2. 1 Sampling points
The sampling site is located at the Meteorological Service Center of Jinchang City, and the sampling took place from the 19th to the 25th of each month in 2020. The samples were collected continuously for six days per month and 24 hours per day. The sampling date, sampling volume and some meteorological conditions were recorded. Before sampling, the quartz filter membranes were roasted in a muffle furnace at high temperature (450℃) for 5 hours to remove organic impurities and reduce the effects of organic residues. PM2.5 samples were then collected with a medium flow sampler at a flow rate of 100 L-min− 1. After collection, the samples were frozen and stored at a low temperature of -20℃ (Li et al. 2020).
2. 2 Extraction of PAHs in PM
2. 2 Extraction of PAHs in PM2.5
The filter membrane was extracted using Soxhlet extractor using 150 mL of dichloromethane as solvent. The quartz fiber filter membrane was cut into three small round membranes with a diameter of 25mm. A quantitative deuterium standard mixed solution (acenaphthene-d10, chrysene-d12) was added and then put into the rope extractor. Then, 150ml of dichloromethane was poured into a distillation flask and extracted continuously for 8 hours. The solution was collected after the 8 hours and concentrated to 1-2mL using a rotary evaporator, and then separated and purified using a chromatography column. After adding the sample to the chromatography column, 20mL of n-hexane was added when the liquid level is tangent to the column to fully distribute the sample in the chromatography column. When the n-hexane was tangent to the column, a 1:1 mixture of n-hexane and dichloromethane (35mL + 35mL) was added for elution. The sample was then collected and filtered. After sample analysis and purification, the sample was first concentrated by rotary evaporation to 1-2mL, and 10mL of n-hexane added for solvent replacement. The sample was then concentrated again to about 1mL, transferred to a nitrogen blown vial with 0.5mL nitrogen, transferred to a sample injection vial, and brought to a constant volume of 1mL. A deuterium internal standard mixture (naphthalene-d8, anthracene-d10, pyrene-d10, perlene-d12) was finally added and 16 kinds of polycyclic aromatic hydrocarbons were detected by gas chromatography-mass spectrometry.
2. 3 Backward trajectory model
The HYSPLIT trajectory model (Hybrid Single Particle Lagrangian Integrated Trajectory Model) was developed by the Air Resources Laboratory ARL of NOAA (National Oceanic and Atmospheric Administration) in cooperation with the Australian Bureau of Meteorology for applications in the calculation and analysis of atmospheric pollutant transport and dispersion trajectories, the calculation of air mass trajectories, and the simulation of complex dispersion and deposition. It has been widely used in the study of the transport and dispersion of many pollutants in various regions. The model is a hybrid Eulerian-Lagrangian computational model with both advection and dispersion calculations using the Lagrangian method (Gao et al. 2021). The HYSPLIT trajectory model is commonly used to track the direction of movement of particles or gases carried by air currents, allowing real-time forecasting of wind field situations, analysis of precipitation, and study of trajectories. The backward trajectory of the HYSPLIT trajectory model analyzes the incoming direction of air currents. The TrajStat module is based on the HYSPLIT model based on multivariate statistical analysis, which can calculate, display, and query the bulk trajectories of air masses and perform trajectory clustering analysis on them, by adding the observed data to the trajectory file and then analyzing the paths and sources of atmospheric constituents at the observation station (Zhang et al. 2021; Li et al. 2019). Cluster analysis is to group a large number of backward trajectories according to the horizontal movement speed and direction of air masses, and the principle of grouping is to achieve great differences between groups and minimal differences within groups, so as to obtain different groups of transport trajectories, and then to make preliminary judgments about the potential source areas of trajectory atmospheric pollutants (Zhao et al. 2014).
In this study, the backward trajectory and clustering analysis of the meteorological data of Jinchang City in 2020 were performed using TrajStat model. Using the monitoring site as the receptor point of the backward trajectory with a starting height of 500m, the continuous 24-h backward airflow trajectory was calculated and clustering analysis was performed to observe the incoming direction of the trajectory and determine the path, direction, and transmission speed of the air mass. The meteorological data were obtained from the Global Data Synoptic Assimilation System (GDAS).
2. 4 Potential Source Contribution Factor Analysis (PSCF)
The PSCF is a method for qualitatively identifying potential pollution sources based on conditional probability functions, and the PSCF initially identifies the location of an emission source by combining the trajectory of an air mass with the value of the element corresponding to that trajectory (Arimoto et al. 2006; Li et al. 2016).The PSCF method is a conditional probability function that describes the spatial distribution of the geographic location of an energy-possible area with the help of a backward trajectory, which is the conditional probability of an air mass passing through a region (i, j) arriving at The conditional probability of the value of an element corresponding to the observation point with its set threshold[19].The PSCF is calculated as:
\({\text{P}\text{S}\text{C}\text{F}}_{\text{i}\text{j}}={\text{m}}_{\text{i}\text{j}} /{\text{n}}_{\text{i}\text{j}}\)
where, nij represents the total number of trajectories passing through the grid; mij represents the number of endpoints passing through the grid points (i, j) corresponding to the freely set critical value (exceeding the limit) of the pollutant concentration in the target city. The study area was set up as a 0.2°x 0.2°grid and the corresponding PSCF value was calculated. The grid with high PSCF values is the potential source area affecting PM2.5 in the atmosphere.
2. 5 Characteristic ratio method
For the source analysis of PAHs in atmospheric particulate matter, the commonly used methods include principal component analysis (Zhang et al. 2015), contour plot method (Li et al. 2017), and characteristic ratio method, etc. The sources of PAHs are complex, and the production pathways of 16 PAHs are different, and the sources of PAHs are mainly the combustion of fossil fuels and biomass, etc. However, the content of PAH monomers produced during the combustion of different fuels is also different, and the specific sources of PAHs can be roughly inferred from the contribution of these monomers. The characteristic ratio method is a relatively simple and effective method for source analysis, in which the source of a substance can be determined based on the known concentration of the specific substance and its characteristic ratio. Fla, Pyr, Chry, and BbF are considered to be signature pollutants for coal combustion; BaA, BbF, BkF, DahA, BghiP, and InP are considered to be signature pollutants for gasoline and diesel combustion (Lwa et al. 2019; Xu et al 2021); and Ace, Acy, Flu, and Phe are considered to be signature pollutants for volatilization from petroleum products and crude oil spills (Wang et al. 2009; He et al. 2022). For some high molecular weight PAHs, such as benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), benzo[a]pyrene (BaP), indenebenzene(1,2,3-cd)pyrene (InP), and benzo[ghi]perylene (BghiP), traffic sources are the main sources (Wang et al. 2008; Rangaswamy et al.2010). Some studies have shown that the values of BaP/ BghiP are related to traffic pollution sources, the values of InP/ (InP + BghiP) are related to petroleum combustion, and the values of BaA/ (BaA + Chr) are related to petroleum, combustion, and coking (Jiang et al. 2015).