Multivariate analysis of monsoon seasonal variation and prediction of particulate matter episode using regression and hybrid models

Journal article


Nazif, A., Mohammed, N.I., Malakahmad, A. and Abualqumboz, M.S. (2018). Multivariate analysis of monsoon seasonal variation and prediction of particulate matter episode using regression and hybrid models. International Journal of Environmental Science and Technology. 16 (3), pp. 2587- 2600. https://doi.org/10.1007/s13762-018-1905-6
AuthorsNazif, A., Mohammed, N.I., Malakahmad, A. and Abualqumboz, M.S.
Abstract

Prediction of particulate matter (PM10) 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 PM10, temperature (T), humidity (H), wind speed and wind direction data for 5 years (2006–2010), from two industrial air quality monitoring stations. These data were used to evaluate the impact of meteorological parameters and PM10 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 PM10 concentration level. The PCA result (seasonal variability) showed that peculiar relationship exists between PM10 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 (R2) 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 PM10 concentration levels.

KeywordsAir pollution · Meteorology · Prediction · Regression
Year2018
JournalInternational Journal of Environmental Science and Technology
Journal citation16 (3), pp. 2587- 2600
PublisherSpringer
ISSN1735-2630
Digital Object Identifier (DOI)https://doi.org/10.1007/s13762-018-1905-6
Publication dates
Print14 Jul 2018
Publication process dates
Accepted19 Apr 2016
Deposited30 Jan 2023
Accepted author manuscript
License
File Access Level
Open
Additional information

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s13762-018-1905-6

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https://openresearch.lsbu.ac.uk/item/92839

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Accepted author manuscript
11Particulate Matter episode using Multivariate and Hybrid Models.pdf
License: Springer Bespoke License
File access level: Open

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