Identifying the bioavailability and release-desorption mechanism of heavy metals (HMs) in soil is critical to understand the release risk of HMs. Simultaneously, the mechanistic investigation of affecting the bioavailability of HMs in soil is necessary, such as the grain-size distribution and soil mineralogy. Herein, the bioavailability of HMs (Cu, Cd, Ni, Pb, and Zn) in different area soils near a typical copper-smelter was evaluated by the sequential extraction technique (BCR), diffusive gradients in thin-films (DGT), and DGT-induced fluxes in sediments (DIFS) model. Results showed that the HMs proportion of the residual fraction in all soils was the highest. The average bioavailability concentration (CDGT) of Cu and Cd in industrial soil was the highest, with 45.12 μg· L⁻¹ and 9.06 μg· L⁻¹. The result of DIFS model revealed that the decreased order of the mean value of desorption rate constant (K₋₁) was Cd > Zn > Ni > Cu > Pb, 5.91 × 10⁻⁵, 4.96 × 10⁻⁵, 2.89 × 10⁻⁵, 9.64 × 10⁻⁶, and 8.69 × 10⁻⁶, respectively. According to the spatial distribution of release potential (R-value), the release potential of labile-Cu in agricultural soil was the highest, which was mainly attributed to fertilizer application in farmland. Simultaneously, the reduced hydroxyl was also related to the agricultural activities, resulting in the weakened adsorption capacity of HMs by soil. Redundancy analysis (RDA) results showed that the bioavailability of Cd, Ni, and Zn was mainly driven by soil pH, while the bioavailability of Cu and Pb was primarily driven by dissolved organic carbon (DOC). Meanwhile, carbonate minerals had a positive correlation with the bioavailability of Cd, Ni, and Zn, which could promote the release of HMs in mining soil as chemical weathering progresses. In conclusion, this study provides a structured method which can be used as a standard approach for similar scenarios to determine the geochemical fractionation, bioavailability, and release kinetics of heavy metals in soils.

Mulch film (MF) residues is an important source of microplastics (MPs) in farmland, but its transportation risk to the wider environment was still unknown. Some researches have pursued the sources of MPs found in exorheic rivers. Even so, a systematic study depicting the occurrence, source and fate of microplastics derived from mulch films (MPMF), the crucial component of MPs in farmlands, in exorheic rivers still lacking. Here, the combination of UV–Vis Raman spectroscopy and X-ray photoelectron spectroscopy (XPS) was used to identify the full-size MPMF (1–5000 μm) in field sediment samples collected by single-diagonal systematic sampling. This study verified that MPMF, a polyethylene-matrix composite doped with additives, contributed a considerable part of MPs detected in upstream farmland soil and riverine sediments, and even had an abundance of 38 ± 11 items/kg to 82 ± 15 items/kg, accounting for 9.0%–13.7% of the total MPs in estuary sediments. Notably, upstream farmland was identified to the main source of the riverine MPMF by partial least square path modeling (PLS-PM), contributing to 94.7% of MPMF in riverside sediments and 85.0% of MPMF in estuary sediments. Our study first demonstrates that MPMF constitutes a non-negligible component of MPs in estuarine sediments and underlines the urgency of strengthening the management of MPs pollution in drainage areas with a high agricultural intensity.

Optimizing landscape pattern to reduce the risk of non-point source (NPS) pollution is an effective measure to improve river water quality. The “source-sink” landscape theory is a recent research tool for landscape pattern analysis that can effectively integrate landscape type, area, spatial location, and topographic features to depict the spatial heterogeneity of NPS pollution. Based on this theory, we quantitatively analyzed the influence of “source-sink” landscape pattern on the river water quality in one of the most intensive agricultural watersheds in Southeastern China. The results indicated that the proportion of “sink” landscape (68.59%) was greater than that of “source” landscape (31.41%) in the study area. In addition, when elevation and slope increased, the “source” landscape proportion decreased, and the “sink” landscape proportion increased. Nitrogen (N) and phosphorus (P) pollutants in rivers showed significant seasonal and spatial variations. Farmland was the primary source of nitrate nitrogen (NO₃⁻-N) and total nitrogen (TN) pollution, whereas residential land was the primary source of ammonium nitrogen (NH₄⁺-N) and total phosphorus (TP) pollution. Intensively cultivated areas and densely inhabited areas degraded water quality despite high proportions of forest land. The four “source-sink” landscape indices (LWLI, LWLI'e, LWLI's, LWLI'd) had significant positive correlations with NO₃⁻-N and TN and weak correlations with NH₄⁺-N and TP. The capacity of LWLI to quantify the NPS pollution was greater in agricultural areas than in residential areas. The “source-sink” landscape thresholds resulted in abrupt changes in water quality. When LWLI was ∼0.35, the probability of river water quality degradation increased sharply. The results suggest the importance of optimizing the “source-sink” landscape pattern for mitigating agricultural NPS pollution and provide policy makers with adequate new information on the agroecosystem-environmental interface in highly developed agricultural watersheds.

Nitrogen (N) is an essential nutrient but may become a pollution source in the environment when the N concentration exceeds a certain threshold for humans and nature. Nitrate is a major N species in river water with notable spatial and temporal variations under the influences of natural factors and anthropogenic N inputs. We analyzed the relationship between riverine N (focusing on nitrate) concentration and various factors (land use, climate, basin topography, atmospheric N deposition, agricultural N sources and human-derived N) in 104 rivers located throughout the Japanese Archipelago except small remote islands. We aimed to better understand processes and mechanisms to explain the spatial and temporal changes in riverine nitrate concentration. A publicly available river water quality database observed in the 1980s (1980–1989) and 2000s (2000–2009) was used. This study is the first to evaluate the long-term scale of 20 years in the latter half of Japan's economic growth period at the national level. A geographic information system (GIS) was employed to determine average values of each variable collected from multiple sources of statistical data. We then performed regression analysis and structural equation modeling (SEM) for each period. The forestland area influenced by the basin topography, climate (i.e., air temperature) and other land uses (i.e., farmland and urban area) played a major role in decreasing nitrate concentrations in both the 1980s and 2000s. Atmospheric N deposition (especially N oxides) and agricultural N sources (fertilizer and manure) were also significant variables regarding the spatial variations in riverine nitrate concentrations. The SEM results suggested that human-derived N (via food consumption) intensified by demographic shifts during the 2000s increased riverine nitrate concentrations over other variables within the context of spatial variation. These findings facilitate better decision making regarding land use, agricultural practices, pollution control and individual behaviors toward a sustainable society.

Heavy metal risks to human health in farmland of wastewater-irrigated areas have long been recognized. It remains to be shown whether farmland heavy metals from wastewater irrigation can migrate to deeper soil at a regional scale. In this study, nine soil cores deep to 30 m from three transects (A, B and C) of a linear wastewater reservoir and the adjacent farmland topsoils and wheat grains were sampled. Heavy metals including As, Cd, Cr, Cu, Pb and Zn in the soils and wheat grains were determined, and the grains’ health risks were assessed using the Target Hazard Quotient (THQ). Considerably high contents of heavy metals in both total and soluble forms were detected in deep soils, especially for the transect B where total As of 73.0 mg kg⁻¹ at 29 m, Cd of 3.80 mg kg⁻¹ at 13 m and Pb of 214 mg kg⁻¹ at 30 m were detected. The silty clayey and silty layers of the transect B had higher contents of As, Cr, Cu, Pb and Zn compared with the sandy layers. Across the studied area, 19.5%–34.1% of the topsoil samples were contaminated by As, Cd, Cu, Pb and Zn, and 34.1% and 19.5% of the wheat grains were contaminated by Cd and Pb, respectively. Wheat grains from all the sampling sites had a combined target hazard quotient (TTHQ) value of >1, with As and Cd being the most important contributors. Our study revealed a wider and deeper risk of typical heavy metals originated from long-term wastewater irrigation in the sampling area, which may pose substantial health risks to the local residents via wheat grains and groundwater.

Drilled seeds are an important food resource for many farmland birds but may pose a serious risk when treated with pesticides. Most compounds currently used as seed treatment in the EU have low acute toxicity but may still affect birds in a sub-chronic or chronic way, especially considering that the sowing season lasts several weeks or months, resulting in a long exposure period for birds. Tebuconazole is a triazole fungicide widely used in agriculture but its toxicity to birds remains largely unknown. Our aim was to test if a realistic scenario of exposure to tebuconazole treated seeds affected the survival and subsequent reproduction of the red-legged partridge (Alectoris rufa). We fed captive partridges with wheat seeds treated with 0%, 20% or 100% of tebuconazole application rate during 25 days in late winter (i.e. tebuconazole dietary doses were approximately 0.2 and 1.1 mg/kg bw/day). We studied treatment effects on the physiology (i.e. body weight, biochemistry, immunology, oxidative stress, coloration) and reproduction of partridges. Exposed birds did not reduce food consumption but presented reduced plasmatic concentrations of lipids (triglycerides at both exposure doses, cholesterol at high dose) and proteins (high dose). The coloration of the eye ring was also reduced in the low dose group. Exposure ended 60 days before the first egg was laid, but still affected reproductive output: hatching rate was reduced by 23% and brood size was 1.5 times smaller in the high dose group compared with controls. No significant reproductive effects were found in the low dose group. Our results point to the need to study the potential endocrine disruption mechanism of this fungicide with lagged effects on reproduction. Risk assessments for tebuconazole use as seed treatment should be revised in light of these reported effects on bird reproduction.

Understanding wetland water quality dynamics and associated influencing factors is important to assess the numerous ecosystem services they provide. We present a combined self-organizing map (SOM) and linear mixed-effects model (LMEM) to relate water quality variation of multipond systems (MPSs, a common type of non-floodplain wetlands in agricultural regions of southern China) to their extrinsic and intrinsic influences for the first time. Across the 6 test MPSs with environmental gradients, ammonium nitrogen (NH₄⁺-N), total nitrogen (TN), and total phosphate (TP) almost always exceeded the surface water quality standard (2.0, 2.0, and 0.4 mg/L, respectively) in the up- and midstream ponds, while chlorophyll-a (Chl-a) exhibited hypertrophic state (≥28 μg/L) in the midstream ponds during the wet season. Synergistic influences explained 69±12% and 73±10% of the water quality variations in the wet and dry season, respectively. The adverse, extrinsic influences were generally 1.4, 6.9, 3.2, and 4.3 times of the beneficial, intrinsic influences for NH₄⁺-N, nitrate nitrogen (NO₃⁻-N), TP, and potassium permanganate index (CODMₙ), respectively, although the influencing direction and degree of forest and water area proportion were spatiotemporally unstable. While CODMₙ was primarily linked with rural residential areas in the midstream, higher TN and TP concentrations in the up- and midstream were associated with agricultural land, and NH₄⁺-N reflected a small but non-negligible source of free-range poultry feeding. Pond surface sediments exhibited consistent, adverse effects with amplifications during rainfall, while macrophyte biomass can reflect the biological uptake of CODMₙ and Chl-a, especially in the mid- and downstream during the wet season. Our study advances nonpoint source pollution (NPSP) research for small water bodies, explores nutrient “source-sink” dynamics, and provides a timely guide for rural planning and pond management. The modelling procedures and analytical results can inform refined assessment of similar NFWs elsewhere, where restoration efforts are required.

Mapping soil contamination enables the delineation of areas where protection measures are needed. Traditional soil sampling on a grid pattern followed by chemical analysis and geostatistical interpolation methods (GIMs), such as Kriging interpolation, can be costly, slow and not well-suited to highly heterogeneous soil environments. Here we propose a novel method to map soil contamination by combining high-resolution aerial imaging (HRAI) with machine learning algorithms. To support model establishment and validation, 1068 soil samples were collected from an arsenic (As) contaminated area in Zhongxiang, Hubei province, China. The average arsenic concentration was 39.88 mg/kg (SD = 213.70 mg/kg), with individual sample points determined as low risk (66.9%), medium risk (29.4%), or high risk (3.7%), respectively. Then, identified features were extracted from a HRAI image of the study area. Four machine learning algorithms were developed to predict As risk levels, including (i) support vector machine (SVM), (ii) multi-layer perceptron (MLP), (iii) random forest (RF), and (iii) extreme random forest (ERF). Among these, we found that the ERF algorithm performed best overall and that its prediction performance was generally better than that of traditional Kriging interpolation. The accuracy of ERF in test area 1 reached 0.87, performing better than RF (0.81), MLP (0.78) and SVM (0.77). The F1-score of ERF for discerning high-risk points in test area 1 was as high as 0.8. The complexity of the distribution of points with different risk levels was a decisive factor in model prediction ability. Identified features in the study area associated with fertilizer factories had the most important contribution to the ERF model. This study demonstrates that HRAI combined with machine learning has good potential to predict As soil risk levels.

Microplastic (MP) pollution in the environment has aroused great concern. However, our knowledge of MP abundance and distribution in soil environment is scarce. This work investigated the MPs in the farmland and grassland at a remote area of China, namely, the eastern area of the Qinghai–Tibet Plateau (QTP). The average numbers of MPs were 53.2 ± 29.7 and 43.9 ± 22.3 items/kg in shallow and deep soil, respectively, from 35 soil samples. A remarkable difference in MP abundances was observed among soil samples from mulch farmland, greenhouses, farmland without covering, and grassland. The MPs were mostly in the form of a film and transparent in color in this study. The dominant polymers of MPs in the soil samples were polyester (PE) and polypropylene (PP). This study revealed the characteristics of MP distribution among different land use at the QTP, and MPs may stem from the fragmentation of plastic mulch in farmland soil. Notably, MP abundance increased with the increase in mulching time in facility agriculture. Additionally, human disturbances and increased mulching time in facility agriculture promote the fragmentation of soil MPs. This study provides important data for follow-up research on MPs in a plateau terrestrial ecosystem.