By Laurence Davidson, P.Eng., EarthFx Inc.
Advances in information technology software have enabled considerable progress in environmental data management techniques. Desktop relational databases are rapidly replacing spreadsheets for data storage, and a variety of data management systems are emerging to streamline the process of integrating and mapping data, with some including basic logic sequences, providing decision support functionality. Simultaneously, we are also collecting more information as data logger technology now enables remote satellite links for virtually continuous data streams of water level, water quality data and the like, and accreditation programs, such as ISO 14000, promote environmental awareness and auditing within firms.
As a profession, we should consider becoming more efficient in the ways we manage our data. The rewards are considerable:
Following is a brief explanation of the merits of data management, and descriptions and examples specific to the environmental sciences. It is intended that this collective wisdom is sufficient to serve as a guide to those undertaking an environmental database for the first time, yet also offer insight for more advanced users.
Once contamination is suspected at a site, we embark on a two-step process to manage the site, and ideally return it to a reasonable land use. The first step is to collect data from the site to characterize the geologic/hydrogeologic conditions, and quantify the extent and composition of the contaminants. Based on these findings, the second step involves making decisions on what action is necessary. Do we remediate, and how? Or is monitor/natural attenuation the most cost-effective solution with the current technology pool, or is more study necessary?
To make these decisions, we use the available data, our expertise and, recently, software for advanced analysis, visualization and decision support to ensure a transparent yet consistent and reproducible evaluation and sensitivity analysis of remedial alternatives.
The annual variation in a water table is an example of a common technical input variable that can be easily queried from a database. Data management, therefore, becomes an important component of a defensible DS system. This is further emphasized by considering the types and sheer volume of data required.
Using a Dense Non-Aqueous Phase Liquid (DNAPL) in a fractured rock site in Canada as an example, Table 1 lists some of the data collected (the site has benefited from considerable research work, and thus offers an unusually large suite of data and may not be representative of more typical projects).
This tabular data creates a database file likely exceeding 200 megabytes, with a possible additional 800 megabytes of core photographs, site plans, air photos and borehole geophysical logs. If printed, the tabular data alone would occupy in excess of 15,000 pages. Understandably, a database system designed for rapid searching of such data becomes an important tool for satisfying the information needs for effective project management and decision-making.
Other benefits include streamlined compliance reporting where new data is automatically loaded into the data management system, compared to the appropriate regulatory criteria for the site and standard reports generated, displaying compliance with regulatory instruments, any exceedances, trends, etc. Furthermore, data management systems are effective quality control mechanisms as they ensure parameter names and units are consistent throughout, thus eliminating errors or omissions due to incorrect nomenclature or units of measurement.
The environmental earth science community is undergoing a significant revolution in how we manage our data, stepping from the 'report-centric' to the 'data-centric' world where the final delivery of a project is a CD containing the project database.
To capitalize on, and arguably survive these trends, environmental scientists and engineers are adopting more rigorous data management methods. As learned from the petroleum geoscience sector, this encourages two important outcomes. First, it will enhance our ability as earth scientists to analyze and understand the physical and chemical systems bearing on our projects as we are more efficient with storing, retrieving and analyzing data. Second, it will promote the value of the data itself. With proper storage, the data has value to others, offering a means of offsetting the initial collection costs.
However, such advances come with new challenges. There is a need to incorporate basic database design constraints to ensure flexibility in our systems. There is a need to give special consideration to the scale at which we want the data to perform. There is a need for data models that establish standards for how data is stored, thus facilitating the sharing and sale of data, yet be implemented within a framework that recognizes that these models will evolve.
And finally, there is a need to develop an understanding of how to judge, select and use the numerous new earth science software products reaching the market every day, to ensure work processes are enhanced, not burdened by these tools.
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