Combining PM 2.5 Component Data from Multiple Sources: Data Consistency and Characteristics Relevant to Epidemiological Analyses of Predicted Long-Term Exposures | Zendy

SunYoung Kim | Zendy; Lianne Sheppard | Zendy; Timothy V. Larson | Zendy; Joel D. Kaufman | Zendy; Sverre Vedal | Zendy

Open Access

Combining PM _2.5 Component Data from Multiple Sources: Data Consistency and Characteristics Relevant to Epidemiological Analyses of Predicted Long-Term Exposures

Author(s) -

SunYoung Kim,

Lianne Sheppard,

Timothy V. Larson,

Joel D. Kaufman,

Sverre Vedal

Publication year - 2015

Publication title -

environmental health perspectives

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.257

H-Index - 282

eISSN - 1552-9924

pISSN - 0091-6765

DOI - 10.1289/ehp.1307744

Subject(s) - comparability , data mining , consistency (knowledge bases) , computer science , data collection , component (thermodynamics) , sampling (signal processing) , data science , statistics , mathematics , artificial intelligence , physics , filter (signal processing) , combinatorics , computer vision , thermodynamics

Regulatory monitoring data have been the exposure data resource most commonly applied to studies of the association between long-term PM2.5 components and health. However, data collected for regulatory purposes may not be compatible with epidemiological studies.

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