Multivariate model-based investigation of the temperature dependence of ozone concentration in Finnish boreal forest

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.

Automated summary

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.