Applying quality assurance procedures to environmental monitoring data: a case study

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.

How to ensure threatened species monitoring leads to threatened species conservation

“…However, data management is often neglected and its value apparent only when it fails (Caughlan & Oakley 2001). For example, if data analysis requirements are poorly estimated during program design, there may be a failure to make reliable inferences about threatened species (Houston & Hiederer 2009). Similarly, budget blow-outs resulting from a lack of accounting for the cost of data management (Caughlan & Oakley 2001), or data loss resulting from insufficient data security (Whitlock 2011), highlight the need for good data management practices.…”

Homogenisation of climate time series from ICP Forests Level II monitoring sites in Germany based on interpolated climate data

“…The sub daily data were aggregated to daily values either as sum or mean, max and min and submitted to the Level II database. Within the international Level II database a quality assessment programme was implemented (Houston and Hiederer, 2009). This consists of single parameter tests which check for outliers with range tests and global thresholds.…”

“…Typically, of a monitoring programme the largest amount of effort is allocated to data collection, while data quality assessment and data evaluation are often underrepresented (Ferretti et al, 1999;Houston and Hiederer, 2009). To assess the impact of climate extremes on forest health it is crucial to have accurate climate time series.…”

“…Different statistical methods were developed to test if climate time series are free of non-climatic inhomogenities (Peterson et al, 1998). Within the ICP Forests Level II monitoring programme the quality of the meteorological data was routinely estimated to a certain level, but tests for homogeneity have not been performed (Houston and Hiederer, 2009 intended to improve the German part of the ICP Forests Level II climate data set by homogenisation.…”

Quality Assessment of Soil Pollution Monitoring: Focus on Representativeness

“…Soil chemical monitoring produces such data -but are its quantity and quality sufficient? Since monitoring programs are only as good as the information (data) they produce (Houston & Hiederer, 2009), comprehensive and reliable quality assessment of soil monitoring is crucial.…”