Abstract. Air pollution is an important cause of adverse health effects, even in the Nordic countries, which have relatively good air quality. Modelling-based air quality assessment of the health impacts relies on reliable model estimates of ambient air pollution concentrations, which furthermore rely on good-quality spatially resolved emission data. While quantitative emission estimates are the cornerstone of good emission data, description of the spatial distribution of the emissions is especially important for local air quality modelling at high resolution. In this paper we present a new air pollution emission inventory for the Nordic countries with high-resolution spatial allocation (1 km × 1 km) covering the years 1990, 1995, 2000, 2005, 2010, 2012, and 2014. The inventory is available at https://doi.org/10.5281/zenodo.10571094 (Paunu et al., 2023). To study the impact of applying national data and methods to the spatial distribution of the emissions, we compared road transport and machinery and off-road sectors to CAMS-REGv4.2, which used a consistent spatial distribution method throughout Europe for each sector. Road transport is a sector with well-established proxies for spatial distribution, while for the machinery and off-road sector, the choice of proxies is not as straightforward as it includes a variety of different type of vehicles and machines operating in various environments. We found that CAMS-REGv4.2 was able to produce similar spatial patterns to our Nordic inventory for the selected sectors. However, the resolution of our Nordic inventory allows for more detailed impact assessment than CAMS-REGv4.2, which had a resolution of 0.1° × 0.05° (longitude–latitude, roughly 5.5 km × 3.5–6.5 km in the Nordic countries). The EMEP/EEA Guidebook chapter on spatial mapping of emissions has recommendations for the sectoral proxies. Based on our analysis we argue that the guidebook should have separate recommendations for proxies for several sub-categories of the machinery and off-road sectors, instead of including them within broader sectors. We suggest that land use data are the best starting point for proxies for many of the subsectors, and they can be combined with other suitable data to enhance the spatial distribution. For road transport, measured traffic flow data should be utilized where possible, to support modelled data in the proxies.

中文翻译：

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使用北欧国家高分辨率空间参数的空气污染排放清单以及道路运输、机械和越野行业 PM2.5 空间分布分析

摘要。空气污染是造成不良健康影响的一个重要原因，即使在空气质量相对较好的北欧国家也是如此。基于模型的空气质量健康影响评估依赖于环境空气污染浓度的可靠模型估计，进而依赖于高质量的空间分辨排放数据。虽然定量排放估算是良好排放数据的基石，但排放空间分布的描述对于高分辨率的当地空气质量建模尤其重要。在本文中，我们提出了北欧国家新的空气污染排放清单，具有高分辨率空间分配（1 km × 1 km），涵盖 1990、1995、2000、2005、2010、2012 和 2014 年。该清单可用网址：https://doi.org/10.5281/zenodo.10571094（Paunu 等人，2023）。为了研究将国家数据和方法应用于排放空间分布的影响，我们将道路运输和机械以及越野行业与 CAMS-REGv4.2 进行了比较，后者在整个欧洲的每个行业中使用了一致的空间分布方法。道路运输是一个具有完善的空间分布代理的行业，而对于机械和越野行业来说，代理的选择并不那么简单，因为它包括在不同环境中运行的各种不同类型的车辆和机器。我们发现 CAMS-REGv4.2 能够为选定的部门生成与我们的北欧库存类似的空间模式。然而，我们北欧清单的分辨率可以比 CAMS-REGv4.2 进行更详细的影响评估，CAMS-REGv4.2 的分辨率为 0.1° × 0.05°（经度-纬度，北欧国家约为 5.5 km × 3.5-6.5 km）。EMEP/EEA 指南中有关排放空间测绘的章节对部门代理提出了建议。根据我们的分析，我们认为指南应该对机械和越野行业的几个子类别的代理提出单独的建议，而不是将它们包含在更广泛的行业中。我们建议，土地利用数据是许多子部门代理的最佳起点，它们可以与其他合适的数据相结合，以增强空间分布。对于道路运输，应尽可能利用测量的交通流量数据来支持代理中的建模数据。