Variability of polycyclic aromatic hydrocarbons and their oxidative derivatives in wintertime Beijing, China

Ambient particulate matter (PM) can contain a mix of different toxic species derived from a wide variety of sources. This study quantifies the diurnal variation and nocturnal abundance of 16 polycyclic aromatic hydrocarbons (PAHs), 10 oxygenated PAHs (OPAHs) and 9 nitrated PAHs (NPAHs) in ambient PM in central Beijing during winter. Target compounds were identified and quantified using gas chromatography-time-of-flight mass spectrometry (GC-Q-ToF-MS). The total concentration of PAHs varied between 18 and 297 ngm-3 over 3 h daytime filter samples and from 23 to 165 ngm-3 in 15 h night-time samples. The total concentrations of PAHs over 24 h varied between 37 and 180 ngm-3 (mean: 97±43 ngm-3). The total daytime concentrations during high particulate loading conditions for PAHs, OPAHs and NPAHs were 224, 54 and 2.3 ngm-3, respectively. The most abundant PAHs were fluoranthene (33 ngm-3), chrysene (27 ngm-3), pyrene (27 ngm-3), benzo[a]pyrene (27 ngm-3), benzo[b]fluoranthene (25 ngm-3), benzo[a]anthracene (20 ngm-3) and phenanthrene (18 ngm-3). The most abundant OPAHs were 9,10-anthraquinone (18 ngm-3), 1,8-naphthalic anhydride (14 ngm-3) and 9-fluorenone (12 ngm-3), and the three most abundant NPAHs were 9-nitroanthracene (0.84 ngm-3), 3-nitrofluoranthene (0.78 ngm-3) and 3-nitrodibenzofuran (0.45 ngm-3). Σ PAHs and Σ OPAHs showed a strong positive correlation with the gas-phase abundance of NO, CO, SO2 and HONO, indicating that PAHs and OPAHs can be associated with both local and regional emissions. Diagnostic ratios suggested emissions from traffic road and coal combustion were the predominant sources of PAHs in Beijing and also revealed the main source of NPAHs to be secondary photochemical formation rather than primary emissions. PM2.5 and NPAHs showed a strong correlation with gas-phase HONO. 9- Nitroanthracene appeared to undergo a photodegradation during the daytime and showed a strong positive correlation with ambient HONO (R D 0:90, P <0:001). The lifetime excess lung cancer risk for those species that have available toxicological data (16 PAHs, 1 OPAH and 6 NPAHs) was calculated to be in the range 10-5 to 10-3 (risk per million people ranges from 26 to 2053 cases per year)

A comparison of PM2.5-bound polycyclic aromatic hydrocarbons in summer Beijing (China) and Delhi (India)

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants in air, soil, and water and are known to have harmful effects on human health and the environment. The diurnal and nocturnal variations of 17 PAHs in ambient particle-bound PAHs were measured in urban Beijing (China) and Delhi (India) during the summer season using gas-chromatography–quadrupole time-of-flight mass spectrometry (GC-Q-TOF-MS). The mean concentration of particles less than 2.5 µm (PM2.5) observed in Delhi was 3.6 times higher than in Beijing during the measurement period in both the daytime and night-time. In Beijing, the mean concentration of the sum of the 17 PAHs (P17 PAHs) was 8.2 ± 5.1 ng m−3 in daytime, with the highest contribution from indeno[1,2,3-cd]pyrene (12 %), while at nighttime the total PAHs was 7.2 ± 2.0 ng m−3, with the largest contribution from benzo[b]fluoranthene (14 %). In Delhi, the mean P17 PAHs was 13.6 ± 5.9 ng m−3 in daytime and 22.7 ± 9.4 ng m−3 at night-time, with the largest contribution from indeno[1,2,3-cd]pyrene in both the day (17 %) and night (20 %). Elevated mean concentrations of total PAHs in Delhi observed at night were attributed to emissions from vehicles and biomass burning and to meteorological conditions leading to their accumulation from a stable and low atmospheric boundary layer. Local emission sources were typically identified as the major contributors to total measured PAHs in both cities. Major emission sources were characterized based on the contribution from each class of PAHs, with the four-, five- and six-ring PAHs accounting ∼ 95 % of the total PM2.5-bound PAHs mass in both locations. The high contribution of five-ring PAHs to total PAH concentration in summer Beijing and Delhi suggests a high contribution from petroleum combustion. In Delhi, a high contribution from six-ring PAHs was observed at night, suggesting a potential emission source from the combustion of fuel and oil in power generators, widely used in Delhi. The lifetime excess lung cancer risk (LECR) was calculated for Beijing and Delhi, with the highest estimated risk attributed to Delhi (LECR = 155 per million people), which is 2.2 times higher than the Beijing risk assessment value (LECR = 70 per million people). Finally, we have assessed the emission control policies in each city and identified those major sectors that could be subject to mitigation measures

Atmospheric conditions and composition that influence PM2.5 oxidative potential in Beijing, China

Epidemiological studies have consistently linked exposure to PM2.5 with adverse health effects. The oxidative potential (OP) of aerosol particles has been widely suggested as a measure of their potential toxicity. Several acellular chemical assays are now readily employed to measure OP; however, uncertainty remains regarding the atmospheric conditions and specific chemical components of PM2.5 that drive OP. A limited number of studies have simultaneously utilised multiple OP assays with a wide range of concurrent measurements and investigated the seasonality of PM2.5 OP. In this work, filter samples were collected in winter 2016 and summer 2017 during the atmospheric pollution and human health in a Chinese megacity campaign (APHH-Beijing), and PM2.5 OP was analysed using four acellular methods: ascorbic acid (AA), dithiothreitol (DTT), 2,7-dichlorofluorescin/hydrogen peroxidase (DCFH) and electron paramagnetic resonance spectroscopy (EPR). Each assay reflects different oxidising properties of PM2.5, including particle-bound reactive oxygen species (DCFH), superoxide radical production (EPR) and catalytic redox chemistry (DTT/AA), and a combination of these four assays provided a detailed overall picture of the oxidising properties of PM2.5 at a central site in Beijing. Positive correlations of OP (normalised per volume of air) of all four assays with overall PM2.5 mass were observed, with stronger correlations in winter compared to summer. In contrast, when OP assay values were normalised for particle mass, days with higher PM2.5 mass concentrations (µgm−3) were found to have lower mass-normalised OP values as measured by AA and DTT. This finding supports that total PM2.5 mass concentrations alone may not always be the best indicator for particle toxicity. Univariate analysis of OP values and an extensive range of additional measurements, 107 in total, including PM2.5 composition, gas-phase composition and meteorological data, provided detailed insight into the chemical components and atmospheric processes that determine PM2.5 OP variability. Multivariate statistical analyses highlighted associations of OP assay responses with varying chemical components in PM2.5 for both mass- and volume-normalised data. AA and DTT assays were well predicted by a small set of measurements in multiple linear regression (MLR) models and indicated fossil fuel combustion, vehicle emissions and biogenic secondary organic aerosol (SOA) as influential particle sources in the assay response. Mass MLR models of OP associated with compositional source profiles predicted OP almost as well as volume MLR models, illustrating the influence of mass composition on both particle-level OP and total volume OP. Univariate and multivariate analysis showed that different assays cover different chemical spaces, and through comparison of mass- and volume-normalised data we demonstrate that mass-normalised OP provides a more nuanced picture of compositional drivers and sources of OP compared to volume-normalised analysis. This study constitutes one of the most extensive and comprehensive composition datasets currently available and provides a unique opportunity to explore chemical variations in PM2.5 and how they affect both PM2.5 OP and the concentrations of particle-bound reactive oxygen species

Using highly time-resolved online mass spectrometry to examine biogenic and anthropogenic contributions to organic aerosol in Beijing

Organic aerosols, a major constituent of fine particulate mass in megacities, can be directly emitted or formed from secondary processing of biogenic and anthropogenic volatile organic compound emissions. The complexity of volatile organic compound emission sources, speciation and oxidation pathways leads to uncertainties in the key sources and chemistry leading to formation of organic aerosol in urban areas. Historically, online measurements of organic aerosol composition have been unable to resolve specific markers of volatile organic compound oxidation, while offline analysis of markers focus on a small proportion of organic aerosol and lack the time resolution to carry out detailed statistical analysis required to study the dynamic changes in aerosol sources and chemistry. Here we use data collected as part of the joint UK–China Air Pollution and Human Health (APHH-Beijing) collaboration during a field campaign in urban Beijing in the summer of 2017 alongside laboratory measurements of secondary organic aerosol from oxidation of key aromatic precursors (1,3,5-trimethyl benzene, 1,2,4-trimethyl benzene, propyl benzene, isopropyl benzene and 1-methyl naphthalene) to study the anthropogenic and biogenic contributions to organic aerosol. For the first time in Beijing, this study applies positive matrix factorisation to online measurements of organic aerosol composition from a time-of-flight iodide chemical ionisation mass spectrometer fitted with a filter inlet for gases and aerosols (FIGAERO-ToF-I-CIMS). This approach identifies the real-time variations in sources and oxidation processes influencing aerosol composition at a near-molecular level. We identify eight factors with distinct temporal variability, highlighting episodic differences in OA composition attributed to regional influences and in situ formation. These have average carbon numbers ranging from C5–C9 and can be associated with oxidation of anthropogenic aromatic hydrocarbons alongside biogenic emissions of isoprene, α-pinene and sesquiterpenes