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

Rybarczyk, Yves; Žalakevičiūtė, Rasa

doi:10.3390/app8122570

Cited by 176 publications

(101 citation statements)

References 68 publications

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“…It should be noted that recently Rybarczyk and Zalakeviciute published a paper about 'Machine learning approaches for outdoor air quality modelling: A systematic review' [11]. By reviewing this paper, we have defined key features which were taken into consideration during our study.…”

Section: Search Strategy and Inclusion/exclusion Criteriamentioning

confidence: 99%

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

2020

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The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features.

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Section: Search Strategy and Inclusion/exclusion Criteriamentioning

confidence: 99%

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

2020

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“…However, relying on satellite AOD as a proxy for near-ground air pollution can be misleading, as AOD reflects the extinction of radiation in an atmospheric column, while particulate matter concentrations reflect a highly localized dry mass concentration of particles of a certain size distribution typically measured near ground (Wang & Christopher, 2003). Several studies have trained statistical models on the relationship between AOD and PM, accounting for a range of additional parameters, and mostly with a focus on applications (see review by Rybarczyk, 2018). Methods include linear regression models (Arvani et al, 2016), multiple-additive regression models (Gupta & Christopher, 2009a;Zhang et al, 2018), land-use models (Kloog et al, 2011;Nordio et al, 2013), or a combination of the latter two (Chudnovsky et al, 2014;Hu et al, 2014;Kloog et al, 2012).…”

Section: Motivation and Research Questionsmentioning

confidence: 99%

Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning

et al. 2020

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The quantification of factors leading to harmfully high levels of particulate matter (PM) remains challenging. This study presents a novel approach using a statistical model that is trained to predict hourly concentrations of particles smaller than 10 μm (PM10) by combining satellite‐borne aerosol optical depth (AOD) with meteorological and land‐use parameters. The model is shown to accurately predict PM10 (overall R 2 = 0.77, RMSE = 7.44 μg/m 3) for measurement sites in Germany. The capability of satellite observations to map and monitor surface air pollution is assessed by investigating the relationship between AOD and PM10 in the same modeling setup. Sensitivity analyses show that important drivers of modeled PM10 include multiday mean wind flow, boundary layer height (BLH), day of year (DOY), and temperature. Different mechanisms associated with elevated PM10 concentrations are identified in winter and summer. In winter, mean predictions of PM10 concentrations >35 μg/m 3 occur when BLH is below ∼500 m. Paired with multiday easterly wind flow, mean model predictions surpass 40 μg/m 3 of PM10. In summer, PM10 concentrations seemingly are less driven by meteorology, but by emission or chemical particle formation processes, which are not included in the model. The relationship between AOD and predicted PM10 concentrations depends to a large extent on ambient meteorological conditions. Results suggest that AOD can be used to assess air quality at ground level in a machine learning approach linking it with meteorological conditions.

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“…Air quality has had a huge impact on human health and climate, so the effective management of air quality and its accurate assessment and prediction have received widespread attention in recent years. As one of the important air quality indices (AQIs), the prediction of PM2.5 concentration is a necessary evaluation parameter for air quality forecast [1,2]. It is difficult to obtain an accurate long-term prediction of PM2.5 concentration because PM2.5 comprises typical complex nonlinear time-series data [3], where the vector field of state dynamics is a nonlinear function of state variables.…”

Section: Introductionmentioning

confidence: 99%

Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction

et al. 2019

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Featured Application: This work can be used in the intelligent system for the smart city, smart agriculture, etc.Abstract: It is crucial to predict PM2.5 concentration for early warning regarding and the control of air pollution. However, accurate PM2.5 prediction has been challenging, especially in long-term prediction. PM2.5 monitoring data comprise a complex time series that contains multiple components with different characteristics; therefore, it is difficult to obtain an accurate prediction by a single model. In this study, an integrated predictor is proposed, in which the original data are decomposed into three components, that is, trend, period, and residual components, and then different sub-predictors including autoregressive integrated moving average (ARIMA) and two gated recurrent units are used to separately predict the different components. Finally, all the predictions from the sub-predictors are combined in fusion node to obtain the final prediction for the original data. The results of predicting the PM2.5 time series for Beijing, China showed that the proposed predictor can effectively improve prediction accuracy for long-term prediction. Appl. Sci. 2019, 9, 4533 2 of 14 models [14] and vector auto-regression (VAR) [15]. ARIMA is a linear modeling method that provides accurate predictions for approximate linear relationships. However, its prediction performance is not good enough for nonlinear prediction problems.To capture the complex nonlinearity of PM2.5, a back propagation (BP) neural network has been widely applied in time-series data prediction [16][17][18]. Other shallow nonlinear networks based on a machine-learning mechanism such as the improved gray neural network model [19] and the radial basis function (RBF) neural network [20] have also been used to predict time-series data. Xu et al. [3] proposed a supplementary leaky integrator echo state network (SLI-ESN), which added the historical state term of the historical moment to the calculation of a leaky integrator reservoir. Compared with an echo state network (ESN), a leaky integrator ESN (LI-ESN), an extreme learning machine (ELM), a hierarchical ELM (H-ELM), a stacked auto-encoder (SAE), and a traditional SLI-ESN, the proposed SLI-ESN of Xu et al.[3] could achieve good prediction results, but its long-term predictions were not satisfactory.As a shallow network still cannot effectively extract the complex nonlinearity of the data, decomposition must be used; that is, the data must be decomposed into multiple components to reduce its complexity, and then multiple sub-models are used to improve the prediction performance. For example, García et al. [21] decomposed a long-term data series into smaller seasonal component patterns. Jesús et al. [22] divided a pollen-concentration data series into seasonal and random parts, used partial least-squares regression (PLSR) to fit the residuals, and established an airborne pollen time-series model to predict the daily pollen concentration. Ming et al.[23] extracted accurate seasonal signals, a...

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

Cited by 176 publications

References 68 publications

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning

Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction

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