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Source identification of episodic rain pollutants by a new approach: Combining satellite observations and backward air mass trajectories

Research output: Contribution to journalArticlepeer-review

Abstract

In this study, various source apportionment methods, viz., enrichment factor, relative source contribution, and factor analysis in addition to cluster analysis coupled with satellite observations, were used to investigate the origin of rainwater pollutants in an arid urban area of Saudi Arabia. The rainwater samples were collected by an automatic wet-only sequential rain sampler and analyzed for their pH, electrical conductivity (EC), and major ions (Cl, NO3–, SO4 2, HCO3–, Na+, K+, NH4+, Ca2+, and Mg2+). The results revealed that the chemical composition was dominated by Ca2+, SO4 2, Cl, HCO3–, and Na+, which represented 80% of the total ionic equivalent concentration. Ca2+, the most abundant ion, exhibited a mean concentration of 480 µeq L–1 and accounted for 30% of the total ionic equivalent concentration. The study area received a total ionic wet deposition flux (FWD) of 4.07 tons km–2 y–1, and SO4 2– and NO3 – contributed 76% and 24%, respectively, of the rainwater acidity. Ca2+ was responsible for 80% of the rainwater’s neutralization. The source apportionment indicated that intense local human activity and medium-to long-range transport from Kuwait, Iraq, Qatar, Riyadh, and the Western Province produced the majority of the anthropogenic components (SO4 2, NO3–, and NH4+), which represented 47% of the total ionic FWD. However, moderate to high particulate matter loads over the southern part of Saudi Arabia and long-range transport from Iraq and Kuwait contributed the crustal components (Ca2+, HCO3–, K+, and Mg2+), which accounted for 36% of the total ionic FWD, whereas the Arabian Gulf was the primary source of the marine components (Na+ and Cl). We found combining satellite observations and cluster analysis of backward air mass trajectories to be an effective new approach for assessing the source-receptor relationships for atmospheric air pollutants.

Original languageEnglish
Pages (from-to)2827-2843
Number of pages17
JournalAerosol and Air Quality Research
Volume19
Issue number12
DOIs
StatePublished - Dec 2019

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Keywords

  • Backward trajectory
  • Cluster analysis
  • Rainwater chemical composition
  • Satellite observation
  • Source apportionment
  • Wet deposition flux

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