Volatile Organic Compounds Monitored Online at Three Photochemical Assessment Monitoring Stations in the Pearl River Delta (PRD) Region during Summer 2016: Sources and Emission Areas

Volatile organic compounds (VOCs) were monitored online at three photochemical assessment monitoring stations (MDS, WQS and HGS) in the Pearl River Delta region during the summer of 2016. Measured levels of VOCs at the MDS, WQS and HGS sites were 34.78, 8.54 and 8.47 ppbv, respectively, with aromatics and alkenes as major ozone precursors and aromatics as major precursors to secondary organic aerosol (SOA). The positive matrix factorization (PMF) model revealed that VOCs at the sites mainly came from vehicle exhaust, petrochemical industry, and solvent use. Vehicle exhaust and industrial processes losses contributed most to ozone formation potentials (OFP) of VOCs, while industrial processes losses contributed most to SOA formation potentials of VOCs. Potential source contribution function (PSCF) analysis revealed a north-south distribution for source regions of aromatics occurring at MDS with emission sources in Guangzhou mainly centered in the Guangzhou central districts, and source regions of aromatics at WQS showed an east-west distribution across Huizhou, Dongguan and east of Guangzhou, while that at HGS showed a south-north distribution across Guangzhou, Foshan, Zhaoqing and Yangjiang. This study demonstrates that multi-point high time resolution data can help resolve emission sources and locate emission areas of important ozone and SOA precursors

Regional Predictions of Air Pollution in Guangzhou: Preliminary Results and Multi-Model Cross-Validations

A precise air pollution forecast is the basis for targeted pollution control and sustained improvements in air quality. It is desirable and crucial to select the most suitable model for air pollution forecasting (APF). To achieve this goal, this paper provides a comprehensive evaluation of performances of different models in simulating the most common air pollutants (e.g., PM2.5, NO2, SO2, and CO) in Guangzhou (23.13° N, 113.26° E), China. To simulate temporal variations of the above-mentioned air pollutant concentrations in Guangzhou in September and October 2020, we use a numerical forecasting model (i.e., the Weather Research and Forecasting model with Chemistry (WRF-Chem)) and two artificial intelligence models (i.e., the back propagation neural network (BPNN) model and the long short-term memory (LSTM) model). WRF-Chem is also used to simulate the meteorological elements (e.g., the 2 m temperature (T2), 2 m relative humidity (RH), and 10 m wind speed and direction (WS, WD)). In order to investigate the simulation accuracies of classical APF models, we simultaneously compare the simulations of the WRF-Chem, BPNN, and LSTM models to ground truth observations. Comparative assessment results show that WRF-Chem simulated air pollutant (i.e., PM2.5, NO2, SO2, and CO) concentrations have the best correlations with ground measurements (i.e., Pearson correlation coefficient R = 0.88, 0.73, 0.61, and 0.61, respectively). Furthermore, to evaluate model performance in terms of accuracy and stability, the normalized mean bias (NMB, %) and mean fractional bias (MFB, %) are adopted as the standard performance metrics (SPMs) proposed by Boylan et al. The comparison results indicate that when simulating PM2.5, WRF-Chem was more effective than the BPNN but less effective than the LSTM. While simulating concentrations of NO2, SO2, and CO, the WRF-Chem model performed better than the BPNN and LSTM models. With regards to WRF-Chem, the NMBs and MFBs for the PM2.5 simulations are, respectively, 6.49% and 0.02%, –11.96% and –0.031% for NO2, 7.93% and 0.019% for CO, and 5.04% and 0.012% for SO2. Our results suggest that WRF-Chem has superior performance and better accuracy than the NN-based prediction models, making it a promising and useful tool to accurately predict and forecast regional air pollutant concentrations on a city scale

Characteristics of Volatile Organic Compounds in the Pearl River Delta Region, China: Chemical Reactivity, Source, and Emission Regions

Volatile organic compounds (VOCs) are important precursors of photochemical ozone and secondary organic aerosol (SOA). Here, hourly variations of ambient VOCs were monitored with an online system at an urban site (Panyu, PY) in the Pearl River Delta region during August–September of 2020 in order to identify reactive VOC species and major sources of VOCs, OH loss rate (LOH), SOA formation potential (SOAFP), and corresponding emission source regions. The average concentration of VOCs at PY was 31.80 ± 20.82 ppbv during the campaign. The C2–C5 alkanes, aromatics, and ≥C6 alkanes contributed for the majority of VOC, alkenes and aromatics showed the highest contribution to LOH and SOAFP. Further, m/p-xylene, propene, and toluene were found to be the top three most reactive anthropogenic VOC species, with respective contributions of 11.6%, 6.1%, and 5.8% to total LOH. Toluene, m/p-xylene, and o-xylene constituted a large fraction of calculated SOAFP. Seven major sources were identified by using positive matrix factorization model. Vehicle exhaust made the most significant contribution to VOCs, followed by liquefied petroleum gas and combustion sources. However, industrial-related sources (including industrial solvent use and industrial process emission) had the largest contribution to LOH and SOAFP. By combining source contribution with wind direction and wind speed, the regions of different sources were further identified. Based on high-resolution observation data during ozone pollution, this study clearly exhibits key reactive VOC species and the major emission regions of different VOC sources, and thus benefits the accurate emission control of VOCs in the near future

Characteristics of Volatile Organic Compounds in the Pearl River Delta Region, China: Chemical Reactivity, Source, and Emission Regions

Volatile organic compounds (VOCs) are important precursors of photochemical ozone and secondary organic aerosol (SOA). Here, hourly variations of ambient VOCs were monitored with an online system at an urban site (Panyu, PY) in the Pearl River Delta region during August–September of 2020 in order to identify reactive VOC species and major sources of VOCs, OH loss rate (LOH), SOA formation potential (SOAFP), and corresponding emission source regions. The average concentration of VOCs at PY was 31.80 ± 20.82 ppbv during the campaign. The C2–C5 alkanes, aromatics, and ≥C6 alkanes contributed for the majority of VOC, alkenes and aromatics showed the highest contribution to LOH and SOAFP. Further, m/p-xylene, propene, and toluene were found to be the top three most reactive anthropogenic VOC species, with respective contributions of 11.6%, 6.1%, and 5.8% to total LOH. Toluene, m/p-xylene, and o-xylene constituted a large fraction of calculated SOAFP. Seven major sources were identified by using positive matrix factorization model. Vehicle exhaust made the most significant contribution to VOCs, followed by liquefied petroleum gas and combustion sources. However, industrial-related sources (including industrial solvent use and industrial process emission) had the largest contribution to LOH and SOAFP. By combining source contribution with wind direction and wind speed, the regions of different sources were further identified. Based on high-resolution observation data during ozone pollution, this study clearly exhibits key reactive VOC species and the major emission regions of different VOC sources, and thus benefits the accurate emission control of VOCs in the near future

Real time analysis of lead-containing atmospheric particles in Guangzhou during wintertime using single particle aerosol mass spectrometry

The toxic effects of lead on human health and the environment have long been a focus of research. To explore sources of lead in Guangzhou, China, we investigated atmospheric lead-containing particles (LCPs) during wintertime using a single particle aerosol mass spectrometer (SPAMS). Based on mass spectral features, LCPs were classified into eight major particle types, including Pb-Cl and Pb-Cl-Li (coal combustion and waste incineration), Pb-Cl-EC and Pb-Cl-OC (diesel trucks and coal combustion), Pb-Cl-Fe (iron and steel industry), Pb-Cl-AlSi (dust), Pb-Sec (secondary formation), and Pb-Cl-Zn (industrial process); these sources (in parentheses) were identified by comparing atmospheric LCP mass spectra with authentic Pb emission source mass spectra. Sampling periods with LCP number fractions (NFs) more than three times the average LCP NF (APF = 4.35%) and below the APF were defined as high LCP NF periods (HLFPs: H1, H3, and H5) and low LCP NF APF periods (LLFPs: L2 and L4), respectively. Diurnal patterns and high Pb-Sec content during LLFPs indicate that photochemical activity and heterogeneous reactions may have controlled Pb-Sec particle formation. The inverse Pb-Cl and Pb-Sec particle diurnal trends during LLFPs suggest the replacement of Cl by sulfate and nitrate. On average over the five periods, similar to 76% of the LCPs likely arose from coal combustion and/or waste incineration, which were dominant sources during all five periods, followed by diesel trucks during LLFPs and iron- and steel-related sources during HLFPs; HLFP LCPs arose mainly from primary emissions. These results can be used to more efficiently control Pb emission sources and prevent harm to human and environmental health from Pb toxicity

Non-agricultural source dominates the ammonium aerosol in the largest city of South China based on the vertical delta N-15 measurements

Ammonia (NH3) is the most prevalent alkaline gas in the atmosphere and plays a critical role in air pollution and public health. However, scientific debate remains over whether agricultural emissions (e.g., livestock and fertilizer application) dominate NH3 in urban atmosphere in China, which is one of the largest NH3 emitters in the world. In this study, we first simultaneously collected the fine atmospheric particles (PM2.5) at two heights (ground and 488 m) using the atmospheric observatories in Canton Tower, Guangzhou city, China for the measurements of stable nitrogen isotope composition in ammonium (delta N-15-NH4+). Our results showed that the average delta N-15-NH4+ value at the ground and the 488 m observatory were 16.9 parts per thousand and 3.8 parts per thousand, respectively, implying that NH4+ aerosols between the two heights probably have different sources. Moreover, we found that the delta N-15-NH4+ value would sharply decrease to - 16.7 parts per thousand when the air masses came from western Guangzhou, where the urbanization is limited compared to other surrounding areas. The Bayesian mixing model indicated that NH4+ aerosol at the ground observatory was mainly derived from non-agricultural activities (76 %, e.g., vehicular exhaust), with the rest from agricultural sources (24 %). As for the 488 m observatory, the contribution of non-agricultural sources was 53 %, which is lower than the ground observatory. This is expected as the lower air receives more impacts from the local urban emission. However, the current "bottom-up" emission inventory illustrates that only similar to 20 % NH3 in Guangzhou is associated with non-agricultural emissions, which is significantly lower than our delta N-15-based results. Overall, our findings strongly imply that non-agricultural sources dominate the urban NH3 in Guangzhou or maybe in adjacent cities of the Pearl River Delta region as well, suggesting that the emission inventory of NH3 in this region probably is urgently needed to be revisited in future studies

Decadal changes in emissions of volatile organic compounds (VOCs) from on-road vehicles with intensified automobile pollution control: Case study in a busy urban tunnel in south China

In the efforts at controlling automobile emissions, it is important to know in what extent air pollutants from on-road vehicles could be truly reduced. In 2014 we conducted tests in a heavily trafficked tunnel in south China to characterize emissions of volatile organic compounds (VOC) from on-road vehicle fleet and compared our results with those obtained in the same tunnel in 2004. Alkanes, aromatics, and alkenes had average emission factors (EFs) of 338, 63, and 42 mg km-1 in 2014 against that of 194, 129, and 160 mg km-1 in 2004, respectively. In 2014, LPG-related propane, n-butane and i-butane were the top three non-methane hydrocarbons (NMHCs) with EFs of 184 ± 21, 53 ± 6 and 31 ± 3 mg km-1; the gasoline evaporation marker i-pentane had an average EF of 17 ± 3 mg km-1; ethylene and propene were the top two alkenes with average EFs of 16 ± 1 and 9.7 ± 0.9 mg km-1, respectively; isoprene had no direct emission from vehicles; toluene showed the highest EF of 11 ± 2 mg km-1 among the aromatics; and acetylene had an average EF of 7 ± 1 mg km-1. While EFs of total NMHCs decreased only 9% from 493 ± 120 mg km-1 in 2004 to 449 ± 40 mg km-1 in 2014, their total ozone formation potential (OFP) decreased by 57% from 2.50 × 103 mg km-1 in 2004 to 1.10 × 103 mg km-1 in 2014, and their total secondary organic aerosol formation potential (SOAFP) decreased by 50% from 50 mg km-1 in 2004 to 25 mg km-1 in 2014. The large drop in ozone and SOA formation potentials could be explained by reduced emissions of reactive alkenes and aromatics, due largely to fuel transition from gasoline/diesel to LPG for taxis/buses and upgraded vehicle emission standards

Amplification of black carbon light absorption induced by atmospheric aging: temporal variation at seasonal and diel scales in urban Guangzhou

Black carbon (BC) aerosols have been widely recognized as a vital climate forcer in the atmosphere. Amplification of light absorption can occur due to coatings on BC during atmospheric aging, an effect that remains uncertain in accessing the radiative forcing of BC. Existing studies on the absorption enhancement factor (E-abs) have poor coverage on both seasonal and diurnal scales. In this study, we applied a recently developed minimum R squared (MRS) method, which can cover both seasonal and diurnal scales, for E-abs quantification. Using field measurement data in Guangzhou, the aims of this study are to explore (1) the temporal dynamics of BC optical properties at seasonal (wet season, 31 July-10 September; dry season, 15 November 2017-15 January 2018) and did scales (1 h time resolution) in the typical urban environment and (2) the influencing factors on E-abs temporal variability. Mass absorption efficiency at 520 nm by primary aerosols (MAE(p520)) determined by the MRS method exhibited a strong seasonality (8.6 m(2) g(-1) in the wet season and 16.8 m(2) g(-1) in the dry season). E-abs520 was higher in the wet season (1.51 +/- 0.50) and lower in the dry season (1.29 +/- 0.28). Absorption Angstrom exponent (AAE(470-660)) in the dry season (1.46 +/- 0.12) was higher than that in the wet season (1.37 +/- 0.10). Collective evidence showed that the active biomass burning (BB) in the dry season effectively altered the optical properties of BC, leading to elevated MAE, MAE(p) and AAE in the dry season compared to those in the wet season. Diurnal E(abs520 )was positively correlated with AAE470-660 (R-2 = 0.71) and negatively correlated with the AE33 aerosol loading compensation parameter (k) (R-2 = 0.74) in the wet season, but these correlations were significantly weaker in the dry season, which may be related to the impact of BB. This result suggests that during the wet season, the lensing effect was more likely dominating the AAE diurnal variability rather than the contribution from brown carbon (BrC). Secondary processing can affect Eabs diurnal dynamics The E-abs520 exhibited a clear dependency on the ratio of secondary organic carbon to organic carbon (SOC/OC), confirming the contribution of secondary organic aerosols to E-abs; Ea(bs520) correlated well with nitrate and showed a clear dependence on temperature. This new finding implies that gas-particle partitioning of semivolatile compounds may potentially play an important role in steering the diurnal fluctuation of E-abs520. In the dry season, the diurnal variability in E-abs520 was associated with photochemical aging as evidenced by the good correlation (R-2 =0.69) between oxidant concentrations (O-x = O-3 + NO2) and E-abs520