Source Contributions to PM2.5under Unfavorable Weather Conditions in Guangzhou City,China

Nan WANG,Zhenhao LING,Xuejiao DENG,Tao DENG,Xiaopu LYU,Tingyuan LI,Xiaorong GAO,and Xi CHEN

1Institute of Tropical and Marine Meteorology,China Meteorological Administration,Guangzhou 510000,China

2School of Atmospheric Sciences,Sun Yat-sen University,Guangzhou 510000,China

3Department of Civil and Environmental Engineering,The Hong Kong Polytechnic University,Hong Kong 999077,China

4Ecological Meteorology Center,Guangdong Provincial Meteorological Bureau,Guangzhou 510000,China

5Guangzhou Meteorological Observatory,Guangzhou 510000,China

6School of Environmental Science and Engineering,Sun Yat-sen University,Guangzhou 510000,China

1.Introduction

Haze pollution,characterized by high levels of fi ne particles(PM2.5—particles with a diameter less than 2.5 µm)and low visibility,is of increasing concern in China.Ambient particles can a ff ect air quality and climate because they can absorb and scatter solar irradiation(Qiu et al.,1985;Watson,2002;Hyslop,2009;Cao et al.,2012).They also pose an adverse e ff ect on human health.Studies have shown that high concentrations of PM2.5can increase morbidity and mortality,as well as a ff ect the cardiovascular system(Dockery et al.,1993;Pope et al.,2009;Tie et al.,2009;IPCC,2013).

Guangzhou is the capital of Guangdong Province and a megacity located in the city clusters of the Pearl River Delta(PRD)region(the PRD often refers to nine cities in Guangdong Province—namely,Guangzhou,Shenzhen,Dongguan,Foshan,Huizhou,Zhuhai,Zhongshan,Jiangmen,and Zhaoqing).With a population of more than 10 million,the city contributed 1.96×104hundred million yuan to the GDP,ranking fi rst in Guangdong Province,in 2016(Guangdong Statistical Yearbook,2016).However,rapid urbanization and economic growth have resulted in this city su ff ering from severe air pollution.In fact,Guangzhou has often been under low-visibility conditions since the 1980s(Deng et al.,2008).During some extreme events,the daily average visibility can be less than 2 km,with the aerosol optical depth higher than 1.2(Wu et al.,2005).High concentrations of PM2.5accompany these events(Zheng et al.,2011;Tao et al.,2012;Deng etal.,2014;Huangetal.,2014;Anderssonetal.,2015;Maiet al.,2016).Facing enormous public pressure to ensure better air quality,the Chinese government has exerted considerable e ff ort in mitigating air pollution.Each of its Five-Year Plans has set targets for primary emissions control,and volatile organic compound(VOC)emissions are going to be tightened,in addition to controlling emissions of sulfur dioxide(SO2)and nitrogen oxides(NOx)in the 13th Five-Year Plan(from 2016 to 2020).In 2013,the State Council released an“Air Pollution Action Plan”,setting speci fi c targets for developed areas,among which a 15%reduction goal was set for PRD PM2.5concentrations.In the local region,a series of emission control strategies have been implemented by the joint governments of Guangdong and Hong Kong.One example is the endorsed“PRD Air Pollutant Emission Reduction Plan Up to 2020”.As a result,a declining trend of PM2.5in Guangzhou was measured between 2010 and 2015,with annual concentrations of 55,41,51,53,49 and 32 µg m−3,respectively(Guangdong Year Book,2010–15).Though the situation is becoming positive,there is still a long way to go,as the annual PM2.5concentration signi fi cantly exceeds the WHO guideline(annual mean of 10 µg m−3).For policymakers,understanding source contributions to ambient PM2.5concentrationsisthebasistofurtherimproveairqualityinthe PRD.

In recent years,a series of studies have been carried out to improve our knowledge of emissions and their contributions to ambient PM2.5in Guangzhou by using di ff erent methods—for instance,receptor models,tracing technology,and chemical transport models(CTMs)(Wang et al.,2005;Louie et al.,2005;Guo et al.,2009;Zhang et al.,2012;Gao et al.,2013).Xiao et al.(2011)used observations and Positive Matrix Factorization(PMF)to identify sources of submicron organic aerosols in a rural site of Guangzhou,reporting that primary organic aerosol constituted ∼34–47%of organic aerosol,and secondary organic aerosol constituted∼53–66%of regional organic aerosol.Zheng et al.(2011)used the Chemical Mass Balance method to conduct source apportionment of carbonaceous aerosols in Guangzhou.It was found that primary emissions were important sources of excess organic carbon in the PRD.In addition,tracing technology,such as the use of isotopes,is also a good method.Liu et al.(2014)used radiocarbon(14C)isotopes and tracers to conduct source apportionment of carbonaceous aerosols during winter in Guangzhou.Gao et al.(2012)combined PMF with tracer data and concluded that vehicular emissions,biomass burning and coal combustion were the main sources of polycyclic aromatic hydrocarbons in Guangzhou.Another mainstream method is the CTM.Wu et al.(2013)adopted CAMx-PSAT(Comprehensive Air Quality Model coupled with Particulate Source Apportionment Technology)in Hong Kong/the PRD and revealed that super-regional transport and mobile vehicles were the two major fi ne-particle sources in most cities of the PRD.Cui et al.(2015)claimed that stationary sources(industrial and power)made the largest contribution to PM2.5in Guangzhou,after conducting simulations with the Weather Research and Forecasting(WRF)Chemistry model.However,most previous studies were conducted to determine the possible contributions of individual sources,whereas few studies have focused on the impact of weather conditions on source contributions.In fact,the lifetime of PM2.5components and their precursors are di ff erent—both the generated PM2.5components and their precursors can be transported from region to region,depending on the synoptic weather conditions(Tan et al.,2009;Li et al.,2012;Liu et al.,2014).Furthermore,meteorological conditions also play important roles in the formation of secondary aerosols(Wangetal.,2009;Tanetal.,2013;Zhaoetal.,2017).Therefore,to control PM2.5pollution in a speci fi c location,in addition to identifying the impact of local sources,the impact of regional/super-regional sources,as well as meteorology,must be identi fi ed—especially under unfavorable weather conditions.Understanding to what degree ambient PM2.5originates from di ff erent sources under unfavorable weather conditions is critical for improving air quality in the PRD region.In the current work,source contribution studies were combined with synoptic meteorology.This combination can help to strengthen our knowledge of the characteristics of weather conditions during haze pollution,as well as help advance our knowledge of the contribution characteristics of a source region under a speci fi c weather system.Since the prediction ability and forecast period of weather systems are currently relatively better and longer,respectively,than those of PM2.5,the results from this study can provide guidance to policymakers so that targeted emissions control measures can be implemented in advance.

Historical haze episodes(2013–16)in Guangzhou were identi fi ed and classi fi ed according to weather systems.Case studies were performed by choosing two of the most unfavorable weather systems.We used the “brute-force method”with the aid of the WRF-CMAQ(CMAQ:Community Multiscale Air Quality)model to obtain the contributions of di ff erent sources to PM2.5in Guangzhou.Section 2 introduces the data used,the model system and the study method.Section 3 provides weather system analysis and observation analysis.Section 4 presents an evaluation of the modeling performance.The impact of source contributions and weather conditions are discussed in section 5.Finally,section 6 summarizes the results.

2.Model,data and methods

2.1.Model system

The CMAQ model,version 4.7.1(http://cmascenter.org/cmaq/),was used to simulate PM2.5concentrations under two di ff erent typical meteorological conditions.Developed by the U.S.Environmental Protection Agency,CMAQ is a three-dimensional Eulerian CTM.It is designed for applications such as atmospheric air quality research and pol-icy analysis and regulation.The WRF model,version 3.3.1(http://www.wrf-model.org/index.php),was run to provide offline meteorological fi elds.The Meteorology Chemistry Interface Processor(version 3.6)was used to convert the WRF-produced meteorological data to the format required by CMAQ.Previous studies have shown that the WRF-CMAQ system is a powerful tool in simulating air quality,ranging from the city-to mesoscale level,in China(Wang et al.,2010;Che et al.,2011;Wang et al.,2015).

The WRF-CMAQ con fi gurations were generally consistent with a previous study(Wang et al.,2016).In brief,a two-way nested domain was adopted with a grid resolution of 27 km and 9 km,with horizontal grids of 283×184 and 223×163,respectively.The outer domain covered the whole continentalregionofChina,andtheinnerdomaincoveredthe entire region of Guangdong Province,with strong focus on the PRD region(see Fig.1).The physical and chemical parameterization con fi gurations were consistent with those presented in Wang et al.(2016).

We used the 2012-based Multi-resolution Emission Inventory for China(MEIC)to provide anthropogenic emissions for all selected scenarios.MEIC is a series of emission inventories for China and has a grid resolution of 0.25◦×0.25◦(He,2012).It was developed by Tsing Hua University and considered fi ve emission categories—namely,transportation,agriculture,power plants,industry and residence.Inthisstudy,MEICwaslinearlyinterpolatedtothemodeldomain with consideration of Earth’s curvature e ff ect.For grids outside of China,the INDEX-B(Intercontinental Chemical Transport Experiment-Phase B)Asian emission inventory(Zhang et al.,2009)was used.The natural emissions for all scenarios were calculated offline using MEGAN(Model of Emissions of Gases and Aerosols from Nature),version 2.04(Guenther et al.,2006).

Fig.1.Map of Guangdong Province,with nine cities(GZ,Guangzhou;FS,Foshan;SZ,Shenzhen;HZ,Huizhou;DG,Dongguan;ZQ,Zhaoqing;JM,Jiangmen;ZH,Zhuhai;ZS,Zhongshan)of the inner PRD highlighted.

2.2.Unfavorable weather systems

Studies have shown that air pollution in the PRD region is an integrated result of local(sources in the city areas),regional(other city sources within the region)and superregional(all sources outside the study region)contributions(Kemball-Cook et al.,2009;Li et al.,2012).Understanding the speci fi c contribution is fundamental for designing effective emission control strategies.One challenge is that the source contribution varies if weather conditions are di ff erent.In order to take the impact of weather into account,historical haze episodes from 2013 to 2016 were identi fi ed and classi fi ed according to weather systems.In this study,a haze episodewasde fi nedasaperiodwithatleastthreeconsecutive haze days(a haze day refers to daily visibility less than 10 km and daily relative humidity(RH)less than 90%;rainy days are not considered).A similar method can be found in Wu et al.(2007).These data were collected from 86 operational automatic weather monitoring stations throughout Guangdong,and the data quality was well controlled by the China Meteorological Administration(CMA).Weather systems were classi fi ed by analyzing the weather charts of the selected haze episode.Asimilarmethodwasusedinpreviousstudies(Chan and Chan,2000;Huang et al.,2006;Saskia Buchholza et al.,2010;Li et al.,2011,2016).According to this method,four types of weather systems—namely,“foreside of a cold front”(FC),“sea high pressure”(SP),“equalizing pressure”(EP)and “others”(Os,such as mainland high pressure and tropical cyclones)—were diagnosed as unfavorable weather systems in Guangdong Province(see Table 1).A general introduction to these weather systems is presented in the supplementary material(Fig.S1,see Electronic Supplementary Material),and more detailed information about the statistical data is given by Gao et al.aGao et al.,2018:Characteristics and analysis on regional pollution process and circulation weather types over Guangdong Province,submitted..On the one hand,the fouryear statistical result showed that the haze episodes exhibited a declining trend.The total number of haze episodes from 2013 to 2016 was 61,35,30 and 19,respectively,which indicated that the emission control e ff orts were e ff ective.The variation of annual concentrations of PM2.5also veri fi ed this positive aspect,with PM2.5concentrations of 53,49,32 and 30µg m−3,respectively.On the other hand,SP was the most frequent unfavorable weather system,accounting for 49.0%from 2013 to 2016.Among all the SP episodes,the lowest visibility(dailyaveraged)ranged from0.7km to3.8km,with the highest concentrations of PM2.5(daily averaged)ranging from 80 µg m−3to 184 µg m−3,respectively.FC was the second most dominant unfavorable weather system,the occurrence of which comprised 26.9%of the total.Low visibility(0.6–5.4 km)and high PM2.5(79–222 µg m−3)concentrations were also recorded.EP and Os were the last two,with occurrences of 16.6%and 7.6%,respectively.Since the total occurrence of haze episodes under SP and FC was over 75%,signi fi cantly higher than that under EP and Os(p<0.05),targeted investigations were conducted by examining an SP-a ff ected event(19–25 November 2014)and an FC-a ff ected event(19–25 November 2010),with the aim of explaining the characteristicsofsourcecontributionsunderthesetwotypical unfavorable weather systems.

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2.3. Scenario setting

Figure 2 shows emissions categorized by administrative boundaries of provinces(Fig.2a)and cities(Fig.2b).It can beseen thatemissions(SO2,NOx,Particulatematterbetween 2.5 and 10µm(PMC),PM2.5,carbon monoxide and VOCs)from Guangdong were the highest among the six provinces in southern China,with a total amount of 10.9×106Mg yr−1(unit area:60.7 Mg yr−1km−2).Hunan ranked the second(total area:9.9×106Mg yr−1;unit area:46.7 Mg yr−1km−2),followed by comparative emissions from Guangxi(6.3×106Mg yr−1;unit area:26.6 Mg yr−1km−2),Jiangxi(total area:5.9× 106Mg yr−1;unit area:35.4 Mg yr−1km−2)and Fujian(4.1 × 106Mg yr−1;unit area:33.1 Mg yr−1km−2).For cities within Guangdong Province,the inland nine PRD citiesaccounted for almost70%of theemissionsofthewhole province.In particular,emissions from the GFS(Guangzhou,Foshan and Shenzhen)region were high:emissions from Guangzhou were the highest(total area:1.7×106Mg yr−1;unit area:0.23× 103Mg yr−1km−2),followed by Shenzhen(total area:1.4×106Mg yr−1;unit area:0.7×103Mg yr−1km−2)and Foshan(total area:1.2 × 106Mg yr−1;unit area:0.31×103Mg yr−1km−2).In fact,these three cities are topographically close to each other,so the contribution of GFS emissions is of interest in the following analysis.

Since the PRD is in fl uenced by Asian monsoon circulations and the synoptic wind becomes a northerly wind in autumn and winter,the above emission patterns suggest that air quality in Guangzhou is likely to be in fl uenced not only by local emissions but also emissions from nearby cities or provinces.With the objective of studying source contributions to Guangzhou,the term “local”in this paper indicates emissions from Guangzhou City(GZ),the term“region”refers to city sources within the PRD region,and the term“super-region”implies contributions from sources(both anthropogenic and biogenic)outside Guangdong Province.One baseline emission scenario(BASE)and four emission reduction scenarios(ONLYGD,NO PRD,NO_GFS and NO_GZ)were used to study the possible source contributions under two unfavorable weather conditions(Table 2).

Fig.2.Emissions categorized(a)by province in southern China and(b)by city in Guangdong Province.The blue frame highlights all cities of the inner PRD,and the red frame highlights three cities of the inner PRD—namely,Guangzhou,Foshan and Shenzhen.

BASE refers to the scenario where PM2.5concentrations are simulated with the participation of the entire countylevel emission inventories;the result represents the overall PM2.5contribution from all local and regional/superregional sources without any reductions.ONLY GD excludes all emissions except those from Guangdong,while NO PRD,NO_GFS and NOGZ pertain to emission inventories without emissions from the respective PRD region,GFS region,and GZ.By comparing the reduction scenarios with the baseline,contributions from di ff erent source regions could be identi fi ed.For example,the di ff erence between ONLY_GD and BASE is the contribution from other provinces(super-regional contribution);similarly,the di ff erence between NO_PRD and BASE,the di ff erence between NO GFS and BASE,and the di ff erence between NOGZ and BASE,are the contributions from the PRD region,the GFS region,and the local region,respectively.Hence,the di ff erence(∆)and source region contribution ratio(SRCR)are defi ned to quantify their speci fi c contributions:

where C is the PM2.5concentration simulated under an emission reduction scenario;B is the baseline PM2.5concentration;ngridis the total grid number of Guangzhou coverage in the model domain;and BiandCirepresent the PM2.5concentration within Guangzhou simulated under the baseline and an emission reduction scenario,respectively.To sum up,∆(mass concentration variation)and SRCR(percentage variation)can re fl ect the impact of a source region on Guangzhou.

2.4.Data used and model evaluation

Hourly values of surface meteorological parameters such as temperature,wind,RH and pressure were adopted from the CMA.Meteorological data were collected at nine automatic weather stations in nine cities in the inland PRD region and used either for case analysis or for model validation.The air quality monitoring data used in this study were obtained fromthe GuangdongProvinceEnvironmentalAdministration(GDEPA)andthePRDRegionalAirQualityMonitoringNetwork.The network consists of 16 sites,13 of which lie within the inner PRD and three in Hong Kong.Previous studies have shown that these sites are well maintained and perform well in both scienti fi c research and operational services(Zhong et al.,2013;Wang et al.,2016).In order to understand the two episodes controlled by unfavorable weather systems in Guangzhou,analyses of observed data were conducted to obtain the speci fi c characteristics.

Statistical metrics,including mean values(Obsmeanand Simmean),mean bias(MB;MB=Obsmean−Simmean),normalized mean bias(NMB;),root-mean-square error(RMSE;),and the index of agreement(IOA;),were used to assess the simulated results:

where n is the total number of samples,i indicates the ith sample,and Sim(i)and Obs(i)represent the ith simulated and observed values.Usually,a modeling result is acceptable if the NMB and RMSE values are close to 0 and the IOA value is close to 1(Streets et al.,2007;Jiang et al.,2008,Xing et al.,2011;Wu et al.,2013;Wang et al.,2015,2016).

3.Characteristics of observations

3.1. Analysis of weather systems

Figure 3 depicts the weather charts of a typical FC-a ff ected event(2000 LST 21 November 2010)and a typical SP-a ff ected event(0800 LST 20 November 2014).Usually,an FC-a ff ected weather system occurs in a relatively cold season such as autumn,winter or early spring.On 19 Novem-ber 2010,southern China experienced a mainland high(see Fig.S2),while a low-pressure system occurred over the Tibetan Plateau on 20 November,and this low pressure developed in an easterly direction(see Fig.S2).Then,the Tibetan low reached the mainland high on 21 November,and fi nally formed a cold front to the northwest of the PRD(Fig.3a).Since the PRD was located on the foreside of this cold front,the weather conditions in the PRD were directly in fl uenced.Usually,the northern parts of Guangdong experience a northerly cold wind,while the southern parts experience a southerly warm and humid air fl ow from the South China Sea.A convergence could elevate the warm and humid air fl ow,forming an inversion layer over the PRD region.Indeed,an inversion layer was observed during this FC-a ff ected event,based on temperature sounding data at Qingyuan(in Guangzhou)and Kings Park(in Hong Kong)(see Fig.S3 in the supplementary material),con fi rming the accuracy of the weather system analysis.Under such a weather condition,air pollutants brought by the northerly wind from upwind areas could easily be trapped and accumulate in the PRD,thus causing air pollution.

Table 2.Emission scenario setting for an FC-a ff ected event and an SP-a ff ected event.

Fig.3.Weather charts of(a)an FC-a ff ected event at 2000 LST 21 November 2010 and(b)an SP-a ff ected event at 0800 LST 20 November 2014.The red frame shows the PRD.

An SP-a ff ected weather system usually occurs in autumn,winter and spring.In most haze-related cases,the predecessor of a sea high is a mainland high(only a few are directly formed by a western Paci fi c subtropical high in summer).On 19 November 2014,a cold mainland high was moving easterly to the sea(Fig.S4);this cold high transformed once it encountered the warm marine atmosphere,and fi nally a slightly weaker but much warmer sea high was formed on 20 November(Fig.3b).With the high-pressure center located over the sea,the ridge of this high extended southwesterly to the PRD and became the dominant weather system in the PRD.The PRD surface was dominated by downward anticyclonic air fl ows resulting in static wind on the ground(this was confi rmed by the observed surface wind).Hence,such a weather condition would not be conducive to atmospheric dispersion and di ff usion.

3.2.Analysis of observations

Figure 4 shows the patterns of wind,RH,pressure,temperature,PM2.5concentration and visibility during the FC-a ff ected event.During this event,relatively strong northerly winds(daily mean wind speed=2 m s−1)were observed before the haze day(21 November),indicating the possible transport of air pollutants from Guangzhou to its downwind areas.In addition,a noticeable decreasing trend of diurnal pressure was observed compared to the relatively stable diurnal variations of RH and temperature.This was consistent withtheweathersystemanalysisinsection3.1,astheTibetan low that reached southern China was conducive to regional transport.After the convergence of the northerly prevailing wind caused by the cold front and the southerly prevailing wind from the South China Sea(see discussion in section 3.1),haze days were observed on 21 November,22 November,and 24 November,with the highest PM2.5concentrations of 110µg m−3,102µg m−3,and 105µg m−3,and the lowest visibility of 0.9 km,4 km and 2 km,respectively.Signi fi cant inverse relationships were observed between PM2.5concentration and visibility(r=−0.61)during these haze days.In addition,likely caused by the inversion layer,the wind pattern changed to weak southerly winds on the haze days(i.e.,the daily mean wind speed on 21 November was only 1.0 m s−1),suggesting the possible accumulation of air pollutants.Therefore,the relationship between the wind and PM2.5concentrations during this FC-a ff ected event revealed that relatively strong northerly winds occurred before haze days(see 19 November and 21 November in Fig.4),which may have played a role in reducing pollutant concentrations in Guangzhou,while the static southerly wind controlled the atmosphere on the haze days(see 21 November,22 November and 24 November in Fig.4),which might play a role in accumulating pollutants in Guangzhou.

The SP-a ff ected event showed a di ff erent pattern(Fig.5):there were three consecutive haze days starting from 20 Novemberthrough22November,withthehighestPM2.5concentration of 112 ug m−3and the lowest visibility of 0.9 km during the haze episode.Though the wind directions varied considerably,the wind speed remained at rather low levels(mean wind speed=0.9 m s−1)during the haze event(20–22 November).The low wind speed indicated a stagnant atmospheric situation,since the PRD was fully controlled by SP.RH showed a stable variation,with a mean value of 68.5%,indicating that the atmosphere was relatively dry.An increasing trend of temperature and a decreasing trend of pressure couldbeseenfrom19Novemberto20November.This could be explained by the weather system evolution.The mainland cold high transformed when it encountered the warm marine atmosphere(see section 3.1).Though the pressure in the PRD decreased,the magnitude of the pressure under this SP-a ff ected event remained high;the mean value during this episode was 1018 hPa—noticeably higher than that of the FC-a ff ected event(1011 hPa).Therefore,the characteristic weather conditions under an SP-a ff ected haze episode include a relatively high temperature,low humidity,static winds and high pressure.The atmosphere was not conducive to air pollutant dispersion,and local emissions appeared to be the dominant sources.

Fig.4.Diurnal variation of surface wind,RH,pressure,temperature,PM2.5and visibility under the FC-a ff ected weather system in November 2010.

Fig.5.Diurnal variation of surface wind,RH,pressure,temperature,PM2.5and visibility under the SP-a ff ected weather system in November 2014.

4.Evaluation of model performance

Given the analyses of weather systems in section 3.1 and the analyses of meteorological factors in section 3.2,it was found that the characteristics of the SP-a ff ected event and the FC-a ff ected event chosen in this study were consistent with the general overview(Li et al.,2016)about feature analysis of the typical FC weather system and SP weather system.Therefore,the two selected events could represent the general characteristics of SP and FC weather systems,respectively.Prior to using the modeling result to study the source contribution,an evaluation of model performance is necessary to verify the model simulation.The evaluation of the FC-a ff ected event was presented previously(Wang et al.,2016),and the result showed that the simulation was well matched with the observation.Here,we present the evaluation of the SP-a ff ected event.

Table 3 shows the average statistical comparisons between the simulated and observed values in this region.In general,the MB and NMB of temperature,RH and wind speed ranged between−1.9 and 1.2,and−0.01 and 1.3,respectively,showing the magnitudes matched well with the observations.The high values of IOA for temperature(0.9)and RH(0.8)also indicated a good agreement between the observed and simulated values.It was noted that the simulated wind speed values were generally overestimated,which could have been due to the model resolution and the urban canopy structure.The grid resolution(9 km×9 km)might be relatively too coarse to reveal the real geography,and the PBL scheme does not consider the urban canopy structure;therefore,uncertainties exist in wind calculations,as the distribution of urban morphology a ff ects the surface-atmosphere energy budget(Jiang et al.,2008;Wang et al.,2015).Moreover,land surface modules,such as soil moisture,also result in uncertainty in the wind shear calculation(Li et al.,2013).Generally,the results were within the typical range of meteorological modeling studies(Chen et al.,2007;Wu et al.,2013;Cui et al.,2015;He et al.,2015;Wang et al.,2016).

Table 3.Averaged model performance for temperature,RH,wind,PM2.5andO3duringtheSP-a ff ectedevent(20–25November2014).

The simulated PM2.5and ozone(O3)concentrations were also evaluated.Three typical stations were selected as representatives(Huadu—a suburban site in northern Guangzhou;Panyu Middle School—an urban site in southern Guangzhou;and Xixiang—a coastal urban site in Shenzhen).Figure 6 shows that the time series of simulated PM2.5and O3values matched well with the observations.The simulated average PM2.5was 55.2 µg m−3,which was close to the observed value(53.1µg m−3).Surface O3was slightly overestimated,with an MB of−12.9 ppb.The trends of the simulation were also comparable with those observed(IOA values were 0.5 and 0.8 for PM2.5and O3,respectively).In addition,an area comparison was conducted for PM2.5evaluation.ObservedPM2.5concentrationscollected from GDEPAmonitoring sites were interpolated throughout Guangdong(see Fig.7a,which was downloaded from the Environmental Meteorology Operation-platform of Southern-China,EMOS,website:http://10.12.12.211/emos/index)and used to compare with simulations.It could be seen that high PM2.5concentrations were located in central Guangdong,i.e.,the area where Guangzhou and Foshan are located;the concentrations were relatively well reproduced by the model.Overall,the spatial distributions showed that modelled PM2.5values agreed well with observations,indicating that the model also captured the regional pollution characteristics.

5.Source contributions

5.1.Impact of local,regional and super-regional emissions

The spatial impact of emissions from di ff erent source regions during the SP-a ff ected event is shown in Fig.8.Under such a stagnant weather condition,emissions outside Guangdong had limited in fl uence on most cities throughout Guangdong.It could be seen that the main a ff ected areas were those provincial boundaries where PM2.5concentrations decreased by 15–18 µg m−3.However,a rather limited in fl uence was observed in most PRD cities—for example,the PM2.5concentration in Guangzhou only decreased by 0–3 µg m−3.Indeed,the average∆between ONLY_GD and BASE was 2.3µg m−3in Guangzhou(Table 4).Compared to the in fl uence ofemissionsoutsideGuangdong,emissionsfromthePRDregion,GFS region and GZ mainly a ff ected the PM2.5concentrations in areas within their speci fi c administrative boundaries(Figs.8b–d).Noticeable reductions in PM2.5were measuredinGuangzhou;the∆was19.6µgm−3,16.4µgm−3and 14.3µg m−3without the consideration of PRD regional emissions,GFS regional emissions and Guangzhou local emissions,respectively(Table 4).However,little in fl uence could be found in areas outside these source regions.Such results suggested that the atmosphere was not favorable for regional transport,and local emissions became the dominant sources under the SP-a ff ected weather conditions.

The spatial impact of super-regional,regional and local emissions indicated di ff erent patterns under the FC-a ff ected event(Fig.9).Emissions from outside Guangdong played a dominant role in contributing PM2.5to most areas of Guangdong.The area-mean PM2.5concentrations were reduced by 15.5 µg m−3if these emissions were not considered.In fact,the average∆between ONLY_GD and BASE was 14.3µg m−3(Table 4),which was signi fi cantly higher than that of the SP-a ff ected event(p<0.05),indicating that superregional transport became an important contributor under the FC-a ff ected weather system.With respect to the impact of regional emissions(NO_PRD minus BASE and NOGFS minus BASE)and local emissions(NOGZ minus BASE),the spatial di ff erences in PM2.5concentrations displayed a“tongue-like”shape extending from the source regions to the southwest.The average∆of the PRD region,the GFS region and local Guangzhou was 10.3 µg m−3,8.7 µg m−3and 7.6 µg m−3,with maximal reductions of 37 µg m−3,27.3 µg m−3and 22.2 µg m−3,respectively(see Table 4).It should be noted that these three source regions’contributions decreased,while the contributions from emissions outside Guangdong increased,compared to those under the SP-a ff ected event.Such phenomena were consistent with the weather system analysis,i.e.,the cold front resulted in a northerly wind in the PRD that transported air pollutants from source regions to downwind areas,thus resulting in the“tongue-like”shape.This fi nding also explained why superregional transport became dominant under this condition,while the SP-a ff ected event was a more stable condition since downward air fl ow dominated the atmosphere,trapping and accumulating emissions from source regions.

Table 4.Statistics of PM2.5concentrations from di ff erent source regions during haze days in Guangzhou.

Fig.6.Comparisons of simulated and observed PM2.5and O3at Huadu(a suburban site in northern Guangzhou),Panyu School(an urban site in southern Guangzhou)and Xixiang(a coastal urban site in Shenzhen)during the SP-a ff ected event.

Fig.7.Spatial comparison between observed PM2.5(units:ug m−3)and simulated PM2.5over the PRD:(a)observed PM2.5,in which the pins represent GDEPA monitoring stations and the contours were interpolated using monitoring values;(b)modelled PM2.5,in which the bottom-right inset is a map of the South China Sea Islands.

Fig.8.Contributions from di ff erent source regions to 0800 LST average PM2.5during the haze days of the SP-a ff ected event:(a)impact of emissions from outside Guangdong;(b)impact of emissions from the PRD;(c)impact of emissions from the GFS;(d)impact of emissions from Guangzhou.

The contributions of speci fi c source regions to PM2.5under these two typical unfavorable weather systems are presented in Fig.10.When the sea high pressure system controlled weather conditions in the PRD,the SRCR of the PRD region(SRCRPRDregion),the SRCR of the GFS region(SRCRGFSregion)and the SRCR of Guangzhou(SRCRGuangzhou)were 44.0%,38.8%and 36.0%,respectively,which were all signi fi cantly higher than that of the super-region(SRCRsuper-region;7.0%).It should be noted that both the SRCRPRDregionand the SRCRGFSregioncontained the source region contributions from Guangzhou;we used the di ff erence between the regional values and the local values to deduct the contributions from Guangzhou.For example,the di ff erence between SRCRPRDregionand SRCRGuangzhouis the source contribution from the eight other inland PRD cities(excluding Guangzhou).It was found that the eight other inland PRD cities contributed 8%,whereas Foshan and Shenzhen contributed 2.8%.The results implied that local emissions were the major contributors to haze pollution,and it is strongly suggested that local emissions be reduced when formulating emission control strategies under a high-pressure system.With regard to the FC-a ff ected event,the super-regional source became the dominant contributor(Fig.10b)in Guangzhou.The SRCRsuper-regionwas 34.8%,which was signi fi cantly higher than that of the SP-a ff ected event(SRCR=7%;p<0.05).In addition,the“tongue-like”shape in Fig.9 indicated that regional transport should be noticeable,and the calculation of SRCR showed that regional source contributions were rather close to that of the local region(SRCRPRD region,SRCRGFS regionand SRCRGuangzhou were 16.0%,13.6%and 12.0%,respectively).This is because Guangzhou is upwind in the PRD region,and the regional impact had greater in fl uence in the downwind areas than in Guangzhou.Consequently,the super-regional contributions(emissions from the upwind area of Guangzhou)became dominant.Our results suggest that merely controlling local emissions under this weather condition is somewhat ine ff ective.By contrast,regional joint prevention and control should be considered to ensure a more reasonable control of haze pollution.

Fig.9.Contributions from di ff erent source regions to 0800 LST average PM2.5during the haze days of the FC-a ff ected event:(a)impact of emissions from outside Guangdong;(b)impact of emissions from the PRD;(c)impact of emissions from the GFS;(d)impact of emissions from Guangzhou.

Fig.10.PM2.5contributions from di ff erent source regions during the(a)SP-and(b)FC-a ff ected events.

5.2.Impact of weather conditions

The above analysis demonstrates that source contributions to PM2.5in Guangzhou exhibit di ff erent characteristics under di ff erent weather systems.In fact,haze pollution is the integrated result of unfavorable weather conditions and emissions.In this section,we propose a simple method to distinguish the relative impact of meteorology and emissions.

Fig.11.A simple method to quantify the impact of meteorology and emissions.

Fig.12.Relative impact of meteorology and emissions between the FC-and SP-a ff ected events.

Figure 11 presents the fl owchart of this method.The observeddi ff erence(OBSdiff)canbedirectlycalculatedbycomparing the observed PM2.5concentration of the SP-a ff ected event and the FC-a ff ected event.This OBSdiffcan be regarded as the result of the di ff erence between meteorology(METEdiff)and that of emissions(EMISdiff)during two different periods.Then,METEdiffcan be obtained with the aid of a CTM.By using the same emissions(EMIS2012-MEIC—the November emission inventory from 2012-MEIC)and different meteorologies(METEFC-affectedand METESP-a ff ected,produced by the WRF model)as input to drive the CMAQ model,the di ff erence of the two simulations is METEdiff.Finally,EMISdiffcan also be quanti fi ed after calculating OBSdiffand METEdiff.We know this method has uncertainties;the main uncertainty is introduced through the accuracy of model performance.For example,the emission inventory contains uncertainties itself;parameter schemes in numerical models can also bring uncertainties;and the overestimated surface wind may reduce air pollutant concentrations in upwindareasandincreasethesimulatedresultsindownwindareas.However,since the simulated results have already passed strict evaluation,the results are considered acceptable.Furthermore,through the rapid development of numerical models,the uncertainty associated with the model has shown a decreasing trend.Therefore,this simple algorithm can be regardedasameanstoquantifytherelativeimpactofemissions and meteorology.

Figure 12 presents the relative impact of meteorology and emissions in two events,i.e.,35%and−18%,respectively.This result is consistent with the above analysis.On the one hand,the SP-a ff ected event was a stagnant condition,and local emissions were the major contributors.On the other hand,super-regional transport was more important than local emissions for the FC-a ff ected event.Therefore,the relative impact of meteorology was positive,while the impact of emissions was negative,when comparing the FC and SP-a ff ected events.

6.Summary

In this study,source contribution studies were combined with synoptic meteorology to understand the contribution characteristics of source regions under di ff erent unfavorable weather systems.

Historical haze episodes(2013–16)in Guangzhou were examined and classi fi ed according to synoptic weather systems.Four types of weather systems were diagnosed as unfavorable:SP was ranked fi rst(49.0%),followed by FC(26.9%),EP(16.6%)and Os(7.6%),in terms of the total number of haze events.Since the occurrence of FC and SP were the highest(>75%of the total haze episodes),tar-geted case studies were performed of an SP-a ff ected event and an FC-a ff ected event.Monitoring data showed that SP was characterized by low winds(0.9 m s−1),relatively high pressure(1018 hPa)and low RH(68.5%).The high pressurecontrolled system resulted in a rather stable atmosphere,and thus pollutants were trapped and not easily di ff used.By contrast,FC was more favorable for pollutant transport.The cold front moved in a southeasterly direction,and synoptic winds prevailed in Guangdong.Northerly winds dominated and persisted at relatively high levels(2 m s−1).Such a wind pattern was conducive to the transport of pollutants from upwind to downwind areas.

The numerical source regions’contribution results were consistent with the meteorological analysis.The source regions’contributions depicted di ff erent characteristics when di ff erent weather systems dominated.SP was a stagnant weather condition;the SRCR showed that the local region was a major contributor(37%),while the contributions of the PRD region(excluding Guangzhou),GFS region(excluding Guangzhou)and super-region were only 8%,2.8%and 7%,respectively.By contrast,FC favored regional transport.The super-region became noticeable,contributing 34.8%,while the local region decreased to 12%.A simple method was proposed to quantify the relative impact of meteorology and emissions.Themeteorologicalimpactwas35%,whilethatof emissions was−18%,when comparing the FC-a ff ected event with the SP-a ff ected event.

Our results highlight the importance of the meteorological impact on the source region contribution.Attention should be paid to local emissions when an SP-type system dominates,while regional joint control should be initiated when an FC-type system is in control.This study can provide guidance to policymakers to implement e ff ective control strategies under unfavorable weather systems.

Acknowledgements.This study was supported by the National Key R&D Program of China:Task 3(Grant No.2016 YFC0202000);Guangzhou Science and Technology Plan(Grant No.201604020028);National Natural Science Foundation of China(Grant No.41775037 and 41475105);Science and Technology Innovative Research Team Plan of Guangdong Meteorological Bureau(Grant No.201704);Guangdong Natural Science Foundation-Major Research Training Project(2015A030308014);and a science and technology study project of Guangdong Meteorological Bureau(Grant No.2015Q03).The author also thanks Tsinghua University for providing MEIC.

Electronic supplementary material Supplementary material is available in the online version of this article at https://doi.org/10.1007/s00376-018-7212-9.

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