Understanding the chemical impacts of biogenic volatile organic compounds and the physical drivers of their observed seasonality

Emissions from natural ecosystems, broadly classified as biogenic volatile organic compounds (BVOCs), contribute 90% to the VOC budget. Individual BVOCs vary widely in their reaction rates with atmospheric oxidants, making their atmospheric impact highly dependent on VOC composition. Their emissions are also dependent on vegetative make up and a number of meteorological and ecological variables. However, the ecological and physical drivers of their emissions is becoming more variable in a changing climate, leading to greater uncertainties in models. Increasing the monitoring of individual compounds can improve our understanding of the drivers of these emissions and the impact of individual chemical species on atmospheric composition. Improved understanding of BVOC composition can better emission models and, SOA and ozone formation predictions. To study the atmospheric impacts and physical drivers of BVOCs, a GC-FID was adapted for automated hourly sampling and analysis. The details of the hardware and software used for the system are described in detail to enable future long-term BVOC measurements in additional locations. The instrument was deployed at a measurement tower in a forest in central Virginia for year-round collection of BVOC concentrations. Using two years of collected hourly data, this work assesses the chemical impacts of individual BVOCs on time scales ranging from hour to year. This work identifies the importance of both concentration and chemical structure in determining atmospheric impacts. Additionally, seasonality in the concentration of some biogenic species has large implications for atmospheric reactivity in the warmest months of the year, particularly ozone reactivity. Using ecological and meteorological data collected at the site in conjunction with the BVOC data, the drivers of BVOC concentrations and their seasonality are identified. Comparison between this data and current models, reveal important deviations which may lead to large modeled uncertainties. Furthermore, the collected data has been made publicly available to aid in future research regarding BVOCs.