Source apportionment of the Fine Particulate Matter in Beijing during extremely heavy Haze Episodes...

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Source apportionment of the Fine

Particulate Matter in Beijing during

extremely heavy Haze Episodes

Yangjun Wang, Shuxiao Wang, Yongtao Hu, Ted Russell

2015-10-06

Beijing on Jan. 23, 2013

Simulation System WRFV3.4/CAMx5.4

Mechanism Land surface mechanism: PL-X Radiation mechanism: rrtmg Gas-phase/aerosol CB05&AERO5

Modeling domain China and Jing-Jin-Ji region

Horizontal Resolution: 36km×36km 12km ×12km

Vertical layers 14 layers

Time Period: January 2013

Source Apportionment Receptor: urban center of Beijing 15 emission regions

Species PM2.5

1. Model setup

2. Evaluation of Model Performance

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(b)Tianjin

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average

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Measured

average

(μg/m3)

Number

of data

pairs

BIAS

(μg/m3)

ERROR

(μg/m3)

RMSE

(μg/m3)FBIAS FERROR IOA

Beijing 163.68 180.32 483 -16.64 109.92 149.19 3.71% 67.98% 0.647

Tianjin 150.56 176.05 427 -25.50 74.72 99.02 -7.07% 49.01% 0.678

Jinan 174.03 252.08 418 -78.05 103.46 131.56 -34.55% 47.15% 0.526

Quantitative evaluation of predicted PM2.5 concentrations with hourly observations

Average modeled ground-level concentrations of PM2.5 in the second domain for January 06-23 2013(LT)

3.1 modeled concentrations of PM2.5

3. Results and discussions

Temporal variation of contributions from different emission regions to PM2.5 concentrations at the urban center of Beijing during January 06-23, 2013(LT)

3.2 Temporal variation of source apportionment results

Hourly distribution of velocity field and PBL on January 12, 2013(LT)

(a) 17:00 (b) 18:00 (c) 19:00 (d) 20:00

Trajectories analysis

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tribu

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BeijingHenanShandongZhangjiakouChengdeCangzhouLangfangQinhuangdaoBaodingTangshanHengshuiHandanXingtaiShijiazhuangTianjinOthers within D2Out side of D2

Temporal contribution percentages of emission regions toPM2.5 concentrations in central Beijing during January 06-23,2013(LT)

Average contribution percentages of different emission regions to PM2.5 concentrations in central Beijing during January 06-23, 2013.

3.3 Contribution evolution during local dominated haze episodes

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P1 P2 P3 P4 P5 P6

3.4 Contribution analysis in a non-local dominated case (January 13, 2013(LT))

(a) Beijing (b) JJJ_nonBeijing

(c) other in D2 (d) Boundary Condition

Average wind velocity field and PBL distribution in simulation domain during 10:00 - 15:00 on January 13, 2013

Contribution percentages from regions

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Cont

ributi

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erce

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Time

TangshanTianjinShijiazhuangLangfangBaodingCangzhouXingtaiHandanHengshuiZhangjiakouChengdeQinhuangdaoShandongHenanother

on January 13,2013(LT)0% 2% 4% 6% 8%

TianjinShijiazhuang

XingtaiHandan

HengshuiTangshan

BaodingQinhuangdao

LangfangCangzhouChengde

Zhangjiakou

Contribution percent

Nam

e of

cit

y

4. Conclusions

Controlling local emissions will be the most important step and should be given priority for Beijing government to mitigate the extremely heavy haze pollution.

it is impossible to eliminate the haze episodes in Beijing without controlling emissions in other cities in Jing-Jin-Ji region, even in long-range cities.

The big contributors from outside Beijing are Shandong, Tangshan, Tianjin, Shijiazhuang, Baoding, Cangzhou.

Acknowledgments:

•Tsinghua University

•Georgia Tech

•Peking University

Shanghai Municipality is devoted to establishing the city into both an innovation center of science and technology and a professional hub of global innovation. In order to fulfill its role, Shanghai University has been actively recruiting overseas elites and planning its Overseas Job Fair in 2015.

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