气候变化领域集成服务门户(CCPortal): Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania

CCPortal

DOI	10.3390/s24051532
	Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania
	El Mghouchi, Youness; Udristioiu, Mihaela Tinca; Yildizhan, Hasan
发表日期	2024
EISSN	1424-8220
起始页码	24
结束页码	5
卷号	24 期号:5
英文摘要	Inadequate air quality has adverse impacts on human well-being and contributes to the progression of climate change, leading to fluctuations in temperature. Therefore, gaining a localized comprehension of the interplay between climate variations and air pollution holds great significance in alleviating the health repercussions of air pollution. This study uses a holistic approach to make air quality predictions and multivariate modelling. It investigates the associations between meteorological factors, encompassing temperature, relative humidity, air pressure, and three particulate matter concentrations (PM10, PM2.5, and PM1), and the correlation between PM concentrations and noise levels, volatile organic compounds, and carbon dioxide emissions. Five hybrid machine learning models were employed to predict PM concentrations and then the Air Quality Index (AQI). Twelve PM sensors evenly distributed in Craiova City, Romania, provided the dataset for five months (22 September 2021-17 February 2022). The sensors transmitted data each minute. The prediction accuracy of the models was evaluated and the results revealed that, in general, the coefficient of determination (R2) values exceeded 0.96 (interval of confidence is 0.95) and, in most instances, approached 0.99. Relative humidity emerged as the least influential variable on PM concentrations, while the most accurate predictions were achieved by combining pressure with temperature. PM10 (less than 10 mu m in diameter) concentrations exhibited a notable correlation with PM2.5 (less than 2.5 mu m in diameter) concentrations and a moderate correlation with PM1 (less than 1 mu m in diameter). Nevertheless, other findings indicated that PM concentrations were not strongly related to NOISE, CO2, and VOC, and these last variables should be combined with another meteorological variable to enhance the prediction accuracy. Ultimately, this study established novel relationships for predicting PM concentrations and AQI based on the most effective combinations of predictor variables identified.
英文关键词	air pollution; hybrid machine learning; low-cost sensors; PM sensor; urban monitoring
语种	英语
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:001182990700001
来源期刊	SENSORS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/302247
作者单位	Moulay Ismail University of Meknes; University of Craiova; Adana Alparslan Turkes Science & Technology University
推荐引用方式 GB/T 7714	El Mghouchi, Youness,Udristioiu, Mihaela Tinca,Yildizhan, Hasan. Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania[J],2024,24(5).
APA	El Mghouchi, Youness,Udristioiu, Mihaela Tinca,&Yildizhan, Hasan.(2024).Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania.SENSORS,24(5).
MLA	El Mghouchi, Youness,et al."Multivariable Air-Quality Prediction and Modelling via Hybrid Machine Learning: A Case Study for Craiova, Romania".SENSORS 24.5(2024).