Four phenylurea herbicides (PUHs) were assessed for degradation and transformation into N-nitrosodimethylamine (NDMA) under three oxidation conditions (chlorine (Cl₂), chlorine dioxide (ClO₂), and ozone (O₃)) from an aqueous solution. Removal ratios correlated with the numbers of halogen elements contained in PUHs (isoproturon ₍₀₎ > chlorotoluron ₍₁ Cₗ₎ > diuron ₍₂ Cₗ₎ > fluometuron ₍₃ F₎), and the degradation efficiencies of oxidants from fastest to slowest were: O₃, ClO₂, and Cl₂. NDMA can be generated directly from the ozonation of PUHs. Further, compared with chloramination alone, ozonation prominently promoted NDMA formation potential (NDMA-FP) during post-chloramination, and NDMA-FPs increased approximately 23–68 times than those during ozonation only at 2.5 mg/L O₃ over 10 min; molar yields of NDMA from highest to lowest were 11.1% (isoproturon), 1.17% (chlorotoluron), 1.0% (diuron), and 0.73% (fluometuron). The PUH degradation kinetics data during ozonation agreed with the pseudo-first-order model. The rate constant kₒbₛ were 0.31 × 10⁻³–19.8 × 10⁻³ s⁻¹. The kₒbₛ and removal ratios of PUHs during ozonation partially scaled with the mass, LogKₒw, and Henry’s constants of PUHs. Comparisons of measured NDMA-FPs with calculated NDMA-FPs from residual PUH after oxidation showed that the intermediates produced during ozonation facilitated NDMA-FPs; this contribution was also observed for chlorotoluron and isoproturon during ClO₂ oxidation. Examination of reaction mechanisms revealed that tertiary amine ozonation, N-dealkylation, hydroxylation, the cleavage of N–C bonds, ammonification, and nitrification occurred during the ozonation of PUHs, and the dimethylamine (DMA) functional groups could be decomposed directly and transformed into NDMA via the formation of the intermediate unsymmetrical dimethylhydrazine. NDMA is also formed from the reaction between DMA and phenylamino-compounds. Clarifying primary degradation products of PUHs and transformation pathways of NDMA during oxidation processes is useful to optimize treatment processes for water supplies.

Quantifying the sources of atmospheric particles is essential to air quality control but remains challenging, especially for the source apportionment of particles based on number concentration with wide size range. Here, particle number concentrations (PNC) with size range 19–20,000 nm involving four modes Nucleation, Aitken, Accumulation, and Coarse are used to do source apportionment of PNC at the Guangdong Atmospheric Supersite (Heshan) during July–October 2015 by nonnegative matrix factorization (NMF) with 6 factors. For July 2015, separated source apportionments for three different size ranges from collocated instruments nano scanning mobility particle sizer (NSMPS), SMPS, and aerodynamic particle sizer (APS) and for two different size ranges (below and above 100 nm) show similar quantitative source information with that for the one whole size range. The mean absolute difference of contribution percentages of total particle number concentrations (TPNC) based on 5 unique apportioned sources is 5.6 % (4.3–7.6 %) for the instrument segregated apportionment and 4.2 % (0–5.3 %) for the size range segregated apportionment respectively, relative to the one whole apportionment. Moreover, the contribution percentages of TPNC are close to the weighted sum of contribution percentages of all size bins, with a mean absolute difference of 1.1 % (0–3.4 %). In both these two aspects, the consistency among different technical paths proves the matrix factorization by NMF is practically desirable and the simplicity of reducing some steps or calculations saves time. Besides, dust can be identified with the wide size range including larger than 3000 nm. Six apportioned sources in the 4 months are Accumulation (32.4 %), Nucleation (20.0 %), Aitken (15.2 %), traffic (14.6 %), dust (10.6 %), and Coarse (7.1 %). Therefore, NMF would serve as a promising tool for PNC source apportionment with wide size range and conducting the apportionment with the whole size range in one matrix factorization procedure and using the single TPNC contribution percentage are feasible.

Antibiotic resistance genes (ARGs) are now viewed as emerging contaminants posing a potential worldwide human health risk. The degree to which ARGs are transferred to other bacteria via mobile genetic elements (MGEs), including insertion sequences (ISs), plasmids, and phages, has a strong association with their likelihood to function as resistance transfer determinants. Consequently, understanding the structure and function of MGEs is paramount to assessing future health risks associated with ARGs in an environment subjected to strong antibiotic pressure. In this study we used whole genome sequencing, done using MinION and HiSeq platforms, to examine antibiotic resistance determinants among four multidrug resistant bacteria isolated from fish farm effluent in Jeju, South Korea. The combined data was used to ascertain the association between ARGs and MGEs. Hybrid assembly using HiSeq and MinION reads revealed the presence of IncFIB(K) and pVPH2 plasmids, whose sizes were verified using pulsed field gel electrophoresis. Twenty four ARGs and 95 MGEs were identified among the 955 coding sequences annotated on these plasmids. More importantly, 22 of 24 ARGs conferring resistance to various antibiotics were found to be located near MGEs, whereas about a half of the ARGs (11 out of 21) were so in chromosomes. Our results also suggest that the total phenotypic resistance exhibited by the isolates was mainly contributed by these putatively mobilizable ARGs. The study gives genomic insights into the origins of putatively mobilizable ARGs in bacteria subjected to selection pressure.

Considerable human data have shown that the exposure to perfluorooctane sulfonate (PFOS) correlates to the risk of metabolic diseases, however the underlying effects are not clearly elucidated. In this study, we investigated the impacts of PFOS treatment, using in-vivo, ex-vivo and in-vitro approaches, on pancreatic β-cell functions. Mice were oral-gavage with 1 and 5 μg PFOS/g body weight/day for 21 days. The animals showed a significant increase in liver triglycerides, accompanied by a reduction of triglycerides in blood sera and glycogen in livers and muscles. Histological examination of pancreases showed no noticeable changes in the size and number of islets from the control and treatment groups. Immunohistochemistry showed a reduction of staining intensities of insulin and the transcriptional factors (Pdx-1, islet-1) in islets of pancreatic sections from PFOS-treated groups, but no changes in the intensity of Glut2 and glucagon were noted. Transcriptomic study of isolated pancreatic islets treated ex vivo with 1 μM and 10 μM PFOS for 24 h, underlined perturbations of the insulin signaling pathways. Western blot analysis of ex-vivo PFOS-treated islets revealed a significant reduction in the expression levels of the insulin receptor, the IGF1 receptor-β, Pdk1-Akt-mTOR pathways, and Pdx-1. Using the mouse β-cells (Min-6) treated with 1 μM and 10 μM PFOS for 24 h, Western blot analysis consistently showed the PFOS-treatment inhibited Akt-pathway and reduced cellular insulin contents. Moreover, functional studies revealed the inhibitory effects of PFOS on glucose-stimulated insulin-secretion (GSIS) and the rate of ATP production. Our data support the perturbing effects of PFOS on animal metabolism and demonstrate the underlying molecular targets to impair β-cell functions.

It has been shown that chemical modification of chitosan with sulfur (S) functional groups could significantly enhance its chelating capability with heavy metals included Cd(II). However, a molecular level understanding has been lacking. Here, we carried out X-ray absorption fine structure (XAFS) and Fourier transformed infrared (FTIR) spectra studies to bridge this knowledge gap. The results indicate that both Cd–O/N and Cd–S bonds exist in the complex of Cd(II) with dithiocarbamate chitosan (DTC-CTS). S functional groups (dithiocarbamate) in DTC-CTS play the major role in complexation with Cd(II) and S content affects the adsorption mechanism. At low S content, Cd(II) is mainly adsorbed on DTC-CTS as an outer-sphere complex with two monodentate amino groups and two water molecules in tetrahedral configuration. At high S content, Cd adsorption dominantly occurs by formation of an inner-sphere complex with two bidentate mononuclear S ligands in tetrahedral configuration. This investigation provides information on the effectiveness and mechanisms of Cd(II) removal that is critical for evaluating modified chitosan applications for stabilization of Cd(II) in surface water, groundwater, soils and sediments.

Evaluation of the toxicity of pesticide residues on non-target organisms in the ecosystem is an important part of pesticide environmental risk assessment. Flupyradifurone is a new type of butenolide insecticide produced by Bayer, who claims it to be “low toxic” to non-target organisms in the environment. However, there is little evidence in the literature to show how flupyradifurone affects aquatic organism development. In the current study, zebrafish embryos were treated with 0.1, 0.15, and 0.2 mg/mL of flupyradifurone within 6.0–72 h past fertilization (hpf). We found that the half-lethal concentration (LC₅₀) of flupyradifurone for zebrafish embryos at 96 hpf was 0.21 mg/mL. Flupyradifurone decreases the heart rate, survival rate, and body length of zebrafish embryos. The flupyradifurone treatment also led to the failure of heart looping, and pericardial edema. Moreover, flupyradifurone increased the level of reactive oxygen species (ROS) and decreased the enzymatic catalysis of catalase (CAT) and superoxide dismutase (SOD). Alterations were induced in the transcription of apoptosis-related genes (bcl-2, bax, bax/bcl-2, p53 and caspase-9) and the heart development-related genes (gata4, myh6, nkx2.5, nppa, tbx2b, tbx5 and vmhc). In the current study, new evidences have been provided regarding the toxic effects of flupyradifurone and the risk of its residues in agricultural products and the environment.

Despite the increased attention for temporal aspects of stressor interactions and for effects of warming in ecotoxicological studies, we lack knowledge on how different exposure durations to warming may affect pesticide sensitivity. We tested how three types of exposure duration to 4 °C warming (acute, developmental and transgenerational exposure to 24 °C vs 20 °C) shape the effect of the pesticide chlorpyrifos on two ecologically relevant fitness-related traits of mosquito larvae: heat tolerance and antipredator behaviour. Transgenerational (from the parental generation) and developmental (from the egg stage) warming appeared energetically more stressful than acute warming (from the final instar), because (i) only the latter resulted in an adaptive increase of heat tolerance, and (ii) especially developmental and transgenerational warming reduced the diving responsiveness and diving time. Exposure to chlorpyrifos decreased the heat tolerance, diving responsiveness and diving time. The impact of chlorpyrifos was lower at 24 °C than at 20 °C indicating that the expected increase in toxicity at 24 °C was overruled by the observed increase in pesticide degradation. Notably, although our results suggest that transgenerational warming was energetically more stressful, it did reduce the chlorpyrifos-induced negative effects at 24 °C on heat tolerance and the alarm escape response compared to acute warming. Our results provide important evidence that the exposure duration to warming may determine the impact of a pesticide under warming, thereby identifying a novel temporal aspect of stressor interactions in risk assessment.

Air pollution is a complex process and is affected by meteorological conditions and other chemical components. Numerous studies have demonstrated that data-driven spatio-temporal prediction models of PM₂.₅ concentration are comparable with the model-driven model. However, data-driven models are usually depending on the statistical correlation between PM₂.₅ and other factors and have challenges in dealing with causality in complex systems. In this paper, we argue that domain knowledge should be incorporated into data-driven models to enhance prediction accuracy and make the model more physically realistic. We focus on the influence of dynamic wind-field on PM₂.₅ concentration distribution and fuse the pollution diffusion distance with the deep learning model based on a wind-field surface. In order to model spatial dependence between monitoring stations, which is dynamic and anisotropic because of the wind-field, we proposed a hybrid deep learning framework, dynamic directed spatio-temporal graph convolution networks (DD-STGCN). It expanded the ability to deal with space-time prediction in the continuous and dynamic wind-field. We used a directed graph time-series to describe the vertex state and topological relationship between vertices and replaced traditional Euclidean distance with wind-field diffusion distance to describe the proximity relationship between vertices. Our experiment results demonstrated that the DD-STGCN model achieved a better prediction ability than LSTM, GC-LSTM, and STGCN models. Compared to the best comparison model, MAPE, MAE, and RMSE were improved by 10.2%, 9.7%, and 9.6% in 12 h on an average, respectively. The performance of our model was further tested during a haze period. In the case that two models both considered the effect of wind, compared with the pure data-driven model, our model performed better in prediction distribution and showed the benefit of spatial interpretability provided by domain knowledge.

Motor vehicles emit a variety of pollutants including metals, petroleum hydrocarbons and polycyclic aromatic hydrocarbons (PAHs). The relationships between metals, petroleum hydrocarbons and PAHs, soil respiration and microbial diversity (fungi and bacteria) were studied using control (n = 3) and roadside soils (n = 27) with different exposure periods to vehicle emissions (2–63 years). Bacterial diversity was found to be higher than control sites (P = 0.002) but was the same across different categories of road age (P = 0.328). Significant (r = −0.49, P = 0.007) contrasting behaviour of fungal and bacterial diversity was reported, with diversity increasing across all road types for bacteria and decreasing across all road types for fungi compared to control soils. Analysis of the bacterial community identified three distinct clusters, separated on age of contamination, suggesting that roadside bacterial communities change over time with pollution from vehicles with the potential development of metal resistant bacteria in roadside soils. In contrast, for fungal communities, a reduction in diversity with time of exposure to roadside vehicle emissions was observed suggesting the potential for reduced ecosystem functionality and soil health in roadside soils. This is the first study in the published literature to include both bacterial and fungal responses from aged roadside soils. The results from this study suggest that normal functionality of soil ecosystem services is being affected in roadside soils, potentially globally.

The Yellow River Delta (YRD) wetland, the most important estuary wetland in eastern China, has an important ecosystem service function. Rapid and intensive development has inevitably led to the accumulation of potentially toxic elements (PTEs) in soils. Therefore, identifying quantitative sources and spatial distributions of PTEs is essential for soil environmental protection in the YRD. A total of 240 topsoil samples (0–20 cm) were collected in the Yellow River Delta Nature Reserve (YRDNR) and analyzed the PTE contents. To avoid the biases of the single receptor model, positive matrix factorization, factor analysis with nonnegative constraints, and maximum likelihood principal component analysis-multivariate curve resolution-alternating least squares were used for source apportionment of soil PTEs. To promote the efficiency of multivariate geostatistical simulation, a minimum/maximum autocorrelation factor-sequential Gaussian simulation was built to map the spatial patterns of PTEs. Three factors were derived by the three receptor models, and their contributions to the source explanation were similar. As, Cr, Cu, Mn, Ni, and Zn originated from natural sources, with contributions of 85.6%–96.4 %. A total of 61.5 % of Hg was associated with atmospheric deposition of coal combustion and wastewater from upstream. Agricultural activities and oil exploitation contributed 33.5 % and 15.9 % of the Cd and Pb concentrations. Spatial distributions of soil PTEs were controlled by sedimentary grain size. A total of 47.2 % of the total study area was identified as hazardous area for Cd, 10.3 % for As, and 5.4 % for Hg. This work is expected to provide references for soil pollution assessment and management of YRDNR.