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

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

About Tetrachloroethene

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

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)

End-Use Pattern in 70s and 90s

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)

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

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

Quality Standard for Tetrachloroethene Maximum Contaminant Level (MCL) in drinking water - 0.005 mg/L Surface Water Quality Standard in New Jersey - 0.388 μg/L N.J. adopted more stringent standard

Monitoring Data

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

Monitoring Data – Histogram Raw Data Log-Transformed Data

Monitoring Data – Statistical Moments Raw (μg/L) Log-transformed (log-μg/L) # of records 369 Mean 0.156965 -2.597410 Standard Deviation 0.304271 1.043757 Coef. of skewness 5.212834 1.119187 Coef. of kurtosis 39.505912 4.080646

Distribution of Data Points

Distribution of Data Points

Distribution of Data Values

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

Details of BME Method

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

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

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

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

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

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

BME Analysis

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

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

Mean Trend – Temporal Component Increase from Jan. 1999 to Jan. 2003 Decrease from Jan. 2003~

Mean Trend – Spatial Component Contaminated Area Northeastern region Southwestern region

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

Covariance for Residual S/TRF

Covariance for Residual S/TRF

Covariance Surface Experimental Data Covariance Model

Results of BME Analysis

BME Estimation – Temporal Fluctuation

BME Estimation – Spatial Distribution

BME Estimation – Spatial Distribution

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

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

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

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%

New Criterion

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

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%

Fraction of River Miles

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

Contribution of WMAs Highly Likely in Non-Attainment

Contribution of WMAs More Likely Than Not in Non-Attainment

Fraction of River Miles in WMAs

Model Comparison

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

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

Model Comparison – Cross-Validation Purely Spatial Analysis Space/Time Analysis Improvement (st-s)/s*100 Mean Error -0.10728 -0.039363 -64.3% Mean Absolute Error 0.62133 0.33275 -46.4% Mean Square Error 0.83508 0.3676 -56.0%

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

Conclusion

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

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

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