Improving Urban Air Quality Measurements by a Diffusion Charger Based Electrical Particle Sensors - A Field Study in Beijing, China

Abstract

High aerosol loadings contribute significantly to the air quality problems of Asian megacities. To address this, monitoring data for aerosol mass and number that is spatially and temoprally of high resolution is needed, while the cost of obtaining such data remains high. Here, we present a field study in a polluted megacity, Beijing, using a diffusion-chargebased electrical aerosol sensor, the Pegasor PPS-M, which is a robust and comparatively low-cost instrument for the monitoring of both aerosol mass and number simultaneously. We present data over several months in the year 2014, and for varying aerosol size distributions, and analyze the performance against particle number and mass (volume) measured using a wide range particle sizer (WPS) and beta-attenuation-based PM 2.5 observations. We show that using a single trap voltage, the PPS-M correlates well with particle mass, but not so well with particle number due to the variability in particle size distributions. However, the instrument response to number was improved by running the instrument with a variable trap voltage, and using the ratio of the different signals to gain information on the particle average volume. With this method, we were able to improve the correlation of the PPS-M; with the observed particle number from R = 0.14 to R = 0.72 for the measurement time period. Altogether, the PPS-M instrument displayed robustness and low maintenance requirements, and it showed good correlation with the other instruments in this study.

Long-term sensor measurements of lung deposited surface area of particulate matter emitted from local vehicular and residential wood combustion sources

“…The Pegasor PPS-M sensor was used in ambient measurements for the first time in Helsinki in two different urban environments by J€ arvinen et al 2015. In addition, Pegasor PPS-M sensors have been used in a heavily polluted urban area in Beijing during several months (Dal Maso et al 2016). All the improvements suggested in these two studies (J€ arvinen et al, 2015;Dal Maso et al, 2016) for the performance of the Pegasor PPS-M sensor for long-term ambient measurements have been taken into account in designing the Pegasor AQ Urban, including the trap voltage settings and heating of the sensor unit.…”

“…alveolar region) (e.g. Dal Maso et al 2016;Pirjola et al 2017;Hennig et al 2018). Unlike conventional PM 2.5 and PM 10 parameters with predefined daily mean threshold values, LDSA parameter remains unregulated and hence largely unmonitored (European Council 2008).…”

Input-adaptive linear mixed-effects model for estimating alveolar Lung Deposited Surface Area (LDSA) using multipollutant datasets

“…The sampling lines and the sensor unit are heated 40℃ above the ambient temperature (1) to dry the aerosol sample, (2) to prevent interference from humidity, and (3) to prevent any water condensation inside the sensor. The performance of the Pegasor PPS-M sensors for long-term ambient measurements has been improved after they were tested in Helsinki and Beijing (Dal Maso et al, 2016). The suggestions have been considered for the design of the current form of the Pegasor AQ Urban in this study.…”

Variations in <i>N</i><sub>cn</sub> and <i>N</i><sub>ccn</sub> over marginal seas in China related to marine traffic emissions, new particle formation and aerosol aging

“…However, large uncertainties still exist in various marine atmospheres, e.g., the sources of aerosols, the concentrations of bulk cloud condensation nuclei (CCN) and aerosol CCN activation under various supersaturations. (Clarke et al, 2006;Decesari et al, 2011;Quinn and Bates, 2011;Saliba et al, 2019;Rosenfeld et al, 2019). These uncertainties are mainly determined by limited observations in marine atmospheres, although a few additional observations of the number concentrations of aerosols (N cn ) and CCN (N ccn ) were recently reported in different marine atmospheres, e.g., over the Mediterranean (Bougiatioti et al, 2009), Sea of Japan (Yamashita et al, 2011), Bay of Bengal (Ramana and Devi, 2016), coast of California (Ruehl et al, 2009) and northwest Pacific Ocean (Wang et al, 2019).…”