PM2.5 stack sampling emmission concentration analysis: EPA method 201A versus EPA method 17 coupled with particles size analysis

Authors: TAYLOR, LILLY - Texas A&M University; BUSER, MICHAEL - Texas A&M University; Whitelock, Derek

Submitted to: National Cotton Council Beltwide Cotton Conference
Publication Type: Proceedings
Publication Acceptance Date: 1/15/2025
Publication Date: 4/9/2025
Citation: Taylor, L., Buser, M., Whitelock, D.P. 2025. PM2.5 stack sampling emmission concentration analysis: EPA method 201A versus EPA method 17 coupled with particles size analysis. National Cotton Council Beltwide Cotton Conference, New Orleans, LA, January 14-16, 2025. 11 p.

Interpretive Summary: Past research has shown that EPA source samplers may over-estimate emissions of particles less than or equal to 2.5 microns in diameter (PM2.5) when the size of the particulate sampled is larger than the target-size of the sampler. This over-estimation or oversampling can lead to facilities whose emitted dust particles are typically larger, like cotton gins, being over-regulated and forced to comply with more stringent air quality regulatory enforcement than necessary. This project uses data from a previous ARS national PM emission factor development study for cotton gins to determine potential PM2.5 stack sampling errors. “True” PM2.5 emissions were determined from total particulate sampling coupled with particle size analyses and were compared to PM2.5 values measured with EPA Method 201a stack sampling to determine the differences between the two methods or errors. EPA Method 201a sampling overestimated PM2.5 emissions from cotton gins by as much as 19 to 1,686 % depending on the cotton gin processing system. This level of oversampling can lead to implementation of more stringent abatement technologies or management practices to reduce PM2.5 emissions levels based on measurements that have substantial errors and result in more financial burden with little to no impact on local ambient PM2.5 concentration. This data will assist agricultural processing facilities in regulatory compliance and be a fundamental pillar needed to address issues with compliance, implementation of costly controls, and human health.

Technical Abstract: The EPA recently released new annual PM2.5 (particulate matter with an aerodynamic diameter = 2.5 micrometers) standards that will significantly impact various industries in the U.S., including cotton gins (Buser et al, 2013-a). This project uses data from a national PM emission factor development study for cotton gins to determine potential PM2.5 stack sampling errors. “True” PM2.5 emission concentrations were calculated by multiplying EPA Method 17 stack sampling concentrations by the percent less than 2.5 microns determined from particle size analysis of the PM capture during the Method 17 sampling. These values were compared to the EPA Method 201a stack sampling data to determine the differences between the two methods or errors. The objectives of this project were to determine if there were Method 201a stack sampling errors and estimate the magnitude of errors in gins across the cotton belt. If the calculated errors are positive, then oversampling has occurred. Oversampling can lead to a source (e.g. cotton gin) being over-regulated and forced to comply with more stringent air quality regulatory enforcement than a source where no oversampling or under sampling has occurred. From a health effects perspective, these sampling errors can lead to incorrect conclusions by epidemiologists about a source's impact on human health and regulatory agency conclusions about a source's impact on ambient air quality. These incorrect conclusions can result in industries spending more money to meet regulatory requirements with limited or no local or regional benefits to ambient air quality in terms of reducing PM2.5 levels.