Applying Quality Assurance Procedures to Environmental Monitoring Data: A Case Study. Houston Durrant, T. & Hiederer, R. 11(4):774–781. ![Applying Quality Assurance Procedures to Environmental Monitoring Data: A Case Study [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Managing data in the context of environmental monitoring is associated with a number of particular difficulties. These can be broadly split into issues originating from the inherent heterogeneity of the parameters sampled, problems related to the long time scale of most monitoring programmes and situations that arise when attempting to maximise cost-effectiveness. The complexity of environmental systems is reflected in the considerable effort and cost required to collect good quality data describing the influencing factors that can improve our understanding of the interrelationships and allow us to draw conclusions about how changes will affect the systems. The resulting information is also frequently elaborate, costly and irreplaceable. Since the quality of the results obtained from analysing the data can only be as good as the data, proper management practices should be considered at all stages of the monitoring activity, if the value of the information is to be properly exploited. Using a Quality Assurance system can aid considerably in improving the overall quality of a database, and good metadata will help in the interpretation of the results. The benefits of implementing Quality Assurance principles to project management and data validation are demonstrated for the information collected for the long-term monitoring of the effects of air pollution on the forest environment under Forest Focus. However, there are limits in the ability of any computer system to detect erroneous or poor quality data, and the best approach is to minimise errors at the collection phase of the project as far as possible.
@article{houstondurrantApplyingQualityAssurance2009,
title = {Applying Quality Assurance Procedures to Environmental Monitoring Data: A Case Study},
author = {Houston Durrant, Tracy and Hiederer, Roland},
date = {2009},
journaltitle = {Journal of Environmental Monitoring},
volume = {11},
pages = {774--781},
issn = {1464-0333},
doi = {10.1039/b818274b},
url = {https://doi.org/10.1039/b818274b},
abstract = {Managing data in the context of environmental monitoring is associated with a number of particular difficulties. These can be broadly split into issues originating from the inherent heterogeneity of the parameters sampled, problems related to the long time scale of most monitoring programmes and situations that arise when attempting to maximise cost-effectiveness. The complexity of environmental systems is reflected in the considerable effort and cost required to collect good quality data describing the influencing factors that can improve our understanding of the interrelationships and allow us to draw conclusions about how changes will affect the systems. The resulting information is also frequently elaborate, costly and irreplaceable. Since the quality of the results obtained from analysing the data can only be as good as the data, proper management practices should be considered at all stages of the monitoring activity, if the value of the information is to be properly exploited. Using a Quality Assurance system can aid considerably in improving the overall quality of a database, and good metadata will help in the interpretation of the results. The benefits of implementing Quality Assurance principles to project management and data validation are demonstrated for the information collected for the long-term monitoring of the effects of air pollution on the forest environment under Forest Focus. However, there are limits in the ability of any computer system to detect erroneous or poor quality data, and the best approach is to minimise errors at the collection phase of the project as far as possible.},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-4388178,~to-add-doi-URL,check-list,data,data-acquisition,data-errors,data-heterogeneity,data-lineage,metadata,uncertainty},
number = {4}
}
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