scholarly journals Calibration of CO, NO2, and O3 Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7977
Author(s):  
Marian-Emanuel Ionascu ◽  
Nuria Castell ◽  
Oana Boncalo ◽  
Philipp Schneider ◽  
Marius Darie ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Quality ◽  
State Of The Art ◽  
Low Cost ◽  
Unit Cost ◽  
Measured Data ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Similar Accuracy

During the last decade, extensive research has been carried out on the subject of low-cost sensor platforms for air quality monitoring. A key aspect when deploying such systems is the quality of the measured data. Calibration is especially important to improve the data quality of low-cost air monitoring devices. The measured data quality must comply with regulations issued by national or international authorities in order to be used for regulatory purposes. This work discusses the challenges and methods suitable for calibrating a low-cost sensor platform developed by our group, Airify, that has a unit cost five times less expensive than the state-of-the-art solutions (approximately €1000). The evaluated platform can integrate a wide variety of sensors capable of measuring up to 12 parameters, including the regulatory pollutants defined in the European Directive. In this work, we developed new calibration models (multivariate linear regression and random forest) and evaluated their effectiveness in meeting the data quality objective (DQO) for the following parameters: carbon monoxide (CO), ozone (O3), and nitrogen dioxide (NO2). The experimental results show that the proposed calibration managed an improvement of 12% for the CO and O3 gases and a similar accuracy for the NO2 gas compared to similar state-of-the-art studies. The evaluated parameters had different calibration accuracies due to the non-identical levels of gas concentration at which the sensors were exposed during the model’s training phase. After the calibration algorithms were applied to the evaluated platform, its performance met the DQO criteria despite the overall low price level of the platform.

scholarly journals Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 394
Author(s):  
Mannam Veera Narayana ◽  
Devendra Jalihal ◽  
S.M. Shiva Nagendra
Keyword(s):  
Air Quality ◽  
State Of The Art ◽  
Low Cost ◽  
Personal Exposure ◽  
Reliable Data ◽  
The State ◽  
Quality Monitoring ◽  
Quality Data ◽  
Air Quality Monitoring ◽  
Step Procedure

Low-cost sensors (LCS) are becoming popular for air quality monitoring (AQM). They promise high spatial and temporal resolutions at low-cost. In addition, citizen science applications such as personal exposure monitoring can be implemented effortlessly. However, the reliability of the data is questionable due to various error sources involved in the LCS measurement. Furthermore, sensor performance drift over time is another issue. Hence, the adoption of LCS by regulatory agencies is still evolving. Several studies have been conducted to improve the performance of low-cost sensors. This article summarizes the existing studies on the state-of-the-art of LCS for AQM. We conceptualize a step by step procedure to establish a sustainable AQM setup with LCS that can produce reliable data. The selection of sensors, calibration and evaluation, hardware setup, evaluation metrics and inferences, and end user-specific applications are various stages in the LCS-based AQM setup we propose. We present a critical analysis at every step of the AQM setup to obtain reliable data from the low-cost measurement. Finally, we conclude this study with future scope to improve the availability of air quality data.

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 AIR QUALITY MONITORING OF KALABAHI-ALOR’S SEAPORT-EAST OF NUSA TENGGARA

2019 ◽  
Vol 3 (2) ◽  
pp. 130-133
Author(s):  
Rikson Siburian ◽  
Minsyahril Bukit ◽  
Herlince Sihotang ◽  
Saur Lumban Raja ◽  
Minto Supeno ◽  
...  
Keyword(s):  
Air Quality ◽  
Government Regulation ◽  
The Other ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Other Hand ◽  
The People ◽  
The Government

Evaluation of environment of seaport is needed as well as our responsibility to nature sustainability. The Alor’s seaport belongs to Pelindo III. In order to know the air quality of Alor’s seaport, we did this study. Our aims are to know level quality of air at Alor’s seaport and compare to the government regulation. This study refers to Pararosaniline (SOx), Saltzman (NOx), Particle Calculation (dust) and decibel (noisy) methods. We used four locations, those are A-1 (Entrance gate of PELINDO (8013’09.12”S, 124031’07.21”E)); A-2 (In front of passengers terminal (8013’08.75”S, 124031’01.60”E)); A-3 (Exit  gate Kalabahi’s seaport (8013’08.2”S, 124031’00.87”E)) and A-4 (In front of port of the people (8011’09.12”S, 124031’07.21”E)). Results show that the averages level of SOx, NOx and dust of A-1, A-2 and A-3 are 103.01, 104.65 and 107.47 (µg/Nm3), 37.87, 30.62, and 39.73 (µg/Nm3), 56.64, 47.47 and 50.72 (µg/Nm), respectively. On the other hand, the level of noisy of A-1, A-2, A-3 and A-4 are 68.76, 65.69, 65.20 and 73.60 (dBA), respectively. Base on all of data, we conclude that the air quality of Alor’s seaport is still appropriate according to government regulation (PP. No. 4, 1999).

Sign in / Sign up

Export Citation Format

Share Document