Background: Particulate air pollution, especially PM_2.5, is highly correlated with various adverse health impacts and, ultimately, economic losses for society, however, few studies have undertaken a spatiotemporal assessment of PM_2.5-related economic losses from health impacts covering all of the main cities in China. Methods: PM_2.5 concentration data were retrieved for 190 Chinese cities for the period 2014–2016. We used a log-linear exposure–response model and monetary valuation methods, such as value of a statistical life (VSL), amended human capital (AHC), and cost of illness to evaluate PM_2.5-related economic losses from health impacts at the city level. In addition, Monte Carlo simulation was used to analyze uncertainty. Results: The average economic loss was 0.3% (AHC) to 1% (VSL) of the total gross domestic product (GDP) of 190 Chinese cities from 2014 to 2016. Overall, China experienced a downward trend in total economic losses over the three-year period, but the Beijing–Tianjin–Hebei, Shandong Peninsula, Yangtze River Delta, and Chengdu-Chongqing regions experienced greater annual economic losses. Conclusions: Exploration of spatiotemporal variations in PM_2.5-related economic losses from long-term health impacts could provide new information for policymakers regarding priority areas for PM_2.5 pollution prevention and control in China.

Accurate watershed delineation is a precondition for runoff and water quality simulation. Traditional digital elevation model (DEM) may not generate realistic drainage networks due to large depressions and subtle elevation differences in local-scale plains. In this study, we propose a new method for solving the problem of watershed delineation, using the Taihu Basin as a case study. Rivers, lakes, and reservoirs were obtained from Sentinel-2A images with the Canny algorithm on Google Earth Engine (GEE), rather than from DEM, to compose the drainage network. Catchments were delineated by modifying the flow direction of rivers, lakes, reservoirs, and overland flow, instead of using DEM values. A watershed was divided into the following three types: Lake, reservoir, and overland catchment. A total of 2291 river segments, seven lakes, eight reservoirs, and 2306 subwatersheds were retained in this study. Compared with results from HydroSHEDS and Arc Hydro, the proposed method retains crisscross structures in the topology and prevented erroneous streamlines in large lakes. High-resolution Sentinel-2A images available on the GEE have relatively greater merits than DEMs for precisely representing drainage networks and catchments, especially in the plains area. Because of the higher accuracy, this method can be used as a new solution for watershed division in the plains area.

Landslide susceptibility mapping (LSM) is a vital tool for proactive disaster mitigation. Although numerous studies utilize slope units (SUs) for LSM, the limited integration of adjacency information, including spatial autocorrelation, often reduces predictive accuracy. In this study, GRASS GIS was utilized to generate slope units, and a spatial logistic regression (SLR) model was developed to incorporate the adjacency information of the slope units to predict the landslide susceptibility. Then, the spatial stratification heterogeneity patterns of landslide susceptibility were analyzed using GeoDetector. The results showed that the SLR model achieved an area under the curve (AUC) of 0.89, a notable improvement of 0.26 compared to the traditional logistic regression (LR) model that does not incorporate adjacency information. This indicates that incorporating adjacency information effectively enhances LSM accuracy by mitigating spatial autocorrelation. Furthermore, lithology, PGV, and distance to the epicenter were identified as the primary factors contributing to the formation of the spatial stratification heterogeneity of landslide susceptibility. Among these, the interaction between lithology and PGV exhibits the strongest nonlinear enhancement. By integrating both mapping units and their adjacency information, this study provides a novel approach to improving the predictive accuracy of LSM. Moreover, by analyzing the driving factors of spatial stratification heterogeneity in landslide susceptibility maps, the study advances the practical utility of LSM for disaster management and mitigation.