Deterministic Modeling Approach Research Articles

Stochastic analysis of Ebola infection in small zoonotic niches.

The size of fruit bat colonies ranges from dozens to hundreds of thousands of individuals, depending on the species. While a deterministic modelling approach is appropriate for large colonies, the role of population fluctuations can be all-important for small colonies. From this perspective, we analyse the infection dynamics in small zoonotic niches due to filoviruses, e.g. Ebola. To this end, we perform stochastic numerical simulations and analytical calculations. The inherent stochasticity in ecological processes may play a significant role in driving small populations towards extinction. Here, we reveal that fluctuations can either lead to virus eradication or to sustain infection compared with the deterministic dynamics, depending on the size of the zoonotic niche. Altogether, our findings reveal non-trivial stochastic effects, which can shed light on the infection dynamics in small- and medium-sized bat colonies and help design preventive measures for zoonotic diseases.

The cost of CO2 emissions abatement in a micro energy community in a Belgian context

This paper investigates and quantifies the benefits and the cost of CO2 emissions abatement of a Micro Energy Community (MEC) in a tiny residential cluster of three houses in a Belgian context. A simulation-based comparative analysis is performed of two individual and two MEC concepts, i.e. a reference scenario, an individual electrification scenario, an electricity sharing scenario, and an energy community scenario. A deterministic dynamic white-box modelling approach, including optimal control, is used, considering heat for space heating and domestic hot water, and electricity for electrical appliances. The energy system that achieves the greatest emission reduction at the lowest cost, has a collective heat pump and a photovoltaic installation sized based on the plug load demand. This system reduces the annual CO2 emissions by 11.48 tons at a cost of 179 EUR/ton CO2 considering 2021 dynamic retail electricity prices with a median of 251 EUR/MWh and a fixed retail gas price of 64 EUR/MWh. However, the results are strongly dependent on the gas-electricity price ratio. The highest value of the retail gas price in 2021 leads to a negative CO2 emissions abatement cost of 154 EUR/ton CO2, putting energy community concepts on the radar for a cost-effective energy transition.

A deterministic fluid model for production and energy mode control of a single machine

Improving machines’ energy efficiency through dynamic energy mode control to meet demand requirements with minimal energy consumption is a promising approach. This study considers a machine operating in working, idle, off, and warmup energy modes with different energy consumption in each mode. A deterministic fluid model is developed to analyze an energy mode control policy that determines when to keep the machine working, off or idle, and switch to other modes based on the inventory/backlog level to minimize the total energy, inventory, and backlog costs. This approach facilitates the derivation of closed-form expressions for the optimal thresholds and the associated costs. This modeling approach allows us to prove that a policy that operates the machine between the working and off modes or the working and idle modes is always better than a hybrid policy that operates the machine in working, off, and idle modes simultaneously. We use the solution of the deterministic fluid model to propose an approximate policy for machines with stochastic production, warmup, and demand processes. We compare the results of the proposed approximation method with the optimal solution of a stochastic system where the production and warmup times are exponential and the demand inter-arrival times have Erlang distribution. The optimal solution for the stochastic system is determined by solving a Markovian Decision Process (MDP). Our numerical experiments show that the proposed approximation method predicts the optimal policy type for the stochastic case with a 89.3% accuracy, and the average error between the optimal cost and the cost obtained with the approximation method is 1.37% for 729 different cases tested. Furthermore, the computational efficiency of the proposed approximation is around 250 times better than the effort to determine the optimal policy using an MDP approach. We propose this approximation method where the control parameters are given in closed form as an easy-to-implement and effective policy to control energy modes to minimize the total energy, inventory, and backlog costs. Furthermore, we present the deterministic fluid modeling approach as a versatile approach to analyze energy mode control problems.

Geological Modeling of Shale Oil in Member 7 of the Yanchang Formation, Heshui South Area, Ordos Basin

In recent years, the Chang 7 member of the Mesozoic Triassic Yanchang Formation in the Ordos Basin has emerged as a significant repository of abundant and distinctive unconventional oil resources. The Heshui area boasts substantial shale oil reserves, with reported third-level reserves surpassing 600 million tons. However, the region in the southern part of Heshui is marked by pronounced variability in reservoir quality, intricate oil–water dynamics, low formation energy, and suboptimal fluid properties, leading to divergent development outcomes for horizontal wells. There is an imperative need to devise and refine new geological models to underpin the efficient exploitation of shale oil in the southern Heshui area. This study focuses on the shale oil reservoir of the Chang 7 member in the southern Heshui area of the Ordos Basin, conducting detailed stratigraphic correlation and establishing a refined isochronous stratigraphic framework. Utilizing PetrelTM modeling software (version 2018), we integrate deterministic and stochastic modeling approaches, adhering to the principles of isochronous and phased modeling. By assessing the thickness of sand and mudstone layers and the overall stratigraphic sequence, we derive a geological probability surface. Subsequently, this surface is harnessed to constrain the lithofacies, yielding a constrained lithofacies model. Employing sequential indicator simulation and sequential Gaussian stochastic simulation, we develop a reservoir attribute model that is anchored in the lithofacies model and its controls, culminating in a robust and dependable static model. Employing the geological probability surface constraint method, we meticulously construct the reservoir matrix model, amalgamating individual well data with the inherent certainty and randomness of reservoir plane thickness. This approach further enhances the model’s accuracy and mitigates the uncertainty and randomness associated with inter-well interpolation to a significant degree.

Systematic review of the uncertainty of coral reef futures under climate change

Climate change impact syntheses, such as those by the Intergovernmental Panel on Climate Change, consistently assert that limiting global warming to 1.5 °C is unlikely to safeguard most of the world’s coral reefs. This prognosis is primarily based on a small subset of available models that apply similar ‘excess heat’ threshold methodologies. Our systematic review of 79 articles projecting coral reef responses to climate change revealed five main methods. ‘Excess heat’ models constituted one third (32%) of all studies but attracted a disproportionate share (68%) of citations in the field. Most methods relied on deterministic cause-and-effect rules rather than probabilistic relationships, impeding the field’s ability to estimate uncertainty. To synthesize the available projections, we aimed to identify models with comparable outputs. However, divergent choices in model outputs and scenarios limited the analysis to a fraction of available studies. We found substantial discrepancies in the projected impacts, indicating that the subset of articles serving as a basis for climate change syntheses may project more severe consequences than other studies and methodologies. Drawing on insights from other fields, we propose methods to incorporate uncertainty into deterministic modeling approaches and propose a multi-model ensemble approach to generating probabilistic projections for coral reef futures.

Interpretable machine learning for predicting the fate and transport of pentachlorophenol in groundwater

Pentachlorophenol (PCP) is a commonly found recalcitrant and toxic groundwater contaminant that resists degradation, bioaccumulates, and has a potential for long-range environmental transport. Taking proper actions to deal with the pollutant accounting for the life cycle consequences requires a better understanding of its behavior in the subsurface. We recognize the huge potential for enhancing decision-making at contaminated groundwater sites with the arrival of machine learning (ML) techniques in environmental applications. We used ML to enhance the understanding of the dynamics of PCP transport properties in the subsurface, and to determine key hydrochemical and hydrogeological drivers affecting its transport and fate. We demonstrate how this complementary knowledge, provided by data-driven methods, may enable a more targeted planning of monitoring and remediation at two highly contaminated Swedish groundwater sites, where the method was validated. We evaluated 6 interpretable ML methods, 3 linear regressors and 3 non-linear (i.e., tree-based) regressors, to predict PCP concentration in the groundwater. The modeling results indicate that simple linear ML models were found to be useful in the prediction of observations for datasets without any missing values, while tree-based regressors were more suitable for datasets containing missing values. Considering that missing values are common in datasets collected during contaminated site investigations, this could be of significant importance for contaminated site planners and managers, ultimately reducing site investigation and monitoring costs. Furthermore, we interpreted the proposed models using the SHAP (SHapley Additive exPlanations) approach to decipher the importance of different drivers in the prediction and simulation of critical hydrogeochemical variables. Among these, sum of chlorophenols is of highest significance in the analyses. Setting that aside from the model, tetra chlorophenols, dissolved organic carbon, and conductivity found to be of highest importance. Accordingly, ML methods could potentially be used to improve the understanding of groundwater contamination transport dynamics, filling gaps in knowledge that remain when using more sophisticated deterministic modeling approaches.

Stochastic modeling of spreading an infection with standard incidence rate

This paper studies and models the random spread of an infection in a population. It extends the traditional deterministic modeling approach by incorporating discretetime stochastic modeling using Markov chains. The probability of extinction and disease persistence is then investigated using the branching chain method, with a focus on a quantity in the non-random model, which is called the basic reproductive number. Moreover, the random model is transformed into a system of stochastic differential equations by approximating the probability distribution function using the forward Kolmogorov equation. An equivalent stochastic differential system is also introduced and examined for the stochastic model. Finally, numerical simulations of the stochastic models are conducted to evaluate the theoretical findings presented in the paper.

Analysis of age-specific fertility in India: Deterministic and non-deterministic modeling approaches

The main objective of this study is to investigate the pattern of age-specific fertility rates (ASFRs) in India using deterministic and non-deterministic approaches. Toward this end, we proposed statistical polynomial regression models to study the distributional pattern of ASFRs for total, rural, and urban women in India. Further, a comparative study considering selected skewed regression models was undertaken. For this study, secondary data on ASFR were collected from Sample Registration System, Statistical Report-2020, and from National Family Health Survey 5 (NFHS-5; 2019 – 2021). It was found that all three subcategories of ASFRs, namely, the total, rural, and urban ASFRs, followed the reciprocal biquadratic polynomial model. On the other hand, all three subcategories of ASFR follow the skew-normal type 2 distribution. Similar findings were also obtained and validated based on NFHS-5 data. Further, the chosen statistical models’ validity and stability were tested using various model validation techniques and model selection criteria.

Unraveling pine wilt disease: Comparative study of stochastic and deterministic model using spectral method

The pinewood nematode, which causes a disease known as pine wilt, is a serious threat to pine forests all over the world. In this paper, we analyze the dynamics of pine wilt disease and its effects on forest ecosystems using mathematical modeling of pine wilt disease, its numerical simulations, and analytical techniques. Understanding the nematode’s spread, evaluating the efficiency of management efforts, and foreseeing the long-term effects on pine populations are some of our research’s goals. To gain a comprehensive understanding of the dynamics of the disease, we categorize pine trees and vectors systematically into susceptible, exposed, and infected groups. We first presented disease dynamics through a flow diagram which is then transformed to system of ordinary differential equations along with non-negative initial conditions. The deterministic model is then transformed to a stochastic model. Using both deterministic and stochastic modeling approaches, this study explores the complex transmission dynamics of Pine Wilt disease. Basic reproduction number has been calculated via next generation technique and some cases of stability analysis on the basis of the values of reproduction number has been discussed. For the solution of stochastic model Legendre Spectral collocation method has been used as it is known for its high accuracy in approximating solutions to complex stochastic models. It can achieve exponential convergence rates, making it suitable for problems where high precision is required. The results of both deterministic and stochastic models are compared via different figures. We find crucial thresholds for disease transmission and highlight significant factors affecting the spread of diseases through simulations. Our research shows that preventing the severe impacts of pine wilt disease requires early detection and intervention. Additionally, our mathematical model is a useful tool for enhancing disease management plans. This research advances our knowledge of the dynamics of the pine wilt disease and provides useful advice for managing forests and promoting conservation. Our research highlights the need for quick action to protect pine forests from this harmful virus.

Stochastic modeling of decarbonizing strategy, policy, and market-induced incentives for the US electricity sector

In line with the global pursuit of achieving net-zero carbon emissions, integrating carbon capture and storage (CCS) and renewable energy (RE) technologies is important in power production. This study evaluates the profitability of CCS and RE technologies as alternative ways of achieving climate change goals. While past research focused on costs, technological advancements, and capture methods, there is a need for more studies on assessing the financial feasibility of these climate change solutions under uncertain conditions, alongside specific performance goals and strategies to entice power producers. Using a comprehensive framework featuring deterministic and stochastic modeling approaches, this research explores the impact of policy and market incentives on CCS and RE investments within the U.S. power sector. It analyzes the interactions of variables such as market uncertainties, technical factors, and policy dynamics on the financial viability of adopting CCS and RE for targeted CO2 reductions. The results reveal that, given the status quo of policies, RE and CCS exhibit annualized net present values of $4.62 and $1.76, respectively, for each metric ton (MT) of CO2. Uncertainties in policy incentives emerge as a primary hindrance to achieving cost-effective carbon reduction mandates using CCS, while changes in the green electricity price premium cause high variability in RE returns. The study proposes a hypothetical market, featuring the sale of CCS-linked net-zero electricity at a distinctive premium price of $0.03/kWh. The study's findings underscore the importance of both policy and market incentives to enable power producers to deploy carbon management technologies at a large scale.

Modelling the transmission of dengue, zika and chikungunya: a scoping review protocol

IntroductionAedes mosquitoes are the primary vectors for the spread of viruses like dengue (DENV), zika (ZIKV) and chikungunya (CHIKV), all of which affect humans. Those diseases contribute to global public...

(Keynote) On Interpretation of Impedance Spectroscopy Data

Impedance spectroscopy represents a rich area of science that has been applied to many research disciplines, including those associated with corrosion and corrosion control, energy systems, and biological systems. In laboratory settings, impedance has been used to study the kinetics and mechanisms of electrochemical reactions. Impedance spectroscopy has also been applied in industrial applications to quality control and corrosion monitoring and constitutes the basis of a class of sensors [1].Impedance spectra are easily measured by used of modern instrumentation. The difficulty lies in interpretation of impedance spectra in terms of physically meaningful information. While graphical methods [2], distribution of relaxation times analyses [3], and measurement model analyses [4] can guide model development, interpretation is generally based on use of a deterministic model that describes the physics of the system under study [5]. Such models may be coupled with electrical circuits that account for the double layer formed at the electrified interface and for the electrolyte resistance. The deterministic model approach is often perceived as being much more complicated than use of electrical circuits alone because it requires mathematical expertise.In general, interpretation of the impedance response requires both a quantitative evaluation of the error structure of the impedance measurement and insight into the physics and chemistry of the system under study. Analysis of the error structure yields a standard deviation for the stochastic errors, which can be used to weight subsequent regressions. The error analysis also identifies the range of frequencies that are consistent with the Kramers–Kronig relations and are, therefore, suitable for regression analysis. The physical insight is used to guide the interpretation model development. It should be noted that interpretation models are not unique. The models may suggest other experiments which can be used to reject the proposed model. Greater confidence may be assigned to a model that explains a variety of experimental observations.The objective of this paper is to review our work on interpretation of impedance data, including the identification and quantification of the error structure associated with impedance spectroscopy measurements. The presentation makes use of a program that allows users to access the power of the measurement model concept and to fit custom process models [6].References S. Wang, J. Zhang, O. Gharbi, V. Vivier, M. Gao, and M. E. Orazem, “Electrochemical Impedance Spectroscopy,” Nat. Rev. Methods Primers, 1, 41 (2021), 1-21.M. E. Orazem, and N. Pébère, and B. Tribollet, “Enhanced Graphical Representation of Electrochemical Impedance Data,” J. Electrochem.Soc., 153 (2006), B129-B136.B. A. Boukamp, “Distribution (Function) of Relaxation Times, Successor to Complex Nonlinear Least Squares Analysis of Electrochemical Impedance Spectroscopy?” J. Phys. Energy, 4 (2020), 042001.H. Liao, W. Watson, A. Dizon, B. Tribollet, V. Vivier, and M. E. Orazem, “Physical Properties Obtained from Measurement Model Analysis of Impedance Measurements,” Electrochim. Acta, 354 (2020), 136747.V. Vivier and M. E. Orazem, “Impedance Analysis of Electrochemical Systems,” Chem. Rev., 122 (2022), 11131-11168.W. Watson and M. E. Orazem, EIS: Measurement Model Program, ECSArXiv, 2020, https://ecsarxiv.org/kze9x/.

Deterministic and Probabilistic Approaches to Model and Update Dynamic Systems

Updating simulation models, based on flight test or other data, or creating a model using only experimental data is a necessary process that can be costly and laborious. Simulation updates are necessary for high-fidelity simulations used in analysis, control development, handling qualities prediction, fleet training, etc. This paper presents methods to update aircraft models based on deterministic and probabilistic approaches. The deterministic approach is based on a collection of signal processing, statistical analysis, and maximum likelihood estimators embodied in the Algorithms to Update Simulation Parameters with Experimental Data tool. Mathematically sound and statistically rigorous techniques are used to calculate updates of current model parameters and suggest new terms capturing unmodeled dynamics, thereby improving correlation with observed vehicle response. The probabilistic approach is based on generalized polynomial chaos theory included in the Algorithms for Uncertainty Representation and Analysis (AURA) tool. The AURA tool allows the user to update probabilistic models based on experimental data, formulated as a Bayesian inference problem. This paper will present the methodology for both deterministic and probabilistic approaches and provide examples of their application to different aircraft designs, including tilt-rotor and rotary-wing unmanned air system vehicles. The results for both deterministic and probabilistic modeling approaches demonstrate excellent results. The probabilistic approach is novel in its expressive power to propagate and update probabilistic models and provides users with an intuitive and powerful modeling approach.

Assessing electric mobility and renewable energy synergy in a small New Caledonia Island community

ABSTRACT In this paper, we evaluate the synergy between variable renewable energy (VRE), electric mobility, and Vehicle to Grid (V2G) deployment for a small community transitioning to a low-carbon economy. We propose an integrated deterministic modeling approach that accurately describes electric mobility activity within the electric grid in the ETEM-SG long-term capacity expansion model. This approach allowed us to keep the entire approach in the linear programming domain. We then perform a scenario analysis on the Isle of Pines case. The results show that V2G is essential for electric vehicles to make a positive contribution to the electric system by providing a supply–demand balance service. The deployment of electric vehicles allows reaching a VRE penetration rate of up to 94%, at no additional cost compared to a solution without electric vehicles, and even with a global-cost reduction of up to 5.3% thanks to a decrease of the capacity of stationary battery packs needed for storage.

Fungal infections in Algeria

Invasive and superficial fungal infections are increasingly reported in Algeria, testifying to the increase in their frequency in parallel with the increase in risk factors and the availability of diagnostic means, at least in university hospitals (CHU). The latter, located in the major northern cities, are equipped with high-performance diagnostic tools compared to hospitals in the interior of the country. A comprehensive search of published and grey literature was undertaken. Prevalence and incidence of discrete fungal diseases were estimated using a deterministic modelling approach based on populations at risk. Population (2021) and major underlying disease risk groups were obtained from UNAIDS, WHO Tuberculosis and the international transplant registries as well as published data for asthma and COPD. The health service profile was summarised from national documentation. Among the 43.6 million, including 12.9 million children, living in Algeria, the most prevalent fungal diseases are tinea capitis (>1.5 million), recurrent vaginal candidiasis (>500,000) and allergic fungal lung and sinus disorders (>110,000) and chronic pulmonary aspergillosis (>10,000). Life-threatening invasive fungal infection incidence includes 774 Pneumocystis pneumonia in AIDS, 361 cryptococcal meningitis, 2272 candidaemia and 2639 invasive aspergillosis cases. Fungal keratitis probably affects >6000 eyes each year. Fungal infections are underestimated in Algeria because they are sought in patients with risk factors only after bacterial infections when they should be sought in parallel. The diagnosis is only accessible in hospitals in large cities and the work carried out in mycology is rarely published, making the estimation of the burden of these conditions difficult.

Insights into Cytomegalovirus Transmission Dynamics: Bridging the Gap between Mathematical Modeling and Public Health Strategies

This mini-review delves into the crucial role of mathematical modeling in understanding cytomegalovirus (CMV) transmission dynamics. While infectious disease modeling often focuses on the population level, it is essential to recognize that immune development takes place at the individual level. This review explores recent advancements in both stochastic and deterministic modeling approaches and their contributions to our understanding of CMV transmission. Stochastic modeling enables us to investigate the efficiency and risk factors associated with CMV transmission in high- risk populations, such as hematopoietic stem cell transplantation (HCT) recipients, shedding light on factors influencing transmission. Deterministic modeling emphasizes the importance of viral replication, immune response, transmission efficiency, and the role of vectors. It underlines the need for a comprehensive understanding of these factors to develop effective prevention and control strategies. Integrated deterministic and stochastic models offer a holistic perspective, explaining phenomena like prolonged oral CMV shedding during primary infection and highlighting the importance of timing and immune suppression levels in transplacental transmission risk. In conclusion, these modeling insights can be integrated into public health strategies for CMV management, including targeted pre- transplant screening, enhanced post-transplant surveillance, optimized immunebased interventions, minimized transmission risks in HCT, vector control, and timing-sensitive intervention guidelines. This comprehensive approach can substantially enhance CMV prevention and control efforts, benefiting vulnerable populations and the broader community.

Goal programming and genetic algorithm in multiple objective optimization model for project portfolio selection: a review

Many scientific fields, such as engineering, data analytics, and deep learning, focus on optimization. Optimization problems are classified into two types based on the number of optimized objective functions: single objective and multi objective optimization problems. In this paper, a comparative review since 2000 using one of the deterministic and stochastic modelling approaches called goal programming (GP) and genetic algorithm (GA) in multi-objective optimization problem is discussed. This study gives a prime review of the application of GP and GA in various criteria of project portfolio selection problem. GP is a method for solving large-scale multi-objective optimization problems to assist decision makers in finding solutions that satisfy several competing goals. GA on the other hand are global meta-heuristic search algorithms that are used to provide approximation or optimal solutions to large-scale optimization problems. Of the 23 articles considered in this review showed that, from more than 100 projects, GA proved near optimal, feasible solution and efficient frontier in projects ranking, projects interaction and a preferred decision support tool of project portfolio selection. In addition, the two models select projects on risk-based approach, but GA proved to be more effective in terms of number of projects proposed, central processing unit (CPU) time and accuracy. The review concludes that, in multi-objective optimization model for project portfolio selection problems on a large-scale, very large or complex problems and less CPU time, GA is more effective than GP in multi-objective optimization problems. The review also showed gaps in previous studies of GP and GA application on project portfolio selection problem (PPSP). This review will aid scholars and demanding practitioners in gaining a broader understanding of goal programming and genetic algorithms in the context of project portfolio selection problems.

Numerical Simulation of the Elastic–Ideal Plastic Material Behavior of Short Fiber-Reinforced Composites Including Its Spatial Distribution with an Experimental Validation

For the numerical simulation of components made of short fiber-reinforced composites, the correct prediction of the deformation including the elastic and plastic behavior and its spatial distribution is essential. When using purely deterministic modeling approaches, the information of the probabilistic microstructure is not included in the simulation process. One possible approach for the integration of stochastic information is the use of random fields. In this study, numerical simulations of tensile test specimens were conducted utilizing a finite deformation elastic–ideal plastic material model. A selection of the material parameters covering the elastic and plastic domain are represented by cross-correlated second-order Gaussian random fields to incorporate the probabilistic nature of the material parameters. To validate the modeling approach, tensile tests until failure were carried out experimentally, which confirmed the assumption of the spatially distributed material behavior in both the elastic and plastic domain. Since the correlation lengths of the random fields cannot be determined by pure analytic treatments, additionally numerical simulations were performed for different values of the correlation length. The numerical simulations endorsed the influence of the correlation length on the overall behavior. For a correlation length of 5 mm, a good conformity with the experimental results was obtained. Therefore, it was concluded that the presented modeling approach was suitable to predict the elastic and plastic deformation of a set of tensile test specimens made of short fiber-reinforced composite sufficiently.

Correlation analysis of the elastic‐ideal plastic material behavior of short fiber‐reinforced composites

AbstractFor the numerical simulation of short fiber‐reinforced composites and the correct analysis of the deformation, information about the plastic behavior and its spatial distribution is essential. When using purely deterministic modeling approaches information of the probabilistic microstructure is not included in the simulation process. One possible approach for the integration of stochastic information is the use of random fields, which requires information about the correlation structure of all material input parameters. In this study the correlation structure for finite strain elasto‐plastic material behavior of short fiber‐reinforced composites is analyzed. This approach combines the use of already established procedures for linear‐elastic material behavior with a homogenization method for plasticity. The obtained results reveal a complex correlation structure, which is approximated with triangle and exponential correlation functions influenced by the window size. Due to the dependence of the hyperelastic and plastic material parameters on the fiber mass fraction, the strain‐energy density function coefficients are cross‐correlated with the yield strength of the composite. With this knowledge at hand, in a subsequent work numerical simulations of tensile tests are conducted that cover the elastic and plastic domain and include spatially distributed material properties.

A suitable deterministic modeling approach of urban stormwater nutrient, metal and organic pollutant removal in plant biofiltration

ABSTRACT Urban water managers regard stormwater runoff and its treatment as one of the greatest challenges, due to its diffused nature and pollutant range. In response, plant biofiltration, a water-sensitive urban design technology, is increasingly preferred. However, due to varying removal performances influenced by design and operational conditions, as well as pollutant removal process complexity, it is under-utilised. This note reviews relevant pollutant removal processes and pathways; and subsequently proffers a first step summary of fundamental processes and pathways applicable to generic plant biofiltration for removal of typical stormwater pollutants. The treatment model combines relevant existing models applicable to the removal pathways of organics, NH3, NO3-, PO4 3- and metal pollutant change, as well as recommends further refinements. Advancing the understanding of plant biofilter complexity represents a significant in the urban drainage management strategy, from simple flood reduction to the recognition of the stochastic nature of interactions influencing water quality.