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Ozone production efficiency calculated for different cities in North China. M.Xue, J. Z. Ma ([email protected]) Chinese Academy of Meteorological Sciences, Beijing, China. Results. Introduction - PowerPoint PPT Presentation
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IntroductionNorth China, or Huabei region, located between 32°- 42°N latitude in eastern China, is one of the most severely polluted regions in China. There are many large and strong emission sources in Beijing (BJ), Tianjin (TJ), Tangshan (TS) and Shijiazhuang (SJZ) in Huabei. The chemical characteristics of air masses from these cities are expected to be very different. A regional chemical transport model coupled with the tracer tagging method are used to investigate the ozone production efficiency (OPEx) from those polluted cities.
Model description
The regional chemical transport model coupled with an on-line tracer tagging method was used （ Ma et al., 2002 ） . The meteorological information was provided by MM5. The model domain covers the entire Huabei region. In the horizontal, the model includes 91×61 grid with 10km resolution. In the vertical the model is divided unequally into 30 layers. The anthropogenic emissions in Huabei region are obtained from Zhao et al (2012) with 10km resolution as shown in Fig.1. The initial and boundary conditions of chemical species were from the EMAC global model. The simulation period was from 8:00 UTC 1 April to 23:00 UTC16 May 2006.
Results Results
M.Xue, J. Z. Ma ([email protected])Chinese Academy of Meteorological Sciences, Beijing, China
ReferenceXue et al., AE, 71, 122-130,. doi:http://dx.doi.org/10.1016/j.atmosenv.2013.01.045,2013.
Fig. 2 Average distributions of O3, NOx/NOy, P(O3), NOx, NOy and NOz
at 14:00 BJT for the simulation period
Ozone production efficiency calculated for different cities in North China
0 20 40 60 80N O z(ppbv)
0
50
100
150
200
250
Ox(
ppbv
)
0 20 40 60 80N O z(ppbv)
0
40
80
120
160
Ox(
ppbv
)
0 10 20 30 40 50N O z(ppbv)
0
50
100
150
200
250
Ox(
ppbv
)
0 40 80 120 160N O z(ppbv)
0
100
200
300
400
500
Ox(
ppbv
)
Y = 2.75 * X + 22.85 R 2=0.53
Y = 3.35 * X + 17.63 R 2=0.77
Y = 1.43 * X + 23.54 R 2=0.26
Y = 2.33 * X + 26.65 R 2=0.81
BJ TJ
TS SJZ
Fig. 3 Average contributions of emitted NOx from tagged regions (BJ,
TJ, TS and SJZ) to the NOx and NOy concentraions at 14:00 BJT for
the simulation period
Fig. 4 Calculated OPEx for BJ 、 TJ 、 TS and SJZ urban plumes
for 12:00-14:00 BJT during the simulation period
Fig. 5 Simulated gases and NOz attributions to different emission
regions and categories at Xin’an for 14:00 BJT at each cloud-free day.
Colors for the label of days represents the site was influenced
dominantly by air masses from BJ (read), TJ (green) or TS (blue) on
that day
Fig.1 Anthropogenic emissions of CO (a), VOC (b), NOx (c), and
NOx from industrial (d), traffic (e) and other (f) in the central area
of Huabei. Other refers to emissions from civil and biomass
burning. Dark solid cycle indicates the Xin’an site.
ConclusionsThe estimated OPEx for BJ,TJ,TS and SJZ plumes is very different, with the values of 3.35, 2.75, 1.43 and 2.33 mol mol-1 respectively. The estimated OPEx in BJ, TJ, TS air masses arriving at Xin’an are comparable to those in their general pollution plumes.A lower OPEx in TS than BJ and TJ air masses indicates a remarkable difference in the chemical characteristics of pollution plumes from different pollution centers in North China.
0 10 20 30 40 50NO z(ppbv)
0
40
80
120
160
Ox(
ppb
v)
Y = 1.42 * X + 21.95R 2=0.81
Y = 2.52 * X + 17.00R 2=0.92
Y = 2.98 * X + 12.37R 2=0.77
Fig. 6 Calculated OPEx at Xin’an site for 12:00-14:00 BJT. Red, green and blue data points indicate the days influenced dominantly by BJ, TJ and TS air masses
Literature citedMa et al., JGR, 107(D22), 4660, doi:10.1029/2001jd001354,2002.Zhao et al., ACP, 12, 481-501, doi:10.5194/acp-12-481-2012, 2012.
D ay of April/M ay 2006
0369
1215
PA
N/H
NO3
00.20.40.60.8
1
NO
x/NOy
01020304050
NO
z(ppbv) Tra
Ind
Rest
01020304050
NOz(ppbv) BJ
TJTSRest
04080
120160
Ox(ppbv) NO 2
O 3
2 3 4 5 7 8 9 14 16 17 19 20 21 22 23 24 25 27 28 29 30 1 2 3 5 6 7 10 13 14 15 16
April M ay2 3 4 5 7 8 9 14 16 17 19 20 21 22 23 24 25 27 28 29 30 1 2 3 5 6 7 10 13 14 15 16
2 3 4 5 7 8 9 14 16 17 19 20 21 22 23 24 25 27 28 29 30 1 2 3 5 6 7 10 13 14 15 16
2 3 4 5 7 8 9 14 16 17 19 20 21 22 23 24 25 27 28 29 30 1 2 3 5 6 7 10 13 14 15 16
2 3 4 5 7 8 9 14 16 17 19 20 21 22 23 24 25 27 28 29 30 1 2 3 5 6 7 10 13 14 15 16
