DOI: 10.1016/j.atmosenv.2017.05.025
Scopus记录号: 2-s2.0-85020266768
论文题名: A candidate framework for PM2.5 source identification in highly industrialized urban-coastal areas
作者: Mateus V ; L ; , Gioda A
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2017
卷: 164 起始页码: 147
结束页码: 164
语种: 英语
英文关键词: Coastal urban atmosphere
; Conditional bivariate probability function
; Conditional inference trees
; PM2.5
; Secondary inorganic aerosols
; Steelworks
Scopus关键词: Calcium
; Decision trees
; Fog
; Forestry
; Pollution
; Sulfur dioxide
; Conditional inference
; Probability functions
; Secondary inorganic aerosol
; Steelworks
; Urban atmospheres
; Pollution detection
; calcium ion
; nitrogen dioxide
; ozone
; sulfur dioxide
; aerosol
; chemical composition
; coastal zone
; gas
; guideline
; industrial emission
; industrial location
; inorganic compound
; mitigation
; numerical method
; particulate matter
; policy making
; pollution control
; pollution monitoring
; pollution policy
; probability
; source identification
; speciation (chemistry)
; urban area
; Article
; chemical composition
; chemoluminescence
; environmental monitoring
; gas waste
; industrialization
; particulate matter
; plume
; priority journal
; random forest
; Rio de Janeiro (state)
; seashore
; total organic carbon
; urban area
; water pollution
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: The variability of PM sources and composition impose tremendous challenges for police makers in order to establish guidelines. In urban PM, sources associated with industrial processes are among the most important ones. In this study, a 5-year monitoring of PM2.5 samples was carried out in an industrial district. Their chemical composition was strategically determined in two campaigns in order to check the effectiveness of mitigation policies. Gaseous pollutants (NO2, SO2, and O3) were also monitored along with meteorological variables. The new method called Conditional Bivariate Probability Function (CBPF) was successfully applied to allocate the observed concentration of criteria pollutants (gaseous pollutants and PM2.5) in cells defined by wind direction-speed which provided insights about ground-level and elevated pollution plumes. CBPF findings were confirmed by the Theil-Sen long trend estimations for criteria pollutants. By means of CBPF, elevated pollution plumes were detected in the range of 0.54–5.8 μg m−3 coming from a direction associated to stacks. With high interpretability, the use of Conditional Inference Trees (CIT) provided both classification and regression of the speciated PM2.5 in the two campaigns. The combination of CIT and Random Forests (RF) point out NO3 − and Ca+2 as important predictors for PM2.5. The latter predictor mostly associated to non-sea-salt sources, given a nss-Ca2+ contribution equal to 96%. © 2017 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82523
Appears in Collections: 气候变化事实与影响
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作者单位: Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Department of Chemistry, Rio de Janeiro, Brazil
Recommended Citation:
Mateus V,L,, Gioda A. A candidate framework for PM2.5 source identification in highly industrialized urban-coastal areas[J]. Atmospheric Environment,2017-01-01,164