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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.

Exposure to outdoor fine particulate matter (PM2.5) is a leading risk factor for mortality. We develop global estimates of annual PM2.5 concentrations and trends for 1998-2018 using advances in satellite observations, chemical transport modeling, and ground-based monitoring. Aerosol optical depths (AODs) from advanced satellite products including finer resolution, increased global coverage, and improved long-term stability are combined and related to surface PM2.5 concentrations using geophysical relationships between surface PM2.5 and AOD simulated by the GEOS-Chem chemical transport model with updated algorithms. The resultant annual mean geophysical PM2.5 estimates are highly consistent with globally distributed ground monitors (R2 = 0.81; slope = 0.90). Geographically weighted regression is applied to the geophysical PM2.5 estimates to predict and account for the residual bias with PM2.5 monitors, yielding even higher cross validated agreement (R2 = 0.90-0.92; slope = 0.90-0.97) with ground monitors and improved agreement compared to all earlier global estimates. The consistent long-term satellite AOD and simulation enable trend assessment over a 21 year period, identifying significant trends for eastern North America (-0.28 ± 0.03 μg/m3/yr), Europe (-0.15 ± 0.03 μg/m3/yr), India (1.13 ± 0.15 μg/m3/yr), and globally (0.04 ± 0.02 μg/m3/yr). The positive trend (2.44 ± 0.44 μg/m3/yr) for India over 2005-2013 and the negative trend (-3.37 ± 0.38 μg/m3/yr) for China over 2011-2018 are remarkable, with implications for the health of billions of people.

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 ...

Australia has relatively diverse sources and low concentrations of ambient fine particulate matter (

Air pollution is a leading global disease risk factor. Tracking progress (e.g., for Sustainable Development Goals) requires accurate, spatially resolved, routinely updated exposure estimates. A Bayesian hierarchical model was developed to estimate annual average fine particle (PM2.5) concentrations at 0.1° × 0.1° spatial resolution globally for 2010–2016. The model incorporated spatially varying relationships between 6003 ground measurements from 117 countries, satellite-based estimates, and other predictors. Model coefficients indicated larger contributions from satellite-based estimates in countries with low monitor density. Within and out-of-sample cross-validation indicated improved predictions of ground measurements compared to previous (Global Burden of Disease 2013) estimates (increased within-sample R2 from 0.64 to 0.91, reduced out-of-sample, global population-weighted root mean squared error from 23 μg/m3 to 12 μg/m3). In 2016, 95% of the world’s population lived in areas where ambient PM2.5 lev...

Efforts to introduce more efficient stoves increasingly leverage carbon-finance to scale up dissemination of interventions. We conducted a randomized intervention study to evaluate a Clean Development Mechanism approved stove replacement impact on fuelwood usage, and climate and health-relevant air pollutants. We randomly assigned 187 households to either receive the intervention or to continue using traditional stoves. Measurements of fine particulate matter (PM2.5) and absorbance were conducted in cooking areas, village center and at upwind background site. There were minor and overlapping seasonal differences (post- minus preintervention change) between control and intervention groups for median (95% CI) fuel use (-0.60 (-1.02, -0.22) vs -0.52 (-1.07, 0.00) kg day(-1)), and 24 h absorbance (35 (18, 60) vs 36 (22, 50) × 10(-6) m(-1)); for 24 h PM2.5, there was a higher (139 (61,229) vs 73(-6, 156) μg m(-3))) increase in control compared to intervention homes between the two seasons. Forty percent of the intervention homes continued using traditional stoves. For intervention homes, absorbance-to-mass ratios suggest a higher proportion of black carbon in PM2.5 emitted from intervention compared with traditional stoves. Absent of field-based evaluation, stove interventions may be pursued that fail to realize expected carbon reductions or anticipated health and climate cobenefits.

We estimated global fine particulate matter (PM2.5) concentrations using information from satellite-, simulation- and monitor-based sources by applying a Geographically Weighted Regression (GWR) to global geophysically based satellite-derived PM2.5 estimates. Aerosol optical depth from multiple satellite products (MISR, MODIS Dark Target, MODIS and SeaWiFS Deep Blue, and MODIS MAIAC) was combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations for 1998-2014. The GWR predictors included simulated aerosol composition and land use information. The resultant PM2.5 estimates were highly consistent (R(2) = 0.81) with out-of-sample cross-validated PM2.5 concentrations from monitors. The global population-weighted annual average PM2.5 concentrations were 3-fold higher than the 10 μg/m(3) WHO guideline, driven by exposures in Asian and African regions. Estimates in regions with high contributions from mineral dust were associated with higher uncertainty, resulting from both sparse ground-based monitoring, and challenging conditions for retrieval and simulation. This approach demonstrates that the addition of even sparse ground-based measurements to more globally continuous PM2.5 data sources can yield valuable improvements to PM2.5 characterization on a global scale.

This paper presents a Geographical Information System (GIS) based probabilistic simulation framework to estimate PM2.5 population exposure in New Delhi, India. The framework integrates PM2.5 output from spatiotemporal LUR models and trip distribution data using a Gravity model based on zonal data for population, employment and enrollment in educational institutions. Time-activity patterns were derived from a survey of randomly sampled individuals (n = 1012) and in-vehicle exposure was estimated using microenvironmental monitoring data based on field measurements. We simulated population exposure for three different scenarios to capture stay-at-home populations (Scenario 1), working population exposed to near-road concentrations during commutes (Scenario 2), and the working population exposed to on-road concentrations during commutes (Scenario 3). Simulated annual average levels of PM2.5 exposure across the entire city were very high, and particularly severe in the winter months: ∼200 μg m(-3) in November, roughly four times higher compared to the lower levels in the monsoon season. Mean annual exposures ranged from 109 μg m(-3) (IQR: 97-120 μg m(-3)) for Scenario 1, to 121 μg m(-3) (IQR: 110-131 μg m(-3)), and 125 μg m(-3) (IQR: 114-136 μ gm(-3)) for Scenarios 2 and 3 respectively. Ignoring the effects of mobility causes the average annual PM2.5 population exposure to be underestimated by only 11%.

Exposure to ambient fine particulate matter (PM2.5) is a leading risk factor for the global burden of disease. However, uncertainty remains about PM2.5 sources. We use a global chemical transport model (GEOS-Chem) simulation for 2014, constrained by satellite-based estimates of PM2.5 to interpret globally dispersed PM2.5 mass and composition measurements from the ground-based surface particulate matter network (SPARTAN). Measured site mean PM2.5 composition varies substantially for secondary inorganic aerosols (2.4–19.7 μg/m3), mineral dust (1.9–14.7 μg/m3), residual/organic matter (2.1–40.2 μg/m3), and black carbon (1.0–7.3 μg/m3). Interpretation of these measurements with the GEOS-Chem model yields insight into sources affecting each site. Globally, combustion sectors such as residential energy use (7.9 μg/m3), industry (6.5 μg/m3), and power generation (5.6 μg/m3) are leading sources of outdoor global population-weighted PM2.5 concentrations. Global population-weighted organic mass is driven by the res...

Ambient fine particulate matter (PM2.5) has a large and well-documented global burden of disease. Our analysis uses high-resolution (10 km, global-coverage) concentration data and cause-specific integrated exposure-response (IER) functions developed for the Global Burden of Disease 2010 to assess how regional and global improvements in ambient air quality could reduce attributable mortality from PM2.5. Overall, an aggressive global program of PM2.5 mitigation in line with WHO interim guidelines could avoid 750 000 (23%) of the 3.2 million deaths per year currently (ca. 2010) attributable to ambient PM2.5. Modest improvements in PM2.5 in relatively clean regions (North America, Europe) would result in surprisingly large avoided mortality, owing to demographic factors and the nonlinear concentration-response relationship that describes the risk of particulate matter in relation to several important causes of death. In contrast, major improvements in air quality would be required to substantially reduce mortality from PM2.5 in more polluted regions, such as China and India. Moreover, forecasted demographic and epidemiological transitions in India and China imply that to keep PM2.5-attributable mortality rates (deaths per 100 000 people per year) constant, average PM2.5 levels would need to decline by ∼20-30% over the next 15 years merely to offset increases in PM2.5-attributable mortality from aging populations. An effective program to deliver clean air to the world's most polluted regions could avoid several hundred thousand premature deaths each year.

China emits a considerable amount of air pollutants when producing goods for export. Previous efforts have emphasized the magnitude of export-related emissions; however, their health consequences on the Chinese population have not been quantified. Here, we present an interdisciplinary study to estimate the health impact of export-related air pollution. The results show that export-related emissions elevated the annual mean population weighted PM2.5 by 8.3 μg/m(3) (15% of the total) in 2007, causing 157,000 deaths and accounting for 12% of the total mortality attributable to PM2.5-related air pollution. Compared to the eastern coastal provinces, the inner regions experience much larger export-related health losses relative to their economic production gains, owing to huge inter-regional disparities in export structures and technology levels. A shift away from emission-intensive production structure and export patterns, especially in inner regions, could significantly help improve national exports while alleviating the inter-regional cost-benefit inequality. Our results provide the first quantification of health consequences from air pollution related to Chinese exports. The proposed policy recommendations, based on health burden, economic production gains, and emission analysis, would be helpful to develop more sustainable and effective national and regional export strategies.

Air pollution in New Delhi, India, is a significant environmental and health concern. To assess determinants of variability in air pollutant concentrations, we develop land use regression (LUR) models for fine particulate matter (PM2.5), black carbon (BC), and ultrafine particle number concentrations (UFPN). We used 136 h (39 sites), 112 h (26 sites), 147 h (39 sites) of PM2.5, BC, and UFPN data respectively, to develop separate morning (0800-1200) and afternoon (1200-1800) models. Continuous measurements of PM2.5 and BC were also made at a single fixed rooftop site located in a high-income residential neighborhood. No continuous measurements of UFPN were available. In addition to spatial variables, measurements from the fixed continuous monitoring site were used as independent variables in the PM2.5 and BC models. The median concentrations (and interquartile range) of PM2.5, BC, and UFPN at LUR sites were 133 (96-232) μg m(-3), 11 (6-21) μg m(-3), and 40 (27-72) × 10(3) cm(-3) respectively. In addition (a) for PM2.5 and BC, the temporal variability was higher than the spatial variability; (b) the magnitude and spatial variability in pollutant concentrations was higher during morning than during afternoon hours. Further, model R(2) values were higher for morning (for PM2.5, BC, and UFPN, respectively: 0.85, 0.86, and 0.28) than for afternoon models (0.73, 0.69, and 0.23); (c) the PM2.5 and BC concentrations measured at LUR sites all over the city were strongly correlated with measured concentrations at a fixed rooftop site; (d) spatial patterns were similar for PM2.5 and BC but different for UFPN; (e) population density and road variables were statistically significant predictors of pollutant concentrations; and (f) available geographic predictors explained a much lower proportion of variability in measured PM2.5, BC, and UFPN than observed in other LUR studies, indicating the importance of temporal variability and suggesting the existence of uncharacterized sources.

Methods to characterize chronic exposure to ultrafine particles (UFP) can help to clarify potential health effects. Since UFP are not routinely monitored in North America, spatiotemporal models are one potential exposure assessment methodology. Portable condensation particle counters were used to measure particle number concentrations (PNC) to develop a land use regression (LUR) model. PNC, wind speed and direction were measured for sixty minutes at eighty locations during a two-week sampling campaign. We conducted continuous monitoring at four additional locations to assess temporal variation. LUR modeling utilized 135 potential geographic predictors including: road length, vehicle density, restaurant density, population density, land use and others. A novel approach incorporated meteorological data through wind roses as alternates to traditional circular buffers. The range of measured (sixty-minute median) PNC across locations varied seventy-fold (1500-105000 particles/cm(3), mean [SD] = 18200 [15900] particles/cm(3)). Correlations between PNC and concurrently measured two-week average NOX concentrations were 0.6-0.7. A PNC LUR model (R(2) = 0.48, leave-one-out cross validation R(2) = 0.32) including truck route length within 50 m, restaurant density within 200 m, and ln-distance to the port represents the first UFP LUR model in North America. Models incorporating wind roses did not explain more variability in measured PNC.

Recent evidence has suggested that woodsmoke exposure in developed countries is associated with acute and chronic health impacts. Accordingly, it is increasingly important to investigate the much higher woodsmoke exposures associated with the use of wood and other biomass for cooking and heating in developing countries. Particulate concentrations were measured in rural Mexican kitchens using biomass combustion for cooking. To investigate differences in indoor particle concentrations between kitchens using different fuels and stove types, measurements were made in eight kitchens using only biomass, six using only liquefied petroleum gas (LPG), six using a combination of biomass and LPG, and three using biomass in ventilated stoves. Outdoor samples were collected at the same time as the indoor samples. PM10 and PM2.5 measurements were made with inertial impactors, and particle light scattering was measured continuously with an integrating nephelometer. Nephelometer and particulate mass measurements were hig...

Experimental studies have shown that nitrogen acid species, particularly nitrous acid, are formed indoors during unvented combustion and by heterogeneous reactions of nitrogen dioxide. Limited measurements support the occurrence of nitrous acid production in occupied homes. The authors report additional measurements of HONO and NO[sub 2] in homes located in Albuquerque, NM, and assess the relationship with housing variables. Indoor HONO concentrations were found to be well correlated with indoor NO[sub 2] levels; HONO concentrations ranged from 5% to 15% of the measured NO[sub 2] concentrations. Given the correlation between HONO and NO[sub 2] in indoor environments, and the plausibility of HONO respiratory toxicity, investigations of respiratory health effects of unvented combustion should consider HONO, in addition to NO[sub 2], as a potentially hazardous indoor pollutant. 38 refs., 2 figs., 2 tabs.

This paper discusses the acidity of polluted atmospheres, mainly in the USA, and considers the evidence for the effects of acid air on health. There have been steady increases in the emission of the primary inorganic pollutant gases, sulphur dioxide and nitrogen oxides; car exhausts account for about half the emmisions of nitrogen oxides. Controls on car emissions, required by the Clean Air Act, have stabilised annual nitrogen oxides emissions at 21 million tons. However, these emissions are expected to rise sharply during the next 50 years, due to the increasing number of vehicle miles travelled in the USA, combined with the projected increase in electric power generation. Ambient concentrations of acids are also expected to rise, and the increased number of cars in use has led to substantial rises in nitric acid concentrations in large cities throughout the USA. Although millions of North Americans are breathing acidic air pollutants, there is only circumstantial evidence that these exposures harm their health. This evidence is derived from in vivo and in vitro laboratory experiments and from controlled human exposure studies. Almost no information has been gathered on the health effects of exposure to nitric or nitrous acids so that it is not yet known how harmful car emissions are. The needs for further research on public health impacts of acidic air are stated. (TRRL)

L'utilisation d'appareillage de controle individuel de type denudeur montre que la mesure de l'exposition des personnes aux aerosols et gaz acides peut differer significativement de la mesure estimee a partir d'un site de controle fixe