Analyze environmental data.

Analyze environmental data. is a detailed work activity in O*NET — a concrete unit of work shared across 10 occupations and seen in 13 occupation-specific tasks. It rolls up into the broader work activity Analyze environmental or geospatial data. in Analyzing Data or Information .

Detailed work activities are the most granular shared layer in O*NET's work-activity hierarchy (Generalized → Intermediate → Detailed → occupation-specific task). The figures below describe how this activity shows up across the economy and what independent studies measure about AI and this kind of work — not a prediction that the work will be automated.

AI exposure

Of the 13 tasks under this activity that the OpenAI / Eloundou “GPTs are GPTs” study rated, 12 (92%) are flagged as directly exposed to language models (E1) or exposed via model-powered tools (E2).

The Anthropic Economic Index observes real AI use on 5 of these tasks, with a mean mapped-usage share of 0.003% per task.

Exposure estimates overlap with model capabilities — whether a model could speed the task up — not whether the work will be done by software. Observed AI use is augmentation and assistance today, not jobs replaced.

Member tasks

Occupation-specific tasks O*NET maps to this detailed work activity, most important first.

• Analyze samples, such as air or water samples, for contaminants or other elements. · Nuclear Monitoring Technicians · importance 4.3 · no direct exposure
• Collect and analyze data to determine environmental conditions and restoration needs. · Environmental Restoration Planners · importance 4.3 · exposure with tools
• Formulate predictions by interpreting environmental data, such as meteorological, atmospheric, oceanic, paleoclimate, climate, or related information. · Atmospheric and Space Scientists · importance 4.3 · exposure with tools
• Identify risks for natural disasters, such as mudslides, earthquakes, or volcanic eruptions. · Geoscientists, Except Hydrologists and Geographers · importance 4.2 · exposure with tools
• Use geospatial technology to develop soil sampling grids or identify sampling sites for testing characteristics such as nitrogen, phosphorus, or potassium content, pH, or micronutrients. · Precision Agriculture Technicians · importance 4.2 · exposure with tools
• Identify spatial coordinates, using remote sensing and Global Positioning System (GPS) data. · Precision Agriculture Technicians · importance 4.0 · exposure with tools
• Study public water supply issues, including flood and drought risks, water quality, wastewater, and impacts on wetland habitats. · Hydrologists · importance 3.9 · exposure with tools
• Compile or interpret biodata to determine extent or type of wetlands or to aid in program formulation. · Conservation Scientists · importance 3.8 · exposure with tools
• Conduct experiments to investigate the underlying mechanisms of plant growth and response to the environment. · Soil and Plant Scientists · importance 3.7 · exposure with tools
• Perform statistical analysis of environmental data. · Environmental Science and Protection Technicians, Including Health · importance 3.3 · direct LLM exposure
• Perform environmentally extended input-output (EE I-O) analyses. · Industrial Ecologists · importance 3.2 · exposure with tools
• Identify areas in need of pesticide treatment by analyzing geospatial data to determine insect movement and damage patterns. · Precision Agriculture Technicians · importance 3.0 · exposure with tools
• Analyze ecological data about the impact of pollution, erosion, floods, and other environmental problems on bodies of water. · Hydrologic Technicians · exposure with tools

Occupations that perform this

• Nuclear Monitoring Technicians
• Environmental Restoration Planners
• Atmospheric and Space Scientists
• Geoscientists, Except Hydrologists and Geographers
• Precision Agriculture Technicians
• Hydrologists
• Conservation Scientists
• Soil and Plant Scientists
• Environmental Science and Protection Technicians, Including Health
• Hydrologic Technicians

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

• O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
• Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
• “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic

Data compiled June 2, 2026. Figures are estimates, not advice.

@misc{singulariki-analyze-environmental-data,
  title  = {Analyze environmental data.},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130. Accessed June 7, 2026},
  url    = {https://singulariki.com/detailed-activities/analyze-environmental-data}
}