Source Apportionment of Urban Road Dust Using Four Multivariate Receptor Models

DOI: https://doi.org/10.21203/rs.3.rs-163837/v1

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 contributor 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.

Full Text

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Tables

Table 1: List of sampling locations

Sl No.

Latitude

Longitude

Location

1

12.96786

79.15671

VIT main gate 

2

12.96594

79.13746

Chittoor bus stop

3

12.96092

79.13747

Indian Oil petrol pump

4

12.93875

79.13844

Palar river bridge

5

12.93365

79.13882

New bus station front exit

6

12.93426

79.1443

Highway to Bangalore

7

12.93473

79.152

District administrative office

8

12.93395

79.14449

Highway to Chennai

9

12.93209

79.13821

Green circle

10

12.92963

79.13413

National circle

11

12.92365

79.13334

CMC road

12

12.92078

79.13206

Old bus station

13

12.91388

79.13236

Vellore Fort

14

12.92695

79.12909

Old bypass road

15

12.93168

79.13539

Highway underpass

16

12.93489

79.13559

New bus station rear exit

17

12.94849

79.13703

Gandhinagar main road

18

12.95804

79.13718

Lorry owner's association petrol pump


Table 2: Principal component scores from PCA analysis (significant values are in bold)

 

PC1

PC2

PC3

PC4

PC5

Al

-0.201

0.068

-0.258

0.268

-0.370

Ba

-0.162

0.223

-0.177

0.161

0.334

Ca

-0.085

-0.086

0.055

-0.512

-0.159

Mg

-0.283

0.131

-0.303

0.256

0.031

Sr

-0.296

-0.014

-0.318

0.114

0.065

Co

-0.291

-0.119

-0.204

-0.204

-0.238

Cr

-0.248

-0.181

0.377

0.196

-0.123

Cu

-0.215

-0.134

0.345

0.083

0.280

Fe

-0.282

-0.028

-0.082

-0.462

0.142

Ga

-0.372

-0.210

0.026

-0.134

-0.189

Zn

-0.160

0.093

0.058

0.022

0.559

In

0.052

-0.422

-0.283

0.051

0.116

K

-0.122

0.423

0.166

0.122

-0.224

Li

-0.179

0.451

0.077

0.010

-0.117

Mn

-0.407

-0.188

0.024

-0.025

-0.001

Na

-0.063

-0.219

-0.229

0.302

0.094

Ni

-0.115

-0.201

0.371

0.321

-0.233

Pb

-0.188

-0.081

0.295

0.037

0.243

Rb

-0.235

0.334

0.039

-0.170

0.067

% Variance

26.829

19.458

14.448

8.580

7.368

% Cumulative Variance

26.829

46.287

60.734

69.315

76.683