Prediction of Soil Nutrients Based on Topographic Factors and Remote Sensing Index in a Coal Mining Area, China

(1) Background: Coal mining operations caused severe land subsidence and altered the distributions of soil nutrients that influenced by multiple environmental factors at different scales. However, the prediction performances for soil nutrients based on their scale-specific relationships with influencing factors remains undefined in the coal mining area. The objective of this study was to establish prediction models of soil nutrients based on their scale-specific relationships with influencing factors in a coal mining area. (2) Methods: Soil samples were collected based on a 1 × 1 km regular grid, and contents of soil organic matter, soil available nitrogen, soil available phosphorus, and soil available potassium were measured. The scale components of soil nutrients and the influencing factors collected from remote sensing and topographic factors were decomposed by two-dimensional empirical mode decomposition (2D-EMD), and the predictions for soil nutrients were established using the methods of multiple linear stepwise regression or partial least squares regression based on original samples (MLSROri or PLSROri), partial least squares regression based on bi-dimensional intrinsic mode function (PLSRBIMF), and the combined method of 2D-EMD, PLSR, and MLSR (2D-EMDPM). (3) Results: The correlation types and correlation coefficients between soil nutrients and influencing factors were scale-dependent. The variances of soil nutrients at smaller scale were stochastic and non-significantly correlated with influencing factors, while their variances at the larger scales were stable. The prediction performances in the coal mining area were better than those in the non-coal mining area, and 2D-EMDPM had the most stable performance. (4) Conclusions: The scale-dependent predictions can be used for soil nutrients in the coal mining areas

Occurrence, Distribution and Risk Assessment of Mercury in Multimedia of Soil-Dust-Plants in Shanghai, China

The urban environment is a complex ecosystem influenced by strong human disturbances in multi-environmental media, so it is necessary to analyze urban environmental pollutants through the comprehensive analysis of different media. Soil, road dust, foliar dust, and camphor leaves from 32 sample sites in Shanghai were collected for the analysis of mercury contamination in soil–road dust–leaves–foliar dust systems. Mercury concentrations in surface soils in Shanghai were the highest, followed by road dust, foliar dust, and leaves, successively. The spatial distribution of mercury in the four environmental media presented different distribution patterns. Except for the significant correlation between mercury concentrations in road dust and mercury concentrations in leaves (r = 0.56, p < 0.001), there was no significant correlation between the other groups in the four media. Besides this, there was no significant correlation between mercury concentrations and land types. The LUR (Land use regression) model was used to assess the impact of urbanization factors on mercury distribution in the environment. The results showed that soil mercury was affected by factories and residential areas. Foliar dust mercury was affected by road density and power plants. Leaf mercury was affected by power plants and road dust mercury was affected by public service areas. The highest average HI (Hazard index) value of mercury in Shanghai was found in road dust, followed by surface soil and foliar dust. The HI values for children were much higher than those for adults. However, the HI values of mercury exposure in all sampling sites were less than one, suggesting a lower health risk level. The microscopic mechanism of mercury in different environmental media was suggested to be studied further in order to learn the quantitative effects of urbanization factors on mercury concentrations

Spatiotemporal Variation and Influence Factors of Habitat Quality in Loess Hilly and Gully Area of Yellow River Basin: A Case Study of Liulin County, China

China has set up ecological protection and high-quality development of the Yellow River Basin as its national strategy. However, the fragile natural ecosystem and intensive human disturbances pose challenges to it. This study evaluates habitat quality change and analyzes its drivers in a representative county of this region, aiming to provide scientific basis for ecological protection and sustainable development. We took Liulin, a representative county of middle Yellow River Basin as the study area and evaluated the spatiotemporal variation of habitat quality from 2000 to 2020 with the InVEST model. Further, the influencing factors of habitat quality pattern were explored using GeoDetector, and their gradient ranges dominating the habitat quality change were determined by gradient analysis. The results showed that: (1) Areas of low and medium-low habitat quality grades were distributed interactively in the whole county; medium grade areas were scattered in the northeast and southwest parts of the county; and medium-high and high grades area were distributed sporadically along the Yellow River and its branches. (2) Habitat quality of the county almost unchanged from 2000 to 2010. However, from 2010 to 2020, with the rapid expansion of construction land (increased by 9.62 times), the area proportion of medium, medium-high, and high habitat quality grades decreased from 7.01% to 5.31%, while that of low and medium-low habitat quality grades increased from 92.99% to 94.69%. (3) The habitat quality was influenced by multiple natural-human factors. The main influencing factor was land use, followed by elevation. (4) Most changes of habitat quality occurred in areas with lower elevation, gentler slope, and higher vegetation coverage, which were affected by intensive human activities. These results suggest that in future land use policy making and the construction land expansion in Liulin County should be restricted, and differentiated ecological protection and restoration strategies should be implemented in areas with different habitat quality