Water quality monitoring and assessment: general principles and fitness for purpose Deborah V Chapman GEMS/Water Capacity development Centre University College Cork
Considerations Purpose of monitoring and assessment What do we expect to achieve from the monitoring? How can we make sure we meet our expectations? What can we do to help generate monitoring data that is fit for purpose?
Definitions Monitoring is the systematic collection of data over temporal or spatial scales in order to define: Current environmental conditions/status Trends Assessment is the overall process of evaluation of the physical, chemical and biological nature of the environment in relation to natural quality, human impacts and intended uses Includes the use of monitoring data (practical, desk based or on line data collection) Includes interpretation of the data Aims to define environmental conditions, detect trends, establish cause effect relationships
Purpose of monitoring A. Provides data to aid management of water resources Conservation and protection Availability and suitability for specific uses Optimising treatment processes Determining impacts on water quantity and quality Determining impacts on aquatic ecosystems and ecosystem services Modelling impacts on future resources
Purpose of monitoring B. Provides information for development and implementation of water policies and their effectiveness Determining efficiency of treatments, controls, use restrictions Preparation and implementation of Conventions, Directives, Regulations Setting of taxes and charges, i.e., pricing Sharing information for regional and global policy
Good assessment and interpretation Water quality data depends on: Reliable and comparable (quality assurance) At appropriate spatial and/or temporal scales Related hydrological information Targets, indicators or background information
Objectives are fundamental to a monitoring programme The objectives must be realistic in relation to the resources (human, technical and financial) available Well defined objectives: lead to more focused and cost effective monitoring inform the design of the monitoring programme
Objectives inform monitoring programme design Objectives must: Specify clearly the questions that need to be answered, and Identify the anticipated information to be gained from the monitoring programme Examples: To determine long term trends in fluxes of nutrients to coastal waters from river basins To check suitability of reservoir waters for drinking water treatment
Design: monitoring variables How many variables should be included in the monitoring programme? Will a few specific variables be adequate to meet the objectives? General variables Temperature Dissolved oxygen (mg l -1 ) ph Total dissolved solids (mg l -1 ) Total suspended solids (mg l -1 ) BOD (mg l -1 O 2 ) Turbidity (NTU) Ammonium (mg l -1 ) Nitrate as N (mg l -1 ) Nitrite (mg l -1 ) Total phosphorus (mg l -1 ) Sodium (mg l -1 ) Chloride (mg l -1 ) Chlorine (mg l -1 ) Sulphate (mg l -1 ) Sulphide (mg l -1 ) Chlorophyll a (mg l -1 ) E. Coli (No per 100 ml) Specific variables, e.g. Lead Mercury Total hydrocarbons Detergents
Design: site selection Single site, e.g. abstraction point, point of use, effluent discharge Multiples sites, e.g. national survey, dispersion from point source Multiple stations, e.g. depth samples in lakes and groundwater Background or control sites, e.g. upstream of discharge point
Design: frequency of sampling Must take into consideration duration of emission and persistence in the environment Must be adequate to show any time related changes in the variables being monitored May be defined by guidelines or licence conditions Trend monitoring needs regular intervals over long time periods Surveys require many sites in as short a time period as possible to ta l C r g L -1 Dissolved oxygen (mg L -1 ) 60 50 40 30 20 10 0 Jan Mar Apr May Jul Oct Nov Dec Sampling site 1 Sampling site 2 Sampling site 3 Dissolved oxygen in bottom water (309m) 12 10 8 6 4 2 0 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 Years
Design: Hydrological information Residence time in lakes and reservoirs Discharge in rivers Thermal stratification in lakes and reservoirs Saline stratification in estuaries Direction of groundwater flow and piezometric level
Design: Sample handling and analysis Appropriate sample containers and sample preservatives (if required) Minimal disturbance during sampling and transportation, e.g. maintain ambient temperature Minimum storage time in appropriate conditions Analytical methods that are capable of required accuracy and level of detection Application of good laboratory quality control procedures
Every monitoring programme should have an associated quality programme Minimizes errors in data acquisition but cannot totally prevent them Helps to quantify errors in measurement Gives credibility to the data Ensures comparability and compatibility between data collectors Quality assurance should be applied at all stages of a monitoring programme, i.e. field, laboratory and data management Approximately 10 20% of the total resources needed for a monitoring programme should be devoted to quality assurance, i.e. financial, technical and personnel
Credible data Data that can be believed and defended. Achieved by: Using recognised national or international standardised methods, e.g. ISO (see www.iso.ch), APHA, DIN Applying quality assurance Demonstrating the level of confidence in the data, e.g. by providing standard deviations or confidence limits
To ensure a valid field sample Variations in sampling procedures can have a marked effect on the results of analysis Appropriate location of sampling sites and stations Appropriate sample container Correct method of collection Appropriate sample fixation/preservation Careful field technique
Sources of error in field sampling Carryover of analyte from sampling equipment Incomplete decontamination of equipment between samples/sampling trips Cross contamination between samples Absorption of volatile chemicals from the air during transportation and storage Poorly maintained and functioning equipment The sampler!
Laboratory internal quality control (IQC) Used for continuous assessment of the quality of the results of individual analytical procedures Used to check: Precision: the likelihood of the analytical method giving the same value if the same sample was analysed more than once. Precision can be expressed as the standard deviation Accuracy: the nearness of the measured value to the true value
Quality control samples Quality control samples are extra samples taken during the sampling and analytical process Sample blanks used to assess potential contamination Field and laboratory instrument blanks used to determine carry over of contamination from one sample to the next Replicates/duplicates used to determine the sampling and/or analytical precision Spiked samples used to obtain percentage recovery, and therefore accuracy
External quality control GEMS/Water laboratory performance evaluation 1. The reference laboratory sends out sets of specimens with known and unknown concentrations of analytes to all of the participating laboratories 2. Each participant laboratory analyses the specimens for the specified analytes and reports the results to the reference laboratory 3. The reference laboratory reports on the performance of the participating laboratories Poor performance means that laboratory procedures need to be checked and deficiencies corrected
Reasons why monitoring programmes fail to provide the expected information for management The objectives of the programme were not defined properly The monitoring programme was installed with insufficient knowledge of the environment to be sampled There was inadequate planning of the sample collection, handling, storage and analysis The information obtained was poorly archived The data were not properly or adequately interpreted and reported
Key messages Successful and useful monitoring depends on: Well defined objectives Careful selection of appropriate monitoring regimes Collection of related hydrological information Credible and defensible data (quality assurance) Assessment and interpretation of the data Presentation of the data in meaningful and understandable formats
Strive for quality! Errors can be introduced at all stages of sampling and analysis Data are not credible if their quality cannot be assured Quality assurance plans and the associated procedures help to assure monitoring data