2026-06-17 ミシガン大学
A cloud water collection system set up outside of the Lakes of the Clouds hut, maintained by the Appalachian Mountain Club. As clouds pass over the mountain, water condenses on the strings and filters into collection vials. Cloud and rainwater samples collected here over 19 summers helped researchers understand how upwind rain impacts air quality. Image credit: Adriana Bailey, Michigan Engineering.
＜関連情報＞
- https://news.umich.edu/air-quality-rainfall-history-matters-as-much-as-where-the-air-came-from/
- https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2025EA004888
ニューハンプシャー州ワシントン山における雲と雨の汚染濃度に対する発生源と降水の影響の分離 Separating Source and Precipitation Effects on Cloud and Rain Pollution Concentrations on Mount Washington, NH
Lauren Richards, Adriana Bailey, Georgia Murray, Eric Kelsey
Earth and Space Science Published: 15 May 2026
DOI:https://doi.org/10.1029/2025EA004888
Abstract
Precipitation processes are critical for removing pollutants from the atmosphere, yet in many mid-latitude continental regions, the effects of rainout are difficult to distinguish from the broader influence of atmospheric transport. In this study, we analyze 582 cloud and rain samples collected during summers 1996–2014 from Mount Washington, New Hampshire. We use the sulfate ion concentration (SO42−) of each sample as a proxy for anthropogenic pollution loading and the water isotopic composition (δD) as a tracer of the water-cycle history associated with each sample’s air mass. Since the δD signal records both exchange with near-surface air (a source of moisture and pollutants) and rainout, we use it to evaluate how these same source and sink processes influence pollution concentrations. To isolate the effects of rainout from source, we compare the ln(SO42−) variability explained by sample δD with that explained by a more traditional back-trajectory analysis. Using trajectory cluster, sample type (cloud or rain), and time as predictors in a multivariate regression, we explain 40% of the observed ln(SO42−) variability. In comparison, substituting δD for cluster, or using δD and back-trajectory information in combination, increases the explained variance to 51% and 56%, respectively. After accounting for sample type and time, roughly 14% of the remaining variability in ln(SO42−) is due to precipitation effects. These results quantitatively demonstrate the importance of cloud and precipitation processes in determining pollution concentrations during air mass transport.
Plain Language Summary
Precipitation plays a key role in cleaning pollution out of the atmosphere, but it is challenging to determine to what extent this rainout effect matters as air travels from more polluted regions to remote mountain sites. This research examines pollution concentrations in cloud and rain water samples—collected during summers (1996–2014) on Mount Washington, New Hampshire—to evaluate the importance of the rainout effect. We use concentrations of sulfate to represent the amount of pollution in the water samples and the water isotopic composition as an indicator of the combined effects of both the source of atmospheric moisture and how much rain was produced enroute to the mountain site. We also consider the atmospheric pathways the air travels on its way to Mount Washington. We find that the geographic pathway (along with sample date and sample type) explains about 40% of the variation in pollution, but using the water isotope information as an indicator of both moisture source and rainout helps explain more than half. The results suggest that rainout is equally important as source in predicting pollution concentration.
