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.

Reduced Ambient PM2.5 Was Associated with a Decreased Risk of Chronic Kidney Disease: A Longitudinal Cohort Study.

Many countries have dedicated to the mitigation of air pollution in the past several decades. However, evidence of beneficial effects of air quality improvement on chronic kidney disease (CKD) remains limited. We thus investigated the effects of dynamic changes (including deterioration and improvement) in air quality on the incidence of CKD in a longitudinal study in Taiwan. During 2001-2016, this study recruited a total of 163,197 Taiwanese residents who received at least two standard physical examinations. The level of fine particle matter (PM2.5) was estimated using a high-resolution (1 km2) satellite-based spatio-temporal model. We defined changes of PM2.5 concentrations (ΔPM2.5) as the difference between the two-year average measurements during follow-up and during the immediately preceding visit. The time-dependent Cox regression model was adopted to evaluate the relationships between ΔPM2.5 and the incidence of CKD after adjusting for a series of covariates. The concentrations of PM2.5 in Taiwan peaked around 2004 and began to decrease since 2005. We observed an approximate linear concentration-response relationship of ΔPM2.5 with CKD incidence. Every 5 µg/m3 decrease in the ambient concentration of PM2.5 was associated with a 25% reduced risk of CKD development [hazard ratio (HR): 0.75; 95% CI: 0.73, 0.78]. In conclusion, this study demonstrated that the improvement of PM2.5 air quality might be associated with a lower risk of CKD development. Our findings indicate that reducing air pollution may effectively prevent the development of CKD.

Predicting personal nitrogen dioxide exposure in an elderly population: integrating residential indoor and outdoor measurements, fixed-site ambient pollution concentrations, modeled pollutant levels, and time-activity patterns.

Predicting chronic exposure to air pollution at the intra-urban scale has been recognized as a priority area of research for environmental epidemiology. Exposure assessment models attempt to predict and proxy for individuals' personal exposure to ambient air pollution, and there are no studies to date that explicitly attempt to compare and cross-validate personal exposure concentrations with pollutants modeled at the intra-urban level using methods such as interpolated surfaces and land-use regression (LUR) models. This study aimed to identify how well personal exposure to NO(2) (nitrogen dioxide) can be predicted from ambient exposure measurements and intra-urban exposure estimates using LUR and what other factors contribute to predicting variations in personal exposure beyond measured pollutant levels within home. Personal, indoor and outdoor NO(2) were measured in a population of older adults (>65 yr old) living in Hamilton, Canada. Our results show that personal NO(2) was most strongly associated with contemporaneously collected indoor and outdoor concentrations of NO(2). Predicted NO(2) exposures from intra-urban LUR models were not associated with personal NO(2), whereas interpolated surfaces of particulates and ozone were modestly associated. Combinations of variables that best predicted personal NO(2) variability were derived from time-activity diaries, interpolated surfaces of ambient particulate pollutants, and a city wide temporally matched average of NO(2). The nonsignificant associations between personal NO(2) and the modeled ambient NO(2) concentrations suggest that observed associations between NO(2) generated by LUR models and health effects are probably not produced by NO(2), but by other pollutants that follow a similar spatial pattern.