Exploring the Uncertainty Associated with Satellite-Based Estimates of Premature Mortality due to Exposure to Fine Particulate Matter

A gap-filling hybrid approach for hourly PM2.5 prediction at high spatial resolution from multi-sourced AOD data

Despite a growing interest in the satellite derived estimation of ground-level PM_2.5 concentrations, modeling hourly PM_2.5 levels at high spatial resolution with complete coverage for a large study domain remains a challenge. The primary modeling challenges lie in the presence of missing data in aerosol optical depth (AOD) and the limited data resolution for a single-platformed satellite AOD product. To address these issues, we developed a gap-filling hybrid approach to estimate full coverage hourly ground-level PM_2.5 concentrations at a high spatial resolution of 1 km using multi-platformed and multi-scale satellite derived AOD products. Specifically, we filled the gaps and downscaled the multi-sourced AOD from Geostationary Ocean Color Imager (GOCI), Multi-Angle Implementation of Atmospheric Correction (MAIAC), and Modern-Era Retrospective Analysis for Research and Applications - version 2 (MERRA-2), using a hybrid data fusion approach. The fused hourly AOD with full coverage was then used for hourly PM_2.5 predictions at a high spatial resolution of 1 km. We demonstrated the application of the proposed approach and assessed its performance using the data collected from northeastern Asia from 2015 to 2019. Our fused hourly AOD data showed high accuracy with the mean absolute error of 0.14 and correlation coefficient of 0.94, in validation against Aerosol Robotic Network (AERONET) AOD. Our AOD-based PM_2.5 prediction model showed a good prediction accuracy with cross-validated R² of 0.85 and root mean squared error of 12.40 μg/m³, respectively. Given that the highly resolved PM_2.5 predictions captured both the temporal trend and the peak of PM_2.5 pollution scenarios, we concluded that the proposed hybrid approach can effectively combine multi-sourced satellite AOD and derive subsequent PM_2.5 distributions at high spatial and temporal resolutions.

Influence of Spatial Resolution on Satellite-Based PM2.5 Estimation: Implications for Health Assessment

Satellite-based PM2.5 estimation has been widely used to assess health impact associated with PM2.5 exposure and might be affected by spatial resolutions of satellite input data, e.g., aerosol optical depth (AOD). Here, based on Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD in 2020 over the Yangtze River Delta (YRD) and three PM2.5 retrieval models, i.e., the mixed effects model (ME), the land-use regression model (LUR) and the Random Forest model (RF), we compare these model performances at different spatial resolutions (1, 3, 5 and 10 km). The PM2.5 estimations are further used to investigate the impact of spatial resolution on health assessment. Our cross-validated results show that the model performance is not sensitive to spatial resolution change for the ME and LUR models. By contrast, the RF model can create a more accurate PM2.5 prediction with a finer AOD spatial resolution. Additionally, we find that annual population-weighted mean (PWM) PM2.5 concentration and attributable mortality strongly depend on spatial resolution, with larger values estimated from coarser resolution. Specifically, compared to PWM PM2.5 at 1 km resolution, the estimation at 10 km resolution increases by 7.8%, 22.9%, and 9.7% for ME, LUR, and RF models, respectively. The corresponding increases in mortality are 7.3%, 18.3%, and 8.4%. Our results also show that PWM PM2.5 at 10 km resolution from the three models fails to meet the national air quality standard, whereas the estimations at 1, 3 and 5 km resolutions generally meet the standard. These findings suggest that satellite-based health assessment should consider the spatial resolution effect.

Assessing the health estimation capacity of air pollution exposure prediction models

BACKGROUND

The era of big data has enabled sophisticated models to predict air pollution concentrations over space and time. Historically these models have been evaluated using overall metrics that measure how close predictions are to monitoring data. However, overall methods are not designed to distinguish error at timescales most relevant for epidemiologic studies, such as day-to-day errors that impact studies of short-term health associations.

METHODS

We introduce frequency band model performance, which quantifies health estimation capacity of air quality prediction models for time series studies of air pollution and health. Frequency band model performance uses a discrete Fourier transform to evaluate prediction models at timescales of interest. We simulated fine particulate matter (PM_2.5), with errors at timescales varying from acute to seasonal, and health time series data. To compare evaluation approaches, we use correlations and root mean squared error (RMSE). Additionally, we assess health estimation capacity through bias and RMSE in estimated health associations. We apply frequency band model performance to PM_2.5 predictions at 17 monitors in 8 US cities.

RESULTS

In simulations, frequency band model performance rates predictions better (lower RMSE, higher correlation) when there is no error at a particular timescale (e.g., acute) and worse when error is added to that timescale, compared to overall approaches. Further, frequency band model performance is more strongly associated (R² = 0.95) with health association bias compared to overall approaches (R² = 0.57). For PM_2.5 predictions in Salt Lake City, UT, frequency band model performance better identifies acute error that may impact estimated short-term health associations.

CONCLUSIONS

For epidemiologic studies, frequency band model performance provides an improvement over existing approaches because it evaluates models at the timescale of interest and is more strongly associated with bias in estimated health associations. Evaluating prediction models at timescales relevant for health studies is critical to determining whether model error will impact estimated health associations.

Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty

Annual global satellite-based estimates of fine particulate matter (PM2.5) are widely relied upon for air-quality assessment. Here, we develop and apply a methodology for monthly estimates and uncertainties during the period 1998-2019, which combines satellite retrievals of aerosol optical depth, chemical transport modeling, and ground-based measurements to allow for the characterization of seasonal and episodic exposure, as well as aid air-quality management. Many densely populated regions have their highest PM2.5 concentrations in winter, exceeding summertime concentrations by factors of 1.5-3.0 over Eastern Europe, Western Europe, South Asia, and East Asia. In South Asia, in January, regional population-weighted monthly mean PM2.5 concentrations exceed 90 μg/m3, with local concentrations of approximately 200 μg/m3 for parts of the Indo-Gangetic Plain. In East Asia, monthly mean PM2.5 concentrations have decreased over the period 2010-2019 by 1.6-2.6 μg/m3/year, with decreases beginning 2-3 years earlier in summer than in winter. We find evidence that global-monitored locations tend to be in cleaner regions than global mean PM2.5 exposure, with large measurement gaps in the Global South. Uncertainty estimates exhibit regional consistency with observed differences between ground-based and satellite-derived PM2.5. The evaluation of uncertainty for agglomerated values indicates that hybrid PM2.5 estimates provide precise regional-scale representation, with residual uncertainty inversely proportional to the sample size.

Analysis of spatial factors, time-activity and infiltration on outdoor generated PM2.5 exposures of school children in five European cities

Atmospheric particles are a major environmental health risk. Assessments of air pollution related health burden are often based on outdoor concentrations estimated at residential locations, ignoring spatial mobility, time-activity patterns, and indoor exposures. The aim of this work is to quantify impacts of these factors on outdoor-originated fine particle exposures of school children. We apply nested WRF-CAMx modelling of PM_2.5 concentrations, gridded population, and school location data. Infiltration and enrichment factors were collected and applied to Athens, Kuopio, Lisbon, Porto, and Treviso. Exposures of school children were calculated for residential and school outdoor and indoor, other indoor, and traffic microenvironments. Combined with time-activity patterns six exposure models were created. Model complexity was increased incrementally starting from residential and school outdoor exposures. Even though levels in traffic and outdoors were considerably higher, 80-84% of the exposure to outdoor particles occurred in indoor environments. The simplest and also commonly used approach of using residential outdoor concentrations as population exposure descriptor (model 1), led on average to 26% higher estimates (15.7 μg/m³) compared with the most complex model (# 6) including home and school outdoor and indoor, other indoor and traffic microenvironments (12.5 μg/m³). These results emphasize the importance of including spatial mobility, time-activity and infiltration to reduce bias in exposure estimates.

Contribution of fine particulate matter to present and future premature mortality over Europe: A non-linear response

The World Health Organization estimates that around 7 million people die every year from exposure to fine particles (PM2.5) inpolluted air. Here, the number of premature deaths in Europe from different diseases associated to the ambient exposure to PM2.5 have here been studied both for present (1991-2010) and future periods (2031-2050, RCP8.5 scenario). This contribution combines different state-of-the-art approaches (use of high-resolution climate/chemistry simulations over Europe for providing air quality data; use of different baseline mortality data for specific European regions; inclusion of future population projections and dynamical changes for 2050 obtained from the United Nations (UN) Population Projections or use of non-linear exposure-response functions) to estimate the premature mortality due to PM2.5. The mortality endpoints included in this study are Lung Cancer (LC), Chronic Obstructive Pulmonary Disease (COPD), Cerebrovascular Disease (CEV), Ischemic Heart Disease (IHD), Lower Respiratory Infection (LRI) and other Non-Communicable Diseases (other NCDs). Different risk ratio and baseline mortalities for each disease end each age range have been estimated individually. The results indicate that the annual excess mortality rate from fine particulate matter in Europe is 904,000 [95% confidence interval (95% CI) 733,100-1,067,800], increasing by 73% in 2050s (1,560,000; 95% CI 1,260,000-1,840,000); meanwhile population decreases from 808 to 806 million according to the UN estimations. The results show that IHD is the main cause of premature mortality in Europe associated to PM2.5 (around 48%) both for the present and future periods. Despite several marked regional differences, premature deaths associated to all the endpoints included in this study will increase in the future period due to the climate penalty but especially because of changes in the population projected and its aging.

Particulate Matter and Premature Mortality: A Bayesian Meta-Analysis

BACKGROUND

We present a systematic review of studies assessing the association between ambient particulate matter (PM) and premature mortality and the results of a Bayesian hierarchical meta-analysis while accounting for population differences of the included studies.

METHODS

The review protocol was registered in the PROSPERO systematic review registry. Medline, CINAHL and Global Health databases were systematically searched. Bayesian hierarchical meta-analysis was conducted using a non-informative prior to assess whether the regression coefficients differed across observations due to the heterogeneity among studies.

RESULTS

We identified 3248 records for title and abstract review, of which 309 underwent full text screening. Thirty-six studies were included, based on the inclusion criteria. Most of the studies were from China (n = 14), India (n = 6) and the USA (n = 3). PM_2.5 was the most frequently reported pollutant. PM was estimated using modelling techniques (22 studies), satellite-based measures (four studies) and direct measurements (ten studies). Mortality data were sourced from country-specific mortality statistics for 17 studies, Global Burden of Disease data for 16 studies, WHO data for two studies and life tables for one study. Sixteen studies were included in the Bayesian hierarchical meta-analysis. The meta-analysis revealed that the annual estimate of premature mortality attributed to PM_2.5 was 253 per 1,000,000 population (95% CI: 90, 643) and 587 per 1,000,000 population (95% CI: 1, 39,746) for PM₁₀.

CONCLUSION

253 premature deaths per million population are associated with exposure to ambient PM_2.5. We observed an unstable estimate for PM₁₀, most likely due to heterogeneity among the studies. Future research efforts should focus on the effects of ambient PM₁₀ and premature mortality, as well as include populations outside Asia. Key messages: Ambient PM_2.5 is associated with premature mortality. Given that rapid urbanization may increase this burden in the coming decades, our study highlights the urgency of implementing air pollution mitigation strategies to reduce the risk to population and planetary health.

Examining PM2.5 concentrations and exposure using multiple models

Epidemiologic studies have found associations between fine particulate matter (PM_2.5) exposure and adverse health effects using exposure models that incorporate monitoring data and other relevant information. Here, we use nine PM_2.5 concentration models (i.e., exposure models) that span a wide range of methods to investigate i) PM_2.5 concentrations in 2011, ii) potential changes in PM_2.5 concentrations between 2011 and 2028 due to on-the-books regulations, and iii) PM_2.5 exposure for the U.S. population and four racial/ethnic groups. The exposure models included two geophysical chemical transport models (CTMs), two interpolation methods, a satellite-derived aerosol optical depth-based method, a Bayesian statistical regression model, and three data-rich machine learning methods. We focused on annual predictions that were regridded to 12-km resolution over the conterminous U.S., but also considered 1-km predictions in sensitivity analyses. The exposure models predicted broadly consistent PM_2.5 concentrations, with relatively high concentrations on average over the eastern U.S. and greater variability in the western U.S. However, differences in national concentration distributions (median standard deviation: 1.00 μg m^-3) and spatial distributions over urban areas were evident. Further exploration of these differences and their implications for specific applications would be valuable. PM_2.5 concentrations were estimated to decrease by about 1 μg m^-3 on average due to modeled emission changes between 2011 and 2028, with decreases of more than 3 μg m^-3 in areas with relatively high 2011 concentrations that were projected to experience relatively large emission reductions. Agreement among models was closer for population-weighted than uniformly weighted averages across the domain. About 50% of the population was estimated to experience PM_2.5 concentrations less than 10 μg m^-3 in 2011 and PM_2.5 improvements of about 2 μg m^-3 due to modeled emission changes between 2011 and 2028. Two inequality metrics were used to characterize differences in exposure among the four racial/ethnic groups. The metrics generally yielded consistent information and suggest that the modeled emission reductions between 2011 and 2028 would reduce absolute exposure inequality on average.

Ground PM2.5 prediction using imputed MAIAC AOD with uncertainty quantification

Satellite-derived aerosol optical depth (AOD) has been widely used to predict ground-level fine particulate matter (PM_2.5) concentrations, although its utility can be limited due to missing values. Despite recent attempts to address this issue by imputing missing satellite AOD values, the uncertainty associated with the AOD imputation and its impacts on PM_2.5 predictions have been understudied. To fill this gap, we developed a missing data imputation model for the AOD derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) and PM_2.5 prediction models using several machine learning methods. We also examined how the uncertainty associated with the imputed AOD and a choice of machine learning algorithm were propagated to PM_2.5 predictions. The application of the proposed imputation model to the data from New York State in the U.S. achieved a superior performance than those related studies, with a cross-validated R² of 0.94 and a Root Mean Square Error of 0.017. We also found that there was considerable uncertainty in PM_2.5 predictions associated with the use of imputed AOD values, although it was not as high as the uncertainty from the machine learning algorithms used in PM_2.5 prediction models. We concluded that the quantification of uncertainties for both AOD imputation and its propagation to AOD-based PM_2.5 prediction is necessary for accurate and reliable PM_2.5 predictions.

Review: Strategies for using satellite-based products in modeling PM2.5 and short-term pollution episodes

Short-term air pollution episodes motivate improved understanding of the association between air pollution and acute morbidity and mortality episodes, and triggers required mitigation plans. A variety of methods have been employed to estimate exposure to air pollution episodes, including GIS-based dispersion models, interpolation between sparse monitoring sites, land-use regression models, optimization models, line- or area-dispersion plume models, and models using information from imaging satellites, often including land-use and meteorological variables. There has been increasing use of satellite-borne aerosol products for assessing short-term air quality events. They provide better spatial coverage, but currently at the price of low temporal coverage and rather crude spatial resolution. This is a brief review on using satellite data for modeling short-term air quality and pollution events. The review can be pursued as a practical guide for modeling air quality with satellite-based products, as it includes important questions that should be considered in both the study design as well as the model development stages. Progress in this field is detailed and includes published models and their use in environmental and health studies. Both current and future satellite-borne capabilities are covered. It also provides links to access and download relevant datasets and some example R code for data processing and modeling.

Using Low-Cost Measurement Systems to Investigate Air Quality: A Case Study in Palapye, Botswana

Exposure to particulate air pollution is a major cause of mortality and morbidity worldwide. In developing countries, the combustion of solid fuels is widely used as a source of energy, and this process can produce exposure to harmful levels of particulate matter with diameters smaller than 2.5 microns (PM2.5). However, as countries develop, solid fuel may be replaced by centralized coal combustion, and vehicles burning diesel and gasoline may become common, changing the concentration and composition of PM2.5, which ultimately changes the population health effects. Therefore, there is a continuous need for in-situ monitoring of air pollution in developing nations, both to estimate human exposure and to monitor changes in air quality. In this study, we present measurements from a 5-week field experiment in Palapye, Botswana. We used a low-cost, highly portable instrument package to measure surface-based aerosol optical depth (AOD), real-time surface PM2.5 concentrations using a third-party optical sensor, and time-integrated PM2.5 concentration and composition by collecting PM2.5 onto Teflon filters. Furthermore, we employed other low-cost measurements of real-time black carbon and time-integrated ammonia to help interpret the observed PM2.5 composition and concentration information during the field experiment. We found that the average PM2.5 concentration (9.5 µg∙m−3) was below the World Health Organization (WHO) annual limit, and this concentration closely agrees with estimates from the Global Burden of Disease (GBD) report estimates for this region. Sulfate aerosol and carbonaceous aerosol, likely from coal combustion and biomass burning, respectively, were the main contributors to PM2.5 by mass (33% and 27% of total PM2.5 mass, respectively). While these observed concentrations were on average below WHO guidelines, we found that the measurement site experienced higher concentrations of aerosol during first half our measurement period (14.5 µg∙m−3), which is classified as “moderately unhealthy” according to the WHO standard.

Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models

Fine particulate matter (PM2.5) is a well-established risk factor for public health. To support both health risk assessment and epidemiological studies, data are needed on spatial and temporal patterns of PM2.5 exposures. This review article surveys publicly available exposure datasets for surface PM2.5 mass concentrations over the contiguous U.S., summarizes their applications and limitations, and provides suggestions on future research needs. The complex landscape of satellite instruments, model capabilities, monitor networks, and data synthesis methods offers opportunities for research development, but would benefit from guidance for new users. Guidance is provided to access publicly available PM2.5 datasets, to explain and compare different approaches for dataset generation, and to identify sources of uncertainties associated with various types of datasets. Three main sources used to create PM2.5 exposure data are ground-based measurements (especially regulatory monitoring), satellite retrievals (especially aerosol optical depth, AOD), and atmospheric chemistry models. We find inconsistencies among several publicly available PM2.5 estimates, highlighting uncertainties in the exposure datasets that are often overlooked in health effects analyses. Major differences among PM2.5 estimates emerge from the choice of data (ground-based, satellite, and/or model), the spatiotemporal resolutions, and the algorithms used to fuse data sources.Implications: Fine particulate matter (PM2.5) has large impacts on human morbidity and mortality. Even though the methods for generating the PM2.5 exposure estimates have been significantly improved in recent years, there is a lack of review articles that document PM2.5 exposure datasets that are publicly available and easily accessible by the health and air quality communities. In this article, we discuss the main methods that generate PM2.5 data, compare several publicly available datasets, and show the applications of various data fusion approaches. Guidance to access and critique these datasets are provided for stakeholders in public health sectors.

Air pollution intervention and life-saving effect in China

As a critical air pollutant, PM_2.5 is proved to be associated with numerous adverse health impacts and pose serious challenges to human life. This situation is especially important for China as the most populous and one of the heaviest PM_2.5 polluted country in the world. However, health burden estimations reported for China in previous studies may be biased due to the usage of PM_2.5 concentrations at a coarsely spatial resolution, as well as the ignorance of the spatial discrepancies of parameters (e.g. respiratory rate) employed in the exposure-response function. This study therefore utilized a hybrid remote sensing-geostatistical approach to refine PM_2.5 concentrations at 1 km resolution across mainland China from 2013 to 2017. Meanwhile, nationwide exposure parameters were for the first time introduced to weight the integrated exposure response (IER) function to calculate and spatially reallocate the corresponding PM_2.5-attributable premature deaths at 1 km resolution. Results showed that annually averaged PM_2.5 concentrations in mainland China decreased by 39.5%, from 59.1 μg/m³ in 2013 to 35.8 μg/m³ in 2017. Subsequently, PM_2.5 attributable premature deaths reduced 12.6%, from 1.20 million (95% CI: 0.57; 1.71) in 2013 to 1.05 million (95% CI: 0.44; 1.44) in 2017. This declining trend was found in most parts of China except some areas in Xinjiang, Jilin, and Heilongjiang provinces. As a result, 214,821 (95% CI: 96,675; 302,897) life were saved with an estimated monetary value of US$ 210.14 billion (2011 values). However, it has to be acknowledged that, the central and northern China within priority areas of air pollution control were still experiencing high numbers of premature deaths due to the severe PM_2.5 pollution and high-density population. But more worrying than these priority areas are those Harbin-Changchun Metropolitan Region, City Belt in Central Henan and Yangtze-Huaihe City Belt in non-priority areas, which also have been seriously suffering PM_2.5 attributable premature deaths over 28, 000 cases per year. In conclusion, despite the huge gain in life-saving effects in China over the past five years with the help of air pollution intervention policy, future work on cleaner air and better human health is still strongly needed, especially in non-priority areas of air pollution control.

Future Fire Impacts on Smoke Concentrations, Visibility, and Health in the Contiguous United States

Fine particulate matter (PM2.5) from U.S. anthropogenic sources is decreasing. However, previous studies have predicted that PM2.5 emissions from wildfires will increase in the midcentury to next century, potentially offsetting improvements gained by continued reductions in anthropogenic emissions. Therefore, some regions could experience worse air quality, degraded visibility, and increases in population-level exposure. We use global climate model simulations to estimate the impacts of changing fire emissions on air quality, visibility, and premature deaths in the middle and late 21st century. We find that PM2.5 concentrations will decrease overall in the contiguous United States (CONUS) due to decreasing anthropogenic emissions (total PM2.5 decreases by 3% in Representative Concentration Pathway [RCP] 8.5 and 34% in RCP4.5 by 2100), but increasing fire-related PM2.5 (fire-related PM2.5 increases by 55% in RCP4.5 and 190% in RCP8.5 by 2100) offsets these benefits and causes increases in total PM2.5 in some regions. We predict that the average visibility will improve across the CONUS, but fire-related PM2.5 will reduce visibility on the worst days in western and southeastern U.S. regions. We estimate that the number of deaths attributable to total PM2.5 will decrease in both the RCP4.5 and RCP8.5 scenarios (from 6% to 4-5%), but the absolute number of premature deaths attributable to fire-related PM2.5 will double compared to early 21st century. We provide the first estimates of future smoke health and visibility impacts using a prognostic land-fire model. Our results suggest the importance of using realistic fire emissions in future air quality projections.

Fine Particle Emissions From Tropical Peat Fires Decrease Rapidly With Time Since Ignition

Southeast Asia experiences frequent fires in fuel-rich tropical peatlands, leading to extreme episodes of regional haze with high concentrations of fine particulate matter (PM_2.5) impacting human health. In a study published recently, the first field measurements of PM_2.5 emission factors for tropical peat fires showed larger emissions than from other fuel types. Here we report even higher PM_2.5 emission factors, measured at newly ignited peat fires in Malaysia, suggesting that current estimates of fine particulate emissions from peat fires may be underestimated by a factor of 3 or more. In addition, we use both field and laboratory measurements of burning peat to provide the first mechanistic explanation for the high variability in PM_2.5 emission factors, demonstrating that buildup of a surface ash layer causes the emissions of PM_2.5 to decrease as the peat fire progresses. This finding implies that peat fires are more hazardous (in terms of aerosol emissions) when first ignited than when still burning many days later. Varying emission factors for PM_2.5 also have implications for our ability to correctly model the climate and air quality impacts downwind of the peat fires. For modelers able to implement a time-varying emission factor, we recommend an emission factor for PM_2.5 from newly ignited tropical peat fires of 58 g of PM_2.5 per kilogram of dry fuel consumed (g/kg), reducing exponentially at a rate of 9%/day. If the age of the fire is unknown or only a single value may be used, we recommend an average value of 24 g/kg.

Estimating premature mortality attributable to PM2.5 exposure and benefit of air pollution control policies in China for 2020

In past decade of rapid industrial development and urbanization, China has witnessed increasingly persistent severe haze and smog episodes, posing serious health hazards to the Chinese population, especially in densely populated cities. Quantification of health impacts attributable to PM_2.5 (particulates with aerodynamic diameter≤2.5μm) has important policy implications to tackle air pollution. The Chinese national monitoring network has recently included direct measurements of ground level PM_2.5, providing a potentially more reliable source for exposure assessment. This study reports PM_2.5-related long-term mortality of year 2015 in 161 cities of nine regions across China using integrated exposure risk (IER) model for PM_2.5 exposure-response functions (ERF). It further provides an estimate of the potential health benefits by year 2020 with a realization of the goals of Air Pollution Prevention and Control Action Plan (APPCAP) and the three interim targets (ITs) and Air Quality Guidelines (AQG) for PM_2.5 by the World Health Organization (WHO). PM_2.5-related premature mortality in 161 cities was 652 thousand, about 6.92% of total deaths in China during year 2015. Among all premature deaths, contributions of cerebrovascular disease (stroke), ischemic heart disease (IHD), chronic obstructive pulmonary disease (COPD), lung cancer (LC) and acute lower respiratory infections (ALRIs) were 51.70, 26.26, 11.77, 9.45 and 0.82%, respectively. The premature mortality in densely populated cities is very high, such as Tianjin (12,533/year), Beijing (18,817/year), Baoding (10,932/year), Shanghai (18,679/year), Chongqing (23,561/year), Chengdu (11,809/year), Harbin (9037/year) and Linyi (9141/year). The potential health benefits will be 4.4, 16.2, 34.5, 63.6 and 81.5% of the total present premature mortality when PM_2.5 concentrations in China meet the APPCAP, WHO IT-1, IT-2, IT-3 and AQG respectively, by the year 2020. In the current situation, by the end of year 2030, even if Chines government fulfills its own target to meet national ambient air quality standard of PM_2.5 (35μg/m³), total premature mortality attributable to PM_2.5 will be 574 thousand across 161 cities. The present methodology will greatly help policy makers and pollution control authorities to further analyze cost and benefits of air pollution management programs in China.

The Potential Impact of Satellite-Retrieved Cloud Parameters on Ground-Level PM2.5 Mass and Composition

Satellite-retrieved aerosol optical properties have been extensively used to estimate ground-level fine particulate matter (PM2.5) concentrations in support of air pollution health effects research and air quality assessment at the urban to global scales. However, a large proportion, ~70%, of satellite observations of aerosols are missing as a result of cloud-cover, surface brightness, and snow-cover. The resulting PM2.5 estimates could therefore be biased due to this non-random data missingness. Cloud-cover in particular has the potential to impact ground-level PM2.5 concentrations through complex chemical and physical processes. We developed a series of statistical models using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product at 1 km resolution with information from the MODIS cloud product and meteorological information to investigate the extent to which cloud parameters and associated meteorological conditions impact ground-level aerosols at two urban sites in the US: Atlanta and San Francisco. We find that changes in temperature, wind speed, relative humidity, planetary boundary layer height, convective available potential energy, precipitation, cloud effective radius, cloud optical depth, and cloud emissivity are associated with changes in PM2.5 concentration and composition, and the changes differ by overpass time and cloud phase as well as between the San Francisco and Atlanta sites. A case-study at the San Francisco site confirmed that accounting for cloud-cover and associated meteorological conditions could substantially alter the spatial distribution of monthly ground-level PM2.5 concentrations.

Spatial and temporal estimates of population exposure to wildfire smoke during the Washington state 2012 wildfire season using blended model, satellite, and in situ data

In the western U.S., smoke from wild and prescribed fires can severely degrade air quality. Due to changes in climate and land management, wildfires have increased in frequency and severity, and this trend is expected to continue. Consequently, wildfires are expected to become an increasingly important source of air pollutants in the western U.S. Hence, there is a need to develop a quantitative understanding of wildfire-smoke-specific health effects. A necessary step in this process is to determine who was exposed to wildfire smoke, the concentration of the smoke during exposure, and the duration of the exposure. Three different tools have been used in past studies to assess exposure to wildfire smoke: in situ measurements, satellite-based observations, and chemical-transport model (CTM) simulations. Each of these exposure-estimation tools has associated strengths and weakness. We investigate the utility of blending these tools together to produce estimates of PM2.5 exposure from wildfire smoke during the Washington 2012 fire season. For blending, we use a ridge-regression model and a geographically weighted ridge-regression model. We evaluate the performance of the three individual exposure-estimate techniques and the two blended techniques by using leave-one-out cross validation. We find that predictions based on in situ monitors are more accurate for this particular fire season than the CTM simulations and satellite-based observations because of the large number of monitors present; therefore, blending provides only marginal improvements above the in situ observations. However, we show that in hypothetical cases with fewer surface monitors, the two blending techniques can produce substantial improvement over any of the individual tools.

Impact of diurnal variability and meteorological factors on the PM2.5 - AOD relationship: Implications for PM2.5 remote sensing

PM_2.5 retrieval from space is still challenging due to the elusive relationship between PM_2.5 and aerosol optical depth (AOD), which is further complicated by meteorological factors. In this work, we investigated the diurnal cycle of PM_2.5 in China, using ground-based PM measurements obtained at 226 sites of China Atmosphere Watch Network during the period of January 2013 to December 2015. Results showed that nearly half of the sites witnessed a PM_2.5 maximum in the morning, in contrast to the least frequent occurrence (5%) in the afternoon when strong solar radiation received at the surface results in rapid vertical diffusion of aerosols and thus lower mass concentration. PM_2.5 tends to peak equally in the morning and evening in North China Plain (NCP) with an amplitude of nearly twice or three times that in the Pearl River Delta (PRD), whereas the morning PM_2.5 peak dominates in Yangtze River Delta (YRD) with a magnitude lying between those of NCP and PRD. The gridded correlation maps reveal varying correlations around each PM_2.5 site, depending on the locations and seasons. Concerning the impact of aerosol diurnal variation on the correlation, the averaging schemes of PM_2.5 using 3-h, 5-h, and 24-h time windows tend to have larger R biases, compared with the scheme of 1-h time window, indicating diurnal variation of aerosols plays a significant role in the establishment of explicit correlation between PM_2.5 and AOD. In addition, high cloud fraction and relative humidity tend to weaken the correlation, regardless of geographical location. Therefore, the impact of meteorology could be one of the most plausible alternatives in explaining the varying R values observed, due to its non-negligible effect on MODIS AOD retrievals. Our findings have implications for PM_2.5 remote sensing, as long as the aerosol diurnal cycle, along with meteorology, are explicitly considered in the future.

Population exposure to hazardous air quality due to the 2015 fires in Equatorial Asia

Vegetation and peatland fires cause poor air quality and thousands of premature deaths across densely populated regions in Equatorial Asia. Strong El-Niño and positive Indian Ocean Dipole conditions are associated with an increase in the frequency and intensity of wildfires in Indonesia and Borneo, enhancing population exposure to hazardous concentrations of smoke and air pollutants. Here we investigate the impact on air quality and population exposure of wildfires in Equatorial Asia during Fall 2015, which were the largest over the past two decades. We performed high-resolution simulations using the Weather Research and Forecasting model with Chemistry based on a new fire emission product. The model captures the spatio-temporal variability of extreme pollution episodes relative to space- and ground-based observations and allows for identification of pollution sources and transport over Equatorial Asia. We calculate that high particulate matter concentrations from fires during Fall 2015 were responsible for persistent exposure of 69 million people to unhealthy air quality conditions. Short-term exposure to this pollution may have caused 11,880 (6,153-17,270) excess mortalities. Results from this research provide decision-relevant information to policy makers regarding the impact of land use changes and human driven deforestation on fire frequency and population exposure to degraded air quality.