The multi-mode transport sector plays a crucial role in economic, social, and political development, but it also contributes to global energy consumption, greenhouse gas emissions, and atmospheric pollution. To effectively address these issues, accurate and detailed emission inventories are essential. This study introduces a comprehensive approach (the BigAir concept), which integrates open-source datasets with traditional methods to estimate multi-mode transport emissions with high spatial (< 500 m) and hourly temporal resolutions. The accuracy, replicability, and scalability of the BigAir approach were evaluated through multi-scale air quality modelling simulations using Portugal as a case study. According to the results obtained, three main conclusions emerged: i) differences in emission magnitudes between BigAir and available inventories were mainly due to the use of outdated datasets; ii) notable spatial distribution differences were observed, particularly for road transport and civil aviation activities; iii) BigAir demonstrated greater accuracy in simulating PM10 levels, with higher correlation coefficients (0.23–0.47, p-value <0.01), and lower errors (7.82–14.0 μg.m−3) while no significant improvement was observed for NO2.. Overall, the BigAir approach strengthens the reliability of the developed methodology and emerges as a powerful tool to guide policymakers in promoting sustainable, clean, resilient, and healthy cities.

中文翻译：

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加强多模式交通排放清单：将开源数据与传统方法相结合


多式联运部门在经济、社会和政治发展中发挥着至关重要的作用，但它也导致了全球能源消耗、温室气体排放和大气污染。为了有效解决这些问题，准确、详细的排放清单至关重要。本研究引入了一种综合方法（BigAir 概念），它将开源数据集与传统方法相结合，以高空间分辨率（< 500 m）和每小时时间分辨率来估算多模式运输排放。以葡萄牙为案例研究，通过多尺度空气质量建模模拟评估了 BigAir 方法的准确性、可复制性和可扩展性。根据获得的结果，得出三个主要结论： i) BigAir 与现有清单之间排放量的差异主要是由于使用了过时的数据集； ii) 观察到显着的空间分布差异，特别是道路运输和民航活动； iii) BigAir 在模拟 PM10 水平方面表现出更高的准确性，具有更高的相关系数（0.23–0.47，p 值 <0.01）和更低的误差（7.82–14.0 μg.m−3），而 NO2 没有观察到显着改善.. 总体而言，BigAir 方法增强了所开发方法的可靠性，并成为指导政策制定者促进可持续、清洁、有弹性和健康城市的强大工具。