Prioritizing county-level well-being: Moving toward assessment of Gulf Coast counties impacted by the Deepwater Horizon Industrial Disaster

To develop a portfolio of indicators and measures that could best measure changes in the social, economic, environmental and health dimensions of well-being in coastal counties we convened a group of experts March 8-9, 2011 in Charleston, SC, U.S.A. The region of interest was of the northern Gulf of Mexico, specifically, those coastal counties most impacted during the explosion and subsequent oil spill from the Macondo Prospect wellhead during the summer of 2010. Over the course of the two-day workshop participants moved through presentations and facilitated sessions to identify and prioritize potential indicators and measures deemed most valuable for capturing changes in well-being related to changes in or disruption of ecosystem services. The experts reached consensus on a list of indicators that are now being operationalized by NOAA researchers. The ultimate goal of this research project is to determine whether a meaningful set of social and economic indicators can be developed to document changes in well-being that occur as a result of changes in ecosystem services. The outcomes and outputs from the workshop that is the subject of this report helped us to identify high-quality indicators useful for measuring well-being.

Deepwater Horizon oil spill: assessment of potential impacts on the deep soft-bottom benthos; interim data summary report

A study was initiated in May 2011, under the direction of the Deepwater Horizon (DWH) Natural Resource Damage Assessment (NRDA) Deepwater Benthic Communities Technical Working Group (NRDA Deep Benthic TWG), to assess potential impacts of the DWH oil spill on sediments and resident benthic fauna in deepwater (> 200 meters) areas of the Gulf. Key objectives of the study were to complete the analysis of samples from 65 priority stations sampled in September-October 2010 on two DWH Response cruises (Gyre and Ocean Veritas) and from 38 long-term monitoring sites (including a subset of 35 of the original 65) sampled on a follow-up NRDA cruise in May-June 2011. The present progress report provides a brief summary of results from the initial processing of samples from fall 2010 priority sites (plus three additional historical sites). Data on key macrofaunal, meiofaunal, and abiotic environmental variables are presented for each of these samples and additional maps are included to depict spatial patterns in these variables throughout the study region. The near-field zone within about 3 km of the wellhead, where many of the stations showed evidence of impaired benthic condition (e.g. low taxa richness, high nematode/harpacticoid-copepod ratios), also is an area that contained some of the highest concentrations of total petroleum hydrocarbons (TPH), total polycyclic aromatic hydrocarbons (total PAHs), and barium in sediments (as possible indicators of DWH discharges). There were similar co-occurrences at other sites outside this zone, especially to the southwest of the wellhead out to about 15 km. However, there also were exceptions to this pattern, for example at several farther-field sites in deeper-slope and canyon locations where there was low benthic species richness but no evidence of exposure to DWH discharges. Such cases are consistent with historical patterns of benthic distributions in relation to natural controlling factors such as depth, position within canyons, and availability of organic matter derived from surface-water primary production.

Deep-sea benthic footprint of the Deepwater Horizon blowout

The Deepwater Horizon (DWH) accident in the northern Gulf of Mexico occurred on April 20, 2010 at a water depth of 1525 meters, and a deep-sea plume was detected within one month. Oil contacted and persisted in parts of the bottom of the deep-sea in the Gulf of Mexico. As part of the response to the accident, monitoring cruises were deployed in fall 2010 to measure potential impacts on the two main soft-bottom benthic invertebrate groups: macrofauna and meiofauna. Sediment was collected using a multicorer so that samples for chemical, physical and biological analyses could be taken simultaneously and analyzed using multivariate methods. The footprint of the oil spill was identified by creating a new variable with principal components analysis where the first factor was indicative of the oil spill impacts and this new variable mapped in a geographic information system to identify the area of the oil spill footprint. The most severe relative reduction of faunal abundance and diversity extended to 3 km from the wellhead in all directions covering an area about 24 km2. Moderate impacts were observed up to 17 km towards the southwest and 8.5 km towards the northeast of the wellhead, covering an area 148 km2. Benthic effects were correlated to total petroleum hydrocarbon, polycyclic aromatic hydrocarbons and barium concentrations, and distance to the wellhead; but not distance to hydrocarbon seeps. Thus, benthic effects are more likely due to the oil spill, and not natural hydrocarbon seepage. Recovery rates in the deep sea are likely to be slow, on the order of decades or longer.

Assessing the impacts of the Deepwater Horizon oil spill: the National Status and Trends Program response; a summary report of coastal contamination

NOAA’s National Status and Trends Program (NS&T) collected oyster tissue and sediments for quantification of polycyclic aromatic hydrocarbons (PAHs) and petroleum associated metals before and after the landfall of oil from the Deepwater Horizon incident of 2010. These new pre- and post- landfall measurements were put into a historical context by comparing them to data collected in the region over three decades during Mussel Watch monitoring. Overall, the levels of PAHs in both sediment and oysters both pre- and post-landfall were within the range of historically observed values for the Gulf of Mexico. Some specific sites did have elevated PAH levels. While those locations generally correspond to areas in which oil reached coastal areas, it cannot be conclusively stated that the contamination is due to oiling from the Deepwater Horizon incident at these sites due to the survey nature of these sampling efforts. Instead, our data indicate locations along the coast where intensive investigation of hydrocarbon contamination should be undertaken. Post-spill concentrations of oil-related trace metals (V, Hg, Ni) were generally within historically observed ranges for a given site, however, nickel and vanadium were elevated at some sites including areas in Mississippi Sound and Galveston, Terrebonne, Mobile, Pensacola, and Apalachicola Bays. No oyster tissue metal body burden exceeded any of the United States Food and Drug Administration’s (FDA) shellfish permissible action levels for human consumption.

The infinite HMM for unsupervised PoS tagging

We extend previous work on fully unsupervised part-of-speech tagging. Using a non-parametric version of the HMM, called the infinite HMM (iHMM), we address the problem of choosing the number of hidden states in unsupervised Markov models for PoS tagging. We experiment with two non-parametric priors, the Dirichlet and Pitman-Yor processes, on the Wall Street Journal dataset using a parallelized implementation of an iHMM inference algorithm. We evaluate the results with a variety of clustering evaluation metrics and achieve equivalent or better performances than previously reported. Building on this promising result we evaluate the output of the unsupervised PoS tagger as a direct replacement for the output of a fully supervised PoS tagger for the task of shallow parsing and compare the two evaluations. © 2009 ACL and AFNLP.

Min-Max Moving Horizon Estimation for a Class of Hybrid Systems

In this paper, a strategy for min-max Moving Horizon Estimation (MHE) of a class of uncertain hybrid systems is proposed. The class of hybrid systems being considered are Piecewise Affine systems (PWA) with both continuous valued and logic components. Furthermore, we consider the case when there is a (possibly structured) norm bounded uncertainty in each subsystem. Sufficient conditions on the time horizon and the penalties on the state at the beginning of the estimation horizon to guarantee convergence of the MHE scheme will be provided. The MHE scheme will be implemented as a mixed integer semidefinite optimisation for which an efficient algorithm was recently introduced.

Robust variable horizon MPC with move blocking

This paper introduces a new formulation of variable horizon model predictive control (VH-MPC) that utilises move blocking for reducing computational complexity. Various results pertaining to move blocking are derived, following which, a generalised blocked VH-MPC controller is formulated for linear discrete-time systems. Robustness to bounded disturbances is ensured through the use of tightened constraints. The resulting time-varying control scheme is shown to guarantee robust recursive feasibility and finite-time completion. An example is then presented for a particular choice of blocking regime, as would be applicable to vehicle manœuvring problems. Simulations demonstrate the efficacy of the formulation. © 2012 Elsevier B.V. All rights reserved.

An Infinite Latent Attribute Model for Network Data

Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network into clusters; the probability of a link between any two nodes then depends only on their cluster assignment. Currently available models can be classified by whether clusters are disjoint or are allowed to overlap. These models can explain a "flat" clustering structure. Hierarchical Bayesian models provide a natural approach to capture more complex dependencies. We propose a model in which objects are characterised by a latent feature vector. Each feature is itself partitioned into disjoint groups (subclusters), corresponding to a second layer of hierarchy. In experimental comparisons, the model achieves significantly improved predictive performance on social and biological link prediction tasks. The results indicate that models with a single layer hierarchy over-simplify real networks.

Optimal constraint tightening policies for robust variable horizon model predictive control

This paper develops a technique for improving the region of attraction of a robust variable horizon model predictive controller. It considers a constrained discrete-time linear system acted upon by a bounded, but unknown time-varying state disturbance. Using constraint tightening for robustness, it is shown how the tightening policy, parameterised as direct feedback on the disturbance, can be optimised to increase the volume of an inner approximation to the controller's true region of attraction. Numerical examples demonstrate the benefits of the policy in increasing region of attraction volume and decreasing the maximum prediction horizon length. © 2012 IEEE.