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Journal of Sensors
Volume 2018, Article ID 5096540, 16 pages
Research Article

Evaluation of Low-Cost Sensors for Ambient PM2.5 Monitoring

1Department of Air Conditioning, Heating, Gas Engineering and Air Protection, Faculty of Environmental Engineering, Wrocław University of Science and Technology, 50-373 Wrocław, Poland
2INSYSPOM, 54-427 Wrocław, Poland
3Department of Climatology and Atmosphere Protection, Institute of Geography and Regional Development, Faculty of Earth Science and Environmental Management, University of Wrocław, 51-621 Wrocław, Poland

Correspondence should be addressed to Marek Badura; lp.ude.rwp@arudab.keram

Received 28 February 2018; Revised 3 June 2018; Accepted 30 July 2018; Published 31 October 2018

Academic Editor: Michele Penza

Copyright © 2018 Marek Badura et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Low-cost sensors are an opportunity to improve the spatial and temporal resolution of particulate matter data. However, such sensors should be calibrated under conditions close to the final ones before any monitoring actions. The paper presents the results of a collocated comparison of four models of low-cost optical sensors with a TEOM 1400a analyser. SDS011 (Nova Fitness), ZH03A (Winsen), PMS7003 (Plantower), and OPC-N2 (Alphasense) sensors were used in this research. Three copies of each sensor model were placed in a common box to compare the sensor performance under the same measurement conditions. Monitoring of the PM2.5 fraction was conducted for almost half a year from 21 August 2017 to 19 February 2018 in Wrocław (Poland). Reproducibility between sensor units was assessed on the basis of coefficient of variation (CV). CV values were lower than 7% in the case of SDS011 and PMS7003 sensors and equal to 20% for OPC-N2 units. CV was higher than 50% for ZH03A, mainly due to malfunctions. During the measurements, the trends of outputs from sensors were generally similar to TEOM data, but significant overestimation of PM2.5 concentrations was observed for the sensor raw data. A high linear relationship between TEOM and sensors was noticed for 1 min, 15 min, and 1-hour averaged data for PMS7003 sensors (–0.89), for SDS011 units (–0.86), and for one unit of ZH03A (–0.81). values for daily averages were at the level 0.91–0.93 for PMS7003, 0.87–0.90 for SDS011, and 0.89 for ZH03A. OPC-N2 had only a moderate linear relationship with TEOM (–0.69 for daily data and 0.43–0.61 for shorter time averages). Quite large dispersion of data and high relative errors of PM2.5 estimation were observed for concentration ranges below 20–30 μg/m3. The impact of high relative humidity level was observed for SDS011 and OPC-N2 devices—clear overestimation of outputs was observed above 80% RH.