profiling areas of ground water contamination by pesticides in california: phase ii – evaluation and modification of a statistical model

A well sampling study was conducted to evaluate anempirical approach to classifying areasof land in California as vulnerable to ground watercontamination by pesticides (Troiano et al., 1994). Wells were sampled from sections of land that had noprevious detections of pesticideresidues. The sections had been classified into vulnerablesoil clusters or into a not-classified groupusing a procedure based on Principal Components Analysis(PCA). Grape, citrus, and olive growingareas of Fresno and Tulare Counties were targeted, areas wherepre-emergence herbicide residues hadbeen detected in well water. Overall, herbicide residues weredetected in 75 of 176 sampled wells, ahigh frequency of detection in relation to results fromprevious targeted well sampling studies. Sinceresidues were also detected in the not-classified group, theclassification procedure was modified usingan approach based on Canonical Variates Analysis (CVA). Moresections were classified intovulnerable soil clusters with the CVA approach than with thePCA method. Data from two otherexplanatory variables, depth to ground water and amount ofpesticide used per section, were includedto illustrate how additional information can be incorporatedinto this approach of identifying vulnerable areas.     

HEAVY RAINFALL DISTRIBUTIONS BY SEASON IN LOUISIANA: SYNOPTIC INTERPRETATIONS AND QUANTILE ESTIMATES1

ABSTRACT: In most studies, quantile estimates of extreme 24-hour rainfall are given in annual probabilities. The probability of experiencing an excessive storm event, however, differs throughout the year. As a result, this paper explored the differences between heavy rainfall distributions by season in Louisiana. It was concluded by using the Kruskal-Wallis and Mann-Whitney tests that the distribution of heavy rainfall events differs significantly between particular seasons at the sites near the Gulf Coast. Furthermore, seasonal frequency curves varied dramatically at the four sites examined. Mixed distributions within these data were not found to be problematic, but the mechanisms that produced the events were found to change seasonally. Extreme heavy rainfall events in winter and spring were primarily generated by frontal weather systems, while summer and fall events had high proportions of events produced by tropical disturbances and airmass (free-convective) conditions.     

NUTRIENT CONCENTRATION CRITERIA AND CHARACTERIZATION OF PATTERNS IN TROPHIC STATE FOR RIVERS IN HETEROGENEOUS LANDSCAPES1

ABSTRACT: A method is demonstrated for the development of nutrient concentration criteria and large scale assessment of trophic state in environmentally heterogeneous landscapes. The method uses the River Environment Classification (REC) as a spatial framework to partition rivers according to differences in processes that control the accrual and loss of algae biomass. The method is then applied to gravel bed rivers with natural flow regimes that drain hilly watersheds in New Zealand's South Island. An existing model is used to characterize trophic state (in terms of chlorophyll a as a measure of maximum biomass) using nutrient concentration, which controls the rate of biomass accrual, and flood frequency, which controls biomass loss. Variation in flood frequency was partitioned into three classes, and flow data measured at 68 sites was used to show that the classes differ with respect to flood frequency. Variation in nutrient concentration was partitioned at smaller spatial scales by subdivision of higher level classes into seven classes. The median of flood frequency in each of the three higher level classes was used as a control variable in the model to provide spatially explicit nutrient concentration criteria by setting maximum chlorophyll a to reflect a desired trophic state. The median of mean monthly soluble reactive phosphorus and soluble inorganic nitrogen measured at 68 water quality monitoring sites were then used to characterize the trophic state of each of the seven lower level classes. The method models biomass and therefore allows variation in this response variable to provide options for trophic state and the associated nutrient concentrations to achieve these. Thus it is less deterministic than using reference site water quality. The choice from among these options is a sociopolitical decision, which reflects the management objectives rather than purely technical considerations.     

Techniques for accuracy assessment of tree locations extracted from remotely sensed imagery

Remotely sensed imagery is becoming a common source of environmental data. Consequently, there is an increasing need for tools to assess the accuracy and information content of such data. Particularly when the spatial resolution of imagery is fine, the accuracy of image processing is determined by comparisons with field data. However, the nature of error is more difficult to assess. In this paper we describe a set of tools intended for such an assessment when tree objects are extracted and field data are available for comparison. These techniques are demonstrated on individual tree locations extracted from an IKONOS image via local maximum filtering. The locations of the extracted trees are compared with field data to determine the number of found and missed trees. Aspatial and spatial (Voronoi) analysis methods are used to examine the nature of errors by searching for trends in characteristics of found and missed trees. As well, analysis is conducted to assess the information content of found trees.     

Recent climate change in the Prince Edward County winegrowing region,Ontario, Canada: implications for adaptation in a fledgling wine industry

There is mounting evidence that climate change is already having an impact on the wine industry, with effects being region specific. In order to understand the capacity of regional wine sectors to adapt to changing climate, it is useful to document the conditions that are important to producers and to identify adaptation and management strategies that are employed in the industry. This paper analyzes climatic conditions and adaptation strategies in the wine region of Prince Edward County Ontario, Canada. Wine producers identified the climate variables most important to their operations and described strategies they use to manage climate-related conditions. The identified variables were analyzed for trends over the study period 1987–2011, and interview data were analyzed in order to categorize adaptive strategies. Results indicate that the wine sector is very sensitive to climate, and the region is already experiencing the effects of climate change, especially with regard to increasing growing season mean minimum daily temperatures, increasing total summer rainfall, and later onset of fall frosts. Adaptive strategies employed by producers are largely learned though collaborative efforts and trial and error. The adaptations are mostly tactical and reactive in the short term, but with continued climate change, these strategies may develop into strategic, anticipatory measures. Climate change has the potential to present both challenges and opportunities to Prince Edward County wine producers, and adaptations will continue to require strong networking and collaborative efforts.     

An improvement selection methodology for key performance indicators

Key performance indicators (KPIs) are critical measures for determining the health of a manufacturing plant in relationship to the plant’s goals. In today’s competitive environment, manufacturers cannot be careless about their business; in fact, they must ensure that their KPIs are effective and use them to make improvements when necessary. This paper describes a method for suggesting improvements to a manufacturer’s KPIs, based on the results achieved from a workshop to score the KPI on a number of predefined criteria. The approach uses a prospect theory approach to weight the scoring. Different problem formulations were derived that allow for both recommendations for improvements and the recommendations for disinvestments to over-performing KPIs. The authors applied the developed approach to two workshop outputs, each from independent manufacturers, and the results highlighted the significant difference between the two manufacturers in terms of improvement priorities and KPI assessment. The optimal improvement suggestions were compared to those found through a fast heuristic. It was determined that given the underlying assumptions of the approach that the heuristic solutions were just as adequate as the optimal ones.     

Toward a robust analytical method for separating trace levels of nano-materials in natural waters: cloud point extraction of nano-copper(II) oxide

Cloud point extraction (CPE) factors, namely Triton X-114 (TX-114) concentration, pH, ionic strength, incubation time, and temperature, were optimized for the separation of nano-sized copper(II) oxide (nCuO) in aqueous matrices. The kinetics of phase transfer was studied using UV–visible spectroscopy. From the highest separation rate, the most favorable conditions were observed with 0.2 %?w/v of TX-114, pH?=?9.0, ionic strength of 10 mM NaCl, and incubation at 40 °C for 60 min, yielding an extraction efficiency of 89.2?±?3.9 % and a preconcentration factor of 86. The aggregate size distribution confirmed the formation of very large nCuO–micelle assemblies (11.9 μm) under these conditions. The surface charge of nCuO was also diminished effectively. An extraction efficiency of 91 % was achieved with a mixture of TX-100 and TX-114 containing 30 wt.% of TX-100. Natural organic and particulate matters, represented by humic acid (30 mg/L) and micron-sized silica particles (50 mg/L), respectively, did not significantly reduce the CPE efficiency (<10 %). The recovery of copper(II) ions (20 mg/L) in the presence of humic acid was low (3–10 %). The spiked natural water samples were analyzed either directly or after CPE by inductively coupled plasma mass spectrometry following acid digestion/microwave irradiation. The results indicated the influence of matrix effects and their reduction by CPE. A delay between spiking nCuO and CPE may also influence the recovery of nCuO due to aggregation and dissolution. A detection limit of 0.04 μg Cu/L was achieved for nCuO.     

Utilizing international networks for accelerating research and learning in transformational sustainability science

A promising approach for addressing sustainability problems is to recognize the unique conditions of a particular place, such as problem features and solution capabilities, and adopt and adapt solutions developed at other places around the world. Therefore, research and teaching in international networks becomes critical, as it allows for accelerating learning by sharing problem understandings, successful solutions, and important contextual considerations. This article identifies eight distinct types of research and teaching collaborations in international networks that can support such accelerated learning. The four research types are, with increasing intensity of collaboration: (1) solution adoption; (2) solution consultation; (3) joint research on different problems; and (4) joint research on similar problems. The four teaching types are, with increasing intensity of collaboration: (1) adopted course; (2) course with visiting faculty; (3) joint course with traveling faculty; and (4) joint course with traveling students. The typology is illustrated by extending existing research and teaching projects on urban sustainability in the International Network of Programs in Sustainability, with partner universities from Europe, North America, Asia, and Africa. The article concludes with challenges and strategies for extending individual projects into collaborations in international networks.