2020
DOI: 10.1155/2020/1561823
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Effects of Population Weighting on PM10 Concentration Estimation

Abstract: Particulate matter with an aerodynamic diameter of 10 μm or less (PM10) pollution poses a considerable threat to human health, and the first step in quantifying health impacts of human exposure to PM10 pollution is exposure assessment. Population-weighted exposure level (PWEL) estimation is one of the methods that provide a more refined exposure assessment as it includes the spatiotemporal distribution of the population into the pollution concentration estimation. This study assessed the population weighting e… Show more

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Cited by 15 publications
(8 citation statements)
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“…Conversely, strong, warm dry winds increased the rate at which dust was lifted and spread causing more PM 10 levels in the breathing zone causing more URTI complaints. Other studies have also shown that PM 10 is washed off by raindrops and results in a reduction [19].…”
Section: Resultsmentioning
confidence: 91%
“…Conversely, strong, warm dry winds increased the rate at which dust was lifted and spread causing more PM 10 levels in the breathing zone causing more URTI complaints. Other studies have also shown that PM 10 is washed off by raindrops and results in a reduction [19].…”
Section: Resultsmentioning
confidence: 91%
“…Historical predictions (2005–2018) were obtained by matching annual time-varying predictor variables for each year and using the 2019 regression coefficients. Population-weighted annual average NO 2 levels for the 358 township-level divisions across NHAR (mean township population 2005–2018 = 17,602 people; mean township area = 144 km 2 ) were calculated by combining the predicted NO 2 concentrations with 100-m gridded annual population estimates [ 39 , 40 ].…”
Section: Methodsmentioning
confidence: 99%
“…All air pollutant output was analysed only at surface level. For some analysis, we weighted PM 2.5 and O 3 by population using the formula outlined in Abdul Shakor et al (2020). We used time-varying gridded population projections for each SSP from Jones & O'Neill (2016).…”
Section: Model Outputmentioning
confidence: 99%