Compartment model of aerobic and anaerobic biodegradation in a municipal solid waste landfill

The mathematical formulations in a one-dimensional compartment model of the biodegradation of organic landfill components are described. The model is designed to switch between anaerobic and aerobic conditions, depending on the local oxygen concentration. The model also includes the effect of environmental factors, such as moisture content, pH, and temperature, on reaction rates. The model includes not only biodegradation processes for carbon compounds (acetate, CO2, CH4), but also for nitrogen compounds involved in nitrification and denitrification due to their significance in landfills. Two example runs to simulate anaerobic and aerobic waste were conducted for a single landfill unit cell by changing the organic content and diffusion coefficient.     

The proboscis extension reflex to evaluate learning and memory in honeybees (Apis mellifera): some caveats

The proboscis extension reflex (PER) is widely used in a classical conditioning (Pavlovian) context to evaluate learning and memory of a variety of insect species. The literature is particularly prodigious for honeybees (Apis mellifera) with more than a thousand publications. Imagination appears to be the only limit to the types of challenges to which researchers subject honeybees, including all the sensory modalities and a broad diversity of environmental treatments. Accordingly, some remarkable insights have been achieved using PER. However, there are several challenges to evaluating the PER literature that warrant a careful and thorough review. We assess here variation in methods that makes interpretation of studies, even those researching the same question, tenuous. We suggest that the numerous variables that might influence experimental outcomes from PER be thoroughly detailed by researchers. Moreover, the influence of individual variables on results needs to carefully evaluated, as well as among two or more variables. Our intent is to encourage investigation of the influence of numerous variables on PER results.     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.     

Mini-review on river eutrophication and bottom improvement techniques,with special emphasis on the Nakdong River

Water quality in rivers is vital to humans and to maintenance of biotic and ecological integrity. During the Four Major Rivers restoration of South Korea, remarkable attempts have been made to decrease external nutrient loads and moveable weirs were designed to discharge silt that may deposit in pools. However, recently eutrophication of the Nakdong River, which was limited to the lower reaches, is seen to be spreading upstream. The reduction of external nutrient loads to rivers is a long-term goal that is unlikely to lead to reductions in algal blooms for many years because of the time required to implement effective land management strategies. It would therefore be desirable to implement complementary strategies. Regulating the amount of water released is effective at preventing algae blooms in weir pools; so, the relationship between discharge, stratification and bloom formation should be understood in this regard. However, pollutants are likely to accumulate in the riverbed upstream from release points. Thus, to control phosphorus levels, total phosphorus density should be lowered by applying in-river techniques as well. As many ecosystem properties are controlled by multiple processes, simultaneous river bottom improvement techniques, such as combined dissolved oxygen supply and nutrient inactivation, are likely to be effective. The purpose of this review is to present a series of technological approaches that can be used to improve the river bottom area and hence sediment nutrient release, and to illustrate the application of these techniques to the Nakdong River.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Greenhouse gas emissions during co-composting of calf mortalities with manure

Composting may be a viable on-farm option for disposal of cattle carcasses. This study investigated greenhouse gas emissions during co-composting of calf mortalities with manure. Windrows were constructed that contained manure + straw (control compost [CK]) or manure + straw + calf mortalities (CM) using two technologies: a tractor-mounted front-end loader or a shredder bucket. Composting lasted 289 d. The windrows were turned twice (on Days 72 and 190), using the same technology used in their creation. Turning technology had no effect on greenhouse gas emissions or the properties of the final compost. The CO2 (75.2 g d(-1) m(-2)), CH4 (2.503 g d(-1) m(-2)), and N2O (0.370 g d(-1) m(-2)) emissions were higher (p < 0.05) in CM than in CK (25.7, 0.094, and 0.076 g d(-1) m(-2) for CO2, CH4, and N2O, respectively), which reflected differences in materials used to construct the compost windrows and therefore their total C and total N contents. The final CM compost had higher (p < 0.05) total N, total C, and mineral N content (NO3*+ NO2* + NH4+) than did CK compost and therefore has greater agronomic value as a fertilizer.     

An Interpretive Study of Yosemite National Park Visitors’ Perspectives Toward Alternative Transportation in Yosemite Valley

The National Park Service (NPS) is increasingly focusing on alternative transportation systems in national parks to address environmental and social problems arising from a historical reliance on personal automobiles as the primary means of visitor access. Despite the potential advantages, alternative transportation may require a reorientation in the way that Americans have experienced national parks since the advent of auto-tourism in the early twentieth century. Little research exists, however, on visitor perspectives towards alternative transportation or the rationale underlying their perspectives. It remains unclear how transportation systems affect visitors’ experiences of the park landscape or the factors influencing their travel behavior in the parks. This report presents an interpretive study of visitor perspectives toward transportation management in the Yosemite Valley area of Yosemite National Park, California. Qualitative analysis of 160 semi-structured interviews identified individual psychological factors as well as situational influences that affect visitors’ behavior and perspectives. Individual psychological factors include perceived freedom, environmental values and beliefs, prior experience with Yosemite National Park and other national parks, prior experience with alternative transportation in national parks, and sensitivity to subjective perceptions of crowding. Situational factors included convenience, access, and flexibility of travel modes, as well as type of visit, type of group, and park use level. Interpretive communication designed to encourage voluntary visitor use of alternative transportation should focus on these psychological and situational factors. Although challenges remain, the results of this study suggest approaches for shaping the way Americans visit and experience their national parks to encourage environmental sustainability.     

Economic benefits of preventing bird collisions in South Korea: findings from a choice experiment survey

Bird deaths due to collisions with artificial structures, such as glass windows of buildings and transparent noise barriers, are continuing to occur in South Korea. The government is trying to prevent bird collisions by increasing the attachment of specially designed tapes to help birds avoid windows. This article estimates the economic benefits arising from the prevention of collisions by applying a choice experiment (CE). For this purpose, a CE survey of 1000 South Korean interviewees was conducted. The four attributes to be attached with the tapes for the CE application were a transparent soundproof wall window on an expressway, a transparent soundproof wall window on a general road, a glass window in a public building, and a glass window in a private building. The unit was the percentage of each structure with the tapes attached to the window. The marginal values of a one-unit (1%p) increase in each attribute were computed to be KRW 534 (USD 0.46), KRW 233 (USD 0.20), KRW 1,318 (USD 1.13), and KRW 12,930 (USD 11.05), respectively. This quantitative information will be an important reference for implementing the prevention policy. For example, based on the collision prevention of 1000 birds per structure, the priority for attaching tapes can be placed in the order of expressways, public buildings, private buildings, and general roads.