Environmental Engineering Reference
In-Depth Information
Acknowledgments
We'd like to acknowledge the Federal Department of Environment, Water,
Heritage and the Arts for providing funding in support of this project. We would also like to
acknowledge Suzanne Quigley and Charles Xu and for providing air quality data, the GMR
motor vehicle emissions inventory and also much helpful advice during the course of this project.
References
Cope, ME, Lee, S, Physick, B, Abbs, D, Nguyen, K & McGregor, J (2008
) 'A methodology for
determining the impact of Climate Changes on Ozone Levels in Urban Area', Final CARP 11
report to the Federal Department of Environment, Water, Heritage and the Arts, Australia.
6. Questions and Answers
Question:
When introducing electric cars where does the (supposedly no polluting)
electricity comes from?
Answer:
For the scenario that petrol fuelled passenger cars are replaced by pure
electric cars, it is assumed that their batteries will be recharged by renewable
green energy such as power generated by solar panel and wind turbine.
Question:
How was synoptic typing performed and how representable are
synoptic patterns?
Answer:
The method (based on Yanal 1993) is grid based and uses a correlation
analysis to group together days which have similar mean sea level pressure
(MSLP) patterns. The methodology involves multiple passes through a data set
of gridded MSLP. (1) Cycle through each day in the MSLP data set calculating
the Pearson product-moment (r
xy
) for every other day in the data set.
Consider 2 days to have similar synoptic patterns if r
xy
> 0.7. (2) The day
which has the greatest number of matches is designated as “key day 1”. This
day and all days having similar synoptic patterns are removed from the data set
and step 1 is repeated to identify “key day 2”. (3) Repeat 1 and 2 until all of the
key days have been identified. (4) Once the key days have been identified, a
second pass is undertaken and all days in the data set are again correlated with
each key day. (5) In a third pass, any remaining days which are unclassified are
again correlated with the key days and are assigned to a key day if r
xy
> 0.5. It
was undertaken for days in the period 1996-2005 where peak 1-h ozone con-
centrations exceeded 100 ppb. In the case of the observed ozone data set, days
were first filtered to remove instances where bushfire smoke may have influ-
enced ozone production. Once a set of days had been generated, a data base of
NCEP 00 UTC MSLP fields were analysed to generate the key days. The
pattern analysis was also run for the modelled data set and then matched with
the observed key day patterns. For key day 1, observed pattern represent 55%
while modeled pattern represents 40%. For key day 2, observed pattern
