Italy was the rst, among all the European countries, to be strongly hit by the Covid-19 pandemic outbreak caused by the severe acute respiratory syndrome coronavirus 2 (Sars-CoV-2). The virus, proven to be very contagious, infected more than 9 million people worldwide (in June 2020). Nevertheless, it is not clear the role of air pollution and meteorological conditions on virus transmission. In this study, we quantitatively assessed how the meteorological and air quality parameters are correlated to the Covid-19 transmission in Lombardy (Northern Italy), the region epicenter of the virus outbreak. Our main ndings highlight that temperature and humidity related variables are negatively correlated to the virus transmission, whereas air pollution (PM 2.5) shows a positive correlation. In other words, Covid-19 pandemic transmission prefers dry and cool environmental conditions, as well as polluted air. For these reasons, the virus might easier spread in un ltered air-conditioned environments. Those results will be supporting decision makers to contain new possible outbreaks.

This nationwide investigation provided robust evidence of the associations between short-term exposure to PM and increased mortality from various cardiopulmonary diseases in China. The magnitude of associations was lower than those reported in Europe and North America.

To estimate PM concentrations, many parametric regression models have been developed, while nonparametric machine learning algorithms are used less often and national-scale models are rare. In this paper, we develop a random forest model incorporating aerosol optical depth (AOD) data, meteorological fields, and land use variables to estimate daily 24 h averaged ground-level PM concentrations over the conterminous United States in 2011. Random forests are an ensemble learning method that provides predictions with high accuracy and interpretability. Our results achieve an overall cross-validation (CV) R value of 0.80. Mean prediction error (MPE) and root mean squared prediction error (RMSPE) for daily predictions are 1.78 and 2.83 μg/m, respectively, indicating a good agreement between CV predictions and observations. The prediction accuracy of our model is similar to those reported in previous studies using neural networks or regression models on both national and regional scales. In addition, the incorporation of convolutional layers for land use terms and nearby PM measurements increase CV R by ∼0.02 and ∼0.06, respectively, indicating their significant contributions to prediction accuracy. A pair of different variable importance measures both indicate that the convolutional layer for nearby PM measurements and AOD values are among the most-important predictor variables for the training process.

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Background:Few large multicity studies have been conducted in developing countries to address the acute health effects of atmospheric ozone pollution.Objective:We explored the associations between ozone and daily cause-specific mortality in China.Methods:We performed a nationwide time-series analysis in 272 representative Chinese cities between 2013 and 2015. We used distributed lag models and over-dispersed generalized linear models to estimate the cumulative effects of ozone (lagged over 0–3 d) on mortality in each city, and we used hierarchical Bayesian models to combine the city-specific estimates. Regional, seasonal, and demographic heterogeneity were evaluated by meta-regression.Results:At the national-average level, a 10-μg/m3 increase in 8-h maximum ozone concentration was associated with 0.24% [95% posterior interval (PI): 0.13%, 0.35%], 0.27% (95% PI: 0.10%, 0.44%), 0.60% (95% PI: 0.08%, 1.11%), 0.24% (95% PI: 0.02%, 0.46%), and 0.29% (95% PI: 0.07%, 0.50%) higher daily mortality from all nonaccidental causes, cardiovascular diseases, hypertension, coronary diseases, and stroke, respectively. Associations between ozone and daily mortality due to respiratory and chronic obstructive pulmonary disease specifically were positive but imprecise and nonsignificant. There were no statistically significant differences in associations between ozone and nonaccidental mortality according to region, season, age, sex, or educational attainment.Conclusions:Our findings provide robust evidence of higher nonaccidental and cardiovascular mortality in association with short-term exposure to ambient ozone in China. https://doi.org/10.1289/EHP1849

Background The evidence for an association between particulate air pollution and type 2 diabetes mellitus (T2DM) in developing countries was very scarce. Objective To investigate the associations of long-term exposure to fine particulate matter (PM2.5) with T2DM prevalence and with fasting glucose and glycosylated hemoglobin (HbA1c) levels in China. Methods This is a cross-sectional study based on a nation-wide baseline survey of 11,847 adults who participated in the China Health and Retirement Longitudinal Study from June 2011 to March 2012. The average residential exposure to PM2.5 for each participant in the same period was estimated using a satellite-based spatial statistical model. We determined the association between PM2.5 and T2DM prevalence by multivariable logistic regression models. We also evaluated the association between PM2.5 and fasting glucose and HbA1c levels using multivariable linear regression models. Stratification analyses were conducted to explore potential effect modification. Results We identified 1,760 cases of T2DM, corresponding to 14.9% of the study population. The average PM2.5 exposure for all participants was 72.6 μg/m3 during the study period. An interquartile range increase in PM2.5 (41.1μg/m3) was significantly associated with increased T2DM prevalence (prevalence ratio, PR=1.14), and elevated levels of fasting glucose (0.26 mmol/L) and HbA1c (0.08%). The associations of PM2.5 with T2DM prevalence and with fasting glucose and HbA1c were stronger in several subgroups. Conclusions This nationwide cross-sectional study suggested that long-term exposure to PM2.5 might increase the risk of T2DM in China.

Public Welfare Research Program.