{"id":64658,"date":"2026-05-21T07:55:32","date_gmt":"2026-05-21T11:55:32","guid":{"rendered":"https:\/\/stateofthenation.info\/?p=64658"},"modified":"2026-05-21T08:30:38","modified_gmt":"2026-05-21T12:30:38","slug":"burgeoning-public-health-disaster-nationwide","status":"publish","type":"post","link":"https:\/\/stateofthenation.info\/?p=64658","title":{"rendered":"

THE BURGEONING PUBLIC HEALTH DISASTER NO ONE IS TALKING ABOUT<\/h2>"},"content":{"rendered":"

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\"\"SOTN Editor’s Note: Why is no one is talking about the profound and pervasive human health impacts caused by these AI data centers across the USA?\u00a0 The plethora of serious medical ailments and chronic health conditions will only further debilitate and incapacitate and\u00a0 the U.S. citizenry.\u00a0 And the out-of-pocket medical expenses and health insurance costs will only skyrocket as these data centers blanket the American landscape further bankrupting both families and individuals alike.\u00a0 Not to mention the nation.<\/p>\n

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We Need to Talk About AI\u2019s Impact on Public Health<\/strong> > Data-center pollution is linked to asthma, heart attacks, and more<\/h1>\n

Adam Wierman & Shaolei Ren<\/p>\n

Adam Wierman is the Carl F. Braun professor of computing and mathematical sciences at Caltech.
\nShaolei Ren is an associate professor of electrical and computer engineering at the University of California, Riverside.<\/p>\n

\"\"<\/p>\n

Most people have heard about the environmental impact of today\u2019s AI boom, stemming from sprawling\u00a0data centers<\/a>\u00a0packed with power-hungry servers. In the United States alone, the demand for AI is projected to push data-center electricity consumption to\u00a06.7 to 12.0 percent of the nation\u2019s total by 2028<\/a>. By that same date,\u00a0water consumption<\/a>\u00a0for cooling these data-center facilities is predicted to double, or even quadruple, compared to the 2023 level.<\/p>\n

But many people haven\u2019t made the connection between data centers and\u00a0public health<\/a>. The power plants and backup generators needed to keep data centers working generate harmful air pollutants, such as fine particulate matter and nitrogen oxides (NOx). These pollutants take an immediate toll on human health, triggering\u00a0asthma<\/a>\u00a0symptoms, heart attacks, and even cognitive decline.<\/p>\n

But AI\u2019s contribution to\u00a0air pollution<\/a>\u00a0and the public health burden is often missing from conversations about responsible AI design. Why?<\/p>\n

Because ambient air pollution is a\u00a0\u201csilent killer.\u201d<\/a>\u00a0While concerns about the\u00a0public health impacts of data centers<\/a>, including potential links to\u00a0cancer rate increases<\/a>, are beginning to surface, most AI-model developers, practitioners, and users simply aren\u2019t aware of the serious health risks tied to the energy and infrastructure powering modern AI systems.<\/p>\n

The Danger of Ambient Air Pollution<\/h2>\n

Ambient air pollution is responsible for\u00a0approximately 4 million premature deaths<\/a>\u00a0worldwide each year. The biggest culprit are tiny particles 2.5 micrometers or less in diameter (referred to as\u00a0PM 2.5<\/a>), which can travel deep into the respiratory tract and lungs. Along with high blood pressure, smoking, and high blood sugar, air pollution is a leading health risk factor. The World Bank estimates the\u00a0global cost of air pollution at US $8.1 trillion<\/a>, equivalent to 6.1 percent of global gross domestic product.<\/p>\n

Contrary to common belief, air pollutants don\u2019t stay near their emission sources: They can travel hundreds of miles. Moreover,\u00a0PM 2.5 is considered a \u201cnonthreshold\u201d pollutant<\/a>, meaning that there\u2019s no safe level of exposure.<\/p>\n

With the danger of this pollution well established, the question becomes: How much is AI responsible for? In\u00a0our research<\/a>\u00a0as professors at Caltech and the University of California, Riverside, we\u2019ve set out to answer that question.<\/p>\n

Quantifying the Public Health Cost of AI<\/h2>\n

To ensure that AI services are available even during grid outages, data centers rely on large sets of backup generators that usually burn\u00a0diesel<\/a>\u00a0fuel. While the total operation time of backup generators is limited and regulated by local environmental agencies, their emission rates are high. A typical diesel generator can release\u00a0200 to 600 times more NOx<\/a>\u00a0than a natural gas power plant producing the same amount of electricity.<\/p>\n

A recent\u00a0report<\/a>\u00a0by the state of Virginia revealed that backup generators at Virginia\u2019s data centers emitted about 7 percent of what permits allowed in 2023. According to the U.S. Environmental Protection Agency\u2019s\u00a0COBRA modeling tool<\/a>, which maps how air pollution affects human health at the local, state, and federal levels, the public health cost of those emissions in Virginia is estimated at $150 million, affecting communities as far away as Florida. Imagine the impact if data centers maxed out their permitted emissions.<\/p>\n

Further compounding the public health risk, a large set of data-center generators in a region may operate simultaneously during grid outages or grid shortages as part of demand-response programs, potentially triggering short-term spikes in PM2.5 and NOx emissions that are especially\u00a0harmful to people with lung problems<\/a>.<\/p>\n

Next, let\u2019s look beyond the backup generators to the supply of energy from the grid. The bulk of the electricity powering AI data centers comes from power plants that burn\u00a0fossil fuels<\/a>, which release harmful air pollutants, including PM 2.5 and NOx. Despite years of progress, power plants remain a\u00a0leading source of air pollution<\/a>\u00a0in the United States.<\/p>\n

We calculated that training a single large\u00a0generative AI<\/a>\u00a0model in the United States, such as Meta\u2019s Llama 3.1, can produce as much PM 2.5 as more than\u00a010,000 round trips by car<\/a>\u00a0between Los Angeles and New York City.<\/p>\n

According to\u00a0our research<\/a>, in 2023, air pollution attributed to U.S. data centers was responsible for an\u00a0estimated $6 billion in public health damages<\/a>. If the current AI growth trend continues, this number is projected to reach $10 billion to $20 billion per year by 2030, rivaling the impact of emissions from California\u2019s 30 million vehicles.<\/p>\n

Why Carbon and\u00a0Energy Efficiency<\/a>\u00a0Aren\u2019t the Whole Story<\/h2>\n

To date, efforts to mitigate AI\u2019s environmental footprint have focused mostly on\u00a0carbon emissions<\/a>\u00a0and energy efficiency. These efforts are important, but they may not alleviate health impacts, which strongly depend on where the emissions occur.<\/p>\n

Carbon anywhere is carbon everywhere. The climate impact of\u00a0carbon dioxide<\/a>\u00a0is largely the same no matter where it\u2019s emitted. But the health impact of air pollution depends heavily on regional factors such as local sources of energy, wind patterns, weather, and population density.<\/p>\n

Even though\u00a0carbon emissions<\/a>\u00a0and health-damaging air pollutants have some shared sources, an exclusive focus on cutting carbon does not necessarily reduce, and could even exacerbate, public health risks. For instance, our latest (and unpublished) research has shown that redistributing Meta\u2019s energy loads in 2023 across its U.S. data centers to prioritize carbon reductions could potentially lower overall carbon emissions by 7.2 percent, but would increase public health costs by 2.8 percent.<\/p>\n

Likewise, focusing solely on energy efficiency can reduce air pollutant emissions, but doesn\u2019t guarantee a decrease in health impact. That\u2019s because training the same AI model using the same amount of energy can yield vastly different health outcomes depending on the location. Across Meta\u2019s U.S. data centers, we\u2019ve found that the public health cost of training the same model can vary by more than a factor of 10.<\/p>\n

We Need Health-Informed AI<\/h2>\n

Supply-side solutions, such as using\u00a0alternative fuels<\/a>\u00a0for backup generators and sourcing electricity from clean fuels, can reduce AI\u2019s public health impact, but they come with significant challenges.<\/p>\n

Clean backup generators that offer the same level of reliability as diesel are still limited. And despite advancements in\u00a0renewable energy<\/a>, fossil fuels remain deeply embedded in the energy fuel mix. The U.S. Energy Information Administration\u00a0projects<\/a>\u00a0that coal-based electricity generation in 2050 will remain at approximately 30 percent of the 2024 level under the alternative electricity scenario, in which power plants continue operating under rules existing prior to April 2024. Globally, the share of coal and other fossil fuels in electricity generation has\u00a0remained nearly flat<\/a>\u00a0over the past four decades, underscoring the difficulty of entirely changing the energy supply that powers data centers.<\/p>\n

We believe that demand-side strategies that consider the spatial and temporal variations in health impacts can provide effective and actionable solutions immediately. These strategies are particularly well-suited for AI data centers with substantial operational flexibility. For example, AI training can often run at any available data centers and typically do not face hard deadlines, so those jobs can be routed to locations or deferred to times that have less impact on public health. Similarly, inference jobs\u2014the work a model does to create an output\u2014can be routed among multiple data centers without affecting\u00a0user experience<\/a>.<\/p>\n

By incorporating public health impact as a key performance metric, these flexibilities can be harnessed to reduce AI\u2019s growing health burden. Crucially, this health-informed approach to AI requires minimal changes to existing systems. Companies simply need to consider public health costs when making decisions.<\/p>\n

While the public health cost of AI is growing rapidly, AI also holds tremendous promise for advancing public health. For example, within the energy sector, AI can navigate the complex decision space of real-time power plant dispatch. By aligning grid stability with public health objectives, AI can help minimize health costs while maintaining a reliable power supply.<\/p>\n

AI is rapidly becoming a public utility and will continue to reshape society profoundly. Therefore, we must examine AI through a public lens, with its public health impact as a critical consideration. If we continue to overlook it, the public health cost of AI will only grow. Health-informed AI offers a clear path forward for advancing AI while promoting cleaner air and healthier communities.<\/p>\n

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