Downscaling national road transport emission to street level: A case study in Dublin, Ireland

Downscaling national road transport emission to street level: A case study in Dublin, Ireland Emissions from road transport are routinely prepared at the national scale in many countries under different national and international policies, directives and legislation. Scaling down this emission to the smaller geographical area is considered as a top-down approach. Several methods have been previously applied to scaling down emission, however these have often reported inconsistent findings in comparison with emission distribution using a bottom-up approach. Carbon dioxide and particulate matter (smaller than about 2.5 μm) emissions from a national road transport estimation in Ireland were disaggregated among four counties in the Greater Dublin Area and subsequently distributed at a finer spatial scale (0.5 × 0.5 km2). Spatial coverage of the proxy variables, spatial weight distribution and appropriate representation of the fleet characteristics were identified as main sources of difference in distributed spatial emissions between top-down and bottom-up approaches. The first two issues were addressed in this paper by predicting missing or absent traffic volume from limited datasets, and the later was addressed by considering the fleet and mileage data from national annual vehicle test data at county level. A neural network model was applied to predict traffic volume which showed a 51% precision in prediction performance. Emission distribution was also performed for comparison purposes using a more conventional road density-based approach, where a correlation analysis showed an inconsistency between the two approaches. The results of this study highlighted that if the fleet characteristics at county level were not considered, the estimated emission would be different by −1.6 to −8.6% (Carbon dioxide) and −12.6 to 0.03% (Particulate matter) for passenger cars and −3.57–13.6% (Carbon dioxide) and −0.054–16.8% (Particulate matter) for light and heavy duty vehicles, depending on the counties in question. This study revealed that a share of 22.6% and 21.1% of national carbon dioxide and particulate matter emission occurred in Dublin County alone, and Dublin city was attributed to approximately 10.5% carbon dioxide and 9.8% particulate matter of the national total. The particulate matter in Dublin County was 14.3–22.4% higher than surrounding counties, and carbon dioxide emissions in Dublin city were two times higher than that of the towns and urban areas in the surrounding three counties. This study provides a combination of methods for producing finer scale spatial estimation of emission to facilitate abatement strategies and mitigation action plans at county and municipality level for the reduction of emission, better air quality and climate. The study highlights the necessity of reliable spatial distribution methods for assigning emission at a finer scale. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Cleaner Production Elsevier

Downscaling national road transport emission to street level: A case study in Dublin, Ireland

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0959-6526
D.O.I.
10.1016/j.jclepro.2018.02.206
Publisher site
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Abstract

Emissions from road transport are routinely prepared at the national scale in many countries under different national and international policies, directives and legislation. Scaling down this emission to the smaller geographical area is considered as a top-down approach. Several methods have been previously applied to scaling down emission, however these have often reported inconsistent findings in comparison with emission distribution using a bottom-up approach. Carbon dioxide and particulate matter (smaller than about 2.5 μm) emissions from a national road transport estimation in Ireland were disaggregated among four counties in the Greater Dublin Area and subsequently distributed at a finer spatial scale (0.5 × 0.5 km2). Spatial coverage of the proxy variables, spatial weight distribution and appropriate representation of the fleet characteristics were identified as main sources of difference in distributed spatial emissions between top-down and bottom-up approaches. The first two issues were addressed in this paper by predicting missing or absent traffic volume from limited datasets, and the later was addressed by considering the fleet and mileage data from national annual vehicle test data at county level. A neural network model was applied to predict traffic volume which showed a 51% precision in prediction performance. Emission distribution was also performed for comparison purposes using a more conventional road density-based approach, where a correlation analysis showed an inconsistency between the two approaches. The results of this study highlighted that if the fleet characteristics at county level were not considered, the estimated emission would be different by −1.6 to −8.6% (Carbon dioxide) and −12.6 to 0.03% (Particulate matter) for passenger cars and −3.57–13.6% (Carbon dioxide) and −0.054–16.8% (Particulate matter) for light and heavy duty vehicles, depending on the counties in question. This study revealed that a share of 22.6% and 21.1% of national carbon dioxide and particulate matter emission occurred in Dublin County alone, and Dublin city was attributed to approximately 10.5% carbon dioxide and 9.8% particulate matter of the national total. The particulate matter in Dublin County was 14.3–22.4% higher than surrounding counties, and carbon dioxide emissions in Dublin city were two times higher than that of the towns and urban areas in the surrounding three counties. This study provides a combination of methods for producing finer scale spatial estimation of emission to facilitate abatement strategies and mitigation action plans at county and municipality level for the reduction of emission, better air quality and climate. The study highlights the necessity of reliable spatial distribution methods for assigning emission at a finer scale.

Journal

Journal of Cleaner ProductionElsevier

Published: May 10, 2018

References

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