Spatio-temporal variation of ambient particulate matter (PM10 and PM2.5) in Punjab: role of stubble burning and meteorological factors

被引:0
作者
Asif, Mohammad [1 ]
Bhatti, Manpreet Singh [1 ]
Prabhu, Vignesh [2 ]
机构
[1] Guru Nanak Dev Univ, Dept Bot & Environm Sci, Amritsar, India
[2] Univ Galway, Ryan Inst Ctr Climate & Air Pollut Studies, Sch Nat Sci, Galway, Ireland
关键词
Ambient air quality; Crop residue burning; PM10; PM2.5; Inverse distance weighting (IDW); Planetary boundary layer height (PBLH); Multiple linear regression (MLR); Concentration weighted trajectory (CWT); AIR-POLLUTION; POLLUTANTS; WINTER; CITY;
D O I
10.1007/s40808-024-02234-6
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Crop residue burning is a major source of air pollution, releasing large amounts of particulate matter and greenhouse gases, worsening air quality. The impact of rice crop residue burning and meteorology conditions were examined on spatial and temporal variation of particulate matter during pre-stubble burning, stubble burning and post stubble burning periods over the state of Punjab. PM2.5 levels were below national ambient air quality standards (NAAQS) during pre-stubble burning but increased significantly during the stubble burning period exceeding NAAQS of 60 mu g/m(3). Percentage change shows that PM2.5 increase of (252%) was maximum at Bathinda and minimal for Mandi-Gobindgarh (92%) during stubble burning compared to the pre-stubble burning period. Similarly, PM10 shows a percentage increase of 62-115%. PM2.5/PM10 ratio differed significantly between low stubble burning period (0.38) as compared to high stubble-burning period (0.57). Spatiotemporal variation showed Bathinda with maximum PM2.5 concentration was hotspot during stubble burning period out of seven major cities of Punjab. Spatial distribution maps using inverse distance weighting analysis showed significantly higher RMSE during stubble burning period. Multiple linear regression analysis gave significant (p <= 0.05) relationship between planetary boundary layer height and temperature with PM2.5 during October-December 2023 period as most significant meteorological parameters associated with accumulation of particulate of particulate matter in the ambient air.
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