Health benefits from the rapid reduction in ambient exposure to air pollutants after China's clean air actions: progress in efficacy and geographic equality.

Clean air actions (CAAs) in China have been linked to considerable benefits in public health. However, whether the beneficial effects of CAAs are equally distributed geographically is unknown. Using high-resolution maps of the distributions of major air pollutants (fine particulate matter [PM2.5] and ozone [O3]) and population, we aimed to track spatiotemporal changes in health impacts from, and geographic inequality embedded in, the reduced exposures to PM2.5 and O3 from 2013 to 2020. We used a method established by the Global Burden of Diseases Study. By analyzing the changes in loss of life expectancy (LLE) attributable to PM2.5 and O3, we calculated the gain of life expectancy (GLE) to quantify the health benefits of the air-quality improvement. Finally, we assessed the geographic inequality embedded in the GLE using the Gini index (GI). Based on risk assessments of PM2.5 and O3, during the first stage of CAAs (2013 to 2017), the mean GLE was 1.87 months. Half of the sum of the GLE was disproportionally distributed in about one quarter of the population exposed (GI 0.44). During the second stage of CAAs (2017 to 2020), the mean GLE increased to 3.94 months and geographic inequality decreased (GI 0.18). According to our assessments, CAAs were enhanced, from the first to second stages, in terms of not only preventing premature mortality but also ameliorating health inequalities. The enhancements were related to increased sensitivity to the health effects of air pollution and synergic control of PM2.5 and O3 levels. Our findings will contribute to optimizing future CAAs.

Associations between hemoglobin levels and source-specific exposure to ambient fine particles among children aged <5 years in low- and middle-income countries.

OBJECTIVE: We investigated associations between source-specific fine particulate matter (PM2.5) exposure and hemoglobin levels among children in low- and middle-income countries (LMICs). METHOD: 36,675 children aged < 5 years were collected in 11 LMICs during 2017. We associated child hemoglobin with 20 source-specific PM2.5, and calculated changes in hemoglobin that could be attributed to different PM2.5-mixture scenarios, established using real-world data from 88 Asian and African LMICs (AA-LMICs). RESULTS: Multiple-source analysis revealed PM2.5 produced by solvents (change in hemoglobin for 1-µg/m3 increment in PM2.5: -10.34 g/L, 95% CI -14.88 to -5.91), industrial coal combustion (-0.51 g/L, 95% CI -9.25 to -0.08), road transportation (-0.50 g/L, 95% CI -6.96 to -0.29), or waste handling and disposal (-0.34 g/L, 95% CI -4.38 to -0.23) was significantly associated with a decrease in hemoglobin level. Decreases in hemoglobin attributable to the PM2.5 mixtures were co-determined by the concentrations and their source profiles. The largest PM2.5-related change in hemoglobin was -10.25 g/L (95% CI -15.54 to -5.27) for a mean exposure of 61.01 µg/m3 in India. CONCLUSION: Association between PM2.5 and a decrease in hemoglobin was affected by variations in PM2.5 source profiles. Source-oriented interventions are warranted to protect children in LMICs from air pollution.

The association of birthweight with fine particle exposure is modifiable by source sector: Findings from a cross-sectional study of 17 low- and middle-income countries.

BACKGROUND: Low birthweight attributable to fine particulate matter (PM2.5) exposure is a global issue affecting infant health, especially in low- and middle-income countries (LMICs). However, large-population studies of multiple LMICs are lacking, and little is known about whether the source of PM2.5 is a determinant of the toxic effect on birthweight. OBJECTIVE: We examined the effect on birthweight of long-term exposure to PM2.5 from different sources in LMICs. METHODS: The birthweights of 53,449 infants born between September 16, 2017 and September 15, 2018 in 17 LMICs were collected from demographic and health surveys. Long-term exposure to PM2.5 in 2017 produced by 20 different sources was estimated by combining chemical transport model simulations with satellite-based concentrations of total mass. Generalized linear regression models were used to investigate the associations between birthweight and each source-specific PM2.5 exposure. A multiple-pollutant model with a ridge penalty on the coefficients of all 20-source-specific components was employed to develop a joint exposure-response function (JERF) of the PM2.5 mixtures. The estimated JERF was then used to quantify the global burden of birthweight reduction attributable to PM2.5 mixtures and to PM2.5 from specific sources. RESULTS: The fully adjusted single-pollutant model indicated that exposure to a 10 µg/m3 increase in total PM2.5 was significantly associated with a -6.6 g (95% CI -11.0 to -2.3) reduction in birthweight. In single- and multiple-pollutant models, significant birthweight changes were associated with exposure to PM2.5 produced by international shipping (SHP), solvents (SLV), agricultural waste burning (GFEDagburn), road transportation (ROAD), waste handling and disposal (WST), and windblown dust (WDUST). Based on the global average exposure to PM2.5 mixtures, the JERF showed that the overall change in birthweight could mostly be attributed to PM2.5 produced by ROAD (-37.7 g [95% CI -49.2 to -24.4] for a global average exposure of 2.2 µg/m3), followed by WST (-27.5 g [95% CI -42.6 to -10.7] for a 1.6-µg/m3 exposure), WDUST (-19.5 g [95% CI -26.7 to -12.6] for a 8.6-µg/m3 exposure), and SHP (-19.0 g [95% CI -32.3 to -5.7] for a 0.2-µg/m3 exposure), which, with the exception of WDUST, are anthropogenic sources. The changes in birthweight varied geographically and were co-determined by the concentration as well as the source profile of the PM2.5 mixture. CONCLUSION: PM2.5 exposure is associated with a reduction in birthweight, but our study shows that the magnitude of the association differs depending on the PM2.5 source. A source-targeted emission-control strategy that considers local features is therefore critical to maximize the health benefits of air quality improvement, especially with respect to promoting maternal and child health.