Air pollution monitoring and modeling

Askariyeh, Mohammad Hashem; Khreis, Haneen; Vallamsundar, Suriya

doi:10.1016/b978-0-12-818122-5.00005-3

Cited by 10 publications

(4 citation statements)

References 79 publications

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“…It is found that, EEMD-LSTM performs the best at 6-h and 2-h time lag at Cheras and Batu Muda monitoring stations, respectively. The (7) 3 lists the parameters of the LSTM model for forecasting PM2.5 concentration. One of the main hyperparameters in the deep learning model is the optimizer.…”

Section: Methodsmentioning

confidence: 99%

PM2.5 forecasting for an urban area based on deep learning and decomposition method

Zaini

Ean

Ahmed

et al. 2022

Sci Rep

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Rapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the health and well-being of human society. Therefore, accurate forecasting of air pollutant concentration is crucial to mitigate the associated health risk. This study aims to predict the hourly PM2.5 concentration for an urban area in Malaysia using a hybrid deep learning model. Ensemble empirical mode decomposition (EEMD) was employed to decompose the original sequence data of particulate matter into several subseries. Long short-term memory (LSTM) was used to individually forecast the decomposed subseries considering the influence of air pollutant parameters for 1-h ahead forecasting. Then, the outputs of each forecast were aggregated to obtain the final forecasting of PM2.5 concentration. This study utilized two air quality datasets from two monitoring stations to validate the performance of proposed hybrid EEMD-LSTM model based on various data distributions. The spatial and temporal correlation for the proposed dataset were analysed to determine the significant input parameters for the forecasting model. The LSTM architecture consists of two LSTM layers and the data decomposition method is added in the data pre-processing stage to improve the forecasting accuracy. Finally, a comparison analysis was conducted to compare the performance of the proposed model with other deep learning models. The results illustrated that EEMD-LSTM yielded the highest accuracy results among other deep learning models, and the hybrid forecasting model was proved to have superior performance as compared to individual models.

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Section: Methodsmentioning

confidence: 99%

PM2.5 forecasting for an urban area based on deep learning and decomposition method

Zaini

Ean

Ahmed

et al. 2022

Sci Rep

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“…atmospheric chemical transport model (CTM), traditional statistical model, and machine learning model. The first type predicts air pollution concentration based on the simulation of atmospheric chemistry with consideration of the transformation and interaction of air pollutants [23,24]. The successful conduction of CTM requires atmospheric expertise and adequate data support.…”

Section: Introductionmentioning

confidence: 99%

Prediction of PM2.5 Concentration Using Spatiotemporal Data with Machine Learning Models

Ma,

Chen,

et al. 2023

Atmosphere

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Among the critical global crises curbing world development and sustainability, air quality degradation has been a long-lasting and increasingly urgent one and it has been sufficiently proven to pose severe threats to human health and social welfare. A higher level of model prediction accuracy can play a fundamental role in air quality assessment and enhancing human well-being. In this paper, four types of machine learning models—random forest model, ridge regression model, support vector machine model, extremely randomized trees model—were adopted to predict PM2.5 concentration in ten cities in the Jing-Jin-Ji region of north China based on multi-sources spatiotemporal data including air quality and meteorological data in time series. Data were fed into the model by using the rolling prediction method which is proven to improve prediction accuracy in our experiments. Lastly, the comparative experiments show that at the city level, RF and ExtraTrees models have better predictive results with lower mean absolute error (MAE), root mean square error (RMSE), and higher index of agreement (IA) compared to other selected models. For seasonality, level four models all have the best prediction performances in winter time and the worst in summer time, and RF models have the best prediction performance with the IA ranging from 0.93 to 0.98 with an MAE of 5.91 to 11.68 μg/m3. Consequently, the demonstration of how each model performs differently in each city and each season is expected to shed light on environmental policy implications.

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“…Atmospheric chemical transport modelling is a powerful tool widely used to study source apportionment, contribution assessments, and policy evaluation studies [25][26]. This study aims to assess the contribution of biomass burning in Southeast Asia to PM2.5 concentration in two major cities of Thailand: Bangkok and Chiang Mai using Weather Research and Forecasting (WRF) meteorological model coupled with Community Multiscale Air Quality (CMAQ) atmospheric chemical transport model.…”

Section: Introductionmentioning

confidence: 99%

Impact assessment of biomass burning in Southeast Asia to 2019 annual average PM2.5 concentration in Thailand using atmospheric chemical transport model

et al. 2023

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Agricultural residues burning as a means of land preparation commonly practiced in many Southeast Asian countries causes significant deterioration of ambient air quality and public health. In this study, WRF-CMAQ Atmospheric Chemical Transport Model was used to conduct a year-round simulation (1 January - 31 December 2019) of PM2.5 spatio-temporal variation over Southeast Asia. The model utilized the Fire emission Inventory from NCAR (FINNv1.5) from National Center for Atmospheric Research (NCAR) as a biomass burning emission input. The model performance was evaluated by comparing simulated values with observed values from monitoring stations in nine major cities. The model shows acceptable performance reproducing the PM2.5 concentration with 14.9% normalized mean bias (NMB) and correlation coefficient of 0.89. After that, the simulation was conducted again with emission from FINNv1.5 turned off. The results from FINNv1.5 on and off cases were then compared to evaluate contribution of biomass burning to PM2.5 concentration in two major cities of Thailand: Bangkok and Chiang Mai. The comparison shows that biomass burning contributes to 49.1% and 13.1% of PM2.5 annual average concentration in Chiang Mai and Bangkok respectively with highest month being April for Chiang Mai (70.7% contribution) and March for Bangkok (35.5% contribution).

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Air pollution monitoring and modeling

Cited by 10 publications

References 79 publications

PM2.5 forecasting for an urban area based on deep learning and decomposition method

PM2.5 forecasting for an urban area based on deep learning and decomposition method

Prediction of PM2.5 Concentration Using Spatiotemporal Data with Machine Learning Models

Impact assessment of biomass burning in Southeast Asia to 2019 annual average PM2.5 concentration in Thailand using atmospheric chemical transport model

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