Evaluation of Different Machine Learning Approaches to Forecasting PM2.5 Mass Concentrations

Karimian, Hamed; Li, Qi; Wu, Chunlin; Qi, Yanlin; Mo, Yuqin; Gong, Chen; Zhang, Xianfeng; Sachdeva, Sonali

doi:10.4209/aaqr.2018.12.0450

Cited by 121 publications

(44 citation statements)

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“…After cleaning and pre-processing the data, it is subjected to further analysis including time-series analysis and analyzing the overall impact of every feature on the PM2.5 value [ 43 ].…”

Section: Data Acquisition and Analysismentioning

confidence: 99%

A machine learning-based model to estimate PM2.5 concentration levels in Delhi's atmosphere

Kumar

Mishra

Singh

2020

Heliyon

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During the last many years, the air quality of the capital city of India, Delhi had been hazardous. A large number of people have been diagnosed with Asthma and other breathing-related problems. The basic reason behind this has been the high concentration of life-threatening PM2.5 particles dissolved in its atmosphere. A good model, to forecast the concentration level of these dissolved particles, may help to prepare the residents with better prevention and safety strategies in order to save them from many health-related diseases. This work aims to forecast the PM2.5 concentration levels in various regions of Delhi on an hourly basis, by applying time series analysis and regression, based on various atmospheric and surface factors such as wind speed, atmospheric temperature, pressure, etc. The data for the analysis is obtained from various weather monitoring sites, set-up in the city, by the Indian Meteorological Department (IMD). A regression model is proposed, which uses Extra-Trees regression and AdaBoost, for further boosting. Experimentation for comparative study with the recent works is done and results indicate the efficacy of the proposed model.

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“…After cleaning and pre-processing the data, it is subjected to further analysis including time-series analysis and analyzing the overall impact of every feature on the PM2.5 value [ 43 ].…”

Section: Data Acquisition and Analysismentioning

confidence: 99%

A machine learning-based model to estimate PM2.5 concentration levels in Delhi's atmosphere

Kumar

Mishra

Singh

2020

Heliyon

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“…The prediction results of neural networks are better than that of linear model, and the maximum prediction error 21 hours ahead is 32% (Perez and Menares, 2018). The long short-term memory (LSTM) can effectively forecast air pollution and achieve the best results (Karimian et al, 2019). The prediction of the 2016 ozone season using generalized additive models are in good agreement with the relevant measurement results (R 2 = 0.70) (Pernak et al, 2019).…”

Section: Comparison With Other Modelsmentioning

confidence: 61%

Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions

Guo

et al. 2020

Aerosol Air Qual. Res.

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Air quality forecasting is a significant method of protecting public health because it provides early warning of harmful air pollutants. In this study, we used correlation analysis and artificial neural networks (ANNs; including wavelet ANNs [WANNs]) to identify the linear and nonlinear associations, respectively, between the air pollution index (API) and meteorological variables in Xi'an and Lanzhou. Evaluating twelve algorithms and nineteen network topologies for the ANN and WANN models, we discovered that the optimal input variables for an API forecasting model were the APIs from the 3 preceding days and sixteen selected meteorological factors. Additionally, the API could be accurately predicted based solely on the value recorded 3 days earlier. Based on the correlation coefficients between the air pollution index of the targeted day and the tested variables, the API displayed the closest relationship with the API 1 day earlier as well as stronger correlations with the average temperature, average water vapor pressure, minimum temperature, maximum temperature, API 2 days earlier, and API 3 days earlier. When Bayesian regularization was applied as a training algorithm, the WANN and ANN models accurately reproduced the APIs in both Xi'an and Lanzhou, although the WANN model (R = 0.8846 for Xi'an and R = 0.8906 for Lanzhou) performed better than the ANN (R = 0.8037 for Xi'an and R = 0.7742 for Lanzhou) during the forecasting stage. These results demonstrate that WANNs are effective in short-term API forecasting because they can recognize historic patterns and thereby identify nonlinear relationships between the input and output variables. Thus, our study may provide a theoretical basis for environmental management policies.

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“…High levels of airborne particles also present a significant health risk, and their effects on human health are well-documented in the The combination of ground-level concentrations and satellite retrievals into a robust model is not a straightforward task, but machine learning techniques have proven to be a valuable tool in this task. The use of machine learning techniques, including artificial neural networks (ANNs), for air quality applications has been explored for some time [24], and its feasibility has been demonstrated [25]. A neural network uses artificial neurons, which are the smallest units in data processing [26].…”

Section: Introductionmentioning

confidence: 99%

“…The types of ANN include the back-propagation neural network (BPNN) [27,28], multilayer perceptron (MLP) [29,30], radial basis function (RBF) [31,32], and adopted neuro-fuzzy inference systems (ANFIS) [33][34][35]. While most of the available work is aimed at long-term forecasting of criteria pollutants using meteorological variables and source emissions as predictors [36], the use of satellite-derived aerosol optical depth (AOD) variables or reanalysis data for training and testing ANNs is also being investigated with favorable results [25]. Still, the majority of satellite applications have not been tested in developing countries, typically having higher PM 2.5 levels and distinctive emission source profiles [36,37].…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Distribution of PM2.5 Pollution over Northeastern Mexico: Application of MERRA-2 Reanalysis Datasets

et al. 2020

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Aerosol and meteorological remote sensing data could be used to assess the distribution of urban and regional fine particulate matter (PM2.5), especially in locations where there are few or no ground-based observations, such as Latin America. The objective of this study is to evaluate the ability of Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA-2) aerosol components to represent PM2.5 ground concentrations and to develop and validate an ensemble neural network (ENN) model that uses MERRA-2 aerosol and meteorology products to estimate the monthly average of PM2.5 ground concentrations in the Monterrey Metropolitan Area (MMA), which is the main urban area in Northeastern Mexico (NEM). The project involves the application of the ENN model to a regional domain that includes not only the MMA but also other municipalities in NEM in the period from January 2010 to December 2014. Aerosol optical depth (AOD), temperature, relative humidity, dust PM2.5, sea salt PM2.5, black carbon (BC), organic carbon (OC), and sulfate (SO42−) reanalysis data were identified as factors that significantly influenced PM2.5 concentrations. The ENN estimated a PM2.5 monthly mean of 25.62 μg m−3 during the entire period. The results of the comparison between the ENN and ground measurements were as follows: correlation coefficient R ~ 0.90; root mean square error = 1.81 μg m−3; mean absolute error = 1.31 μg m−3. Overall, the PM2.5 levels were higher in winter and spring. The highest PM2.5 levels were located in the MMA, which is the major source of air pollution throughout this area. The estimated data indicated that PM2.5 was not distributed uniformly throughout the region but varied both spatially and temporally. These results led to the conclusion that the magnitude of air pollution varies among seasons and regions, and it is correlated with meteorological factors. The methodology developed in this study could be used to identify new monitoring sites and address information gaps.

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Evaluation of Different Machine Learning Approaches to Forecasting PM2.5 Mass Concentrations

Cited by 121 publications

References 0 publications

A machine learning-based model to estimate PM2.5 concentration levels in Delhi's atmosphere

A machine learning-based model to estimate PM2.5 concentration levels in Delhi's atmosphere

Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions

Spatial and Temporal Distribution of PM2.5 Pollution over Northeastern Mexico: Application of MERRA-2 Reanalysis Datasets

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