Scholarly article on topic 'Rapid formation of a severe regional winter haze episode over a mega-city cluster on the North China Plain'

Rapid formation of a severe regional winter haze episode over a mega-city cluster on the North China Plain Academic research paper on "Earth and related environmental sciences"

CC BY-NC-ND
0
0
Share paper
Academic journal
Environmental Pollution
Keywords
{"Beijing-Tianjin-Hebei megacity cluster" / Haze / "Regional transport" / "Source apportionment" / "Vertical profile"}

Abstract of research paper on Earth and related environmental sciences, author of scientific article — Jie Li, Huiyun Du, Zifa Wang, Yele Sun, Wenyi Yang, et al.

Abstract The Nested Air Quality Prediction Model System (NAQPMS) was used to investigate an extreme regional haze episode persisting over the Beijing-Tianjin-Hebei megacity cluster from November 26 to December 1, 2015. During this extreme haze event, the regional daily mean PM2.5 exceeded 500 μg/m3. We found that local emissions were the main source of haze over Beijing and Hebei in the early formational stage of this episode. The accumulation of regionally transported, highly aged secondary inorganic aerosols (SIA) along the foot of the mountains was responsible (60%) for the rapid increase of surface PM2.5 in Beijing between November 30 and December 1, although PM2.5 concentrations in the source regions of Hebei province were lower. The height of regional transport ranged from 200 to 700 m above ground level, with a slow increase with increasing distance of the source regions from Beijing. This indicates that more attention should be given to point sources at heights of 200–500 m in order to reduce the contribution of transport. The contribution of local emissions to haze in Beijing was mostly concentrated below 300 m above ground level, and was more significant for black carbon (BC) and organic matter (OM) than SIA. Tagging of pollutants by emission time showed that PM2.5 had been aged before it arrived at Beijing, and PM2.5 formed one or more days prior to arrival was twice that formed on the arrival day. This suggests that control measures would be more effective if they were implemented two days prior to haze episodes. In contrast to Beijing, haze in Tianjin was governed by transport from outside sources, whereas in cities located in Hebei province this episode resulted from local emissions.

Academic research paper on topic "Rapid formation of a severe regional winter haze episode over a mega-city cluster on the North China Plain"

Environmental Pollution xxx (2017) 1—11

Contents lists available at ScienceDirect

Environmental Pollution

journal homepage: www.elsevier.com/locate/envpol

Rapid formation of a severe regional winter haze episode over a megacity cluster on the North China Plain

Jie Li a *, Huiyun Du a b, Zifa Wang a, Yele Sun a, Wenyi Yang a, Jianjun Li c, Xiao Tang a, Pingqing Fu a

a LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China b University of Chinese Academy of Sciences, Beijing, China c China National Environmental Monitoring Center, Beijing, China

ARTICLE INFO

ABSTRACT

Article history: Received 4 August 2016 Received in revised form 3 January 2017 Accepted 22 January 2017 Available online xxx

Keywords:

Beijing-Tianjin-Hebei megacity cluster Haze

Regional transport Source apportionment Vertical profile

The Nested Air Quality Prediction Model System (NAQPMS) was used to investigate an extreme regional haze episode persisting over the Beijing-Tianjin-Hebei megacity cluster from November 26 to December 1, 2015. During this extreme haze event, the regional daily mean PM25 exceeded 500 mg/m3. We found that local emissions were the main source of haze over Beijing and Hebei in the early formational stage of this episode. The accumulation of regionally transported, highly aged secondary inorganic aerosols (SIA) along the foot of the mountains was responsible (60%) for the rapid increase of surface PM25 in Beijing between November 30 and December 1, although PM25 concentrations in the source regions of Hebei province were lower. The height of regional transport ranged from 200 to 700 m above ground level, with a slow increase with increasing distance of the source regions from Beijing. This indicates that more attention should be given to point sources at heights of 200—500 m in order to reduce the contribution of transport. The contribution of local emissions to haze in Beijing was mostly concentrated below 300 m above ground level, and was more significant for black carbon (BC) and organic matter (OM) than SIA. Tagging of pollutants by emission time showed that PM25 had been aged before it arrived at Beijing, and PM2.5 formed one or more days prior to arrival was twice that formed on the arrival day. This suggests that control measures would be more effective if they were implemented two days prior to haze episodes. In contrast to Beijing, haze in Tianjin was governed by transport from outside sources, whereas in cities located in Hebei province this episode resulted from local emissions.

© 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND

license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction

During the rapid urbanization and industrialization of China, clusters of mega-cities have become the principal centers of industrial production and economic growth. Densely populated areas constitute about 12% of the land in China and generate over 50.4% of the Gross Domestic Product (GDP), enhancing the possibility of forming regional hazes over megacity clusters. Since the 1980s, there has been a rapid increase in the number of days with haze in megacity clusters, increasing from about 50 days to about 100 days in the 2000s (Wu, 2010). In 2015,179 haze days happened in Beijing (http://www.bjepb.gov.cn/bjepb/413526/331443/331937/333896/ 4382832/index.html). In January 2013, the maximum hourly

* Corresponding author. E-mail address: lijie8074@mail.iap.ac.cn (J. Li).

concentration of particulate matter with an aerodynamic diameter less than 2.5 mm (PM2.5) was more than 600 mg/m3 in Beijing (Sun et al., 2014), nearly sixty times the World Health Organization (WHO) safe level of 10 mg/m3, nearly eight times the Chinese National Ambient Air Quality Standard (GB 3095—2012) second level standard of 75 mg/m3. Problems related to haze now constitute a top priority in future urban planning to ensure city growth does not irreversibly damage the natural environment of China and other countries.

Beijing, Tianjin and Hebei (BTH) form the largest city cluster in China and suffer the most severe haze pollution in China. In 2014, eight of the ten Chinese cities with the worst air quality belonged to this mega-city cluster and had annual PM2.5 concentrations exceeding 100 mg/m3 (http://jcs.mep.gov.cn). Extensive research has been conducted to investigate the source and formation of the haze in this region. Chemical analysis has shown that inorganic aerosols constitute the bulk of the material during haze rapid

http://dx.doi.org/10.1016/j.envpol.2017.01.063

0269-7491/© 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

formation (Sun et al., 2014). Low temperatures and high relative humidity accelerate the chemical transformation from gaseous precursors to aerosols (Zheng et al., 2015). Temperature inversions stratify the local emissions of particulates into thin mixing layers, resulting in high surface PM2.5 concentrations around source areas (Zhao et al., 2013). Recently, regional transport was also found to play an important role during haze episodes. Wang et al. (2014b) and Sun et al. (2014) reported that regional sources were responsible for half of the observed PM25 concentrations (200—400 mg/ m3) in Beijing during haze episodes in January 2013. Ge et al. (2012) showed that the contribution of regional transport to urban haze plumes was higher than that of local emissions in the BTH cluster during other haze episodes. The heterogeneous chemical composition of the aerosols (e.g. mineral dust) in the transported haze likely amplified the contribution of regional transport (Li et al., 2012; He et al., 2014). Chen et al. (2015) indicated that regional transport within the BTH city cluster appeared within 0.5—2.5 km in vertical. Previously, Wu et al. (2011) had argued that local emissions contributed in large part to the high PM10 concentrations in Beijing haze.

In 2013, the Chinese State Council released the "Atmospheric Pollution Prevention and Control Action Plan" to implement a megacity cluster-scale joint prevention and control strategy program (http://www.gov.cn/zwgk/2013-09/12/content_2486773. htm). As a result, the PM2.5 in Beijing decreased from 89.5 mg/m3 in 2013 to 85.9 mg/m3 in 2014, and 80.6 mg/m3 in 2015. However, the number of polluted days still remains very high. The episode from November 26 to December 1, 2015, lasted five days which was unusually long relative to other haze episodes reported in previous studies, with the hourly PM2.5 maximum concentrations exceeding 1000 mg/m3.

In this study, we employed a multi-scale chemical transport model to investigate the sources and formation mechanisms of haze during the 5-day severe haze episode of November 26 to December 1, 2015. We thoroughly analyzed the vertical structure of the regional transport and the local contributions to PM25. In particular, the elapsed time since emission for PM2.5 and its gaseous precursors was presented for the first time for the BTH region. This can be viewed as an indicator of the degree of PM2.5 aging. We hope that our results on the causes of the high PM2.5 episode are helpful to policy-makers in this region.

2. Methodology

2.1. Model description

The Nested Air Quality Prediction Model System (NAQPMS) used in this study is a 3D Eulerian chemical transport model with terrain-following coordinates, developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP/CAS). It includes modules that represent horizontal and vertical advection and diffusion, dry and wet deposition, and gaseous, aqueous, aerosol and heterogeneous chemistry (Wang et al., 2001; Li et al., 2007). The advection algorithm was developed by Walcek and Aleksic (1998). Diffusion processes are based on the procedure described in Byun and Dennis (1995). Modules concerning the dry deposition of gas and aerosols use the parameters defined by Wesely (1989) and Zhang et al. (2001). The wet deposition and aqueous chemistry schemes are based on the Regional Acid Deposition Model (RADM) (Stockwell et al., 1990) mechanism used in CMAQ v4.6. The gaseous chemical mechanism (CBM-Z) with additional dimethyl sulfide (DMS) reactions includes 71 species and 134 chemical reactions (Zaveri and Peters, 1999). Inorganic aerosols are simulated with the ISORROPIA v1.7 (Nenes et al., 1998) model using an ammonia—sulfate—nitrate—chloride—sodium—water

system. Six Secondary Organic Aerosols (SOA) were processed by a two-product module (Odum et al., 1997; Pandis et al., 1991). The simulation of heterogeneous chemical processes involves 14 chemical compounds and 28 reactions including dust, sea salt, sulfate and black carbon. Further details of NAQPMS can be found in Liet al. (2007, 2011; 2012).

NAQPMS quantitatively identifies the regional contribution to the concentrations of air pollutants through simulations using an on-line tracer-tagging module similar to the Particulate Matter Source Apportionment Technology (Wagstrom et al., 2008). The module attributes pollutant concentrations to different geographical locations at each step of the simulation without influencing the standard calculations. All secondary particulate matter (PM) components are attributed to a specific precursor gas species (e.g., sulfate to sulfur dioxide, nitrate to gaseous nitric acid (HNO3), ammonium to ammonia (NH3), SOA to semi-volatile gases). In particular, the duration of transport is also labeled for PM2.5 and its precursors to provide the time since emission for pollutants. Note that the tagging is made for precursors rather than the secondary aerosols. A detailed description of the module can be found in Li et al. (2008, 2014; 2016). The module has been validated by the Ministry of Environmental Protection of China (CMEP, 2013).

Fig. 1 c illustrates the nine regions selected for study, six of which (Chengde, Zhangjiakou and Qinhuangdao (CZQ); Beijing (BJ); Tangshan (TS); Langfang and Tianjin (LT); Hengshui, Cangzhou, Baoding (HCB); Xingtai, Handan and Shijiazhuang (XHS)) are part of the BTH megacity cluster. As the central megacity, Beijing has 19.6% of the population of the BTH cluster, but only emits 8% of the primary PM25 (Table 1). Regions to the south of BJ within BTH have higher emissions. The pollutants may be transported to Beijing under favorable meteorological conditions. Note that Shandong (SD) and Henan (HN) regions are located south of the BTH cluster and are highly polluted. These regions may affect the Beijing area by transport of pollutants under prevailing southerly winds (Chen et al., 2015).

2.2. Model structure

Fig. 1a shows the three nested model domains used in these studies. The coarsest domain (D1) covers most of China and East Asia at a 27 km horizontal resolution. The second domain (D2) includes most anthropogenic emissions within the BTH cluster and its surrounding provinces at a 9 km resolution. The innermost domain (D3) covers the BTH megacity cluster at a 3 km resolution and is the focus of this study. The first level of model above the surface is 30 m in height, and the average vertical layer spacing between 30 m and 1 km is around 100 m.

The MIX (http://www.meicmodel.org/dataset-mix.html) anthropogenic emission inventory that includes power, industry, residential, transportation, and agricultural sources was used (Li et al., 2015). The original resolution and initial year of the MIX inventory were 0.25° (about 25 km at middle latitudes) and 2010, respectively. The biogenic emission inventory was obtained from the National Center for Atmospheric Research (NCAR), and was derived from a run of the Model of Emissions of Gases and Aerosols from Nature (MEGAN v2.0) (Guenther et al., 2006). Emissions from burning biomass were provided by the Global Fire Emissions Database version 2 (GFEDv2) (Werf et al., 2006). Fig. 1b shows the hourly average primary PM2.5 emission rate in the study period.

The Weather Research and Forecasting model (WRF-ARW v3.6.1) (http://www.wrf-model.org/) driven by the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) data, was used to provide the NAQPMS meteorological fields. The NAQPMS initial and lateral boundary conditions were obtained from the global chemistry transport model MOZART-v2.4

90S 100E HOE 120E 130E 140E

(b) (c)

Fig. 1. (a) Model domain and (b) location of sites investigated in this study. (b) Hourly mean emission rates of primary PM2 5 (mgm2/s) and (c) the regions used for on-line tracer-tagging which are described in Table 1 are also shown.

(Hauglustaine et al., 1998). The simulation was conducted from November 11, 2015 to December 2, 2015, and the first 15 days were set aside as a spin-up period.

2.3. Observation data

Hourly surface and sounding meteorological parameters including temperature, relative humidity, wind direction and wind

speed were obtained from the China Meteorological Administration (http://data.cma.gov.cn/). Hourly surface PM2.5 concentrations from eight cities were provided by the China National Environmental Monitoring Center (http://106.37.208.233:20035/). Surface weather charts during the haze period were downloaded from the Korea Meteorological Administration website (http://Web.KMA.go. KR).

Table 1

Tagged aerosol source regions and their primary PM2.5 emissions during 26 November—December 1, 2015 in this study.a

Regions Description Area 103 km2 Population 106 Emissions 109g

BTH BJ Beijing 16.4 21.5 1.94

LT Langfang and Tianjian 18.4 19.7 3.94

HCB Hengshui, Cangzhou and Baoding 44.4 22.2 4.86

XHS Xingtai, Handan and Shijiazhuang 40.3 27.4 6.8

TS Tangshan 13.4 7.7 4.21

CZQ Chengde, Zhangjiakou, Qinhuangdao 83.3 11.4 2.44

HN Henan provinces 167 94.8 7.22

SD Shandong provinces 158 97.8 9.26

OT Others

Region locations are shown in Fig. 1.

3. Model evaluation

3.1. Observed PM2.5 concentrations

Fig. 2 shows the observed hourly PM2.5 concentrations from eight cities in the BTH megacity cluster, the locations of which are shown in Fig. 1b. We divided this episode into four stages:

than that of the January 2013 episode (Sun et al., 2014). In other cities, the PM2.5 concentrations showed a slow decrease.

(IV) Clearance stage (December 2). During this stage, the eight cities entered a dispersion stage with PM2.5 concentrations decreasing dramatically to low levels (50—120 mg/m3).

(I) Slow formational stage (November 27—28). In this stage, the PM2.5 concentrations increased from 30 to 50 mg/m3 to 200—400 mg/m3.

(II) Peak-sustaining stage except Beijing (November 29—30). PM2.5 concentrations slowly continued rising to maximum values of 300—800 mg/m3 and persisted for one day, with the exception of Beijing. PM2.5 concentrations in Beijing showed a short-time decrease to 50—100 mg/m3, which was likely caused by local circulation.

(III) Rapid increasing stage in Beijing (December 1). For Beijing, PM25 concentration increased rapidly to 626 mg/m3. The rate of formation reached 57.5 mg/m3/h, which was much higher

3.2. Model performance

To evaluate the model performance, we presented the correlation coefficient (R) and Normalized Mean Bias (NMB) as statistical parameters. As shown in Fig. 2, NAQPMS reproduced the four stages observed during the spatial and temporal evolution of PM2.5 concentrations. The obtained R and NMB parameters varied between 0.67 and 0.82 and -0.06—0.47, respectively. For PM2 5 composition, model simulations also showed good skill for Beijing (Fig. SI). Model simulation reproduced the sudden PM2 5 increase in Stage III in Beijing, with the increase rate reaching 30.2 mg/m3 mg/m3/h, almost three times of that in Stage II. This is underestimated a little

Fig. 2. Comparison between the observed (blue) and simulated (red) PM2 5 hourly concentrations for eight cities in the BTH cluster from November 26 to December 2,2015. Stages I, II, III, IV are defined in the text. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

compared with the observed increase rate of 57.5 mg/m3 mg/m3/h, and this underestimate is similar to that found with other models in previous model studies (Wang et al., 2013; He et al., 2015). The simulation overestimated the observations in Baoding at the end of Stage II and in Stage III, and this was likely related to the failure to capture the change in local wind direction caused by the complex terrain in Baoding and the relative coarse resolution in the simulation.

The good correspondence for the aerosol simulation is partly due to the successful simulation of the general meteorological conditions. Fig. 3 illustrates the simulated and observed surface hourly wind vectors for the eight cities, presenting variations in NMB and R values of 0.25-1.3 and 0.5-0.89, respectively. Low speed winds (less than 2 m/s) during stages I and II, which helped the accumulation of pollutants, were reproduced. In particular, a sudden change in wind speed from calm during the afternoon of November 30 to a strong southerly wind (3-4 m/s) along the Handan-Xingtai-Cangzhou-Tianjin-Beijing region until December 1, was simulated. This indicates that southern to northern transport contributed substantially to the high PM2.5 concentrations in Beijing during Stage III. Fig. S2 and Fig. S3 show the good modeling of temperature and relative humidity. The simulated meteorological parameters were validated further by comparing results of the model with observed vertical profiles collected over Beijing (Fig. S4). The observations and simulation reveal a temperature inversion at an altitude of 1 km during Stage I, which accelerated the PM2.5 accumulation at the surface. During Stage III, the inversion layer climbed up to 1 -2 km above the surface. The prevailing southerly winds present at 1 km, below the inversion, brought pollutants from the southern part of the BTH cluster to Beijing.

4. Results and discussion

4.1. Spatial distribution of surface PM25 concentrations

Fig. 4 shows the simulation of the daily mean surface PM2.5 concentrations within the BTH city cluster. Before the haze episode (e.g. November 26), the daily PM25 concentrations in most areas of the BTH cluster was less than 100 mg/m3 under a prevailing clean northerly air flow. In Stage I (November 27-28), the BTH was under a uniform pressure field, with the high-pressure center lying over the western Pacific (Figs. S5b and c). The area characterized by winds less than 2 m/s expanded from the northern BTH to the entire megacity cluster (Fig. 4b and c). The boundary layer became more stable, which constrained the PM2.5 within the low mixing layer. Consequently, a high PM2.5 belt with concentrations over 200 mg/m3 covered the entire BTH cluster. During Stage II (November 29-30), a uniform high pressure still controlled most of the BTH (Figs. S5d and e), and daily PM2.5 concentrations increased to 300 mg/m3 in most cities (Fig. 4d and e). However, on November 29, Beijing was affected by unpolluted northwesterly winds which resulted in a short decrease in PM2.5 concentrations. During Stage III (December 1), the BTH cluster was located at the front of a low pressure centered in Mongolia (Fig. S5f), with prevailing southerly winds over 3 m/s in the southern BTH region. The southern BTH pollutants were transported to the north. The PM2.5 accumulated at the base of the Taihang-Yanshan Mountains and the concentrations reached a maximum for the 2015 haze episode in Beijing (over 500 mg/m3) (Fig. 4f). Finally, a strong Siberian high-pressure passed over the BTH cluster bringing a clear northeasterly air mass (Fig. 4g). Daily PM2.5 concentrations in the eight cities decreased to 50-120 mg/m3.

Fig. 3. Comparison between the observed (blue) and simulated (red) hourly wind vectors for eight cities in the BTH cluster from November 26 to December 2, 2015. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

J. Li et al. / Environmental Pollution xxx (2017) 1—11

Fig. 4. Simulated daily average PM25 (shaded: mg/m3) and wind vectors (arrows: m/s) over the BTH megacity cluster from November 26 to December 2, 2015.

4.2. Source apportionment of PM2.5

4.21. Surface PM25

Fig. 5 presents the contribution of different source regions to the surface PM25 concentrations. For Beijing, local emissions principally produced the slow PM2 5 accumulation (Stages I and II) in which contributions accounted for about 60%, 80% respectively. The highest PM2.5 concentrations (about 550 mg/m3) occurred during Stage III and were mostly related to regional transport (Fig. 5c). The contribution of the southern BTH cities (e.g. HCB) increased to 25—30% (135—165 mg/m3). Local emissions only contributed 40% to the PM2.5 concentrations. This differs from the results published by Tang et al. (2015) believed the rapid increase in PM25 concentrations from October 15 to November 30, 2014 was dominated by local emissions in Beijing. For Tianjin, regional particulate transport was prevalent during the 2015 haze episode and contributed 50—90% of the PM2 5 during four stages. The contribution from outside the BTH (e.g. SD) reached about 30% in Stage III (Fig. 5c) due to the southerly flow of air (Fig. 4f). The southern cities of the BTH cluster (Shijiazhuang, Handan, Xingtai, Baoding, Cangzhou and Hengshui) experienced a haze episode attributed to local emissions, which contributed up to 60—85% in the rising Stage I and II (Fig. 5a and b). For example, at Baoding, the city with the second worst PM2 5 pollution in China in 2015, PM2 5 concentrations from local emissions reached 80%. During Stage III, local contributions declined to 30—60%, while regional transport of pollutants from outside increased rapidly on December 1. It should be pointed out that the high concentration in Baoding shown in Fig. 5c is overestimated compared with observations. An interesting point is that the largest megacity (Beijing) became a net importer of PM2.5 from its surrounding cities, which is in strong contrast to conditions typically displayed by European and North American cities (Kleinman et al., 2000; White et al., 1976).

Fig. 6 presents the temporal contributions to surface PM2.5 for the eight cities during the four stages. For Beijing, fresh PM2.5 emitted during the current day contributed 60—70% of the total surface concentrations during Stages I and II (Fig. 6a and b). During Stage III, the fraction of highly aged PM2 5 emitted one day or more previously, increased to 30% and 25%, respectively (Fig. 6c). The degree of aging of the PM2.5 increased significantly. This suggests that joint emergency measures should be made two days prior to the culmination of the haze episode in Beijing to avoid the rapid accumulation to the highest PM2.5 concentrations. Interestingly, the change in temporal contributions from Stage I to Stage II in HCB and XHS is very similar to the change from Stage I to Stage III in Beijing, which indicates that the very high episodes in Beijing and HCB had the same cause with a one-day delay for Beijing. The situation in Tianjin was different from that in Beijing and Hebei, with more than 60% of PM2.5 emitted one or two days previously from stage I to III.

Fig. 7 presents the sources of surface PM2.5 composition during the four stages for Beijing. It is clear that SIA accounts for the largest fraction of the rapid increase of PM2.5 in Stage III. SIA concentrations increased by a factor of five from Stage II to III, while BC and OM increased by less than a factor of two (Fig. 7a). During Stage III, regional transport with highly aged air masses was the largest contributor of SIA for Beijing. 80% of SIA in Beijing in Stage III came from emissions in other cities in Hebei province (HCB, XHS) and farther provinces (SD and HN) one or two days previously (Fig. 7a—b). In contrast to SIA, BC and OM mostly came from local sources on the day of emission at all stages (60—90%). These results indicate that regional controls on SIA precursors would be the most efficient way to decelerate the rapid growth of PM2.5 in Beijing during this episode. This was similar to the situation during the 2014 Asia-Pacific Economic Cooperation (APEC) summit, when regional control measures caused greater reductions in SIA than in primary matter in Beijing (Sun et al., 2016).

J. Li et al. / Environmental Pollution xxx (2017) 1—11

Fig. 5. Contribution of different source regions to surface PM2 5 for eight cities in the BTH cluster (BJ: Beijing; TJ: Tianjin; BD: Baoding; CZ: Cangzhou; HS, Hengshui; SJZ: Shi-jiazhuang; XT: Xingtai; HD: Handan) during the four stages of the haze episode (line: mg/m3; column bar: %). The shaded legend represents names of source regions. The characters of x-axis represent the names of receptors. Stages I, II, III, IV are defined in the text.

Fig. 6. Contribution of PM25 classified by time since emission to surface PM25 for eight cities in the BTH cluster (<1day: contributions on the day of emission; 1—2 days: emissions one day previously; >2 days: two or more days previously). City names in x-axis are same in Fig. 5. Stages I, II, III, IV are defined in the text.

To control air pollution, we must define precisely the sectors. Fig. S6 reveals that the residential sector was the main contributor to pollution during the four stages in all eight cities, ranging from 50% to 60%. This is related to the structure of energy production in the megacity cluster. Winter heating in the BTH, except for Beijing, is based on coal burning and is technically inefficient compared to electric, gas and oil heating systems prevailing in developed countries (Almond et al., 2009). Industrial production and power plants contribute 20-30% of the PM2.5 concentrations, followed by emissions from motor vehicles (10-20%). The dominant sector

(residential) in this episode was different from the one affecting the BTH in February 2014, in which industrial production was the most important source (Chen et al., 2015). There is a high possibility that the control measures adopted since 2015 are partly responsible for this change between the two episodes. For example, the Hebei provincial government initiated a "Heavy Air Pollution Emergency Response Program" in December 2014. The energy structure in BTH also has an important impact on source apportionment. In mega-cities like Beijing and Tianjin, the contribution of motor vehicles (15—20%) was higher than in other cities. In industrial cities

Fig. 7. Relative contribution of different regions (a) and sustained time intervals (b) to surface BC, SIA and OM in Beijing during the four stages (bars). Also shown are the mean concentrations of BC, SIA and OM in Beijing during the four stages (blue dotted lines). Colorful shaded legends represent the source cities. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

(Baoding, Cangzhou and Shijiazhuang), industrial production and power plants were more important (25—30%). This highlights the complexity of the air pollution problem prevailing in the BTH city cluster.

4.2.2. Vertical profiles in Beijing

Fig. 8a—d presents the contribution from different source regions to the PM2.5 at different height during the four stages identified in Beijing. Contribution from local emissions decreased rapidly with height, while contribution from regional transport increased. Regional transport from other BTH cities and surrounding provinces was concentrated in the 150—1000 m and 500—2000 m layers during Stages I and II, respectively. During Stage I, the HCB contributed most of the PM2.5 from regional sources (20%) with the exception of OT. In Stage II, the contribution from the HCB reached 25% at the height of 700 m. Local contribution decreased substantially from 80% at the surface to 20% at 1000 m height. During Stage III, regional transport defined the bulk of the PM2.5 from the surface to the height of 1500 m, with varying percentages (40—100%), resulting from the flow of southerly air masses. Four regional sources (HCB, XHS, HN and SD) supplied a similar proportion, indicating that pollutants were well mixed in the boundary layer in Beijing. Therefore, multi-regional joint emission controls are needed.

Fig. 8e—h displays the vertical profile of the temporal contributions at each stage. During Stage III, the degree of aging of the PM2.5 increased significantly due to the effect of regional transport. Fresh PM2.5 from local emissions was under low level. A high fraction of the PM2.5 at the altitude of 150—2000 m was emitted two days prior to the haze event confirming the age of the PM2.5 in Beijing and demonstrating that control measures have to be enforced at least two days prior to the haze episode to effectively reduce the regional level of pollution.

4.2.3. Regional transport corridor of PM25 to Beijing

Fig. 9 presents the vertical profile of regionally transported

PM2.5 during Stage III along a cross-section shown in Fig. 4a. The accumulation of transported PM2.5 in Beijing is clearly related to terrain. For example, high PM25 concentrations (more than 200 mg/ m3) in the HCB were transported northward and encountered 1000—1500 m high mountains north of Beijing. The stable boundary layer and weak winds (less than 3 m/s) caused the PM2.5 to accumulate at the base of the mountains, with concentrations comparable to particulates from local emissions (100—200 mg/m3). The PM2.5 from source regions located far away from the Beijing was also blocked by the mountain range north of Beijing. Therefore, the concentrations of PM2.5 became very high (more than 500 mg/ m3) and showed a rapid increase that persisted in Beijing for one day. This transport corridor suggests that observations in HCB one day in advance can be regarded as a good indicator to predict heavy pollution in Beijing.

The altitude of the main polluted air mass slowly increases within the stable boundary layer below 1000 m with increasing distance of the source from Beijing (Fig. 9a—e). For HCB (Fig. 9b) close to Beijing, the PM2.5 contribution decreased from 200 mg/m3 below 350 m to 70—100 mg/m3 in the 700—1000 m-high layer over Beijing. For XHS (Fig. 9c), located 400 km from Beijing, the main transport occurs near the ground south of 38.5° N latitude, the air climbed to 200—400 m until it reached Beijing. The contribution attained 70—100 mg/m3 at Beijing, which is double the concentration at ground level. For HN province (Fig. 9e) situated 800 km from Beijing, the main transport occurs within 300—700 m from the southernmost edge of the BTH (36° N latitude) to Beijing, with concentrations of 50—120 mg/m3. Maximum concentrations (120 mg/m3) appeared at the height of 200—500 m above Baoding (35.5° N—36° N latitude). This upward shift below 1000 m in the stable boundary layer has been rarely reported in previous studies. In the medium haze episode in Beijing in February 2014, Chen et al. (2015) reported that the main regional transport occurred at about 1 km altitude. Wang et al. (2014a) suggested the transport of pollutants from the southern Hebei Province to Beijing occurred at 700—1500 m above ground level during the haze period of January

PM25(ng/m3) PM25(ng/m3) PM25(ng/m3) PM^^g/m3)

0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100

0 150 300 450 600 0 150 300 450 600 0 150 300 450 600 0 150 300 450 600

0.05' ■ ■ ■ >...... ■ ■ N....... .........1 ■ IV.........

0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100

Contribution(%) Contribution(%) Contribution(%) Contribution(%)

Fig. 8. Vertical profiles showing the contribution (shaded area: %) from different source regions (a—d) and different time intervals (e—h) to PM25 in Beijing for each stage: stage I (a, e), stage II (b, f), stage III (c, g), and stage IV (d, h). The total PM25 concentrations (lines: mg/m3) for each stage are also shown.

10 20 30 50 70 100 120 150 200

Fig. 9. Vertical profile of PM25 transported from different source regions along the cross-section shown by the dark line in Fig. 4a during Stage III (shaded area: mg/m3; contour: %). Panels a, b, c, d and e show the contributions from BJ, HCB, XHS, SD and HN, respectively.

J. Li et al. / Environmental Pollution xxx (2017) 1—11

2013. Our results suggest that more regional observation stations located at 200—500 m above ground level should be established to accurately assess regional transport. More attention should be paid to the sources of particulate emissions at 200—500 m height over the BTH cluster and surrounding provinces.

5. Summary and conclusions

We employed the NAQPMS to investigate the formations and sources of a 5-day extreme regional haze episode occurring over the BTH megacity cluster during the period of November 26 to December 1, 2015. During this episode, surface PM25 experienced a slow increase and a sustained period of high concentrations in most cities (Stages I and II). However Beijing experienced an additional rapid increase (Stage III) with a PM2.5 accumulation rate of 57.5 mg/ m3 mg/m3/h. NAQPMS reproduced the evolution of the haze episode accurately in most cities.

Our results show that the rapid increase of surface PM2.5 in Beijing (Stage III) was caused by regional transport of pollutants emitted one or two days prior to the haze episode, although concentrations of surface PM2.5 in the source regions were lower than that in Beijing. For PM2 5 composition, the increase of SIA concentrations was the most significant and 80% of SIA in Beijing came from emissions over other cities in Hebei province and further afield. BC and OM mostly came from local sources on the day of emission (60—90%). During stage I and II, the concentrations of surface PM2.5 were controlled by local emissions (60—80%).

As for emission sectors to the surface PM2.5, the main contributions for the eight cities came from the residential sector (50—60%), followed by industrial production and power plants (20—30%) and motor vehicles (10—20%).

There was a large difference in the PM2.5 sources between the surface (lower than 300 m) and higher altitudes (300—2000 m) over Beijing. During the slow increase stages (Stage I and II), local emissions dominated surface PM2.5, while regional transport dominated PM2.5 at higher altitudes. During the rapid increase stage, regional transport played a dominant role throughout the boundary layer (below 1500 m), with contributions varying from 40 to 100%. The altitude of the main transport pathway was around 200—500 m. This suggests that more attention should be paid to emission sources at 200—500 m height within the BTH cluster and surrounding provinces.

The time since emission provides a good indicator of the contribution of aged aerosol. In this episode, PM2.5 from the previous one or more days contributed a high fraction, which indicates that PM2 5 has been fully aged before it arrived at Beijing. Therefore, control measures should be implemented at least two days before the occurrence of a haze episode to effectively reduce the levels of regional pollution.

Acknowledgements

This work was supported by the Chinese Key Projects in the National Science & Technology Pillar Program (2014BAC06B03), the National Key Project of Basic Research (2014CB447900), the Chinese Academy of Sciences Strategic Priority Research Program (XDB05030101), the Chinese Ministry of Environmental Protection's Special Funds for Scientific Research on Public Welfare (201309016), and the Natural Science Foundation of China (41571130034; 41275138). We thank Dr. Wild Oliver for improving the English language.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://

dx.doi.org/10.1016/j.envpol.2017.01.063. References

Almond, D., Chen, Y., Greenstone, M., Li, H., 2009. Winter heating or clean air? Unintended impacts of China's Huai River policy. Am. Econ. Rev. 99 (2), 184-190. http://dx.doi.org/10.1257/aer.99.2.184.

Byun, D.W., Dennis, R., 1995. Design artifacts in Eulerian air- quality modelsevaluation of the effects of layer thickness and vertical profile correction on surface ozone concentrations. Atmos. Environ. 29, 105-126. http://dx.doi.org/ 10.1016/1352-2310(94)00225-A.

Chen, H., Li, J., Ge, B., Yang, W., Wang, Z., Huang, S., Wang, Y., Yan, P., Li, J., Zhu, L., 2015. Modeling study of source contributions and emergency control effects during a severe haze episode over the Beijing-Tiajin-Hebei area. Sci. China Chem. 58 (9), 1403-1415. http://dx.doi.org/10.1007/s11426-015-5458-y.

China Ministry of Environmental Protection, 2013. Technical Guidelines for Source Apportionment of Atmospheric Particulate Matter (For Trial Implementation) (in Chinese). http://www.mep.gov.cn/gkml/hbb/bwj/201308/t20130820_ 257699.htm.

Ge, B.Z., Xu, X.B., Lin, W.L., Li, J., Wang, Z.F., 2012. Impact of the regional transport of urban Beijing pollutants on downwind areas in summer: ozone production efficiency analysis. Tellus B 64, 17348. http://dx.doi.org/10.3402/tellusb.v64i0. 17348.

Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P.I., Geron, C., 2006. Estimates of global terrestrial isoprene emissions using MEGAN (model of emissions of Gases and aerosols from nature). Atmos. Chem. Phys. 6 (11), 3181-3210. http://dx.doi.org/10.5194/acp-6-3181-2006.

He, H., Tie, X., Zhang, Q., Liu, X., Gao, Q., Gao, Y., 2015. Analysis of the causes of heavy aerosol pollution in Beijing, China: a case study with the WRF-Chem model. Particuology 20 (3), 32-40. http://dx.doi.org/10.1016/j.partic.2014.06.004.

He, H., Wang, Y., Ma, Q., Ma, J., Chu, B., Ji, D., Tang, G., Liu, C., Zhang, H., Hao, J., 2014. Mineral dust and NOx promote the conversion of SO2 to sulfate in heavy pollution days. Sci. Rep. 4 (8), 4172. http://dx.doi.org/10.1038/srep04172.

Hauglustaine, D.A., Brasseur, G.P., Walters, S., Rasch, P.J., Müller, J.F., Emmons, L.K., et al., 1998. Mozart, a global chemical transport model for ozone and related chemical tracers: 2. model results and evaluation. J. Geophys. Res. - Atmos. 103 (D21), 28265-28289. http://dx.doi.org/10.1029/98JD02398.

Kleinman, L.I., Daum, P.H., Imre, D.G., Lee, J.H., Yin-Nan, L., Nunnermacker, L.J., 2000. Ozone production in the New York City urban plume. J. Geophys. Res. Atmos. 105 (D11), 14495-14512. http://dx.doi.org/10.1029/2000JD900011.

Li, J., Wang, Z., Akimoto, H., Gao, C., Pochanart, P., Wang, X., 2007. Modeling study of ozone seasonal cycle in lower troposphere over East Asia. J. Geophys. Res. 112 (D22), 22-25. http://dx.doi.org/10.1029/2006JD008209.

Li, J., Wang, Z., Akimoto, H., Tang, J., Uno, I., 2008. Near-ground ozone source attributions and outflow in central eastern China during MTX2006. Atmos. Chem. Phys. 8 (24), 7335-7351. http://dx.doi.org/10.5194/acp-8-7335-2008.

Li, J., Wang, Z.F., Wang, X., Yamaji, K., Takigawa, M., Kanaya, Y., Pochanart, P., Liu, Y., Irir, H., Tanimoto, H., Akimoto, H., 2011. Impacts of aerosols on summertime tropospheric photolysis frequencies and photochemistry over Central Eastern China. Atmos. Environ. 45 (10), 1817-1829. http://dx.doi.org/10.1016/j. atmosenv.2011.01.016.

Li, J., Wang, Z.F., Zhuang, G., Luo, G., Sun, Y., Wang, Q., 2012. Mixing of Asian mineral dust with anthropogenic pollutants over East Asia: a model cast study of a super-duststorm in March 2010. Atmos. Chem. Phys. 12 (16), 7591-7607. http:// dx.doi.org/10.5194/acp-12-7591-2012.

Li, J., Yang, W., Wang, Z., Chen, H., Hu, H., Li, J.J., Sun, Y.L., Huang, Y., 2014. A modeling study of source-receptor relationships in atmospheric particulate matter over Northeast Asia. Atmos. Environ. 91,40-51. http://dx.doi.org/10.1016/j.atmosenv. 2014.03.027.

Li, J., Yang, W., Wang, Z., Chen, H., Hu, H., Li, J.J., Sun, Y.L., Zhang, Y., 2016. Modeling study of surface ozone source-receptor relationships in East Asia. Atmos. Res. 167, 77-88. http://dx.doi.org/10.1016/j.atmosres.2015.07.010.

Li, M., Zhang, Q., Kurokawa, J., Woo, J.H., He, K.B., Lu, Z., Ohara, T., Song, Y., Streets, D.G., Carmichael, G.R., Cheng, Y.F., Hong, C.P., Huo, H., Jiang, X.J., Kang, S.C., Liu, F., Su, H., Zheng, B., 2015. MIX: a mosaic Asian anthropogenic emission inventory for the MICS-Asia and the HTAP projects. Atmos. Chem. Phys. Discuss. 15 (23), 34813-34869. http://dx.doi.org/10.5194/acpd-15-34813-2015.

Nenes, A., Pandis, S.N., Pilinis, C., 1998. Isorropia: a new thermodynamic equilibrium model for multiphase multicomponent inorganic aerosols. Aquat. Geochem. 4 (1), 123-152. http://dx.doi.org/10.1023/A:1009604003981.

Odum, J.R., Jungkamp, T.P.W., Griffin, R.J., Flagan, R.C., Seinfeld, J.H., 1997. The atmospheric aerosol-forming potential of whole gasoline vapor. Science 276 (5309), 96-99. http://dx.doi.org/10.1126/science.276.5309.96.

Pandis, S.N., Harley, R.A., Cass, G.R., Seinfeld, J.H., 1991. Secondary organic aerosol formation and transport. Atmos. Environ. 26 (13), 2269-2282. http://dx.doi. org/10.1016/0960-1686(92)90358-R.

Stockwell, W.R., Middleton, P., Chang, J.S., Tang, X., 1990. The second generation regional acid deposition model chemical mechanism for regional air quality modeling. J. Geophys. Res. - Atmos. 95 (D10), 16343-16367. http://dx.doi.org/ 10.1029/JD095iD10p16343.

Sun, Y.L., Jiang, Q., Wang, Z., Fu, P.Q., Li, J., Yang, T., Yan, L., 2014. Investigation of the sources and evolution processes of severe haze pollution in Beijing in January 2013. J. Geophys. Res. 119 (7), 4380-4398. http://dx.doi.org/10.1002/

2014JD021641.

Sun, Y.L., Wang, Z., Wild, O., Xu, W., Chen, C., et al., 2016. "APEC Blue": secondary aerosol reductions from emission controls in Beijing. Sci. Rep. 6, 20668. http:// dx.doi.org/10.1038/srep20668.

Tang, G., Zhu, X., Hu, B., Xin, J., Wang, L., Munkel, C., Mao, G., Wang, Y., 2015. Impact of emission controls on air quality in Beijing during APEC 2014: lidar ceilometer observations. Atmos. Chem. Phys. 15 (21), 12667—12680. http://dx.doi.org/10. 5194/acp-15-12667-2015.

Wagstrom, K.M., Pandis, S.N., Yarwood, G., Wilson, G.M., Morris, R.E., 2008. Development and application of a computationally efficient particulate matter apportionment algorithm in a three-dimensional chemical transport model. Atmos. Environ. 42 (22), 5650—5659. http://dx.doi.org/10.1016/j.atmosenv. 2008.03.012.

Walcek, C.J., Aleksic, N.M., 1998. A simple but accurate mass conservative, peak-preserving, mixing ratio bounded advection algorithm with Fortran code. Atmos. Environ. 32, 3863—3880. http://dx.doi.org/10.1016/S1352-2310(98) 00099-5.

Wang, H., Xu, J., Zhang, M., Yang, Y., Shen, X., Wang, Y., Chen, D., Guo, J., 2014a. A study of the meteorological causes of a prolonged and severe haze episode in January 2013 over central-eastern China. Atmos. Environ. 98,146—98,157. http:// dx.doi.org/10.1016/j.atmosenv.2014.08.053.

Wang, L.T., Wei, Z., Yang, J., Zhang, Y., Zhang, F.F., Su, J., Meng, C.C., Zhang, Q., 2013. The 2013 severe haze over the southern Hebei, China: model evaluation, source apportionment, and policy implications. Atmos. Chem. Phys. 13 (11), 28395—28451. http://dx.doi.org/10.5194/acp-14-3151-2014.

Wang, Z., Maeda, T., Hayashi, M., Hsiao, L.F., Liu, K.Y., 2001. A nested air quality prediction modeling system for urban and regional scales, application for high-ozone episode in Taiwan. Water, Air, Soil Pollut. 130, 391—396. http://dx.doi.org/ 10.1023/A:1013833217916.

Wang, Z.F., Li, J., Wang, Z., Yang, W., Tang, X., Ge, B., Yan, P., Zhu, L., Chen, X., Chen, H., Wang, W., Li, J., Liu, B., Wang, X., Wang, W., Zhao, Y., Lu, N., Su, D., 2014b. Modeling study of regional severe hazes over mid-eastern China in January 2013 and its implications on pollution prevention and control. Sci. China Earth

Sci. 57 (1), 3—13. http://dx.doi.org/10.1007/s11430-013-4793-0.

Werf, G.R.V.D., et al., 2006. Interannual variability in global biomass burning emissions from 1997 to 2004. Atmos. Chem. Phys. 6 (11), 3423—3441. http://dx. doi.org/10.5194/acp-6-3423-2006.

Wesely, M.L., 1989. Parameterization of surface resistances to gaseous dry deposition in regional-scale numerical models. Atmos. Environ. 23, 1293—1304. http:// dx.doi.org/10.1016/0004-6981(89)90153-4.

White, W., Anderson, J., Blumenthal, D., Husar, R., Gillani, N., Husar, J., Wilson, W., 1976. Formation and transport of secondary air pollutants: ozone and aerosols in the St. Louis urban plume. Science 194 (4261), 187—189. http://dx.doi.org/10. 1126/science.959846.

Wu, D., 2010. Temporal and spatial variation of haze during 1951-2005 in Chinese mainland. Acta Meteorol. Sin. 68 (5), 680—688. http://dx.doi.org/10.11676/ qxxb2010.066.

Wu, Q., Wang, Z., Gbaguidi, A., Gao, C., Li, L., Wang, W., 2011. A numerical study of contributions to air pollution in Bejing during CARE-Beijing-2006. Atmos. Chem. Phys. 11 (12), 5997—6011. http://dx.doi.org/10.5194/acp-11-5997-20H.

Zaveri, R.A., Peters, L.K., 1999. A new lumped structure photochemical mechanism for large-scale applications. J. Geophys. Res. 104 (D23), 30387—30415. http://dx. doi.org/10.1029/1999JD900876.

Zhang, L., Gong, S., Padro, J., Barrie, L., 2001. A size-segregated particle dry deposition scheme for an atmospheric aerosol module. Atmos. Environ. 35 (3), 549—560. http://dx.doi.org/10.1016/S1352-2310(00)00326-5.

Zhao, X.J., Zhao, P.S., Xu, J., Meng, W., Pu, W., Dong, F., 2013. Analysis of a winter regional haze event and its formation mechanism in the North China Plain. Atmos. Chem. Phys. 13 (11), 5685—5696. http://dx.doi.org/10.5194/acp-13-5685-2013.

Zheng, B., Zhang, Q., Zhang, Y., He, K., Wang, K., Zheng, G.J., Duan, F.K., Ma, Y.L., Kimoto, T., 2015. Heterogeneous chemistry: a mechanism missing in current models to explain secondary inorganic aerosol formation during the January 2013 haze episode in North China. Atmos. Chem. Phys. 15 (4), 2031—2049. http://dx.doi.org/10.5194/acp-15-2031-2015.