APPENDIX G: HEALTH BENEFITS AND METHODOLOGY

APPENDIX G1: HEALTH BENEFITS ASSOCIATED WITH REDUCTIONS IN DIESEL PM EMISSIONS FROM OFF-ROAD DIESEL VEHICLES

APPENDIX G2: METHODOLOGY FOR ESTIMATING AMBIENT CONCENTRATIONS OF PARTICULATE MATTER FROM DIESEL-FUELED ENGINE EMISSIONSG.1 - 1

APPENDIX G.1: HEALTH BENEFITS ASSOCIATED WITH REDUC TIONS

IN DIESEL PM EMISSIONS FROM OFF-ROAD DIESEL VEHICLES

I. Overview

The estimation of premature death related to PM2.5 exposure presented below is based on a peer-reviewed methodology developed by t he U.S. Environmental Protection Agency (U.S. EPA, 2010) and an incidents -per-ton (IPT) methodology (ARB, 2006). Staff estimates that approximately 1, 900 tons of PM2.5 and 34,000 tons of NO X

emitted from off-road diesel vehicles will be redu ced through implementation of this regulation in the years 2010 -2029. The reduction of premature deaths associated with emission reduction s of both primary PM and secondary PM (produced in the atmosphere from the p recursor NO X ) are presented in Table 1.

Table 1: Estimate of Premature Deaths Avoided Assoc iated with Emission

Reductions from Implementation of the Off-Road Vehi cle Regulation (2010-2029)*

Pollutant # of Cases

95% C.I. (Lower Bound) # of Cases

(Mean) # of Cases

95% C.I. (Upper Bound)
PM 190 250 310

NOx 170 220 270

Total 360 470 570 * Health effects from primary and secondary PM are la beled PM and NO X , respectively.

Details of the methodology are described below. Se ctions II through VII describe how the health impact estimation methodology was de veloped, including development of the IPT factor and its application for the Off-Road Rule.

II. Incidence-per-ton Factors

ARB used a methodology known as the incidence-per-t on (IPT) to quantify the health benefits of regulatory controls to reduce PM 2.5. This method, which was peer reviewed in 2006 (ARB, 2006), yields results s imilar to those of a more sophisticated modeling analysis, but can be used mo re efficiently. It is similar in concept to the methodology developed by the U.S. EP A for similar estimations (Fann et al., 2009). Details on the methodology us ed to calculate these estimates can be found in Appendix A of the Emissio n Reduction Plan for Ports and Goods Movement in California, as documented in the Emission Reduction Plan for Ports and Goods Movement (ARB, 2006).G.1 - 2

The basis of the IPT methodology is the approximate ly linear relationship which holds between changes in emissions and estimated ch anges in health outcomes. This is a consequence of the following observations : (1) Across the range of ambient PM2.5 concentration s encountered in California, modeled changes in premature mortality are approximately proportional to changes in ambient pollutant concen trations. (2) For primary pollutants such as diesel particula te matter, changes in ambient concentrations are approximately proportion al to changes in emissions. (3) For secondary pollutants such as ammonium nitra te aerosol, a linear relationship may be used as a first-order approxima tion to the relationship between ambient concentration and emissions of NO X . There may be cases where the relationship between emission of NO X and ammonium nitrate aerosol is greater than or less than linear

. Therefore, premature death is approximately proport ional to emissions, and can be estimated by multiplying emissions by a scaling factor, the IPT factor. IPT factors are derived by calculating the premature de ath incidence associated with a PM2.5 source in an air basin, and dividing by the emissions of that PM2.5 source. The methodology for estimation of premature death incidence is described in Section III. The emission inventories for NO X were developed for 2008, adjusted to account for changes due to the re cession, updates to the ocean going vessels, trucks/buses, locomotives, and construction equipment models. Separate IPT factors are used for each air basin and for each pollutant. Since the total incidence of heath effects is propo rtional to population, the result is adjusted by the ratio of the population in the t arget year to the population in the base year for which the IPT factors were developed.

III. Mortality Incidence

Background

For estimating the health benefits of emission redu ctions brought about by implementation of this regulation, ARB applied the methodology used by U.S. EPA in the 2010 Quantitative Health Risk Asses sment that was developed to estimate premature deaths associated with PM2.5 exposure. In this assessment of health benefits, ARB calculated estim ates for premature cardiopulmonary death. ARB is emphasizing cardiopu lmonary deaths because they are the most frequent causes of death, and cat egory of deaths most strongly related to PM2.5 exposure (U.S. EPA, 2010).

Calculation of the premature death incidence associ ated with PM2.5 exposure requires baseline incidence rates, population data, ambient concentration of PM2.5, and a concentration-response function relati ng changes in PM2.5 exposure to changes in mortality incidence. These data are available in a spreadsheet as part of the rulemaking package. Cal culations were made basedG.1 - 3

on both primary and secondary PM2.5 exposure. The sources and derivation of these parameters are described in sections IV – VII

. Estimating population exposure to PM2.5

Estimation of the PM2.5 exposure is a several step process, involving estimation of the annual-average concentration at each PM2.5 m onitor in the state, and interpolation of concentrations between monitors to estimate exposure for each census tract. Since diesel engines emit particles d irectly (primary particles), as well as gases that convert to PM2.5 through atmosph eric chemical reactions (secondary particles), exposure estimates are made for both, in order to capture the full impact of diesel engines on premature deat h.

Population-weighted exposure to primary and seconda ry PM2.5 was estimated based on monitor-specific concentrations. Even wit h an extensive air quality monitoring network, the mortality quantification me thod requires estimation of exposure between monitors across a geographic area. ARB uses a standard spatial interpolation method known as inverse dista nce-squared weighting which was peer reviewed in 2007 (Shepard, 1968; Goodin an d McRae, 1979). This method yields reasonable accuracy in estimating pol lutant concentrations near monitoring stations, although when distance from th e monitoring station increases, the uncertainty in the interpolated conc entration also increases. This method gives more accurate estimates of concentrati on in areas with a large number of monitors with good spatial coverage and l ow variability in concentration. When data are abundant, most simple interpolation techniques give similar results (Jarvis et al., 2001). When da ta are sparse, however, the assumption made about the underlying variation in P M2.5 concentration, along with the choice of interpolation method and its parameters can be critical to avoid misleading results.

Aggregating results to county, air basin and state

To aggregate results from census tracts to larger g eographical subdivisions such as counties or air basins, we used a GIS technique called areal interpolation. Areal interpolation is a procedure for translating spatial data from one set of geographical subdivisions to another when the bound aries do not exactly overlap. Numerous variants of the technique exist, but for the purpose of this analysis the simplest form, which uses area of poly gon intersection, was employed (Goodchild and Lam, 1980; Fotheringham and Rogerson, 1994).

The precision of areal interpolation based on area of intersection depends on the relative size of the geographical subdivisions, and the homogeneity of the spatial distribution of the quantity being apportioned. In urban areas, where census tracts are small and population is distributed more evenly, areal interpolation to larger subdivisions such as air basins yields relat ively precise estimates. In rural areas where the population is distributed unevenly over large census tracts, estimates are less precise.G.1 - 4

IV. Population at the Census Tract Level

Age-resolved population data at the census tract le vel, for the 2000 Census, were obtained from the United States Census Bureau (U.S. Census Bureau). These were projected to 2006-2008 using age-resolve d county population projections from the California Department of Finan ce (CDOF).

Age-specific growth factors for each county, for ea ch year, were computed from the CDOF projections by dividing each county popula tion for the target year by the county population for the year 2000. Since eac h census tract lies entirely in a county, these growth factors were applied to each c ensus tract in the county, each age group separately. Population was projecte d for ten-year age groups 25-34 through 75-84, and for age 85 and older.

This method of projection reflects growth in overal l county population, but does not model changes in population distribution within counties, such as expansion of urban areas into surrounding rural land.

V. Baseline Cardiopulmonary Mortality Incidence Rat e

Baseline incidence rates vary by age bracket. Inci dence was estimated separately for ten-year age groups 25-34 through 75 -84, and age 85 and older.

Baseline cardiopulmonary mortality incidence rates were estimated at the county level from individual death records for the year 20 05, obtained from the California Department of Public Health (CDPH). Cardiopulmonary mortality was defined as ICD9 codes 161-187 and 192-214.

The county of residence of the decedent was general ly not recorded. However, the Federal Information Processing Standards (FIPS) city code and the ZIP code were usually recorded. The FIPS city code unambigu ously identifies the county, but was sometimes invalid, unrecorded, or recorded as “unknown”. When the FIPS code was not available it was sometimes possib le to identify the county from the ZIP code, but ZIP codes can overlap multip le counties. In cases where 90% or more of the area of the decedent’s zip code lay entirely within a county, the death was assigned to that county. A handful o f records included invalid dates. The breakdown of records was as follows:

County identified by FIPS code 231,181 96.6% County identified by ZIP code 4,196 1.8% Unidentified or invalid 3,851 1.6%

Because the county could not be determined for 1.6% of the records, the incidence is slightly underestimated. No adjustmen t was made to compensate for excluded records.G.1 - 5

In some cases the cardiopulmonary incidence was ext remely low, because some counties only have a population of a few thousand, and the population is further subdivided into age groups. In such cases the vari ability of the incidence is high. However, since this represents a very small fractio n of California’s population the effect on statewide mortality estimates is negligib le. Large counties show little year-to-year variability.

Baseline mortality rates are subject to other sourc es of uncertainty. For example, the baseline incidence level is treated as uniform throughout the county of interest. In addition, baseline incidence rates can change over time as lifestyles, health care, income, and other factors evolve.

VI. Annual Diesel PM Concentrations

Annual diesel PM concentrations were estimated indi rectly from annual average NO X concentrations by applying a conversion factor,  , of 0.022. Derivation of the conversion factor is described in Appendix G.2. Annual average NO X concentrations for 2006-2008 were retrieved from AR B’s air quality database (ARB ADAM).

Because there is statistical uncertainty in  , this uncertainty carries through in the mortality estimates from diesel PM. The overall un certainty in the mortality incidence associated with diesel PM exposure was es timated by taking into account both the variability in  and the uncertainty in the coefficient of the concentration-response function (described in Secti on III). Since this is very difficult to estimate analytically, parametric boot strapping was used to obtain uncertainty estimates.

Parametric bootstrapping (Efron and Tibshirani, 199 3) is a technique for estimating statistical properties of a complicated function of parameters with uncertain values. Probability distributions are as sumed for the parameters, random samples are drawn from those distributions, and the function is calculated for each choice of parameters. The stat istical properties of interest, in this case the confidence interval, can then be esti mated from the values the function takes over the random sample.

In this case,  and were assumed to have normal (Gaussian) distributio ns. The conversion factor  was assumed to have a mean of 0.022 and standard deviation of 0.05 as described in section VII. The mean of the coefficient was taken to be 0.01293, and the standard deviation was back-calculated from the 0.95 confidence interval given in Appendix C of (U. S. EPA, 2009) as (0.01587 – 0.01007) / (1.96 × 2) = 0.001480.

Using the R statistical programming environment (CRAN), 100,000 sets of values for  (one for each NO X monitor) and values for were randomly generated. TheG.1 - 6

corresponding cardiopulmonary incidence for each ch oice of parameters was then computed. Since the sample exhibited a fairlysymmetrical distribution, we computed the 0.025 and 0.975 quantiles of the sampl e to estimate the confidence interval.

VII. Annual PM2.5 ammonium nitrate concentrations

In addition to directly emitted PM, diesel exhaust contains NO X , which is the precursor to nitrates, secondary diesel-related PM formed in the atmosphere. Secondary PM can lead to additional health impacts beyond those associated with directly emitted diesel PM. To quantify such impacts, staff developed annual average ammonium nitrate concentrations for 2006-20 08. The concentrations were computed from ambient nitrate ion concentratio ns, using PM10 data combined from two sources: the regular air quality monitoring network and the IMPROVE visibility network (IMPROVE Visibility Netw ork).

The ARB and air pollution control districts operate a network of PM10 monitors around the state, mostly in urban areas (ARB AQMN). PM10 samples are collected as 24-hour filter samples, once every sixdays, using size-selective inlet (SSI) sampler. Samples from some monitors are furt her analyzed to determine the concentration of nitrate and other constituents . During 2006-2008, nitrate data were available from 45 monitors. Since nitrat e particles form a fine aerosol, essentially all of the nitrate mass falls into the PM2.5 fraction, so the PM10 nitrate concentration may be regarded as equivalentto PM2.5 nitrate concentration. SSI data were retrieved from ARB’s ADAM air quality database (ARB ADAM).

In addition to urban PM10 nitrate monitoring, the n ational IMPROVE visibility network operated 18 PM2.5 nitrate monitors, mainly in national parks and other remote locations (IMPROVE Visibility Network). The se instruments collect one sample every three days. The IMPROVE samplers are more efficient than the SSI samplers, and tend to recover a higher fraction of ambient ammonium nitrate than the SSI samplers. However, since the IMPROVE samplers are located at remote locations where PM2.5 concentrations are clo se to natural background levels, the effect of instrument bias is considered negligible, and the data were treated as equivalent to the SSI data. IMPROVE dat a were retrieved from the project web site (IMPROVE Visibility Network).

Daily samples were aggregated by monitor to obtain annual averages. In order to avoid potential seasonal bias due to missed samp les, the samples were aggregated into quarterly means, and the four quart erly means were averaged to obtain annual means. For a quarterly average to be considered valid, the data were required to be at least 75% complete. For a y ear to be considered valid, all four valid quarters were required.G.1 - 7

To convert from nitrate ion concentration to ammoni um nitrate (NH 4 NO 3 ) concentration, the annual averages were multiplied by the ratio of the molecular weight of ammonium nitrate to that of the nitrate i on, 1.29.

References

ARB ADAM. Air Quality Database web site. Available at: http://www.arb.ca.gov/adam/

ARB AQMN. Air Quality Monitoring Network web site. Available at: http://www.arb.ca.gov/aqd/aqmoninca.htm

CDOF. California Department of Finance population p rojection web site. Available at: http://www.dof.ca.gov/research/demographic/reports/view.php

CDPH. California Department of Public Health statis tics web site. Available at: http://www.cdph.ca.gov/data/statistics/Pages/defaul t.aspx

CRAN . The Comprehensive R Archive Network. Availa ble at: http://cran.rproject.org/

.
Efron E, Tibshirani R. 1993. An Introduction to th e Bootstrap. Chapman and Hall. New York.

Fann N, Fulcher CM, Hubbell BJ. 2009. The influence of location, source, and emission type in estimates of the human health bene fits of reducing a ton of air pollution Air Quality. Atmosphere & Health. 2:169-1 76.

Fotheringham S, Rogerson P, editors. 1994. Spatial Analysis and GIS. London. Taylor and Francis.

Goodchild M, Lam N. 1980. Areal interpolation: a variant of the traditional spatial problem. Geo-processing, 1: 297-312.

Goodin, WR, McRae, GJ, Seinfeld JH. 1979. A Compar ison of Interpolation Methods for Sparse Data: Application to Wind and Co ncentration Fields. J Applied Meteor. 18:761-771.

IMPROVE Visibility Network web site. Available at: http://vista.cira.colostate.edu/improve/Overview/Ov erview.htm

Jarvis, CH Stuart, N. 2001. A comparison among st rategies for interpolating maximum and minimum daily air temperatures. Part ! !: The interaction between number of guiding variables and the type of interpo lation method. J. Appl. Metero. 40, 1075-1084.G.1 - 8

Shepard D. 1968. A two-dimensional interpolation fu nction for irregularly-spaced data. Proceedings of the 1968 ACM National Confere nce. Pp. 517–524.

US Census Bureau. American Fact Finder web site. Available at: http://factfinder.census.gov/home/saff/main.html?_l ang=en

U.S. EPA. 2009. Integrated Science Assessment for P M (Final Report). Available at: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_isa.html

U.S. EPA. 2010. Quantitative Health Risk Assessmen t for Particulate Matter Available at: http://www.epa.gov/ttn/naaqs/standards/pm/data/PM_RA_FINAL_June_2010.pdfG.2 - 1

APPENDIX G.2: METHODOLOGY FOR ESTIMATING AMBIENT

CONCENTRATIONS OF PARTICULATE MATTER FROM DIESEL-FUELED ENGINE EMISSIONS

Introduction

This appendix outlines a method to estimate annual average concentrations of diesel particulate matter (DPM) over large spatial scales. It consists of a simple variation of receptor modeling, which uses measurem ents of ambient chemical concentrations to infer source contributions, known as the tracer species method. 1 A basic assumption in this method is that the ambi ent concentration of a tracer species, C , may be used alone to infer the ambient concentrat ion of a pollutant from a specific source, S :

S =  C, (1)

where α is a scaling factor that is assumed to be independ ent of geographical location. In the estimation of DPM, we take C to be the annual-average ambient concentration of NO x

and Sto be the annual-average ambient concentration of DPM less than 2.5  m in diameter (DPM2.5). The factor α relates the concentration of PM2.5 produced by diesel-fueled en gine emissions to the concentration of NO x

produced by all sources (NO x is not a unique tracer for diesel emissions). In the following section, we dem onstrate that estimates for α based on the emission inventory (EI) and on source apportionment (SA) studies agree within calculated uncertainties. We approxima te the distribution of α values over counties by a Gaussian distribution with mean 0.023 and standard deviation 0.006 for the year 2000. Using data for 2008, we es timate a mean of 0.022 and standard deviation of 0.0005. The 2008 values are appropriate for estimating health impacts based on recent NO x data. A single value for α and associated dispersion may be used to infer DPM concentrations from measurements of ambient NO x

concentrations in all air basins.

Background

The primary interest of the California Air Resource s Board in the estimation of ambient DPM concentrations is for assessment of pre mature deaths. For this purpose, annual-average ambient concentrations of D PM are needed. These values are used to calculate lifetime average dailydoses 2 ; multiplication of the average daily inhalation dose over 70 years with a cancer potency factor gives inhalation cancer risk estimates. In previous estim ates 3of diesel particulate matter less than 10  m in diameter (DPM10) concentrations, the Air Resou rces Board (1998) used a method based on ambient total P M10 concentrations. For this approach, one of two factors, rural or urban -which were determined from a combination of chemical mass balance source apporti onment studies (CMB) and emission inventory estimates (EI) - was used to sca le ambient PM10G.2 - 2

measurement values to obtain estimates of DPM10 con centrations. Air basins that had DPM10 to total PM10 emissions (emission in ventory estimates) greater or less than the base case had these DPM10 estimate s scaled by another factor (that was determined from the EI): the ratio of air basin to base case value of the relative DPM10 to total PM10 emissions. Application of this method, therefore, depends on several elements, the most important of which are: 1) measurements of ambient PM10 concentrations, 2) previous source apportionment work in specific air basins (base cases), and 3) emission i nventory estimates. These components are also the primary weaknesses of the m ethod: 1) PM10 contains predominantly crustal material, and the fraction as sociated with diesel PM is very small - at most approximately 6.5%; 2) early CMB st udies may not be as accurate as more recent organic marker species-base d CMB methods; and 3) early emission inventory estimates may not be as ac curate in accounting for all source emissions as more recent models.

We believe the use of scaled ambient NO x concentrations is more direct than the PM10 method to estimate DPM concentrations for seve ral reasons, foremost among these are the close linkage 4 of diesel-engine emitted NO x to total emitted NO x – about half of total NO x emissions are from diesel sources – and the relatively good correlation of ambient with recent emission inventory estimates for α . In addition, NO x

emissions in California are predominantly from mob ile sources, with diesel vehicle emissions accounting for approximately half the onroad mobile contribution and greater than 60% of th e off-road contribution. The limited variation in different air basins of the di esel engine emissions contribution to total NO x

is a reflection of the similarity of on- and off-road fleet composition and activity. In this respect, California likely di ffers from other regions of the country in the scarcity of important point stationa ry sources (such as power plants and refineries). Contributions from such poi nt sources would introduce proximity dependencies and preclude the use of a si mple NO x-scaling methodology to approximate DPM.

Methods

In this section, we develop estimates for the scali ng factor α, the ratio of ambient DPM to total ambient NO x concentrations. First, we compare the ratio of amb ient concentrations (DPM/NO x ) SAfrom several source apportionment (SA) studies done in the late 1990's with the ratio of annual em issions (DPM/NO x ) EI from the calendar year 2000 emission inventory (EI). Current ly, the source apportionment studies are considered the best available methods for determining ambient DPM concentrations (at selected monitoring sites); agre ement between the SA and EI estimates of α

is used to support the use of a single α value for the whole state of California. Second, based on this favorable comp arison, we use the distribution of county emission inventory estimates for the (DPM/NO x ) EI to determine an average and standard deviation for α for a baseline year 2008.G.2 - 3

In the following section, we estimate the ratio of DPM to NO x concentrations in ambient air for two year-long and several short-ter m source apportionment modeling studies with co-located NO x measurements. These studies utilize specific organic chemical species, or molecular mar kers, in chemical mass balance (CMB) apportionment of PM, which is conside red to be essential for the accurate separation of gasoline from diesel-fueled engine emissions. Even with such molecular markers, however, CMB modeling relie s on several important, though typically unverifiable, assumptions. First, all chemical species used in the CMB mass balance equations must be conserved during transport from source to receptor site. That is, these species in the partic le phase must not be either depleted through volatilization or chemical reactio n or increased through atmospheric reactions of precursor species. Second, all major sources that contribute chemical species used in CMB apportionme nt must be chemically characterized and included in the model, and the so urce profiles constructed must be sufficiently different from one another tha t problems of collinearity are not encountered. Recent work has investigated how w ell these assumptions are fulfilled in practice. 5In these studies, several challenges in applicatio n of the CMB method have been determined: emissions from veh icles exhibit significant profile-to-profile variability, which causes diffic ulties in construction of a single source profile; molecular markers undergo significa nt oxidation across a range of experimental conditions, which indicates that simil ar aging effects may affect apportionment studies; CMB analysis is very sensiti ve to source profile selection; and well defined source profiles may not exist for some emission categories. Another source of uncertainty in the apportionment of PM to diesel emission sources in all these studies is in the off-road die sel source contribution. These sources are captured by CMB modeling only to the extent the emissions are similar in chemical composition to those of on-road diesel trucks, for which source profiles are available. In light of the emis sion inventory estimate that approximately half the diesel contribution to PM an d NO x is from off-road sources, this poorly understood aspect of SA modeli ng warrants further qualifications in all CMB estimates of DPM.

The first PM source apportionment work considered i n this document was a yearlong effort conducted as part of the Children's Hea lth Study (CHS 1995), in which James Schauer carried out organic chemical PM CMB s tudies for eleven sites in the South Coast Air Basin. 6,7 Hence, eleven annual average values for DPM10 and NO x concentrations are available from this work. Four of the sites are centrally located, while the rest are in more or le ss outlying areas. The second considered SA study was carried out as part of the California Regional Particulate Air Quality Study (CRPAQS 2000) by Dese rt Research Institute (DRI) in the San Joaquin Valley. 8From this work, seven estimates of annual average DPM2.5 and associated NO x are available. Most of these sites are in urban areas (with the exception of Bethel Island). Although J. Chow of DRI used a different methodology to measure elemental and organic carbon (IMPROVE method) than used by J. Schauer for CHS (NIOSH method), DRI util ized similar specific organic chemical markers for combustion sources. In addition to these long-termG.2 - 4

measurements, side-by-side, organic marker CMB mode ling was done by E. Fujita and J. Schauer at two sites for one week eac h in Southern California in 1999 as part of the Diesel-Gasoline Particulate Matter Split Study (DGPM 2000). 9,10,11

An unexpected result from the Diesel-Gasoline Parti culate Split Study is that apportionment of PM2.5 depends on the specific carb on measurement method utilized (to determine relative organic/elemental c arbon). Such differences in apportionment are currently not incorporated into u ncertainty estimates. We also note that the DGPM study raised several important, but still unresolved, questions in the interpretation of CMB modeling res ults. Specifically, SA estimates may be very sensitive to the choice of so urce profiles used; e.g. the characteristics of the “average” driving cycle, cat egories of vehicles, composition of the fleet (e.g. inclusion of high emitter catego ries such as gasoline “smoker” vehicles) and, information about average high emitt er organic species emissions. These aspects bear directly upon SA attribution est imates in a poorly understood manner. Results from several recent short-term appo rtionment studies that do not utilize CMB modeling are also included below; t hese studies provide further evidence for a wide range of DPM estimates. Based o n a favorable comparison of SA and EI results, we develop an estimate of the DPM/ NO x ratio based on the EI.

Results

Source apportionment of PM collected in the South C oast Air Basin was done by J. Schauer as part of the Children's Health Study ( CHS) in 1995. The sampling sites are described in the CHS Final Report and rep resent eleven communities in the South Coast Air Basin; these include four urban sites (Mira Loma, North Long Beach, Riverside, Upland), two sites in a mountaino us region (Alpine and Lake Arrowhead), one desert site (Lancaster), three rura l coastal sites (Atascadero, Lompoc, and Santa Maria), and one rural inland site (Lake Elsinore). NO x measurements and filter samples (organic chemical m arker measurements) were taken at the same locations. Although each filter P M sample was collected over a two week interval, filters from each site were comp osited into three seasonal time periods. Each composited sample was analyzed for organic marker compounds and utilized in chemical mass balance source apport ionment modeling. We concentrate on using annual average results from th e apportionment study, and show seasonal trends below. Figure 1 shows site-to -site variation of source apportionment estimates of the ratio (annual averag e DPM10 concentration)/(annual average total NO x concentration) from the CHS (1995). A straight average over all eleven sites of the ratio DPM10/NO x , gives the mean value as 0.030 (0.010), where here and in the follo wing text the value in parentheses denotes the standard deviation. An alte rnative estimate based on regression of DPM10 concentrations against ambient NO x concentrations (over eleven sites) gives 0.027 (0.001); see Fig. 2. In t his, and all following regressions, the intercept is set to zero, which ma kes the regression lessG.2 - 5

sensitive to scatter and is physically meaningful, as one expects that diesel emissions tend to zero with total NO x emissions.

As expected, the dispersion in α is much larger over individual measurements of DPM/NO x

than it is for the regression coefficient. It is u nclear which choice of error is best for use in personal exposure estimate s that use population weighting. The site-specific DPM/NO x values, Fig. 1, are best estimates for local DPM/NO x

ratios, though specific meteorology and lack of po pulation weighting may emphasize unrepresentative values. Similarly, D PM/NO x ratios obtained from linear regression (with zero intercept) are hi ghly influenced by data with large NO x

and/or DPM values. Because individual measurements for the ratio DPM/NO x

retain site-specific variability in concentrations , we believe the statistics from individual measurements are better estimates t han regression coefficients for DPM exposure-related work. We take the standard deviation of the distribution of DPM/NO x values as the measure of uncertainty in α for SA studies.G.2 - 6

Figure 3 shows DPM10 vs. NO x plots of the CHS data for each of the three composited seasons (January – April; May – October; November – December). The slope exhibits a clear seasonal dependence with largest value in summer and smallest in winter. This variation can not be e xplained completely by EI estimates (summer, winter), which show much less se asonal variability, and indicates further sources of uncertainty in the use of short timescales for scaling NO x . Figure 1

Figure 2

CHS 1995 Ambient DPM10 vs. NO x

y = 0.027x

0 0.5 1 1.5 2 2.5 3 3.5 4

0 20 40 60 80 100 120 140

NO x ( μg/m 3 )DPM10 (μg/m3)G.2 - 7 Figure 3

The other year-long SA estimate for α is from CRPAQS (DRI, 2000) for the San Joaquin Valley (SJV). As in the CHS, this study als o utilizes organic chemical species CMB for apportionment, though it departs in many details of application, which affect apportionment results. Several aspects which bear directly on the results are usage of different carbon analysis meth ods (the National Institute for Occupational Safety and Health (NIOSH) method in CH S and the Interagency Monitoring of Protected Visual Environments (IMPROV E) protocol, also known as thermal/optical reflectance (TOR), in CRPAQS), d ifferent chemical markers and source profiles (see refs. 6-8). A straight av erage of the ratios of SA DPM2.5 to NO x

concentrations for seven sites in the SJV gives a value of 0.018 (0.008). Figure 4 shows a regression of SA ambient DPM against NO x , which gives a slope of 0.016 (0.004). As for the previous SA work, we take the standard deviation (0.008) from the distribution of DPM/NO x values as an indicator of the variability in ambient ratios.

We note that the relative variability of DPM/NO x in both studies is very large: the coefficient of variation (standard deviation/averag e) 0.5. We believe this large uncertainty in SA estimates best captures local var iation of source composition, mixing, chemical reactions and other factors. Hence , this order of uncertainty is expected in any estimate of DPM based on ambient NO x concentrations. A recent short-term SA modeling study investigated the sources of uncertainties in the relative contributions of diesel and gasolin e vehicle emissions to PM2.5 in the South Coast (2001) – the Gasoline/Diesel PM Spl it Study. 9,10,11 In this work, James Schauer (University of Wisconsin, Madison) an d Eric Fujita (Desert Research Institute) collected samples side-by-side for sources (57 light duty gasoline and 34 heavy duty diesel vehicles) and amb ient air (two sites Los Angeles, N. Main, and Azusa), and carried out indep endent chemical and SA data analyses. The DRI study did not directly appor tion PM2.5 to diesel sources; instead, apportionment of total carbon and elementa l carbon were determined. Children's Health Study 1995

y = 0.0353x y = 0.0214x

y = 0.0208x

0 1 2 3 4 5 6

0 50 100 150 200 250

NO
x ( μg/m 3 )DPM10 (μg/m3)

Jan-April May-Oct
Nov-DecG.2 - 8

To estimate corresponding DPM2.5 concentrations, we scale reported diesel EC values by a factor determined from DRI's diesel sou rce profile (DPM2.5 = 1.36 DEC2.5, where DEC2.5 denotes elemental carbon in PM 2.5 apportioned to diesel engine emissions). The resulting SA results show a lack of agreement between DPM2.5 estimates: apportionment of PM to di esel emission sources by the two groups differ by approximately a factor of two; see Figures 5 and 6. Estimates for DPM2.5/NO x are: 0.010 (0.003) for Schauer and 0.023 (0.004) for Fujita. Because ambient and vehicle emission sample s were collected side-byside, these results indicate that the disparity in DPM estimates is driven by differences in source apportionment methodology, wh ich includes differences in carbon measurement methods (NIOSH vs. IMPROVE), org anic marker chemical species, and source profiles for vehicles. Without a priori information about which method is more accurate, we believe both estimates should be weighted equally, giving DPM/NO x

= .017 (.009).

Figure 4

Two recent studies that used simpler methods to app ortion PM2.5 to diesel sources were done by Livermore National Laboratory (LLNL) in 2007 and ARB's Monitoring and Laboratory Division (MLD) in 2003. L LNL utilized fossil carbon measurements, based on Carbon 14, and MLD utilized n-octadecane as a diesel tracer. In these studies, DPM concentrations were e stimated to be on the order of 1  g/m 3 ; these values would presumably support a lower DPM /NO x ratio of .01 (with a likely relative uncertainty of 50%). More s pecifically, LLNL determined the average fossil elemental carbon (FEC) at Wilmington as approximately 1.05
 g/m 3 (based on the limited data), and the average FEC a t Roseville as approximately 0.65  g/m 3 . Assuming that all FEC is from diesel emissions an d that OC emissions from diesels are small in compari son, these values may be considered upper bound DPM concentrations. MLD's st udy yielded estimates of DPM for Wilmington as 1.2  g/m 3 and Sacramento as 0.8  g/m 3 , and the statewide average as 1.0  g/m 3 . These values, however, differ by over a factor of two from the recent MATES III organic marker CMB es timate of >3  g/m 3 in 2004-2005 (in Wilmington). Therefore, while these two independent estimates CRPAQS 2000

y = 0.016x

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

0 20 40 60 80 100
NOx ( μg/m 3 )DPM (μg/m3)G.2 - 9

provide support for the lower end of DPM/NO x ratio, considerable uncertainty remains in their interpretation.

Figure 5

Figure 6

A comparison of the above SA estimates with the emi ssion inventory can not be made directly, as emission inventory estimates are for whole counties while SA estimates are specific to monitoring sites and impl icitly take into account meteorology, chemistry and deposition. Hence we com pare average values for DPM/ NO x

from the previous SA studies with EI estimates of DPM to total NO x emission ratios. For this purpose, the EI estimates for DPM and total NO x emission rates for individual counties are utilized

. 12 These estimates may be visualized as tons of pollutants emitted each day i nto a well-mixed box covering each county, with removal rates of DPM and NO x proportionately the same. The assumption of approximately equal removal rates is difficult to verify, given that the rates are caused by a variety of processes: dep osition, chemical reactions, and flow into and out of air basins. Further, while the atmospheric lifetimes for DPM and NO x

are typically very different (greater and less tha n a few days, respectively), which would bias the ratio of DPM/ N O x toward higher values, the mean residence time of an air parcel in a coastal a ir basin is typically less than a day, which would dominate the reaction and depositi on rates and effectively Gasoline-Diesel PM Split Study

y = 0.010x y = 0.021x

0 0.5 1 1.5 2 2.5 3

0 50 100 150 NOx ( μg/m 3 )DPM2.5 (μg/m3) Fujita
SchauerG.2 - 10

make the rate of removal for NO x and PM the same. In the following, we assume this dominance of air parcel residence time on remo val rates, and take the removal rates for NO x

and DPM as equal.

Figure 7

Figure 8

To compare the above source apportionment estimates of DPM/NO x with emission inventory estimates, we utilize ARB emissi on inventory estimates for the year 2000 (the SA studies were conducted in 199 5 CHS in SoCAB, 2000 CRPAQS in SJV, and 2001 GDPM in SoCAB). The emissio n inventory estimates incorporate spatial and temporal averaging over large scales and therefore may be used to estimate average ambient DPM/NO x ratios directly (in the following figures for the emission inventory, we abbreviate t otal NO x by tNO x ). A plot of (DPM/tNO x ) e

e against tNO x e

e for all counties in California is shown in Fig. 7. This scatter plot shows that the county-wide ratios DPM/tNO x are clustered about an average and that the dispersion depends on the average annual tNO x emission rate. The plot also shows that a separation of high -NO x from lower-NO x

emission counties occurs with a division around an annual av erage of 80 tons per day. 2000 Emission Inventory DPM2.5 / tNOx vs. tNOx

0 0.01 0.02 0.03 0.04 0.05

0 200 400 600 800

tNOx (tpd)DPM2.5/tNOxG.2 - 11

(High-NO x

counties are listed in the next section of this do cument.) In California, NO x inventories are dominated by on-road and off-road mobile sources; overall, diesel engine emissions contribute approximately ha lf of the on-road NO x emissions and greater than 60% of the off-road mobi le emissions. To better capture exposure-related estimates of DPM/tNO x , each county value is weighted by its population; weighted histograms are approxim ated by normal distributions. Figure 8 shows histograms for the ratio of DPM/tNO x over high- and low-NO x emission counties. The mean and standard deviation for these distributions are: 0.023 (0.003) for the high-NO x counties and 0.023 (0.006) for the low-NO x counties. Hence, population weighted distributions for α in high and low-NO x counties may be described by normal distributions w ith same mean value and a dispersion that depends on NO x emissions characteristics. To develop a single California-wide approximation, we take the (larger) dispersion for the ratio of DPM/tNO x

in low-NO x

counties as measure of the variability that is enc ountered locally within air basins.

Figure 9

The above estimates of the ratio DPM/tNO xfrom the 2000 EI populationweighted and SA studies compare well, given the rel atively large uncertainties: 2000 EI county average 0.023 (0.006); and SA: 1995 CHS 0.030 (0.010), 2000 CRPAQS 0.018 (0.008), and 2001 GDPM 0.017 (.009). F igure 9 shows a plot of these estimates. This overall agreement in α values motivates adoption of a single scaling factor for the whole state of Califo rnia for years close to 2000: the average from the 2000 EI estimates, α = 0.023 (0.006).

Conclusions

Based on the relatively good agreement between SA a nd EI estimates for the scaling factor α

- the ratio of DPM to total NO x - for years close to 2000, we propose the use of a single value of 0.023 for αin estimating the populationweighted annual average ambient DPM concentration i n California. The resultant DPM estimates from this approach depend upon the ne twork of ambient NO x measurements from the ARB monitoring sites. In the following, we outline a DPM/NOx Estimates

0 0.01 0.02 0.03 0.04 0.05

EI-2000 CHS-1995 CRPAQS2000 GDPM2001G.2 - 12

method to calculate such averages. First, the annua l average DPM concentration at each monitoring site is estimated as the productof annual average NO x concentration value and α . The uncertainty associated with this DPM estimate is the product of the annual average NO x measurement value and the low- NO x county standard deviation, .006. The following twel ve counties are considered high-NO x emission counties (annual average NO x > .80 tons per day): Los Angeles, San Bernardino, Kern, San Diego, Orange, R iverside, Alameda, Fresno, Santa Clara, Contra Costa, San Joaquin, and Sacramento. The remaining 46 counties are considered low-NO x emission counties. From this set of spatially discrete DPM concentration estimates a smooth DPM concentration surface may be constructed using kriging or other i nterpolation methods. In remote areas without monitoring sites, the smoothin g method may be modified to incorporate a minimum concentration, which would re flect a nonzero background value (or such areas may be removed, if the populat ion is sufficiently small). Second, census data for California is used to appro ximate a population density surface (population fraction per unit area) and the (pointwise) product of the population density and DPM concentration surfaces i s taken. This product may be integrated over any region and divided by the fraction of California population within that region to give a population-weighted av erage DPM concentration; in particular, integration of the product may be perfo rmed over the state to give an average population-weighted ambient DPM concentrati on. Once ambient diesel PM concentrations have been estimated for a baselin e year (2000), linear rollback techniques may be used to project concentrations for future years.

A comparison of DPM concentration estimates for the year 2000 using the proposed NO x-scaling method with the projections from the previ ous PM10- scaling method 3 is given in Table 1. The overall agreement between DPM concentration estimates for the six highest populat ion air basins is very good. More specifically, the six highest population air b asins contain over 90% of the population of California and contribute greater tha n 96% of the population weighted DPM concentration; in each of these air ba sins, the difference between the proposed and the previous DPM concentrations is less than 20% of the previous estimate. It should be noted that the prev ious estimates use a baseline year 1990 and are projected forward by a decade bas ed on linear rollback, and so do not constitute the best approximation for yea r 2000. Greater variation of agreement between proposed and previous methods is found for lower population air basins. Many factors contribute to t his variability, several of which are: the larger dispersion in the DPM to NO x ratio (.006), uncertainty in application of PM10 scaling method to regions less similar to the SJV, and greater influence of localized emission sources. Al together, the proposed, population-weighted DPM concentration for Californi a is increased by 11% over the previous estimate. This high level of agreementbetween the populationweighted DPM estimates gives confidence that the proposed method is consistent with the previous technique and represen ts a viable approach to estimate DPM exposure.G.2 - 13

A final application of the NO x-scaling approach is to estimate α for a more recent year - 2008 (to be used as a baseline for health im pact studies). An analysis of an updated ARB 2008 emissions inventory, similar to that carried out for 2000, yields the value α

= 0.022 (0.005). This result is based on an unweighte d fitting of the distribution of county DPM2.5/NO x ratios to a normal distribution; see Fig. 10. [Emissions from ocean going vessels were down-w eighted by an adjustment factor, 0.10 in the South Coast and 0.25 in other a reas, to reflect their decreased contribution to inland areas. 14 ] As expected, because of the relatively slow turnover of diesel engines, the mean and standard d eviations are similar to those for 2000. The low- and high-NO x counties, exhibit the same α values: α

= 0.022 with (0.006) for low-NO x counties and (0.003) for high-NO x emission counties (the demarcation between low- and high-NOx counties was taken as 60 tpd NO x ). It is expected that α will slowly vary with year due to improvements in technology and turnover of emission sources, though large changes may be expected based on preferential DPM control measures . Such divergences will necessitate reexamination of estimates for α ; although time intervals for such reviews can not be prescribed, revisions in α are necessary when the difference in estimates approaches the uncertainty (dispersion ).

A rough comparison of this 2008 EI estimate may be made with measurements from recently completed field work – the Harbor Com munity Monitoring Study (HCMS). 13 This program was conducted in 2007 to characterize the spatial variations in concentrations of toxic air contamina nts (TACs) and their copollutants within the communities of Wilmington, We st Long Beach, and San

Pedro in California’s South Coast Air Basin. These communities were chosen because of the close proximity of residents to many emission sources, which include the Ports of Los Angeles and Long Beach, pe troleum refineries, intermodal rail facilities and the greatest concent ration of diesel traffic in the Los Angeles metropolitan area. To avoid sites heavily i mpacted by near sources, we concentrate on neighborhood sites from the saturati on monitoring network operated by the Desert Research Institute (denoted LWIN, WCOL, WGUL, WLAK, WMAR, WMCD and WMCF in the report). This stud y consisted of 7-day Figure 1 0 2008 EI: DPM2.5/NOx vs. NOx

0 0.01 0.02 0.03 0.04

0 100 200 300 400 500
NOx (tpd)DPM2.5/NOxG.2 - 14

time-integrated sampling for four consecutive weeks in four seasons. NO x was measured with Ogawa passive samplers and PM2.5 was collected on 7-day integrated Teflon and quartz filters with Airmetric s MiniVol samplers. Organic carbon (OC) and elemental carbon (EC) were analyzed by thermal optical reflectance (TOR) method using the IMPROVE (Interag ency Monitoring of Protected Visual Environments) temperature/oxygen cycle (IMPROVE TOR). Several caveats should be noted in the interpretati on of this data. First, the high density of emission sources in the HCMS area may produce ambient NO x and DPM concentrations that are different from those in the greater region, and hence, less well described by EI estimates. Second, source apportionment was not carried out for this study; instead, the tracer used for diesel-engine PM emissions is elementary carbon (EC) from PM2.5, whi ch is predominantly but not exclusively from diesel emissions. Under these limi tations, DPM2.5 was estimated from EC concentrations as 65% of the tota l carbon from diesel engine emissions (DTC): EC=0.65 DTC. This conversion is ad opted because the carbon fraction of EC in heavy-duty diesel truck emissions during city suburban driving has been measured as approximately 0.65 (City Subur ban Heavy Vehicle Route dynamometer cycle, ref. 10) and the general agreeme nt between TC collected on quartz filters and PM2.5 mass collected on Teflon f ilters for diesel emissions in dynamometer testing (Gasoline/Diesel PM Split Study ). 9,10 A full consideration of various methods to estimate DPM using EC, and possi bly other co-pollutants, was not attempted; such an investigation would yiel d a confidence interval for DPM estimates, and possibly provide a better case for comparison. A different scaling method was adopted by DRI in the Final Repo rt for the HCMS saturation study. 11 In their work, ambient diesel particulate carbon ( DPC) concentrations for each site (for a given season) were taken as direct ly proportional to measured EC concentrations, where the proportionality consta nt was determined from the slope of the correlation between total carbon and E C at the near road sampling locations. These slopes varied between 1.5 and 2.2. Diesel particulate matter was then estimated as the sum of the elemental carb on and organic matter portions of DPM:

DPM = EC + 1.4 6 (DPC – EC), (2)

where 1.46 is the ratio of diesel particulate organ ic matter to DPC from the Gasoline/Diesel PM Split dynamometer testing of die sel trucks by DRI. A third estimate of DPM may be obtained from a method used in the Multiple Air Toxics Exposure Study III (MATES III) - a monitoring and e valuation study conducted in the South Coast Air Basin (Basin) from April 2004 t o March 2005. In this study, a scaling factor for EC of 1.72 was developed based o n the 2005 emission inventory ratio of diesel particulate to elemental carbon emissions. Application of these three DPM estimation methods to measurements from the neighborhood sites yields the following average values for the ratio of DPM concentration to NOx concentration: 0.038 (0.005), 0.025 (0.004), an d 0.028 (0.004), for the DRI, EC/0.65, and MATES III methods, respectively. These results show that all sites exhibited similar DPM to NO x ratios and that the largest source of uncertainty isG.2 - 15

in the choice of scaling factors for EC (and total carbon). The largest value of DPM/NO x

was obtained using DRI's methodology, which may be regarded as an estimate of the "upper-bound ambient concentration" of DPM. 12 In light of the caveats and simple EC-scaling, these estimates agre e relatively well with the 2008 EI estimate of 0.022 (.005) and provide furthe r support for use of a NO xscaling methodology (with associated uncertainty in terval) to estimate DPM in California.

In summary, the proposed method to estimate ambientDPM concentrations has distinct advantages over the previous PM10 method a s well as several important limitations. The primary strengths of the method in clude the significant connection between DPM and NO x , simple application, estimates of uncertainty intervals, and ability to capture sub-county variat ions in DPM concentrations. In addition to these strengths, the approach is tied d irectly to the ARB emission inventory, and links bottom-up EI estimates with to p-down SA estimates. Several limitations and caveats also bear on applications o f the method. The limitations include all assumptions sufficient for application of EI estimates to ambient air, such as well-mixed air parcels on county-wide scale s, proportional removal rates for NO x and DPM (including air basin outflow), proportiona lly time-uniform emission rates for all NO x and DPM sources, etc. Verification of these assumptions is in general not possible; instead, ag reement between EI and SA estimates is taken as best available evidence for s upport. The uncertainty intervals produced by the estimation method are bas ed on variations between low-NO x counties and reflect differences in relative emiss ion sources (primarily diesel vs. non-diesel mobile sources). As such, the uncertainty describes the confidence in α

to accurately describe local NO x emission sources. For areas outside California, in which the NO x emission inventory has a significant contribution from non-mobile sources (e.g. power pl ants or refineries), the value of α is likely to be different from that for California and vary with source proximity. Further work is needed in strengthening the underst anding of the contribution of various emission sources to ambient concentrations of both gases and particles. In this respect, source apportionment work that uti lizes organic marker species is the probably best available approach; ideally, high ly time-resolved studies would allow better characterization and support for singl e species scaling estimates, such as the NO x-scaling method. Finally, off-road diesel sources, which are a large source of uncertainty in current CMB modeling , need to be included explicitly in future source apportionment studies.G.2 - 16

References

1 Henry R., Lewis C., Hopke P., and Williamson H., Review of Receptor Model Fundamentals(1984) Atmospheric Environment 18, 1507-1515.

2 Air Toxics Hot Spots Program Risk Assessment Guidel ines, Appendix D Risk Assessment Procedures to Evaluate Particulate Emiss ions from DieselFueled Engines, 2003, Office of Environmental Health Hazard Assessment.

3 Estimate of Ambient PM10 Concentrations from Direct ly Emitted Emissions from Diesel Engines, Appendix III, Part A, ExposureAssessment from Proposed Identification of Diesel Exhaust as a Toxic Air Contaminant (1998). Report to the California Air Resources Board.

4 Marmur A., Unal A., Mulholland J.A., and Russell A., Optimization-Based Source Apportionment of PM2.5 Incorporating Gas-to- Particle Ratios (2005) Environmental Science and Technology 39, 3245-3254.

5 Robinson, A., Adams, P., and Donahue, N., Final R eport: SourceApportionment of Primary Organic Carbon in the East ern United States Combining Receptor-Models, Chemical Transport Model s, and Laboratory Oxidation Experiments (USEPA National Center for En vironmental Research, 2007). The report and links to associated papers are available at: http://cfpub.epa.gov/ncer_abstracts/INDEX.cfm/fusea ction/display.abstractDet ail/abstract/7503/report/F

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6 Manchester J., Schauer J., and Cass G. Determination of the Elemental Carbon, Organic Compounds and Source Contributions to Atmospheric Particles During the Southern California Children’sHealth Study: Part B; The Distribution of Particle-Phase Organic Compounds in the Atmosphere and Source Contributions to Atmospheric Particulate Matter Concentrations During the Southern California Children’s Health Study, 1995 . Final Report for California Air Resources Board.

7 Manchester-Neesvig J. B., Schauer J. J., and Cass G. R., The Distribution of Particle-Phase Organic Compounds in the Atmosphere and their Use for Source Apportionment during the Southern California Children’s Health Study (2003) Journal of the Air and Waste Management Asso ciation. 53, 1065- 1079.

8 Chow J., Chen L.W., Lowenthal D., Doraiswamy P., Park K., Kohl S., Trimble D., and Watson J. California Regional PM10/PM2.5 Air Quality Study (CRPAQS): Initial Data Analysis of Field Program Me asurements, Final Report, 2005

for the California Air Resources Board.G.2 - 17

9 The DOE Gasoline/Diesel PM Split Study, Presentat ions by D. Lawson, E. Fujita and J. Schauer, California Air Resources Boa rd Seminars webpage; and DOE/NREL Gasoline/Diesel PM Split Study webpage

. 10 Fujita E., Campbell D., Arnott W.P., Chow J., an d Zelinska B., Evaluations of the Chemical Mass Balance Method for Determining Co ntributions of Gasoline and Diesel Exhaust to Ambient CarbonaceousAerosols (2007) Journal of the Air and Waste Management Association . 57, 721-740.

11 Lough G. and Schauer J., Sensitivity of Source Apportionment of Urban Particulate Matter to Uncertainty in Motor Vehicle Emissions Profiles(2007) Journal of the Air and Waste Management Association . 57, 1200-1213.

12 California Air Resources Board, 2007 Emission I nventory, Planning and Technical Support Division (extraction data 7/2008)

.

13 California Air Resources Board – Harbor Communi ties Monitoring Study website ( http://www.arb.ca.gov/research/mobile/hcm/hcm.htm ).

14 California Air Resources Board – Goods Movement Emission Reduction Plan, Appendix A (page A-52): ( http://www.arb.ca.gov/planning/gmerp/gmerp.htm ).