Characteristics of PM<sub>2.5</sub> mass concentrations and chemical species in urban and background areas of China: emerging results from the CARE-China network

Abstract

Abstract. The “Campaign on Atmospheric Aerosol Research” network of China (CARE-China) is a long-term project for the study of the spatio-temporal distributions of physical aerosol characteristics as well as the chemical components and optical properties of aerosols over China. This study presents the first long-term data sets from this project, including 3 years of observations of online PM2.5 mass concentrations (2012–2014) and 1 year of observations of PM2.5 compositions (2012–2013) from the CARE-China network. The average PM2.5 concentration at 20 urban sites is 73.2 µg m−3 (16.8–126.9 µg m−3), which was 3 times higher than the average value from the 12 background sites (11.2–46.5 µg m−3). The PM2.5 concentrations are generally higher in east-central China than in the other parts of the country due to their relatively large particulate matter (PM) emissions and the unfavourable meteorological conditions for pollution dispersion. A distinct seasonal variability in PM2.5 is observed, with highs in the winter and lows during the summer at urban sites. Inconsistent seasonal trends were observed at the background sites. Bimodal and unimodal diurnal variation patterns were identified at both urban and background sites. The chemical compositions of PM2.5 were analysed at six paired urban and background sites located within the most polluted urban agglomerations – North China Plain (NCP), Yangtze River delta (YRD), Pearl River delta (PRD), North-east China region (NECR), South-west China region (SWCR) – and the cleanest region of China – the Tibetan Autonomous Region (TAR). The major PM2.5 constituents across all the urban sites are organic matter (OM, 26.0 %), SO42- (17.7 %), mineral dust (11.8 %), NO3- (9.8 %), NH4+ (6.6 %), elemental carbon (EC) (6.0 %), Cl− (1.2 %) at 45 % RH and unaccounted matter (20.7 %). Similar chemical compositions of PM2.5 were observed at background sites but were associated with higher fractions of OM (33.2 %) and lower fractions of NO3- (8.6 %) and EC (4.1 %). Significant variations of the chemical species were observed among the sites. At the urban sites, the OM ranged from 12.6 µg m−3 (Lhasa) to 23.3 µg m−3 (Shenyang), the SO42- ranged from 0.8 µg m−3 (Lhasa) to 19.7 µg m−3 (Chongqing), the NO3- ranged from 0.5 µg m−3 (Lhasa) to 11.9 µg m−3 (Shanghai) and the EC ranged from 1.4 µg m−3 (Lhasa) to 7.1 µg m−3 (Guangzhou). The PM2.5 chemical species at the background sites exhibited larger spatial heterogeneities than those at urban sites, suggesting different contributions from regional anthropogenic or natural emissions and from long-range transport to background areas. Notable seasonal variations of PM2.5-polluted days were observed, especially for the megacities in east-central China, resulting in frequent heavy pollution episodes occurring during the winter. The evolution of the PM2.5 chemical compositions on polluted days was consistent for the urban and nearby background sites, where the sum of sulfate, nitrate and ammonia typically constituted much higher fractions (31–57 %) of PM2.5 mass, suggesting fine-particle pollution in the most polluted areas of China assumes a regional tendency, and the importance of addressing the emission reduction of secondary aerosol precursors including SO2 and NOx. Furthermore, distinct differences in the evolution of [NO3-]/[SO42-] ratio and OC∕EC ratio on polluted days imply that mobile sources and stationary (coal combustion) sources are likely more important in Guangzhou and Shenyang, respectively, whereas in Beijing it is mobile emission and residential sources. As for Chongqing, the higher oxidation capacity than the other three cities suggested it should pay more attention to the emission reduction of secondary aerosol precursors. This analysis reveals the spatial and seasonal variabilities of the urban and background aerosol concentrations on a national scale and provides insights into their sources, processes and lifetimes.

Molecular Characterization of Organosulfates in Highly Polluted Atmosphere Using Ultra‐High‐Resolution Mass Spectrometry

“…The sum of all quantified OS and nitrooxy‐OS concentrations ranged 5.85–161 ng m −3 , with an average of 56.8 ng m −3 . By assuming an OM/OC ratio of 1.6 based on recent studies (Liu et al, 2018; Turpin & Lim, 2001; Xing et al, 2013), the calculated contribution of the total quantified OSs and nitrooxy‐OSs (TOSs) constituted 0.65% of the total particulate OM mass in summer, as compared to 0.17% of OM in winter. Even if identification and number of detected OSs may greatly vary, the absolute levels of OSs in this study were generally on the same order of magnitude as those reported in Beijing (41.4 ng m −3 ) (Wang, Hu, et al, 2018) and less than those in the forest sites of Belgium (2–290 ng m −3 ) (Gomez‐Gonzalez et al, 2012).…”

Assessing the formation and evolution mechanisms of severe haze pollution in the Beijing–Tianjin–Hebei region using process analysis

“…Meteorological initial and lateral boundary conditions used in the WRF-Chem model are taken from the NCEP (National Center for Environmental Prediction) (Final) Operational Global Analysis data with a spatial resolution of 1 • ×1 • . Four-dimensional data assimilation (FDDA) with the nudging coefficient of 3.0 × 10 −4 for wind (in and above the PBL), temperature (above the PBL), and water vapor mixing ratio (above the PBL) is adopted to improve the accuracy of simulation results (no analysis nudging is included for the inner domain) (Lo et al, 2008;Otte, 2008;Werner et al, 2016). The forecasts from the MOZART-4 global chemical transport model are processed to provide the chemical initial and boundary conditions for the WRF-Chem model (Emmons et al, 2010).…”

Characteristics and Source Apportionment of Metallic Elements in PM2.5 at Urban and Suburban Sites in Beijing: Implication of Emission Reduction

“…The concentrations of the elements in PM 2.5 were calculated by measuring the concentrations of the elements and the sampling volume. More detailed information, such as instrument optimization, calibration, and quality control, is given in [19].…”