Development and application of a mass closure PM<sub>2.5</sub> composition online monitoring system

Abstract

Abstract. Online instruments have been widely applied for the measurement of PM2.5 and its chemical components; however, these instruments have a major shortcoming in terms of the lack or limited number of species in field measurements. To this end, a new mass closure PM2.5 online integrated system was developed and applied in this work to develop more comprehensive information on chemical species in PM2.5. For the new system, one isokinetic sampling system for PM2.5 was coupled with an aerosol chemical speciation monitor (Aerodyne, ACSM), an aethalometer (Magee, AE-31), an automated multi-metals monitor (Cooper Corporation, Xact-625) and a hybrid synchronized ambient particulate real-time analyzer monitor (Thermo Scientific, SHARP-5030i) to enable high-resolution temporal (1 h) measurements of organic matter, SO42-, NO3-, Cl−, NH4+, black carbon, important elements and PM2.5 mass concentrations. The new online integrated system was first deployed in Shenzhen, China, to measure the PM2.5 composition from 25 September to 30 October 2019. Our results showed that the average PM2.5 concentration in this work was 33 µg m−3, and the measured species reconstructed the PM2.5 well and almost formed a mass closure (94 %). The multi-linear engine (ME-2) model was employed for the comprehensive online PM2.5 chemical dataset to apportion the sources with predetermined constraints, in which the organic ion fragment m/z 44 in ACSM data was used as the tracer for secondary organic aerosol (SOA). Nine sources were determined and obtained reasonable time series and diurnal variations in this study, including identified SOA (23 %), secondary sulfate (22 %), vehicle emissions (18 %), biomass burning (11 %), coal burning (8.0 %), secondary nitrate (5.3 %), fugitive dust (3.8 %), ship emissions (3.7 %) and industrial emissions (2.1 %). The potential source contribution function (PSCF) analysis indicated that the major source area could be the region north of the sampling site. This is the first system in the world that can perform online measurements of PM2.5 components with a mass closure, thus providing a new powerful tool for PM2.5 long-term daily measurement and source apportionment.

Trends and Challenges Regarding the Source-Specific Health Risk of PM2.5-Bound Metals in a Chinese Megacity from 2014 to 2020

“…32 Differently, V and Ni showed high levels in summer due to the impact of ship emissions on the sea, and the high levels of Ca observed in summer and fall were related to dust from urban construction activities. 31 Although meteorological conditions have an effect on the diffusion of pollutant concentrations, long-term concentration changes in metals are less affected because of the low correlation between metal concentrations and typical meteorological conditions such as wind speed, temperature, wind direction, etc. (Table S5).…”

“…Our previous work revealed that vehicle emissions were a major source of PM 2.5 in Shenzhen, accounting for ∼20% of the PM 2.5 mass concentrations. 31,32 Therefore, Shenzhen is a unique megacity offering an ideal environment for studying the sources and challenges of PM 2.5 health risks, especially those from vehicles including new energy cars.…”

“…In addition, the population of vehicles in Shenzhen reached 3.59 million in 2020 with the largest number of new energy cars (0.48 million) in the world, and the vehicle density (per kilometer) in Shenzhen was over 500, ranking first in China (). Our previous work revealed that vehicle emissions were a major source of PM 2.5 in Shenzhen, accounting for ∼20% of the PM 2.5 mass concentrations. , Therefore, Shenzhen is a unique megacity offering an ideal environment for studying the sources and challenges of PM 2.5 health risks, especially those from vehicles including new energy cars.…”

Substantial changes of chemical composition and sources of fine particles during the period of COVID-19 pandemic in Taiyuan, Northern China

“…Factor 4 can be identified as a source of dust source (DS). This factor was distinguished by higher loadings of crustal elements such as Ca, Fe, and Ba (Lyu et al 2016;Su et al 2020). In factor 5, Cr, Mn, Fe, and Ni accounted for a relatively higher proportion (Taiwo et al 2014).…”

“…The potential source contribution function (PSCF) (Ashbaugh et al 1985;Su et al 2020) was commonly used to access the potential sources of pollutants, but this method can only reflect the proportion of pollution trajectories in each grid and cannot distinguish the differences between moderate and severe sources when PM 2.5 concentrations are slightly or much higher than the standard. In order to emphasize the spatial variation of the concentration of pollution source, the concentration-weighted trajectory (CWT) was used to calculate the potential source concentration weight of airflow trajectory in area, to show the variation of sources of PM 2.5 before and after the outbreak of the epidemic more visually.…”