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Hazardous Materials Removal Workers

Occupation · SOC 47-4041.00

Identify, remove, pack, transport, or dispose of hazardous materials, including asbestos, lead-based paint, waste oil, fuel, transmission fluid, radioactive materials, or contaminated soil. Specialized training and certification in hazardous materials handling or a confined entry permit are generally required. May operate earth-moving equipment or trucks.

Also called: Asbestos Abatement Worker · Asbestos Remover · Asbestos Worker · Waste Handling Technician · Abatement Worker · Asbestos Hazard Abatement Worker · Decontamination and Decommissioning Operator (D and D Operator) · Hazmat Technician (Hazardous Materials Technician) · Asbestos Coverer · Asbestos Handler · Asbestos Technician · Decontamination Worker

Job family: Construction and Extraction Occupations

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Download .md

A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-47-4041-00/context.md directly.

AI work map

A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.

Often handed to AI

Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.

  • Identify or separate waste products or materials for recycling or reuse. · 0.4%
See how AI is used here →

Keep a human in the loop

Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.

  • Identify or separate waste products or materials for recycling or reuse. · 100.0% need a human
See the boundary tasks →

13th-percentile task overlap — yet about 5,000 openings a year (+1% projected, BLS) . What exposure means →

AI & job outlook

What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.

Exposure to current AI

Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.

Measure Rank vs all occupations Percentile Score
Overall AI exposure (Felten et al.) Low 29th -0.7
LLM task exposure, γ (OpenAI / Eloundou) Low 17th 0.1
AI assistant applicability (Microsoft) Low 3rd 0.0

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.0), with simple added tooling (β 0.1), and including AI-powered software (γ 0.1). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.

This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.

Historical automation estimate (2013)

A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.

Frey–Osborne probability 0.5 · 48th percentile among occupations · Moderate

How AI is actually used in this job

Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.

Identify or separate waste products or materials for recycling or reuse. 0.4%

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

Outlook About average · +1.0% by 2034
Projected annual openings 5,000
Employment 2024 → 2034 51,300 → 51,800

“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.

Where this work sits on the global GenAI gradient

The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.

9% mean task exposure (2025)
2nd percentile of 427 placed occupations
+1 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Building Frame and Related Trades Workers Not Elsewhere Classified · 7119 9% Not exposed

Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.

Working with AI in this job

How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.

Most common way people use AI here Directive · AI does it; you give the instruction
Typical AI autonomy 3.0 / 5 · higher = AI acts more independently

What people delegate to AI

The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.

Task How Usage
Identify or separate waste products or materials for recycling or reuse. Directive 0.4%

Where a human is still needed

Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.

Identify or separate waste products or materials for recycling or reuse. 100.0%

What people most often hand AI here

Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.

  • Help me identify or separate waste products or materials for recycling or reuse.

    From: Identify or separate waste products or materials for recycling or reuse. · 0.4% of measured AI use · directive

Tasks

All 21 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

Work activities

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Abilities

Problem Sensitivity 4.0
Oral Comprehension 3.9
Control Precision 3.8
Oral Expression 3.6
Near Vision 3.6
Written Expression 3.5
Deductive Reasoning 3.5
Category Flexibility 3.5
Arm-Hand Steadiness 3.5
Speech Recognition 3.5
Inductive Reasoning 3.4
Information Ordering 3.4
Multilimb Coordination 3.4
Written Comprehension 3.3
Visualization 3.3
Manual Dexterity 3.3
Speech Clarity 3.3
Selective Attention 3.1
Extent Flexibility 3.1

Essential skills

Monitoring 3.6
Critical Thinking 3.5
Active Listening 3.3
Reading Comprehension 3.1
Speaking 3.1
Writing 3.0
Active Learning 3.0

Knowledge

Public Safety and Security 3.6
Administration and Management 3.5
Transportation 3.4
Customer and Personal Service 3.4
Building and Construction 3.2
Mechanical 3.1
English Language 3.1

Transferable skills

Operation and Control 3.4
Operations Monitoring 3.3
Social Perceptiveness 3.0
Coordination 3.0
Complex Problem Solving 3.0
Quality Control Analysis 3.0
Judgment and Decision Making 3.0

Skills in demand

Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.

Tools & technology

Example Category
Jenkins CI Enterprise application integration software Hot technology
Microsoft Excel Spreadsheet software Hot technology
Microsoft Office software Office suite software Hot technology
Microsoft Outlook Electronic mail software Hot technology
Microsoft PowerPoint Presentation software Hot technology
Microsoft Word Word processing software Hot technology
SAP software Enterprise resource planning ERP software Hot technology
Computerized maintenance management system software CMMS Facilities management software
Database software Data base user interface and query software
Inventory management systems Inventory management software
Xactware Xactimate Data base user interface and query software

Work context

How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.

Face-to-Face Discussions with Individuals and Within Teams 4.8
Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets 4.6
Wear Specialized Protective or Safety Equipment such as Breathing Apparatus, Safety Harness, Full Protection Suits, or Radiation Protection 4.5
Health and Safety of Other Workers 4.3
Exposed to Contaminants 4.1
Telephone Conversations 4.1
Work With or Contribute to a Work Group or Team 4.1
Contact With Others 4.0
Time Pressure 3.9
Spend Time Standing 3.9
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 3.9
Importance of Being Exact or Accurate 3.8
Work Outcomes and Results of Other Workers 3.7
Indoors, Not Environmentally Controlled 3.7
Physical Proximity 3.7
Spend Time Walking or Running 3.6
Outdoors, Exposed to All Weather Conditions 3.6
Coordinate or Lead Others in Accomplishing Work Activities 3.6
Spend Time Making Repetitive Motions 3.5
Exposed to Very Hot or Cold Temperatures 3.5
Impact of Decisions on Co-workers or Company Results 3.4
Indoors, Environmentally Controlled 3.4
Deal With External Customers or the Public in General 3.4
Frequency of Decision Making 3.4
Outdoors, Under Cover 3.3
Freedom to Make Decisions 3.3
Exposed to Cramped Work Space, Awkward Positions 3.3
Consequence of Error 3.3
Exposed to Hazardous Equipment 3.2
Conflict Situations 3.2
Written Letters and Memos 3.2
Importance of Repeating Same Tasks 3.2
Determine Tasks, Priorities and Goals 3.2
Exposed to High Places 3.1
Spend Time Kneeling, Crouching, Stooping, or Crawling 3.1
Spend Time Climbing Ladders, Scaffolds, or Poles 3.1
Spend Time Bending or Twisting Your Body 3.1
Exposed to Hazardous Conditions 3.1
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 3.0
In an Enclosed Vehicle or Operate Enclosed Equipment 3.0

How to get in

Job zone
Zone 3 — Job Zone Three: Medium Preparation Needed
Education
Most occupations in this zone require training in vocational schools, related on-the-job experience, or an associate's degree.
Typical entry-level education
High school diploma or equivalent · BLS, the typical path — not a requirement
Related experience
Previous work-related skill, knowledge, or experience is required for these occupations. For example, an electrician must have completed three or four years of apprenticeship or several years of vocational training, and often must have passed a licensing exam, in order to perform the job.
Preparation level
SVP (6.0 to < 7.0) — total schooling plus on-the-job experience.

What to study: Engineering/Engineering-Related Technologies/Technicians . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.

Education of current workers

Share of people in this occupation at each level of education.

Less than a High School Diploma 26.0%
High School Diploma 24.9%
Post-Secondary Certificate 19.7%
Bachelor's Degree 13.8%
Some College Courses 13.7%
Associate's Degree (or other 2-year degree) 1.9%

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Career interests (Holland / RIASEC)

Realistic 7.0
Conventional 4.4
Investigative 2.6

Interest areas

Physical/Manual Labor 6.3
Transportation/Machine Operation 4.3
Construction/Woodwork 2.3
Protective Service 2.3
Mechanics/Electronics 2.3
Engineering 1.9

Work styles

Dependability 5.0
Attention to Detail 4.0
Integrity 3.0
Cautiousness 2.7
Stress Tolerance 2.2
Perseverance 2.0
Self-Control 1.8

Wages & employment

U.S. · annual wages (BLS OEWS)

$37k10th$43k25th$48kMedian$62k75th$82k90th
Annual wages by percentile — U.S. (BLS OEWS). The light band spans the 10th–90th percentile; the darker band is the middle half (25th–75th); the line is the median.
51k202452k2034 (proj.)+1.0% · About average
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $37,330
25th percentile $42,710
Median (50th) $48,490
75th percentile $62,150
90th percentile $82,480
People employed 50,570

Industries that employ this occupation

Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.

Industry Workers National median pay
Administrative and Support and Waste Management and Remediation Services · Sector 41,500 $47,660
Construction · Sector 3,160 $54,690
Temporary Help Services · National industry 2,770 $39,360
Professional, Scientific, and Technical Services · Sector 2,420 $60,080
Engineering Services · National industry 760 $81,420
Health Care and Social Assistance · Sector 260 $53,090
Manufacturing · Sector 220 $59,920
Utilities · Sector 190 $105,890
Nuclear Electric Power Generation · National industry 160 $110,210
Other Building Equipment Contractors · National industry 160 $58,730
Wholesale Trade · Sector 160 $61,330
Plumbing, Heating, and Air-Conditioning Contractors · National industry 150 $54,970

Where this work is most concentrated

Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).

Industry Concentration Workers
Administrative and Support and Waste Management and Remediation Services · Sector 14.01× 41,500
Nuclear Electric Power Generation · National industry 13.14× 160
Temporary Help Services · National industry 3.19× 2,770
Other Building Equipment Contractors · National industry 3.18× 160
Engineering Services · National industry 2× 760
Construction · Sector 1.19× 3,160
Utilities · Sector 1× 190
Professional, Scientific, and Technical Services · Sector 0.69× 2,420

Part of the Advanced Manufacturing and Energy & Natural Resources career clusters.

Exposure quadrant: AI task-overlap percentile vs Median pay Hazardous Materials Removal Workers sits at the 13th percentile of AI task-overlap and the 29th percentile of median pay, placed here against 11 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Hazardous Materials Removal Workers Construction Laborers Recycling and Reclamation Workers Cleaners of Vehicles and Equipment Explosives Workers, Ordnance Handling Experts, and Blasters Environmental Science and Protection Technicians, Including Health Environmental Engineering Technologists and Technicians AI task-overlap percentile → ↑ Median pay
AI task-overlap percentile (horizontal) vs. median-pay percentile (vertical), across all scored occupations. This occupation is highlighted; related occupations are plotted alongside it. Overlap measures shared tasks with AI, not automation.

Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.

What you can do with this

Options the data surfaces for Hazardous Materials Removal Workers — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Skills that travel

Capabilities this work builds that are used across many other occupations.

Adjacent roles

Occupations O*NET rates as related — the nearby moves on the map.

Paths in

How people typically prepare for this work.

Zoom out

On the global GenAI exposure gradient this work sits around the 2nd percentile of 427 international occupations.

Write a report on thisheadline · factoids · citation

Hazardous Materials Removal Workers show 13th-percentile AI task overlap — and about 5,000 annual U.S. openings

  • Hazardous Materials Removal Workers rank in the 13th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
  • The occupation is projected to see about 5,000 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
  • BLS projects employment to be about average (+1%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $48,490, across about 50,570 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Hazardous Materials Removal Workers show 13th-percentile AI task overlap — and about 5,000 annual U.S. openings

• Hazardous Materials Removal Workers rank in the 13th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE)
• The occupation is projected to see about 5,000 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34)
• BLS projects employment to be about average (+1%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $48,490, across about 50,570 U.S. workers. (BLS OEWS (May 2024))

Source: Singulariki — "Hazardous Materials Removal Workers". https://singulariki.com/roles/role-47-4041-00
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.

AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom

Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.

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.

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

Cite this page
Plain

Singulariki. "Hazardous Materials Removal Workers." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-47-4041-00

APA

Singulariki. (2026). Hazardous Materials Removal Workers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-47-4041-00

BibTeX 
@misc{singulariki-role-47-4041-00,
  title  = {Hazardous Materials Removal Workers},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
  url    = {https://singulariki.com/roles/role-47-4041-00}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.

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