Definition
of Discipline Activities: Environmental Chemistry
The
environmental chemistry discipline encompasses a number of related fields
of chemistry and is complimentary to other disciplines offered by ELA such
as environmental geology,
hydrogeology, mining
and remediation, toxicology,
risk assessment, microbiology
and bioremediation, and environmental
and metallurgical engineering.
This discipline also is involved in corrosion
studies and related failure
analysis, and in health
and safety-related issues.
Environmental chemistry is essentially the
science of identifying and measuring the amount of chemicals species in the
environment, natural or manmade. It also includes the study of the fate and
effects of these chemicals species in the environment. It includes such tasks
as defining the intended use of analytical data, preparing sampling plans
to satisfy the intended use, selecting appropriate analytical methods, advising
on the collection of samples in the field, interpreting laboratory analytical
results, and assuring the validity and legal defensibility of analytical results.
In determining fate and effects, it often involves an evaluation or organic
and inorganic chemical reactions as well as physical processes such as volatilization,
cosolvency effects, and soil adsorption. As such, it is a key ingredient in
all other disciplines offered by ELA, such as hydrogeology, remediation, toxicology,
risk assessment, bioremediation, corrosion studies and materials failure analysis,
and in industrial health and safety-related issues. The broad area of environmental
chemistry encompasses a number of related fields, including: analytical chemistry,
chemical engineering, organic chemistry, data quality assurance, radiation
chemistry, and inorganic chemistry
Data Quality Objectives
Environmental chemistry includes such tasks as definition of intended
use of analytical data, preparation of sampling plans to satisfy intended
use, selection of appropriate analytical methods, sample collection in the
field, interpretation of laboratory results, and the fate and effects of chemicals
in the environment. Intended use of the data can include such purposes as
site characterization, compliance monitoring, determination of extent of contamination,
toxicological risk assessment, personnel monitoring, remediation alternative
studies, and remediation verification. The selection of appropriate sampling
and analysis methods must satisfy the applicable state and federal regulations.
The appropriate sampling and analytical methods are determined during the
Data Quality Objective (DQO) process.
DQOs are qualitative and quantitative statements derived from the outputs
of the first six steps of the DQO process. DQOs clarify the study objective,
define the most appropriate type of data to collect, determine the most appropriate
conditions from which to collect the data, and specify tolerance limits on
the data used to make decision. DQOs are not restatements of laboratory limits
for precision and accuracy, but statements that result from the DQO process.
The seven steps of the DQO process are:
1)
State the problem - concisely describe the problem to be studied,
2)
Identify the decision - identify what the study will resolve,
3)
Identify the inputs to the decision - identify the information that
is needed,
4)
Define the boundaries of the study - specify time periods and spatial areas,
5)
Develop a decision rule - define the statistical parameter of interest,
6)
Specify tolerable limits on decision errors - define the decision maker's
error limits, and
7) Optimize
the design for obtaining data - generate alternative data collection designs.
By following the DQO process, one can improve the effectiveness, efficiency,
and defensibility of decisions in a resource-effective manner. Sample collection
must be carried out in a manner that will not compromise the intended use.
Interpretation of laboratory data will involve working with the users of the
data (geologists, hydrogeologists, toxicologists, and environmental engineers)
to determine the suitability of the data for their intended use. Fate
and effects studies address those reactions that may occur due to physical
and chemical processes in the environment and often involve evaluation of
the role of organic and inorganic chemical reaction mechanisms as well as
physical processes such as volatilization, water transport, and soil adsorption.
For additional information on the DQO process, click
here.
Data Quality Assessment
Data Quality Assessment (DQA) is a formal, rigorous scientific and
statistical evaluation to determine if environmental data are of the right
type, quality, and quantity to support their intended use. The process involves
review of data quality objectives (DQOs), sampling purpose, sampling design,
sampling methods, documentation, analytical procedures, validation procedures,
data reduction procedures, review of data base procedures, and review of statistical
methods used for decision making. The five steps in the DQA process are:
1)
Review the DQOs and Sampling Design
2)
Conduct a Preliminary Data Review
3)
Select the Statistical Test
4)
Verify the Assumptions of the Statistical Test
5)
Draw Conclusions from the Data
DQA frequently requires data validation. Data validation is a process
to verify that the laboratory has complied with all the requirements (Quality
Control Checks) of the specified analytical method. Data qualification is
the process of qualifying (flagging) data to reflect any failures to meet
the requirements according to the sets of pre-established functional guidelines.
The USEPA has published generic functional guidelines for flagging environmental
analytical data, much of which is applicable to forensic data. The qualifications
of the data are considered with respect to the intended use of the data to
determine the suitability (or technical validity) which can range from
complete acceptance to partial restriction to complete rejection. Often, data
that has been rejected during the validation process can be "rescued." Data
rescue involves techniques to salvage data that appears to be unsuitable upon
completion of qualification.
Quality Assurance (QA) is the total integrated process for assuring the
defensibility and reliability of decisions based on chemical analysis data.
The principal goal of the QA process is to provide the necessary documentation
for a comprehensive user Data Quality Assessment. The DQA determines if the
data quality is adequate for its intended use and involves a four step process:
1) data validation, 2) data qualification, 3) data rescue, and (4) data suitability
determination. The qualifications of the data are considered with respect
to the intended use of the data to determine the suitability (or technical
validity) which can range from complete acceptance through partial restriction
to complete rejection.
Radioactive Materials
Radioactive materials encountered in the environment arise from numerous
sources including both man-made and natural materials. Naturally- occurring
radioactive material (NORM) contains radioactive nuclides from the decay of
uranium and thorium. NORM frequently forms as scale in pipes or monitoring
wells. As insoluble calcium compounds are formed, radium and other decay products
of uranium and thorium co-precipitate. Daughter products of radium such as
various isotopes of radon, polonium, bismuth, and lead are deposited
in the scale. NORM is often associated with petroleum production, phosphate
and titanium dioxide mining and processing, and with coal ash. Treatment and
disposal of materials contaminated with NORM is strictly controlled by the
NRC and the EPA.
Radiation detectors are extremely sensitive with counting efficiencies
of close to 100% for some types of radiation. This is possible because of
the large amounts of energy deposited in the detector by the radiation. Selection
of the type of detector used is primarily based on the type (and hence the
penetration power) of radiation. Alpha particles have little penetrating ability
and are best detected by thin-window or windowless gas proportional counting
or by liquid scintillation. Beta particles have limited penetrating ability
and are best detected by liquid scintillation. Gamma rays have high penetrating
ability and are best detected with semi-conductor counters such as GeLi or
HPGe.
Sampling Program Design
The design of a sampling scheme and selection of sampling methods is a
multidisciplinary process that often requires input from environmental
chemistry, environmental geology, environmental microbiology, and
risk assessment. The purpose of sampling is to obtain a fraction of
some lot of material that accurately represents the characteristics of the
entire lot. The "lot" is what we are sampling. It may be a landfill, a tank
car, a drum, sediments under a pond, or a jar in the analytical laboratory.
In practice, it is not possible to obtain a perfectly representative sample
of soils or sediments. Sampling procedures should be designed to minimize
the sampling error and to document an estimate of the overall error, which
includes: sampling error, sample handling error, and analytical error. Some
sources of sampling error cannot be eliminated, however with proper understanding
of sampling theory and with clear understanding the purpose of the sampling,
error can be minimized and documented.
Incorrect sampling design or incorrect sampling procedures are just as
damaging to the defensibility of data as are incorrect analytical methods.
Planning for sampling and assessment of sampling methods should be given the
same consideration as analytical data validation.
Inorganic Modeling
Powerful modeling programs (such as the U.S. Geological Survey's PHREEQC)
allow one to calculate: speciation, saturation-index, reaction-path and advective-transport,
mixing of solution, mineral and gas equilibria, surface-complexation reactions,
ion-exchange reactions, and inverse modeling (which finds sets of mineral
and gas transfers that account for compositional differences between aquifers).
Although PHREEQC's database contains a large amount of thermodynamic data,
complete understanding of the chemical composition of the aquifer is required.
Interpretation of the modeling results from such programs requires a thorough
knowledge of chemical equilibrium and physical/chemical processes. The handling
and misuse of analytical data are common subjects of litigation.
Data Validation
Historically, the term "validation" has been used to denote the systematic review of analytical data. In CERCLA (Superfund) programs, the data are compared with the USEPA Contract Laboratory Program (CLP) Inorganic/Organic Statements of Work. In a similar manner, analytical data generated under RCRA are compared to Test Methods for Evaluating Solid Waste, SW-846. In both instances, qualifiers (such as U, J, or R) are added to the data. These data qualifier flags provide data users with information about the quality of the data.
The data validation process has been enormously successful in improving the quality of analytical data. The review process determines the quality of a data set, but does not improve it. However, it is an audit program that has pushed the laboratories to improve their procedure and their adherence to method requirements. Validation is the standard for data that are being collected under litigious or potentially litigious circumstances.
For further information on the discipline, the Institute
of Environmental Technology sponsors an Internet Resources Portal, click (here).
The ELA Principal responsible for the discipline's activities include:
Michael D. Campbell, P.G., P.H.
and
Richard E. Woodward, M.A.
Note: The environmental field is multi-disciplinary by nature
and, for maximum effectiveness, ELA incorporates input from complimentary
disciplines when appropriate in most projects undertaken.
Last Update: January 10, 2005
