Water quality monitoring programs often collect large amounts of data with limited attention given to the assessment of the dominant drivers of spatial and temporal water quality variations at the catchment scale. This study uses a multi-model approach: a) to identify the influential catchment characteristics affecting spatial variability in water quality; and b) to predict spatial variability in water quality more reliably and robustly. Tropical catchments in the Great Barrier Reef (GBR) area, Australia, were used as a case study. We developed statistical models using 58 catchment characteristics to predict the spatial variability in water quality in 32 GBR catchments. An exhaustive search method coupled with multi-model inference approaches were used to identify important catchment characteristics and predict the spatial variation in water quality across catchments. Bootstrapping and cross-validation approaches were used to assess the uncertainty in identified important factors and robustness of multi-model structure, respectively. The results indicate that water quality variables were generally most influenced by the natural characteristics of catchments (e.g., soil type and annual rainfall), while anthropogenic characteristics (i.e., land use) also showed significant influence on dissolved nutrient species (e.g., NOX, NH₄ and FRP). The multi-model structures developed in this work were able to predict average event-mean concentration well, with Nash-Sutcliffe coefficient ranging from 0.68 to 0.96. This work provides data-driven evidence for catchment managers, which can help them develop effective water quality management strategies.

Aquatic hyphomycetes (AHs), a group of saprotrophic fungi adapted to submerged leaf litter, play key functional roles in stream ecosystems as decomposers and food source for higher trophic levels. Fungicides, controlling fungal pathogens, target evolutionary conserved molecular processes in fungi and contaminate streams via their use in agricultural and urban landscapes. Thus fungicides pose a risk to AHs and the functions they provide. To investigate the impacts of fungicide exposure on the composition and functioning of AH communities, we exposed four AH species in monocultures and mixed cultures to increasing fungicide concentrations (0, 5, 50, 500, and 2500 μg/L). We assessed the biomass of each species via quantitative real-time PCR. Moreover, leaf decomposition was investigated. In monocultures, none of the species was affected at environmentally relevant fungicide levels (5 and 50 μg/L). The two most tolerant species were able to colonize and decompose leaves even at very high fungicide levels (≥500 μg/L), although less efficiently. In mixed cultures, changes in leaf decomposition reflected the response pattern of the species most tolerant in monocultures. Accordingly, the decomposition process may be safeguarded by tolerant species in combination with functional redundancy. In all fungicide treatments, however, sensitive species were displaced and interactions between fungi changed from complementarity to competition. As AH community composition determines leaves’ nutritional quality for consumers, the data suggest that fungicide exposures rather induce bottom-up effects in food webs than impairments in leaf decomposition.

An industrial warehouse illegally storing a large quantity of unknown chemical and industrial waste ignited in an urban area in Melbourne, Australia. The multiday fire required firefighters to use large amounts of fluorine-free foam that carried contaminated firewater runoff into an adjacent freshwater creek. In this study, the occurrence and fate of 42 per- and polyfluoroalkyl substances (PFASs) was determined from triplicate surface water samples (n = 45) from five locations (upstream, point-source, downstream; 8 km) over three sampling campaigns from 2018 to 2020. Out of the 42 target PFASs, perfluorocarboxylates (PFCAs: C4–C14), perfluoroalkane sulfonates (PFSAs: C4–C10), and perfluoroalkyl acid precursors (e.g. 6:2 fluorotelomer sulfonate (6:2 FTSA)) were ubiquitously detected in surface waters (concentration ranges: <0.7–3000 ng/L). A significant difference in ΣPFAS concentration was observed at the point-source (mean 5500 ng/L; 95% CI: 4800, 6300) relative to upstream sites (mean 100 ng/L; 95% CI: 90, 110; p ≤ 0.001). The point-source ΣPFAS concentration decreased from 5500 ± 1200 ng/L to 960 ± 42 ng/L (−83%) after two months and to 430 ± 15 ng/L (−98%) two years later. 6:2 FTSA and perfluorooctanesulfonate (PFOS) dominated in surface water, representing on average 31% and 20% of the ΣPFAS, respectively. Emerging PFASs including a cyclic perfluoroalkanesulfonate (PFECHS) and a C4 perfluoroalkane sulfonamide (FBSA) were repeatedly present in surface water (concentration ranges <0.3–77 ng/L). According to the updated Australian PFAS guidelines for ecological conservation, the water samples collected at the time of monitoring may have posed a short-term risk to aquatic organisms in regard to PFOS levels. These results illustrate that acute high dose exposure to PFASs can result from industrial fires at sites storing or stockpiling PFAS-based waste products. Continued monitoring will be crucial to evaluate potential long-term risk to wildlife in the region.

Recent studies have indicated that Galleria mellonella larvae ingest polyethylene films and the degradation mechanism could inspire biotechnological exploitation for degrading plastic to eliminate global pollution from plastic waste. In this study, we tested the chemical compositions of masticated and ingested different plastic types by G. mellonella. High throughput sequencing of 16S rRNA gene was used to characterize the alteration of the microbial communities derived from salivary glands, gut contents and whole G. mellonella larvae. Our results indicated that G. mellonella is able to masticate polyethylene (PE), expanded polystyrene (EPS) and polypropylene (PP) and convert it to small particles with very large and chemically modified surfaces. The characteristics of the polymer affect the rate of damage. Formation of functional carbonyl groups on the appearance of oxidized metabolic intermediates of polyolefins in the frass samples observed. We found that the mastication of EPS, PP or PE could significantly alter the microbial composition in the gut content while it did not appear to influence the salivary glands microbial community. Representatives of Desulfovibrio vulgaris and Enterobacter grew with the PE diet while mastication of polystyrene and polypropylene increased the abundance of Enterococcus. The evaluation of bacterial communities in whole larvae confirmed the obtained result and additionally showed that the abundance of Paenibacillus, Corynebacterium and Commamonadaceae increased by Styrofoam (EPS) consumption.

The Central Plains Economic Region (CPER) located along the transport path to the Beijing-Tianjin-Hebei area has experienced severe PM₂.₅ pollution in recent years. However, few modeling studies have been performed on the sources of PM₂.₅, especially the impacts of emission reduction strategies. In this study, the Nested Air Quality Prediction Model System (NAQPMS) with an online tracer-tagging module was adopted to investigate source sectors of PM₂.₅ and a series of sensitivity tests were conducted to investigate the impacts of different sector-based mitigation strategies on PM₂.₅ pollution. The response surfaces of pollutants to sector-based emission changes were built. The results showed that resident-related sector (resident and agriculture), fugitive dust, traffic and industry emissions were the main sources of PM₂.₅ in Zhengzhou, contributing 49%, 19%, 15% and 13%, respectively. Response surfaces of pollutants to sector-based emission changes in Henan revealed that the combined reduction of resident-related sector and industry emissions efficiently decreased PM₂.₅ in Zhengzhou. However, reduced emissions in only the Henan region barely satisfied the national air quality standard of 75 μg/m³, whereas a 50%–60% reduction in resident-related sector and industry emissions over the whole region could reach this goal. On severely polluted days, even a 60% reduction in these two sectors over the whole region was insufficient to satisfy the standard of 75 μg/m³. Moreover, a reduction in traffic emissions resulted in an increase in the O₃ concentration. The results of the response surface method showed that PM₂.₅ in Zhengzhou decreased by 19% in response to the COVID-19 lockdown, which approached the observed reduction of 21%, indicating that the response surface method could be employed to study the impacts of the COVID-19 lockdown on air pollution. This study provides a scientific reference for the formulation of pollution mitigation strategies in the CPER.

High delicate particulate matter (PM₂.₅) concentration can seriously reduce air quality, destroy the environment, and even jeopardize human health. Accordingly, accurate prediction for PM₂.₅ plays a vital role in taking precautions against upcoming air ambient pollution incidents. However, due to the disturbance of seasonal and nonlinear characteristics in the raw series, pronounced forecasts are confronted with tremendous handicaps, even though for seasonal grey prediction models in the preceding researches. A novel seasonal nonlinear grey model is initially designed to address such issues by integrating the seasonal adjustment factor, the conventional Weibull Bernoulli grey model, and the cultural algorithm, simultaneously depicting the seasonality and nonlinearity of the original data. Experimental results from PM₂.₅ forecasting of four major cities (Shanghai, Nanjing, Hangzhou, and Hefei) in the YRD validate that the proposed model can obtain more accurate predictive results and stronger robustness, in comparison with grey prediction models (SNGBM(1,1) and SGM(1,1)), conventional econometric technology (SARIMA), and machine learning methods (LSSVM and BPNN) by employing accuracy levels. Finally, the future PM₂.₅ concentration is forecasted from 2020 to 2022 using the proposed model, which provides early warning information for policy-makers to develop PM₂.₅ alleviation strategies.

Nitrocompounds are the major prime water contaminants. In this investigative study, toxic nitrocompounds (4-nitrophenol, 2,4-dinitrophenol, 2,4,6-trinitrophenol) were removed by using magnetic CuFe₂O₄, CoFe₂O₄, and NiFe₂O₄ material systems. The metal ferrites were synthesized through hydrothermal method and also followed with calcination process. The properties of metal ferrites were confirmed through using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS) and field emission scanning electron microscopy (FE-SEM) studies and results there on were presented. For the first time, the synthesized CuFe₂O₄, CoFe₂O₄, and NiFe₂O₄ material systems were used for the reduction of 4-nitrophenol (NP), 2,4-dinitrophenol (DNP), and 2,4,6-trinitrophenol (TNP) in aqueous medium. The UV–visible spectrometry was employed to monitor the removal of nitro compounds and formation of aminophenol. Among, the three catalysts, the CuFe₂O₄ displayed excellent removal activity for nitrocompounds. The CuFe₂O₄ nanoparticles completely removed the NP, DNP and TNP within 2, 5, 10 min, respectively. The NP reduction reaction follows the pseudo-first-order kinetics. Further, the investigated and proposed CuFe₂O₄, catalyst has given and demonstrated excellent kinetic rate constants 0.990, 0.317, 0.184 min⁻¹ for 4-NP, DNP and TNP respectively, which was very fast kinetic than the already published reports. Also, the aminophenol formation was confirmed for the above mentioned and select nitrocompounds. The obtained results confirm suggest that CuFe₂O₄ nanoparticles based material system could be one of the promising catalysts for nitro compounds removal process.

Marine bivalves are bioindicators of coastal environmental pollution, integrating monitoring programs worldwide. Nonetheless, the choice of particular species as an indicator requires validation, achieved by understanding the differences in element concentrations among and within species. The present study compares the chemical composition of whole body soft tissues of four common bivalve species from the Bijagós Archipelago, a pristine region of West Africa. Significant differences were recorded in the concentrations of various elements among studied species, which likely arise from species-specific uptake and bioaccumulation processes. Overall, there was a segregation between a group including the bloody cockle Senilia senilis and oyster Crassostrea tulipa (with high Cd and Zn concentrations) versus the two other species, Austromacoma nymphalis (with low Cu and high Mn, Se, Hg, Pb concentrations) and Diplodonta spp. (with high values of Cu, Al, Fe, V, Cr, Hg, Pb). C. tulipa was sampled in two different substrates (rock beds and mangrove roots), and the two groups revealed different chemical profiles, with significantly higher concentrations of P, Si, Zn and Cr and lower Cu in specimens fixed in mangrove roots. These results strongly suggest the influence of small-scale environmental variability on the accumulation of particular elements. We found extremely high Cd concentrations in S. senilis (27.1 ± 7.53 μg g⁻¹ DW) and identified C. tulipa as another high Cd-accumulating species (ca. 10 μg g⁻¹ DW). Our results suggest a detoxifying mechanism linked with the presence of Se to reduce the potential toxic effects of Cd in these two species. Cadmium concentrations reported for some bivalve species in this area largely exceed the maximum values proposed by the European Union, emphasizing the need for a regular contamination assessment.

Antibiotics are commonly used in livestock and poultry breeding along with organic arsenic. Through long-term accumulation, they can enter into the surrounding soil through various pathways and contaminate the soil. In this paper, tetracycline antibiotics (TCs) and roxarsone (ROX) contaminated soil were used as the representatives of the two kinds of veterinary drugs contaminated soil, respectively, to study the thermal desorption behavior and arsenic stabilization process. Different parameters like heating temperatures, heat duration, stabilizer type and dosage were optimized for effective removal of TCs and ROX. Furthermore, TCs and ROX removal path and ROX stabilization mechanism were explored. Results of the study showed that over 98% of tetracycline antibiotics and roxarsone were effectively removed at 300 °C for 60 min. The heat treatment process of TCs contaminated soil was controlled by the first-order kinetics. Based on the detection of degradation products and thermogravimetric analysis, the possible thermal degradation path of TCs and ROX was proposed. Addition of FeSO₄.7H₂O (10% by weight) as stabilizer during the heat treatment process yielded 96.7% stabilization rate. Through the analysis of arsenic fractions, valence and the characterization of soil samples collected after the heat treatment, mechanism of arsenic stabilization in ROX was explored. The results show that thermal treatment combined with chemical stabilization technology can not only degrade TCs and ROX efficiently and completely, but also convert organic arsenic into inorganic state, which is conducive to better stabilization, and finally achieve effective and safe remediation of this kind of contaminated soil.