Beijing-Tianjin-Hebei area is one of the most polluted areas in China. This paper used the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5) and Model-3/Community Multiscale Air Quality (CMAQ) modeling system to quantify the source contribution to PM2.5 in Hebei cities in order to obtain an in-depth understanding haze process in January and February 2013, using the Multiresolution Emission Inventory for China (MEIC). The result showed that PM2.5 were mainly originated from the southern Hebei (SHB) with the fractions of 70.8% and 66.4% to Shijiazhuang, 70.6% and 63.9% to Xingtai, and 68.5% and 63.0% to Handan in January and February 2013, respectively. The northern Hebei (NHB) contributed 69.8% and 70.7% to Zhangjiakou, 68.7% and 66.2% to Chengde, and 57.7% and 59.6% to Qinhuangdao in January and February. In Cangzhou, Hengshui, and Langfang, regional joint policy making should be implemented due to the pollution of multiple sources. In Baoding and Tangshan, industrial emissions contributed 38.1% and 41.9% of PM2.5 to Baoding and 39.8% and 45.8% to Tangshan in January and February, respectively. Industrial and domestic emissions should be controlled in Tangshan and Baoding, especially for industrial emissions of NHB.
An extreme regional haze episode with extensive influence area and high particulate matters (PM) concentration has occurred in centre-eastern China during January 2013, which has attracted wide attentions and concerns over the world. This haze event is the most serious pollution event since 1961 [
Along with economic development and environmental deterioration, the government looks forward to quantify the source contributions of PM2.5. Several cities have released the detailed source apportionment results of PM2.5, as shown in Table
The source contribution (%) of PM2.5 in different cities of China.
Regional | Local | |||||
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Coal combustion | Industry | Dust | Transportation | Others | ||
Beijing | 28–36 | 22.4 | 18.1 | 14.3 | 31.1 | 14.1 |
Tianjin | 22–34 | 27 | 17 | 30 | 20 | 6 |
Shijiazhuang | 23–30 | 28.5 | 25.2 | 22.5 | 15.0 | 8.8 |
Jinan | 20–32 | 27 | 18 | 24 | 15 | 16 |
Shanghai | 16–36 | 13.5 | 28.9 | 13.4 | 29.2 | 15.0 |
Beijing:
Tianjin:
Shijiazhuang:
Jinan:
Shanghai:
An offline MM5-CMAQ model is preformed over two nested domains: as shown in Figure
The model domain (Hebei province is divided into the southern Hebei (SHB, which includes Shijiazhuang, Xingtai, and Handan) and northern Hebei (NHB, which includes Zhangjiakou, Chengde, Qinhuangdao, Tangshan, Langfang, Baoding, Cangzhou, and Hengshui).
Using MM5 model (version 3.7) combined with four-dimensional data assimilation (FDDA) to produce meteorological field for CMAQ model. The input data of MM5 and terrain and land use data are drawn from US Geological Survey Database (
Models-3/CMAQ is a three-dimensional Eulerian atmospheric chemistry and transport modeling system, which can simulate almost all major components including SO2, NO2, CO, O3, PM2.5, and PM10 throughout the troposphere. The SAPRC-99 chemical mechanism with aqueous and aerosol extensions and AERO5 model are selected for the gaseous chemistry and aerosol modules, respectively. The aqueous-phase chemistry mechanism is the Regional Acid Deposition Model (RADM). It is noted that online dust emissions are not included in CMAQ v4.7.1; that is to say, this paper does not calculate the contribution of dust emission.
In this paper, CMAQ is applied with Brute-Force method as a source sensitivity method for quantifying source contributions of PM2.5 by zeroing out emissions from a specific source [
According to the conclusions of BTH-Steel version 1.0 (Emissions Inventory Of Steel Industry in the Beijing-Tianjin-Hebei Area, BTH-Steel version 1.0), the three cities of Xingtai, Handan, and Shijiazhuang are regarded as the SHB in this paper, where the steel and iron industry are centered (
This paper used the same model and configurations details introduced by Wang et al. [
Table
The average spatial source contribution (%) to PM2.5 concentrations in Hebei cities.
SHB | NHB | BJTJ | SX | SD | HN | Sum | ||
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Shijiazhuang | Jan. | 70.8 | 14.1 | 2.3 | 4.9 | 1.4 | 1.4 | 94.9 |
Feb. | 66.4 | 15.0 | 3.2 | 3.8 | 3.8 | 1.9 | 94.1 | |
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Xingtai | Jan. | 70.6 | 9.4 | 2.2 | 4.9 | 2.5 | 5.6 | 95.2 |
Feb. | 63.9 | 10.1 | 3.2 | 3.6 | 5.8 | 7.6 | 94.2 | |
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Handan | Jan. | 68.5 | 7.6 | 1.9 | 4.1 | 2.9 | 9.9 | 94.9 |
Feb. | 63.0 | 7.9 | 2.8 | 2.9 | 6.2 | 10.9 | 93.7 | |
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Zhangjiakou | Jan. | 0.5 | 69.8 | 1.6 | 2.5 | 0.2 | 0.5 | 75.1 |
Feb. | 2.0 | 70.7 | 1.6 | 2.7 | 0.8 | 0.4 | 78.2 | |
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Chengde | Jan. | 0.7 | 68.7 | 4.0 | 1.1 | 0.6 | 0.4 | 75.5 |
Feb. | 0.7 | 66.2 | 7.6 | 0.7 | 2.4 | 0.6 | 78.2 | |
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Qinhuangdao | Jan. | 0.8 | 57.7 | 2.7 | 0.6 | 2.0 | 0.6 | 64.4 |
Feb. | 0.8 | 59.6 | 4.4 | 0.6 | 5.7 | 0.6 | 71.7 | |
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Cangzhou | Jan. | 3.1 | 54.1 | 14.0 | 2.1 | 10.7 | 3.7 | 87.7 |
Feb. | 2.6 | 45.9 | 16.1 | 2.0 | 18.7 | 3.1 | 88.4 | |
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Hengshui | Jan. | 10.2 | 54.3 | 4.2 | 3.0 | 11.0 | 7.0 | 89.7 |
Feb. | 6.2 | 48.6 | 6.5 | 2.7 | 18.7 | 6.3 | 89 | |
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Langfang | Jan. | 2.3 | 30.0 | 52.9 | 1.5 | 1.4 | 0.8 | 88.9 |
Feb. | 1.5 | 31.5 | 48.9 | 1.3 | 5.9 | 0.8 | 89.9 | |
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Baoding | Jan. | 3.7 | 82.1 | 5.5 | 1.8 | 1.3 | 0.9 | 95.3 |
Feb. | 3.5 | 78.2 | 6.8 | 1.4 | 4.4 | 1.1 | 95.4 | |
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Tangshan | Jan. | 0.8 | 81.6 | 4.7 | 0.6 | 1.2 | 0.5 | 89.4 |
Feb. | 0.9 | 79.0 | 6.3 | 0.6 | 4.4 | 0.6 | 91.8 |
SHB: southern Hebei; NHB: northern Hebei; BJTJ: Beijing and Tianjin; SX: Shanxi; SD: Shandong; HN: Henan.
The source contributions ranges by spatial source to PM2.5 concentrations in 11 cities of Hebei (SHB: southern Hebei; NHB: northern Hebei; BJTJ: Beijing and Tianjin; SX: Shanxi; SD: Shandong; HN: Henan).
In Zhangjiakou, Chengde, and Qinhuangdao, NHB was the largest contributor shown in Figure
In Cangzhou, Hengshui, and Langfang, all of SD, BJTJ, SHB, SX, and HN contributed a considerable amount of PM2.5 shown in Figure
In Tangshan and Baoding, NHB contributed 81.6% of January and 79.0% of February in Tangshan and 82.1% and 78.2% in Baoding, respectively. Additionally, the sum contributions were 89.4–91.8% in Tangshan and 95.3-95.4% in Baoding. It is indicated that MM5-CMAQ is available to calculate source contribution to PM2.5 in the two cities. And local emissions dominate the source contribution of PM2.5. BJTJ is the second largest contributor. This paper inferred that transport process may be the process of northeast and southwest, both of which influence BJTJ and vice versa. Further, Wang et al. [
Table
The sectoral contributions to the PM2.5 concentrations in 11 cities of Hebei.
PO | IN | DO | TR | AG | ||
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Shijiazhuang | Jan. | 0.5 | 35.1 | 39.4 | 4.1 | 15.4 |
Feb. | 0.4 | 38.0 | 36.3 | 4.4 | 17.5 | |
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Xingtai | Jan. | 0.0 | 33.6 | 42.4 | 1.2 | 17.3 |
Feb. | 0.7 | 35.5 | 38.1 | 2.4 | 20.8 | |
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Handan | Jan. | −0.1 | 35.4 | 40.7 | 2.2 | 17.4 |
Feb. | 1.0 | 37.0 | 35.1 | 4.0 | 20.9 | |
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Zhangjiakou | Jan. | 0.9 | 30.4 | 31.3 | 3.7 | 9.7 |
Feb. | 1.4 | 35.1 | 27.3 | 4.6 | 11.2 | |
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Chengde | Jan. | 0.4 | 16.7 | 43.1 | 2.0 | 11.5 |
Feb. | 1.1 | 21.9 | 36.4 | 3.1 | 16.5 | |
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Qinhuangdao | Jan. | 1.1 | 28.7 | 25.3 | 2.6 | 9.0 |
Feb. | 1.7 | 33.0 | 22.9 | 3.5 | 13.5 | |
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Cangzhou | Jan. | −0.4 | 22.7 | 43.6 | 0.1 | 20.8 |
Feb. | 1.7 | 26.7 | 38.0 | 2.4 | 24.3 | |
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Hengshui | Jan. | −1.8 | 21.3 | 46.5 | −0.4 | 23.7 |
Feb. | 1.2 | 24.4 | 39.0 | 2.1 | 26.7 | |
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Langfang | Jan. | 0.0 | 26.6 | 49.0 | 1.4 | 12.9 |
Feb. | 0.6 | 28.6 | 47.0 | 2.0 | 16.1 | |
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Baoding | Jan. | −0.1 | 38.1 | 42.0 | 3.0 | 11.8 |
Feb. | 0.5 | 41.9 | 36.9 | 3.8 | 14.8 | |
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Tangshan | Jan. | 1.8 | 39.8 | 36.4 | 1.4 | 10.4 |
Feb. | 2.3 | 45.8 | 30.2 | 2.2 | 14.4 |
Domestic, industrial, and agricultural emissions are the top three contributors in Zhangjiakou, Chengde, and Qinhuangdao as shown in Figure
The contribution of sectors to PM2.5 concentrations in Hebei from 1 January to 28 February 2013.
In Hengshui, the source contributions of domestic emissions were, respectively, 46.5% and 39.0% to PM2.5 in January and February, followed by 23.7% and 26.7% of agricultural emissions and 21.3% and 24.4% of industrial emissions. It is worthy of concern that the contributions of agricultural emissions exceed industrial emissions and have been the secondary largest contributor results from fewer industries located in Hengshui. Regional transport process is an important impact factor; that is the neighboring SD province is the largest agricultural province, which would bring amounts of PM2.5 to Hengshui. Additionally, agricultural emissions are important sources of ammonia, which react into ammonium in pattern of fine particulate matter in the atmosphere. Therefore, the control of agricultural emissions should be considered in here. In Cangzhou, domestic emissions contributed 43.6% and 38.0% of PM2.5 concentrations in January and February, respectively, followed by 22.7% and 26.7% of industrial emissions and 20.8% and 24.3% of agricultural emissions. Although the contributions of agricultural emissions ranked number three, its contributions were slightly lower than industrial emissions. Similar to Cangzhou and Hengshui, domestic emissions were the largest contributor, which contributed 49.0% and 47.0% in January and February in Langfang, respectively, followed by 26.6% and 28.6% of industrial emissions. But agricultural emissions contributed 12.9% and 16.1% of PM2.5, which were lower than in Cangzhou and Hengshui. In summary, domestic emissions dominate the contributions of PM2.5 in the three cities, especially for Langfang. At the same time, this paper found that more attention should be paid to agricultural emissions, compared to other cities.
Tangshan and Baoding are different from the other cities of Hebei; industrial emissions were the largest contributor and contributed 39.8% and 45.8% of PM2.5 in January and February in Tangshan and 38.1% and 41.9% in Baoding, respectively. As statistic of China Environmental Impact Assessment (
Industrial emissions of SHB contributed 29.7%, 27.3%, and 28.8% of PM2.5 to Shijiazhuang, Xingtai, and Handan. Domestic emissions of SHB contributed 24.6%, 27.6%, and 25.1% of PM2.5 to Shijiazhuang, Xingtai, and Handan, respectively. In addition to the source contribution of SHB, domestic emissions of NHB contributed nonignorable PM2.5 with 8.0%, 5.1%, and 4.1% to Shijiazhuang, Xingtai, and Handan. The great importance of establishing a regional joint framework of policy making and action system would mitigate air pollution in this area, but control of local emission should be considered firstly.
In Zhangjiakou and Qinhuangdao, the largest contributor was industrial emissions of NHB, with contributions of 31.2% and 28.2%, followed by 26.9% and 20.1% of domestic emissions of NHB and 8.5% and 8.2% of agricultural emissions of NHB as shown in Table
The average source contributions (%) to PM2.5 concentrations in Hebei cities by source regions and sectors from 1 January to 28 February 2013.
IN | PO | DO | TR | AG | IN | PO | DO | TR | AG | IN | PO | DO | TR | AG | |
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Shijiazhuang | Xingtai | Handan | |||||||||||||
SHB | 29.7 | 0.6 | 24.6 | 4.4 | 9.4 | 27.3 | 0.5 | 27.6 | 1.8 | 9.9 | 28.8 | 0.6 | 25.1 | 3.0 | 8.8 |
NHB | 3.3 | 0.0 | 8.0 | 0.0 | 2.7 | 2.2 | 0.0 | 5.1 | 0.0 | 2.2 | 1.7 | −0.1 | 4.1 | 0.0 | 1.8 |
BJTJ | 0.9 | 0.0 | 1.5 | 0.0 | 0.2 | 1.0 | 0.0 | 1.5 | 0.0 | 0.3 | 0.8 | 0.0 | 1.2 | 0.0 | 0.2 |
SD | 0.4 | −0.1 | 1.3 | −0.1 | 0.9 | 0.7 | −0.1 | 2.0 | 0.0 | 1.3 | 0.7 | −0.1 | 2.3 | 0.0 | 1.4 |
HN | 0.3 | −0.1 | 0.7 | −0.1 | 0.7 | 1.5 | −0.1 | 2.4 | 0.0 | 2.7 | 2.5 | −0.2 | 3.7 | 0.0 | 4.1 |
SX | 1.5 | 0.1 | 1.9 | 0.0 | 0.5 | 1.6 | 0.1 | 1.9 | 0.0 | 0.5 | 1.3 | 0.0 | 1.6 | 0.0 | 0.4 |
Sum | 36.1 | 0.5 | 38 | 4.2 | 14.4 | 34.3 | 0.4 | 40.5 | 1.8 | 16.9 | 35.8 | 0.2 | 38 | 3 | 16.7 |
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Zhangjiakou | Chengde | Qinhuangdao | |||||||||||||
SHB | 0.2 | 0.0 | 0.4 | 0.0 | 0.5 | 0.1 | 0.0 | 0.3 | 0.0 | 0.3 | 0.2 | 0.0 | 4.2 | 0.0 | 0.2 |
NHB | 31.2 | 0.9 | 26.9 | 4.0 | 8.5 | 16.9 | 0.6 | 35.8 | 2.4 | 10.8 | 28.2 | 1.3 | 20.1 | 3.0 | 8.2 |
BJTJ | 0.4 | 0.0 | 0.8 | 0.1 | 0.4 | 1.5 | 0.0 | 2.8 | 0.1 | 1.2 | 1.0 | 0.0 | 1.8 | 0.0 | 0.5 |
SD | 0.1 | 0.0 | 0.2 | 0.0 | 0.3 | 0.2 | 0.0 | 0.5 | 0.0 | 0.6 | 0.9 | 0.0 | 1.2 | 0.1 | 1.4 |
HN | 0.1 | 0.0 | 0.1 | 0.0 | 0.3 | 0.1 | 0.0 | 0.1 | 0.0 | 0.3 | 0.1 | 0.0 | 0.2 | 0.0 | 0.3 |
SX | 0.7 | 0.1 | 1.1 | 0.0 | 0.7 | 0.3 | 0.0 | 0.4 | 0.0 | 0.2 | 0.2 | 0.0 | 0.3 | 0.0 | 0.1 |
Sum | 32.7 | 1 | 29.5 | 4.1 | 10.7 | 19.1 | 0.6 | 39.9 | 2.5 | 13.4 | 30.6 | 1.3 | 27.8 | 3.1 | 10.7 |
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Cangzhou | Hengshui | Langfang | |||||||||||||
SHB | 0.6 | −0.1 | 1.5 | −0.1 | 0.8 | 2.0 | −0.2 | 4.7 | −0.1 | 1.6 | 0.5 | 0.0 | 1.0 | 0.0 | 0.5 |
NHB | 14.0 | 0.3 | 23.9 | 0.8 | 10.4 | 13.5 | 0.2 | 24.6 | 0.9 | 11.1 | 9.1 | 0.2 | 14.9 | 0.3 | 5.0 |
BJTJ | 5.3 | 0.2 | 7.5 | 0.3 | 1.1 | 1.9 | 0.0 | 2.7 | 0.0 | 0.4 | 16.0 | 0.2 | 29.4 | 1.4 | 3.6 |
SD | 2.9 | 0.2 | 5.8 | 0.1 | 4.6 | 2.6 | −0.2 | 6.9 | −0.1 | 4.6 | 0.7 | 0.0 | 1.5 | 0.0 | 1.1 |
HN | 0.5 | −0.2 | 1.3 | −0.1 | 1.6 | 0.2 | −0.3 | 2.8 | −0.2 | 2.8 | 0.1 | −0.1 | 0.3 | 0.0 | 0.4 |
SX | 0.7 | 0.0 | 1.0 | 0.0 | 0.3 | 1.0 | −0.1 | 1.5 | 0.0 | 0.4 | 0.4 | 0.0 | 0.7 | 0.0 | 0.2 |
Sum | 24 | 0.4 | 41 | 1 | 18.8 | 21.2 | −0.6 | 43.2 | 0.5 | 20.9 | 26.8 | 0.3 | 47.8 | 1.7 | 10.8 |
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Baoding | Tangshan | ||||||||||||||
SHB | 0.9 | −0.1 | 1.8 | −0.1 | 0.8 | 0.2 | 0.0 | 0.4 | 0.0 | 0.2 | |||||
NHB | 35.4 | 0.3 | 31.9 | 3.4 | 9.2 | 40.0 | 2.0 | 28.6 | 1.7 | 9.2 | |||||
BJTJ | 2.2 | 0.0 | 3.3 | 0.0 | 0.4 | 1.5 | 0.0 | 3.0 | 0.0 | 0.8 | |||||
SD | 0.5 | −0.1 | 1.3 | 0.0 | 0.9 | 0.5 | 0.1 | 1.0 | 0.0 | 1.0 | |||||
HN | 0.2 | −0.1 | 0.4 | 0.0 | 0.5 | 0.1 | 0.0 | 0.2 | 0.0 | 0.3 | |||||
SX | 0.5 | 0.0 | 0.7 | 0.0 | 0.2 | 0.2 | 0.0 | 0.3 | 0.0 | 0.1 | |||||
Sum | 39.7 | 0.0 | 39.4 | 3.3 | 12 | 42.5 | 2.1 | 33.5 | 1.7 | 11.6 |
The contribution of multiple sectors is an obvious characteristic in Cangzhou, Hengshui, and Langfang. This paper noted that 7.5% of domestic emissions of BJTJ, 5.8% of domestic emissions of SD, 5.3% of industrial emissions of BJTJ, and 4.6% of agricultural emissions of SD contributed to Cangzhou. More detailed contributions were presented in Table
As for Baoding and Tangshan, industrial emissions of NHB were the most essential contributor, with contributions of 35.4% and 40.0%, followed by 31.9% and 28.6% of domestic emissions of NHB and 9.2% of agricultural emissions of NHB, respectively. Controls of industrial emissions of NHB should be considered firstly, especially for Tangshan.
This paper used MM5-CMAQ model to assess the source contributions of PM2.5 in Hebei cities. In southern cities of Hebei, SHB contributed 70.6%, 70.8%, and 68.5% of PM2.5 to Xingtai, Shijiazhuang, and Handan in January, respectively. Because of the geography situation, NHB contributed more PM2.5 to Shijiazhuang, and HN contributed more PM2.5 to Handan and Xingtai. In the three cities, domestic emissions were the most important contributors in January, with contributions of 39.4% to Shijiazhuang, 42.4% to Xingtai, and 40.7% to Handan. Simultaneously, industrial and agricultural emissions were nonignored sources. As for the contributions of regions and sectors, the local emissions are major sources.
In Zhangjiakou, Chengde, and Qinhuangdao, NHB was the most essential spatial contributors, which contributed 69.8%, 68.7%, and 57.7% of PM2.5 in January, respectively. And domestic and industrial emissions were the major sectoral contributors. Domestic emissions contributed 31.3% and 27.3% of PM2.5 to Zhangjiakou, 43.1% and 36.4% to Chengde, and 25.3% and 22.9% to Qinhuangdao in January and February, respectively. Furthermore, domestic and industrial emissions of NHB are the most important spatial-sector sources.
NHB was the most significant contributor in Cangzhou, with the fractions of 54.1% and 45.9% of PM2.5 in January and February. This paper found that BJTJ and SD contributed 14.0% and 10.7% of PM2.5 to Cangzhou in January, which cannot be ignored. Similar to Cangzhou, the outside regions contributed quite a part of PM2.5. It was found that more complicated multiple sources contributions induce the high PM2.5 in the three cities. That was to say, regional transport process is the major cause for formation of haze event. Therefore, control strategies should be focused on regional joint policy making in here. Additionally, more attention should be paid to agricultural emissions in Cangzhou and Hengshui, which contributed 20.8% and 24.3% to Cangzhou and 23.7% and 26.7% of PM2.5 to Hengshui in January and February, respectively.
In Baoding and Tangshan, the most essential contributors of NHB contributed 82.1% and 78.2% of PM2.5 to Baoding and 81.6% and 79.0% to Tangshan in January and February. Domestic and industrial emissions were the largest contributors in Baoding (42.0% and 38.1% in January) and Tangshan (36.4% and 39.8% in January). Industrial and domestic emissions of NHB were the major contributors, with contributions of 35.4% and 31.9% of PM2.5 to Baoding and 40.0% and 28.6% of PM2.5 to Tangshan. All in all, industrial and domestic emissions should be considered in Tangshan and Baoding, especially for industrial emissions controls of emissions of NHB.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This study was sponsored by the National Natural Science Foundation of China (no. 41475131 and no. 41105105), the State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex (no. SCAPC201307), the Program for the Outstanding Young Scholars of Hebei Province, the Excellent Young Scientist Foundation of Hebei Education Department (no. YQ2013031), and the Environmental Protection Bureau of Handan, Hebei, China.