Long-term trends in the ambient PM2.5- and O3-related mortality burdens in the United States under emission reductions from 1990 to 2010

Concentrations of both fine particulate matter (PM2.5) and ozone (O3) in the United States (US) have decreased significantly since 1990, mainly because of air quality regulations. Exposure to these air pollutants is associated with premature death. Here we quantify the annual mortality burdens from PM2.5 and O3 in the US from 1990 to 2010, estimate trends and inter-annual variability, and evaluate the contributions to those trends from changes in pollutant concentrations, population, and baseline mortality rates. We use a fine-resolution (36 km) self-consistent 21-year simulation of air pollutant concentrations in the US from 1990 to 2010, a health impact function, and annual county-level population and baseline mortality rate estimates. From 1990 to 2010, the modeled population-weighted annual PM2.5 decreased by 39 %, and summertime (April to September) 1 h average daily maximum O3 decreased by 9 % from 1990 to 2010. The PM2.5-related mortality burden from ischemic heart disease, chronic obstructive pulmonary disease, lung cancer, and stroke steadily decreased by 54 % from 123 700 deaths year−1 (95 % confidence interval, 70 800– 178 100) in 1990 to 58 600 deaths year−1 (24 900–98 500) in 2010. The PM2.5-related mortality burden would have decreased by only 24 % from 1990 to 2010 if the PM2.5 concentrations had stayed at the 1990 level, due to decreases in baseline mortality rates for major diseases affected by PM2.5. The mortality burden associated with O3 from chronic respiratory disease increased by 13 % from 10 900 deaths year−1 (3700– 17 500) in 1990 to 12 300 deaths year−1 (4100–19 800) in 2010, mainly caused by increases in the baseline mortality rates and population, despite decreases in O3 concentration. The O3-related mortality burden would have increased by 55 % from 1990 to 2010 if the O3 concentrations had stayed at the 1990 level. The detrended annual O3 mortality burden has larger inter-annual variability (coefficient of variation of 12 %) than the PM2.5-related burden (4 %), mainly from the inter-annual variation of O3 concentration. We conclude that air quality improvements have significantly decreased the mortality burden, avoiding roughly 35 800 (38 %) PM2.5-related deaths and 4600 (27 %) O3-related deaths in 2010, compared to the case if air quality had stayed at 1990 levels (at 2010 baseline mortality rates and population). Published by Copernicus Publications on behalf of the European Geosciences Union. 2 Y. Zhang et al.: Long-term trends in the ambient PM2.5and O3-related mortality burdens


Introduction
The 2015 Global Burden of Disease (GBD) study lists air pollution as the fourth-highest ranking global mortality risk factor (GBD 2016). The most recent GBD estimates that exposure to ambient particulate matter (particulate size less than 2.5 µm, PM 2.5 ) causes 4.2 million (95 % confidence interval (CI), 3.7-4.8 million) deaths globally, with an additional 254 000 (97 000-422 000) deaths globally caused by exposure to ozone (O 3 ) . For the United States (US), the same study estimated a mortality burden attributable to ambient PM 2.5 of 88 400 (66 800-115 000) deaths, and 11 700 (4400-19 600) deaths attributable to O 3 in 2015 . Adopting similar methods, the US burden of disease study lists ambient PM 2.5 and O 3 pollution as the 8th and 15th leading risk factors in the US in 2010 (Murray and Collaborators US Burden of Disease, 2013).
Over recent decades, emissions of air pollutants within the US have significantly decreased and air quality has improved. For example, between 1990 and 2010, total US anthropogenic emissions are estimated to have declined by 48 %, 49 %, 67 %, 60 % and 34 %, for NO x (= NO + NO 2 ), non-methane volatile organic compounds (NMVOCs), SO 2 , CO and PM 2.5 , respectively . EPA observations showed that the US average annual PM 2.5 concentration declined by 42 % from 2000 to 2016, and maximum daily 8 h average (MDA8) O 3 declined by 22 % from 1990 to 2016 (US EPA, 2017). These air quality improvements were likely mainly driven by ambient air quality standards, and federal and state implementation of stationary and mobile source regulations, especially the 1990 Clean Air Act (CAA) Amendments, the 2002 NO x State Implementation Plans (SIP) Call, and the Cross-State Air Pollution Rule (Chestnut and Mills, 2005;US EPA, 2011), together with other rules to reduce anthropogenic emissions from light duty, heavy duty, and nonroad vehicles (Fann et al., 2012b;US EPA, 2014). Other changes in energy and emission control technology that occurred concurrently with air quality regulations also helped to improve air quality. These decreased concentrations are expected to have brought substantial benefits for public health in the US, but assessing the health benefits requires quantification of changes in human exposure relating where air quality has improved to how population and baseline mortality are distributed.
Several recent studies have assessed the global (Anenberg et al., 2010;GBD, 2015GBD, , 2016Lelieveld et al., 2015;Silva et al., 2013Silva et al., , 2016b or national (Fann et al., 2012a;Punger and West, 2013) burdens of disease attributable to air pollution. However, less effort has been made to understand how these burdens evolve over time. Cohen et al. (2017) used estimates of air pollutant concentrations from a combination of air quality model simulations and satellite and surface observations to study the global and national temporal trends of the burdens of disease attributable to ambient PM 2.5 and O 3 (only model results were used for O 3 ), at 5-year inter-vals from 1990 to 2015. Two other studies (Butt et al., 2017;Wang et al., 2017) used coarse-resolution model simulations to study the global/hemispheric PM 2.5 mortality burdens for the past few decades. Fann et al. (2017) estimated the annual mean PM 2.5 concentration in the US from monitoring data and its all-cause mortality burden in 1980, 1990, 2000 and 2010. Epidemiological studies have also inferred how health effects have changed through time in the US (Correia et al., 2013;Pope et al., 2009). However, previous studies have not estimated mortality burdens for both PM 2.5 and O 3 in the US for several years in succession or investigated the different drivers for mortality trends or the inter-annual variability of the mortality burdens. The inter-annual variability analysis indicates whether the mortality burden at a given year, such as the results from Cohen et al. (2017) and Fann et al. (2017), is representative of years around it. In the US, such changes in the air pollution mortality burdens can support decision making on air pollution control policies. For the public, analyzing trends can effectively illustrate the benefits of past air pollution controls, as well as the challenges for future policy efforts.
Here we aim to quantify air pollution-related mortality in the continental US in each year from 1990 to 2010, mainly to understand the trends over this time period. We also analyze the contributions of changes in air pollutant concentrations, population, and baseline mortality rates to the overall trend, and analyze the inter-annual variability in mortality burden estimates. To achieve this, we use a 21-year (1990-2010) model simulation of PM 2.5 and O 3 concentrations over the continental US (CONUS) from 1990 to 2010. We also use annual county-level baseline mortality rates and population archived by the US Centers for Disease Control (CDC WONDER, https://wonder.cdc.gov/mortSQL.html, last access: 5 October 2018).

Air quality simulations
We use simulations of air quality over the CONUS from 1990 to 2010 by Gan et al. (2015Gan et al. ( , 2016. These simulations used the WRF-CMAQ model which coupled the Weather Research and Forecasting (WRF, v3.4) model and the Community Multiscale Air Quality (CMAQ, v5.02) model (Wong et al., 2012), covering the CONUS at 36 km × 36 km. These simulations used an internally consistent emission inventory from 1990 to 2010 for the US developed by Xing et al. (2013), three-dimensional meteorological fields constrained by reanalysis of available surface and aloft measurements of the atmospheric state, and time-varying lateral boundary conditions provided by the hemispheric CMAQ Xing et al., 2015). The model system used the CB05 chemical mechanism with the AERO6 module for aerosols. Gan et al. (2015Gan et al. ( , 2016 showed that the modeled trends of total PM 2.5 and its components generally matched both the CASTNET (Clean Air Status and Trend Network) and IM-PROVE (Interagency Monitoring of Protection Visual) observational data from 1995 to 2010, with correlation coefficients usually larger than 0.87 for total PM 2.5 and its components. Moreover, the trends for PM 2.5 and its species were similar in direction and magnitude . There was a small or nearly no trend for PM 2.5 in the western US for both the model and observations, but a dramatic decreasing trend in the eastern US, with a larger decreasing trend from the model (−0.44 µg m −3 year −1 ) than from the IM-PROVE observations (−0.30 µg m −3 year −1 ).

Mortality burden attributable to ambient air pollution
The mortality burdens attributable to ambient PM 2.5 and O 3 ( Mort) are estimated using the health impact function (HIF) following Eq. (1): where y 0 is the baseline mortality rate for specific diseases, AF is the attributable fraction calculated as 1 − 1/RR, with RR as the relative risk of death from a specific disease, and Pop is exposed population age 25 years and greater. For PM 2.5 , RR is calculated using the integrated exposureresponse (IER) model (Burnett et al., 2014), which has been extensively used by recent studies, including Liu et al. (2017), Silva et al. (2016a, b), Wang et al. (2017), and the World Health Organization (2016). The RR is calculated as a function of PM 2.5 concentration following Eq. (2): for where C is the annual average ambient PM 2.5 concentration, C 0 is the PM 2.5 threshold concentration (5.8-8.0 µg m −3 ), below which no additional risk is assumed, and the parameter values of α, γ , and δ are given by distributions (Burnett et al., 2014). For this study, the RRs are downloaded from the GBD website (Global Health Data Exchange (GHDx), 2013).
For the O 3 -related mortality burden, RR = exp β X , where β is the concentration response factor, and X is the difference in O 3 concentration (summertime 1 h daily maximum O 3 ) between the current year (1990 to 2010) and the lowconcentration threshold. For RR, we use the value of 1.040 (with 95 % CI: 1.013, 1.067) from Jerrett et al. (2009) following recent studies (e.g., Cohen et al., 2017;GBD, 2015GBD, , 2016Lim et al., 2012). Turner et al. (2016) found a larger RR for respiratory mortality (RR, 1.12; 95 % CI, 1.08-1.16) associated with the annual average of MDA8 O 3 , and using these results would likely lead to a larger O 3 mortality burden (Malley et al., 2017). We account for all chronic respiratory disease (RESP), to be consistent with Jerrett et al. (2009). The counterfactual concentration of 37.6 ppbv (Lim et al., 2012;Lelieveld et al., 2015) is used in our study, to be comparable with Cohen et al. (2017). We use adults above 25 years, to be comparable with other calculations of PM 2.5 mortality burden following Silva et al. (2016a, b), even though the estimated RRs from Jerrett et al. (2009) were for adults above 30 years old only. Uncertainties in air pollutionrelated mortality burden calculations are based on the uncertainty in RRs only, ignoring those in modeled air pollutant concentrations, and population and baseline mortality rates, which may be larger than that from the RRs but are not in the scope of our study.
We use baseline mortality rates from each year to calculate deaths from air pollution in each year, as changes in baseline mortality rates from other socioeconomic determinants are likely more important than changes in deaths from air pollution. To estimate the annual baseline mortality rates (y 0 ) for each disease associated with PM 2.5 (chronic obstructive pulmonary disease, COPD; ischemic heart disease, IHD; lung cancer, LC; cerebrovascular disease and ischemic stroke, STROKE) and O 3 (RESP), we acquire US county-level specific causes of mortality data for each year from the National Center for Health Statistics (NCHS) (CDC, 2017). We then aggregate the county-level mortality data to each model grid cell at 36 km × 36 km. The specific causes of mortality data for some counties are sometimes suppressed when the total deaths are lower than 10 year −1 to protect privacy (Jian et al., 2016), missing or considered "unreliable" when the total deaths are less than 20 year −1 , and are corrected following established procedures (BenMAP, 2017;Fann et al., 2017; also see the Supplement).
Definitions of each disease follow the GBD study (Lim et al., 2012; Table S1 in the Supplement). Note that the CDC changed the disease codes from the International Classification of Diseases 9th Revision (ICD 9) in 1998 to ICD10 in 1999, and there were discontinuities in the death counts of specific diseases (Anderson et al., 2001;Anderson and Rosenberg, 2003). To account for the discontinuities, we group the total deaths for each of the five diseases for ICD9 and ICD10 using the results of Anderson et al. (2001) and Anderson and Rosenberg (2003), who reported deaths for 135 specific causes in 1996 for both the ICD9 and ICD10 codes, and calculate comparability ratios (Table S1). We then recalculate comparability ratios for the five diseases (RESP, COPD, IHD, LC and STROKE) as the ratios of deaths for ICD9 and ICD10 (Table S2). Finally, we apply these ratios to the ICD9 baseline mortality rates from 1990 to 1998.
Annual population in the US at county level was taken from the US Bureau of Census, which reported populations associated with the 1990, 2000, and 2010 censuses and estimated population for each year in between (CDC 2017; https://www.cdc.gov/nchs/data/nvss/bridged_ race/Documentation_bridge_postcenv2017.pdf, last access: 5 September 2018). The adult population above 25 years in the US has steadily grown between 1990 and 2010, with an average 1.23 % year −1 rate of increase ( Fig. S1 in the Supplement).

The contribution of different factors to mortality trends
The overall trends in PM 2.5 -and O 3 -related mortality between 1990 and 2010 are a combination of contributions from trends in population, baseline mortality rates, and concentration. Here we separate the contributions of each fac-tor by assuming that only a single factor was changing from 1990 to 2010, with the other two constant at 1990 levels. For example, the mortality burden change associated with air pollution changes in year y ( Mort y P ), relative to 1990, is calculated following Eq. (4): (4) Similarly, we also calculate the mortality burden change without accounting for ambient air pollution changes ( Mort y noP ) following Eq. (5): 3 Results

Air quality trends
From 1990 to 2010, annual average PM 2.5 in the model decreases significantly in the eastern US (Fig. 1c), but slightly decreases or even increases in the northwest, southwest and west ( Fig. S2 and Table S3; also see Fig. S3 for the US 9 regions defined by the National Oceanic and Atmospheric Administration, Zhang et al., 2016). The dramatic decreasing trends of PM 2.5 in the eastern US were also reported in previous studies Xing et al., 2015) due to emission reductions. The increasing trend in the western central area is due in part to frequent wildfires (Dennison et al., 2014;Hand et al., 2013Hand et al., , 2014Jaffe et al., 2008;Murphy et al., 2011;Spracklen et al., 2007). In general, the decadal decreasing trends in the east are larger than 2 µg m −3 decade −1 from 1990 to 2010, especially in the central area (−3.48 µg m −3 decade −1 ) and northeast (−3.14 µg m −3 decade −1 ). The summertime average of 1 h daily maximum O 3 decreases significantly in the central and eastern US, generally at a rate greater than 4 ppbv decade −1 . It also decreases in the western US, but at a much smaller rate than in the east, generally less than 1 ppbv decade −1 (Fig. 1f; Table S3). In Fig. 2, both the spatial average and populationweighted average (PWA) annual PM 2.5 exhibit smooth decreasing trends (Fig. 2, top): the spatial average of annual PM 2.5 has decreased by 29 %, from 9.07 µg m −3 in 1990 to 6.45 µg m −3 in 2010, with a decadal rate of decrease of 1.1 µg m −3 decade −1 . The corresponding PWA PM 2.5 decreases by 39 %, from 17.61 µg m −3 in 1990 to 10.73 µg m −3 in 2010, with a decadal decreasing rate of 3.2 µg m −3 decade −1 . Years with high PM 2.5 , such as in 1994, 1996, and 2000, are mainly caused by increases in organic carbon due to large wildfires in the western US (Spracklen et al., 2007). Both the spatial average and PWA O 3 also exhibit decreasing trends over the past 2 decades, with greater inter-annual variability resulting from meteorological variability (Porter et al., 2017). The spatial average O 3 concentration decreases by 9 %, from 55.02 ppbv in 1990 to 49.99 ppbv in 2010, decreasing at a rate of 2.4 ppbv decade −1 . The PWA O 3 also decreased by 9 %, from 58.96 ppbv in 1990 to 53.57 ppbv in 2010, decreasing at a rate of 3.0 ppbv decade −1 . We also calculate the air quality and mortality burden trends separately for two 11-year periods, 1990 to 2000 and 2000 to 2010, following Astitha et al. (2017). Both PM 2.5 and O 3 decrease more strongly in the second decade than in the first decade for both spatial average and PWA (Table S5), consistent with previous findings (Astitha et al., 2017;Gan et al., 2015;Porter et al., 2017;Xing et al., 2015).
We then calculate trends in the number of days annually that exceed the daily PM 2.5 standard (35 µg m −3 ), and the daily MDA8 O 3 standard (70 ppbv) (Fig. S4). The exceedance days decrease for both PM 2.5 and O 3 , especially in the eastern US. In 2010, fewer than 5 days exceed the air quality standard for the majority of the US (Fig. S4b, e). We also calculate the population exposure exceedances by multiplying the population (adults > 25 years old) by the number of air quality exceedance days in each grid cell. The PM 2.5 population exposure exceedances have decreased from 5340 million people-days in 1990 to 1042 million people-days in 2010, and the O 3 population exposure exceedances has decreased from 4691 million people-days in 1990 to 2236 million people-days in 2010 (Fig. S1). These decreases in population exposure exceedances occur despite population growth over this period.

Mortality burden trends and contributing factors
The mortality burdens associated with exposure to ambient PM 2.5 in the US steadily decreased by 54 %, from 123 700 (95 % confidence interval considering the uncertainty in relative risk only, 70 800-178 100) deaths year −1 in 1990 to 58 600 (24 900-98 500) deaths year −1 in 2010 (Fig. 3). The leading cause of PM 2.5 -related mortality is IHD, which decreases by 55 %, from 96 500 (62 600-132 500) deaths year −1 in 1990 to 43 600 (21 500-68 700) deaths year −1 , followed by LC, which has decreased by 44 %, from 12 500 (2500-21 000) deaths year −1 in 1990 to 7000 (900-13 400) deaths year −1 in 2010 (Table S4). The PM 2.5 mortality burden per 100 000 adults is much higher in the east than the west for both 1990 and 2010 (Fig. 4), due to the higher PM 2.5 concentrations (Fig. 1). Table 1 shows the mortality burdens for PM 2.5 and O 3 in 2010, and also the burden changes since 1990 from different contributing factors. From the table, we see that the PM 2.5 - Table 1. The total mortality burdens in 2010 and the burdens in 2010 due to changes since 1990 in each of three factors (concentration, baseline mortality rates and population) and where the concentration change is excluded, for PM 2.5 and O 3 , and the relative changes between 2010 and 1990. The relative changes are calculated as   , if the PM 2.5 concentrations had stayed constant over the period 1990-2010, due to decreases in the baseline mortality rates for the specific causes of death that PM 2.5 influences (Fig. 3), especially IHD (Fig. 5), despite the population increase. Therefore, the reduction in PM 2.5 concentrations from 1990 to 2010 significantly accelerates the decrease in the mortality burden. The decreased PM 2.5 concentration avoided roughly 35 800 (38 %) PM 2.5 -related deaths in 2010, compared to the case if current air quality stays at level in 1990 (estimated as the 2010 mortality burden minus the "concentration change excluded" case in 2010). The benefit of the decreased PM 2.5 concentration could also be estimated as the "concentration change only" case in Fig. 3, yielding 78 900 (35 700-129 200) deaths year −1 in 2010, decreasing by 36 % (−44 800 deaths year −1 ) compared with 1990. The population increases from 1991 to 2010 would lead to increases in the PM 2.5 mortality burden, but that increase is smaller than the combined reduction from decreasing PM 2.5 concentrations and baseline mortality rates (Figs. S5 and S6).
When separating the two 11-year periods, the PM 2.5related mortality burden decreased by 45 % from 2000 to 2010 (decreasing trend of −4400 deaths year −1 ), much higher than the 15 % decrease from 1990 to 2000 (decreasing trend of −2100 deaths year −1 ) ( Table S5). The detrended annual PM 2.5 -related mortality burden has a coefficient of variation (CV, standard deviation divided by the average) of 4 %, mainly caused by inter-annual variation in PM 2.5 concentrations (Table S6 and Fig. S6).
We also calculate burdens and trends for each state individually ( Table 2). The three states with the highest PM 2.5 mortality burden in 1990 are New York (NY, 13 700 deaths year −1 ), California (CA, 9500 deaths year −1 ) and Pennsylvania (PA, 9200 deaths year −1 ); and in 2010, NY (5100 deaths year −1 ), Texas (TX, 4200 deaths year −1 ) and Ohio (OH, 3900 deaths year −1 ). NY has seen the largest benefits of mortality burden decreases (−8500 deaths year −1 ), followed by CA (−6100 deaths year −1 ) and PA (−5500 deaths year −1 ). For the relative mortality burden changes, generally large percent decreases in PM 2.5 -related mortality are seen in western, northern, and northeastern states (including Nevada, Utah, Colorado, Montana, Maine and Vermont) (Fig. 6) Figure 3. Trends in the total mortality burden (black) for PM 2.5 (a, as a total of ischemic heart disease (IHD) + stroke (STROKE) + chronic obstructive pulmonary disease (COPD) + lung cancer (LC)) and O 3 (b, chronic respiratory disease (RESP)), and mortality burdens considering the air quality change only (blue), and with air quality changes excluded (red). Units are deaths year −1 . The error bars are the 95 % CI for the total mortality burden (black). 2010 are very low or even fall below the low-concentration threshold in these states (Fig. 1), as confirmed by the mortality burden changes from concentration changes alone (Table S7). For other states in the eastern US with large relative mortality burden changes, the contributing factors are different. For example, for Connecticut, the relative mortality burden changes from the decrease in PM 2.5 concentration are larger than that from the decrease in the baseline mortality rates. However, for Massachusetts, NY and PA, the decreases in baseline mortality rates have a slightly larger effect than that from the decrease in PM 2.5 concentration. For CA, the effects from the decrease in baseline mortality rates and PM 2.5 concentration are comparable (Table S7).
The mortality burden associated with exposure to O 3 from RESP has increased by 13 %, from 10 900 (3700-17 500) deaths year −1 in 1990 to 12 300 (4100-19 800) deaths year −1 in 2010 (Fig. 3). The O 3 mortality burden per 100 000 adults is highest in the midwest and southwest (Fig. 4). The O 3 -related mortality burden in 2010 would have increased by 55 % (10 600 deaths year −1 in 2010, 95 % CI, 3600-17 100) compared with that in 1990 if the O 3 concentration had stayed constant over the period 1990-2010 (Fig. 3), due to increases in both population and baseline mortality rates (Fig. S5). The decreased O 3 concentration  The change in O 3 generally reduces the mortality burden relative to 1990 with some inter-annual variation (Fig. S6) due to meteorology and wildfires (Porter et al., 2017), while the increases in population and baseline mortality rates generally increase the mortality burden, with a larger contribution from the population change (Fig. S6). When separating the O 3 mortality trends into 2 decades, we find that the burdens decrease slightly (−70 deaths year −1 ) from 2000 to 2010, compared with the increasing trend from 1990 to 2000 (240 deaths year −1 ) (Table S5). The increasing trend in the first decade is caused by the combined effect of increases in baseline mortality rates and population, while the decreasing trend in the second decade is dominated by decreases in O 3 concentration (Fig. S6). The inter-annual variability for the detrended annual O 3 mortality burden from 1990 to 2010 (CV of 12 %) is larger than PM 2.5 (CV of 4 %), caused mainly by variations in O 3 concentrations from 1990 to 2010 (Table S6).
The three states with the highest O 3 mortality burden in 1990 are CA (910 deaths year −1 ), Florida (FL, 740 deaths year −1 ) and NY (700 deaths year −1 ); and in 2010, CA (1270 deaths year −1 ), TX (900 deaths year −1 ) and FL (770 deaths year −1 ) ( Table 2). CA has seen the largest O 3 mortality burden increases (360 deaths year −1 ), followed by TX (230 deaths year −1 ) and Arizona (AZ, 140 deaths year −1 ), with the greatest decrease in NY (−90 deaths year −1 ). For the relative mortality burden changes, large percent decreases in O 3 -related mortality are seen in the northwestern (Washington and Oregon) and northeastern US (Fig. 6), mainly caused by significant O 3 decreases (Table S7), while the greatest percent increases occur in the southwestern US driven mainly by large population increases, and also the baseline mortality rate increases.
Previous health impact assessments have used national baseline mortality rates Silva et al., 2016a, b, etc.), but baseline mortality rates can vary strongly within individual counties (Fig. 5;Dwyer-Lindgren et al., 2016). We performed sensitivity analyses by applying the national average baseline mortality rates for each disease to every county in the mortality burden calculations. We find that the PM 2.5 mortality burden calculated from the national average baseline mortality rates is lower than those calculated from the county-level baseline mortality rates, ranging among individual years from −2.2 % to −1.3 % (Table S8). For the O 3 mortality burden, the differences between using the national average baseline mortality rates and our best estimates range from −1.1 % to 2.0 % (Table S8). However, using the national average baseline mortality rates fails to capture regional mortality burden hotspots for both PM 2.5 and Figure 5. The baseline mortality rates for specific causes of death related to PM 2.5 , including chronic obstructive pulmonary disease (a), lung cancer (b), ischemic heart disease (c) and stroke (d), and respiratory diseases related to O 3 (e). The bottom whiskers, bottom border, middle line, top border and top whiskers of the boxes indicate the 5th, 25th, 50th, 75th, and 95th percentiles, respectively, across all counties; the red circles are the national average rate. Baseline mortality rates are shown for 1990-1998 after they are corrected to ensure comparability between ICD9 and ICD10 codes. The units on the y axis are per 100 000 people. O 3 (Figs. S7-S8), demonstrating the value of using countylevel baseline mortality rates where possible.

Comparison with previous studies
The mortality burden associated with PM 2.5 calculated in our study generally aligns with several previous findings ( Fig. 7; also Table S9). Our PM 2.5 mortality burden is higher than that reported by Cohen et al. (2017Cohen et al. ( ) in 1990Cohen et al. ( (17 % higher) and 1995, and lower in 2000 (−0.5 %), 2005 (−17 %) and 2010 (−30 %) (Fig. 7). The overestimation of PM 2.5 mortality burdens in the early 2000s are likely due to the higher population-weighted PM 2.5 con-centration simulated by WRF-CMAQ (Fig. 2), compared with Cohen et al. (2017), in which they estimated the PM 2.5 concentration based on data fusion of air quality model outputs, satellite retrievals and ground observations. The lower mortality burdens in the second decade (from 2000 to 2010) in our study likely reflect that  included hemorrhagic stroke and lower respiratory infections in the PM 2.5 -related mortality burden, in addition to COPD, LC, IHD and STROKE, and used an updated integrated exposure-response function. While the absolute value is similar, our results show a stronger decreasing trend (−3000 deaths year −1 ) than Cohen et al. (2017) (−1000 deaths year −1 ), which may result from the overestimation of PM 2.5 decreasing trends in our model relative to ground observations (Gan et al., 2016). The PM 2.5 mortality burdens estimated in our study are much lower than those from Fann et al. (2017), but the temporal patterns are similar, mainly because Fann et al. (2017) estimated the total all-cause mortality with a different HIF.
To compare with Cohen et al. (2017), who reported the O 3 mortality burden from the COPD, which is a subset of RESP, we recalculate the O 3 mortality burden from the COPD (Table S4). The newly calculated O 3 mortality burden from the COPD is generally lower than the estimate of  by 8 %-30 % (Fig. 7). This could be caused by the fact that for the O 3 changes, we use the summertime (April to September) average of the 1 h daily maximum, while Cohen et al. (2017) used the 3-month average, which will be higher. The temporal trend for the O 3 mortality burdens from our study is similar to that from Cohen et al. (2017), except that the burden decreases after 2005 in our study but increases in Cohen et al. (2017). The O 3 mortality burden from the RESP disease in 2005 estimated from our study is much lower than two previous studies (Fann et al., 2012a;Punger and West, 2013; Table S10). As discussed in the methods, the lower US background O 3 concentration used in these two studies (22 ppb in the eastern US and 30 ppb in the western US) could lead to a higher O 3 mortality burden. We then did sensitivity analysis by using the pre-industrial O 3 concentration simulated by an ensemble of model outputs from the Atmospheric Chemistry and Climate Model Intercomparison Project see Fig. S9 and Sect. 2 in the Supplement) as the counterfactual risk exposure factor, and recalculated the O 3 mortality burden with RESP. The new calculated O 3 mortality burdens are estimated to be 64 %-100 % higher than the current estimation from RESP using the low-concentration threshold (Table S10). In Fig. 7, we see that the new estimated O 3 mortality burden from RESP in 2005 (dashed line) is now comparable with the two previous studies.  We also estimate the inter-annual variability in mortality burdens considering air pollutant concentrations in individual years and annual county-level baseline mortality rates, and find that the O 3 mortality burdens are more variable (CV of 12 %) than for PM 2.5 (CV of 4 %), mainly because of interannual variability in concentrations. We found that the interannual variability is small for PM 2.5 but larger for ozone, which has not been shown previously that we are aware of.
The uncertainties in air pollution-related mortality estimates presented in this study are based on the uncertainty in relative risks for the specific causes of death only, and do not account for uncertainties in population and baseline mortality rates (which are likely small), nor for uncertainty in the modeled air pollutant concentration. Previous studies have shown that the uncertainties from the modeled air pollutant concentrations may be greater than uncertainties in baseline mortality or relative risk, so the use of model ensembles is suggested to better quantify the uncertainty (Silva et al., 2016a;Liang et al., 2018). Uncertainties also exist due to the assumption of equal toxicity for different components of PM 2.5 (Li et al., 2016). For our analysis, we use modeled air pollutant concentration without any bias correction based on either in situ observation or satellite data Hogrefe et al., 2009;van Donkelaar et al., 2015;Xu et al., 2016). In our study, the PM 2.5 mortality burden trend may be overestimated, and O 3 mortality burdens underestimated, based on comparing the modeled air pollution trends with the observations. Despite these uncertainties, this study illustrates the importance of past air pollutant reductions for public health in the US, and of continued air pollution controls to reduce air pollution-related mortality.
Data availability. The 21-year model outputs for the coupled WRF-CMAQ model, as well as the annual county-level baseline mortality rates, can be obtained by contacting the corresponding author Yuqiang Zhang (yuqiangzhang.thu@gmail.com, yuqiang.zhang@duke.edu).
Author contributions. YZ designed the study, JX, CG and DW performed the model simulations, and YZ performed the data analysis and prepared the figures and tables. YZ wrote the paper with comments from all the coauthors.
Competing interests. The authors declare that they have no conflicts of interest.
Disclaimer. Although this work has been reviewed and approved for publication by the US EPA, the views expressed in this paper are those of the authors and do not necessarily represent the views or policies of the US EPA.
Acknowledgements. This research was supported in part by an appointment to the Research Participation Program at the US EPA, Office of Research and Development (ORD), administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy and the US EPA, and also in part by National Aeronautics and Space Administration Health and Air Quality Applied Science Team no. NNX16AQ80G. This work was also supported in part by a China Ministry of Science and Technology National Key R & D program (2016YFC0207601). We thank Raquel Silva and Neal Fann from the US EPA for useful discussions. We greatly acknowledge Ana Rappold and Geoffrey Peterson from the US EPA for their comments and suggestions on the initial version of this paper. We also appreciate the free usage of the county-level baseline mortality and population data from the US Centers for Disease Control and Prevention (CDC). We thank two anonymous reviewers for their helpful comments.
Edited by: Robert Harley Reviewed by: two anonymous referees