Deriving Nitrogen Oxide emissions from inland waterway vessels using MAX-DOAS and in-situ measurements: First results from Koblenz, Germany

Simona Ripperger-Lukosiunaite, Steffen Ziegler,Philipp Eger,Sebastian Donner,Peter Hoor,Thomas Wagner

crossref(2024)

引用 0|浏览1
暂无评分
摘要
Nitrogen oxides (NOx, i.e., NO and NO2) are a major contributor to urban air pollution. They have negative impacts on human health and play an important role in tropospheric chemistry. NO2 causes respiratory and cardiovascular problems and is a precursor of secondary particulate matter and tropospheric ozone, which are also associated with adverse effects on human health. Long-lasting diesel engines of inland waterway vessels operate at high temperatures and are strong NOx emitters. These emissions are concentrated in the vicinity of waterways and have the potential to be a significant source of local air pollution, affecting the air quality and health of people living near waterways. To assess the impact of inland ships on air quality, it is important to have a better understanding of their emission levels by obtaining real-world emission data. Remote sensing techniques, such as Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS), offer several advantages in determining inland ship emissions. They can be directed to the emission plume from a distance (e.g., from the shore of the river) and provide integrative measurements, yielding the integrated trace gas concentration across the plume, which allows quantitative emission estimates. By using plume scans, the second across plume dimension can be captured, and when combined with wind information, total trace gas emissions can be calculated. The combination of MAX-DOAS and in-situ measurements could lead to more accurate estimates of ship emissions by providing important additional information on the composition of the plume and its chemical evolution. Here we present the first results retrieved from MAX-DOAS and in-situ observations conducted along the Rhine River in Koblenz, Germany.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要