Abstract
Air quality monitoring has traditionally been conducted using sparsely distributed, expensive reference monitors. To understand variations in PM2.5 on a finely resolved spatiotemporal scale a dense network of over 40 low-cost monitors was deployed throughout and around Pittsburgh, Pennsylvania, USA. Monitor locations covered a wide range of site types with varying traffic and restaurant density, varying influences from local sources, and varying socioeconomic (environmental justice, EJ) characteristics. Variability between and within site groupings was observed. Concentrations were higher near the source-influenced sites than the Urban or Suburban Residential sites. Gaseous pollutants (NO2 and SO2) were used to differentiate between traffic (higher NO2 concentrations) and industrial (higher SO2 concentrations) sources of PM2.5. Statistical analysis proved these differences to be significant (coefficient of divergence > 0.2). The highest mean PM2.5 concentrations were measured downwind (east) of the two industrial facilities while background level PM2.5 concentrations were measured at similar distances upwind (west) of the point sources. Socioeconomic factors, including the fraction of non-white population and fraction of population living under the poverty line, were not correlated with increases in PM2.5 or NO2 concentration. The analysis conducted here highlights differences in PM2.5 concentration within site groupings that have similar land use thus demonstrating the utility of a dense sensor network. Our network captures temporospatial pollutant patterns that sparse regulatory networks cannot.
Highlights
Poor air quality has deleterious health effects
This suggests that about 40% of the variation in PM2.5 at sites 41 and 42 (East of the Coke Plant sites), which is significantly higher than any of the other source impacted sites. These two sites saw significantly higher PM2.5 than the Urban and Suburban Residential sites. This suggests that the elevated PM2.5 concentrations at sites East of the Coke plant are more heavily influenced by emissions from the Coke plant when compared to the other source impacted sites in the area, and even among sites east of the Coke plant, there can be differences that are revealed by a high-density sensor network
PM2.5 and percent of the population belonging to a minority group is low (0.01). This means that the relationship between mean PM2.5 concentration and socioeconomic (EJ) variables cannot be described by a monotonic function; PM2.5 concentration does not increase with increasing Environmental justice (EJ) indicators
Summary
Poor air quality has deleterious health effects. Particulate matter with a diameter of less than. We utilize our dense network of air quality monitors to investigate whether the EJ areas in Pittsburgh do have lower air quality in comparison to non-EJ areas This definition of environmental injustice fits most closely with disparate exposure inequality. We use the RAMP (Real-time Affordable Multi-Pollutant sensor package) [22,23,24], a lower-cost monitor consisting of electrochemical gas sensors and PM2.5 nephelometers, to investigate spatial patterns in air pollution and exposure inequality in Pittsburgh. The expansiveness of our dense, low-cost sensor network, which was deployed for over a calendar year, captures pollutant measurements over various socio-economic areas within a city, allowing us to compare measurements taken in different EJ and non-EJ communities over a significant amount of time. Public Health 2019, 16, x that socio‐economic (EJ) factors do not necessarily determine PM2.5 exposures in different parts of Pittsburgh
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