Abstract
Road dust is one of the biggest contributors to airborne particulate matter (PM) in many urban regions. Due to the inherent heterogeneity of road dust, it is important that its sources are identified and mitigated. Multivariate receptor models are used for source apportionment of PM in many cities. In recent years, these receptor models are finding more applications outside the scope of PM source apportionment. In this study, four multivariate receptor models (Unmix, Positive Matrix Factorization, Principal Component Analysis, and Multiple Curve Regression) are used for source apportionment of road dust at Vellore City, India. The elemental composition of road dust samples from 18 locations and for three seasons (summer, winter, and monsoon) are measured using acid digestion followed by Inductively Coupled Plasma–Optical Emission Spectroscopy. Irrespective of models, results showed that crustal material (100–68%) and resuspended road dust (82–15%) are the biggest contributors to road dust in the study region. Brake wear, tire wear, biomass combustion, vehicular emission, and industrial sources are some of the other sources identified by the receptor models. Receptor modeling performance of MCR and PCA models are unsatisfactory. PMF and Unmix models gave acceptable results. From comparing the performance characteristics, Unmix is found to be the ideal receptor model for this dataset. This research clarifies the constraints of different receptor models and the source apportionment information obtained is critical for development of future policy and regulation.
Highlights
Chemometric methods of data analysis is a cornerstone for air pollution control in urban environments (Azid et al 2015)
Road dust is the loose, mostly crustal material settled in road surfaces that is resuspended by the action of wind or wake from vehicular movement (Abu-Allaban et al 2003; Amato et al 2009)
The major objectives of this study are, (1) Identifying and apportioning the sources of road dust in the city, thereby helping future endeavors in regulation and policy, (2) Recognizing the constraints of different receptor models used in this study, and (3) Most importantly, this research could prove to be instrumental in reinvigorating receptor modeling studies on road dust
Summary
Chemometric methods of data analysis is a cornerstone for air pollution control in urban environments (Azid et al 2015). Isolating and quantifying the contribution of various sources to pollution at a location is one of the most common applications of chemometrics in environmental data This is commonly referred to as receptor modeling (Devi and Yadav 2018). Receptor modeling started gaining popularity during the mid-2000s and continues to be a major player in urban air quality management (Zhang et al 2017). These models reconstruct the contribution of individual sources to pollution in a region using the ambient pollutant concentration information (Henry et al, 1984) and are frequently used for source apportionment of particulate matter (PM). Non-vehicular sources of road dust include crustal material transported by wind (Mao et al 2013), construction and demolition activities (Marín et al 2011)
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