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This paper is aimed at developing an air quality monitoring system using machine learning (ML), Internet of Things (IoT), and other elements to predict the level of particulate matter and gases in the air based on the air quality index (AQI). It is an air quality assessor and therefore a means of achieving the Sustainable Development Goals (SDGs), in particular, SDG 3.9 (substantial reduction of the health impacts of hazardous substances) and SDG 11.6 (reduction of negative impacts on cities and populations). AQI quantifies and informs the public about air pollutants and their adverse effects on public health. The proposed air quality monitoring device is low-cost and operates in real-time. It consists of a hardware unit that detects various pollutants to assess air quality as well as other airborne particles such as carbon dioxide (CO 2 ), methane (CH 4 ), volatile organic compounds (VOCs), nitrogen dioxide (NO 2 ), carbon monoxide (CO), and particulate matter with an aerodynamic diameter of 2.5 microns or less (PM 2.5 ). To predict air quality, the device was deployed from November 1, 2022, to February 4, 2023, in certain bauxite-rich areas of Adamawa and certain volcanic sites in western Cameroon. Therefore, machine learning algorithm models, namely, multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), XGBoost (XGB), and K-nearest neighbors (KNN) were applied to analyze the collected concentrations and predict the future state of air quality. The performance of these models was evaluated using mean absolute error (MAE), coefficient of determination (R-square), and root mean square error (RMSE). The obtained data in this study show that these pollutants are present in selected localities albeit to different extents. Moreover, the AQI values obtained range from 10 to 530, with a mean of 132.380 ± 63.705, corresponding to moderate air quality state but may induce an adverse effect on sensitive members of the population. This study revealed that XGB regression performed better in air quality forecasting with the highest R-squared (test score of 0.9991 and train score of 0.9999) and lowest RMSE (test score of 1.5748 and train score of 0. 0073) and MAE (test score of 0.0872 and train score of 0.0020), while the KNN model had the worst prediction (lowest R-squared and highest RMSE and MAE). This embryonic work is a prototype for projects in Cameroon as measurements are underway for a national spread over a longer period of time.

Titel:	IoT-based monitoring system and air quality prediction using machine learning for a healthy environment in Cameroon.
Autor/in / Beteiligte Person:	Folifack Signing, VR ; Mbarndouka Taamté, J ; Kountchou Noube, M ; Hamadou Yerima, A ; Azzopardi, J ; Tchuente Siaka, YF ; Saïdou
Link:	Full Text Finder (Volltext)
Zeitschrift:	Environmental monitoring and assessment, Jg. 196 (2024-06-15), Heft 7, S. 621
Veröffentlichung:	1998- : Dordrecht : Springer ; <i>Original Publication</i>: Dordrecht, Holland ; Boston : D. Reidel Pub. Co., c1981-, 2024
Medientyp:	academicJournal
ISSN:	1573-2959 (electronic)
DOI:	10.1007/s10661-024-12789-7
Schlagwort:	Cameroon Volatile Organic Compounds analysis Nitrogen Dioxide analysis Carbon Monoxide analysis Carbon Dioxide analysis Methane analysis Environmental Monitoring methods Machine Learning Air Pollutants analysis Air Pollution statistics & numerical data Particulate Matter analysis
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Environ Monit Assess] 2024 Jun 15; Vol. 196 (7), pp. 621. <i>Date of Electronic Publication: </i>2024 Jun 15. MeSH Terms: Environmental Monitoring* / methods ; Machine Learning* ; Air Pollutants* / analysis ; Air Pollution* / statistics & numerical data ; Particulate Matter* / analysis ; Cameroon ; Volatile Organic Compounds / analysis ; Nitrogen Dioxide / analysis ; Carbon Monoxide / analysis ; Carbon Dioxide / analysis ; Methane / analysis References: Abu Alfeilat, H. A., Hassanat, A. B., Lasassmeh, O., Tarawneh, A. S., Alhasanat, M. B., Eyal Salman, H. S., & Prasath, V. S. (2019). Effects of distance measure choice on k-nearest neighbor classifier performance: A review. Big data, 7(4), 221-248. https://www.liebertpub.com/doi/abs/10.1089/big.2018.0175. ; Adak, P., & Kour, N. (2021). A review on the effects of environmental factors on plants tolerance to air pollution. 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IoT-based monitoring system and air quality prediction using machine learning for a healthy environment in Cameroon.