Assessing temporal representativeness of water quality monitoring data
| Autor: | Mirva Ketola, Saku Anttila, Timo Kairesalo, Kirsi Vakkilainen |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Chlorophyll
010504 meteorology & atmospheric sciences 010501 environmental sciences Management Monitoring Policy and Law 01 natural sciences Standard deviation Time Water Quality Sampling design Statistics Finland 0105 earth and related environmental sciences Block (data storage) Chlorophyll A Water Pollution Public Health Environmental and Occupational Health Phycocyanin Sampling (statistics) General Medicine Variance (accounting) 6. Clean water Standard error Sample size determination Calibration Environmental science Water quality |
| Zdroj: | Journal of environmental monitoring : JEM. 14(2) |
| ISSN: | 1464-0333 |
| Popis: | The effectiveness of different monitoring methods in detecting temporal changes in water quality depends on the achievable sampling intervals, and how these relate to the extent of temporal variation. However, water quality sampling frequencies are rarely adjusted to the actual variation of the monitoring area. Manual sampling, for example, is often limited by the level of funding and not by the optimal timing to take samples. Restrictions in monitoring methods therefore often determine their ability to estimate the true mean and variance values for a certain time period or season. Consequently, we estimated how different sampling intervals determine the mean and standard deviation in a specific monitoring area by using high frequency data from in situ automated monitoring stations. Raw fluorescence measurements of chlorophyll a for three automated monitoring stations were calibrated by using phycocyanin fluorescence measurements and chlorophyll a analyzed from manual water samples in a laboratory. A moving block bootstrap simulation was then used to estimate the standard errors of the mean and standard deviations for different sample sizes. Our results showed that in a temperate, meso-eutrophic lake, relatively high errors in seasonal statistics can be expected from monthly sampling. Moreover, weekly sampling yielded relatively small accuracy benefits compared to a fortnightly sampling. The presented method for temporal representation analysis can be used as a tool in sampling design by adjusting the sampling interval to suit the actual temporal variation in the monitoring area, in addition to being used for estimating the usefulness of previously collected data. |
| Databáze: | OpenAIRE |
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