Spatiotemporal and temporal forecasting of ambient air pollution levels through data-intensive hybrid artificial neural network models

Outdoor air pollution (AP) is a serious public threat which has been linked to severe respiratory and cardiovascular illnesses, and premature deaths especially among those residing in highly urbanised cities. As such, there is a need to develop early-warning and risk management tools to alleviate its effects. The main objective of this research is to develop AP forecasting models based on Artificial Neural Networks (ANNs) according to an identified model-building protocol from existing related works. Plain, hybrid and ensemble ANN model architectures were developed to estimate the temporal and spatiotemporal variability of hourly NO2 levels in several locations in the Greater London area. Wavelet decomposition was integrated with Multilayer Perceptron (MLP) and Long Short-term Memory (LSTM) models to address the issue of high variability of AP data and improve the estimation of peak AP levels. Block-splitting and crossvalidation procedures have been adapted to validate the models based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Willmott’s index of agreement (IA). The results of the proposed models present better performance than those from the benchmark models. For instance, the proposed wavelet-based hybrid approach provided 39.15% and 28.58% reductions in RMSE and MAE indices, respectively, on the performance of the benchmark MLP model results for the temporal forecasting of NO2 levels. The same approach reduced the RMSE and MAE indices of the benchmark LSTM model results by 12.45% and 20.08%, respectively, for the spatiotemporal estimation of NO2 levels in one site at Central London. The proposed hybrid deep learning approach offers great potential to be operational in providing air pollution forecasts in areas without a reliable database. The model-building protocol adapted in this thesis can also be applied to studies using measurements from other sites.Outdoor air pollution (AP) is a serious public threat which has been linked to severe respiratory and cardiovascular illnesses, and premature deaths especially among those residing in highly urbanised cities. As such, there is a need to develop early-warning and risk management tools to alleviate its effects. The main objective of this research is to develop AP forecasting models based on Artificial Neural Networks (ANNs) according to an identified model-building protocol from existing related works. Plain, hybrid and ensemble ANN model architectures were developed to estimate the temporal and spatiotemporal variability of hourly NO2 levels in several locations in the Greater London area. Wavelet decomposition was integrated with Multilayer Perceptron (MLP) and Long Short-term Memory (LSTM) models to address the issue of high variability of AP data and improve the estimation of peak AP levels. Block-splitting and crossvalidation procedures have been adapted to validate the models based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Willmott’s index of agreement (IA). The results of the proposed models present better performance than those from the benchmark models. For instance, the proposed wavelet-based hybrid approach provided 39.15% and 28.58% reductions in RMSE and MAE indices, respectively, on the performance of the benchmark MLP model results for the temporal forecasting of NO2 levels. The same approach reduced the RMSE and MAE indices of the benchmark LSTM model results by 12.45% and 20.08%, respectively, for the spatiotemporal estimation of NO2 levels in one site at Central London. The proposed hybrid deep learning approach offers great potential to be operational in providing air pollution forecasts in areas without a reliable database. The model-building protocol adapted in this thesis can also be applied to studies using measurements from other sites

Short- and long-term forecasting of ambient air pollution levels using wavelet-based non-linear autoregressive artificial neural networks with exogenous inputs

Roadside air pollution is a major issue due to its adverse effects on human health and the environment. This highlights the need for parsimonious and robust forecasting tools that help vulnerable members of the public reduce their exposure to harmful air pollutants. Recent results in air pollution forecasting applications include the use of hybrid models based on non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable inputs (NARX) and wavelet decomposition techniques. However, attempts employing both methods into one hybrid modelling system have not been widely made. Hence, this work further investigates the utilisation of wavelet-based NARX-ANN models in the shortand long-term prediction of hourly NO2 concentration levels. The models were trained using emissions and meteorological data collected from a busy roadside site in Central London, United Kingdom from January to December 2015. A discrete wavelet transformation technique was then implemented to address the highly variable characteristic of the collected NO2 concentration data. Overall results exhibit the superiority of the wavelet-based NARX-ANN models improving the accuracy of the benchmark NARX-ANN model results by up to 6% in terms of explained variance. The proposed models also provide fairly accurate long-term forecasts, explaining 68–76% of the variance of actual NO2 data. In conclusion, the findings of this study demonstrate the high potential of wavelet-based NARX-ANN models as alternative tools in short- and long-term forecasting of air pollutants in urban environments

A review of artificial neural network models for ambient air pollution prediction

Research activity in the field of air pollution forecasting using artificial neural networks (ANNs) has increased dramatically in recent years. However, the development of ANN models entails levels of uncertainty given the black-box nature of ANNs. In this paper, a protocol by Maier et al. (2010) for ANN model development is presented and applied to assess journal papers dealing with air pollution forecasting using ANN models. The majority of the reviewed works are aimed at the long-term forecasting of outdoor PM10, PM2.5, and oxides of nitrogen, and ozone. The vast majority of the identified works utilised meteorological and source emissions predictors almost exclusively. Furthermore, ad-hoc approaches are found to be predominantly used for determining optimal model predictors, appropriate data subsets and the optimal model structure. Multilayer perceptron and ensemble-type models are predominantly implemented. Overall, the findings highlight the need for developing systematic protocols for developing powerful ANN models

Hybrid artificial neural network models for effective prediction and mitigation of urban roadside NO2 pollution

Traffic-related air pollution has been a serious concern amongst policy-makers and the public due to its physiological and environmental impacts. An early warning system based on accurate forecasting tools must therefore be implemented to circumvent the adverse effects of exposure to major air pollutants. A multilayer perceptron neural network was trained and developed using air pollution and meteorological data over a two-year period from a monitoring site in Marylebone Road, Central London to predict roadside concentration values of NO2 24 hours ahead. Several hybrid models were also developed by applying feature selection techniques such as stepwise regression, principal component analysis, and Classification and Regression Trees to the neural network model. Most roadside pollutant variables, e.g., oxides of nitrogen, were found to be significant in predicting NO2. The statistical results reveal overall prediction superiority of the hybrid models to the standalone neural network model

Methods used for handling and quantifying model uncertainty of artificial neural network models for air pollution forecasting

The use of data-driven techniques such as artificial neural network (ANN) models for outdoor air pollution forecasting has been popular in the past two decades. However, research activity on the uncertainty surrounding the development of ANN models has been limited. Therefore, this review outlines the approaches for addressing model uncertainty according to the steps for building ANN models. Based on 128 articles published from 2000 to 2022, the review reveals that input uncertainty was predominantly addressed while less focus was given to structure, parameter and output uncertainties. Ensemble approaches have been mostly employed, followed by neuro-fuzzy networks. However, the direct measurement of uncertainty received less attention. The use of bootstrapping, Bayesian, and Monte Carlo simulation techniques which can quantify uncertainty was also limited. In conclusion, this review recommends the development and application of approaches that can both handle and quantify uncertainty surrounding the development of ANN models

Spatial estimation of outdoor NO2 levels in Central London using deep neural networks and a wavelet decomposition technique

Outdoor air pollution remains a major environmental threat to the public, especially those who reside in highly urbanised areas. Recent studies have revealed the effectiveness of early-warning mechanisms that enable the public reduce their exposure to air pollutants. This highlights the need for accurate air quality forecasts. However, air quality in many developing and highly urbanised countries remains unmonitored. Hence, a novel spatiotemporal interpolation modelling approach based on a deep learning and wavelet pre-processing technique was proposed in this paper. In more detail, Long Short-term Memory (LSTM) neural networks and Discrete Wavelet Transformation (DWT) were utilised to model the spatial variability of hourly NO2 levels at six urban sites in Central London, the United Kingdom. The models were trained using only the NO2 concentration data from the neighbouring sites. Benchmark models such as plain LSTM and Multilayer Perceptron (MLP) models were also developed to validate the effectiveness of the proposed models. The proposed wavelet-based spatiotemporal models were found to provide superior forecasting results, explaining 77% to 93% of the variability of the actual NO2 concentration levels at most sites. The overall results reveal the very promising potential of the proposed models for the spatiotemporal characterisation of outdoor air pollution