Environmental Engineering Technologists and Technicians

Apply theory and principles of environmental engineering to modify, test, and operate equipment and devices used in the prevention, control, and remediation of environmental problems, including waste treatment and site remediation, under the direction of engineering staff or scientists. May assist in the development of environmental remediation devices.

The occupation of Environmental Engineering Technologists and Technicians has an automation risk of 48.3%, which is very close to its calculated base risk of 49.0%. This moderate level of risk is due to the nature of many job tasks that, while structured and routine, still require oversight and applied judgment. Many components of the job involve collecting and maintaining data, conducting repetitive quality checks, and following standardized procedures. These tasks are often well-suited to automation, particularly as digital technologies, sensors, and data management platforms become increasingly sophisticated and widespread in environmental monitoring. The three tasks most susceptible to automation in this field include maintaining project logbook records or computer program files, recording laboratory or field data such as numerical information, test results, or photographs, and performing environmental quality work in both field and office settings. These duties typically involve repetitive actions, structured data collection, and documentation—areas where automated systems like data loggers, digital forms, and basic AI analytics tools can be directly applied to increase efficiency, reduce human error, and lower costs. Such technologies can seamlessly substitute much of the manual effort, enabling faster data processing and communication of results. However, the automation risk is counterbalanced by several more resistant areas of the job. Tasks such as obtaining product information, identifying vendors or suppliers, or ordering materials require judgment, negotiation, and adaptability to changing circumstances. Improving chemical processes to reduce toxic emissions typically demands creativity and specialized expertise beyond the reach of current AI. Likewise, creating sophisticated models to demonstrate or predict the movement and impact of pollutants in environments depends on a mix of scientific understanding, modeling skills, and interpretative ability. The bottleneck skill of originality, at a low level (3.0%), plays a role here: while most tasks are procedural, the small yet important need for original problem-solving and innovation remains a critical barrier to full automation in this occupation.