Towards retrieving aerosol chemical composition from temporal variations
of total PM mass concentrations: Theoretical approach, insights, and the
promise of machine learning techniques
Abstract
Fine and ultrafine ambient particulate matter (PM) has major health and
climate impacts. Chemical composition of PM is required for better
estimation of these impacts but is considerably expensive to measure as
compared to the total PM mass concentrations. We explore the indirect
estimation of PM chemical composition by analysing the temporal
variation of, relatively inexpensively measured, total PM.
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