Heat wave vulnerability and exposure mapping for Osaka City,Japan

Heat waves and heat-related stresses are increasing environmental concerns in urban areas. The impact of heat waves is dependent on the intensity and duration of each event and on underlying environmental and socio-demographic factors which influence population vulnerability. In order to develop effective adaptation strategies, it is important to develop a method to clearly identify the most vulnerable areas based on these factors. The purpose of this study is to develop and map a heat wave vulnerability index combined with heat exposure analysis to identify areas where interventions can be targeted. The vulnerability index was derived from a principle component analysis of eight key variables that influence heat wave vulnerability. Eight proxy measures of vulnerability were obtained from 2010 census and land-use data for the 1904 census districts of Osaka City. Three principle components explained >77 % of the variance (age, employment and education; social isolation; density and lack of green space). The components were combined and weighted to produce a vulnerability score for each census district. The vulnerability scores ranged from 0 to 106, were categorised into eight vulnerability levels and were overlaid with fine-scale air temperature observations. The resulting output identified the distribution of population vulnerability and exposure. This assessment of vulnerability, combining exposure and sensitivity components, can provide precedent for efficient, targeted action to be taken to reduce the impact of heat waves at present and under climate change.     

Determinants of Individual Attitudes Toward Animal Welfare-Friendly Food Products

Animal welfare involves societal and human values, ethical concerns and moral considerations since it incorporates the belief of what is right or what is wrong in animal treatment and care. This paper aims to ascertain whether the different dimensions of individual attitudes toward animal welfare in food choices may be characterized by general human values, as identified by Schwartz. For this purpose, an EU-wide survey was carried out, covering almost 2500 nationally representative individuals from five European countries. Compared with the previous literature this study shows a twofold novelty: (1) it develops a general framework to link individual enduring beliefs and attitudes toward animal welfare attributes in food choices; (2) the framework is analyzed within a broad-based cross-country study. Our empirical results prove that human values related to self-transcendence are strongly associated to overall animal welfare attitudes and especially to those explicitly related to food choices, while values related to the spheres of self-enhancement and conservatism are significantly associated to less sensitive attitudes to animal welfare. Moreover, our results appear to indicate that a determinant of animal welfarism in food choices is potentially associated to individual concerns regarding food safety issues.     

Carbon emission reduction and cost–benefit of methane digester systems on hog farms in China

Three different sizes of hog farms were selected to analyze the carbon emissions reduction and the cost–benefit of three methane digester systems. The sizes of the digesters are 2,200, 2,200 and 800 m³, respectively. The sales of slaughter hogs from them are 50,000, 35,000 and 10,000 head, respectively. The carbon emissions reductions were 5,237, 4,017, and 1,334 tons, respectively. The results show that while the methane digester systems have a significant effect on carbon emissions reduction, it is difficult to operate the systems sustainably. If the carbon emissions reduction can be traded at high enough prices in the carbon offset markets, then the systems will be profitable and sustainable. Newly established China's domestic carbon offset market could provide this possibility, but more government support is needed. In addition, this study shows that scale economies make the digester adoption relatively more profitable for larger farms than smaller ones.     

Assessing local planning capacity to promote environmentally sustainable residential development

Smart growth and sustainability planning have, in recent years, become central issues in planning discourse. Scholars have argued that planning capacity at the local government level is critical for smart growth planning, and that planners have a fundamental role to play in advancing local and regional sustainability. In this paper, we investigate the extent to which local planning capacity enables communities to promote more sustainable, smart growth residential development. Based on a 2013 survey of 38 county and 53 municipal governments in the state of Wisconsin, USA, this study finds that the majority of the sample communities have permitted residential developments characterized as transit-oriented, New Urbanist, mixed use, infill developments, or conservation subdivisions as alternatives to low-density, automobile-dependent conventional developments. The study also finds that jurisdictions with higher planning capacities are more likely to overcome significant barriers to more sustainable residential development.     

Benchmarking Optical/Thermal Satellite Imagery for Estimating Evapotranspiration and Soil Moisture in Decision Support Tools

Generally, one expects evapotranspiration (ET) maps derived from optical/thermal Landsat and MODIS satellite imagery to improve decision support tools and lead to superior decisions regarding water resources management. However, there is lack of supportive evidence to accept or reject this expectation. We “benchmark” three existing hydrologic decision support tools with the following benchmarks: annual ET for the ET Toolbox developed by the United States Bureau of Reclamation, predicted rainfall‐runoff hydrographs for the Gridded Surface/Subsurface Hydrologic Analysis model developed by the U.S. Army Corps of Engineers, and the average annual groundwater recharge for the Distributed Parameter Watershed Model used by Daniel B. Stephens & Associates. The conclusion of this benchmark study is that the use of NASA/USGS optical/thermal satellite imagery can considerably improve hydrologic decision support tools compared to their traditional implementations. The benefits of improved decision making, resulting from more accurate results of hydrologic support systems using optical/thermal satellite imagery, should substantially exceed the costs for acquiring such imagery and implementing the remote sensing algorithms. In fact, the value of reduced error in estimating average annual groundwater recharge in the San Gabriel Mountains, California alone, in terms of value of water, may be as large as $1 billion, more than sufficient to pay for one new Landsat satellite.     

Augmenting Watershed Model Calibration with Incorporation of Ancillary Data Sources and Qualitative Soft Data Sources

Watershed simulation models such as the Soil & Water Assessment Tool (SWAT) can be calibrated using “hard data” such as temporal streamflow observations; however, users may find upon examination of model outputs, that the calibrated models may not reflect actual watershed behavior. Thus, it is often advantageous to use “soft data” (i.e., qualitative knowledge such as expected denitrification rates that observed time series do not typically exist) to ensure that the calibrated model is representative of the real world. The primary objective of this study is to evaluate the efficacy of coupling SWAT‐Check (a post‐evaluation framework for SWAT outputs) and IPEAT‐SD (Integrated Parameter Estimation and Uncertainty Analysis Tool‐Soft & hard Data evaluation) to constrain the bounds of soft data during SWAT auto‐calibration. IPEAT‐SD integrates 59 soft data variables to ensure SWAT does not violate physical processes known to occur in watersheds. IPEAT‐SD was evaluated for two case studies where soft data such as denitrification rate, nitrate attributed from subsurface flow to total discharge ratio, and total sediment loading were used to conduct model calibration. Results indicated that SWAT model outputs may not satisfy reasonable soft data responses without providing pre‐defined bounds. IPEAT‐SD provides an efficient and rigorous framework for users to conduct future studies while considering both soft data and traditional hard information measures in watershed modeling.     

Evaluating Infiltration Requirements for New Development Using Extreme Storm Transposition: A Case Study from Dane County,WI

Changes in land use and extreme rainfall trends can lead to increased flood vulnerability in many parts of the world, especially for urbanized watersheds. This study investigates the performance of existing stormwater management strategies for the Upper Yahara watershed in Dane County, WI to determine whether they are adequate to protect urban and suburban development from an extreme rainfall. Using extreme storm transposition, we model the performance of the stormwater infiltration practices required for new development under current county ordinances. We find during extreme rainfall the volume of post‐development runoff from impervious surfaces from a typical site would increase by over 55% over pre‐development conditions. We recommend the ordinance be strengthened to reduce vulnerability to flooding from future urban expansion and the likely increase in the magnitude and frequency of extreme storms.     

Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition

Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base‐flow conditions. Factors that affect instream biological components, based on the Index of Biotic Integrity (IBI), were also analyzed. Seasonal BRT models at two spatial scales (watershed and riparian buffered area [RBA]) for nitrite‐nitrate (NO₂‐NO₃), total Kjeldahl nitrogen, and total phosphorus (TP) and annual models for the IBI score were developed. Two primary factors — location within the watershed (i.e., geographic position, stream order, and distance to a downstream confluence) and percentage of urban land cover (both scales) — emerged as important predictor variables. Latitude and longitude interacted with other factors to explain the variability in summer NO₂‐NO₃ concentrations and IBI scores. BRT results also suggested that location might be associated with indicators of sources (e.g., land cover), runoff potential (e.g., soil and topographic factors), and processes not easily represented by spatial data indicators. Runoff indicators (e.g., Hydrological Soil Group D and Topographic Wetness Indices) explained a substantial portion of the variability in nutrient concentrations as did point sources for TP in the summer months. The results from our BRT approach can help prioritize areas for nutrient management in mixed‐use and heavily impacted watersheds.     

Monitoring of Phenol in Wastewater Bioremediation by HPLC

Bioremediation emphasizes the detoxification and destruction of toxic substances by microorganisms. Wastewater obtained from an industrial concern was solvent extracted with methyl alcohol and dichloromethane and analysed by GC/MS. Besides phenol, a large variety of organic compounds were detected. Under controlled laboratory conditions, the wastewater was innoculated with a mixed culture of microorganisms specially selected for their abilities to degrade phenol. Samples were collected at regular intervals from the stirred tank bioreactor and analysed for phenol by reverse phase HPLC with a C18 column. Results shows that from an initial phenol concentration of 987 ppm, slightly more than 50% was destroyed within 163 hours. The dry weight of the microorganisms and the plate count (CFU/ml) shows a steady increase from 0.5238 gms to 0.5355 gms and from 1.1E+9 to 1.94E+13 respectively over the same period. This suggested that the phenol was consumed by the microorganisms as the sole carbon source.