Automatic detection method of atmospheric pollutant concentration based on multi-sensor data fusion. (18th January 2023)

• Record Type: Journal Article
• Authors: Meng, Mingchuan; Lu, Dawei; Zhao, Jiong; Zhang, Wei; Zhou, Xuehao
• Is Part Of: International journal of data science. Volume 7:Number 4(2022)
• Page Start: 365
• Page End: 382
• Publication Date: 2023-01-18
• Subjects: multi-sensor -- data fusion -- air pollutants -- concentration detection; Big data -- Periodicals; Data mining -- Periodicals; Information storage and retrieval systems -- Periodicals; Decision making -- Data processing -- Periodicals; 005.7
• Languages: English
• ISSNs: 2053-0811

Abstract

Aiming at the problem that the traditional air pollutant concentration detection method cannot accurately detect the concentration of multiple pollutants, an automatic detection method of air pollutant concentration based on multi-sensor data fusion is proposed. A multi-sensor environment detection system is constructed by the temperature and humidity sensor DHT22, the NO2 sensor ZE08-CH2O and the dust sensor ZPH01, and the multi-sensor data fusion is realised by the Kalman filter algorithm. The improved BP network is used to classify pollution source data, and the detection data of the same pollution source is back calculated by adaptive simulated annealing algorithm to realise the automatic detection of the concentration of similar air pollutants. The experimental results show that the detection errors of this method for the concentration of atmospheric pollutants NO2, TVOC and PM2.5 inhalable particulate matter are 0.03 μg·m -3, 0.02 μg·m -3, 0.03 μg·m -3 respectively, which proves the effectiveness of this method.