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
Online publish date: 2017-03-22
Publish date: 2017-03-22
Submission date: 2016-07-26
Final revision date: 2016-10-09
Acceptance date: 2016-10-09
 
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.
eISSN:2083-5906
ISSN:1230-1485