Seasonal characteristics of chemically apportioned aerosol optical properties at Seoul and Gosan,Korea

Seasonal variations of aerosol optical properties in Seoul (polluted urban site) and Gosan (coastal background site), Korea, with an emphasis on the relative humidity were investigated using ground-based aerosol measurements and optical model calculations. The mass fraction of elemental carbon was 9–20%, but the optical contribution of these particles to light extinction was higher, up to 33–55% in Seoul. In Gosan, the contribution of non-sea-salt water-soluble aerosols on extinction was 81–93% due to the high mass fraction of these particles. Based on daily MODIS datasets, our analysis showed that the aerosol optical depths at Seoul and Gosan were highest in spring due to the influence of dust particles. The aerosol water content at Gosan, calculated using a thermodynamic equilibrium model, was higher than that at Seoul; this was attributed to the high relative humidity and high fraction of water-soluble aerosols at Gosan. At Seoul, despite abundant water vapors in summer, the possibility of hygroscopic growth of water-soluble aerosols was not more significant than that at Gosan.     

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.     

A new approach to characterize biodegradation of organics by molecular mass distribution in landfill leachate

This study provides biodegradability of organics in leachate according to their molecular mass distributions (<0.5, 0.5 to 1, 1 to 3, 10, and >10 KDa). Organics with molecular mass values lower than 0.5 KDa were the predominant species in the raw leachate filtrate, and the aerated lagoon process was very effective in treating these highly biodegradable organics; the Fenton's oxidation process was very effective in treating not-so-biodegradable organics with molecular mass values higher than 0.5 KDa, but a portion of these organics were converted into organics <0.5 KDa after Fenton's oxidation. An oxygen uptake measurement using a respirometer was more sensitive than a conventional biochemical oxygen demand measurement to evaluate bioactivities, especially when bioactivities were low.     

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.     

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