Environmental impact of urine analyses: Comparison of manual, semi and fully automated analytical workflows
Abstract
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.
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BibTeX
@article{brunier2026environmental,
title = {{Environmental impact of urine analyses: Comparison of manual, semi and fully automated analytical workflows}},
author = {Julien Brunier and Clara Mourgues and Fanny Auger and Gérard Lina and François Vandenesch and Céline Ramanantsoa and Olivier Dauwalder},
journal = {The Journal of Climate Change and Health},
year = {2026},
month = sep,
doi = {10.1016/j.joclim.2026.100737},
url = {https://ehesp.hal.science/hal-05751941v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Environmental impact of urine analyses: Comparison of manual, semi and fully automated analytical workflows
AU - Julien Brunier
AU - Clara Mourgues
AU - Fanny Auger
AU - Gérard Lina
AU - François Vandenesch
AU - Céline Ramanantsoa
AU - Olivier Dauwalder
PY - 2026
DA - 2026/09/10
JO - The Journal of Climate Change and Health
DO - 10.1016/j.joclim.2026.100737
UR - https://ehesp.hal.science/hal-05751941v1
AB - 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.
SN - 2667-2782
ER -