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Volume 2022 Number 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.

© 2022 The Author(s), licensee HBKU Press.
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2022-02-28
2024-11-09
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References

  1. World Health Organization. WHO air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide. Global update 2005. Geneva, Switzerland: WHO Press; 2005.
    [Google Scholar]
  2. Apte JS, Marshall JD, Cohen AJ, Brauer M. Addressing global mortality from ambient PM2.5. Environmental Science & Technology. 2015; 49:(13):8057–8066.
     [Google Scholar]
  3. Nasser Z, Salameh P, Nasser W, Abou Abbas L, Elias E, Leveque A. Outdoor particulate matter (PM) and associated cardiovascular diseases in the Middle East. International Journal of Occupational Medicine and Environmental Health. 2015; 28:(4)641–661.
     [Google Scholar]
  4. Leiva GM, Santibanez DA, Ibarra ES, Matus CP, Seguel R. A five-year study of particulate matter (PM2.5) and cerebrovascular diseases. Environmental Pollution. 2013;181:1–6.
     [Google Scholar]
  5. Madrigano J, Kloog I, Goldberg R, Coull BA, Mittleman MA, Schwartz J. Long-term exposure to PM2.5 and incidence of acute myocardial infarction. Environmental Health Perspectives. 2013 121:(2):192–196.
     [Google Scholar]
  6. Bowe B, Xie Y, Li T, Yan Y, Xian H, Al-Aly Z. The 2016 global and national burden of diabetes mellitus attributable to PM2.5 air pollution. The Lancet Planetary Health. 2018 2:(7):e301–e312.
     [Google Scholar]
  7. Braithwaite I, Zhang S, Kirkbride JB, Osborn DPJ, Hayes JF. Air pollution (particulate matter) exposure and associations with depression, anxiety, bipolar, psychosis and suicide risk: A systematic review and meta-analysis. Environmental Health Perspectives. 2019 127:(12):126002.
     [Google Scholar]
  8. Javed W, Iakovides M, Garaga R, Stephanou EG, Kota SH, Ying Q, et al. Source apportionment of organic pollutants in fine and coarse atmospheric particles in Doha, Qatar. Journal of the Air & Waste Management Association. 2019 69:(11):1277–1292.
     [Google Scholar]
  9. Ouyang X, Wei X, Li Y, Wang XC, Klemes JJ. Impacts of urban land morphology on PM2.5 concentration in the urban agglomerations of China. Journal of Environmental Management. 2021;:283:112000.
     [Google Scholar]
  10. Isaifan RJ, Al-Thani H, Ayoub M, Aissa B, Koç M. The economic value of common urban trees in the State of Qatar from an air quality control perspective.Journal of Environmental Science and Pollution Research. 2018 4:(3):285–288.
     [Google Scholar]
  11. Isaifan RJ, Baldauf RW. Estimating economic and environmental benefits of urban trees in desert regions. Frontiers in Ecology and Evolution. 2020;:8:1–14.
     [Google Scholar]
  12. Alsalama T, Koç M, Isaifan RJ. Mitigation of urban air pollution with green vegetation for sustainable cities: A review. International Journal of Global Warming. 2021; 25:(3/4):498–515.
     [Google Scholar]
  13. Wang J, Ogawa S. Effects of meteorological conditions on PM2.5 concentrations in Nagasaki, Japan. International Journal of Environmental Research and Public Health. 2015 12:(8):9089–9101.
     [Google Scholar]
  14. Lu HC, Fang GC. Estimating the frequency distributions of PM10 and PM2.5 by the statistics of wind speed at Sha-Lu, Taiwan. Science of the Total Environment. 2002; 298:(1–3):119–130.
     [Google Scholar]
  15. Al-Thani H, Koç M, Isaifan RJ. Investigations on deposited dust fallout in urban Doha: Characterization, source apportionment and mitigation. Environment and Ecology Research. 2018 6:(5):493–506.
     [Google Scholar]
  16. Zhai K, Bhatti M, Khalil O, Khalil L, Al-Hail M, Yousef MS. Real-time air pollution (PM2.5) measurements in Education City, Doha, Qatar: Evaluating data from two different photometric monitors. QScience Connect. 2020; 2020:(1). https://doi.org/10.5339/connect.2020.5
     [Google Scholar]
  17. Al-Thani H, Koç M, Fountoukis C, Isaifan RJ. Evaluation of particulate matter emissions from non-passenger diesel vehicles in Qatar. Journal of the Air & Waste Management Association. 2020 70:(2):228–242.
     [Google Scholar]
  18. Lanouar C, Al-Malk AY, Al Karbi K. Air pollution in Qatar: Causes and challenges. White Paper. 2016 1:(3):1–7.
     [Google Scholar]
  19. Javed W, Iakovides M, Stephanou EG, Wolfson JM, Koutrakis P, Guo B. Concentrations of aliphatic and polycyclic aromatic hydrocarbons in ambient PM2.5 and PM10 particulates in Doha, Qatar. Journal of the Air & Waste Management Association. 2019 69:(2):162–177.
     [Google Scholar]
  20. Taylor CC, Yousif AE, Mwitondi KS. Statistical analysis of particulate matter data in Doha, Qatar. In: Casares J, Passerini G, Barnes J, Longhurst J, Perillo G, editors. WIT transactions on ecology and the environment. Vol. 230. Southampton, UK: WIT Press; 2018. pp. 107–118.
     [Google Scholar]
  21. Cuesta HA, Coffman DL, Branas C, Murphy HM. Using decision trees to understand the influence of individual- and neighborhood-level factors on urban diabetes and asthma. Health & Place. 2019;58:102119.
     [Google Scholar]
  22. Poucke SV, Kovacevic A, Vukicevic M. Early prediction of patient mortality based on routine laboratory tests and predictive models in critically ill patients. In: Thomas C, editor. Data mining. London: IntechOpen; 2018. pp. 93–106.
     [Google Scholar]
/content/journals/10.5339/connect.2022.2
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Data mining indicates an association between ambient PM2.5 levels and wind speed in an urban environment (Education City, Doha, Qatar)
QScience Connect 2022, 2 (2022); https://doi.org/10.5339/connect.2022.2
/content/journals/10.5339/connect.2022.2
/content/journals/10.5339/connect.2022.2
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  • Article Type: Research Article
Keyword(s): PM2.5, particulate matter, air pollution, meteorological factors, data mining

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