Multivariate methods for indoor PM10 and PM2.5 modelling in naturally ventilated schools buildings

Elbayoumi, Maher; Ramli, Nor Azam; Yusof, Noor Faizah Fitri Md; Yahaya, Ahmad Shukri; Madhoun, Wesam Al; Ul-Saufie, Ahmad Zia

doi:10.1016/j.atmosenv.2014.05.007

Cited by 97 publications

(58 citation statements)

References 46 publications

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“…This study builds upon other studies predicting the PM 2.5 levels, providing evidence that PM 2.5 levels can be predicted using the household characteristics, occupant activities, and outdoor pollution sources . The study provides confidence that the model can be used to predict residential indoor PM 2.5 exposure for participants in the MACE cohort.…”

Section: Resultsmentioning

confidence: 68%

“…Other factors are known to be important when modeling indoor air quality such as the penetration efficiency; deposition rate, air exchange rate, and infiltration rate were also not studied. The importance of the meteorological factors such as relative humidity and temperature has been shown to have significant effects on the PM 2.5 levels and were not included in the modeling . Another limitation was the fact that within‐household variability was much higher than the between‐household's variability.…”

Section: Limitations and Advantages Of The Studymentioning

confidence: 99%

“…Various models have been used in modeling indoor PM 2.5 levels and they include, among others the use of statistical models (such as linear or logistic regression models), micro‐environmental models (such as mass balance models), and computational fluid dynamics (CFD) models …”

Section: Introductionmentioning

confidence: 99%

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Developing a predictive model for fine particulate matter concentrations in low socio-economic households in Durban, South Africa

et al. 2017

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In low-resource settings, there is a need to develop models that can address contributions of household and outdoor sources to population exposures. The aim of the study was to model indoor PM using household characteristics, activities, and outdoor sources. Households belonging to participants in the Mother and Child in the Environment (MACE) birth cohort, in Durban, South Africa, were randomly selected. A structured walk-through identified variables likely to generate PM . MiniVol samplers were used to monitor PM for a period of 24 hours, followed by a post-activity questionnaire. Factor analysis was used as a variable reduction tool. Levels of PM in the south were higher than in the north of the city (P < .05); crowding and dwelling type, household emissions (incense, candles, cooking), and household smoking practices were factors associated with an increase in PM levels (P < .05), while room magnitude and natural ventilation factors were associated with a decrease in the PM levels (P < .05). A reasonably robust PM predictive model was obtained with model R of 50%. Recognizing the challenges in characterizing exposure in environmental epidemiological studies, particularly in resource-constrained settings, modeling provides an opportunity to reasonably estimate indoor pollutant levels in unmeasured homes.

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Section: Resultsmentioning

confidence: 68%

Section: Limitations and Advantages Of The Studymentioning

confidence: 99%

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Developing a predictive model for fine particulate matter concentrations in low socio-economic households in Durban, South Africa

et al. 2017

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“…In the present study, this technique was applied to reduce variables and to identify the most relevant variables in O 3 variations (Dominick et al, 2012). PCA is widely known due to its capability to detect the most significant variables in dataset with minimum loss of the original information (Dominick et al, 2012;Elbayoumi et al, 2014). Principal components (PCs) were extracted, such that the first PC (PC1) accounted for the largest amount of total variation in the data set, whereas the following components accounted for the remaining variations that were not considered in PC1 (Kovac-Andric et al, 2009).…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

The Influence of Spatial Variability of Critical Conversion Point (CCP) in Production of Ground Level Ozone in the Context of Tropical Climate

Awang

Elbayoumi

Ramli

et al. 2016

Aerosol Air Qual. Res.

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Critical conversion point (CCP) is a very crucial step in production of the ground level O 3 chemistry. Thus, a multivariate analysis was applied on the dataset of nine selected locations in Malaysia from 1999 to 2010. It incorporated hierarchical agglomerative cluster analysis (HACA) to explore the spatial variability of CCP and principal component analysis (PCA) to determine the major sources of the air pollutants that influence ozone CCP. High variability in CCP was observed between the monitoring stations that occurred during critical conversion time (CCT) from 8:00 a.m. to 11:00 a.m. The HACA results grouped the nine monitoring stations into three different clusters, based on the characteristics of ozone concentrations during CCT period. Results of PCA for the three clusters showed that the contributions to O 3 level variation during CCT by meteorological variables (UVB, temperature, relative humidity, and wind speed) are higher at 51.6%, 48.5%, and 33.3% than that of primary air pollutants (NO 2 , SO 2 , PM 10 ) at 19.2%, 21.4%, and 15.2% for cluster 1, cluster 2, and cluster 3, respectively. Therefore, applying a targeted spatial control strategy for ground level O 3 precursors during the CCT period is a crucial step.

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“…A small COD (r < 0.2) indicates similar pollutant concentrations between two sites, whereas a value approaching unity indicates a significant difference in absolute concentrations and subsequent spatial nonuniformity between the sites. Pearson correlation coefficient was employed for univariate analysis to measure the linear association and the strength and direction of the relationship between selected variables and a dependent variable (Awang et al 2015;Elbayoumi et al 2014). Equation 2 shows equation for calculating r. r = (2) where x and y are selected variables and and are the means of the variables.…”

Section: Site Descriptionmentioning

confidence: 99%

Distribution and Temporal Behaviour of O3 and NO2 Near Selected Schools in Seberang Perai, Pulau Pinang and Parit Buntar, Perak, Malaysia

Zainordin¹,

Ramli²,

Elbayoumi³

2017

JSM

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Multivariate methods for indoor PM10 and PM2.5 modelling in naturally ventilated schools buildings

Cited by 97 publications

References 46 publications

Developing a predictive model for fine particulate matter concentrations in low socio-economic households in Durban, South Africa

Developing a predictive model for fine particulate matter concentrations in low socio-economic households in Durban, South Africa

The Influence of Spatial Variability of Critical Conversion Point (CCP) in Production of Ground Level Ozone in the Context of Tropical Climate

Distribution and Temporal Behaviour of O3 and NO2 Near Selected Schools in Seberang Perai, Pulau Pinang and Parit Buntar, Perak, Malaysia

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