In recent decades, microplastic (MP) pollution has become a severe problem in aquatic environments. Yet the behavioral and selective responses of fish toward different types of MPs remain unclear. We therefore conducted laboratory-based video observations to investigate the behavioral responses of hybrid grouper juveniles (tiger grouper Epinephelus fuscoguttatus♀ × giant grouper E. lanceolatus♂) to eight different types of MPs. We observed four distinct feeding behaviors: (i) normal ingestion of MPs, which rarely occurred (0%–6%); (ii) pursuit, capture, and tasting of MPs, after which MPs were quickly spat out; (iii) detection and rejection of MPs without attack; and (iv) no significant response to MPs. Our results indicate that juveniles can distinguish MPs as inedible particle and behave differently between MPs with different sizes, colors, and materials, primarily using visual and gustatory senses. Notably, 50%–90% of MP rejection events occurred before capture. Juveniles spent double the time evaluating large nylon particles than they did evaluating large polyvinyl chloride particles before capture, but half the time tasting after capture. Although we observed no sub-lethal or lethal effects of MPs, we conclude that the presence of MPs can still have an impact on groupers in aquaculture. For instance, in the densely stocked conditions of an aquaculture unit, the fish could lose visibility and can inadvertently ingest MPs, thus suffering from their toxic impacts.

Ammonium molybdophosphate (AMP) exhibits high selectivity towards Cs but it cannot be directly applied in column packing, so it is necessary to prepare AMP–based adsorbents into an available form to improve its practicality. This work provided two AMP immobilized cellulose microspheres (MCC@AMP and MCC-g-AMP) as adsorbents for Cs removal by radiation grafting technique. MCC-g-AMP was prepared by radiation graft polymerization of GMA on microcrystalline cellulose microspheres (MCC) followed by reaction with AMP suspension, and MCC@AMP was synthesized by radiation hybrid grafting of AMP and GMA onto MCC through one step. The different structures and morphologies of two adsorbents were characterized by FTIR and SEM. The adsorption properties of two adsorbents against Cs were investigated and compared in batch and column experiments under different conditions. Both adsorbents were better obeyed pseudo-second-order kinetic model and Langmuir model. MCC-g-AMP presented faster adsorption kinetic and more stable structure, whereas MCC@AMP presented more facile synthesis and higher adsorption capacity. MCC@AMP was pH independent in the range of pH 1–12 but MCC-g-AMP was sensitive to pH for Cs removal. The saturated column adsorption capacities of MCC@AMP and MCC-g-AMP were 5.4 g-Cs/L-ad and 0.75 g-Cs/L-ad in column adsorption experiments at SV 10 h⁻¹. Both adsorbents exhibited very high radiation stability and can maintain an adsorption capacity of >85% even after 1000 kGy γ-irradiation. On the basis, two AMP-loaded adsorbents possessed promising application in removal of Cs from actual contaminated groundwater. These findings provided two efficient adsorbents for treatment of Cs in radioactive waste disposal.

We propose a methodology to estimate single and multiple emission sources of atmospheric contaminants. It combines hybrid metaheuristic/gradient-descent optimization techniques and Tikhonov-type regularization. The dispersion problem is solved by the Galerkin/Least-squares finite element formulation, which allows more realistic modeling. The accuracy of the proposed inversion model is tested under different contexts with experimental data. To identify single and multiple emissions, we use experimental field data. We consider different configurations for both the Tikhonov-type functional and optimization techniques. Several single and composite data misfit functions are tested. We also use a discrepancy-based choice rule for the regularization parameter. The resulting inversion tool is highly versatile and presents accurate results under different contexts with a competitive computational cost.

Although anthropogenic emissions decreased, polluted days still occurred in the Beijing–Tianjin–Hebei (BTH) region during the initial outbreak of the coronavirus disease (COVID-19). Analysis of the characteristics and source distribution of large-scale air pollution episodes during the COVID-19 outbreak (from 23 January to April 8, 2020) in the BTH region is helpful for exploring the efficacy of control measures and policy making. The results indicated that the BTH region suffered two large-scale air pollution episodes (23–28 January and 8–13 February), which were characterized by elevated PM₂.₅, SO₂, NO₂, and CO concentrations, while the O₃ concentration decreased by 1.5%–33.9% (except in Shijiazhuang, where it increased by 16.6% during the second episode). These large-scale air pollution episodes were dominated by unfavorable meteorological conditions comprising a low wind speed and increased relative humidity. The transport pathways and source distribution were explored using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT), potential source contribution function (PSCF), and concentration weighted trajectory (CWT) models. The air pollution in the BTH region was mainly affected by local emission sources during the first episode, which contributed 51.6%–60.6% of the total trajectories in the BTH region with a PM₂.₅ concentration ranging from 146.2 μg/m³ to 196.7 μg/m³. The short-distance air masses from the southern and southwestern areas of the BTH region were the main transport pathways of airflow arriving in the BTH region during the second episode. These contributed 51.9%–57.9% of the total trajectories and originated in Hebei, Henan, central Shanxi, and Shaanxi provinces, which were the areas contributing the most to the PM₂.₅ level and exhibited the highest PSCF and CWT values. Therefore, on the basis of local emission reduction, enhancing regional environmental cooperation and implementing a united prevention and control of air pollution are effective mitigation measures for the BTH region.

The emergence of omics approaches in environmental research has enhanced our understanding of the mechanisms underlying toxicity; however, extrapolation from molecular effects to whole-organism and population level outcomes remains a considerable challenge. Using environmentally relevant, sublethal, concentrations of two metals (Cu and Ni), both singly and in binary mixtures, we integrated data from traditional chronic, partial life-cycle toxicity testing and metabolomics to generate a statistical model that was predictive of reproductive impairment in a Daphnia pulex-pulicaria hybrid that was isolated from an historically metal-stressed lake. Furthermore, we determined that the metabolic profiles of organisms exposed in a separate acute assay were also predictive of impaired reproduction following metal exposure. Thus we were able to directly associate molecular profiles to a key population response – reproduction, a key step towards improving environmental risk assessment and management.

Antimony (Sb), as a toxic metalloid, has been gaining increasing research concerns due mainly to its severe pollution in many places. Rice has been identified to be the dominant intake route of Sb by residents close to the Sb mining areas. A hydroponic experiment was conducted to investigate the difference in uptake, translocation and transformation of Sb in rice seedlings of four cultivars exposed to 0.2 or 1.0 mg/L of Sb(V). The results showed that mass concentration of iron plaque (mg/kg FW) formed at the root surfaces of cultivar N was the highest among all tested cultivars at both low and high exposure levels of Sb(V). The accumulated Sb concentration in iron plaque significantly increased with an increase in mass concentration of iron plaque formed at the rice root. The total amount of iron plaque (mg/pot) at rice root generally increased with increasing exposed Sb(V) concentration, which was closely associated with the increasing lipid peroxidation in roots. Concentration percentage of Sb in rice root significantly reduced as the corresponding value in the iron plaque increased, suggesting that iron plaque formation strongly suppressed uptake of Sb by rice root. Sb concentration in rice tissues followed an order: root > stem, leaf. The japonica rice (cultivars N and Z) exhibited a stronger translocation tendency of Sb from root to stem than indica hybrid rice (cultivars F and G). Translocation of Sb from root of cultivar F to its stem and leaf was sharply enhanced with increasing Sb exposure concentration. Sb(V) could be reduced to Sb(III) in rice tissues, especially in stems (10–26% of the total Sb). For the sake of food safety, the difference in uptake, translocation and transformation of Sb in rice species planted in Sb-contaminated soils should be taken into consideration.

Road traffic contributes considerably to ground-level air pollution and is therefore likely to affect roadside ecosystems. Differences in growth and leaf traits among 13 hybrid aspen (Populus tremula×P. tremuloides) clones were studied in relation to distance from a motorway. The trees sampled were growing 15 and 30m from a motorway and at a background rural site in southern Finland. Litter decomposition was also measured at both the roadside and rural sites. Height and diameter growth rate and specific leaf area were lowest, and epicuticular wax amount highest in trees growing 15m from the motorway. Although no significant distance×clone interactions were detected, clone-based analyses indicated differences in genotypic responses to motorway proximity. Leaf N concentration did not differ with distance from the motorway for any of the clones. Leaf litter decomposition was only temporarily retarded in the roadside environment, suggesting minor effects on nutrient cycling.

Phosphor imager autoradiography is a technique for rapid, sensitive analysis of the localization of xenobiotics in plant tissues. Use of this technique is relatively new to research in the field of plant science, and the potential for enhancing visualization and understanding of plant uptake and transport of xenobiotics remains largely untapped. Phosphor imager autoradiography is used to investigate the uptake and translocation of the explosives 1,3,5-trinitro-1,3,5-triazine (RDX) and 2,4,6-trinitrotoluene within Populus deltoides × nigra DN34 (poplar) and Panicum vigratum Alamo (switchgrass). In both plant types, TNT and/or TNT-metabolites remain predominantly in root tissues while RDX and/or RDX-metabolites are readily translocated to leaf tissues. Phosphor imager autoradiography is further investigated for use in semi-quantitative analysis of uptake of TNT by switchgrass. Phosphor imager autoradiography allows for rapid localization and quantification of RDX, TNT, and/or metabolites in plant tissues.

The objective of this paper was to incorporate source-meteorological interaction information from two commonly employed atmospheric dispersion models into the land use regression technique for predicting ambient nitrogen dioxide (NO₂), sulphur dioxide (SO₂), and particulate matter (PM₁₀). The study was undertaken across two regions in Durban, South Africa, one with a high industrial profile and a nearby harbour, and the other with a primarily commercial and residential profile. Multiple hybrid models were developed by integrating air pollution dispersion modelling predictions for source specific NO₂, SO₂, and PM₁₀ concentrations into LUR models following the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to characterise exposure, in Durban. Industrial point sources, ship emissions, domestic fuel burning, and vehicle emissions were key emission sources. Standard linear regression was used to develop annual, summer and winter hybrid models to predict air pollutant concentrations. Higher levels of NO₂ and SO₂ were predicted in south Durban as compared to north Durban as these are industrial related pollutants. Slightly higher levels of PM₁₀ were predicted in north Durban as compared to south Durban and can be attributed to either traffic, bush burning or domestic fuel burning. The hybrid NO₂ models for annual, summer and winter explained 60%, 58% and 63%, respectively, of the variance with traffic, population and harbour being identified as important predictors. The SO₂ models were less robust with lower R² annual (44%), summer (53%) and winter (46%), in which industrial and traffic variables emerged as important predictors. The R² for PM₁₀ models ranged from 80% to 85% with population and urban land use type emerging as predictor variables.

Ambient fine particulate matter (PM₂.₅) has been ranked as the sixth leading risk factor globally for death and disability. Modelling methods based on having access to a limited number of monitor stations are required for capturing PM₂.₅ spatial and temporal continuous variations with a sufficient resolution. This study utilized a land use regression (LUR) model with machine learning to assess the spatial-temporal variability of PM₂.₅. Daily average PM₂.₅ data was collected from 73 fixed air quality monitoring stations that belonged to the Taiwan EPA on the main island of Taiwan. Nearly 280,000 observations from 2006 to 2016 were used for the analysis. Several datasets were collected to determine spatial predictor variables, including the EPA environmental resources dataset, a meteorological dataset, a land-use inventory, a landmark dataset, a digital road network map, a digital terrain model, MODIS Normalized Difference Vegetation Index (NDVI) database, and a power plant distribution dataset. First, conventional LUR and Hybrid Kriging-LUR were utilized to identify the important predictor variables. Then, deep neural network, random forest, and XGBoost algorithms were used to fit the prediction model based on the variables selected by the LUR models. Data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were used to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 58% and 89% of PM₂.₅ variations, respectively. When XGBoost algorithm was incorporated, the explanatory power of the models increased to 73% and 94%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed the other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm for estimating the spatial-temporal variability of PM₂.₅ exposures.