Advertisement

Estimating Impacts of Environmental Interventions in Monitoring Programs Requires Conceptual Data Models and Robust Statistical Processing

(Position Paper)
  • Ladislav Dušek
  • Jana Klánová
  • Jiří Jarkovský
  • Jakub Gregor
  • Richard Hůlek
  • Ivan Holoubek
  • Jiří Hřebíček
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 413)

Abstract

This paper discusses main problems associated with evaluation of performance and impact of long-term environmental programs. Lack of data standards, incompleteness of archived datasets and insufficient statistical power were identified as main limits in functionality of monitoring networks. To avoid these failures, environmental programs should be designed with inception to incorporate data management as their integral part. Especially in global programs, local and regional data managers should invest significant proportion of their effort to handle documentation in terms of standardized coding, data formats, metadata coding and consistency of records over time. Up-to-date trends in building knowledge-based infrastructures are illustrated using example of monitoring of atmospheric pollution by persistent organic pollutants (POPs). Conceptual model usable to facilitate the integration and analysis of data on POPs concentrations is introduced with its multilayer hierarchy of entities (POPs as nomenclature classes, couples “observation – measurement” as content classes). Robust set of statistical methods for processing of time series of concentration data is discussed from the viewpoint of practical implementation within running monitoring programs. It consists of the following components: baseline pollution estimates, uncertainty analyses, spatial extrapolations, effect size estimates, time trend identification and quantification. Development of tools supporting standardized environmental data management is rapidly expanding field of science which results in the following challenges for applied informatics and statistics: log-term sustainability of information systems, data-related metadata coding and archiving, tools for automated integration and reporting of data.

Keywords

environmental monitoring persistent organic pollutants data model data standards statistics 

References

  1. 1.
    Mickwitz, P.: A framework for evaluating environmental policy instruments: Context and key concepts. Evaluation 9, 415–436 (2003)CrossRefGoogle Scholar
  2. 2.
    Crabbé, A., Leroy, P.: The handbook of environmental policy evaluation. Earthscan, London (2008)Google Scholar
  3. 3.
    Goodchild, M.F.: Communicating the results of accuracy assessment: metadata, digital libraries, and assessing fitness for use. In: Mowrer, H.T., Congalton, R.G. (eds.) Quantifying Spatial Uncertainty in Natural Resources: Theory and Applications for GIS and Remote Sensing, pp. 3–15. CRC Press (2000)Google Scholar
  4. 4.
    Rönnbäck, B.I., Nordberg, M.L., Olsson, A., Östman, A.: Evaluation of environmental monitoring strategies. Ambio 32, 495–501 (2003)Google Scholar
  5. 5.
    Gray, J., Liu, D., Nieto-Santisteban, M., Szalay, A., DeWitt, D., Heber, G.: Scientific Data Management in the Coming Decade. CTWatch Quarterly 1, 17–26 (2005)Google Scholar
  6. 6.
    Brunt, J.W., McCartney, P., Baker, K., Stafford, S.G.: The future of eco-informatics in long-term ecological research. The International Institute of Informatics and Systemics IIIS 7. In: Proceedings of the 6th World Multi-Conference on Systematics, Cybernetics and Informatics, Orlando, FL, USA, July 14–18, pp. 367–372 (2002)Google Scholar
  7. 7.
    Dardari, D., Conti, A., Buratti, C., Verdone, R.: Mathematical evaluation of environmental monitoring estimation error through energy-efficient wireless sensor networks. IEEE. Trans. Mob. Comput. 6, 790–802 (2007)CrossRefGoogle Scholar
  8. 8.
    Svenfelt, A., Engström, R., Hojer, M.: Use of explorative scenarios in environmental policy-making-Evaluation of policy instruments for management of land, water and the built environment. Futures 42, 1166–1175 (2010)CrossRefGoogle Scholar
  9. 9.
    Mermet, L., Bille, R., Leroy, M.: Concern-Focused Evaluation for Ambiguous and Conflicting Policies: An Approach From the Environmental Field. Am. J. Eval. 31, 180–198 (2010)CrossRefGoogle Scholar
  10. 10.
    EPA (Environmental Protection Agency): EPA requirements for quality assurance project plans. EPA QA/R-5, United States Environmental Protection Agency, Washington, DC (2001)Google Scholar
  11. 11.
    EPA (Environmental Protection Agency): STORET, EPA’s largest computerized environmental data system (2008), http://www.epa.gov/storet/
  12. 12.
    Michener, W.K., Brunt, J.W.: Ecological Data: Design, Management and Processing. Blackwell Science, Oxford (2000)Google Scholar
  13. 13.
    Jones, M.B., Schildhauer, M., Reichman, O.J., Bowers, S.: The New Bioinformatics: integrating ecological data from the gene to the biosphere. Ann. Rev. Ecol. Evol. Syst. 37, 519–544 (2006)CrossRefGoogle Scholar
  14. 14.
    Elmagarmid, A., Rusinkiewicz, M., Sheth, A.: Management of Heterogeneous and Autonomous Database Systems, vol. 4. Morgan Kaufmann, San Francisco (1999)Google Scholar
  15. 15.
    Grossman, D.A., Frieder, O.: Information Retrieval: Algorithms and Heuristics. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    Darwin Core: Darwin Core Schema (version 1.3), a draft standard of the Taxonomic Database Working Group (TDWG), http://wiki.tdwg.org/DarwinCore
  17. 17.
  18. 18.
    Huang, P.S., Shih, L.H.: Effective environmental management through environmental knowledge management. Int. J. Environ. Sci. Tech. 6, 35–50 (2009)CrossRefGoogle Scholar
  19. 19.
    Madin, J., Bowers, S., Schildhauer, M., Krivov, S., Pennington, D., Villa, F.: An ontology for describing and synthesizing ecological observation data. Int. J. Ecol. Informatics 2, 279–296 (2007)CrossRefGoogle Scholar
  20. 20.
    Williams, R.J., Martinez, N.D., Golbeck, J.: Ontologies for ecoinformatics. J. Web Semant. 4, 237–242 (2006)CrossRefGoogle Scholar
  21. 21.
    Hale, S.S., Hollister, J.W.: Beyond data management: how ecoinformatics can benefit environmental monitoring programs. Environ. Monit. Assess. 150, 227–235 (2009)CrossRefGoogle Scholar
  22. 22.
    Stockholm Convention on Persistent Organic Pollutants (POPs): Interim Secretariat for the Stockholm Convention, United Nations Environmental Programme (UNEP) Chemicals: Geneva, Switzerland (October 2001), http://chm.pops.int
  23. 23.
    Klanova, J., Harner, T.: The challenge of producing reliable results under highly variable conditions and the role of passive air samplers in the Global Monitoring Plan. Trends Anal. Chem. 46, 139–149 (2013)CrossRefGoogle Scholar
  24. 24.
    Secretariat of the Stockholm Convention: UNEP Report of the First Expert Meeting to update the Guidance on the Global Monitoring Plan for Persistent Organic Pollutants (2010), http://chm.pops.int/Programmes/GlobalMonitoringPlan/Meetings/GMP1stExpertMeeting2010/tabid/760/ctl/Download/mid/3261/language/en-US/Default.aspx
  25. 25.
    United Nations Environment Programme: Guidance on the Global Monitoring Plan for Persistent Organic Pollutants. Secretariat of the Stockholm Convention on Persistent Organic Pollutants, Geneva (2007)Google Scholar
  26. 26.
    Berkley, C., Jones, M.B., Bojilova, J., Higgins, D.: Metacat: a schema-independent XML database system. In: Proc. of the 13th Intl. Conf. on Scientific and Statistical Database Management. IEEE Computer Society Press (2001)Google Scholar
  27. 27.
    Borgida, A.: Description logics in data management. IEEE Trans. Knowl. Data Eng. 7, 671–682 (1995)CrossRefGoogle Scholar
  28. 28.
    Cook, R.B., Olson, R.J., Kanciruk, P., Hook, L.A.: Best practices for preparing ecological data sets to share and archive. Bulleting of the Ecological Society of America 82, 138–141 (2001)Google Scholar
  29. 29.
    Cornélis, B., Brunet, S.: A policy-maker point of view on uncertainties in spatial decisions. In: Shi, W., Fisher, P.F., Goodchild, M.F. (eds.) Spatial Data Quality, pp. 168–185. Taylor & Francis (2002)Google Scholar
  30. 30.
    Hartigan, J.A., Hartigan, P.M.: The Dip Test of Unimodality. Ann of Stat. 13, 70–84 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Hůlek, R., Jarkovský, J., Borůvková, J., Kalina, J., Gregor, J., Šebková, K., Schwarz, D., Klánová, J., Dušek, L.: Global Monitoring Plan of the Stockholm Convention on Persistent Organic Pollutants: visualization and on-line analysis of data from the monitoring reports. Masaryk University (2013), http://www.pops-gmp.org/visualization
  32. 32.
    Christensen, S.W., Brandt, C.C., McCracken, M.K.: Importance of data management in a long-term biological monitoring program. Environ. Manage. 47, 1112–1124 (2011)CrossRefGoogle Scholar
  33. 33.
    Emerson, K.J., Merz, C.R., Catchen, J.M., Hohenlohe, P.A., Cresko, W.A., Bradshaw, W.E., Holzapfel, C.M.: Resolving postglacial phylogeography using high-throughput sequencing. Proc. Natl. Acad. Sci. USA 107, 16196–16200 (2010)CrossRefGoogle Scholar
  34. 34.
    Sork, V.L., Waits, L.: Introduction: contributions of landscape genetics approaches, insights, and future potential. Mol. Ecol. 19, 3489–3495 (2010)CrossRefGoogle Scholar
  35. 35.
    Metzger, K.J., Klaper, R., Thomas, M.A.: Implications of informatics approaches in ecological research. Ecological Informatics 6, 4–12 (2011)CrossRefGoogle Scholar
  36. 36.
    Ozmen-Ertekin, D., Ozbay, K.: Dynamic data maintenance for quality data, quality research. Int. J. Inform. Manage. 32, 282–293 (2012)CrossRefGoogle Scholar
  37. 37.
    Michener, W.K.: Meta-information concepts for ecological data management. Ecol. Inform. 1, 3–7 (2006)CrossRefGoogle Scholar
  38. 38.
    Horsburgh, J.S., Tarboton, D.G., Piasecki, M., Maidment, D.R., Zaslavsky, I., Valentine, D., Whitenack, T.: An integrated system for publishing environmental observations data. Environ. Modell. Softw. 24, 879–888 (2009)CrossRefGoogle Scholar
  39. 39.
    Bertzky, M., Stoll-Kleemann, S.: Multi-level discrepancies with sharing data on protected areas: What we have and what we need for the global village. J. Environ. Manage. 90, 8–24 (2009)CrossRefGoogle Scholar
  40. 40.
    Michener, W.K., Jones, M.B.: Ecoinformatics: supporting ecology as a data-intensive science Trends Ecol. Evol. 27, 85–93 (2012)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Ladislav Dušek
    • 1
    • 2
  • Jana Klánová
    • 2
  • Jiří Jarkovský
    • 1
  • Jakub Gregor
    • 1
    • 2
  • Richard Hůlek
    • 2
  • Ivan Holoubek
    • 2
  • Jiří Hřebíček
    • 1
  1. 1.Institute of Biostatistics and AnalysesMasaryk UniversityBrnoCzech Republic
  2. 2.Research Centre for Toxic Compounds in the EnvironmentMasaryk UniversityBrnoCzech Republic

Personalised recommendations