DOI: 10.1016/j.atmosenv.2014.05.007
Scopus记录号: 2-s2.0-84900841050
论文题名: Multivariate methods for indoor PM10 and PM2.5 modelling in naturally ventilated schools buildings
作者: Elbayoumi M ; , Ramli N ; A ; , Md Yusof N ; F ; F ; , Yahaya A ; S ; B ; , Al Madhoun W ; , Ul-Saufie A ; Z
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2014
卷: 94 起始页码: 11
结束页码: 21
语种: 英语
英文关键词: Air pollution
; PCA
; Regression models
Scopus关键词: Air pollution
; Carbon dioxide
; Linear regression
; Meteorological variables
; Multiple linear regressions
; Multivariate statistical method
; Outdoor concentrations
; PCA
; Performance indicators
; Principal component regression
; Regression model
; Principal component analysis
; carbon dioxide
; carbon monoxide
; building
; concentration (composition)
; human activity
; indoor air
; multivariate analysis
; particulate matter
; statistical analysis
; ventilation
; air temperature
; article
; bivariate analysis
; concentration (parameters)
; correlation analysis
; humidity
; indoor air pollution
; multiple linear regression analysis
; multivariate analysis
; particulate matter
; prediction
; principal component analysis
; priority journal
; school
; seasonal variation
; statistical analysis
; wind
; Gaza Strip
; Occupied Territories
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: In this study the concentrations of PM10, PM2.5, CO and CO2 concentrations and meteorological variables (wind speed, air temperature, and relative humidity) were employed to predict the annual and seasonal indoor concentration of PM10 and PM2.5 using multivariate statistical methods. The data have been collected in twelve naturally ventilated schools in Gaza Strip (Palestine) from October 2011 to May 2012 (academic year). The bivariate correlation analysis showed that the indoor PM10 and PM2.5 were highly positive correlated with outdoor concentration of PM10 and PM2.5. Further, Multiple linear regression (MLR) was used for modelling and R2 values for indoor PM10 were determined as 0.62 and 0.84 for PM10 and PM2.5 respectively. The Performance indicators of MLR models indicated that the prediction for PM10 and PM2.5 annual models were better than seasonal models. In order to reduce the number of input variables, principal component analysis (PCA) and principal component regression (PCR) were applied by using annual data. The predicted R2 were 0.40 and 0.73 for PM10 and PM2.5, respectively. PM10 models (MLR and PCR) show the tendency to underestimate indoor PM10 concentrations as it does not take into account the occupant's activities which highly affect the indoor concentrations during the class hours. © 2014 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81174
Appears in Collections: 气候变化事实与影响
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作者单位: Clean Air Research Group, School of Civil Engineering, Universiti Sains Malaysia, Penang, Malaysia; Environment and Earth Science Department, The Islamic University at Gaza, Palestine; Faculty of Computer and Mathematical Sciences, Universiti Teknologi Mara, Malaysia
Recommended Citation:
Elbayoumi M,, Ramli N,A,et al. Multivariate methods for indoor PM10 and PM2.5 modelling in naturally ventilated schools buildings[J]. Atmospheric Environment,2014-01-01,94