Data Fusion/Assimilation of Low-Cost Sensors for Air Pollution Exposure

Air pollution is a major issue in urban areas. High population density is related to excess anthropogenic emissions, impacting upon the environment and health. According to the World Health Organization (WHO), each year approximately 7 million human deaths are attributed to exposure to ambient air pollution. To perform air quality monitoring in urban areas, there is a need to measure the pollution with high resolution. Indeed, highly accurate and reliable air quality monitoring stations have been established to continuously monitor air quality. However, these monitoring stations are expensive and complex to establish, operate, and maintain. Therefore, it is not feasible to deploy these stations massively in urban areas. Alternatively, the dense deployment of low-cost air quality sensors in urban areas enables the detection of the pollution hot spots in real-time. Low-cost air quality sensors are also beneficial to be deployed indoors, where official monitoring stations are not able to perform measurements.

Due to the impact of air pollution on human health, as well as the increasing popularity of low-cost air quality sensors, this Special Issue aims to publish new research and reviews to assess and examine the adverse health effects via the assimilation of data from low-cost air quality sensors both indoors and outdoors. The scope of this Special Issue also includes Internet of things technology and several data science methods, such as data mining and machine learning for processing and analyzing air quality data for investigating health exposure. We are also interested in air quality data fusion and its association to human health as well as environmental epidemiology.

Prof. Tareq Hussein
Dr. Martha A. Zaidan
Guest Editors

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