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Pregled bibliografske jedinice broj: 722102

Primjena multivariantnih metoda u istraživanju utjecaja NO2, SO2, CO, PM10 i meteoroloških faktora na koncentracije O3 u urbanom području


Kovač-Andrić, Elvira; Gvozdić, Vlatka; Brana, Josip; Malatesti, Nela; Roland, Danijela
Primjena multivariantnih metoda u istraživanju utjecaja NO2, SO2, CO, PM10 i meteoroloških faktora na koncentracije O3 u urbanom području // Međunarodni znanstveno-stručni skup XIV. Ružičkini dani 2012 / Jukić, Ante (ur.).
Kutina: HDKI, 2012. str. 110-110 (poster, nije recenziran, sažetak, znanstveni)


Naslov
Primjena multivariantnih metoda u istraživanju utjecaja NO2, SO2, CO, PM10 i meteoroloških faktora na koncentracije O3 u urbanom području
(Application of multivariate methods in an investigation of the effect of NO2, SO2, CO, PM10 and meteorological factors on ozone concentrations in an urban area)

Autori
Kovač-Andrić, Elvira ; Gvozdić, Vlatka ; Brana, Josip ; Malatesti, Nela ; Roland, Danijela

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
Međunarodni znanstveno-stručni skup XIV. Ružičkini dani 2012 / Jukić, Ante - Kutina : HDKI, 2012, 110-110

ISBN
978-953-6894-46-8

Skup
XIV. Ružičkini dani " Danas znanost-sutra industrija"

Mjesto i datum
Vukovar, Hrvatska, 13.-15.09.2012

Vrsta sudjelovanja
Poster

Vrsta recenzije
Nije recenziran

Ključne riječi
Atmospheric pollutants; meteorological factors; principal component regression

Sažetak
Presents an investigation of the importance of meteorological and air pollutants' variables in predicting ozone concentrations by using linear regression, principal component analysis, and principal component regression method. O3, NO2, CO, SO2 and PM10 concentrations determined in urban area in summer period are presented for the first time. The study focuses on the evaluation of the impact of temperature (T), relative humidity (RH), wind speed (WS), wind direction (WD), NO2, SO2, CO and PM10 concentrations on ozone variability. The principal component regression method showed that RH, T, WS, the wind vector component that explains air mass movement on the axis east to west, NO2, CO and SO2 were responsible for most variations in ozone concentrations (R2≈0.82). Any remaining variability could be attributed to other causes i.e parameters that were not monitored in this study. Results showed that the use of principal components as inputs improved multiple regression models prediction by reducing their complexity and eliminating data multicollinearity.

Izvorni jezik
Engleski

Znanstvena područja
Kemija



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