The Value of Sample Information for Water Quality Management

There is considerable interest in watershed-based pollution water quality protection but the approach can be highly information intensive (USEPA 2004, NRC 2000). This study examines the value of different types and levels of information for water quality management in the Conestoga watershed. For this estimation, a Monte Carlo procedure is used to construct the posterior expected value. Then, an Evolutionary Optimization Strategy with Covariance Matrix Adaptation (CMA-ES) is used to compute the expected value of optimized resources allocations given posterior information structures for specific sample sizes. This posterior optimization is nested within a second Monte Carlo simulation that computes the preposterior expectation (a nested Monte Carlo procedure). Thus, this paper provides some insight about the relative values of these alternative types of information for controlling water pollution from agriculture, and the gains from more intensive sampling.Environmental Economics and Policy,

The Expected Value of Sample Information Analysis for Nonpoint Water Quality Management

There is considerable interest in watershed-based water quality protection. However, the approach can be highly information intensive, necessitating decisions about the types and amounts of data used to guide decisions. This study examines the Bayesian value of different types and amounts of sample information for reducing nutrient pollution in the Conestoga watershed of Pennsylvania, focusing on nitrogen from agricultural sources. Uncertainty is modeled from the perspective of a social planner seeking to maximize the economic efficiency of water quality control. A nested Monte Carlo procedure combined with an Evolutionary Optimization Strategy with Covariance Matrix Adaptation is used to compute resource allocation that optimizes the expected net benefit after updating for varying sample sizes and information types (broadly classified as pertaining to abatement costs, pollution fate and transport, and benefits of environmental protection). The results provide insights the returns from information investments to improve water quality management

The Expected Value of Sample Information Analysis for Nonpoint Water Quality Management

There is considerable interest in watershed-based water quality protection. However, the approach can be highly information intensive, necessitating decisions about the types and amounts of data used to guide decisions. This study examines the Bayesian value of different types and amounts of sample information for reducing nutrient pollution in the Conestoga watershed of Pennsylvania, focusing on nitrogen from agricultural sources. Uncertainty is modeled from the perspective of a social planner seeking to maximize the economic efficiency of water quality control. A nested Monte Carlo procedure combined with an Evolutionary Optimization Strategy with Covariance Matrix Adaptation is used to compute resource allocation that optimizes the expected net benefit after updating for varying sample sizes and information types (broadly classified as pertaining to abatement costs, pollution fate and transport, and benefits of environmental protection). The results provide insights the returns from information investments to improve water quality management.water quality management, value of sample information, Monte Carlo simulation, Environmental Economics and Policy,

The Value of Sample Information for Water Quality Management

There is considerable interest in watershed-based pollution water quality protection but the approach can be highly information intensive (USEPA 2004, NRC 2000). This study examines the value of different types and levels of information for water quality management in the Conestoga watershed. For this estimation, a Monte Carlo procedure is used to construct the posterior expected value. Then, an Evolutionary Optimization Strategy with Covariance Matrix Adaptation (CMA-ES) is used to compute the expected value of optimized resources allocations given posterior information structures for specific sample sizes. This posterior optimization is nested within a second Monte Carlo simulation that computes the preposterior expectation (a nested Monte Carlo procedure). Thus, this paper provides some insight about the relative values of these alternative types of information for controlling water pollution from agriculture, and the gains from more intensive sampling

Magnetic states of atomic vacancies in graphite probed by scanning tunneling microscopy

Intrinsic defects in graphitic materials, like vacancies and edges, have been expected to possess magnetic states from the many-body interaction of localized electrons. However, charge screening from graphite bulk carriers significantly reduces the localization effect and hinders the observation of those magnetic states. Here, we use an ultra-low-temperature scanning tunneling microscope with a high magnetic field to observe the magnetic states of atomic vacancies in graphite generated by ion sputtering. Scanning tunneling spectroscopy reveals localized states at the vacancies, which exhibit splitting at a certain magnetic field whose separation increases with the field strength. The transition is well described by the Anderson model,which describes the emergence of localized magnetic states inside the metallic reservoir through electron-electron interaction. The interaction strength is estimated to be between 1 meV and 3 meV, which is supported by the density functional theory calculation. The observation provides an important foundation for application of intrinsic defects to carbon-based spintronic devices. © 2020 Author(s).1