Xiaodong Li

High-altitude saline lakes on the Tibetan Plateau host some of the world’s most arsenic-rich waters, yet controls on arsenic (As) transformation remain poorly resolved. By integrating depth-resolved geochemistry with metagenomic profiling in Lake Tosen, we identified a redox framework for As cycling that is not fully explained by the Fe-centric model derived from groundwater and freshwater. In summer, oxic-suboxic-anoxic stratification supported three distinct pathways: (i) oxic waters exhibited a microbial detoxification-oxidation loop sustaining As(V) dominance; (ii) suboxic waters were associated with nitrate-supported As(III) oxidation linked to denitrifying taxa; and (iii) anoxic sulfidic waters showed sulfate-reduction-related As transformation, with sulfur cycling likely modifying the fate and mobility of released As under sulfidic conditions. Meanwhile, canonical Fe(III)-respiration markers were not prominent, and saturation indices indicated stable goethite/hematite under in situ conditions. Together, these findings indicate that, under the observed stratified conditions in Lake Tosen, nitrate- and sulfate-associated pathways were more closely linked to arsenic transformation than canonical Fe(III)-respiration signals. This nonclassical coupling may become increasingly relevant as stratification, nitrate loading, and salinization intensify, with implications for As risk prediction in endorheic basins under changing climates.

To clarify the pollution characteristics and source composition of heavy metals in the paddy soils of the Yellow River irrigation district of Ningxia, a total of 515 surface soil samples were collected, and the concentrations of As, Hg, Pb, Cd, and Cr were measured. Regional-scale pollution assessment and source apportionment were conducted using the geo-accumulation index, spatial interpolation analysis, and the Positive matrix factorization (PMF) model. Based on the regional pollution assessment and spatial distribution patterns, a representative area with relatively elevated Cd accumulation and more pronounced anthropogenic influence was selected for local-scale isotope investigation. Eight paddy soil samples and potential end-member samples were collected, and, combined with literature-based end-member data, Pb and Cd isotopes were analyzed using the MixSIAR Bayesian (version 3.1.12) mixing model to further constrain the sources of Pb and Cd in village soils. The results showed that some sampling points in the study area exceeded the soil background values, but none of the points surpassed the screening values for agricultural soil pollution risk, indicating that the overall risk of paddy soils in the study area remained low. Geo-accumulation index results indicated that As, Pb, and Cr were predominantly classified as unpolluted, whereas Hg and Cd showed more pronounced accumulation, with most sampling points reaching unpolluted to moderately polluted or higher. PMF results revealed that heavy metals in the study area primarily originated from natural sources, agricultural activities, coal combustion-related sources, and industrial–traffic mixed sources. Cd was mainly influenced by agricultural sources, Hg was primarily affected by coal combustion and related industrial activities, and Pb exhibited a mixture of multiple sources. Local isotope analysis in the representative area further indicated that industrial and agricultural sources were the main contributors to soil Cd, accounting for 34.3% and 33.1%, respectively. Pb was primarily derived from agricultural activities (38.1%), while coal emissions, industrial sources, natural sources, and traffic contributed 19.2%, 18.3%, 16.9%, and 7.5%, respectively, indicating a complex mixture of agricultural, industrial, coal-combustion, natural, and traffic-related inputs. The combined application of PMF and Pb/Cd isotopes allowed for constraints on heavy metal sources at both regional and local scales, providing a scientific basis for pollution control and agricultural safety management in paddy soils of the Yellow River irrigation district.

Denitrification and anaerobic ammonium oxidation (anammox) are key processes in mitigating nitrogen (N) pollution in river ecosystems. However, there has been insufficient investigation of the riverine N‐removal processes on the Qinghai‐Tibet Plateau (QTP). This study investigated the driving mechanisms of denitrification and anammox in 23 small rivers using multiple approaches (remote sensing, ¹⁵ N pairing, and molecular techniques). The rivers span large elevational and climatic gradients, offering an ideal window for studying the riverine N‐removal processes, especially in the context of global change. The denitrification rates were 1.37 ± 2.88 mg N/kg/d in summer and 0.21 ± 0.26 mg N/kg/d in winter, which dominated the total riverine N removal (96% and 73%, respectively). Structural equation models (SEM) revealed that geographic factors (elevation and land use), water properties (water temperature, pH, etc.), sediment parameters (moisture, NO ₃ ⁻ ‐N, NH ₄ ⁺ ‐N, etc.), and microbial gene abundances collectively explained over 60% of the variation in N‐removal rates, with sediment properties being the primary regulating factors in summer. In winter, SEM showed that the contribution of geographic factors increased. We proposed a framework combining cross‐perspective methods for revealing in‐river N removal mechanisms and predicted that, under global change, N removal processes in rivers on the QTP are likely to intensify in the future. The integrated approaches systematically offered critical insights into the processes and drivers of biogeochemical N cycling on the QTP.

Approximately 1.1% of global soils exceed the safety thresholds. Yinchuan is one of the key grain production bases, and the quality of its agricultural soil directly impacts the quality of agricultural products. To investigate the heavy metal contamination status of surface agricultural soil in Yinchuan, this study collected 325 agricultural soil samples from the city to analyze the concentrations of five heavy metal elements—As, Hg, Pb, Cd, and Cr—and conducted a risk assessment and quantitative source apportionment of soil heavy metal contamination. The results indicate that the majority of the study area is classified as having a lightly polluted level with moderate ecological risks. The order of the over-standard rates is Hg > Cd > Pb > Cr > As. The soil in the study area is generally weakly alkaline, which has a relatively low impact on the migration and transformation of heavy metal elements. High-value areas of heavy metals are all located near the Yellow River floodplain. They are significantly affected by the agricultural and industrial wastewater discharge from the upper reaches of the Yellow River. The Absolute Principal Component Score-Multiple Linear Regression (APCS-MLR) model analysis identified the sources of soil heavy metal pollution as natural sources (37.29%), agricultural sources (25.50%), coal combustion sources (20.18%), and industrial-transportation sources (17.04%). The positive matrix factorization (PMF) model explained that the sources of heavy metals in the soil were natural sources (22.42%), agricultural activities (24.46%), coal combustion sources (26.70%), and traffic sources (26.42%). Overall, the results indicate that there is a certain degree of metal pollution in the agricultural soil of Yinchuan, which is significantly influenced by human activities.

Contained arsenic (As) and unsafe brackish groundwater irrigation can lead to serious As pollution and increase the ecological risk in cultivated soils. However, little is known about how Fe oxides and microbes affect As migration during soil irrigation processes involving arsenic-contaminated brackish groundwater. In this study, the samples (porewater and soil) were collected through the dynamic soil column experiments to explore the As migration process and its effect factors during soil irrigation. The results showed that the As concentration in porewater samples from the topsoil was enriched compared to that in the subsoil, and the main solid As fractions were strongly adsorbed or bound to amorphous and crystalline Fe oxides. The aqueous As concentration and the solid As fractions indicated that reductive dissolution and desorption from amorphous Fe oxides were the primary mechanisms of As release at the topsoil and subsoil, respectively. Meanwhile, Sphingomonas_sp., Microvirga_ossetica and Acidobacteriota_bacterium were the dominant microbes affecting As biotransformation by arsenate reductase gene (arsC) expression. Accompanied by the Eh and competitive ions concentration change, amorphous Fe oxide dissolution increased to facilitate the As release, and the changes in the microbial community structure related to As reduction may have enhanced As mobilization in soils irrigated by As-containing brackish groundwater.

Stable nitrogen isotopic composition (δ¹⁵N) has proven to be a valuable tool for identifying sources of nitrates (NO₃^–) in PM_2.5. However, the absence of a systematic study on the δ¹⁵N values of domestic NOx sources hinders accurate identification of NO₃^– sources in China. Here, we systematically determined and refined δ¹⁵N values for six categories of NOx sources in the local Tianjin area using an active sampling method. Moreover, the δ¹⁵N values of NO₃^– in PM_2.5 were measured during pre-heating, mid-heating and late-heating periods, which are the most heavily polluted in Tianjin. Results shown that the representative nature and region-specific characteristics of isotopic fingerprints for six categories of NOx sources in Tianjin. The Bayesian isotope mixing (MixSIAR) model demonstrated that coal combustion, biomass burning, and vehicle exhaust collectively contributed more than 60 %, dominating the sources of NO₃^– during sampling periods in Tianjin. However, failure to consider the isotopic signatures of local NOx sources could result in an underestimation of the contribution from coal combustion. Additionally, the absence of industrial sources, an uncharacterized source in previous studies, may directly result in the contribution fraction of other sources being overestimated by the model more than 15 %. Notably, as the number of sources input to the model increased, the contribution of various NOx sources was becoming more stable, and the inter-influence between various sources significantly reduced. This study demonstrated that the refined isotopic fingerprint in a region-specific context could more effectively distinguish source of NO₃^–, thereby providing valuable insights for controlling NO₃^– pollution.

Stable nitrogen isotopic composition (δ¹⁵N) has proven to be a valuable tool for identifying sources of nitrates (NO₃^–) in PM_2.5. However, the absence of a systematic study on the δ¹⁵N values of domestic NOx sources hinders accurate identification of NO₃^– sources in China. Here, we systematically determined and refined δ¹⁵N values for six categories of NOx sources in the local Tianjin area using an active sampling method. Moreover, the δ¹⁵N values of NO₃^– in PM_2.5 were measured during pre-heating, mid-heating and late-heating periods, which are the most heavily polluted in Tianjin. Results shown that the representative nature and region-specific characteristics of isotopic fingerprints for six categories of NOx sources in Tianjin. The Bayesian isotope mixing (MixSIAR) model demonstrated that coal combustion, biomass burning, and vehicle exhaust collectively contributed more than 60 %, dominating the sources of NO₃^– during sampling periods in Tianjin. However, failure to consider the isotopic signatures of local NOx sources could result in an underestimation of the contribution from coal combustion. Additionally, the absence of industrial sources, an uncharacterized source in previous studies, may directly result in the contribution fraction of other sources being overestimated by the model more than 15 %. Notably, as the number of sources input to the model increased, the contribution of various NOx sources was becoming more stable, and the inter-influence between various sources significantly reduced. This study demonstrated that the refined isotopic fingerprint in a region-specific context could more effectively distinguish source of NO₃^–, thereby providing valuable insights for controlling NO₃^– pollution.

The mechanism governing sulfur cycling in nitrate reduction within sulfate-rich reservoirs during seasonal hypoxic conditions remains poorly understood. This study employs nitrogen and oxygen isotope fractionation in nitrate, along with metagenomic sequencing to elucidate the intricacies of the coupled sulfur oxidation and nitrate reduction process in the water column. In the Aha reservoir, a typical seasonally stratified water body, we observed the coexistence of denitrification, bacterial sulfide oxidation, and bacterial sulfate reduction in hypoxic conditions. This is substantiated by the presence of abundant N/S-related genes (nosZ and aprAB/dsrAB) and fluctuations in N/S species. The lower ¹⁵ε_NO3/¹⁸ε_NO3 ratio (0.60) observed in this study, compared to heterotrophic denitrification, strongly supports the occurrence of sulfur-driven denitrification. Furthermore, we found a robust positive correlation between the metabolic potential of bacterial sulfide oxidation and denitrification (p < 0.05), emphasizing the role of sulfide produced via sulfate reduction in enhancing denitrification. Sulfide-driven denitrification relied on ∑S^2- as the primary electron donor preferentially oxidized by denitrification. The pivotal genus, Sulfuritalea, emerged as a central player in both denitrification and sulfide oxidation processes in hypoxic water bodies. Our study provides compelling evidence that sulfides assume a critical role in regulating denitrification in hypoxic water within an ecosystem where their contribution to the overall nitrogen cycle was previously underestimated.