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Tanker Accident Rates and Expected Consequences in U.S. Ports and High Seas Regions

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Tanker Accident Rates and Expected Consequences in U.S. Ports and High Seas Regions Special Hazard Situations 161 Tanker Accident Rates and Expected Consequences in U.S. Ports and High Seas Regions Mark Abkowitz and Jorge Galarraga ABSTRACT As increasing amounts of hazardous materials carqo are transported by the marine mode, the associated risk to public safety and the environment bas been a significant concern. The development of representative accident rates and consequences for marine transport has been hindered by difficulties in working with various data bases that are requ1red to responsibly address this problem. A methodology that was developed to derive accident rates of hazardous materi­ als marine transport in U.S. ports and high seas regions by using data col­ lected from multiple sources is described in this paper. The focus of the study was tanker and tanker barge movements because these vessels are responsible for almost all hazardous materials movement by water. Several U.S. ports and high seas reg ions in the Gulf of Mexico and the Atlantic Ocean were selected for study. The resulting accident rates were coupled with conditional spill rates and consequences to derive expected release amounts for each geographic area. Several important findings emerged, includinq the relative risk of hazardous material movements in the Atlantic and Gulf regions. The paper concludes with a comparison of the research findings with other studies of domestic and inter­ national tanker transport. As increasing amounts of hazardous materials cargo accident records. Weighted averages of Atlantic and are transported by the marine mode, the associated Gulf regions were also derived for comparison. risk to public safety and the environment has been a In addition to the estimation of accident rates, significant concern. The development of representa­ expected release rates were derived by using the ac­ tive accident rates and consequences for marine cident rates, conditional spill rates, and associ­ transport has been hindered by difficulties in work­ ated consequences. The results of this project are ing with various data bases that are required to also compared to findings from previous studies of responsibly address this problem. domestic and international tanker transport. A methodology that was developed to derive acci­ dent rates of hazardous materials marine transport in U.S. ports and high seas regions is described in ANALYSIS METHODOLOGY this paper. It is based on the use of empirical data on vessel movements and accidents i these data are The analysis was segmented according to the follow­ maintained by various federal agencies. Detailed in­ ing geographic classification: formation on domestic vessel movements and aggregate data on foreign vessel movements in u.s. waters is 1. Vessel movements and related incidents in the maintained by the Corps of Engineers, u.s. Depart­ harbors and bays where the ports are located, and ment of the Army, and was made available for use in 2. Vessel movements and related incidents in the this analysis. Data on shipping accidents, known as high seas of the Gulf of Mexico and the Atlantic the Commercial Vessel Casualty File, are recorded hy Ocean. the U.S. Coast Guard and were also used in this study. Collectively, these data bases allow for the The analysis was also segmented according to the development of accident rates segmented by many dif­ following accident types: ferent shipping characteristics. The focus of the study was tanker and tanker 1. Collision, barge movements because these vessels are responsi­ 2. Fire or explosion, ble for almost all hazardous materials movement by 3. Grounding, and water. Several U.S. ports and high seas regions in 4. Other (including structural failure). the Gulf of Mexico and the Atlantic Ocean were se­ lected for study. They include Mobile Bay, Houston For each geographic location-accident type pair, Ship Channel, Corpus Christi, Delaware Bay, and the following accident and damage statistics were Providence. These ports were chosen because of their compiled (including frequencies and histograms): geographic locations and levels of tanker and tanker barge activity. In most cases, port definitions were 1. Number of accidents, expanded to consider the entire harbor or bay loca­ 2. Number of fatalities and injuries, and tion to establish consistency between movement and 3. Monetary loss. 162 TRB State-of-the-Art Report 3 TABLE 1 Summary of Analysis Methodology Data Weight of Data Base Vessel Type Vessel (grt) Location Accident Type Available Measures Accident Tankers and >500 Harbor or bay, Collision, fire or exp]osion, 1976-1980 No. of accldents; no. of fatalities and injuries; mone­ tanker barges or high seas grounding, other tary loss Movement Tankers and >333 Harbor or bay, 1980 No. of vessel trips; no. of tons shipped; no. of tanker barges or high seas vessel-miles; no. of ton-miles For each geographic location, the following ship­ nel case). For every harbor and bay under considera­ ping volume measures were computed: tion, with the segmentation just described, there remain a small number of shipments that are included 1. Vessel movements (trips), in the shipping population that are carrying commod­ 2. Tons shipped, ities that are not hazardous. The only three commod­ 3. Vessel-miles, and ity types that are definitely known to be hazardous 4. Ton-miles. in this list are crude petroleum, petroleum prod­ ucts, and chemicals, in the limiting case. However, These measures, combined with the accident measures these three commodities together constituted more previously described, serve as denominators from than 98 percent of all shipments transported by which to derive accident rates. self-propelled tankers and tanker barges in and out The analysis methodology is summarized in Table of any of the ports; this validates the appropriate­ 1. Three aspects of the movement analysis should he ness of using this methodology to represent hazard­ noted: ous materials transport by tanker. It is important to note the relatively small pop­ 1. A sample of Gulf and Atlantic port shipping ulation of self-propelled tanker movements. Ideally, records was analyzed for high seas measures; results self-propelled tankers would best represent ocean were applied to the remainder of the shipping rec­ transport vessels. It is interesting to note, how­ ords. ever, that several tanker barge trips were found to 2. Domestic shipping results were extrapolated leave the ports and travel into ocean waters, sug­ to foreign shipping traffic by using aggregate for­ gesting that there may be little compromise in rep­ eign tonnage by commodity and domestic coastal resentativeness by including these vessels in the travel only. shipping population being considered. Apparently the 3. Extrapolation to the period 1976 to 1979 was use of a tanker barge in coastal ocean travel is done by using aggregate tonnage from previous years. common practice for shippers of hazardous materials because the regulations for barge movement are often A more detailed description of this methodology is less stringent on manpower requirements. contained in the following discussion. Similar vessel categories can be established for Establishing tbe vessel class to focus on re­ the casualty file to create consistency between quires identifying descriptors in both the volume movement analysis and accident analysis. Accident and incident files that describe a consistent vessel analysis was performed on all tanker and tanker type. The two factors considered were gross regis­ barge vessels greater than 500 grt. To facilitate tered tons (grt) and vessel classification codes. this, the following vessel types were included from Number of gross registered tons appears in the casu­ the casualty file: (a) tanker ships, (b) tank alty file and can be derived for the movement file, barges, (c) public vessels-tanker, (d) flag vessels­ which contains net registered tons. Vessels that tanker, and (e) tank barges-hazardous cargo. weigh more than 500 grt were selected because ves­ It should be noted that the accident data base sels of this size would be indicative of cargo ves­ includes incidents involving both foreign and domes­ sels that are seaworthy. The vessel classifications tic vessels, whereas the movement records described selected for the analysis were tankers and tanker previously involve only domestic vessels. However, barges. aggregate movements of foreign vessels were also ob­ For each harbor and bay, a frequency table was tained and classified by commodity type, which al­ constructed of all shipping and receiving movements lows for extrapolation across commodity type to for tankers and tanker barges that weigh more than derive total domestic and foreign movement measures. 500 grti the table was segmented by type of commod­ The commodities to be used in the extr~polation ity carried (see Table 2 for the Houston Ship Chan- process can be determined by looking at their rela­ tive share of the domestic tanker and tanker barge market and whether the commodity would be considered hazardous cargo. Previously in this paper it was TABLE 2 Tonnage of Tankers and Tanker Barges Weighing shown that petroleum and chemical products consti­ More Than 500 grt, Segmented by Type of Commodity tute more than 98 percent of all tanker and tanker Carried, in the Houston Ship Channel barge cargo transport; therefore, it would appear logical to extrapolate across these commodities. Type of Commodity Self-Propelled Tanker However, it must also be shown that by using these (tons) Tanker Barge Total commodities, other vessel types are not being erro­ neously included in the extrapolation process. Fish products 0 3,509 3,509 Crude petroleum 4,637,312 1,828,244 6,465,556 To examine the question about extrapolation, all Non-metallic minerals 0 364,521 364,521 movements of vessels weighing more than 500 grt that Food products 37,496 42,085 79,581 carry petroleum and chemical products were identi­ Chemicals 1,079,850 7,871,327 8,951, 177 fied (see Table 3 for the Houston Ship Channel Petroleum products 14,590,835 16,582,345 31,173,168 Basic metals 0 3,427 3,427 case).
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