crta
Hrvatska znanstvena Sekcija img
bibliografija
3 gif
 Naslovna
 O projektu
 FAQ
 Kontakt
4 gif
Pregledavanje radova
Jednostavno pretraživanje
Napredno pretraživanje
Skupni podaci
Upis novih radova
Upute
Ispravci prijavljenih radova
Ostale bibliografije
Slični projekti
 Bibliografske baze podataka

Pregled bibliografske jedinice broj: 722102

Zbornik radova

Autori: Kovač-Andrić, Elvira; Gvozdić, Vlatka; Brana, Josip; Malatesti, Nela; Roland, Danijela
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 )
Izvornik: Međunarodni znanstveno-stručni skup XIV. Ružičkini dani 2012 / Jukić, Ante (ur.). - 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.
Ključne riječi: atmospheric pollutants; meteorological factors; principal component regression
( 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.
Vrsta sudjelovanja: Poster
Vrsta prezentacije u zborniku: Sažetak
Vrsta recenzije: Nema recenziju
Izvorni jezik: eng
Kategorija: Znanstveni
Znanstvena područja:
Kemija
Upisao u CROSBI: Elvira Kovač Andrić (eakovac@kemija.unios.hr), 21. Lis. 2014. u 11:40 sati



Verzija za printanje   za tiskati


upomoc
foot_4