Improving emissions inputs via mobile measurements to estimate fine-scale Black Carbon monthly concentrations through geostatistical space-time data fusion

Spatial distribution characteristics and prediction of fluorine concentration in groundwater based on driving factors analysis

Excess fluoride (F^-) in groundwater can be hazardous to human health. A total of 360 ground water samples was collected from northern Anhui, China, to study the levels, distribution, and source of F^-. And on this basis, predicting the spatial distribution of F^- in a wider scale space. The range of F^- was 0.1-5.8 mg/L, with a mean value of 1.2 mg/L, and 26.4 % of the samples exceeded the acceptable level of 1.5 mg/L. Moreover, the water-rock interaction (fluorite dissolution) and cation alternate adsorption were considered to be two main driving factors of high F^- in groundwater. To further illustrate the spatial effects, the BME-RF model was established by combining the main environmental factors. The spatial distribution of F^- was quantitatively predicted, and the response to environmental variables was analyzed. The R² of BME-RF model reached 0.93, the prediction results showed that the region with 1.0-1.5 mg/L of F^- accounts for 47.2 % of the total area. The predicted F^- content of nearly 70 % of groundwater in this area has exceeded 1.0 mg/L, which was dominated by Na⁺ and HCO₃^- type. The spatial variability of F^- in the study area was mainly affected by hydrogeological conditions, and the vertical distribution characteristics were related to the spatial variation of slope, distance from runoff, and hydrochemical types. The results of the study provide new insights into the F^- concentration prediction in underground environment, especially in the borehole gap area.

Assessing public health and economic loss associated with black carbon exposure using monitoring and MERRA-2 data

Black carbon (BC) exposure in China continues to be relatively high, prompting researchers to assess BC exposure levels using data from monitoring sites, satellite remote sensing, and models. However, data regarding the application of a combined strategy comprising the analysis of monitoring data and various types of data to simulate BC exposure levels are lacking. Hence, the current study seeks to estimate short- and long-term BC exposure levels by combining national monitoring data with data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2). Furthermore, this study attempts to improve the spatio-temporal resolution of BC exposure levels using Bayesian maximum entropy (BME). The BME model performed well in terms of estimating short- (R² = 0.74 and RMSE = 1.76 μg/m³) and long-term (R² = 0.76 and RMSE = 1.3 μg/m³) exposure. Premature mortalities and economic losses were also assessed by applying localised concentration-response coefficients simulated in China. A total of 74,500 (95% confidence interval (CI): 23,900-124,500) and 538,400 (95% CI: 495,000-581,300) all-cause premature mortality cases were found to be associated with short- and long-term BC exposure, respectively. Meanwhile, short-term BC exposure was associated with economic losses ranging from 7.5 to 13.2 billion US dollars (USD) (1 USD = 6.36 RMB on January 19, 2022) based on amended human capital (AHC) and willingness to pay (WTP), accounting for 0.06%-0.1% of China's total gross domestic product (GDP) in 2017 (1.2 × 10⁴ billion USD), respectively. The economic losses for long-term exposure varied from 53 to 93.2 billion USD based on AHC and WTP, accounting for 0.4%-0.8% of China's total GDP in 2017, respectively.

Quantification of SO2 Emission Variations and the Corresponding Prediction Improvements Made by Assimilating Ground-Based Observations

In this research, a new time-resolved emission inversion system was developed to investigate variations in SO2 emission in China during the COVID-19 (Corona Virus Disease 2019) lockdown period based on a four-dimensional variational (4DVar) inversion method to dynamically optimize the SO2 inventory by assimilating the ground-based hourly observation data. The inversion results obtained were validated in the North China Plain (NCP). Two sets of experiments were carried out based on the original and optimized inventories during the pre-lockdown and lockdown period to quantify the SO2 emission variations and the corresponding prediction improvement. The SO2 emission changes due to the lockdown in the NCP were quantified by the differences in the averaged optimized inventories between the pre-lockdown and lockdown period. As a response to the lockdown control, the SO2 emissions were reduced by 20.1% on average in the NCP, with ratios of 20.7% in Beijing, 20.2% in Tianjin, 26.1% in Hebei, 18.3% in Shanxi, 19.1% in Shandong, and 25.9% in Henan, respectively. These were mainly attributed to the changes caused by the heavy industry lockdown in these areas. Compared to the model performance based on the original inventory, the optimized daily SO2 emission inventory significantly improved the model SO2 predictions during the lockdown period, with the correlation coefficient (R) value increasing from 0.28 to 0.79 and the root-mean-square error (RMSE) being reduced by more than 30%. Correspondingly, the performance of PM2.5 was slightly improved, with R-value increasing from 0.67 to 0.74 and the RMSE being reduced by 8% in the meantime. These statistics indicate the good optimization ability of the time-resolved emission inversion system.