Toxicoproteomic analysis of steel industry ambient particulate matter (PM) that contain high concentrations of PAHs and metals was done by treating human lung cancer cell-line, A549 and the cell lysates were analysed using quantitative label-free nano LC-MS/MS. A total of 18,562 peptides representing 1576 proteins were identified and quantified, with 196 proteins had significantly altered expression in the treated cells. Enrichment analyses revealed that proteins associated to redox homeostsis, metabolism, and cellular energy generation were inhibited while, proteins related to DNA damage and repair and other stresses were over expressed. Altered activities of several tumor associated proteins were observed. Protein-protein interaction network and biological pathway analysis of these differentially expressed proteins were carried out to obtain a systems level view of proteome changes. Together it could be inferred that PM exposure induced oxidative stress which could have lead into DNA damage and tumor related changes. However, lowering of cellular metabolism, and energy production could reduce its ability to overcome these stress. This kind of disequilibrium between the DNA damage and ability of the cells to repair the DNA damage may lead into genomic instability that is capable of acting as the driving force during PM induced carcinogenesis.

Exposure to fine particulate matter (PM₂.₅) remains a worldwide public health issue. However, epidemiological studies on the chronic health impacts of PM₂.₅ in the developing countries are hindered by the lack of monitoring data. Despite the recent development of using satellite remote sensing to predict ground-level PM₂.₅ concentrations in China, methods for generating reliable historical PM₂.₅ exposure, especially prior to the construction of PM₂.₅ monitoring network in 2013, are still very rare. In this study, a high-performance machine-learning model was developed directly at monthly level to estimate PM₂.₅ levels in North China Plain. We developed a random forest model using the latest Multi-angle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD), meteorological parameters, land cover and ground PM₂.₅ measurements from 2013 to 2015. A multiple imputation method was applied to fill the missing values of AOD. We used 10-fold cross-validation (CV) to evaluate model performance and a separate time period, January 2016 to December 2016, was used to validate our model's capability of predicting historical PM₂.₅ concentrations. The overall model CV R² and relative prediction error (RPE) were 0.88 and 18.7%, respectively. Validation results beyond the modeling period (2013–2015) shown that this model can accurately predict historical PM₂.₅ concentrations at the monthly (R² = 0.74, RPE = 27.6%), seasonal (R² = 0.78, RPE = 21.2%) and annual (R² = 0.76, RPE = 16.9%) level. The annual mean predicted PM₂.₅ concentration from 2013 to 2016 in our study domain was 67.7 μg/m3 and Southern Hebei, Western Shandong and Northern Henan were the most polluted areas. Using this computationally efficient, monthly and high-resolution model, we can provide reliable historical PM₂.₅ concentrations for epidemiological studies on PM₂.₅ health effects in China.

Introducing of earthworms to constructed wetlands (CWs) has been considered as a new approach to solve the clogging problems in the long-established systems. Despite its potential advantage, the correlational researches are still in the stage of preliminary observation and speculation. This paper presents a comprehensive and in-depth research about the positive effects of earthworms (Eisenia foetida) on clog matter (CM) reduction through different pathways, including in vivo metabolism and uptake, conversion, transport, and promotion of microorganism quantities. The results showed that the metabolism and uptake by Eisenia foetida could effectively reduce the CM content at an average removal rate of 0.155 mg g−1 d−1, which was obviously higher than the rate of CM decomposition by microorganisms alone. Through the metabolism of earthworms, the amounts of proteins and polysaccharides in CM were decreased, while the amounts of humin and nucleic acids were increased. Simultaneously, the viscosity of CM was reduced by 0.0082 mPa s g−1 d−1, and the quantity of microorganisms was increased by 0.0109 mg g−1 d−1, which finally made the treated CM can be easily washed away and decomposed. Furthermore, earthworms could reduce the CM content in the clogging layer by transporting the metabolic products out. A regression model was further performed for describing the interaction between earthworm and CM. The simulated value of porosity fitted well with the measured one, suggesting that the earthworms can increase the substrate porosity at a rate of 0.33 mL g−1 d−1. This study quantitively depicted the mechanisms of earthworms on the decrement of CM content in CWs, which is of great benefit for the engineering management of constructed wetlands in the future. We also proposed that the density of introduced earthworms should exceed a certain threshold for effectively increasing the substrate porosity and solving the clogging problems.

Acetochlor is a widely used chloroacetanilide herbicide and has posed environmental risks in soil and water due to its toxicity and high leaching capacity. Earthworm represents the dominant invertebrate in soil and can promote the decomposition of organic pollutants. The effect of earthworm on acetochlor degradation in soil was studied by soil column experiment with or without acetochlor and earthworm in sterile and natural soils. The degradation capacities of drilosphere components to acetochlor were investigated by microcosm experiments. Bacterial and fungal acetochlor degraders stimulated by earthworm were identified by high-throughput sequencing. The degradation kinetics of acetochlor suggested that both indigenous microorganisms and earthworm played important roles in acetochlor degradation. Acetochlor degradation was quicker in soil with earthworms than without earthworms, with the degradation rates increased by 62.3 ± 15.2% and 9.7 ± 1.7% in sterile and natural treatments respectively. The result was related to the neutralized pH, higher enzyme activities and enhanced soil microbial community diversity and richness in the presence of earthworms. Earthworm cast was the degradation hotpot in drilosphere and exhibited better anaerobic degradation capacity in microcosm experiments. The acetochlor degradation rate of cast in anaerobic environment was 12.0 ± 0.1% quicker than that in aerobic environment. Residual acetochlor in soil conferred a long-term impairment on fungal community, and this inhibition could be repaired by earthworm. Earthworm stimulated indigenous degraders like Sphingomonas and Microascales and carried suspected intestinal degraders like Mortierella and Escherichia_coli to degradation process. Cometabolism between nutrition cycle species and degraders in casts also contributed to its faster degradation rates. The study also presented some possible anaerobic degradation species like Rhodococcus, Pseudomonas_fulva and Methylobacillus.

Plant uptake factors (PUFs) are of great importance in human cadmium (Cd) exposure risk assessment while it has been often treated in a generic way. We collected 1077 pairs of vegetable-soil samples from production fields to characterize Cd PUFs and demonstrated their utility in assessing Cd exposure risks to consumers of locally grown vegetables. The Cd PUFs varied with plant species and pH and organic matter content of soils. Once normalized PUFs against soil parameters, the PUFs distributions were log-normal in nature. In this manner, the PUFs were represented by definable probability distributions instead of a deterministic figure. The Cd exposure risks were then assessed using the normalized PUF based on the Monte Carlo simulation algorithm. Factors affecting the extent of Cd exposures were isolated through sensitivity analyses. Normalized PUF would illustrate the outcomes for uncontaminated and slightly contaminated soils. Among the vegetables, lettuce was potentially hazardous for residents due to its high Cd accumulation but low Zn concentration. To protect 95% of the lettuce production from causing excessive Cd exposure risks, pH of soils needed to be 5.9 and above.

Dicamba (DIC) and 2,4-dichlorophenoxyacetic acid (2,4-D) are two of the most applied auxinic herbicides worldwide, both individually and as part of a mixture. However, the toxicity and interactions achieved when applied as a mixture have not yet been characterised. The equitoxic and non-equitoxic acute toxicity exerted by binary mixtures of Banvel® (57.71% DIC) and DMA® (58.4% 2,4-D) on the Neotropical fish Cnesterodon decemmaculatus were evaluated. Results revealed mean values of 1.02 (range, 0.96–1.08) for the toxic unit (TU) that induced 50% mortality (TU50 96 h) to the fish exposed to binary equitoxic mixtures of the commercial formulations Banvel®–DMA®. These results suggest that the mixture is nearly concentration additive. Furthermore, results demonstrated the occurrence of synergistic interaction when non-equitoxic combinations of Banvel®-or DMA®-formulated herbicides were assayed. In this context and regardless of their concentrations, either Banvel®- or DMA®-induced toxicity were synergised by the presence of the counterpart within mixtures. The present study represents the first evidence of the lethality exerted by mixtures of two auxinic herbicides—namely, DIC and 2,4-D—reported to date for fish and other biotic matrices. When C. decemmaculatus is used as the target organism, a synergistic pattern is observed following exposure to a mixture of both herbicides.

Anthropogenic activities are driving an increase in sediment contamination in coastal areas. This poses significant challenges for the management of estuarine ecosystems and their adjacent seas worldwide. However, few studies have been conducted on how dynamic mechanisms affect the sediment toxicity in the estuarine environment. This study was designed to investigate the linkages between sediment toxicity and hydrodynamics in the Yangtze River Estuary (YRE) area. High sediment toxicity was found in the Yangtze River mouth (Region I), the depocenter of the Yangtze River Delta (Region II), and the southeastern area of the adjacent sea (Region III), while low sediment toxicity was found in the northeastern offshore region (Region IV). A spatial comparison analysis and regression model indicated that the distributed pattern of sediment toxicity was likely related to hydrodynamics and circumfluence in the East China Sea (ECS) shelf. Specifically, high sediment toxicity in Region I may be affected by the Yangtze River Pump (YRP) and the low hydrodynamics there, and high toxicity in Region II can be influenced by the low sediment dynamics and fine sediment in the depocenter. The high sediment toxicity in Region III might be related to the combination of the YRP and Taiwan Warm Current, while the low toxicity in Region IV may be influenced by the local coarse-grained relict sand with strong sediment dynamics there. The present research results further suggest that it is necessary to link hydrodynamics and the spatial behavior of sediment and sediment-derived pollutants when assessing the pollution status of estuarine environments, especially for those mega-estuaries and their neighboring ocean environments with complex waves, tides and ocean currents.

Ibuprofen (IBU), as a commonly used non-steroidal anti-inflammatory drug (NSAID) and pharmaceutical and personal care product (PPCP), is frequently prescribed by doctors to relieve pain. It is widely released into environmental water and soil in the form of chiral enantiomers by the urination and defecation of humans or animals and by sewage discharge from wastewater treatment plants. This study focused on the alteration of metabolism in the adult zebrafish (Danio rerio) brain after exposure to R-(-)-/S-(+)-/rac-IBU at 5 μg L−1 for 28 days. A total of 45 potential biomarkers and related pathways, including amino acids and their derivatives, purine and its derivatives, nucleotides and other metabolites, were observed with untargeted metabolomics. To validate the metabolic disorders induced by IBU, 22 amino acids and 3 antioxidant enzymes were selected to be quantitated and determined using targeted metabolomics and enzyme assay. Stereoselective changes were observed in the 45 identified biomarkers from the untargeted metabolomics analysis. The 22 amino acids quantitated in targeted metabolomics and 3 antioxidant enzymes determined in enzyme assay also showed stereoselective changes after R-(-)-/S-(+)-/rac-IBU exposure. Results showed that even at a low concentration of R-(-)-/S-(+)-/rac-IBU, disorders in metabolism and antioxidant defense systems were still induced with stereoselectivity. Our study may enable a better understanding of the risks of chiral PPCPs in aquatic organisms in the environment.

The increased use of pesticides during recent years necessitates a reevaluation of the effect of those compounds by extending the range of nontarget species commonly used in risk assessment. In the present work, we thus determined the impact of the pesticides glyphosate, carbendazim, and malathion on the parasite Chordodes nobilii in both natural and reconstituted freshwater as the assay medium and tested the sensitivity of three of this species's ecologically relevant parameters—e. g., embryo nonviablity and the infective capability of larvae exposed for 48 or 96 h either in ovo or after hatching via the infection index mean abundance—to compare those parameters to data from previous trials with reconstituted freshwater. In natural-freshwater assays, at environmentally relevant concentrations, all three pesticides inhibited the preparasitic-stage endpoints; with carbendazim being the most toxic pesticide and the subsequent infectivity of larvae exposed in ovo the most sensitive endpoint. In general, the 50%-inhibitory concentrations assayed in reconstituted freshwater were higher than those obtained in natural freshwater, indicating a certain protective effect; whereas the maximal toxicity of the three pesticides in both aqueous environments was essentially similar. The sensitivity of C. nobilii to these agents demonstrated that this species is one of the most susceptible to toxicity by all three pesticides. These findings with the assay methodology provide relevant information for a future assessment of the risk of toxicity to aquatic ecosystems and furthermore underscore the need to include parasitic organisms among the nontarget species canvassed. We also recommend that in the bioassays in which the risk assessment is carried out, water from a nontarget species's natural environment be used in parallel in order to obtain more conclusive results.

Ambient concentrations of fine particulate matter (PM₂.₅) concentration is often used as an exposure surrogate to estimate PM₂.₅ health effects in epidemiological studies. Ignoring the potential variations in the amount of outdoor PM₂.₅ infiltrating into indoor environments will cause exposure misclassification, especially when people spend most of their time indoors. As it is not feasible to measure the PM₂.₅ infiltration factor (Fᵢₙf) for each individual residence, we aimed to build models for residential PM₂.₅Fᵢₙf prediction and to evaluate seasonal Fᵢₙf variations among residences. We repeated collected paired indoor and outdoor PM₂.₅ filter samples for 7 continuous days in each of the three seasons (hot, cold and transitional seasons) from 48 typical homes of Shanghai, China. PM₂.₅-bound sulfur on the filters was measured by X-ray fluorescence for PM₂.₅Fᵢₙf calculation. We then used stepwise-multiple linear regression to construct season-specific models with climatic variables and questionnaire-based predictors. All models were evaluated by the coefficient of determination (R²) and root mean square error (RMSE) from a leave-one-out-cross-validation (LOOCV). The 7-day mean (±SD) of PM₂.₅Fᵢₙf across all observations was 0.83 (±0.18). Fᵢₙf was found higher and more varied in transitional season (12–25 °C) than hot (>25 °C) and cold (<12 °C) seasons. Air conditioning use and meteorological factors were the most important predictors during hot and cold seasons; Floor of residence and building age were the best transitional season predictors. The models predicted 60.0%–68.4% of the variance in 7-day averages of Fᵢₙf, The LOOCV analysis showed an R² of 0.52 and an RMSE of 0.11. Our finding of large variation in residential PM₂.₅Fᵢₙf between seasons and across residences within season indicated the important source of outdoor-generated PM₂.₅ exposure heterogeneity in epidemiologic studies. Our models based on readily available data may potentially improve the accuracy of estimates of the health effects of PM₂.₅ exposure.