Modeling PM<sub>2.5</sub> Urban Pollution Using Machine Learning and Selected Meteorological Parameters

Deters, Jan Kleine; Žalakevičiūtė, Rasa; González, Mario; Rybarczyk, Yves

doi:10.1155/2017/5106045

Cited by 118 publications

(59 citation statements)

References 27 publications

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“…Instead of predicting a spectrum of concentrations through a regression technique, this study is interested in a binary discrimination between high levels of contamination, which present a risk for public health, versus acceptable levels. This choice is supported by the recommendations of the WHO, which defines a standard threshold, and related work proposing a machine learning approach to classify air pollution [26,[35][36][37][38].…”

Section: Data Preparation and Assessmentmentioning

confidence: 99%

“…The second explanation is related to the height of the planetary boundary layer (PBL). PBL is low in the early morning and keeps growing all morning long, due to the intensification of solar radiation, which, in result, increases the dilution of PM 2.5 in the atmosphere [36], especially after 10:00 (Figure 8b). These two phenomena account for the two thresholds (around 9:00 and 10:00) for which the time feature is split in the models presented in Figure 8.…”

Section: Pm 25 Prediction From Traffic and Time Of The Daymentioning

confidence: 99%

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A Traffic-Based Method to Predict and Map Urban Air Quality

et al. 2020

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As global urbanization, industrialization, and motorization keep worsening air quality, a continuous rise in health problems is projected. Limited spatial resolution of the information on air quality inhibits full comprehension of urban population exposure. Therefore, we propose a method to predict urban air pollution from traffic by extracting data from Web-based applications (Google Traffic). We apply a machine learning approach by training a decision tree algorithm (C4.8) to predict the concentration of PM2.5 during the morning pollution peak from: (i) an interpolation (inverse distance weighting) of the value registered at the monitoring stations, (ii) traffic flow, and (iii) traffic flow + time of the day. The results show that the prediction from traffic outperforms the one provided by the monitoring network (average of 65.5% for the former vs. 57% for the latter). Adding the time of day increases the accuracy by an average of 6.5%. Considering the good accuracy on different days, the proposed method seems to be robust enough to create general models able to predict air pollution from traffic conditions. This affordable method, although beneficial for any city, is particularly relevant for low-income countries, because it offers an economically sustainable technique to address air quality issues faced by the developing world.

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Section: Data Preparation and Assessmentmentioning

confidence: 99%

Section: Pm 25 Prediction From Traffic and Time Of The Daymentioning

confidence: 99%

A Traffic-Based Method to Predict and Map Urban Air Quality

et al. 2020

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“…As a globally optimal method, support machine vector has been increasingly used for PM 2.5 estimation [97][98][99]. However, the scalability of this method is constrained in studies with a large sample size (e.g., high spatiotemporal resolution AOD over a long period and a large region).…”

Section: Strengths Of Bagging Of Residual Networkmentioning

confidence: 99%

A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5

2020

Remote Sensing

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Accurate estimation of fine particulate matter with diameter ≤2.5 μm (PM2.5) at a high spatiotemporal resolution is crucial for the evaluation of its health effects. Previous studies face multiple challenges including limited ground measurements and availability of spatiotemporal covariates. Although the multiangle implementation of atmospheric correction (MAIAC) retrieves satellite aerosol optical depth (AOD) at a high spatiotemporal resolution, massive non-random missingness considerably limits its application in PM2.5 estimation. Here, a deep learning approach, i.e., bootstrap aggregating (bagging) of autoencoder-based residual deep networks, was developed to make robust imputation of MAIAC AOD and further estimate PM2.5 at a high spatial (1 km) and temporal (daily) resolution. The base model consisted of autoencoder-based residual networks where residual connections were introduced to improve learning performance. Bagging of residual networks was used to generate ensemble predictions for better accuracy and uncertainty estimates. As a case study, the proposed approach was applied to impute daily satellite AOD and subsequently estimate daily PM2.5 in the Jing-Jin-Ji metropolitan region of China in 2015. The presented approach achieved competitive performance in AOD imputation (mean test R2: 0.96; mean test RMSE: 0.06) and PM2.5 estimation (test R2: 0.90; test RMSE: 22.3 μg/m3). In the additional independent tests using ground AERONET AOD and PM2.5 measurements at the monitoring station of the U.S. Embassy in Beijing, this approach achieved high R2 (0.82–0.97). Compared with the state-of-the-art machine learning method, XGBoost, the proposed approach generated more reasonable spatial variation for predicted PM2.5 surfaces. Publically available covariates used included meteorology, MERRA2 PBLH and AOD, coordinates, and elevation. Other covariates such as cloud fractions or land-use were not used due to unavailability. The results of validation and independent testing demonstrate the usefulness of the proposed approach in exposure assessment of PM2.5 using satellite AOD having massive missing values.

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“…Statistical models have been applied for air pollution prediction on the basis of meteorological data [31][32][33][34][35]. However, existing studies on statistical modeling have mostly been restricted to simply utilizing standard classification or regression models, which have neglected the nature of the problem itself or ignored the correlation between sub-models in different time slots.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization

Zhu

Cai

Yang

et al. 2018

BDCC

130

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Abstract:In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., ozone, particle matter (PM 2.5 ) and sulfur dioxide). Machine learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exist some works applying machine learning to air quality prediction, most of the prior studies are restricted to several-year data and simply train standard regression models (linear or nonlinear) to predict the hourly air pollution concentration. In this work, we propose refined models to predict the hourly air pollution concentration on the basis of meteorological data of previous days by formulating the prediction over 24 h as a multi-task learning (MTL) problem. This enables us to select a good model with different regularization techniques. We propose a useful regularization by enforcing the prediction models of consecutive hours to be close to each other and compare it with several typical regularizations for MTL, including standard Frobenius norm regularization, nuclear norm regularization, and 2,1 -norm regularization. Our experiments have showed that the proposed parameter-reducing formulations and consecutive-hour-related regularizations achieve better performance than existing standard regression models and existing regularizations.

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Modeling PM_2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters

Cited by 118 publications

References 27 publications

A Traffic-Based Method to Predict and Map Urban Air Quality

A Traffic-Based Method to Predict and Map Urban Air Quality

A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5

A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization

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Modeling PM2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters

Cited by 118 publications

References 27 publications

A Traffic-Based Method to Predict and Map Urban Air Quality

A Traffic-Based Method to Predict and Map Urban Air Quality

A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5

A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization

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Modeling PM_2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters