DataWatch: Wisconsin air pollution reaches record highs as wildfire smoke engulfs the state 72%

By Hongyu Liu0% Wisconsin Watch12%

7/17/2026, 4:16:56 PM

BS Summary: This article contains 18 faulty reasoning types, including Appeal to Authority, Post Hoc (False Cause), and Ambiguity (Equivocation), with Negativity Bias as the most egregious example at 30.2% saturation with 106 hits. Analysis detected 682 faulty-reasoning hits from 351 analyzed words, generating a BS Score of 64% and a BS Rank of 72% (6,303 of 21,886 articles). This article is worse (more manipulative) than 71.20% of the article peer group.

Reading Time: minute 
Wisconsinites breathed record-breaking levels of air pollution Thursday as wildfire smoke from northern Minnesota and Canada wafted across much of the Great Lakes region and the Northeast. 
Many monitoring stations across the state recorded pollution considered hazardous for the first time in at least 16 years  the span covered by most monitoring stations’ records. 
As of 8 a.m. on Friday, Air Quality Index, or AQI, levels in Madison had peaked above 450  far above the AQI’s 301 threshold for “hazardous” air quality, where everyone  not just sensitive groups  should take precautions to avoid breathing dangerous air. 
Milwaukee’s AQI reached 644 earlier on Thursday  more than twice the hazardous threshold. 
Air quality deteriorated even further in northwestern Wisconsin, with the index reaching 967 along the Minnesota border earlier in the day. 
Many Wisconsin communities, including Milwaukee, Waukesha, Odanah and Appleton, were still observing AQI over the 301 threshold Friday morning. 
The index categories typically run only from zero to 500. 
Thursday’s pollution surpassed levels once considered unprecedented. 
Before Canadian wildfire smoke pushed the AQI to record highs across Wisconsin in July 2023, cities including Madison, Milwaukee and Waukesha had not recorded an AQI above 200 since at least 2010, according to a Wisconsin Watch analysis of historical AQI data. 
But wildfire smoke has elevated air pollution in Wisconsin each summer since 2023. 
Major cities have periodically recorded higher AQI readings around July, though Thursday’s levels were the first to surpass those recorded in 2023. 
“This is a hazardous air quality episode that we have not experienced before,” Craig Czarnecki, air management outreach coordinator for the Wisconsin Department of Natural Resources, said in an email. 
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DataWatch: Wisconsin air pollution reaches record highs as wildfire smoke engulfs the state is a post from Wisconsin Watch , a non-profit investigative news site covering Wisconsin since 2009. 
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Article reasoning-pattern comparisonThis article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 2.1%Confirmation Bias0.0%This article: 12.0%Hongyu Liu: 10.6%Wisconsin Watch: 1.1%Anchoring Bias12.0%This article: 9.7%Hongyu Liu: 3.2%Wisconsin Watch: 2.2%Availability Heuristic9.7%This article: 5.4%Hongyu Liu: 1.8%Wisconsin Watch: 0.9%Representativeness Heuristic5.4%This article: 6.3%Hongyu Liu: 2.1%Wisconsin Watch: 0.3%Hindsight Bias6.3%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.7%Overconfidence Bias0.0%This article: 12.8%Hongyu Liu: 9.8%Wisconsin Watch: 3.8%Framing Effect12.8%This article: 2.6%Hongyu Liu: 0.9%Wisconsin Watch: 1.1%Loss Aversion2.6%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.6%Status Quo Bias0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.1%Sunk Cost Effect0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 2.5%Optimism Bias0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 1.7%Pessimism Bias0.0%This article: 30.2%Hongyu Liu: 21.7%Wisconsin Watch: 5.3%Negativity Bias30.2%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 1.1%Self-Serving Bias0.0%This article: 8.0%Hongyu Liu: 2.7%Wisconsin Watch: 0.6%Fundamental Attribution Error8.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.1%Actor-Observer Bias0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 1.1%In-Group Bias0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.1%Out-Group Homogeneity Bias0.0%This article: 10.3%Hongyu Liu: 3.4%Wisconsin Watch: 1.6%Halo Effect10.3%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.1%Horn Effect0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.0%Dunning-Kruger Effect0.0%This article: 9.7%Hongyu Liu: 4.5%Wisconsin Watch: 0.9%Recency Bias9.7%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.3%Primacy Effect0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.0%Blind-Spot Bias0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.2%Ad Hominem0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.1%Straw Man0.0%This article: 20.5%Hongyu Liu: 9.7%Wisconsin Watch: 3.1%Appeal to Authority20.5%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 1.2%False Dilemma0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.4%Slippery Slope0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.1%Circular Reasoning0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 2.3%Hasty Generalization0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.0%Red Herring0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.6%Bandwagon0.0%This article: 2.0%Hongyu Liu: 0.7%Wisconsin Watch: 4.0%Appeal to Emotion2.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.5%Begging the Question0.0%This article: 15.7%Hongyu Liu: 5.2%Wisconsin Watch: 2.3%Post Hoc (False Cause)15.7%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.1%Tu Quoque0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.3%Burden of Proof0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.4%Appeal to Nature0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.3%Composition/Division0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 2.8%Anecdotal0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.1%No True Scotsman0.0%This article: 14.8%Hongyu Liu: 4.9%Wisconsin Watch: 1.2%Ambiguity (Equivocation)14.8%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.1%Middle Ground0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.0%Personal Incredulity0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.0%Special Pleading0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.1%Genetic Fallacy0.0%This article: 8.5%Hongyu Liu: 2.8%Wisconsin Watch: 0.6%Unattributed Quote8.5%This article: 8.5%Hongyu Liu: 2.8%Wisconsin Watch: 1.3%Quote-first Misdirection8.5%This article: 11.4%Hongyu Liu: 3.8%Wisconsin Watch: 1.3%Biased Writer Voice11.4%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 1.6%Indoctrination0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.3%Politically Left Leaning Bias0.0%This article: 0.0%Hongyu Liu: 0.0%Wisconsin Watch: 0.2%Politically Right Leaning Bias0.0%This article: 6.0%Hongyu Liu: 6.0%Wisconsin Watch: 1.8%Attempt to Sell a Product or S…6.0%

351 words analyzed.

Speakers

2speakers17%attributed speech292writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 13 words • 100.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 27 words • 100.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 45 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 42 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageCraig Czarnecki • 30 words • 100.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 12 words • 100.0% coverageWisconsin Watch • 29 words • 0.0% coverageWriter's voice • 9 words • 100.0% coverage
Selected voice

Craig Czarnecki

100%flagged-word coverage
30 attributed words51% of attributed speech96% writer coverage
0%50.0%100.0%Unattributed Quote+100.0 ptsWriter: 0.0%Craig Czarnecki: 100.0%100.0%Quote-first Misdirection+100.0 ptsWriter: 0.0%Craig Czarnecki: 100.0%100.0%Biased Writer Voice-13.7 ptsWriter: 13.7%Craig Czarnecki: 0.0%0.0%Attempt to Sell a Product -7.2 ptsWriter: 7.2%Craig Czarnecki: 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.