Machine learning approaches for predicting the performance of stormwater biofilters in heavy metal removal and risk mitigation

Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest

The world's largest mangrove forest (Sundarbans) is facing an imminent threat from heavy metal pollution, posing grave ecological and human health risks. Developing an accurate predictive model for heavy metal content in this area has been challenging. In this study, we used machine learning techniques to model sediment pollution by heavy metals in this vital ecosystem. We collected 199 standardized sediment samples to predict the accumulation of eleven heavy metals using ten different machine learning algorithms. Among them, the extremely randomized tree model exhibited the best performance in predicting Fe (0.87), Cr (0.89), Zn (0.85), Ni (0.83), Cu (0.87), Co (0.62), As (0.68), and V (0.90), achieving notable R2 values. On the other hand, the random forest outperformed for predicting Cd (0.72) and Mn (0.91), whereas the decision tree model showed the best performance for Pb (0.73). The feature attribute analysis identified FeV, CrV, CuZn, CoMn, PbCd, and AsCd relationships resembled with correlation coefficients among them. Based on the established models, the prediction of the contamination factor of metals in sediments showed very high Cd contamination (CF ≥ 6). The Moran's I index for Cd, Cr, Pb, and As were 0.71, 0.81, 0.71, and 0.67, respectively, indicating strong positive spatial autocorrelation and suggesting clustering of similar contamination levels. Conclusively, this research provides a comprehensive framework for predicting heavy metal sediment pollution in the Sundarbans, identifying key areas needing urgent conservation. Our findings support the adoption of integrated management strategies and targeted remedial actions to mitigate the harmful effects of heavy metal contamination in this vital ecosystem.

Mathematical modeling and machine learning-based optimization for enhancing biofiltration efficiency of volatile organic compounds

Biofiltration is a method of pollution management that utilizes a bioreactor containing live material to absorb and destroy pollutants biologically. In this paper, we investigate mathematical models of biofiltration for mixing volatile organic compounds (VOCs) for instance hydrophilic (methanol) and hydrophobic ( α -pinene). The system of nonlinear diffusion equations describes the Michaelis-Menten kinetics of the enzymic chemical reaction. These models represent the chemical oxidation in the gas phase and mass transmission within the air-biofilm junction. Furthermore, for the numerical study of the saturation of α -pinene and methanol in the biofilm and gas state, we have developed an efficient supervised machine learning algorithm based on the architecture of Elman neural networks (ENN). Moreover, the Levenberg-Marquardt (LM) optimization paradigm is used to find the parameters/ neurons involved in the ENN architecture. The approximation to a solutions found by the ENN-LM technique for methanol saturation and α -pinene under variations in different physical parameters are allegorized with the numerical results computed by state-of-the-art techniques. The graphical and statistical illustration of indications of performance relative to the terms of absolute errors, mean absolute deviations, computational complexity, and mean square error validates that our results perfectly describe the real-life situation and can further be used for problems arising in chemical engineering.

Innovative graph neural network approach for predicting soil heavy metal pollution in the Pearl River Basin, China

Predicting soil heavy metal (HM) content is crucial for monitoring soil quality and ensuring ecological health. However, existing methods often neglect the spatial dependency of data. To address this gap, our study introduces a novel graph neural network (GNN) model, Multi-Scale Attention-based Graph Neural Network for Heavy Metal Prediction (MSA-GNN-HMP). The model integrates multi-scale graph convolutional network (MS-GCN) and attention-based GNN (AGNN) to capture spatial relationships. Using surface soil samples from the Pearl River Basin, we evaluate the MSA-GNN-HMP model against four other models. The experimental results show that the MSA-GNN-HMP model has the best predictive performance for Cd and Pb, with a coefficient of determination (R2) of 0.841 for Cd and 0.886 for Pb, and the lowest mean absolute error (MAE) of 0.403 mg kg-1 for Cd and 0.670 mg kg-1 for Pb, as well as the lowest root mean square error (RMSE) of 0.563 mg kg-1for Cd and 0.898 mg kg-1 for Pb. In feature importance analysis, latitude and longitude emerged as key factors influencing the heavy metal content. The spatial distribution prediction trend of heavy metal elements by different prediction methods is basically consistent, with the high-value areas of Cd and Pb respectively distributed in the northwest and northeast of the basin center. However, the MSA-GNN-HMP model demonstrates superior detail representation in spatial prediction. MSA-GNN-HMP model has excellent spatial information representation capabilities and can more accurately predict heavy metal content and spatial distribution, providing a new theoretical basis for monitoring, assessing, and managing soil pollution.

Urban Stormwater Phosphorus Export Control: Comparing Traditional and Low-impact Development Best Management Practices

Data from the International Stormwater Best Management Practices (BMP) Database were used to compare the phosphorus (P) control performance of six categories of stormwater BMPs representing traditional systems (stormwater pond, wetland basin, and detention basin) and low-impact development (LID) systems (bioretention cell, grass swale, and grass strip). Machine learning (ML) models were trained to predict the reduction or enrichment factors of surface runoff concentrations and loadings of total P (TP) and soluble reactive P (SRP) for the different categories of BMP systems. Relative to traditional BMPs, LIDs generally enriched TP and SRP concentrations in stormwater surface outflow and yielded poorer P runoff load control. The SRP concentration reduction and enrichment factors of LIDs also tended to be more sensitive to variations in climate and watershed characteristics. That is, LIDs were more likely to enrich surface runoff SRP concentrations in drier climates, when inflow SRP concentrations were low, and for watersheds exhibiting high impervious land cover. Overall, our results imply that stormwater BMPs do not universally attenuate urban P export and that preferentially implementing LIDs over traditional BMPs may increase TP and SRP export to receiving freshwater bodies, hence magnifying eutrophication risks.

Review of trace organic chemicals in urban stormwater: Concentrations, distributions, risks, and drivers

Urban stormwater, increasingly seen as a potential water resource for cities and towns, contains various trace organic chemicals (TrOCs). This study, conducted through a comprehensive literature review of 116 publications, provides a detailed report on the occurrence, concentration distribution, health, and ecological risks of TrOCs, as well as the impact of land use and rainfall characteristics on their concentrations. The review uncovers a total of 629 TrOCs detected at least once in urban stormwater, including 228 pesticides, 132 pharmaceutical and personal care products (PPCPs), 29 polycyclic aromatic hydrocarbons (PAHs), 30 per- and polyfluorinated substances (PFAS), 28 flame retardants, 24 plasticizers, 22 polychlorinated biphenyls (PCBs), nine corrosion inhibitors, and 127 other industrial chemicals/intermediates/solvents. Concentration distributions were explored, with the best fit being log-normal distribution. Risk assessment highlighted 82 TrOCs with high ecological risk quotients (ERQ > 1.0) and three with potential health risk quotients (HQ > 1.0). Notably, 14 TrOCs (including six PAHs, five pesticides, three flame-retardants, and one plasticizer) out of 68 analyzed were significantly influenced by land-use type. Relatively weak relationships were observed between rainfall characteristics and pollutant concentrations, warranting further investigation. This study provides essential information about the occurrence and risks of TrOCs in urban stormwater, offering valuable insights for managing these emerging chemicals of concern.

Fungal bioleaching of metals from WPCBs of mobile phones employing mixed Aspergillus spp.: Optimization and predictive modelling by RSM and AI models

In the present study, optimization and prediction models for fungal bioleaching for effective metal extraction from waste printed circuit boards (WPCBs) of mobile phones were developed employing central composite design (CCD) of response surface methodology (RSM), and two artificial intelligence (AI) models, i.e., artificial neural network (ANN) and, support vector machine (SVM), respectively. Two continuous process parameters, such as pH (4-9) and pulp density (1-10 g/L), and the bioleaching approaches, viz., one-step and two-step, were experimentally optimized for the extraction of targeted metals, i.e., Cu, Ni, and Zn from WPCBs by mixed cultures of Aspergillus niger and Aspergillus tubingensis. Datasets were then used for predictive modelling using AI tools. Results showed that the highest simultaneous bioleaching of Cu, Ni, and Zn, with an extraction efficacy of about 86%, 51%, and 100%, respectively, achieved at an optimal condition of pH 5.7 and pulp density of 3 g/L following the two-step bioleaching approach. Effective metal extraction in the two-step approach could be attributed to the abundant production of organic acids with a content of about 16.3 g/L, 8.4 g/L, and 0.5 g/L of citric acid, oxalic acid, and malic acid, respectively. Further, the predictive modelling revealed that the ANN model was found to predict the fungal bioleaching responses more accurately as compared to the SVM model with R2 values exceeding 0.96 for all targeted metals. This research demonstrates the applicability of the optimization and prediction models for efficient metal extraction from WPCBs using mixed Aspergillus spp. following the two-step approach.

Insights into the transport and bio-degradation of dissolved inorganic nitrogen in the biochar-pyrite amended stormwater biofilter using dynamic modeling

The stormwater biofilter is a prevailing green infrastructure for urban stormwater management, but it is less effective in dissolved nitrogen removal, especially for nitrate. The mechanism that governs the nitrate leaching and performance stability of stormwater biofilters is poorly understood. In this study, a water quality model was developed to predict the ammonium and nitrate dynamics in a biochar-pyrite amended stormwater biofilter. The transport of dissolved nitrogen species was described by advection-dispersion models. The kinetics of adsorption and pyrite-based autotrophic denitrification are included in the model and simulated with a steady-state saturated flow. The model was calibrated and validated using eleven storm events. The modeling results reveal that the contribution of pyrite-based autotrophic denitrification to nitrate leaching alleviation improves with the increased drying duration. The nitrate removal efficiency was affected by a series of design parameters. Pyrite filling rate has a minor effect on nitrate removal promotion. Service area ratio and submerged zone depth are the key parameters to prevent nitrate leaching, as they influence the emergence and discharge time of nitrate breakthrough. The high inflow volume (high service area ratio) and small submerged zone can lead to earlier and increased discharge of peak nitrate otherwise the peak nitrate could be retained in the submerged zone and denitrified during the drying period. The developed mechanistic model provides a useful tool to evaluate the treatment ability of stormwater biofilters under varying conditions and offers a guideline for biofilter design optimization.

Performance of a gross pollutant trap-biofilter and sand filter treatment train for the removal of organic micropollutants from highway stormwater (field study)

This field study assessed the occurrence, event mean concentrations (EMCs), and removal of selected organic micro-pollutants (OMPs), namely, polycyclic aromatic hydrocarbons (PAHs), petroleum hydrocarbons (PHCs), nonylphenol (NP), 4-t-octylphenol (OP), and bisphenol A (BPA), in a gross pollutant trap (GPT)-biofilter/sand filter stormwater treatment train in Sundsvall, Sweden. The effects of design features of each treatment unit, including pre-sedimentation (GPT), sand filter medium, vegetation, and chalk amendment, were investigated by comparing the units' removal performances. Overall, the treatment train removed most OMPs from highway runoff effectively. The results showed that although the sand filter provided moderate (<50 % for phenolic substances) to high (50-80 % for PAHs and PHCs) removal of OMPs, adding a vegetated soil layer on top of the sand filter considerably improved the removal performance (by at least 30 %), especially for BPA, OP, and suspended solids. Moreover, GTP did not contribute to the treatment significantly. Uncertainties in the removal efficiencies of PAHs and PHCs by the filter cells increased substantially when the ratio of the influent concentration to the limit of quantification decreased. Thus, accounting for such uncertainties due to the low OMP concentrations should be considered when evaluating the removal performance of biofilters.

Metal bioleaching from printed circuit boards by bio-Fenton process: Optimization and prediction by response surface methodology and artificial intelligence models

Recycling printed circuit boards (PCBs) in the e-waste stream is essential for ecological protection and metal recycling for a circular economy. Efficient metal recovery from PCBs is highly dependent on the determination of the optimum combination of inputs in the recycling process. In this study, optimization and predictive modelling of the bio-Fenton process were performed employing the response surface methodology (RSM) and the artificial intelligence (AI) models for efficient enzymatic metal bioleaching from discarded cellphone PCBs. The Box-Behnken design (BBD) of RSM was chosen as the design matrix. Further, two AI models, i.e., support vector machine (SVM) and artificial neural network (ANN) were employed to predict complex metal bioleaching process. Experiments were performed based on variations of four input process parameters, namely, glucose oxidase (GOx) content (100-1000 U/L), Fe²⁺ content (10-50 mM), PCB pulp density (1-10 g/L), and shaking speed (150-450 rpm). Results revealed that the maximum simultaneous enzymatic metal extraction of 100% Cu, 70% Ni, 40% Pb, and 100% Zn was attained at the optimized conditions: GOx content: 300 U/L, Fe²⁺ content: 10 mM, pulp density: 1 g/L, and shaking speed: 335 rpm. A comparative analysis of the AI models suggested that the ANN-based model predicting more accurate results than the SVM-based model with coefficient of determination values > 0.99 for all the targeted metals. The FTIR analysis confirmed the partial disintegration of PCB polymeric base by OH radicals (OH•), which helped in liberating and exposing the embedded metals to the bio-Fenton solution. Further, the oxidation of metals by ferric ions produced from GOx-mediated oxidation of ferrous ions ensued efficient enzymatic metal bioleaching. Selective metal recovery of >99% was obtained by the chemical precipitation of bioleachate.

Extraction of multi-scale features enhances the deep learning-based daily PM2.5 forecasting in cities

Characterising the daily PM2.5 concentration is crucial for air quality control. To govern the status of the atmospheric environment, a novel hybrid model for PM2.5 forecasting was proposed by introducing a two-stage decomposition technology of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD); subsequently, a deep learning approach of long short-term memory (LSTM) was proposed. Five cities with unique meteorological and economic characteristics were selected to assess the predictive ability of the proposed model. The results revealed that PM2.5 pollution was generally more severe in inland cities (66.98 ± 0.76 μg m^-3) than in coastal cities (40.46 ± 0.40 μg m-3). The modelling comparison showed that in each city, the secondary decomposition algorithm improved the accuracy and prediction stability of the prediction models. When compared with other prediction models, LSTM effectively extracted featured information and achieved relatively accurate time-series prediction. The hybrid model of CEEMDAN-VMD-LSTM achieved a better prediction in the five cities (R2 = 0.9803 ± 0.01) compared with the benchmark models (R2 = 0.7537 ± 0.03). The results indicate that the proposed approach can identify the inherent correlations and patterns among complex datasets, particularly in time-series analysis.

Experimental and numerical study of steady state stability in a toluene biodegrading biofilter

Different steady states in a toluene biodegrading biofilter were explored experimentally and numerically. Experimental results showed that a gradual increase of the toluene inlet concentration over several weeks leads to a consistently low exit concentration, with a drastic increase at an inlet concentration change from 7.7 to 8.5 g m⁻³, indicating an alteration in steady state. A significant and sudden drop in the removal efficiency from 88 to 46% was observed. A model that includes nitrogen and biomass dynamics predicted results matching the experimental biofilter performance well, but the timing of the concentration jump was not reproduced exactly. A model that assumes a gradual increase of toluene inlet concentration of 0.272 g m⁻³ per day, accurately reproduced the experimental relationship between inlet and outlet concentration. Although there was variation between experimental and simulated results, a clear confirmation of the jump from one steady state to another was found.

Can water composition and weather factors predict fecal indicator bacteria removal in retention ponds in variable weather conditions?

Retention ponds provide benefits including flood control, groundwater recharge, and water quality improvement, but changes in weather conditions could limit the effectiveness in improving microbial water quality metrics. The concentration of fecal indicator bacteria (FIB), which is used as regulatory standards to assess microbial water quality in retention ponds, could vary widely based on many factors including local weather and influent water chemistry and composition. In this critical review, we analyzed 7421 data collected from 19 retention ponds across North America listed in the International Stormwater BMP Database to examine if variable FIB removal in the field conditions can be predicted based on changes in these weather and water composition factors. Our analysis confirms that FIB removal in retention ponds is sensitive to weather conditions or seasons, but temperature and precipitation data may not describe the variable FIB removal. These weather conditions affect suspended solid and nutrient concentrations, which in turn could affect FIB concentration in the ponds. Removal of total suspended solids and total P only explained 5% and 12% of FIB removal data, respectively, and TN removal had no correlation with FIB removal. These results indicate that regression-based modeling with a single parameter as input has limited use to predict FIB removal due to the interactive nature of their effects on FIB removal. In contrast, machine learning algorithms such as the random forest method were able to predict 65% of the data. The overall analysis indicates that the machine learning model could play a critical role in predicting microbial water quality of surface waters under complex conditions where the variation of both water composition and weather conditions could deem regression-based modeling less effective.

Using Random Forest, a machine learning approach to predict nitrogen, phosphorus, and sediment event mean concentrations in urban runoff

Estimating pollutant loads from developed watersheds is vitally important to reduce nonpoint source pollution from urban areas, as a key tool in meeting water quality goals is the implementation of Stormwater Control Measures (SCMs). SCMs are selected and sized based on influent pollutant loads. A common method used to estimate pollutant loads in urban runoff is the Event Mean Concentration (EMC) method. In this study, we develop and apply data-driven models using Random Forest (RF), a machine learning approach, to predict Total Nitrogen (TN), Total Phosphorus (TP), Total Suspended Solids (TSS), and Ortho-Phosphorus (Ortho-P) EMCs in urban runoff. The parameters considered in this study were climatological characteristics (i.e., Antecedent Dry Period or ADP, Precipitation Depth or P, Duration or D, and Intensity or I) and catchment characteristics including land use-related parameters including Imperviousness or Imp, Saturated Hydraulic Conductivity or K_sat, and Available Water Capacity or AWC), and site-specific parameters including Slope (S), and Catchment Size (A). Stormwater quality data for this study were obtained from the National Stormwater Quality Database (NSQD), which is the largest repository of stormwater quality data in the U.S. Results demonstrate that land use-related characteristics (i.e., Imp, K_sat, and AWC) were the most effective variables for predicting all EMCs. For TP, TSS, and Ortho-P, site-specific characteristics (S and A) had a greater effect than climatological characteristics (i.e., ADP, P, D, and I). However, for TN, climatological characteristics had a greater effect than site-specific characteristics (S and A). In addition, for TN, TP, and TSS, precipitation characteristics (P, D, and I) were found to be more effective parameters for estimating EMCs than ADP. This study highlights the most influential parameters affecting EMCs which can be used by stakeholders and SCMs designers to improve estimates of nutrients and sediment EMCs. The selection and design of the highest performing SCMs is essential in achieving effective treatment of stormwater, attaining water quality goals, and protecting downstream waterbodies.

Antibiotic resistance genes removals in stormwater bioretention cells with three kinds of environmental conditions

Recently, increasing attention has been paid to antibiotic resistance genes (ARGs) in stormwater runoff. However, there is still no available literature about ARGs removals through stormwater bioretention cells. Batch experiments were conducted to investigate target ARGs (bla_TEM, tetR and aphA) removals under three environmental conditions, including substrate (weight ratios of sand to soil), hydraulic loading rate (HLR) and submerged area depth. The target ARGs removals were the largest (more than 5 log in the bottom outlets) in bioretention cells with 8:2 ratio of sand to soil, HLR 0.044 cm³/cm²/min and 150 mm of submerged area depth. The proportion for both iARGs and eARGs had little effect on target ARGs removals (expect extracellular bla_TEM), although distributions of target ARGs were different in substrate layers. Adsorption behavior tests indicated that both kinetics and isotherms of target ARGs adsorption by biofilms were more suitable to explain their best removals for bioretention cells with 8:2 ratio of sand to soil than that by substrate. At phylum and genus levels, there were respectively 6 dominant microflora related significantly to target ARGs levels, and their relationships changed obviously under different environmental conditions, suggesting that regulating the dominant microflora (like Verrucomicrobia and Actinobacteria) could be feasible to change ARGs removals.

Natural aging of expanded shale, clay, and slate (ESCS) amendment with heavy metals in stormwater increases its antibacterial properties: Implications on biofilter design

Aging is often expected to decrease the pathogen removal capacity of media because of exhaustion of attachment sites by adsorption of co-contaminants and dissolved organics. In contrast, the adsorption of metals naturally present in stormwater during aging could have a positive impact on pathogen removal. To examine the effect of adsorbed metals on pathogen removal, biofilter media amended with expanded clay, shale, and slate (ESCS) aggregates, a lightweight aggregate, were exposed to metals by intermittently injecting natural stormwater spiked with Cu, Pb, and Zn, and the capacity of aged and unaged media to remove Escherichia coli (E. coli), a pathogen indicator, were compared. Metal adsorption on ESCS media decreased their net negative surface charge and altered the surface properties as confirmed by zeta potential measurement and Fourier-Transform Infrared Spectroscopy (FTIR) analysis. These changes increased the E. coli adsorption capacity of aged media compared with unaged media and decreased overall remobilization of attached E. coli during intermittent infiltration of stormwater. A live-dead analysis confirmed that the adsorbed metals inactivated attached E. coli, thereby replenishing the adsorption capacity. Overall, the results confirmed that natural aging of biofilter media with adsorbed metals could indeed have a net positive effect on E. coli removal in biofilters and therefore should be included in the conceptual model predicting long-term removal of pathogens from stormwater containing mixed pollutants.

Using an ensemble model coupled with portable X-ray fluorescence and visible near-infrared spectroscopy to explore the viability of mapping and estimating arsenic in an agricultural soil

Increasing concentrations of potentially toxic elements (PTE) in agricultural soils remain a major source of public concern. Monitoring PTEs in an agricultural field with no history of contaminants necessitate adequate analysis utilizing a robust model to accurately uncover hidden PTEs. Detecting and mapping the distribution of soil properties using portable X-ray fluorescence (pXRF) and proximal sensing techniques is not only rapid, but also relatively inexpensive. In this study, an ensemble model, consisting of partial least square regression (PLSR), support vector machine (SVM), random forest (RF) and cubist, was used for the prediction and mapping of soil As content in an agricultural field with no history of pollution. The datasets were collected using pXRF and field spectroscopy techniques. The main goal was to compare the ensemble model to each of the calibration techniques in terms of prediction accuracy of As content in such a field. Other components [e.g., soil organic carbon (SOC), Mn, S, soil pH, Fe] that are known to influence As levels in the soil were also retrieved to assess their correlation with soil As. The models were evaluated using the root mean squared error (RMSE_CV), the coefficient of determination (R²_CV) and the ratio of performance to interquartile range (RPIQ). In terms of prediction accuracy, the ensemble model outperformed each of the individual techniques (R²_CV = 0.80/0.75) and obtained the least error margin (RMSE_CV = 1.91/2.16). Overall, all the predictive techniques were able to detect both low and high estimated values of soil As within the study field, but with the ensemble model resembling the measurements better. The ensemble model, a promising tool as demonstrated by the current study, is highly recommended to be included in future studies for more accurate estimation of As and other PTEs in other agricultural fields.

A review on biofiltration techniques: Recent advancements in the removal of volatile organic compounds and heavy metals in the treatment of polluted water

Good quality of water determines the healthy life of living beings on this earth. The cleanliness of water was interrupted by the pollutants emerging out of several human activities. Industrialization, urbanization, heavy population, and improper disposal of wastes are found to be the major reasons for the contamination of water. Globally, the inclusion of volatile organic compounds (VOCs) and heavy metals released by manufacturing industries, pharmaceuticals, and petrochemical processes have created environmental issues. The toxic nature of these pollutants has led researchers, scientists, and industries to exhibit concern towards the complete eradication of them. In this scenario, the development of wastewater treatment methodologies at low cost and in an eco-friendly way had gained importance at the international level. Recently, bio-based technologies were considered for environmental remedies. Biofiltration based works have shown a significant result for the removal of volatile organic compounds and heavy metals in the treatment of wastewater. This was done with several biological sources such as bacteria, fungi, algae, plants, yeasts, etc. The biofiltration technique is cost-effective, simple, biocompatible, sustainable, and eco-friendly compared to conventional techniques. This review article provides deep insight into biofiltration technologies engaged in the removal of volatile organic compounds and heavy metals in the wastewater treatment process.

Heavy metal-contained wastewater in China: Discharge, management and treatment

A large amount of heavy metal-contained wastewater (HMW) was discharged during Chinese industry development, which has caused many environmental problems. This study reviewed discharge, management and treatment of HMW in China through collecting and analyzing data from China's official statistical yearbook, standards, technical specifications, government reports, case reports, and research paper. Results showed that industry wastewater discharged by an amount of about 221.6 × 10⁸ t (in 2012), where emission of heavy metals including Pb, Hg, Cd, Cr(VI), T-Cr was around 388.4 t (in 2012). Heavy metal emission with wastewater in east China and central south China was observed to be graver than that in other areas. However, control of heavy metals in Pb and Cd in northwest China was more difficult compared with other areas. In terms of management, China's government has issued many wastewater discharge standards, strict management policies for controlling HMW discharge in recent years, resulting in reduced HMW discharge. In addition, main HMW treatment technology in China was chemical precipitation, and other technologies such as membrane separation, adsorption, ion exchange, electrochemical and biological methods were also occasionally applied. In the future, chemical industries will be concentrated in northwest China, therefore control of HMW discharge should be paid much more attention in those areas. In addition, more effective and environment-friendly heavy metal removal and regeneration technologies should be developed, such as biomaterials adsorbent.

Competitive sorption of Cd, Cr, Cu, Ni, Pb and Zn from stormwater runoff by five low-cost sorbents; Effects of co-contaminants, humic acid, salinity and pH

For a comprehensive estimation of metals removal by sorbents in stormwater systems, it is essential to evaluate the impacts of co-contaminants. However, most studies consider only metals (single or multiple), which may overestimate performance. This study employed a batch method to investigate the performance of five low-cost sorbents - coconut coir fiber (CCF), blast furnace slag (BFS), waste tire crumb rubber (WTCR), biochar (BC), and iron coated biochar (FeBC) - for simultaneous removal of Cd, Cr, Cu, Ni, Pb and Zn from simulated stormwater (SSW) containing other contaminants (nutrients and polycyclic aromatic hydrocarbons). BFS and CCF demonstrated the highest sorption capacity of all metals (> 95% removal) in all systems (single and multi-contaminant). However, the presence of other contaminants in solution reduced metals removal for other sorbents, as follows (highest to lowest removal): single-metal > multi-metal > multi-contaminant solutions, and removal efficiency ranking among metals was generally Cr~Cu~Pb > Ni > Cd > Zn. Humic acid (HA) negatively affected the metal sorption, likely due to the formation of soluble HA-metal complexes; NaCl concentration did not impact removal, but alkaline pH improved removal. These findings indicate that sorbents need to be tested under realistic stormwater solution chemistry including co-contaminants to appropriately characterize performance prior to implementation.

Modeling defluoridation of real-life groundwater by a green adsorbent aluminum/olivine composite: Isotherm, kinetics, thermodynamics and novel framework based on artificial neural network and support vector machine

The kinetic, isotherm, and thermodynamics of adsorptive removal of fluoride from the real-life groundwater was evaluated to assess the applicability of a green adsorbent, aluminum/olivine composite (AOC). The isotherm and kinetics were demonstrated by the Freundlich and Elovich model indicating significant surface heterogeneity of AOC in favouring the fluoride sorption. The fluoride removal efficiency of AOC was achieved as 87.5% after 240 min of contact time. The diffusion kinetic model exhibited that both the intra-particle and film diffusion together control the rate-limiting step of fluoride adsorption. A negative value of ΔG⁰ (-19.919 kJ/mol) at 303 K confirmed the spontaneous adsorption reaction of fluoride, and its endothermic nature was supported by the negative value of ΔH⁰ (39.504 kJ/mol). A novel framework for a predictive model by artificial neural network (ANN), and support vector machine (SVM) considering the real and synthetic fluoride-containing water was developed to assess the efficiency of adsorbent under different scenarios. ANN model was observed to be statistically significant (RMSE: 1.0955 and R²: 0.9982) and the proposed method may be instrumental in a similar area for benchmarking the synthetic and real-life samples. The low desorption potential of the spent adsorbent exhibited safe disposal of sludge and the secondary-pollutant-free treated water by the efficient and green adsorbent AOC enhanced the field-scale applicability of the green technology.