A Monitoring and Modeling Study to Investigate Regional Transport and Characteristics of PM2.5 Pollution

Jianlei Lang1, Shuiyuan Cheng1*, Jianbing Li2, Dongsheng Chen1, Ying Zhou1, Xiao Wei1, Lihui Han1, Haiyan Wang1

1 College of Environmental & Energy Engineering, Beijing University of Technology, Beijing 100124, China 2 Environmental Engineering Program, University of Northern British Columbia, Prince George, British Columbia V2N 4Z9, Canada

ABSTRACT

In this study, the regional transport and characteristics of PM2.5 pollution were examined through a case study in Beijing, China. The results from an intensive monitoring program indicate that the inorganic particles (sulfate, nitrate, and ammonium), organic carbons, and the elements accounted for 35.5, 24.2, and 15.3% of the total PM2.5 on an annual average basis, respectively. The proportions of such PM2.5 components also showed clear seasonal variations. An integrated MM5- CMAQ modeling system was then developed to examine the regional transport of PM2.5 and its components in Beijing within four typical months of 2010. The results indicate that the annual average total trans-boundary contribution ratio (TBCR) is 42.2, 46.3, 77.4, and 61.6% for the concentrations of PM2.5, sulfate, nitrate, and ammonium, respectively. A logarithmic relationship was found between the total TBCR and the PM2.5 concentrations in Beijing for different seasons. Further investigations showed that trans-boundary transport played a major role in Beijing’s PM2.5 concentrations during the period of high pollution levels, with an annual average TBCR of 54.6%. As a result, the control of PM2.5 pollution in Beijing needs effective cooperation between Beijing and its surrounding regions, especially during periods of heavy pollution.

Keywords: Trans-boundary transport; Beijing; Emission contribution; MM5-CMAQ; Air quality.

INTRODUCTION atmospheric visibility and the human health. For example, previous studies indicated that the PM2.5 was the main cause With continuous rapid economic development and sharp of reduced visibility in Beijing (Wang et al., 2006; Zhou et growth of vehicle population, the pollution of PM2.5 (i.e., al., 2012a). Such fine particles can also result in serious the fine particles with aerodynamic diameter of ≤ 2.5 μm) human health problems (e.g., cardiovascular diseases, has become one major environmental problem. This problem respiratory irritation, and pulmonary dysfunction) since they has received wide concerns among experts, governments, contain microscopic solids or liquid droplets which are toxic media, and the public in China and around the world, and can get deep into the human body (Cao et al., 2012; particularly after the occurrence of a severe haze event in Haberzettl et al., 2012). Consequently, the study of PM2.5 Beijing (i.e., the capital of China) in December 2011 (Lang pollution is of great importance for effectively improving et al., 2012; Yuan et al., 2012). It was reported that the the air quality and the public health. In fact, a new National annual average PM2.5 concentrations in Beijing from 2000 Ambient Air Quality Standard (NAAQS) was proposed in to 2010 were 101.0, 93.6, 101.5, 100.0, 102.2, 85.2, 93.5, China in the beginning of 2012 (http://www.zhb.gov.cn/gkml 84.5, 76.8, 79.6, and 71.9 μg/m3, respectively (Zheng et al., /hbb/bwj/201203/t20120302_224147.htm). This new standard 2005; Song et al., 2007; Zhao et al., 2009; Wang et al., introduced the control of PM2.5 for the first time in China. 2012). However, the PM2.5 concentrations were only 9.8– Generally, the pollution of particulate matters (PM) is a 13.6 μg/m3 during the same period in the United States (http:// complex regional transport problem as confirmed from www.epa.gov/airtrends/pm.html). The high concentration many previous studies (Hatakeyama et al., 2011; Hussein of PM2.5 could have significant adverse impacts on the et al., 2011; Ibn Azkar et al., 2012; Squizzato et al., 2012; Tao et al., 2012). Particularly, air quality models have been commonly applied to investigate such regional air pollutant transport (Chen et al., 2007). Among various models, the * Corresponding author. Tel.: +86 10 67391656; CMAQ/Model-3 developed by US EPA has obtained Fax: +86 10 67391983 extensive applications. For example, Chen et al. (2007) E-mail address: [email protected] examined the trans-boundary transport of PM10 to Beijing,

944 Lang et al., Aerosol and Air Quality Research, 13: 943–956, 2013 and obtained the trans-boundary contribution ratios (TBCRs) gradually from its northwest to southeast towards the Bohai from its surrounding regions using a meteorological model Bay. This basin-like topography plays an important role in (MM5) and an air quality model (CMAQ). Wang et al. the transport of pollutants from the industrial provinces (2010) utilized the HYSPLIT and MM5-CMAQ models to (e.g., Hebei, Shanxi) to Beijing. The total population of study the PM10 pollution problem in Beijing, and found Beijing was 19.62 million in 2010, accounting for 1.47% that the southwest transport pathway was closely associated of the national population. The GDP of Beijing was 213.1 with the increasing phase of its PM10 pollution processes. billion US dollars in 2010, accounting for 3.23% of the Zhu et al. (2011) investigated the transport pathways and national GDP. There are 14 subordinate districts and 2 potential sources of PM10 concentration in Beijing based counties in Beijing as shown in Fig. 1, covering a total area 2 on backward trajectories and PM10 concentration records. of 16410.5 km . The central urban area of Beijing includes Che et al. (2011) evaluate the effects of different vehicle 6 subordinate districts (e.g., Dongcheng, Xicheng, Chaoyang, emission control measures on the air quality of SO2, NO2, Haidian, Fengtai, and Shijngshan), covering 8.3% of Beijing’s PM10, and O3. Zhou et al. (2012b) investigated the source- geographical area, accounting for 63.2% of Beijing’s total receptor relationships of PM10 in Tangshan of northern population, and contributing to 69.7% of the total GDP in China using the integrated MM5-CMAQ model. Other Beijing. As mentioned before, the annual average PM2.5 application of CMAQ can be found in Zhang et al. (2007), concentration during the past decade in Beijing was in the Mueller and Mallard (2011) and Han et al. (2012). range of 71.9–101.0 μg/m3, which is much higher than the 3 Most of the previous studies on the transport of particulate proposed class II standard of PM2.5 (i.e., 35 μg/m ) in the matters were focused on PM10, and the transport of PM2.5 new Chinese NAQQS, indicating severe PM2.5 pollution in has received less attention (Streets et al., 2007). As compared Beijing. with PM10, the diameter of PM2.5 is much smaller with more complex components that contain a higher percentage METHODOLOGY of inorganic particles, which are mainly secondary pollutants produced via complex reactions (Ianniello et al., 2011). This MM5-CMAQ Modeling System makes the PM2.5 can stay in the atmosphere for a longer The meteorological model MM5 is a limited-area, non- time and facilitates a long distance transport. As a result, hydrostatic, terrain-following sigma-coordinate model the transport of PM2.5 may be quite different from that of designed to simulate meso-scale and regional-scale PM10. However, few previous studies were reported to study atmospheric circulation. It was developed by Penn State the transport characteristics of PM2.5 components and their University and National Center for Atmospheric Research temporal variations (Streets et al., 2007). In fact, the (PSU/NCAR) as a community meso-scale model in 1970s understanding of such information is very valuable for the and has been continuously upgraded since then to its latest effective decision making of air quality management. The fifth version (Grell et al., 2000). The air quality model objective of this study is then to examine the regional CMAQ/Model-3, which was developed by US EPA, is the transport of PM2.5 and its components through conducting a third generation of CMAQ air quality prediction tool. It is case study in Beijing, which has typical and representative a sophisticated modeling system which can simulate the PM2.5 pollution problem in China. An intensive monitoring concentrations of fine particulate, tropospheric ozone, acid program was implemented in the study area to measure the deposition, visibility, and other atmospheric pollutants at concentrations of PM2.5 components within different seasons, regional and urban scales (Byun and Ching, 1999). In the and to provide necessary data for establishing an integrated context of “one atmospheric” perspective, the CMAQ model MM5-CMAQ modeling system. The modeling system was can well simulate the complex physical and chemical then applied to facilitate the investigation of trans-boundary processes. In addition, the model contains a capacity of PM2.5 transport. The concentrations of PM2.5 and three multiple levels nested-grid. The above characteristics of selected components (sulfate, nitrate, and ammonium) in the model can reduce the uncertainty and increase the Beijing were simulated under various emission reduction accuracy of the simulation results. In this study, the Carbon scenarios, and the corresponding trans-boundary contribution Bond-IV Mechanism (CB-IV) and the AER03 aerosol ratios (TBCRs) of Beijing’s surrounding areas were then mechanism were chosen for the simulations of secondary calculated. Moreover, the TBCRs associated with different particles formation and other relative atmospheric chemical PM2.5 pollution levels were analyzed. The results can provide reactions. sound decision making basis for the mitigation of PM2.5 In this study, the MM5 and CMAQ models were coupled pollution in Beijing. to predict PM2.5 concentrations in Beijing. The MM5 model was used for providing the 4D meteorological data OVERVIEW OF THE STUDY AREA required by the CMAQ model. The details of the required data by MM5, such as the topography data, 3D first-guess Beijing is located in northern China, with Tianjin meteorological fields, and the meteorological measurements, municipality on its eastern border and Hebei province on as well as the physical options used were the same as those its other three borders as shown in Fig. 1. Topographically, described in Chen et al. (2007) and Zhou et al. (2012c). As Beijing is characterized as a semi-basin region, with Taihang for the CMAQ model, emission inventory is another Mountains on its southwest and Yan Mountains on its important input data. The county-level air pollutant emission northwest. The mean sea level of its land tends to decrease inventories (including pollutants of PM10, PM2.5, SO2, NOx,

Lang et al., Aerosol and Air Quality Research, 13: 943–956, 2013 945

0 250 kilometers

Beijing Do‐2 Do‐1 Tianjin Hebei Shanxi 0 750 1,500 kilometers Shandong

Henan

Note: A.T.: Aoti D .S.: Dongsi 02550 Do‐3 T.T.: Tiantan G.C.: Gucheng kilometers W.L.: Wanliu G.Y.: Guanyuan N.Z.G.: Nongzhanguan F.T.H.Y.: Fengtaihuayuan Yanqing Miyun W.S.X.G.: Wanshouxigong Huairou BNU: Beijing Normal University

Changping Pinggu Shunyi BNU A.T. W.L. Mentougou H.D. D.S. S.J.S. X. D. C.Y. G.Y. C. C. Note: F.T. Tong D.C. : Dongcheng G.C. N.Z.G. zhou X.C. : Xicheng C.Y. : Chaoyang T.T. Fangshan F.T.H.Y. Daxing H.D. : Haidian W.S.X.G. F.T. : Fengtai S.J.S.: Shijingshan E v aluation grid cell area ( EGC) Fig. 1. Design of three-level modeling domains.

NH3, VOCs and CO) were obtained from the Environmental and its surrounding regions (including Tianjin, Hebei, Shanxi, Protection Bureaus (EPBs) of Beijing and its surrounding and parts of Shandong and Henan); modeling domain 3 regions. These emission inventories were processed by the (both for MM5 and CMAQ simulation) was divided into modified Sparse Matrix Operator Kernel Emissions Modeling 49 × 49 grid cells with a spatial resolution of 4 km × 4 km, System (SMOKE) (Houyoux et al., 2000) to generate covering Beijing. Vertically, 35 sigma levels were designed emission inputs with high spatial and temporal resolution for the grid structure in the MM5 simulation, with the top as required by the CMAQ model. The space distribution of height being 15 km at the pressure of 100 mbar. The first PM2.5 emissions in Beijing and surrounding regions was 20 vertical levels were distributed within 2 km from the shown in Fig. 2. ground level. These 35 vertical layers for the MM5 model A three-level nested-grid architecture (shown in Fig. 1) were collapsed into 12 layers for the vertical domain of the was designed for the implementation of the MM5-CMAQ CMAQ model. The MCIP (Meteorology-Chemistry Interface modeling system. Modeling domain 1 (only for MM5 Processor) was used to process and transform the hourly simulation) was divided into 43 × 49 grid cells with a spatial MM5 outputs from 35 vertical levels into 12 levels with resolution of 36 km × 36 km, covering most areas of the format required by the CMAQ model. More detailed northeastern China; modeling domain 2 (both for MM5 descriptions of the MM5-CMAQ modeling system and setup and CMAQ simulation) was divided into 70 × 76 grid cells can be found in previous papers (Chen et al., 2007; Zhou with a 12 km × 12 km spatial resolution, covering Beijing et al., 2012c).

946 Lang et al., Aerosol and Air Quality Research, 13: 943–956, 2013

42.0N

37.0N

113.2E 120.0E

Fig. 2. Space distributions of PM2.5 emissions in Beijing and surrounding regions.

Observation of PM2.5 Components were measured using a Thermal/Optical Carbon Analyzer The transport of PM2.5 components is of importance as (DRI, Model 2001). mentioned above, and was thus investigated in this study. The observation of PM2.5 components was performed at a Evaluation of Modeling Performance monitoring station located on the rooftop of a building at The observations from nine air quality monitoring Beijing Normal University (BNU), about 45m above the stations in Beijing were used for modeling performance ground level. The BNU is located between the north second assessment of PM2.5 simulation. These stations include and third Ring Roads, which is a heavily traffic area of AoTi, NongZhanGuan, DongSi, TianTan, WanShouXiGong, Beijing. The PM2.5 samples were simultaneously collected FengTaiHuaYuan, GuCheng, GuanYuan, and WanLiu, as on the Whatmans 41 filters (Whatman Inc., Maidstone, UK) illustrated in Fig. 1. As mentioned above, the PM2.5 and the quartz filters (Whatman Inc., Maidstone, UK) using components were measured at a monitoring station in the medium-volume samplers made by Wuhan Instrument Beijing Normal University (BNU). An evaluation grid cell Co., Ltd., with a flow rate of 40 L/min and 100L/min, area (EGC) with a total number of 7 × 10 grid cells was respectively. The samplings were carried out during winter, selected within modeling domain 3 (Fig. 1). It covers the spring, summer, and autumn from December 2010 to January above ten monitoring stations and most of the central urban 2012 on a 24-h basis. One or two respective months of per area of Beijing. As a result, the data obtained from these season were selected for the PM2.5 samplings. About 50-60 monitoring stations can well represent the characteristics samples per season and a total number of 212 samples were of the urban PM2.5 pollution in Beijing. The MM5-CMAQ collected. All the procedures were strictly quality controlled model was applied to investigate Beijing’s PM2.5 pollution to avoid any possible contamination of the samples. Such as, within four target months in 2010. These four target months the samples collected were put in the polyethylene plastic (January, April, July, and October) were used to represent bags right after sampling and reserved in a refrigerator. All winter, spring, summer, and autumn, respectively. In order the filters were weighed before and after sampling using a to assess the modeling performance, the average simulated One Over Ten-thousand Analytical Balance under constant daily concentrations of PM2.5 within the grid cells containing temperature (20 ± 5°C) and relative humidity (40 ± 2%). the nine monitoring stations during different target months The samples collected on Whatmans 41 filters were used were compared with the average observation results in 2010 for the analysis of 10 selected ions and 22 selected elements, from these nine stations. Meanwhile, the simulated monthly while the samples collected on the quartz filters were used concentrations of the three PM2.5 components (sulfate, nitrate, for the detection of Organic Carbon (OC) and Element and ammonium) within the grid cell containing the BNU 2– – + Carbon (EC). The SO4 , NO3 , NH4 , and other ions monitoring station during different target months were also 2+ 2+ + + 3– – – (Mg , Ca , K , Na , PO4 , F , and Cl ) were analyzed by compared with the observation results from that monitoring ion chromatograph (IC, Metrohm 861 Advanced Compact station. IC). The analysis of 22 elements (Na, Mg, Al, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Sr, Cd, Sb, Ce, Eu, Calculation of Trans-boundary Contribution Ratio and Pb) was based on the inductively coupled plasma-mass (TBCR) spectrometry (ICP-MS, 7500a, Thermo). The OC and EC In order to examine the trans-boundary transport of PM2.5,

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a parameter called trans-boundary contribution ratio (TBCR) of PM2.5, sulfate, nitrate, ammonium, and other fine particles was introduced in this study. A zero-out method was applied under the ZERS scenario; CERS represents the hourly to calculate TBCR using two scenarios, including the Zero pollutant concentration under different ERS scenarios. The Emission Reduction Scenario (ZERS) and the Emission CERS under the scenarios of S1, S3, and S4 was used to Reduction Scenario (ERS) (Chen et al., 2007). The ZERS calculate the TBCR from all of Beijing’s surrounding regions, was corresponding to the base scenario under which the Tianjin, and Hebei, respectively. The CERS under the scenario MM5-CMAQ model was run using the original emission of S2 can be used to calculate the contribution ratio of local inventories. In terms of the ERS, four sub-scenarios were emissions within Beijing. introduced, including Zero-Surrounding-Emission (S1), Zero- Beijing-Emission (S2), Zero-Tianjin-Emission (S3), and RESULTS AND DISCUSSION Zero-Hebei-Emission (S4). The S1, S2, S3, and S4 scenarios correspond to the situations where the emissions (including Seasonal Characteristics of PM2.5 in Beijing pollutants of PM10, PM2.5, SO2, NOx, NH3, VOCs and CO) Fig. 3 presents the observed proportions of different PM2.5 from Beijing’s surrounding regions, Beijing, Tianjin, and components in Beijing. It can be found that the inorganic 2– – + Hebei were set to zero, respectively. particles of SO4 , NO3 , and NH4 were important The average simulated hourly concentrations of PM2.5 and components in PM2.5 in Beijing. They accounted for an its three components as well as other fine particles within the annual average percentage of 35.5% of the total PM2.5. – EGC were used to calculate TBCR as described below: Among these three components, the nitrate (NO3 ) had the highest percentage. In addition to the three inorganic CC particles, organic carbons (OC) and elements were another TBCRZERS ERS 100% (1) two important components in the PM in Beijing. The CZERS 2.5 annual average percentages were 24.2 and 15.3% for OC where CZERS represents the simulated hourly concentration and elements, respectively. The annual average ratio of

Elements Ammonium Elements 26.2% 11.8% 11.6%

Ammonium 8.5% Nitrate 16.2% OC OC 20.0% 22.2% Nitrate 14.2% Sulfate EC 14.9% Sulfate 3.7% EC 6.5% 5.8% Others Others 21.0% 17.6% (a) Spring (b) Summer

Elements Elements 10.5% Ammonium 13.0% Ammonium 9.0% 9.0% OC 30.2% Nitrate OC 15.5% 24.5% Nitrate 13.0%

EC Sulfate EC Sulfate 4.1% 12.8% 3.3% 10.6% Others Others 22.6% 21.9% (c) Autumn (d) Winter

Fig. 3. Proportion of chemical components in PM2.5 during different seasons in Beijing (“Elements” include Na, Mg, Al, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Sr, Cd, Sb, Ce, Eu, and Pb).

948 Lang et al., Aerosol and Air Quality Research, 13: 943–956, 2013

– 2– NO3 /SO4 was 1.23, indicating that the motor vehicles for all of the four target months. The correlation coefficients accounted for an important emission source for the formation (CCs) between the simulated and observed concentrations of PM2.5 pollution in Beijing (Wang et al., 2005). The were 0.789, 0.541, 0.641, and 0.591 for January, April, proportion of inorganic particle components also showed July, and October, respectively. The correlation coefficients obvious seasonal variation. For example, the proportions were generally greater than 0.50. This indicates that the of the three inorganic particles in PM2.5 were higher in modeling performance for simulating PM2.5 concentrations summer and winter, while those for elements were the highest in Beijing is acceptable (Chen et al., 2007). in spring. The three inorganic components accounted for The modeling performance for simulating the 29.2, 42.9, 32.5, and 37.2% of the total PM2.5 during concentrations of the three inorganic particle components 2– – + spring, summer, autumn, and winter, respectively. In terms in PM2.5 (SO4 , NO3 , NH4 ) were also evaluated. Table 1 of the elements, the proportions were 26.2, 11.6, 10.5, and lists the monthly comparison of the simulation and 13.0% in the four seasons, respectively. The reasons for observation results. Generally, most of the simulation errors these seasonal characteristics of PM2.5 components may be of PM2.5 components were ranging from –30% to 10% for explained as follows: (a) the high O3 concentration in the various seasons. The simulation errors of the annual atmosphere in summer could facilitate the formation of average concentrations were –21.8, –15.7, and –18.4% for secondary particles; (b) the higher emissions of SO2 and sulfate, nitrate, and ammonium, respectively. Considering NOx in the heating season of winter could result in a higher the inherent uncertain nature of meteorological and air concentrations of inorganic particles as compared to the quality simulation, the modeling performance of the MM5- spring and autumn, and (c) the higher occurrence frequency CMAQ for simulating the concentration of inorganic of sandstorms in spring could lead to higher percentage of particle components of PM2.5 is also acceptable (Zhang et elements in the PM2.5. al., 2007; Wang et al., 2011a).

Modeling Performance Emission Contribution to PM2.5 Pollution in Beijing Fig. 4 presents the scatter plots of the simulated PM2.5 Figs. 5–6 present the hourly emission contribution ratios concentrations versus the observation results during the four to the PM2.5 concentration in Beijing during the four target target months in 2010. It can be found that most of the data months from all of Beijing’s surrounding regions, as well points are adjacently distributed on both sides of the y = x line as from Beijing, Tianjin, and Hebei, respectively. It can be

300 200

250

) 160 -3 )

200 -3 g m

 120 g m 150 

80 100

Simulation ( 50 40 Simulation ( Simulation

0 0 0 50 100 150 200 250 300 0 40 80 120 160 200 -3 -3 Observation (g m ) Observation (g m ) (a) January (b) April 150 200

120 160 ) ) -3 -3

90 g m 120 g m  

60 80

30 40 Simulation ( Simulation Simulation ( Simulation

0 0 0 306090120150 0 40 80 120 160 200 -3 -3 Observation (g m ) Observation (g m ) (c) July (d) October

Fig. 4. Scatter plots of the simulated PM2.5 concentrations versus observations during the target months in 2010.

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3 Table 1. Comparison of simulated results with observations of inorganic PM2.5 particles (μg/m ) Spring Summer Autumn Winter Annual average Observation 4.5 20.7 11.5 13.0 11.9 Sulfate Simulation 4.2 10.2 9.1 13.9 9.3 Relative error –7.8% –50.9% –21.1% 7.1% –21.8% Observation 9.8 22.6 14.1 15.7 14.7 Nitrate Simulation 8.2 20.4 14.3 6.7 12.4 Relative error –16.9% –9.8% 1.1% –57.5% –15.7% Observation 5.9 16.4 9.8 9.2 9.4 Ammonium Simulation 4.3 10.9 7.4 8.1 7.7 Relative error –26.3% –33.5% –24.0% –11.4% –18.4%

80 100

60 80 60 40 40

TBCR (%) 20 20 TBCR (%) 0 0 3 6 9 12 15 18 21 24 27 30 3 6 9 12151821242730 (a) January (b) April

100 100 80 80 60 60 40 40 20

20 TBCR (%) TBCR (%) 0 0 3 6 9 12151821242730 3 6 9 12151821242730 (c) July (d) October

100 100 80 80 60 60 40 40 20 20 Contribution ratio (%) ratio Contribution Contribution ratio ratio (%) Contribution 0 3 6 9 12 15 18 21 24 27 30 3 6 9 12151821242730

(e) January (f) April 100 100 80 80 60 60 40 40 20 20 Contribution ratio (%)

0 Contribution ratio (%) 0 3 6 9 12151821242730 3 6 9 12151821242730 (g) July (h) October

Fig. 5. Hourly emission contribution ratio for PM2.5 concentration in Beijing from local emissions and all of Beijing’s surrounding regions, (a)–(d): TBCR from surrounding regions; (e)–(h): contribution ratios from local Beijing.

found that significant hourly variations of the contribution PM2.5 concentration in Beijing generally corresponds to the ratios exist for all of the study months. Fig. 7 presents the high trans-boundary PM2.5 contribution. Meanwhile, the low simulated daily PM2.5 concentrations in the EGC area in PM2.5 concentration in Beijing was corresponding to the low Beijing for the four target months in 2010. By comparing trans-boundary contribution. Further regression analysis the daily PM2.5 concentration trend shown in Fig. 7 with results showed a logarithmic relationship between the the trend of emission contribution ratio for Beijing’s TBCRs from Beijing’s surrounding regions and the PM2.5 surrounding regions (Fig. 5), it can be found that the high concentrations in Beijing (Fig. 8).

950 Lang et al., Aerosol and Air Quality Research, 13: 943–956, 2013

40 15

30 12 9 20 6 TBCR (%) 10 (%) TBCR 3 0 0 3 6 9 12151821242730 3 6 9 12 15 18 21 24 27 30 (a) January (b) April

25 50 20 40 15 30 10 20 TBCR (%) TBCR TBCR (%) TBCR 5 10 0 0 3 6 9 12 15 18 21 24 27 30 3 6 9 12151821242730 (c) July (d) October

60 100

45 80 60 30 40

15 TBCR (%)

TBCR (%) 20 0 0 3 6 9 12151821242730 3 6 9 12151821242730

(e) January (f) April 100 100 80 80 60 60 40 40 TBCR (%) 20 TBCR (%) 20 0 0 3 6 9 12151821242730 3 6 9 12151821242730 (h) October (g) July

Fig. 6. Hourly TBCR to PM2.5 concentration in Beijing from Tianjin and Hebei, (a)–(d): TBCR from Tianjin; (e)–(h): TBCR from Hebei.

The monthly contribution ratios to PM2.5 concentration under the control of Siberian cold high pressure, the in Beijing were also calculated based on the hourly results prevailing wind direction is northwest (with a frequency shown in Figs. 5–8, and are listed in Table 2. Table 2 also about 22%). In this case, the PM2.5 transport is mainly from lists the calculated annual contribution ratios from different the northwest of Hebei, where there has a lower PM2.5 emission regions based on the simulated hourly PM2.5 emission (Fig. 2). In addition, the temperature inversion is concentrations. It can be found that the monthly average universal and the frequency is high in the winter of North emission contribution ratios from all of Beijing’s surrounding China. This could make the emissions stay in the lower regions were 24.9, 34.4, 75.2, and 33.6% for January, atmosphere for a long time and facilitates the pollutants April, July, and October, respectively. The trans-boundary accumulation on the surface. As a result, the PM2.5 transport from Beijing’s surrounding regions had a much concentration is mainly caused by the local emission higher contribution ratio in July as compared to the three sources, and the ratio of transport PM2.5 is lower. However, other months. The reasons for this may be explained as in spring and autumn, the wind directions are relative even, follows. Firstly, North China has a typical temperate and resulting in the intermediate level of PM2.5 TBCRs between monsoonal climate with four clearly distinct seasons. In summer and winter. Secondly, the formation of secondary summer, it is mainly under the control of temperate oceanic particles is strongly associated with the O3 concentrations air mass. The prevailing wind directions are south and in July. When the emissions from Beijing’s surrounding southwest (with a frequency about 34%) and this could regions were set to zero, the O3 concentrations in Beijing facilitate the transport of high PM2.5 emissions from the was declined by about 50%, resulting in the reduction of southern and southwestern regions (Fig. 2) to Beijing along secondary fine particles formation. The annual average the Taihang Mountains. In winter, North China is mainly contribution ratio to PM2.5 concentration in Beijing from

Lang et al., Aerosol and Air Quality Research, 13: 943–956, 2013 951

250 150 3 3 g/m g/m 200 120 150 90 100 60 50 30 0 0 6 9 12 15 18 21 24 27 30 6 9 12 15 18 21 24 27 30 (a) January (b) April

120 180 3 3 g/m g/m Daily PM concentration 100 150 2.5 3 80 120 Class II level (75 g/m ) 3 Class I level (35 g/m ) 60 90 60 40 30 20 0 6 9 12 15 18 21 24 27 30 6 9 12 15 18 21 24 27 30 (c) July (d) Octorber

Fig. 7. Simulated daily PM2.5 concentration in the EGC area in Beijing during four target months in 2010.

80% 80%

y = 0.1301ln(x) -0.2752 60% R² = 0.4796 60%

40% 40% TBCRs TBCRs 20% 20% y = 0.2537ln(x) -0.7128 R² = 0.6191

0% 0% 0 50 100 150 200 250 300 0 306090120150180 3 3 PM2.5 concentration (μg/m ) PM2.5 concentration (μg/m ) (a) January (b) April 100% 100%

80% 80%

60% 60% y = 0.3719ln(x) -0.7491 40% R² = 0.6752 40%

TBCRs TBCRs y = 0.2032ln(x) -0.4008 R² = 0.6221 20% 20%

0% 0% 0 306090120 0 40 80 120 160 200 3 3 PM2.5 concentration (μg/m ) PM2.5 concentration (μg/m ) (c) July (d) October

Fig. 8. Relationship between TBCR from Beijing’s surrounding regions and PM2.5 concentration in Beijing (“x” represents PM2.5 concentration, “y” represents TBCR).

its surrounding regions was 42.2% as listed in Table 2. has a smaller aerodynamic size than PM10, and as a result Previous studies based on the MM5/CMAQ modeling system can stay in the atmosphere for a longer time. This can illustrated that the contribution ratios to PM10 concentration in facilitate the long-distance transport. Beijing from its surrounding regions were about 34.7% Table 2 also lists the monthly emission contribution ratios (annual average) (Chen et al., 2007), 22.0% (in winter) and from Beijing, Tianjin, and Hebei. It can be found that the 40.0% (in summer) (Wang et al., 2008). The result based contribution from Hebei accounted for a major part in the on backward trajectories shows that about 26.0% of PM10 total contributions from Beijing’s surrounding regions during concentration in Beijing was contributed from long transport all of the study months. This may be explained by the fact (Zhu et al., 2011). The differences between the results for that most part of Beijing is bordered by Hebei province PM10 and PM2.5 can be explained by the fact that the PM2.5 (Fig. 1) which can facilitate the transport of PM2.5 emitted

952 Lang et al., Aerosol and Air Quality Research, 13: 943–956, 2013

Table 2. The average emission contribution ratio to the concentrations of PM2.5 and inorganic particles in Beijing from Beijing and its surrounding regions. Emission regions January April July October Annual PM2.5 All of the surrounding regions 24.9% 34.4% 75.2% 33.6% 42.2% Beijing 70.8% 62.5% 30.7% 60.1% 55.4% Tianjin 1.9% 5.0% 4.4% 9.7% 5.1% Hebei 19.4% 28.7% 72.3% 28.4% 38.0% Sulfate All of the surrounding regions 31.6% 40.8% 79.3% 32.8% 46.3% Beijing 44.6% 23.8% 8.7% 30.1% 26.5% Tianjin 3.0% 2.3% 1.3% 2.2% 2.2% Hebei 25.3% 37.6% 75.5% 30.2% 43.5% Nitrate All of the surrounding regions 60.5% 77.2% 96.9% 74.8% 77.4% Beijing 0.5% 10.1% 21.3% 13.5% 10.7% Tianjin –3.2% 3.6% 6.6% 2.5% 2.3% Hebei 27.2% 67.8% 92.0% 66.1% 64.6% Ammonium All of the surrounding regions 39.4% 61.1% 90.4% 55.1% 61.6% Beijing 33.6% 14.6% 15.8% 19.3% 20.4% Tianjin 1.6% 2.6% 4.4% 2.1% 2.7% Hebei 26.0% 55.1% 86.1% 49.3% 55.6% Other particles All of the surrounding regions 20.6% 29.0% 48.6% 22.6% 30.3% Beijing 79.0% 70.7% 51.4% 76.6% 69.0% Tianjin 1.9% 5.3% 4.6% 4.8% 4.2% Hebei 17.4% 23.1% 42.8% 17.5% 26.0%

3 and formed in Hebei to Beijing. The annual emission was highest (more than 20 μg/m ), the NH3 reduction ratio contribution ratios to the PM2.5 concentration from Beijing, can even reach more than 50%. This will significantly Tianjin, and Hebei were 55.4, 5.1, and 38.0%, respectively decrease the nitrate formation in local Beijing. Both the (Table 2), indicating that nearly half of Beijing’s PM2.5 is nitrate concentration reductions in local Beijing and from a due to trans–boundary transport. The control of Beijing’s long distance transport result in the high nitrate TBCR. As a PM2.5 pollution needs coordinated efforts in Beijing and its result, the actual nitrate contributions may be lower than the surrounding regions, especially Hebei province. The monthly results above. The contribution ratios for the concentration average contributions for the three inorganic components and of ammonium (i.e., 61.6%) and sulfate (i.e., 46.3%) in other particles of PM2.5 were also calculated and presented Beijing from its surrounding regions were also higher than in Table 2. Similar to PM2.5, the contribution ratios to the that for the PM2.5 concentration (i.e., 42.2%). However, the PM2.5 component concentrations from Beijing’s surrounding contribution ratio for the concentration of other particles regions were much higher in July as compared to the three from Beijing’s surrounding regions (i.e., 30.3%) was lower other months. The trans–boundary transport impact on nitrate than that for PM2.5 concentration. concentration in Beijing from its surrounding regions was It is worth noting that the sum of emission contribution the greatest (i.e., 77.4%) among all of the fine particles. ratios from Tianjin and Hebei exceeds the contribution This is mainly because that the formation of nitrate can be ratio from all of Beijing’s surrounding regions. The sum of affected more strongly by the emission of NH3 as compared the emission contribution ratios from Beijing and all of its to sulfate and other particles (Behera and Sharma, 2012). surrounding regions is also not equal to 100% in this study. As a result, when the emission of NH3 was reduced, it The calculated TBCR from Tianjin in January is less than would bring more negative influence to the formation of zero as well. The following descriptions may explain these nitrate. Liu et al.’s (2005) study found that if there were no phenomena. Except for the impact of the corresponding NH3 emissions, the sulfate concentration in North China precursors, the formation of sulfate, nitrate, and ammonium would reduce about 30%, however the nitrate will nearly can also be affected by the atmospheric oxidization capacity change to zero. In this study, when the NH3 emissions of and the emissions of other related pollutants. Take sulfate Beijing surrounding regions were set to zero, the nitrate in for an example, its formation can be influenced by the surrounding regions and transport to Beijing will also existence of SO2, NOx, NH3, and oxidizing substance such as become zero. In addition, the NH3 concentration in Beijing O3 (Wang et al., 2011; Behera and Sharma, 2012b). When will decrease by an annual average ratio of about 34%. In the pollutants emissions of Hebei (for example) were set to summer, the season during which the nitrate concentration zero, the sulfate in Beijing would changes in the following

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ways: (1) the primary sulfate (directly emitted from various PM2.5 concentration standard. The class I level corresponds 3 sources) transport from Hebei would decrease; (2) the to the daily average PM2.5 concentration of 35μg/m , while secondary sulfate formed by chemical reactions in Hebei and the class II level corresponds to the daily average PM2.5 3 during the transport would decrease, causing a reduction of concentration of 75 μg/m . Based on the PM2.5 standards sulfate transport to Beijing; (3) the SO2, NOx transport from and the simulated concentrations (Fig. 7) in Beijing under Hebei to Beijing would also decrease, and this will reduce emission scenarios S0 and S1, the emission contributions to the SO2, NOx concentration and change the O3 formation, PM2.5 and its components in Beijing from its surrounding consequently impact the formation of sulfate in local Beijing. regions under different PM2.5 pollution levels were And the influence on the O3 concentration in (3) would be calculated. The results are listed in Table 3. Three different either positive or negative. It is dependent on the NOx and PM2.5 pollution levels were examined, namely the daily VOC concentrations in the receptor district – Beijing (Xing PM2.5 concentration of 0–35, 35–75, and greater than 75 et al., 2011). As a result, the sulfate concentration changes μg/m3, respectively. The results of January were discussed in (3) may be plus or minus. But in generally, if the as an example. As shown in Fig. 7(a), the daily PM2.5 3 influence on the O3 formation would be not so significant, concentrations in January were higher than 75μg/m for 7 the secondary particles concentration decrease in Beijing days, including January 8–10 and 16–19. The average trans– would be plus because of the reduction of NOx, SO2 and boundary contribution ratio during these periods was 35.4, NH3. However, the actual sulfate transport from Hebei to 56.3, 59.3, 56.4, and 26.8% for the concentrations of PM2.5, Beijing just contains (1) and (2). The existence of course (3) sulfate, nitrate, ammonium, and other particles, respectively results in the error between the results based on the zero–out (Table 3). During the periods when the PM2.5 concentrations 3 method and the actual sulfate transport contributions. The were 35–75 μg/m , the TBCRs in January for PM2.5, sulfate, zero–out method could make similar influence on nitrate and nitrate, ammonium, and other particles were 24.2, 25.7, 58.6, ammonium. This is also the reason that results in the 35.8, and 21.5%, respectively. These were 1.1–54.4% lower phenomena mentioned in the beginning of this paragraph. than those during the periods with PM2.5 concentration of As a result, due to the natural uncertainty, the zero–out over 75 μg/m3. When the air quality was excellent (i.e., the 3 would generally bring higher TBCR of secondary inorganic PM2.5 concentration was lower than 35 μg/m ), the TBCRs particles. And the actual TBCR may be a little lower than in January were 16.6, 19.5, 61.5, 29.8, and 14.5% for PM2.5, the results in this study. Further studies should be conducted sulfate, nitrate, ammonium and other particles, respectively. to avoid the errors brought by the zero–out method for The results listed in Table 3 confirm the positive correlation calculating the emission contribution ratio. This may include between TBCRs from Beijing’s surrounding regions and the adoption of source apportionment technology, such as its PM2.5 concentrations. PSAT (Particulate matter Source Apportionment Technology) Based on the PM2.5 standards and the simulated integrated in CAMx (Comprehensive Air Quality Model concentrations in Beijing under emission scenarios S0 and with extensions) model (Huang et al., 2010). S2, the annual local emission contribution ratios to PM2.5 in Beijing under different PM2.5 pollution levels were also Emission Contributions under Different PM2.5 Pollution calculated, and the results are presented Fig. 9. As different Levels from the trans–boundary impact from Beijing’s surrounding As mentioned before, a new National Ambient Air regions, a negative correlation exists between the local Quality Standard (NAAQS) was proposed in China in the emission contribution ratio and the PM2.5 concentration in beginning of 2012, with the introduction of two levels of Beijing. The annual average emission contribution ratios

Table 3. The average contributions to PM2.5 and inorganic particles in Beijing from its surrounding regions under different PM2.5 pollution levels. 3 Daily PM2.5 concentration (μg/m ) January April July October Annual > 75 35.4% 45.9% 88.3% 51.1% 54.6% PM2.5 35–75 24.2% 26.6% 71.0% 42.8% 46.1% 0–35 16.6% 10.5% 37.9% 15.3% 17.4% > 75 56.3% 50.0% 92.3% 50.0% 61.6% Sulfate 35–75 25.7% 38.8% 76.9% 46.9% 50.6% 0–35 19.5% 5.5% 36.0% 10.2% 15.4% > 75 59.3% 86.8% 99.3% 80.3% 83.2% Nitrate 35–75 58.6% 72.3% 94.4% 82.8% 79.1% 0–35 61.5% 55.2% 88.0% 61.7% 63.5% > 75 56.4% 73.9% 97.1% 66.0% 74.8% Ammonium 35–75 35.8% 56.4% 89.2% 69.0% 65.7% 0–35 29.8% 23.5% 57.5% 33.2% 33.2% > 75 26.8% 38.9% 60.9% 29.4% 40.5% Other particles 35–75 21.5% 20.2% 44.7% 29.1% 30.7% 0–35 14.5% 10.5% 26.3% 11.5% 14.1%

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100 3 3 3 >75g/m 35-75g/m 0-35g/m

20 contributions from Local Beijing (%) Beijing from Local contributions 2.5

PM 0 January April July October Annual

Fig. 9. The average local emission contribution ratio to PM2.5 concentration in Beijing under different PM2.5 pollution levels.

from Beijing were 70.8, 62.5, and 30.7% for the PM2.5 concentration in Beijing, but a negative correlation existed 3 concentration levels of 0–35, 35–75, and over 75 μg/m , between the local emission contribution ratio and the PM2.5 respectively. However, the corresponding annual average concentration. The results also illustrated that the trans– TBCRs from Beijing’s surrounding regions were 17.4, 46.1, boundary transport played a major role in Beijing’s PM2.5 and 54.6%, respectively. This indicates that the trans– concentration during the high pollution level period (i.e., > 3 boundary transport played a major role in Beijing’s PM2.5 75 μg/m ), while the local emission in Beijing played a major concentration during the high pollution levels (i.e., > 75 role in its PM2.5 concentration during the lower pollution level 3 3 μg/m ), while the local emission in Beijing played a major period (i.e., < 75 μg/m ). The high PM2.5 concentration in role in its PM2.5 concentration during the lower pollution Beijing may pose serious threats to the public health, and thus 3 levels (i.e., < 75 μg/m ). As a result, the mitigation of PM2.5 needs effective mitigation. The results obtained from this pollution in Beijing not only requires the reduction of local study indicated that the control of Beijing’s PM2.5 pollution emissions, but also needs the cooperation of its surrounding should require coordinated efforts in both Beijing and its regions, especially during the heavy pollution periods. surrounding regions. In addition, the natural uncertainty of zero–out method using for the investigation of secondary CONCLUSIONS particles transport was discussed. Generally, if the influence on the O3 formation of the receptor region is not so An intensive monitoring and modeling program was significant, the TBCR calculated based on the zero–out implemented to investigate the regional PM2.5 transport method may be higher than the actual ones. problem, with a case study being conducted in Beijing, China. 2– – It was found that the inorganic components of SO4 , NO3 , ACKNOWLEDGMENTS + and NH4 particles accounted for a significant proportion of the total PM2.5 in Beijing, while the nitrate had the highest This research was supported by the Natural Sciences percentage. To further investigate the emission contributions Foundation of China (No. 51038001 & 51208010), the to Beijing’s PM2.5 from different emission regions, a coupled "Beijing Science and Technology Project" MM5–CMAQ modeling system was developed. The (D09040903670801) of the Beijing Municipal Science & concentrations of PM2.5 and its various components (sulfate, Technology Commission, and the Ministry of Environmental nitrate, ammonium, and other particles) in Beijing during Protection Special Funds for Scientific Research on Public four selected months in 2010 were simulated. Acceptable Causes (No. 201209003&200909008). The authors would modeling performance was obtained. A zero–out method also like to thank Beijing NOVA Program of China (No. was used to calculate the emission contribution ratio from 2009B07), Innovation Team Project of Beijing Municipal different emission regions. It was found that the high PM2.5 Education Commission (PHR201007105) as well as the concentration in Beijing generally corresponds to the high Cultivation Fund of the Key Scientific and Technical trans–boundary emission contribution. And the contribution Innovation Project, Ministry of Education of China (708017) from Hebei province accounted for a major part in the total for supporting this work. trans–boundary contributions. It was also found that the trans–boundary transport from Beijing’s surrounding regions REFERENCES had the highest contribution to Beijing’s PM2.5 pollution in July. The trans–boundary transport impact on nitrate Behera, S. and Sharma, M. (2012). Transformation of concentration in Beijing was the greatest among all of the Atmospheric Ammonia and Acid Gases into Components PM2.5 particles. A positive correlation was found between of PM2.5: An Environmental Chamber Study. Environ. the trans–boundary emission contribution ratio and the PM2.5 Sci. Pollut. Res. 19: 1187–1197.

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