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Few studies have evaluated the short-term association between hospital admissions and individual exposure to ambient particulate matter (PM2.5). Particularly, no studies focused on hospital admissions for chronic obstructive pulmonary disease (COPD) at individual level. We assessed the short-term effects of PM2.5 on hospitalization admissions for COPD in Guangzhou, China, during 2014–2015, based on satellite-derived estimates of ambient PM2.5 concentrations at 1-km resolution near the residential address as individual-level exposure for each patient. 40,002 patients with COPD admitted to 110 hospitals were included in this study. A time-stratified case-crossover design with conditional logistic regression models was applied to assess the effects of PM2.5 based on 1-km grid data of aerosol optical depth provided by National Aeronautics and Space Administration on hospital admissions for COPD. Further, we performed stratified analyses by individual demographic characteristics and season of hospital admission. 10 µg/m3 increase in individual-level PM2.5 was associated with an increase of 1.6% (95% confidence interval [CI]: 0.6%, 2.7%) in hospitalization for COPD at lag of 0–5 days. The impact of PM2.5 on hospitalization for COPD was greater significantly in male and patients admitted in summer and accordingly the impacts may be exacerbated in the context of global warming. Our study strengthened the evidence for the adverse effect of PM2.5 based on satellite-based individual-level exposure data.

It is unclear how to develop a model based on the combined satellite data and ground monitoring data to accurately estimate daily NO2 levels. Furthermore, the conventional cross-validation (CV) results represent average levels but the model performance may vary greatly from grid to grid. It is an essential issue to evaluate model's prediction ability in different grids and determine the factors affecting model extrapolating ability, which have never been well examined to date. The aim of this study was to compare the ability of three different methods to estimate the daily NO2 across mainland China during 2014–2016; and to develop a novel two-stage meta-analysis method for exploring the influence of the number and the distribution of nearby sites on grid-level prediction accuracy. For better estimating the daily NO2 level, we developed and compared three methods, including universal kriging model, satellite-based method (Non-linear exposure-lag-response model & Extreme gradient boosting combined technique) and the kriging-calibrated satellite method. For exploring influencing factors, the two-stage meta-analysis method was purposed. The kriging-calibrated satellite method had an overall CV R-square and root mean square error (RMSE) of 0.85 and 7.87μg/m3, better than the Universal Kriging model and the satellite-based method (CV R2 = 0.57 and 0.81). The two-stage meta-analysis method revealed that the model performance did decrease with the sparser distribution of nearby sites. And adding 5 sites within 50 km in the random mode can bring 17.51% improvement in model extrapolating ability. The kriging-calibration can help satellite-based machine learning to provide more accurate NO2 prediction. Our novel evaluation method can provide the suggestion of adding new sites effectively within a limit budget.

BackgroundLimited evidence is available on the health effects of particulate matter (i.e. PM2.5, particulate matter with an aerodynamic diameter < 2.5μm; PM10, < 10μm; PM2.5-10, 2.5-10μm) during the pandemic of COVID-19 in Italy.ObjectivesTo examine the associations between all-cause mortality and daily PM2.5, PM2.5-10, and PM10in the pandemic period, and compare them to the normal periods (2015-2019) in Italy.MethodsWe collected daily data regarding all-cause (stratified by age and gender), and PM2.5, PM2.5-10, and PM10for 107 Italian provinces from 1, January 2015 to 31, May 2020. A time-stratified case-cross design with the distributed lag non-linear model was used to examine the association between PM and all-cause mortality during the first three months of the COVID-19 outbreak (March to May in 2020) and the same months in 2015-2019. We also compared the counts and fractions of death attributable to PM in two periods.ResultsOverall, Italy saw an increase in daily death counts while slight decreases in PM concentrations in 2020 pandemic period compared to same months of 2015-2019. Mortality effects were significant in lag 0-3 days for PM2.5, lag 0-2 for PM10, and lag 0-1 for PM2.5-10. Each 10 µg/m3increase in PM was associated much higher increase in daily all-cause mortality during 2020 pandemic period compared to the same months during 2015-2019 [increased mortality rate: 7.24 % (95%CI: 4.84%, 9.70%) versus 1.69% (95%CI: 1.12%, 2.25%) for PM2.5; 3.45 % (95%C: 2.58%, 4.34%) versus 1.11% (95%CI: 0.79%, 1.42%) for PM10, 4.25% (95%CI: 2.99%, 5.52%) versus 1.76% (95%CI: 1.14%, 2.38%) for PM2.5-10]. The counts and fractions of deaths attributable to PM were higher in 2020 than the normal periods for PM2.5(attributable death counts: 20,062 in 2020 versus 3,927 per year in 2015-2019; attributable fractions: 10.2% versus 2.4%), PM10(15,112 versus 3,999; 7.7% versus 2.5%), and PM2.5-10(7,193 versus 2303; 3.7% versus 1.4%).ConclusionsCOVID-19 pandemic increased the vulnerability and excess cases of all-cause mortality associated with short-term exposure to PM2.5, PM2.5-10and PM10in Italy, despite a decline in air pollution level. This suggests using historical PM-mortality association to calculate health benefits associated with reduction in PMs has big uncertainties.

Due to the COVID-19 outbreak, the Chinese government implemented nationwide traffic restrictions and self-quarantine measures from January 23 to April 8 (in Wuhan), 2020. We estimated how these measures impacted ambient air pollution and the subsequent consequences on health and the health-related economy in 367 Chinese cities. A random forests modeling was used to predict the business-as-usual air pollution concentrations in 2020, after adjusting for the impact of long-term trend and weather conditions. We calculated changes in mortality attributable to reductions in air pollution in early 2020 and health-related economic benefits based on the value of statistical life (VSL). Compared with the business-as-usual scenario, we estimated 1239 (95% CI: 844-1578) PM2.5-related deaths were avoided, as were 2777 (95% CI: 1565-3995) PM10-related deaths, 1587 (95% CI: 98-3104) CO-related deaths, 4711 (95% CI: 3649-5781) NO2-related deaths, 215 (95% CI: 116-314) O3-related deaths, and 1088 (95% CI: 774-1421) SO2-related deaths. Based on the reduction in deaths, economic benefits for in PM2.5, PM10, CO, NO2, O3, and SO2 were 1.22, 2.60, 1.36, 4.05, 0.20, and 0.95 billion USD, respectively. Our findings demonstrate the substantial benefits in human health and health-related costs due to improved urban air quality during the COVID lockdown period in China in early 2020.

Background: Limited evidence is available on the health effects of particulate matter (PM including PM2.5 with an aerodynamic diameter ≤ 2.5 μm; PM10, ≤ 10 μm; PM2.5–10, 2.5–10 μm) during the pandemic of COVID-19 in Italy. The aims of the study were to examine the associations between all-cause mortality and PM in the pandemic period and compare them to the normal periods (2015–2019). Methods: We collected daily data regarding all-cause mortality (stratified by age and gender), and PM concentrations for 107 Italian provinces from 1 January 2015 to 31 May 2020. A time-stratified case-cross design with the distributed lag non-linear model was used to examine the association between PM and all-cause mortality. We also compared the counts and fractions of death attributable to PM in two periods. Results: Italy saw an increase in daily death counts while slight decreases in PM concentrations in pandemic period. Each 10 µg/m3 increase in PM was associated with much higher increase in daily all-cause mortality during the pandemic period compared to the same months during 2015–2019 (increased mortality rate: 7.24% (95%CI: 4.84%, 9.70%) versus 1.69% (95%CI: 1.12%, 2.25%) for PM2.5; 3.45% (95%CI: 2.58%, 4.34%) versus 1.11% (95%CI: 0.79%, 1.42%) for PM10; 4.25% (95%CI: 2.99%, 5.52%) versus 1.76% (95%CI: 1.14%, 2.38%) for PM2.5–10). The counts and fractions of deaths attributable to PM were higher in 2020 for PM2.5 (attributable death counts: 20,062 versus 3927 per year in 2015–2019; attributable fractions: 10.2% versus 2.4%), PM10 (15,112 versus 3999; 7.7% versus 2.5%), and PM2.5–10 (7193 versus 2303; 3.7% versus 1.4%). Conclusion: COVID-19 pandemic increased the vulnerability and excess cases of all-cause mortality associated with short-term exposure to PM2.5, PM2.5–10, and PM10 in Italy, despite a decline in air pollution level.