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Time-activity Patterns and PM2.5 Exposure of the Elderly in Urban and Rural Areas
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 Title & Authors
Time-activity Patterns and PM2.5 Exposure of the Elderly in Urban and Rural Areas
Lim, Chaeyun; Guak, Sooyoung; Lee, Kiyoung; Hong, Yun-Chul;
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Objectives: Personal exposure to air pollution is affected by contact over time and by location. The purpose of this study was to determine the relationship between personal exposure to and the time-activity patterns of the elderly in urban and rural areas. Methods: A total of 44 elderly participants were recruited for a 24-hour personal exposure measurement. Twenty-four were from Seoul (urban area) and 20 were from Asan (rural area). Energy expenditure and spatiotemporal positioning were monitored through measurement. Spearman correlation analysis was conducted to determine the relationship between and time-activity pattern. Results: Daily average personal exposures were in Seoul and in Asan. Although outdoor exposure was higher in Seoul than in Asan, residential indoor exposure was higher in Asan than in Seoul. Higher personal exposure in Asan could be explained by longer time in residential indoor environments and higher indoor concentrations. Seoul elderly had higher energy expenditure, which may be due to the use of mass transportation. Conclusion: Personal exposure to was higher among Asan elderly than Seoul elderly because of high residential indoor concentrations and longer residential time. Lack of energy spent and higher personal exposure to might have led to higher risk among the Asan elderly.
Air pollution;personal exposure;;regional variation;time-activity patterns;
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Sarnat SE, Coull BA, Schwartz J, Gold DR, Suh HH. Factors affecting the association between ambient concentrations and personal exposures to particles and gases. Environmental Health Perspectives. 2006; 114(5): 649-54.

Lee K, Sohn H, Putti K. In-vehicle exposures to particulate matter and black carbon. Journal of the Air & Waste Management Association. 2010; 60(2): 130-136. crossref(new window)

Dons E, Panis LI, Van Poppel M, Theunis J, Willems H, Torfs R, et al. Impact of time-activity patterns on personal exposure to black carbon. Atmospheric Environment. 2011; 45(21): 3594-3602. crossref(new window)

Buonanno G, Stabile L, Morawska L. Personal exposure to ultrafine particles: the influence of time-activity patterns. Science of the Total Environment. 2014; 468-469: 903-907. crossref(new window)

Lim S, Kim J, Kim T, Lee K, Yang W, Jun S, et al. Personal exposures to $PM_{2.5}$ and their relationships with microenvironmental concentrations. Atmospheric Environment. 2012; 47: 407-412. crossref(new window)

National Institute of Environmental Research (NIER). Research for personal exposure assessment by time activity patterns on a nation. Korea National Institute of Environmental Research. 2010.

Querol X, Alastuey A, Moreno T, Viana M, Castillo S, Pey J, et al. Spatial and temporal variations in airborne particulate matter ($PM_{10}$ and $PM_{2.5}$) across Spain 1999-2005. Atmospheric Environment. 2008; 42(17): 3964-3979. crossref(new window)

Dionisio KL, Arku RE, Hughes AF, Vallarino J, Carmichael H, Spengler JD, et al. Air pollution in Accra neighborhoods: spatial, socioeconomic, and temporal patterns. Environmental Science & Technology. 2010; 44(7): 2270-2276. crossref(new window)

Baxter LK, Burke J, Lunden M, Turpin BJ, Rich DQ, Thevenet-Morrison K, et al. Influence of human activity patterns, particle composition, and residential air exchange rates on modeled distributions of $PM_{2.5}$ exposure compared with central-site monitoring data. Journal of Exposure Science and Environmental Epidemiology. 2013; 23(3): 241-247. crossref(new window)

Qu W, Arimoto R, Zhang X, Zhao C, Wang Y, Sheng L, et al. Spatial distribution and interannual variation of surface PM 10 concentrations over eighty-six Chinese cities. Atmospheric Chemistry and Physics. 2010; 10(12): 5641-5662. crossref(new window)

Pinto JP, Lefohn AS, Shadwick DS. Spatial variability of PM2. 5 in urban areas in the United States. Journal of the Air & Waste Management Association. 2004; 54(4): 440-449. crossref(new window)

Brasche S, Bischof W. Daily time spent indoors in German homes-baseline data for the assessment of indoor exposure of German occupants. International Journal of Hygiene and Environmental Health. 2005; 208(4): 247-253. crossref(new window)

Schweizer C, Edwards RD, Bayer-Oglesby L, Gauderman WJ, Ilacqua V, Jantunen MJ, et al. Indoor time-microenvironment-activity patterns in seven regions of Europe. Journal of Exposure Science and Environmental Epidemiology. 2007; 17(2): 170-181. crossref(new window)

Diez Roux AV, Auchincloss AH, Dvonch JT, Brown PL, Barr RG, Daviglus ML, et al. Associations between recent exposure to ambient fine particulate matter and blood pressure in the Multi-Ethnic Study of Atherosclerosis. Environmental Health Perspectives Online. 2008.

Kim T, Lee K, Yang W, Do Yu S. A new analytical method for the classification of time-location data obtained from the global positioning system (GPS). Journal of Environmental Monitoring. 2012; 14(8): 2270-2274. crossref(new window)

Adgate JL, Ramachandran G, Pratt G, Waller L, Sexton K. Spatial and temporal variability in outdoor, indoor, and personal $PM_{2.5}$ exposure. Atmospheric Environment. 2002; 36(20): 3255-3265. crossref(new window)

Olsen DA, Burke JM. Distributions of PM2.5 source strengths for cooking from the research triangle park particulate matter panel study. Environmental Science & Technology. 2006; 40: 163-169. crossref(new window)

Wallace L, Williams R, Rea A, Croghan C. Continuous weeklong measurements of personal exposures and indoor concentrations of fine particles for 37 health-impaired North Carolina residents for up to four seasons. Atmospheric Environment. 2006; 40: 7659-7660. crossref(new window)