Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed) University, Pune 412115, Maharashtra, India.
Innovation Department, Technology University of Denmark, Copenhagen 2800, Denmark.
Sensors (Basel). 2020 Sep 22;20(18):5448. doi: 10.3390/s20185448.
Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO, and O, for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants.
空气污染是 21 世纪面临的一个严重问题，它也对周围环境和社会健康产生了重大影响。最近，先前的研究已经对空气污染和空气质量监测进行了广泛的研究。尽管如此，空气污染和空气质量监测领域仍然存在许多未解决的问题。在这项研究中，提出了污染天气预测系统（PWP），用于对各种污染参数的户外站点进行空气污染预测。在本研究工作中，我们引入了一个配置有污染感应单元的 PWP 系统，例如 SDS021、MQ07-CO、NO2-B43F 和 Aeroqual 臭氧（O）。这些感应单元用于在印度马哈拉施特拉邦浦那的 Symbiosis International University 收集和测量各种污染物水平，例如 PM2.5、PM10、CO、NO 和 O，为期 90 天。数据收集时间为 2019 年 12 月至 2020 年 2 月的冬季。调查结果验证了所提出的 PWP 系统的成功。在进行的实验中，进行了线性回归和基于人工神经网络（ANN）的空气质量指数（AQI）预测。此外，本研究还发现，定制的线性回归方法优于其他机器学习方法，例如线性、岭、Lasso、贝叶斯、Huber、Lars、Lasso-lars、随机梯度下降（SGD）和 ElasticNet 回归方法，以及在进行的实验中使用的定制 ANN 回归方法。基于所有提出的空气污染物的 AQI 值的总和，计算了空气污染物的整体 AQI 值。最后，开发了网络和移动界面，以显示各种空气污染物的空气污染预测值。
