Prioritizing research for trace pollutants and emerging contaminants in the freshwater environment

Organic chemicals have been detected at trace concentrations in the freshwater environment for decades. Though the term trace pollutant indicates low concentrations normally in the nanogram or microgram per liter range, many of these pollutants can exceed an acceptable daily intake (ADI) for humans. Trace pollutants referred to as emerging contaminants (ECs) have recently been detected in the freshwater environment and may have adverse human health effects. Analytical techniques continue to improve; therefore, the number and frequency of detections of ECs are increasing. It is difficult for regulators to restrict use of pollutants that are a human health hazard; scientists to improve treatment techniques for higher priority pollutants; and the public to modify consumption patterns due to the vast number of ECs and the breadth of literature on the occurrence, use, and toxicity. Hence, this paper examines literature containing occurrence and toxicity data for three broad classes of trace pollutants and ECs (industrials, pesticides, and pharmaceuticals and personal care products (PPCPs)), and assesses the relevance of 71 individual compounds. The evaluation indicates that widely used industrials (BPF) and PPCPs (AHTN, HHCB, ibuprofen, and estriol) occur frequently in samples from the freshwater environment but toxicity data were not available; thus, it is important to establish their ADI. Other widely used industrials (BDE-47, BDE-99) and pesticides (benomyl, carbendazim, aldrin, endrin, ethion, malathion, biphenthrin, and cypermethrin) have established ADI values but occurrence in the freshwater environment was not well documented. The highest priority pollutants for regulation and treatment should include industrials (PFOA, PFOS and DEHP), pesticides (diazinon, methoxychlor, and dieldrin), and PPCPs (EE2, carbamazepine, βE2, DEET, triclosan, acetaminophen, and E1) because they occur frequently in the freshwater environment and pose a human health hazard at environmental concentrations.     

The toxicology of climate change: Environmental contaminants in a warming world

Climate change induced by anthropogenic warming of the earth's atmosphere is a daunting problem. This review examines one of the consequences of climate change that has only recently attracted attention: namely, the effects of climate change on the environmental distribution and toxicity of chemical pollutants. A review was undertaken of the scientific literature (original research articles, reviews, government and intergovernmental reports) focusing on the interactions of toxicants with the environmental parameters, temperature, precipitation, and salinity, as altered by climate change. Three broad classes of chemical toxicants of global significance were the focus: air pollutants, persistent organic pollutants (POPs), including some organochlorine pesticides, and other classes of pesticides. Generally, increases in temperature will enhance the toxicity of contaminants and increase concentrations of tropospheric ozone regionally, but will also likely increase rates of chemical degradation. While further research is needed, climate change coupled with air pollutant exposures may have potentially serious adverse consequences for human health in urban and polluted regions. Climate change producing alterations in: food webs, lipid dynamics, ice and snow melt, and organic carbon cycling could result in increased POP levels in water, soil, and biota. There is also compelling evidence that increasing temperatures could be deleterious to pollutant-exposed wildlife. For example, elevated water temperatures may alter the biotransformation of contaminants to more bioactive metabolites and impair homeostasis. The complex interactions between climate change and pollutants may be particularly problematic for species living at the edge of their physiological tolerance range where acclimation capacity may be limited. In addition to temperature increases, regional precipitation patterns are projected to be altered with climate change. Regions subject to decreases in precipitation may experience enhanced volatilization of POPs and pesticides to the atmosphere. Reduced precipitation will also increase air pollution in urbanized regions resulting in negative health effects, which may be exacerbated by temperature increases. Regions subject to increased precipitation will have lower levels of air pollution, but will likely experience enhanced surface deposition of airborne POPs and increased run-off of pesticides. Moreover, increases in the intensity and frequency of storm events linked to climate change could lead to more severe episodes of chemical contamination of water bodies and surrounding watersheds. Changes in salinity may affect aquatic organisms as an independent stressor as well as by altering the bioavailability and in some instances increasing the toxicity of chemicals. A paramount issue will be to identify species and populations especially vulnerable to climate–pollutant interactions, in the context of the many other physical, chemical, and biological stressors that will be altered with climate change. Moreover, it will be important to predict tipping points that might trigger or accelerate synergistic interactions between climate change and contaminant exposures.     

Algorithms and analytical solutions for rapidly approximating long-term dispersion from line and area sources

Predicting long-term mean pollutant concentrations in the vicinity of airports, roads and other industrial sources are frequently of concern in regulatory and public health contexts. Many emissions are represented geometrically as ground-level line or area sources. Well developed modelling tools such as AERMOD and ADMS are able to model dispersion from finite (i.e. non-point) sources with considerable accuracy, drawing upon an up-to-date understanding of boundary layer behaviour. Due to mathematical difficulties associated with line and area sources, computationally expensive numerical integration schemes have been developed. For example, some models decompose area sources into a large number of line sources orthogonal to the mean wind direction, for which an analytical (Gaussian) solution exists. Models also employ a time-series approach, which involves computing mean pollutant concentrations for every hour over one or more years of meteorological data. This can give rise to computer runtimes of several days for assessment of a site. While this may be acceptable for assessment of a single industrial complex, airport, etc., this level of computational cost precludes national or international policy assessments at the level of detail available with dispersion modelling. In this paper, we extend previous work [S.R.H. Barrett, R.E. Britter, 2008. Development of algorithms and approximations for rapid operational air quality modelling. Atmospheric Environment 42 (2008) 8105–8111] to line and area sources. We introduce approximations which allow for the development of new analytical solutions for long-term mean dispersion from line and area sources, based on hypergeometric functions. We describe how these solutions can be parameterized from a single point source run from an existing advanced dispersion model, thereby accounting for all processes modelled in the more costly algorithms. The parameterization method combined with the analytical solutions for long-term mean dispersion are shown to produce results several orders of magnitude more efficiently with a loss of accuracy small compared to the absolute accuracy of advanced dispersion models near sources. The method can be readily incorporated into existing dispersion models, and may allow for additional computation time to be expended on modelling dispersion processes more accurately in future, rather than on accounting for source geometry.     

Differential toxicity of silver and titanium dioxide nanoparticles on Drosophila melanogaster development, reproductive effort, and viability: size, coatings and antioxidants matter

Silver and titanium dioxide nanoparticles are known to induce oxidative stress in vitro and in vivo. Here we test if they impact development, mating success, and survivorship in Drosophila melanogaster, and if so, if these effects are reversible by antioxidants. Ingestion of nanotitanium dioxide during the larval stage of the life cycle showed no effects on development or survivorship, up to doses of 200 μg mL⁻¹. Conversely, ingestion of nanosilver had major dose, size, and coating-dependent effects on each of these aspects of life history. Each of these effects was partially or fully reversible by vitamin C. Larvae growing on nanosilver supplemented with vitamin C showed a greater than twofold increase in survivorship compared to flies reared on nanosilver alone, and a threefold increase in mating success. Vitamin C also rescued cuticular and pigmentation defects in nanosilver fed flies. Biochemical assays of superoxide dismutase and glutathione show these markers respond to nanotitanium dioxide and nanosilver induced oxidative stress, and this response is reduced by vitamin C. These results indicate that life history effects of nanosilver ingestion result from oxidative stress, and suggest antioxidants as a potential remediation for nanosilver toxicity. Conversely, the lack of nanotitanium dioxide life history toxicity shows that oxidative stress does not necessarily result in whole organism effects, and argues that nanoparticle toxicity needs to be examined at different levels of biological organization.     

Water quality performance of a batch-type stormwater detention basin.

The objective of this research was to modify an extended detention basin to provide batch treatment of stormwater runoff. An automated valve/controller was developed and placed on the outlet of a detention basin in Austin, Texas, which allowed the water quality volume to be retained in the basin for a preset length of time. The influent and effluent of the modified basin were monitored for total suspended solids (TSS), nutrients, chemical oxygen demand (COD), and total and dissolved metals. Statistically significant removal of total metals, COD, total nitrogen, total phosphorus, and TSS was observed, with a discharge event mean TSS concentration of 7 mg/L and a TSS removal efficiency of 91%. The modified basin has substantially better pollutant removal than conventional extended detention basins and is comparable with that of Austin sand filters, which are a common structural stormwater treatment system in the Austin area. The valve also can be used to isolate hazardous material spills.     

Relationship between antecedent dry period and highway pollutant: conceptual models of buildup and removal processes

This paper investigated the highway stormwater quality at two Texas cities-Austin and College Station. Two highways with high average daily traffic were monitored using passive stormwater samplers for collecting first-flush runoff during a 16-month period. Detailed traffic and weather data were collected at College Station sites, but only weather data were obtained at Austin sites. A stepwise regression analysis on College Station data identifies the antecedent dry period (ADP) as the most significant predictor of pollutant concentration. Specifically, the College Station data show an unexpected result that pollutant event mean concentrations significantly decrease with increasing ADP for all analyzed pollutants. However, the runoff concentrations observed in Austin were not significantly correlated with ADP. The result from College Station data provides a different insight to the pollutant buildup and removal process on highways. Conceptual highway pollutant buildup and removal models are proposed for generating further discussion and research interest.     

The clearinghouse concept: a model for geospatial data centralization and dissemination in a disaster

The disaster clearinghouse concept originates with the earthquake community as an effort to coordinate research and data collection activities. Though prior earthquake clearinghouses are small in comparison to what was needed in response to Hurricane Katrina, these seminal structures are germane to the establishment of our current model. On 3 September 2005, five days after Katrina wrought cataclysmic destruction along the Gulf Coast, FEMA and Louisiana State University personnel met to establish the LSU GIS Clearinghouse Cooperative (LGCC), a resource for centralization and dissemination of geospatial information related to Hurricane Katrina. Since its inception, the LGCC has developed into a working model for organization, dissemination, archiving and research regarding geospatial information in a disaster. This article outlines the formation of the LGCC, issues of data organization, and methods of data dissemination and archiving with an eye towards implementing the clearinghouse model as a standard resource for addressing geospatial data needs in disaster research and management.     

Abundance of denitrification genes under different peizometer depths in four Irish agricultural groundwater sites

This study examined the relationship between the abundance of bacterial denitrifiers in groundwater at four sites, differing with respect to overlaying land management and peizometer depth. Groundwater was sourced from 36 multilevel piezometers, which were installed to target different groundwater zones: (1) subsoil, (2) subsoil to bedrock interface, and (3) bedrock. The gene copy concentrations (GCCs), as gene copies per liter, for bacterial 16S rRNA genes and the denitrifying functional genes, nirK, nirS, and nosZ, were determined using quantitative polymerase chain reaction assays. The results were related to gaseous nitrogen emissions and to the physicochemical properties of the four sites. Overall, nirK and nirS abundance appeared to show no significant correlation to N₂O production (P?=?0.9989; P?=?0.3188); and no significant correlation was observed between nosZ and excess N₂ concentrations (P?=?0.0793). In the majority of piezometers investigated, the variation of nirK and nirS gene copy concentrations was considered significant (P?<?0.0001). Dissolved organic carbon (DOC) decreased with aquifer depth and ranged from 1.0–4.0 mg l^?1, 0.9–2.4 mg l^?1, and 0.8–2.4 mg l^?1 within piezometers located in the subsoil, subsoil/bedrock interface, and bedrock depths, respectively. The availability of increasing DOC and the depth of the water table were positively correlated with increasing nir and nosZ GCCs (P?=?0.0012). A significant temporal correlation was noted between nirS and piezometer depth (P?<?0.001). Interestingly, the nirK, nirS, and nosZ GCCs varied between piezometer depths within specific sites, while GCCs remained relatively constant from site to site, thus indicating no direct impact of agricultural land management strategies investigated on denitrifier abundance.     

Assessing conservation status with extensive but low-resolution data: Application of frequentist and Bayesian models to endangered Athabasca River rainbow trout

Use of extensive but low-resolution abundance data is common in the assessment of species at-risk status based on quantitative decline criteria under International Union for Conservation of Nature (IUCN) and national endangered species legislation. Such data can be problematic for 3 reasons. First, statistical power to reject the null hypothesis of no change is often low because of small sample size and high sampling uncertainty leading to a high frequency of type II errors. Second, range-wide assessments composed of multiple site-specific observations do not effectively weight site-specific trends into global trends. Third, uncertainty in site-specific temporal trends and relative abundance are not propagated at the appropriate spatial scale. A common result is the propensity to underestimate the magnitude of declines and therefore fail to identify the appropriate at-risk status for a species. We used 3 statistical approaches, from simple to more complex, to estimate temporal decline rates for a designatable unit (DU) of rainbow trout in the Athabasca River watershed in western Canada. This DU is considered a native species for purposes of listing because of its genetic composition characterized as >0.95 indigenous origin in the face of continuing introgressive hybridization with introduced populations in the watershed. Analysis of abundance trends from 57 time series with a fixed-effects model identified 33 sites with negative trends, but only 2 were statistically significant. By contrast, a hierarchical linear mixed model weighted by site-specific abundance provided a DU-wide decline estimate of 16.4% per year and a 3-generation decline of 93.2%. A hierarchical Bayesian mixed model yielded a similar 3-generation decline trend of 91.3% and the posterior distribution showed that the estimate had a >99% probability of exceeding thresholds for an endangered listing. We conclude that the Bayesian approach was the most useful because it provided a probabilistic statement of threshold exceedance in support of an at-risk status recommendation.