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.

在许多国家，公路运输的排放通常是根据国家和国际上不同的国家和国际政策，指令和法规在全国范围内准备的。将排放量缩小到较小的地理区域被认为是自上而下的方法。以前已经采用了几种方法来缩小排放量，但是与使用自下而上方法的排放量分布相比，这些方法经常报告不一致的发现。在爱尔兰的一次全国公路运输估计中，二氧化碳和颗粒物（小于约2.5μm）的排放量被汇总在大都柏林地区的四个县中，并随后以更精细的空间规模（0.5×0.5 km ^2）分布）。代用变量的空间覆盖，空间权重分布以及舰队特征的适当表示被确定为自上而下和自下而上方法之间分布空间排放差异的主要来源。本文通过预测有限数据集中缺少或缺少的交通量来解决前两个问题，然后通过考虑来自县级国家年度车辆测试数据的车队和里程数据来解决后两个问题。应用神经网络模型来预测交通量，该交通量在预测性能上显示出51％的精度。为了进行比较，还使用更常规的基于道路密度的方法进行了排放分配，其中相关性分析显示了这两种方法之间的不一致。这项研究的结果强调，如果不考虑县一级的车队特征，乘用车和-轻型和重型车辆的二氧化碳含量分别为3.57–13.6％（二氧化碳）和−0.054–16.8％（颗粒物），具体取决于所涉及的县。这项研究表明，仅都柏林县就发生了22.6％和21.1％的国家二氧化碳和颗粒物排放，而都柏林市则被归因于全国总量的约10.5％二氧化碳和9.8％颗粒物。都柏林县的颗粒物比周围的县高14.3–22.4％，都柏林市的二氧化碳排放量是周边三个县的城镇和市区的两倍。这项研究提供了产生更精细的排放空间估算方法的组合，以促进县和市一级的减排战略和减缓行动计划，以减少排放，改善空气质量和气候。该研究强调了可靠的空间分布方法的必要性，以便在更细的范围内分配排放。