Selecting optimal monitoring site locations for peak ambient particulate material concentrations using the MM5-CAMx4 numerical modelling system.
Installation of temporary or long term monitoring sites is expensive, so it is important to rationally identify potential locations that will achieve the requirements of regional air quality management strategies. A simple, but effective, numerical approach to selecting ambient particulate matter (PM) monitoring site locations has therefore been developed using the MM5-CAMx4 air pollution dispersion modelling system. A new method, 'site efficiency,' was developed to assess the ability of any monitoring site to provide peak ambient air pollution concentrations that are representative of the urban area. 'Site efficiency' varies from 0 to 100%, with the latter representing the most representative site location for monitoring peak PM concentrations. Four heavy pollution episodes in Christchurch (New Zealand) during winter 2005, representing 4 different aerosol dispersion patterns, were used to develop and test this site assessment technique. Evaluation of the efficiency of monitoring sites was undertaken for night and morning aerosol peaks for 4 different particulate material (PM) spatial patterns. The results demonstrate that the existing long term monitoring site at Coles Place is quite well located, with a site efficiency value of 57.8%. A temporary ambient PM monitoring site (operating during winter 2006) showed a lower ability to capture night and morning peak aerosol concentrations. Evaluation of multiple site locations used during an extensive field campaign in Christchurch (New Zealand) in 2000 indicated that the maximum efficiency achieved by any site in the city would be 60-65%, while the efficiency of a virtual background site is calculated to be about 7%. This method of assessing the appropriateness of any potential monitoring site can be used to optimize monitoring site locations for any air pollution measurement programme.
Centre for Atmospheric Research, University of Canterbury, Christchurch, New Zealand. andrew.sturman@canterbury.ac.nz
Andrew
A
Sturman
Mikhail
M
Titov
Peyman
P
Zawar-Reza
D000393:Air Pollutants / Q000032:analysis* / Q000737:chemistry; D000397:Air Pollution / Q000706:statistics & numerical data; D003661:Decision Support Techniques; D004784:Environmental Monitoring / Q000295:instrumentation / Q000379:methods*; D008956:Models, Chemical; D009520:New Zealand; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*
D000393:Air Pollutants; D052638:Particulate Matter