Time Series Analysis of Atmospheric Particulate Matter of Bengaluru City

Shrivallabha S.¹ , Kumaresh P. Nelavigi²

Section:Research Paper, Product Type: Journal-Paper
Vol.6 , Issue.5 , pp.83-85, Oct-2019

Online published on Oct 31, 2019

Copyright © Shrivallabha S., Kumaresh P. Nelavigi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Particulate matter (PM) is composed of inert carbonaceous cores with multiple layers of various absorbed molecules, including metals, organic pollutants, acid salts and biological elements, such as endotoxins, allergens and pollen fragments. PM is classified in the following types -“Total suspended particulates” (TSP) is a name given to particles of sizes up to about 50μm. The larger particles in this class are too big to pass through our noses or throats and so, they cannot enter our lungs. They are often from wind-blown dust and may cause soiling of buildings and clothes. However, TSP samples may also contain the small PM10 and PM2.5 particles that may enter into our lungs. Total suspended particulates (TSP) with additional subcategories of particles smaller than 10μm (PM10) and particles smaller than 2.5μm (PM2.5) are discussed. Size and chemical composition are among the most important parameters influencing the way in which airborne particles interact with the environment. This paper presents a time series analysis of particulate matter (PM10) in Bengaluru city, Karnataka, India from April 2018 to November 2018. An ARIMA(Auto-Regressive Integrated Moving Average ) model of time series analysis is used for analysis and forecasting of the future concentration of the air pollutant. The data set of daily average PM10 concentration collected from Karnataka State Pollution Control Board was good fitted with an ARIMA model as per Ljung –Box test. The cross validation of model is done using residual analysis

Key-Words / Index Term :
Air pollution, Forecasting, Particulate matter, stationary, non-stationary, Time series analysis, ARIMA, ARMA, PM10

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