Deterministic Approach for Temporal Patterns of Particle Pollution Analysis

Abstract

This paper presents a detailed analysis of air pollutants trend in Vijayawada. Vijayawada, designated as part of the state capital Amaravati, smoke and existing pollution levels in the city has exceeded the standard levels due to increase in population and the constructional activities being taken up in the recent years after the bifurcation of the Telugu states. This has made a profound influence to carry out the study on pollution in Vijayawada using data analytics. Descriptive analysis has been carried out to study the trends of air pollutants like Suspended Particulate Matter (SPM) (PM2.5 and PM10), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Carbon Monoxide (CO), Ozone (O3) based on Air Quality Index (AQI) scale. AQI scale is a standard scale defined by the governments of respective countries. Then the contamination characteristics of particulate matters were analysed, which further served to determine the characteristics of temporal patterns pollution variations of NO2, SO2, CO, O3. 

Most of the air pollution monitoring systems in India are ground-based and are dependent on meteorological data which reflects inaccurate predictions of pollutant concentrations. Geo-Spatial data integrated with Deep Learning techniques facilitates an increased awareness on the geospatial diversity, scalable to different locations. The proposed work intends to model, predict the particulate pollutant levels before they reach abnormal levels, predict the chronic disease patterns caused by particulate matter, thereby creating relevant human-health awareness. 

Keywords: Temporal Patterns, Air Pollutants, Suspended Particulate Matter, Spatio-Temporal Patterns.