TY - GEN
T1 - A personalized air quality sensing system-A preliminary study on assessing the air quality of London underground stations
AU - Zhang, Ruizhe
AU - Ravi, Daniele
AU - Yang, Guang Zhong
AU - Lo, Benny
N1 - Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/5/30
Y1 - 2017/5/30
N2 - Recent studies have shown that air pollution has a negative impact on people's health, especially for patients with respiratory and cardiac diseases (e.g. COPD, asthma, ischemic heart disease). Although there are already many air quality monitoring stations in major cities, such as London, these stations are sparsely located, and the periodic collection of information is insufficient to provide the granularity needed to assess the environmental risk for an individual (e.g.To avoid exacerbation). Wearable devices, on the other hand, are more suitable in this context, providing a better estimation of the air quality in the proximity of the person. Therefore, relevant warnings and information on health risks can be provided in real-Time. As a proof of concept, we have developed a wearable sensor for continuous monitoring of air quality around the user, and a preliminary study was conducted to validate the sensor and assess the air quality in London underground stations. Based on the PM2.5 (particulate matter with a diameter of 2.5 μm), temperature and location information, a model is generated for predicting the air quality of each station at different times. Our preliminary results have shown that there are significant differences in air quality among stations and metro lines. It also demonstrates that wearable sensors can provide necessary information for users to make travel arrangements that minimize their exposure to polluted air.
AB - Recent studies have shown that air pollution has a negative impact on people's health, especially for patients with respiratory and cardiac diseases (e.g. COPD, asthma, ischemic heart disease). Although there are already many air quality monitoring stations in major cities, such as London, these stations are sparsely located, and the periodic collection of information is insufficient to provide the granularity needed to assess the environmental risk for an individual (e.g.To avoid exacerbation). Wearable devices, on the other hand, are more suitable in this context, providing a better estimation of the air quality in the proximity of the person. Therefore, relevant warnings and information on health risks can be provided in real-Time. As a proof of concept, we have developed a wearable sensor for continuous monitoring of air quality around the user, and a preliminary study was conducted to validate the sensor and assess the air quality in London underground stations. Based on the PM2.5 (particulate matter with a diameter of 2.5 μm), temperature and location information, a model is generated for predicting the air quality of each station at different times. Our preliminary results have shown that there are significant differences in air quality among stations and metro lines. It also demonstrates that wearable sensors can provide necessary information for users to make travel arrangements that minimize their exposure to polluted air.
UR - http://www.scopus.com/inward/record.url?scp=85025457447&partnerID=8YFLogxK
U2 - 10.1109/BSN.2017.7936020
DO - 10.1109/BSN.2017.7936020
M3 - Conference contribution
AN - SCOPUS:85025457447
T3 - 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2017
SP - 111
EP - 114
BT - 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2017
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 14th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2017
Y2 - 9 May 2017 through 12 May 2017
ER -