Chapter 9. Welfare Effects 

9.1. Introduction 

This chapter is a synthesis and evaluation of the most policy-relevant science used to help 

form the scientific foundation for review of the secondary (welfare-based) NAAQS aimed at 

protecting against welfare effects of ambient airborne PM. Specifically, Chapter 9 assesses the 

effects of atmospheric PM on the environment, including: (1) effects on visibility; (2) effects on 

climate; (3) ecological effects; and (4) effects on materials. These sections initially highlight the 

conclusions from the 2004 PM AQCD (U.S. EPA, 2004, 056905), followed by an evaluation of 

recent publications and assessment of the expanded body of evidence. In some sections, few new 

publications are available, and the discussion is primarily a brief overview of the key conclusions 

from the previous review. 

As discussed in Chapter 1, the effects of particulate NOX and SOX have recently been 

evaluated in the ISA for Oxides of Nitrogen and Sulfur – Ecological Criteria (U.S. EPA, 2008, 

157074). That ISA focused on the effects from deposition of gas- and particle-phase pollutants 

related to ambient NOX and SOX concentrations that can lead to acidification and nutrient 

enrichment, as well as on the potential for increased mercury methylation from SO4

2– deposition. 

Thus, emphasis in this document is placed on the effects of airborne PM on visibility and climate, 

and on the deposition effects of PM constituents other than NOX and SOX, primarily metals and 

carbonaceous compounds. 

Chapter 2 of this assessment provides an integrative overview of the major welfare effects 

evaluated. EPA’s framework for causality, described in Chapter 1, is applied throughout the 

evaluation and the causal determinations are highlighted. 

9.2. Effects on Visibility 

9.2.1. Introduction 

In recent years, most visibility research involved characterizing visibility conditions and trends 

over broad regional scales, improving the understanding of the atmospheric processes and pollutants 

responsible for the regional impacts, and attribution of visibility-impairing pollutants to emission 

sources, source types, and regions. The motivation for much of this work has come from the 

visibility protection provisions of the 1977 Clean Air Act Amendments (CAAA) that called for the 

development of regulations to address reduction of regional haze in 156 NPs and wilderness areas to 

natural conditions, and from the subsequent Regional Haze Rule (RHR) promulgated in 1999 by 

EPA in response to the CAA mandate. Implementation of the RHR entails planned emissions 

reductions to reach natural haze conditions in these protected areas by 2064 in six 10-year planning 

steps. 

Haze conditions caused solely by PM from natural sources are generally much lower than 

contemporary conditions. The largest difference is between natural and current conditions for the 

inorganic salts ammonium sulfate and ammonium nitrate, with natural concentrations taken to be 

just a few tenths of a μg/m3

 each (Trijonis et al., 1990, 157058), while current conditions of both 

over large regions of the country are an order of magnitude or more larger (DeBell, 2006, 156388). 

 

 Note: Hyperlinks to the reference citations throughout this document will take you to the NCEA HERO database (Health and 

Environmental Research Online) at http://epa.gov/hero. HERO is a database of scientific literature used by U.S. EPA in the process of 

developing science assessments such as the Integrated Science Assessments (ISA) and the Integrated Risk Information System (IRIS). 

December 2009 9-1 
However, natural source PM can be substantial on an episodic basis for crustal mineral PM 

components during high windblown dust conditions and for carbonaceous PM from biomass 

combustion during wildfire and prescribed burning episodes. The need for information to generate 

RHR implementation plans has resulted in extensive use of continental-scale air quality simulation 

modeling and assessment of expanded ambient monitoring data sets. 

Unlike the substantial remote-area visibility investigations that have been conducted in 

response to the RHR, relatively little work on urban visibility effects has been done in recent years. 

For example, there has been relatively little new research on the optical and human perceptual 

aspects of atmospheric visibility over the last decade or more. These topics have been the subjects of 

numerous earlier investigations that have been summarized in detail elsewhere (Latimer and Ireson, 

1980, 035723; Middleton, 1952, 016324; Tombach and McDonald, 2004, 157054; Trijonis et al., 

1990, 157058; U.S. EPA, 1979, 157065; Watson et al., 2002, 035623), including past criteria 

documents on PM, SO2 and NOX (U.S. EPA, 1982, 017610; U.S. EPA, 1993, 017649; U.S. EPA, 

2004, 056905). 

In spite of this fact, the understanding of urban visibility conditions has continued to improve. 

By applying a well established algorithm that relates PM and haze conditions to data currently 

collected from routine filter-based PM chemical speciation monitors located in numerous urban areas 

(Jayanty, 2003, 156605), and to data collected from the more recently deployed high time- and 

size-resolved PM speciation monitors located in several cities such as those in the PM Supersites 

program (Solomon and Hopke, 2008, 156997) urban visibility conditions can be better characterized. 

Comparisons between urban and remote area data in the same region afford the opportunity to 

differentiate between regional and local visibility impacts. The availability of better size and time 

resolution PM composition data, compared to that available from the routine monitoring programs, 

reduces the number of simplifying assumptions required to estimate visibility conditions in these 

areas, thereby reducing the uncertainty of the estimates. Thus, the state of the science supporting 

urban visibility assessments continues to improve. 

The background section below contains an overview of long-available information to help 

provide context to the more recently published literature summarized in subsequent sections. 

9.2.2. Background 

Air pollution-induced visibility impairment is caused by the loss of image-forming light (i.e., 

signal) and the addition of non-image forming light (i.e., noise) between an observer and the object 

being viewed. These changes to the light reaching the observer are a result of light being scattered 

and absorbed by particles and gases in the sight path (see the schematic in Figure 9-1). 

Electromagnetic theory developed to characterize the interaction of light with matter (Mie, 1908, 

155983) permits the calculation of light scattering and absorption by particles and gas molecules 

where the index of refraction and shape of particles by size are known (Van de Hulst, 1981, 191972). 

The ability of human observers to visually detect distant objects or identify changes in their 

appearance depends on the apparent contrast of the object against its background. The apparent 

contrast is affected by changes in the physicochemical characteristics of the atmosphere caused by 

air pollution as well as factors not related to air quality such as length of the sight path, scenic 

lighting and the physical characteristics of the viewed object and other elements of the scene. To 

rigorously determine the perceived visual effects of changes in the optical properties of the 

atmosphere requires the use of radiative transfer modeling to determine changes in light from the 

field of view experienced by the observer, followed by the use of psychophysical modeling to 

determine the response to the light by the eye-brain system. The complexity of such an approach 

discourages its common use. 

Atmospheric light extinction is a fundamental atmospheric optics metric used to characterize 

air pollution impacts on visibility. It is the fractional loss of intensity in a light beam per unit distance 

due to scattering and absorption by the gases and particles in the air. Light extinction (bext) can be 

expressed as the sum of light scattering by particles (bs,p), scattering by gases (bs,g), absorption by 

particles (ba,p) and absorption by gases (ba,g). Light extinction and its components are expressed in 

units of inverse length, typically either inverse kilometers (km-1) or, as will be the convention in this 

document, inverse megameters (Mm-1). Traditionally, for visibility-protection applications, the most 

sensitive portion of the spectrum for human vision (550 nm) has been used to characterize light 

extinction and its components. 

December 2009 9-2 
Source: Malm (1999, 025037). 

Figure 9-1. Important factors involved in seeing a scenic vista are outlined. Image-forming 

information from an object is reduced (scattered and absorbed) as it passes 

through the atmosphere to the human observer. Air light is also added to the 

sight path by scattering processes. Sunlight, light from clouds, and 

ground-reflected light all impinge on and scatter from particulates located in the 

sight path. Some of this scattered light remains in the sight path, and at times it 

can become so bright that the image essentially disappears. A final important 

factor in seeing and appreciating a scenic vista are the characteristics of the 

human observer. 

A parametric analysis has shown that a constant fractional change in light extinction results in 

a similar perceptual change regardless of certain baseline conditions (Pitchford et al., 1990, 156871). 

From this assessment, the deciview haze index, which is a log transformation of light extinction, 

similar in many ways to the decibel index for acoustic measurements, was developed (Pitchford and 

Malm, 1994, 044922). A one deciview (1dv) change is about a 10% change in light extinction, which 

is a small change that is detectable for sensitive viewing situations. The haze index in deciview units 

is an appropriate metric for expressing the extent of haze changes where the perceptibility of the 

change is an issue. The RHR has adopted the deciview haze index as the metric for tracking 

long-term haze trends of visibility-protected federal lands (U.S. EPA, 2001, 157068). Light 

extinction and its components are more useful metrics for characterizing the apportionment of haze 

to its pollutant components due to the approximately linear relationship between pollutant species 

concentrations and their contributions to light extinction. 

December 2009 9-3 
Figure 9-2. Schematic of remote-area (top) and urban (bottom) nighttime sky visibility 

showing the effects of PM and light pollution. 

Daytime visibility has dominated the attention of those who have studied the visibility effects 

of air pollution, though nighttime visibility is also known to be affected by air pollution. Stargazing 

is a popular human activity in urban and remote settings. The reduction in visibility of the night sky 

is primarily dependent on the addition of light into the sight path, the brightness of the night sky, and 

the reduction in contrast of stars against the background (see the schematic in Figure 9-2). These are 

December 2009 9-4 
controlled by the addition of PM, which enhances scattering, and the addition of anthropogenic 

sources of light. Scattering of anthropogenic light contributes to the “skyglow” within and over 

populated areas, adding to the total sky brightness. The visual result is a reduction of the number of 

visible stars and the disappearance of diffuse or subtle phenomena such as the Milky Way. The 

extinction of starlight is a secondary and minor effect also caused by increased scattering and 

absorption. Anthropogenic light sources include artificial outdoor lighting, which varies dramatically 

across space. Natural sources include the Moon, planets, and stars that have a predictable rhythm 

across time. 

The nighttime visual environment has some important differences to note. Light sources and 

ambient conditions are typically five to seven orders of magnitude dimmer at night than in sunlight. 

Moonlight, like sunlight, introduces light throughout an observer’s sight path at a constant angle. On 

the other hand, dim starlight emanates from all over the celestial hemisphere while artificial lights 

are concentrated in cities and illuminate the atmosphere from below. Sight paths are often inclined 

upward at night as targets may be nearby terrain features or celestial phenomena. Extinction behaves 

the same at night as during the day, lowering the contrast of scenes through scattering and 

absorption; nevertheless the different light sources will yield variable changes in visibility as 

compared to what has been established for the daytime scenario. Little research has been conducted 

on nighttime visibility. Even if the air quality-visibility interactions are shown to be similar between 

day and night settings, the human psychophysical response at night is expected to differ. Though 

recent advances in the ability to instrument and quantify nighttime scenes (Duriscoe et al., 2007, 

156411) have been made and can be utilized to evaluate nocturnal visibility, the state of the science 

is not yet comparable to that associated with daytime visibility impairment. The remainder of this 

document focuses exclusively on daytime visibility. 

9.2.2.1. Non-PM Visibility Effects 

Light extinction due to the gaseous components of the atmosphere is relatively well 

understood and well estimated for any atmospheric conditions. Absorption of visible light by gases 

in the atmosphere is primarily by NO2, and can be directly and accurately estimated from NO2

concentrations by multiplying by the absorption efficiency. Scattering by gases is described by the 

Rayleigh scattering theory. 

NO2 absorbs more light in the short wavelength blue portion of the spectrum than at longer 

wavelengths. For this reason a plume or layer of NO2 removes more of the blue light from the scene 

viewed through the layer giving a yellow or brown appearance to the layer or plume. This filtering of 

blue light by NO2 can deepen the brown appearance of hazes over urban areas, although it is not the 

sole cause of such discoloration (U.S. EPA, 1993, 017649). The photopic-weighted absorption 

efficiency at the 550 nm wavelength is incorporated into the revised version of the algorithm for 

estimating light extinction from aerosol data that is used for implementing the RHR (Pitchford et al., 

2007, 098066). However, NO2 is not routinely measured at any of the monitoring sites representing 

visibility protected areas where its impacts are assumed to be inconsequential compared to those of 

PM. At background concentrations NO2 absorption is generally less than five percent of the light 

scattering by clean air (Rayleigh scattering), making it imperceptible. Plume visibility models are 

available to assess both achromatic contrast and discoloration associated with NO2 light absorption, 

for point source emissions (Latimer and Ireson, 1980, 035723; Seigneur et al., 1984, 156965). 

9.2.2.2. PM Visibility Effects 

Particle light extinction is more complex than that caused by gaseous components. PM is 

responsible for most visibility impairment except under near-pristine conditions, where Rayleigh 

scattering is the largest contributor to light extinction or in plumes of combustion sources that are 

well-controlled for particulate emissions (e.g., coal-fired power plants with bag houses), where light 

absorption by NO2 may dominate the light extinction. 

Light-absorbing carbon (e.g., DE soot and smoke) and some crustal minerals are the only 

commonly occurring airborne particle components that absorb light. All particles scatter light, and 

generally particle light scattering is the largest of the four light extinction components. While a 

larger particle scatters more light than a similar shaped smaller particle of the same composition, the 

light scattered per unit of mass concentration (i.e., mass scattering efficiency in units of 

Mm-1/[μg/m3

] which reduces to m2/g) is greatest for particles with diameters from ~0.3-1.0 μm. If 

December 2009 9-5 
the index of refraction, particle shape and concentration as a function of particle size are well 

characterized, Mie theory can be used to accurately calculate the light scattering and absorption by 

those particles. However, it is rare that these particle properties are known, so assumptions are used 

in place of missing information to develop a simplified calculation scheme that provides an estimate 

of the particle light extinction from available data sets. 

Particles composed of water soluble inorganic salts (i.e., ammoniated sulfate, ammonium 

nitrate, sodium chloride, etc.) are hygroscopic in that they absorb water as a function of relative 

humidity to form a liquid solution droplet. Aside from the chemical consequences of this water 

growth, the droplets become larger when relative humidity increases, resulting in increased light 

scattering. Hence, the same PM dry concentration produces more haze. Figure 9-3 shows the effect 

of water growth as a function of relative humidity on light scattering for two size distributions of 

ammonium nitrate and ammonium sulfate particles as well as for internal and external mixtures (i.e., 

mixed within the same particle and in separate particles, respectively) of the two components. This 

figure illustrates a number of important points. The water growth effect is substantial with an 

increase in light scattering by about a factor of 10 between 40% and 97% relative humidity for the 

same dry particle concentrations. The amount of scattering is significantly dependent on the dry 

particle size distribution. However the growth curves for ammonium sulfate, ammonium nitrate and 

mixtures of the two particle components are similar at any of the dry particle size distributions. 

Water growth curves are also available for sodium chloride, the major component in sea salt, which 

is an important PM component at coastal locations. 

Source: Reprinted with Permission of the American Geophysical Union from Tang (1996, 157042). 

Figure 9-3. Effect of relative humidity on light scattering by mixtures of ammonium nitrate 

and ammonium sulfate. 

Using Mie theory, the scattering and absorption of any wavelength of light by a particle of 

known size and index of refraction (a function of the wavelength of light) can be calculated 

(Van de Hulst, 1981, 191972). Particle density is used to convert the particle light extinction to its 

mass extinction efficiency (i.e., the ratio of particle light extinction to its mass). To expand the 

calculations from one particle at a time to the multitude of particles in ambient aerosol, information 

December 2009 9-6 
about the aerosol size and composition distributions are needed. Aerosol mixture refers to how the 

major components that make up the particles are mixed. Methods have been developed to treat 

simple mixture models ranging from external mixtures where the various components are assumed to 

be in separate particles, to multi-component mixtures where individual particles contain several 

components (Ouimette and Flagan, 1982, 025047). The latter includes internally mixed particles 

where two or more components are mixed within the particles, and layered aerosol with a core of 

one component covered by a shell of another component. 

The Mie theory solution for an external mixture can be simplified to a linear relationship 

where the light extinction is the sum of the mass concentration of each species multiplied by its 

specific mass extinction efficiency (Ouimette and Flagan, 1982, 025047). This formulation promotes 

the concept of apportioning the light extinction among the various PM species. For internally mixed 

aerosol, the light extinction response to adding or removing mass of any component to the aerosol is 

dependent on how such changes would affect the particle size, density and index of refraction 

distribution of the aerosol. However, a number of investigators have shown that the differences 

among the calculated light extinction values using external and various internal mixture assumptions 

are generally less than about 10% (Lowenthal et al., 1995, 045134; Ramsey, 1966, 013946; Sloane, 

1983, 025039; Sloane, 1984, 025040; Sloane, 1986, 045954; Sloane and Wolff, 1985, 045953; Wolff, 

1985, 044680). This provides a basis to accept the apportionment of light extinction to the PM 

components calculated using an external mixture assumption as a meaningful surrogate for their 

contributions. 

Ambient aerosols are usually a complex and unknown combination of both internal and 

external mixtures of the particle components. Despite these complexities, PM light scattering can be 

accurately calculated for any relative humidity if the chemical composition as a function of dry 

particle size is known (Hand et al., 2002, 190367; Malm and Pitchford, 1997, 002519). However, 

most routinely available ambient monitoring programs do not include data with sufficient detail to 

make such calculations. The IMPROVE network with its greater than 150 remote area monitoring 

sites (DeBell, 2006, 156388) and the CSN (Jayanty, 2003, 156605) with its greater than 150 urban 

area monitoring sites collect 24-h duration fine particle samples (PM2.5) that are analyzed for the 

major PM components including SO4

2–, nitrate, and carbonaceous particulate. CSN also analyzes for 

ammonium ion, but does not monitor coarse mass (PM10-2.5), while IMPROVE measures coarse mass 

but does not analyze for ammonium ion. Neither data set has sufficient size resolution to make Mie 

theory calculations of light extinction, nor does either program routinely monitor NO2

concentrations, which would be required to calculate its contribution to light extinction by 

absorption. 

A simple algorithm similar in form to the linear equation that results from Mie theory applied 

with an external mixture assumption is frequently used to estimate light extinction from the 

concentrations of the major components. The concentration of each of the major aerosol components 

is multiplied by a dry extinction efficiency value and for the hygroscopic components (e.g., 

ammoniated sulfate and ammonium nitrate) an additional multiplicative term to account for the 

water growth to estimate that components contribution to light extinction. Both the dry extinction 

efficiency and water growth terms are developed by some combination of empirical assessment and 

theoretical calculation using typical particle size distributions associated with each of the major 

aerosol components, and they are evaluated by comparing the algorithm estimates of light extinction 

with coincident optical measurements. Summing the contribution of each component gives the 

estimate of total light extinction. The most commonly used of these is referred to as the IMPROVE 

algorithm because it was developed specifically to use the IMPROVE aerosol monitoring data and 

was evaluated using IMPROVE optical measurements at the subset of sites that make those 

measurements (Malm et al., 1994, 044920). The formula for the traditional IMPROVE algorithm is 

shown below. 

December 2009 9-7 
[ ]

[ ]

[ ]

[ ]

[ ]

[ ]

10

6.0

1

10

4

)(3

)(3

+

×+

×+

×+

×+

××+

≈ × ×

Coarse Mass

SoilFine

Elemental Carbon

Organic Mass

RHf Nitrate

RHfb Sulfate ext

Equation 9-1 

Source: DeBell (2006, 156388)

Light extinction (bext) is in units of Mm-1, the mass concentrations of the components indicated 

in brackets are in μg/m3

, and f(RH) is the unitless water growth term that depends on relative 

humidity. The dry extinction efficiency for particulate organic mass is larger than those for 

particulate SO4

2– and nitrate principally because the density of the dry inorganic compounds is 

higher than that assumed for the PM organic mass components. Since IMPROVE does not include 

ammonium ion monitoring, the assumption is made that all SO4

2– is fully neutralized ammonium 

sulfate and all nitrate is assumed to be ammonium nitrate. Though often reasonable, neither 

assumption is always true (see Section 9.2.3.1). In the eastern U.S. during the summer there is 

insufficient ammonia in the atmosphere to neutralize the SO4

2– fully. Fine particle nitrates can 

include sodium or calcium nitrate, which are the fine particle fraction of generally much coarser 

particles due to nitric acid interactions with sea salt at near-coastal areas (sodium nitrate) or nitric 

acid interactions with calcium carbonate in crustal aerosol (calcium nitrate). Despite the simplicity of 

the algorithm, it performs reasonably well and permits the contributions to light extinction from each 

of the major components (including the water associated with the SO4

2– and nitrate compounds) to 

be separately approximated. 

The f(RH) terms inflate the particulate SO4

2– and nitrate light scattering for high relative 

humidity conditions. For relative humidity below 40% the f(RH) value is 1, but it increases to 2 at 

~66%, 3 at ~83%, 4 at ~90%, 5 at ~93% and 6 at ~95% relative humidity. The result is that both 

particulate SO4

2– and nitrate are more efficient per unit mass than any other aerosol component for 

relative humidity above ~85% where its total light extinction efficiency exceeds the 10m2/g 

associated with EC. Based on this algorithm, particulate SO4

2– and nitrate are estimated to have 

comparable light extinction efficiencies (i.e., the same dry extinction efficiency and f(RH) water 

growth terms), so on a per unit mass concentration basis at any specific relative humidity they are 

treated as equally effective contributors to visibility effects. The strong relationship demonstrated 

between dry light scattering and fine PM mass concentration or ambient light extinction and fine PM 

mass concentration under low relative humidity conditions noted by a number of investigators 

(Charlson et al., 1968, 095355; Chow et al., 2002, 037784; Chow et al., 2002, 036166; McMurry, 

2000, 081517; Samuels et al., 1973, 070601; Waggoner and Weiss, 1980, 070152; Waggoner et al., 

1981, 095453) is reasonable based on this algorithm when the PM fractional composition is either 

relatively constant or varies most among PM components with similar dry extinction efficiency 

values (e.g., SO4

2–, nitrate and organic mass efficiencies). 

9.2.2.3. Direct Optical Measurements 

Light extinction and its components (i.e., scattering and absorption by particles and gases) can 

be determined directly by optical measurements using commercially available instruments (Trijonis 

et al., 1990, 157058). Though these measurements are all wavelength dependent, the convention for 

visibility monitoring purposes is to make measurement at or near 550 nm, which is the wavelength 

of maximum eye response. Direct PM light extinction, scattering and absorption measurements offer 

a number of advantages compared to estimates using an algorithm applied to PM speciation data. 

The direct optical measurements are considered more accurate because they do not depend on the 

assumed particle characteristics (e.g., size, shape, density, component mixture, etc.) thought to be 

associated with the major PM species. Also the optical measurements are made with high time 

resolution (e.g., minutes to hourly) compared with the filter composition based estimates that are 

December 2009 9-8 
typically 24-h duration, allowing the former to better characterize sub-daily temporal patterns which 

can help in identifying influential source categories and characterize atmospheric phenomenon. The 

higher time resolution attainable with direct light extinction measurements are also more 

commensurate than the 24-h light extinction estimates from PM samples with the short exposure 

time associated with perceived visibility effects. 

Path-averaged light extinction can be determined by long-path transmissometers that monitors 

the intensity of light that has traversed a known distance from a known initial intensity light source. 

Transmission (i.e., the ratio of the final to the initial light intensity) is the natural logarithm of the 

product of the path-averaged light extinction and the distance the light has traversed. 

Transmissometer path-length establishes the useful range of light extinction over which the 

measurements can be accurately measured, with path-lengths of 10 km or more required for pristine 

conditions and <1 km more appropriate for hazier situations or to measure the visibility impacts 

associated with fogs or precipitation events. The National Park Service (NPS) operated long-path 

transmissometers at up to 25 locations from 1986 through 2004 (DeBell, 2006, 156388), but have 

more recently discontinued their use at all but one remote area location due to the cost of 

maintenance and the difficulties of performing calibration. Transmissometers are currently in routine 

service at five urban areas. 

A number of instruments measure the light scattered by particles and gases from a source of 

known intensity. These include forward scattering, back scattering, polar, and integrating 

nephelometers. Of these the integrating nephelometers with its high sensitivity and sample control 

options has been more widely used for air quality-related visibility and PM monitoring purposes, 

while the robust design of the open air forward scattering instruments have seen extensive use by the 

National Weather Service (NWS) Automated Surface Observing System (ASOS) for characterizing 

visibility principally for transportation safety purposes (NOAA, 1998). The potential utility of the 

ASOS visibility network at about 900 locations for air quality monitoring has been established, but 

the lack of resolution in the reported data is a serious impediment to this use of the data (Richards et 

al., 1996, 190476). 

Integrating nephelometers draw air into a sample chamber, making it possible to modify the 

sample either by changing its humidity or controlling the particle size range that is measured. This 

feature makes it possible to use sample-controlled nephelometers to investigate the effects of 

ambient PM size and water growth characteristic on light scattering (Covert et al., 1972, 072055; 

Malm and Day, 2001, 190431; Rood et al., 1987, 046397). For instance the coarse particle 

contribution to light scattering can be estimated using a nephelometer that alternately samples 

through a 2.5 μm size selective inlet and a 10 μm size selective inlet. This separation by size may be 

useful in that it would allow correction of the underestimated light scattering of larger particles due 

to nephelometer angular truncation errors (Anderson and Ogren, 1998, 156213). For routine 

monitoring, integrating nephelometers are typically either used to measure the PM component of 

light scattering when operated at ambient relative humidity or to measure dry PM light scattering as 

a high-time resolution surrogate for PM mass concentration when operated with a heater or other 

sample air drier. Integrating nephelometers operated at ambient conditions by the IMPROVE 

program have replaced the long-path transmissometer as the principal optical measurement at about 

30 locations (DeBell, 2006, 156388). 

PM light absorption can also be inferred from measured changes in the light transmitted 

through a filter used to sample the PM compared to an identical clean filter (Bond et al., 1999, 

156281). Such measurements can be made subsequent to sampling (Campbell et al., 1995, 190171) 

or continuously during sampling by using specifically designed sampler (Hansen et al., 1982, 

190368; Hansen et al., 1984, 002396). All of the filter-based methods require adjustments to the 

optical measurements to account for filter and sampled particle light scattering effects associated 

with particles concentrated on and within the matrix of the filters (Bond et al., 1999, 156281). Often 

PM light absorption measurements are used to infer BC concentration by assuming it is the dominant 

PM contributor to light absorption with a near constant absorption efficiency (Allen et al., 1999, 

048923; Babich et al., 2000, 156239). In fact commercially available aethalometers incorporate an 

absorption efficiency value so they can directly report BC concentrations. Like nephelometers, 

commercially available aethalometers can be obtained with either single or multiple-wavelength 

measurement capabilities, where the multi-wavelength data can be used to better characterize the 

PM. More recently these have been used to distinguish BC that absorbs light strongly over the full 

visible light spectrum (e.g., DE) from brown carbon that absorbs appreciably more at shorter 

wavelengths than at long wavelengths (e.g., WS) (Andreae and Gelencsér, 2006, 156215). 

December 2009 9-9 
Other approaches to measure light absorption include a photoacoustic instrument that 

measures the heating associated with absorbed light by suspended PM (Arnott et al., 1999, 020650; 

Moosmuller et al., 1998, 020657), as well as by the difference between light extinction and light 

scattering measurements (Bond et al., 1999, 156281). 

9.2.2.4. Value of Good Visual Air Quality 

The term visual air quality (VAQ) is used here to refer to the visibility effects caused solely by 

air quality conditions. For example, it excludes the reduced visibility caused by fog. Two broadly 

different approaches have traditionally been used to define and quantify the value of good VAQ. One 

approach assesses the monetary value associated with visibility changes; the other assesses the 

psychological value of visual air quality. With respect to the latter, reduced VAQ is considered an 

environmental stressor (Campbell, 1983, 190172) that is associated with heightened amounts of 

anxiety, tension, anger, fatigue, depression, and feelings of helplessness (Evans et al., 1987, 190347; 

Zeidner and Shechter, 1988, 189973). Though the relationship between impaired VAQ and mental 

health is poorly understood, there are greater emergency calls associated with psychiatric 

disturbances during periods with reduced VAQ (Rotton and Frey, 1982, 190477). Studies have 

shown that reduced VAQ affects people’s behavior, including reductions in outdoor activities, and 

increased hostility and aggressive behavior (Cunningham, 1979, 191974; Evans et al., 1982, 190521; 

Jones and Bogat, 1978, 190396; Rotton et al., 1979, 190478). 

The value of VAQ (both monetary and non-monetary) has been investigated in two broadly 

different settings, non-recreational or urban settings and recreational settings, such as the NPs and 

wilderness areas where visibility is protected by the RHR (Trijonis et al., 1990, 157058). In urban 

settings, public surveys have shown that greater than 80% of the participants are aware of poor VAQ 

conditions (Cohen et al., 1986, 190182), though attitudes towards poor VAQ have been shown to 

vary by socio-economic status, health, and length of residence in the urban setting (Barker, 1976, 

072137). The economic importance of urban visibility has been examined by a number of studies 

designed to quantify the benefits (or willingness to pay) associated with potential improvements in 

urban visibility. Urban visibility valuation research prior to 1997 was summarized in Chestnut and 

Dennis (1997, 014525), and was also described in the 2004 PM AQCD (U.S. EPA, 2004, 056905) 

and the 2005 PM Staff Paper (U.S. EPA, 2005, 090209). These reviews summarize 34 estimates 

(based on different cities or model specifications) from six different studies. Since the mid 1990s, 

however, only one new valuation study of urban visibility has been published (Beron et al., 2001, 

156270) which is summarized below (Section 9.2.4.6). 

In recreational settings, experience based demand models have been developed using on-site 

and mail-in surveys to judge the relative importance to NP visitors of various park attributes 

including good VAQ, to assess visitor awareness of VAQ conditions, and to explore possible 

relationships between VAQ and visitor satisfaction (Ross et al., 1985, 044287; Ross et al., 1987, 

037420). At the three western and two eastern NPs where this survey was conducted, visitors rated 

the attribute identified as “clean, clear air” among the most important features of the parks. A 

random sample of 1,800 visitors at one of the parks (Grand Canyon) showed that visitor awareness 

of VAQ impacts increased as measured visibility conditions decreased, and that overall park 

enjoyment and satisfaction decreased with reduced VAQ. Grand Canyon visitors when asked to 

indicate how they would budget their time (e.g., between visiting an archaeological site or a view 

lookout point) indicated that they would to be willing to significantly alter their behavior to 

experience views under improved VAQ (Malm et al., 1984, 044292). 

9.2.3. Monitoring and Assessment 

Monitoring and the assessment of monitoring data serve a number of goals with regard to the 

visibility effects of PM, including improving the understanding of the physio/chemical/optical 

properties of the aerosol, characterizing spatial and temporal air quality patterns, and assessing the 

causes (i.e., pollution sources and atmospheric processes) that are responsible for visibility 

impairment. Information generated by special studies employing sophisticated instrumentation are 

typically needed to advance the understanding of aerosol properties, while characterizing trends is 

the product of analyzing routine monitoring data. Whereas, assessing the causes of haze usually 

involves a weight-of-evidence approach applied to special study and/or routine monitoring data sets 

December 2009 9-10 
plus the use of air quality simulation modeling. This section summarizes recently available 

information that is based on monitoring data. 

9.2.3.1. Aerosol Properties 

Particle size is the most influential physical property of aerosols with respect to their dry light 

extinction efficiency. Chemical composition by size is used to ascertain density (needed to convert 

aerodynamic to physical size and to determine particle mass as a function of size) and to identify the 

water growth characteristics of the aerosol (needed to calculate the particle size, density and index of 

refraction at ambient RH). To characterize aerosol properties of interest for visibility effects, field 

monitoring programs typically include particle size distribution monitoring, high size resolution 

particle sampling with subsequent compositional analysis, and optical monitoring. These generate 

data that permit optical closure assessments where the light scattering and/or light extinction 

estimates from the aerosol data are compared to corresponding optical data. Since component 

contributions to visibility are generally assessed by applying the IMPROVE or some similar 

algorithm to measured or modeled aerosol concentration data, this section will include recent 

investigations that evaluate or address various assumptions inherent in the use of these simple 

algorithms. 

One component of the Big Bend Regional Aerosol and Visibility Observational (BRAVO) 

Study, conducted at Big Bend NP, TX in the summer and fall of 1999, entailed use of detailed 

measurements of aerosol chemical composition, size distribution, water growth, and optical 

properties to characterize the aerosol and assess the relationship between aerosol physical, chemical 

and optical properties (Malm et al., 2003, 190434; Schichtel et al., 2004, 179902). Fine ammoniated 

sulfate during the BRAVO Study was about half the fine particle mass concentration and was shown 

to be responsible for about 35% of the light extinction. Rayleigh scattering was the second largest 

contributor at about 25%, followed by coarse particle (about 18%), and organic compounds (about 

13%). There was little fine particle nitrate (less than 5% of the mass concentration) and most of it is 

apparently in the form of sodium nitrate and two thirds of it was found in the coarse mode where it 

comprises about 8% of the coarse particle mass concentration. Both the composition of the nitrate 

and the fact of much of it being in the coarse size mode (2.5 μm >D>10 μm) are inconsistent with 

the implied assumptions of the IMPROVE algorithm. 

A year-long special study of coarse particle speciation was conducted at nine IMPROVE 

remote area monitoring sites during 2003-2004 to provide additional information about the 

geographic and seasonal variations in coarse particle composition (Malm et al., 2007, 156730). The 

same sampling and analytical methodologies procedures were used for the PM10 samples as are 

routinely used on the IMPROVE PM2.5 samples. The IMPROVE coarse particle speciation study did 

not include ammonium analysis, so SO4

2– and nitrate ions were assumed to be ammonium sulfate 

and ammonium nitrate. As expected crustal minerals were the largest contributors to coarse mass 

overall (about 60%), though at Mt. Rainier the fraction of coarse PM that was organic exceeded the 

crustal mineral by nearly two to one (i.e., 59.2% compared to 33.5%) On average across sites the 

organic particulate contributed significantly at about one quarter of the coarse mass, while 

ammonium nitrate was the third largest contributor to coarse mass (about 8%). Sea salt was 

negligible overall but high at the one coastal site (i.e., 12% at Brigantine, NJ). The two California 

sites, San Gorgonio and Sequoia, had the highest coarse nitrate concentrations, 0.74 μg/m3

 and 

0.69 μg/m3, and high fine nitrates concentrations on average, 2.66 μg/m3 and 2.14 μg/m3, 

respectively. Brigantine, a coastal site in New Jersey, had the highest fraction of total nitrate in the 

coarse size range (36%). The authors speculate that Brigantine’s particulate nitrate is likely sodium 

nitrate, the result of nitric acid reactions with sodium chloride. The nine-site average fraction of total 

nitrate in the coarse size range is 26%. By contrast, coarse SO4

2– concentrations are small with only 

about ~1% of the total SO4

2– in the coarse fraction. 

Routine IMPROVE monitoring data include the mass concentration, but not the composition 

of the coarse PM fraction, so the algorithm used to estimate light extinction does not include any 

provision for varied coarse PM composition as shown in this study. This study shows that about 10% 

of the coarse mass across the nine monitoring sites is composed of hygroscopic materials (i.e., 

ammonium sulfate, ammonium nitrate and sea salt), which during high humidity conditions will 

scatter more light than estimated by the current algorithm (e.g., ~20% bias at ~90% relative 

humidity). However, at coastal sites such as the Brigantine, NJ, IMPROVE site where the combined 

concentration of the inorganic salts (i.e., sea salt, nitrate and SO4

2–) constitute a significant fraction 

December 2009 9-11 
(~24% on average) of the coarse mass concentration, the IMPROVE algorithm underestimation of 

light extinction by coarse PM can be significant for high relative humidity conditions (~60% at 

~90% relative humidity). The resulting underestimation of total light extinction can be much smaller 

since fine particle light extinction generally exceeds that contributed by coarse particles. Another 

issue with regard to estimating light extinction from coarse PM concentration when the composition 

is not crustal minerals, as has been assumed, has to do with the lower average density of the coarse 

mode particles that results in greater particle numbers and/or larger particles and therefore a greater 

light extinction efficiency (Malm and Hand, 2007, 155962). 

Special studies with more complete, higher time resolution and size resolved particulate 

inorganic ion species chemistry and precursor gases were conducted at seven of the nine sites with 

IMPROVE coarse particle speciation monitoring (Lee et al., 2008, 156686). This work confirmed 

the presence of sodium and calcium nitrate (referred to as mineral nitrate) primarily in the coarse 

particle size range in addition to fine particle ammonium nitrate where low temperatures, high 

humidity and excess ammonium (beyond that required to neutralize the particulate SO4

2–) favored 

particle phase equilibrium. Figure 9-4 is a map showing the locations and sample times and 

estimated composition of the total particulate nitrate for the seven locations for this special study. 

Sites with a high fraction of ammonium nitrate (e.g., San Gorgonio, Bondville, and Brigantine) have 

the highest nitrate contributions to total mass concentration and haze, whereas sites with high 

mineral nitrates tend to have low total nitrate contributions. This work shows that the common 

assumption that particulate nitrate is in the fine particle size range and consists principally of 

ammonium nitrate is not necessarily true. 

Source: Reprinted with Permission of Atmospheric Environment from Lee et al. (2008, 156686) 

Figure 9-4. Estimated fractions of total particulate nitrate during each field campaign 

comprised of ammonium nitrate, reacted sea salt nitrate (shown as NaNO3), and 

reacted soil dust nitrate (shown as Ca(NO3)2). 

Extinction efficiencies for individual particle species can be theoretically calculated from 

sized-resolved aerosol measurements and can be inferred using multiple linear regression applied to 

aerosol composition and light extinction measurement data. In a recent publication, Hand and Malm 

(2007, 155825) reviewed the literature since 1990 in which aerosol mass scattering efficiency values 

were calculated or inferred. From these they have compiled normalized dry scattering efficiency 

December 2009 9-12 
values for the individual species. Based on 93 separate determinations including marine, remote 

continental and urban areas data sets, the average dry mass scattering efficiency for ammonium 

sulfate is 2.5 ± 0.6 m2

/g. Average values tended to be somewhat lower for the marine aerosol 

(~2 m2/g) than for remote continental (~2.7 m2/g) and urban (2.6 m2/g) areas, and values also tended 

to be lower for fairly clean arid locations compared with more humid polluted areas. 

Based on 48 separate determinations including remote area and urban area data sets, the 

average dry mass scattering efficiency for ammonium nitrate is 2.7 ± 0.5 m2

/g (Hand and Malm, 

2007, 155825). Average values were higher in remote locations (2.8 ± 0.5 m2

/g) compared to urban 

locations (2.2 ± 0.5 m2

/g) though this might be accounted for by the predominate use of multiple 

linear regression for the remote areas, which can be biased high, compared to the use of theoretical 

calculations for the urban data sets. 

Organic fine PM extinction efficiency of 3.9 ± 1.5 m2

/g is based on 58 separate 

determinations, though much higher values (~6 m2/g) resulted for locations influenced by industrial 

and biomass combustion sources (Hand and Malm, 2007, 155825). These organic fine PM extinction 

efficiency values were adjusted to use a consistent ratio of organic mass to OC (OC) of 1.8 for each 

determination of the mass concentration. This value is generally associated with aged organic PM, 

while for more freshly emitted PM, such as in an urban environment, a smaller ratio (e.g., 1.4) would 

be more appropriate. This could explain the discrepancy between two approaches used to estimate 

the organic PM light extinction efficiency for Phoenix (Hand and Malm, 2006, 156517), which 

resulted in a significantly lower value where a site specific regression method was used compared to 

the value obtained from a method optimized for remote-area monitoring (2.47 m2

/g compared to 

3.71 m2/g). However in Fresno both the mass balance and light scattering balance was improved by 

using a ratio of 1.8 instead of 1.4 to estimate the organic compound mass (Watson and Chow, 2007, 

157127). Another possible or partial factor with respect to urban light extinction efficiency for 

organic PM may be that the size distribution of freshly emitted organic PM in urban areas extends 

significantly into the ultra-fine particle size range (Demerjian and Mohnen, 2008, 156392) that is 

less efficient per mass concentration at light scattering than the generally larger-sized aged organic 

PM such as from a distant forest fire as was measured at the Baltimore Supersite. 

Hand and Malm (2007, 155825) also reviewed and made recommendations for extinction 

efficiencies for the other PM components including mixed coarse mode (1.0 ± 0.9 m2

/g based on 51 

determinations) and fine mode dust or soil (3.3 ± 0.6 m2/g based on 23 determinations, but 

recommending 1.0 m2

/g for use with data from realistic collection efficiency samplers) and fine sea 

salt (4.5 ± 0.9 m2

/g based on 25 determinations, but recommending 1.0 m2/g to 1.3 m2/g for use with 

data from realistic collection efficiency samplers). This work did not address light absorption 

efficiency of EC, CB, or crustal PM. 

The Hand and Malm (2007, 155825) average dry mass light scattering efficiency values are 

generally consistent with the values for the IMPROVE algorithm (as shown in Equation 9-1). 

However the adoption of the IMPROVE algorithm by EPA for calculating the haze metric used to 

track trends and assess the nominal pace of progress for the RHR (U.S. EPA, 2001, 157068) resulted 

in much greater scrutiny of its performance in estimating extinction (Lowenthal and Kumar, 2003, 

156712; Malm, 1999, 025037; Malm and Hand, 2007, 155962; Ryan et al., 2005, 156934). Among 

the issues raised is that the algorithm tended to underestimate the light extinction for the haziest 

conditions and overestimate light extinction for the clearest conditions in regions such as the 

southeastern U.S., though it generally worked well in the arid western U.S. Furthermore, they 

showed the lack of mass or light scattering closure at coastal sites due to sea salt that was not 

accounted for by the IMPROVE algorithm. These assessments used mass concentration and light 

extinction closure and regression analysis methods to infer that the dry extinction efficiency for the 

major fine particle components would need to vary in order to avoid the biased estimates of light 

extinction at the extremes. Theoretical calculations of SO4

2– dry extinction efficiencies for 41 days of 

size-resolved chemical composition data for Big Bend, TX as part of the BRAVO Study produced a 

range of results from ~2.4 m2

/g to ~4.1 m2/g, with the larger dry extinction efficiency values tending 

to be associated with higher ammonium sulfate mass concentration and narrower size distributions 

(Schichtel et al., 2004, 179902). 

In response to the technical concerns raised about the performance of the IMPROVE 

algorithm, a revised algorithm was developed (Pitchford et al., 2007, 098066). The revised version 

of the algorithm differs from the original algorithm by: including a fine sea salt term related to the 

measured chloride ion concentration; increasing by about 30% the mass concentration of the organic 

aerosol component by changing the ratio of organic compound mass to OC mass from 1.4 to 1.8; 

December 2009 9-13 
using site elevation dependent Rayleigh scattering in place of 10 Mm-1 that had been used at every 

site; adding a NO2 light absorption term; and employing a split component model for the secondary 

particulate components (i.e., SO4

2–, nitrate and organic species) with new water growth terms to 

better estimate their extinction at the high and low extremes of the range. The revised algorithm is 

displayed below in Equation 9-2. 

])([33.0

( )

[6.0 ]

][)(7.1

][1

[10 ]

[8.2 arg[1.6] ]

[)(4.2 arg[)(1.5] ]

[)(2.2 arg[)(8.4] ]

2 ppbNO

RayleighScattering Site Specific

CoarseMass

RHf SaltSea

SoilFine

ElementalCarbon

SmallOrganicMass OrganiceL Mass

RHf Small Nitrate NitrateeLRHf

b RHf Small Sulfate SulfateeLRHf

ss

s L

ext s L

×+

+

×+

×+ ×

×+

×+

×+ ×+

×+ × ×+ ×

×≈ × ×+ ×

Equation 9-2 

Source: Reprinted with Permission of the Air & Wast Management Association from Pitchford et al. (2007, 098066) 

Small and large SO4

2–, nitrate and organic mass are used to refer to the splitting of the 

concentrations of each of those three species into two size distributions. This approach accounts for 

increased light extinction efficiency with mass by using a simple mixing model that assume that each 

of these three components are comprised of an external mixture of small and large particle size 

modes. Conceptually, the large mode particles represent aged or cloud-processed aerosol, while the 

small mode particles represent relatively newly generated particles from the gas phase precursors. 

The former are more likely to be associated with high concentrations while the latter are likely to be 

at relatively low concentration. 

The geometric mean diameter and standard deviations assumed for these two size modes are 

0.5 μm and 1.5 for the large mode particles and 0.2 μm and 2.2 for the small mode particles. Mie 

theory applied to these size distributions for the three species results in dry extinction efficiencies for 

the small and large mode ammonium sulfate (2.2 m2

/g and 4.8 m2/g), ammonium nitrate (2.4 m2/g 

and 5.1 m2

/g) and organic mass (2.8 m2/g and 6.1 m2/g). Water growth terms specifically derived for 

the small and large size distribution using the upper branch of the hygroscopic growth curves for 

ammonium sulfate are applied to both the SO4

2– and nitrate PM. No water growth is assumed for 

organic PM. 

A simple empirically developed apportionment approach that was evaluated by testing the new 

algorithms estimated light scattering at the 21 IMPROVE sites that have nephelometer-measured 

light scattering data. For each sample, the fraction of the fine particle component (SO4

2–, nitrate, or 

organic mass) that is assigned to the large mode is calculated by dividing the total concentration of 

the component by 20 μg/m3

 (e.g., if the total fine particle nitrate concentration is 4 μg/m3, the large 

mode concentration is 1/5 of 4 μg/m3

 or 0.8 μg/m3, leaving 3.2 μg/m3 in the small mode). If the total 

concentration of a component exceeds 20 μg/m3

, all of it is assumed to be in the large mode. 

Figure 9-5 and Figure 9-6 are scatterplots of the estimated versus measured light scattering for 

the two algorithms. The revised algorithm has noticeably reduced bias at the upper and lower 

extremes. However, the new algorithm estimates have somewhat reduced precision (i.e., the points 

are more broadly scattered). States have adopted the new algorithm for the technical assessments that 

support their RHR State Implementation Plans, but the revised algorithm was too recently developed 

to be incorporated into any of the peer-reviewed technical literature reported on below. In general the 

differences resulting from use of the original versus the revised IMPROVE algorithm in identifying 

best and worst haze conditions and the apportionment of the various PM components are small with 

exception of coastal locations where sea salt may be a significant contributor. 

December 2009 9-14 
0

50

100

150

200

250

300

350

0 50 100 150 200 250 300 350

Measured Bsp

IMPROVE Bsp

Source: Reprinted with Permission of the Air & Waste Management Associaiton from Pitchford, et al. (2007, 098066) 

Figure 9-5. A scatter plot of the original IMPROVE algorithm estimated particle light 

scattering versus measured particle light scattering. 

0

50

100

150

200

250

300

350

0 50 100 150 200 250 300 350

Measured Bsp

Split Component Method Bsp

Source: Reprinted with Permission of the Air & Waste Management Associaiton from Pitchford, et al. (2007, 098066). 

Figure 9-6. Scatter plot of the revised algorithm estimates of light scattering versus 

measured light scattering. 

December 2009 9-15 
9.2.3.2. Spatial Patterns 

The IMPROVE network is the basis for much of what is known about particulate species 

spatial and temporal patterns for remote areas of the U.S. Though IMPROVE includes some urban 

monitoring sites, most of what is known about urban particle speciation trends is based on the EPA 

Speciation Trend Network (STN) and other similarly operated state particle speciation sites jointly 

referred to as the Chemical Speciation Network (CSN) (Jayanty, 2003, 156605). The number of 

IMPROVE network sites has increased considerably beginning in 2000, first to increase its ability to 

generate data representative of the 156 visibility-protected NPs and wilderness areas, then later as 

the states in the central U.S. requested additional remote-area monitoring to better understand their 

contributions to regional haze. The expansion of the network into the central U.S. significantly 

improved the understanding of spatial trends in a region of the country that had little speciation 

monitoring. Except as otherwise noted most of the information in this section was from the 

IMPROVE Report IV (DeBell, 2006, 156388) and displays of data that are readily generated using 

the Visibility Information Exchange Web Site (VIEWS). VIEWS, the ambient monitoring data 

system, is one of several websites (as described in Table 9-1) sponsored by the Regional Planning 

Organizations (RPO) that documents substantial, though often otherwise unpublished, technical 

information generated to support implementation of the RHR. 

Figure 9-7 shows maps of remote area light extinction estimates from PM speciation data for 

two years selected to demonstrate the additional information available due to the expansion of the 

IMPROVE network into the central U.S. The locations of monitoring sites supplying the data shown 

as color contours are shown as dot on the maps. Users of such contour maps are usually cautioned 

that the contours are only there to guide the eye to sites with similar measurements and that nothing 

should be implied about spatial patterns where there are no monitoring sites. Certainly these plots 

give proof to the wisdom of such warnings. Prior to 2001 there were no IMPROVE or any other 

remote-area aerosol speciation monitoring sites in the central states between northern Minnesota and 

Michigan to the north and Arkansas and Kentucky to the south. The lack of monitoring over such a 

large region in the center of the country hid the presence of high average regional haze over the 

midwestern U.S. Smaller scale differences are seen in the rest of the country and some of those are 

due to interannual variations as well as to better spatial resolution made possible by a more dense 

monitoring network. 

December 2009 9-16 
Table 9-1. Regional Planning Organization websites with visibility characterization and source 

attribution assessment information. 

Type of 

Information Name and Web Address RPO Information Content and Comments 

Western Regional Air Partnership 

http://www.wrapair.org/ WRAP 

Central Regional Air Planning Association 

http://www.cenrap.org/ CENRAP 

Midwest Regional Planning Organization 

http://64.27.125.175/mrpo.html MRPO 

Visibility Improvement State and Tribal 

Association of the Southeast 

http://www.vistas-sesarm.org/ 

VISTAS 

RPO Home Pages 

Mid-Atlantic/Northeast Visibility Union 

http://www.manevu.org/ 

http://www.nescaum.org/topics/regional-haze 

http://www.marama.org/visibility/ 

MANE-VU 

NESCAUM 

MARAMA 

Organizational structure, plans, projects, reports and links to other sites with 

additional information. 

MANE-VU works in close cooperation with Northeast States for Coordinated 

Air Use Management (NESCAUM) and Mid-Atlantic Regional Air 

Management Association (MARAMA) to develop the technical information for 

RHR in the Northeast. All three web sites contain unique technical support 

information. 

Visibility - Air Quality 

Monitoring Data 

Visibility Information Exchange Web Site 

http://vista.cira.colostate.edu/views/ All RPOs All IMPROVE and most other PM speciation data, RHR compatible derived 

parameters, and user-friendly tools to summarize and display data. 

Emission Inventory 

Data 

Emissions Data Management System 

http://www.wrapedms.org/default_login.asp WRAP WRAP emission inventory data warehouse and tools that provides a 

consistent approach to regional emissions tracking 

Monitoring Data 

Assessment 

Causes of Haze Assessment 

http://www.coha.dri.edu/ 

WRAP 

CENRAP 

Monitoring site-specific descriptive characterizations and maps, seasonal and 

trends analysis, air flow analysis, & receptor modeling. 

Visibility Modeling 

U. of California-Riverside Modeling Center 

http://pah.cert.ucr.edu/aqm/308/ 

http://pah.cert.ucr.edu/aqm/cenrap/index.shtml

http://pah.cert.ucr.edu/vistas/ 

WRAP 

CENRAP 

VISTAS 

Descriptions of input data, performance, and results of regional scale 

modeling (CMAQ & CAMx) & source attribution for base and future year 

regional haze. 

Integrated Information 

to Support RHR SIP 

Preparations 

Technical Support System 

http://vista.cira.colostate.edu/tss/ WRAP 

Provides access and common formats to display and summarize emissions 

inventory information, monitoring data/ assessment and regional haze 

modeling result to aid state and tribal analyst prepare RHR implementation 

plans. 

December 2009 9-17 
Source: VIEWS (http://vista.cira.colostate.edu/views/) 

Figure 9-7. IMPROVE network PM species estimated light extinction for 2000 (left) and for 

2004 (right). 

 

Source: VIEWS (http://vista.cira.colostate.edu/views/) 

Figure 9-8. Mean estimated light extinction from PM speciation measurements for the first 

(top left), second (top right), third (bottom left), and fourth (bottom right) calendar 

quarters of 2004. 

Figure 9-8 shows the seasonal pattern of PM species estimated light extinction using maps of 

mean values for each of the calendar quarter for 2004. The first quarter has the highest region of 

haze centered in the midwestern U.S.; the warmer second and third quarters have the region of 

highest haze over the Ohio River Valley; and the fourth quarter is a composite with high haze in both 

December 2009 9-18 
the Midwest and Ohio River Valley. Smaller regions of haze show up in the Columbia River Valley 

(border between Washington and Oregon) in the colder first and fourth quarters and in Southern 

California in the warmer second and third quarters. 

The IMPROVE algorithm permits each PM component contribution to light extinction to be 

separately estimated. Figure 9-9, Figure 9-10, and Figure 9-11 display the seasonal variation of the 

percent contribution to aerosol light extinction by the various component estimates. Figure 9-9

shows the contributions by SO4

2– and nitrate particulate including the haze enhancement caused by 

the absorbed water in humid conditions. As shown in Figure 9-9, a large regional pattern of high 

contribution to haze by nitrate PM is centered in the Midwest, and during the cooler months the 

nitrate PM is the dominant cause of haze in the region responsible for a third to a half of the 

particulate light extinction. Midwestern particulate nitrate is responsible for the regional pattern of 

the highest haze conditions shifting from the Ohio River Valley during summer to the Midwest in the 

winter as shown in Figure 9-8. Particulate nitrate is also a significant contributor to particulate light 

extinction year-around in parts of California, where it generally contributes 20%-40%. The Pacific 

Northwest, parts of Idaho and Utah experience large contributions to particulate light extinction by 

nitrates during the colder seasons, with contributions of 20%-30%. Figure 9-9 also shows that 

particulate SO4

2– is the predominate contributor in the eastern U.S., where it contributes 40% or 

more on average and during the summer months up to three quarters of the particulate light 

extinction over much of the East. In the western U.S. particulate SO4

2– generally contribute 20-50% 

of the particle light extinction. Regions of the lowest fractional contributions by particulate SO4

2–

and nitrate for any calendar quarter are generally in the western U.S. 

Figure 9-10 shows the contributions to haze by the carbonaceous PM components (i.e., organic 

mass and EC). They show broadly similar patterns with the greatest contributions in the western U.S. 

especially during the warmer months of the year. For the most part this spatial pattern results from 

the dominant contributions to haze by SO4

2– and nitrate PM in the eastern half of the U.S., leaving 

relatively little for other component contributions. The fractional contribution to haze by organic PM 

is generally two to five times that of EC. In absolute terms, both carbonaceous components tend to 

have two to three times higher concentrations in the eastern U.S. than in the non-coastal western 

states. 

December 2009 9-19 
Source: VIEWS (http://vista.cira.colostate.edu/views/) 

Figure 9-9. Percent contributions of ammonium nitrate (left column) and ammonium sulfate 

(right column) to particulate light extinction for each calendar quarter of 2004 

(first through fourth quarter arranged from top to bottom). Note that the contour 

intervals are not the same for the two species contributions. 

December 2009 9-20 
Source: VIEWS (http://vista.cira.colostate.edu/views/) 

Figure 9-10. Percent contributions of organic mass (left column) and EC (right column) to 

particulate light extinction for each calendar quarter of 2004 (first through fourth 

quarter arranged from top to bottom). Note that the contour intervals are not the 

same for the two species contributions. 

December 2009 9-21 
Source: VIEWS (http://vista.cira.colostate.edu/views/) 

Figure 9-11. Percent contributions of coarse mass (left column) and fine soil (right column) to 

particulate light extinction for each calendar quarter of 2004 (first through fourth 

quarter arranged from top to bottom). Note that the contour intervals are not the 

same for the two species contributions. 

December 2009 9-22 
Figure 9-11 shows the contributions to haze by coarse mass and fine soil components. As with 

the carbonaceous components, these crustal dominated components have a similar spatial pattern 

with regions of highest contribution to haze in the western U.S., and just as for the carbonaceous 

PM, the explanation for low contributions in the eastern U.S. is the dominant contributions to haze 

by SO4

2– and nitrate PM leaving relatively little for other components. The crustal components 

contribute more to haze in the arid regions of the west including the southwestern deserts. In 

absolute terms, coarse mass concentrations are as high in the rural areas of the center of the country 

(including Oklahoma, Arkansas, Kansas, Missouri, and Iowa) as they are in the Desert Southwest. 

Typically coarse mass contributions to haze exceed those of fine mass by a factor of 2-4. 

9.2.3.3. Urban and Regional Patterns 

Using a combination of IMPROVE and CSN data, it is possible to compare urban PM2.5

concentrations and composition to corresponding remote-area regional values. These are shown as 

paired color contours maps for IMPROVE and IMPROVE plus CSN (see Figure 9-12 through 

Figure 9-23). The degree of comparability of the data from these 2 networks was assessed by an 

analysis of two years of co-located IMPROVE and CSN data from 6 urban areas. The CSN organic 

mass data were adjusted for a positive sampling artifact prior to inclusion in this assessment, in a 

fashion similar to that used for the IMPROVE data set (pages 29-30, DeBell, 2006, 156388). Note 

that the contour scales are different between the two maps for each component pair of maps so that 

each contains as much information as possible using ten concentration contours. To assess the degree 

to which urban areas have higher PM component concentrations compared to regional background 

note how many contour intervals surround the urban monitoring sites. The U.S. EPA (2004, 190219) 

used the pairing of IMPROVE and CSN monitoring sites at 13 selected urban areas to separate local 

and regional contributions of three major PM2.5 components as shown in Figure 9-24. 

In Figure 9-12 and Figure 9-13, urban PM2.5 concentrations are systematically higher than 

those in the surrounding non-urban regions. The urban excess is generally much higher in the 

western U.S. than in the East (e.g., there are five contour intervals separating Salt Lake City from its 

remote regional area compared to only two for Indianapolis). This implies that eastern and western 

urban PM2.5 concentrations and resulting visibility are less different than the eastern and western 

regional concentrations and visibility. 

December 2009 9-23 
Source: Debell (2006, 156388). 

Figure 9-12. IMPROVE Mean PM2.5 mass concentration determined by summing the major 

components for the 2000-2004. 

Source: Debell (2006, 156388). 

Figure 9-13. IMPROVE and CSN (STN) mean PM2.5 mass concentration determined by 

summing the major components for 2000-2004. 

December 2009 9-24 
Source: Debell (2006, 156388). 

Figure 9-14. IMPROVE mean ammonium nitrate concentrations for 2000-2004. 

Source: Debell (2006, 156388). 

Figure 9-15. IMPROVE and CSN (STN) mean ammonium nitrate concentrations for 2000-2004. 

December 2009 9-25 
Source: Debell (2006, 156388). 

Figure 9-16. IMPROVE mean ammonium sulfate concentrations for 2000-2004. 

Source: Debell (2006, 156388). 

Figure 9-17. IMPROVE and CSN (STN) mean ammonium sulfate concentrations for 2000-2004. 

Figure 9-14, Figure 9-15, and Figure 9-24 show the PM2.5 nitrate in remote and urban areas. 

Here the western states have urban particulate nitrate concentrations that far exceed twice the remote 

December 2009 9-26 
area regional concentrations. For the Central Valley of California and Los Angeles areas, the urban 

excess of ammonium nitrate exceeds regional concentrations by 2 μg/m3

 to 12 μg/m3. In the region 

of the Midwest nitrate bulge, the urban concentrations were less than twice the regional 

concentrations for an annual urban excess of about 1 μg/m3

. Northeast and southeast of the Midwest 

nitrate bulge, annual urban particulate nitrate concentrations are several tenths to about 1 μg/m3

above the remote area regional concentrations, with warmer southern locations tending to have the 

smaller concentrations of both regional and urban excess particulate nitrate. 

As shown in Figure 9-16, Figure 9-17, and Figure 9-24, annual-averaged urban particulate 

SO4

2– concentrations are generally not much higher than the regional values, with urban excess 

generally of less than about 0.5 μg/m3. The exceptions apparent by comparing Figure 9-16 and 

Figure 9-17 are in Texas and Louisiana where urban excess particulate SO4

2– are >1 μg/m3

, perhaps 

caused by local emissions (e.g., from oil refineries). Urban contributions are a larger fraction of the 

total particulate SO4

2– concentrations in the western U.S. because the regional concentrations are 

much lower than in the East. The modest additional particulate SO4

2– concentrations associated with 

urban areas suggests that most particulate SO4

2– is regionally distributed, and that IMPROVE and 

CSN monitoring sites can be used together to enhance the ability to delineate particulate SO4

2–

spatial distributions. For example, note that the additional data from urban sites shown in Figure 

9-17 extends north and south of the distribution of the high particulate SO4

2– loading shown in 

Figure 9-16 over Tennessee and Kentucky, as well as the high loadings over southern Pennsylvania, 

eastern West Virginia and northern Virginia. (The color-contour suggested dip in concentrations 

between the two eastern particulate SO4

2– high concentrations regions may not exist in the 

atmosphere, but this cannot be verified without speciation monitoring sites in southern Ohio, the 

border of Kentucky and West Virginia and western Virginia.) 

Urban and remote area carbonaceous PM2.5 are displayed in Figure 9-18 and Figure 9-19

(organic mass), Figure 9-20 and Figure 9-21 (EC), and Figure 9-24 (total carbon = organic + EC 

concentration). Just as with particulate nitrate, both organic mass and EC concentrations are more 

than twice the remote-area background concentrations for western urban monitoring locations. One 

of the more interesting pairing of sites is for the Virgin Islands compared to the urban site at San 

Juan, Puerto Rico (see the map cutout Figure 9-18 through Figure 9-21). The San Juan urban excess 

OC is moderate, while the EC value is among the most extreme inferred in this manner. For eastern 

urban areas, approximately half the total carbon is local while the other half is regional. In eastern 

urban areas, carbonaceous and SO4

2– particulate are the two major components of PM2.5, with 

roughly equal contributions, and account for over 80% of the mass concentration. Edgerton et al. 

(2004, 156413) showed that carbonaceous PM2.5 is responsible for most of the urban excess above 

regional concentrations at four urban/rural paired Southeastern Aerosol Research and 

Characterization (SEARCH) monitoring sites in the southeastern U.S. However, the higher overall 

light extinction efficiency for SO4

2– resulting from its hydrophilicity gives it ~ 2:1 dominance in 

responsibility for eastern urban light extinction. 

Urban and remote area soil PM2.5 concentrations are displayed in Figure 9-22 and Figure 9-23. 

Urban fine soil concentrations are at most a few tenths of a μg/m3

 higher than the regional 

background concentrations and in some regions they are much less. Just as with carbonaceous PM2.5, 

the Virgin Island, San Juan, Puerto Rico pair are interesting for fine soil. In this case, both of these 

island monitoring sites have high concentrations of fine soil, which is caused by the influence of the 

trans-Atlantic transport path of dust from Africa (Prospero, 1996, 156889). 

No urban-remote pair of coarse mass concentration maps is available because CSN does not 

monitor coarse mass. In Malm et al. (2004, 156728) a map of annual mean coarse mass 

concentration is shown for 2003 which includes the values for IMPROVE urban sites, including two 

in the western U.S. with much more coarse mass than the nearby remote areas monitoring sites 

(i.e., ~24 μg/m3

 compared to ~9 μg/m3 for Phoenix, AZ, and ~6 μg/m3 compared to ~2 μg/m3 for 

Puget Sound, WA) and one eastern IMPROVE site at Washington, DC with less coarse mass than the 

surrounding remote area values (~2 μg/m3

 compared to ~4 μg/m3). 

December 2009 9-27 
Source: Debell (2006, 156388). 

Figure 9-18. IMPROVE monitored mean organic mass concentrations for 2000-2004. 

Source: Debell (2006, 156388). 

Figure 9-19. IMPROVE and CSN (STN) mean organic mass concentrations for 2000-2004. 

December 2009 9-28 
Source: Debell (2006, 156388). 

Figure 9-20. IMPROVE mean EC concentrations for 2000-2004. 

Source: Debell (2006, 156388). 

Figure 9-21. IMPROVE and CSN (STN) mean EC concentrations for 2000-2004. 

December 2009 9-29 
Source: Debell (2006, 156388). 

Figure 9-22. IMPROVE mean fine soil concentrations for 2000-2004. 

Source: Debell (2006, 156388). 

Figure 9-23. IMPROVE and CSN (STN) fine soil concentrations, 2000-2004. 

Figure 9-25 shows the remote area coarse mass concentrations as measured by the IMPROVE 

network. The pattern of high coarse mass concentrations from Oklahoma to Iowa is comparable to 

the high concentrations in the desert southwest, though as shown in Figure 9-11 it contributes a 

December 2009 9-30 
smaller relative share of the light extinction because of the higher contributions to haze by 

particulate nitrate and sulfate in this agricultural region of the country. Comparing Figure 9-22 and 

Figure 9-25 shows that the coarse mass and fine soil concentration patterns are similar for the desert 

southwest but there is a much lower fine soil to coarse mass concentration ratio for the agricultural 

center of the country, suggesting a regional difference in the size distribution of the coarse mass, 

perhaps due to differences in suspendable soil materials. 

9.2.3.4. Temporal Trends 

Visibility trend analysis requires relatively long data records to avoid having meteorologically 

driven interannual variability obscure more meaningful emissions-driven air quality trends. A 

requirement for long-term data limits the number of monitoring sites useful for trend analysis. Maps 

that show haze trends for IMPROVE sites for the 10-year period 1995-2004 for the mean of the 20% 

best and the 20% worst haze days where sites are required to have a minimum of 6 complete years of 

data during the 10-year period are shown in Figure 9-26 and Figure 9-27, respectively. The best haze 

days have improving haze at most sites (32 of 47), no trend at several sites (10 of 47) and degrading 

visibility at just one site (Great Sand Dunes, CO). The worst haze days have improving haze 

conditions at several sites (13 of 47), no trend at most sites (30 of 47), and degrading visibility at a 

few western sites (4 of 47). 

December 2009 9-31 
Source: U.S. EPA (2004, 190219) 

Figure 9-24. Regional and local contributions to annual average PM2.5 by particulate SO4

2–, 

nitrate and total carbon (i.e., organic plus EC) for select urban areas based on 

paired IMPROVE and CSN monitoring sites. 

December 2009 9-32 
Source: Debell (2006, 156388). 

Figure 9-25. IMPROVE mean coarse mass concentrations for 2000-2004. 

Eight-, ten-, and sixteen-year trends analysis conducted for the Western Regional Air 

Partnership (WRAP) as part of the Causes of Haze Assessment (http://www.wrapair.or) show that 

improving trends for the 20% best haze days for the sites in the western U.S. generally correspond to 

improving trends for all of the major components with the exception of particulate nitrate. Trends 

assessment for the worst haze days at western sites show consistent reductions in particulate SO4

2–, 

but otherwise have mixed increasing and decreasing haze component trends, many of which are not 

statistically significant. Substantial interannual and shorter term spatial and temporal wildfire 

activity variations have been shown to have a significant impact on the variability of haze in the 

western U.S. (Park et al., 2006, 190469; Spracklen et al., 2007, 190485). Edgerton et al. (2004, 

156413) showed a decreasing trend in PM2.5 of about 18% (corresponding to 1 μg/m3 to 2 μg/m3) for 

4 urban-rural paired SEARCH sites in the Southeastern U.S. corresponding to similar reductions in 

SO4

2– and carbonaceous particulate. 

December 2009 9-33 
Source: Debell (2006, 156388) 

Figure 9-26. Ten-year (1995-2004) haze trends for the mean of the 20% best annual haze 

conditions. 

December 2009 9-34 
Source: Debell (2006, 156388) 

Figure 9-27. Ten-year (1995-2004) haze trends for the mean of the 20% worst annual haze 

conditions. 

Malm et al. (2002, 156727) conducted 10-year (1988-1998) trends analyses on the 

combination of IMPROVE and CASTNET (Clean Air Status and Trends Network) (Baumgardner et 

al., 1999, 011308) particulate SO4

2– concentration datasets, which were shown to produce 

comparable SO4

2– concentrations at 23 co-located monitoring sites. Figure 9-28 shows time plots of 

80th percentile particulate SO4

2– concentrations and annual average SO2 emissions from the National 

Emissions Trends (U.S. EPA, 2000, 012211) database for four regions of the U.S. Note that the 

concentration and emissions scales on the plots are each a factor of three, so that an equal percentage 

change in particulate SO4

2– and SO2 emissions slope in any plot will have the same trend line slope. 

Each plot shows a strong correspondence between 80th percentile particulate SO4

2– and SO2

emissions trends. The western U.S. had steadily declining trends in both, for an overall decrease of 

about 15%. The northeastern U.S. had a decrease of about 27% over the 10-year period with the 

largest 1-year decrease of about 20% between 1994 and 1995 as a result of the Phase I 

implementation of the Acid Rain Program. The southwestern U.S. (Texas, New Mexico and 

Colorado) had about a 15% increase in particulate SO4

2– and SO2 emissions over the 10-year period. 

The southeastern U.S. had a declining trend for the early 1990s followed by an increasing trend for 

the later half of the decade, with a net decrease over the decade of less than 10%. Others have shown 

similar decreasing particulate SO4

2– concentration trends and a correspondence in trends between 

SO2 emissions and particulate SO4

2– concentration by region (Holland et al., 1999, 092051; 

U.S. EPA, 2004, 056905). 

Holland et al. (1999, 092051) developed and compared NOX emissions trends from 1989 to 

1995 to corresponding trends in total nitrogen concentration (defined as particulate nitrate plus 

gaseous nitric acid) for the eastern U.S. (states between Louisiana to Minnesota and further east) 

based on data from 34 rural CASTNet dry deposition monitoring sites. They found a decrease in 

nitrogen median values of about 8% associated with a decrease of 5.4% in non-biogenic NOX

emissions. Trends in haze associated with particulate SO4

2– and nitrate concentrations should 

correspond fairly well with trends in their concentration due to the simple relationship between 

concentration and light extinction at any relative humidity. However, nitrogen as defined in the 

Holland et al. (1999, 092051) trend analysis includes the nitrogen from particulate nitrate and 

gaseous nitric acid, but nitric acid does not contribute to light extinction. For situations with limited 

December 2009 9-35 
atmospheric ammonia or elevated temperatures, trends in nitrogen may be principally in nitric acid 

with no net change in nitrate light extinction. Alternately with abundant ammonia and low 

temperatures the trend in nitrogen may be principally in particulate nitrate and the nitrate component 

of haze. 

Source: Reprinted with Permission of the American Geophysical Union from Malm et al. (2002, 156727). 

Figure 9-28. Ten-year trends in the 80th percentile particulate SO4

2– concentration based on 

IMPROVE and CASTNet monitoring and net SO2 emissions from the National 

Emissions Trends (NET) data base by region of the U.S. 

Ten-year trends (1994-2003) of particulate nitrate contribution to light extinction during the 

20% worst haze conditions conducted as part of the Causes of Haze Assessment (see the link in 

Table 9-1) are shown in Figure 9-29. This indicates that haze from particulate nitrates is increasing 

across the western U.S. at a rate of several Mm-1 per year in parts of California and at a rate of 

several tenths of an Mm-1 across the Four-Corners states. A similar particulate nitrate trends map (not 

shown here) for the 20% best haze conditions that is available at the same web site shows decreasing 

particulate nitrate contribution to light extinction at nearly all of the western monitoring sites. While 

statistically significant, these trends for both the 20% worst and 20% best haze periods are 

influenced by an unexplained nationwide period of depressed nitrate concentrations measured by the 

IMPROVE network during a 4-yr period from the winter of 1996-1997 through the winter of 

2000-2001. Extensive examinations of plausible monitoring methodological explanations have failed 

December 2009 9-36 
to offer any evidence that the data are invalid (McDade, 2004, 192075), but no satisfactory 

atmospheric or emissions-related explanation has been offered to account for this 4-yr depression of 

nitrate. Similar analyses of particulate nitrate haze trends are not available for the rest of the country. 

Maps of remote-area 8-, 10-, and 16-yr trends for carbonaceous and crustal PM species based 

on IMPROVE monitoring are available for the western U.S. conducted as part of the Causes of Haze 

Assessment. Generally these show a broad range of results (i.e., a mixture of statistically significant 

upward or downward trends and insignificant trends often with neighboring sites having opposing 

trends) that vary considerably depending on the number of years selected (i.e., 8, 10, or 16) and 

whether trends are for the best, worst, or middle of the haze distribution data. The scatter in these 

results is undoubtedly due to the high interannual variability and varying locations of wildfire and 

wind-suspended dust emissions that dominate the remote-area concentrations of the carbonaceous 

and crustal PM species in the western U.S. 

Source: http://www.coha.dri.edu/ 

Figure 9-29. Map of 10-yr trends (1994-2003) in haze by particulate nitrate contribution to haze 

for the worst 20% annual haze periods. The orientation, size and color of the 

arrows indicate the direction, magnitude and statistical level of significance of 

the trends. These consistent upward trends may be a misleading result due to an 

unexplained sampling issue (see text for additional information). 

9.2.3.5. Causes of Haze 

In order to attribute haze to emissions from individual sources, source types, or source regions, 

generally, any of a number of receptor and air quality simulation modeling approaches are applied. 

When using multiple approaches, the results of each are reconciled using a weight-of-evidence 

methodology. Commonly this methodology has been applied to the extensive datasets generated by 

special studies designed to estimate source-receptor relationships for a few receptor locations or for 

individual emission sources (Pitchford et al., 1999, 156873; Pitchford et al., 2005, 156874; Schichtel 

December 2009 9-37 
et al., 2005, 156957). More recently the Regional Planning Organizations (RPOs) have sponsored 

extensive regional haze source attribution assessments using weight-of-evidence methodologies to 

reconcile attribution results for virtually all of the remote-area IMPROVE sites to support the 

development of State Implementation Plans for the RHR. Additionally, a number of recent urban 

special studies, including those sponsored by EPA PM Supersites program (Solomon and Hopke, 

2008, 156997), have addressed the causes of and sources contributing to urban excess PM 

concentrations above region concentrations. Attribution results uncertainties are generally larger than 

those of the measurements upon which they are based because they also include model uncertainties 

and assumptions, as well as issues of representativeness (e.g., How well does the data used in the 

analysis represent the typical emissions, air quality and meteorological conditions of interest?). As 

such it is advisable to treat the attribution results reported below as semi-quantitative. 

The relative importance of the PM species that contribute to haze varies by region of the U.S. 

and time of year as shown in Figure 9-9, Figure 9-10, and Figure 9-11, above. Generally haze in the 

western half of the U.S. is not dominated by any one or two PM species. In the eastern half of the 

U.S., SO4

2–, especially during summer, and nitrate during the winter in the Midwest are the dominant 

haze species. As described above, urban haze can be viewed as a composite of the regional and local 

contributions where local contributions seem to be dominated by carbonaceous and, to a lesser 

extent, nitrate and crustal PM components. There have been far fewer urban investigations that 

explicitly consider visibility impacts, though there are numerous studies of urban PM source 

attribution. The order of discussion below on the cause of haze is by region beginning in the western 

U.S. and proceeding to the east, analogous to dominant air flow patterns across the lower 48 states 

and will include information from urban studies along side those of remote-area haze investigations. 

Based on modeling of an episode (September 23-25, 1996) in the California South Coast Air 

Basin (SCAB) and another episode (January 4-6, 1996) in the San Joaquin Valley (SJV) by Ying and 

Kleeman (2006, 098359), about 80% of the particulate SO4

2– for both regions is from upwind 

sources (i.e., likely from offshore sources including marine shipping, long-range transport and 

natural marine sources), with most of the remaining is associated with diesel and high-sulfur fuel 

combustion. Kleeman et al. (1999, 011286), using a combination of measurements and modeling, 

showed that the upwind particulate SO4

2– source region for the SCAB was over the Pacific Ocean 

(confirmed by measurements on Santa Catalina Island) and that these particles subsequently grew 

with accumulation of additional secondary aerosol material, principally ammonium nitrate as they 

traversed the SCAB. The majority of the nitric acid that forms particulate nitrate in the SCAB is 

from diesel and gasoline combustion (~63%), while much of the ammonia is from agricultural 

sources (~40%), catalyst equipped gasoline combustion (~16%), and upwind sources (~18%). The 

majority of the OC found in SCAB was attributed in this study to primary emissions by 

transportation-related sources, including diesel (~13%) and gasoline (~44%) engines and paved road 

dust (~12%). At the Fullerton site in the middle of the SCAB the concentration of locally generated 

organics is roughly double that of the locally generated nitrates (~5.6 μg/m3

 compared to 

~2.4 μg/m3

), while at Riverside on the east edge of the SCAB and near the large agricultural sources 

of ammonia emissions, the particulate nitrate concentrations are nearly double that of organic PM 

(~17 μg/m3 compared to ~10 μg/m3). 

Ying and Kleeman (2006, 098359) showed that during the winter 1996 episode in the SJV 

most of the nitric acid that forms particulate nitrate is from upwind sources (~57%) with diesel and 

gasoline combustion contributing most of the rest (30%), while much of the ammonia is from 

upwind sources (~39%) and a combination of area, soil and fertilizer sources (~52%). In an 

assessment of PM particle size and composition in the SJV during the winter of 2000-2001, Herner 

et al. (2006, 135981) showed that fresh emissions of carbonaceous PM from combustion sources in 

urban locations (Sacramento, Modesto, and Bakersfield, CA) move quickly from ultrafine particle 

size (i.e., diameter ~0.1 μm) to accumulation mode by condensation with accumulation mode 

(i.e., diameter ~0.5 μm) particles, and that secondary nitrate particle formation occurs preferentially 

on the surface of hydrated ammonium sulfate particles during the afternoon when gas-phase nitric 

acid is at peak photo-chemical production from NOX. Given the abundance of ammonia emissions 

and low ambient temperatures, particulate nitrate production in this way is only limited by the 

availability of nitric acid. Due to the cool winter conditions there was little SOA production during 

this study. Sea salt was shown to dominate the larger coarse particle mode during on-shore wind at 

the background coastal monitoring site at Bodega Bay, north of San Francisco, CA. 

Using a regression analysis to find the dependence of particulate SO4

2– concentration 

measured over a 3-year period (2000-2002) at 84 western IMPROVE monitoring sites on the 

December 2009 9-38 
modeled transport trajectories to the sites for each sample period, Xu et al. (2006, 102706) were able 

to infer the source regions that supplied particulate SO4

2– in the western U.S. Among the source 

regions included in this analysis was the near coastal Pacific Ocean (i.e., a 300-km zone off the coast 

of California, Oregon, and Washington). Up to half of the particulate SO4

2– measured at Southern 

California monitoring sites was associated with this source region. As shown in Figure 9-30 the zone 

of impact from this source region included large regions of California, Arizona, and Nevada. The 

authors made the case that high sulfur content fuel used in marine shipping and port emissions may 

be largely responsible. As a result, the WRAP RPO emissions inventory was modified to include 

marine shipping and a Pacific Offshore source region was added to source attribution by air quality 

simulation modeling. 

The SO4

2– attribution results of the WRAP air quality modeling (results available on the 

Technical Support System (TSS) website, see Table 9-1 for the web-link) credit the Pacific offshore 

source region with somewhat smaller contributions than those from the trajectory regression work by 

Xu et al. (2006, 102706) with concentrations at the peak impact site in California that are about 45% 

compared to 50% by regressions and even greater differences for more distant monitoring sites. 

Based on the modeling attribution the Pacific offshore source region was responsible for 10-20% of 

the nitrate measured in Southern California. 

Source: Reprinted with Permission of Atmospheric Environment from Xu et al. (2006, 102706). 

Figure 9-30. Contributions of the Pacific Coast area to the ammonium sulfate (μg/m3) at 84 

remote-area monitoring sites in western U.S. based on trajectory regression for 

all sample periods from 2000-2002 (dots denote locations of the IMPROVE 

aerosol monitoring sites). 

A coordinated effort by federal, state, and county air quality organizations to determine the 

causes of haze in the Columbia River Gorge (a deep and narrow gap in the Cascade Mountains on 

the Washington/Oregon border) through extensive multiyear measurements and high spatial 

resolution air quality modeling of typical episodes demonstrated the multitude of emission sources 

that contribute to its impairment (Pitchford et al., 2007, 098066). During the summer, gorge winds 

are generally from the west and relatively dry. More than half of the haze during a typical summer 

episode is from a combination of international and other distant sources (~22% at the western end of 

the gorge) plus regional natural sources including wildfire and secondary organic PM from biogenic 

emissions (~39% at the eastern end of the gorge). The Portland/Vancouver metropolitan area was 

responsible for a significant amount of the haze during the summer (~20% on in the western end of 

December 2009 9-39 
the gorge), while sources within the gorge were responsible for a moderate amount of haze (~6% and 

~9% at the western and eastern ends of the gorge). The wind is much more often from the east 

during the winter. The highest haze conditions in the gorge are during the winter and are associated 

with fog conditions that rapidly convert precursor gaseous emissions of NOX and SO2 from local and 

regional combustion sources and NH4 from local and regional agricultural activities to secondary 

nitrate and SO4

2– PM that persist as a post-fog intense haze. Contributions by these sources east of 

the gorge contribute ~57% of the haze on the eastern end of the gorge, with half of the nitrate and 

SO4

2– particulate from electric utility emissions and most of the rest from transportation sources. 

Other sources contributing during the winter haze at the eastern end of the gorge are from sources 

outside the modeling domain (i.e., most of Washington and Oregon) and within the gorge (~23% and 

~10%, respectively). 

An assessment of concurrent measurements at the nearby Mt. Hood IMPROVE monitoring 

site (45 km south of the Columbia River at 1,531 m ASL) shows that Columbia River Gorge haze 

conditions and especially the wintertime high nitrate/SO4

2– contributions to haze are not typical of 

the generally higher elevation remote areas of the region (Pitchford et al., 2007, 098066). However 

Gorge-like high wintertime nitrate and SO4

2– are found at the Hells Canyon IMPROVE site, which is 

similarly situated in a narrow canyon of the Snake River almost 400 km east of the Gorge (from the 

VIEWS web site, see Table 9-1), implying that there may be a substantial vertical concentration 

gradient during winter in this complex terrain. 

Several example monitoring locations distributed across the northern and southern portions of 

the western U.S. have been selected to illustrate the attribution results from the WRAP-sponsored 

attribution analysis tools that estimate the relative responsibility for haze of the various PM species 

by source region and source type. The selected sites include Olympic NP, WA; Yellowstone NP, WY; 

and Badlands NP, SD across the north, and San Gorgonio Wilderness (W), CA; Grand Canyon NP, 

AZ and Salt Creek W, NM across the south as shown in Figure 9-31. 

December 2009 9-40 
Source: http://vista.cira.colostate.edu/airdata/linkbrowser/LinkBrowserNormal.aspx 

Figure 9-31. Shows the IMPROVE monitoring sites in the WRAP region with at least three 

years of valid data and identifies the six sites selected to demonstrate the 

apportionment tools. Pie diagrams show the composition for the mean of the 

20% worst haze conditions and the mean light extinction (Mm-1) by the size of the 

circle (see figure key). 

WRAP-sponsored CMAQ modeling for 2002 used virtual tracers of SO2 and NOX emissions 

that tracked the source region and category through the transport and transformation processes to 

particulate SO4

2– and nitrate. This was used to produce pie diagrams of particulate SO42– and nitrate 

attribution results by source region for each of these sites as shown in Figure 9-32 (produced using 

the TSS, see Table 9-1). Based on these sites, over half of the particulate SO4

2– in remote areas of the 

Pacific coastal states is from outside of the U.S. (Pacific offshore and outside of the domain). The 

outside of the domain values were derived by simulating the fate of the boundary condition 

concentrations, which for the WRAP air quality modeling were obtained using output from the 

GEOS-CHEM global air quality model (Fiore et al., 2003, 047805). The SO4

2– fraction from the 

region labeled outside of domain was approximately uniform throughout the western U.S. with 

site-to-site variation in the fraction caused mostly by variations in total SO4

2– concentration. The 

more northerly sites have impacts from Canadian emissions, while the southern sites have impacts 

from Mexican emissions. Half of the Salt Creek, New Mexico SO4

2– is from the domestic source 

emissions further to the east, which also contribute about 20% to Badlands particulate SO4

2–

concentrations. A breakout of the emission sources from within the WRAP region by source type 

(not shown) has most of the emissions from point sources, with the combination of motor vehicle, 

December 2009 9-41 
area and wildfire emissions contributing from a few percent at the furthest eastern sites to about half 

at San Gorgonio. 

By comparison, the particulate nitrate is much more from domestic regional emission sources, 

with ~60-80% being from emissions within the WRAP region. For the west coast sites about 25% of 

the nitrate is from a combination of Pacific offshore emissions (i.e., marine shipping) and outside 

domain regions. Canadian emissions are responsible for about 10-30% of the particulate nitrate for 

the three northern sites, but Mexican emissions do not contribute appreciably to particulate nitrate 

for the three southern sites. Motor vehicles are the largest contributing NOX source category 

responsible for particulate nitrate for these six WRAP sites, with a combination of point, area and 

wildfire source categories also contributing from about 10-50% of the WRAP regional emissions. 

WRAP only used the virtual tracer approach to investigate source locations and categories for 

SO2 and NOX emissions. A different type of virtual tracer modeling tool was used to track the 

various OC compounds and sort them into three groups for 2002. The first group labeled primary 

organics includes all of the organics that are emitted directly as PM from any source type and 

location. The second group labeled anthropogenic secondary organics is PM produced in the 

atmosphere by aromatic VOCs. The third category labeled biogenic secondary organics is PM 

produced in the atmosphere by biogenic VOCs. Organic PM in the biogenic secondary category 

includes those that would functionally be considered man-made emissions (e.g., those from 

agricultural crops and urban landscapes), though in most remote areas of the west these man-made 

VOC emissions are small compared to those of the natural biogenic sources. Figure 9-33, Figure 

9-34, and Figure 9-35 show the monthly averaged apportionment of organic PM for the 6 selected 

monitoring locations. 

December 2009 9-42 
Figure 9-32. Particulate SO4

2– (a) and nitrate (b) source attribution by region using CAMx 

modeling for six western remote area monitoring sites : top left to right Olympic 

NP, WA; Yellowstone NP, WY; Badlands NP, SD; bottom left to right San Gorgonio 

W, CA; Grand Canyon NP, AZ; and Salt Creek W, NM. WRAP includes ND, SD, WY, 

CO, NM and all states further west. CENRAP includes all states east of WRAP and 

west of the Mississippi River including MN. Eastern U.S. includes all states east 

of CENRAP. The Pacific Offshore extends 300km to the west of CA, OR, and WA. 

Outside Domain refers to the modeling domain, which extend hundreds of 

kilometers into the Pacific and Atlantic Oceans and from Hudson Bay Canada to 

just north of Mexico City. This figure was assembled from site-specific diagrams 

produced on the TSS web site (see Table 9-1) for 2002. 

December 2009 9-43 
Source: From the TSS website, see Table 9-1. 

Figure 9-33. Monthly averaged model predicted organic mass concentration apportioned into 

primary PM and anthropogenic and biogenic secondary PM categories for the 

Olympic NP (top) and San Gorgonio W (bottom) monitoring sites. 

Based on the modeling results for these six sites and confirmed by measurements (see, e.g., 

Figure 9-10), a west-to-east decreasing gradient of organic mass exists with annual concentrations 

from ~2 μg/m3

 for the coastal state sites to ~1 μg/m3 for the intermountain west sites, to <1 μg/m3 for 

the sites just east of the Rocky Mountains, discounting the large fire impacts for July at Yellowstone 

NP which raised its annual mean to ~2 μg/m3

. At all of these remote-area sites anthropogenic 

secondary PM is estimated to be a small fraction of the organic mass, with the largest fractional 

contribution at the San Gorgonio monitoring site immediately downwind of the major Southern 

California urban areas, yet having <10% of the monthly mean organic mass from anthropogenic 

secondary PM. Of the six selected monitoring sites, San Gorgonio has the highest fraction of the 

organic PM from primary emissions (~57%), followed by Yellowstone (~55%), then the two 

eastern-most sites (Badlands ~42% and Salt Creek 41%), and with Grand Canyon and Olympic NPs 

the lowest fraction by primary emissions (~37%). Yellowstone NP would have had the lowest 

fraction of organic PM by primary emissions had it not been for the month of July (the 11-mo mean 

was 29%) when wild fire smoke contributed. Results from recent chamber and field studies, and 

modeling would seem to call into question apportionment of primary and secondary carbon done by 

traditional air quality model simulations of OC, as described above, due to the combined effects of 

December 2009 9-44 
extensive evaporation of semivolatile primary emissions when diluted and to photochemical 

reactions of low volatility gas phase species that substantially increases the amount of secondary 

organic PM (Robinson et al., 2007, 156053). 

Source: From the TSS website, see Table 9-1. 

Figure 9-34. Monthly averaged model predicted organic mass concentration apportioned into 

primary PM and anthropogenic and biogenic secondary PM categories for the 

Yellowstone NP (top) and Grand Canyon (Hopi Point) (bottom) monitoring sites. 

December 2009 9-45 
Source: From the TSS website, see Table 9-1. 

Figure 9-35. Monthly averaged model predicted organic mass concentration apportioned into 

primary PM and anthropogenic and biogenic secondary PM categories for the 

Badland NP (top) and Salt Creek W (bottom) monitoring sites. 

Radiocarbon (14C) dating techniques were used to group ambient PM carbon into fossil and 

contemporary source categories at 12 IMPROVE monitoring sites across the U.S., 8 of which are in 

the WRAP region (Schichtel et al., 2008, 156958). Results of this work showed that contemporary 

carbon accounts for about half the carbon in urban areas, 70-97% in near-urban areas (i.e., San 

Gorgonio) and 82-100% in remote areas. Comparing these radiocarbon dating results with the 

WRAP virtual tracer modeling results for organic aerosol (above), and presuming that the modeled 

anthropogenic secondary organic is fossil carbon and the biogenic secondary is contemporary 

carbon, suggests that a large fraction of the model-determined regional primary organic PM is from 

contemporary carbon sources (e.g., smoke from wildfires). 

Schichtel et al. (2008, 156958) compared radiocarbon measurements at two sets of urban/rural 

paired sites in the west (Mount Rainer/Seattle, and Tonto/Phoenix). Figure 9-36 shows that most of 

total carbon urban excess (i.e., urban site concentration minus the regional site concentration) in the 

summer is from fossil carbon sources (87% and 79%, respectively), while in the winter there is a 

surprisingly high fraction of the urban excess at both sites that is from contemporary carbon sources 

(41% and 47%, respectively). This implies that urban, and therefore anthropogenic, activities 

December 2009 9-46 
generate almost as much PM2.5 carbon from contemporary sources (e.g., residential wood 

combustion) as from the fossil sources during the winter for these two western urban areas. 

Source: Reprinted with Permission of the American Geophysical Union from Schichtel et al. (2008, 156958). 

Figure 9-36. Comparison of carbon concentrations between Seattle (Puget Sound site) and Mt. 

Rainer (left) and between Phoenix and Tonto (right) showing the background site 

concentration (gray) and the urban excess concentration (black) for total, fossil 

and contemporary carbon during the summer and winter studies. 

December 2009 9-47 
Figure 9-37. Average contemporary fraction of PM2.5 carbon for the summer (top) and winter 

(bottom), estimated from IMPROVE monitoring data (June 2004-February 2006) 

based on EC/TC ratios. The contemporary values from radiocarbon dating for the 

12 monitoring sites are indicated in by colored circles with the site names. Color 

contours are shown to aid in showing sites with similar values. Site locations are 

indicated by circles for remote area sites and triangles for urban sites. 

December 2009 9-48 
Contemporary carbon estimates for all of the IMPROVE network monitoring sites for data 

from two summer seasons (June, July and August, 2004/2005) and two winter seasons (December, 

2004/2005, January and February, 2005/2006) were calculated from the measured EC to total carbon 

(EC/TC) ratios using the 12-site empirical relationship between radiocarbon determined 

contemporary carbon fraction and IMPROVE measured EC/TC ratio (Schichtel et al., 2008, 

156958). The results are displayed in color contour maps in Figure 9-37, which also shows the 

radiocarbon determined contemporary carbon for the 12 sites. The lowest contemporary carbon 

estimates (<60%) in both seasons are for urban areas. In the rural West, most of the sites have over 

90% of their PM carbon from contemporary carbon sources during the summer and from 60% to 

over 90% during the winter. In the rural East, most of the sites have 45-90% of their PM carbon from 

contemporary carbon sources during the summer and from 60% to over 90% in the winter. 

Schichtel et al. (2008, 156958) showed a strong relationship between the site-averaged EC/TC 

ratios and the site-averaged fraction of fossil carbon separately for the summer and winter data sets 

(i.e., R2 of 0.71 and 0.87, respectively). Using regression analysis they estimated that the summer 

and winter EC/TC ratios associated with purely fossil carbon were 0.35 ± 0.039 and 0.46 ± 0.028, 

respectively, and for purely contemporary carbon the EC/TC ratios were 0.12 ± 0.011 and 

0.19 ± 0.0095. These ratios are shown to be consistent with corresponding ratios from the literature 

for source testing primary fossil and contemporary combustion sources respectively. They are also 

shown to be consistent with the 90 percentile value of the EC/TC ratio from the urban IMPROVE 

monitoring sites (0.41 and 0.44 for summer and winter) and the 10th percentile values of the EC/TC 

ratio for remote areas IMPROVE monitoring sites (0.07 and 0.16 for summer and winter), which 

they argue are dominated by fossil and contemporary carbon, respectively. 

The largest sources of contemporary carbon are primary emissions from biomass burning and 

SOA from biogenic precursor gases (e.g., terpenes from conifer forests). Schichtel et al. (2008, 

156958) estimated the 12-site overall contribution by secondary organic PM to the summer 

contemporary carbon fraction as 36 ± 6.4% by assuming the EC/TC ratio for contemporary carbon 

during the winter represented the ratio of primary emissions only (i.e., no secondary organic PM 

formation in the winter) and that the EC/TC ratio for primary emissions is independent of seasons. 

This approach should provide a lower bound estimate of the secondary OC species. The same 

method applied to the fossil carbon fraction yielded an estimate of 23 ± 10% of the fossil carbon PM 

from secondary organic formation in the atmosphere during the summer. These estimates correspond 

to over 40% of the contemporary and over 35% of the fossil OC being from secondary PM 

formation. 

December 2009 9-49 
Source: From the TSS website (see Table 9-1) 

Figure 9-38. Results of the weighted emissions potential tool applied to primary OC emissions 

(top) and EC emissions (bottom) for the baseline and projected 2018 emissions 

inventories for Olympic NP. Only source regions (WRAP states and other 

regions) with the largest estimated contributions are shown (i.e., Canada, 

Oregon, Pacific Off-Shore, and Washington from left to right). The scale is 

normalized (i.e., unitless) one over distance weighted emissions multiplied by 

trajectory residence time. 

December 2009 9-50 
Source: From the TSS website (see Table 9-1). 

Figure 9-39. Results of the weighted emissions potential tool applied to primary OC emissions 

(top) and EC emissions (bottom) for the baseline and projected 2018 emissions 

inventories for San Gorgonio W. Only source regions (WRAP states and other 

regions) with the largest estimated contributions are shown (i.e., California and 

Pacific Off-Shore from left to right). The scale is normalized (i.e., unitless) one 

over distance weighted emissions multiplied by trajectory residence time. 

December 2009 9-51 
WRAP applied a weighted emissions potential analysis tool that combined gridded emissions data with back-trajectory analysis that simulated the transport pathway to the 

various monitoring sites to infer likely source region and emission categories for the 20% best and 20% worst haze conditions for each of the IMPROVE PM speciation monitoring 

locations in the West. Unlike the virtual tracer approach that uses a full regional air quality simulation model, this method does not explicitly account for chemistry or removal 

processes and it does not incorporate the sophisticated dispersion estimates (i.e., it uses one over distance weighting for dispersion), so it should be considered a screening tool 

that has been found to be helpful in identifying the likely sources contributing to haze. More information on this approach is available elsewhere (see the link to the TSS in Table 

9-1). Primary OC and EC PM species results from the weighted emissions potential tool for the worst 20% haze days using the 2000-2004 base years’ emissions and trajectories, 

and the same trajectories with 2018-projected emissions for each of the 6 selected western monitoring locations are shown in Source: From the TSS website (see Table 9-1) 

Figure 9-38 through Figure 9-43. For Olympic NP (Source: From the TSS website (see Table 9-1) 

Figure 9-38), most of the primary OC as well as most of the EC PM is likely to be from the 

state of Washington during the worst haze days. This is because the multi-day trajectories that 

transport emissions on its worst days tend to be short (within 200 km based on maps available on 

TSS, see Table 9-1). Area sources, which include emissions from residential wood heating, 

watercraft, non-mobile urban and other sources too small to be labeled as point sources, are the big 

contributors to primary organic, while on- and off-road mobile emissions plus area sources are large 

contributors to the EC at Olympic NP. The 2018 projected growth in area sources and decrease in 

emissions of mobile source emissions is anticipated to increase the haze by primary OC while 

reducing the haze by EC at Olympic NP The same analysis applied to San Gorgonio (Figure 9-39), is 

similar in that the majority of the emissions with the potential to contribute to primary OC and EC 

PM is from the home state, California in this case. However, the likely importance of natural fire 

emissions for carbonaceous PM species sites is substantially greater at San Gorgonio W. than it was 

for Olympic NP. 

The weighted emissions potential results applied to Yellowstone NP and Grand Canyon NP 

(Figure 9-40 and Figure 9-41) show the likely dominance of natural fire emissions in the 

intermountain western U.S. to primary OC and EC PM during worst haze conditions for these two 

locations. Numerous states have emissions that have the potential to contribute noticeably to these 

carbonaceous species, due to relatively long multi-day trajectories (500-1,000 km) on worst haze 

days, though for both sites the home state has the greatest potential based on this inverse distance 

weighting approach. On- and off-road mobile sources in Arizona and California have significant 

potential to contribute to Grand Canyon carbonaceous particles, especially EC concentrations, 

probably due to some of the trajectories being over the populated areas of these two states to the 

south and southwest of Grand Canyon. 

December 2009 9-52 
Source: From the TSS website (see Table 9-1). 

Figure 9-40. Results of the weighted emissions potential tool applied to primary OC emissions 

(top) and EC emissions (bottom) for the baseline and projected 2018 emissions 

inventories for Yellowstone NP. Only source regions (WRAP states and other 

regions) with the largest estimated contributions are shown (i.e., California, 

Idaho, Montana, Oregon, Utah, Washington, and Wyoming from left to right). The 

scale is normalized (i.e., unitless) one over distance weighted emissions 

multiplied by trajectory residence time. 

December 2009 9-53 
Source: From the TSS website (see Table 9-1). 

Figure 9-41. Results of the weighted emissions potential tool applied to primary OC emissions 

(top) and EC emissions (bottom) for the baseline and projected 2018 emissions 

inventories for Grand Canyon NP. Only source regions (WRAP states and other 

regions) with the largest estimated contributions are shown (i.e., Arizona, 

California, Mexico, New Mexico, Nevada, Oregon, Pacific Off-shore and Utah from 

left to right). The scale is normalized (i.e., unitless) one over distance weighted 

emissions multiplied by trajectory residence time. 

December 2009 9-54 
Source: From the TSS website (see Table 9-1). 

Figure 9-42. Results of the weighted emissions potential tool applied to primary OC emissions 

(top) and EC emissions (bottom) for the baseline and projected 2018 emissions 

inventories for Badlands NP. Only source regions (WRAP states and other 

regions) with the largest estimated contributions are shown (i.e., Arizona, 

California, Canada, CenRAP, Colorado, eastern U.S., Idaho, Montana, North 

Dakota, Nevada, Oregon, South Dakota, Utah, Washington, and Wyoming from 

left to right). The scale is normalized (i.e., unitless) one over distance weighted 

emissions multiplied by trajectory residence time. 

December 2009 9-55 
Source: From the TSS website (see Table 9-1). 

Figure 9-43. Results of the weighted emissions potential tool applied to primary OC emissions 

(top) and EC emissions (bottom) for the baseline and projected 2018 emissions 

inventories for Salt Creek W. Only source regions (WRAP states and other 

regions) with the largest estimated contributions are shown (i.e., Arizona, 

California, CenRAP, Colorado, eastern U.S., Idaho, Mexico, Montana, New Mexico, 

Nevada, Oregon, Utah, and Wyoming from left to right). The scale is normalized 

(i.e., unitless) one over distance weighted emissions multiplied by trajectory 

residence time. 

December 2009 9-56 
For the most easterly of the selected WRAP sites, Badlands NP and Salt Creek, the weighted 

emissions potential results for primary OC and EC (Figure 9-42 and Figure 9-43) show potential 

contributions from a greater number of states and multi-state regions than for selected sites further to 

the west. This may be due in part to trajectories associated with worst haze conditions for these two 

sites being moderately long (~500 km) and in multiple directions. Natural fire emissions have the 

greatest potential to contribute to organic species PM at Badlands NP, but are less likely to be 

dominant at Salt Creek or at either site in its contribution to EC PM concentrations. The 

contributions by emissions from area and mobile sources from the home states and states to the east 

(Central States Regional Air Partnership states are labeled “CEN” in the figures) are potentially 

greater than by natural fire; this is especially true for contributions to EC PM. 

WRAP applied the weighted emissions potential tool to assess likely source types and regions 

contributing to coarse mass concentrations. The results for the six selected monitoring sites (not 

shown) are as follows. Most dust at Olympic NP is likely to be from fugitive dust sources in 

Washington state, while at San Gorgonio it is likely more from road dust with smaller amounts from 

fugitive dust sources. The amount from wind-blown dust is small for both of these far westerly sites. 

Wind-blown dust is likely the largest source contributing to coarse mass at Grand Canyon NP, 

Badlands NP and Salt Creek W with most of it originating in the home-state for those sites. The 

weighted emissions potential results for coarse mass at Yellowstone are different from those of the 

other five selected sites in that Idaho and Montana each have a higher potential to contribute to 

coarse mass on the worst haze days than the home state (Wyoming), and that wind-blown and road 

dust both contribute substantially as does fugitive dust and natural fire. 

In another WRAP-sponsored effort to better understand the causes of remote area haze in the 

western U.S., each of the worst haze days for all western IMPROVE monitoring sites where dust 

(defined as the sum of coarse mass and fine soil PM) was the largest contributor to light extinction 

was separately assessed to categorize the most likely dust source (Kavouras et al., 2007, 156630; 

Kavouras et al., 2009, 191976) and the Causes of Haze Website – see Table 9-1). Elemental 

composition was used to assess the likelihood that the dust was associated with long-range transport 

from Asia. A regression analysis at each site between dust concentrations and coincident local wind 

speed was used to generate site-specific estimates of local windblown dust for each sample period. 

Finally, back trajectory analysis combined with maps constructed of wind erosion potential 

(i.e., developed by combining soil types and land cover classifications) are used in a manner similar 

to the weighted emissions potential analysis to identify the likelihood of regionally transported 

wind-blown dust as the source. These assessments were conducted on each of the 610 so-called 

“worst dust haze days” at 70 monitoring sites for data from 2001-2003 to classify each day by its 

likely contributions from Asian dust, local windblown dust, upwind transport and undetermined. The 

undetermined category includes those sample periods that failed to be classified into one of the other 

three source categories suggesting that mechanically suspended dust activities (e.g., unpaved road 

dust, agricultural, construction and mining activities) may be responsible. 

Of the 610 “worst dust haze days” at the 70 WRAP monitoring sites, 55 sample periods are 

classified as Asian dust influenced, almost exclusively in the spring; 201 sample period are classified 

as local windblown dust, mostly in the spring but some in all seasons; 240 sample periods are 

classified as upwind transported dust, with a broader seasonal distribution centered on summer and 

few instances during winter; and 114 are in the undetermined category with most in summer and 

least in winter. Most dust days occurred in the deserts of Arizona, New Mexico, Colorado, western 

Texas and southern California, and these were dominated by local and regionally transported 

wind-blown dust (e.g., 84% for Salt Creek W). Asian dust caused only a few of the worst dust days 

during the 3-year assessment period, though it is an important source (i.e., 10-40% of the worst dust 

days) for sites in the more northern regions of the West with greater vegetative land-cover where 

local and regionally transported wind-blown dust was infrequent. The frequency of worst dust events 

classified as undetermined was greatest for sites in the vicinity of large urban and agricultural areas 

such as those in California and southern Arizona. 

December 2009 9-57 
Source: Reprinted with Permission of the Air & Waste Management Associaiton from Pitchford et al. (2005, 156874) 

Figure 9-44. BRAVO study haze contributions for Big Bend NP, TX during a 4-mo period in 

1999. Shown are impacts by various particulate SO4

2– sources, as well as the 

total light extinction (black line) and Rayleigh or clear air light scattering. 

December 2009 9-58 
Figure 9-45. Maps of spatial patterns for average annual particulate nitrate measurements 

(top), and for ammonia emissions for April 2002 from the WRAP emissions 

inventory (bottom). 

December 2009 9-59 
Figure 9-46. Maps of spatial patterns of annual NO (left) and NO2 (right) emissions for 2002 

from the WRAP emissions inventory. 

Source attribution of the particulate SO4

2– contribution to haze at Big Bend NP, TX was a 

primary motivation for the BRAVO study. Schichtel et al. (2005, 156957) showed that during the 

four-month field monitoring study (July-October 1999), SO2 emissions sources in the U.S. and 

Mexico were responsible for ~55% and ~38% of the particulate SO4

2–, respectively. Among U.S. 

source regions, Texas was responsible for ~16%, eastern U.S. ~30%, and the western U.S. ~9%. A 

large coal fired power plant, the Carbón facility in Mexico, just south of Eagle Pass, TX, was 

responsible for ~19%, making it the largest single contributor. Pitchford et al. (2005, 156874) put 

these results into the context of other component contributions to regional haze, plus seasonal and 

longer-term variations in haze by particulate components. Figure 9-44 shows the temporal variation 

of the contributions by the various SO2 emissions source regions plus the Carbón facility during the 

BRAVO study period. The largest particulate SO4

2– peak haze periods are dominated by infrequent 

large contribution by emission sources in TX and the eastern U.S., while Mexican sources including 

the Carbón facility are more frequent contributors to haze, but at generally lower light extinction 

values. Particulate nitrate contributions to haze at Big Bend NP are among the lowest measured in 

the U.S. (~3% of light extinction on average and for worst haze episodes). 

Nitrate concentrations are a significant contributor to light extinction further to the north of 

Texas in the center of the country. While SO4

2– can be in particulate form though not fully 

neutralized by ammonia, nitric acid from NOX emissions requires neutralization by ammonium to 

become particulate ammonium nitrate. One way to explore the causes of the Midwest nitrate bulge is 

to compare its spatial distribution with the spatial distributions of NOX and ammonia emissions. 

Figure 9-45 shows a map of the annual average particulate nitrate concentrations (top) with a map of 

ammonia emissions directly below it. Animal agriculture is responsible for most of the ammonia 

emissions in the Midwest. The striking similarity between the ambient particulate nitrate 

concentration and the ammonia emissions spatial patterns with regional maximum centered on Iowa 

is in contrast to the NOX (i.e., NO + NO2) emissions spatial patterns, shown in Figure 9-46. NOX

emissions are high over a broad region of the country associated with the larger population densities 

and greater numbers of fossil fuel electric generation plant generally to the east of the Midwest 

nitrate bulge. While both ammonia and nitric acid are needed to form particulate ammonium nitrate, 

the maps suggest the Midwest nitrate bulge is due primarily to the abundance of free ammonia (i.e., 

the amount beyond what is required to neutralize the acidic particulate SO4

2–). By contrast, the 

region to the east of the Midwest nitrate bulge should have plenty of nitric acid given the higher 

emissions of NOX, but apparently has a deficiency of free ammonia. The few eastern monitoring 

sites with locally high particulate nitrate (near southeastern PA) are located within a small region of 

high density animal agriculture that shows up as a high ammonia emissions region in Figure 9-45. 

Note that California’s South Coast and Central Valley have both high ammonia and high NOX

emissions, explaining the high particulate nitrate contribution to haze there. 

December 2009 9-60 
To better understand the role of ammonia in the formation of the Midwest nitrate bulge, the 

Midwest RPO and Central States Regional Air Partnership deployed a measurement program from 

late 2003 through early 2005 at 10 locations (9 rural and 1 urban) in the region (see Figure 9-47) to 

monitor particulate SO4

2–, nitrate, and ammonium ions, plus the precursor gases sulfur dioxide, nitric 

acid, and ammonia (Kenski et al., 2004, 192078; Sweet et al., 2005, 180038). These data have been 

used as input for thermodynamic equilibrium modeling to assess the changes in PM concentrations 

that would result from changes to precursor concentrations (Blanchard and Tanenbaum, 2006, 

190005; Blanchard et al., 2007, 098659). Blanchard and Tanenbaum (2006, 190005) and Blanchard 

et al. (2007, 098659) conclude that the current conditions at nine of the ten sites are near the point of 

transition between the precursor species (nitric acid and ammonia) that limits the formation of 

particulate nitrate. If excess ammonia increases, either by greater ammonia emissions or by 

anticipated decreases in SO2 emissions, then nitric acid concentration would need to be reduced (via 

lower NOX emissions) in order to reduce the particulate nitrate concentration. 

Given the comparability of particulate SO4

2– and nitrate with regard to their light extinction 

efficiencies, their visibility impacts are proportional to the sum of their mass concentrations. A 

reduction in SO4

2– caused by SO2 emission reductions would reduce the particulate SO42–

concentration, though according to the thermodynamic equilibrium modeling for these sites the 

particulate nitrate concentration will be increased somewhat. However, the total particulate SO4

2–

plus nitrate concentration would be reduced so visibility impacts would be decreased. At current 

ammonium concentrations the predicted response of changes to SO4

2– and nitric acid concentrations 

(i.e., SO2 and NOX emissions changes) are similar in respect to the resulting magnitude of changes to 

the total particulate SO4

2– plus nitrate concentrations. At all but two sites the total particulate SO42–

plus nitrate concentrations would decrease if either ammonia or nitric acid where reduced. 

Source: Kenski et al. (2004, 192078) 

Figure 9-47. Midwest ammonia monitoring network. 

A further degree of complications in understanding the response of particulate nitrate to 

changes in precursor concentrations results from the temperature and humidity dependence of the 

partition between particulate ammonium nitrate and the disassociated gaseous nitric acid and 

ammonia. This dependence causes seasonal and even diurnal differences in the expected responses 

of particulate nitrate concentrations to changes in precursor concentrations. As expected, during the 

colder times of the year the total particulate concentrations are more sensitive to changes in ammonia 

and nitric acid concentrations than during the warmer seasons when SO4

2– concentrations are greater. 

December 2009 9-61 
As shown in Figure 9-48, results of an air transport assessment to identify emission source 

areas associated with high particulate nitrate at five monitoring locations in the East (four 

remote-area sites and Toronto, Canada) implicate the high ammonia emissions region of the Midwest 

as a common source region (Canada-US Air Quality Committee, 2004, 190519). This assessment 

does not preclude local sources of the precursor gases responsible for particulate ammonium nitrate, 

but does suggest that long-range transport of particulate nitrate or ammonia from the high emissions 

region of the Midwest is also contributing to eastern nitrate episodes. 

Source: Canada-U.S. Air Committee (2004, 190519). 

Figure 9-48. Upwind transport probability fields associated with high particulate nitrate 

concentrations measured at Toronto, Canada; Boundary Water Canoe Area, MN; 

Shenandoah NP, VA; Lye Brook, VT; and Great Smoky Mountains NP, TN. 

In a similar air transport assessment for measurements at Underhill, VT and at Brigantine, NJ, 

Hopke et al. (2005, 156567) identified separate regions associated with particulate SO4

2–

accompanied by trace particulate components associated with coal burning (e.g., Se) and 

accompanied by trace particulate components associated with oil burning (e.g., V). As shown in 

Figure 9-49, the coal-burning related particulate SO4

2– for these two monitoring sites is associated 

with long-range transport from the Ohio River Valley, while oil-burning related particulate SO4

2– is 

from more nearby emissions in the high population region of coastal New York, New Jersey, 

Massachusetts, and Connecticut. 

December 2009 9-62 
Source: Reprinted with Permission of Environmental Science and Technology from Hopke, et al. (2005, 156567). 

Figure 9-49. Trajectory probability fields for periods with high particulate SO4

2– measured at 

Underhill, VT and Brigantine, NJ (shown as white stars) associated with 

oil-burning trace components (left) and with coal-burning trace components 

(right). Shown for comparison are the interpolated SO2 emissions areal density 

contours for oil combustion sources (emissions times 10) and coal combustion 

sources, displayed as yellow and red contour lines, respectively. 

The Regional Aerosol Intensive Network (RAIN) was established by MANE-VU to generate 

enhanced continuous visibility, plus fine particle mass and composition monitoring data at a string of 

three monitoring locations along the transport path from the Ohio River Valley to coastal Maine 

(NESCAUM, 2006, 156802). The dominant role of particulate SO4

2– in the northeast is well 

demonstrated by a scatter plot of RAIN data that shows the relationship between particulate SO4

2–

extinction, calculated using the IMPROVE algorithm plotted against directly measured particle light 

scattering for hourly data over a 8-mo period, beginning in July 2004 at the Acadia NP, ME 

monitoring site (see Figure 9-50). Particulate SO4

2– explains 90% of the variance in particulate light 

scattering even though it is responsible for only about 64% of the total light extinction (annual 

averaged value from the VIEWS web site). Adding the contribution by the second-largest regional 

contributor to light extinction, particulate OC with about 14%, does not improve the variance 

explained, but does increase the slope to 0.78. The noticeable difference between these two plots is 

that the particulate SO4

2– alone underestimates light scattering during low haze periods (points on the 

plot are below the regression line for light scattering <70 Mm-1), while the agreement is improved 

with the addition of particulate OC contributions to haze (regression slope is nearer to one and 

reduced bias for low haze periods). This implies that particulate SO4

2– and any other co-varying PM 

species are largely responsible for the highly impacted periods, while OC and other co-varying PM 

species contribute more during the less extreme haze periods. Particulate nitrate contribution to light 

extinction at Acadia is about 10% on average. 

December 2009 9-63 
Source: RAIN Preliminary Data Analysis Report (NESCAUM, 2006, 156802) 

Figure 9-50. Scatter plots of particulate SO4

2– (left) and particulate SO42– and organic mass 

(right) versus nephelometer measured particle light scattering for Acadia NP, ME. 

Particulate nitrate concentrations are considerably lower in the SO4

2–-dominated warmer 

southeastern U.S. than in the Northeast and upper Midwest. Blanchard et al. (2007, 098659) 

conducted thermodynamic equilibrium modeling on data from the eight SEARCH monitoring sites 

and found that total particulate nitrate plus SO4

2– is much more responsive to changes in SO2

concentrations than to changes in nitric acid concentrations, which in turn is more responsive than 

changes in ammonia concentrations. 

The VISTAS RPO commissioned an emissions sensitivity study using CMAQ modeling on 

winter and summer 2009 emissions projected from the 2002 emissions inventory (NCDENR, 2007, 

156798). Figure 9-51 contains bar plots for two North Carolina Class I areas that indicate projected 

changes in light extinction for the worst haze day due to 30% emissions reductions by particulate 

species, source types and location across the Southeastern states modeling domain (i.e., as far west 

as Texas, as far north as Pennsylvania, as far south as the Florida Keys, and as far east as ~300 km 

from the North Carolina coast). Great Smoky Mountains in the southern Appalachian Mountains has 

the greatest sensitivity to changes by SO2 emissions from electrical generation units (EGU) and to a 

lesser extent other SO2 emission sources in the region. Reductions of NOX emissions from ground or 

point sources are not nearly as effective as SO2 reductions in reducing the light extinction at Great 

Smoky Mountains. This is due principally to the worst days at Great Smoky Mountains occurring 

during the summer, when temperatures are too high to support high particulate nitrate 

concentrations. For the same reason, ammonia emission reductions are also ineffective. Swanquarter 

W, NC is a coastal location where some of the worst haze days are during the winter and include 

contributions from particulate ammonium nitrate. Both SO2 and ammonia emissions reductions 

would be effective at reducing worst haze days at the Swanquarter W, though NOX emissions are not 

as effective presumably because the atmosphere is ammonia-limited for particulate nitrate 

production. 

December 2009 9-64 
Source: NCDENR (2007, 156798). 

Figure 9-51. CMAQ air quality modeling projections of visibility responses on the 20% worst 

haze days at Great Smoky Mountains NP, NC (top) and Swanquarter W, NC 

(bottom) to 30% reductions. This is from a projected 2009 emission inventory of 

visibility-reducing pollutants by source category and geographic areas. 

9.2.4. Urban Visibility Valuation and Preference 

The Clean Air Act §302(h) defines public welfare to include the effects of air pollution on 

“...visibility, ... and personal comfort and wellbeing.” Though good visibility conditions in Class I 

(e.g., NPs) and wilderness areas have long been recognized as important to the public welfare (see 

discussions in EPA (2004, 056905; 2005, 090209) and Chestnut and Dennis (1997, 014525), 

visibility conditions in urban areas also contribute to the public welfare. Although visibility 

impairment may be caused by either natural or manmade conditions (or both), it is only impairment 

that occurs as a result of air pollution (either alone or in combination with water vapor or other 

atmospheric conditions) that can be mitigated by regulations such as the RHR (40 CFR 51.300 

through 309) or the Secondary NAAQS. The term visual air quality (VAQ) is used here to refer to 

December 2009 9-65 
the visibility effects caused solely by air quality conditions, so for example it excludes the reduced 

visibility caused by fog. Visibly poor air quality causes people to be concerned about substantive 

health risks, but degraded VAQ adversely affects people in additional ways. These include the 

aesthetic and wellbeing benefits of better visibility, improved road and air safety, and enhanced 

recreation in activities like hiking and bicycling. Because the human health impacts of air pollution 

are assessed under the Primary NAAQS, it is necessary to separate out these non-health components 

associated with the visibility condition produced by a given amount of air pollution when assessing 

the need for additional regulation to protect the public welfare effect of visibility under the 

Secondary NAAQS. The degree to which previous human preference and valuation studies for VAQ 

have adequately made this distinction and separation is an important issue in applying results from 

available studies in a Secondary NAAQS (or benefits estimation for any policy affecting VAQ) 

context. The remainder of this discussion is focused on those aesthetic and wellbeing qualities 

associated with a given VAQ in urban areas. 

The term “urban visibility” is used to refer to VAQ throughout a city or metropolitan area. 

Urban visibility includes the VAQ conditions in all locations that people experience in their daily 

lives, including scenes such as residential streets and neighborhood parks, commercial and industrial 

areas, highway and commuting corridors, central downtown areas, and views from elevated locations 

providing a broad overlook of the metropolitan area. Thus urban visibility includes VAQ conditions 

in major cities and smaller towns and encompasses all the VAQ an individual resident sees on a 

regular basis. Visibility conditions in urban and suburban locations are therefore distinct from 

visibility in rural or wilderness settings such as the Class 1 areas defined by the Clean Air Act, which 

include NPs and similar natural settings. 

Visibility has direct significance to people’s enjoyment of daily activities and their overall 

wellbeing. Visibility conditions can be described both as an aesthetic quality as well as a 

scientifically measurable set of atmospheric conditions. Due to the subjective nature of aesthetics, 

people’s preferences with respect to visibility are difficult to express or quantify, but people have 

expressed in many different ways that they enjoy and value a clear view. A number of social science 

studies have been undertaken to link perceived urban visibility to an array of effects reflecting the 

overall desire for good VAQ, and the benefits of improving currently degraded VAQ. This wide 

range of diverse studies have identified types of benefits of good VAQ. 

For example, psychological research has demonstrated that people are emotionally affected by 

low VAQ such that their overall sense of wellbeing is diminished (e.g., Bickerstaff and Walker, 2001, 

156271). Researchers have also shown that perception of pollution is correlated with stress, 

annoyance, and symptoms of depression (Evans and Jacobs, 1982, 179899; Jacobs et al., 1984, 

156596; Mace et al., 2004, 180255). Sociological research has demonstrated that VAQ is deeply 

intertwined with a “sense of place,” effecting people’s sense of the desirability of a neighborhood 

quite apart from the actual physical conditions of the area (e.g., ABT, 2002, 156186; Day, 2007, 

156386; Elliot et al., 1999, 010716; Howel et al., 2002, 156571). Public policy research finds that 

people think it is important to protect visibility, and accept the concept of setting standards to protect 

visibility (e.g., ABT, 2001, 156185; BBC Research & Consulting, 2002, 156258; Ely et al., 1991, 

056599; Pryor, 1996, 056598). Finally, economic valuation research has measured the amount of 

money that people are willing to pay to protect or improve both urban visibility (e.g., summary 

review in Beron et al., 2001, 156270; Chestnut and Dennis, 1997, 014525) and natural locations such 

as NPs and other locations defined by the Clean Air Act as Class I visibility area (e.g., summary 

review in Chestnut and Dennis, 1997, 014525). 

Urban visibility has been examined in two types of studies directly relevant to the NAAQS 

review process: urban visibility preference studies and urban visibility valuation studies. The 

purpose of the remainder of this section is to review preference studies in four urban areas, as well as 

one new urban visibility valuation study not previously discussed in previous EPA Criteria 

Documents or OAQPS Staff Papers. 

Both types of studies are designed to evaluate individuals’ desire (or demand) for good VAQ 

where they live, using different metrics to evaluate demand. Urban visibility preference studies 

examine individuals’ demand by investigating what amount of visibility degradation is unacceptable 

while economic studies examine demand by investigating how much one would be willing to pay to 

improve visibility. 

December 2009 9-66 
9.2.4.1. Urban Visibility Preference Studies 

One group of urban visibility research projects focused on identifying preferences for urban 

VAQ without necessarily estimating the economic value of improving visibility. This group of 

preference studies used a common focus group method to estimate the visibility impairment 

conditions that respondents described as “acceptable.” The specific definition of acceptable was 

largely left to each individual respondent, allowing each to identify their own preferences. 

There are three completed studies that used this method, and two pilot studies that provided 

additional information (Table 9-2). The completed studies were conducted in Denver, Colorado (Ely 

et al., 1991, 056599), two cities in British Columbia, Canada (Pryor, 1996, 056598), and Phoenix, 

Arizona (BBC Research & Consulting, 2002, 156258). The additional studies were conducted in 

Washington, DC (ABT, 2001, 156185; Smith and Howell, 2009, 198803). 

Each study collected information in a focus group setting, presenting slides depicting various 

visibility conditions. All four studies used photographs of a single scene from the study’s city; each 

photo included images of the broad downtown area and spreading out to the hills or mountains 

composing the scene’s backdrop. The maximum sight distance under good conditions varied by city, 

ranging from 8 km in Washington, DC to mountains hundreds of kilometers away in Denver. 

Multiple photos of the same scene were used to present approximately 20 different visibility 

impairment conditions. The Denver and British Columbia studies used actual photographs taken in 

the same location to depict various visibility conditions. The Phoenix and Washington, DC studies 

used photographs prepared using the WinHaze software from Air Resource Specialists (ARS). 

WinHaze is a computer-imaging software program that simulates visual air quality differences of 

various scenes, allowing the user to “degrade” an original near-pristine visibility condition 

photograph to create a photograph of each desired VAQ condition. 

A common characteristic of the three visibility preference studies was that each was conducted 

in the West where distant mountains were shown in the photograph used to elicit local participant 

responses about visibility. Among other issues, the Washington D.C. pilot study was the first step in 

a process to expand the results to other regions where typical scenes may have different sensitivity to 

perceived visibility changes in PM air quality and where participants may have different acceptable 

visibility preference values. 

The range of median preference values for an acceptable amount of visibility degradation from 

the 4 urban areas was approximately 19-33 dv. Measured in terms of visual range (VR), these 

median acceptable values were between approximately 59 and 20 km. 

Table 9-2. Summary of urban visibility preference studies. 

 Denver, CO Phoenix, AZ 2 British Columbia cities Washington, DC 

(2001) 

Washington, DC 

(2009) 

Report Date 1991 2003 1996 2001 2009 

Duration of 

session 45 min 50 min 2 h 

Compensation None (civic groups) $50 None (class room exercise) $50 None 

# focus group 

sessions 17 

27 total at 6 locations, 

Including 3 in Spanish 

4 1 3 tests 

# participants 214 385 180 9 64 

Age range Adults 18-65+ University students 27-58 Adults 

Annual or 

seasonal Wintertime Annual Summertime Annual Annual 

# total scenes 

presented 

Single scene of 

downtown with 

mountains in 

background 

Single scene of 

downtown and 

mountains, 42 km 

maximum distance 

Single scene from each city 

Single scene of DC Mall 

and downtown, 8 km 

maximum sight 

Single scene of DC 

Mall and downtown, 8 

km maximum sight 

# of total visibility 

conditions 

presented 

20 conditions (+ 5 

duplicates) 

21 conditions (+ 4 

duplicates) 20 conditions (10 each from each city) 20 conditions (+ 5 duplicates) 22 conditions 

December 2009 9-67 
 Denver, CO Phoenix, AZ 2 British Columbia cities Washington, DC 

(2001) 

Washington, DC 

(2009) 

Source of slides 

Actual photos taken 

between 9am and 

3pm 

WinHaze Actual photos taken at 1 p.m. or 4 p.m. WinHaze WinHaze 

Medium of 

presentation Slide projection Slide projection Slide projection Slide projection Slide projection 

Ranking scale 

used 7 point scale 7 point scale 7 point scale 7 point scale 7 point scale 

Visibility range 

presented 11 to 40 dv 15 to 35 dv 13-25 dv (Chilliwack) 13.5-31.5 dv (Abbotsford) 9-38 dv 9-45 dv 

Health issue 

directions 

Ignore potential 

health impacts; 

visibility only 

Judge solely on 

visibility, do not 

consider health 

Judge solely on visibility, do not 

consider health 

Health never 

mentioned, “Focus only 

on visibility” 

Health never 

mentioned, “Focus only 

on visibility” 

Key questions 

asked 

a) Rank VAQ (1-7 

scale) 

a) Rank VAQ (1-7 

scale) a) Rank VAQ (1-7 scale) a) Rank VAQ (1-7 scale) 

a) Rank VAQ (1-7 

scale) 

b) Is each slide 

“acceptable” 

b) Is each slide 

“acceptable” b) Is each slide “acceptable” b) Is each slide “acceptable” 

b) Is each slide 

“acceptable” 

c) “How much haze 

is too much?” 

c) How many days a 

year would this picture 

be “acceptable” 

 

c) if this hazy, how 

many hs would it be 

acceptable (3 slides 

only) 

 

 

 d) valuation question 

Mean dv found 

“acceptable” 20.3 dv 23-25 dv ~23 dv(Chilliwack), 

~19 dv(Abbotsford) ~20 dv (range 20-25) ~30 dv 

9.2.4.2. Denver, Colorado Urban Visibility Preference Study 

The Denver urban visibility preference study (Ely et al., 1991, 056599) was conducted on 

behalf of the Colorado Department of Public Health and Environment (CDPHE). The study 

conducted a series of focus group sessions with 17 civic and community groups in which a total of 

214 individuals were asked to rate slides. The slides depicted varying values of VAQ for a 

well-known Denver vista, including a broad view of downtown Denver with the mountains to the 

west composing the scene’s background. The participants were instructed to base their judgments on 

three factors: 

1. The standard was for an urban area, not a pristine NP area where the standards might be 

more strict; 

2. The value of an urban visibility standard violation should be set at a VAQ value considered 

to be unreasonable, objectionable, and unacceptable visually; and 

3. Judgments of standards violations should be based on visibility only, not on health effects. 

Participants were shown 25 randomly ordered slides of actual photographs. The visibility 

conditions presented in the slides ranged from 11-40 dv, approximating the 10th-90th percentile of 

wintertime visibility conditions in Denver. The participants rated the 25 slides based on a scale of 

1 (poor) to 7 (excellent), with 5 duplicates included. They were then asked to judge whether the slide 

would violate what they would consider to be an appropriate urban visibility standard (i.e., whether 

the amount of impairment was “acceptable” or “unacceptable”). The individual’s judgment of a 

slide’s VAQ and whether the slide violated a visibility standard were highly correlated (Pearson 

correlation coefficient >80%), as were the VAQ ratings and the yes/no “acceptable” response. The 

participant’s median response was that a visibility condition of 20.3 dv (extinction coefficient 

bext = 76Mm-1, or VR ~51 km) was judged as “acceptable.” The CDPHE subsequently established a 

Denver visibility standard at this value (defined as bext = 76Mm-1), based on the median 50% 

acceptability findings from the study. 

December 2009 9-68 
9.2.4.3. Phoenix, Arizona Urban Visibility Preference Study 

The Phoenix urban visibility preference study (BBC Research & Consulting, 2002, 156258) 

was conducted on behalf of the Arizona Department of Environmental Quality. The Phoenix study 

patterned its focus group survey process after the Denver study. The study included 385 participants 

in 27 separate focus group sessions. Participants were recruited using random digit dialing to obtain 

a sample group designed to be demographically representative of the larger Phoenix population. 

Focus group sessions were held at six neighborhood locations throughout the metropolitan area to 

improve the participation rate. Three sessions were held in Spanish in one region of the city with a 

large Hispanic population (25%), although the final overall participation of native Spanish speakers 

(18%) in the study was modestly below the targeted value. Participants received $50 as an 

inducement to participate. 

Participants were shown a series of 25 images of the same vista of downtown Phoenix, with 

South Mountain in the background at a distance of about 40 km. Photographic slides of the images 

were developed using WinHaze. The visibility impairment conditions ranged from 15-35 dv (the 

extinction coefficient, bext, range was approximately 45 Mm-1 to 330 Mm-1, or a visual range of 

87-12 km). Participants first individually rated the randomly shown slides on a VAQ scale of 

1 (unacceptable) to 7 (excellent). Participants were instructed to rate the photographs solely on 

visibility, and to not base their decisions on either health concerns or what it would cost to have 

better visibility. Next, the participants individually rated the randomly ordered slides as “acceptable” 

or “unacceptable,” defined as whether the visibility in the slide is unreasonable or objectionable. 

Better visibility conditions (15 dv and 20 dv) were judged “acceptable” by 90% of all participants. 

At 24 dv nearly half of all participants thought the VAQ was “unacceptable,” with almost 

three-quarters judging 26 dv as unacceptable. 

The Phoenix urban visibility study formed the basis of the decision of the Phoenix Visibility 

Index Oversight Committee for a visibility index for the Phoenix Metropolitan Area (Arizona DEQ, 

2000, 019164). The Phoenix Visibility Index establishes an indexed system with 5 categories of 

visibility conditions, ranging from “Excellent” (14 dv or less) to “Very Poor” (29 dv or greater). The 

“Good” range is 15-20 dv. The environmental goal of the Phoenix urban visibility program is to 

achieve continued progress through 2018 by moving the number of days in lower quality categories 

into better quality categories. 

9.2.4.4. British Columbia, Canada Urban Visibility Preference Study 

The British Columbia urban visibility preference study (Pryor, 1996, 056598) was conducted 

on behalf of the Ministry of Environment. The study conducted focus group sessions that were also 

developed following the methods used in the Denver study. Participants were students at the 

University of British Columbia, who participated in one of four focus group sessions with between 7 

and 95 participants. A total of 180 respondents completed surveys (29 did not complete the survey). 

Participants in the study were shown slides of two suburban locations in British Columbia: 

Chilliwack and Abbotsford. Using the same general protocol as the Denver study, Pryor found that 

responses from this study found the acceptable level of visibility was 23 dv in Chilliwack and 19 dv 

in Abbotsford. Pryor (1996, 056598) discusses some possible reasons for the variation in standard 

visibility judgments between the two locations. Factors discussed include the relative complexity of 

the scenes, potential bias of the sample population (only University students participated), and the 

different amounts of development at each location. Abbotsford (population 130,000) is an ethnically 

diverse suburb adjacent to the Vancouver Metro area, while Chilliwack (population 70,000) is an 

agricultural community 100 km east Vancouver in the Frazier Valley. 

The British Columbia urban visibility preference study is being considered by the B.C. 

Ministry of the Environment as a part of establishing urban and wilderness visibility goals in British 

Columbia. 

9.2.4.5. Washington, DC Urban Visibility Preference Studies 

The Washington, DC urban visibility pilot study (ABT, 2001, 156185) was conducted on 

behalf of the EPA, and was designed to be a pilot focus group study, an initial developmental trial 

run of a larger study. The intent of the pilot study was to study both focus group method design and 

potential survey questions. Due to funding limitations, only a single focus group session was held, 

December 2009 9-69 
consisting of one extended session with nine participants. No further urban visibility focus group 

sessions were held in Washington, DC. 

Due to the small number of participants, it is not possible to make statistical inferences about 

the opinions of the general population. The study does, however, provide additional useful 

information about urban visibility studies, potentially helping to both better understand previous 

studies as well as design future studies. 

The study also adopted the general Denver study method, modifying it as appropriate to be 

applicable in an eastern urban setting which has substantially different visibility conditions than any 

of the three western locations of the other preference studies. Washington’s (and the entire East) 

visibility is typically substantially worse than western cities, and has different characteristics. 

Washington’s visibility impairment is primarily a uniform whitish haze dominated by sulfates, 

relative humidity values are higher, the low lying terrain provides substantially shorter maximum 

sight distances, and many residents are not well informed that anthropogenic emissions impair 

visibility on hazy days. 

The Washington focus group session included questions on valuation, as well as on 

preferences. The focus group was asked to state its preferences measured in an increase in the 

general cost of living for certain increments of improvement in visibility on a typical summer day. A 

general cost of living approach is one payment vehicle approach that can be used in willingness to 

pay studies, especially for environmental issues arising from multiple diverse emission sources (e.g., 

transportation, electricity generation, industry, etc.) making a specific price increase potentially 

misleading. 

The first part of the focus group session was designed to be an hour long, and was comparable 

to the focus group sessions in the Denver and Phoenix studies. A single scene was used; a panoramic 

shot of the Potomac River, Washington mall and downtown Washington, DC. In the first part of the 

session people were asked to rate the VAQ of 25 photographs (prepared using WinHaze, and 

projected on a large screen), judge the acceptability of visibility condition in each slide, and answer 

the valuation questions. The second half of the session, however, was a moderated discussion session 

about the format and content of the first phase of the session. In this moderated discussion, 

participants were asked about their understanding of each question asked in the first half of the 

session. Particular issues in designing a focus group session were also explored. Important 

participant comments included: 

1. Participants had been asked how they reacted to the initial direction to base their answers 

only on visibility, but health was never explicitly mentioned by the focus group moderator. 

Participants strongly agreed with the decision to not mention that health effects are 

associated with visibility impairment. They understood the directions as meaning they should 

ignore health issues, and said their answers would have been different if they included health 

as well as visibility in their judgments. 

2. Differentiating between haze and weather conditions was difficult. Weather was not 

discussed in the focus group session, and the photographs were WinHaze altered photos with 

identical weather conditions. Participants mentioned they were still confused about the role 

of weather and humidity in the different visibility conditions presented in the photos. 

3. Questions about how many hours an impairment level would be acceptable were confusing. 

Most participants were normally indoors during most of the day, so questions about duration 

of outdoor conditions were difficult to answer. 

4. Participants strongly agreed that not mentioning the purpose of the study, or the sponsor, 

until the very end (after all the questions were answered) was viewed as very important. 

Most felt this information would have influenced their answers. 

The Smith and Howell (2009, 198803) study recreated the same WinHaze images used in the 

2001 Washington, DC urban visibility preference study, and followed a shortened version of the 

same question protocol as the 2001 study. The WinHaze images were presented to a total of 

64 participants who were all employees of CRA International, Inc. (Smith and Howell also are CRA 

International employees). 

The stated purpose of the Smith and Howell (2009, 198803) study was to explore the 

robustness of the 2001 pilot study results. To investigate this issue, Smith and Howell (2009, 

198803) conducted three different tests concerning urban visibility preferences. Each participant was 

involved with only one test. Test 1 was designed to replicate the 2001 study. Test 2 reduced the 

December 2009 9-70 
upper end of the range of VAQ by eliminating the 11 images used in Test 1 with a VAQ above 

27.1 dv. Test 3 increased the upper end of the range of VAQ by including two new images of worse 

VAQ; the two new images had a VAQ of 42 dv and 45 dv. Smith and Howell (2009, 198803) 

concluded that changing the range of VAQ presented to the participants affects the responses about 

whether a particular VAQ is acceptable. 

9.2.4.6. Urban Visibility Valuation Studies 

The one recent urban visibility benefit assessment not included in earlier reviews is “The 

Benefits of Visibility Improvement: New Evidence from the Los Angeles Metropolitan Area” (Beron 

et al., 2001, 156270). Rather than a contingent valuation method (CVM) technique used in the 

majority of other urban visibility valuation studies, Beron et al. (2001, 156270) used a housing 

market hedonic technique. The housing hedonic methods were used in previous urban visibility 

studies by Murdoch and Thayer (1988, 156788) and Trijonis et al. (1985, 078468). A housing market 

hedonic study views a housing unit as composed of a bundle of attributes, and uses housing sale 

price data from a large number of units in a metropolitan area to estimate the value of each 

component. Hedonic pricing has been used to estimate economic values for environmental effects 

that have a direct effect on housing market values. It relies on the measurement of differentials in 

property values under various environmental quality conditions including air pollution, visibility and 

other environmental amenities such as access to nearby beaches and parks, as well as by physical 

attributes of the house and attributes of the neighborhood. 

Beron et al. (2001, 156270) obtained data on approximately 840,000 owner-occupied, single 

family housing sales between 1980 and 1995 from the California South Coast Air Basin (composed 

of Los Angeles and Orange Counties, and the portions of Riverside and San Bernardino Counties in 

the greater metropolitan area). The real estate data included information on the sale price of the 

house, 13 housing attributes (square footage, number of bathrooms, etc.), 9 neighborhood attributes 

(percent poverty, school quality, FBI crime index, etc.), and three air pollution variables: ozone, 

particulates (measured by total suspended particulates, or TSP), and visibility. Visibility was 

measured as the annual average of visual range, measured in miles, and was obtained from seven 

airports within the study region. The visibility range was from 12.4 miles (Los Angeles International 

Airport, 1991) to 31.9 miles (Palm Springs Airport, 1995). Ozone data (39 monitors) and TSP data 

(40 monitors) were obtained from the South Coast Air Quality Management District. Annual mean 

values for each year were calculated for ozone and TSP. 

Beron et al. (2001, 156270) presented results for a hypothetical basin-wide 20% visibility 

improvement, or an increase from 15.3 to 18.4 miles, which is equivalent to approximately 27.6 dv 

to 25.8 dv. The initial results reflect the change in the purchase price of a house associated with this 

difference in VAQ, which can be interpreted as a present value of a stream of annual values over the 

lifetime of the house. The authors therefore selected a time horizon (30 yrs) and an interest rate (8%) 

to calculate an annual per household benefit per dv ranging from $484 to $1,756. The Beron results 

are higher than the CVM-based values summarized in Chestnut and Dennis (1997, 014525), which 

ranged from $12 to $132 per dv. It should be noted that the $132 CVM values cited by Chestnut and 

Dennis (1997, 014525) is from a study in the Los Angeles area (Brookshire, 1979, 156298). The 

Beron et al. (2001, 156270) results are also higher than the Trijonis et al. (1990, 157058) hedonic 

study in the Los Angeles area, which had a range of $134 to $360 per dv per year. All values 

reported here are in terms of 1994 prices. 

A critical question for all urban visibility valuation studies is the extent to which the estimated 

values strictly reflect preferences for visibility, and do not include a component of preferences for 

reducing health risk from air pollution. The ability to isolate the value of visibility from within the 

collection of intertwined benefits from visual air quality, which is inherently multi-attributed, is a 

challenge for all visibility valuation studies. Each study attempts to isolate visibility from other 

effect categories, but different studies take different approaches. 

Beron et al. (2001, 156270) include two measures of air pollution directly related to health 

effects in their housing market hedonic study, ozone and particulates (using TSP as the metric for 

particulates), as well as visibility. They argue that the presence of the two health-related pollution 

conditions results in an estimated hedonic demand function for visibility that successfully separates 

the health component of demand for overall air quality from the visibility component. An alternative 

interpretation is that the estimated visibility function still includes a component of health risk 

because the housing market data does not support completely isolating the demand for visibility (due 

December 2009 9-71 
to correlated variables, omitted variables, measurement error, model specification error, etc.) from 

demand for health risk reductions measured by the two health related air quality metrics. 

A key issue in interpreting the Beron et al. (2001, 156270) results is whether the objective 

measures of air quality characteristics (e.g., visibility, PM concentrations, etc.) capture people’s 

perceptions of the different aspects of air quality in a given location. To the extent the people 

simultaneously use what they see regarding VAQ as an indicator of the overall air quality including 

potential health risks, then including all the measures in the equation is not necessarily sufficient to 

isolate one effect from the other. 

9.2.5. Summary of Effects on Visibility 

Visibility impairment is caused by light scattering and absorption by suspended particles and 

gases. NO2 is the only commonly occurring atmospheric pollutant gas that absorbs visible spectrum 

radiation, though in most situations the amount of light absorption by NO2 is overwhelmed by the 

higher amounts of particulate light extinction (i.e., the combination of scattering and absorption) 

usually accompanying high NO2 concentrations. Light scattering by gases in a pollutant-free 

atmosphere provides a limit to visibility in pristine conditions and is the largest contributor to the 

total light extinction during the least visibility-impaired periods in remote regions of the western 

U.S. There is strong and consistent evidence that PM is the overwhelming source of visibility 

impairment in both urban and remote areas. EC and some crustal minerals are the only commonly 

occurring airborne particle components that absorb light. All particles scatter light, and generally 

light scattering by particles is the largest of the four light extinction components. Although a larger 

particle scatters more light than a similarly shaped smaller particle of the same composition, the light 

scattered per unit of mass is greatest for particles with diameters from approximately 0.3-1.0 μm. 

For studies where detailed data on particle composition by size data are available, accurate 

calculations of light extinction can be made. However, routinely available PM speciation data can be 

used to make reasonable estimates of light extinction using relatively simple algorithms that multiply 

the concentrations of each of the major PM species by its dry extinction efficiency and by a water 

growth term that accounts for particle size change as a function of relative humidity for hygroscopic 

species (e.g., SO4

2–, nitrate, and sea salt). This permits the visibility impairment associated with each 

of the major PM components to be separately approximated from PM speciation monitoring data. 

There are six major PM components: PM2.5 SO4

2– usually assumed to be ammonium sulfate, PM2.5

nitrate usually assumed to be ammonium nitrate, PM2.5 OC, PM2.5 EC, PM2.5 crustal material 

(referred to as fine soil), and PM10-2.5 or coarse mass. 

Direct optical measurement of light extinction measured by transmissometer, or by combining 

the PM light scattering measured by integrating nephelometers with the PM light absorption 

measured by an aethalometer offer a number of advantages compared to algorithm estimates of light 

extinction based on PM composition and relative humidity data. The direct measurements are not 

subject to the uncertainties associated with assumed scattering and absorption efficiencies used in the 

PM algorithm approach. The direct measurements have higher time resolution (i.e., minutes to 

hours), which is more commensurate with the visibility effects compared with calculated light 

extinction using routinely available PM speciation data (i.e., 24-h duration). 

Particulate SO4

2– and nitrate are produced in the atmosphere from gaseous precursors, making 

them secondary PM species. They both have comparable light extinction efficiencies (haze impacts 

per unit mass concentration) at any relative humidity value, their light scattering per unit mass 

concentration increases with increasing relative humidity, and at sufficiently high humidity values 

(RH>85%) they are the most efficient particulate species contributing to haze. Particulate SO4

2– is 

the dominant source of regional haze in the eastern U.S. (>50% of the particulate light extinction) 

and an important contributor to haze elsewhere in the country (>20% of particulate light extinction). 

Particulate nitrate is a minor component of remote-area regional haze in the non-California 

western and eastern U.S., but an important contributor in much of California and in the upper 

Midwestern U.S. especially during winter when it is the dominant contributor to particulate light 

extinction. While both nitric acid (a reaction product of NOX emissions) and ammonia are needed to 

form ammonium nitrate, the apparent reason for the Midwest nitrate bulge (i.e., region of high winter 

PM nitrate) is an abundance of atmospheric ammonia in this region principally from agricultural 

emissions. There is evidence that transport from the Midwest nitrate bulge region is responsible for 

some of the ammonium nitrate episodes experienced in downwind regions far to the east. Urban 

particulate nitrate concentrations are significantly elevated above surrounding remote-area 

December 2009 9-72 
background concentrations with the largest urban contributions in the western U.S. Particulate 

ammonium nitrate concentrations in California and the Midwestern nitrate bulge region are an order 

of magnitude greater than estimated natural ammonium nitrate concentrations. Thermodynamic and 

air quality simulation modeling show that particulate nitrate concentrations are sensitive to changes 

in either NOX emissions (from a combination of mobile and point sources) or ammonia emissions 

(principally from agricultural sources), with the responsiveness of particulate nitrate to emissions 

changes depending on the relative abundance of ammonia and nitric acid in the atmosphere. 

EC and OC have the highest dry extinction efficiencies of the major PM species and are 

responsible for a large fraction of the haze especially in the Northwestern U.S., though absolute 

concentrations are as high in the eastern U.S. Both are a product of incomplete combustion of fuels, 

including those used in internal combustion processes (gasoline and diesel emissions) and open 

biomass burning (smoke from wild and prescribed fire). OC PM species are also produced by 

atmospheric transformation of precursor gaseous emissions. Smoke plume impacts from large 

wildfires dominate many of the worst haze periods in the western U.S. Carbonaceous PM is 

generally the largest component of urban excess PM2.5 (i.e., the difference between urban and 

regional background concentration). Western urban areas have more than twice the average 

concentrations of carbonaceous PM than remote areas sites in the same region. In eastern urban 

areas, PM2.5 is dominated by about equal concentrations of carbonaceous and SO4

2– components, 

though the usually high relative humidity in the East causes the hydrated SO4

2– particles to be 

responsible for about twice as much of the urban haze as that caused by the carbonaceous PM. 

Radiocarbon dating of carbonaceous PM from twelve sites (eight in the West, two of which are 

urban) showed that about half of the urban area carbonaceous PM is from contemporary as opposed 

to fossil sources, while in remote areas the fraction that is contemporary ranges from 82-100%. 

Summer urban excess carbonaceous PM is dominated by fossil carbon for the two western urban 

areas (Phoenix, AZ and Seattle, WA), but nearly half of the winter urban excess for these two urban 

areas are from contemporary carbon sources (e.g., residential wood combustion). An empirical 

relationship between the radiocarbon analysis results and the more widely measured EC and OC data 

set was used to estimate the fraction of contemporary carbon at about 150 monitoring locations 

nationwide. The highest fraction of contemporary carbon is for the western remote areas sites during 

the summer (>90% contemporary) and the least was for eastern urban areas during the summer 

(<45% contemporary). Winter tended to have less extreme fractions of contemporary carbon for both 

remote and urban areas. A lower bound estimate of 40% of the contemporary and 35% of the fossil 

carbon is from secondary conversion of gaseous precursor during the summer at the twelve 

radiocarbon monitoring sites, suggesting that primary carbonaceous PM whether from fossil or 

contemporary sources represent less than two thirds of the total carbonaceous PM. 

PM2.5 crustal material (referred to as fine soil) and coarse mass (i.e., PM10 minus PM2.5) are 

significant contributors to haze for remote areas sites in the arid Southwestern U.S. where they 

contribute a quarter to a third of the haze, with coarse mass usually contributing twice that of fine 

soil. Coarse mass concentrations are as high in the Central Great Plains as in the Southwestern 

deserts though there are no corresponding high concentrations of fine soil as in the Southwest. Also, 

the relative contribution to haze by the high coarse mass in the Great Plains is much smaller because 

of the generally higher haze values caused by the high concentrations of SO4

2– and nitrate PM in that 

region. 

A comprehensive assessment of the 610 worst haze sample periods over a 3-yr period in the 

western U.S. where dust is the major contributor categorized each site/sampler period into four 

causal groups: Asian dust, local windblown dust, transported regional windblown dust, and 

undetermined dust (i.e., not in one of the three other groups). Most dust days occurred at sites in 

Arizona, New Mexico, Colorado, western Texas, and southern California, and these were dominated 

by local and regionally transported wind-blown dust. Asian dust caused only a few of the worst dust 

days during the 3-year assessment period, though it is an important source of dust for the more 

northerly regions of the West (responsible for 10-40% of their worst dust periods) where there is 

rarely any windblown dust probably due to the greater ground cover. The frequency of worst dust 

events classified as undetermined was greatest for sites in the vicinity of large urban and agricultural 

areas such as those in California and Southern Arizona. 

Visibility has direct significance to people’s enjoyment of daily activities and their overall 

sense of wellbeing. For example, psychological research has demonstrated that people are 

emotionally affected by poor VAQ such that their overall sense of wellbeing is diminished. Urban 

visibility has been examined in two types of studies directly relevant to the NAAQS review process: 

December 2009 9-73 
urban visibility preference studies and urban visibility valuation studies. Both types of studies are 

designed to evaluate individuals’ desire for good VAQ where they live, using different metrics. 

Urban visibility preference studies examine individuals’ preferences by investigating the amount of 

visibility degradation considered unacceptable, while economic studies examine the value an 

individual places on improving VAQ by eliciting how much the individual would be willing to pay 

for different amounts of VAQ improvement. 

There are three urban visibility preference studies and two additional pilot studies that have 

been conducted to date that provide useful information on individuals’ preferences for good VAQ in 

the urban setting. The completed studies were conducted in Denver, Colorado (Ely et al., 1991, 

056599), two cities in British Columbia, Canada (Pryor, 1996, 056598) and Phoenix, AZ 

(BBC Research & Consulting, 2002, 156258). The additional studies were conducted in Washington, 

DC (ABT, 2001, 156185; Smith and Howell, 2009, 198803). The range of median preference values 

for an acceptable amount of visibility degradation from the 4 urban areas was approximately 

19-33 dv. Measured in terms of visual range (VR), these median acceptable values were between 

approximately 59 and 20 km. 

The economic importance of urban visibility has been examined by a number of studies 

designed to quantify the benefits (or willingness to pay) associated with potential improvements in 

urban visibility. Urban visibility valuation research prior to 1997 was summarized in Chestnut and 

Dennis (1997, 014525), and was also described in the 2004 PM AQCD (U.S. EPA, 2004, 056905) 

and the 2005 PM Staff Paper (U.S. EPA, 2005, 090209). Since the mid-1990s, little new information 

has become available regarding urban visibility valuation. 

Collectively, the evidence is sufficient to conclude that a causal relationship exists 

between PM and visibility impairment.

9.3. Effects on Climate 

While most of this ISA is restricted to consideration of the emissions, transport and 

transformation, resulting concentrations, and effects from PM in the U.S., because the effects 

endpoint here is climate, a larger spatial domain is needed. However, this assessment is not intended 

to be comprehensive even as a survey of the enormous range and volume of science related to 

climate effects from PM; rather, particular attention has been paid to data relevant to the U.S. 

The two principal sources for material in this section are Chapter 2, “Changes in Atmospheric 

Constituents and in Radiative Forcing,” (Forster et al., 2007, 092936) in the comprehensive Working 

Group I report in the Fourth Assessment Report (AR4) from the Intergovernmental Panel on Climate 

Change (IPCC), Climate Change 2007: The Physical Science Basis (IPCC, 2007, 092765), hereafter 

IPCC AR4; and the U.S. Climate Change Science Program Synthesis and Assessment Product 2.3, 

“Atmospheric Aerosol Properties and Climate Impacts,” by Chin et al. (2009, 192130), hereafter 

CCSP SAP2.3. The EPA is a constituent agency member of the U.S. federated CCSP along with 

NOAA and NASA, which led production of CCSP SAP2.3 incorporating significant sections from 

EPA data and reports related particularly to U.S. emissions and measurements. Sections from each of 

these recent comprehensive reports are included here in their entirety or as emended as noted where 

they represent the most thorough summary of the climate effects of aerosols. (In the sections 

included from IPCC AR4 and CCSP SAP2.3, ‘aerosols’ is more frequently used than “PM” and that 

word is retained.) 

9.3.1. The Climate Effects of Aerosols 

Section 9.3.1 comes directly from CCSP SAP2.3 Chapter 1 Section 1.2, with section, table, 

and figure numbers changed to be internally consistent with this ISA. 

Aerosols exert a variety of impacts on the environment. Aerosols (sometimes referred to 

particulate matter or “PM,” especially in air quality applications), when concentrated near the 

surface, have long been recognized as affecting pulmonary function and other aspects of 

human health. Sulfate and nitrate aerosols play a role in acidifying the surface downwind of 

gaseous sulfur and odd nitrogen sources. Particles deposited far downwind might fertilize ironpoor waters in remote oceans, and Saharan dust reaching the Amazon Basin is thought to 

contribute nutrients to the rainforest soil. 

December 2009 9-74 
Aerosols also interact strongly with solar and terrestrial radiation in several ways. Figure 

9-52 offers a schematic overview. First, they scatter and absorb sunlight (Charlson and Pilat, 

1969, 190025; McCormick and Ludwig, 1967, 190528; Mitchell, 1971, 190546); these are 

described as “direct effects” on shortwave (solar) radiation. Second, aerosols act as sites at 

which water vapor can accumulate during cloud droplet formation, serving as cloud 

condensation nuclei or CCN. Any change in number concentration or hygroscopic properties 

of such particles has the potential to modify the physical and radiative properties of clouds, 

altering cloud brightness (Twomey, 1977, 190533) and the likelihood and intensity with which 

a cloud will precipitate (e.g., Albrecht, 1989, 045783; Gunn and Phillips, 1957, 190595; Liou 

and Ou, 1989, 190407). 

Collectively changes in cloud processes due to anthropogenic aerosols are referred to as 

aerosol indirect effects. Finally, absorption of solar radiation by particles is thought to 

contribute to a reduction in cloudiness, a phenomenon referred to as the semi-direct effect. 

This occurs because absorbing aerosol warms the atmosphere, which changes the atmospheric 

stability, and reduces surface flux. 

The primary direct effect of aerosols is a brightening of the planet when viewed from 

space, as much of Earth’s surface is dark ocean, and most aerosols scatter more than 90% of 

the visible light reaching them. The primary indirect effects of aerosols on clouds include an 

increase in cloud brightness, change in precipitation and possibly an increase in lifetime; thus 

the overall net impact of aerosols is an enhancement of Earth’s reflectance (shortwave albedo). 

This reduces the sunlight reaching Earth’s surface, producing a net climatic cooling, as well as 

a redistribution of the radiant and latent heat energy deposited in the atmosphere. These effects 

can alter atmospheric circulation and the water cycle, including precipitation patterns, on a 

variety of length and time scales (e.g., Ramanathan et al., 2001, 042681; Zhang et al., 2006, 

190933). 

Source: IPCC (2007, 092765) modified from Haywood and Boucher (2000, 156531). 

Figure 9-52. Aerosol radiative forcing. Airborne particles can affect the heat balance of the 

atmosphere, directly, by scattering and absorbing sunlight, and indirectly, by 

altering cloud brightness and possibly lifetime. Here small black dots represent 

aerosols, circles represent cloud droplets, and straight lines represent shortwave radiation, and wavy lines, long-wave radiation. LWC is liquid water content, 

and CDNC is cloud droplet number concentration. Confidence in the magnitudes 

of these effects varies considerably (see Chapter 3). Although the overall effect of 

aerosols is a net cooling at the surface, the heterogeneity of particle spatial 

distribution, emission history, and properties, as well as differences in surface 

reflectance, mean that the magnitude and even the sign of aerosol effects vary 

immensely with location, season and sometimes inter-annually. The humaninduced component of these effects is sometimes called “climate forcing.” 

Several variables are used to quantify the impact aerosols have on Earth’s energy balance; 

these are helpful in describing current understanding, and in assessing possible future steps. 

For the purposes of this report, aerosol radiative forcing (RF) is defined as the net energy 

flux (downwelling minus upwelling) difference between an initial and a perturbed aerosol 

loading state, at a specified level in the atmosphere. (Other quantities, such as solar radiation, 

December 2009 9-75 
are assumed to be the same for both states.) This difference is defined such that a negative 

aerosol forcing implies that the change in aerosols relative to the initial state exerts a cooling 

influence, whereas a positive forcing would mean the change in aerosols exerts a warming 

influence. 

There are a number of subtleties associated with this definition: 

(1) The initial state against which aerosol forcing is assessed must be specified. For direct 

aerosol radiative forcing, it is sometimes taken as the complete absence of aerosols. IPCC AR4 

(2001, 156587) uses as the initial state their estimate of aerosol loading in 1750. That year is 

taken as the approximate beginning of the era when humans exerted accelerated influence on 

the environment. 

(2) A distinction must be made between aerosol RF and the anthropogenic contribution to 

aerosol RF. Much effort has been made to distinguishing these contributions by modeling and 

with the help of space-based, airborne, and surface-based remote sensing, as well as in situ 

measurements. These efforts are described in subsequent chapters (of the CCSP SAP2.3). 

(3) In general, aerosol RF and anthropogenic aerosol RF include energy associated with 

both the shortwave (solar) and the long-wave (primarily planetary thermal infrared) 

components of Earth’s radiation budget. However, the solar component typically dominates, so 

in this document, these terms are used to refer to the solar component only, unless specified 

otherwise. The wavelength separation between the short- and long-wave components is usually 

set at around three or four micrometers. 

(4) The IPCC AR4 (2007, 092765) defines radiative forcing as the net downward minus 

upward irradiance at the tropopause due to an external driver of climate change. This 

definition excludes stratospheric contributions to the overall forcing. Under typical conditions, 

most aerosols are located within the troposphere, so aerosol forcing at TOA and at the 

tropopause are expected to be very similar. Major volcanic eruptions or conflagrations can 

alter this picture regionally, and even globally. 

(5) Aerosol radiative forcing can be evaluated at the surface, within the atmosphere, or at 

top-of-atmosphere (TOA). In this document, unless specified otherwise, aerosol radiative 

forcing is assessed at TOA. 

(6) As discussed subsequently, aerosol radiative forcing can be greater at the surface than 

at TOA if the aerosols absorb solar radiation. TOA forcing affects the radiation budget of the 

planet. Differences between TOA forcing and surface forcing represent heating within the 

atmosphere that can affect vertical stability, circulation on many scales, cloud formation, and 

precipitation, all of which are climate effects of aerosols. In this document, unless specified 

otherwise, these additional climate effects are not included in aerosol radiative forcing.) 

(7) Aerosol direct radiative forcing can be evaluated under cloud-free conditions or under 

natural conditions, sometimes termed “all-sky” conditions, which include clouds. Cloud-free 

direct aerosol forcing is more easily and more accurately calculated; it is generally greater than 

all-sky forcing because clouds can mask the aerosol contribution to the scattered light. Indirect 

forcing, of course, must be evaluated for cloudy or all-sky conditions. In this document, unless 

specified otherwise, aerosol radiative forcing is assessed for all-sky conditions. 

(8) Aerosol radiative forcing can be evaluated instantaneously, daily (24 h) averaged, or 

assessed over some other time period. Many measurements, such as those from polar-orbiting 

satellites, provide instantaneous values, whereas models usually consider aerosol RF as a daily 

average quantity. In this document, unless specified otherwise, daily averaged aerosol radiative 

forcing is reported. 

(9) Another subtlety is the distinction between a “forcing” and a “feedback.” As different 

parts of the climate system interact, it is often unclear which elements are “causes” of climate 

change (forcings among them), which are responses to these causes, and which might be some 

of each. So, for example, the concept of aerosol effects on clouds is complicated by the impact 

clouds have on aerosols; the aggregate is often called aerosol-cloud interactions. This 

distinction sometimes matters, as it is more natural to attribute responsibility for causes than 

for responses. However, practical environmental considerations usually depend on the net 

result of all influences. In this report, “feedbacks” are taken as the consequences of changes in 

surface or atmospheric temperature, with the understanding that for some applications, the 

accounting may be done differently. 

December 2009 9-76 
Source: IPCC (2007, 092765). 

Figure 9-53. Global average radiative forcing (RF) estimates and uncertainty ranges in 2005, 

relative to the pre-industrial climate. Anthropogenic CO2, methane (CH4), nitrous 

oxide (N2O), ozone, and aerosols as well as the natural solar irradiance variations 

are included. Typical geographical extent of the forcing (spatial scale) and the 

assessed level of scientific understanding (LOSU) are also given. Forcing is 

expressed in units of watts per square meter (W/m2). The total anthropogenic 

radiative forcing and its associated uncertainty are also given. 

December 2009 9-77 
Source: Adapted from IPCC (2007, 092765). 

Figure 9-54. Probability distribution functions (PDFs) for anthropogenic aerosol and GHG 

RFs. Dashed red curve: RF of long-lived greenhouse gases plus ozone; dashed 

blue curve: RF of aerosols (direct and cloud albedo RF); red filled curve: 

combined anthropogenic RF. The RF range is at the 90% confidence interval. 

In summary, aerosol radiative forcing, the fundamental quantity about which this report is 

written, must be qualified by specifying the initial and perturbed aerosol states for which the 

radiative flux difference is calculated, the altitude at which the quantity is assessed, the 

wavelength regime considered, the temporal averaging, the cloud conditions, and whether total 

or only human-induced contributions are considered. The definition given here, qualified as 

needed, is used throughout the report. 

Although the possibility that aerosols affect climate was recognized more than 40 years 

ago, the measurements needed to establish the magnitude of such effects, or even whether 

specific aerosol types warm or cool the surface, were lacking. Satellite instruments capable of 

at least crudely monitoring aerosol amount globally were first deployed in the late 1970s. But 

scientific focus on this subject grew substantially in the 1990s (e.g., Charlson and Wigley, 

1994, 189989; Charlson et al., 1991, 045793; 1992, 045794; Penner et al., 1992, 045825), in 

part because it was recognized that reproducing the observed temperature trends over the 

industrial period with climate models requires including net global cooling by aerosols in the 

calculation (IPCC, 1995, 190991; 1996, 190990), along with the warming influence of 

enhanced atmospheric greenhouse gas (GHG) concentrations – mainly carbon dioxide, 

methane, nitrous oxide, chlorofluorocarbons, and ozone. 

Improved satellite instruments, ground- and ship-based surface monitoring, more 

sophisticated chemical transport and climate models, and field campaigns that brought all 

these elements together with aircraft remote sensing and in situ sampling for focused, 

coordinated study, began to fill in some of the knowledge gaps. By the Fourth IPCC 

Assessment Report, the scientific community consensus held that in global average, the sum of 

direct and indirect top-of-atmosphere (TOA) forcing by anthropogenic aerosols is negative 

(cooling) of about -1.3 W/m2

 (-2.2 to -0.5 W/m2). This is significant compared to the positive 

forcing by anthropogenic GHGs (including ozone), about 2.9 ± 0.3 W/m2 (IPCC, 2007, 

092765). However, the spatial distribution of the gases and aerosols are very different, and 

they do not simply exert compensating influences on climate. 

The IPCC aerosol forcing assessments are based largely on model calculations, 

constrained as much as possible by observations. At present, aerosol influences are not yet 

quantified adequately, according to Figure 9-53, as scientific understanding is designated as 

“Medium-Low” and “Low” for the direct and indirect climate forcing, respectively. The IPCC 

AR4 (2007, 092765) concluded that uncertainties associated with changes in Earth’s radiation 

budget due to anthropogenic aerosols make the largest contribution to the overall uncertainty 

in radiative forcing of climate change among the factors assessed over the industrial period 

(Figure 9-54). 

December 2009 9-78 
Source: Adapted with Permission of theAmerican Geophysical Union from from Zhou et al. (2005, 156183). 

Figure 9-55. The clear-sky forcing efficiency Eτ, defined as the diurnally averaged aerosol 

direct radiative effect (W/m2) per unit AOD at 550 nm, calculated at both TOA and 

the surface, for typical aerosol types over different geographical regions. The 

vertical black lines represent ± one standard deviation of Eτ for individual aerosol 

regimes and A is surface broadband albedo. 

Although AOD, aerosol properties, aerosol vertical distribution, and surface reflectivity 

all contribute to aerosol radiative forcing, AOD usually varies on regional scales more than the 

other aerosol quantities involved. Forcing efficiency (Eτ), defined as a ratio of direct aerosol 

radiative forcing to AOD at 550 nm, reports the sensitivity of aerosol radiative forcing to 

AOD, and is useful for isolating the influences of particle properties and other factors from 

that of AOD. Eτ is expected to exhibit a range of values globally, because it is governed 

mainly by aerosol size distribution and chemical composition (which determine aerosol singlescattering albedo and phase function), surface reflectivity, and solar irradiance, each of which 

exhibits pronounced spatial and temporal variations. To assess aerosol RF, Eτ is multiplied by 

the ambient AOD. 

Figure 9-55 shows a range of Eτ, derived from AERONET surface sun photometer 

network measurements of aerosol loading and particle properties, representing different 

aerosol and surface types, and geographic locations. It demonstrates how aerosol direct solar 

radiative forcing (with initial state taken as the absence of aerosol) is determined by a 

combination of aerosol and surface properties. For example, Eτ due to southern African 

biomass burning smoke is greater at the surface and smaller at TOA than South American 

smoke because the southern African smoke absorbs sunlight more strongly, and the magnitude 

of Eτ for mineral dust for several locations varies depending on the underlying surface 

reflectance. Figure 9-55 illustrates one further point, that the radiative forcing by aerosols on 

surface energy balance can be much greater than that at TOA. This is especially true when the 

particles have SSA substantially less than 1, which can create differences between surface and 

TOA forcing as large as a factor of five (e.g., Zhou et al., 2005, 156183). 

December 2009 9-79 
Table 9-3. Top-of-atmosphere, cloud-free, instantaneous direct aerosol radiative forcing 

dependence on aerosol and surface properties. Here TWP, SGP, and NSA are the 

Tropical West Pacific island, Southern Great Plains, and North Slope Alaska 

observation stations maintained by the DOE ARM program, respectively. 

Instantaneous values are given at specific solar zenith angle. Upper and middle 

parts are from McComiskey et al. (2008, 190523). Representative, parameterspecific measurement uncertainty upper bounds for producing 1 W/m2

instantaneous TOA forcing accuracy are given in the lower part, based on 

sensitivities at three sites from the middle part of the table. 

Parameters TWP SGP NSA 

AEROSOL PROPERTIES (AOD, SSA, G), SOLAR ZENITH ANGLE (SZA), SURFACE ALBEDO (A), AND AEROSOL DIRECT RF AT 

TOA (F) 

AOD 0.05 0.1 0.05 

SSA 0.97 0.95 0.95 

g 0.8 0.6 0.7 

A 0.05 0.1 0.9 

SZA 30 45 70 

F (W/m2) -2.2 -6.3 2.6 

SENSITIVITY OF CLOUD-FREE, INSTANTANEOUS, TOA DIRECT AEROSOL RADIATIVE FORCING TO AEROSOL AND SURFACE 

PROPERTIES, W/M2

 PER UNIT CHANGE IN PROPERTY 

dFld(AOD) -45 -64 51 

dFld(SSA) -11 -50 -60 

dFldg 13 23 2 

dFldA 8 24 6 

REPRESENTATIVE MEASUREMENT UNCERTAINTY UPPER BOUNDS FOR PRODUCING 1 W/M2 ACCURACY OF AEROSOL RF 

AOD 0.022 0.016 0.020 

SSA 0.091 0.020 0.017 

g 0.077 0.043 

A 0.125 0.042 0.167 

 

Table 9-3 presents estimates of cloud-free, instantaneous, aerosol direct RF dependence 

on AOD, and on aerosol and surface properties, calculated for three sites maintained by the 

U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) program, where 

surface and atmospheric conditions span a significant range of natural environments 

(McComiskey et al., 2008, 190523). Here aerosol RF is evaluated relative to an initial state 

that is the complete absence of aerosols. Note that aerosol direct RF dependence on individual 

parameters varies considerably, depending on the values of the other parameters, and in 

particular, that aerosol RF dependence on AOD actually changes sign, from net cooling to net 

warming, when aerosols reside over an exceedingly bright surface. Sensitivity values are given 

for snapshots at fixed solar zenith angles, relevant to measurements made, for example, by 

polar-orbiting satellites. 

The lower portion of Table 9-3 presents upper bounds on instantaneous measurement 

uncertainty, assessed individually for each of AOD, SSA, g, and A, to produce a 1 W/m2 top-of 

atmosphere, cloud-free aerosol RF accuracy. The values are derived from the upper portion of 

the table, and reflect the diversity of conditions captured by the three ARM sties. Aerosol RF 

sensitivity of 1 W/m2 is used as an example; uncertainty upper bounds are obtained from the 

partial derivative for each parameter by neglecting the uncertainties for all other parameters. 

These estimates produce an instantaneous AOD measurement uncertainty upper bound 

between about 0.01 and 0.02, and SSA constrained to about 0.02 over surfaces as bright as or 

brighter than the ARM Southern Great Plains site, typical of mid-latitude, vegetated land. 

Other researchers, using independent data sets, have derived ranges of Eτ and aerosol RF 

December 2009 9-80 
sensitivity similar to those presented here, for a variety of conditions (Christopher and Jones, 

2008, 189985; Yu et al., 2006, 156173; Zhou et al., 2005, 156183). 

These uncertainty bounds provide a baseline against which current and expected nearfuture instantaneous measurement capabilities are assessed in Chapter 2 (of the CCSP 

SAP2.3). Model sensitivity is usually evaluated for larger-scale (even global) and longer-term 

averages. When instantaneous measured values from a randomly sampled population are 

averaged, the uncertainty component associated with random error diminishes as something 

like the inverse square root of the number of samples. As a result, the accuracy limits used for 

assessing more broadly averaged model results corresponding to those used for assessing 

instantaneous measurements would have to be tighter, as discussed in Chapter 4 (of the CCSP 

SAP2.3). 

In summary, much of the challenge in quantifying aerosol influences arises from large 

spatial and temporal heterogeneity, caused by the wide variety of aerosol sources, sizes and 

compositions, the spatial non-uniformity and intermittency of these sources, the short 

atmospheric lifetime of most aerosols, and the spatially and temporally non-uniform chemical 

and microphysical processing that occurs in the atmosphere. In regions having high 

concentrations of anthropogenic aerosol, for example, aerosol forcing is much stronger than 

the global average, and can exceed the magnitude of GHG warming, locally reversing the sign 

of the net forcing. It is also important to recognize that the global-scale aerosol TOA forcing 

alone is not an adequate metric for climate change (NRC, 2005, 057409). Due to aerosol 

absorption, mainly by soot, smoke, and some desert dust particles, the aerosol direct radiative 

forcing at the surface can be much greater than the TOA forcing, and in addition, the radiative 

heating of the atmosphere by absorbing particles can change the atmospheric temperature 

structure, evolution, and possibly large-scale dynamical systems such as the monsoons (Kim et 

al., 2006, 190917; Lau et al., 2009, 190229). By realizing aerosol’s climate significance and 

the challenge of charactering highly variable aerosol amount and properties, the U.S. Climate 

Change Research Initiative (CCRI) identified research on atmospheric concentrations and 

effects of aerosols specifically as a top priority (NRC, 2001, 053303). 

9.3.2. Overview of Aerosol Measurement Capabilities 

9.3.2.1. Satellite Remote Sensing 

Section 9.3.2 with the exception of the final paragraph, comes directly from CCSP SAP2.3 

Chapter 2, Section 2.2 with section, table, and figure numbers changed to be internally consistent 

with this ISA. 

A measurement-based characterization of aerosols on a global scale can be realized only 

through satellite remote sensing, which is the only means of characterizing the large spatial 

and temporal heterogeneities of aerosol distributions. Monitoring aerosols from space has been 

performed for over two decades and is planned for the coming decade with enhanced 

capabilities (Forster et al., 2007, 092936; King et al., 1999, 190635; Lee et al., 2006, 190358; 

Mischenko et al., 2007, 190543). Table 9-4 summarizes major satellite measurements currently 

available for the tropospheric aerosol characterization and radiative forcing research. 

Early aerosol monitoring from space relied on sensors that were designed for other 

purposes. The Advanced Very High Resolution Radiometer (AVHRR), intended as a cloud and 

surface monitoring instrument, provides radiance observations in the visible and near infrared 

wavelengths that are sensitive to aerosol properties over the ocean (Husar et al., 1997, 045900; 

Mishchenko et al., 1999, 190541). Originally intended for ozone monitoring, the ultraviolet 

(UV) channels used for the Total Ozone Mapping Spectrometer (TOMS) are sensitive to 

aerosol UV absorption with little surface interferences, even over land (Torres et al., 1998, 

190503). This UV-technique makes TOMS suitable for monitoring biomass burning smoke 

and dust, though with limited sensitivity near the surface (Herman et al., 1997, 048393) and 

for retrieving aerosol single-scattering albedo from space (Torres et al., 2005, 190507). (A new 

sensor, the Ozone Monitoring Instrument (OMI) aboard Aura, has improved on such UVtechnique advantages, providing higher spatial resolution and more spectral channels; see 

(Veihelmann et al., 2007, 190627). Such historical sensors have provided multi-decadal 

climatology of aerosol optical depth that has significantly advanced the understanding of 

aerosol distributions and long-term variability (e.g.,Geogdzhayev et al., 2002, 190574; Massie 

et al., 2004, 190492; Mishchenko and Geogdzhayev, 2007, 190545; Mishchenko et al., 2007, 

190542; Torres et al., 2002, 190505; Zhao et al., 2008, 190935). 

Over the past decade, satellite aerosol retrievals have become increasingly sophisticated. 

Now, satellites measure the angular dependence of radiance and polarization at multiple 

wavelengths from UV through the infrared (IR) at fine spatial resolution. From these 

observations, retrieved aerosol products include not only optical depth at one wavelength, but 

also spectral optical depth and some information about particle size over both ocean and land, 

as well as more direct measurements of polarization and phase function. In addition, cloud 

screening is much more robust than before and onboard calibration is now widely available. 

December 2009 9-81 
Examples of such new and enhanced sensors include the MODerate resolution Imaging 

Spectroradiometer (MODIS, see Box 2.1 of CCSP SAP2.3), the Multi-angle Imaging 

SpectroRadiometer (MISR, see Box 2.2 of CCSP SAP2.3), Polarization and Directionality of 

the Earth’s Reflectance (POLDER, see Box 2.3 of CCSP SAP2.3), and OMI, among others. 

The accuracy for AOD measurement from these sensors is about 0.05 or 20% of AOD (Kahn 

et al., 2005, 190966; Remer et al., 2005, 190221) and somewhat better over dark water, but 

that for aerosol microphysical properties, which is useful for distinguishing aerosol air mass 

types, is generally low. The Clouds and the Earth’s Radiant Energy System (CERES, see Box 

2.4 of CCSP SAP2.3) measures broadband solar and terrestrial radiances. The CERES 

radiation measurements in combination with satellite retrievals of aerosol optical depth can be 

used to determine aerosol direct radiative forcing. 

Table 9-4. Summary of major satellite measurements currently available for the tropospheric 

aerosol characterization and radiative forcing research. 

Category Properties Sensor/platform Parameters Spatial coverage Temporal coverage 

AVHRR/NOAA-series ~daily coverage of 

global ocean 1981-present 

TOMS/Nimbus, ADEOSI, EP 1979-2001 

POLDER-1,2, PARASOL 1997-present 

MODIS/Terra, Aqua 

~daily coverage of 

global land and ocean 2000-present (Terra) 

2002-present (Aqua) 

MISR/Terra 

~weekly coverage of 

global land and ocean, 

including bright desert 

and nadir sun-glint 

2000-present 

Loading 

OMI/Aura 

Optical depth 

~daily coverage of 

global land and ocean 2005-present 

AVHRR/NOAA-series Ångström exponent Global ocean 1981-present 

POLDER-1,2, PARASOL 

Fine-mode fraction, 

Ångström exponent, 

non-spherical fraction 

Global land and ocean 1997-present 

Fine-mode fraction 

Ångström exponent 

Global land and ocean 

(better quality over 

ocean) 

Effective radium 

MODIS/Terra, Aqua 

Asymmetry factor 

Global ocean 

2000-present (Terra) 

2002-present (Aqua) 

Size, shape 

MISR/Terra 

Ångström exponent, 

small, medium large 

fractions, non-spherical 

fractions 

Global land and ocean 2000-present 

TOMS/Nimbus, ADEOSI, EP 1979-2001 

OMI/Aura 

Absorbing aerosol index, 

single-scattering albedo, 

absorbing optical depth 2005-present 

Column-integrated 

Absorption 

MISR/Terra Single-scattering albedo 

(2-4 bins) 

Global land and ocean 

2000-present 

GLAS/ICESat Extinction/backscatter 2003-present 

(~3 mo/yr) 

Vertical-resolved Loading, size, and 

shape 

CALIOP/CALIPSO 

Extinction/backscatter, 

color ratio, 

depolarization ratio 

Global land and ocean, 

16-day repeating cycle, 

single-nadir 

measurement 2006-present 

December 2009 9-82 
Complementary to these passive sensors, active remote sensing from space is also now 

possible and ongoing (see Box 2.5 of CCSP SAP2.3). Both the Geoscience Laser Altimeter 

System (GLAS) and the Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP) are 

collecting essential information about aerosol vertical distributions. Furthermore, the 

constellation of six afternoon-overpass spacecrafts (as illustrated in Figure 9-60), the so-called 

A-Train (Stephens et al., 2002, 190412) makes it possible for the first time to conduct near 

simultaneous (within 15 minutes) measurements of aerosols, clouds, and radiative fluxes in 

multiple dimensions with sensors in complementary capabilities. 

The improved accuracy of aerosol products (mainly AOD) from these new-generation 

sensors, together with improvements in characterizing the earth’s surface and clouds, can help 

reduce the uncertainties associated with estimating the aerosol direct radiative forcing (Yu et 

al., 2006, 156173); and references therein). The retrieved aerosol microphysical properties, 

such as size, absorption, and non-spherical fraction can help distinguish anthropogenic 

aerosols from natural aerosols and hence help assess the anthropogenic component of aerosol 

direct radiative forcing (Bellouin et al., 2005, 155684; Christopher et al., 2006, 155729; 

Kaufman et al., 2005, 155891; Yu et al., 2006, 156173). However, to infer aerosol number 

concentrations and examine indirect aerosol radiative effects from space, significant efforts are 

needed to measure aerosol size distribution with much improved accuracy, characterize aerosol 

type, account for impacts of water uptake on aerosol optical depth, and determine the fraction 

of aerosols that is at the level of the clouds (Kapustin et al., 2006, 190961; Rosenfeld, 2006, 

190233). In addition, satellite remote sensing is not sensitive to particles much smaller than 

0.1 micrometer in diameter, which comprise of a significant fraction of those that serve as 

cloud condensation nuclei. 

Finally, algorithms are being developed to retrieve aerosol absorption or SSA from 

satellite observations (e.g., Kaufman et al., 2002, 190955; Torres et al., 2005, 190507). The 

NASA Glory mission, scheduled to launch in 2009 and to be added to the A-Train, will deploy 

a multi-angle, multispectral polarimeter to determine the global distribution of aerosol and 

clouds. It will also be able to infer microphysical property information, from which aerosol 

type (e.g., marine, dust, pollution, etc.) can be inferred for improving quantification of the 

aerosol direct and indirect forcing on climate (Mischenko et al., 2007, 190543). 

In summary, major advances have been made in both passive and active aerosol remote 

sensing from space in the past decade, providing better coverage, spatial resolution, retrieved 

AOD accuracy, and particle property information. However, AOD accuracy is still much 

poorer than that from surface-based sun photometers (0.01-0.02), even over vegetated land and 

dark water where retrievals are most reliable. Although there is some hope of approaching this 

level of uncertainty with a new generation of satellite instruments, the satellite retrievals entail 

additional sensitivities to aerosol and surface scattering properties. It seems unlikely that 

satellite remote sensing could exceed the sun photometer accuracy without introducing some 

as-yet-unspecified new technology. Spacebased lidars are for the first time providing global 

constraints on aerosol vertical distribution, and multi-angle imaging is supplementing this with 

maps of plume injection height in aerosol source regions. Major advances have also been made 

during the past decade in distinguishing aerosol types from space, and the data are now useful 

for validating aerosol transport model simulations of aerosol air mass type distributions and 

transports, particularly over dark water. But particle size, shape, and especially SSA 

information has large uncertainty; improvements will be needed to better distinguish 

anthropogenic from natural aerosols using space-based retrievals. The particle microphysical 

property detail required to assess aerosol radiative forcing will come largely from targeted in 

situ and surface remote sensing measurements, at least for the near-future, although estimates 

of measurement-based aerosol RF can be made from judicious use of the satellite data with 

relaxed requirements for characterizing aerosol microphysical properties. 

MODerate resolution Imaging Spectroradiometer 

MODIS performs near global daily observations of atmospheric aerosols. Seven of 36 

channels (between 0.47 and 2.13 μm) are used to retrieve aerosol properties over cloud and 

surface-screened areas (Li et al., 2004, 190386; Martins et al., 2002, 190470). Over vegetated 

land, MODIS retrieves aerosol optical depth at three visible channels with high accuracy of ± 

0.05 ± 0.2τ (Chu et al., 2002, 190001; Kaufman and Fraser, 1997, 190958; Levy et al., 2007, 

190379; Remer et al., 2005, 190221). Most recently a deep blue algorithm (Hsu et al., 2004, 

190622) has been implemented to retrieve aerosols over bright deserts on an operational basis, 

with an estimated accuracy of 20-30%. Because of the greater simplicity of the ocean surface, 

MODIS has the unique capability of retrieving not only aerosol optical depth with greater 

accuracy, i.e., ± 0.03 ± 0.05τ (Remer et al., 2002, 190218; 2005, 190221; Remer et al., 2008, 

190224; Tanré et al., 1997, 190452), but also quantitative aerosol size parameters (e.g., 

effective radius, fine-mode fraction of AOD) (Kaufman et al., 2002, 190956; Kleidman et al., 

2005, 190175; Remer et al., 2005, 190221). The fine-mode fraction has been used as a tool for 

separating anthropogenic aerosol from natural ones and estimating the anthropogenic aerosol 

direct climate forcing (Kaufman et al., 2005, 155891). Figure 9-56 shows composites of 

MODIS AOD and fine-mode fraction that illustrate seasonal and geographical variations of 

aerosol types. Clearly seen from the figure is heavy pollution over East Asia in both months, 

biomass burning smoke over South Africa, South America, and Southeast Asia in August, 

December 2009 9-83 
heavy dust storms over North Atlantic in both months and over Arabian Sea in August, and a 

mixture of dust and pollution plume swept across North Pacific in April. 

Multi-Angle Imaging SpectroRadiometer 

MISR, aboard the sun-synchronous, polar orbiting satellite Terra, measures upwelling 

solar radiance in four visible-near-IR spectral bands and at nine view angles spread out in the 

forward and aft directions along the flight path (Diner et al., 2002, 189967). It acquires global 

coverage about once per week. A wide range of along-track view angles makes it feasible to 

more accurately evaluate the surface contribution to the TOA radiances and hence retrieve 

aerosols over both ocean and land surfaces, including bright desert and sunglint regions (Diner 

et al., 1998, 189962; Kahn et al., 2005, 190966; Martonchik et al., 1998, 190472; 2002, 

190490). MISR AODs are within 20% or ± 0.05 of coincident AERONET measurements 

(Abdou et al., 2005, 190028; Kahn et al., 2005, 189961). The MISR multi-angle data also 

sample scattering angles ranging from about 60° to 160° in midlatitudes, yielding information 

about particle size (Chen et al., 2008, 189984; Kahn et al., 1998, 190970; 2001, 190969; 2005, 

190966) and shape (Kalashnikova and Kahn, 2006, 190962). The aggregate of aerosol 

microphysical properties can be used to assess aerosol airmass type, a more robust 

characterization of MISR-retrieved particle property information than individual attributes. 

MISR also retrieves plume height in the vicinity of wildfire, volcano, and mineral dust aerosol 

sources, where the plumes have discernable spatial contrast in the multi-angle imagery (Kahn 

et al., 2007, 190964). Figure 9-57 is an example that illustrates MISR’s ability to characterize 

the load, optical properties, and stereo height of near-source fire plumes. 

POLarization and Directionality of the Earth’s Reflectance 

POLDER is a unique aerosol sensor that consists of wide field-of-view imaging spectroradiometer capable of measuring multi-spectral, multi-directional, and polarized radiances 

(Deuzé et al., 2001, 192013). The observed radiances can be exploited to better separate the 

atmospheric contribution from the surface contribution over both land and ocean. POLDER -1 

and -2 flew onboard the ADEOS (Advanced Earth Observing Satellite) from November 1996 

to June 1997 and April to October of 2003, respectively. A similar POLDER instrument flies 

on the PARASOL satellite that was launched in December 2004. 

Figure 9-58 shows global horizontal patterns of AOD and Ångström exponent over the 

oceans derived from the POLDER instrument for June 1997. The oceanic AOD map (Figure 

9-58a) reveals near-coastal plumes of high AOD, which decrease with distance from the coast. 

This pattern arises from aerosol emissions from the continents, followed by atmospheric 

dispersion, transformation, and removal in the downwind direction. In large-scale flow fields, 

such as the trade winds, these continental plumes persist over several thousand kilometers. The 

Ångström exponent shown in Figure 9-58 exhibits a very different pattern from that of the 

aerosol optical depth; specifically, it exhibits high values downwind of industrialized regions 

and regions of biomass burning, indicative of small particles arising from direct emissions 

from combustion sources and/or gas-to-particle conversion, and low values associated with 

large particles in plumes of soil dust from deserts and in sea salt aerosols. 

December 2009 9-84 
Source: Adapted from (Chin et al., 2007, 190062); original figure from Yoram Kaufman and Reto Stöckli. 

Figure 9-56. A composite of MODIS/Terra observed aerosol optical depth (at 550 nm, green 

light near the peak of human vision) and fine-mode fraction that shows spatial 

and seasonal variations of aerosol types. Industrial pollution and biomass 

burning aerosols are predominately small particles (shown as red), whereas 

mineral dust and sea salt consist primarily of large particles (shown as green). 

Bright red and bright green indicate heavy pollution and dust plumes, 

respectively. 

December 2009 9-85 
a b c 

Source: Reprinted with Permission of theAmerican Geophysical Union from Kahn et al. (2007, 190964). 

Figure 9-57. Oregon fire on September 4, 2003, as observed by MISR: (a) MISR nadir view of 

the fire plume, with five patch locations numbered and wind-vectors superposed 

in yellow; (b) MISR aerosol optical depth at 558 nm; and (c) MISR stereo height 

without wind correction for the same region. 

9.3.2.2. Focused Field Campaigns 

Over the past two decades, numerous focused field campaigns have examined the 

physical, chemical, and optical properties and radiative forcing of aerosols in a variety of 

aerosol regimes around the world, as listed in Table 9-5. These campaigns, which have been 

designed with aerosol characterization as the main goal or as one of the major themes in more 

interdisciplinary studies, were conducted mainly over or downwind of known continental 

aerosol source regions, but in some instances in low-aerosol regimes, for contrast. During each 

of these comprehensive campaigns, aerosols were studied in great detail, using combinations 

of in situ and remote sensing observations of physical and chemical properties from various 

platforms (e.g., aircraft, ships, satellites, and ground-based stations) and numerical modeling. 

In spite of their relatively short duration, these field studies have acquired comprehensive data 

sets of regional aerosol properties that have been used to understand the properties and 

evolution of aerosols within the atmosphere and to improve the climatology of aerosol 

microphysical properties used in satellite retrieval algorithms and CTMs. 

December 2009 9-86 
Source : Reproduced with permission of Laboratoire d’Optique Atmospherique (LOA), Lille, FR; Laboratoire des Sciences du Climat et de l’Environnement 

(LSCE), Gif sur Yvette, FR; Centre National d’etudes Spatiales (CNES), Toulouse, FR; and NAtional Space Development Agency (NASDA), Japan. 

Figure 9-58. Global maps at 18 km resolution showing monthly average (a) AOD at 865 nm 

and (b) Ångström exponent of AOD over water surfaces only for June, 1997, 

derived from radiance measurements by the POLDER. 

9.3.2.3. Ground-Based In Situ Measurement Networks 

Major U.S.-operated surface in situ and remote sensing networks for tropospheric aerosol 

characterization and climate forcing research are listed in Table 9-6. These surface in situ 

stations provide information about long-term changes and trends in aerosol concentrations and 

properties, the influence of regional sources on aerosol properties, climatologies of aerosol 

radiative properties, and data for testing models and satellite aerosol retrievals. The NOAA 

Earth System Research Laboratory (ESRL) aerosol monitoring network consists of baseline, 

regional, and mobile stations. These near-surface measurements include submicrometer and 

sub-10 micrometer scattering and absorption coefficients from which the extinction coefficient 

and single-scattering albedo can be derived. Additional measurements include particle 

concentration and, at selected sites, CCN concentration, the hygroscopic growth factor, and 

chemical composition. 

Several of the stations, which are located across North America and world-wide, are in 

regions where recent focused field campaigns have been conducted. The measurement 

protocols at the stations are similar to those used during the field campaigns. Hence, the station 

data are directly comparable to the field campaign data so that they provide a longer-term 

measure of mean aerosol properties and their variability, as well as a context for the shorterduration measurements of the field campaigns. 

The Interagency Monitoring of Protected Visual Environment (IMPROVE), which is 

operated by the NP Service Air Resources Division, has stations across the U.S. located within 

NPs (Malm et al., 1994, 044920). Although the primary focus of the network is air pollution, 

the measurements are also relevant to climate forcing research. Measurements include fine and 

coarse mode (PM2.5 and PM10) aerosol mass concentration; concentrations of elements, sulfate, 

nitrate, organic carbon, and elemental carbon; and scattering coefficients. 

December 2009 9-87 
In addition, to these U.S.-operated networks, there are other national and international 

surface networks that provide measurements of aerosol properties including, but not limited to, 

the World Meteorological Organization (WMO) Global Atmospheric Watch (GAW) network 

(http://www.wmo.int/pages/prog/arep/gaw/monitoring.html), the European Monitoring and 

Evaluation Programme (EMEP) (http://www.emep.int/), the Canadian Air and Precipitation 

Monitoring Network (CAPMoN) (http://www.msc-smc.ec.gc.ca/capmon/index_e.cfm), and the 

Acid Deposition Monitoring Network in East Asia (EANET) (http://www.eanet.cc/eanet.html). 

Clouds and the Earth’s Radiant Energy System 

CERES measures broadband solar and terrestrial radiances at three channels with a large 

footprint (e.g., 20 km for CERES/Terra) (Wielicki et al., 1996, 190637). It is co-located with 

MODIS and MISR aboard Terra and with MODIS on Aqua. The observed radiances are 

converted to TOA irradiances or fluxes using the Angular Distribution Models (ADMs) that 

are functions of viewing angle, sun angle, and scene type (Loeb and Kato, 2002, 190432; Loeb 

et al., 2005, 190436; Zhang et al., 2005, 190929). Such estimates of TOA solar flux in clearsky conditions can be compared to the expected flux for an aerosol-free atmosphere, in 

conjunction with measurements of aerosol optical depth from other sensors (e.g., MODIS and 

MISR) to derive the aerosol direct radiative forcing (Christopher et al., 2006, 155729; Loeb 

and Manalo-Smith, 2005, 190433; Patadia et al., 2008, 190558; Zhang and Christopher, 2003, 

190928; Zhang et al., 2005, 190930). The derived instantaneous value is then scaled to obtain 

a daily average. A direct use of the coarse spatial resolution CERES measurements would 

exclude aerosol distributions in partly cloudy CERES scenes. Several approaches that 

incorporate coincident, high spatial and spectral resolution measurements (e.g., MODIS) have 

been employed to overcome this limitation (Loeb and Manalo-Smith, 2005, 190433; Zhang et 

al., 2005, 190930). 

Active Remote Sensing of Aerosols 

Following the success of a demonstration of lidar system aboard the U.S. Space Shuttle 

mission in 1994, i.e., Lidar In-space Technology Experiment (LITE) (Winker et al., 1996, 

190914), the Geoscience Laser Altimeter System (GLAS) was launched in early 2003 to 

become the first polar orbiting satellite lidar. It provides global aerosol and cloud profiling for 

a one-month period out of every three-to-six months. It has been demonstrated that GLAS is 

capable of detecting and discriminating multiple layer clouds, atmospheric boundary layer 

aerosols, and elevated aerosol layers (e.g., Spinhirne et al., 2005, 190410). The Cloud-Aerosol 

Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), launched on April 28, 2006, 

is carrying a lidar instrument (Cloud and Aerosol Lidar with Orthogonal Polarization - 

CALIOP) that has been collecting profiles of the attenuated backscatter at visible and nearinfrared wavelengths along with polarized backscatter in the visible channel (Winker et al., 

2003, 192017). CALIOP measurements have been used to derive the above-cloud fraction of 

aerosol extinction optical depth (Chand et al., 2008, 189974), one of the important factors 

determining aerosol direct radiative forcing in cloudy conditions. Figure 9-59 shows an event 

of trans-Atlantic transport of Saharan dust captured by CALIPSO. Flying in formation with the 

Aqua, AURA, POLDER, and CloudSat satellites, the vertically resolved information is 

expected to greatly improve passive aerosol and cloud retrievals as well as allow the retrieval 

of vertical distributions of aerosol extinction, fine- and coarse-mode separately (Huneeus and 

Boucher, 2007, 190624; Kaufman et al., 2003, 190954; Léon et al., 2003, 190366). 

December 2009 9-88 
Source: Reprinted with Permission of the American Geophysical Union from Liu et al. (2008, 156709). 

Figure 9-59. A dust event that originated in the Sahara desert on 17 August 2007 and was 

transported to the Gulf of Mexico. Red lines represent back trajectories 

indicating the transport track of the dust event. Vertical images are 532 nm 

attenuated backscatter coefficients measured by CALIOP when passing over the 

dust transport track. The letter “D” designates the dust layer, and “S” represents 

smoke layers from biomass burning in Africa (17-19 August) and South America 

(22 August). The track of the high-spectral-resolution-lidar (HSRL) measurement 

is indicated by the white line superimposed on the 28 August CALIPSO image. 

The HSRL track is coincident with the track of the 28 August CALIPSO 

measurement off the coast of Texas between 28.75°N and 29.08°N. 

9.3.2.4. In Situ Aerosol Profiling Programs 

In addition to long-term ground based measurements, regular long-term aircraft in situ 

measurements recently have been implemented at several locations. These programs provide a 

statistically significant data set of the vertical distribution of aerosol properties to determine 

spatial and temporal variability through the vertical column and the influence of regional 

sources on that variability. In addition, the measurements provide data for satellite and model 

validation. As part of its long-term ground measurements, NOAA has conducted regular flights 

over Bondville, Illinois since 2006. Measurements include light scattering and absorption 

coefficients, the relative humidity dependence of light scattering, aerosol number 

concentration and size distribution, and chemical composition. The same measurements with 

the exception of number concentration, size distribution, and chemical composition were made 

by NOAA during regular overflights of DOE ARM’s Southern Great Plains (SGP) site from 

2000-2007 (Andrews et al., 2004, 190058) 

(http://www.esrl.noaa.gov/gmd/aero/net/index.html). 

In summary of Sections 9.3.2.2, 9.3.2.3, and 9.3.2.4, in situ measurements of aerosol 

properties have greatly expanded over the past two decades as evidenced by the number of 

focused field campaigns in or downwind of aerosol source regions all over the globe, the 

continuation of existing and implementation of new sampling networks worldwide, and the 

implementation of regular aerosol profiling measurements from fixed locations. In addition, in 

situ measurement capabilities have undergone major advancements during this same time 

December 2009 9-89 
period. These advancements include the ability to measure aerosol chemical composition as a 

function of size at a time resolution of seconds to minutes (e.g., Jayne et al., 2000, 190978), 

the development of instruments able to measure aerosol absorption and extinction coefficients 

at high sensitivity and time resolution and as a function of relative humidity (e.g., Baynard et 

al., 2007, 151669; Lack et al., 2006, 096032), and the deployment of these instruments across 

the globe on ships, at ground-based sites, and on aircraft. However, further advances are 

needed to make this newly developed instrumentation more affordable and turn-key so that it 

can be deployed more widely to characterize aerosol properties at a variety of sites worldwide. 

Figure 9-60. A constellation of five spacecraft that overfly the Equatorat about 1:30 p.m., the 

so-called A-Train, carries sensors having complementary capabilities, offering 

unprecedented opportunities to study aerosols from space in multiple 

dimensions. 

December 2009 9-90 
Table 9-5. List of major intensive field experiments that are relevant to aerosol research in a variety 

of aerosol regimes around the globe conducted in the past two decades. 

Intensive Field Experiments Aerosol Regimes 

Name Location Time Period 

Major References 

TARFOX North Atlantic July 1996 Russell et al. (1999, 190363) 

NEAQS North Atlantic July-August 2002 Quinn and Bates (2003, 049189) 

SCAR-A North America 1993 Remer et al. (1997, 190216) 

CLAMS East Coast of U.S. July-August 2001 Smith et al. (2005, 190401) 

INTEX-NA, ICARTT North America Summer 2004 Fehsenfeld et al. (2006, 190531) 

DOE AIOP Northern Oklahoma May 2003 Ferrare et al. (2006, 190561) 

MILAGRO Mexico City, Mexico March 2006 Molina et al. (2008, 192019) 

TexAQS/GoMACCS Texas and Gulf of Mexico August-September 2006 Jiang et al. (2008, 156609); 

Lu et al. (2008, 190455) 

ARCTS North-central Alaska to 

Greenland (Arctic haze) 

March-April 2008 

ARCTAS Northern Canada (smoke) June-July 2008 

http://www.espo.nasa.gov/arctas/ 

MINOS Mediterranean region July-August 2001 Lelieveld et al. (2002, 190361) 

LACE98 Lindberg, Germany July-August 1998 Ansmann et al. (2002) 

Anthropogenic aerosol 

and boreal forest from 

North America and West 

Europe 

Aerosols99 Atlantic January-February 1999 Bates et al. (2001, 043385) 

INDOEX Indian subcontinent and 

Indian Ocean 

Brown haze in South Asia January-April 1998 & 1999 Ramanathan et al. (2001, 190196) 

ABC South and East Asia Ongoing Ramanathan and Crutzen (2003, 190198) 

EAST-AIRE China March-April 2005 Li et al. (2007, 190392) 

INTEX-B Northeastern Pacific April 2006 Singh et al. (2008, 190394) 

ACE-Asia April 2001 Huebert et al. (2003, 190623); 

Seinfeld et al. (2004, 190388) 

TRACE-P 

East Asian and Northwest 

Pacific 

March-April 2001 Jacob et al. (2003, 190987) 

Anthropogenic aerosol 

and desert dust mixture 

from East Asia 

PEM-West A & B Western Pacific off East 

Asia 

September-October 1991 

February-March 1994 

Hoell et al. (1996, 190607; 1997, 057373) 

BASE-A Brazil 1989 Kaufman et al. (1992, 044557) 

SCAR-B Brazil August-September 1995 Kaufman et al. (1998, 089989) 

LBA-SMOCC Amazon basin September-November 

2002 

Andreae et al. (2004, 155658) 

SAFARI2000 August-September 2000 King et al. (2003, 094395) 

SAFARI92 

South Africa and South 

Atlantic 

September-October 1992 Lindesay et al. (1996, 190403) 

TRACE-ADABEX South Atlantic September-October 1992 Fishman et al. (1996, 190566) 

Biomass burning smoke 

in the tropics 

DABEX West Africa January-February 2006 Haywood et al. (2008, 190602) 

SAMUM Southern Morocco May-June 2006 Heintzenberg et al. (2008, 190605) 

SHADE West coast of North Africa September 2000 Tanrė et al. (2003, 190454) 

PRIDE Puerto Rico June-July 2000 Reid et al. (2003, 190213) 

Mineral dusts from North 

Africa and Arabian 

Peninsula 

UAE2

 Arabian Peninsula August-September 2004 Reid et al. (2008, 190214) 

Remote oceanic aerosol ACE-1 Southern Oceans December 1995 Bates et al. ((1998, 190063) ; 

Quinn and Coffman (1998, 190918) 

Source: Yu (2006, 156173)

December 2009 9-91 
Table 9-6. Summary of major U.S. surface in situ and remote sensing networks for the 

tropospheric aerosol characterization and radiative forcing research. All the reported 

quantities are column-integrated or column-effective, except as indicated. 

Measured/Derived Parameters 

Surface Network 

Loading Size, Shape Absorption Chemistry 

Spatial 

Coverage 

Temporal 

Coverage 

NOAA ESRL aerosol monitoring 

(http://www.esrl.noaa.gov/gmd/aero/) 

Near-surface 

extinction 

coefficient, 

optical depth, 

CN/CNN 

number 

concentrations 

Ångström 

exponent, 

hemispheric 

backscatter 

fraction, 

asymmetry 

factor, 

hygroscopic 

growth 

Singlescattering 

albedo, 

absorption 

coefficient 

Chemical 

composition in 

selected sites 

and periods 

5 baseline 

stations, 

several 

regional 

stations, 

aircraft and 

mobile 

platforms 

In situ 1976 onward

NPS/EPA IMPROVE 

(http://vista.cira.colostate.edu/improve/) 

Near-surface 

mass 

concentrations 

and derived 

extinction 

coefficients by 

species 

Fine and coarse 

separately 

Single-scattering 

albedo, 

absorption 

coefficient 

Ions, ammonium 

SO4

2–, 

ammonium 

nitrate organics, 

EC, fine soil 

156 NPs and 

wilderness 

areas in the 

U.S. 

1988 onward 

NASA AERONET 

(http://aeronet.gsfc.nasa.gov) 

~200 sites 

over global 

land and 

islands 

1993 onward

DOE ARM (http://www.arm.gov) 

Fine-mode 

fraction, 

Ångström 

exponents, 

asymmetry 

factor, phase 

function, nonspherical 

fraction 

Singlescattering 

albedo, 

absorption 

optical depth, 

refractive 

indices 

N/A 

6 sites and 1 

mobile facility 

in N. America, 

Europe, and 

Asia 

1989 onward

NOAA SURFRAD 

(http://www.srrb.noaa.gov/surfrad/) 

7 sites in the 

U.S. 

1995 onward

AERONET-MAN 

(http://aeronet.gsfc.nasa.gov/maritime_aero

sol_network.html) 

Optical depth 

N/A N/A N/A 

Global Ocean 2004-present 

periodically 

Remote 

Sensing 

NASA MPLNET 

(http://mplnet.gsfc.nasa.gov/) 

Vertical profiles 

of backscatter/

extinction 

coefficient 

N/A N/A N/A ~30 sites in 

major 

continents, 

usually 

co-located 

with 

AERONET 

and ARM sites

2000 onward

December 2009 9-92 
Figure 9-61. Geographical coverage of active AERONET sites in 2006. 

9.3.2.5. Ground-Based Remote Sensing Measurement Networks 

The Aerosol Robotic Network (AERONET) program is a federated ground-based remote 

sensing network of well-calibrated sun photometers and radiometers 

(http://aeronet.gsfc.nasa.gov). 

AERONET includes about 200 sites around the world, covering all major tropospheric 

aerosol regimes (Holben et al., 1998, 155848; 2001, 190618), as illustrated in Figure 9-61. Spectral measurements of sun and sky radiance are calibrated and screened for cloud-free 

conditions (Smirnov et al., 2000, 190397). AERONET stations provide direct, calibrated 

measurements of spectral AOD (normally at wavelengths of 440, 670, 870, and 1020 nm) with 

an accuracy of ± 0.015 (Eck et al., 1999, 190390). In addition, inversion-based retrievals of a 

variety of effective, column-mean properties have been developed, including aerosol singlescattering albedo, size distributions, fine-mode fraction, degree of non-sphericity, phase 

function, and asymmetry factor (Dubovik and King, 2000, 190197; Dubovik et al., 2000, 

190177; Dubovik et al., 2002, 190202; O'Neill et al., 2003, 180187). The SSA can be retrieved 

with an accuracy of ± 0.03, but only for AOD >0.4 (Dubovik et al., 2002, 190202), which 

precludes much of the planet. These retrieved parameters have been validated or are 

undergoing validation by comparison to in situ measurements (e.g., Haywood et al., 2003, 

190599; Leahy et al., 2007, 190232; Magi et al., 2005, 190468). 

Recent developments associated with AERONET algorithms and data products include: 

 simultaneous retrieval of aerosol and surface properties using combined AERONET 

and satellite measurements (Sinyuk et al., 2007, 190395) with surface reflectance 

taken into account (which significantly improves AERONET SSA retrieval 

accuracy) (Eck et al., 2008, 190409); 

 the addition of ocean color and high frequency solar flux measurements; and 

 the establishment of the Maritime Aerosol Network (MAN) component to monitor 

aerosols over the World oceans from ships of- opportunity (Smirnov et al., 2006, 

190400). 

Because of consistent calibration, cloud-screening, and retrieval methods, uniformly 

acquired and processed data are available from all stations, some of which have operated for 

over 10 years. These data constitute a high-quality, ground-based aerosol climatology and, as 

such, have been widely used for aerosol process studies as well as for evaluation and 

validation of model simulation and satellite remote sensing applications (e.g., Chin et al., 

2002, 189996; Kahn et al., 2005, 190966; Remer et al., 2005, 190221; Yu et al., 2003, 156171; 

2006, 156173). In addition, AERONET retrievals of aerosol size distribution and refractive 

indices have been used in algorithm development for satellite sensors (Levy et al., 2007, 

190377; Remer et al., 2005, 190221). A set of aerosol optical properties provided by 

AERONET has been used to calculate the aerosol direct radiative forcing (Procopio et al., 

2004, 190571; Zhou et al., 2005, 156183), which can be used to evaluate both satellite remote 

sensing measurements and model simulations. 

AERONET measurements are complemented by other ground-based aerosol networks 

having less geographical or temporal coverage, such as the Atmospheric Radiation 

Measurement (ARM) network (Ackerman and Stokes, 2003, 192080), NOAA’s national 

surface radiation budget network (SURFRAD) (Augustine et al., 2008, 189913) and other 

networks with multifilter rotating shadowband radiometer (MFRSR) (Harrison et al., 1994, 

045805; Michalsky et al., 2001, 190537), and several lidar networks including: 

December 2009 9-93 
 NASA Micro Pulse Lidar Network (MPLNET) (Welton et al., 2001, 157133; Welton 

et al., 2002, 190631); 

 Regional East Atmospheric Lidar Mesonet (REALM) in North America (Hoff and 

McCann, 2002, 190612; Hoff et al., 2004, 190617); 

 European Aerosol Research Lidar Network (EARLINET) (Matthias et al., 2004, 

155971); and 

 Asian Dust Network (AD-Net) (e.g., Murayama et al., 2001, 155992). 

Obtaining accurate aerosol extinction profile observations is pivotal to improving aerosol 

radiative forcing and atmospheric response calculations. The values derived from these lidar 

networks with state-of-the-art techniques (Schmid et al., 2006, 190372) are helping to fill this 

need. 

9.3.2.6. Synergy of Measurements and Model Simulations 

Individual approaches discussed above have their own strengths and limitations, and are 

usually complementary. None of these approaches alone is adequate to characterize large 

spatial and temporal variations of aerosol physical and chemical properties and to address 

complex aerosol-climate interactions. The best strategy for characterizing aerosols and 

estimating their radiative forcing is to integrate measurements from different satellite sensors 

with complementary capabilities from in situ and surface based measurements. Similarly, 

while models are essential tools for estimating regional and global distributions and radiative 

forcing of aerosols at present as well as in the past and the future, observations are required to 

provide following, several synergistic approaches to studying aerosols and their radiative 

forcing are discussed. 

Closure Experiments 

During intensive field studies, multiple platforms and instruments are deployed to sample 

regional aerosol properties through a well-coordinated experimental design. Often, several 

independent methods are used to measure or derive a single aerosol property or radiative 

forcing. This combination of methods can be used to identify inconsistencies in the methods 

and to quantify uncertainties in measured, derived, and calculated aerosol properties and 

radiative forcings. This approach, often referred to as a closure experiment, has been widely 

employed on both individual measurement platforms (local closure) and in studies involving 

vertical measurements through the atmospheric column by one or more platforms (column 

closure) (Quinn et al., 1996, 192021; Russell et al., 1997, 190359). 

Past closure studies have revealed that the best agreement between methods occurs for 

submicrometer, spherical particles such that different measures of aerosol optical properties 

and optical depth agree within 10-15% and often better (e.g., Clarke et al., 1996, 190003; 

Collins et al., 2000, 190059; Quinn et al., 2004, 190937; Schmid et al., 2000, 190369). Larger 

particle sizes (e.g., sea salt and dust) present inlet collection efficiency issues and nonspherical particles (e.g., dust) lead to differences in instrumental responses. In these cases, 

differences between methods for determining aerosol optical depth can be as great as 35% 

(Doherty et al., 2005, 190027; Wang et al., 2003, 157106). Closure studies on aerosol clear-sky 

DRF reveal uncertainties of about 25% for sulfate/carbonaceous aerosol and 60% for dustcontaining aerosol (Bates et al., 2006, 189912). Future closure studies could integrate surfaceand satellite-based radiometric measurements of AOD with in situ optical, microphysical, and 

aircraft radiometric measurements for a wide range of situations. There is also a need to 

maintain consistency in comparing results and expressing uncertainties (Bates et al., 2006, 

189912). 

Constraining Models with In Situ Measurements 

In situ measurements of aerosol chemical, microphysical, and optical properties with 

known accuracy, based in part on closure studies, can be used to constrain regional CTM 

simulations of aerosol direct forcing, as described by Bates et al. (2006, 189912). A key step in 

the approach is assigning empirically derived optical properties to the individual chemical 

components generated by the CTM for use in a Radiative Transfer Model (RTM). Specifically, 

regional data from focused, short-duration field programs can be segregated according to 

aerosol type (sea salt, dust, or sulfate/carbonaceous) based on measured chemical composition 

and particle size. Corresponding measured optical properties can be carried along in the sorting 

process so that they, too, are segregated by aerosol type. The empirically derived aerosol 

properties for individual aerosol types, including mass scattering efficiency, single-scattering 

albedo, and asymmetry factor, and their dependences on relative humidity, can be used in place 

of assumed values in CTMs. Short-term, focused measurements of aerosol properties (e.g., 

aerosol concentration and AOD) also can be used to evaluate CTM parameterizations on a 

regional basis, to suggest improvements to such uncertain model parameters, such as emission 

factors and scavenging coefficients (e.g., Koch et al., 2007, 190185). Improvements in these 

parameterizations using observations yield increasing confidence in simulations covering 

regions and periods where and when measurements are not available. To evaluate the aerosol 

December 2009 9-94 
properties generated by CTMs on broader scales in space and time, satellite observations and 

long-term in situ measurements are required. 

Improved Model Simulations with Satellite Measurements 

Global measurements of aerosols from satellites (mainly AOD) with well-defined 

accuracies offer an opportunity to evaluate model simulations at large spatial and temporal 

scales. The satellite measurements can also be used to constrain aerosol model simulations and 

hence the assessment of aerosol DRF through data assimilation or objective analysis process 

(e.g., Collins et al., 2001, 189987; Liu et al., 2005, 190414; Yu et al., 2003, 156171; 2004, 

190926; 2006, 156173; Zhang et al., 2008, 190932). Both satellite retrievals and model 

simulations have uncertainties. The goal of data integration is to minimize the discrepancies 

between them, and to form an optimal estimate of aerosol distributions by combining them, 

typically with weights inversely proportional to the square of the errors of individual 

descriptions. Such integration can fill gaps in satellite retrievals and generate global 

distributions of aerosols that are consistent with ground-based measurements (Collins et al., 

2001, 189987; Liu et al., 2005, 190414; Yu et al., 2003, 156171; 2006, 156173). Recent efforts 

have also focused on retrieving global sources of aerosol from satellite observations using 

inverse modeling, which may be valuable for reducing large aerosol simulation uncertainties 

(Dubovik et al., 2007, 190211). Model refinements guided by model evaluation and integration 

practices with satellite retrievals can then be used to improve aerosol simulations of the preand post-satellite eras. Current measurement-based understanding of aerosol characterization 

and radiative forcing is assessed in Section 9.3.3 through intercomparisons of a variety of 

measurement-based estimates and model simulations published in literature. This is followed 

by a detailed discussion of major outstanding issues in Section 9.3.4. 

Source: Bates et al. (2006, 189912). 

Figure 9-62. Comparison of the mean concentration (μg/m3) and standard deviation of the 

modeled (STEM) aerosol chemical components with shipboard measurements 

during INDOEX, ACE-Asia, and ICARTT. 

Further complexity is added when attempting to relate surface PM2.5 to aerosol optical depths. 

The main approach to derive surface PM2.5 from satellite optical depths from MISR is based on the 

December 2009 9-95 
use of model derived profiles to determine ratios of aerosol optical depth to surface PM2.5 (Liu and 

Koutrakis, 2007, 187007; Liu et al., 2007, 098197; Van Donkelaar et al., 2006, 192108). 

Van Donkelaar et al. (2006, 192108) derived r = 0.69 (MODIS) and 0.58 (MISR) with annual 

average ground based PM2.5 across the U.S. and Canada. For comparison, r between AERONET total 

AOD and surface measurements was 0.71. On average, MODIS tended to overestimate surface PM2.5

by ~5 μg/m3

, while MISR estimates were biased high by about 3 μg/m3. Liu et al. (2007, 098197) 

and Liu and Koutrakis (2007, 187007) used MISR derived fractional AODs in their analysis and 

found improvement in the retrievals when fractional AODs were used instead of total AOD, allowing 

for better fits to the radiance data. They found that fractional AODs can explain 13-62% of the 

variability in PM2.5 and its components in the eastern U.S. and 28-56% of the variability in the 

western U.S. The models tended to underpredict PM2.5 by ~7-8% in both the East and West. The 

relative errors in surface PM2.5 were estimated to be 30% in the East and 34% in the West. For AODs 

>0.15 (nominal continental background values), dust particles could be distinguished from other 

particles with an estimated error of 4%. Performance improves substantially over polluted urban 

areas because they have much larger AOD. For example, Gupta et al. (2006, 137694) derived a 

Pearson r between MODIS AOD and surface PM2.5 of 0.96 for several urban areas around the world. 

The MODIS Aerosol Optical Depth (AOD)/ In-situ PM2.5 correlation summary plot shown in Figure 

9-63 below illustrates the correlation between AOD and surface PM2.5 across the U.S. and parts of 

Canada. The correlation is based on coincident MODIS AOD pixels and 1-h PM2.5 concentrations 

from the in-situ continuous surface monitors. The parameter plotted is the monitoring site-specific 

running correlation coefficient during the preceding 60 days (in color scale). The correlation 

coefficient has values between 1 (perfectly correlated) and -1 (perfectly anti-correlated). A value of 

zero indicates that the two measurements vary independently of each other. 

The running time period of the correlation determination is given in the plot title, 20090704-

20090901. The size of the point at each site indicates the number of coincidences between MODIS 

AOD pixels and the measured surface 1-h PM2.5 concentrations for that period. Correlation 

significance generally increases with increasing number of coincidences. Higher correlations suggest 

the MODIS AOD pixel is reflective of in-situ surface PM2.5 mass concentrations at the monitor 

location. 

December 2009 9-96 
Figure 9-63. Correlations between one-hour PM2.5 surface measurements in the U.S. and 

southern Canada reported to AIRNOW and MODIS satellite AOD values for the 

period between 4 July and 1 September 2009. Symbol size indicates number of 

coincident points at that location in that period; symbol color is indexed to 

degree of correlation from -1 (cooler) to +1 (warmer). 

The data and image of the aerosol comparison shown in Figure 9-63 were taken from the 

multi-agency project, Infusing satellite Data into Environmental Air Quality Applications (IDEA), a 

partnership of NASA, NOAA, and EPA designed to improve air quality assessment, management, 

and prediction by infusing NASA satellite measurements into NOAA and EPA analyses for public 

benefit. IDEA is funded by these three agencies and managed by the University of Maryland 

Baltimore County and NOAA. 

9.3.3. Assessments of Aerosol Characterization and Climate Forcing 

Sections 9.3.3 through 9.3.6 come directly from CCSP SAP2.3 Chapters 2, Section 2.3 

through Chapter 3, Section 3.8, with section, table, and figure numbers changed to be internally 

consistent with this ISA. 

December 2009 9-97 
This section focuses on the assessment of measurement-based aerosol characterization 

and its use in improving estimates of the direct radiative forcing on regional and global scales. 

In situ measurements provide highly accurate aerosol chemical, microphysical, and optical 

properties on a regional basis and for the particular time period of a given field campaign. 

Remote sensing from satellites and ground-based networks provide spatial and temporal 

coverage that intensive field campaigns lack. Both in situ measurements and remote sensing 

have been used to determine key parameters for estimating aerosol direct radiative forcing 

including aerosol single scattering albedo, asymmetry factor, optical depth remote sensing has 

also been providing simultaneous measurements of aerosol optical depth and radiative fluxes 

that can be combined to derive aerosol direct radiative forcing at the TOA with relaxed 

requirement for characterizing aerosol properties. Progress in using both satellite and surfacebased measurements to study aerosol-cloud interactions and aerosol indirect forcing is also 

discussed. 

9.3.3.1. The Use of Measured Aerosol Properties to Improve Models 

The wide variety of aerosol data sets from intensive field campaigns provides a rigorous 

“testbed” for model simulations of aerosol properties and distributions and estimates of DRF. 

As described in Section 9.3.2.6, in situ measurements can be used to constrain regional CTM 

simulations of aerosol properties, DRF, anthropogenic component of DRF, and to evaluate 

CTM parameterizations. In addition, in situ measurements can be used to develop simplifying 

parameterizations for use by CTMs. 

Several factors contribute to the uncertainty of CTM calculations of size-distributed 

aerosol composition including emissions, aerosol removal by wet deposition, processes 

involved in the formation of secondary aerosols and the chemical and microphysical evolution 

of aerosols, vertical transport, and meteorological fields including the timing and amount of 

precipitation, formation of clouds, and relative humidity. In situ measurements made during 

focused field campaigns provide a point of comparison for the CTM-generated aerosol 

distributions at the surface and at discrete points above the surface. Such comparisons are 

essential for identifying areas where the models need improvement. 

December 2009 9-98 
Source: Data from Zhang et al. (2007, 189998). 

Figure 9-64. Location of aerosol chemical composition measurements with aerosol mass 

spectrometers. Colors for the labels indicate the type of sampling location: 

urban areas (blue), <100 mi downwind of major cites (black), and rural/remote 

areas >100 miles downwind (pink). Pie charts show the average mass 

concentration and chemical composition: organics (green), SO4

2– (red), nitrate 

(blue), ammonium (orange), and chloride (purple), of non-refractory PM1. 

Figure 9-62 shows a comparison of submicrometer and supermicrometer aerosol chemical 

components measured during INDOEX, ACEAsia, and ICARTT onboard a ship and the same 

values calculated with the STEM Model (e.g., Bates et al., 2004, 189958; Carmichael et al., 

2002, 148319; Carmichael et al., 2003, 190042; Streets et al., 2006, 157019; Tang et al., 2003, 

190441; Tang et al., 2004, 190445). To permit direct comparison of the measured and modeled 

values, the model was driven by analyzed meteorological data and sampled at the times and 

locations of the shipboard measurements every 30 min along the cruise track. The best 

agreement was found for submicrometer sulfate and BC. The agreement was best for sulfate; 

this is attributed to greater accuracy in emissions, chemical conversion, and removal for this 

component. Underestimation of dust and sea salt is most likely due to errors in modelcalculated emissions. Large discrepancies between the modeled and measured values occurred 

for submicrometer particulate organic matter (POM) (INDOEX), and for particles in the 

supermicrometer size range such as dust (ACE-Asia), and sea salt (all regions). The model 

underestimated the total mass of the supermicrometer aerosol by about a factor of 3. POM 

makes up a large and variable fraction of aerosol mass throughout the anthropogenically 

influenced northern hemisphere, and yet models have severe problems in properly representing 

this type of aerosol. Much of this discrepancy follows from the models inability to represent 

the formation of secondary organic aerosols (SOA) from the precursor volatile organic 

compounds (VOC). Figure 9-64 shows a summary of the results from aerosol mass 

spectrometer measurements at 30 sites over North America, Europe, and Asia. Based on 

aircraft measurements of urban-influenced air over New England, de Gouw et al. (2005, 

190020) found that POM was highly correlated with secondary anthropogenic gas phase 

species suggesting that the POM was derived from secondary anthropogenic sources and that 

the formation took one day or more. 

December 2009 9-99 
Source: Data from de Guow et al. (2005, 190020). 

Figure 9-65. Scatterplots of the submicrometer POM measured during NEAQS versus 

A) acetylene and B) iso-propyl nitrate. The colors of the data points in A) denote 

the photochemical age as determined by the ratios of compounds of known OH 

reactivity. The gray area in A) shows the range of ratios between submicrometer 

POM and acetylene observed by Kirchstetter et al. (1999, 010642) in tunnel 

studies. 

Figure 9-65 shows scatterplots of submicrometer POM versus acetylene (a gas phase 

primary emitted VOC species) and isopropyl nitrate (a secondary gas phase organic species 

formed by atmospheric reactions). The increase in submicrometer POM with increasing 

photochemical age could not be explained by the removal of VOC alone, which are its 

traditionally recognized precursors. This result suggests that other species must have 

contributed and/or that the mechanism for POM formation is more efficient than assumed by 

models. Similar results were obtained from the 2006 MILAGRO field campaign conducted in 

Mexico City (Kleinman et al., 2008, 190074), and comparisons of GCM results with several 

long-term monitoring stations also showed that the model underestimated organic aerosol 

concentrations (Koch et al., 2007, 190185). Recent laboratory work suggests that isoprene may 

be a major SOA source missing from previous atmospheric models (Henze and Seinfeld, 2006, 

190606; Kroll et al., 2006, 190195), but underestimating sources from certain economic 

sectors may also play a role (Koch et al., 2007, 190185). Models also have difficulty in 

representing the vertical distribution of organic aerosols, underpredicting their occurrence in 

the free troposphere (FT) (Heald et al., 2005, 190603). While organic aerosol presents models 

with some of their greatest challenges, even the distribution of well-characterized sulfate 

aerosol is not always estimated correctly in models (Shindell et al., 2008, 190391). 

Comparisons of DRF and its anthropogenic component calculated with assumed optical 

properties and values constrained by in situ measurements can help identify areas of 

uncertainty in model parameterizations. In a study described by Bates et al. (2006, 189912), 

two different CTMs (MOZART and STEM) were used to calculate dry mass concentrations of 

the dominant aerosol species (sulfate, organic carbon, BC, sea salt, and dust). 

In situ measurements were used to calculate the corresponding optical properties for each 

aerosol type for use in a radiative transfer model. Aerosol DRF and its anthropogenic 

component estimated using the empirically derived and a priori optical properties were then 

compared. The DRF and its anthropogenic component were calculated as the net downward 

solar flux difference between the model state with aerosol and of the model state with no 

aerosol. It was found that the constrained optical properties derived from measurements 

increased the calculated AOD (34 ± 8%), TOA DRF (32 ± 12%), and anthropogenic 

component of TOA DRF (37 ± 7%) relative to runs using the a priori values. These increases 

were due to larger values of the constrained mass extinction efficiencies relative to the a priori 

values. In addition, differences in AOD due to using the aerosol loadings from MOZART 

versus those from STEM were much greater than differences resulting from the a priori vs. 

constrained RTM runs. In situ observations also can be used to generate simplified 

parameterizations for CTMs and RTMs thereby lending an empirical foundation to uncertain 

parameters currently in use by models. CTMs generate concentration fields of individual 

aerosol chemical components that are then used as input to radiative transfer models (RTMs) 

for the calculation of DRF. Currently, these calculations are performed with a variety of 

simplifying assumptions concerning the RH dependence of light scattering by the aerosol. 

December 2009 9-100 
Chemical components often are treated as externally mixed each with a unique RH 

dependence of light scattering. However, both model and measurement studies reveal that 

POM, internally mixed with water-soluble salts, can reduce the hygroscopic response of the 

aerosol, which decreases its water content and ability to scatter light at elevated relative 

humidity (e.g., Carrico et al., 2005, 190052; Saxena et al., 1995, 077273). 

The complexity of the POM composition and its impact on aerosol optical properties 

requires the development of simplifying parameterizations that allow for the incorporation of 

information derived from field measurements into calculations of DRF (Quinn et al., 2005, 

156033). Measurements made during INDOEX, ACE-Asia, and ICARTT revealed a 

substantial decrease in fσsp(RH) with increasing mass fraction of POM in the accumulation 

mode. Based on these data, a parameterization was developed that quantitatively describes the 

relationship between POM mass fraction and fσsp(RH) for accumulation mode sulfate-POM 

mixtures (Quinn et al., 2005, 156033). This simplified parameterization may be used as input 

to RTMs to derive values of fσsp(RH) based on CTM estimates of the POM mass fraction. 

Alternatively, the relationship may be used to assess values of fσsp(RH) currently being used 

in RTMs. 

9.3.3.2. Intercomparisons of Satellite Measurements and Model Simulation of Aerosol 

Optical Depth 

As aerosol DRF is highly dependent on the amount of aerosol present, it is of first-order 

importance to improve the spatial characterization of AOD on a global scale. This requires an 

evaluation of the various remote sensing AOD data sets and comparison with model-based 

AOD estimates. The latter comparison is particularly important if models are to be used in 

projections of future climate states that would result from assumed future emissions. Both 

remote sensing and model simulation have uncertainties and satellite-model integration is 

needed to obtain an optimum description of aerosol distribution. 

Figure 9-65 shows an intercomparison of annual average AOD at 550 nm from two recent 

satellite aerosol sensors (MODIS and MISR), five model simulations (GOCART, GISS, 

SPRINTARS, LMDZ-LOA, LMDZ-INCA) and three satellite-model integrations (MO_GO, 

MI_GO, MO_MI_GO). These model-satellite integrations are conducted by using an optimum 

interpolation approach (Yu et al., 2003, 156171) to constrain GOCART simulated AOD with 

that from MODIS, MISR, or MODIS over ocean and MISR over land, denoted as MO_GO, 

MI_GO, and MO_MI_GO, respectively. MODIS values of AOD are from Terra Collection 4 

retrievals and MISR AOD is based on early post launch retrievals. MODIS and MISR 

retrievals give a comparable average AOD on the global scale, with MISR greater than 

MODIS by 0.01~0.02 depending on the season. However, differences between MODIS and 

MISR are much larger when land and ocean are examined separately: AOD from MODIS is 

0.02-0.07 higher over land but 0.03-0.04 lower over ocean than the AOD from MISR. Several 

major causes for the systematic MODIS-MISR differences have been identified, including 

instrument calibration and sampling differences, different assumptions about ocean surface 

boundary conditions made in the individual retrieval algorithms, missing particle property or 

mixture options in the look-up tables, and cloud screening (Kahn et al., 2007, 190963). The 

MODIS-MISR AOD differences are being reduced by continuous efforts on improving 

satellite retrieval algorithms and radiance calibration. The new MODIS aerosol retrieval 

algorithms in Collection 5 have resulted in a reduction of 0.07 for global land mean AOD 

(Levy et al., 2007, 190379), and improved radiance calibration for MISR removed ~40% of 

AOD bias over dark water scenes (Kahn et al., 2005, 190965). 

The annual and global average AOD from the five models is 0.19 ± 0.02 (mean ± standard 

deviation) over land and 0.13 ± 0.05 over ocean, respectively. Clearly, the model-based mean 

AOD is smaller than both MODIS- and MISR-derived values (except the GISS model). A 

similar conclusion has been drawn from more extensive comparisons involving more models 

and satellites (Kinne et al., 2006, 155903). On regional scales, satellite-model differences are 

much larger. These differences could be attributed in part to cloud contamination (Kaufman et 

al., 2005, 155891; Zhang et al., 2005, 190931) and 3D cloud effects in satellite retrievals 

(Kaufman et al., 2005, 155891; Wen et al., 2006, 179964) or to models missing important 

aerosol sources/sinks or physical processes (Koren et al., 2007, 190192). Integrated satellitemodel products are generally in-between the satellite retrievals and the model simulations, and 

agree better with AERONET measurements (e.g., Yu et al., 2003, 156171). As in comparisons 

between models and in situ measurements (Bates et al., 2006, 189912), there appears to be a 

relationship between uncertainties in the representation of dust in models and the uncertainty 

in AOD, and its global distribution. 

For example, the GISS model generates more dust than the other models (Figure 9-67), resulting in a closer agreement with MODIS and MISR in the global mean (Source: Data 

taken from Kinne et al. (2006, 155903) 

Figure 9-66). However, the distribution of AOD between land and ocean is quite different 

from MODIS- and MISR-derived values. 

Figure 9-67 shows larger model differences in the simulated percentage contributions of 

individual components to the total aerosol optical depth on a global scale, and hence in the 

simulated aerosol single-scattering properties (e.g., single-scattering albedo, and phase 

function), as documented in Kinne et al. (2006, 155903). This, combined with the differences 

December 2009 9-101 
in aerosol loading (as characterized by AOD) determines the model diversity in simulated 

aerosol direct radiative forcing, as discussed later. However, current satellite remote sensing 

capability is not sufficient to constrain model simulations of aerosol components. 

Source: Data taken from Kinne et al. (2006, 155903) 

Figure 9-66. Comparison of annual mean aerosol optical depth (AOD). 

Source: Data taken from Kinne et al. (2006, 155903). 

Figure 9-67. Percentage contributions of individual aerosol components. SU – sulfate, BC – 

BC, POM – particulate organic matter, DU – dust, SS – sea salt; to the total 

aerosol optical depth (at 550 nm) on a global scale simulated by the five models. 

9.3.3.3. Satellite-Based Estimates of Aerosol Direct Radiative Forcing 

Table 9-7 summarizes approaches to estimating the aerosol direct radiative forcing, 

including a brief description of methods, identifies major sources of uncertainty, and provides 

references. These estimates fall into three broad categories, namely (A) satellite-based, (B) 

satellite-model integrated, and (C) model-based. As satellite aerosol measurements are 

generally limited to cloud-free conditions, the discussion here focuses on assessments of clearsky aerosol direct radiative forcing, a net (downwelling minus upwelling) solar flux difference 

between with aerosol (natural + anthropogenic) and in the absence of aerosol. 

December 2009 9-102 
Global Distributions 

Figure 9-68 shows global distributions of aerosol optical depth at 550 nm (left panel) and 

diurnally averaged clear-sky TOA DRF (right panel) for March-April-May (MAM) based on 

the different approaches. The DRF at the surface follows the same pattern as that at the TOA 

but is significantly larger in magnitude because of aerosol absorption. It appears that different 

approaches agree on large-scale patterns of aerosol optical depth and the direct radiative 

forcing. In this season, the aerosol impacts in the Northern Hemisphere are much larger than 

those in the Southern Hemisphere. Dust outbreaks and biomass burning elevate the optical 

depth to more than 0.3 over large parts of North Africa and the tropical Atlantic. In the tropical 

Atlantic, TOA cooling as large as -10 W/m2 extends westward to Central America. In eastern 

China, the optical depth is as high as 0.6-0.8, resulting from the combined effects of industrial 

activities and biomass burning in the south, and dust outbreaks in the north. The Asian impacts 

also extend to the North Pacific, producing a TOA cooling of more than -10 W/m2. Other areas 

having large aerosol impacts include Western Europe, midlatitude North Atlantic, and much of 

South Asia and the Indian Ocean. Over the “roaring forties” in the Southern Hemisphere, high 

winds generate a large amount of sea salt. Elevated optical depth, along with high solar zenith 

angle and hence large backscattering to space, results in a band of TOA cooling of more than -

4 W/m2. However, there is also some question as to whether thin cirrus (e.g., Zhang et al., 

2005, 190931) and unaccounted-for whitecaps contribute to the apparent enhancement in AOD 

retrieved by satellite. Some differences exist between different approaches. For example, the 

early post-launch MISR retrieved optical depths over the southern hemisphere oceans are 

higher than MODIS retrievals and GOCART simulations. Over the “roaring forties,” the 

MODIS derived TOA solar flux perturbations are larger than the estimates from other 

approaches. 

December 2009 9-103 
Table 9-7. Summary of approaches to estimating the aerosol direct radiative forcing in three 

categories: (1) satellite retrievals; (2) satellite-model integrations; and (3) model 

simulations. 

Category Product Brief Description Identified Sources of Uncertainty Major References 

MODIS 

Using MODIS retrievals of a linked 

set of AOD, ω0, and phase function 

consistently in conjunction with a 

radiative transfer model (RTM) to 

calculate TOA fluxes that best match 

the observed radiances. 

Radiance calibration, cloud-aerosol discrimination, 

instantaneous-to-diurnal scaling, RTM 

parameterizations 

Remer and Kaufman (2006, 

190222) 

MODIS_A 

Splitting MODIS AOD over ocean into 

mineral dust, sea salt, and biomassburning and pollution; using AERONET 

measurements to derive the size 

distribution and single-scattering albedo 

for individual components. 

Satellite AOD and FMF retrievals, overestimate due to 

summing up the compositional direct forcing, use of a 

single AERONET site to characterize a large region 

Bellouin et al. (2005, 155684) 

CERES_A Using CERES fluxes in combination 

with standard MODIS aerosol. 

CERES_B 

Using CERES fluxes in combination 

with NOAA NESDIS aerosol from 

MODIS radiances. 

Loeb and Manalo-Smith 

(2005, 190433); Loeb and 

Kato (2002, 190432) 

CERES_C 

Using CERES fluxes in combination 

with MODIS (ocean) and MISR (nondesert land) aerosol with new angular 

models for aerosols. 

Calibration of CERES radiances, large CERES 

footprint, satellite AOD retrieval, radiance-to-flux 

conversion (ADM), instantaneous-to-diurnal scaling, 

narrow-to-broadband conversion Zhang et al. (2005, 086743; 

2005, 157185); Zhang and 

Christopher (2003, 190928); 

Christopher et al. (2006, 

155729); Patadia et al. (2008, 

190558) 

A Satellite 

retrievals 

POLDER 

Using POLDER AOD in combination 

with prescribed aerosol models 

(similar to MODIS). 

Similar to MODIS 

Boucher and Tanré (2000, 

190041); Bellouin et al. 

(2003, 189911) 

MODIS_G 

MISR_G 

Using GOCART simulations to fill 

AOD gaps in satellite retrievals. 

MO_GO Integration of MODIS and GOCART 

AOD. 

MO_MI_GO 

Integration of GOCART AOD with 

retrievals from MODIS (Ocean) and 

MISR (Land). 

Propagation of uncertainties associated with both 

satellite retrievals and model simulations (but the 

model-satellite integration approach does result in 

improved AOD quality for MO_GO, and O_MI_GO) 

*Aerosol single-scattering 

albedo and asymmetry factor 

are taken from GOCART 

simulations 

*Yu et al. (2003, 156171; 

2004, 190926; 2006, 156173)

B. Satellitemodel 

integrations 

SeaWiFS Using SeaWiFS AOD and assumed 

aerosol models. 

Similar to MODIS_G and MISR_G, too weak aerosol 

absorption Chou et al. (2002, 190008) 

GOCART 

Offline RT calculations using 

monthly avg aerosols with a time step 

of 30 min (without the presence of 

clouds). 

Chin et al. (2002, 189996); Yu 

et al. (2004, 190926) 

SPRINTARS Online RT calculations every 3 hrs 

(cloud fraction = 0). 

Takemura et al. (2002, 

190438; 2005, 190439) 

GISS Online model simulations and 

weighted by clear-sky fraction. 

Koch and Hansen (2005, 

190183); Koch et al. (2006, 

190184) 

LMDZ-INCA Online RT calculations every 2 hrs 

(cloud fraction = 0). 

Balkanski et al. (2007, 

189979); Schulz et al. (2006, 

190381); Kinne et al. (2006, 

155903) 

C. Model 

simulations 

LMDZ-LOA Online RT calculations every 2 hrs 

(cloud fraction = 0). 

Emissions, parameterizations of a variety of sub-grid 

aerosol processes (e.g., wet and dry deposition, cloud 

convection, aqueous-phase oxidation), assumptions on 

aerosol size, absorption, mixture, and humidification of 

particles, Meteorology fields, not fully evaluated 

surface albedo schemes, RT parameterizations 

Reddy et al. (2005, 190207; 

2005, 190208) 

Source: Adapted from Yu et al. (2006, 156173).

December 2009 9-104 
Source: Yu et al. (2006, 156173) 

Figure 9-68. Geographical patterns of seasonally (MAM) averaged aerosol optical depth at 550 

nm (left panel) and the diurnally averaged clear-sky aerosol direct radiative (solar 

spectrum) forcing (W/m2) at the TOA (right panel) derived from satellite (Terra) 

retrievals. MODIS (Remer and Kaufman, 2006, 190222; Remer et al., 2005, 

190221); MISR (Kahn et al., 2005, 190966); and CERES_A (Loeb and ManaloSmith, 2005, 190433); GOCART simulations (Chin et al., 2002, 189996; Yu et al., 

2004, 190926); and GOCART-MODIS-MISR integrations (MO_MI_GO) (Yu et al., 

2006, 156173). 

December 2009 9-105 
Global Mean 

Figure 9-69 summarizes the measurement- and model-based estimates of clear-sky 

annual-averaged DRF at both the TOA and surface from 60°S to 60°N. Seasonal DRF values 

for individual estimates are summarized in Table 9-8 and Table 9-9 for ocean and land, 

respectively. Mean, median and standard error ε (ε=σ/(n-1)1/2), where σ is standard deviation 

and n is the number of methods) are calculated for measurement- and model-based estimates 

separately. Note that although the standard deviation or standard error reported here is not a 

fully rigorous measure of a true experimental uncertainty, it is indicative of the uncertainty 

because independent approaches with independent sources of errors are used (see Table 9-7; in 

the modeling community, this is called the “diversity;” see Section 9.3.6). 

Ocean 

For the TOA DRF, a majority of measurement-based and satellite-model integration-based 

estimates agree with each other within about 10%. On annual average, the measurement-based 

estimates give the DRF of -5.5 ± 0.2 W/m2 (mean ± ε) at the TOA and -8.7 ± 0.7 W/m2 at the 

surface. This suggests that the ocean surface cooling is about 60% larger than the cooling at 

the TOA. Model simulations give wide ranges of DRF estimates at both the TOA and surface. 

The ensemble of five models gives the annual average DRF (mean ± ε) of -3.2 ± 0.6 W/m2 and 

-4.9 ± 0.8 W/m2 at the TOA and surface, respectively. On average, the surface cooling is about 

37% larger than the TOA cooling, smaller than the measurement-based estimate of surface and 

TOA difference of 60%. However, the ‘measurement-based’ estimate of surface DRF is 

actually a calculated value, using poorly constrained particle properties. 

Land 

It remains challenging to use satellite measurements alone for characterizing complex 

aerosol properties over land surfaces with high accuracy. As such, DRF estimates over land 

have to rely largely on model simulations and satellite-model integrations. On a global and 

annual average, the satellite-model integrated approaches derive a mean DRF of -4.9 W/m2 at 

the TOA and -11.9 W/m2 at the surface respectively. The surface cooling is more than a factor 

of 2 larger than the TOA cooling because of aerosol absorption. Note that the TOA DRF of -

4.9 W/m2 agrees quite well with the most recent satellite-based estimate of -5.1 ± 1.1 W/m2

over non-desert land based on coincident measurements of MISR AOD and CERES solar flux 

(Patadia et al., 2008, 190558). For comparisons, an ensemble of five model simulations 

derives a DRF (mean ± ε) over land of -3.0 ± 0.6 W/m2 at the TOA and -7.6 ± 0.9 W/m2 at the 

surface, respectively. Seasonal variations of DRF over land, as derived from both 

measurements and models, are larger than those over ocean. 

Source: Yu et al. (2006, 156173) 

Figure 9-69. Summary of observation- and model-based (denoted as OBS and MOD, 

respectively) estimates of clear-sky, annual average DRF at the TOA and at the 

surface. The box and vertical bar represent median and standard error, 

respectively. 

December 2009 9-106 
Table 9-8. Summary of seasonal and annual average clear-sky DRF (W/m2

) at the TOA and the 

surface (SFC) over global OCEAN derived with different methods and data. 

DJF MAM JJA SON ANN 

Products 

TOA SFC TOA SFC TOA SFC TOA SFC TOA SFC 

MODIS -5.9 -5.8 -6.0 -5.8 -5.9 

MODIX_A* -6.0 -8.2 -6.4 -8.9 -6.5 -9.3 -6.4 -8.9 -6.4 -8.9 

CERES_A -5.3 -6.1 -5.4 -5.1 -5.5 

CERES_B -3.8 -4.3 -3.5 -3.6 -3.8 

CERES_C -5.3 -5.4 -5.2 -5.3 

MODIS_G -5.5 -9.1 -5.7 -10.4 -6.0 -10.6 -5.5 -9.8 -5.7 -10.0 

MISR_G** -6.4 -10.3 -6.5 -11.4 -7.0 -11.9 -6.3 -10.9 -6.5 -11.1 

MO_GO -4.9 -7.8 -5.1 -9.3 -5.4 -9.4 -5.0 -8.7 -5.1 -8.8 

MO_MI_GO -4.9 -7.9 -5.1 -9.2 -5.5 -9.5 -5.0 -8.6 -5.1 -8.7 

POLDER -5.7 -5.7 -5.8 -5.6 

-5.7 

-5.2*** -7.7*** 

SeaWiFS -6.0 -6.6 -5.2 -5.8 -4.9 -5.6 -5.3 -5.7 -5.4 -5.9 

Obs. Mean -5.4 -8.3 -5.6 -9.2 -5.6 -9.4 -5.4 -8.8 -5.5 -8.7 

Obs. Median -5.5 -8.1 -5.7 -9.3 -5.5 -9.5 -5.4 -8.8 -5.5 -8.8 

Obs. σ 0.72 1.26 0.64 1.89 0.91 2.10 0.79 1.74 0.70 1.65 

Obs. ε 0.23 0.56 0.20 0.85 0.29 0.94 0.26 0.78 0.21 0.67 

GOCART -3.6 -5.7 -4.0 -7.2 -4.7 -8.0 -4.0 -6.8 -4.1 -6.9 

SPRINTARS -1.5 -2.5 -1.5 -2.5 -1.9 -3.3 -1.5 -2.5 -1.6 -2.7 

GISS -3.3 -4.1 -3.5 -4.6 -3.5 -4.9 -3.8 -5.4 -3.5 -4.8 

LMDZ-INCA -4.6 -5.6 -4.7 -5.9 -5.0 -6.3 -4.8 -5.5 -4.7 -5.8 

LMDZ-LOA -2.2 -4.1 -2.2 -3.7 -2.5 -4.4 -2.2 -4.1 -2.3 -4.1 

Mod. Mean -3.0 -4.4 -3.2 -4.8 -3.5 -5.4 -3.3 -4.9 -3.2 -4.9 

Mod Median -3.3 -41. -3.5 -4.6 -3.5 -4.9 -3.8 -5.4 -3.5 -4.8 

Mod. σ 1.21 1.32 1.31 1.84 1.35 1.82 1.36 1.63 1.28 1.60 

Mod. ε 0.61 0.66 0.66 0.92 0.67 0.91 0.68 0.81 0.64 0.80 

Mod./Obs. .60 .51 .61 .50 .64 .52 .70 .61 .64 .55 

* High bias may result from adding the DRF of individual components to derive the total DRF (Bellouin et al., 2005, 155684). 

** High bias most likely results from an overall overestimate of 20% in early post-launch MISR optical depth retrievals (Kahn et al., 2005, 190966). 

*** Bellouin et al. (2003, 189911) use AERONET retrieval of aerosol absorption as a constraint to the method in Boucher and Tanré (2000, 190041), deriving aerosol direct radiative forcing both at the TOA and 

the surface. 

Sources of data: MODIS (Remer & Kaufman, 2006), MODIS_A (Bellouin et al., 2005, 155684), POLDER (Bellouin et al., 2003, 189911; Boucher and Tanré, 2000, 190041), CERES_A and CERES_B (Loeb 

and Manalo-Smith, 2005, 190433), CERES_C (Zhang et al., 2005, 190930), MODIS_G, MISR_G, MO_GO, MO_MI_GO (Yu et al., 2004, 190926; 2006, 156173), SeaWiFS (Chou et al., 2002, 190008), 

GOCART (Chin et al., 2002, 189996; Yu et al., 2004, 190926), SPRINTARS (Takemura et al., 2002, 190438), GISS (Koch and Hansen, 2005, 190183; Koch et al., 2006, 190184), LMDZ-INCA (Kinne et al., 

2006, 155903; Schulz et al., 2006, 190381), LMDZ-LOA (Reddy et al., 2005, 190207; Reddy et al., 2005, 190208). Mean, median, standard deviation (σ), and standard error (ε) are calculated for observations 

(Obs) and model simulations (Mod) separately. The last row is the ratio of model median to observational median (taken from Yu et al., 2006, 156173). 

December 2009 9-107 
Table 9-9. Summary of seasonal and annual average clear-sky DRF (W/m2

) at the TOA and the 

surface (SFC) over global LAND derived with different methods and data. 

DJF MAM JJA SON ANN 

Products 

TOA SFC TOA SFC TOA SFC TOA SFC TOA SFC 

MODIS_G -4.1 -9.1 -5.8 -14.9 -6.6 -17.4 -5.4 -12.8 -5.5 -13.5 

MISR_G -3.9 -8.7 -5.1 -13.0 -5.8 -14.6 -4.6 -10l.7 -4.9 -11.8 

MO_GO -3.5 -7.5 -5.1 -12.9 -5.8 -14.9 -4.8 -10.9 -4.8 -11.6 

MO_MI_GO -3.4 -7.4 -4.7 -11.8 -5.3 -13.5 -4.3 -9.7 -4.4 -10.6 

Obs. Mean -3.7 -8.2 -5.2 -13.2 -5.9 -15.1 -4.8 -11.0 -4.9 -11.9 

Obs. Median -3.7 -8.1 -5.1 -13.0 -5.8 -14.8 -4.7 -10.8 -4.9 -11.7 

Obs. σ 0.33 0.85 0.46 1.29 0.54 1.65 0.46 1.29 0.45 1.20 

Obs. ε 0.17 0.49 0.26 0.74 0.31 0.85 0.27 0.75 0.26 0.70 

GOCART 02.9 -6.1 -4.4 -10.9 -4.8 -12.3 -4.3 -9.3 -4.1 -9.7 

SPRINTARS -1.4 -4.0 -1.5 -4.6 -2.0 -6.7 -1.7 -5.2 -1.7 -5.1 

GISS -1.6 -3.9 -3.2 -7.9 -3.6 -9.3 -2.5 -6.6 -2.8 -7.2 

LMDZ-INCA -3.0 -5.8 -4.0 -9.2 -6.0 -13.5 -4.3 -8.2 -4.3 -9.2 

LMDZ-LOA -1.3 -5.4 -1.8 -6.4 -2.7 -8.9 -2.1 -6.7 -2.0 -6.9 

Mod. Mean -2.0 -5.0 -3.0 -7.8 -3.8 -10.1 -3.0 -7.2 -3.0 -7.6 

Mod Median -1.6 -5.4 -3.2 -7.9 -3.6 -9.3 -2.5 -6.7 -2.8 -7.2 

Mod. σ 0.84 1.03 1.29 2.44 1.61 2.74 1.24 1.58 1.19 1.86 

Mod. ε 0.42 0.51 0.65 1.22 0.80 1.37 0.62 0.79 0.59 0.93 

Mod./Obs. 0.43 0.67 0.63 0.61 0.62 0.62 0.53 0.62 0.58 0.62 

Sources of data: MODIS_G, MISR_G, MO_GO, MO_MI_GO (Yu et al., 2004, 190926; 2006, 156173), GOCART (Chin et al., 2002, 189996; Yu et al., 2004, 190926), 

SPRINTARS (Takemura et al., 2002, 190438), GISS (Koch and Hansen, 2005, 190183; Koch et al., 2006, 190184), LMDZ-INCA (Balkanski et al., 2007, 189979; Kinne et al., 

2006, 155903; Schulz et al., 2006, 190381), LMDZ-LOA (Reddy et al., 2005, 190207; Reddy et al., 2005, 190208). Mean, median, standard deviation (σ), and standard error (ε) 

are calculated for observations (Obs) and model simulations (Mod) separately. The last row is the ratio of model median to observational median. (Taken from Yu et al., 2006, 

156173). 

The above analyses show that, on a global average, the measurement-based estimates of 

DRF are 55-80% greater than the model-based estimates. The differences are even larger on 

regional scales. Such measurement-model differences are a combination of differences in 

aerosol amount (optical depth), single-scattering properties, surface albedo, and radiative 

transfer schemes (Yu et al., 2006, 156173). As discussed earlier, MODIS retrieved optical 

depths tend to be overestimated by about 10-15% due to the contamination of thin cirrus and 

clouds in general (Kaufman et al., 2005, 155891). Such overestimation of optical depth would 

result in a comparable overestimate of the aerosol direct radiative forcing. Other satellite AOD 

data may have similar contamination, which however has not yet been quantified. On the other 

hand, the observations may be measuring enhanced AOD and DRF due to processes not well 

represented in the models including humidification and enhancement of aerosols in the vicinity 

of clouds (Koren et al., 2007, 190192). 

From the perspective of model simulations, uncertainties associated with 

parameterizations of various aerosol processes and meteorological fields, as documented under 

the AEROCOM and Global Modeling Initiative (GMI) frameworks (Kinne et al., 2006, 

155903; Liu et al., 2007, 190427; Textor et al., 2006, 190456), contribute to the large 

measurement-model and model-model discrepancies. Factors determining the AOD should be 

major reasons for the DRF discrepancy and the constraint of model AOD with well evaluated 

and bias reduced satellite AOD through a data assimilation approach can reduce the DRF 

discrepancy significantly. Other factors (such as model parameterization of surface reflectance, 

and model-satellite differences in single-scattering albedo and asymmetry factor due to 

satellite sampling bias toward cloud-free conditions) should also contribute, as evidenced by 

the existence of a large discrepancy in the radiative efficiency (Yu et al., 2006, 156173). 

Significant effort will be needed in the future to conduct comprehensive assessments. 

December 2009 9-108 
9.3.3.4. Satellite-Based Estimates of Anthropogenic Component of Aerosol Direct 

Radiative Forcing 

Satellite instruments do not measure the aerosol chemical composition needed to 

discriminate anthropogenic from natural aerosol components. Because anthropogenic aerosols 

are predominantly sub-micron, the fine-mode fraction derived from POLDER, MODIS, or 

MISR might be used as a tool for deriving anthropogenic aerosol optical depth. This could 

provide a feasible way to conduct measurement-based estimates of anthropogenic component 

of aerosol direct radiative forcing (Kaufman et al., 2002, 190956). Such method derives 

anthropogenic AOD from satellite measurements by empirically correcting contributions of 

natural sources (dust and maritime aerosol) to the sub-micron AOD (Kaufman et al., 2005, 

155891). The MODIS-based estimate of anthropogenic AOD is about 0.033 over oceans, 

consistent with model assessments of 0.030~0.036 even though the total AOD from MODIS is 

25-40% higher than the models (Kaufman et al., 2005, 155891). This accounts for 21 ± 7% of 

the MODIS-observed total aerosol optical depth, compared with about 33% of anthropogenic 

contributions estimated by the models. The anthropogenic fraction of AOD should be much 

larger over land (i.e., 47 ± 9% from a composite of several models) (Bellouin et al., 2005, 

155684), comparable to the 40% estimated by Yu et al. (2006, 156173). Similarly, the nonspherical fraction from MISR or POLDER can be used to separate dust from spherical aerosol 

(Kahn et al., 2001, 190969; Kalashnikova and Kahn, 2006, 190962), providing another 

constraint for distinguishing anthropogenic from natural aerosols. 

There have been several estimates of anthropogenic component of DRF in recent years. 

Table 9-10 lists such estimates of anthropogenic component of TOA DRF that are from model 

simulations (Schulz et al., 2006, 190381) and constrained to some degree by satellite 

observations (Bellouin et al., 2005, 155684; Bellouin et al., 2008, 189999; Christopher et al., 

2006, 155729; Chung et al., 2005, 155733; Kaufman et al., 2005, 155891; Matsui et al., 2006, 

190495; Quaas et al., 2008, 190916; Yu et al., 2006, 156173; Zhao et al., 2008, 190936). The 

satellite-based clear-sky DRF by anthropogenic aerosols is estimated to be -1.1 ± 0.37 W/m2

over ocean, about a factor of 2 stronger than model simulated -0.6 W/m2. Similar DRF 

estimates are rare over land, but a few studies do suggest that the anthropogenic DRF over 

land is much more negative than that over ocean (Bellouin et al., 2005, 155684; Bellouin et al., 

2008, 189999; Yu et al., 2006, 156173). On global average, the measurement-based estimate of 

anthropogenic DRF ranges from -0.9 to -1.9 W/m2, again stronger than the model-based 

estimate of -0.8 W/m2. Similar to DRF estimates for total aerosols, satellite-based estimates of 

anthropogenic component of DRF are rare over land. 

On global average, anthropogenic aerosols are generally more absorptive than natural 

aerosols. As such the anthropogenic component of DRF is much more negative at the surface 

than at TOA. Several observation-constrained studies estimate that the global average, clearsky, anthropogenic component of DRF at the surface ranges from -4.2 to -5.1 W/m2

 (Bellouin 

et al., 2005, 155684; Chung et al., 2005, 155733; Matsui et al., 2006, 190495; Yu et al., 2004, 

190926), which is about a factor of 2 larger in magnitude than the model estimates (e.g., 

Reddy et al., 2005, 190208). 

Uncertainties in estimates of the anthropogenic component of aerosol DRF are greater 

than for the total aerosol, particularly over land. An uncertainty analysis (Yu et al., 2006, 

156173) partitions the uncertainty for the global average anthropogenic DRF between land and 

ocean more or less evenly. Five parameters, namely fine-mode fraction (ff) and anthropogenic 

fraction of fine-mode fraction (faf) over both land and ocean, and τ over ocean, contribute 

nearly 80% of the overall uncertainty in the anthropogenic DRF estimate, with individual 

shares ranging from 13-20% (Yu et al., 2006, 156173). These uncertainties presumably 

represent a lower bound because the sources of error are assumed to be independent. 

Uncertainties associated with several parameters are also not well defined. Nevertheless, such 

uncertainty analysis is useful for guiding future research and documenting advances in 

understanding. 

December 2009 9-109 
Table 9-10. Estimates of anthropogenic components of aerosol optical depth (Tant) and clear-sky 

DRF at the TOA from model simulations. 

Ocean Land Global 

Data Sources 

Τant DRF 

(W/m2) Τant DRF (W/m2) Τant DRF (W/m2) 

Estimated uncertainty or 

model diversity for DRF 

Kaufman et al. (2005, 155891) 0.033 -1.4 30% 

Bellouin et al. (2005, 155684) 0.028 -0.8 0.13 0.062 -1.9 15% 

Chung et al. (2005, 155733) -1.1

Yu et al. (2006, 156173) 0.031 -1.1 -.088 -1.8 0.048 -1.3 47% (ocean), 845 (land), and 

62% (global) 

Christopher et al. (2006, 

155729) -1.4 65% 

Matsui and Pielke (2006, 

190495) -1.6 30°S-30°N oceans 

Quaas et al. (2008, 190916) -0.7 -1.8 -0.9 45% 

Bellouin et al. (2008, 189999) 0.021 -0.6 0.107 -3.3 0.043 -1.3 

Update to Bellouin et al. (2005, 

155684) with MODIS Collection 5 

data 

Zhao et al. (2008, 190936) -1.25 35% 

Schulz et al. (2006, 190381) 0.022 -0.59 0.065 -1.14 0.036 -0.77 30-40%; same emissions 

prescribed for all models 

Sources: Schulz et al., (2006, 190381) approaches constrained by satellite observations, Kaufman et al. (2005, 155891); Bellouin et al. (2005, 155684) 2008; Chung et al. (2005, 155733); Yu et al. (2006, 

156173); Christopher et al. (2006, 155729); Matsui and Pielke (2006, 190495); Quaas et al. (2008, 190916); Zhao et al. (2008, 190936). 

9.3.3.5. Aerosol-Cloud Interactions and Indirect Forcing 

Satellite views of the Earth show a planet whose albedo is dominated by dark oceans and 

vegetated surfaces, white clouds, and bright deserts. The bright white clouds overlying darker 

oceans or vegetated surface demonstrate the significant effect that clouds have on the Earth’s 

radiative balance. Low clouds reflect incoming sunlight back to space, acting to cool the 

planet, whereas high clouds can trap outgoing terrestrial radiation and act to warm the planet. 

In the Arctic, low clouds have also been shown to warm the surface (Garrett and Zhao, 2006, 

190570). Changes in cloud cover, in cloud vertical development, and cloud optical properties 

will have strong radiative and therefore, climatic impacts. Furthermore, factors that change 

cloud development will also change precipitation processes. These changes may alter amounts, 

locations and intensities of local and regional rain and snowfall, creating droughts, floods and 

severe weather. 

Cloud droplets form on a subset of aerosol particles called cloud condensation nuclei 

(CCN). In general, an increase in aerosol leads to an increase in CCN and an increase in drop 

concentration. Thus, for the same amount of liquid water in a cloud, more available CCN will 

result in a greater number but smaller size of droplets (Twomey, 1977, 190533). A cloud with 

smaller but more numerous droplets will be brighter and reflect more sunlight to space, thus 

exerting a cooling effect. This is the first aerosol indirect radiative effect, or “albedo effect.” 

The effectiveness of a particle as a CCN depends on its size and composition so that the degree 

to which clouds become brighter for a given aerosol perturbation, and therefore the extent of 

cooling, depends on the aerosol size distribution and its size-dependent composition. In 

addition, aerosol perturbations to cloud microphysics may involve feedbacks; for example, 

smaller drops are less likely to collide and coalesce; this will inhibit growth, suppressing 

precipitation, and possibly increasing cloud lifetime (Albrecht, 1989, 045783). In this case 

clouds may exert an even stronger cooling effect. 

A distinctly different aerosol effect on clouds exists in thin Arctic clouds (LWP <25 gm-2) 

having low emissivity. Aerosol has been shown to increase the longwave emissivity in these 

clouds, thereby warming the surface (Garrett and Zhao, 2006, 190570; Lubin and Vogelmann, 

2006, 190466). 

Some aerosol particles, particularly black carbon and dust, also act as ice nuclei (IN) and 

in so doing, modify the microphysical properties of mixed-phase and ice-clouds. An increase 

in IN will generate more ice crystals, which grow at the expense of water droplets due to the 

December 2009 9-110 
difference in vapor pressure over ice and water surfaces. The efficient growth of ice particles 

may increase the precipitation efficiency. In deep convective, polluted clouds there is a delay 

in the onset of freezing because droplets are smaller. These clouds may eventually precipitate, 

but only after higher altitudes are reached that result in taller cloud tops, more lightning and 

greater chance of severe weather (Andreae et al., 2004, 155658; Rosenfeld and Lansky, 1998, 

190230). The present state of knowledge of the nature and abundance of IN, and ice formation 

in clouds is extremely poor. There is some observational evidence of aerosol influences on ice 

processes, but a clear link between aerosol, IN concentrations, ice crystal concentrations and 

growth to precipitation has not been established. This report therefore only peripherally 

addresses ice processes. More information can be found in a review by the WMO/IUGG 

International Aerosol-Precipitation Scientific Assessment (Levin and Cotton, 2008, 190375). 

In addition to their roles as CCN and IN, aerosols also absorb and scatter light, and 

therefore they can change atmospheric conditions (temperature, stability, and surface fluxes) 

that influence cloud development and properties (Ackerman et al., 2000, 002987; Hansen et 

al., 1997, 043104). Thus, aerosols affect clouds through changing cloud droplet size 

distributions, cloud particle phase, and by changing the atmospheric environment of the cloud. 

9.3.3.6. Remote Sensing of Aerosol-Cloud Interactions and Indirect Forcing 

The AVHRR satellite instruments have observed relationships between columnar aerosol 

loading, retrieved cloud microphysics, and cloud brightness over the Amazon Basin that are 

consistent with the theories explained above (Feingold et al., 2001, 190544; Kaufman and 

Fraser, 1997, 190958; Kaufman and Nakajima, 1993, 190959), but do not necessarily prove a 

causal relationship. Other studies have linked cloud and aerosol microphysical parameters or 

cloud albedo and droplet size using satellite data applied over the entire global oceans (Han et 

al., 1998, 190594; Nakajima et al., 2001, 190552; Wetzel and Stowe, 1999, 190636). Using 

these correlations with estimates of aerosol increase from the pre-industrial era, estimates of 

anthropogenic aerosol indirect radiative forcing fall into the range of -0.7 to -1.7 W/m2

(Nakajima et al., 2001, 190552). 

Introduction of the more modern instruments (POLDER and MODIS) has allowed more 

detailed observations of relationships between aerosol and cloud parameters. Cloud cover can 

both decrease and increase with increasing aerosol loading (Kaufman et al., 2005, 155891; 

Koren et al., 2004, 190187; Koren et al., 2005, 190188; Matheson et al., 2005, 190494; 

Sekiguchi et al., 2003, 190385; Yu et al., 2007, 093173). The same is true of LWP (Han et al., 

2002, 049181; Matsui et al., 2006, 190498). Aerosol absorption appears to be an important 

factor in determining how cloud cover will respond to increased aerosol loading (Jiang and 

Feingold, 2006, 190976; Kaufman and Koren, 2006, 190951; Koren et al., 2008, 190193). 

Different responses of cloud cover to increased aerosol could also be correlated with 

atmospheric thermodynamic and moisture structure (Yu et al., 2007, 093173). Observations in 

the MODIS data show that aerosol loading correlates with enhanced convection and greater 

production of ice anvils in the summer Atlantic Ocean (Koren et al., 2005, 190188), which 

conflicts with previous results that used AVHRR and could not isolate convective systems 

from shallow clouds (Sekiguchi et al., 2003, 190385). 

In recent years, surface-based remote sensing has also been applied to address aerosol 

effects on cloud microphysics. This method offers some interesting insights, and is 

complementary to the global satellite view. Surface remote sensing can only be applied at a 

limited number of locations, and therefore lacks the global satellite view. However, these 

surface stations yield high temporal resolution data and because they sample aerosol below, 

rather than adjacent to clouds they do not suffer from “cloud contamination.” With the 

appropriate instrumentation (lidar) they can measure the local aerosol entering the clouds, 

rather than a column-integrated aerosol optical depth. Under well-mixed conditions, surface in 

situ aerosol measurements can be used. Surface remote-sensing studies are discussed in more 

detail below, although the main science issues are common to satellite remote sensing. 

Feingold et al. (2001, 190544) used data collected at the ARM Southern Great Plains 

(SGP) site to allow simultaneous retrieval of aerosol and cloud properties. A combination of a 

Doppler cloud radar and a microwave radiometer was used to retrieve cloud drop effective 

radius re profiles in non-precipitating (radar reflectivity Z <-17 dBZ), ice-free clouds. 

Simultaneously, sub-cloud aerosol extinction profiles were measured with a lidar to quantify 

the response of drop sizes to changes in aerosol properties. Cloud data were binned according 

to liquid water path (LWP) as measured with a microwave radiometer, consistent with 

Twomey’s (1977, 190533) conceptual view of the aerosol impact on cloud microphysics. With 

high temporal/spatial resolution data (on the order of 20s or 100s of meters), realizations of 

aerosol-cloud interactions at the large eddy scale were obtained, and quantified in terms of the 

relative decrease in re in response to a relative increase in aerosol extinction (dln re/dln 

extinction), as shown in Figure 9-70. Examining the dependence in this way reduces reliance 

on absolute measures of cloud and aerosol parameters and minimizes sensitivity to 

measurement error, provided errors are unbiased. This formulation permitted these responses 

to be related to cloud microphysical theory. Restricting the examination to updrafts only (as 

determined from the radar Doppler signal) permitted examination of the role of updraft in 

determining the response of re to changes in aerosol (via changes in drop number 

December 2009 9-111 
concentration Nd). Analysis of data from 7 days showed that turbulence intensifies the aerosol 

impact on cloud microphysics. 

In addition to radar/microwave radiometer retrievals of aerosol and cloud properties, 

measurements of cloud optical depth by surface based radiometers such as the MFRSR 

(Michalsky et al., 2001, 190537) have been used in combination with measurements of cloud 

LWP by microwave radiometer to measure an average value of re during daylight when the 

solar elevation angle is sufficiently high (Min and Harrison, 1996, 190538). Using this 

retrieval, Kim et al. (2003, 155899) performed analyses of the re response to changes in 

aerosol at the same continental site, using a surface measurement of the aerosol light scattering 

coefficient instead of using extinction near cloud base as a proxy for CCN. Variance in LWP 

was shown to explain most of the variance in cloud optical depth, exacerbating detection of an 

aerosol effect. Although a decrease in re was observed with increasing scattering coefficient, 

the relation was not strong, indicative of other influences on re and/or decoupling between the 

surface and cloud layer. A similar study was conducted by Garrett et al. (2004, 190568) at a 

location in the Arctic. 

Source: Adapted with Permission of the American Geophysical Union from Feingold et al. (2003, 190551). 

Figure 9-70. Scatter plots showing mean cloud drop effective radius (re) versus aerosol 

extinction coefficient (unit: km-1) for various liquid water path (LWP) bands on 

April 3, 1998 at ARM SGP site. 

They suggested that summertime Arctic clouds are more sensitive to aerosol perturbations 

than clouds at lower latitudes. The advantage of the MFRSR/microwave radiometer 

combination is that it derives re from cloud optical depth and LWP and it is not as sensitive to 

large drops as the radar is. A limitation is that it can be applied only to clouds with extensive 

horizontal cover during daylight hours. 

More recent data analyses by Feingold et al. (2003, 190551), Kim et al. (2008, 130785) 

and McComiskey et al. (2008, 190525) at a variety of locations, and modeling work (Feingold, 

2003, 190547) have investigated (i) the use of different proxies for cloud condensation nuclei, 

such as the light scattering coefficient and aerosol index; (ii) sensitivity of cloud 

microphysical/optical properties to controlling factors such as aerosol size distribution, 

entrainment, LWP, and updraft velocity; (iii) the effect of optical- as opposed to radarretrievals of drop size; and (iv) spatial heterogeneity. These studies have reinforced the 

importance of LWP and vertical velocity as controlling parameters. They have also begun to 

reconcile the reasons for the large discrepancies between various approaches, and platforms 

(satellite, aircraft in situ, and surface-based remote sensing). These investigations are 

important because satellite measurements that use a similar approach are being employed in 

GCMs to represent the albedo indirect effect (Quaas and Boucher, 2005, 190573). In fact, the 

weakest albedo indirect effect in IPCC (2007, 092765) derives from satellite measurements 

that have very weak responses of re to changes in aerosol. The relationship between these 

aerosol-cloud microphysical responses and cloud radiative forcing has been examined by 

December 2009 9-112 
McComiskey and Feingold (2008, 190517). They showed that for plane-parallel clouds, a 

typical uncertainty in the logarithmic gradient of a re-aerosol relationship of 0.05 results in a 

local forcing error of -3 to -10 W/m2, depending on the aerosol perturbation. This sensitivity 

reinforces the importance of adequate quantification of aerosol effects on cloud microphysics 

to assessment of the radiative forcing, i.e., the indirect effect. Quantification of these effects 

from remote sensors is exacerbated by measurement errors. For example, LWP is measured to 

an accuracy of 25 g m-2 at best, and since it is the thinnest clouds (i.e., low LWP) that are most 

susceptible (from a radiative forcing perspective) to changes in aerosol, this measurement 

uncertainty represents a significant uncertainty in whether the observed response is related to 

aerosol, or to differences in LWP. The accuracy and spatial resolution of satellite-based LWP 

measurements is much poorer and this represents a significant challenge. In some cases 

important measurements are simply absent, e.g., updraft is not measured from satellite-based 

remote sensors. 

Finally, cloud radar data from CloudSat, along with the A-train aerosol data, is providing 

great opportunity for inferring aerosol effects on precipitation (e.g., Stephens and Haynes, 

2007, 190413). The aerosol effect on precipitation is far more complex than the albedo effect 

because the instantaneous view provided by satellites makes it difficult to establish causal 

relationships. 

9.3.3.7. In Situ Studies of Aerosol-Cloud Interactions 

In situ observations of aerosol effects on cloud microphysics date back to the 1950s and 

1960s (Brenguier et al., 2000, 189966; Gunn and Phillips, 1957, 190595; Leaitch et al., 1992, 

045270; Radke et al., 1989, 156034; Squires, 1958, 045608; Warner, 1968, 157114; Warner 

and Twomey, 1967, 045616; to name a few). These studies showed that high concentrations of 

CCN from anthropogenic sources, such as industrial pollution or the burning of sugarcane can 

increase cloud droplet number concentration Nd, thus increasing cloud microphysical stability 

and potentially reducing precipitation efficiency. As in the case of remote sensing studies, the 

causal link between aerosol perturbations and cloud microphysical responses (e.g., re or Nd) is 

much better established than the relationship between aerosol and changes in cloud fraction, 

LWC, and precipitation (see also Levin and Cotton, 2008, 190375). 

In situ cloud measurements are usually regarded as “ground truth” for satellite retrievals 

but in fact there is considerable uncertainty in measured parameters such liquid water content 

(LWC), and size distribution, which forms the basis of other calculations such as drop 

concentration, re and extinction. It is not uncommon to see discrepancies in LWC on the order 

of 50% between different instruments, and cloud drop size distributions are difficult to 

measure, particularly for droplets <10 μm where Mie scattering oscillations generate 

ambiguities in drop size. Measurement uncertainty in re from in situ probes is assessed, for 

horizontally homogeneous clouds, to be on the order of 15- 20%, compared to 10% for 

MODIS and 15-20% for other spectral measurements (Feingold et al., 2003, 190551). As with 

remote measurements it is prudent to consider relative (as opposed to absolute) changes in 

cloud microphysics related to relative changes in aerosol. An added consideration is that in situ 

measurements typically represent a very small sample of the atmosphere akin to a thin pencil 

line through a large volume. For an aircraft flying at 100 m/s and sampling at 1 Hz, the sample 

volume is on the order of 10 cm3. The larger spatial sampling of remote sensing has the 

advantage of being more representative but it removes small-scale (i.e., sub sampling volume) 

variability, and therefore, may obscure important cloud processes. 

Measurements at a wide variety of locations around the world have shown that increases 

in aerosol concentration lead to increases in Nd. However the rate of this increase is highly 

variable and always sub-linear, as exemplified by the compilation of data in Ramanathan et al. 

(2001, 042681). This is because, as discussed previously, Nd is a function of numerous 

parameters in addition to aerosol number concentration, including size distribution, updraft 

velocity (Leaitch et al., 1996, 190354), and composition. In stratocumulus clouds, 

characterized by relatively low vertical velocity (and low supersaturation) only a small fraction 

of particles can be activated whereas in vigorous cumulus clouds that have high updraft 

velocities, a much larger fraction of aerosol particles is activated. Thus the ratio of Nd to 

aerosol particle number concentration is highly variable. 

In recent years there has been a concerted effort to reconcile measured Nd concentrations 

with those calculated based on observed aerosol size and composition, as well as updraft 

velocity. These so-called “closure experiments” have demonstrated that on average, agreement 

in Nd between these approaches is on the order of 20% (Conant et al., 2004, 190010). This 

provides confidence in theoretical understanding of droplet activation, however, measurement 

accuracy is not high enough to constrain the aerosol composition effects that have magnitudes 

<20%. 

One exception to the rule that more aerosol particles result in larger Nd is the case of giant 

CCN (sizes on the order of a few microns), which, in concentrations on the order of 1 cm3

 (i.e., 

~ 1% of the total concentration) can lead to significant suppression in cloud supersaturation 

and reductions in Nd (O'Dowd et al., 1999, 090414). The measurement of these large particles 

is difficult and hence the importance of this effect is hard to assess. These same giant CCN, at 

concentrations as low as 1/liter, can significantly affect the initiation of precipitation in 

December 2009 9-113 
moderately polluted clouds (Johnson, 1982, 190973) and in so doing alter cloud albedo 

(Feingold et al., 1999, 190540). 

The most direct link between the remote sensing of aerosol-cloud interactions discussed 

in Section 9.3.3.6 and in situ observations is via observations of relationships between drop 

concentration Nd and CCN concentration. Theory shows that if re-CCN relationships are 

calculated at constant LWP or LWC, their logarithmic slope is -1/3 that of the Nd-CCN 

logarithmic slope (i.e., dlnre/dlnCCN = -1/3 dlnNd/ dlnCCN). In general, Nd-CCN slopes 

measured in situ tend to be stronger than equivalent slopes obtained from remote sensing – 

particularly in the case of satellite remote sensing (McComiskey and Feingold, 2008, 190517). 

There are a number of reasons for this: (i) in situ measurements focus on smaller spatial scales 

and are more likely to observe the droplet activation process as opposed to remote sensing that 

incorporates larger spatial scales and includes other processes such as drop coalescence that 

reduce Nd, and therefore the slope of the Nd- CCN relationship (McComiskey et al., 2008, 

190525). (ii) Satellite remote sensing studies typically do not sort their data by LWP, and this 

has been shown to reduce the magnitude of the re-CCN response (Feingold, 2003, 190547). 

In conclusion, observational estimates of aerosol indirect radiative forcings are still in 

their infancy. Effects on cloud microphysics that result in cloud brightening have to be 

considered along with effects on cloud lifetime, cover, vertical development and ice 

production. For in situ measurements, aerosol effects on cloud microphysics are reasonably 

consistent (within ~ 20%) with theory but measurement uncertainties in remote sensing of 

aerosol effects on clouds, as well as complexity associated with three-dimensional radiative 

transfer, result in considerable uncertainty in radiative forcing. The higher order indirect 

effects are poorly understood and even the sign of the microphysical response and forcing may 

not always be the same. Aerosol type and specifically the absorption properties of the aerosol 

may cause different cloud responses. Early estimates of observationally based aerosol indirect 

forcing range from -0.7 to -1.7 W/m2

 (Nakajima et al., 2001, 190552) and -0.6 to -1.2 W/m2

(Sekiguchi et al., 2003, 190385), depending on the estimate for aerosol increase from preindustrial times and whether aerosol effects on cloud fraction are also included in the estimate. 

9.3.4. Outstanding Issues 

Despite substantial progress, as summarized in Sections 9.3.2 and 9.3.3, most 

measurement-based studies so far have concentrated on influences produced by the sum of 

natural and anthropogenic aerosols on solar radiation under clear sky conditions. Important 

issues remain: 

 Because accurate measurements of aerosol absorption are lacking and land surface 

reflection values are uncertain, DRF estimates over land and at the ocean surface are 

less well constrained than the estimate of TOA DRF over ocean. 

 Current estimates of the anthropogenic component of aerosol direct radiative forcing 

have large uncertainties, especially over land.  Because there are very few measurements of aerosol absorption vertical distribution, 

mainly from aircraft during field campaigns, estimates of direct radiative forcing of 

above-cloud aerosols and profiles of atmospheric radiative heating induced by 

aerosol absorption are poorly constrained.  There is a need to quantify aerosol impacts on thermal infrared radiation, especially 

for dust.  The diurnal cycle of aerosol direct radiative forcing cannot be adequately 

characterized with currently available, sun-synchronous, polar orbiting satellite 

measurements.  Measuring aerosol, cloud, and ambient meteorology contributions to indirect 

radiative forcing remains a major challenge.  Long-term aerosol trends and their relationship to observed surface solar radiation 

changes are not well understood. 

The current status and prospects for these areas are briefly discussed below. 

Measuring Aerosol Absorption and Single-Scattering Albedo 

Currently, the accuracy of both in situ and remote sensing aerosol SSA measurements is 

generally ± 0.03 at best, which implies that the inferred accuracy of clear sky aerosol DRF 

would be larger than 1 W/m2

 (see Chapter 1 of the CSSP SAP2.3). Recently developed 

photoacoustic (Arnott et al., 1999, 020650) and cavity ring down extinction cell (Strawa et al., 

2002, 190421) techniques for measuring aerosol absorption produce SSA with improved 

accuracy over previous methods. However, these methods are still experimental, and must be 

deployed on aircraft. Aerosol absorption retrievals from satellites using the UV-technique have 

large uncertainties associated with its sensitivity to the height of the aerosol layer(s) (Torres et 

al., 2005, 190507), and it is unclear how the UV results can be extended to visible 

wavelengths. Views in and out of sunglint can be used to retrieve total aerosol extinction and 

scattering, respectively, thus constraining aerosol absorption over oceans (Kaufman et al., 

2002, 190955). However, this technique requires retrievals of aerosol scattering properties, 

including the real part of the refractive index, well beyond what has so far been demonstrated 

December 2009 9-114 
from space. In summary, there is a need to pursue a better understanding of the uncertainty in 

SSA from both in situ measurements and remote sensing retrievals and, with this knowledge, 

to synthesize different data sets to yield a characterization of aerosol absorption with welldefined uncertainty (Leahy et al., 2007, 190232). Laboratory studies of aerosol absorption of 

specific known composition are also needed to interpret in situ measurements and remote 

sensing retrievals and to provide updated database of particle absorbing properties for models. 

Estimating the Aerosol Direct Radiative Forcing over Land 

Land surface reflection is large, heterogeneous, and anisotropic, which complicates 

aerosol retrievals and DRF determination from satellites. Currently, the aerosol retrievals over 

land have relatively lower accuracy than those over ocean (Section 9.3.2.5) and satellite data 

are rarely used alone for estimating DRF over land (Section 9.3.3). Several issues need to be 

addressed, such as developing appropriate angular models for aerosols over land (Patadia et 

al., 2008, 190558) and improving land surface reflectance characterization. MODIS and MISR 

measure land surface reflection wavelength dependence and angular distribution at high 

resolution (Martonchik et al., 1998, 190484; Martonchik et al., 2002, 190490; Moody et al., 

2005, 190548). This offers a promising opportunity for inferring the aerosol direct radiative 

forcing over land from satellite measurements of radiative fluxes (e.g., CERES) and from 

critical reflectance techniques (Fraser and Kaufman, 1985, 190567; Kaufman, 1987, 190960). 

The aerosol direct radiative forcing over land depends strongly on aerosol absorption and 

improved measurements of aerosol absorption are required. 

Distinguishing Anthropogenic from Natural Aerosols

Current estimates of anthropogenic components of AOD and direct radiative forcing have 

larger uncertainties than total aerosol optical depth and direct radiative forcing, particularly 

over land (see Section 9.3.3.4), because of relatively large uncertainties in the retrieved aerosol 

microphysical properties (see Section 9.3.2). Future measurements should focus on improved 

retrievals of such aerosol properties as size distribution, particle shape, and absorption, along 

with algorithm refinement for better aerosol optical depth retrievals. Coordinated in situ 

measurements offer a promising avenue for validating and refining satellite identification of 

anthropogenic aerosols (Anderson et al., 2005, 189993; 2005, 189991). For satellite-based 

aerosol type characterization, it is sometimes assumed that all biomass-burning aerosol is 

anthropogenic and all dust aerosol is natural (Kaufman et al., 2005, 155891). The better 

determination of anthropogenic aerosols requires a quantification of biomass burning ignited 

by lightning (natural origin) and mineral dust due to human induced changes of land 

cover/land use and climate (anthropogenic origin). Improved emissions inventories and better 

integration of satellite observations with models seem likely to reduce the uncertainties in 

aerosol source attribution. 

Profiling the Vertical Distributions of Aerosols 

Current aerosol profile data are far from adequate for quantifying the aerosol radiative 

forcing and atmospheric response to the forcing. The data have limited spatial and temporal 

coverage, even for current spaceborne lidar measurements. Retrieving aerosol extinction 

profile from lidar measured attenuated backscatter is subject to large uncertainties resulting 

from aerosol type characterization. Current space-borne Lidar measurements are also not 

sensitive to aerosol absorption. Because of lack of aerosol vertical distribution observations, 

the estimates of DRF in cloudy conditions and dust DRF in the thermal infrared remain highly 

uncertain (Lubin et al., 2002, 190463; Schulz et al., 2006, 190381; Sokolik et al., 2001, 

190404). It also remains challenging to constrain the aerosol-induced atmospheric heating rate 

increment that is essential for assessing atmospheric responses to the aerosol radiative forcing 

(e.g., Feingold et al., 2005, 190550; Lau et al., 2006, 190223; Yu et al., 2002, 190923). 

Progress in the foreseeable future is likely to come from (1) better use of existing, global, 

space-based backscatter lidar data to constrain model simulations, and (2) deployment of new 

instruments, such as high-spectral-resolution lidar (HSRL), capable of retrieving both 

extinction and backscatter from space. The HSRL lidar system will be deployed on the 

EarthCARE satellite mission tentatively scheduled for 2013 

(http://asimov/esrin.esi.it/esaLP/ASESMYNW9SC_Lpearthcare_1.html). 

Characterizing the Diurnal Cycle of Aerosol Direct Radiative Forcing 

The diurnal variability of aerosol can be large, depending on location and aerosol type 

(Smirnov et al., 2002, 190398), especially in wildfire situations, and in places where boundary 

layer aerosols hydrate or otherwise change significantly during the day. This cannot be 

captured by currently available, sun-synchronous, polar orbiting satellites. Geostationary 

satellites provide adequate time resolution (Christopher and Zhang, 2002, 190031; Wang et al., 

2003, 157106), but lack the information required to characterize aerosol types. Aerosol type 

information from low earth orbit satellites can help improve accuracy of geostationary satellite 

December 2009 9-115 
aerosol retrievals (Costa et al., 2004, 190006; 2004, 192022). For estimating the diurnal cycle 

of aerosol DRF, additional efforts are needed to adequately characterize the anisotropy of 

surface reflection (Yu et al., 2004, 190926) and daytime variation of clouds. 

Studying Aerosol-Cloud Interactions and Indirect Radiative Forcing 

Remote sensing estimates of aerosol indirect forcing are still rare and uncertain. 

Improvements are needed for both aerosol characterization and measurements of cloud 

properties, precipitation, water vapor, and temperature profiles. Basic processes still need to be 

understood on regional and global scales. Remote sensing observations of aerosol-cloud 

interactions and aerosol indirect forcing are for the most part based on simple correlations 

among variables, from which cause-and-effects cannot be deduced. One difficulty in inferring 

aerosol effects on clouds from the observed relationships is separating aerosol from 

meteorological effects, as aerosol loading itself is often correlated with the meteorology. In 

addition, there are systematic errors and biases in satellite aerosol retrievals for partly cloudfilled scenes. Stratifying aerosol and cloud data by liquid water content, a key step in 

quantifying the albedo (or first) indirect effect, is usually missing. Future work will need to 

combine satellite observations with in situ validation and modeling interpretation. A 

methodology for integrating observations (in situ and remote) and models at the range of 

relevant temporal/spatial scales is crucial to improve understanding of aerosol indirect effects 

and aerosol-cloud interactions. 

Quantifying Long-Term Trends of Aerosols at Regional Scales 

Because secular changes are subtle and are superposed on seasonal and other natural 

variability, this requires the construction of consistent, multi-decadal records of climate-quality 

data. To be meaningful, aerosol trend analysis must be performed on a regional basis. Longterm trends of aerosol optical depth have been studied using measurements from surface 

remote sensing stations (e.g., Augustine et al., 2008, 189913; Hoyt and Frohlich, 1983, 

190621; Luo et al., 2001, 190467) and historic satellite sensors (Massie et al., 2004, 190492; 

Mishchenko and Geogdzhayev, 2007, 190545; Mishchenko et al., 2007, 190542; Zhao et al., 

2008, 190935). An emerging multiyear climatology of high quality AOD data from modern 

satellite sensors (e.g., Kahn et al., 2005, 190966; Remer et al., 2008, 190224) has been used to 

examine the interannual variations of aerosol (e.g., Koren et al., 2007, 190189; Mishchenko 

and Geogdzhayev, 2007, 190545) 

and contribute significantly to the study of aerosol trends. Current observational 

capability needs to be continued to avoid any data gaps. A synergy of aerosol products from 

historical, modern and future sensors is needed to construct as long a record as possible. Such 

a data synergy can build upon understanding and reconciliation of AOD differences among 

different sensors or platforms (Jeong et al., 2005, 190977). This requires overlapping data 

records for multiple sensors. A close examination of relevant issues associated with individual 

sensors is urgently needed, including sensor calibration, algorithm assumptions, cloud 

screening, data sampling and aggregation, among others. 

Linking Aerosol Long-Term Trends with Changes of Surface Solar Radiation 

Analysis of the long-term surface solar radiation record suggests significant trends during 

past decades (e.g., Alpert et al., 2005, 190047; Pinker et al., 2005, 190569; Stanhill and Cohen, 

2001, 042121; Wild et al., 2005, 156156). Although a significant and widespread decline in 

surface total solar radiation (the sum of direct and diffuse irradiance) occurred up to 1990 (socalled solar dimming), a sustained increase has been observed during the subsequent decade. 

Speculation suggests that such trends result from decadal changes of aerosols and the interplay 

of aerosol direct and indirect radiative forcing (Norris and Wild, 2007, 190555; Ruckstuhl et 

al., 2008, 190356; Stanhill and Cohen, 2001, 042121; Streets et al., 2006, 190425; Wild et al., 

2005, 156156). However, reliable observations of aerosol trends are required to test these 

ideas. In addition to aerosol optical depth, changes in aerosol composition must also be 

quantified, to account for changing industrial practices, environmental regulations, and 

biomass burning emissions (Novakov et al., 2003, 048398; Streets and Aunan, 2005, 156106; 

Streets et al., 2004, 190423). Such compositional changes will affect the aerosol SSA and size 

distribution, which in turn will affect the surface solar radiation (e.g., Qian et al., 2007, 

190572). However, such data are currently rare and subject to large uncertainties. Finally, a 

better understanding of aerosol-radiation-cloud interactions and trends in cloudiness, cloud 

albedo, and surface albedo is badly needed to attribute the observed radiation changes to 

aerosol changes with less ambiguity. 

9.3.5. Concluding Remarks 

Since the concept of aerosol-radiation-climate interactions was first proposed around 

1970, substantial progress has been made in determining the mechanisms and magnitudes of 

December 2009 9-116 
these interactions, particularly in the last 10 years. Such progress has greatly benefited from 

significant improvements in aerosol measurements and increasing sophistication of model 

simulations. As a result, knowledge of aerosol properties and their interaction with solar 

radiation on regional and global scales is much improved. Such progress plays a unique role in 

the definitive assessment of the global anthropogenic radiative forcing, as “virtually certainly 

positive” in IPCC AR4 (Haywood and Schulz, 2007, 190600). 

In Situ Measurements of Aerosols 

New in situ instruments such as aerosol mass spectrometers, photoacoustic techniques, 

and cavity ring down cells provide high accuracy and fast time resolution measurements of 

aerosol chemical and optical properties. Numerous focused field campaigns and the emerging 

ground-based aerosol networks are improving regional aerosol chemical, microphysical, and 

radiative property characterization. Aerosol closure studies of different measurements indicate 

that measurements of submicrometer, spherical sulfate and carbonaceous particles have a 

much better accuracy than that for dust-dominated aerosol. The accumulated comprehensive 

data sets of regional aerosol properties provide a rigorous “test bed” and strong constraint for 

satellite retrievals and model simulations of aerosols and their direct radiative forcing. 

Remote Sensing Measurements of Aerosols 

Surface networks, covering various aerosol regimes around the globe, have been 

measuring aerosol optical depth with an accuracy of 0.01~0.02, which is adequate for 

achieving the accuracy of 1 W/m2

 for cloud-free TOA DRF. On the other hand, aerosol 

microphysical properties retrieved from these networks, especially SSA, have relatively large 

uncertainties and are only available in very limited conditions. Current satellite sensors can 

measure AOD with an accuracy of about 0.05 or 15-20% in most cases. The implementation of 

multi-wavelength, multi-angle, and polarization measuring capabilities has also made it 

possible to measure particle properties (size, shape, and absorption) that are essential for 

characterizing aerosol type and estimating anthropogenic component of aerosols. However, 

these microphysical measurements are more uncertain than AOD measurements. 

Observational Estimates of Clear-Sky Aerosol Direct Radiative Forcing 

Closure studies based on focused field experiments reveal DRF uncertainties of about 

25% for sulfate/carbonaceous aerosol and 60% for dust at regional scales. The high-accuracy 

of MODIS, MISR and POLDER aerosol products and broadband flux measurements from 

CERES make it feasible to obtain observational constraints for aerosol TOA DRF at a global 

scale, with relaxed requirements for measuring particle microphysical properties. Major 

conclusions from the assessment are: 

 A number of satellite-based approaches consistently estimate the clear-sky diurnally 

averaged TOA DRF (on solar radiation) to be about -5.5 ± 0.2 W/m2

 (mean ± 

standard error from various methods) over global ocean. At the ocean surface, the 

diurnally averaged DRF is estimated to be -8.7 ± 0.7 W/m2. These values are 

calculated for the difference between today’s measured total aerosol (natural plus 

anthropogenic) and the absence of all aerosol. 

 Overall, in comparison to that over ocean, the DRF estimates over land are more 

poorly constrained by observations and have larger uncertainties. A few satellite 

retrieval and satellite-model integration yield the overland clear-sky diurnally 

averaged DRF of -4.9 ± 0.7 W/m2

 and -11.8 ± 1.9 W/m2 at the TOA and surface, 

respectively. These values over land are calculated for the difference between total 

aerosol and the complete absence of all aerosol.  Use of satellite measurements of aerosol microphysical properties yields that on a 

global ocean average, about 20% of AOD is contributed by human activities and the 

clear-sky TOA DRF by anthropogenic aerosols is -1.1 ± 0.4 W/m2. Similar DRF 

estimates are rare over land, but a few measurement-model integrated studies do 

suggest much more negative DRF over land than over ocean.  These satellite-based DRF estimates are much greater than the model-based 

estimates, with differences much larger at regional scales than at a global scale. 

Measurements of Aerosol-Cloud Interactions and Indirect Radiative Forcing 

In situ measurement of cloud properties and aerosol effects on cloud microphysics suggest 

that theoretical understanding of the activation process for water cloud is reasonably wellunderstood. Remote sensing of aerosol effects on droplet size associated with the albedo effect 

tends to underestimate the magnitude of the response compared to in situ measurements. 

Recent efforts trace this to a combination of lack of stratification of data by cloud water, the 

relatively large spatial scale over which measurements are averaged (which includes variability 

in cloud fields, and processes that obscure the aerosol-cloud processes), as well as 

measurement uncertainties (particularly in broken cloud fields). It remains a major challenge 

December 2009 9-117 
to infer aerosol number concentrations from satellite measurements. The present state of 

knowledge of the nature and abundance of IN, and ice formation in clouds is extremely poor. 

Despite the substantial progress in recent decades, several important issues remain, such 

as measurements of aerosol size distribution, particle shape, absorption, and vertical profiles, 

and the detection of aerosol long-term trend and establishment of its connection with the 

observed trends of solar radiation reaching the surface, as discussed in Section 9.3.4. 

Furthering the understanding of aerosol impacts on climate requires a coordinated research 

strategy to improve the measurement accuracy and use the measurements to validate and 

effectively constrain model simulations. Concepts of future research in measurements are 

discussed in Chapter 4 “Way Forward” (of the CCSP SAP2.3). 

9.3.6. Modeling the Effect of Aerosols on Climate 

9.3.6.1. Introduction 

The IPCC Fourth Assessment Report (AR4) (IPCC, 2007, 092765) concludes that man’s 

influence on the warming climate is in the category of “very likely”. This conclusion is based 

on, among other things, the ability of models to simulate the global and, to some extent, 

regional variations of temperature over the past 50-100 years. When anthropogenic effects are 

included, the simulations can reproduce the observed warming (primarily for the past 50 

years); when they are not, the models do not get very much warming at all. In fact, all of the 

models runs for the IPCC AR4 assessment (more than 20) produce this distinctive result, 

driven by the greenhouse gas increases that have been observed to occur. 

These results were produced in models whose average global warming associated with a 

doubled CO2 forcing of 4 W/m2

 was about 3°C. This translates into a climate sensitivity 

(surface temperature change per forcing) of about 0.75°C/(W/m2). The determination of 

climate sensitivity is crucial to projecting the future impact of increased greenhouse gases, and 

the credibility of this projected value relies on the ability of these models to simulate the 

observed temperature changes over the past century. However, in producing the observed 

temperature trend in the past, the models made use of very uncertain aerosol forcing. The 

greenhouse gas change by itself produces warming in models that exceeds that observed by 

some 40% on average (IPCC, 2007, 092765). Cooling associated with aerosols reduces this 

warming to the observed level. Different climate models use differing aerosol forcings, both 

direct (aerosol scattering and absorption of short and longwave radiation) and indirect (aerosol 

effect on cloud cover reflectivity and lifetime), whose magnitudes vary markedly from one 

model to the next. Kiehl (2007, 190949) using nine of the IPCC (2007, 092765) AR4 climate 

models found that they had a factor of three forcing differences in the aerosol contribution for 

the 20th century. The differing aerosol forcing is the prime reason why models whose climate 

sensitivity varies by almost a factor of three can produce the observed trend. It was thus 

concluded that the uncertainty in IPCC (2007, 092765) anthropogenic climate simulations for 

the past century should really be much greater than stated (Kerr, 2007, 190950; Schwartz et al., 

2007, 190384), since, in general, models with low/high sensitivity to greenhouse warming 

used weaker/stronger aerosol cooling to obtain the same temperature response (Kiehl, 2007, 

190949). Had the situation been reversed and the low/high sensitivity models used strong/ 

weak aerosol forcing, there would have been a greater divergence in model simulations of the 

past century. 

December 2009 9-118 
Figure 9-71. Sampling the Arctic Haze. Pollution and smoke aerosols can travel long 

distances, from mid-latitudes to the Arctic, causing “Arctic Haze.” Photo taken 

from the NASA DC-8 aircraft during the ARCTAS field experiment over Alaska in 

April 2008. Credit: Mian Chin, NASA. 

Therefore, the fact that a model has accurately reproduced the global temperature change 

in the past does not imply that its future forecast is accurate. This state of affairs will remain 

until a firmer estimate of radiative forcing (RF) by aerosols, in addition to that by greenhouse 

gases, is available. 

Two different approaches are used to assess the aerosol effect on climate. “Forward 

modeling” studies incorporate different aerosol types and attempt to explicitly calculate the 

aerosol RF. From this approach, IPCC (2007, 092765) concluded that the best estimate of the 

global aerosol direct RF (compared with preindustrial times) is -0.5 (-0.9 to -0.1) W/m2. The 

RF due to the cloud albedo or brightness effect (also referred to as first indirect or Twomey 

effect) is estimated to be -0.7 (-1.8 to -0.3) W/m2. No estimate was specified for the effect 

associated with cloud lifetime. The total negative RF due to aerosols according to IPCC (2007, 

092765) estimates is then -1.3 (-2.2 to -0.5) W/m2. In comparison, the positive radiative 

forcing (RF) from greenhouse gases (including tropospheric ozone) is estimated to be +2.9 ± 

0.3 W/m2; hence tropospheric aerosols reduce the influence from greenhouse gases by about 

45% (15-85%). This approach however inherits large uncertainties in aerosol amount, 

composition, and physical and optical properties in modeling of atmospheric aerosols. The 

consequences of these uncertainties are discussed in the next section. 

The other method of calculating aerosol forcing is called the “inverse approach” – it is 

assumed that the observed climate change is primarily the result of the known climate forcing 

contributions. If one further assumes a particular climate sensitivity (or a range of 

sensitivities), one can determine what the total forcing had to be to produce the observed 

temperature change. The aerosol forcing is then deduced as a residual after subtraction of the 

greenhouse gas forcing along with other known forcings from the total value. Studies of this 

nature come up with aerosol forcing ranges of -0.6 to -1.7 W/m2 (Knutti et al., 2002, 190178; 

December 2009 9-119 
Knutti et al., 2003, 190180); IPCC AR4 Chap.9); -0.4 to -1.6 W/m2

 (Gregory et al., 2002, 

190593); and -0.4 to -1.4 W/m2 (Stott, 2006, 190419). This approach however provides a 

bracket of the possible range of aerosol forcing without the assessment of current knowledge 

of the complexity of atmospheric aerosols. 

This chapter of the CCSP SAP2.3 reviews the current state of aerosol RF in the global 

models and assesses the uncertainties in these calculations. First representation of aerosols in 

the forward global chemistry and transport models and the diversity of the model simulated 

aerosol fields are discussed; then calculation of the aerosol direct and indirect effects in the 

climate models is reviewed; finally the impacts of aerosols on climate model simulations and 

their implications are assessed. 

9.3.6.2. Modeling of Atmospheric Aerosols 

The global aerosol modeling capability has developed rapidly in the past decade. In the 

late 1990s, there were only a few global models that were able to simulate one or two aerosol 

components, but now there are a few dozen global models that simulate a comprehensive suite 

of aerosols in the atmosphere. As introduced in Chapter 1 (of the CCSP SAP2.3), aerosols 

consist of a variety of species including dust, sea salt, sulfate, nitrate, and carbonaceous 

aerosols (black and organic carbon) produced from natural and man-made sources with a wide 

range of physical and optical properties. Because of the complexity of the processes and 

composition, and highly inhomogeneous distribution of aerosols, accurately modeling 

atmospheric aerosols and their effects remains a challenge. Models have to take into account 

not only the aerosol and precursor emissions, but also the chemical transformation, transport, 

and removal processes (e.g., dry and wet depositions) to simulate the aerosol mass 

concentrations. Furthermore, aerosol particle size can grow in the atmosphere because the 

ambient water vapor can condense on the aerosol particles. This “swelling” process, called 

hygroscopic growth, is most commonly parameterized in the models as a function of relative 

humidity. 

Estimates of Emissions

Aerosols have various sources from both natural and anthropogenic processes. Natural 

emissions include wind-blown mineral dust, aerosol and precursor gases from volcanic 

eruptions, natural wild fires, vegetation, and oceans. Anthropogenic sources include emissions 

from fossil fuel and biofuel combustion, industrial processes, agriculture practices, and humaninduced biomass burning. 

Following earlier attempts to quantify manmade primary emissions of aerosols (Penner et 

al., 1993, 045457; Turco et al., 1983, 190529) systematic work was undertaken in the late 

1990s to calculate emissions of black carbon (BC) and organic carbon (OC), using fuel-use 

data and measured emission factors (Cooke and Wilson, 1996, 190046; Cooke et al., 1999, 

156365; Liousse et al., 1996, 078158). The work was extended in greater detail and with 

improved attention to source-specific emission factors in Bond et al. (2004, 056389), which 

provides global inventories of BC and OC for the year 1996, with regional and sourcecategory discrimination that includes contributions from industrial, transportation, residential 

solid-fuel combustion, vegetation and open biomass burning (forest fires, agricultural waste 

burning, etc.), and diesel vehicles. 

Emissions from natural sources—which include wind-blown mineral dust, wildfires, sea 

salt, and volcanic eruptions—are less well quantified, mainly because of the difficulties of 

measuring emission rates in the field and the unpredictable nature of the events. Often, 

emissions must be inferred from ambient observations at some distance from the actual source. 

As an example, it was concluded (Lewis and Schwartz, 2004, 192023) that available 

information on size-dependent sea salt production rates could only provide order-of-magnitude 

estimates. The natural emissions in general can vary dramatically over space and time. 

Aerosols can be produced from trace gases in the atmospheric via chemical reactions, and 

those aerosols are called secondary aerosols, as distinct from primary aerosols that are directly 

emitted to the atmosphere as aerosol particles. For example, most sulfate and nitrate aerosols 

are secondary aerosols that are formed from their precursor gases, sulfur dioxide (SO2) and 

nitrogen oxides (NO and NO2, collectively called NOX), respectively. Those sources have been 

studied for many years and are relatively well known. By contrast, the sources of secondary 

organic aerosols (SOA) are poorly understood, including emissions of their precursor gases 

(called volatile organic compounds, VOC) from both natural and anthropogenic sources and 

the atmospheric production processes. 

Globally, sea salt and mineral dust dominate the total aerosol mass emissions because of 

the large source areas and/or large particle sizes. However, sea salt and dust also have shorter 

atmospheric lifetimes because of their large particle size, and are radiatively less active than 

aerosols with small particle size, such as sulfate, nitrate, BC, and particulate organic matter 

(POM, which includes both carbon and non-carbon mass in the organic aerosol), most of 

which are anthropogenic in origin. 

Because the anthropogenic aerosol RF is usually evaluated (e.g., by the IPCC) as the 

anthropogenic perturbation since the pre-industrial period, it is necessary to estimate the 

December 2009 9-120 
historical emission trends, especially the emissions in the pre-industrial era. Compared to 

estimates of present-day emissions, estimates of historical emission have much larger 

uncertainties. Information for past years on the source types and strengths and even locations 

are difficult to obtain, so historical inventories from preindustrial times to the present have to 

be based on limited knowledge and data. Several studies on historical emission inventories of 

BC and OC (e.g., Bond et al., 2007, 190050; Fernandes et al., 2007, 190554; Ito and Penne, 

2005, 190626; Junker and Liousse, 2008, 190971; Novakov et al., 2003, 048398), SO2 (Stern, 

2005, 190416), and various species (Dentener et al., 2006, 088434; Van Aardenne et al., 2001, 

055564) are available in the literature; there are some similarities and some differences among 

them, but the emission estimates for early times do not have the rigor of the studies for 

present-day emissions. One major conclusion from all these studies is that the growth of 

primary aerosol emissions in the 20th century was not nearly as rapid as the growth in CO2 

emissions. This is because in the late 19th and early 20th centuries, particle emissions such as 

BC and POM were relatively high due to the heavy use of biofuels and the lack of particulate 

controls on coal-burning facilities; however, as economic development continued, traditional 

biofuel use remained fairly constant and particulate emissions from coal burning were reduced 

by the application of technological controls (Bond et al., 2007, 190050). Thus, particle 

emissions in the 20th century did not grow as fast as CO2 emissions, as the latter are roughly 

proportional to total fuel use—oil and gas included. Another challenge is estimating historical 

biomass burning emissions. A recent study suggested about a 40% increase in carbon 

emissions from biomass burning from the beginning to the end of last century (Mouillot et al., 

2006, 190549), but it is difficult to verify. 

Table 9-11. Anthropogenic emissions of aerosols and precursors for 2000 and 1750. 

Source Species* Emission# 2000 

(Tg/yr) 

Emission 1750 

(Tg/yr) 

Biomass burning 

BC 

POM 

S 

3.1 

34.7 

4.1 

1.03 

12.8 

1.46 

Biofuel 

BC 

POM 

S 

9.1 

9.6 

0.39 

1.56 

0.12 

Fossil fuel 

BC 

POM 

S 

3.0 

3.2 

98.9 

# Data source for 2000 emission: biomass burning – Global Fire Emission Dataset (GFED); biofuel BC and POM – Speciated Pollutant Emission Wizard (SPEW); biofuel sulfur – International Institute for 

Applied System Analysis (IIASA); fossil fuel BC and POM – SPEW; fossil fuel sulfur – Emission Database for Global Atmospheric Research (EDGAR) and IIASA. Fossil fuel emission of sulfur (S) is the sum of 

emission from industry, power plants, and transportation listed in Dentener et al. (2006, 088434). 

* S=sulfur, including SO2 and particulate SO4

2–. Most emitted as SO2, and 2.5% emitted as SO42–. 

Source: Adapted from Dentener et al. (2006, 088434)

.

As an example, Table 9-11 shows estimated anthropogenic emissions of sulfur, BC and 

POM in the present day (year 2000) and pre-industrial time (1750) compiled by Dentener et al. 

(2006, 088434) These estimates have been used in the Aerosol Comparisons between 

Observations and Models (AeroCom) project (Experiment B, which uses the year 2000 

emission; and Experiment PRE, which uses pre-industrial emissions), for simulating 

atmospheric aerosols and anthropogenic aerosol RF. The AeroCom results are discussed below 

and in Section 9.3.6.3. 

Aerosol Mass Loading and Optical Depth 

In the global models, aerosols are usually simulated in the successive steps of sources 

(emission and chemical formation), transport (from source location to other area), and removal 

processes (dry deposition, in which particles fall onto the surface, and wet deposition by rain) 

that control the aerosol lifetime. Collectively, emission, transport, and removal determine the 

amount (mass) of aerosols in the atmosphere. Aerosol optical depth (AOD), which is a 

measure of solar or thermal radiation being attenuated by aerosol particles via scattering or 

absorption, can be related to the atmospheric aerosol mass loading as follows: 

December 2009 9-121 
AOD = • MMEE

 Equation 9-3 

where M is the aerosol mass loading per unit area (g m-2), MEE is the mass extinction 

efficiency or specific extinction in unit of m2/g, which is 

∫•

π =

eff

est

pr4

Q3 MEE

Equation 9-4 

where Qext is the extinction coefficient (a function of particle size distribution and 

refractive index), reff is the aerosol particle effective radius, ρ is the aerosol particle density, 

and f is the ratio of ambient aerosol mass (wet) to dry aerosol mass M. Here, M is the result 

from model-simulated atmospheric processes and MEE embodies the aerosol physical 

(including microphysical) and optical properties. Since Qext varies with radiation wavelength, 

so do MEE and AOD. AOD is the quantity that is most commonly obtained from remote 

sensing measurements and is frequently used for model evaluation (see Chapter 2 of the CCSP 

SAP2.3). AOD is also a key parameter determining aerosol radiative effects. 

Here the results from the recent multiple global-model studies by the AeroCom project are 

summarized, as they represent the current assessment of model-simulated atmospheric aerosol 

loading, optical properties, and RF for the present-day. AeroCom aims to document differences 

in global aerosol models and compare the model output to observations. Sixteen global models 

participated in the AeroCom Experiment A (AeroCom-A), for which every model used their 

own configuration, including their own choice of estimating emissions (Kinne et al., 2006, 

155903; Textor et al., 2006, 190456). Five major aerosol types: sulfate, BC, POM, dust, and 

sea salt, were included in the experiments, although some models had additional aerosol 

species. Of those major aerosol types, dust and sea-salt are predominantly natural in origin, 

whereas sulfate, BC, and POM have major anthropogenic sources. 

Table 9-12 summarizes the model results from the AeroCom-A for several key 

parameters: Sources (emission and chemical transformation), mass loading, lifetime, removal 

rates, and MEE and AOD at a commonly used, mid-visible, wavelength of 550 nanometer 

(nm). These are the globally averaged values for the year 2000. Major features and conclusions 

are: 

 Globally, aerosol source (in mass) is dominated by sea salt, followed by dust, sulfate, 

POM, and BC. Over the non-desert land area, human activity is the major source of 

sulfate, black carbon, and organic aerosols.  Aerosols are removed from the atmosphere by wet and dry deposition. Although sea 

salt dominates the emissions, it is quickly removed from the atmosphere because of 

its large particle size and near-surface distributions, thus having the shortest lifetime. 

The median lifetime of sea salt from the AeroCom-A models is less than half a day, 

whereas dust and sulfate have similar lifetimes of 4 days and BC and POM 6-7 days.  Globally, small-particle-sized sulfate, BC, and POM make up a little over 10% of 

total aerosol mass in the atmosphere. However, they are mainly from anthropogenic 

activity, so the highest concentrations are in the most populated regions, where their 

effects on climate and air quality are major concerns.  Sulfate and BC have their highest MEE at mid-visible wavelengths, whereas dust is 

lowest among the aerosol types modeled. That means for the same amount of aerosol 

mass, sulfate and BC are more effective at attenuating (scattering or absorbing) solar 

radiation than dust. This is why the sulfate AOD is about the same as dust AOD even 

though the atmospheric amount of sulfate mass is 10 times less than that of the dust.  There are large differences, or diversities, among the models for all the parameters 

listed in Table 9-12. The largest model diversity, shown as the % standard deviation 

from the all-model-mean and the range (minimum and maximum values) in Table 

9-12, is in sea salt emission and removal; this is mainly associated with the 

differences in particle size range and source parameterizations in each model. The 

diversity of sea salt atmospheric loading however is much smaller than that of 

sources or sinks, because the largest particles have the shortest lifetimes even though 

they comprise the largest fraction of emitted and deposited mass. 

December 2009 9-122 
Table 9-12. Summary of statistics of AeroCom Experiment A results from 16 global models. 

 Quantity Mean Median Range Stddev/mean* 

SOURCES (TG/YR) 

 SO4

2– 179 186 98-232 22% 

 BC 11.9 11.3 7.8-19.4 23% 

 Organic matter 96.6 96.0 53-138 26% 

 Dust 1840 1640 672-4040 49% 

 Sea salt 16600 6280 2180-121000 199% 

REMOVAL RATE (DAY-) 

 SO4

2– 0.25 0.24 0.19-0.39 18% 

 BC 0.15 0.15 0.066-0.19 21% 

 Organic matter 0.16 0.16 0.09-0.23 24% 

 Dust 0.31 0.25 0.14-0.79 62% 

 Sea salt 5.07 2.50 0.95-35.0 188% 

LIFETIME (DAY) 

 SO4

2– 4.12 4.13 2.6-5.4 18% 

 BC 7.12 6.54 5.3-15 33% 

 Organic matter 6.54 6.16 4.3-11 27% 

 Dust 4.14 4.04 1.3-7.0 43% 

 Sea salt 0.48 0.41 0.03-1.1 58% 

MASS LOADING (TG) 

 SO4

2– 1.99 1.98 0.92-2.70 25% 

 BC 0.24 0.21 0.046-0.51 42% 

 Organic matter 1.70 1.76 0.46-2.56 27% 

 Dust 19.2 20.5 4.5-29.5 40% 

 Sea salt 7.52 6.37 2.5-13.2 54% 

MEE AT 550 NM (M2G-1) 

 SO4

2– 11.3 9.5 4.2-28.3 56% 

 BC 9.4 9.2 5.3-18.9 36% 

 Organic matter 5.7 5.7 3.7-9.1 26% 

 Dust 0.99 0.95 0.46-2.05 45% 

 Sea salt 3.0 3.1 0.97-7.5 55% 

AOD AT 550 NM 

 SO4

2– 0.035 0.034 0.015-0.051 33% 

 BC 0.004 0.004 0.002-0.009 46% 

 Organic matter 0.018 0.019 0.006-0.030 36% 

 Dust 0.032 0.033 0.012-0.054 44% 

 Sea salt 0.033 0.030 0.02-0.067 42% 

TOTAL AOT AT 550 NM 0.124 0.127 0.056-0.151 18% 

Stddev/mean was used as the term “diversity” in Textor et al. (2006, 190456). 

Source: Textor et al. (2006, 190456) and Kinne et al. (2006, 155903), and AeroCom website http://nansen.ipsl.jussieu.fr/AEROCOM/data.html

December 2009 9-123 
 Among the key parameters compared in Table 9-12, the models agree best for 

simulated total AOD – the % of standard deviation from the model mean is 18%, 

with the extreme values just a factor of 2 apart. The median value of the multi-model 

simulated global annual mean total AOD, 0.127, is also in agreement with the global 

mean values from recent satellite measurements. However, despite the general 

agreement in total AOD, there are significant diversities at the individual component 

level for aerosol optical thickness, mass loading, and mass extinction efficiency. This 

indicates that uncertainties in assessing aerosol climate forcing are still large, and 

they depend not only on total AOD but also on aerosol absorption and scattering 

direction (called asymmetry factor), both of which are determined by aerosol 

physical and optical properties. In addition, even with large differences in mass 

loading and MEE among different models, these terms could compensate for each 

other (Equation 9-3) to produce similar AOD. This is illustrated in Figure 9-72. For 

example, model LO and LS have quite different mass loading (44 and 74 mg m-2, 

respectively), especially for dust and sea salt amount, but they produce nearly 

identical total AOD (0.127 and 0.128, respectively). 

 Because of the large spatial and temporal variations of aerosol distributions, regional 

and seasonal diversities are even larger than the diversity for global annual means. 

To further isolate the impact of the differences in emissions on the diversity of simulated 

aerosol mass loading, identical emissions for aerosols and their precursor were used in the 

AeroCom Experiment B exercise in which 12 of the 16 AeroCom-A models participated 

(Textor et al., 2007, 190458). The comparison of the results and diversity between AeroCom-A 

and –B for the same models showed that using harmonized emissions does not significantly 

reduce model diversity for the simulated global mass and AOD fields, indicating that the 

differences in atmospheric processes, such as transport, removal, chemistry, and aerosol 

microphysics, play more important roles than emission in creating diversity among the models. 

This outcome is somewhat different from another recent study, in which the differences in 

calculated clear-sky aerosol RF between two models (a regional model STEM and a global 

model MOZART) were attributed mostly to the differences in emissions (Bates et al., 2006, 

189912), although the conclusion was based on only two model simulations for a few focused 

regions. It is highly recommended from the outcome of AeroCom-A and -B that, although 

more detailed evaluation for each individual process is needed, multi-model ensemble results, 

e.g., median values of multi-model output variables, should be used to estimate aerosol RF, 

due to their greater robustness, relative to individual models, when compared to observations 

(Schulz et al., 2006, 190381; Textor et al., 2006, 190456; Textor et al., 2007, 190458). 

9.3.6.3. Calculating Aerosol Direct Radiative Forcing 

The three parameters that define the aerosol direct RF are the AOD, the single scattering 

albedo (SSA), and the asymmetry factor (g), all of which are wavelength dependent. AOD is 

indicative of how much aerosol exists in the column, SSA is the fraction of radiation being 

scattered versus the total attenuation (scattered and absorbed), and the g relates to the direction 

of scattering that is related to the size of the particles (see Chapter 1 of the CCSP SAP2.3). An 

indication of the particle size is provided by another parameter, the Ångström exponent (Å), 

which is a measure of differences of AOD at different wavelengths. For typical tropospheric 

aerosols, Å tends to be inversely dependent on particle size; larger values of Å are generally 

associated with smaller aerosols particles. These parameters are further related; for example, 

for a given composition, the ability of a particle to scatter radiation decreases more rapidly 

with decreasing size than does its ability to absorb, so at a given wavelength varying Å can change SSA. Note that AOD, SSA, g, Å, and all the other parameters in Equation 9-3 and 

Equation 9-4 vary with space and time due to variations of both aerosol composition and 

relative humidity, which influence these characteristics. 

December 2009 9-124 
Source: Adapted from Kinne et al. (2006, 155903) 

Figure 9-72. Global annual averaged AOD (upper panel) and aerosol mass loading (lower 

panel) with their components simulated by 15 models in AeroCom- A (excluding 

one model which only reported mass). SU=SO4

2–, BC=black carbon, 

POM=particulate OC, DU=dust, SS=sea salt. Model abbreviations: LO=LOA (Lille, 

Fra), LS=LSCE (Paris, Fra), UL=ULAQ (L’Aquila, Ita), SP=SPRINTARS (Kyushu, 

Jap), CT=ARQM (Toronto, Can), MI=MIRAGE (Richland, USA), EH=ECHAM5 (MPIHamburg, Ger), NF=CCM-Match (NCAR Boulder, USA), OT=Oslo-CTM (Oslo, Nor), 

OG=OLSO-GCM (Oslo, Nor) [prescribed background for DU and SS], IM=IMPACT 

(Michigan, USA), GM=GFDLMozart (Princeton, NJ, USA), GO=GOCART (NASAGSFC, Washington DC, USA), GI=GISS (NASA-GISS, New York, USA), TM=TM5 

(Utrecht, Net). Also shown in the upper panel are the averaged observation data 

from AERONET (Ae) and the satellite composite (S*). 

In the recent AeroCom project, aerosol direct RF for the solar spectral wavelengths (or 

shortwave) was assessed based on the 9 models that participated in both Experiment B and 

PRE in which identical, prescribed emissions for present (year 2000) and pre-industrial time 

(year 1750) listed in Table 9-11 were used across the models (Schulz et al., 2006, 190381). The 

anthropogenic direct RF was obtained by subtracting Aero- Com-PRE from AeroCom-B 

simulated results. Because dust and sea salt are predominantly from natural sources, they were 

not included in the anthropogenic RF assessment although the land use practice can contribute 

to dust emissions as “anthropogenic”. Other aerosols that were not considered in the AeroCom 

forcing assessment were natural sulfate (e.g., from volcanoes or ocean) and POM (e.g., from 

biogenic hydrocarbon oxidation), as well as nitrate. The aerosol direct forcing in the AeroCom 

assessment thus comprises three major anthropogenic aerosol components sulfate, BC, and 

POM. 

December 2009 9-125 
Table 9-13. SO4

2– mass loading, MEE and AOD at 550 nm, shortwave radiative forcing at the top of 

the atmosphere, and normalized forcing with respect to AOD and mass. All values refer 

to anthropogenic perturbation. 

Model Mass load 

(mg m-2) 

MEE 

(m2g-1) 

AOD att 

550 nm 

TOA Forcing 

(W/m2) 

Forcing/AOD 

(W/m2) 

Forcing/Mass

(W g-1) 

PUBLISHED SINCE IPCC 2001 

A CCM3 2.23 -0.56 -251 

 B GEOSCHEM 1.53 11.8 0.018 -0.33 -18 -216 

 C GISS 3.30 6.7 0.022 -0.65 -30 -197 

D GISS 3.27 -0.96 -294 

E GISS* 2.12 -0.57 -269 

 F SPRINTARS 1.55 9.7 0.015 -0.21 -135 

G LMD 2.76 -0.42 -152 

 H LOA 3.03 9.9 0.03 -0.41 -14 -135 

I GATORG 3.06 -0.32 -105 

 J PNNL 5.50 7.6 0.042 -0.44 -10 -80 

 K UIO-CTM 1.79 10.6 0.019 -0.37 -19 -207 

L UIO-GCM 2.28 -0.29 -127 

AEROCOM: IDENTICAL EMISSIONS USED FOR YEAR 2000 AND 1750 

 M UMI 2.64 7.6 0.02 -0.58 -29 -220 

 N UIO-CTM 1.70 11.2 0.019 -0.36 -19 -212 

 O LOA 3.64 9.6 0.035 -0.49 -14 -135 

 P LSCE 3.01 7.6 0.023 -0.42 -18 -140 

 Q ECHAMS-HAM 2.47 6.5 0.016 -0.46 -29 -186 

 R GISS** 1.34 4.5 0.006 -0.19 -32 -142 

 S UIO-GCM 1.72 7.0 0.012 -0.25 -21 -145 

 T SPRINTARS 1.19 10.9 0.013 -0.16 -12 -134 

U ULAQ 1.62 12.3 0.02 -0.22 -11 -136 

 Average A-L 2.70 9.4 0.024 -00.46 -18 -181 

 Average M-U 2.15 8.6 0.018 -0.35 -21 -161 

 Minimum A-U 1.19 4.5 0.006 -0.96 -32 -294 

 Maximum A-U 5.50 12.3 0.042 -0.16 -10 -80 

 Std dev A-L 1.09 1.9 0.010 0.202 7 68 

 Std dev M-U 0.83 2.6 0.008 0.149 8 35 

%Stddev/avg A-L 40% 20% 41% 44% 38% 385 

%Stddev/avg M-U 39% 30% 45% 43% 37% 22% 

Model abbreviations: CCM3=Community Climate Model; GEOSCHEM=Goddard Earth Observing System-Chemistry; GISS=Goddard nstitute for Space Studies; SPRINTARS=Spectral Radiation-Transport 

Model for Aerosol Species; LMD=Laboratoire de Meteorologie Dynamique; LOA=Laboratoire d’Optique Atmospherique; GATORG=Gas, Aerosol Transport and General circulation model; PNNL=Pacific 

Northwest National Laboratory; UIO-CTM=Univeristy of Oslo CTM; UIO-GCM=University of Oslo GCM; UMI=University of Michigan; LSCE=Laboratoire des Sciences du Climat et de l’Environment; 

ECHAMS5-HAM=European Centre Hamburg with Hamburg Aerosol Module; ULAQ=University of lL’Aquila. 

Source: Adapted from IPCC AR4 (2007, 092765) and Schulz et al. (2006, 190381)

December 2009 9-126 
Table 9-14. Particulate organic matter (POM) and BC mass loading, AOD at 550 nm, shortwave 

radiative forcing at the top of the atmosphere, and normalized forcing with respect to 

AOD and mass. 

Model 

Mass 

load 

(mg m-2) 

MEE 

(m2g-1) 

AOD at 

550 nm 

TOA 

Forcing

(W/m2) 

Forcing/

AOD 

(W/m2) 

Forcing/

Mass

(W g-1) 

Mass 

load 

(mg m-2)

MEE 

(m2g-1) 

AOD at 

550 nm 

TOA 

Forcing 

(W/m2) 

Forcing/

AOD 

(W/m2) 

Forcing/

Mass

(W g-1) 

PUBLISHED SINCE IPCC 2001 

A SPRINTARS -0.24 -107 0.36 

B LOA 2.33 6.9 0.016 -0.25 -16 -140 0.37 0.55 

C GISS 1.86 9.1 0.017 -0.26 -15 -161 0.29 0.61 

D GISS 1.86 8.1 0.015 -0.30 -20 -75 0.29 0.35 

E GISS* 2.39 -0.18 -92 0.39 0.50 

F GISS 2.49 -0.23 -101 0.43 0.53 

G SPRINTARS 2.67 10.9 0.029 -0.27 -9 -23 0.53 0.42 

H GATORG 2.56 -0.06 -112 0.39 0.55 

I MOZGN 3.03 5.9 0.018 -0.34 -19 

J CCM 0.33 0.34 

K UIO-CTM 0.30 0.19 

AEROCOM: IDENTICAL EMISSIONS FOR YEAR 2000 & 1750 

 L UMI 1.16 5.2 0.0060 -0.23 -38 -198 0.19 6.8 1.29 0.25 194 1316 

 M UIO-CTM 1.12 5.2 0.0058 -0.16 -28 -143 0.19 7.1 1.34 0.22 164 1158 

 N LOA 1.41 6.0 0.0085 -0.16 -19 -113 0.25 7.9 1.98 0.32 162 1280 

 OLSCE 1.50 5.3 0.0079 -0.17 -22 -113 0.25 4.4 1.11 0.30 270 1200 

 P ECHAMS-HAM 1.00 7.7 0.0077 -0.10 -13 -100 0.16 7.7 1.23 0.20 163 1250 

 Q GISS** 1.22 4.9 0.0060 -0.14 -23 -115 0.24 7.6 1.83 0.22 120 917 

 R UIO-GCM 0.88 5.2 0.0046 -0.06 -13 -68 0.19 10.3 1.95 0.36 185 1895 

 S SPRINTARS 1.84 10.9 0.0200 -0.10 -5 -54 0.37 9.5 3.50 0.32 91 865 

 T ULAQ 1.71 4.4 0.0075 -0.09 -12 -53 0.38 7.6 2.90 0.08 28 211 

Average A-K 2.40 8.2 0.019 -0.24 -16 -102 0.37 0.44 1242 

 Average L-T 1.32 6.1 0.008 -0.13 -19 -106 0.25 7.7 1.90 0.25 153 1121 

 Minimum A-T 0.88 4.4 0.005 -0.34 -38 -198 0.16 4.4 1.11 0.08 28 211 

 Maximum A-T 3.03 10.9 0.029 -0.06 -5 -23 0.53 10.3 3.50 0.61 270 2103 

Std dev A-K 0.39 1.7 0.006 0.09 4 41 0.08 0.06 384 

 Std dev L-T 0.32 2.0 0.005 0.05 10 46 0.08 1.6 0.82 0.09 68 450 

 %Stddev/avg A-K 16% 21% 30% 36% 26% 41% 22% 23% 31% 

 %Stddev/avg L-T 25% 33% 56% 39% 52% 43% 32% 21% 43% 34% 45% 40% 

Source: Based on IPCC AR4 (2007, 092765) and Schulz et al. (2006, 190381).

December 2009 9-127 
The IPCC AR4 (2007, 092765) assessed anthropogenic aerosol RF based on the model 

results published after the IPCC TAR in 2001, including those from the AeroCom study 

discussed above. These results (adopted from IPCC AR4) are shown in Table 9-13 for sulfate 

and Table 9-14 for carbonaceous aerosols (BC and POM), respectively. All values listed in 

Table 9-13 and Table 9-14 refer to anthropogenic perturbation, i.e., excluding the natural 

fraction of these aerosols. In addition to the mass burden, MEE, and AOD, Table 9-13 and 

Table 9-14 also list the “normalized forcing”, also known as “forcing efficiency”, one for the 

forcing per unit AOD, and the other the forcing per gram of aerosol mass (dry). For some 

models, aerosols are externally mixed, that is, each aerosol particle contains only one aerosol 

type such as sulfate, whereas other models allow aerosols to mix internally to different 

degrees, that is, each aerosol particle can have more than one component, such as black carbon 

coated with sulfate. For models with internal mixing of aerosols, the component values for 

AOD, MEE, and forcing were extracted (Schulz et al., 2006, 190381). 

Considerable variation exists among these models for all quantities in Table 9-13 and 

Table 9-14. The RF for all the components varies by a factor of 6 or more: Sulfate from 0.16 to 

0.96 W/m2, POM from -0.06 to -0.34 W/m2, and BC from +0.08 to +0.61 W/m2, with the 

standard deviation in the range of 30 to 40% of the ensemble mean. It should be noted that 

although BC has the lowest mass loading and AOD, it is the only aerosol species that absorbs 

strongly, causing positive forcing to warm the atmosphere, in contrast to other aerosols that 

impose negative forcing to cool the atmosphere. As a result, the net anthropogenic aerosol 

forcing as a whole becomes less negative when BC is included. The global average 

anthropogenic aerosol direct RF at the top of the atmosphere (TOA) from the models, together 

with observation-based estimates (see Chapter 2 of the CCSP SAP2.3), is presented in Figure 

9-73. Note the wide range for forcing in Figure 9-73. The comparison with observation-based 

estimates shows that the model estimated forcing is in general lower, partially because the 

forcing value from the model is the difference between present-day and pre-industrial time, 

whereas the observation-derived quantity is the difference between an atmosphere with and 

without anthropogenic aerosols, so the “background” value that is subtracted from the total 

forcing is higher in the models. The discussion so far has dealt with global average values. The 

geographic distributions of multi-model aerosol direct RF has been evaluated among the 

AeroCom models, which are shown in Figure 9-74 for total and anthropogenic AOD at 550 nm 

and anthropogenic aerosol RF at TOA, within the atmospheric column, and at the surface. 

Globally, anthropogenic AOD is about 25% of total AOD (Figure 9-74A and B) but is more 

concentrated over polluted regions in Asia, Europe, and North America and biomass burning 

regions in tropical southern Africa and South America. At TOA, anthropogenic aerosol causes 

negative forcing over mid-latitude continents and oceans with the most negative values (-1 to 

-2 W/m2

) over polluted regions (Figure 9-74C). Although anthropogenic aerosol has a cooling 

effect at the surface with surface forcing values down to -10 W/m2 over China, India, and 

tropical Africa (Figure 9-74E), it warms the atmospheric column with the largest effects again 

over the polluted and biomass burning regions. This heating effect will change the atmospheric 

circulation and can affect the weather and precipitation (e.g., Kim et al., 2006, 190917). 

December 2009 9-128 
Aerosol Direct Radiative Forcing 

Bellouin et al. (2005, 155684) 

Chung et al. (2005, 155733) 

Observation 

based 

Yu et al. (2006, 156173) 

GISS_1 (Liao and Seinfeld, 2005, 199892) 

GISS_2 (Liao and Seinfeld, 2005, 199892) 

LOA (Reddy et al., 2005, 190208) 

SPRINTARS (Takemura et al., 2005, 190439) 

UIO-GCM (Kirkevåg and Iversen, 2002, 199893) 

GATORG (Jacobson, 2001, 043110) 

GISS (Hansen et al., 2005, 190596) 

Models published since TAR 

GISS (Koch, 2001, 192054) 

UMI 

UIO_CTM 

LOA 

LSCE 

ECHAM50HAM 

GISS 

UIO-GCM 

SPRINTARS 

AeroCom models 

ULAQ 

 

 Radiative Forcing (W/m2

) 

Source: IPCC (2007, 092765). 

Figure 9-73. Aerosol direct radiative forcing in various climate and aerosol models. Observed 

values are shown in the top section. 

December 2009 9-129 
A C 

B D 

E 

.

Source: Schulz et al. (2006, 190381) and AeroCom image catalog (http://nansen.ipsl.jussieu.fr/AEROCOM/aerocomhome.html) 

Figure 9-74. Aerosol optical thickness and anthropogenic shortwave all-sky radiative forcing 

from the AeroCom study. Shown in the figure: total AOD (A) and anthropogenic 

AOD (B) at 550 nm, and radiative forcing at TOA (C), atmospheric column (D), and 

surface (E). 

December 2009 9-130 
Aerosol Direct Radiative Forcing 

Hadley (Jones et al., 2001, 199895) 

Hadley (Williams et al., 2001, 199898) 

CSIRO (Rotstayn and Penner, 2001, 193754) 

GISS (Menon et al., 2002, 155978) 

CSIRO (Rotstayn and Liu, 2003, 199905) 

LMDZ (Quaas et al., 2004, 199907) 

LMDZ/POLDER (Dufresne et al., 2005, 199908) 

Aerosol species: S, SS, OC 

GFDL (Ming et al., 2005, 199909) 

ECHAM (Lohmann et al., 2000, 199910) 

PNNL (Ghan et al., 2001, 199911) 

NCAR-CCM (Chuang et al., 2002, 199912) 

NCAR-CCM (Kristjánsson et al., 2002, 199916) 

SPRINTARS (Suzuki et al., 2004, 199917) 

SPIRNTARS (Takemura et al., 2005, 190439) 

GISS (Hansen et al., 2005, 190596) 

LMDZ/CTRL (Quaas and Boucher, 2005, 190573) 

LMDZ/POLDER (Quaas and Boucher, 2005, 190573) 

LMDZ/MODIS (Quaas and Boucher, 2005, 190573) 

UM_ctrl (Chen and Penner, 2005, 199918) 

UM-1 (Chen and Penner, 2005, 199918) 

UM_2 (Chen and Penner, 2005, 199918) 

UM_3 (Chen and Penner, 2005, 199918) 

UM_4 (Chen and Penner, 2005, 199918) 

UM_5 (Chen and Penner, 2005, 199918) 

UM_6 (Chen and Penner, 2005, 199918) 

Oslo (Penner et al., 2006, 190564) 

LMDZ (Penner et al., 2006, 190564) 

Aerosol species: S, SS, OC, BC, D, N 

CCSR (Penner et al., 2006, 190564) 

 

 Radiative Forcing (W/m2

) 

Source: IPCC (2007, 092765). 

Figure 9-75. Radiative forcing from the cloud albedo effect (1st aerosol indirect effect) in the 

global climate models used from IPCC (2007, 092765), Chapter 2, Figure 2.14, of 

the IPCC AR4. Species included in the lower panel are SO4

2–, sea salt, organic 

and BC, dust and nitrates; in the top panel, only SO4

2–, sea salt and OC are 

included. 

December 2009 9-131 
Basic conclusions from forward modeling of aerosol direct RF are: 

 The most recent estimate of all-sky shortwave aerosol direct RF at TOA from 

anthropogenic sulfate, BC, and POM (mostly from fossil fuel/biofuel combustion and 

biomass burning) is -0.22 ± 0.18 W/m2

 averaged globally, exerting a net cooling 

effect. This value would represent the low-end of the forcing magnitude, since some 

potentially significant anthropogenic aerosols, such as nitrate and dust from human 

activities are not included because of their highly uncertain sources and processes. 

IPCC AR4 had adjusted the total anthropogenic aerosol direct RF to -0.5 ± 0.4 W/m2

by adding estimated anthropogenic nitrate and dust forcing values based on limited 

modeling studies and by considering the observation-based estimates (see Chapter 2 

of the CCSP SAP2.3). 

 Both sulfate and POM cause negative forcing whereas BC causes positive forcing 

because of its highly absorbing nature. Although BC comprises only a small fraction 

of anthropogenic aerosol mass load and AOD, its forcing efficiency (with respect to 

either AOD or mass) is an order of magnitude stronger than sulfate and POM, so its 

positive shortwave forcing largely offsets the negative forcing from sulfate and 

POM. This points out the importance of improving the model ability to simulate each 

individual aerosol components more accurately, especially black carbon. Separately, 

it is estimated from recent model studies that anthropogenic sulfate, POM, and BC 

forcings at TOA are -0.4, -0.18, +0.35 W/m2

, respectively. The anthropogenic nitrate 

and dust forcings are estimated at -0.1 W/m2 for each, with uncertainties exceeds 

100% (IPCC, 2007, 092765). 

 In contrast to long-lived greenhouse gases, anthropogenic aerosol RF exhibits 

significant regional and seasonal variations. The forcing magnitude is the largest over 

the industrial and biomass burning source regions, where the magnitude of the 

negative aerosol forcing can be of the same magnitude or even stronger than that of 

positive greenhouse gas forcing.  There is a large spread of model-calculated aerosol RF even in the global annual 

averaged values. The AeroCom study shows that the model diversity at some 

locations (mostly East Asia and African biomass burning regions) can reach ± 3 

W/m2

, which is an order of magnitude above the global averaged forcing value of -

0.22 W/m2. The large diversity reflects the low level of current understanding of 

aerosol radiative forcing, which is compounded by uncertainties in emissions, 

transport, transformation, removal, particle size, and optical and microphysical 

(including hygroscopic) properties. 

 In spite of the relatively small value of forcing at TOA, the magnitudes of 

anthropogenic forcing at the surface and within the atmospheric column are 

considerably larger: -1 to -2 W/m2

 at the surface and +0.8 to +2 W/m2 in the 

atmosphere. Anthropogenic aerosols thus cool the surface but heat the atmosphere, 

on average. Regionally, the atmospheric heating can reach annually averaged values 

exceeding 5 W/m2. Source: Schulz et al. (2006, 190381) and AeroCom Image 

Catalog (http://nansen.ipsl.jussieu.fr/AEROCOM/aerocomhome.html ) 

 Figure 9-74D). These regional effects and the negative surface forcing are expected 

to exert an important effect on climate through alteration of the hydrological cycle. 

9.3.6.4. Calculating Aerosol Indirect Forcing 

Aerosol Effects on Clouds 

A subset of the aerosol particles can act as cloud condensation nuclei (CCN) and/or ice 

nuclei (IN). Increases in aerosol particle concentrations, therefore, may increase the ambient 

concentrations of CCN and IN, affecting cloud properties. For a fixed cloud liquid water 

content, a CCN increase will lead to more cloud droplets so that the cloud droplet size will 

decrease. That effect leads to brighter clouds, the enhanced albedo then being referred to as the 

“cloud albedo effect” (Twomey, 1977, 190533), also known as the first indirect effect. If the 

droplet size is smaller, it may take longer to rainout, leading to an increase in cloud lifetime, 

hence the “cloud lifetime” effect (Albrecht, 1989, 045783), also called the second indirect 

effect. Approximately one-third of the models used for the IPCC 20th century climate change 

simulations incorporated an aerosol indirect effect, generally (though not exclusively) 

considered only with sulfates. 

Shown in Figure 9-75 are results from published model studies indicating the different RF 

values from the cloud albedo effect. The cloud albedo effect ranges from -0.22 to -1.85 W/m2; 

the lowest estimates are from simulations that constrained representation of aerosol effects on 

clouds with satellite measurements of drop size vs. aerosol index. In view of the difficulty of 

quantifying this effect remotely (discussed later), it is not clear whether this constraint 

provides an improved estimate. The estimate in the IPCC AR4 ranges from +0.4 to -1.1 W/m2

, 

with a “best-guess” estimate of 0.7 W/m2. 

The representation of cloud effects in GCMs is considered below. However, it is 

becoming increasingly clear from studies based on high resolution simulations of aerosolDecember 2009 9-132 
cloud interactions that there is a great deal of complexity that is unresolved in climate models. 

This point is examined below (High Resolution Modeling). 

Most models did not incorporate the “cloud lifetime effect.” Hansen et al. (2005, 059087) 

compared this latter influence (in the form of time-averaged cloud area or cloud cover 

increase) with the cloud albedo effect. In contrast to the discussion in IPCC (2007, 092765), 

they argue that the cloud cover effect is more likely to be the dominant one, as suggested both 

by cloud-resolving model studies (Ackerman et al., 2004, 190056) and satellite observations 

(Kaufman et al., 2005, 155891). The cloud albedo effect may be partly offset by reduced cloud 

thickness accompanying aerosol pollutants, producing a meteorological (cloud) rather than 

aerosol effect (see the discussion in Lohman and Feichter, 2005, 155942). The distinction 

between meteorological feedback and aerosol forcing can become quite opaque; as noted 

earlier, the term feedback is restricted here to those processes that are responding to a change 

in temperature. Nevertheless, both aerosol indirect effects were utilized in Hansen et al. (2005, 

059087), with the second indirect effect calculated by relating cloud cover to the aerosol 

number concentration, which in turn is a function of sulfate, nitrate, black carbon and organic 

carbon concentration. Only the low altitude cloud influence was modeled, principally because 

there are greater aerosol concentrations at low levels, and because low clouds currently exert 

greater cloud RF. The aerosol influence on high altitude clouds, associated with IN changes, is 

a relatively unexplored area for models and as well for process-level understanding. 

Hansen et al. (2005, 059087) used coefficients to normalize the cooling from aerosol 

indirect effects to between -0.75 and -1 W/m2, based on comparisons of modeled and observed 

changes in the diurnal temperature range as well as some satellite observations. The response 

of the GISS model to the direct and two indirect effects is shown in Figure 9-76. As 

parameterized, the cloud lifetime effect produced somewhat greater negative RF (cooling), but 

this was the result of the coefficients chosen. Geographically, it appears that the “cloud cover” 

effect produced slightly more cooling in the Southern Hemisphere than did the “cloud albedo” 

response, with the reverse being true in the Northern Hemisphere (differences on the order of a 

few tenths °C). 

Model Experiments 

There are many different factors that can explain the large divergence of aerosol indirect 

effects in models (Figure 9-75). To explore this in more depth, Penner et al. (2006, 190564) 

used three general circulation models to analyze the differences between models for the first 

indirect effect, as well as a combined first plus second indirect effect. The models all had 

different cloud and/or convection parameterizations. In the first experiment, the monthly 

average aerosol mass and size distribution of, effectively, sulfate aerosol were prescribed, and 

all models followed the same prescription for parameterizing the cloud droplet number 

concentration (CDNC) as a function of aerosol concentration. In that sense, the only difference 

among the models was their separate cloud formation and radiation schemes. The different 

models all produced similar droplet effective radii, and therefore shortwave cloud forcing, and 

change in net outgoing whole sky radiation between pre-industrial times and the present. 

Hence the first indirect effect was not a strong function of the cloud or radiation scheme. The 

results for this and the following experiments are presented in Figure 9-77, where the 

experimental results are shown sequentially from left to right for the whole sky effect and in 

Table 9-15 for the clear-sky and cloud forcing response as well. 

 

December 2009 9-133 
Source: Reprinted with Permission of Bioresource Technology from Hansen et al. (2005, 059087). 

Figure 9-76. Anthropogenic impact on cloud cover, planetary albedo, radiative flux at the 

surface (while holding sea surface temperatures and sea ice fixed) and surface 

air temperature change from the direct aerosol forcing (top row), the first indirect 

effect (second row) and the second indirect effect (third row). The temperature 

change is calculated from year 81-120 of a coupled atmosphere simulation with 

the GISS model. 

The change in cloud forcing is the difference between whole sky and clear sky outgoing 

radiation in the present day minus pre-industrial simulation. The large differences seen 

between experiments 5 and 6 are due to the inclusion of the clear sky component of aerosol 

scattering and absorption (the direct effect) in experiment 6. 

In the second experiment, the aerosol mass and size distribution were again prescribed, 

but now each model used its own formulation for relating aerosols to droplets. In this case one 

of the models produced larger effective radii and therefore a much smaller first indirect aerosol 

effect (Figure 9-77, Table 9-15. However, even in the two models where the effective radius 

change and net global forcing were similar, the spatial patterns of cloud forcing differ, 

especially over the biomass burning regions of Africa and South America. 

The third experiment allowed the models to relate the change in droplet size to change in 

precipitation efficiency (i.e., they were now also allowing the second indirect effect – smaller 

droplets being less efficient rain producers – as well as the first). The models utilized the same 

relationship for autoconversion of cloud droplets to precipitation. Changing the precipitation 

efficiency results in all models producing an increase in cloud liquid water path, although the 

effect on cloud fraction was smaller than in the previous experiments. The net result was to 

increase the negative radiative forcing in all three models, albeit with different magnitudes: for 

two of the models the net impact on outgoing shortwave radiative increased by about 20%, 

whereas in the third model (which had the much smaller first indirect effect), it was magnified 

by a factor of three. 

December 2009 9-134 
Source: Adapted from Penner et al. (2006, 190564). 

Figure 9-77. Global average present-day short wave cloud forcing at TOA (top) and change in 

whole sky net outgoing shortwave radiation (bottom) between the present-day 

and pre-industrial simulations for each model in each experiment. 

December 2009 9-135 
Table 9-15. Differences in present day and pre-industrial outgoing solar radiation (W/m2

) in the 

different experiments. 

Model EXP 1 EXP 2 EXP 3 EXP 4 EXP 5 EXP 6 

WHOLE-SKY 

CAM-Oslo -0.648 -0.726 -0.833 -0.580 -0.365 -0.518 

LMD-Z -0.682 -0.597 -0.722 -1.194 -1.479 -1.553 

CCSR -0.739 -0.218 -0.733 -0.350 -1.386 -1.386 

CLEAR-SKY 

CAM-Oslo -0.063 -0.066 -0.026 0.014 -0.054 -0.575 

 LMD-Z -0.054 0.019 0.030 -0.066 -0.126 -1.034 

CCSR 0.018 -0.007 -0.045 -0.008 0.018 -1.160 

CLOUD-FORCING 

CAM-Oslo -0.548 -0.660 -0.807 -0.595 -0.311 0.056 

LMD-Z -0.628 -0.616 -0.752 -1.128 -1.353 -0.518 

CCSR -0.757 -0.212 -0.728 -0.345 -1.404 -0.200 

EXP1: tests cloud formation and radiation schemes 

EXP2: tests formulation for relating aerosols to droplets 

EXP3: tests inclusion of droplet size influence on precipitation efficiency 

EXP4: tests formulation of droplet size influence on precipitation efficiency 

EXP5: tests model aerosol formulation from common sources 

EXP6: added the direct aerosol effect 

Source: Adapted from Penner et al. (2006, 190564).

In the fourth experiment, the models were now each allowed to use their own formulation 

to relate aerosols to precipitation efficiency. This introduced some additional changes in the 

whole sky shortwave forcing (Figure 9-77). 

In the fifth experiment, models were allowed to produce their own aerosol concentrations, 

but were given common sources. This produced the largest changes in the RF in several of the 

models. Within any one model, therefore, the change in aerosol concentration has the largest 

effect on droplet concentrations and effective radii. This experiment too resulted in large 

changes in RF. 

In the last experiment, the aerosol direct effect was included, based on the full range of 

aerosols used in each model. While the impact on the whole-sky forcing was not large, the 

addition of aerosol scattering and absorption primarily affected the change in clear sky 

radiation (Table 9-15). 

The results of this study emphasize that in addition to questions concerning cloud physics, 

the differences in aerosol concentrations among the models play a strong role in inducing 

differences in the indirect effect(s), as well as the direct one. 

Observational constraints on climate model simulations of the indirect effect with satellite 

data (e.g., MODIS) have been performed previously in a number of studies (e.g., Lohmann et 

al., 2006, 190451; Menon et al., 2008, 190534; Quaas et al., 2006, 190915; Storlevmo et al., 

2006, 190418). 

These have been somewhat limited since the satellite retrieved data used do not have the 

vertical profiles needed to resolve aerosol and cloud fields (e.g., cloud droplet number and 

liquid water content); the temporal resolution of simultaneous aerosol and cloud product 

retrievals are usually not available at a frequency of more than one a day; and higher level 

clouds often obscure low clouds and aerosols. Thus, the indirect effect, especially the second 

indirect effect, remains, to a large extent, unconstrained by satellite observations. However, 

improved measurements of aerosol vertical distribution from the newer generation of sensors 

on the A-train platform may provide a better understanding of changes to cloud properties 

from aerosols. Simulating the top-of-atmosphere reflectance for comparison to satellite 

measured values could be another way to compare model with observations, which would 

eliminate the inconsistent assumptions of aerosol optical properties and surface reflectance 

encountered when compared the model calculated and satellite retrieved AOD values. 

December 2009 9-136 
Additional Aerosol Influences 

Various observations have empirically related aerosols injected from biomass burning or 

industrial processes to reductions in rainfall (e.g., Andreae et al., 2004, 155658; Eagan et al., 

1974, 190231; Rosenfeld, 2000, 002234; Warner, 1968, 157114). There are several potential 

mechanisms associated with this response. 

In addition to the two indirect aerosol effects noted above, a process denoted as the 

“semidirect” effect involves the absorption of solar radiation by aerosols such as black carbon 

and dust. The absorption increases the temperature, thus lowering the relative humidity and 

producing evaporation, hence a reduction in cloud liquid water. The impact of this process 

depends strongly on what the effective aerosol absorption actually is; the more absorbing the 

aerosol, the larger the potential positive forcing on climate (by reducing low level clouds and 

allowing more solar radiation to reach the surface). This effect is responsible for shifting the 

critical value of SSA (separating aerosol cooling from aerosol warming) from 0.86 with fixed 

clouds to 0.91 with varying clouds (Hansen et al., 1997, 043104). Reduction in cloud cover 

and liquid water is one way aerosols could reduce rainfall. 

More generally, aerosols can alter the location of solar radiation absorption within the 

system, and this aspect alone can alter climate and precipitation even without producing any 

change in net radiation at the top of the atmosphere (the usual metric for climate impact). By 

decreasing solar absorption at the surface, aerosols (from both the direct and indirect effects) 

reduce the energy available for evapotranspiration, potentially resulting in a decrease in 

precipitation. This effect has been suggested as the reason for the decrease in pan evaporation 

over the last 50 years (Roderick and Farquhar, 2002, 042788). The decline in solar radiation at 

the surface appears to have ended in the 1990s (Wild et al., 2005, 156156), perhaps because of 

reduced aerosol emissions in industrial areas (Kruger and Grasl, 2002, 190200), although this 

issue is still not settled. 

Energy absorption by aerosols above the boundary layer can also inhibit precipitation by 

warming the air at altitude relative to the surface, i.e., increasing atmospheric stability. The 

increased stability can then inhibit convection, affecting both rainfall and atmospheric 

circulation (Chung and Zhang, 2004, 190054; Ramanathan et al., 2001, 042681). To the extent 

that aerosols decrease droplet size and reduce precipitation efficiency, this effect by itself could 

result in lowered rainfall values locally. 

In their latest simulations, Hansen et al. (2007, 190597) did find that the indirect aerosol 

effect reduced tropical precipitation; however, the effect is similar regardless of which of the 

two indirect effects is used, and also similar to the direct effect. So it is likely that the 

reduction of tropical precipitation is because of aerosol induced cooling at the surface and the 

consequent reduced evapotranspiration. Similar conclusions were reached by Yu et al. (2002, 

190923) and Feingold et al. (2005, 190550). In this case, the effect is a feedback and not a 

forcing. 

The local precipitation change, through its impacts on dynamics and soil moisture, can 

have large positive feedbacks. Harvey (2004, 190598) concluded from assessing the response 

to aerosols in eight coupled models that the aerosol impact on precipitation was larger than on 

temperature. He also found that the precipitation impact differed substantially among the 

models, with little correlation among them. 

Recent GCM simulations have further examined the aerosol effects on hydrological cycle. 

Ramanathan et al. (2005, 190199) showed from fully coupled ocean-atmosphere GCM 

experiments that the “solar dimming” effect at the surface, i.e., the reduction of solar radiation 

reaching the surface, due to the inclusion of absorbing aerosol forcing causes a reduction in 

surface evaporation, a decrease in meridional sea surface temperature (SST) gradient and an 

increase in atmospheric stability, and a reduction in rainfall over South Asia. Lau and Kim 

(2006, 190226) examined the direct effects of aerosol on the monsoon water cycle variability 

from GCM simulations with prescribed realistic global aerosol forcing and proposed the 

“elevated heat pump” effect, suggesting that atmospheric heating by absorbing aerosols (dust 

and black carbon), through water cycle feedback, may lead to a strengthening of the South 

Asia monsoon. These model results are not necessarily at odds with each other, but rather 

illustrate the complexity of the aerosol-monsoon interactions that are associated with different 

mechanisms, whose relative importance in affecting the monsoon may be strongly dependent 

on spatial and temporal scales and the timing of the monsoon. These results may be model 

dependent and should be further examined. 

High Resolution Modeling 

Largely by its nature, the representation of the interaction between aerosol and clouds in 

GCMs is poorly resolved. This stems in large part from the fact that GCMs do not resolve 

convection on their large grids (order of several hundred km), that their treatment of cloud 

microphysics is rather crude, and that as discussed previously, their representation of aerosol 

needs improvement. Superparametrization efforts (where standard cloud parameterizations in 

the GCM are replaced by resolving clouds in each grid column of the GCM via a cloud 

resolving model) (e.g.,Grabowski, 2004, 190590) could lead the way for the development of 

more realistic cloud fields and thus improved treatments of aerosol cloud interactions in largescale models. However, these are just being incorporated in models that resolve both cloud and 

December 2009 9-137 
aerosols. Detailed cloud parcel models have been developed to focus on the droplet activation 

problem (that asks under what conditions droplets actually start forming) and questions 

associated with the first indirect effect. The coupling of aerosol and cloud modules to 

dynamical models that resolve the large turbulent eddies associated with vertical motion and 

clouds [large eddy simulations (LES) models, with grid sizes of ~100 m and domains ~10 km] 

has proven to be a powerful tool for representing the details of aerosol-cloud interactions 

together with feedbacks (e.g., Ackerman et al., 2004, 190056; Feingold et al., 1994, 190535; 

1999, 190540; Kogan et al., 1994, 190186; Stevens et al., 1996, 190417). 

 This section explores some of the complexity in the aerosol indirect effects revealed by 

such studies to illustrate how difficult parameterizing these effects properly in GCMs could 

really be. 

The First Indirect Effect 

The relationship between aerosol and drop concentrations (or drop sizes) is a key piece of 

the first indirect effect puzzle. (It should not, however, be equated to the first indirect effect 

which concerns itself with the resultant RF). A huge body of measurement and modeling work 

points to the fact that drop concentrations increase with increasing aerosol. The main 

unresolved questions relate to the degree of this effect, and the relative importance of aerosol 

size distribution, composition and updraft velocity in determining drop concentrations (for a 

review, see Mcfiggans et al., 2006, 190532). Studies indicate that the aerosol number 

concentration and size distribution are the most important aerosol factors. Updraft velocity 

(unresolved by GCMs) is particularly important under conditions of high aerosol particle 

number concentration. 

Although it is likely that composition has some effect on drop number concentrations, 

composition is generally regarded as relatively unimportant compared to the other parameters 

(Dusek et al., 2006, 155756; Ervens et al., 2005, 190527; Feingold et al., 2003, 190551; 

Fitzgerald, 1975, 095417). Therefore, it has been stated that the significant complexity in 

aerosol composition can be modeled, for the most part, using fairly simple parameterizations 

that reflect the soluble and insoluble fractions (e.g., Rissler et al., 2004, 190225). However, 

composition cannot be simply dismissed. Furthermore, chemical interactions also cannot be 

overlooked. A large uncertainty remains concerning the impact of organic species on cloud 

droplet growth kinetics, thus cloud droplet formation. Cloud drop size is affected by wet 

scavenging, which depends on aerosol composition especially for freshly emitted aerosol. And 

future changes in composition will presumably arise due to biofuels/biomass burning and a 

reduction in sulfate emissions, which emphasizes the need to include composition changes in 

models when assessing the first indirect effect. The simple soluble/insoluble fraction model 

may become less applicable than is currently the case. 

The updraft velocity, and its change as climate warms, may be the most difficult aspect to 

simulate in GCMs because of the small scales involved. In GCMs it is calculated in the 

dynamics as a grid box average, and parameterized on the small scale indirectly because it is a 

key part of convection and the spatial distribution of condensate, as well as droplet activation. 

Numerous solutions to this problem have been sought, including estimation of vertical velocity 

based on predicted turbulent kinetic energy from boundary layer models (Larson et al., 2001, 

190212; Lohmann et al., 1999, 190443) and PDF representations of subgrid quantities, such as 

vertical velocity and the vertically-integrated cloud liquid water (‘liquid water path,’ or LWP) 

(Golaz et al., 2002, 190587; 2002, 190589; Larson et al., 2005, 190220; Pincus and Klein, 

2000, 190565). Embedding cloud-resolving models within GCMs is also being actively 

pursued (Grabowski et al., 1999, 190592; Randall et al., 2003, 190201). Numerous other 

details come into play; for example, the treatment of cloud droplet activation in GCM 

frameworks is often based on the assumption of adiabatic conditions, which may overestimate 

the sensitivity of cloud to changes in CCN (Sotiropoulou et al., 2006, 190406; Sotiropoulou et 

al., 2007, 190405). This points to the need for improved theoretical understanding followed by 

new parameterizations. 

Other Indirect Effects 

The second indirect effect is often referred to as the “cloud lifetime effect”, based on the 

premise that non-precipitating clouds will live longer. In GCMs the “lifetime effect” is 

equivalent to changing the representation of precipitation production and can be parameterized 

as an increase in cloud area or cloud cover (e.g., Hansen et al., 2005, 059087). The second 

indirect effect hypothesis states that the more numerous and smaller drops associated with 

aerosol perturbations, suppress collision-induced rain, and result in a longer cloud lifetime. 

Observational evidence for the suppression of rain in warm clouds exists in the form of 

isolated studies (e.g., Warner, 1968, 157114) but to date there is no statistically robust proof of 

surface rain suppression (Levin and Cotton, 2008, 190375). Results from ship-track studies 

show that cloud water may increase or decrease in the tracks (Coakley and Walsh, 2002, 

192025) and satellite studies suggest similar results for warm boundary layer clouds (Han et 

al., 2002, 049181). Ackerman et al. (2004, 190056) used LES to show that in stratocumulus, 

cloud water may increase or decrease in response to increasing aerosol depending on the 

relative humidity of the air overlaying the cloud. Wang et al. (2003, 157106) showed that all 

December 2009 9-138 
else being equal, polluted stratocumulus clouds tend to have lower water contents than clean 

clouds because the small droplets associated with polluted clouds evaporate more readily and 

induce an evaporation-entrainment feedback that dilutes the cloud. This result was confirmed 

by Xue and Feingold (2006, 190920) and Jiang and Feingold (2006, 190976) for shallow 

cumulus, where pollution particles were shown to decrease cloud fraction. Furthermore, Xue et 

al. (2008, 190921) suggested that there may exist two regimes: the first, a precipitating regime 

at low aerosol concentrations where an increase in aerosol will suppress precipitation and 

increase cloud cover (Albrecht, 1989, 045783); and a second, non-precipitating regime where 

the enhanced evaporation associated with smaller drops will decrease cloud water and cloud 

fraction. 

The possibility of bistable aerosol states was proposed earlier by Baker and Charlson 

(1990, 190016) based on consideration of aerosol sources and sinks. They used a simple 

numerical model to suggest that the marine boundary layer prefers two aerosol states: a clean, 

oceanic regime characterized by a weak aerosol source and less reflective clouds; and a 

polluted, continental regime characterized by more reflective clouds. On the other hand, study 

by Ackerman et al. (1994, 189975) did not support such a bistable system using a somewhat 

more sophisticated model. Further observations are needed to clarify the nature of 

cloud/aerosol interactions under a variety of conditions. 

Finally, the question of possible effects of aerosol on cloud lifetime was examined by 

Jiang et al. (2006, 133165), who tracked hundreds of cumulus clouds generated by LES from 

their formative stages until they dissipated. They showed that in the model there was no effect 

of aerosol on cloud lifetime, and that cloud lifetime was dominated by dynamical variability. 

It could be argued that the representation of these complex feedbacks in GCMs is not 

warranted until a better understanding of the processes is at hand. Moreover, until GCMs are 

able to represent cloud scales, it is questionable what can be obtained by adding microphysical 

complexity to poorly resolved clouds. A better representation of aerosol-cloud interactions in 

GCMs therefore depends on the ability to improve representation of aerosols and clouds, as 

well as their interaction, in the hydrologic cycle. This issue is discussed further in the next 

chapter. 

9.3.6.5. Aerosol in the Climate Models 

Aerosol in the IPCC AR4 Climate Model Simulations 

To assess the atmospheric and climate response to aerosol forcing, e.g., changes in surface 

temperate, precipitation, or atmospheric circulation, aerosols, together with greenhouse gases 

should be an integrated part of climate model simulation under the past, present, and future 

conditions. Table 9-16 lists the forcing species that were included in 25 climate modeling 

groups used in the IPCC AR4 (2007, 092765) assessment. All the models included long-lived 

greenhouse gases, most models included sulfate direct forcing, but only a fraction of those 

climate models considered other aerosol types. In other words, aerosol RF was not adequately 

accounted for in the climate simulations for the IPCC AR4. Put still differently, the current 

aerosol modeling capability has not been fully incorporated into the climate model 

simulations. As pointed out in Section 9.3.6.4, fewer than one-third of the models incorporated 

an aerosol indirect effect, and most considered only sulfates. 

The following discussion compares two of the IPCC AR4 climate models that include all 

major forcing agencies in their climate simulation: the model from the NASA Goddard 

Institute for Space Studies (GISS) and from the NOAA Geophysical Fluid Dynamics 

Laboratory (GFDL). The purpose in presenting these comparisons is to help elucidate how 

modelers go about assessing their aerosol components, and the difficulties that entail. A 

particular concern is how aerosol forcings were obtained in the climate model experiments for 

IPCC AR4. Comparisons with observations have already led to some improvements that can 

be implemented in climate models for subsequent climate change experiments (e.g., Koch et 

al., 2006, 190184, for GISS model). This aspect is discussed further in Chapter 4 of the CCSP 

SAP2.3. 

December 2009 9-139 
Table 9-16. Forcings used in IPCC AR4 simulations of 20th century climate change. This table is 

adapted from SAP 1.1 Table 5.2 (compiled using information provided by the 

participating modeling centers, see http://www.pcmdi.llnl.gov/ipcc/modeldocumentation/ipcc_ model_documentation.php) plus additional information from 

that website. Eleven different forcings are listed: well-mixed greenhouse gases (G), 

tropospheric and stratospheric ozone (O), SO4

2– aerosol direct (SD) and indirect 

effects (S), black carbon (BC) and organic carbon aerosols (OC), mineral dust (MD), 

sea salt (SS), land use/land cover (LU), solar irradiance (SO), and volcanic aerosols 

(V). Check mark denotes inclusion of a specific forcing. As used here, “inclusion” 

means specification of a time-varying forcing, with changes on interannual and 

longer timescales. 

Model Country G O SD SI BC OC MD SS LU SO V 

1 BCC-CMI China √ √ √

2 BCCR-BCM2.0 Norway √ √ √ √

3 CCSM3 USA √ √ √ √ √ √ √

4 CGCM3.1(T47) Canada √ √

5 CGCM3.1(T63) Canada √ √

6 CNRM-CM3 France √ √ √ √

7 CSIRO-Mk3.0 Australia √ √

8 CSIRO-Mk3.5 Australia √ √

9 ECHAMS/MPI-OM Germany √ √ √ √

10 ECHO-G Germany/ 

Korea √ √ √ √ √ √

11 FGOALS-g1.0 China √ √

12 GFDL-CM2.0 USA √ √ √ √ √ √ √

13 GFDL-CM2.1 USA √ √ √ √ √ √ √

14 GISS-AOM USA √ √ √

15 GISS-EH USA √ √ √ √ √ √ √ √ √ √ √

16 GISS-ER USA √ √ √ √ √ √ √ √ √ √ √

17 INGV-SXG Italy √ √ √

18 INM-CM3.0 Russia √ √ √

19 IPSL-CM4 France √ √ √

20 MICROC3.2(hires) Japan √ √ √ √ √ √ √ √ √ √

21 MICROC3.2(medres) Japan √ √ √ √ √ √ √ √ √ √

22 MRI-CGCM2.3.2 Japan √ √ √ √

23 PCM USA √ √ √ √ √

24 UKMO-HasCM3 U.K. √ √ √ √

25 UKMO-HadGEM1 U.K. √ √ √ √ √ √ √ √

The GISS Model 

There have been many different configurations of aerosol simulations in the GISS model 

over the years, with different emissions, physics packages, etc., as is apparent from the 

multiple GISS entries in the preceding figures and tables. There were also three different GISS 

GCM submissions to IPCC AR4, which varied in their model physics and ocean formulation. 

(Note that the aerosols in these three GISS versions are different from those in the 

AeroCom simulations described in Sections 9.3.6.2 and 9.3.6.3.) The GCM results discussed 

December 2009 9-140 
below all relate to the simulations known as GISS model ER (Schmidt et al., 2006, 190373) 

(see Table 9-16). Although the detailed description and model evaluation have been presented 

in Liu et al. (2006, 190422), below are the general characteristics of aerosols in the GISS ER: 

Aerosol fields: The aerosol fields used in the GISS ER is a prescribed “climatology” 

which is obtained from chemistry transport model simulations with monthly averaged mass 

concentrations representing conditions up to 1990. Aerosol species included are sulfate, nitrate, 

BC, POM, dust, and sea salt. Dry size effective radii are specified for each of the aerosol types, 

and laboratory-measured phase functions are employed for all solar and thermal wavelengths. 

For hygroscopic aerosols (sulfate, nitrate, POM, and sea salt), formulas are used for the 

particle growth of each aerosol as a function of relative humidity, including the change in 

density and optical parameters. With these specifications, the AOD, single scattering albedo, 

and phase function of the various aerosols are calculated. While the aerosol distribution is 

prescribed as monthly mean values, the relative humidity component of the extinction is 

updated each hour. The global averaged AOD at 550 nm is about 0.15. 

Global distribution: When comparing with AOD from observations by multiple satellite 

sensors of MODIS, MISR, POLDER, and AVHRR and surface based sunphotometer network 

AERONET (see Chapter 2 of the CCSP SAP2.3 for detailed information about data), 

qualitative agreement is apparent, with generally higher burdens in Northern Hemisphere 

summer, and seasonal variations of smoke over southern Africa and South America, as well as 

wind blown dust over northern African and the Persian Gulf. Aerosol optical depth in both 

model and observations is smaller away from land. There are, however, considerable 

discrepancies between the model and observations. Overall, the GISS GCM has reduced 

aerosol optical depths compared with the satellite data (a global, clear-sky average of about 

80% compared with MODIS and MISR data), although it is in better agreement with 

AERONET ground-based measurements in some locations (note that the input aerosol values 

were calibrated with AERONET data). The model values over the Sahel in Northern 

Hemisphere winter and the Amazon in Southern Hemisphere winter are excessive, indicative 

of errors in the biomass burning distributions, at least partially associated with an older 

biomass burning source used (the source used here was from (Liousse et al., 1996, 078158)). 

Seasonal variation: A comparison of the seasonal distribution of the global AOD between 

the GISS model and satellite data indicates that the model seasonal variation is in qualitative 

agreement with observations for many of the locations that represent major aerosol regimes, 

although there are noticeable differences. For example, in some locations the seasonal 

variations are different from or even opposite to the observations. 

Particle size parameter: The Ångström exponent (Å), which is determined by the contrast 

between the AOD at two or more different wavelengths and is related to aerosol particle size 

(discussed in Section 9.3.6.3). This parameter is important because the particle size 

distribution affects the efficiency of scattering of both short and long wave radiation, as discussed earlier. Å from the GISS model is biased low compared with AERONET, MODIS, 

and POLDER data, although there are technical differences in determining the Å. This low 

bias suggests that the aerosol particle size in the GISS model is probably too large. The 

average effective radius in the GISS model appears to be 0.3-0.4 μm, whereas the 

observational data indicates a value more in the range of 0.2-0.3 μm (Liu et al., 2006, 190422). 

Single scattering albedo: The model-calculated SSA (at 550 nm) appears to be generally 

higher than the AERONET data at worldwide locations (not enough absorption), but lower 

than AERONET data in Northern Africa, the Persian Gulf, and the Amazon (too much 

absorption). This discrepancy reflects the difficulties in modeling BC, which is the dominant 

absorbing aerosol, and aerosol sizes. Global averaged SSA at 550 nm from the GISS model is 

at about 0.95. 

Aerosol direct RF: The GISS model calculated anthropogenic aerosol direct shortwave RF 

is -0.56 W/m2 at TOA and -2.87 W/m2 at the surface. The TOA forcing (upper left, Figure 

9-78) indicates that, as expected, the model has larger negative values in polluted regions and 

positive forcing at the highest latitudes. At the surface (lower left, Figure 9-78) GISS model 

values exceed -4 W/m2 over large regions. Note there is also a longwave RF of aerosols (right 

column), although they are much weaker than the shortwave RF. 

There are several concerns for climate change simulations related to the aerosol trend in 

the GISS model. One is that the aerosol fields in the GISS AR4 climate simulation (version 

ER) are kept fixed after 1990. In fact, the observed trend shows a reduction in tropospheric 

aerosol optical thickness from 1990 through the present, at least over the oceans (Mishchenko 

and Geogdzhayev, 2007, 190545). Hansen et al. (2007, 190597) suggested that the deficient 

warming in the GISS model over Eurasia post-1990 was due to the lack of this trend. Indeed, a 

possible conclusion from the Penner et al. (2002, 190562) study was that the GISS model 

overestimated the AOD (presumably associated with anthropogenic aerosols) poleward of 

30°N. However, when an alternate experiment reduced the aerosol optical depths, the polar 

warming became excessive (Hansen et al., 2007, 190597). The other concern is that the GISS 

model may underestimate the organic and sea salt AOD, and overestimate the influence of 

black carbon aerosols in the biomass burning regions (Liu et al., 2006, 190422; deduced from 

Penner et al., 2002, 190562). To the extent that is true, it would indicate the GISS model 

underestimates the aerosol direct cooling effect in a substantial portion of the tropics, outside 

of biomass burning areas. Clarifying those issues requires numerous modeling experiments 

and various types of observations. 

December 2009 9-141 
The GFDL Model 

A comprehensive description and evaluation of the GFDL aerosol simulation are given in 

Ginoux et al. (2006, 190582). Below are the general characteristics: 

Aerosol fields: The aerosols used in the GFDL climate experiments are obtained from 

simulations performed with the MOZART 2 model (Model for Ozone and Related chemical 

Tracers) (Horowitz, 2006, 190620; Horowitz et al., 2003, 057770). The exceptions were dust, 

which was generated with a separate simulation of MOZART 2, using sources from Ginoux et 

al. (2001, 190579) and wind fields from NCEP/NCAR reanalysis data; and sea salt, whose 

monthly mean concentrations were obtained from a previous study by Haywood et al. (1999, 

040453). It includes most of the same aerosol species as in the GISS model (although it does 

not include nitrates), and, as in the GISS model, relates the dry aerosol to wet aerosol optical 

depth via the model’s relative humidity for sulfate (but not for organic carbon); for sea salt, a 

constant relative humidity of 80% was used. Although the parameterizations come from 

different sources, both models maintain a very large growth in sulfate particle size when the 

relative humidity exceeds 90%. 

Global distributions: Overall, the GFDL global mean aerosol mass loading is within 30% 

of that of other studies (Chin et al., 2002, 189996; Reddy et al., 2005, 190207; Tie et al., 2005, 

190459), except for sea salt, which is 2 to 5 times smaller. However, the sulfate AOD (0.1) is 

2.5 times that of other studies, whereas the organic carbon value is considerably smaller (on 

the order of 1/2). Both of these differences are influenced by the relationship with relative 

humidity. In the GFDL model, sulfate is allowed to grow up to 100% relative humidity, but 

organic carbon does not increase in size as relative humidity increases. Comparison of AOD 

with AVHRR and MODIS data for the time period 1996-2000 shows that the global mean 

value over the ocean (0.15) agrees with AVHRR data (0.14) but there are significant 

differences regionally, with the model overestimating the value in the northern mid latitude 

oceans and underestimating it in the southern ocean. Comparison with MODIS also shows 

good agreement globally (0.15), but in this case indicates large disagreements over land, with 

the model producing excessive AOD over industrialized countries and underestimating the 

effect over biomass burning regions. Overall, the global averaged AOD at 550 nm is 0.17, 

which is higher than the maximum values in the AeroCom-A experiments (Table 9-12) and 

exceeds the observed value too (Ae and S* in Figure 9-72). 

December 2009 9-142 
Source: Figure provided by A. Lacis, GISS. 

Figure 9-78. Direct radiative forcing by anthropogenic aerosols in the GISS model (including 

sulfates, BC, OC and nitrates). Short wave forcing at TOA and surface are shown 

in the top left and bottom left panels. The corresponding thermal forcing is 

indicated in the right hand panels. 

Composition: Comparison of GFDL modeled species with in situ data over North 

America, Europe, and over oceans has revealed that the sulfate is overestimated in spring and 

summer and underestimated in winter in many regions, including Europe and North America. 

Organic and black carbon aerosols are also overestimated in polluted regions by a factor of 

two, whereas organic carbon aerosols are elsewhere underestimated by factors of 2 to 3. Dust 

concentrations at the surface agree with observations to within a factor of 2 in most places 

where significant dust exists, although over the southwest U.S. it is a factor of 10 too large. 

Surface concentrations of sea salt are underestimated by more than a factor of 2. Over the 

oceans, the excessive sulfate AOD compensates for the low sea salt values except in the 

southern oceans. 

Size and single-scattering albedo: No specific comparison was given for particle size or 

single-scattering albedo, but the excessive sulfate would likely produce too high a value of 

reflectivity relative to absorption except in some polluted regions where black carbon (an 

absorbing aerosol) is also overestimated. 

As in the case of the GISS model, there are several concerns with the GFDL model. The 

good global-average agreement masks an excessive aerosol loading over the Northern 

Hemisphere (in particular, over the northeast U.S. and Europe) and an underestimate over 

biomass burning regions and the southern oceans. Several model improvements are needed, 

including better parameterization of hygroscopic growth at high relative humidity for sulfate 

and organic carbon; better sea salt simulations; correcting an error in extinction coefficients; 

and improved biomass burning emissions inventory (Ginoux et al., 2006, 190582). 

Comparisons between GISS and GFDL Model 

Both GISS and GFDL models were used in the IPCC AR4 climate simulations for climate 

sensitivity that included aerosol forcing. It would be constructive, therefore, to compare the 

similarities and differences of aerosols in these two models and to understand what their 

December 2009 9-143 
impacts are in climate change simulations. Figure 9-79 shows the percentage AOD from 

different aerosol components in the two models. 

Sulfate: The sulfate AOD from the GISS model is within the range of that from all other 

models (Table 9-13), but that from the GFDL model exceeds the maximum value by a factor of 

2.5. An assessment in SAP 3.2 (CCSP, 2008, 192028; Shindell et al., 2008, 190393) also 

concludes that GFDL had excessive sulfate AOD compared with other models. The sulfate 

AOD from GFDL is nearly a factor of 4 large than that from GISS, although the sulfate burden 

differs only by about 50% between the two models. Clearly, this implies a large difference in 

sulfate MEE between the two models. 

BC and POM: Compared to observations, the GISS model appears to overestimate the 

influence of BC and POM in the biomass burning regions and underestimate it elsewhere, 

whereas the GFDL model is somewhat the reverse: it overestimates it in polluted regions, and 

underestimates it in biomass burning areas. The global comparison shown in Table 9-14

indicates the GISS model has values similar to those from other models, which might be the 

result of such compensating errors. The GISS and GFDL models have relatively similar 

global-average black carbon contributions, and the same appears true for POM. 

Sea salt: The GISS model has a much larger sea salt contribution than does GFDL (or 

indeed other models). 

Global and regional distributions: Overall, the global averaged AOD is 0.15 from the 

GISS model and 0.17 from GFDL. However, as shown in Figure 9-79, the contribution to this 

AOD from different aerosol components shows greater disparity. For example, over the 

Southern Ocean where the primary influence is due to sea salt in the GISS model, but in the 

GFDL it is sulfate. The lack of satellite observations of the component contributions and the 

limited available in situ measurements make the model improvements at aerosol composition 

level difficult. 

Climate simulations: With such large differences in aerosol composition and distribution 

between the GISS and GFDL models, one might expect that the model simulated surface 

temperature might be quite different. Indeed, the GFDL model was able to reproduce the 

observed temperature change during the 20th century without the use of an indirect aerosol 

effect, whereas the GISS model required a substantial indirect aerosol contribution (more than 

half of the total aerosol forcing) (Hansen et al., 2007, 190597). It is likely that the reason for 

this difference was the excessive direct effect in the GFDL model caused by its overestimation 

of the sulfate optical depth. The GISS model direct aerosol effect (see Section 9.3.6.6) is close 

to that derived from observations (Chapter 2 of the CCSP SAP2.3); this suggests that for 

models with climate sensitivity close to 0.75°C/(W/m2) (as in the GISS and GFDL models), an 

indirect effect is needed. 

December 2009 9-144 
Figure 9-79. Percentage of aerosol optical depth in the GISS, left, based on Liu et 

al. (2006, 190422), provided by A. Lacis, GISS, and GFDL, right, from Ginoux et al. 

(2006, 190582). Models associated with the different components: SO4

2– (1st 

row), BC (2nd row), OC (3rd row), sea-salt (4th row), dust (5th row), and nitrate 

(last row). Nitrate not available in GFDL model). Numbers on the GISS panels are 

global average, but on the GFDL panels are maximum and minimum. 

December 2009 9-145 
Additional Considerations 

Long wave aerosol forcing: So far only the aerosol RF in the shortwave (solar) spectrum 

has been discussed. Figure 9-78 (right column) shows that compared to the shortwave forcing, 

the values of anthropogenic aerosol long wave (thermal) forcing in the GISS model are on the 

order of 10%. Like the shortwave forcing, these values will also be affected by the particular 

aerosol characteristics used in the simulation. 

Aerosol vertical distribution: Vertical distribution is particularly important for absorbing 

aerosols, such as BC and dust in calculating the RF, particularly when longwave forcing is 

considered (e.g., Figure 9-78) because the energy they reradiate depends on the temperature 

(and hence altitude), which affects the calculated forcing values. Several model intercomparison studies have shown that the largest difference among model simulated aerosol 

distributions is the vertical profile (e.g., Lohmann et al., 2001, 190448; Penner et al., 2002, 

190562; Textor et al., 2006, 190456), due to the significant diversities in atmospheric 

processes in the models (e.g., Table 9-12). In addition, the vertical distribution also varies with 

space and time, as illustrated in Figure 9-80 from the GISS ER simulations for January and 

July showing the most probable altitude of aerosol vertical locations. In general, aerosols in the 

northern hemisphere are located at lower altitudes in January than in July, and vice versa for 

the southern hemisphere. 

Mixing state: Most climate model simulations incorporating different aerosol types have 

been made using external mixtures, i.e., the evaluation of the aerosols and their radiative 

properties are calculated separately for each aerosol type (assuming no mixing between 

different components within individual particles). Observations indicate that aerosols 

commonly consist of internally mixed particles, and these “internal mixtures” can have very 

different radiative impacts. For example, the GISS-1 (internal mixture) and GISS-2 (external 

mixture) model results shows very different magnitude and sign of aerosol forcing from 

slightly positive (implying slight warming) to strong negative (implying significant cooling) 

TOA forcing (Figure 9-73), due to changes in both radiative properties of the mixtures, and in 

aerosol amount. The more sophisticated aerosol mixtures from detailed microphysics 

calculations now being used/developed by different modeling groups may well end up 

producing very different direct (and indirect) forcing values. 

Cloudy sky vs. clear sky: The satellite or AERONET observations are all for clear sky 

only because aerosol cannot be measured in the remote sensing technique when clouds are 

present. However, almost all the model results are for all-sky because of difficulty in extracting 

cloud-free scenes from the GCMs. So the AOD comparisons discussed earlier are not 

completely consistent. Because AOD can be significantly amplified when relative humidity is 

high, such as near or inside clouds, all-sky AOD values are expected to be higher than clear 

sky AOD values. On the other hand, the aerosol RF at TOA is significantly lower for all-sky 

than for clear sky conditions; the IPCC AR4 and AeroCom RF study (Schulz et al., 2006, 

190381) have shown that on average the aerosol RF value for all-sky is about 1/3 of that for 

clear sky although with large diversity (63%). These aspects illustrate the complexity of the 

system and the difficulty of representing aerosol radiative influences in climate models whose 

cloud and aerosol distributions are somewhat problematic. And of course aerosols in cloudy 

regions can affect the clouds themselves, as discussed in Section 9.3.6.5. 

December 2009 9-146 
Source: A. Lacis, GISS. 

Figure 9-80. Most probable aerosol altitude (in pressure, hPa) from the GISS model in January 

(top) and July (bottom). 

9.3.6.6. Impacts of Aerosols on Climate Model Simulations 

Surface Temperature Change 

It was noted in the introduction that aerosol cooling is essential in order for models to 

produce the observed global temperature rise over the last century, at least models with climate 

sensitivities in the range of 3°C for doubled CO2 (or ~0.75°C/(W/m2)). The implications of 

this are discussed here in somewhat more detail. Hansen et al. (2007, 190597) show that in the 

GISS model, well-mixed greenhouse gases produce a warming of close to 1°C between 1880 

and the present (Table 9-17). The direct effect of tropospheric aerosols as calculated in that 

model produces cooling of close to -0.3°C between those same years, while the indirect effect 

(represented in that study as cloud cover change) produces an additional cooling of similar 

magnitude (note that the general model result quoted in IPCC AR4 is that the indirect RF is 

twice that of the direct effect). 

December 2009 9-147 
Table 9-17. Climate forcings (1880-2003) used to drive GISS climate simulations, along with the 

surface air temperature changes obtained for several periods. 

Forcing Agent Forcing Wm-2 (1880-2003) ΔT Surface °C (year to 2003) 

Fi Fa Fs Fe 1880 1900 1950 1979 

Well-mixed GHGs 2.62 2.50 2.65 2.72 0.96 0.93 0.74 0.43 

Stratospheric H2O 0.06 0.05 0.03 0.01 0.05 0.00 

O3 0.44 0.28 0.26 0.23 0.p08 0.05 0.00 -0.01 

Land use -0.09 -0.09 -0.05 -0.p07 -0.04 -0.02 

Snow albedo 0.05 0.05 0.14 0.14 0.03 0.00 0.02 -0.01 

Solar irradiance 0.23 0.24 0.23 0.22 0.07 0.07 0.01 0.02 

Stratospheric aerosols 0.00 0.00 0.00 0.00 -0.08 -0.03 -0.06 0.04 

Trop. aerosol direct forcing -0.41 -0.38 -0.52 -0.60 -0.28 -0.23 -0.18 -0.10 

Trop. aerosol indirect forcing -0.87 -0.77 -0.27 -0.29 -0.14 -0.05 

Sum of above 1.86 1.90 0.49 0.44 0.40 0.30 

All forcings at once 1.77 1.75 0.53 0.61 0.44 0.29 

Source: Reprinted with Permission of Springer Publishing from Hansen et al. (2007, 190597).

 Instantaneous (Fi), adjusted (Fa), fixed SST (Fs) and effective (Fe) forcings are defined in Hansen et al. (2005, 059087)

.

The time dependence of the total aerosol forcing used as well as the individual species 

components is shown in Figure 9-81. The resultant warming, 0.53 (± 0.04) °C including these 

and other forcings (Table 9-17), is less than the observed value of 0.6-0.7°C from 1880-2003. 

Hansen et al. (2007, 190597) further show that a reduction in sulfate optical thickness and the 

direct aerosol effect by 50%, which also reduced the aerosol indirect effect by 18%, produces a 

negative aerosol forcing from 1880-2003 of -0.91 W/m2 (down from -1.37 W/m2 with this 

revised forcing). The model now warms 0.75°C over that time. Hansen et al. (2007, 190597) 

defend this change by noting that sulfate aerosol removal over North America and western 

Europe during the 1990s led to a cleaner atmosphere. Note that the comparisons shown in the 

previous section suggest that the GISS model already underestimates aerosol optical depths; it 

is thus trends that are the issue here. 

The magnitude of the indirect effect used by Hansen et al. (2005, 190596) is roughly 

calibrated to reproduce the observed change in diurnal temperature cycle and is consistent with 

some satellite observations. However, as Anderson et al. (2003, 054820) note, the forward 

calculation of aerosol negative forcing covers a much larger range than is normally used in 

GCMs; the values chosen, as in this case, are consistent with the inverse reasoning estimates of 

what is needed to produce the observed warming, and hence generally consistent with current 

model climate sensitivities. The authors justify this approach by claiming that paleoclimate 

data indicate a climate sensitivity of close to 0.75 (± 0.25)°C/(W/m2

), and therefore something 

close to this magnitude of negative forcing is reasonable. Even this stated range leaves 

significant uncertainty in climate sensitivity and the magnitude of the aerosol negative forcing. 

Furthermore, IPCC (2007, 092765) concluded that paleoclimate data are not capable of 

narrowing the range of climate sensitivity, nominally 0.375-1.13 °C/(W/m2), because of 

uncertainties in paleoclimate forcing and response; so from this perspective the total aerosol 

forcing is even less constrained than the GISS estimate. Hansen et al. (2007, 190597) 

acknowledge that “an equally good match to observations probably could be obtained from a 

model with larger sensitivity and smaller net forcing, or a model with smaller sensitivity and 

larger forcing”. 

December 2009 9-148 
Source: Reprinted with Permission of Springer from Hansen et al. (2007, 190597). 

Figure 9-81. Time dependence of aerosol optical thickness (left) and climate forcing (right). 

Note that as specified, the aerosol trends are all “flat” from 1990-2000. 

The GFDL model results for global mean ocean temperature change (down to 3 km depth) 

for the time period 1860-2000 is shown in Figure 9-82, along with the different contributing 

factors (Delworth et al., 2005, 190055). This is the same GFDL model whose aerosol 

distribution was discussed previously. The aerosol forcing produces a cooling on the order of 

50% that of greenhouse warming (generally similar to that calculated by the GISS model, 

Table 9-17). Note that this was achieved without any aerosol indirect effect. 

The general model response noted by IPCC, as discussed in the introduction, was that the 

total aerosol forcing of -1.3 W/m2 reduced the greenhouse forcing of near 3 W/m2 by about 

45%, in the neighborhood of the GFDL and GISS forcings. Since the average model sensitivity 

was close to 0.75 °C/(W/m2), similar to the sensitivities of these models, the necessary 

negative forcing is therefore similar. The agreement cannot therefore be used to validate the 

actual aerosol effect until climate sensitivity itself is better known. 

Is there some way to distinguish between greenhouse gas and aerosol forcing that would 

allow the observational record to indicate how much of each was really occurring? This 

question of attribution has been the subject of numerous papers, and the full scope of the 

discussion is beyond the range of this (CCSP SAP2.3) report. It might be briefly noted that 

Zhang et al. (2006, 157722) using results from several climate models and including both 

spatial and temporal patterns, found that the climate responses to greenhouse gases and sulfate 

aerosols are correlated, and separation is possible only occasionally, especially at global scales. 

This conclusion appears to be both model and method-dependent: using time-space 

distinctions as opposed to trend detection may work differently in different models (Gillett et 

al., 2002, 190576). Using multiple models helps primarily by providing larger-ensemble sizes 

for statistics (Gillett et al., 2002, 190578). However, even separating between the effects of 

different aerosol types is difficult. Jones et al. (2005, 155885) concluded that currently the 

pattern of temperature change due to black carbon is indistinguishable from the sulfate aerosol 

pattern. In contrast, Hansen et al. (2005, 059087) found that absorbing aerosols produce a 

different global response than other forcings, and so may be distinguishable. Overall, the 

similarity in response to all these very different forcings is undoubtedly due to the importance 

of climate feedbacks in amplifying the forcing, whatever its nature. 

December 2009 9-149 
Source: Courtesy of the American Geogphysical Union from Delworth et al. (2005, 190055). 

Figure 9-82. Change in global mean ocean temperature (left axis) and ocean heat content 

(right axis) for the top 3000 m due to different forcings in the GFDL model. 

WMGG includes all greenhouse gases and ozone; NATURAL includes solar and 

volcanic aerosols (events shown as green triangles on the bottom axis). 

Observed ocean heat content changes are shown as well. 

Distinctions in the climate response do appear to arise in the vertical, where absorbing 

aerosols produce warming that is exhibited throughout the troposphere and into the 

stratosphere, whereas reflective aerosols cool the troposphere but warm the stratosphere 

(Hansen et al., 2005, 059087; IPCC, 2007, 092765). Delworth et al. (2005, 190055) noted that 

in the ocean, the cooling effect of aerosols extended to greater depths, due to the thermal 

instability associated with cooling the ocean surface. Hence the temperature response at levels 

both above and below the surface may provide an additional constraint on the magnitudes of 

each of these forcings, as may the difference between Northern and Southern Hemisphere 

changes (IPCC, 2007, 092765, Chapter 9). The profile of atmospheric temperature response 

will be useful to the extent that the vertical profile of aerosol absorption, an important 

parameter to measure, is known. 

Implications for Climate Model Simulations 

The comparisons in Sections 9.3.6.2 and 9.3.6.3 suggest that there are large differences in 

model calculated aerosol distributions, mainly because of the large uncertainties in modeling 

the aerosol atmospheric processes in addition to the uncertainties in emissions. The fact that 

the total optical depth is in better agreement between models than the individual components 

means that even with similar optical depths, the aerosol direct forcing effect can be quite 

different, as shown in the AeroCom studies. Because the diversity among models and 

discrepancy between models and observations are much larger at the regional level than in 

global average, the assessment of climate response (e.g., surface temperature change) to 

aerosol forcing would be more accurate for global average than for regional or hemispheric 

differentiation. However, since aerosol forcing is much more pronounced on regional than on 

global scales because of the highly variable aerosol distributions, it is insufficient or even 

misleading to just get the global average right. 

The indirect effect is strongly influenced by the aerosol concentrations, size, type, mixing 

state, microphysical processes, and vertical profile. As shown in previous sections, very large 

differences exist in those quantities even among the models having similar AOD. Moreover, 

modeling aerosol indirect forcing presents more challenges than direct forcing because there is 

so far no rigorous observational data, especially on a global scale, that one can use to test the 

model simulations. As seen in the comparisons of the GISS and GFDL model climate 

simulations for IPCC AR4, aerosol indirect forcing was so poorly constrained that it was 

completely ignored by one model (GFDL) but used by another (GISS) at a magnitude that is 

more than half of the direct forcing, in order to reproduce the observed surface temperature 

trends. A majority of the climate models used in IPCC AR4 do not consider indirect effects; 

the ones that did were mostly limited to highly simplified sulfate indirect effects (Table 9-16). 

Improvements must be made to at least the degree that the aerosol indirect forcing can no 

longer be used to mask the deficiencies in estimating the climate response to greenhouse gas 

and aerosol direct RF. 

December 2009 9-150 
9.3.6.7. Outstanding Issues 

Clearly there are still large gaps in assessing the aerosol impacts on climate through 

modeling. Major outstanding issues and prospects of improving model simulations are 

discussed below. 

Aerosol composition 

Many global models are now able to simulate major aerosol types such as sulfate, black 

carbon, and POM, dust, and sea salt, but only a small fraction of these models simulate nitrate 

aerosols or consider anthropogenic secondary organic aerosols. And it is difficult to quantify 

the dust emission from human activities. As a result, the IPCC AR4 estimation of the nitrate 

and anthropogenic dust TOA forcing was left with very large uncertainty. The next generation 

of global models should therefore have a more comprehensive suite of aerosol compositions 

with better-constrained anthropogenic sources. 

Aerosol absorption 

One of the most critical parameters in aerosol direct RF and aerosol impact on 

hydrological cycles is the aerosol absorption. Most of the absorption is from BC despite its 

small contribution to total aerosol short and long-wave spectral ranges, whereas POM absorbs 

in the near UV. The aerosol absorption or SSA, will have to be much better represented in the 

models through improving the estimates of carbonaceous and dust aerosol sources, their 

atmospheric distributions, and optical properties. 

Aerosol indirect effects 

The activation of aerosol particles into CCN depends not only on particle size but 

chemical composition, with the relative importance of size and composition unclear. In current 

aerosol-climate modeling, aerosol size distribution is generally prescribed and simulations of 

aerosol composition have large uncertainties. Therefore the model estimated “albedo effect” 

has large uncertainties. How aerosol would influence cloud lifetime/ cover is still in debate. 

The influence of aerosols on other aspects of the climate system, such as precipitation, is even 

more uncertain, as are the physical processes involved. Processes that determine aerosol size 

distributions, hygroscopic growth, mixing state, as well as CCN concentrations, however, are 

inadequately represented in most of the global models. It will also be difficult to improve the 

estimate of indirect effects until the models can produce more realistic cloud characteristics. 

Aerosol impacts on surface radiation and atmospheric heating 

Although these effects are well acknowledged to play roles in modulating atmospheric 

circulation and water cycle, few coherent or comprehensive modeling studies have focused on 

them, as compared to the efforts that have gone to assessing aerosol RF at TOA. They have not 

yet been addressed in the previous IPCC reports. Here, of particular importance is to improve 

the accuracy of aerosol absorption. 

Long-term trends of aerosol 

To assess the aerosol effects on climate change the long-term variations of aerosol amount 

and composition and how they are related to the emission trends in different regions have to be 

specified. Simulations of historical aerosol trends can be problematic since historical emissions 

of aerosols have shown large uncertainties – as information is difficult to obtain on past source 

types, strengths, and even locations. The IPCC AR4 simulations used several alternative 

aerosol emission histories, especially for BC and POM aerosols. 

Climate modeling 

Current aerosol simulation capabilities from CTMs have not been fully implemented in 

most models used in IPCC AR4 climate simulations. Instead, a majority employed simplified 

approaches to account for aerosol effects, to the extent that aerosol representations in the 

GCMs, and the resulting forcing estimates, are inadequate. The oversimplification occurs in 

part because the modeling complexity and computing resource would be significantly 

increased if the full suite of aerosols were fully coupled in the climate models. 

Observational constraints 

Model improvement has been hindered by a lack of comprehensive datasets that could 

provide multiple constraints for the key parameters simulated in the model. The extensive 

AOD coverage from satellite observations and AERONET measurements has helped a great 

December 2009 9-151 
deal in validating model-simulated AOD over the past decade, but further progress has been 

slow. Large model diversities in aerosol composition, size, vertical distribution, and mixing 

state are difficult to constrain, because of lack of reliable measurements with adequate spatial 

and temporal coverage (see Chapter 2 of the CCSP SAP2.3). 

Aerosol radiative forcing 

Because of the large spatial and temporal differences in aerosol sources, types, emission 

trends, compositions, and atmospheric concentrations, anthropogenic aerosol RF has profound 

regional and seasonal variations. So it is an insufficient measure of aerosol RF scientific 

understanding, however useful, for models (or observation-derived products) to converge only 

on globally and annually averaged TOA RF values and accuracy. More emphasis should be 

placed on regional and seasonal comparisons, and on climate effects in addition to direct RF at 

TOA. 

9.3.6.8. Conclusions 

From forward modeling studies, as discussed in the IPCC (2007, 092765) , the direct 

effect of aerosols since pre-industrial times has resulted in a negative RF of about -0.5 ± 0.4 

W/m2. The RF due to cloud albedo or brightness effect is estimated to be -0.7 (-1.8 to -0.3) 

W/m2. Forcing of similar magnitude has been used in some modeling studies for the effect 

associated with cloud lifetime, in lieu of the cloud brightness influence. The total negative RF 

due to aerosols according to IPCC (2007, 092765) estimates is therefore -1.3 (-2.2 to -0.5) 

W/m2. With the inverse approach, in which aerosols provide forcing necessary to produce the 

observed temperature change, values range from -1.7 to -0.4 W/m2 (IPCC, 2007, 092765) . 

These results represent a substantial advance over previous assessments (e.g., IPCC TAR), as 

the forward model estimated and inverse approach required aerosol TOA forcing values are 

converging. However, large uncertainty ranges preclude using the forcing and temperature 

records to more accurately determine climate sensitivity. 

There are now a few dozen models that simulate a comprehensive suite of aerosols. This 

is done primarily in the CTMs. Model inter-comparison studies have shown that models have 

merged at matching the global annual averaged AOD observed by satellite instruments, but 

they differ greatly in the relative amount of individual components, in vertical distributions, 

and in optical properties. Because of the great spatial and temporal variations of aerosol 

distributions, regional and seasonal diversities are much larger than that of the global annual 

mean. Different emissions and differences in atmospheric processes, such as transport, 

removal, chemistry, and aerosol microphysics, are chiefly responsible for the spread among the 

models. The varying component contributions then lead to differences in aerosol direct RF, as 

aerosol scattering and absorption properties depend on aerosol size and type. They also impact 

the calculated indirect RF, whose variations are further amplified by the wide range of cloud 

and convective parameterizations in models. Currently, the largest aerosol RF uncertainties are 

associated with the aerosol indirect effect. Most climate models used for the IPCC AR4 

simulations employed simplified approaches, with aerosols specified from stand-alone CTM 

simulations. Despite the uncertainties in aerosol RF and widely varying model climate 

sensitivity, the IPCC AR4 models were generally able to reproduce the observed temperature 

record for the past century. This is because models with lower/higher climate sensitivity 

generally used less/more negative aerosol forcing to offset the greenhouse gas warming. An 

equally good match to observed surface temperature change in the past could be obtained from 

a model with larger climate sensitivity and smaller net forcing, or a model with smaller 

sensitivity and larger forcing (Hansen et al., 2007, 190597). Obviously, both greenhouse gases 

and aerosol effects have to be much better quantified in future assessments. 

Progress in better quantifying aerosol impacts on climate will only be made when the 

capabilities of both aerosol observations and representation of aerosol processes in models are 

improved. The primary concerns and issues discussed in this chapter of the CCSP SAP2.3 

include:  Better representation of aerosol composition and absorption in the global models 

 Improved theoretical understanding of subgrid-scale processes crucial to aerosolcloud interactions and lifetime  Improved aerosol microphysics and cloud parameterizations 

 Better understanding of aerosol effects on surface radiation and hydrological cycles 

 More focused analysis on regional and seasonal variations of aerosols 

 More reliable simulations of aerosol historic long-term trends 

 More sophisticated climate model simulations with coupled aerosol and cloud 

processes  Enhanced satellite observations of aerosol type, SSA, vertical distributions, and 

aerosol radiative effect at TOA; more coordinated field experiments to provide 

constraints on aerosol chemical, physical, and optical properties. 

December 2009 9-152 
9.3.7. Fire as a Special Source of PM Welfare Effects 

Much interest has developed in defining more precisely the role of pyrogenic C in the boreal 

C cycle. This is due to: (1) the resistance of pyrogenic C to decomposition; (2) its influence on soil 

processes; and (3) the absorption of solar radiation by soot aerosols (Preston and Schmidt, 2006, 

156030). 

Preston and Schmidt (2006, 156030) reviewed the current state of knowledge regarding 

atmospheric emissions of pyrogenic C in the boreal zone. They considered chemical structures, 

analytical methods, formation, characteristics in soil, loss mechanisms, and longevity. Biomass is 

largely converted to gaseous forms during burning, but up to several percent is converted to 

pyrogenic C, and this includes charcoal and BC. Charcoal is defined visually; BC is defined 

chemically by its resistance to oxidation in the laboratory. Andreae and Gelencsér (2006, 156215) 

reviewed a different category of light-absorbing carbon, referred to as brown carbon. 

Within the boreal zone, fire is a critical driver of ecosystem process and nutrient cycling 

(Hicke et al., 2003, 156545). For example, Bachelet et al. (2005, 156241) estimated that 61% of the 

C gained in Alaska by primary production of boreal forests between 1922 and 1996 was lost to fire. 

An updated modeling effort to evaluate the radiative effects of aerosols was presented by Stier 

et al. (2007, 157012). Inclusion of refractive indices recommended byBond and Bergstrom (2005, 

155696) significantly increased aerosol RF and resulted in better agreement with sun-photometer 

estimates. Although this stage of climate modeling improved the representation of aerosols, large 

uncertainties remain regarding the effects of aerosol mixing and aerosol-cloud interactions. 

Furthermore, Stier et al. (2007, 157012) emphasized that these types of modeling efforts are 

dependent upon emission estimates that are likely to vary by a factor of 2 or more. 

One important reason for the acknowledged uncertainty in estimating global emissions of 

carbonaceous aerosols is the influence of intermittent fires that can occur at scales large enough to 

affect hemispheric aerosol concentrations. To better quantify the effects of large-scale fire, Generoso 

et al. (2007, 155786) used satellite observations of boreal fires in Russia in 2003 to evaluate the 

performance of a global chemistry and transport model in simulating aerosol optical thickness, 

transport, and deposition. Emissions estimates of BC and OC were adjusted in the model to better 

match satellite observations of pollutant transport over the North Pacific. This resulted in an increase 

in optical thickness and BC deposition by a factor of 2. The adjusted model estimated that the fires 

contributed 16-33% of the optical thickness and 40-56% of BC deposition north of 75o

 N in the 

spring and summer of 2003. 

Large fires also occurred over the Iberian Peninsula and Mediterranean coast during 2003. A 

meso-scale atmospheric transport model was used with ground-based measurements and satellite 

optical measurements to characterize the dispersion of emitted smoke particles and quantify radiative 

effects across Europe (Hodzic et al., 2007, 156553). The modeled wildfire emissions resulted in 

increases in PM2.5 concentrations from 20 to 200%. The increased aerosol concentration was 

estimated to increase radiative forcing by 10-35 W/m2

 during the period of strong fire influence. 

Absorption of radiation by BC was also estimated to decrease rates of photolysis by 30%. In this 

simulation, all particles were assumed to be internally mixed, and secondary aerosol formation was 

not considered. Meteorological conditions in Europe during the exceptionally hot summer of 2003 

were linked to enhanced photochemically derived pollutants, increased wild fires, and elevated 

aerosol concentrations in an analysis by Vautard et al. (2007, 106012). 

In addition to incidental fires, routine biomass burning, usually associated with agriculture in 

eastern Europe, also has been shown to contribute to hemispheric concentrations of carbonaceous 

aerosols. In the spring of 2006, the most severe air pollution levels in the Arctic to date were 

recorded (Stohl et al., 2006, 156100). Atmospheric transport modeling coupled with satellite fire 

detection data identified biomass burning for agriculture as the primary cause of the high pollution 

levels. Concentrations of PM2.5 peaked during the pollution episode at values of an order of 

magnitude greater than those recorded prior to the episode. The increased transport of pollution into 

the Arctic during 2006 was attributed to weather conditions that delayed preparations for crop 

planting into May. Weather patterns favorable for pollutant transport into the Arctic were related to 

unusually warm weather in late April and May, when the majority of agricultural biomass burning 

took place that year. 

In the summer of 2004, 2.7 million ha were burned by wildfire in Alaska and 3.1 million ha 

were burned in Canada. Effects on atmospheric air quality were measured throughout the Arctic, 

although the concentrations of particulates varied considerably. Aerosol optical depths were also 

December 2009 9-153 
increased at all measurement stations, which indicated that the fires were likely to have had a 

significant effect on the atmospheric radiation budget for the Arctic (Stohl et al., 2006, 156100). At 

one site, a pronounced drop in albedo was observed due presumably to high deposition of light 

absorbing particulates on the snow surface by the North American fires in 2004. 

Investigations of the effects of large fires on climate forcing have typically focused on the 

absorptive effects of BC. However, these fires also release large amounts of CO2 and CH4, as well as 

light scattering compounds such as OC, and can enhance cloud formation. These fires also increase 

radiative surface absorption through BC deposition on snow and ice, and alter surface albedo and 

ecosystem energy budgets within the burn perimeter. Randerson et al. (2006, 156038) estimated the 

net climate forcing of greenhouse gases, aerosols, BC deposition on snow and ice and changes in 

albedo for the year subsequent to a fire and for 80 years in the future in interior Alaska. The net 

effect of the fire in the first year was an increase in radiative forcing, but over the 80-year recovery 

period, average net annual radiative forcing was decreased by the fire. 

9.3.8. Radiative Effects of Volcanic Aerosols 

Section 9.3.8.1. comes directly from IPCC AR4 Chapter 2, Section 2.7.2, with section, table, 

and figure numbers changed to be internally consistent with this ISA. 

9.3.8.1. Explosive Volcanic Activity 

Radiative Effects of Volcanic Aerosols 

Volcanic sulfate aerosols are formed as a result of oxidation of the sulfur gases emitted by 

explosive volcanic eruptions into the stratosphere. The process of gas-to-particle conversion 

has an e-folding time of roughly 35 days (Bluth et al., 1992, 192029; Read et al., 1993, 

192031). The e-folding time (by mass) for sedimentation of sulfate aerosols is typically about 

12 to 14 months (Baran and Foot, 1994, 192032; Barnes and Hofmann, 1997, 192044; Bluth et 

al., 1997, 192045; Lambert et al., 1993, 192231). Also emitted directly during an eruption are 

volcanic ash particulates (siliceous material). These are particles usually larger than 2 μm that 

sediment out of the stratosphere fairly rapidly due to gravity (within three months or so), but 

could also play a role in the radiative perturbations in the immediate aftermath of an eruption. 

Stratospheric aerosol data incorporated for climate change simulations tends to be mostly that 

of the sulfates (Ammann et al., 2003, 192057; Hansen et al., 2002, 049177; Ramachandran et 

al., 2000, 192050; Sato et al., 1993, 192046; Stenchikov et al., 1998, 192049; Tett et al., 2002, 

192053). As noted in the Second Assessment Report (SAR) and the TAR, explosive volcanic 

events are episodic, but the stratospheric aerosols resulting from them yield substantial 

transitory perturbations to the radiative energy balance of the planet, with both shortwave and 

longwave effects sensitive to the microphysical characteristics of the aerosols (e.g., size 

distribution). 

Long-term ground-based and balloon-borne instrumental observations have resulted in an 

understanding of the optical effects and microphysical evolution of volcanic aerosols (Deshler 

et al., 2003, 192058; Hofmann et al., 2003, 192062). Important ground-based observations of 

aerosol characteristics from pre-satellite era spectral extinction measurements have been 

analysed by Stothers (2001, 192233; 2001, 192232), but they do not provide global coverage. 

Global observations of stratospheric aerosol over the last 25 years have been possible owing to 

a number of satellite platforms, for example, TOMS and TOVS have been used to estimate 

SO2 loadings from volcanic eruptions (Krueger et al., 2000, 192234; Prata et al., 2003, 

192235). The Stratospheric Aerosol and Gas Experiment (SAGE) and Stratospheric Aerosol 

Measurement (SAM) projects (e.g., McCormick and Trepte, 1987, 192328) have provided 

vertically resolved stratospheric aerosol spectral extinction data for over 20 years, the longest 

such record. This data set has significant gaps in coverage at the time of the El Chichón 

eruption in 1982 (the second most important in the 20th century after Mt. Pinatubo in 1991) 

and when the aerosol cloud is dense; these gaps have been partially filled by lidar 

measurements and field campaigns (e.g., Antuña et al., 2003, 192251; Thomason and Peter, 

2006, 192248). 

Volcanic aerosols transported in the atmosphere to polar regions are preserved in the ice 

sheets, thus recording the history of the Earth’s volcanism for thousands of years (Kruysse, 

1971, 192236; Mosley-Thompson et al., 2003, 192255; Palmer et al., 2002, 192319). However, 

the atmospheric loadings obtained from ice records suffer from uncertainties due to imprecise 

knowledge of the latitudinal distribution of the aerosols, depositional noise that can affect the 

signal for an individual eruption in a single ice core, and poor constraints on aerosol 

microphysical properties. The best-documented explosive volcanic event to date, by way of 

reliable and accurate observations, is the 1991 eruption of Mt. Pinatubo. The growth and decay 

December 2009 9-154 
of aerosols resulting from this eruption have provided a basis for modeling the RF due to 

explosive volcanoes. There have been no explosive and climatically significant volcanic events 

since Mt. Pinatubo. As pointed out in Ramaswamy et al. (2001, 156899), stratospheric aerosol 

concentrations are now at the lowest concentrations since the satellite era and global coverage 

began in about 1980. Altitude dependent stratospheric optical observations at a few 

wavelengths, together with columnar optical and physical measurements, have been used to 

construct the time-dependent global field of stratospheric aerosol size distribution formed in 

the aftermath of volcanic events. The wavelength-dependent stratospheric aerosol singlescattering characteristics calculated for the solar and longwave spectrum are deployed in 

climate models to account for the resulting radiative (shortwave plus longwave) perturbations. 

Using available satellite- and ground-based observations, Hansen et al. (2002, 049177) 

constructed a volcanic aerosols data set for the 1850-1999 period (Sato et al., 1993, 192046). 

This has yielded zonal mean vertically resolved aerosol optical depths for visible wavelengths 

and column average effective radii. Stenchikov et al. (2006, 192260) introduced a slight 

variation to this data set, employing UARS observations to modify the effective radii relative 

to Hansen et al. (2002, 049177), thus accounting for variations with altitude. Ammann et al. 

(2003, 192057) developed a data set of total aerosol optical depth for the period since 1890 

that does not include the Krakatau eruption. The data set is based on empirical estimates of 

atmospheric loadings, which are then globally distributed using a simplified parameterization 

of atmospheric transport, and employs a fixed aerosol effective radius (0.42 μm) for 

calculating optical properties. The above data sets have essentially provided the bases for the 

volcanic aerosols implemented in virtually all of the models that have performed the 20thcentury climate integrations (Stenchikov et al., 2006, 192260). Relative to Sato et al. (1993, 

192046), the Ammann et al. (2003, 192057) estimate yields a larger value of the optical depth, 

by 20 to 30% in the second part of the 20th century, and by 50% for eruptions at the end of 

19th and beginning of 20th century, for example, the 1902 Santa Maria eruption (Figure 9-83). 

The global mean RF calculated using the Sato et al. (1993, 192046) data yields a peak in 

radiative perturbation of about -3 W/m2 for the strong (rated in terms of emitted SO2) 1860 and 

1991 eruptions of Krakatau and Mt. Pinatubo, respectively. The value is reduced to about -2 

W/m2 for the relatively less intense El Chichón and Agung eruptions (Hansen et al., 2002, 

049177). As expected from the arguments above, Ammann’s RF is roughly 20 to 30% larger 

than Sato’s RF. 

Not all features of the aerosols are well quantified, and extending and improving the data 

sets remains an important area of research. This includes improved estimates of the aerosol 

size parameters (Bingen et al., 2004, 192262), a new approach for calculating aerosol optical 

characteristics using SAGE and UARS data (Bauman et al., 2003, 192265), and 

intercomparison of data from different satellites and combining them to fill gaps (Randall et 

al., 2001, 192268). While the aerosol characteristics are better constrained for the Mt. Pinatubo 

eruption, and to some extent for the El Chichón and Agung eruptions, the reliability degrades 

for aerosols from explosive volcanic events further back in time as there are few, if any, 

observational constraints on their optical depth and size evolution. 

The radiative effects due to volcanic aerosols from major eruptions are manifest in the 

global mean anomaly of reflected solar radiation; this variable affords a good estimate of 

radiative effects that can actually be tested against observations. However, unlike RF, this 

variable contains effects due to feedbacks (e.g., changes in cloud distributions) so that it is 

actually more a signature of the climate response. In the case of the Mt. Pinatubo eruption, 

with a peak global visible optical depth of about 0.15, simulations yield a large negative 

perturbation as noted above of about -3 W/m2

 (Hansen et al., 2002, 049177; Ramachandran et 

al., 2000, 192050) (see also Section 9.2 of the IPCC AR4). This modeled estimate of reflected 

solar radiation compares reasonably with ERBS observations (Minnis et al., 1993, 190539). 

However, the ERBS observations were for a relatively short duration, and the modelobservation comparisons are likely affected by differing cloud effects in simulations and 

measurements. It is interesting to note (Stenchikov et al., 2006, 192260) that, in the Mt. 

Pinatubo case, the Goddard Institute for Space Studies (GISS) models that use the Sato et al. 

(1993, 192046) data yield an even greater solar reflection than the National Center for 

Atmospheric Research (NCAR) model that uses the larger (Ammann et al., 2003, 192057) 

optical depth estimate. 

December 2009 9-155 
Figure 9-83. Visible (wavelength 0.55 μm) optical depth estimates of stratospheric SO4

2–

aerosols formed in the aftermath of explosive volcanic eruptions that occurred 

between 1860 and 2000. Results are shown from two different data sets that have 

been used in recent climate model integrations. Note that the Ammann et al. 

(2003, 192057) data begins in 1890. 

Thermal, Dynamical and Chemistry Perturbations Forced by Volcanic Aerosols

 Four distinct mechanisms have been invoked with regards to the climate response to 

volcanic aerosol RF. First, these forcings can directly affect the Earth’s radiative balance and 

thus alter surface temperature. Second, they introduce horizontal and vertical heating 

gradients; these can alter the stratospheric circulation, in turn affecting the troposphere. Third, 

the forcings can interact with internal climate system variability (e.g., El Niño-Southern 

Oscillation, North Atlantic Oscillation, Quasi- Biennial Oscillation) and dynamical noise, 

thereby triggering, amplifying or shifting these modes (see Section 9.2 of the IPCC AR4) 

(Stenchikov et al., 2004, 192274; Yang and Schlesinger, 2001, 192270). Fourth, volcanic 

aerosols provide surfaces for heterogeneous chemistry affecting global stratospheric ozone 

distributions (Chipperfield et al., 2003, 192275) and perturbing other trace gases for a 

considerable period following an eruption. Each of the above mechanisms has its own spatial 

and temporal response pattern. In addition, the mechanisms could depend on the background 

state of the climate system, and thus on other forcings (e.g., due to well-mixed gases) (Meehl 

et al., 2004, 192279), or interact with each other. 

The complexity of radiative-dynamical response forced by volcanic impacts suggests that 

it is important to calculate aerosol radiative effects interactively within the model rather than 

prescribe them (Andronova et al., 1999, 192286; Broccoli et al., 2003, 192283). Despite 

differences in volcanic aerosol parameters employed, models computing the aerosol radiative 

effects interactively yield tropical and global mean lower-stratospheric warmings that are fairly 

consistent with each other and with observations (Hansen et al., 2002, 049177; Ramachandran 

et al., 2000, 192050; Ramaswamy et al., 2006, 192284; Stenchikov et al., 2004, 192274; Yang 

and Schlesinger, 2001, 192270); however, there is a considerable range in the responses in the 

December 2009 9-156 
polar stratosphere and troposphere. The global mean warming of the lower stratosphere is due 

mainly to aerosol effects in the longwave spectrum, in contrast to the flux changes at the TOA 

that are essentially due to aerosol effects in the solar spectrum. The net radiative effects of 

volcanic aerosols on the thermal and hydrologic balance (e.g., surface temperature and 

moisture) have been highlighted by recent studies (Free and Angell, 2002, 192281; Jones et al., 

2003, 192278) (see Chapter 6 of the IPCC AR4). See Chapter 9 (of the IPCC AR4) for 

significance of the simulated responses and model-observation comparisons for 20th-century 

eruptions. A mechanism closely linked to the optical depth perturbation and ensuing warming 

of the tropical lower stratosphere is the potential change in the cross-tropopause water vapour 

flux (Joshi and Shine, 2003, 192327) (see Section 2.3.7 of the IPCC AR4). 

Anomalies in the volcanic-aerosol induced global radiative heating distribution can force 

significant changes in atmospheric circulation, for example, perturbing the equator-to-pole 

heating gradient (Ramaswamy et al., 2006, 192273; Stenchikov et al., 2002, 192277) (see 

Section 9.2 of the IPCC AR4) and forcing a positive phase of the Arctic Oscillation that in turn 

causes a counterintuitive boreal winter warming at middle and high latitudes over Eurasia and 

North America (Miller et al., 2005, 192258; Perlwitz and Graf, 2001, 192271; Perlwitz and 

Harnik, 2003, 192264; Rind et al., 2005, 192261; Shindell et al., 2003, 192069; Shindell et al., 

2004, 192267; Stenchikov et al., 2002, 192277; Stenchikov et al., 2004, 192274; Stenchikov et 

al., 2006, 192260). 

Stratospheric aerosols affect the chemistry and transport processes in the stratosphere, 

resulting in the depletion of ozone (Brasseur and Granier, 1992, 192256; Chipperfield et al., 

2003, 192275; Solomon et al., 1996, 192252; Tie et al., 1994, 192253). Stenchikov et al. 

(2002, 192277) demonstrated a link between ozone depletion and Arctic Oscillation response; 

this is essentially a secondary radiative mechanism induced by volcanic aerosols through 

stratospheric chemistry. Stratospheric cooling in the polar region associated with a stronger 

polar vortex initiated by volcanic effects can increase the probability of formation of polar 

stratospheric clouds and therefore enhance the rate of heterogeneous chemical destruction of 

stratospheric ozone, especially in the NH (Tabazadeh et al., 2002, 192250). The above studies 

indicate effects on the stratospheric ozone layer in the wake of a volcanic eruption and under 

conditions of enhanced anthropogenic halogen loading. Interactive microphysics-chemistryclimate models (Dameris et al., 2005, 192055; Rozanov et al., 2002, 192070; Rozanov et al., 

2004, 192072; Shindell et al., 2003, 192069; Timmreck et al., 2003, 192068) indicate that 

aerosol-induced stratospheric heating affects the dispersion of the volcanic aerosol cloud, thus 

affecting the spatial RF. However the models’ simplified treatment of aerosol 

microphysics introduces biases; further, they usually overestimate the mixing at the tropopause 

level and intensity of meridional transport in the stratosphere (Douglass et al., 2003, 057260; 

Schoeberl et al., 2003, 057262). For present climate studies, it is practical to utilize simpler 

approaches that are reliably constrained by aerosol observations. 

Because of its episodic and transitory nature, it is difficult to give a best estimate for the 

volcanic RF, unlike the other agents. Neither a best estimate nor a level of scientific 

understanding was given in the TAR. For the well-documented case of the explosive 1991 Mt. 

Pinatubo eruption, there is a good scientific understanding. However, the limited knowledge of 

the RF associated with prior episodic, explosive events indicates a low level of scientific 

understanding. 

9.3.9. Other Special Sources and Effects 

International shipping has been identified as an additional source of carbonaceous aerosols. 

Simulations with a climate model that included aerosol effects and 3 different emissions inventories 

showed that shipping contributed 2.3-3.6% of the total SO4

2– atmospheric aerosol content and 

0.4-1.4% of the total BC atmospheric aerosol content, based on global means in 2000. This modeling 

also showed that aerosol optical thickness over the Indian Ocean, the Gulf of Mexico, and the 

northeastern Pacific Ocean varied by 8 to 10%. The corresponding all-sky (that includes both cloudy 

and clear skies) direct radiative forcings ranged from -0.011 to -0.013 W/m2

. The greatest effect of 

aerosols emitted from global shipping is likely to be an increase in cloud formation and the resulting 

change in reflectivity of shortwave radiation. Aerosols from shipping were estimated to contribute 

17-39% of the total anthropogenic aerosol radiation forcing effect. 

When BC is deposited to the surface of ice or snow, solar absorption and heating occur at the 

surface. This can melt additional snow or ice at the surface and the reflectivity of the surface can 

change. Both factors affect aspects of climate. Jacobson (2003, 155866; 2004, 155870) and Jacobson 

et al. (2004, 180362) estimated the warming due to fossil fuel BC and organic matter using the Gas, 

Aerosol, Transport, Radiation, General Circulation, Mesoscale and Ocean Model 

(GATOR-GCMOM). The modeling effort included consideration of the BC cycle, accounting for 

emissions, transport, aerosol coagulation, aerosol growth, cloud activation, aerosol-cloud 

coagulation, cloud-cloud coagulation, rainout, washout, dry deposition, and processes of precipitated 

and dry-deposited BC in snow and sea ice. Results suggested that BC absorption in snow and sea ice 

December 2009 9-157 
increased near-surface temperatures over a 10-year simulation by about 0.06o

K (Jacobson, 2003, 

155868). 

BC soot is a potentially important agent of climate warming in the Arctic, and northern boreal 

wildfires may contribute substantially to this effect. Soot is approximately twice as effective as CO2

in altering surface air temperature, and can reduce sea ice formation and snow albedo (Hansen and 

Nazarenko, 2004, 156521). 

Kim et al. (2005, 155900) investigated the relationships between northern boreal wildfires and 

reductions in Arctic sea ice and glacial coverage. They modeled the FROSTFIRE boreal forest 

control burn (Hinzman et al., 2003, 155845) with respect to BC aerosol transport, dispersion, and 

deposition. Model results suggested that boreal wildfires could be a major source of BC soot to sea 

ice and glaciers in Alaska. This may exacerbate summer melting of sea ice and reduce recruitment of 

first-year ice into multiyear ice, thereby leading to an overall reduction in sea ice. Similarly, 

increased BC soot on glaciers would be expected to increase summer melting and lead to an overall 

reduction in glacial coverage (Kim et al., 2005, 155900). 

Jacobson (2002, 155865) proposed, based on model simulations with 12 identifiable effects of 

aerosol particles on climate, emission reductions of fossil fuel particulate BC and associated organic 

matter could potentially slow warming for a specific period more than reduction of CO2 or CH4 for a 

specific period. Jacobson’s (2006, 156599) calculations suggested that fossil fuel BC plus organic 

matter emissions reductions could eliminate 8-18% of total anthropogenic warming, and 20-45% of 

net warming after accounting for aerosol cooling, within a period of 3-5 years (Chock et al., 2003, 

155727). See also conflicting discussions (Feichter et al., 2003, 155772; Penner, 2003, 156851); and 

further responses (Jacobson, 2003, 155867; Jacobson, 2003, 155868; Jacobson, 2003, 155869; 

Penner, 2003, 156851). 

Bond and Sun (2005, 156282) reviewed published data regarding the warming potential of 

BC, compared with CO2 and other GHG. Climatic effects of GHG are generally compared on the 

basis of top-of-the-atmosphere, globally averaged changes in radiative balance. On that basis, BC is 

one of the largest individual warming agents, after CO2 and perhaps CH4 (Bond and Sun, 2005, 

156282; Jacobson, 2000, 056378; Sato et al., 2003, 156947). 

Reddy and Boucher (2007, 156042) conducted an analysis that provided regional estimates of 

BC emissions from fossil fuels and biofuels. These estimates indicated that East and Southeast Asia 

contributed over 50% of the global BC burden and its associated direct radiative forcing. Europe was 

found to be the largest BC contributor in the northern latitudes. The indirect effect of BC deposition 

on snow was also estimated to be highest for Europe. 

To improve understanding of the role of aerosols in climate forcing, Chung and Seinfeld 

(2002, 155732) estimated the global distribution of BC, primary organic particles (those directly 

emitted from combustion), secondary organic particles (primary organic compounds partially 

oxidized in the atmosphere), and SO4

2– aerosols to model the overall radiative forcing of these 

groups of compounds. The model was run with the assumption that the BC particles do not combine 

with OC or SO4

2– particles (termed an external mixture), and with the assumption that the particles 

are represented by a core of BC surrounded by a shell of light scattering aerosols. Modeling results 

suggested an overall radiative cooling effect from aerosols ranging from -0.39 to -0.78 W/m2. 

Roberts and Jones (2004, 156052) used a climate modeling approach to compare possible 

effects of BC on climate warming to those attributable to emissions from greenhouse gases. Results 

suggested that the warming effect from atmospheric BC aerosols may not be large relative to that 

from greenhouse gases. A different modeling approach by Roeckner et al. (2006, 156920) evaluated 

the effects of BC and primary OC on climate under two scenarios of carbonaceous aerosol 

emissions. In the first scenario, BC and primary OC emissions decreased over Europe and China, but 

increased at lower latitudes. In the second scenario, emissions were frozen at 2000 levels. The effects 

of both scenarios on mean global temperature were found to be small, but higher aerosol emissions 

at low latitudes did result in atmospheric heating and corresponding land surface cooling that led to 

increased precipitation and runoff in this simulation. 

Study of BC effects in tropical climates was undertaken by Wang (2007, 156147). Substantial 

effects of direct radiative forcing by BC on the tropical Pacific were shown in model results that 

were similar to the El Niño Southern Oscillation activities both in the nature and scale of effects with 

enhancement of the Indian monsoon circulation. The model suggested that atmospheric heating by 

radiation absorption by BC can form temperature and pressure anomalies that favor propagation of 

convection from western to central and eastern Pacific. More work will be needed to distinguish 

between the aerosol signal and natural factors in controlling tropical precipitation in this region. 

December 2009 9-158 
Table 9-18. Overview of the different aerosol indirect effects and their sign of the net radiative flux 

change at the top of the atmosphere (TOA). 

Effect Cloud Types 

Affected Process Sign of Change in TOA Radiation

Potential 

Magnitude 

Scientific 

Understanding 

Cloud albedo 

effect All clouds For the same cloud water or ice content more but smaller cloud particles reflect more solar radiation Negative Medium Low 

Cloud lifetime 

effect All clouds Smaller cloud particles decrease the precipitation efficiency thereby presumably prolonging cloud lifetime Negative Medium Very low 

Semi-direct effect All clouds 

Absorption of solar radiation by absorbing aerosols affects 

static stability and the surface energy budget, and may lead to 

an evaporation of cloud particles 

Positive or Negative Small Very low 

Glaciation indirect 

effect 

Mixed-phase 

clouds An increase in IN increases the precipitation efficiency Positive Medium Very low 

Thermodynamic 

effect 

Mixed-phase 

clouds 

Smaller cloud droplets delay freezing causing super-cooled 

clouds to extend to colder temperatures Positive or Negative Medium Very low 

Source: Reprinted with Permission of Cambridge University Press from Denman (2007, 156394)

Table 9-19. Overview of the different aerosol indirect effects and their implications for the global 

mean net shortwave radiation of the surface Fsfc (columns 2-4) and for precipitation 

(columns 5-7). 

Effect Sign of Change 

in Fsfc 

Potential 

Magnitude 

Scientific 

Understanding 

Sign of Change in 

Precipitation 

Potential 

Magnitude 

Scientific 

Understanding 

Cloud albedo effect Negative Medium Low n.a. n.a. n.a. 

Cloud lifetime effect Negative Medium Very low Negative Small Very low 

Semi-direct effect Negative Large Very low Negative Large Very low 

Glaciation indirect 

effect Positive Medium Very low Positive Medium Very low 

Thermodynamic 

effect 

Positive or 

Negative Medium Very low Positive or Negative Medium Very low 

Source: Reprinted with Permission of Cambridge University Press from Denman (2007, 156394)

There are several other kinds of climate effects from aerosol PM. None is well understood or 

well quantified. The semi-direct effect, which involves absorption of solar radiation by soot particles 

followed by re-emission as thermal radiation, is expected to heat the air mass and increase its static 

stability relative to the surface. The semi-direct effect can also cause evaporation of cloud droplets, 

thereby partially offsetting the cloud albedo indirect effect. The glaciation effect involves an increase 

in IN, which is expected to cause rapid glaciation of a super-cooled liquid water cloud due to the 

differences in vapor pressure over ice and water. Unlike cloud droplets, these ice crystals can quickly 

reach precipitation size, with the potential to turn a non-precipitating cloud into a precipitating cloud. 

The thermodynamic effect involves a delay in freezing by the smaller cloud droplets, which can 

cause super cooled clouds to occur under colder temperatures. The possible consequences to 

radiative flux of all of the processes are outlined in Table 9-18 (top of the atmosphere effects) and 

Table 9-19 (surface radiative and precipitation effects) (Denman et al., 2007, 156394), though 

significant uncertainties remain. Nevertheless, the individual processes cannot be considered in 

isolation because of the numerous feedbacks, and because atmospheric aerosol concentrations and 

climate are intimately coupled (Denman et al., 2007, 156394; Dentener et al., 2006, 088434). 

December 2009 9-159 
9.3.9.1. Glaciers and Snowpack 

Organic compounds are incorporated into snow by wet and dry deposition processes (Lei and 

Wania, 2004, 127880; Roth et al., 2004, 056431). Atmospherically deposited organics appear to be 

ubiquitous in snowpacks at appreciable concentrations (Grannas et al., 2007, 156492). Examples 

include PAHs, phthalates, alkanes, phenols, low molecular weight carbonyls, POPs, and low 

molecular weight organic acids (Halsall, 2004, 155822; Nakamura et al., 2000, 156792; Villa et al., 

2003, 156139). Humic-like substances found in the snowpack may release VOCs into the 

atmosphere via photo-oxidation (Grannas et al., 2004, 155803; Grannas et al., 2007, 156492). 

Several thousand organic species were identified by Grannas et al. (2006, 155804), based on 

molecular weight, from a single ice core collected in Russia. Little information is available, however, 

regarding the chemical properties of these chemical constituents. In addition to the diversity of 

chemicals that are deposited into the snowpack, there are also biological organisms, including 

bacteria and algae. Their role in influencing snow chemistry and volatilization processes is not 

understood (Grannas et al., 2007, 156492). 

Recent research has explored connections between the atmosphere and the cryosphere (land or 

sea covered by snow or ice). A seasonal maximum of 40% of the Earth’s land surface is covered by 

snow or ice, as well as several percent of the oceans. Particulate deposition to snow and ice surfaces 

can affect melting rates. Deposition of PM to glacial ice surfaces can affect the subsequent rate of 

melting. A thin cover of debris contributes to accelerated melting. A thicker cover of debris, such as 

that which may result from a volcanic eruption, retards melting. The difference is due to the 

changing balance between enhanced absorption of shortwave radiation by PM and conductive heat 

flow (insulation) through a buildup of material having low heat conduction (Kirkbride and Dugmore, 

2003, 156645).This issue is particularly important for deposition of large quantities of volcanic 

material. To a lesser extent, however, the same principles apply to PM deposition derived from air 

pollution. Under a thin layer of debris, ablation rates are higher than for clean ice. However, as the 

thickness of the debris layer increases, ablation rates systematically decline (Nicholson and Benn, 

2006, 156806). The threshold debris thickness that separates ablation increase from decrease is site 

specific and depends on local climate and the nature of the debris particles. Nicholson and Benn 

(2006, 156806) presented a surface energy balance model to calculate ice melt beneath a surface 

debris layer, based on meteorological data and basic debris characteristics. Modeled melting rates 

matched observed rates, suggesting that the model produced useful results. 

Long-range atmospheric transport of PM delivers a large fraction of the total input of POPs to 

the Arctic region (Halsall, 2004, 155822). These contaminants can accumulate in Arctic food webs 

and have become the focus of international research and concern. Nevertheless, fate and transport of 

POPs within terrestrial and marine Arctic ecosystems are not well understood and are strongly 

affected by the presence of snow and ice. Sea ice provides a barrier to air-water exchange, and this 

hinders volatilization and re-emission of previously deposited contaminants (Halsall, 2004, 155822). 

Thus, the effects of greenhouse gasses and PM on climate in the Arctic region have feedbacks to 

POP fate, transport, and toxicity. The transfer of POPs among the major abiotic environmental 

compartments in the Arctic are summarized in Figure 9-84 from Halsall (2004, 155822). Recent 

studies detailing rate and transport of POPs are summarized in Table 9-20. 

December 2009 9-160 
Table 9-20. Recent studies highlighting POP occurrence and fate in the major arctic compartments. 

ATMOSPHERE 

 1 Annual time-series of OC and PCB concentrations in the Norwegian Arctic Oehme et al. (1996, 156001) 

 2 Long-term analysis of the chlordane-group and their input to the Arctic with changing sources Bidleman et al. (2002, 155691) 

 3 PAH occurrence at monitoring sites across the Arctic, seasonality and gas/particle partitioning Halsall et al. (1997, 155821) 

 4 PCB occurrence at monitoring sites across the Arctic, spatial differences and seasonality Stern et al. (1997, 156096) 

 5 Long-term analysis of PCB and OC trends in the Canadian Arctic and seasonal patterns Hung et al. (2001, 155856; 2002, 155857) 

 6 Trans-Pacific LRAT and impact of Asian sources on the western Canadian Arctic Bailey et al. (2000, 155670) 

FRESHWATER 

 7 Annual avg water concentrations in major Russian rivers for selected OC pesticides Alexeeva et al. (2001, 155651) 

 8 Long-term (decades) PCB deposition profile in Arctic lake sediments Muir et al. (1996, 155991) 

 9 Mass balance of selected OCs in Canadian Arctic lake conducted with data collected over 3 yrs Helm et al. (2002, 155835) 

 10 Examining the biodegradation of HCHs in Canadian Arctic watersheds Helm et al. (2000, 155834) 

MARINE 

 11 Transport and entry of β-HCH into western Arctic Ocean via Pacific surface waters Li et al. (2002, 156691) 

 12 Occurrence of current use pesticides in air, fog and surface seawater in the western Arctic Ocean Chermyak et al. (1996, 155726) 

 13 Resolving petrogenic and anthropogenic PAH input to marine sediments in coastal Arctic seas Yunker et al. (1996, 156175) 

 14 Quantifying abiotic and biotic degradation of HCHs in the Arctic Ocean water column Harner et al. (2000, 155829) 

 15 PCBs and OCs in surface ocean water—Bering and Chukchi seas Strachan et al. (2001, 156103) 

 16 Spatial patterns of HCHs and toxaphene in Arctic Ocean surface water Jantunen and Bidleman (1998, 155877) 

SNOW/AIR-FRESHWATER 

 17 PAHs (and inorganics) in surface snow layers (snowpit) at Summit, Greenland Masclet et al. (2000, 155966) 

 18 PAHs measured in snow and ice layers on Agassiz ice-cap, Ellesmere Island, Canada Peters et al. (1995, 156856) 

 19 Modeling OC behaviour and fate in the surface seasonal snow pack at Amituk Lake, Canada Wania et al. (1998, 156148) 

 20 OCs, PCBs and PAHs in snow and ice of the Ob-Yenisey watershed of the Russian Arctic Melnikov et al. (2003, 156753) 

OCEAN/AIR 

 21 Transfer of α-HCH across the air/water interface in the western Arctic ocean Jantunen and Bidleman (1996, 155876) 

 22 Calculated seasonality of OC air/water fluxes in the Canadian high Arctic Hargrave et al. (1997, 155827) 

OCEAN/ICE 

 23 Transport potential of contaminants across the Arctic ocean via sea-ice drift Pfirman et al. (1997, 156864) 

 24 The importance of eastern Arctic sea-ice drift as a source of contaminants to the Norwegian sea Korsnes et al. (2002, 156657) 

Source: Reprinted with Permission of Elsevier Ltd. from Halsall (2004, 155822)

December 2009 9-161 
Figure 9-84. The transfer of POPs between the major abiotic compartments of the Arctic. 

Shaded arrows represent inputs/outputs of POPs to the Arctic. The numbers refer 

to selected studies detailing the occurrence and behavior of POPs, and are listed 

in Table 9-20. Question marks represent those areas that are least well 

understood. LRAT-long range atmospheric transport; LROT – long range oceanic 

transport. 

9.3.9.2. Radiative Forcing by Anthropogenic Surface Albedo Change: BC in Snow and 

Ice 

Section 9.3.9.2 comes directly from IPCC AR4 Chapter 2, Section 2.5.4, with section, table, 

and figure numbers changed to be internally consistent with this ISA. 

The presence of soot particles in snow could cause a decrease in the albedo of snow and 

affect snowmelt. Initial estimates by Hansen et al. (2000, 042683) suggested that BC could 

thereby exert a positive RF of +0.2 W/m2. This estimate was refined by Hansen and Nazarenko 

(2004, 156521), who used measured BC concentrations within snow and ice at a wide range of 

geographic locations to deduce the perturbation to the surface and planetary albedo, deriving 

an RF of +0.15 W/m2. The uncertainty in this estimate is substantial due to uncertainties in 

whether BC and snow particles are internally or externally mixed, in BC and snow particle 

shapes and sizes, in voids within BC particles, and in the BC imaginary refractive index. 

Jacobson (2004, 155870) developed a global model that allows the BC aerosol to enter snow 

via precipitation and dry deposition, thereby modifying the snow albedo and emissivity. They 

found modeled concentrations of BC within snow that were in reasonable agreement with 

those from many observations. The model study found that BC on snow and sea ice caused a 

decrease in the surface albedo of 0.4% globally and 1% in the NH, although RFs were not 

reported. Hansen et al. (2005, 059087) allowed the albedo change to be proportional to local 

BC deposition according to Koch (2001, 192054) and presented a further revised estimate of 

0.08 W/m2. They also suggested that this RF mechanism produces a greater temperature 

response by a factor of 1.7 than an equivalent CO2 RF, that is, the ‘efficacy’ may be higher for 

this RF mechanism (see Section 2.8.5.7 of the IPCC AR4). This report adopts a best estimate 

December 2009 9-162 
for the BC on snow RF of +0.10 ± 0.10 W/m2

, with a low level of scientific understanding 

(Section 2.9, Table 2.11, of the IPCC AR4). 

9.3.9.3. Effects on Local and Regional Climate 

Most effects of PM on climate, as assessed by IPCC (Stohl et al., 2007, 157015) and 

summarized in this assessment, focus on global-scale processes and responses. In addition, it is also 

possible that PM emissions contribute to local and regional climate changes. These might include 

short-term cycles in rainfall or temperature and rainfall suppression, especially near cities and for 

orographic precipitation. Rainfall suppression, in particular, is believed to exacerbate water supply 

problems which are substantial in many regions, especially in the western U.S. 

Aerosol particles, directly and through cloud enhancement, may reduce near-surface wind 

speeds locally. Slower winds, in turn, reduce evaporation. The overall impact can be a reduction in 

local precipitation. Jacobson and Kaufman (2006, 090942) investigated the effects of PM on 

spatially-distributed wind speeds and resulting feedbacks to precipitation using the 

GATOR-GCMOM (Jacobson, 2001, 155864) and supporting evidence from satellite data. The study 

focused on the South Coast Air Basin (SCAB) in California during February and August, 2002-2004. 

The modeled precipitation decrease over land in California was 2% of the baseline 1.5 mm/day due 

to emissions of anthropogenic aerosol particle and precursor gasses in the SCAB domain. However, 

the reduction over much of the Sierra Nevada, where most precipitation falls, was up to 0.5 mm/day, 

or 4-5% of the baseline 10-13 mm/day in that mountainous region (Jacobson, 2006, 156599). The 

probable mechanism was described as follows. Aerosol particles and aerosol-enhanced clouds reduce 

wind speeds below them by stabilizing the air, reducing the vertical transport of horizontal 

momentum. In turn, the reduced wind speeds, and associated reduced evaporation and increased 

cloud lifetime, contributes to reduced local and regional precipitation (Jacobson, 2006, 156599). 

Effects of air pollution on regional precipitation were quantified by Givati and Rosenfeld 

(2004, 156475). They found a 15-25% reduction in the orographic component of precipitation 

downwind of major coastal urban areas during the 20th century. Their study focused on 

orographically-forced clouds because these short-lived, shallow clouds are expected to exhibit the 

largest effect of air pollution on precipitation. Substantially larger precipitation suppression due to 

aerosol particulate pollution was found between Fresno and Sacramento in California by Givati and 

Rosenfeld (2004, 156475). Precipitation losses over topographical barriers in the Sierra Nevada 

amounted to 15-25% of the annual precipitation at elevations less than 2,000 m. This precipitation 

suppression occurred mainly in the relatively shallow orographic clouds within the cold air mass of 

cyclones. The suppression that occurred on the upslope side of the mountains was coupled with 

similar percentage (but lower absolute volume) enhancement on the drier downslope eastern side 

(Givati and Rosenfeld, 2004, 156475). Similar results were found in studies by Griffith et al. (2005, 

156497), Jirak and Cotton (2006, 156612), Rosenfeld and Givati (2006, 156924), and Rosenfeld et 

al. (2007, 156057). At all of these study locations (California, Israel, Utah, Colorado, China), 

orographic precipitation decreased by 15-30% downwind of pollution sources, likely due to creation 

of more and smaller cloud droplets and resulting suppression of precipitation. 

The study of Givati and Rosenfeld (2004, 156475) was the first to quantify the microphysical 

effect of mesoscale precipitation. Following the findings of Givati and Rosenfeld (2004, 156475), 

the effects of aerosol air pollution on precipitation at high elevation sites in the Front Range of 

Colorado adjacent to urban areas were investigated by Jirak and Cotton (2006, 156612). 

Examination of precipitation trends showed that the ratio of upslope precipitation during easterly 

flows at high elevation west of Denver and Colorado Springs to the upwind urban sites decreased by 

about 30% over the past half century. These results provide further support for the hypothesis that 

aerosol pollution suppresses orographic precipitation downwind of pollution source areas. 

Griffith et al. (2005, 156497) found similar reductions in mountainous precipitation in Utah, 

downwind of Salt Lake City and Provo. The ratio of precipitation at mountain stations located in 

rural settings in Utah and Nevada remained stable, supporting the hypothesis that air pollution 

decreases Ro (the ratio of precipitation at the downwind site to precipitation at the upwind pollution 

source) over the mountains to the east of Salt Lake City. 

Rosenfeld and Givati (2006, 156924) extended the investigation of the suppression of 

precipitation by aerosol pollutants to a larger scale by examining the ratio between precipitation 

amounts over the hills to precipitation over upwind lowland areas throughout the western U.S. from 

the Pacific Coast to the Rocky Mountains. They found in these paired analyses a pattern of 

December 2009 9-163 
decreasing precipitation by as much as 24% from the Mexican border to central California, with no 

decrease in northern California and Oregon and smaller decrease of 14% in Washington east of 

Seattle and Puget Sound. Similar decreases were found over Arizona and New Mexico (Rosenfeld, 

2006, 190233), Utah (Griffith et al., 2005, 156497), and the east slope of the Colorado Rockies 

(Jirak and Cotton, 2006, 156612). 

Suppression of winter orographic precipitation appears to occur up to hundreds of kilometers 

inland of coastal urban areas (Rosenfeld, 2006, 190233). Decreases in this precipitation ratio 

occurred during winter orographic precipitation, but not during convective summer precipitation 

over the same mountain ranges. This finding agrees with the expectation that aerosol-induced 

changes in the rate of precipitation formation would cause a decrease in precipitation from shallow 

and short-lived orographic clouds, but not necessarily from deeper and longer-lived thermally-driven 

convective clouds. 

Results of these studies of aerosol effects on orographic precipitation suggest that 

human-caused air pollution, and fine particles in particular, have had a large effect on precipitation 

well beyond the local scales of the pollution sources (Rosenfeld, 2006, 190233). 

9.3.10.Summary of Effects on Climate 

Aerosols affect climate through direct and indirect effects. The direct effect is primarily 

realized as planet brightening when seen from space because most aerosols scatter most of the 

visible spectrum light that reaches them. The IPCC AR4 reported that the radiative forcing from this 

direct effect was -0.5 (±0.4) W/m2

 and identified the level of scientific understanding of this effect as 

'Medium-low'. The global mean direct radiative forcing effect from individual components of 

aerosols was estimated for the first time in the IPCC AR4 where they were reported to be (all in 

W/m2

 units): -0.4 (±0.2) for sulfate, -0.05 (±0.05) for fossil fuel-derived organic carbon, +0.2 (±0.15) 

for fossil fuel-derived black carbon, +0.03 (±0.12) for biomass burning, -0.1 (±0.1) for nitrates, and 

-0.1 (±0.2) for mineral dust. Global loadings of anthropogenic dust and nitrates remain very 

troublesome to estimate, making the radiative forcing estimates for these constituents particularly 

uncertain. 

Numerical modeling of aerosol effects on climate has sustained remarkable progress since the 

time of the last PM AQCD, though model solutions still display large heterogeneity in their estimates 

of the direct radiative forcing effect from anthropogenic aerosols. The clear-sky direct radiative 

forcing over ocean due to anthropogenic aerosols is estimated from satellite instruments to be on the 

order of -1.1 (±0.37) W/m2

 while model estimates are -0.6 W/m2. The models' low bias over ocean is 

carried through for the global average: global average direct radiative forcing from anthropogenic 

aerosols is estimated from measurements to range from -0.9 to -1.9 W/m2, larger than the estimate of 

-0.8 W/m2

 from the models. 

Aerosol indirect effects on climate are primarily realized as an increase in cloud brightness 

(termed the 'first indirect' or Twomey effect), changes in precipitation, and possible changes in cloud 

lifetime. The IPCC AR4 reported that the radiative forcing from the Twomey effect was -0.7 (range: 

-1.1 to +4) and identified the level of scientific understanding of this effect as 'Low' in part owing to 

the very large unknowns concerning aerosol size distributions and important interactions with 

clouds. Other indirect effects from aerosols are not considered to be radiative forcing. 

December 2009 9-164 
Taken together, direct and indirect effects from aerosols increase Earth's shortwave albedo or 

reflectance thereby reducing the radiative flux reaching the surface from the Sun. This produces net 

climate cooling from aerosols. The current scientific consensus reported by IPCC AR4 is that the 

direct and indirect radiative forcing from anthropogenic aerosols computed at the top of the 

atmosphere, on a global average, is about -1.3 (range: -2.2 to -0.5) W/m2

. While the overall global 

average effect of aerosols at the top of the atmosphere and at the surface is negative, absorption and 

scattering by aerosols within the atmospheric column warms the atmosphere between the Earth's 

surface and top of the atmosphere. In part, this is owing to differences in the distribution of aerosol 

type and size within the vertical atmospheric column since aerosol type and size distributions 

strongly affect the aerosol scattering and reradiation efficiencies at different altitudes and 

atmospheric temperatures. And, although the magnitude of the overall negative radiative forcing at 

the top of the atmosphere appears large in comparison to the analogous IPCC AR4 estimate of 

positive radiative forcing from anthropogenic GHG of about +2.9 (± 0.3) W/m2

, the horizontal, 

vertical, and temporal distributions and the physical lifetimes of these two very different radiative 

forcing agents are not similar; therefore, the effects do not simply off-set one another. 

Overall, the evidence is sufficient to conclude that a causal relationship exists between 

PM and effects on climate, including both direct effects on radiative forcing and indirect 

effects that involve cloud feedbacks that influence precipitation formation and cloud 

lifetimes.

9.4. Ecological Effects of PM 

9.4.1. Introduction 

PM is heterogeneous with respect to chemical composition and size; therefore, it can cause a 

variety of ecological effects, which have been previously described by the U.S. EPA (2004, 056905) 

and by Grantz et al. (2003, 155805). Atmospheric PM has been defined, for regulatory purposes, 

mainly by size fractions and less clearly so in terms of chemical nature, structure, or source. Both 

fine and coarse-mode particles may affect plants and other organisms; however, PM size classes do 

not necessarily relate to ecological effects (U.S. EPA, 1996, 079380). More often the chemical 

constituents drive the ecosystem response to PM (Grantz et al., 2003, 155805). 

The previous PM assessment (U.S. EPA, 2004, 056905) included the acidifying effects of 

particulate N and S. The 2008 NOXSOX ISA (U.S. EPA, 2008, 157074) assessed the effects of 

particle- and gas-phase N and S pollution on acidification, N enrichment, and Hg methylation. 

Acidification of ecosystems is driven primarily by deposition resulting from SOX, NOX, and NHX

pollution. Acidification from the deposition resulting from current emission levels causes a cascade 

of effects that harm susceptible aquatic and terrestrial ecosystems, including slower growth and 

injury to forests and localized extinction of fishes and other aquatic species. In addition to 

acidification, atmospheric deposition of reactive N resulting from current NOX and NHX emissions 

along with other non-atmospheric sources (e.g., fertilizers and wastewater), causes a suite of 

ecological changes within sensitive ecosystems. These include increased primary productivity in 

most N-limited ecosystems, biodiversity losses, changes in C cycling, and eutrophication and 

harmful algal blooms in freshwater, estuarine, and ocean ecosystems. In some watersheds, additional 

SO4

2– from atmospheric deposition increases Hg methylation rates by increasing both the number 

and activity of S-reducing bacteria. Methylmercury is a powerful toxin that can bioaccumulate to 

toxic amounts in food webs at higher trophic levels. 

This assessment of PM effects on ecosystems considers both direct and indirect exposure 

pathways. Atmospheric PM may affect ecological receptors directly following deposition on surfaces 

or indirectly by changing the soil chemistry or by changing the amount of radiation reaching the 

Earth’s surface. Indirect effects acting through the soil are often thought to be most significant 

because they can alter nutrient cycling and inhibit nutrient uptake (U.S. EPA, 2004, 056905; 

U.S. EPA, 2008, 157074). The U.S. EPA (2004, 056905) reported that the effects of PM can be both 

chemical and physical. Physical effects of particle deposition on vegetation may include abrasion 

and radiative heating. However, chemical effects may be more significant (U.S. EPA, 2008, 157074). 

December 2009 9-165 
In general, anthropogenic stressors can result in damaged ecosystems that do not recover 

readily (Odum, 1993, 076742; Rapport and Whitford, 1999, 004595). Ecosystems sometimes lack 

the capacity to adapt to an anthropogenic stress and are unable to maintain their normal structure and 

functions unless the stressor is removed (Rapport and Whitford, 1999, 004595). These stresses result 

in a process of ecosystem degradation marked by a decrease in biodiversity, reduced primary and 

secondary production, and a lower capacity to recover and return to the original ecosystem state. In 

addition, there can be an increased prevalence of disease, reduced nutrient cycling, increased 

dominance of exotic species, and increased dominance by smaller, short-lived opportunistic species 

(Odum, 1985, 039482; Rapport and Whitford, 1999, 004595). 

Ecosystems are often subjected to multiple stressors, of which atmospheric PM deposition is 

only one. Additional stressors are also important, including O3 exposure, climatic variation, natural 

and human disturbance, the occurrence of invasive non-native plants, native and non-native insect 

pests, disease, acidification, and eutrophication among others. PM deposition interacts with these 

other stressors to affect ecosystem patterns and processes. 

The possible effects of particulate (and other) air pollutants on ecosystems have been 

categorized by Guderian (1977, 004150) as follows: 

 accumulation of pollutants in plants and other ecosystem components (such as soil and 

surface- and groundwater), 

 damage to consumers as a result of pollutant accumulation, 

 changes in species diversity because of shifts in competition, 

 disruption of biogeochemical cycles, 

 disruption of stability and reduction in the ability to self-regulate, 

 breakdown of stands and associations, and 

 expansion of denuded zones. 

The general conclusion of the last PM assessment (U.S. EPA, 2004, 056905) was that 

ecosystem response to PM can be difficult to determine because the changes are often subtle. For 

example, changes in the soil may not be observed until pollutant deposition has occurred for many 

decades, except in the most severely polluted areas around heavily industrialized point sources. The 

presence of co-occurring pollutants generally makes it difficult to attribute ecological effects to PM 

alone or to one constituent in the deposited PM. In other words, the potential for alteration of 

ecosystem function and structure exists but can be difficult to quantify except in cases of extreme 

amounts of deposition, especially when there are other pollutants present in the ambient air that may 

produce additive or synergistic responses. 

New information on the ecological effects of coarse and fine particle PM is presented in the 

following discussion in the context of effects that were known from the last PM AQCD (U.S. EPA, 

2004, 056905). The general effects of the chemical constituents of PM are discussed; however, a 

rigorous assessment of each chemical constituent (e.g., Hg, Cd, Pb, etc.) is not given. Both direct and 

indirect effects will be discussed and the strength of the scientific evidence will be evaluated using 

the causality framework. 

9.4.1.1. Ecosystem Scale, Function, and Structure 

Information presented in this section was collected at multiple scales, ranging from the 

physiology of a given species to population, community, and ecosystem-level investigations. For this 

assessment, “ecosystem” is defined as a functional entity consisting of interacting groups of living 

organisms and their abiotic (chemical and physical) environment. Ecosystems cover a hierarchy of 

spatial scales and can comprise the entire globe, biomes at the continental scale, or small, wellcircumscribed systems such as a small pond. 

Ecosystems have both structure and function. Structure may refer to a variety of measurements 

including the species richness, abundance, community composition and biodiversity as well as 

December 2009 9-166 
landscape attributes. Competition among and within species and their tolerance to environmental 

stresses are key elements of survivorship. When environmental conditions are shifted, for example, 

by the presence of anthropogenic air pollution, these competitive relationships may change and 

tolerance to stress may be exceeded. “Function” refers to the suite of processes and interactions 

among the ecosystem components and their environment that involve nutrient and energy flow as 

well as other attributes including water dynamics and the flux of trace gases. Plant processes 

including photosynthesis, nutrient uptake, respiration, and C allocation, are directly related to 

functions of energy flow and nutrient cycling. The energy accumulated and stored by vegetation (via 

photosynthetic C capture) is available to other organisms. Energy moves from one organism to 

another through food webs, until it is ultimately released as heat. Nutrients and water can be 

recycled. Air pollution alters the function of ecosystems when elemental cycles or the energy flow 

are altered. This alteration can also be manifested in changes in the biotic composition of 

ecosystems. 

There are at least three levels of ecosystem response to pollutant deposition: (1) the individual 

organism and its environment; (2) the population and its environment; and (3) the biological 

community composed of many species and their environment (Billings, 1978, 034165). Individual 

organisms within a population vary in their ability to withstand the stress of environmental change. 

The response of individual organisms within a population is based on their genetic constitution, stage 

of growth at time of exposure to stress, and the microhabitat in which they are growing (Levine and 

Pinto, 1998, 029599). The range within which organisms can exist and function determines the 

ability of the population to survive. Those able to cope with the stresses survive and reproduce. 

Competition among different species results in succession (community change over time) and, 

ultimately, produces ecosystems composed of populations of species that have the capability to 

tolerate the stresses (Guderian, 1985, 019325; Rapport and Whitford, 1999, 004595). Available 

information on individual, population and community response to PM will be discussed. 

9.4.1.2. Ecosystem Services 

Ecosystem structure and function may be translated into ecosystem services. Ecosystem 

services identify the varied and numerous ways that ecosystems are important to human welfare. 

Ecosystems provide many goods and services that are of vital importance for the functioning of the 

biosphere and provide the basis for the delivery of tangible benefits to human society. Hassan et al. 

(2005, 092759) define these to include supporting, provisioning, regulating, and cultural services: 

 Supporting services are necessary for the production of all other ecosystem services. 

Some examples include biomass production, production of atmospheric O2, soil 

formation and retention, nutrient cycling, water cycling, and provisioning of habitat. 

Biodiversity is a supporting service that is increasingly recognized to sustain many of the 

goods and services that humans enjoy from ecosystems. These provide a basis for three 

higher-level categories of services. 

 Provisioning services, such as products (Gitay et al., 2001, 092761), i.e., food (including 

game, roots, seeds, nuts and other fruit, spices, fodder), fiber (including wood, textiles), 

and medicinal and cosmetic products (including aromatic plants, pigments). 

 Regulating services that are of paramount importance for human society such as (a) C 

sequestration, (b) climate and water regulation, (c) protection from natural hazards such 

as floods, avalanches, or rock-fall, (d) water and air purification, and (e) disease and pest 

regulation. 

 Cultural services that satisfy human spiritual and aesthetic appreciation of ecosystems 

and their components. 

9.4.2. Deposition of PM 

Deposition of PM is discussed in Chapter 3.3.4. Additional material specifically related to 

ecosystems is discussed in this section. 

December 2009 9-167 
9.4.2.1. Forms of Deposition 

Research summarized by the previous NAAQS PM assessment illustrated the complexity of 

deposition processes. Airborne particles, their gas-phase precursors, and their transformation 

products are removed from the atmosphere by wet and dry deposition processes. These deposition 

processes transfer PM pollutants to other environmental media where they can alter the structure, 

function, diversity, and sustainability of complex ecosystems. Dry deposition of PM is most effective 

for coarse particles. These include primary geologic materials and elements such as iron and 

manganese. By contrast, wet deposition is more effective for fine particles of secondary atmospheric 

origin and elements such as cadmium, chromium, lead, nickel, and vanadium (Reisinger, 1990, 

046737; Smith, 1990, 084015; U.S. EPA, 2004, 056905). The relative magnitudes of the different 

deposition modes vary with ecosystem type, location, elevation, and chemical burden of the 

atmosphere (U.S. EPA, 2004, 056905). There are differences in the deposition behavior of fine and 

coarse particles. Coarse particles generally settle nearer their site of formation than do fine particles. 

In addition, the chemical constitution of individual particles is correlated with size. For example, 

much of the base cation and heavy metal burden is present on coarse particles. 

Fine PM is often a secondary pollutant that forms within the atmosphere, rather than being 

directly emitted from a pollution source. It derives from atmospheric gas-to-particle conversion 

reactions involving nucleation, condensation, and coagulation, and from evaporation of water from 

contaminated fog and cloud droplets. Fine PM may also contain condensates of VOCs, volatilized 

metals, and products of incomplete combustion, including polycyclic aromatic hydrocarbons (PAH) 

and BC (soot) (U.S. EPA, 2004, 056905). 

Fine PM may act as a carrier for materials such as herbicides that are phytotoxic. Fine PM 

provides much of the surface area of particles suspended in the atmosphere, whereas coarse PM 

provides much of the mass of airborne particles. Surface area can influence ecological effects 

associated with the oxidizing capacity of fine particles, their interactions with other pollutants, and 

their adsorption of organic compounds. Fine and coarse particles also respond to changes in 

atmospheric humidity, precipitation, and wind, and these can alter their deposition characteristics. 

Coarse PM is mainly a primary pollutant, having been emitted from pollution sources as fully 

formed particles derived from abrasion and crushing processes, soil disturbances, desiccation of 

marine aerosol emitted from bursting bubbles, hygroscopic fine PM expanding with humidity to 

coarse mode, and/or gas condensation directly onto preexisting coarse particles. Suspended primary 

coarse PM may contain iron, silica, aluminum, and base cations from soil, plant and insect 

fragments, pollen, fungal spores, bacteria, and viruses, as well as fly ash, brake lining particles, and 

automobile tire fragments. Coarse mode particles can be altered by chemical reactions and/or 

physical interactions with gaseous or liquid contaminants. 

Exposure to a given mass concentration of PM may lead to widely differing phytotoxic and 

other environmental outcomes depending upon the particular mix of PM constituents involved. 

Especially important in this regard are S and N components of PM, which are addressed in the 2008 

NOXSOX ISA, and effects of particulate heavy metals and organic contaminants. This variability has 

not been characterized adequately. Though effects of specific chemical fractions of PM have been 

described to some extent, there has been relatively little research aimed at defining the effects of 

unspeciated PM on plants or ecosystems. 

9.4.2.2. Components of PM Deposition 

Trace Metals 

Atmospheric deposition can be the primary source of some metals to some watersheds. Metal 

inputs can include the primary crustal elements (Al, Ca, K, Fe, Mg, Si, Ti) and the primary 

anthropogenic elements (Cu, Zn, Cd, Cr, Mn, Pb, V, Hg). The crustal elements are derived largely 

from weathering and erosion, whereas the anthropogenic elements are derived from combustion, 

industrial sources, and other man-made sources (Goforth and Christoforou, 2006, 088353). 

Heavy metal deposition to ecosystems depends on their location as well as upwind emissions 

source strength. The deposition velocity tends to be dependent on particle size and chemical species. 

Larger particles deposit more efficiently than smaller particles. Heavy metals preferentially associate 

December 2009 9-168 
with fine particles. Fine particles also have the longest atmospheric residence times. Depending on 

climate and topography, fine particles may remain airborne for days to months and may be 

transported thousands of kilometers from their source. 

Ecosystems immediately downwind of major heavy metal emissions sources may receive 

locally heavy dry deposition. Trace element investigations conducted in roadside, industrial, and 

urban environments have also shown that substantial amounts of particulate heavy metals can 

accumulate on surfaces. 

A significant trace metal component of PM is mercury (Hg). Hg is toxic and can move readily 

through environmental compartments. Atmospheric and depositional inputs of Hg include both 

natural and anthropogenic sources. Natural geologic contributions to Hg in the environment include 

geothermal and volcanic activity, geologic metal deposits, and organic-rich sedimentary rocks. These 

natural emissions combine with anthropogenic emissions from such sources as power plants, 

landfills, sewage sludge, mine waste, and incineration (Gustin, 2003, 155816; Schroeder and 

Munthe, 1998, 014559). Emissions from natural sources are controlled by geologic features, 

including substrate Hg content, rock type, the degree of hydrothermal activity, and the presence of 

heat sources (Gustin, 2003, 155816). The significance of natural Hg sources relative to 

anthropogenic sources varies geographically. For example, Nevada occurs within a global 

mercuriferous belt, with area emissions about three times higher than the value assumed for global 

modeling (Gustin, 2003, 155816). In Nevada, natural and anthropogenic Hg emissions are 

approximately equal (Gustin, 2003, 155816). 

 The U.S. EPA (1997, 157066) compiled an assessment of the sources and environmental 

effects of Hg in the U.S. A variety of factors were found to influence Hg deposition, fate and 

transport (Table 9-21). Such factors relate in particular to speciation of the Hg that is emitted, the 

forms in which it is deposited from the atmosphere, and transformations that occur within the 

atmosphere and within the aquatic, transitional, and terrestrial compartments of the receiving 

watershed. There have been studies that have reconstructed, from lake sediment records, the 

atmospheric depositional history of trace metals and PAHs in lakes adjacent to coal-fired power 

plants. For example, Donahue et al. (2006, 155751) analyzed sediment from Wababun Lake, which 

is located in Alberta, Canada in proximity (within 35 km) to 4 power plants built since 1950. Trace 

metal concentrations of Hg, Cu, Pb, As, and Se in lake sediment increased by 1.2- to 4-fold. The 

total PAH flux to surface sediments was 730-1,100 μg/m2

/yr, which was two to five times higher 

than in 2 lakes situated 20 km to the north and 70 km to the south. Further discussion of Hg effects 

on ecosystems can be found in Section 9.4.5. 

December 2009 9-169 
Table 9-21. Factors potentially important in estimating Hg exposure. 

Factor Importance and Possible Effect on Mercury Exposure 

Type of anthropogenic source of mercury 

Different combustion and industrial process sources are anticipated to have different local scale impacts due to 

physical source characteristics (e.g., stack height), the method of waste generation (e.g., incineration or mass 

burn) or mercury control devices and their effectiveness. 

Mercury emission rates from stack Increased emissions will result in a greater chance of adverse impacts on environment. 

Mercury species emitted from stack More soluble species will tend to deposit closer to the source. 

Form of mercury emitted from stack Transport properties can be highly dependent on form. 

Deposition differences between vapor and 

particulate-bound mercury Vapor-phase forms may deposit significantly faster than particulate-bound forms. 

Transformations of mercury after emission from 

source Relatively nontoxic forms emitted from source may be transformed into more toxic compounds. 

Transformation of mercury in watershed soil Reduction and revolatilization of mercury in soil limits the buildup of concentration. 

Transport of mercury from watershed soils to 

water body Mercury in watershed soils can be a significant source to water bodies and subsequently to fish. 

Transformation of mercury in water body Reduction, methylation, and demethylation of mercury in water bodies affect the overall concentration and the 

MHg fraction, which is bioaccumulated in fish. 

Facility locations Effects of meteorology and terrain may be significant. 

Location relative to local mercury source Receptors located downwind are more likely to have higher exposures. Influence of distance depends on source 

type. 

Contribution from non-local sources of mercury Important to keep predicted impacts of local sources in perspective. 

Uncertainty Reduces confidence in ability to estimate exposure accurately. 

Source: Modified from U.S. EPA (1997, 157066)

Organics 

Organic compounds that may be associated with deposited PM include persistent organic 

pollutants (POPs), pesticides, SOCs, polyaromatic hydrocarbons (PAHs) and flame retardants among 

others. Organic compounds partition between gas and particle phases, and organic particulate 

deposition depends largely on the particle sizes available for adsorption (U.S. EPA, 2004, 056905). 

Dry deposition of organic materials is often dominated by the coarse fraction. Gas-particle phase 

interconversions are important in determining the amount of dry deposition. 

Most persistent organic pollutants (POPs) enter the biosphere via human activities, including 

synthetic pesticide application, output of polychlorinated dibenzo dioxins (PCDD) from incinerators, 

and accidental release of PCBs from transformers (Lee, 2006, 088968). Once they are introduced 

into the environment, their accumulation and magnification in biological systems are determined by 

physiochemical properties and environmental conditions (Section 9.4.6). Uptake by plants can occur 

at the soil/plant interface and at the air/plant interface (Krupa et al., 2008, 198696). For lipophilic 

POPs, such as PCDDs and PCBs, the air/plant response route generally dominates (Lee, 2006, 

088968; Thomas et al., 1998, 156118), but uptake through above-ground plant tissue also occurs. In 

a study of zucchini (Cucurbita pepo), Lee et al. (2006, 088968) found chlordane pesticide 

components in all vegetation tissues examined: root, stem, leaves, fruits. 

Many pesticides and SOCs are carcinogenic or estrogenic and pose potential threats to aquatic 

and terrestrial biota. Although deposition of SOCs was previously reported for the Sierra Nevada 

Mountains in California and the Rocky Mountains in Colorado, little was previously known about 

the occurrence, distribution, or sources of SOCs in alpine, sub-Arctic, and Arctic ecosystems in the 

western U.S. The snowpack is efficient at scavenging of both particulate and gas phase pesticides 

from the atmosphere (Halsall, 2004, 155822; Lei and Wania, 2004, 127880). Analysis of pesticides 

in snowpack samples from seven NPs in the western U.S. by Hageman et al. (2006, 156509) 

illustrated the deposition and fate of 47 pesticides and their degradation products. Correlation 

analysis with latitude, temperature, elevation, PM, and two indicators of regional pesticide use 

suggested that regional patterns in historic and current agricultural practices are largely responsible 

December 2009 9-170 
for the distribution of pesticides in the NPs. Pesticide deposition to parks in Alaska was attributed to 

long-range atmospheric transport. 

PAHs include hundreds of different compounds that are characterized by possessing two or 

more fused benzene rings. They are widespread contaminants in the environment, and are formed by 

incomplete combustion of fossil fuels and other organic materials. Eight PAHs are considered 

carcinogenic and 16 are classified by EPA as priority pollutants. They are common air pollutants in 

metropolitan areas, derived from vehicular traffic and other urban sources. Especially high 

concentrations have been found near Söderberg aluminum production industries and areas where 

heating during winter via wood burning is common. Other sources, in addition to gasoline and diesel 

engines, include forest fires and various forms of fossil fuel combustion (Sanderson and Farant, 

2004, 156942). 

The behavior of PAHs is strongly determined by their chemical characteristics, especially their 

nonpolarity and hydrophobicity. They readily adsorb to particulates in the air and to sediments in 

water. Srogi (2007, 180049) provided a thorough review of PAH concentrations in various 

environmental compartments and their use for assessing environmental risks and possible effects on 

ecosystems and human health. 

Deposition and fate of PAH has been an important area of research. Because they are 

carcinogenic, PAHs are important environmental contaminants. Root-soil behavior of PAHs is an 

area of active study. Soil-bound PAHs are associated with soil organic matter and are therefore 

generally not easily available for root uptake. PAHs are readily adsorbed to root surfaces but there 

seems to be little movement to the interior of the root or movement up to the shoots (Gao and Zhu, 

2004, 155782). Paddy rice is the main food crop planted in China. As an aquatic plant having aerial 

roots, the movement of PAHs into rice roots may be different than their movement into more widely 

studied land-grown food crops. PAH concentrations in the rice roots were more correlated with the 

water and air compartments than with the soil (Jiao et al., 2007, 155879). 

The total PAH concentration in grasses adjacent to a highway have been measured to be about 

eight times higher than in grasses from reference sites not close to a highway (Crépineau et al., 2003, 

155741). Howe et al. (2004, 155854) found that concentrations of PAHs and hexachlorobenzene 

(HCB) in spruce (Picea spp.) needles at 36 sites in eastern Alaska varied by an order of magnitude. 

Samples collected near the city of Fairbanks generally had higher concentrations than samples 

collected from rural areas. The relative importance of combustion sources versus petrogenic sources 

was highest in the near-coastal areas, as reflected in variation in the concentration of ratios of 

isomeric PAHs. 

Use of flame retardants has increased in recent years in response to fire product safety 

regulations. However, some flame retardant chemicals are toxic and are readily transported 

atmospherically. Use of some has been banned in Europe and some of the United States because of 

their persistence and tendency to bioaccumulate (Hoh et al., 2006, 190378). 

Base Cations 

With respect to ecosystem effects from PM deposition, the inclusion of base cations 

(especially Ca, Mg, and K) in atmospheric deposition is generally considered to be a positive effect. 

Base cations are important plant nutrients that are in some locations present in short supply and that 

are further depleted by the acidic components of deposition. Increased base cation deposition can 

help to ameliorate adverse effects of acidification of soils and surface waters and reduce the toxicity 

of inorganic Al to plant roots and aquatic biota. These topics are covered in detail in the recent 2008 

NOXSOX ISA (U.S. EPA, 2008, 157074). 

Although the effects of base cation deposition inputs to terrestrial ecosystems are most 

commonly considered to be positive, under very high base cation deposition, plant health can be 

adversely affected. Dust that is high in base cations can settle on leaves and other plant structures 

and remain for extended periods of time. This is especially likely in arid environments because 

rainfall can serve to wash dry deposited materials off the foliage. Extended dust coverage can result 

in a variety of adverse impacts on plant physiology (Grantz et al., 2003, 155805). For example, van 

Heerden et al. (2007, 156131) documented decreased chlorophyll content, inhibition of CO2

assimilation, and uncoupling of the oxygen-evolving complex in desert shrubs exposed to high 

limestone dust deposition near a limestone quarry in Namibia. 

December 2009 9-171 
Based on the Integrated Forest Study (IFS) data, the U.S. EPA (2004, 056905) concluded that 

particulate deposition has a greater effect on base cation inputs to soils than on base cation losses 

associated with the inputs of sulfur, nitrogen, and H+

. These atmospheric inputs of base cations have 

considerable significance, not only to the base cation status of these ecosystems, but also to the 

potential of incoming precipitation to acidify or alkalize the soils in these ecosystems. This topic is 

discussed in detail in the recent NOXSOX ISA (U.S. EPA, 2008, 157074). 

9.4.2.3. Magnitude of Dry Deposition 

Using Vegetation for Estimating Atmospheric Deposition 

Whereas direct real-time measurement of deposition or air concentrations of atmospheric 

contaminants is desirable, it is not always practical (Howe et al., 2004, 155854). Instead, passive 

time-integrative methods are frequently used. These can involve analysis of vegetative tissues as a 

record of pollutant exposure, or analysis of lake sediment cores or ice cores to determine changes in 

pollutant input over time. There is a general assumption that the concentration of an analyte in 

vegetation reflects the time-integrated concentration of that analyte in the air. The development of 

deposition layers in sediment or ice cores allows the possibility of determining the effects of changes 

in the atmospheric concentration over periods of years, decades, or longer. 

Biomonitoring methods are important in air pollution assessment and provide a complement 

for more typical instrumental analyses. It is well known that mosses can accumulate large amounts 

of heavy metals in response to atmospheric deposition. Mosses accumulate dissolved materials and 

PM deposited from the atmosphere and have been used extensively in Europe as surrogate collectors 

for estimating bulk (wet plus dry) deposition of metals. The ease and low cost of this method has 

enabled regional assessments to be conducted throughout Europe. 

Despite its wide use, however, several papers have pointed out complications in the use of 

mosses to quantify metal deposition rates. Zechmeister (1998, 156178) found that the uptake 

efficiency for 12 heavy metals in three species of moss was similar, but that uptake efficiency in a 

fourth species was uncorrelated with the other species for about half the metals considered. 

Zechmeister (1998, 156178) also showed that productivity of an individual species can vary greatly 

among sites. To calculate atmospheric deposition of metals from accumulation in mosses, both the 

metal concentration and the rate of biomass production is needed. Further complication was shown 

in the study of Shakya et al. (2008, 156081), which revealed that accumulation of Cu, Zn and Pb 

decreased chlorophyll content. Sites with greater deposition amounts may therefore have lower rates 

of productivity than cleaner sites. 

Differences in uptake efficiencies among species and productivity among sites has led to the 

use of a single moss species placed in mesh bags that can be distributed to areas where that species 

of moss does not grow naturally. Studies to standardize this passive deposition monitoring approach 

have been limited. Adamo et al. (2007, 155644) evaluated the effects of washing with water, oven 

drying, and acid washing as preteatments and found little difference in uptake efficiencies, although 

the ratio of the collecting surface area to mass was found to be a key factor in uptake efficiency. 

Couto et al. (2004, 155739) investigated dry versus bulk deposition of metals using 

transplanted moss bags. This study showed that at some sites dry deposition exceeded bulk 

deposition, a likely outcome of wash-off of dry deposited particles. This study also documented 

intercationic displacement and leaching as a result of acidic precipitation. The authors concluded that 

the accumulated metal concentration represented an unstable equilibrium between inputs and outputs 

of elements that were a function of the local environment and weather during the exposure period. 

They also concluded that it was not possible to extrapolate calibrations between metal accumulation 

in moss and atmospheric deposition of metals to areas with different weather conditions, 

precipitation pH, and air contaminant concentrations. Zechmeister et al. (2003, 157175) also 

presented results demonstrating the problems with dry deposited particles that can be washed off by 

rain. These studies indicate that moss is not a completely effective collector of total particle 

deposition. Deposition estimates from moss accumulation probably represent values that fall 

between wet deposition and total deposition. 

A European moss biomonitoring network has been in place since 1990 (Harmens et al., 2007, 

155828). Sampling surveys are repeated every five years. The survey conducted in 2005/2006 

December 2009 9-172 
occurred in 32 countries at over 7,000 sites. The network reports metal concentrations associated 

with live moss tissue. Trends analysis of these data showed statistically significant decreases over 

time in moss concentrations for As, Cu, V, and Zn. Trends were not observed for Cr, Fe, or Ni. 

Results for individual countries participating in the survey have also been published. In Hungary, 

major pollution sources were readily detected by moss sampling (Otvos et al., 2003, 156831). 

Somewhat higher metal concentrations in mosses in 1997 than in other European countries were 

attributed to the use of a different moss species in the Hungarian survey (Otvos et al., 2003, 156831). 

Similar sampling in Romania showed regions with contamination that were among the highest in 

Europe. These results were consistent with known air quality problems in Romania (Lucaciu et al., 

2004, 155947). Because particulate deposition is not well characterized using this method, spatial 

patterns and temporal trends for particulate metal deposition in Europe only provide crude estimates 

of relative deposition patterns. 

The use of moss to assess heavy metal deposition has received much less attention in the U.S. 

than in Europe. A study conducted in the Blue Ridge Mountains, VA, found that metal concentrations 

in moss were related to elevation and canopy species at some sites (Schilling and Lehman, 2002, 

113075). However, metal concentrations in moss were not related to concentrations in the O horizon 

of the soil. Other measurement methods for trace metal deposition were not available to compare 

with moss concentrations. 

Epiphytic lichens have also been used to evaluate heavy metal accumulation. Helena et al. 

(2004, 155833) found substantially increased concentrations of metals in lichens transplanted from a 

relatively clean region to an area in proximity to a metal smelter. The presence of specific species of 

bryophyte or lichen can serve as an effective bioindicator of metal contamination (Cuny et al., 2004, 

155742). In some studies, tree bark has been used as a biomonitor for atmospheric deposition of 

heavy metals (Baptista et al., 2008, 155673; Pacheco and Freitas, 2004, 156011; Rusu et al., 2006, 

156062). 

Biomonitoring using mosses, lichens, or other types of vegetation has been established as a 

means of identifying spatial patterns in atmospheric deposition of heavy metals in relation to power 

plants, industry, and other point and regional emissions sources. More recently, a number of studies 

(López et al., 2002, 155943; 2003, 155944; 2003, 155945) have used cattle that have been reared 

predominantly on local forage as a means of monitoring atmospheric inputs of Cu, Ar, Zn, and Hg. 

For example, Hg emissions from coal fired power plants in Spain had a substantial effect on Hg 

accumulation by calves (López et al., 2003, 155944). Accumulation of Hg by cattle extended to 

~140-200 km downwind from the source. 

Yang and Zhu (2007, 156168) investigated the effectiveness of pine needles as passive air 

samplers for SOCs, such as PAHs, that are partially or completely particle-associated in the 

atmosphere. PAH distribution patterns are complicated by their properties, which span a broad range 

of octanol-air partition coefficients. This allows them to be present in both vapor and particle phases. 

In addition, the air-plant partitioning of PAHs is affected by air temperature and atmospheric stability 

(Krupa et al., 2008, 198696; Yang and Chen, 2007, 092847). DeNicola et al. (2005, 155747) 

documented the suitability of a Mediterranean evergreen oak (Quercus ilex) to serve as a passive 

biomonitor for atmospheric contamination with PAH in Italy. 

Deposition to Canopies 

Tree canopies have been shown to increase dry deposition from the atmosphere, including 

deposition of PM. Dry deposition rates in the canopy are commonly estimated by the difference 

between throughfall deposition and deposition measured by an open collector, although the use of 

this approach to specifically quantify particulate deposition is complicated by gaseous deposition to 

leaf surfaces and, for some elements, leaching and uptake. Avila and Rodrigo (2004, 155664) found 

that trace metal deposition in throughfall in a Spanish oak forest were higher than bulk deposition for 

Cu, Pb, Mn, V, and Ni, but not for Cd and Zn. This study also found that dry deposition of Cu, Pb, 

Zn, Cd and V occurred, but that canopy uptake of Zn and Cd also occurred. Leaching of Mn and Ni 

from the foliage was observed as well. Leaching of Ni, Cu, Mn, Rb, and Sr from a red spruce-balsam 

fir canopy by acidic cloud water was also measured in a study by Lawson et al. (2003, 089371). 

These studies suggest that leaching of trace metals from forest canopies varies with tree species and 

the acidity of precipitation. Throughfall therefore cannot be assumed to represent total deposition of 

heavy metals without evaluating uptake and leaching at the specific study site. Physical models have 

December 2009 9-173 
provided an alternative to estimating dry deposition to canopies with throughfall measurements. 

Recently, Pryor and Binkowski (2004, 116805) identified an additional complication in that models 

typically hold particle size constant. Nevertheless, there may be significant modification of particle 

size distributions during the deposition process. 

The use of pine and oak canopies as bioindicators of atmospheric trace metal pollution was 

investigated by Aboal et al. (2004, 155642). As an ecosystem pool, metals in leaves were likely to be 

much more important than those in mosses. The authors concluded, however, that these tree species 

were not effective bioindicators of atmospheric deposition of heavy metals. Metal concentrations in 

leaves were found to be one to three orders of magnitude lower than in mosses collected in this 

study. 

The effectiveness of tree canopies in capturing particulates was investigated as a method for 

improving air quality by Freer-Smith et al. (2004, 156451). This study showed that with 

consideration of planting design, location of pollution source, and tree species, planting of trees can 

be effective at reducing particulate air pollution. However, this approach does not address the 

possible effects of the captured pollution on trees, soils and surface waters. 

High-elevation forests generally receive larger particulate deposition loadings than equivalent 

low elevation sites. Higher wind speeds at high elevation enhance the rate of aerosol impaction. 

Orographic effects enhance rainfall intensity and composition and increase the duration of occult 

deposition. High-elevation forests are often dominated by coniferous species with needle-shaped 

leaves that enhance impaction and retention of PM delivered by all three deposition modes. 

Deposition to Soil 

As with mosses, accumulation of heavy metals in surface soils provides a general reflection of 

the spatial distribution of industrial pollution. The distribution of toxic elements in urban soils has 

been an important area of study (Madrid et al., 2002, 155956; Markiewicz et al., 2005, 155963). 

Generally, Cu, Pb, Zn, and Ni have accumulated in urban soils compared with their rural 

counterparts (Yuangen et al., 2006, 156174). In the study of Romic and Romic (2003, 156055), 

relationships were found between urban activities and concentrations of metals in soils in developed 

areas surrounding Zagreb, Croatia. Goodarzi et al. (2002, 155801) compared deposition estimated by 

moss bags to concentrations of metals in A-horizon soils in the vicinity of a large smelter. 

Statistically significant correlations were observed between the moss bag deposition estimates and 

the soil metal concentrations for Cd, Pb, Zn, and in some cases also Cu. These correlations suggested 

that atmospheric deposition of metals caused elevated metal concentrations in the upper mineral 

horizon of these soils. No correlations were found for Hg or As in this study. 

Studies have also been conducted to assess metal accumulation in peat because of the tendency 

of most metals to be immobilized through binding with organic matter. Steinnes et al. (2005, 

156095) presented geographical patterns of metal concentrations in surface peat throughout Norway 

that corresponded to pollution sources, although the peat samples were collected in 1979. Zaccone et 

al. (2007, 179930) found that variations of metal concentrations with depth in a single Swiss peat 

core corresponded with the depositional history that would be expected from the industrial 

revolution, although Cs137 activity exhibited a distribution in the profile that was not fully consistent 

with the Chernobyl nuclear reactor accident. A detailed study of Finnish peat showed that 

relationships between depth profiles of metal concentrations and deposition history can match well 

for some metals at some sites, but not well for the same metals at other sites (Roberts et al., 2003, 

156051). They also found that Zn and Cd accumulation rates were independent of deposition history 

at each of three study sites. 

Metal deposition to soil is also a significant concern adjacent to roadways. Urban stormwater 

can be rich in heavy metals and other contaminants derived from atmospheric deposition, and can be 

a major source of pollutant inputs to water bodies in urban settings. Urban stormwater runoff can 

also be toxic to aquatic biota, partly due to trace metal concentrations (Greenstein et al., 2004, 

155808; Sabin et al., 2005, 088300; Schiff et al., 2002, 156959). These processes are largely a 

function of the impervious nature of much of the ground surface in urban areas (i.e., buildings, roads, 

sidewalks, parking lots, construction sites). Dry-deposited pollutants can build up, especially in arid 

and semi-arid environments, and then be washed into surface waters with the first precipitation 

event. The concentrations of Cd, Ca, Cu, Pb, and Zn in road runoff were found to be significantly 

higher during winter in Sweden. This seasonal pattern was attributed to the intense wearing of the 

December 2009 9-174 
pavement that occurred during winter due to the use of studded tires in combination with chemical 

effects of deicing salts (Bäckström et al., 2003, 156242). 

9.4.3. Direct Effects of PM on Vegetation 

Exposure to airborne PM can lead to a range of phytotoxic responses, depending on the 

particular mix of deposited particles. This was well known at the time of the previous PM criteria 

assessment, as summarized below. Most direct phytotoxic effects occur in severely polluted areas 

surrounding industrial point sources, such as limestone quarries and other mining activities, cement 

kilns, and metal smelting facilities (U.S. EPA, 2004, 056905). Experimental application of PM 

constituents to foliage typically elicits little response at the more common ambient concentrations. 

The diverse chemistry and size characteristics of ambient PM and the lack of clear distinction 

between effects attributed to phytotoxic particles and to other air pollutants further confound the 

understanding of the direct effects on foliar surfaces. 

Deposition of PM can cause the accumulation of heavy metals on vegetative surfaces. Low 

solubility limits foliar uptake and direct heavy metal toxicity because trace metals must be brought 

into solution before they can enter into the leaves or bark of vascular plants. In those instances when 

trace metals are absorbed, they are frequently bound in leaf tissue and are lost when the leaf drops 

off (Hughes, 1981, 053595). 

Depending on the size of the particles, the PM deposited on the leaf surface can affect the 

plant’s metabolism and photosynthesis by blocking light, obstructing stomatal apertures, increasing 

leaf temperature and altering pigment and mineral content (Naidoo and Chirkoot, 2004, 190449) 

(Section 9.4.3.1.). Fine PM has been shown to enter the leaf through the stomata and penetrate into 

the mesophyll layers where it alters leaf chemistry (Da et al., 2006, 190190). Kuki et al. (2008, 

155346) also showed increased leaf permeability and increased activity of enzymes in response to 

fine PM lead (Section 9.4.5.). 

Studies of the direct toxic effects of particles on vegetation have not yet advanced to the stage 

of reproducible exposure experiments. In general, phytotoxic gases are deposited more readily, 

assimilated more rapidly, and lead to greater direct injury of vegetation as compared with most 

common particulate materials. The dose-response functions obtained in early experiments following 

the exposure of plants to phytotoxic gases generally have not been observed following the 

application of particles (U.S. EPA, 2004, 056905). 

9.4.3.1. Effects of Coarse-mode Particles 

The current state of scientific knowledge regarding the direct effects of coarse PM on plants 

has not changed since publication of the previous PM criteria assessment (U.S. EPA, 2004, 056905). 

The summary provided here is taken from that report. In many rural areas and some urban areas, the 

majority of the mass in the coarse particle mode derives from the elements Si, Al, Ca, and Fe, 

suggesting a crustal origin as fugitive dust from disturbed land, roadways, agriculture tillage, or 

construction activities. Large particles tend to deposit near their source (Grantz et al., 2003, 155805) 

and rapid sedimentation of coarse particles tends to restrict their direct effects on vegetation largely 

to roadsides and forest edges, which often receive the greatest deposition (U.S. EPA, 2004, 056905) 

Dust 

Dust can cause both physical and chemical effects. Consequences are often mediated via 

impacts on leaf cuticles and waxes. Deposition of inert PM on above-ground plant organs sufficient 

to coat them with a layer of dust may result in changes in radiation received, a rise in leaf 

temperature, and the blockage of stomata. Crust formation can reduce photosynthesis and the 

formation of carbohydrates needed for normal growth, induce premature leaf-fall, damage leaf 

tissues, inhibit growth of new tissue, and reduce starch storage. Dust may decrease photosynthesis, 

respiration, and transpiration; and it may result in the condensation and reactivity of gaseous 

pollutants with PM, thereby causing visible injury symptoms and decreased productivity (U.S. EPA, 

2004, 056905). Leaves with trichomes may be more prone to the accumulation of dust on leaf 

surfaces (Kuki et al., 2008, 155346). 

December 2009 9-175 
The chemical composition of PM is usually the key phytotoxic factor leading to plant injury. 

For example, cement-kiln dust liberates calcium hydroxide on hydration. It can then penetrate the 

epidermis and enter the mesophyll, causing an increase in leaf surface pH. In turn, surface pH can be 

important for surface microbial colonization and wax formation and degradation. 

Salt 

Sea-salt particles can serve as nuclei for the absorption and subsequent reaction of other 

gaseous and particulate air pollutants. Direct effects on vegetation reflect these inputs and salt injury 

caused by the sodium and chloride that constitute the bulk of these particles. The source of most salt 

spray near the coast is aerosolized ocean water. Sea salt can cause damage to plants; however, it is 

not covered in this assessment because it is not of anthropogenic origin. However particulate salt 

may be input to an ecosystem from deicing salt. 

Injury to vegetation from the application of deicing salt is caused by salt spray blown or 

drifting from the highways (Viskari and Karenlampi, 2000, 019101). The most severe injury is often 

observed nearest the highway. Conifers planted near roadway margins in the eastern U.S. often 

exhibit foliar injury due to toxic amounts of saline aerosols deposited from deicing solutions 

(U.S. EPA, 2004, 056905). 

9.4.4. PM and Altered Radiative Flux 

The effects of PM on radiative flux and the subsequent effects on vegetation have been 

described in Section 4.2.3.2 of the previous PM assessment (U.S. EPA, 2004, 056905); a brief 

overview is presented below. Atmospheric PM can affect ambient radiation, which can be considered 

in both its direct and diffuse components. Foliar interception by canopy elements occurs for both upand down-welling radiation. Therefore, the effect of atmospheric PM on atmospheric turbidity 

influences canopy processes both by radiation attenuation and by changing the efficiency of 

radiation interception in the canopy through conversion of direct to diffuse radiation (Hoyt, 1978, 

046638). Diffuse radiation is more uniformly distributed throughout the canopy and increases 

canopy photosynthetic productivity by distributing radiation to lower leaves. The enrichment in 

photosynthetically active radiation (PAR) present in diffuse radiation may offset a portion of the 

effect of an increased atmospheric albedo due to atmospheric particles. Mercado et al. (2009, 

190444) estimated the effects of variations in diffuse light on the terrestrial carbon sink during the 

last century using a global model. The results indicated that the terrestrial carbon sink increased by 

approximately 25% during the “global dimming”’ period (1950-1980), likely driven by increased 

diffuse light despite decreased PAR. However, under a future scenario in which SO4

2– and BC 

aerosols decline, the diffuse-radiation and the associated terrestrial C sink also decline (Mercado et 

al., 2009, 190444). 

The effects of regional haze on the yield of crops because of reduction in solar radiation were 

examined by Chameides et al. (1999, 011184) in China, where regional haze is especially severe. 

Based on model results, it was estimated that approximately 70% of crops were being depressed by 

at least 5-30% by regional scale air pollution and its associated haze (Chameides et al., 1999, 

011184; U.S. EPA, 2004, 056905). 

9.4.5. Effects of Trace Metals on Ecosystems 

Trace metals may enter the ecosystems as both fine and coarse particles. All but 10 of the 90 

elements that comprise the inorganic fraction of the soil occur at concentrations of <0.1% 

(1,000 μg/g) and are termed “trace” elements or trace metals. Trace metals with a density greater 

than 6 g/cm3

, referred to as “heavy metals” (e.g., Cd, Cu, Pb, Cr, Hg, Ni, Zn), are of particular 

interest because of their potential toxicity to plants and animals. Although some trace metals are 

essential for vegetative growth or animal health, they are all toxic in large quantities. Most trace 

elements exist in the atmosphere in particulate form as metal oxides (Ormrod, 1984, 046892). 

Aerosols containing trace elements derive predominantly from industrial activities. Generally, only 

the heavy metals Cd, Cr, Ni, and Hg are released from stacks in the vapor phase (McGowan et al., 

1993, 046731). Atmospherically deposited PM can interact with a variety of biogeochemical 

December 2009 9-176 
processes. The potential pathways of accumulation of trace metals in terrestrial ecosystems, as well 

as the possible consequences of trace metal deposition on ecosystem functions, are summarized in 

Figure 9-85 (U.S. EPA, 2004, 056905). A number of mass balance approaches (Macleod et al., 2005, 

155954; Toose and Mackay, 2004, 156123), and metal speciation and transport models (Bhavsar et 

al., 2004, 155689; Bhavsar et al., 2004, 155690; Gandhi et al., 2007, 155781) have been developed 

in recent years. 

Atmospheric Pb is a component of PM in some regions. The effects of Pb on ecosystems were 

discussed in the 2006 Pb AQCD (U.S. EPA, 2006, 090110), which concluded that, due to the 

deposition of Pb from past human practices (e.g., leaded gasoline, ore smelting) and the long 

residence time of Pb in many aquatic and terrestrial ecosystems, a legacy of environmental Pb 

burden exists, over which is superimposed much lower contemporary atmospheric Pb loadings. The 

potential for ecological effects of the combined legacy and contemporary Pb burden to occur is a 

function of the bioavailability or bioaccessibility of the Pb. This, in turn, is highly dependent upon 

numerous site factors (e.g., soil OC content, pH, water hardness). Although the more localized 

ecosystem impacts observed around smelters are often striking, effects generally cannot be attributed 

solely to Pb, because of the presence of many other stressors (e.g., other heavy metals, oxides of 

sulfur and nitrogen) that can also act singly or in concert with Pb to cause readily observable 

environmental effects (U.S. EPA, 2004, 056905; U.S. EPA, 2008, 157074). 

Effects of fine particle trace elements were described by the U.S. EPA (2004, 056905), and 

some additional more recent research has also been conducted, especially on the topic of vegetative 

uptake of trace elements from the soil. The state of scientific understanding as presented by the U.S. 

EPA (2004, 056905) as well as a discussion of more recent research findings are presented below. 

December 2009 9-177 
Source: U.S. EPA (2004, 056905) 

Figure 9-85. Relationship of plant nutrients and trace metals with vegetation. Compartments 

(roman numerals) represent potential storage sites; whereas arrows (Arabic 

numerals) represent potential transfer routes. 

9.4.5.1. Effects on Soil Chemistry 

Trace metals are naturally found in small amounts in soils, ground water, and vegetation. 

Many are essential micronutrients required for growth by plants and animals. Naturally occurring 

mineralization can produce metal concentrations in soils and vegetation that are high compared to 

atmospheric sources. Many metals are bound by chemical processes in the soil, reducing their 

availability to biota. However, epiphytic or parasitic root colonizing microorganisms can solubilize 

and transport metals for root uptake (Lingua et al., 2008, 155935). It can be difficult to assess the 

extent to which observed heavy metal concentrations in soil are of anthropogenic origin. This is 

because soil parent material, pedogenesis, and anthropogenic inputs all influence the amounts and 

distribution of trace elements in soil. Trace element concentrations in some natural soils that are 

remote from air pollution can be higher than soils derived from other parent materials that receive 

anthropogenic inputs (Burt et al., 2003, 155709). The general effects of metals from atmospheric 

deposition are presented in the following discussion. 

There is not a standard method available for quantifying the bioavailability of heavy metals in 

soil. A variety of models, isotopic studies, and sequential extraction methods have been used (Collins 

et al., 2003, 155737; Feng et al., 2005, 155774; Shan et al., 2003, 156972). Total metal concentration 

in soil does not give a good indication of potential biological effects because soils vary in their 

December 2009 9-178 
ability to bind metals in forms that are not bioavailable. There are various methods available for 

assessing bioavailability of metals, but soils are heterogeneous and there is no ideal method for 

evaluating what conditions the soil biota experience. Almås et al. (2004, 155654) argued that the 

actual measurement of biological effects is the best criterion for determining bioavailability. In 

particular, the replacement of metal-sensitive microorganisms by metal-tolerant organisms within 

each functional group may be one of the most sensitive indicators of metal exposure. An increase in 

microbial trace metal tolerance per se would not be problematic if it was not for the fact that this 

increase in tolerance is generally accompanied by a decrease in microbial diversity (Almas et al., 

2004, 155654; Lakzian et al., 2002, 156671). 

Heavy metals deposited from the atmosphere to forests accumulate either in the organic forest 

floor or in the upper mineral soil layers and metal concentration tends to decrease with soil depth. 

The accumulation of heavy metals in soil is influenced by a variety of soil characteristics, including 

pH, Fe and Al oxide content, amount of clay and organic material, and cation exchange capacity 

(CEC) (Hernandez et al., 2003, 155841). Thus, the pattern of distribution of heavy metals in soils 

depends on both the soil characteristics and the metal characteristics. 

Burt et al. (2003, 155709) investigated the concentrations and chemical forms of trace metals 

in smelter-contaminated soils collected in the Anaconda and Deer Lodge Valley area of Montana, 

one of the major mining districts of the world for over a century (1864-1983). The relative 

distributions of trace metals within the more soluble soil extraction forms were similar to their 

respective total concentrations. This suggested a relationship between the concentrations of total 

trace elements and concentrations of soluble mobile fractions. Sequential extractions do not provide 

direct characterization of trace metal speciation, but rather an indication of chemical reactivity (Burt 

et al., 2003, 155709; Ramos et al., 1994, 046736). Soluble and exchangeable forms are considered 

readily mobile and bioavailable. Those bound to clay minerals or organic matter are considered 

generally unavailable. 

There is concern that Pb contamination of forest soil could move into groundwater. This would 

be important in view of the large quantity of Pb deposited from the atmosphere in the 1960s and 

1970s in response to combustion of leaded gasoline. This issue was investigated by Watmough et al. 

(2004, 077809) who applied a stable isotope (207Pb) to the forest floors of white pine (Pinus strobus) 

and sugar maple (Acer saccharum) stands. Added Pb was rapidly lost from the forest floor, likely 

due to high litter turnover in these forest types. However, Pb concentrations in the upper 30 cm of 

mineral soil were strongly correlated with soil OM, suggesting that Pb does not readily move down 

the soil profile to the ground water, but rather is associated with the organic content of the upper soil 

layers (Watmough et al., 2004, 077809). 

The upper soil layers are typically an active site of litter decomposition and plant root uptake, 

both processes may be affected by metal components of PM. Surface litter decomposition is reduced 

in soils having high metal concentrations. This is likely due to the sensitivity to metals of microbial 

decomposers and reduced palatability of plant litter having high metal concentration (Johnson and 

Hale, 2008, 155881). Root decomposition is a key component of nutrient cycling. Johnson and Hale 

(2008, 155881) measured in situ fine root decomposition at Sudbury, Ontario and Rouyn-Noranda, 

Quebec. Elevated soil metal concentrations (Cu, Ni, Pb, Zn) did not necessarily reduce fine root 

decomposition. Only at sites having high concentrations of metals did decomposing roots show 

increased metal concentrations over time. 

9.4.5.2. Effects on Soil Microbes and Plant Uptake via Soil 

Upon entering the soil environment, PM pollutants can alter ecological processes of energy 

flow and nutrient cycling, inhibit nutrient uptake, change ecosystem structure, and affect ecosystem 

biodiversity. Many of the most important effects occur in the soil. The soil environment is one of the 

most dynamic sites of biological interaction in nature. It is inhabited by microbial communities of 

bacteria, fungi, and actinomycetes. These organisms are essential participants in the nutrient cycles 

that make elements available for plant uptake. Changes in the soil environment that influence the 

role of the bacteria and fungi in nutrient cycling determine plant and ultimately ecosystem response. 

Many of the major indirect plant responses to PM deposition are chiefly soil-mediated and 

depend on the chemical composition of the individual components of deposited PM. Effects may 

result in changes in biota and in soil conditions that affect ecological processes, such as nutrient 

cycling and uptake by plants. 

December 2009 9-179 
The soil environment is rich in biota. Bacteria and fungi are usually most abundant in the 

rhizosphere, the soil around plant roots that all mineral nutrients must pass through. Bacteria and 

fungi benefit from the nutrients that are present in root exudates and make mineral nutrients 

available for plant uptake. The soil-mediated ecosystem impacts of PM are largely determined by 

effects on the growth of bacteria and mycorrhizal fungi that are involved in nutrient cycling and 

plant nutrient uptake. 

Soil Nutrient Cycling 

Accumulation of heavy metals in litter can interfere with nutrient cycling. Microorganisms are 

responsible for decomposition of organic matter, which contributes to soil fertility. Toxic effects on 

the microflora can be caused by Zn, Cd, and Cu. The U.S. EPA (2004, 056905) judged that addition 

of only a few mg of Zn per kg of soil can inhibit sensitive microbial processes. Enzymes involved in 

the cycling of N, P, and S (especially arylsulfatase and phosphatase) seem to be most affected 

(Kandeler et al., 1996, 094392). 

Soil organic matter cycling is known to be sensitive to disturbance due to heavy metal 

pollution. This can cause increased litter accumulation at sites close to metal emissions point 

sources. The relative importance of the various processes that might be responsible for this 

observation is poorly known. Boucher et al. (2005, 155699) conducted CO2 evolution studies in 

microcosms having metal-rich and metal-poor plant materials. Their results suggested that there was 

a pool of less readily decomposable C that appeared to be preferentially preserved in the presence of 

high metal (Zn, Pb, Cd) concentrations in the leaves of the metallophyte Arabidopsis halleri. An 

additional possibility is that increased lignification of the cell walls increased the amount of 

insoluble C (Mayo et al., 1992, 155974). 

Yuangen et al. (2006, 156174) found that urban soil basal respiration rates were positively 

correlated with soil acetic acid-extractable Cd, Cu, Ni, and Zn. The soil microbial biomass was 

negatively correlated with the concentrations of Pb fractions, but not with other metals. Overall 

microbial biomass was lower for urban soils as compared with rural soils (Yuangen et al., 2006, 

156174). 

Metal Toxicity to Microbial Communities 

It is believed that increased accumulation of litter in metal-contaminated areas is due to the 

effects of metal toxicity on microorganisms. Smith (1991, 042566) reported the effects of Cd, Cu, 

Ni, and Zn on the symbiotic activity of fungi, bacteria, and actinomycetes. In particular, the 

formation of mycorrhizae has been shown to be reduced when Zn, Cu, Ni, and Cd were added to the 

soil. 

Most studies of the effects of heavy metals on soils have been conducted under laboratory 

conditions. However, Oliveira and Pampulha (2006, 156827) performed a field study to evaluate 

long-term changes in soil microbiological characteristics in response to heavy metal contamination. 

Dehydrogenase activity, soil ATP content, and enumeration of major soil microbial groups illustrated 

the effects of contamination. There was a marked decrease in total numbers of the different microbial 

groups. In particular, asymbiotic nitrogen-fixers and heterotrophic bacteria were found to be 

sensitive. Dehydrogenase activity was confirmed to be a good assay for determining the effect of 

heavy metals on physiologically active soil microbial biomass. 

The toxic effects of heavy metals on soil microorganisms are well known. However, less is 

known about the relative sensitivity of different types of soil microorganisms (Rajapaksha et al., 

2004, 156035). Vaisvalavicius et al. (2006, 157080) assessed the toxicity of high concentrations of 

Pb (839 mg/kg), Zn (844 mg/kg), and Cu (773 mg/kg) in the upper 0-0.1 m soil layer. Microbial 

abundance of all groups was reduced and enzymatic activity was lower than for uncontaminated soil. 

In particular, actinomycetes, oligonitrophobic and mineral N assimilating bacteria were most 

affected. 

Effects of heavy metals in soil on microbes depends on soil pH, organic content, and the type 

of heavy metal exposure (Kucharski and Wyszkowska, 2004, 156662). Some studies have shown 

that heavy metals inhibit microbial activity in soil (Smejkalova et al., 2003, 156987; Vasundhara et 

al., 2004, 156133). However, Wyszkowska et al. (2007, 179948) showed that heavy metals can either 

December 2009 9-180 
inhibit or stimulate the growth of soil microbes. Populations of Azotobacter spp. decreased, but 

populations of oligotrophic and copiotrophic bacteria, actinomyces, and fungi increased in response 

to heavy metal exposure. Acute metal stress causes a decrease in microbial biomass as 

metal-sensitive microbes are inhibited (Joynt et al., 2006, 155887). 

Studies of the impacts of metal stress on the microbial community composition in soil have 

generally been based on microbial culturing techniques that can select only a subset of the natural 

soil population of microbes. More recent culture-independent studies have been conducted using 

phospholipids or nucleic acid biomarkers to reveal information regarding changes in microbial 

community structure (Joynt et al., 2006, 155887). Using this approach, Joynt et al.(2006, 155887) 

demonstrated that soils contaminated with both metals (Pb, Cr) and organic solvent compounds over 

a period of several decades had undergone changes in community composition, but still contained a 

phytogenetically diverse group of bacteria. This may reflect adaptation to the potentially toxic 

conditions through such processes as natural selection, gene exchange, and immigration. 

Comparison between a severely contaminated soil with a similar soil that had much lower amounts 

of contamination showed considerably lower microbial diversity in the contaminated soil, 

particularly for asymbiotic nitrogen fixers and heterotrophic bacteria (Oliveira and Pampulha, 2006, 

156827). 

As pollution increases, it is expected that the more sensitive species will be lost and the more 

tolerant species remain. This gives rise to the concept of pollution-induced community tolerance 

(PICT), which has been demonstrated for populations of bacteria and fungi (Davis et al., 2004, 

155744). These researchers assessed the effects of long-term Zn exposure on the metabolic diversity 

and tolerance to Zn of a soil microbial community across a gradient of Zn pollution. PICT was found 

to correlate better with total soil Zn than with the concentration of Zn in soil pore water. 

Soil Microbe Interactions with Plant Uptake of Metals 

Atmospherically-deposited metals accumulate in upper soil horizons where fine roots are most 

developed. The availability for plant uptake of metals in soil depends on metal speciation and soil 

pH. In addition, metal binding to dissolved organic matter (DOM) reduces bioavailability (Sauvé, 

2001, 156948). Because organic matter typically decreases with soil depth, the affinity of metals for 

organic matter can influence metal bioavailability at different soil depths. Fine roots (<2 mm 

diameter) provide the major site of uptake and transport to the above-ground plant and generally 

contain a large proportion of the total metals found in plants (Gordon and Jackson, 2000, 155802). 

Fine roots are often colonized by mycorrhiza and interact with other soil microbes. Recent 

published evidence supports that mycorrizha and bacteria influence plant uptake and tolerance of 

metals. Mycorrhiza are fungi that colonize plant roots to form a symbiosis. Mycorrhiza take up 

nutrients from the soil and transfer them to the plant in exchange for carbon from the plant. Like 

plants, some species and strains of mycorrhiza are more tolerant of metals in the soil (Ray et al., 

2005, 190473), so that unpolluted and polluted sites may host different species and strains of 

mycorrhiza (Vogel-Mikus et al., 2005, 190501). 

Mycorrhiza have been observed to cause a range of effects on plants. In some cases, plants 

colonized with mycorrhiza showed improved nutrient uptake and decreased metal uptake (Berthelsen 

et al., 1995, 078058; Nogueira et al., 2004, 190460; Vogel-Mikus et al., 2006, 190502). Mycorrhiza 

have been shown to accumulate metals and act as a sink (Berthelsen et al., 1995, 078058; Carvalho 

et al., 2006, 155715) often preventing the metals in the roots from allocation to shoots (Kaldorf et 

al., 1999, 190399; Soares and Siqueira, 2008, 190482; Zhang et al., 2005, 192083). For example, 

estuarine salt marshes are often located close to urban and industrial areas and receive elevated 

amounts of trace metal contaminants from point and non-point (including atmospheric deposition) 

sources. Vegetation is important in the retention and accumulation of heavy metals in salt marshes. 

Carvalho et al. (2006, 155715) conducted experiments on the effects of arbuscular mycorrhizal fungi 

(AMF) on the uptake of Cd and Cu by Aster tripolium, a common plant species in polluted salt 

marshes and a host of AMF. Carvalho et al. (2006, 155715) found that AMF colonization increased 

metal accumulation in the root system of Aster tripolium without enhancing translocation to the 

shoot. By trapping toxic metals in the roots, this plant species may reduce the extent of vegetative 

stress caused by metal exposure and act as an effective sink for these metals. In a review paper 

Christie et al. (2004, 190174) concluded that mycorrhiza may directly improve plant tolerance to 

December 2009 9-181 
metals by binding and immobilizing metals and indirectly improve plant tolerance by improving 

uptake of nutrients that increase plant growth. 

There is recent evidence that bacteria and mycorrhiza act together to improve plant tolerance 

to metals. Like mycorrhiza some bacteria are more tolerant to metals than others (Vivas et al., 2003, 

190499). Combined inoculation of Trifolium sp. by the arbuscular mycorrhiza, Glomus mosseae, and 

the bacterium, Brevivacillus sp., conferred tolerance to Cd by increasing nutrient status and rooting 

development and by decreasing Cd uptake by the plant (Vivas et al., 2003, 190499). A similar result 

was observed for Zn uptake (Vivas et al., 2006, 190500). 

In some cases, mycorrhizae will not prevent metal uptake (Weissenhorn et al., 1995, 073826). 

In fact, mycorrhiza may facilitate the accumulation of metals in plants and enhance the translocation 

of metals from the root to the shoot (Citterio et al., 2005, 190176; Vogel-Mikus et al., 2005, 190501; 

Zimmer et al., 2009, 192085). There is evidence of variable responses depending on the combination 

of mycorrhiza and bacteria species. Zimmer et al. (2009, 192085) recently showed that the willow 

tree (Salix sp.) colonized with the ectomycorrhizal fungus, Hebeloma crustuliniforme, and the 

bacteria, Microcuccus luters, increased total Cd and Zn accumulation due to enhanced mycorrhizal 

formation. In these cases where soil microbes cause increased metal accumulation, there is a 

potential to use the system for phytoremediation. 

Plants also vary in the extent to which they take up heavy metals from the soil. Variability has 

been shown to occur in response to different plant species and different metals. For example, Szabó 

and Fodor (2006, 156109) exposed winter wheat (Triticum aestivum), maize (Zea mays) and 

sunflower (Helianthus annuus) to a variety of micro-pollutants. Cadmium accumulation was 

significant in both vegetative and reproductive plant parts. Vegetative winter wheat accumulated 

substantial amounts of Hg, but the other species did not. Lead, Cu, and Zn showed only moderate 

enrichment in crops (Szabó and Fodor, 2006, 156109). 

There is some evidence to support that shallow-rooted plant species are most likely to take up 

metals from the soil (Martin and Coughtrey, 1981, 047727). However, there is little evidence 

confirming this observation. It may be more likely that shallow roots of species are likely to take up 

metals because the metal often accumulates in shallow soil layers. Even though atmospheric PM will 

usually deposit on soils before being taken up by plants, it could also be deposited to aquatic systems 

with subsequent transfer to terrestrial plants. Contamination of stream sediments by heavy metals 

can impact adjacent terrestrial ecosystems when high flows cause resuspension and subsequent 

streamside deposition of sediment particles. For example, Ozdilek et al. (2007, 156010) showed that 

metal concentrations in vegetation along the Blackstone River in Massachusetts and Rhode Island 

were generally inversely related to the distance from the riverbank, with higher metal concentrations 

in plant tissues located near the river. The ability of plants to take up metals from soil is an important 

part of metal cycling in the environment. This uptake process allows the metals to enter the food 

web, where they might exert mutagenic, carcinogenic, and teratogenic effects (Hunaiti et al., 2007, 

156579). 

9.4.5.3. Plant Response to Metals 

Some metals, including Cu, Co, Ni, and Zn, are essential micronutrients needed for plant 

growth. Others, including Hg, Cd, and Pb are not essential for plants. Though all heavy metals can 

be directly toxic at sufficiently high concentrations, only Cu, Ni, and Zn have been documented as 

being frequently toxic to plants (U.S. EPA, 2004, 056905), while toxicity due to Cd, Co, and Pb has 

been observed less frequently (Smith, 1990, 046896). Toxic doses depend on the type of ion, ion 

concentration, plant species and the stage of plant growth (Memon and Schroder, 2009, 190442). 

Toxicity response is also dependent on the nutritional status of the plant and the development of 

mycorrhizae (Strandberg et al., 2006, 156105). Plants respond to high concentrations of metals in 

soil through a variety of mechanisms and there are substantial differences among plant species in 

their response to heavy metal exposure. Mechanisms of metal tolerance included exclusion or 

excretion rates, genetics (Patra et al., 2004, 081976; Yang et al., 2005, 192104), mycorrhizal 

interactions (Gohre and Paszkowski, 2006, 190355), storage capability and accumulation (Clemens, 

2006, 190179), and various cellular detoxification mechanisms (Gratao et al., 2005, 190364; Hall, 

2002, 190365). 

One of the most important mechanisms that increases plant tolerance to metals is chelation 

with phytochelatins, such as metallothioneins and peptide ligands that are synthesized within the 

plant from glutathione (Memon and Schroder, 2009, 190442). Phytochelatins are intracellular 

December 2009 9-182 
metal-binding peptides that act as specific indicators of metal stress. Because they are produced by 

plants as a response to sublethal concentrations of heavy metals, they can indicate that heavy metals 

play a role in forest decline (Gawel et al., 1996, 012278). Phytochelatin concentrations have 

previously been measured in coniferous trees in the northeastern U.S. The U.S. EPA (2004, 056905) 

and Grantz et al. (2003, 155805) summarized studies indicating that both the number of dead red 

spruce trees and phytochelatin concentrations increased sharply with elevation in the northeastern 

U.S. Red spruce stands showing varying degrees of decline indicated a systematic and significant 

increase in phytochelatin concentrations associated with the extent of tree injury. These data suggest 

that metal stress might contribute to tree injury and forest decline in the northeastern U.S. The extent 

to which low to moderate amounts of heavy metal deposition, which might occur at locations that are 

not in close proximity to a large point source, contribute to adverse impacts on forest vegetation is 

not known. Although the phytochelatin data suggest a linkage, more direct experimental data would 

be needed to confirm such a finding. 

 In general, plant growth is negatively correlated with trace metal and heavy metal 

concentration in soils and plant tissue (Audet and Charest, 2007, 190169). Trace metals, particularly 

heavy metals, can influence forest growth. Growth suppression of foliar microflora has been shown 

to result from Fe, Al, and Zn. These three metals can also inhibit fungal spore formation, as can Cd, 

Cr, Mg, and Ni (see Smith, 1990, 046896). Metals cause stress and decreased photosynthesis 

(Kucera et al., 2008, 190408) and disrupt numerous enzymes and metabolic pathways (Strydom et 

al., 2006, 190486). Excessive concentrations of metals result in phytotoxicity through: (i) changes in 

the permeability of the cell membrane; (ii) reactions of sulfydryl (-SH) groups with cations; (iii) 

affinity for reacting with phosphate groups and active groups of ADP or ATP; and (iv) replacement 

of essential ions (Patra et al., 2004, 081976). 

In addition to disrupting photosynthesis and other metabolic pathways, metals have been 

shown to alter frost hardiness and impair nutrition. A recent review by Taulavuori (2005, 190489) 

suggests that metal- induced stress reduces frost hardiness of plants, a particular concern at high 

elevation sites. Kim et al. (2003, 155899) found decreased concentration of K in needles and Ca in 

stems of Pinus sylvestris seedlings exposed to Cd addition. This response suggests a disturbance of 

nutrition in response to Cd. Pollutant-caused needle loss can reduce the interception of pollutants 

from the atmosphere, and therefore reduce their concentrations in stemflow. This may be responsible 

for the observation that species diversity of lichens is sometimes higher on trees affected by die-back 

(Hauck, 2003, 155830). 

Da Silva et al. (2006, 190190) have shown that PM had anatomical and physiological effects 

on plants growing near an iron pelletization factory in Brazil. The effects of PM occurred due to 

foliar uptake. Structural characteristics such as peltate trichomes may have formed a barrier 

lessening the penetration of metallic iron into the mesophyll in some species. Iron was shown to 

penetrate the trichomes, epidermic cells (adaxial and abaxial surfaces), stomata, xylem cells, 

collenchyma, endodermis and mesophyll tissues. Once entering the stomata, the PM penetrates 

within the mesophyll, it may modify the chemical balance of the mesophyll (Da et al., 2006, 

190190). 

A greenhouse study evaluated the combined effects of iron dust on restinga vegetation (coastal 

vegetation of Brazil) that commonly grows near iron ore industries. Kuki et al. (2008, 155346) found 

that iron dust had differing effects on gas exchange, chlorophyll content, iron content and antioxidant 

enzyme activity on two plant species common to the restinga. Schinus terebinthifolius (an invasive 

exotic in the U.S.) was not affected by the iron dust. However, Sophora tomentosa showed increased 

iron content and membrane permeability to the leaves, increasing activity of antioxidant enzymes. 

These results showed that the plants used different strategies to cope with PM pollution. S. 

terebinthifolius avoided stress, while S. tomentosa used antioxidant enzyme systems to partially 

neutralize oxidative stress. 

Plant foliage can accumulate elemental Hg over time in response to air exposure and 

concentrations in soil (Ericksen et al., 2003, 155769; Frescholtz et al., 2003, 190352). A mesocosm 

experiment was conducted by Ericksen et al. (2003, 155769) where aspen trees were grown in 

gas-exchange chambers in Hg-enriched soil (12.3 ± 1.3 μg/g) and the Hg content in the foliage was 

analyzed. Foliar Hg increased with leaf age for 2-3 mo and then stabilized at leaf concentrations near 

150 ng/g. About 80% of the Hg found in above-ground biomass was present in the leaves. The 

concentration of Hg in trees grown in the same mesocosms in containers of low Hg soil 

(0.03 ± 0.01 μg/g) exhibited foliar Hg concentrations that were similar to those of trees grown in 

Hg-enriched soil. Almost all of the foliar Hg originated from the atmosphere. Clearly, plant foliage 

December 2009 9-183 
can be a major sink for airborne Hg, which can subsequently enter the soil after litterfall (Ericksen et 

al., 2003, 155769). However, this study did not determine the extent to which atmospheric Hg was 

dry-deposited on the foliage, as opposed to gaseous uptake through the stomata. Foliar/air Hg 

exchange has been shown to be dynamic and bi-directional (Millhollen et al., 2006, 190447). These 

investigators compared foliar Hg accumulation over time in three tree species with fluxes measured 

using a plant gas-exchange system subsequent to soil amendment with HgCl2. Root tissue Hg 

concentrations were strongly correlated with soil Hg concentrations, suggesting that below-ground 

accumulation of Hg by roots may be an important process in the biogeochemical cycling of Hg in 

soil systems. Nevertheless, measured foliar Hg fluxes indicated that deposition of atmospheric Hg 

constituted the dominant flux of Hg to the leaf surface (Millhollen et al., 2006, 190447). Grigal 

(2003, 155811) also found that Hg in vegetation is derived almost exclusively from the atmosphere. 

Mercury uptake from soil is limited, partly because roots adsorb Hg but transport it to foliage very 

poorly (Grigal, 2002, 156498). Grigal (2003, 155811) provided a thorough review of the 

sequestration of Hg in forest and peatland ecosystems. A fundamental aspect of Hg cycling is its 

strong relationship to organic matter. For that reason, peatlands sequester much larger quantities of 

Hg than would be expected on the basis of their land area. Thus, if global climate change affects C 

storage, it may indirectly affect Hg storage because of the strong relationship between Hg and 

organic matter (Grigal, 2003, 155811). 

Arbuscular mycorrhizal (AM) fungi can play important roles in mitigating toxicity of heavy 

metals in plants. For example, AM symbiosis is known to be involved in plant adaptation to 

As-contaminated soils. Higher plants that are adapted to As contaminated soils are generally 

associated with mycorrhizal fungi (Gonzalez-Chavez et al., 2002, 155800). It has also been shown 

that AM symbioses can influence plant coexistence and community diversity (O’Connor, 2002). 

Some plants associated with AM fungi can successfully colonize sites that are heavily contaminated 

by heavy metals (Pennisi, 2004, 156018). 

Dong et al. (2008, 192106) cultivated white clover (Trifolium repens) and ryegrass (Lolium 

perenne) in As-contaminated soil (water extractable As 82.7 mg/kg). The growth and P nutrition of 

both species largely depended on AM symbiosis. The AM-inoculated plants showed selective uptake 

and transfer of P over As. 

PM pollution has the potential to alter species composition over long time scales. Kuki et al. 

(2009, 190411) showed that early establishment stages of Sophora tomentosa species were 

negatively affected by the combination of iron ore and acidifying particles. The deleterious effects of 

the PM included deficient germination and toxic concentrations in roots. In contrast, S. 

terebinthifolius was not affected by the PM revealing species resistance to the pollution. The 

difference among species response suggests that over a long time period the imbalance will likely 

change the species composition (Kuki et al., 2009, 190411). 

The process of removing toxins from soil or water using photoautotrophs is referred to as 

phytoremediation. Some plant species have good ability to extract heavy metals from soil, thereby 

offering potential for phytoremediation (Clemens, 2006, 190179; Hooda, 2007, 190382; 

Padmavathiamma and Li, 2007, 190465) . For example, several species of willow (Salix spp.) 

accumulate high concentrations of Zn and Cd in aboveground biomass (Lunácková et al., 2003, 

155948; Meers et al., 2007, 155977; Rosselli et al., 2003, 156058). A first estimation of the order of 

magnitude of potential metal removal by willow was 2 to 27 kg/ha/yr of Zn and 0.25 to 0.65 kg/ha/yr 

for Cd (Meers et al., 2007, 155977). Build-up of high concentrations of trace metals in soil is 

difficult to remediate because of the long residence times of metals in the environment. Plants that 

survive on heavy metal-contaminated soils have been studied to elucidate the mechanisms that allow 

them to tolerate such conditions and interactions between soil contamination and vegetation 

composition (Becker and Brändel, 2007, 156260; Hall, 2002, 190365). There are numerous other 

plants that have been investigated for application to phytoremediation. Plants that hyperaccumulate 

metals have special potential for remediation of metal-contaminated sites. About 400 species have 

been reported. Brassicaceae has the largest numbers of taxa, with 11 genera and 87 species known to 

hyperaccumulate one or more metal contaminants (Prasad and DeOliveira, 2003, 156885). 

Plant uptake is often the first step for a metal to enter higher levels of the food web. 

Consumers of vegetation may often receive heavy loading of metals from their diets. Metals may 

also bioaccumulate in some species and tissue concentrations are magnified at the higher trophic 

levels, so-called biomagnifications (see Section 9.4.5.7. on Biomagnification). 

December 2009 9-184 
9.4.5.4. Effects on Aquatic Ecosystems 

The atmospheric deposition of PM into the ocean has important implications for primary 

productivity and carbon sequestration. In part, metals in PM deposition may limit phytoplankton 

growth in parts of the ocean (Crawford et al., 2003, 156370). In particular, Fe and Zn can influence 

the productivity of algae that are involved in CaCO3 production. The production of both particulate 

organic C and CaCO3 drive the ocean’s biological carbon pump (Shulz et al., 2004, 156087). Thus, 

in oceanic areas of trace metal limitation, changes in trace metal atmospheric deposition can affect 

biogenic calcification, with potential consequences for CO2 partitioning between the ocean and 

atmosphere. 

A study by Sheesley et al. (2004, 156084) illustrated the value of bioassay procedures to 

provide an initial screening of ambient PM toxicity. They used two species of green algae and two 

extraction methods to compare the toxicities of atmospheric PM collected at two urban/industrial 

sites and one rural site near the southern shore of Lake Michigan. Toxicities varied by site, by 

extraction solvent, and by bioassay. Results suggested that toxicity was not related to the total mass 

of PM in the extract, but to the chemical components of the PM. It is noteworthy that the 

concentrations of contaminants in PM in this type of short-term and acute toxicity testing are much 

higher than would be found in the natural environment. Thus, the purpose of this type of testing is to 

provide an initial screening-level comparison of relative toxicities of atmospheric PM from different 

source areas. It does not provide the data that would be needed to assess risk (Sheesley et al., 2004, 

156084). 

9.4.5.5. Effects on Animals 

There has been little work focusing on animal indicators of PM effects in the field. However, 

there have been several recent studies on snails, amphibians, earthworms, and bivalves that are 

discussed below. 

Bioindicator organisms can be especially useful for monitoring PM effects over geographical 

and temporal scales. Terrestrial invertebrates have been used to monitor contaminants in both air and 

soil. Snails (Helix sp.) accumulate trace metals and agrochemicals, and can be used as effective 

biomonitors for urban air pollution (Beeby and Richmond, 2002, 155680; Regoli et al., 2006, 

156046; Viard et al., 2004, 055675). Demonstrated biological effects include growth inhibition, 

impairment of reproduction, and induction of metallothioneins that are involved in metal 

detoxification (Gomot-de and Kerhoas, 2000, 155798; Regoli et al., 2006, 156046). The use of 

sentinel species to detect the effects of complex mixtures of air pollutants is of particular value 

because the chemical constituents are difficult to characterize, exhibit varying bioavailability, and are 

subject to various synergistic effects. 

Regoli et al. (2006, 156046) caged land snails (Helix aspersa) at five locations in the urban 

areas of Ancona, Italy. After four weeks of exposure to ambient air pollution, the snails were 

analyzed for trace metals and PAHs. Biomarkers were measured that correlated with contaminant 

accumulation, including concentrations of metallothioneins, activity of biotransformation enzymes, 

and peroxisomal proliferation. In addition, indicators of oxidative stress were measured, such as 

oxyradical scavenging capacity, onset of cellular damage, and loss of DNA integrity. Results 

documented substantial accumulation of metals and PAHs in snail digestive tissues in urban areas 

having high traffic congestion. Cellular reactivity was also found, suggesting that this species is an 

effective bioindicator for multipollutant air quality and PM monitoring. 

Some amphibian ecotoxicological research has focused on heavy metal exposure. Contaminant 

uptake can occur by oral, pulmonary, and dermal exposure (James et al., 2004, 155874; Johnson et 

al., 1999, 155880; Lambert, 1997, 155916). This is potentially important because of documented 

declines in amphibian populations in the U.S. and elsewhere in recent decades (Houlahan et al., 

2000, 155853). Toads were shown to be fairly tolerant of Cd exposure (James et al., 2004, 155874). 

It is not clear whether current amounts of terrestrial metal contamination pose an increased risk to 

amphibians in general. 

Estuarine and marine bivalves provide potential bioindicators for Hg bioaccumulation. For 

example, Coelho et al. (2006, 190181) investigated Hg concentrations in Scrobicularia plana, a 

long-lived, deposit-feeding bivalve in southern Europe. Annual bioaccumulation rates were shown to 

be strongly correlated with Hg concentrations in suspended particulate matter (SPM), a response to 

December 2009 9-185 
their deposit-feeding tactics (Verdelhos et al., 2005, 190497). The ability to predict annual 

accumulation rates for indicator species, such as this bivalve, may facilitate management actions to 

avoid deleterious effects on humans through consumption of bivalves above a certain age/size class. 

Earthworms often constitute a large percentage of soil animal biomass and they are considered 

to be relatively sensitive indicators of soil metal contamination. They are continuously exposed to 

the soil via dermal contact in the soil solution or ingestion of large quantities of soil pore water, 

polluted food and/or soil particles (Lanno et al., 2004, 190415). Hobbelen et al. (2006, 190371) 

determined the important metal pools for bioaccumulation by earthworms Lumbricus rubellus, 

which live in the upper 5cm of soil and Aporrectodea caliginosa, which live in the upper 25 cm of 

soil. Soil concentration explained much of earthworm concentrations, however Cd concentration in 

A. calinginosa was best explained by pore water concentrations and no variable tested explained Zn 

tissue concentrations. Massicotte et al. (2003, 155968) compared the cell viability and phagocytic 

potential of three earthworm species (Lumbricus terrestris, Eisenia andrei, and Aporrectodea 

tuberculata) in response to atmospheric emissions of metals from a cement factory in Quebec, 

Canada. Cell viability actually increased in proximity (0.5 km) to the cement factory for A. 

tuberculata, and this might have been due to beneficial effects of increased Ca deposition. There 

were no significant differences observed for the other two species (Massicotte et al., 2003, 155968). 

Biogeochemical cycling of Hg in the Arctic has been investigated, in part because observed 

Hg concentrations in marine animals may pose health risks for local human populations. The lifetime 

of gaseous elemental Hg (GEM) in the atmosphere, which constitutes about 95% of atmospheric Hg, 

is generally about one year (Lin and Pehkonen, 1999, 190426). However, during spring (typically 

March through June), the lifetime of GEM in the Arctic is much shorter, and atmospheric GEM can 

be depleted in less than one day during atmospheric Hg depletion episodes (AMDE) (Lindberg et al., 

2002, 190429; Skov et al., 2004, 190481). During the AMDE, GEM is rapidly oxidized to reactive 

gaseous Hg that can be deposited to the ground surface (Skov et al., 2004, 190481). Because of the 

increased solar flux to the Arctic during spring and seasonal melting of sea ice, there may be an 

increased efficiency of Hg bioaccumulation in Arctic food webs than would be expected based on 

data collected at mid-latitudes. Skov et al. (2004, 190481) developed a simple parameterization for 

AMDE and included it in the Danish Eulerian Hemispheric Model (DEHM). The model was shown 

to reproduce the general structure of AMDE, suggesting that the limiting factor for AMDE may be 

the surface temperature of sea ice. 

9.4.5.6. Biomagnification across Trophic Levels 

Biomagnification is the progressive accumulation of chemicals with increasing trophic level 

(LeBlanc, 1995, 155921). Organic Hg is the most likely metal to biomagnify, in part because 

organisms can efficiently assimilate methylmercury and it is slowly eliminated (Croteau et al., 2005, 

156373; Reinfelder et al., 1998, 156047). Of the trace metals, there is also evidence that Cd, Pb, Zn, 

Cu and Se biomagnify. 

The study of trophic transfer and biomagnification is limited by the difficulty in discriminating 

food webs and the uncertainty associated with assignment of trophic position to individual species 

(Croteau et al., 2005, 156373). Use of stable isotopes can help to establish linkages. However, it is 

difficult to determine the extent to which biomagnification occurs in a given ecosystem without 

thoroughly investigating physiological biodynamics, habitat, food web structure, and trophic position 

of relevant species. Thus, development of an understanding of ecosystem complexity is necessary to 

determine what species might be at greatest risk from toxic metal exposure (Croteau et al., 2005, 

156373). 

Terrestrial 

Bioaccumulation of heavy metals can occur through the plant-herbivore and litter-detrivore 

food webs. The U.S. EPA (2004, 056905) concluded that Cd and Zn can bioaccumulate in 

earthworms. Other invertebrates inhabiting soil litter may also accumulate metals. Although food 

web accumulation of a metal may not result in mortality, it might reduce breeding potential or result 

in other non-lethal effects that adversely affect organism responses to environmental cues. 

Metal accumulation in litter can be found mainly around brass works, cement factories, and Pb 

and Zn smelters. Organisms that feed on earthworms living in soils with elevated metal 

December 2009 9-186 
concentrations may also accumulate Pb and Zn. Increased concentrations of heavy metals have been 

found in a variety of mammals living in areas with elevated heavy metal concentrations in the soils. 

The transfer of metals from plants to terrestrial snails is an interesting system for 

biomagnifications because snails accumulate metals in their soft tissue and can contribute 

significantly to the transfer of pollutants to primary consumers and terrestrial predators (Dallinger et 

al., 2001, 192109). Notten et al. (2005, 190461) studied the transfer of Cu, Zn, Cd and Pb in 

terrestrial soil-plant-snail food chains in metal-polluted soils of the Netherlands. The food chain 

included perennial plant species Urtica dioica and the herbivorous snail Cepaea nemoralis. The 

transfer of metal from the soil to the plant compartment was low (coefficient of determination R2 

= 

0.20). Total concentration of metals in soils was a poor predictor of leaf concentration. Low metal 

concentration in the leaves was thought to be due to low pore water metal concentrations and was 

also thought to be partly caused by low translocation from roots within the plant. The Cu, Zn and Cd 

concentrations in the snails were always higher than concentrations in the leaves indicating 

bioaccumulation. The metal transfer from the leaf to snail was highest among all routes tested, 

suggesting that transfer from diet is important. Similar results were found by Beeby and Richmond 

(2002, 155680) with the snail, Helix aspersa, and the plant, Taraxacum sp., for Zn, Pb, Cd, but not 

for Cu. 

Many types of predators including shrews, thrushes and beetle larvae include snails as part of 

their diet (Gomot-De and Pihan, 2002, 190357; Seifert et al., 1999, 190480). Seifert et al. (1999, 

190480) found the shrews eating snails with elevated Cd had critical levels of Cd in their kidneys. 

Scheifler et al. (2007, 190379) found that Cd in snails lead to toxic levels in beetle larvae that caused 

increased amounts of mortality. 

Aquatic 

In general, it has been assumed that metal biomagnification in aquatic ecosystems is an 

exception rather than the rule (Gray, 2002, 155806). More recent research has demonstrated aquatic 

biomagnification of certain metals. For example, Stewart et al. (2004, 156097) used stable isotopes 

of C and N to show biomagnification of Se in San Francisco Bay food webs. Croteau et al. (2005, 

156373) identified trophic position of estuarine organisms and food web structure in the delta of San 

Francisco Bay to document Cd biomagnification in invertebrates that live on macrophytes and also 

in fish. Concentrations of Cd were biomagnified 15 times within two trophic links in each food web. 

In contrast, no tendency towards biomagnification was observed for Cu. 

In aquatic ecosystems, biomagnification of trace metals does not necessarily occur. Nguyen et 

al. (2005, 155997) found biodiminution for most metals in Lake Balaton, Hungary, with the 

exception of slight enrichment of Zn from PM to zooplankton and of Cd from sediment to mussels. 

Once transported to aquatic ecosystems, trace metals often preferentially bind to sediment 

particles. Some of these sediment-bound metals may be unavailable to biota; in contrast, metals 

bound to sediment organic matter may exhibit varying degrees of bioavailability (Di Toro et al., 

2005, 155750). Piol et al. (2006, 156028) studied the bioavailability of sediment-bound Cd to the 

freshwater oligochaete Lumbriculus variegatus. They found that Cd uptake depended on the amount 

of free dissolved Cd(II), and the Cd contribution from sedimentary particles to biological uptake was 

negligible. 

Marine bivalve mollusks bioaccumulate trace metals and other contaminants (LaBrecque et 

al., 2004, 155913) and therefore may be used as bioindicators of contamination. In addition, they 

constitute an important link to human health by virtue of their importance as a food source 

(Cheggour et al., 2005, 155723; Li et al., 2002, 156691). 

9.4.5.7. Effects near Smelters and Roadsides 

The high PM concentrations in proximity to mining, smelting, roadsides and other industrial 

sources result in heavy metal loadings that may be particularly damaging to nearby ecosystems. 

December 2009 9-187 
Smelters 

The Harjavalta region is one of the most intensively studied heavy metal polluted areas in the 

world. Kiikkilä et al. (2003, 156637) reviewed available data on heavy metal deposition and 

environmental effects in this area. Emissions from the smelter were as high as 1,100 t/yr of dust, 140 

t/yr Cu, 96 t/yr Ni, 162 t/yr Zn, and 94 t/yr Pb in 1987. Deposition amounts decreased substantially 

after 1990, to only a few percent of the amounts that occurred during the 1980s. 

Kiikkilä (2003, 156637) investigated the effects of heavy metal pollution in proximity to a 

Cu-Ni smelter at Harjavalta, Finland. The deposition of heavy metals increased within 30 km of the 

smelter. Only slight changes in the understory vegetation were observed at distances greater than 8 

km from the smelter. At 4 km distance, species composition of vegetation, insects, birds, and soil 

microbiota changed and tree growth was reduced. Within about 1 km, only the most resistant 

organisms were surviving. 

The number of soil organisms clearly decreased and their community structure was altered 

close to the Harjavalta smelter (Kiikkilä, 2003, 156637). However, this effect was only pronounced 

within about 2 km of the smelter. This suggests that the soil microfauna are relatively resistant to 

metal pollution effects. 

Soil microbial activity decreased close to the Harjavalta smelter (Kiikkilä, 2003, 156637), as 

reflected by microbial respiration, distribution of species within physiological groups, and microbial 

and fungal biomass. The fungi appeared to be more sensitive to metal contamination than the 

bacteria (Pennanen et al., 1996, 156016). The rate of litter decomposition decreased, causing an 

accumulation of needle litter on top of the forest floor near the smelter (Fritze et al., 1989, 079635). 

Inhibition of nutrient cycling and displacement by Cu and Ni of base cations from cation 

exchange sites on the soil resulted in a decrease in base cation concentrations in the organic soil 

layer (Derome and Lindroos, 1998, 155749; Kiikkilä, 2003, 156637) close to the Harjavalta smelter. 

In addition, Mg, Ca, and Mn concentrations in Scots pine (Pinus sylvestris) needles were low, and 

this was attributed by Kiikkilä (2003, 156637) to the toxic effects of Cu and Ni to plant fine roots 

and also to ectomycorrhizal root tips (Helmisaari et al., 1999, 155836). Nutrient translocation during 

fall was also affected close to the smelter; as a consequence needle concentrations of K were 

relatively high (Nieminen et al., 1999, 155998). 

Tree growth (Scots pine) has been poor (Malkönen et al., 1999, 155961) and most vegetation 

was absent within 0.5 km of the smelter. Effects on plant species occurrence close to the smelter 

were almost entirely negative. In contrast, some animal species responded positively, including a leaf 

miner, three species of aphid, and some ants, beetles, and spiders. 

Salemaa et al. (2004, 156069) investigated heavy metal concentrations in understory plant 

species growing at varying distances from the Harjavalta Cu-Ni smelter. Heavy metal concentrations 

(except Mn) were highest in bryophytes, followed by lichens, and were lowest in vascular plants. 

Vascular plants are generally able to restrict the uptake of toxic elements, and therefore were able to 

grow closer to the smelter than lichens. A pioneer moss (Pohlia nutans) was unusual in that it 

survived close to the smelter despite its accumulation of high amounts of Cu and Ni. 

Changes in breeding success of cavity-nesting passerine birds close to the Harjavalta smelter 

were attributed to habitat changes in response to metal toxicity (Eeva et al., 2000, 155761; Kiikkilä, 

2003, 156637). Calcium supply is also well known to be important for breeding success in passerine 

bird species. Eggshell thickness, egg size, clutch size, and hatchability of pied flycatcher (Ficedula 

hypoleuca) were found to be depressed near the Cu smelter at Harjavalta, SW Finland (Eeva and 

Lehikoinen, 2004, 155762). Availability of Ca-rich food to the birds was estimated by counting snail 

shells in the nests postfledging. The number of snail shells correlated positively with the Ca 

concentration of nestling feces and adult breeding success. In addition, the negative impact of Cu on 

the number of fledglings was stronger at locations where Ca concentration was low (Eeva and 

Lehikoinen, 2004, 155762). 

Documentation of effects on individual species, such as was reported above, does not reveal 

what the impacts might be on ecosystem function. Nevertheless, the mere fact that multiple species, 

operating at different trophic levels, have been shown to be affected by the ambient deposition in 

proximity to the smelter suggests that effects on ecosystem function may indeed have occurred. 

More research is needed, however, to fully evaluate effects on function as opposed to abundance of 

individual species. 

December 2009 9-188 
Roadsides 

Heavy metal particles are important constituents of road dust. These particles accumulate on 

the road surface from brake linings, road paint, tire debris, diesel exhaust, road construction 

materials, and catalyst materials. Road dust can be suspended in the atmosphere and contribute 

metals to soil, air, and urban runoff (Adachi and Tainosho, 2004, 081380; Davis et al., 2001, 024933; 

Smolders and Degryse, 2002, 156091). In particular, Zn oxide comprises 0.4-4.3% of tire tread 

(Smolders and Degryse, 2002, 156091) and tire wear is a substantial source of environmental Zn 

pollution. Adachi and Tainosho (2004, 081380) used a field emission screening electron microscope 

equipped with an energy dispersive x-ray spectrometer to characterize heavy metal particles 

embedded in tire dust. Samples were classified into four likely source categories, based on cluster 

analysis. Based on morphology and chemical composition, the samples were identified as having 

derived from yellow paint (CrPbO4 particles), brake dust (particulate Ti, Fe, Cu, Sb, Zr, Ba and 

heavy minerals [Y, Zr, La, Ce]), and tire tread (Zn oxide). 

Since publication of EPA’s 2004 PM criteria assessment, some additional research has been 

conducted on the effects of windblown PM. Effects on physical, chemical, and biological attributes 

of both plants and animals have been documented (Englert, 2004, 087939; Gleason et al., 2007, 

155794; Kappos et al., 2004, 087922). Experiments by Gleason et al. (2007, 155794) suggest that 

most direct effects on plants of windblown PM originating from on-road surfaces occur within 40 m 

of the source. Windblown PM from roads or agriculture can cover plant photosynthetic structures 

(Sharifi et al., 1999, 156082), cause impact damage (Armbrust and Retta, 2002, 156225), or interfere 

with physiological mechanisms (Burkhardt et al., 2002, 155708). As previously discussed in Section 

9.4.5.5, land snails in urban areas have been shown to be a good indicator of traffic pollution. 

9.4.5.8. Toxicity to Mosses and Lichens 

At the time of the most recent air quality criteria report for PM (U.S. EPA, 2004, 056905), 

trace metal toxicity to lichens had been demonstrated in relatively few cases. Nash (1975, 016763) 

documented Zn toxicity in the vicinity of a Zn smelter near Palmerton, PA. Experimental data had 

suggested that lichen tolerance to Zn and Cd generally ranges between 200 and 600 ppm (Nash, 

1975, 016763). 

The effects of deposited metals on the mosses have not been well studied. Tremper et al. 

(2004, 156126) exposed mosses of two species to roadside conditions and sampled them over a 

period of 3 mo. Under field conditions, chlorophyll concentrations in moss tissue were not affected 

by metal contamination and accumulation 

Mosses and lichens readily take up metals from atmospheric deposition. Otnyukova (2007, 

156009) demonstrated vertical gradients within a coniferous forest canopy in the fruticose lichen 

genus Usnea with respect to lichen thallus morphology and heavy metal concentration. Abnormal 

thalli at the tree-top level contained higher concentrations of Al, Fe, Zn, F, Sr, and Pb. This vertical 

pattern within the tree canopy is in general accordance with known deposition of PM to plants 

(Otnyukova, 2007, 156009). 

There is an extensive literature on the use of mosses and lichens for estimating deposition 

(biomonitors) and indicating metal exposure in ecosystems (bioindicators) (see Section 9.4.2.3.). 

9.4.6. Organic Compounds 

VOCs in the atmosphere are partitioned between the gas and particle phases. As described by 

the U.S. EPA (2004, 056905), the partitioning depends on vapor pressure, temperature, surface area 

of the particles, and the nature of the particles and of the chemical being adsorbed. A wide variety of 

organic contaminants are deposited from the atmosphere. These include chemicals such as DDT, 

PCBs, and PAHs. 

Important organic atmospheric contaminants are generally those that are transported long 

distances in the atmosphere, subsequently deposited into remote locations, and bioaccumulated to 

sufficient concentrations that they can affect humans, wildlife, or other biota (Swackhamer et al., 

2004, 190488). Certain physical and chemical properties facilitate the movement of these 

contaminants from land and water surfaces into the atmosphere, provide stability, and enhance 

accumulation in lipids. Some, including the relatively small (up to 4 rings) PAHs degrade relatively 

December 2009 9-189 
rapidly in the atmosphere or at the surface subsequent to atmospheric deposition. Below is a 

summary of the findings of the U.S. EPA (2004, 056905), followed by discussion of more recent 

research findings. 

Plants may be used as passive monitors to compare the deposition of organic compounds 

between sites. Vegetation can be used semi-quantitatively to indicate organic pollutant amounts if the 

mechanism of accumulation is considered. Organic compounds can enter the plant via the roots or be 

deposited as particles on the leaves and be taken up through the cuticle or stomata. The pathways 

depend on the chemical and its physical properties. These include, for example, lipophilicity, water 

solubility, vapor pressure, and Henry’s law constant. Environmental conditions can also be 

important, including temperature and organic content of soil, plant species, and the foliar surface 

area and lipid content. 

Organic particulates in the atmosphere are diverse in their makeup and sources. Vegetation 

itself is an important source of hydrocarbon aerosols. Terpenes, particularly α-pinene, β-pinene, and 

limonene, released from tree foliage may react in the atmosphere to form submicron particles. These 

naturally generated organic particles contribute significantly to the blue-haze aerosols formed 

naturally over forested areas (Geron et al., 2000, 019095; U.S. EPA, 2004, 056905). 

The low water solubility and high lipo-affinity of many organic xenobiotics control their 

interaction with the vegetative components of natural ecosystems. Foliar surfaces are covered with a 

waxy cuticle layer that helps reduce moisture loss and short-wave radiation stress. This epicuticular 

wax consists largely of long-chain esters, polyesters, and paraffins, which accumulate lipophilic 

compounds. Organic air contaminants in the particulate or vapor phase can be adsorbed to, and 

accumulate in, the epicuticular wax of leaf surfaces. Direct uptake of organic contaminants through 

the cuticle and vapor-phase uptake through the stomata are not well characterized for most trace 

organics. 

Soil acts as an important storage compartment for POPs, including PCBs and PAHs. There is a 

continuous process of partitioning between the soil pool and the atmosphere, and this controls the 

regional and global transport of these compounds (Backe et al., 2004, 155668; Wania and Mackay, 

1993, 157110). Over time, POPs move towards equilibrium between the environmental 

compartments, and this process can be described using the fugacity concept (Backe et al., 2004, 

155668; Mackay, 1991, 042941). Fugacity reflects the tendency of a chemical constituent to escape 

one environmental compartment and move to another. When an equilibrium distribution is achieved, 

the fugacity quotient values in each compartment will be equal. Soil/air partitioning is controlled by 

a variety of factors. These include soil properties, such as organic matter content, moisture, porosity, 

texture, and structure, as well as the physiochemical properties of the pollutant, including vapor 

pressure and water solubility. 

The accumulation of PAHs in vegetation, due to their lipophilic nature, could contribute to 

human and other animal exposure via food consumption. As a result, plant uptake of PAHs has been 

an important area of research (Gao and Zhu, 2004, 155782). Most bioaccumulation of PAHs by 

plants occurs by leaf uptake (Tao et al., 2006, 156112). Root uptake also occurs. It appears that roots 

preferentially accumulate the lower molecular weight PAHs due to their greater water solubility 

(Wild and Jones, 1992, 156155). 

Various models have been developed to simulate plant uptake of organic contaminants. The 

simple partition-limited model of Chiou et al. (2001, 156342) has been further expanded to increase 

complexity and to include root uptake pathways (e.g., Fryer and Collins, 2003, 156454; Yang et al., 

2005, 192104; Zhu et al., 2004, 156184). 

In evaluating receptor choice for studies of contaminant exposure to plants, and also 

remediation potential, it is important to consider differences among species. For example, Parrish et 

al. (2006, 156014) assessed the bioavailability of PAHs in soil. During the first growing season, 

zucchini (Cucurbita pepo ssp. pepo) accumulated significantly more PAHs than did other related 

plant species, including up to three orders of magnitude greater concentrations of the six-ring PAHs. 

Parrish et al. (2006, 156014) also noted differences in PAH uptake by two different species of 

earthworm. 

The leaves of Quercus ilex have been shown to readily accumulate PAHs in situ. Young leaves 

accumulated PAHs within three weeks of bud break. Mature leaves showed seasonality, with higher 

PAH concentrations during winter (Alfani et al., 2005, 154319). 

It is difficult to discriminate between PAHs that are adsorbed to plant root surfaces as opposed 

to those that are actually taken up by the roots. In general, soil bound PAHs are associated with soil 

organic matter and are therefore not readily available for root uptake (Fismes et al., 2002, 141156; 

December 2009 9-190 
Jiao et al., 2007, 155879). Wild et al. (2005, 156156) used two-photon excitation microscopy to 

visualize the uptake and transport of two PAHs (anthracene and phenanthrene) from a contaminated 

soil into living wheat and maize roots. Jiao et al. (2007, 155879) developed a sequential extraction 

method to discriminate between PAH adsorption in rice roots. 

Maize roots and tops of plants have been shown to directly accumulate PAHs from aqueous 

solution and from air in proportion to exposure amounts. Root concentration factors are log-linear 

functions of log-based octanol-water partition coefficients (log Kow); similarly, leaf concentration 

factors are log-linear functions of log-based octanol-air partition coefficients (log Koa) (Lin et al., 

2007, 155933). Although the bulk concentrations of PAHs in various plant tissues can differ greatly, 

the observed differences disappear after they are normalized to lipid content (Lin et al., 2007, 

155933). This suggests that the lipid content of different plant tissues may influence PAH 

distribution within the plant. 

Previously, there was relatively little information available regarding incorporation of 

atmospherically deposited PAHs into aquatic food webs. It is known that PAHs can be transferred to 

higher trophic levels, including fish, and that this transfer can be mediated by aquatic invertebrates, 

which generally comprise an important part of fish diets. High mountain lakes offer an effective 

receptor for quantification of biomagnification in aquatic ecosystems from atmospheric PM 

deposition. There are typically no sources of organic contaminants in their watersheds, and 

atmospheric inputs dominate as sources of contamination. In addition, such lakes tend to have 

relatively simple food webs. Vives et al. (2005, 157099) investigated PAH content of brown trout 

(Salmo trutta) and their food items. Total PAH concentrations tended to be highest in organisms that 

occupy littoral habitats, and lowest in pelagic organisms. This may reflect more efficient transfer of 

PAHs to underlying sediments in shallower water and associated degradation within the water 

column. 

Some atmospheric organic contaminants have been shown to accumulate in biota at remote 

locations. For example, polybrominated diphenyl ethers (PBDEs), which are man-made chemicals 

used as flame retardants in materials manufacturing, have been found to accumulate in lichens and 

mosses collected at King George Island, maritime Antarctica (Yogui and Sericano, 2008, 189971). 

Because contaminant concentrations were not statistically different at sites close to and distant from 

human facilities in Antarctica, the authors concluded that long-range atmospheric transport was the 

likely primary source of PBDEs to King George Island. Law et al. (2003, 190420) reviewed 

available data for accumulation of PBDEs and other brominated flame retardants in wildlife. These 

compounds have become widely distributed in the environment, including in the deep-water, oceanic 

food webs. 

Ohyama et al. (2004, 190462) chose salmonid fish, mainly rainbow trout (Oncorhyncus 

mykiss), as an indicator species to evaluate the transport and bioaccumulation of organochloride 

compounds in the northern and central Sierra Nevada. They found that elevation was an important 

factor affecting residual concentrations of polychlorinated biphenyls (PCBs) in fish muscle tissue. 

On this basis, Ohyama et al. (2004, 190462) concluded that PCB residue in rainbow trout, a widely 

distributed salmonid species, provided a good monitoring tool for studying the effects of 

mountainous topography on the long-range transport and distribution of persistent organic pollutants. 

Semivolatile compounds can undergo repeated volatilization on surfaces, such as plant foliage, 

in response to diel changes in temperature. As a consequence, such compounds can be deposited, reemitted, and re-deposited multiple times. This behavior can cause these compounds to move large 

distances in a leap-frog fashion (Krupa et al., 2008, 198696). It is believed that POPs can be 

atmospherically transported throughout the world because of their volatility and response to changes 

in temperature. This “global distillation theory” (Holmqvist et al., 2006, 190380; Wania and Mackay, 

1993, 157110) predicts that POPs in the northern hemisphere are generally transported towards the 

Arctic, and in the southern hemisphere they are transported toward the Antarctic. In general, POP 

concentrations measured in the Arctic are higher than in the Antarctic. They have been detected in all 

levels of the Arctic food web (Oehme et al., 1995, 011267). Bioconcentration of organochlorines has 

been shown in the Arctic food web, including fish, seals, and polar bears (Oehme et al., 1995, 

011267). Concentrations measured in Arctic polar bears are especially high (AMAP, 2004, 190168). 

Holmqvist et al. (2006, 190380) measured levels of PCBs in longfin eels (Anguilla 

dieffenbachii) in 17 streams on the west coast of South Island, New Zealand. The PCBs were at low 

levels, and were believed to originate from atmospheric transport from industrial areas in Asia. 

Characteristics of the longfin eel that make it susceptible to bioaccumulation of lipophilic persistent 

December 2009 9-191 
pollutants include high lipid content (up to 40%), long lifespan (up to 90 yrs), and position near the 

top of the food chain (Holmqvist et al., 2006, 190380). 

Long-range transport of atmospherically deposited contaminants can be augmented by 

biotransport. A good example of this phenomenon was documented by Ewald et al. (1998, 190348), 

who showed that biotransport by migrating sockeye salmon (Oncorhynchus nerka) in the Copper 

River watershed, Alaska, had a greater influence than atmospheric transport on bioaccumulation of 

PCBs and DDT in lake food webs. Organic pollutants accumulated by salmon during their ocean 

residence were effectively transferred 410 km inland to their spawning lake. Arctic grayling 

(Thymallus arcticus) in the salmon spawning lake were found to contain organic pollutants more 

than twice as high as arctic grayling in a near-by salmon-free lake. The pollutant composition of the 

grayling in the salmon spawning lake was similar to that of the migrating salmon (Ewald et al., 1998, 

190348), suggesting that salmon migration contributed to bioaccumulation of organic contaminants 

in the lake used for spawning by the salmon. 

An assessment of the ecological effects of airborne metals and SOCs was conducted for eight 

NPs by the Western Airborne Contaminants Assessment Project (WACAP) (Landers et al., 2008, 

191181). From 2002-2007, WACAP researchers conducted analysis of the biological effects of 

airborne contaminants in seven ecosystem compartments: air, snow, water, sediments, lichens, 

conifer needles and fish. The goals were to identify where the pollutants were accumulating, identify 

ecological indicators for those pollutants causing ecological harm, and to determine the source of the 

air masses most likely to have transported the contaminants to the parks. 

The results from WACAP were summarized by Landers et al. (2008, 191181), which 

concluded that bioaccumulation of SOCs were observed throughout park ecosystems. Vegetation 

tended to accumulate PAHs, CUPs, and HCHs. Conifer needles were a good indicator of pesticides, 

however the ecological consequences of this accumulation are unexamined. SOCs in vegetation and 

air showed different patterns, possibly because each medium absorbs different types of SOCs with 

varying efficiencies. Mean ammonium nitrate concentration in ambient fine particulates <2.5 μm 

diameter was a good predictor of dacthal, endosulfan, chloradane, trifluralin, DDT and PAH 

concentrations in vegetation. 

Concentrations of SOCs were five to seven orders of magnitude higher in fish tissue than in 

sediments. Fish accumulated more PCBs, chlordanes, DDT and dieldrin than vegetation. Fish lipid 

and age were the most reliable predictors of SOC concentrations. Most fish appeared normal during 

field necropsies; however, individuals with both male and female reproductive organs were collected 

at two sites. The incidence of this condition has increased since the pre-organic pollutant era. 

Additionally, elevated concentrations of vitellogenin, a female protein involved in egg production, 

were found in male fish from three sites, and directly related to the concentration of several 

organochlorines at one site. 

The lake sediment records showed steadily increasing mercury deposition over time at lakes in 

two parks, Mt. Ranier NP and Rocky Mountain NP. Apportionment of the mercury to its atmospheric 

sources is not quantified at this time; however, the pattern in the sediment suggests a local source 

rather than a global source. Mercury concentrations in fish exceeded contaminant health thresholds 

for some piscivorous fish, mammals and birds in most parks. The average mercury concentration in 

fish from one site and individual fish from three additional sites exceeded the U.S. EPA contaminant 

health thresholds for humans. 

Although this assessment focuses on chemical species that are components of PM, it does not 

specifically assess the effects of particulate versus gas-phase forms; therefore, in most cases it is 

difficult to apply the results to this assessment based on particulate concentration and size fraction. 

9.4.7. Summary of Ecological Effects of PM 

Ecological effects of PM include direct effects to metabolic processes of plant foliage; 

contribution to total metal loading resulting in alteration of soil biogeochemistry and microbiology, 

plant and animal growth and reproduction; and contribution to total organics loading resulting in 

bioaccumulation and biomagnification across trophic levels. These effects were well-characterized in 

the 2004 PM AQCD (U.S. EPA, 2004, 056905). Thus, the summary below builds upon the 

conclusions provided in that review. 

PM deposition comprises a heterogeneous mixture of particles differing in origin, size, and 

chemical composition. Exposure to a given concentration of PM may, depending on the mix of 

deposited particles, lead to a variety of phytotoxic responses and ecosystem effects. Moreover, many 

December 2009 9-192 
of the ecological effects of PM are due to the chemical constituents (e.g., metals, organics, and ions) 

and their contribution to total loading within an ecosystem. 

Investigations of the direct effects of PM deposition on foliage have suggested little or no 

effects on foliar processes, unless deposition levels were higher than is typically found in the 

ambient environment. However, consistent and coherent evidence of direct effects of PM has been 

found in heavily polluted areas adjacent to industrial point sources such as limestone quarries, 

cement kilns, and metal smelters (Sections 9.4.3 and 9.4.5.7). Where toxic responses have been 

documented, they generally have been associated with the acidity, trace metal content, surfactant 

properties, or salinity of the deposited materials. 

An important characteristic of fine particles is their ability to affect the flux of solar radiation 

passing through the atmosphere, which can be considered in both its direct and diffuse components. 

Foliar interception by canopy elements occurs for both up- and down-welling radiation. Therefore, 

the effect of atmospheric PM on atmospheric turbidity influences canopy processes both by radiation 

attenuation and by changing the efficiency of radiation interception in the canopy through 

conversion of direct to diffuse radiation. Crop yields can be sensitive to the amount of radiation 

received, and crop losses have been attributed to increased regional haze in some areas of the world 

such as China. On the other hand, diffuse radiation is more uniformly distributed throughout the 

canopy and may increase canopy photosynthetic productivity by distributing radiation to lower 

leaves. The enrichment in photosynthetically active radiation (PAR) present in diffuse radiation may 

offset a portion of the effect of an increased atmospheric albedo due to atmospheric particles. Further 

research is needed to determine the effects of PM alteration of radiative flux on the growth of 

vegetation in the U.S. 

The deposition of PM onto vegetation and soil, depending on its chemical composition, can 

produce responses within an ecosystem. The ecosystem response to pollutant deposition is a direct 

function of the level of sensitivity of the ecosystem and its ability to ameliorate resulting change. 

Many of the most important ecosystem effects of PM deposition occur in the soil. Upon entering the 

soil environment, PM pollutants can alter ecological processes of energy flow and nutrient cycling, 

inhibit nutrient uptake, change ecosystem structure, and affect ecosystem biodiversity. The soil 

environment is one of the most dynamic sites of biological interaction in nature. It is inhabited by 

microbial communities of bacteria, fungi, and actinomycetes, in addition to plant roots and soil 

macro-fauna. These organisms are essential participants in the nutrient cycles that make elements 

available for plant uptake. Changes in the soil environment can be important in determining plant 

and ultimately ecosystem response to PM inputs. 

There is strong and consistent evidence from field and laboratory experiments that metal 

components of PM alter numerous aspects of ecosystem structure and function. Changes in the soil 

chemistry, microbial communities and nutrient cycling, can result from the deposition of trace 

metals. Exposures to trace metals are highly variable, depending on whether deposition is by wet or 

dry processes. Although metals can cause phytotoxicity at high concentrations, few heavy metals 

(e.g., Cu, Ni, Zn) have been documented to cause direct phytotoxicity under field conditions. 

Exposure to coarse particles and elements such as Fe and Mg are more likely to occur via dry 

deposition, while fine particles, which are more often deposited by wet deposition, are more likely to 

contain elements such as Ca, Cr, Pb, Ni, and V. Ecosystems immediately downwind of major 

emissions sources can receive locally heavy deposition inputs. Phytochelatins produced by plants as 

a response to sublethal concentrations of heavy metals are indicators of metal stress to plants. 

Increased concentrations of phytochelatins across regions and at greater elevation have been 

associated with increased amounts of forest injury in the northeastern U.S. 

Overall, the ecological evidence is sufficient to conclude that a causal relationship is 

likely to exist between deposition of PM and a variety of effects on individual organisms 

and ecosystems, based on information from the previous review and limited new 

findings in this review. However, in many cases, it is difficult to characterize the nature and 

magnitude of effects and to quantify relationships between ambient concentrations of PM and 

ecosystem response due to significant data gaps and uncertainties as well as considerable variability 

that exists in the components of PM and their various ecological effects. 

December 2009 9-193 
9.5. Effects on Materials 

Effects of air pollution on materials are related to both aesthetic appeal and physical damage. 

Deposited particles, primarily carbonaceous compounds, cause soiling of building materials and 

culturally important items, such as statues and works of art. Physical damage from dry deposition of 

PM also can accelerate natural weathering processes. The major deterioration phenomenon affecting 

building materials in response to atmospheric deposition is most likely sulfation, leading to 

secondary salt crystallization which forms gypsum (Marinoni et al., 2003, 092520). 

This section (a) summarizes information on exposure-related effects on materials associated 

with particulate pollutants as addressed in the 2004 PM AQCD (U.S. EPA, 2004, 056905); and (b) 

presents relevant information derived from very limited research conducted and published since 

completion of that document. Most recent work on this topic has been conducted outside the U.S. 

There is a variety of factors that contribute to the deterioration of monuments and buildings of 

cultural significance. They include: (1) biodeterioration processes; (2) weathering of materials 

exposed to the air; and (3) air pollution from both anthropogenic and natural sources (Herrera and 

Videla, 2004, 155843). Because of the diversity in climate, proximity to marine aerosol sources, and 

pollution of various types, the magnitude and relative importance of these causal agents vary by 

location. 

Much existing literature on damage to structural materials of cultural heritage has not seriously 

considered the importance of biodeterioration processes and the relationship that often exists 

between environmental characteristics and the microbial communities that colonize monuments and 

buildings. In general, high humidity, high temperature, and air pollution often enhance the 

biodeterioration hazard. Herrera and Videla (2004, 155843) concluded that heterotrophic bacteria, 

fungi, and cyanobacteria were the main microbial colonizers of buildings that they investigated in 

Latin America. Their analyses suggested that the major deterioration mechanism of limestone at the 

Mayan site of Uxmal in a non-polluted rural environment was biosolubilization induced by 

metabolic acids produced by bacteria and fungi. The rock decay at Tulum, near the seashore, was 

mainly attributed to the marine influence. At Medellin, it appeared that biodeterioration effects from 

microbes synergistically enhanced the effects of atmospheric factors on material decay. Deterioration 

of structural material in the Cathedral of La Plata, located in a mixed urban/industrial environment, 

was attributed mainly to atmospheric pollutants (Herrera and Videla, 2004, 155843). 

Ambient particles can cause soiling of man-made surfaces. Soiling generally is considered an 

optical effect. Soiling changes the reflectance from opaque materials and reduces the transmission of 

light through transparent materials. Soiling can represent a significant detrimental effect, requiring 

increased frequency of cleaning of glass windows and concrete structures, washing and repainting of 

structures, and, in some cases, reduces the useful life of the object. Particles, especially carbon, may 

also help catalyze chemical reactions that result in the deterioration of materials (U.S. EPA, 2004, 

056905). 

Soiling is dependent on atmospheric particle concentration, particle size distribution, 

deposition rate, and the horizontal or vertical orientation and texture of the exposed surface (Haynie, 

1986, 157198). The chemical composition and morphology of the particles and the optical properties 

of the surface being soiled will determine the time at which soiling is perceived by human observers 

(Nazaroff and Cass, 1991, 044577). 

Ferm et al. (2006, 155135) reported development of a simple passive particle collector for 

estimating dry deposition to objects of cultural heritage. The observed mass of deposited particles 

mainly belonged to the coarse particulate mode. The sampler collects particles from all directions. It 

replicates at least some of the complexity of particle deposition to actual objects, and is easier to 

analyze than a precious object (Ferm et al., 2006, 155135). 

Soiling of urban buildings constitutes a visual nuisance that leads to the loss of architectural 

value. Soiling can include reversible darkening of the building surfaces and also irreversible damage. 

Water runoff patterns on the building surfaces are influenced by the type of surface material, 

architectural elements, and climate. Therefore, soiling does not occur uniformly across the building. 

Public perception of soiling entails complex interactions between the extent of soiling, architecture, 

and aesthetics (Grossi and Brimblecombe, 2004, 155813). 

One of the most significant air pollution damage features affecting urban buildings and 

monuments is the formation of black crusts. Quantification of different forms of carbon in black 

crusts is difficult. There is often a carbonate component which is derived from the building material, 

plus OC and EC, derived from air pollution. EC is considered to be a tracer for combustion sources, 

December 2009 9-194 
whereas OC may derive from multiple sources, including atmospheric deposition of primary and 

secondary pollutants, and the decay of protective organic treatments (Bonazza et al., 2005, 155695). 

Bonazza et al. (2005, 155695) quantified OC and EC in damage layers on European cultural heritage 

structures. OC predominated over EC at almost all locations investigated. Traffic appeared to be the 

major source of fine carbonaceous particles, with organic matter as the main component (Putaud et 

al., 2004, 055545). Viles and Gorbushina (2003, 156138) found that soiling in Oxford, U.K. showed 

a relationship with traffic and NO2 concentrations. 

In addition to the soiling effects of EC, much soiling appears to be largely of microbiological 

origin (Viles and Gorbushina, 2003, 156138). Microbial biofilms, composed mainly of fungi, can 

stain exposed rock surfaces with yellow, orange, brown, gray, or black colors. Microorganisms may 

be able to trap PM more efficiently than the stone surface itself. In addition, microbial growth may 

be stimulated by organic or nutrient constituents in PM deposition. 

Viles et al. (2002, 156137) investigated the nature of soiling on limestone tablets in relation to 

ambient air pollution and climate at three contrasting sites in Great Britain over periods of one to 

eight years. Spectrophotometer and microscope observations suggested that there were not consistent 

trends in soiling over time at the study sites. Each site behaved differently in terms of the temporal 

development of soiling and the differences between sheltered and exposed limestone tablets. In 

addition, organisms played important roles in the soiling response, even at the highly polluted site. 

Some work has been conducted on public perception regarding the lightness of historic 

buildings and the aesthetic need for cleaning subsequent to soiling by air pollution. Brimblecombe 

and Grossi (2005, 155703) found a strong relationship between the perceived lightness of a building 

and the opinion that it was dirty. This relationship was used to establish levels of blackening that 

might be publicly acceptable. 

Recently, the importance of organic contaminant deposition to the overall air pollution damage 

to building materials has been recognized. Low molecular weight organic anions such as formate, 

acetate, and oxalate are ubiquitous in black crusts in damage layers on stones and mortars sampled 

from monuments and buildings throughout Europe (Sabbioni et al., 2003, 049282). This has been 

observed at urban, suburban, and rural sites. 

9.5.1. Effects on Paint 

Studies have evaluated the soiling effects of particles on painted surfaces (U.S. EPA, 2004, 

056905). Particles composed of EC, acids, and various other constituents are responsible for the 

soiling of structural painted surfaces. Coarse-mode particles (>2.5 μm) initially contribute more 

soiling of horizontal and vertical painted surfaces than do fine-mode particles (<2.5 μm), but are 

more easily removed by rain (Haynie and Lemmons, 1990, 044579). Rain interacts with coarse 

particles, dissolving the particle and leaving stains on the painted surface (Creighton et al., 1990, 

044578; Haynie and Lemmons, 1990, 044579). Particle deposition contributes to increased 

frequency of cleaning of painted surfaces and physical damage to the painted surface. Air pollution 

affects the durability of paint finishes by promoting discoloration, chalking, loss of gloss, erosion, 

blistering, and peeling (U.S. EPA, 2004, 056905). There have been no new developments in this field 

subsequent to the review of the U.S. EPA (2004, 056905). 

9.5.2. Effects on Metal Surfaces 

Metals undergo natural weathering processes. The effects of air pollutants on natural 

weathering processes depend on the nature of the pollutant(s), the deposition rate, and the presence 

of moisture (U.S. EPA, 2004, 056905). Pollutant effects on metal surfaces are governed by such 

factors as the presence of protective corrosion films and surface electrolytes, the orientation of the 

metal surface, and surface moisture. Surface moisture facilitates particulate deposition and promotes 

corrosive reactions. Formation of hygroscopic salts increases the duration of surface wetness and 

enhances corrosion. 

A corrosion film, such as for example the rust layer on the surface of some metals, may 

provide some protection against further corrosion. Its effectiveness in retarding the corrosion process 

is affected by the solubility of the corrosion layer and the pollutant exposure. Other than the effects 

of acidifying compounds, there has not been additional research conducted in recent years on the 

effects of PM deposition on metal corrosion. 

December 2009 9-195 
9.5.3. Effects on Stone 

Air pollutants can enhance the natural weathering processes on building stone. The 

development of crusts on stone monuments has been attributed to the interaction of the stone’s 

surface with pollutants, wet or dry deposition of atmospheric particles, and dry deposition of gypsum 

particles. Because of a greater porosity and specific surface, mortars have a high potential for 

reacting with environmental pollutants (Zappia et al., 1998, 012037). 

Most research evaluating the effects of air pollutants on stone structures has concentrated on 

gaseous pollutants (U.S. EPA, 2004, 056905). The dark color of gypsum is attributed to soiling by 

carbonaceous particles. A lighter gray colored crust is attributed to soil dust and metal deposits 

(Ausset et al., 1998, 040480; Camuffo, 1995, 076278; Lorusso et al., 1997, 084534; Moropoulou et 

al., 1998, 040485). Lorusso et al. (1997, 084534) attributed the need for frequent cleaning and 

restoration of historic monuments in Rome to exposure to total suspended particulates. 

Grossi et al. (2003, 155812) investigated the black soiling rates of building granite, marble, 

and limestone in two urban environments with different climates. Horizontal specimens were 

exposed, both sheltered and unsheltered from rainfall. Limestone showed soiling proportional to the 

square root of the time of exposure, but granite and marble did not. 

Black soiling is caused mainly by particulate EC (PEC). For that reason, it is most prevalent in 

urban environments due to the formation of carbonaceous fine particles from the incomplete 

combustion of fossil fuels. Traffic emissions, especially from diesel engines, and wood burning are 

important sources of PEC (Grossi et al., 2003, 155812). 

Kamh (2005, 155888) studied the effects of weathering on Conway Castle, an historical 

structure in Great Britain built about 1289 AC. The weathering was identified as honeycomb, 

blackcrust, exfoliation, and discoloration, with white salt efflorescence at some parts. These features 

are diagnostic for salt weathering (Goudie et al., 2002, 156486), and this was confirmed by 

laboratory analyses, including scanning electron microscopy and x-ray diffraction. The authors 

concluded that the salt was derived from three sources: sea spray, chemical alteration of the 

carbonate in mortar into SO4

2– salts by acidic deposition, and wet deposition of air pollutants on the 

stone surface. The salt content on the rock surface fills the rock pores and then exerts high pressure 

on the rock texture due to hydration of the salt in the cold humid environment. In particular, CaSO4

and Na2SO4 exert enough pressure on hydration as to deteriorate construction rock at both the microand macroscale (Moses and Smith, 1994, 156785). 

9.5.4. Summary of Effects on Materials 

Building materials (metals, stones, cements, and paints) undergo natural weathering processes 

from exposure to environmental elements (wind, moisture, temperature fluctuations, sunlight, etc.). 

Metals form a protective film of oxidized metal (e.g., rust) that slows environmentally induced 

corrosion. However, the natural process of metal corrosion is enhanced by exposure to 

anthropogenic pollutants. For example, formation of hygroscopic salts increases the duration of 

surface wetness and enhances corrosion. 

A significant detrimental effect of particle pollution is the soiling of painted surfaces and other 

building materials. Soiling changes the reflectance of opaque materials and reduces the transmission 

of light through transparent materials. Soiling is a degradation process that requires remediation by 

cleaning or washing, and, depending on the soiled surface, repainting. Particulate deposition can 

result in increased cleaning frequency of the exposed surface and may reduce the usefulness of the 

soiled material. Attempts have been made to quantify the pollutant exposure at which materials 

damage and soiling have been perceived. However, to date, insufficient data are available to advance 

the knowledge regarding perception thresholds with respect to pollutant concentration, particle size, 

and chemical composition. Nevertheless, the evidence is sufficient to conclude that a causal 

relationship exists between PM and effects on materials. 

December 2009 9-196 
Chapter 9 References 

Abdou W; Diner D; Martonchik J; Bruegge C; Kahn R; Gaitley B; Crean K; Remer L; Holben B (2005). Comparison of 

coincident Multiangle Imaging Spectroradiometer and Moderate Resolution Imaging Spectroradiometer aerosol 

optical depths over land and ocean scenes containing Aerosol Robotic Network sites. J Geophys Res, 110: D10S07. 

190028

Aboal JR; Fernández JA; Carballeira A (2004). Oak leaves and pine needles as biomonitors of airborne trace elements 

pollution. Environ Exp Bot, 51: 215-225. 155642

ABT (2001). Assessing Public Opinions on Visibility Impairment Due to Air Pollution: Summary Report. 156185

ABT (2002). Sense of Place and Stewardship: Final Focus Group Report. 156186

Ackerman A; Kirkpatrick M; Stevens D; Toon O (2004). The impact of humidity above stratiform clouds on indirect 

aerosol climate forcing. Nature, 432: 1014-1017. 190056

Ackerman AS; Toon OB; Hobbs PV (1994). Reassessing the dependence of cloud condensation nucleus concentration on 

formation rate. Nature, 367: 445-447. 189975

Ackerman AS; Toon OB; Stevens DE; Heymsfield AJ; Ramanathan V; Welton EJ (2000). Reduction of tropical cloudiness 

by soot. Sci New York, 288: 1042-1047. 002987

Ackerman T; Stokes G (2003). The atmospheric radiation measurement program. Phys Today, 56: 38-44. 192080

Adachi K; Tainosho Y (2004). Characterization of heavy metal particles embedded in tire dust. Environ Int, 30: 1009-1017. 

081380

Adamo P; Crisafulli P; Giordano S; Minganti V; Modenesi P; Monaci F; Pittao E; Tretiach M; Bargagli R (2007). Lichen 

and moss bags as monitoring devices in urban areas. Part II: Trace element content in living and dead biomonitors 

and comparison with synthetic materials. Environ Pollut, 146: 392-399. 155644

Albrecht B (1989). Aerosols, cloud microphysics, and fractional cloudiness. Science, 245: 1227-1230. 045783

Alexeeva LB; Strachan WMJ; Shlychkova VV; Nazarova AA; Nikanorov AM; Korotova LG (2001). Organochlorine 

pesticides and trace metal monitoring of Russian rivers flowing to the Arctic Ocean. Mar Pollut Bull, 43: 71-85. 

155651

Alfani A; De Nicola F; Maisto G; Prati MV (2005). Long-term PAH accumulation after bud break in Quercus ilex L leaves 

in a polluted environment. Atmos Environ, 39: 307-314. 154319

Allen GA; Lawrence J; Koutrakis P (1999). Field validation of a semi-continuous method for aerosol black carbon 

(aethalometer) and temporal patterns of summertime hourly black carbon measurements in southwestern PA. Atmos 

Environ, 33: 817-823. 048923

Almas AR; Bakken LR; Mulder J (2004). Changes in tolerance of soil microbial communities in Zn and Cd contaminated 

soils. Soil Biol Biochem, 36: 805-813. 155654

Alpert P; Kishcha P; Kaufman Y; Schwarzbard R (2005). Global dimming or local dimming?: Effect of urbanization on 

sunlight availability. Geophys Res Lett, 32: 17. 190047

AMAP (2004). AMAP Assessment 2002: Persistent Organic Pollutants in the Arctic. Arctic Monitoring and Assessment 

Programme. Oslo, Norway. 190168

Ammann CM; Meehl GA; Washington WM; Zender CS (2003). A monthly and latitudinally varying volcanic forcing 

dataset in simulations of 20th century climate. Geophys Res Lett, 30: 1657. 192057

Anderson HR; Atkinson RW; Bremner SA; Marston L (2003). Particulate air pollution and hospital admissions for 

cardiorespiratory diseases: are the elderly at greater risk? Eur Respir J, 40: 39s-46s. 054820

 

Note: Hyperlinks to the reference citations throughout this document will take you to the NCEA HERO database (Health and 

Environmental Research Online) at http://epa.gov/hero. HERO is a database of scientific literature used by U.S. EPA in the process of 

developing science assessments such as the Integrated Science Assessments (ISA) and the Integrated Risk Information System (IRIS). 

December 2009 9-197 
Anderson T; Charlson R; Bellouin N; Boucher O; Chin M; Christopher S; Haywood J; Kaufman Y; Kinne S; Ogren J 

(2005). An “A-Train” strategy for quantifying direct climate forcing by anthropogenic aerosols. Bull Am Meteorol 

Soc, 86: 1795-1809. 189993

Anderson T; Wu Y; Chu D; Schmid B; Redemann J; Dubovik O (2005). Testing the MODIS satellite retrieval of aerosol 

fine-mode fraction. J Geophys Res, 110: D18204. 189991

Anderson TL; Ogren JA (1998). Determining aerosol radiative properties using the TSI 3563 integrating nephelometer. 

Aerosol Sci Technol, 29: 57-69. 156213

Andreae MO; Gelencsér A (2006). Black carbon or brown carbon? The nature of light-absorbing carbonaceous aerosols. 

Atmos Chem Phys, 6: 3131-3148. 156215

Andreae MO; Rosenfeld D; Artaxo P; Costa AA; Frank GP; Longo KM; Silva-Dias MAF (2004). Atmospheric science: 

smoking rain clouds over the Amazon. Sci New York, 303: 1337-1341. 155658

Andrews E; Sheridan PJ; Ogren JA; Ferrare R (2004). In situ aerosol profiles over the Southern Great Plains cloud and 

radiation test bed site: 1. Aerosol optical properties. J Geophys Res, 109: D06208. 190058

Andronova NG; Rozanov EV; Yang F; Schlesinger ME; Stenchikov GL (1999). Radiative forcing by volcanic aerosols 

from 1850 to 1994. J Geophys Res, 104: 16807–16826. 192286

Antuña JC; Robock A; Stenchikov G; Zhou J; David C; Barnes J; Thomason L (2003). Spatial and temporal variability of 

the stratospheric aerosol cloud produced by the 1991 Mount Pinatubo eruption. J Geophys Res, 108: 4624. 192251

Arizona DEQ (2000). Phoenix metropolitan area visibility briefing paper. Arizone Department of Environmental Quality. 

Phoenix, AZ. 019164

Armbrust DV; Retta A (2002). Wind and sandblast damage to growing vegetation. Ann Arid Zone, 39: 273-284. 156225

Arnott WP; Moosmuller H; Rogers CF; Jin T; Bruch R (1999). Photoacoustic spectrometer for measuring light absorption 

by aerosol; instrument description. Atmos Environ, 33: 2845-2852. 020650

Audet P; Charest C (2007). Heavy metal phytoremediation from a meta-analytical perspective. Environ Pollut, 147: 231-

237. 190169

Augustine J; Hodges G; Dutton E; Michalsky J; Cornwall C (2008). An aerosol optical depth climatology for NOAA's 

national surface radiation budget network (SURFRAD). J Geophys Res, 113: D11204. 189913

Ausset P; Bannery F; Del Monte M; Lefevre RA (1998). Recording of pre-industrial atmospheric environment by ancient 

crusts on stone monuments. Atmos Environ, 32: 2859-2863. 040480

Avila A; Rodrigo A (2004). Trace metal fluxes in bulk deposition, throughfall and stemflow at two evergreen oakstands in 

NE Spain subject to different exposure to the industrial environment. Atmos Environ, 38: 171-180. 155664

Babich P; Wang PY; Allen G; Sioutas C; Koutrakis P (2000). Development and Evaluation of a Continuous Ambient 

PM2.5 Mass Monitor. Aerosol Sci Technol, 32: 309-324. 156239

Bachelet D; Lenihan J; Neilson R; Drapek R; Kittel T (2005). Simulating the response of natural ecosystems and their fire 

regimes to climatic variability in Alaska. Can J For Res, 35: 2244-2257. 156241

Backe C; Cousins IT; Larsson P (2004). PCB in soils and estimated soil-air exchange fluxes of selected PCB congeners in 

the south of Sweden. Environ Pollut, 128: 59-72. 155668

Bäckström M; Nilsson U; Håkansson K; Allard B; Karlsson S (2003). Speciation of heavy metals in road runoff and 

roadside total deposition. Water Air Soil Pollut, 147: 343-366. 156242

Bailey R; Barrie LA; Halsall CJ; Fellin P; Muir DCG (2000). Atmospheric organochlorine pesticides in the western 

Canadian Arctic: evidence of trans-Pacific transport. J Geophys Res, 105: 11805-11811. 155670

Baker M; Charlson R (1990). Bistability of CCN concentrations and thermodynamics in the cloud-topped boundary layer. 

Nature, 345: 142-145. 190016

Balkanski Y; Schulz M; Claquin T; Guibert S (2007). Reevaluation of mineral aerosol radiative forcings suggests a better 

agreement with satellite and AERONET data. Atmos Chem Phys, 7: 81-95. 189979

Baptista MS; Vasconcelos MTSD; Cabral JP; Freitas MC; Pacheco AMG (2008). Copper, nickel and lead in lichen and tree 

bark transplants over different periods of time. Environ Pollut, 151: 408-413. 155673

December 2009 9-198 
Baran AJ; Foot JS (1994). New application of the operational sounder HIRS in determining a climatology of sulphuric acid 

aerosol from the Pinatubo eruption. J Geophys Res, 99: 25673-25679. 192032

Barker ML (1976). Planning for environmental indices: observer appraisals of air quality. In K Craig; E Zube 

(Ed.),Perceiving Environmental Quality (pp. 175-203). New York: Plenum Press. 072137

Barnes JE; Hofmann DJ (1997). Lidar measurements of stratospheric aerosol over Mauna Loa Observatory. Geophys Res 

Lett, 24: 1923?1926. 192044

Bates T; Anderson T; Baynard T; Bond T; Boucher O; Carmichael G; Clarke A; Erlick C; Guo H; Horowitz L (2006). 

Aerosol direct radiative effects over the northwest Atlantic, northwest Pacific, and North Indian Oceans: estimates 

based on in-situ chemical and optical measurements and chemical transport modeling. Atmos Chem Phys Discuss, 

6: 175-362. 189912

Bates T; Huebert B; Gras J; Griffiths B; Durkee P (1998). The International Global Atmospheric Chemistry (IGAC) 

Project?s First Aerosol Characterization Experiment (ACE 1)-Overview. J Geophys Res, 103: 16297-16318. 

190063

Bates T; Quinn P; Coffman D; Covert D; Miller T; Johnson J; Carmichael G; Uno I; Guazzotti S; Sodeman D (2004). 

Marine boundary layer dust and pollutant transport associated with the passage of a frontal system over eastern 

Asia. J Geophys Res, 109: D19S19. 189958

Bates TS; Quinn PK; Coffman DJ; Johnson JE; Miller TL; Covert DS; Wiedensohler A; Leinert S; Nowak A; Neususs C 

(2001). Regional physical and chemical properties of the marine boundary layer aerosol across the Atlantic during 

Aerosols99: an overview. J Geophys Res, 106: 20,767-20,782. 043385

Bauman JJ; Russell PB; Geller MA; Hamill P (2003). A stratospheric aerosol climatology from SAGE II and CLAES 

measurements: 2. Results and comparisons. J Geophys Res, 108: 4383. 192265

Baumgardner RE Jr; Isil SS; Bowser JJ; Fitzgerald KM (1999). Measurements of rural sulfur dioxide and particle sulfate: 

analysis of CASTNet data, 1987 through 1996. J Air Waste Manag Assoc, 49: 1266-1279. 011308

Baynard T; Lovejoy ER; Pettersson A; Brown SS; Lack D; Osthoff H; Massoli P; Ciciora S; Dube WP; Ravishankara AR 

(2007). Design and application of a pulsed cavity ring-down aerosol extinction spectrometer for field 

measurements. Aerosol Sci Technol, 41: 447-462. 151669

BBC Research & Consulting (2002). Phoenix Area Visibility Survey. Draft Report. Available at 

http://www.azdeq.gov/environ/air/download/vis_021903f.pdf. 156258

Becker T; Brändel M (2007). Vegetation-environment relationships in a heavy metal-dry grassland complex. Folia Geobot, 

42: 11-28. 156260

Beeby A; Richmond L (2002). Evaluating Helix aspersa as a sentinel for mapping metal pollution. Ecol Indicat, 1: 261-270. 

155680

Bellouin N; Boucher O; Haywood J; Reddy MS (2005). Global emissions of aerosol direct radiative forcing from satellite 

measurements. Nature, 438: 1138-1141. 155684

Bellouin N; Boucher O; Tanré D; Dubovik O (2003). Aerosol absorption over the clear-sky oceans deduced from 

POLDER-1 and AERONET observations. Geophys Res Lett, 30: 1748. 189911

Bellouin N; Jones A; Haywood J; Christopher S (2008). Updated estimate of aerosol direct radiative forcing from satellite 

observations and comparison against the Hadley Centre climate model. J Geophys Res, 113: D10205. 189999

Beron K; Murdoch J; Thayer M (2001). The Benefits of Visibility Improvement: New Evidence from the Los Angeles 

Metropolitan Area. J R Estate Finance Econ, 22: 319-337. 156270

Berthelsen BO; Olsen RA; Steinnes E (1995). Ectomycorrhizal heavy metal accumulation as a contributing factor to heavy 

metal levels in organic surface soils. Sci Total Environ, 170: 141-149. 078058

Bhavsar SP; Diamond ML; Evans LJ; Gandhi N; Nilsen J; Antunes P (2004). Development of a coupled metal speciation–

fate model for surface aquatic systems. Environ Toxicol Chem, 23: 1376– 1385. 155689

Bhavsar SP; Diamond ML; Gandhi N; Nilsen J (2004). Dynamic coupled metal transport–speciation model: application to 

assess a zinc-contaminated lake. Environ Toxicol Chem, 23: 2410–2420. 155690

Bickerstaff K; Walker G (2001). Public understandings of air pollution: the ‘localisation’of environmental risk. Global 

Environ Change, 11: 133-145. 156271

December 2009 9-199 
Bidleman TF; Jantunen LMM; Helm PA; Bronstrom-Lunden E; Juntto S (2002). Chlordane isomers and enantiomers 

suggest changing sources in Arctic air. Environ Sci Technol, 36: 539-544. 155691

Billings WD (1978). Plants and the ecosystem. Belmont, CA: Wadsworth Publishing Company, Inc. 034165

Bingen C; Fussen D; Vanhellemont F (2004). A global climatology of stratospheric aerosol size distribution parameters 

derived from SAGE II data over the period 1984?2000: 2. Reference data . J Geophys Res, 102: D06202. 192262

Blanchard C; Tanenbaum S (2006). Weekday/weekend differences in ambient air pollutant concentrations in Atlanta and 

the southeastern United States. J Air Waste Manag Assoc, 56: 271-284. 190005

Blanchard CL; Tanenbaum S; Hidy GM (2007). Effects of sulfur dioxide and oxides of nitrogen emission reductions on 

fine particulate matter mass concentrations: regional comparisons. J Air Waste Manag Assoc, 57: 1337-50. 098659

Bluth GJS; Doiron SD; Schnetzler CC; Krueger AJ; Walter LS (1992). Global tracking of the SO2 clouds from the June, 

1991 Mount Pinatubo eruptions . Geophys Res Lett, 19: 151-154. 192029

Bluth GJS; Rose WI; Sprod IE (1997). Stratospheric loading of sulfur from explosive volcanic eruptions. J Geol, 105: 671-

683. 192045

Bonazza A; Sabbioni C; Ghedini N (2005). Quantitative data on carbon fractions in interpretation of black crusts and 

soiling on European built heritage. Atmos Environ, 39: 2607-2618. 155695

Bond T; Bhardwaj E; Dong R; Jogani R; Jung S; Roden C; Streets D; Trautmann N (2007). Historical emissions of black 

and organic carbon aerosol from energy-related combustion, 1850-2000. Global Biogeochem Cycles, 21: GB2018. 

190050

Bond TC; Anderson TL; Campbell D (1999). Calibration and Intercomparison of Filter-Based Measurements of Visible 

Light Absorption by Aerosols. Aerosol Sci Technol, 30: 582-600. 156281

Bond TC; Bergstrom RW (2005). Light absorption by carbonaceous particles: an investigative review. Aerosol Sci Technol, 

39: 1-41. 155696

Bond TC; Streets DG; Yarber KF; Nelson SM; Woo J-H; Klimont Z (2004). A technology-based global inventory of black 

and organic carbon emissions from combustion. J Geophys Res, 109: D14203. 056389

Bond TC; Sun H (2005). Can reducing black carbon emissions counteract global warming? Environ Sci Technol, 39: 5921-

5926. 156282

Boucher O; Tanré D (2000). Estimation of the aerosol perturbation to the Earth's radiative budget over oceans using 

POLDER satellite aerosol retrievals. Geophys Res Lett, 27: 1103-1106. 190041

Boucher U; Balabane M; Lamy I; Cambier P (2005). Decomposition in soil microcosms of leaves of the metallophyte 

Arabidopsis halleri: Effect of leaf-associated heavy metals on biodegradation. Environ Pollut, 135: 187-194. 

155699

Brasseur G; Granier C (1992). Mount Pinatubo aerosols, chlorofluorocarbons, and ozone depletion. Science, 257: 1239-

1242. 192256

Brenguier J; Chuang P; Fouquart Y; Johnson D; Parol F; Pawlowska H; Pelon J; Schuller L; Schroder F; Snider J (2000). 

An overview of the ACE-2 CLOUDYCOLUMN closure experiment. Tellus B Chem Phys Meteorol, 52: 815-827. 

189966

Brimblecombe P; Grossi CM (2005). Aesthetic thresholds and blackening of stone buildings. Sci Total Environ, 349: 175-

189. 155703

Broccoli AJ; Dixon KW; Delworth TL; Knutson TR; Stouffer RJ; Zeng F (2003). Twentieth-century temperature and 

precipitation trends in ensemble climate simulations including natural and anthropogenic forcing . J Geophys Res, 

108: 4798. 192283

Brookshire D (1979). Methods Development for Assessing Air Pollution Control Benefits. Volume 2: Experiments in 

Valuing Nonmarket Goods. A Case Study of Alternative Benefit Measures of Air Pollution in the South Coast Air 

Basin of Southern California. EPA. Washington, DC. 156298

Burkhardt J; Kaiser H; Kappen L; Goldbach HE (2002). The possible role of aerosols on stomatal conductivity for water 

vapour. Basic Appl Ecol, 2: 351–364. 155708

December 2009 9-200 
Burt R; Wilson MA; Mays MD; Lee CW (2003). Major and trace elements of selected pedons in the USA. J Environ Qual, 

32: 2109-2121. 155709

Campbell D; Copeland S; Cahill T (1995). Measurement of Aerosol Absorption Coefficient from Teflon Filters using 

Integrating Plate and Integrating Sphere Techniques. Aerosol Sci Technol, 22: 287-292. 190171

Campbell FW (1983). Ambient Stressors. Environ Behav, 15: 355-380. 190172

Camuffo D (1995). Physical weathering of stones. Presented at In: Saiz-Jimenez, C., ed. The deterioration of monuments: 

proceedings of the 2nd international symposium on biodeterioration and biodegradation; January 1994; Sevilla, 

Spain. Sci. Total Environ. 167: 1-14. 076278

Canada-US Air Quality Committee (2004). Canada-United States transboundary particulate matter science assessment. 

Meteorological Service of Canada. Toronto, Ontario, Canada. En56-203/2004E. http://www.mscsmc.ec.gc.ca/saib/smog/transboundary/toc_e.html. 190519

Carmichael G; Tang Y; Kurata G; Uno I; Streets D; Thongboonchoo N; Woo J; Guttikunda S; White A; Wang T (2003). 

Evaluating regional emission estimates using the TRACE-P observations. J Geophys Res, 108: 8810. 190042

Carmichael GR; Calori G; Hayami H; Uno I; Cho SY; Engardt M; Kim SB; Ichikawa Y; Ikeda Y; Woo JH; Ueda H; Amann 

M (2002). The MICS-Asia study: model intercomparison of long-range transport and sulfur deposition in East Asia. 

Atmos Environ, 36: 175-199. 148319

Carrico C; Kreidenweis S; Malm W; Day D; Lee T; Carrillo J; McMeeking G; Collett J (2005). Hygroscopic growth 

behavior of a carbon-dominated aerosol in Yosemite National Park. Atmos Environ, 39: 1393-1404. 190052

Carvalho LM; Caçador I; Martins-Loução MA (2006). Arbuscular mycorrhizal fungi enhance root cadmium and copper 

accumulation in the roots of the salt marsh plant Aster tripolium L. Plant Soil, 285: 161-169. 155715

CCSP (2008). Climate Projections Based on Emissions Scenarios for Long-lived and Short-lived Radiatively Active Gases 

and Aerosols. A Report by the U.S. Climate Change Science, Program and the Subcommittee on Global Change 

Research . Department of Commerce. Washington, DC. 192028

Chameides WL; Yu H; Liu SC; Bergin M; Zhou X; Mearns L; Wang G; Kiang CS; Saylor RD; Luo C; Huang Y; Steiner A; 

Giorgi F (1999). Case study of the effects of atmospheric aerosols and regional haze on agriculture: an opportunity 

to enhance crop yields in China through emission controls? PNAS, 96: 13626-13633. 011184

Chand D; Anderson T; Wood R; Charlson R; Hu Y; Liu Z; Vaughan M (2008). Quantifying above-cloud aerosol using 

spaceborne lidar for improved understanding of cloudy-sky direct climate forcing. J Geophys Res, 113: D13206. 

189974

Charlson R; Pilat M (1969). Climate: the influence of aerosols. J Appl Meteorol, 8: 1001-1002. 190025

Charlson R; Wigley T (1994). Sulfate aerosol and climate change. Sci Am, 270: 28-35. 189989

Charlson RJ; Ahlquist NC; Horvath H (1968). On the generality of correlation of atmospheric aerosol mass concentration 

and light scatter. Atmos Environ, 2: 455-464. 095355

Charlson RJ; Langner J; Rodhe H; Leovy CB; Warren SG (1991). Perturbation of the northern hemisphere radiative 

balance by backscattering from anthropogenic sulfate aerosols. Tellus Dyn Meteorol Oceanogr, 43AB: 152-163. 

045793

Charlson RJ; Schwartz SE; Hales JM; Cess RD; Coakley JA Jr; Hansen JE; Hofmann DJ (1992). Climate forcing by 

anthropogenic aerosols. Science, 255: 423-430. 045794

Cheggour M; Chafik A; Fisher NS; Benbrahim S (2005). Metal concentrations in sediments and clams in four Moroccan 

estuaries. Mar Environ Res, 59: 119-137. 155723

Chen W; Kahn R; Nelson D; Yau K; Seinfeld J (2008). Sensitivity of multiangle imaging to the optical and microphysical 

properties of biomass burning aerosols. J Geophys Res, 113: D10203. 189984

Chen Y; Penner JE (2005). Uncertainty analysis of the first indirect aerosol effect. Atmos Chem Phys Discuss, 5: 4507–

4543. 199918

Chernyak SM; Rice CP; McConnell LL (1996). Evidence of currently used pesticides in air, ice, fog, seawater and surface 

microlayer in the Bering and Chukchi Seas. Mar Pollut Bull, 32: 410-419. 155726

December 2009 9-201 
Chestnut LG; Dennis RL (1997). Economic benefits of improvements in visibility: acid rain Provisions of the 1990 Clean 

Air Act Amendments. J Air Waste Manag Assoc, 47: 395-402. 014525

Chin M; Diehl T; Ginoux P; Malm W (2007). Intercontinental transport of pollution and dust aerosols: implications for 

regional air quality. Atmos Chem Phys, 7: 5501-5517. 190062

Chin M; Ginoux P; Kinne S; Torres O; Holben B; Duncan B; Martin R; Logan J; Higurashi A; Nakajima T (2002). 

Tropospheric aerosol optical thickness from the GOCART model and comparisons with satellite and Sun 

photometer measurements. J Atmos Sci, 59: 461-483. 189996

Chin M; Kahn RA; Remer LA; Yu H; Rind D; Feingold G; Quinn PK; Schwartz SE; Streets DG; DeCola P; Halthorne R 

(2009). Atmospheric aerosol properties and climate impacts. Synthesis and assessment product 2.3. A report by the 

U.S. Climate Change Science Program and the Subcommittee on Global Change Research. National Aeronautics 

and Space Administration. Washington, DC. 192130

Chiou CT; Sheng G; Manes M (2001). A partition-limited model for the plant uptake of organic contaminants from soil and 

water. Environ Sci Technol, 35: 1437–1444. 156342

Chipperfield MP; Randel WJ; Bodeker GE; Johnston P (2003). Global ozone: past and future. In Scientific Assessment of 

Ozone Depletion: 2002 (pp. 4.1-4.90). Geneva: World Meteorological Organization. 192275

Chock DP; Song Q; Hass H; Schell B; Ackermann I (2003). Comment on "Control of fossil-fuel particulate black carbon 

and organic matter, possibly the most effective method of slowing global warming" by Jacobson, M. Z. J Geophys 

Res, 108: ACH12.1-ACH13.4. 155727

Chou M; Chan P; Wang M (2002). Aerosol radiative forcing derived from SeaWiFS-retrieved aerosol optical properties. J 

Atmos Sci, 59: 748-757. 190008

Chow JC; Watson JG; Edgerton SA; Vega E (2002). Chemical composition of PM10 and PM25 in Mexico City during 

winter 1997. Sci Total Environ, 287: 177-201. 036166

Chow JC; Watson JG; Lowenthal DH; Richards LW (2002). Comparability between PM25 and particle light scattering 

measurements. Environ Monit Assess, 79: 29-45. 037784

Christie P; Li XL; Chen BD (2004). Arbuscular mycorrhiza can depress translocation of zinc to shoots of host plants in 

soils moderately polluted with zinc. Plant Soil, 261: 209-217. 190174

Christopher S; Jones T (2008). Short-wave aerosol radiative efficiency over the global oceans derived from satellite data. 

Tellus B Chem Phys Meteorol, 60: 636-640. 189985

Christopher SA; Zhang J (2002). Daytime variation of shortwave direct radiative forcing of biomass burning aerosols from 

GOES-8 imager. J Atmos Sci, 59: 681-691. 190031

Christopher SA; Zhang J; Kaufman YJ; Remer L (2006). Satellite-based assessment of the top of the atmosphere 

anthropogenic aerosol radiative forcing over cloud-free oceans. Geophys Res Lett, 111: L15816. 155729

Chu D; Kaufman Y; Ichoku C; Remer L; Tanré D; Holben B (2002). Validation of MODIS aerosol optical depth retrieval 

over land. Geophys Res Lett, 29: 8007. 190001

Chuang CC; Penner JE; Prospero JM; Grant KE; Rau GH; Kawamoto K (2002). Cloud susceptibility and the first aerosol 

indirect forcing: Sensitivity to black carbon and aerosol concentrations. J Geophys Res, 107: 4564. 199912

Chung C; Zhang G (2004). Impact of absorbing aerosol on precipitation: Dynamic aspects in association with convective 

available potential energy and convective parameterization closure and dependence on aerosol heating profile. J 

Geophys Res, 109: D22103. 190054

Chung CE; Ramanathan V; Kim D; Podgorny IA (2005). Global anthropogenic aerosol direct forcing derived from satellite 

and ground-based observations. J Geophys Res, 110: D24207. 155733

Chung SH; Seinfeld JH (2002). Global distribution and climate forcing of carbonaceous aerosols. J Geophys Res, 107: 

4407. 155732

Citterio S; Prato N; Fumagalli P; Aina R; Massa N; Santagostino A; Sgorbati S; Berta G (2005). The arbuscular 

mycorrhizal fungus Glomus mosseae induces growth and metal accumulation changes in Cannabis sativa L. 

Chemosphere, 59: 21-29. 190176

December 2009 9-202 
Clarke A; Porter J; Valero F; Pilewskie P (1996). Vertical profiles, aerosol microphysics, and optical closure during the 

Atlantic Stratocumulus Transition Experiment: Measured and modeled column optical properties. J Geophys Res, 

101: 4443-4453. 190003

Clemens S (2006). Toxic metal accumulation, responses to exposure and mechanisms of tolerance in plants. Biochimie, 88: 

1707-1719. 190179

Coakley JA; Walsh CD (2002). Limits to the aerosol indirect radiative effect derived from observations of ship tracks. J 

Atmos Sci, 59: 668-680. 192025

Coelho JP; Rosa M; Pereira E; Duarte A; Pardal MA (2006). Pattern and annual rates of Scrobicularia plana mercury 

bioaccumulation in a human induced mercury gradient (Ria de Aveiro, Portugal). Estuar Coast Mar Sci, 69: 629-

635. 190181

Cohen S; Evans GW; Stokols D; Krantz DS (1986). Behavior, Health, and Environmental Stress. New York: Plenum Press. 

190182

Collins D; Johnsson H; Seinfeld J; Flagan R; Gasso S; Hegg D; Russell P; Schmid B; Livingston J; Ostrom E (2000). In 

situ aerosol-size distributions and clear-column radiative closure during ACE-2. Tellus B Chem Phys Meteorol, 52: 

498-525. 190059

Collins RN; Merrington G; McLaughlin MJ; Morel JL (2003). Organic ligand and pH effects on isotopically exchangeable 

cadmium in polluted soils. Soil Sci Soc Am J, 67: 112-121. 155737

Collins W; Rasch P; Eaton B; Khattatov B; Lamarque J; Zender C (2001). Simulating aerosols using a chemical transport 

model with assimilation of satellite aerosol retrievals: Methodology for INDOEX. J Geophys Res, 106: 7313–7336. 

189987

Conant W; VanReken T; Rissman T; Varutbangkul V; Jonsson H; Nenes A; Jimenez J; Delia A; Bahreini R; Roberts G 

(2004). Aerosol-cloud drop concentration closure in warm cumulus. J Geophys Res, 109: D13204. 190010

Cooke W; Wilson J (1996). A global black carbon aerosol model. J Geophys Res, 101: 19,395–19,409. 190046

Cooke WF; Liousse C; Cachier H; Feichter J (1999). Construction of a 1 ° x 1 ° fossil fuel emission data set for 

carbonaceous aerosol and implementation and radiative impact in the ECHAM 4 model. J Geophys Res, 104: 

22137-22162. 156365

Costa M; Silva A; Levizzani V (2004). Aerosol characterization and direct radiative forcing assessment over the ocean. Part 

I: Methodology and sensitivity analysis. J Appl Meteorol, 43: 1799-1817. 190006

Costa MJ; Levizzani V; Silva AM (2004). Aerosol characterization and direct radiative forcing assessment over the ocean. 

Part II: Application to test cases and validation. J Appl Meteorol, 43: 1818-1833. 192022

Couto JA; Fernández JA; Aboal JR; Carballeira A (2004). Active biomonitoring of element uptake with terrestrial mosses: 

a comparison of bulk and dry deposition. Sci Total Environ, 324: 211-222. 155739

Covert DS; Charlson RJ; Ahlquist NC (1972). A study of the relationship of chemical composition and humidity to light 

scattering by aerosols. J Appl Meteorol, 11: 968-976. 072055

Crawford DW; Lipsen MS; Purdie DA; Lohan MC; Statham PJ; Whitney FA; Putland JN; Johnson WK; Sutherland N; 

Peterson TD; Harrison PJ; Wong CS (2003). Influence of zinc and iron enrichments on phytoplankton growth in the 

northeastern subarctic Pacific. Limnol Oceanogr, 48: 1583-1600. 156370

Creighton PJ; Lioy PJ; Haynie FH; Lemmons TJ; Miller JL; Gerhart J (1990). Soiling by atmospheric aerosols in an urban 

industrial area. J Air Waste Manag Assoc, 40: 1285-1289. 044578

Crépineau C; Rychen G; Feidt C; Le Roux Y; Lichtfouse E; Laurent F (2003). Contamination of pastures by polycyclic 

aromatic hydrocarbons (PAHs) in the vicinity of a highway. J Agric Food Chem, 51: 4841-4845. 155741

Croteau MN; Luoma SN; Stewart AR (2005). Trophic transfer of metals along freshwater food webs: Evidence of cadmium 

biomagnification in nature. Limnol Oceanogr, 50: 1511-1519. 156373

Cunningham M (1979). Weather, mood, and helping behavior: Quasi experiments with the sunshine Samaritan. J Pers Soc 

Psychol, 37: 1947-1956. 191974

Cuny D; Denayer FO; De Foucault B; Schumacker R; Colein P; Van Haluwyn C (2004). Patterns of metal soil 

contamination and changes in terrestrial cryptogamic communities. Environ Pollut, 129: 289-297. 155742

December 2009 9-203 
Da Silva LC; Oliva MA; Azevedo AA; De Araujo JM (2006). Responses of restinga plant species to pollution from an iron 

pelletization factory. Water Air Soil Pollut, 175: 241-256. 190190

Dallinger R; Wang Y; Berger B; Mackay EA; Kägi JH (2001). Spectroscopic characterization of metallothionein from the 

terrestrial snail, Helix pomatia. Eur J Med Chem, 268: 4126-4133. 192109

Dameris M; Grewe V; Ponater M; Deckert R; Eyring V; Mager F; Matthes S; Schnadt C; Stenke A; Steil B; Brühl C; 

Giorgetta MA (2005). Long-term changes and variability in a transient simulation with a chemistry-climate model 

employing realistic forcing. Atmos Chem Phys Discuss, 5: 2121-2145. 192055

Davis AP; Shokouhian M; Ni S (2001). Loading estimates of lead, copper, cadmium, and zinc in urban runoff from specific 

sources. Chemosphere, 44: 997-1009. 024933

Davis MRH; Zhao FJ; McGrath SP (2004). Pollution-induced community tolerance of soil microbes in response to a zinc 

gradient. Environ Toxicol Chem, 23: 2665-2672. 155744

Day R (2007). Place and the experience of air quality. Health Place, 13: 249-260. 156386

DeBell L (2006). Spatial and seasonal patterns and temporal variaibility of haze and its constituents in the United States: 

Report IV. 1Cooperative Institute for Research in the Atmosphere. Colorado State University Fort Collins, CO. 

156388

Delworth T; Ramaswamy V; Stenchikov G (2005). The impact of aerosols on simulated ocean temperature and heat content 

in the 20th century. Geophys Res Lett, 32: N/A. 190055

Demerjian KL; Mohnen VA (2008). Synopsis of the temporal variation of particulate matter composition and size. J Air 

Waste Manag Assoc, 58: 216-233. 156392

Denman KL; Brasseur G; Chidthaisong A; Ciais P; Cox PM; Dickinson RE; Hauglustaine D; Heinze C; Holland E; Jacob 

D; Lohmann U; Ramachandran S; da Silva Dias PL; Wofsy SC; Zhang X (2007). Couplings Between Changes in 

the Climate System and Biogeochemistry. In Climate Change 2007: The Physical Science Basis. Contribution of 

Working Group I to the Fourth AssessmentReport of the Intergovernmental Panel on Climate Change (pp. 499-

587). Cambridge and New York: Cambridge University Press. 156394

Dentener F; Stevenson D; Ellingsen K; Van Noije T; Schultz M; Amann M; Atherton C; Bell N; Bergmann D; Bey I; 

Bouwman L; Butler T; Cofala J; Collins B; Drevet J; Doherty R; Eickhout B; Eskes H; Fiore A; Gauss M; 

Hauglustaine D; Horowitz L; Isaksen ISA; Josse B; Lawrence M; Krol M; Lamarque JF; Montanaro V; Muller JF; 

Peuch VH; Pitari G; Pyle J; Rast S; Rodriguez J; Sanderson M; Savage NH; Shindell D; Strahan S; Szopa S; Sudo 

K; Van Dingenen R; Wild O; Zeng G (2006). The global atmospheric environment for the next generation. Environ 

Sci Technol, 40: 3586-3594. 088434

Derome J; Lindroos A-J (1998). Effects of heavy metal contamination on macronutrient availability and acidification 

parameters in forest soil in the vicinity of the Harjavalta Cu-Ni smelter, SW Finland. Environ Pollut, 99: 225–232. 

155749

Deshler T; Hervig ME; Hofmann DJ; Rosen JM; Liley JB (2003). Thirty years of in situ stratospheric aerosol size 

distribution measurements from Laramie, Wyoming (41°N), using balloon-borne instruments . J Geophys Res, 108: 

4167. 192058

Deuzé J; Bréon F; Devaux C; Goloub P; Herman M; Lafrance B; Maignan F; Marchand A; Nadal F; Perry G (2001). 

Remote sensing of aerosols over land surfaces from POLDER-ADEOS-1 polarized measurements. J Geophys Res, 

106: 4913–4926. 192013

De Gouw J; Middlebrook A; Warneke C; Goldan P; Kuster W; Roberts J; Fehsenfeld F; Worsnop D; Canagaratna M; 

Pszenny A (2005). Budget of organic carbon in a polluted atmosphere: Results from the New England Air Quality 

Study in 2002. J Geophys Res, 110: D16305. 190020

De Nicola F; Maisto G; Prati MV; Alfani A (2005). Temporal variations in PAH concentrations in Quercus ilex L. (holm 

oak) leaves in an urban area. Chemosphere, 61: 432-440. 155747

Diner D; Beckert J; Bothwell G; Rodriguez J (2002). Performance of the MISR instrument during its first 20 months in 

Earth orbit. IEEE Trans Geosci Remote Sens, 40: 1449-1466. 189967

Diner D; Beckert J; Reilly T; Bruegge C; Conel J; Kahn R; Martonchik J; Ackerman T; Davies R; Gerstl S (1998). Multiangle Imaging SpectroRadiometer(MISR) instrument description and experiment overview. IEEE Trans Geosci 

Remote Sens, 36: 1072-1087. 189962

December 2009 9-204 
Di Toro DM; McGrath JH; Berry WJ; Paquin PR; Mathew R; Wu KB; Santore RC (2005). Predicting sediment metal 

toxicity using a sediment biotic ligand model: Methodology and initial application. Environ Toxicol Chem, 24: 

2410-2427. 155750

Doherty S; Quinn P; Jefferson A; Carrico C; Anderson T; Hegg D (2005). A comparison and summary of aerosol optical 

properties as observed in situ from aircraft, ship, and land during ACE-Asia. J Geophys Res, 110: D04201. 190027

Donahue WF; Allen EW; Schindler DW (2006). Impacts of coal-fired power plants on trace metals and polycyclic aromatic 

hydrocarbons (PAHs) in lake sediments in central Alberta, Canada. J Paleolimnol, 35: 111-128. 155751

Dong Y; Zhu YG; Smith FA; Wang Y; Chen B (2008). Arbuscular mycorrhiza enhanced arsenic resistance of both white 

clover (Trifolium repens Linn.) and ryegrass (Lolium perenne L.) plants in an arsenic-contaminated soil. Environ 

Pollut, 155: 174-181. 192106

Douglass AR; Schoeberl MR; Rood RB; Pawson S (2003). Evaluation of transport in the lower tropical stratosphere in a 

global chemistry and transport model. J Geophys Res, 108: 4259. 057260

Dubovik O; Holben B; Eck TF; Smirnov A; Kaufman YJ; King MD; Tanre D;Slutsker I (2002). Variability of absorption 

and optical properties of key aerosol types observed in worldwide locations . J Atmos Sci, 59: 590-608. 190202

Dubovik O; King MD (2000). A flexible inversion algorithm for retrieval of aerosol optical properties from sun and sky 

radiance measurements. J Geophys Res, 105: 20673-20696. 190197

Dubovik O; Lapyonok T; Kaufman Y; Chin M; Ginoux P; Sinyuk A (2007). Retrieving global sources of aerosols from 

MODIS observations by inverting GOCART model. Atmos Chem Phys Discuss, 7: 3629-3718. 190211

Dubovik O; Smirnov A; Holben BN; King MD;Kaufman YJ; Eck TF; Slutsker I (2000). Accuracy assessments of aerosol 

optical properties retrieved from AERONET sun and sky radiance measurements. J Geophys Res, 105: 9791-9806. 

190177

Dufresne L-L; Quaas J; Boucher O; Denvil S; Fairhead L (2005). Contrasts in the effects on climate of anthropogenic 

sulfate aerosols between the 20th and the 21st century. Geophys Res Lett, 32: L21703. 199908

Duriscoe DM; Luginbuhl CB; Moore CA (2007). Measuring Night-Sky Brightness with a Wide-Field CCD Camera. Publ. 

Astron. Soc. Pac., 119: 192-213. 156411

Dusek R; Frank GP; Hildebrandt L; Curtius J; Schneider J; Walter S; Chand D; Drewnick F; Hings S; Jung D; Borrmann S; 

Andreae MO (2006). Size matters more than chemistry for cloud-nucleating ability of aerosol particles. Science, 

312: 1375-1378. 155756

Eagan RC; Hobbs PV; Radke LF (1974). Measurements of cloud condensation nuclei and cloud droplet size distributions in 

the vicinity of forest fires. J Appl Meteorol, 13: 553-557. 190231

Eck TF; Holben BN; Reid JS; Dubovik O; Smirnov A; O'Neill NT; Slutsker I; Kinne S (1999). Wavelength dependence of 

the optical depth of biomass burning, urban, and desert dust aerosols . J Geophys Res, 104: 31333-31350. 190390

Eck TF; Holben BN; Reid JS; Sinyuk A; Dubovik O; Smirnov A; Giles D; O'Neill NT; Tsay SC; Ji Q; Mandoos AAI; Khan 

MR; Reid EA; Schafer JS; Sorokine M; Newcomb W; Slutsker I (2008). Spatial and temporal variability of 

column- integrated aerosol optical properties in the southern Arabian Gulf and United Arab Emirates in summer. J 

Geophys Res, 113: in press. 190409

Edgerton ES; Hartsell BE; Jansen JJ; Hansen DA; Waid CJ; Kandasamy K (2004). 5-Year Trend Analysis of PM2.5 Data 

from the SEARCH Network. Presented at Regional and Global Perspectives on Haze: Causes, Consequences and 

Controversies, Visibility, Asheville, NC. 156413

Eeva T; Lehikoinen E (2004). Rich calcium availability diminishes heavy metal toxicity in Pied Flycatcher. Funct Ecol, 18: 

548-553. 155762

Eeva T; Tanhuanpää S; Råbergh C; Airaksinen S; Nikinmaa M; Lehikoinen E (2000). Biomarkers and fluctuating 

asymmetry as indicators of pollution- induced stress in two hole-nesting passerines. Funct Ecol, 14: 235–243. 

155761

Elliot SJ; Cole DC; Krueger P; Voorberg N; Wakefield S (1999). The power of perception: health risk attributed to air 

pollution in an urban industrial neighborhood. Risk Anal, 19: 621-634. 010716

December 2009 9-205 
Ely DW; Leary JT; Stewart TR; Ross DM (1991). The establishment of the Denver Visibility Standard. Presented at 

Presented at: 84th annual meeting & exhibition of the Air & Waste Management Association; June; Vancouver, 

British Columbia. Pittsburgh, PA: Air & Waste Management Association; paper no. 91-48.4. 056599

Englert N (2004). Fine particles and human health - a review of epidemiological studies. Toxicol Lett, 149: 235-242. 

087939

Ericksen JA; Gustin MS; Schorran DE; Johnson DW; Lindberg SE; Coleman JS (2003). Accumulation of atmospheric 

mercury in forest foliage. Atmos Environ, 37: 1613-1622. 155769

Ervens B; Feingold G; and SMKreidenweis SM (2005). The influence of water-soluble organic carbon on cloud drop 

number concentration. J Geophys Res, 110: D18211.1-D18211.14. 190527

Evans GW; Jacobs SV (1982). Air Pollution and Human Behavior. In Evans, GW (Ed.),Environmental Stress (pp. 105-

132). New York, NY: Cambridge University Press. 179899

Evans GW; Jacobs SV; Dooley D; Catalano R (1987). The Interaction of Stressful Life Events and Chronic Strains on 

Community Mental Health. Am J Community Psychol, 15: 23-24. 190347

Evans GW; Jacobs SV; Frager NB (1982). Behavioral responses to air pollution. In A Baum; J Singer (Ed.),Advances in 

Environmental Psychology (pp. 237-269). Hillsdale, NJ: Erlbaum. 190521

Ewald G; Larsson P; Linge H; Okla L; Szarzi M (1998). Biotransport of organic pollutants to an inland Alaska lake by 

migrating sockeye salmon (Oncorhynchus nerka). Arctic, 51: 40-47. 190348

Fehsenfeld FC; Ancellet G; Bates TS; Goldstein AH; Hardesty RM; Honrath R; Law KS; Lewis AC; Leaitch R; McKeen S; 

Meagher J; Parrish DD; Pszenny AAP; Russell PB; Schlager H; Seinfeld J; Talbot R; Zbinden R (2006). 

International consortium for atmospheric research on transport and transformation (ICARTT): North America to 

Europe -overview of the 2004 summer field study . J Geophys Res, 111: D23S01.1-D23S01.36. 190531

Feichter J; Sausen R; Graßl H; Fiebig M (2003). Comment on "Control of fossil-fuel particulate black carbon and organic 

matter, possibly the most effective method of slowing global warming" by M. Z. Jacobson. J Geophys Res, 108: 

4767. 155772

Feingold G (2003). Modeling of the first indirect effect: Analysis of measurement requirements. Geophys Res Lett, 30: 

ASC7.1-ASC7.4. 190547

Feingold G; Furrer R; Pilewskie P; Remer LA; Min Q; Jonsson; Haflidi (2003). Aerosol indirect effect studies at southern 

great plains during the May 2003 intensive operations period . J Geophys Res, 111: D5. 190551

Feingold G; Jiang H; Harrington J (2005). On smoke suppression of clouds in Amazonia. Geophys Res Lett, 32: L02804.1-

L02804.4. 190550

Feingold G; Remer LA; Ramaprasad J; Kaufman YJ (2001). Analysis of smoke impact on clouds in Brazilian biomass 

burning regions: An extension of Twomey's approach. J Geophys Res, 106: 22907-22922. 190544

Feingold G; Stevens B; Cotton WR; Walko RL (1994). An explicit microphysics/LES model designed to simulate the 

Twomey Effect. Atmos Res, 33: 207-233. 190535

Feingold GW; Cotton WR; Kreidenweis SM; Davis JT (1999). The impact of giant cloud condensation nuclei on drizzle 

formation in stratocumulus: Implications for cloud radiative properties. J Atmos Sci, 56: 4100-4117. 190540

Feng MH; Shan XQ; Zhang SZ; Wen B (2005). Comparison of a rhizosphere-based method with other one-step extraction 

methods for assessing the bioavailability of soil metals to wheat. Chemosphere, 59: 939-949. 155774

Ferm M; Watt J; O'Hanlon S; De Santis F: Varotsos C (2006). Deposition measurement of particulate matter in connection 

with corrosion studies. Anal Bioanal Chem, 384: 1320-30. 155135

Fernandes SD; Trautmann NM; Streets DG; Roden CA; Bond TC (2007). Global biofuel use, 1850-2000. Global 

Biogeochem Cycles, 21: GB2019.1-GB2019.15. 190554

Ferrare R; Feingold G; Ghan S; Ogren J Schmid B; Schwartz SE; Sheridan P (2006). Preface to special section: 

Atmospheric radiation measurement program May 2003 intensive operations period examining aerosol properties 

and radiative influences . J Geophys Res, 111: D05S01. 190561

Fiore AM; Jacob DJ; Mathur R; Martin RV (2003). Application of empirical orthogonal functions to evaluate ozone 

simulations with regional and global models. J Geophys Res, 108: 4431. 047805

December 2009 9-206 
Fishman J; Hoell JM; Bendura RD; McNeal RJ; Kirchhoff V (1996). NASA GTE TRACE A experiment (SeptemberOctober 2002): Overview. J Geophys Res, 101: 23865-23880. 190566

Fismes J; Perrin-Ganier C; Empereur-Bissonnet P; Morel JL (2002). Soil-to-root transfer and translocation of polycyclic 

aromatic hydrocarbons by vegetables grown on industrial contaminated soils. J Environ Qual, 31: 1649-1656. 

141156

Fitzgerald JW (1975). Approximation formulas for the equilibrium size of an aerosol particle as a function of its dry size 

and composition and the ambient relative humidity. J Appl Meteor Climatol, 14: 1044-1049. 095417

Forster P; Ramaswamy V; Artaxo P; Berntsen T; Betts R; Fahey DW; Haywood J; Lean J; Lowe DC; Myhre G; Nganga J; 

Prinn R; Raga G; Schultz M; Van Dorland R (2007). Changes in atmospheric constituents and in radiative forcing, 

Chapter 2. In Solomon S, Qin D; Manning M; Chen Z; Marquis M; Averyt KB; Tignor M; Miller HL (Ed.),IPCC 

Fourth Assessment Report (AR4): Climate Change 2007: Working Group I Report: The Physical Science Basis. 

(pp. 129-234). Cambridge, U.K. and New York, NY: Intergovernmental Panel on Climate Change; Cambridge 

University Press. 092936

Fraser R; Kaufman Y (1985). The relative importance of aerosol scattering and absorption in remote sensing. IEEE Trans 

Geosci Remote Sens, GE-23: 625-633. 190567

Free M; Angell JK (2002). Effect of volcanoes on the vertical temperature profile in radiosonde data. J Geophys Res, 107: 

4101. 192281

Freer-Smith PH; El-khatib A; Taylor G (2004). Capture of particulate pollution by trees: a comparison of species typical of 

semi-arid areas (Ficus nitida and Eucalyptus globulus) with European and North American species. Water Air Soil 

Pollut, 155: 173-187. 156451

Frescholtz TF; Gustin MS; Schorran DE; Fernandez GCJ (2003). Assessing the source of mercury in the foliar tissue of 

quaking aspen. Environ Toxicol Chem, 22: 2114-2119. 190352

Fritze H; Niini S; Mikkola K; Makinen A (1989). Soil microbial effects of a Cu-Ni smelter in southwestern Finland. Biol 

Fertil Soils, 8: 87-94. 079635

Fryer ME; Collins CD (2003). Model intercomparison for the uptake of organic chemicals by plants. Environ Sci Technol, 

37: 1617–1624. 156454

Gandhi N; Bhavsar SP; Diamond ML; Kuwabara JS; Marvin-DiPasquale M; Krabbenhoft DP (2007). Development of a 

mercury speciation, fate, and biotic uptake (BIOTRANSPEC) model: Application to Lahontan Reservoir (Nevada, 

USA). Environ Toxicol Chem, 26: 2260-2273. 155781

Gao YZ; Zhu LZ (2004). Plant uptake, accumulation and translocation of phenanthrene and pyrene in soils. Chemosphere, 

55: 1169-1178. 155782

Garrett T; Zhao C; Dong X; Mace G; Hobbs P (2004). Effects of varying aerosol regimes on low-level Arctic stratus. 

Geophys Res Lett, 31: L17105.1-L17105.4. 190568

Garrett TJ; Zhao C (2006). Increased Arctic cloud longwave emissivity associated with pollution from mid-latitudes. 

Nature, 440: 787-789. 190570

Gawel JE; Ahner BA; Friedland AJ; Morel FMM (1996). Role for heavy metals in forest decline indicated by phytochelatin 

measurements. Nature, 381: 64-65. 012278

Generoso S; Bey I; Attié J-L; Bréon F-M (2007). A satellite- and model-based assessment of the 2003 Russian fires: impact 

on the arctic region. J Geophys Res, 112: 5302. 155786

Geogdzhayev I; Mishchenko M; Rossow W; Cairns B; Lacis AA (2002). Global two-channel AVHRR retrievals of aerosol 

properties over the ocean for the period of NOAA-9 observations and preliminary retrievals using NOAA-7 and 

NOAA-11 data. J Atmos Sci, 59: 262-278. 190574

Geron C; Rasmussen R; Arnts RR; Guenther A (2000). A review and synthesis of monoterpene speciation from forests in 

the United States. Atmos Environ, 34: 1761-1781. 019095

Ghan S; Laulainen N; Easter R; Wagener R; Nemesure S; Chapman E; Zhang Y; Leung R (2001). Evaluation of aerosol 

direct radiative forcing in MIRAGE. J Geophys Res, 106: 5295-5316. 199911

Gillett NP; Hegerl GC; Allen MR; Stott PA; Schnur R (2002). Reconciling two approaches to the detection of 

anthropogenic influence on climate. J Clim, 15: 326-329. 190576

December 2009 9-207 
Gillett NP; Zwiers FW; Weaver AJ; Hegerl GC; Allen MR; Stott PA (2002). Detecting anthropogenic influence with a 

multi-model ensemble. J Geophys Res, 29: 31.1-31.4. 190578

Ginoux P; Chin M; Holben B; Lin SJ; Tegen I; Prospero JM; Dubovik O (2001). Sources and distributions of dust aerosols 

simulated with the GOCART model. J Geophys Res, 20: 20255-20273. 190579

Ginoux P; Horowitz LW; Ramaswamy V; Geogdzhayev IV; Holben BN; Stenchikov G; Tie X (2006). Evaluation of aerosol 

distribution and optical depth in the geophysical fluid dynamics laboratory coupled model CM2.1 for present 

climate. J Geophys Res, 111: D22210. 190582

Gitay H; Brown S; Easterling W; Jallow B (2001). Ecosystems and their goods and services. In Climate change 2001: 

impacts, adaptation and vulnerability. Contribution of working group II to the third assessment report of the 

Intergovernmental Panel on Climate Change (pp. 237-342). Cambridge, United Kingdom: Cambridge University 

Press. 092761

Givati A; Rosenfeld D (2004). Quantifying precipitation suppression due to air pollution. J Appl Meteorol, 43: 1038-1056. 

156475

Gleason SM; Faucette DT; Toyofuku MM; Torres CA; Bagley CF (2007). Assessing and mitigating the effects of 

windblown soil on rare and common vegetation. Environ Manage, 40: 1016-1024. 155794

Goforth MR; Christoforou CS (2006). Particle size distribution and atmospheric metals measurements in a rural area in the 

South Eastern USA. Sci Total Environ, 356: 217-227. 088353

Gohre V; Paszkowski U (2006). Contribution of the arbuscular mycorrhizal symbiosis to heavy metal phytoremediation. 

Planta, 223: 1115-1122. 190355

Golaz JC; Larson VE; Cotton WR (2002). A PDF based model for boundary layer clouds. Part I: Method and model 

description. J Atmos Sci, 59: 3540-3551. 190587

Golaz JC; Larson VE; Cotton WR (2002). A PDF-Based Model for Boundary Layer Clouds. Part II: Model Results. J 

Atmos Sci, 59: 3552-3571. 190589

Gomot-de Vaufleury A; Kerhoas I (2000). Effects of cadmium on the reproductive system of the land snail Helix aspersa. 

Bull Environ Contam Toxicol, 64: 434-442. 155798

Gomot-De Vaufleury A; Pihan F (2002). Methods for toxicity assessment of contaminated soil by oral or dermal uptake in 

land snails: Metal bioavailability and bioaccumulation. Environ Toxicol Chem, 21: 820-827. 190357

Gonzalez-Chavez C; Harris PJ; Dodd J; Meharg AA (2002). Arbuscular mycorrhizal fungi confer enhanced arsenate 

resistance on Holcus lanatus. New Phytol, 155: 163-171. 155800

Goodarzi F; Sanei H; Garrett RG; Duncan WF (2002). Accumulation of trace elements on the surface soil around the Trail 

smelter, British Columbia, Canada. Environ Geol, 43: 29-38. 155801

Gordon W; Jackson R (2000). Nutrient concentrations in fine roots. Ecology, 81: 275–280. 155802

Goudie AS; Elaine W; Viles HA (2002). The roles of salt and fog in weathering: a laboratory simulation of conditions the 

northern Atacama Desert, Chile. Catena, 48: 255–266. 156486

Grabowski WW (2004). An improved framework for superparameterization. [embedding of cloud-resolving model in each 

column of large-scale model for small-scale and mesoscale processes representation] . J Atmos Sci, 61: 1940-1952. 

190590

Grabowski WW; Wu X; Moncrieff MW (1999). Cloud resolving modeling of tropical cloud systems during phase III of 

GATE. Part III: Effects of cloud microphysics . J Atmos Sci, 56: 2384-2402. 190592

Grannas AM; Hockaday WC; Hatcher PG; Thompson LG; Mosley-Thompson E (2006). New revelations on the nature of 

organic matter in ice cores. J Geophys Res, 111: D04304. 155804

Grannas AM; Jones AE; Dibb J; Ammann M; Anastasio C; Beine HJ; Bergin M; Bottenheim J; Boxe CS; Carver G; Chen 

G; Crawford JH; Dominé F; Frey MM; Guzmán MI; Heard DE; Helmig D; Hoffmann MR; Honrath RE; Huey LG; 

Hutterli M; Jacobi HW; Klán P; Lefer B; McCo (2007). An overview of snow photochemistry: evidence, 

mechanisms and impacts. Atmos Chem Phys, 7: 4329-4373. 156492

Grannas AM; Shepson PB; Filley TR (2004). Photochemistry and nature of organic matter in Arctic and Antarctic snow. 

Global Biogeochem Cycles, 18: GB1006. 155803

December 2009 9-208 
Grantz DA; Garner JHB; Johnson DW (2003). Ecological effects of particulate matter. Environ Int, 29: 213-239. 155805

Gratao PL; Polle A; Lea PJ; Azevedo RA (2005). Making the life of heavy metal-stressed plants a little easier. Funct Plant 

Biol, 32: 481-494. 190364

Gray JS (2002). Biomagnification in marine systems: The perspective of an ecologist. Mar Pollut Bull, 45: 46-52. 155806

Greenstein D; Tiefenthaler L; Bay S (2004). Toxicity of parking lot runoff after application of simulated rainfall. Arch 

Environ Contam Toxicol, 47: 199-206. 155808

Gregory JM; Stouffer RJ; Raper SCB; Stott PA; Rayner NA (2002). An observationally based estimate of the climate 

sensitivity. J Clim, 15: 3117-3121. 190593

Griffith DA; Solak ME; Yorty DP (2005). Is air pollution impacting winter orographic precipitation in Utah? Weather 

modification association. J Weather Modif, 37: 14-20. 156497

Grigal DF (2002). Inputs and outputs of mercury from terrestrial watersheds: a review. Environ Rev, 10: 1–39. 156498

Grigal DF (2003). Mercury sequestration in forests and peatlands: A review. J Environ Qual, 32: 393-405. 155811

Grossi CM; Brimblecombe P (2004). Aesthetics of simulated soiling patterns on architecture. Environ Sci Technol, 38: 

3971-3976. 155813

Grossi CM; Esbert RM; Díaz-Pache F; Alonso FJ (2003). Soiling of building stones in urban environments. Build Environ, 

38: 147-159. 155812

Guderian R (1977). Accumulation of pollutants in plant organs. In Air pollution: phytotoxicity of acidic gases and its 

significance in air pollution control (pp. 66-74). Berlin, Germany: Springer-Verlag. 004150

Guderian R (1985). Air pollution by photochemical oxidants: formation, transport, control, and effects on plants. New 

York: Springer-Verlag. 019325

Gunn R; Phillips BB (1957). An experimental investigation of the effect of air pollution on the initiation of rain. J Atmos 

Sci, 14: 272-280. 190595

Gupta P; Christopher SA; Wang J; Gehrig R; Lee Y; Kumar N (2006). Satellite remote sensing of particulate matter and air 

quality assessment over global cities. Atmos Environ, 40: 5880-5892. 137694

Gustin MS (2003). Are mercury emissions from geologic sources significant? A status report. Sci Total Environ, 304: 153-

167. 155816

Hageman KJ; Simonich SL; Campbell DH; Wilson GR; Landers DH (2006). Atmospheric deposition of current-use and 

historic-use pesticides in snow at national parks in the western United States. Environ Sci Technol, 40: 3174-3180. 

156509

Hall JL (2002). Cellular mechanisms for heavy metal detoxification and tolerance. J Exp Bot, 53: 1-11. 190365

Halsall CJ (2004). Investigating the occurrence of persistent organic pollutants (POPs) in the Arctic: their atmospheric 

behaviour and interaction with the seasonal snow pack. Environ Pollut, 128: 163-175. 155822

Halsall CJ; Barrie LA; Fellin P; Muir DCG; Rovinski FY; Kononov EY (1997). Spatial and temporal variation of 

polycyclic aromatic hydrocarbons in the arctic atmosphere. Environ Sci Technol, 31: 3593-3599. 155821

Han Q; Rossow WB; Chou J; Welch RM (1998). Global survey of the relationship of cloud albedo and liquid water path 

with droplet size using ISCCP. J Clim, 11: 1516-1528. 190594

Han Q; Rossow WB; Zeng J (2002). Three different behaviors on liquid water path of clouds in aerosol-cloud interactions. 

J Atmos Sci, 59: 726-735. 049181

Hand JL; Kreidenweis SM; Sherman DE; Collett JL Jr; Hering SV; Day DE; Malm WC (2002). Aerosol size distributions 

and visibility estimates during the big bend regional aerosol visibility and observational study (BRAVO). Atmos 

Environ, 36: 5043-5055. 190367

Hand JL; Malm WC (2006). Review of the IMPROVE Equation for Estimating Ambient Light Extinction CoefficientsFinal Report. 156517

Hand JL; Malm WC (2007). Review of aerosol mass scattering efficiencies from ground-based measurements since 1990. J 

Geophys Res, 112: D16203. 155825

Hansen AC; Zhang Q; Lyne PWL (2005). Ethanol-diesel fuel blends--a review. Bioresour Technol, 96: 277-285. 059087

December 2009 9-209 
Hansen AD; Rosen AH; Novakov T (1982). Real-Time Measurement of the Absorption Coefficient of Aerosol Particles. 

Appl Opt, 21: 3060-3062. 190368

Hansen ADA; Rosen H; Novakov T (1984). The aethalometer - an instrument for the real-time measurement of optical 

absorption by aerosol particles. Sci Total Environ, 36: 191-196. 002396

Hansen J; Nazarenko L (2004). Soot climate forcing via snow and ice albedos. PNAS, 101: 423-428. 156521

Hansen J; Sato M; Mazarenko L; Ruedy R; Lacis A; Koch D; Tegen I; Hall T; Shindell D; Santer B; Stone P; Novakov T; 

Thomason L; Wang R; Wang Y; Jacob D; Hollandsworth S; Bishop L; Logan J; Thompson A; Stolarski R; Lean J; 

Willson R; Levitus S; Antonov J; Rayner N; Parker D; Christy J (2002). Climate forcings in Goddard Institute for 

Space Studies S12000 simulations. J Geophys Res, 107: 4347. 049177

Hansen J; Sato M; Ruedy R; Kharecha P; Lacis A; Miller R; Nazarenko L; Lo K; Schmidt GA; Russell G; Aleinov I; Bauer 

S; Baum E; Cairns B; Canuto V; Chandler M; Cheng Y; Cohen A; Del Genio A; Faluvegi G; Fleming E; Friend A; 

Hall T; Jackman C; Jonas J; Kelley M; Kiang NY; Koch D; Labow G; Lerner J; Menon S; Novakov T; Oinas V; 

Perlwitz JA; Perlwitz Ju; Rind D; Romanou A; Schmunk R; Shindell D; Stone P; Sun S; Streets D; Tausnev N; 

Thresher D; Unger N; Yao M; Zhang S (2007). Climate simulations for 1880-2003 with GISS modelE. Clim 

Dynam, 29: 661-696. 190597

Hansen J; Sato M; Ruedy R; Lacis A; Oinas V (2000). Global warming in the twenty-first century: an alternative scenario. 

PNAS, 97: 9875-9880. 042683

Hansen J; Sato M; Ruedy R; Nazarenko L; Lacis A; Schmidt GA; Russell G; Aleinov I; Bauer M; Bauer S; Bell N; Cairns 

B; Canuto V; Chandler M; Cheng Y; Del Genio A; Faluvegi G; Fleming E; Friend A; Hall T; Jackman C; Kelley M; 

Kiang N; Koch D; Lean J; Lerner J; Lo K; Menon S; Miller R; Minnis P; Novakov T; Oinas V; Perlwitz JA; 

Perlwitz Ju; Rind D; Romanou A; Shindell D; Stone P; Sun S; Tausnev N; Thresher D; Wielicki B; Wong T; Yao M; 

Zhang S (2005). Efficacy of climate forcings. J Geophys Res, 110: D18104. 190596

Hansen JE; Sato M; Ruedy R (1997). Radiative forcing and climate response. J Geophys Res, 102: 6831-6864. 043104

Hargrave BT; Barrie LA; Bidleman TF; Welch HE (1997). Seasonality in exchange of organochlorines between arctic air 

and seawater. Environ Sci Technol, 31: 3258-3266. 155827

Harmens H; Norris DA; Koerber GR; Buse A; Steinnes E; Rühling A (2007). Temporal trends in the concentration of 

arsenic, chromium, copper, iron, nickel, vanadium and zinc in mosses across Europe between 1990 and 2000. 

Atmos Environ, 41: 6673-6687. 155828

Harner T; Jantunen LMM; Bidleman TF; Barrie LA; Kylin H; Strachan WMJ; Macdonald RW (2000). Microbial 

degradation is a key elimination pathway of hexachlorocyclohexanes from the Arctic Ocean. Geophys Res Lett, 27: 

1155-1158. 155829

Harrison L; Michalsky J; Berndt J (1994). Automated multifilter rotating shadow-band radiometer: an instrument for 

optical depth and radiation measurements. Appl Opt, 33: 5118-5125. 045805

Harvey LDD (2004). Characterizing the annual-mean climatic effect of anthropogenic CO2 and aerosol emissions in eight 

coupled atmosphere-ocean GCMs. Clim Dynam, 23: 569-599. 190598

Hassan R; Scholes R; Ash N (2005). Ecosystems and human well-being: current state and trends, volume 1. United 

Kingdom: Shearwater Books. 092759

Hauck M (2003). Epiphytic lichen diversity and forest dieback: The role of chemical site factors. Bryologist, 106: 257-269. 

155830

Haynie FH (1986). Environmental factors affecting corrosion of weathering steel. Presented at Materials degradation 

caused by acid rain: developed from the 20th state-of-the-art symposium of the American Chemical Society; June 

1985; Arlington, VA., Washington, DC. 157198

Haynie FH; Lemmons TJ (1990). Particulate matter soiling of exterior paints at a rural site. Aerosol Sci Technol, 13: 356-

367. 044579

Haywood J; Boucher O (2000). Estimates of the direct and indirect radiative forcing due to tropospheric aerosols: A review. 

Rev Geophys, 38: 513-543. 156531

Haywood J; Francis P; Osborne S; Glew M; Loeb N; Highwood E; Tanré D; Myhre G; Formenti P; Hirst E (2003). 

Radiative properties and direct radiative effect of Saharan dust measured by the C-130 aircraft during SHADE: 1. 

Solar spectrum : The Saharan Dust Experiment (SHADE). J Geophys Res, 108: SAH 4-1. 190599

December 2009 9-210 
Haywood J; Schulz M (2007). Causes of the reduction in uncertainty in the anthropogenic radiative forcing of climate 

between IPCC (2001) and IPCC (2007). Geophys Res Lett, 34: L20701. 190600

Haywood JM; Pelon J; Formenti P; Bharmal N; Brooks M; Capes G; Chazette P; Chou C; Christopher S; Coe H; Cuesta J; 

Derimian Y; Desboeufs K; Greed G; Harrison M; Heese B; Highwood EJ; Johnson B; Mallet M; Marticorena B; 

Marsham J; Milton S; Myhre G; Osborne SR; Parker DJ; Rajot JL; Schulz M; Slingo A; Tanré D; Tulet P (2008). 

Overview of the dust and biomass burning experiment and African monsoon multidisciplinary analysis special 

observing Period-0. J Geophys Res, 113: D00C17. 190602

Haywood JM; Ramaswamy V; Soden BJ (1999). Tropospheric aerosol climate forcing in clear-sky satellite observations 

over the oceans. Science, 283: 1299-1303. 040453

Heald CL; Jacob DJ; Park RJ; Russell LM; Huebert BJ; Seinfeld JH; Liao H; Weber RJ (2005). A large organic aerosol 

source in the free troposphere missing from current models. Geophys Res Lett, 32: L18809.1-L18809.4. 190603

Heintzenberg J (2008). The SAMUM-1 experiment over Southern Morocco: overview and introduction. Tellus B Chem 

Phys Meteorol, 61: 2-11. 190605

Helena P; Franc B; Cvetka RL (2004). Monitoring of short-term heavy metal deposition by accumulation in epiphytic 

lichens (Hypogymnia Physodes (L.) Nyl.). J Atmos Chem, 49: 223-230. 155833

Helm PA; Diamond ML; Semkin R; Bidleman TF (2000). Degradation as a loss mechanism in the fate of αhexachlorocyclohexane in Arctic watersheds. Environ Sci Technol, 34: 812-818. 155834

Helm PA; Diamond ML; Semkin R; Strachan WMJ; Teixeira C; Gregor D (2002). A mass balance model describing multiyear fate of organochlorine compounds in a high arctic lake. Environ Sci Technol, 36: 996-1003. 155835

Helmisaari H-S; Makkonen K; Olsson M; Viksna A; Mälkönen E (1999). Fine-root growth, mortality and heavy metal 

concentrations in limed and fertilized Pinus silvestris (L.) stands in the vicinity of a Cu-Ni smelter in SW Finland. 

Plant Soil, 209: 193-200. 155836

Henze DK; Seinfeld JH (2006). Global secondary organic aerosol from isoprene oxidation. Geophys Res Lett, 33: 

L09812.1-L09812.4. 190606

Herman JR; Bhartia PK; Torres O; Hsu C; Seftor C; Celarier E (1997). Global distribution of UV-absorbing aerosols from 

Nimbus 7/TOMS data. J Geophys Res, 102: 16911-16922. 048393

Hernandez L; Probst A; Probst JL; Ulrich E (2003). Heavy metal distribution in some French forest soils: Evidence for 

atmospheric contamination. Sci Total Environ, 312: 195-219. 155841

Herner JD; Ying Q; Aw J; Gao O; Chang DPY; Kleeman MJ (2006). Dominant Mechanisms that Shape the Airborne 

Particle Size and Composition Distribution in Central California. Aerosol Sci Technol, 40: 827-844. 135981

Herrera LK; Videla HA (2004). The importance of atmospheric effects on biodeterioration of cultural heritage 

constructional materials. Int Biodeterior Biodegradation, 54: 125-134. 155843

Hicke JA; Asner GP; Kasischike ES; French NHF; Randerson JT; Collatz GJ; Stocks BJ; Tucker CJ; Los SO; Field CB 

(2003). Postfire response of North American boreal forest net primary productivity analysed with satellite 

observations. Global Change Biol, 9: 1145-1157. 156545

Hinzman LD; Fukuda M; Sandberg DV; Chapin III FS; Dash D (2003). FROSTFIRE: an experimental approach to 

predicting the climate feedbacks from the changing boreal fire regime. J Geophys Res, 108: 8153. 155845

Hobbelen PHF; Koolhaas JE; van Gestel CAM (2006). Bioaccumulation of heavy metals in the earthworms Lumbricus 

rubellus and Aporrectodea caliginosa in relation to total and available metal concentrations in field soils. Environ 

Pollut, 144: 639-646. 190371

Hodzic A; Madronich S; Bohn B; Massie S; Menut L; Wiedinmyer C (2007). Wildfire particulate matter in Europe during 

summer 2003: meso-scale modeling of smoke emissions, transport and radiative effects. Atmos Chem Phys, 7: 

4043-4064. 156553

Hoell JM; Davis DD; Liu SC; Newell R; Shipham M; Akimoto H; McNeal RJ; Bemdura RJ; Drewry JW (1996). Pacific 

exploratory mission-west A (PEM-WEST A): September- October, 1991. J Geophys Res, 101: 1641-1653. 190607

Hoell JM; Davis DD; Liu SC; Newell RE; Akimoto H; McNeal RJ; Bendura RJ (1997). The Pacific exploratory missionwest phase B: February-March, 1994. J Geophys Res, 102: 28223-28239. 057373

December 2009 9-211 
Hoff R; Engel-Cox J; Krotkov N; Palm S; Rogers R; McCann K; Sparling L; Jordan N; Torres O; Spinhirne J (2004). 

Long-range transport observations of two large forest fire plumes to the northeastern U.S. In Proceedings of the 

22nd International Laser Radar Conference (pp. 683-686.). Paris: European Space Agency. 190617

Hoff RM; McCann KJ (2002). Regional East Atmospheric Lidar Mesonet: REALM. In L. Bissonette; G Roy; G Vallée 

(Ed.),Lidar Remote Sensing in Atmospheric and Earth Sciences (pp. 281-284). Val-Bélair, Quebec: Def. R&D Can. 

Valcartier. 190612

Hofmann D; Barnes J; Dutton E; Deshler T; Jäger H; Keen R; Osborn M (2003). Surface-based observations of volcanic 

emissions to stratosphere . In Volcanism and the Earth’s Atmosphere (pp. 57–73). Washington, DC: American 

Geophysical Union. 192062

Hoh E; Zhu L; Hites RA (2006). Dechlorane plus, a chlorinated flame retardant, in the Great Lakes. Environ Sci Technol, 

40: 1184-1189. 190378

Holben BN; Eck TF; Slutsker I; Tanré D; Buis JP; Setzer A; Vermote E; Reagan JA; Kaufman YJ; Nakajima T; Lavenu F; 

Jankowiak I; Smirnov A (1998). AERONET: A federated instrument network and data archive for aerosol 

characterization. Rem Sens Environ, 66: 1-16. 155848

Holben BN; Smirnov A; Eck TF; Slutsker I; Abuhassan N; Newcomb WW; Schafer JS; Tanre D; Chatenet B; Lavenu F 

(2001). An emerging groundbased aerosol climatology: aerosol optical depth from AERONET. J Geophys Res, 106: 

12067-12098. 190618

Holland EA; Dentener FJ; Braswell BH; Sulzman JM (1999). Contemporary and pre-industrial global reactive nitrogen 

budgets. Biogeochemistry, 46: 7-43. 092051

Holmqvist M; Stenroth P; Berglund O; Nystrom P; Olsson K; Jellyman D; McIntosh AR; Larsson P (2006). Low levels of 

persistent organic pollutants (POPs) in New Zealand eels reflect isolation from atmospheric sources. Environ 

Pollut, 141: 532-538. 190380

Hooda V (2007). Phytoremediation of toxic metals from soil and waste water. J Environ Biol, 28: 367-376. 190382

Hopke PK; Zhou L; Poirot RL (2005). Reconciling Trajectory Ensemble Receptor Model Results with Emissions. Environ 

Sci Technol, 39: 7980-7983. 156567

Horowitz L (2006). Past, present, and future concentrations of tropospheric ozone and aerosols: Methodology, ozone 

evaluation, and sensitivity to aerosol wet removal. J Geophys Res, 111: D22211.1-D22211.16. 190620

Horowitz LW; Walters S; Mauzerall DL; Emmons LK; Rasch PJ; Granier C; Tie X; Lamarque J-F; Schultz MG; Tyndall 

GS; Orlando JJ; Brasseur GP (2003). A global simulation of tropospheric ozone and related tracers: Description and 

evaluation of MOZART, version 2. J Geophys Res, 108: D24. 057770

Houlahan JE; Findlay CS; Schmidt BR; Meyer AH; Kuzmin SL (2000). Quantitative evidence for global amphibian 

population declines. Nature, 404: 752-755. 155853

Howe TS; Billings S; Stolzberg RJ (2004). Sources of polycyclic aromatic hydrocarbons and hexachlorobenzene in spruce 

needles of eastern Alaska. Environ Sci Technol, 38: 3294-3298. 155854

Howel D; Moffatt S; Prince H; Bush J; Dunn CE (2002). Urban Air Quality in North-East England: Exploring the 

Influences on Local Views and Perceptions. Risk Anal, 22: 121-130. 156571

Hoyt D; Frohlich C (1983). Atmospheric transmission at Davos, Switzerland 1909-1979. Clim Change, 5: 61-71. 190621

Hoyt DV (1978). A model for the calculation of solar global insolation. Solar Energy, 21: 27-35. 046638

Hsu NC; Tsay SC; King MD; Herman JR (2004). Aerosol properties over bright-reflecting source regions. IEEE Trans 

Geosci Remote Sens, 42: 557-569. 190622

Huebert BJ; Bates T; Russell PB; Shi G; Kim YJ; Kawamura K; Carmichael G; Nakajima T (2003). An overview of ACEAsia: Strategies for quantifying the relationships between Asian aerosols and their climatic impacts. J Geophys Res, 

108: 8633. 190623

Hughes MK (1981). Cycling of trace metals in ecosystems. In NW Lepp (Ed.),Effect of heavy metal pollution on plants. 

Volume 2: metals in the environment (pp. 95-118). London, United Kingdom: Applied Science Publishers. 053595

Hunaiti AA; Al-Oqlah A; Shannag NM; Abukhalaf IK; Silvestrov NA; Von Deutsch DA; Bayorh MA (2007). Toward 

understanding the influence of soil metals and sulfate content on plant thiols. J Toxicol Environ Health A Curr Iss, 

70: 559-567. 156579

December 2009 9-212 
Huneeus N; Boucher O (2007). One-dimensional variational retrieval of aerosol extinction coefficient from synthetic 

LIDAR and radiometric measurements. J Geophys Res, 112: D14303.1-D14303.14. 190624

Hung HH; Halsall CJ; Blanchard P (2001). Are PCBs in the Canadian Arctic atmosphere declining? Evidence from 5 years 

of monitoring. Environ Sci Technol, 35: 1303-1311. 155856

Hung HH; Halsall CJ; Blanchard P; Li HH; Fellin P; Stern G; Rosenberg B (2002). Temporal trends of organochlorine 

pesticides in the Canadian Arctic atmosphere. Environ Sci Technol, 36: 862-868. 155857

Husar RB; Prospero JM; Stowe LL (1997). Characterization of tropospheric aerosols over the oceans with the NOAA 

advanced very high resolution radiometer optical thickness operational product. J Geophys Res, 102: 16,889-

16,909. 045900

IPCC (1995). Radiative forcing of climate change and an evaluation of the IPCC IS92 emission scenarios. In Climate 

Change 1994 (pp. 193-204). New York: Cambridge University Press. 190991

IPCC (1996). Radiative forcing of climate change. In Climate Change 1995 (pp. 65-131). New York: Intergovernmental 

Panel on Climate Change. 190990

IPCC (2001). Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment 

Report (TAR) of the Intergovernmental Panel on Climate Change [Houghton, J.T.,Y. Ding, D.J. Griggs, M. Noguer, 

P.J. van der Linden, X. Dai, K. Maskell, and C.A. Johnson (eds.)]. Cambridge, UK and New York, NY, USA: 

Intergovernmental Panel on Climate Change, Cambridge University Press. 156587

IPCC (2007). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth 

Assessment Report (AR4) of the Intergovernmental Panel on Climate Change . Cambridge, UK and New York, NY: 

Intergovernmental Panel on Climate Change, Cambridge University Press. 092765

Ito A; Penne JE (2005). Historical emissions of carbonaceous aerosols from biomass and fossil fuel burning for the period 

1870-2000. Global Biogeochem Cycles, 19: GB2028.1-GB2028.14. 190626

Jacob DJ; Crawford JH; Kleb MM; Connors VS; Bendura RJ; Raper JL; Sachse GW; Gille JC; Emmons L; Heald CL 

(2003). The Transport and Chemical Evolution over the Pacific (TRACE-P) aircraft mission: design, execution, and 

first results. J Geophys Res, 108: 9000. 190987

Jacobs SV; Evans GW; Catalano R; Dooley D (1984). Air pollution and depressive symptomatology: Exploratory analyses 

of intervening psychosocial factors. Popul Environ, 7: 260-272. 156596

Jacobson MZ (2000). A physically-based treatment of elemental carbon optics: implications for global direct forcing of 

aerosols. Geophys Res Lett, 27: 217-220. 056378

Jacobson MZ (2001). GATOR-GCMM: a global through urban scale air pollution and weather forecast model: 1. Model 

design and treatment of subgrid soil, vegetation, roads, rooftops, water, sea ice, and snow. J Geophys Res, 106: 

5385-5402. 155864

Jacobson MZ (2001). Global direct radiative forcing due to multicomponent anthropogenic and natural aerosols. J Geophys 

Res, 106: 1551-1568. 043110

Jacobson MZ (2002). Control of fossil-fuel particulate black carbon and organic matter, possibly the most effective method 

of slowing global warming. J Geophys Res, 107: 4410. 155865

Jacobson MZ (2003). Development of mixed-phase clouds from multiple aerosol size distributions and the effect of the 

clouds on aerosol removal. J Geophys Res, 108: 4245. 155866

Jacobson MZ (2003). Reply to comment by D. P. Chock et al. on "Control of fossil-fuel particulate black carbon and 

organic matter, possibly the most effective method of slowing global warming". J Geophys Res, 108: 4770. 155867

Jacobson MZ (2003). Reply to comment by J. E. Penner on "Control of fossil-fuel particulate black carbon and organic 

matter, possibly the most effective method of slowing global warming". J Geophys Res, 108: 4772. 155868

Jacobson MZ (2003). Reply to comment by J. Feichter et al. on "Control of fossil-fuel particulate black carbon and organic 

matter, possibly the most effective method of slowing global warming". J Geophys Res, 108: 4768. 155869

Jacobson MZ (2004). Climate response of fossil fuel and biofuel soot, accounting for soot's feedback to snow and sea ice 

albedo and emissivity. J Geophys Res, 109, : D21201. 155870

Jacobson MZ (2006). Effects of externally-through-internally-mixed soot inclusions within clouds and precipitation on 

global climate. J Phys Chem B, 110: 6860-6873. 156599

December 2009 9-213 
Jacobson MZ; Kaufman YJ (2006). Wind reduction by aerosol particles. Geophys Res Lett, 33: L24814. 090942

Jacobson MZ; Seinfeld JH; Carmichael GR; Streets DG (2004). The effect on photochemical smog of converting the U.S. 

fleet of gasoline vehicles to modern diesel vehicles. Geophys Res Lett, 31: L02116. 180362

James SM; Little EE; Semlitsch RD (2004). Effects of multiple routes of cadmium exposure on the hibernation success of 

the American toad (Bufo americanus). Arch Environ Contam Toxicol, 46: 518-527. 155874

Jantunen LMM; Bidleman TF (1996). Air-water gas exchange of hexachlorocyclohexanes (HCHs) and the enantiomers of 

α-HCH in Arctic regions. J Geophys Res, 101: 28837-28846. 155876

Jantunen LMM; Bidleman TF (1998). Organochlorine pesticides and enantiomers of chiral pesticides in Arctic Ocean 

water. Arch Environ Contam Toxicol, 35: 218-228. 155877

Jayanty R (2003). Overview of PM2.5 Chemical Speciation Nationwide Network Program. Presented at Specialty 

Conference of the American Association for Aerosol Research, Pittsburgh, PA, April, 2003, Research Triangle Park, 

NC. 156605

Jayne JT; Leard DC; Zhang X; Davidovits P; Smith KA; Kolb CE; Worsnop DR (2000). Development of an aerosol mass 

spectrometer for size and composition analysis of submicron particles. Aerosol Sci Technol, 33: 49-70. 190978

Jeong MJ; Li Z; Chu DA; Tsay SC (2005). Quality and Compatibility Analyses of Global Aerosol Products Derived from 

the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer. J Geophys 

Res, 110: D10S09. 190977

Jiang H; Feingold G (2006). Effect of aerosol on warm convective clouds: Aerosol-cloud-surface flux feedbacks in a new 

coupled large eddy model. J Geophys Res, 111: D01202. 190976

Jiang J; Oberdrster G; Elder A; Gelein R; Mercer P; Biswas P (2008). Does nanoparticle activity depend upon size and 

crystal phase? Nanotoxicology, 2: 33-42. 156609

Jiang W; Smyth S; Giroux E; Roth H; Yin D (2006). Differences between CMAQ fine mode particle and PM 25 

concentrations and their impact on model performance evaluation in the lower Fraser valley. Atmos Environ, 40: 

4973-4985. 133165

Jiao XC; Xu FL; Dawson R; Chen SH; Tao S (2007). Adsorption and absorption of polycyclic aromatic hydrocarbons to 

rice roots. Environ Pollut, 148: 230-235. 155879

Jirak IL; Cotton WR (2006). Effect of air pollution on precipitation along the Front Range of the Rocky Mountains. J Appl 

Meteorol, 45: 236-245. 156612

Johnson D; Hale B (2008). Fine root decomposition and cycling of Cu, Ni, Pb, and Zn at forest sites near smelters in 

Sudbury, ON, and Rouyn-Noranda, QU, Canada. Hum Ecol Risk Assess, 14: 41-53. 155881

Johnson DB (1982). The role of giant and ultragiant aerosol particles in warm rain initiation. J Atmos Sci, 39: 448-460. 

190973

Johnson MS; Franke LS; Lee RB; Holladay SD (1999). Bioaccumulation of 2,4,6-trinitrotoluene and polychlorinated 

biphenyls through two routes of exposure in a terrestrial amphibian: Is the dermal route significant? Environ 

Toxicol Chem, 18: 873–878. 155880

Jones A; Roberts DL; Woodage MJ (2001). Indirect sulphate aerosol forcing in a climate model with an interactive sulphur 

cycle. J Geophys Res, 106: 20293-20301. 199895

Jones GS; Jones A; Roberts DL; Stott PA; Williams KD (2005). Sensitivity of global-scale climate change attribution 

results to inclusion of fossil fuel black carbon aerosol. Geophys Res Lett, 32: L14701. 155885

Jones JW; Bogat GA (1978). Air pollution and human aggression. Psychol Rep, 43: 721-722. 190396

Jones PD; Moberg A; Osborn TJ; Briffa KR (2003). Surface climate responses to explosive volcanic eruptions seen in long 

European temperature records and mid-to-high latitude tree-ring density around the Northern Hemisphere. In 

Robock A; Oppenheimer C (Ed.),Volcanism and the earth's atmosphere (pp. 239–254). Washington, DC: American 

Geophysical Union. 192278

Joshi M; Shine K (2003). A GCM study of volcanic eruptions as a cause of increased stratospheric water vapor. J Clim, 16: 

3525-3534. 192327

December 2009 9-214 
Joynt J; Bischoff M; Turco R; Konopka A; Nakatsu CH (2006). Microbial community analysis of soils contaminated with 

lead, chromium and petroleum hydrocarbons. Microb Ecol, 51: 209-219. 155887

Junker C; Liousse C (2008). A global emission inventory of carbonaceous aerosol from historic records of fossil fuel and 

biofuel consumption for the period 1860-1997. Atmos Chem Phys, 8: 1195-1207. 190971

Kahn R; Banerjee P; McDonald D (2001). The sensitivity of multiangle imaging to natural mixtures of aerosols over ocean. 

J Geophys Res, 106: 18219-18238. 190969

Kahn R; Banerjee P; McDonald D; Diner D (1998). Sensitivity of multiangle imaging to aerosol optical depth, and to pureparticle size distribution and composition over ocean. J Geophys Res, 103: 32195-32213. 190970

Kahn R; Gaitley B; Martonchik J; Diner D; Crean K; Holben B (2005). Multiangle Imaging Spectroradiometer (MISR) 

global aerosol optical depth validation based on 2 years of coincident Aerosol Robotic Network (AERONET) 

observations. J Geophys Res, 110: D10S04. 189961

Kahn R; Li W; Martonchik J; Bruegge C; Diner D; Gaitley B; Abdou W; Dubovik O; Holben B; Smirnov A; Jin Z; Clark D 

(2005). MISR low-light-level calibration, and implications for aerosol retrieval over dark water. Science, 62: 1032-

1052. 190965

Kahn R; Li W; Moroney C; Diner D; Martonchik J; Fishbein E (2007). Aerosol source plume physical characteristics from 

space-based multiangle imaging. J Geophys Res, 112: D11205. 190964

Kahn RA; Gaitley BJ; Martonchik JV; Diner DJ; Crean KA; Holben B (2005). MISR global aerosol optical depth 

validation based on two years of coincident AERONET observations. J Geophys Res, 110: D10S04. 190966

Kahn RA; Garay MJ; Nelson DL; Yau KK; Bull MA; Gaitley BJ; Martonchik JV; Levy R (2007). Satellite-derived aerosol 

optical depth over dark water from MISR and MODIS: comparisons with AERONET and implications for 

climatological studies. J Geophys Res, 112: D18205. 190963

Kalashnikova O; Kahn R (2006). Ability of multiangle remote sensing observations to identify and distinguish mineral dust 

types: Part 2 Sensitivity over dark water. J Geophys Res, 111: D11207. 190962

Kaldorf M; Kuhn AJ; Schroder WH; Hildebrandt U; Bothe H (1999). Selective element deposits in maize colonized by a 

heavy metal tolerance conferring arbuscular mycorrhizal fungus. J Plant Physiol, 154: 718-728. 190399

Kamh GME (2005). The impact of landslides and salt weathering on Roman structures at high latitudes - Conway Castle, 

Great Britain: A case study. Environ Geol, 48: 238-254. 155888

Kandeler E; Kampichler C; Horak O (1996). Influence of heavy metals on the functional diversity of soil microbial 

communities. Biol Fertil Soils, 23: 299-306. 094392

Kappos AD; Bruckmann P; Eikmann T; Englert N; Heinrich U; Hoppe P; Koch E; Krause GH; Kreyling WG; Rauchfuss 

K; Rombout P; Schulz-Klemp V; Thiel WR; Wichmann HE (2004). Health effects of particles in ambient air. Int J 

Hyg Environ Health, 207: 399-407. 087922

Kapustin VN; Clarke AD; Shinozuka Y; Howell S; V Brekhovskikh V; Nakajima T; Higurashi A (2006). On the 

determination of a cloud condensation nuclei from satellite: Challenges and possibilities. J Geophys Res, 111: 

D04202. 190961

Kaufman Y (1987). Satellite sensing of aerosol absorption. J Geophys Res, 92: 4307-4317. 190960

Kaufman Y; Boucher O; Tanré D; Chin M; Remer L; Takemura T (2005). Aerosol anthropogenic component estimated 

from satellite data. Geophys Res Lett, 32: L17804. 155891

Kaufman Y; Fraser R (1997). The effect of smoke particles on clouds and climate forcing. Science, 277: 1636-1639. 

190958

Kaufman Y; Haywood J; Hobbs P; Hart W; Kleidman R; Schmid B (2003). Remote sensing of vertical distributions of 

smoke aerosol off the coast of Africa. Geophys Res Lett, 30: 1831. 190954

Kaufman YD; Tanré D; Boucher O (2002). A satellite view of aerosols in the climate system. Nature, 419: 215-223. 190956

Kaufman YJ; Hobbs PV; Kirchoff VWJH; Artaxo P; Remer LA; Holben BN; King MD; Ward DE; Prins EM; Longo KM; 

Mattos LF; Nobre CA; Spinhirne JD; Ji Q; Thompson AM; Gleason JF; Christopher SA; Tsay S-C (1998). Smoke, 

clouds, and radiation--Brazil (SCAR-B) experiment. J Geophys Res, 103: 31783-31808. 089989

Kaufman YJ; Koren I (2006). Smoke and pollution aerosol effect on cloud cover. Science, 313: 655-658. 190951

December 2009 9-215 
Kaufman YJ; Martins JV; Remer LA; Schoeberl MR; Yamasoe MA (2002). Satellite retrieval of aerosol absorption over the 

oceans using sunglint. Geophys Res Lett, 29: 34.1-34.4. 190955

Kaufman YJ; Nakajima T (1993). Effect of Amazon smoke on cloud microphysics and albedo—Analysis from satellite 

imagery. J Appl Meteorol, 32: 729-744. 190959

Kaufman YJ; Setzer A; Ward D; Tanre D; Holben BN; Menzel P; Pereira MC; Rasmussen R (1992). Biomass Burning 

Airborne and Spaceborne Experiment in the Amazonas (BASE-A). J Geophys Res, 97: 14581-14599. 044557

Kavouras IG; Etyemezian V; DuBois DW; Xu J; Pitchford M (2009). Source reconciliation of atmospheric dust causing 

visibility impairment in Class I areas of the western United States. J Geophys Res, 114: D02308. 191976

Kavouras IG; Etyemezian V; Xu J; DuBois DW; Green M; Pitchford M (2007). Assessment of the local windblown 

component of dust in the western United States. J Geophys Res, 112: D08211. 156630

Kenski DM; Gay D; Fitzsimmons S (2004). Ammonia and its role in midwestern haze. Presented at Regional and Global 

Perspectives on Haze: Causes, Consequences and Controversies Visibility Specialty Conference, Asheville, NC. 

192078

Kerr R (2007). Another global warming icon comes under attack. Science, 317: 28-29. 190950

Kiehl JT (2007). Twentieth century climate model response and climate sensitivity. Geophys Res Lett, 34: 1-4. 190949

Kiikkilä O (2003). Heavy-metal pollution and remediation of forest soil around the Harjavalta Cu-Ni smelter, in SW 

Finland. Silva Fennica, 37: 399-415. 156637

Kim CG; Bell JNB; Power SA (2003). Effects of soil cadmium on Pinus sylvestris L. seedlings. Plant Soil, 257: 443-449. 

155899

Kim MK; Lau KM; Chin M; Kim KM; Sud Y; Walker GK (2006). Atmospheric teleconnection over Eurasia induced by 

aerosol radiative forcing during boreal spring. PNAS, 19: 4700-4718. 190917

Kim SW; Yoon SC; Kim J (2008). Columnar Asian dust particle properties observed by sun/sky radiometers from 2000 to 

2006 in Korea. Atmos Environ, 42: 492-504. 130785

Kim Y; Hatsushika H; Muskett RR; Yamazaki K (2005). Possible effect of boreal wildfire soot on Arctic sea ice and Alaska 

glaciers. Atmos Environ, 39: 3513-3520. 155900

King M; Kaufman Y; Tanré D;Nakajima T (1999). Remote sensing of tropospheric aerosols from space: Past, present, and 

future. Bull Am Meteorol Soc, 80: 2229-2259. 190635

King MD; Platnick S; Moeller CC; Revercomb HE; Chu DA (2003). Remote sensing of smoke, land, and clouds from the 

NASA ER-2 during SAFARI 2000. J Geophys Res, 108: SAF38.1-SAF38.12. 094395

Kinne S; Schulz M; Textor C; Guibert S; Balkanski Y; Bauer SE; Berntsen T; Berglen TF; Boucher O; Chin M; Collins W; 

Dentener F; Diehl T; Easter R; Feichter J; Fillmore D; Ghan S; Ginoux P; Gong S; Grini A; Hendricks J; Herzog M; 

Horowitz L; Isaksen I; Iversen T; Kirkevåg A; Kloster S; Koch D; Kristjansson JE; Krol M; Lauer A; Lamarque JF; 

Lesins G; Liu X; Lohmann U; Montanaro V; Myhre G; Penner J; Pitari G; Reddy S; Seland O; Stier P; Takemura T; 

Tie X (2006). An AeroCom initial assessment—optical properties in aerosol component modules of global models. 

Atmos Chem Phys, 6: 1815-1834. 155903

Kirchstetter TW; Harley RA; Kreisberg NM; Stolzenberg MR; Hering SV (1999). On-road measurement of fine particle 

and nitrogen oxide emissions from light- and heavy-duty motor vehicles. Atmos Environ, 33: 2955-2968. 010642

Kirkbride MP; Dugmore JA (2003). Glaciological response to distal tephra fallout from the 1947 eruption of Hekla, south 

Iceland. J Glaciol, 49: 420-428. 156645

Kirkevåg A; Iversen T (2002). Global direct radiative forcing by process-parametrized aerosol optical properties. J 

Geophys Res, 107: 4433. 199893

Kleeman MJ; Hughes LS; Allen JO; Cass GR (1999). Source contributions to the size and composition distribution of 

atmospheric particles: southern California in September 1996. Environ Sci Technol, 33: 4331-4341. 011286

Kleidman R; O’Neill N; Remer L; Kaufman Y; Eck T; Tanré D; Dubovik O; Holben B (2005). Comparison of Moderate 

Resolution Imaging Spectroradiometer (MODIS) and Aerosol Robotic Network (AERONET) remote-sensing 

retrievals of aerosol fine mode fraction over ocean. J Geophys Res, 110: 1-16. 190175

December 2009 9-216 
Kleinman MT; Araujo JA; Nel A; Sioutas C; Campbell A; Cong PQ; Li H; Bondy SC (2008). Inhaled ultrafine particulate 

matter affects CNS inflammatory processes and may act via MAP kinase signaling pathways. Toxicol Lett, 178: 

127-130. 190074

Knutti R; Stocker TF; Joos F; Plattner GK (2002). Constraints on radiative forcing and future climate change from 

observations and climate model ensembles. Nature, 416: 719-723. 190178

Knutti R; Stocker TF; Joos F; Plattner GK (2003). Probabilistic climate change projections using neural networks. Clim 

Dynam, 21: 257-272. 190180

Koch D (2001). Transport and direct radiative forcing of carbonaceous and sulfate aerosols in the GISS GCM. J Geophys 

Res, 106: 20311-20332. 192054

Koch D; Bond TC; Streets D; Unger N; van der Werf GR (2007). Global impact of aerosols from particular source regions 

and sectors. J Geophys Res, 112: D02205. 190185

Koch D; Hansen J (2005). Distant origins of Arctic black carbon: A Goddard Institute for Space Studies ModelE 

experiment. J Geophys Res, 110: D04204. 190183

Koch D; Schmidt G; Field C (2006). Sulfur, sea salt, and radionuclide aerosols in GISS ModelE. J Geophys Res, 111: 

D06206. 190184

Kogan YL; Lilly DK; Kogan ZN; Filyushkin V (1994). The effect of CCN regeneration on the evolution of stratocumulus 

cloud layers. Atmos Res, 33: 137-150. 190186

Koren I; Kaufman YJ; Remer LA; Martins JV (2004). Measurement of the effect of Amazon smoke on inhibition of cloud 

formation. Science, 303: 1342-1345. 190187

Koren I; Kaufman YJ; Rosenfeld D; Remer LA; Rudich Y (2005). Aerosol invigoration and restructuring of Atlantic 

convective clouds. Geophys Res Lett, 32: L14828. 190188

Koren I; Martins JV; Remer LA; Afargan H (2008). Smoke invigoration versus inhibition of clouds over the Amazon. 

Science, 321: 946-949. 190193

Koren I; Remer KA; Kaufman YJ; Rudich Y; Martins JV (2007). On the twilight zone between clouds and aerosols. 

Geophys Res Lett, 34: L08805. 190192

Koren I; Remer LA; Longo K (2007). Reversal of trend of biomass burning in the Amazon. Geophys Res Lett, 34: L20404. 

190189

Korsnes R; Pavlova O; Godtliebsen F (2002). Assessment of potential transport of pollutants into the Barents Sea via sea 

ice--an observational approach. Mar Pollut Bull, 44: 861-869. 156657

Kristjánsson JE; Staple A; Kristiansen J; Kaas E (2002). A new look at possible connections between solar activity, clouds 

and climate. Geophys Res Lett, 29: 2107. 199916

Kroll JH; Ng NL; Murphy SM; Flagan RC; Seinfeld JH (2006). Secondary organic aerosol formation from isoprene 

photooxidation. Environ Sci Technol, 40: 1869-1877. 190195

Krueger AJ; Schaefer SJ; Krotkov N; Bluth G; Barker S (2000). Ultraviolet remote sensing of volcanic emissions. In 

Remote Sensing of Active Volcanism (pp. 25-43). Washington, DC: American Geophysical Union. 192234

Kruger O; Grasl H (2002). The indirect aerosol effect over Europe. Geophys Res Lett, 29: 1925. 190200

Krupa S; Booker F; Bowersox V; Lehmann CT; Grantz D (2008). Uncertainties in the current knowledge of some 

atmospheric trace gases associated with U.S. agriculture: A review. J Air Waste Manag Assoc, 58: 986-993. 198696

Kruysse A (1971). Acute inhalation toxicity of acrolein in hamsters. Central Institute for Nutrition and Food Research. The 

Netherlands. R 3516. 192236

Kucera T; Horakova H; Sonska A (2008). Toxic metal ions in photoautotrophic organisms. Photosynthetica, 46: 481-489. 

190408

Kucharski J; Wyszkowska J (2004). Inter-relationship between number of microorganisms and spring barley yield and 

degree of soil contamination with copper. Plant Soil Environ, 50: 243–249. 156662

Kuki KN; Oliva MA; Costa AC (2009). The simulated effects of iron dust and acidity during the early stages of 

establishment of two coastal plant species. Water Air Soil Pollut, 196: 287-295. 190411

December 2009 9-217 
Kuki KN; Oliva MA; Pereira EG; Costa AC; Cambraia J (2008). Effects of simulated deposition of acid mist and iron ore 

particulate matter on photosynthesis and the generation of oxidative stress in Schinus terebinthifolius Radii and 

Sophora tomentosa L. Sci Total Environ, 403: 207-214. 155346

LaBrecque JJ; Benzo Z; Alfonso JA; Cordoves Manuelita Quintal PR; Gomez CV; Marcano E (2004). The concentrations 

of selected trace elements in clams, Trivela mactroidea along the Venezuelan coast in the state of Miranda. Mar 

Pollut Bull, 49: 659-667. 155913

Lack DA; Lovejoy ER; Baynard T; Pettersson A; Ravishankara AR (2006). Aerosol Absorption Measurement using 

Photoacoustic Spectroscopy: Sensitivity, Calibration, and Uncertainty Developments. Aerosol Sci Technol, 40: 697 

- 708. 096032

Lakzian A; Murphy P; Turner A; Beynon JL; Giller KE (2002). Rhizobium leguminosarum bv. viciae populations in soils 

with increasing heavy metal contamination: abundance, plasmid profiles, diversity and metal tolerance. Soil Biol 

Biochem, 34: 519-529. 156671

Lambert A; Grainger RG; Remedios JJ; Rodgers CD; Corney M; Taylor FW (1993). Measurements of the evolution of the 

Mt. Pinatubo aerosol cloud by ISAMS. Geophys Res Lett, 20: 1287–1290. 192231

Lambert MRK (1997). Environmental effects of heavy spillage from a destroyed pesticide store near Hargeisa (Somaliland) 

assessed during the dry season, using reptiles and amphibians as bioindicators. Arch Environ Contam Toxicol, 32: 

80–93. 155916

Landers DH; Simonich SL; Jaffe DA; Geiser LH; Campbell DH; Schwindt AR; Schreck CB; Kent ML; Hafner WD; Taylor 

HE; Hageman KJ; Usenko S; Ackerman LK; Schrlau JE; Rose NL; Blett TF; Erway MM (2008). The fate, 

transport, and ecological impacts of airborne contaminants in western national parks (USA). U.S. Environmental 

Protection Agency, Office of Research and Development, NHEERL, Western Ecology Division. Corvallis, Oregon. 

EPA/600/R-07/138. http://www.nature.nps.gov/air/studies/air_toxics/WACAPreport.cfm. 191181

Lanno R; Wells J; Conder J; Bradham K; Basta N (2004). The bioavailability of chemicals in soil for earthworms. 

Ecotoxicol Environ Saf, 57: 39-47. 190415

Larson VE; Golaz J-C; Jiang H; Cotton WR (2005). Supplying local microphysics parameterizations with information 

about subgrid variability: Latin hypercube sampling. J Atmos Sci, 62: 4010-4026. 190220

Larson VE; Wood R; Field PR; Golaz J-C (2001). Small-scale and mesoscale variability of scalars in cloudy boundary 

layers: One-dimensional probability density functions. J Atmos Sci, 58: 1978-1996. 190212

Latimer DA; Ireson RG (1980). Workbook for estimating visibility impairment. U.S. Enviornmental Protection Agency. 

Research Triangle Park, NC. EPA-450/4-80-031. 035723

Lau K-M; Kim K-M (2006). Observational relationships between aerosol and Asian monsoon rainfall, and circulation. 

Geophys Res Lett, 33: L21810. 190226

Lau K; Kim M; Kim K (2006). Asian summer monsoon anomalies induced by aerosol direct forcing—the role of the 

Tibetan Plateau. Clim Dynam, 36: 855-864. 190223

Lau KM; Kim KM; Sud YC; Walker GK (2009). A GCM study of the response of the atmospheric water cycle of West 

Africa and the Atlantic to Saharan dust radiative forcing. Ann Geophys, 27: 4023-4037. 190229

Law RJ; Alaee M; Allchin CR; Boon JP; Lebeuf M; Lepom P; Ster GA (2003). Levels and trends of polybrominated 

diphenylethers and other brominated flame retardants in wildlife. Environ Int, 29: 757-770. 190420

Lawson ST; Scherbatskoy TD; Malcolm EG; Keeler GJ (2003). Cloud water and throughfall deposition of mercury and 

trace elements in a high elevation spruce-fir forest at Mt Mansfield, Vermont. J Environ Monit, 5: 578-583. 089371

Leahy L; Anderson T; Eck T; Bergstrom R (2007). A synthesis of single scattering albedo of biomass burning aerosol over 

southern Africa during SAFARI 2000. Geophys Res Lett, 34: L12814. 190232

Leaitch WR; Banic CM; Isaac GA; Couture MD; Liu PSK; Gultepe I; Li S-M; Kleinman L; Daum PH; MacPherson JI 

(1996). Physical and chemical observations in marine stratus during the 1993 North Atlantic Regional Experiment: 

Factors controlling cloud droplet number concentrations. J Geophys Res, 101: 29123-29135. 190354

Leaitch WR; Isaac GA; Strapp JW; Banic CM; Wiebe HA (1992). The relationship between cloud droplet number 

concentrations and anthropogenic pollution: observations and climatic implications. J Geophys Res, 97: 2463-2474. 

045270

December 2009 9-218 
LeBlanc GA (1995). Trophic-level differences in the bioconcentration of chemicals: Implications in assessing 

environmental biomagnification. Environ Sci Technol, 29: 154-160. 155921

Lee JK (2006). Toxicity and tissue distribution of magnetic nanoparticles in mice [erratum]. Toxicol Sci, 90: 267. 088968

Lee T; Yu XY; Ayres B; Kreidenweis SM; Malm WC; Collett JL (2008). Observations of fine and coarse particle nitrate at 

several rural locations in the United States. Atmos Environ, 42: 2720-2732. 156686

Lee TE; Miller SD; Turk FJ; Schueler C; Julian R; Deyo S; Dills P; Wang S (2006). The NPOESS VIIRS day/night visible 

sensor. Bull Am Meteorol Soc, 87: 191-199. 190358

Lei YD; Wania F (2004). Is rain or snow a more efficient scavenger of organic chemicals? Atmos Environ, 38: 3557-3571. 

127880

Lelieveld J; Berresheim H; Borrmann S; Crutzen PJ; Dentener FJ; Fischer H; Feichter J; Flatau PJ; Heland J; Holzinger R; 

Korrmann R; Lawrence MG; Levin Z; Markowicz KM; Mihalopoulos N; Minikin A; Ramanathan V; De Reus M; 

Roelofs GJ; Scheeren HA; Sciare J; Schlager H; Schultz M; Siegmund P; Steil B; Stephanou EG; Stier P; Traub M; 

Warneke C; Williams J; Ziereis H (2002). Global air pollution crossroads over the Mediterranean. Science, 298: 

794-799. 190361

Léon J; Tanré D; Pelon J; Kaufman Y; Haywood J; Chatenet B (2003). Profiling of a Saharan dust outbreak based on a 

synergy between active and passive remote sensing. J Geophys Res, 108: 8575. 190366

Levin Z; Cotton WR (2008). Report from the WMO/ IUGG International Aerosol Precipitation Science, Assessment Group 

(IAPSAG). In Aerosol pollution impact on precipitation: A scientific review (pp. 482). Geneva, Switzerland: World 

Meteorological Organization. 190375

Levine JS; Pinto JP (1998). The production of CO by biomass burning. Presented at Proceedings of the International 

Conference on Atmospheric Carbon Monoxide and Its Environmental Effects, Washington, DC. 029599

Levy R; Remer L; Dubovik O (2007). Global aerosol optical properties and application to MODIS aerosol retrieval over 

land. J Geophys Res, 112: D13210. 190377

Levy R; Remer L; Mattoo S; Vermote E; Kaufman Y (2007). Second-generation algorithm for retrieving aerosol properties 

over land from MODIS spectral reflectance. J Geophys Res, 112: D13211. 190379

Lewis ER; Schwartz SE (2004). Sea salt aerosol production: Mechanisms, methods, measurements and models: A critical 

review. Washington, DC: American Geophysical Union. 192023

Li R-R; Kaufman YJ; Hao WM; Salmon JM; Gao B-C (2004). A technique for detecting burn scars using MODIS data. 

IEEE Trans Geosci Remote Sens, 42: 1300-1308. 190386

Li Y-F; MacDonald RW; Jantunen LMM; Harner T; Bidleman TF; Stachen WMJ (2002). The transport of βhexachlorocyclohexane to the western Arctic Ocean: a contrast to αHCH. Sci Total Environ, 291: 229-246. 156691

Li Z; Chen H; Cribb M; Dickerson R; Holben B; Li C; Lu D; Luo Y; Maring H; Shi G; Tsay S-C; Wang P; Wang Y; Xia X; 

Zheng Y; Yuan T; Zhao F (2007). Preface to special section on East Asian studies of tropospheric aerosols: An 

international regional experiment (EAST-AIRE). J Geophys Res, 112: D22S00. 190392

Liao H; Seinfeld JH (2005). Global impacts of gas-phase chemistryaerosol interactions on direct radiative forcing by 

anthropogenic aerosols and ozone. J Geophys Res, 110: D18208. 199892

Lin CJ; Pehkonen SO (1999). The chemistry of atmospheric mercury: a review. Atmos Environ, 33: 2067-2070. 190426

Lin H; Tao S; Zuo Q; Coveney RM (2007). Uptake of polycyclic aromatic hydrocarbons by maize plants. Environ Pollut, 

148: 614-619. 155933

Lindberg SE; Brooks S; Linn CJ; Scott KJ; Landis MS; Stevens RK; Goodsite M; Richter A (2002). Dynamic oxidation of 

gaseous mercury in the Arctic troposphere at polar sunrise. Environ Sci Technol, 36: 1245-1256. 190429

Lindesay JA; Andreae MO; Goldammer JG; Harris G; Annegarn HJ; Garstang M; Scholes RJ; van Wilgen BW (1996). 

International Geosphere Biosphere Programme/International Global Atmospheric Chemistry SAFARI-92 field 

experiment: Background and overview. J Geophys Res, 23521-23530: 101. 190403

Lingua G; Franchin C; Todeschini V; Castiglione S; SBiondi; Burlando B; Parravicini V; PTorrigiani; GBerta (2008). 

Arbuscular mycorrhizal fungi differentially affect the response to high zinc concentrations of two registered poplar 

clones. Environ Pollut, 153: 137-147. 155935

December 2009 9-219 
Liou KN; Ou S-C (1989). The role of cloud microphysical processes in climate: an assessment from a one-dimensional 

perspective. J Geophys Res, 94: 8599-8607. 190407

Liousse C; Penner JE; Chuang C; Walton JJ; Eddleman H; Cachier H (1996). A global three-dimensional model study of 

carbonaceous aerosols. J Geophys Res, 101: 19,411-19,432. 078158

Liu H; Pinker R; Holben B (2005). A global view of aerosols from merged transport models, satellite, and ground 

observations. J Geophys Res, 110: D10S15. 190414

Liu J; Ballaney M; Al-Alem U; Quan C; Jin X; Perera F; Chen LC; Miller RL (2008). Combined Inhaled Diesel Exhaust 

Particles and Allergen Exposure Alter Methylation of T Helper Genes and IgE Production In Vivo. Toxicol Sci, 102: 

76-81. 156709

Liu L; Lacis AA; Carlson BE; Mishchenko MI; Cairns B (2006). Assessing Goddard Institute for Space Studies ModelE 

aerosol climatology using satellite and ground-based measurements. J Geophys Res, 111: D20212. 190422

Liu X; Penner J; Das B; Bergmann D; Rodriguez J; Strahan S; Wang M; Feng Y (2007). Uncertainties in global aerosol 

simulations: Assessment using three meteorological data sets. J Geophys Res, 112: D11212. 190427

Liu Y; Koutrakis P (2007). Estimating Fine Particulate Matter Component Concentrations and Size Distributions Using 

Satellite- Retrieved Fractional Aerosol Optical Depth: Part 1—Method Development. J Air Waste Manag Assoc, 57: 

1351-1359. 187007

Liu Y; Koutrakis P; Kahn R; Turquety S; Yantosca RM (2007). Estimating fine particulate matter component 

concentrations and size distributions using satellite-retrieved fractional aerosol optical depth: part 2--a case study. J 

Air Waste Manag Assoc, 57: 1360-9. 098197

Loeb N; Kato S (2002). Top-of-atmosphere direct radiative effect of aerosols over the tropical oceans from the Clouds and 

the Earth’s Radiant Energy System (CERES) satellite instrument. PNAS, 15: 1474-1484. 190432

Loeb N; Manalo-Smith N (2005). Top-of-Atmosphere direct radiative effect of aerosols over global oceans from merged 

CERES and MODIS observations. J Clim, 18: 3506-3526. 190433

Loeb NG; Kato S; Loukachine K; Smith NM (2005). Angular distribution models for top-of-atmosphere radiative flux 

estimation from the Clouds and the Earth’s Radiant Energy System instrument on the Terra Satellite. part I: 

Methodology. J Atmos Ocean Tech, 22: 338–351. 190436

Lohman U; Feichter J (2005). Global indirect aerosol effects: a review. Atmos Chem Phys, 5: 715-737. 155942

Lohmann U; Feichter J; Chuang CC; Penner JE (1999). Prediction of the number of cloud droplets in the ECHAM GCM. J 

Geophys Res, 104: 9169-9198. 190443

Lohmann U; Feichter J; Penner JE; Leaitch WR (2000). Indirect effect of sulfate and carbonaceous aerosols: A mechanistic 

treatment. J Geophys Res, 105: 12193-12206. 199910

Lohmann U; Koren I; Kaufman YJ (2006). Disentangling the role of microphysical and dynamical effects in determining 

cloud properties over the Atlantic. Geophys Res Lett, 33: L09802. 190451

Lohmann U; Leaitch WR; Barrie L; Law K; Yi Y; Bergmann D; Bridgeman C; Chin M; Christensen J; Easter R; Feichter J; 

Jeuken A; Kjellstrom E; Koch D; Land C; Rasch P; Roelofs GJ (2001). Vertical distributions of sulfur species 

simulated by large scale atmospheric models in COSAM: comparison with observations. Tellus B Chem Phys 

Meteorol, 53: 646 - 672. 190448

López Alonso M; Benedito JL; Miranda M; Castillo C; Hernández J; Shore RF (2002). Cattle as biomonitors of 

environmental semi-metal and trace metal concentrations in Galicia (NW Spain). Arch Environ Contam Toxicol, 

43: 103-108. 155943

López Alonso M; Benedito JL; Miranda M; Castillo C; Hernández J; Shore RF (2003). Mercury concentrations in cattle 

from NW Spain. Sci Total Environ, 302: 93-100 . 155945

López Alonso M; Benedito JL; Miranda M; Fernández JA; Castillo C; Hernández J; Shore RF (2003). Large-scale spatial 

variation in mercury concentrations in cattle in NW Spain. Environ Pollut, 125: 173-181. 155944

Lorusso S; Marabelli M; Troili M (1997). Air pollution and the deterioration of historic monuments. J Environ Pathol 

Toxicol Oncol, 16: 171-173. 084534

Lowenthal DH; Kumar N (2003). PM2.5 mass and light extinction reconstruction in IMPROVE. J Air Waste Manag Assoc, 

53: 1109-1120. 156712

December 2009 9-220 
Lowenthal DH; Rogers CF; Saxena P; Watson JG; Chow JC (1995). Sensitivity of estimated light extinction coefficients to 

model assumptions and measurement errors. Atmos Environ, 29: 751-766. 045134

Lu M-L; Feingold G; Jonsson H; Chuang P; Gates H; Flagan RC; Seinfeld JH (2008). Aerosol-cloud relationships in 

continental shallow cumulus. J Geophys Res, 113: D15201. 190455

Lubin D; Vogelmann AM (2006). A climatologically significant aerosol longwave indirect effect in the Arctic. Nature, 439: 

453-456. 190466

Lubin DS; Satheesh S; McFarquar G; Heymsfield A (2002). Longwave radiative forcing of Indian Ocean tropospheric 

aerosol. J Geophys Res, 107: 8004. 190463

Lucaciu A; Timofte L; Culicov O; Frontasyeva MV; Oprea C; Cucu-Man S; Mocanu R; Steinnes E (2004). Atmospheric 

deposition of trace elements in Romania studied by the moss biomonitoring technique. J Atmos Chem, 49: 533-548. 

155947

Lunácková L; Masarovicová E; Kráová K; Stresko V (2003). Response of fast growing woody plants from family 

Salicaceae to cadmium treatment. Bull Environ Contam Toxicol, 70: 576-585. 155948

Luo Y; Lu D; Zhou X; Li W; He Q (2001). Characteristics of the spatial distribution and yearly variation of aerosol optical 

depth over China in last 30 years. J Geophys Res, 106: 14501. 190467

Mace BL; Bell PA; Loomis RJ (2004). Visibility and Natural Quiet in National Parks and Wilderness Areas: Psychological 

Considerations. Environ Behav, 36: 5-31. 180255

Mackay D (1991). Multimedia environmental models: the fugacity approach. Chelsea, MI: Lewis Publishers. 042941

Macleod M; McKone TE; Mackay D (2005). Mass balance for mercury in the San Francisco Bay area. Environ Sci 

Technol, 39: 6721–6729. 155954

Madrid L; Diaz-Barrientos E; Madrid F (2002). Distribution of heavy metal contents of urban soils in parks of Seville. 

Chemosphere, 49: 1301–1309. 155956

Magi B; Hobbs P; Kirchstetter T; Novakov T; Hegg D; Gao S; Redemann J; Schmid B (2005). Aerosol properties and 

chemical apportionment of aerosol optical depth at locations off the United States East Coast in July and August 

2001. J Atmos Sci, 62: 919-933. 190468

Malkönen E; Derome J; Fritze H; Helmisaari H-S; Kukkola M; Kytö M; Saarsalmi A; Salemaa M (1999). Compensatory 

fertilization of Scots pine stands polluted by heavy metals. Nutr Cycling Agroecosyst, 55: 239-268. 155961

Malm W; Bell P; McGlothin GE (1984). Field testing a methodology for assessing the importance of good visual air 

quality. Presented at Air Pollution Control Association, June, 1984, San Francisco, CA. 044292

Malm WC (1999). Introduction to visibility. Cooperative Institute for Research in the Atmosphere. Fort Collins, CO. 

025037

Malm WC; Day DE (2001). Estimates of aerosol species scattering characteristics as a function of relative humidity. Atmos 

Environ, 35: 2845-2860. 190431

Malm WC; Day DE; Kreidenweis SM; Collett JL; Lee T (2003). Humidity dependent optical properties of fine particle 

during the Big Bend Regional Aerosol and Visibility Observational Study (BRAVO). J Geophys Res, 108: 4279. 

190434

Malm WC; Hand JL (2007). An examination of the physical and optical properties of aerosols collected in the IMPROVE 

program. Atmos Environ, 41: 3407-3427. 155962

Malm WC; Pitchford ML (1997). Comparison of calculated sulfate scattering efficiencies as estimated from size-resolved 

particle measurements at three national locations. Atmos Environ, 31: 1315-1325. 002519

Malm WC; Pitchford ML; McDade C; Ashbaugh LL (2007). Coarse particle speciation at selected locations in the rural 

continental United States. Atmos Environ, 41: 2225-2239. 156730

Malm WC; Schichtel BA; Ames RB; Gebhart KA (2002). A ten-year spatial and temporal trend of sulfate across the United 

States. J Geophys Res, 107: 4627. 156727

Malm WC; Schichtel BA; Pitchford ML; Ashbaugh LL; Eldred RA (2004). Spatial and monthly trends in speciated fine 

particle concentration in the United States. J Geophys Res, 109: 3306. 156728

December 2009 9-221 
Malm WC; Trijonis J; Sisler J; Pitchford M; Dennis RL (1994). Assessing the effect of SO2 emission changes on visibility. 

Atmos Environ, 28: 1023-1034. 044920

Marinoni N; Birelli MP; Rostagno C; Pavese A (2003). The effects of atmospheric multipollutants on modern concrete. 

Atmos Environ, 37: 4701-4712. 092520

Markiewicz Patkowska J; Hursthouse A; Przybyla-Kij H (2005). The interaction of heavy metals with urban soils: sorption 

behaviour of Cd, Cu, Cr, Pb and Zn with a typical mixed brownfield deposit. Environ Int, 31: 513–521. 155963

Martin MH; Coughtrey PJ (1981). Impact of metals on ecosystem function and productivity. In Lepp, N. W. (Ed.),Effect of 

heavy metal pollution on plants: volume 2, metals in the environment (pp. 119-158). Barking, United Kingdom: 

Applied Science Publishers. 047727

Martins J; Tanré D; Remer L; Kaufman Y; Mattoo S; Levy R (2002). MODIS cloud screening for remote sensing of aerosol 

over oceans using spatial variability. Geophys Res Lett, 29: MOD4. 190470

Martonchik J; Diner D; Crean K; Bull M (2002). Regional aerosol retrieval results from MISR. IEEE Trans Geosci Remote 

Sens, 40: 1520-1531. 190490

Martonchik J; Diner D; Pinty B; Verstraete M; Myneni R; Knjazikhin Y; Gordon H (1998). Determination of land and 

ocean reflective, radiative, and biophysical properties using multiangle imaging. IEEE Trans Geosci Remote Sens, 

36: 1266-1281. 190484

Martonchik JD; Diner D; Kahn R; Verstraete M; Pinty B; Gordon H; Ackerman T (1998). Techniques for the Retrieval of 

aerosol properties over land and ocean using multiangle data. IEEE Trans Geosci Remote Sens, 36: 1212-1227. 

190472

Masclet PP; Hoyau V; Jaffrezo JL; Cachier H (2000). Polycyclic aromatic hydrocarbons deposition on the ice sheet of 

Greenland. Part I. Superficial snow. Atmos Environ, 34: 3195-3207. 155966

Massicotte R; Robidoux PY; Sauve S; Flipo D; Fournier M; Trottier B (2003). Immune response of earthworms 

(Lumbricus terrestris, Eisenia andrei and Aporrectodea tuberculata) following in situ soil exposure to atmospheric 

deposition from a cement factory. J Environ Monit, 5: 774-779. 155968

Massie S; Torres O; Smith S (2004). Total ozone mapping spectrometer (TOMS) observations of increases in Asian aerosol 

in winter from 1979 to 2000. J Geophys Res, 109: D18211. 190492

Matheson MA; Coakley JA; Tahnk WR (2005). Aerosol and cloud property relationships for summertime stratiform clouds 

in the northeastern Atlantic from Advanced Very High Resolution Radiometer observations. J Geophys Res, 110: 

D24204. 190494

Matsui T; Masunaga H; Kreidenweis SM; Pielke RA; Tao W-K; Chin M; Kaufman YJ (2006). Satellite-based assessment 

of marine low cloud variability associated with aerosol, atmospheric stability, and the diurnal cycle. J Geophys Res, 

111: D17204. 190498

Matsui T; Pielke R; (2006). Measurement-based estimation of the spatial gradient of aerosol radiative forcing. Geophys Res 

Lett, 33: L11813. 190495

Matthias I; Ansmann A; Müller D; Wandinger U; Althausen D (2004). Multiyear aerosol observations with dual wavelength 

Raman lidar in the framework of EARLINET. J Geophys Res, 109: 1-15. 155971

Mayo JM; Legge AH; Yeung EC; Krupa SV; Bogner JC (1992). The effects of sulphur gas and elemental sulphur dust 

deposition on Pinus contorta x Pinus banksiana: Cell walls and water relations. Environ Pollut, 76: 43-50. 155974

McComiskey A; Feingold G (2008). Quantifying error in the radiative forcing of the first aerosol indirect effect. Geophys 

Res Lett, 35: L20810. 190517

McComiskey A; Feingold G; Frisch AS; Turner D; Miller M; Chiu JC; Min Q; Ogren JA (2008). An assessment of aerosolcloud interactions in marine stratus clouds based on surface remote sensing. J Geophys Res, 114: D09203. 190525

McComiskey A; Schwartz SE; Schmid B; Guan H; Lewis ER; Ricchiazzi P; Ogren JA (2008). Direct aerosol forcing: 

Calculation from observables and sensitivity to inputs. J Geophys Res, 113: D09202. 190523

McCormick M; Trepte C (1987). Polar stratospheric optical depth observed between 1978 and 1985. J Geophys Res, 92: 

4297-4306. 192328

McCormick R; Ludwig J (1967). Climate modification by atmospheric aerosols. Science, 9: 1358 - 1359. 190528

December 2009 9-222 
McDade C (2004). Summary of IMPROVE nitrate measurements. Retrieved 29-JUL-09, from 

http://vista.cira.colostate.edu/improve/publications/GrayLit/gray_literature.htm. 192075

Mcfiggans G; Artaxo P; Baltensperger U; Coe H; Facchini MC; Feingold G; Fuzzi S; Gysel M; Laaksonen A; Lohmann U; 

Mentel TF; Murphy DM; O'Dowd CD; Snider JR; Weingartner E (2006). The effect of physical and chemical 

aerosol properties on warm cloud droplet activation. Atmos Chem Phys, 6: 2593-2649. 190532

McGowan TF; Lipinski GE; Santoleri JJ (1993). New rules affect the handling of waste fuels. Chem Eng, 100: 122-128. 

046731

McMurry PH (2000). A review of atmospheric aerosol measurements. Atmos Environ, 34: 1959-1999. 081517

Meehl GA; Washington WM; Ammann CM; Arblaster JM; Wigley TML; Tebaldi C (2004). Combinations of natural and 

anthropogenic forcings in twentieth-century climate. J Clim, 17: 3721–3727. 192279

Meers E; Vangronsveld J; Tack FMG; Vandecasteele B; Ruttens A (2007). Potential of five willow species (Salix spp.) for 

phytoextraction of heavy metals. Environ Exp Bot, 60: 57-68. 155977

Melnikov S; Carroll J; Gorshkov A; Vlasov S; Dahle S (2003). Snow and ice concentrations of selected persistent organic 

pollutants in the Ob-Yenisey River watershed. Sci Total Environ, 306: 27-37. 156753

Memon AR; Schroder P (2009). Implications of metal accumulation mechanisms to phytoremediation. Environ Sci Pollut 

Res Int, 16: 162-175. 190442

Menon S; Del Genio AD; Kaufman Y; Bennartz R; Koch D; Loeb N; Orlikowski D (2008). Analyzing signatures of aerosol 

cloud interactions from satellite retrievals and the GISS GCM to constrain the aerosol indirect effect. J Geophys 

Res, 113: D14S22. 190534

Menon S; Hansen J; Nazarenko L; Luo Y (2002). Climate effects of black carbon aerosols in China and India. Science, 

297: 2250-2253. 155978

Mercado LM; Bellouin N; Sitch S; Boucher O; Huntingford C; Wild M; Cox PM (2009). Impact of changes in diffuse 

radiation on the global land carbon sink. Nature, 458: 1014-U1087. 190444

Michalsky J; Schlemmer J; Berkheiser W; (2001). Multiyear measurements of aerosol optical depth in the Atmospheric 

Radiation Measurement and Quantitative Links program. J Geophys Res, 106: 12099-12108. 190537

Middleton WEK (1952). Vision through the atmosphere. Toronto, ON, Canada: University of Toronto Press. 016324

Mie G (1908). Beiträge zur Optik trüber Medien, speziell kolloidaler Metallösungen. Ann Phys, 330: 377-445. 155983

Miller RL; Schmidt GA; Shindell DT (2005). Forced annular variations in the 20th century Intergovernmental Panel on 

Climate Change Fourth Assessment Report models. J Geophys Res, 111: D18101. 192258

Millhollen AG; Gustin MS; Obrist D (2006). Foliar mercury accumulation and exchange for three tree species. Environ Sci 

Technol, 40: 6001-6006. 190447

Min Q; Harrison LC (1996). Cloud properties derived from surface MFRSR measurements and comparison with GEOS 

results at the ARM SGP site. Geophys Res Lett, 23: 1641- 1644. 190538

Ming Y; Ramaswamy V; Ginoux PA; Horowitz LW; Russell LM (2005). Geophysical Fluid Dynamics Laboratory general 

circulation model investigation of the indirect radiative effects of anthropogenic sulfate aerosol. J Geophys Res, 

110: D22206. 199909

Minnis P; Harrison EF; Stowe LL; Gibson GG; Denn FM; Doelling DR; Smith WL Jr (1993). Radiative Climate Forcing 

by the Mount Pinatubo Eruption. Science, 259: 1411-1415. 190539

Mischenko M; Cairns B; Kopp G; Schueler CF; Fafaul BA; Hansen JE; Hooker RJ; Itchkawich T; Maring HB; Travis LD 

(2007). Accurate monitoring of terrestrial aerosols and total solar irradiance. Bull Am Meteorol Soc, 88: 677-691. 

190543

Mishchenko MI; Geogdzhayev IV (2007). Satellite remote sensing reveals regional tropospheric aerosol trends. Optics 

Express, 15: 7423-7438. 190545

Mishchenko MI; Geogdzhayev IV; Cairns B; Rossow WB; Lacis AA (1999). Aerosol retrievals over the ocean by use of 

channels 1 and 2 AVHRR data: sensitivity analysis and preliminary results. Appl Opt, 38: 7325-7341. 190541

Mishchenko MI; Geogdzhayev IV; Rossow WB; Cairns B; Carlson BE; Lacis AA; Liu L; Travis LD (2007). Long-term 

satellite record reveals likely recent aerosol trend. Science, 315: 1543. 190542

December 2009 9-223 
Mitchell JM Jr (1971). The effect of atmospheric aerosols on climate with special reference to temperature near the Earth's 

surface. J Appl Meteorol, 10: 703-714. 190546

Molina LT; Madronich S; Gaffney JS; Singh HB (2008). Overview of MILAGRO/INTEX-B Campaign. International 

Global Atmospheric Chemistry (Newsletter). Seattle, 

WA.http://www.igac.noaa.gov/newsletter/igac38/Apr_2008_IGAC_38.pdf. 192019

Moody E; King M; Platnick S; Schaaf C; Gao F (2005). Spatially complete global spectral surface albedos: value-added 

datasets derived from Terra MODIS land products. IEEE Trans Geosci Remote Sens, 43: 144-158. 190548

Moosmuller H; Arnott WP; Rogers CF (1998). Photoacoustic and filter measurements related to aerosol light absorption 

during the Northern Front Range Air Quality Study (Colorado 1996/1997). J Geophys Res, 103: 28,149-28,157. 

020657

Moropoulou A; Bisbikou K; Torfs K; Van Grieken R; Zezza F; Macri F (1998). Origin and growth of weathering crusts on 

ancient marbles in industrial atmosphere. Atmos Environ, 32: 967-982. 040485

Moses CA; Smith BJ (1994). Limestone weathering in the supratidal zone: an example from Mallorca. In DA Robinson; 

RBG Williams (Ed.),Rock weathering and landform evolution (pp. 432-451). Chichester, West Sussex, United 

Kingdom: John Wiley & Sons. 156785

Mosley-Thompson E; Mashiotta TA; Thompson LG (2003). High resolution ice core records of Late Holocene volcanism: 

Current and future contributions from the Greenland PARCA cores. In Volcanism and the Earth’s Atmosphere (pp. 

153–164). Washington, DC: American Geophysical Union. 192255

Mouillot F; Narasimha A; Balkanski Y; Lamarque J-F (2006). Global carbon emissions from biomass burning in the 20th 

century. Geophys Res Lett, 33: L01801. 190549

Muir DCG; Omelchenko A; Grift NP; Savoie DA; Lockhart WL; Wilkinson P; Brunskill GJ (1996). Spatial trends and 

historical deposition of polychlorinated biphenyls in Canadian midlatitude and arctic lake sediments. Environ Sci 

Technol, 30: 3609-3617. 155991

Murayama T; Sugimoto N; Uno I; Kinoshita K; Aoki K; Hagiwara N; Liu Z; Matsui I; Sakai T; Shibata T; Arao K; Sohn BJ; Won J-G; Yoon S-C; Li T; Zhou J; Hu H; Abo M; Iokibe K; Koga R; Iwasaka Y (2001). Ground-based network 

observation of Asian dust events of April 1998 in East Asia. J Geophys Res, 106: 18345-18360. 155992

Murdoch JC; Thayer MA (1988). Hedonic price estimation of variable urban air quality. J Environ Econ Manage, 15: 143-

146. 156788

Naidoo G; Chirkoot D (2004). The effects of coal dust on photosynthetic performance of the mangrove, Avicennia marina 

in Richards Bay, South Africa. Environ Pollut, 127: 359-366. 190449

Nakajima T; Higurashi A; Kawamoto K; Penner JE (2001). A possible correlation between satellite-derived cloud and 

aerosol microphysical parameters. Geophys Res Lett, 28: 1171-1174. 190552

Nakamura K; Nakawo M; Ageta Y; Goto-Azuma K; Kamiyam K (2000). Post-depositional loss of nitrate in surface snow 

layers of the Antarctic ice sheet. Bull. Glaciol. Res., 17: 11-16. 156792

Nash TH (1975). Influence of effluents from a zinc factory on lichens. Ecol Monogr, 45: 183-198. 016763

Nazaroff WW; Cass GR (1991). Protecting museum collections from soiling due to the deposition of airborne particles. 

Atmos Environ, 25: 841-852. 044577

NCDENR (2007). Technical Analyses Supporting Regional Haze State Implementation Plan. , North Carolina Department 

of Environment and Natural Resources. NC. 156798

NESCAUM (2006). Regional Aerosol Intensive Network (RAIN) Preliminary Data Analysis. Northeast States for 

Coordinated Air Use Management. Boston, MA.http://www.nescaum.org/documents/2006-05-memo8-rain.pdf/. 

156802

Nguyen HL; Leermakers M; Elskens M; De Ridder F; Doan TH; Baeyens W (2005). Correlations, partitioning and 

bioaccumulation of heavy metals between different compartments of Lake Balaton. Sci Total Environ, 341: 211-

226. 155997

Nicholson L; Benn DI (2006). Calculating ice melt beneath a debris layer using meteorological data. J Glaciol, 52: 463-

470. 156806

December 2009 9-224 
Nieminen T; Derome J; Helmisaari H-S (1999). Interactions between precipitation and Scots pine canopies along a heavymetal pollution gradient. Environ Pollut, 106: 129–137. 155998

Nogueira MA; Magalhaes GC; Cardoso EJBN (2004). Manganese toxicity in mycorrhizal and phosphorus-fertilized 

soybean plants. J Plant Nutr, 27: 141-156. 190460

Norris J; Wild M (2007). Trends in aerosol radiative effects over Europe inferred from observed cloud cover, solar 

“dimming”, and solar “brightening”. J Geophys Res, 112: D08214. 190555

Notten MJM; Oosthoek AJP; Rozema J; Aerts R (2005). Heavy metal concentrations in a soil-plant-snail food chain along 

a terrestrial soil pollution gradient. Environ Pollut, 138: 178-190. 190461

Novakov T; Ramanathan V; Hansen JE; Kirchstetter TW; Sato M; Sinton JE; Sathaye JA (2003). Large historical changes 

of fossil-fuel black carbon aerosols. Geophys Res Lett, 30: 1-4. 048398

NRC (2001). Climate change science: an analysis of some key questions. National Research Council. National Academy 

Press. Washington, DC. 053303

NRC (2005). Radiative forcing of climate change: expanding the concept and addressing uncertainties. Washington DC: 

National Research Council; The National Academies Press. 057409

O'Dowd C; McFiggans G; Creasey DJ; Pirjola L; Hoell C; Smith MH; Allan BJ; Plane JMC; Heard DE; Lee JD; Pilling 

MJ; Kulmala M (1999). On the photochemical production of new particles in the coastal boundary layer. Geophys 

Res Lett, 26: 1707-1710. 090414

O'Neill NT; Eck TF; Smirnov A; Holben BN; Thulasiraman S (2003). Spectral discrimination of coarse and fine mode 

optical depth. J Geophys Res, 108: 4559. 180187

Odum EP (1985). Trends expected in stressed ecosystems. Bioscience, 35: 419-422. 039482

Odum EP (1993). Major ecosystem types of the world. In Odum, E. P. (Ed.),Ecology and our endangered life-support 

systemsSunderland, MA: Sinauer Associates, Inc. 076742

Oehme M; Biseth A; Schlabach M; Wiig O (1995). Concentrations of polychlorinated dibenzo-p-dioxins, dibenzofurans 

and non-ortho substituted biphenyls in polar bear milk from Svalbard (Norway). Environ Pollut, 90: 401-407. 

011267

Oehme M; Haugen J-E; Schlabach M (1996). Seasonal changes and relation between levels of organochlorines in Arctic 

ambient air: First results from an all-round-year monitoring program at Ny-Ålesund, Norway. Environ Sci Technol, 

30: 2294-2304. 156001

Ohyama K; Angermann J; Dunlap DY; Matsumura F (2004). Distribution of polychlorinated biphenyls and chlorinated 

pesticide residues in trout in the Sierra Nevada. J Environ Qual, 33: 1752-1764. 190462

Oliveira A; Pampulha ME (2006). Effects of long-term heavy metal contamination on soil microbial characteristics. J 

Biosci Bioeng, 102: 157-161. 156827

Ormrod DP (1984). Impact of trace element pollution on plants. In Treshow, M. (Ed.),Air pollution and plant life (pp. 291-

314). Chichester, United Kingdom: John Wiley & Sons Ltd. 046892

Otnyukova T (2007). Epiphytic lichen growth abnormalities and element concentrations as early indicators of forest 

decline. Environ Pollut, 146: 359-365. 156009

Otvos E; Pazmandi T; Tuba Z (2003). First national survey of atmospheric heavy metal deposition in Hungary by the 

analysis of mosses. Sci Total Environ, 309: 151-160. 156831

Ouimette JR; Flagan RC (1982). The extinction coefficient of multicomponent aerosols. Atmos Environ, 16: 2405-2420. 

025047

Ozdilek HG; Mathisen PP; Pellegrino D (2007). Distribution of heavy metals in vegetation surrounding the Blackstone 

River, USA: Considerations regarding sediment contamination and long term metals transport in freshwater riverine 

ecosystems. J Environ Biol, 28: 493-502. 156010

Pacheco AMG; Freitas MC (2004). Are lower epiphytes really that better than higher plants for indicating airborne 

contaminants? An insight into the elemental contents of lichen thalli and tree bark by INAA. Journal of Radioanal 

Chem, 259: 27-33. 156011

December 2009 9-225 
Padmavathiamma PK; Li LY (2007). Phytoremediation technology: Hyper-accumulation metals in plants. Water Air Soil 

Pollut, 184: 105-126. 190465

Palmer AS; Morgan VI; Curran MAJ; van Ommen TD; Mayewski PA (2002). Antarctic volcanic flux ratios from Law 

Dome ice cores. Ann Glaciol, 35: 329-332. 192319

Park RJ; Jacob DJ; Kumar N; Yantosca RM (2006). Regional visibility statistics in the United States: Natural and 

transboundary pollution influences, and implications for the regional haze rule. Atmos Environ, 40: 5405-5423. 

190469

Parrish ZD; White JC; Isleyen M; Gent MPN; Iannucci-Berger W; Eitzer BD; Kelsey JW; Mattina MI (2006). 

Accumulation of weathered polycyclic aromatic hydrocarbons (PAHs) by plant and earthworm species. 

Chemosphere, 64: 609-618. 156014

Patadia F; Gupta P; Christopher SA (2008). First observational estimates of global clear-sky shortwave aerosol direct 

radiative effect over land. J Geophys Res, 35: L04810. 190558

Patra M; Bhowmik N; Bandopadhyay B; Sharma A (2004). Comparison of mercury, lead and arsenic with respect to 

genotoxic effects on plant systems and the development of genetic tolerance. Environ Exp Bot, 52: 199-223. 

081976

Pennanen T; Frostegård Å; Fritze H; Bååth E (1996). Phospholipid fatty acid composition and heavy metal tolerance of soil 

microbial communities along two heavy metal-polluted gradients in coniferous forests. Appl Environ Microbiol, 62: 

420–428. 156016

Penner J; Quaas J; Storelvmo T; Takemura T; Boucher O; Guo H; Kirkevag A; Kristjansson JE; Seland O (2006). Model 

intercomparison of indirect aerosol effects. Atmos Chem Phys, 6: 3391-3405. 190564

Penner JE (2003). Comment on "Control of fossil-fuel particulate black carbon and organic matter, possibly the most 

effective method of slowing global warming. J Geophys Res, 108: 4771. 156851

Penner JE; Dickinson RE; O'Neill CA (1992). Effects of aerosol from biomass burning on the global radiation budget. 

Science, 256: 1432-1433. 045825

Penner JE; Eddleman H; Novakov T (1993). Towards the development of a global inventory for black carbon emissions. 

Atmos Environ, 27: 1277-1295. 045457

Penner JE; Zhang SY; Chin M; Chuang CC; Feichter J; Feng Y; Geogdzhayev IV; Ginoux P; Herzog M; Higurashi A; Koch 

D; Land C; Lohmann U; Mishchenko M; Nakajima T; Pitari G; Soden B; Tegen I; Stowe L (2002). A comparison of 

model- and satellite derived aerosol optical depth and reflectivity. J Atmos Sci, 59: 441-460. 190562

Pennisi E (2004). The secret life of fungi. Science, 304: 1620-1622. 156018

Perlwitz J; Graf HF (2001). Trosposphere-stratosphere dynamic coupling under strong and weak polar vortex conditions . 

Geophys Res Lett, 28: 271-274. 192271

Perlwitz J; Harnik N (2003). Observational evidence of a stratospheric influence on the troposphere by planetary wave 

reflection. J Clim, 16: 3011-3026. 192264

Peters AJ; Gregor DJ; Teixeira CF; Jones NP; Spencer C (1995). The recent depositional trend of polycyclic aromatic 

hydrocarbons and elemental carbon to the Agassiz Ice cap, Ellesmere Island, Canada. Sci Total Environ, 160/161: 

167-179. 156856

Pfirman SL; Kögeler JW; Rigor I (1997). Potential for rapid transport of contaminants from the Kara Sea. Sci Total 

Environ, 202: 111-122. 156864

Pincus R; Klein SA (2000). Unresolved spatial variability and microphysical process rates in large-scale models. J Geophys 

Res, 105: 27059-27065. 190565

Pinker R; Zhang B; Dutton E (2005). Do satellites detect trends in surface solar radiation? Science, 308: 850-854. 190569

Piol MN; López AG; Miño LA; Dos Santos Afonso M; Verrengia Guerrero NR (2006). The impact of particle-bound 

cadmium on bioavailability and bioaccumulation: A pragmatic approach. Environ Sci Technol, 40: 6341-6347. 

156028

Pitchford M; Green M; Kuhns H; Tombach I; Malm W; Scruggs M; Farber R; Mirabella V; White WH; McDade C (1999). 

Project MOHAVE Final Report. 156873

December 2009 9-226 
Pitchford M; Maim W; Schichtel B; Kumar N; Lowenthal D; Hand J (2007). Revised algorithm for estimating light 

extinction from IMPROVE particle speciation data. J Air Waste Manag Assoc, 57: 1326-36. 098066

Pitchford ML; Malm WC (1994). Development and applications of a standard visual index. Atmos Environ, 28: 1049-1054. 

044922

Pitchford ML; Polkowsky BV; McGown MR; Malm WC; Molenar JV; Mauch L (1990). Percent change in extinction 

coefficient: a proposed approach for federal visibility protection strategy. In CV Mathai (Ed.),Transactions: 

Visibility and Fine Particles (pp. 37-49). Pittsburgh, PA: Air and Waste Management Association. 156871

Pitchford ML; Schichtel BA; Gebhart KA; Barna MG; Malm WC; Tombach IH; Knipping EM (2005). Reconciliation and 

Interpretation of the Big Bend National Park Light Extinction Source Apportionment: Results from the Big Bend 

Regional Aerosol and Visibility Observational Study- Part II. J Air Waste Manag Assoc, 55: 1726-1732. 156874

Prasad MNV; DeOliveira FHM (2003). Metal hyperaccumulation in plants: Biodiversity prospecting for phytoremediation 

technology. Electron J Biotechnol, 6: 110-146. 156885

Prata A; Rose W; Self S; O’Brien D (2003). Global, long-term sulphur dioxide measurements from TOVS data: a new tool 

for studying explosive volcanism and climate . In Volcanism and the Earth’s Atmosphere (pp. 75–92). Washington, 

DC: American Geophysical Union. 192235

Preston CM; Schmidt MWI (2006). Black (pyrogenic) carbon: a synthesis of current knowledge and uncertainties with 

special consideration of boreal regions. Biogeosciences, 3: 397-420. 156030

Procopio AS; Artaxo P; Kaufman YJ; Remer LA; Schafer JA; Holben BN (2004). Multiyear analysis of Amazonian 

biomass burning smoke radiative forcing of climate. J Geophys Res, 31: L03108. 190571

Prospero JM (1996). Saharan dust transport over the North Atlantic Ocean and Mediterranean: an overview. In Guerzoni S; 

Chester R (Ed.),The Impact of Desert Dust Across the Mediterranean (pp. 133–152). The Netherlands: Kluwer 

Academic Publishers. 156889

Pryor SC (1996). Assessing public perception of visibility for standard setting exercises. Atmos Environ, 30: 2705-2716. 

056598

Pryor SC; Binkowski FS (2004). An analysis of the time scales associated with aerosol processes during dry deposition. 

Aerosol Sci Technol, 38: 1091-1098. 116805

Putaud J-P; Raes F; Van Dengenen R; Bruggemann E; Facchini M-C; Decesari S; Fuzzi S; Gehrig R; Huglin C; Laj P; 

Lorbeer G; Maenhaut W; Mihalopoulos N; Muller K; Querol X; Rodriguez S; Schneider J; Spindler G; ten Brink H; 

Torseth K; Wiedensohler A (2004). A European aerosol phenomenology--2: chemical characteristics of particulate 

matter at kerbside, urban, rural and background sites in Europe. Atmos Environ, 38: 2579-2595. 055545

Qian Y; Wang W; Leung L; Kaiser D (2007). Variability of solar radiation under cloud-free skies in China: The role of 

aerosols. Geophys Res Lett, 34: L12804. 190572

Quaas J; Boucher O (2005). Constraining the first aerosol indirect radiative forcing in the LMDZ GCM using POLDER 

and MODIS satellite data. Geophys Res Lett, 32: L17814. 190573

Quaas J; Boucher O; Bellouin N; Kinne S (2008). Satellite- based estimate of the direct and indirect aerosol climate 

forcing. J Geophys Res, 113: D05204. 190916

Quaas J; Boucher O; Breon F-M (2004). Aerosol indirect effects in POLDER satellite data and the Laboratoire de 

Météorologie Dynamique-Zoom (LMDZ) general circulation model. J Geophys Res, 109: D08205. 199907

Quaas J; Boucher O; Lohmann U (2006). Constraining the total aerosol indirect effect in the LMDZ GCM and ECHAM4 

GCMs using MODIS satellite data. Atmos Chem Phys Discuss, 5: 9669-9690. 190915

Quinn PK; Anderson T; Bates T; Dlugi R; Heintzenberg J; Von Hoyningen-Huene W; Kumula M; Russel P; Swietlicki E 

(1996). Closure in tropospheric aerosol-climate research : A review and future needs for addressing aerosol direct 

shortwave radiative forcing. Contrib Atmos Phys, 69: 547-577. 192021

Quinn PK; Bates TS (2003). North American, Asian, and Indian haze: Similar regional impacts on climate? Geophys Res 

Lett, 30: 1555. 049189

Quinn PK; Bates TS; Baynard T; Clarke AD; Onasch TB; Wang W; Rood MJ; Andrews E; Allan J; Carrico CM; Coffman 

D; Worsnop D (2005). Impact of particulate organic matter on the relative humidity dependence of light scattering: 

A simplified parameterization. Geophys Res Lett, 32: 1-4. 156033

December 2009 9-227 
Quinn PK; Coffman D; Kapustin V; Bates TS; Covert DS (1998). Aerosol optical properties in the marine boundary layer 

during ACE 1 and the underlying chemical and physical aerosol properties. J Geophys Res, 103: 16547-16563. 

190918

Quinn PK; Coffman DJ; Bates TS; Welton EJ; Covert DS; Miller TL; Johnson JE; Maria S; Russell L; Arimoto R; Carrico 

CM; Rood MJ; Anderson J (2004). Aerosol optical properties measured aboard the Ronald H. Brown during ACEAsia as a function of aerosol chemical composition and source region. J Geophys Res, 109: 109. 190937

Radke LF; Coakley JA; King MD (1989). Direct and remote sensing observations of the effects of ships on clouds. 

Science, 246: 1146-1149. 156034

Rajapaksha R; MT-K; Bååth E (2004). Metal toxicity affects fungal and bacterial activities in soil differently. Appl Environ 

Microbiol, 5: 2966 – 2973. 156035

Ramachandran S; Ramaswamy V; Stenchikov GL; Robock A (2000). Radiative impact of the Mount Pinatubo volcanic 

eruption: Lower stratospheric response. J Geophys Res, 105: 24409-24429. 192050

Ramanathan V; Chung C; Kim D; Bettge T; Buja L; Kiehl JT; Washington WM; Fu Q; Sikka DR; Wild M (2005). 

Atmospheric brown clouds: Impact on South Asian climate and hydrologic cycle. PNAS, 102: 5326-5333. 190199

Ramanathan V; Crutzen P (2003). Atmospheric Brown “Clouds”. Atmos Environ, 37: 4033-4035. 190198

Ramanathan V; Crutzen PJ; Kiehl JT; Rosenfeld D (2001). Aerosols, climate and the hydrological cycle. Science, 294: 

2119-2124. 042681

Ramanathan V; Crutzen PJ; Lelieveld J; Mitra AP; Althausen D; Anderson J; Andreae MO; Cantrell W; Cass GR; Chung 

CE; Clarke AD; Coakley JA; Collins WD; Conant WC; Dulac F; Heintzenberg J; Heymsfield AJ; Holben B; 

Howell S; Hudson J; Jayaraman A; Kiehl JT; Krishnamurti TN; Lubin D; McFarquhar G; Novakov T; Ogren JA; 

Podgorny IA; Prather K; Priestley K; Prospero JM; Quinn PK: Rajeev K; Rasch P; Rupert S; Sadourny R; Satheesh 

SK; Shaw GE; Sheridan P; Valero FPJ (2001). Indian Ocean Experiment: An integrated analysis of the climate 

forcing and effects of the great Indo-Asian haze. J Geophys Res, 106: 28371-38398. 190196

Ramaswamy V; Boucher O; Haigh J; Hauglustaine D; Haywood J; Myhre G; Nakajima T; Shi G; Solomon S (2001). 

Radiative forcing of climate change, Chapter 6. In Houghton JT; Ding Y; Griggs DJ; Noguer M; van der Linden PJ; 

Da X; Maskell K; Johnson CA (Ed.),IPCC Third Assessment Report (TAR): Climate Change 2001: Working Group 

I: The Scientific Basis (pp. 349-416). Cambridge, U.K. and New York, NY: Intergovernmental Panel on Climate 

Change; Cambridge University Press. 156899

Ramaswamy V; Ramachandran S; Stenchikov G; Robock A (2006). A model study of the effect of Pinatubo volcanic 

aerosols on the stratospheric temperatures+. In Kiehl JT; Ramanathan V (Ed.),Frontiers of climate modeling (pp. 

152–178). Cambridge, UK: Cambridge University Press. 192273

Ramaswamy V; Schwarzkopf MD; Randel WJ; Santer BD; Soden BJ; Stenchikov GL (2006). Anthropogenic and natural 

influences in the evolution of lower stratospheric cooling. Science, 311: 1138-1141. 192284

Ramos L; Hernandez LM; Gonzalez MJ (1994). Sequential fractionation of copper, lead, cadmium and zinc in soils from or 

near Donana National Park. J Environ Qual, 23: 50-57. 046736

Ramsey JM (1966). Concentrations of carbon monoxide at traffic intersections in Dayton, Ohio. Arch Environ Occup 

Health, 13: 44-46. 013946

Randall CE; Bevilacqua RM; Lumpe JD; Hoppel KW (2001). Validation of POAM III aerosols: Comparison to SAGE II 

and HALOE. J Geophys Res, 106: 27525–27536. 192268

Randall D; Khairoutdinov M; Arakawa A; Grabowski W (2003). Breaking the cloud parameterization deadlock. Bull Am 

Meteorol Soc, 84: 1547-1564. 190201

Randerson JT; Liu H; Flanner MG; Chambers SD; Jin Y; Hess PG; Pfister G; Mack MC; Treseder KK; Welp LR; Chapin 

FS; Harden JW; Goulden ML; Lyons E; Neff JC; Schuur EAG; Zender CS (2006). The impact of boreal forest fire 

on climate warming. Science, 314: 1130-1132. 156038

Rapport DJ; Whitford WG (1999). How ecosystems respond to stress: Common properties of arid and aquatic systems. 

Bioscience, 49: 193-203. 004595

Ray P; Reddy UG; Lapeyrie F; Adholeya A (2005). Effect of coal ash on growth and metal uptake by some selected 

ectomycorrhizal fungi in vitro. Int J Phytoremediation, 7: 199-216. 190473

December 2009 9-228 
Read WG; Froidevaux L; Waters JW (1993). Microwave limb sounder measurement of stratospheric SO2 from the Mt. 

Pinatubo Volcano. Geophys Res Lett, 20: 1299–1302. 192031

Reddy M; Boucher O; Balkanski Y; Schulz M (2005). Aerosol optical depths and direct radiative perturbations by species 

and source type. Geophys Res Lett, 32: L12803. 190208

Reddy M; Boucher O; Bellouin N; Schulz M; Balkanski Y; Dufresne J; Pham M (2005). Estimates of multi-component 

aerosol optical depth and direct radiative perturbation in the LMDZT general circulation model. J Geophys Res, 

110: D10S16. 190207

Reddy MS; Boucher O (2007). Climate impact of black carbon emitted from energy consumption in the world's regions. 

Geophys Res Lett, 34: 1802. 156042

Regoli F; Gorbi S; Fattorini D; Tedesco S; Notti A; Machella N; Bocchetti R; Benedetti M; Piva F (2006). Use of the land 

snail Helix aspersa sentinel organism for monitoring ecotoxicologic effects of urban pollution: An integrated 

approach. Environ Health Perspect, 114: 63-69. 156046

Reid J (2008). An overview of UAE2 flight operations: Observations of summertime atmospheric thermodynamic and 

aerosol profiles of the southern Arabian Gulf. J Geophys Res, 113: D14213. 190214

Reid JS; Kinney JE; Westphal DL; Holben BN; Welton EJ; Tsay S-C; Eleuterio DP; Campbell JR; Christopher SA; Colarco 

PR; Jonsson HH; Livingston JM; Maring HB; Meier ML; Pilewskie P; Prospero JM; Reid EA; Remer LA; Russell 

PB; Savoie DL; Smirnov A; Tanré D (2003). Analysis of measurements of Saharan dust by airborne and groundbased remote sensing methods during the Puerto Rico Dust Experiment (PRIDE). J Geophys Res, 108: 8586. 

190213

Reinfelder JR; Fisher NS; Luoma SN; Nichols JW; Wang W-X (1998). Trace element trophic transfer in aquatic organisms: 

A critique of the kinetic model approach. Sci Total Environ, 219: 117-135. 156047

Reisinger LM (1990). Analysis of airborne particles sampled in the southern Appalachian Mountains. Water Air Soil Pollut, 

50: 149-162. 046737

Remer L; Gassó; Hegg D; Kaufman Y; Holben B (1997). Urban/industrial aerosol: ground based sun/sky radiometer and 

airborne in situ measurements. J Geophys Res, 102: 16849-16859. 190216

Remer L; Kaufman Y (2006). Aerosol direct radiative effect at the top of the atmosphere over cloud free ocean derived 

from four years of MODIS data. Atmos Chem Phys, 6: 237-253. 190222

Remer L; Kaufman Y; Tanré D; Mattoo S; Chu D; Martins J; Li R; Ichoku C; Levy R; Kleidman R; Eck T; Vermote E; 

Holben B (2005). The MODIS aerosol algorithm, products and validation. J Atmos Sci, 62: 947-973. 190221

Remer L; Kleidman RG; Levy RC; Kaufman YJ; Tanre D; Mattoo S; Vanderlei Martins J; Ichoku C; Koren I; Yu H; 

Holben BN (2008). An emerging aerosol climatology from the MODIS satellite sensors. J Geophys Res, 113: 

D14S01. 190224

Remer L; Tanré D; Kaufman Y; Ichoku C; Mattoo S; Levy R; Chu D; Holben B; Dubovik O; Smirnov A; Martins J; Li R; 

Ahman Z (2002). Validation of MODIS aerosol retrieval over ocean. Geophys Res Lett, 29: 8008. 190218

Richards LW; Dye TS; Arthur M; Byars MS (1996). Analysis of ASOS Data for Visibility Purposes, Final Report . Systems 

Applications International, Inc.. San Rafael, CA. STI-996231-1610-FR. 190476

Rind D; Perlwitz J; Lonergan P (2005). AO/NAO response to climate change: 1. Respective influences of stratospheric and 

tropospheric climate changes. J Geophys Res, 110: D12107. 192261

Rissler J; Swietlicki E; Zhou J; Roberts G; Andreae MO; Gatti LV; Artaxo P (2004). Physical properties of the submicrometer aerosol over the Amazon rain forest during the wet-todry season transition—comparison of modeled 

and measured CCN concentrations. Atmos Chem Phys, 4: 2119-2143. 190225

Roberts DL; Jones A (2004). Climate sensitivity to black carbon aerosol from fossil fuel combustion. J Geophys Res, 109: 

6202. 156052

Roberts ES; Richards JH; Jaskot R; Dreher KL (2003). Oxidative stress mediates air pollution particle-induced acute lung 

injury and molecular pathology. Inhal Toxicol, 15: 1327-1346. 156051

Robinson AL; Donahue NM; Shrivastava MK; Weitkamp EA; Sage AM; Grieshop AP; Lane TE; Pierce JR; Pandis SN 

(2007). Rethinking organic aerosols: Semivolatile emissions and photochemical aging. Science, 315: 1259-1262. 

156053

December 2009 9-229 
Roderick ML; Farquhar GD (2002). The cause of decreased pan evaporation over the past 50 years. Science, 298: 1410-

1411. 042788

Roeckner E; Stier P; Feichter J; Kloster S; Esch M; Fischer-Bruns L (2006). Impact of carbonaceous aerosol emissions on 

regional climate change. Clim Dynam, 27: 553-571. 156920

Romic M; Romic D (2003). Heavy metals distribution in agricultural topsoils in urban area. Environ Geol, 43: 795-805. 

156055

Rood MJ; Covert DS; Larson TV (1987). Hygroscopic properties of atmospheric aerosol in Riverside, California. Tellus B 

Chem Phys Meteorol, 39B: 383-397. 046397

Rosenfeld D (2000). Suppression of rain and snow by urban and industrial air pollution. Science, 287: 1793-1796. 002234

Rosenfeld D (2006). Aerosols, clouds, and climate. Science, 312: 1. 190233

Rosenfeld D; Dai J; Yu X; Yao Z; Xu X; Yang X; Du C (2007). Inverse relations between amounts of air pollution and 

orographic precipitation. Science, 315: 1396-1398. 156057

Rosenfeld D; Givati A (2006). Evidence of orographic precipitation suppression by air pollution-induced aerosols in the 

western United States. J Appl Meteor Climatol, 45: 893-911. 156924

Rosenfeld D; Lansky I (1998). Satellite-based insights into precipitation formation processes in continental and maritime 

convective clouds. Bull Am Meteorol Soc, 79: 2457-2476. 190230

Ross DM; Malm WC; Loomis RJ (1985). The psychological valuation of good visual air quality by national park visitors. 

Presented at 78th annual meeting of the Air Pollution Control Association, June, 1985, Detroit, MI. 044287

Ross DM; Malm WC; Loomis RJ (1987). An examination of the relative importance of park attributes at several national 

parks. In Bhardwaja, P. S. (Ed.),Visibility protection: research and policy aspects, an APCA international specialty 

conferenceGrand Teton National Park, WY; Pittsburgh, PA: Air Pollution Control Association. 037420

Rosselli W; Keller C; Boschi K (2003). Phytoextraction capacity of trees growing on a metal contaminated soil. Plant Soil, 

256: 265-272. 156058

Roth CM; Goss K-U; Schwarzenbach RP (2004). Sorption of diverse organic vapors to snow. Environ Sci Technol, 38: 

4078-4084. 056431

Rotstayn LD; Liu Y (2003). Sensitivity of the first indirect aerosol effect to an increase of the cloud droplet spectral 

dispersion with droplet number concentration. J Clim, 16: 3476-3481. 199905

Rotstayn LD; Penner JE (2001). Indirect aerosol forcing, quasi-forcing, and climate response. J Clim, 14: 2960-2975. 

193754

Rotton J; Barry T; Milligan M; Fitzpatrick M (1979). The air pollution experience and interpersonal aggression. Appl 

Psychol, 9: 397-412. 190478

Rotton J; Frey J (1982). Atmospheric conditions, seasonal trends, and psychiatric emergencies. In Replications and 

Extensions (pp. Unknown). Washington, DC: American Psychological Association. 190477

Rozanov EV; Schlesinger ME; Andronova NG; Yang F; Malyshev SL; Zubov VA; Egorova TA; Li B (2002). 

Climate/chemistry effects of the Pinatubo volcanic eruption simulated by the UIUC stratosphere/troposphere GCM 

with interactive photochemistry . J Geophys Res, 107: ACL 12-1. 192070

Rozanov EV; Schlesinger ME; Egorova TA; Li B; Andronova N; Zubov VA (2004). Atmospheric response to the observed 

increase of solar UV radiation from solar minimum to solar maximum simulated by the University of Illinois at 

Urbana-Champaign climate-chemistry model . J Geophys Res, 109: D01110.1-D01110.16. 192072

Ruckstuhl C; Philipona R; Behrens K; Coen MC; Dürr B; Heimo A; Mätzler C; Nyeki S; Ohmura A; Vuilleumier L; Weller 

M; Wehrli C; Zelenka A (2008). Aerosol and cloud effects on solar brightening and recent rapid warming. Geophys 

Res Lett, 35: L12708. 190356

Russell P; Kinne S; Bergstrom R (1997). Aerosol climate effects: local radiative forcing and column closure experiments. J 

Geophys Res, 102: 9397-9407. 190359

Russell P; Livingston J; Hignett P; Kinne S; Wong J; Chien A; Bergstrom R; Durkee P; Hobbs P (1999). Aerosol-induced 

radiative flux changes off the United States mid-Atlantic coast: comparison of values calculated from sun 

photometer and in situ data with those measured by airborne pyranometer. J Geophys Res, 104: 2289-2307. 190363

December 2009 9-230 
Rusu A-M; Jones GC; Chimonides PDJ; Purvis OW (2006). Biomonitoring using the lichen Hypogymnia physodes and 

bark samples near Zlatna, Romania immediately following closure of a copper ore-processing plant. Environ Pollut, 

143: 81-88. 156062

Ryan PA; Lowenthal D; Kumar N (2005). Improved light extinction reconstruction in interagency monitoring of protected 

visual environments. J Air Waste Manag Assoc, 55: 1751-1759. 156934

Sabbioni C; Ghedini N; Bonazza A (2003). Organic anions in damage layers on monuments and buildings. Atmos Environ, 

37: 1261-1269. 049282

Sabin LD; Lim JH; Stolzenbach KD; Schiff KC (2005). Contribution of trace metals from atmospheric deposition to 

stormwater runoff in a small impervious urban catchment. Water Res, 39: 3929-3937. 088300

Salemaa M; Derome J; Helmisaari HS; Nieminen T; Vanha-Majamaa I (2004). Element accumulation in boreal bryophytes, 

lichens and vascular plants exposed to heavy metal and sulfur deposition in Finland. Sci Total Environ, 324: 141-

160. 156069

Samuels HJ; Twiss S; Wong EW (1973). Visibility, light scattering and mass concentration of particulate matter: report of 

the California Tri-City Aerosol Sampling Project. Air Resources Board. CA. 070601

Sanderson EG; Farant JP (2004). Indoor and outdoor polycyclic aromatic hydrocarbons in residences surrounding a 

Soderberg aluminum smelter in Canada. Environ Sci Technol, 38: 5350-5356. 156942

Sato M; Hansen J; Koch D; Lucis A; Ruedy R; Dubovik O; Holben B; Chin M; Novakov T (2003). Global atmospheric 

black carbon inferred from AAEONET. Presented at Proceedings of the National Academy of Science. 156947

Sato M; Hansen JE; McCormick MP; Pollack JB (1993). Stratospheric aerosol optical depths, 1850-1990. J Geophys Res, 

98: 22987–22994. 192046

Sauvé S (2001). Speciation of metals in soils. Presented at , Pensacola, FL. 156948

Saxena P; Hildemann LM; McMurry PH; Seinfeld JH (1995). Organics alter hygroscopic behavior of atmospheric 

particles. J Geophys Res, 100: 18,755-18,770. 077273

Schichtel BA; Gebhart K; Barna MG; Malm WC; Green MC (2004). Big Bend Regional Aerosol and Visibility 

Observational (BRAVO) Study Results: Air Quality Data and Source Attribution Analyses Results from the 

National Park Service. Colorado State University CIRA. Ft. Collins, CO 

.http://vista.cira.colostate.edu/improve/Studies/BRAVO/Studybravo.htm. 179902

Schichtel BA; Gebhart KA; Malm WC; Barna MG; Pitchford ML; Knipping EM; Tombach IH (2005). Reconciliation and 

interpretation of big bend national park particulate sulfur source apportionment: Results from the big bend regional 

aerosol and visibility observational study-part I. J Air Waste Manag Assoc, 55: 1709-1725. 156957

Schichtel BA; Malm WC; Bench G; Fallon S; McDade CE; Chow JC; Watson JG (2008). Fossil and contemporary fine 

particulate carbon fractions at 12 rural and urban sites in the United States. J Geophys Res, 113: D02311. 156958

Schiff K; Bay S; Stransky C (2002). Characterization of stormwater toxicants from an urban watershed to freshwater and 

marine organisms. Urban Water, 4: 215-227. 156959

Schilling JS; Lehman ME (2002). Bioindication of atmospheric heavy metal deposition in the Southeastern US using the 

moss Thuidium delicatulum. Atmos Environ, 36: 1611-1618. 113075

Schmid B; Ferrare R; Flynn C; Elleman R; Covert D; Strawa A; Welton E; Turner D; Jonsson H; Redemann J; Eilers J; 

Ricci K; Hallar AG; Clayton M; Michalsky J; Smirnov A; Holben B; Barnard J (2006). How well do state-of-the-art 

techniques measuring the vertical profile of tropospheric aerosol extinction compare? J Geophys Res, 111: 1-25. 

190372

Schmid B; Livingston JM; Russell PB; Durkee PA; Jonsson HH; Collins DR; Flagan RC; Seinfeld JH; Gassó S; Hegg 

DA; Öström E; Noone KJ; Welton EJ; Voss KJ; Gordon HR Formenti, P.(9) Andreae, M.O (2000). Clear sky 

closure studies of lower tropospheric aerosol and water vapor during ACE-2 using airborne sunphotometer, airborne 

in situ, space-borne, and ground-based measurements. Tellus B Chem Phys Meteorol, 52: 568-593. 190369

Schmidt GA; Ruedy R; Hansen JE; Aleinov I; Bell N; Bauer M; Bauer S; Cairns B; Canuto V; Cheng Y; del Genio A; 

Faluvegi G; Friend AD; Hall TM; Hu Y; Kelley M; Kiang NY; Koch D; Lacis AA; Lerner J; Lo KK; Miller RL; 

Nazarenko L; Oinas V; Perlwitz J; Perlwitz J; Rind D; Romanou A; Russell GL; Sato MKI; Shindell DT; Stone PH; 

Sun S; Tausnev N; Thresher D; Yao MS (2006). Present-day atmospheric simulations using GISS Model E: 

Comparison to in situ, satellite and reanalysis data. J Clim, 19: 153-192. 190373

December 2009 9-231 
Schoeberl MR; Douglass AR; Zhu Z; Pawson S (2003). A comparison of the lower stratospheric age spectra derived from a 

general circulation model and two data assimilation systems. J Geophys Res, 108: 4113. 057262

Schroeder WH; Munthe J (1998). Atmospheric mercury -- an overview. Atmos Environ, 32: 809-822. 014559

Schulz M; Textor C; Kinne S; Balkanski Y; Bauer S; Berntsen T; Berglen T; Boucher O; Dentener F; Guibert S; Isaksen 

ISA; Iversen T; Koch D; Kirkevag A; Liu X; Montanaro V; Myhre G; Penner JE; Pitari G; Reddy S; Seland O; Stier 

P; Takemura T (2006). Radiative forcing by aerosols as derived from the AeroCom present-day and preindustrial 

simulations. Atmos Chem Phys, 6: 5225-5246. 190381

Schwartz SE; Charlson RJ; Rodhe H (2007). Quantifying climate change- Too rosy a picture? Nature, 2: 23-24. 190384

Seifert M; Anke S; Holzinger S; Jaritz M; Arnhold W; Anke M (1999). Cadmium and strontium content of mice, shrews. 

and some invertebrates. J. Trace Microprobe Tech., 17: 357-365. 190480

Seigneur C; Johnson CD; Latimer DA; Bergstrom RW; Hogo H (1984). Users manual for the Plume Visibility Model 

(PLUVUE II). Final report 23 Feb-29 Aug 83. Systems Applications, Inc. San Rafael, CA . PB-84-158302. 156965

Seinfeld JH; Carmichael GR; Arimoto R; Conant WC; Brechtel FJ; Bates TS; Cahill TA; Clarke AD; Doherty SJ; Flatau 

PJ; Huebert BJ; Kim J; Markowicz KM; Quinn PK; Russell LM; Russell PB; Shimizu A; Shinozuka Y; Song CH; 

Tang Y; Uno I; Vogelmann AM; Weber RJ; Woo J-H; Zhang XY (2004). ACEAsia: Regional climatic and 

atmospheric chemical effects of Asian dust and pollution. Bull Am Meteorol Soc, 85: 367-380. 190388

Sekiguchi M; Nakajima T; Suzuki K; Kawamoto K; Higurashi A; Rosenfeld D; Sano I; Mukai S (2003). A study of the 

direct and indirect effects of aerosols using global satellite data sets of aerosol and cloud parameters. J Geophys 

Res, 108 D22: 4699. 190385

Shakya K; Chettri MK; Sawidis T (2008). Impact of heavy metals (copper, zinc, and lead) on the chlorophyll content of 

some mosses. Arch Environ Contam Toxicol, 54: 412-421. 156081

Shan XQ; Wang ZW; Wang WS; Zhang SZ; Wen B (2003). Labile rhizosphere soil solution fraction for prediction of 

bioavailability of heavy metals and rare earth elements to plants. Anal Bioanal Chem, 375: 400-407. 156972

Sharifi MR; Gibson AC; Rundel PW (1999). Phenological and physiological responses of heavily dusted creosote bush 

(Larrea tridentata) to summer irrigation in the Mojave Desert. Flora, 14: 369–378. 156082

Sheesley RJ; Schauer JJ; Hemming JD; Barman MA; Geis SW; Tortorelli JJ (2004). Toxicity of ambient atmospheric 

particulate matter from the Lake Michigan (USA) airshed to aquatic organisms. Environ Toxicol Chem, 23: 133-

140. 156084

Shindell DT; Levy H; Schwarzkopf II MD; Horowitz LW; Lamarque JF; Faluvegi G (2008). Multimodel projections of 

climate change from short-lived emissions due to human activities. J Geophys Res, 113: D11109. 190393

Shindell DT; Schmidt GA; Mann ME; Faluvegi G (2004). Dynamic winter climate response to large tropical volcanic 

eruptions since 1600. J Geophys Res, 109: D05104. 192267

Shindell DT; Schmidt GA; Miller RL; Mann M (2003). Volcanic and solar forcing of climate change during the 

preindustrial era. J Clim, 16: 4094–4107. 192069

Shindell DT; Teich H; Chin M; Dentener F; Doherty RM; Faluvegi G; Fiore AM; Hess P; MacKenzie IA; Sanderson MG; 

Schultz MG; Schulz M; Stevenson DS; Textor C; Wild O; Bergmann DJ; Bian H; Cuvelier C; Duncan BN; Folberth 

G; Horowitz LW; Jonson J; Kaminski JW; Marmer E; Park R; Pringle KJ; Schroeder S; Szopa S; Takemura T; Zeng 

G; Keating TJ; Zuber A (2008). A multi-model assessment of pollution transport to the Arctic. Atmos Chem Phys, 

8: 5353-5372. 190391

Shulz KG; Zondervan I; Gerringa LJA; Timmermans KR; Veldhuls MJW; Riebesell U (2004). Effect of trace metal 

availability on coccolithophorid calcification. Nature, 430: 673-676. 156087

Singh HB; Brune WH; Crawford JH; Flocke F; Jacob DJ (2008). Chemistry and transport of pollution over the Gulf of 

Mexico and the Pacific: spring 2006 INTEX-B campaign overview and first results. Atmos Chem Phys Discuss, 9: 

2301-2318. 190394

Sinyuk A; Dubovik O; Holben B; Eck TF; Breon FM; Martonchik J; Kahn R; Diner DJ; Vermote EF; Roger JC; Lapyonok 

T; Slutsker I (2007). Simultaneous retrieval of aerosol and surface properties from a combination of AERONET and 

satellite data. Rem Sens Environ, 107: 90-108. 190395

December 2009 9-232 
Skov H; Christensen JH; Goodsite ME; Heidam NZ; Jensen B; Wahlin P; Geernaert G (2004). Fate of elemental mercury in 

the Arctic during atmospheric mercury depletion episodes and the load of atmospheric mercury to the Arctic. 

Environ Sci Technol, 38: 2373-2382. 190481

Sloane CS (1983). Optical properties of aerosols--comparison of measurements with model calculations. Atmos Environ, 

17: 409-416. 025039

Sloane CS (1984). Optical properties of aerosols of mixed composition. Atmos Environ, 18: 871-878. 025040

Sloane CS (1986). Effect of composition on aerosol light scattering efficiencies. Atmos Environ, 20: 1025-1037. 045954

Sloane CS; Wolff GT (1985). Prediction of ambient light scattering using a physical model responsive to relative humidity: 

validation with measurements from Detroit. Atmos Environ, 19: 669-680. 045953

Smejkalova M; Mikanova O; Boruvka L (2003). Effects of heavy metal concentrations on biological activity of soil 

microorganisms. Plant Soil Environ, 49: 321?326. 156987

Smirnov A; Holben B; Eck T; Dubovik O; Slutsker I (2000). Cloud screening and quality control algorithms for the 

AERONET database. Rem Sens Environ, 73: 337-349. 190397

Smirnov A; Holben B; Eck T; Slutsker I; Chatenet B; Pinker R (2002). Diurnal variability of aerosol optical depth observed 

at AERONET (Aerosol Robotic Network) sites. Geophys Res Lett, 29: 2115. 190398

Smirnov A; Holben B; Sakerin S; Kabanov DM; Slutsker I; Chin M; Diehl TL; Remer LA; Kahn R; Ignatov A; Liu L; 

Mishchenko M; Eck TF; Kucsera TL; Giles D; Kopelevich OV (2006). Shipbased aerosol optical depth 

measurements in the Atlantic Ocean, comparison with satellite retrievals and GOCART model. Geophys Res Lett, 

33: L14817. 190400

Smith AE; Howell S (2009). An assessment of the robustness of visual air quality preference study results. CRA 

International. Washington, 

DC.http://yosemite.epa.gov/sab/sabproduct.nsf/B55911DF9796E5E385257592006FB737/$File/CRA+VAQ+Pref+

Robustness+Study+3+30+09+final.pdf. 198803

Smith WH (1990). Forest nutrient cycling: toxic ions. In Air pollution and forests: interactions between air contaminants 

and forest ecosystemsNew York, NY: Springer-Verlag. 046896

Smith WH (1990). Forests as sinks for air contaminants: vegetative compartment. In Air pollution and forests: interactions 

between air contaminants and forest ecosystems (pp. 147-180). New York, NY: Springer-Verlag. 084015

Smith WH (1991). Air pollution and forest damage. Chem Eng News, 6945: 30-43. 042566

Smith WL Jr; Charlock TP; Kahn R; Martins JV; Remer LA; Hobbs PV; Redemann J; Rutledge CK (2005). EOS Terra 

aerosol and radiative flux validation: An overview of the Chesapeake Lighthouse and aircraft measurements from 

satellites (CLAMS) experiment. J Atmos Sci, 62: 903-918. 190401

Smolders E; Degryse F (2002). Fate and effect of zinc from tire debris in soil. Environ Sci Technol, 36: 3706–3710. 

156091

Soares CRFS; Siqueira JO (2008). Mycorrhiza and phosphate protection of tropical grass species against heavy metal 

toxicity in multi-contaminated soil. Biol Fertil Soils, 44: 833-841. 190482

Sokolik I; Winker D; Bergametti G; Gillette DA; Carmichael G; Kaufman YJ; Gomes L; Schuetz L; Penner JE (2001). 

Introduction to special section: outstanding problems in quantifying the radiative impacts of mineral dust. J 

Geophys Res, 106: 18015-18027. 190404

Solomon PA; Hopke PK (2008). A Special Issue of JA&WMA Supporting Key Scientific and Policy-and Health-Relevant 

Findings from EPA's Particulate Matter Supersites Program and Related Studies: An Integration and Synthesis of 

Results. J Air Waste Manag Assoc, 58: 137. 156997

Solomon S; Portmann RW; Garcia RR; Thomason LW; Poole LR; McCormick MP (1996). The role of aerosol variations in 

anthropogenic ozone depletion at northern midlatitudes. J Geophys Res, 101: 6713-6727. 192252

Sotiropoulou REP; Medina J; Nenes A (2006). CCN predictions: is theory sufficient for assessments of the indirect effect? 

Geophys Res Lett, 33: L05816. 190406

Sotiropoulou REP; Nenes A; Adams PJ; Seinfeld JH (2007). Cloud condensation nuclei prediction error from application of 

Kohler theory: Importance for the aerosol indirect effect. J Geophys Res, 112: D12202. 190405

December 2009 9-233 
Spinhirne J; Palm S; Hart W; Hlavka D; Welton E (2005). Cloud and Aerosol Measurements from the GLAS Space Borne 

Lidar: initial results. Geophys Res Lett, 32: L22S03. 190410

Spracklen DV; Logan JA; Mickley LJ; Park RJ; Yevich R; Westerling AL; Jaffe DA (2007). Wildfires drive interannual 

variability of organic carbon aerosol in the western U.S. in summer. Geophys Res Lett, 34: L16816. 190485

Squires P (1958). The microstructure and colloidal stability of warm clouds: Part I - the relation between structure and 

stability. Tellus Dyn Meteorol Oceanogr, 10: 256-261. 045608

Srogi (2007). Monitoring of environmental exposure to polycyclic aromatic hydrocarbons: a review. Environ Chem Lett, 5: 

169-195. 180049

Stanhill G; Cohen S (2001). Global dimming: a review of the evidence for a widespread and significant reduction in global 

radiation with discussion of its probable causes and possible agricultural consequences. Agr Forest Meteorol, 107: 

255-278. 042121

Steinnes E; Hvatum OØ; Bølviken B; Varskog P (2005). Atmospheric supply of trace elements studied by peat samples 

from ombrotrophic bogs. J Environ Qual, 34: 192-197. 156095

Stenchikov G; Hamilton K; Robock A; Ramaswamy V; Schwarzkopf MD (2004). Arctic Oscillation response to the 1991 

Pinatubo eruption in the SKYHI GCM with a realistic quasi-biennial oscillation . J Geophys Res, 109: D03112. 

192274

Stenchikov G; Hamilton K; Stouffer RJ; Robock A; Ramaswamy V; Santer B; Graf H-F (2006). Arctic Oscillation response 

to volcanic eruptions in the IPCC AR4 climate models. J Geophys Res, 111: D07107. 192260

Stenchikov G; Robock A; Ramaswamy V; Schwarzkopf MD; Hamilton K; Ramachandran S (2002). Arctic Oscillation 

response to the 1991 Mount Pinatubo eruption: Effects of volcanic aerosols and ozone depletion. J Geophys Res, 

107: D24. 192277

Stenchikov GL; Kirchner I; Robock A; Graf H-F; Antuña JC; Grainger RG; Lambert A; Thomason L (1998). Radiative 

forcing from the 1991 Mount Pinatubo volcanic eruption. J Geophys Res, 103: 13837-13857. 192049

Stephens G; Vane DG; Boain RJ; Mace GG; Sassen K; Wang Z; Illingworth AJ; O'Connor EJ; Rossow WB; Durden SL; 

Miller SD; Austin RT; Benedetti A; Mitrescu C; The CloudSat Science Team (2002). The CloudSat mission and the 

A-Train. Bull Am Meteorol Soc, 83: 1771-1790. 190412

Stephens GL; Haynes JM (2007). Near global observations of the warm rain coalescence process. Geophys Res Lett, 34: 

L20805. 190413

Stern DI (2005). Global sulfur emissions from 1850 to 2000. Chemosphere, 58: 163-175. 190416

Stern GA; Halsall CJ; Barrie LA; Muir DCG; Fellin P; Rosenberg B (1997). Polychlorinated biphenyls in arctic air. 1. 

Temporal and spatial trends: 1992-1994. Environ Sci Technol, 31: 3619-3628. 156096

Stevens B; Feingold G; Walko RL; Cotton WR (1996). On elements of the microphysical structure of numerically 

simulated non-precipitating stratocumulus. J Atmos Sci, 53: 980-1006. 190417

Stewart AR; Luoma SN; Schlekat CE; Doblin MA; Hieb KA (2004). Food web pathway determines how selenium affects 

aquatic ecosystems. Environ Sci Technol, 38: 4519-4526. 156097

Stier P; Seinfeld JH; Kinne S; Boucher O (2007). Aerosol absorption and radiative forcing. Atmos Chem Phys, 7: 5237-

5261. 157012

Stohl A; Andrews E; Burkhart JF; Forster C; Herber A; Hoch SW; Kowal D; Lunder C; Mefford T; Ogren JA; Sharma S; 

Spichtinger N; Stebel K; Stone R; Ström J; Tørseth K; Wehrli C; Yttri KE (2006). Pan-Arctic enhancements of light 

absorbing aerosol concentrations due to North American boreal forest fires during summer 2004. J Geophys Res, 

111: D22214. 156100

Stohl A; Berg T; Burkhart JF; Fjæraa AM; Forster C; Herber A; Hov Ø; Lunder C; McMillan WW; Oltmans S; Shiobara M; 

Dimpson D; Solberg S; Stebel K; Ström J; Tørseth K; Treffeisen R; Virkkunen K; Yttri KE (2007). Arctic smoke - 

record high air pollution levels in the European Arctic due to agricultural fires in Eastern Europe in spring 2006. 

Atmos Chem Phys, 7: 511-534. 157015

Storlevmo T; Kristjansson JE; Myhre G; Johnsud M; Stordal F (2006). Combined observational and modeling based study 

of the aerosol indirect effect. Atmos Chem Phys, 6: 3583-3601. 190418

December 2009 9-234 
Stothers RB (2001). A chronology of annual mean effective radii of stratospheric aerosols from volcanic eruptions during 

the twentieth century as derived from ground-based spectral extinction measurements. J Geophys Res, 106: 32043–

32049. 192233

Stothers RB (2001). Major optical depth perturbations to the stratosphere from volcanic eruptions: Stellar extinction period, 

1961–1978. J Geophys Res, 106: 2993–3003. 192232

Stott PA (2006). Observational constraints on past attributable warming and predictions of future global warming. J Clim, 

19: 3055-3069. 190419

Strachan WMJ; Burniston DA; Williamson M; Bohdanowicz H (2001). Spatial difference in persistent organochlorine 

pollutant concentrations between the Bering and Chukchi Seas. Mar Pollut Bull, 43: 132-142. 156103

Strandberg B; Axelsen JA; Pedersen MB; Jensen J; Attrill MJ (2006). Effect of a copper gradient on plant community 

structure. Environ Toxicol Chem, 25: 743-753. 156105

Strawa A; Castaneda R; Owano T; Baer P; Paldus B (2002). The measurement of aerosol optical properties using 

continuous wave cavity ring-down techniques. J Atmos Ocean Tech, 20: 454-465. 190421

Streets D; Bond T; Lee T; Jang C (2004). On the future of carbonaceous aerosol emissions. J Geophys Res, 109: D24212. 

190423

Streets D; Wu Y; Chin M (2006). Two-decadal aerosol trends as a likely explanation of the global dimming/brightening 

transition. Geophys Res Lett, 33: L15806. 190425

Streets DG; Aunan K (2005). The importance of China's household sector for black carbon emissions. Geophys Res Lett, 

32: L12708. 156106

Streets DG; Zhang Q; Wang L; He K; Hao J; Wu Y; Tang Y; Carmichael GR (2006). Revisiting China?s CO emissions after 

the Transport and Chemical Evolution over the Pacific (TRACE-P) mission: Synthesis of inventories, atmospheric 

modeling, and observations. J Geophys Res, 111: D14306. 157019

Strydom C; Robinson C; Pretorius E; Whitcutt JM; Marx J; Bornman MS (2006). The effect of selected metals on the 

central metabolic pathways in biology: A review. WaterSA, 32: 543-554. 190486

Suzuki K; Nakajima T; Numaguti A; Takemura T; Kawamoto K; Higurashi A (2004). A study of the aerosol effect on a 

cloud field with simultaneous use of GCM modeling and satellite observation. J Atmos Sci, 61: 179-194. 199917

Swackhamer DL; Paerl HW; Eisenreich SJ; Hurley J; Hornbuckle KC; McLachlan M; Mount D; Muir D; Schindler D 

(2004). Impacts of atmospheric pollutants on aquatic ecosystems. Issues in Ecology, 12: 1-24. 190488

Sweet C; Caughey M; Gay D (2005). Midwest Ammonia Monitoring Project: Summary for October 2003 through 

November 2004. Illinois State Water Survey. 

IL.http://www.ladco.org/reports/rpo/monitoring/ammonia_study_monitoring_report.pdf. 180038

Szabó L; Fodor L (2006). Uptake of microelements by crops grown on heavy metal-amended soil. Commun Soil Sci Plant 

Anal, 37: 2679-2689. 156109

Tabazadeh A; Drdla K; Schoeberl MR; Hamill P; Toon OB (2002). Arctic "ozone hole" in a cold volcanic stratosphere. 

PNAS, 99: 2609-2612. 192250

Takemura T; Nakajima T; Dubovik O; Holben B; Kinne S (2002). Single-scattering albedo and radiative forcing of various 

aerosol species with a global three-dimensional model. PNAS, 15: 333-352. 190438

Takemura T; Nozawa T; Emori S; Nakajima T; Nakajima T (2005). Simulation of climate response to aerosol direct and 

indirect effects with aerosol transport-radiation model. J Geophys Res, 110: D02202. 190439

Tang IN (1996). Chemical and size effects of hygroscopic aerosols on light scattering coefficients. J Geophys Res, 101: 

245?219. 157042

Tang Y; Carmichael GR; Seinfeld JH; Dabdub D; Weber RJ; Huebert B; Clarke AD; Guazzotti SA; Sodeman DA; Prather 

KA; Uno I; Woo JH; Yienger JJ; Streets DG; Quinn PK; Johnson JE; Song CH; Grassian VH; Sandu A; Talbot RW; 

Dibb JE (2004). Three-dimensional simulations of inorganic aerosol distributions in East Asia during spring 2001. J 

Geophys Res, 109: D19S23. 190445

December 2009 9-235 
Tang Y; Carmichael GR; Woo JH; Thongboonchoo N; Kurata G; Uno I; Streets DG; Blake DR; Weber RJ; Talbot RW; 

Kondo Y; Singh HB; Wang T (2003). Influences of biomass burning during the Transport and Chemical Evolution 

Over the Pacific (TRACE-P) experiment identified by the regional chemical transport model. J Geophys Res, 108: 

8824. 190441

Tanré D; Haywood J; Pelon J; Léon JF; Chatenet B; Formenti P; Francis P; Goloub P; Highwood EJ; Myhre G (2003). 

Measurement and modeling of the Saharan dust radiative impact: Overview of the Saharan Dust Experiment 

(SHADE). J Geophys Res, 108: 8574. 190454

Tanré D; Kaufman Y; Herman M; Mattoo S (1997). Remote sensing of aerosol properties over oceans using the MODIS/ 

EOS spectral radiances. J Geophys Res, 102: 16971-16988. 190452

Tao S; Jiao X; Chen S; Xu F; Li Y; Liu F (2006). Uptake of vapor and particulate polycyclic aromatic hydrocarbons by 

cabbage. Environ Pollut, 140: 13–15. 156112

Taulavuori K; Prasad MNV; Taulavuori E; Laine K (2005). Metal stress consequences on frost hardiness of plants at 

northern high latitudes: a review and hypothesis. Environ Pollut, 135: 209-220. 190489

Tett SFB; Jones GS; Stott PA; Hill DC; Mitchell JFB; Allen MR; Ingram WJ; Johns TC; Johnson CE; Jones A; Roberts DL; 

Sexton DMH; Woodage MJ (2002). Estimation of natural and anthropogenic contributions to twentieth century 

temperature change. J Geophys Res, 107: 4306. 192053

Textor C; Schulz M; Guibert S; Kinne S; Balkanski Y; Bauer S; Berntsen T; Berglen T; Boucher O; Chin M; Dentener F; 

Diehl T; Easter R; Feichter H; Fillmore D; Ghan S; Ginoux P; Gong S; Grini A; Hendricks J; Horowitz L; Huang P; 

Isaksen I; Iversen T; Kloster S; Koch D; Kirkevåg A; Kristjansson JE; Krol M; Lauer A; Lamarque JF; Liu X 

Montanaro V; Myhre G; Penner J; Pitari G; Reddy S; Sel Ø; Stier P; Takemura T; Tie X (2006). Analysis and 

quantification of the diversities of aerosol life cycles within AEROCOM. Atmos Chem Phys, 6: 1777-1813. 190456

Textor C; Schulz M; Guibert S; Kinne S; Balkanski Y; Bauer S; Berntsen T; Berglen T; Boucher O; Chin M; Dentener F; 

Diehl T; Feichter J; Fillmore D; Ginoux P; Gong S; Grini A; Hendricks J; Horowitz L; Huang P; Isaksen ISA; 

Iversen T; Kloster S; Koch D; Kirkevag A; Kristjansson JE; Krol M; Lauer A; Lamarque JF; Liu X; Montanaro V; 

Myhre G; Penner JE; Pitari14 G; Reddy MS; Seland O; Stier P; Takemura T; Tie X (2007). The effect of 

harmonized emissions on aerosol properties in global models?an AeroCom experiment. Atmos Chem Phys, 7: 

4489-4501. 190458

Thomas GO; Smith KEC; Sweetman AJ; Jones KC (1998). Further studies of the air-pasture transfer of polychlorinated 

biphenyls. Environ Pollut, 102: 119–128. 156118

Thomason LW; Peter T (2006). Assessment of Stratospheric Aerosol Properties (ASAP): Report on the Assessment KickOff Workshop, Paris, France, 4-6 November 2001 . Presented at Assessment of Stratospheric Aerosol Properties , 

Paris, France. 192248

Tie X; Madronich S; Walters S; Edwards DP; Ginoux P; Mahowald N; Zhang R; Lou C; Brasseur C (2005). Assessment of 

the global impact of aerosols on tropospheric oxidants. J Geophys Res, 110: 1-32. 190459

Tie XX; Brasseur GP; Briegleb B; Granier C (1994). Two-dimensional simulation of Pinatubo aerosol and its effect on 

stratospheric ozone. J Geophys Res, 99: 20545-20562. 192253

Timmreck C; Graf H-F; Steil B (2003). Aerosol chemistry interactions after the Mt. Pinatubo Eruption . In A Robock and C 

Oppenheimer (Ed.),Volcanism and the Earth’s Atmosphere (pp. 213-226). Washington, DC: American 

Geophysical Union. 192068

Tombach I; McDonald K (2004). Visibility and Radiative Balance Effects. In P.H. McMurry MFS, and J.S. Vickery 

(Ed.),Particulate Matter Science for Policy Makers: A NARSTO Assessment (pp. 325-354). Cambridge, MA: 

Cambridge University Press. 157054

Toose LK; Mackay D (2004). Adaptation of fugacity models to treat speciating chemicals with constant species 

concentration ratios. Environ Sci Technol, 38: 4619–4626. 156123

Torres O; Bhartia P; Herman J; Ahmad Z; Gleason J (1998). Derivation of aerosol properties from satellite measurements 

of backscattered ultraviolet radiation: Theoretical bases. J Geophys Res, 103: 17009-17110. 190503

Torres O; Bhartia P; Herman J; Sinyuk A; Ginoux P; Holben B (2002). A long-term record of aerosol optical depth from 

TOMS observations and comparison to AERONET measurements. J Atmos Sci, 59: 398-413. 190505

December 2009 9-236 
Torres O; Bhartia P; Sinyuk A; Welton E; Holben B (2005). Total Ozone Mapping Spectrometer measurements of aerosol 

absorption from space: Comparison to SAFARI 2000 groundbased observations. J Geophys Res, 110: D10S18. 

190507

Tremper AH; Agneta M; Burton S; Higgs DEB (2004). Field and laboratory exposures of two moss species to low level 

metal pollution. J Atmos Chem, 49: 111-120. 156126

Trijonis J; Thayer M; Murdoch J; Hagemen R (1985). Air quality benefit analysis for Los Angeles and San Francisco based 

on housing values and visibility. California Air Resources Board. Sacramento, CA. 078468

Trijonis JC; Malm WC; Pitchford M; White WH; Charlson R (1990). Acidic deposition: State of science and technology. 

Report 24. Visibility: Existing and historical conditions-causes and effects. Final report. 157058

Turco RP; Toon OB; Whitten RC; Pollack JB; Hamill P (1983). The global cycle of particulate elemental carbon: a 

theoretical assessment. In Pruppacher HR (Ed.),Precipitation Scavenging, Dry Deposition, and Resuspension (pp. 

1337- 1351). New York: Elsevier Science. 190529

Twomey S (1977). The influence of pollution on the shortwave albedo of clouds. J Atmos Sci, 34: 1149-1152. 190533

U.S. EPA (1979). Protecting Visibility, an EPA Report to Congress. U.S. Environmental Protection Agency. Washington, 

DC. 157065

U.S. EPA (1982). Air quality criteria for particulate matter and sulfur oxides, Vol 1, 2, 3. U.S. Environmental Protection 

Agency, Environmental Criteria and Assessment Office. Washington, D.C.. EPA 600/8-82-029a; EPA 600/8-82-

029b; EPA 600/8-82-029c. http://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=3000188Z.txt; 

http://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=300018EV.txt; 

http://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=300053KV.txt. 017610

U.S. EPA (1993). Air quality criteria for oxides of nitrogen, 3 Volumes. Environmental Criteria and Assessment Office, 

Office of Health and Environmental Assessment, U.S. Environmental Protection Agency. Research Triangle Park, 

NC. EPA/600/8-91/049. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=40179. 017649

U.S. EPA (1996). Air quality criteria for particulate matter. U.S. Environmental Protection Agency. Research Triangle Park, 

NC. EPA/600/P-95/001aF-cF. 079380

U.S. EPA (1997). Mercury Study Report to Congress. U.S. Environmental Protection Agency. Washington, DC. 157066

U.S. EPA (2000). National air pollutant emission trends, 1900-1998. U.S. Environmental Protection Agency. Washington, 

DC. 012211

U.S. EPA (2001). Draft Guidance for tracking progress under the regional haze rule. U.S. Environmental Protection 

Agency. Washington, DC. 157068

U.S. EPA (2004). Air quality criteria for particulate matter. U.S. Environmental Protection Agency. Research Triangle Park, 

NC. EPA/600/P-99/002aF-bF. 056905

U.S. EPA (2004). The Particle Pollution Report: Current Understanding of Air Quality and Emissions through 2003. U.S. 

Environmental Protection Agency. Research Triangle Park, NC. EPA 454-R-04-002. 190219

U.S. EPA (2005). Review of the national ambient air quality standards for particulate matter: Policy assessment of 

scientific and technical information OAQPS staff paper. U.S. Environmental Protection Agency. Washington, DC. 

EPA/452/R-05-005a. http://www.epa.gov/ttn/naaqs/standards/pm/data/pmstaffpaper_20051221.pdf . 090209

U.S. EPA (2006). Air quality criteria for lead, in 2 Volumes. Office of Health and Environmental Assessment, 

Environmental Criteria and Assessment Office, Office of Research and Development, U.S. Environmental 

Protection Agency. Research Triangle Park, NC. EPA-600/R-5/144aF-bF. 090110

U.S. EPA (2008). Integrated science assessment for oxides of nitrogen and sulfur: Ecological criteria. U.S. Environmental 

Protection Agency. Research Triangle Park, NC. EPA/600/R-08/082F. 157074

Vaisvalavicius R; Motuzas A; Prosycevas I; Levinskaite L; Zakarauskaite D; Grigaliuniene K; Butkus V (2006). Effect of 

heavy metals on microbial communities and enzymatic activity in soil column experiment. Arch Agron Soil Sci, 52: 

161-169. 157080

Van Aardenne JA; Dentener FJ; Olivier JGJ; Klein Goldewijk CGM; Lelieveld J (2001). A 1"degree" x 1"degree" 

resolution data set of historical anthropogenic trace gas emissions for the period 1980-1990. Global Biogeochem 

Cycles, 15: 909-928. 055564

December 2009 9-237 
Van de Hulst H (1981). Light scattering by small particles. New York: Dover. 191972

Van Donkelaar A; Martin RV; Park RJ (2006). Estimating ground-level PM2.5 using aerosol optical depth determined from 

satellite remote sensing . J Geophys Res, 111: D21. 192108

van Heerden PDR; Krüger GHJ; Kilbourn Louw M (2007). Dynamic responses of photosystem II in the Namib Desert 

shrub, Zygophyllum prismatocarpum, during and after foliar deposition of limestone dust. Environ Pollut, 146: 34-

45. 156131

Vasundhara G; Jayashree G; Muraleedhara-Kurup G (2004). Sequestration of nickel and copper by Azotobacter 

chroococcum SB1. Bull Environ Contam Toxicol, 72: 1122–1127. 156133

Vautard R; Maidi M; Menut L; Beekmann M; Colette A (2007). Boundary layer photochemistry simulated with a twostream convection scheme. Atmos Environ, 41: 8275-8287. 106012

Veihelmann B; Levelt PF; Stammes P; Veefkind JP (2007). Simulation study of the aerosol information content in OMI 

spectral reflectance measurements. Atmos Chem Phys, 7: 3115-3127. 190627

Verdelhos T; Neto JM; Marques JC; Pardal MA (2005). The effect of eutrophication abatement on the bivalve Scrobicularia 

plana. Estuar Coast Shelf Sci, 63: 261-268. 190497

Viard B; Pihan F; Promeyrat S; Pihan J-C (2004). Integrated assessment of heavy metal (Pb, Zn, Cd) highway pollution: 

bioaccumulation in soil, graminaceae and land snails. Chemosphere, 55: 1349-1314. 055675

Viles HA; Gorbushina AA (2003). Soiling and microbial colonisation on urban roadside limestone: A three year study in 

Oxford, England. Build Environ, 38: 1217-1224. 156138

Viles HA; Taylor MP; Yates TJS; Massey SW (2002). Soiling and decay of N.M.E.P. limestone tablets. Sci Total Environ, 

292: 215-229. 156137

Villa S; Vighi M; Maggi V; Finizio A; Bolzacchini E (2003). Historical trends of organochlorine pesticides in an Alpine 

glacier. J Atmos Chem, 46: 295-311. 156139

Viskari E-L; Karenlampi L (2000). Roadside Scots pine as an indicator of deicing salt use - a comparative study from two 

consecutive winters. Water Air Soil Pollut, 122: 405-419. 019101

Vivas A; Barea JM; Biro B; Azcon R (2006). Effectiveness of autochthonous bacterium and mycorrhizal fungus on 

Trifolium growth, symbiotic development and soil enzymatic activities in Zn contaminated soil. J Appl Microbiol, 

100: 587-598. 190500

Vivas A; Voros A; Biro B; Barea JM; Ruiz-Lozano JM; Azcon R (2003). Beneficial effects of indigenous Cd-tolerant and 

Cd-sensitive Glomus mosseae associated with a Cd-adapted strain of Brevibacillus sp in improving plant tolerance 

to Cd contamination. Appl Soil Ecol, 24: 177-186. 190499

Vives I; Grimalt JO; Ventura M; Catalan J (2005). Distribution of polycyclic aromatic hydrocarbons in the food web of a 

high mountain lake, Pyrenees, Catalonia, Spain. Environ Toxicol Chem, 24: 1344-1352. 157099

Vogel-Mikus K; Drobne D; Regvar M (2005). Zn, Cd and Pb accumulation and arbuscular mycorrhizal colonisation of 

pennycress Thlaspi praecox Wulf. (Brassicaceae) from the vicinity of a lead mine and smelter in Slovenia. Environ 

Pollut, 133: 233-242. 190501

Vogel-Mikus K; Pongrac P; Kump P; Necemer M; Regvar M (2006). Colonisation of a Zn, Cd and Pb hyperaccumulator 

Thlaspi praecox Wulfen with indigenous arbuscular mycorrhizal fungal mixture induces changes in heavy metal 

and nutrient uptake. Environ Pollut, 139: 362-371. 190502

Waggoner AP; Weiss RE (1980). Comparison of fine particle mass concentration and light scattering extinction in ambient 

aerosol. Atmos Environ, 14: 623-626. 070152

Waggoner AP; Weiss RE; Ahlquist NC; Covert DS; Will S; Charlson RJ (1981). Optical characteristics of atmospheric 

aerosols. Atmos Environ, 15: 1891-1909. 095453

Wang C (2007). Impact of direct radiative forcing of black carbon aerosols on tropical convective precipitation. Geophys 

Res Lett, 34: 5709. 156147

Wang Y-Z; Ingram JL; Walters DM; Rice AB; Santos JH; Van Houten B; Bonner JC (2003). Vanadium-induced STAT-1 

activation in lung myofibroblasts requires H2O2 and P38 MAP kinase. Free Radic Biol Med, 35: 845-855. 157106

December 2009 9-238 
Wania F; Hoff JT; Jia CQ; Mackay D (1998). The effects of snow and ice on the environmental behaviour of hydrophobic 

organic chemicals. Environ Pollut, 102: 43-51. 156148

Wania F; Mackay D (1993). Global fractionation and cold condensation of low volatility organochlorine compounds in 

polar regions. Ambio, 22: 10-18. 157110

Warner J (1968). A reduction in rainfall associated with smoke from sugar-cane fires—an inadvertent weather 

modification? J Appl Meteorol, 7: 247-251. 157114

Warner J; Twomey S (1967). The production of cloud nuclei by cane fires and the effect on cloud droplet concentration. J 

Atmos Sci, 24: 704-706. 045616

Watmough SA; Hutchinson TC; Dillon PJ (2004). Lead dynamics in the forest floor and mineral soil in south-central 

Ontario. Biogeochemistry, 71: 43-68. 077809

Watson JG; Chow JC (2007). Receptor models for source apportionment of suspended particles. In Introduction to 

Environmental Forensics (pp. 273-298). San Diego, CA: Academic Press. 157127

Watson JG; Zhu T; Chow JC; Engelbrecht J; Fujita EM; Wilson WE (2002). Receptor modeling application framework for 

particle source apportionment. Chemosphere, 49: 1093-1136. 035623

Weissenhorn I; Leyval C; Berthelin J (1995). Bioavailability of heavy metals and abundance of arbuscular mycorrhiza in a 

soil polluted by atmospheric deposition from a smelter. Biol Fertil Soils, 19: 22-28. 073826

Welton E; Voss K; Quinn P; Flatau P; Markowicz K; Campbell J; Spinhirne J; Gordon H; Johnson J (2002). Measurements 

of aerosol vertical profiles and optical properties during INDOEX 1999 using micro-pulse lidars. J Geophys Res, 

107: 8019. 190631

Welton EJ; Campbell JR; Spinhirne JD; Scott VS (2001). Global monitoring of clouds and aerosols using a network of 

micro-pulse lidar systems. In Singh, U.N. T. Itabe N. Sugimoto (Ed.),Lidar remote sensing for industry and 

environment monitoring (pp. 151-158). Bellingham, WA: Society of Photo-Optical Instrumentation Engineers. 

157133

Wen T; Wang Y; Chang SY; Liu G (2006). On-line measurement of water-soluble ions in ambient particles. Adv Atmos Sci, 

23: 586-592. 179964

Wetzel MA; Stowe LL (1999). Satellite-observed patterns in stratus microphysics, aerosol optical thickness, and shortwave 

radiative forcing. J Geophys Res, 104: 31287-31299. 190636

Wielicki B; Barkstrom B; Harrison E; Lee R; Smith G; Cooper J (1996). Clouds and the Earth’s radiant energy system 

(CERES): An Earth observing system experiment. Bull Am Meteorol Soc, 77: 853-868. 190637

Wild E; Dent J; Thomas GO; Jones KC (2005). Direct observation of organic contaminant uptake, storage, and metabolism 

within plant roots. Environ Sci Technol, 39: 3695-3702. 156156

Wild SR; Jones KC (1992). Polynuclear aromatic hydrocarbons uptake by carrots grown in sludge amended soil. J Environ 

Qual, 21: 217– 225. 156155

Williams KD; Jones A; Roberts DL; Senior CA; Woodage MJ (2001). The response of the climate system to the indirect 

effects of anthropogenic sulfate aerosols. Clim Dynam, 17: 846-856. 199898

Winker D; Couch R; McCormick M (1996). An overview of LITE: NASA's Lidar In-Space Technology Experiment. IEEE 

Micro, 84: 164-180. 190914

Winker DM; Pelon J; McCormick MP (2003). The CALIPSO mission: spaceborne lidar for observation of aerosols and 

clouds. PNAS, 4893: 1-11. 192017

Wolff GT (1985). Characteristics and consequences of soot in the atmosphere. Environ Int, 11: 259-269. 044680

Wyszkowska J; Boros E; Kucharski J (2007). Effect of interactions between nickel and other heavy metals on the soil 

microbiological properties. Plant Soil Environ, 53: 544-552. 179948

Xu J; DuBois D; Pitchford M; Green M; Etyemezian V (2006). Attribution of sulfate aerosols in Federal Class I areas of 

the western United States based on trajectory regression analysis. Atmos Environ, 40: 3433-3447. 102706

Xue H; Feingold G (2006). Large eddy simulations of tradewind cumuli: Investigation of aerosol indirect effects. J Atmos 

Sci, 63: 1605-1622. 190920

December 2009 9-239 
Xue H; Feingold G; Stevens B (2008). Aerosol effects on clouds, precipitation, and the organization of shallow cumulus 

convection. J Atmos Sci, 65: 392- 406. 190921

Yang CY; Chen CJ (2007). Air pollution and hospital admissions for chronic obstructive pulmonary disease in a subtropical 

city: Taipei, Taiwan. J Toxicol Environ Health A Curr Iss, 70: 1214-1219. 092847

Yang F; Schlesinger M (2001). Identification and separation of Mount Pinatubo and El Niño-Southern Oscillation land 

surface temperature anomalies. J Geophys Res, 106: 14757–14770. 192270

Yang XE; Jin XF; Feng Y; Islam E (2005). Molecular mechanisms and genetic basis of heavy metal 

tolerance/hyperaccumulation in plants. J Integr Plant Biol, 47: 1025-1035. 192104

Yang Z; Zhu L (2007). Performance of the partition-limited model on predicting ryegrass uptake of polycyclic aromatic 

hydrocarbons. Chemosphere, 67: 402-409. 156168

Ying Q; Kleeman MJ (2006). Source contributions to the regional distribution of secondary particulate matter in California. 

Atmos Environ, 40: 736-752. 098359

Yogui G; Sericano J (2008). Polybrominated diphenyl ether flame retardants in lichens and mosses from King George 

Island, maritime Antarctica. Chemosphere, 73: 1589-1593. 189971

Yu H; Dickinson R; Chin M; Kaufman Y; Zhou M; Zhou L; Tian Y; Dubovik O; Holben B (2004). The direct radiative 

effect of aerosols as determined from a combination of MODIS retrievals and GOCART simulations. J Geophys 

Res, 109: D03206. 190926

Yu H; Kaufman YJ; Chin M; Feingold G; Remer LA; Anderson TL; Balkanski Y; Bellouin N; Boucher O; Christopher S; 

Decola P; Kahn R; Koch D; Loeb N; Reddy MS; Schulz M; Takemura T; Zhou M (2006). A review of 

measurement-based assessments of the aerosol direct radiative effect and forcing. Atmos Chem Phys, 6: 613-666. 

156173

Yu H; Liu S; Dickinson R (2002). Radiative effects of aerosols on the evolution of the atmospheric boundary layer. J 

Geophys Res, 107: 4142. 190923

Yu IJ; Park JD; Park ES; Song KS; Han KT; Han JH; Chung YH; Choi BS; Chung KH; Cho MH (2003). Manganese 

distribution in brains of Sprague-Dawley rats after 60 days of stainless steel welding-fume exposure. 

Neurotoxicology, 24: 777-785. 156171

Yu LE; Yung LL; Ong C; Tan Y; Balasubramaniam KS; Hartono D; Shui G; Wenk MR; Ong W (2007). Translocation and 

effects of gold nanoparticles after inhalation exposure in rats. Nanotoxicology, 1: 235-242. 093173

Yuangen Y; Campbell CD; Clark L; Camerson CM; Paterson E (2006). Microbial indicators of heavy metal contamination 

in urban and rural soils. Chemosphere, 63: 1942-1952. 156174

Yunker MB; Snowdon LR; MacDonald RW; Smith JN; Fowler MG; Skibo DN (1996). Polycyclic aromatic hydrocarbon 

composition and potential sources for sediment samples from the Beufort and Barents Seas. Environ Sci Technol, 

30: 1310-1320. 156175

Zaccone C; Cocozza C; Cheburkin AK; Shotyk W; Miano TM (2007). Highly Organic Soils as “Witnesses” of 

Anthropogenic Pb, Cu, Zn, and 137Cs Inputs During Centuries. Water Air Soil Pollut, 186: 263-271. 179930

Zappia G; Sabbioni C; Riontino C; Gobbi G; Favoni O (1998). Exposure tests of building materials in urban atmosphere. 

Sci Total Environ, 224: 235-244. 012037

Zechmeister HG (1998). Annual growth of four pleurocarpous moss species and their applicability for biomonitoring heavy 

metals. Environ Monit Assess, 52: 441-451. 156178

Zechmeister HG; Hohenwallner D; Riss A; Hanus-Illnar A (2003). Variation in heavy metal concentrations in the moss 

species Abietinella abietina (Hedw.) Fleisch. according to sampling time, within site variability and increase in 

biomass. Sci Total Environ, 301: 55-65. 157175

Zeidner M; Shechter M (1988). Psychological responses to air pollution: some personality and demographic correlates. J 

Environ Psychol, 8: 191-208. 189973

Zhang J; Christopher S (2003). Longwave radiative forcing of Saharan dust aerosols estimated from MODIS, MISR, and 

CERES observations on Terra. Geophys Res Lett, 30: 2188. 190928

Zhang J; Christopher S; Remer L; Kaufman Y (2005). Shortwave aerosol radiative forcing over cloud-free oceans from 

Terra. I: Angular models for aerosols. J Geophys Res, 110: D10S23. 190929

December 2009 9-240 
Zhang J; Christopher S; Remer L; Kaufman Y (2005). Shortwave aerosol radiative forcing over cloud-free oceans from 

Terra. II: Seasonal and global distributions. J Geophys Res, 110: D10S24. 190930

Zhang J; Reid JS; Holben BN (2005). An analysis of potential cloud artifacts in MODIS over ocean aerosol optical 

thickness products. Geophys Res Lett, 32: L15803. 190931

Zhang J; Reid JS; Westphal DL; Baker NL; Hyer EJ (2008). A system for operational aerosol optical depth data 

assimilation over global oceans. J Geophys Res, 113: D10208. 190932

Zhang KM; Wexler AS; Niemeier DA; Zhu YF; Sioutas W; Sioutas C (2005). Evolution of particle number distribution 

near roadways Part III: traffic on-road size resolved particulate emission factors. Atmos Environ, 39: 4155-4166. 

086743

Zhang Q; Jimenez JL; Canagaratna MR; Allan JD; Coe HL Ulbrich I; Alfarra MR; Takami A; Middlebrook AM; Sun YL; 

Dzepina K; Dunlea E; Docherty K; DeCarlo PF; Salcedo D; Onasch T; Jayne JR; Miyoshi T; Shimono A; 

Hatakeyama S; Takegawa N; Kondo Y; Schneider J; Drewnick F; Borrmann S; Weimer S; Demerjian K; Williams 

P; Bower K; Bahreini R; Cottrell L; Griffin RJ; Rautiainen J; Sun JR; Zhang YM; Worsnop DR (2007). Ubiquity 

and dominance of oxygenated species in organic aerosols in anthropogenically-influenced Northern Hemisphere 

midlatitudes. Geophys Res Lett, 34: L13801. 189998

Zhang Q; Jimenez JL; Canagaratna MR; Jayne JT; Worsnop DR (2005). Time- and size-resolved chemical composition of 

submicron particles in Pittsburgh: Implications for aerosol sources and processes. J Geophys Res, 110: 1-19. 

157185

Zhang X; Zwiers FW; Stott PA (2006). Multi-model multisignal climate change detection at regional scale. J Clim, 19: 

4294-4307. 190933

Zhang XH; Zhu YG; Chen BD; Lin AJ; Smith SE; Smith FA (2005). Arbuscular mycorrhizal fungi contribute to resistance 

of upland rice to combined metal contamination of soil. J Plant Nutr, 28: 2065-2077. 192083

Zhang XZ; Sun HW; Zhang ZY (2006). [Bioaccumulation of titanium dioxide nanoparticles in carp]. Huanjing Kexue, 27: 

1631-5. 157722

Zhao TXP; Laszlo I; Guo W; Heidinger A; Cao C; Jelenak A; Tarpley D; Sullivan J (2008). Study of long-term trend in 

aerosol optical thickness observed from operational AVHRR satellite instrument. J Geophys Res, 113: D07201. 

190935

Zhao TXP; Yu H; Laszlo I; Chin M; Conant WC (2008). Derivation of component aerosol direct radiative forcing at the top 

of atmosphere for clear-sky oceans. J Quant Spectrosc Radiat Transf, 109: 1162-1186. 190936

Zhou M; Yu H; Dickinson RE; Dubovik O; Holben BN (2005). A normalized description of the direct effect of key aerosol 

types on solar radiation as estimated from aerosol robotic network aerosols and moderate resolution imaging 

spectroradiometer albedos. J Geophys Res, 110: D19202. 156183

Zhu Y; Hinds WC; Shen S; Sioutas C (2004). Seasonal trends of concentration and size distribution of ultrafine particles 

near major highways in Los Angeles. Aerosol Sci Technol, 38: 5-13. 156184

Zimmer D; Baum C; Leinweber P; Hrynkiewicz K; Meissner R (2009). Associated bacteria increase the phytoextraction of 

cadmium and zinc from a metal-contaminated soil by mycorrhizal willows. Int J Phytoremediation, 11: 200-213. 

192085

December 2009 9-241