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| Titel |
Taking potential probability function maps to the local scale and matching them with land use maps |
| VerfasserIn |
Saryu Garg, Vinayak Sinha, Baerbel Sinha |
| Konferenz |
EGU General Assembly 2013
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 15 (2013) |
| Datensatznummer |
250072011
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| Zusammenfassung |
| Source-Receptor models have been developed using different methods. Residence-time
weighted concentration back trajectory analysis and Potential Source Contribution Function
(PSCF) are the two most popular techniques for identification of potential sources of a
substance in a defined geographical area. Both techniques use back trajectories
calculated using global models and assign values of probability/concentration to
various locations in an area. These values represent the probability of threshold
exceedances / the average concentration measured at the receptor in air masses
with a certain residence time over a source area. Both techniques, however, have
only been applied to regional and long-range transport phenomena due to inherent
limitation with respect to both spatial accuracy and temporal resolution of the of
back trajectory calculations. Employing the above mentioned concepts of residence
time weighted concentration back-trajectory analysis and PSCF, we developed a
source-receptor model capable of identifying local and regional sources of air pollutants like
Particulate Matter (PM), NOx, SO2 and VOCs. We use 1 to 30 minute averages of
concentration values and wind direction and speed from a single receptor site or
from multiple receptor sites to trace the air mass back in time. The model code
assumes all the atmospheric transport to be Lagrangian and linearly extrapolates
air masses reaching the receptor location, backwards in time for a fixed number
of steps. We restrict the model run to the lifetime of the chemical species under
consideration. For long lived species the model run is limited to < 4 hrs as spatial
uncertainty increases the longer an air mass is linearly extrapolated back in time.
The final model output is a map, which can be compared with the local land use
map to pinpoint sources of different chemical substances and estimate their source
strength.
Our model has flexible space- time grid extrapolation steps of 1-5 minutes and 1-5 km
grid resolution. By making use of high temporal resolution data, our model can produce maps
for different times of the day, thus accounting for temporal changes and activity profiles of
different sources.
The main advantage of our approach compared to geostationary numerical methods that
interpolate measured concentration values of multiple measurement sites to produce maps
(gridding) is that the maps produced are more accurate in terms of spatial identification of
sources. The model was applied to isoprene and meteorological data recorded during clean
post-monsoon season (1 October- 7 October, 2012) between 11 am and 4 pm at a
receptor site in the North-West Indo-Gangetic Plains (IISER Mohali, 30.665° N,
76.729°E, 300 m asl), near the foothills of the Himalayan range. Considering the
lifetime of isoprene, the model was run only 2 hours backward in time. The map
shows highest residence time weighted concentration of isoprene (up to 3.5 ppbv)
over agricultural land with high number of trees (>180 trees/gridsquare); moderate
concentrations for agricultural lands with low tree density (1.5-2.5 ppbv for 250 μg/m3 for traffic hotspots in Chandigarh
City are observed. Based on the validation against the land use maps, the model
appears to do an excellent job in source apportionment and identifying emission
hotspots.
Acknowledgement: We thank the IISER Mohali Atmospheric Chemistry Facility for data
and the Ministry of Human Resource Development (MHRD), India and IISER Mohali for
funding the facility. Chinmoy Sarkar is acknowledged for technical support, Saryu Garg
thanks the Max Planck-DST India Partner Group on Tropospheric OH reactivity and VOCs
for funding the research. |
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