Paul S. Fischbeck

Global nitrogen and sulfur inventories for oceangoing ships

We present geographically resolved global inventories of nitrogen and sulfur emissions from international maritime transport for use in global atmospheric models. Current inventories of globally resolved sources of natural and anthropogenic emissions do not include the significant contribution of SO2 or NOx from oceangoing ships [Benkovitz et al., 1996]. We estimate the global inventory of ship emissions, using current emission test data for ships [Carlton et al., 1995] and a fuel-based approach similar to that used for automobile inventories [Singer and Harley, 1996]. This study estimates the 1993 global annual NOx and SO2 emissions from ships to be 3.08 teragrams (Tg, or 1012 g) as N and 4.24 Tg S, respectively. Nitrogen emissions from ships are shown to account for more than 14% of all nitrogen emissions from fossil fuel combustion, and sulfur emissions exceed 5% of sulfur emitted by all fuel combustion sources including coal. Ship sulfur emissions correspond to about 20% of biogenic dimethylsulfide (DMS) emissions. In regions of the Northern Hemisphere, annual sulfur emissions from ships can be of the same order of magnitude as estimates of the annual flux of DMS [Chin et al., 1996]. Monthly inventories of ship sulfur and nitrogen emissions presented in this paper are geographically characterized on a 2° × 2° resolution. Temporal and spatial characteristics of the inventory are presented. Uncertainty in inventory estimates is assessed: the fifth and ninety-fifth percentile values for global nitrogen emissions are 2.66 Tg N and 4.00 Tg N, respectively; the fifth and ninety-fifth percentile values for sulfur emissions are 3.29 Tg S and 5.61 Tg S, respectively. We suggest that these inventories, available via the Ship Emissions Assessment (SEA) web site, be used in models along with the Global Emissions Inventory Activity (GEIA) inventories for land-based anthropogenic emissions and modeled with ocean-biogenic inventories for DMS.

Effects of ship emissions on sulphur cycling and radiative climate forcing over the ocean

The atmosphere overlying the ocean is very sensitive—physically, chemically and climatically—to air pollution. Given that clouds over the ocean are of great climatic significance, and that sulphate aerosols seem to be an important control on marine cloud formation, anthropogenic inputs of sulphate to the marine atmosphere could exert an important influence on climate. Recently, sulphur emissions from fossil fuel burning by international shipping have been geographically characterized, indicating that ship sulphur emissions nearly equal the natural sulphur flux from ocean to atmosphere in many areas. Here we use a global chemical transport model to show that these ship emissions can be a dominant contributor to atmospheric sulphur dioxide concentrations over much of the worlds oceans and in several coastal regions. The ship emissions also contribute significantly to atmospheric non-seasalt sulphate concentrations over Northern Hemisphere ocean regions and parts of the Southern Pacific Ocean, and indirect radiative forcing due to ship-emitted particulate matter (sulphate plus organic material) is estimated to contribute a substantial fraction to the anthropogenic perturbation of the Earths radiation budget. The quantification of emissions from international shipping forces a re-evaluation of our present understanding of sulphur cycling and radiative forcing over the ocean.

Categorizing Risks for Risk Ranking

Any practical process of risk ranking must group hazards into a manageable number of categories. Defining such categories requires value choices that can have important implications for the rankings that result. Most risk-management organizations will find it useful to begin defining categories in terms of environmental loadings or initiating events. However, the resulting categories typically need to be modified in light of other considerations. Risk-ranking projects can benefit from considering several alternative categorization strategies and drawing upon elements of each in developing their final categorization of risks. In principle, conducting multiple ranking exercises by using different categorizations could be interesting and useful. In practice, agencies are unlikely to have either the resources or patience to do this, but other groups in society might. Done well, such additional independent rankings could add valuable inputs to democratic risk-management decision making.

A Deliberative Method for Ranking Risks (I): Overview and Test Bed Development

Risk ranking offers a potentially powerful means for gathering public input to help set risk-management priorities. In most rankings conducted to date, the categories and attributes used to describe the risks have varied widely, the materials and procedures have not been designed to facilitate comparisons among risks on all important attributes, and the validity and reproducibility of the resulting rankings have not been assessed. To address these needs, a risk-ranking method was developed in which risk experts define and categorize the risks to be ranked, identify the relevant risk attributes, and characterize the risks in a set of standardized risk summary sheets, which are then used by lay or other groups in structured ranking exercises. To evaluate this method, a test bed involving 22 health and safety risks in a fictitious middle school was created. This article provides an overview of the risk-ranking method and describes the challenges faced in designing the middle school test bed. A companion article in this issue reports on the validity of the ranking procedures and the level of agreement among risk managers regarding ranking of risks and attributes.

Using expert elicitation to link foodborne illnesses in the united states to foods

U.S. foodborne illness risk analysis would benefit greatly from better information on the relationship between the incidence of foodborne illness and exposure to foodborne pathogens. In this study, expert elicitation was used to attribute U.S. foodborne illnesses caused by the nine FoodNet pathogens, Toxoplasma gondii, and noroviruses to consumption of foods in 11 broad categories. Forty-two nationally recognized food safety experts responded to a formal written expert elicitation survey. For each pathogen, respondents gave their best estimate of the distribution of foodborne illnesses associated with each of the food categories and the 90% confidence bounds on each of their estimates. Based on the work of Paul Mead and his coauthors, food attribution percentage estimates from this study were used to attribute case, hospitalization, and death incidence estimates to foods according to pathogen. These attribution estimates indicate that 15 food-pathogen pairs account for 90% of the illnesses, 25 pairs account for 90% of hospitalizations, and 21 pairs account for 90% of deaths.

An environmental sensor network to determine drinking water quality and security

Finding patterns in large, real, spatio/temporal data continues to attract high interest (e.g., sales of products over space and time, patterns in mobile phone users; sensor networks collecting operational data from automobiles, or even from humans with wearable computers). In this paper, we describe an interdisciplinary research effort to couple knowledge discovery in large environmental databases with biological and chemical sensor networks, in order to revolutionize drinking water quality and security decision making. We describe a distribution and operation protocol for the placement and utilization of in situ environmental sensors by combining (1) new algorithms for spatialtemporal data mining, (2) new methods to model water quality and security dynamics, and (3) a sophisticated decision-analysis framework. The project was recently funded by NSF and represents application of these research areas to the critical current issue of ensuring safe and secure drinking water to the population of the United States.

PRA as a management tool: organizational factors and risk-based priorities for the maintenance of the tiles of the space shuttle orbiter

Abstract A probabilistic risk assessment (PRA) model, developed for the Thermal Protection System (TPS) of the space shuttle orbiter and presented in the previous paper, is used as a management tool to identify root-cause, organizational factors of the various failure modes. The objective is to set priorities in the process of resource allocation to minimize the risk of accident caused by the failure of the TPS. Starting with the technical characteristics of the system and the inputs of the risk assessment model, the approach is to identify the human decisions and actions and the key organizational factors that influence the risk. Among the management factors that affect the reliability of the TPS are time pressures that have occurred in the past, liability concerns and conflicts among contractors, the low status of the tile work and material technicians among maintenance personnel, the absence of priorities in tile testing, and under-recognized couplings among subsystems (such as the external tank insulation as a source of debris that may hit the tiles). It is shown here how using the PRA results to set priorities in the maintenance of the tiles can allow reduction of the overall risk, and how critical zones of debris sources can be identified on the surface of the external tank and the solid rocket booster. It was found, for instance, that detecting and fixing loose tiles in the most risk-critical areas and securing insulation by up to 80%, and securing the insulation of external systems in specified areas could reduce the TPS risk by about 75%.

Traffic accident statistics and risk perceptions in Japan and the United States

Several recent studies have concluded that Japan and the US have different risk cultures. This study examines the actual risk environments faced by citizens in the two countries, in the domain of traffic safety, as a possible source of differences in risk perceptions. The study contrasts traffic-accident risks from several points of view (e.g. car drivers, motorcyclists, bicyclists and pedestrians) and risk statistics (e.g. death rates, relative fatality risks, and accident lethality). Results clarify the traffic risks in the two countries and confirm their potential for explaining cross-national differences in risk perceptions.

OPTIMIZING SENSOR PLACEMENTS IN WATER DISTRIBUTION SYSTEMS USING SUBMODULAR FUNCTION MAXIMIZATION

Drinking water distribution networks represent complex systems. Water flow rates in a water distribution system vary with time, with periodic features that reflect temporal variations in water demand by consumers. The intentional introduction of a contaminant disrupts the system and could theoretically be detected by a sensor or network of sensors placed at nodes (pipe junctions, reservoirs, storage tanks, or even individual consumer taps) in the system. Determining the best locations for placement of these sensors represents a significant research question, because the system has multiple states, the number of possible intrusion points is large, and the likely high cost of these sensors limits the number that can realistically be deployed. The optimal placement of these sensors to minimize the effect of an introduced contaminant on the population is a critical issue. Sensor placement for intrusion detection exhibits an important diminishing returns property: adding a sensor to a sensor network improves the detection ability less than adding it to a subset of the sensor network. We prove that this submodularity property holds for the objective functions that we consider for placing sensors, and exploit it by applying algorithms for maximizing monotonic submodular functions. Unlike existing optimization algorithms for selecting sensor placements, our efficient optimization procedure has strong theoretical performance guarantees. In spite of the problem’s complexity, our algorithm is guaranteed to always find a solution that is at least within 63% of the optimum, and will often find a (near-)optimal solution. This method is applied to two hypothetical distribution systems (129 nodes and 12,527 nodes) to determine optimal sensor placements for a sensor network of 5 or 20 sensors. Optimization was based on multiple criteria including: (1) minimizing time to detection, (2) minimizing population affected prior to detection, (3) minimizing expected demand for contaminated water prior to detection, and (4) maximizing detection likelihood. A base scenario and three derivative scenarios were used to test the sensor location optimization for the hypothetical systems. In order to compute accurately the objective criteria, we exhaustively simulated all possible attack scenarios, using distributed computation. Five optimization objective functions were considered (i.e., optimization on each of the four objectives independently and then an equally weighted multi-objective optimization). The two networks analyzed in this project illustrate how a sensor network of 20 sensors is more than “adequate” for the example distribution system of 129 nodes, while a much larger sensor network would be needed for “adequate” detection in the example large network of 12,527 nodes. The developed algorithms generalize to networks of arbitrary size and can be constrained by expert knowledge or rankings of scenario likelihood. Further, the optimization algorithms have potential applications for placement of sensors in other complex, dynamic systems.

Probabilistic risk analysis and risk-based priority scale for the tiles of the space shuttle

Abstract The thermal protection system of the space shuttle is one of its most critical subsystems because it protects the orbiter from heavy heat loads at reentry into the atmosphere. To optimize NASAs allocation of risk management resources, a probabilistic risk analysis model is developed for the black tiles, and a risk-criticality index is computed for each tile based on its contribution to the overall probability of loss of vehicle and crew (LOV/C). This assessment is based on the susceptibility of the tiles (i.e. their probabilities of debonding), and on the vulnerability of the orbiter to specific tile losses given the criticality of the subsystems under the aluminum skin in various locations. The two main initiating events are linked to the debonding of a tile, caused either by debris hits or by a weak bond because of poor tile installation. The PRA model relies on a partition of the orbiters surface according to four parameters: the probability of debris hits, the probability of secondary tile loss once a first tile has debonded, the probability of burnthrough given a failure patch of specified size, and the probability of LOV given a hole in the orbiters aluminum skin. The results show that the contribution of the tiles to the overall probability of LOV is about 10%. They also include a map of the orbiters surface showing the relative risk-criticality of tiles at various locations. It was found that 85% of the risk can be attributed to 15% of the tiles, thus allowing the management to allocate more effort and resources to the maintenance of the most risk-critical tiles.