Jagoda Crawford 1, Alan Griffiths1, David D. Cohen1, Ningbo Jiang2, Eduard Stelcer1

  • 1 Australian Nuclear Science and Technology Organisation, Locked Bag 2001 Kirrawee DC NSW 2232, Australia
  • 2 Office of Environment and Heritage, NSW Department of Premier and Cabinet, Sydney, Australia

Received: March 16, 2015
Revised: July 22, 2015
Accepted: August 30, 2015
Download Citation: ||https://doi.org/10.4209/aaqr.2015.02.0081 


Cite this article:
Crawford, J., Griffiths, A., Cohen, D.D., Jiang, N. and Stelcer, E. (2016). Particulate Pollution in the Sydney Region: Source Diagnostics and Synoptic Controls. Aerosol Air Qual. Res. 16: 1055-1066. https://doi.org/10.4209/aaqr.2015.02.0081


HIGHLIGHTS

  • Ion Bean Analysis was used to obtain elemental composition of PM2.5.
  • High PM2.5 concentrations were associated with high pressure systems.
  • WRF was used to generate meteorological data of 12 km resolution.
  • PM2.5 sources were identified using low and high resolution meteorological data.
  • Terrain resolution was important for low altitude back trajectories.

 

ABSTRACT


Airborne particulate matter (PM2.5) was sampled at Richmond and Liverpool, located in the Sydney Basin, Australia, and ion beam analysis was used to obtain the elemental composition. Using self-organising maps to classify synoptic weather systems, it was found that high PM2.5 concentrations were associated with high pressure systems located to the east of the sampling sites. The highest median sulfur was associated with weak synoptic conditions and high soil dust days were more often associated with frontal systems.

To investigate the effect of local flows in the Sydney Basin, the Weather Research and Forecasting model (WRF) was used to generate meteorological data of 12 km resolution. A comparison was made between back trajectories generated using the higher-resolution WRF data, the 0.5° by 0.5° Climate Forecast System data and the 1° by 1° Global Data Assimilation System data. It was found that for high soil dust days, there were small differences between the different back trajectories. However, under weak synoptic conditions (high sulfur days), the back trajectories generated from higher resolution data showed larger variations over a 24 hr period. This was attributed to the meandering of local winds and sea-breezes.

Lower altitude back trajectories, generated from low resolution data, passed more often over the power stations located on the western side of the Great Dividing Range (while the sampling sites are on the east). This demonstrates the need for higher resolution meteorological data for generating low altitude back trajectories when the source and receptor are separated by hilly terrain.

In estimating the number of high sulfur days for which a power station was crossed, there was up to 20% difference at Liverpool and up to 10% difference at Richmond, between back trajectories starting at different altitudes and generated from meteorological data of three different resolutions.


Keywords: PM2.5; Secondary sulfate; WRF; Back trajectory; SOM


Impact Factor: 2.735

5-Year Impact Factor: 2.827


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