BioMedware is delighted to share the article recently published by our Chief Scientist and colleagues titled “Space–Time Distribution of Trichloroethylene Groundwater Concentrations: Geostatistical Modeling and Visualization”. Appearing in the journal Mathematical Geosciences, the article presents a novel geostatistical approach to model and visualize the space—time distribution of groundwater contaminants. Illustrated using data from one of the world’s largest plumes of trichloroethylene (TCE) contamination that has polluted drinking water wells in northern Michigan.
What is Trichloroethylene?
Trichloroethylene (TCE) is a nonflammable, colorless liquid that evaporates quickly into the air. It’s used as a synthetic solvent to remove grease from metal parts and in the manufacture of other chemicals, such as refrigerants. TCE is also used as a solvent for greases, oils, fats, waxes, and tars; in textile processing to clean cotton, wool, and other fabrics; in dry cleaning; and as a component of adhesives, lubricants, paints, varnishes, paint strippers, pesticides, and metal cleaners. TCE is classified as a human carcinogen by the US Department of Health and Human Services, the International Agency for Research on Cancer (IARC) and as “carcinogenic in humans by all routes of exposure” by the US Environmental Protection Agency. It can cause kidney cancer, as well as non-Hodgkin’s lymphoma and can induce headaches or dizziness after short-term exposure.
Because TCE is a volatile organic compound, it forms chemical vapors when present in soil or groundwater. These vapors could migrate upward and enter overlaying buildings through cracks and openings in the foundation. Residents are then exposed to TCE vapor intrusion by inhaling contaminated indoor air.
Research such as this underpins BioMedware’s core mission—reducing the global burden of environmental exposures that lead to illness and death. Follow this link to the article https://rdcu.be/doAd2. The abstract is attached below.
This paper describes a geostatistical approach to model and visualize the space—time distribution of groundwater contaminants. It is illustrated using data from one of the world’s largest plume of trichloroethylene (TCE) contamination, extending over23 km2, which has polluted drinking water wells in northern Michigan. A total of 613 TCE concentrations were recorded at 36 wells between May 2003 and October 2018. To account for the non-stationarity of the spatial covariance, the data were ﬁrst projected in a new space using multidimensional scaling. During this spatial deformation the domain is stretched in regions of relatively lower spatial correlation (i.e.,higher spatial dispersion), while being contracted in regions of higher spatial correlation. The range of temporal autocorrelation is 43 months, while the spatial range is 11 km. The sample semivariogram was ﬁtted using three different types of non-separable space–time models, and their prediction performance was compared using cross-validation. The sum-metric and product-sum semivariogram models performed equally well, with a mean absolute error of prediction corresponding to 23% of the mean TCE concentration. The observations were then interpolated every 6 months to the nodes of a 150 m s pacing grid covering the study area and results were visualized using a three-dimensional space–time cube. This display highlights how TCE concentrations increased over time in the northern part of the study area, as the plume is ﬂowing to the so-called Chain of Lakes.