A near real-time system for continuously monitoring airborne subtilisin-type enzymes in the industrial atmosphere

We describe the development and validation of a portable system comprising an air sampler coupled to an automated flow injection analysis device. The system is able to monitor airborne concentrations of subtilisin-type enzymes in the workplace atmosphere on a continuous basis. Sampling is in two stages: using a sampling head that is designed to mimic human respiration at approx. 1 m s(-1) at a sampling rate of 600 l min(-1). In the second stage, the captured particles are deposited by impaction from the air stream onto the inner surface of a cyclone that is continuously washed with a jet of buffer solution. Deposited particles are then washed into a reservoir from which samples are taken every 5-6 min and injected automatically into a continuous flow injection analysis system. Proteolytic enzyme in the sample passes through a bioreactor maintained at about 40 degrees C. This contains a cellulose solid phase matrix on which is covalently immobilised Texas Red-labelled gelatin as substrate. The passing enzyme partially digests the substrate releasing fluorophore that is detected down stream in a flow cell coupled to a fluorimeter. The system is calibrated using enzyme standards and the intensity of the resulting peaks from the ex-air samples is converted to airborne concentrations using a mathematical model programmed into a PC. The system has a limit of detection of 4.8 ng m(-3) and a dynamic range of 5-60 ng m(-3). The within assay precision (RSD) is 6.3-9.6% over this range. The within batch precision is 20.3% at 20 ng m(-3) and the corresponding between batch value is 19.5%. The system has been run for periods up to 8 h in the laboratory and for up to 4 h at a factory site and the values obtained compared with time-averaged values obtained from a conventional Galley sampler and in-house analysis when reasonable agreement of the results was observed. The stability of the system over 21 days of continuous use with standards injected periodically was studied. Linearity was observed for all the standard plots throughout. At the end of 21 days, after a total exposure equivalent to 2395 ng ml(-1) of Savinase, the signal due to the 5.0 ng ml(-1) standard was still easily detectable.     

Temporal variation in total phosphorus concentrations revealed from a multidecadal monitoring program on Big Platte Lake,Michigan

Effective water quality management depends on enactment of appropriately designed monitoring programs to reveal current and forecasted conditions. Because water quality conditions are influenced by numerous factors, commonly measured attributes such as total phosphorus (TP) can be highly temporally varying. For highly varying processes, monitoring programs should be long-term and periodic quantitative analyses are needed so that temporal trends can be distinguished from stochastic variation, which can yield insights into potential modifications to the program. Using generalized additive mixed modeling, we assessed temporal (yearly and monthly) trends and quantified other sources of variation (daily and subsampling) in TP concentrations from a multidecadal depth-specific monitoring program on Big Platte Lake, Michigan. Yearly TP concentrations decreased from the late 1980s to late 1990s before rebounding through the early 2000s. At depths of 2.29 to 13.72 m, TP concentrations have cycled around stationary points since the early 2000s, while at the surface and depths ≥?18.29 concentrations have continued declining. Summer and fall peaks in TP concentrations were observed at most depths, with the fall peak at deeper depths occurring 1 month earlier than shallower depths. Daily sampling variation (i.e., variation within a given month and year) was greatest at shallowest and deepest depths. Variation in subsamples collected from depth-specific water samples constituted a small fraction of total variation. Based on model results, cost-saving measures to consider for the monitoring program include reducing subsampling of depth-specific concentrations and reducing the number of sampling depths given observed consistencies across the program period.     

A Comparison of Statistical Approaches for Predicting Stream Temperatures Across Heterogeneous Landscapes1

Abstract: Estimating stream temperatures across broad spatial extents is important for regional conservation of running waters. Although statistical models can be useful in this endeavor, little information exists to aid in the selection of a particular statistical approach. Our objective was to compare the accuracy of ordinary least‐squares multiple linear regression, generalized additive modeling, ordinary kriging, and linear mixed modeling (LMM) using July mean stream temperatures in Michigan and Wisconsin. Although LMM using low‐rank thin‐plate smoothing splines to measure the spatial autocorrelation in stream temperatures was the most accurate modeling approach; overall, there were only slight differences in prediction accuracy among the evaluated approaches. This suggests that managers and researchers can select a stream temperature modeling approach that meets their level of expertise without sacrificing substantial amounts of prediction accuracy. The most accurate models for Michigan and Wisconsin had root mean square errors of 2.0‐2.3°C, suggesting that only relatively coarse predictions can be produced from landscape‐based statistical models at regional scales. Explaining substantially more variability in stream temperatures likely will require the collection of finer‐scale hydrologic and physiographic data, which may be cost prohibitive for monitoring and assessing stream temperatures at regional scales.     

Developments in the Framing of Climate Change as a Public Health Issue in US Newspapers

News media are major channels for the transmission of information to the public and deliver news about the latest developments regarding health issues such as climate change. How the media frame such information may enhance public understanding and enable appropriate responses by individuals and communities. This study follows up on previous research examining media portrayals of climate change in US newspapers from 1 January 2007 to 31 December 2008. Here, we content analyze 270 news stories on climate change as a public health issue from five US newspapers between 1 January 2011 and 31 December 2012. Findings indicate that the total number of articles about climate change declined while emphasis on the public health dimension of climate change increased. The types of generic news frames (i.e., dramatic/substantive) most frequently used did not considerably change across the two time periods, however. To explain this, we discuss ways in which people may assess and spark change in news framing of public issues to better reach and influence a range of audiences.     

Quantitative Identification of Disturbance Thresholds in Support of Aquatic Resource Management

The identification of disturbance thresholds is important for many aspects of aquatic resource management, including the establishment of regulatory criteria and the identification of stream reference conditions. A number of quantitative or model-based approaches can be used to identify disturbance thresholds, including nonparametric deviance reduction (NDR), piecewise regression (PR), Bayesian changepoint (BCP), quantile piecewise constant (QPC), and quantile piecewise linear (QPL) approaches. These methods differ in their assumptions regarding the nature of the disturbance-response variable relationship, which can make selecting among the approaches difficult for those unfamiliar with the methods. We first provide an overview of each of the aforementioned approaches for identifying disturbance thresholds, including the types of data for which the approaches are intended. We then compare threshold estimates from each of these approaches to evaluate their robustness using both simulated and empirical datasets. We found that most of the approaches were accurate in estimating thresholds for datasets with drastic changes in responses variable at the disturbance threshold. Conversely, only the PR and QPL approaches performed well for datasets with conditional mean or upper boundary changes in response variables at the disturbance threshold. The most robust threshold identification approach appeared to be the QPL approach; this method provided relatively accurate threshold estimates for most of the evaluated datasets. Because accuracy of disturbance threshold estimates can be affected by a number of factors, we recommend that several steps be followed when attempting to identify disturbance thresholds. These steps include plotting and visually inspecting the disturbance-response data, hypothesizing what mechanisms likely generate the observed pattern in the disturbance-response data, and plotting the estimated threshold in relation to the disturbance-response data to ensure the appropriateness of the threshold estimate.     

What if Dutch investors started worrying about flood risk? Implications for disaster risk reduction

Regional Environmental Change - Increasingly, roles and responsibilities of the public sector in flood risk management are receiving attention in research and policy. Part of the debate suggests...     

Global phylogeography of<Emphasis Type="Italic"> Cassiopea</Emphasis> (Scyphozoa: Rhizostomeae): molecular evidence for cryptic species and multiple invasions of the Hawaiian Islands

The upside-down jellyfish Cassiopea is a globally distributed, semi-sessile, planktonically dispersed scyphomedusa. Cassiopea occurs in shallow, tropical inshore marine waters on sandy mudflats and is generally associated with mangrove-dominated habitats. Controversy over the taxonomy of upside-down jellyfishes precedes their introduction to the Hawaiian Islands during the Second World War, and persists today. Here we address the global phylogeography and molecular systematics of the three currently recognized species: Cassiopea andromeda, C. frondosa, and C. xamachana. Mitochondrial cytochrome c oxidase I (COI) sequences from Australia, Bermuda, Fiji, the Florida Keys, the Hawaiian Islands, Indonesia, Palau, Panama, Papua New Guinea, and the Red Sea were analyzed. Highly divergent COI haplotypes within the putative species C. andromeda (23.4% Kimura 2-parameter molecular divergence), and shared haplotypes among populations of two separate putative species, C. andromeda and C. xamachana from different ocean basins, suggest multiple anthropogenic introductions and systematic confusion. Two deeply divergent Oahu haplotypes (20.3%) from morphologically similar, geographically separate invasive populations indicate long-term (14–40 million years ago) reproductive isolation of phylogenetically distinct source populations and cryptic species. Data support at least two independent introductions to the Hawaiian Islands, one from the Indo-Pacific, another from the western Atlantic/Red Sea. Molecular phylogenetic results support six species: (1) C. frondosa, western Atlantic (2) C. andromeda, Red Sea/western Atlantic/Hawaiian Islands (3) C. ornata, Indonesia/Palau/Fiji (4) Cassiopea sp. 1, eastern Australia (5) Cassiopea sp. 2, Papua New Guinea and (6) Cassiopea sp. 3, Papua New Guinea/Hawaiian Islands.Communicated by P.W. Sammarco, Chauvin     

The spatial pattern of nitrogen cycling in the Adirondack Park, New York

Maps of canopy nitrogen obtained through analysis of high-resolution, hyperspectral, remotely sensed images now offer a powerful means to make landscape-scale to regional-scale estimates of forest N cycling and net primary production (NPP). Moreover, recent research has suggested that the spatial variability within maps of canopy N may be driven by environmental gradients in such features as historic forest disturbance, temperature, species composition, moisture, geology, and atmospheric N deposition. Using the wide variation in these six features found within the diverse forest ecosystems of the 2.5 million ha Adirondack Park, New York, USA, we examined linkages among environmental gradients and three measures of N cycling collected during the 2003 growing season: (1) field survey of canopy N, (2) field survey of soil C:N, and (3) canopy N measured through analysis of two 185 x 7.5 km Hyperion hyperspectral images. These three measures of N cycling strongly related to forest type but related poorly to all other environmental gradients. Further analysis revealed that the spatial pattern in N cycling appears to have distinct inter- and intraspecific components of variability. The interspecific component, or the proportional contribution of species functional traits to canopy biomass, explained 93% of spatial variability within the field canopy N survey and 37% of variability within the soil C:N survey. Residual analysis revealed that N deposition accounted for an additional 2% of variability in soil C:N, and N deposition and historical forest disturbance accounted for an additional 2.8% of variability in canopy N. Given our finding that 95.8% of the variability in the field canopy N survey could be attributed to variation in the physical environment, our research suggests that remotely sensed maps of canopy N may be useful not only to assess the spatial variability in N cycling and NPP, but also to unravel the relative importance of their multiple controlling factors.     

Delineation and Validation of River Network Spatial Scales for Water Resources and Fisheries Management

Identifying appropriate spatial scales is critically important for assessing health, attributing data, and guiding management actions for rivers. We describe a process for identifying a three-level hierarchy of spatial scales for Michigan rivers. Additionally, we conduct a variance decomposition of fish occurrence, abundance, and assemblage metric data to evaluate how much observed variability can be explained by the three spatial scales as a gage of their utility for water resources and fisheries management. The process involved the development of geographic information system programs, statistical models, modification by experienced biologists, and simplification to meet the needs of policy makers. Altogether, 28,889 reaches, 6,198 multiple-reach segments, and 11 segment classes were identified from Michigan river networks. The segment scale explained the greatest amount of variation in fish abundance and occurrence, followed by segment class, and reach. Segment scale also explained the greatest amount of variation in 13 of the 19 analyzed fish assemblage metrics, with segment class explaining the greatest amount of variation in the other six fish metrics. Segments appear to be a useful spatial scale/unit for measuring and synthesizing information for managing rivers and streams. Additionally, segment classes provide a useful typology for summarizing the numerous segments into a few categories. Reaches are the foundation for the identification of segments and segment classes and thus are integral elements of the overall spatial scale hierarchy despite reaches not explaining significant variation in fish assemblage data.