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This past April, I brought an air quality monitor to the Rhode Island State House to testify, in my personal capacity, about a bill mandating indoor air quality inspections in public schools. Room 35 was packed — well over 100 people in a space designed for maybe 70. I left my monitor in the corner and watched from the hallway as CO₂ climbed past 2,000 ppm, past 5,000 ppm (OSHA’s permissible exposure limit for industrial settings), and settled at 6,000 ppm for the rest of the night. It was the worst air quality I have personally measured.
That experience prompted me to build the model below. Using the Wells-Riley equation — the foundational framework in airborne transmission epidemiology — it estimates expected infections from a single infectious person in a shared room, across the full range of indoor CO₂ concentrations. CO₂ is one of the best real-time proxies we have for ventilation quality: the same physics that allows exhaled CO₂ to accumulate also allows exhaled aerosols to accumulate. The model uses quanta emission rates from the published literature for both COVID-19 and influenza, assumes a realistic activity mix (mostly quiet, with brief talking), and includes confidence intervals to reflect genuine uncertainty across strains and individuals.
The takeaway from that hearing room: at 6,000 ppm, with 60 people and one infectious person over four hours, the math suggests roughly 9 to 10 people would have gone home infected. Not a worst case — just the arithmetic of shared air. The tool below lets you see how that number changes across the full range of ventilation conditions, and where the thresholds that matter actually sit.
Model inputs and assumptions
The model assumes a room of 60 occupants (1 infectious, 59 susceptible) sharing air for four hours, with each person talking for approximately 2.5 minutes and seated quietly otherwise. Breathing rate is set at 0.30 m³/hr, consistent with light sedentary activity. Ventilation is derived directly from CO₂ concentration: each person produces approximately 18 litres of CO₂ per hour, and the steady-state excess above outdoor background (420 ppm) is used to back-calculate total room airflow. Quanta emission rates — the currency of the Wells-Riley model — are time-weighted averages of resting and talking emission, drawn from Buonanno et al. (2020) and Miller et al. (2021): 28.4 quanta/hr for COVID-19 and 13.6 quanta/hr for influenza at the central estimate. Confidence intervals reflect a ×0.5 to ×2 range on the central quanta value, capturing uncertainty across pathogen strains, individual variation, and measurement methodology. The model does not account for masking, room geometry, near-field exposure, or variation in susceptibility.
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The dominant narrative around the Colorado River crisis—repeated again in a recent article the Times—casts the problem as one of people versus scarcity, or states versus states. That framing is deeply misleading. The uncomfortable truth is that cities are not draining the river. Agriculture is.
In his Feb 2 guest essay (“These Four States Are in Denial Over a Looming Water Crisis”), Mr. Roth does not acknowledge the most important fact in this debate: irrigated agriculture accounts for roughly 74% of human water use from the Colorado River. Framing this as a human-centric or population-driven conflict between states is therefore not just incomplete — it is fundamentally wrong.
If we want an honest debate about the Colorado River’s future, coverage must stop treating this as a fight over water for people and start acknowledging what it actually is: a dispute over agricultural water allocations built for a different century. By highlighting lawn removal and “water cops,” as Mr. Roth does, the discussion shifts blame onto individual behavior while sidestepping the sector that uses most of the water. Taking a shorter shower won’t save the Colorado River; talking seriously about farming might.
]]>I gave a talk titled “Your Brain Runs on (Clean) Air” on Nov 19, nestled between talks about open-source microfabrication, medical imaging tools, and the future of computing. It might seem unusual to bring indoor air quality to a crypto conference, but the connection is real: the air we breathe indoors directly shapes how well our brains function. We spend 90% of our time indoors, and studies show that air pollutants, high CO₂, and airborne viruses can meaningfully impair decision-making, reaction time, learning, and memory.
For a community obsessed with performance – whether it’s improving coordination, writing better code, or making smarter governance decisions – this matters. High CO₂ can reduce cognitive performance by up to 50%. And studies with airplane pilots found that as CO₂ ticks up to 2500 ppm (not uncommon in classrooms), the odds of making mistakes on plane maneuvers went up by 50%. Yikes! Particulate matter lowers test scores: adding air purifiers inside classrooms during a natural experiment in California showed that scores improved by about 8%. And test scores during big national exams were lower on days with higher outdoor particulate matter. Bioaerosols like the virus particles that cause Covid can have lasting cognitive effects. A large cohort study found that IQ dropped by 3-6 points after one symptomatic infection. If we care about clearer thinking and better outcomes, clean indoor air is one of the highest-leverage tools we have.
There’s also a natural connection to the Ethereum and d/acc ethos. Clean air systems include low-cost sensors, open-source filtration designs, and transparent real-time data – all of which can be decentralized, permissionless, and community-driven. They make the invisible visible, turning every room into something you can measure, verify, and improve without gatekeepers.
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