RELATIONSHIP BETWEEN COLUMN DENSITY AND SURFACE MIXING RATIO FOR O3 AND NO2: IMPLICATIONS FOR SATELLITE OBSERVATIONS AND THE IMPACTS OF VERTICAL MIXING

Satellites have great potential for diagnosis of surface air quality conditions, though reduced sensitivity of satellite instrumentation to the lower troposphere currently impedes their applicability. One objective of the NASA DISCOVER-AQ project is to provide information relevant to improving our ability to relate satellite-observed columns to surface conditions for key trace gases and aerosols. In support of DISCOVER-AQ, this dissertation investigates the degree of correlation between O3 and NO2 column abundance and surface mixing ratio during the four DISCOVER-AQ deployments; characterize the variability of the aircraft in situ and model-simulated O3 and NO2 profiles; and use the WRF-Chem model to further investigate the role of boundary layer mixing in the column-surface connection for the Maryland 2011 deployment, and determine which of the available boundary layer schemes best captures the observations. Simple linear regression analyses suggest that O3 partial column observations from future satellite instruments with sufficient sensitivity to the lower troposphere may be most meaningful for surface air quality under the conditions associated with the Maryland 2011 campaign, which included generally deep, convective boundary layers, the least wind shear of all four deployments, and few geographical influences on local meteorology, with exception of bay breezes. Hierarchical clustering analysis of the in situ O3 and NO2 profiles indicate that the degree of vertical mixing (defined by temperature lapse rate) associated with each cluster exerted an important influence on the shapes of the median cluster profiles for O3, as well as impacted the column vs. surface correlations for many clusters for both O3 and NO2. However, comparisons to the CMAQ model suggest that, among other errors, vertical mixing is overestimated, causing too great a column-surface connection within the model. Finally, the WRF-Chem model, a meteorology model with coupled chemistry, is used to further investigate the impact of vertical mixing on the O3 and NO2 column-surface connection, for an ozone pollution event that occurred on July 26-29, 2011. Five PBL schemes were tested, with no one scheme producing a clear, consistent “best” comparison with the observations for PBLH and pollutant profiles; however, despite improvements, the ACM2 scheme continues to overestimate vertical mixing.

Dynamics of a large submersed plant bed in upper Chesapeake Bay

A large SAV bed in upper Chesapeake Bay has experienced several abrupt shifts over the past half-century, beginning with near-complete loss after a record-breaking flood in 1972, followed by an unexpected, rapid resurgence in the early 2000’s, then partial decline in 2011 following another major flood event. Together, these trends and events provide a unique opportunity to study a recovering SAV ecosystem from several different perspectives. First, I analyzed and synthesized existing time series datasets to make inferences about what factors prompted the recovery. Next, I analyzed existing datasets, together with field samples and a simple hydrodynamic model to investigate mechanisms of SAV bed loss and resilience to storm events. Finally, I conducted field deployments and experiments to explore how the bed affects internal physical and biogeochemical processes and what implications those effects have for the dynamics of the system. I found that modest reductions in nutrient loading, coupled with several consecutive dry years likely facilitated the SAV resurgence. Furthermore, positive feedback processes may have played a role in the sudden nature of the recovery because they could have reinforced the state of the bed before and after the abrupt shift. I also found that scour and poor water clarity associated with sediment deposition during the 2011 flood event were mechanisms of plant loss. However, interactions between the bed, water flow, and waves served as mechanisms of resilience because these processes created favorable growing conditions (i.e., clear water, low flow velocities) in the inner core of the bed. Finally, I found that that interactions between physical and biogeochemical processes led to low nutrient concentrations inside the bed relative to outside the bed, which created conditions that precluded algal growth and reinforced vascular plant dominance. This work demonstrates that positive feedbacks play a central role in SAV resilience to both chronic eutrophication as well as acute storm events. Furthermore, I show that analysis of long-term ecological monitoring data, together with field measurements and experiments, can be an effective approach for understanding the mechanisms underlying ecosystem dynamics.