The journal of climate change and health · 2026
Environmental impact of urine analyses: Comparison of manual, semi and fully automated analytical workflows.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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Brunier Julien, Mourgues Clara, Auger Fanny, Lina Gérard, Vandenesch François, Ramanantsoa Céline, Dauwalder Olivier
Аннотация
OBJECTIVES: Urinary tract infection is the most common infection requiring urinalysis (UA). Due to economical constraints, many laboratories have merged to use full laboratory automation instruments for high volume of urine samples. However, the environmental impact (EI) of UA is still unknown, particularly according to automation levels. The present study aimed to document the environmental impact of UA for the first time.
METHODS: We used the life cycle assessment (LCA) methodology (ISO 14040/44, 2006) to compare manual, semi-automated and fully-automated analytical workflows for Escherichia coli UA (ECUA) including cytology, isolation, and antibiotic susceptibility tests (AST). Using a multicenter approach, we measured midpoints and endpoints levels defined by the impact assessment method ReCiPe 2016.
RESULTS: ECUA carbon footprint was 1.9, 2.6 and 1.1 kgCO2eq using manual, semi-automated and fully-automated workflows respectively. AST processes accounted for 66.8%, 79.0%, and 59.9% of emissions, respectively. The fully-automated workflow had the lowest impact for 12/18 midpoint indicators and had the lowest impact on human health (2.9 × 10-6 disability-adjusted life years (DALY) vs 3.7 × 10-6 for manual and 6.9 × 10-6 for semi-automated workflows). When scaled to 50,000 ECUA/year, the manual workflow had the lowest impact for 10/18 midpoints and human health (3.7 × 10-6 DALY), but its impact remained higher than fully-automated in real conditions.
CONCLUSIONS: The EI of UA depends on both the automation level and the organizational scale. While automation does not always reduce impacts linearly, full automation offers the best carbon and health performance in high-throughput settings. Integrating LCA into laboratory strategy may support the ecological transition of microbiology.
METHODS: We used the life cycle assessment (LCA) methodology (ISO 14040/44, 2006) to compare manual, semi-automated and fully-automated analytical workflows for Escherichia coli UA (ECUA) including cytology, isolation, and antibiotic susceptibility tests (AST). Using a multicenter approach, we measured midpoints and endpoints levels defined by the impact assessment method ReCiPe 2016.
RESULTS: ECUA carbon footprint was 1.9, 2.6 and 1.1 kgCO2eq using manual, semi-automated and fully-automated workflows respectively. AST processes accounted for 66.8%, 79.0%, and 59.9% of emissions, respectively. The fully-automated workflow had the lowest impact for 12/18 midpoint indicators and had the lowest impact on human health (2.9 × 10-6 disability-adjusted life years (DALY) vs 3.7 × 10-6 for manual and 6.9 × 10-6 for semi-automated workflows). When scaled to 50,000 ECUA/year, the manual workflow had the lowest impact for 10/18 midpoints and human health (3.7 × 10-6 DALY), but its impact remained higher than fully-automated in real conditions.
CONCLUSIONS: The EI of UA depends on both the automation level and the organizational scale. While automation does not always reduce impacts linearly, full automation offers the best carbon and health performance in high-throughput settings. Integrating LCA into laboratory strategy may support the ecological transition of microbiology.