# Chapter 9. Welfare Ef fects

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, in cluding: (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 expand ed 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 NO X 

### and SO X 

### 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 deposi tion of gas- and particle-phase pollutants related to ambient NO X 

### and SO X 

### concentrations that can lead to acidification and nutrient enrichment, as well as on the potential for increased mercury methylation from SO 4 2– 

### deposition. Thus, emphasis in this document is placed on the ef fects of airborne PM on visibility and climate, and on the deposition effects of PM constituents other than NO X 

### and SO X 

### , primarily metals and carbonaceous compounds. Chapter 2 of this assessment provides an inte grative overview of the major welfare effects evaluated. EPA’s framework for causality, desc ribed 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 characterizi ng visibility conditions and trends over broad regional scales, improving the understanding of the atmospheric processes and pollutants responsible for the regional impacts, and attributi on 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 ni trate, with natural concentrations taken to be just a few tenths of a μg/m 3 

### 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)

.

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### However, natural source PM can be substantial on an episodic basis for crustal mineral PM components during high windblown dust conditi ons 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 ne w research on the optical and human perceptual aspects of atmospheric visibility over the last deca de 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, SO 2 

### and NO X 

### (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 rela tes 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 seve ral 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 lo cal 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 requi red to estimate visibility conditions in these areas, thereby reducing the uncertainty of the esti mates. Thus, the state of the science supporting urban visibility assessments continues to improve. The background section below contains an overvi ew of long-available information to help provide context to the more re cently published literature summa rized 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 th e 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 par ticles by size are known (Van de Hulst, 1981, 191972 ). The ability of human observers to visually det ect 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 physicoch emical characteristics of the atmosphere caused by air pollution as well as factors not related to ai r quality such as length of the sight path, scenic lighting and the physical characteristics of the view ed 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, fo llowed 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 ( b ext 

### ) can be expressed as the sum of light scattering by particles ( b s,p 

### ), scattering by gases ( b s,g 

### ), absorption by particles ( b a,p 

### ) and absorption by gases ( b a,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 9-2Source: Malm (1999, 025037 ).



## Figure 9-1. Important factors involved in see ing 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 obse rver. 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 fra ctional change in light extinction results in a similar perceptual change regardless of ce rtain 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 acou stic 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 vi ewing situations. The haze index in deciview units is an appropriate metric for expressing the extent of haze changes where th e perceptibility of the change is an issue. The RHR has adopted the d eciview haze index as the metric for tracking long-term haze trends of visibility-pro tected federal lands (U.S. EPA, 2001, 157068 ). Light extinction and its components are more useful metr ics for characterizing the apportionment of haze to its pollutant components due to the approximate ly 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 al so known to be affected by air pollution. Stargazing is a popular human activity in urban and remote se ttings. 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 agai nst the background (see the schematic in Figure 9-2 ). These are

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### 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. Th e 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 artifi cial 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 impor tant differences to note. Light sources and ambient conditions are typically five to seven orde rs 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 atm osphere 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 da ytime scenario. Little research has been conducted on nighttime visibility. Even if the air quality-visib ility 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 a nd 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 NO 2 

### , and can be directly and accurately estimated from NO 2 

### concentrations by multiplying by the absorption efficiency. Scattering by gases is described by the Rayleigh scattering theory. NO 2 

### absorbs more light in the short wavelength blue portion of the spectrum than at longer wavelengths. For this reason a plume or layer of NO 2 

### 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 NO 2 

### 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, NO 2 

### is not routinely measured at any of the monitoring sites representing visibility protected areas where its impacts are as sumed to be inconsequential compared to those of PM. At background concentrations NO 2 

### 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 contra st and discoloration associated with NO 2 

### 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 th an 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 NO 2 

### 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 th at 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 

### /[ μ g/m 3 

### ] which reduces to m 2 

### /g) is greatest for particles with diameters from ~0.3-1.0 μ m. If

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### the index of refraction, particle shape and conc entration 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 pa rticle properties are known, so assumptions are used in place of missing information to develop a simp lified 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 th at 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 dr y 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 pa rticles, 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 cu rves for ammonium sulfate, ammonium nitrate and mixtures of the two particle components are simila r 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 partic le light extinction to its mass). To expand the calculations from one particle at a time to the mul titude of particles in ambient aerosol, information

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### about the aerosol size and composition distributions are needed. Aerosol mixture refers to how the major components that make up the particles ar e mixed. Methods have been developed to treat simple mixture models ranging from external mixtur es where the various components are assumed to be in separate particles, to multi-component mi xtures 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 mixt ure 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 a pportionment 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. Desp ite 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 (PM 2.5 

### ) that are analyzed for the major PM components including SO 4 2– 

### , nitrate, and carbonaceous particulate. CSN also analyzes for ammonium ion, but does not monitor coarse mass (PM 10-2.5 

### ), while IMPROVE measures coarse mass but does not analyze for ammonium ion. Neither da ta set has sufficient size resolution to make Mie theory calculations of light extinction, nor does either program routinely monitor NO 2 

### concentrations, which would be required to calculate its contribution to light extinction by absorption. A simple algorithm similar in form to the lin ear 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 concen tration 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 contri bution 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 compar ing 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.

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# [ ] [ ] [ ]

[ ] [ ] [ ]

10 6.0 1 10 4 )(3 )(3

+ ×+ ×+ ×+ ×+ ××+ × × ≈

Mass Coarse SoilFine Carbon

Elemental Mass Organic Nitrate RHf Sulfate RHfb

ext

Equation 9-1

Source: DeBell (2006, 156388 )



### Light extinction ( b ext 

### ) is in units of Mm -1



### , the mass concentrations of the components indicated in brackets are in μ g/m 3 

### , 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 SO 4 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 SO 4 2– 

### is fully neutralized ammonium sulfate and all nitrate is assumed to be ammo nium 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 SO 4 2– 

### fully. Fine particle nitrates can include sodium or calcium nitrate, which are the fi ne 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 ae rosol (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 SO 4 2– 

### and nitrate compounds) to be separately approximated. The f(RH) terms inflate the particulate SO 4 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 SO 4 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 10m 2 

### /g associated with EC. Based on this algorithm, particulate SO 4 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 visib ility effects. The strong relationship demonstrated between dry light scattering and fine PM mass concen tration 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., SO 4 2– 

### , nitrate and organic mass efficiencies).



# 9.2. 2.3. Di rect 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 measur ement at or near 550 nm, which is the wavelength of maximum eye response. Direct PM light extincti on, 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 mo re 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 op tical measurements are made with high time resolution (e.g., minutes to hourly) compared with the filter composition based estimates that are

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### typically 24-h duration, allowing the former to better characterize sub-daily temporal patterns which can help in identifying influential source categor ies 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 use ful range of light extinction over which the measurements can be accurately measured, with pa th-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 calibra tion. Transmissometers are currently in routine service at five urban areas. A number of instruments measure the light sca ttered by particles and gases from a source of known intensity. These include forward scattering, back scattering, polar, and integrating nephelometers. Of these the integrating nephelomet ers with its high sensitivity and sample control options has been more widely used for air quality-r elated 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 seri ous 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 control ling 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 characteris tic 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 estimate d using a nephelometer that alternately samples through a 2.5 μ m size selective inlet and a 10 μ m size selective inlet. This separation by size may be useful in that it would allow correction of the under estimated 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 concentr ation 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 transmissomete r 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 specifi cally 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 fo r filter and sampled particle li ght 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 obtain ed 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 dist inguish 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 ).

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### Other approaches to measure light absorpti on include a photoacoustic instrument that measures the heating associated with absor bed 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. Val ue 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 wi th visibility changes; the other assesses the psychological value of visual air quality. With resp ect 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 recreationa l 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 resear ch 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 vi sibility has been published (Beron et al., 2001, 156270 ) which is summarized below (Section 9.2.4.6 ). In recreational settings, experience based de mand models have been developed using on-site and mail-in surveys to judge the relative importanc e to NP visitors of various park attributes including good VAQ, to assess visitor awaren ess 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 wher e 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 visibi lity 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., be tween 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 pro cesses) 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

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### plus the use of air quality simulation modeling. This section summarizes recently available information that is based on monitoring data.



# 9.2. 3.1. Aer osol Properties

Particle size is the most influential physical prope rty 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 prope rties of interest for visibility effects, field monitoring programs typically include particle size distribution monitoring, high size resolution particle sampling with subsequent compositional anal ysis, 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 a ssessed by applying the IMPROVE or some similar algorithm to measured or modeled aerosol concen tration data, this sect ion will include recent investigations that evaluate or address various a ssumptions inherent in the use of these simple algorithms. One component of the Big Bend Regional Aer osol 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 co ncentration. Both the composition of the nitrate and the fact of much of it being in the coarse size mode (2.5 μ m >D>10 μ m) 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 coar se particle composition (Malm et al., 2007, 156730 ). The same sampling and analytical methodol ogies procedures were used for the PM 10 

### samples as are routinely used on the IMPROVE PM 2.5 

### samples. The IMPROVE coarse particle speciation study did not include ammonium analysis, so SO 4 2– 

### and nitrate ions were assumed to be ammonium sulfate and ammonium nitrate. As expected crustal minera ls were the largest contributors to coarse mass overall (about 60%), though at Mt. Rainier the fracti on 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 hi ghest coarse nitrate concentrations, 0.74 μ g/m 3 

### and 0.69 μ g/m 3 

### , and high fine nitrates concentrations on average, 2.66 μ g/m 3 

### and 2.14 μ g/m 3



### , respectively. Brigantine, a coastal site in New Jers ey, 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 SO 4 2– 

### concentrations are small with only about ~1% of the total SO 4 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 SO 4 2– 

### ) constitute a significant fraction

December 2009 9-11

### (~24% on average) of the coarse mass concentr ation, 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 exc eeds 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 numb ers 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 SO 4 2– 

### ) favored particle phase equilibrium. Figure 9-4 is a map showing the locations and sample times and estimated composition of the total particulate nitr ate 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 co ncentration 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 Atmo spheric 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 NaNO 3 

## ), and reacted soil dust nitrate (shown as Ca(NO 3 

## ) 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 wh ich 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 sepa rate determinations including marine, remote continental and urban areas data sets, the average dry mass scattering efficiency for ammonium sulfate is 2.5 ± 0.6 m 2 

### /g. Average values tended to be somewhat lower for the marine aerosol (~2 m 2 

### /g) than for remote continental (~2.7 m 2 

### /g) and urban (2.6 m 2 

### /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 m 2 

### /g (Hand and Malm, 2007, 155825 ). Average values were higher in remote locations (2.8 ± 0.5 m 2 

### /g) compared to urban locations (2.2 ± 0.5 m 2 

### /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 m 2 

### /g is based on 58 separate determinations, though much higher values (~6 m 2 

### /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 va lue is generally associated with aged organic PM, while for more freshly emitted PM, such as in an urba n environment, a smaller ratio (e.g., 1.4) would be more appropriate. This could explain the discre pancy 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 m 2 

### /g compared to 3.71 m 2 

### /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 resp ect 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 m 2 

### /g based on 51 determinations) and fine mode dust or soil (3.3 ± 0.6 m 2 

### /g based on 23 determinations, but recommending 1.0 m 2 

### /g for use with data from realistic collection efficiency samplers) and fine sea salt (4.5 ± 0.9 m 2 

### /g based on 25 determinations, but recommending 1.0 m 2 

### /g to 1.3 m 2 

### /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 SO 4 2– 

### dry extinction efficiencies for 41 days of size-resolved chemical composition data for Big Be nd, TX as part of the BRAVO Study produced a range of results from ~2.4 m 2 

### /g to ~4.1 m 2 

### /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 NO 2 

### light absorption term; and employing a split component model for the secondary particulate components (i.e., SO 4 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 ] arg[1.6] [8.2 ] arg[)(1.5] [)(4.2 ] arg[)(8.4] [)(2.2

2 

### ppbNO Specific Site Scattering Rayleigh Mass Coarse SaltSea RHf SoilFine Carbon

Elemental Mass OrganiceL Mass Organic Small NitrateeLRHf Nitrate Small RHf SulfateeLRHf Sulfate Small RHf b

ss L s L s ext



### ×+ + ×+ × ×+ ×+ ×+ ×+ ×+ × ×+ × ×+ × ×+ × ×≈

Equation 9-2

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



### Small and large SO 4 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 re present aged or cloud-processed aerosol, while the small mode particles represent relatively newly ge nerated 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 μ m and 1.5 for the large mode particles and 0.2 μ m and 2.2 for the small mode particles. Mie theory applied to these size distributions for the th ree species results in dry extinction efficiencies for the small and large mode ammonium sulfate (2.2 m 2 

### /g and 4.8 m 2 

### /g), ammonium nitrate (2.4 m 2 

### /g and 5.1 m 2 

### /g) and organic mass (2.8 m 2 

### /g and 6.1 m 2 

### /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 SO 4 2– 

### and nitrate PM. No water growth is assumed for organic PM. A simple empirically developed apportionment ap proach that was evaluated by testing the new algorithms estimated light scattering at the 21 IM PROVE sites that have nephelometer-measured light scattering data. For each sample, the fraction of the fine particle component (SO 4 2– 

### , nitrate, or organic mass) that is assigned to the large mode is calculated by dividing the total concentration of the component by 20 μ g/m 3 

### (e.g., if the total fine particle nitrate concentration is 4 μ g/m 3 

### , the large mode concentration is 1/5 of 4 μ g/m 3 

### or 0.8 μ g/m 3 

### , leaving 3.2 μ g/m 3 

### in the small mode). If the total concentration of a component exceeds 20 μ g/m 3 

### , 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 th e new algorithm for the technical assessments that support their RHR State Implementation Plans, but th e revised algorithm was too recently developed to be incorporated into any of the peer-reviewed te chnical 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 se a salt may be a significant contributor.

December 2009 9-140 50 100 150 200 250 300 350

0 50 100 150 200 250 300 350
Measured BspIMPROVE 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 BspSplit 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. Spa tial 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 ur ban 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 increase d 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 additi onal remote-area monitoring to better understand their contributions to regional haze. The expansion of th e network into the central U.S. significantly improved the understanding of spatial trends in a re gion 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 extincti on 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 loca tions 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 assessmen t 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 Improvemen t 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 dev elop the technical information for RHR in the Northeast. All three web site s contain unique technical support information.

Visibility - Air Quality Monitoring Data Visibility Informati on Exchange Web Site

http://vista.cira.colostate.edu/views/ All RPOs All IMPROVE and most other PM spec iation 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 tri bal analyst prepare RHR implementation plans.

December 2009 9-17Source: 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 cold er 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 exti nction by the various component estimates. Figure 9-9 shows the contributions by SO 4 2– 

### and nitrate particulate includ ing 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 particulat e 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, wher e 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 SO 4 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 SO 4 2– 

### generally contribute 20-50% of the particle light extinction. Regions of th e lowest fractional contributions by particulate SO 4 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 w ith 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 SO 4 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 th e eastern U.S. than in the non-coastal western

states.

December 2009 9-19Source: 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 9-20Source: 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 calend ar quarter of 2004 (first through fourth quarter arranged from top to bottom). No te that the contour intervals are not the same for the two species contributions.

December 2009 9-21Source: 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 calend ar quarter of 2004 (first through fourth quarter arranged from top to bottom). No te 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 easte rn U.S. is the dominant contributions to haze by SO 4 2– 

### and nitrate PM leaving relatively little for other components. The crustal components contribute more to haze in the arid regions of th e west including the southwestern deserts. In absolute terms, coarse mass concentrations are as hi gh 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. Urb an and Regional Patterns

Using a combination of IMPROVE and CSN data, it is possible to compare urban PM 2.5 

### 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 artif act 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 ur ban monitoring sites. The U.S. EPA (2004, 190219 ) used the pairing of IMPROVE and CSN monitoring site s at 13 selected urban areas to separate local and regional contributions of three major PM 2.5 

### components as shown in Figure 9-24

. In Figure 9-12 and Figure 9-13 , urban PM 2.5 

### 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 PM 2.5 

### concentrations and resulting visibility are less different than the eastern and western regional concentrations and visibility.

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



## Figure 9-12. IMPROVE Mean PM 2.5 

## mass concentration determined by summing the major components for the 2000-2004.

Source: Debell (2006, 156388 ).



## Figure 9-13. IMPROVE and CSN (STN) mean PM 2.5 

## mass concentration determined by summing the major components for 2000-2004.

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



## Figure 9-14. IMPROVE mean ammonium ni trate concentrations for 2000-2004.

Source: Debell (2006, 156388 ).



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

December 2009 9-25Source: 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 PM 2.5 

### nitrate in remote and urban areas. Here the western states have urban particulate nitr ate 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 μ g/m 3 

### to 12 μ g/m 3 

### . In the region of the Midwest nitrate bulge, the urban con centrations were less than twice the regional concentrations for an annual urban excess of about 1 μ g/m 3 

### . Northeast and southeast of the Midwest nitrate bulge, annual urban particulate nitrate co ncentrations are several tenths to about 1 μg/m 3 

### 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 SO 4 2– 

### concentrations are generally not much higher than the regional values, with urban excess generally of less than about 0.5 μg/m 3 

### . The exceptions apparent by comparing Figure 9-16 and Figure 9-17 are in Texas and Louisiana where urban excess particulate SO 4 2– 

### are >1 μ g/m 3 

### , perhaps caused by local emissions (e.g., from oil refineries). Urban contributions are a larger fraction of the total particulate SO 4 2– 

### concentrations in the western U.S. b ecause the regional concentrations are much lower than in the East. The modest additional particulate SO 4 2– 

### concentrations associated with urban areas suggests that most particulate SO 4 2– 

### is regionally distributed, and that IMPROVE and CSN monitoring sites can be used together to e nhance the ability to delineate particulate SO 4 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 SO 4 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. (T he color-contour suggested dip in concentrations between the two eastern particulate SO 4 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 PM 2.5 

### 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 SO 4 2– 

### particulate are the two major components of PM 2.5 

### , with roughly equal contributions, and account for over 80% of the mass concentration. Edgerton et al. (2004, 156413 ) showed that carbonaceous PM 2.5 

### 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 th e southeastern U.S. However, the higher overall light extinction efficiency for SO 4 2– 

### resulting from its hydrophilicity gives it ~ 2:1 dominance in responsibility for eastern urban light extinction. Urban and remote area soil PM 2.5 

### concentrations are displayed in Figure 9-22 and Figure 9-23

. Urban fine soil concentrations are at most a few tenths of a μg/m 3 

### higher than the regional background concentrations and in some regions they are much less. Just as with carbonaceous PM 2.5 

### , the Virgin Island, San Juan, Puerto Rico pair are in teresting for fine soil. In this case, both of these island monitoring sites have high concentrations of fi ne 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 concentr ation 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 ma ss than the nearby remote areas monitoring sites (i.e., ~24 μ g/m 3 

### compared to ~9 μ g/m 3 

### for Phoenix, AZ, and ~6 μ g/m 3 

### compared to ~2 μ g/m 3 

### for Puget Sound, WA) and one eastern IMPROVE site at Washington, DC with less coarse mass than the surrounding remote area values (~2 μ g/m 3 

### compared to ~4 μ g/m 3 

### ).

December 2009 9-27Source: 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 9-28Source: 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 9-29Source: 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 concentr ations 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 con centration 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 differen ce in the size distribution of the coarse mass, perhaps due to differences in suspendable soil materials.



# 9.2. 3.4. Tem poral 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 mo st sites (30 of 47), and degrading visibility at a few western sites (4 of 47).

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



## Figure 9-24. Regional and local contributions to annual average PM 2.5 

## by particulate SO 4 2–



## , nitrate and total carbon (i.e., organic plus EC) for select urban areas based on paired IMPROVE and CSN monitoring sites.

December 2009 9-32Source: 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 wester n sites show consistent reductions in particulate SO 4 2–



### , but otherwise have mixed increasing and decreasi ng haze component trends, many of which are not statistically significant. Substantial interannua l and shorter term spatial and temporal wildfire activity variations have been shown to have a si gnificant 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 PM 2.5 

### of about 18% (corresponding to 1 μ g/m 3 

### to 2 μ g/m 3 

### ) for 4 urban-rural paired SEARCH sites in the Southe astern U.S. corresponding to similar reductions in SO 4 2– 

### and carbonaceous particulate.

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



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

December 2009 9-34Source: 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 SO 4 2– 

### concentration datasets, which were shown to produce comparable SO 4 2– 

### concentrations at 23 co-located monitoring sites. Figure 9-28 shows time plots of 80th percentile particulate SO 4 2– 

### concentrations and annual average SO 2 

### 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 th at an equal percentage change in particulate SO 4 2– 

### and SO 2 

### emissions slope in any plot will have the same trend line slope. Each plot shows a strong correspondence between 80th percentile particulate SO 4 2– 

### and SO 2 

### emissions trends. The western U.S. had steadily dec lining 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. Th e southwestern U.S. (Texas, New Mexico and Colorado) had about a 15% increase in particulate SO 4 2– 

### and SO 2 

### emissions over the 10-year period. The southeastern U.S. had a declining trend for th e early 1990s followed by an increasing trend for the later half of the decade, with a net decrease ove r the decade of less than 10%. Others have shown similar decreasing particulate SO 4 2– 

### concentration trends and a correspondence in trends between SO 2 

### emissions and particulate SO 4 2– 

### concentration by region (Holland et al., 1999, 092051 ; U.S. EPA, 2004, 056905 ). Holland et al. (1999, 092051 ) developed and compared NO X 

### emissions trends from 1989 to 1995 to corresponding trends in total nitrogen concen tration (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 NO X 

### emissions. Trends in haze associated with particulate SO 4 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 contribut e 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 extincti on. 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 SO 4 2– 

## concentration based on IMPROVE and CASTNet monitoring and net SO 2 

## emissions from the National Emissions Trends (NET) data base by region of the U.S.

Ten-year trends (1994-2003) of particulate nitr ate 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 m onitoring 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 tr ends 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 tr ends often with neighboring sites having opposing trends) that vary considerably depending on the numb er 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 re mote-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 ha ze by particulate nitrat e 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. Cau ses 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 qu ality 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 estimat e source-receptor relationships fo r 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 assessme nts 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 nu mber of recent urban special studies, including those sponsored by EPA PM Supersites program (Solomon and Hopke, 2008, 156997 ), have addressed the causes of and so urces contributing to urban excess PM concentrations above region concentr ations. Attribution results uncertainties are generally larger than those of the measurements upon which they are ba sed because they also include model uncertainties and assumptions, as well as issues of representa tiveness (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 c ontribute 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 on e or two PM species. In the eastern half of the U.S., SO 4 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 ca use of haze is by region beginning in the western U.S. and proceeding to the east, analogous to domi nant 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 SO 4 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 remaini ng 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 SO 4 2– 

### source region for the SCAB was over the Pacific Ocean (confirmed by measurements on Santa Catalina Is land) and that these particles subsequently grew with accumulation of additional secondary aerosol ma terial, 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%), whil e 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 attri buted 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 μ g/m 3 

### compared to ~2.4 μ g/m 3 

### ), while at Riverside on the east edge of the SCAB and near the large agricultural sources of ammonia emissions, the particulate nitrate conc entrations are nearly double that of organic PM (~17 μ g/m 3 

### compared to ~10 μ g/m 3 

### ). Ying and Kleeman (2006, 098359 ) showed that during the winter 1996 episode in the SJV most of the nitric acid that forms particulate ni trate 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 car bonaceous PM from combustion sources in urban locations (Sacramento, Modesto, and Bakersfiel d, CA) move quickly from ultrafine particle size (i.e., diameter ~0.1 μ m) to accumulation mode by condensation with accumulation mode (i.e., diameter ~0.5 μ m) particles, and that secondary nitrate particle formation occurs preferentially on the surface of hydrated ammonium sulfate par ticles during the afternoon when gas-phase nitric acid is at peak photo-chemical production from NO X 

### . 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 wi nter conditions there was li ttle 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 B odega Bay, north of San Francisco, CA. Using a regression analysis to find the dependence of particulate SO 4 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 SO 4 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 SO 4 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 SO 4 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 Atmosp heric Environment from Xu et al. (2006, 102706 ).



## Figure 9-30. Contributions of the Pacific Coast area to the ammonium sulfate ( μ g/m 3 

## ) 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 extensi ve multiyear measurements and high spatial resolution air quality modeling of typical episode s 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 w ildfire 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 du ring 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 c onvert precursor gaseous emissions of NO X 

### and SO 2 

### from local and regional combustion sources and NH 4 

### from local and regional agricult ural activities to secondary nitrate and SO 4 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 SO 4 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/SO 4 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 SO 4 2– 

### are found at the Hells Canyon IMPROVE site, which is similarly situated in a narrow canyon of the Snak e 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 illust rate the attribution results from the WRAP-sponsored attribution analysis tools that estimate the rela tive 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 9-40Source: 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 SO 2 

### and NO X 

### emissions that tracked the source region and category through the transport and transformation processes to particulate SO 4 2– 

### and nitrate. This was used to produce pie diagrams of particulate SO 4 2– 

### 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 SO 4 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 qual ity modeling were obtained using output from the GEOS-CHEM global air quality model (Fiore et al., 2003, 047805 ). The SO 4 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 SO 4 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 SO 4 2– 

### is from the domestic source emissions further to the east, which also contribute about 20% to Badlands particulate SO 4 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 perc ent 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 off shore 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 NO X 

### source category responsible for particulate nitrate for these six WRAP sites, with a combination of point, area and wildfire source categories also contributing from a bout 10-50% of the WRAP regional emissions. WRAP only used the virtual tracer approach to investigate source locations and categories for SO 2 

### and NO X 

### 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. Or ganic PM in the biogenic secondary category includes those that would functionally be co nsidered man-made emissions (e.g., those from agricultural crops and urban landscapes), though in m ost 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 SO 4 2– 

## (a) and nitrate (b) source attribution by region using CAMx modeling for six western remote area moni toring 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 9-43Source: 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 biogen ic 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 μ g/m 3 

### for the coastal state sites to ~1 μ g/m 3 

### for the intermountain west sites, to <1 μ g/m 3 

### 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 μ g/m 3 

### . 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 site s, 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. Re sults from recent chamber and field studies, and modeling would seem to call into question appor tionment 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 primar y 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 biogen ic secondary PM categories for the Yellowstone NP (top) and Grand Canyon (Hopi Point) (bottom) monitoring sites.

December 2009 9-45Source: 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 biogen ic secondary PM categories for the Badland NP (top) and Salt Creek W (bottom) monitoring sites.

Radiocarbon ( 14 

### C) 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 urba n 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 mode l-determined regional primary organic PM is from contemporary carbon sources (e.g., smoke from wildfires). Schichtel et al. (2008, 156958 ) compared radiocarbon measuremen ts 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 concentrat ion 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 PM 2.5 

### 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 To nto (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 PM 2.5 

## 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 IM PROVE 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 calcu lated from the measured EC to total carbon (EC/TC) ratios using the 12-site empirical relationship between radiocarbon determined contemporary carbon fraction and IMPROVE measur ed 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 a nd 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., R 2 

### 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 car bon the EC/TC ratios were 0.12 ± 0.011 and 0.19 ± 0.0095. These ratios are shown to be consi stent 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 wint er) 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 an d 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 prim ary 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 9-49Source: 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 th e 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 dist ance weighted emissions multiplied by trajectory residence time.

December 2009 9-50Source: 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 th e 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). Th e scale is normalized (i.e., unitless) one over distance weighted emissions multiplied by trajectory residence time.

December 2009 9-51WRAP applied a weighted emissions potential analysis tool that combined gridded emissions data with back-trajectory analysis th at simulated the transport pathway to the various monitoring sites to infer likely source region and emi ssion categories for the 20% best and 20% worst haze conditions f or 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 d oes 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 dis persion), 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 em issions potential tool for the worst 20% haze days using the 2000-2 004 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 pr ojected 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 mult i-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 carbonace ous 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 9-52Source: 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 th e 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 9-53Source: 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 th e 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 9-54Source: 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 th e 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, So uth 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 9-55Source: 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 th e 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 mu lti-state regions than for selected sites further to the west. This may be due in part to trajectorie s associated with worst haze conditions for these two sites being moderately long (~500 km) and in mult iple 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 orig inating 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 Mont ana 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 m ost 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 betw een dust concentrations and coincident local wind speed was used to generate site-specific estimate s 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 cove r 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 fo r 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 mechanica lly suspended dust activities (e.g., unpaved road dust, agricultural, construction and mi ning 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 exclusi vely 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 de serts 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). As ian 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 th e 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 9-57Source: Reprinted with Permission of the Air & Wast e 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 SO 4 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 Ap ril 2002 from the WRAP emissions inventory (bottom).

December 2009 9-59

## Figure 9-46. Maps of spatial patterns of annual NO (left) and NO 2 

## (right) emissions for 2002 from the WRAP emissions inventory.

Source attribution of the particulate SO 4 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), SO 2 

### emissions sources in the U.S. and Mexico were responsible for ~55% and ~38% of the particulate SO 4 2– 

### , respectively. Among U.S. source regions, Texas was responsible for ~16%, east ern 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 SO 2 

### emissions source regions plus the Carbón facility during the BRAVO study period. The largest particulate SO 4 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 SO 4 2– 

### can be in particulate form though not fully neutralized by ammonia, nitric acid from NO X 

### 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 NO X 

### 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 agri culture is responsible for most of the ammonia emissions in the Midwest. The striking similar ity between the ambient particulate nitrate concentration and the ammonia emissions spatial pa tterns with regional maximum centered on Iowa is in contrast to the NO X 

### (i.e., NO + NO 2 

### ) emissions spatial patterns, shown in Figure 9-46 . NO X 

### emissions are high over a broad region of the countr y 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 ar e 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 SO 4 2– 

### ). By contrast, the region to the east of the Midwest nitrate bulge should have plenty of nitric acid given the higher emissions of NO X 

### , but apparently has a deficiency of free ammonia. The few eastern monitoring sites with locally high particulate nitrate (near sout heastern 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 NO X 

### 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 SO 4 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 modeli ng to assess the changes in PM concentrations that would result from changes to precursor co ncentrations (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 increase s, either by greater ammonia emissions or by anticipated decreases in SO 2 

### emissions, then nitric acid concentr ation would need to be reduced (via lower NO X 

### emissions) in order to reduce the particulate nitrate concentration. Given the comparability of particulate SO 4 2– 

### and nitrate with regard to their light extinction efficiencies, their visibility impacts are propor tional to the sum of their mass concentrations. A reduction in SO 4 2– 

### caused by SO 2 

### emission reductions would reduce the particulate SO 4 2– 

### concentration, though according to the thermodyn amic equilibrium modeling for these sites the particulate nitrate concentration will be increa sed somewhat. However, the total particulate SO 4 2– 

### plus nitrate concentration would be reduced so vi sibility impacts would be decreased. At current ammonium concentrations the predicted response of changes to SO 4 2– 

### and nitric acid concentrations (i.e., SO 2 

### and NO X 

### emissions changes) are similar in respect to the resulting magnitude of changes to the total particulate SO 4 2– 

### plus nitrate concentrations. At all but two sites the total particulate SO 4 2– 

### 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 ev en 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 conc entrations are more sensitive to changes in ammonia and nitric acid concentrations than during the warmer seasons when SO 4 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, Ca nada) implicate the high ammonia emissions region of the Midwest as a common source region (Canad a-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 par ticulate nitrate or ammonia from the high emissions region of the Midwest is also contri buting 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 measurem ents at Underhill, VT and at Brigantine, NJ, Hopke et al. (2005, 156567 ) identified separate regions associated with particulate SO 4 2– 

### accompanied by trace particulate components associated with coal burning (e.g., Se) and accompanied by trace particulate components associa ted with oil burning (e.g., V). As shown in Figure 9-49 , the coal-burning related particulate SO 4 2– 

### for these two monitoring sites is associated with long-range transport from the Ohio River Valley, while oil-burning related particulate SO 4 2– 

### is from more nearby emissions in the high populat ion region of coastal New York, New Jersey, Massachusetts, and Connecticut.

December 2009 9-62Source: 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 SO 4 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 SO 2 

## 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 (RAI N) 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 SO 4 2– 

### in the northeast is well demonstrated by a scatter plot of RAIN data that shows the relationship between particulate SO 4 2– 

### extinction, calculated using the IMPROVE algorithm pl otted 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 SO 4 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 SO 4 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 SO 4 2– 

### and any other co-varying PM species are largely responsible for the highly impa cted 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 9-63Source: RAIN Preliminary Data Analysis Report (NESCAUM, 2006, 156802 )



## Figure 9-50. Scatter plots of particulate SO 4 2– 

## (left) and particulate SO 4 2– 

## and organic mass (right) versus nephelometer measured particle light scattering for Acadia NP, ME.

Particulate nitrate concentrations are considerably lower in the SO 4 2– 

### -dominated warmer southeastern U.S. than in the Northeast a nd upper Midwest. Blanchard et al. (2007, 098659 ) conducted thermo dynamic equilibrium modeling on data fr om the eight SEARCH monitoring sites and found that total particulate nitrate plus SO 4 2– 

### is much more responsive to changes in SO 2 

### 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 fro m 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 Southeaste rn 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 SO 2 

### emissions from electrical generation units (EGU) and to a lesser extent other SO 2 

### emission sources in the region. Reductions of NO X 

### emissions from ground or point sources are not nearly as effective as SO 2 

### 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 emi ssion 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 SO 2 

### and ammonia emissions reductions would be effective at reducing worst haze days at the Swanquarter W, though NO X 

### emissions are not as effective presumably because the atmospher e is ammonia-limited for particulate nitrate production.

December 2009 9-64Source: 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 so urce category and geographic areas.



# 9.2.4. Urban Visibility Valuation and Preference

The Clean Air Act §302(h) defines public welfar e to include the effects of air pollution on “...visibility, ... and personal comfort and wellbe ing.” Though good visibility conditions in Class I (e.g., NPs) and wilderness areas have long been re cognized 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 contri bute 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 al one or in combination with water vapor or other atmospheric conditions) that can be mitigate d by regulations such as the RHR (40 CFR 51.300 through 309) or the Secondary NAAQS. The term visua l 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 affect s people in additional ways. These include the aesthetic and wellbeing benefits of better visibi lity, 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 Prim ary 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 hu man preference and valuation studies for VAQ have adequately made this distinction and separa tion 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 fo cused 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 al l locations that people experience in their daily lives, including scenes such as residential street s and neighborhood parks, commercial and industrial areas, highway and commuting corridors, central dow ntown areas, and views from elevated locations providing a broad overlook of the metropolitan ar ea. Thus urban visibility includes VAQ conditions in major cities and smaller towns and encompasse s all the VAQ an individual resident sees on a regular basis. Visibility conditions in urban a nd 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 cond itions. 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 demonstra ted 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 percepti on 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 de monstrated that VAQ is deeply intertwined with a “sense of place,” effecting peop le’s sense of the desirability of a neighborhood quite apart from the actual physical co nditions 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 se tting 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 Ai r 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 studi es 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 evalua te 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. Urb an Visibility Preference Studies

One group of urban visibility research project s 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 describe d as “acceptable.” The specific definition of acceptable was largely left to each individual respondent, allo wing 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 phot ographs 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 Co lumbia 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 sof tware from Air Resource Specialists (ARS). WinHaze is a computer-imaging software program th at 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 Wa shington D.C. pilot study was the first step in a process to expand the results to other regions wher e 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 ac ceptable 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 be tween 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 9-67Denver, 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. Den ver, Colorado Urban Vi sibility 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 vi olation 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 s lides of actual photographs. The visibility conditions presented in the slides ranged from 11-4 0 dv, approximating the 10th-90th percentile of wintertime visibility conditions in Denver. The par ticipants 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 a ppropriate urban visibility standard (i.e., whether the amount of impairment was “acceptable” or “u nacceptable”). The individual’s judgment of a slide’s VAQ and whether the slide violated a vi sibility 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 b ext 

### = 76Mm -1 

### , or VR ~51 km) was judged as “acceptab le.” The CDPHE subsequently established a Denver visibility standard at this value (defined as b ext 

### = 76Mm -1 

### ), based on the median 50% acceptability findings from the study.

December 2009 9-68

# 9.2. 4.3. Pho enix, Arizona Urban Vi sibility 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 re presentative 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 he ld in Spanish in one region of the city with a large Hispanic population (25%), although the final ove rall 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, b ext 

### , 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 visibili ty 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 ba sis 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 establishe s 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 th e number of days in lower quality categories into better quality categories.



# 9.2. 4.4. Bri tish Columbia, Canada Urba n Visibility Preference Study

The British Columbia urban visibi lity 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 Denv er 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 comp leted 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. Fact ors 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 ar ea, while Chilliwack (population 70,000) is an agricultural community 100 km east Vancouver in the Frazier Valley. The British Columbia urban visibility pref erence 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. Was hington, DC Urban Visi bility 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 pilo t 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 lim itations, only a single focus group session was held,

December 2009 9-69

### consisting of one extended session with nine partic ipants. 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, poten tially 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 su bstantially different visibility conditions than any of the three western locations of the other pref erence 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 ly ing terrain provides substantially shorter maximum sight distances, and many residents are not we ll 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 it s preferences measured in an increase in the general cost of living for certain increments of im provement 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 aris ing from multiple diverse emission sources (e.g., transportation, electricity generation, industry, et c.) 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 down town 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 foc us 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 explic itly mentioned by the focus group moderator. Participants strongly agreed with the decision to not mention that health effects are associated with visibility impa irment. 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 menti oned 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 Wi nHaze images were presented to a total of 64 participants who were all employees of CRA In ternational, 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 urba n 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. Urb an 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 valuati on 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 me thods 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 me tropolitan area to estimate the value of each component. Hedonic pricing has been used to estima te 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 n earby 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 particul ates, 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 ra nge was from 12.4 miles (Los Angeles International Airport, 1991) to 31.9 miles (Palm Springs Airpor t, 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 mile s, 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 valuati on 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 th at the presence of the two health-related pollution conditions results in an estimated hedonic demand f unction for visibility that successfully separates the health component of demand for overall air qual ity 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, measur ement error, model specification error, etc.) from demand for health risk reductions measured by th e 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., visi bility, 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 scat tering and absorption by suspended particles and gases. NO 2 

### is the only commonly occurring atmospheric pollutant gas that absorbs visible spectrum radiation, though in most situations the amount of light absorption by NO 2 

### is overwhelmed by the higher amounts of particulate light extinction (i.e., the combination of scattering and absorption) usually accompanying high NO 2 

### 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-i mpaired periods in remote regions of the western U.S. There is strong and consistent evidence th at 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 par ticles with diameters from approximately 0.3-1.0 μ m. For studies where detailed data on particle co mposition 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., SO 4 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: PM 2.5 

### SO 4 2– 

### usually assumed to be ammonium sulfate, PM 2.5 

### nitrate usually assumed to be ammonium nitrate, PM 2.5 

### OC, PM 2.5 

### EC, PM 2.5 

### crustal material (referred to as fine soil), and PM 10-2.5 

### 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 hum idity 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 visi bility effects compared with calculated light extinction using routinely available PM speciation data (i.e., 24-h duration). Particulate SO 4 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 particulat e species contributing to haze. Particulate SO 4 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 ac id (a reaction product of NO X 

### emissions) and ammonia are needed to form ammonium nitrate, the apparent reason for th e 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 experi enced 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 ammo nium nitrate concentrations. Thermodynamic and air quality simulation modeling show that particulat e nitrate concentrations are sensitive to changes in either NO X 

### 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 especi ally 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 gase ous 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 PM 2.5 

### (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, PM 2.5 

### is dominated by about equal concentrations of carbonaceous and SO 4 2– 

### components, though the usually high relative humidity in the East causes the hydrated SO 4 2– 

### particles to be responsible for about twice as much of the urba n 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 frac tion 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 ha lf 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 cont emporary carbon at about 150 monitoring locations nationwide. The highest fraction of contemporary ca rbon 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 pr imary carbonaceous PM whether from fossil or contemporary sources represent less than two thirds of the total carbonaceous PM.
PM 2.5 

### crustal material (referred to as fine soil) and coarse mass (i.e., PM 10 

### minus PM 2.5 

### ) are significant contributors to haze for remote areas si tes in the arid Southwestern U.S. where they contribute a quarter to a third of the haze, with co arse mass usually contributing twice that of fine soil. Coarse mass concentrations are as high in th e 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 ca used by the high concentrations of SO 4 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/sam pler 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, a nd 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 gr eater ground cover. The frequency of worst dust events classified as undetermined was greatest for s ites 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 thei r 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 visibi lity valuation studies. Both types of studies are designed to evaluate individuals’ desire for goo d VAQ where they live, using different metrics. Urban visibility preference studies examine indivi duals’ 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 studi es 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 co nducted 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 degrada tion 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 resear ch 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 effe cts 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 attentio n 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 me mber of the U.S. fede rated CCSP along with NOAA and NASA, which led produc tion of CCSP SAP2.3 incorpor ating 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 th e climate effects of aerosols. (In the sections included from IPCC AR4 and CCSP SA P2.3, ‘aerosols’ is more frequen tly 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 e nvironment. Aerosols (sometimes referred to particulate matter or “PM,” especially in air quali ty applications), when concentrated near the surface, have long been recognized as affec ting 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 9-74Aerosols also interact strongly with solar and terrestrial radiation in several ways. Figure 9-52 offers a schematic overview. First, they sca tter and absorb sunlight (Charlson and Pilat, 1969, 190025 ; McCormick and Ludwig, 1967, 190528 ; Mitchell, 1971, 190546 ); these are described as “direct effects” on shortwave (sola r) 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 phe nomenon 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 indir ect effects of aerosols on clouds include an increase in cloud brightness, change in precipita tion and possibly an increase in lifetime; thus the overall net impact of aerosols is an enhan cement 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 life time. Here small black dots representaerosols, 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 concen tration. Confidence in the magnitudes of these effects varies considerably (see Ch apter 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 varyimmensely 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 understandi ng, 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 be tween an initial and a perturbed aerosol loading state, at a specified level in the atmosp here. (Other quantities, such as solar radiation,

December 2009 9-75are assumed to be the same for both states.) Th is 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 forci ng 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 surf ace-based remote sensing, as well as in situ measurements. These efforts are described in s ubsequent 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 refe r to the solar component only, unless specified otherwise. The wavelength separation between th e 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 troposphe re, 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 radiativ e 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-s ky” conditions, which in clude clouds. Cloud-free direct aerosol forcing is more easily and more accu rately calculated; it is generally greater than all-sky forcing because clouds can mask the aeros ol 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 forc ing is assessed for all-sky conditions. (8) Aerosol radiative forcing can be evaluate d instantaneously, daily (24 h) averaged, or assessed over some other time period. Many meas urements, such as those from polar-orbiting satellites, provide instantaneous values, whereas m odels usually consider aerosol RF as a daily average quantity. In this document, unless specifi ed 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 ofte n unclear which elements are “causes” of climate change (forcings among them), which are response s 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 of ten called aerosol-cloud interactions. This distinction sometimes matters, as it is more na tural to attribute responsibility for causes than for responses. However, practical environmenta l considerations usually depend on the net result of all influences. In this report, “feedback s” 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 9-76Source: IPCC (2007, 092765 ).



## Figure 9-53. Global average radiative forcing (RF) estimates and uncertainty ranges in 2005, relative to the pre-industrial climate. Anthropogenic CO 2 

## , methane (CH 4 

## ), nitrous oxide (N 2 

## O), 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/m 2 

## ). The total anthropogenic radiative forcing and its associated uncertainty are also given.

December 2009 9-77Source: 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 f undamental 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 aver aging, 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 substan tially 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- a nd 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/m 2 (-2.2 to -0.5 W/m 2 ). This is significant compared to the positive forcing by anthropogenic GHGs (including ozone), about 2.9 ± 0.3 W/m 2 (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 indi rect 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 th e factors assessed over the industrial period ( Figure 9-54 ).

December 2009 9-78Source: Adapted with Permission of theAmerican Geophysical Union from from Zhou et al. (2005, 156183 ).



## Figure 9-55. The clear-sky forcing efficiency E τ , defined as the diurnally averaged aerosol direct radiative effect (W/m 2 

## ) 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 E τ for individual aerosol regimes and A is surface broadband albedo.

Although AOD, aerosol properties, aerosol vert ical 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 (E τ ), 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. E τ 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 refl ectivity, and solar irradiance, each of which exhibits pronounced spatial and temporal variations. To assess aerosol RF, E τ is multiplied by the ambient AOD. Figure 9-55 shows a range of E τ , derived from AERONET surface sun photometer network measurements of aeros ol loading and particle prope rties, 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 surf ace properties. For example, E τ 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 absorb s sunlight more strongly, and the magnitude of E τ 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/m 2 

## 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/m 2 ) -2.2 -6.3 2.6

SENSITIVITY OF CLOUD-FREE, INSTANTANEOUS, TOA DIRECT AEROSOL RADIATIVE FORCING TO AEROSOL AND SURFACE PROPERTIES, W/M 2 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/M 2 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, instan taneous, aerosol direct RF dependence on AOD, and on aerosol and su rface 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 exceedingl y 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/m 2 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/m 2 is used as an example; uncerta inty upper bounds are obtained from the partial derivative for each parameter by neglect ing the uncertainties for all other parameters. These estimates produce an instantaneou s 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 E τ and aerosol RF

December 2009 9-80sensitivity 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 capabilitie s 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 va lues 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 quant ifying 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 thes e 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 exam ple, 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 r ecognize 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 partic les 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 ae rosol amount and propertie s, 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. Sat ellite Remote Sensing

Section 9.3.2 with the exception of the final para graph, comes directly from CCSP SAP2.3 Chapter 2, Section 2.2 with section, table, and fi gure numbers changed to be internally consistent with this ISA.
A measurement-based characteri zation 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 distri butions. Monitoring aerosols from space has been performed for over two decades and is pl anned 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 character ization 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 inte rferences, even over land (Torres et al., 1998, 190503 ). This UV-technique makes TOMS suitable for monitoring biomass burning smoke and dust, though with limited sensitivit y 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 (O MI) 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 retrie vals 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 pr oducts 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 polar ization and phase function. In addition, cloud screening is much more robust than before a nd onboard calibration is now widely available.

December 2009 9-81Examples 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 se nsors 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 satell ite 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 9-82Complementary to these passive sensors, ac tive 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 aer osol products (mainly AOD) from these new-generation sensors, together with improvements in charact erizing the earth’s surface and clouds, can help reduce the uncertainties associated with estimati ng the aerosol direct radiative forcing (Yu et al., 2006, 156173 ); and references therein). The retr ieved 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 radi ative effects from space, significant efforts are needed to measure aerosol size distribution w ith 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 dete rmine the global distribution of aerosol and clouds. It will also be able to infer microphys ical 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 informa tion. However, AOD accuracy is still much poorer than that from surface-based sun photomet ers (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, a nd multi-angle imaging is supplementing this with maps of plume injection height in aerosol sour ce 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 simulation s of aerosol air mass type distributions and transports, particularly over dark water. Bu t 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 radiativ e 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 characterizi ng aerosol microphysical properties.


## MODerate resolution Imaging Spectroradiometer

MODIS performs near global daily observation s of atmospheric aerosols. Seven of 36 channels (between 0.47 and 2.13 μ m) are used to retrieve aer osol 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 th ree visible channels with high accuracy of ± 0.05 ± 0.2 τ (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 ± 0.05 τ (Remer et al., 2002, 190218 ; 2005, 190221 ; Remer et al., 2008, 190224 ; Tanré et al., 1997, 190452 ), but also quantitative aeros ol size parameters (e.g., effective radius, fine-mode fracti on 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 illustr ate 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 9-83heavy dust storms over North Atlantic in both months and over Arabian Sea in August, and a mixture of dust and pollution plume sw ept 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 a ssess aerosol airmass type, a more robust characterization of MISR-retrieved particle prop erty information than individual attributes. MISR also retrieves plume height in the vicini ty of wildfire, volcano, and mineral dust aerosol sources, where the plumes have discernable spat ial 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-spectra l, 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 contri bution 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 9-58 a) 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 re moval in the downwind direction. In large-scale flow fields, such as the trade winds, these continental plum es 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 dus t from deserts and in sea salt aerosols.

December 2009 9-84Source: 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 primar ily 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-v ectors 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. Foc used Field Campaigns

Over the past two decades, numerous focu sed field campaigns have examined the physical, chemical, and optical properties and ra diative 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., aircra ft, ships, satellites, and ground-base d stations) and numerical modeling. In spite of their relatively short duration, thes e 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 9-86Source : Reproduced with permission of Laboratoire d’Optique Atmo spherique (LOA), Lille, FR; Laborat oire des Sci ences du Climat et de l’Environnement (LSCE), Gif sur Yvette, FR; Centre National d’etudes Spatiales (CNES), Toulouse, FR; and NAtional Space Development Agency (NAS DA), 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. Gro und-Based In Situ Measurement Networks

Major U.S.-operated surface in situ and remo te 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 re gional sources on aerosol propertie s, 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-su rface measurements include submicrometer and sub-10 micrometer scattering and absorption coef ficients from which the extinction coefficient and single-scattering albedo can be derived. Additional measurements include particle concentration and, at selected sites, CCN con centration, 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 us ed 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 vari ability, 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 (PM 2.5 and PM 10 ) aerosol mass concentration; concen trations of elements, sulfate, nitrate, organic carbon, and elemental carbon; and scattering coefficients.

December 2009 9-87In addition, to these U.S.-operated networks, there are other national and international surface networks that provide measurements of aerosol propertie s 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 expect ed 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 radi ative 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 cl oudy 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 lid ar system aboard the U.S. Space Shuttle mission in 1994, i.e., Lidar In-space Technolog y 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 mont hs. It has been demonstrated that GLAS is capable of detecting and discriminating multiple layer clouds, atmospheric boundary layer aerosols, and elevated aerosol laye rs (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 backs catter 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 9-88Source: Reprinted with Permission of the Amer ican 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 du st 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 Texa s between 28.75°N and 29.08°N.



# 9.3. 2.4. In Situ Aerosol Profiling Programs

In addition to long-term ground based measurem ents, regular long-term aircraft in situ measurements recently have been implemented atseveral 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 m easurements, NOAA has conducted regular flights over Bondville, Illinois since 2006. Measuremen ts include light scattering and absorption coefficients, the relative humidity dependence of light scattering, aerosol number concentration and size distribution, and chemic al 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 fi xed locations. In addition, in situ measurement capabilities have undergone major advancements during this same time

December 2009 9-89period. These advancements include the ability to measure aerosol chem ical composition as a function of size at a time resolution of sec onds to minutes (e.g., Jayne et al., 2000, 190978 ), the development of instruments able to measur e 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 instrument ation more affordable and turn-key so that it can be deployed more widely to characterize aeros ol properties at a variety of sites worldwide.



## Figure 9-60. A constellation of five spacecraft that overfly the Equatora t about 1:30 p.m., the so-called A-Train, carries sensors having complementary capabilities, offering unprecedented opportunities to study ae rosols 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 Su mmer 2004 Fehsenfeld et al. (2006, 190531 )

DOE AIOP Northern Oklahoma Ma y 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-Augu st 2001 Lelieveld et al. (2002, 190361 )

LACE98 Lindberg, Germany July-Augu st 1998 Ansmann et al. (2002) Anthropogenic aerosol and boreal forest from North America and West Europe

Aerosols99 Atlantic January-Febr uary 1999 Bates et al. (2001, 043385 )

INDOEX Indian subcontinent and Indian Ocean January-April 1998 & 1999 Ramanathan et al. (2001, 190196 ) Brown haze in South Asia

ABC South and East Asia Ongoi ng 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 Kaufm an 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 Atl antic September-October 1992 Fishman et al. (1996, 190566 ) Biomass burning smoke in the tropics

DABEX West Afri ca January-February 2006 Haywood et al. (2008, 190602 )

SAMUM Southern Morocco May-June 2006 Heintzenberg et al. (2008, 190605 )

SHADE West coast of Nort h Africa September 2000 Tanr ė et al. (2003, 190454 )

PRIDE Puerto Rico June-July 2000 Reid et al. (2003, 190213 ) Mineral dusts from North Africa and Arabian Peninsula

UAE 2 Arabian Peninsula August-Sept ember 2004 Reid et al. (2008, 190214 )

Remote oceanic aerosol ACE-1 Southern Oc eans 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 sen sing 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 1976 onward In situ

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 SO 4 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, phasefunction, 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.nas a.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. Gro und-Based Remote Sensing Measurement Networks

The Aerosol Robotic Network (AERONET) pr ogram is a federated ground-based remote sensing network of well-calibrate d 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 radian ce 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 f actor (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 retrieve d parameters have been validated or are undergoing validation by comparison to in si tu 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 Aero sol Network (MAN) component to monitor aerosols over the World oceans from ship s of- opportunity (Smirnov et al., 2006, 190400 ). Because of consistent calibration, cloud-sc reening, 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-qu ality, ground-based aerosol climatology and, as such, have been widely used for aerosol pr ocess studies as well as for evaluation and validation of model simulation and satellite remo te 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 Netw ork (EARLINET) (Matthias et al., 2004, 155971 ); and

 Asian Dust Network (AD-Net) (e.g., Murayama et al., 2001, 155992 ). Obtaining accurate aerosol extinction profile ob servations is pivotal to improving aerosol radiative forcing and atmospheric response calcu lations. 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. Syn ergy of Measurements and Model Simulations

Individual approaches discussed above have their own strengths and limitations, and are usually complementary. None of these approach es alone is adequate to characterize large spatial and temporal variations of aerosol phys ical and chemical properties and to address complex aerosol-climate interactions. The be st strategy for characterizing aerosols and estimating their radiative forcing is to integrat e measurements from diff erent 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 th e 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-coordi nated 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 us ed 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 th e best agreement between methods occurs for submicrometer, spherical particles such that di fferent measures of aer osol 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) presen t inlet collection efficiency issues and nonspherical particles (e.g., dust) lead to differ ences in instrumental responses. In these cases, differences between methods for determining aer osol 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 ra nge 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 fi eld 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 aeros ol 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 9-94properties generated by CTMs on broader scales in space and time, satellite observations and long-term in situ meas urements are required.


## Improved Model Simulations with Satellite Measurements

Global measurements of aerosols from sate llites (mainly AOD) with well-defined accuracies offer an opportunity to evaluate mode l simulations at large spatial and temporal scales. The satellite measurements can also be us ed 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 estimat e 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 understandi ng of aerosol characterization and radiative forcing is assessed in Section 9.3.3 through intercomparisons of a variety of measurement-based estimates a nd 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 ( μ g/m 3 

## ) and standard deviation of the modeled (STEM) aerosol chemical com ponents with shipboard measurements during INDOEX, ACE-Asia, and ICARTT.

Further complexity is added when attempting to relate surface PM 2.5 

### to aerosol optical depths. The main approach to derive surface PM 2.5 

### 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 PM 2.5 

### (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 PM 2.5 

### 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 PM 2.5 

### by ~5 μg/m 3 

### , while MISR estimates were biased high by about 3 μg/m 3 

### . 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 PM 2.5 

### and its components in the eastern U.S. and 28-56% of the variability in the western U.S. The models tended to underpredict PM 2.5 

### by ~7-8% in both the East and West. The relative errors in surface PM 2.5 

### 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 PM 2.5 

### of 0.96 for several urban areas around the world. The MODIS Aerosol Optical Depth (AOD)/ In-situ PM 2.5 

### correlation summary plot shown in Figure 9-63 below illustrates the correla tion between AOD and surface PM 2.5 

### across the U.S. and parts of Canada. The correlation is based on coincident MODIS AOD pixels and 1-h PM 2.5 

### concentrations from the in-situ continuous surface monitors. The parameter plotted is the monitoring site-specific running correlation coefficient during the prec eding 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 PM 2.5 

### 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 PM 2.5 

### mass concentrations at the monitor location.

December 2009 9-96

## Figure 9-63. Correlations between one-hour PM 2.5 

## 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 measur ements into NOAA and EPA analyses for public benefit. IDEA is funded by these three agenci es and managed by the University of Maryland Baltimore County and NOAA.



# 9.3.3. Assessments of Aerosol Character ization 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 9-97This section focuses on the assessment of measurement-based aeros ol characterization and its use in improving estimates of the direct radiative forcing on regional and global scales. In situ measurements provide highly accurat e aerosol chemical, microphysical, and optical properties on a regional basis and for the partic ular 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. Bo th 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 dir ect radiative forcing at the TOA with relaxed requirement for characterizing aerosol propertie s. Progress in using both satellite and surfacebased measurements to study aerosol-cloud interac tions 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, anth ropogenic component of DRF, and to evaluate CTM parameterizations. In addition, in situ m easurements can be used to develop simplifying parameterizations for use by CTMs. Several factors contribute to the uncertain ty 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 transpor t, 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 9-98Source: 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), SO 4 2– 

## (red), nitrate (blue), ammonium (orange), and chloride (purple), of non-refractory PM 1


## .

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 ev ery 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 thiscomponent. 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 supermicro meter 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 aerosol s (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 9-99Source: 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 submicromete r POM versus acetylene (a gas phase primary emitted VOC species) and isopropyl nitrate (a secondary gas phase organic species formed by atmospheric reactions). The incr ease in submicrometer POM with increasing photochemical age could not be explained by the removal of VOC alone, which are its traditionally recognized precursors. This resu lt 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 th at 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 organi c 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 correct ly 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, or ganic carbon, BC, sea salt, and dust). In situ measurements were used to calcula te the corresponding optical properties for each aerosol type for use in a radiative transf er model. Aerosol DRF and its anthropogenic component estimated using the empirically derive d 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 wi th aerosol and of the model state with no aerosol. It was found that the constrained opt ical properties derived from measurements increased the calculated AOD (34 ± 8%), TOA DRF (32 ± 12%), and anthropogenic component of TOA DRF (37 ± 7%) relative to r uns 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 cal culations are performed with a variety of simplifying assumptions concerning the RH dependence of light scattering by the aerosol.

December 2009 9-100Chemical 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 ab ility 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 a nd 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 f σ sp(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 f σ sp(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 f σ sp(RH) based on CTM estimates of the POM mass fraction. Alternatively, the relationship may be used to assess values of f σ sp(RH) currently being used in RTMs.



# 9.3. 3.2. Int ercomparisons of Satellite Measurements and M odel 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 comparis on is particularly important if models are to be used in projections of future climate states that w ould 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 inte grations are conducted by using an optimum interpolation approach (Yu et al., 2003, 156171 ) to constrain GOCART simulated AOD with that from MODIS, MISR, or MO DIS over ocean and MISR over land, denoted as MO_GO, MI_GO, and MO_MI_GO, respectively. MODIS va lues 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 ar e 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 retrie val 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 cal ibration. 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 fi ve 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-d erived 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, sate llite-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 measur ements (e.g., Yu et al., 2003, 156171 ). As in comparisons between models and in situ meas urements (Bates et al., 2006, 189912 ), there appears to be a relationship between uncertainties in the repres entation 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 propertie s (e.g., single-scattering albedo, and phase function), as documented in Kinne et al. (2006, 155903 ). This, combined with the differences

December 2009 9-101in 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 contribu tions of individual aerosol comp onents. 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. Sat ellite-Based Estimates of Aero sol Direct Radiative Forcing

Table 9-7 summarizes approaches to estimating the aerosol direct radiative forcing, including a brief description of methods, identif ies 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-base d. As satellite aerosol measurements are generally limited to cloud-free conditions, the di scussion here focuses on assessments of clearsky aerosol direct radiative forcing, a net ( downwelling minus upwelling) solar flux difference between with aerosol (natur al + 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 aer osol 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 Nort h Africa and the tropical Atlantic. In the tropical Atlantic, TOA cooling as large as -10 W/m 2 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/m 2 . Other areas having large aerosol impacts include Western Europe, midlatitude North Atlantic, and much of South Asia and the Indian Ocean. Over the “roa ring 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/m 2 . 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 dept hs over the southern hemisphere oceans are higher than MODIS retrievals and GOCART si mulations. 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, ω 0 , and phase function consistently in c onjunction 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 intomineral 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 retrieva ls, 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 combinationwith 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), instant aneous-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 integrati on 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 oxi dation), assumptions on aerosol size, absorption, mi xture, 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 9-104Source: 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/m 2 

## ) at the TOA (right panel) de rived 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 calculate d for measurement- and model-based estimates separately. Note that although the standard devi ation or standard error reported here is not a fully rigorous measure of a true experimental un certainty, it is indicative of the uncertainty because independent approaches with inde pendent sources of errors are used (see Table 9-7 ; in the modeling community, this is ca lled 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/m 2 (mean ± ε ) at the TOA and -8.7 ± 0.7 W/m 2 at the surface. This suggests that the ocean surface cool ing 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/m 2 and -4.9 ± 0.8 W/m 2 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 ‘measur ement-based’ estimate of surface DRF is actually a calculated value, using poorly constrained particle properties.



## Land

It remains challenging to use satellite meas urements alone for characterizing complex aerosol properties over land surfaces with hi gh 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/m 2 at the TOA and -11.9 W/m 2 at the surface respectively. The surfa ce cooling is more than a factor of 2 larger than the TOA cooling because of aer osol absorption. Note that the TOA DRF of - 4.9 W/m 2 agrees quite well with the most recent satellite-based estimate of -5.1 ± 1.1 W/m 2

over non-desert land based on coincident measur ements 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/m 2 at the TOA and -7.6 ± 0.9 W/m 2 at the surface, respectively. Seasonal variations of DRF over land, as derived from both measurements and models, are la rger 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/m 2 

## ) 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 e t 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/m 2 

## ) 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) s eparately. The last row is the ratio of model median to obse rvational median. (Taken from Yu et al., 2006,
156173 ).

The above analyses show that, on a global av erage, the measuremen t-based estimates of DRF are 55-80% greater than the model-based es timates. The differences are even larger on regional scales. Such measurement-model differ ences are a combination of differences in aerosol amount (optical depth), single-scatteri ng 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 enha nced AOD and DRF due to processes not well represented in the models including humidificati on and enhancement of aerosols in the vicinity of clouds (Koren et al., 2007, 190192 ). From the perspective of model simula tions, 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-m odel 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-s cattering albedo and asymmetry factor due to satellite sampling bias toward cloud-free conditi ons) 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. Sat ellite-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 measuremen t-based estimates of anthropogenic component of aerosol direct radiative forcing (Kaufman et al., 2002, 190956 ). Such method derives anthropogenic AOD from satel lite 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 anthr opogenic 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 an thropogenic fraction of AOD should be much larger over land (i.e., 47 ± 9% from a compos ite 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 anth ropogenic from natural aerosols. There have been several estimates of anthr opogenic component of DRF in recent years. Table 9-10 lists such estimates of anthropogenic co mponent 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 aer osols is estimated to be -1.1 ± 0.37 W/m 2 over ocean, about a factor of 2 str onger than model simulated -0.6 W/m 2 . 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 m easurement-based estimate of anthropogenic DRF ranges from -0.9 to -1.9 W/m 2 , again stronger than the model-based estimate of -0.8 W/m 2 . Similar to DRF estimates for total aer osols, 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/m 2 (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 anthropoge nic component of aerosol DRF are greater than for the total aerosol, particularly over la nd. 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, na mely 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 (T ant 

## ) and clear-sky DRF at the TOA from model simulations.

Ocean Land Global

Data Sources

Τ ant DRF (W/m 2 ) Τ ant DRF (W/m 2 ) Τ ant DRF (W/m 2 ) 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. Aer osol-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 dese rts. The bright white clouds overlying darker oceans or vegetated surface demonstrate the signifi cant 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 show n 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 lead s 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 bri ghter and reflect more sunlight to space, thus exerting a cooling effect. This is the first aeroso l 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 aeros ol perturbation, and th erefore the extent of cooling, depends on the aerosol size distribut ion and its size-dependent composition. In addition, aerosol perturbations to cloud microp hysics may involve feedbacks; for example, smaller drops are less likely to collide and coalesce; this will inhibit growth, suppressing precipitation, and possibly increasi ng 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 car bon 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 9-110difference 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 smal ler. 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 natu re and abundance of IN, and ice formation in clouds is extremely poor. There is some obser vational 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 establis hed. 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. Rem ote Sensing of Aerosol-Cloud Interactions and Indirect Forcing

The AVHRR satellite instruments have observed relationships between columnar aerosol loading, retrieved cloud microphysics, and clou d 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 in crease from the pre-industr ial era, estimates of anthropogenic aerosol indirect radiative forcin g fall into the range of -0.7 to -1.7 W/m 2 (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 increas ed 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 offe rs some interesting insights, and is complementary to the global satellite view. Su rface 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 st udies 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 (ra dar reflectivity Z <-17 dBZ), ice-free clouds. Simultaneously, sub-cloud aerosol extinction profil es were measured with a lidar to quantify the response of drop sizes to changes in aeros ol 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 orde r 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 relati ve 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) perm itted examination of the role of updraft in determining the response of re to change s in aerosol (via changes in drop number

December 2009 9-111concentration 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 surf ace 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 obse rved 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 adva ntage 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 ch anges in aerosol. The relationship between these aerosol-cloud microphysical responses and cloud radiative forcing has been examined by

December 2009 9-112McComiskey 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/m 2 , 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 th e thinnest clouds (i.e., low LWP) that are most susceptible (from a radiative forcing perspectiv e) 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 a nd this represents a significant challenge. In some cases important measurements are simp ly 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 s howed that high concentrations of CCN from anthropogenic sources, such as industr ial 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 cl oud 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 unc ommon 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 μ m 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 th e 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 samp le 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 cm 3 . 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 obs cure 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 prev iously, Nd is a function of numerous parameters in addition to aerosol number con centration, including size distribution, updraft velocity (Leaitch et al., 1996, 190354 ), and composition. In stratocumulus clouds, characterized by relatively low vertical veloci ty (and low supersaturation) only a small fraction of particles can be activated whereas in vi gorous cumulus clouds that have high updraft velocities, a much larger fraction of aerosol pa rticles 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 aeros ol 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 ae rosol 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), wh ich, in concentrations on the order of 1 cm 3 (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 eff ect 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 9-113moderately polluted clouds (Johnson, 1982, 190973 ) and in so doing alter cloud albedo (Feingold et al., 1999, 190540 ). The most direct link between the remote sens ing of aerosol-cloud interactions discussed in Section 9.3.3.6 and in situ observations is via obs ervations of relationships between drop concentration Nd and CCN concentration. Theo ry 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 e quivalent slopes obtained from remote sensing – particularly in the case of satellite remo te sensing (McComiskey and Feingold, 2008, 190517 ). There are a number of reasons for this: (i) in s itu measurements focus on smaller spatial scales and are more likely to observe the droplet activ ation process as opposed to remote sensing that incorporates larger spatial scales and include s 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 typical ly 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 aeros ol 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, aeros ol effects on cloud microphysics are reasonably consistent (within ~ 20%) with theory but m easurement 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/m 2 (Nakajima et al., 2001, 190552 ) and -0.6 to -1.2 W/m 2 (Sekiguchi et al., 2003, 190385 ), depending on the estimate for aerosol increase from preindustrial times and whether aerosol effects on cl oud 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 concen trated 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 compon ent 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 at mospheric radiative heating induced by aerosol absorption are poorly constrained.

 There is a need to quantify aerosol impact s 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 me teorology contributions to indirect radiative forcing remains a major challenge.

 Long-term aerosol trends and their relati onship 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 re mote 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/m 2 (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, thes e methods are still experimental, and must be deployed on aircraft. Aerosol absorption retrieva ls 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 aer osol absorption over oceans (Kaufman et al., 2002, 190955 ). However, this technique requires retr ievals of aerosol s cattering properties, including the real part of the refractive index, well beyond what has so far been demonstrated

December 2009 9-114from space. In summary, there is a need to pur sue 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 ch aracterization of aerosol absorption with welldefined uncertainty (Leahy et al., 2007, 190232 ). Laboratory studies of aerosol absorption of specific known composition are also needed to in terpret 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, heterogene ous, and anisotropic, which complicates aerosol retrievals and DRF determination from sa tellites. 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 angul ar 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 fo r 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 la nd 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 de pth 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 dist ribution, particle shape, and absorption, along with algorithm refinement for better aerosol opt ical depth retrievals. Coordinated in situ measurements offer a promising avenue for vali dating 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 mode ls seem likely to reduce the uncertainties in aerosol source attribution.


## Profiling the Vertical Distributions of Aerosols

Current aerosol profile data are far from ade quate 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 backscatte r 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 atmosphe ric 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 si gnificantly during the day. This cannot be captured by currently available, sun-synchr onous, 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 he lp improve accuracy of geostationary satellite

December 2009 9-115aerosol 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 indi rect forcing are still rare and uncertain. Improvements are needed for both aerosol characterization and m easurements of cloud properties, precipitation, water vapor, and temperature profiles. Basic processes still need to be understood on regional and global scales. Remo te 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. Inaddition, 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 s itu and remote) and models at the range of relevant temporal/spatial scales is crucial to improve understanding of aer osol indirect effects and aerosol-cloud interactions.


## Quantifying Long-Term Trends of Aerosols at Regional Scales

Because secular changes are subtle and ar e superposed on seasonal and other natural variability, this requires the construction of co nsistent, 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 meas urements 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 sens or 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 radia tion 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 fr om 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 aeros ol trends are required to test these ideas. In addition to aerosol optical depth, ch anges 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 su rface solar radiation (e.g., Qian et al., 2007, 190572 ). However, such data are cu rrently rare and subject to large uncertainties. Finally, a better understanding of aerosol-radiation-cloud in teractions 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-clim ate interactions was first proposed around 1970, substantial progress has been made in de termining the mechanisms and magnitudes of

December 2009 9-116these interactions, particularly in the last 10 years. Such progress has greatly benefited from significant improvements in aerosol measuremen ts 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 anthropoge nic 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 ma ss spectrometers, photoacoustic techniques, and cavity ring down cells provide high accuracy and fast time resolution measurements of aerosol chemical and optical properties. Nume rous focused field campaigns and the emerging ground-based aerosol networks are improving regional aerosol chemical, microphysical, and radiative property characterization. Aerosol clos ure studies of differen t measurements indicate that measurements of submicrometer, spheri cal sulfate and carbonaceous particles have a much better accuracy than that for dust-domin ated 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 accu racy of 0.01~0.02, which is adequate for achieving the accuracy of 1 W/m 2 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 pr operties (size, shape, and abso rption) that are essential for characterizing aerosol type and estimating an thropogenic component of aerosols. However, these microphysical measurements are mo re 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% fo r dust at regional scales. The high-accuracy of MODIS, MISR and POLDER aerosol pr oducts 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 measur ing 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/m 2 (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/m 2 . 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 o cean, 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/m 2 and -11.8 ± 1.9 W/m2 at the TOA and surface, respectively. These values over land are cal culated for the difference between total aerosol and the complete absence of all aerosol.

 Use of satellite measurements of aerosol mi crophysical 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/m 2 . 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 suggestthat theoretical understanding of the activation process for water cloud is reasonably wellunderstood. Remote sensing of aeros ol effects on droplet size associated with the albedo effect tends to underestimate the magnitude of the re sponse 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 (partic ularly in broken cloud fields). It remains a major challenge

December 2009 9-117to infer aerosol number concentrations from sa tellite measurements. The present state of knowledge of the nature and abundance of IN, a nd ice formation in clouds is extremely poor. Despite the substantial progress in recent decad es, several important issues remain, such as measurements of aerosol size distribution, particle shape, ab sorption, and vertical profiles, and the detection of aerosol long-term trend and establishment of its connection with the observed trends of solar radiation reachi ng 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. Concep ts of future research in measurements are discussed in Chapter 4 “Way Fo rward” (of the CCSP SAP2.3).



# 9.3.6. Modeling the Effect of Aerosols on Climate

9.3. 6.1. Int roduction

The IPCC Fourth Assessment Report (AR4) (IPCC, 2007, 092765 ) concludes that man’s influence on the warming climate is in the categ ory 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 ob served warming (primarily for the past 50 years); when they are not, the models do not get ve ry much warming at all. In fact, all of the models runs for the IPCC AR4 assessment (mor e than 20) produce this distinctive result, driven by the greenhouse gas increases that have been observed to occur. These results were produced in models whos e average global warming associated with a doubled CO 2 forcing of 4 W/m 2 was about 3°C. This translates into a climate sensitivity (surface temperature change per forcing) of about 0.75°C/(W/m 2 ). The determination of climate sensitivity is crucial to projecting the fu ture 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 warmi ng 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 climat e models use differing aerosol forcings, both direct (aerosol scattering and absorption of shor t 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 fo rcing 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 mu ch greater than stated (Kerr, 2007, 190950 ; Schwartz et al., 2007, 190384 ), since, in general, models with lo w/high sensitivity to greenhouse warming used weaker/stronger aerosol cooling to obtai n 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. Po llution 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 accurate ly reproduced the global temperature change in the past does not imply that its future forecas t 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 prei ndustrial times) is -0.5 (-0.9 to -0.1) W/m 2 . 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/m 2 . No estimate was specified for the effect associated with cloud lifetime. The total negati ve RF due to aerosols according to IPCC (2007, 092765 ) estimates is then -1.3 (-2.2 to -0.5) W/m 2 . In comparison, the positive radiative forcing (RF) from greenhouse gases (including tr opospheric ozone) is estimated to be +2.9 ± 0.3 W/m 2 ; hence tropospheric aerosols reduce the influence from greenhouse gases by about 45% (15-85%). This approach however inher its large uncertainties in aerosol amount, composition, and physical and optical propertie s 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 par ticular 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 forci ngs from the total value. Studies of this nature come up with aerosol forc ing ranges of -0.6 to -1.7 W/m 2 (Knutti et al., 2002, 190178 ;

December 2009 9-119Knutti et al., 2003, 190180 ); IPCC AR4 Chap.9); -0.4 to -1.6 W/m 2 (Gregory et al., 2002, 190593 ); and -0.4 to -1.4 W/m 2 (Stott, 2006, 190419 ). This approach however provides a bracket of the possible range of aerosol forc ing 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 ca lculations. 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 impact s of aerosols on climate model simulations and their implications are assessed.



# 9.3. 6.2. Mod eling 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. Becau se of the complexity of the processes and composition, and highly inhomogeneous distri bution 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 tran sformation, 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 na tural 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, industri al processes, agriculture practices, and humaninduced biomass burning. Following earlier attempts to quantify manmad e 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 (B C) and organic carbon (OC), using fuel-use data and measured emission f actors (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 em ission 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 includ e 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 observa tions 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 prim ary 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 (SO 2 ) and nitrogen oxides (NO and NO 2 , collectively called NO X ), 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, in cluding 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 part icle size, and are radiatively less active than aerosols with small particle size, such as sulf ate, 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 us ually evaluated (e.g., by the IPCC) as the anthropogenic perturbation since the pre-industria l period, it is necessary to estimate the

December 2009 9-120historical emission trends, especially the emissions in the pre-industr ial 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 inventorie s 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 ), SO 2 (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 time s 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 par ticulate 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 includ ed. Another challenge is estimating historical biomass burning emissions. A recent study su ggested 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 Pollutan t 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 R esearch (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 SO 2 and particulate SO 4 2– . Most emitted as SO 2 , and 2.5% emitted as SO 4 2–

. 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-indus trial 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 si mulated in the successive steps of sources (emission and chemical formation), transport (fr om source location to other area), and removal processes (dry deposition, in which particles fa ll 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 at tenuated by aerosol particles via scattering or absorption, can be related to the atmospheric aerosol mass loading as follows:

December 2009 9-121

## MMEE AOD • = 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 m 2 /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 processe s 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 glob al-model studies by the AeroCom project are summarized, as they represent the current assess ment of model-simulated atmospheric aerosol loading, optical properties, and RF for the pres ent-day. AeroCom aims to document differences in global aerosol models and compare the mode l output to observations. Sixteen global models participated in the AeroCom Experiment A (A eroCom-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 fro m the AeroCom-A for several key parameters: Sources (emission and chemical tran sformation), mass loadi ng, lifetime, removal rates, and MEE and AOD at a commonly used, mid-visible, wavelength of 550 nanometer (nm). These are the globally averaged values fo r the year 2000. Major features and conclusions

are:

 Globally, aerosol source (in mass) is dominat ed 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 distri butions, thus having the shortest lifetime. The median lifetime of sea salt from the Ae roCom-A models is less than half a day, whereas dust and sulfate have similar lifetim es 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. Howe ver, they are mainly from anthropogenic activity, so the highest concentrations ar e 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; th is is mainly associated with the differences in particle size range and s ource parameterizations in each model. The diversity of sea salt atmospheric loading however is much smaller than that of sources or sinks, because the largest partic les have the shortest lifetimes even though they comprise the largest fraction of emitted and deposited mass.

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## Table 9-12. Summary of statistics of AeroCom Experiment A results from 16 global models.

Quantity Mean Median Range Stddev/mean*

SOURCES (TG/YR)

SO 4 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-)

SO 4 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)

SO 4 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)

SO 4 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)

SO 4 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

SO 4 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 measur ements. 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), bot h 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 va riations 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 us ing 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 out come of AeroCom-A and -B that, although more detailed evaluation for each individual pro cess is needed, multi-model ensemble results, e.g., median values of multi-model output variab les, should be used to estimate aerosol RF, due to their greater robustness, relative to in dividual models, when compared to observations (Schulz et al., 2006, 190381 ; Textor et al., 2006, 190456 ; Textor et al., 2007, 190458 ).



# 9.3. 6.3. Cal culating 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), al l 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 pa rticle size; larger values of Å are generally associated with smaller aerosols particles. Thes e 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 ab sorb, 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 vari ations of both aerosol composition and relative humidity, which influence these characteristics.

December 2009 9-124Source: Adapted from Kinne et al. (2006, 155903 )



## Figure 9-72. Global annual averaged AOD (uppe r panel) and aerosol mass loading (lower panel) with their component s simulated by 15 models in AeroCom- A (excluding one model which only reported mass). SU=SO 4 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) a nd 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 ar e 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 nitrat e. The aerosol direct forcing in the AeroCom assessment thus comprises thre e major anthropogenic aerosol components sulfate, BC, and POM.

December 2009 9-125

## Table 9-13. SO 4 2– 

## mass loading, MEE and AOD at 550 nm, sh ortwave radiative forcing at the top of the atmosphere, and normalized forc ing with respect to AOD and mass. All values refer to anthropogenic perturbation.

Model Mass load (mg m -2 ) MEE (m 2 g -1 ) AOD att 550 nm TOA Forcing (W/m 2 ) Forcing/AOD (W/m 2 ) 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, Aero sol Transport and General circulation model; PNNL=Pacific Northwest National Laboratory; UIO-CTM=Univeristy of Oslo CTM; UI O-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 normaliz ed forcing with respect to AOD and mass.

Model Mass load (mg m -2 ) MEE (m 2 g -1 ) AOD at 550 nm TOA Forcing (W/m 2 ) Forcing/ AOD (W/m 2 ) Forcing/ Mass (W g -1 ) Mass load (mg m -2 ) MEE (m 2 g -1 ) AOD at 550 nm TOA Forcing (W/m 2 ) Forcing/ AOD (W/m 2 ) 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 9-127The 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 forci ng per gram of aerosol mass (dry). For some models, aerosols are externally mixed, that is, e ach 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/m 2 , POM from -0.06 to -0.34 W/m 2 , and BC from +0.08 to +0.61 W/m 2 , 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 at mosphere, 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 a nd 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 9-74 A 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, anth ropogenic aerosol causes negative forcing over mid-latitude continents and oceans with the most negative values (-1 to -2 W/m 2 ) over polluted regions ( Figure 9-74 C). Although anthropogenic aerosol has a cooling effect at the surface with surface forcing values down to -10 W/m 2 over China, India, and tropical Africa ( Figure 9-74 E), 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 9-128Aerosol Direct Radiative Forcing

Bellouin et al. (2005, 155684 )

Chung et al. (2005, 155733 )Observationbased

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

SPRINTARSAeroCom models

ULAQ

Radiative Forcing (W 

## / m 2 )

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 9-129A 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 9-130Aerosol 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 

## / m 2 ) 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 SO 4 2– 

## , sea salt, organic and BC, dust and nitrates; in the top panel, only SO 4 2– 

## , sea salt and OC are included.

December 2009 9-131Basic conclusions from forward modeling of aerosol direct RF are:

 The most recent estimate of all-sky s hortwave 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/m 2 averaged globally, exerting a net cooling effect. This value would represent the low-end of the forcing magnitude, since some potentially significant anthropogenic aerosol s, such as nitrate and dust from human activities are not included because of thei r highly uncertain sources and processes. IPCC AR4 had adjusted the total anthropogenic aerosol direct RF to -0.5 ± 0.4 W/m 2 by adding estimated anthropogenic nitrate and dust forcing values based on limited modeling studies and by considering the obs ervation-based estimates (see Chapter 2 of the CCSP SAP2.3).

 Both sulfate and POM cause negative fo rcing 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 magnit ude 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 im proving 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/m 2 , respectively. The anthropogenic nitrate and dust forcings are estimated at -0.1 W/m 2 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-calcula ted 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/m 2 , which is an order of magnitude above the global averaged forcing value of - 0.22 W/m 2 . The large diversity reflects the low level of current understanding of aerosol radiative forcing, which is compounded by uncertainties in emissions, transport, transformation, removal, par ticle 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/m 2 at the surface and +0.8 to +2 W/m 2 in the atmosphere. Anthropogenic aerosols thus c ool the surface but heat the atmosphere, on average. Regionally, the atmospheric h eating can reach annually averaged values exceeding 5 W/m 2 . Source: Schulz et al. (2006, 190381 ) and AeroCom Image Catalog ( http://nansen.ipsl.jussieu. fr/AEROCOM/aerocomhome.html )

 Figure 9-74 D). These regional effects and the negative surface forcing are expected to exert an important effect on climate th rough alteration of the hydrological cycle.



# 9.3. 6.4. Cal culating 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 concen trations, therefore, ma y increase the ambient concentrations of CCN and IN, affecting cl oud 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 indir ect effect, generally (though not exclusively) considered only with sulfates. Shown in Figure 9-75 are results from published model st udies indicating the different RF values from the cloud albedo effect. The cloud albedo effect ranges from -0.22 to -1.85 W/m 2 ; the lowest estimates are from simulations that constrained representatio n 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 late r), 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/m 2

, with a “best-guess” estimate of 0.7 W/m 2

. The representation of cloud effects in GCMs is considered below. However, it is becoming increasingly clear from studies base d on high resolution simulations of aerosolDecember 2009 9-132cloud 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 fo rm 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 li kely 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 functi on 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 altitu de clouds, associated with IN changes, is a relatively unexplored area for models a nd as well for process-level understanding. Hansen et al. (2005, 059087 ) used coefficients to norma lize the cooling from aerosol indirect effects to between -0.75 and -1 W/m 2 , based on comparisons of modeled and observed changes in the diurnal temperature range as we ll as some satellite observations. The response of the GISS model to the direct a nd 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 S outhern Hemisphere than did the “cloud albedo” response, with the reverse being true in the Nort hern Hemisphere (differences on the order of a few tenths °C).



## Model Experiments

There are many different factors that can expl ain 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 analy ze 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 parameterizat ions. In the first experiment, the monthly average aerosol mass and size distribution of, eff ectively, sulfate aerosol were prescribed, and all models followed the same prescription for parameterizing the cloud droplet number concentration (CDNC) as a function of aerosol c oncentration. 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 ra dii, and therefore shor twave cloud forcing, and change in net outgoing whole sky radiation be tween pre-industrial ti mes 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 9-133Source: Reprinted with Permission of Biores ource 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 incl usion 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 regi ons of Africa and South America. The third experiment allowed the models to rela te 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 we ll 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 thr ee 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 sm aller first indirect effect), it was magnified by a factor of three.

December 2009 9-134Source: Adapted from Penner et al. (2006, 190564 ).



## Figure 9-77. Global average present-day short wa ve cloud forcing at TOA (top) and change in whole sky net outgoing shortwave radiat ion (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/m 2 

## ) 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 allowe d 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, th erefore, the change in aerosol concentration has the largest effect on droplet concentrations and effective ra dii. This experiment too resulted in large changes in RF. In the last experiment, the aerosol direct ef fect 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 pr imarily 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 simula tions of the indirect effect with satellite data (e.g., MODIS) have been performed previ ously 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 a nd 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-atmosphe re reflectance for comparison to satellite measured values could be another way to compare model with observations, which would eliminate the inconsistent assumptions of aer osol 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 so lar 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 forcin g 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 cool ing 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 locati on 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 atmos phere (the usual metric for climate impact). By decreasing solar absorption at the surface, aerosol s (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 industr ial 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 su rface, 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, th e 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 precipita tion 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 realis tic 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 nece ssarily at odds with each other, but rather illustrate the complexity of the aerosol-monsoon inte ractions that are associated with different mechanisms, whose relative importance in aff ecting 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 seve ral hundred km), that their treatment of cloud microphysics is rather crude, and that as discu ssed 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 tr eatments of aerosol cloud interactions in largescale models. However, these are just being incorporated in models that resolve both cloud and

December 2009 9-137aerosols. Detailed cloud parcel models have been developed to focus on the droplet activation problem (that asks under what conditions dropl ets 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, w ith grid sizes of ~100 m and domains ~10 km] has proven to be a powerful tool for represen ting 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 para meterizing these effects properly in GCMs could really be.



## The First Indirect Effect

The relationship between aerosol and drop con centrations (or drop sizes) is a key piece of the first indirect effect puzzle. (It should not, how ever, 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 veloc ity 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 factor s. 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. Furtherm ore, 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 fro m 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 improve d 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 co llision-induced rain, and result in a longer cloud lifetime. Observational evidence for the suppression of ra in 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 decr ease in the tracks (Coakley and Walsh, 2002, 192025 ) and satellite studies suggest similar resu lts 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 res ponse to increasing aerosol depending on the relative humidity of the air overlaying the cloud. Wang et al. (2003, 157106 ) showed that all

December 2009 9-138else being equal, polluted stratocumulus clouds tend to have lower water contents than clean clouds because the small droplets associated w ith 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 regi mes: the first, a precipitating regime at low aerosol concentrations where an incr ease 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 bounda ry layer prefers two aerosol states: a clean, oceanic regime characterized by a weak aer osol source and less reflective clouds; and a polluted, continental regime characterized by mo re 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 observati ons 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 li fetime 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 impr ove 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. Aer osol 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 mode l 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 th e IPCC AR4. Put still differently, the current aerosol modeling capability has not been fu lly 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 si mulation: 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 experi ments (e.g., Koch et al., 2006, 190184 , for GISS model). This aspect is discussed further in Chapter 4 of the CCSP SAP2.3.

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## Table 9-16. Forcings used in IPCC AR4 si mulations of 20th cen tury 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.ph p ) plus additional information from that website. Eleven different forcings are listed: well-mixed greenhouse gases (G), tropospheric and stratospheric ozone (O), SO 4 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 9-140below 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 characteris tics 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 ra dii 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 sp ecifications, 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 relati ve 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 AVH RR 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 s outhern 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 satell ite 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 meas urements in some locations (not e 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 loca tions 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 scatteri ng of both short and long wave radiation, as discussed earlier. Å from the GISS model is biased low compared w ith 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 GI SS model appears to be 0.3-0.4 μ m, whereas the observational data indicates a value more in the range of 0.2-0.3 μ m (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 diff iculties in modeling BC, which is the dominant absorbing aerosol, and aerosol sizes. Global aver aged 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/m 2 at TOA and -2.87 W/m 2 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 latitu des. At the surface (lower left, Figure 9-78 ) GISS model values exceed -4 W/m 2 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 fiel ds 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 wa s 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 sa lt 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 i ssues 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 mode l, 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 la rge growth in sulfate particle size when the relative humidity exceeds 90%. Global distributions: Overall, the GFDL globa l 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 or ganic 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 AV HRR 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 valu es in the AeroCom-A experiments ( Table 9-12 ) and exceeds the observed value too (Ae and S* in Figure 9-72 ).

December 2009 9-142Source: Figure provided by A. Lacis, GISS.



## Figure 9-78. Direct radiative fo rcing 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 GF DL modeled species with in situ data over North America, Europe, and over oceans has revealed th at 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 overest imated in polluted regions by a factor of two, whereas organic carbon aerosols are elsewher e underestimated by factors of 2 to 3. Dust concentrations at the surface agree with observati ons to within a factor of 2 in most places where significant dust exists, although over the sout hwest U.S. it is a factor of 10 too large. Surface concentrations of sea salt are underestimat ed by more than a factor of 2. Over the oceans, the excessive sulfate AOD compensate s 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 su lfate 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 m odel, there are several concerns with the GFDL model. The good global-average agreement masks an exces sive 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 ocea ns. 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 woul d be constructive, ther efore, to compare the similarities and differences of aerosols in these two models and to understand what their

December 2009 9-143impacts 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 ex ceeds 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 AO D compared with other models. The sulfate AOD from GFDL is nearly a factor of 4 larg e 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, th e 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 revers e: it overestimates it in polluted regions, and underestimates it in biomass burning ar eas. 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 obser vations 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 differ ences 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 centu ry without the use of an indirect aerosol effect, whereas the GISS model required a substa ntial 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); th is suggests that for models with climate sensitivity close to 0.75°C/(W/m 2 ) (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: SO 4 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 aer osol RF in the shortwave (solar) spectrum has been discussed. Figure 9-78 (right column) shows that compar ed to the shortwave forcing, the values of anthropogenic aerosol long wave (t hermal) 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 larg est 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 aeros ol mixtures from detailed microphysics calculations now being used/developed by di fferent 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 AO D comparisons discussed earlier are not completely consistent. Because AOD can be si gnificantly 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 pr oblematic. And of course aerosols in cloudy regions can affect the clouds them selves, as discussed in Section 9.3.6.5

.

December 2009 9-146Source: 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. Imp acts of Aerosols on Climate Model Simulations

Surface Temperature Change

It was noted in the introduction that aerosol co oling is essential in order for models to produce the observed global temperature rise over th e last century, at least models with climate sensitivities in the range of 3°C for doubled CO2 (or ~0.75°C/(W/m 2 )). 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 pr oduce a warming of close to 1°C between 1880 and the present ( Table 9-17 ). The direct effect of tropospheri c 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 simula tions, along with the surface air temperature changes obtained for several periods.

Forcing Agent Forcing Wm -2 (1880-2003) Δ T Surface °C (y ea r 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 H 2 O 0.06 0.05 0.03 0.01 0.05 0.00

O 3 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 Spri nger 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/m 2 (down from -1.37 W/m 2 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 aer osol removal over North America and western Europe during the 1990s led to a cleaner atmosphe re. 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 eff ect used by Hansen et al. (2005, 190596 ) is roughly calibrated to reproduce the observed change in diur nal 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 co nsistent 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/m 2 ), 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 sensiti vity, nominally 0.375-1.13 °C/(W/m 2 ), because of uncertainties in paleoclimate forcing and respons e; 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 forc ing, or a model with smaller sensitivity and larger forcing”.

December 2009 9-148Source: 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 tr ends 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 sim ilar to that calculated by the GISS model, Table 9-17 ). Note that this was achieved with out 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/m 2 reduced the greenhouse forcing of near 3 W/m 2 by about 45%, in the neighborhood of the GFDL and GISS forcings. Since the average model sensitivity was close to 0.75 °C/(W/m 2 ), similar to the sensitivities of these models, the necessary negative forcing is therefore similar. The agre ement cannot therefore be used to validate the actual aerosol effect until climate se nsitivity 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 cl imate 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 wo rk differently in different models (Gillett et al., 2002, 190576 ). Using multiple models helps primar ily 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, an d so may be distinguishable. Overall, the similarity in response to all these very differ ent forcings is undoubtedly due to the importance of climate feedbacks in amplifying the forcing, whatever its nature.

December 2009 9-149Source: Courtesy of the American Geogphysi cal 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 an d 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 exhibite d 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 su rface. 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 at mospheric temperature response will be useful to the extent that the vertical profile of aerosol ab sorption, 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, th e aerosol direct forcing effect can be quite different, as shown in the AeroCom studi es. 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 res ponse (e.g., surface temperature change) to aerosol forcing would be more accurate for gl obal average than for regional or hemispheric differentiation. However, since aerosol forci ng is much more pronounced on regional than on global scales because of the highly variable aeros ol 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 prof ile. As shown in previous sections, very large differences exist in those quantities even am ong the models having similar AOD. Moreover, modeling aerosol indirect forcing presents more ch allenges 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 comparis ons of the GISS and GFDL model climate simulations for IPCC AR4, aeros ol 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 obs erved 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 hi ghly simplified sulfate indirect effects ( Table 9-16 ). Improvements must be made to at least the de gree 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. Out standing Issues

Clearly there are still large gaps in asse ssing the aerosol impacts on climate through modeling. Major outstanding issues and pros pects 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 sm all fraction of these models simulate nitrate aerosols or consider anthropogenic secondary or ganic aerosols. And it is difficult to quantify the dust emission from human activities. As a re sult, 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 importan ce 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 i nvolved. 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 em ission 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 in formation is difficult to obtain on past source types, strengths, and even locations. The IPCC AR4 simulations used several alternative aerosol emission histories, esp ecially for BC and POM aerosols.


## Climate modeling

Current aerosol simulation capabilities from CT Ms have not been fully implemented in most models used in IPCC AR 4 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 in adequate. 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 parame ters simulated in the model. The extensive AOD coverage from satellite observations and AERONET measurements has helped a great

December 2009 9-151deal in validating model-simulated AOD over th e past decade, but further progress has been slow. Large model diversities in aerosol compos ition, 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 di fferences in aerosol sources, types, emission trends, compositions, and atmospheric concentr ations, anthropogenic aerosol RF has profound regional and seasonal variations. So it is an in sufficient 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. Con clusions

From forward modeling studies, as discussed in the IPCC (2007, 092765 ) , the direct effect of aerosols since pre-i ndustrial times has resulted in a negative RF of about -0.5 ± 0.4 W/m 2 . The RF due to cloud albedo or brightness eff ect is estimated to be -0.7 (-1.8 to -0.3) W/m 2 . 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 therefor e -1.3 (-2.2 to -0.5) W/m 2 . With the inverse approach, in which aeros ols provide forcing necessary to produce the observed temperature change, values range from -1.7 to -0.4 W/m 2 (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 simula te a comprehensive suite of aerosols. This is done primarily in the CTMs. Model inter-comp arison 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 respons ible for the spread among the models. The varying component contributions then lead to differences in aerosol direct RF, as aerosol scattering and absorption properties depe nd 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. Curre ntly, the largest aerosol RF uncertainties are associated with the aerosol indirect effect. Most climate models used for the IPCC AR4 simulations employed simplified approaches, w ith aerosols specified from stand-alone CTM simulations. Despite the uncertainties in aerosol RF and widely varying model climate sensitivity, the IPCC AR4 models were genera lly able to reproduce the observed temperature record for the past century. Th is is because models with lower/higher climate sensitivity generally used less/more negative aerosol forc ing 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 sm aller 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 bett er quantified in future assessments. Progress in better quantifying aerosol imp acts on climate will only be made when the capabilities of both aerosol observations and repres entation of aerosol processes in models are improved. The primary c oncerns and issues discussed in th is chapter of the CCSP SAP2.3 include:

 Better representation of aerosol compositi on and absorption in the global models

 Improved theoretical understanding of s ubgrid-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 aeros ol 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 pyrog enic C to decomposition; (2) its influence on soil processes; and (3) the absorption of solar radi ation 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-absorbi ng carbon, referred to as brown carbon. Within the boreal zone, fire is a critical dr iver 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 m odel in simulating aerosol optical thickness, transport, and deposition. Emissions es timates of BC and OC were adjusted in the model to better match satellite observations of pollu tant 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 75 o 

### 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 w ith ground-based measurements and satellite optical measurements to character ize 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 PM 2.5 

### concentrations from 20 to 200%. Th e increased aerosol concentration was estimated to increase radiative forcing by 10-35 W/m 2 

### 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 inte rnally mixed, and secondary aerosol formation was not considered. Meteorological conditions in Euro pe during the exceptionally hot summer of 2003 were linked to enhanced photoche mically 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 contri bute to hemispheric concen trations 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 agricu lture as the primary cause of the high pollution levels. Concentrations of PM 2.5 

### peaked during the pollution episode at values of an order of magnitude greater than those recorded prior to th e 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 poll utant transport into the Arctic were related to unusually warm weather in late April and May, wh en the majority of agricultural biomass burning took place that year. In the summer of 2004, 2.7 million ha were bu rned by wildfire in Alaska and 3.1 million ha were burned in Canada. Effects on atmospheric ai r 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 indi cated 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 CO 2 

### and CH 4 

### , as well as light scattering compounds such as OC, and can e nhance 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. Exp losive Volcanic Activity

Radiative Effects of Volcanic Aerosols

Volcanic sulfate aerosols are formed as a resu lt of oxidation of the sulfur gases emitted by explosive volcanic eruptions into the stratosphe re. 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 sedimenta tion 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 μ m 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 perturba tions in the immediate aftermath of an eruption. Stratospheric aerosol data incorporated for climat e 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 microphysi cal evolution of volcanic aerosols (Deshler et al., 2003, 192058 ; Hofmann et al., 2003, 192062 ). Important ground-ba sed observations of aerosol characteristics from pre-satellite era sp ectral 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 SO 2 loadings from volcanic erup tions (Krueger et al., 2000, 192234 ; Prata et al., 2003, 192235 ). The Stratospheric Aerosol and Gas Expe riment (SAGE) and Stratospheric Aerosol Measurement (SAM) projects (e.g., McCormick and Trepte, 1987, 192328 ) have provided vertically resolved stratospheric aerosol spectra l 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 Eart h’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 record s 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 e xplosive volcanic event to date, by way of reliable and accurate observations, is the 1991 erup tion of Mt. Pinatubo. The growth and decay

December 2009 9-154of 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 concentra tions 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 st ratospheric aerosol size distribution formed inthe 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 di stributed using a simplified parameterization of atmospheric transport, and employs a fixed aerosol effective radius (0.42 μ m) for calculating optical properties. The above data sets have essentially provided the bases for thevolcanic 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/m 2 for the strong (rated in terms of emitted SO 2 ) 1860 and 1991 eruptions of Krakatau and Mt. Pinatubo, resp ectively. The value is reduced to about -2 W/m 2 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 qua ntified, and extending and improving the data sets remains an important area of research. Th is 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 furthe r back in time as there are few, if any, observational constraints on their optical depth and size evolution. The radiative effects due to volcanic aerosol s 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 respons e. 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/m 2 (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 Studi es (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.

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## Figure 9-83. Visible (wavelength 0.55 μ m) optical depth estimates of stratospheric SO 4 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 di rectly affect the Earth’s radiative balance and thus alter surface temperature. Second, th ey 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 ch emistry 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 fo rcings (e.g., due to we ll-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 eff ects interactively within the model rather than prescribe them (Andronova et al., 1999, 192286 ; Broccoli et al., 2003, 192283 ). Despite differences in volcanic aerosol parameters empl oyed, 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 9-156polar stratosphere and troposphere. The global mean warming of the lower stratosphere is due mainly to aerosol effects in the longwave spectru m, 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 hydrolog ic 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 m odel-observation comparis ons for 20th-century eruptions. A mechanism closely linked to the optical depth perturbation and ensuing warming of the tropical lower stratosphere is the poten tial 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 m odels’ simplified treatment of aerosol microphysics introduces biases; further, they us ually 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 cons trained 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 un derstanding. However, the limited knowledge of the RF associated with prior episodic, explosiv e 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 SO 4 2– 

### atmospheric aerosol content and 0.4-1.4% of the total BC atmospheric aerosol cont ent, 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/m 2 

### . 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 fu el BC and organic matter using the Gas, Aerosol, Transport, Radiation, General Circulation, Mesoscale and Ocean Model (GATOR-GCMOM). The modeling effort included c onsideration of the BC cycle, accounting for emissions, transport, aerosol coagulation, ae rosol 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 ov er a 10-year simulation by about 0.06 o 

### K (Jacobson, 2003, 155868 ). BC soot is a potentially important agent of clim ate warming in the Arctic, and northern boreal wildfires may contribute substantially to this effect . Soot is approximately twice as effective as CO 2 

### 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 leadi ng to an overall reduction in sea ice. Similarly, increased BC soot on glaciers would be expected to increase summer melting a nd 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 f ossil fuel particulate BC and associated organic matter could potentially slow warming for a specific period more than reduction of CO 2 

### or CH 4 

### 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 CO 2 

### and other GHG. Climatic effects of GHG are generally compared on the basis of top-of-the-atmosphere, globally averaged chan ges in radiative balance. On that basis, BC is one of the largest individual warming agents, after CO 2 

### and perhaps CH 4 

### (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 it s 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 SO 4 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 SO 4 2– 

### particles (termed an external mixture), and with the assumption that the particles are represented by a core of BC surrounded by a she ll of light scattering aerosols. Modeling results suggested an overall radiative cooling effect from aerosols ranging from -0.39 to -0.78 W/m 2



### . Roberts and Jones (2004, 156052 ) used a climate modeling approach to compare possible effects of BC on climate warming to those attri butable 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 scenar io, 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 tr opical 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. Th e model suggested that atmospheric heating by radiation absorption by BC can form temperature a nd pressure anomalies that favor propagation of convection from western to central and eastern P acific. More work will be needed to distinguish between the aerosol signal and natural factors in cont rolling tropical precipitation in this region.

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## 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 Camb ridge 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 Camb ridge University Press from Denman (2007, 156394 )



### There are several other kinds of climate effect s from aerosol PM. None is well understood or well quantified. The semi-direct effect, which invol ves absorption of solar radiation by soot particles followed by re-emission as thermal ra diation, is expected to heat the air mass and increase its static stability relative to the surface. The semi-direct ef fect 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 cau se 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, th e 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 ).

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# 9.3. 9.1. Gla ciers 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, lo w 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 id entified 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 volatili zation processes is not understood (Grannas et al., 2007, 156492 ). Recent research has explored connections betw een 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 glaci al ice surfaces can affect the subsequent rate of melting. A thin cover of debris contributes to accel erated 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 pr inciples apply to PM deposition derived from air pollution. Under a thin layer of debris, ablation rate s are higher than for clean ice. However, as the thickness of the debris layer increases, ablati on rates systematically decline (Nicholson and Benn, 2006, 156806 ). The threshold debris thickness that separa tes 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 ba sic 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 eco systems 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 previ ously 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

.

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## Table 9-20. Recent studies highlighting POP occurrence and fate in the major arctic compartments.

A TMOSPHERE

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 biodegr adation of HCHs in Canadian Arctic watersheds Helm et al. (2000, 155834 )

MARINE

11 Transport and entry of β -HCH into western Ar ctic Ocean via Pacific surfac e 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 colu mn 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 Arct ic Ocean surface water J antunen 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 interf ace in the western Arctic oc ean 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 )

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## 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 occurren ce 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. Rad iative 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 coul d cause a decrease in the albedo of snow and affect snowmelt. Initial estim ates by Hansen et al. (2000, 042683 ) suggested that BC could thereby exert a positive RF of +0.2 W/m 2 . This estimate was refined by Hansen and Nazarenko (2004, 156521 ), who used measured BC concentrations w ithin 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/m 2 . 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 modi fying 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% globall y 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/m 2 . 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 9-162for the BC on snow RF of +0.10 ± 0.10 W/m 2 , with a low level of scientific understanding (Section 2.9, Table 2.11, of the IPCC AR4).



# 9.3. 9.3. Eff ects 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 pa rticular, is believed to exacerbate water supply problems which are substantial in many regions, especially in the western U.S. Aerosol particles, directly and through cl oud 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 Cali fornia was 2% of the baseline 1.5 mm/day due to emissions of anthropogenic aerosol particle a nd precursor gasses in the SCAB domain. However, the reduction over much of the Sierra Nevada, wh ere 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, an d 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 Fre sno 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 upsl ope side of the mountains was coupled with similar percentage (but lower absolute volume) e nhancement 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% downwi nd 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 th e 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 precipit ation 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 dow nwind 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 s cale by examining the ratio between precipitation amounts over the hills to precipitation over upwi nd lowland areas throughout the western U.S. from the Pacific Coast to the Rocky Mountains. They found in these paired analyses a pattern of

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### 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 a ppears 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 effect s on orographic precipitation suggest that human-caused air pollution, and fine particles in pa rticular, 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 indir ect 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/m 2 

### 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/m 2 

### 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 st ill 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/m 2 

### while model estimates are -0.6 W/m 2 

### . 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/m 2 

### , larger than the estimate of -0.8 W/m 2 

### from the models. Aerosol indirect effects on climate are primarily realized as an increase in cloud brightness (termed the 'first indirect' or Twomey effect), ch anges 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 ar e not considered to be radiative forcing.

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### Taken together, direct and indirect effects fro m aerosols increase Earth's shortwave albedo or reflectance thereby reducing the radiative flux reach ing the surface from the Sun. This produces net climate cooling from aerosols. The current scientif ic 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/m 2 

### . While the overall global average effect of aerosols at the t op 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 reradia tion 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/m 2 

### , the horizontal, vertical, and temporal distributions and the physical lifetimes of these two very different radiative forcing agents are not similar; therefore, th e 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 NO X 

### SO X 

### ISA (U.S. EPA, 2008, 157074 ) assessed the effects of particle- and gas-phase N and S pollution on acidi fication, N enrichment, and Hg methylation. Acidification of ecosystems is driven primarily by deposition resulting from SO X 

### , NO X 

### , and NH X 

### pollution. Acidification from the deposition resul ting from current emission levels causes a cascade of effects that harm susceptible aquatic and terre strial 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 NO X 

### and NH X 

### emissions along with other non-atmospheric sources (e.g., fertilizers and wastewater), causes a suite of ecological changes within sensitive ecosystems. Th ese include increased primary productivity in most N-limited ecosystems, biodiversity losses, changes in C cycling, and eutrophication and harmful algal blooms in freshwater, estuarine, an d ocean ecosystems. In some watersheds, additional SO 4 2– 

### from atmospheric deposition increases Hg methylation rates by increasing both the number and activity of S-reducing bacteria. Methylmercur y 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 th e 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 eff ects may be more significant (U.S. EPA, 2008, 157074 ).

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### 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 stre ssors, of which atmospheric PM deposition is only one. Additional stressors are also important, including O 3 

### exposure, climatic variation, natural and human disturbance, the occurrence of invasi ve 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 othe r) 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 dete rmine 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. Eco system 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 ofspatial 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. Struct ure may refer to a variety of measurements including the species richness, abundance, communi ty composition and biodiversity as well as

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### 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” re fers 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, respirati on, 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 or ganisms. 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 manife sted in changes in the biotic composition of ecosystems. There are at least three levels of ecosystem re sponse 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 populat ion 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 popula tions 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. Eco system Services

Ecosystem structure and function may be tra nslated 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 ta ngible 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 O 2 

### , 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 (i ncluding 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.

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# 9.4. 2.1. For ms 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 environm ental media where they can alter the structure, function, diversity, and sustainability of comple x ecosystems. Dry deposition of PM is most effective for coarse particles. These include primary ge ologic 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 ge nerally settle nearer their site of formation than do fine particles. In addition, the chemical constitution of individua l particles is correlated with size. For example, much of the base cation and heavy metal bur den 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 deri ves 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 ma y 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 particle s. 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, ha ving 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, h ygroscopic 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 particle s 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 NO X 

### SO X 

### 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. Com ponents 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 anthr opogenic elements are derived from combustion, industrial sources, and other man-made sour ces (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 smalle r particles. Heavy metals preferentially associate

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### with fine particles. Fine particles also have th e longest atmospheric residence times. Depending on climate and topography, fine particles may rema in 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 inves tigations 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 merc ury (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 de posits, and organic-rich sedimentary rocks. These natural emissions combine with anthropogenic emissions from such sources as power plants, landfills, sewage sludge, mine wa ste, 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 de gree of hydrothermal activity, and the presence of heat sources (Gustin, 2003, 155816 ). The significance of natu ral Hg sources relative to anthropogenic sources varies geographically. For example, Nevada occurs within a global mercuriferous belt, with area emissions about thre e 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 factor s 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 atmospher e, 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 (with in 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 μ g/m 2 

### /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

.

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## Table 9-21. Factors potentially impo rtant 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 Increa sed 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 revola tilization of mercury in soil limits the buildup of concentrat ion.

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 meteor ology and terrain ma y 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 ab ility to estimate ex posure 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) en ter the biosphere via human activities, including synthetic pesticide application, output of polychl orinated 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 exam ined: 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 th e western U.S. by Hageman et al. (2006, 156509 ) illustrated the deposition and fate of 47 pesticid es 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

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### for the distribution of pesticides in the NPs. Pestic ide 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 cont aminants 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 impor tant area of research. Because they are carcinogenic, PAHs are important environmental cont aminants. 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 site s 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 Al aska varied by an order of magnitude. Samples collected near the city of Fairbanks ge nerally had higher concentrations than samples collected from rural areas. The relative importa nce 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 ch emicals are toxic and are readily transported atmospherically. Use of some has been banned in Eu rope 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 depositi on 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. Th ese topics are covered in detail in the recent 2008 NO X 

### SO X 

### 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 CO 2 

### assimilation, and uncoupling of the oxygen-evol ving complex in desert shrubs exposed to high limestone dust deposition near a limestone quarry in Namibia.

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### Based on the Integrated Forest St udy (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 NO X 

### SO X 

### ISA (U.S. EPA, 2008, 157074 ).



# 9.4. 2.3. Mag nitude of Dry Deposition

Using Vegetation for Estimating Atmospheric Deposition

Whereas direct real-time measurement of deposi tion 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. Th ese 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 assu mption 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 speci es 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 standa rdize 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 upt ake 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 dr y 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 accumula tion probably represent values that fall between wet deposition and total deposition. A European moss biomonitoring network has be en in place since 1990 (Harmens et al., 2007, 155828 ). Sampling surveys are repeated every five years. The survey conducted in 2005/2006

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### occurred in 32 countries at over 7,000 sites. Th e network reports metal concentrations associated with live moss tissue. Trends analysis of these da ta 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 det ected 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 c ontamination 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 Ri dge Mountains, VA, found that metal concentrations in moss were related to elevation and canopy speci es 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 bioi ndicator 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 emi ssions 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 mon itoring 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 par tially 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 th em 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 measur ed by an open collector, although the use of this approach to specifically quantify particulat e deposition is complicated by gaseous deposition to leaf surfaces and, for some elements, l eaching 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

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### provided an alternative to estimating dry deposi tion 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 dep osition of heavy metals. Metal concentrations in leaves were found to be one to three orders of ma gnitude 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 pollu tion 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 polluti on 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 el evation enhance the rate of aerosol impaction. Orographic effects enhance rainfall intensity an d 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 observ ed 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 p eat 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 Cs 137 

### 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 co ncentrations and deposition history can match well for some metals at some sites, but not well for th e same metals at other sites (Roberts et al., 2003, 156051 ). They also found that Zn and Cd accumulati on rates were independent of deposition history at each of three study sites. Metal deposition to soil is also a significant conc ern adjacent to roadways. Urban stormwater can be rich in heavy metals and other contaminan ts 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-deposi ted 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

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### 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 we ll 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 re sponse 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 li ght, 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 dire ct injury of vegetation as compared with most common particulate materials. The dose-response f unctions obtained in early experiments following the exposure of plants to phytotoxic gases gene rally have not been observed following the application of particles (U.S. EPA, 2004, 056905 ).



# 9.4. 3.1. Eff ects of Coarse-mode Particles

The current state of scientific knowledge regardi ng the direct effects of coarse PM on plants has not changed since publication of the prev ious PM criteria assessment (U.S. EPA, 2004, 056905 ). The summary provided here is taken from that repo rt. In many rural areas and some urban areas, the majority of the mass in the coarse particle m ode derives from the elements Si, Al, Ca, and Fe, suggesting a crustal origin as fugitive dust from di sturbed 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 ).

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### The chemical composition of PM is usually the key phytotoxic factor leading to plant injury. For example, cement-kiln dust liberates calcium hyd roxide 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 effect s 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. S ea 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 pr evious PM assessment (U.S. EPA, 2004, 056905 ); a brief overview is presented below. Atmospheric PM can af fect ambient radiation, which can be considered in both its direct and diffuse components. Foliar in terception 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 a ttenuation and by changing the efficiency of radiation interception in the canopy through conversi on 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 indi cated 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. Howeve r, under a future scenario in which SO 4 2– 

### and BC aerosols decline, the diffuse-radiation and the associ ated 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 μ g/g) and are termed “trace” elements or trace metals. Trace metals with a density greater than 6 g/cm 3 

### , 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 particul ate form as metal oxides (Ormrod, 1984, 046892 ). Aerosols containing trace elements derive predomin antly 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

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### 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 le gacy 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 ot her 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 9-177Source: 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. Eff ects 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 chem ical 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 an thropogenic 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 quan tifying the bioavailability of heavy metals in soil. A variety of models, isotopic studies, and se quential 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

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### ability to bind metals in forms that are not bi oavailable. There are various methods available for assessing bioavailability of metals, but soils ar e 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 be st criterion for determining bioavailability. In particular, the replacement of metal-sensitive micr oorganisms 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 meta l concentration tends to decrease with soil depth. The accumulation of heavy metals in soil is influe nced 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 worl d for over a century (1864-1983). The relative distributions of trace metals within the more sol uble 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 mob ile 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 cl ay minerals or organic matter are considered generally unavailable. There is concern that Pb contamination of fore st 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 ( 207 

### Pb) 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 concen trations of metals did decomposing roots show increased metal concentrations over time.



# 9.4. 5.2. Eff ects 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. Ch anges in the soil environment that influence the role of the bacteria and fungi in nutrient cycli ng 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.

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### The soil environment is rich in biota. Bacter ia and fungi are usually most abundant in the rhizosphere, the soil around plant roots that all mi neral 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 inte rfere 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 arylsulf atase 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 accumul ation 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 CO 2 

### 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 lignifi cation 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 co mpared with rural soils (Yuangen et al., 2006, 156174 ).



## Metal Toxicity to Microbial Communities

It is believed that increased accumulation of li tter 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 enumer ation 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 mi croorganisms 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 ac tivity 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 depe nds 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

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### inhibit or stimulate the growth of soil microbes. Populations of Azotobacter spp. decreased, but populations of oligotrophic and copiotrophic bacter ia, actinomyces, and fungi increased in response to heavy metal exposure. Acute metal stress ca uses 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 me tals (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 se lection, gene exchange, and immigration. Comparison between a severely contaminated soil with a similar soil that had much lower amounts of contamination showed considerably lower micr obial 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 th e concept of pollution-induced community tolerance (PICT), which has been demonstrated for populati ons of bacteria and fungi (Davis et al., 2004, 155744 ). These researchers assessed the effects of l ong-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 organi c matter (DOM) reduces bioavailability (Sauvé, 2001, 156948 ). Because organic matter typically decreases w ith 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 a nd interact with other soil microbes. Recent published evidence supports that mycorrizha and bact eria influence plant uptake and tolerance of metals. Mycorrhiza are fungi that colonize plant r oots to form a symbiosis. Mycorrhiza take up nutrients from the soil and transfer them to the pl ant 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 ma y 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 nutrien t 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 a nd 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

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### metals by binding and immobilizing metals and i ndirectly 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 to lerant 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 pr event 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 spe cies. 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 di d 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 sha llow soil layers. Even though atmospheric PM will usually deposit on soils before being taken up by pl ants, it could also be deposited to aquatic systems with subsequent transfer to terrestrial plants. C ontamination of stream sediments by heavy metals can impact adjacent terrestrial ecosystems wh en high flows cause resuspension and subsequent streamside deposition of sediment particle s. 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, carcinogen ic, and teratogenic effects (Hunaiti et al., 2007, 156579 ).



# 9.4. 5.3. Pla nt 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 es sential for plants. Though all heavy metals can be directly toxic at sufficiently high concentrati ons, 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

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### 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, contri bute 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 ca n 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) reacti ons 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 nut rition. A recent review by Taulavuori (2005, 190489 ) suggests that metal- induced stress reduces frost ha rdiness 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 con centrations in stemflow. This may be responsible for the observation that species diversity of lichens is sometimes higher on tr ees 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 factor y 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 leav es, 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 μ g/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 sa me mesocosms in containers of low Hg soil (0.03 ± 0.01 μ g/g) exhibited foliar Hg concentrations that were similar to those of trees grown in Hg-enriched soil. Almost all of the foliar Hg orig inated from the atmosphere. Clearly, plant foliage

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### 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 HgCl 2 

### . 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 fl uxes 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 ecosystem s. 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 be cause of the strong relationship between Hg and organic matter (Grigal, 2003, 155811 ). Arbuscular mycorrhizal (AM) fungi can play im portant 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 ad apted 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 successful ly 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 a nd 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 interac tions between soil contamination and vegetation composition (Becker and Brändel, 2007, 156260 ; Hall, 2002, 190365 ). There are numerous other plants that have been investigated for applicat ion 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 cont aminants (Prasad and DeOliveira, 2003, 156885 ). Plant uptake is often the first step for a meta l 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).

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# 9.4. 5.4. Eff ects 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 CaCO 3 

### production. The production of both particulate organic C and CaCO 3 

### 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 CO 2 

### 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 at mospheric 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 co mponents 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 re lative 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. Eff ects on Animals

There has been little work focusing on animal i ndicators 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 usefu l 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 mi xtures of air pollutants is of particular value because the chemical constituents are difficult to ch aracterize, 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 metallothi oneins, activity of biotransformation enzymes, and peroxisomal proliferation. In addition, indicators of oxidative stress were measured, such as oxyradical scavenging capacity, onset of cellula r 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, a nd 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. a nd 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 terrestri al metal contamination pose an increased risk to amphibians in general. Estuarine and marine bivalves provide potentia l 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 Eur ope. Annual bioaccumulation rates were shown to be strongly correlated with Hg concentrations in suspended particulate matter (SPM), a response to

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### 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 meta l 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 pr oximity (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 th e 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 he alth 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 an d 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. Bio magnification across Trophic Levels

Biomagnification is the progressive accumulati on 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 assign ment 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 biom agnification occurs in a given ecosystem without thoroughly investigating physiological biodynamics, hab itat, food web structure, and trophic position of relevant species. Thus, development of an unde rstanding of ecosystem complexity is necessary to determine what species might be at greatest ri sk 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 mort ality, 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

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### concentrations may also accumulate Pb and Zn. Increa sed 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 primar y 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 R 2 

### = 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 Sa n Francisco Bay food webs. Croteau et al. (2005, 156373 ) identified trophic position of estuarine organism s 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 me tals often preferentially bind to sediment particles. Some of these sediment-bound metals ma y 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 fro m sedimentary particles to biological uptake was negligible. Marine bivalve mollusks bioaccumulate trace meta ls and other contaminants (LaBrecque et al., 2004, 155913 ) and therefore may be used as bioindicator s 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. Eff ects near Smelters and Roadsides

The high PM concentrations in proximity to mi ning, smelting, roadsides and other industrial sources result in heavy metal loadings that may be particularly damaging to nearby ecosystems.

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## 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 decrease d 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. Effect s on plant species occurrence close to the smelter were almost entirely negative. In contrast, some animal species re sponded 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 Harj avalta Cu-Ni smelter. Heavy metal concentrations (except Mn) were highest in bryophytes, followed by li chens, and were lowest in vascular plants. Vascular plants are generally able to restrict the up take 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 sm elter at Harjavalta, SW Finland (Eeva and Lehikoinen, 2004, 155762 ). Availability of Ca-rich food to th e birds was estimated by counting snail shells in the nests postfledging. The number of sn ail shells correlated positively with the Ca concentration of nestling feces and adult breeding su ccess. 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, su ch as was reported above, does not reveal what the impacts might be on ecosystem function. Neve rtheless, the mere fact that multiple species, operating at different trophic levels, have been s hown 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 evalua te effects on function as opposed to abundance of individual species.

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## Roadsides

Heavy metal particles are importa nt 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 spectro meter to characterize heavy metal particles embedded in tire dust. Samples were classified in to four likely source categories, based on cluster analysis. Based on morphology and chemical composition, the samples were identified as having derived from yellow paint (CrPbO 4 

### 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. Effect s 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. Tox icity 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 sm elter near Palmerton, PA. Experimental data had suggested that lichen tolerance to Zn and Cd ge nerally ranges between 200 and 600 ppm (Nash, 1975, 016763 ). The effects of deposited metals on the mosses ha ve not been well studied. Tremper et al. (2004, 156126 ) exposed mosses of two species to roa dside 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 co niferous 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 concentr ations of Al, Fe, Zn, F, Sr, and Pb. This vertical pattern within the tree canopy is in general accord ance 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 pr essure, 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 huma ns, wildlife, or other bi ota (Swackhamer et al., 2004, 190488 ). Certain physical and chemical proper ties 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

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### rapidly in the atmosphere or at the surface s ubsequent 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-quantit atively 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 di verse 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 th e 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 a nd short-wave radiation stress. This epicuticular wax consists largely of long-chain esters, polyest ers, and paraffins, which accumulate lipophilic compounds. Organic air contaminants in the par ticulate or vapor phase can be adsorbed to, and accumulate in, the epicuticular wax of leaf surfa ces. 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 eq uilibrium 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 w ill 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 wei ght 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 conside r 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 th at 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 ;

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### 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 K

ow 

### ); similarly, leaf concentration factors are log-linear functions of log-base d octanol-air partition coefficients (log K

oa 

### ) (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 info rmation available regarding incorporation of atmospherically deposited PAHs into aquatic food we bs. It is known that PAHs can be transferred to higher trophic levels, including fish, and that this tra nsfer 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 biomagnificati on in aquatic ecosystems from atmospheric PM deposition. There are typically no sources of orga nic contaminants in their watersheds, and atmospheric inputs dominate as sources of contamin ation. 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 organi sms. 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 be en 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 manufacturi ng, have been found to accumulate in lichens and mosses collected at King George Island, mari time Antarctica (Yogui and Sericano, 2008, 189971 ). Because contaminant concentrations were not statisti cally different at sites close to and distant from human facilities in Antarctica, the authors concl uded 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 polyc hlorinated 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 volat ilization 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 becau se of their volatility and response to changes in temperature. This “global disti llation theory” (Holmqvist et al., 2006, 190380 ; Wania and Mackay, 1993, 157110 ) predicts that POPs in the northern hemi sphere are generally transported towards the Arctic, and in the southern hemisphere they are tr ansported 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 fi sh, 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 sus ceptible to bioaccumulation of lipophilic persistent

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### 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 de posited 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 pollu tants accumulated by salmon during their ocean residence were effectively transferred 410 km in land 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-b y 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 air borne 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 conducte d analysis of the biological effects of airborne contaminants in seven ecosystem compar tments: air, snow, water, sediments, lichens, conifer needles and fish. The goals were to identif y where the pollutants were accumulating, identify ecological indicators for those pollutants causing ecol ogical harm, and to determine the source of the air masses most likely to have transport ed 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. Conife r 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 concen tration 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 orde rs 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 ha s increased since the pre-organic pollutant era. Additionally, elevated concentrations of vitellogeni n, a female protein involved in egg production, were found in male fish from three sites, and dir ectly 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 m ost parks. The average mercury concentration in fish from one site and individual fish from three addi tional sites exceeded th e 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 ga s-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 eff ects to metabolic processes of plant foliage; contribution to total metal loading resulting in a lteration of soil biogeochemistry and microbiology, plant and animal growth and reproduction; and contribution to total organics loading resulting in bioaccumulation and biomagnification across trophic leve ls. 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 phytot oxic responses and ecosystem effects. Moreover, many

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### 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 leve ls 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 indust rial 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 turb idity 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 radia tion is more uniformly distributed throughout the canopy and may increase canopy photosynthetic produc tivity by distributing radiation to lower leaves. The enrichment in photosynthetically active ra diation (PAR) present in diffuse radiation may offset a portion of the effect of an increased atmo spheric 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 bi ological interaction in nature. It is inhabited by microbial communities of bacteria, fungi, and ac tinomycetes, 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 ecosyst em 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 phytotoxi city 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 of ten deposited by wet deposition, are more likely to contain elements such as Ca, Cr, Pb, Ni, an d V. Ecosystems immediately downwind of major emissions sources can receive locally heavy depositi on inputs. Phytochelatins produced by plants as a response to sublethal concentrations of heavy meta ls are indicators of metal stress to plants. Increased concentrations of phytochelatins across regions and at greater elevation have been associated with increased amounts of fore st 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 diff icult to characterize the nature and magnitude of effects and to quantify relationshi ps between ambient concentrations of PM and ecosystem response due to significant data gaps and unc ertainties as well as considerable variability that exists in the components of PM and their various ecological effects.

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# 9.5. Effects on Materials

Effects of air pollution on materials are related to both aesthetic appeal and physical damage. Deposited particles, primarily carbonaceous com pounds, cause soiling of building materials and culturally important items, such as statues and wo rks of art. Physical damage from dry deposition of PM also can accelerate natural weathering processe s. The major deterioration phenomenon affecting building materials in response to atmospheric depo sition is most likely sulfation, leading to secondary salt crystallization which forms gypsum (Marinoni et al., 2003, 092520 ). This section (a) summarizes information on e xposure-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) biodeterior ation 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 temp erature, and air pollution often enhance the biodeterioration hazard. Herrera and Videla (2004, 155843 ) concluded that heterotrophic bacteria, fungi, and cyanobacteria were the main microbial co lonizers of buildings that they investigated in Latin America. Their analyses suggested that the ma jor 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 Mede llin, 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 Plat a, 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 fro m opaque materials and reduces the transmission of light through transparent materials. Soiling can re present a significant detrimental effect, requiring increased frequency of cleaning of glass windows a nd concrete structures, washing and repainting of structures, and, in some cases, reduces the useful lif e 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 orie ntation 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 heri tage. The observed mass of deposited particles mainly belonged to the coarse particulate mode. Th e sampler collects particles from all directions. It replicates at least some of the complexity of par ticle 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 nuisa nce 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 interact ions between the extent of soiling, architecture, and aesthetics (Grossi and Brimblecombe, 2004, 155813 ). One of the most significant air pollution dama ge features affecting urban buildings and monuments is the formation of black crusts. Quan tification 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,

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### whereas OC may derive from multiple sources, including atmospheric deposition of primary and secondary pollutants, and the decay of protec tive organic treatments (Bonazza et al., 2005, 155695 ). Bonazza et al. (2005, 155695 ) quantified OC and EC in damage la yers 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 NO 2 

### 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 co lors. 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 contras ting sites in Great Britain over periods of one to eight years. Spectrophotometer and microscope obse rvations 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 mo lecular 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 μ m) initially contribute more soiling of horizontal and vertical painted surfaces than do fine-mode particles (<2.5 μ m), 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 moistu re 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. It s effectiveness in retarding the corrosion process is affected by the solubility of the corrosion laye r and the pollutant exposure. Other than the effects of acidifying compounds, there has not been additi onal research conducted in recent years on the effects of PM deposition on metal corrosion.

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# 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 sp ecific surface, mortars have a high potential for reacting with environmental pollutants (Zappia et al., 1998, 012037 ). Most research evaluating the effects of air po llutants 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, espe cially 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 thre e sources: sea spray, chemical alteration of the carbonate in mortar into SO 4 2– 

### salts by acidic deposition, and wet deposition of air pollutants on the stone surface. The salt content on the rock surface f ills 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, CaSO 4 

### and Na 2 

### SO 4 

### 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, a nd paints) undergo natural weathering processes from exposure to environmental elements (wind, mo isture, 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, formati on 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 qu antify 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 respe ct 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.

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# Chapter 9 References

Abdou W; Diner D; Martonchik J; Bruegge C; Kahn R; Gaitle y B; Crean K; Remer L; Holben B (2005). Comparison of coincident Multiangle Imaging Spectroradiometer and M oderate Resolution Imaging Spectroradiometer aerosol optical depths over land and ocean scenes containing Aero sol 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 de pendence of cloud condensation nucleus concentration on formation rate. Nature, 367: 445-447. 189975

Ackerman AS; Toon OB; Stevens DE; Heymsfield AJ; Ramana than 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 pa rticles 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, a nd 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 Pollu tants 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 centur y 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 doc ument 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 9-197Anderson 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 clim ate 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 sa tellite 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 car bon 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 optic al properties. J Geophys Res, 109: D06208. 190058

Andronova NG; Rozanov EV; Yang F; Schlesinger ME; Stench ikov 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 descripti on. 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). Record ing of pre-industrial atmospheric environment by ancient

crusts on stone monuments. At mos Environ, 32: 2859-2863. 040480

Avila A; Rodrigo A (2004). Trace metal fluxes in bulk depositi on, throughfall and stemflow at two evergreen oakstands in NE Spain subject to different exposure to the i ndustrial 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 estima ted 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 Re s, 105: 11805-11811. 155670

Baker M; Charlson R (1990). Bistability of CCN concentratio ns and thermodynamics in the cloud-topped boundary layer. Nature, 345: 142-145. 190016

Balkanski Y; Schulz M; Claquin T; Guibert S (2007). Reevaluati on 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 9-198Baran AJ; Foot JS (1994). New application of the operational sounder HIRS in determining a climatology of sulphuric acid aerosol from the Pinatubo erupti on. J Geophys Res, 99: 25673-25679. 192032

Barker ML (1976). Planning for environmental indices: obser ver 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 m easurements of stratospheric aerosol over Mauna Loa Observatory. Geophys Res Lett, 24: 1923?1926. 192044

Bates T; Anderson T; Baynard T; Bond T; Boucher O; Carmic hael G; Clarke A; Erlick C; Guo H; Horowitz L (2006).

Aerosol direct radiative effects over the northwest Atlantic , northwest Pacific, and Nort h Indian Oceans: estimates based on in-situ chemical and optical measurements and ch emical 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 associat ed 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; Cove rt 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 co mparisons. 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; Ost hoff 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 Sc i 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-en vironment relationships in a heavy metal-dry grassland complex. Folia Geobot,

42: 11-28. 156260

Beeby A; Richmond L (2002). Evaluating Helix aspersa as a se ntinel for mapping metal pollution. Ecol Indicat, 1: 261-270.

155680

Bellouin N; Boucher O; Haywood J; Reddy MS (2005). Global emi ssions of aerosol direct radi ative forcing from satellite measurements. Nature, 438: 1138-1141. 155684

Bellouin N; Boucher O; Tanré D; Dubovik O (2003). Aero sol 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 es timate of aerosol direct radiative forcing from satellite observations and comparison against the Hadley Ce ntre 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 9-199Bidleman TF; Jantunen LMM; Helm PA ; Bronstrom-Lunden E; Junt to 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. Be lmont, CA: Wadsworth Publishing Company, Inc. 034165

Bingen C; Fussen D; Vanhellemont F (2004). A global climatol ogy 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 oxi des of nitrogen emission reductions on fine particulate matter mass concentrations: regiona l 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; St reets D; Trautmann N (2007). Hi storical emissions of black and organic carbon aerosol from energy-related combusti on, 1850-2000. Global Biogeochem Cycles, 21: GB2018.

190050

Bond TC; Anderson TL; Campbell D (1999). Calibration and Inte rcomparison 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; Klim ont Z (2004). A technology-based global inventory of black and organic carbon emissions from co mbustion. 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 pert urbation 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). Decomp osition in soil microcosms of leaves of the metallophyte Arabidopsis halleri: Effect of leaf-associated heavy metals on biodegradation. E nviron Pollut, 135: 187-194.

155699

Brasseur G; Granier C (1992). Mount Pinatubo aerosols, ch lorofluorocarbons, 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 expe riment. Tellus B Chem Phys Meteorol, 52: 815-827.

189966

Brimblecombe P; Grossi CM (2005). Aesthetic thresholds an d blackening of stone buildings. Sci Total Environ, 349: 175-

189. 155703

Broccoli AJ; Dixon KW; Delworth TL; Knutson TR; Stouffe r RJ; Zeng F (2003). Twentieth-century temperature and precipitation trends in ensemble climate simulations in cluding 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 Bene fit 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 po ssible role of aerosols on stomatal conductivity for water vapour. Basic Appl Ecol, 2: 351–364. 155708

December 2009 9-200Burt 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 Stress ors. Environ Behav, 15: 355-380. 190172

Camuffo D (1995). Physical weathering of stones. Presented at In: Saiz-Jimenez, C., ed. Th e deterioration of monuments: proceedings of the 2nd international symposium on biodete rioration and biodegradation; January 1994; Sevilla, Spain. Sci. Total Environ. 167: 1-14. 076278

Canada-US Air Quality Committee (2004). Canada-United Stat es transboundary particulate matter science assessment. Meteorological Service of Canada. Toronto, Ontario, Canada. En56-203/2004E. http://www.mscsmc.ec.gc.ca/saib/smog/tr ansboundary/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 inte rcomparison of long-range transport a nd 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 Yose mite National Park. Atmos Environ, 39: 1393-1404. 190052

Carvalho LM; Caçador I; Martins-Loução MA (2006). Arbuscu lar 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 Sc ience, 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 atmospheri c aerosols and regional haze on agriculture: an opportunity to enhance crop yields in China th rough 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 di rect climate forcing. J Geophys Res, 113: D13206.

189974

Charlson R; Pilat M (1969). Climate: the influen ce 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 generalit y 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). Sensitiv ity of multiangle imaging to the optical and microphysical properties of biomass burning aerosol s. 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, ic e, fog, seawater and surface microlayer in the Bering and Chukchi Seas. Mar Pollut Bull, 32: 410-419. 155726

December 2009 9-201Chestnut LG; Dennis RL (1997). Economic be nefits 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). Gene va: 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 de rived 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). Co mparability between PM25 and particle light scattering

measurements. Environ M onit 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 ef ficiency 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 shortwav e 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). Satell ite-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; Kawa moto K (2002). Cloud susceptibility and the first aerosol indirect forcing: Sensitivity to black carbon an d aerosol concentrations. J Geophys Res, 107: 4564. 199912

Chung C; Zhang G (2004). Impact of absorb ing 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 an thropogenic aerosol direct forcing derived from satellite and ground-based observations. J Geophys Res, 110: D24207. 155733

Chung SH; Seinfeld JH (2002). Global distribution and clim ate forcing of carbonaceous aerosols. J Geophys Res, 107:

4407. 155732

Citterio S; Prato N; Fumagalli P; Aina R; Massa N; Sa ntagostino 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 9-202Clarke 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). Estu ar 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; Ze nder C (2001). Simulating aerosol s using a chemical transport

model with assimilation of satellite aerosol retrie vals: 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). Constr uction of a 1 ° x 1 ° fossil fuel emission data set for carbonaceous aerosol and implementation and radiative im pact 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 characterizati on 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 upt ake 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 re lationship 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 zi nc 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; Gerh art 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). Contaminatio n 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: Quas i 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 crypt ogamic communities. Environ Pollut, 129: 289-297. 155742

December 2009 9-203Da Silva LC; Oliva MA; Azevedo AA; De Araujo JM (2006). Re sponses 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 comm unity 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 vari aibility of haze and its constituents in the United States: Report IV. 1Cooperative Institute for Research in the At mosphere. 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 particulat e 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 Cha nge 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth AssessmentReport of th e 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; Lawr ence 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 atmo spheric 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 vicini ty 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 Lara mie, Wyoming (41°N), using balloon-bo rne 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 m easurements. 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). Performa nce 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 experi ment overview. IEEE Trans Geosci Remote Sens, 36: 1072-1087. 189962

December 2009 9-204Di 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). Arbuscula r mycorrhiza enhanced arsenic resistance of both white clover (Trifolium repens Linn.) and ryeg rass (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 fo r 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; Si nyuk 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). Accur acy assessments of aerosol optical properties retrieved from AERONET sun and s ky 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 cl oud-nucleating ability of aerosol particles. Science,

312: 1375-1378. 155756

Eagan RC; Hobbs PV; Radke LF (1974). Meas urements of cloud condensation nuclei a nd 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, a nd 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; Sl utsker 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 Gl obal 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 ne ighborhood. Risk Anal, 19: 621-634. 010716

December 2009 9-205Ely DW; Leary JT; Stewart TR; Ross DM (1991). The establis hment 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 Ma nagement Association; paper no. 91-48.4. 056599

Englert N (2004). Fine particles and human health - a revi ew 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). Th e 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 Beha vior. 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 Inte raction 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). Biot ransport of organic pollutants to an inland Alaska lake by migrating sockeye salmon (Oncorhync hus 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 gl obal warming" by M. Z. Jac obson. J Geophys Res, 108:

4767. 155772

Feingold G (2003). Modeling of the first indirect effect: An alysis of measurement require ments. 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 suppressi on 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 proper ties. 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 so il metals to wheat. Chemosphere, 59: 939-949. 155774

Ferm M; Watt J; O'Hanlon S; De Santis F: Varotsos C ( 2006). Deposition measurem ent 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; Schw artz 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). Appli cation of empirical orthogonal functions to evaluate ozone simulations with regional and global models. J Geophys Res, 108: 4431. 047805

December 2009 9-206Fishman 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 so ils. 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 humidit y. 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). Change s in atmospheric constituents and in radiative forcing, Chapter 2. In Solomon S, Qin D; Manning M; Chen Z; Ma rquis 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 abso rption 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 tr ees: 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 ef fects of a Cu-Ni smelter in southwestern Finland. Biol Fertil Soils, 8: 87-94. 079635

Fryer ME; Collins CD (2003). Model intercomparison for the upt ake 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 translo cation 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 AV HRR retrievals of aerosol properties over the ocean for the peri od of NOAA-9 observations and preliminary retrieva ls 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 9-207Gillett NP; Zwiers FW; Weaver AJ; Hegerl GC; Allen MR; Stott PA (2002). Detecting anthropogenic influence with a multi-model ensemble. J Geop hys 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 flui d dynamics laboratory coupled model CM2.1 for present climate. J Geophys Res, 111: D22210. 190582

Gitay H; Brown S; Easterling W; Jallow B (2001). Ecosyste ms 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 vegeta tion. Environ Manage, 40: 1016-1024. 155794

Goforth MR; Christoforou CS (2006). Particle size distribution a nd 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 arbuscula r 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 syst em of the land snail Helix aspersa. Bull Environ Contam Toxicol, 64: 434-442. 155798

Gomot-De Vaufleury A; Pihan F (2002). Methods for toxicity a ssessment of contaminated soil by oral or dermal uptake in land snails: Metal bioavailability and bioaccum ulation. 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). Accumu lation 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 concentr ations 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 superparam eterization. [embedding of cloud-resolving model in each column of large-scale model for small-scale and mesos cale processes representation] . J Atmos Sci, 61: 1940-1952.

190590

Grabowski WW; Wu X; Moncrieff MW (1999). Cloud resolvin g 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. At mos 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 9-208Grantz DA; Garner JHB; Johnson DW (2003). Ecological ef fects 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 park ing 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 te rrestrial watersheds: a review. Environ Rev, 10: 1–39. 156498

Grigal DF (2003). Mercury sequestration in forests a nd 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 e nvironments. 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: fo rmation, transport, control, and effects on plants. New York: Springer-Verlag. 019325

Gunn R; Phillips BB (1957). An experimental investigation of th e 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 de toxification and tolerance. J Exp Bot, 53: 1-11. 190365

Halsall CJ (2004). Investigating the occurre nce of persistent organic pollutants (POPs) in the Arctic: their atmospheric

behaviour and interaction with the seasona l 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 Equati on for Estimating Ambient Light Extinction CoefficientsFinal Report. 156517

Hand JL; Malm WC (2007). Review of aerosol mass scatteri ng efficiencies from ground-base d measurements since 1990. J Geophys Res, 112: D16203. 155825

Hansen AC; Zhang Q; Lyne PWL (2005). Ethanol-diesel fu el blends--a review. Bioresour Technol, 96: 277-285. 059087

December 2009 9-209Hansen 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 forci ng 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; Bi shop L; Logan J; Thompson A; Stolarski R; Lean J; Willson R; Levitus S; Antonov J; Rayner N; Parker D; Chri sty 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). Clim ate 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 fo rcings. 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; R ühling A (2007). Temporal trends in the concentration of arsenic, chromium, copper, iron, nickel, vanadium a nd 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 multif ilter rotating shadow-band radi ometer: an instrument for optical depth and radiation measurements. Appl Opt, 33: 5118-5125. 045805

Harvey LDD (2004). Characterizing the annual-mean climatic ef fect 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 f actors. 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 exte rior paints at a rural site. Aerosol Sci Technol, 13: 356-

367. 044579

Haywood J; Boucher O (2000). Estimates of the direct and indir ect 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 9-210Haywood 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; Hees e 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; SeinfeldJH; Liao H; Weber RJ (2005). A large organic aerosol source in the free troposphere missing from curren t models. Geophys Res Le tt, 32: L18809.1-L18809.4. 190603

Heintzenberg J (2008). The SAMUM-1 experiment over Sout hern Morocco: overview and introduction. Tellus B Chem Phys Meteorol, 61: 2-11. 190605

Helena P; Franc B; Cvetka RL (2004). Monitoring of shor t-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 α

hexachlorocyclohexane 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; Mälköne n E (1999). Fine-root growth, mortality and heavy metal

concentrations in limed and fertilized Pinus silvestris (L.) stands in the vicini ty of a Cu-Ni smelter in SW Finland. Plant Soil, 209: 193-200. 155836

Henze DK; Seinfeld JH (2006). Global secondary organi c aerosol from isoprene oxidation. Geophys Res Lett, 33:

L09812.1-L09812.4. 190606

Herman JR; Bhartia PK; Torres O; Hsu C; Seftor C; Celari er 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. Sc i 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 Cent ral California. Aerosol Sci Technol, 40: 827-844. 135981

Herrera LK; Videla HA (2004). The importance of atmos pheric effects on biodeterioration of cultural heritage

constructional materials. Int Biodete rior 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 fore st 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 changi ng boreal fire regime. J Geophys Res, 108: 8153. 155845

Hobbelen PHF; Koolhaas JE; van Gestel CAM (2006). Bioaccu mulation of heavy metals in the earthworms Lumbricus rubellus and Aporrectodea caliginosa in relation to total a nd available metal concentrations in field soils. Environ Pollut, 144: 639-646. 190371

Hodzic A; Madronich S; Bohn B; Massie S; Menut L; Wiedin myer C (2007). Wildfire particulate matter in Europe during summer 2003: meso-scale modeling of smoke emissions, tran sport 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): Septem ber- October, 1991. J Geophys Res, 101: 1641-1653. 190607

Hoell JM; Davis DD; Liu SC; Newell RE; Akimoto H; McN eal RJ; Bendura RJ (1997). The Pacific exploratory missionwest phase B: February-March, 1994. J Geophys Res, 102: 28223-28239. 057373

December 2009 9-211Hoff 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 Sc iences (pp. 281-284). Val-Béla ir, Quebec: Def. R&D Can.

Valcartier. 190612

Hofmann D; Barnes J; Dutton E; Deshler T; Jäger H; K een R; Osborn M (2003). Surface-ba sed observations of volcanic emissions to stratosphere . In Volcanism and the Eart h’s Atmosphere (pp. 57–73). Washington, DC: American Geophysical Union. 192062

Hoh E; Zhu L; Hites RA (2006). Dechlorane plus, a chlorinate d 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 in strument 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-industr ial 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 concentrat ions of tropospheric ozone and aerosols: Methodology, ozone evaluation, and sensitivity to aerosol wet re moval. 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 hydro carbons 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 pr operties 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; Silv estrov NA; Von Deutsch DA; Bayorh MA (2007). Toward understanding the influence of soil metals and sulfate conten t on plant thiols. J Toxicol Environ Health A Curr Iss,

70: 559-567. 156579

December 2009 9-212Huneeus N; Boucher O (2007). One-dimensional variational re trieval 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 Cana dian Arctic atmosphere declining? Evidence from 5 years of monitoring. Environ Sc i 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). Characterizati on of tropospheric aerosols over the oceans with the NOAA advanced very high resolution radiometer optical th ickness operational product. J Geophys Res, 102: 16,889- 16,909. 045900

IPCC (1995). Radiative forcing of climate change and an ev aluation 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 Clim ate 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 Ch ange [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 Cy cles, 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 pollu tion and depressive symptomatology: Exploratory analyses of intervening psychosocial fact ors. 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 globa l 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 mixe d-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 "C ontrol 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 fo ssil 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 inclusi ons within clouds and precipitation on global climate. J Phys Chem B, 110: 6860-6873. 156599

December 2009 9-213Jacobson 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 dies el 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). Ar ch 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 Specia tion Nationwide Network Program. Presented at Specialty

Conference of the American Association for Aerosol Resear ch, Pittsburgh, PA, April, 2003, Research Triangle Park,

NC. 156605

Jayne JT; Leard DC; Zhang X; Davidovits P; Smith KA; Kolb CE; Worsnop DR (2000). Devel opment of an aerosol mass spectrometer for size and composition analysis of s ubmicron particles. Aerosol Sci Technol, 33: 49-70. 190978

Jeong MJ; Li Z; Chu DA; Tsay SC (2005). Quality and Compat ibility 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). Adsorp tion 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 precipita tion along the Front Range of the Rocky Mountains. J Appl Meteorol, 45: 236-245. 156612

Johnson D; Hale B (2008). Fine root decom position 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). Bioaccu mulation of 2,4,6-trinitrotoluene and polychlorinated biphenyls through two routes of exposure in a terrestri al amphibian: Is the dermal route significant? Environ Toxicol Chem, 18: 873–878. 155880

Jones A; Roberts DL; Woodage MJ (2001). Indirect sulphate aero sol 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 expl osive 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 9-214Joynt J; Bischoff M; Turco R; Konopka A; Nakatsu CH (2006). Microbial community analysis of soils contaminated with lead, chromium and petroleum hydr ocarbons. Microb Ecol, 51: 209-219. 155887

Junker C; Liousse C (2008). A global emission inventory of car bonaceous aerosol from historic records of fossil fuel and biofuel consumption for the period 1860-1 997. Atmos Chem Phys, 8: 1195-1207. 190971

Kahn R; Banerjee P; McDonald D (2001). The sensitivity of mu ltiangle 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 ofmultiangle imaging to aerosol optical depth, and to pureparticle size distribution and compositi on 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 AE RONET 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 weatheri ng 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 h eavy 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 Br ekhovskikh 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 aeros ol absorption. J Geophys Res, 92: 4307-4317. 190960

Kaufman Y; Boucher O; Tanré D; Chin M; Remer L; Ta kemura 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; Schm id 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 cl oud cover. Science, 313: 655-658. 190951

December 2009 9-215Kaufman 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; Me nzel 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 Contr oversies 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 respons e 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. seed lings. 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 partic le 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 ef fect 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 sens ing of tropospheric aerosols fro m 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 proper ties 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 meas urement 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). Glaciolog ical 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 forc ing by process-parametrized aer osol optical properties. J Geophys Res, 107: 4433. 199893

Kleeman MJ; Hughes LS; Allen JO; Cass GR (1999). Source c ontributions 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 frac tion over ocean. J Geophys Res, 110: 1-16. 190175

December 2009 9-216Kleinman 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; Plattn er 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 pr ojections 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 fr om particular source regions and sectors. J Geophys Res, 112: D02205. 190185

Koch D; Hansen J (2005). Distant origins of Arctic bl ack 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, a nd 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). Measurem ent 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 poten tial 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 w ith U.S. agriculture: A review. J Air Waste Manag Assoc, 58: 986-993. 198696

Kruysse A (1971). Acute inhalation toxicity of acrolein in ha msters. 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 coppe r. Plant Soil Environ, 50: 243–249. 156662

Kuki KN; Oliva MA; Costa AC (2009). The simulated effect s of iron dust and acidity dur ing the early stages of establishment of two coastal plant species. Water Air Soil Pollut, 196: 287-295. 190411

December 2009 9-217Kuki 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 To tal Environ, 403: 207-214. 155346

LaBrecque JJ; Benzo Z; Alfonso JA; Cordoves Manuelita Qu intal 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; Ravish ankara 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, plas mid 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 fro m a destroyed pesticide store near Hargeisa (Somaliland) assessed during the dry season, using re ptiles 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 ai rborne contaminants in western nationa l 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 dens ity functions. J Atmos Sci, 58: 1978-1996. 190212

Latimer DA; Ireson RG (1980). Workbook for estimating visibili ty 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 betw een aerosol and Asian monsoon rainfall, and circulation. Geophys Res Lett, 33: L21810. 190226

Lau K; Kim M; Kim K (2006). Asian summ er 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 a nd trends of polybrominated diphenylethers and other brominated flame retard ants 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 synthesi s 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 st ratus during the 1993 North Atlantic Regional Experiment: Factors controlling cloud droplet number c oncentrations. 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 9-218LeBlanc GA (1995). Trophic-level differences in the bioc oncentration of chemicals: Implications in assessing environmental biomagnification. Environ Sci Technol, 29: 154-160. 155921

Lee JK (2006). Toxicity and tissue distribution of magneti c 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 s cavenger 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 po llution crossroads over the Mediterranean. Science, 298:

794-799. 190361

Léon J; Tanré D; Pelon J; Kaufman Y; Haywood J; Chaten et B (2003). Profiling of a Sa haran 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 Inte rnational Aerosol Precipitation Science, Assessment Group (IAPSAG). In Aerosol pollution impact on precipitation: A sc ientific review (pp. 482). Geneva, Switzerland: World Meteorological Organization. 190375

Levine JS; Pinto JP (1998). The production of CO by biomas s burning. Presented at Proceedings of the International Conference on Atmospheric Carbon Monoxide and It s 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). Se cond-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 β

hexachlorocyclohexane to the western Arctic Ocean: a contrast to α HCH. 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 sect ion 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 ch emistryaerosol interactions on direct radiative forcing by anthropogenic aerosols and ozone. J Geophys Res, 110: D18208. 199892

Lin CJ; Pehkonen SO (1999). The chemis try of atmospheric mercury: a re view. Atmos Environ, 33: 2067-2070. 190426

Lin H; Tao S; Zuo Q; Coveney RM (2007). Uptake of polyc yclic aromatic hydrocarbons by ma ize plants. Environ Pollut,

148: 614-619. 155933

Lindberg SE; Brooks S; Linn CJ; Scott KJ; Landis MS; Steven s RK; Goodsite M; Richter A (2002). Dynamic oxidation of gaseous mercury in the Arctic troposphere at pol ar sunrise. Environ Sc i 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/Interna tional 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 respons e to high zinc concentrations of two registered poplar clones. Environ Pollut, 153: 137-147. 155935

December 2009 9-219Liou KN; Ou S-C (1989). The role of cloud microphysical pro cesses 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 Geop hys Res, 101: 19,411-19,432. 078158

Liu H; Pinker R; Holben B (2005). A global view of aeros ols 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 He lper 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-ba sed measurements. J Geophys Res, 111: D20212. 190422

Liu X; Penner J; Das B; Bergmann D; Rodriguez J; Straha n S; Wang M; Feng Y (2007). Uncertainties in global aerosol simulations: Assessment using three meteorolog ical 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 par ticulate matter component concentrations and size distributions using satellite-retrieve d 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 ef fect 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). Globa l indirect aerosol effects: a re view. Atmos Chem Phys, 5: 715-737. 155942

Lohmann U; Feichter J; Chuang CC; Penner JE (1999). Predic tion 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 ro le 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; Christense n J; Easter R; Feichter J; Jeuken A; Kjellstrom E; Koch D; Land C; Rasch P; Roel ofs GJ (2001). Vertical distri butions 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; He rnández 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; Herná ndez 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 cattl e 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 extincti on reconstruction in IMPROVE. J Air Waste Manag Assoc,

53: 1109-1120. 156712

December 2009 9-220Lowenthal 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; Flag an 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 mo ss 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 th e 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 Qu iet in National Parks and Wilderness Areas: Psychological Considerations. Environ Behav, 36: 5-31. 180255

Mackay D (1991). Multimedia environmen tal models: the fugacity approach . Chelsea, MI: Lewis Publishers. 042941

Macleod M; McKone TE; Mackay D (2005). Mass balance fo r 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 hea vy metals. Nutr Cycling Agroecosyst, 55: 239-268. 155961

Malm W; Bell P; McGlothin GE (1984). Field testing a me thodology 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 At mosphere. Fort Collins, CO.

025037

Malm WC; Day DE (2001). Estimates of aerosol species scatteri ng characteristics as a functio n 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 an d optical properties of aerosols collected in the IMPROVE program. Atmos Envi ron, 41: 3407-3427. 155962

Malm WC; Pitchford ML (1997). Comparison of calculated sulfate scattering ef ficiencies as estimat ed 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 9-221Malm 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 ef fects 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 productivit y. 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 aeros ol 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 us ing multiangle imaging. IEEE Trans Geosci Remote Sens,

36: 1266-1281. 190484

Martonchik JD; Diner D; Kahn R; Verstraete M; Pinty B; Go rdon 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 tube rculata) 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 spectrom eter (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, at mospheric 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). Th e effects of sulphur gas and elemental sulphur dust deposition on Pinus contorta x Pinus banksiana: Cell wa lls and water relations. Environ Pollut, 76: 43-50. 155974

McComiskey A; Feingold G (2008). Quantifying error in the radi ative 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 sensitivit y to inputs. J Geophys Res, 113: D09202. 190523

McCormick M; Trepte C (1987). Polar stratospheric opti cal 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 9-222McDade C (2004). Summary of IMPROVE nitr ate measurements. Retr ieved 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; Weinga rtner 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 aeros ol 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 accumula tion 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 effect s 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 optic al depth in the Atmospheric Radiation Measurement and Quantitative Li nks program. J Geophys Res, 106: 12099-12108. 190537

Middleton WEK (1952). Vision through the atmosphere. Tor onto, ON, Canada: University of Toronto Press. 016324

Mie G (1908). Beiträge zur Optik trüber Medien, spezie ll 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 Re port models. J Geophys Res, 111: D18101. 192258

Millhollen AG; Gustin MS; Obrist D (2006). Foliar mercury acc umulation 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 radiativ e effects of anthropogenic su lfate 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 to tal solar irradiance. Bull Am Meteorol Soc, 88: 677-691.

190543

Mishchenko MI; Geogdzhayev IV (2007). Satellite remote sens ing reveals regional tropospheric aerosol trends. Optics Express, 15: 7423-7438. 190545

Mishchenko MI; Geogdzhayev IV; Cairns B; Rossow WB; Laci s 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; Carl son BE; Lacis AA; Liu L; Travis LD (2007). Long-term satellite record reveals likely recen t aerosol trend. Science, 315: 1543. 190542

December 2009 9-223Mitchell JM Jr (1971). The effect of atmospheric aerosols on c limate 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). Ov erview of MILAGRO/INTEX-B Campaign. International Global Atmospheric Chemistr y (Newsletter). Seattle, WA.http://www.igac.noaa.gov/newsle tter/igac38/Apr_2008_IGAC_38.pdf. 192019

Moody E; King M; Platnick S; Schaaf C; Gao F (2005). Spat ially 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 a nd 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 atmo sphere. Atmos Environ, 32: 967-982. 040485

Moses CA; Smith BJ (1994). Limestone weathering in the supr atidal zone: an example from Mallorca. In DA Robinson; RBG Williams (Ed.),Rock weathering and landform evol ution (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 PARC A 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 Cana dian 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 pho tosynthetic 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; Kamiya m 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 compar tments 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 9-224Nieminen 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 eff ects 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 me tal 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 aerosol s. 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: expandi ng 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 st ressed 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). Concentr ations 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 a nd 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). Di stribution 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 h eavy 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 To tal 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 contaminat ion 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 9-225Padmavathiamma 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 fo r 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 hydr ocarbons (PAHs) by plant and earthworm species. Chemosphere, 64: 609-618. 156014

Patadia F; Gupta P; Christopher SA (2008). First observationa l estimates of global clear-s ky shortwave aerosol direct radiative effect over land. J Geophys Res, 35: L04810. 190558

Patra M; Bhowmik N; Bandopadhyay B; Sharma A (2004). Compar ison 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 ef fects. 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 aer osol from biomass burning on the global radiation budget. Science, 256: 1432-1433. 045825

Penner JE; Eddleman H; Novakov T (1993). Towards the deve lopment 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 dept h 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 dyna mic 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; J ones NP; Spencer C (1995). The recent de positional 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 a nd microphysical process rates in large-scale models. J Geophys Res, 105: 27059-27065. 190565

Pinker R; Zhang B; Dutton E (2005). Do satellites detect tr ends in surface solar radiation? Science, 308: 850-854. 190569

Piol MN; López AG; Miño LA; Dos Santos Afonso M; Verr engia Guerrero NR (2006). The impact of particle-bound cadmium on bioavailability and bioaccumulation: A pr agmatic approach. Environ Sci Technol, 40: 6341-6347.

156028

Pitchford M; Green M; Kuhns H; Tombach I; Malm W; Scr uggs M; Farber R; Mirabella V; White WH; McDade C (1999). Project MOHAVE Final Report. 156873

December 2009 9-226Pitchford M; Maim W; Schichtel B; Kumar N; Lowentha l 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). Developmen t and applications of a standard visu al index. Atmos Environ, 28: 1049-1054.

044922

Pitchford ML; Polkowsky BV; McGown MR; Malm WC; Mole nar 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). Pittsbur gh, 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 Volcanis m 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 Atla ntic Ocean and Mediterranean: an overview. In Guerzoni S; Chester R (Ed.),The Impact of Desert Dust Across th e 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 processe s 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: chem ical 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 sola r radiation under cloud-free skies in China: The role of aerosols. Geophys Res Lett, 34: L12804. 190572

Quaas J; Boucher O (2005). Constraining th e 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 ef fects 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 tota l 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 aer osol-climate research : A review and fu ture needs for addressing aerosol direct shortwave radiative forcing. Contrib Atmos Phys, 69: 547-577. 192021

Quinn PK; Bates TS (2003). North Ameri can, 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 9-227Quinn 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 propertie s 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 funga l 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). Ae rosols, climate and the hydrological cycle. Science, 294:

2119-2124. 042681

Ramanathan V; Crutzen PJ; Lelieveld J; Mitra AP; Althau sen 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-As ian 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 Assessme nt 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; S oden 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). Vali dation 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 EA G; 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 st ress: 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 9-228Read 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 eco toxicologic 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 Arab ian 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 appro ach. 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). Urba n/industrial aerosol: ground ba sed 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 fro m 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, In c.. San Rafael, CA. STI-996231-1610-FR. 190476

Rind D; Perlwitz J; Lonergan P (2005). AO/NAO response to clim ate 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 trans ition—comparison of modeled

and measured CCN concentrations . Atmos Chem Phys, 4: 2119-2143. 190225

Roberts DL; Jones A (2004). Climate sensitivity to black carbo n aerosol from fossil fuel combustion. J Geophys Res, 109:

6202. 156052

Roberts ES; Richards JH; Jaskot R; Dreher KL (2003). Oxidat ive 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 emissi ons and photochemical aging. Science, 315: 1259-1262.

156053

December 2009 9-229Roderick ML; Farquhar GD (2002). The cause of decreased pa n evaporation over the past 50 years. Science, 298: 1410-

1411. 042788

Roeckner E; Stier P; Feichter J; Kloster S; Esch M; Fisc her-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 agri cultural topsoils in urban ar ea. Environ Geol, 43: 795-805.

156055

Rood MJ; Covert DS; Larson TV (1987). Hygroscopic properties of atmospheric aerosol in Rive rside, California. Tellus B Chem Phys Meteorol, 39B: 383-397. 046397

Rosenfeld D (2000). Suppression of rain and snow by ur ban 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 prec ipitation 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 valuat ion of good visual air quality by national park visitors. Presented at 78th annual meeting of the Air Polluti on Control Association, June, 1985, Detroit, MI. 044287

Ross DM; Malm WC; Loomis RJ (1987). An ex amination of the relative importance of park attributes at several national parks. In Bhardwaja, P. S. (Ed.),Visibility protection: re search 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 aer osol effect to an increase of the cloud droplet spectral dispersion with droplet number c oncentration. J Clim, 16: 3476-3481. 199905

Rotstayn LD; Penner JE (2001). Indirect aerosol forcing, quasi-forcing, and climate re sponse. J Clim, 14: 2960-2975.

193754

Rotton J; Barry T; Milligan M; Fitzpatrick M (1979). The ai r pollution experience and interpersonal aggression. Appl Psychol, 9: 397-412. 190478

Rotton J; Frey J (1982). Atmospheric conditions, seasonal tr ends, 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 so lar 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-Atla ntic 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 9-230Rusu 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-proce ssing plant. Environ Pollut,

143: 81-88. 156062

Ryan PA; Lowenthal D; Kumar N (2005). Improved light extinc tion reconstruction in interage ncy 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 scatte ring 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 hydr ocarbons 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 Pr oceedings of the National Academy of Science. 156947

Sato M; Hansen JE; McCormick MP; Pollack JB (1993). Stra tospheric aerosol optical de pths, 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/im prove/Studies/BRAVO/Studybravo.htm. 179902

Schichtel BA; Gebhart KA; Malm WC; Barna MG; Pitchfor d ML; Knipping EM; Tombach IH (2005). Reconciliation and interpretation of big bend national park particulate sulf ur 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 site s in the United States. J Geophys Res, 113: D02311. 156958

Schiff K; Bay S; Stransky C (2002). Characterization of stor mwater 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 pr ofile of tropospheric aerosol extinc tion 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 va por during ACE-2 using airbor ne sunphotometer, airborne in situ, space-borne, and ground-ba sed 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). Presen t-day atmospheric simulations using GISS Model E: Comparison to in situ, satellite and reanalysis data. J Clim, 19: 153-192. 190373

December 2009 9-231Schoeberl MR; Douglass AR; Zhu Z; Pawson S (2003). A comparis on 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 merc ury -- an overview. At mos 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; A nke 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; Brechte l 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 a nd pollution. Bull Am Meteorol Soc, 85: 367-380. 190388

Sekiguchi M; Nakajima T; Suzuki K; Kawamoto K; Higuras hi A; Rosenfeld D; Sano I; Mukai S (2003). A study of the direct and indirect effects of aerosols using global sate llite data sets of aerosol and cloud parameters. J Geophys Res, 108 D22: 4699. 190385

Shakya K; Chettri MK; Sawidis T (2008). Impact of heavy me tals (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 elem ents to plants. Anal Bioanal Chem, 375: 400-407. 156972

Sharifi MR; Gibson AC; Rundel PW (1999). Phenological and physiological responses of h eavily 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) airshe d to aquatic organisms. Environ Toxicol Chem, 23: 133-

140. 156084

Shindell DT; Levy H; Schwarzkopf II MD; Horowitz LW; La marque 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). Dyna mic 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 overv iew 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 9-232Skov H; Christensen JH; Goodsite ME; Heidam NZ; Jensen B; Wa hlin 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 proper ties of aerosols of mixed compos ition. 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 scat tering using a physical model responsive to relative humidity:

validation with measurem ents from Detroit. At mos 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 sa tellite retrievals and GOCART model. Geophys Res Lett,

33: L14817. 190400

Smith AE; Howell S (2009). An assessment of the robustn ess of visual air quality preference study results. CRA International. Washington, DC.http://yosemite.epa.gov/sab/sabproduct.nsf/B 55911DF9796E5E385257592006FB737/$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: vegetati ve compartment. In Air pollution and forests: interactions between air contaminants and forest ecosystem s (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 th e 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 fro m tire debris in soil. Environ Sci Technol, 36: 3706–3710.

156091

Soares CRFS; Siqueira JO (2008). Mycorrhiza and phosphate pr otection 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 norther n midlatitudes. J Geophys Res, 101: 6713-6727. 192252

Sotiropoulou REP; Medina J; Nenes A (2006). CCN predictions: is theory sufficient for assessme nts of the indirect effect? Geophys Res Lett, 33: L05816. 190406

Sotiropoulou REP; Nenes A; Adams PJ; Seinfeld JH (2007). Cl oud condensation nuclei prediction error from application of Kohler theory: Importance for the aerosol i ndirect effect. J Geophys Res, 112: D12202. 190405

December 2009 9-233Spinhirne 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 polycyc lic aromatic hydrocarbons: a review. Environ Chem Lett, 5:

169-195. 180049

Stanhill G; Cohen S (2001). Global dimming: a review of the ev idence 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 E nviron Qual, 34: 192-197. 156095

Stenchikov G; Hamilton K; Robock A; Ramaswamy V; Schw arzkopf 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 c limate 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 fr om 1850 to 2000. Chemosphere, 58: 163-175. 190416

Stern GA; Halsall CJ; Barrie LA; Muir DCG; Fellin P; Rosenbe rg 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 el ements 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. Envir on 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; We hrli C; Yttri KE (2006). Pan-Ar ctic 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; Tr effeisen R; Virkkunen K; Yttri KE (2007). Arctic smoke - record high air pollution levels in the European Arctic due to agricultural fires in Ea stern 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 9-234Stothers RB (2001). A chronology of annual mean effective radii of stratospheric aerosols from volcanic eruptions during the twentieth century as derived from ground-based spect ral 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-d ecadal aerosol trends as a likely explanation of the global dimming/brightening transition. Geophys Res Lett, 33: L15806. 190425

Streets DG; Aunan K (2005). The importa nce 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 a nd satellite observation. J Atmos Sci, 61: 179-194. 199917

Swackhamer DL; Paerl HW; Eisenreich SJ; Hurley J; Hor nbuckle KC; McLachlan M; Mount D; Muir D; Schindler D (2004). Impacts of atmospheric pollutants on aqua tic 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/monito ring/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 (20 02). 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 (2 005). Simulation of climate res ponse to aerosol direct and indirect effects with aerosol transport-ra diation model. J Geophys Res, 110: D02202. 190439

Tang IN (1996). Chemical and size effects of hygroscopic aer osols 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 9-235Tang 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 re gional 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 se nsing of aerosol properties ov er 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). Upta ke of vapor and particulate pol ycyclic aromatic hydrocarbons by cabbage. Environ Pollut, 140: 13–15. 156112

Taulavuori K; Prasad MNV; Taulavuori E; Laine K (2005). Me tal stress consequences on frost hardiness of plants at northern high latitudes: a review and hypot hesis. Environ Pollut, 135: 209-220. 190489

Tett SFB; Jones GS; Stott PA; Hill DC; Mitchell JFB; Allen MR; Ingram WJ; Johns TC; Johns on 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 cy cles 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 tr ansfer of polychlorinated biphenyls. Environ Pollut, 102: 119–128. 156118

Thomason LW; Peter T (2006). Assessment of Stratospheric Ae rosol 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 troposphe ric oxidants. J Geophys Res, 110: 1-32. 190459

Tie XX; Brasseur GP; Briegleb B; Gran ier 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). Ae rosol chemistry interactions after the Mt. Pinatubo Eruption . In A Robock and C Oppenheimer (Ed.),Volcanism and the Earth’s Atmo sphere (pp. 213-226). Washington, DC: American Geophysical Union. 192068

Tombach I; McDonald K (2004). Visibility and Radiative Ba lance 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 propertie s from satellite measurements

of backscattered ultraviolet radiation: Theo retical 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 AERONE T measurements. J Atmos Sci, 59: 398-413. 190505

December 2009 9-236Torres O; Bhartia P; Sinyuk A; Welton E; Holben B (2005). To tal 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 a nd 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. Californi a 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.),Precip itation Scavenging, Dry Deposition, and Resuspension (pp. 1337- 1351). New York: Elsevier Science. 190529

Twomey S (1977). The influence of pollution on the shor twave albedo of clouds. J Atmos Sci, 34: 1149-1152. 190533

U.S. EPA (1979). Protecting Visibility, an EPA Report to C ongress. 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 Offi ce. 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 Unde rstanding of Air Quality and Emissions through 2003. U.S. Environmental Protection Agency. Resear ch 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 Volu mes. 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 Gold ewijk 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 9-237Van de Hulst H (1981). Light scattering by small particles. New York: Dover. 191972

Van Donkelaar A; Martin RV; Park RJ (2006). Estimating gr ound-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). Dyna mic responses of photosystem II in the Namib Desert shrub, Zygophyllum prismatocarpum, duri ng and after foliar deposition of lime stone 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 C ontam 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). Si mulation 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 ef fect 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 hea vy 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 colo nisation 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). Hi storical 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 wi th 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). Distributi on of polycyclic aromatic hydroc arbons 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 ma ss 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 ch aracteristics of atmospheric

aerosols. Atmos E nviron, 15: 1891-1909. 095453

Wang C (2007). Impact of direct radiative forcing of bl ack carbon aerosols on tropical convective precipitation. Geophys Res Lett, 34: 5709. 156147

Wang Y-Z; Ingram JL; Walters DM; Rice AB; Santos JH; Va n Houten B; Bonner JC (2003). Vanadium-induced STAT-1 activation in lung myofibroblasts requires H2O2 a nd P38 MAP kinase. Free Radic Biol Med, 35: 845-855. 157106

December 2009 9-238Wania F; Hoff JT; Jia CQ; Mackay D (1998). The effects of snow and ice on the environmental behaviour of hydrophobic organic chemicals. Envi ron 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 a pportionment 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; Wi lson 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; Cam pbell J; Spinhirne J; Gordon H; Johnson J (2002). Measurements of aerosol vertical profiles and op tical properties during INDOEX 1999 usi ng 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-l ine 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 opti cal 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 cont aminant uptake, storage, and metabolism within plant roots. Environ Sci Technol, 39: 3695-3702. 156156

Wild SR; Jones KC (1992). Polynuclear ar omatic hydrocarbons uptake by carrots grow n 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 aer osols. 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 mi ssion: spaceborne lidar for observation of aerosols and clouds. PNAS, 4893: 1-11. 192017

Wolff GT (1985). Characteristics and consequences of soot in the at mosphere. Environ Int, 11: 259-269. 044680

Wyszkowska J; Boros E; Kucharski J (2007). Effect of inter actions between nickel and other heavy metals on the soil microbiological properties. Pl ant 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 9-239Xue 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 th e 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 aer osols 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; Hart ono 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). Polycy clic aromatic hydrocarbon composition and potential sources for sediment samples fr om 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 thei r 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.) Fl eisch. 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). Shortw ave aerosol radiative forcing over cloud-free oceans from Terra. I: Angular models for aerosols. J Geophys Res, 110: D10S23. 190929

December 2009 9-240Zhang J; Christopher S; Remer L; Kaufman Y (2005). Shortw ave 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 pot ential 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; Si outas C (2005). Evolution of particle number distribution near roadways Part III: traffic on-ro ad 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; Rau tiainen 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: Imp lications for aerosol sources and pr ocesses. 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 contamin ation of soil. J Plant Nutr, 28: 2065-2077. 192083

Zhang XZ; Sun HW; Zhang ZY (2006). [Bioaccumulation of ti tanium 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). Deriva tion of component aerosol direct radiative forcing at the top of atmosphere for clear-sky oceans. J Quan t Spectrosc Radiat Transf, 109: 1162-1186. 190936

Zhou M; Yu H; Dickinson RE; Dubovik O; Holben BN (2005). A nor malized 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 myco rrhizal willows. Int J Phytoremediation, 11: 200-213.

192085

December 2009 9-241