Application of Deep Learning Techniques for Air Quality Prediction: A Case Study in Macau DOI Open Access
Thomas M. T. Lei, Jianxiu Cai,

Wan-Hee Cheng

et al.

Processes, Journal Year: 2025, Volume and Issue: 13(5), P. 1507 - 1507

Published: May 14, 2025

To better inform the public about ambient air quality and associated health risks prevent cardiovascular chronic respiratory diseases in Macau, local government authorities apply Air Quality Index (AQI) for management within its jurisdiction. The application of AQI requires first determining sub-indices several pollutants, including respirable suspended particulates (PM10), fine (PM2.5), nitrogen dioxide (NO2), ozone (O3), sulfur (SO2), carbon monoxide (CO). Accurate prediction is crucial providing early warnings to before pollution episodes occur. improve accuracy, deep learning methods such as artificial neural networks (ANNs) long short-term memory (LSTM) models were applied forecast six pollutants commonly found AQI. data this study was accessed from Macau High-Density Residential Monitoring Station (AQMS), which located an area with high traffic population density near a 24 h land border-crossing facility connecting Zhuhai Macau. novelty work lies potential enhance operational forecasting ANN LSTM run five times, average pollutant forecasts obtained each model. Results demonstrated that both accurately predicted concentrations upcoming h, PM10 CO showing highest predictive reflected Pearson Correlation Coefficient (PCC) between 0.84 0.87 Kendall’s Tau (KTC) 0.66 0.70 values low Mean Bias (MB) 0.06 0.10, Fractional (MFB) 0.09 0.11, Root Square Error (RMSE) 0.14 0.21, Absolute (MAE) 0.11 0.17. Overall, model consistently delivered PCC (0.87) KTC (0.70) lowest MB (0.06), MFB (0.09), RMSE (0.14), MAE (0.11) across all SD (0.01), indicating greater precision reliability. As result, concludes outperforms offering more accurate consistent tool management.

Language: Английский

Application of Deep Learning Techniques for Air Quality Prediction: A Case Study in Macau DOI Open Access
Thomas M. T. Lei, Jianxiu Cai,

Wan-Hee Cheng

et al.

Processes, Journal Year: 2025, Volume and Issue: 13(5), P. 1507 - 1507

Published: May 14, 2025

To better inform the public about ambient air quality and associated health risks prevent cardiovascular chronic respiratory diseases in Macau, local government authorities apply Air Quality Index (AQI) for management within its jurisdiction. The application of AQI requires first determining sub-indices several pollutants, including respirable suspended particulates (PM10), fine (PM2.5), nitrogen dioxide (NO2), ozone (O3), sulfur (SO2), carbon monoxide (CO). Accurate prediction is crucial providing early warnings to before pollution episodes occur. improve accuracy, deep learning methods such as artificial neural networks (ANNs) long short-term memory (LSTM) models were applied forecast six pollutants commonly found AQI. data this study was accessed from Macau High-Density Residential Monitoring Station (AQMS), which located an area with high traffic population density near a 24 h land border-crossing facility connecting Zhuhai Macau. novelty work lies potential enhance operational forecasting ANN LSTM run five times, average pollutant forecasts obtained each model. Results demonstrated that both accurately predicted concentrations upcoming h, PM10 CO showing highest predictive reflected Pearson Correlation Coefficient (PCC) between 0.84 0.87 Kendall’s Tau (KTC) 0.66 0.70 values low Mean Bias (MB) 0.06 0.10, Fractional (MFB) 0.09 0.11, Root Square Error (RMSE) 0.14 0.21, Absolute (MAE) 0.11 0.17. Overall, model consistently delivered PCC (0.87) KTC (0.70) lowest MB (0.06), MFB (0.09), RMSE (0.14), MAE (0.11) across all SD (0.01), indicating greater precision reliability. As result, concludes outperforms offering more accurate consistent tool management.

Language: Английский

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