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QA / QC
 
 
  Quality Assurance and Quality Control
 

Proper Quality Assurance and Quality Control (QA/QC) protocols are essential to WOW. We have gone to great lengths to assure the accuracy of our data - the following sections describe these measures in detail. What are Quality Assurance and Quality Control?

QA/QC basically refers to all those things good investigators do to make sure their measurements are right on (accurate; the absolute true value), reproducible (precise; consistent), and have a good estimate of their uncertainty. In the regulatory arena, this aspect of data collection is as crucial to the final outcome of a confrontation as the numbers themselves. It specifically involves following established rules in the field and lab to assure everyone that the sample is representative of the site, free from outside contamination by the sample collector (no dirty hands touching the water) and that it has been analyzed following standard QA/QC methods. This typically involves comparing the sample to a set of known samples for estimating accuracy and by replicating the measurement to estimate its precision. The U.S. Environmental Protection Agency has lots to add should you wish to learn more of the technical aspects of a Quality Assurance Program (QAP). See also the WOW Lessons about data quality and interpretation for further information.

http://www.epa.gov/owowwtr1/monitoring/volunteer/qappexec.htm contains The Volunteer Monitor’s Guide to: Quality Assurance Project Plans. 1996. EPA 841-B-96-003, Sep 1996, U.S. EPA, Office of Wetlands, Washington, D.C. 20460, USA

Conventional data quality assurance procedures follow guidelines set by the U.S.EPA (1987; 1989a,b), and APHA (1998). Water chemistry and manual field profiles are collected by trained staff limnologists and technicians at both Hennepin Parks (HP under Principal Investigator/Limnologist John Barten's supervision) and the Natural Resources Research Institute (NRRI under Co-Principal Investigator / Limnologist Rich Axler). Both the Hennepin Parks Water Quality Laboratory and the NRRI Central Analytical Laboratory are certified annually by the Minnesota Department of Health for Federal Safe Drinking Water Act and Clean Water Act parameters (Ameel et al. 1993, 1998; Axler and Owen 1994; Archer and Barten 1995, 1996; Barten 1997; MCWD 1997). The certification procedure involves blind analyses of certified performance standards and an in-depth site inspection and interview approximately every other year. The NRRI lab has also been certified over the past decade by the Minnesota Pollution Control Agency and the Minnesota Department of Natural Resources for low-level water quality analyses in pristine, acid-sensitive lake monitoring programs and for sediment contaminant analyses in the St. Louis River and Upper Mississippi Rivers.


LAKE DATA QA/QC

LAKE DATA TYPES
There are basically two sets of environmental data that are collected for WOW Lake sites:

(1) conventional water quality parameters such as nutrients (N- and P-series of nutrients), chlorophyll, clarity, fecal coliform bacteria, manual field profiles for temp, DO, EC, etc. These are based upon traditional methods where a trained staff person records measurements at different depths from a sensor lowered over the side of a boat and collects water from discrete depths that are returned to the lab for analysis.

(2) remotely sensed and controlled R.U.S.S. (Remote Underwater Sampling System) units that control the depth and sampling interval of water quality sondes housing depth, temperature, DO, pH, EC and turbidity probes. Data may be transmitted via cellular phone/modem to our base computer/website immediately upon completion of a depth profile, or may be stored on board the RUSS and downloaded less frequently (each morning, currently) to save connection costs.

RUSS QA/QC is performed at a number of levels. The sensors are either Hydrolab H20 or YSI 6820 probe/sonde instruments; both HP and NRRI staff follow the Instrument Manuals for calibration and maintenance procedures. Our staff also have extensive experience with these calibration procedures and with their importance in interpreting field data and distinguishing systematic errors associated with deteriorating, or bio-fouled probes. In 1998 and 1999 we gained considerable experience in dealing with problems associated with continuous sensor deployment; the resultant protocols are included in our WOW efforts. Other aspects of the data management process are discussed in Host et al. (2000a, 2000b).

NRRI contributed to the initial development of the RUSS technology. During the preliminary and early stages of Water on the Web, numerous tests were conducted in regard to the accuracy and precision of in-situ data. Since both the YSI and Hydrolab systems are well established and used for numerous state and federal monitoring programs, the principal concerns related to the time allowed for sensor equilibration at each depth . Of all the sensors that we use, dissolved oxygen is most susceptible to erroneous values from inadequate stabilization- the error being greatest in regions with steep depth-gradients in DO. Following our collaborative work on this topic with Apprise Technologies, Inc., the company subsequently ran a nearly yearlong experiment in Lake Waco, Texas with Hydrolab, Inc. comparing RUSS-transmitted data to conventional datalogger data. The data sets agreed within sensor specifications. Both sensor companies have internal quality control systems (YSI is ISO14001 registered) that guarantee the consistent quality of their sensors. Apprise has worked independently with both companies to integrate these sensor packages with their RUSS units. As a part of these programs, the RUSS technology was independently field-tested by both companies and both YSI and Hydrolab have audited the Apprise facilities for QA/QC compliance. Apprise has also implemented an internal quality system based on the ISO9000 system and has been extremely helpful in dealing with problems that occasionally arise with the Lake Access and WOW units. A more complete description of our current protocols follows:

RUSS SENSOR RESOLUTION & REPORTING LIMITS

On the RUSS unit, the on-board computer processes a user-submitted instruction sequence, the sensor package is sent to a specified depth, and a series of feedback corrections are made until the sensors are stabilized within 0.2 m of the specified depth. Output from the sensors is monitored to assess when the readings on all parameters have stabilized to a specified criterion, usually a coefficient of variation <20% for a running set of 10 consecutive measurements over an interval of ~1 minute. Dissolved oxygen typically requires the most amount of time to stabilize on average, in part because of the occurrence of steeper depth gradients for this parameter. Depending on the site characteristics and the specific O2-sensor, as much as 3-5 minutes may be required for complete equilibration. Once stabilized, readings on all parameters are stored in buffer memory on the on-board computer. The raw data stream is a simple string of comma-delimited ASCII text containing a time signature, depth, and parameter values (Table 1).

Table 1. Output from Lake Access RUSS unit on Halsteds Bay, Lake Minnetonka, MN, 6/4/2000.
Unit: EMPT2 site: Halsteds Bay
Site
Sample
Sample
Depth
Temp
pH
EC @ 25 C
O2
O2
Turb
 
Date
Time
(m)
oC
 
(uS/cm)
(mg/L)
(% sat)
(NTU)
Halsteds
06/04/2000
00:10:58
1
18.3
8.4
406
10.0
107
11
Halsteds
06/04/2000
00:11:43
2
18.3
8.4
407
10.1
107
6
Halsteds
06/04/2000
00:13:34
3
18.2
8.4
407
10.0
106
3
Halsteds
06/04/2000
00:15:13
4
17.9
8.3
410
9.1
97
15
Halsteds
06/04/2000
00:17:04
5
17.6
8.2
411
8.0
84
5
Halsteds
06/04/2000
00:18:55
6
17.3
8.0
414
6.7
70
4
Halsteds
06/04/2000
00:20:34
7
16.7
7.8
419
4.9
50
9
Halsteds
06/04/2000
00:22:25
8
16.3
7.6
425
1.8
18
14

To date we have set the reporting limits for RUSS data based on instrument specifications and prior knowledge of the magnitude of typical field variations. This information is presented within the RUSS data section of the WOW web site. The resolution, i.e. the smallest reading shown for a particular parameter is likely to be considerably lower than the error associated with differences in time, with depth fluctuations, and with sensor drift and calibration accuracy. Periodic examination of the RUSS data stream with Apprise Technologies, Inc. has generally confirmed the estimated accuracy reported below (Table 2). An important, and greatly underestimated element of the WOW projects has been to assess the accuracy of these data by comparison with approximately biweekly manual profiles. However, it is likely that the relative precision of the data between depths within a water column profile and within a few hours to a day will be better than from week-to-week.

Ideally, if all of the RUSS sensors behaved according to sensor-manufacturer's specifications (Table 2) we could simply post the data on the Lake Access web site and assume it is accurate to these levels. However, except for temperature, all of the sensors require routine maintenance and calibration. When using these sensors for manual profiling, that is, visiting lake sites by boat, we always re-calibrate the pH, EC and turbidity sensors using individual standard solutions with known values, and the DO by air calibration. Experience has taught us that the sensors remain stable during the course of a sampling day.

Table 2. Reporting limits for RUSS sensor data (Hydrolab or YSI sensors)
Depth
(m)
Temp
(oC)
DO
(mg/L)
DO
% saturation
pH
EC
(uS/cm)
Turbidity
(NTUs)
Resolution (what is reported by the RUSS sensors)
0.12
0.1
0.1
0.1
0.1
1
1
Estimated Accuracy (what we really trust)
0.3
0.15
0.2
2
0.2
10
~3
However, when deployed for continuous operation, as for the RUSS unit, the sensors are colonized gradually by a biofilm of algae and less noticeably by bacteria and fungi as well. As this material builds up, its metabolic activity interferes with the sensor's ability to accurately sample the surrounding water. One can easily picture the effect of fine filaments of algae wafting intermittently between the electrodes of the EC sensor or in the light path of the turbidimeter giving seemingly erratic values with wide swings as the sensors move up and down. An anomalous spike in the Ice Lake EC data during July 1998 (see shaded region in the Surface Trends for Ice Lake on the WOW site), are a good example of this effect and are the basis of a lab lesson (Increased Conductivity: Are Culverts The Culprits? in draft). DO and turbidity probes are most susceptible to these changes, followed by pH and EC.

SENSOR MAINTENANCE AND CALIBRATION

Lake Access and WOW staff set up the following protocols to minimize these biofouling and instrument drift effects to quality assure the RUSS data:

* Clean and re-calibrate sensors frequently (about every 2 weeks) and perform manual profiles with an independent instrument at the same time

* Compare independent manual profiles with near-simultaneous RUSS data prior to cleaning (re-calibration). This provides assurance that data from the previous period are accurate. We calculate test statistics for each parameter as:

and

 
 
for each parameter. They PASS according to rules in Table 3
Table 3. Quality Assurance Criteria for RUSS Sensors
SENSOR
RPD
DELTA
Temperature
< 5%
< 0.2 oC
DO
< 10 %
< 0.5 mg O2/L
EC
< 10 %
< 5 uS/cm
pH
< 10 %
< 0.2 units
turbidity
< 10 %
< 5 NTUS

If the data "passes", it is considered acceptable for the previous period. If not, we examine it in the context of our understanding of the limnology of the individual lake and other data (nutrients, chlorophyll, trends, etc.) and then either delete it from the database or allow it to be posted. We have to be careful not to delete anomalous data that may simply reveal real dynamic changes. The sheer volume of data (218,720,430,742,644,316,434,172,687,130 values to date) has been taxing and we lack the resources to always be as current as we would like. In the interim, data are posted as provisional . Dates of calibrations and these manual data are posted in the DATA section of WOW and are available within easily accessible Excel files - these will soon be posted on the Lake Access site as well. The three Data Visualization Tools (DVTs) developed for Lake Access and Water on the Web are also helpful in rapidly displaying the data in a variety of formats to help identify anomalous data. We are currently in the process of adding 'calibration date flags' to the control panels of the Profile Plotter and Color Mapper DVTs and to the DxT Profiler to allow the user to more easily keep track of calibration dates as the data stream is being viewed. The first year of WOW, 1998, taught us that we were understaffed for the frequency of maintenance required for continuous RUSS operation at Ice Lake and Lake Independence. With an additional three units being deployed for 1999 and 2000, we set up collaborations with Itasca Soil and Water Conservation District (for Ice Lake), Hennepin Parks (for Lake Independence and Lake Minnetonka), the Minnesota Department of Natural Resources Regional Fisheries for Grindstone Lake, and the Minnesota Pollution Control Agency staff for the St Louis River site (still in development as of July 2000). Lake Access and WOW staff work with these folks to clean and re-calibrate all sensors approximately every 1-3 weeks depending on the site. The less productive sites (Grindstone and Ice lakes) generally require less maintenance.

DATA TRANSMISSION AND INITIAL QA SCREENING

The program that imports the RUSS data currently is scheduled to run every day at 7:30 AM. The RUSS base station software is used to call each RUSS and download data that has been collected since the last call. A file containing real-time data (RTD) collected during the duration of the call is also created. These new profile data and RTD files are stored on the base station computer as plain ASCII text files, one file for each day's data. The data files from each site are stored in a separate directory on the computer. Table 1 (above) is an sample of an original profile data file created by the RUSS base station.

The Conversion Process

A program (the importer) is now launched. It reads data files that have been created or changed since the last time it was run, and converts the data to the format used by the report generating and data visualization programs. Additionally, the original data files are copied to the web server so they are accessible for immediate QA/QC. Profile data files are copied to http://wow.nrri.umn.edu/data/ and RTD files are copied to http://wow.nrri.umn.edu/rtd/ .

The importer parses the first line of a new or modified RUSS data file and tests to make sure that the Unit and Site correspond to what is expected. If not, an error message is generated and no further action is taken with this file. This will catch errors that could occur if, for example, a data file from Halsteds Bay was somehow stored in the Lake Independence directory. Next, it reads the line containing the column descriptions, and compares it with what is expected. If it differs, an error message is generated and no further action is taken with this file. This will catch errors that could occur if, for example, a new parameter is being read by the RUSS, but the importer hasn't been updated to handle the change. Now, each data line is read and converted to a "Reading". A set of readings is combined to form a "Profile" in the data base. Specific data is rejected by the importing program if it is outside these ranges:

temperature < -1 or > 35 oC
pH < 5 or > 10
specific conductance (EC25) < 1 or > 600 uS/cm
dissolved oxygen (DO) < -1 or > 20 mg/L O2/L
DO % saturation < -5 or > 200 %
turbidity < -5 or > 100NTU (note: turbidity values between -5 and 0 are set = 0)

There is no direct indication in the raw data files of where one profile ends and the next begins, so the importer applies some heuristics to decide how to assign readings to profiles. The values listed below are those in current use, but they can be changed. Since only the actual time is reported on each data line, the importer assigns a "scheduled" time to the new profile, using the nearest :00 or :30 minute time value before the time reported for the first reading in the profile. Subsequent readings are added to the same profile provided that:
     1) the reading is from a lower depth, and
     2) the reading was taken within 30 minutes of the previous one

When the importer either comes to a line where the reading no longer qualifies for the current profile or it reaches the end of the data file, it will add the new profile to the data base provided that:
     1) the first reading starts within 3 meters of the surface,
     2) there are at least 4 readings in the profile, and
     3) the date is not in the future

Instead it will generate an appropriate error message in the log file, and disregard the profile. This helps eliminate partial or invalid profiles that could be caused by RUSS hardware problems.

If it is winter and the RUSS is installed on ice, we set the minimum upper depth to 1 or 2 meters to minimize the risk of the unit becoming trapped in the hole through the ice. The data importer then creates a default reading at 0 meters, listing a temperature of 0 oC, with all other parameters blank (since we don't know what their true values are). The time for this reading is set equal to the scheduled time. The timestamp of each reading is expected to be unique, and can be used as the key value in a database. There is the possibility that the first actual reading in the profile could have the same timestamp as the bogus reading, so the readings in the profile are checked for duplicate times. If found, 5 seconds are added to the time of the deeper reading, and the change is noted in the log file.

Sometimes a sensor for a particular parameter at a particular site will go bad. In this case, the importer program can be customized to reject that parameter when importing the data from the site. Data stored in buffer memory is transmitted to the base station via cellular phone at specified intervals, or at the request of the user. Standard parity-based error correction techniques are used to ensure that data were not altered during transmission. At the base station, a JAVA based application adds the raw data to a standardized relational database (DBMS) file. For archival purposes the original ASCII data are stored in a compressed data format (ZIP) file. The ASCII and DBMS files are periodically downloaded to an off-site location via File Transfer Protocol (FTP).

FINAL DATA REVIEW & POSTING

At present (July 2000), funding limitations have precluded adherence to a rigorous schedule for removing the provisional label from RUSS data. In part this is a due to the need to review ancillary water chemistry data before making final decisions when the RUSS data are questionable. All water chemistry data posted on the Lake Access and WOW sites however, have passed QA/QC prior to being posted, although this typically takes from 30-60 days after collection.

Despite regular maintenance and calibration schedules, occasional RUSS data anomalies still occur. To date, they have virtually always been associated with DO and/or turbidity data although there have been recurring problems with the pH probe at the WOW Grindstone Lake site.

The most troublesome anomalies are those that occur within the calibration window of time, are not flagged by our automated screening tools and are not unreasonable values in terms of the range of values previously measured for that depth stratum and time of year. These errors have not been trivial to identify and require careful examination in a complete limnological (lake/watershed/climate) context by a professional limnologist. The process is adequately described as Best Professional Judgement (BPJ). In some cases we have decided to adjust data by calculating correction factors when there is accurate calibration data spanning the period in question and when the results estimated by interpolation are consistent with the rest of the data set. In other cases we have simply rejected the data - omitting it from the website. Data deletions are summarized and circulated to all limnological staff and archived in a hidden section of the Lake Access and WOW websites. The WOW project sends a periodic e-mail newsletter providing data updates to all teachers and researchers using the site for educational or research purposes; you can subscribe to this newsletter at http://wow.nrri.umn.edu/wow/contactus.html.


STREAM AND RIVER QA/QC

DATA TYPES FOR RIVERS AND STREAMS

(1) water quality parameters analyzed in the lab from "grab" samples of water such as nutrients (N- and P-series of nutrients), pH and alkalinity, turbidity, total suspended solids (TSS), total dissolved solids (TDS), color, chloride, fecal coliform bacteria, total volatile solids (TVS) and biochemical oxygen demand (TVS and BOD5 are both measures of organic matter);

(2) water quality measurements made in the field at the time of water sample collection such as sensor measurements of temperature, water velocity (to estimate flow), dissolved oxygen (DO), specific electrical conductivity (EC25) and transparency tube measurements of water transparency or clarity, etc.; and

(3) remotely sensed water quality measurements collected, logged and transmitted by electronic sensors deployed in the streams that we call SMU's for Stream Monitoring Units. These instruments are programmed to record temperature, stream elevation (also called stage height), EC25 (a measure of total salt concentration), and turbidity (used as a measure of suspended sediment). In addition, the SMU at the Kingsbury Creek site is connected to an automated sampling device (ISCO 6712) that upon preset increases in stage height, signifying a storm event, initiates sample collection of up to 24 individual water samples into one liter plastic bottles. We have initially programmed the unit to sample at 2 hour intervals during storm events.

The stream sensor data is typically collected every 15 minutes, stored throughout the day and then transmitted via cellular phone/modem to our base computer/website each morning. We can collect the data more frequently, and call up the unit to see what the latest data looks like, but this adds to our phone bill. A final intensive data set is collected at the Duluth-Superior Harbor/ St. Louis River outlet to Lake Superior where a sensor array measures and logs temperature, EC25 and turbidity at 15 minute intervals. This unit was installed on the channel wall above the USGS Superior Bay Duluth Ship Channel (Station 464646092052900) where an acoustical velocity meter (AVM) system with a two-path transducer installation is used to measure river discharge into Lake Superior. We have previously established a data-sharing partnership with USGS (Madison, WI, P. Hughes) to link our on-line water quality data with their discharge data.

(4) volunteer monitoring data. During this grant period, DuluthStreams staff will work with the MPCA to coordinate existing volunteer monitoring with the goal of achieving more consistent methodology and developing a repository and database for stream-related information in the City of Duluth. Programs include the state-wide Citizens Stream Monitoring Program (MPCA sponsored), St. Louis River Watch and a variety of less formal efforts by local schools and environmental learning centers. DuluthStreams initiated school-based stream monitoring programs at sites on Chester, Tischer and Kingsbury Creeks in October 2002 as part of the National Monitoring Day, nation-wide celebration (http://www.yearofcleanwater.org) of the 30th anniversary of the Clean Water Act on October 15. It was subsequently decided to include these schools in the St. Louis River Watch program which already includes over 30 area schools (see http://www.duluthstreams.org/citizen/involvement.html for further information).

Parameters will vary depending on the level of effort available, but at a minimum will include temperature, transparency tube depth, turbidity and EC25 during seasonal baseflow conditions, snowmelt runoff and rainstorm runoff. Training will be provided by River Watch and DuluthStreams staff, and turbidity and EC25 measurements will be performed by NRRI on discrete samples collected by the schools. Biological monitoring of macroinvertebrate communities is fundamental to River Watch, and DuluthStreams will work to encourage standardized sampling protocols that conform to peer-reviewed research standards and existing state and federal guidelines. The goals of this effort are to initiate a comprehensive database, standardize monitoring methods, and educate students and citizens

STREAM SENSOR RESOLUTION & REPORTING LIMITS
Ideally, if all of the sensors behaved according to the sensor manufacturer's specifications (Table 1) we could simply post the data on the DuluthStreams web site and assume it is accurate to these levels. However, except for temperature, all of the sensors require routine maintenance and calibration. When visiting sites we re-calibrate the EC and turbidity sensors using individual standard solutions with known values for EC25 and turbidity. Experience has taught us that the sensors remain stable during the course of a sampling day. The depth sensor is also calibrated at this time to read zero when the sensor is set at the water surface.

When deployed for continuous operation the sensors are colonized gradually by a biofilm of algae and less noticeably, by bacteria and fungi as well. As this material builds up, the biofilm interferes with the sensor's ability to accurately sample the surrounding water. One can easily picture fine filaments of algae, like you see on the rocks in summer, wafting intermittently across the electrodes of the EC sensor or in the light path of the turbidimeter. This would produce erratic values with wide swings even during periods of low and relatively constant flow when the stream is running clear. Turbidity probes are most susceptible to these changes, followed by dissolved oxygen (not measured automatically at this time) and EC. Fine particulate sediment will also be trapped and gradually contribute to erroneous readings if not cleaned on a regular basis. Turbidity sensors are "notorious" for their maintenance problems - the YSI 6136 sensor we are using is called a "self-wiping" sensor that mechanically wipes the optical window used for the measurement just before a reading is taken. Although this feature certainly reduces errors due to fouling, the wiper occasionally "sticks", creating apparent "spikes" in the data set (see below)



Other sources of variability include the natural variations that occur in a stream due to eddies and larger debris suddenly breaking free, events less likely to be seen in the calmer water in deeper stream pools or in lakes. Also, the sensor is actually "sampling" only a tiny (millimeter scale) patch of water for less than a second every 15 minutes. Table 2 compares the theoretical sensor resolution with our best estimate of the true accuracy of the sensor readings, incorporating all sources of known error.

Table 1. Reported automated sensor specifications
Model Probe Resolution Accuracy
Chester /Tischer
YSI 6920 sonde
YSI 6136 turbidity sensor
temperature 0.01 °C ± 0.15 °C
specific conductance (EC25) 1 µS/cm ± 0.5% reading +1 µS/cm
turbidity 0.1 NTU ± 5 % of reading or 2 NTU
(the greater of the two)
stage height 0.0003 m ± 0.003 m (± 0.01 ft)
Kingsbury
YSI 6820 sonde
YSI 6136
turbidity sensor as above; a 6820 sonde instead of a 6920 sonde is used for cost and data logging considerations associated with the automated water sampler
stage height sensor triggers ISCO 6712 automatic water sampler
St. Louis River YSI 6820 sensors as above (w/o stage height)
data logger/controller: Apprise Technologies RepDAR unit
(see http://lakeaccess.org/QAQC.html for details)



Table 2. DuluthStreams Reporting limits for SMU sensor data (YSI 6920/6820)
The resolution, i.e. the smallest reading displayed for a particular parameter, is likely to be considerably lower than the error associated with differences in time, with sensor drift and calibration accuracy.
Depth
(m)
Temp
(°C)
EC25
(µS/cm)
Turbidity
(NTU's)
Resolution (what is reported by the SMU sensors)
± 0.0003 ± 0.01 ± 1 ± 0.1
Manufacturers Reported Accuracy (what we really trust)
± 0.003 ± 0.15 ± 5 ±~5


SMU PLACEMENT, FLOW CALIBRATION , SAMPLE COLLECTION

We attempted to place the water quality probes at representative measurement points in the stream cross-section with allowances made for protecting the units from debris and sediment where necessary. Probes are inspected, calibrated, and maintained following the manufacturers recommendations in addition to following USGS (2000a, 2000b) procedures. The exact location of each unit within a watershed was chosen based upon considerations of security, stability regarding anticipated road and bridge construction activities, and on upstream land use characteristics. Because of this, summary interpretations as to water quality differences between streams must be done with caution since such a direct comparison was not intended.

Stream discharge is determined as in Anderson et al (2000) and USGS (2000b) with rating curves developed and subsequently used to calibrate flow. Stream depth is measured remotely using a pressure transducer and discharge is determined using rating curves based upon a set of cross-sectional and discrete depth in-stream velocity measurements made with a Marsh-McBirney stream velocity meter over a range of discharge conditions.

Discrete water samples are collected at Kingsbury Creek at various points along the hydrograph using an ISCO 6712 slaved to the YSI 6820 stream elevation sensor. Periodic intensive manual collections throughout high water periods as well as during baseflow periods are conducted for Chester and Tischer Creeks.

SENSOR CALIBRATION

NRRI staff follow the Instrument Manuals for calibration and maintenance procedures. Our staff also have extensive (i.e. decades collectively) experience with these calibration procedures and with their importance in interpreting field data and distinguishing systematic errors associated with deteriorating, or bio-fouled probes. Our Lake Access, EMPACT project is a companion to an earlier NSF-funded Advanced Technology Education project entitled Water on the Web (WOW: http://wow.nrri.umn.edu), now in its fifth year, that involved establishment of Apprise Technologies, Inc. robotic water quality sensing RUSS units on five Minnesota lakes. Since 1998 we have gained extensive experience in dealing with the problems associated with continuous sensor deployment and the resultant protocols are included also in the Lake Access and WOW websites.

The following protocols have been set up to minimize these biofouling and instrument drift effects to quality assure the DuluthStreams data:

(1)Clean and re-calibrate sensors frequently (about every 2 weeks) and perform manual measurements with an independent instrument at the same time (temperature and EC25) or soon after in the lab (turbidity). The depth sensor is also calibrated at this time by re-setting it to equal zero depth when placed just above the water surface.

(2) Compare independent manual measurements with near-simultaneous SMU data prior to cleaning (re-calibration). This provides assurance that the previous period of data is accurate. We calculate test statistics for each parameter as:

RPD (relative % difference) = |Manual-SMU| x 100 , and
[SMU]

DELTA = |Manual – SMU| for each parameter. They PASS according to rules in Table 3.

Table 3. Quality Assurance Criteria for SMU Sensors
SENSOR RPD DELTA
temperature < 5% < 0.2 °C
EC25 < 10% < 5 µS/cm
turbidity < 10% < 5 NTUs
depth    

If the data "passes," it is considered acceptable for the previous period. If not, we examine it in the context of our understanding of the instrumentation, the stream's prior automated and "manual" water quality data, its watershed and the recent weather pattern and then either delete it from the database or allow it to be posted. We have to be careful not to delete anomalous data that may simply reveal real dynamic changes. The sheer volume of data has been taxing and we lack the resources to always be as current as we would like. In the interim, data are posted as provisional. Dates of calibrations and manual data are both posted in the DATA section of the website and are available within easily accessible Excel files. The stream data visualization tool (DVT) is also helpful in rapidly displaying the data in a variety of formats to help identify anomalous data. Calibration "date flags" will be added to the control panel of the DVT to allow the user to more easily keep track of calibration dates as the data stream is being viewed.

Although not yet implemented (as of December 2002), we are also exploring a data quality classification rating system such as used by the US Geological Survey for certain continuous water quality records. The table below is taken from USGS (2000a; Guidelines and Standard Procedures for Continuous Water-Quality Monitors: Site Selection, Field Operation, Calibration, Record Computation, and Reporting, U.S. Geological Survey, WRIR 00-4252, by R.J. Wagner, H.C. Mattraw, G.F. Ritz, and B. A. Smith; http://water.usgs.gov/pubs/wri/wri004252/htdocs/record_comp.html#table9.
This USGS document also discusses the use of such data when there are extensive periods without data as well as other considerations that are beyond the scope of our present objectives.

Table 9. Rating continuous water-quality records , < less than or equal to; +, plus or minus value shown; °C, degree Celsius; >, greater than; %, percent; mg/L, milligram per liter; pH unit, standard pH unit

Measured physical
property
Ratings
Excellent
Good
Fair
Poor
Water temperature 0.2 °C > 0.2 to 0.5 °C > 0.5 to 0.8 °C > 0.8 °C
Specific conductance 3% > 3 to 10% > 10 to 15% > 15 %
Dissolved oxygen > 0.3 mg/L > 0.3 to 0.5 mg/L > 0.5 to 0.8 mg/L > 0.8 mg/L
pH 0.2 unit > 0.2 to 0.5 unit > 0.5 to 0.8 unit > 0.8 unit
Turbidity 5% > 5 to 10% 10 to 15% > 15 %


OTHER CONTINUOUS WATER MONITORING DATA

Water quality data and discharge measurements are also being collected at Amity Creek, by the MPCA (2001 and 2002) in collaboration with the USGS with separate funding and when available these data will be posted on the DuluthStreams website. The MPCA maintains a continuous stage height monitor in Amity and collects 20-30 grab samples throughout different flow regimes (~75% should represent the higher flow storm and snowfall periods) for analysis by the Minnesota Department of Health. The MPCA, in collaboration with the South S. Louis County Soil and Water Conservation District (SSLCSWCD) has also operated several continuous stage height monitors on Miller Creek, and these data will also be posted. A limited number of water quality samples have also been collected at the St. Louis River/Lake Superior entry site.

DATA TRANSMISSION AND INITIAL QA SCREENING
Each SMU is called daily and data that has been collected since the last call is downloaded. These new data files are stored on the base station computer as comma-delimited ASCII text files. The Kingsbury station is polled twice daily, at 6 AM and 2 PM. The Chester and Tischer sites are scheduled to be polled at 6AM each day because of cell phone costs.

The Conversion Process
A program (the data importer) is now launched. It reads any data files that have been created or changed since the last time it was run, and converts the data to the format used by the report generating and data visualization programs. The data importer examines the beginning of these files and checks to make sure that the contents (e.g. site name, parameter names and units) correspond to what is expected. If not, an error message is generated and no further action is taken with this file. This will catch errors that could occur if, for example, a new parameter is being read by the SMU, but the property files used by the data importer for that site haven't been updated to handle the change.

Now, each data line is read and converted to the "DataTable" format used for storage. The importing program can reject specific data if it is outside of a pre-defined range for the given parameter. Data are automatically rejected if outside the following ranges:

stage height negative reading
temperature < -1 °C or > 35 °C.
EC25 < 50 µS/cm or > 3000 µS/cm
(subject to review after we've experienced spring snowmelt)
Turbidity < - 5 & > 2000, with values from - 5 to 0 set equal to 0

If it encounters a value outside of the range it will generate an appropriate error message in the log file, and disregard the value. This helps eliminate invalid readings that could be caused by sensor drift or SMU hardware problems. After all of the new data files have been imported the data importer triggers the appropriate programs that will create and send updated reports and data-visualization data files to the website.

FINAL DATA REVIEW & POSTING
At present (December 2002), funding limitations have precluded adherence to a rigorous schedule for removing the provisional label from DuluthStreams automated data. In part this is due to the need to review ancillary water chemistry data before making final decisions when the data is questionable. All manually sampled water quality data posted on the DuluthStreams however, have passed QA/QC prior to being posted, although this typically takes from 30-60 days after collection.

Despite regular maintenance and calibration schedules, occasional SMU data anomalies still occur. To date, they have generally been associated with the turbidity sensor although there was great difficulty during summer 2002 in getting the stage height sensors to function properly. Additional difficulties were caused by the Minnesota DNR modifying the Chester Creek stream channel in the vicinity of our SMU, causing excessive turbidity values and requiring a new empirical calibration of the stage height-flow relationship. There have also been start up problems associated with precipitation monitoring and the modem link to Kingsbury Creek. All of these problems were resolved by about September 1, 2002.

The most troublesome anomalies are those that occur within the calibration window of time, are not flagged by our automated screening tools and are not unreasonable values in terms of the range of values previously measured or expected for the hydrologic conditions at the time. These errors will not be trivial to identify and will require careful examination in a complete fluvial/limnological (stream/watershed/climate) context by professionals. The process is adequately described as Best Professional Judgement (BPJ). In some cases data will need to be adjusted by calculating correction factors when there is accurate calibration data spanning the period in question and when the results estimated by interpolation are consistent with the rest of the data set. In other cases, it will be necessary to simply reject the data - omitting it from the website. A log of data deletions will be maintained on the website, and an e-mail announcement sent to all teachers and researchers known to be using the site for educational or research purposes related to curricula associated with DuluthStreams and Water-on-the-Web (http://wow.nrri.umn.edu).

Data collected under ice: When streams are ice covered the depth readings and flow calculations are subject to errors:

  • The stream can be running under the ice without an air gap, essentially pressurizing the stream -- increasing the apparent depth and velocity.
  • The streams can be running on top of the ice (probably unaccounted for flow).
  • Anchor Ice may change the channel cross section, making our rating curves unreliable.
  • Ice dams below the site can change flows.
  • Bank ice can constrict the channel.

All of these things can happen at the same place over the course of time as well.

We have chosen to flag this data as 'collected under ice cover'. It will still show relative changes, especially over the short-term, but it should be noted that the reported depths and flows are not necessarily accurate.

WATER SAMPLE ANALYSES
Data from the monitoring programs will follow established procedures of the NRRI Central Analytical Laboratory (Ameel et al. 1998) and WLSSD (2000a,b). Both laboratories are annually certified for Clean Water Act and Safe Drinking Water Act water quality parameters by the Minnesota Department of Health.

Sample Collection: Samples for micronutrients ([nitrate+nitrite]-nitrogen, ammonium-nitrogen, total-nitrogen [TN], orthophosphate-phosphorus [OP], total phosphorus [TP], alkalinity, chloride, BOD5, total suspended solids [TSS], total volatile solids [TVS], and total dissolved solids [TDS] and fecal coliform bacteria are collected exclusively by trained personnel (City of Duluth, WLSSD, MPCA, NRRI Cental Analytical Laboratory personnel), according to individual analysis requirements using an ISCO 6712 automated sampler at the Kingsbury site. Manual samples are collected at various times of the year to characterize Chester and Tischer Creeks at their SMU sites. Several intensive sampling surveys will be conducted to characterize high flow storm events and the snowmelt runoff period in the Spring. Grab samples for fecal coliform bacteria will be collected using sterile technique (APHA 1998). All samples are transported to the laboratory on ice in dark coolers on the day of collection, within a few hours after collection. A field data sheet accompanies each sample. Sample sites for the collection of duplicate samples and preparation of field blanks are chosen at random.

Sample Preservation: All samples are initially preserved by refrigeration and when filtration is necessary it is performed as soon as possible after return to the lab. If delay in analysis is anticipated, additional preservation will be incorporated, as provided in individual methods and according to approved guidelines (e.g. acidification or freezing). The container label is marked when preservative is added.

Sample Identification and Tracking: Sample containers are labeled in the field with date and time of collection, location of sample, and person collecting the sample. Field data sheets include all of the above information, and also conditions specific to the project and lists the individual parameters for which analysis is requested. For stream monitoring, and other sampling events for which field analysis are performed (such as conductivity, dissolved oxygen, pH, temperature), an additional sheet is used which lists site, stream conditions and other pertinent information. Water samples delivered to the laboratory are assigned a laboratory identification code and entered into a computerized database (Microsoft Access) along with location, collection date and required analytes. The code is used to keep a running tally of all samples received, and also to track analysis as they are completed.

Instrumentation: Laboratory instruments are calibrated, operated, and maintained properly according to manufacturer's recommendations and this is a requirement for State Laboratory Certification. Instruments are calibrated each time they are used, if applicable. A log is kept of each instrument where maintenance and general comments on instrument performance are documented. For spectroscopy procedures, the calibration is performed with reagent blanks and standards of a matrix closely matching the samples. A typical standard curve consists of at least five concentrations. The linearity of each standard curve is monitored and must have a regression coefficient (r2) greater than 0.99. Additional quality assurance procedures that are a part of every micro- and macronutrient run include blanks, QCCS (quality control check standards), and internal spikes (recoveries of 80-120% or 85-115% are typically required depending on analyte).

Data Storage and Archiving: After analysis, raw data from labsheets are entered into spreadsheets to calculate concentrations and compute the relevant QC statistics. The analyst checks the quality control data against laboratory limits, and if the data meets the requirements, the data is saved and printed in hard copy. Both the original data sheet as well as the spreadsheet printout are saved. On the hard copy, the analyst checks the data, shows the quality control calculations, and initials the completed sheet. This data is reviewed by the Laboratory QA officer who also initials the sheet. At this point the data is considered valid and reportable. Data from spreadsheets is imported electronically into the water quality database on the DuluthStreams-NRRI file server. The server is backed up daily to prevent data loss.

SUMMARY

The QA/QC of near-real time remotely collected sensor data has provided challenges that were not present under traditional sampling regimes. We have attempted to develop rigorous protocols for each step of the data aquisition effort, and believe these protocols suit the needs of projects such as Lake Access and Water on the Web. Nonetheless, as these technologies become more common in resource management, future efforts must be directed toward the unique problems posed by real-time data collection.

ACKNOWLEGEMENTS

RIchard Axler, Elaine Ruzycki, and Norm Will contributed to the development, testing, and documentation of these QA/QC protocols.

REFERENCES


Ameel, J.J., Axler, R.P. and Owen, C.J. 1993. Persulfate digestion for determination of total nitrogen and phosphorus in low-nutrient waters. Amer. Environ. Labor. October 1993, p.1-11.

Ameel, J., E. Ruzycki and R.P. Axler. 1998 (reviewed annually). Analytical chemistry and quality assurance procedures for natural water samples. 6th edition. Central Analytical Laboratory, NRRI Tech. Rep. NRRI/TR98/03.

Anderson, J., T. Estabrooks, and J. McDonnel. 2000. Duluth Metropolitan Area streams snowmelt runoff study. Minnesota Pollution Control Agency, St. Paul, MN. March 2000.

APHA. 1998. Standard methods for the examination of water and wastewater. American Public Health Association, Washington, D.C.

Archer, A. and J. Barten. 1995. Quality assurance manual. Hennepin Parks Water Quality Laboratory. September 1995. Hennepin Parks, 3800 County Road 4, Maple Plain, MN 55359.

Archer, A. and J. Barten. 1996. Laboratory Procedures Manual. Hennepin Parks Water Quality Laboratory. October 1996. Hennepin Parks, 3800 County Road 4, Maple Plain, MN 55359.

Axler, R.P. and C.J. Owen.1994. Fluorometric measurement of chlorophyll and phaeophytin: Whom should you believe? Lake and Reservoir Management 8:143-151.

Barten, J. 1997. Water quality monitoring plan. Hennepin Parks, 3800 County Road 4, Maple Plain, MN 55359.

EPA. 2000. Delivering timely water quality information to your community: The Lake Access-Minneapolis project. EPA/625/R-00/012, September 2000, U. S. Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, 45268, USA.

EPA. 1998. Guidance for Quality Assurance Project Plans EPA QA/G-5. EPA/600/R-98/018, Feb 1998 (http://www.epa.gov/quality1/qs-docs/g5-final.pdf ). U.S. EPA, Washington, D.C. 20460

EPA 1989a. Preparing perfect project plans. US EPA Risk Reduction Engineering Laboratory, Cincinnati, OH, EPA/600/9-89/087.

EPA.1989b. Handbook of methods for acid deposition studies-Field operations for surface water chemistry. EPA/600/4-89-020.

EPA. 1987. Handbook of methods for acid deposition studies-Laboratory analysis for water chemistry. EPA/600/4-87-026

EPA. 1996. The Volunteer Monitor's Guide to: Quality Assurance Project Plans. EPA 841-B-96-003, Sep 1996, U.S. EPA, Office of Wetlands, Washington, D.C. 20460, USA (http://www.epa.gov/owowwtr1/monitoring/volunteer/qappexec.htm)

Host, G., N. Will, R.Axler, C. Owen and B. Munson. 2000a. Interactive technologies for collecting and visualizing water quality data. URISA Journal (In Press; refereed: http:// wow.nrri.umn.edu/urisa)

Host, G.E. , B. H. Munson, R. P. Axler, C. A. Hagley, G. Merrick and C. J. Owen. 2000b. Water on the Web: Students monitoring Minnesota rivers and lakes over the Internet. AWRA Spec.Ed. (Dec., 1999). (refereed: www.awra.org/proceedings/www99/w74/index.htm.).

MCWD. 1997. Quality assurance - quality control assessment report. Lake Minnetonka Monitoring Program 1997. Minnehaha Creek Watershed District, 2500 Shadywood Road, Excelsior, MN 55331-9578.
 
USGS. 2000a. Guidelines and standard procedures for continuous water-quality monitors: Site selection, field operation, calibration, record computation, and reporting. R.J. Wagner, H.C. Mattraw, G.F. Fritz and B.A. Smith. U.S. Geological Survey Techniques of Water-Resources Investigations Report 00-4252 (http://water.usgs.gov/pubs/wri/wri004252/). U.S. Geological Survey, Reston, Virginia, USA.

USGS. 2000b. National field manual for the collection of water-quality data. U.S. Geological Survey Techniques of Water-Resources Investigations, book 9, chaps. A1-A9, 2 v., variously paged.

WLSSD. 2000a. Laboratory Procedures Manual. Revision 2, Oct 1998. Western Lake Superior Sanitary District, Duluth, MN. 55807.

WLSSD. 2000b. Laboratory Quality Assurance Manual. (Revision 2, May 1998 with annual minor revisions). Western Lake Superior Sanitary District., Duluth, MN 55807. 


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date last updated: Wednesday January 19 2005