Eight kinds of heavy metals in soil within 0–2 km from the banks of Shuimo River in Urumqi were analyzed by using an X-ray fluorescence spectrometer and national standard detection methods. Unmix and PMF models are comprehensively used to analyze potential pollutant sources and contribution rates. Soil samples are sampled in three layers of 0–20, 20–40, and 40–60 cm, and each group of sample points in each layer is 5 m, 1 km, and 2 km away from the riverbank, respectively. Only the average concentration of Mn in each layer of soil is lower than the background value, according to the analytical results, while the average concentration of other heavy metals surpasses the background value. The highest proportion of exceeding the background value is Ni in the 40–60 cm soil layer, up to 1.92 times. Unmix and PMF models are used to analyze pollutants’ source quantity and contribution rate, respectively. The results show that the two models can identify two pollution sources at the three soil layers, and their contribution rates are similar, and each index of the analysis results of the two models is within the required range of model reliability. By comparing with the Pearson correlation coefficient and distribution map of heavy metal concentration in surface soil, it is concluded that Zn, Pb, Cr, and Cu are mainly from industrial sewage and air pollution from coal combustion, while As, Mn, Ni, and V are mainly from agricultural pollution and light industrial pollution. In future research, it is necessary to investigate the change of heavy metal concentration in detail from the time dimension to further quantitatively calculate the potential pollutant source and contribution rate.

Soil samples were collected from the upstream, midstream and downstream areas of the Shuimo River in three layers of 0–20, 20–40 and 40–60 cm, and each group of sample points was located 5 m, 1 km and 2 km away from the river bank, respectively. The analysis was carried out. Based on the investigation and research, six indicators, including As, Pb, Zn, Cu, Ni and Cr, were designated as evaluation factors in combination with the results of the sample collection with low or no detectable values of Cd and Hg. The mean values of the samples measured in the upper, middle and downstream layers were taken, and the degree and source of pollution were evaluated and jointly analyzed using the gray correlation analysis and factor analysis methods. By using the gray correlation analysis, it was found that the evaluation results of the upstream and middle reaches of the soil along the Shuimo River were both level 3, with slight pollution, and the evaluation results of the downstream areas were level 2, with good soil quality. There are two main sources of pollution obtained through the factor analysis; source 1 is mainly heavy metals such as Zn, Cu, Cr, Pb and Ni, while source 2 is mainly heavy metals such as As, Pb and Ni. The amount of pollution sources is inferred from the heavy metal types of each source and the soil environment along the Shuimo River as industrial and human sources of pollution. From the analysis results, the combination of the gray correlation analysis model and factor analysis model is convenient and fast and can accurately quantify the source contribution of various pollution sources. Not only can it reflect the actual situation more objectively and realistically in the evaluation of soil heavy metal pollution and pollution sources, but also the calculation is simple and easily applied with low data requirements.

Heavy metal pollution in the surface sediments of urban rivers has a significant influence on the safety of city residents. This study explores the features of heavy metal pollution in the surface sediments of the Shuimo River and provides a theoretical basis for decision makers regarding river management and restoration. This study uses principal component analysis and kriging interpolation to analyse the pH values and pollution characteristics of nine heavy metals (As, Pb, Zn, Cu, Ni, Fe, Mn, Cr, and V) in 23 surface sediments of the Shuimo River. The results showed that the pH value of the surface sediments along the direction of water flow had a quadratic curve trend. Kriging interpolation revealed consistency in the spatial distribution of heavy metals and Fe, and the peak value was from Qidaowan to Weihuliang. There were significant positive correlations (p < 0.05) between Fe and Pb; Mn, Cr, V, Cu, and Zn; and Mn, Cr, and V. The principal component analysis showed that the main heavy metals in the surface sediments of the Shuimo River were Fe, Zn, Cu, and As. The total amount of heavy metals was in the order of Fe > Mn > Zn > V > Cr > Ni > Cu > Pb > As, ranging from 11.27~18,760.97 mg·kg⁻¹. The cluster analysis classified the nine heavy metals into four categories: Zn and Cu in the first category; Ni in the second; As and Pb in the third; and V, Cr, Mn, and Fe in the fourth.

Saline–alkali soils in arid regions are increasingly recognized as critical yet underrepresented components of the global carbon cycle. However, their CO₂ flux dynamics under warming remain poorly understood. In this study, we conducted controlled growth-chamber experiments using typical saline–alkali soils from the Taklamakan Desert, where temperature, soil moisture, and atmospheric CO₂ concentrations were systematically manipulated. We quantified how warming reshaped moisture–temperature interactions regulating soil CO₂ fluxes. The results revealed a pronounced diurnal variation pattern, characterized by daytime CO₂ release and nighttime uptake. Temperature was identified as the dominant driver (R² > 0.93, p < 0.001), whereas soil moisture primarily modulated flux intensity; at 0.8 cm³ cm⁻³, fluxes declined by up to 61% compared with the baseline. Warming enhanced the temperature–moisture synergy (−43%, p < 0.01) and simultaneously reduced baseline fluxes (−56%, p < 0.01). These shifts fundamentally altered the regulation of CO₂ flux dynamics. Our findings highlight the necessity of integrating salt dynamics and carbonate equilibria into multiphase reactive transport models to improve regional carbon sink assessments. Ultimately, this study refines estimates of the contribution of saline–alkali soils to the global “missing carbon sink” (~1.7 Pg C a⁻¹) and emphasizes their overlooked role in the Earth’s carbon budget under a warming climate.