Estimating contributions of vehicular emissions to PM2.5 in a roadside environment: A multiple approach study

Yee Ka Wong; X H Hilda Huang; Yuk Ying Cheng; Peter K K Louie; Alfred L C Yu; Alice W Y Tang; Damgy H L Chan; Jian Zhen Yu

doi:10.1016/j.scitotenv.2019.03.463

Estimating contributions of vehicular emissions to PM_2.5 in a roadside environment: A multiple approach study

Sci Total Environ. 2019 Jul 1:672:776-788. doi: 10.1016/j.scitotenv.2019.03.463. Epub 2019 Apr 1.

Authors

Yee Ka Wong¹, X H Hilda Huang¹, Yuk Ying Cheng², Peter K K Louie³, Alfred L C Yu³, Alice W Y Tang³, Damgy H L Chan³, Jian Zhen Yu⁴

Affiliations

¹ Division of Environment & Sustainability, Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong.
² Department of Chemistry, Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong.
³ Hong Kong Environmental Protection Department, 47/F, Revenue Tower, 5 Gloucester Road, Wan Chai, Hong Kong.
⁴ Division of Environment & Sustainability, Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong; Department of Chemistry, Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong. Electronic address: [email protected].

PMID: 30974367
DOI: 10.1016/j.scitotenv.2019.03.463

Abstract

Vehicular emissions (VE) are among the major sources of airborne fine particulate matter (PM_2.5) in urban atmospheres, which adversely impact the environment and public health. Receptor models are widely used for estimating PM_2.5 source contributions from VE (PM_vehicle), but often give inconsistent results due to different modelling principles and assumptions. During December 2015-May 2017, we collected nine-months of hourly organic carbon (OC) and elemental carbon (EC) data, as well as 24-h PM_2.5 speciation data including major species and organic tracers on select days from an ad hoc roadside site in Hong Kong. The weekday vs. holiday and diurnal variations of EC tracked closely with those of traffic flow volume, indicating EC as a reliable tracer for PM_vehicle in this area. We applied multiple approaches to estimate the PM_vehicle, including the EC-tracer method with the hourly OC-EC data, and chemical mass balance (CMB) and positive matrix factorization (PMF) analyses with the filter-based speciation data. Considering source profile variability, CMB gave the lowest PM_vehicle estimate among the three approaches, possibly due to the degradation of organic markers (i.e., hopanes). The PM_vehicle derived from the EC-tracer method and PMF were comparable, accounting for ~12% (3.4-4.0 μg/m³) of PM_2.5 averaged across 20 samples in both approaches, but a larger sample size is needed for a more robust PMF solution. The monthly PM_vehicle derived from the EC-tracer method was in the range of 3.2-6.6 μg/m³. The continuous measurement reveals a decreasing trend in PM_vehicle throughout the entire sampling period, indicating the effectiveness of a recent vehicle control measures implemented by the Government in phasing out pre-Euro IV diesel commercial vehicles. This work implies that hourly OC-EC monitoring at strategically located spots is an effective way of monitoring vehicle control measures. It provides reasonable estimate of PM_vehicle through comparing with other more sophisticated receptor models.

Keywords: EC-tracer method; PM(2.5); Receptor model; Source apportionment; Vehicular emissions.