Effects of roof and rainwater characteristics on copper concentrations in roof runoff

Copper sheeting is a common roofing material used in many parts of the world. However, copper dissolved from roof sheeting represents a source of copper ions to watersheds. Researchers have studied and recently developed a simple and efficient model to predict copper runoff rates. Important input parameters include precipitation amount, rain pH, and roof angle. We hypothesized that the length of a roof also positively correlates with copper concentration (thus, runoff rates) on the basis that runoff concentrations should positively correlate with contact time between acidic rain and the copper sheet. In this study, a novel system was designed to test and model the effects of roof length (length of roof from crown to the drip edge) on runoff copper concentrations relative to rain pH and roof angle. The system consisted of a flat-bottom copper trough mounted on an apparatus that allowed run length and slope to be varied. Water of known chemistry was trickled down the trough at a constant rate and sampled at the bottom. Consistent with other studies, as pH of the synthetic rainwater decreased, runoff copper concentrations increased. At all pH values tested, these results indicated that run length was more important in explaining variability in copper concentrations than was the roof slope. The regression equation with log-transformed data (R ² = 0.873) accounted for slightly more variability than the equation with untransformed data (R ² = 0.834). In log-transformed data, roof angle was not significant in predicting copper concentrations.     

Visions and strategies for growth management: Teller County/Woodland Park example

This article is the third in a series prepared to explain the Teller County growth management planning process. Once it was determined by local decision-makers that Teller County, Colorado, would pursue a policy of directed growth, concepts based on this policy were developed. These concepts presented visions and options for the future. Landscape plans then were developed for Teller County and the City of Woodland Park planning area so that these options could be visualized by local leaders and the public. The landscape plans were used as part of the ongoing citizen involvement process to inform the public about the options for growth management. To provide even more information, detailed designs were developed for specific areas in the county. From the landscape plan, public involvement effort, and detailed designs, specific implementation measures were identified, discussed, and adopted. These measures included guidelines for administration, so that county and city goals could be linked to the actual day-to-day management of development proposals. Paper 3 in a series of 3.     

Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting,generalized additive models,and tree-based methods

Many efforts are underway to produce broad-scale forest attribute maps by modelling forest class and structure variables collected in forest inventories as functions of satellite-based and biophysical information. Typically, variants of classification and regression trees implemented in Rulequest's^© See5 and Cubist (for binary and continuous responses, respectively) are the tools of choice in many of these applications. These tools are widely used in large remote sensing applications, but are not easily interpretable, do not have ties with survey estimation methods, and use proprietary unpublished algorithms. Consequently, three alternative modelling techniques were compared for mapping presence and basal area of 13 species located in the mountain ranges of Utah, USA. The modelling techniques compared included the widely used See5/Cubist, generalized additive models (GAMs), and stochastic gradient boosting (SGB). Model performance was evaluated using independent test data sets. Evaluation criteria for mapping species presence included specificity, sensitivity, Kappa, and area under the curve (AUC). Evaluation criteria for the continuous basal area variables included correlation and relative mean squared error. For predicting species presence (setting thresholds to maximize Kappa), SGB had higher values for the majority of the species for specificity and Kappa, while GAMs had higher values for the majority of the species for sensitivity. In evaluating resultant AUC values, GAM and/or SGB models had significantly better results than the See5 models where significant differences could be detected between models. For nine out of 13 species, basal area prediction results for all modelling techniques were poor (correlations less than 0.5 and relative mean squared errors greater than 0.8), but SGB provided the most stable predictions in these instances. SGB and Cubist performed equally well for modelling basal area for three species with moderate prediction success, while all three modelling tools produced comparably good predictions (correlation of 0.68 and relative mean squared error of 0.56) for one species.     

Adequacy of Wildlife Habitat Relation Models for Estimating Spatial Distributions of Terrestrial Vertebrates

In the U.S. National Biological Service's gap analysis, potential distributions of terrestrial vertebrate species are based on the synthesis of wildlife habitat relation data and then modeled using a vegetation cover map derived from Landsat Thematic Mapper imagery. Using long-term species lists from eight National Parks in Utah, we evaluated the adequacy of the wildlife habitat relations data generated by gap analysis in predicting species distributions at landscape scales. Omission and commission error rates were estimated for major taxonomic groups and for each national park. Depending on the taxonomic group, omission error ranged from 0 to 25%, whereas commission error ranged from 4 to 33%. Error rates were highest in amphibians and reptiles and lowest for birds and mammals. In general, the error rate declined as the size of the park increased. The Utah wildlife habitat relation models performed well when used to predict the presence or absence of terrestrial vertebrates in eight national parks in Utah and should provide valuable information for making conservation decisions. They also provide a measure of support for the use of these models within the gap analysis framework. Although it is likely that accuracy of wildlife habitat relation models will vary from state to state, and even considerably within a state, the modeling process seems robust enough to provide a reasonably high level of accuracy for use in conservation planning at the ecoregion level.     

Knowledge and Perceptions in Costa Rica Regarding Environment, Population, and Biodiversity Issues

Public understanding of general environment- and population-related issues is critical for successful conservation efforts. We interviewed 300 residents of Costa Rica, distributed among three socioeconomic groups, to survey knowledge and perceptions about the environment. We focused on Costa Rica because it is an international model for sustainable development and because of its direct economic interest in biodiversity preservation for ecotourism. Our results indicate that Costa Ricans generally have a limited awareness of environment- and population-related problems. In all socioeconomic groups environmental problems were considered less important than various other problems, but 91% of respondents indicated a willingness to pay more for water or electricity if the extra money were used to protect biodiversity. Residents of San Jose's lower-class neighborhoods more strongly perceived the effects of population growth and environmental degradation than did residents of urban, upper-class, or rural neighborhoods. Of those interviewed, 69% thought the rate of population growth should decrease. Only 52% of those surveyed, however, perceived any link between population size or growth rate and environmental quality; of those, 33% could not describe the link. Our findings and others suggest a number of ways to enhance the effectiveness of environmental education programs worldwide: (1) include information on the relationship between population and environmental quality; (2) explain the socioeconomic implications of environmental degradation; (3) in addition to formal education, utilize the mass media more; (4) tailor messages to local circumstances.     

A Preliminary Assessment of the Montréal Process Indicators of Air Pollution for the United States

Air pollutants pose a risk to forest health and vitality in the United States. Here we present the major findings from a national scale air pollution assessment that is part of the United States' 2003 Report on Sustainable Forests. We examine trends and the percent forest subjected to specific levels of ozone and wet deposition of sulfate, nitrate, and ammonium. Results are reported by Resource Planning Act (RPA) reporting region and integrated by forest type using multivariate clustering. Estimates of sulfate deposition for forested areas had decreasing trends (1994-2000) across RPA regions that were statistically significant for North and South RPA regions. Nitrate deposition rates were relatively constant for the 1994 to 2000 period, but the South RPA region had a statistically decreasing trend. The North and South RPA regions experienced the highest ammonium deposition rates and showed slightly decreasing trends. Ozone concentrations were highest in portions of the Pacific Coast RPA region and relatively high across much of the South RPA region. Both the South and Rocky Mountain RPA regions had an increasing trend in ozone exposure. Ozone-induced foliar injury to sensitive species was recorded in all regions except for the Rocky Mountain region. The multivariate analysis showed that the oak-hickory and loblolly-shortleaf pine forest types were generally exposed to more air pollution than other forest types, and the redwood, western white pine, and larch forest types were generally exposed to less. These findings offer a new approach to national air pollution assessments and are intended to help focus research and planning initiatives related to air pollution and forest health.     

Comparison of recycling outcomes in three types of recycling collection units

Commercial institutions have many factors to consider when implementing an effective recycling program. This study examined the effectiveness of three different types of recycling bins on recycling accuracy by determining the percent weight of recyclable material placed in the recycling bins, comparing the percent weight of recyclable material by type of container used, and examining whether a change in signage increased recycling accuracy. Data were collected over 6 weeks totaling 30 days from 3 different recycling bin types at a Midwest University medical center. Five bin locations for each bin type were used. Bags from these bins were collected, sorted into recyclable and non-recyclable material, and weighed. The percent recyclable material was calculated using these weights. Common contaminates found in the bins were napkins and paper towels, plastic food wrapping, plastic bags, and coffee cups. The results showed a significant difference in percent recyclable material between bin types and bin locations. Bin type 2 was found to have one bin location to be statistically different (p = 0.048), which may have been due to lack of a trash bin next to the recycling bin in that location. Bin type 3 had significantly lower percent recyclable material (p < 0.001), which may have been due to lack of a trash bin next to the recycling bin and increased contamination due to the combination of commingled and paper into one bag. There was no significant change in percent recyclable material in recycling bins post signage change. These results suggest a signage change may not be an effective way, when used alone, to increase recycling compliance and accuracy. This study showed two or three-compartment bins located next to a trash bin may be the best bin type for recycling accuracy.     

Limited gene flow in the brooding coral Favia fragum (Esper, 1797)

Understanding population connectivity in corals is particularly important as these organisms are increasingly threatened by abiotic and biotic factors. This study examined the population genetic structure of the brooding coral Favia fragum across four locations in the Caribbean and Western Atlantic using mitochondrial and nuclear markers. Morphological features were also compared to test whether genetic diversity corresponds with skeletal morphology. When comparing across distantly related Caribbean and Bermudian locations, F _ST values were high and significant, indicating strong genetic structure. At a local scale, significant genetic structure was found among reefs in Panama, while no genetic structure was found among reefs within Barbados, Bermuda or Jamaica. Surprisingly, a single haplotype for each of the three markers examined was found in Bermuda, where samples varied significantly from all other locations in three out of four morphological features analyzed. These data indicate that gene flow of F. fragum may occur locally among reefs but is highly restricted at distant locations. Furthermore, isolated populations, such as that of Bermuda, must be self-seeding to maintain the observed genetic uniformity.     

Assessing Linkages in Stream Habitat,Geomorphic Condition,and Biological Integrity Using a Generalized Regression Neural Network

Watershed managers often use physical geomorphic and habitat assessments in making decisions about the biological integrity of a stream, and to reduce the cost and time for identifying stream stressors and developing mitigation strategies. Such analysis is difficult since the complex linkages between reach‐scale geomorphic and habitat conditions, and biological integrity are not fully understood. We evaluate the effectiveness of a generalized regression neural network (GRNN) to predict biological integrity using physical (i.e., geomorphic and habitat) stream‐reach assessment data. The method is first tested using geomorphic assessments to predict habitat condition for 1,292 stream reaches from the Vermont Agency of Natural Resources. The GRNN methodology outperforms linear regression (69% vs. 40% classified correctly) and improves slightly (70% correct) with additional data on channel evolution. Analysis of a subset of the reaches where physical assessments are used to predict biological integrity shows no significant linear correlation, however the GRNN predicted 48% of the fish health data and 23% of macroinvertebrate health. Although the GRNN is superior to linear regression, these results show linking physical and biological health remains challenging. Reasons for lack of agreement, including spatial and temporal scale differences, are discussed. We show the GRNN to be a data‐driven tool that can assist watershed managers with large quantities of complex, nonlinear data.