Ecological environment quality reflects the overall condition and health of the environment. Analyzing the spatiotemporal dynamics and driving factors of ecological environment quality across large regions is crucial for environmental protection and policy-making. This study utilized the Google Earth Engine (GEE) platform to efficiently process large-scale remote sensing data and construct a multi-scale Remote Sensing Ecological Index (RSEI) based on Landsat and Sentinel data. This approach overcomes the limitations of traditional single-scale analyses, enabling a comprehensive assessment of ecological environment quality changes across provincial, municipal, and county levels in Fujian Province. Through the Mann–Kendall mutation test and Sen + Mann–Kendall trend analysis, the study identified significant change points in the RSEI for Fujian Province and revealed the temporal dynamics of ecological quality from 1987 to 2023. Additionally, Moran’s I statistic and Geodetector were employed to explore the spatial correlation and driving factors of ecological quality, with a particular focus on the complex interactions between natural factors. The results indicated that: (1) the integration of Landsat and Sentinel data significantly improved the accuracy of RSEI construction; (2) the RSEI showed a consistent upward trend across different scales, validating the effectiveness of the multi-scale analysis approach; (3) the ecological environment quality in Fujian Province experienced significant changes over the past 37 years, showing a trend of initial decline followed by recovery; (4) Moran’s I analysis demonstrated strong spatial clustering of ecological environment quality in Fujian Province, closely linked to human activities; and (5) the interaction between topography and natural factors had a significant impact on the spatial patterns of RSEI, especially in areas with complex terrain. This study not only provides new insights into the dynamic changes in ecological environment quality in Fujian Province over the past 37 years, but also offers a scientific basis for future environmental restoration and management strategies in coastal areas. By leveraging the efficient data processing capabilities of the GEE platform and constructing multi-scale RSEIs, this study significantly enhances the precision and depth of ecological quality assessment, providing robust technical support for long-term monitoring and policy-making in complex ecosystems.

Soil heavy metal (HM) control has become a pressing global ecological and environmental issue, with a significant focus on the western Dabie Mountain in China, a region that is recognized for its ecological barriers and water sources. We analyzed the concentrations, pollution status, and potential ecological risks of 10 HMs (i.e., Cu, +Pb, Zn, Cr, Ni, Co, Cd, As, Hg, and Mn) in the topsoil of different ecosystems in this region. The effects of four soil groups (Anthrosol, Fluvisols, Gleysol, and Alisols) and three soil texture types (sandy, clayey, and loamy soils) on the above aspects were also analyzed. Positive Matrix Factorization (PMF) models were performed to identify and quantify the potential sources and contribution rates of these HMs. Results showed that the level of HM pollution may be negligible, and there was no potential ecological risk. Of all metals, Pb and Cd exhibited the highest levels of pollution. The potential ecological risk associated with soil HMs decreased from croplands to urban and further to forests. Cu and Co were more likely to accumulate in Anthrosols, while Alisols were more likely to accumulate Pb, Cd, and Hg and had a higher overall potential ecological risk. The overall risk in clayey and loamy soils is both higher than in sandy soils. The HM accumulation in topsoil was closely related to the soil organic matter and clay contents. The potential main sources of HMs in this region can be agricultural activities, soil parent material, mining, industrial activities, and vehicle emissions. These findings suggest that special attention should be paid to soil Pb and Cd in western Dabie Mountain. Future efforts can focus on reducing mining, smelting, and industrial emissions, as well as strengthening the research on the migration patterns and mechanisms of HMs.

In this study, the separation conditions of UHPLC-QTOF-MS and the extraction conditions of QuEChERS were optimized. The analytical process for determining Broflanilide residues in different soil types was successfully established and applied to its adsorption, desorption, and leaching in soil. Broflanilide was extracted from soil with acetonitrile and purified using PSA and MgSO₄. The modified UHPLC-QTOF-MS method was used for quantification. The average recovery of Broflanilide was between 87.7% and 94.38%, with the RSD lower than 7.6%. In the analysis of adsorption, desorption, and leaching quantities in four soil types, the RSD was less than 9.2%, showing good stability of the method, which can be applied to determine the residue of Broflanilide in different soils.

In this study, the separation conditions of UHPLC-QTOF-MS and the extraction conditions of QuEChERS were optimized. The analytical process for determining Broflanilide residues in different soil types was successfully established and applied to its adsorption, desorption and leaching in soil. Broflanilide was extracted from soil with acetonitrile as extraction solvent, and purified with 50 mg PSA + 150 mg MgSO4. The modified UHPLC-QTOF-MS method was used for determination. In different types of soil, the average recovery of Broflanilide was 87.7% -94.38%, and the RSD was lower than 7.6%. In the analysis and determination of the adsorption, desorption and leaching properties in four soils, the RSD was less than 9.2%, showing good stability and can be applied to the analysis and determination of the residue of Broflanilide in different soils.