Effects of land use, climate, and imperviousness on urban stormwater quality: A meta-analysis.

Many natural and anthropogenic factors cause degradation of urban stormwater quality, resulting in negative consequences to receiving waters. In order to improve water quality models at a variety of scales, accurate estimates of pollutant (nutrients, total suspended solids, and heavy metal) concentrations are needed using potential explanatory variables. To this end, a meta-analysis was performed on aggregated stormwater quality data from the published literature from 360 urban catchments worldwide to understand how urban land use and land cover (LULC), climate (i.e., KÓ§ppen-Geiger zone), and imperviousness (1) affect runoff quality, and (2) whether they are able to predict stormwater pollutant concentrations. Runoff pollutant concentrations were more influenced by LULC and climate than imperviousness. Differences in LULC significantly affected the generation of metals and some nitrogen species. Road, city center, and commercial LULCs generally produced the most elevated pollutant concentrations. Changes in climate zones resulted in significant differences in concentrations of nutrients and metals. Continental and arid climate zones produced runoff with the highest pollutant concentrations. Rainfall patterns seemed to have a more important role in affecting runoff quality than seasonal temperature. Differences in imperviousness only significantly affected chromium and nickel concentrations, although increased imperviousness led to slightly (not significantly) elevated concentrations of nutrients, suspended solids, and other heavy metals. Multiple linear regression models were created to predict the quality of urban runoff. Predictive equations were significant (p < 0.05) for 67% of the pollutants analyzed (ammonia, total Kjeldahl nitrogen, total nitrogen, total phosphorus, cadmium, nickel, lead, and zinc) suggesting that LULC, climate, and imperviousness are useful predictors of stormwater quality when local field monitoring or modeling is not practical. This study provides useful relationships to better inform urban stormwater quality models and regulations such as total maximum daily loads.

Evaluating management options to reduce Lake Erie algal blooms using an ensemble of watershed models.

Reducing harmful algal blooms in Lake Erie, situated between the United States and Canada, requires implementing best management practices to decrease nutrient loading from upstream sources. Bi-national water quality targets have been set for total and dissolved phosphorus loads, with the ultimate goal of reaching these targets in 9-out-of-10 years. Row crop agriculture dominates the land use in the Western Lake Erie Basin thus requiring efforts to mitigate nutrient loads from agricultural systems. To determine the types and extent of agricultural management practices needed to reach the water quality goals, we used five independently developed Soil and Water Assessment Tool models to evaluate the effects of 18 management scenarios over a 10-year period on nutrient export. Guidance from a stakeholder group was provided throughout the project, and resulted in improved data, development of realistic scenarios, and expanded outreach. Subsurface placement of phosphorus fertilizers, cover crops, riparian buffers, and wetlands were among the most effective management options. But, only in one realistic scenario did a majority (3/5) of the models predict that the total phosphorus loading target would be met in 9-out-of-10 years. Further, the dissolved phosphorus loading target was predicted to meet the 9-out-of-10-year goal by only one model and only in three scenarios. In all scenarios evaluated, the 9-out-of-10-year goal was not met based on the average of model predictions. Ensemble modeling revealed general agreement about the effects of several practices although some scenarios resulted in a wide range of uncertainty. Overall, our results demonstrate that there are multiple pathways to approach the established water quality goals, but greater adoption rates of practices than those tested here will likely be needed to attain the management targets.

Pyrosequencing reveals unique microbial signatures associated with healthy and failing dental implants.

AIM: Although it is established that peri-implantitis is a bacterially induced disease, little is known about the bacterial profile of peri-implant communities in health and disease. The purpose of the present investigation was to examine the microbial signatures of the peri-implant microbiome in health and disease. MATERIALS AND METHODS: Subgingival and submucosal plaque samples were collected from forty subjects with periodontitis, peri-implantitis, periodontal and peri-implant health and analysed using 16S pyrosequencing. RESULTS: Peri-implant biofilms demonstrated significantly lower diversity than subgingival biofilms in both health and disease, however, several species, including previously unsuspected and unknown organisms, were unique to this niche. The predominant species in peri-implant communities belonged to the genera Butyrivibrio, Campylobacter, Eubacterium, Prevotella, Selenomonas, Streptococcus, Actinomyces, Leptotrichia, Propionibacterium, Peptococcus, Lactococcus and Treponema. Peri-implant disease was associated with lower levels of Prevotella and Leptotrichia and higher levels of Actinomyces, Peptococcus, Campylobacter, non-mutans Streptococcus, Butyrivibrio and Streptococcus mutans than healthy implants. These communities also demonstrated lower levels of Prevotella, non-mutans Streptococcus, Lactobacillus, Selenomonas, Leptotrichia, Actinomyces and higher levels of Peptococcus, Mycoplasma, Eubacterium, Campylobacter, Butyrivibrio, S. mutans and Treponema when compared to periodontitis-associated biofilms. CONCLUSION: The peri-implant microbiome differs significantly from the periodontal community in both health and disease. Peri-implantitis is a microbially heterogeneous infection with predominantly gram-negative species, and is less complex than periodontitis.

Target region selection is a critical determinant of community fingerprints generated by 16S pyrosequencing.

Pyrosequencing of 16S rRNA genes allows for in-depth characterization of complex microbial communities. Although it is known that primer selection can influence the profile of a community generated by sequencing, the extent and severity of this bias on deep-sequencing methodologies is not well elucidated. We tested the hypothesis that the hypervariable region targeted for sequencing and primer degeneracy play important roles in influencing the composition of 16S pyrotag communities. Subgingival plaque from deep sites of current smokers with chronic periodontitis was analyzed using Sanger sequencing and pyrosequencing using 4 primer pairs. Greater numbers of species were detected by pyrosequencing than by Sanger sequencing. Rare taxa constituted nearly 6% of each pyrotag community and less than 1% of the Sanger sequencing community. However, the different target regions selected for pyrosequencing did not demonstrate a significant difference in the number of rare and abundant taxa detected. The genera Prevotella, Fusobacterium, Streptococcus, Granulicatella, Bacteroides, Porphyromonas and Treponema were abundant when the V1-V3 region was targeted, while Streptococcus, Treponema, Prevotella, Eubacterium, Porphyromonas, Campylobacter and Enterococcus predominated in the community generated by V4-V6 primers, and the most numerous genera in the V7-V9 community were Veillonella, Streptococcus, Eubacterium, Enterococcus, Treponema, Catonella and Selenomonas. Targeting the V4-V6 region failed to detect the genus Fusobacterium, while the taxa Selenomonas, TM7 and Mycoplasma were not detected by the V7-V9 primer pairs. The communities generated by degenerate and non-degenerate primers did not demonstrate significant differences. Averaging the community fingerprints generated by V1-V3 and V7-V9 primers provided results similar to Sanger sequencing, while allowing a significantly greater depth of coverage than is possible with Sanger sequencing. It is therefore important to use primers targeted to these two regions of the 16S rRNA gene in all deep-sequencing efforts to obtain representational characterization of complex microbial communities.