S-Space: Seoul National University
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Is replacing missing values of PM2.5 constituents with estimates using machine learning better for source apportionment than exclusion or median replacement?
East Asian countries have been conducting source apportionment of fine particulate matter (PM 2.5 ) by applying positive matrix factorization (PMF) to hourly constituent concentrations. However, some of the constituent data from the supersites in South Korea was missing due to instrument maintenance
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