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.

Modeling the die-off of E. coli and Ascaris in wastewater-irrigated vegetables: implications for microbial health risk reduction associated with irrigation cessation.

This study assessed the die-off of Escherichia coli (E. coli) and Ascaris suum on lettuce (Great Lakes 118) and cabbage (Brassica oleracea var capitata) in wastewater-irrigated fields using comparative mathematical die-off models. The study revealed that none of the survival curves of E. coli and A. suum was best fitted with the log-linear model, indicating that the classical first-order kinetic approach is inadequate in many cases. The biphasic die-off model best described the die-off of E. coli on lettuce (kmax1 = 2.62 day(-1) and kmax2 = 0.22 day(-1)) and cabbage (kmax1 = 1.06 day(-1) and kmax2 = 0.53 day(-1)). The die-off of A. suum on lettuce was best described by the biphasic model (kmax1 = 0.48 day(-1) and kmax2 = 0.01 day(-1)) and best described by log linear + tail (kmax = 0.44) on cabbage. A comparative health risk assessment associated with the consumption of lettuce showed significant underestimation of the number of days of irrigation cessation required to achieve E. coli O157:H7 and Ascaris tolerable annual infection risk when using biphasic die-off rates compared with other die-off rates. The study stresses the need to test different die-off models as inputs for quantitative microbial risk assessment (QMRA) particularly for interventions associated with health risk reduction.