Share and intensity of work current AI systems can materially affect.
SOC 47-4041
Hazardous Materials Removal Workers AI displacement risk
Removing asbestos, lead, and contaminated materials is regulated physical work in protective gear inside containment zones. Monitoring devices and documentation software assist; nobody robots their way through an abatement.
Likely potential for exposed tasks to move to software after workflow integration.
Certification requirements (HAZWOPER, state asbestos licenses) and the physical containment process anchor this role. Environmental cleanup demand is durable and regulation-driven, not technology-driven.
Distribution
Where Hazardous Materials Removal Workers sits across 620 tracked roles
Displacement pressure 14 — higher than 8% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-15. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
19 O*NET task statements matched to SOC 47-4041. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $49,450 (May 2025, US national). The latest BLS row matched SOC 47-4041.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Hazardous Materials Removal Workers
SOC 47-4041 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 14/100 role score and are not an occupation forecast.
+1.1% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +1.6% GDP and 3.9% unemployment.
+5.9% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +8.3% GDP and 4.6% unemployment.
+33.6% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Hazardous Materials Removal Workers
The current evidence import matched 19 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks19
SOC47-4041
- Core task / ID 11591
Remove asbestos or lead from surfaces, using hand or power tools such as scrapers, vacuums, or high-pressure sprayers.
- Core task / ID 20796
Build containment areas prior to beginning abatement or decontamination work.
- Core task / ID 20797
Prepare hazardous material for removal or storage.
- Core task / ID 11584
Comply with prescribed safety procedures or federal laws regulating waste disposal methods.
- Core task / ID 11585
Record numbers of containers stored at disposal sites, specifying amounts or types of equipment or waste disposed.
- Core task / ID 11589
Clean contaminated equipment or areas for reuse, using detergents or solvents, sandblasters, filter pumps, or steam cleaners.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Remove hazardous materials by hand
Exposure 14, automation 5%, augmentation 22%.
O*NET evidence: Remove or limit contamination following emergencies involving hazardous substances. (ID 19872)
Build containment and follow safety protocols
Exposure 20, automation 8%, augmentation 36%.
O*NET evidence: Build containment areas prior to beginning abatement or decontamination work. (ID 20796)
Identify hazards with monitoring devices
Exposure 34, automation 15%, augmentation 54%.
O*NET evidence: Identify asbestos, lead, or other hazardous materials to be removed, using monitoring d... (ID 11594)
Document disposal and track waste
Exposure 46, automation 24%, augmentation 56%.
O*NET evidence: Comply with prescribed safety procedures or federal laws regulating waste disposal meth... (ID 11584)
TaskExposureAutomationAugmentation
Remove hazardous materials by hand145%22%
Build containment and follow safety protocols208%36%
Identify hazards with monitoring devices3415%54%
Document disposal and track waste4624%56%
Transition pathways
Adjacent moves that preserve existing skills
credentialed transition
LowAbatement Supervisor
Training horizon: 3-6 months. Skill overlap 76. Wage preservation signal 122.
- Earn supervisor certification
- Own project safety plans
- Manage disposal documentation
adjacent role
LowEnvironmental Technician
Training horizon: 3-9 months. Skill overlap 58. Wage preservation signal 112.
- Learn sampling methods
- Study remediation systems
- Document site assessments
Comparison guides
Compare the next move before you commit
3-6 months
Hazardous Materials Removal Workers to Abatement Supervisor
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Hazardous Materials Removal Workers into Abatement Supervisor.
3-9 months
Hazardous Materials Removal Workers to Environmental Technician
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Hazardous Materials Removal Workers into Environmental Technician.
What the AI risk score means for Hazardous Materials Removal Workers
The displacement pressure score for Hazardous Materials Removal Workers is 14. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Document disposal and track waste carries 24% automation pressure, while Document disposal and track waste carries 56% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $49,450 (May 2025, US national). Employment context: Remediation trade with asbestos and environmental demand. Typical education: High school diploma plus HAZWOPER certification.
Wage vulnerability is 56, while transition feasibility is 62. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Very low displacement pressure
- Regulated physical remediation
- Environmental cleanup demand is durable
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Hazardous Materials Removal Workers, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Abatement technique
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Safety compliance
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Hazard identification
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Documentation
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Abatement Supervisor, such as earn supervisor certification.
- By 90 days, compare internal openings and external postings for Abatement Supervisor or Environmental Technician and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Hazardous Materials Removal Workers
Will AI replace Hazardous Materials Removal Workers?
Removing asbestos, lead, and contaminated materials is regulated physical work in protective gear inside containment zones. Monitoring devices and documentation software assist; nobody robots their way through an abatement. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Hazardous Materials Removal Workers work are most exposed to AI?
Document disposal and track waste and Identify hazards with monitoring devices show the strongest automation pressure in this model. Document disposal and track waste and Identify hazards with monitoring devices are better treated as AI-augmented work.
What should Hazardous Materials Removal Workers learn next?
Start with Abatement technique, Safety compliance, Hazard identification. The most practical adjacent paths in this model are Abatement Supervisor and Environmental Technician.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
OECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics