ALBERT

Description: There is a continued need to understand how human activities along the northern Gulf of Mexico coast are impacting the natural ecosystems. The gulf coast is experiencing rapid population growth and associated land cover/land use change. Mobile Bay, AL is a designated pilot region of the Gulf of Mexico Alliance (GOMA) and is the focus area of many current NASA and NOAA studies, for example. This is a critical region, both ecologically and economically to the entire United States because it has the fourth largest freshwater inflow in the continental USA, is a vital nursery habitat for commercially and recreational important fisheries, and houses a working waterfront and port that is expanding. Watershed and hydrodynamic modeling has been performed for Mobile Bay to evaluate the impact of land use change in Mobile and Baldwin counties on the aquatic ecosystem. Watershed modeling using the Loading Simulation Package in C++ (LSPC) was performed for all watersheds contiguous to Mobile Bay for land use Scenarios in 1948, 1992, 2001, and 2030. The Prescott Spatial Growth Model was used to project the 2030 land use scenario based on observed trends. All land use scenarios were developed to a common land classification system developed by merging the 1992 and 2001 National Land Cover Data (NLCD). The LSPC model output provides changes in flow, temperature, sediments and general water quality for 22 discharge points into the Bay. These results were inputted in the Environmental Fluid Dynamics Computer Code (EFDC) hydrodynamic model to generate data on changes in temperature, salinity, and sediment concentrations on a grid with four vertical profiles throughout the Bay s aquatic ecosystems. The models were calibrated using in-situ data collected at sampling stations in and around Mobile bay. This phase of the project has focused on sediment modeling because of its significant influence on light attenuation which is a critical factor in the health of submerged aquatic vegetation. The impact of land use change on sediment concentrations was evaluated by analyzing the LSPC and EFDC sediment simulations for the four land use scenarios. Such analysis was also performed for storm and non-storm periods. In- situ data of total suspended sediments (TSS) and light attenuation were used to develop a regression model to estimate light attenuation from TSS. This regression model was used to derive marine light attenuation estimates throughout Mobile bay using the EFDC TSS outputs. The changes in sediment concentrations and associated impact on light attenuation in the aquatic ecosystem were used to perform an ecological analysis to evaluate the impact on seagreasses and Submerged Aquatic Vegetation (SAV) habitat. This is the key product benefiting the Mobile Bay coastal environmental managers that integrates the influences of sediments due to land use driven flow changes with the restoration potential of SAVs.

Description: Coincident with global expansion of urban areas has been an increase in hypertension. It is unclear how much the urban environment contributes as a risk factor for blood pressure differences, and how much is due to a variety of environmental, lifestyle, and demographic correlates of urbanization. Objectives/Purpose: The purpose of this study is to examine the relationship between living environment (defined as urban, suburban, or rural) and hypertension in selected regions from the REasons for Geographic And Racial Differences in Stroke (REGARDS) cohort. Methods: REGARDS is a national cohort of 30,228 participants from the 48 contiguous United States. We used data from 4 metropolitan regions (Philadelphia, Atlanta, Minneapolis and Chicago) for this study (n=3928). We used Land Cover/Land Use (LCLU) information from the 30-meter National Land Cover Data. Results: Overall, 1996 (61%) of the participants were hypertensive. We characterized participants into urban, suburban or rural living environments using the LCLU data. In univariate models, we found that living environment is associated with hypertension, but that after adjustment for known hypertension risk factors, the relationship was no longer present at the 95% confidence level. Conclusions: LCLU data can be utilized to characterize the living environment, which in turn can be applied to studies of public health outcomes. Further study regarding the relationship between hypertension and living environment should focus on additional characteristics of the associated environment.