1887
Volume 2022, Issue 1
  • EISSN: 2223-506X

Abstract

Elevated PM levels pose serious health hazards and are implicated in numerous acute and chronic conditions. Delineating the contributions of meteorological factors to PM levels is a daunting task, especially in confined or semiconfined urban spaces. This study aims to (1) characterize the influence of wind speed and direction on outdoor PM levels within a semiconfined urban environment, and (2) develop a simple and readily accessible data mining method for this purpose. The ultimate goal is to evaluate the extent to which PM correlations demonstrated in open spaces hold in semiconfined outdoor settings with irregular terrain. In this study, data mining techniques were applied to retrieve patterns pertaining to the effects of meteorological factors on PM levels. As a proof of concept, a feasible framework was developed to elucidate the associations between wind speed and direction and PM levels during May 2020 in Education City, Doha, Qatar. The results showed a modest negative correlation between wind speed and PM levels, at low to moderate, but not high, PM readings. Meanwhile, no correlation was detected between wind direction and PM levels. Limited by the geographical location, microenvironment, and duration of this study, it can be said with moderate statistical confidence that low PM readings are associated with high wind speeds. As a result, increasing wind speed may be beneficial at low to moderate PM levels. However, delineating a single contributing factor to high PM readings may prove infeasible. Moreover, an association with wind direction was not immediately obvious, possibly due to microenvironmental limitations. These findings underscore the applicability of data mining and the importance of microenvironmental factors in air quality research and mitigation.

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2022-02-28
2022-09-30
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