Ambient fine particulate matter (PM_2.5) pollution is a major health risk. Networks of low-cost sensors (LCS) are increasingly being used to understand local-scale air pollution variation. However, measurements from LCS have uncertainties that can act as a potential barrier to effective decision making. LCS data thus need adequate calibration to obtain good quality PM_2.5 estimates. In order to develop calibration factors, one or more LCS are typically co-located with reference monitors for short or long periods of time. A calibration model is then developed that characterizes the relationships between the raw output of the LCS and measurements from the reference monitors. This calibration model is then typically transferred from the co-located sensors to other sensors in the network. Calibration models tend to be evaluated based on their performance only at co-location sites. It is often implicitly assumed that the conditions at the relatively sparse co-location sites are representative of the LCS network overall and that the calibration model developed is not overfitted to the co-location sites. Little work has explicitly evaluated how transferable calibration models developed at co-location sites are to the rest of an LCS network, even after appropriate cross-validation. Further, few studies have evaluated the sensitivity of key LCS use cases, such as hotspot detection, to the calibration model applied. Finally, there has been a dearth of research on how the duration of co-location (short-term or long-term) can impact these results. This paper attempts to fill these gaps using data from a dense network of LCS monitors in Denver deployed through the city's “Love My Air” program. It offers a series of transferability metrics for calibration models that can be used in other LCS networks and some suggestions as to which calibration model would be most useful for achieving different end goals.

Background: Fine particulate matter (PM_2.5) has been consistently linked to cardiovascular disease (CVD). Although studies have reported modification by income, to our knowledge, no study to date has examined this relationship among adults in Medicaid, which provides health coverage to low-income and/or disabled Americans. Methods: We estimated the association between short-term PM_2.5 exposure (average of PM_2.5 on the day of hospitalization and the preceding day) and CVD admissions rates among adult Medicaid enrollees in the continental United States (2000–2012) using a time-stratified case–crossover design. We repeated this analysis at PM_2.5 concentrations below the World Health Organization daily guideline of 25 μg/m³. We compared the PM_2.5–CVD association in the Medicaid ≥65 years old versus non-Medicaid-eligible Medicare enrollees (≥65 years old). Results: Using information on 3,666,657 CVD hospitalizations among Medicaid adults, we observed a 0.9% (95% CI = 0.6%, 1.1%) increase in CVD admission rates per 10 μg/m³ PM_2.5 increase. The association was stronger at low PM_2.5 levels (1.3%; 95% CI = 0.9%, 1.6%). Among Medicaid enrollees ≥65 years old, the association was 0.9% (95% CI = 0.6%, 1.3%) vs. 0.8% (95% CI = 0.6%, 0.9%) among non-Medicaid-eligible Medicare enrollees ≥65 years old. Conclusion: We found robust evidence of an association between short-term PM_2.5 and CVD hospitalizations among the vulnerable subpopulation of adult Medicaid enrollees. Importantly, this association persisted even at PM_2.5 levels below the current national standards.

PurpleAir sensors are widely used to measure PM_2.5 levels in cities around the world. However, little is known about the change in sensor performance over time. This paper fills this gap.