Environmental data is data about the environment—local, national, and international. It measures changes in the environment itself and in the plants and animals that inhabit it (including humans).
Environmental data comes from public and private institutions or from companies. Institutions measure an entire environment or aspect of it (air quality only, for example). Meanwhile, private institutions report their effects on the local environment, as required by law.
Environmental data attributes depend on the local or national government and on the industry providing the data. Yet no matter the source or how specific the data set, there is always an enormous amount of data to work with. Everything from surface and groundwater quality, waste production, dust production, air particulate production, fuel use, landfill vs recycling measures, the weather (of course), and more.
Naturally, conservationists, disaster response teams and planners, nonprofits, and educators use this data in the course of their regular duties. Increasingly, though, companies across various industries (like mining and metallurgy) use this data to ensure they comply with environmental regulations. Even more, by complying with or even exceeding these regulations, the companies can improve their public image.
To test environmental data quality, you must first define your needs and requirements. After that, you can determine whether your data set meets these parameters.
Secondly, regular tests can be performed by sending fabricated samples to the lab to check that their results are consistent and their equipment up to par. For automated sensors collecting data, just keep watch that there are no missing details, as that may indicate a technical problem with the sensory equipment.
Additionally, good environmental data is maintained by a system that requires frequent, consistent, and full reporting of data: without this, with the large amount of data that perhaps hundreds or more individuals record, the possibility of human errors, delays, and difficulty compiling information into one data set worsen.
To vet this data from a data vendor, check for a list of other companies they supply data or data services to and how often they secure and upgrade their system. Additionally, ask what the licensing arrangements may be and whether the software system is accredited.
If, instead, you are creating your own data set, make sure the data is precise, consistent, complete, and sensitive to changes. For example, make sure you set up the data feed to flag you when two successive values exceed a certain value.
There are about as many options for validating data as there are data sets catered to a specific need. Think about the attributes and vetting process from the beginning and look for examples on sampling and analyzation procedures from organizations like the ASTM.
City of Marion – Safeguarding the quality of a water aquifer with MonitorPro
White Paper: EHS Data Ltd – 2018: The Role of Good Environmental Data Management in Reducing Risk, and the Challenges Involved.
The forests Ingka Investments manages teem with life. The properties in Romania alone contain over 70 species of trees. Brown bears, wolves, and lynx cross paths on the woodland floor while black storks and golden eagles soar overhead. Rare orchids paint hidden slopes and endangered frogs dwell in the wetlands. Blagu monitors this rich biodiversity through aerial imagery and real-time mobile data uploaded to a GIS platform by field agents.
simulation results. This dataset is not publicly accessible because: I don’t own the data. It can be accessed through the following means: Please contact Dr. Jordan Schnell (email@example.com) to obtain a copy of the data. Format: N/A.This dataset is associated with the following publication: Schnell, J., D. Peters, D. Wong, X. Lu, H. Gao, H. Zhang, P. Kinney, and D. Horton. Potential for Electric Vehicle Adoption to Mitigate Extreme Air Quality Events in China. Earth’s Future. John Wiley & Sons, Inc., Hoboken, NJ, USA, 9(2): e2020EF001788, (2021).
All data for the EPA Report, Identifying and Evaluating Vapor Intrusion through Preferential Migration Routes and Points of Entry into Buildings.
excell spreadsheet.This dataset is associated with the following publication: Kim, Y.H., S. Warren, I. Kooter, W. Williams, I. George, S. Vance, M. Hays, M. Higuchi, S. Gavett, D. Demarini, I. Jaspers, and M. Gilmour. Chemistry, lung toxicity and mutagenicity of burn pit smoke-related particulate matter. Particle and Fibre Toxicology. BioMed Central Ltd, London, UK, 18(1): 45, (2021).