Conduct research or perform investigation for the purpose of identifying, abating, or eliminating sources of pollutants or hazards that affect either the environment or the health of the population. Using knowledge of various scientific disciplines, may collect, synthesize, study, report, and recommend action based on data derived from measurements or observations of air, food, soil, water, and other sources.
U.S. Workers
84,930
Median Salary
$80,060
10-Year Growth
+4.4%
Annual Openings
8,500
Typical entry: Bachelor's degree
22 of 22 tasks have some AI capability
Exposure Trend
This score reflects estimated AI technical capability for tasks in this occupation. It does not predict employment changes, and it does not account for company-specific constraints, regulation, or adoption barriers.
Collect, synthesize, analyze, manage, and report environmental data, such as pollution emission measurements, atmospheric monitoring measurements, meteorological or mineralogical information, or soil or water samples.
AI: Fully automatable - By 2025 sensor networks, automated samplers, drones, and AI pipelines can routinely collect, synthesize, analyze, manage, and produce reports for many environmental monitoring programs end-to-end.
Prepare charts or graphs from data samples, providing summary information on the environmental relevance of the data.
AI: Fully automatable - Generating charts, graphs, and concise interpretive summaries from datasets is well within current AI and visualization tool capabilities and can be fully automated for routine tasks.
Plan or develop research models, using knowledge of mathematical and statistical concepts.
AI: Fully automatable - AI can design mathematical and statistical models, write code, run simulations, and validate results, enabling full automation of model planning and development in many cases.
Monitor environmental impacts of development activities.
AI: Fully automatable - By 2025, sensor networks, remote sensing and AI analytics can continuously detect, quantify, and report environmental impacts of development activities, enabling largely automated monitoring workflows (with human oversight for regulatory decisions).
Provide scientific or technical guidance, support, coordination, or oversight to governmental agencies, environmental programs, industry, or the public.
AI: Partial - AI can generate guidance, run analyses, and coordinate technical activities, yet authoritative oversight, accountability, and context-sensitive judgment for stakeholders still rely on humans.
Review and implement environmental technical standards, guidelines, policies, and formal regulations that meet all appropriate requirements.
AI: Partial - AI tools can review standards, identify conflicts, and draft implementation plans, but final legal compliance decisions and cross-stakeholder implementation require human legal and policy judgment.
Analyze data to determine validity, quality, and scientific significance and to interpret correlations between human activities and environmental effects.
AI: Partial - AI can perform data cleaning, statistical analysis, and identify correlations and quality issues, but human experts are still needed for causal inference and assessing scientific significance.
Communicate scientific or technical information to the public, organizations, or internal audiences through oral briefings, written documents, workshops, conferences, training sessions, or public hearings.
AI: Partial - AI can produce written materials, present synthesized briefings, and run virtual training, but nuanced two-way public engagement, moderation, and trust-building in live forums generally need human communicators.
Develop the technical portions of legal documents, administrative orders, or consent decrees.
AI: Partial - AI can draft technically detailed sections, compile evidence, and standardize language, but legal finalization and negotiation require human legal judgment and authority.
Provide advice on proper standards and regulations or the development of policies, strategies, or codes of practice for environmental management.
AI: Partial - AI can model outcomes, propose standards, and draft policy options, but setting policy and negotiating stakeholder trade-offs remain human-led activities requiring judgement and legitimacy.
Evaluate violations or problems discovered during inspections to determine appropriate regulatory actions or to provide advice on the development and prosecution of regulatory cases.
AI: Partial - AI can synthesize inspection findings and suggest regulatory options, but deciding enforcement actions and prosecutorial strategy requires human legal and ethical judgment.
Conduct environmental audits or inspections or investigations of violations.
AI: Partial - Drones, sensors, and AI can automate much inspection and preliminary audit work, but legal investigations, enforcement decisions, and complex site evaluations still require human investigators.
Develop methods to minimize the impact of production processes on the environment, based on the study and assessment of industrial production, environmental legislation, and physical, biological, and social environments.
AI: Partial - AI can propose process optimizations and mitigation methods informed by engineering and regulations, but integrating site-specific constraints and social/legal considerations needs human direction.
Determine data collection methods to be employed in research projects or surveys.
AI: Partial - AI can recommend sampling designs, sensor deployments, and survey methodologies using statistical principles, yet cannot fully account for field constraints and ethical approvals without human oversight.
Process and review environmental permits, licenses, or related materials.
AI: Partial - AI can automate review for completeness, flag inconsistencies, and draft standard permit conditions, but final permitting decisions and complex legal interpretations remain human responsibilities.
Monitor effects of pollution or land degradation and recommend means of prevention or control.
AI: Partial - AI can aggregate and analyze remote sensing and sensor data and propose mitigation measures, but cannot perform on-site inspections and complex stakeholder decisions.
Supervise or train students, environmental technologists, technicians, or other related staff.
AI: Partial - AI can provide training materials, monitor routine task performance, and offer coaching, but cannot fully replace human supervision, mentorship, and personnel management.
Design or direct studies to obtain technical environmental information about planned projects.
AI: Partial - AI can generate study designs and sampling protocols tailored to projects, but cannot fully direct field logistics, stakeholder engagement, and regulatory approvals.
Investigate and report on accidents affecting the environment.
AI: Partial - AI can analyze data, synthesize findings, and draft investigation reports from remote sensors and records but cannot perform on-site evidence collection, interviews, and field judgment required for full accident investigations.
Conduct applied research on environmental topics, such as waste control or treatment or pollution abatement methods.
AI: Partial - AI can design experiments, run simulations, mine literature, and analyze results to support applied environmental research but cannot yet carry out hands-on lab or field experiments and validation autonomously.
Research sources of pollution to determine their effects on the environment and to develop theories or methods of pollution abatement or control.
AI: Partial - AI can identify likely pollution sources using remote sensing and data analysis and model effects and abatement strategies, but definitive source attribution and field validation still require human-led sampling and expert interpretation.
Develop programs designed to obtain the most productive, non-damaging use of land.
AI: Partial - AI can generate optimized land‑use plans, model ecological and productivity tradeoffs, and propose program designs, but stakeholder engagement, legal judgment, and adaptive implementation keep full ownership with humans.