The LUCAS topsoil database and derived information on the regional variability of cropland topsoil properties in the European Union

Approximately 20,000 topsoil samples were collected in 25 European Union (EU) Member States (EU-27 except Bulgaria and Romania) with the aim to produce the first coherent pan-European physical and chemical topsoil database, which can serve as baseline information for an EU wide harmonized soil monitoring. The soil sampling was undertaken within the frame of the Land Use/Land Cover Area Frame Survey (LUCAS), a project to monitor changes in the management and character of the land surface of the EU. Soil samples have been analysed for basic soil properties, including particle size distribution, pH, organic carbon, carbonates, NPK, cation exchange capacity (CEC) and multispectral signatures. Preliminary studies show the outstanding potential of the dataset for enhancing the knowledge base on soils in the EU. The current paper provides an introduction to the LUCAS Topsoil 2009 project and provides an example of data applicability for cropland assessment by highlighting initial results for regional and national comparisons.     

Development of visibility forecasting modeling framework for the Lower Fraser Valley of British Columbia using Canada’s Regional Air Quality Deterministic Prediction System

Visibility degradation, one of the most noticeable indicators of poor air quality, can occur despite relatively low levels of particulate matter when the risk to human health is low. The availability of timely and reliable visibility forecasts can provide a more comprehensive understanding of the anticipated air quality conditions to better inform local jurisdictions and the public. This paper describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada’s operational Regional Air Quality Deterministic Prediction System (RAQDPS) for the Lower Fraser Valley of British Columbia. A baseline model (GM-IMPROVE) was constructed using the revised IMPROVE algorithm based on unprocessed forecasts from the RAQDPS. Three additional prototypes (UMOS-HYB, GM-MLR, GM-RF) were also developed and assessed for forecast performance of up to 48 hr lead time during various air quality and meteorological conditions. Forecast performance was assessed by examining their ability to provide both numerical and categorical forecasts in the form of 1-hr total extinction and Visual Air Quality Ratings (VAQR), respectively. While GM-IMPROVE generally overestimated extinction more than twofold, it had skill in forecasting the relative species contribution to visibility impairment, including ammonium sulfate and ammonium nitrate. Both statistical prototypes, GM-MLR and GM-RF, performed well in forecasting 1-hr extinction during daylight hours, with correlation coefficients (R) ranging from 0.59 to 0.77. UMOS-HYB, a prototype based on postprocessed air quality forecasts without additional statistical modeling, provided reasonable forecasts during most daylight hours. In terms of categorical forecasts, the best prototype was approximately 75 to 87% correct, when forecasting for a condensed three-category VAQR. A case study, focusing on a poor visual air quality yet low Air Quality Health Index episode, illustrated that the statistical prototypes were able to provide timely and skillful visibility forecasts with lead time up to 48 hr.

Implications: This study describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada’s operational Regional Air Quality Deterministic Prediction System. The main applications include tourism and recreation planning, input into air quality management programs, and educational outreach. Visibility forecasts, when supplemented with the existing air quality and health based forecasts, can assist jurisdictions to anticipate the visual air quality impacts as perceived by the public, which can potentially assist in formulating the appropriate air quality bulletins and recommendations.     

Socio-environmental consideration of phosphorus flows in the urban sanitation chain of contrasting cities

Understanding how cities can transform organic waste into a valuable resource is critical to urban sustainability. The capture and recycling of phosphorus (P), and other essential nutrients, from human excreta is particularly important as an alternative organic fertilizer source for agriculture. However, the complex set of socio-environmental factors influencing urban human excreta management is not yet sufficiently integrated into sustainable P research. Here, we synthesize information about the pathways P can take through urban sanitation systems along with barriers and facilitators to P recycling across cities. We examine five case study cities by using a sanitation chains approach: Accra, Ghana; Buenos Aires, Argentina; Beijing, China; Baltimore, USA; and London, England. Our cross-city comparison shows that London and Baltimore recycle a larger percentage of P from human excreta back to agricultural lands than other cities, and that there is a large diversity in socio-environmental factors that affect the patterns of recycling observed across cities. Our research highlights conditions that may be “necessary but not sufficient” for P recycling, including access to capital resources. Path dependencies of large sanitation infrastructure investments in the Global North contrast with rapidly urbanizing cities in the Global South, which present opportunities for alternative sanitation development pathways. Understanding such city-specific social and environmental barriers to P recycling options could help address multiple interacting societal objectives related to sanitation and provide options for satisfying global agricultural nutrient demand.

     

Industrial pollution,spatial stigma and economic decline: the case of Asopos river basin through the lens of local small business owners

This paper explores the notion of environmentally induced spatial stigma through an analysis of data from interviews across public attitudes to pollution within the Asopos river basin in central Greece. The area has a 40 year plus history of legal and illicit industrial waste disposal and public debate on the associated environmental degradation. The study focuses on the perceptions and beliefs of a sector of the community likely to be directly and negatively affected by stigma, that is small business owners in the tourism and hospitality sector. The qualitative analysis explores awareness and viewpoints on environmental degradation and water quality within the local context, implications for the local economy and the individual's own enterprise, views on industrial environmental management as well as corporate responsibility and future prospects for the environmental problems of Asopos. Findings reveal a noticeable variation in views on industrial pollution and ecosystem deterioration among the respondents, but overall a strong environmentally induced stigmatization of the area. They also uncover an information asymmetry and lack of credible commitment by government bodies and industry members in disclosing accurate information, a situation likely to increase speculation and uncertainty within the community. The paper concludes by addressing implications of the findings to policy-making and managerial considerations, along with future research perspectives which aim to increase considerations of sustainability aspects for local development.     

Methane biofiltration in the presence of ethanol vapor under steady and transient state conditions: an experimental study

Methane (CH₄) removal in the presence of ethanol vapors was performed by a stone-based bed and a hybrid packing biofilter in parallel. In the absence of ethanol, a methane removal efficiency of 55 ± 1% was obtained for both biofilters under similar CH₄ inlet load (IL) of 13 ± 0.5 g_CH4 m^?3 h^?1 and an empty bed residence time (EBRT) of 6 min. The results proved the key role of the bottom section in both biofilters for simultaneous removal of CH₄ and ethanol. Ethanol vapor was completely eliminated in the bottom sections for an ethanol IL variation between 1 and 11 g_ethanol m^?3 h^?1. Ethanol absorption and accumulation in the biofilm phase as well as ethanol conversion to CO₂ contributed to ethanol removal efficiency of 100%. In the presence of ethanol vapor, CO₂ productions in the bottom section increased almost fourfold in both biofilters. The ethanol concentration in the leachate of the biofilter exceeding 2200 g_ethanol m^?3 _leachate in both biofilters demonstrated the excess accumulation of ethanol in the biofilm phase. The biofilters responded quickly to an ethanol shock load followed by a starvation with 20% decrease of their performance. The return to normal operations in both biofilters after the transient conditions took less than 5 days. Unlike the hybrid packing biofilter, excess pressure drop (up to 1.9 cmH₂O m^?1) was an important concern for the stone bed biofilter. The biomass accumulation in the bottom section of the stone bed biofilter contributed to 80% of the total pressure drop. However, the 14-day starvation reduced the pressure drop to 0.25 cmH₂O m^?1.     

A Comprehensive Python Toolkit for Accessing High‐Throughput Computing to Support Large Hydrologic Modeling Tasks

The National Flood Interoperability Experiment (NFIE) was an undertaking that initiated a transformation in national hydrologic forecasting by providing streamflow forecasts at high spatial resolution over the whole country. This type of large‐scale, high‐resolution hydrologic modeling requires flexible and scalable tools to handle the resulting computational loads. While high‐throughput computing (HTC) and cloud computing provide an ideal resource for large‐scale modeling because they are cost‐effective and highly scalable, nevertheless, using these tools requires specialized training that is not always common for hydrologists and engineers. In an effort to facilitate the use of HTC resources the National Science Foundation (NSF) funded project, CI‐WATER, has developed a set of Python tools that can automate the tasks of provisioning and configuring an HTC environment in the cloud, and creating and submitting jobs to that environment. These tools are packaged into two Python libraries: CondorPy and TethysCluster. Together these libraries provide a comprehensive toolkit for accessing HTC to support hydrologic modeling. Two use cases are described to demonstrate the use of the toolkit, including a web app that was used to support the NFIE national‐scale modeling.     

Designing and Implementing a Network for Sensing Water Quality and Hydrology across Mountain to Urban Transitions

Water resources are increasingly impacted by growing human populations, land use, and climate changes, and complex interactions among biophysical processes. In an effort to better understand these factors in semiarid northern Utah, United States, we created a real‐time observatory consisting of sensors deployed at aquatic and terrestrial stations to monitor water quality, water inputs, and outputs along mountain to urban gradients. The Gradients Along Mountain to Urban Transitions (GAMUT) monitoring network spans three watersheds with similar climates and streams fed by mountain winter‐derived precipitation, but that differ in urbanization level, land use, and biophysical characteristics. The aquatic monitoring stations in the GAMUT network include sensors to measure chemical (dissolved oxygen, specific conductance, pH, nitrate, and dissolved organic matter), physical (stage, temperature, and turbidity), and biological components (chlorophyll‐a and phycocyanin). We present the logistics of designing, implementing, and maintaining the network; quality assurance and control of numerous, large datasets; and data acquisition, dissemination, and visualization. Data from GAMUT reveal spatial differences in water quality due to urbanization and built infrastructure; capture rapid temporal changes in water quality due to anthropogenic activity; and identify changes in biological structure, each of which are demonstrated via case study datasets.     

Comparison of Stream Invertebrate Response Models for Bioassessment Metrics1

Waite, Ian R., Jonathan G. Kennen, Jason T. May, Larry R. Brown, Thomas F. Cuffney, Kimberly A. Jones, and James L. Orlando, 2012. Comparison of Stream Invertebrate Response Models for Bioassessment Metrics. Journal of the American Water Resources Association (JAWRA) 48(3): 570-583. DOI: 10.1111/j.1752-1688.2011.00632.x Abstract: We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R² from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R² values than RICHTOL for the two regions tested. Modeled O/E R² values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.     

Enhanced resistance of yeast mutants deficient in low-affinity iron and zinc transporters to stannous-induced toxicity

Tin or stannous (Sn²⁺) compounds are used as catalysts, stabilizers in plastic industries, wood preservatives, agricultural biocides and nuclear medicine. In order to verify the Sn²⁺ up-take and toxicity in yeast cells we utilized a multi-elemental analysis known as particle-induced X-ray emission (PIXE) along with cell survival assays and quantitative real-time PCR. The detection of Sn²⁺ by PIXE was possible only in yeast cells in stationary phase of growth (STAT cells) that survive at 25 mM Sn²⁺ concentration. Yeast cells in exponential phase of growth (LOG cells) tolerate only micro-molar Sn²⁺ concentrations that result in intracellular concentration below of the method detection limit. Our PIXE analysis showed that STAT XV185-14c yeast cells demonstrate a significant loss of intracellular elements such as Mg, Zn, S, Fe and an increase in P levels after 1 h exposure to SnCl₂. The survival assay showed enhanced tolerance of LOG yeast cells lacking the low-affinity iron and zinc transporters to stannous treatment, suggesting the possible involvement in Sn²⁺ uptake. Moreover, our qRT-PCR data showed that Sn²⁺ treatment could generate reactive oxygen species as it induces activation of many stress-response genes, including SOD1, YAP1, and APN1.     

Interference with xenobiotic metabolic activity by the commonly used vehicle solvents dimethylsulfoxide and methanol in zebrafish (Danio rerio) larvae but not Daphnia magna

Organic solvents, such as dimethylsulfoxide (DMSO) and methanol are widely used as vehicles to solubilise lipophilic test compounds in toxicity testing. However, the effects of such solvents upon innate detoxification processes in aquatic organisms are poorly understood. This study assessed the effect of solvent exposure upon cytochrome P450 (CYP)-mediated xenobiotic metabolism in Daphnia magna and zebrafish larvae (4 d post fertilisation). Adult D. magna were demonstrated to have a low, but detectable, metabolism of ethoxyresorufin in vivo and this activity was not modulated by pre-exposure to DMSO or methanol (24 h, up to 0.1% and 0.05% v/v, respectively). In contrast, the metabolism of ethoxyresorufin in zebrafish larvae was significantly reduced by both solvents (0.1% and 0.05% v/v, respectively) after 24 h of exposure. In zebrafish, these observed decreases in activity towards ethoxyresorufin were accompanied by decreased expression of a variety of genes coding for drug metabolising enzymes (corresponding to CYP1, CYP2, CYP3 and UDP-glucuronyl transferase [UGT] family enzymes), measured by quantitative PCR. Reduction of gene expression and CYP1 enzyme activities by methanol (0.05% v/v) in zebrafish larvae was partially reversed by co-exposure with Aroclor 1254 (100 μg L⁻¹). Overall this study suggests that relatively low concentrations of organic solvents can impact upon the biotransformation of certain xenobiotics in zebrafish larvae, and that this warrants consideration when assessing compounds for metabolism and toxicity in this species.