ORIGINAL RESEARCH
Source Identification of Emission Sources
for Hydrocarbon with Backward Trajectory Model
and Statistical Methods
Tai-Yi Yu
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Department of Risk Management and Insurance, Ming Chuan University, Taipei, Taiwan
Submission date: 2016-07-26
Final revision date: 2016-10-09
Acceptance date: 2016-10-09
Online publication date: 2017-03-22
Publication date: 2017-03-22
Pol. J. Environ. Stud. 2017;26(2):893-902
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ABSTRACT
Several statistical techniques were combined with a backward trajectory model and emission inventory to
locate sources of total hydrocarbon (THC) emissions and to calculate contributed ratios of emission sources.
Emission attraction, a novel method of combining emission inventory and residence time, was introduced
to confirm respective contributions of specific emission sources with detailed meteorological and emissions
data. This research combined four techniques – residence time, conditional probability function, emission
inventory and principal component analysis – to locate possible regions and sources on severe surface ozone
episodes and non-episode days. Temporal and spatial interpolation manners were performed on surface and
rawinsonde meteorological stations, and complex terrain effects were corrected with a variation-kinematic
model. Emission inventory of THC and maximum incremental reactivity (MIR) scales were utilized to
calculate the accounted contributions of distinct emission sources from various jurisdictions. Conditional
probability function combined with emission attraction could reveal potential regions that emitted high THC
emissions and MIR scales during ozone episodes.
The ratios of emission attraction for 11-h backward trajectories indicated that 68% of THC emissions
and 74% of MIR scales were from the target air-quality basin during non-episode days; the respective
figures during ozone episodes were 81% and 75%. The combination of emission attraction and conditional
probability function could identify specific locations that cause severe ground ozone pollution and provide
more detailed information about source regions compared to traditional RTA and PSCF approaches.