Population-weighted PM2 Research Articles

Exploring the impacts of anthropogenic emission sectors on PM<sub>2.5</sub> and human health in South and East Asia

Abstract. To improve poor air quality in Asia and inform effective emission-reduction strategies, it is vital to understand the contributions of different pollution sources and their associated human health burdens. In this study, we use the WRF-Chem regional atmospheric model to explore the air quality and human health benefits of eliminating emissions from six different anthropogenic sectors (transport, industry, shipping, electricity generation, residential combustion, and open biomass burning) over South and East Asia in 2014. We evaluate WRF-Chem against measurements from air quality monitoring stations across the region and find the model captures the spatial distribution and magnitude of PM2.5 (particulate matter with an aerodynamic diameter of no greater than 2.5 µm). We find that eliminating emissions from residential energy use, industry, or open biomass burning yields the largest reductions in population-weighted PM2.5 concentrations across the region. The largest human health benefit is achieved by eliminating either residential or industrial emissions, averting 467 000 (95 % uncertainty interval (95UI): 409 000–542 000) or 283 000 (95UI: 226 000–358 000) annual premature mortalities, respectively, in India, China, and South-east Asia, with fire prevention averting 28 000 (95UI: 24 000–32 000) annual premature mortalities across the region. We compare our results to previous sector-specific emission studies. Across these studies, residential emissions are the dominant cause of particulate pollution in India, with a multi-model mean contribution of 42 % to population-weighted annual mean PM2.5. Residential and industrial emissions cause the dominant contributions in China, with multi-model mean contributions of 29 % for both sectors to population-weighted annual mean PM2.5. Future work should focus on identifying the most effective options within the residential, industrial, and open biomass-burning emission sectors to improve air quality across South and East Asia.

Source Contributions to Ambient Fine Particulate Matter for Canada.

Understanding the sectoral contribution of emissions to fine particulate matter (PM2.5) offers information for air quality management, and for investigation of association with health outcomes. This study evaluates the contribution of different emission sectors to PM2.5 in 2013 for Canada using the GEOS-Chem chemical transport model, downscaled with satellite-based PM2.5. Despite the low population-weighted PM2.5 concentrations of 5.5 μg m-3 across Canada, we find that over 70% of population-weighted PM2.5 originates from Canadian sources followed by 30% from the contiguous United States. The three leading sectoral contributors to population-weighted PM2.5 over Canada are wildfires with 1.0 μg m-3 (17%), transportation with 0.96 μg m-3 (16%), and residential combustion with 0.91 μg m-3 (15%). The relative contribution to population-weighted PM2.5 of different sectors varies regionally with residential combustion as the leading contributor in Central Canada (19%), while wildfires dominate over Northern Canada (59%), Atlantic Canada (34%), and Western Canada (18%). The contribution from U.S. sources is larger over Central Canada (33%) than over Western Canada (17%), Atlantic Canada (17%), and Northern Canada (<2%). Sectoral trend analysis showed that the contribution from anthropogenic sources to population-weighted PM2.5 decreased from 7.1 μg m-3 to 3.4 μg m-3 over the past two decades.

Energy and air pollution benefits of household fuel policies in northern China

In addition to many recent actions taken to reduce emissions from energy production, industry, and transportation, a new campaign substituting residential solid fuels with electricity or natural gas has been launched in Beijing, Tianjin, and 26 other municipalities in northern China, aiming at solving severe ambient air pollution in the region. Quantitative analysis shows that the campaign can accelerate residential energy transition significantly, and if the planned target can be achieved, more than 60% of households are projected to remove solid fuels by 2021, compared with fewer than 20% without the campaign. Emissions of major air pollutants will be reduced substantially. With 60% substitution realized, emission of primary PM2.5 and contribution to ambient PM2.5 concentration in 2021 are projected to be 30% and 41% of those without the campaign. With 60% substitution, average indoor PM2.5 concentrations in living rooms in winter are projected to be reduced from 209 (190 to 230) μg/m3 to 125 (99 to 150) μg/m3 The population-weighted PM2.5 concentrations can be reduced from 140 μg/m3 in 2014 to 78 μg/m3 or 61 μg/m3 in 2021 given that 60% or 100% substitution can be accomplished. Although the original focus of the campaign was to address ambient air quality, exposure reduction comes more from improved indoor air quality because ∼90% of daily exposure of the rural population is attributable to indoor air pollution. Women benefit more than men.

Impacts of air pollutants from rural Chinese households under the rapid residential energy transition

Rural residential energy consumption in China is experiencing a rapid transition towards clean energy, nevertheless, solid fuel combustion remains an important emission source. Here we quantitatively evaluate the contribution of rural residential emissions to PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) and the impacts on health and climate. The clean energy transitions result in remarkable reductions in the contributions to ambient PM2.5, avoiding 130,000 (90,000–160,000) premature deaths associated with PM2.5 exposure. The climate forcing associated with this sector declines from 0.057 ± 0.016 W/m2 in 1992 to 0.031 ± 0.008 W/m2 in 2012. Despite this, the large remaining quantities of solid fuels still contributed 14 ± 10 μg/m3 to population-weighted PM2.5 in 2012, which comprises 21 ± 14% of the overall population-weighted PM2.5 from all sources. Rural residential emissions affect not only rural but urban air quality, and the impacts are highly seasonal and location dependent.

Estimates of the 2016 global burden of kidney disease attributable to ambient fine particulate matter air pollution

ObjectiveTo quantitate the 2016 global and national burden of chronic kidney disease (CKD) attributable to ambient fine particulate matter air pollution ≤ 2.5 μm in aerodynamic diameter (PM2.5).DesignWe used the...

Influence of spatial resolution on population PM2.5 exposure and health impacts

Health effect estimates depend on the methods of evaluating exposures. Due to non-linearities in the exposure-response relationships, both the predicted mean exposures as well as its spatial variability are significant. The aim of this work is to systematically quantify the impact of the spatial resolution on population-weighted mean concentration (PWC), its variance, and mortality attributable to fine particulate matter (PM2.5) exposure in Finland in 2015. The atmospheric chemical transport model SILAM was used to estimate the ambient air PM2.5 concentrations at 0.02° longitudinal × 0.01° latitudinal resolution (ca. 1 km), including both the national PM2.5 emissions and the long-range transport. The decision-support model FRES source-receptor matrices applied at 250-m resolution was used to model the ambient air concentrations of primary fine particulate matter (PPM2.5) from local and regional sources up to 10 km and 20 km distances. Numerical averaging of population and concentrations was used to produce the results for coarser resolutions. Population-weighted PM2.5 concentration was 11% lower at a resolution of 50 km, compared with the corresponding computations at a resolution of 1 km. However, considering only the national emissions, the influences of spatial averaging were substantially larger. The average population-weighted local PPM2.5 concentration originated from Finnish sources was 70% lower at a resolution of 50 km, compared with the corresponding result obtained using a resolution of 250 m. The sensitivity to spatial averaging, between the finest 250-m and the coarsest 50-km resolution, was highest for the emissions of PPM2.5 originated from national vehicular traffic (about 80% decrease) and lowest for the national residential combustion (60% decrease). Exposure estimates in urban areas were more sensitive to the changes of model resolution (14% and 74% decrease for PM2.5 and local PPM2.5, respectively), compared with estimates in rural areas (2% decrease for PM2.5 and 36% decrease for PPM2.5). We conclude that for the evaluation of the health impacts of air pollution, the resolution of the model computations is an important factor, which can potentially influence the predicted health impacts by tens of percent or more, especially when assessing the impacts of national emissions.

Estimated Long-Term (1981-2016) Concentrations of Ambient Fine Particulate Matter across North America from Chemical Transport Modeling, Satellite Remote Sensing, and Ground-Based Measurements.

Accurate data concerning historical fine particulate matter (PM2.5) concentrations are needed to assess long-term changes in exposure and associated health risks. We estimated historical PM2.5 concentrations over North America from 1981 to 2016 for the first time by combining chemical transport modeling, satellite remote sensing, and ground-based measurements. We constrained and evaluated our estimates with direct ground-based PM2.5 measurements when available and otherwise with historical estimates of PM2.5 from PM10 measurements or total suspended particle (TSP)measurements. The estimated PM2.5 concentrations were generally consistent with direct ground-based PM2.5 measurements over their duration from 1988 onward ( R2 = 0.6 to 0.85) and to a lesser extent with PM2.5 inferred from PM10 measurements from 1985 to 1998 ( R2 = 0.5 to 0.6). The collocated comparison of the trends of population-weighted annual average PM2.5 from our estimates and ground-based measurements was highly consistent (RMSD = 0.66 μg m-3). The population-weighted annual average PM2.5 over North America decreased from 22 ± 6.4 μg m-3 in 1981, to 12 ± 3.2 μg m-3 in 1998, andto 7.9 ± 2.1 μg m-3 in 2016, with an overall trend of -0.33 μg m-3 yr-1 (95% CI: -0.35, -0.31).

Indian annual ambient air quality standard is achievable by completely mitigating emissions from household sources

Exposures to ambient and household fine-particulate matter (PM2.5) together are among the largest single causes of premature mortality in India according to the Global Burden of Disease Studies (GBD). Several recent investigations have estimated that household emissions are the largest contributor to ambient PM2.5 exposure in the country. Using satellite-derived district-level PM2.5 exposure and an Eulerian photochemical dispersion model CAMx (Comprehensive Air Quality Model with Extensions), we estimate the benefit in terms of population exposure of mitigating household sources--biomass for cooking, space- and water-heating, and kerosene for lighting. Complete mitigation of emissions from only these household sources would reduce India-wide, population-weighted average annual ambient PM2.5 exposure by 17.5, 11.9, and 1.3%, respectively. Using GBD methods, this translates into reductions in Indian premature mortality of 6.6, 5.5, and 0.6%. If PM2.5 emissions from all household sources are completely mitigated, 103 (of 597) additional districts (187 million people) would meet the Indian annual air-quality standard (40 μg m-3) compared with baseline (2015) when 246 districts (398 million people) met the standard. At 38 μg m-3, after complete mitigation of household sources, compared with 55.1 μg m-3 at baseline, the mean annual national population-based concentration would meet the standard, although highly polluted areas, such as Delhi, would remain out of attainment. Our results support expansion of programs designed to promote clean household fuels and rural electrification to achieve improved air quality at regional scales, which also has substantial additional health benefits from directly reducing household air pollution exposures.

Major Source Contributions to Ambient PM2.5 and Exposures within the New South Wales Greater Metropolitan Region

The coupled Conformal Cubic Atmospheric Model (CCAM) and Chemical Transport Model (CTM) (CCAM-CTM) was undertaken with eleven emission scenarios segregated from the 2008 New South Wales Greater Metropolitan Region (NSW GMR) Air Emission Inventory to predict major source contributions to ambient PM2.5 and exposure in the NSW GMR. Model results illustrate that populated areas in the NSW GMR are characterised with annual average PM2.5 of 6–7 µg/m3, while natural sources including biogenic emissions, sea salt and wind-blown dust contribute 2–4 µg/m3 to it. Summer and winter regional average PM2.5 ranges from 5.2–6.1 µg/m3 and 3.7–7.7 µg/m3 across Sydney East, Sydney Northwest, Sydney Southwest, Illawarra and Newcastle regions. Secondary inorganic aerosols (particulate nitrate, sulphate and ammonium) and sodium account for up to 23% and 18% of total PM2.5 mass in both summer and winter. The increase in elemental carbon (EC) mass from summer to winter is found across all regions but particularly remarkable in the Sydney East region. Among human-made sources, “wood heaters” is the first or second major source contributing to total PM2.5 and EC mass across Sydney in winter. “On-road mobile vehicles” is the top contributor to EC mass across regions, and it also has significant contributions to total PM2.5 mass, particulate nitrate and sulphate mass in the Sydney East region. “Power stations” is identified to be the third major contributor to the summer total PM2.5 mass across regions, and the first or second contributor to sulphate and ammonium mass in both summer and winter. “Non-road diesel and marine” plays a relatively important role in EC mass across regions except Illawarra. “Industry” is identified to be the first or second major contributor to sulphate and ammonium mass, and the second or third major contributor to total PM2.5 mass across regions. By multiplying modelled predictions with Australian Bureau of Statistics 1-km resolution gridded population data, the natural and human-made sources are found to contribute 60% (3.55 µg/m3) and 40% (2.41 µg/m3) to the population-weighted annual average PM2.5 (5.96 µg/m3). Major source groups “wood heaters”, “industry”, “on-road motor vehicles”, “power stations” and “non-road diesel and marine” accounts for 31%, 26%, 19%, 17% and 6% of the total human-made sources contribution, respectively. The results in this study enhance the quantitative understanding of major source contributions to ambient PM2.5 and its major chemical components. A greater understanding of the contribution of the major sources to PM2.5 exposures is the basis for air quality management interventions aiming to deliver improved public health outcomes.

Mitigation pathways of air pollution from residential emissions in the Beijing-Tianjin-Hebei region in China

Air pollution is one of the most harmful consequences of China's rapid economic development and urbanization. Particularly in the Beijing-Tianjin-Hebei (BTH) regions, particulate matter concentrations have consistently exceeded the national air quality standards. Over the last years, China implemented ambitious measures to reduce emissions from the power, industry and transportation sectors, with notable success during the 11th and 12th Five Year Plan (FYP) periods. However, such strategies appear to be insufficient to reduce the ambient PM2.5 concentration below the National Air Quality Standard of 35 μg m−3 across the BTH region within the next 15 years. We find that a comprehensive mitigation strategy for the residential sector in the BTH region would deliver substantial air quality benefits. Beyond the already planned expansion of district heating and natural gas distribution in urban centers and the foreseen curtailment of coal use for households, such a strategy would redirect some natural gas from power generation units towards the residential sector. Rural households would replace biomass for cooking by liquid petroleum gas (LPG) and electricity, and substitute coal for heating by briquettes. Jointly, these measures could reduce the primary PM2.5 and SO2 emissions by 28% and 11%, respectively, and the population-weighted PM2.5 concentrations by 13%, i.e., from 68 μg m−3 to 59 μg m−3. We estimate that such a strategy would reduce premature deaths attributable to ambient and indoor air pollution by almost one third.

Abstract WMP57: Geographic Disparities in Stroke and Life Expectancy Significantly Attributable to Ambient PM2.5 Pollution Across the Counties of the United States

Background: We aimed to test the hypotheses that ambient particulate matter (PM2.5) is significantly associated with the risk of stroke mortality and life expectancy (LE) reduction, and the degrees of these associations differ across the counties and regions of the United States (U.S). Methods: Annual average county-level data (n=1561 counties) from U.S. Centers for Disease Control and Prevention, and Environmental Protection Agency on PM2.5 measures in 2005-2010, age-adjusted stroke mortality in adults aged ≥35 in 2013-2015, and LE in 2010, as well as % of poverty rate, number of primary care physicians per 1000 residents (as a marker of healthcare support) and county-level % of rural population as covariates were analyzed using linear and multilevel hierarchical regression analyses. Results: Significant county-level variations in annual average PM2.5 levels (range: 7.2-14.7 ug/ 3 ), stroke mortality (30.1-219.0 per 100,000 population in males, and 35.0-249.0 in females), and LE (65.0-81.7 years in males and 72.7-85.0 in females) were observed across the nation. Population-weighted PM2.5 levels were significantly and positively associated with stroke mortality in males (r=0.49, p&lt;.0001), and in females (r=0.42, p&lt;.0001), and negatively associated with LE in males (r=-0.39, p&lt;.0001) and females (r=-0.37, p&lt;.0001). Adjusted multilevel analysis indicates that PM2.5 was significantly associated with stroke mortality rates (β=1.27, p&lt;.0001) in both sexes, and with LE in males (β=-0.18, p=&lt;.001), and in females (β=-0.09, p&lt;.001). The adverse impacts of PM2.5 were even worse in counties with higher % of poverty rates and poorer healthcare support compared to their counterparts. Analyses by regions indicate that the highest risk of PM2.5 for stroke (assessed by regression coefficients) was shown for males and females living in the South, followed by Midwest, Northeast, and West. Similar results of the risk of PM2.5 for LE reduction were observed across the regions. Conclusion: The study, using U.S. national representative datasets provides new and important evidence that geographic disparities in stroke mortality and LE are significantly attributable to ambient PM2.5 pollution across the nation.

Satellite-derived PM2.5 concentration trends over Eastern China from 1998 to 2016: Relationships to emissions and meteorological parameters

Fine particulate matter (PM2.5) pollution in Eastern China (EC) has raised concerns due to its adverse effects on air quality, climate, and human health. This study investigated the long-term variation trend in satellite-derived PM2.5 concentrations and how it was related to pollutant emissions and meteorological parameters over EC and seven regions of interest (ROIs) during 1998–2016. Over EC, the annual mean PM2.5 increased before 2006 due to the enhanced emissions of primary PM2.5, NOx and SO2, but decreased with the reduced SO2 emissions after 2006 evidently in response to China's clean air policies. In addition, results from statistical analyses indicated that in the North China Plain (NCP), Northeast China (NEC), Sichuan Basin (SCB) and Central China (CC) planetary boundary layer height (PBLH) was the dominant meteorological driver for the PM2.5 decadal changes, and in the Pearl River Delta (PRD) wind speed is the leading factor. Overall, the variation in meteorological parameters accounted for 48% of the variances in PM2.5 concentrations over EC. The population-weighted PM2.5 over EC increased from 36.4 μg/m3 in 1998–2004 (P1) to 49.4 μg/m3 in 2005–2010 (P2) then decreased to 46.5 μg/m3 in 2011–2016 (P3). In the NCP and NEC, the percentages of the population living above the World Health Organization (WHO) Interim Target-1 (IT-1, 35 μg/m3) have risen steadily over the past 20 yr, reaching maxima of 97.3% and 78.8% in P3, respectively, but decreases of ∼30% from P2 to P3 were found for the SCB and PRD.

Mitigating air pollution: planetary health awaits a cosmopolitan moment

Mitigating air pollution: planetary health awaits a cosmopolitan moment

Mapping daily PM2.5 at 500 m resolution over Beijing with improved hazy day performance

The application of satellite-derived aerosol optical depth (AOD) to infer surface PM2.5 has significantly increased the spatial coverage and resolutions (1–10 km) of ground-level PM2.5 mapping as required for accurate exposure estimation. The remaining challenge is to further increase the mapping resolution to the sub-km level with improved algorithms to minimize misrepresentation of severe haze as clouds. In this study, we provide the first daily PM2.5 estimation over Beijing at a 500 m resolution using AOD from the Simplified Aerosol Retrieval Algorithm (SARA) and linear mixed effects model. A novel cloud screen method is developed which significantly improves data availability during hazy days. The cross-validation R2 for PM2.5 estimations is 0.82 with the cloud-screened SARA AOD. Based on the satellite-predicted high-resolution PM2.5 map, all-day population-weighted PM2.5 is estimated to be 81.4 μg m−3 over Beijing (2.3 times higher than China's NAAQS of 35 μg m−3). Compared to the standard MODIS Dark Target 3 km product which presents a significant percentage of missing data, the 500 m resolution PM2.5 mapping derived from SARA AOD reveals distinct pollution patterns and population exposure conditions during severe hazy days, thereby providing valuable information for pollution control and epidemiological studies.

Applying Integrated Exposure-Response Functions to PM2.5 Pollution in India.

Fine particulate matter (PM2.5, diameter ≤2.5 μm) is implicated as the most health-damaging air pollutant. Large cohort studies of chronic exposure to PM2.5 and mortality risk are largely confined to areas with low to moderate ambient PM2.5 concentrations and posit log-linear exposure-response functions. However, levels of PM2.5 in developing countries such as India are typically much higher, causing unknown health effects. Integrated exposure-response functions for high PM2.5 exposures encompassing risk estimates from ambient air, secondhand smoke, and active smoking exposures have been posited. We apply these functions to estimate the future cause-specific mortality risks associated with population-weighted ambient PM2.5 exposures in India in 2030 using Greenhouse Gas-Air Pollution Interactions and Synergies (GAINS) model projections. The loss in statistical life expectancy (SLE) is calculated based on risk estimates and baseline mortality rates. Losses in SLE are aggregated and weighted using national age-adjusted, cause-specific mortality rates. 2030 PM2.5 pollution in India reaches an annual mean of 74 μg/m3, nearly eight times the corresponding World Health Organization air quality guideline. The national average loss in SLE is 32.5 months (95% Confidence Interval (CI): 29.7–35.2, regional range: 8.5–42.0), compared to an average of 53.7 months (95% CI: 46.3–61.1) using methods currently applied in GAINS. Results indicate wide regional variation in health impacts, and these methods may still underestimate the total health burden caused by PM2.5 exposures due to model assumptions on minimum age thresholds of pollution effects and a limited subset of health endpoints analyzed. Application of the revised exposure-response functions suggests that the most polluted areas in India will reap major health benefits only with substantial improvements in air quality.

Health benefit assessment of PM2.5 reduction in Pearl River Delta region of China using a model-monitor data fusion approach

The Pearl River Delta (PRD), one of the most polluted and populous regions of China, experienced a 28% reduction in fine particulate matter (PM2.5) concentration between 2013 (47 μg/m3) and 2015 (34 μg/m3) under a stringent national policy known as the Air Pollution Prevention and Control Action Plan (hereafter Action Plan). In this study, the health and economic benefits associated with PM2.5 reductions in PRD during 2013–2015 were estimated using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) software. To create reliable gridded PM2.5 surfaces for BenMAP-CE calculations, a data fusion tool which incorporates the accuracy of monitoring data and the spatial coverage of predictions from the Community Multiscale Air Quality (CMAQ) model has been developed. The population-weighted average PM2.5 concentration over PRD was predicted to decline by 24%. PM2.5-related mortality was estimated to decrease by more than 3800 due to decreases in stroke (48%), ischemic heart disease (IHD) (35%), chronic obstructive pulmonary disease (COPD) (10%), and lung cancer (LC) (7%). A 13% reduction in PM2.5-related premature deaths from these four causes yielded a large economic benefit of about 1300 million US dollars. Our research suggests that the Action Plan played a major role in reducing emissions and additional measures should be implemented to further reduce PM2.5 pollution and protect public health in the future.

The impact of air pollution on deaths, disease burden, and life expectancy across the states of India: the Global Burden of Disease Study 2017

SummaryBackgroundAir pollution is a major planetary health risk, with India estimated to have some of the worst levels globally. To inform action at subnational levels in India, we estimated the exposure to air pollution and its impact on deaths, disease burden, and life expectancy in every state of India in 2017.MethodsWe estimated exposure to air pollution, including ambient particulate matter pollution, defined as the annual average gridded concentration of PM2.5, and household air pollution, defined as percentage of households using solid cooking fuels and the corresponding exposure to PM2.5, across the states of India using accessible data from multiple sources as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017. The states were categorised into three Socio-demographic Index (SDI) levels as calculated by GBD 2017 on the basis of lag-distributed per-capita income, mean education in people aged 15 years or older, and total fertility rate in people younger than 25 years. We estimated deaths and disability-adjusted life-years (DALYs) attributable to air pollution exposure, on the basis of exposure–response relationships from the published literature, as assessed in GBD 2017; the proportion of total global air pollution DALYs in India; and what the life expectancy would have been in each state of India if air pollution levels had been less than the minimum level causing health loss.FindingsThe annual population-weighted mean exposure to ambient particulate matter PM2·5 in India was 89·9 μg/m3 (95% uncertainty interval [UI] 67·0–112·0) in 2017. Most states, and 76·8% of the population of India, were exposed to annual population-weighted mean PM2·5 greater than 40 μg/m3, which is the limit recommended by the National Ambient Air Quality Standards in India. Delhi had the highest annual population-weighted mean PM2·5 in 2017, followed by Uttar Pradesh, Bihar, and Haryana in north India, all with mean values greater than 125 μg/m3. The proportion of population using solid fuels in India was 55·5% (54·8–56·2) in 2017, which exceeded 75% in the low SDI states of Bihar, Jharkhand, and Odisha. 1·24 million (1·09–1·39) deaths in India in 2017, which were 12·5% of the total deaths, were attributable to air pollution, including 0·67 million (0·55–0·79) from ambient particulate matter pollution and 0·48 million (0·39–0·58) from household air pollution. Of these deaths attributable to air pollution, 51·4% were in people younger than 70 years. India contributed 18·1% of the global population but had 26·2% of the global air pollution DALYs in 2017. The ambient particulate matter pollution DALY rate was highest in the north Indian states of Uttar Pradesh, Haryana, Delhi, Punjab, and Rajasthan, spread across the three SDI state groups, and the household air pollution DALY rate was highest in the low SDI states of Chhattisgarh, Rajasthan, Madhya Pradesh, and Assam in north and northeast India. We estimated that if the air pollution level in India were less than the minimum causing health loss, the average life expectancy in 2017 would have been higher by 1·7 years (1·6–1·9), with this increase exceeding 2 years in the north Indian states of Rajasthan, Uttar Pradesh, and Haryana.InterpretationIndia has disproportionately high mortality and disease burden due to air pollution. This burden is generally highest in the low SDI states of north India. Reducing the substantial avoidable deaths and disease burden from this major environmental risk is dependent on rapid deployment of effective multisectoral policies throughout India that are commensurate with the magnitude of air pollution in each state.FundingBill & Melinda Gates Foundation; and Indian Council of Medical Research, Department of Health Research, Ministry of Health and Family Welfare, Government of India.

Population Exposure to Ambient PM2.5 at the Subdistrict Level in China.

Fine-particulate pollution is a major public health concern in China. Accurate assessment of the population exposed to PM2.5 requires high-resolution pollution and population information. This paper assesses China’s potential population exposure to PM2.5, maps its spatiotemporal variability, and simulates the effects of the recent air pollution control policy. We relate satellite-based Aerosol Optical Depth (AOD) retrievals to ground-based PM2.5 observations. We employ block cokriging (BCK) to improve the spatial interpolation of PM2.5 distribution. We use the subdistrict level population data to estimate and map the potential population exposure to PM2.5 pollution in China at the subdistrict level, the smallest administrative unit with public demographic information. During 8 April 2013 and 7 April 2014, China’s population-weighted annual average PM2.5 concentration was nearly 7 times the annual average level suggested by the World Health Organization (WHO). About 1322 million people, or 98.6% of the total population, were exposed to PM2.5 at levels above WHO’s daily guideline for longer than half a year. If China can achieve its Action Plan on Prevention and Control of Air Pollution targets by 2017, the population exposed to PM2.5 above China’s daily standard for longer than half a year will be reduced by 85%.

The fraction of lung cancer incidence attributable to fine particulate air pollution in France: Impact of spatial resolution of air pollution models

Outdoor air pollution is a leading environmental cause of death and cancer incidence in humans. We aimed to estimate the fraction of lung cancer incidence attributable to fine particulate matter (PM2.5) exposure in France, and secondarily to illustrate the influence of the input data and the spatial resolution of information on air pollution levels on this estimate. The population attributable fraction (PAF) was estimated using a nationwide spatially refined chemistry-transport model with a 2-km spatial resolution, neighbourhood-scale population density data, and a relative risk from a published meta-analysis. We used the WHO guideline value for PM2.5 exposure (10 μg/m3) as reference. Sensitivity analyses consisted in attributing the nation-wide median exposure to all areas and using alternative input data such as reference of PM2.5 exposure level and relative risk. Population-weighted median PM2.5 level in 2005 was 13.8 μg/m3; 87% of the population was exposed above the guideline value. The burden of lung cancer attributable to PM2.5 exposure corresponded to 1466 cases, or 3.6% of all cases diagnosed in 2015. Sensitivity analyses showed that the use of a national median of PM2.5 exposure would have led to an underestimation of the PAF by 11% (population-weighted median) and by 72% (median of raw concentration), suggesting that our estimates would have been higher with even more finely spatially-resolved models. When the PM2.5 reference level was replaced by the 5th percentile of country-scale exposure (4.9 μg/m3), PAF increased to 7.6%. Other sensitivity analyses resulted in even higher PAFs. Improvements in air pollution are crucial for quantitative health impacts assessment studies. Actions to reduce PM2.5 levels could substantially reduce the burden of lung cancer in France.

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).