Advances and Applications in Statistics
Volume 61, Issue 2, Pages 169 - 181
(April 2020) http://dx.doi.org/10.17654/AS061020169 |
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HARNESSING THE POTENTIAL OF REAL-TIME DATA ANALYSIS AND STATISTICAL MODELING FOR MAPPING EXPOSURE TO AIR POLLUTION
Ibrahim Sidi Zakari
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Abstract: This paper aims at highlighting some aspects of the current state of open data sources for real-time air quality monitoring and statistical modeling options for calibrating low-cost sensors ($100-500).
Our findings include that there is a lack of real-time air quality monitoring ground stations in African cities and data about population-weighted exposure to particulate matter (PM) of aerodynamic diameter of less than 1 micrometer (PM1) are not publicly available (as open data) for all countries worldwide.
Finally, although the next generation of low-cost sensors (using the latest microsensing technology) could contribute in monitoring personal exposure to air pollution, it is important to pursue efforts towards designing data science and machine learning methods for improving the calibration of these wearable devices. |
Keywords and phrases: statistical modeling, pollution maps, sustainable development goals, low-cost sensors calibration, open data, air quality index.
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