IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i6p5341-d1100059.html
   My bibliography  Save this article

Application of ANN, XGBoost, and Other ML Methods to Forecast Air Quality in Macau

Author

Listed:
  • Thomas M. T. Lei

    (Institute of Science and Environment, University of Saint Joseph, Macau, China)

  • Stanley C. W. Ng

    (Institute of Science and Environment, University of Saint Joseph, Macau, China)

  • Shirley W. I. Siu

    (Institute of Science and Environment, University of Saint Joseph, Macau, China)

Abstract

Air pollution in Macau has become a serious problem following the Pearl River Delta’s (PRD) rapid industrialization that began in the 1990s. With this in mind, Macau needs an air quality forecast system that accurately predicts pollutant concentration during the occurrence of pollution episodes to warn the public ahead of time. Five different state-of-the-art machine learning (ML) algorithms were applied to create predictive models to forecast PM 2.5 , PM 10, and CO concentrations for the next 24 and 48 h, which included artificial neural networks (ANN), random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR), to determine the best ML algorithms for the respective pollutants and time scale. The diurnal measurements of air quality data in Macau from 2016 to 2021 were obtained for this work. The 2020 and 2021 datasets were used for model testing, while the four-year data before 2020 and 2021 were used to build and train the ML models. Results show that the ANN, RF, XGBoost, SVM, and MLR models were able to provide good performance in building up a 24-h forecast with a higher coefficient of determination (R 2 ) and lower root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). Meanwhile, all the ML models in the 48-h forecasting performance were satisfactory enough to be accepted as a two-day continuous forecast even if the R 2 value was lower than the 24-h forecast. The 48-h forecasting model could be further improved by proper feature selection based on the 24-h dataset, using the Shapley Additive Explanations (SHAP) value test and the adjusted R 2 value of the 48-h forecasting model. In conclusion, the above five ML algorithms were able to successfully forecast the 24 and 48 h of pollutant concentration in Macau, with the RF and SVM models performing the best in the prediction of PM 2.5 and PM 10 , and CO in both 24 and 48-h forecasts.

Suggested Citation

  • Thomas M. T. Lei & Stanley C. W. Ng & Shirley W. I. Siu, 2023. "Application of ANN, XGBoost, and Other ML Methods to Forecast Air Quality in Macau," Sustainability, MDPI, vol. 15(6), pages 1-17, March.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5341-:d:1100059
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/6/5341/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/6/5341/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yaolin Lin & Jiale Zou & Wei Yang & Chun-Qing Li, 2018. "A Review of Recent Advances in Research on PM 2.5 in China," IJERPH, MDPI, vol. 15(3), pages 1-29, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yanzhao Wang & Jianfei Cao, 2023. "Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China’s Cities Based on Spatial Autocorrelation Analysis and MGWR Model," IJERPH, MDPI, vol. 20(4), pages 1-23, February.
    2. Nan Jia & Yinshuai Li & Ruishan Chen & Hongbo Yang, 2023. "A Review of Global PM 2.5 Exposure Research Trends from 1992 to 2022," Sustainability, MDPI, vol. 15(13), pages 1-15, July.
    3. Wei Xue & Qingming Zhan & Qi Zhang & Zhonghua Wu, 2019. "Spatiotemporal Variations of Particulate and Gaseous Pollutants and Their Relations to Meteorological Parameters: The Case of Xiangyang, China," IJERPH, MDPI, vol. 17(1), pages 1-23, December.
    4. Sungwan Son & Aya Elkamhawy & Choon-Man Jang, 2022. "Active Soil Filter System for Indoor Air Purification in School Classrooms," IJERPH, MDPI, vol. 19(23), pages 1-16, November.
    5. Bo Li & Qingfeng Cao & Muhammad Mohiuddin, 2020. "Factors Influencing the Settlement Intentions of Chinese Migrants in Cities: An Analysis of Air Quality and Higher Income Opportunity as Predictors," IJERPH, MDPI, vol. 17(20), pages 1-18, October.
    6. Hugo Wai Leung Mak & Daisy Chiu Yi Ng, 2021. "Spatial and Socio-Classification of Traffic Pollutant Emissions and Associated Mortality Rates in High-Density Hong Kong via Improved Data Analytic Approaches," IJERPH, MDPI, vol. 18(12), pages 1-27, June.
    7. Xiaoyan Dai & Chao Wei & Liguo Zhou & Ping Li, 2022. "Identification and Characterization of PM 2.5 Emission Sources in Shanghai during COVID-19 Pandemic in the Winter of 2020," Sustainability, MDPI, vol. 14(17), pages 1-15, September.
    8. Ali Asghar Heidari & Mehdi Akhoondzadeh & Huiling Chen, 2022. "A Wavelet PM2.5 Prediction System Using Optimized Kernel Extreme Learning with Boruta-XGBoost Feature Selection," Mathematics, MDPI, vol. 10(19), pages 1-35, September.
    9. Daoru Liu & Qinli Deng & Zeng Zhou & Yaolin Lin & Junwei Tao, 2018. "Variation Trends of Fine Particulate Matter Concentration in Wuhan City from 2013 to 2017," IJERPH, MDPI, vol. 15(7), pages 1-18, July.
    10. Aleksei S. Kholodov & Irina A. Tarasenko & Ekaterina A. Zinkova & Michele Teodoro & Anca Oana Docea & Daniela Calina & Aristidis Tsatsakis & Kirill S. Golokhvast, 2021. "The Study of Airborne Particulate Matter in Dalnegorsk Town," IJERPH, MDPI, vol. 18(17), pages 1-14, September.
    11. Timofey Leshukov & Konstantin Legoshchin & Olga Yakovenko & Sebastian Bach & Dmitriy Russakov & Daria Dimakova & Evgeniya Vdovina & Elizaveta Baranova & Kirill Avdeev & Elena Kolpina & Karina Oshchepk, 2022. "Fractional Composition and Toxicity Coal–Rock of PM 10 -PM 0.1 Dust near an Opencast Coal Mining Area and Coal-Fired Power Station," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    12. Ming Zeng & Jiang Du & Weike Zhang, 2019. "Spatial-Temporal Effects of PM 2.5 on Health Burden: Evidence from China," IJERPH, MDPI, vol. 16(23), pages 1-23, November.
    13. Zifeng Liang & Manli Zhang & Qingduo Mao & Bingxin Yu & Ben Ma, 2018. "Improvement of Eco-Efficiency in China: A Comparison of Mandatory and Hybrid Environmental Policy Instruments," IJERPH, MDPI, vol. 15(7), pages 1-20, July.
    14. Hone-Jay Chu & Muhammad Zeeshan Ali, 2020. "Establishment of Regional Concentration–Duration–Frequency Relationships of Air Pollution: A Case Study for PM 2.5," IJERPH, MDPI, vol. 17(4), pages 1-13, February.
    15. Bowen He & Qun Guan, 2021. "A Risk and Decision Analysis Framework to Evaluate Future PM 2.5 Risk: A Case Study in Los Angeles-Long Beach Metro Area," IJERPH, MDPI, vol. 18(9), pages 1-23, May.
    16. Xingchuan Yang & Lei Jiang & Wenji Zhao & Qiulin Xiong & Wenhui Zhao & Xing Yan, 2018. "Comparison of Ground-Based PM 2.5 and PM 10 Concentrations in China, India, and the U.S," IJERPH, MDPI, vol. 15(7), pages 1-16, July.
    17. Zhao, Chuanmin & Xie, Rui & Ma, Chunbo & Han, Feng, 2022. "Understanding the haze pollution effects of China's development zone program," Energy Economics, Elsevier, vol. 111(C).
    18. Dipak Kumar Mandal & Sharmistha Bose & Nirmalendu Biswas & Nirmal K. Manna & Erdem Cuce & Ali Cemal Benim, 2024. "Solar Chimney Power Plants for Sustainable Air Quality Management Integrating Photocatalysis and Particulate Filtration: A Comprehensive Review," Sustainability, MDPI, vol. 16(6), pages 1-31, March.
    19. Xiaowei Xu & Daxin Dong & Yilun Wang & Shiying Wang, 2019. "The Impacts of Different Air Pollutants on Domestic and Inbound Tourism in China," IJERPH, MDPI, vol. 16(24), pages 1-15, December.
    20. Le Yang & Jiahao Zhang & Yufeng Zhang, 2021. "Environmental Regulations and Corporate Green Innovation in China: The Role of City Leaders’ Promotion Pressure," IJERPH, MDPI, vol. 18(15), pages 1-21, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5341-:d:1100059. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.