High-density samples are usually a prerequisite for obtaining high-precision source apportionment results in large-scale areas. In-situ field portable X-ray fluorescence spectrometry (FPXRF) is a fast and cheap way to increase the sample size of soil heavy metals (HMs). Moreover, categorical land-use types may be closely associated with source contributions. However, the above information has rarely been incorporated into the source apportionment. In this study, robust geographically weighted regression (RGWR) was first used to correct the spatially varying effect of the related soil factors (e.g., soil water and soil organic matter) on in-situ FPXRF in an urban-rural fringe of Wuhan City, China, and the correction accuracy of RGWR was compared with those of the traditionally non-spatial multiple linear regression (MLR) and basic GWR. Then, the effect of land-use types on HM concentrations was partitioned using analysis of variance (ANOVA). Last, based on the robust spatial receptor model (i.e., robust absolute principal component scores/RGWR [RAPCS/RGWR]), this study proposed RAPCS/RGWR with categorical land-use types and RGWR-corrected in-situ FPXRF data (RAPCS/RGWR_LU&FPXRF), and its performance was compared with those of RAPCS/RGWR with none or one kind of auxiliary data. Results showed that (i) the performances of the correction models for in-situ FPXRF data were in the order of RGWR > GWR > MLR, and the relative improvement of RGWR over MLR ranged from 52.6% to 70.71% for each HM; (ii) categorical land-use types significantly affected the concentrations of soil Zn, Cu, As, and Pb; (iii) the highest estimation accuracy for source contributions was obtained by RAPCS/RGWR_LU&FPXRF, and the lowest estimation accuracy was obtained by basic RAPCS/RGWR. It is concluded that land-use types and RGWR-corrected in-situ FPXRF data are closely associated with the source contribution, and RAPCS/RGWR_LU&FPXRF is a cost-effective source apportionment method for soil HMs in large-scale areas.

The oxidative potential (OP) of atmospheric fine particulate matter (PM₂.₅) has been linked to organic content, which includes polycyclic aromatic hydrocarbons (PAHs). The OP of 135 individual PAHs (including six subclasses) was measured using the dithiolthreitol (DTT) consumption assay. The DTT assay results were used to compute the concentration of each PAH needed to consume 50% of the DTT concentration in the assay (DTT₅₀), and the reduction potential of the PAHs (ΔGᵣₓₙ). Computed reduction potential results were found to match literature reduction potential values (r² = 0.97), while DTT₅₀ results had no correlations with the computed ΔGᵣₓₙ values (r² < 0.1). The GINI equality index was used to assess the electron distribution across the surface of unreacted and reacted PAHs. GINI values correlated with ΔGᵣₓₙ in UPAH, HPAH, and OHPAH subclasses, as well as with all 135 PAHs in this study but did not correlate with DTT₅₀, indicating that electron dispersion is linked to thermodynamic reactions and structural differences in PAHs, but not linked to the OP of PAHs. Three ambient PM₂.₅ filters extracts were measured in the DTT assay, alongside mixtures of analytical standards prepared to match PAH concentrations in the filter extracts to test if the OP follows an additive model of toxicity. The additive prediction model did not accurately predict the DTT consumption in the assay for any of the prepared standard mixtures or ambient PM₂.₅ filter extracts, indicating a much more complex model of toxicity for the OP of PAHs in ambient PM₂.₅. This study combined computed molecular properties with toxicologically relevant assay results to probe the OP of anthropogenically driven portions of ambient PM₂.₅, and results in a better understanding of the complexity of ambient PM₂.₅ OP.

In this study, a novel biosorbent is prepared from the pods of Bauhinia variegata is used for defluoridation of the fluoride contaminated water. It is an eco-friendly and economically feasible material. Comparison of adsorption capacity of Physically Treated Bauhinia (PTB) and Chemically Treated Bauhinia (CTB) are carried in this work. Characterization studies like SEM, EDS, FTIR, and XRD are executed to analyze surface morphology and functional groups in PTB and CTB. The experimental procedure was implemented in a batch process where the operating constraints such as dosage, pH, initial fluoride concentration, time, and temperature are varied to attain optimized efficiency. PTB and CTB yield an adsorption capacities of 10.90 mg/g and 15.45 mg/g respectively in the batch process. PTB adheres fluoride in monolayer formation whereas CTB forms multilayer adsorption. The adsorption process was described by the Pseudo first-order model to state the mechanism of physisorption. The negative values of thermodynamic parameters indicate spontaneity and favorable conditions for adsorption process. As CTB has a higher adsorption capacity than PTB, the batch study has been extended to column adsorption. Bed depth, initial fluoride concentration, and flow rate are the experimental variables used to acquire breakthrough curves. Simplified column models like Adam-Bohart, Thomas, and Yoon-Nelson models were analyzed. In column studies, Yoon-Nelson model fitted well in describing the process of adsorption. The maximum adsorption capacity acquired during the column process was found to be 1.176 mg/g with a bed depth of 5 cm and a flow rate of 5 ml/min. Thus, the innocuous and sustainable adsorbent is developed and serves as an excellent defluoridation agent.

Urban Heat Island (UHI) is posing a significant challenge due to growing urbanisations across the world. Green infrastructure (GI) is popularly used for mitigating the impact of UHI, but knowledge on their optimal use is yet evolving. The UHI effect for large cities have received substantial attention previously. However, the corresponding effect is mostly unknown for towns, where appreciable parts of the population live, in Europe and elsewhere. Therefore, we analysed the possible impact of three vegetation types on UHI under numerous scenarios: baseline/current GI cover (BGI); hypothetical scenario without GI cover (HGI-No); three alternative hypothetical scenarios considering maximum green roofs (HGR-Max), grasslands (HG-Max) and trees (HT-Max) using a dispersion model ADMS-Temperature and Humidity model (ADMS-TH), taking a UK town (Guildford) as a case study area. Differences in an ambient temperature between three different landforms (central urban area, an urban park, and suburban residential area) were also explored. Under all scenarios, the night-time (0200 h; local time) showed a higher temperature increase, up to 1.315 °C due to the lowest atmospheric temperature. The highest average temperature perturbation (change in ambient temperature) was 0.563 °C under HGI-No scenario, followed by HG-Max (0.400 °C), BGI (0.343 °C), HGR-Max (0.326 °C) and HT-Max (0.277 °C). Furthermore, the central urban area experienced a 0.371 °C and 0.401 °C higher ambient temperature compared with its nearby suburban residential area and urban park, respectively. The results allow to conclude that temperature perturbations in urban environments are highly dependent on the type of GI, anthropogenic heat sources (buildings and vehicles) and the percentage of land covered by GI. Among all other forms of GI, trees were the best-suited GI which can play a viable role in reducing the UHI. Green roofs can act as an additional mitigation measure for the reduction of UHI at city scale if large areas are covered.

Source apportionment can be an effective tool in mitigating soil pollution but its efficacy is often limited by a lack of information on the factors that influence the accumulation of pollutants at a site. In response to this limitation and focusing on a suite of heavy metals identified as priorities for pollution control, the study established a comprehensive pollution control framework using factor identification coupled with spatial agglomeration for agricultural soils in an industrialized part of Zhejiang Province, China. In addition to elucidating the key role of industrial and traffic activities on heavy metal accumulation through implementing a receptor model, specific influencing factors were identified using a random forest model. The distance from the soil sample location to the nearest likely industrial source was the most important factor in determining cadmium and copper concentrations, while distance to the nearest road was more important for lead and zinc pollution. Soil parent materials, pH, organic matter, and clay particle size were the key factors influencing accumulation of arsenic, chromium, and nickel. Spatial auto-correlation between levels of soil metal pollution and industrial agglomeration can enable a more targeted approach to pollution control measures. Overall, the approach and results provide a basis for improved accuracy in source apportionment, and thus improved soil pollution control, at the regional scale.

Organic fraction municipal solid waste (OFMSW) has a high potential for energy and value-added product recovery due to its carbon- and nutrient-rich composition; however, traditional value chains have treated OFMSW as an undesired by-product. This study focuses on value chain optimisation to assist the transition to resource recovery value chains. To achieve this, this work combined two stage stochastic mathematical optimisation with geographical spatial analysis and time series waste generation analysis. Existing infrastructure in England, including anaerobic digestion plants and road transportation networks, were included in the model. To account for uncertainty in waste generation, multiple scenarios and their associated probabilities were developed based on environmental variables. The optimisation problem was solved to further advance the understanding of economically optimal waste-to-resource value chains under waste generation variability. The pertinent decision variables included sizing, technology selection, waste flows and location of thermochemical treatment sites. The model highlights the potential reduction in system profitability as a result of different operating constraints, such as minimum plant operating capacity factors and landfill taxation. The latter was shown to have the largest impact on profitability as overconservative systems designs were implemented to hedge against the waste variability. Such computer-aided models offer opportunities to overcome the challenges posed by waste generation variability and waste to resource value chain transformation.

The vadose zone is the first natural layer preventing groundwater pollution. Understanding virus transport and retention in the vadose zone is necessary. The effects of different interfaces and mechanisms on virus transport and retention were investigated by studying Escherichia coli phage migration in laboratory-scale columns under unsaturated conditions. The E. coli phage was used as a model virus. Colloid filtration theory, extended Derjagin–Landau–Verwey–Overbeek theory and two−site kinetic deposition model were used to calculate fitted parameters and interaction energies to assess virus retention at different interfaces. The collector diameters and the size of E. coli phages in the influent and effluent were compared to assess the effect of straining. The results indicated that the roles of solid–water interfaces (SWIs) and air–water interfaces (AWIs) in retaining E. coli phages are strongly controlled by the moisture content and hydrochemical conditions. Decreasing the moisture content and increasing the ionic strength (IS) of the suspension increased E. coli phage retention. At suspension ISs of 0.01 or 0.03 M and various moisture contents, E. coli phages were mainly retained at the SWIs rather than AWIs. When the IS was increased to 0.06 M, the viruses were strongly retained by becoming attached to both SWIs and AWIs. The role of straining in virus retention could not be ignored. Viruses were retained more at the SWIs and less straining occurred under acidic conditions than under neutral or alkaline conditions. This was mainly because of the effects of the pH and IS on surface charges and the model virus particle size. This study has important implications for modeling and predicting virus transport in soil affected by rainfall, snowmelt, and human activities (e.g., irrigation and artificial groundwater recharging).

The present paper reports on the interrelationships of fish, parasites and the bioaccumulation of hazardous organic compounds in the Zemplínska Šírava water reservoir in eastern Slovakia, which is heavily polluted with polychlorinated biphenyls (PCBs). The concentrations of these contaminants were measured in various fish matrices (dorsal and abdominal muscle tissues, hepatopancreas, intestine wall and adipose tissue) of the freshwater bream, Abramis brama (Cyprinidae), and in its intestinal parasite Caryophyllaeus laticeps (Cestoda), which was used for the first time as a model for a PCB bioaccumulation study. Regarding the fish, the highest concentrations of PCBs were found in the intestine, followed by hepatopancreas and muscle tissues. The amounts of PCBs were higher in abdominal muscles than in their dorsal parts. Concentrations of ∑PCBs above the limits set by European regulations were detected in both muscle parts in the fish, confirming the persistent unfavorable conditions in this locality and high risk for biota and humans. Based on bioconcentration factor values (BCFs), PCBs reached much higher levels in cestodes compared to bream matrices. Some significant differences in PCB amounts between infected and uninfected bream were determined. Fulton's condition factor (CF) significantly differed in infected and non-infected fish (p ˂ 0.05), with CF values surprisingly lower in fish free of parasites compared to parasitized fish, which suggests a “mutualistic” relationship between the parasite and its host.

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.

Tropospheric ozone is a dangerous atmospheric pollutant for forest ecosystems when it penetrates stomata. Thresholds for ozone-risk assessment are based on accumulated stomatal ozone fluxes such as the Phytotoxic Ozone Dose (POD). In order to identify the effect of ozone on a Holm oak forest in central Italy, four flux-based ozone impact response functions were implemented and tested in a multi-layer canopy model AIRTREE and evaluated against Gross Primary Productivity (GPP) obtained from observations of Eddy Covariance fluxes of CO₂. To evaluate if a clear phytotoxic threshold exists and if it changes during the year, six different detoxifying thresholds ranging between 0 and 5 nmol O₃ m⁻² s⁻¹ were tested.The use of species-specific rather than more general response functions based on plant functional types (PFT) increased model accuracy (RMSE reduced by up to 8.5%). In the case of linear response functions, a threshold of 1 nmol m⁻² s⁻² produced the best results for simulations of the whole year, although the tolerance to ozone changed seasonally, with higher tolerance (5 nmol m⁻² s⁻¹ or no ozone impact) for Winter and Spring and lower thresholds in Summer and Fall (0–1 nmol m⁻² s⁻¹). A “dynamic threshold” obtained by extracting the best daily threshold values from a range of different simulations helped reduce model overestimation of GPP by 213 g C m⁻² y⁻¹ and reduce RMSE up to 7.7%. Finally, a nonlinear ozone correction based on manipulative experiments produced the best results when no detoxifying threshold was applied (0 nmol O₃ m⁻² s⁻¹), suggesting that nonlinear functions fully account for ozone detoxification. The evidence of seasonal changes in ozone tolerance points to the need for seasonal thresholds to predict ozone damage and highlights the importance of performing more species-specific manipulative experiments to derive response functions for a broad range of plant species.