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Mapping and Prediction of Coal Workers’ Pneumoconiosis with Bioavailable Iron Content in the Bituminous Coals

Overview of attention for article published in this source, April 2005
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Title
Mapping and Prediction of Coal Workers’ Pneumoconiosis with Bioavailable Iron Content in the Bituminous Coals
Published by
Environmental Health Perspectives, April 2005
DOI 10.1289/ehp.7679
Pubmed ID
Authors

Xi Huang, Weihong Li, Michael D. Attfield, Arthur Nádas, Krystyna Frenkel, Robert B. Finkelman

Abstract

Based on the first National Study of Coal Workers' Pneumoconiosis (CWP) and the U.S. Geological Survey database of coal quality, we show that the prevalence of CWP in seven coal mine regions correlates with levels of bioavailable iron (BAI) in the coals from that particular region (correlation coefficient r = 0.94, p < 0.0015). CWP prevalence is also correlated with contents of pyritic sulfur (r = 0.91, p < 0.0048) or total iron (r = 0.85, p < 0.016) but not with coal rank (r = 0.59, p < 0.16) or silica (r = 0.28, p < 0.54). BAI was calculated using our model, taking into account chemical interactions of pyrite, sulfuric acid, calcite, and total iron. That is, iron present in coals can become bioavailable by pyrite oxidation, which produces ferrous sulfate and sulfuric acid. Calcite is the major component in coals that neutralizes the available acid and inhibits iron's bioavailability. Therefore, levels of BAI in the coals are determined by the available amounts of acid after neutralization of calcite and the amount of total iron in the coals. Using the linear fit of CWP prevalence and the calculated BAI in the seven coal mine regions, we have derived and mapped the pneumoconiotic potencies of 7,000 coal samples. Our studies indicate that levels of BAI in the coals may be used to predict coal's toxicity, even before large-scale mining.

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Geographical breakdown

Country Count As %
United States 1 2%
Unknown 54 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 22%
Student > Master 9 16%
Student > Bachelor 6 11%
Other 4 7%
Researcher 4 7%
Other 10 18%
Unknown 10 18%
Readers by discipline Count As %
Medicine and Dentistry 12 22%
Engineering 6 11%
Environmental Science 5 9%
Agricultural and Biological Sciences 4 7%
Chemistry 3 5%
Other 13 24%
Unknown 12 22%