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.

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.

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.

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.

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.

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.

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.

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.

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.

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.