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

<p>This study was carried out within the framework of the Improving Air Quality in West Africa<strong> </strong>(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<sub>2.5</sub>), ozone (O<sub>3</sub>), nitrogen oxides (NOx), sulfur dioxide (SO<sub>2</sub>), and carbon monoxide (CO) in Abidjan and Accra, two major West African capitals, through the deployment of Real-time Affordable Multi-Pollutant (RAMP) monitors.</p><p>Since February 2020, five RAMPs have been installed and are operating continuously at various sites 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.</p>

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 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 Robust statistical calibration and characterization of portable low-cost air quality monitoring sensors to quantify real-time O<sub>3</sub> and NO<sub>2</sub> concentrations in diverse environments

2020 ◽  
Author(s):  
Ravi Sahu ◽  
Ayush Nagal ◽  
Kuldeep Kumar Dixit ◽  
Harshavardhan Unnibhavi ◽  
Srikanth Mantravadi ◽  
...  
Keyword(s):  
Air Quality ◽  
Low Cost ◽  
Metric Learning ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Local Calibration ◽  
Calibration Models ◽  
Calibration Methods ◽  
Calibration Algorithm

Abstract. Rising awareness of the health risks posed by elevated levels of ground-level O3 and NO2 have led to an increased demand for affordable and dense spatio-temporal air quality monitoring networks. Low-cost sensors used as a part of Internet of Things (IoT) platforms offer an attractive solution with greater mobility and lower maintenance costs, and can supplement compliance regulatory monitoring stations. These commodity low-cost sensors have reasonably high accuracy but require in-field calibration to improve precision. In this paper, we report the results of a deployment and calibration study on a network of seven air quality monitoring devices built using the Alphasense O3 (OX-B431) and NO2 (NO2-B43F) electrochemical gas sensors. The sensors were deployed at sites situated within two mega-cities with diverse geographical, meteorological and air quality parameters – Faridabad (Delhi National Capital Region) and Mumbai, India. The deployment was done in two phases over a period of three months. A unique feature of our deployment is a swap-out experiment wherein four of these sensors were relocated to different sites in the two deployment phases. Such a diverse deployment with sensors switching places gives us a unique opportunity to ablate the effect of seasonal, as well as geographical variations on calibration performance. We perform an extensive study of more than a dozen parametric as well as non-parametric calibration algorithms and find local calibration methods to offer the best performance. We propose a novel local calibration algorithm based on metric-learning that offers, across deployment sites and phases, an average R2 coefficient of 0.873 with respect to reference values for O3 calibration and 0.886 for NO2 calibration. This represents an upto 9 % increase over R2 values offered by classical local calibration methods. In particular, our proposed model far outperforms the default calibration models offered by the gas sensor manufacturer. We also offer a critical analysis of the effect of various data preparation and model design choices on calibration performance. The key recommendations emerging out of this study include (1) incorporating ambient relative humidity and temperature as free parameters (or features) into all calibration models, (2) assessing the relative importance of various features with respect to the calibration task at hand, by using an appropriate feature weighing or metric learning technique, (3) the use of local (or even hyper-local) calibration techniques such as k-NN that seem to offer the best performance in high variability conditions such as those encountered in field deployments, (4) performing temporal smoothing over raw time series data, say by averaging sensor signals over small windows, but being careful to not do so too aggressively, and (5) making all efforts at ensuring that data with enough diversity is demonstrated to the calibration algorithm while training to ensure good generalization. These results offer insights into the strengths and limitations of these sensors, and offer an encouraging opportunity at using them to supplement and densify compliance regulatory monitoring networks.

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 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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