Emerging Tools for Continuous Nutrient Monitoring Networks: Sensors Advancing Science and Water Resources Protection

Sensors and enabling technologies are becoming increasingly important tools for water quality monitoring and associated water resource management decisions. In particular, nutrient sensors are of interest because of the well‐known adverse effects of nutrient enrichment on coastal hypoxia, harmful algal blooms, and impacts to human health. Accurate and timely information on nutrient concentrations and loads is integral to strategies designed to minimize risk to humans and manage the underlying drivers of water quality impairment. Using nitrate sensors as the primary example, we highlight the types of applications in freshwater and coastal environments that are likely to benefit from continuous, real‐time nutrient data. The concurrent emergence of new tools to integrate, manage, and share large datasets is critical to the successful use of nutrient sensors and has made it possible for the field of continuous monitoring to rapidly move forward. We highlight several near‐term opportunities for federal agencies, as well as the broader scientific and management community, that will help accelerate sensor development, build and leverage sites within a national network, and develop open data standards and data management protocols that are key to realizing the benefits of a large‐scale, integrated monitoring network. Investing in these opportunities will provide new information to guide management and policies designed to protect and restore our nation's water resources.     

Supporting Diverse Data Providers in the Open Water Data Initiative: Communicating Water Data Quality and Fitness of Use

Shared, trusted, timely data are essential elements for the cooperation needed to optimize economic, ecologic, and public safety concerns related to water. The Open Water Data Initiative (OWDI) will provide a fully scalable platform that can support a wide variety of data from many diverse providers. Many of these will be larger, well‐established, and trusted agencies with a history of providing well‐documented, standardized, and archive‐ready products. However, some potential partners may be smaller, distributed, and relatively unknown or untested as data providers. The data these partners will provide are valuable and can be used to fill in many data gaps, but can also be variable in quality or supplied in nonstandardized formats. They may also reflect the smaller partners' variable budgets and missions, be intermittent, or of unknown provenance. A challenge for the OWDI will be to convey the quality and the contextual “fitness” of data from providers other than the most trusted brands. This article reviews past and current methods for documenting data quality. Three case studies are provided that describe processes and pathways for effective data‐sharing and publication initiatives. They also illustrate how partners may work together to find a metadata reporting threshold that encourages participation while maintaining high data integrity. And lastly, potential governance is proposed that may assist smaller partners with short‐ and long‐term participation in the OWDI.     

A spatial model approach for assessing windbreak growth and carbon stocks

Agroforestry, the deliberate integration of trees into agricultural operations, sequesters carbon (C) while providing valuable services on agricultural lands. However, methods to quantify present and projected C stocks in these open-grown woody systems are limited. As an initial step to address C accounting in agroforestry systems, a spatial Markov random field model for predicting the natural logarithm (log) of the mean aboveground volume of green ash ( Marsh.) within a shelterbelt, referred to as the log of aboveground volume, was developed using data from an earlier study and web-available soil and climate information. Windbreak characteristics, site, and climate variables were used to model the large-scale trend of the log of aboveground volume. The residuals from this initial model were correlated among sites up to 24 km from a point of interest. Therefore, a spatial dependence parameter was used to incorporate information from sites within 24 km into the prediction of the log of the aboveground volume. Age is an important windbreak characteristic in the model. Thus, the log of aboveground volume can be predicted for a given windbreak age and for values of other explanatory variables associated with a site of interest. Such predictions can be exponentiated to obtain predictions of aboveground volume for windbreaks without repeated inventory. With the capability of quantifying uncertainty, the model has the potential for large regional planning efforts and C stock assessments for many deciduous tree species used in windbreaks and riparian buffers once it is calibrated.     

Integrating Environmental and Socio-Economic Indicators of a Linked Catchment–Coastal System Using Variable Environmental Intensity

Can we develop land use policy that balances the conflicting views of stakeholders in a catchment while moving toward long term sustainability? Adaptive management provides a strategy for this whereby measures of catchment performance are compared against performance goals in order to progressively improve policy. However, the feedback loop of adaptive management is often slow and irreversible impacts may result before policy has been adapted. In contrast, integrated modelling of future land use policy provides rapid feedback and potentially improves the chance of avoiding unwanted collapse events. Replacing measures of catchment performance with modelled catchment performance has usually required the dynamic linking of many models, both biophysical and socio-economic—and this requires much effort in software development. As an alternative, we propose the use of variable environmental intensity (defined as the ratio of environmental impact over economic output) in a loose coupling of models to provide a sufficient level of integration while avoiding significant effort required for software development. This model construct was applied to the Motueka Catchment of New Zealand where several biophysical (riverine water quantity, sediment, E. coli faecal bacteria, trout numbers, nitrogen transport, marine productivity) models, a socio-economic (gross output, gross margin, job numbers) model, and an agent-based model were linked. An extreme set of land use scenarios (historic, present, and intensive) were applied to this modelling framework. Results suggest that the catchment is presently in a near optimal land use configuration that is unlikely to benefit from further intensification. This would quickly put stress on water quantity (at low flow) and water quality (E. coli). To date, this model evaluation is based on a theoretical test that explores the logical implications of intensification at an unlikely extreme in order to assess the implications of likely growth trajectories from present use. While this has largely been a desktop exercise, it would also be possible to use this framework to model and explore the biophysical and economic impacts of individual or collective catchment visions. We are currently investigating the use of the model in this type of application.     

Variability in the content and composition of alkaloid found in Canadian ergot. iii. triticale and barley∗

Abstract

The total alkaloid content and individual alkaloid composition were determined by colorimetry and high performance liquid chromatography, respectively, for Canadian triticale and barley ergot (Claviceps purpurea). The total alkaloid content was highly variable between individual sclerotia from the same or different sources and ranged from 0.042 to 0.752% for triticale and from 0.082 to 1.04% for barley; average values were 0.239% for Ottawa triticale, 0.289% for Prairie triticale, and 0.264% for barley. Ergocristine and its isomer ergocristinine were the major constituents in both grains. On average, Canadian ergot pooled from rye, wheat, triticale, and barley consists of ergocristine (31%), ergocristinine (13%), ergocristinine (13%), ergotamine (17%), ergotaminine (8%), ergocryptine (5%), ergocryptinine (3%), ergo‐metrine (5%), ergometrinine (2%), ergosine (4%), ergosinine (2%), ergocornine (4%), ergocorninine (2%), and unidentified alkaloids (3%) and an average total alkaloid content of 0.236%. With the exception of rye and barley ergot from the maritime regions, Canadian rye, wheat, triticale, and barley ergot is fairly uniform in individual alkaloid composition.     

Environmental Awareness, Economic Orientation, and Farming Practices: A Comparison of Organic and Conventional Farmers

/ This study examines similarities and differences between organic and conventional farmers. We explore the factors that underlie farmers' conservation attitudes and behaviors, including demographic and farm characteristics, awareness of and concern for environmental problems associated with agriculture, economic orientation toward farming, and self-reported conservation practices. A series of intensive personal interviews was conducted with 25 farmers in Washtenaw County, Michigan, USA, using both qualitative and quantitative survey methods. The findings indicate that both groups of farmers share a concern for the economic risks associated with farming, although the organic farmers reported a significantly greater concern for long-term sustainability and a greater willingness to incur present risk to gain future benefits. Organic farmers expressed a greater awareness of and concern for environmental problems associated with agriculture. Organic farmers also scored significantly higher on a multifaceted measure of conservation practices, although both groups had a fairly high adoption rate. Implications of these findings are discussed, relative to economic risks of farming, implications for new farmers, effectiveness of conservation education and government programs, and impact of farm size and crop diversity.KEY WORDS: Environmental attitudes; Conservation behaviors; Organic farming; Agricultural sustainability     

Development of a high-efficiency rotary dryer for sewage sludge

A rotary drum dryer having an internal rotating body was designed and tested in this study. It was shown that the developed dryer is effective for drying sewage sludge. The best operating conditions in the dryer were low energy input and almost 10% moisture content. The conditions are 255°C for the rotary drum temperature, 17 min for the sludge residence time, and 55 kg/m³ h for the dryer load. Under these conditions, the drying efficiency was 84.8%. The average diameter of dried sludge was less than 8 mm, and the weight reduction rate was 80%. Parametric screening studies achieved the following results. The drying efficiency increased with the increase of the internal temperature and the sludge residence time in the rotary drum, while the drying efficiency decreased when increasing the dryer load. In addition, it was shown that NH₃ and CO₂ were the primary components released from the sewage sludge drying process. The amounts of both of these components increased when the rotary drum temperature was increased.     

Process design for the environment: A multi-objective framework under uncertainty

Designing chemical processes for the environment requires consideration of several indexes of environmental impact including ozone depletion, global warming potentials, human and aquatic toxicity, photochemical oxidation, and acid rain potentials. Current methodologies, such as the generalized waste reduction algorithm (WAR), provide a first step towards evaluating these impacts. However, to address the issues of accuracy and the relative weights of these impact indexes, one must consider the problem of uncertainties. Environmental impacts must also be weighted and balanced against other concerns, such as their cost and long-term sustainability. These multiple, often conflicting, goals pose a challenging and complex optimization problem, requiring multi-objective optimization under uncertainty. This paper will address the problem of quantifying and analyzing the various objectives involved in process design for the environment. Towards this goal, we proposed a novel multi-objective optimization framework under uncertainty. This framework is based on new and efficient algorithms for multi-objective optimization and for uncertainty analysis. This approach finds a set of potentially optimal designs where trade-offs can be explicitly identified, unlike cost-benefit analysis, which deals with multiple objectives by identifying a single fundamental objective and then converting all the other objectives into this single currency. A benchmark process for hydrodealkylation (HDA) of toluene to produce benzene modeled in the ASPEN simulator is used to illustrate the usefulness of the approach in finding environmentally friendly and cost-effective designs under uncertainty.