DOI: 10.1016/j.atmosenv.2014.04.017
Scopus记录号: 2-s2.0-84899583407
论文题名: Point source influence on observed extreme pollution levels in a monitoring network
作者: Ensor K ; B ; , Ray B ; K ; , Charlton S ; J
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2014
卷: 92 起始页码: 231
结束页码: 239
语种: 英语
英文关键词: Count regression
; Extreme pollution
; Model based clustering
; Point source
; Zero inflation
Scopus关键词: Air quality
; Atmospheric movements
; Decision making
; Mathematical models
; Regression analysis
; Atmospheric dispersion
; Count regression
; Gaussian plume models
; Model-based clustering
; Monitoring network
; Point sources
; Time-series regression
; Zero inflation
; Pollution
; benzene
; air quality
; atmospheric plume
; benzene
; decision making
; dispersion
; industrial emission
; point source
; pollution monitoring
; port
; regression analysis
; time series
; air monitor
; air monitoring
; air pollution
; air pollution control
; air quality
; article
; atmospheric dispersion
; decision making
; gaussian plume model
; health hazard
; industrial area
; kernel method
; priority journal
; probability
; time series analysis
; Houston Ship Channel
; Texas
; United States
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: This paper presents a strategy to quantify the influence major point sources in a region have on extreme pollution values observed at each of the monitors in the network. We focus on the number of hours in a day the levels at a monitor exceed a specified health threshold. The number of daily exceedances are modeled using observation-driven negative binomial time series regression models, allowing for a zero-inflation component to characterize the probability of no exceedances in a particular day. The spatial nature of the problem is addressed through the use of a Gaussian plume model for atmospheric dispersion computed at locations of known emissions, creating covariates that impact exceedances. In order to isolate the influence of emitters at individual monitors, we fit separate regression models to the series of counts from each monitor. We apply a final model clustering step to group monitor series that exhibit similar behavior with respect to mean, variability, and common contributors to support policy decision making. The methodology is applied to eight benzene pollution series measured at air quality monitors around the Houston ship channel, a major industrial port. © 2014 The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80800
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Statistics, MS 138, Rice University, Houston, TX 77251-1892, United States; Business Analytics and Math Sciences, IBM T. J. Watson Research Center, United States
Recommended Citation:
Ensor K,B,, Ray B,et al. Point source influence on observed extreme pollution levels in a monitoring network[J]. Atmospheric Environment,2014-01-01,92