Applications of mathematical modelling for assessing microplastic transport and fate in water environments: a comparative review.

Microplastics in the environment are considered complex pollutants as they are chemical and corrosive-resistant, non-biodegradable and ubiquitous. These microplastics may act as vectors for the dissemination of other pollutants and the transmission of microorganisms into the water environment. The currently available literature reviews focus on analysing the occurrence, environmental effects and methods of microplastic detection, however lacking a wide-scale systematic review and classification of the mathematical microplastic modelling applications. Thus, the current review provides a global overview of the modelling methodologies used for microplastic transport and fate in water environments. This review consolidates, classifies and analyses the methods, model inputs and results of 61 microplastic modelling studies in the last decade (2012-2022). It thoroughly discusses their strengths, weaknesses and common gaps in their modelling framework. Five main modelling types were classified as follows: hydrodynamic, process-based, statistical, mass-balance and machine learning models. Further, categorisations based on the water environments, location and published year of these applications were also adopted. It is concluded that addressed modelling types resulted in relatively reliable outcomes, yet each modelling framework has its strengths and weaknesses. However, common issues were found such as inputs being unrealistically assumed, especially biological processes, and the lack of sufficient field data for model calibration and validation. For future research, it is recommended to incorporate macroplastics' degradation rates, particles of different shapes and sizes and vertical mixing due to biofouling and turbulent conditions and also more experimental data to obtain precise model inputs and standardised sampling methods for surface and column waters.

Effect of selected wastewater characteristics on estimation of SARS-CoV-2 viral load in wastewater.

Wastewater-based epidemiology has been used as a tool for surveillance of COVID-19 infections. This approach is dependent on the detection and quantification of SARS-CoV-2 RNA in untreated/raw wastewater. However, the quantification of the viral RNA could be influenced by the physico-chemical properties of the wastewater. This study presents the first use of Adaptive Neuro-Fuzzy Inference System (ANFIS) to determine the potential impact of physico-chemical characteristics of wastewater on the detection and concentration of SARS-CoV-2 RNA in wastewater. Raw wastewater samples from four wastewater treatment plants were investigated over four months. The physico-chemical characteristics of the raw wastewater was recorded, and the SARS-CoV-2 RNA concentration determined via amplification with droplet digital polymerase chain reaction. The wastewater characteristics considered were chemical oxygen demand, flow rate, ammonia, pH, permanganate value, and total solids. The mean SARS-CoV-2 RNA concentrations ranged from 648.1(±514.6) copies/mL to 1441.0(±1977.8) copies/mL. Among the parameters assessed using the ANFIS model, ammonia and pH showed significant association with the concentration of SARS-CoV-2 RNA measured. Increasing ammonia concentration was associated with increasing viral RNA concentration and pH between 7.1 and 7.4 were associated with the highest SARS-CoV-2 concentration. Other parameters, such as total solids, were also observed to influence the viral RNA concentration, however, this observation was not consistent across all the wastewater treatment plants. The results from this study indicate the importance of incorporating wastewater characteristic assessment into wastewater-based epidemiology for a robust and accurate COVID-19 surveillance.

Variability of leachate quality and polluting potentials in light of leachate pollution index (LPI) - A global perspective.

From a holistic perspective, this review is the first to comprehensively assess and characterise leachate quality from waste disposal facilities (WDFs), landfills and dumpsites, located in 61 countries worldwide. A continent wise grouping approach was adopted to identify the variability of leachate quality and polluting abilities in light of leachate pollution index (LPI). The literature data on leachate quality included 428 samples, with eighteen leachate parameters, classified under, organic, inorganic, and heavy metals. Statistically significant differences in LPI were found between different continents and WDFs demographic data, i.e., type, status, age, rainfall, etc. A negative correlation was found between pH and the majority of studied parameters, especially for heavy metals such as Pb, Zn, As, Hg, Cy, as the decrease in pH intensifies heavy metals' solubility. Based on the studied worldwide leachate data and WDFs age, an LPI rating was identified, where high, intermediate, and low contaminated leachate are typically classified with having an average of 26.5, 23.6 and 17.5, respectively. The provided database in this review could be of great importance in establishing a more comprehensive global databank by including other countries- and site-specific factors that are vital in enhancing the accuracy of LPI and formatting a more representative leachate diagnosis index.

Fuzzy inference optimization algorithms for enhancing the modelling accuracy of wastewater quality parameters.

To ensure the safe discharge of treated wastewater to the environment, continuous efforts are vital to enhance the modelling accuracy of wastewater treatment plants (WWTPs) through utilizing state-of-art techniques and algorithms. The integration of metaheuristic modern optimization algorithms that are natlurally inspired with the Fussy Inference Systems (FIS) to improve the modelling performance is a promising and mathematically suitable approach. This study integrates four population-based algorithms, namely: Particle swarm optimization (PSO), Genetic algorithm (GA), Hybrid GA-PSO, and Mutating invasive weed optimization (M-IWO) with FIS system. A full-scale WWTP in South Africa (SA) was selected to assess the validity of the proposed algorithms, where six wastewater effluent parameters were modeled, i.e., Alkalinity (ALK), Sulphate (SLP), Phosphate (PHS), Total Kjeldahl Nitrogen (TKN), Total Suspended Solids (TSS), and Chemical Oxygen Demand (COD). The results from this study showed that the hybrid PSO-GA algorithm outperforms the PSO and GA algorithms when used individually, in modelling all wastewater effluent parameters. PSO performed better for SLP and TKN compared to GA, while the M-IWO algorithm failed to provide an acceptable modelling convergence for all the studied parameters. However, three out of four algorithms applied in this study proven beneficial to be optimized in enhancing the modelling accuracy of wastewater quality parameters.

Emerging contaminants in South African water environment- a critical review of their occurrence, sources and ecotoxicological risks.

The release of emerging contaminants (ECs) to the environment is a serious concern due to its health implications on humans, aquatic species, and the development of anti-microbial resistance. This review focuses on the critical analysis of available literature on the prevalence of ECs in the aquatic environment and their removal from wastewater treatment plants (WWTPs) in South Africa. Besides, a risk assessment is performed on the reported ECs from the South African surface water to augment the knowledge towards mitigation of EC pollution, and prioritisation of ECs to assist future monitoring plans and regulation framework. A zone wise classification approach was carried out to identify the spatial inferences and data deficiencies that revealed a non-uniformity in the monitoring of ECs throughout South Africa, with few zones rendering no data. The overarching data mining further revealed that unmanaged urine diverted toilets could be a potential source of EC pollution to groundwater in South Africa. Based on the available literature, it can be deduced that the complete adoption of EC management practices from developed countries might only contribute partly in the mitigation of EC pollution in South Africa. Therefore, an EC monitoring programme specific to the country is recommended which should be based on their occurrence levels, sources and removal in WWTPs.

Proposed formulation of surface water quality and modelling using gene expression, machine learning, and regression techniques.

The rising water pollution from anthropogenic factors motivates further research in developing water quality predicting models. The available models have certain limitations due to limited timespan data and the incapability to provide empirical expressions. This study is devoted to model and derive empirical equations for surface water quality of upper Indus river basin using a 30-year dataset with machine learning techniques and then to determine the most reliable model capable to accurately predict river water quality. Total dissolve solids (TDS) and electrical conductivity (EC) were used as dependent variables, whereas eight parameters were used as independent variables with 70 and 30% data for model training and testing, respectively. Various evaluation criteria, i.e., Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE), were used to assess the performance of models. The data is also validated with the help of k-fold cross-validation using R2 and RMSE. The results indicated a strong correlation with NSE and R2 both above 0.85 for all the developed models. Gene expression programming (GEP) outperformed both artificial neural network (ANN) and linear and non-linear regression models for TDS and EC. The sensitivity and parametric analyses revealed that bicarbonate is the most sensitive parameter influencing both TDS and EC models. Two equations were derived and formulated to represent the novel results of GEP model to help authorities in the effective monitoring of river water quality.

Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill.

Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environment. In developing countries, the accurate evaluation of leachate generation rates has often considered a challenge due to the lack of reliable data and high measurement costs. Leachate generation is related to several factors, including meteorological data, waste generation rates, and landfill design conditions. The high variations in these factors lead to complicating leachate modeling processes. This study aims at identifying the key elements contributing to leachate production and developing various AI-based models to predict leachate generation rates. These models included Artificial Neural Network (ANN)-Multi-linear perceptron (MLP) with single and double hidden layers, and support vector machine (SVM) regression time series algorithms. Various performance measures were applied to evaluate the developed model's accuracy. In this study, input optimization process showed that three inputs were acceptable for modeling the leachate generation rates, namely dumped waste quantity, rainfall level, and emanated gases. The initial performance analysis showed that ANN-MLP2 model-which applies two hidden layers-achieved the best performance, then followed by ANN-MLP1 model-which applies one hidden layer and three inputs-while SVM model gave the lowest performance. Ranges and frequency of relative error (RE%) also demonstrate that ANN-MLP models outperformed SVM models. Furthermore, low and peak flow criterion (LFC and PFC) assessment of leachate inflow values in ANN-MLP model with two hidden layers made more accurate values than other models. Since minimizing data collection and processing efforts as well as minimizing modeling complexity are critical in the hydrological modeling process, the applied input optimization process and the developed models in this study were able to provide a good performance in the modeling of leachate generation efficiently.

Predicting sanitary landfill leachate generation in humid regions using ANFIS modeling.

Landfill leachate is one of the sources of surface water pollution in Selangor State (SS), Malaysia. Leachate volume prediction is essential for sustainable waste management and leachate treatment processes. The accurate estimation of leachate generation rates is often considered a challenge, especially in developing countries, due to the lack of reliable data and high measurement costs. Leachate generation is related to several variable factors, including meteorological data, waste generation rates, and landfill design conditions. Large variations in these factors lead to complicated leachate modeling processes. The aims of this study are to determine the key elements contributing to leachate production and then develop an adaptive neural fuzzy inference system (ANFIS) model to predict leachate generation rates. Accuracy of the final model performance was tested and evaluated using the root mean square error (RMSE), the mean absolute error (MAE), and the correlation coefficient (R). The study results defined dumped waste quantity, rainfall level, and emanated gases as the most significant contributing factors in leachate generation. The best model structure consisted of two triangular fuzzy membership functions and a hybrid training algorithm with eight fuzzy rules. The proposed ANFIS model showed a good performance with an overall correlation coefficient of 0.952.

Analysing the accuracy of machine learning techniques to develop an integrated influent time series model: case study of a sewage treatment plant, Malaysia.

The function of a sewage treatment plant is to treat the sewage to acceptable standards before being discharged into the receiving waters. To design and operate such plants, it is necessary to measure and predict the influent flow rate. In this research, the influent flow rate of a sewage treatment plant (STP) was modelled and predicted by autoregressive integrated moving average (ARIMA), nonlinear autoregressive network (NAR) and support vector machine (SVM) regression time series algorithms. To evaluate the models' accuracy, the root mean square error (RMSE) and coefficient of determination (R2) were calculated as initial assessment measures, while relative error (RE), peak flow criterion (PFC) and low flow criterion (LFC) were calculated as final evaluation measures to demonstrate the detailed accuracy of the selected models. An integrated model was developed based on the individual models' prediction ability for low, average and peak flow. An initial assessment of the results showed that the ARIMA model was the least accurate and the NAR model was the most accurate. The RE results also prove that the SVM model's frequency of errors above 10% or below - 10% was greater than the NAR model's. The influent was also forecasted up to 44 weeks ahead by both models. The graphical results indicate that the NAR model made better predictions than the SVM model. The final evaluation of NAR and SVM demonstrated that SVM made better predictions at peak flow and NAR fit well for low and average inflow ranges. The integrated model developed includes the NAR model for low and average influent and the SVM model for peak inflow.

Assessment of carbon footprint emissions and environmental concerns of solid waste treatment and disposal techniques; case study of Malaysia.

Malaysian authorities has planned to minimize and stop when applicable unsanitary dumping of waste as it puts human health and the environment at elevated risk. Cost, energy and revenue are mostly adopted to draw the blueprint of upgrading municipal solid waste management system, while the carbon footprint emissions criterion rarely acts asa crucial factor. This study aims to alert Malaysian stakeholders on the uneven danger of carbon footprint emissions of waste technologies. Hence, three scenarios have been proposed and assessed mainly on the carbon footprint emissions using the 2006 IPCC methodology. The first scenario is waste dumping in sanitary landfills equipped with gas recovery system, while the second scenario includes anaerobic digestion of organics and recycling of recyclable wastes such as plastic, glass and textile wastes. The third scenario is waste incineration. Besides the carbon footprint emissions criterion, other environmental concerns were also examined. The results showed that the second scenario recorded the lowest carbon footprint emissions of 0.251t CO2 eq./t MSW while the third scenario had the highest emissions of 0.646t CO2 eq./t MSW. Additionally, the integration between anaerobic digestion and recycling techniques caused the highest avoided CO2 eq. emissions of 0.74t CO2 eq./t MSW. The net CO2 eq. emissions of the second scenario equaled -0.489t CO2 eq./t MSW due to energy recovery from the biogas and because of recycled plastic, glass and textile wastes that could replace usage of raw material. The outcomes also showed that the first scenario generates huge amount of leachate and hazardous air constituents. The study estimated that a ton of dumped waste inside the landfills generates approximately 0.88m3 of trace risky compounds and 0.188m3 of leachate. As for energy production, the results showed that the third scenario is capable of generating 639kWh/t MSW followed by the second scenario with 387.59kWh/t MSW. The first scenario produced 296.79kWh/t MSW. In conclusion, the outcomes of this study recommend an integrated scenario of anaerobic digestion and recycling techniques to be employed in Malaysia.