Tree growth inference and prediction from diameter censuses and ring widths.

Estimation of tree growth is based on sparse observations of tree diameter, ring widths, or increments read from a dendrometer. From annual measurements on a few trees (e.g., increment cores) or sporadic measurements from many trees (e.g., diameter censuses on mapped plots), relationships with resources, tree size, and climate are extrapolated to whole stands. There has been no way to formally integrate different types of data and problems of estimation that result from (1) multiple sources of observation error, which frequently result in impossible estimates of negative growth, (2) the fact that data are typically sparse (a few trees or a few years), whereas inference is needed broadly (many trees over many years), (3) the fact that some unknown fraction of the variance is shared across the population, and (4) the fact that growth rates of trees within competing stands are not independent. We develop a hierarchical Bayes state space model for tree growth that addresses all of these challenges, allowing for formal inference that is consistent with the available data and the assumption that growth is nonnegative. Prediction follows directly, incorporating the full uncertainty from inference with scenarios for "filling the gaps" for past growth rates and for future conditions affecting growth. An example involving multiple species and multiple stands with tree-ring data and up to 14 years of tree census data illustrates how different levels of information at the tree and stand level contribute to inference and prediction.     

Forecasting air quality time series using deep learning

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O₃) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O₃ exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours.

Implications: Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.     

Characterizing emissions from open burning of military food waste and ration packaging compositions

Emissions from open burning of military food waste and ration packaging compositions were characterized in response to health concerns from open burning disposal of waste, such as at military forward operating bases. Emissions from current and prototype Meals, Ready-to-Eat (MREs), and material options for their associated fiberboard packaging were quantified to assess contributions of the individual components. MREs account for 67–100% of the particulate matter (PM), volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), and polychlorinated dibenzo-p-dioxins and -furans (PCDDs/PCDFs) emissions when burned in unison with the current fiberboard container and liner. The majority of the particles emitted from these burns are of median diameter 2.5 µm (PM_2.5). Metal emission factors were similar regardless of waste composition. Measurements of VOCs and PAHs indicate that targeted replacement of MRE components may be more effective in reducing emissions than variation of fiberboard-packaging types. Despite MRE composition variation, equivalent emission factors for PM, PAH, VOC, and PCDD/PCDF were seen. Similarly, for fiberboard packaging, composition variations exhibited essentially equivalent PM, PAH, VOC, and PCDD/PCDF emission factors amongst themselves. This study demonstrated a composition-specific analysis of waste burn emissions, assessing the impact of waste component substitution using military rations.     

Energy Communication: Theory and Praxis Towards a Sustainable Energy Future

This essay comments and expands upon an emerging area of research, energy communication, that shares with environmental communication the fraught commitment to simultaneously study communication as an ordinary yet potentially transformative practice, and a strategic endeavour to catalyse change. We begin by defining and situating energy communication within ongoing work on the discursive dimensions of energy extraction, production, distribution, and consumption. We then offer three generative directions for future research related to energy transitions as communicative processes: analysing campaigns’ strategic efforts, critically theorizing energy’s transnational power dynamics, and theorizing the energy democracy movement.     

Differentiating the effects of climate and land use change on European biodiversity: A scenario analysis

Current observed as well as projected changes in biodiversity are the result of multiple interacting factors, with land use and climate change often marked as most important drivers. We aimed to disentangle the separate impacts of these two for sets of vascular plant, bird, butterfly and dragonfly species listed as characteristic for European dry grasslands and wetlands, two habitats of high and threatened biodiversity. We combined articulations of the four frequently used SRES climate scenarios and associated land use change projections for 2030, and assessed their impact on population trends in species (i.e. whether they would probably be declining, stable or increasing). We used the BIOSCORE database tool, which allows assessment of the effects of a range of environmental pressures including climate change as well as land use change. We updated the species lists included in this tool for our two habitat types. We projected species change for two spatial scales: the EU27 covering most of Europe, and the more restricted biogeographic region of ‘Continental Europe’. Other environmental pressures modelled for the four scenarios than land use and climate change generally did not explain a significant part of the variance in species richness change. Changes in characteristic bird and dragonfly species were least pronounced. Land use change was the most important driver for vascular plants in both habitats and spatial scales, leading to a decline in 50–100% of the species included, whereas climate change was more important for wetland dragonflies and birds (40–50 %). Patterns of species decline were similar in continental Europe and the EU27 for wetlands but differed for dry grasslands, where a substantially lower proportion of butterflies and birds declined in continental Europe, and 50 % of bird species increased, probably linked to a projected increase in semi-natural vegetation. In line with the literature using climate envelope models, we found little divergence among the four scenarios. Our findings suggest targeted policies depending on habitat and species group. These are, for dry grasslands, to reduce land use change or its effects and to enhance connectivity, and for wetlands to mitigate climate change effects.     

A science of integration: frameworks,processes, and products in a place-based,integrative study

Integrative research is increasingly a priority within the scientific community and is a central goal for the evolving field of sustainability science. While it is conceptually attractive, its successful implementation has been challenging and recent work suggests that the move towards interdisciplinarity and transdisciplinarity in sustainability science is being only partially realized. To address this from the perspective of social-ecological systems (SES) research, we examine the process of conducting a science of integration within the Southcentral Alaska Test Case (SCTC) of Alaska-EPSCoR as a test-bed for this approach. The SCTC is part of a large, 5 year, interdisciplinary study investigating changing environments and adaptations to those changes in Alaska. In this paper, we review progress toward a science of integration and present our efforts to confront the practical issues of applying proposed integration frameworks. We: (1) define our integration framework; (2) describe the collaborative processes, including the co-development of science through stakeholder engagement and partnerships; and (3) illustrate potential products of integrative, social-ecological systems research. The approaches we use can also be applied outside of this particular framework. We highlight challenges and propose improvements for integration in sustainability science by addressing the need for common frameworks and improved contextual understanding. These insights may be useful for capacity-building for interdisciplinary projects that address complex real-world social and environmental problems.     

Superstorm Sandy and the academic achievement of university students

Much of the literature on the consequences of natural disasters has focused on their physical and psychological ramifications. Few researchers have considered how the impacts of a natural disaster can influence academic achievement. This study analyses data collected from nearly 300 students at a mid‐sized, private university in the northeast United States to determine if the effects of Cyclone Sandy in 2012 are associated with measures of academic achievement. The findings reveal that experiencing headaches after the event resulted in a higher likelihood of students suffering a loss of academic motivation. In addition, experiencing headaches and a loss of academic motivation were correlated with a lower grade point average (GPA) during the semester in which Sandy made landfall. However, the more direct effects of the superstorm, including displacement and a loss of power, did not have a significant bearing on academic achievement. Lastly, the paper examines the implications for higher education policy and future research.     

Escherichia coli loading at or near base flow in a mixed-use watershed

This study analyzed the occurrence of Escherichia coli in a mixed land-use watershed with human, cattle, and wildlife fecal inputs located in a karstic geologic region using synoptic monitoring (samples taken throughout the watershed system) during base-flow conditions. The objective of the study was to evaluate the occurrence of E. coli during base-flow conditions for several months at seven different main channel and nine different tributary sampling sites in the Stock Creek watershed, a 49.3-km(2) basin located in Knoxville, TN. Escherichia coli densities were measured using the Colilert (Defined Substrate Technology) method. The instantaneous loads for E. coli were determined from measured flow rates and E. coli densities, with the highest loading rates observed in the late fall. The study indicated a strong correlation between E. coli load rate (colony-forming units [CFU]/d), 7-d antecedent precipitation, and turbidity. Water quality data, however, also exhibited a spatial dependency; for example, the E. coli load rate was better correlated with turbidity in the slower draining basin tailwater sampling sites than in the faster draining upstream headwater sampling sites. In the headwater sites, the E. coli load rate was better correlated with 7-d antecedent precipitation than turbidity.     

Simulating long-term and residual effects of nitrogen fertilization on corn yields, soil carbon sequestration, and soil nitrogen dynamics

Soil carbon sequestration (SCS) has the potential to attenuate increasing atmospheric CO2 and mitigate greenhouse warming. Understanding of this potential can be assisted by the use of simulation models. We evaluated the ability of the EPIC model to simulate corn (Zea mays L.) yields and soil organic carbon (SOC) at Arlington, WI, during 1958-1991. Corn was grown continuously on a Typic Argiudoll with three N levels: LTN1 (control), LTN2 (medium), and LTN3 (high). The LTN2 N rate started at 56 kg ha(-1) (1958), increased to 92 kg ha(-1) (1963), and reached 140 kg ha(-1) (1973). The LTN3 N rate was maintained at twice the LTN2 level. In 1984, each plot was divided into four subplots receiving N at 0, 84, 168, and 252 kg ha(-1). Five treatments were used for model evaluation. Percent errors of mean yield predictions during 1958-1983 decreased as N rate increased (LTN1 = -5.0%, LTN2 = 3.5%, and LTN3 = 1.0%). Percent errors of mean yield predictions during 1985-1991 were larger than during the first period. Simulated and observed mean yields during 1958-1991 were highly correlated (R2 = 0.961, p < 0.01). Simulated SOC agreed well with observed values with percent errors from -5.8 to 0.5% in 1984 and from -5.1 to 0.7% in 1990. EPIC captured the dynamics of SOC, SCS, and microbial biomass. Simulated net N mineralization rates were lower than those from laboratory incubations. Improvements in EPIC's ability to predict annual variability of crop yields may lead to improved estimates of SCS.     

Risk assessment test for lead bioaccessibility to waterfowl in mine-impacted soils

Due to variations in soil physicochemical properties, species physiology, and contaminant speciation, Pb toxicity is difficult to evaluate without conducting in vivo dose-response studies. Such tests, however, are expensive and time consuming, making them impractical to use in assessment and management of contaminated environments. One possible alternative is to develop a physiologically based extraction test (PBET) that can be used to measure relative bioaccessibility. We developed and correlated a PBET designed to measure the bioaccessibility of Pb to waterfowl (W-PBET) in mine-impacted soils located in the Coeur d'Alene River Basin, Idaho. The W-PBET was also used to evaluate the impact of P amendments on Pb bioavailability. The W-PBET results were correlated to waterfowl-tissue Pb levels from a mallard duck [Anas platyrhynchos (L.)] feeding study. The W-PBET Pb concentrations were significantly less in the P-amended soils than in the unamended soils. Results from this study show that the W-PBET can be used to assess relative changes in Pb bioaccessibility to waterfowl in these mine-impacted soils, and therefore will be a valuable test to help manage and remediate contaminated soils.