Forecasting particulate matter concentration using nonlinear autoregression with exogenous input model

• Main Authors: M.I. Rumaling, Chee Fuei Pien, H.W.J. Chang, Carolyn Melissa Payus, S.K. Kong, Jedol Dayou, Justin Sentian
• Format: Article
• Language: English
• Published: Global Journal of Environmental Science and Management 2021
• Subjects: QC851-999 Meteorology. Climatology Including the earth's atmosphere
• Online Access: https://eprints.ums.edu.my/id/eprint/31079/1/Forecasting%20particulate%20matter%20concentration%20using%20nonlinear%20autoregression%20with%20exogenous%20input%20model-ABSTRACT.pdf; https://eprints.ums.edu.my/id/eprint/31079/; https://www.gjesm.net/article_245403.html; https://doi.org/10.22034/GJESM.2022.01.03
• Institution: Universiti Malaysia Sabah

Air quality in some developing countries is dominated by particulate matter, especially those with size 10 micrometers and smaller or PM10. They can be inhaled and sometimes can get deep into lungs; some may even get into bloodstream and cause serious health problems. Therefore, future PM10 concentr...