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1,2,3-Trichloropropane (TCP)

1,2,3-Trichloropropane (TCP) is a chlorinated volatile organic compound (CVOC) that has been used in chemical production processes, in agriculture, and as a solvent, resulting in point and non-point source contamination of soil and groundwater. TCP is mobile and highly persistent in soil and groundwater. TCP is not currently regulated at the national level in the United States, but maximum contaminant levels (MCLs) have been developed by some states. Current treatment methods for TCP are limited and can be cost prohibitive. However, some treatment approaches, particularly in situ chemical reduction (ISCR) with zero valent zinc (ZVZ) and in situ bioremediation (ISB), have recently been shown to have potential as practical remedies for TCP contamination of groundwater.

Related Article(s):

• Anaerobic Bioremediation
• In Situ Chemical Reduction (ISCR)
• In Situ Chemical Oxidation (ISCO)

Contributor(s):

• Alexandra J. Salter-Blanc
• Paul G. Tratnyek
• John Merrill
• Alyssa Saito
• Lea Kane
• Eric Suchomel
• Rula Deeb

Key Resource(s):

• Prospects for Remediation of 1,2,3-Trichloropropane by Natural and Engineered Abiotic Degradation Reactions. Strategic Environmental Research and Development Program (SERDP), Project ER-1457.

• Verification Monitoring for In Situ Chemical Reduction Using Zero-Valent Zinc, A Novel Technology for Remediation of Chlorinated Alkanes. Strategic Environmental Research and Development Program (SERDP), Project ER-201628.

Introduction

Figure 1. Ball and stick representation of the molecular structure of TCP (Salter-Blanc and Tratnyek, unpublished)

1,2,3-Trichloropropane (TCP) (Figure 1) is a man-made chemical that was used in the past primarily as a solvent and extractive agent, as a paint and varnish remover, and as a cleaning and degreasing agent.. Currently, TCP is primarily used in chemical synthesis of compounds such as polysulfone liquid polymers used in the aerospace and automotive industries; hexafluoropropylene used in the agricultural, electronic, and pharmaceutical industries; polysulfide polymers used as sealants in manufacturing and construction; and 1,3-dichloropropene used in agriculture as a soil fumigant. TCP may also be present in products containing these chemicals as an impurity. For example, the 1,2-dichlropropane/1,3-dichloropropene soil fumigant mixture (trade name D-D), which is no longer sold in the United States, contained TCP as an impurity and has been linked to TCP contamination in groundwater. Soil fumigants currently in use which are composed primarily of 1,3-dichloropropene may also contain TCP as an impurity, for instance Telone II has been reported to contain up to 0.17 percent TCP by weight.

TCP contamination is problematic because it is “reasonably anticipated to be a human carcinogen” based on evidence of carcinogenicity to animals. Toxicity to humans appears to be high relative to other chlorinated solvents, suggesting that even low-level exposure to TCP could pose a significant human health risk.

Environmental Fate

TCP’s fate in the environment is governed by its physical and chemical properties (Table 1). TCP does not adsorb strongly to soil, making it likely to leach into groundwater and exhibit high mobility. In addition, TCP is moderately volatile and can partition from surface water and moist soil into the atmosphere. Because TCP is only slightly soluble and denser than water, it can form a dense non-aqueous phase liquid (DNAPL) as observed at the Tyson’s Dump Superfund Site. TCP is generally resistant to aerobic biodegradation, hydrolysis, oxidation, and reduction under naturally occurring conditions making it persistent in the environment.

Table 1. Physical and chemical properties of TCP

Property	Value
Chemical Abstracts Service (CAS) Number	96-18-4
Physical Description (at room temperature)	Colorless to straw-colored liquid
Molecular weight (g/mol)	147.43
Water solubility at 25°C (mg/L)	1,750 (slightly soluble)
Melting point (°C)	-14.7
Boiling point (°C)	156.8
Vapor pressure at 25°C (mm Hg)	3.10 to 3.69
Density at 20°C (g/cm3)	1.3889
Octanol-water partition coefficient (logKow)	1.98 to 2.27 (temperature dependent)
Organic carbon-water partition coefficient (logKoc)	1.70 to 1.99 (temperature dependent)
Henry’s Law constant at 25°C (atm-m3/mol)	3.17x10-4 to 3.43x10-4

Table 2. Advantages and limitations of TCP treatment technologies

Technology	Advantages	Limitations
ZVZ	Can degrade TCP at relatively high and low concentrations Faster reaction rates than ZVI Material is commercially available	Higher cost than ZVI Difficult to distribute in subsurface in situ applications
Groundwater Extraction and Treatment	Can cost-effectively capture and treat larger, more dilute groundwater plumes than in situ technologies Well understood and widely applied technology	Requires construction, operation and maintenance of aboveground treatment infrastructure Typical technologies (e.g. GAC) may be expensive due to treatment inefficiencies
ZVI	Can degrade TCP at relatively high and low concentrations Lower cost than ZVZ Material is commercially available	Lower reactivity than ZVZ, therefore may require higher ZVI volumes or thicker PRBs Difficult to distribute in subsurface in situ applications
ISCO	Can degrade TCP at relatively high and low concentrations Strategies to distribute amendments in situ are well established Material is commercially available	Most effective oxidants (e.g., base-activated or heat-activated persulfate) are complex to implement Secondary water quality impacts (e.g., high pH, sulfate, hexavalent chromium) may limit ability to implement
In Situ Bioremediation	Can degrade TCP at moderate to high concentrations Strategies to distribute amendments in situ are well established Materials are commercially available and inexpensive	Slower reaction rates than ZVZ or ISCO

There are two main approaches to downscaling. One method, commonly referred to as “statistical downscaling”, uses the empirical-statistical relationships between large-scale weather phenomena and historical local weather data. In this method, these statistical relationships are applied to output generated by global climate models. A second method uses physics-based numerical models (regional-scale climate models or RCMs) of weather and climate that operate over a limited region of the earth (e.g., North America) and at spatial resolutions that are typically 3 to 10 times finer than the global-scale climate models. This method is known as “dynamical downscaling”. These regional-scale climate models are similar to the global models with respect to their reliance on the principles of physics, but because they operate over only part of the earth, they require information about what is coming in from the rest of the earth as well as what is going out of the limited region of the model. This is generally obtained from a global model. The primary differences between statistical and dynamical downscaling methods are summarized in Table 1.

It is important to realize that there is no “best” downscaling method or dataset, and that the best method/dataset for a given problem depends on that problem’s specific needs. Several data products based on downscaling higher level spatial data are available (USGS, MACA, NARCCAP, CORDEX-NA). The appropriate method and dataset to use depends on the intended application. The method selected should be able to credibly resolve spatial and temporal scales relevant for the application. For example, to develop a risk analysis of frequent flooding, the data product chosen should include precipitation at greater than a diurnal frequency and over multi-decadal timescales. This kind of product is most likely to be available using the dynamical downscaling method. SERDP reviewed the various advantages and disadvantages of using each type of downscaling method and downscaling dataset, and developed a recommended process that is publicly available. In general, the following recommendations should be considered in order to pick the right downscaled dataset for a given analysis:

• When a problem depends on using a large number of climate models and emission scenarios to perform preliminary assessments and to understand the uncertainty range of projections, then using a statistical downscaled dataset is recommended.
• When the assessment needs a more extensive parameter list or is analyzing a region with few long-term observational data, dynamically downscaled climate change projections are recommended.

Uncertainty in Projections

Table 2. Downscaling model characteristics and output

Model or Dataset Name	Model Method	Output Variables	Output Format	Spatial Resolution	Time Resolution
Statistical Downscaled Datasets
WorldClim	Delta	T(min, max, avg), Pr	NetCDF	grid: 30 arc sec to 10 arc min	month
Bias Corrected / Spatial Disaggregation (BCSD)	Empirical Quantile Mapping	Runoff, Streamflow	NetCDF	grid: 7.5 arc min	day
Asynchronous Regional Regression Model (ARRM v.1)	Parameterized Quantile Mapping	T(min, max), Pr	NetCDF	stations plus grid: 7.5 arc min	day
Statistical Downscaling Model (SDSM)	Weather Generator	T(min, max), Pr	PC Code	stations	day
Multivariate Adaptive Constructed Analogs (MACA)	Constructed Analogues	10 Variables	NetCDF	grid: 2.5 arc min	day
Localized Constructed Analogs (LOCA)	Constructed Analogues	T(min, max), Pr	NetCDF	grid: 3.75 arc min	day
NASA Earth Exchange Downscaled Climate Projections (NEX-DCP30)	Bias Correction / Spatial Disaggregation	T(min, max), Pr	NetCDF	grid: 30 arc sec	month
Dynamical Downscaled Datasets
North American Regional Climate Change Assessment Program (NARCCAP)	Multiple Models	49 Variables	NetCDF	grid: 30 arc min	3 hours
Coordinated Regional Climate Downscaling Experiment (CORDEX)	Multiple Models	66 Variables	NetCDF	grid: 30 arc min	3 hours
Strategic Environmental Research and Development Program (SERDP)	Weather Research and Forecasting (WRF v3.3)	80+ Variables	NetCDF	grid: 6.5 arc min	3 hours

A primary cause of uncertainty in climate change projections, especially beyond 30 years into the future, is the uncertainty in the greenhouse gas (GHG) emission scenarios used to make climate model projections. The best method of accounting for this type of uncertainty is to apply a climate change model to multiple GHG emission scenarios (see also: Wikipedia: Representative Concentration Pathway).

The uncertainties in climate projections over shorter timescales, less than 30 years out, are dominated by something known as “internal variability” in the models. Different approaches are used to address the uncertainty from internal variability. A third type of uncertainty in climate modeling, known as scientific uncertainty, comes from our inability to numerically solve every aspect of the complex earth system. We expect this scientific uncertainty to decrease as we understand more of the earth system and improve its representation in our numerical models. As discussed in Climate Change Primer, numerical experiments based on global climate models are designed to address these uncertainties in various ways. Downscaling methods evaluate this uncertainty by using several independent regional climate models to generate future projections, with the expectation that each of these models will capture some aspects of the physics better than the others, and that by using several different models, we can estimate the range of this uncertainty. Thus, the commonly accepted methods for accounting for uncertainty in climate model projections are either using projections from one model for several emission scenarios, or applying multiple models to project a single scenario.

A comparison of the currently available methods and their characteristics is provided in Table 2 (adapted from Kotamarthi et al., 2016). The table lists the various methodologies and models used for producing downscaled data, and the climate variables that these methods produce. These datasets are mostly available for download from the data servers and websites listed in the table and in a few cases by contacting the respective source organizations.

The most popular and widely used format for atmospheric and climate science is known as NetCDF, which stands for Network Common Data Form. NetCDF is a self-describing data format that saves data in a binary format. The format is self-describing in that a metadata listing is part of every file that describes all the data attributes, such as dimensions, units and data size and in principal should not need additional information to extract the required data for analysis with the right software. However, specially built software for reading and extracting data from these binary files is necessary for making visualizations and further analysis. Software packages for reading and writing NetCDF datasets and for generating visualizations from these datasets are widely available and obtained free of cost (NetCDF-tools). Popular geospatial analysis tools such as ARC-GIS, statistical packages such as ‘R’ and programming languages such as Fortran, C++, and Python have built in libraries that can be used to directly read NetCDF files for visualization and analysis.

See Also

Climate Change Impacts to Department of Defense Installations, SERDP Project RC-2242