This visualization shows how the bacterial profile of the sewage samples changes over time.
Click on a bacteria to learn more about it and see its evolution over 24 hours in the timeline below.
Hover over a different bacteria to compare the two together.
The selected bacteria's taxonomy is displayed here, including individual Green Genes IDs.
Use the controls to watch the visualization evolve over time. Or simply click on a time point in the timeline below.
By Amount By Category
All
Human
Non Human
Show me how it works!
Green Genes Id
Phylum
Class
Order
Family
Genus
No of taximonial hits
0
We imagine a future in which sewage is mined for
real-time information that can inform policy makers, health practitioners, designers, and researchers alike.
Such is the idea behind Underworlds: a cross-disciplinary, open-data platform for monitoring urban health patterns, shaping more inclusive public health strategies, and pushing the boundaries of urban epidemiology. Pioneered by the Senseable City Lab and the Alm Lab, and sponsored by the MIT-Kuwait Center for Natural Resources and the Environment, a prototype smart sewage platform is being developed at MIT consisting of physical infrastructure, biochemical measurement technologies, and the downstream computational tools and analytics necessary to interpret and act on our findings.
The Underworlds project is the first of its kind, and a proof of concept that cities can make use of their waste water system to do near real-time urban epidemiology and understand human health and behavior with a fine spatio-temporal resolution. Probably the most obvious first application of smart sewage technology is infectious disease surveillance, and the prediction of outbreaks.
Early warnings in relation to the presence of new flu strains in urban centers could significantly reduce a community’s medical costs and even help mitigate outbreaks. In addition, smart sewage could impact the
way non-communicable diseases are studied, because biomarkers for diseases such as obesity and diabetes
can be measured at unprecedented scale and temporal resolution.
The implications of this platform extend beyond just disease surveillance to the development of a new type
of human population census. Analyzed in tandem with demographic data, this platform can study the aggregate health of a city to the particular health of a neighborhood.
Underworlds will study the urban geography, network topology, and demographic distribution in conjunction with wastewater loads over time, to propose and validate a model that informs wastewater sampling and correlates to target population samples. Together with the Department of Public Works, this work has already started in a pilot study in Cambridge.
The material on this web site can be used freely
in any publication provided that
Project Lead
Carlo Ratti Principal Investigator
Newsha Ghaeli Project Manager
Engineering Team
Aline Barros, Antoine De Maleprade,
Carlos Graeves, Franco Montalvo, Jessica Snyder,
Peter Kang, Youjin Shin
Web & Visualization
Paul Bouisset, Wonyoung So, Youjin Shin
Network Modeling & Analysis
Alaa Al-Radwan, Aldo Treville, Alexander Belyi,
Yaniv Turgeman, Youjin Shin
Concept Phase Lead
Yaniv Turgeman
Undergraduate Researchers
Anuj Khandelwal, Cyndia Cao, Dang Pham,
Katherine Adler
Biology Lead
Eric Alm Co-Principal Investigator
Biological Research Team
Claire Duvallet, Ilana Brito, Mariana Matus
Concept Phase Lead
Yaniv Turgeman
Undergraduate Researchers
Kathy Lin, Minyi Lee
Together With
Daniela Rus Co-Principal Investigator
Martin Polz Co-Principal Investigator
Jon Runstadler Co-Principal Investigator
Graduate Research Assistant
Christopher Bandoro
Elfatih Eltahir Co-Principal Investigator
Sponsor
Kuwait-MIT Center for Natural Resources and
the Environment
Kuwait Collaborators
Kuwait Institute for Scientific Research
Sameer Al-Zenki Principal Investigator
Kazi M. Jamil Co-Principal Investigator
Tareq Al-Ati Co-Principal Investigator
Ashraf I. Ahmad Co-Principal Investigator
Entesar H. Husain Principal Investigator
Rawa Aljarallah Co-Principal Investigator