Corn Pollution Kills Thousands of Americans a Year, Study Finds

Illustration for article titled Corn Pollution Kills Thousands of Americans a Year, Study Finds
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Midwestern states better buckle up because a new bad guy’s in town. I’m not talking about any gun-toting cowboys, though. I’m talking about corn.

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A study published Monday in Nature Sustainability shows how deadly this crop can be. And eating it isn’t the killer; growing it is. Turns out that the air pollution from growing corn is behind an estimated 4,300 premature deaths a year.

Growing corn results in emissions of particulate matter, a dangerous pollutant that is so small it winds up in your lungs when inhaled and can even affect your heart. This forms from the ammonia, sulfur oxides, nitrogen oxides, and volatile organic compounds released during fertilizer and manure application, the use of farm machinery (like tractors), and dust from ploughing and planting. The fertilizer and manure are the real culprits here, though: They account for 71 percent of the researchers’ corn-attributed deaths.

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“It’s important for farmers to have this information so that they can implement practices that reduce the environmental impact of the crops they grow,” said lead author Jason Hill, an associate professor at the University of Minnesota College of Food, Agricultural, and Natural Resource Sciences, in a statement. “Farmers can greatly improve the environmental profile of their corn by using precision agriculture tools and switching to fertilizers that have lower ammonia emissions.”

The University of Minnesota team calculated all this by running pollution inventory models in each of the top 2,000 corn-growing counties with each county’s agricultural data between 2010 and 2014. The authors also looked at county-level data on the amount of fertilizer used per metric ton of corn and coupled it all with air quality models to determine an estimated number of deaths. The study included not only the emissions directly from the farms themselves, but also their upstream emissions from the fuel and machines they need, their electricity source, and production of agrichemicals.

Different parts of the U.S. are definitely feeling these impacts differently, as the study points out. States like Iowa, Illinois, Nebraska, Minnesota, and Indiana make up a little over half the study’s mortalities. Illinois really has it bad with nearly 800 premature deaths a year thanks to its beloved corn. Cities like Chicago and Minneapolis feel the effects too because of how close the corn production is in the central corn belt.

The study is clear that it doesn’t paint a full picture, either. The researchers didn’t account for the pollution that results from where the corn goes after it grows. Like producing ethanol biofuel or animal feed. And this is a tiny piece of our nation’s—and world’s—air pollution problem. The World Health Organization estimates some 7 million people die prematurely around the world every year because bad air. In the U.S., blacks and Hispanics deal with air pollution the most—air pollution that white people disproportionately create, according to a separate study out earlier this year.

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Anyway, more than 90 million acres go toward growing corn. Maybe it’s time to rethink how we grow it if we want to keep it around while protecting the people who live near its farms.

Yessenia Funes is climate editor at Atmos Magazine. She loves Earther forever.

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DISCUSSION

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Dense non aqueous phase liquid

Industrial scale ag commodities production has problems and the problems are manifold. Corn is just one cash crop of many, BTW.

However...

Epidemiological studies (what this study is) are done more and more because less and less actual environmental chemical dispersion/human health impact field study is done anymore. Chiefly due to funding limitations. Or funding from someone or some entity needing something for something from those being funded.

This study seems to be promoting data driven agriculture (as hawked above). Those promoting big ag big data would be IBM, Microsoft, Silicon Valley, and Climate Corporations.

Here’s some papers on problems with epidemiological studies.

The problems with some epidemiological studies.

https://www.ncbi.nlm.nih.gov/pubmed/17399923

Epidemiological studies provide a valuable tool for the investigation of the causes of disease. However, such studies alone are rarely able to prove cause. No study is perfect and the evaluation of results must take account of the design and execution of the study together with the analytic methods used. Before imputing cause it is important to consider the findings against the criteria set out by Bradford Hill. Some of the problems with studies are discussed including the specification of the data collected, accuracy of information, end point definition, study size and the way results are presented. The issues are illustrated by reference to published papers. It is concluded that unless studies are evaluated and interpreted with care they may result in more harm than good.

Current Methods and Challenges for Epidemiological Studies of the Associations Between Chemical Constituents of Particulate Matter and Health

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4626265/

Epidemiological studies have been critical for estimating associations between exposure to ambient particulate matter (PM) air pollution and adverse health outcomes. Because total PM mass is a temporally and spatially varying mixture of constituents with different physical and chemical properties, recent epidemiological studies have focused on PM constituents. Most studies have estimated associations between PM constituents and health using the same statistical methods as in studies of PM mass. However, these approaches may not be sufficient to address challenges specific to studies of PM constituents, namely assigning exposure, disentangling health effects, and handling measurement error. We reviewed large, population-based epidemiological studies of PM constituents and health and describe the statistical methods typically applied to address these challenges. Development of statistical methods that simultaneously address multiple challenges, for example, both disentangling health effects and handling measurement error, could improve estimation of associations between PM constituents and adverse health outcomes.