scholarly journals A compact, modular and low-cost Internet of Things (IoT) platform for air quality monitoring in urban areas

2020 ◽  
Vol 1710 ◽  
pp. 012004
Author(s):  
Christos Spandonidis ◽  
Stefanos Tsantilas ◽  
Elias Sedikos ◽  
Nektarios Galiatsatos ◽  
Fotios Giannopoulos ◽  
...  
Keyword(s):  
Air Quality ◽  
Internet Of Things ◽  
Urban Areas ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Iot Platform

scholarly journals A Low-Cost Sensing System for Cooperative Air Quality Monitoring in Urban Areas

Sensors ◽  
2015 ◽  
Vol 15 (6) ◽  
pp. 12242-12259 ◽  
Author(s):  
Simone Brienza ◽  
Andrea Galli ◽  
Giuseppe Anastasi ◽  
Paolo Bruschi
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Sensing System

scholarly journals View: implementing low cost air quality monitoring solution for urban areas

2012 ◽  
Vol 1 (1) ◽  
pp. 10 ◽  
Author(s):  
Jahangir Ikram ◽  
Amer Tahir ◽  
Hasanat Kazmi ◽  
Zonia Khan ◽  
Rabi Javed ◽  
...  
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring

scholarly journals Forecasting Model Validation of Particulate Air Pollution by Low Cost Sensors Data

2019 ◽  
Vol 11 (2) ◽  
pp. 63-68 ◽  
Author(s):  
Nicoletta Lotrecchiano ◽  
Filomena Gioiella ◽  
Aristide Giuliano ◽  
Daniele Sofia
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Low Cost ◽  
Emission Control ◽  
Quality Monitoring ◽  
Forecasting Model ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Human Beings ◽  
Monitoring Stations

Environmental pollution in urban areas may be mainly attributed to the rapid industrialization and increased growth of vehicular traffic. As a consequence of air quality deterioration, the health and welfare of human beings are compromised. Air quality monitoring networks usually are used not only to assess the pollutant trend but also in the effective set-up of preventive measures of atmospheric pollution. In this context, monitoring can be a valid action to evaluate different emission control scenarios; however, installing a high space-time resolution monitoring network is still expensive. Merge of observations data from low-cost air quality monitoring networks with forecasting models can contribute to improving significantly emission control scenarios. In this work, a validation algorithm of the forecasting model for the concentration of small particulates (PM10 and PM2.5) is proposed. Results showed a satisfactory agreement between the PM concentration forecast values and the measured data from 3 air quality monitoring stations. Final average RMSE values for all monitoring stations are equal to about 4.5 µg/m3.

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.

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
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