In recent years, atmospheric PM_2.5 pollution in China has become increasingly severe and exploring the relationships among its influencing factors is important in the prevention and control of air pollution. Although previous studies have identified complexity in variations in PM_2.5 concentrations and recognized the interaction of multiple factors, little quantitative information is available on the evolution of the relationships among these factors, their spatial heterogeneity, and the multiscale interactions between them. In this study, geographical detector and multiscale geographically weighted regression models have been used to explore the multiscale interactions among natural and socioeconomic factors and PM_2.5 concentration in China over the period 2000–2015. The results indicate that the relationship between natural factors and PM_2.5 concentration is stronger than that for socioeconomic factors. The type of interaction between each factor is dominated by bivariate and nonlinear enhancement, exhibiting strong interactions between natural factors and anthropogenic factors. Although the effect of each factor on PM_2.5 is complex, the relative influence of both human activities and social factors is shown to have gradually increased over time and population, agriculture, urbanization, and socioeconomic activities in general make important contributions to PM_2.5. In addition, the scale of effects related to natural factors is smaller and more stable compared to the influence of human activities during the period 2000-2015. There are significant differences in the way natural factors and socioeconomic factors affect PM_2.5, and there is strong non-stationarity of spatial relationships. Factors associated with topography, vegetation (NDVI), climate (temperature), natural sources, and agricultural activity are shown to be important determinants of PM_2.5 across China and warrant significant attention in terms of managing atmospheric pollution. The study demonstrates that spatial differences in the direction, intensity, and scale of each factor should be accounted for to improve prevention and control measures and alleviate regional PM_2.5 pollution.

Fine particulate matter in the lower atmosphere (PM_2.5) continues to be a major public health problem globally. Identifying the key contributors to PM_2.5 pollution is important in monitoring and managing atmospheric quality, for example, in controlling haze. Previous research has been aimed at quantifying the relationship between PM_2.5 values and their underlying factors, but the spatial and temporal dynamics of these factors are not well understood. Based on random forest and Shapley additive explanation (SHAP) algorithms, this study analyses the spatiotemporal variations in selected key factors influencing PM_2.5 in Zhejiang Province, China, for the period 2000–2019. The results indicate that, while factors influencing PM_2.5 varied significantly during the period studied, SHAP values suggest that there is consistency in their relative importance as follows: meteorological factors (e.g., atmospheric pressure) > socioeconomic factors (e.g., gross domestic product, GDP) > topography and land cover factors (e.g., elevation). The contribution of GDP and transportation factors initially increased but has declined in the recent past, indicating that economic and infrastructural development does not necessarily result in increased PM_2.5 concentrations. Vegetation productivity, as indicated by changes in NDVI, is demonstrated to have become more important in improving air quality, and the area of the province over which it constrains PM_2.5 concentrations has increased between 2000 and 2019. Mapping of SHAP values suggests that, although the relative importance of industrial emissions has declined during the period studied, the actual area positively impacted by such emissions has actually increased. Despite developments in government policy, greater efforts to conserve energy and reduce emissions are still needed. The study further demonstrates that the combination of random forest and SHAP methods provides a valuable means to identify regional differences in key factors affecting atmospheric PM_2.5 values and offers a reliable reference for pollution control strategies.

Biomass is a key biophysical parameter used to estimate carbon storage and forest productivity. Spatially-explicit estimation of biomass provides invaluable information for carbon stock calculation and scientific forest management. Nevertheless, there still exists large uncertainty concerning the relationship between biomass and influential factors. In this study, aboveground biomass (AGB) was estimated using the random forest algorithm based on remote sensing imagery (Landsat) and field data for three regions with different topographic conditions in Zhejiang Province, China. AGB distribution and change combined with stratified terrain classifications were analyzed to investigate the relations between AGB and topography conditions. The results indicated that AGB in three regions increased from 2010 to 2015 and the magnitude of growth varied with elevation, slope, and aspect. In the basin region, slope had a greater influence on AGB, and we attributed this negative AGB-elevation relationship to ecological forest construction. In the mountain area, terrain features, especially elevation, showed significant relations with AGB. Moreover, AGB and its growth showed positive relations with elevation and slope. In the island region, slope also played a relatively more important role in explaining the relationship. These results demonstrate that AGB varies with terrain conditions and its change is a consequence of interactions between the natural environment and anthropogenic behavior, implying that biomass retrieval based on Landsat imagery could provide considerable important information related to regional heterogeneity investigations.

High-resolution albedo has the advantage of a higher spatial scale from tens to hundreds of meters, which can fill the gaps of albedo applications from the global scale to the regional scale and can solve problems related to land use change and ecosystems. The Sentinel-2 satellite provides high-resolution observations in the visible-to-NIR bands, giving possibilities to generate a high-resolution surface albedo at 10 m. This study attempted to evaluate the performance of the four data-driven machine learning algorithms (i.e., random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and XGBoost (XGBT)) for the generation of a Sentinel-2 albedo over flat and rugged terrain. First, we used the RossThick-LiSparseR model and the 3D discrete anisotropic radiative transfer (DART) model to build the narrowband surface reflectance and broadband surface albedo, which acted as the training and testing datasets over flat and rugged terrain. Second, we used the training and testing datasets to drive the four machine learning models, and evaluated the performance of these machine learning models for the generation of Sentinel-2 albedo. Finally, we used the four machine learning models to generate a Sentinel-2 albedo and compared them with in situ albedos to show the models’ application potentials. The results show that these machine learning models have great performance in estimating Sentinel-2 albedos at a 10 m spatial scale. The comparison with in situ albedos shows that the random forest model outperformed the others in estimating a high-resolution surface albedo based on Sentinel-2 datasets over the flat and rugged terrain, with an RMSE smaller than 0.0308 and R² larger than 0.9472.

Negative air ions (NAIs), which are known as the “air vitamin”, have been widely used as a measure of air cleanness. Field observation provides an alternative way to record site-level NAIs. However, these observations fail to capture the regional distribution of NAIs due to the limited number of sites. In this study, satellite-based bio-geophysical parameters from the climate, topography, air quality, vegetation, and anthropogenic intensity were used to estimate the daily NAIs with the Random Forest model (RF). In situ NAI observations over Zhejiang Province, China were incorporated into the model. Daily NAIs were averaged to capture the spatio-temporal distribution. The results showed that (1) the RF algorithm performed better than traditional regression analysis and the common BP neural network to generate regional NAIs at a spatial scale of 500 m over the larger scale, with an RMSE of 258.62, R² of 0.878 for model training, and R² of 0.732 for model testing; (2) in the variable importance measures (VIM) analysis, 87.96% of the NAI variance was caused by the elevation, aspect, slope, surface temperature, solar-induced chlorophyll fluorescence (SIF), relative humidity (RH), and the concentration of carbon monoxide (CO), while path analysis indicated that SIF was one of the most important factors affecting NAI concentration across the whole region; (3) NAI concentrations in 87.16% of the region were classified above grade III (>500 ions cm⁻³), which was able to meet the needs of human health maintenance; (4) the highest NAI concentration was distributed over the southwest of the Zhejiang Province, where forest land dominates. The lowest NAI concentration was mostly found in the northeast regions, where urban areas are well-developed; and (5) among different land types, the NAI concentrations were ranked as forest land > water bodies > barren > grassland > croplands > urban and built-up. Among different seasons, summer and winter have the highest and lowest NAIs, respectively. Our study provided a substantial reference for ecosystem services assessment in Zhejiang Province.