The early years of Sequoia and Kings Canyon Science: Building a research program

This paper provides a history of the development of the scientific research program at Sequoia and Kings Canyon National Parks (SEKI) during the period 1968–1994 from the perspective of one of the scientists involved. The years following the 1968 hiring of Bruce Kilgore as the first park-based research scientist at SEKI saw the growth of a research program that included three permanent research-grade scientists and their support staff.  This nucleus was successful in attracting both outside funding and leading university and government scientists to work on issues of importance to the parks and to society at large, topics that included fire ecology and management, black bears, wilderness impacts, acid deposition, and climate change. During this time the SEKI scientists’ role expanded from one focused primarily on the personal research on issues of immediate importance to the park, to increasing responsibilities for marketing and coordinating a growing program of collaborative research that also addressed regional and national priorities. This, in turn, required that the park scientists increasingly become generalists, able to converse in a number of scientific disciplines as well as communicate with non-scientists. Finally, keys to success and lessons learned are discussed

Impact of European Water Framework Directive Article 7 on Drinking Water Directive compliance for pesticides: challenges of a prevention-led approach

Article 7 of the European Water Framework Directive (WFD) promotes a prevention-led approach to European Drinking Water Directive (DWD) compliance for those parameters that derive from anthropogenic influences on raw water quality. However, the efficacy of pollution prevention interventions is currently uncertain and likely to be variable, which makes absolute compliance with the drinking water standard a significant challenge. Member State governments, the WFD competent authority, the DWD competent authority, water suppliers and agriculture are all affected by and have a different perspective on the nature of this challenge. This paper presents a discussion of these perspectives applicable to stakeholders in all European Member States; the analysis is supported with examples from England and Wales. Improved understanding of the challenges faced by each group is needed if these groups are to achieve the shared goals of WFD Article 7 compliance and DWD compliance without a disproportionately negative impact on agricultural productivity. In addition, the European Commission needs to be aware of and address a potential incompatibility between WFD Article 7 and the DWD. With this in mind, targeted recommendations for action are presented for each stakeholder group

Carbon Brainprint Case Study: optimising defouling schedules for oil- refinerypreheat trains

In an oil refinery, crude oil is heated to 360-370°C before entering a distillation columnoperating at atmospheric pressure where the gas fraction and several liquid fractions withdifferent boiling points (e.g. gasoline, kerosene, diesel, gas oil, heavy gas oil) are separated off.The crude oil is heated in two stages. The preheat train - a series of heat exchangers - heats itfrom ambient temperature to about 270°C when it enters the furnace, known as the coil inlettemperature. The furnace then heats the oil to the temperature required for distillation.The purpose of the preheat train is to recover heat from the liquid products extracted in thedistillation column. Without this, 2-3% of the crude oil throughput would be used for heating thefurnace; with the preheat train up to 70% of the required heat is recovered. It also serves tocool the refined products: further cooling normally uses air or water. Over time, fouling reduces the performance of the heat exchangers, increasing the amount ofenergy that has to be supplied. It is possible to bypass units to allow them to be cleaned, withan associated cost and temporary loss of performance. The cleaning schedule thus has animpact on the overall efficiency, cost of operation and emissions. The group at the Department of Chemical Engineering and Biotechnology at Cambridgedeveloped a scheduling algorithm for this non-linear optimisation problem. It yields a good,though not-necessarily optimal, schedule and can handle additional constraints, such as thepresence of desalters with specific temperature requirements within the preheat train. This isnow being developed into a commercial software product. Data from two refineries - one operated by Repsol YPF in Argentina and the Esso FawleyRefinery in the UK - were used to model the systems and test the algorithm. For the Repsol YPF refinery, when compared with current practice and including a constrainton the desalter inlet temperature, the most conservative estimate of the emissions reductionwas 773 t CO2/year. This assumed a furnace efficiency of 90%. The emissions reductionincreased to 927 t CO2/year at 75% efficiency and 1730 t CO2/year at 40%. These were basedon a stoichiometric estimate of the emissions from the furnace. Using a standard emissionfactor increased them by 7.4%. For Esso Fawley, the estimated emission reduction compared to no maintenance was1435 t CO2/year at 90% furnace efficiency. This increased to 1725 t CO2/year at 75% and3225 t CO2/year at 40% efficien

Learning policy constraints through dialogue

Publisher PD

Modelling a two-dimensional spatial distribution of mycotoxin concentration in bulk commodities to design effective and efficient sample selection strategies

Mycotoxins in agricultural commodities are a hazard to human and animal health. Their heterogeneous spatial distribution in bulk storage or transport makes it particularly difficult to design effective and efficient sampling plans. There has been considerable emphasis on identifying the different sources of uncertainty associated with mycotoxin concentration estimations, but much less on identifying the effect of the spatial location of the sampling points. This study used a two-dimensional statistical modelling approach to produce detailed information on appropriate sampling strategies for surveillance of mycotoxins in raw food commodities. The emphasis was on deoxynivalenol (DON) and ochratoxin A (OTA) in large lots of grain in storage or bulk transport. The aim was to simulate a range of plausible distributions of mycotoxins in grain from a set of parameters characterising the distributions. For this purpose, a model was developed to generate data sets which were repeatedly sampled to investigate the effect that sampling strategy and the number of incremental samples has on determining the statistical properties of mycotoxin concentration. Results showed that, for most sample sizes, a regular grid proved to be more consistent and accurate in the estimation of the mean concentration of DON, which suggests that regular sampling strategies should be preferred to random sampling, where possible. For both strategies, the accuracy of the estimation of the mean concentration increased significantly up to sample sizes of 40-60 (depending on the simulation). The effect of sample size was small when it exceeded 60 points, which suggests that the maximum sample size required is of this order. Similar conclusions about the sample size apply to OTA, although the difference between regular and random sampling was small and probably negligible for most sample sizes

The Weak Tie Between Natural Gas and Oil Prices

Several recent studies establish that crude oil and natural gas prices are cointegrated, so that changes in the price of oil appear to translate into changes in the price of natural gas. Yet at times in the past, and very powerfully in the last two years, many voices have noted that the two prices series appear to have "decoupled". We explore the apparent contradiction between these two views. Although we also find that the two series are cointegrated, recognition of the statistical fact of cointegration needs to be tempered with two additional points that we think have been insufficiently emphasized in the past literature. First, there is an enormous amount of unexplained volatility in natural gas prices at short horizons. Hence, any simple formulaic relationship between the price of oil and the price of natural gas will leave a large portion of the price of natural gas unexplained. Second, the cointegrating relationship does not appear to be stable through time. Natural gas prices may be tied to oil prices, but the relationship can shift dramatically over time. Therefore, although the two price series are cointegrated, the confidence intervals for both short and long time horizons are large.Massachusetts Institute of Technology. Center for Energy and Environmental Policy Researc

Illusions of gunk

The possibility of gunk has been used to argue against mereological nihilism. This paper explores two responses on the part of the microphysical mereological nihilist: (1) the contingency defence, which maintains that nihilism is true of the actual world; but that at other worlds, composition occurs; (2) the impossibility defence, which maintains that nihilism is necessary true, and so gunk worlds are impossible. The former is argued to be ultimately unstable; the latter faces the explanatorily burden of explaining the illusion that gunk is possible. It is argued that we can discharge this burden by focussing on the contingency of the microphysicalist aspect of microphysical mereological nihilism. The upshot is that gunk-based arguments against microphysical mereological nihilism can be resisted

Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system

The reliability of the water distribution system is critical to maintaining a secure supply for the population, industry and agriculture, so there is a need for proactive maintenance to help reduce water loss and down times. Bayesian networks are one approach to modelling the complexity of water mains, to assist water utility companies in planning maintenance. This paper compares and analyses how accurately the Bayesian network structure can be derived given a large and highly variable dataset. Method one involved using automated learning algorithms to build the Bayesian network, while method two involved a guided method using a combination of historic failure data, prior knowledge and pre-modelling data exploration of the water mains. By understanding common failure types (circumferential, longitudinal, pinhole and joint), the guided learning Bayesian Network was able to capture the interactions of the surrounding soil environment with the physical properties of pipes. The Bayesian network built using data exploration and literature was able to achieve an overall accuracy of 81.2% when predicting the specific type of water mains failure compared to the 84.4% for the automated method. The slightly greater accuracy from the automated method was traded for a sparser Bayes net where the interpretation of the interactions between the variables was clearer and more meaningful

Analysis of the 2007/8 Defra Farm Business Survey Energy Module

Key points This study has delivered an invaluable baseline estimate of energy use and greenhouse gas (GHG) emissions on commercial farms in England. Energy use and GHG emissions associated with particular commodities were quantified and results broadly agreed with those derived by Life Cycle Assessment, but with much scatter in the environmental performance of farms.Direct energy use on farms was generally less that indirect (embedded) energy use, except for horticulture, which is dominated by heating fuel use. In contrast, most GHG emissions are incurred on farms, rather than as embedded emissions.Scatter in both environmental and economic performance underlies the somewhat disappointing finding of no clear positive link between farm financial performance and energy use or GHG emissions. However, the mere existence of these ranges shows that there is scope for improvement in both financial and environmental performance and that there is no apparent barrier for both to be achievable in harmony. The recording of such farm-level energy data is essential for the future, as it should enable improvements to be made in efficiency of energy use. The improved UK agricultural GHG inventory will depend on high quality energy data on agricultural activities. This study will be invaluable in identifying the level of detail needed. Future data requirements include: contractor work rates and fuel use per unit area and per unit time, fertiliser and pesticide use by brand name, enhanced output data, especially animal live weights, and horticultural produce recorded by weight rather than by value