Primer on GRITS/STAT 5.0

GRITS/STAT 4.2 (Groundwater Information Tracking System with statistical analysis capability) enjoys widespread use in the groundwater community. GRITS/STAT is a free PC-based database system capable of performing many of the statistical methods promulgated by the EPA guidance documents: Statistical Analysis of Ground-Water Monitoring Data at RCRA Facilities, 1989, and Addendum to Interim Final Guidance, 1992. GRITS/STAT 4.2, however, suffered from numerous software problems and lagged behind current guidance, particularly in the implementation of statistical intervals. With the release of GRITS/STAT 5.0, many of the software problems that plagued GRITS/STAT 4.2 were addressed and an entirely new Statistical Intervals module was added to bring the software closer to current guidance. The GRITS/STAT 5.0 Beta Test produced a helpful list of work arounds to commonly reported problems. A comparison of the Parametric Confidence Interval methods in GRITS/STAT 4.2 and GRITS/STAT 5.0 illustrates the expanded utility offered by the new Statistics module. The Parametric Confidence Interval in GRITS/STAT 4.2 is only moderately useful for comparing individual wells to an alternate concentration limit (ACL) computed on average background samples. GRITS/STAT 5.0, however, offers users the ability to compare individual and pooled wells to ACLs computed on background averages and risk-based maximum concentration limits (MCLs).     

Effects of interactions between anthropogenic stressors and recurring perturbations on ecosystem resilience and collapse

Insights into declines in ecosystem resilience and their causes and effects can inform preemptive action to avoid ecosystem collapse and loss of biodiversity, ecosystem services, and human well-being. Empirical studies of ecosystem collapse are rare and hampered by ecosystem complexity, nonlinear and lagged responses, and interactions across scales. We investigated how an anthropogenic stressor could diminish ecosystem resilience to a recurring perturbation by altering a critical ecosystem driver. We studied groundwater-dependent, peat-accumulating, fire-prone wetlands known as upland swamps in southeastern Australia. We hypothesized that underground mining (stressor) reduces resilience of these wetlands to landscape fires (perturbation) by diminishing groundwater, a key ecosystem driver. We monitored soil moisture as an indicator of ecosystem resilience during and after underground mining. After landscape fire, we compared responses of multiple state variables representing ecosystem structure, composition, and function in swamps within the mining footprint with unmined reference swamps. Soil moisture declined without recovery in swamps with mine subsidence (i.e., undermined), but was maintained in reference swamps over 8 years (effect size 1.8). Relative to burned reference swamps, burned undermined swamps showed greater loss of peat via substrate combustion; reduced cover, height, and biomass of regenerating vegetation; reduced postfire plant species richness and abundance; altered plant species composition; increased mortality rates of woody plants; reduced postfire seedling recruitment; and extirpation of a hydrophilic animal. Undermined swamps therefore showed strong symptoms of postfire ecosystem collapse, whereas reference swamps regenerated vigorously. We found that an anthropogenic stressor diminished the resilience of an ecosystem to recurring perturbations, predisposing it to collapse. Avoidance of ecosystem collapse hinges on early diagnosis of mechanisms and preventative risk reduction. It may be possible to delay or ameliorate symptoms of collapse or to restore resilience, but the latter appears unlikely in our study system due to fundamental alteration of a critical ecosystem driver. Efectos de las interacciones entre los estresantes antropogénicos y las perturbaciones recurrentes sobre la resiliencia y el colapso de los ecosistemas     

Organochlorine residues in the avifauna of Tamil Nadu (Southeast coast of India)

Pollution due to persistent pesticides is not a regional but a global problem. Organochlorine pesticides are persistent chemicals, stored and accumulated in the tissues of a wide variety of invertebrates and vertebrates including marine species. In the present study the organochlorine residues HCHs, DDTs and PCBs were measured in different trophic groups of birds (scavengers, inland piscivores, coastal piscivores, insectivores, granivores and omnivores) collected from Tamil Nadu Coast, India. The residue accumulation as a function of sex did not depict distinct variation. However, females had lower residue levels than males in terms of their mean weight and feeding habits. A continuous monitoring programme is recommended to establish the studied organisms as indicator species.     

Effects of daily nickel intake on the bio-accumulation,body weight and length in tilapia (Oreochromis niloticus)

Nickel pollution is a serious environmental problem, and its effects may provoke alterations in the ecosystem and in organism of animals and humans. Dermatitis, eczema, and asthma are some illnesses caused by Ni²⁺ poisoning. In this work, fish fed either Ni²⁺-enriched pellets or commercial pellets were studied. The amount of Ni²⁺ in fish were measurements by adsorptive stripping voltammetry (AdSV) with dimethylglyoxime as a complexing agent. The analysis of Ni²⁺ in fish by AdSV established that its accumulation occurs principally in viscera (670.86 ± 5.82 µg g^?1), in the head (697.12 ± 2.77 µg g^?1) and in the muscle (405.82 ± 3.26 µg g^?1), both after 12 months of experiments. Ni²⁺ adsorbs preferentially in organs such as the stomach, the intestine, and the kidneys and acts in the central nervous system as well. Tilapia growth and mass were significantly affected by Ni²⁺ poisoning. From statistical analysis, observed that the results for lengths, weights and metal concentration were different for each sampling at significance level of p < 0.05. The Ni²⁺ concentration in tilapia was enough to cause the death of tilapias; however, it did not occur because the presence of Zn²⁺ might act as protective agent of heavy metals.     

An integrated approach for predicting fates of reintroductions with demographic data from multiple populations

We devised a novel approach to model reintroduced populations whereby demographic data collected from multiple sites are integrated into a Bayesian hierarchical model. Integrating data from multiple reintroductions allows more precise population-growth projections to be made, especially for populations for which data are sparse, and allows projections that account for random site-to-site variation to be made before new reintroductions are attempted. We used data from reintroductions of the North Island Robin (Petroica longipes), an endemic New Zealand passerine, to 10 sites where non-native mammalian predators are controlled. A comparison of candidate models that we based on deviance information criterion showed that rat-tracking rate (an index of rat density) was a useful predictor of robin fecundity and adult female survival, that landscape connectivity and a binary measure of whether sites were on a peninsula were useful predictors of apparent juvenile survival (probably due to differential dispersal away from reintroduction sites), and that there was unexplained random variation among sites in all demographic rates. We used the two best supported models to estimate the finite rate of increase (λ) for populations at each of the 10 sites, and for a proposed reintroduction site, under different levels of rat control. Only three of the reintroduction sites had λ distributions completely >1 for either model. At two sites, λ was expected to be >1 if rat-tracking rates were <5%. At the other five reintroduction sites, λ was predicted to be close to 1, and it was unclear whether growth was expected. Predictions of λ for the proposed reintroduction site were less precise than for other sites because distributions incorporated the full range of site-to-site random variation in vital rates. Our methods can be applied to any species for which postrelease data on demographic rates are available and potentially can be extended to model multiple species simultaneously.     

DIFFERENCES IN WARM-SEASON,RAINSTORM-GENERATED STORMFLOWS FOR NORTHEASTERN ILLINOIS URBANIZED BASINS1

ABSTRACT: Precipitation, streamflow, and population data were analyzed over the 1941–1990 period to determine whether changes in stormflows and net (post. minus pre-rainstorm) stormflows, associated with warm-season large rainstorms, were similar for two urbanized northeastern Illinois basins. Warm season large rainstorms were defined as April through October rainfall events in which ? 5.1 cm occurred in a 48-hour period over the basin. To minimize differences associated with varying large rainstorm amounts over time, the net sthrmflow for each event was divided by the large rainstorm amount. This ratio, Ui, indicated that the two urbanized basins experienced significant, yet different, increases (102 percent and 49 percent) in flow amount per centimeter of rainfall from 1941–1965 to 1966–1990. Results of a regression analysis between Ui and population showed that the increase in U_i per 100,000 increase in population ranged from 0.59 to 0.67 m³s^-1 per cm of rainfall for the two basins. These results demonstrate the varying degree of change that urban planners can expect in stormflows associated with large warm season rainstorms for areas undergoing urbanization.     

Effects of field storage method on <Emphasis Type="Italic">E. coli</Emphasis> concentrations measured in storm water runoff

Storm water runoff is increasingly assessed for fecal indicator organisms (e.g., Escherichia coli, E. coli) and its impact on contact recreation. Concurrently, use of autosamplers along with logistic, economic, technical, and personnel barriers is challenging conventional protocols for sample holding times and storage conditions in the field. A common holding time limit for E. coli is 8 h with a 10 °C storage temperature, but several research studies support longer hold time thresholds. The use of autosamplers to collect E. coli water samples has received little field research attention; thus, this study was implemented to compare refrigerated and unrefrigerated autosamplers and evaluate potential E. coli concentration differences due to field storage temperature (storms with holding times ≤24 h) and due to field storage time and temperature (storms >24 h). Data from 85 runoff events on four diverse watersheds showed that field storage times and temperatures had minor effects on mean and median E. coli concentrations. Graphs and error values did, however, indicate a weak tendency for higher concentrations in the refrigerated samplers, but it is unknown to what extent differing die-off and/or regrowth rates, heterogeneity in concentrations within samples, and laboratory analysis uncertainty contributed to the results. The minimal differences in measured E. coli concentrations cast doubt on the need for utilizing the rigid conventional protocols for field holding time and storage temperature. This is not to say that proper quality assurance and quality control is not important but to emphasize the need to consider the balance between data quality and practical constraints related to logistics, funding, travel time, and autosampler use in storm water studies.     

A new approach to characterize emission contributions from area sources during optical remote sensing technique testing

In the method termed “Other Test Method-10,” the U.S. Environmental Protection Agency has proposed a method to quantify emissions from nonpoint sources by the use of vertical radial plume mapping (VRPM) technique. The surface area of the emitting source and the degree to which the different zones of the emitting source are contributing to the VRPM computed emissions are often unknown. The objective of this study was to investigate and present an approach to quantify the unknown emitting surface area that is contributing to VRPM measured emissions. Currently a preexisting model known as the “multiple linear regression model,” which is described in Thoma et al. (2009 Thoma, E.R., Green, R., Hater, G., Goldsmith, C., Swan, N., Chase, M. and Hashmonay, R. 2010. Development of EPA OTM-10 for landfill applications. J. Environ. Eng., 136: 769–776. [Crossref], [Web of Science ®] , [Google Scholar]), is used for quantifying the unknown surface area.

The method investigated and presented in this paper utilized tracer tests to collect data and develop a model much like that described in Thoma et al. (2009 Thoma, E.R., Green, R., Hater, G., Goldsmith, C., Swan, N., Chase, M. and Hashmonay, R. 2010. Development of EPA OTM-10 for landfill applications. J. Environ. Eng., 136: 769–776. [Crossref], [Web of Science ®] , [Google Scholar]). However, unlike the study used for development of the multiple linear regression model, this study is considered a very limited study due to the low number of pollutant releases performed (seven total releases). It was found through this limited study that the location of an emitting source impacts VRPM computed emissions exponentially, rather than linearly (i.e., the impact that an emitting source has on VRPM measurements decreases exponentially with increasing distances between the emitting source and the VRPM plane). The data from the field tracer tests were used to suggest a multiple exponential regression model. The findings of this study, however, are based on a very small number of tracer tests. More tracer tests performed during all types of climatic conditions, terrain conditions, and different emissions geometries are still needed to better understand the variation of capture efficiency with emitting source location. This study provides a step toward such an objective.

Implications The findings of this study will aid in the advancement of the VRPM technique. In particular, the contribution of this study is to propose a slight improvement in how the area contributing to flux is determined during VRPM campaigns. This will reduce some of the technique's inherent uncertainties when it is employed to estimate emissions from an area source under nonideal conditions.