1. Presentation of the project of Yasuyuki Akita Temporal GIS Fall 2004
Spatiotemporal Analysis of Surface Water Tetrachloroethene in New Jersey
Presentation of the project of Yasuyuki Akita
Temporal GIS Fall 2004

2. Agenda About Tetrachloroethene Monitoring Data Details of BME Method
BME Analysis
Results of BME Analysis
New Criterion
Model Comparison
Conclusion

3. About Tetrachloroethene

4. About Tetrachloroethene
Tetrachloroethene: C2Cl4
Volatile organic compound
Nonflammable colorless liquid at room temperature
Ether-like odor
Synonym: Tetrachloroethylene, Perchloroethylene, and PCE

5. Use and Production
Mainly Used for dry cleaning, chemical intermediates, and industrial solvent
PCE used in dry cleaning industry has been declining during 90s
Recent Demand: 763 million lb (1980)
　　　318 million lb (1999)

6. End-Use Pattern in 70s and 90s

7. Exposure pathway Primary route Widely distributed in environment
Inhalation
Ingestion of contaminated food and water
Widely distributed in environment
38% of surface water sampling sites in the U.S.
771 of the 1430 National Priorities List sites
154 of 174 surface water samples in N.J. (1977~1979)

8. Health Effect of Tetrachloroethene
Acute Effect (inhalation exposure)
Dizziness, headache, sleepiness, confusion, nausea, difficulty in speaking and walking, unconsciousness, and death
Chronic Effect (oral/inhalation exposure)
Detrimental effect to kidney and liver

9. Carcinogenicity
Reasonably anticipated to be a human carcinogen (US DHHS)
Group 2A (Probably carcinogenic to humans) (IARC)
Animal studies: tumors in liver and kidney

10. Quality Standard for Tetrachloroethene
Maximum Contaminant Level (MCL) in drinking water mg/L
Surface Water Quality Standard in New Jersey μg/L
N.J. adopted more stringent standard

11. Monitoring Data

12. Monitoring Dataset for New Jersey
Data Source
NJDEP/USGS Water Quality Network Website
EPA STORET database
Data used in this study
369 measured values
171 monitoring stations
From 1999 to 2003

13. Monitoring Data – Histogram
Raw Data
Log-Transformed Data

14. Monitoring Data – Statistical Moments
Raw
(μg/L)
Log-transformed
(log-μg/L)
# of records
369
Mean
Standard Deviation
Coef. of skewness
Coef. of kurtosis

15. Distribution of Data Points

16. Distribution of Data Points

17. Distribution of Data Values

18. What we want to know is … Challenge of our research
Assess all river reaches
Taking into account the space/time variability
Framework for the space/time estimation
Bayesian Maximum Entropy (BME) analysis of TGIS

19. Details of BME Method

20. Space/Time Random Field
The concentration field is modeled in terms of Space/Time Random Field (S/TRF)
Collection of random variables
S/TRF: Collection of all possible realization
Stochastic characterization of S/TRF is provided by multivariate PDF

21. Knowledge Base General Knowledge Base: G
Describe global characteristics of the random field of interest
Expressed as statistical moments
Site-specific knowledge Base: S
Available monitoring data over the space/time domain of interest
Total Knowledge Base: K
K = G ∪ S

22. General Knowledge Base G
Mean Trend
Global trend of the S/TRF of interest
Covariance
Measure of dependency between two points
Sill = variance = covariance(r=0)
Range shows the extent that co-variability exists

23. BME analysis of Temporal GIS
Prior stage
Examine all general knowledge base G and calculate Prior PDF
Integration stage
Update Prior PDF using Bayesian conditionalization on the site-specific knowledge base S and obtain posterior PDF
Interpretive stage
Obtain estimation value from Posterior PDF

24. BME analysis of Temporal GIS
General KB Prior PDF
Update prior PDF with Site-specific KB
Bayesian conditionalization
Posterior PDF is given by conditional probability

25. Summary of BME analysis of TGIS
General KB
Mean trend
Covariance
Site-Specific KB
Hard Data
BME
long
lati t
fK(ck)
Posterior PDF at estimation point
long
lati t
Estimation Value
Estimation Point
Data Point

26. BME Analysis

27. S/TRF for Log-transformed PCE concentration
S/TRF representing Log-tranformed concentration:
Residual field describes purely stochastic aspect of the concentration field
Mean Trend
Residual Field

28. Mean Trend of Log-transformed concentration field
Mean trend consist of two components
Purely spatial component
Purely temporal component
Each component is calculated by exponential smoothing

29. Mean Trend – Temporal Component
Increase from Jan to Jan. 2003
Decrease from Jan. 2003~

30. Mean Trend – Spatial Component
Contaminated Area
Northeastern region
Southwestern region

31. Homogeneous/Stationary S/TRF
Log-transformed data
Removing the mean trend
Residual data for S/TRF:
Homogeneous/Stationary Random Field
Its mean trend is constant
Its covariance is only function of the spatial lag and temporal lag

32. Covariance for Residual S/TRF

33. Covariance for Residual S/TRF

34. Covariance Surface
Experimental Data
Covariance Model

35. Results of BME Analysis

36. BME Estimation – Temporal Fluctuation

37. BME Estimation – Spatial Distribution

38. BME Estimation – Spatial Distribution

39. BME Estimation – Spatial Distribution
(Apr. 15, 2002)

40. BME Estimation – Contaminated Area
Area above the quality standard: 0.388μg/L
(Apr. 15, 2002)
BME mean estimate
Upper bound of the BME 68% confidence interval
Upper bound of the BME 95% confidence interval

41. BME Estimation – Along River Stream
Equidistance points along river stream
More accurate estimation for surface water

42. BME Estimation – Along River Stream
Fraction of river miles that does not attain the quality standard
Mean
68% CI
95% CI
Feb. 5, 2000
0.79%
1.48%
15.03%
Mar. 11, 2001
0.98%
6.86%
66.96%
Apr. 15, 2002
1.50%
9.04%
69.63%
May 20, 2003
0.59%
3.24%
46.08%

43. New Criterion

44. Assessment Criterion S/TRF is characterized by Posterior PDF
Area under the curve = Probability
Prob[PCE>QSTD]=Area under the curve (QSTD<PCE<∞)

45. Assessment Criterion Prob[Non-Attainment]=Prob[PCE>0.388μｇ]
Highly Likely in Attainment
Prob[Non-Attainment]<10%
Highly Likely in Non-Attainment
Prob[Non-Attainment]>90%
Non-Assessment
10%≦Prob[Non-Attainment]≦90%
More Likely Than Not in Non-Attainment
Prob[Non-Attainment]>50%

46. Fraction of River Miles

47. Identifying Contaminated WMAs
The state of New Jersey is divided into 20 Watershed Management Area (WMA)
Assess which part of the state is contaminated
Contribution of each WMA to the fraction of river miles assessed as
Highly Likely in Non-Attainment
More Likely Than Not in Non-Attainment

48. Contribution of WMAs
Highly Likely in Non-Attainment

49. Contribution of WMAs
More Likely Than Not in Non-Attainment

50. Fraction of River Miles in WMAs

51. Model Comparison

52. Model Comparison – Error Variance
Space/Time Analysis
Purely Spatial Analysis
(Feb. 5, 2000)

53. Model Comparison – Cross-Validation
Space/Time Analysis
Purely Spatial Analysis

54. Model Comparison – Cross-Validation
Purely Spatial Analysis
Space/Time Analysis
Improvement
(st-s)/s*100
Mean Error
-64.3%
Mean Absolute Error
-46.4%
Mean Square Error
0.3676
-56.0%

55. Model Comparison – Fraction of River Miles
Space/Time Analysis
Purely Spatial Analysis

56. Conclusion

57. Conclusion About Monitoring Data Application of BME method of TGIS
Some high concentration values are observed in New Jersey between 1999 to 2003.
Monitoring data shows high Space/Time variability in terms of location of the monitoring point and monitoring value
Application of BME method of TGIS
It enables us to take into account high space/time variability and to estimate the concentration all river reaches

58. Conclusion New Criterion
New criterion takes into account the uncertainty information of posterior PDF
It is used to complementary criterion for the conventional one
Fraction of the river miles assessed as “Highly Likely in Non-Attainment” reached about 0.45% in 2000
Fraction of the river miles assessed by the conventional criterion (More Likely Than~) reached about 1.8% in 2002

59. Conclusion Model Comparison
Space/Time analysis produces more accurate estimation than the conventional purely spatial analysis
Space/Time analysis produced very different estimate
In purely spatial analysis, non-assessment river miles reach about 99%
NJ DEP will be able to better assess PCE concentration in all river reaches by using this method and new criterion