Sediment serves as a sink for metals, thus it is critical to assess its contamination and associated risk. A typical riparian wetland close to a Zn-smelting operation in karst areas in southwest China was investigated. Sediment and reed plant (Phragmites australis) samples from wet and dry seasons were analyzed for total As, Cu, and Zn concentrations. Metal pollution in the sediment was assessed based on geoaccumulation index (Igₑₒ). Further, metals in the sediment were fractionated into exchangeable, water and acid-soluble, reducible, oxidizable, and residual fractions based on the BCR sequential extraction. The results showed that the As, Cu, and Zn concentrations in the sediment were significantly higher than the background values (740–4081, 96–228, and 869–3331 vs. 10, 22, and 70 mg kg⁻¹). With the Igₑₒ being 10–17, the data indicate that the sediment was highly-polluted. While total As, Cu and Zn in the sediment increased from dry to wet season, their available concentrations decreased except Cu. With 62–94% of As, Cu, and Zn being in the residual fraction, metal availability in the sediment was low based on fractionation data. The data are consistent with low metal uptake by reed as their concentration ratios in plant roots to the sediment were 0.01–0.32. The results suggest that the riparian sediment was highly-polluted with As, Cu and Zn, but showing low metal availability and limited plant uptake.

Ingestion of food or cereal products contaminated by deoxynivalenol (DON) and related derivatives poses a threat to the health of humans and animals. However, the toxicity and underlying mechanisms of 3-acetyldeoxynivalenol (3-Ac-DON), an acetylated form of deoxynivalenol, have not been fully elucidated. In the present study, we showed that 3-Ac-DON caused significant oxidative damage, as shown by elevated aspartate aminotransferase (AST), alanine aminotransferase (ALT), and lactic dehydrogenase (LDH) in serum, increased lipid peroxidation products, such as hydrogen peroxide (H₂O₂) and malondialdehyde (MDA), decreased activities of antioxidant enzymes catalase (CAT) and superoxide dismutase (SOD). In addition, 3-Ac-DON exposure led to elevated infiltrations of immune cell, increased apoptosis and autophagy in the liver. Interestingly, 3-Ac-DON-resulted apoptosis and liver injury were partially reduced by autophagy inhibitors. Further study showed that 3-Ac-DON-treated mice had altered ultrastructural changes of endoplasmic reticulum (ER), as well as enhanced protein levels of p-IRE1α, p-PERK, and downstream targets, indicating activation of unfolded protein response (UPR) in the liver. Importantly, 3-Ac-DON induced ER stress, oxidative damage, cell death, infiltration of immune cells, and increased mRNA levels of inflammatory cytokines were significantly abolished by 4-phenylbutyric acid (4-PBA), an ER stress inhibitor, indicating a critical role of UPR signaling for the cellular damage of the liver in response to 3-Ac-DON exposure. In conclusion, using mice as an animal model, we showed that 3-Ac-DON exposure impaired the function of liver, as shown by oxidative damage, cell death, and infiltration of immune cell, in which ER stress played an important role. Restoration of the ER function might be a preventive strategy to reduce the deleterious effect of 3-Ac-DON on the liver of animals.

Insulation materials are essential components in construction, and their main objective is to increase the efficiency of thermal energy by minimizing internal and external thermal exchange. Accordingly, research and development studies are being actively conducted to increase the thermal resistance of insulation materials, and high-performance insulation materials that use organic chemicals have been developed after industrialization. However, thermal insulation comprising chemicals poses a potential risk of pollutant emissions and can cause health problems. In this study, five types of insulation materials and the contaminants generated from the building materials used in insulation construction were quantitatively analyzed. In addition, an empirical study on the discharge of pollutants was conducted using a test bed, and the effects of the pollutants discharged from the insulation material on the indoor environment were examined by analyzing the pollutant concentration for 90 days. In addition, we analyzed the effect of an insulation material on an indoor environment through the standard specifications. Moreover, the necessity of legal management of the emission of contaminants from insulation materials was proposed based on the empirical research results.

Considerable uncertainty exists with regard to the effects of thinning and harvesting on N₂O emissions as a result of changes caused in the belowground environment by tree cutting. To evaluate on the effects of changes in the belowground environment on N₂O emissions from soils, we conducted a tree manipulation experiment in a Japanese cedar (Cryptomeria japonica) stand without soil compaction or slash falling near measurement chambers and measured N₂O emission at distances of 50 and 150 cm from the tree stem (stump) before and after cutting. In addition, we inferred the effects of logging on the emission using a hierarchical Bayesian (HB) model. Our results showed that tree cutting stimulated N₂O emission from soil and that the increase in N₂O emission depended on the distance from the stem (stump); increase in N₂O emission was greater at 50 than at 150 cm from the stem. Tree cutting caused the estimated N₂O emission at 0–40 cm from the stem to double (the % increase in N₂O emission by tree cutting was 54%–213%, 95% predictive credible interval) when soil temperature was 25 °C and WFPS was 60%. Posterior simulation of the HB model predicted that 30% logging would cause a 57% (47%–67%) increase in N₂O emission at our study site (2000 trees ha⁻¹) considering only the effects of belowground changes by tree cutting during the measurement period.

Livestock manure has been widely used in agriculture to improve soil productivity and quality. However, intensive application can significantly enhance soil nitrogen (N) availability and facilitate ammonia (NH₃) volatilization during rice cultivation. The effects of different rates of manure application on the NH₃ volatilization rate, its mechanism, and their relationships have not been comprehensively investigated. In this study, field trials were conducted to investigate NH₃ volatilization in rice paddy soils amended with different livestock manure, cattle manure (CM), and swine manure (SM), at a rate of 0 (NPK), 10, 20, and 40 Mg ha⁻¹ during cultivation. Moreover, the soil physicochemical and biological properties and rice N uptake were investigated. Ultra-fine particulate matter (PM₂.₅) was measured quantitatively and qualitatively. Manure application significantly increased NH₃ emissions compared to the control. Much higher volatilization rates were observed in the SM soils than in the CM soils, even when the same amount of N was applied. This is mainly related to the higher labile NH₄⁺ concentration and urease activity in SM soils. With increasing application levels, NH₃ emission rates proportionally increased in the SM, but there was no significant difference in the CM. Livestock manure application significantly increased NH₃ volatilization, particularly during the initial manure application and additional fertilization stages during rice cultivation. The results showed that the application of livestock manure significantly increased NH₃ volatilization. Moreover, the biochemical properties of manure composts, including labile N and urease activity, mainly affected NH₃ dynamics in rice paddies during cultivation rather than their type. Irrespective of manure application, PM₂.₅, did not show a significant difference at the initial stage of cultivation. NH₃ volatilization was not significantly correlated with the formation of PM₂.₅. It is necessary to develop effective strategies for mitigating NH₃ volatilization and maintaining soil quality without decreasing rice productivity in paddy ecosystems.

Overuse of antibiotics is accelerating the spread of resistance risk in the environment. In drinking water supply systems, the effect of antibiotics on the resistance of biofilm is unclear, and there have been few studies in disinfectant-containing systems. Here, we designed a series of drinking water supply reactors to investigate the effects of antibiotics on biofilm and bacteria in the water. At low concentrations, antibiotics could promote the growth of bacteria in biofilm; among the tested antibiotics (tetracycline, sulfadiazine and chloramphenicol), tetracycline had the strongest ability to promote this. And the antibiotic resistant bacteria (ARB) could inhibit the growth of bacteria in drinking water. Results have shown that antibiotics enhanced the bacterial chlorine resistance in the effluent, but reduced that in the biofilm. Furthermore, metagenomic analysis showed that antibiotics reduced the richness of biofilm communities. The dominant phyla in the biofilm were Proteobacteria, Planctomycetes, and Firmicutes. In tetracycline-treated biofilm, the dominant phylum was Planctomycetes. In sulfadiazine- and chloramphenicol-treated groups, bacteria with complex cell structures preferentially accumulated. The dominant class in biofilm in the ARB-added group was Gammaproteobacteria. The abundance of antibiotic resistant genes (ARGs) was correlated with biofilm community structure. This study shows that antibiotics make the biofilm community structure of drinking water more resistant to chlorine. ARGs may be selective for certain bacteria in the process, and there may ultimately be enhanced chlorine and antibiotic resistance of effluent bacteria in drinking water.

The sulfate pollution in water environment gains more and more concerns in recent years. The discharge of domestic, municipal, and industrial wastewaters increases the riverine sulfate concentrations, which may cause local health and ecological problems. To better understand the sources of sulfate, this study collected water samples in a typical agricultural watershed in East Thailand. The source apportionment of sulfide was conducted by using stable isotopes and receptor models. The δ³⁴SSO₄ value of river water varied from 1.2‰ to 16.4‰, with a median value of 8.9‰. The hydrochemical data indicated that the chemical compositions of Mun river water were affected by the anthropogenic inputs and natural processes such as halite dissolution, carbonate, and silicate weathering. The positive matrix factorization (PMF) model was not suitable to trace source of riverine sulfate, because the meaning of the extracted factors seems to be vague. Based on the elemental ratio and isotopic composition, the inverse model yielded the relative contribution of sulfide oxidation (approximately 46.5%), anthropogenic input (approximately 41.5%), and gypsum dissolution (approximately 12%) to sulfate in Mun river water. This study indicates that the selection of models for source apportionment should be careful. The large contribution of anthropogenic inputs calls an urgent concern of the Thai government to establish effective management strategies in the Mun River basin.

The sorption behavior of phthalate additives in plastic and microplastic litter is an important process controlling the exposure, net health risk and ecotoxicity of these co-occurring pollutants. Plastic crystallinity and particle morphology are hypothesized to be important variables for microplastics sorption behavior, but to date there have been few direct studies to explicitly test for the influence of these parameters. To address this, in this study we explored the sorption of dibutyl phthalate (DBP) as a probe molecule to diverse polyethylene microplastics including irregularly-shaped pure polyethylene microplastics (IPPM), black plastic film microplastics (BPFM), white plastic film microplastics (WPFM), and commercial microspheres (CM), which had crystallinities ranging from 17 to 99%. Sorption kinetics for all materials could be well represented with both a pseudo-first-order (R² = 0.87–0.93) and pseudo-second-order model (R² = 0.87–0.93). Further, sorption was highly linear in the concentration range of 0.5–10 mg L⁻¹, with no greater performance from a linear sorption model (R² = 0.96–0.99) than the non-linear Freundlich or Temkin sorption models. The partition coefficient (Kd) of DBP sorption onto IPPM, BPFM, WPFM and CMs were 1974.55 L kg⁻¹, 1483.85 L kg⁻¹, 1477.45 L kg⁻¹ and 509.37 L kg⁻¹, respectively, showing a significant decrease with increasing crystallinity (r² = 0.98). The particle size of microplastics (27–1000 μm) is, however, an indecisive factor affecting their sorption behavior for DBP in this study. This study provides new insight that crystallinity plays a governing role on the sorption of phthalate from microplastic. This should be considered in future exposure studies and assessments of phthalates from plastics and microplastics.

Real-time river chloride prediction has received a lot of attention for its importance in chloride control and management. In this study, an artificial neural network model (i.e., multi-layer perceptron, MLP) and a statistical inference model (i.e., stepwise-cluster analysis, SCA) are developed for predicting chloride concentration in stream water. Then, an ensemble learning model based on MLP and SCA is proposed to further improve the modeling accuracy. A case study of hourly river chloride prediction in the Grand River, Canada is presented to demonstrate the model applicability. The results show that the proposed ensemble learning model, MLP-SCA, provides the best overall performance compared with its two ensemble members in terms of RMSE, MAPE, NSE, and R² with values of 11.58 mg/L, 27.55%, 0.90, and 0.90, respectively. Moreover, MLP-SCA is more competent for predicting extremely high chloride concentration. The prediction of observed concentrations above 150 mg/L has RMSE and MAPE values of 9.88 mg/L and 4.40%, respectively. The outstanding performance of the proposed MLP-SCA, particularly in extreme value prediction, indicates that it can provide reliable chloride prediction using commonly available data (i.e., conductivity, water temperature, river flow rate, and rainfall). The high-frequency prediction of chloride concentration in the Grand River can supplement the existing water quality monitoring programs, and further support the real-time control and management of chloride in the watershed. MLP-SCA is the first ensemble learning model for river chloride prediction and can be extended to other river systems for water quality prediction.

Indoor environment constitutes an important source of industrial additive chemicals to human exposure. We hypothesized that the influence of residential environment on human exposure varies among different types of additive chemicals and differs between children and mothers. This study determined a suite of additive chemicals in house dust from South China dwellings (n = 47) and urine from child-mother pairs. Concentrations of phthalates (PAEs; median 601 μg/g) were 2–3 orders of magnitude greater than those of parabens (0.82 μg/g), bisphenols (3.31 μg/g), and benzophenone-related chemicals (2.69 μg/g). Urinary concentrations differed between children and mothers, but the pattern of differences varied between chemical groups. Children exhibited greater urinary levels of mono-PAEs than mothers (510 versus 395 ng/mL, p = 0.152), while the latter population exhibited greater levels of parabens and benzophenones. Regression analyses indicate a lack of association between dust and urinary levels for most chemicals, suggesting that other exposure pathways can complicate human exposure scenarios. Indeed, we estimated that the daily intake via dust ingestion only constituted 0.002–0.81% of total daily intake estimated based on urine data for mothers and 0.04–5.61% for children. Future efforts are needed to better characterize source-specific exposure for different populations.