Low-Cost Sensor Network for Air Quality Assessment in Cabo Verde Islands

This study explores the application of low-cost sensor networks for air quality monitoring in Cabo Verde islands, utilizing Clarity Node-S sensors to measure fine particulate matter with diameters equal to or smaller than 10 µm (PM10) and 2.5 µm (PM2.5) and nitrogen dioxide (NO<sub>2</sub>) gasses, across various locations. The sensors were strategically placed and calibrated to ensure coverage of the whole archipelago and accurate data collection. The results consistently revealed seasonal patterns of dust variation across the archipelago, with concentrations of particulate matter exceeding World Health Organization (WHO) limits in all regions. However, Praia frequently exhibits the highest levels of air pollution, exceeding a 200 µg/m<sup>3</sup> daily average, particularly during the dry season. Seasonal variations indicated that pollutants are significantly higher from November to March due to Saharan dust flux (a phenomenon locally know as <i>Bruma Seca</i>). Other cities showed more stable and lower pollutant concentrations. This study highlights the potential of low-cost sensors to provide extensive and real-time air quality data, enabling better environmental assessment and policy formulation. However, the variability in equipment accuracy and the limited geographical coverage remain the main limitations to be overcome. Future research should focus on these issues, and a sensor network integrated with reference methods could be a great asset to enhance data accuracy and improve outcomes of air quality monitoring in the country.

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Publication Details

Journal
Sensors
Published
2024-11-29
DOI
https://doi.org/10.3390/s24237656
Citations
4
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
0.82

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article

Low-Cost Sensor Network for Air Quality Assessment in Cabo Verde Islands

José P. S. Aniceto, Myriam Alexandra dos Santos Batalha Dias Nunes Lopes, ANA PAULA ALMEIDA DA COSTA
4 citations
Sensors
Air Quality Monitoring and Forecasting
0.82
article

Low-Cost Sensor Network for Air Quality Assessment in Cabo Verde Islands

José P. S. Aniceto, Myriam Alexandra dos Santos Batalha Dias Nunes Lopes, ANA PAULA ALMEIDA DA COSTA
article en
4 citations

Abstract

This study explores the application of low-cost sensor networks for air quality monitoring in Cabo Verde islands, utilizing Clarity Node-S sensors to measure fine particulate matter with diameters equal to or smaller than 10 µm (PM10) and 2.5 µm (PM2.5) and nitrogen dioxide (NO<sub>2</sub>) gasses, across various locations. The sensors were strategically placed and calibrated to ensure coverage of the whole archipelago and accurate data collection. The results consistently revealed seasonal patterns of dust variation across the archipelago, with concentrations of particulate matter exceeding World Health Organization (WHO) limits in all regions. However, Praia frequently exhibits the highest levels of air pollution, exceeding a 200 µg/m<sup>3</sup> daily average, particularly during the dry season. Seasonal variations indicated that pollutants are significantly higher from November to March due to Saharan dust flux (a phenomenon locally know as <i>Bruma Seca</i>). Other cities showed more stable and lower pollutant concentrations. This study highlights the potential of low-cost sensors to provide extensive and real-time air quality data, enabling better environmental assessment and policy formulation. However, the variability in equipment accuracy and the limited geographical coverage remain the main limitations to be overcome. Future research should focus on these issues, and a sensor network integrated with reference methods could be a great asset to enhance data accuracy and improve outcomes of air quality monitoring in the country.

SensorsVol. 24(23)
University of Aveiro (PT)
Universidade de Aveiro
Openalex Percentile: Top 29%
Air Quality Monitoring and Forecasting
0.82
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Low-Cost Sensor Network for Air Quality Assessment in Cabo Verde Islands — José P. S. Aniceto, Myriam Alexandra dos Santos Batalha Dias Nunes Lopes, et al. · Sensors (2024) | TGRS Research Map | TGRS