International Journal of Biometeorology

, Volume 62, Issue 8, pp 1521–1534 | Cite as

Temporal and spatial variation in personal ambient temperatures for outdoor working populations in the southeastern USA

  • Margaret M. SuggEmail author
  • Christopher M. Fuhrmann
  • Jennifer D. Runkle
Original Paper


Excessive ambient temperature exposure can result in significant morbidity and mortality, especially among vulnerable occupational groups like outdoor workers. Average temperatures in the USA are projected to increase in frequency and intensity, placing future worker populations at greater risk for unhealthy levels of exposure. Unlike previous research focused on aggregate-level temperature exposures from in situ weather station data, this study will measure location-based personal ambient temperatures (PAT) at the individual-level by piloting the use of wearable sensor technology. A total of 66 outdoor workers in three geographically and climatologically diverse regions in the Southeast USA were continuously sampled during the workday for a 1-week period throughout July 11 to August 8 2016. Results indicate significant worker variation in temperature exposure within and between study locations; with PAT characterized by less pronounced variability as workers moved between indoor and outdoor environments. Developed land covers, a factor often associated with higher temperatures, were poorly correlated with PAT. Future analysis should focus on a worker’s physiological response to PAT and mapping of spatial patterns of PAT for a larger worker population to produce innovative and targeted heat prevention programs.



We gratefully acknowledge support for this work by the American Association of Geographers (AAG), the Oak Ridge Associated Universities (ORAU) Travel Grant, and the Appalachian State University’s University Research Council Grant. The authors also thank Scott Stevens at the North Carolina Institute for Climate Studies for his expertise in data management and data merger and our ground workers partners at the NCSU, ASU, and MSU. This work would not be possible without their support. We also wish to thank the anonymous reviewers for their constructive feedback that greatly improved the manuscript.

Compliance with ethical standards

This project received human subjects approval from the institutional review board (IRB) at ASU (IRB no. 16-0303), NCSU (IRB no. 7986), and MSU (IRB no. 16-254).

Supplementary material

484_2018_1553_MOESM1_ESM.docx (7.2 mb)
ESM 1 (DOCX 7.19 mb)


  1. Adam-Poupart A, Smargiassi A, Busque MA, Duguay P, Fournier M, Zayed J, Labrèche F (2014) Summer outdoor temperature and occupational heat-related illnesses in Quebec (Canada). Environ Res 134:339–344CrossRefGoogle Scholar
  2. Anderson BG, Bell ML (2009) Weather-related mortality. Epidemiology 20(2):205–213CrossRefGoogle Scholar
  3. Arguez A, Durre I, Applequist S, Vose RS, Squires MF, Yin X, Heim RR Jr, Owen TW (2012) NOAA’s 1981–2010 U.S. climate normals. Bull Am Meteorol Soc 93(11):1687–1697CrossRefGoogle Scholar
  4. Basu R (2009) High ambient temperature and mortality: a review of epidemiologic studies from 2001 to 2008. Environ Health 8(1):40CrossRefGoogle Scholar
  5. Basu R, Samet J (2002) An exposure assessment study of ambient heat exposure in an elderly population in Baltimore, Maryland. Environ Health Perspect 110(12):1219–1224CrossRefGoogle Scholar
  6. Bernhard MC, Kent ST, Sloan ME, Evans MB, McClure LA, Gohlke JM (2015) Measuring personal heat exposure in an urban and rural environment. Environ Res 137:410–418 Available at: CrossRefGoogle Scholar
  7. Birenboim A, Shoval N (2016) Mobility research in the age of the smartphone. Ann Am Assoc Geogr 106(2):283–291Google Scholar
  8. Bonauto D, Anderson R, Rauser E, Burke B (2007) Occupational heat illness in Washington State, 1995–2005. Am J Prev Med 50:940–950Google Scholar
  9. Center for Disease Control (CDC) (2010) Proportion of workers who were work-injured and payment by workers’ compensation systems—10 states, 2007Google Scholar
  10. Chan WR, Joh J, Sherman MH (2013) Analysis of air leakage measurements of US houses. Energ Buildings 66:616–625Google Scholar
  11. Collins P, Al-Nakeeb Y, Nevill A, Lyons M (2012) The impact of the built environment on young people’s physical activity patterns: a suburban-rural comparison using GPS. Int J Env Res Pub He 9(9):3030–3050CrossRefGoogle Scholar
  12. Coombes E, van Sluijs E, Jones A (2013) Is environmental setting associated with the intensity and duration of children’s physical activity? Findings from the SPEEDY GPS study. Health and Place 20:62–65CrossRefGoogle Scholar
  13. Dumas J, Jagger M, Kintzinger K (2016) Where to wear iButtons: individual level temperature and humidity observations for public health surveillance (96th American Meteorological Society Annual Meeting). In American Meteorological Society. New Orleans. Available at: [Accessed September 14, 2017]
  14. Embedded DataSystems (2017) DS1921G-F5#—Thermochron iButton—40 °C thru 85 °C. Available at: [Accessed September 14, 2017]
  15. Fouillet A, Rey G, Laurent F, Pavillon G, Bellec S, Guihenneuc-Jouyaux C, Clavel J, Jougla E, Hémon D (2006) Excess mortality related to the August 2003 heat wave in France. Int Arch Occ Env Hea 80(1):16–24CrossRefGoogle Scholar
  16. Fry JA et al (2011) Completion of the 2006 national land cover database for the conterminous United States. Photogramm Eng Remote Sens 77(9):58–864Google Scholar
  17. GARMIN (2017) vívoactive® HR | Garmin. Available at: [Accessed September 14, 2017]
  18. Glass K, Tait PW, Hanna EG, Dear K (2015) Estimating risks of heat strain by age and sex: a population-level simulation model. Int J Env Res Pub He 12(5):5241–5255CrossRefGoogle Scholar
  19. Gubernot DM, Anderson GB, Hunting KL (2013) The epidemiology of occupational heat-related morbidity and mortality in the United States. Int J Biometeorol 58(8):1779–1788CrossRefGoogle Scholar
  20. Guo Y, Barnett AG, Tong S (2013) Spatiotemporal model or time series model for assessing city-wide temperature effects on mortality? Environ Res 120:55–62CrossRefGoogle Scholar
  21. Harlan SL, Chowell G, Yang S, Petitti DB, Morales BEJ, Ruddell BL, Ruddell DM (2014) Heat-related deaths in hot cities: estimates of human tolerance to high temperature thresholds. Int J Environ Res Public Health 11(3):3304–3326Google Scholar
  22. Hartz DA, Prashad L, Hedquist BC, Golden J, Brazel AJ (2006) Linking satellite images and hand-held infrared thermography to observed neighborhood climate conditions. Remote Sens Environ 104(2):190–200CrossRefGoogle Scholar
  23. Hattis D, Ogneva-Himmelberger Y, Ratick S (2012) The spatial variability of heat-related mortality in Massachusetts. Appl Geogr 33:45–52CrossRefGoogle Scholar
  24. HondulaD (2017), personal communicationGoogle Scholar
  25. Huang G, Zhou W, Cadenasso ML (2011) Is everyone hot in the city? Spatial pattern of land surface temperatures, land cover and neighborhood socioeconomic characteristics in Baltimore, MD. J Environ Manag 92(7):1753–1759CrossRefGoogle Scholar
  26. Imhoff ML, Zhang P, Wolfe RE, Bounoua L (2010) Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sens Environ 114(3):504–513CrossRefGoogle Scholar
  27. Isaaks E, Isaaks M, Srivastava M (1989) Applied geostatistics, New York. Available at: [Accessed September 14, 2017]
  28. Jenerette GD, Harlan SL, Buyantuev A, Stefanov WL, Declet-Barreto J, Ruddell BL, Myint SW, Kaplan S, Li X (2016) Micro-scale urban surface temperatures are related to land-cover features and residential heat related health impacts in Phoenix, AZ USA. Landsc Ecol 31(4):745–760CrossRefGoogle Scholar
  29. Kerr J, Duncan S, Schipperjin J (2011) Using global positioning systems in health research: a practical approach to data collection and processing. Am J Prev Med 41(5):532–540 Available at: CrossRefGoogle Scholar
  30. Klepeis NE et al (2001) The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants. J Expo Anal Environ Epidemiol 11(3):231–252CrossRefGoogle Scholar
  31. Kuras ER et al (2017) Opportunities and challenges for personal heat exposure research. Environ Health Perspect 125(8):1–9CrossRefGoogle Scholar
  32. Kuras ER, Hondula DM, Brown-Saracino J (2015) Heterogeneity in individually experienced temperatures (IETs) within an urban neighborhood: insights from a new approach to measuring heat exposure. Int J Biometeorol 59(10):1363–1372CrossRefGoogle Scholar
  33. Kwan MP (2012) The uncertain geographic context problem. Ann Am Assoc Geogr 102(5):958–968CrossRefGoogle Scholar
  34. Lee M, Shi L, Zanobetti A, Schwartz JD (2016) Study on the association between ambient temperature and mortality using spatially resolved exposure data. Environ Res 151:610–617CrossRefGoogle Scholar
  35. Liang WM, Liu WP, Chou SY, Kuo HW (2008) Ambient temperature and emergency room admissions for acute coronary syndrome in Taiwan. Int J Biometeorol 52(3):223–229CrossRefGoogle Scholar
  36. Lioy PJ, Georgopoulos PG (2011) New Jersey: a case study of the reduction in urban and suburban air pollution from the 1950s to 2010. Environ Health Perspect 119(10):1351–1355CrossRefGoogle Scholar
  37. McCrorie PR, Fenton C, Ellaway A (2014) Combining GPS, GIS, and accelerometry to explore the physical activity and environment relationship in children and young people—a review. Int J Behav Nutr Phy 11(93):1–14Google Scholar
  38. Mirabelli MC, Quandt SA, Crain R, Grzywacz JG, Robinson EN, Vallejos QM, Arcury TA (2010) Symptoms of heat illness among Latino farm workers in North Carolina. Am J Prev Med 39(5):468–471CrossRefGoogle Scholar
  39. Park YM, Kwan MP (2017) Individual exposure estimates may be erroneous when spatiotemporal variability of air pollution and human mobility are ignored. Health & place 43:85–94CrossRefGoogle Scholar
  40. Quinn A, Tamerius JD, Perzanowski M, Jacobson JS, Goldstein I, Acosta L, Shaman J (2014) Predicting indoor heat exposure risk during extreme heat events. Sci Total Environ 490:686–693 Available at: CrossRefGoogle Scholar
  41. R Development Core Team (2008) R: the R project for statistical computing. R Foundation for Statistical Computing. Available at: [Accessed September 14, 2017]
  42. Reis S, Seto E, Northcross A, Quinn NWT, Convertino M, Jones RL, Maier HR, Schlink U, Steinle S, Vieno M, Wimberly MC (2015) Integrating modelling and smart sensors for environmental and human health. Environ Model Softw 74:238–246CrossRefGoogle Scholar
  43. Schlink U, Kindler A, Großmann K, Schwarz N, Franck U (2014) The temperature recorded by simulated mobile receptors is an indicator for the thermal exposure of the urban inhabitants. Ecol Indic 36:607–616 Available at: CrossRefGoogle Scholar
  44. Semenza JC, McCullough J, Flanders WD, McGeehin M, Lumpkin JR (1999) Excess hospital admissions during the July 1995 heat wave in Chicago. Am J Prev Med 16(4):269–277CrossRefGoogle Scholar
  45. ThompsonLC, SuggMM, RunkleJR, FuhrmannCM (2016) Reporting back environmental exposures: a case study of environmental health literacy and individual experience temperature among ground maintenance worker. In 2016 Annual Meeting Information and Registration | Southeastern Division of the American Association of Geographers. Available at: [Accessed September 14, 2017]
  46. Tomlinson CJ et al (2011) Including the urban heat island in spatial heat health risk assessment strategies: a case study for Birmingham, UK. Int J Health Geogr 10(42):1–14 Available at: Google Scholar
  47. Tukey J (2016) Comparing individual means in the analysis of variance. Biometrics 5(2):99–114CrossRefGoogle Scholar
  48. Uejio CK, Tamerius JD, Vredenburg J, Asaeda G, Isaacs DA, Braun J, Quinn A, Freese JP (2016) Summer indoor heat exposure and respiratory and cardiovascular distress calls in New York City, NY, U.S. Indoor Air 26(4):594–604CrossRefGoogle Scholar
  49. Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86(3):370–384CrossRefGoogle Scholar
  50. Whitman S, Good G, Donoghue ER, Benbow N, Shou W, Mou S (1997) Mortality in Chicago attributed to the July 1995 heat wave. Am J Public Health 87(9):1515–1518CrossRefGoogle Scholar
  51. Wickham H (2009) ggplot2: elegant graphics for data analysis. Springer-Verlag. Available at: [Accessed September 14, 2017]
  52. Wiehe SE, Carroll AE, Liu GC, Haberkorn KL, Hoch SC, Wilson JS, Fortenberry J (2008) Using GPS-enabled cell phones to track the travel patterns of adolescents. Int J Health Geogr 7(1):22CrossRefGoogle Scholar
  53. Xiang J, Bi P, Pisaniello D, Hansen A, Sullivan T et al (2014) Association between high temperature and work-related injuries in Adelaide, South Australia, 2001–2010. Occup Environ Med 71(4):246–252CrossRefGoogle Scholar
  54. Yuan F, Bauer ME (2007) Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sens Environ 106(3):375–386CrossRefGoogle Scholar
  55. Zhang Y, Yan C, Kan H, Cao J, Peng L, Xu J, Wang W (2014) Effect of ambient temperature on emergency department visits in Shanghai, China: a time series study. Environ Health 13:1–19. Available at. CrossRefGoogle Scholar

Copyright information

© ISB 2018

Authors and Affiliations

  • Margaret M. Sugg
    • 1
    Email author
  • Christopher M. Fuhrmann
    • 2
  • Jennifer D. Runkle
    • 3
  1. 1.Department of Geography and PlanningAppalachian State UniversityBooneUSA
  2. 2.Department of GeosciencesMississippi State UniversityMississippi StateUSA
  3. 3.North Carolina Institute for Climate StudiesNorth Carolina State UniversityAshevilleUSA

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