Appendix J 

Methodology for Estimating Ambient Concentrations of Particulate Matter 

from Diesel-Fueled Engine Emissions 

And 

Health Benefits Associated with Reductions in Diesel PM Emissions from 

In-Use On-Road Heavy-Duty Diesel-Fueled Vehicles 

J-1 

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 measurements of ambient chemical 

concentrations to infer source contributions, known as the tracer species 

method.1

 A basic assumption in this method is that the ambient concentration of 

a tracer species, C, may be used alone to infer the ambient concentration of a 

pollutant from a specific source, S: 

 S = α C, (1) 

where α is a scaling factor that is assumed to be independent of geographical 

location. In the estimation of DPM, we take C to be the annual-average ambient 

concentration of NOx and S to be the annual-average ambient concentration of 

DPM less than 2.5 μm in diameter (DPM2.5). The factor α relates the 

concentration of PM2.5 produced by diesel-fueled engine emissions to the 

concentration of NOx produced by all sources (NOx is not a unique tracer for 

diesel emissions). In the following section, we demonstrate that estimates for α

based on the emission inventory (EI) and on source apportionment (SA) studies 

agree within calculated uncertainties. We approximate 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 estimate a mean of 0.022 and 

standard deviation of 0.0005. The 2008 values are appropriate for estimating 

health impacts based on recent NOx data. A single value for α and associated 

dispersion may be used to infer DPM concentrations from measurements of 

ambient NOx concentrations in all air basins. 

Background 

The primary interest of the California Air Resources Board in the estimation of 

ambient DPM concentrations is for assessment of premature deaths. For this 

purpose, annual-average ambient concentrations of DPM are needed. These 

values are used to calculate lifetime average daily doses2

; multiplication of the 

average daily inhalation dose over 70 years with a cancer potency factor gives 

inhalation cancer risk estimates. In previous estimates3

 of diesel particulate 

matter less than 10 μm in diameter (DPM10) concentrations, the Air Resources 

Board (1998) used a method based on ambient total PM10 concentrations. For 

this approach, one of two factors, rural or urban - which were determined from a 

combination of chemical mass balance source apportionment studies (CMB) and 

emission inventory estimates (EI) - was used to scale ambient PM10 

measurement values to obtain estimates of DPM10 concentrations. Air basins 
J-2 

that had DPM10 to total PM10 emissions (emission inventory estimates) greater 

or less than the base case had these DPM10 estimates 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 inventory estimates. These 

components are also the primary weaknesses of the method: 1) PM10 contains 

predominantly crustal material, and the fraction associated with diesel PM is very 

small - at most approximately 6.5%; 2) early CMB studies may not be as 

accurate as more recent organic marker species-based CMB methods; and 3) 

early emission inventory estimates may not be as accurate in accounting for all 

source emissions as more recent models. 

We believe the use of scaled ambient NOx concentrations is more direct than the 

PM10 method to estimate DPM concentrations for several reasons, foremost 

among these are the close linkage4 of diesel-engine emitted NOx to total emitted 

NOx – about half of total NOx emissions are from diesel sources – and the 

relatively good correlation of ambient with recent emission inventory estimates 

for α. In addition, NOx emissions in California are predominantly from mobile 

sources, with diesel vehicle emissions accounting for approximately half the onroad mobile contribution and greater than 60% of the off-road contribution. The 

limited variation in different air basins of the diesel engine emissions contribution 

to total NOx is a reflection of the similarity of on- and off-road fleet composition 

and activity. In this respect, California likely differs from other regions of the 

country in the scarcity of important point stationary sources (such as power 

plants and refineries). Contributions from such point sources would introduce 

proximity dependencies and preclude the use of a simple NOx-scaling 

methodology to approximate DPM. 

Methods 

In this section, we develop estimates for the scaling factor α, the ratio of ambient 

DPM to total ambient NOx concentrations. First, we compare the ratio of ambient 

concentrations (DPM/NOx)SA from several source apportionment (SA) studies 

done in the late 1990's with the ratio of annual emissions (DPM/NOx)EI from the 

calendar year 2000 emission inventory (EI). Currently, the source apportionment 

studies are considered the best available methods for determining ambient DPM 

concentrations (at selected monitoring sites); agreement 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 comparison, we use the 

distribution of county emission inventory estimates for the (DPM/NOx)EI to 

determine an average and standard deviation for α for a baseline year 2008. 

In the following section, we estimate the ratio of DPM to NOx concentrations in 

ambient air for two year-long and several short-term source apportionment 
J-3 

modeling studies with co-located NOx measurements. These studies utilize 

specific organic chemical species, or molecular markers, in chemical mass 

balance (CMB) apportionment of PM, which is considered to be essential for the 

accurate separation of gasoline from diesel-fueled engine emissions. Even with 

such molecular markers, however, CMB modeling relies 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 particle phase must not be either 

depleted through volatilization or chemical reaction or increased through 

atmospheric reactions of precursor species. Second, all major sources that 

contribute chemical species used in CMB apportionment must be chemically 

characterized and included in the model, and the source profiles constructed 

must be sufficiently different from one another that problems of collinearity are 

not encountered. Recent work has investigated how well these assumptions are 

fulfilled in practice.5 In these studies, several challenges in application of the 

CMB method have been determined: emissions from vehicles exhibit significant 

profile-to-profile variability, which causes difficulties in construction of a single 

source profile; molecular markers undergo significant oxidation across a range of 

experimental conditions, which indicates that similar aging effects may affect 

apportionment studies; CMB analysis is very sensitive 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 diesel 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 emission inventory estimate that 

approximately half the diesel contribution to PM and NOx is from off-road 

sources, this poorly understood aspect of SA modeling warrants further 

qualifications in all CMB estimates of DPM. 

The first PM source apportionment work considered in this document was a yearlong effort conducted as part of the Children's Health Study (CHS 1995), in which 

James Schauer carried out organic chemical PM CMB studies for eleven sites in 

the South Coast Air Basin.6,7 Hence, eleven annual average values for DPM10 

and NOx concentrations are available from this work. Four of the sites are 

centrally located, while the rest are in more or less outlying areas. The second 

considered SA study was carried out as part of the California Regional 

Particulate Air Quality Study (CRPAQS 2000) by Desert Research Institute (DRI) 

in the San Joaquin Valley.8 From this work, seven estimates of annual average 

DPM2.5 and associated NOx 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 utilized similar specific 

organic chemical markers for combustion sources. In addition to these long-term 

measurements, side-by-side, organic marker CMB modeling was done by E. 

Fujita and J. Schauer at two sites for one week each in Southern California in 
J-4 

1999 as part of the Diesel-Gasoline Particulate Matter Split Study (DGPM 

2000).9,10,11

 

An unexpected result from the Diesel-Gasoline Particulate Split Study is that 

apportionment of PM2.5 depends on the specific carbon measurement method 

utilized (to determine relative organic/elemental carbon). Such differences in 

apportionment are currently not incorporated into uncertainty estimates. We also 

note that the DGPM study raised several important, but still unresolved, 

questions in the interpretation of CMB modeling results. Specifically, SA 

estimates may be very sensitive to the choice of source profiles used; e.g. the 

characteristics of the “average” driving cycle, categories of vehicles, composition 

of the fleet (e.g. inclusion of high emitter categories such as gasoline “smoker” 

vehicles) and, information about average high emitter organic species emissions. 

These aspects bear directly upon SA attribution estimates in a poorly understood 

manner. Results from several recent short-term apportionment studies that do 

not utilize CMB modeling are also included below; these studies provide further 

evidence for a wide range of DPM estimates. Based on a favorable comparison 

of SA and EI results, we develop an estimate of the DPM/ NOx ratio based on the 

EI. 

Results 

Source apportionment of PM collected in the South Coast 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 represent eleven communities in 

the South Coast Air Basin; these include four urban sites (Mira Loma, North Long 

Beach, Riverside, Upland), two sites in a mountainous region (Alpine and Lake 

Arrowhead), one desert site (Lancaster), three rural coastal sites (Atascadero, 

Lompoc, and Santa Maria), and one rural inland site (Lake Elsinore). NOx

measurements and filter samples (organic chemical marker measurements) were 

taken at the same locations. Although each filter PM sample was collected over a 

two week interval, filters from each site were composited into three seasonal time 

periods. Each composited sample was analyzed for organic marker compounds 

and utilized in chemical mass balance source apportionment modeling. We 

concentrate on using annual average results from the apportionment study, and 

show seasonal trends below. Figure 1 shows site-to-site variation of source 

apportionment estimates of the ratio (annual average DPM10 

concentration)/(annual average total NOx concentration) from the CHS (1995). A 

straight average over all eleven sites of the ratio DPM10/NOx, gives the mean 

value as 0.030 (0.010), where here and in the following text the value in 

parentheses denotes the standard deviation. An alternative estimate based on 

regression of DPM10 concentrations against ambient NOx concentrations (over 

eleven sites) gives 0.027 (0.001); see Fig. 2. In this, and all following 

regressions, the intercept is set to zero, which makes the regression less 

sensitive to scatter and is physically meaningful, as one expects that diesel 

emissions tend to zero with total NOx emissions. 
J-5 

As expected, the dispersion in α is much larger over individual measurements of 

DPM/NOx than it is for the regression coefficient. It is unclear which choice of 

error is best for use in personal exposure estimates that use population 

weighting. The site-specific DPM/NOx values, Fig. 1, are best estimates for local 

DPM/NOx ratios, though specific meteorology and lack of population weighting 

may emphasize unrepresentative values. Similarly, DPM/NOx ratios obtained 

from linear regression (with zero intercept) are highly influenced by data with 

large NOx and/or DPM values. Because individual measurements for the ratio 

DPM/NOx retain site-specific variability in concentrations, we believe the statistics 

from individual measurements are better estimates than regression coefficients 

for DPM exposure-related work. We take the standard deviation of the 

distribution of DPM/NOx values as the measure of uncertainty in α for SA studies. 

Figure 3 shows DPM10 vs. NOx 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 

 Figure 1 

 Figure 2 

CHS 1995 Ambient DPM10 vs. NOx

 y = 0.027x

0

0.5

1

1.5

2

2.5

3

3.5

4

0 20 40 60 80 100 120 140

NOx (μg/m3)

DPM10 (μg/m

3) 
J-6 

and smallest in winter. This variation can not be explained completely by EI 

estimates (summer, winter), which show much less seasonal variability, and 

indicates further sources of uncertainty in the use of short timescales for scaling 

NOx. 

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 also 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 methods (the National Institute for 

Occupational Safety and Health (NIOSH) method in CHS and the Interagency 

Monitoring of Protected Visual Environments (IMPROVE) protocol, also known 

as thermal/optical reflectance (TOR), in CRPAQS), different chemical markers 

and source profiles (see refs. 6-8). A straight average of the ratios of SA 

DPM2.5 to NOx 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 NOx, 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/NOx values as an indicator of the 

variability in ambient ratios. 

We note that the relative variability of DPM/NOx in both studies is very large: the 

coefficient of variation (standard deviation/average) ≈ 0.5. We believe this large 

uncertainty in SA estimates best captures local variation of source composition, 

mixing, chemical reactions and other factors. Hence, this order of uncertainty is 

expected in any estimate of DPM based on ambient NOx concentrations. 

A recent short-term SA modeling study investigated the sources of uncertainties 

in the relative contributions of diesel and gasoline vehicle emissions to PM2.5 in 

the South Coast (2001) – the Gasoline/Diesel PM Split Study.9,10,11 In this work, 

James Schauer (University of Wisconsin, Madison) and Eric Fujita (Desert 

Research Institute) collected samples side-by-side for sources (57 light duty 

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

NOx

 (μg/m3)

DPM10 (μg/m

3

)

Jan-April

May-Oct

Nov-Dec
J-7 

gasoline and 34 heavy duty diesel vehicles) and ambient air (two sites Los 

Angeles, N. Main, and Azusa), and carried out independent chemical and SA 

data analyses. The DRI study did not directly apportion PM2.5 to diesel sources; 

instead, apportionment of total carbon and elemental carbon were determined. 

To estimate corresponding DPM2.5 concentrations, we scale reported diesel EC 

values by a factor determined from DRI's diesel source profile (DPM2.5 = 1.36 

DEC2.5, where DEC2.5 denotes elemental carbon in PM2.5 apportioned to 

diesel engine emissions). The resulting SA results show a lack of agreement 

between DPM2.5 estimates: apportionment of PM to diesel emission sources by 

the two groups differ by approximately a factor of two; see Figures 5 and 6. 

Estimates for DPM2.5/NOx are: 0.010 (0.003) for Schauer and 0.023 (0.004) for 

Fujita. Because ambient and vehicle emission samples were collected side-byside, these results indicate that the disparity in DPM estimates is driven by 

differences in source apportionment methodology, which includes differences in 

carbon measurement methods (NIOSH vs. IMPROVE), organic 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/NOx = .017 (.009). 

Figure 4 

Two recent studies that used simpler methods to apportion PM2.5 to diesel 

sources were done by Livermore National Laboratory (LLNL) in 2007 and ARB's 

Monitoring and Laboratory Division (MLD) in 2003. LLNL utilized fossil carbon 

measurements, based on Carbon 14, and MLD utilized n-octadecane as a diesel 

tracer. In these studies, DPM concentrations were estimated to be on the order 

of 1 μg/m3; these values would presumably support a lower DPM/NOx ratio of .01 

(with a likely relative uncertainty of 50%). More specifically, LLNL determined the 

average fossil elemental carbon (FEC) at Wilmington as approximately 1.05 

μg/m3 (based on the limited data), and the average FEC at Roseville as 

approximately 0.65 μg/m3

. Assuming that all FEC is from diesel emissions and 

that OC emissions from diesels are small in comparison, these values may be 

considered upper bound DPM concentrations. MLD's study yielded estimates of 

DPM for Wilmington as 1.2 μg/m3 and Sacramento as 0.8 μg/m3, and the 

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/m3)

DPM (μg/m

3

)
J-8 

statewide average as 1.0 μg/m3. These values, however, differ by over a factor of

two from the recent MATES III organic marker CMB estimate of >3 μg/m3

 in 

2004-2005 (in Wilmington). Therefore, while these two independent estimates 

provide support for the lower end of DPM/NOx ratio, considerable uncertainty 

remains in their interpretation. 

Figure 5 

Figure 6 

A comparison of the above SA estimates with the emission inventory can not be 

made directly, as emission inventory estimates are for whole counties while SA 

estimates are specific to monitoring sites and implicitly take into account 

meteorology, chemistry and deposition. Hence we compare average values for 

DPM/ NOx from the previous SA studies with EI estimates of DPM to total NOx

emission ratios. For this purpose, the EI estimates for DPM and total NOx

emission rates for individual counties are utilized.

12 These estimates may be 

visualized as tons of pollutants emitted each day into a well-mixed box covering 

each county, with removal rates of DPM and NOx 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: deposition, chemical reactions, 

and flow into and out of air basins. Further, while the atmospheric lifetimes for 

DPM and NOx are typically very different (greater and less than a few days, 

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/m3

)

DPM2.5 (μg/m

3

)

Fujita

Schauer
J-9 

respectively), which would bias the ratio of DPM/ NOx toward higher values, the 

mean residence time of an air parcel in a coastal air basin is typically less than a 

day, which would dominate the reaction and deposition rates and effectively 

make the rate of removal for NOx and PM the same. In the following, we assume 

this dominance of air parcel residence time on removal rates, and take the 

removal rates for NOx and DPM as equal. 

Figure 7 

Figure 8 

To compare the above source apportionment estimates of DPM/NOx with 

emission inventory estimates, we utilize ARB emission inventory estimates for 

the year 2000 (the SA studies were conducted in 1995 CHS in SoCAB, 2000 

CRPAQS in SJV, and 2001 GDPM in SoCAB). The emission inventory estimates 

incorporate spatial and temporal averaging over large scales and therefore may 

be used to estimate average ambient DPM/NOx ratios directly (in the following 

figures for the emission inventory, we abbreviate total NOx by tNOx). A plot of 

(DPM/tNOx)ee against tNOxee for all counties in California is shown in Fig. 7. This 

scatter plot shows that the county-wide ratios DPM/tNOx are clustered about an 

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/tNOx
J-10 

average and that the dispersion depends on the average annual tNOx emission 

rate. The plot also shows that a separation of high-NOx from lower-NOx emission 

counties occurs with a division around an annual average of 80 tons per day. 

(High-NOx counties are listed in the next section of this document.) In California, 

NOx inventories are dominated by on-road and off-road mobile sources; overall, 

diesel engine emissions contribute approximately half of the on-road NOx

emissions and greater than 60% of the off-road mobile emissions. To better 

capture exposure-related estimates of DPM/tNOx, each county value is weighted 

by its population; weighted histograms are approximated by normal distributions. 

Figure 8 shows histograms for the ratio of DPM/tNOx over high- and low-NOx

emission counties. The mean and standard deviation for these distributions are: 

0.023 (0.003) for the high-NOx counties and 0.023 (0.006) for the low-NOx

counties. Hence, population weighted distributions for α in high and low-NOx

counties may be described by normal distributions with same mean value and a 

dispersion that depends on NOx emissions characteristics. To develop a single 

California-wide approximation, we take the (larger) dispersion for the ratio of 

DPM/tNOx in low-NOx counties as measure of the variability that is encountered 

locally within air basins. 

Figure 9 

The above estimates of the ratio DPM/tNOx from the 2000 EI populationweighted and SA studies compare well, given the relatively 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). Figure 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 California 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 and EI estimates for the 

scaling factor α - the ratio of DPM to total NOx - for years close to 2000, we 

propose the use of a single value of 0.023 for α in estimating the populationDPM/NOx Estimates

0

0.01

0.02

0.03

0.04

0.05

EI-2000 CHS-1995 CRPAQS2000GDPM2001
J-11 

weighted annual average ambient DPM concentration in California. The resultant 

DPM estimates from this approach depend upon the network of ambient NOx

measurements from the ARB monitoring sites. In the following, we outline a 

method to calculate such averages. First, the annual average DPM concentration 

at each monitoring site is estimated as the product of annual average NOx

concentration value and α. The uncertainty associated with this DPM estimate is 

the product of the annual average NOx measurement value and the low- NOx

county standard deviation, .006. The following twelve counties are considered 

high-NOx emission counties (annual average NOx > .80 tons per day): Los 

Angeles, San Bernardino, Kern, San Diego, Orange, Riverside, Alameda, 

Fresno, Santa Clara, Contra Costa, San Joaquin, and Sacramento. The 

remaining 46 counties are considered low-NOx emission counties. From this set 

of spatially discrete DPM concentration estimates a smooth DPM concentration 

surface may be constructed using kriging or other interpolation methods. In 

remote areas without monitoring sites, the smoothing method may be modified to 

incorporate a minimum concentration, which would reflect a nonzero background 

value (or such areas may be removed, if the population is sufficiently small). 

Second, census data for California is used to approximate a population density 

surface (population fraction per unit area) and the (pointwise) product of the 

population density and DPM concentration surfaces is 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 average DPM concentration; in 

particular, integration of the product may be performed over the state to give an 

average population-weighted ambient DPM concentration. Once ambient diesel 

PM concentrations have been estimated for a baseline 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 NOx-scaling method with the projections from the previous PM10-

scaling method3

 is given in Table 1. The overall agreement between DPM 

concentration estimates for the six highest population air basins is very good. 

More specifically, the six highest population air basins contain over 90% of the 

population of California and contribute greater than 96% of the population 

weighted DPM concentration; in each of these air basins, the difference between 

the proposed and the previous DPM concentrations is less than 20% of the 

previous estimate. It should be noted that the previous estimates use a baseline 

year 1990 and are projected forward by a decade based on linear rollback, and 

so do not constitute the best approximation for year 2000. Greater variation of 

agreement between proposed and previous methods is found for lower 

population air basins. Many factors contribute to this variability, several of which 

are: the larger dispersion in the DPM to NOx ratio (.006), uncertainty in 

application of PM10 scaling method to regions less similar to the SJV, and 

greater influence of localized emission sources. Altogether, the proposed, 

population-weighted DPM concentration for California is increased by 11% over 

the previous estimate. This high level of agreement between the populationweighted DPM estimates gives confidence that the proposed method is 
J-12 

consistent with the previous technique and represents a viable approach to 

estimate DPM exposure. 

A final application of the NOx-scaling approach is to estimate α for a more recent 

year - 2008 (to be used as a baseline for health impact 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 unweighted fitting 

of the distribution of county DPM2.5/NOx ratios to a normal distribution; see Fig. 

10. [Emissions from ocean going vessels were down-weighted by an adjustment 

factor, 0.10 in the South Coast and 0.25 in other areas, to reflect their decreased 

contribution to inland areas. 14] As expected, because of the relatively slow 

turnover of diesel engines, the mean and standard deviations are similar to those 

for 2000. The low- and high-NOx counties, exhibit the same α values: α = 0.022 

with (0.006) for low-NOx counties and (0.003) for high-NOx emission counties 

(the demarcation between low- and high-NOx counties was taken as 60 tpd 

NOx). 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 Community Monitoring Study 

(HCMS).13 This program was conducted in 2007 to characterize the spatial 

variations in concentrations of toxic air contaminants (TACs) and their copollutants within the communities of Wilmington, West 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, petroleum refineries, 

intermodal rail facilities and the greatest concentration of diesel traffic in the Los 

Angeles metropolitan area. To avoid sites heavily impacted by near sources, we 

 Figure 10

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/NOx
J-13 

concentrate on neighborhood sites from the saturation monitoring network 

operated by the Desert Research Institute (denoted LWIN, WCOL, WGUL, 

WLAK, WMAR, WMCD and WMCF in the report). This study consisted of 7-day 

time-integrated sampling for four consecutive weeks in four seasons. NOx was 

measured with Ogawa passive samplers and PM2.5 was collected on 7-day 

integrated Teflon and quartz filters with Airmetrics MiniVol samplers. Organic 

carbon (OC) and elemental carbon (EC) were analyzed by thermal optical 

reflectance (TOR) method using the IMPROVE (Interagency Monitoring of 

Protected Visual Environments) temperature/oxygen cycle (IMPROVE TOR). 

Several caveats should be noted in the interpretation of this data. First, the high 

density of emission sources in the HCMS area may produce ambient NOx 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, which is predominantly but not 

exclusively from diesel emissions. Under these limitations, DPM2.5 was 

estimated from EC concentrations as 65% of the total carbon from diesel engine 

emissions (DTC): EC=0.65 DTC. This conversion is adopted because the carbon 

fraction of EC in heavy-duty diesel truck emissions during city suburban driving 

has been measured as approximately 0.65 (City Suburban Heavy Vehicle Route 

dynamometer cycle, ref. 10) and the general agreement between TC collected on 

quartz filters and PM2.5 mass collected on Teflon filters 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 possibly other co-pollutants, 

was not attempted; such an investigation would yield 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 Report 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 directly proportional to measured 

EC concentrations, where the proportionality constant was determined from the 

slope of the correlation between total carbon and EC 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 carbon and organic matter 

portions of DPM: 

 

 DPM = EC + 1.46 (DPC – EC), (2) 

where 1.46 is the ratio of diesel particulate organic matter to DPC from the 

Gasoline/Diesel PM Split dynamometer testing of diesel 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 evaluation study conducted in 

the South Coast Air Basin (Basin) from April 2004 to March 2005. In this study, a 

scaling factor for EC of 1.72 was developed based on 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 
J-14 

NOx concentration: 0.038 (0.005), 0.025 (0.004), and 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 NOx ratios and that the largest source of uncertainty is 

in the choice of scaling factors for EC (and total carbon). The largest value of 

DPM/NOx 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 agree relatively well with the 

2008 EI estimate of 0.022 (.005) and provide further support for use of a NOxscaling methodology (with associated uncertainty interval) to estimate DPM in 

California. 

In summary, the proposed method to estimate ambient DPM concentrations has 

distinct advantages over the previous PM10 method as well as several important 

limitations. The primary strengths of the method include the significant 

connection between DPM and NOx, simple application, estimates of uncertainty 

intervals, and ability to capture sub-county variations in DPM concentrations. In 

addition to these strengths, the approach is tied directly to the ARB emission 

inventory, and links bottom-up EI estimates with top-down SA estimates. Several 

limitations and caveats also bear on applications of 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 scales, proportional removal rates 

for NOx and DPM (including air basin outflow), proportionally time-uniform 

emission rates for all NOx and DPM sources, etc. Verification of these 

assumptions is in general not possible; instead, agreement between EI and SA 

estimates is taken as best available evidence for support. The uncertainty 

intervals produced by the estimation method are based on variations between 

low-NOx counties and reflect differences in relative emission sources (primarily 

diesel vs. non-diesel mobile sources). As such, the uncertainty describes the 

confidence in α to accurately describe local NOx emission sources. For areas 

outside California, in which the NOx emission inventory has a significant 

contribution from non-mobile sources (e.g. power plants 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 understanding of the contribution of 

various emission sources to ambient concentrations of both gases and particles. 

In this respect, source apportionment work that utilizes organic marker species is 

the probably best available approach; ideally, highly time-resolved studies would 

allow better characterization and support for single species scaling estimates, 

such as the NOx-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. 
J-15 

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 Guidelines, Appendix D Risk 

Assessment Procedures to Evaluate Particulate Emissions from DieselFueled Engines, 2003, Office of Environmental Health Hazard Assessment. 

3 Estimate of Ambient PM10 Concentrations from Directly Emitted Emissions 

from Diesel Engines, Appendix III, Part A, Exposure Assessment 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 Report: SourceApportionment of Primary Organic Carbon in the Eastern United States 

Combining Receptor-Models, Chemical Transport Models, and Laboratory 

Oxidation Experiments (USEPA National Center for Environmental Research, 

2007). The report and links to associated papers are available at: 

http://cfpub.epa.gov/ncer_abstracts/INDEX.cfm/fuseaction/display.abstractDet

ail/abstract/7503/report/F. 

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’s Health 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 Association. 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 Measurements, Final 

Report, 2005 for the California Air Resources Board. 
J-16 

9 The DOE Gasoline/Diesel PM Split Study, Presentations by D. Lawson, E. 

Fujita and J. Schauer, California Air Resources Board Seminars webpage; 

and DOE/NREL Gasoline/Diesel PM Split Study webpage. 

10 Fujita E., Campbell D., Arnott W.P., Chow J., and Zelinska B., Evaluations of 

the Chemical Mass Balance Method for Determining Contributions of 

Gasoline and Diesel Exhaust to Ambient Carbonaceous Aerosols (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 Inventory, Planning and 

Technical Support Division (extraction data 7/2008). 

13 California Air Resources Board – Harbor Communities 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).
J-17 

Health Benefits Associated with Reductions in Diesel 

PM Emissions from In-Use On-Road Heavy-Duty 

Diesel-Fueled Vehicles 

I. Overview 

The estimation of premature death related to PM2.5 exposure presented below is 

based on a peer-reviewed methodology developed by the U.S. Environmental 

Protection Agency (U.S. EPA, 2010) and an incidents-per-ton (IPT) methodology 

(ARB, 2006). Staff estimates that approximately 21,000 tons of PM2.5 and 

200,000 tons of NOX emitted from on-road trucks, buses and drayage vehicles 

are reduced through implementation of this regulation in the years 2010-2025. 

The reduction of premature deaths associated with emission reductions of both 

primary PM and secondary PM (produced in the atmosphere from the precursor 

NOX) are presented in Table 1. 

Table 1: Estimate of Premature Deaths Avoided Associated with Emission 

Reductions from Implementation of the On-Road Truck

Regulation (2010-2025)* 

 

Pollutant 

# of Cases 

95% C.I. 

(Lower Bound)

# of Cases 

(Mean) 

# of Cases 

95% C.I. 

(Upper Bound) 

PM 1,700 2,300 2,800

NOx 1,000 1,300 1,600

Total 2,700 3,500 4,400

* Health effects from primary and secondary PM are labeled PM and NOX, respectively. 

Details of the methodology are described below. Sections II through VIII 

describe how the health impact estimation methodology was developed, 

including development of the IPT factor and its application for the Truck, Bus, 

and Drayage Rule. 

II. Incidents-per-ton Factors 

ARB used a methodology known as the incidents-per-ton (IPT) to quantify the 

health benefits of regulatory controls to reduce PM2.5. This method, which was 

peer reviewed in 2006 (ARB, 2006), yields results similar to those of a more 

sophisticated modeling analysis, but can be used more efficiently. It is similar in 
J-18 

concept to the methodology developed by the U.S. EPA for similar estimations 

(Fann et al., 2009). Details on the methodology used to calculate these 

estimates can be found in Appendix A of the Emission Reduction Plan for Ports 

and Goods Movement in California, as documented in the Emission Reduction 

Plan for Ports and Goods Movement (ARB, 2006). 

The basis of the IPT methodology is the approximately linear relationship which 

holds between changes in emissions and estimated changes in health outcomes. 

This is a consequence of the following observations: 

(1) Across the range of ambient PM2.5 concentrations encountered in 

California, modeled changes in premature mortality are approximately 

proportional to changes in ambient pollutant concentrations. 

(2) For primary pollutants such as diesel particulate matter, changes in 

ambient concentrations are approximately proportional to changes in 

emissions. 

(3) For secondary pollutants such as ammonium nitrate aerosol, a linear 

relationship may be used as a first-order approximation to the relationship 

between ambient concentration and emissions of NOX. There may be 

cases where the relationship between emission of NOX and ammonium 

nitrate aerosol is greater than or less than linear. 

Therefore, premature death is approximately proportional to emissions, and can 

be estimated by multiplying emissions by a scaling factor, the IPT factor. IPT 

factors are derived by calculating the premature death 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 NOX were developed for 

2008, adjusted to account for changes due to the recession, 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 proportional to population, the result 

is adjusted by the ratio of the population in the target 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 reductions brought about by 

implementation of this regulation, ARB applied the methodology used by 

U.S. EPA in the 2010 Quantitative Health Risk Assessment that was developed 

to estimate premature deaths associated with PM2.5 exposure. In this 

assessment of health benefits, ARB calculated estimates for premature 

cardiopulmonary death. ARB is emphasizing cardiopulmonary deaths because 

they are the most frequent causes of death, and category of deaths most strongly 

related to PM2.5 exposure (U.S. EPA, 2010). 
J-19 

Calculation of the premature death incidence associated with PM2.5 exposure 

requires baseline incidence rates, population data, ambient concentration of 

PM2.5, and a concentration-response function relating changes in PM2.5 

exposure to changes in mortality incidence. This data is available in a 

spreadsheet as part of the rulemaking package. Calculations were made based 

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 monitor in the state, and 

interpolation of concentrations between monitors to estimate exposure for each 

census tract. Since diesel engines emit particles directly (primary particles), as 

well as gases that convert to PM2.5 through atmospheric chemical reactions 

(secondary particles), exposure estimates are made for both, in order to capture 

the full impact of diesel engines on premature death. 

Population-weighed exposure to primary and secondary PM2.5 was estimated 

based on monitor-specific concentrations. Even with an extensive air quality 

monitoring network, the mortality quantification method requires estimation of 

exposure between monitors across a geographic area. ARB uses a standard 

spatial interpolation method known as inverse distance-squared weighting which 

was peer reviewed in 2007 (Shepard, 1968; Goodin and McRae, 1979). This 

method yields reasonable accuracy in estimating pollutant concentrations near 

monitoring stations, although when distance from the monitoring station 

increases, the uncertainty in the interpolated concentration also increases. This 

method gives more accurate estimates of concentration in areas with a large 

number of monitors with good spatial coverage and low variability in 

concentration. When data are abundant, most simple interpolation techniques 

give similar results (Jarvis et al., 2001). When data are sparse, however, the 

assumption made about the underlying variation in PM2.5 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 geographical 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 boundaries do not exactly 

overlap. Numerous variants of the technique exist, but for the purpose of this 

analysis the simplest form, which uses area of polygon intersection, was 

employed (Goodchild and Lam, 1980; Fotheringham and RogersonFlowerdew 

and Green, 1994). 
J-20 

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 relatively precise estimates. In rural 

areas where the population is distributed unevenly over large census tracts, 

estimates are less precise. 

IV. Population at the Census Tract Level 

Age-resolved population data at the census tract level, for the 2000 Census, 

were obtained from the U.S. Census Bureau (U.S. Census Bureau). These were 

projected to 2006-2008 using age-resolved county population projections from 

the California Department of Finance (CDOF). 

Age-specific growth factors for each county, for each year, were computed from 

the CDOF projections by dividing each county population for the target year by 

the county population for the year 2000. Since each census tract lies entirely in a 

county, these growth factors were applied to each census tract in the county, 

each age group separately. Population was projected for ten-year age groups 

25-34 through 75-84, and for age 85 and older. 

This method of projection reflects growth in overall 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 Rate 

Baseline incidence rates vary by age bracket. Incidence 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 2005, 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 generally not recorded. However, 

the Federal Information Processing Standards (FIPS) city code and the ZIP code 

were usually recorded. The FIPS city code unambiguously identifies the county, 

but was sometimes invalid, unrecorded, or recorded as “unknown”. When the 

FIPS code was not available it was sometimes possible to identify the county 

from the ZIP code, but ZIP codes can overlap multiple 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 of records included invalid 

dates. The breakdown of records was as follows: 

 
J-21 

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 adjustment was made to compensate 

for excluded records. 

In some cases the cardiopulmonary incidence was extremely 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 variability of the incidence is high. 

However, since this represents a very small fraction of California’s population the 

effect on statewide mortality estimates is negligible. Large counties show little 

year-to-year variability. 

Baseline mortality rates are subject to other sources 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 indirectly from annual average 

NOX concentrations by applying a conversion factor, α, of 0.022. Derivation of 

the conversion factor is described in the first section of this Appendix, 

Methodology for Estimating Ambient Concentrations of Particulate Matter from 

Diesel-Fueled Engine Emissions. Annual average NOX concentrations for 2006-

2008 were retrieved from ARB’s air quality database (ARB ADAM). 

Because there is statistical uncertainty in α, this uncertainty carries through in the 

mortality estimates from diesel PM. The overall uncertainty in the mortality 

incidence associated with diesel PM exposure was estimated by taking into 

account both the variability in α and the uncertainty in the β coefficient of the 

concentration-response function (described in Section III). Since this is very 

difficult to estimate analytically, parametric bootstrapping was used to obtain 

uncertainty estimates. 

Parametric bootstrapping (Efron and Tibshirani, 1993) is a technique for 

estimating statistical properties of a complicated function of parameters with 

uncertain values. Probability distributions are assumed for the parameters, 

random samples are drawn from those distributions, and the function is 

calculated for each choice of parameters. The statistical properties of interest, in 

this case the confidence interval, can then be estimated from the values the 

function takes over the random sample. 
J-22 

In this case, α and β were assumed to have normal (Gaussian) distributions. 

The conversion factor α was assumed to have a mean of 0.022 and standard 

deviation of 0.05 as described in section VII. The mean of the β 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, 100,000 sets of values for α

(one for each NOX monitor) and values for β were randomly generated. The 

corresponding cardiopulmonary incidence for each choice of parameters was 

then computed. Since the sample exhibited a fairly symmetrical distribution, we 

computed the 0.025 and 0.975 quantiles of the sample to estimate the 

confidence interval. 

VII. Annual PM2.5 ammonium nitrate concentrations 

In addition to directly emitted PM, diesel exhaust contains NOX, 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 2006-2008. The concentrations 

were computed from ambient nitrate ion concentrations, using PM10 data 

combined from two sources: the regular air quality monitoring network and the 

IMPROVE visibility network (IMPROVE Visibility Network). 

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 six days, using size-selective inlet 

(SSI) sampler. Samples from some monitors are further analyzed to determine 

the concentration of nitrate and other constituents. During 2006-2008, nitrate 

data were available from 45 monitors. Since nitrate 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 equivalent to 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 national IMPROVE visibility 

network operated 18 PM2.5 nitrate monitors, mainly in national parks and other 

remote locations (IMPROVE Visibility Network). These 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 close to natural background 

levels, the effect of instrument bias is considered negligible, and the data were 

treated as equivalent to the SSI data. IMPROVE data were retrieved from the 

project web site (IMPROVE Visibility Network). 
J-23 

Daily samples were aggregated by monitor to obtain annual averages. In order 

to avoid potential seasonal bias due to missed samples, the samples were 

aggregated into quarterly means, and the four quarterly 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 year to be considered valid, all 

four valid quarters were required. 

To convert from nitrate ion concentration to ammonium nitrate (NH4NO3) 

concentration, the annual averages were multiplied by the ratio of the molecular 

weight of ammonium nitrate to that of the nitrate ion, 1.29. 
J-24 

References 

ARB ADAM. Air Quality Database web site. Available at: 

http://www.arb.ca.gov/adam/

(File: Compiled Appendix Tables ONROAD 25oct10.xls)

ARB AQMN. Air Quality Monitoring Network web site. Available at: 

http://www.arb.ca.gov/aqd/aqmoninca.htm

(File: ARB AQMN.pdf) 

CDOF. California Department of Finance population projection web site. 

Available at: http://www.dof.ca.gov/research/demographic/reports/view.php

(File: Compiled Appendix Tables ONROAD 25oct10.xls)

CDPH. California Department of Public Health statistics web site. Available at: 

http://www.cdph.ca.gov/data/statistics/Pages/default.aspx

(File: Compiled Appendix Tables ONROAD 25oct10.xls)

Efron E, Tibshirani R. 1993. An Introduction to the 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 benefits of reducing a ton of air 

pollution Air Quality. Atmosphere & Health. 2:169-176. 

Flowerdew R, Green M. 1994. Areal interpolation and types of data. Chapter 7 in: 

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 Comparison of Interpolation 

Methods for Sparse Data: Application to Wind and Concentration Fields. J 

Applied Meteor. 18:761-771. 

IMPROVE Visibility Network web site. Available at:

http://vista.cira.colostate.edu/improve/Overview/Overview.htm

(File: Compiled Appendix Tables ONROAD 25oct10.xls)

Jarvis, CH Stuart, N. 2001. A comparison among strategies for interpolating 

maximum and minimum daily air temperatures. Part II: The interaction between 

number of guiding variables and the type of interpolation method. J. Appl. 

Meteor. 40, 1075-1084. 
J-25 

Shepard D. 1968. A two-dimensional interpolation function for irregularly-spaced 

data. Proceedings of the 1968 ACM National Conference. Pp. 517–524. 

US Census Bureau. American Fact Finder web site. Available at: 

http://factfinder.census.gov/home/saff/main.html?_lang=en 

(File: Compiled Appendix Tables ONROAD 25oct10.xls)

U.S. EPA. 2009. Integrated Science Assessment for PM (Final Report). Available 

at: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_isa.html

U.S. EPA. 2010. Quantitative Health Risk Assessment for Particulate Matter 

Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/data/PM_RA_FINAL_June_2010.pdf