Influence of traffic on build-up of polycyclic aromatic hydrocarbons on urban road surfaces: A Bayesian network modelling approach

Abstract

Due to their carcinogenic effects, Polycyclic Aromatic Hydrocarbons (PAHs) deposited on urban surfaces are a major concern in the context of stormwater pollution. However, the design of effective pollution mitigation strategies is challenging due to the lack of reliability in stormwater quality modelling outcomes. Current modelling approaches do not adequately replicate the interdependencies between pollutant processes and their influential factors. Using Bayesian Network modelling, this research study characterised the influence of vehicular traffic on the build-up of the sixteen US EPA classified priority PAHs. The predictive analysis was conditional on the structure of the proposed BN, which can be further improved by including more variables. This novel modelling approach facilitated the characterisation of the influence of traffic as a source of origin and also as a key factor that influences the re-distribution of PAHs, with positive or negative relationship between traffic volume and PAH build-up. It was evident that the re-distribution of particle-bound PAHs is determined by the particle size rather than the chemical characteristics such as volatility. Moreover, compared to commercial and residential land uses, mostly industrial land use contributes to the PAHs load released to the environment. Carcinogenic PAHs in industrial areas are likely to be associated with finer particles, while PAHs, which are not classified as human carcinogens, are likely to be found in the coarser particle fraction.

Assessing Anthropogenic Impacts on Chemical and Biochemical Oxygen Demand in Different Spatial Scales with Bayesian Networks

“…Second, based on conditional dependencies between variables, it estimates the conditional coefficients and conditional probabilities for continuous variables and discrete variables, respectively. Thus, each variable could be analyzed without knowing the precise information about global model distribution [6,36,37]. In the study, the BN was developed to model the complex processes between human activities (land use and sewage outfalls) and oxygen-consuming organic matter indicators (COD and BOD) in dry and wet seasons in the HRB (Figure 3).…”

“…In the research, the impacts of human activities (land use and sewage outfalls) on COD and BOD in the HRB were assessed in dry and wet seasons and different spatial scales (from local to catchment scales). In order to conveniently model the complex processes between anthropogenic activities and water quality, Bayesian networks (BNs) were applied, which can decompose the global model distribution of all variables into the local conditional probability distribution of each variable by the directed acyclic graph (DAG) [34][35][36]. In this way, both quantitative variables (such as water quality and land use data) and qualitative variables (different season scenarios) can be easily incorporated into one model.…”

Bayesian-Based Approaches to Exploring the Long-Term Alteration in Trace Metals of Surface Water and Its Driving Forces

“…The BN is a probabilistic graphical modeling approach. It has a structure consisting of nodes representing variables and arcs representing conditional probability relationships . The Markovian property of the model accounts for the probabilistic conditional dependencies, and each variable depends only on its immediate parent variables.…”

“…It has a structure consisting of nodes representing variables and arcs representing conditional probability relationships. 58 The Markovian property of the model accounts for the probabilistic conditional dependencies, and each variable depends only on its immediate parent variables. In this study, the metal concentrations were modeled as the child (or dependent) variable, while hydrochemical and anthropogenic factors were parent (or independent) variables.…”