The making of unnatural pollutants: The stealth ‘Unknown Knowns’ that shape regulatory monitoring

Authors: Katherine Clifford*,
Topics: Cultural and Political Ecology, Rural Geography, Medical and Health Geography
Keywords: environmental regulations, monitoring, uncertainty, pollution, rural
Session Type: Paper
Day: 4/5/2019
Start / End Time: 8:00 AM / 9:40 AM
Room: Roosevelt 5, Marriott, Exhibition Level
Presentation File: No File Uploaded


The regulatory monitoring system for particulate matter—which includes dust—is deeply, yet furtively, built upon a problematic assumptions around the urban and rural. More specifically that urban environments are shaped by human processes and rural environments considered natural or un-impacted by society. These stealth ‘unknown knowns’ underpin the very notion of pollution, and correspondingly impact all attempts to understand it. If the definition of pollution is “matter out of place,” then this raises questions about our expectations for these places and the different matter—pollutants—within them, expectations that are tied to these two problematic divides. This paper examines the ‘unknown knowns’ that shape how we understand pollution and environmental monitoring, specifically in regard to dust, and highlights the impacts of two fundamental assumptions. The first assumption is that only man-made pollution occurs and is dangerous, and correspondingly that pollution cannot be produced through ‘natural’ processes. This results in monitors limiting collection to man-made pollutants and looking for pollutants where lots of people are, urban areas. Thinking of pollution as urban results in a second assumption: rural environments are natural and unpolluted. Air quality monitors are required based on population density, leaving most rural areas unmonitored. However, if understandings of pollution were delinked from being man-made, it would undermine monitoring logic based on population density. Explicitly examining these ‘unknown knowns’ can help challenge the problematic divisions that produce uncertainty at best, and false assurances of safety at worst.

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