scholarly journals A Review of Low-Cost Particulate Matter Sensors from the Developers’ Perspectives

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6819
Author(s):  
Brigida Alfano ◽  
Luigi Barretta ◽  
Antonio Del Giudice ◽  
Saverio De Vito ◽  
Girolamo Di Francia ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Health Effects ◽  
Low Cost ◽  
Reference Method ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Extensive Review ◽  
Research Groups ◽  
International Regulations

The concerns related to particulate matter’s health effects alongside the increasing demands from citizens for more participatory, timely, and diffused air quality monitoring actions have resulted in increasing scientific and industrial interest in low-cost particulate matter sensors (LCPMS). In the present paper, we discuss 50 LCPMS models, a number that is particularly meaningful when compared to the much smaller number of models described in other recent reviews on the same topic. After illustrating the basic definitions related to particulate matter (PM) and its measurements according to international regulations, the device’s operating principle is presented, focusing on a discussion of the several characterization methodologies proposed by various research groups, both in the lab and in the field, along with their possible limitations. We present an extensive review of the LCPMS currently available on the market, their electronic characteristics, and their applications in published literature and from specific tests. Most of the reviewed LCPMS can accurately monitor PM changes in the environment and exhibit good performances with accuracy that, in some conditions, can reach R2 values up to 0.99. However, such results strongly depend on whether the device is calibrated or not (using a reference method) in the operative environment; if not, R2 values lower than 0.5 are observed.

scholarly journals ASSESSMENT OF LOW-COST PARTICULATE MATTER SENSORS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 671-677
Author(s):  
K. Lehmann ◽  
A. Minhans ◽  
M. K. Fajari ◽  
M. Hahn
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Meteorological Parameters ◽  
Low Cost ◽  
The State ◽  
Quality Monitoring ◽  
Quality Data ◽  
Air Quality Monitoring ◽  
Low Costs ◽  
German Cities

Abstract. The effect of particulate matter is increasingly gaining significance due to its harmful effects on human and urban ecosystems. In view of it, many communities worldwide are collecting air quality data privately to influence their policy makers to make stricter provisions for reducing harmful emissions and thereby improving their quality of life. Likewise, in many German cities, a community of air quality monitors which rely on low-cost PM Sensors is gathering momentum. Such communities possess privately-owned & low-cost air quality monitoring devices that claim to accurately measure PM concentrations and are openly accessible via internet. One such initiative is an air quality monitoring network viz. “luftdaten.info”, which contains of more than 300 low-cost sensors that consistently obtains PM data, colloquially referred as fine dust, in the city of Stuttgart as well as its surrounding districts. Besides, eight stations are continuously monitoring PM concentration in Stuttgart; these are operated by the State Environmental Agency (LuBW- Landesanstalt für Umwelt Baden-Württemberg). Stuttgart University of Applied Sciences (HFT) has currently installed 7 low-cost PM sensors to monitor and study PM concentration in one of its projects. This study endeavors to relate PM 2.5 and PM 10.0 using low-cost sensors. It intends to investigate the reliability of the measured PM concentration using such low-costs sensors once these are placed horizontally and vertically apart and comparing the measures of the 7 sensors. Another objective is to compare the PM concentration measurements with a meteorological station operated by the State of Baden-Wuerttemberg in the vicinity. A correlation analysis is performed to develop understanding of relationships of PM concentration with meteorological parameters, viz. with respect to ambient temperature, air pressure, humidity, wind speed and wind direction. Furthermore, it attempts to develop a regression model using above listed meteorological parameters. Finally, deficiencies in the measurement of low-costs and its placement effects are commented. Further suggestions are made for improving the data capturing and analytical procedures while using low-cost sensors.

scholarly journals Analyzing and Improving the Performance of a Particulate Matter Low Cost Air Quality Monitoring Device

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 251
Author(s):  
Evangelos Bagkis ◽  
Theodosios Kassandros ◽  
Marinos Karteris ◽  
Apostolos Karteris ◽  
Kostas Karatzas
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Urban Areas ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Quality Monitoring ◽  
Reference Station ◽  
Air Quality Monitoring ◽  
Pollution Levels

Air quality (AQ) in urban areas is deteriorating, thus having negative effects on people’s everyday lives. Official air quality monitoring stations provide the most reliable information, but do not always depict air pollution levels at scales reflecting human activities. They also have a high cost and therefore are limited in number. This issue can be addressed by deploying low cost AQ monitoring devices (LCAQMD), though their measurements are of far lower quality. In this paper we study the correlation of air pollution levels reported by such a device and by a reference station for particulate matter, ozone and nitrogen dioxide in Thessaloniki, Greece. On this basis, a corrective factor is modeled via seven machine learning algorithms in order to improve the quality of measurements for the LCAQMD against reference stations, thus leading to its on-field computational improvement. We show that our computational intelligence approach can improve the performance of such a device for PM10 under operational conditions.

Recent advancements in low-cost portable sensors for urban and indoor air quality monitoring

2021 ◽  
Author(s):  
A. Hernández-Gordillo ◽  
S. Ruiz-Correa ◽  
V. Robledo-Valero ◽  
C. Hernández-Rosales ◽  
S. Arriaga
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Portable Sensors

scholarly journals Low cost air quality monitoring

2020 ◽  
Author(s):  
Chekwube A. Okigbo ◽  
Amar Seeam ◽  
Shivanand P. Guness ◽  
Xavier Bellekens ◽  
Girish Bekaroo ◽  
...  
Keyword(s):  
Air Quality ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring

scholarly journals Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3338
Author(s):  
Ivan Vajs ◽  
Dejan Drajic ◽  
Nenad Gligoric ◽  
Ilija Radovanovic ◽  
Ivan Popovic
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Relative Humidity ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Lower Accuracy ◽  
Wide Range ◽  
The Impact ◽  
Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

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