Regional ecological security assessment is a significant methodology for environmental protection, land utilisation, and human development. This study aims to reveal the regional constraints of ecological resources to overcome the difficulties and complexities in quantification of current models used in land ecosystems. For this purpose, the technique for order preference by similarity to an ideal solution (TOPSIS) was linked to a grey relational analysis and integrated with a geographic information system. The obtained method was used to construct a land eco-security evaluation on a regional scale for application in a traditional mining city, Daye, in central China. Parameter analysis was introduced to the method to produce a more realistic spatial distribution of eco-security. Subsequently, based on the pressure–state–response framework, the eco-security index was calculated, and the carrying capacity of land resources and population for each sub-region were analysed. The results showed that: (i) very insecure and insecure classes comprised 5.65% and 18.2% of the total area, respectively, highlighting the vulnerable eco-environmental situation; (ii) moderate secure classes areas comprised a large amount of arable land, spanning an area of 494.5 km²; (iii) secure areas were distributed in the northwest, containing mostly water and wetland areas and accounting for 426.3 km²; and (iv) very secure areas were located on the southeastern region, involving traditional woodland with a better vegetation cover and an overall higher eco-environmental quality. In addition, for each sub-region, the extremely low and low ecological security areas were mainly arable and urban lands, which amounted to 305 and 190 km², respectively. Under the current ecological constraints, sub-region 1 cannot continue supporting the population size in Daye City. The present results demonstrate the accuracy of our methodology, and our method may be used by local managers to make effective decisions for regional environment protection and sustainable use of land resources.

We adopted a weight of evidence approach to establish a causal analysis of an impaired land ecosystem on a regional scale; namely, Daye, a traditional mining city in China. Working processes, including problem statements, a list of candidate causes, and a conceptual model were developed to represent a causal hypothesis for describing land degradation. Causal criteria were applied to integrate multiple lines of evidence. Then, various pieces of evidence were scored to either strengthen or weaken our causal assumptions. Results showed that habitat alteration, heavy metal accumulation, organic pollutants, water eutrophication, and nutrient runoff were the probable causes of land ecosystem impairment in Daye. Meanwhile, noxious gas, toxicants, altered underground runoff, atmospheric deposition, and acid rain were identified as possible causes. The most unlikely causes were altered hydrology, altered earth surface runoff, and soil erosion. Soil salinization, soluble inorganic salts, biological species invasion, and pathogens were deferred as delayed causes due to lack of adequate information. The causal analysis approach was applied to identify the primary causes of land degradation and implement accurate protective measures in an impaired land ecosystem.

Land use changes are an important factor contributing to the increasingly severe deterioration of the ecological environment. Therefore, regional analyses of land use and ecological risk should be developed for the restoration of ecological functions. In this study, a comprehensive ecological risk assessment was constructed on a regional scale and applied to Daye, a traditional mining city in China. Cointegration analyses and Granger causality tests were used to explore the complex relationship between land use and ecological risks in the study area from 2007 to 2021. The results show a long-term and stable relationship between land use changes in different sub-regions and ecological risks, albeit with distinct Granger causality relationships. This research presents the development trend of the relationship between land use change and ecological risks in a mining city, from rapid economic growth to economic restructuring and full-region ecological governance.

Heavy metal contamination in urban land has become a serious environmental problem in large cities. Visible and near-infrared spectroscopy (vis-NIR) has emerged as a promising method for monitoring copper (Cu), which is one of the heavy metals. When using vis-NIR spectroscopy, it is crucial to consider sample similarity. However, there is limited research on studying sample similarities and determining their relative importance. In this study, we compared three types of similarities: spectral, compositional, and spatial similarities. We collected 250 topsoil samples (0–20 cm) from Shenzhen City in southwest China and analyzed their vis-NIR spectroscopy data (350–2500 nm). For each type of similarity, we divided the samples into five groups and constructed Cu measurement models. The results showed that compositional similarity exhibited the best performance ($R_p^2$ = 0.92, RPD = 3.57) and significantly outperformed the other two types of similarity. Spatial similarity ($R_p^2$ = 0.73, RPD = 1.88) performed slightly better than spectral similarity ($R_p^2$ = 0.71, RPD = 1.85). Therefore, we concluded that the ranking of the Cu measurement model’s performance was as follows: compositional similarity > spatial similarity > spectral similarity. Furthermore, it is challenging to maintain high levels of similarity across all three aspects simultaneously.

Heavy mental contamination in urban land is a serious environmental issue for large cities. Visible and near-infrared spectroscopy has been rapidly developed as a new method for estimating copper (Cu) levels, which is one of the heavy metals. Spectral pretreatment is essential for reducing noise and enhancing analysis. In the traditional method, the entire spectrum is uniformly pretreated. However, in reality, the influence of pretreatment on the spectrum may vary depending on the wavelengths. Limited research has been conducted on breaking down the entire spectrum into distinct parts for individualized pretreatment, an innovative method called piecewise pretreatment. This study gathered 250 topsoil samples (0–20 cm) in Shenzhen City, southwest China, and obtained their vis-NIR spectra (350–2500 nm) in the laboratory. This study divided the spectrum into three parts, each processed by six commonly used spectral pretreatments. The number of pretreated parts varied from 1 to 3, resulting in 342 PLSR models being built. Compared to the traditional method, piecewise pretreatment showed an increase in mean residual predictive deviation (RPD) from 1.55 to 1.71 and an increase in the percentage of positive outcomes in ∆RPD from 33.33% to 55.56%. Thus, we concluded that piecewise pretreatment generally outperforms the traditional method. Furthermore, piecewise pretreatment aims to choose the most effective pretreatment method for each part to optimize the Cu estimation model.

Soil heavy metal contamination in urban land can affect biodiversity, ecosystem functions, and the health of city residents. Visible and near-infrared (Vis-NIR) spectroscopy is fast, inexpensive, non-destructive, and environmentally friendly compared to traditional methods of monitoring soil Cu, a common heavy metal found in urban soils. However, there has been limited research on using spatially nearby samples to build the Cu estimation model. Our study aims to investigate how spatially nearby samples influence the Cu estimation model. In our study, we collected 250 topsoil samples (0–20 cm) from China’s third-largest city and analyzed their spectra (350–2500 nm). For each unknown validation sample, we selected its spatially nearby samples to construct the Cu estimation model. The results showed that compared to the traditional method ($R_p^2$ = 0.75, RMSEP = 8.56, RPD = 1.73), incorporating nearby samples greatly improved the model ($R_p^2$ = 0.93, RMSEP = 4.02, RPD = 3.89). As the number of nearby samples increased, the performance of the Cu estimation model followed an inverted U-shaped curve—initially increasing and then declining. The optimal number of nearby samples is 125 (62.5% of the total), and the mean distance between validation and calibration samples is 17 km. Therefore, we conclude that using nearby samples significantly enhances the Cu estimation model. The optimal number of nearby samples should strike a balance, covering a moderate area without there being too few or too many.