Joint effects of green space and air pollutant exposure on preterm birth: evidence from a nationwide study in China.

An association between green space exposure and preterm birth has been reported. However, evidence on the joint effects of air pollutant and green space exposure on preterm birth from nationwide research is limited in China. Based on a nationwide cohort, this study aims to explore the effect of green space exposure on preterm birth and analyze the joint effects of green space and air pollutant. Logistic regression models were developed to analyze the effects of green space exposure, and interaction effects were evaluated by adding interaction terms between green space and air pollutants. From 2013 to 2019, this study included 2,294,188 records of newborn births, of which 82,921 were preterm births. The results show that for buffer zones with 250 m, 500 m, 1000 m, and 1500 m, every 0.1 unit increase in NDVI exposure was associated with a decrease in the risk of preterm birth by 5.5% (95% CI: 4.6-6.4%), 5.8% (95% CI: 4.9-6.6%), 6.1% (95% CI: 5.3-7.0%), and 5.6% (95% CI: 4.7-6.5%), respectively. Under high-level exposure to air pollutants, high-level NDVI exposure was more strongly negatively correlated with preterm birth than low-level NDVI exposure. High-level green space exposure might mitigate the adverse effect of air pollutants on preterm birth by promoting physical activity, reducing stress, and adsorbing pollutants. Further investigation is needed to explore how green space and air pollution interact and affect preterm birth, in order to improve risk management and provide a reference for newborn health.

A data-augmentation approach to deriving long-term surface SO2 across Northern China: Implications for interpretable machine learning.

Until recently, Northern China was one of the most SO2 polluted regions in the world. The lack of long-term and spatially resolved surface SO2 data hinders retrospective evaluation of relevant environmental policies and human health effects. This study aims to derive the spatiotemporal distribution of surface SO2 across Northern China during 2005-2019. As "concept drift" causes substantial estimation bias in back-extrapolation, we propose a new approach named the robust back-extrapolation via data augmentation approach (RBE-DA) to model the long-term surface SO2. The results show that the population-weighted regional SO2 ([SO2]pw) increased from 2005 to 2007 and decreased steadily afterwards. The [SO2]pw decreased by 80.4% from 74.2 ± 28.4 µg/m3 in 2007 to 14.6 ± 4.8 µg/m3 in 2019. The predicted spatial distributions for each year show that the SO2 pollution was severe (more than 20 µg/m3) in most areas of Northern China until 2017. By using model interpretation methods, we visually reveal the mechanism of estimation bias in the back-extrapolation. Specifically, the training data is severely imbalanced with respect to the satellite-retrieved SO2 column densities (i.e., it is short on high-value samples), so the benchmark model is unable to extrapolate the effects of this important predictor. This study provides long-term surface SO2 data for post hoc evaluation and human exposure assessment in Northern China, while demonstrating that the interpretable machine learning approach is critical for model diagnostics and refinement. Leveraging satellite retrievals, the RBE-DA approach can be applied worldwide to back-extrapolate various measures of air quality.

Short-term air quality forecasting model based on hybrid RF-IACA-BPNN algorithm.

Despite the apparent improvement in air quality in recent years through a series of effective measures, the concentration of PM2.5 and O3 in Chengdu city remains high. And both the two pollutants can cause serious damage to human health and property; consequently, it is imperative to accurately forecast hourly concentration of PM2.5 and O3 in advance. In this study, an air quality forecasting method based on random forest (RF) method and improved ant colony algorithm coupled with back-propagation neural network (IACA-BPNN) are proposed. RF method was used to screen out highly correlated input variables, and the improved ant colony algorithm (IACA) was adopted to combine with BPNN to improve the convergence performance. Two datasets based on two different kinds of monitoring stations along with meteorological data were applied to verify the performance of this proposed model and compared with another five plain models. The results showed that the RF-IACA-BPNN model has the minimum statistical error of the mean absolute error, root mean square error, and mean absolute percentage error, and the values of R2 consistently outperform other models. Thus, it is concluded that the proposed model is suitable for air quality prediction. It was also detected that the performance of the models for the forecasting of the hourly concentrations of PM2.5 were more acceptable at suburban station than downtown station, while the case is just the opposite for O3, on account of the low variability dataset at suburban station.

The effects of vertical incision on fascia in repeated cesarean section delivery / 대한산부인과학회잡지

No abstract available.