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|Title:||Modelling the size separated particulate matter (SSPM10) from vehicular exhaust at traffic intersections in Mumbai|
|Publisher:||KLUWER ACADEMIC PUBL|
|Citation:||ENVIRONMENTAL MONITORING AND ASSESSMENT, 98(1-3), 23-40|
|Abstract:||The study was carried out to predict the size separated particulate matter below 10 mum size (SSPM10) from vehicular exhausts at traffic intersections using modified general finite line source model (GFLSM). Two air quality control regions (AQCRs) were selected in Mumbai City for this study. One was industrial area (AQCR(1)) containing the busy intersection, i.e. Marol link road, with the heavy inflow of two-three wheelers. And, the other was commercial busy district area (AQCR(2)) containing the busy intersection, i.e. Dadar circle, with a heavy traffic flow especially cars. The model was applied at both the traffic intersections. The data were collected for modelling study for three winter months in 1995 using cascade impactor of nine size ranges. The prediction results revealed that modified GFLSM underpredicted the SSPM10 concentrations for all the size ranges. However, showed considerable correlation between observed and predicted values for the size range below 4.7 mum at both the intersections. The relative high concentrations observed in the coarser range of 10-4.7 mum are attributed to the resuspension of the roadside particulate matter. Hence, the amount of underprediction was more for this range, which was due to the characteristics of model that does not take into account the factor for resuspension of roadside particulate matter caused by traffic movements. The model was also applied to predict the total particulate matter for downwind distances from the road intersection. The statistical evaluation of model was done, which indicated that the model's performance was good for the finer range of particles (below 4.7 mum) with r-square values of 0.49 and 0.57 found at both the intersections in AQCR(1) and AQCR(2), respectively. However, it is not unusual that the model uncertainty is likely to exist due to data input errors and stochastic fluctuations irrespective of the models accurateness. The statistical distribution model was therefore identified using Kolmogorov-Smirnov test. At both the intersections, SSPM10 concentration data were found lognormally distributed.|
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