p-ISSN: 0972-6268 Nature Environment and Pollution Technology (Print copies up to 2016) Vol. 20 No.3 pp. 1087-1096 2021 An International Quarterly Scientific Journal e-ISSN: 2395-3454

Original Research Paper Originalhttps://doi.org/10.46488/NEPT.2021.v20i03.016 Research Paper Open Access Journal

Spatial and Temporal Characteristics of PM2.5 Sources and Pollution Events in a Low Industrialized City

R. Xu*, Q. Tian*, H. Wan**†, J. Wen***, Q. Zhang* and Y. Zhang* *College of Computer and Information Security, University of Electronic Technology, Guilin 541004, **Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou, 510006, China ***Guilin Environmental Monitoring Center Station, Guilin 541002, China †Corresponding author: H. Wan; [email protected]

ABSTRACT Nat. Env. & Poll. Tech. Website: www.neptjournal.com In recent years, cities in southern China have experienced severe air pollution, despite having few sources of pollutants. To study the pollution characteristics of PM in these “low industrialized” cities, Received: 28-08-2020 2.5 Revised: 04-11-2020 a numerical method based on the HYSPLIT4 Model and Kriging Spatial Interpolation Technology was Accepted: 08-12-2020 established. Simulation results showed that the PM2.5 pollution in Guilin was affected by both internal and external sources. The backward air mass trajectory from July 2017 to June 2018 was simulated Key Words: using the HYSPLIT model. The cluster analysis results indicated that the direction of trajectory  PM2.5 accounted for 63.09% of the air pollution in the city. The average concentration of PM2.5 pollution Air pollution was 45.94 μg.m-3. The pollutant originated from the “Xiang-Gui Corridor.” The location of the sources Pollution events was collocated with high industry regions. The spatial characteristics of the four pollution processes Spatial characteristics in the winter of 2017 were analyzed using a spatial interpolation method. The results showed that the transport of air masses in the direction of trajectory  was obstructed by a mountain system in the northeast. Therefore, two air pollution accumulation centers and a topographic weakening zone dominated by internal and external sources were formed. It can be inferred that the air pollution in Guilin is affected by both internal and external factors. These results provide important theoretical and technical support for regional air pollution control and environmental protection.

INTRODUCTION example, Zhang et al. (2019) employed the Comprehen- sive Air Quality Model Extensions (CAMx) based on the The Chinese government implemented a series of air pollu- Particulate Source Apportionment Technology (PSAT) to tion prevention and control measures to improve the country’s simulate and analyze the sources of PM in Beijing. They air quality. Under this situation, air quality in most regions 2.5 found that 47.6% of the PM2.5 originated from local sourc- has improved, but pollution remains serious in some areas es. Zhang et al. (2018) used the Community Multiscale Air (Luan et al. 2018, Jiang et al. 2018, Cui et al. 2019, Sun et Quality Modeling System (CMAQ) to simulate the changes al. 2019). It is worth noting that tourist areas in southern of surface PM in Qingdao during winter. They found that China, such as Guilin, have also suffered serious air pollution, 2.5 PM2.5 accounted for 72.7%–93.2% of the daily emissions. despite having fewer sources of pollutants. However, there is This percentage would decrease by about 21% when con- no clear conclusion about the cause of air pollution in tourist sidering the pollution process, and the proportion of aerosol areas, which are quite different from those in industrial cities accumulation would increase by about 6%. Another study by because of their characteristics. In recent years, with the Yang et al. (2019) used the WRF-SMOKE-CMAQ model to development of tourism, the eco-environmental problems analyze the life cycle of PM in Xi’an from 2014 to 2017. in Guilin have been a cause of concern for environmental 2.5 The study concluded that the PM2.5 concentrations increased workers. Bai et al. (2017) studied the impact of climate re-� from 82.4 μg.m-3 to 95.4 μg.m-3 as a result of dust emissions sources on the tourism development of Guilin International into the atmosphere. Wang et al. (2015) analyzed the air Resort and found that air pollution would affect tourism pollution in the Yangtze River Delta in December 2013 using development. As a tourism city, ensuring the air quality of a Principal Component Analysis (PCA). They found that Guilin is necessary and important. the concentration of fine particulate matter increased due to Scholars adopted several tools to analyze the sources of the anthropogenic emission of dust from Northwest China. air pollutants and characterize pollution characteristics. For This method of research uses a meteorological grid, which 1088 R. Xu et al. needs to be analyzed with an internal source list data. This topography. In this paper, the HYSPLIT method is adopted research model is suitable for the pollution source analysis to determine the trajectory of an air mass and estimate air of large-scale regions but is ineffective when extrapolat- pollution sources. Then, the PSCF and CWT methods are ing the source of a pollutant from pollution monitoring used to quantify and analyze the internal pollution sources. points. Furthermore, spatial interpolation and topographic element The Hybrid Single-Particle Lagrangian Integrated Tra- methods are adopted to determine the temporal and spatial jectory (HYSPLIT) model can be used to trace the transport distribution characteristics of pollutants. and diffusion trajectory of pollutants from the monitoring points. Arif et al. (2018) used the HYSPLIT4 model to study MATERIALS AND METHODS the source of atmospheric pollutants in Patna in 2015. The Study Object and Data Concentration Weighted Trajectory (CWT) method was also used to analyze pollution levels in source regions. Mukherjee Guilin was selected as the research area. Guilin covers an 2 and Agrawal (2018) studied the sources of PM2.5 pollution area of 27,809 km and has a population of over 5 million. in Varanasi, India, from 2014 to 2017. They quantified The research area borders Yongzhou and Shaoyang of the contribution from traffic, road dust, and internal com- Province in the northeast and in the south. All three bustion activities, and found that Northwest India was the are heavy industrial cities. main source area. Liu et al. (2013) used the CWT method The topographical distribution of the study area is to track the origin and transport of atmospheric pollutants presented in Fig. 1. The west, north, and southeast regions in Lanzhou. Results showed that the HYSPLIT model and are mountainous and have higher terrain. The central regions CWT analysis methods are effective tools when analyzing have relatively low terrain. There is a long narrow channel external sources and their corresponding contributions (Sun in the northeast between the Yuechengling and Dupangling- et al. 2015, Perrone et al. 2018). However, it is difficult to Haiyangshan Mountain chains. The depth of this channel, clarify the temporal and spatial characteristics of air pollution also known as the “Xiang-Gui Corridor,” ranges from 600 to processes under the combined action of internal and external 1600 m between the mountain peak and the basin. sources in small-scale regions. Guilin has lower pollution emissions than surrounding The internal sources of air pollution are studied using set cities, but its air quality is significantly worse. The overall pair analysis and the spatial interpolation method used by amount of PM remains high, especially in some scholars. Zhou et al. (2016) constructed the set pair 2.5 County, , , and downtown analysis method for internal and external sources of PM 2.5 Guilin. and then studied the proportion of internal to external sourc- es in Dongguan. However, this study did not consider the The research area contains 19 fixed atmospheric envi- spatial and temporal characteristics of the pollutants. Yang ronmental quality automatic monitoring stations (“fixed et al. (2018) employed a Spatiotemporal Ordinary Kriging stations” in short) and 51 mini ones (“mini stations” in short). (STOK) technique and analyzed the daily concentrations of Guilin University of Electronic Technology Yaoshan Station (Yaoshan Station) was selected as the background value PM2.5 in southern Jiangsu province in 2014. Their results showed that 29.3% of the area was polluted by PM2.5 in reference station because it was considered to be free from 2014. Additionally, the number of polluted days varied from the influence of internal sources. Yaoshan Station is 13 km 59 to 164 in different parts of the study region. The spatial from downtown Guilin. Vegetation covers more than 78% interpolation method can be used to study the spatial and of this station, and consequently, this station is only weakly temporal distribution of pollutants, but previous studies influenced by the air pollution from downtown. typically focused on the correlation between a pollutant and The following basic data was used to analyze the sourc- various meteorological parameters. The correlation analyses es and spatial distribution of air pollution in the research speculated the causes of regional pollution but failed to area: hourly average data of the 19 fixed stations in Guilin, consider that transport, emissions, and accumulation are including wind direction, wind speed, temperature, rainfall, affected by topography. barometric pressure, humidity, and PM2.5 concentration; Previous studies failed to capture the complete life cycle Landsat8 satellite 30 m resolution full-band image, adminis- of a pollutant (Zdun et al. 2016, Zhao et al. 2015, Liu et al. trative division data, and elevation DEM image data provided 2017). To fully understand the emission and transport of pol- by Geographic Cloud; and the GDAS data (spatial resolution lution, this study needs to track the trajectory of a pollutant, 1°×1°) provided by US National Centers for Environmental locate and quantify its sources, and consider the effects of Prediction (Table 1).

Vol. 20, No. 3, 2021 • Nature Environment and Pollution Technology

SPATIAL AND TEMPORAL CHARACTERISTICS OF PM2.5 SOURCES 1089

Fig. 1: Topography of the study area and distribution of monitoring stations. Fig. 1: Topography of the study area and distribution of monitoring stations. Table 1: Datasheet.

Data category The researchData contents area contains 19 fixed atmosphericData source environmental qualityTime automatic range Resolution

Meteorological data PM2.5 Guilin Environmental 2014.3–2019.6 Day monitoring stations (“fixed stations” in short)Monitoring and Station 51 mini ones (“mini stations” in Meteorologicalshort). analysis Guilin Wind University speed, wind of direction, Electronic temperature, Technology Guilin Environmental Yaoshan Station (Yaoshan2017.7–2018.6 Station)Day data rainfall, barometric pressure, moisture Monitoring Station was selected ascontent the background value reference station because it was considered to be Air mass trajectory data GDAS data NCEP 2017.7–2018.6 1°×1° free from the influence of internal sources. Yaoshan Station is 13 km from downtown Geographic data DEM data, Administrative division data China Geographic cloud space website 2017.7–2018.6 30m×30m Guilin. Vegetation covers more than 78% of this station, and consequently, this station Study Methodsis only weakly influenced by the air pollutionspatial from characteristics downtown. of air pollution in the study area under the influence of meteorological and topographic changes The internal and external sources and the transport pattern were discussed. of the air pollutantsThe following in the research basic area data were was analyzed used to analyze the sources and spatial distribution using the data described above. First, the trajectory of the External Source Transmission air mass ofwas air simulated pollution for 365in theconsecutive research days area: using hourly the average data of the 19 fixed stations in The HYSPLIT4 model was used to simulate the trajectory HYSPLIT4Guilin, model. including Using a windspatial direction, clustering windmethod speed,, it temperature, rainfall, barometric pressure, was found that air pollution was transported from several of an air mass for continuous periods to determine the di- directions.humidity, Second, theand PSCF PM 2.5method concentration; was used to gridLandsat8 the rectionsatellite of air30 pollutantm resolution transmission full-band from image,external sources. potential source areas. Third, the CWT method was employed The HYSPLIT4 model is a hybrid single-particle orbit model to quantifyadministrative the grid value ofdivision the pollutants data, inand the elevation potential DEMdeveloped image by datathe Air provided Resources by Laboratory Geographic (ARL) under source areaCloud;s. Fourth, and four the periods GDAS with data severe (spatial pollution resolution were the 1°×1°) US National provided Oceanic by US and NationalAtmospheric Centers Administration selected. The changes of PM2.5 were calculated using the (NOAA). This model is used to calculate and analyze the spatial interpolationfor Environmental method, which Prediction analyzed (Tablethe spatial 1). and trajectories of atmospheric pollutants and diffusion. Trajec- temporal distributions of pollutants. Finally, the temporal and tories were categorized according to their spatial variation Table 1: Datasheet.

Nature Environment and Pollution TechnologyTime •Resolu Vol. 20, No.3, 2021 Data category Data contents Data source range tion

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Equation (3): Equation(1) (3): Potential Source Contribution Function - PSCF Equation (3): Equation (3): The PSCF method determines Equationthe location (3): of a pollution source by setting the Equation, (3): Equation (3): , …(3) …(3) 1090 , Equation (3): R. Xu et al. …(3) atmospheric backward trajectory and the meteorologicalEquation, values. (3): The PSCF function is …(3) , , …(3) …(3) (SPVAR) to obtaindefined the relationship as the conditionalbetween the total ,probabi where spatial lity that the denotes value theof an concentration element corresponding grid values… toof(3) thePM 2.5; n stands for the number where denotes the concentration, grid values of PM 2.5 ; n stands for the number …(3) where variationdenotes (TSV) the(the concentrationsum of SPVAR) andgrid n values(the number of PM of 2.5; n stands for the number , , … (3) …(3) monitoring grid, namely PMof2.5 stations concentration,where used for PMexceedsdenotes2.5 interpolation; the the set concentration threshold whengridis the values an average air of PM PM2.52.5; n concentrationstands for the numb of i-er trajectories). ofBased stations on the used clustering for PM results,2.5 interpolation; the locations is the average PM2.5 concentration of i- where wheredenotes the concentrationdenotes the concentration grid values of grid PM values2.5; n stands of for the number of stationsof used source for regions PM2.5 (1)were interpolation; Potential determinedwhere Source using Contribution thedenotes PSCFis the method. theaverage Function concentration whereof PM -stations PSCF2.5 concentration grid used values fordenotes PM of PM2.5of the interpolation;i2.5- ; concentrationn stands for the gridnumbis valuestheer average of PM PM2.52.5; nconcentration stands for the of inumb- er 2.5 mass passingwhere through andenotes areath reachesstation the concentrationPM , theand monitoring; nP standsi iswhere grid thefor grid. the valuesundetermined number The ofdenotes PSCF PMof stations thevalueweighting; n concentration standsused of for the for coefficient.PM i -theth grid numb values erAfter of PM the2.5 ; unbiasedn stands for the number Then CWT methodth station was used, and to quantify Pi is the the pollution undeterminedof stations weight used weightingfor2.5 PM2.5 interpolation; coefficient. Afteris thethe averageunbiased PM 2.52.5 concentration of i- (1)th Potential station ,Source and PContributioni is the undetermined Functionof stations - PSCF usedweighting for PM2.5 coefficient. interpolation;ofth stationsstation After , usedand the is Pfortheunbiasedi isaveragePM the2.5 undeterminedinterpolation; PM2.5 concentration weighting of iis- thecoefficient. average AfterPM2.5 theconcentration unbiased of i- and thus estimategridThe the total PSCFwithin pollutantsof method stationsthe research carried determines used out. area for minimumthePM is shownlocation2.5 interpolation; interpolation; variancein of Equation a pollution estimation of (1):stations source isis the usedisthe by averageexecuted, average settingfor PM PM the2.5 PM2.5Equation interpolation; concentration2.5 concentration (4) isof derived ofis thei- by average introducing PM2.5 concentration the of i- minimum variance estimationth isstation executed,, and EquationPi is the undetermined(4) is derived weighting by introducing coefficient. the After the unbiased th station, and Pi is the undetermined weighting coefficient. After the unbiased minimum (1)variance Potential estimation Source Contribution is executed, Function Equation - PSCF (4) is derivedthminimumi -thstation station, by variance,introducing andand P i isP theiestimation is undetermined thethe undetermined is executed, weighting Equation coefficient.weighting (4) iscoefficient. derived by introducingAfter the theunbiased The PSCF method determinesatmospheric the backwardlocationth station oftrajectory ,a pollutionand andPi Lagrange theis source themeteorological undetermined bycoefficient setting values. the thQ :weighting station The PSCF, and function coefficient.Pi is the is undetermined After the unbiasedweighting coefficient. After the unbiased The PSCF methodLagrange determines coefficientminimum the location variance Q: of estimationminimuma pollution variance is executed,After theestimation unbiasedEquation is minimumexecuted,(4) is derived variance Equation by introducingestimation (4) is derived is the exe -by introducing the Lagrange coefficientdefined Q: as the conditional probability that the minimumvalueLagrangecuted, of Equation an coefficient elementvariance (4) correspondingis estimationQderived: by introducing isto executed,the the Lagrange Equation (4) is derived by introducing the atmospheric backwardsource trajectory by setting andthe atmospheric the meteorological minimum backward variance values.trajectory Lagrange estimation The and coefficientPSCF is function executed, Q: minimumis Equation variance (4) isestimation derived isby… executed, (1)introducing Equation the (4) is derived by introducing the Lagrange coefficient Q: coefficientQ : defined as the conditionalthe meteorological probabimonitoringlity values. that grid, The the namely PSCFvalue function ofPM an2.5 element concentration,is defined corresponding as Lagrange exceeds tothe coefficient the Lagrangeset threshold Qcoefficient: when an Q :air …(4) the conditional probability thatLagrange the value ofcoefficient an element correQ: - …(4) …(4) where ni and mi separately represent the relative standing … time(4) of backward air quality monitoring grid, namely PMmass2.5 concentration, passing through exceeds an area thereaches set thresholdthe monitoring when grid. an Theair PSCF value of the i-th …(4) …(4) sponding to the monitoring grid, namely PM2.5 concentration, …(4) … (4) … (4) mass passing throughexceeds an areathe gridset reaches threshold withinand pollutantthe the when monitoring research an airconcentration massarea grid. ispassing shown The through PSCFinin Equationthe valueretention (1): of the area. i-th The PSCF method calculates the …(4) an area reaches the monitoring grid. The PSCF value of the , , …, (5) … …(5)(5) grid within the research area is showncontribution in Equation of the (1): grid , to pollutant concentrations in the … research(5) area. …(5) i-th grid within the research area is shown in Equation (1): , , … (5) …(5) …(1) , , … (5) … (5) (2) Concentration -Weightedwhere…(1) Trajectorywherewhere (CWT)is the co isis -the,thevariance co-variance co -variance function function function of of of andand and… (5) in Equationin Equation (4). (4). where is the co - variance where function is… of the (1) co-variance and function inof Equationand (4). in Equation (4). where is thewhere co n-variancei and wheremi separately function represent ofis the theco - variancerelativeand standing functioninin Equation time Equationof of backward(4). Equations(4).and air quality(4) andin (5)Equation are combined (4). n m Equations Equations (4) and (4)(5) andare (5)combined are combined to get to the get weighting the weighting coefficient coefficient where i and Equationsi separately (4) represent and (5) the are relative combined standing to getto getthe theweighting weightingwhere coefficient coefficientis the co-variance functionand of and in Equation (4). Equations (4) and (5)and are pollutant combinedTheEquations concentration PSCF to get (4)method the and in weighting the(5) Equationscan areretention determinecombined coefficient (4) wherearea. and to Thethe(5) get are PSCFcorrelationthe combined weighting methodis the betweento coefficientcalculatesco get- variance the theweighting the corresponding function coefficient of and in Equation (4). where ni and mi separatelytime of backward represent air qualitythe relativewhere and pollutant standing concentration timeis and theof backward Lagrange coin -varianceandLagrange Lagrange air coefficient quality function coefficient coefficient Q of.Q Then, . Then, Q. Then, thetheand interpolation the interpolation estimationin estimation Equation estimation of(4). any of any point point within within the the the retention andarea. LagrangeThe PSCF methodcoefficient calculates Q. and Then,the Lagrangecontri the- interpolation coefficient Q Equationsestimation. Then, the (4) ofinterpolation and any (5) point are estimationcombinedwithin the toof getany the point weighting within the coefficient and pollutantand Lagrange concentration coefficient contributionin theelement Q retention. Then, ofand value the theLagrange area.grid andinterpolation to The thepollutant coefficient thresholdPSCF concentrationsestimation method Q .value Then,Equations calculatesof of theof any inth anyinterpolation ethe point trajectory point research (4)the within and within area.estimationin(5)the the research arethe grid. combined of area Therefore,any canpoint to be getwithin obtainedthe the CWT the weighting by coefficient bution of the grid to pollutant concentrations in the research research area can be obtained by Equation (3). research areaEquations can be obtained (4) andresearch by (5)research Equation are area combined area canEquat (3). becanion obtained be(3).to getobtainedand bytheLagrange Equation weighting by Equation coefficient (3). coefficient (3). Q. Then, the interpolation estimation of any point within the contributionresearch of the areaarea. grid can to bepollutant obtainedmethod concentrations byresearch isEquation used areato in (3).quantify thecan researchbe obtained and area.analyze by and Equation Lagrangethe weighted (3). coefficient concentration Q. Then, of pollutants.the interpolation The estimation of any point within the (2) Concentrationand Lagrange-Weighted coefficient Trajectory Q(CWT). Then,The theKriging interpolationresearch interpolation area canestimation method be obtained is employed of any by Equation pointto reflect within (3). the (2) Concentration-Weighted Trajectory (CWT) The Kriging interpolation method is employed to reflect the accumulation of PM2.5 weighted concentration of gridThe i refersThe Kriging Kriging theto the accumulationinterpolation time interpolation-averaged of method PM2.5 methodconcentration andis employed the changesis employed to of inreflect monitoringits concentra to the reflect accumulation- the accumulation of PM2.5 of PM2.5 The KrigingThe interpolation Kriging interpolation method methodisresearch employed is employed area to can reflect tobe reflect obtainedthe accumulation the accumulation by Equation of PMof (3). PM2.5 2.5 (2) ConcentrationThe PSCF-Weighted method TheTrajectory can PSCF determine method(CWT) the correlationcan determine between the correlationtion in different between areas. the The corresponding landscape’s influence on PM The Kriging interpolation methodresearch is employed area can beto reflectobtained theand by accumulationthe Equation changes (3).in Theof its PM Krigingco2.5ncentration interpolation in different method areas. is2.5 employed The landscape’s to reflect the influence accumulation on of PM2.5 the correspondingpoints element passing value andthrough the threshold gridand andi , the asvalue theshochanges wnchanges in in Equation its in co itsncentration co (2):ncentration in different in different areas. The areas. landscape’s The landscape’s influence oninfluence on elementand the value changesand and the the in changesthreshold its co ncentrationin value its co ofncentration th ein trajectory differenttransport in differentin istheareas. also grid. areas.determined. The Therefore, landscape’sThe landscape’sTherefore, the CWT influence theinfluence air pollution on on Theand PSCF the changesmethodof the trajectory incan its determine co in ncentrationthe grid. the Therefore, correlation in different the CWT between areas. method The theis PMlandscape’scorrespondingemission2.5The transport Krigingis influenced andinfluence is the alsointerpolation changes bydetermined. onboth thein itstransportmethod Therefore,concentration isof employedair thepollution inair different pollution to reflect areas. emission the The accumulation landscape’sis influenced of influence PM2.5 on PM2.5 transportThe Kriging is alsoPM interpolation PMdetermined.2.5 transport2.5 transport Therefore,ismethod also is determined.also is theemployed determined. air pollution Therefore, to reflect Therefore, emission the the air accumulation ispollution the influenced air pollution emission of PM isemission 2.5influenced is influenced used to quantifymethodPM and2.5 isanalyzetransport used tothe quantify isweighted also determined.and concentration analyze the Therefore, of weightedfrom external concentrationthe air sources, pollution andof pollutants. the emission diffusion The ofis airinfluenced pollution from elementPM value2.5 transportand the threshold is also determined.value of the trajectoryTherefore, in thethe airgrid. pollution Therefore,by bothemission the the CWT transport PMis influenced2.5 transport of air pollutionis also determined. from external Therefore, sources, the andair pollutionthe diffusion emission of air is influenced pollutants. The weighted concentration of grid, i refersby both to the the and transport the changes of air pollution in its cofromncentration external sources,in different …and(2) theareas. diffusion The landscape’sof air influence on weighted concentrationbyand both the the ofchanges transportgrid i refers in ofby itsto air both theco pollution ncentrationtime theinternal-averaged transport from sources. in externalconcentration differentof air sources, pollution areas. of monitoring and The from the landscape’s diffusion external of sources, airinfluence and on the diffusion of air method is used to time-averagedquantify andby concentration analyze both the the transport ofweighted monitoring of concentration airpoints pollution passing of from pollutionpollutants. external from Theby sources,inter bothnal the sources. and transport the diffusion of air pollution of air from external sources, and the diffusion of air by both the transport of air pollutionpollution from from external internalpollution sources.sources, from PM and inter2.5 transportthenal sources.diffusion is also of determined.air Therefore, the air pollution emission is influenced through gridpoints ipollution, as shown passing infrom EquationthroughPM inter2.5 transport gridnal(2): sources.i, as shois alsownpollution in determined. Equation fromRESULTS (2): inter Therefore, nalAND sources. DISCUSSION the air pollution emission is influenced weightedpollution concentration from interof gridnal i referssources. to the time-averaged concentrationby of both monitoring the transportpollution from of air inter pollutionnal sources. from external sources, and the diffusion of air by both the transportRESULTS of air ANDRESULTS pollution DISCUSSIONS ANDfrom DISCUSSIONSexternal sources, and the diffusion of air points passing through grid i, as shoWherewn in CWTRESULTS Equationi denotes (2):AND the DISCUSSIONS weighted concentrationPollution Characteristics of grid i, l denotes the serial number RESULTS AND DISCUSSIONS, RESULTS … (2) pollution AND fromDISCUSSIONSRESULTS internal sources.AND DISCUSSIONS… (2) RESULTS AND DISCUSSIONSpollution from internal sources.PollutionData was collectedCharacteristics from Yaoshan Station, Longyin Station, of the trajectories,Pollution Characteristics t denotesPollution the total Characteristics number of trajectories, nil denotes the time spent , Guilin Environmental…(2) Monitoring Station, and No. 8 Middle Pollution Characteristics PollutionRESULTS Characteristics ANDPollution DISCUSSIONS Characteristics by l-th trajectory passing through gridSchool i, andData StationC wil asis collected thefrom PM 20142.5 from concentrationto 2019, Yaoshan and thenStation, of the trajectory dataLongyin was Station, Guilin Environmental Pollution CharacteristicsWhere CWTi denotes theRESULTS weightedData was concentrationcollectedAND DISCUSSIONS fromData of Yaoshanw as collected Station, from Longyin Yaoshan Station, Station, Guilin Longyin Environmental Station, Guilin Environmental i l Where CWTi denotes the weighted concentrationt analyzed of grid to i, determinel denotes changesthe serial in PMnumberin different regions. grid , denotes the serial number of the trajectories, de- Monitoring Station,Data and w No.as collected 8 Middle2.5 from School Yaoshan Station Station, from Longyin2014 to 2019,Station, and Guilin then theEnvironmental l whileData wMonitoringpassingas collected through Station, from grid Monitoringand Yaoshan i .No. Data 8 Middle Station,Station,Pollution Aswas shown Schoolcollected andLongyin Characteristics inNo. StationFig. 8 from Station,Middle2, thefrom Yaoshan Yaoshan School 2014Guilin to StationStation,Station 2019,Environmental presented andfrom Longyin then 2014 lowerthe toStation, 2019, andGuilin then Environmental the notes the totalof thenumber trajectories, of trajectories, t denotes nil denotesthe total the number time of trajectories, nil denotes the time spent Data was collected from YaoshanPollution Station, Characteristics Longyin Station, Guilin Environmental Where CWTi denotesspent the by weightedl-th trajectory concentration passing through of grid ii, ,and l denotes C is the the data concentrations.serial was number analyzedMonitoring The to PM determine2.5 concentrationsStation, changes and No. at thisin 8 PM Middlestation2.5 in were Schooldifferent Station regions. from As 2014 shown to 2019,in Fig and. then the data was analyzed todata determineMonitoring ilw as analyzed changes Station, to in determine PM and2.5 in No. differentchanges 8 Middle inregions. PM School2.5 inAs different shown Station in regions. Figfrom. 2014As shown to 2019, in Fig and. then the byMonitoring l-th trajectory Station, passing and through No. grid8 Middle i, and CSchoolil is the StationPM2.5 concentration from 2014 ofto trajectory 2019, and then the MonitoringPM Station,2.5 concentration and INo.nternal of 8 trajectory Middle Source Schooll while Diffusion passing Station through from 2014 highest toData 2019, from w asDecember and collected then to the January from Yaoshanof the following Station, year Longyinand Station, Guilin Environmental of the trajectories, t denotes the total number of trajectories, nil denotes2, the the time Yaoshan spentdata Station was analyzedpresented to lower determine concentrations. changes in The PM PM2.5 in2.5 different concentrations regions. at As this shown in Fig. grid i. 2, the YaoshanData w Stationas collected2, thepresenteddata Yaoshan w fromas loweranalyzedlowest YaoshanStation concentrations. from presented to Station,June determine to July.lower LongyinThe changes concentrations.PM2.5 Station, concentrations in PM Guilin The2.5 in PM atdifferent Environmental this2.5 concentrations regions. As at this shown in Fig. l datawhile w passingas analyzed through to grid determine i. changes in PM2.5 in different regions. As shown in Fig. by l-th trajectorydata was analyzedpassing through to determine grid i, andchanges Cil is inthe PM PM2.52.5 in concentration different Monitoringstationregions. of trajectory were As Station,highestshown2, the Yaoshan fromin and Fig DecemberNo.. Station 8 Middle presented to January School lower of Station theconcentrations. following from 2014 year The andto PM 2019, lowest2.5 concentrations and from then the at this Internal Source DiffusionA spatialstationMonitoring wereinterpolation highest Station,station from method2, and the December were No.Yaoshan was highest 8 Middle usedTheto StationJanuary from pollution for School Decemberthe presentedof internaltheand Station followingmeteorological to lowersourceJanuary from year concentrations. diffusion2014of andconditions the lowesttofollowing 2019, analysis differedfrom Theand year . PMthen and2.5 thelowest concentrations from at this 2, the Yaoshan Station presented lower concentrations.significantly from The March PM to2.5 August concentrations (Period 1) and at Septemberthis l while 2,passing the Yaoshan through Station grid Ii.nternal presented Source lower Diffusion concentrations. June toThe July. PMdataJune 2.5 wconcentrationstoas July. analyzed station towere at determine this highest fromchanges December in PM to2.5 January in different of the regions. following As year shown and lowestin Fig .from A spatial interpolationThe regional methodJunedata to was w changesJuly.as used analyzed for in theconcentration tostationinternal determine wereto February werehighestchanges estimated, offrom in the PM followingDecember2.5 and in different theyear toinfluence (Period January regions. 2). Tableofof Astheinternal 2 shown followingsum- in Fig year. and lowest from station were highest from December to January of theJune following to July. year and lowest from station weresource highest diffusion from analysis.A Decemberspatial The interpolation regional to January changes method of inthe was concen following used- for2,marizes the the year Yaoshaninternal the and results sourcelowest Station from diffusion from presentedFig. 2. analysisThe loweratmospheric. concentrations. pressure The PM2.5 concentrations at this Internal Source Diffusion sources was2, the analyzed, Yaoshan as Station were the presented topographic lower characteristics. concentrations. The PM2.5 concentrations at this tration were estimated,June to July. and the influence of internalJune sources to July.is less correlated with the meteorological parameter (PM , June to July. The regional changes in concentration were stationestimated, were and highest the influence from Decemberof internal to January of2.5 the following year and lowest from was analyzed, as were the topographicstation were characteristics. highest from DecemberPM10), and to the January humidity of is the directly following related yearto rainfall. and lowestThe from A spatial interpolation method wasIf usedit is forassumed the internal that there source are diffusion n points analysis of actual. measurements within the area of If it is assumedsources that was there analyzed, are n points as were of actual the topographic measure- June characteristics.corresponding to July. statistical results were8 not listed in8 Table 2. The regional changes in concentration wereJune estimated, to July. and the influence of internal8 ments within the areapoint of point x0, xnamely0, namely .. Then, Then, the the (1) Kriging PM2.5 concentration:interpolation During method Period is 1,expressed PM2.5 concen as- 8 -3 sources was analyzed,Kriging as interpolwere theationIf topographic it methodis assumed is expressed characteristics. that there as Equationare n points (3): of actual trations measurements were greater within than the 70 area μg.m of and less than 20 8 8 7 point x0, namely . 8Then, the Kriging interpolation method is expressed as If it is assumed that there are n points of actual measurements within the area of Vol. 20, No. 3, 2021 • Nature Environment and Pollution Technology 7 8 point x0, namely . Then, the Kriging interpolation method is expressed as 8

7

SPATIAL AND TEMPORAL CHARACTERISTICS OF PM2.5 SOURCES 1091

100 Yaoshan station monitor ststion 90 longyin station 8th school station 80 mean value

70

60 ) 3 - 50 100 g·m μ Yaoshan station (

2.5 40 monitor ststion 90 longyin station PM 30 8th school station 80 mean value 20

70 10 Mar-14 Sep-14 Mar-15 Sep-15 Mar-16 Sep-16 Mar-17 Sep-17 Mar-18 Sep-18 60

) time(month) 3 - 50

g·m Fig. 2: Comparison of atmospheric environmental quality data of fixed stations in 2014-2019. μ Fig.( 2: Comparison of atmospheric environmental quality data of fixed stations in 2014-2019.

2.5 40 μg.m-3, and between these two values accounted for 2%, accounted for 16% and 12%, respectively. During Period PM 30 44%, and The54% pollutionof the observed and meteorologicalPM2.5 concentrations, conditions 2, differed the temperatures significantly between from 10°C Marchand 25°C to accounted respectively.20 During Period 2, PM2.5 concentrations for 62% of the observed temperature, while tempera- wereAugust greater than(Period 70 μg.m 1) -3and and Septemberless than 20 μg/m to February3, and of turesthe following below 10°C year and above(Peri od25°C 2). accounted Table 2 for 25% between these10 two values accounted for 21%, 20%, and and 13%, respectively. summarizesMar-14 theSep results-14 Mar from-15 FigSep.- 152. TheMar -atmospheric16 Sep-16 Marpressure-17 Sep is-17 lessMar correlated-18 Sep- 18with the 59%, of the observed PM2.5 concentrations, respectively. (5) Rainfall: During Period 1, there was no rainfall 41% time(month) (2) Windmeteorological direction: During parameter Period 1, eight (PM wind2.5, directionsPM10), and the humidityof the time. is Rainfall directly between related 0.1 to and rainfall. 20 mm occurred had Therelatively corresponding uniform values, statistical with westerly results winds were and not listedon 56%in Table of the 2. days, and more than 20 mm of rainfall Fig.northwesterly 2: Comparison winds occurring of atmospheric 4% and 5% environmental of the time, qualityoccurred data on 3% of offixed the days. stations During in Period 2014 2,-2019 there .was respectively. During Period 2, northerly and northwest- no rainfall 79% of the time, rainfall between 0.1 and 20 erly windsTable were most2: Proportion frequent andof pollution accounted characteristics for 43% in mmGuilin occurred from July21% 2014 of the to time, June and 2019 more. than 20 mm andThe 25% pollution of all the winds, and respectively. meteorological conditionsrainfall differed occurred significantly 0% of the time. from March to PM2.5 Period one Period two Period one Period two wind speed Period one Period two temperture Period one Period two rainfall Period one Period two wind direction (3) Wind speed:3 （ During） （ ）Period 1, weak（ ） winds（ ） occurred（ ） （ ） （ ） （ ） （ ） （ ） （ ） （ ） （ ） August（ μg/m(Period） % 1) %and September% % to Februarym/s % ofTo% the summarize, following %Period %1year typicallymm (Peri had%od temperatures 2).% Table rang 2- the most0~20 frequently.44% 20% As shownN in Table12 2,25 95%0~0.5 of the 27 ing from12 15°C0~5℃ to 30°C.0 This2 period0 also41 had weak,79 uniform -1 observed21~30 wind23% was less15% thanNE 3.5 m.s20 . During43 Period0.6~1.0 2, 26 wind20 directions,6~10 larger3 amounts23 0.1~1 of precipitation,21 9 bad mete- summarizes31~40 the16% results16% fromE 9Fig. 2.5 The1.0~1.5 atmospheric13 14 11~15 pressure13 is24 less1~2 correlated8 3 with the strong winds occurred with greaterSE frequency, and only orological conditions for pollutant diffusion, and low PM 41~50 8% 1% 10 4 1.6~2.0-1 11 11 16~20 12 17 2~3 4 3 2.5 83% of51~60 the observed5% 15% winds Swere less17 than3 3.5 m.s2.1~2.5 . 8 12 21~25 29 21 3~5 6 2 meteorological parameter (PM2.5, PM10), and concentrations.the humidity Period is 2directly primarily hadrelated temperatures to rainfall. between 61~70 2% 12% SW 23 6 2.6~3.0 6 10°C9 and 26~3025°C, strong31 northeasterly10 5~10 and7 northerly3 winds, (4) Temperature:71~90 During2% Period11% 1,W the temperatures4 5 between3.1~3.5 4 8 31~35 11 3 10~20 10 1 The15°C corresponding ≥and90 30°C0% accounted10% statisticalNW for 72%5 results of the9 observed were≥3.6 not 5 little listed14 rainfall, in≥35 Tablegood1 meteorological 2. 0 >20 conditions3 0for pollutant temperature.Note: Period Temperatures 1 is from below March 15°C to August, and above and 30°C Period diffusion, 2 is from and September high PM 2.5to concentrations.February of the Tablefollowing 2: Proportion year of pollution characteristics in Guilin from July 2014 to June 2019. Table 2: Proportion of pollution characteristics in Guilin from July 2014 to June 2019.

PM2.5 Period one Period two2.5 Period one Period two wind speed Period one Period two2.5 temperture Period one Period two rainfall Period one Period two (1) PM wind concentration: direction During Period 1, PM concentrations were greater than （μg/m3） （%） （%） （%） （%） （m/s） （%） （%） （ ） （%） （%） （mm） （%） （%） 70 μg.m-3 and less than 20 μg.m-3, and between these two values accounted for 2%, 0~20 44% 20% N 12 25 0~0.5 27 12 0~5℃ 0 2 0 41 79 21~30 23% 15% NE 20 43 0.6~1.0 26 20 6~10 3 23 0.1~1 21 9 44%, and 54% of the observed PM2.5 concentrations, respectively. During Period 2, 31~40 16% 16% E 9 5 1.0~1.5 13 14 11~15 13 24 1~2 8 3 41~50 8% 1% SE 10 4 1.6~2.0 11 -311 16~20 12 17 3 2~3 4 3 PM2.5 concentrations were greater than 70 μg.m and less than 20 μg/m , and between 51~60 5% 15% S 17 3 2.1~2.5 8 12 21~25 29 21 3~5 6 2 61~70 2% 12% SW 23 6 2.6~3.0 6 9 26~30 31 10 5~10 7 3 these two values accounted for 21%, 20%, and 59%, of the observed PM2.5 71~90 2% 11% W 4 5 3.1~3.5 4 8 31~35 11 3 10~20 10 1 ≥90 concentrations,0% 10% NWrespectively.5 9 ≥3.6 5 14 ≥35 1 0 >20 3 0 Note:Note: Period Period 1 is from 1 isMarch from to August, March and Periodto August, 2 is from Septemberand Period to February 2 is of from the following September year. to February of the following year(2) Wind direction: During Period 1, eight wind directions had relatively uniform Nature Environment and Pollution Technology • Vol. 20, No.3, 2021 values, with westerly winds and northwesterly winds occurring 4% and 5% of the time, (1)respectively. PM2.5 concentration: During Period During 2, northerly Period and 1,northwesterly PM2.5 concentrations winds were most were frequent greater than -3 -3 70 μg.m and less than 20 μg.m , and between9 these two values accounted for 2%,

44%, and 54% of the observed PM2.5 concentrations, respectively. During Period 2,

-3 3 PM2.5 concentrations were greater than 70 μg.m and less than 20 μg/m , and between these two values accounted for 21%, 20%, and 59%, of the observed PM2.5 concentrations, respectively.

(2) Wind direction: During Period 1, eight wind directions had relatively uniform values, with westerly winds and northwesterly winds occurring 4% and 5% of the time, respectively. During Period 2, northerly and northwesterly winds were most frequent

9

1092 R. Xu et al.

The research area is located in a subtropical zone, with Trajectory fractal number and spatial distribution are little demand for heat in the winter and little seasonal differ- shown in Table 4. ence in atmospheric pollution emissions. Period 1 had more The majority of the air masses were transported along unfavorable meteorological conditions for pollutant diffusion Trajectory . This trajectory also had the highest PM2.5 than Period 2, but the air quality was better. This indicates concentrations. The two parameters during Period 2 were that the transport of pollutants from external sources needs higher than those in Period 1, which demonstrates better to be focused on. atmospheric quality during Period 1 (Table 2). Table 3 showed the variation of PM2.5 concentration Trajectory  originated from the south of the research from December 8 to 19 in 2019. As a background station, area, and 25.62% of the air masses were transported along the variation of PM2.5 concentration at Yaoshan station was this path. The average concentration of PM2.5 for this quite different from the other three stations. In the monitoring trajectory was 39.47 μg.m-3, the lowest among the three period, PM2.5 concentration at Yaoshan station increased trajectories. Its daily frequency of pollution was 12.63%. rapidly compared to other stations. After that, PM2.5 con- The trajectory was the largest and originated from the centration at the other three stations just began to increase, northeast. Its average concentration of PM2.5 was 45.94 which reflected the spatial differences of stations. μg.m-3, however, the daily frequency of pollution was Analysis of External Source Transmission 14.41%, which was significantly higher than trajectories  and . The trajectory  had a length of 662.9 km and The HYSPLIT4 model was used to calculate the backward originated from the southwest, and had the least quantity air mass trajectory in the research area, while the PSCF of air masses (11.29%). Its average concentration of PM2.5 and CWT methods were used to locate and quantify the was 55.27 μg.m-3, however, its daily frequency of pollution trajectory sources. was only 9.7%. Analysis of trajectory simulation results: The first simula- Yaoshan station was regarded as the background value tion used the Guilin Environmental Monitoring Station as the station of external pollution. The clustering results of the air target. The simulated altitude was 1000 m, which corresponds masses and PM2.5 concentrations were used to divide pollu- to 700 m in Guilin. The simulation ran from July 1, 2017, tion sources into internal and external sources and calculate to June 30, 2018, and 48 hours was used as the backward their proportion. The results are shown in Table 5. trajectory parameter. The TSV changed over 30%, indicating External sources were responsible for more than 76% an acceptable allocated clustering number. The 365-day air of the pollution, on average. Referring back to Table 4, mass trajectory lines were organized into three categories: the air quality was good, and the pollution in period 1 was Trajectories ,  and  (Fig. 3). generally lower.

Table 3: PM2.5 concentration at four stations from December 8 to 19 in 2019.

- PM2.5(μg.m ³) 8th School station Longyin station Monitor station Yaoshan station Dec-8 31 34 32 66 Dec-9 38 41 39 72 Dec-10 75 75 68 90 Dec-11 92 93 89 123 Dec-12 94 101 98 128 Dec-13 98 103 93 93 Dec-14 129 136 121 85 Dec-15 128 134 122 68 Dec-16 95 98 96 45 Dec-17 82 88 85 36 Dec-18 75 78 76 30 Dec-19 49 65 59 42

Vol. 20, No. 3, 2021 • Nature Environment and Pollution Technology

SPATIAL AND TEMPORAL CHARACTERISTICS OF PM2.5 SOURCES 1093

In the second period, pollution was more severe. Ex- PSCF result analysis: The daily mean concentration of -3 ternal sources contributed 84.7% of the total pollution, and PM2.5 (75 µg.m ) was used as the threshold. The total resi- Trajectory  accounted for the highest proportion (95.1%). dence time of the trajectory in the grid and the daily average External sources accounted for 83% of the annual pollution, value was 48 h. The simulation was run for 365 days, and 10 of which Trajectory  accounted for the highest proportion days was used as the average residence time in the grid. To (94.2%). The contribution of external pollution from the “Xiang Gui corridor” (Trajectory) was 83.2%. Trajectory  mainly represented pollution from external sources. In Trajectory , the contribution of pollution from external sources was high, but few air masses were transported along this trajectory. Therefore, this trajectory could not represent the primary cause of pollution in the study region. The majority of the air masses traveled along Trajecto- ry . It is not surprising that this trajectory carried more

pollutants and had higher PM2.5 concentrations. Trajectory also had high concentrations of PM2.5, but fewer air masses were transported along this trajectory. The trajectory 1 con-

centration of PM2.5 was close to the value of the target area. Trajectories  and  had longer transmission distances

and low PM2.5 concentrations. These trajectories do not trans- port enough pollution from external sources and should be taken as the main direction for the diffusion of internal source pollutionFig. in the 3: researchThe WPSCF area. Additionally, distribution by comparisonand clustering analysis results of backward trajectory in Table 4, Fig. 3, and Fig. 1, it was found that Trajectory  was coincident with the “Xiang-Gui Corridor.” It showed that this area should be taken as the key point for impact analysis Trajectory fractal number and spatial distributionFig. are3: The shown WPSCF distribution in Table and clustering 4. analysis results of of topographical changes. Fig. 3: The WPSCF distribution andbackward clustering trajectory analysis results of backward trajectory Table 4: Result analysis of trajectory clustering. Table 4: Result analysis of trajectory clustering. Trajectory fractal number and spatial distribution are shown in Table 4. day period one period one period two period two year trajectory year year trajectory PM2.5>75 pollution cluster trajectory PM2.5 trajectory PM2.5 path Tablelength（ km4:） Resultnumber analysis ratio of trajectoryPM2.5 clustering. (number) （number） frequency (number) (number) （%） (μg/m³) (μg/m³) (μg/m³) （ ） % day period one period one period two period two year trajectory year 1 34 47.9 61 33.21 95 448.5 year25.62 trajectory 39.47 12 12.63 PM2.5>75 pollution cluster trajectory PM trajectory PM path length（km） number ratio PM 2 125 54.64 104 35.48 229 2.5 276.2 2.5 63.09(number) 45.94 33 14.41 2.5 （number） frequency (number) (μg/m³) (number) (μg/m³) （%） (μg/m³) 3 23 -3 75.33 18 29.63 41 662.9 11.29 55.27 4 9.7 （%） wasall 55.27 μg182 .m , 57.37however,183 its daily134.15 frequency34 365 47.9 of1387.6 pollution61 33.21 was10095 only45.05 9.7%.448.5 50 25.6213.71 39.47 12 12.63 2 125 54.64 104 35.48 229 276.2 63.09 45.94 33 14.41 Note: Period 1 is from March to August, and Period 2 is from September to February of the following year Note: Period 1 is from March to August, and3 Period23 2 is from75.33 September18 to29.63 February41 of the following662.9 year 11.29 55.27 4 9.7 Table 5: Proportion of internalall and 182external57.37 pollution183 sources34.15 in365 different1387.6 periods. 100 45.05 50 13.71 Table 5: Proportion of internal and external pollution sources in different periods. Note: Period 1 is from March to August, and Period 2 is from September to February of the following year The majority of the air masses were transported along Trajectory ○2 . This Period 1 PM2.5 Period 1 PM2.5 Period 2 PM2.5 Period 2 PM2.5 Annual PM2.5 Annual PM2.5 proportion of proportion of proportion of proportion of proportion of proportion of ○ . trajectory also had the highest PMThe2.5 concentrations.majority of the Theair masses two parameters were transported during along Period Trajectory 2 This cluster external internal external internal external internal 2 were higherpollution than thosepollution in Periodtrajectory 1, also pollutionwhich had thedemonstrates highestpollution PM2.5 better concentrations.pollution atmospheric Thepollution two quality parameters during Period sources sources2 were highersources than those sourcesin Period 1, whichsources demonstratessources better atmospheric quality during1 Period 76%1 (Table 2). 23% 95.10% 4.90% 73.80% 26.20% 2 76.40% 23.60%during Period84% 1 (Table 2). 16% 83.20% 16.80% 3 Trajectory80.10% ○1 originated19.90% from the88% south of the 12%research area,94.20% and 25.62%5.80% of the Trajectory ○1 originated from the south of the research area, and 25.62% of the ALL 76.70% 23.30% 84.70% 15.30% 83% 17% air masses were transported alongair masses this path.were transported The average along concentration this path. The averageof PM2.5 concentration for this of PM2.5 for this Nature Environment and Pollution Technology • Vol. 20, No.3, 2021 trajectoryYaoshan was station 39.47 wasμg.m regarded-3, thetrajectory lowest as the was among background 39.47 theμg. mthree-3, valuethe trajectories. lowest station among of Its the external daily three frequencytrajectories. pollution. Its daily frequency ○ Theof pollutionclustering was results 12.63%. of the T heairof tpollutionmassesrajectory wasand ○2 12.63%.PM was2.5 the concentrationsThe largest trajectory and 2 owere riginatedwas theused largest from to divide and the o riginated from the -3 northeast. Its average concentration of PM-3 2.5 was 45.94 μg.m , however, the daily pollutinortheast.on sources Its average into internal concentration and external of PM sources2.5 was and 45.94 calculate μg.m ,their however, proportion. the daily The frequency of pollution was 14.41%, which was significantly higher than trajectories ○1 resultsfrequency are shown of pollution in Table wa s5 14.41%,. which was significantly higher than trajectories ○1 and ○3 . The trajectory ○3 had a length of 662.9 km and originated from the southwest, and ○3 . The trajectory ○3 had a length of 662.9 km and originated from the southwest, External sources were responsibleand had thefor least more quantity than of76% air massesof the (11.29%).pollution, Its onaverage average. concentration of PM2.5 and had the least quantity of air masses (11.29%). Its average concentration12 of PM2.5 Referring back to Table 4, the air quality was12 good, and the pollution in period 1 was generally lower.

In the second period, pollution was more severe. External sources contributed 84.7% of the total pollution, and Trajectory ○1 accounted for the highest proportion (95.1%). External sources accounted for 83% of the annual pollution, of which Trajectory ○3 accounted for the highest proportion (94.2%). The contribution of external pollution from the “Xiang Gui corridor” (Trajectory○2 ) was 83.2%. Trajectory ○2 mainly represented pollution from external sources. In Trajectory ○3 , the contribution of pollution from external sources was high, but few air masses were transported along this trajectory. Therefore, this trajectory could not represent the primary cause of pollution in the study region.

The majority of the air masses traveled along Trajectory ○2 . It is not surprising that this trajectory carried more pollutants and had higher PM2.5 concentrations. Trajectory

○3 also had high concentrations of PM2.5, but fewer air masses were transported along this trajectory. The trajectory ○1 concentration of PM2.5 was close to the value of the target area.

Trajectories ○1 and ○3 had longer transmission distances and low PM2.5 concentrations. These trajectories do not transport enough pollution from external sources and should be taken as the main direction for the diffusion of internal source

13

pollution in the research area. Additionally, by comparison in Table 4, Fig. 3, and Fig. 1, it was found that Trajectorypollution ○2 was in coincident the research with area. theAdditionally, “Xiang-Gui by comparison Corridor.” in It Table 4, Fig. 3, and Fig. showed that this area should1, beit wastaken found as that the Trajectory key point ○2 forwas impactcoincident ana withlysis the of “Xiang -Gui Corridor.” It topographical changes. showed that this area should be taken as the key point for impact analysis of topographical changes. -3 PSCF result analysis: The daily mean concentration of PM2.5 (75 µg.m ) was used as -3 The daily mean concentration of PM2.5 (75 µg.m ) was used as the threshold. The total residencePSCF time result of the analysis trajectory: in the grid and the daily average the threshold. The total residence time of the trajectory in the grid and the daily average value was 48 h. The simulation was run for 365 days, and 10 days was used as the value was 48 h. 1094The simulation was run for 365 days, and 10 daysR. wasXu et used al. as the average residence time in the grid. To avoid distortion caused by too high PSCF values average residence time in the grid. To avoid distortion caused by too high PSCF values in grids , empirical weight function, wasavoid introduced: distortion caused by too high PSCF values in grids ni, As shown in Table 4, PM2.5 concentrations peaked be- , W tween September 2017 and February 2018. Four pollution in grids , empiricalempirical weight weight function, function, , i,was was introduced: introduced: events were selected to interpolate the mean PM2.5 concen- trations from 19 stations using the Kriging interpolation . … (6) method. The four events chosen were from October 31 to . . … (6) … (6) November 9, December 13 to December 22, December 28 to January 6, and January 23 to February 1. The results are shown in Fig. 5. W n This formula indicates values of when This formulatrajectories indicates pass values through of i whengrids i trajectories This formula indicates valuesi of when trajectories pass throughPM grids2.5 concentrations were high in pass through grids at the above time points. The PSCF value at the above time points. The PSCFat the valueabove timeis thenis points. then given given The by byPSCF value is then given by.. and urban. areas. However, the PM2.5 concentration in Longsheng County and Gongcheng County was relative- The WPSCF values were distributed as the grid values ly low. This is consistent with Fig. 3 and Fig. 4, which The WPSCFof values Fig. 3. wereThe northeast distributed grid as had the large grid gray values values, of Figwhich. 3. The northeast The WPSCF values were distributed as the grid values of Fig. 3. The northeast showed that the external source of pollution came from indicated that the air pollution was transported along Trajec- grid had large gray values, which indicated that the air pollution was transportedthe northeast. along During the four pollution events, Ziyuan grid had large gray values, which indicated thattory the  .air Therefore, pollution the wassource transported was located inalong the grid in the County, the northeast of Xing’an County, and Guanyang Trajectory ○2 . Therefore,northeast of Trajectorythe source  .was Trajectories located  in and the  hadgrid smaller in the northeast of Trajectory ○2 . Therefore, the source was located in the grid in the northeast of in the “Xiang-Gui Corridor” showed relatively low PM grid values outside the research area and larger values within 2.5 Trajectory ○2 . Trajectories ○1 and ○3 had smaller grid values outside theconcentrations. research Trajectory ○2 . Trajectories ○1 and ○3 had smallerthe research grid area, values which outsidesuggests thatthe the research internal pollution The distribution of the blue belt varied with the pollution area and larger values withinarea the and research larger sourcevaluesarea, waswhichwithin consistent thesuggests research with thethat area, results the which internalobtained suggests from thethat the internal trajectory data. events. Its core area was northeast of Xing’an County, with pollution source was consistent with the results obtained from the trajectorythe data. Yuechengling Mountains to the northwest and the Ocean pollution source was consistent with the resultsCWT obtained results from analysis the :trajectory The PSCF data.method was used to Mountains in the Dupangling Mountains to the southeast. analyze the influence of pollution in the research area from CWT results analysis: The PSCF method was used to analyze the influenceThe peaks ofaffect the direct transport of the air masses and CWT results analysis: The PSCF method a wassemi-quantitative used to analyze perspective. the The influence CWT method of was also form obvious weakening zones. The Yaoshan Mountains to pollution in the researchused to determine area from the a source semi- quantitativeof the pollutants. perspective. The backward The CWT method pollution in the research area from a semi-quantitative perspective. The CWT method the northeast of the urban areas act as barriers and prevent was also used to trajectoriesdetermine theare shownsource inof Fig the. pollutants.4. The trajectories The backward from the trajectories are was also used to determine the source of the pollutants.northeast and Thesouth backward are presented trajectories as dark grids inare a consistent shown in Fig. 4. directionThe trajectories of the cluster from trajectory the northeast line. By and comparison, south are areas presented as dark shown in Fig. 4. The trajectories from the northeast and south are presented as dark grids in a consistentwith direction dark grids of could the coincidecluster trajectoryin Fig. 3 and line. Fig .By 4, indicating comparison, areas with grids in a consistent direction of the cluster trajectorythat the sources line. wereBy comparison, located in the northeastareas with and south of dark grids could coincidethe research in area.Fig. 3 and Fig. 4, indicating that the sources were located dark grids could coincide in Figin. the3 and northeast Fig. 4, andCharacteristics indicating south of the that researchof the external sources area. source were transmission located : Ac- in the northeast and south of the research area.cording to the WCWT distribution and clustering results of backward trajectories, it is known that (1) Trajectory  accounted for 63.09%, with an average PM2.5 concentration of 45.94 μg.m-3. (2) The dark grids were in the northeast, implying that the “Xiang-Gui Corridor” is the source of the pollutants. (3) Dark grids were also located in the north and northeast of the research area, where there is a lot of indus- tries. In other areas of the research region, the darker grids coincided with trajectories  14and . Based on the above analysis, it can be inferred that the air 14pollution in the research area was emitted from a combination of external sources (i.e., from the “Xiang-Gui Corridor”) and internal sources.

Analysis of Internal Source Pollution Note: 0 Quanzhou County, 1 Ziyuan County, 2 Longsheng County, 3 Note: 0 Quanzhou County,Xing’an 1 County,Ziyuan 4 County,Lingchuan 2 County, Longsheng 5 Guanyang County, County, 3 Xing’an6 Lingui County, 4 Lingchuan The previous section analyzed external sources of air pollu- County, 7 Yongfu County, 8 the urban area, 9 Gongcheng County, 10 tion and air mass trajectories. It is natural County,to next consider5 Guanyang County,Yangshuo 6 Lingui County, County, 11 Pingle 7 County, Yongfu and County, 12 Lipu County 8 the urban area, 9 Gongcheng how external and internal sources jointly impactCounty, pollution 10 Yangshuo Fig. County, 4: The WCWT11 Pingle distribution County, and and clustering 12 Lipu analysis County results of back- using a spatial interpolation method. ward trajectory Fig. 4: The WCWT distribution and clustering analysis results of backward trajectory Vol. 20, No. 3, 2021 • Nature Environment and Pollution Technology Characteristics of external source transmission: According to the WCWT distribution and clustering results of backward trajectories, it is known that (1)

Trajectory ○2 accounted for 63.09%, with an average PM2.5 concentration of 45.94 μg.m-3. (2) The dark grids were in the northeast, implying that the “Xiang-Gui Corridor” is the source of the pollutants. (3) Dark grids were also located in the north and northeast of the research area, where there is a lot of industries. In other areas of the research region, the darker grids coincided with trajectories ○1 and ○3 .

Based on the above analysis, it can be inferred that the air pollution in the research area was emitted from a combination of external sources (i.e., from the “Xiang-Gui Corridor”) and internal sources.

Analysis of Internal Source Pollution

The previous section analyzed external sources of air pollution and air mass trajectories. It is natural to next consider how external and internal sources jointly impact pollution using a spatial interpolation method.

As shown in Table 4, PM2.5 concentrations peaked between September 2017 and

15

SPATIAL AND TEMPORAL CHARACTERISTICS OF PM2.5 SOURCES 1095 the air masses from directly entering the urban areas, causing CONCLUSIONS the four pollution events. This study focused on the sources and transport of PM2.5 The four periods in Fig. 5 all showed that Quanzhou in the city of Guilin. A combination of PM2.5 daily average County in the northeast and the urban areas in the center and meteorological parameters data, and simulations using contain high concentrations of pollution. This shows the HYSPLIT model were used to analyze pollutant sources, the Februaryeffect of the 2018. internal Four and externalpollution sources events and sugwere- internalselected source to diffusion,interpolate and thethe effect mean of topographyPM2.5 on gests that while Quanzhou County is affected by the pollutants in the research area. Conclusions could be drawn northeasternconcentrations air mass, fromthe internal 19 stations sources using also contrib the Kriging- as follows. interpolation method. The four events ute toward the accumulation of pollution in the urban 1. From 2014 to 2019, heavy pollution periods in the areas.chosen were from October 31 to November 9, Decemberresearch are13a to occurred December from September 22, December to February. 28 to January 6, and January 23 to February 1. The results are shown in Fig. 5.

a b

c d Note: A: October 31 to November 9, B: December 13 to December 22, C: December 28 to January 6, D: January 23 to February 1 Note: A: October 31 toFig. November 5: The interpolation 9, B: resultsDecember of four pollution13 to December processes in Winter 22, C:in Guilin. December 28 to January 6, D: January 23 to February 1 Nature Environment and Pollution Technology • Vol. 20, No.3, 2021 Fig. 5: The interpolation results of four pollution processes in Winter in Guilin.

PM2.5 concentrations were high in Quanzhou County and urban areas. However,

the PM2.5 concentration in Longsheng County and Gongcheng County was relatively low. This is consistent with Fig. 3 and Fig. 4, which showed that the external source of pollution came from the northeast. During the four pollution events, Ziyuan County, the northeast of Xing’an County, and Guanyang in the “Xiang-Gui Corridor” showed

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Sources and geographic origin of particulate matter in urban areas southern, and southeastern sources, formed a second of the Danube macro-region: The cases of Zagreb (Croatia), Budapest regional center. (Hungary), and Sofia (Bulgaria). Sci. Tot. Environ., 619-620: 1515-1529. Sun, K., Liu, X., Gu, J., Li, Y., Qu, Y., An, J. Wang, J. 2015. Chemical char- acterization of size-resolved aerosols in four seasons and hazy days in ACKNOWLEDGMENTS the megacity Beijing of China. J. Environ. Sci., 32: 5-167. Sun, R., Fan, L. and Chen, Z., 2019. Temporal and spatial distribution charac- This work was supported by the National Natural Sci- teristics of atmospheric PM 2.5 concentrations in Guiyang, China. Nat. ence Foundation of China [grant number 41967042]; the Environ. Pollut. Technol., 18(2). State-supported Special Fund Project for Local Science and Wang, M., Cao, C., Li, G. and Singh, R. P. 2015. 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Vol. 20, No. 3, 2021 • Nature Environment and Pollution Technology