atmosphere

Article Characteristics of the Transport of a Typical Pollution Event in the Area Based on Remote Sensing Data and Numerical Simulations

Ying Zhang 1,2,*, Zhihong Liu 1, Xiaotong Lv 3, Yang Zhang 4 and Jun Qian 3

1 College of Resources and Environment, Chengdu University of Information Technology, Chengdu 610225, China; [email protected] 2 Wenjiang Meteorological Bureau, Chengdu 611130, China 3 Academy of Environmental Sciences, Chengdu 610041, China; [email protected] (X.L.); [email protected] (J.Q.) 4 Environment Protection Key Laboratory of Satellite Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, 100101, China; [email protected] * Correspondence: [email protected]; Tel.: +86-28-8596-6930

Academic Editor: Giovanni Pitari Received: 27 July 2016; Accepted: 30 September 2016; Published: 9 October 2016

Abstract: A heavy air pollution event occurred in Chengdu between 7 May 2014 and 8 May 2014. The present study established tracer sources based on HJ-1 satellite data, micropulse light detection and ranging (LiDAR) remote sensing data, and backward trajectories simulated using the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model. Additionally, the present study analyzed the diffusion conditions for the sources and characteristics of the pollutant transport in this pollution event through simulation using a mesoscale atmospheric chemistry transport model—the weather research forecasting model with chemistry (WRF–CHEM). The results show that the change in the boundary-layer height over Chengdu had a relatively large effect on the vertical diffusion of pollutants. During the pollution event, Chengdu, Meishan, and were areas of significantly low mean ventilation coefficients (VH). In Chengdu, the VH was extremely low at night, and there was a temperature inversion near the ground, resulting in the continuous accumulation of pollutants at night and a continuous worsening of the pollution. During the period of heavy pollution, there were straw-burning sites in Meishan, , , , and . On 7 May 2014, the pollutants in Chengdu mainly originated from Meishan. The accumulation in Chengdu of pollutants originating in Meishan and Deyang led to highly concentrated pollution on 8 May 2014, to which the pollutants originating in Deyang were the main contributor. The transport of pollutants resulting from straw burning in the study area and the relatively poor conditions for the pollutant diffusion in Chengdu collectively led to the heavy air pollution event investigated in the present study.

Keywords: remote sensing; weather research forecasting model with chemistry; air pollution; transport simulation

1. Introduction A continuous rise in the frequency of heavy pollution events and a continuous increase in the area affected by heavy pollution has recently been observed in China; additionally, regional atmospheric environmental problems involving inhalable and fine particulate matter as the characteristic pollutants have become increasingly prominent and attracted extensive attention from all sectors of society. Therefore, based on the current regional and complex characteristics of atmospheric pollution in China, it is necessary to strengthen the fundamental research on atmospheric pollution sources and regional pollution transport. Such research will provide a basis for regional heavy pollution weather monitoring

Atmosphere 2016, 7, 127; doi:10.3390/atmos7100127 www.mdpi.com/journal/atmosphere Atmosphere 2016, 7, 127 2 of 16 and warning and the joint prevention and control of regional atmospheric pollution in key areas [1]. Currently, many studies on the environmental problems caused by the transport of pollutants have been conducted in China [1]. Wang et al. [2] simulated the dust-haze pollution that occurred in southern Hebei between 2001 and 2010 using the community multi-scale air quality model and observed that 65% of the particulate matter less than 2.5 µm in diameter (PM2.5) in and Xingtai had originated from local emissions and that the remainder originated from Shanxi and northern Hebei. He et al. [3] analyzed a severe dust-haze pollution event that occurred in Beijing in 2013 through simulations using the weather research forecasting model with chemistry (WRF–CHEM) and noted that the relatively low wind speed and relatively low boundary layer were the main causes of the peak PM2.5 concentrations, and that the local pollution sources contributed to as much as 78% of this pollution event. Cheng et al. [4] analyzed the sources of sulfur dioxide (SO2) in a heavy pollution event that occurred in Beijing based on the comprehensive air quality model with extensions and noted that external pollution sources contributed to as much as 83% of the SO2 concentration during this pollution event. Zhu et al. [5] analyzed an air pollution event that occurred in and its surrounding area through the comprehensive use of satellite monitoring data, meteorological observation data, and a backward trajectory simulation model and observed that the atmospheric pollutants resulting from straw burning in central and northern Jiangsu was the main pollutant source of this event. Regarding these types of studies, two methods have been used in the literature. One method conducts analysis based on monitoring data such as air quality and meteorological conditions. Monitoring data can truly reflect the intensity of pollution events. In addition, monitoring data combined with a receptor model can be used to quantitatively analyze the main sources of atmospheric pollutants, and can also reflect the characteristics of pollutant sources at different times and different monitoring points relatively well [6,7]. However, monitoring data cannot visually reflect the transboundary transport or transport processes of pollutants. The other method conducts analysis through model simulation by inputting a list of pollution sources compiled from statistical data on pollutants. However, these pollutant emission inventories have certain inherent errors. Consequently, it is difficult to know the location and distribution of pollutants in a timely and accurate manner based on an emission inventory. Therefore, the two aforementioned methods cannot accurately capture or predict the occurrence and development of typical heavy pollution events, thereby significantly reducing the capacity of the state to respond to heavy pollution events. The present study obtained relevant pollutant information based on satellite remote sensing data, ground-based remote sensing data, and backward trajectories simulated using the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model and simulated the pollutant gas transport process during a typical pollution event using WRF–CHEM. The present study attempts to simulate and monitor typical heavy pollution processes by comprehensively using remote sensing data and models, and is important for the monitoring, prevention, and control of regional atmospheric pollution. Remote sensing techniques are characterized by large spatial scales and can be employed to adequately obtain information on the spatial distribution of pollutants. The HJ-1 satellite system is an Earth observation system specifically designed for environmental and disaster monitoring; can perform wide-range, long-term, and high-precision dynamic monitoring of environmental change in China; and rapidly obtain information in areas where pollutants are distributed [8–10]. Of the ground-based remote sensing systems, micropulse light detection and ranging (LiDAR) (MPL) is a new-generation, high-tech product that rapidly monitors the atmospheric environment. The detector of an MPL device uses a single-channel photomultiplier module to count single photons, and each counter channel has a minimum temporal resolution of 200 ns. An MPL device can rapidly monitor the atmospheric environment of an area of several kilometers in real time. This system can obtain the characteristics of the vertical profile and temporal evolution of the atmospheric aerosol extinction coefficient (AEC) [11,12]. Additionally, the HYSPLIT model can be used to understand the trajectory of tracer air mass movements or atmospheric particles and is currently widely used to research the transport of environmental atmospheric pollution [13,14]. WRF–CHEM is a new generation of regional Atmosphere 2016, 7, 127 3 of 16

Atmosphere 2016 7 and is currently, , 127widely used to research the transport of environmental atmospheric pollution [133, of14 16]. WRF–CHEM is a new generation of regional air quality model, and its meteorological and chemistry modulesair quality are model, completely and its coupled meteorological online. and WRF chemistry–CHEM can modules not only are completely simulate the coupled evolution online. of WRF–CHEMmeteorological can conditions not only simulate, but also the pre evolutiondict and of meteorological simulate the conditions, transport and but also spatiotemporal predict and distributionsimulate the of transport pollutants and [1 spatiotemporal5–18]. distribution of pollutants [15–18]. The is located in central-easterncentral-eastern Sichuan Province and is surrounded by high mountains. Because Because of of its its closed closed topography, topography, the the wind wind speed speed in the in Sichuan the Sichuan Basin Basin is perennially is perennially low, whichlow, which is unfavorable is unfavorable for pollutant for pollutant diffusion. diffusion. Currently, Currently, the Sichuan the Sichuan Basin is Basin one of is the one four of thenotable four heavynotable dust heavy-haze dust-haze areas in areas China in [ China19]. Chengdu [19]. Chengdu is the is capital the capital of Sichuan of Sichuan Province Province and and a hub a hub for economicfor economic development development in western in western China. China. In January, In January, February, February, May, October May, October,, and December, and December, heavy pollutionheavy pollution occurs continuously occurs continuously and at high and frequency at high frequency in the Chengdu in the Chengdu area. Heavy area. pollution Heavy occurring pollution betweenoccurring December between December and February and February of subsequent of subsequent years results years in results seasonal in seasonal variations variations of heavy of pollutionheavy pollution related relatedto meteorological to meteorological conditions. conditions. This heavy This pollution heavy pollution mainly mainlyresults resultsfrom the from heavy the pollutantheavy pollutant emission emission load, low load, precipitation, low precipitation, relatively relatively few rainy few days rainy, and days, frequent and frequent occurrence occurrence of static andof static stable and atmospheric stable atmospheric weather c weatheronditions conditions during this during period, this which period, are which unfavorable are unfavorable for pollutant for dispersion.pollutant dispersion. May and October May and compose October the compose harvesting the season harvesting for the season main staple for the crops main (rapeseed staple crops and paddy(rapeseed rice) and grown paddy in rice)the farmlands grown in thearound farmlands Chengdu, around during Chengdu, which duringtime farmers which start time to farmers burn large start amountsto burn large of straw. amounts Therefore, of straw. the Therefore, heavy pollutionthe heavy occurringpollution occurring in May and in May October and Octoberis from is straw from burningstraw burning [20,21 []20. The,21]. severe The severe pollution pollution situation situation in Chengdu in Chengdu has has had had a a significant significant impact impact on on the economic development of the region and the living conditions of its residents. A severe atmospheric pollution event occurred in the Chengdu area in earlyearly MayMay 2014.2014. Figure 11 showsshows thethe monitoringmonitoring results obtained obtained from from the the automatic automatic air air-monitoring-monitoring substation substation in Sichuan in Sichuan Province. Province. The value The value of each of indexeach index continuously continuously decreased decreased from from 1 May1 May to 5to May 5 May,, but but the the overall overall air air quality quality remained remained at at the moderate pollution pollution level. level. The The concentration concentration of each of each pollutant pollutant started started to increase to increase on 6 May on 6 and May rapidly and rapidly increased to the maximum on 7 May. The peak PM2.5 and PM10 concentrations on 7 May increased to the maximum on 7 May. The peak PM2.5 and PM10 concentrations on 7 May reached 180reached and 180284 andµg/m 2843, μg/m respectively,3, respectively, reflecting reflecting that the that air the quality air quality had reached had reached a severe a severe pollution pollution level. Additionally,level. Additionally, analysis analysis of black of carbonblack carbon using using a black a carbonblack carbon analyzer analyzer showed showed that the that daily the meandaily meanconcentration concentration of black of black carbon carbon was was 8.69 8.69µg/m μg/m3 on3 on 7 May 7 May and and 9.20 9.20µ g/mμg/m3 3on on 88 May,May, whichwhich were much higher than the values onon otherother daysdays ((<4.0<4.0 µμg/mg/m3).). The The sudden sudden increase increase in in the black carbon concentration reflectedreflected the effect of straw burning. burning. This This atmospheric pollution pollution event event gave gave rise to heavy pollution conditions in the Chengdu Chengdu area area in in 2014 2014 that that were were relatively relatively typical typical of of straw straw burning. burning. Therefore, thethe present present study study analyzed analyzed the diffusion the diffusion conditions conditions related to relatedthe sources to andthe characteristicssources and characteristicsof pollutant transport of pollutant during transport this air pollution during event this throughair pollution simulation eventusing through a mesoscale simulation atmospheric using a mesoscalechemistry transportatmospheric model—WRF–CHEM. chemistry transport model—WRF–CHEM.

Figure 1. DailyDaily concentration concentration of pollutants pollutants in Chengdu between 1 May 2014 and 9 May May 2014 2014,, extracted extracted from ground ground-based-based monitoring data of an automatic air-monitoringair-monitoring substation in Sichuan Province ◦ ◦ (30.63°NN,, 104.07 °EE).). Atmosphere 2016, 7, 127 4 of 16

2. DataAtmosphere Sources 2016, 7, 127 and Research Methods 4 of 16

2.1.2. Data Data Sources Sources and Research Methods

2.1.The Data air Sources mass concentration data were obtained from the automatic air-monitoring substation ◦ ◦ in SichuanThe air Province mass concentration (30.63 N, 104.07 data wereE) obtained and included from the the automatic hourly PMair-monitoring2.5 data between substation 7 May in 2014 andSichuan8 May Province 2014, and (30.63 the daily°N, 104.07 mean°E) PM and2.5 included, PM10, andthe hourly black carbonPM2.5 data aerosol between data 7 betweenMay 2014 1 and May 2014 and8 9May May 2014 2014, and. The the PMdaily2.5 meanand PM 2.510, PMconcentrations10, and black carbon were measuredaerosol data using between a BAM-1020 1 May 2014 particulateand monitor,9 May 2014. which The produced PM2.5 and PM by10 American concentrations Met were One measured company. using This a particulateBAM-1020 particulate monitor monitor automatically, measureswhich produced and records by American the particulate Met One concentrations company. This in particulate the air based monitor on the automaticallyβ-ray attenuation measures principle andand has records been verified the particulate by the concentrations U.S. Environmental in the air Protection based on the Agency β-ray (EPA).attenuation Black principle carbon and aerosols has were observedbeen verified using an by Aethalometer the U.S. Environmental™, which is Protection produced Agency by American (EPA). MageeBlack carbon company. aerosols This were instrument observed using an Aethalometer™, which is produced by American Magee company. This can be employed to observe atmospheric black carbon aerosols simultaneously at seven wavelengths in instrument can be employed to observe atmospheric black carbon aerosols simultaneously at seven the ultraviolet, visible, and near-infrared spectra and has been verified by the U.S. EPA’s Environmental wavelengths in the ultraviolet, visible, and near-infrared spectra and has been verified by the U.S. TechnologyEPA’s Environmental Verification Technology Program. Verification The aerosol Progra opticalm. depthThe aerosol (AOD) optical data depth were (AOD) obtained data by were inverting theobtained monitoring by inverting data acquired the monitoring by the data HJ-1B acquired satellite by ofthe the HJ- HJ-11B satellite satellite of the system. HJ-1 satellite The AEC system. data were obtainedThe AEC by inverting data were the obtained LiDAR by data inverting acquired the from LiDAR the data comprehensive acquired from substation the comprehensive at the Chengdu Universitysubstation of Information at the Chengdu Technology University (30.58 of◦ N, Information 103.99◦E). Technology The meteorological (30.58°N, data 103.99 for°E the). HYSPLIT4The modelmeteorological originated data from for the the U.S. HYSPLIT4 National model Oceanic originated and Atmospheric from the U.S. Administration’s National Oceanic global and data assimilationAtmospheric system Administration (GDAS).’sThe global meteorological data assimilation data system for (GDAS). the HYSPLIT4 The meteorological model had data a horizontal for resolutionthe HYSPLIT4 of 1◦ model× 1◦, had 12 isobaric a horizontal layers resolution vertically of 1° from × 1°, 12 1000 isobaric to 50 layers hPa, vertically and a time from interval 1000 to of 6 h. 50 hPa, and a time interval of 6 h. The final (FNL) reanalysis data (spatial resolution: 1° × 1°; time interval: The final (FNL) reanalysis data (spatial resolution: 1◦ × 1◦; time interval: 6 h) released by the U.S. 6 h) released by the U.S. National Centers for Environmental Prediction were used as the meteorological National Centers for Environmental Prediction were used as the meteorological field data required field data required by WRF–CHEM. Figure 2 shows the locations of the automatic air-monitoring by WRF–CHEM.substation in Sichuan Figure 2 Province shows theand locations the comprehensive of the automatic substation air-monitoring at the Chengdu substation University in of Sichuan ProvinceInforma andtion the Technology. comprehensive substation at the Chengdu University of Information Technology.

Figure 2. The locations of the automatic air-monitoring substation in Sichuan Province and the Figure 2. The locations of the automatic air-monitoring substation in Sichuan Province and the comprehensive substation at the Chengdu University of Information Technology (notes: the red dot comprehensive substation at the Chengdu University of Information Technology (notes: the red dot signifies the automatic air monitoring substation in Sichuan Province, and the green dot signifies the signifies the automatic air monitoring substation in Sichuan Province, and the green dot signifies the comprehensive substation at the Chengdu University of Information Technology). comprehensive substation at the Chengdu University of Information Technology). 2.2. Research Methods 2.2. Research Methods 2.2.1. Acquisition of Tracer Sources 2.2.1. AcquisitionThe tracer of sources Tracer used Sources in the WRF–CHEM analysis were obtained by comprehensively analyzingThe tracer the sources AOD data, used the in AEC the WRF–CHEMdata obtained analysisfrom inverting were obtainedthe MPL data, by comprehensively and the background analyzing the AOD data, the AEC data obtained from inverting the MPL data, and the background trajectories simulated using the HYSPLIT4 model. The charge-couple device and infrared-scanning radiometer Atmosphere 2016, 7, 127 5 of 16

Atmosphere 2016, 7, 127 5 of 16 installedtrajectories on the simulated HJ-1B satellite using the were HYSPLIT4 used tomodel. invert The the charge high-resolution-couple device AOD and datainfrared for-scanning the Sichuan Basinradiometer on 7 May installed 2014, and on thethe tracerHJ-1B sourcessatellite were were used determined to invert based the high on-resolution the inversion AOD results. data for The the time at whichSichuan pollutants Basin on 7 were May 2014, emitted and fromthe tracer the tracersources sources were determined was determined based on basedthe inversion on the results. AEC data obtainedThe time from at thewhich inverted pollutants LiDAR were data. emitted The from transport the tracer trajectories sources was of the determined air masses based at different on the AEC heights (50,500data and obtained 1000 from m) over the inverted Chengdu LiDAR (30.67 data.◦N, 104.06The transport◦E) were trajectories simulated of usingthe air themasses HYSPLIT4 at different model. Theheights trajectories (50,500 over and the1000 course m) over of Chengdu the 72 h (30.67 prior°N to, 104.06 0000UTC°E) were on simulated 9 May, at using which the time HYSPLIT4 the heavy pollutionmodel. process The trajectories was nearly over over, the course were traced.of the 72 The h prior general to 00 directions00UTC on from 9 May which, at which the pollutants time the in Chengduheavy originated pollution process were determined was nearly based over, on were the traced. trajectories. The general directions from which the pollutants in Chengdu originated were determined based on the trajectories. Figure3 shows the results of the inverted AOD data for the Sichuan Basin on 7 May 2014, and Figure 3 shows the results of the inverted AOD data for the Sichuan Basin on 7 May 2014, and the backward trajectories of pollutant transport in the Sichuan Basin. On 7 May, the air quality was the backward trajectories of pollutant transport in the Sichuan Basin. On 7 May, the air quality was relatively poor in Chengdu, Deyang, Suining, and , and the air pollution was relatively relatively poor in Chengdu, Deyang, Suining, and Nanchong, and the air pollution was relatively severesevere in Ziyang, in Ziyang, Zigong, Zigong, Neijiang, Neijiang and, and western western Meishan. In In Chengdu Chengdu,, the the AOD AOD ranged ranged from from 1.1– 1.1–1.81.8 andand exceeded exceeded 1.4 1.4 in mostin most areas. areas. These These values values were were significantly significantly higherhigher than the the annual annual mean mean AOD AOD in Chengduin Chengdu between between 2008 2008 and and 2012 2012 (0.70–0.95) (0.70–0.95) [22 [],22 which,], which, to to a a certain certain degree, degree, indicates the the severity severity of the pollution.of the pollution. Additionally, Additionally, based based on the on backward the backward trajectories, trajectories, the airthe massesair masses at 30 at and30 and 500 500 m mainlym movedmainly from moved Meishan, from NeijiangMeishan, Neijiang and Zigong and Zigong to Chengdu, to Chengdu, whereas whereas the air the masses air masses at 1000 at 1000 m mainly m originatedmainly originated from Ziyang. from Meishan, Ziyang. Meishan, Neijiang, Neijiang, Zigong, Zigong and Ziyang, and Ziyang contained contained small small areas areas of highof high AOD. Initially,AOD. it Initially, was believed it was thatbelieved the aforementionedthat the aforementioned areas corresponded areas correspond to straw-burninged to straw-burning sites andsites that the resultingand that the pollutants resulting werepollutants continuously were continuously transported transported to Chengdu. to Chengdu.

Figure 3. Aerosol optical depth (AOD) in the Sichuan Basin and backward trajectories of air mass Figure 3. Aerosol optical depth (AOD) in the Sichuan Basin and backward trajectories of air mass transporttransport over over Chengdu Chengdu (30,500 (30,500 and and 1000 1000 m).m). Note:Note: the the AOD AOD data data originated originated from from the thedata data acquired acquired by theby the HJ-1 HJ satellite-1 satellite on on 7 May7 May 2014. 2014.

Figure 4 displays that the variation in the PM2.5 concentration was essentially consistent with Figure4 displays that the variation in the PM 2.5 concentration was essentially consistent with that of thethat AEC. of the Therefore, AEC. Therefore it is believed, it is believed that the that AEC the variation AEC variation could reflectcould reflect the variation the variation in the in pollutant the pollutant concentration during the pollution event. Because only the hourly PM2.5 concentration data concentration during the pollution event. Because only the hourly PM2.5 concentration data between between 7 May 2014 and 8 May 2014 were available, the variation pattern of the PM2.5 concentration 7 May 2014 and 8 May 2014 were available, the variation pattern of the PM concentration before before 7 May 2014 could not be analyzed. Therefore, the present study used the2.5 AEC data to analyze 7 May 2014 could not be analyzed. Therefore, the present study used the AEC data to analyze the time the time at which the tracer sources emitted pollutants. Figure 5 shows the hourly change in the AEC at whichbetween the tracer4 May sources 2014 and emitted 9 May pollutants. 2014. Figure Figure 5 shows5 shows that the the hourly AEC change exhibited in the a small AEC between peak 4 May(approximately 2014 and 9 0.8 May km 2014.−1) at 1700 Figure UTC5 shows on 4 May that and the was AEC less exhibited than 0.3 akm small−1 during peak the (approximately other time 0.8 km−1) at 1700 UTC on 4 May and was less than 0.3 km−1 during the other time periods, indicating that it could be neglected. The AEC started to increase at 0800 UTC on 6 May and reached its peak at 2300 UTC on 6 May, after which it started to decrease and reached its minimum at 0800 UTC on 7 May. Atmosphere 2016,, 7,, 127 6 of 16 Atmosphere 2016, 7, 127 6 of 16 periods, indicating that it could be neglected. The AEC started to increase at 0800 UTC on 6 May and reached its peak at 2300 UTC on 6 May,, afterafter whichwhich itit startedstarted toto decreasedecrease andand reachedreached itsits minimumminimum Thisat minimum 0800 UTC was on 7 greater May.. ThisThis than minimumminimum the AEC was during greater the than morning the AEC rush during hours the on morning5 May ,rush suggesting hours on that other5 May factors,, suggestingsuggesting must have thatthat been otherother contributing factorsfactors mustmust havehave to the beenbeen overall contributingcontributing increase toto the inthethe overalloverall number increaseincrease of aerosol inin thethe numbernumber particles. Betweenof aerosol 0800 UTC particles. on 7 Between May and 08 000000 UTC UTC on on 7 8May May, and the 0 AEC000 UTC exhibited on 8 May a variation,, the the AEC AEC trend exhibited exhibited identical aa to variationiation trendtrend identicalidentical toto thatthat which occurred during the same time period on the previous day: that which occurred during the same time period on the previous day: reaching its peak (approximately reaching its peak (approximately 2.7 km−−11)) atat 00000 UTC on 8 May,, whichwhich waswas comparativelycomparatively greatergreater 2.7 km−1) at 0000 UTC on 8 May, which was comparatively greater than the peak AEC on 6 May. thanthan thethe peakpeak AECAEC onon 6 May.. AfterAfter 00000 UTC on 8 May,, thethe AECAEC graduallygradually decreaseddecreased andand reachedreached aa Aftervalley 0000 UTCat 0700 on UTC, 8 May, which the AEC was gradually comparatively decreased greater and than reached the valley a valley on 7 at May 0700.. Thus, Thus, UTC, it it which can can be be was comparativelyconcluded that greater the pollution than the valleywas more on severe 7 May. on Thus, 8 May it canthanthan be on concluded 7 May.. TheThe that AECAEC the onon 9 pollution May was was lower more severethanthan on that that 8 May on on than the the previ previ on 7ous May. two The days, AEC and on the9 May pollutants was lower gradually than that dissipated. on the previous The AEC two increased days, and the pollutantssuddenly on gradually 7 May and dissipated. 8 May.. Thus,Thus, The ititAEC waswas inferredinferred increased thatthat suddenly externalexternal factorsfactors on 7 May resultedresultedand 8inin May. thethe significantsignificant Thus, it was inferredincreaseincrease that inin external thethe numbernumber factors ofof aerosolaerosol resulted particlesparticles in the onon significant 7 May and increase 8 May.. BasedBased in the onon number thethe blackblack of carbon aerosol monitoring particles on 7 Mayresults, and 8it May.was determined Based on thethat blackstraw carbonwas burned monitoring on 7 May results, and 8 May it was,, andand determined thatthat thethe burningburning that strawstartedstarted was burnedat 08 on00 7UTC May on and 6 May 8 May, and and7 May that.. the burning started at 0800 UTC on 6 May and 7 May.

FigureFigure 4. Hourly 4.. Hourly change change in in the the aerosol aerosol extinction extinction coefficientcoefficient ( (AEC)AEC)) data data for for Chengdu Chengdu and and the the observed observed

PM2.5PM(particulate2.52.5 (particulate(particulate matter mattermatter less lessless than thanthan 2.5 2.52.5 µµmµmm inin diameter)diameter) diameter) concentration concentration during during the pollution the pollution event. event.

FigureFigure 5. Hourly 5.. Hourly change change in in the the AEC AEC from from 1600 1600UTC UTC onon 4 May 2014, 2014, to to 1600 1600 UTC UTC on on 9 May 9 May 2014. 2014. Note Note thatthat 0000that 00000000 UTC UTCUTC is the isis thethe same samesame 0800 00800 local llocal sidereal siderealidereal time ttimeime(LST). (LST).(LST).

Based on the AOD data inversion results, the backward trajectories of the pollutants in Chengdu andBased the on AEC the simulation AOD data results, inversion it was results, inferred the that backward straw--burning trajectories sites of were the pollutantslocated in Meishan, in Chengdu andZiyang, the AEC Neijiang simulation,, and results,Zigong, itsouth was of inferred Chengdu that,, and straw-burning that the burning sites started were at located 0800 UTC. in Meishan, The Ziyang,pollutants Neijiang, resulting and from Zigong, straw southburning of continuously Chengdu, andconverg thatinging the inin Chengdu,Chengdu, burning therebythereby started resultingresulting at 0800 inin UTC. The pollutantsthethe heavyheavy pollutionpollution resulting event.event. from However,However, straw burning consideringconsidering continuously thatthat DeyangDeyang converging andand SuiningSuining in Chengdu, areare nextnext toto thereby Chengdu,Chengdu, resulting thethe in the heavy pollution event. However, considering that Deyang and Suining are next to Chengdu, the AOD in most areas exceeded 1.4, and the GDAS data used in the backward trajectory simulations of the HYSPLIT model were of low resolution, the effect of pollutants originating in Deyang and Suining on this pollution event could not be excluded. Therefore, the present study simulated this Atmosphere 2016, 7, 127 7 of 16

Atmosphere 2016, 7, 127 7 of 16 pollution event using the areas with high AOD as the locations of the tracer sources (Figure3). Carbon monoxideAOD in was most used areas as exceeded the tracer 1.4 gas, and for the the GDAS simulation. data used The in the areas backward with an trajectory AOD greater simulations than of 1.4 in the HYSPLIT model were of low resolution, the effect of pollutants originating in Deyang and Suining Deyang, Suining, Meishan, Ziyang, Neijiang, and Zigong were set as the areas where pollutants were on this pollution event could not be excluded. Therefore, the present study simulated this pollution emitted from the tracer sources. The emission intensity of the tracer sources was 100 mol/(km2·h) [23]. event using the areas with high AOD as the locations of the tracer sources (Figure 3). Carbon Additionally,monoxide the was tracer used as sources the tracer were gas added for the atsimulation. 0800 UTC The on areas 6 May with during an AOD the greater simulation than 1.4 period.in Furthermore,Deyang, Suining, a survey Meishan, conducted Ziyang, by theNeijiang environmental, and Zigong protection were set as departmentthe areas where of pollutants Chengdu were revealed that noemitted straw-burning from the tracer sites sources. were The located emission in Chengdu intensity of [ 24the]. tracer Therefore, sources was to trace 100 mol/(k the sourcesm2·h) [23 of]. the pollutantsAdditionally, in Chengdu, the tracer the pollutionsources were sources added in at Chengdu 0800 UTC were on 6 removed.May during the simulation period. Furthermore, a survey conducted by the environmental protection department of Chengdu revealed 2.2.2.that Design no straw of the-burning Simulation sites Scheme were located for the in Heavy Chengdu Pollution [24]. Therefore, Transport to Processtrace the sources of the pollutants in Chengdu, the pollution sources in Chengdu were removed. The present study simulated the atmospheric diffusion conditions for and the characteristics of pollutant2.2.2. transport Design of duringthe Simulation the heavy Scheme pollution for the eventHeavy betweenPollution 0000Transport UTC Process on 6 May and 0000 UTC on 9 May 2014, using WRF–CHEM. The FNL reanalysis data were used as the initial meteorological field The present study simulated the atmospheric diffusion conditions for and the characteristics of data required by WRF–CHEM, and a three-layer nested structure was used for the simulation (Table1). pollutant transport during the heavy pollution event between 0000 UTC on 6 May and 0000◦ UTC on ◦ Figure96 May shows 2014, the using simulation WRF–CHEM. area. The FNL center reanalysis of the simulation data were used area as was the locatedinitial meteorological at 29.71 N, 101.79field E, and 20data unequally required spacedby WRF layers–CHEM, were and createda three-layer vertically. nested Thestructure pressure was used at the for top the ofsimulation the model (Table was 1). set to 50 hPa.Figure The 6 optimal shows the parameter simulation configuration area. The center for of thethe simulation typical pollution area was event located was at 29.71 previously°N, 101.79 obtained°E, throughand an20 experimentalunequally spaced study layers [25 were]. The created Weather vertically. Research The pressure and Forecasting at the top of single-moment the model was set 3-class schemeto was50 hPa selected. The optimal for the parametermicrophysical configuration process. for The the rapid typical radiative pollution transfer event model was previously scheme was selectedobtained for longwave through an radiation. experimental The study Dudhia [25] scheme. The Weather wasselected Researchfor and shortwave Forecasting radiation. single-moment The slab land surface3-class scheme model was was selected used for for thethe microphysical land surface process.process. The The rapid Mellor–Yamada–Janjic radiative transfer model (MYJ) scheme scheme was selected for longwave radiation. The Dudhia scheme was selected for shortwave radiation. The was selected for the planetary boundary layer. The scheme used for the surface layer was combined slab land surface model was used for the land surface process. The Mellor–Yamada–Janjic (MYJ) with the MYJ boundary-layer scheme, and thus the Eta scheme was selected for the surface layer. scheme was selected for the planetary boundary layer. The scheme used for the surface layer was The Grell–Devenyicombined with the scheme MYJ boundary (with a- closedlayer scheme, third layer)and thus was the selectedEta scheme for was cumulus selected parameterization.for the surface The chemistrylayer. The Grell tracer–Devenyi module scheme within (with the a closed chemistry third layer) module was wasselected initiated. for cumulus Atmospheric parameterization. chemical processes,The chemistry such as gas-phase tracer module chemistry within the and chemistry aerosol evolution, module was were initiated. deactivated Atmospheric to emphasize chemical the pollutantprocesses, gas transport such as gas process.-phase chemistry and aerosol evolution, were deactivated to emphasize the pollutant gas transport process. Table 1. Design of the model simulation areas. Table 1. Design of the model simulation areas.

AreasAreas Mesh Mesh Numbers Numbers Spacing Spacing (km) (km) Dom1Dom1 109 × 109 68 × 6827 27 Dom2Dom2 136 × 136 106× 1069 9 Dom3 130 × 105 3 Dom3 130 × 105 3

(a) (b)

Figure 6. The three nested areas (a) and innermost area (b) of simulation using weather research Figure 6. The three nested areas (a) and innermost area (b) of simulation using weather research forecasting model with chemistry (WRF–CHEM) (notes: the red dot in (b) signifies the automatic air forecasting model with chemistry (WRF–CHEM) (notes: the red dot in (b) signifies the automatic air monitoring substation in Sichuan Province, and the green dot in (b) signifies the comprehensive substation at the Chengdu University of Information Technology). Atmosphere 2016, 7, 127 8 of 16

3. Analysis of the Pollution Transport Process through Simulation Using WRF–CHEM

3.1. Analysis of the Conditions for Pollutant Diffusion The height of the atmospheric boundary layer and wind speed within the atmospheric boundary layer reflect the capacity of the atmospheric turbulent flows within the boundary layer to diffuse pollutants. Zhang [26] defines the product of the height of the boundary layer and the mean 2 −1 wind speed within the boundary layer as the ventilation coefficient (VH (unit: m ·s )), which represents the diffusion conditions in the atmospheric boundary layer. VH can be calculated using the following equation: n VH = ∑ Ui (Zi) ·∆Zi (1) i=1 where n represents the number of vertical model layers within the height of the boundary layer, Ui (Zi) −1 represents the horizontal wind speed in each model layer within the boundary layer (m·s ), and ∆zi represents the height of each model layer within the boundary layer (m). The present study calculated the VH for Chengdu during the pollution event using the aforementioned equation. Figure7 shows the distribution of the mean value of VH in the study area on 7 May 2014 and 8 May 2014. On 7 May, the mean VH over Meishan and Leshan in the central and southern regions of the study area was relatively low, and the VH over Chengdu’s city center was only 600 m2·s−1. Additionally, the diffusion capacity of the atmospheric boundary layer over the entire basin was relatively weak. On 8 May, Chengdu, Meishan, and Leshan in the area west of the Longquan Mountains had significantly low mean VH, with a value at the center of this area of less than 600 m2·s−1, thus indicating a weak capacity of the atmospheric turbulent flows within the boundary layer over this area to diffuse pollutants. Therefore, pollutants could not diffuse in a timely fashion and continuously accumulated in the area, thus resulting in a rapid increase in concentration.

2 −1 Figure 7. Distribution of the mean ventilation coefficient VH (m ·s ) on 7 May 2014 (a) and 8 May 2014 (b).

Figure8 shows the hourly change in the AEC and VH. During the heavy pollution period, the 2 −1 VH over Chengdu at night was extremely low at approximately 100 m ·s . The persistently poor diffusion conditions led to the continuous accumulation of pollutants at night. The AEC reached its peak value at approximately 0000 UTC on both 7 May and 8 May. After 0000 UTC, as solar radiation continuously increased and the boundary layer gradually rose, the VH continuously increased and reached its peak at approximately 0800 UTC. The change in VH was opposite that observed for the AEC. The AEC decreased to a minimum during the time period when the VH was highest. Thus, the VH could characterize the diffusion capacity of the atmosphere. The lower the VH, the more unfavorable Atmosphere 2016, 7, 127 9 of 16

Atmosphere 2016, 7, 127 9 of 16 AEC. The AEC decreased to a minimum during the time period when the 푉퐻 was highest. Thus, the AEC. The AEC decreased to a minimum during the time period when the 푉퐻 was highest. Thus, the 푉퐻 could characterize the diffusion capacity of the atmosphere. The lower the 푉퐻 , the more 푉퐻 could characterize the diffusion capacity of the atmosphere. The lower the 푉퐻 , the more theunfavorable conditions the for conditions pollutant diffusion,for pollutant and diffusion, vice versa. and Analysis vice versa of the. Analysis hourly change of the inhourly the thickness change in of the temperaturethickness of inversionthe temperature layer (Figure inversion9) indicated layer (Figure that this 9) layerindicated always that occurred this layer at night.always On occurred 6 May, theat night. thickness On 6 of Ma they, temperaturethe thickness inversion of the temperature layer gradually inversion increased layer in gradually the early increased morning and in the reached early 200morning m at 1900and UTC,reached which 200 was m at maintained 1900 UTC until, which 0000 was UTC maintained on 7 May. Theuntil thickness 0000 UTC of theon temperature7 May. The inversionthickness of layer the reachedtemperature 100 m inversion on the night layer of reached 8 May. 100 The m temperature on the night inversion of 8 May layer. The occurredtemperature for inversion layer occurred for 11 h. During the heavy pollution event, the extremely low value of 푉퐻 11inversion h. During layer the occurred heavy pollution for 11 h. event, During the the extremely heavy pollution low value event, of VH theat extremely night and relativelylow value strong of 푉퐻 temperatureat night and inversion relatively resulted strong temperature in the continuous inversion accumulation resulted in of the pollutants, continuous thereby accumulation causing the of occurrencepollutants, thereby and development causing the of occurrenc atmospherice and pollution. development of atmospheric pollution.

Figure 8. Hourly change in the AEC and 푉퐻 over Chengdu during the pollution event (notes: the Figure 8 8.. HourlyHourly change change in in the the AEC AEC and and 푉퐻V overH over Chengdu Chengdu during during the thepollution pollution event event (notes: (notes: the volume fraction of pollutants was the simulation result obtained based on the mesh closest to the thevolume volume fraction fraction of pollutants of pollutants was was the the simulation simulation result result obtained obtained based based on on the the mesh mesh closest closest to the comprehensive substation at the Chengdu University of Information Technology). comprehensive substationsubstation at the Chengdu University ofof InformationInformation Technology).Technology).

Figure 9. Figure 9. HourlyHourly change change in in the the thickness thickness of of the the temperature temperature inversion inversion layer layer over over Chengdu Chengdu during during the theFigure pollution 9. Hourly event change (notes: in thethe thicknessthickness of the temperature inversion layer over was theChengdu simulation during result the pollution event (notes: the thickness of the temperature inversion layer was the simulation result obtainedpollution based event on(notes: the mesh the thickness closest to of the the comprehensive temperature inversion substation layer at the was Chengdu the simulation University result of obtained based on the mesh closest to the comprehensive substation at the Chengdu University of Informationobtained based Technology). on the mesh closest to the comprehensive substation at the Chengdu University of Information Technology). 3.2. Analysis of Pollutant Sources and Transport Characteristics 3.2. Analysis of Pollutant Sources and Transport Characteristics

Figure 10 showsshows thethe simulatedsimulated volumevolume fractionfraction ofof pollutantspollutants andand hourlyhourly changechange inin thethe PMPM2.52.5 Figure 10 shows the simulated volume fraction of pollutants and hourly change in the PM2.5 concentration over Chengdu duringduring thethe pollutionpollution event.event. TheThe concentrationconcentration ofof PMPM2.52.5 increased fromfrom concentration over Chengdu during the pollution event. The concentration of PM2.5 increased from the early morning of 7 May, reaching the first peak at 0000 UTC and the second peak at 0600 UTC. the early morning of 7 May, reaching the first peak at 0000 UTC and the second peak at 0600 UTC. Subsequently, the PM PM2.52.5 concentrationconcentration decreased decreased rapidly rapidly and and fluctuated fluctuated after after reaching reaching the minimum. the minimum. The Subsequently, the PM2.5 concentration decreased rapidly and fluctuated after reaching the minimum. The µ 3 ThePM2.5 PM concentration2.5 concentration rapidly rapidly increased increased at 2100 at 2100 UTC UTC on 7 on May 7 May and and reached reached its itsmaximum maximum (375 (375 µg/mg/m3) at) PM2.5 concentration rapidly increased at 2100 UTC on 7 May and reached its maximum (375 µg/m3) at at 0100 UTC, after which it continued to decrease. The volume fraction of pollutants started to increase Atmosphere 2016, 7, 127 10 of 16 Atmosphere 2016, 7, 127 10 of 16 0100 UTC, after which it continued to decrease. The volume fraction of pollutants started to increase at 1600 UTC on 6 May, which was consistent with the time point at which the PM2.5 concentration at 1600 UTC on 6 May, which was consistent with the time point at which the PM2.5 concentration started to increase. Similar to the PM2.5 concentration, the volume fraction of pollutants also had two started to increase. Similar to the PM2.5 concentration, the volume fraction of pollutants also had twopeaks peaks in the in theearly early morning morning of 7 of May 7 May.. The The volume volume fraction fraction of ofpollutants pollutants started started to to increase increase at atapproximately approximately 1600 1600 UTC UTC on on 7 7 May May and and reached reached a a peak peak at at 02 020000 UTC UTC on on 8 8 MayMay,, after which which it it continuouslycontinuously decreased. The The aforementioned aforementioned analysis analysis demonstrates demonstrates a slight a slight difference difference between between the time at which the peaks in the volume fraction of pollutants and PM2.5 concentration occurred. This the time at which the peaks in the volume fraction of pollutants and PM2.5 concentration occurred. Thistime timedifference difference was mainly was mainly caused caused by the by exclusion the exclusion of the of local the pollut local pollutionion sources sources in Chengdu in Chengdu in the insimulation. the simulation. However, However, the correlation the correlation coefficient coefficient between betweenthe hourly the change hourly in change the volume in the fraction volume of pollutants and PM2.5 concentration reached 0.78, thus indicating that the volume fraction of pollutants fraction of pollutants and PM2.5 concentration reached 0.78, thus indicating that the volume fraction and PM2.5 concentration had similar variation patterns. This result suggests that the transport of the of pollutants and PM2.5 concentration had similar variation patterns. This result suggests that the transportpollutants of resulting the pollutants from straw resulting burning from was straw the burning main cause was theof the main pollution cause ofevent. the pollution event.

−4 FigureFigure 10.10. HourlyHourly changechange inin the the simulated simulated volume volume fraction fraction of of pollutants pollutants (10 (10−4)) andand observed observed PM PM2.52.5 concentrationconcentration inin Chengdu Chengdu during during the the pollution pollution event event (notes: (notes: the the volume volume fraction fraction of pollutants of pollutants was was the simulationthe simulation result result obtained obtained based based on the on mesh the closest mesh to closest the comprehensive to the comprehensive substation substation at the Chengdu at the UniversityChengdu University of Information of Information Technology). Technology).

3.2.1.3.2.1. HorizontalHorizontal TransportTransport ofof PollutantsPollutants FigureFigure 1111 showsshows thethe spatiotemporalspatiotemporal changechange inin thethe groundground pollutantspollutants duringduring thethe entireentire pollutionpollution event,event, simulatedsimulated usingusing WRF–CHEM.WRF–CHEM. FigureFigure 1111aa showsshows thatthat thethe atmosphericatmospheric diffusiondiffusion conditionsconditions werewere poorpoor at 1200 UTC on on 6 6 May May and and that that pollutants pollutants continuously continuously accumulated accumulated near near the the ground. ground. A Aweak weak convergence convergence formed formed over over Chengdu Chengdu because because of of the the convergence convergence of of a a southerly airflow airflow and anan airflowairflow directeddirected fromfrom thethe mountains.mountains. PollutantsPollutants originatingoriginating inin MeishanMeishan werewere slowlyslowly transportedtransported toto ChengduChengdu byby thethe southerlysoutherly airflow,airflow, whereaswhereas pollutantspollutants originatingoriginating inin Ziyang,Ziyang, Neijiang,Neijiang, andand ZigongZigong werewere controlledcontrolled byby aa northerlynortherly airflowairflow andand movedmoved southward.southward. The firstfirst peakpeak periodperiod ofof thethe heavyheavy pollutionpollution event event occurred occurred at at0000 0000 UTC UTC on 7on May. 7 May Figure. Figure 11b shows 11b shows that pollutants that pollutants originating originating in Ziyang, in Neijiang,Ziyang, Neijiang and Zigong, and diffusedZigong diffused eastward eastward and that and pollutants that pollutants originating originating in Meishan in Meishan were thewere main the pollutionmain pollution source source affecting affecting Chengdu Chengdu during thisduring period. this Additionally,period. Additionally, the wind the speeds wind were speeds low, were and therelow, and were there continuous were continuous southerly windssoutherly over winds the area. over Consequently, the area. Consequently, the pollutants the couldpollutants not diffuse could innot a diffuse timely fashion.in a timely The fashion. pollutants The originatingpollutants originating in Meishan in continued Meishan continued to be transported to be transported to Chengdu to withChengdu the airflow. with the These airflow. aforementioned These aforementioned factors collectively factors ledcollec to thetively highly led to concentrated the highly pollution.concentrated At 0400pollution. UTC on At 704 May00 UTC (Figure on 11 7 c), May because (Figure of the 11c), effect because of a southerly of the effect airflow of a over southerly Chengdu, airflow the wind over speedChengdu, increased the wind and boundaryspeed increased layer rose. and As boundary a result, the layer ground rose. pollutantsAs a result, diffused the ground northward pollutants with thediffused airflow, northward and the concentration with the airflow, of pollutants and the concentration decreased. of pollutants decreased. AtAt 12001200 UTCUTC onon 77 May (Figure(Figure 11d),d), the area near Chengdu was controlled by a southerly wind, andand thethe pollutantspollutants fromfrom thethe areaarea southsouth ofof ChengduChengdu continuouslycontinuously convergedconverged inin Chengdu.Chengdu. BeginningBeginning atat 20002000 UTCUTC onon 77 MayMay (Figure(Figure 1111),), thethe areaarea northnorth ofof ChengduChengdu waswas controlledcontrolled byby aa northerlynortherly wind,wind, whichwhich convergedconverged withwith thethe southerlysoutherly airflowairflow fromfrom southsouth ofof ChengduChengdu over Chengdu at 22002200 UTC,UTC, Atmosphere 2016, 7, 127 11 of 16 Atmosphere 2016, 7, 127 11 of 16 formingforming a significanta significant convergence convergence center. center. At At 0000 0000 UTCUTC onon 8 May ( (FigureFigure 1111e),e), the the convergence convergence center center disappeared,disappeared, and and the the airflows airflows over over Chengdu Chengdu turned turned intointo a northerly airflow. airflow. The The second second peak peak period period ofof the the heavy heavy pollution pollution event event occurred occurred at at 00000000 UTCUTC onon 88 May.May. Figure 11ee shows shows that that the the pollut pollutantsants originatingoriginating in in Meishan Meishan and and Deyang Deyang were were the the mainmain externalexternal pollution sources sources during during the the second second peak peak periodperiod of theof the heavy heavy pollution pollution event. event. The The pollutants pollutants carried carried by by the the southerly southerly airflow airflow before before 2200 2200 UTC UTC on 7 Mayon 7 mainly May mainly accumulated accumulated in southern in southern Chengdu. Chengdu. Controlled Con bytrolled the northerly by the northerly airflow, the airflow, pollutants the originatingpollutants in originating Deyang moved in Deyang to Chengdu. moved to The Chengdu. accumulation The accumulation in Chengdu in of Chengdu the pollutants of the pollutants originating inoriginating Meishan and in Meishan Deyang collectivelyand Deyang ledcollectively to the second led to the high-concentration second high-concentration pollution pollution event. The event. daily meanThe concentrationdaily mean conce ofntration black carbonof black carbon was comparatively was comparatively higher higher on on 8 May 8 May than than 7 7 MayMay (Figure (Figure 11),), indicatingindicating that that straw-burning straw-burning sites sites were were also also presentpresent inin Deyang.Deyang. At At 0400 0400 UTC UTC on on 8 8May May (Figure (Figure 11 11f),f), thethe northerly northerly airflow airflow intensified, intensified, and and the the pollutants pollutants graduallygradually dissipated.dissipated.

Figure 11. Spatiotemporal change in the volume fraction of ground pollutants (10−4) and the 10-m Figure 11. Spatiotemporal change in the volume fraction of ground pollutants (10−4) and the 10-m wind field (m·s−1) in the study area: (a) 1200 UTC on 6 May; (b) 0000 UTC on 7 May; (c) 0400 UTC on wind field (m·s−1) in the study area: (a) 1200 UTC on 6 May; (b) 0000 UTC on 7 May; (c) 0400 UTC on 7 May; (d) 1200 UTC on 7 May; (e) 0000 UTC on 8 May; and (f) 0400 UTC on 8 May. 7 May; (d) 1200 UTC on 7 May; (e) 0000 UTC on 8 May; and (f) 0400 UTC on 8 May. Atmosphere 2016, 7, 127 12 of 16

Atmosphere 2016, 7, 127 12 of 16 3.2.2. Vertical Transport of Pollutants 3.2.2. Vertical Transport of Pollutants The backward trajectory results and horizontal distribution of pollutants simulated using AtmosphereThe backward 2016, 7, 127 trajectory results and horizontal distribution of pollutants simulated12 of using 16 WRF–CHEM indicated that the pollutants originating in Meishan and Deyang were the main pollution WRF–CHEM indicated that the pollutants originating in Meishan and Deyang were the main sourcespollution3.2.2. of Vertical the sources heavy Transport of the pollution heavy of Pollutants pollution event. The event. backward The backward trajectory trajectory results results show show that that the the airflows airflows at 1000 m mainly originated from the direction of Ziyang. Therefore, the present study analyzed the at 1000 Them mainly backward originated trajectory from results the direction and horizontal of Ziyang. distribution Therefore, of the pollutants present study simulated analyzed using the verticalverticalWRF pollutant –pollutantCHEM diffusion indicated diffusion along that along the lines lines pollutants AB AB and and CD originatingCD shown shown inin in Figure Meishan 66b.b. and The profile Deyangprofile in in were the the AB AB the direction direction main (Figure(Figurepollution 12) 12 shows) shows sources that that of the the the Longquan heavy Longquan pollution Mountains Mountains event. The (altitude: (altitude: backward approximately trajectory results 1000 1000 show m m)) separate that separate the airflows Chengdu Chengdu andand Ziyang,at Ziyang,1000 andm mainly and primarily primarily originated easterly easterly from airflows the airflows direction occurred occurredof Ziyang. at 1000 at Therefore, m1000 over m Ziyang overthe present Ziyang during study duringthe analyzed heavy the pollution heavythe event;pollutionvertical however, event; pollutant the however, vertical diffusion diffusion the along vertical lines capacity diffusion AB and of CD the capacity sh atmosphereown of in theFigure atmosphere was 6b. relatively The profile was weak, relativelyin the and AB thedirection weak, pollution and layerthe at( Figurepollution 1000 12 m) waslayershows essentially at that 1000 the m Longquan was nonexistent. essentially Mountains The nonexistent. pollutants (altitude: Theapproximately originating pollutants in 1000originating Ziyang m) separate could in Ziyang Chengdu not cross could the Longquannotand cross Ziyang, Mountains the Longquan and and,primarily Mountains therefore, easterly and, had airflows therefore, no significant occurred had n effect o at significant 1000 on mChengdu. over effect Ziyang on Chengdu. during the heavy pollution event; however, the vertical diffusion capacity of the atmosphere was relatively weak, and the pollution layer at 1000 m was essentially nonexistent. The pollutants originating in Ziyang could not cross the Longquan Mountains and, therefore, had no significant effect on Chengdu.

−4 −1 FigureFigure 12. 1Vertical2. Vertical profile profile of of the the volume volume fraction fraction of of pollutants pollutants (10 (10−4) and and wind wind ((m (m·s−)1 )(along (along line line AB AB

in Figurein Figure6b): 6 (b):a) (a 0000) 0000 UTC UTC on on 7 7 May May and and ( b(b)) 1800 1800 UTCUTC onon 7 May May.. Note: Note: the the gray gray shaded shaded area area signifies signifies −4 −1 thethe topography.Figure topography. 12. Vertical profile of the volume fraction of pollutants (10 ) and wind ((m·s ) (along line AB in Figure 6b): (a) 0000 UTC on 7 May and (b) 1800 UTC on 7 May. Note: the gray shaded area signifies Figurethe topography. 13 shows the mean height of the boundary layer in the area where line CD (Figure 6b) was Figure 13 shows the mean height of the boundary layer in the area where line CD (Figure6b) located in the simulated inner area. The height of the boundary layer was greater in this area during the wasday located thanFigure at in night. the 13 shows simulated The height the mean inner of theheight area.boundary of Thethe boundary heightlayer at of nightlayer the inwas boundary the below area where 200 layer m, line waswhich CD greater (Figurewas unfavorable in6b) this was area duringfor locatedpollutant the day in the diffusion. than simulated at night. Beginning inner The area. at height The0000 height UTC of the of, the the boundary boundary boundary layer layer atwasrose, night greater and was the in thisdiffusion below area 200during capacity m, the which of wasthe unfavorableday atmosphere than at night. forincreased. pollutant The height After diffusion. of 10the00 boundary UTC Beginning, the layer boundary at at night 0000 layer was UTC, belowgradually the 200 boundary m,lowered. which layerwasThe unfavorable height rose, andof the the for pollutant diffusion. Beginning at 0000 UTC, the boundary layer rose, and the diffusion capacity of diffusionboundary capacity layer ofat thenight atmosphere on 8 May was increased. greater After than 1000that on UTC, 7 May. the boundaryThe height layer of the gradually boundary lowered. layer the atmosphere increased. After 1000 UTC, the boundary layer gradually lowered. The height of the Theduring height the of day the on boundary 8 May decreased layer at by night approximately on 8 May 300 was m greater compared than to thatthat on on 7 7 May. May. The height of boundary layer at night on 8 May was greater than that on 7 May. The height of the boundary layer the boundary layer during the day on 8 May decreased by approximately 300 m compared to that on during the day on 8 May decreased by approximately 300 m compared to that on 7 May. 7 May.

Figure 13. Mean height of the boundary layer in the area where line CD (Figure 6b) was located in the FigureFigure 13. Mean 13. Mean height height of the of the boundary boundary layer layer in in the the areaarea where line line CD CD (Figure (Figure 6b6)b) was was located located in the in the simulated inner area. simulatedsimulated inner inner area. area. Atmosphere 2016, 7, 127 13 of 16

Atmosphere 2016, 7, 127 13 of 16 Figure 14 shows the vertical profile of the volume fraction of pollutants and the wind along line Figure 14 shows the vertical profile of the volume fraction of pollutants and the wind along line CDCD shown shown in in Figure Figure6b. 6b. The The pollutants pollutants were were concentrated concentrated withinwithin thethe boundary layer, layer, and and there there were were fewfew pollution pollution layers layers above above 1000 1000 m. At m 0000. At UTC0000 onUTC 7May on 7 (Figure May ( 14Figurea), there 14a), were there consistent were consistent southerly airflowssoutherly from airflows the ground from to the 1700 ground m. The to pollutants1700 m. The originating pollutants in originating Leshan and in Meishan Leshan diffusedand Meishan along thediffused airflow along direction. the airflow The mean direction. height The of mean the boundary height of the layer, boundary through layer, which through line CDwhich passes, line CD was approximatelypasses, was approx 150 mimately (Figure 13150). m Limited (Figure by 13 the). Limited boundary by layer,the boundary the pollutants layer, werethe pollutants concentrated were in theconcentrated ground layer. in the During ground this layer. time During period, this the time diffusion period, capacity the diffusion of the capacity atmosphere of the over atmosphere Chengdu wasover poor, Chengdu and the temperaturewas poor, and inversion the temperature near the ground inversion had notnear dissipated. the ground Additionally, had not dissipated. pollutants continuouslyAdditionally, accumulated, pollutants continuously and a high-pollution accumulated, center and appeared a high-pollution in south center Chengdu. appeared At 0400in south UTC onChengdu. 7 May, the At mean 0400 heightUTC on of 7 theMay boundary, the mean layer height increased of the boundary to 1000 m, layer the increased diffusion to capacity 1000 m of, the the atmospherediffusion capacity over Chengdu of the atmosphere increased, and over there Che werengdu consistentincreased, southerlyand there windswere consistent within the southerly boundary layer.winds The within pollutants the boundary were also layer. transported The pollutants by the were turbulent also transported flows to the by topthe turbulent of the boundary flows to layerthe whiletop diffusingof the boundary northward, layer and while the diffusing concentration northward, of pollutants and the near concentration the ground of decreased pollutants (Figure near the14b). Betweenground 1800 decreased UTC ( andFigure 2200 14b). UTC Between on 7 May1800 UTC (Figure and 14 2200), the UTC southerly on 7 May wind (Figure in 14 the), th grounde southerly layer graduallywind in turnedthe ground into alayer northerly gradually wind. turned At 0000 into UTC a northerly on 8 May, wind. the At ground 0000 UTC layer on was 8 May controlled, the ground by the northerlylayer was airflow, controlled and theby the wind northerly speed in airflow, the ground and the layer wind increased speed in to the some ground extent layer compared increased to thatto onsome 7 May. extent Pollution compared centers to werethat on located 7 May in. Pollution the southern centers and were northern located areas in the of Chengdu.southern and The northern pollution concentrationareas of Chengdu. in the northern The pollution center concentration was relatively in high, the northern and the pollutioncenter was layer relatively in the northernhigh, and center the waspollutio significantlyn layer higherin the northern than that center in the was southern significantly center. higher The pollutants than that originatingin the southern in Deyang center. The were pollutants originating in Deyang were the notable contributor to the pollution in Chengdu on 8 May the notable contributor to the pollution in Chengdu on 8 May (Figure 14c). Figure 14d shows that the (Figure 14c). Figure 14d shows that the concentration of pollutants in Chengdu decreased as the concentration of pollutants in Chengdu decreased as the boundary layer rose at 0400 UTC; however, boundary layer rose at 0400 UTC; however, the height of the boundary layer during the day on 8 May the height of the boundary layer during the day on 8 May was significantly lower than that on 7 May. was significantly lower than that on 7 May. Therefore, the concentration of pollutants in Chengdu at Therefore, the concentration of pollutants in Chengdu at 0400 UTC on 8 May remained higher than 0400 UTC on 8 May remained higher than that at 0400 UTC on 7 May (Figure 10). that at 0400 UTC on 7 May (Figure 10).

−4 · −1 FigureFigure 14. 14Vertical. Vertical profile profile of of the the volume volume fraction fraction of of pollutants pollutants (10 (10−4) and wind wind ((m (m ·ss−1) )(along (along line line CD CD in Figurein Figure6b): 6 (b):a) ( 0000a) 0000 UTC UTC on on 7 May; 7 May; (b) ( 0400b) 04 UTC00 UTC on 7on May; 7 May; (c) 0000(c) 00 UTC00 UTC on 8on May; 8 May; and and (d) 0400(d) 04 UTC00 onUTC 8 May. on Note:8 May the. Note: gray the shaded gray shaded area signifies area signifies the topography. the topography. Atmosphere 2016, 7, 127 14 of 16

The aforementioned analysis indicates that the transport of pollutants resulting from straw burning was the leading cause of the typical pollution event. The pollutant transport process was mainly concentrated within 1000 m. During the typical pollution event, the mean value of VH in Chengdu, Meishan, and Leshan was relatively low, and the diffusion capacity of the atmosphere over the aforementioned areas was relatively weak. On 7 May, the pollutants originating in Meishan were continuously transported to Chengdu. The extremely low value of VH in Chengdu at night and the relatively strong temperature inversion resulted in the large accumulation of pollutants in the ground layer, thereby causing the high-concentration pollution event on the morning of 7 May. On 8 May, the pollutants originating in Meishan and Deyang converged in Chengdu. The pollutants originating in Deyang were the notable contributor to the worsening pollution in Chengdu on 8 May. The pollutants could not diffuse because of the relatively low height of the boundary layer during the day on 8 May. Therefore, the pollution concentration on 8 May was higher than that on 7 May.

4. Conclusions The present study established the tracer sources required for the simulation based on the AOD data obtained from the following: inversion of HJ-1 satellite data, backward trajectories simulated using the HYSPLIT model and MPL AEC data. Additionally, the present study also analyzed a typical heavy pollution event that occurred in Chengdu in early May 2014 using a mesoscale atmospheric chemistry transport model—WRF–CHEM. The main conclusions of the present study are as follows:

1. The multisource remote sensing data indicated that the regional transport of pollutants resulting from straw burning was the direct cause of the heavy air pollution event that occurred in Chengdu between 7 May 2014 and 8 May 2014. There were straw-burning sites in Meishan, Ziyang, Neijiang, and Zigong, south of Chengdu, beginning at 0800 UTC on 6 May and 7 May. 2. The numerical simulation results indicated that Chengdu, Meishan, and Leshan were areas with significantly low mean VH during the typical pollution event. The VH in Chengdu at night was extremely low, and there was a continuous temperature inversion near the ground in Chengdu. The unfavorable meteorological conditions for diffusion were a key factor in the maintenance and worsening of the pollution event. The change in the boundary layer height over Chengdu had a relatively large effect on vertical pollutant diffusion. The boundary layer was low at night, and the capacity of the atmosphere to vertically diffuse pollutants was poor. Therefore, pollutants were essentially concentrated in the ground layer. During the day, as the boundary layer continuously rose, the capacity of the atmosphere to vertically diffuse pollutants increased, and the concentration of pollutants near the ground consequently decreased.

The remote sensing data and numerical simulation results indicated that the pollutants resulting from straw burning in Meishan and Deyang were the main pollution source of the pollution event. On 7 May, the pollutants in Chengdu mainly originated from Meishan. The accumulation in Chengdu of pollutants originating in Meishan and Deyang resulted in the highly concentrated pollution on 8 May, to which the pollutants originating in Deyang were the notable contributor.

Acknowledgments: This work is financially supported by grants from the Air Pollution Prevention and Control Countermeasures of Sichuan and Chengdu Region (2013HBZX03), the Environmental Protection Science and Technology Program Project in Sichuan Province in 2015 (the Air Pollution Prevention and Control Countermeasures of Southern Sichuan). Author Contributions: All authors made great contributions to the work. Ying Zhang and Zhihong Liu conceived and designed the experiments, analyzed the data and model results and wrote the article. Yang Zhang performed the AOD algorithm research. Xiaotong Lv and Jun Qian examined the experimental data and checked the experimental results. All authors agreed to the submission of the manuscript. Conflicts of Interest: The authors declare no conflict of interest. Atmosphere 2016, 7, 127 15 of 16

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