Wildfires are reversing America’s progress on ozone pollution, the main ingredient in smog 38%

By Weizhi Deng0% Jun Wang0% Meng Zhou0%

6/4/2026, 6:00:02 PM

BS Summary: This article contains 27 faulty reasoning types, including Biased Writer Voice, Negativity Bias, and Indoctrination, with Post Hoc (False Cause) as the most egregious example at 30% saturation with 246 hits. Analysis detected 1,719 faulty-reasoning hits from 819 analyzed words, generating a BS Score of 44% and a BS Rank of 38% (13,236 of 21,172 articles). This article is better (less manipulative) than 62.50% of the article peer group.

For decades, the United States made steady progress in reducing surface ozone pollution, the main ingredient in smog. 
But that progress  achieved as vehicles, industries and power sources became cleaner  is increasingly being overshadowed by a different and growing source of ozone pollution: wildfires. 
Our team of atmospheric and wildfire scientists analyzed wildfires’ contribution to surface ozone levels from 2003 to 2024 across the United States. 
We found that the gases in wildfire smoke have reversed the national ozone trend, forcing a shift from declining ozone levels prior to 2015 to increasing ozone levels after 2015. 
We also found that the number of ozone-related premature deaths due to wildfires has been increasing by about 300 deaths per year since then. 
Battling smog 
Most people know ozone as the protective layer of the atmosphere high above the Earth that shields the planet from harmful ultraviolet radiation. 
But ozone has two very different faces. 
High in the atmosphere, ozone is beneficial. 
Near the ground, it is a harmful air pollutant that can irritate the lungs and worsen respiratory diseases. 
Los Angeles made ozone visible to the nation in the 1940s and 1950s, as thick, eye-stinging smog often blanketed the city. 
It turned an invisible chemistry problem into a public-health crisis people could see and feel. 
That crisis helped motivate decades of air pollution control efforts in California and, later, across the United States. 
After the passage of the Clean Air Act and its amendments in the 1970s, the U.S. made steady progress in cleaning up surface ozone. 
Regulations on vehicles, power plants and industrial sources reduced emissions of nitrogen oxides and other ozone-forming chemicals. 
To monitor the progress, the U.S. 
Environmental Protection Agency has over 1,000 stations that measure ozone around the country. 
They cover many places, but mostly urban areas, and do not measure ground-level ozone everywhere at the neighborhood scale. 
We were able to fill in the gaps by combining those monitoring station measurements with satellite-derived information about air pollution and human activity, along with weather and air quality model simulations. 
We then used artificial intelligence to estimate daily surface ozone levels everywhere in the contiguous U.S., with data every square kilometer, over the past 22 years. 
The results show that national progress in reducing surface ozone reversed around 2015 as North America began to face more severe wildfires. 
In many regions, ozone levels are now increasing, especially in the western U.S. and the Midwest, where smoke and gases from wildfires are becoming more common as they are transported through the air. 
Overall, surface ozone levels that had been falling by about 0.65 part per billion per year from 2003–2015 have since increased by about 0.13 parts per billion per year. 
If wildfires hadn’t been an influence, we found, the trend of falling surface ozone levels would have continued instead. 
People often think of wildfires as a problem for the western U.S., but smoke and gas pollutants from their emissions can travel thousands of miles, affecting communities far from the fires themselves. 
The 2023 Canadian wildfires offered a vivid example. 
In much of the Midwest, ozone reached unhealthy levels for more than a week. 
The impact of wildfire smoke reached as far as Georgia and New York. 
That year, an additional 43 million Americans lived in areas with ozone exceeding healthy standards compared to previous years because of increased wildfire emissions. 
As the Earth and its atmosphere warm, wildfire seasons are becoming longer and more severe across many parts of North America, and the trend is predicted to continue. 
In line with projections, Canada experienced its most devastating wildfire seasons on record in 2023 and 2025. 
In January 2025, destructive fires burned more than 16,000 homes and businesses in and around Los Angeles during a time of year when such events have historically been uncommon. 
The shift toward more fires suggests that the rising ozone problem could become even greater in the future. 
That’s a problem for human health. 
Reducing exposure to ozone and its health risks 
People can reduce their exposure to ozone pollution by checking air quality forecasts and limiting outdoor activities when wildfires are sending smoke into the air. 
But protecting public health in the long run will require broader actions to reduce ground-level ozone itself. 
That includes efforts to mitigate fire risk by improving wildfire management, such as reducing brush and other dry undergrowth that can fuel fires, and also scaling back the causes of rising global temperatures, such as the burning of fossil fuels. 
As temperatures rise, the ground loses moisture, creating conditions for more extreme fires. 
Protecting public health also means strengthening air quality forecasting systems to provide accurate early warnings, so people can take precautions, and maintaining air pollution monitoring networks and investing in satellite sensors to continue measuring progress, so problems can be identified and fixed. 
Article reasoning-pattern comparisonThis article: 4.4%Weizhi Deng: 1.6%The Conversation: 3.0%Confirmation Bias4.4%This article: 2.7%Weizhi Deng: 0.7%The Conversation: 0.7%Anchoring Bias2.7%This article: 7.9%Weizhi Deng: 5.2%The Conversation: 2.7%Availability Heuristic7.9%This article: 3.9%Weizhi Deng: 1.0%The Conversation: 1.0%Representativeness Heuristic3.9%This article: 3.7%Weizhi Deng: 0.9%The Conversation: 1.0%Hindsight Bias3.7%This article: 9.6%Weizhi Deng: 7.0%The Conversation: 3.2%Overconfidence Bias9.6%This article: 3.7%Weizhi Deng: 2.2%The Conversation: 5.3%Framing Effect3.7%This article: 2.1%Weizhi Deng: 0.5%The Conversation: 0.4%Loss Aversion2.1%This article: 2.9%Weizhi Deng: 0.7%The Conversation: 0.5%Status Quo Bias2.9%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.2%Sunk Cost Effect0.0%This article: 7.3%Weizhi Deng: 1.8%The Conversation: 2.3%Optimism Bias7.3%This article: 5.6%Weizhi Deng: 3.4%The Conversation: 1.8%Pessimism Bias5.6%This article: 15.8%Weizhi Deng: 5.6%The Conversation: 6.1%Negativity Bias15.8%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.9%Self-Serving Bias0.0%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.8%Fundamental Attribution Error0.0%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.1%Actor-Observer Bias0.0%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.7%In-Group Bias0.0%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.2%Out-Group Homogeneity Bias0.0%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 1.3%Halo Effect0.0%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.0%Horn Effect0.0%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.0%Dunning-Kruger Effect0.0%This article: 12.9%Weizhi Deng: 3.2%The Conversation: 1.1%Recency Bias12.9%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.4%Primacy Effect0.0%This article: 2.3%Weizhi Deng: 0.6%The Conversation: 0.1%Blind-Spot Bias2.3%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.4%Ad Hominem0.0%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.5%Straw Man0.0%This article: 7.4%Weizhi Deng: 2.7%The Conversation: 4.2%Appeal to Authority7.4%This article: 2.1%Weizhi Deng: 0.5%The Conversation: 1.7%False Dilemma2.1%This article: 12.2%Weizhi Deng: 3.1%The Conversation: 1.5%Slippery Slope12.2%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.1%Circular Reasoning0.0%This article: 8.8%Weizhi Deng: 3.2%The Conversation: 4.8%Hasty Generalization8.8%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.1%Red Herring0.0%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.6%Bandwagon0.0%This article: 2.6%Weizhi Deng: 1.0%The Conversation: 2.5%Appeal to Emotion2.6%This article: 2.3%Weizhi Deng: 1.2%The Conversation: 0.9%Begging the Question2.3%This article: 30.0%Weizhi Deng: 12.2%The Conversation: 2.9%Post Hoc (False Cause)30.0%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.0%Tu Quoque0.0%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.3%Burden of Proof0.0%This article: 3.4%Weizhi Deng: 0.9%The Conversation: 0.6%Appeal to Nature3.4%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.6%Composition/Division0.0%This article: 10.4%Weizhi Deng: 2.6%The Conversation: 1.9%Anecdotal10.4%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.1%No True Scotsman0.0%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 2.0%Ambiguity (Equivocation)0.0%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.3%Middle Ground0.0%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.0%Personal Incredulity0.0%This article: 4.9%Weizhi Deng: 1.2%The Conversation: 0.1%Special Pleading4.9%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.1%Genetic Fallacy0.0%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 1.5%Unattributed Quote0.0%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.6%Quote-first Misdirection0.0%This article: 20.9%Weizhi Deng: 5.2%The Conversation: 6.0%Biased Writer Voice20.9%This article: 15.1%Weizhi Deng: 7.6%The Conversation: 2.1%Indoctrination15.1%This article: 4.9%Weizhi Deng: 1.2%The Conversation: 1.3%Politically Left Leaning Bias4.9%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.1%Politically Right Leaning Bias0.0%This article: 0.0%Weizhi Deng: 0.0%The Conversation: 0.4%Attempt to Sell a Product or S…0.0%

819 words analyzed.

Speakers

1speaker1.6%attributed speech806writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 13 words • 100.0% coverageWriter's voice • 18 words • 100.0% coverageWriter's voice • 28 words • 100.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 30 words • 100.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 2 words • 100.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 21 words • 100.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageU.S. Environmental Protection Agency • 13 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 31 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 22 words • 100.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 32 words • 0.0% coverageWriter's voice • 8 words • 100.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 29 words • 100.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 25 words • 100.0% coverageWriter's voice • 17 words • 100.0% coverageWriter's voice • 40 words • 100.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 42 words • 100.0% coverage
100%flagged-word coverage
13 attributed words100% of attributed speech98% writer coverage
0%12.5%25.0%Biased Writer Voice-21.2 ptsWriter: 21.2%U.S. Environmental Protection Agency: 0.0%0.0%Indoctrination-15.4 ptsWriter: 15.4%U.S. Environmental Protection Agency: 0.0%0.0%Politically Left Leaning B-5.0 ptsWriter: 5.0%U.S. Environmental Protection Agency: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.