Mobile air quality monitoring of particulate matter at the neighborhood scale: challenges and opportunities through the incorporation of geospatial technologies

Although more than 44 million people live in an area with unhealthy levels of particulate matter (PM) pollution, according to the American Lung Association (ALA), “the burden of air pollution is not evenly shared” (2014). Higher exposure is experienced by low-income individuals, certain racial and ethnic groups, less-educated individuals and those living closer to major sources such as roadways and emission sites. As a compounding factor the overburdened populations may also lack access to resources such as healthcare and suitable employment. Furthermore, the complexity of the health burden experienced by those living in such neighborhoods may exaggerate the exposures and impacts from PM exposure (ALA, 2014). Currently the monitoring of airborne PM occurs at over 500 stations across the United States (EPA, 2013). This network provides a sufficient understanding of a regional level of PM but is not dense enough to capture local patterns. Some environmental professionals and researchers have turned to mobile platforms as an alternative to the costly option of installing more stationary air monitors. This allows for a finer-scale of data collection and analysis which may identify pockets of PM pollution missed by a regional monitor. Furthermore the mobile platform facilitates the identification of specific areas within a neighborhood with higher levels of PM which can be compared to other fine scale data as health outcomes, housing conditions or even crime. The combination of all these data layers can help in understanding health disparities, and then identifying interventions for those neighborhoods most at risk.

The purpose of this research is to investigate the variability in PM levels at the neighborhood scale in a post-industrial city in northeast Ohio using a mobile air quality monitoring device in tandem with geospatial video, and then demonstrate how such data can enhance the understanding of health and quality of life within a neighborhood. This paper describes the process used to measure PM in two lower income neighborhoods in Akron, Ohio, using the Dylos DC1700 air monitor. Spatial video provides a visual snapshot of the environment at the time of data collection which can be coded and mapped in a GIS and compared to air quality readings of the same location, also mapped in a GIS. This toolset not only enhances understanding of the immediate conditions affecting the residents of a neighborhood, but when collected repeatedly over the duration of months, can identify changes and patterns over time. Furthermore, the layering of data related to health and quality of life within a GIS, such as transportation, environmental hazards, housing conditions, walkability, mortality data, crime, and perceptions of the neighborhood, facilitates the ability to draw critical connections between environment, health outcomes and behavior.