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Aerosol and Air Quality Research, 18: 1720–1733, 2018 Copyright © Taiwan Association for Aerosol Research ISSN: 1680-8584 print / 2071-1409 online doi: 10.4209/aaqr.2017.10.0406 Understanding the Regional Transport Contributions of Primary and Secondary PM2.5 Components over Beijing during a Severe Pollution Episodes Wei Wen1,2, Xiaodong He1*, Xin Ma3**, Peng Wei4, Shuiyuan Cheng5, Xiaoqi Wang5, Lei Liu2

1Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China 2 Key Laboratory of Atmospheric Chemistry, China Meteorological Administration, Beijing 100081, China 3 National Meteorological Center, Beijing 100081, China

4 Chinese Research Academy of Environment Science, Beijing 100012, China 5 Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China ABSTRACT

This study applied the CAMx model to study the regional transport of various PM2.5 components in Beijing during a severe pollution episodes. The results revealed that during the episodes, Beijing had the average PM2.5 pollution value of 119 µg m–3. It was 1.58 times of the PM2.5 national air quality standard (75 µg m–3 Level II). The wind speed was low (< 2 m s–1) and relative humidity reached 98%. The anticyclone in Eastern China showed weak local flow fields and southerly winds at the surface and strong temperature inversion under 1000 m, which promote pollution accumulation. The contribution of monthly regional transport to primary PM2.5 components and SO4

2–, NO3–, and secondary organic aerosol

concentrations in Beijing were 29.6%, 41.5%, 58.7%, and 60.6%, respectively. The emissions from Baoding had the greatest effect on the primary components of PM2.5 (6.1%) in Beijing. The emissions from Tianjin had the greatest influence on the secondary components of PM2.5 concentrations. These values indicated that the secondary components of Beijing’s PM2.5 are more easily affected by transboundary transport than are the primary components. The present findings suggest that control strategies for PM2.5 pollution should include coordinated efforts aimed at reducing secondary aerosol precursors (SO2, NOx, and VOCs) from long-range transport and local generation in addition to primary particulate emissions. Keywords: PM2.5 pollution; Regional transport; Secondary component; Air quality modeling. INTRODUCTION

PM2.5 pollution is a severe environmental problem in China. It poses a considerable threat to human health because it comprises various metallic elements and toxic organic materials (Cao et al., 2012). PM2.5 pollution is a complex regional transport problem, as the particles can be transported over long distances (Hatakeyama et al., 2011; Squizzato et al., 2012; Tao et al., 2012; Lang et al., 2013). Previous studies have reported that in North China, a large fraction of PM2.5 is attributable to secondary aerosols (Huang et al., 2014; Zheng et al., 2015). Secondary components, such as SO4

2–, NO3–, and secondary organic aerosols (SOAs), are

transformed from precursor gases in the atmosphere (SO2, * Corresponding author. Tel.: +86 10 68400750 E-mail address: [email protected]

** Corresponding author. Tel.: +86 10 58993295 E-mail address: [email protected]

NOx, and volatile organic compounds [VOCs], respectively) through not only photochemical reaction but also aqueous and heterogeneous reaction etc. (Huang et al., 2014). PM2.5 pollution can be controlled through coordinated regional control. Emphasis should be placed not only on controlling primary PM2.5 pollutants from local emissions but also on coordinating the control of precursor emissions (SO2, VOCs, and NOx) in the region. Beijing is the political and cultural center of China. It’s located in the north of China, the most polluted region. This region is characterized by high concentrations and frequencies of PM2.5 pollution (MEP, 2015). The pollution in Beijing has attracted increasing attention from the government and public of China. To improve the overall air quality and control PM2.5 pollution in the region, a series of PM2.5 pollution control strategies were implemented by the government (China State Council 2013). For example, in 2014 and 2015, the government executed strict emission control strategies to ensure that the Asia-Pacific Economic Cooperation meeting and 70th anniversary of the victory of the Anti-Japanese War celebration were successfully. Emission control in Beijing and surrounding provinces (Hebei, Shanxin, Inner Mongolia, Shandong, and Henan) has been correspondingly implemented (Wen et al.,

Wen et al., Aerosol and Air Quality Research, 18: 1720–1733, 2018 1721

2015; Wang et al., 2016). Several studies and projects have investigated the regional

transport of PM2.5. Chuang et al. (2008) reported that during a typical pollution episode in Taiwan, the SO4

2– concentration (35%) was contributed by Shanghai. Wang et al. (2008) showed that 28.1% and 59.5% of PM pollution in urban and suburban Beijing was contributed by PM transport from surrounding regions. Streets et al. (2007) found that 50%–70% of PM pollutants in Beijing were transported from the southern areas of Hebei Province. Wang et al. (2014) used the CAMx/PSAT (Comprehensive air quality model with extensions/ particulate source apportionment technology) model to analyze a heavy pollution process (November 2010) in Shanghai; the results revealed that nearly 50% of PM2.5 pollution was from surrounding regions. Wang et al. (2012) applied the CMAQ model in Hebei Province to investigate haze pollution and found that regional transport accounted for approximately 35% and 41% of PM2.5 pollution in Shijiazhuang and Xingtai, respectively. Lin et al. (2016) extended the CAMx model and reported that in August, local sources respectively accounted for 23.8% and 16.6% of anthropogenic and biogenic SOAs in Beijing. Furthermore, Wang et al. (2015) applied the MM5–CMAQ model to estimate the regional source contribution to PM2.5 concentrations in most polluted cities in Hebei Province. They reported that regional transport plays an important role in PM2.5 pollution and that the regional joint air pollution controls are crucial. Concurrently, the long-distance transport of particulate matter and also the species has been widely investigated. For example, PM2.5 sulfate in Beirut is mainly from the Eastern European region (Saliba et al., 2007). A study reported that pollution in Asia affects the air quality of the United States and Canada, showing that the long-distance emission from Asia can be transport and transmitted across the Pacific Ocean (Heald et al., 2006). However, most studies on the transport of particulate matter are focused on total PM2.5. Few studies have used model approaches to comprehensively analyze the transport of primary and secondary PM2.5 components separately in the Beijing-Tianjin-Hebei (BTH) region under one study framework. The locations where secondary PM2.5 forms might differ from where its precursors are emitted, because the precursors might not be directly converted into PM2.5 after emitted into the atmosphere. The time for secondary particle formation to occur depends on the abundance of precursor, temperature, humidity, and other factors. PM2.5 is the mixture of four aspects (1) Locally emitted primary PM2.5 and secondary components; (2) Secondary PM2.5 components from local precursor emissions; (3) Secondary PM2.5 components from precursor emissions transport from surroundings, which later formed into PM2.5 components in that focused region; (4) Primary PM2.5 and secondary components transport from surrounding regions that already formed there. Therefore, the amount of PM2.5 in a specific region is the mixture of PM2.5 formed and transported from different sources, which can be distinguished according to source regions and emission categories. To control PM2.5 pollution, it is necessary to determine the contribution of the regional transport of primary and secondary PM2.5

components to local PM2.5 concentrations. In this study, the offline model CAMx was applied to

study the transport contribution of primary PM2.5 and SO42–,

NO3–, and SOA. The particulate source apportionment

technology (PSAT) was implemented in CAMx. This model was used to analyze the contributions of emission categories and different source regions. The model not only determines the source region and contributions but also provides source apportionments for both primary and secondary particulate matter. Furthermore, the relative importance of different source regions can be ranked and further provide a major contribution amount, which is important for air quality management. Studies on the transport of primary and secondary PM2.5 components can provide scientific and technological support for the collaborative prevention of air pollution.

Model Setting and Application Model System

In the present study, the transport components of primary and secondary PM2.5 in the Beijing region were simulated using the CAMx model. The meteorological factors were simulated using The Weather Research and Forecasting model (WRF; Version 3.3). The WRF initial conditions were used as the final global tropospheric analysis data, which were provided by the National Centers for Environmental Prediction. The PSAT module in CAMx is a framework for apportioning six categories of PM components and gaseous precursors. PSAT has been widely used in the CAMx model (Fann et al., 2012; Zhang et al., 2013). In the present study, two nested grid architectures were designed to implement the modeling system (Fig. 1(a)). During October 2014, several typical severe pollution episodes occurred in the BTH region. The model was applied for a 35-day period in Beijing, starting from September 25, 2014. The first 5 days were considered as spin-up and were excluded from the analysis. Beijing is enclosed by Hebei Province except to the southeast, where Tianjin Province is located. The modeling domains had the spatial resolution for the modeling domains are 27 km and 9 km respectively. The emission source regions included Beijing and other regions, which were defined on the basis of geographical borders. The emission inventories were obtained from the Multi resolution Emission Inventory for China of the year 2012, which was developed by Tsinghua University. We have previously reported additional details of the emission inventory, physical options, and model setting (Wen et al., 2015; Wen et al., 2016). WRF Version 3.3 was used to analyze the meteorological conditions. The physics options included the New Thompson, Goddard short-wave, rapid radiative transport model long-wave, Yonsei University planetary boundary layer meteorology, Noah land surface model, and New Grell schemes. Source Apportionment Technology

The particulate source apportionment technology (PSAT) module in CAMx was designed for the apportionment of primary and secondary PM2.5 components: Sulfate (SO4), Particulate nitrate (NO3), Secondary organic aerosol (SOA),


(a) Double nesting domains utilized in model simulation

(b) Different source regions for PSAT in the domain

Fig. 1. Nesting domains and PSAT source regions used for model simulation.


and Six categories of primary PM (Elemental carbon, Primary organic aerosol, Crustal fine, Other fine, Crustal coarse, and Other coarse). In general, a single tracer can track primary PM species whereas secondary PM species require several tracers to track the relationship between gaseous precursors and the resulting PM. The PSAT tracers for each type of PM are listed: Sulfur (Primary SO2 emissions and Particulate sulfate ion from primary emissions plus secondarily formed sulfate), Nitrogen (Reactive gaseous nitrogen including primary NOx emissions plus nitrate radical, nitrous acid, dinitrogen pentoxide, Gaseous peroxyl acetyl nitrate plus peroxy nitric acid, Organic nitrates, etc.), Secondary Organic (Aromatic secondary organic aerosol precursors, Isoprene secondary organic aerosol precursors, etc.). The source apportionment results actually includes the contributions from two aspects: (1) transport of precursors from surrounding regions to Beijing, e.g., SO2 for sulfate, NOx for nitrate, VOC for SOA, which later transformed into PM2.5 components in Beijing; (2) direct transport of specific PM2.5 components that formed in surrounding regions. The model estimates the contribution of specific emission sources to receptor PM2.5 concentrations. It can determine the spatial and temporal relationship between ambient PM2.5 levels and various emission source regions and categories, which can be used to rank the relative importance of various source regions to further develop control policies (Wu et al., 2013). On the basis of the PSAT results, the important sources can be controlled to achieve certain air quality targets. PSAT has been used as a source apportionment tool in many studies. The PSAT module can provide apportion of particulate matter in each single-grid cell among different sources and regions. The different source groups in the PSAT methodology are defined in terms of their geographical region and emission category. In the present study, Beijing was selected as the receptor region. The CAMx receptor region setting was not a point in Beijing, but a group of

grid cells that are averaged together to provide multi-cell average tracer concentrations. The entire domain was divided into 14 source regions, namely Beijing, Tianjin, Zhangjiakou, Chengde, Qinhuangdao, Tangshan, Baoding, Langfang, Cangzhou, Shijiazhuang, Hengshui, Xingtai, Handan, the non-BTH region, as shown in Fig. 1(b). These regions were defined according to their geographical borders. Detailed PSAT algorithms are reported in the manual of the CAMx model (http://www.camx.com). RESULTS AND DISCUSSION Model Verification

In this study, the model simulation results were assessed in terms of normalized mean bias (NMB), normalized mean gross error (NME), and correlation coefficient (RC). Daily average PM2.5 concentration monitoring data for Beijing were collected from the China National Environmental Monitoring Centre (CNEMC) for the target month; these data included an average concentration of 23 monitoring sites. The simulated particle components (SO4

2–, NO3–, SOA)

were compared with the monitoring data. The components monitoring data were from our previous study paper, which sampling site was at the Beijing Normal University (BNU) (wen et al., 2015). Table 1 presents the statistical results of the comparison the CAMx simulation results with the daily average concentrations of the monitored PM2.5 and its components. The simulated meteorological parameters were also evaluated against observations. The meteorological data were obtained from the Meteorological Information Comprehensive Analysis and Process system of the China Meteorological Administration. The evaluation results of the meteorological parameters affecting air pollution, including temperature (T2), relative humidity (RH), wind speed at 10 m (WSP10), and sea-level pressure (P) are also provided in Table 1. The near-surface temperature simulation

Table 1. Comparison of simulated results with monitored data (24 h average).

WSP10 Simulation (m s–1) 2.3 P Simulation (hpa) 989.5 Monitored (m s–1) 1.9 Monitored (hpa) 1020.6 NMB 19.22% NMB –3.04% NME 28.94% NME 3.04% RC 0.76 RC 0.99

T2 Simulation (°C) 13.58 RH Simulation (%) 50.4 Monitored (°C) 13.28 Monitored (%) 65.5 NMB 1.75% NMB –21.09% NME 9.13% NME 21.32% RC 0.84 RC 0.86

PM2.5 Simulation (µg m–3) 136.7 SO42– Simulation (µg m–3) 8.09

Monitored (µg m–3) 131.2 Monitored (µg m–3) 10.56 NMB 4.50% NMB –6.38% NME 29.60% NME 48.56% RC 0.89 RC 0.68

NO3– Simulation (µg m–3) 30.62 SOA Simulation (µg m–3) 4.8

Monitored (µg m–3) 35.40 Monitored (µg m–3) 5.2 NMB –13.51% NMB –92.28% NME 63.46% NME 93.11% RC 0.82 RC 0.35


results showed a closer fit to the observations. The NMB and RC for the domain were 1.75% and 0.84, respectively. WSP10 was slightly over predicted with NMBs (19.22%). RH and sea-level pressure were in good agreement with the RC at 0.86 and 0.99, respectively. In general, the comparison results indicate that an acceptable agreement between the simulated and observed concentrations has been achieved the guidelines of the U.S. Environmental Protection Agency (2007). WRF yielded acceptable performance.

These acceptable WRF-simulated meteorological results in addition to emission categories can drive the air quality model. Fig. 2 presents the simulated and monitored PM2.5 concentrations in Beijing. PM2.5 pollution was much more frequent in October 2014 and was the highest on October 9, 2014 (330 µg m–3). The value was 4.4 times of the PM2.5 national air quality standard (75 µg m–3 Level II). The trend of the simulated values matched well with the observation data. The average monthly spatial distribution of PM2.5

Fig. 2. Comparison between the simulated and observed daily concentrations of PM2.5.

(simulated) (monitored)

Fig. 3. Average simulated and monitored monthly spatial distribution of PM2.5 pollution (µg m–3).


pollution simulated data and the monitored data were plotted in Fig. 3, which also confirmed the well simulation and observation trends. The correlation coefficient between the simulated and observed PM2.5 concentrations was 0.89. The simulation results obtained using the CAMx model can be considered to reflect the change in PM2.5 concentrations in October.

The NMB value for the CAMx simulation of PM2.5 in Beijing was 4.5%. The NME of CAMx was 29.6%. It was slightly overestimated by CAMx. The model did not simulate well during heavy pollution events. For example, the observed PM2.5 concentration was 330 µg m–3 in Beijing in October 9th 2014; however, the simulated PM2.5 concentration was only 279 µg m–3. The simulation NMB and NME values of secondary components were relatively larger than PM2.5 concentration. The most of correlation coefficients of secondary species between simulation and monitored data were strong (SO4

2– with 0.68, NO3- with 0.82), except SOA

results (0.35). The model underpredicted PM2.5 concentrations on heavy polluted days and PM2.5 secondary species possibly due to its mechanisms. These underpredictions may also reflect the meteorological simulation bias, which oversimulated of the WS and underestimated RH. The meteorological simulation bias resulted in accelerating pollutant diffusion. The model lacks chemical reaction processes such as missing model calculated oxidants reactions and stat-of-science concerning SOA formation (Li et al., 2015), which may also lead to some underestimation. According to Morris et al. (2006), the performance of the in the simulation of organic carbons are usually poor. The NMB of carbons simulation results usually over 100% (Zhang et al., 2013). These underpredictions are the inherent characteristics of the model (Wang et al., 2013). The model results can clearly simulate the PM2.5 pollution process despite of the underestimations. The CAMx simulation results in this study were acceptable compared with those reported by related studies (Liu et al., 2010; Wu et al., 2013; Zhang et al., 2013).

Regional PM2.5 Pollution Characteristics and Meteorological Conditions

The meteorological conditions and PM2.5 concentration in the BTH region affected the transport of PM2.5 components in Beijing. The data (CNEMC) on PM2.5 concentration monitoring in all cities in the BTH region during October were determined (Fig. 4).

The cities in the BTH region were divided into three categories (northern, eastern, and southern) for calculation. The daily PM2.5 concentrations in these cities are plotted in Fig. 4. The BTH region experienced several severe PM2.5 pollution episodes in October 2014. The monitoring data of different cites showed similar trends of PM2.5 changes with time, indicating that the BTH region experienced similar PM2.5 pollution processes in October. This observation also revealed that PM2.5 pollution can be characterized as a regional pollution concern. Severe pollution episodes occurred during October 2014, notably from the 6th to the 12th, 16th to the 21st, and 22nd to the 26th. Fig. 4 shows that PM2.5 concentrations in the southern BTH region were typically

higher than those in the northern BTH region. In the northern BTH region, Langfang had the highest PM2.5 monthly average value of 131 µg m–3. Beijing had a PM2.5 pollution value of 119 µg m–3, which was 1.58 times of the PM2.5 national air quality standard (75 µg m–3 Level II). In the southern BTH region, Xingtai had the highest PM2.5 monthly average value of 167 µg m–3, which was 2.22 times of the national standard. In the eastern BTH region, Tangshan yielded the highest PM2.5 monthly average value of 106 µg m–3. According to the calculation data, the average monthly PM2.5 concentrations were lowest in Zhangjiakou in the BTH region (26 µg m–3). The emissions, meteorological conditions, and terrain could be obvious reasons for such a spatial distribution of PM2.5 concentrations. Industrial activities in Shijiazhuang and Xingtai in the southern BTH region is intensive (i.e., cement, coke, and metallurgical industries); thus, PM2.5 emissions were higher than those in the northern BTH region according to the emission inventory (i.e., Shijiazhuang [9.0 ton year–1 area–1], Xingtai [9.6 ton year–1 area–1], and Zhangjiakou 2.7 ton year–1 area–1]) (Wen et al., 2015). Meanwhile, the atmosphere over the southern areas of Hebei was highly stable. Shijiazhuang and Xingtai are located in the Taihang Piedmont Plain. Southerly or easterly air currents over this region coincide with the westerlies, which cross the Taihang Mountain range. These air flows sink and converge, resulting in a highly stable atmosphere. This unique meteorological factor and terrain tend to trap precursor gases as well as primary PM2.5 emissions, which contributes to pollution in the region. Even with the same emission densities as in other regions, the southern BTH region typically experiences heavier PM2.5 pollution than do other regions (Wang et al., 2013).

The meteorological conditions were highly conducive to PM2.5 pollution. The meteorological parameters in the typical pollution episodes (6–9th) in October were rigorously analyzed (Fig. 4). The meteorological parameters affecting air pollution, including wind direction, WS, total cloud amount, and RH. As shown in Fig. 4, the heavy air pollution can be divided into several stages PM2.5 pollution ascent, continuous high-value stage and descent. As shown in Fig. 5(a), in the significant ascent stage of PM2.5 concentrations (October 6–9, 2014), the wind direction changed between 0° and 350°. The WS was relatively low (< 2 m s–1). Static wind occurred at night or in early morning (UTC). Under stable weather conditions, the RH and cloud amount had significantly increased [Fig. 4(b)]. These conditions were conducive to pollutant accumulation in the local areas, which resulted in a significant increase in PM2.5 concentrations. In the continuous high-value stage of PM2.5 (October 8–10, 2014), the wind direction changed between 50° and 200°. The easterly and southerly winds WS was still low (< 2 m s–1). The clouds were more stable, and the RH increased slowly. The RH reached 98%, which was close to saturation and was conducive to secondary pollutant formation. In the PM2.5 concentration descent stage (October 11–12, 2014), the wind direction was mainly toward the north. The WS increased from 2 m s–1 to 6 m s–1; the RH was 12%–20%. The amount of cloud change was scarce, and the PM2.5 concentration decreased rapidly. During heavy pollution,


Fig. 4. Daily average concentration monitoring of PM2.5 in the Beijing-Tianjin-Hebei region.

obvious variations were observed in the WS, wind direction, cloud amount, and RH in each stage of PM2.5 pollution. The results reveal that pollution was dominated by meteorological factors. Furthermore, the large-scale synoptic pattern influences the changes in meteorological factors, which results in changes in PM2.5 concentration. The special weather background often results in large-scale fog haze and heavy pollution events in the area. To verify this point, the large-scale synoptic pattern were studied using the WRF model. Fig. 6 shows the daily average large-scale synoptic pattern the PM2.5 concentration ascent stage, which included sea-level pressure and WSP10 on the ground. The eastern lands of China were controlled by high pressure, and the pressure

center was located over the North China Plain. Under the high pressure control, the wind was weaker, with speeds less than 4 m s–1. The north was dominated by weak local flow fields and southerly winds, while the south was controlled by weaker northerly currents. Furthermore, the anticyclone in Eastern China showed a weak local flow fields and southerly winds at the surface and strong temperature inversion near the ground (Fig. 7), which promote pollution accumulation. In the severe pollution episodes, the surface weather system in the BTH region was highly stable. The anticyclone provides a stable large-scale synoptic pattern for the heavy pollution events in autumn. The weaker wind field on the ground and the subsidence inversion layer at a high altitude


(a)

(b)

Fig. 5. Variations in meteorological factors over time in Beijing. (0–200 m) were beneficial for the local accumulation. Since October 11, 2014, the main weather situation effect changed in Eastern China. The high pressure was from Mongolia. The frontal of high pressure control almost the entire continent. This meteorological background in Northern China was consistent with systemic northerly winds. The wind was strong, reaching 10 m s–1, which is highly favorable for pollution removal. It was the main systematic pattern for the PM2.5 concentration descent. Regional Transport of Primary and Secondary PM2.5 Components

Table 2 presents the contribution of monthly transport to primary PM2.5, SO4

2–, NO3–, and SOA concentrations in

Beijing during the simulation months from the surrounding regions.

Significant variation was observed in the contribution ratios among different pollutant compositions. The contribution for different components transported from surrounding regions to Beijing was 30%–61%. The contribution of local emission sources to primary PM2.5, SO4

2–, NO3–, and SOA

concentrations in Beijing were 70.4%, 58.5%, 41.3%, and 39.4%, respectively. Furthermore, the contribution of monthly regional transport to primary PM2.5, SO4


concentrations in Beijing were 29.6%, 41.5%, 58.7%, and 60.6%, respectively, indicating that secondary components

of Beijing’s PM2.5 pollution are more easily affected by transboundary transport than are primary components. This behavior of secondary components was also reported by a previous study that revealed that the average contribution of local emissions to primary PM2.5 was 64% and that less than 10% of SOA was contributed by local emission sources in Paris (Skyllakou et al., 2014). This is mainly because component formation is more complex and can be affected by other factors. First, secondary components are mainly enriched in smaller particles, which are conducive to long-distance transport (Li et al., 2013). Second, various ways of secondary particles are transported (i.e., SO4

2–). (1) Primary sulfates (directly emitted from various sources) are transported from other region. (2) Secondary sulfates are formed by chemical reactions in the region and are transported to Beijing. (3) SO2 and NOx are transported from the region to Beijing, where SO4

2–, NO3–, and SOA are formed through

oxidation. Table 2 shows that the effect of transboundary transport from the surrounding regions was the highest on the concentration of SOA (61%) among the studied fine particles components in Beijing. Identical results were reported in a previous study, in which a simulation indicated that long-distance sources dominated SOA in Beijing (Lin et al., 2016). The reasons are as follows: (1) Compared with the surrounding region, Beijing has modest VOC emissions; the formation of SOA from freshly emitted


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VOCs required several hours (Li et al., 2013). During the reaction, VOCs emitted in Beijing were transported out of Beijing before oxidation and converted into semivolatile oxidation products, which can condense to form SOA (Wagstrom et al., 2012; Skyllakou et al., 2014). (2) SO4

2– was formed in the atmosphere primarily through the oxidation of SO2, which is mainly emitted from coal burning. Moreover, NO3

– was derived from the emission of NOx, which is mainly an exhaust gas from power plants and vehicles. In contrast, the emission sources of SOA precursor gases were highly uncertain; the sources include biogenic sources and those from other anthropogenic areas. Although VOCs cannot be transported too far because of their short life span, during transport, they are consistent of the

abundant emissions of SOA precursors to form semivolatile oxidation products and condense to SOAs (Kim et al., 2011). (3) SOAs comprise all types of secondary compounds, which are formed by the reactions of evaporated primary organic aerosols, intermediate VOCs, and VOCs. SOA formation processes are very complicated, thus making it very difficult to simulate SOA concentration in the atmosphere. The module used in this study may not contain all reaction processes, which may result in some errors. The CAMx SOA module consists of two parts: gas-phase oxidation chemistry that forms condensable gases (CGs), and equilibrium partitioning between gas and aerosol phases for each CG/SOA pair. The simulated SOA concentration using the two-product approach are substantially underestimated,


Table 2. Regional transport of primary and secondary PM2.5 components (%). PM2.5 (Primary) SO4

2– NO3– SOA

Beijing 70.4% 58.5% 41.3% 39.4% Baoding 6.7% 6.5% 10.1% 9.6% Tianjin 6.1% 9.4% 12.9% 10.2% Tangshan 3.4% 5.6% 5.8% 4.6% Zhangjiakou 2.2% 3.3% 3.1% 8.5% Shijiazhuang 2.1% 3.5% 6.1% 3.8% Langfang 2.0% 2.8% 4.0% 5.1% Chengde 1.8% 2.9% 3.4% 7.9% Xingtai 1.6% 2.4% 4.0% 2.2% Cangzhou 1.5% 1.2% 3.5% 3.9% Hengshui 1.1% 1.3% 2.3% 2.9% Handan 1.0% 2.3% 2.9% 1.3% Qinhuangdao 0.1% 0.3% 0.6% 0.6%

when compared to the corresponding observations in most cases. A large OA source in the free troposphere was not included in the model (Lin et al., 2016).

The daily variations in the contributions of emission sources from the surrounding regions to different PM2.5 components in Beijing under different PM2.5 pollution levels are shown in Fig. 8. During the periods of higher PM2.5 concentrations, the contribution of regional transport to different PM2.5 components was also higher (i.e., on October 8, 2014: PM2.5 concentration, 258 µg m–3 vs. regional contribution ratio of NO3

–, 68%). Relatively weaker polluted days corresponded to lower regional contributions (i.e., on October 13, 2014: PM2.5 concentration, 13 µg m–3 vs. regional contribution ratio of NO3

–, 13%). A positive correlation was observed between the contribution of the transport from surrounding regions and local PM2.5 concentration in Beijing. The result indicated a major role of transboundary transport in Beijing’s PM2.5 concentrations during the high pollution episodes. The sea-level pressure and 10-m wind field (Fig. 6) revealed that during the PM2.5 ascent stage, the weak wind field to the south was advantageous for transporting pollutants from the south of Hebei Province. The meteorological conditions were favorable for pollution accumulation. Table 2 and Fig. 9 show the average contributions of transport from different regions to PM2.5 components. Pollution emissions from Baoding and Tianjin had the greatest effect on PM2.5 concentrations in Beijing because these cities are close to Beijing and have high pollutant emissions. The emissions from Tianjin had the greatest influence on the secondary components of PM2.5 concentrations in Beijing; SO4

2–, NO3–,

and SOA concentrations were 9.4%, 12.9%, and 10.2%, respectively. The emissions from Baoding had the greatest effect on the primary components of PM2.5 (6.7%) in Beijing.

Based on the PM2.5 standards, the contribution ratios of emission contributions to PM2.5 components in Beijing from its surrounding regions under different PM2.5 pollution levels were calculated. The results are listed in Table 3. Two different PM2.5 pollution levels were examined, namely the daily PM2.5 concentration of 0–75 µg m–3, and greater than 75 µg m–3, respectively. During the periods when the PM2.5 concentrations were 0–75 µg m–3, the contribution of

transport for Beijing PM2.5 primary , sulfate, nitrate, SOA concentration were 22.6, 26.8, 42.0, and 48.0%, respectively. These were lower than those during the periods with PM2.5 concentration of over 75 µg m–3. During the high pollution levels (i.e., > 75 µg m–3), the transportation were 36.7, 45.5, 62.0, and 63.7% for PM2.5, sulfate, nitrate, SOA, respectively. Moreover the contribution from further south of Hebei (e.g. Handan, Xingtai, Shijiazhuang) had a greater contribution to Beijing pollution, when PM2.5 concentration was in high pollution level. The results listed in Table 3 confirm the positive correlation between transport from regions and its PM2.5 concentrations. The results suggest that the mitigation of PM2.5 pollution not only requires the reduction of local emissions, but also needs the cooperation of its surrounding regions, especially during the heavy pollution periods. CONCLUSIONS

In this study, the offline model CAMx (PSAT) was used to study the transport of primary and secondary PM2.5 components (SO4

2–, NO3–, and SOA) in October 2014 in

Beijing. That month, several severe pollution episodes occurred in Beijing. The model performance reflects the process of PM2.5 pollution and agree well with the observations. The southern BTH region experienced heavier PM2.5 pollution than did the other regions. By studying the meteorological conditions, it revealed that the large-scale synoptic in the BTH region was highly stable during the severe pollution episodes. The anticyclone in Eastern China showed weak local flow fields and southerly winds at the surface and strong temperature inversion, which promote pollution accumulation. The contribution of monthly regional transport to primary PM2.5, SO4


concentrations in Beijing were 29.6%, 41.5%, 58.7%, and 60.6%, respectively. The secondary components of Beijing’s PM2.5 were more easily affected by transboundary transport than were the primary components. The higher concentration of PM2.5 pollutants corresponded to the greater contribution of regional transport to PM2.5 components. The severe pollution episodes were promoted by the long-range transport-and-transmission generation processes from the


south of Hebei Province. Emissions from Baoding and Tianjin had the greatest effect on PM2.5 concentrations in

Beijing. The results suggest that the regional strategies aimed at reducing PM2.5 pollution will require coordinated effort to

Fig. 8. Daily variations in the contribution of emission transport to different PM2.5 components, as studied using the CAMx model (TR-SO4

2–: transportation contributions to SO42–; TR-NO3

–: transportation contributions to NO3–; TR-SOA:

transportation contributions to SOA).

Fig. 9. Average contribution of transport to different PM2.5 components from different regions (%).


Table 3. Regional transport of primary and secondary components under different PM2.5 concentration level. PM SO4 NO3 SOA

> 75 µg m–3 < 75 µg m–3 > 75 µg m–3 < 75 µg m–3 > 75 µg m–3 < 75 µg m–3 > 75 µg m–3 < 75 µg m–3 Beijing 63.3% 77.4% 54.5% 73.2% 38.0% 58.0% 36.3% 52.0% Tianjin 9.2% 3.0% 12.2% 3.9% 15.5% 7.7% 12.1% 5.7% Tangshan 4.6% 2.3% 6.4% 3.8% 5.9% 4.9% 6.2% 3.2% Baoding 6.9% 6.6% 6.3% 4.9% 10.5% 6.8% 8.9% 7.9% Shijiazhuang 2.2% 2.0% 3.3% 2.8% 5.7% 5.0% 3.4% 3.0% Langfang 2.6% 1.2% 3.2% 1.5% 4.5% 2.8% 5.5% 3.6% Handan 1.5% 0.4% 3.0% 0.8% 3.5% 1.4% 2.6% 0.5% Xingtai 2.1% 1.0% 2.7% 1.3% 4.3% 2.5% 4.9% 1.3% Zhangjiakou 2.0% 2.8% 2.6% 3.6% 2.0% 4.1% 6.1% 10.5% Chengde 1.7% 2.0% 2.2% 2.9% 2.3% 3.9% 5.0% 8.9% Cangzhou 2.2% 0.7% 1.6% 0.5% 4.5% 1.5% 5.0% 1.7% Hengshui 1.5% 0.5% 1.6% 0.6% 2.7% 1.1% 3.4% 1.3% Qinhuangdao 0.2% 0.1% 0.4% 0.2% 0.7% 0.4% 0.7% 0.4%

reduce both the long-range transport and also local generation of SO2, NOx, and VOCs, in addition to particulate primary emissions. The techniques used in this study are effective for developing PM2.5 pollution control policies. ACKNOWLEDGMENT

This study was supported by the Beijing Natural Science Foundation (No. 8172051), LAC/CMA (No. 2017B05), Natural Sciences Foundation of China (No. 41305130 and No. 41305104), Natural Sciences Foundation of Beijing (No. 8161004), National Science and Technology Support Project of China (No. 2014BAC23B03), National Science and Technology Support Project of Beijing (No. Z151100002115045), and Special Scientific Research Fund of Meteorological Public Welfare Profession of China (No. GYHY201306061). The authors thank the editors and anonymous reviewers for their insightful comments. REFERENCES Cao, J.J., Xu, H.M., Xu, Q., Chen, B.H. and Kan, H.D.

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Received for review, October 22, 2017 Revised, December 19, 2017

Accepted, December 23, 2017

understanding the regional transport contributions of ... · 2.5 pollution; regional transport;...

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