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Technical Paper

Characterization of Major Chemical Components of Fine Particulate Matter in North Carolina

, , , &
Pages 1099-1107 | Published online: 27 Feb 2012
 

Abstract

This paper presents measurements of daily sampling of fine particulate matter (PM2.5) and its major chemical components at three urban and one rural locations in North Carolina during 2002. At both urban and rural sites, the major insoluble component of PM2.5 is organic matter, and the major soluble components are sulfate (SO4 2−), ammonium (NH4 +), and nitrate (NO3 ). NH4 + is neutralized mainly by SO4 2− rather than by NO3 , except in winter when SO4 2− concentration is relatively low, whereas NO3 concentration is high. The equivalent ratio of NH4 + to the sum of SO4 2− and NO3 is <1, suggesting that SO4 2−and NO3 are not completely neutralized by NH4 +. At both rural and urban sites, SO4 2−concentration displays a maximum in summer and a minimum in winter, whereas NO3 displays an opposite seasonal trend. Mass ratio of NO3 to SO4 2−is consistently <1 at all sites, suggesting that stationary source emissions may play an important role in PM2.5 formation in those areas. Organic carbon and elemental carbon are well correlated at three urban sites although they are poorly correlated at the agriculture site. Other than the daily samples, hourly samples were measured at one urban site. PM2.5 mass concen trations display a peak in early morning, and a second peak in late afternoon. Back trajectory analysis shows that air masses with lower PM2.5 mass content mainly originate from the marine environment or from a continental environment but with a strong subsidence from the upper troposphere. Air masses with high PM2.5 mass concentrations are largely from continental sources. Our study of fine particulate matter and its chemical composition in North Carolina provides crucial information that may be used to determine the efficacy of the new National Ambient Air Quality Standard (NAAQS) for PM fine. Moreover, the gas-to-particle conversion processes provide improved prediction of long-range transport of pollutants and air quality.

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