Hyper resoluted Air Quality maps in urban environment with crowdsensed data from intelligent low cost sensors
Authors:
- 29 May 2022
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-4
Abstract
Air pollution is a complex phenomenon driven by emissions and their (photo)chemistry along with weather conditions and physical characteristics of the affected scenario. Urban areas remain an extremely challenging scenario for canyon effects and traffic car emissions which exacerbate the spatio temporal variability of air pollutants concentrations. Low cost Air Quality Sensors based crowdsensing offers a solution for hyper resolution AQ mapping for spatial analysis of the phenomenon. This work presents the design of an IoT crowdsensing platform supporting intelligent low cost sensors and spatial data processing along with its deployment and results for hyper resolution AQ mapping in an urban scenario.Keywords
This publication has 4 references indexed in Scilit:
- Crowdsensing IoT Architecture for Pervasive Air Quality and Exposome Monitoring: Design, Development, Calibration, and Long-Term ValidationSensors, 2021
- Adaptive machine learning strategies for network calibration of IoT smart air quality monitoring devicesPattern Recognition Letters, 2020
- A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoringAtmospheric Measurement Techniques, 2018
- Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?Environment International, 2017