Human exposure to organic contaminants is widespread. Many of these contaminants show adverse health effects on human population. Human biomonitoring (HBM) follows the levels and the distribution of biomarkers of exposure (BoE), but it is usually done in a targeted manner. Suspect and non-targeted screening (SS/NTS) tend to find BoE in an agnostic way, without preselection of compounds, and include finding evidence of exposure to predicted, unpredicted known and unknown chemicals. This study describes the application of high-resolution mass spectrometry (HRMS)-based SS/NTS workflow for revealing organic contaminants in urine of a cohort of 200 children from Slovenia, aged 6–9 years. The children originated from two regions, urban and rural, and the latter were sampled in two time periods, summer and winter. We tentatively identified 74 BoE at the confidence levels of 2 and 3. These BoE belong to several classes of pharmaceuticals, personal care products, plasticizers and plastic related products, volatile organic compounds, nicotine, caffeine and pesticides. The risk of three pesticides, atrazine, amitraz and diazinon is of particular concern since their use was limited in the EU. Among BoE we tentatively identified compounds that have not yet been monitored in HBM schemes and demonstrate limited exposure data, such as bisphenol G, polyethylene glycols and their ethers. Furthermore, 7 compounds with unknown use and sources of exposure were tentatively identified, either indicating the entry of new chemicals into the market, or their metabolites and transformation products. Interestingly, several BoE showed location and time dependency. Globally, this study presents high-throughput approach to SS/NTS for HBM. The results shed a light on the exposure of Slovenian children and raise questions on potential adverse health effects of such mixtures on this vulnerable population.

Methyl Chloride (CH₃Cl) is the largest source of stratospheric chlorine, which has a significant impact on the depletion of the stratospheric ozone layer. Detailed information on anthropogenic CH₃Cl emissions in China is still lacking. This study establishes a comprehensive bottom-up inventory of anthropogenic CH₃Cl emissions in China during 2000–2020. Results show that China's anthropogenic CH₃Cl emissions have increased significantly, from 34.1 ± 11.6 Gg/yr (gigagrams per year) in 2000 to 128.5 ± 26.5 Gg/yr in 2018 with a slight decrease to 124.9 ± 26.0 Gg/yr in 2020. The main sources of anthropogenic emissions of CH₃Cl in China are chemical production (37.1%), solvent use (35.4%), and coal combustion (13.6%) in 2020. China's contribution to global anthropogenic emissions of CH₃Cl reached almost 50%. Moreover, the ratios of CH₃Cl CFC-11-eq emissions relative to emissions of ozone-depleting substances (ODSs) controlled under the Montreal Protocol in China have increased from 0.8% in 2000 to 11.6% in 2020 and are estimated to continue increasing in the future. In summary, China's anthropogenic CH₃Cl emissions have shown an increasing trend in the past two decades, made a huge contribution to the total global anthropogenic emissions, and presented a potential increasing impact on the depletion of the ozone layer and global warming.

Many researches mention the need to identify the land-based sources of riverine macrolitter but few field data on litter amount, composition and sources are available in the scientific literature. Describing macrolitter hotspot dynamics would actually allow a better estimation of fluxes in the receiving environments and a better identification of the more appropriate mitigation strategies. This study provides new insights in roadway macrolitter production rates, typologies and input sources (i.e. deliberate or accidental). The macrolitter from an 800 m portion of a highly frequented roadway (around 90,000 vehicles per day) was collected during almost one year. Typologies were defined using the OSPAR/TGML classification. Results show high annual loads of macrolitter (42.8 kg/yr/ha), suggesting significant contributions of the road runoff to the litter fluxes in urban stormwater. Over the course of a year, 88.5 kg of debris were collected, including 53.2 kg (60%) of plastic debris. In total, 36,439 items were characterized, of which 84% were plastics. The macrodebris collected present a low diversity of components with Top 10 items accounting for 92% by count and a majority of small and lightweight items like plastic fragments (31%) or cigarette butts (18%). Input sources were estimated for 43% of the mass collected in which 37.2% were deliberately littered and 62.8% were accidental leaks, illustrating a major contribution of uncovered trucks and unsecured loads. The accumulation rates show a linear correlation with the road traffic. Such data are of prime interest since they enable to determine the potential contribution of road traffic to plastic fluxes to the environment.

Ecological risk assessment of contaminated sediment has become a fundamental component of water quality management programs, supporting decision-making for management actions or prompting additional investigations. In this study, we proposed a machine learning (ML)-based approach to assess the ecological risk of contaminated sediment as an alternative to existing index-based methods and costly toxicity testing. The performance of three widely used index-based methods (the pollution load index, potential ecological risk index, and mean probable effect concentration) and three ML algorithms (random forest, support vector machine, and extreme gradient boosting [XGB]) were compared in their prediction of sediment toxicity using 327 nationwide data sets from Korea consisting of 14 sediment quality parameters and sediment toxicity testing data. We also compared the performances of classifiers and regressors in predicting the toxicity for each of RF, SVM, and XGB algorithms. For all algorithms, the classifiers poorly classified toxic and non-toxic samples due to limited information on the sediment composition and the small training dataset. The regressors with a given classification threshold provided better classification, with the XGB regressor outperforming the other models in the classification. A permutation feature importance analysis revealed that Cr, Cu, Pb, and Zn were major contributors to toxicity prediction. The ML-based approach has the potential to be even more useful in the future with the expected increase in available sediment data.

Solidification/stabilization technology is one of the most desirable technologies for the remediation of heavy metal contaminated soils due to its convenience and effectiveness. The annual production of alkaline industrial wastes in China is in the hundreds of millions of tons. Alkaline industrial wastes have the potential to replace conventional stabilizers because of their cost effectiveness and performance in stabilizing heavy metals in soils. This paper systematically summarizes the use of four alkaline industrial wastes (soda residue, steel slag, carbide slag, and red mud) for the solidification/stabilization of heavy metal contaminated soils and provides a comprehensive analysis of the three mechanisms of action (hydration, precipitation, and adsorption) and factors that influence the process. In addition, the environmental risks associated with the use of alkaline industrial wastes are highlighted. We found that soda residues, steel slag and carbide slag are appropriate for solidification/stabilization of Pb, Cd, Zn and Cu, while red mud is a potential passivation agent for the stabilization of As in soils. However, implementation of remediation methods using alkaline industrial wastes has been limited because the long-term effectiveness, synergistic effects, and usage in soils containing multiple heavy metals have not been thoroughly studied. This review provides the latest knowledge on the mechanisms, risks, and challenges of using alkaline industrial wastes for solidification/stabilization of heavy metal contaminated soils.

Arsenic (As) toxicity is a problem that needs to be solved in terms of both human health and agricultural production in the vast majority of the world. The presence of As causes biomass loss by disrupting the balance of biochemical processes in plants and preventing growth/water absorption in the roots and accumulating in the edible parts of the plant and entering the food chain. A critical method of combating As toxicity is the use of biosafe, natural, bioactive compounds such as hesperidin (HP) or chlorogenic acid (CA). To this end, in this study, the physiological and biochemical effects of HP (100 μM) and CA (50 μM) were investigated in Zea mays under arsenate stress (100 μM). Relative water content, osmotic potential, photosynthesis-related parameters were suppressed under stress. It was determined that stress decreased the activities of the antioxidant system and increased the level of saturated fatty acids and, gene expression of PHT transporters involved in the uptake and translocation of arsenate. After being exposed to stress, HP and CA improved the capacity of superoxide dismutase (SOD), catalase (CAT), peroxidase (POX), glutathione S-transferase (GST) and glutathione peroxidase (GPX) and then ROS accumulation (H₂O₂) and lipid peroxidation (TBARS) were effectively removed. These phenolic compounds contributed to maintaining the cellular redox status by regulating enzyme/non-enzyme activity/contents involved in the AsA-GSH cycle. HP and CA reversed the adverse effects of excessive metal ion accumulation by re-regulated expression of the PHT1.1 and PHT1.3 genes in response to stress. Exogenously applied HP and CA effectively maintained membrane integrity by regulating saturated/unsaturated fatty acid content. However, the combined application of HP and CA did not show a synergistic protective activity against As stress and had a negative effect on the antioxidant capacity of maize leaves. As a result, HP and CA have great potentials to provide tolerance to maize under As stress by reducing oxidative injury and preserving the biochemical reactions of photosynthesis.

The seasonal variation, spatial distribution, and ecological risks of thirteen organophosphate pesticides (OPPs) were studied in the Sundays and Swartkops estuaries in South Africa. Ten pesticides were detected in surface water samples from both estuaries, while all OPPs were detected in sediments. The highest concentration of OPPs (18.8 μg pyrazophos L⁻¹) was detected in surface water samples from Swartkops Estuary, while 48.7 μg phosalone kg⁻¹ dw was the highest in sediments collected from Sundays Estuary. There was no clear seasonal pattern in OPPs occurrence in surface water from both systems. However, their occurrence in sediments was in the following order: winter > autumn > summer > spring, perhaps indicating major pesticide input in the winter seasons. Results from ecological risk assessment showed that pyraclofos and chlorpyrifos (CHL) in surface water from both systems are respectively likely to cause high acute and chronic toxicity to fish (risk quotient – RQ > 1). For sediments of both estuaries, the highest acute and chronic RQs for fish were calculated for isazophos and CHL respectively. The majority of the detected OPPs in sediments posed potential high risks to Daphnia magna from both systems. These results suggest that these aquatic organisms (fish, and Daphnia), if present in the studied estuaries, can develop certain forms of abnormalities due to OPP exposure. To this end, proper measures should be taken to reduce OPP input into the estuarine systems.

This study investigated the control of dissolved organic matter (DOM) molecular compositions by microbial community shifts under temperature regulation (range from 5 to 35 °C), using riverine DOM and in situ microorganisms as examples. The functioning of different microbial metabolisms, including the utilization and generation processes, was comprehensively analyzed. Though the overall quantity of DOM was less temperature-affected, more molecules were identified at moderate temperatures (e.g., 15 and 25 °C) and their accumulated mass peak intensities increased with the temperature. The results were ascribed to 1) the microbial production of macromolecular (m/z > 600) CHO, CHON, and CHONS species was stimulated at higher temperatures; 2) the microorganisms consumed more DOM molecules at both higher and lower temperatures; and 3) the simultaneously decreased utilization and increased generation of recalcitrant CHO and CHON molecules with m/z < 600 at higher temperatures. The strong correlations among the temperature, community structures, and DOM chemodiversity suggested that temperature promoted the community evenness to increase the DOM generation. In addition, the higher temperature decreased the abundance of microorganisms that utilized more recalcitrant molecules and produced fewer new molecules (e.g., Proteobacteria, Acinetobacter, and Erythrobacter) while increased others that functioned the opposite (e.g., Verrucomicrobia, Bacteroidetes, and Flavobacterium) to increase the DOM production. The constructed temperature-community-DOM chemistry relationship deepened the molecular-level understanding of DOM variations and provided implications for the warming future.

Pharmaceutical contaminants in surface water have raised significant concerns because of their potential ecological risks. In particular, coronavirus disease 2019 (COVID-19)-related pharmaceuticals can be released to surface water and reduce environmental water quality. Therefore, reliable and robust sampling tools are required for monitoring pharmaceuticals. In this study, passive sampling devices of diffusive gradients in thin films (DGTs) were developed for sampling 35 pharmaceuticals in surface waters. The results demonstrated that hydrophilic–lipophilic balance (HLB) was more suitable for DGT-based devices compared with XAD18 and XDA1 resins. For most pharmaceuticals, the performance of the HLB-DGT devices were independent of pH (5.0–9.0), ionic strength (0.001–0.5 M), and flow velocity (0–400 rpm). The HLB-DGT devices exhibited linear pharmaceutical accumulation for 7 days, and time-weighted average concentrations provided by the HLB-DGT were comparable to those measured by conventional grab sampling. Compared to previous studies, we extended DGT monitoring to include three antiviral drugs used for COVID-19 treatment, which may inspire further exploration on identifying the effects of COVID-19 on ecological and human health.