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Aerosol and Air Quality Research, 13: 943–956, 2013 Copyright © Taiwan Association for Aerosol Research ISSN: 1680-8584 print / 2071-1409 online doi: 10.4209/aaqr.2012.09.0242

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

With continuous rapid economic development and sharp growth of vehicle population, the pollution of PM2.5 (i.e., the fine particles with aerodynamic diameter of ≤ 2.5 μm) has become one major environmental problem. This problem has received wide concerns among experts, governments, media, and the public in China and around the world, particularly after the occurrence of a severe haze event in Beijing (i.e., the capital of China) in December 2011 (Lang et al., 2012; Yuan et al., 2012). It was reported that the annual average PM2.5 concentrations in Beijing from 2000 to 2010 were 101.0, 93.6, 101.5, 100.0, 102.2, 85.2, 93.5, 84.5, 76.8, 79.6, and 71.9 μg/m3, respectively (Zheng et al., 2005; Song et al., 2007; Zhao et al., 2009; Wang et al., 2012). However, the PM2.5 concentrations were only 9.8–13.6 μg/m3 during the same period in the United States (http:// www.epa.gov/airtrends/pm.html). The high concentration of PM2.5 could have significant adverse impacts on the * Corresponding author. Tel.: +86 10 67391656; Fax: +86 10 67391983 E-mail address: [email protected]

atmospheric visibility and the human health. For example, previous studies indicated that the PM2.5 was the main cause of reduced visibility in Beijing (Wang et al., 2006; Zhou et al., 2012a). Such fine particles can also result in serious human health problems (e.g., cardiovascular diseases, respiratory irritation, and pulmonary dysfunction) since they contain microscopic solids or liquid droplets which are toxic and can get deep into the human body (Cao et al., 2012; Haberzettl et al., 2012). Consequently, the study of PM2.5 pollution is of great importance for effectively improving the air quality and the public health. In fact, a new National Ambient Air Quality Standard (NAAQS) was proposed in China in the beginning of 2012 (http://www.zhb.gov.cn/gkml /hbb/bwj/201203/t20120302_224147.htm). This new standard introduced the control of PM2.5 for the first time in China.

Generally, the pollution of particulate matters (PM) is a complex regional transport problem as confirmed from many previous studies (Hatakeyama et al., 2011; Hussein 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 CMAQ/Model-3 developed by US EPA has obtained extensive applications. For example, Chen et al. (2007) examined the trans-boundary transport of PM10 to Beijing,

Lang et al., Aerosol and Air Quality Research, 13: 943–956, 2013 944

and obtained the trans-boundary contribution ratios (TBCRs) from its surrounding regions using a meteorological model (MM5) and an air quality model (CMAQ). Wang et al. (2010) utilized the HYSPLIT and MM5-CMAQ models to study the PM10 pollution problem in Beijing, and found that the southwest transport pathway was closely associated with the increasing phase of its PM10 pollution processes. Zhu et al. (2011) investigated the transport pathways and potential sources of PM10 concentration in Beijing based on backward trajectories and PM10 concentration records. Che et al. (2011) evaluate the effects of different vehicle emission control measures on the air quality of SO2, NO2, PM10, and O3. Zhou et al. (2012b) investigated the source-receptor relationships of PM10 in Tangshan of northern China using the integrated MM5-CMAQ model. Other application of CMAQ can be found in Zhang et al. (2007), Mueller and Mallard (2011) and Han et al. (2012).

Most of the previous studies on the transport of particulate matters were focused on PM10, and the transport of PM2.5 has received less attention (Streets et al., 2007). As compared with PM10, the diameter of PM2.5 is much smaller with more complex components that contain a higher percentage of inorganic particles, which are mainly secondary pollutants produced via complex reactions (Ianniello et al., 2011). This makes the PM2.5 can stay in the atmosphere for a longer time and facilitates a long distance transport. As a result, the transport of PM2.5 may be quite different from that of PM10. However, few previous studies were reported to study the transport characteristics of PM2.5 components and their temporal variations (Streets et al., 2007). In fact, the understanding of such information is very valuable for the effective decision making of air quality management. The objective of this study is then to examine the regional transport of PM2.5 and its components through conducting a case study in Beijing, which has typical and representative PM2.5 pollution problem in China. An intensive monitoring program was implemented in the study area to measure the concentrations of PM2.5 components within different seasons, and to provide necessary data for establishing an integrated MM5-CMAQ modeling system. The modeling system was then applied to facilitate the investigation of trans-boundary PM2.5 transport. The concentrations of PM2.5 and three selected components (sulfate, nitrate, and ammonium) in Beijing were simulated under various emission reduction scenarios, and the corresponding trans-boundary contribution ratios (TBCRs) of Beijing’s surrounding areas were then calculated. Moreover, the TBCRs associated with different PM2.5 pollution levels were analyzed. The results can provide sound decision making basis for the mitigation of PM2.5 pollution in Beijing. OVERVIEW OF THE STUDY AREA

Beijing is located in northern China, with Tianjin municipality on its eastern border and Hebei province on its other three borders as shown in Fig. 1. Topographically, Beijing is characterized as a semi-basin region, with Taihang Mountains on its southwest and Yan Mountains on its northwest. The mean sea level of its land tends to decrease

gradually from its northwest to southeast towards the Bohai Bay. This basin-like topography plays an important role in the transport of pollutants from the industrial provinces (e.g., Hebei, Shanxi) to Beijing. The total population of Beijing was 19.62 million in 2010, accounting for 1.47% of the national population. The GDP of Beijing was 213.1 billion US dollars in 2010, accounting for 3.23% of the national GDP. There are 14 subordinate districts and 2 counties in Beijing as shown in Fig. 1, covering a total area of 16410.5 km2. The central urban area of Beijing includes 6 subordinate districts (e.g., Dongcheng, Xicheng, Chaoyang, Haidian, Fengtai, and Shijngshan), covering 8.3% of Beijing’s geographical area, accounting for 63.2% of Beijing’s total population, and contributing to 69.7% of the total GDP in Beijing. As mentioned before, the annual average PM2.5 concentration during the past decade in Beijing was in the range of 71.9–101.0 μg/m3, which is much higher than the proposed class II standard of PM2.5 (i.e., 35 μg/m3) in the new Chinese NAQQS, indicating severe PM2.5 pollution in Beijing. METHODOLOGY

MM5-CMAQ Modeling System

The meteorological model MM5 is a limited-area, non-hydrostatic, terrain-following sigma-coordinate model designed to simulate meso-scale and regional-scale atmospheric circulation. It was developed by Penn State University and National Center for Atmospheric Research (PSU/NCAR) as a community meso-scale model in 1970s and has been continuously upgraded since then to its latest fifth version (Grell et al., 2000). The air quality model CMAQ/Model-3, which was developed by US EPA, is the third generation of CMAQ air quality prediction tool. It is a sophisticated modeling system which can simulate the concentrations of fine particulate, tropospheric ozone, acid deposition, visibility, and other atmospheric pollutants at regional and urban scales (Byun and Ching, 1999). In the context of “one atmospheric” perspective, the CMAQ model can well simulate the complex physical and chemical processes. In addition, the model contains a capacity of multiple levels nested-grid. The above characteristics of the model can reduce the uncertainty and increase the accuracy of the simulation results. In this study, the Carbon Bond-IV Mechanism (CB-IV) and the AER03 aerosol mechanism were chosen for the simulations of secondary particles formation and other relative atmospheric chemical reactions.

In this study, the MM5 and CMAQ models were coupled to predict PM2.5 concentrations in Beijing. The MM5 model was used for providing the 4D meteorological data required by the CMAQ model. The details of the required data by MM5, such as the topography data, 3D first-guess meteorological fields, and the meteorological measurements, as well as the physical options used were the same as those described in Chen et al. (2007) and Zhou et al. (2012c). As for the CMAQ model, emission inventory is another important input data. The county-level air pollutant emission inventories (including pollutants of PM10, PM2.5, SO2, NOx,


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Evaluation grid cell area (EGC) Fig. 1. Design of three-level modeling domains.

NH3, VOCs and CO) were obtained from the Environmental Protection Bureaus (EPBs) of Beijing and its surrounding regions. These emission inventories were processed by the modified Sparse Matrix Operator Kernel Emissions Modeling System (SMOKE) (Houyoux et al., 2000) to generate emission inputs with high spatial and temporal resolution as required by the CMAQ model. The space distribution of PM2.5 emissions in Beijing and surrounding regions was shown in Fig. 2.

A three-level nested-grid architecture (shown in Fig. 1) was designed for the implementation of the MM5-CMAQ modeling system. Modeling domain 1 (only for MM5 simulation) was divided into 43 × 49 grid cells with a spatial resolution of 36 km × 36 km, covering most areas of northeastern China; modeling domain 2 (both for MM5 and CMAQ simulation) was divided into 70 × 76 grid cells with a 12 km × 12 km spatial resolution, covering Beijing

and its surrounding regions (including Tianjin, Hebei, Shanxi, and parts of Shandong and Henan); modeling domain 3 (both for MM5 and CMAQ simulation) was divided into 49 × 49 grid cells with a spatial resolution of 4 km × 4 km, covering Beijing. Vertically, 35 sigma levels were designed for the grid structure in the MM5 simulation, with the top height being 15 km at the pressure of 100 mbar. The first 20 vertical levels were distributed within 2 km from the ground level. These 35 vertical layers for the MM5 model were collapsed into 12 layers for the vertical domain of the CMAQ model. The MCIP (Meteorology-Chemistry Interface Processor) was used to process and transform the hourly MM5 outputs from 35 vertical levels into 12 levels with the format required by the CMAQ model. More detailed descriptions of the MM5-CMAQ modeling system and setup can be found in previous papers (Chen et al., 2007; Zhou et al., 2012c).


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

Observation of PM2.5 Components The transport of PM2.5 components is of importance as

mentioned above, and was thus investigated in this study. The observation of PM2.5 components was performed at a monitoring station located on the rooftop of a building at Beijing Normal University (BNU), about 45m above the ground level. The BNU is located between the north second and third Ring Roads, which is a heavily traffic area of Beijing. The PM2.5 samples were simultaneously collected on the Whatmans 41 filters (Whatman Inc., Maidstone, UK) and the quartz filters (Whatman Inc., Maidstone, UK) using the medium-volume samplers made by Wuhan Instrument Co., Ltd., with a flow rate of 40 L/min and 100L/min, respectively. The samplings were carried out during winter, spring, summer, and autumn from December 2010 to January 2012 on a 24-h basis. One or two respective months of per season were selected for the PM2.5 samplings. About 50-60 samples per season and a total number of 212 samples were collected. All the procedures were strictly quality controlled to avoid any possible contamination of the samples. Such as, the samples collected were put in the polyethylene plastic bags right after sampling and reserved in a refrigerator. All the filters were weighed before and after sampling using a One Over Ten-thousand Analytical Balance under constant temperature (20 ± 5°C) and relative humidity (40 ± 2%).

The samples collected on Whatmans 41 filters were used for the analysis of 10 selected ions and 22 selected elements, while the samples collected on the quartz filters were used for the detection of Organic Carbon (OC) and Element Carbon (EC). The SO4

2–, NO3–, NH4

+, and other ions (Mg2+, Ca2+, K+, Na+, PO4

3–, F–, and Cl–) were analyzed by ion chromatograph (IC, Metrohm 861 Advanced Compact 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, and Pb) was based on the inductively coupled plasma-mass spectrometry (ICP-MS, 7500a, Thermo). The OC and EC

were measured using a Thermal/Optical Carbon Analyzer (DRI, Model 2001). Evaluation of Modeling Performance

The observations from nine air quality monitoring stations in Beijing were used for modeling performance assessment of PM2.5 simulation. These stations include AoTi, NongZhanGuan, DongSi, TianTan, WanShouXiGong, FengTaiHuaYuan, GuCheng, GuanYuan, and WanLiu, as illustrated in Fig. 1. As mentioned above, the PM2.5 components were measured at a monitoring station in Beijing Normal University (BNU). An evaluation grid cell area (EGC) with a total number of 7 × 10 grid cells was selected within modeling domain 3 (Fig. 1). It covers the above ten monitoring stations and most of the central urban area of Beijing. As a result, the data obtained from these monitoring stations can well represent the characteristics of the urban PM2.5 pollution in Beijing. The MM5-CMAQ model was applied to investigate Beijing’s PM2.5 pollution within four target months in 2010. These four target months (January, April, July, and October) were used to represent winter, spring, summer, and autumn, respectively. In order to assess the modeling performance, the average simulated daily concentrations of PM2.5 within the grid cells containing the nine monitoring stations during different target months were compared with the average observation results in 2010 from these nine stations. Meanwhile, the simulated monthly concentrations of the three PM2.5 components (sulfate, nitrate, and ammonium) within the grid cell containing the BNU monitoring station during different target months were also compared with the observation results from that monitoring station. Calculation of Trans-boundary Contribution Ratio (TBCR)

In order to examine the trans-boundary transport of PM2.5,


a parameter called trans-boundary contribution ratio (TBCR) was introduced in this study. A zero-out method was applied to calculate TBCR using two scenarios, including the Zero Emission Reduction Scenario (ZERS) and the Emission Reduction Scenario (ERS) (Chen et al., 2007). The ZERS was corresponding to the base scenario under which the MM5-CMAQ model was run using the original emission inventories. In terms of the ERS, four sub-scenarios were introduced, including Zero-Surrounding-Emission (S1), Zero-Beijing-Emission (S2), Zero-Tianjin-Emission (S3), and Zero-Hebei-Emission (S4). The S1, S2, S3, and S4 scenarios correspond to the situations where the emissions (including pollutants of PM10, PM2.5, SO2, NOx, NH3, VOCs and CO) from Beijing’s surrounding regions, Beijing, Tianjin, and Hebei were set to zero, respectively.

The average simulated hourly concentrations of PM2.5 and its three components as well as other fine particles within the EGC were used to calculate TBCR as described below:

ZERS ERS

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of PM2.5, sulfate, nitrate, ammonium, and other fine particles under the ZERS scenario; CERS represents the hourly pollutant concentration under different ERS scenarios. The CERS under the scenarios of S1, S3, and S4 was used to calculate the TBCR from all of Beijing’s surrounding regions, Tianjin, and Hebei, respectively. The CERS under the scenario of S2 can be used to calculate the contribution ratio of local emissions within Beijing. RESULTS AND DISCUSSION Seasonal Characteristics of PM2.5 in Beijing

Fig. 3 presents the observed proportions of different PM2.5 components in Beijing. It can be found that the inorganic particles of SO4

2–, NO3–, and NH4

+ were important components in PM2.5 in Beijing. They accounted for an annual average percentage of 35.5% of the total PM2.5. Among these three components, the nitrate (NO3

–) had the highest percentage. In addition to the three inorganic particles, organic carbons (OC) and elements were another two important components in the PM2.5 in Beijing. The annual average percentages were 24.2 and 15.3% for OC and elements, respectively. The annual average ratio of

(a) Spring (b) Summer

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Sulfate6.5%

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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).


NO3–/SO4

2– was 1.23, indicating that the motor vehicles accounted for an important emission source for the formation of PM2.5 pollution in Beijing (Wang et al., 2005). The proportion of inorganic particle components also showed obvious seasonal variation. For example, the proportions of the three inorganic particles in PM2.5 were higher in summer and winter, while those for elements were the highest in spring. The three inorganic components accounted for 29.2, 42.9, 32.5, and 37.2% of the total PM2.5 during spring, summer, autumn, and winter, respectively. In terms of the elements, the proportions were 26.2, 11.6, 10.5, and 13.0% in the four seasons, respectively. The reasons for these seasonal characteristics of PM2.5 components may be explained as follows: (a) the high O3 concentration in the atmosphere in summer could facilitate the formation of secondary particles; (b) the higher emissions of SO2 and NOx in the heating season of winter could result in a higher concentrations of inorganic particles as compared to the spring and autumn, and (c) the higher occurrence frequency of sandstorms in spring could lead to higher percentage of elements in the PM2.5. Modeling Performance

Fig. 4 presents the scatter plots of the simulated PM2.5 concentrations versus the observation results during the four target months in 2010. It can be found that most of the data points are adjacently distributed on both sides of the y = x line

for all of the four target months. The correlation coefficients (CCs) between the simulated and observed concentrations were 0.789, 0.541, 0.641, and 0.591 for January, April, July, and October, respectively. The correlation coefficients were generally greater than 0.50. This indicates that the modeling performance for simulating PM2.5 concentrations in Beijing is acceptable (Chen et al., 2007).

The modeling performance for simulating the concentrations of the three inorganic particle components in PM2.5 (SO4

2–, NO3–, NH4

+) were also evaluated. Table 1 lists the monthly comparison of the simulation and observation results. Generally, most of the simulation errors of PM2.5 components were ranging from –30% to 10% for various seasons. The simulation errors of the annual average concentrations were –21.8, –15.7, and –18.4% for sulfate, nitrate, and ammonium, respectively. Considering the inherent uncertain nature of meteorological and air quality simulation, the modeling performance of the MM5-CMAQ for simulating the concentration of inorganic particle components of PM2.5 is also acceptable (Zhang et al., 2007; Wang et al., 2011a). Emission Contribution to PM2.5 Pollution in Beijing

Figs. 5–6 present the hourly emission contribution ratios to the PM2.5 concentration in Beijing during the four target months from all of Beijing’s surrounding regions, as well as from Beijing, Tianjin, and Hebei, respectively. It can be

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Table 1. Comparison of simulated results with observations of inorganic PM2.5 particles (μg/m3)

Spring Summer Autumn Winter Annual average

Sulfate Observation 4.5 20.7 11.5 13.0 11.9 Simulation 4.2 10.2 9.1 13.9 9.3

Relative error –7.8% –50.9% –21.1% 7.1% –21.8%

Nitrate Observation 9.8 22.6 14.1 15.7 14.7 Simulation 8.2 20.4 14.3 6.7 12.4

Relative error –16.9% –9.8% 1.1% –57.5% –15.7%

Ammonium Observation 5.9 16.4 9.8 9.2 9.4 Simulation 4.3 10.9 7.4 8.1 7.7

Relative error –26.3% –33.5% –24.0% –11.4% –18.4%

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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 ratios exist for all of the study months. Fig. 7 presents the simulated daily PM2.5 concentrations in the EGC area in Beijing for the four target months in 2010. By comparing the daily PM2.5 concentration trend shown in Fig. 7 with the trend of emission contribution ratio for Beijing’s surrounding regions (Fig. 5), it can be found that the high

PM2.5 concentration in Beijing generally corresponds to the high trans-boundary PM2.5 contribution. Meanwhile, the low PM2.5 concentration in Beijing was corresponding to the low trans-boundary contribution. Further regression analysis results showed a logarithmic relationship between the TBCRs from Beijing’s surrounding regions and the PM2.5 concentrations in Beijing (Fig. 8).


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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 in Beijing were also calculated based on the hourly results shown in Figs. 5–8, and are listed in Table 2. Table 2 also lists the calculated annual contribution ratios from different emission regions based on the simulated hourly PM2.5 concentrations. It can be found that the monthly average emission contribution ratios from all of Beijing’s surrounding regions were 24.9, 34.4, 75.2, and 33.6% for January, April, July, and October, respectively. The trans-boundary transport from Beijing’s surrounding regions had a much higher contribution ratio in July as compared to the three other months. The reasons for this may be explained as follows. Firstly, North China has a typical temperate and monsoonal climate with four clearly distinct seasons. In summer, it is mainly under the control of temperate oceanic air mass. The prevailing wind directions are south and southwest (with a frequency about 34%) and this could facilitate the transport of high PM2.5 emissions from the southern and southwestern regions (Fig. 2) to Beijing along the Taihang Mountains. In winter, North China is mainly

under the control of Siberian cold high pressure, the prevailing wind direction is northwest (with a frequency about 22%). In this case, the PM2.5 transport is mainly from the northwest of Hebei, where there has a lower PM2.5 emission (Fig. 2). In addition, the temperature inversion is universal and the frequency is high in the winter of North China. This could make the emissions stay in the lower atmosphere for a long time and facilitates the pollutants accumulation on the surface. As a result, the PM2.5 concentration is mainly caused by the local emission sources, and the ratio of transport PM2.5 is lower. However, in spring and autumn, the wind directions are relative even, resulting in the intermediate level of PM2.5 TBCRs between summer and winter. Secondly, the formation of secondary particles is strongly associated with the O3 concentrations in July. When the emissions from Beijing’s surrounding regions were set to zero, the O3 concentrations in Beijing was declined by about 50%, resulting in the reduction of secondary fine particles formation. The annual average contribution ratio to PM2.5 concentration in Beijing from


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CR

s


y = 0.1301ln(x) - 0.2752R² = 0.4796

0%

20%

40%

60%

80%

0 50 100 150 200 250 300

TB

CR

s


y = 0.2032ln(x) - 0.4008R² = 0.6221

0%

20%

40%

60%

80%

100%

0 40 80 120 160 200

TB

CR

s


(b) April

(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. Previous studies based on the MM5/CMAQ modeling system illustrated that the contribution ratios to PM10 concentration in Beijing from its surrounding regions were about 34.7% (annual average) (Chen et al., 2007), 22.0% (in winter) and 40.0% (in summer) (Wang et al., 2008). The result based on backward trajectories shows that about 26.0% of PM10 concentration in Beijing was contributed from long transport (Zhu et al., 2011). The differences between the results for PM10 and PM2.5 can be explained by the fact that the PM2.5

has a smaller aerodynamic size than PM10, and as a result can stay in the atmosphere for a longer time. This can facilitate the long-distance transport.

Table 2 also lists the monthly emission contribution ratios from Beijing, Tianjin, and Hebei. It can be found that the contribution from Hebei accounted for a major part in the total contributions from Beijing’s surrounding regions during all of the study months. This may be explained by the fact that most part of Beijing is bordered by Hebei province (Fig. 1) which can facilitate the transport of PM2.5 emitted


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%

and formed in Hebei to Beijing. The annual emission contribution ratios to the PM2.5 concentration from Beijing, Tianjin, and Hebei were 55.4, 5.1, and 38.0%, respectively (Table 2), indicating that nearly half of Beijing’s PM2.5 is due to trans–boundary transport. The control of Beijing’s PM2.5 pollution needs coordinated efforts in Beijing and its surrounding regions, especially Hebei province. The monthly average contributions for the three inorganic components and other particles of PM2.5 were also calculated and presented in Table 2. Similar to PM2.5, the contribution ratios to the PM2.5 component concentrations from Beijing’s surrounding regions were much higher in July as compared to the three other months. The trans–boundary transport impact on nitrate concentration in Beijing from its surrounding regions was the greatest (i.e., 77.4%) among all of the fine particles. This is mainly because that the formation of nitrate can be affected more strongly by the emission of NH3 as compared to sulfate and other particles (Behera and Sharma, 2012). As a result, when the emission of NH3 was reduced, it would bring more negative influence to the formation of nitrate. Liu et al.’s (2005) study found that if there were no NH3 emissions, the sulfate concentration in North China would reduce about 30%, however the nitrate will nearly change to zero. In this study, when the NH3 emissions of Beijing surrounding regions were set to zero, the nitrate in surrounding regions and transport to Beijing will also become zero. In addition, the NH3 concentration in Beijing will decrease by an annual average ratio of about 34%. In summer, the season during which the nitrate concentration

was highest (more than 20 μg/m3), the NH3 reduction ratio can even reach more than 50%. This will significantly decrease the nitrate formation in local Beijing. Both the nitrate concentration reductions in local Beijing and from a long distance transport result in the high nitrate TBCR. As a result, the actual nitrate contributions may be lower than the results above. The contribution ratios for the concentration of ammonium (i.e., 61.6%) and sulfate (i.e., 46.3%) in Beijing from its surrounding regions were also higher than that for the PM2.5 concentration (i.e., 42.2%). However, the contribution ratio for the concentration of other particles from Beijing’s surrounding regions (i.e., 30.3%) was lower than that for PM2.5 concentration.

It is worth noting that the sum of emission contribution ratios from Tianjin and Hebei exceeds the contribution ratio from all of Beijing’s surrounding regions. The sum of the emission contribution ratios from Beijing and all of its surrounding regions is also not equal to 100% in this study. The calculated TBCR from Tianjin in January is less than zero as well. The following descriptions may explain these phenomena. Except for the impact of the corresponding precursors, the formation of sulfate, nitrate, and ammonium can also be affected by the atmospheric oxidization capacity and the emissions of other related pollutants. Take sulfate for an example, its formation can be influenced by the existence of SO2, NOx, NH3, and oxidizing substance such as O3 (Wang et al., 2011; Behera and Sharma, 2012b). When the pollutants emissions of Hebei (for example) were set to zero, the sulfate in Beijing would changes in the following


ways: (1) the primary sulfate (directly emitted from various sources) transport from Hebei would decrease; (2) the secondary sulfate formed by chemical reactions in Hebei and during the transport would decrease, causing a reduction of sulfate transport to Beijing; (3) the SO2, NOx transport from Hebei to Beijing would also decrease, and this will reduce the SO2, NOx concentration and change the O3 formation, consequently impact the formation of sulfate in local Beijing. And the influence on the O3 concentration in (3) would be either positive or negative. It is dependent on the NOx and VOC concentrations in the receptor district – Beijing (Xing et al., 2011). As a result, the sulfate concentration changes in (3) may be plus or minus. But in generally, if the influence on the O3 formation would be not so significant, the secondary particles concentration decrease in Beijing would be plus because of the reduction of NOx, SO2 and NH3. However, the actual sulfate transport from Hebei to Beijing just contains (1) and (2). The existence of course (3) results in the error between the results based on the zero–out method and the actual sulfate transport contributions. The zero–out method could make similar influence on nitrate and ammonium. This is also the reason that results in the phenomena mentioned in the beginning of this paragraph. As a result, due to the natural uncertainty, the zero–out would generally bring higher TBCR of secondary inorganic particles. And the actual TBCR may be a little lower than the results in this study. Further studies should be conducted to avoid the errors brought by the zero–out method for calculating the emission contribution ratio. This may include the adoption of source apportionment technology, such as PSAT (Particulate matter Source Apportionment Technology) integrated in CAMx (Comprehensive Air Quality Model with extensions) model (Huang et al., 2010). Emission Contributions under Different PM2.5 Pollution Levels

As mentioned before, a new National Ambient Air Quality Standard (NAAQS) was proposed in China in the beginning of 2012, with the introduction of two levels of

PM2.5 concentration standard. The class I level corresponds to the daily average PM2.5 concentration of 35μg/m3, while the class II level corresponds to the daily average PM2.5 concentration of 75 μg/m3. Based on the PM2.5 standards and the simulated concentrations (Fig. 7) in Beijing under emission scenarios S0 and S1, the emission contributions to PM2.5 and its components in Beijing from its surrounding regions under different PM2.5 pollution levels were calculated. The results are listed in Table 3. Three different PM2.5 pollution levels were examined, namely the daily PM2.5 concentration of 0–35, 35–75, and greater than 75 μg/m3, respectively. The results of January were discussed as an example. As shown in Fig. 7(a), the daily PM2.5 concentrations in January were higher than 75μg/m3 for 7 days, including January 8–10 and 16–19. The average trans–boundary contribution ratio during these periods was 35.4, 56.3, 59.3, 56.4, and 26.8% for the concentrations of PM2.5, sulfate, nitrate, ammonium, and other particles, respectively (Table 3). During the periods when the PM2.5 concentrations were 35–75 μg/m3, the TBCRs in January for PM2.5, sulfate, nitrate, ammonium, and other particles were 24.2, 25.7, 58.6, 35.8, and 21.5%, respectively. These were 1.1–54.4% lower than those during the periods with PM2.5 concentration of over 75 μg/m3. When the air quality was excellent (i.e., the PM2.5 concentration was lower than 35 μg/m3), the TBCRs in January were 16.6, 19.5, 61.5, 29.8, and 14.5% for PM2.5, sulfate, nitrate, ammonium and other particles, respectively. The results listed in Table 3 confirm the positive correlation between TBCRs from Beijing’s surrounding regions and its PM2.5 concentrations.

Based on the PM2.5 standards and the simulated concentrations in Beijing under emission scenarios S0 and S2, the annual local emission contribution ratios to PM2.5 in Beijing under different PM2.5 pollution levels were also calculated, and the results are presented Fig. 9. As different from the trans–boundary impact from Beijing’s surrounding regions, a negative correlation exists between the local emission contribution ratio and the PM2.5 concentration in 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.

Daily PM2.5 concentration (μg/m3) January April July October Annual

PM2.5 > 75 35.4% 45.9% 88.3% 51.1% 54.6%

35–75 24.2% 26.6% 71.0% 42.8% 46.1% 0–35 16.6% 10.5% 37.9% 15.3% 17.4%

Sulfate > 75 56.3% 50.0% 92.3% 50.0% 61.6%

35–75 25.7% 38.8% 76.9% 46.9% 50.6% 0–35 19.5% 5.5% 36.0% 10.2% 15.4%

Nitrate > 75 59.3% 86.8% 99.3% 80.3% 83.2%

35–75 58.6% 72.3% 94.4% 82.8% 79.1% 0–35 61.5% 55.2% 88.0% 61.7% 63.5%

Ammonium > 75 56.4% 73.9% 97.1% 66.0% 74.8%

35–75 35.8% 56.4% 89.2% 69.0% 65.7% 0–35 29.8% 23.5% 57.5% 33.2% 33.2%

Other particles > 75 26.8% 38.9% 60.9% 29.4% 40.5%

35–75 21.5% 20.2% 44.7% 29.1% 30.7% 0–35 14.5% 10.5% 26.3% 11.5% 14.1%


0

20

40

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80

100P

M2.

5 c

ontr

ibut

ions

from

Loc

al B

eijin

g (%

) >75g/m3 35-75g/m3 0-35g/m3

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 levels of 0–35, 35–75, and over 75 μg/m3, respectively. However, the corresponding annual average TBCRs from Beijing’s surrounding regions were 17.4, 46.1, and 54.6%, respectively. This indicates that the trans–boundary transport played a major role in Beijing’s PM2.5 concentration during the high pollution levels (i.e., > 75 μg/m3), while the local emission in Beijing played a major role in its PM2.5 concentration during the lower pollution levels (i.e., < 75 μg/m3). As a result, the mitigation of PM2.5 pollution in Beijing not only requires the reduction of local emissions, but also needs the cooperation of its surrounding regions, especially during the heavy pollution periods. CONCLUSIONS

An intensive monitoring and modeling program was implemented to investigate the regional PM2.5 transport problem, with a case study being conducted in Beijing, China. It was found that the inorganic components of SO4

2–, NO3–,

and NH4+ particles accounted for a significant proportion

of the total PM2.5 in Beijing, while the nitrate had the highest percentage. To further investigate the emission contributions to Beijing’s PM2.5 from different emission regions, a coupled MM5–CMAQ modeling system was developed. The concentrations of PM2.5 and its various components (sulfate, nitrate, ammonium, and other particles) in Beijing during four selected months in 2010 were simulated. Acceptable modeling performance was obtained. A zero–out method was used to calculate the emission contribution ratio from different emission regions. It was found that the high PM2.5 concentration in Beijing generally corresponds to the high trans–boundary emission contribution. And the contribution from Hebei province accounted for a major part in the total trans–boundary contributions. It was also found that the trans–boundary transport from Beijing’s surrounding regions had the highest contribution to Beijing’s PM2.5 pollution in July. The trans–boundary transport impact on nitrate concentration in Beijing was the greatest among all of the PM2.5 particles. A positive correlation was found between the trans–boundary emission contribution ratio and the PM2.5

concentration in Beijing, but a negative correlation existed between the local emission contribution ratio and the PM2.5 concentration. The results also illustrated that the trans–boundary transport played a major role in Beijing’s PM2.5 concentration during the high pollution level period (i.e., > 75 μg/m3), while the local emission in Beijing played a major role in its PM2.5 concentration during the lower pollution level period (i.e., < 75 μg/m3). The high PM2.5 concentration in Beijing may pose serious threats to the public health, and thus needs effective mitigation. The results obtained from this study indicated that the control of Beijing’s PM2.5 pollution should require coordinated efforts in both Beijing and its surrounding regions. In addition, the natural uncertainty of zero–out method using for the investigation of secondary particles transport was discussed. Generally, if the influence on the O3 formation of the receptor region is not so significant, the TBCR calculated based on the zero–out method may be higher than the actual ones. ACKNOWLEDGMENTS

This research was supported by the Natural Sciences Foundation of China (No. 51038001 & 51208010), the "Beijing Science and Technology Project" (D09040903670801) of the Beijing Municipal Science & Technology Commission, and the Ministry of Environmental Protection Special Funds for Scientific Research on Public Causes (No. 201209003&200909008). The authors would also like to thank Beijing NOVA Program of China (No. 2009B07), Innovation Team Project of Beijing Municipal Education Commission (PHR201007105) as well as the Cultivation Fund of the Key Scientific and Technical Innovation Project, Ministry of Education of China (708017) for supporting this work. REFERENCES Behera, S. and Sharma, M. (2012). Transformation of

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Received for review, September 13, 2012 Accepted, November 27, 2012

a monitoring and modeling study to investigate regional ... · data by mm5, such as the topography...

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