scholarly journals ON THE ORGANIZATION AND VALIDATION OF A PILOT TEST OF A MOBILE CROWDSOURCED AIR QUALITY MONITORING SYSTEM

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 361-366
Author(s):  
D. Garcia ◽  
F. Vázquez-Gallego ◽  
M. E. Parés
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Continuous Monitoring ◽  
Low Cost ◽  
Sensor Nodes ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Using Data ◽  
Pilot Tests ◽  
Data Calibration

Abstract. The development of new tools that allow continuous monitoring of air quality is essential for the study of actions, in order to improve the levels of pollutants in the air that are harmful to the health of citizens. Cardiovascular and respiratory diseases have been identified as risk factors for death in patients with COVID-19; at the same time, exposure to air pollution is associated with these diseases. In this article, we present the pilot tests of the Crowdsourced Air Quality Monitoring (C-AQM) system, which allows the generation of reliable air pollution maps, using data provided by low-cost sensor nodes. The results verify that the system is correct after performing a data calibration; an improvement in NO2 pollution has been observed on weekends, as well as a situation of less air pollution by NO2 between the first and second pandemic waves in Spain.

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 C-AQM: A CROWD-SOURCED AIR QUALITY MONITORING SYSTEM

2018 ◽  
Vol XLII-4 ◽  
pp. 491-497
Author(s):  
M. E. Parés ◽  
F. Vázquez-Gallego
Keyword(s):  
Air Quality ◽  
Communication Systems ◽  
Low Cost ◽  
City Council ◽  
Sensor Nodes ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Energy Sensor ◽  
Environment Service ◽  
Quality Maps

<p><strong>Abstract.</strong> European cities are currently facing one of the main evolutions of the last fifty years. “Cities for the citizens” is the new leitmotiv of modern societies, and citizens are demanding, among others, a greener environment including non-polluted air. Improved sensors and improved communication systems open the door to the design of new systems based on citizen science to better monitor the air quality. In this paper, we present a system that relies on the already available Copernicus Environment Service, on Air Quality Monitoring reference stations and on a cluster of new low-cost, low-energy sensor nodes that will improve the resolution of air quality maps. The data collected by this system will be stored in a time series database, and it will be available both to city council managers for decision making and to citizens for informative purposes. In this paper, we present the main challenges imposed by Air Quality Monitoring systems, our proposal to overcome those challenges, and the results of our preliminary tests.</p>

MOPGA/Improving Air Quality in West Africa: Low-cost sensors as a solution to improve the understanding of spatial and temporal variability in urban air pollution

2021 ◽  
Author(s):  
Julien Bahino ◽  
Michael Giordano ◽  
Véronique Yoboué ◽  
Arsène Ochou ◽  
Corinne Galy-Lacaux ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
West Africa ◽  
Low Cost ◽  
West African ◽  
Passive Samplers ◽  
Quality Monitoring ◽  
Spatial And Temporal Variability ◽  
Air Quality Monitoring ◽  
Calibration Models

&lt;p&gt;This study was carried out within the framework of the Improving Air Quality in West Africa&lt;strong&gt;&amp;#160;&lt;/strong&gt;(IAQWA) project funded by the Make Our Planet Great Again (MOPGA) program. In recent years, West African countries have experienced an economic upturn driven by GDP growth of nearly 3.7% in 2019 (AfDB, 2020). This economic boom is mainly felt in the cities where it promotes the construction of highway infrastructure, real estate development, and industry. All these activities are sources of air pollution. Unfortunately, there is almost no air quality monitoring in these cities partly due to the high cost of monitoring instruments. Low-cost air quality monitoring instruments can help improve the spatial and temporal resolution of measurements at relatively lower cost. However, the installation of these instruments in West African environments characterized by high relative humidity requires their calibration through collocation with reference instruments. The IAQWA project aims to improve our understanding of air pollutants such as fine particulate matter mass (PM&lt;sub&gt;2.5&lt;/sub&gt;), ozone (O&lt;sub&gt;3&lt;/sub&gt;), nitrogen oxides (NOx), sulfur dioxide (SO&lt;sub&gt;2&lt;/sub&gt;), and carbon monoxide (CO) in Abidjan and Accra, two major West African capitals, through the deployment of Real-time Affordable Multi-Pollutant (RAMP) monitors.&lt;/p&gt;&lt;p&gt;Since February 2020, five RAMPs have been installed and are operating continuously at various sites&amp;#160;in Abidjan and Lamto in Cote d'Ivoire, and four RAMPs have been operating in Accra, Ghana. Some of the RAMPs have been collocated with PM and/or NOx reference instruments. At other sites the RAMPs have been collocated with INDAAF passive samplers and passive aerosol collectors. These collocations have allowed for the development of calibration models for these low-cost sensors. The performance of these calibration models is presented here along with the diurnal and seasonal variations of air pollution at the different sites in Abidjan and Accra. These results will eventually be used to improve our understanding of the drivers of air pollution in these major West African cities, which is essential to choosing sustainable development pathways in the future.&lt;/p&gt;

scholarly journals IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction

2019 ◽  
Vol 9 (9) ◽  
pp. 1831 ◽  
Author(s):  
Xiaozheng Lai ◽  
Ting Yang ◽  
Zetao Wang ◽  
Peng Chen
Keyword(s):  
Kalman Filter ◽  
Air Quality ◽  
Low Cost ◽  
Moving Average ◽  
High Accuracy ◽  
Edge Computing ◽  
Sensor Nodes ◽  
Quality Monitoring ◽  
Raspberry Pi ◽  
Air Quality Monitoring

In order to obtain high-accuracy measurements, traditional air quality monitoring and prediction systems adopt high-accuracy sensors. However, high-accuracy sensors are accompanied with high cost, which cannot be widely promoted in Internet of Things (IoT) with many sensor nodes. In this paper, we propose a low-cost air quality monitoring and real-time prediction system based on IoT and edge computing, which reduces IoT applications dependence on cloud computing. Raspberry Pi with computing power, as an edge device, runs the Kalman Filter (KF) algorithm, which improves the accuracy of low-cost sensors by 27% on the edge side. Based on the KF algorithm, our proposed system achieves the immediate prediction of the concentration of six air pollutants such as SO2, NO2 and PM2.5 by combining the observations with errors. In the comparison experiments with three common predicted algorithms including Simple Moving Average, Exponentially Weighted Moving Average and Autoregressive Integrated Moving Average, the KF algorithm can obtain the optimal prediction results, and root-mean-square error decreases by 68.3% on average. Taken together, the results of the study indicate that our proposed system, combining edge computing and IoT, can be promoted in smart agriculture.

scholarly journals Performance Assessment of a Low-Cost PM2.5 Sensor for a near Four-Month Period in Oslo, Norway

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 41 ◽  
Author(s):  
Hai-Ying Liu ◽  
Philipp Schneider ◽  
Rolf Haugen ◽  
Matthias Vogt
Keyword(s):  
Air Quality ◽  
Relative Humidity ◽  
Low Cost ◽  
Quality Monitoring ◽  
Monitoring Station ◽  
Air Quality Monitoring ◽  
Potential Improvement ◽  
Relative Errors ◽  
Short Time ◽  
Data Calibration

The very low-cost Nova particulate matter (PM) sensor SDS011 has recently drawn attention for its use for measuring PM mass concentration, which is frequently used as an indicator of air quality. However, this sensor has not been thoroughly evaluated in real-world conditions and its data quality is not well documented. In this study, three SDS011 sensors were evaluated by co-locating them at an official, air quality monitoring station equipped with reference-equivalent instrumentation in Oslo, Norway. The sensors’ measurement results for PM2.5 were compared with data generated from the air quality monitoring station over almost a four-month period. Five performance aspects of the sensors were examined: operational data coverage, linearity of response and accuracy, inter-sensor variability, dependence on relative humidity (RH) and temperature (T), and potential improvement of sensor accuracy, by data calibration using a machine-learning method. The results of the study are: (i) the three sensors provide quite similar results, with inter-sensor correlations exhibiting R values higher than 0.97; (ii) all three sensors demonstrate quite high linearity against officially measured concentrations of PM2.5, with R2 values ranging from 0.55 to 0.71; (iii) high RH (over 80%) negatively affected the sensor response; (iv) data calibration using only the RH and T recorded directly at the three sensors increased the R2 value from 0.71 to 0.80, 068 to 0.79, and 0.55 to 0.76. The results demonstrate the general feasibility of using these low cost SDS011 sensors for indicative PM2.5 monitoring under certain environmental conditions. Within these constraints, they further indicate that there is potential for deploying large networks of such devices, due to the sensors’ relative accuracy, size and cost. This opens up a wide variety of applications, such as high-resolution air quality mapping and personalized air quality information services. However, it should be noted that the sensors exhibit often very high relative errors for hourly values and that there is a high potential of abusing these types of sensors if they are applied outside the manufacturer-provided specifications particularly regarding relative humidity. Furthermore, our analysis covers only a relatively short time period and it is desirable to carry out longer-term studies covering a wider range of meteorological conditions.

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 MAPPING AIR QUALITY WITH A MOBILE CROWDSOURCED AIR QUALITY MONITORING SYSTEM (C-AQM)

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 685-690
Author(s):  
M. E. Parés ◽  
D. Garcia ◽  
F. Vázquez-Gallego
Keyword(s):  
Air Quality ◽  
Communication Systems ◽  
Low Cost ◽  
City Council ◽  
Sensor Nodes ◽  
Quality Monitoring ◽  
Strong Impact ◽  
Air Quality Monitoring ◽  
Energy Sensor ◽  
Quality Maps

Abstract. World cities are currently facing one of the major crisis of the last century. Some preliminary studies on COVID-19 pandemia have shown that air pollutants may have a strong impact on virus effects. Improved gas sensors and wireless communication systems open the door to the design of new air monitoring systems based on citizen science to better monitor and communicate the air quality levels. In this paper, we present the Crowdsourced Air Quality Monitoring (C-AQM) system, which relies on Air Quality Monitoring reference stations and a cluster of new low-cost and low-energy sensor nodes, in order to improve the resolution of air quality maps. The data collected by the C-AQM system is stored in a time series database and is available both to city council managers for decision making and to citizens for informative purposes. In this paper, we present the main bases of the C-AQM system as well as the measurements validation campaign carried out.

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.

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