The BErkeley Atmospheric CO<sub>2</sub> Observation Network: Field Calibration and Evaluation of Low-cost Air Quality Sensors

Abstract

Abstract. The newest generation of air quality sensors is small, low cost, and easy to deploy. These sensors are an attractive option for developing dense observation networks in support of regulatory activities and scientific research. They are also of interest for use by individuals to characterize their home environment and for citizen science. However, these sensors are difficult to interpret. Although some have an approximately linear response to the target analyte, that response may vary with 10 time, temperature, and/or humidity, and the cross-sensitivity to non-target analytes can be large enough to be confounding.Standard approaches to calibration that are sufficient to account for these variations require a quantity of equipment and labor that negates the attractiveness of the sensors' low cost. Here we describe a novel calibration strategy for a set of sensors including CO, NO, NO 2 , and O 3 that makes use of multiple co-located sensors, a priori knowledge about the chemistry of NO, NO 2 , and O 3 , as well as an estimate of mean emission factors for CO and the global background of CO. 15The strategy requires one or more well calibrated anchor points within the network domain, but it does not require direct calibration of any of the individual low-cost sensors. The procedure nonetheless accounts for temperature and drift, in both the sensitivity and zero offset. We demonstrate this calibration on a subset of the sensors comprising BEACO 2 N, a distributed network of approximately 50 sensor "nodes," each measuring CO 2 , CO, NO, NO 2 , O 3 and particle matter at 10 second time resolution at approximately 2km spacing in locations surrounding the San Francisco Bay Area. 20

Optimising Citizen-Driven Air Quality Monitoring Networks for Cities

“…The spatial arrangement of sensors usually influence how well the spatial distribution of air pollutants and their impact can be captured [39]. Although various approaches have been proposed to identify the optimal locations for air pollution monitoring sensor placement [51], few studies focus on general location selection aspect for deployment of low-cost sensors [52,53]. To our knowledge, no previous studies have considered the application of an optimisation method to enable systematic location selection for robust LUR estimations during the crowdsourcing of air pollution data for cities.…”

Propuesta híbrida para la captación y medición de CO2 edáfico empleando cámaras de incubación estáticas y secuestramiento de gases

“…Ninguna variable presentó relación con el CO 2 ; sin embargo, hubo un indicio de una potencial relación con la densidad aparente y el nivel de salinidad del suelo, ya que ambas propiedades del suelo se ubicaron en el mismo cuadrante del CO 2 y se encontraron asociadas al sector positivo del CP1. Esta relación entre respiración y propiedades del suelo ha sido reportado con anterioridad [17,19,29,42,43] Ostaiza Demera y col. Los resultados aquí descritos revelaron que efectivamente fue posible encontrar diferencias significativas entre los sistemas evaluados; no obstante, se hace necesario seguir aplicando la metodología obteniendo mayor número de datos, explorando otros ecosistemas naturales e intervenidos, para hacerlo más robusto y poder entonces realizar estudios más profundos como flujos y balances de carbón edáfico, establecer factores de emisión de CO 2 y, de ser necesario, proponer medidas mitigativas y adaptativas frente al cambio climático a escala local y nacional.…”

Comparing Building and Neighborhood-Scale Variability of CO2 and O3 to Inform Deployment Considerations for Low-Cost Sensor System Use

“…One of the barriers to widespread sensor use has been concerns over data quality and reliability. There is a growing body of research demonstrating the ability of sensors to quantify pollutants at levels relevant to ambient investigations [ 4 , 5 , 6 , 7 ]. However, other issues have received less attention, for example, strategies for siting low-cost sensors.…”