Scientists turn pee into power in Uganda
At the edge of a dirt road flanked by trees and green mountains outside the town of Kisoro, Uganda, sits the concrete building that houses Sesame Girls School, where girls aged 11 to 19 can live, learn and, at least for a while, safely use a toilet. In many developing regions, toileting at night is especially dangerous for children. Without electrical power for lighting, kids may fall into the deep pits of the latrines through broken or unsteady floorboards. Girls are sometimes assaulted by men who hide in the dark.
For the Sesame School girls, though, bright LED lights, connected to tiny gadgets, chased the fears away. They got to use new, clean toilets lit by the power of their own pee. Some girls even used the light provided by the latrines to study.
Urine, whether animal or human, is more than waste. It’s a cheap and abundant resource. Each day across the globe, 8.1 billion humans make 4 billion gallons of pee. Cows, pigs, deer, elephants and other animals add more. By spending money to get rid of it, we waste a renewable resource that can serve more than one purpose. Microorganisms that feed on nutrients in urine can be used in a microbial fuel cell that generates electricity – or "pee power," as the Sesame girls called it.
Plus, urine contains water, phosphorus, potassium and nitrogen, the key ingredients plants need to grow and survive. Human urine could replace about 25 percent of current nitrogen and phosphorous fertilizers worldwide and could save water for gardens and crops. The average U.S. resident flushes a toilet bowl containing only pee and paper about six to seven times a day, which adds up to about 3,500 gallons of water down per year. Plus cows in the U.S. produce 231 gallons of the stuff each year.
Pee power
A conventional fuel cell uses chemical reactions to produce energy, as electrons move from one electrode to another to power a lightbulb or phone. Ioannis Ieropoulos, a professor and chair of Environmental Engineering at the University of Southampton in England, realized the same type of reaction could be used to make a fuel from microbes in pee.
Bacterial species like Shewanella oneidensis and Pseudomonas aeruginosa can consume carbon and other nutrients in urine and pop out electrons as a result of their digestion. In a microbial fuel cell, one electrode is covered in microbes, immersed in urine and kept away from oxygen. Another electrode is in contact with oxygen. When the microbes feed on nutrients, they produce the electrons that flow through the circuit from one electrod to another to combine with oxygen on the other side. As long as the microbes have fresh pee to chomp on, electrons keep flowing. And after the microbes are done with the pee, it can be used as fertilizer.
These microbes are easily found in wastewater treatment plants, ponds, lakes, rivers or soil. Keeping them alive is the easy part, says Ieropoulos. Once the cells start producing stable power, his group sequences the microbes and keeps using them.
Like many promising technologies, scaling these devices for mass consumption won’t be easy, says Kevin Orner, a civil engineering professor at West Virginia University. But it’s moving in the right direction. Ieropoulos’s device has shrunk from the size of about three packs of cards to a large glue stick. It looks and works much like a AAA battery and produce about the same power. By itself, the device can barely power a light bulb, but when stacked together, they can do much more—just like photovoltaic cells in solar panels. His lab has produced 1760 fuel cells stacked together, and with manufacturing support, there’s no theoretical ceiling, he says.
Although pure urine produces the most power, Ieropoulos’s devices also work with the mixed liquids of the wastewater treatment plants, so they can be retrofit into urban wastewater utilities.
This image shows how the pee-powered system works. Pee feeds bacteria in the stack of fuel cells (1), which give off electrons (2) stored in parallel cylindrical cells (3). These cells are connected to a voltage regulator (4), which smooths out the electrical signal to ensure consistent power to the LED strips lighting the toilet.
Courtesy Ioannis Ieropoulos
Key to the long-term success of any urine reclamation effort, says Orner, is avoiding what he calls “parachute engineering”—when well-meaning scientists solve a problem with novel tech and then abandon it. “The way around that is to have either the need come from the community or to have an organization in a community that is committed to seeing a project operate and maintained,” he says.
Success with urine reclamation also depends on the economy. “If energy prices are low, it may not make sense to recover energy,” says Orner. “But right now, fertilizer prices worldwide are generally pretty high, so it may make sense to recover fertilizer and nutrients.” There are obstacles, too, such as few incentives for builders to incorporate urine recycling into new construction. And any hiccups like leaks or waste seepage will cost builders money and reputation. Right now, Orner says, the risks are just too high.
Despite the challenges, Ieropoulos envisions a future in which urine is passed through microbial fuel cells at wastewater treatment plants, retrofitted septic tanks, and building basements, and is then delivered to businesses to use as agricultural fertilizers. Although pure urine produces the most power, Ieropoulos’s devices also work with the mixed liquids of the wastewater treatment plants, so they can be retrofitted into urban wastewater utilities where they can make electricity from the effluent. And unlike solar cells, which are a common target of theft in some areas, nobody wants to steal a bunch of pee.
When Ieropoulos’s team returned to wrap up their pilot project 18 months later, the school’s director begged them to leave the fuel cells in place—because they made a major difference in students’ lives. “We replaced it with a substantial photovoltaic panel,” says Ieropoulos, They couldn’t leave the units forever, he explained, because of intellectual property reasons—their funders worried about theft of both the technology and the idea. But the photovoltaic replacement could be stolen, too, leaving the girls in the dark.
The story repeated itself at another school, in Nairobi, Kenya, as well as in an informal settlement in Durban, South Africa. Each time, Ieropoulos vowed to return. Though the pandemic has delayed his promise, he is resolute about continuing his work—it is a moral and legal obligation. “We've made a commitment to ourselves and to the pupils,” he says. “That's why we need to go back.”
Urine as fertilizer
Modern day industrial systems perpetuate the broken cycle of nutrients. When plants grow, they use up nutrients the soil. We eat the plans and excrete some of the nutrients we pass them into rivers and oceans. As a result, farmers must keep fertilizing the fields while our waste keeps fertilizing the waterways, where the algae, overfertilized with nitrogen, phosphorous and other nutrients grows out of control, sucking up oxygen that other marine species need to live. Few global communities remain untouched by the related challenges this broken chain create: insufficient clean water, food, and energy, and too much human and animal waste.
The Rich Earth Institute in Vermont runs a community-wide urine nutrient recovery program, which collects urine from homes and businesses, transports it for processing, and then supplies it as fertilizer to local farms.
One solution to this broken cycle is reclaiming urine and returning it back to the land. The Rich Earth Institute in Vermont is one of several organizations around the world working to divert and save urine for agricultural use. “The urine produced by an adult in one day contains enough fertilizer to grow all the wheat in one loaf of bread,” states their website.
Notably, while urine is not entirely sterile, it tends to harbor fewer pathogens than feces. That’s largely because urine has less organic matter and therefore less food for pathogens to feed on, but also because the urinary tract and the bladder have built-in antimicrobial defenses that kill many germs. In fact, the Rich Earth Institute says it’s safe to put your own urine onto crops grown for home consumption. Nonetheless, you’ll want to dilute it first because pee usually has too much nitrogen and can cause “fertilizer burn” if applied straight without dilution. Other projects to turn urine into fertilizer are in progress in Niger, South Africa, Kenya, Ethiopia, Sweden, Switzerland, The Netherlands, Australia, and France.
Eleven years ago, the Institute started a program that collects urine from homes and businesses, transports it for processing, and then supplies it as fertilizer to local farms. By 2021, the program included 180 donors producing over 12,000 gallons of urine each year. This urine is helping to fertilize hay fields at four partnering farms. Orner, the West Virginia professor, sees it as a success story. “They've shown how you can do this right--implementing it at a community level scale."
Researchers probe extreme gene therapy for severe alcoholism
Story by Freethink
A single shot — a gene therapy injected into the brain — dramatically reduced alcohol consumption in monkeys that previously drank heavily. If the therapy is safe and effective in people, it might one day be a permanent treatment for alcoholism for people with no other options.
The challenge: Alcohol use disorder (AUD) means a person has trouble controlling their alcohol consumption, even when it is negatively affecting their life, job, or health.
In the U.S., more than 10 percent of people over the age of 12 are estimated to have AUD, and while medications, counseling, or sheer willpower can help some stop drinking, staying sober can be a huge struggle — an estimated 40-60 percent of people relapse at least once.
A team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
According to the CDC, more than 140,000 Americans are dying each year from alcohol-related causes, and the rate of deaths has been rising for years, especially during the pandemic.
The idea: For occasional drinkers, alcohol causes the brain to release more dopamine, a chemical that makes you feel good. Chronic alcohol use, however, causes the brain to produce, and process, less dopamine, and this persistent dopamine deficit has been linked to alcohol relapse.
There is currently no way to reverse the changes in the brain brought about by AUD, but a team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
To find out, they tested it in heavy-drinking monkeys — and the animals’ alcohol consumption dropped by 90% over the course of a year.
How it works: The treatment centers on the protein GDNF (“glial cell line-derived neurotrophic factor”), which supports the survival of certain neurons, including ones linked to dopamine.
For the new study, a harmless virus was used to deliver the gene that codes for GDNF into the brains of four monkeys that, when they had the option, drank heavily — the amount of ethanol-infused water they consumed would be equivalent to a person having nine drinks per day.
“We targeted the cell bodies that produce dopamine with this gene to increase dopamine synthesis, thereby replenishing or restoring what chronic drinking has taken away,” said co-lead researcher Kathleen Grant.
To serve as controls, another four heavy-drinking monkeys underwent the same procedure, but with a saline solution delivered instead of the gene therapy.
The results: All of the monkeys had their access to alcohol removed for two months following the surgery. When it was then reintroduced for four weeks, the heavy drinkers consumed 50 percent less compared to the control group.
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
The researchers then took the alcohol away for another four weeks, before giving it back for four. They repeated this cycle for a year, and by the end of it, the treated monkeys’ consumption had fallen by more than 90 percent compared to the controls.
“Drinking went down to almost zero,” said Grant. “For months on end, these animals would choose to drink water and just avoid drinking alcohol altogether. They decreased their drinking to the point that it was so low we didn’t record a blood-alcohol level.”
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
Looking ahead: Dopamine is involved in a lot more than addiction, so more research is needed to not only see if the results translate to people but whether the gene therapy leads to any unwanted changes to mood or behavior.
Because the therapy requires invasive brain surgery and is likely irreversible, it’s unlikely to ever become a common treatment for alcoholism — but it could one day be the only thing standing between people with severe AUD and death.
“[The treatment] would be most appropriate for people who have already shown that all our normal therapeutic approaches do not work for them,” said Grant. “They are likely to create severe harm or kill themselves or others due to their drinking.”
This article originally appeared on Freethink, home of the brightest minds and biggest ideas of all time.
Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the book The Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.