How do combined sewer overflows affect water quality in Philadelphia?

Analysis shows some watersheds are more vulnerable and more frequent sewage overflows could have a lasting effect on ecosystems

Drexel University

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In Philadelphia, and many urban areas across the country with sewer systems dating back to the 1800s, more frequent flooding is posing a threat not only to people and property, but also to surrounding ecosystems. In these areas stormwater and sewage drain into the same pipes and when flooding occurs they are designed to spill into nearby waterways, rather than inundating streets. Researchers at Drexel University, in collaboration with the Philadelphia Water Department, are looking at the long-term water quality impact of these events — called combined sewer overflows — to understand what steps could be taken to mitigate their negative effects.

Their latest finding, published in the Journal of Environmental Management, compared how different watershed regions in Philadelphia respond to the environmental stress of a combined sewer overflow to determine the environmental and infrastructural elements that factor most prominently into the response.

“In Philadelphia County more than 8 billion gallons of combined sewer and stormwater are discharged into rivers and streams each year during flooding events,” said Amanda Carneiro Marques, PhD, an assistant professor in the Nick Howley College of Engineering and Computing’s School of Engineering. “For the city to address the negative impact this has on water quality, it is important to have a full picture of what happens in an ecosystem during these events.”

Marques and her team drew on more than a decade of U.S. Geological Survey water quality monitoring, combined with precipitation data from the National Oceanic and Atmospheric Administration and the Philadelphia Water Department’s combined sewer overflow records to create a detailed model of the overall water quality impact of an overflow event.

Their research centered on fluctuations in dissolved oxygen, a measure of the amount of oxygen present in water, which is an important water quality metric because it indicates a body of water’s potential to sustain aquatic life, like fish and macroinvertebrates.

Dissolved oxygen levels tend to fluctuate with sustained changes in temperature, flow volume and abundance of oxygen-consuming microorganisms. Because of this, it is often a lagged indicator of the residual effects of a deleterious event, such as flooding or infiltration of a pollutant.

“Dissolved oxygen is one of the most important indicators for aquatic ecosystem health as it can reflect cumulative environmental impacts over time – like recording a video, rather than just getting a snapshot of a moment in time,” Marques said. “For example, an influx of organic matter or nutrients may cause rapid algal growth. When that algae dies, it may trigger a boom in aerobic microorganisms that decompose the organic matter. These microorganisms consume vast amounts of dissolved oxygen during respiration, which can lead to very low dissolved oxygen levels in the water.”

The team looked at data from four Philadelphia watersheds: Darby-Cobbs Creek (in parts of Chester, Delaware, Mongomery and Philadelphia counties), Pennypack Creek (in Northeast Philadelphia), Tacony-Frankford Creek (in Montgomery and Philadelphia counties) and the portion of the Delaware River Basin that borders Philadelphia, from 2013-2024. Each watershed contains at least one designated combined sewer overflow area and Geological Survey monitoring stations both up- and downstream of it.

Looking at more than a decade of data from the same monitoring sites allowed the researchers to observe each watershed’s regular dissolved oxygen fluctuations tied to seasonal temperature and flow rate changes.

For example, the Tacony-Frankford Creek watershed reported dissolved oxygen levels below the Environmental Protection Agency’s minimum threshold for fish growth and reproduction in 40% of observations in July, with low levels persisting through September. By contrast, Pennypack Creek and Darby-Cobbs Creek watersheds rarely reported levels below the threshold throughout the entire year.

Overlaying this information with the Water Department’s combined sewer overflow records from the same time period illuminated how each watershed responded to a rapid influx of stormwater and organic matter from sewage.

“Each watershed had a slightly different dissolved oxygen baseline profile, depending on its physical and hydrological characteristics, such as size, land coverage and flow magnitude, for example,” Marques said. “These preexisting conditions also seemed to play a role in their ability to stabilize dissolved oxygen levels after a sewage overflow event.”

The Delaware River watershed showed very little change in dissolved oxygen in the aftermath of overflows, likely owing to its flow rate and relatively large volume which enabled it to dilute the organic matter from the sewage, according to the researchers. Smaller watersheds, like Tacony-Cobbs Creek, saw more than a 1 mg/L decrease in dissolved oxygen that persisted for days after the discharge.

Surrounding infrastructure and land use also plays a role, according to the researchers. The flow rate and volume of the overflow plume entering the creek or river affects how well it can dilute the organic matter it carries.

“These factors suggest that combined sewer overflow impacts are far from uniform across watersheds, but they are highly dependent on variations in flow rate and volume and likely the surrounding infrastructure and geography,” Marques said. “It also means that for municipalities looking to reduce the negative effects of combined sewer overflows, it is important to understand the underlying vulnerability of a watershed as they weigh mitigation strategies.”

One of the strategies that urban areas with combined sewers are currently employing to reduce the frequency of overflow events is attempting to reduce or delay the stormwater entering the sewer system. This includes installing water retention areas, permeable surfaces and residential cistern and rain barrel systems – which have proven to be effective in certain areas.

The group’s future research will continue to explore the interplay between the built environment and combined sewer overflow resiliency. Marques also noted that gaining a better understanding of the composition of the overflows could provide important insight into how municipalities could better manage their impact.

“This is an important first step toward providing urban areas with aging sewer systems the information they need to make data-informed decisions about infrastructure upgrades and mitigation strategies,” Marques said. “More monitoring and testing of overflow water could help to identify areas that are facing the greatest ecological risk during combined sewer overflows.”

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Journal

Journal of Environmental Management

DOI

10.1016/j.jenvman.2026.130970

Method of Research

Data/statistical analysis

Article Title

Seasonal drivers and infrastructure impacts on dissolved oxygen dynamics in complex environmental systems

 

Why exposure to firearm suicide may not mean safer gun storage

Awareness doesn’t automatically lead to more secure firearm storage, says a Rutgers professor who led the study

Rutgers University

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Knowing someone who died by firearm suicide is associated with greater recognition of suicide risk, but may not lead to safer storage practices, according to a Rutgers study published in Psychiatry Research.

Previous research has linked firearms in the home to increased suicide risk, with additional risk when firearms are stored unsecured. Yet relatively little is known about whether personally knowing someone who died by firearm suicide is associated with recognizing that risk or practicing secure storage.

Researchers analyzed survey responses from 7,810 U.S. adults. Participants were asked about whether they knew someone who died by firearm suicide, whether firearms were typically kept in or around their home, their beliefs about suicide risk and whether any household firearm was typically stored both unlocked and loaded.

More than one in four participants, 27.8%, reported personally knowing someone who died by firearm suicide. Firearm access at home was associated with greater odds of reporting such a loss.

Among firearm owners, knowing someone who died by firearm suicide was associated with stronger beliefs that having a firearm at home increases suicide risk. It was also associated with storing at least one firearm both unlocked and loaded.

"Personal experience with firearm suicide appears to increase awareness of the risk, but awareness does not automatically lead to more secure firearm storage,” said Allison E. Bond, lead author of the study and an assistant professor at the New Jersey Gun Violence Research Center and the Department of Urban-Global Public Health at the Rutgers School of Public Health. “That disconnect is important for suicide prevention. We need to move beyond simply telling people that firearms can increase suicide risk and find ways to make secure storage feel like a practical part of protecting their families and homes.”

The researchers suggest some firearm owners may recognize suicide risk in general while viewing themselves or their families as unlikely to experience it. Concerns about outside threats, such as a home invasion, may also influence storage decisions. The study didn’t directly test these explanations.

The researchers said the survey captured experiences, beliefs and storage practices at one point in time. It can’t establish cause and effect or determine whether a firearm suicide loss preceded participants’ storage choices, they added.

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Journal

Psychiatry Research

DOI

10.1016/j.psychres.2026.117472

Method of Research

Survey

Subject of Research

People

Article Title

The paradox of experience: firearm access, beliefs about risk, and storage behaviors among those exposed to firearm suicide

Article Publication Date

4-Oct-2026

 

Forecasting "Tipping Points" with AI 

Caltech-led researchers take key steps in developing deep-learning models that can predict irreversible shifts in climate and other systems

California Institute of Technology

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The writer Malcolm Gladwell popularized the term "tipping points" to refer to the moment when a trend or social behavior crosses a threshold and rapidly spreads everywhere. In physics, tipping points occur when systems—say, Earth's climate—reach an unstable point and topple over into a sea of change that is sometimes irreversible. Examples include global warming or the melting of polar ice caps. 

 

While scientists have previously used AI models to predict smaller changes in weather, such as day-to-day variations, forecasting tipping points comes with more uncertainty and has thus proven more challenging. Now, a new Caltech-led paper demonstrates that an AI program—specifically a type of neural network called a recurrent neural operator (RNO)—can be used to predict when tipping points occur in simple models, including models of a climate-change system and simulations of turbulent airflow over a plane's wing. 

 

The paper was published in the Proceedings of the National Academy of Sciences (PNAS).

 

"A tipping point is where you don't have that point of return anymore because it's nonstationary," says Anima Anandkumar, Caltech's Bren Professor of Computing and Mathematical Sciences and the principal author of the study. "Parts of a system are changing in a way so that you cannot bring the system back to its original state. That's why it is seen as something that could be catastrophic."

 

The new research, cited in a recent report from the United Nations on the importance of tipping points in climate science, was performed by Anandkumar and her colleagues at Caltech, the University of Colorado Boulder (CU Boulder), and the tech company NVIDIA. Because tipping points are so universal, the AI tool has potential applications in the fields of climate science, ecology, epidemiology, aeronautical design, and more. 

 

"This work lays the foundations for characterizing tipping points and predicting when they might occur in the future," Anandkumar says. "We demonstrated it on a simplified climate model and a model of air turbulence over a wing—systems that weren't tractable with earlier methods."

 

In one part of the study, the researchers looked at a specific climate scenario in which low clouds over the oceans rapidly disappear due to rising carbon dioxide levels—a situation that has been predicted to occur in the future but has not yet been observed. This "point-of-no-return" loss of low cloud cover would further add to global warming. The researchers tested their AI program on simulated data to see if it could forecast when the clouds would disappear. The RNO was able to look more than two years ahead of the expected tipping point in the simulated data and predict its timing with an error of about 11 days.

 

"In the past, we used AI to learn mostly stationary systems that assume the weather from before will be the same as weather in future," Anandkumar says. "Our group has trained such AI-based weather models successfully in the past and can even predict extreme events like hurricanes with good accuracy. On the other hand, if you want to now model how that climate is changing and account for changes such as the carbon dioxide content, the system is no longer stationary, and you can no longer use standard machine learning principles."

 

The group's latest AI approach, based on an RNO, differs from other machine learning programs because it incorporates a built-in "memory" that tracks how the physics change over time. The RNO detects when the AI's predictions about the physics of a system no longer make sense, indicating a tipping point is imminent. In other words, when the AI's predictions no longer follow the rules of previously defined physics, a significant shift in the model is about to occur.

 

"The system is drifting away over time, and the laws from physics are no longer holding at the tipping point," Anandkumar says. "We can use the principles of physics on top of recurrent neural operators to make these predictions of when the tipping point is likely to 

 

The PNAS paper also describes testing the RNO on simulations of airflow over a tilting plane wing to see if it could forecast a tipping point that occurs when the wing's lift drops precipitously. The RNO succeeded in predicting the tipping point, suggesting these kinds of AI programs could potentially help with future aeronautical and aerospace designs. 

 

"In aerodynamics, quantities like the angle of attack can change gradually while the flow itself undergoes a much more abrupt transition, such as the onset of stall or a change in the wake," explains study coauthor Jane Bae (BS '12), assistant professor of aerospace at Caltech and a Susan Wu Scholar. "What is exciting about this work is that the model can recognize when the physics it has learned no longer adequately describes what is happening, providing a warning that the system may be approaching a tipping point. Being able to anticipate these qualitative changes before they occur could ultimately be very useful for aerodynamic prediction and design."

 

The paper is titled "Tipping Point Forecasting in Non-Stationary Dynamics on Function Spaces." In addition to Anandkumar, other Caltech authors are alumni Miguel Liu-Schiaffini (BS '25) and Sze Chai Leung (MS '24). Additional authors are Clare E. Singer (PhD '24) of CU Boulder and Nikola Kovachki (BS '16, PhD '22) and Kamyar Azizzadenesheli of NVIDIA. Funding was provided by the Mellon Mays Undergraduate Fellowship; Eric and Wendy Schmidt, through the AI2050 Senior Fellow Schmidt Sciences program; Caltech trustee Charles Trimble (BS '63, MS '64); the Office of Naval Research; the Carver Mead New Adventures Fund; and Caltech's Center for Autonomous Systems and Technologies (CAST). Computational time for the airfoil simulations was provided by the Pittsburgh Supercomputing Center, supported by the National Science Foundation.

 

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Journal

Proceedings of the National Academy of Sciences

DOI

10.1073/pnas.2600331123 

Does generative AI dilute markets for human-created work?

Summary author: Walter Beckwith

American Association for the Advancement of Science (AAAS)

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In a Policy Forum, Pamela Samuelson and colleagues discuss whether generative AI causes “market dilution,” i.e., crowding human-authors works out of markets and weakening incentives for people to create original works. Generative AI holds considerable promise for advancing scientific research, but its future depends in part on unresolved questions about copyright law. Because foundation models are trained on vast collections of material, including copyrighted books, images, and other human-authored works, more than 100 active US lawsuits are examining whether this practice is lawful. Although two federal courts have ruled that such training qualifies as “fair use,” a legal doctrine that permits certain uses of copyrighted material without permission, the broader legal dispute remains unsettled. One emerging argument, known as market dilution, proposes that AI-generated content could undermine entire markets for human-created works, even when it does not directly harm any copyrighted work. Here, Samuelson et al. question the conceptual basis of this theory and argue that it oversimplifies the relationship between human and AI creativity. According to Samuelson et al., any assessment of market dilution must account for how AI is used, the extent of human contribution, and the specific creators and markets affected. Claims of harm must be evaluated through rigorous empirical research focused on particular AI systems and creative markets, rather than broad assumptions about AI’s impact on human authorship. The authors suggest that collaboration between AI developers and researchers, alongside longitudinal data and surveys of creative practices, could help clarify these dynamics. If evidence eventually establishes that AI harms particular creative markets, they argue that legislatures, not courts resolving individual copyright disputes, are better equipped to develop targeted policies, such as licensing or funding schemes. They warn that court rulings restricting the use of copyrighted works to train AI models could create widespread legal uncertainty and significantly affect scientific researchers and developers who rely on these technologies. “If courts decide that developers of generative-AI models are likely diluting markets for human-authored works, rulings against fair uses may substantially raise the liability risks for all developers and users of generative AI in ways that are difficult to predict,” write the authors. “This could have considerable impacts on scientist users and builders of generative-AI systems, including open-source and domain-specific models.”

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Journal

Science

DOI

10.1126/science.ael4888

Article Title

Market dilution, fair use, and generative AI

Article Publication Date

15-Oct-2026

 

Study finds common chemicals may alter normal breast development

Exposure to hormone-disrupting chemicals may leave molecular changes that could someday help identify people at higher risk of breast cancer

Tufts University

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A breast tissue organoid that has been artificially colored. 

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Credit: The Kuperwasser Lab

Exposure to common chemicals may alter how normal human breast tissue develops, according to a new study from researchers at Tufts University School of Medicine and Boston University. The findings, published in the Proceedings of the National Academy of Sciences, suggest that exposure to some chemicals—particularly multiple bisphenols at once—may create molecular changes in breast tissue that could influence susceptibility to breast disease later in life.

The findings lay the groundwork for scientists to investigate whether these molecular changes persist in human tissues, contribute to disease later in life, and could yield biomarkers to help assess breast cancer risk. 

“If these results are validated by additional studies, they could change how we think about cancer prevention,” said Charlotte Kuperwasser, the study’s senior author and a professor of developmental, molecular, and chemical biology at Tufts University School of Medicine.

A growing body of research has raised questions about whether exposure to endocrine-disrupting chemicals—substances that can interfere with the body’s hormones—may contribute to breast cancer risk. But much of what scientists know about how chemicals affect breast tissue comes from animal models or laboratory-grown cells that cannot fully reproduce the complexity of human tissue. 

“The breast also is unusual in that it undergoes major developmental changes well after birth, including during puberty and pregnancy,” said Kuperwasser. “And the concern is that breast tissue cells may be highly sensitive to environmental cues, such as chemical exposure, during critical windows of mammary gland development.”

To better capture this complexity, the researchers used three-dimensional models called organoids, grown from breast tissue donated by 13 healthy young women. The organoids contain multiple types of breast cells and can reproduce some aspects of how human breast tissue actually develops.

As the organoids developed over about three weeks, the researchers exposed them to a panel of individual chemicals and mixtures, including endocrine-disrupting chemicals, chemicals that affect metabolism, and known or suspected carcinogens. They then analyzed molecular changes, including changes in gene activity and DNA modifications.

While different chemicals affected tissue development in different ways, the most striking finding involved three common bisphenols: BPA, BPS, and BPF. Individually, the chemicals produced relatively few changes in the organoids. But when combined at very low concentrations—comparable to levels detected in human blood and urine—the mixture disrupted normal breast development in organoids from most of the donors.

“We intentionally used concentrations of chemicals that were relevant to real-world exposure rather than the very high doses sometimes used in toxicology studies,” said Kuperwasser. “We also tested a mixture of these because people are typically exposed to multiple chemicals at the same time.”

The team then compared the molecular changes associated with the bisphenol mixture with molecular data from human breast cancers. The exposure-associated pattern was most strongly represented in data from patients with invasive lobular carcinoma—a type of breast cancer that may not cause a detectable lump and can be difficult to detect with a mammogram.

The finding does not establish that the chemicals cause cancer or show how the observed molecular changes might contribute to breast disease. Instead, the researchers say the research demonstrates a new way to study how environmental exposures may affect normal human tissue before cancer develops.

“Historically, we have thought about cancer risk largely in terms of inherited mutations, lifestyle factors, and exposures that directly damage DNA,” said Kuperwasser. “These findings raise another possibility: Environmental exposures may subtly alter how normal tissues develop, leaving behind molecular changes that do not cause cancer by themselves but may make the tissue more permissive to cancer many years later.”

She noted that such persistent molecular changes could eventually serve as a kind of biological record—like a “scar” of past exposure. If validated in people, such a marker could potentially help identify individuals at higher risk and inform earlier or more targeted screening.

Future studies involving larger and more diverse groups of donors, as well as a broader range of chemicals and mixtures, are needed to determine how widely the findings apply and if molecular changes observed in organoids are associated with breast disease in people.

Meadow Parrish and Meloryn Seraj, both PhD students in genetics, molecular, and cellular biology at Tufts Graduate School of Biomedical Sciences, are co-first authors on the study.

Research reported in this article was supported by Find The Cause Breast Cancer Foundation. Complete information on additional authors, methodology, limitations, and conflicts of interest is available in the published paper.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the funder.

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Journal

Proceedings of the National Academy of Sciences

DOI

10.1073/pnas.2613054123

Article Title

Environmental chemical mixtures reprogram mammary epithelial development to epigenetic states associated with breast cancer

Article Publication Date

15-Oct-2026

 

FSU review shows molecular fingerprints of human impacts on global water systems

Florida State University

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Rob Spencer, the Verta Cox Professor of Oceanography in the Department of Earth, Ocean, and Atmospheric Science.

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Credit: Courtesy of Rob Spencer

A Florida State University-led review shows how ultrahigh-resolution mass spectrometry can help researchers identify the molecular fingerprints of human activity in water systems around the world.

Mass spectrometry is a powerful analytical tool that lets researchers identify unknown compounds and determine the structure and chemical properties of molecules. Rob Spencer, the Verta Cox Professor of Oceanography in the Department of Earth, Ocean, and Atmospheric Science, has used ultrahigh-resolution mass spectrometry to investigate environmental changes for almost 20 years. He led a review of the technology featured as the cover article in the September issue of Nature Water.

“Our research aims to understand how organic matter within a water sample from anywhere in the world can tell us something about human-induced change in that aquatic environment,” Spencer said. “The security and stability of our water resources are important, and we found a new way to address these issues in ways we never thought possible. With the application of this technology, we’re seeing an almost diagnostic response to human activities at the molecular level.”

How it works: Molecular fingerprints

Dissolved organic matter, or DOM, is a complex mixture found in all global waters that plays many roles in the environment. It’s an energy source for aquatic life, affects drinking water treatment and plays an important role in major biogeochemical cycles. Understanding the composition of DOM is critical to distinguishing the roles it plays in the environment. Ultrahigh- and high-resolution mass spectrometry, or UHRMS, have transformed researchers’ ability to understand how human activities alter that mixture.

“By synthesizing what we’ve learned across many different aquatic systems, Rob and his colleagues highlight both the power and current limitations of UHRMS and help chart a path toward better identifying and understanding the molecular fingerprints of human impacts, combining aquatic biogeochemistry with cutting-edge analytical techniques to address fundamental questions about our changing planet,” said Mike Stukel, chair of the FSU Department of Earth, Ocean, and Atmospheric Science.

Research at the National High Magnetic Field Laboratory

At the National Science Foundation-funded, FSU-headquartered National High Magnetic Field Laboratory, researchers like Spencer combine mass spectrometry with high magnetic fields for a range of research applications at the lab’s Ion Cyclotron Resonance Facility. UHRMS differs from conventional mass spectrometry in its exceptional accuracy and precision, allowing researchers to examine tens of thousands of data points — in this case, organic markers and molecular formulas — that provide valuable diagnostic data.

“At FSU, we’re uniquely positioned to conduct this research thanks to the National MagLab’s facilities,” Spencer said. “When analyzing tens of thousands of molecular formulae in a sample, we might end up with 10 diagnostic markers in the Amazon River Basin that track land-use change, for example. Similar research in the Mississippi Basin found a small number of organic markers linked to agriculture, and it was noted that these molecular formulae were very similar to those from the Amazon. This ties big human impacts, like converting pristine lands to agricultural land, to molecular-level changes in our global water systems.”

Understanding human impacts and the global carbon cycle

Changes in DOM’s molecular composition can also reveal the effects of urbanization, fires, and overall changes in climate and precipitation patterns.

“One motivation for this review was to encourage researchers to make their data openly accessible so we can improve identification of these diagnostic indicators and organic markers in conjunction with different human impacts,” Spencer said. “Ultimately, that tells us how things will respond to changes downstream. It was really interesting to look across different temperate and tropical systems and see similar responses at the molecular level with common major human impacts.”

Another focus of the review was Arctic change, as the region is warming faster than anywhere else on Earth. Nearly two-thirds of all soil carbon on Earth is frozen in Arctic permafrost. As the region thaws, carbon that has been stored for thousands of years can enter the modern carbon cycle. By identifying organic markers linked to thawing permafrost, scientists can more confidently predict how this carbon will interact with the environment, whether it moves from long-term storage in soils to long-term ocean storage or is consumed by microbes and released into the atmosphere as carbon dioxide.

“In addition to advancing our understanding of the global carbon cycle, this work addresses societally important issues such as agriculture, urbanization, fire effects and climate change,” Stukel said. “Rob’s work also highlights the tremendous opportunities created by combining EOAS expertise with the world-class analytical capabilities of the National MagLab. This invited review reflects both his team’s leadership in aquatic biogeochemistry and their ability to help define where the field should go next.”

Collaborators and support

This research was supported by the National Science Foundation, NASA, the Novo Nordisk Foundation and the National Institute of Food and Agriculture through the U.S. Department of Agriculture. Researchers from the University of Wisconsin-Madison and Colorado State University were co-authors on this study.

Visit the Department of Earth, Ocean, and Atmospheric Science website for more information about research conducted in the department. Visit the National High Magnetic Field Laboratory to learn more about how this facility supports groundbreaking research at FSU and beyond.

Research led by Rob Spencer, the Verta Cox Professor of Oceanography in the Department of Earth, Ocean, and Atmospheric Science, was featured on cover of the September issue of Nature Water.

Credit

Courtesy of Nature

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Journal

Nature Water

DOI

10.1038/s44221-026-00682-1

Article Title

The use of ultrahigh- and high-resolution mass spectrometry to examine the anthropogenic signature of dissolved organic matter in aquatic systems