Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

Atmospheric Environment

Volumn 151, Issue , 2017, Pages 1-11

  Brokamp, Cole ^a,b   Jandarov, Roman ^b   Rao, M B ^b   LeMasters, Grace ^a,b   Ryan, Patrick ^a,b  

^a CINCINNATI CHILDREN'S HOSPITAL MEDICAL CENTER   (United States)

^b UNIVERSITY OF CINCINNATI COLLEGE OF MEDICINE   (United States)

Author keywords

Elemental PM2.5; Land use regression; Random forest

Indexed keywords

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;

EID: 84999663771     PISSN: 13522310     EISSN: 18732844     Source Type: Journal    
DOI: 10.1016/j.atmosenv.2016.11.066     Document Type: Article

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