Computational Intelligence Estimation of Natural Background Ozone Level and its Distribution for Air Quality Modelling and Emission Control

Abstract

ABSTRACT:Background ozone, known as the ozone that occurs in the troposphere as a result of biogenic emissions without photochemical influences, has a close relationship with human health risk. The prediction of the background ozone level by an air quality model could cover a wider region, whereas a measurement method can only record at monitoring sites. The problem is that simulation with deterministic models is quite tedious because of the nonlinear nature of some particular chemical reactions involved in the pollutant formulation. In this work, we present a reliable method for determination of the background ozone using the ambient measurement data. Our proposed definition can be used to determine the background level at any part of the globe and in any seasons without relying on data obtained at remote sites.A statistical model approach will be used for the estimation of the background ozone concentration, and a method for extrapolating the site data will be utilised to approximate the spatial distribution on the region. The proposed method will be applied in the Sydney basin to evaluate its effectiveness in background ozone determination. The results show the advantage of the proposed approach as a globally generic and computationally efficient way for the background ozone estimation with a reasonable accuracy.

A Hybrid Model for Spatiotemporal Air Quality Prediction Based on Interpretable Neural Networks and a Graph Neural Network

“…Classic statistical methods, such as the auto regression integrated moving average (ARIMA) [5,[20][21][22] and geographically weighted regression (GWR) [23], have been used with statistical models for predicting small datasets and univariate time series of air quality. In addition, traditional machine learning methods along with statistical models have also been applied to AQP, such as random forest (RF) [24], SVM [22,[25][26][27], improved SVM (LSSVM) [28], LR [29,30], ANNs [31,32], and the improved neural network models BPNN [33], GRNN [32], RBFNN [31], and other models for processing. Based on restricted datasets, the typical machine learning approach can only capture limited nonlinear temporal and spatial correlation aspects affecting air quality.…”

Study and development of hybrid and ensemble forecasting models for air quality index forecasting

“…To handle the nonlinear aspect efficiently, an artificial neural network (ANN) is the most widely used ML predictor, miming the structure of the human brain and nervous system. The ANNs like BPNN (Kamal et al, 2006), RBFNN (Wahid et al, 2011), WNN (Li et al, 2018; Zhang et al, 2018), and GRNN (Antanasijević et al, 2013) have already been applied for air pollution prediction. Harish Kumar and Yogesh (Harishkumar et al, 2020) compared various ML regression models (Linear regression, lasso, ridge, Random Forest, Gradient boost, K‐Nearest Neighbours, and MLP) to predict PM2.5 concentration in Taiwan.…”