Quality Assurance/Quality Control Plan for Agricultural

Nonpoint Source Pollution Monitoring Research

Tamim Younos, Associate Director

Virginia Water Resources Research Center

Saied Mostaghimi, Professor

Carol Newell, Laboratory Manager

Phillip McClellan, Systems Analyst

Biological Systems Engineering Department, Virginia Polytechnic Institute and State University

Blacksburg, VA 24061

 

Abstract

Monitoring of surface runoff and stream discharge, and pollutant concentration and load in surface water systems constitute major components of a surface water monitoring and nonpoint source pollution research program. Advances in field instrumentation, data transfer, and laboratory analyses have made the "real time" sampling and data processing feasible. However, there is a critical need for developing quality assurance/quality control plans to improve on protocols for water sampling, sample custody, storage, and analysis. The overall goal of a quality assurance/quality control plan is to create a high quality database. The objective of this paper is to discuss major components of a quality assurance/quality control plan for an agricultural nonpoint source pollution monitoring research program. The article is based on the authors’ collective experiences with developing quality assurance/quality control plans for several nonpoint source pollution research projects and other relevant information.

Introduction

The objectives of a nonpoint source monitoring research program are to evaluate the extent and characteristics of nonpoint source pollution (NPS), its short- and long-term impacts on receiving waters, and the effectiveness of NPS control measures. To achieve these goals, field and laboratory measurements should be designed to create a scientifically sound database. Important criteria for creating such a database are conducting timely and accurate sampling, and implementation of standard protocols for sample custody, storage, and analysis. Recent advances in field instrumentation, data transfer, and laboratory analyses have made the "real time" sampling and data processing feasible. However, there is a critical need to improve on protocols for water sampling procedures and analyses.

Recently, the U.S. Environmental Protection Agency (USEPA) published the monitoring guidance for determining the effectiveness of NPS controls (USEPA 1997). The objective of this paper is to focus on the standard protocols "quality assurance/quality control plan" for evaluation of agricultural NPS impacts in a monitoring-research program. This article is based on the authors’ collective experiences with developing quality assurance/quality control plans for several nonpoint source pollution research projects conducted at Virginia Tech (Mostaghimi 1989; Younos 1994) as well as other relevant information from the literature.

Definition of Terms

Detailed descriptions of data quality terms relevant to nonpoint source pollution research can be found in several publications (USEPA 1997; USEPA 1994; Erickson et al 1991). Briefly, Quality Assurance (QA) is defined as an integrated system of management procedures designed to evaluate the quality of data and to verify that the quality control system is operating within acceptable limits. Quality Control (QC) is defined as a system of technical procedures designed to ensure the integrity of analyses by proper operation and maintenance of equipment and instruments.

The overall goal of a QA/QC plan is to create a high quality database that is characterized by accuracy, precision, representativeness, comparability, and completeness. Accuracy is defined as the degree of agreement of a measurement (or an average of measurements) with an accepted reference value and is estimated by calculating the standard deviation of the differences between the measured and referenced values over a typical range of data. Precision is a measure of mutual agreement among individual measurements of the same property and is calculated in terms of the standard deviation of various measurements. Comparability is the quality that makes data obtained from one study comparable to data from other studies. For example, consistent sampling methodology, handling, and analyses are necessary to ensure comparability. Representativeness is a measure of how the collected data is compared to the value the same parameter has within the population being measured. Sampling must be designed to ensure that the samples are representative of the population being sampled. Completeness is the amount of valid data obtained from the measurement system (field and laboratory) compared to the amount that was expected to be obtained under anticipated sampling/analytical conditions. It can also be expressed as percent recovery.

Components of a QA/QC Plan

Major components of a QA/QC plan for NPS research include: project description and organization; QA/QC objectives; sampling methods and sample custody procedures; procedures for instrument calibration and maintenance; laboratory protocols; procedures for data reduction and validation; procedures for internal quality control and auditing; guidelines for corrective action; and guidelines for report preparation. Details of a QA/QC plan for NPS research are described below.

Project Description and Organization

A QA/QC plan shall contain a comprehensive description of the proposed project. The major components of a proposed research are: Justification statement for the proposed research, review of literature to support the stated justification, research goal and specific objectives, research methods (experimental design, field and laboratory procedures, methods of computation and statistical analysis), plan of work, expected results, and projected budget.

Project organization is essential to the success of a project. A working team should be formed at the outset when the project is in the planning stage. Possible team members and their responsibilities are: project director, responsible for overall project design and completion; project manager, responsible for daily activities and project reports; quality assurance officer, responsible for the design and implementation of the QA/QC plan; laboratory liaison officer, responsible for coordinating field and laboratory tasks; field coordinator, responsible for the task coordination between researcher and the property owner/field operator; project engineer, responsible for installing field instruments, and regular and/or emergency maintenance; laboratory personnel, responsible for laboratory analysis and computations; data reduction personnel, responsible for digitizing and analysis of rainfall and runoff charts; field observer, responsible for attending to field instrumentation on a daily basis; sampling team, responsible for regular grab water sampling and retrieval of auto-samples.

QA/QC Objectives

The QA/QC objectives are to meet the data quality requirements (accuracy, precision, representativeness, comparability, and completeness) described earlier. Tables 1 and 2 show the overall data quality standards for field and laboratory data pertinent to a NPS pollution monitoring-research program.

Sampling Methods and Sample Custody Procedures

Two types of samples are taken in a NPS research project, i.e., grab samples and auto-samples. Usually, a grab sample indicates the ambient or baseline water quality condition while auto-samples indicate the rainfall-runoff impact on a "real time" basis. Table 3 shows standards for sampling methods and frequency, sample preservation and allowable holding times for a NPS research program.

Proper chain of custody is very important in order to maintain a high data quality. A custody form should be developed for transferring the field samples to the laboratory. Separate forms should be developed for grab sampling and auto-sampling. A sample custody form should contain the following information: watershed identification; farm identification (farm address and owner or operator); field number; type of activity (animal or cropping production); sampling site identification; sampling date and time; sample number; sample bottle number; weather condition; name of sampler; name of individual who transported samples to the laboratory; number of samples collected; number of samples delivered to the laboratory; sample arrival date and time in the laboratory; and name of individual who received samples in the laboratory.

Use of triplicate sample custody forms is recommended. The first copy shall be retained at sampling location for future reference and other two copies should be submitted to the laboratory when the sample is delivered. The laboratory manager should sign off on the form, retain one copy and submit the second copy to the project manager for record keeping.

Instrument Calibration and Preventive Maintenance

Essentially, three types of field data collection instruments are used in a NPS research program: mechanical, electrical/mechanical, and electronic instruments. The mechanical instruments (for example, a Universal rain gauge) use paper charts and are driven by wind-up or battery clocks. The electrical/mechanical instruments (for example, an auto-sampler) contain electronic devices (battery powered) that control mechanical devices (AC or battery powered). The electronic instruments (for example, a data logger) are driven by battery power. Major laboratory instruments that require calibration and maintenance include various types of auto-analyzers, the digestion block, and water bath.

To meet QA/QC requirements, before any data collection and analysis is initiated, all field and laboratory instruments should be calibrated and a regular maintenance schedule for each should be established according to standard procedures provided by the instrument manufacturer. After the project initiation, if the regular instrument maintenance indicates that the required accuracy and precision limits are not met, the instrument should be recalibrated and subsequently it should be regularly checked to ensure that it is functioning properly.

Preventive maintenance of instruments should be conducted regularly to ensure that all instruments remain in good working order. Preventive maintenance may include replacing or cleaning various instrument parts, rejuvenation of electrodes, etc. Spare parts should be readily available so there will be no interference with data collection in case of a breakdown. Instrument calibration and maintenance procedures and maintenance schedule should be documented in detail and attached as an appendix to the QA/QC plan.

Procedures for Laboratory Analysis

Requirements established by the USEPA for laboratory analysis are documented in Table 2. All laboratory analysis should be conducted in accordance to standard procedures and the procedures should be documented and attached as an appendix to the QA/QC plan.

Procedures for Data Reduction and Validation

For a NPS research program, two types of field data are required, i.e., hydrologic (Table 1) and water quality (Table 2). Recent advances in instrumentation allow direct hydrologic data transfer to the laboratory. However, in many cases strip charts should be digitized in the laboratory. Protocols should be developed for hydrologic data transfer, database management, and data interpretation and how the hydrologic data should be integrated with the water quality data. As an example, Figure 1 shows the QA/QC data management system developed at the Biological Systems Engineering Department at Virginia Tech.

Internal Quality Control and Auditing

Internal quality control is an integral part of determining the quality of both field and laboratory data. Quality control checks for field instrumentation were discussed earlier. The quality control check for hydrologic and quality data can be performed by a data management system (e.g. Figure 1). Procedures are needed for the performance evaluation of the laboratory analysis to maintain precision and accuracy within the required confidence level. In general, as a measure of precision, a duplicate sample analysis is recommended. Duplicate samples are used to document the variance of the analytical results. The results of duplicate testing are entered into an ` R control chart and the data is checked to make sure that the results fall within the 95% confidence interval. As a measure of accuracy, either matrix spike (sample-standard samples), check standards (standard-reference samples) or both can be used. The results are entered separately into` X-control charts for both matrix spikes and check standards to measure accuracy within the 95% confidence interval. For performance evaluation of analytical methods, an independent laboratory should be sub-contracted to evaluate the NPS laboratory’s performance against other participating laboratories on the same set of standards.

A system of semi-annual internal audits should be established to review and assess the ongoing assurance practices for compliance with the quality assurance program. The audit should be undertaken by an ‘Audit Committee’. Its members should include the project director, project manager, laboratory liaison officer, laboratory manager, project engineer, and quality assurance officer. The committee will be responsible for verifying both compliance and performance and identifying discrepancies that may exist. This task can best be achieved by developing a field and laboratory quality assurance audit form. Completed audit forms should be reviewed to assure that a) all laboratory procedures are up-to-date and that control samples are analyzed at the specified intervals, b) all field and laboratory equipment and instrumentation are checked and calibrated according to the specified procedures, c) a logbook is maintained on problems encountered and corrective measures taken, d) the precision and accuracy requirements for all data is enforced, and e) all reports to the sponsoring agency are screened for QA/QC.

Corrective Action

If the audit review outlined in the previous section indicates that any of the QA/QC requirements are violated, then corrective measures should be taken. Out-of-control situations may occur in the field or in the laboratory as a result of instrument breakdown despite careful planning. The corrective action may include repairing, recalibrating, or adjusting the malfunctioning instrument or substituting an alternative piece of instrument. Problems and corrective actions should be documented in the field logbook. Field and laboratory personnel should be notified of any corrective action and changes in procedures.

Report Preparation

The project director in cooperation with the project manager should prepare a quarterly report for submission to the sponsoring agency. Each quarterly report should address the following topics: a) performance evaluation and system audit results, b) evaluation of compliance with the QA/QC goals, c) evaluation of data quality measurement trends, and d) identification of problems, needs, and recommendations for solutions.

Summary

In this article, data quality terms (accuracy, precision, representativeness, comparability, and completeness) relevant to QA/QC plan for a NPS pollution research and monitoring program were defined. Detailed descriptions were provided for major components of a QA/QC plan for agricultural NPS pollution monitoring research program. Major components of a QA/QC plan include: project description and organization; QA/QC objectives; sampling methods and sample custody procedures; procedures for instrument calibration and maintenance; laboratory protocols; procedures for data reduction and validation; procedures for internal quality control and auditing; guidelines for corrective action; and guidelines for report preparation.

It is expected that this paper will serve as guideline for developing and implementing uniform QA/QC plans for agricultural NPS research programs.

Literature Cited

Dux, J.P. 1986. Handbook of Quality Assurance for the Analytical Chemistry Laboratory. VNR Co., Inc.

Erickson, H.E., M. Morrison, J. Kern, L. Hughes, J. Malcolm, and K. Thornton. 1991. Watershed Manipulation Project: Quality Assurance Implementation Plan for 1986-1989. EPA-600/3-91/008.

Mostaghimi, S. 1989. Quality Assurance/Quality Control Project Plan for Watershed/Water Quality Monitoring and Evaluating BMP Effectiveness. Biological Systems Engineering Department, Virginia Tech, Blacksburg, Virginia. Submitted to the Virginia Department of Conservation and Recreation, Richmond, Virginia.

Taylor, J. K. 1987. Quality Assurance of Chemical Measurements. Lewis, Pub., Inc.

U.S. Environmental Protection Agency. 1979. Methods of Chemical Analysis of Water and Wastes. EPA-600/4-79-020.

U.S. Environmental Protection Agency. 1994. Guidance for the Data Quality Objectives Process. EPA-QA/G-4.

U.S. Environmental Protection Agency. 1997. Monitoring Guidance for Determining the Effectiveness of Nonpoint Source Controls. EPA/841-B-96-004.

Younos, T. 1994. Quality Assurance/Quality Control Project Plan for the Dairy Loafing Lot Rotational Management System – Nonpoint Source Pollution Assessment and Demonstration Project. Biological Systems Engineering Department, Virginia Tech, Blacksburg, Virginia. Submitted to the Virginia Department of Conservation and Recreation, Richmond, Virginia.

 

 

 

 



Hydrologic Quantity Data Water Quality Data



Data Loggers Strip Charts Punched Paper Tape Automatic/Manual Sampling



Telephone Computer Digitizing Translation Sample Log Sheets/Samples



Laboratory Analysis

Editing, PC Storage









Time vs. Value Entry, Sample Log & Analysis

Error Detection & Correction

REDUCE (Discharge, Intensity, Loading, etc.)



Summaries, Tables, Graphs Archiving, Recall, Magnetic Tape Utilities Modeling, Reports



 

Figure 1. Database Management System

 

 

 

Table 1. Data Quality Standards for Field Measurements

 

Parameter

Accuracy Precision Completeness Reference for accuracy calculations
Rainfall

 

 

4% 0.01 inch 80% laboratory calibrated weights

graduated pipette (with an equivalent 0.01 rainfall graduation)

Stage

Stream

0.01 foot 0.0002 foot 95% land surveyor’s level hook gage
Air Temperature

Water (Stream) Temperature

1.6 C

 

2.0 C

0.1C

 

0.1 C

95%

 

80%

laboratory grade thermometer with 0.2 C resolution

same as above

Time Data Archival 5 minutes 1 second 95% digital watch referenced to the university mainframe computer clock and observed to be accurate within 1 second per month
Time of rainfall samples

Time of wet weather stream samples

10 minutes

 

5 minutes

1 second

 

1 second

90%

 

90%

same as above

 

same as above

 

 

 

 

 

Table 2. Data Quality Standards for Laboratory Analysis

 

Parameter

Detection Limit (mg/l) Recovery Precision (mg/l) QC Protocol* Method
Ammonia

(NH3 - N)

 

 

0.01

98 – 102%

recovery

0.06 1 dup. per 20 samples

1 EPA QA-QC standards

per 40 samples

1 spike per 40 samples

1 blank run daily

EPA

350.l

Nitrate

N03 – N

 

0.05

96 – 100% 0.026 1 dup. per 20 samples

1 EPA QA-QC standards per 40 samples

1 spike per 40 samples

1 blank run daily

EPA

353.2

Orthophosphate

(P04 – P)

 

0.01

89 – 94% 0.013 1 EPA QA-QC standards

per 40 samples

1 spike per 40 samples

1 blank run daily

EPA

365.1

TKN

 

 

0.10

97 – 101% 0.126 1 dup. per 17.5 samples

2 EPA QA-QC standards

per 35 samples

1 blank per 35 samples

1 spike per 35 samples

EPA

351.2

Total-P  

0.05

91 - 94% 0.056 1 dup. per 17.5 samples

2 EPA QA-QC standards

per 35 samples

1 blank per 35 samples

1 spike per 35 samples

EPA

365.1

Total Suspended Solids

 

 

0.02

5%

relative error

0.74 1 dup. per 40 samples

1 blank per 40 samples

1 EPA standard per 200 samples

EPA

160.2

COD  

10

5%

relative error

4.23 1 dup. for each sample

1 EPA standard per 20 samples

EPA

410.4

Fecal/Total Coliform  

MPN or

Membrane

Filtration

95% confidence limit   1 dup. for each sample APHA 909

*This QA/QC plan was developed using several references (USEPA 1979; Dux 1986; Taylor 1987).

 

Table 3. Sampling Method, Frequency, Preservation, and Holding Times

Parameter Collection Method Collection
Frequency
Volume Required Container Type (polyethylene) Preservation Method Immediately after Sampling Maximum Holding Time Prior to Analysis
Ammonia

 

Automatic

 

Grab

Every 3 cm change in stage

Bi-weekly

500 ml ISCO

 

Nalgene

Cool 4C

(H2SO4 to pH <2)

28 days

 

48 hours

Nitrate & Nitrite Automatic

 

Grab

Every 3 cm change in stage

Bi-weekly

500 ml ISCO

 

Nalgene

Cool 4C

(H2SO4 to pH <2)

28 days
Nitrate Automatic

 

Grab

Every 3 cm change in stage

Bi-weekly

500 ml ISCO

 

Nalgene

Cool 4C 14 days

 

48 hours

TKN Automatic

 

Grab

Every 3 cm change in stage

Bi-weekly

500 ml ISCO

 

Nalgene

Cool 4C

(H2SO4 to pH <2)

28 days
Orthophosphate Automatic

 

Grab

Every 3 cm change in stage

Bi-weekly

500 ml ISCO

 

Nalgene

Cool 4C 14 days

 

48 hours

Total-P Automatic

 

Grab

Every 3 cm change in stage

Bi-weekly

500 ml ISCO

 

Nalgene

Cool 4C 28 days
COD Automatic

 

Grab

Every 3 cm change in stage

Bi-weekly

500 ml ISCO

 

Nalgene

Cool 4C 28 days
TSS Automatic

 

Grab

Every 3 cm change in stage

Bi-weekly

500 ml ISCO

 

Nalgene

Cool 4C 14 days

 

7 days

Fecal Coliform Grab Bi-weekly 100 ml Nalgene Cool 4C

(0.08% NA2S203)

24 hours
Total Coliform Grab Bi-weekly 100 ml Nalgene Cool 4C

(0.08% Na2S203)

24 hours