Literature DB >> 15487685

Statistical model for assessing the portion of fine particulate matter transported regionally and long range to urban air.

Ari Karppinen1, Jari Härkönen, Jaakko Kukkonen, Päivi Aarnio, Tarja Koskentalo.   

Abstract

OBJECTIVES: This study attempted to develop a simple statistical model for assessing the contribution of aerosols transported regionally and those transported long range to the concentrations of fine particulate matter (PM2.5) in urban air in Helsinki.
METHODS: The construction and testing of the linear regression model was based on PM2.5 measurement data from two locations in the City of Helsinki (Vallila & Kallio) and on ion concentration data obtained from the three nearest monitoring stations of The Co-operative Programme for Monitoring and Evaluating of the Long-range Transmission of Air Pollutants in Europe (EMEP). The "ion sum" was calculated on the basis of the following daily measured EMEP parameters in 1998--2000: (i) sulfate (SO4(2-)), (ii) the sum of nitrate (NO3-) and nitrogen acid (HNO3), and (iii) the sum of ammonium (NH4+) and ammonia (NH3). The ion sum was compared with sulfate as the proxy variable for PM2.5 transported long range.
RESULTS: The correlation of the daily average PM2.5 concentration with the ion sum (R2=0.59-0.61) was higher than that with sulfate (R2 = 0.48-0.50). The regression estimates showed relatively small year-to-year variation. The contribution of long-range transport to the measured PM2.5 concentration in urban air in Helsinki was estimated to be 64-76%.
CONCLUSIONS: The results showed a strong association between the ion sum interpolated from the EMEP data and the PM2.5 concentration measured at urban sites in Helsinki. This association can be utilized in local dispersion modeling of the PM2.5 concentration in urban air.

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Year:  2004        PMID: 15487685

Source DB:  PubMed          Journal:  Scand J Work Environ Health        ISSN: 0355-3140            Impact factor:   5.024


  1 in total

1.  Response Characterization of an Inexpensive Aerosol Sensor.

Authors:  Joel Kuula; Timo Mäkelä; Risto Hillamo; Hilkka Timonen
Journal:  Sensors (Basel)       Date:  2017-12-15       Impact factor: 3.576

  1 in total

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