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Preparing for a pandemic that never came ended up setting off another − how an accidental virus release triggered 1977’s ‘Russian flu’

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theconversation.com – Donald S. Burke, Dean Emeritus and Distinguished University Professor Emeritus of Health Science and Policy, and of Epidemiology, at the School of Public Health, University of Pittsburgh – 2024-09-04 07:28:24

Vaccine research quickly picked up to try to prevent a possible flu pandemic in 1976.

AP Photo

Donald S. Burke, University of Pittsburgh

Nineteen-year-old U.S. Army Pvt. David Lewis set out from Fort Dix on a 50-mile hike with his unit on Feb. 5, 1976. On that bitter cold day, he collapsed and died. Autopsy specimens unexpectedly tested positive for an H1N1 swine influenza virus.

Virus disease surveillance at Fort Dix found another 13 cases among recruits who had been hospitalized for respiratory illness. Additional serum antibody testing revealed that over 200 recruits had been infected but not hospitalized with the novel swine H1N1 strain.

masked nurse and military man stand above patient in bed

Officials worried about a repeat of something like the 1918 flu pandemic, which took hold in soldiers and spread globally.

PhotoQuest/Getty Images

Alarm bells instantly went off within the epidemiology community: Could Pvt. Lewis’ death from an H1N1 swine flu be a harbinger of another global pandemic like the terrible 1918 H1N1 swine flu pandemic that killed an estimated 50 million people worldwide?

The U.S. government acted quickly. On March 24, 1976, President Gerald Ford announced a plan to “inoculate every man, woman, and child in the United States.” On Oct. 1, 1976, the mass immunization campaign began.

Meanwhile, the initial small outbreak at Fort Dix had rapidly fizzled, with no new cases on the base after February. As Army Col. Frank Top, who headed the Fort Dix virus investigation, later told me, “We had shown pretty clearly that (the virus) didn’t go anywhere but Fort Dix … it disappeared.”

Nonetheless, concerned by that outbreak and witnessing the massive crash vaccine program in the U.S., biomedical scientists worldwide began H1N1 swine influenza vaccine research and development programs in their own countries. Going into the 1976-77 winter season, the world waited – and prepared – for an H1N1 swine influenza pandemic that never came.

piles of cardboard boxes and two men lifting them

By September 1976, New York State Health Department workers were unloading cartons of swine flu vaccine for distribution.

AP Photo/Jim McKnight

But that wasn’t the end of the story. As an experienced infectious disease epidemiologist, I make the case that there were unintended consequences of those seemingly prudent but ultimately unnecessary preparations.

What was odd about H1N1 Russian flu pandemic

In an epidemiological twist, a new pandemic influenza virus did emerge, but it was not the anticipated H1N1 swine virus.

In November 1977, health officials in Russia reported that a human – not swine – H1N1 influenza strain had been detected in Moscow. By month’s end, it was reported across the entire USSR and soon throughout the world.

Compared with other influenzas, this pandemic was peculiar. First, the mortality rate was low, about a third that of most influenza strains. Second, only those younger than 26 were regularly attacked. And finally, unlike other newly emerged pandemic influenza viruses in the past, it failed to displace the existing prevalent H3N2 subtype that was that year’s seasonal flu. Instead, the two flu strains – the new H1N1 and the long-standing H3N2 – circulated side by side.

Here the story takes yet another turn. Microbiologist Peter Palese applied what was then a novel technique called RNA oligonucleotide mapping to study the genetic makeup of the new H1N1 Russian flu virus. He and his colleagues grew the virus in the lab, then used RNA-cutting enzymes to chop the viral genome into hundreds of pieces. By spreading the chopped RNA in two dimensions based on size and electrical charge, the RNA fragments created a unique fingerprint-like map of spots.

dark spots in a funnel shape on a lighter background

Researchers were surprised to see the ‘genetic fingerprint’ for the 1977 H1N1 Russian flu strain closely matched that of an extinct influenza virus.

Peter Palese

Much to Palese’s surprise, when they compared the spot pattern of the 1977 H1N1 Russian flu with a variety of other influenza viruses, this “new” virus was essentially identical to older human influenza H1N1 strains that had gone extinct in the early 1950s.

So, the 1977 Russian flu virus was actually a strain that had disappeared from the planet a quarter century early, then was somehow resurrected back into circulation. This explained why it attacked only younger people – older people had already been infected and become immune when the virus circulated decades ago in its earlier incarnation.

But how did the older strain come back from extinction?

black and white photo of people sitting on subway in Moscow, 1977

Though called the Russian flu, the virus appears to have been circulating elsewhere before being identified in the Soviet population.

Gilbert UZAN/Gamma-Rapho via Getty Images

Refining the timeline of a resurrected virus

Despite its name, the Russian flu probably didn’t really start in Russia. The first published reports of the virus were from Russia, but subsequent reports from China provided evidence that it had first been detected months earlier, in May and June of 1977, in the Chinese port city of Tientsin.

In 2010, scientists used detailed genetic studies of several samples of the 1977 virus to pinpoint the date of their earliest common ancestor. This “molecular clock” data suggested the virus initially infected people a full year earlier, in April or May of 1976.

So, the best evidence is that the 1977 Russian flu actually emerged – or more properly “re-emerged” – in or near Tientsin, China, in the spring of 1976.

A frozen lab virus

Was it simply a coincidence that within months of Pvt. Lewis’ death from H1N1 swine flu, a heretofore extinct H1N1 influenza strain suddenly reentered the human population?

Influenza virologists around the world had for years been using freezers to store influenza virus strains, including some that had gone extinct in the wild. Fears of a new H1N1 swine flu pandemic in 1976 in the United States had prompted a worldwide surge in research on H1N1 viruses and vaccines. An accidental release of one of these stored viruses was certainly possible in any of the countries where H1N1 research was taking place, including China, Russia, the U.S., the U.K. and probably others.

Years after the reemergence, Palese, the microbiologist, reflected on personal conversations he had at the time with Chi-Ming Chu, the leading Chinese expert on influenza. Palese wrote in 2004 that “the introduction of the 1977 H1N1 virus is now thought to be the result of vaccine trials in the Far East involving the challenge of several thousand military recruits with live H1N1 virus.”

Although exactly how such an accidental release may have occurred during a vaccine trial is unknown, there are two leading possibilities. First, scientists could have used the resurrected H1N1 virus as their starting material for development of a live, attenuated H1N1 vaccine. If the virus in the vaccine wasn’t adequately weakened, it could have become transmissible person to person. Another possibility is that researchers used the live, resurrected virus to test the immunity provided by conventional H1N1 vaccines, and it accidentally escaped from the research setting.

Whatever the specific mechanism of the release, the combination of the detailed location and timing of the pandemic’s origins and the stature of Chu and Palese as highly credible sources combine to make a strong case for an accidental release in China as the source of the Russian flu pandemic virus.

black and grey bubbly blobs

The H1N1 influenza virus identified at Fort Dix wasn’t the one that ended up causing a pandemic.

CDC/Dr. E. Palmer, R.E. Bates, 1976 via Getty Images

A sobering history lesson

The resurrection of an extinct but dangerous human-adapted H1N1 virus came about as the world was scrambling to prevent what was perceived to be the imminent emergence of a swine H1N1 influenza pandemic. People were so concerned about the possibility of a new pandemic that they inadvertently caused one. It was a self-fulfilling-prophecy pandemic.

I have no intent to lay blame here; indeed, my main point is that in the epidemiological fog of the moment in 1976, with anxiety mounting worldwide about a looming pandemic, a research unit in any country could have accidentally released the resurrected virus that came to be called the Russian flu. In the global rush to head off a possible new pandemic of H1N1 swine flu from Fort Dix through research and vaccination, accidents could have happened anywhere.

Of course, biocontainment facilities and policies have improved dramatically over the past half-century. But at the same time, there has been an equally dramatic proliferation of high-containment labs around the world.

woman fully contained in personal protective gear reaches across glass bottles

Across the globe, researchers work on dangerous pathogens in labs that are part of biocontainment facilities.

AP Photo/Michael Probst

Overreaction. Unintended consequences. Making matters worse. Self-fulfilling prophecy. There is a rich variety of terms to describe how the best intentions can go awry. Still reeling from COVID-19, the world now faces new threats from cross-species jumps of avian flu viruses, mpox viruses and others. It’s critical that we be quick to respond to these emerging threats to prevent yet another global disease conflagration. Quick, but not too quick, history suggests.The Conversation

Donald S. Burke, Dean Emeritus and Distinguished University Professor Emeritus of Health Science and Policy, and of Epidemiology, at the School of Public Health, University of Pittsburgh

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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The Conversation

Contaminated milk from one plant in Illinois sickened thousands with Salmonella in 1985 − as outbreaks rise in the US, lessons from this one remain true

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theconversation.com – Michael Petros, Clinical Assistant Professor of Environmental and Occupational Health Sciences, University of Illinois Chicago – 2025-05-07 07:34:00

A valve that mixed raw milk with pasteurized milk at Hillfarm Dairy may have been the source of contamination. This was the milk processing area of the plant.
AP Photo/Mark Elias

Michael Petros, University of Illinois Chicago

In 1985, contaminated milk in Illinois led to a Salmonella outbreak that infected hundreds of thousands of people across the United States and caused at least 12 deaths. At the time, it was the largest single outbreak of foodborne illness in the U.S. and remains the worst outbreak of Salmonella food poisoning in American history.

Many questions circulated during the outbreak. How could this contamination occur in a modern dairy farm? Was it caused by a flaw in engineering or processing, or was this the result of deliberate sabotage? What roles, if any, did politics and failed leadership play?

From my 50 years of working in public health, I’ve found that reflecting on the past can help researchers and officials prepare for future challenges. Revisiting this investigation and its outcome provides lessons on how food safety inspections go hand in hand with consumer protection and public health, especially as hospitalizations and deaths from foodborne illnesses rise.

Contamination, investigation and intrigue

The Illinois Department of Public Health and the U.S. Centers for Disease Control and Prevention led the investigation into the outbreak. The public health laboratories of the city of Chicago and state of Illinois were also closely involved in testing milk samples.

Investigators and epidemiologists from local, state and federal public health agencies found that specific lots of milk with expiration dates up to April 17, 1985, were contaminated with Salmonella. The outbreak may have been caused by a valve at a processing plant that allowed pasteurized milk to mix with raw milk, which can carry several harmful microorganisms, including Salmonella.

Overall, labs and hospitals in Illinois and five other Midwest states – Indiana, Iowa, Michigan, Minnesota and Wisconsin – reported over 16,100 cases of suspected Salmonella poisoning to health officials.

To make dairy products, skimmed milk is usually separated from cream, then blended back together in different levels to achieve the desired fat content. While most dairies pasteurize their products after blending, Hillfarm Dairy in Melrose Park, Illinois, pasteurized the milk first before blending it into various products such as skim milk and 2% milk.

Subsequent examination of the production process suggested that Salmonella may have grown in the threads of a screw-on cap used to seal an end of a mixing pipe. Investigators also found this strain of Salmonella 10 months earlier in a much smaller outbreak in the Chicago area.

Microscopy image of six rod-shaped bacteria against a black background
Salmonella is a common cause of food poisoning.
Volker Brinkmann/Max Planck Institute for Infection Biology via PLoS One, CC BY-SA

Finding the source

The contaminated milk was produced at Hillfarm Dairy in Melrose Park, which was operated at the time by Jewel Companies Inc. During an April 3 inspection of the company’s plant, the Food and Drug Administration found 13 health and safety violations.

The legal fallout of the outbreak expanded when the Illinois attorney general filed suit against Jewel Companies Inc., alleging that employees at as many as 18 stores in the grocery chain violated water pollution laws when they dumped potentially contaminated milk into storm sewers. Later, a Cook County judge found Jewel Companies Inc. in violation of the court order to preserve milk products suspected of contamination and maintain a record of what happened to milk returned to the Hillfarm Dairy.

Political fallout also ensued. The Illinois governor at the time, James Thompson, fired the director of the Illinois Public Health Department when it was discovered that he was vacationing in Mexico at the onset of the outbreak and failed to return to Illinois. Notably, the health director at the time of the outbreak was not a health professional. Following this episode, the governor appointed public health professional and medical doctor Bernard Turnock as director of the Illinois Department of Public Health.

In 1987, after a nine-month trial, a jury determined that Jewel officials did not act recklessly when Salmonella-tainted milk caused one of the largest food poisoning outbreaks in U.S. history. No punitive damages were awarded to victims, and the Illinois Appellate Court later upheld the jury’s decision.

YouTube video
Raw milk is linked to many foodborne illnesses.

Lessons learned

History teaches more than facts, figures and incidents. It provides an opportunity to reflect on how to learn from past mistakes in order to adapt to future challenges. The largest Salmonella outbreak in the U.S. to date provides several lessons.

For one, disease surveillance is indispensable to preventing outbreaks, both then and now. People remain vulnerable to ubiquitous microorganisms such as Salmonella and E. coli, and early detection of an outbreak could stop it from spreading and getting worse.

Additionally, food production facilities can maintain a safe food supply with careful design and monitoring. Revisiting consumer protections can help regulators keep pace with new threats from new or unfamiliar pathogens.

Finally, there is no substitute for professional public health leadership with the competence and expertise to respond effectively to an emergency.The Conversation

Michael Petros, Clinical Assistant Professor of Environmental and Occupational Health Sciences, University of Illinois Chicago

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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The post Contaminated milk from one plant in Illinois sickened thousands with Salmonella in 1985 − as outbreaks rise in the US, lessons from this one remain true appeared first on theconversation.com



Note: The following A.I. based commentary is not part of the original article, reproduced above, but is offered in the hopes that it will promote greater media literacy and critical thinking, by making any potential bias more visible to the reader –Staff Editor.

Political Bias Rating: Centrist

The article provides an analytical, factual recounting of the 1985 Salmonella outbreak, with an emphasis on public health, safety standards, and lessons learned from past mistakes. It critiques the failures in leadership and oversight during the incident but avoids overt ideological framing. While it highlights political accountability, particularly the firing of a public health official and the appointment of a medical professional, it does so in a balanced manner without assigning blame to a specific political ideology. The content stays focused on the public health aspect and the importance of professional leadership, reflecting a centrist perspective in its delivery.

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Predictive policing AI is on the rise − making it accountable to the public could curb its harmful effects

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theconversation.com – Maria Lungu, Postdoctoral Researcher of Law and Public Administration, University of Virginia – 2025-05-06 07:35:00

Data like this seven-day crime map from Oakland, Calif., feeds predictive policing AIs.
City of Oakland via CrimeMapping.com

Maria Lungu, University of Virginia

The 2002 sci-fi thriller “Minority Report” depicted a dystopian future where a specialized police unit was tasked with arresting people for crimes they had not yet committed. Directed by Steven Spielberg and based on a short story by Philip K. Dick, the drama revolved around “PreCrime” − a system informed by a trio of psychics, or “precogs,” who anticipated future homicides, allowing police officers to intervene and prevent would-be assailants from claiming their targets’ lives.

The film probes at hefty ethical questions: How can someone be guilty of a crime they haven’t yet committed? And what happens when the system gets it wrong?

While there is no such thing as an all-seeing “precog,” key components of the future that “Minority Report” envisioned have become reality even faster than its creators imagined. For more than a decade, police departments across the globe have been using data-driven systems geared toward predicting when and where crimes might occur and who might commit them.

Far from an abstract or futuristic conceit, predictive policing is a reality. And market analysts are predicting a boom for the technology.

Given the challenges in using predictive machine learning effectively and fairly, predictive policing raises significant ethical concerns. Absent technological fixes on the horizon, there is an approach to addressing these concerns: Treat government use of the technology as a matter of democratic accountability.

Troubling history

Predictive policing relies on artificial intelligence and data analytics to anticipate potential criminal activity before it happens. It can involve analyzing large datasets drawn from crime reports, arrest records and social or geographic information to identify patterns and forecast where crimes might occur or who may be involved.

Law enforcement agencies have used data analytics to track broad trends for many decades. Today’s powerful AI technologies, however, take in vast amounts of surveillance and crime report data to provide much finer-grained analysis.

Police departments use these techniques to help determine where they should concentrate their resources. Place-based prediction focuses on identifying high-risk locations, also known as hot spots, where crimes are statistically more likely to happen. Person-based prediction, by contrast, attempts to flag individuals who are considered at high risk of committing or becoming victims of crime.

These types of systems have been the subject of significant public concern. Under a so-called “intelligence-led policing” program in Pasco County, Florida, the sheriff’s department compiled a list of people considered likely to commit crimes and then repeatedly sent deputies to their homes. More than 1,000 Pasco residents, including minors, were subject to random visits from police officers and were cited for things such as missing mailbox numbers and overgrown grass.

YouTube video
Lawsuits forced the Pasco County, Fla., Sheriff’s Office to end its troubled predictive policing program.

Four residents sued the county in 2021, and last year they reached a settlement in which the sheriff’s office admitted that it had violated residents’ constitutional rights to privacy and equal treatment under the law. The program has since been discontinued.

This is not just a Florida problem. In 2020, Chicago decommissioned its “Strategic Subject List,” a system where police used analytics to predict which prior offenders were likely to commit new crimes or become victims of future shootings. In 2021, the Los Angeles Police Department discontinued its use of PredPol, a software program designed to forecast crime hot spots but was criticized for low accuracy rates and reinforcing racial and socioeconomic biases.

Necessary innovations or dangerous overreach?

The failure of these high-profile programs highlights a critical tension: Even though law enforcement agencies often advocate for AI-driven tools for public safety, civil rights groups and scholars have raised concerns over privacy violations, accountability issues and the lack of transparency. And despite these high-profile retreats from predictive policing, many smaller police departments are using the technology.

Most American police departments lack clear policies on algorithmic decision-making and provide little to no disclosure about how the predictive models they use are developed, trained or monitored for accuracy or bias. A Brookings Institution analysis found that in many cities, local governments had no public documentation on how predictive policing software functioned, what data was used, or how outcomes were evaluated.

YouTube video
Predictive policing can perpetuate racial bias.

This opacity is what’s known in the industry as a “black box.” It prevents independent oversight and raises serious questions about the structures surrounding AI-driven decision-making. If a citizen is flagged as high-risk by an algorithm, what recourse do they have? Who oversees the fairness of these systems? What independent oversight mechanisms are available?

These questions are driving contentious debates in communities about whether predictive policing as a method should be reformed, more tightly regulated or abandoned altogether. Some people view these tools as necessary innovations, while others see them as dangerous overreach.

A better way in San Jose

But there is evidence that data-driven tools grounded in democratic values of due process, transparency and accountability may offer a stronger alternative to today’s predictive policing systems. What if the public could understand how these algorithms function, what data they rely on, and what safeguards exist to prevent discriminatory outcomes and misuse of the technology?

The city of San Jose, California, has embarked on a process that is intended to increase transparency and accountability around its use of AI systems. San Jose maintains a set of AI principles requiring that any AI tools used by city government be effective, transparent to the public and equitable in their effects on people’s lives. City departments also are required to assess the risks of AI systems before integrating them into their operations.

If taken correctly, these measures can effectively open the black box, dramatically reducing the degree to which AI companies can hide their code or their data behind things such as protections for trade secrets. Enabling public scrutiny of training data can reveal problems such as racial or economic bias, which can be mitigated but are extremely difficult if not impossible to eradicate.

Research has shown that when citizens feel that government institutions act fairly and transparently, they are more likely to engage in civic life and support public policies. Law enforcement agencies are likely to have stronger outcomes if they treat technology as a tool – rather than a substitute – for justice.The Conversation

Maria Lungu, Postdoctoral Researcher of Law and Public Administration, University of Virginia

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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The post Predictive policing AI is on the rise − making it accountable to the public could curb its harmful effects appeared first on theconversation.com



Note: The following A.I. based commentary is not part of the original article, reproduced above, but is offered in the hopes that it will promote greater media literacy and critical thinking, by making any potential bias more visible to the reader –Staff Editor.

Political Bias Rating: Center-Left

The article provides an analysis of predictive policing, highlighting both the technological potential and ethical concerns surrounding its use. While it presents factual information, it leans towards caution and skepticism regarding the fairness, transparency, and potential racial biases of these systems. The framing of these issues, along with an emphasis on democratic accountability, transparency, and civil rights, aligns more closely with center-left perspectives that emphasize government oversight, civil liberties, and fairness. The critique of predictive policing technologies without overtly advocating for their abandonment reflects a balanced but cautious stance on technology’s role in law enforcement.

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Worsening allergies aren’t your imagination − windy days create the perfect pollen storm

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theconversation.com – Christine Cairns Fortuin, Assistant Professor of Forestry, Mississippi State University – 2025-05-05 07:45:00

Windy days can mean more pollen and more sneezing.
mladenbalinovac/E+ via Getty Images

Christine Cairns Fortuin, Mississippi State University

Evolution has fostered many reproductive strategies across the spectrum of life. From dandelions to giraffes, nature finds a way.

One of those ways creates quite a bit of suffering for humans: pollen, the infamous male gametophyte of the plant kingdom.

In the Southeastern U.S., where I live, you know it’s spring when your car has turned yellow and pollen blankets your patio furniture and anything else left outside. Suddenly there are long lines at every car wash in town.

A car covered in yellow. Someone drew a smiley face with the words 'LOLLEN,' with LOL underlined.
On heavy pollen days, cars can end up covered in yellow grains.
Scott Akerman/Flickr, CC BY

Even people who aren’t allergic to pollen – clearly an advantage for a pollination ecologist like me – can experience sneezing and watery eyes during the release of tree pollen each spring. Enough particulate matter in the air will irritate just about anyone, even if your immune system does not launch an all-out attack.

So, why is there so much pollen? And why does it seem to be getting worse?

2 ways trees spread their pollen

Trees don’t have an easy time in the reproductive game. As a tree, you have two options to disperse your pollen.

Option 1: Employ an agent, such as a butterfly or bee, that can carry your pollen to another plant of the same species.

The downside of this option is that you must invest in a showy flower display and a sweet scent to advertise yourself, and sugary nectar to pay your agent for its services.

A bee noses into a white flower.
A bee enjoys pollen from a cherry blossom. Pollen is a primary source of protein for bees.
Ivan Radic/Flickr, CC BY

Option 2, the budget option, is much less precise: Get a free ride on the wind.

Wind was the original pollinator, evolving long before animal-mediated pollination. Wind doesn’t require a showy flower nor a nectar reward. What it does require for pollination to succeed is ample amounts of lightweight, small-diameter pollen.

Why wind-blown pollen makes allergies worse

Wind is not an efficient pollinator, however. The probability of one pollen grain landing in the right location – the stigma or ovule of another plant of the same species – is infinitesimally small.

Therefore, wind-pollinated trees must compensate for this inefficiency by producing copious amounts of pollen, and it must be light enough to be carried.

For allergy sufferers, that can mean air filled with microscopic pollen grains that can get into your eyes, throat and lungs, sneak in through window screens and convince your immune system that you’ve inhaled a dangerous intruder.

Tiny flowers on a live oak tree.
When wind blows the tiny pollen grains of live oaks, allergy sufferers feel it.
Charles Willgren/Flickr, CC BY

Plants relying on animal-mediated pollination, by contrast, can produce heavier and stickier pollen to adhere to the body of an insect. So don’t blame the bees for your allergies – it’s really the wind.

Climate change has a role here, too

Plants initiate pollen release based on a few factors, including temperature and light cues. Many of our temperate tree species respond to cues that signal the beginning of spring, including warmer temperatures.

Studies have found that pollen seasons have intensified in the past three decades as the climate has warmed. One study that examined 60 location across North America found pollen seasons expanded by an average of 20 days from 1990 to 2018 and pollen concentrations increased by 21%.

That’s not all. Increasing carbon dioxide levels may also be driving increases in the quantity of tree pollen produced.

Why the Southeast gets socked

What could make this pollen boost even worse?

For the Southeastern U.S. in particular, strong windstorms are becoming more common and more intense − and not just hurricanes.

Anyone who has lived in the Southeast for the past couple of decades has likely noticed this. The region has more tornado warnings, more severe thunderstorms, more power outages. This is especially true in the mid-South, from Mississippi to Alabama.

A map showing windiest events in the Southeast are over Alabama and Mississippi.
Severity of wind and storm events mapped from NOAA data, 2012-2019, shows high activity over Mississippi and Alabama. Red areas have the most severe events.
Christine Cairns Fortuin

Since wind is the vector of airborne pollen, windier conditions can also make allergies worse. Pollen remains airborne for longer on windy days, and it travels farther.

To make matters worse, increasing storm activity may be doing more than just transporting pollen. Storms can also break apart pollen grains, creating smaller particles that can penetrate deeper into the lungs.

Many allergy sufferers may notice worsening allergies during storms.

The peak of spring wind and storm season tends to correspond to the timing of the release of tree pollen that blankets our world in yellow. The effects of climate change, including longer pollen seasons and more pollen released, and corresponding shifts in windy days and storm severity are helping to create the perfect pollen storm.The Conversation

Christine Cairns Fortuin, Assistant Professor of Forestry, Mississippi State University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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The post Worsening allergies aren’t your imagination − windy days create the perfect pollen storm appeared first on theconversation.com



Note: The following A.I. based commentary is not part of the original article, reproduced above, but is offered in the hopes that it will promote greater media literacy and critical thinking, by making any potential bias more visible to the reader –Staff Editor.

Political Bias Rating: Centrist

The content is a scientific and educational article focusing on the biology of pollen, its effects on allergies, and the influence of climate change on pollen production. It presents factual information supported by research studies and references, without taking a partisan stance. While it acknowledges climate change as a factor, the discussion remains grounded in scientific observation rather than political opinion, leading to a neutral, centrist tone.

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