Skip to Content

Data Gathering and Validation

Data Gathering

Fundamental to water-quality sampling is the fact that the analytical results can be no better than the sample on which the analysis was performed. Thus, the sample collector must accept primary responsibility for the quality and integrity of the sample up to the time that it is delivered to the analyzing laboratory or office. Communication and collaboration between field and laboratory personnel is essential to producing valid data from the sampling effort.

Creating Study Design

Before sample collection begins, field personnel must take steps to ensure that the samples collected will be representative of the water system being investigated. A representative water sample is a sample that typifies ("represents") in time and space that part of the aqueous system to be studied and is delineated by the objectives and scope of the study. Data collection efforts often take a whole-system approach, meaning that data-collection methods ensure representation of an entire stream reach or aquifer volume. A modified approach is needed for studies in which samples are representative of a specific part or aspect of an aqueous system instead of the entire system; for example, a study of aquatic ecology may establish nearshore boundaries on the system, and an oil-spill study may target only the surface of a water-table aquifer.

  • Be alert to sample representativeness. The data are no better than the confidence that can be placed in how well the sample represents the aqueous system.
  • Plan to collect quality-control samples. Quality-control checks applied during laboratory analyses of the samples cannot compensate for data that are biased because samples were not representative of the aqueous system or because samples were improperly collected and processed.

Site Selection

Consider the study objectives, types of data needed, equipment needs, and sampling methods.

  • Obtain all available historical information.
  • Consider physical characteristics of the area, such as size and shape, land use, tributary and runoff characteristics, geology, point and nonpoint sources of contamination, hydraulic conditions, climate, water depth, and fluvial-sediment transport characteristics.
  • Consider chemical and biological characteristics of the area (aquatic and terrestrial).

Sampling Methodologies (both field and lab)

Use appropriate methods and quality-assurance measures to ensure that the field sites selected and the samples collected accurately represent the environment intended for study and can fulfill data-quality objectives.

Frequency

Many factors need to be considered in establishing a sampling frequency for a monitoring program. Depending on the natural environment, land use, and constituent of concern being monitored, temporal changes in ground-water quality may or may not be substantial. For example, ground-water quality in areas where a major land-use change has taken place, such as rangeland being converted to urban land use, has the potential to change in a short period of time (months or years). On the other hand, ground-water quality in areas where almost no land use change has taken place, such as in unharvested forest lands, probably will not change much even over long periods such as decades. Also, the proximity of a sampling site to a known potential source of contamination is important. For example, frequent sampling for VOCs in an area many miles from known potential sources would not be an efficient use of resources. Because collecting, processing, analyzing, and interpreting ground-water-quality samples can be expensive, it is important that these factors be considered in determining the sampling frequency for a water-quality monitoring network. Sampling frequency in a monitoring program could range from sampling for every constituent of interest at a regular time interval such as yearly, to sampling for different groups of constituents at different time intervals. A mixture of these two approaches may provide for the most efficient monitoring program. For example, the initial sampling could consist of testing for all constituents of concern in an area to establish a base line. If initial sampling results show that a given constituent of concern is not present or only present in concentrations that are much less than USEPA and State of Colorado standards, it probably is not necessary to sample for that constituent during every sampling cycle. If elevated concentrations of constituents of concern are detected in samples from the initial sampling round, testing for those constituents during subsequent sampling cycles would be beneficial until a trend can be established. If the trend shows increasing concentrations of the constituent, continued monitoring for that constituent would be beneficial.

Lab Methods and Selection

Accurate data, using the best possible methods, are useless if they do not address the study or program objectives. The strategy for collecting the data needed to meet the goals of a water-quality monitoring project involves a process that is interwoven iteratively with the scientific approach developed for the project as whole, with particular emphasis on monitoring objectives and study design, but also including managing and interpreting the data. Thus, the field and laboratory methods that should be used in a project will depend on the questions being asked, the decisions to be made based on the data collected, and the acceptable degree of risk in reaching an incorrect conclusion or decision.

Documentation (SAP or QAPP)

Everything discussed so far could be a part of either your Sampling & Analysis Plan (SAP) or your Quality Assurance Project Plan (QAPP). The CWQMC worked with partners, Colorado Monitoring Framework and Colorado Water Quality Control Division in developing a SAP to meet the needs of a permitted facility for Regulation 85 monitoring requirements. That document can be found here.

Other resources are: EPA SAMPLING AND ANALYSIS PLAN (SAP) GUIDANCE AND TEMPLATE http://www.epa.gov/region9/qa/pdfs/sap_ot6_gov.pdf

Data Validation

Validation of data requires that appropriate quality assurance and quality control (QA/QC) procedures be followed, and that adequate documentation be included for all data generated both in the laboratory and in the field.

QA/QC

Quality assurance and quality control measures are two distinct types of activities and is defined as follows:

  • Quality Assurance (QA) – a planned system of review procedures conducted by personnel not involved in the inventory development process.
  • Quality Control (QC) – data management and documentation for laboratory analysis.