The effect of climate and meteorological changes on particulate matter in Pune, India

被引:28
作者
Yadav, Suman [1 ]
Praveen, O. Divya [1 ]
Satsangi, P. Gursumeeran [1 ]
机构
[1] Savitribai Phule Pune Univ, Dept Chem, Pune 411007, Maharashtra, India
关键词
Particulate matter; Seasonal variation; Meteorology; Long-range transport; Chemometrics; AIR-POLLUTANTS; PM2.5; COARSE; DNA; CONSTITUENTS; EMISSIONS; AEROSOL; REPAIR; FINE;
D O I
10.1007/s10661-015-4634-z
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper presents the distinctiveness of particulate matter (PM) mass concentrations (PM10, PM2.5, and PM10-(2.5)) and meteorological effect in Pune city during 2011-2012. The PM samples were collected using Mini-Vol TAS air sampler (Airmetrics Co. Inc., 5 l min(-1) flow rate). The meteorological parameters were also measured during the study period. The analysis of 24-h average PM10, PM2.5, and PM10-(2.5) concentrations showed the maximum during winter (267.267.2, 180.6-55.6, 138.9-11.7 mu g m(-3)) followed by summer (236.1-55.5, 138.8-27.7, 125-13.8 mu g m(-3)) and post-monsoon (153.3-82.3, 138.9-41.7, 41.714.4 mu g m(-3)) and showed the lowest concentration during monsoon (98.9-27.8, 83.3-13.9, 40.06.0 mu gm(-3)) seasons in the entire study. PM10 comprised a vast fraction of PM2.5 (61 % of PM2.5), while the estimated PM2.5/PM10 ratios for monsoon, post-monsoon, winter, and summer seasons were ranged between 0.5 and 0.9, 0.51 and 0.91, 0.3 and 0.9, and 0.3 and 0.8, respectively. The 7-day back trajectories analysis for whole year shows that the air masses transported to Pune were mixed mainland-maritime such as from southwesterly, north, northwest. Chemometric analysis was applied as a tool to evaluate and predict the particulate mass concentration from available meteorological data. To achieve this, a calibration model was developed by partial least squares regression (PLSR) method and was further used to predict the PM concentrations based on meteorological data. On predicting the PM concentration from local meteorological data, the model performance and quality was found very good in case of PM10 compared to PM2.5.
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页数:14
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