Evaluation of Multi-level Social Learning for Sustainable Landscapes: Perspective of a Development Initiative in Bergslagen, Sweden

To implement policies about sustainable landscapes and rural development necessitates social learning about states and trends of sustainability indicators, norms that define sustainability, and adaptive multi-level governance. We evaluate the extent to which social learning at multiple governance levels for sustainable landscapes occur in 18 local development initiatives in the network of Sustainable Bergslagen in Sweden. We mapped activities over time, and interviewed key actors in the network about social learning. While activities resulted in exchange of experiences and some local solutions, a major challenge was to secure systematic social learning and make new knowledge explicit at multiple levels. None of the development initiatives used a systematic approach to secure social learning, and sustainability assessments were not made systematically. We discuss how social learning can be improved, and how a learning network of development initiatives could be realized.     

Towards a renewed research agenda in ecotoxicology

New concerns about biodiversity, ecosystem services and human health triggered several new regulations increasing the need for sound ecotoxicological risk assessment. The PEER network aims to share its view on the research issues that this challenges. PEER scientists call for an improved biologically relevant exposure assessment. They promote comprehensive effect assessment at several biological levels. Biological traits should be used for Environmental risk assessment (ERA) as promising tools to better understand relationships between structure and functioning of ecosystems. The use of modern high throughput methods could also enhance the amount of data for a better risk assessment. Improved models coping with multiple stressors or biological levels are necessary to answer for a more scientifically based risk assessment. Those methods must be embedded within life cycle analysis or economical models for efficient regulations. Joint research programmes involving humanities with ecological sciences should be developed for a sound risk management.     

Comparison of Spill Frequencies and Amounts at Vapor Recovery and Conventional Service Stations in California

Based on a survey conducted in 1989 and 1990 at 21 conventional nozzle and 33 vapor recovery nozzle equipped service stations in California, the California Air Resources Board staff conclude that conventional nozzles in California produce a greater number of quantifiable spills (≥1 ml) and a greater average volume per spill than do vapor recovery equipped nozzles. This conclusion is based on survey results showing conventional nozzles having spills 30.3 percent of the time at four fueling intervals vs 22.3 percent for vapor recovery nozzles, and having an average spill volume of 13.3 ml vs 10.6 ml for vapor recovery nozzles. However, if spillage noted from a conventional nozzle during a very large spill event is subtracted from the total quantified spill volume, the average volume per quantifiable spill for a conventional nozzle would drop to 11.0 ml. This value is very close to the 10.6 ml average volume per quantifiable spill with a vapor recovery nozzle. In addition, conventional nozzles in California produce about 14 percent more drop (<1 ml) spillage than do vapor recovery nozzles. Drop spillage has not been quantified as to the volume of gasoline it represents.     

Diagnostics for Determining Influential Species in the Chemical Mass Balance Receptor Model

ABSTRACT

The chemical mass balance (CMB) model can be applied to estimate the amount of airborne particulate matter (PM) coming from various sources given the ambient chemical composition of the particles measured at the receptor and the chemical composition of the source emissions. Of considerable practical importance is the identification of those chemical species that have a large effect on either the source contributions or errors estimated by the CMB model. This paper details a study of a number of influential diagnostics for application of the CMB software. Some of the diagnostics studied are standard regression diagnostics based on single-row deletion diagnostics. A number of new diagnostics were developed specifically for the CMB application, based on the pseudo-inverse of the source composition matrix and called nondeletion diagnostics to distinguish them from the standard deletion diagnostics. Simulated data sets were generated to compare the diagnostics and their response to controlled amounts of random error.

A particular diagnostic called a modified pseudoinverse matrix (MPIN), developed for this study, was found to be the best choice for CMB model application. The MPIN diagnostic contains virtually all the information present in both deletion and nondeletion diagnostics. Since the MPIN diagnostic requires only the source profiles, it can be used to identify influential species in advance without sampling the ambient data and to improve CMB results through possible remedial actions for the influential species. Specific recommendations are given for interpretation and use of the MPIN diagnostic with the CMB model software. Elements with normalized MPIN absolute values of 1 to 0.5 are associated with influential elements. Noninfluential elements have normalized MPIN absolute values of 0.3 or less. Elements with absolute values between 0.3 and 0.5 are ambiguous but should generally be considered noninfluential.     

Day-to-Day Particulate Exposures and Health Changes in Los Angeles Area Residents with Severe Lung Disease

ABSTRACT

We measured particulate matter (PM_2.5 and PM₁₀) exposures, home temperature, arterial blood oxygen saturation, blood pressure, and lung function in 30 volunteer Los Angeles area residents during four-day intervals. Continuous Holter electrocardiograms were recorded in a subgroup on the first two days. Subjects recorded symptoms and time-activity patterns in diaries during monitoring, and during a reference period one week earlier/later. All subjects had severe chronic obstructive pulmonary disease. PM₁₀ (24-hr mean) at monitoring stations near subjects’ homes averaged 33 μg/m³, and ranged from 9 to 84 μpg/m³. In longitudinal analyses, day-to-day changes in PM_2.5 and PM₁₀ outside subjects’ homes significantly tracked concurrent station PM10 (r² = 0.22 and 0.44, respectively). Indoor and personal concentrations were less related to station readings (r² ≤ 0.1), but tracked each other (r² ≥ 0.4). In-home temperatures tracked outdoor temperatures more for lows (r² = 0.27) than for highs (r² = 0.10). These longitudinal relationships of subject-oriented and station PM measurements were generally similar to cross-sectional relationships observed previously in similar subjects. Among health measurements, only blood pressure showed reasonably consistent unfavorable longitudinal associations with particulates, more with station or outdoor PM than with indoor or personal PM.     

Modeling Sorbent Injection for Mercury Control in Baghouse Filters: I—Model Development and Sensitivity Analysis

Abstract

A two-stage mathematical model for Hg removal using powdered activated carbon injection upstream of a bag-house filter was developed, with the first stage accounting for removal in the ductwork and the second stage accounting for additional removal caused by the retention of carbon particles on the filter. The model shows that removal in the ductwork is minimal, and the additional carbon detention time from the entrapment of the carbon particles in the fabric filter enhances the Hg removal from the gas phase. A sensitivity analysis on the model shows that Hg removal is dependent on the isotherm parameters, the carbon pore radius and tortuosity, the C/Hg ratio, and the carbon particle radius.     

Chlorinated vapor intrusion indicators,tracers, and surrogates (ITS): Supplemental measurements for minimizing the number of chemical indoor air samples—Part 1: Vapor intrusion driving forces and related environmental factors

Vapor intrusion (VI) assessment is complicated by spatial and temporal variability, largely due to compounded interactions among the many individual factors that influence the vapor migration pathway from subsurface sources to indoor air. Past research on highly variable indoor air datasets demonstrates that conventional sampling schemes can result in false negative determinations of potential risk corresponding to reasonable maximum exposures (RME). While high‐frequency chemical analysis of individual chlorinated volatile organic compounds (CVOCs) in indoor air is conceptually appealing, it remains largely impractical when numerous buildings are involved and particularly for long‐term monitoring. As more is learned about the challenges with indoor air sampling for VI assessment, it has become clear that alternative approaches are needed to help guide discrete sampling efforts and reduce sampling requirements while maintaining acceptable confidence in exposure characterization. Indicators, tracers, and surrogates (ITS), which include a collection of quantifiable metrics and tools, have been suggested as a potential solution for making VI pathway assessment and long‐term monitoring more informative, efficient, and cost‐effective. This review, compilation, and evaluation of ITS demonstrates how even low numbers of indoor air CVOC samples can provide high levels of confidence for representing the RME levels (e.g., 95th percentile) often sought by regulatory agencies for less than chronic effects. A two‐part compilation of available evidence for select low‐cost ITS is presented, with Part 1 focused on introducing the concepts of ITS, meteorologically based ITS, and the evidence from data‐rich studies to support lower cost CVOC VI assessments. Part 1 includes the results of quantitative analyses on two robust residential building VI datasets, where numerous supplemental metrics were collected concurrently with indoor air concentration data. These are supplemented with additional less‐intensive studies in different circumstances. These analyses show that certain ITS metrics and tools, including differential temperature, differential pressure, and radon (in Part 2), can provide benefits to VI assessment and long‐term monitoring. This includes indicators that narrow the assessment period needed to capture RME conditions, tracers that enhance understanding of the conceptual site model, and aid in the identification of preferential pathways and surrogates that support or substitute for CVOC sampling results. The results of this review provide insight into the scientifically supportable uses of ITS.     

Predicting estuarine sediment metal concentrations and inferred ecological conditions: an information theoretic approach

Empirically derived relationships associating sediment metal concentrations with degraded ecological conditions provide important information to assess estuarine condition. Resources limit the number, magnitude, and frequency of monitoring activities to acquire these data. Models that use available information and simple statistical relationships to predict sediment metal concentrations could provide an important tool for environmental assessment. We developed 45 predictive models for the total concentrations of copper, lead, mercury, and cadmium in estuarine sediments along the Southern New England and Mid-Atlantic regions of the United States. Using information theoretic model-averaging approaches, we found total developed land and percent silt/clay of estuarine sediment were the most important variables for predicting the presence of all four metals. Estuary area, river flow, tidal range, and total agricultural land varied in their importance. The model-averaged predictions explained 78.4, 70.5, 56.4, and 50.3% of the variation for copper, lead, mercury, and cadmium, respectively. Overall prediction accuracies of selected sediment benchmark values (i.e., effects ranges) were 83.9, 84.8, 78.6, and 92.0% for copper, lead, mercury, and cadmium, respectively. Our results further support the generally accepted conclusion that sediment metal concentrations are best described by the physical characteristics of the estuarine sediment and the total amount of urban land in the contributing watershed. We demonstrated that broad-scale predictive models built from existing monitoring data with information theoretic model-averaging approaches provide valuable predictions of estuarine sediment metal concentrations and show promise for future environmental modeling efforts in other regions.