Estimation of PM2.5 Concentration Efficiency and Potential Public Mortality Reduction in Urban China

A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5

Accurate estimation of fine particulate matter with diameter ≤2.5 μm (PM2.5) at a high spatiotemporal resolution is crucial for the evaluation of its health effects. Previous studies face multiple challenges including limited ground measurements and availability of spatiotemporal covariates. Although the multiangle implementation of atmospheric correction (MAIAC) retrieves satellite aerosol optical depth (AOD) at a high spatiotemporal resolution, massive non-random missingness considerably limits its application in PM2.5 estimation. Here, a deep learning approach, i.e., bootstrap aggregating (bagging) of autoencoder-based residual deep networks, was developed to make robust imputation of MAIAC AOD and further estimate PM2.5 at a high spatial (1 km) and temporal (daily) resolution. The base model consisted of autoencoder-based residual networks where residual connections were introduced to improve learning performance. Bagging of residual networks was used to generate ensemble predictions for better accuracy and uncertainty estimates. As a case study, the proposed approach was applied to impute daily satellite AOD and subsequently estimate daily PM2.5 in the Jing-Jin-Ji metropolitan region of China in 2015. The presented approach achieved competitive performance in AOD imputation (mean test R2: 0.96; mean test RMSE: 0.06) and PM2.5 estimation (test R2: 0.90; test RMSE: 22.3 μg/m3). In the additional independent tests using ground AERONET AOD and PM2.5 measurements at the monitoring station of the U.S. Embassy in Beijing, this approach achieved high R2 (0.82–0.97). Compared with the state-of-the-art machine learning method, XGBoost, the proposed approach generated more reasonable spatial variation for predicted PM2.5 surfaces. Publically available covariates used included meteorology, MERRA2 PBLH and AOD, coordinates, and elevation. Other covariates such as cloud fractions or land-use were not used due to unavailability. The results of validation and independent testing demonstrate the usefulness of the proposed approach in exposure assessment of PM2.5 using satellite AOD having massive missing values.

Evaluating Economic and Environmental Performance of the Chinese Industry Sector

This study assesses economic and environmental performance in the Chinese industry sector across 30 provinces during the period of 2006–2017. The study relies on a nonparametric framework and we apply a novel decomposition of the overall inefficiency scores into three components of technical, scale and mix inefficiency at the aggregate level by incorporating undesirable outputs. As we rely on by-production technology, industry performance is split into economic and environmental dimensions. Our results show that Chinese industry inefficiency is equally due to economic and environmental performance during 2006–2017, whereas technical and scale inefficiencies are relatively higher for environmental sub-technology (which relates energy to CO2 emission) if opposed to the economic sub-technology (which relates all the inputs to the economic value added). This implies that Chinese industry still requires improvements in environmental performance. The eastern region shows a relatively low average economic overall inefficiency if compared to other regions, yet its total OI (overall inefficiency) is the highest among the regions. Thus, environmental performance and misallocation of resources constitute the underlying causes of the total inefficiency. Therefore, structural reforms are necessary besides improvements in the production processes in the eastern region. This is important since China has experienced economic growth, but also policy must pay attention to environmental issues and sustainability.

Long-term exposure to ambient fine particulate pollution, sleep disturbance and their interaction effects on suicide attempts among Chinese adolescents

BACKGROUND

There is a lack of literature about the joint effects of PM_2.5 exposure and sleep disturbance on suicide attempts. This study aimed to estimate the association of PM_2.5 exposure or sleep disturbance with suicide attempts among Chinese adolescents and to test their interaction effects on both additive and multiplicative scales.

METHODS

Data was drawn from a subsample of the School-based Chinese Adolescents Health Survey (SCAHS) during 2014-2015 in Guangdong province, including 21,780 eligible participants. The 3-year (2011-2013) annual average concentration of PM_2.5 was estimated using satellite data. Multi-level logistic regression models with weights were fitted, and both multiplicative and additive interactions for PM_2.5 and sleep disturbance were tested.

RESULTS

A total of 608 students (2.8%) reported having suicide attempts. After adjusting for significant demographics and depressive symptoms, PM_2.5 exposure (Adjusted odds ratio [AOR] = 1.25, 95% CI = 1.03-1.56) and sleep disturbance (AOR = 1.69, 95% CI = 1.41-2.02) were positively associated with suicide attempts, respectively. The adjusted additive interaction effect of PM_2.5 and sleep disturbance was 2.42 (95% CI = 1.80-3.26) with a synergistic index of 1.31. The multivariable multi-level logistic regression models did not find any significant multiplicative interaction item (P > 0.05).

LIMITATION

The school-based cross-sectional study design CONCLUSION: Long-term exposure to PM_2.5 may elevate the risks of suicide attempts among Chinese adolescents. Moreover, the significant interaction effects of PM_2.5 exposure and sleep disturbance on suicide attempts were found in the additive model, suggesting decreasing long-term exposure to a higher level of PM_2.5 may be helpful to reduce the risk of suicide attempts among adolescents with sleep disturbance.

Ovariectomy predisposes female rats to fine particulate matter exposure's effects by altering metabolic, oxidative, pro-inflammatory, and heat-shock protein levels

The reduction of estrogen levels, as a result of menopause, is associated with the development of metabolic diseases caused by alterations in oxidative stress (OS), inflammatory biomarkers, and 70-kDa heat-shock protein (HSP70) expression. Additionally, exposure to fine particulate matter air pollution modifies liver OS levels and predisposes organisms to metabolic diseases, such as type 2 diabetes (T2DM). We investigated whether ovariectomy affects hepatic tissue and alters glucose metabolism in female rats exposed to particulate air pollution. First, 24 female Wistar rats received an intranasal instillation of saline or particles suspended in saline 5 times per week for 12 weeks. The animals then received either bilateral ovariectomy (OVX) or false surgery (sham) and continued to receive saline or particles for 12 additional weeks, comprising four groups: CTRL, Polluted, OVX, and Polluted+OVX. Ovariectomy increased body weight and adiposity and promoted edema in hepatic tissue, hypercholesterolemia, glucose intolerance, and a pro-inflammatory profile (reduced IL-10 levels and increased IL-6/IL-10 ratio levels), independent of particle exposure. The Polluted+OVX group showed an increase in neutrophils and neutrophil/lymphocyte ratios, decreased antioxidant defense (SOD activity), and increased liver iHSP70 levels. In conclusion, alterations in the reproductive system predispose female organisms to particulate matter air pollution effects by affecting metabolic, oxidative, pro-inflammatory, and heat-shock protein expression.