Characterization of biological responses under different environmental conditions: A hierarchical modeling approach

A natural river system is organized as a nested hierarchy of interconnected habitats with specific environmental conditions to which the biological community has adapted. Due to this hierarchical structure, identifying the role of different stressors on the biological community is a formidable task. Efforts trying to link stressors to biological integrity have always been bound to the geographic scale of the selected study area, leading to scale-specific results. In this research, an attempt is made to lift this limitation and develop a hierarchical, scale-sensitive methodology that can identify the significant environmental stressors to the biological community at different scales. Sites with similar background environmental conditions are clustered using self-organizing maps (SOM). This is used to identify stressors which affect the biological community throughout the area of study - called environmental gradients or large-scale stressors. Subsequently, these clusters of similar observations (sampling sites) are progressively sub-divided using environmental variables with a significant but localized effect on the biological community - called small-scale stressors. A parent group of sites is split only when the resulting sub-groups have significantly different biological responses. At the end of this recursive sites decomposition procedure, the original set of observations is organized as a tree of environmentally homogeneous groups of observations characterized by unique biological responses to multiple stressors with different geographic extents. The developed hierarchical analysis methodology has been validated using a large-size dataset of environmental observations from the State of Ohio. Our results show that habitat degradation and increased nutrient loading are the large-scale stressors with a widespread impact in Ohio. Other stressors, such as heavy metals, pH or nitrate concentrations have significant albeit localized effects on biological integrity.     

Quantification of risk perception: Development and validation of the construction worker risk perception (CoWoRP) scale

Introduction: The construction sector is leading in the number of accidents and fatalities; risk perception is the key to driving these numbers. Previous construction safety studies on risk perception quantification have not considered affective risk perception of construction workers or conducted comprehensive reliability and validity testing. Thus, this study aims to fill this need by developing a psychometrically sound instrument – the Construction Worker Risk Perception (CoWoRP) Scale – to assess the risk perception of construction workers. Method: Four phases of scale development, namely, item development, factor analysis, reliability assessment, and validity assessment were conducted with the collection and testing of data from a group (n = 469) of voluntary construction workers in Hong Kong. Results: The CoWoRP Scale with 13 items was shown to have acceptable test–retest reliability, internal consistency reliability, as well as content, convergent, discriminant, and criterion-related validity. Also, the CoWoRP Scale was affirmed to have three dimensions of worker risk perception, namely risk perception – probability, risk perception – severity, risk perception – worry and unsafe. These three dimensions of worker risk perception were negatively correlated with their risk-taking behavior. Conclusions: The CoWoRP Scale is a reliable and valid instrument for measuring the risk perception of construction workers and is expected to facilitate the construction safety studies that take risk perception of construction workers into account. Practical applications: The CoWoRP Scale could serve as an aptitude test to identify the characteristics of construction workers most likely to perceive lower risk in risky work situations. In turn, this information could help safety management provide safety training programs to those workers to enhance their risk perception and thereby minimizing their risk-taking behavior, reducing unnecessary training costs, and improving the construction safety performance.     

Effect of scale and dimensionality on the surfactant-enhanced solubilization of a residual DNAPL contamination

The mass transfer rate from residual dense non-aqueous phase liquids (DNAPLs) to the mobile aqueous phase is an important parameter for the efficiency of surfactant-enhanced remediation through solubilization of this type of contamination. The mass transfer kinetics are highly dependent on the dimensionality of the system. In this study, irregularly shaped residual TCE saturations in two-dimensional saturated flow fields were flushed with a 2% polyoxyethylene sorbitan (20) monooleate (POESMO) solution until complete removal had been achieved. A numerical model was developed and used for the simulation of the various surfactant-flushing experiments with different initial saturation patterns and flow rates. Through optimization against in situ concentration and saturation data, a phenomenological power-law model for the relationship between the mass transfer rate from the DNAPL to the mobile aqueous phase on the one hand and the residual DNAPL saturation and the flow velocity on the other hand was derived. The obtained mass transfer rate parameters provide a reasonable fit to the experimental data, predicting the cleanup time and the general saturation and concentration pattern quite well but failing to predict the concentration curves at every individual sampling port. The obtained mass transfer rate model gives smaller values for the predicted mass transfer rate but shows a comparable dependence on water flow and saturation as in earlier published one-dimensional column experiments with identical characteristics for porous medium, DNAPL and surfactant. Mass transfer rate predictions were about one order of magnitude lower in the 2-D flow cell experiment than in 1-D column experiments. These results give an indication for the importance of dimensionality during surfactant remediation.     

基于环境承载力的适度人口规模研究——以北海市为例

以北海市为例,以环境承载力的几个主要分量作为人口规模的限制因子,分别计算各限制因子约束下的适度人口规模,根据木桶原理,取最小值作为环境承载力约束下的城市适度人口规模。结果表明,能源和大气环境、水资源和土地资源承载力均不会限制人口规模;在不排海情景下,水环境容量明显不足,在排海情景下,COD和NH_3~-N的环境容量对应的适度人口分别为133.05万人和153.72万人,仍无法满足北海市水环境功能要求。北海市的适度人口规模取决于水环境容量,具体在130~150万之间。     

Measuring knowledge of indoor environmental hazards

People spend most of their time indoors, where air pollution levels rival and often exceed those outdoors for a number of important pollutants. Yet, little is known about people’s knowledge of indoor environmental hazards. The purpose of the current study was to construct a measure of indoor environmental knowledge. A set of 78 true/false items were developed with input from a panel of experts. The set of items was truncated with traditional item analyses, resulting in a reliable set of 21 items (α = .79). Concurrent validity was established by a significant correlation between the indoor environmental knowledge (IEK) scale and an established measure of science literacy (r = 0.44, p < .001). Schema theory guided the assumption that the two measures should be related. Convergent validity was established by the significant regression of science literacy, formal education in science and math, and status as an engineering student on IEK scale score, accounting for 25% of the variance in the IEK scale score. Future research avenues are proposed and limitations are discussed.     

Issues in developing a capacity for integrated analysis of mitigation and adaptation

As policymakers and stakeholders increasingly consider relative merits and complementarities of climate change mitigation and adaptation strategies, it is important to improve analytical capacities to support this process. Because a single analytical approach is unlikely to fit all needs, this paper explores potentials for an integrated analytical framework that incorporates both top–down and bottom–up approaches.     

Collaboration mobilises institutions with scale-dependent comparative advantage in landscape-scale biodiversity conservation

Landscape-scale approaches are emerging as central to ecosystem management and biodiversity conservation globally, triggering the requirement for collaboration between multiple actors and associated risks including knowledge asymmetries; institutional fragmentation; uncertainty; power imbalances; “invisible” slow-changing variables; and entrenched socio-economic inequities. While social science has elucidated some dimensions required for effective collaboration, little is known about how collaboration manages these risks, or of its effects on associated social-ecological linkages. Our analysis of four different Australian contexts of collaboration shows they mobilised institutions matched to addressing environmental threats, at diverse scales across regulatory and non-regulatory domains. The institutions mobilised included national regulatory controls on development that threatened habitat, incentives to farmers for practice-change, and mechanisms that increased resources for on-ground fire and pest management. Knowledge-sharing underpinned effective risk management and was facilitated through the use of boundary objects, enhanced multi-stakeholder peer review processes, interactive spatial platforms, and Aboriginal-driven planning. Institutions mobilised in these collaborations show scale-dependent comparative advantage for addressing environmental threats. The findings confirm the need to shift scientific attention away from theorising about the ideal-scale for governance. We argue instead for a focus on understanding how knowledge-sharing activities across multiple scales can more effectively connect environmental threats with the most capable institution to address these threats.     

Large-Scale Effects of Timber Harvesting on Stream Systems in the Ouachita Mountains, Arkansas, USA

Using Basin Area Stream Survey (BASS) data from the United States Forest Service, we evaluated how timber harvesting influenced patterns of variation in physical stream features and regional fish and macroinvertebrate assemblages. Data were collected for three years (1990–1992) from six hydrologically variable streams in the Ouachita Mountains, Arkansas, USA that were paired by management regime within three drainage basins. Specifically, we used multivariate techniques to partition variability in assemblage structure (taxonomic and trophic) that could be explained by timber harvesting, drainage basin differences, year-to-year variability, and their shared variance components. Most of the variation in fish assemblages was explained by drainage basin differences, and both basin and year-of-sampling influenced macroinvertebrate assemblages. All three factors modeled, including interactions between drainage basins and timber harvesting, influenced variability in physical stream features. Interactions between timber harvesting and drainage basins indicated that differences in physical stream features were important in determining the effects of logging within a basin. The lack of a logging effect on the biota contradicts predictions for these small, hydrologically variable streams. We believe this pattern is related to the large scale of this study and the high levels of natural variability in the streams. Alternatively, there may be time-specific effects we were unable to detect with our sampling design and analyses.