A good example of a bad environmental justice study


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Biden’s so-called environmental justice push is going to bring on a big wave of bogus research. A really big report just came out so I want to chop on it a bit. Not just for its specific fallacies, but also as typical of what we are going to see a lot of. As with the climate scare, there is a careful combination of bad modeling plus goofy statistics.

In fact the term “environmental justice” just means unhappy environmental statistics with a racial or ethnic focus. That the situation described is somehow deliberately unjust is assumed but often false. It certainly is false in this case. The concept of environmental injustice tends to be more emotional than rational.

In this case the Washington Post’s screaming headline is typical: “Deadly air pollutant disproportionately and systematicallyharms Americans of color, study finds“. The fine print sub-headline summarizes the supposed science: “Black, Latino and Asian Americans face higher levels of exposure to fine particulate matter from traffic, construction and other sources“.

The research report in question is “PM2.5 polluters disproportionately and systemically affect people of color in the United States” by Assistant Professor Christopher Tessum et al. It appears in Science Advances, the open access adjunct to the once prestigious Science magazine, which is now fully climate alarmist. Environmental justice alarmism is the new wave.

There are three central issues here: (1) harm, (2) disproportionately and (3) systematically. Let’s look at each in turn.

The supposedly deadly pollutant here is not a substance, just a size. It is called fine particulates, or more technically PM2.5. Anything smaller than 2.5 microns is included, which is so small it is invisible. Natural cases include viruses, bacteria, and ordinary dust. Your dust cloths and sweeper bag are full of it. Human sources include soot from combustion and dust from construction. There are also a host of large molecules that form in the air via chemical reactions with emitted gasses. These can be either human or natural, such as the vast quantities of volatile organic compounds produced by forests.

Alarmists claim that PM2.5 is deadly, but skeptics say this is simply false. One of the leading critics is Steve Milloy, founder of the famous JunkScience.com blog. Milloy even has a great book on PM2.5, titled: “Scare Pollution: Why and How to Fix the EPA“.

Here is a succinct description: “The U.S Environmental Protection Agency (EPA) claims that outdoor air kills hundreds of thousands of Americans every year. EPA has used this claim to: wreck the coal industry; justify expensive and job-killing air quality and climate rules; and to scare Americans about the air they breathe. Milloy not only debunks the outrageous EPA’s claims and exposes them as rank scientific fraud in no uncertain terms, but offers a roadmap for fixing the rogue and out-of-control EPA.” Note that EPA funded the study in question here.

The reported research is not about harm, but the harm hype makes it potentially very dangerous as far as policy goes. What Tessum et al claim to do is look at exposure to human caused PM2.5, specifically exposure on the basis of race and ethnicity. They then claim to find that exposure levels are disproportionately high among the three nonwhite groups analyzed — Blacks, Latinos and Asians — when compared to Whites. They even do this for various specific types of sources of PM2.5.

Their goal is impressive but it is far from clear that they achieve it. Just as with a lot of climate studies, this looks more like speculation presented as established fact. I expect a lot of so-called environmental justice studies are going to look like this. Let’s look at some of the problems.

The research proceeds in two steps. First the human caused PM2.5 concentration is estimated everywhere in the contiguous 48 states. This is done with a computer model. So the obvious first question is how good is the model?

The unfortunate reality is that this is a deliberately crude model. In engineering terms it is a “quick and dirty” model. It is specifically designed to be much faster than the good models. This was explained four years ago when the model was introduced: “Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions.

So the model is not as good as the existing comprehensive models. We need to know how much not as good? This is nowhere said and it could easily be enough to nullify the results, since these often indicate relatively small differences in exposure.

I am here reminded of the rapid climate attribution models. Major climate models take months to run on supercomputers. The quick models take just a few days to supposedly say how much human climate change helped cause a destructive weather event. They fit the news cycle but their results are nonsense.

In this regard it is worth noting how incredibly complex the phenomena being modeled are. They start with a whopping 5434 TYPES of emission sources. Some types have millions of instances, such as auto exhausts and gas cook stoves. There is also interstate transport of particulates, wherever the wind blows, plus the complex chemical reactions that create or destroy PM2.5 along the way. And of course there are myriad natural sources, which this model ignores.

In short it is clearly possible, likely even, that these crude exposure estimates are inaccurate enough to nullify the results.

The second step is to estimate how much PM2.5 each person, in each of the four racial/ethnic groups is exposed to, on average. In fact they do this for every one of the 5434 types!

Except they do not, of course, because there is no way to know where people go. They just do it for each home address, using census data to say how many people from each group live there. That most people do not spend that much time at home, and little of that is spent outdoors, is simply ignored.

It is thus completely wrong to say they have estimated how much PM2.5 these people have been exposed to.

But the third problem is perhaps the greatest. This is what is called in statistics a “confounding factor”. In simple terms a confounding factor is something other than the claimed explanation that explains the result.

In this case the confounding factor is urbanization. The biggest causes of greater PM2.5 levels at non-white residences, compared to white residences, are cars, trucks, construction and industrial activity. All of these are highly concentrated in urban areas and that is where the non-white population’s residences are also concentrated. Whites make up about 60% of the population but only about 40% of the urban residential population. In fact something like 70% of Blacks and Latinos live in urban areas.

This means that a lot of the supposedly “disproportionate” exposure to PM2.5 is due simply to the disproportionate way that people do or do not live in urban areas. I see no environmental injustice here.

In addition to these huge macro problems, there are specific results that seem highly questionable. For example, residential gas combustion is relatively good for Whites but bad for non-whites. It is about three times worse for Asians than for Hispanics. (Good here means that their PM2.5 exposure is below the average for all groups, while bad makes it above average.) How are these big differences possible since we all have the same gas stoves, furnaces and such?

Likewise, agriculture is good for Hispanics but bad for Blacks. Coal fired power plants are bad for Blacks and Whites, but good for Asians and especially Hispanics. Residential wood combustion is good for Hispanics but bad for Asians, while neutral for the rest.

None of these specific differences make any sense which strongly suggests the results have a random aspect.

The conclusion seems clear. The model and methods used to do this study are too crude to make the results believable, so there is no reason to believe them.

Nor is there anything “systematic” about these results, except that they are systematically unreliable. Moreover the supposed harm is probably nonexistent.

More generally, environmental justice is going down the same wrong road as climate change. Speculation that serves a political agenda is taken to be true, because it so serves. False terms like “systematic” and “systemic” are being wielded like clubs.

By and large environmental justice is a nonsensical concept.

  • David Wojick, Ph.D. is an independent analyst working at the intersection of science, technology and policy. For origins see http://www.stemed.info/engineer_tackles_confusion.html For over 100 prior articles for CFACT see http://www.cfact.org/author/david-wojick-ph-d/ Available for confidential research and consulting.

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