While it is generally known that various types of pollution can cause detrimental effects to human health, the relationship of different pollutant types, such as ozone, is less clear for given ailments. The effects of some pollutants are less clear particularly over space and time. GIS has been used to assist in showing strong or weak links for given health conditions to different types of pollutants.
One study, which investigated fine particulate matter (PM2.5), sulfur dioxide (SO2), and ozone (O3), demonstrated that high concentrations of sulfur dioxide and fine particle matter have much greater effect than ozone. Asthma concentrations and case admission over time show that as sulfur dioxide and fine particulate matter changed more positive correlations in asthma hospital visits were shown. Kriging was used to estimate concentration of pollutants from different monitoring sites. In a study on the state of California, O3 and NO2 was positively correlated with ischemic heart disease mortality. In fact, NO2 and fine particle matter was associated with nearly every disease that caused mortality. Spatial Cox regression and survival models were utilized in this study. While the New York study was able to look at data at the county level, the California study utilized zip codes to demonstrate relationships.
Technologies now can now produce much finer spatial resolution over time to understand the relationship of health and pollution. A study in Palermo, Italy utilized wireless body area networks (WBANs) that integrate body sensor data, including monitoring of vitals, and pollution monitors placed throughout the city provide real time data via mobile devices to show how a person’s heartbeat and other vitals change as he/she enters zones where pollution levels for given particles are elevated. This allows real time monitoring, while also allowing the user to select different types of potential pollutants to monitor. Other studies use mobile phone data to show how people’s travel patterns throughout a city relate to pollution health effects. This has allowed more accurate ways to assess the association of where people are located in a given time with patterns of pollution for different particles. In other words, with mobile devices, spatial understanding of health and pollution are now become much more fine-scale than previous studies.
 For more on the relationship between asthma and certain pollutants, see: Gorai, Amit, Francis Tuluri, and Paul Tchounwou. 2014. “A GIS Based Approach for Assessing the Association between Air Pollution and Asthma in New York State, USA.” International Journal of Environmental Research and Public Health 11 (5): 4845–69.
 For more information on California death rates and air pollution. See: Jerrett, Michael, Richard T. Burnett, Bernardo S. Beckerman, Michelle C. Turner, Daniel Krewski, George Thurston, Randall V. Martin, et al. 2013. “Spatial Analysis of Air Pollution and Mortality in California.” American Journal of Respiratory and Critical Care Medicine 188 (5): 593–99.
 For more on WBANs, see: Filipe, Luis, Florentino Fdez-Riverola, Nuno Costa, and António Pereira. 2015. “Wireless Body Area Networks for Healthcare Applications: Protocol Stack Review.” International Journal of Distributed Sensor Networks 2015: 1–23. doi:10.1155/2015/213705.
 For more on time-based monitoring of movement and health monitoring, see: Dewulf, Bart, Tijs Neutens, Wouter Lefebvre, Gerdy Seynaeve, Charlotte Vanpoucke, Carolien Beckx, and Nico Van de Weghe. 2016. “Dynamic Assessment of Exposure to Air Pollution Using Mobile Phone Data.” International Journal of Health Geographics 15 (1).