{"id":357101,"date":"2024-12-21T18:30:51","date_gmt":"2024-12-21T17:30:51","guid":{"rendered":"https:\/\/climatescience.press\/?p=357101"},"modified":"2024-12-21T18:30:54","modified_gmt":"2024-12-21T17:30:54","slug":"the-climate-change%e2%88%92air-quality%e2%88%92public-health-fallacy","status":"publish","type":"post","link":"https:\/\/climatescience.press\/?p=357101","title":{"rendered":"The Climate Change\u2212Air Quality\u2212Public Health Fallacy"},"content":{"rendered":"\n
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From Watts Up With That?<\/a><\/p>\n\n\n\n

Guest post by S. Stanley Young and Warren Kindzierski<\/p>\n\n\n\n

We were asked by Heritage Foundation to write two papers on the alleged link between climate change, predicted changes in air quality, and public health effects. Both papers are available on their website. One paper is a discussion of a\u00a0climate change\u2212<\/a>fine particulate matter<\/a>\u00a0(PM2.5) \u2212public health link (or lack thereof). The second paper is a discussion of how\u00a0ozone<\/a>\u00a0fits (or rather how it fails to fit) into a climate change\u2212ozone\u2212asthma link. A summary of both papers is provided, and an open question is posed to WUWT readers below.<\/p>\n\n\n\n

First and foremost, for both PM2.5 and ozone, ALL of the studies we refer to in our two papers are not founded on proven biological plausibility of these factors causing diseases or deaths. They are founded on an assumption of what\u00a0may<\/em>\u00a0be a cause of disease or death\u2014for example, PM2.5 or ozone.<\/p>\n\n\n\n

PM2.5<\/em><\/p>\n\n\n\n

Our first paper<\/a> examined the two key public health endpoints that the U.S. Environmental Protection Agency (EPA) claims<\/a> result from PM2.5 exposure\u2014nonfatal heart attacks and premature deaths. For both endpoints, we show that health effect studies\u2014observational epidemiology studies\u2014cited by the EPA to support their claims do not take proper accounting of hidden biases, nor do they apply rigorous tests for reproducibility of these studies.<\/p>\n\n\n\n

The most glaring biases to us, but hidden to most readers, are use of questionable (poor) research practices<\/a>, multiple hypotheses testing<\/a>, and irreproducibility (falseness<\/a>) of the research claims. Our paper also points to numerous null association studies in scholarly literature showing that PM2.5 does not cause nonfatal heart attacks and premature death.<\/p>\n\n\n\n

It is up to the interpretation of the scientific method to answer the PM2.5\u2212nonfatal heart attack\/premature death claims: If the methods are flawed, so is the evidence. Studies engaging in poor research practices should be treated as untrustworthy<\/a> until proven otherwise. Studies that perform many statistical comparisons tend to produce more errors of false-positive associations<\/a> in the absence of statistical corrections. Finally, PM2.5\u2212heart attack\/premature death association studies failed our statistical reproducibility tests using p-value plots.<\/p>\n\n\n\n

Herein lies the problem with PM2.5\u2212health effect studies relied upon by the EPA: They are non-randomized designs with no effective researcher controls. They allow infinite researcher flexibilities in designing, analyzing, interpretating, and reporting of their results. Their data sets are not available to others for independent reproducibility testing. These types of designs and lack of controls cannot address biases and confounding. Spurious risk statistics that resemble genuine effects can (and do) easily occur in these studies when, in fact, they are nothing more than artifacts of hidden biases and confounding.<\/p>\n\n\n\n

With no reproducible causal effects and given persistent, hidden biases and confounding in PM2.5 health research, any purported link between PM2.5 and public health is entirely unsupported and should not be taken seriously.<\/p>\n\n\n\n

Ozone<\/em><\/p>\n\n\n\n

Our second paper<\/a> shows that the EPA\u2019s own data for emissions of ozone precursors and ozone levels recorded at air monitoring stations in the U.S. over the past several decades show very different trends (declines) to what climate change models are predicting in the future. For example:<\/p>\n\n\n\n