DOI: 10.1016/j.atmosenv.2015.08.016
Scopus记录号: 2-s2.0-84940527038
论文题名: Application of nonparametric regression and statistical testing to identify the impact of oil and natural gas development on local air quality
作者: Cheng H ; , Small M ; J ; , Pekney N ; J
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 119 起始页码: 381
结束页码: 392
语种: 英语
英文关键词: Air pollution
; Block bootstrap
; Directional analysis
; Nonparametric regression
; Oil and natural gas
; Statistical methods
Scopus关键词: Air pollution
; Air quality standards
; Density of gases
; Gases
; Ionization of gases
; Natural gas
; Natural gas wells
; Natural gasoline plants
; Oil fields
; Petroleum prospecting
; Pollution
; Quality control
; Regression analysis
; Statistical methods
; Statistics
; Block bootstraps
; Directional Analysis
; National ambient air quality standards
; Non-parametric regression
; Oil and natural gas
; Pollutant concentration
; Statistical inference
; Statistical significance
; Air quality
; natural gas
; air quality
; ambient air
; anthropogenic effect
; atmospheric pollution
; bootstrapping
; concentration (composition)
; data acquisition
; environmental monitoring
; gas well
; hypothesis testing
; management practice
; natural gas
; oil well
; regression analysis
; statistical analysis
; wind direction
; air monitoring
; air pollutant
; air quality
; Article
; bootstrapping
; data analysis
; density
; kernel method
; nonparametric test
; oil and gas field
; priority journal
; statistical analysis
; statistical significance
; wind
; Allegheny National Forest
; Pennsylvania
; 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: The objective of the current work was to develop a statistical method and associated tool to evaluate the impact of oil and natural gas exploration and production activities on local air quality. Nonparametric regression of pollutant concentrations on wind direction was combined with bootstrap hypothesis testing to provide statistical inference regarding the existence of a local/regional air quality impact. The block bootstrap method was employed to address the effect of autocorrelation on test significance. The method was applied to short-term air monitoring data collected at three sites within Pennsylvania's Allegheny National Forest. All of the measured pollutant concentrations were well below the National Ambient Air Quality Standards, so the usual criteria and methods for data analysis were not sufficient. Using advanced directional analysis methods, test results were first applied to verify the existence of a regional impact at a background site. Next the impact of an oil field on local NOx and SO2 concentrations at a second monitoring site was identified after removal of the regional effect. Analysis of a third site also revealed air quality impacts from nearby areas with a high density of oil and gas wells. All results and conclusions were quantified in terms of statistical significance level for the associated inferences. The proposed method can be used to formulate hypotheses and verify conclusions regarding oil and gas well impacts on air quality and support better-informed decisions for their management and regulation. © 2015 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81537
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, United States; National Energy Technology Laboratory, 626 Cochrans Mill Rd., P.O. Box 10940, Pittsburgh, PA, United States
Recommended Citation:
Cheng H,, Small M,J,et al. Application of nonparametric regression and statistical testing to identify the impact of oil and natural gas development on local air quality[J]. Atmospheric Environment,2015-01-01,119