Combining biochar with irrigation management to alter the microbial community is a sustainable method for remediating soils contaminated by heavy metals. However, studies on how these treatments promote Cr(VI) reduction are limited, and the corresponding microbial mechanisms are unclear. Therefore, we conducted a pot experiment to explore the responses of soil microbial communities to combined biochar amendment and irrigation management strategies and their involvement in Cr transformation in paddy soils. Six treatments were established using varying concentrations of biochar (0, 1, and 2% [w/w]) combined with two irrigation management strategies (continuous flooding [CF] and dry–wet alternation [DWA]). The results showed that the combined biochar addition and irrigation management strategy significantly altered soil pH, redox potential, organic matter content, and Fe(II) and sulfide concentrations. In addition, the Cr(VI) concentration under CF irrigation management was conspicuously lower (48.2–54.4%) than that under DWA irrigation management. Biochar amendment also resulted in a substantial reduction (8.8–27.4%) in Cr(VI) concentration. Moreover, the changes in soil physicochemical properties remarkably affected the soil microbial community. The microbial diversity and abundance significantly increased with biochar amendment. Furthermore, the combined biochar amendment and CF strategy stimulated the growth of Geobacter- and Anaeromyxobacter-related Fe(III)-reducing bacteria, Gallionella-related Fe(II)-oxidizing bacteria, and Desulfovibro- and Clostridium-related sulfate-reducing bacteria, which simultaneously facilitated the generation of Fe(II) and sulfide, thereby enhancing Cr(VI) reduction. Consequently, our results suggest that the effectively increased abundance of Fe-reducing/oxidizing bacteria and sulfate-reducing bacteria via combined CF irrigation management and biochar addition may be a key factor in reducing Cr(VI) in paddy soil. The keystone genera responsible for Cr(VI) reduction were Geobacter, Anaeromyxobacter, Gallionella, Desulfovibro, and Clostridium. This study provides novel insights into the coupling mechanism of the Fe/S/Cr transformation mediated by Fe-reducing/oxidizing bacteria and sulfate-reducing bacteria.

Trace elements contamination is mainly originated from industrial emission, sewage irrigation and pesticides, and poses a threat to the environment and human health. This study analyzed the trace element pollutants in peanut-soil systems, the enrichment and translocation capacity of peanut to trace elements, and the potential risk of trace elements to environment and human health. The results indicated that Cd and Ni in peanut kernels exceeded the standard limits in 2019, and the exceeding rate were 9% and 31%, respectively. Cd in 8% of soil samples and As in 98% of soil samples exceeded the risk screening value of trace elements. The concentration of trace elements in peanuts was related to varieties and planting regions. In addition, there was a significant positive correlation between the concentration of Cd in peanut kernel and its concentration in soil. Compared with other trace elements, peanut kernels had stronger ability to enrich and transport Cd, Cu, and Zn, the BFs were 0.45, 0.51 and 0.47, respectively. After oil extraction, trace elements were mainly concentrated in peanut meal, and only 0.25% of Cd was in oil. The RI of trace elements was less than 150, indicating that the study area was under low degree of ecological risk. However, As and Cd might pose moderate risk to environment. Trace elements in soil and peanut could not cause non-carcinogenic and carcinogenic risks to human, but the HI and CR value of As (0.59 and 9.54 × 10⁻⁵) in soil and CRᵢₙg value of Cd (9.25 × 10⁻⁷) in peanut were close to the critical value. We conclude that Cd pollution in peanut kernel, and Cd and As pollution in soil should be monitored to enter into the food chain or environment and to avoid the possible health hazards and environment risks.

Trace metal (loid) contamination in the atmosphere is widely monitored, but there is a gap in understanding its long-term patterns, especially in North China, which is currently a global contamination hotspot mainly caused by heavy industry emissions and coal combustion. Herein, historical trends of atmospheric As, Cd, Cr, Cu, Hg, Ni, Pb and Zn contamination in North China over the past ∼500 years are comparatively studied with sediment cores from two subalpine lakes (Gonghai and Muhai). Arsenic, Pb, Cd and Hg were main pollutants according to Pb isotopes and enrichment factors. Mercury contamination has increased continuously since the late 1800s and increasing As, Pb and Cd contamination started in the 1950s in Gonghai. In contrast, the contamination in Muhai lagged two decades for As, Cd and Pb and a half-century for Hg behind that in Gonghai, although the trends were similar. This contamination lag was attributed to the low sensitivity of Muhai sediment to early weak atmospheric metal contamination under 2.1-fold higher detrital sedimentation. As, Pb and Cd contamination has intensified since the 1980s, and the metals showed similar sedimentary fluxes in the cores. However, sedimentary fluxes of Hg contamination were 3.4-fold higher in Gonghai than in Muhai due to combination with organic matter. No obvious Cr, Cu and Ni contamination in the cores was mainly because of the low atmospheric deposition from anthropogenic sources relative to detrital input, although some of their atmospheric emissions were higher than those of As, Cd and Hg. Atmospheric As, Pb and Cd contamination was mainly from domestic sources of coal combustion and nonferrous smelting. Mercury contamination was mainly from global and Asian sources in the first half of the 20th century, and domestic emissions gradually dominated Hg contamination after the mid-1900s.

Aquifer vulnerability mapping to pollution is topical research activity, and common frameworks such as the basic DRASTIC framework (BDF) suffer from the inherent subjectivity. This paper formulates an artificial intelligence modeling strategy based on Convolutional Neural Network (CNN) to decrease subjectivity. This formulation considers three definitions of intrinsic, specific, and total vulnerabilities. Accordingly, three CNN models are trained and tested to calculate IVI, SVI, and TVI, respectively referring to the intrinsic, specific, and total vulnerability indices. The formulation is applied in an unconfined aquifer northwest of Iran and delineates hotspots within the aquifer. The area under curve (AUC) values derived by the receiver operating curves evaluate the vulnerability indices versus nitrate concentrations. The AUC values for BDF, IVI, SVI, and TVI are 0.81, 0.91, 0.95, and 0.95, respectively. Therefore, CNNs significantly improve the results compared to BDF, but IVI, SVI, and TVI have approximately identical performances. However, the visual comparison between their results provides evidence that significant differences exist between the spatial patterns despite identical AUC values.

This study aims to investigate the effects of long-term nicosulfuron residue on an herbicide factory ecosystem. High-throughput sequencing was used to investigate the environmental microbial community structure and interactions. The results showed that the main contributor to the differences in the microbial community structure was the sample type, followed by oxygen content, pH and nicosulfuron residue concentration. Regardless of the presence or absence of nicosulfuron, soil, sludge, and sewage were dominated by groups of Bacteroidetes, Actinobacteria, and Proteobacteria. Long-term exposure to nicosulfuron increased alpha diversity of bacteria and archaea but significantly decreased the abundance of Bacteroidetes and Acidobateria compared to soils without nicosulfuron residue. A total of 81 possible nicosulfuron-degrading bacterial genera, e.g., Rhodococcus, Chryseobacterium, Thermomonas, Stenotrophomonas, and Bacillus, were isolated from the nicosulfuron factory environmental samples through culturomics. The co-occurrence network analysis indicated that the keystone taxa were Rhodococcus, Stenotrophomonas, Nitrospira, Terrimonas, and Nitrosomonadaceae_MND1. The strong ecological relationship between microorganisms with the same network module was related to anaerobic respiration, the carbon and nitrogen cycle, and the degradation of environmental contaminants. Synthetic community (SynCom), which provides an effective top-down approach for the critical degradation strains obtained, enhanced the degradation efficiency of nicosulfuron. The results indicated that Rhodococcus sp. was the key genus in the environment of long-term nicosulfuron exposure.

Cyanobacterial blooms are important environmental problems in aquatic ecosystems. Researchers have found that cyanobacterial blooms cannot be completely prevented by controlling and/or eliminating pollutants (nutrients). Thus, more in-depth basic research on the mechanism of cyanobacterial blooms is urgently needed. Cyanobacteria, being primordial microorganisms, provide habitats and have various forms of interactions (reciprocity and competition) with microorganisms, thus having a significant impact on themselves. However, little is known about how environmental conditions and microbial communities in both water and sediment jointly affect cyanobacterial blooms or about the co-occurrence patterns and interactions of microbial communities. We investigated changes in environmental factors and microbial communities in water and sediment during different cyanobacterial blooms and revealed their interacting effects on cyanobacteria. Cyanobacteria had greater competitive and growth advantages than other microorganisms and had antagonistic and aggressive effects on them when resources (such as nutrients) were abundant. Furthermore, microbial networks from cyanobacterial degradation periods may be more complex and stable than those from bloom periods, with more positive links among the microbial networks, suggesting that microbial community structures strengthen interconnections with each other to degrade cyanobacteria. In addition, we found that sediment-enriched cyanobacteria play a key role in cyanobacterial blooms, and sediment microorganisms promote the nutrient release, further promoting cyanobacterial blooms in the water bodies. The study contributes to further our understanding of the mechanisms for cyanobacterial blooms and microbial community structural composition, co-occurrence patterns, and responses to cyanobacteria. These results can contribute to future management strategies for controlling cyanobacterial blooms in freshwater ecosystems.

Effects of human activities on atmospheric nitrate (NO₃⁻) formation remain unclear, though the knowledge is critical for improving atmospheric chemistry models and nitrogen deposition reduction strategies. A potentially useful way to explore this is to compare NO₃⁻ oxidation processes in urban and rural atmospheres based upon the oxygen stable isotope composition of NO₃⁻ (Δ¹⁷O–NO₃⁻). Here we compared the Δ¹⁷O–NO₃⁻ from three-years of daily-based bulk deposition in urban (Shenyang) and forested rural sites (Qingyuan) in northeast China and quantified the relative contributions of different formation pathways based on the SIAR model. Our results showed that the Δ¹⁷O in Qiangyuan (26.2 ± 3.3‰) is significantly higher (p < 0.001) than in Shenyang (24.0 ± 4.0‰), and significantly higher in winter (Shenyang: 26.1 ± 6.7‰, Qingyuan: 29.6 ± 2.5‰) than in summer (Shenyang: 22.7 ± 2.9‰, Qingyuan: 23.8 ± 2.4‰) in both sites. The lower values in the urban site are linked with conditions that favored a higher relative contribution of nitrogen dioxide reaction with OH pathway (0.76-0.91) than in rural site (0.47-0.62), which should be induced by different levels of human activities in the two sites. The seasonal variations of Δ¹⁷O–NO₃⁻ in both sites are explained by a higher relative contribution of ozone-mediated oxidation chemistry and unfavorable conditions for the OH pathway during winter relative to summer, which is affected by human activities and seasonal meteorological condition change. Based on Δ¹⁷O, wintertime conditions led to a contribution of O₃ related pathways (NO₃ + DMS/HC and N₂O₅ hydrolysis) of 0.63 in Qingyuan and 0.42 in Shenyang, while summertime conditions led to 0.15 in Qingyuan and 0.05 in Shenyang. Our comparative study on Δ¹⁷O–NO₃⁻ between urban and rural sites reveals different anthropogenic effects on nitrate formation processes on spatial and temporal scales, illustrating different responses of reactive nitrogen chemistry to changes in human activities.

The occurrence, spatial distribution, and partitioning behavior of 17 marine lipophilic phycotoxins (MLPs) in surface and bottom seawater, particulate organic matter (POM), and surface sediment from the Pearl River Estuary (PRE) were investigated to understand current contamination and the potential risks to marine ecosystems in this region. Nine MLPs were detected, including azaspiracid1−3, gymnodimine, okadaic acid, dinophysistoxin 1−2, pectenotoxin2 (PTX2), and homoyessotoxin, with Σ₁₇MLP concentrations ranging 545–12,600 pg L⁻¹ and 619−8,800 pg L⁻¹ in surface and bottom seawater, respectively; 0–294 ng g⁻¹ and 0.307–300 ng g⁻¹ dry weight (dw) in surface and bottom POM, respectively; and 3.90–982 pg g⁻¹ dw in surface sediment. Lower Σ₁₇MLP levels in the seawater were found at the mouth of the PRE, and gradually increased with increasing distance offshore. According to the calculated partition coefficient, the affinity of MLPs for the aquatic environment components was as follows (from highest to lowest): POM > seawater > sediment. Overall, the distribution and migration of MLPs in the PRE may depend on partition coefficients, the organic carbon fraction, and environmental factors.

Radioiodine is one of the long-lived fission products and also an important radionuclide released during nuclear accidents, which generates interest in its environmental fate. Its sorption has been studied in a wide range of materials, but no equivalent study exists for microplastics, an emerging environmental vector. Weathering and biofilm formation on microplastics can enhance radioiodine sorption. For the first time, we're reporting how radioiodine interacts with different types of polyethylene derived microplastics (pristine, irradiated, and biofilm developed microplastics). This study revealed that exposure to radiation and the marine environment significantly alters the physico-chemical properties of microplastics. In particular, in marine-exposed samples, a signature of biofilm development was detected. Speciation study indicates that iodine exists in the iodide form in the studied marine environment. The study revealed that, iodide ions attach to biofilm-developed microplastics via electrostatic, ion-dipole, pore filling, and van der Waals interactions. Pore filling, ion-dipole, and van der Waals interactions may cause iodide binding to irradiated microplastics, whereas pore-filling and van der Waals interactions cause iodide binding to pristine microplastics. The distribution coefficient (Kd) of iodine on microplastics is positively correlated with biofilm biomass, which signifies the role of biofilm in radioiodine uptake. The Kd indicates microplastics are potential iodide accumulators and could be a possible vector in the marine system.

Traditional soil heavy metal (HM) investigation usually costs a lot of human and material resources. In-situ portable X-ray fluorescence spectrometry (PXRF) is a cheap and rapid HM analysis method, but its analysis accuracy is usually affected by spatially non-stationary field environment factors. In this study, residual sequential Gaussian co-simulation (RCoSGS) was first proposed to incorporate both continuous and categorical auxiliary variables for spatial simulation of soil Cu. Next, additional in-situ PXRF sampling sites (n = 300) were allocated in the subareas with high, medium, and low conditional variances in the proportions of 50%, 33.33%, and 16.67%, respectively. Then, robust geographically weighted regression (RGWR) was established to correct the spatially non-stationary effects of field environmental factors on in-situ PXRF and further compared with the traditionally-used multiple linear regression (MLR) and basic GWR in correction accuracy. Finally, RCoSGS with the RGWR-corrected in-situ PXRF as part of hard data (RCoSGS-PXRF) was established and further compared with the model with one or multiple auxiliary variables in the spatial simulation accuracy. Results showed that (i) RCoSGS effectively incorporated both SOM and land-use types and obtained higher spatial simulation accuracy (RI = 37.52%) than residual sequential Gaussian simulation with land-use types (RI = 19.44%) and sequential Gaussian co-simulation with SOM (RI = 20.92%); (ii) RGWR significantly weakened the spatially non-stationary effects of field environmental factors on in-situ PXRF, and RGWR (RI = 58.96%) and GWR (RI = 39.61%) obtained higher correction accuracy than MLR; (iii) the RGWR-corrected in-situ PXRF (RI = 66.57%) brought higher spatial simulation accuracy than both land-use types and SOM (RI = 37.52%); (iv) RCoSGS-PXRF obtained the highest spatial simulation accuracies (RI = 83.74%). Therefore, the proposed method is cost-effective for the rapid and high-precision investigation of soil HMs at a regional scale.