Fine particulate matter (PM₂.₅) has been recognized as a key air pollutant that can influence population health risk, especially during extreme cases such as wildfires. Previous studies have applied geospatial techniques such as land use regression to map the ground-level PM₂.₅, while some recent studies have found that Aerosol Optical Depth (AOD) derived from satellite images and machine learning techniques may be two elements that can improve spatiotemporal prediction. However, there has been a lack of studies evaluating use of different machine learning techniques with AOD datasets for mapping PM₂.₅, especially in areas with high spatiotemporal variability of PM₂.₅.In this study, we compared the performance of eight predictive algorithms with the use of multiple remote sensing datasets, including satellite-derived AOD data, for the prediction of ground-level PM2.5 concentration. Based on the results, Cubist, random forest and eXtreme Gradient Boosting were the algorithms with better performance, while Cubist was the best (CV-RMSE = 2.64 μg/m3, CV-R² = 0.48). Variable importance analysis indicated that the predictors with the highest contributions in modelling were monthly AOD and elevation.In conclusion, appropriate selection of machine learning algorithms can improve ground-level PM2.5 estimation, especially for areas with nonlinear relationships between PM2.5 and predictors caused by complex terrain. Satellite-derived data such as AOD and land surface temperature (LST) can also be substitutes for traditional datasets retrieved from weather stations, especially for areas with sparse and uneven distribution of stations.

A method was proposed to evaluate comprehensive effects of pHs and total metal concentration (TMC) variation for metal speciation human health risk in groundwater. The method used for the health assessment considered comprehensive and mutative effects caused by oral ingestion of groundwater based on human health risk assessment model and MINTEQ simulation. The results demonstrated that the dissolution rate of Ni2+ was affected by pH and Ni total concentration (total-Ni). With the increase of pH, the Ni2+ dissolved rate was smaller in the higher total-Ni at same pH. Ni2+ was dominant components contributed to health risk in groundwater. With the increase of pH in various total-Ni, HINi keep constant at first, and then decreased gradually. The HINi values of Ni speciation above acceptable level only in high total-Ni with alkaline conditions. The obtained results to verify that metals speciation were determined in health risk, and variation factors (pH and metal total concentration) played important role in risk estimation. These results provide basic information of heavy metal pollution control as well as remediation management.

Plastic pollution is a major global concern with several million microplastic particles entering every day freshwater ecosystems via wastewater discharge. Microplastic particles stimulate biofilm formation (plastisphere) throughout the water column and have the potential to affect microbial community structure if they accumulate in pelagic waters, especially enhancing the proliferation of biohazardous bacteria. To test this scenario, we simulated the inflow of treated wastewater into a temperate lake using a continuous culture system with a gradient of concentration of microplastic particles. We followed the effect of microplastics on the microbial community structure and on the occurrence of integrase 1 (int1), a marker associated with mobile genetic elements known as a proxy for anthropogenic effects on the spread of antimicrobial resistance genes. The abundance of int1 increased in the plastisphere with increasing microplastic particle concentration, but not in the water surrounding the microplastic particles. Likewise, the microbial community on microplastic was more similar to the original wastewater community with increasing microplastic concentrations. Our results show that microplastic particles indeed promote persistence of typical indicators of microbial anthropogenic pollution in natural waters, and substantiate that their removal from treated wastewater should be prioritised.

Perfluoroalkyl acids (PFAAs) were measured in aqueous and suspended particulate matter (SPM) fractions in the final effluents from 12 wastewater treatment facilities located around the Connecticut shoreline. Aqueous phase concentrations ranged from 53 to 198 ng/L for ∑PFAAs with ≤7 perfluorinated carbons (CF₂) and 2–73 ng/L for >7 CF₂ PFAAs. Predominant PFAAs associated with effluent derived SPM were perfluorodecanoic acid and perflurorooctane sulfonic acid, detected in 48% and 52% of samples in concentrations ranging from <LOQ–1770 ng/g and <LOQ–2750 ng/g respectively. Based on the range of concentrations detected and the average flow of final effluent to the Long Island Sound (LIS), average total annual PFAA mass loads from wastewater treatment facilities to the LIS is estimated in the range of 70–315 kg/year, with 4–100 kg/year consisting of >7 CF₂ PFAAs. Partitioning coefficients (log KOC) derived for effluent water and SPM phases (4.2 ± 0.3, 4.4 ± 0.4, 5.1 ± 0.2 and 5.3 ± 0.2 for PFOA, PFNA PFDA and PFUnA; 4.5 ± 0.2 and 5.2 ± 0.2 for PFOS and PFHsX respectively) were found to be of similar magnitude to aeration tank particles, though 0.5 to 2 log units greater than sludge solids and to natural system particulates including riverine SPM, estuarine SPM and sediments. Results from this study suggest that effluent derived suspended particulate matter could be an effective vector in the transport of long-chained PFAAs through wastewater treatment into receiving waters, and a potential vector to the local food chain.

Increasing evidence shows that impaired telomere function is associated with male infertility, and various environmental factors are believed to play a pivotal role in telomerase deficiency and telomere shortening. Benzo[a]pyrene (B[a]P), a ubiquitous pollutant of polycyclic aromatic hydrocarbons (PAHs), can act as a reproductive toxicant; however, the adverse effect of B[a]P on telomeres in male reproductive cells has never been studied, and the related mechanisms remain unclear. In this study, we explored the effects of benzo[a]pyrene-7,8-dihydrodiol-9,10-epoxide (BPDE), the active metabolite of B[a]P, on telomere dysfunction in mouse spermatocyte-derived cells (GC-2) and also the potential role of telomerase in BPDE-induced spermatogenic cell damage. The results showed that BPDE induced cell viability inhibition, senescence, and apoptosis in GC-2 cells in a dose-dependent manner. Shortened telomeres, telomere-associated DNA damage, reduced telomerase activity, and TERT expression were also observed in BPDE-treated cells, accompanied with the activation of DNA damage response pathway (ATM/Chk1/p53/p21). Moreover, by establishing the TERT knockdown and re-expression cell models, we found that TERT regulated telomere length and the expression of DNA damage response-related proteins to influence senescence and apoptosis in GC-2 cells. These in vitro findings were further confirmed in vivo in the testicular cells of rats orally administrated with B[a]P for 7 days. B[a]P treatment resulted in histological lesions, apoptosis, and senescence in the testes of rats, which were accompanied by shortened telomeres, reduced levels of TERT protein, and increased expression of DNA damage response-related proteins. In conclusion, it can be concluded that TERT-mediated telomere dysfunction contributes to B[a]P- and BPDE-induced senescence and apoptosis through DNA damage response in male reproductive cells.

Due to the severe degradation and environmental pollution of coastal wetlands by human activities, they have gradually become an important source of greenhouse gases (GHGs) emissions, so exploring the characteristics of their emission is important to reduce greenhouse gas emissions from coastal wetlands. In this study, the dynamics of methane (CH₄) and carbon dioxide (CO₂) emissions were investigated in five kinds of typical tidal flats from the Yellow River delta wetland during the years 2011–2013, and the influences of water level and salinity on their emissions were explored in laboratory experiments. The mean fluxes of CO₂ and CH₄ were −20.98 to 68.12 mg m⁻² h⁻¹ and −0.12 to 0.44 mg m⁻² h⁻¹ across all seasons in the five kinds of representative tidal flats. The highest and lowest mean fluxes of CO₂ were mainly observed during summer and winter, respectively, whereas the seasons with the highest and lowest mean fluxes of CH₄ varied according to the type of tidal flat. The results showed that the summer season and the mud flat environment had the largest contributions to greenhouse gas emissions. In laboratory experiments, the largest sequestration fluxes of CO₂ and CH₄ were observed with +4/+2 cm and −4 cm water levels, respectively, indicating that a moderately high water level was beneficial for CO₂ sequestration but led to the increase of CH₄ emission. In the study of salinity, we found that the largest sequestration fluxes of CO₂ and CH₄ were both detected at 24 g L⁻¹ salinity, indicating that high salinity level was advantageous for CO₂ and CH₄ sequestration in the five simulation devices. Furthermore, a carbon cycle pathway of coastal wetlands was proposed, which could have a vital significance for research into the global carbon cycle. We can reduce GHG emissions by protecting the coastal wetlands and lessening human activities.

Copper nanoparticles (nCu) are widely used in industry and in daily life, due to their unique physical, chemical, and biological properties. Few studies have focused on nCu phytotoxicity, especially with regard to toxicity mechanisms in crop plants. The present study examined the effect of 15.6 μM nCu exposure on the root morphology, physiology, and gene transcription levels of wheat (Triticum aestivum L.), a major crop cultivated worldwide. The results obtained were compared with the effects of exposing wheat to an equivalent molar concentration of ionic Cu (Cu²⁺ released from CuSO₄) and to control plants. The relative growth rate of roots decreased to approximately 60% and the formation of lateral roots was stimulated under nCu exposure, possibly due to the enhancement of nitrogen uptake and accumulation of auxin in lateral roots. The expression of four of the genes involved in the positive regulation of cell proliferation and negative regulation of programmed cell death decreased to 50% in the Cu²⁺ treatment compared to that of the control, while only one gene was down-regulated to about half of the control in nCu treatment. This explained the decreased root cell proliferation and higher extent of induced cell death in Cu²⁺- than in nCu-exposed plants. The increased methane dicarboxylic aldehyde accumulation (2.17-fold increase compared with the control) and decreased antioxidant enzyme activities (more than 50% decrease compared with the control) observed in the Cu²⁺ treatment in relation to the nCu treatment indicated higher oxidative stress in Cu²⁺- than in nCu-exposed plants. Antioxidant (e.g., proline) synthesis was pronouncedly induced by nCu to scavenge excess reactive oxygen species, alleviating phytotoxicity to wheat exposed to this form of Cu. Overall, oxidative stress and root growth inhibition were the main causes of nCu toxicity.

In order to improve the prediction accuracy and real-time of the air pollutant concentration prediction, this paper proposes self-adaptive neuro-fuzzy weighted extreme learning machine (ANFIS-WELM) based on the weighted extreme learning machine (WELM) and the adaptive neuro-fuzzy inference system (ANFIS) combined air pollutant concentration prediction method. Firstly, Gaussian membership function parameters are selected to fuzzify the input values and calculate the membership degree of each input variable. Secondly, corresponding fuzzy rules are activated, and the firing strength is normalized to calculate the output matrix of hidden nodes. Then, the optimal parameters (C, M), weights are assigned to weighted ELM by using locally weighted linear regression, and the regularized WELM target formula with equality constraint is optimized by the Karush–Kuhn–Tucker (KKT) conditions, the output weight matrix is calculated, and finally the prediction output matrix is calculated. Based on the air pollutant concentration data collected in Datong, Taiwan, the data on the pollutants containing carbon monoxide (CO), nitric oxide (NO), PM2.5 (particulate matter) and PM10, are selected by different historical time series lengths, using genetic algorithm-backpropagation neural network (GA-BPNN), support vector regression (SVR), extreme learning machine (ELM), WELM, ANFIS, regularized extreme learning adaptive neuro-fuzzy inference system (R-ELANFIS) and ANFIS-WELM are built for predict the concentration of each pollutant collected by single monitoring point in single-step time series. The experimental results show that the ANFIS-WELM presented in this paper has better prediction accuracy and real-time performance, realizes the prediction of multi-step time series on the basis of the ANFIS-WELM, and realizes the engineering application of the ANFIS-WELM algorithm package on the self-developed mobile source emissions online monitoring data center software system.

Groundwater is an essential public and drinking water supply and its protection is a goal for global policies. Here, we investigated the presence and prevalence of antibiotic residues, antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), and microbial contamination in groundwater environments at various distances from urban areas. Antibiotic concentrations ranged from below detection limit to 917 ng/L, being trimethoprim, macrolide, and sulfonamide the most abundant antibiotic classes. A total of eleven ARGs (aminoglycoside, β-lactam, chloramphenicol, Macrolide-Lincosamide-Streptogramin B - MLSB, sulfonamide, and tetracycline), one antiseptic resistance gene, and two MGEs were detected by qPCR with relative abundances ranging from 6.61 × 10−7 to 2.30 × 10−1 copies/16S rRNA gene copies. ARGs and MGEs were widespread in the investigated groundwater environments, with increased abundances not only in urban, but also in remote areas. Distinct bacterial community profiles were observed, with a higher prevalence of Betaproteobacteria and Bacteroidetes in the less-impacted areas, and that of Firmicutes in the contaminated groundwater. The combined characteristics of increased species diversity, distinct phylogenetic composition, and the possible presence of fecal and/or pathogenic bacteria could indicate different types of contamination. Significant correlations between ARGs, MGEs and specific taxa within the groundwater bacterial community were identified, revealing the potential hosts of resistance types. Although no universal marker gene could be determined, a co-selection of int1, qacEΔ1 and sulI genes, a proxy group for anthropogenic pollution, with the tetC, tetO, tetW resistance genes was identified. As the tet group was observed to follow the pattern of environmental contamination for the groundwater samples investigated in this study, our results strongly support the proposal of this group of genes as an environmental tracer of human impact. Overall, the present study investigated several emerging contaminants in groundwater habitats that may be included in monitoring programs to enable further regulatory and protection measures.

Agriculture is today indispensably connected with enormous use of pesticides. Despite tough regulation, their entrance into soil cannot be excluded and they might enter soil organisms and plants and continue further to terrestrial food chains. This study was conducted to investigate the bioaccumulation of two pesticides currently used in large amounts, the insecticide chlorpyrifos (CLP) and the fungicide tebuconazole (TBZ). Their detailed uptake kinetics in the model earthworm species Eisenia andrei were measured in two arable soils differing in organic carbon content (1.02 and 1.93% respectively). According to our results, a steady state was reached after 3–5 days for both pesticides and soils. The values of bioaccumulation factors calculated at the steady state ranged from 4.5 to 6.3 for CLP and 2.2–13.1 for TBZ. Bioaccumulation factors were also calculated as the ratio of uptake and elimination rate constants with results comparable with steady-state bioaccumulation factors. The results suggested that the degradation and bioaccumulation of tested compounds might be influenced by other factors than only total organic carbon (e.g. clay content). The lower Koc and hydrophobicity of TBZ relative to CLP probably led to higher availability of TBZ through pore water exposure. On the other hand, CLP's higher hydrophobicity probably caused an increase in availability by its additional uptake via ingestion. To enable a proper ecological risk assessment of current pesticides in soils, it is necessary to accurately determine their bioaccumulation in soil invertebrates. We believe that our study not only brings such information for two specific pesticides but also addresses key methodological issues in this area.