AI Summary & Key Takeaways
AI InsightDistinct underlying processes of this source involved convective re-suspension and city-widecleaning associated upliftment of surface deposits back into the ambient environment.
📌 3 Key Takeaways
Özet
A reliable identification of sources and their relative time dependent contributions to ambient aerosolload is an important air pollution research problem. Given the inherent complexity of contributingsources in urban/mega-cities, an appropriate statistical investigation is needed to characterize sourcesand to understand their timeline trend profiles. Daily average ambient particulate matter (PM) loads,PM10 (aerodynamic diameter 10 mm) were collected once a week over 4 years at a receptor site in Delhi. The samples were analyzedto quantify the presence of 17 marker elements. Time series data of PM loads, and that of associatedmarker elements was subjected to Positive Matrix Factorization (PMF) to identify sources and to quantifytheir contributions to each PM fraction with reference to the associated marker elements. The resolvedtime series data of each contributing source was further subjected to Ensemble Empirical ModeDecomposition (EEMD) analysis to extract their timeline trend profile over four years in CPM and PM10load. Three sources contributed to the CPM load: anthropogenic (15%), primary crustal (59%), and finecrustal dust (26%). Four sources contributed to the PM10 load: coarser grain crustal material (9%), finegrain crustal material (12%), industrial and vehicular emissions (23%), and wind assisted transport andre-suspension of surface dust (56%). The timeline trend of sources contributions to CPM and PM10 displayeda non-linearity. The unique composite-PM10 source contributed maximum to the ambient PM10load. Distinct underlying processes of this source involved convective re-suspension and city-widecleaning associated upliftment of surface deposits back into the ambient environment.