The coupling coordination relationship between cultivated land and water resources in arid regions is crucial for ecological security and sustainable food production. This study explores the interaction between these resources to optimize the allocation of water–land resources, ecological resources, and agricultural resources and promote synergistic development. Taking the Turpan–Hami Basin as a case study, this research analyzed the utilization efficiency of cultivated land and agricultural water resources from 2000 to 2023 using a super-efficiency SBM-DEA model. A coupling coordination degree model was constructed to evaluate their coordinated development level, with spatial autocorrelation and other methods used to examine spatiotemporal patterns. Key findings include: (1) The overall utilization efficiency of both resources was relatively low, with mean values of 0.516 and 0.596, showing a fluctuating upward trend and significant spatial heterogeneity; (2) The mean coupling coordination degree (CCD) ranked as follows: Barkol Kazakh Autonomous County (0.587) > Yiwu County (0.563) > Gaochang District (0.494) > Shanshan County (0.437) > Tuokexun County (0.417) > Yizhou District (0.342), with an annual growth rate of 4.6%; (3) Regional disparities were dominated by intra-regional differences (42.0%), followed by transvariation density (30.64%). This study provides scientific evidence for optimizing resource allocation in arid regions.

Plants serve as vital components of ecosystems, with their contamination status acting as sensitive indicators of environmental pollution. Therefore, the precise assessment of plant heavy metal contamination and source identification are crucial for regional ecological conservation and sustainable development. This study investigated heavy metal pollution in four characteristic plant species (Anabasis aphylla L., Alhagi camelorum Fisch., Reaumuria songonica (PalL)Maxim., and Haloxylon ammodendron (C. A. Mey.) Bunge.) within the Kalamaili Mountain Nature Reserve, employing comprehensive methodologies including pollution indices, bioconcentration factors (BCFs), absolute principal component score–multiple linear regression (APCS-MLR), and the random forest model (RF). The key findings revealed the following: The soil exhibited severe Cd and Hg contamination. The plant Cr concentrations exceeded standard limits by 31.89 to 147 fold. The Pb, Hg, and As content in plants showed significant differences. The plants displayed differential metal enrichment capacities, ranked as Cr (BCF = 3.28) > Hg (1.22) > Cd (0.92) > Cu (0.25) > Zn (0.15) > Pb (0.125) > As (0.125), highlighting Cr, Hg, and Cd as priority ecological hazards. Complex interactions were observed, with Reaumuria songonica (PalL)Maxim. showing strong Cd soil–plant correlation (r = 0.78), whereas Alhagi camelorum Fisch. demonstrated negative associations (Cd: r = −0.21). APCS-MLR identified mining/smelting as primary contributors to Cd (63.49%), Zn (55.66%), and Cr (45.51%), while transportation dominated Pb emissions (72.92%). Mercury pollution originated from mixed sources (56.18%), likely involving atmospheric deposition, and RF modeling corroborated these patterns, confirming industrial and transportation synergies for Cd, Zn, Cr, Cu, Hg, and As, with Pb predominantly linked to vehicular emissions. This multidisciplinary approach provides scientific evidence for establishing heavy metal monitoring systems and formulating targeted remediation strategies in arid ecologically fragile regions.

In recent years, due to the shortage of water resources and the fragile ecological environment in arid areas, the relationship between vegetation and water resources has been relatively close. The unreasonable allocation of water resources and the excessive demand for ecological water use have led to ecological and environmental problems such as river interruption, land desertification, and the extensive withering of vegetation in arid areas; therefore, rapid, accurate estimation of the vegetation ecological water demand has become a hot research topic in related fields. In this study, we classified the land use types in the lower reaches of the Kokyar River Basin based on Sentinel-2A data and calculated the water requirements of each type of vegetation using a combination of the area quota method and improved Penman–Monteith (PM) based on different vegetation coverage levels. The results revealed that in 2020, the water demand of planted woodlands within 0–2 km of the watershed will be the highest, and the water demand of naturally growing arboreal woodlands will be the lowest, and the water demand of the surrounding desert riparian vegetation forests will be very small in relation to the ecological base flow and will not affect the downstream water use for agriculture, industry, and domestic use for the time being. The ecological water demand of the vegetation in the study area can be accurately estimated using Sentinel-2A data, and the research results provide technical support and a theoretical basis for rapid estimation of the ecological water demand of vegetation in typical riparian forests in arid areas and for the allocation of water resources.

To investigate the contamination status and analyze the sources of soil toxic metal contamination on the northern slopes of the East Tianshan mountain industrial belt in Xinjiang, northwest China, this study measured the contents of six common toxic metals such as Zn, Cu, Cr, Pb, Hg and As in 82 surface soil (0–20 cm), and using the ground accumulation index, pollution load index, and improved weighted index assessed the contamination characteristics of the soil and using a semi-variance function and APCS-MLR model the identified the potential sources of contamination. The results indicate that the average concentrations of Pb, Hg, and As are significantly higher than the background values in Xinjiang. The average ranking of toxic metal content is as follows: Zn > Cr > Pb > Cu > Hg > as. A single-factor pollution index analysis shows that As and Pb pollution are severe, while Hg and Cu pollution are moderate. The improved weighted index shows that moderate lead pollution accounts for 6.1% and severe lead pollution accounts for 54.88%; 98.88% of arsenic is severely contaminated. The APCS-MLR model identified three main sources of heavy metals: Cu and Cr as industrial production sources, Pb and Zn as transportation and agricultural activity sources, and As, Cr, Cu, Hg, and Zn as natural and mixed pollution sources. This study provides a solid scientific basis for the prevention and control of toxic metal pollution in agricultural soils, thus ensuring food security and sustainable development in the region.