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Spatial-Temporal Analysis of PM 2.5 and NO₂ Concentrations Collected Using Low-Cost Sensors in Peñuelas, Puerto Rico

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Spatial-Temporal Analysis of PM 2.5 and NO₂ Concentrations Collected Using Low-Cost Sensors in Peñuelas, Puerto Rico

Stephen Reece et al. Sensors (Basel).

Abstract

The U.S. Environmental Protection Agency (EPA) is involved in the discovery, evaluation, and application of low-cost air quality (AQ) sensors to support citizen scientists by directly engaging with them in the pursuit of community-based interests. The emergence of low-cost (<$2500) sensors have allowed a wide range of stakeholders to better understand local AQ conditions. Here we present results from the deployment of the EPA developed Citizen Science Air Monitor (CSAM) used to conduct approximately five months (October 2016⁻February 2017) of intensive AQ monitoring in an area of Puerto Rico (Tallaboa-Encarnación, Peñuelas) with little historical data on pollutant spatial variability. The CSAMs were constructed by combining low-cost particulate matter size fraction 2.5 micron (PM2.5) and nitrogen dioxide (NO₂) sensors and distributed across eight locations with four collocated weather stations to measure local meteorological parameters. During this deployment 1 h average concentrations of PM2.5 and NO₂ ranged between 0.3 to 33.6 µg/m³ and 1.3 to 50.6 ppb, respectively. Peak concentrations were observed for both PM2.5 and NO₂ when conditions were dominated by coastal-originated winds. These results advanced the community's understanding of pollutant concentrations and trends while improving our understanding of the limitations and necessary procedures to properly interpret measurements produced by low-cost sensors.

Keywords: Low-cost sensors; Puerto Rico; air quality; citizen science.

Conflict of interest statement

The authors have no conflict of interest or financial ties to disclose.

Figures

Figure 1
Figure 1
The deployment area in southern Puerto Rico is identified on the inset map with a red box. Approximate locations of CSAMs in the deployment area are identified with blue markers and locations with both a CSAM and weather station are identified with red markers. Image credit Google.
Figure 2
Figure 2
(A) Pearson coefficient (r) and (B) coefficient of variation (CV) calculated from 5 min (dashed line) and 1 h averaged (solid line) PM2.5 (black dots) and NO2 (purple dots) concentrations between October 2016–February 2017.
Figure 3
Figure 3
The 1 h average PM2.5 (A) and NO2 (B) concentrations collected during the deployment for each CSAM location. The box represents the interquartile range of 25th and 75th percentile and the whiskers indicate the 5th and 95th percentile. The horizontal line in each box is the median concentration. The x-axis displays the number of 1 h average data points measured at each CSAM location.
Figure 4
Figure 4
1 h average PM2.5 concentrations were time-aligned in pairwise manner to calculate the Person coefficient (r) and Coefficient of Divergence (COD) between each location. The r values are numerically reported in the upper right and increase in font size with improved correlation. Scatter plots in the lower left visually display the correlations and backgrounds are colored either green (homogenous) or yellow (heterogeneous) to represent the COD values.
Figure 5
Figure 5
1 h average NO2 concentrations were time-aligned in pairwise manner to calculate the Person coefficient (r) and Coefficient of Divergence (COD) between each location. The r values are numerically reported in the upper right and increase in font size with improved correlation. Scatter plots in the lower left visually display the correlations and backgrounds are colored either green (homogenous) or yellow (heterogeneous) to represent the COD values.
Figure 6
Figure 6
The change in 1 h average PM2.5 concentrations across 3 CSAM locations (CSAMs 302, 304, and 353/355) were explored as a function of the following wind conditions observed at the South weather station: (A) inactive, (B) ocean-originated, and (C) mainland-originated. COD (blue bars) and r (green bars) values were recalculated in a pairwise fashion between locations for each wind condition and displayed as a bar chart in the bottom left of Figure 6A–C. Wind conditions at the South, East, and North weather stations are depicted by arrows indicating the median wind direction as a function of the conditions at the South weather station and colored by WS. Each CSAM location in Figure 6A–C are similarly colored based on pollutant concentrations.
Figure 7
Figure 7
The change in 1 h average NO2 concentrations across 3 CSAM locations (CSAMs 351, 352, and 353/355) were explored as a function of the following wind conditions observed at the South weather station: inactive (A), ocean-originated (B), and mainland-originated (C). COD (blue bars) and r (green bars) values were recalculated in a pairwise fashion between locations for each wind condition and displayed as a bar chart in the bottom left of Figure 7A–C. Wind conditions at the South, East, and North weather stations are depicted by arrows indicating the median wind direction as a function of the conditions at the South weather station and colored by WS. Each CSAM location in Figure 7A–C are similarly colored based on pollutant concentrations.
Figure 7
Figure 7
The change in 1 h average NO2 concentrations across 3 CSAM locations (CSAMs 351, 352, and 353/355) were explored as a function of the following wind conditions observed at the South weather station: inactive (A), ocean-originated (B), and mainland-originated (C). COD (blue bars) and r (green bars) values were recalculated in a pairwise fashion between locations for each wind condition and displayed as a bar chart in the bottom left of Figure 7A–C. Wind conditions at the South, East, and North weather stations are depicted by arrows indicating the median wind direction as a function of the conditions at the South weather station and colored by WS. Each CSAM location in Figure 7A–C are similarly colored based on pollutant concentrations.
Figure 8
Figure 8
The 1 h average PM2.5 (A) and NO2 (B) time-aligned concentrations were binned hourly on a weekly basis to explore the spatial relationship between CSAM locations as a function of time. Shading indicates the standard deviation binned hourly. Gaps in PM2.5 concentrations during certain periods indicate either 1 or more sensors were non-responsive.

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