Improving Insights on Air Pollutant Mixtures and Their Origins by Enhancing Local Monitoring in an Area of Intensive Resource Development

2020 ◽  
Vol 54 (23) ◽  
pp. 14936-14945
Author(s):  
Sumi N. Wren ◽  
Cris M. Mihele ◽  
Gang Lu ◽  
Zhimei Jiang ◽  
Deyong Wen ◽  
...  
Keyword(s):  
Resource Development ◽  
Air Pollutant ◽  
Local Monitoring ◽  
Pollutant Mixtures

scholarly journals A Toxicogenomic Comparison of Primary and Photochemically Altered Air Pollutant Mixtures

2011 ◽  
Vol 119 (11) ◽  
pp. 1583-1589 ◽  
Author(s):  
Julia E. Rager ◽  
Kim Lichtveld ◽  
Seth Ebersviller ◽  
Lisa Smeester ◽  
Ilona Jaspers ◽  
...  
Keyword(s):  
Air Pollutant ◽  
Pollutant Mixtures

scholarly journals Assessing Agreement in Exposure Classification between Proximity-Based Metrics and Air Monitoring Data in Epidemiology Studies of Unconventional Resource Development

2019 ◽  
Vol 16 (17) ◽  
pp. 3055 ◽  
Author(s):  
Judy Wendt Hess ◽  
Gerald Bachler ◽  
Fayaz Momin ◽  
Krystal Sexton
Keyword(s):  
Resource Development ◽  
Weighted Kappa ◽  
Air Pollutant ◽  
Air Monitoring ◽  
Monitoring Data ◽  
Fine Particulates ◽  
Adverse Health Effects ◽  
Pollutant Concentrations ◽  
Unconventional Resource ◽  
Epidemiology Studies

Recent studies of unconventional resource development (URD) and adverse health effects have been limited by distance-based exposure surrogates. Our study compared exposure classifications between air pollutant concentrations and “well activity” (WA) metrics, which are distance-based exposure proxies used in Marcellus-area studies to reflect variation in time and space of residential URD activity. We compiled Pennsylvania air monitoring data for benzene, carbon monoxide, nitrogen dioxide, ozone, fine particulates and sulfur dioxide, and combined this with data on nearly 9000 Pennsylvania wells. We replicated WA calculations using geo-coordinates of monitors to represent residences and compared exposure categories from air measurements and WA at the site of each monitor. There was little agreement between the two methods for the pollutants included in the analysis, with most weighted kappa coefficients between −0.1 and 0.1. The exposure categories agreed for about 25% of the observations and assigned inverse categories 16%–29% of the time, depending on the pollutant. Our results indicate that WA measures did not adequately distinguish categories of air pollutant exposures and employing them in epidemiology studies can result in misclassification of exposure. This underscores the need for more robust exposure assessment in future analyses and cautious interpretation of these existing studies.

Effects of Air Pollutant Mixtures on the Eye

1962 ◽  
Vol 4 (1) ◽  
pp. 103-107 ◽  
Author(s):  
S. R. Mettier ◽  
H. Karl Boyer ◽  
W. K. McEwen ◽  
Francis Ivanhoe ◽  
F. H. Meyers ◽  
...  
Keyword(s):  
Air Pollutant ◽  
Pollutant Mixtures

Removal of Air Pollutant Mixtures from Museum Display Cases

1991 ◽  
Vol 36 (3) ◽  
pp. 129 ◽  
Author(s):  
Daniel Grosjean ◽  
Sucha S. Parmar
Keyword(s):  
Air Pollutant ◽  
Pollutant Mixtures ◽  
Museum Display

scholarly journals Air pollutant mixtures and autoimmune skin disease prevalence: findings from the Personalized Environment and Genes Study

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Melissa E. Lowe ◽  
Taylor A. Potter ◽  
Farida S. Akhtari ◽  
David C. Fargo ◽  
Charles S. Schmitt ◽  
...  
Keyword(s):  
Skin Disease ◽  
Disease Prevalence ◽  
Air Pollutant ◽  
Pollutant Mixtures

Removal of air pollutant mixtures from museum display cases

1991 ◽  
Vol 36 (3) ◽  
pp. 129-141 ◽  
Author(s):  
Daniel Grosjean ◽  
Sucha S. Parmar
Keyword(s):  
Air Pollutant ◽  
Pollutant Mixtures ◽  
Museum Display
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