研究目的
To compare the performance of four low-cost optical particulate matter sensors with a TEOM analyser in measuring PM2.5 concentrations in ambient air.
研究成果
Plantower PMS7003 and Nova Fitness SDS011 sensors demonstrated good precision and linear correlation with TEOM data, making them suitable for detecting elevated PM concentration events or indicating PM 'hot-spots'. However, all tested sensors exhibited a bias in relation to TEOM responses, highlighting the importance of calibration before deployment in measurement campaigns.
研究不足
The study was limited to a specific location and time period, and the performance of sensors may vary under different environmental conditions. The bias observed in all tested sensors indicates the need for calibration before use in measurement campaigns.
1：Experimental Design and Method Selection:
Collocated comparison of four low-cost PM sensors and a TEOM analyser was conducted over a period of 18 weeks in Wroc?aw, Poland. The study focused on sensor performance characteristics including precision, bias, and linearity with TEOM data.
2：Sample Selection and Data Sources:
Measurements were performed in ambient air at the Meteorological Observatory of the Department of Climatology and Atmosphere Protection of the University of Wroc?aw.
3：List of Experimental Equipment and Materials:
The equipment included TEOM 1400a analyser, Plantower PMS7003, Nova Fitness SDS011, Winsen ZH03A, and Alphasense OPC-N2 sensors, Raspberry Pi microcomputer, USB hubs, and AR235 datalogger for temperature and humidity measurements.
4：Experimental Procedures and Operational Workflow:
Sensors were placed inside a housing made of foamed PVC sheets, connected to a Raspberry Pi microcomputer via USB hubs, and powered with 5V and 12V power supplies. Data was collected with 1- or 2-second resolution and averaged in 1-minute intervals.
5：Data Analysis Methods:
Data analysis included calculation of coefficients of variation for precision assessment, bias calculation, and Pearson correlation coefficients for linearity assessment. Linear regression fitting was also performed.
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