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.
Toward cleaner air for a billion Indians Apte, Joshua S.; Pant, Pallavi
Proceedings of the National Academy of Sciences - PNAS,
05/2019, Volume:
116, Issue:
22
Journal Article
Fine particulate matter (PM2.5) air pollution exposure is the largest environmental health risk factor in the United States. Here, we link PM2.5 exposure to the human activities responsible for PM2.5 ...pollution. We use these results to explore “pollution inequity”: the difference between the environmental health damage caused by a racial–ethnic group and the damage that group experiences. We show that, in the United States, PM2.5 exposure is disproportionately caused by consumption of goods and services mainly by the non-Hispanic white majority, but disproportionately inhaled by black and Hispanic minorities. On average, non-Hispanic whites experience a “pollution advantage”: They experience ∼17% less air pollution exposure than is caused by their consumption. Blacks and Hispanics on average bear a “pollution burden” of 56% and 63% excess exposure, respectively, relative to the exposure caused by their consumption. The total disparity is caused as much by how much people consume as by how much pollution they breathe. Differences in the types of goods and services consumed by each group are less important. PM2.5 exposures declined ∼50% during 2002–2015 for all three racial–ethnic groups, but pollution inequity has remained high.
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 (R 2 = 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 (R 2 = 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.
Delhi, India, routinely experiences some of the world's highest urban particulate matter concentrations. We established the Delhi Aerosol Supersite study to provide long-term characterization of the ...ambient submicron aerosol composition in Delhi. Here we report on 1.25 years of highly time-resolved speciated submicron particulate matter (PM.sub.1) data, including black carbon (BC) and nonrefractory PM.sub.1 (NR-PM.sub.1 ), which we combine to develop a composition-based estimate of PM.sub.1 ("C-PM.sub.1 " = BC + NR-PM.sub.1) concentrations.
High-resolution urban air pollution mapping Apte, Joshua S.; Manchanda, Chirag
Science (American Association for the Advancement of Science),
07/2024, Volume:
385, Issue:
6707
Journal Article
Peer reviewed
Variation in urban air pollution arises because of complex spatial, temporal, and chemical processes, which profoundly affect population exposure, human health, and environmental justice. This Review ...highlights insights from two popular in situ measurement methods—mobile monitoring and dense sensor networks—that have distinct but complementary strengths in characterizing the dynamics and impacts of the multidimensional urban air quality system. Mobile monitoring can measure many pollutants at fine spatial scales, thereby informing about processes and control strategies. Sensor networks excel at providing temporal resolution at many locations. Increasingly sophisticated studies leveraging both methods can vividly identify spatial and temporal patterns that affect exposures and disparities and offer mechanistic insight toward effective interventions. This Review summarizes the strengths and limitations of these methods and discusses their implications for understanding fine-scale processes and impacts.
Exposure to ambient fine particulate matter (PM2.5) air pollution is a major risk for premature death. Here, we systematically quantify the global impact of PM2.5 on life expectancy. Using data from ...the Global Burden of Disease project and actuarial standard life table methods, we estimate global and national decrements in life expectancy that can be attributed to ambient PM2.5 for 185 countries. In 2016, PM2.5 exposure reduced average global life expectancy at birth by ∼1 year with reductions of ∼1.2–1.9 years in polluted countries of Asia and Africa. If PM2.5 in all countries met the World Health Organization Air Quality Guideline (10 μg m–3), we estimate life expectancy could increase by a population-weighted median of 0.6 year (interquartile range of 0.2–1.0 year), a benefit of a magnitude similar to that of eradicating lung and breast cancer. Because background disease rates modulate the effect of air pollution on life expectancy, high age-specific rates of cardiovascular disease in many polluted low- and middle-income countries amplify the impact of PM2.5 on survival. Our analysis adds to prior research by illustrating how mortality from air pollution substantially reduces human longevity.
Wildfires have become an important source of particulate matter (PM
< 2.5-µm diameter), leading to unhealthy air quality index occurrences in the western United States. Since people mainly shelter ...indoors during wildfire smoke events, the infiltration of wildfire PM
into indoor environments is a key determinant of human exposure and is potentially controllable with appropriate awareness, infrastructure investment, and public education. Using time-resolved observations outside and inside more than 1,400 buildings from the crowdsourced PurpleAir sensor network in California, we found that the geometric mean infiltration ratios (indoor PM
of outdoor origin/outdoor PM
) were reduced from 0.4 during non-fire days to 0.2 during wildfire days. Even with reduced infiltration, the mean indoor concentration of PM
nearly tripled during wildfire events, with a lower infiltration in newer buildings and those utilizing air conditioning or filtration.
Abstract
An exposure-based traffic assignment (TA) model and accompanying analysis framework have been developed to quantify primary and secondary fine particulate matter (PM
2.5
) exposure due to ...modeled on-road vehicle flow on a regional network at a high spatial resolution. The Chicago Metropolitan Area transportation network is used to demonstrate the model’s decision-informing power. The study compares the spatially distributed exposure impacts due to traffic emissions of two TA optimization scenarios: a baseline user equilibrium with respect to travel time (UET) and a novel system optimal with respect to pollutant intake (SOI). The UET and SOI scenarios are developed through the use of (a) the TA model used for obtaining vehicle flow patterns and characteristics including emissions, (b) a source-receptor matrix for PM
2.5
developed through a reduced-complexity air quality model to quantify primary and secondary PM
2.5
concentrations across the exposure domain, (c) spatial analysis for assessing exposure profiles at the census tract level, and (d) a health impact model to quantify exposure damages. The SOI scenario yields a 9% – 10% total reduction in exposure damages, with the most impacted census tracts benefiting from up to 20% – 30% of reductions, but leads to a 16% increase in travel time costs. Further reduction to PM
2.5
exposure by the SOI is hindered by network constraints, where travel demand in populous areas around the network must still be satisfied. The model can be used to systematically quantify the mitigation potential of different transportation exposure reduction strategies, to assess the exposure impacts of newly developed transportation infrastructure, and to address the equity implications of PM
2.5
exposure from traffic, all under realistic system behavior and bounded by actual system constraints.
The widespread and rapid social and economic changes from Covid-19 response might be expected to dramatically improve air quality. However, national monitoring data from the US Environmental ...Protection Agency for criteria pollutants (PM2.5, ozone, NO2, CO, PM10) provide inconsistent support for that expectation. Specifically, during stay-at-home orders, average PM2.5 levels were slightly higher (~10% of its multi-year interquartile range IQR) than expected; average ozone, NO2, CO, and PM10 levels were slightly lower (~30%, ~20%, ~27%, and ~1% of their IQR, respectively) than expected. The timing of peak anomaly, relative to the stay-at-home orders, varied by pollutant (ozone: 2 weeks before; NO2, CO: 3 weeks after; PM10: 2 weeks after); but, by 5–6 weeks after stay-at-home orders, the concentration anomalies appear to have ended. For PM2.5, ozone, CO, and PM10, no US state had lower-than-expected pollution levels for all weeks during stay-at-home-orders; for NO2, only Arizona had lower-than-expected levels for all weeks during stay-at-home orders. Our findings show that the enormous changes from the Covid-19 response have not lowered PM2.5 levels across the US beyond their normal range of variability; for ozone, NO2, CO, and PM10 concentrations were lowered but the reduction was modest and transient.
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•Impacts of stay-at-home orders on air pollution were evaluated using EPA monitoring data from 100s of stations across the US.•During stay-at-home orders, ozone, NO2, CO and PM10 were lower and PM2.5 were higher than expected levels by 1%-30% of their IQR.•Concentration anomalies ended only 5-6 weeks after stay-at-home orders were issued.•Ozone, NO2, and CO concentrations returned to expected levels and PM2.5 and PM10 levels were higher than expected.•Reductions in ozone, NO2, and CO levels were modest and short-lived. PM10 levels did not change and PM2.5 levels increased.
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