DOI: 10.1016/j.atmosenv.2016.11.066
Scopus记录号: 2-s2.0-84999663771
论文题名: Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches
作者: Brokamp C ; , Jandarov R ; , Rao M ; B ; , LeMasters G ; , Ryan P
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2017
卷: 151 起始页码: 1
结束页码: 11
语种: 英语
英文关键词: Elemental PM2.5
; Land use regression
; Random forest
Scopus关键词: Aluminum
; Artificial intelligence
; Decision trees
; Economics
; Errors
; Iron compounds
; Learning systems
; Manganese
; Nickel
; Pollution
; Regression analysis
; Silicon
; Zinc
; Elemental PM2.5
; Exposure assessment models
; Land use regression
; Machine learning methods
; Non-linear relationships
; Random forests
; Socioeconomic characteristics
; Spatial and temporal variation
; Land use
; aluminum
; copper
; iron
; lead
; manganese
; nickel
; potassium
; silicon
; sulfur
; vanadium
; zinc
; atmospheric pollution
; chemical composition
; concentration (composition)
; emission inventory
; land use change
; machine learning
; particulate matter
; point source pollution
; pollution exposure
; spatial data
; spatiotemporal analysis
; urban ecosystem
; Article
; comparative study
; environmental exposure
; land use
; machine learning
; measurement accuracy
; Ohio
; particle size
; particulate matter
; pollutant
; priority journal
; random forest
; regression analysis
; urban area
; Cincinnati
; Ohio
; 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: Exposure assessment for elemental components of particulate matter (PM) using land use modeling is a complex problem due to the high spatial and temporal variations in pollutant concentrations at the local scale. Land use regression (LUR) models may fail to capture complex interactions and non-linear relationships between pollutant concentrations and land use variables. The increasing availability of big spatial data and machine learning methods present an opportunity for improvement in PM exposure assessment models. In this manuscript, our objective was to develop a novel land use random forest (LURF) model and compare its accuracy and precision to a LUR model for elemental components of PM in the urban city of Cincinnati, Ohio. PM smaller than 2.5 μm (PM2.5) and eleven elemental components were measured at 24 sampling stations from the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS). Over 50 different predictors associated with transportation, physical features, community socioeconomic characteristics, greenspace, land cover, and emission point sources were used to construct LUR and LURF models. Cross validation was used to quantify and compare model performance. LURF and LUR models were created for aluminum (Al), copper (Cu), iron (Fe), potassium (K), manganese (Mn), nickel (Ni), lead (Pb), sulfur (S), silicon (Si), vanadium (V), zinc (Zn), and total PM2.5 in the CCAAPS study area. LURF utilized a more diverse and greater number of predictors than LUR and LURF models for Al, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all showed a decrease in fractional predictive error of at least 5% compared to their LUR models. LURF models for Al, Cu, Fe, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all had a cross validated fractional predictive error less than 30%. Furthermore, LUR models showed a differential exposure assessment bias and had a higher prediction error variance. Random forest and other machine learning methods may provide more accurate exposure assessment. © 2016 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82278
Appears in Collections: 气候变化事实与影响
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作者单位: Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Environmental Health, University of Cincinnati, Cincinnati, OH, United States; Division of Asthma Research, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
Recommended Citation:
Brokamp C,, Jandarov R,, Rao M,et al. Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches[J]. Atmospheric Environment,2017-01-01,151