Abstract

Abstract. Hourly measurements of elemental carbon (EC) and organic carbon (OC) were made at Mong Kok, a roadside air quality monitoring station in Hong Kong, for a year, from May 2011 to April 2012. The monthly average EC concentrations were 3.8–4.9 μg C m−3, accounting for 9.2–17.7% of the PM2.5 mass (21.5–49.7 μg m−3). The EC concentrations showed little seasonal variation and peaked twice daily, coinciding with the traffic rush hours of a day. Strong correlations were found between EC and NOx concentrations, especially during the rush hours in the morning, confirming vehicular emissions as the dominant source of EC at this site. The analysis by means of the minimum OC / EC ratio approach to determine the OC / EC ratio representative of primary vehicular emissions yields a value of 0.5 for (OC / EC)vehicle. By applying the derived (OC / EC)vehicle ratio to the data set, the monthly average vehicle-related OC was estimated to account for 17–64% of the measured OC throughout the year. Vehicle-related OC was also estimated using receptor modeling of a combined data set of hourly NOx, OC, EC and volatile organic compounds characteristic of different types of vehicular emissions. The OCvehicle estimations by the two different approaches were in good agreement. When both EC and vehicle-derived organic matter (OM) (assuming an OM-to-OC ratio of 1.4) are considered, vehicular carbonaceous aerosols contributed ~ 7.3 μg m−3 to PM2.5, accounting for ~ 20% of PM2.5 mass (38.3 μg m−3) during winter, when Hong Kong received significant influence of air pollutants transported from outside, and ~ 30% of PM2.5 mass (28.2 μg m−3) during summertime, when local emission sources were dominant. A reduction of 3.8 μg m−3 in vehicular carbonaceous aerosols was estimated during 07:00–11:00 (i.e., rush hours on weekdays) on Sundays and public holidays. This could mainly be attributed to less on-road public transportation (e.g., diesel-powered buses) in comparison with non-holidays. These multiple lines of evidence confirm local vehicular emissions as an important source of PM2.5 in an urban roadside environment and suggest the importance of vehicular emission control in reducing exposure to PM2.5 in busy roadside environments.

Highlights

  • Carbonaceous species is an important constituent of PM2.5 (Seinfeld and Pandis, 1998) and a substantial contributor to climate forcing, visibility impairment and adverse health effects (e.g., USEPA, 2004; IPCC, 2007)

  • The carbonaceous material is commonly distinguished in elemental carbon (EC) and organic carbon (OC)

  • These source analysis studies were all based on 24 h filter measurements and they are inherently incapable of capturing the dynamics of pollutant emissions and atmospheric chemical conversion processes that happen on a faster time scale

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Summary

Introduction

Carbonaceous species is an important constituent of PM2.5 (atmospheric particulate matter with aerodynamic diameters less than 2.5 μm) (Seinfeld and Pandis, 1998) and a substantial contributor to climate forcing, visibility impairment and adverse health effects (e.g., USEPA, 2004; IPCC, 2007). Zheng et al (2006) analyzed filter samples collected at three contrasting sampling sites with respect to vehicular emission influence during 2000–2001 They employed a chemical mass balance receptor model in combination with organic tracers to apportion contribution of nine air pollution sources to PM2.5 OC. The results showed that vehicular exhaust contributed 41.0 and 8.4 % to the ambient OC on sampling days that were mainly under the influence of local emissions and regional transport, respectively These source analysis studies were all based on 24 h filter measurements and they are inherently incapable of capturing the dynamics of pollutant emissions and atmospheric chemical conversion processes that happen on a faster time scale. The objectives are to derive the OC / EC ratio representing primary vehicular emissions and to estimate the contributions of vehicular carbonaceous aerosols to PM2.5 in the roadside environment in Hong Kong

Sampling equipment and method
Quality control and data validation
Findings
Estimation using receptor modeling analysis
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