1. Water Quality Portal Semantic e-Science Evan Patton Jin Guang Zheng Ping Wang Theodora Kampelou 11/22/2010

2. Speaking time Ping – 8 min Jin – 12 min Evan – 12 min Theodora – 8 min

3. Agenda Overview Use Case Knowledge Engineering System Architecture Implementation Demonstration Conclusions/Takeaways

4. Overview We want to know if the water from tap is safe to drink or not. We need information about the reservoir from which drinking water originates and corporations located nearby which releases wastes.

5. Get data from USGS, EPA websites

6. How to make life a little easier? Water Quality Portal – Help citizens track issues in water quality – Utilize different data services in different formats – Enable query across various systems using RDF and SPARQL

7. Use Case - Goal A citizen wants to identify potential pollutants that could affect the reservoir from which her drinking water originates, including what tests her municipal water authority performs. She also wishes to include information about corporations located nearby the reservoir that have violated EPA regulations and what pollutants were released into the environment as part of those violations. The portal provides her with a summary of previously reported problems with the water source.

8. Use Case - Actors Primary: Citizen – Interested in water pollution, not expected to be knowledgeable in scientific terminology Primary: Water Pollution Portal – Acts on behalf of the citizen – Collates and reasons about pollution using federal and state regulations Secondary: USGS Water Quality Web Service – Provides data on ground water quality, but data are sparse Secondary: EPA ECHO – Provides data on facilities that must follow the Clean Water Act and other regulations

9. Use Case - Preconditions Existing monitoring of water quality via USGS Existing reporting of violations of EPA regulations via EPA Municipal water supplier reports data regarding water testing to USGS or EPA

10. Use Case - Triggers Observance of above-average cases of illness that are common to water-borne microbes Change in observable water quality, such as, but not limited to, smell, discoloration, presence of large particulate matter

11. Use Case - Basic Flow 1) User (citizen) accesses Water Quality portal 2) User inputs the zip code of the region he/she is interested in 3) Portal requests data about the facilities in the region (e.g. address, violation record) from EPA ECHO 4) Portal requests data about water tests conducted in test sites located in the region from USGS NWIS 5) Portal identifies polluted water sites via SPARQL queries 6) Results are rendered using Google Maps (clicking on a marker reveals additional information about water sites)

12. Use Case - Post Conditions User knows if there are polluted water sites in the region he/she is interested in, what are the pollutants and how severe is the pollution. Portal stores the data acquired during the current session to speed later queries about the same region.

13. Use Case - Activity Diagram

14. Roles Evan: Domain expert, Software engineer, Knowledge engineer, Facilitator Jin: Software engineer, Knowledge engineer Ping: Software engineer Theodora: Software engineer

15. Knowledge Encoding System Architecture

16. Knowledge Engineering I Knowledge & Information Sources USGS Data: – provides measurements of many different chemicals in groundwater and waterways Rhode Island's Water Quality Regulations Data: – provides data about regulations EPA Data: – provides information about specific companies and if or when they have violated EPA regulations

17. Knowledge Engineering II The Ontology Uses OWL 2 to encode the ontology – Needs some OWL 2 features: OWL Restriction, IntersectionOf, etc. … 0.18 … Imports other existing ontologies – Sweet ontology: bodyOfWater, Element, Measurement – Geo ontology: lat, long – Time ontology: instant

18. Ontology Diagram

19. Knowledge Engineering III The Provenance Use PML to encode provenance information The Provenance Information – A water source is marked as polluted water source. Why? Where does the threshold come from? – A facility is marked as it violated regulation. Why? Where does the threshold come from?

20. System Architecture Technologies Encoding Technologies – Web Ontology Language 2 – PML Programming Lanuage – Java Server Side Technologies – Jena + Pellet Query Language – SPARQL Communication Technologies – Asynchronous Javascript and XML (AJAX) Front-end Technologies – HTML, Javascript

21. System & Data Interaction Diagram

22. System Sequence Diagram

23. Implementation

29. Client side implementation: Client enters Zip Code and initiates new session Upon obtaining a session key, four queries are submitted (in series, not parallel) Results are rendered using Google Maps Clicking on a marker reveals additional information – Table of known pollutants Name of pollutant When it was measured Value measured Regulation limit – Links for SPARQL or PML

32. Implementation Some things learned: For performance reasons, application needs to provide state Jena or Pellet (not sure which) has problems with parallel access Data clutter can obscure important information In the future: Better optimization of resources due to lack of asynchronous communications Need for additional information from EPA Better mechanisms for dealing with large number of data points

33. System Architecture

34. Demonstration

35. Conclusions Performance: The portal returns results within 15 seconds on average. The first access may be slower while the system collects data from different resources. Scalability: The portal scales with the number of counties with data available.

36. Conclusions Usability: The portal is easy to use. Anyone who can read/write is able to interact with the system and understand, at a minimum, what water sources are polluted or not and what companies are violating EPA regulations.

37. Conclusions Data: The combination of the 3 different resources gave us more accurate results. The ZIP Code function plays the most significant role.

38. Future Work Except for water pollution, air pollution to be included. Further implementation of the data so as the user to be informed about a possible flooding. Some mathematical models included for better accuracy of the data. Different categories for pollution – Current restrictions are for drinkable quality – Different (higher) values for consuming fish, etc. Expand the water quality portal in other states.

39. Takeaways Group: The diversity of the group and the different levels of knowledge. Pollution awareness: The development of the water quality portal in terms of semantic e-science contributes to the environmental concern and to the health protection of the citizens.

40. Takeaways Semantic e-science: The “semantic” approach of the metadata and its tremendous significance on each field that focuses on, predicts that the future of the web will be semantic. The water quality portal was an example of semantic applications’ power and technological impact. Thank you!!!

41. Resources http://was.tw.rpi.edu/water/ http://code.google.com/p/swqp/ http://www.epa-echo.gov/echo/ http://qwwebservices.usgs.gov/ http://www.dem.ri.gov/

42. Questions?