Identifying Particulate Matter Spatial Variation in the El Paso Del Norte Region Using Land-Use Regression Modeling and Data Obtained From a Network of Low-Cost Sensors
The emergence and rise in popularity of low-cost sensors for atmospheric observation are setting a new precedent in identifying emission hotspots and providing high-resolution spatial and temporal data. Furthermore, low-cost sensors are becoming popular among institutions and the public, allowing community scientists to become more involved in air quality monitoring. However, concerns about the accuracy and precision of low-cost sensors have been questioned. Most recent research has focused on the utility of real-time monitoring and calibration requirements for these sensors. A low-cost monitoring project has deployed sensors in the El Paso del Norte region in low and high annual average daily traffic (AADT), school, and industrial zones. A calibration equation was created for each sensor during a twoweek deployment next to a federal monitoring station; the low-cost sensors showed a high coefficient of determination (R2) of >0.9 for PM2.5 between low-cost sensors and monitoring stations. During the two months that the sensors were in the field, PM2.5 values had a higher concentration in the high AADT zone and higher concentrations in the low AADT zones in Cd. Juarez. The PM values recorded at each site were utilized in the land use regression model to find variables that significantly affected PM concentration. While traffic variables showed an adverse effect on PM, PM concentration would increase per mile decrease to a traffic source; geographic data showed an increase in PM per unit increase in population at a given 500 m buffer zone.
Environmental Studies|Civil engineering|Atmospheric sciences
Vazquez-Raygoza, Leonardo Demetrio, "Identifying Particulate Matter Spatial Variation in the El Paso Del Norte Region Using Land-Use Regression Modeling and Data Obtained From a Network of Low-Cost Sensors" (2022). ETD Collection for University of Texas, El Paso. AAI30242007.