An Intelligent traffic network optimisation by use of Bayesian inference methods to combat air pollution

CCI Group has contributed to the researchTraffic flow related air pollution is one of the major problems in urban areas, and is often difficult to avoid it if the time sequenced dynamic pollution and traffic parameters are not identified and modelled efficiently. In our introduced work here, an artificial intelligence technique such as Bayesian networks are used for a robust traffic data analysis and modelling. The most common challenge in traditional data analysis is a lack of capability of unveiling the hidden links between the distant data attributes (e.g. pollution sources, dynamic traffic parameters, geographic location characteristics, etc.), whereas some subtle effects of these parameters or events may play an important role in pollution on a long term basis

Air quality modelling using chemometric techniques

The datasets of air quality parameters for three years (2012-2014) were applied. HACA gave the result of three different groups of similarity based on the characteristics of air quality parameters. DA shows all seven parameters (CO, O3, PM10, SO2, NOx, NO and NO2) gave the most significant variables after stepwise backward mode. PCA identifies the major source of air pollution is due to combustion of fossil fuels in motor vehicles and industrial activities. The ANN model shows a better prediction compared to the MLR model with R2 values equal to 0.819 and 0.773 respectively. This study presents that the chemometric techniques and modelling become an excellent tool in API assessment, air pollution source identification, apportionment and can be setbacks in designing an API monitoring network for effective air pollution resources management

Air quality modelling using chemometric techniques

The datasets of air quality parameters for three years (2012-2014) were applied. HACA gave the result of three different groups of similarity based on the characteristics of air quality parameters. DA shows all seven parameters (CO, O3, PM10, SO2, NOx, NO and NO2) gave the most significant variables after stepwise backward mode. PCA identifies the major source of air pollution is due to combustion of fossil fuels in motor vehicles and industrial activities. The ANN model shows a better prediction compared to the MLR model with R2 values equal to 0.819 and 0.773 respectively.This study presents that the chemometric techniques and modelling become an excellent tool in API assessment, air pollution source identification, apportionment and can be setbacks in designing an API monitoring network for effective air pollution resources management.Keywords: air pollutant index; chemometric; ANN; ML

Use of Bayesian Inference Method to Model Vehicular Air Pollution in Local Urban Areas

The file attached to this record is the author's final peer reviewed version.Traffic Related Air Pollution (TRAP) studies are usually investigated using different categories such as air pollution exposure for health impacts, urban transportation network design to mitigate pollution, environmental impacts of pollution, etc. All of these subfields often rely on a robust air pollution model, which also necessitates an accurate prediction of future pollutants. As is widely accepted by the heath authorities, TRAP is considered to be the major health issue in urban areas, and it is difficult to keep pollution at harmless levels if the time sequenced dynamic pollution and traffic parameters are not identified and modelled efficiently. In our work here, artificial intelligence techniques, such as Bayesian Networks with an optimized configuration, are used to deliver a probabilistic traffic data analysis and predictive modelling for air pollution (SO2, NO2 and CO) at very local scale of an urban region with up to 85% accuracy. The main challenge for traditional data analysis is a lack of capability to reveal the hidden links between distant data attributes (e.g. pollution sources, dynamic traffic parameters, etc.), whereas some subtle effects of these parameters or events may play an important role in pollution on a long-term basis. This study focuses on the optimisation of Bayesian Networks to unveil hidden links and to increase the prediction accuracy of TRAP considering its further association with a predictive GIS syste

Air Quality Prediction - A Study Using Neural Network Based Approach

India is the 7th largest country by area and 2nd most populated country in the world. The reports prepared by IQAir revels that India is 3rd most polluted country after Bangladesh and Pakistan, on the basis of fine particulates (PM2.5) concentration for the year 2020. In this article, the quality of air in six Indian cities is predicted using data-driven Artificial Neural Network. The data was taken from the 'Kaggle' online source. For six Indian cities, 6139 data sets for ten contaminants (PM2.5, PM10, NO, NO2, NH3, CO, SO2, O3, C6H6 and C7H8) were chosen. The datasets were collected throughout the last five years, from 2016 to 2020, and were used to develop the predictive model. Two machine learning model are proposing in this study namely Artificial Intelligence (AI) and Gaussian Process Regression (GPR) The R-value of ANN and GPR models are 0.9611 and 0.9843 sequentially. The other performance indices such as RMSE, MAPE, MAE of the GPR model are 21.4079, 7.8945% and 13.5884, respectively. The developed model is quite useful to update citizens about the predicted air quality of the urban spaces and protect them from getting affected by the poor ambient air quality. It can also be used to find the proper abatement strategies as well as operational measures