Personal real-time air pollution exposure assessment methods promoted by information technological advances

Examining Personal Air Pollution Exposure, Intake, and Health Danger Zone Using Time Geography and 3D Geovisualization

“…It is impractical to expect the general public to carry both a GPS and a pollutant sensor while conducting daily activities. As pointed out by Fang and Lu [9], the research on real-time assessment of personal air pollution exposure and health effects are limited by the availability of sensors that can genuinely integrate location-detection and air pollutant sampling technologies. This technological limitation has been one major restriction for most of on-going or not-fully-implemented projects seeking to link air sensor to GPS (such as those reviewed in [19]).…”

“…As Kwan [8] pointed out, there is a clear rising need for the assessment of health effects to gear away from deriving environmental effects on the individual level from an aggregated neighborhood level and to move towards assessing the health effects on a personal level. There has emerged in the past few years a number of studies and research projects that piloted the exploration of assessing individual level health effects of air pollution, although most of them are limited in both space and time scales partially due to the technical challenges related to sensors and data collection [9]. Second, AQI maps are 2D maps that reflect the spatial variation of air quality and its health effects at one specific time point or as an average over a period of time.…”

The importance of choosing precipitation datasets

“…In addition to being a community effort, many organizations utilize the data that are generated from the CoCoRaHS network including, but not limited to, the National Oceanic and Atmospheric Administration and the National Hydrologic Warning Council. Furthermore, several peer‐reviewed publications utilize the data ranging from hydrologic analyses (e.g., Bunkers, Smith, Driscoll, & Hoogestraat, ; Grant et al, ; Smith, Smith, Baeck, & Miller, ), atmospheric aerosol analyses (e.g., Fang & Yongmei, ; Kelleners & Verma, ; Kelly et al, ), and verification of precipitation estimations from radar (e.g., Moon et al, ; Kluver et al, ; Martinaitis et al, ; Smith et al, ; Wolfe & Snider, ; Zhang, Qi, Langston, Kaney, & Howard, ). Therefore, the applications of the data generated from the CoCoRaHS network are very broad and impactful in several different fields of science.…”

“…Furthermore, several peer-reviewed publications utilize the data ranging from hydrologic analyses (e.g., Bunkers, Smith, Driscoll, & Hoogestraat, 2015;Grant et al, 2013;Smith, Smith, Baeck, & Miller, 2015), atmospheric aerosol analyses (e.g., Fang & Yongmei, 2012;Kelleners & Verma, 2012;Kelly et al, 2012), and verification of precipitation estimations from radar (e.g., Moon et al, 2009;Kluver et al, 2016;Martinaitis et al, 2014;Smith et al, 2015;Wolfe & Snider, 2012;Zhang, Qi, Langston, Kaney, & Howard, 2014). Therefore, the applications of the data generated from the CoCoRaHS network are very broad and impactful in several different fields of science.…”

Spatial Turn in Health Research

“…(13) could examine the complex interactions between human and animal movements and the spread of malaria in Kenya. From Japan to California, volunteers with GPS-enabled real-time air-quality monitoring systems are assessing exposures to air pollution and radiation at spatial resolution levels and data densities not previously feasible (14). …”