Visitor Evaluations of Management Actions at a Highly Impacted Appalachian Trail Camping Area

Protected area management involves balancing environmental and social objectives. This is particularly difficult at high-use/high-impact recreation sites, because resource protection objectives may require substantial site management or visitor regulation. This study examined visitors’ reactions to both of these types of actions at Annapolis Rocks, Maryland, a popular Appalachian Trail camping area. We surveyed visitors before and after implementation of camping policies that included shifting camping to designated newly constructed campsites and prohibiting campfires. Survey results reveal that visitors were more satisfied with all social and environmental indicators after the changes were enacted. An Importance-Performance analysis also determined that management actions improved conditions for factors of greatest concern to campers prior to the changes. Posttreatment visitors were least satisfied with factors related to reduced freedom and to some characteristics of the constructed campsites. Although there was evidence of visitor displacement, the camping changes met management goals by protecting the camping area’s natural resources and improving social conditions.     

Combined analysis of modeled and monitored SO2 concentrations at a complex smelting facility

Vale Canada Limited owns and operates a large nickel smelting facility located in Sudbury, Ontario. This is a complex facility with many sources of SO₂ emissions, including a mix of source types ranging from passive building roof vents to North America's tallest stack. In addition, as this facility performs batch operations, there is significant variability in the emission rates depending on the operations that are occurring. Although SO₂ emission rates for many of the sources have been measured by source testing, the reliability of these emission rates has not been tested from a dispersion modeling perspective. This facility is a significant source of SO₂ in the local region, making it critical that when modeling the emissions from this facility for regulatory or other purposes, that the resulting concentrations are representative of what would actually be measured or otherwise observed. To assess the accuracy of the modeling, a detailed analysis of modeled and monitored data for SO₂ at the facility was performed. A mobile SO₂ monitor sampled at five locations downwind of different source groups for different wind directions resulting in a total of 168 hr of valid data that could be used for the modeled to monitored results comparison. The facility was modeled in AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model) using site-specific meteorological data such that the modeled periods coincided with the same times as the monitored events. In addition, great effort was invested into estimating the actual SO₂ emission rates that would likely be occurring during each of the monitoring events. SO₂ concentrations were modeled for receptors around each monitoring location so that the modeled data could be directly compared with the monitored data. The modeled and monitored concentrations were compared and showed that there were no systematic biases in the modeled concentrations.

Implications:

This paper is a case study of a Combined Analysis of Modelled and Monitored Data (CAMM), which is an approach promulgated within air quality regulations in the Province of Ontario, Canada. Although combining dispersion models and monitoring data to estimate or refine estimates of source emission rates is not a new technique, this study shows how, with a high degree of rigor in the design of the monitoring and filtering of the data, it can be applied to a large industrial facility, with a variety of emission sources. The comparison of modeled and monitored SO₂ concentrations in this case study also provides an illustration of the AERMOD model performance for a large industrial complex with many sources, at short time scales in comparison with monitored data. Overall, this analysis demonstrated that the AERMOD model performed well.