An Environmental Science and Engineering Framework for Combating Antimicrobial Resistance
On June 20, 2017, members of the environmental engineering and science (EES) community convened at the Association of Environmental Engineering and Science Professors (AEESP) Biennial Conference for a workshop on antimicrobial resistance. With over 80 registered participants, discussion groups focused on the following topics: risk assessment, monitoring, wastewater treatment, agricultural systems, and synergies. In this study, we summarize the consensus among the workshop participants regarding the role of the EES community in understanding and mitigating the spread of antibiotic resistance via environmental pathways. Environmental scientists and engineers offer a unique and interdisciplinary perspective and expertise needed for engaging with other disciplines such as medicine, agriculture, and public health to effectively address important knowledge gaps with respect to the linkages between human activities, impacts to the environment, and human health risks. Recommendations that propose priorities for research within the EES community, as well as areas where interdisciplinary perspectives are needed, are highlighted. In particular, risk modeling and assessment, monitoring, and mass balance modeling can aid in the identification of ‘‘hot spots’’ for antibiotic resistance evolution and dissemination, and can help identify effective targets for mitigation. Such information will be essential for the development of an informed and effective policy aimed at preserving and protecting the efficacy of antibiotics for future generations.
An Environmental Science and Engineering Framework for Combating Antimicrobial Resistance. A. Pruden, R. Alcalde, P. Alvarez, N. Ashbolt, H. Bischel, N. Capiro, E. Crossette, D. Frigon, K. Grimes, C. Haas, K. Ikuma, A. Kappell, T. LaPara, L. Kimbell, M. Li, X. Li, P. McNamara, Y. Seo, M. Sobsey, E. Sozzi, T. Navab-Daneshmand, L. Raskin, M. Riquelme, P. Vikesland, K. Wigginton, Z. Zhou. 2018. Environmental Engineering Science 35:10, pp. 1005-1011. doi.org/10.1089/ees.2017.0520