Metal concentrations in used engine oils: Relevance to site assessments of soils

Used engine oils contain metals, which upon entering soils may pose risks to human health or the environment. In this study, previously published concentrations of 23 metals in 213 used engine oil samples from the early 1970s to the mid-1990s are statistically evaluated. Neat (100%) used engine oils were found to contain relatively high concentrations of lead, calcium, and zinc, attributable to piston blow-by of leaded gasoline, calcium salt detergent additives, and zinc-bearing anti-corrosion/anti-oxidation additives, respectively. Wear metal concentrations were lower. The lead concentration in used engine oils in the U.S. declined between the 1970s and early 1990s, potentially providing a basis to constrain the “age” of used engine oil(s) in soils. The concentrations of 23 metals in used engine oils were compared to soil risk benchmarks in 15 representative jurisdictions in the U.S., Canada, Australia, and Europe. The maximum concentrations in neat (100%) used engine oil of eight metals – Be, Co, Fe, Mn, Ni, Se, Ag, and Ti – were lower than their collective minimum benchmarks in soils for the jurisdictions surveyed, indicating their concentrations in soils could not be reasonably expected to exceed any soil benchmarks. Nine metals (As, Ba, Cd, Cr, Cu, Pb, Sn, V and Zn), but particularly arsenic, cadmium, lead, tin, and zinc, were identified as potential contaminants of concern (PCOC) for soils from locations impacted with used engine oils, owing to their higher median concentrations (i.e., 2.5, 1.4, 1038, 5.0, and 922 mg/kg in oil, respectively) relative to most soil benchmarks. Site-specific benchmarks and metal concentrations at reasonable oil in soil concentrations require consideration when developing the suite of PCOC metal analytes for conducting site assessments of soils impacted by used engine oil.     

Social justice and mobility in coastal Louisiana,USA

Regional Environmental Change - Louisiana faces extensive coastal land loss which threatens the livelihoods of marginalized populations. These groups have endured extreme disruptive events in the...     

Research priorities for managing the impacts and dependencies of business upon food,energy, water and the environment

Delivering access to sufficient food, energy and water resources to ensure human wellbeing is a major concern for governments worldwide. However, it is crucial to account for the ‘nexus’ of interactions between these natural resources and the consequent implications for human wellbeing. The private sector has a critical role in driving positive change towards more sustainable nexus management and could reap considerable benefits from collaboration with researchers to devise solutions to some of the foremost sustainability challenges of today. Yet opportunities are missed because the private sector is rarely involved in the formulation of deliverable research priorities. We convened senior research scientists and influential business leaders to collaboratively identify the top forty questions that, if answered, would best help companies understand and manage their food-energy-water-environment nexus dependencies and impacts. Codification of the top order nexus themes highlighted research priorities around development of pragmatic yet credible tools that allow businesses to incorporate nexus interactions into their decision-making; demonstration of the business case for more sustainable nexus management; identification of the most effective levers for behaviour change; and understanding incentives or circumstances that allow individuals and businesses to take a leadership stance. Greater investment in the complex but productive relations between the private sector and research community will create deeper and more meaningful collaboration and cooperation.     

Modeling and data assessment of longitudinal salinity in a low-gradient estuarine river

Continuous data of vertical-profile salinity were analyzed for four stations located successively upriver in a macrotidal estuary, the lower St. Johns River (Northeast Florida, USA). The data analysis confirmed well-mixed salinity conditions in the river with at most 1.3 ppt of vertical variability at Dames Point (river km 20), where the main variations of salinity are along the longitudinal axis of the river. Given the well-mixed salinity conditions and dominant horizontal structure of salinity variations in the river, we present and apply a barotropic, two-dimensional modeling approach for hydrodynamic-salinity transport simulation in the lower St. Johns River. When properly forced by offshore surge, high-resolution wind fields and freshwater river inflows, the model replicated the salinity measurements remarkably well, including the separation into tidal and sub-tidal components. The data and model results show that, at times, offshore winds and surge can be more influential on longitudinal salinity variations than local winds over the river. We demonstrate the importance of using proper boundary conditions to force the model relative to the minimal sensitivity of the model to parameter adjustment of horizontal mixing and uncertainty-based perturbation of wind and inflow forcings.     

Ecosystem responses to climate change at a Low Arctic and a High Arctic long-term research site

Long-term measurements of ecological effects of warming are often not statistically significant because of annual variability or signal noise. These are reduced in indicators that filter or reduce the noise around the signal and allow effects of climate warming to emerge. In this way, certain indicators act as medium pass filters integrating the signal over years-to-decades. In the Alaskan Arctic, the 25-year record of warming of air temperature revealed no significant trend, yet environmental and ecological changes prove that warming is affecting the ecosystem. The useful indicators are deep permafrost temperatures, vegetation and shrub biomass, satellite measures of canopy reflectance (NDVI), and chemical measures of soil weathering. In contrast, the 18-year record in the Greenland Arctic revealed an extremely high summer air-warming of 1.3 °C/decade; the cover of some plant species increased while the cover of others decreased. Useful indicators of change are NDVI and the active layer thickness.     

Anticipatory capacity in response to global change across an extreme elevation gradient in the Ica Basin,Peru

Regional Environmental Change - Mountainous areas with extreme elevation gradients and corresponding ranges of biophysical and socioeconomic conditions are highly vulnerable to global change. We...     

Analysis of short-term ozone and PM2.5 measurements: Characteristics and relationships for air sensor messaging

Air quality sensors are becoming increasingly available to the general public, providing individuals and communities with information on fine-scale, local air quality in increments as short as 1 min. Current health studies do not support linking 1-min exposures to adverse health effects; therefore, the potential health implications of such ambient exposures are unclear. The U.S. Environmental Protection Agency (EPA) establishes the National Ambient Air Quality Standards (NAAQS) and Air Quality Index (AQI) on the best science available, which typically uses longer averaging periods (e.g., 8 hr; 24 hr). Another consideration for interpreting sensor data is the variable relationship between pollutant concentrations measured by sensors, which are short-term (1 min to 1 hr), and the longer term averages used in the NAAQS and AQI. In addition, sensors often do not meet federal performance or quality assurance requirements, which introduces uncertainty in the accuracy and interpretation of these readings. This article describes a statistical analysis of data from regulatory monitors and new real-time technology from Village Green benches to inform the interpretation and communication of short-term air sensor data. We investigate the characteristics of this novel data set and the temporal relationships of short-term concentrations to 8-hr average (ozone) and 24-hr average (PM_2.5) concentrations to examine how sensor readings may relate to the NAAQS and AQI categories, and ultimately to inform breakpoints for sensor messages. We consider the empirical distributions of the maximum 8-hr averages (ozone) and 24-hr averages (PM_2.5) given the corresponding short-term concentrations, and provide a probabilistic assessment. The result is a robust, empirical comparison that includes events of interest for air quality exceedances and public health communication. Concentration breakpoints are developed for short-term sensor readings such that, to the extent possible, the related air quality messages that are conveyed to the public are consistent with messages related to the NAAQS and AQI.

Implications: Real-time sensors have the potential to provide important information about fine-scale current air quality and local air quality events. The statistical analysis of short-term regulatory and sensor data, coupled with policy considerations and known health effects experienced over longer averaging times, supports interpretation of such short-term data and efforts to communicate local air quality.     

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.     

Are sustainable cities “happy” cities? Associations between sustainable development and human well-being in urban areas of the United States

In this paper, we assess the associations between self-reported happiness, measured using the Gallup Healthways Well-Being Index (2012), and four US city sustainability indices: the Green City Index (2011), Our Green Cities (2012), Popular Science US City Rankings (2008) and the SustainLane US Green City Rankings (2007). Based on the examination of nonparametric, rank-based correlations, we found positive associations between sustainable development and happiness on all scales and statistically significant correlations for two of the four SD indices. Results support previous research, emphasize the value of explicit consideration of happiness when measuring urban sustainability and highlight the need for future research that assesses the influence of specific subsystems of urban development on self-reported happiness.