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Spatial dynamic assessment of health risks for urban river cruises

  • Cheng-Shin JangEmail author
  • Ching-Ping Liang
  • Shih-Kai Chen
Article
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Abstract

River cruising ships move along river courses, and thus health risks to passengers may vary spatially due to the accidental exposure of river fecal pollution. This study performed a spatial dynamic assessment of health risks for river cruises in the highly urbanized Tamsui River Basin. First, the spatial distributions of river Escherichia coli (E. coli) were probabilistically characterized using indicator kriging (IK). Moreover, the current river cruise information was surveyed to obtain cruise routes and transit times. Then, to explore the parametric uncertainty of quantitative microbial risk assessment (QMRA), the ingestion rate (IR) for boating was determined using Monte Carlo simulation (MCS). Moreover, river E. coli distributions were estimated using nonparametric MCS according to multi-threshold IK estimates. Eventually, after combining the distribution of the joint probability of the IR and E. coli in QMRA, the β-Poisson dose–response function was adopted to analyze risks to river cruise passengers at discretized segments of cruise routes. Health risks to river cruise passengers were integrated at the discretized segments to explore suitable recreational strategies for river cruises. The research results indicate that all health risks do not exceed a daily target level of 8 illnesses per 1000 exposures for single-trip cruise routes. However, health risks to passengers can exceed this level for round-trip cruise routes along highly polluted urban river courses.

Keywords

River cruise Indicator kriging Monte Carlo simulation Escherichia coli Quantitative microbial risk assessment Uncertainty 

Notes

Acknowledgements

The authors would like to thank the Taiwan Environmental Protection Administration for generously supporting the data on E. coli in the Tamsui River Basin, and the Taiwan Ministry of Science and Technology for financially supporting this research under Contract No. MOST 106-2410-H-424-020.

Funding information

This study received financial support from Taiwan Ministry of Science and Technology under Contract No. MOST 106-2410-H-424-020.

Supplementary material

10661_2018_7122_MOESM1_ESM.docx (840 kb)
ESM 1 (DOCX 840 kb)

References

  1. Brown, T. C., Taylor, J. G., & Shelby, B. (1991). Assessing the direct effects of streamflow on recreation: a literature review. Water Resources Bulletin, 27(6), 979–989.CrossRefGoogle Scholar
  2. Chen, Y. C., Yeh, H. C., & Wei, C. (2012). Estimation of river pollution index in a tidal stream using kriging analysis. International Journal of Environmental Research and Public Health, 9(9), 3085–3100.CrossRefGoogle Scholar
  3. Chen, S. K., Jang, C. S., & Peng, Y. H. (2013). Developing a probability-based model of aquifer vulnerability in an agricultural region. Journal of Hydrology, 486, 494–504.CrossRefGoogle Scholar
  4. Chica-Olmo, M., Luque-Espinar, J. A., Rodriguez-Galiano, V., Pardo-Igúzquiza, E., & Chica-Rivas, L. (2014). Categorical indicator kriging for assessing the risk of groundwater nitrate pollution: the case of Vega de Granada aquifer (SE Spain). Science of the Total Environment, 470–471, 229–239.CrossRefGoogle Scholar
  5. Chigor, V. N., Sibanda, T., & Okoh, A. I. (2014). Assessment of the risks for human health of adenoviruses, hepatitis A virus, rotaviruses and enteroviruses in the Buffalo River and three source water dams in the Eastern Cape. Food and Environmental Virology, 6, 87–98.CrossRefGoogle Scholar
  6. Department of Transportation, Taipei City Government (DOT-TCG) (2017). River cruise. Department of Transportation, Taipei City Government. http://english.dot.gov.taipei/lp.asp?ctNode=65642&CtUnit=35701&BaseDSD=7&mp=117002. Accessed 25 Oct 2017.
  7. Deutsch, C. V., & Journel, A. G. (1998). GSLIB: geostatistical software library and user’s guide (2nd ed.). New York: Oxford University Press.Google Scholar
  8. Donovan, E., Unice, K., Roberts, J. D., Harris, M., & Finley, B. (2008). Risk of gastrointestinal disease associated with exposure to pathogens in the water of the lower Passaic River. Applied and Environmental Microbiology, 74(4), 994–1003.CrossRefGoogle Scholar
  9. Geosyntec. (2008). Dry and wet weather risk assessment of human health impacts of disinfection vs. no disinfection of the Chicago area waterways system (CWS) (pp. 99–110). Chicago: Metropolitan Water Reclamation District of Greater Chicago.Google Scholar
  10. Gerba, C. P., Rose, J. B., Haas, C. N., & Crabtree, K. D. (1996). Waterborne rotavirus: a risk assessment. Water Research, 30(12), 2929–2940.CrossRefGoogle Scholar
  11. Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation (pp. 259–368). New York: Oxford University Press.Google Scholar
  12. Goovaerts, P., Semrau, J., & Lontoh, S. (2001). Monte Carlo analysis of uncertainty attached to microbial pollutant degradation rates. Environmental Science & Technology, 35, 3924–3930.CrossRefGoogle Scholar
  13. Goovaerts, P., AvRuskin, G., Meliker, J., Slotnick, M., Jacquez, G., & Nriagu, J. (2005). Geostatistical modeling of the spatial variability of arsenic in groundwater of southeast Michigan. Water Resources Research, 41.  https://doi.org/10.1029/2004WR003705.
  14. Haas, C. N. (2015). Microbial dose response modeling: past, present, and future. Environmental Science & Technology, 49(3), 1245–1259.CrossRefGoogle Scholar
  15. Haas, C. N., Thayyar-Madabusi, A., Rose, J. B., & Gerba, C. P. (2000). Development of a dose-response relationship for Escherichia coli O157:H7. International Journal of Food Microbiology, 1748, 153–159.CrossRefGoogle Scholar
  16. Haas, C. N., Rose, J. B., & Gerba, C. P. (2014). Quantitative Microbial Risk Assessment (2nd ed.) (pp.72–73 and pp.267–321)). New York: Wiley.Google Scholar
  17. Health Canada. (2012). Guidelines for Canadian Recreational Water Quality (3rd ed.p. 26). Ottawa: Water, Air and Climate Change Bureau, Healthy Environments and Consumer Safety Branch, Health Canada.Google Scholar
  18. Jang, C. S. (2016). Using probability-based spatial estimation of the river pollution index to assess urban water recreational quality in the Tamsui River watershed. Environmental Monitoring and Assessment, 188(36), 1–17.Google Scholar
  19. Jang, C. S., & Chen, S. K. (2018). Establishing a spatial map of health risk assessment for recreational fishing in a highly urbanized watershed. Stochastic Environmental Research and Risk Assessment, 32(3), 685–699.CrossRefGoogle Scholar
  20. Jang, C. S., & Liang, C. P. (2018). Characterizing health risks associated with recreational swimming at Taiwanese beaches by using quantitative microbial risk assessment. Water Science and Technology, 77(2), 534–547.CrossRefGoogle Scholar
  21. Jang, C. S., Liu, C. W., Lin, K. H., Huang, F. M., & Wang, S. W. (2006). Spatial analysis of potential carcinogenic risks associated with ingesting arsenic in aquacultural tilapia (Oreochromis mossambicus) in blackfoot disease hyperendemic areas. Environmental Science & Technology, 40, 1707–1713.CrossRefGoogle Scholar
  22. Jang, C. S., Liang, C. P., & Wang, S. W. (2013). Integrating the spatial variability of water quality and quantity to probabilistically assess groundwater sustainability for use in aquaculture. Stochastic Environmental Research and Risk Assessment, 27, 1281–1291.CrossRefGoogle Scholar
  23. Juang, K. W., & Lee, D. Y. (1998). Simple indicator kriging for estimating the probability of incorrectly delineating hazardous areas in a contaminated site. Environmental Science & Technology, 32, 2487–2493.CrossRefGoogle Scholar
  24. Lee, J. J., Jang, C. S., Wang, S. W., & Liu, C. W. (2007). Evaluation of potential health risk of arsenic-affected groundwater using indicator kriging and dose-response model. Science of the Total Environment, 384, 151–162.CrossRefGoogle Scholar
  25. Money, E. S., Carter, G. P., & Serre, M. L. (2008). Improving the assessment of E. coli exposure levels along un-monitored stream reaches. Epidemiology, 19(6), S162–S163.Google Scholar
  26. Money, E. S., Carter, G. P., & Serre, M. L. (2009). Modern space/time geostatistics using river distances: data integration of turbidity and E. coli measurements to assess fecal contamination along the Raritan River in New Jersey. Environmental Science & Technology, 43, 3736–3742.CrossRefGoogle Scholar
  27. Prideaux, B., Timothy, D. J., & Cooper, M. (2009). In B. Prideaux & M. Cooper (Eds.), Introducing river tourism: Physical, ecological and human aspects. River tourism (pp. 1–22). Wallingford: CABI Publishing.CrossRefGoogle Scholar
  28. Ren, X., Zeng, G., Tang, L., Wang, J., Wan, J., Liu, Y., Yu, J., Yi, H., Ye, S., & Deng, R. (2018a). Sorption, transport and biodegradation – an insight into bioavailability of persistent organic pollutants in soil. Science of the Total Environment, 610-611, 1154–1163.CrossRefGoogle Scholar
  29. Ren, X., Zeng, G., Tang, L., Wang, J., Wan, J., Feng, H., Song, B., Huang, C., & Tang, X. (2018b). Effect of exogenous carbonaceous materials on the bioavailability of organic pollutants and their ecological risks. Soil Biology and Biochemistry, 116, 70–81.CrossRefGoogle Scholar
  30. Rijal, G., Tolson, J. K., Petropoulou, C., Granato, T. C., Glymph, A., Gerba, C., Deflaun, M. F., O’Connor, C., Kollias, L., & Lanyon, R. (2011). Microbial risk assessment for recreational use of the Chicago area waterway system. Journal of Water and Health, 9(1), 169–186.CrossRefGoogle Scholar
  31. Schilling, K. E., Zhang, Y. K., Hill, D. R., Jones, C. S., & Wolter, C. F. (2009). Temporal variations of Escherichia coli concentrations in a large Midwestern river. Journal of Hydrology, 365(1–2), 79–85.CrossRefGoogle Scholar
  32. Soller, J. A., Schoen, M. E., Bartrand, T., Ravenscroft, J. E., & Ashbolt, N. J. (2010). Estimated human health risks from exposure to recreational waters impacted by human and non-human sources of faecal contamination. Water Research, 44(16), 4674–4691.CrossRefGoogle Scholar
  33. Steyn, M., Jagals, P., & Genthe, B. (2004). Assessment of microbial infection risks posed by ingestion of water during domestic water use and full-contact recreation in a mid-southern African region. Water Science and Technology, 50(1), 301–308.CrossRefGoogle Scholar
  34. Sunger, N., & Haas, C. N. (2015). Quantitative microbial risk assessment for recreational exposure to water bodies in Philadelphia. Water Environment Research, 87(3), 211–222.CrossRefGoogle Scholar
  35. Taiwan Environmental Protection Administration (Taiwan EPA) (2017). Environmental water quality information. Environmental Protection Administration, Executive Yuan, Taiwan. http://wq.epa.gov.tw/WQEPA/Code/?Languages=en. Accessed 1 Aug 2017.
  36. Till, D., McBride, G., Ball, A., Taylor, K., & Pyle, E. (2008). Large-scale freshwater microbiological study: rationale, results and risks. Journal of Water and Health, 6(4), 443–460.CrossRefGoogle Scholar
  37. Tseng, L. Y., & Jiang, S. C. (2012). Comparison of recreational health risks associated with surfing and swimming in dry weather and post-storm conditions at Southern California beaches using quantitative microbial risk assessment (QMRA). Marine Pollution Bulletin, 64(5), 912–918.CrossRefGoogle Scholar
  38. U.S. Environmental Protection Agency (U.S. EPA). (1986). Ambient Water Quality Criteria for Bacteria - 1986 (p. 15) (EPA 440-5-84-002)). Washington, DC: U.S. Environmental Protection Agency.Google Scholar
  39. U.S. Environmental Protection Agency (U.S. EPA). (2001). Risk Assessment Guidance for Superfund (RAGS) Volume III - Part A: Process for Conducting Probabilistic Risk Assessment (pp.3–1~3–27). Washington, DC: Office of Emergency and Remedial Response, U.S. Environmental Protection Agency.Google Scholar
  40. U.S. Environmental Protection Agency (U.S. EPA). (2012). Recreational Water Quality Criteria (p. 14) (EPA-820-F-12-058)). Washington, DC: Office of Water, United States Environmental Protection Agency.Google Scholar
  41. U.S. Environmental Protection Agency (U.S. EPA). (2014). Microbiological Risk Assessment (MRA) Tools, Methods, and Approaches for Water Media (pp.90–94 and pp.104–110) (EPA-820-R-14-009). Washington, DC: Office of Science and Technology Office of Water, U.S. Environmental Protection Agency.Google Scholar
  42. Wang, Y. B., Liu, C. W., Liao, P. Y., & Lee, J. J. (2014). Spatial pattern assessment of river water quality: Implications of reducing the number of monitoring stations and chemical parameters. Environmental Monitoring and Assessment, 186(3), 1781–1792.CrossRefGoogle Scholar
  43. World Health Organization (WHO). (2003). Guidelines for safe recreational water environments. Vol. 1. Coastal and fresh waters (pp. 82–87). Geneva: World Health Organization.Google Scholar
  44. World Health Organization (WHO). (2016). Quantitative microbial risk assessment: application for water safety management (pp. 171–179). Geneva: World Health Organization.Google Scholar
  45. Yu, W. H., Harvey, C. M., & Harvey, C. F. (2003). Arsenic in groundwater in Bangladesh: A geostatistical and epidemiological framework for evaluating health effects and potential remedies. Water Resources Research, 39(6).  https://doi.org/10.1029/2002WR001327.

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Cheng-Shin Jang
    • 1
    Email author
  • Ching-Ping Liang
    • 2
  • Shih-Kai Chen
    • 3
  1. 1.Department of Leisure and Recreation ManagementKainan UniversityTaoyuan CityTaiwan
  2. 2.Department of NursingFooyin UniversityKaohsiung CityTaiwan
  3. 3.Department of Civil EngineeringNational Taipei University of TechnologyTaipei CityTaiwan

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