Meteorological Data Mining and Synthesis for Supplementing On-Site Data for Regulatory Compliance

Ben Sonpon, Shoaib Usman (), Joseph Smith, Sarah Kovaleski and Jason Wibbenmeyer
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Ben Sonpon: Nuclear Engineering and Radiation Science Department, Missouri University of Science and Technology, Rolla, MO 65409, USA
Shoaib Usman: Nuclear Engineering and Radiation Science Department, Missouri University of Science and Technology, Rolla, MO 65409, USA
Joseph Smith: Nuclear Engineering and Radiation Science Department, Missouri University of Science and Technology, Rolla, MO 65409, USA
Sarah Kovaleski: Ameren Callaway Energy Center, 8315 County Rd. 459, Steedman, MO 65077, USA
Jason Wibbenmeyer: Ameren Callaway Energy Center, 8315 County Rd. 459, Steedman, MO 65077, USA

Energies, 2024, vol. 17, issue 15, 1-37

Abstract: Many regulatory requirements add significant delay in the licensing of new nuclear power stations. One area of particular interest is the environmental impact of potential atmospheric release. The purpose of this research is to demonstrate effectiveness of meteorological data mining and synthesis from offsite locations to reduce need for onsite data, hence allowing rapid licensing. The automated procedures tested for data mining and extraction of meteorological data from multiple offsite sources and the data analytic tool developed for data fusion are presented here. Three important meteorological parameters from regulatory compliance are considered for this analysis: wind velocity, wind direction, and atmospheric stability. Callaway Nuclear Power Plant (CNPP) is used as our reference site. CNPP uses the Δ T Δ z approach while we use the Vogt method to estimated stability for the offsite locations. Stability classification correlation coefficients between the reference plant and Columbia Regional Airport range from −0.087 to 0.826 for raw with an average of 0.317 ± 0.313. With travel time, correction changed slightly, i.e., a 10 m observation 0.064 ± 0.249 and 0.028 ± 0.123 and a 60 m observation 0.103 ± 0.265 and 0.063 ± 0.155 for the wind from the reference plant to the airport and vice versa, respectively. For Jefferson City Memorial Airport, raw data correlation was from −0.083 to 0.771, with an average of 0.358 ± 0.321. With travel time, correction changed slightly, i.e., 10 m observation 0.075 ± 0.208 and −0.073 ± 0.255 and 60 m observation 0.018 ± 0.223 and −0.032 ± 0.248 for wind from the reference plant to the airport and vice versa, respectively. Stability classification correlation coefficients between the reference plant and St. Louis Lambert International Airport ranged from −0.083 to 0.763 for raw with an average of 0.314 ± 0.295. With travel time, correction changed slightly, i.e., 10 m observation −0.003 ± 0.307 and −0.030 ± 0.277 and 60 m observation −0.030 ± 0.193 and −0.005 ± 0.215 for wind from the reference plant to the airport and vice versa, respectively. It is important to observe that mathematically. stability class correlation coefficients were not great, but in most cases the predicted and observed values were only off by one stability class. Similar correlations were calculated for wind direction and velocities. Our result, when applied to a proposed nuclear power station, can significantly reduce time and effort to prepare a robust environmental protection plan required for license application.

Keywords: Nuclear Regulatory Commission Compliance; meteorological data; atmospheric stability; data mining; air quality; atmospheric dispersion; Nuclear Reactor Site Licensing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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