TY  - JOUR
Y1  - 2019///
A1  - Nazif, A.
A1  - Mohammed, N.I.
A1  - Malakahmad, A.
A1  - Abualqumboz, M.S.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049916097&doi=10.1007%2fs13762-018-1905-6&partnerID=40&md5=1e1bbfdeac2eb7fadcecb283b4d0efe9
N1  - cited By 4
JF  - International Journal of Environmental Science and Technology
VL  - 16
KW  - Air pollution; Air quality; Atmospheric thermodynamics; Benchmarking; Forecasting; Linear regression; Meteorology; Multivariant analysis; Waste disposal; Wind effects
KW  -  Air quality monitoring stations; Coefficient of determination; Meteorological parameters; Multi variate analysis; Multiple linear regressions; Performance indicators; Principal component regression; Regression
KW  -  Principal component analysis
IS  - 6
TI  - Multivariate analysis of monsoon seasonal variation and prediction of particulate matter episode using regression and hybrid models
N2  - Prediction of particulate matter (PM 10 ) episode in advance enables for better preparation to avert and reduce the impact of air pollution ahead of time. This is possible with proper understanding of air pollutants and the parameters that influence its pattern. Hence, this study analysed daily average PM 10 , temperature (T), humidity (H), wind speed and wind direction data for 5 years (2006â??2010), from two industrial air quality monitoring stations. These data were used to evaluate the impact of meteorological parameters and PM 10 in two peculiar seasons: south-west monsoon and north-east monsoon seasons, using principal component analysis (PCA). Subsequently, lognormal regression (LR), multiple linear regression (MLR) and principal component regression (PCR) methods were used to forecast next-day average PM 10 concentration level. The PCA result (seasonal variability) showed that peculiar relationship exists between PM 10 pollutants and meteorological parameters. For the prediction models, the three methods gave significant results in terms of performance indicators. However, PCR had better predictability, having a higher coefficient of determination (R 2 ) and better performance indicator results than LR and MLR methods. The outcomes of this study signify that PCR models can be effectively used as a suitable format in predicting next-day average PM 10 concentration levels. © 2018, Islamic Azad University (IAU).
SN  - 17351472
EP  - 2600
ID  - scholars11535
AV  - none
SP  - 2587
PB  - Center for Environmental and Energy Research and Studies
ER  -