Contribution of local and surrounding anthropogenic emissions to a particulate matter pollution episode in Zhengzhou, Henan, China

Spatial source, simulating improvement, and short-term health effect of high PM2.5 exposure during mutation event in the key urban agglomeration regions in China

Air quality in China has significantly improved owing to the effective implementation of pollution control measures. However, mutation events caused by short-term spikes in PM2.5 in urban agglomeration regions continue to occur frequently. Identifying the spatial sources and influencing factors, as well as improving the prediction accuracy of high PM2.5 during mutation events, are crucial for public health. In this study, we firstly introduced discrete wavelet transform (DWT) to identify the mutation events with high PM2.5 concentration in the four key urban agglomerations, and evaluated the spatial sources for the polluted scenario using Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Additionally, DWT was combined with a widely used artificial neural network (ANN) to improve the prediction accuracy of PM2.5 concentration seven days in advance (seven-day forecast). Results indicated that mutation events commonly occurred in the northern regions during winter time, which were under the control of both short-range transportation of dirty airmass as well as negative meteorology conditions. Compared with the ANN model alone, the average band errors decreased by 9% when using DWT-ANN model. The average correlation coefficient (R) and root mean square error (RMSE) obtained using the DWT-ANN improved by 10% and 12% compared to those obtained using the ANN, indicating the efficiency and accuracy of simulating PM2.5, by combining the DWT and ANN. The short-term mortality during mutation events was then calculated, with the total averted all-cause, cardiovascular, and respiratory deaths in the four regions, being 4751, 2554, and 582 persons, respectively. A declining trend in prevented deaths from 2018 to 2020 demonstrated that the pollution intensity during mutation events gradually decreased owing to the implementation of the Three-Year Action Plan to Win the Blue Sky Defense War. The method proposed in this study can be used by policymakers to take preventive measures in response to a sudden increase in PM2.5, thereby ensuring public health.

Association between fine particulate matter and fecundability in Henan, China: A prospective cohort study

OBJECTIVE

To investigate the relationship between ambient fine particulate matter (PM2.5) exposure and fecundability.

METHODS

This study included 751,270 female residents from Henan Province who participated in the National Free Pre-conception Check-up Projects during 2015-2017. Ambient cycle-specific PM2.5 exposure was assessed at the county level for each participant using satellite-based PM2.5 concentration data at 1-km resolution. Cox proportional hazards models with time-varying exposure were used to estimate the association between fecundability and PM2.5 exposure, adjusted for potential individual risk factors.

RESULTS

During the study period, 568,713 participants were pregnant, monthly mean PM2.5 concentrations varied from 25.5 to 114.0 µg/m3 across study areas. For each 10 µg/m3 increase in cycle-specific PM2.5, the hazard ratio for fecundability was 0.951 (95 % confidence interval: 0.950-0.953). The association was more pronounced in women who were older, with urban household registration, history of pregnancy, higher body mass index (BMI), hypertension, without exposure to tobacco, or whose male partners were older, with higher BMI, or hypertension.

CONCLUSION

In this population-based prospective cohort, ambient cycle-specific PM2.5 exposure was associated with reduced fecundability. These findings may support the adverse implications of severe air pollution on reproductive health.

Spatiotemporal characteristic analysis of PM2.5 in central China and modeling of driving factors based on MGWR: a case study of Henan Province

Since the start of the twenty-first century, China's economy has grown at a high or moderate rate, and air pollution has become increasingly severe. The study was conducted using data from remote sensing observations between 1998 and 2019, employing the standard deviation ellipse model and spatial autocorrelation analysis, to explore the spatiotemporal distribution characteristics of PM2.5 in Henan Province. Additionally, a multiscale geographically weighted regression model (MGWR) was applied to explore the impact of 12 driving factors (e.g., mean surface temperature and CO2 emissions) on PM2.5 concentration. The research revealed that (1) Over a period of 22 years, the yearly mean PM2.5 concentrations in Henan Province demonstrated a trend resembling the shape of the letter "M", and the general trend observed in Henan Province demonstrated that the spatial center of gravity of PM2.5 concentrations shifted toward the north. (2) Distinct spatial clustering patterns of PM2.5 were observed in Henan Province, with the northern region showing a primary concentration of spatial hot spots, while the western and southern areas were predominantly characterized as cold spots. (3) MGWR is more effective than GWR in unveiling the spatial heterogeneity of influencing factors at various scales, thereby making it a more appropriate approach for investigating the driving mechanisms behind PM2.5 concentration. (4) The results acquired from the MGWR model indicate that there are varying degrees of spatial heterogeneity in the effects of various factors on PM2.5 concentration. To summarize the above conclusions, the management of the atmospheric environment in Henan Province still has a long way to go, and the formulation of relevant policies should be adapted to local conditions, taking into account the spatial scale effect of the impact of different influencing factors on PM2.5.