Download Temperature Effects on Radon Concentrations

Environmental
Effects on Radon
Concentrations
Beth Hall1, Leslie Stoecker1,
Paul Francisco2, Stacy Gloss2, Yigang Sun2
1Midwestern
Regional Climate Center, University of Illinois
2Indoor Climate Research & Training, University of Illinois
Background
• Indoor Radon (Rn) testing (practices, motivation)
• “Conventional wisdom” of seasonal trends
• Greater in winter
• Inverse relationship - outdoor temperatures, Rn
• Past research indicated strong in-ground Rn correlations
to
• Precipitation
• Soil moisture
• Air pressure
Motivation
2
4
6
8
10
2013, 2014 study results –
Contradiction to “conventional wisdom”?
Time
Hourly
1-week
4-day
2-week
Motivation
Combining different case periods –
Contradiction to “conventional wisdom”?
30
Radon Concentration (pCi/L)
25
20
15
10
5
0
1
31
61
91
121
151
181
211
241
271
Day of the Year
Indoor Rn from 3 different studies (2013, 2014, 2016) in
Champaign County
Possibly Seasonal Cycle?
301
331
Study Questions
• Is indoor Rn concentrations seasonally different?
• Does data support seasonal “conventional wisdom”?
• What atmospheric and/or soil parameters influence Rn
concentrations?
• What are climate trends in those parameters?
• Could findings be used to improve:
• Contextual understanding of indoor Rn?
• Timing of indoor Rn testing?
• Future studies?
Methodology
• Analyze various atmospheric, soil parameters to indoor
Rn concentrations
• More sites
• Some overlapping sites
• Examine coincident and lag correlations
• Examine proxy parameters if possible
Indoor Rn Data
• RADStar R5300 CRM
• Living space and foundation (crawl space / basement)
• Hourly sampling
• 4 different study periods across Champaign County
Indoor Rn Data
• Winter 2013/2014 – 5 sites
• Oct ‘13 – Jan ‘14
• Spring 2014 – 5 new sites
• Apr ‘14 – July ‘14
• Summer 2014 – 5 new sites
• Aug ‘14 – Nov ‘14
• Spring/Summer 2016 – 15 sites
•
•
•
•
Apr ‘16 – Aug ‘16
2 from Winter
1 from Spring
1 from Summer
Atmospheric / Soil Data
• 4 data sources: Gridded and point datasets
• Variable list:
•
•
•
•
•
•
Temperature
Air pressure
Precip amts
Wind speed, dir
Specific Humidity
Solar Radiation
•
Soil Moisture
• 0-10 cm
• 0-100 cm
• 0-200 cm
• 10-40cm
• 40-100cm
•
Soil Temperature
• 0-10cm
• 10-40cm
• 40-100cm
• 100-200cm
Results – Part 1
• Inconsistent correlations between sites
• Strongest correlations (r) with NLDAS data:
• SoilM (depths); give ranges of r2; show maps <make locations
larger circles to avoid specific locations>
• SoilT (depths)
• Neither precipitation nor pressure showed strong
correlations – contradicting past research in-ground
Results – Part 1
Correlations (r) – Living space over Basement
Results – Part 1
Correlations (r) – Living space over Crawl Space
Results – Part 1
Correlations (r) – Basement
Results – Part 1
Correlations (r) – Crawl Space
Results – Part 1
• Inconsistent correlations between sites
• Strongest correlations (r) with “in ground” parameters:
• SoilM (varying depths)
• SoilT (varying depths)
• Weakest correlations (r) with “above ground” parameters:
•
•
•
•
Winds
Solar radiation
Precipitation
Air pressure
• Some “above ground” good correlations:
• Air temperature
• Specific humidity
Results – Part 1
Soil Moisture 100-200cm vs Living Space Over Basement
Radon Correlations
Results – Part 2
• Challenges with soilM:
• Extremely variable across space, time (geology)
• Not well modeled
• Sparsely observed
• Proxy for soilM?
• Should be correlated to precipitation and evaporation
• How are greater depths affected?
• Keetch-Byrum Drought Index (KBDI)
• Simple, daily drought index
• Max temperature, precipitation
Results – Part 2
Living Space over Crawl Space
Results – Part 3
• Possible theories:
• Underlying geology
• Structural aspects of homes
• Age of homes
Results – Part 4
Seasonal climatology
of soil moisture
Seasonal
climatology of
KBDI
Results – Part 4
“Possible” seasonal
trends in Rn
concentrations?
Mar ‘16 – Aug ‘16
Oct ‘13 – Jan ‘14
May ‘14 – Jul ‘14
Aug ‘14 – Dec ‘14
Conclusions
• Indoor Rn highly variable in space and time
• “In-ground” variables (e.g., soilM, soilT) more often
have stronger correlations than atmospheric
• Many factors influence indoor Rn
• Needs:
• Test both inside and near outside home for assessing
structural impact (house-shadow effect?)
• Track windows open/closed
• Understand spikes in Rn
• Full annual cycle at sites
• Examine temperature differences (indoorT– outdoorT)
Acknowledgements
Illinois Emergency Management Agency
Patrick Daniels
Thank you!