Current studies show that traditional deterministic models tend to struggle to capture the non-linear relationship between the concentration of air pollutants and their sources of emission and dispersion. To tackle such a limitation, the most promising approach is to use statistical models based on machine learning techniques. Nevertheless, it is puzzling why a certain algorithm is chosen over another for a given task. This systematic review intends to clarify this question by providing the reader with a comprehensive description of the principles underlying these algorithms and how they are applied to enhance prediction accuracy. A rigorous search that conforms to the PRISMA guideline is performed and results in the selection of the 46 most relevant journal papers in the area. Through a factorial analysis method these studies are synthetized and linked to each other. The main findings of this literature review show that: (i) machine learning is mainly applied in Eurasian and North American continents and (ii) estimation problems tend to implement Ensemble Learning and Regressions, whereas forecasting make use of Neural Networks and Support Vector Machines. The next challenges of this approach are to improve the prediction of pollution peaks and contaminants recently put in the spotlights (e.g., nanoparticles).

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Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review

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Air Quality Prediction: Big Data and Machine Learning Approaches

Predicting air quality is a complex task due to the dynamic nature, volatility, and high variability in time and space of pollutants and particulates. At the same time, being able to model, predict, and monitor air quality is becoming more and more relevant, especially in urban areas, due to the observed critical impact of air pollution on citizens’ health and the environment. In this paper, we employ a popular machine learning method, support vector regression (SVR), to forecast pollutant and particulate levels and to predict the air quality index (AQI). Among the various tested alternatives, radial basis function (RBF) was the type of kernel that allowed SVR to obtain the most accurate predictions. Using the whole set of available variables revealed a more successful strategy than selecting features using principal component analysis. The presented results demonstrate that SVR with RBF kernel allows us to accurately predict hourly pollutant concentrations, like carbon monoxide, sulfur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, as well as the hourly AQI for the state of California. Classification into six AQI categories defined by the US Environmental Protection Agency was performed with an accuracy of 94.1% on unseen validation data.

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A Machine Learning Approach to Predict Air Quality in California

Air pollution is the problem of adding harmful substances or other agents into the atmosphere and it is caused by industrial, transport or household activities. It is one of the most serious problems of our times and the determination of the conditions under which we have extreme pollutants’ values is a crucial challenge for the modern scientific community. The innovative and effective hybrid algorithm designed and employed in this research effort is entitled Easy Hybrid Forecasting (EHF). The main advantage of the EHF is that each forecasting does not require measurements from sensors, other hardware devices or data that require the use of expensive software. This was done intentionally because the motivation for this work was the development of a hybrid application that can be downloaded for free and used easily by everyday common people with no additional financial cost, running in devices like smart phones. From this point of view it does not require data from sensors or specialized software and it can offer people reliable information about extreme cases.

Fast and low cost prediction of extreme air pollution values with hybrid unsupervised learning

Predicting minority class for suspended particulate matters level by extreme learning machine

Short-Term Load Forecasting Using Channel and Temporal Attention Based Temporal Convolutional Network

Forecasting of air pollution is a popular and important topic in recent years due to the health impact caused by air pollution. It is necessary to build an early warning system, which provides forecast and also alerts health alarm to local inhabitants by medical practitioners and the local government. Meteorological and pollutions data collected daily at monitoring stations of Macau can be used in this study to build a forecasting system. Support vector machines (SVMs), a novel type of machine learning technique based on statistical learning theory, can be used for regression and time series prediction. SVM is capable of good generalization while the performance of the SVM model is often hinged on the appropriate choice of the kernel.

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Short-term prediction of air pollution in macau using support vector machines

Multi-layer perceptrons (MLP) have been employed to solve a variety of problems. The practical applications of MLP however suffer from different drawbacks such as local minima and over-fitting, such that good generalization may not be obtained. Least squares support vector machines (LS-SVM), a novel type of machine learning technique based on statistical learning theory, can be used for regression and time series prediction. In this study, meteorological and pollutions data are collected daily at monitoring stations of a city. This pollutant-related information can be used to build an early warning system, which provides forecast and also alarms health advice to local inhabitants by medical practicians and local government. Through experiment, we found that LS-SVM could overcome most of the drawbacks of MLP and had been reported to show promising results.

Least Squares Support Vector Prediction for Daily Atmospheric Pollutant Level

Forecasting of air pollution is a popular and important topic in recent year due to the health impact caused by air pollution. It is necessary to build an early warning system, which provides forecast and also alerts health alarm to local inhabitants by medical practicians and local government. Meteorological and pollutions data collected daily at monitoring stations of Macau can be used in this study to build a forecasting system. Support vector machines (SVM), a novel type of machine learning technique based on statistical learning theory, can be used for regression and time series prediction. SVM is capable of good generalization while the performance of the SVM model is often hinged on the appropriate choice of the kernel.

Effect of choice of kernel in support vector machines on ambient air pollution forecasting

By matching air quality index (AQI) data with the household data from China Family Panel Studies (CFPS), we identify the impact of air pollution on household medical expenses from a micro perspective. The results show that higher air pollution will increase household medical expenses and change household consumption structure. This effect is still significant after controlling for cities' relevant household and individual characteristics and economic characteristics. Under different educational backgrounds, income, hukou, gender, and other conditions, air pollution will significantly reduce medical spending. For those females in the urban areas with higher education backgrounds and higher income, the spending elasticity of air pollution is more significant than other corresponding groups. And air pollution will promote medical expenses through stronger individuals' environmental awareness, poor health conditions, bad emotional status, and positive risk aversion. Furthermore, we find that the impact of air pollution on healthcare spending remains significant after instrumental variables regression and geographical regression based on the Qinling Mountains-Huaihe River Line.

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Jing-yi Yang

Papers

Short-Term Load Forecasting Using Channel and Temporal Attention Based Temporal Convolutional Network

Short-term prediction of air pollution in macau using support vector machines

Least Squares Support Vector Prediction for Daily Atmospheric Pollutant Level

Effect of choice of kernel in support vector machines on ambient air pollution forecasting

Air Pollution and Household Medical Expenses: Evidence From China