OH radicals are crucial for the formation of secondary organic aerosol (SOA) and ozone in the atmosphere. HONO photolysis is one of the main sources of OH radicals, especially in winter. HONO chemistry in polluted areas has been a widely discussed in the last five dec-ades. However, HONO chemistry is often absent or simplified in air quality models, making its impact on OH radical chemistry uncertain. In this thesis, I investigated the formation of HONO over snow in a polluted rural area.The platform for atmospheric chemistry and vertical transport in one dimension (PACT-1D) model was used to calculate a 4-day cycle (Jan. 18th -Jan. 21st,2014) of atmospheric composi-tion and HONO figures. The model output was compared with observational data from the Uintah Basin Winter Ozone Study 2014(UBWOS 2014). Modeled HONO mixing ratios compared well with observation. HONO fluxes from surface chemistry in the model shows good agreement of shape and magnitude. In conclusion, PACT-1D model calculations show that chemical HONO formation on the snow explain the observed HONO mixing ratios.

Biomass burning (BB) has worsened in the Western US over the past several decades, a trend that is expected to continue. BB emits particles and precursors of secondary organic aerosols (SOA) and ozone. Ozone and particles are known to have adverse effects on human health, and BB will have a larger impact on the air quality of the Western US. HONO and HCHO are two of the major sources of radicals in BB plumes that drive the chemistry that forms SOA and ozone.

HONO photlyzes quickly, thus providing an early source of OH radicals in smoke. This photolysis complicates the measurement of HONO emissions, as in-situ observations are made after some photolysis has occurred. Remote sensing (RS) provides one solution to this issue, as it can measure fresher smoke and continuously observe smoke as it travels downwind. Unfortunately, little work has been done to understand the radiative transfer (RT) of sunlight through smoke. In this thesis, a radiative transfer model (RTM) was initialized with observations of smoke from an airborne field campaign to understand the ability of RS to observe trace gases in smoke when data is available to constrain the model and how a lack of data impacts that ability.

Results from the RTM suggest a model uncertainty from observations of BB plumes of 16.5%, and greater than 50% if more averaged assumptions are used. The impact of HONO photolysis can also impact RS retrievals by 15% over the first 2 hours after emission. This motivates better modelling of BB plumes for future satellite observations, which are becoming more prevalent. A parameter called the color index may help to inform these assumptions. Early results of HONO to NO2 emission ratios from satellites agree very well with the observations in this work, and RS observations suggest a high variability of HONO emissions, even by a single fire. Modelling studies of BB plumes must take care to accurately represent HONO emissions. The findings of this thesis motivate further improvements on RT modelling of BB plumes and photolysis rates to better account for RS observations and chemical modelling.

The largest source of uncertainty in global climate models is terrestrial carbon cycle feed- backs. One of the most important but most poorly understood vegetation types in the global carbon cycle is evergreen needleleaf forests (ENFs). To address this challenge, a growing appreciation for the stress physiology of photosynthesis has inspired emerging techniques to detect ENF photosynthetic activity with optical signals. This includes the use of solar- induced chlorophyll fluorescence (SIF), a small light signal emitted by plants during the photosynthetic process. SIF has shown a marked improvement over traditional reflectance- based vegetation indices in tracking ENF photosynthesis. However, SIF, as well as other optical signals, in ENF are complicated by photon-plant interactions over complex canopy structures (physical) and unique adaptations to deal with the seasonal stress of winter while retaining their needles (biological).

In this dissertation, we identify the physical and biological drivers of optical signals in ENF and connect remote sensing observations with physiological processes to improve monitoring of evergreen needleleaf photosynthesis. In Chapter 2, we provide a broad overview for non-specialists of the biological basis for using optical signals to track evergreen needleleaf photosynthesis. We then explore these topics in more detail by using tower-based remote sensing data across four ENF sites which span the climatic gradient experienced by ENF (details in Chapter 3). In Chapters 4 and 5 we zoom in to a single site in Canada and explore the temporal dynamics of different optical metrics and their biological underpinnings. In Chapter 6 we then show how to combine multiple metrics across multiple sites to improve predictions of forest carbon uptake.

Ultimately this work advances our understanding of ENF photosynthesis and our ability to predict the fate of ENFs in a changing climate. Future work will help scale and integrate the understandings gleaned in this dissertation to satellite and modeling frameworks.