PM2.5 concentration prediction during COVID-19 lockdown over Kolkata metropolitan city, India using MLR and ANN models

Bera, Biswajit

doi:10.1016/j.envc.2021.100155

Cited by 43 publications

(18 citation statements)

References 50 publications

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“…pre- or post-lockdown ( Das et al, 2021 ; Collivignarelli et al, 2020 ; Tobías et al, 2020 ; Dantas et al, 2020 ) or to the previous years’ records ( Xu et al, 2020 ; Gualtieri et al, 2020 ; Abou El-Magd and Zanaty, 2021 ; Patel et al, 2020 ; Kumari and Toshniwal, 2020 ). The other aspect was to predict air pollutants based on meteorological parameters and other factors such as minor- or major-lockdown ( Briz-Redón et al, 2021 ; Munir et al, 2021 ; Wang et al, 2021 ; Bera et al, 2021 ) to evaluate the reductions of air pollution, however, in those air pollution simulations, traffic variable was missing since it was a very important factor for air pollutants predictions. In order to accurately predict the air pollutants, traffic factor should be considered in the statistical model simulations.…”

Section: Introductionmentioning

confidence: 99%

Investigation of COVID-19-related lockdowns on the air pollution changes in augsburg in 2020, Germany

Cao

Liu

Hadiatullah

et al. 2022

Atmospheric Pollution Research

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Section: Introductionmentioning

confidence: 99%

Investigation of COVID-19-related lockdowns on the air pollution changes in augsburg in 2020, Germany

Cao

Liu

Hadiatullah

et al. 2022

Atmospheric Pollution Research

View full text Add to dashboard Cite

“…Due to the good performance of shallow neural networks, such as support vector regression (SVR) [ 13 , 14 ] and artificial neural network (ANN) [ 15 , 16 , 17 , 18 ], many studies applied shallow learning to prediction tasks. Compared with the linear models and time series models, shallow neural networks have stronger performance and better prediction performance for the nonlinear system.…”

Section: Related Workmentioning

confidence: 99%

A Spatial–Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM2.5 Concentration Prediction

Lin

Zhao

et al. 2022

Entropy

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Accurate and fine-grained prediction of PM2.5 concentration is of great significance for air quality control and human physical and mental health. Traditional approaches, such as time series, recurrent neural networks (RNNs) or graph convolutional networks (GCNs), cannot effectively integrate spatial–temporal and meteorological factors and manage dynamic edge relationships among scattered monitoring stations. In this paper, a spatial–temporal causal convolution network framework, ST-CCN-PM2.5, is proposed. Both the spatial effects of multi-source air pollutants and meteorological factors are considered via spatial attention mechanism. Time-dependent features in causal convolution networks are extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-PM2.5 are tuned by Bayesian optimization. Haikou air monitoring station data are employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results include the following points: (1) For a single station, the RMSE, MAE and R2 values of ST-CCN-PM2.5 decreased by 27.05%, 10.38% and 3.56% on average, respectively. (2) For all stations, ST-CCN-PM2.5 achieve the best performance in win–tie–loss experiments. The numbers of winning stations are 68, 63, and 64 out of 95 stations in RMSE (MSE), MAE, and R2, respectively. In addition, the mean MSE, RMSE and MAE of ST-CCN-PM2.5 are 4.94, 2.17 and 1.31, respectively, and the R2 value is 0.92. (3) Shapley analysis shows wind speed is the most influencing factor in fine-grained PM2.5 concentration prediction. The effects of CO and temperature on PM2.5 prediction are moderately significant. Friedman test under different resampling further confirms the advantage of ST-CCN-PM2.5. The ST-CCN-PM2.5 provides a promising direction for fine-grained PM2.5 prediction.

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“…This model can learn the complex nonlinear dependence between input and output well, and has good robustness and adaptive characteristics, which improves the prediction accuracy and has low time complexity [31]. Compared with time series models, shallow neural networks generally have better performance [31][32][33]. However, they are mostly limited to practical applications, since it is difficult to capture complex spatial-temporal dependency among stations and predict overall IAQI situations in a large-scale network [21,24].…”

Section: Related Workmentioning

confidence: 99%

Fine-Grained Individual Air Quality Index (IAQI) Prediction Based on Spatial-Temporal Causal Convolution Network: A Case Study of Shanghai

et al. 2022

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Accurate and fine-grained individual air quality index (IAQI) prediction is the basis of air quality index (AQI), which is of great significance for air quality control and human health. Traditional approaches, such as time series, recurrent neural network or graph convolutional network, cannot effectively integrate spatial-temporal and meteorological factors and manage the dynamic edge relationship among scattered monitoring stations. In this paper, a ST-CCN-IAQI model is proposed based on spatial-temporal causal convolution networks. Both the spatial effects of multi-source air pollutants and meteorological factors were considered via spatial attention mechanism. Time-dependent features in the causal convolution network were extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-IAQI were tuned by Bayesian optimization. Shanghai air monitoring station data were employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results showed that: (1) For a single station, the RMSE and MAE values of ST-CCN-IAQI were 9.873 and 7.469, decreasing by 24.95% and 16.87% on average, respectively. R2 was 0.917, with an average 5.69% improvement; (2) For all nine stations, the mean RMSE and MAE of ST-CCN-IAQI were 9.849 and 7.527, respectively, and the R2 value was 0.906. (3) Shapley analysis showed PM10, humidity and NO2 were the most influencing factors in ST-CCN-IAQI. The Friedman test, under different resampling, further confirmed the advantage of ST-CCN-IAQI. The ST-CCN-IAQI provides a promising direction for fine-grained IAQI prediction.

show abstract

PM2.5 concentration prediction during COVID-19 lockdown over Kolkata metropolitan city, India using MLR and ANN models

Cited by 43 publications

References 50 publications

Investigation of COVID-19-related lockdowns on the air pollution changes in augsburg in 2020, Germany

Investigation of COVID-19-related lockdowns on the air pollution changes in augsburg in 2020, Germany

A Spatial–Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM2.5 Concentration Prediction

Fine-Grained Individual Air Quality Index (IAQI) Prediction Based on Spatial-Temporal Causal Convolution Network: A Case Study of Shanghai

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