For the first time, a quantitative assessment of this pollutant was made at the bottom of the rivers of the Mekong basin, and the features of its accumulation and dynamics in bottom sediments and relationship with abundance of fish and decapods were investigated. Sampling of materials for the research was carried out by bottom trawls in the Mekong delta in Vietnam. The amount of macroplastic caught by the trawl from the bottom averaged 33.4 g/100 m². The maximum values of the waste content (up to 923.2 g/100 m²) were confined to the districts of large cities. The distribution of macroplastic was characterized by high spatio-temporal variability. Its amount was significantly (p < 0.05) higher in branches with low monthly flow, in shallow-water areas, as well as in the low-water period. During the flood period, which usually lasts from July to November, the amount of macroplastic at the bottom decreased by an average of 2.5 times. In this study a significant relationship between the number of animals and the mass of macroplastics was statistically proven for most species of fish and decapods. The substrate, containing a significant amount of plastic fragments, attracted aquatic organisms. All in all, we present results for poorly understood processes of transport, deposition and influence of plastic debris in large rivers in regions of monsoon subequatorial climate and show that more efforts should be dedicated to further unravel potentially complex pathways of the plastic exposure to water ecosystems.

Intense harmful algal blooms (HABs) can occur in the backwaters of tributaries supplying large-scale reservoirs. Due to the characteristics of process-based models and difficulties in modelling complex nonlinear processes, traditional models have difficulties disentangling the driving factors of HABs. In this study, we used data-driven methods (i.e., correlation analysis and machine-learning models) to identify the most important drivers of HABs in the Xiangxi River, a tributary of the Three Gorges Reservoir, China (2017–2018), for the dry season (from October to mid-April) and wet season (from April to September). We utilized the maximal information coefficient (MIC) combined with a time lag strategy and prior knowledge to quantitatively identify the driving variables of HABs. An extra trees regression (ETR) model was developed to assess the relative importance of causal variables driving algal blooms for the different periods. The results showed that water temperature was the most important driver for the duration of the study, followed by total nitrogen. Nitrogen had a stronger effect on algal blooms than phosphorus during both the wet and dry seasons. HABs were mainly affected by ammonia nitrogen in the wet season and by other forms of nitrogen in the dry season. In contrast, rather than the water temperature and nutrients, the operation of the Three Gorges Dam (difference between inflow and outflow discharge rate) was the most significant factor for algal blooms during the dry season, but its influence sharply declined during the wet season. This study showed that the key drivers of HABs can differ between seasons and suggests that HAB management should take seasonality into account.

According to fire accident statistics, fires in buildings are increasing. The flame-retardant performance of insulation materials is considered an important factor for preventing the spread of fire and ensuring evacuation. This study evaluated the flame-retardant performance and combustion characteristics of four types of organic thermal insulation used as core materials in sandwich panels. The flame-retardant performance evaluation based on total heat release and heat release rate revealed that phenolic foam (PF) satisfied the criteria for non-combustible grade insulation. An analysis of the hazardous gases released while combustion of the four insulation materials indicated that a significant amount of CO was released—an average of 19,000 ppm or higher—in the rigid urethan foam (PIR) and spray-type polyurethane foam (SPU). The fractional effective dose (FED) value was derived from the gas analysis results according to ISO 13344. PIR and SPU had an average FED value of 2.0 or higher and were identified as very dangerous in the case of fire accidents. Moreover, the evacuation time in the case of a fire in a warehouse-type building was comprehensively analyzed considering the material, size, and height for the four types of insulation. PIR was the most vulnerable to fire, and for PF, the danger limit was not reached until the end of the simulation.

Breast milk samples were collected from 51 mothers in a seaside city Dalian, where the residents usually have higher dietary exposure to polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) and polychlorinated biphenyls (PCBs) due to the larger consumption of seafood. The lipid-basis concentrations of ∑Cl₂–₈DD/Fs, ∑Cl₂–₁₀Bs, and total toxicity equivalent (WHO-TEQ) were measured to be in the ranges of 35.7–2727.8 pg/g, 4.91–52.64 ng/g, and 2.27–36.30 pg/g, respectively. The average proportion of ∑Cl₂–₃DD/Fs was higher than that of ∑Cl₄–₇DD/Fs in the collected human breast milk samples, suggesting that the health risk of Cl₂–₃DD/Fs should be especially concerned. The concentration data of PCDD/Fs and PCBs in human breast milk essentially followed a positive skew probability distribution. Women in high-level exposure scenarios exhibited a higher potential to accumulate homologues Cl₄DFs, Cl₇DFs, Cl₈DF, and Cl₆Bs in breast milk. Three PCDD/F congeners (1,2,3,6,7,8-Cl₆DF, 1,2,3,4,7,8-Cl₆DF, and 1,2,3,4,6,7,8-Cl₇DD) and three PCB congeners (PCB 126, PCB 138, and PCB 169) were identified as good indicators for the accumulation of PCDD/Fs and PCBs in human breast milk, respectively. The food-to-milk accumulation factors (FMAF) were calculated to evaluate the accumulation potentials of different PCDD/F and PCB congeners in human breast milk via dietary exposure. The calculated FMAF value presented a non-monotonic variation with the logarithm of n-octanol–water partition coefficient (log KOW) with a peak at a log KOW value of about 7.3 and a valley at a log KOW value of about 8. The mean value of the estimated daily intake (EDI) of total WHO-TEQ for breast-fed infants in Dalian, predicted by Monte Carlo simulation, was 10 folds higher than the upper range of the tolerable daily intake (TDI) value (4 pg WHO-TEQ/kg bw/d), suggesting continued and enhanced efforts should be made to reduce the exposure risk of infants to PCDD/Fs and PCBs.

A better understanding of the spatiotemporal dynamics and influencing factors of sulfonamide antibiotic resistance genes (ARGs) distribution in subsurface flow constructed wetlands is essential to improve the ARGs removal efficiency. The spatiotemporal dynamics of sulfonamide ARGs were explored in the vertical upflow subsurface flow constructed wetland (VUSFCW). The results showed that the absolute abundance of ARGs presented a trend of bottom layer > middle layer > top layer. The relative abundance of ARGs decreased significantly from the bottom layer to the middle layer, but increased in the top layer. The bottom layer was the main stage to remove ARGs. The absolute abundance of ARGs at each point in summer was significantly higher than that in winter. Based on the spatiotemporal distribution of ARGs, the internal mechanism of ARGs dynamic change was explored by the partial least square path analysis model. The results showed that physical-chemical factors, microorganisms and antibiotics indirectly affected the spatiotemporal distribution of ARGs mainly through mobile genetic elements. The indirect influence coefficients of physical-chemical factors, microorganisms and antibiotics on the spatiotemporal distribution of ARGs were 0.505, 0.221 and 0.98 respectively. The direct influence coefficient of MGEs on the spatiotemporal distribution of ARGs was 0.895. The results of network analysis showed that the potential host species of ARGs in summer were more abundant than those in winter. The selection mode of sulfonamide ARGs to potential hosts was nonspecific. There is a risk of sulfonamide ARGs infecting pathogens in VUSFCW. Fortunately, VUSFCW has proven effective in reducing the absolute abundance of ARGs and the potential risk of pathogens carrying ARGs. These findings provide a model simulation and theoretical basis for effectively reducing the threat of ARGs.

Brominated flame retardants (BFRs) are an important group of additives in plastics that increase resistance to ignition and slow down the rate of burning. Because of concerns about their environmental and human health impacts, however, some of the most widely employed BFRs, including hexabromocyclododecane (HBCD) and commercial mixtures of penta-, octa- and deca- (poly)bromodiphenyl ethers (PBDEs), have been restricted or phased out. In this review, the oceanic sources and pathways of PBDEs, the most widely used BFRs, are evaluated and quantified, with particular focus on emissions due to migration from plastics into the atmosphere versus emissions associated with the input of retarded or contaminated plastics themselves. Calculations based on available measurements of PBDEs in the environment suggest that 3.5 and 135 tonnes of PBDEs are annually deposited in the ocean when scavenged by aerosols and through air-water gas exchange, respectively, with rivers contributing a further ∼40 tonnes. Calculations based on PBDE migration from plastic products in use or awaiting or undergoing disposal yield similar net inputs to the ocean but indicate a relatively rapid decline over the next two decades in association with the reduction in the production and recycling of these chemicals. Estimates associated with the input of PBDEs to the ocean when “bound” to marine plastics and microplastics range from about 360 to 950 tonnes per year based on the annual production of plastics and PBDEs over the past decade, and from about 20 to 50 tonnes per annum based on the abundance and distribution of PBDEs in marine plastic litter. Because of the persistence and pervasiveness of plastics in the ocean and diffusion coefficients for PBDEs on the order of 10⁻²⁰ to 10⁻²⁷ m² s⁻¹, microplastics are likely to act as a long-term source of these chemicals though gradual migration. Locally, however, and more important from an ecotoxicological perspective, PBDE migration may be significantly enhanced when physically and chemically weathered microplastics are exposed to the oily digestive fluids conditions of fish and seabirds.

This study aimed to evaluate the impact factors and effectiveness of management policies on the presence of polybrominated diphenyl ethers (PBDEs) in sediment samples in Taiwan from the last 10 years. Twenty-four PBDE congeners were detected in 838 sediment samples collected from 4 stages (2006–2019) in 30 principal rivers, based on the national project for background monitoring of the environmental distribution of chemical substances. The ΣPBDE concentrations in the 4 stages ranged from 30.00 to 147.10 ng/g dw, 6.03–15.30 ng/g dw, 4.99–7.00 ng/g dw, and 1.20–2.10 ng/g dw in the northern, southern, central, and eastern areas, respectively. The concentrations of PBDEs (e.g., penta-BDE and octa-BDE) in sediment samples notably decreased (−6 to −73%) as the Taiwan Environmental Protection Administration implemented policies banning PBDEs (except deca-BDE). The PBDEs levels of the sediment samples collected in the dry season were higher than those collected in the wet season. The levels of ΣPBDEs in sediment samples were affected by season, the amount of general waste present, and nearby PBDE-related factories and e-waste recycling facilities. Reducing the release of PBDEs, especially deca-BDE, through sound waste management and recycling practices is still needed to improve environmental sustainability in Taiwan.

Synthetic microfibers have been identified as the most prevalent type of microplastic in samples from aquatic, atmospheric, and terrestrial environments across the globe. Apparel washing has shown to be a major source of microfiber pollution. We used California as a case study to estimate the magnitude and fate of microfiber emissions, and to evaluate potential mitigation approaches. First, we quantified synthetic microfiber emissions and fate from apparel washing in California by developing a material flow model which connects California-specific data on synthetic fiber consumption, apparel washing, microfiber generation, and wastewater and biosolid management practices. Next, we used the model to assess the effectiveness of different interventions to reduce microfiber emissions to natural environments. We estimate that in 2019 as much as 2.2 kilotons (kt) of synthetic microfibers were generated by apparel washing in California, a 26% increase since 2008. The majority entered terrestrial environments (1.6 kt), followed by landfills (0.4 kt), waterbodies (0.1 kt), and incineration (0.1 kt). California's wastewater treatment network was estimated to divert 95% of microfibers from waterbodies, mainly to terrestrial environments and primarily via land application of biosolids. Our analysis also reveals that application of biosolids on agricultural lands facilitates a directional flow of microfibers from higher-income urban counties to lower-income rural communities. Without interventions, annual synthetic microfiber emissions to California's natural environments are expected to increase by 17% to 2.1 kt by 2026. Further increasing the microfiber retention efficiency at the wastewater treatment plant would increase emissions to terrestrial environments, which suggests that microfibers should be removed before entering the wastewater system. In our model, full adoption of in-line filters in washing machines decreased annual synthetic microfiber emissions to natural environments by 79% to 0.5 kt and offered the largest reduction of all modeled scenarios.

Facing billions of tons of pollutants entering the ocean each year, aquatic toxicity is becoming a crucial endpoint for evaluating chemical adverse effects on ecosystems. Notably, huge amount of toxic chemicals at environmental relevant doses can cause potential adverse effects. However, chronic aquatic toxicity effects of chemicals are much scarcer, especially at population level. Rotifers are highly sensitive to toxicants even at chronic low-doses and their communities are usually considered as effective indicators for assessing the status of aquatic ecosystems. Therefore, the no observed effect concentration (NOEC) for population abundance of rotifers were selected as endpoints to develop machine learning models for the prediction of chemical aquatic chronic toxicity. In this study, forty-eight binary models were built by eight types of chemical descriptors combined with six machine learning algorithms. The best binary model was 1D & 2D molecular descriptors – random trees model (RT) with high balanced accuracy (BA) (0.83 for training and 0.83 for validation set), and Matthews correlation coefficient (MCC) (0.72 for training set and 0.67 for validation set). Moreover, the optimal model identified the primary factors (SpMAD_Dzp, AMW, MATS2v) and filtered out three high alerting substructures [c1cc(Cl)cc1, CNCO, CCOP(=S)(OCC)O] influencing the chronic aquatic toxicity. These results showed that the compounds with low molecular volume, high polarity and molecular weight could contribute to adverse effects on rotifers, facilitating the deeper understanding of chronic toxicity mechanisms. In addition, forecast models had better performances than the common models embedded into ECOSAR software. This study provided insights into structural features responsible for the toxicity of different groups of chemicals and thereby allowed for the rational design of green and safer alternatives.

Decision Support System (DSS) is a novel approach for smart, sustainable controlling of environmental phenomena and purification processes. Toluene is one of the most widely used petroleum products, which adversely impacts on human health. In this study, Fusarium Solani fungi are utilized as the engine of the toluene bioremediation procedure for the monitoring part of DSS. Experiments are optimized by Central Composite Design (CCD) - Response Surface Methodology (RSM), and the behavior of the mentioned fungi is estimated by M5 Pruned model tree (M5P), Gaussian Processes (GP), and Sequential Minimal Optimization (SMOreg) algorithms as the prediction section of DSS. Finally, the control stage of DSS is provided by integrated Petri Net modeling and Failure Modes and Effects Analysis (FMEA). The findings showed that Aeration Intensity (AI) and Fungi load/Biological Waste (F/BW) are the most influential mechanical and biological factors, with P-value of 0.0001 and 0.0003, respectively. Likewise, the optimal values of main mechanical parameters include AI, and the space between pipes (S) are equal to 13.76 m³/h and 15.99 cm, respectively. Also, the optimum conditions of biological features containing F/BW and pH are 0.001 mg/g and 7.56. In accordance with the kinetic study, bioremediation of toluene by Fusarium Solani is done based on a first-order reaction with a 0.034 s-1 kinetic coefficient. Finally, the machine learning practices showed that the GP (R2 = 0.98) and M5P (R2 = 0.94) have the most precision for predicting Removal Percentage (RP) for mechanical and biological factors, respectively. At the end of the present research, it is found that by controlling seven possible risk factors in bioremediation operation through the FMEA- Petri Net technique, efficiency of the process can be adjusted to optimum value.