Details
 
TitleASSESSMENT AND PREDICTION OF PARTICULATE MATTER AIR QUALITY USING IN-SITU AND REMOTE SENSING DATA SETS
AbstractThe effect of air pollution has been extending from local scale to the global scale. Keeping in view of the adverse health hazards caused by air pollution, investigation, assessment, mitigation and prediction of air pollutants is required to provide a healthy environment at air pollution hotspots. Continuous exposure to particulate matter (PM) was found to be the prime reason for these adverse health effects and it was found that, the air quality guidelines provided by CPCB (Central pollution control board) were violated due to the alarming pollution levels. Therefore, in the present research work, ground based observations, satellite derived measurements of PM in conjunction with modeling systems are considered to improve the quality of monitoring, assessment and prediction of air pollutants in response to the local and global phenomenon occurring in the atmosphere. Variation of pollutants diurnally and seasonally using in-situ measurements of particulate matter and dependence of air pollutants on meteorological conditions will be investigated in the present study. Impact of long range transport of pollutants over a particular region will be studied in the present study using source apportionment techniques. Linear and non linear modeling techniques will be employed to establish a statistical relationship between the various observations collected from both in-situ and satellite and governing parameters. Prediction models using artificial neural networks are employed in the study to estimate the future concentrations of particulate matter and their dependence on meteorological parameters. Keywords: Particulate Matter, Source apportionment techniques, linear and non linear modeling, prediction