We investigated the competitive effects of different fractions of wastewater treatment plant effluent organic matter (EfOM) on adsorption of an organic micro pollutant (OMP), propranolol (PRO), in a fixed bed column packed with magnetic tyre char (MTC). The results showed that the presence of EfOM inhibited PRO adsorption in wastewater leading to decreased PRO adsorption capacity from 5.86 to 2.03 mg/g due to competitive effects and pore blockage by smaller EfOM fractions. Characterization of EfOM using size exclusion chromatography (LC-OCD) showed that the principal factor controlling EfOM adsorption was pore size distribution. Low molecular weight neutrals had the highest adsorption onto MTC while humic substances were the least interfering fraction. Effect of important parameters such as contact time, linear velocity and bed height/diameter ratio on MTC performance was studied in large-lab scale columns. Linear velocity and contact time were found to be effective in increasing adsorption capacity of PRO on MTC and delaying breakthrough time. Increase in linear velocity from 0.64 cm/min to 1.29 cm/min increased mass transfer and dispersion, resulting in considerable rise of adsorbed amount (5.86 mg/g to 22.58 mg/g) and increase in breakthrough time (15.8–62.7 h). Efficiency of non-equilibrium Hydrus model considering dispersion and mass transfer mechanism was demonstrated for real wastewater and scale up purposes. Ball milling for degradation of adsorbed PRO and regeneration of MTC resulted in 79% degradation of PRO was achieved after 5 h milling (550 rpm), while the addition of quartz sand increased the efficiency to 92%.

The objective of this study was to demonstrate the feasibility and the relevance of combining biochar with the Fenton process for the simultaneous improvement of polycyclic aromatic hydrocarbons (PAHs) degradation and immobilization of heavy metals (HMs) in real soil remediation processes at circumneutral pH. The evaluation of PAHs degradation results was performed through multivariate statistical tools, including principal component analysis (PCA) and partial least squares (PLS). PCA showed that the level of biochar amendment decisively affected the degree of degradation of total PAHs, highlighting the role of biochar in catalyzing the Fenton reaction. Moreover, the PLS model was used to interpret the important features of each PAH's physico-chemical properties and its correlation to degradation efficiency. The electron affinity of PAHs correlated positively with the degradation efficiency only if the level of biochar amendment sat at 5%, explained by the ability of biochar to transfer the electrons to PAHs, improving the Fenton-like degradation. Moreover, the addition of biochar reduced the mobilization of HMs by their fixation on their surface, reducing the Fenton-induced metal leaching from the destruction of metal-organic complexes. In overall, these results on the high immobilization rate of HMs accompanied with additional moderate PAHs degradation highlighted the advantages of using a biochar-assisted Fenton-like reaction for sustainable remediation of technogenic soil.

The volatilities of ambient organic aerosol (OA) components are important to forecasting OA formation with models. However, providing the OA volatility distribution inputs for models is challenging, and models often rely on measurements from chamber experiments. We measured the volatility of submicron ambient OA in Seoul during May/June of 2019 by connecting a thermodenuder to an Aerodyne Time-of-Flight Aerosol Mass Spectrometer (AMS). We calculated a volatility basis set (VBS) of the organic aerosol with a thermodenuder mass transfer model and data from the thermodenuder set to various temperatures (30–200 °C). We found a large discrepancy between the measured ambient VBS and a reference VBS used in air quality models, with the ambient organics being less volatile. The results suggest that a modeling study that tries to account for this discrepancy may be needed to identify the impact it has on modeling outcomes. Chamber experiments aiming to determine VBSs for specific chemical systems should address limitations caused by wall losses and incomplete modeling parameters.

Particulate Matter (PM₂.₅) pollution in China has been a primary concern for public health in recent years, which requires banks to appropriately control their credit supply to industries with high pollution, high energy consumption, and surplus capacity. For this reason, this paper examines economic determinants of PM₂.₅ concentrations and incorporates the spatial spillover effect of bank credit by employing the spatial Durbin model (SDM) under the stochastic impacts by regression on population, affluence and technology framework. Using China's provincial dataset from 1998 to 2016, the main findings are as follows: First, there is evidence in support of spatial dependence of PM₂.₅ concentrations and their inverted U-shaped relationship with economic growth in China. Second, PM₂.₅ concentrations in a province tend to increase as the level of its own urbanization increases, but they decrease as its own human capital and bank credit increase. Meanwhile, the level of neighboring urbanization positively influences a province's PM₂.₅ concentrations, whereas neighboring population size, industrialization, trade openness, and bank credit present negative impacts. Third, indirect effects of the SDM indicate significant and negative spatial spillover effect of bank credit on PM₂.₅ concentrations. These findings implicate policies on reforming economic growth, urbanization, human capital and bank credit to tackle PM₂.₅ pollution in China from a cross-provincial collaboration perspective.

Exposure to suspended particulate matter (PM), found in the air, is one of the most acute environmental problems that affect the health of modern society. Among the different airborne pollutants, heavy metals (HMs) are particularly relevant because they are bioaccumulated, impairing the functions of living beings. This study aimed to establish a method to predict heavy metal concentrations in leaves and road dust, through their magnetic properties measurements. For this purpose, machine learning, automatic linear regression (MLR), and support vector machine (SVM) were used to establish models for the prediction of airborne heavy metals based on leaves and road dust magnetic properties. Road dust samples and leaves of two common evergreen species (Cupressus lusitanica/Casuarina equisetifolia) were sampled simultaneously during two different years in the Great Metropolitan Area (GMA) of Costa Rica. MLR and SVM algorithms were used to establish the relationship between airborne heavy metal concentrations based on single (χlf) and multiple (χlf y χdf) leaf magnetic properties and road dust. Results showed that Fe, Cu, Cr, V, and Zn concentrations were well-simulated by SVM prediction models, with adjusted R² values ≥ 0.7 in both training and test stages. By contrast, the concentrations of Pb and Ni were not well-simulated, with adjusted R² values < 0.7 in both training and test stages. Heavy metal predicción models using magnetic properties of leaves from Casuarina equisetifolia, as collectors, yielded better prediction results than those based on the leaves of Cupressus lusitanica and road dust, showing relatively higher adjusted R² values and lower errors (MAE and RMSE) in both training and test stages. SVM proved to be the best prediction model with variations between single (χlf) and multiple (χlf y χdf) magnetic properties depending on the element studied.

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.