Amperometric Gas Sensors as a Low Cost Emerging Technology Platform for Air Quality Monitoring Applications: A Review

ACS Sensors ◽  
2017 ◽  
Vol 2 (11) ◽  
pp. 1553-1566 ◽  
Author(s):  
Ronan Baron ◽  
John Saffell
Keyword(s):  
Air Quality ◽  
Gas Sensors ◽  
Low Cost ◽  
Emerging Technology ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Technology Platform ◽  
Monitoring Applications

scholarly journals 15 - Stationary and Mobile Low-Cost Gas Sensor-Systems for Air Quality Monitoring Applications

2015 ◽  
Author(s):  
D. Suriano ◽  
M. Prato ◽  
V. Pfister ◽  
G. Cassano ◽  
G. Camporeale ◽  
...  
Keyword(s):  
Air Quality ◽  
Gas Sensor ◽  
Low Cost ◽  
Quality Monitoring ◽  
Sensor Systems ◽  
Air Quality Monitoring ◽  
Monitoring Applications

scholarly journals Calibration of Low-cost Gas Sensors for Air Quality Monitoring

2021 ◽  
Vol 21 (11) ◽  
pp. 210073
Author(s):  
Dimitris Margaritis ◽  
Christos Keramydas ◽  
Ioannis Papachristos ◽  
Dimitra Lambropoulou
Keyword(s):  
Air Quality ◽  
Gas Sensors ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring

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.

scholarly journals Domain adaptation based deep calibration of low-cost PM2.5 sensors

2021 ◽  
Author(s):  
Sonu Kumar Jha ◽  
Mohit Kumar ◽  
Vipul Arora ◽  
Sachchida Nand Tripathi ◽  
Vidyanand Motiram Motghare ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Domain Adaptation ◽  
Low Cost ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Proposed Model ◽  
Temperature And Humidity ◽  
Calibration Approach

<div>Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.</div>

scholarly journals The impacts of water vapour and co-pollutants on the performance of electrochemical gas sensors used for air quality monitoring

2018 ◽  
Vol 266 ◽  
pp. 674-684 ◽  
Author(s):  
Xiaobing Pang ◽  
Marvin D. Shaw ◽  
Stefan Gillot ◽  
Alastair C. Lewis
Keyword(s):  
Air Quality ◽  
Water Vapour ◽  
Gas Sensors ◽  
Quality Monitoring ◽  
Air Quality Monitoring
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