MyJournals Home  

RSS FeedsSensors, Vol. 18, Pages 4314: Spatial-Temporal Analysis of PM2.5 and NO2 Concentrations Collected Using Low-Cost Sensors in Pe˝uelas, Puerto Rico (Sensors)

 
 

8 december 2018 13:00:08

 
Sensors, Vol. 18, Pages 4314: Spatial-Temporal Analysis of PM2.5 and NO2 Concentrations Collected Using Low-Cost Sensors in Pe˝uelas, Puerto Rico (Sensors)
 




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.


Del.icio.us Digg Facebook Google StumbleUpon Twitter
 
31 viewsCategory: Chemistry, Physics
 
Sensors, Vol. 18, Pages 4315: A Fast Calibration Method for Phased Arrays by Using the Graph Coloring Theory (Sensors)
Sensors, Vol. 18, Pages 4313: A Novel Framework for Road Traffic Risk Assessment with HMM-Based Prediction Model (Sensors)
 
 
blog comments powered by Disqus


MyJournals.org
The latest issues of all your favorite science journals on one page

Username:
Password:

Register | Retrieve

Search:

Physics

Use these buttons to bookmark us:
Del.icio.us Digg Facebook Google StumbleUpon Twitter


Valid HTML 4.01 Transitional
Copyright © 2008 - 2018 Indigonet Services B.V.. Contact: Tim Hulsen. Read here our privacy notice.
Other websites of Indigonet Services B.V.: Nieuws Vacatures News Tweets Travel Photos Nachrichten Indigonet Finances Leer Mandarijn