Arizona Administrative Code › Title 18 - ENVIRONMENTAL QUALITY › Chapter 11 - DEPARTMENT OF ENVIRONMENTAL QUALITY - WATER QUALITY STANDARDS › Article 6 - IMPAIRED WATER IDENTIFICATION › Section R18-11-602 - Credible Data

Current through Register Vol. 30, No. 28, July 12, 2024
A. Data are
				credible and relevant to an impaired water identification or a TMDL decision
				when:
1. Quality Assurance Plan. A monitoring
				entity, which contribute data for an impaired water identification or a TMDL
				decision, provides the Department with a QAP that contains, at a minimum, the
				elements listed in subsections (A)(1)(a) through (A)(1)(f). The Department may
				accept a QAP containing less than the required elements if the Department
				determines that an element is not relevant to the sampling activity and that
				its omission will not impact the quality of the results based upon the type of
				pollutants to be sampled, the type of surface water, and the purpose of the
				sampling.
a. An approval page that includes
				the date of approval and the signatures of the approving officials, including
				the project manager and project quality assurance manager;
b. A project organization outline that
				identifies all key personnel, organizations, and laboratories involved in
				monitoring, including the specific roles and responsibilities of key personnel
				in carrying out the procedures identified in the QAP and SAP, if
				applicable;
c. Sampling design and
				monitoring data quality objectives or a SAP that meets the requirements of
				subsection (A)(2) to ensure that:
i. Samples
				are spatially and temporally representative of the surface water,
ii. Samples are representative of water
				quality conditions at the time of sampling, and
iii. The monitoring is
				reproducible;
d. The
				following field sampling information to assure that samples meet data quality
				objectives:
i. Sampling and field protocols
				for each parameter or parametric group, including the sampling methods,
				equipment and containers, sample preservation, holding times, and any analysis
				proposed for completion in the field or outside of a laboratory;
ii. Field and laboratory methods approved
				under subsection (A)(5);
iii.
				Handling procedures to identify samples and custody protocols used when samples
				are brought from the field to the laboratory for analysis;
iv. Quality control protocols that describe
				the number and type of field quality control samples for the project that
				includes, if appropriate for the type of sampling being conducted, field
				blanks, travel blanks, equipment blanks, method blanks, split samples, and
				duplicate samples;
v. Procedures
				for testing, inspecting, and maintaining field equipment;
vi. Field instrument calibration procedures
				that describe how and when field sampling and analytical instruments will be
				calibrated;
vii. Field notes and
				records that describe the conditions that require documentation in the field,
				such as weather, stream flow, transect information, distance from water edge,
				water and sample depth, equipment calibration measurements, field observations
				of watershed activities, and bank conditions. Indicate the procedures
				implemented for maintaining field notes and records and the process used for
				attaching pertinent information to monitoring results to assist in data
				interpretation;
viii. Minimum
				training and any specialized training necessary to do the monitoring, that
				includes the proper use and calibration of field equipment used to collect
				data, sampling protocols, quality assurance/quality control procedures, and how
				training will be achieved;
e. Laboratory analysis methods and quality
				assurance/quality control procedures that assure that samples meet data quality
				objectives, including:
i. Analytical methods
				and equipment necessary for analysis of each parameter, including
				identification of approved laboratory methods described in subsection (A)(5),
				and laboratory detection limits for each parameter;
ii. The name of the designated laboratory,
				its license number, if licensed by the Arizona Department of Health Services,
				and the name of a laboratory contact person to assist the Department with
				quality assurance questions;
iii.
				Quality controls that describe the number and type of laboratory quality
				control samples for the project, including, if appropriate for the type of
				sampling being conducted, field blanks, travel blanks, equipment blanks, method
				blanks, split samples, and duplicate samples;
iv. Procedures for testing, inspecting, and
				maintaining laboratory equipment and facilities;
v. A schedule for calibrating laboratory
				instruments, a description of calibration methods, and a description of how
				calibration records are maintained; and
vi. Sample equipment decontamination
				procedures that outline specific methods for sample collection and preparation
				of equipment, identify the frequency of decontamination, and describe the
				procedures used to verify decontamination;
f. Data review, management, and use that
				includes the following:
i. A description of
				the data handling process from field to laboratory, from laboratory to data
				review and validation, and from validation to data storage and use. Include the
				role and responsibility of each person for each step of the process, type of
				database or other storage used, and how laboratory and field data qualifiers
				are related to the laboratory result;
ii. Reports that describe the intended
				frequency, content, and distribution of final analysis reports and project
				status reports;
iii. Data review,
				validation, and verification that describes the procedure used to validate and
				verify data, the procedures used if errors are detected, and how data are
				accepted, rejected, or qualified; and
iv. Reconciliation with data quality
				objectives that describes the process used to determine whether the data
				collected meets the project objectives, which may include discarding data,
				setting limits on data use, or revising data quality objectives.