Research Centre for Nuclear Science and Technology, Institute of Geological and Mining Research, P.O. Box 4110, Yaoundé, Cameroon.
Department of Artificial Intelligence, Faculty of Information and Communication Technology, University of Malta, Msida, Malta.
Environ Monit Assess. 2024 Jun 15;196(7):621. doi: 10.1007/s10661-024-12789-7.
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), methane (CH), volatile organic compounds (VOCs), nitrogen dioxide (NO), carbon monoxide (CO), and particulate matter with an aerodynamic diameter of 2.5 microns or less (PM). 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.
本论文旨在开发一个空气质量监测系统，该系统使用机器学习 (ML)、物联网 (IoT) 和其他元素，根据空气质量指数 (AQI) 预测空气中颗粒物和气体的水平。它是空气质量评估器，因此也是实现可持续发展目标 (SDGs) 的一种手段，特别是目标 3.9（大幅减少有害物质对健康的影响）和目标 11.6（减少城市和人口的负面影响）。AQI 量化并告知公众有关空气污染物及其对公众健康的不利影响。拟议的空气质量监测设备成本低，实时运行。它由一个硬件单元组成，该单元检测各种污染物，以评估空气质量以及其他空气传播颗粒，如二氧化碳 (CO)、甲烷 (CH)、挥发性有机化合物 (VOCs)、二氧化氮 (NO)、一氧化碳 (CO) 和空气动力学直径为 2.5 微米或更小的颗粒物 (PM)。为了预测空气质量，该设备于 2022 年 11 月 1 日至 2023 年 2 月 4 日在阿达马瓦的某些富铝土矿地区和喀麦隆西部的某些火山地区进行了部署。因此，应用了机器学习算法模型，即多元线性回归 (MLR)、支持向量回归 (SVR)、随机森林回归 (RFR)、XGBoost (XGB) 和 K-最近邻 (KNN)，以分析收集的浓度并预测未来的空气质量状况。使用平均绝对误差 (MAE)、确定系数 (R-square) 和均方根误差 (RMSE) 评估这些模型的性能。本研究获得的数据表明，这些污染物存在于选定的地点，尽管程度不同。此外，获得的空气质量指数 (AQI) 值范围为 10 至 530，平均值为 132.380 ± 63.705，对应于中等空气质量状态，但可能对人口中的敏感成员产生不利影响。这项研究表明，XGB 回归在空气质量预测中表现更好，具有最高的 R 平方（测试得分 0.9991 和训练得分 0.9999）和最低的 RMSE（测试得分 1.5748 和训练得分 0.0073）和 MAE（测试得分 0.0872 和训练得分 0.0020），而 KNN 模型的预测效果最差（最低的 R 平方和最高的 RMSE 和 MAE）。这项初步工作是喀麦隆项目的原型，因为正在进行全国范围内的长期测量。
