期刊
ATMOSPHERIC ENVIRONMENT
卷 289, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.atmosenv.2022.119315
关键词
Tropospheric ozone; Statistical modelling; Temperature dependence; OVOCs
资金
- ACCC Flagship - Academy of Finland [337549, 337550]
- Academy of Finland [334792, 307537, 311932]
- Academy of Finland competitive
- University of Eastern Finland [325022]
- Jane and Aatos Erkko Foundation
- European Research Council (ERC) project ATM-GTP [742206]
- University of Eastern Finland Doctoral Program in Environmental Physics
- Academy of Finland (AKA) [307537, 307537, 334792, 334792] Funding Source: Academy of Finland (AKA)
The study investigates the temperature dependency of tropospheric ozone concentration in the Finnish boreal forest. It finds that factors like weather conditions, long-range transport of precursors, and hydrocarbon concentration influence this dependency. Moreover, it identifies the role of oxygenated volatile organic compounds in the temperature dependence of ozone concentration in a low-NOx environment. The findings highlight the importance of considering multiple factors and the potential of using mixed effects models for ozone prediction.
Tropospheric ozone (O-3) concentrations are observed to increase with temperature in urban and rural locations. We investigated the apparent temperature dependency of daytime ozone concentration in the Finnish boreal forest in summertime based on long-term measurements. We used statistical mixed effects models to separate the direct effects of temperature from other factors influencing this dependency, such as weather conditions, long-range transport of precursors, and concentration of various hydrocarbons. The apparent temperature dependency of 1.16 ppb ?(-1) based on a simple linear regression was reduced to 0.87 ppb ?(-1) within the canopy for summer daytime data after considering these factors. In addition, our results indicated that small oxygenated volatile organic compounds may play an important role in the temperature dependence of O-3 concentrations in this dataset from a low-NOx environment. Summertime observations and daytime data were selected for this analysis to focus on an environment that is significantly affected by biogenic emissions. Despite limitations due to selection of the data, these results highlight the importance of considering factors contributing to the apparent temperature dependence of the O-3 concentration. In addition, our results show that a mixed effects model achieves relatively good predictive accuracy for this dataset without explicitly calculating all processes involved in O-3 formation and removal.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据