Who plans for health improvement? SEA,HIA and the separation of spatial planning and health planning

This study examines whether there is active planning for health improvement in the English spatial planning system and how this varies across two regions using a combination of telephone surveys and focus group interviews in 2005 and 2010. The spatial planning profession was found to be ill-equipped to consider the health and well-being implications of its actions, whilst health professionals are rarely engaged and have limited understanding and aspirations when it comes to influencing spatial planning. Strategic Environmental Assessment was not considered to be successful in integrating health into spatial plans, given it was the responsibility of planners lacking the capacity to do so. For their part, health professionals have insufficient knowledge and understanding of planning and how to engage with it to be able to plan for health gains rather than simply respond to health impacts. HIA practice is patchy and generally undertaken by health professionals outside the statutory planning framework. Thus, whilst appropriate assessment tools exist, they currently lack a coherent context within which they can function effectively and the implementation of the Kiev protocol requiring the engagement of health professionals in SEA is not to likely improve the consideration of health in planning while there continues to be separation of functions between professions and lack of understanding of the other profession.     

A framework for adaptive monitoring of the cumulative effects of human footprint on biodiversity

Effective ecological monitoring is imperative in a human-dominated world, as our ability to manage functioning ecosystems will depend on understanding biodiversity responses to anthropogenic impacts. Yet, most monitoring efforts have either been narrowly focused on particular sites, species and stressors — thus inadequately considering the cumulative effects of multiple, interacting impacts at scales of management relevance — or too unfocused to provide specific guidance. We propose a cumulative effects monitoring framework that integrates multi-scaled surveillance of trends in biodiversity and land cover with targeted evaluation of hypothesized drivers of change. The framework is grounded in a flexible conceptual model and uses monitoring to generate and test empirical models that relate the status of diverse taxonomic groups to the nature and extent of human “footprint” and other landscape attributes. An adaptive cycle of standardized sampling, model development, and model evaluation provides a means to learn about the system and guide management. Additional benefits of the framework include standardized data on status and trend for a wide variety of biodiversity elements, spatially explicit models for regional planning and scenario evaluation, and identification of knowledge gaps for complementary research. We describe efforts to implement the framework in Alberta, Canada, through the Alberta Biodiversity Monitoring Institute, and identify key challenges to be addressed.     

An integrated environmental and health performance quantification model for pre-occupancy phase of buildings in China

To comprehensively pre-evaluate the damages to both the environment and human health due to construction activities in China, this paper presents an integrated building environmental and health performance (EHP) assessment model based on the Building Environmental Performance Analysis System (BEPAS) and the Building Health Impact Analysis System (BHIAS) models and offers a new inventory data estimation method. The new model follows the life cycle assessment (LCA) framework and the inventory analysis step involves bill of quantity (BOQ) data collection, consumption data formation, and environmental profile transformation. The consumption data are derived from engineering drawings and quotas to conduct the assessment before construction for pre-evaluation. The new model classifies building impacts into three safeguard areas: ecosystems, natural resources and human health. Thus, this model considers environmental impacts as well as damage to human wellbeing. The monetization approach, distance-to-target method and panel method are considered as optional weighting approaches. Finally, nine residential buildings of different structural types are taken as case studies to test the operability of the integrated model through application. The results indicate that the new model can effectively pre-evaluate building EHP and the structure type significantly affects the performance of residential buildings.     

Assessment of Spatiotemporal Varying Relationships Between Rainfall,Land Cover and Surface Water Area Using Geographically Weighted Regression

Traditional regression techniques such as ordinary least squares (OLS) are often unable to accurately model spatially varying data and may ignore or hide local variations in model coefficients. A relatively new technique, geographically weighted regression (GWR) has been shown to greatly improve model performance compared to OLS in terms of higher R ² and lower corrected Akaike information criterion (AIC_C). GWR models have the potential to improve reliabilities of the identified relationships by reducing spatial autocorrelations and by accounting for local variations and spatial non-stationarity between dependent and independent variables. In this study, GWR was used to examine the relationship between land cover, rainfall and surface water habitat in 149 sub-catchments in a predominately agricultural region covering 2.6 million ha in southeast Australia. The application of the GWR models revealed that the relationships between land cover, rainfall and surface water habitat display significant spatial non-stationarity. GWR showed improvements over analogous OLS models in terms of higher R ² and lower AIC_C. The increased explanatory power of GWR was confirmed by the results of an approximate likelihood ratio test, which showed statistically significant improvements over analogous OLS models. The models suggest that the amount of surface water area in the landscape is related to anthropogenic drainage practices enhancing runoff to facilitate intensive agriculture and increased plantation forestry. However, with some key variables not present in our analysis, the strength of this relationship could not be qualified. GWR techniques have the potential to serve as a useful tool for environmental research and management across a broad range of scales for the investigation of spatially varying relationships.     

A Long-Term Electricity Dispatch Model with the TIMES Framework

A new Swiss TIMES (The Integrated MARKAL–EFOM System) electricity model with an hourly representation of inter-temporal detail and a century-long model horizon has been developed to explore the TIMES framework’s suitability as a long-term electricity dispatch model. To understand the incremental insights from this hourly model, it is compared to an aggregated model with only two diurnal timeslices like in most MARKAL/TIMES models. Two scenarios have been analysed with both models to answer the following questions: Are there differences in model solutions? What are the benefits of having a high number of timeslices? Are there any computational limitations? The primary objective of this paper is to understand the differences between the solutions of the two models, rather than Swiss policy implication or potential uncertainties in input parameters and assumptions. The analysis reveals that the hourly model offers powerful insights into the electricity generation schedule. Nevertheless, the TIMES framework cannot substitute for a dispatch model because some features cannot be represented; however, the long model time horizon and integrated system approaches of TIMES provide features not available in conventional dispatch models. The methodology of the model development and insights from the model comparison are described.     

New Unstructured Mesh Water Quality Model for Cooling Water Biocide Discharges

A new unstructured mesh coastal water and air quality model has been developed that includes species transport, nonlinear decay, by-product formation, and mass-exchange between sea and atmosphere. The model has been programmed with a graphical user interface and is applicable to coastal seawater, lakes, and rivers. Focused on species conversion and interaction with the atmosphere, the water and air quality model follows a modular approach. It is a compatible module which simulates distributions based on fluid dynamic field data of underlying existing hydrodynamic and atmospheric simulations. Nonlinear and spline approximations of decay and growth kinetics, by-product formation, and joint sea–atmosphere simulation have been embedded. The Windows application software includes functions allowing error analysis concerning mesh and finite volume approximation. In this work, a submerged residual chlorine cooling water discharge and halogenated matter by-product formation has been simulated. An error analysis has been carried out by varying vertical meshing, time-steps and comparing results based on explicit and implicit finite volume approximation. The new model has been named 3D Simulation for Marine and Atmospheric Reactive Transport, in short 3D SMART.     

The Sustainability Dynamics Framework – A holistic approach to define and evaluate sustainability and unsustainability in the Anthropocene

The significant role and impact of humans in fostering environmental change, at all spatial-temporal scales, has been formally recognised by the development and inclusion of the Anthropocene epoch into the standardised geological time scale. Due to the role and impacts that humans are having, it is necessary to develop an understanding of the co-evolutionary relationship between humans and the environment in the Anthropocene – Specifically, in relation to the fundamental dynamics of sustainability and unsustainability. This requires the development of approaches which are capable of developing new holistic knowledge concerning the fundamental dynamics of sustainability/unsustainability operating at any specified spatial-temporal scale(s) in the Anthropocene. Henceforth, this paper proposes a holistic quantitative framework - the Sustainability Dynamics Framework. The framework integrates and extends four fundamental theories concerning the environment-human relationship into a single framework. The framework is then demonstratively applied for the first time to determine and analyse the fundamental dynamic relationships between indicated levels of sustainability (S-values) and various influencing factors, significantly expanding upon previously obtained results and analysis in Phillips (2015). The demonstrative application indicates that the SDF has a potential contribution towards advancing knowledge concerning the dynamics of sustainability/unsustainability in the Anthropocene. This will be demonstrated through the future applications of the SDF to newly developed case studies of a multi spatial-temporal nature.     

Assessment and prediction of the impacts of the Ok Tedi copper mine on fish catches in the Fly River system,Papua New Guinea

The Ok Tedi copper mine discharges overburden and ore residues into the Ok Tedi, a tributary of the Fly River. These discharges result in elevated suspended solids and dissolved and particulate associated copper. Analyses of covariance were performed to establish statistical model of the relationships between the mine discharges and fish catches between 1983 and 1988. These models were then extrapolated to predict the affects of future mine discharges on fish catches. The models predicted that if the observed effects were caused by particulate associated copper, the period of greatest impact will be between 1989 and 1993, following which catches should be close to 1988 levels for the remainder of mine life. Some additional catches not included in the data set used to derive the models were found to fit the model predictions well. As the predicted period of greatest impact is short and most species reproduce away from the river channel, the ability of the fish communities to undergo partial recovery after 1991 should be maintained.     

Using a relative health indicator (RHI) metric to estimate health risk reductions in drinking water

When a new drinking water regulation is being developed, the USEPA conducts a health risk reduction and cost analysis to, in part, estimate quantifiable and non-quantifiable cost and benefits of the various regulatory alternatives. Numerous methodologies are available for cumulative risk assessment ranging from primarily qualitative to primarily quantitative. This research developed a summary metric of relative cumulative health impacts resulting from drinking water, the relative health indicator (RHI). An intermediate level of quantification and modeling was chosen, one which retains the concept of an aggregated metric of public health impact and hence allows for comparisons to be made across “cups of water,” but avoids the need for development and use of complex models that are beyond the existing state of the science. Using the USEPA Six-Year Review data and available national occurrence surveys of drinking water contaminants, the metric is used to test risk reduction as it pertains to the implementation of the arsenic and uranium maximum contaminant levels and quantify “meaningful” risk reduction. Uranium represented the threshold risk reduction against which national non-compliance risk reduction was compared for arsenic, nitrate, and radium. Arsenic non-compliance is most significant and efforts focused on bringing those non-compliant utilities into compliance with the 10 μg/L maximum contaminant level would meet the threshold for meaningful risk reduction.     

Assessing the Relative Severity of Stressors at a Watershed Scale

Water quality monitoring data are usually used independently to report on the condition of streams and watersheds. For example, watersheds are often rated as good, fair, or poor with regard to a single stressor or with regard to an index of biotic integrity. The utility of monitoring data may be enhanced by integrating stressor-response information with the observed stressor data, and reporting stressor levels in terms of their relative effects upon valued ecological resources. We estimated stressor-response relationships at the regional scale using data collected in the Eastern Cornbelt Plains Ecoregion of Ohio. Generalized additive models were used to visualize stressor-response relationships. Piecewise linear functions and simple linear functions were then used to parameterize the observed responses. Parameters derived from the regional models were used to scale observations of stressors in the Big Darby Creek watershed, OH. After scaling, stressors were compared in terms of their spatial distribution and in terms of the severity with which they influenced the biological endpoint of interest. Stressors most strongly associated with the current ecological condition of the watershed were identified. In the Big Darby Creek watershed, decreases in substrate quality were associated with the most severe decrements in biological condition. At smaller decrements in biological condition, three stressors were important: substrate quality, riparian quality, and increased concentrations of NOx.     

A Rapid Method for Assessing the Environmental Performance of Commercial Farms in the Pampas of Argentina

The generation of reliable updated information is critical to support the harmonization of socio-economic and environmental issues in a context of sustainable development. The agro-environmental assessment and management of agricultural systems often relies on indicators that are necessary to make sound decisions. This work aims to provide an approach to (a) assess the environmental performance of commercial farms in the Pampas of Argentina, and (b) propose a methodological framework to calculate environmental indicators that can rapidly be applied to practical farming. 120 commercial farms scattered across the Pampas were analyzed in this study during 2002 and 2003. Eleven basic indicators were identified and calculation methods described. Such indicators were fossil energy (FE) use, FE use efficiency, nitrogen (N) balance, phosphorus (P) balance, N contamination risk, P contamination risk, pesticide contamination risk, soil erosion risk, habitat intervention, changes in soil carbon stock, and balance of greenhouse gases. A model named Agro-Eco-Index was developed on a Microsoft-Excel support to incorporate on-farm collected data and facilitate the calculation of indicators by users. Different procedures were applied to validate the model and present the results to the users. Regression models (based on linear and non-linear models) were used to validate the comparative performance of the study farms across the Pampas. An environmental dashboard was provided to represent in a graphical way the behavior of farms. The method provides a tool to discriminate environmentally friendly farms from those that do not pay enough attention to environmental issues. Our procedure might be useful for implementing an ecological certification system to reward a good environmental behavior in society (e.g., through tax benefits) and generate a commercial advantage (e.g., through the allocation of green labels) for committed farmers.     

Assessing forest canopy closure in a geospatial medium to address management concerns for tropical islands—Southeast Asia

The present study outlines an approach to classify forest density and to estimate canopy closure of the forest of the Andaman and Nicobar archipelago. The vector layers generated for the study area using satellite data was validated with the field knowledge of the surveyed ground control points. The methodology adopted in this present analysis is three-tiered. First, the density stratification into five zones using visual interpretation for the complete archipelago. In the second step, we identified two island groups from the Andaman to investigate and compare the forest strata density. The third and final step involved more of a localised phytosociological module that focused on the North Andaman Islands. The results based on the analysis of the high-resolution satellite data show that more than 75% of the mangroves are under high- to very high-density canopy class. The framework developed would serve as a significant measure to forest health and evaluate management concerns whilst addressing issues such as gap identification, conservation prioritisation and disaster management—principally to the post-tsunami assessment and analysis.     

水环境质量预报预警大数据平台研究

构建基于水环境模型的水质预报预警大数据平台是一项复杂的系统工程,主要面临模型的规模计算及其引发的模拟结果规模存储、规模分析的挑战.相关挑战极大地限制了水环境质量预报预警业务的深入开展,但目前国内外关于此方面的研究极少.为此,提出了水环境质量预报预警大数据平台框架.该框架以水环境模型体系为基础,以大数据集群计算体系为核心...     

Multi-criteria decision analysis with probabilistic risk assessment for the management of contaminated ground water

Traditionally, environmental decision analysis in subsurface contamination scenarios is performed using cost–benefit analysis. In this paper, we discuss some of the limitations associated with cost–benefit analysis, especially its definition of risk, its definition of cost of risk, and its poor ability to communicate risk-related information. This paper presents an integrated approach for management of contaminated ground water resources using health risk assessment and economic analysis through a multi-criteria decision analysis framework. The methodology introduces several important concepts and definitions in decision analysis related to subsurface contamination. These are the trade-off between population risk and individual risk, the trade-off between the residual risk and the cost of risk reduction, and cost-effectiveness as a justification for remediation. The proposed decision analysis framework integrates probabilistic health risk assessment into a comprehensive, yet simple, cost-based multi-criteria decision analysis framework. The methodology focuses on developing decision criteria that provide insight into the common questions of the decision-maker that involve a number of remedial alternatives. The paper then explores three potential approaches for alternative ranking, a structured explicit decision analysis, a heuristic approach of importance of the order of criteria, and a fuzzy logic approach based on fuzzy dominance and similarity analysis. Using formal alternative ranking procedures, the methodology seeks to present a structured decision analysis framework that can be applied consistently across many different and complex remediation settings. A simple numerical example is presented to demonstrate the proposed methodology. The results showed the importance of using an integrated approach for decision-making considering both costs and risks. Future work should focus on the application of the methodology to a variety of complex field conditions to better evaluate the proposed methodology.     

On a Holistic Modeling Approach for Managing Carbon Emission Ecosystems

Effective use of historical volumes of heterogeneous and multidimensional data is a major challenge, especially projects associated with potential applications of carbon emission ecosystems. Data science in these applications becomes tedious when such varied data are accumulated and or distributed in multiple domains. Design, development, and implementation of sustainable geological storages are crucial for managing carbon dioxide (CO₂) emissions and its modeling process. The purpose of the research is to address major challenges and how best a robust “ontology-based multidimensional data warehousing and mining” approach can resolve issues associated with carbon ecosystems. The conceptualized relationships deduced among multiple domains, integration of domain ontologies, data mining, visualization, and interpretation artefacts are highlights of the study. Several data, plot, and map views are extracted from metadata storage for interpreting new knowledge on carbon emissions. Statistical mining models describe data attributes’ correlations, patterns, and trends that can help in predicting future forecast of CO₂ emissions worldwide.     

Polynomial Surrogates for Open-Channel Flows in Random Steady State

Assessing epistemic uncertainties is considered as a milestone for improving numerical predictions of a dynamical system. In hydrodynamics, uncertainties in input parameters translate into uncertainties in simulated water levels through the shallow water equations. We investigate the ability of generalized polynomial chaos (gPC) surrogate to evaluate the probabilistic features of water level simulated by a 1-D hydraulic model (MASCARET) with the same accuracy as a classical Monte Carlo method but at a reduced computational cost. This study highlights that the water level probability density function and covariance matrix are better estimated with the polynomial surrogate model than with a Monte Carlo approach on the forward model given a limited budget of MASCARET evaluations. The gPC-surrogate performance is first assessed on an idealized channel with uniform geometry and then applied on the more realistic case of the Garonne River (France) for which a global sensitivity analysis using sparse least-angle regression was performed to reduce the size of the stochastic problem. For both cases, Galerkin projection approximation coupled to Gaussian quadrature that involves a limited number of forward model evaluations is compared with least-square regression for computing the coefficients when the surrogate is parameterized with respect to the local friction coefficient and the upstream discharge. The results showed that a gPC-surrogate with total polynomial degree equal to 6 requiring 49 forward model evaluations is sufficient to represent the water level distribution (in the sense of the \(\ell _2\) norm), the probability density function and the water level covariance matrix for further use in the framework of data assimilation. In locations where the flow dynamics is more complex due to bathymetry, a higher polynomial degree is needed to retrieve the water level distribution. The use of a surrogate is thus a promising strategy for uncertainty quantification studies in open-channel flows and should be extended to unsteady flows. It also paves the way toward cost-effective ensemble-based data assimilation for flood forecasting and water resource management.     

A Knowledge-Based Approach to the Assessment of Watershed Condition

The Ecosystem Management Decision Support (EMDS) system is an application framework for knowledge-based decision support of ecological assessments. EMDS integrates geographic information system and knowledge base system technologies to provide an analytical tool for environmental assessment and monitoring. The basic objective of EMDS is to improve the quality and completeness of environmental assessments and the efficiency with which they are performed. The USDA Forest Service and the U.S. Environmental Protection Agency have cooperatively developed an EMDS knowledge base for watershed condition assessment. Specifically, this knowledge base evaluates watershed processes, patterns, general effects of human activity, and fisheries habitat suitability. Such assessments are based on spatially explicit input data concerning current conditions and reference conditions which are subsequently interpreted by user-defined "fuzzy" membership functions. In this paper we describe basic components of our knowledge base for assessing watershed condition and illustrate its application within northern Idaho and northwestern Montana.     

Cluster analysis of Delhi's ambient air quality data

The purpose of this study was to study the spatial patterns of ambient air quality in Delhi in the absence of extensive datasets needed for space-time modeling. A spatial classification was attempted on the basis of ambient air quality data of nine years (1998 is latest year for which published data were available) for three criteria pollutants--nitrogen dioxide, sulfur dioxide, and suspended particulate matter. Monitoring stations take 24-hour samples twice a week. Published monthly average concentration data were used in this study. A hierarchical agglomerative algorithm using the average linkage between groups method and the Euclidean distance metric was used. Cluster analysis indicated that till 1998, by and large, two distinct classes existed. The results of cluster analysis prompted an investigation of systematic biases in the monitored data. No statistically significant differences in the mean concentration of all pollutants were observed between stations belonging to different land-use types (residential and industrial). This fact would be useful, if and when the authorities consider modifying the network or expanding it in Delhi. The results also support the recommendation that Delhi have a uniform standard across all areas. This study has provided a methodology for Indian researchers and practitioners to do an exploratory study of spatial patterns of air pollution and data quality issues in Indian cities using the National Ambient Air Quality Monitoring System data.