Modeling urban air pollution with optimized hierarchical fuzzy inference system

Abstract

Environmental exposure assessments (EEA) and epidemiological studies require urban air pollution models with appropriate spatial and temporal resolutions. Uncertain available data and inflexible models can limit air pollution modeling techniques, particularly in under developing countries. This paper develops a hierarchical fuzzy inference system (HFIS) to model air pollution under different land use, transportation, and meteorological conditions. To improve performance, the system treats the issue as a large-scale and high-dimensional problem and develops the proposed model using a three-step approach. In the first step, a geospatial information system (GIS) and probabilistic methods are used to preprocess the data. In the second step, a hierarchical structure is generated based on the problem. In the third step, the accuracy and complexity of the model are simultaneously optimized with a multiple objective particle swarm optimization (MOPSO) algorithm. We examine the capabilities of the proposed model for predicting daily and annual mean PM2.5 and NO2 and compare the accuracy of the results with representative models from existing literature. The benefits provided by the model features, including probabilistic preprocessing, multi-objective optimization, and hierarchical structure, are precisely evaluated by comparing five different consecutive models in terms of accuracy and complexity criteria. Fivefold cross validation is used to assess the performance of the generated models. The respective average RMSEs and coefficients of determination (R (2)) for the test datasets using proposed model are as follows: daily PM2.5 = (8.13, 0.78), annual mean PM2.5 = (4.96, 0.80), daily NO2 = (5.63, 0.79), and annual mean NO2 = (2.89, 0.83). The obtained results demonstrate that the developed hierarchical fuzzy inference system can be utilized for modeling air pollution in EEA and epidemiological studies.

Tackling Air Pollution in Krakow

“…The analysis of correlations between the major factors impacting the air quality may provide a basis for the building of a model of predicting the level of pollutants depending on, inter alia, the wind velocity, the temperature, the amount of precipitation and the occurrence of inversion [15][16][17]. As part of the construction of the prediction model of air pollutants concentrations for Łódź, among others, analysis of the correlation between pollutant concentrations and components of the weather vector was carried out [18].…”

A Hybrid Fuzzy Inference System Based on Dispersion Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning

“…Evidence from previous researches, including the study by the authors, have proven that the conformity between the hierarchical structure and the studied issue considerably improve the performance [55,56]. …”

“…Having transformed a fuzzy inference system to a number of more simple systems related to each other hierarchically, these systems reduce the number of rules. Through this approach, by considering the physical nature of the problem, a system will be developed with desirable aspects, which has increased the accuracy [56].…”

“…A buffer analysis is applied around all dispersion model receptors to generate the independent variables related to traffic volume and road network area. The radii of the buffers are 500 m. The buffer distance is selected according to evidence, indicating extension of gradients from road network [73][74][75], and LUR studies [10,16,56]. Domain of the independent variables for HFISPM model is tabulated in Table 3.…”

“…It is necessary to note that although the number of rules, as the index of model complexity, and RMSE as the index of model accuracy make a multi-objective problem, the simultaneous optimization of these indexes leads to a better cooperation among the produced rules, thus improving the model accuracy [56].…”