Oil in the Sea IV: Inputs, Fates, and Effects (2022)

Chapter: Appendix C: Estimating Land-Based Sources of Oil in the Sea

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Suggested Citation: "Appendix C: Estimating Land-Based Sources of Oil in the Sea." National Academies of Sciences, Engineering, and Medicine. 2022. Oil in the Sea IV: Inputs, Fates, and Effects. Washington, DC: The National Academies Press. doi: 10.17226/26410.
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Appendix C

Estimating Land-Based Sources of Oil in the Sea

C.1 INTRODUCTION

Due to the scarcity of individual data samples for estimating land-based loads of oil to the sea, the loading estimates calculated in this analysis were based on loading per unit of urban land area. These calculations assume that most land-based runoff of oil is from urban areas. This approach was used for the United States and Canada and then extrapolated to estimate the other regions of the world. This appendix is an update of Oil in the Sea III, Appendix I.

C.1.1 Methodology and Sources of the Data

A review of The Water Quality Portal’s STORET data indicated that oil and grease data had been collected for the major rivers in the United States; however, eight of those rivers had fewer than 10 observations. Only three rivers—Columbia, Mississippi, and Potomac—had petroleum hydrocarbon data.

Quantified estimates of oil and grease and petroleum hydrocarbon loadings were made for the United States and Canada. These estimates were made using unit loadings per urban land area. The annual loadings were calculated according to the coastal zones defined in this study, and the overall loadings for the United States and Canada were extrapolated to the world. For the calculations in the United States and Canada, the land-based sources were separated into two categories: inland basins and coastal basins. It was assumed that inland basins discharged into one of the following major river basins that outlet to the sea along the coast of the United States and Canada (coastal basins were assumed to discharge directly to the sea):

  • Alabama–Tombigbee
  • Apalachicola
  • Altamaha
  • Brazos
  • Colorado (Texas)
  • Columbia
  • Copper (Alaska)
  • Delaware
  • Hudson
  • James
  • Mississippi
  • Neuse
  • Potomac
  • Rio Grande
  • Roanoke
  • Sabine
  • Sacramento
  • San Joaquin
  • Santee
  • Saskatchewan
  • Savannah
  • St. Lawrence
  • Susquehanna

C.1.2 Calculations for the Inland Rivers of the United States and Canada

The following methodology was used to estimate the loading of oil and grease to the sea from inland river basins in the United States and Canada:

  1. Using the locations designated in Oil in the Sea III (see Table C.1), water quality data were requested from STORET. Using the Water Quality Portal, searches were made for all surface water quality data collected within these regions. Data with the Characteristic Group: Organics, Other and the following characteristics:
    • Oil and grease
    • Hydrocarbons, petroleum
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Suggested Citation: "Appendix C: Estimating Land-Based Sources of Oil in the Sea." National Academies of Sciences, Engineering, and Medicine. 2022. Oil in the Sea IV: Inputs, Fates, and Effects. Washington, DC: The National Academies Press. doi: 10.17226/26410.
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  1. Averages of all reported values from STORET were compiled for each river (see Table C.2) with the following assumptions (rivers not shown in Table C.2 did not have any usable oil and grease data):
    • Data were collected from 2000 onward
    • During calculations for each river, NA (not available) data were not included in the averages
  2. An average annual load in tonne/yr was calculated for those rivers with reported oil and grease data by using the following formula:

    Equation C-1

Li = ci Qi,

whereLi = average annual load for river i (tonne/yr),

ci = average oil and grease concentration for river i (mg/L),

Qi = average annual flow for river i (m3/yr), tonne = 106 g.

The average annual flow (per calendar year) was determined from U.S. Geological Survey (USGS) daily flow data available for each of the rivers at either the same station from Oil in the Sea III or the closest non-tidally influenced station to the collection site for the oil and grease samples (see Table C.3). The average annual flow of the major inland rivers is compared to the flows from 1980 to 1999, similar to the period of record in Oil in the Sea III, in order to assess the changes in river flow since the last report (see Table C-4).

  1. Using data obtained from the State and Metropolitan Area Data Book (2010, 2020), unit loads per urban land area were calculated as follows:

    Equation C-2

Image

where lai = unit load per urban land area for river i (g/m2yr),
Aui = 2019 urban land area for river i (m2)

The 2019 urban land area in each river basin was determined by Table B-1 in State and Metropolitan Area Data Book 2020. Metropolitan areas in this table were partitioned in the major river basins identified in Table C.1, coastal areas, the Great Lakes, or areas not discharging to the coast of the United States or Canada (e.g., Great Salt Lake basin). Urban areas contributing to the Great Lakes fall within one of the counties defined by Indiana Business Research Center (2016). Metropolitan areas contributing urban runoff to the Great Lakes or areas not discharging to the coast of the United States or Canada were not included further in the analysis.

  1. For the majority of the inland river basins, no usable oil and grease data were available in STORET. In addition, the number of non-NA observations for the Columbia, Delaware, James, Roanoke, Sacramento, San Joaquin, Susitna, and Susquehanna rivers were very small. It was therefore decided to use an alternative procedure based on the unit loads of oil and grease per urban land area and per capita calculated from Steps 1–4 to estimate the contributions of oil and grease from these other river basins. The procedure was as follows:
    1. The unit loads of oil and grease per urban land area calculated from Steps 1–4 were used for the other river basins with the following assumptions:
      • The Hudson, James, and Susquehanna rivers were assumed to have unit loads of oil and grease per urban land area of 12.34 g/m2yr, the value calculated from four observations on the Delaware River (this small number of observations was deemed sufficient due to the consistency with the values of samples presented in Oil in the Sea III). The high unit loadings on the Delaware River are due to the highly industrialized nature of the waterway, and these three rivers are also very industrialized and in a similar geographic area.
      • It is assumed that the Alaskan rivers Copper and Susitna did not contribute significant loads of oil and grease to the ocean.
      • All other rivers for which the measured data were not adequate or were unavailable were assumed to have unit loads of oil and grease per urban land area of 0.15 g/m2yr. This value was calculated from 404 non-NA observations on the Potomac. Rivers for which this value applied includes the Alabama–Tombigbee, Altamaha, Apalachicola, Brazos, Colorado (Texas), Columbia, Mississippi, Neuse, Rio Grande, Roanoke, Sabine, Sacramento, Santee, San Joaquin, Saskatchewan, Savannah, St. Lawrence, Trinity, and Yukon rivers.
    2. Using data obtained from the State and Metropolitan Area Data Book (2020) and Statistics Canada (2016), the annual loads per unit land area (Lai) were calculated as follows:

      Equation C-3

      Lai = lai Aui (tonne/106 g)

      where lai was the unit load for river i as described in Step 5.a. The urban land area, Aui was calculated in the same manner as described in Step 4 for metropolitan areas

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Suggested Citation: "Appendix C: Estimating Land-Based Sources of Oil in the Sea." National Academies of Sciences, Engineering, and Medicine. 2022. Oil in the Sea IV: Inputs, Fates, and Effects. Washington, DC: The National Academies Press. doi: 10.17226/26410.
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in the United States. For metropolitan areas in Canada, Aui was calculated using data from Statistics Canada (2016). The urban land area and population for each inland river was compared for the 1990s, 2000s, and 2010s to understand the comparative growth of urban land area and population within each river basin (see Table C.5).

C.1.3 Calculations for the Coastal Zones of the United States and Canada

For the United States, metropolitan areas in State and Metropolitan Area Data Book (2020) were classified as contributing to coastal basins if they fell within 1 of the 254 non–Great Lakes bordering coastal counties defined by Culliton et al. (1990). The individual coastal basin metropolitan areas were then aggregated into the appropriate coastal zones. The data for 2019 urban land area for metropolitan areas (State and Metropolitan Area Data Book, 2020) were then compiled for each coastal zone. Similarly, data from Statistics Canada (2016) for Canadian metropolitan areas along the coast were grouped into the appropriate coastal zones.

The annual load Lai was calculated for urban areas in each coastal zone i in the United States and Canada using Equation C-3. The unit load per urban land area for coastal zone i, lai, was 12.34 g/m2yr for Coastal Zone D, and 0.15 g/m2yr for all other coastal zones. The unit loads were set higher for Coastal Zone D because it is the coastal zone to which the Delaware River discharges.

The total oil and grease loading was determined by adding discharges from inland rivers and urban coastal areas to the appropriate coastal zones.

C.1.4 World Estimates of Oil and Grease

The data used for the calculations of oil and grease loading for North American were not available for other regions of the world. Therefore, a method to extrapolate the North American calculations to the rest of the world was used. It is widely thought that land-based contributions of oil and grease are due primarily to vehicle operation and maintenance (Fam et al., 1987; Hoffman and Quinn, 1987a,b; Latimer et al., 1990; Bomboi and Hernández, 1991; Zeng and Vista, 1997; Latimer and Quinn, 1998). Thus, oil and grease loading estimates for the world were based on the number of motor vehicles in different regions of the world as calculated by the motor vehicles per 1000 persons for each country and population of each country (World Bank, 2020). Oil and grease loading per vehicle was based on the calculations from Oil in the Sea III, 0.01573 tonne/vehicle yr. Redoing the original calculations with the new values found in this report produced a similar value of 0.01593 tonne/veh yr, so the value from the original report was used in order to compare more closely between the two reports.

The number of vehicles in regions of the world was determined by applying Equation C-4 to country data and then compiling for each region. These numbers of vehicles were then multiplied by the loading per vehicle in North America calculated in Oil in the Sea III to obtain a world estimate of loading of oil and grease to the sea via land-based contributions. Because data on actual vehicle usage and maintenance in other countries were unavailable, it was assumed that the loadings of oil and grease per vehicle in North America were representative of oil and grease loadings per vehicle in other parts of the world. This assumption was considered reasonable because, while motor vehicles in other countries of the world are not as well maintained as vehicles in North America and therefore would likely contribute more oil and grease per vehicle while running, motor vehicles are less frequently used in other regions of the world.

C.1.5 Estimates of Petroleum Hydrocarbons and Polycyclic Aromatic Hydrocarbons

Within the STORET data, there were hydrocarbons and petroleum data for three rivers: Columbia, Mississippi, and Potomac (see Table C.2). In Oil in the Sea III, petroleum hydrocarbons loadings were estimated to be 1.5% of the oil and grease loadings. In contrast, the Potomac River data presents a proportion of petroleum hydrocarbons to oil and grease of about 90%. Without information to the contrary, the polycyclic aromatic hydrocarbon (PAH) proportion was considered to be 1% of the hydrocarbons and petroleum data as found in Oil in the Sea III.

C.2 RESULTS

The average annual loads of oil and grease discharged to the sea were calculated for those rivers with reported oil and grease data in STORET (see Table C.6). These total loads were then normalized to unit loads per urban land area. The final estimates of land-based contributions of oil and grease to the sea via all major inland river basins in the United States and Canada were then determined using the oil and grease

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Suggested Citation: "Appendix C: Estimating Land-Based Sources of Oil in the Sea." National Academies of Sciences, Engineering, and Medicine. 2022. Oil in the Sea IV: Inputs, Fates, and Effects. Washington, DC: The National Academies Press. doi: 10.17226/26410.
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data for the Delaware and Potomac rivers (see Table C.7) with urban land area data from State and Metropolitan Area Data Book (2020) and Statistics Canada (2016). About one-seventh of the estimated loading in North America was determined from actual measured data in STORET, with the remainder determined using the unit load approach.

The estimates of land-based contributions of oil and grease to the sea from both major inland rivers and coastal areas in the United States and Canada were totaled by coastal basin, based on the loads calculated in Table C.7 (see Table C.8). The total loading for North America (2.9 million tonne/yr; 4.5 million tonne/yr) was used to obtain a world estimate of land-based oil and grease loading (18.8 million tonne/yr; see Table C.10). The regional distribution of this loading shows that Europe, North America, and Asia contribute the majority of land-based oil and grease to the sea. The population and number of vehicle growth between the years 2000 and 2019 was also looked at for each region (see Table C.9).

Based on the calculations of Oil in the Sea III, a factor of 0.015 was applied to the total oil and grease loading to estimate the fraction of hydrocarbons in oil and grease. The estimated worldwide loading of hydrocarbons to the sea from land-based sources was 259,000 tonnes based on Table C.8 (see Table C.11). Based on Oil in the Sea III, a factor of 0.00015 was applied to the total oil and grease loading to estimate the fraction of PAH in oil and grease. The estimated worldwide loading of PAH to the sea from land-based sources was 2,584 tonne/yr based on Table C.8 (see Table C.11).

C.2.1 Discussion

The method used to estimate land-based oil and grease, hydrocarbon, and PAH contributions to the sea involved a large degree of uncertainty due to a number of factors, including (but not limited to):

  • Lack of data; only 10 major rivers in the United States had oil and grease data in the U.S. Environmental Protection Agency’s STORET database, and many of these consisted of very few observations.
  • Estimating the proportion of petroleum-related hydrocarbons and PAH in oil and grease measurements; the data demonstrate a vastly different proportion of petroleum and hydrocarbon in oil and grease measurements in comparison to the original estimates in Oil in the Sea III.

Quantifying the uncertainty in the estimates presented in this analysis was not possible, but a reasonable estimate of the low and high ranges of the calculated oil and grease values was made (see Table C.12). The low estimate is the lowest unit load per urban land area, 0.15 for the Potomac River. The best estimate was either based on the calculation from available oil and grease data for the river (i.e., Columbia, Delaware, James, Potomac, Roanoke, Sacramento, San Joaquin, Savannah, Susitna, and Susquehanna) or, if those data were not available, an estimate of 1.25 from Oil in the Sea III was used. The estimate of 12.34 was the best estimate for Coastal D, Delaware, and Hudson. The high estimate is the highest unit load per urban land area, 15.88 for the Susquehanna River. Based on these estimates, the range of worldwide loadings of land-based sources of oil and grease to the sea was 7.4–82.5 million tonne/yr, with a best estimate of 21.1 million tonne/yr. For the vehicle-based calculations, the loading per vehicle is based on Oil in the Sea III. The low estimate for loading per vehicle is 0.007619, the best estimate is 0.01573, and the high estimate is 0.055799.

To estimate the range of total petroleum hydrocarbons, STORET data from the Columbia, Potomac, and Mississippi rivers was used. The low estimate was the lowest value for each region from Table C.8. The best estimate was built on the oil and grease and the hydrocarbon and petroleum STORET data, namely the average hydrocarbons and petroleum per urban land area and average oil and grease per urban land area (i.e., ~0.2). The high estimate is based on the proportion of Potomac oil and grease, and hydrocarbon and petroleum data (i.e., ~0.9). The best and high estimates were both proportions of the best estimates of oil and grease annual load (see Table C.12). The estimates of PAH followed suit, assuming PAH constitute 1% of total petroleum hydrocarbons (see Table C.13). The range of land-based petroleum hydrocarbon loading to the sea was 254,000–18,127,000 tonne/yr, with a best estimate of 4,028,000 tonne/yr. The range of PAH loading to the sea from land-based sources was 2,536–181,272 tonne/yr, with a best estimate of 40,281 tonne/yr.

C.2.2 Comparison of Estimates of Land-Based Loading with Other Estimates

The calculations of oil and grease loadings presented in this analysis were based on unit loadings per urban land area. Comparison calculations were also made based on unit loadings per capita urban population using 2019 urban populations in the United States obtained from the State and Metropolitan Area Data Book (2020) and 2016 urban populations in Canada from Statistics Canada (2016). These calculations resulted in oil and grease loadings of the same magnitude as calculations based on unit loadings per urban land area (see Table C.14).

The estimates of the land-based loadings of oil and grease were compared to global and regional oil consumption. According to BP Amoco (2020), North America consumed 1,029 million tonnes of oil in 2019. (The 2019 data was used for comparison, rather than 2020, due to the changes in global oil consumption due to the COVID-19 pandemic.) Assuming that all of the 5.8 million tonne/yr of oil and

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Suggested Citation: "Appendix C: Estimating Land-Based Sources of Oil in the Sea." National Academies of Sciences, Engineering, and Medicine. 2022. Oil in the Sea IV: Inputs, Fates, and Effects. Washington, DC: The National Academies Press. doi: 10.17226/26410.
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grease estimated in this study as returning to the sea from land-based sources were petroleum-derived, then only about 0.56% of consumed oil was returned to the sea from land-based sources. Furthermore, BP Amoco (2020) estimated that the North American annual consumption of oil was broken down as follows:

  • Light Distillates (Gasoline, Naphtha): 519.3 million tonne/yr
  • Middle Distillates (Diesel, Kerosene): 359.5 million tonne/yr
  • Fuel Oil: 20.2 million tonne/yr
  • Other: 312.6 million tonne/yr
  • Total: 1211.6 million tonne/yr

Table C.15 shows comparisons of the computed land-based loads presented in the current study for North America and other regions with the BP Amoco (2020) data.

The best estimate of petroleum hydrocarbon loading from land-based sources was about 3 times as large as the best estimate from the National Research Council (1985), and 28 times as large as Oil in the Sea III (see Table C.16). These discrepancies indicate an increase in oil and grease loadings in the past two decades.

TABLE C.1 Regions Searched for Oil and Grease and Hydrocarbon Data from STORET

RiverLatitudeLongitudeRadius (mi)
Alabama-Tombigbee32°00’00”, 30°00’00”-87°15’00”, -88°15’00”a
Altamaha32°31’30”-81°15’45”50
Apalachicolab
Brazos29°34’56”-95°45’27”50
Colorado (Texas)28°58’26”-96°00’44”30
Columbia46°10’55”-123°10’50”50
Copper (Alaska)61°00’00”-144°45’00”50
Delaware39°30’03”-75°34’07”30
Hudson41°43’18”-73°56’28”40
James37°24’00”-77°18’00”50
Mississippi29°16’26”-89°21’00”50
Neuse35°06’33”-77°01’59”50
Potomac38°55’46”-77°07’02”75
Rio Grande25°52’35”-97°27’15”30
Roanoke35°54’54”-76°43’22”70
Sabine30°18’13”-93°44’37”50
Sacramento37°30’00”, 38°30’00”-121°00’00”, -123°00’00”a
San Joaquin37°30’00”, 38°30’00”-121°00’00”, -123°00’00”a
Santee33°14’00”-79°30’00”40
Saskatchewanb
Savannah32°31’30”-81°15’45”50
St. Lawrence45°00’22”-74°47’43”50
Susitna61°35’00”-150°22’00”40
Susquehanna39°42’00”-76°15’00”50
Trinity29°50’10”-94°44’57”30
Yukon62°45’00”-164°30’00”30

a Rectangular polygons formed by the latitudinal and longitudinal coordinates were searched for these rivers

b No data were requested for the Appalachicola and Saskatchewan Rivers.

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Suggested Citation: "Appendix C: Estimating Land-Based Sources of Oil in the Sea." National Academies of Sciences, Engineering, and Medicine. 2022. Oil in the Sea IV: Inputs, Fates, and Effects. Washington, DC: The National Academies Press. doi: 10.17226/26410.
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TABLE C.2 STORET Data Used to Calculate Average Oil and Grease; Hydrocarbons, Petroleum Concentration in Major Inland Rivers

RiverMonitoring LocationCharacteristic NameNumber of Observations
(non-NA)		Date(s) of ObservationsAverage Concentration
(mg/L)
ColumbiaRiver/Stream at Superfund siteHydrocarbons, Petroleum249 (249)4/27/04–11/30/051.35
Oil and Grease6 (6)11/02/04–4/25/055.03
DelawareRiver/Stream 2,000 yds up buoyOil and Grease14 (4)4/24/00–1/26/0512.55
JamesLogging runoff from landOil and Grease6 (6)4/19/01–4/28/156.83
MississippiHurricane Rita/Urban FloodwaterHydrocarbons, Petroleum4 (2)9/30/05–10/09/051.1
PotomacPeter Pan Run StreamHydrocarbons, Petroleum404 (404)1/10/00–9/21/061.43
Oil and Grease413 (404)1/10/00–9/21/061.55
RoanokeOil and Grease2 (2)5/10/01–8/06/015
SacramentoOil and Grease20 (1)1/24/12–3/26/140.68
San JoaquinDELSOil and Grease6 (6)4/26/000
SavannahStream BMP intakeOil and Grease101 (82)4/07/05–11/07/069.74
SusitnaShip Creek, River/StreamOil and Grease10 (5)5/20/11–9/28/112.04
SusquehannaOil and Grease39 (6)2/15/001/26/0511.9

TABLE C.3 Calculations of Average Annual Flows for Major Inland Rivers

RiverStation NamePeriod of Record UsedAverage Annual Flow
(m3/yr)
Alabama–Tombigbee02469761: Tombigbee River at Coffeeville L&D near Coffeeville, AL2000–2012; 2014–2015; 2017–201924,596,158,257
Altamaha02226000: Altamaha River at Doctortown, GA2000–20209,894,382,025
Brazos08114000: Brazos River at Richmond, TX2000–20207,256,268,535
Colorado (Texas)08162500: Colorado River near Bay City, TX2000–20072,565,942,193
Columbia14246900: Columbia River at Port Westward, near Quincy, OR2000–2019204,666,350,394
Copper (Alaska)15214000: Copper River at Million Dollar Bridge near Cordova, AK2010–2011; 2017–201959,627,302,886
Delaware01463500: Delaware River at Trenton, NJ2000–201912,109,284,340
Hudson01335754: Hudson River above Lock 1 near Waterford, NY2000–20198,257,334,855
James02037500: James River Near Richmond, VA2000–20206,636,808,160
Mississippi07289000: Mississippi River at Vicksburg, MS2000–2020689,140,762,304
Neuse02089500: Neuse River at Kinston, NC2000–2015; 2017–20202,391,360,966
Potomac01608500: South Branch Potomac River near Springfield, WV2000–20201,274,215,458
Rio Grande08375300: Rio Grande at Rio Grande Village, Big Bend NP, TX2008–2019682,154,193
Roanoke02080500: Roanoke River at Roanoke Rapids, NC2000–20207,115,455,412
Sabine08030500: Sabine River near Ruliff, TX2000–20207,064,129,254
Sacramento11425500: Sacramento River at Verona, CA2000–202015,926,970,319
San Joaquin11303500: San Joaquin R near Vernalis, CA2000–20205,720,145,091
Santee02198500: Savannah River near Cylo, GA2000–20203,107,890,290
Savannah02171645: Rediv Canal at Santee River near St. Stephen, SC2000–20208,292,980,383
Susitna15292000: Susitna River at Gold Creek, AK2002–20199,086,707,310
Susquehanna01576000: Susquehanna River at Marietta, PA2000–202036,942,927,072
Trinity08066500: Trinity River at Romayor, TX2000–20208,261,002,528
Yukon15565447: Yukon River at Pilot Station, AK2002–2019210,866,737,379
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Suggested Citation: "Appendix C: Estimating Land-Based Sources of Oil in the Sea." National Academies of Sciences, Engineering, and Medicine. 2022. Oil in the Sea IV: Inputs, Fates, and Effects. Washington, DC: The National Academies Press. doi: 10.17226/26410.
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TABLE C.4 Comparison and Change of Average Annual Flow

RiverStation NamePeriod of Record UsedAverage Annual Flow
(m3/yr)		Percent Change
Alabama–Tombigbee02469761: Tombigbee River at Coffeeville L&D near Coffeeville, AL1980–199926,969,450,885
2000–2012; 2014–2015; 2017–201924,596,158,257-8.80%
Altamaha02226000: Altamaha River at Doctortown, GA1980–199912,222,829,113
2000–20209,894,382,025-19.05%
Brazos08114000: Brazos River at Richmond, TX1980–19997,083,041,689
2000–20207,256,268,5352.45%
Colorado (Texas)08162500: Colorado River near Bay City, TX1980–19992,639,422,583
2000–20072,565,942,193-2.78%
Columbia14246900: Columbia River at Port Westward, near Quincy, OR1992–1999225,024,486,103
2000–2019204,666,350,394-9.05%
Copper (Alaska)15214000: Copper River at Million Dollar Bridge near Cordova, AK1989–199456,287,190,436
2010–2011; 2017–201959,627,302,8865.93%
Delaware01463500: Delaware River at Trenton, NJ1980–199910,046,323,612
2000–201912,109,284,34020.53%
Hudson01335754: Hudson River above Lock 1 near Waterford, NY1980–19997,065,137,067
2000–20198,257,334,85516.87%
James02037500: James River Near Richmond, VA1980–19996,606,492,983
2000–20206,636,808,1600.46%
Mississippi07032000: Mississippi River at Memphis, TN1980–1994492,828,070,403
07289000: Mississippi River at Vicksburg, MS2000–2020689,140,762,30439.83%
Neuse02089500: Neuse River at Kinston, NC1983–19992,643,275,872
2000–2015; 2017–20202,391,360,966-9.53%
Potomac01608500: South Branch Potomac River near Springfield, WV1980–19991,327,513,867
2000–20201,274,215,458-4.01%
Rio Grande08361000: Rio Grande Below Elephant Butte Dam, NM1980–19991,001,672,071
08375300: Rio Grande at Rio Grande Village, Big Bend NP, TX2008–2019682,154,193-31.90%
Roanoke02080500: Roanoke River at Roanoke Rapids, NC1980–19997,469,397,536
2000–20207,115,455,412-9.82%
Sabine08030500: Sabine River near Ruliff, TX1980–19997,890,714,278
2000–20207,064,129,254-10.48%
Sacramento11425500: Sacramento River at Verona, CA1980–199918,506,011,951
2000–202015,926,970,319-13.94%
San Joaquin11303500: San Joaquin River near Vernalis, CA1980–19994,962,946,910
2000–20203,107,890,290-37.38%
Santee02171645: Rediv Canal at Santee River near St. Stephen, SC1987–19998,653,431,256
2000–20205,720,145,091-33.90%
Savannah02198500: Savannah River near Cylo, GA1980–1985; 1987–199910,554,679,518
2000–20208,292,980,383-21.43%
Susitna15292000: Susitna River at Gold Creek, AK1980–19868,904,929,565
2002–20199,086,707,3102.04%
Susquehanna01576000: Susquehanna River at Marietta, PA1980–199932,899,408,125
2000–202036,942,927,07212.29%
Trinity08066500: Trinity River at Romayor, TX1980–19998,461,251,087
2000–20208,261,002,528-2.37%
Yukon15565447: Yukon River at Pilot Station, AK1980–1995209,056,554,789
2002–2019210,866,737,3790.86%
Total1980–19991,169,104,231,699
2000–20201,341,483,269,60414.74%
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Suggested Citation: "Appendix C: Estimating Land-Based Sources of Oil in the Sea." National Academies of Sciences, Engineering, and Medicine. 2022. Oil in the Sea IV: Inputs, Fates, and Effects. Washington, DC: The National Academies Press. doi: 10.17226/26410.
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TABLE C.5 Percent Change of Urban Land Area and Population in River Basins

RiverDecadeUrban Land Area
(m2)		Percent ChangePopulationPercent Change
Alabama–Tombigbee199019,848,114,8061,601,369
200035,059,114,9172,270,148
201035,197,938,27977%2,339,40946%
Altamaha19905,498,803,735454,600
20008,114,173,718553,202
20108,482,211,02854%629,15538%
Apalachicola199021,708,503,2584,016,893
200032,739,780,5736,013,671
201034,848,289,88661%6,692,57967%
Brazos199014,384,534,909987,859
200027,235,019,8661,246,704
201031,468,355,415119%1,493,26851%
Colorado (Texas)199014,887,769,5961,173,671
200017,605,703,1091,762,165
201019,992,118,11434%2,349,110100%
Columbia199030,466,548,0181,263,460
2000101,947,370,5912,316,164
2010123,850,640,954307%2,953,885134%
Delaware19905,082,592,647967,893
20006,004,369,4121,211,805
201012,315,393,416142%3,419,661253%
Hudson199021,972,423,0451,432,124
200023,837,991,4741,587,549
201023,789,040,6998%1,598,03612%
James19907,686,825,682354,043
20009,722,802,098440,200
20109,751,305,19727%482,18136%
Mississippi1990464,341,095,53139,900,057
2000654,764,272,61048,226,561
2010699,320,096,89351%53,628,48834%
Neuse199010,472,875,8811,162,035
200011,483,230,2391,692,198
201012,851,520,95223%2,158,28386%
Potomac19901,950,520,03899,122
20006,960,852,018339,811
201012,880,010,821560%793,235700%
Rio Grande199043,843,577,5561,410,081
200052,220,376,0671,689,829
201054,583,999,20824%1,912,04936%
Roanoke19904,830,068,808337,136
20007,478,590,639403,891
20107,461,755,71654%414,86423%
Sabine19906,964,737,000374,973
20007,028,191,708406,023
20109,326,547,14834%519,40839%
Sacramento199030,438,835,1452,152,519
200033,061,198,0972,873,315
201033,037,888,2049%3,159,04347%
San Joaquin199045,968,921,7902,382,323
200049,560,458,2893,062,479
201049,554,242,3178%3,366,05141%
Santee199026,807,930,8283,183,877
200032,722,945,6513,985,324
201040,737,922,82552%5,225,75564%
Savannah19906,342,621,858457,228
20008,492,052,982534,218
20109,015,748,57642%608,98033%
Susquehanna199027,400,261,1062,788,354
200026,140,749,8932,855,770
201027,676,612,8371%3,071,25510%
Trinity199023,581,064,6544,683,013
200023,283,216,0236,300,006
201022,465,556,77925%7,573,13662%
Page 447 Cite Bookmark
Suggested Citation: "Appendix C: Estimating Land-Based Sources of Oil in the Sea." National Academies of Sciences, Engineering, and Medicine. 2022. Oil in the Sea IV: Inputs, Fates, and Effects. Washington, DC: The National Academies Press. doi: 10.17226/26410.
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TABLE C.6 Calculated Annual Load and Unit Load per Urban Land Area for Major Inland Rivers from STORET Data

RiverAverage Annual Flow
(m3/yr) 2000–2020		Average Concentration
(mg/L)		Average Annual Load
(tonne/yr)		Urban Land Area
(m2) (2019)		Unit Load per Urban Land Area
(g/m2/yr)
Columbia204,666,350,3945.031,029,471.74123,850,640,9548.31
Delaware12,109,284,34012.55151,971.5212,315,393,41612.34
James6,636,808,1606.8345,329.49,751,305,1974.65
Potomac1,274,215,4581.551,975.0312,880,010,8210.15
Roanoke7,115,455,412535,577.287,461,755,7164.77
Sacramento15,926,970,3190.6810,830.3433,037,888,2040.33
San Joaquin3,107,890,2900049,554,242,3170
Savannah8,292,980,3839.7480,773.639,015,748,5768.96
Susitna9,086,707,3102.0418,536.8800
Susquehanna36,942,927,07211.9439,620.8327,676,612,83715.88

TABLE C.7 Estimates of Land-Based Contributions of Oil and Grease to the Sea via Major Inland River Basins

RiverNumber of Non-NA ObservationsAverage Concentration of Oil and Grease,
(mg/L)		Average Annual Flow
(m3/yr)		Urban Land Area in Watershed
(m2)		Annual Load
(tonne/yr)		Unit Load per Urban Land Area
(g/m2/yr)
Calculated from STORET data
Delaware412.5512.1 × 10912.3 × 109151,97212.34
Potomac4041.551.3 × 10912.9 × 1091,9750.15
Subtotal25.2 × 109153,947
Calculated from alternative method
Alabama–Tombigbee35.2 × 1095,2800.15
Altamaha8.5 × 1091,2750.15
Apalachicola35.8 × 1095,3700.15
Brazos31.5 × 1094,7250.15
Colorado (Texas)20 × 1093,0000.15
Columbia123.9 × 10918,5850.15
Copper (Alaska)000
Hudson23.8 × 109293,69212.34
James9.8 × 109120,93212.34
Mississippi699.3 × 109104,8950.15
Neuse12.9 × 1091,9350.15
Rio Grande54.6 × 1098,1900.15
Roanoke7.5 × 1091,1250.15
Sabine9.3 × 1091,3950.15
Sacramento33 × 1094,5900.15
Santee40.7 × 1096,1050.15
San Joaquin49.6 × 1097,4400.15
Saskatchewan2.1 × 1093150.15
Savannah9 × 1091,3500.15
St. Lawrence19.7 × 1092,9550.15
Susitna000
Susquehanna27.7 × 109341,81812.34
Trinity22.5 × 1093,3750.15
Yukon19 × 1092,8500.15
Subtotal1,295.4 × 109941,197
Average2.01
Total1,320.6 × 1091,095,144
Page 448 Cite Bookmark
Suggested Citation: "Appendix C: Estimating Land-Based Sources of Oil in the Sea." National Academies of Sciences, Engineering, and Medicine. 2022. Oil in the Sea IV: Inputs, Fates, and Effects. Washington, DC: The National Academies Press. doi: 10.17226/26410.
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TABLE C.8 Estimate of Land-Based Oil and Grease to the Sea by Coastal Zone Based on Table C.7

Coastal ZoneDescriptionUrban Population in Watershed, PiUrban Land Area in Watershed, Aui (m2)Annual Load, Lai (tonne/yr)z
ANo urban areas0000
BCoastal0000
Saskatchewan3,009,1302,113,000,0003170.15
Subtotal3,009,1302,113,000,000317
CCoastal1,680,6531,555,000,0002330.15
St. Lawrence6,278,75819,676,169,3782,9510.15
Coastal7,959,41121,231,169,3783,184
DCoastal51,808,560139,545,968,8401,721,99812.34
Delaware3,419,66112,315,393,416151,96712.34
Hudson1,598,03623,789,040,699293,55612.34
James482,1819,751,305,197120,32712.34
Potomac793,23512,880,010,8211,9320.15
Susquehanna3,071,25527,676,612,837341,53412.34
Subtotal61,172,928225,958,331,8102,631,314
ECoastal17,810,577102,074,021,00915,3110.15
Altamaha629,1558,482,211,0281,2720.15
Neuse2,158,28312,851,520,9521,9280.15
Roanoke414,8647,461,755,7161,1190.15
Santee5,225,75540,737,922,8256,1110.15
Savannah608,9809,015,748,5761,3520.15
Subtotal26,847,614180,623,180,10627,093
FCoastal8,252,78952,675,177,9787,9010.15
Alabama–Tombigbee2,339,40935,197,938,2795,2800.15
Apalachicola6,692,57934,848,289,8865,2270.15
Subtotal17,284,777122,721,406,14318,408
GCoastal12,773,63997,880,830,27514,6820.15
Brazos1,493,26831,468,355,4154,7200.15
Colorado (Texas)2,349,11019,992,118,1442,9990.15
Mississippi53,628,488699,320,096,893104,8980.15
Rio Grande1,912,04954,583,999,2088,1880.15
Sabine519,4089,326,547,1481,3990.15
Trinity7,573,13622,465,556,7793,3700.15
Subtotal80,249,098935,037,503,862140,256
INo urban areas0000
KCoastal22,049,76698,888,335,64614,8330.15
LCoastal9,239,07046,772,595,0987,0160.15
Sacramento3,159,04333,037,888,2044,9560.15
San Joaquin3,366,05149,554,242,3177,4330.15
Subtotal15,764,164129,364,725,61919,405
MCoastal8,542,08372,757,945,70510,9140.15
Columbia2,953,885123,850,640,95418,5780.15
Subtotal11,495,968196,608,586,65929,492
NCoastal1,141,9804,566,149,0206850.15
OCoastal3,011,7191,367,000,0002050.15
PCoastal396,31768,430,075,59010,2650.15
Copper0000
Susitna0000
Subtotal396,31768,430,075,59010,265
QCoastal0000
Yukon96,84918,984,612,7732,8480.15
Subtotal96,84918,984,612,7732,848
Total250,479,7212,005.9 × 1092,880,805
Page 449 Cite Bookmark
Suggested Citation: "Appendix C: Estimating Land-Based Sources of Oil in the Sea." National Academies of Sciences, Engineering, and Medicine. 2022. Oil in the Sea IV: Inputs, Fates, and Effects. Washington, DC: The National Academies Press. doi: 10.17226/26410.
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TABLE C.9 Percent Change of Global Population and Number of Vehicles

RegionYearPopulationPopulation Percent ChangeNumber of VehiclesVehicle Percent Change
Africa2000778,484,00015,569,680
20191,306,033,37568%44,902,492188%
Europe2000729,406,000196,939,620
2019743,131,3572%346,027,25576%
North America2000304,078,000218,936,160
2019365,889,13220%284,430,08430%
Central America2000130,710,00014,378,100
2019217,693,61767%44,441,062209%
South America2000331,889,00029,870,010
2019428,615,77429%84,113,596182%
Asia20003,588,877,000107,666,310
20194,566,180,07127%370,533,424244%
Oceania200029,460,00012,667,800
201942,461,75944%22,194,09375%
Total20005,892,904,000596,027,680
20197,670,005,08530%1,196,642,006101%

TABLE C.10 World Estimates of Land-Based Sources of Oil and Grease to the Sea

RegionPopulationMotor Vehicles per CapitaNumber of VehiclesLoading per VehicleLoading (tonne/year)
Africa1,306,033,3750.0344,902,4920.01573706,316
Europe743,131,3570.47346,027,2550.015735,443,009
North America365,889,1320.78284,430,0840.015734,474,085
Central America217,693,6170.20444,441,0620.01573699,058
South America428,615,7740.19684,113,5960.015731,323,107
Asia4,566,180,0710.08370,533,4240.015735,828,491
Oceania42,461,7590.5222,194,0930.01573349,113
Total7,670,005,0851,196,642,0060.0157318,823,179
Page 450 Cite Bookmark
Suggested Citation: "Appendix C: Estimating Land-Based Sources of Oil in the Sea." National Academies of Sciences, Engineering, and Medicine. 2022. Oil in the Sea IV: Inputs, Fates, and Effects. Washington, DC: The National Academies Press. doi: 10.17226/26410.
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TABLE C.11 Estimates of Worldwide Land-Based Contributions of Hydrocarbons and PAH to the Sea Based on Table C.8

World RegionCoastal ZoneDescriptionHydrocarbon (tonne/year)PAH (tonne/year)
North AmericaANo urban area00
BCoastal00
Saskatchewan50
Subtotal50
CCoastal30
St. Lawrence440
Subtotal470
DCoastal25,830258
Delaware2,28023
Hudson4,40344
James1,80518
Potomac290
Susquehanna5,12351
Subtotal39,470394
ECoastal2302
Altamaha190
Neuse290
Roanoke170
Santee921
Savannah200
Subtotal4073
FCoastal1191
Alabama–Tombigbee791
Apalachicola781
Subtotal2763
GCoastal2202
Brazos711
Colorado (TX)450
Mississippi1,57316
Rio Grande1231
Sabine210
Trinity511
Subtotal2,10421
INo urban areas00
KCoastal2222
LCoastal1051
Sacramento741
San Joaquin1111
Subtotal2903
MCoastal1642
Columbia2793
Subtotal4435
NCoastal100
OCoastal30
PCoastal1542
Copper00
Susitna00
Subtotal1542
QCoastal00
Yukon430
Subtotal430
Subtotal43,474433
Africa10,595106
Europe81,645816
Central America10,486105
South America19,847198
Asia87,427874
Oceania5,23752
TOTAL258,7112,584
Page 451 Cite Bookmark
Suggested Citation: "Appendix C: Estimating Land-Based Sources of Oil in the Sea." National Academies of Sciences, Engineering, and Medicine. 2022. Oil in the Sea IV: Inputs, Fates, and Effects. Washington, DC: The National Academies Press. doi: 10.17226/26410.
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TABLE C.12 Ranges of Worldwide Land-Based Contributions of Oil and Grease to the Sea

World RegionCoastal ZoneDescriptionUnit Load Based on Urban Area (g/m2/yr)Annual Load (tonne/yr)
LowBest EstimateHighLowBest EstimateHigh
North AmericaANo urban area000000
BCoastal000000
Saskatchewan0.151.2515.883172,64133,554
Subtotal3172,64133,554
CCoastal0.151.2515.882331,94424,693
St. Lawrence0.151.2515.882,95124,595312,455
Subtotal3,18426,539337,148
DCoastal0.1512.3415.8820,9321,721,9982,215,990
Delaware12.3412.3412.34151,967151,967151,967
Hudson0.1512.3415.883,568293,556377,769
James0.154.6515.881,46345,342154,846
Potomac0.150.150.151,9321,9321,932
Susquehanna0.1515.8815.884,152439,511439,511
Subtotal184,0142,654,3063,342,015
ECoastal0.151.2515.8815,311127,5931,620,935
Altamaha0.151.2515.881,32611,053140,411
Neuse0.151.2515.881,92816,065204,090
Roanoke0.154.7715.881,11935,594118,497
Santee0.151.2515.886,11150,923646,919
Savannah0.158.9615.881,35280,783143,174
Subtotal27,147322,0112,874,026
FCoastal0.151.2515.887,90165,844836,479
Ala-Tom0.151.2515.885,28043,998558,944
Apalachicola0.151.2515.885,22743,560553,386
Subtotal18,408153,4021,948,859
GCoastal0.151.2515.8814,682122,3511,554,350
Brazos0.151.2515.884,72039,335499,712
Colorado (TX)0.151.2515.882,99924,990317,473
Mississippi0.151.2515.88104,898847,15011,105,202
Rio Grande0.151.2515.888,18868,230866,794
Sabine0.151.2515.881,39911,659148,113
Trinity0.151.2515.883,37028,083356,760
Subtotal140,2561,141,79814,848,404
INo urban areas000000
KCoastal0.151.2515.8814,833123,6101,570,341
LCoastal0.151.2515.887,01658,466742,755
Sacramento0.150.3315.884,95610,903524,643
San Joaquin0.151.2515.887,43361,943786,918
Subtotal19,405131,3122,054,336
MCoastal0.151.2515.8810,91490,9481,155,397
Columbia0.158.3115.8818,5781,029,2021,966,754
Subtotal29,4921,120,1503,122,151
NCoastal0.151.2515.886855,70872,508
OCoastal0.151.2515.882051,70921,708
PCoastal0.151.2515.8810,26585,5381,086,668
Copper000000
Susitna000000
Subtotal10,26585,5381,086,668
QCoastal000000
Yukon0.151.2515.882,84823,731301,482
Subtotal2,84823,731301,482
Subtotal451,0595,792,44531,613,200
Africa342,112706,3162,505,514
Europe2,636,3815,443,00919,307,975
Central America338,597699,0582,479,767
South America640,8621,323,1074,693,455
Asia2,823,0945,828,49120,675,395
Oceania169,097349,1131,238,408
TOTAL7,401,20220,141,549
Page 452 Cite Bookmark
Suggested Citation: "Appendix C: Estimating Land-Based Sources of Oil in the Sea." National Academies of Sciences, Engineering, and Medicine. 2022. Oil in the Sea IV: Inputs, Fates, and Effects. Washington, DC: The National Academies Press. doi: 10.17226/26410.
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TABLE C.13 Ranges of Worldwide Land-Based Contributions of Hydrocarbons and PAH to the Sea

World RegionCoastal ZoneDescriptionHydrocarbons (tonne/yr)PAH (tonne/yr)
LowBest EstimateHighLowBest EstimateHigh
North AmericaANo urban area000000
BCoastal000000
Saskatchewan55282,3770524
Subtotal55282,3770524
CCoastal33891,7500418
St. Lawrence444,91922,136049221
Subtotal475,30823,886053239
DCoastal25,579344,4001,549,7982563,44415,498
Delaware2,25730,393136,770233041,368
Hudson4,40358,711264,200445872,642
James1,7879,06840,8081891408
Potomac293861,7390417
Susquehanna51987,902395,56058793,956
Subtotal34,574530,8602,388,8753465,30923,889
ECoastal23025,519114,83422551,148
Altamaha192,2119,94802299
Neuse293,21314,459032145
Roanoke177,11932,035071320
Santee9210,18545,8311102458
Savannah2016,15772,7050162727
Subtotal40764,414289,81236442,897
FCoastal8813,16959,2601132593
Ala-Tom798,80039,598188396
Apalachicola788,71239,204187392
Subtotal24530,681138,06233071,381
GCoastal22024,470110,11622451,101
Brazos717,86735,402179354
Colorado (TX)454,99822,491050225
Mississippi1,573169,430762,435161,6947,624
Rio Grande12313,64661,4071136614
Sabine212,33210,493023105
Trinity515,61725,274156252
Subtotal2,104228,3601,027,618212,28310,275
INo urban areas000000
KCoastal22224,722111,24922471,112
LCoastal10511,69352,6191117526
Sacramento742,1819,81312298
San Joaquin11112,38955,7491124557
Subtotal29026,263118,18132631,181
MCoastal16418,19081,8532182819
Columbia279205,840926,28232,0589,263
Subtotal443224,0301,008,13552,24010,082
NCoastal101,1425,13701151
OCoastal33421,5380315
PCoastal15417,10876,9842171770
Copper000000
Susitna000000
Subtotal15417,10876,9842171770
QCoastal000000
Yukon414,74621,358047214
Subtotal414,74621,358047214
Subtotal38,5451,158,5045,213,21238511,58352,130
Africa10,595141,263635,6841061,4136,357
Europe81,6451,088,6024,898,70881610,88648,987
Central America10,486139,812629,1521051,3986,292
South America19,847264,6211,190,7961982,64611,908
Asia87,4271,165,6985,245,64287411,65752,456
Oceania5,23769,823314,202526983,142
TOTAL253,7824,028,32318,127,3962,53640,281181,272
Page 453 Cite Bookmark
Suggested Citation: "Appendix C: Estimating Land-Based Sources of Oil in the Sea." National Academies of Sciences, Engineering, and Medicine. 2022. Oil in the Sea IV: Inputs, Fates, and Effects. Washington, DC: The National Academies Press. doi: 10.17226/26410.
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TABLE C.14 Comparison of Estimates of Worldwide of Land-Based Oil and Grease Based on Population and Area

World RegionCoastal ZoneDescriptionAnnual Load Based on Population (tonne/yr)Annual Load Based on Area (tonne/yr)
North AmericaANo urban area00
BCoastal00
Saskatchewan7,329317
Subtotal7,329317
CCoastal4,093233
St. Lawrence15,2932,951
Subtotal19,3863,184
DCoastal2,302,3311,721,998
Delaware151,967151,967
Hudson71,015293,556
James45,34245,342
Potomac1,9321,932
Susquehanna439,511439,511
Subtotal3,012,0982,654,306
ECoastal43,52615,311
Altamaha1,5321,272
Neuse5,2571,928
Roanoke35,59435,594
Santee12,7286,111
Savannah80,78380,783
Subtotal179,420140,999
FCoastal21,1007,901
Alabama–Tombigbee5,6985,280
Apalachicola16,3005,227
Subtotal43,09818,408
GCoastal31,11114,682
Brazos3,6374,720
Colorado (TX)5,7212,999
Mississippi130,617104,898
Rio Grande4,6578,188
Sabine1,2651,399
Trinity18,4453,370
Subtotal195,453140,256
INo urban areas00
KCoastal53,70414,833
LCoastal22,5027,016
Sacramento10,90310,903
San Joaquin8,1987,433
Subtotal41,60325,352
MCoastal20,80510,914
Columbia1,029,2021,029,202
Subtotal1,050,0071,040,116
NCoastal2,781685
OCoastal7,335205
PCoastal96510,265
Copper00
Susitna00
Subtotal96510,265
QCoastal00
Yukon2362,848
Subtotal2362,848
Subtotal4,613,4154,051,774
Africa1,027,411706,316
Europe7,917,4255,443,009
Central America1,016,853699,058
South America1,924,5971,323,107
Asia8,478,1495,828,491
Oceania507,822349,113
TOTAL25,485,67218,400,868
Page 454 Cite Bookmark
Suggested Citation: "Appendix C: Estimating Land-Based Sources of Oil in the Sea." National Academies of Sciences, Engineering, and Medicine. 2022. Oil in the Sea IV: Inputs, Fates, and Effects. Washington, DC: The National Academies Press. doi: 10.17226/26410.
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TABLE C.15 Comparison of Oil Consumption with Estimated Oil and Grease Loading from Land-Based Sources to the Sea

Location2019 Oil Consumption
(million tonne/yr)		Oil and Grease Loading to the Sea from Land-Based Sources
(million tonne/yr)		Ratio of Oil and Grease Loading to the Sea to Oil Consumption
(percent)
North America1,0295.80.56
South and Central America274.220.73
Europe7005.40.77
Africa190.10.70.37
Asia2,229.35.80.26
World4,422.720.10.45

C.3 UNDERSTANDING LAND-BASED INPUTS OF FOSSIL FUEL HYDROCARBONS TRANSPORTED VIA THE ATMOSPHERE

To understand the inputs of fossil fuel hydrocarbons that are emitted from land and transported via the atmosphere, numerous sampling campaigns to measure petroleum hydrocarbons, specifically PAHs, in marine atmospheres and surface waters have been performed. This increase in data covers a broad geographic area and provides insights into the inputs and sources of PAH compounds to marine surface waters (see Table C.17).

The methods used in these studies are a combination of a high-volume sampler (air) or direct intake (water) passed through a filter to remove particulate matter, followed by a polyurethane foam (PUF; air) or resin (water and sometimes air) sampler to collect the compounds of interest (exception is Lohmann et al., 2011, and Zheng et al., 2021, which used passive polyethylene samplers). The PUF or resin is then extracted with an organic solvent and analyzed primarily via gas chromatography–mass spectrometry (GC-MS) in all but one instance (Nizzetto et al., 2008, uses high-performance liquid chromatography). Quality assurance and quality control including laboratory and field blanks, recoveries and analytical limits are reported in all studies described in the Table C.17. For González-Gaya et al. (2016), the quantification of the semivolatile aromatic-like compounds is more challenging as these compounds cannot be resolved by the gas chromatographic techniques used and are therefore not identified. Given this, the source of these compounds cannot be confirmed, and further examination is required to determine what these compounds are and what their source is. For example, there may be potential inputs from dissolved organic matter derived from biogenic sources. Quantification of the semivolatile aromatic-like compounds also requires further verification before this dataset could provide reliable estimates of inputs in the same way that the PAH measurements are.

TABLE C.16 Comparison of Petroleum Hydrocarbon Loading Estimates from Land-Based Sources from This Work and Other Studies

Hydrocarbon Loading (tonne/yr)
ReferenceCommentsLowBest EstimateHigh
World estimates
Baker (1983)Petroleum hydrocarbons from municipal wastes, industrial waste, and runoff700,0001,400,0002,800,000
National Research Council (1985)World estimate of land-based sources600,0001,200,0003,100,000
Van Vleet and Quinn (1978)Petroleum hydrocarbons from municipal wastes only based on Rhode Island treatment plants—200,000—
Oil in the Sea III (2003)World estimate of land-based sources6,800141,0005,000,000
This workWorld estimate of land-based sources254,0004,000,00018,000,000
Ratio (This work: Oil in the Sea III)37283.6
North American estimates
Eganhouse and Kaplan (1982)U.S. input of petroleum hydrocarbons based on mass emission rate for wastewater effluent in southern California—120,600—
Oil in the Sea III (2003)North American estimate of land-based sources2,50052,0001,800,000
This workNorth American estimate of land-based sources38,5001,158,5005,213,200
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Suggested Citation: "Appendix C: Estimating Land-Based Sources of Oil in the Sea." National Academies of Sciences, Engineering, and Medicine. 2022. Oil in the Sea IV: Inputs, Fates, and Effects. Washington, DC: The National Academies Press. doi: 10.17226/26410.
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TABLE C.17 Studies Examining Polycyclic Aromatic Hydrocarbons in Marine Surface Waters and Atmospheres

LocationSample TypeDateProposed SourcesReference
Atlantic and Indian OceanMarine aerosol ~15 m above the sea surface (81 samples)1999Predominantly fossil fuel with some biomassCrimmins et al., 2004
South AtlanticAir and water samples (14 samples)2005Uncombusted fuel, oil spills, gas flaring, ship transport, biomass burningNizzetto et al., 2008
Narragansett Bay, Atlantic OceanAir and water samples (72 of each)2006Fossil fuel combustionLohmann et al., 2011
Mediterranean and Black SeaGas and aerosol (66 samples) and 43 water samples (2–3 m depth)2006
2007		Gas phase is pyrogenic, aerosol phase is mixture of pyrogenic and petrogenicCastro-Jiménez et al., 2012
East and South China Seas, Bay of Bengal, Indian Ocean, Atlantic OceanGaseous (60 samples, 9 PAH) and particle bound (44 samples, 15 PAH) from marine boundary layer2008Coal and coke from Mainland China, biomass burning in Africa and Southeast AsiaXu et al., 2012
Southern OceanGas (22 samples) and aerosol phase (30 samples)2005
2008
2009		Long-range transport and local sources, specifics not providedCabrerizo et al., 2014
Tropical Atlantic OceanWater (57 samples) and air (47 samples)2009Traffic emissions and petroleum combustion productsLohmann et al., 2013
North Pacific toward the Arctic OceanBoundary layer air (17 samples) and surface seawater (18 samples)2010Biomass or coal in air, mixture in seawaterMa et al., 2013
Tropical and subtropical Atlantic, Pacific, Indian OceansGas and aerosol (108 samples) and water samples (68)2010Mixed sources. SALC also calculated, but quantification more challengingGonzález-Gaya et al., 2016
North Pacific-Arctic OceansAtmospheric (32) and surface seawater (16)2014Combustion-derived via long-range transport, and sea ice melting and runoffZheng et al., 2021
Livingston Island, AntarcticaAir (52 samples), seawater (26 samples, 0.5–1 m depth)2014
2015		Mixed sources (long-range and local)Casal et al., 2018
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Next Chapter: Appendix D: Regional Values of Water-to-Oil Ratio for Calculating Inputs from Produced Water
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