Kuang, Yun-chun & Yi-lin Chen Environmental Engineering ...€¦ · Kuang, Yun-chun & Yi-lin Chen...

Ozone simulation over the southern Taiwan

areaKuang, Yun-chun & Yi-lin ChenEnvironmental Engineering Department (I) SinotechEngineering Consultants, LTD. 171, Nankin E. Rd Sec. 5,

Taipei 105, Taiwan, Republic of ChinaEmail: [email protected]

Abstract

Last autumn, ozone became the predominant air pollutant over southern part ofTaiwan and the new era of air quality management began. Full scale simulationsare performed to verify the object of emission reduction in this study. UAMdated 93287 and released by US EPA is used to fulfill the simulations. Thewind fields and mixing height field are composed from surface and soundingdata. The verification comparisons are done for stations NO% and VOCobservations with correlation coefficients of 0.62 and 0.33, respectively. Localemissions from low stacks, traffic and area sources are found to be the mainprecursors by sensitivity tests of emission turning on/off. Plumes from elevatedstacks are blown offshore and have no chance to be mixed with ground levelVOC before sunrise. The results also show that the ground level VOC/NOxsources from petroleum industrial parks are the most urgent candidates foremission reduction. Their plumes are well mixed during the night time andbecoming active in ozone formation after sunrise.

1 Introduction

The ozone air quality of many metropolitans around the world becomes

worse and worse as the result of economical growth. Exhausted gasemitted from industry and the vehicles transform to be the photochemicalsmog that is harmful not only to the human health but also to the cropsand plants. The photochemical smog phenomenon is quite complicatedand case-dependent because the meteorological environment and theemission allocation characteristic is unique for each metropolitan area.

Transactions on Ecology and the Environment vol 21, © 1998 WIT Press, www.witpress.com, ISSN 1743-3541

564 Air Pollution

Finding out the local characteristic is most important issue in the regional

air quality management plan.In this study we focus the ozone problem on southern Taiwan region

where the air quality is worst over the island[l]. Figure 1 is the map ofdesignated region. There are 4 main petroleum refineries and chemical

industrial parks (denoted by CPC, Chinese Petroleum Cooperation) and 3sets of large power plants (denoted by TPC, Taiwan Power Cooperation).

Besides, the steel mill at annual production rate of 80 million Tons is

located at the beach of metropolitan (denoted by CSC, Chinese Steel

Cooperation). The overall population of Kaohsiung and Tainan cities is

about 5 million. With the aid of the international harbor and internationalairport of Kaohsiung, the city is continuously developing to be a highly

industrialized and commercialized metropolitan.

2600000-

2580000-

2560000-

2540000-

2520000-

^ 2500000-

2480000-

2460000-

2440000-

2420000-1 r- 1 , , , —140000 160000 180000 200000 220000 240000 260000

UIME(M)

Figure 1. Map of southern Taiwan area. The air quality stations aredenoted by "A", the surface meteorology stations are denoted by "M",and the sounding station is denoted by "S". The interior area is the most

intensively industrialized area.


565

The ozone precursor emitted from the coastal sources is transported

toward the inland area by the sea breeze. It is reacted to build up high

level of photo-oxidants at inland stations such as Pingtung (A 17) and

Chaochow (A 18). The correlation between ozone concentrations and the

sea breeze direction is very obvious not only at onshore stations, but alsoat the inland stations[2]. The back trajectories[3] from inland stationsshow that the high potential precursor plumes are coming from thehighly industrial and urbanized onshore area. In autumn and winter, the

exceeding frequency of ambient hourly ozone standard 120 PPB isalmost once every 3 days. Over 26% of a year it will violate the 8 hoursaverage ambient standard of 60 PPB The maximum ozone concentrationeven exceeds 200 PPB. The efficient emission abatement plan is

expected to improve the ozone air quality.To achieve the objective, this study applies UAM (Urban Airshed

Model[4]) to simulate the ozone problem. The experience of developing

the research procedure and setting up relevant techniques to prepare the

input data will not be only benefit to the special case and can be

reproduced in elsewhere.

2 Simulation Conditions and Data Preparation

2.1 The pollutant concentrationThe autumn episode dated 10/12/93 is chosen for the ozone

concentration at Pingtung (A 17) is as high as 207 PPB We also have thesounding data from Air force base that date in chance. The maximumconcentrations and peak hours of NO*, NMHC and ozone at selectedstations are listing in Table 1. The precursor concentrations at downwindstations are not as much as the upwind ones while the ozone is muchhigher. Since the air monitoring station is too sparse to reveal theprogress of ozone formation, comprehensive simulations and also more

intensively field observation are still needed.The initialization of the concentration field is done by running 12

hours before sunrise. The ground level NO* concentration field is

obtained by interpolation. The top and lateral boundaries are assume as"clean" as the values in Table 2. Since the back trajectories of inlandstations originate from the points within model domain, the boundary

condition will affect the result ozone in a limited range.


566 Air Pollution

Table 1 Peak values and Peak hours of pollutants

No.

A3

A4

A5

A6

A7

A8

A9

A10

All

A12

A13

A14

A15

A17

A18

A19

Site description

Tainan city

Tainan city

Downwind site

Remote site

Industry park

Industry park

Industry park

Industry park

Industry park

Industry park

Kaohsiung city

Kaohsiung city

Kaohsiung city

Downwind site

Downwind site

Remote site

NMHCPPDMC

-

54-

6

24-

-

19

11

11-

-

12

20-

-

Hr.-

21-

20

3-

-

3

16

9-

-

8

3-

-

NOxPPB

7978

34

70

80

114

77

50

54

83

108

96

106

53

52

-

Hr.

128

18

8

10

20

1310

8

9

9

9

9

23

17-

OsPPB

93

160

132

112

83

116

124

153

102

127

109

93-

207

133

45

Hr.

1211

15

13

13

13

12

12

13

14

13

12-

13

12

1

Note: "-" means not monitored

Table 2 The initial and boundary condition used in simulation.

Name

NO

NO2

O3

ETH

OLE

PAR

BC/IQPPB)

2

8

40

0.1

0.85

53.9

Ref./Note

[1]

[1]

Hsiu[5]

Liao, etal.[6]

Liao, etal.[6]

Liao, etal.[6]

Name

TOL

XYL

FORM

ALD2

ISOP

NMHC

BQIC(PPB)

0.31

0.52

0.4

0.85

0.96

69.03

Ref./Note

Liao, et al

[6]Liao, etal.[61

Liao, etal[6].

Liao, etal[6].

Hsiu[5],Tso

mP]

Note [1] are obtained from the minimum observation of monitoringstation, and [2] is in unit of ppbc and is the summation of all the VOC's


567

2.2 The meteorological module

2.2.1 The wind observation and wind field generationDiurnal cycle of wind direction is obvious from the surface meteorologystations. The wind is blowing southward in the night time as affected bythe monsoon, and switching to the sea breeze of northwestern wind. Thecycle is quite typical for any autumn and winter sunny days. Thecorrelation between wind speed and ozone concentrations is poor, in

facts that the sea breeze could dominant the field and up to 4.5 m/sduring the episode. Since there is no significant obstacle mountain

between the sources and high ozone stations downwind, and the numberof the surface stations is sufficient in the coastal region, the objectiveinterpolation approach is considered to get better representative of windfield over the plains. The wind speed components are interpolated by

using Barnes[8] method.

2.2.2 The sounding data and mixing height generationThere is no apparent diffusion break aloft. But the gradient of thepotential temperature is within 2.5-6.0 degree/ 1 DOOM and is more stablethan usual. This will inhibit the penetration of elevated point sources

through the diffusion break. The maximum surface temperature is over30 degree, so the mixing height can be more than 1000 m as predicted byHolzworth method. The 2-D mixing heights field is generated by

interpolating the ground level potential temperature of surface stations.The marine stations are assumed uniformly distributed over the sea. Andthe potential temperature in mountainous region is obtained also byinterpolating postulated mountain station with same altitude. Theminimum mechanical mixing height is determined by the scale height(friction velocity divided by the Coriolis parameter). The coefficients of

0.25 and 0. 1 are used for the neutral and stable conditions.The resultant wind and mixing height field at 6:00 and 12:00 are

shown in Figure 2. In the night time the radiation cooling makes the air

on the coastal very stable, the wind speed is low and the mixing layer isshallow. At the noon time, the surface is much warmer than the sea, thenthe stronger sea breeze and deeper mixing height is developed to bring

the coastal emission to the inland area.

2.3 The geographic setting and discretizationThe type of land use is greatly related to the aerodynamic roughness,biological emission of VOC's, and also the deposition resistance. The


568 Air Pollution

land use type of each grid is read from the vegetation map. Because ofthe turbulent diffusion effect, the downwind concentration will be morehomogenous, and because the resolution of the VOC's both in industrialpark and the urban area is not so good, finer mesh resolution will notbenefit greatly but increase the computation loading. In this study, the

model domain is set as 120 Km time 190 Km and 60x95x9 gridsresolution, with 2 layers above the diffusion break, 6 layers under it, anda surface layer of 30 M. In sparse vertical resolution, the model willunderestimates the contribution of ground source and exaggerate the

2600000-

140000 180000 220UTME (M) 5 M/sec

180000UTME (M)

6:00 12:00Figure 2. The generated wind field and mixing height at 6:00 and 12:00.

fumigation of elevated source.

2.4 The source module

2.4.1 Overall structure of precursor emissionThe works of emission estimation were performed by ROC EPA. Thebaseline year is designated as 1991. Target years of 2001 and 2011 arealso predicted from the proposal of developing projects includingrefineries, power plants, and other fundamental industries, and also thehighway systems, as well as the economical growth predictions andpopulation predictions. In order to keep simple, this study focus on the

baseline year emission.


Air Pollution 569

The emission data are composed and recompiled into the model

system in 3 categories including industry sources, mobile sources, and

area sources. Besides, the biological emission rates of VOC's are

estimated in this study. The sources are summarized in Table 3, and are

further discussed as followed.

Table 3. The air pollutant emission in baseline year of the region.

Sources

Industry

Mobile

Area

Biological

NO,(Ton/day)

471.3

1806.2

25.8

0

VOC(Ton/day)

185.9

337.2

240.2

150.2

2.4.2 Industry sourcesThe emission rates are estimated by SCC factors[9] and activities data

from inventory data bank. The data of operation hours in a year are usedto break down from annual to hourly base. This approximation may betoo conservative, since the power plants are usually shut down formaintenance in autumn or winter seasons. The operation hours of a dayare also carefully considered, since 7.2% difference may occur betweenday and night time. The effective plume heights are estimated byBriggs'[10] formula and the hourly meteorological data. The penetrationof plume through the diffusion break is determined by the heat flux fromstack and the temperature gradient above the diffusion break suggestedby McRae[ll]. Almost all of the emission from elevated sources injects

into the cells above the surface layer at night. Even at noon time andabove the mixing layer, the ratio is about 50%. This will keep theYOG/NO* ratio high in the mixing layer, since the wind directionsdiverse across the diffusion break. Ground level industry sources include

the short stacks, and the fugitive source.

2.4.3 Mobile sourcesThe emission factors are calculated from MOBILE 5[12] with

corrections of local driving pattern and vehicle testing data set[13, 14].The vehicle are divided into 7 categories of personal/commercialpassenger car, low duty gas/diesel engine truck, high duty truck, 2/4strokes motorcycle. Since the air quality data are highly dependent onthe hourly traffic, the daily emissions are broken down to hourly base byusing traffic counting data separately from national and province


570 /*/

Highway Bureau. The night time traffic on the highway (24% of theaverage volume) is much more than the local traffic(7% of average), and

this part of emissions will has much more time to react.

2.4.4 Area sourceThe area sources which are estimated from top-down material statistics

and open burning, such as VOC's from the coating/solvent and

commercial/consumer products, home-use heater, open burning of

agricultural or municipal wastes, and the fire disaster. The time

variations of the industrial sources are composed from individualoperation patterns. Area sources are distributed by weighting of

populations and in annual base without time variations.

2.4.5 Biological sourcesEight kinds of different vegetation and land use types are taken into

account in this study. The emission rate data are based on the historicalstudies and also broadly used in the BEIS model[15, 16]. Time variations

of biological sources are dependent on the plants types, temperature andsolar radiation. The respiration of plants will be very active in the noon

time when the photosynthesis taking place most actively. The noon timeemission is about 3.2 times of emission at night. Whether the biologicalsources contribute to the high ozone concentration must be determinedby the limiting factors of sufficient reaction time and NOx concentration.

3 Evaluations of Model Performance

3.1 NO, concentrationThe simulation results of NOx at Kaohsiung metropolitan (A 15), refineryparks (A 12, A7) and downwind area (A 17) are compared to theobservations in Figure 3. The concentration level and the variationpattern can both be well simulated by the model. While overestimation ishappened at the station just beside the highway (A7). This is due to thediscretization of highway emission. The lateral diffusion of highwayemission is over exaggerated. The NOx downwind station (A 17) isunderestimated also due to discretization process, since A17 is located inthe center of Pingtung city and the local emission is larger then the gridaverage values. The overall correlation coefficient is 0.62. The averagedNO/NOx ratio is 0.11, which is almost the same as the observed NO/NOx

ofO.13.


Air Pollution 571

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Figure 3 The comparisons of observed NOx (squares) and model results(lines) in PPB at selected stations. The station locations are in Figure 1

3.2 NMHC concentrationThe match between observation and simulation NMHC at selectedstations is pretty good as depicted in Figure 4. Both the concentrationlevel and also the variation pattern are sounds acceptable. Overestimation

at A7 is happened at the rush hour due to the national highway traffic,

which location is just downwind of the highway. At the downwind siteA17, NHHC is lower than the observations. The reason is also due to the

discretization process. The overall correlation coefficient is 0.33.

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Figure 4 The comparisons of observed NMHC (squares) and modelresults (lines) in PPDMC at selected stations. The station locations are in

Figure 1

3.3 Ozone concentrationThe simulation results in Figure 5 are fairly good in order of magnitude.While the simulated peak hour is about 2-3 hour latter than theobservation. The results at A17 located just downwind of refinery park issignificant underestimated, while other downwind station A18 is quitematch. The ozone plume passes over the A17 station by its southern sideas found in the contour Figure 6. This is possible due to the error of windvector interpolating, since there is one observation at A18, but is no anydata around A17. The highest concentration in the ozone plume can

reach 204 PPB


572 Air Pollution

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Figure 5 The comparisons of observed Og (squares) and model results(lines) in PPB at selected stations. The station locations are in Figure 1

4 Abatements of Ozone Precursor

The sensitivity of each sources category is essential to the air quality

management and also the emission abatement plan Here we try to

turning on/off each of the 3 sources categories to find the most sensitive

emission. By cutting-off one source category and keeping others, we can

quantify the contribution of it. The sensitivities are expressed as relativeerror of postulated cases with respect to baseline simulation results in

Figure 6.

4.1 The contribution of industry source categoriesFigure 6a shows that the industry sources have a very significantcontribution on the ozone plume. The contribution shape is very similaras the baseline ozone plume. If shut down all industry source and remainthe others, the maximum ozone concentration of the region will be downto 150 PPB. While comparing each grid point to the baseline case, themaximum relative contribution of industry source is up to 45%, whichlocation is very near to the peak location of baseline ozone plume.Negative contribution of industry sources may occur because of toomuch NOx emission at specific area. While the large point sources willnot affect the ground level ozone significantly, since they are above themixing layer and blown southwestern by upper air.

4.2 The contribution of mobile source categoriesThe contribution of mobile sources is not exactly the same phase as theindustry sources in Figure 6b. The pattern looks like in highway shapeand not in the plume shape. The front of the high contribution regionseems moving longer than the industry sources This can be explained bythe effect of diffusion break. Since the emission of mobile sources istrapped by the night time diffusion break, while the elevated industrysources must wait until the mixing layer is as high as their plume height.


Air Pollution 573

If we eliminate all the vehicle sources, the maximum ozone

concentration will be 167 PPB. The maximum contribution of mobile

sources is 49% which occurs at front of the ozone plume.

Figure 6. The simulated ozone concentration in PPB at 14:00 withcontour interval 20 PPB Figure 6a to 6c are the relative contribution ofindustry, mobile and area sources with interval 0.1. The maximumcontribution locations are denoted by "P", "L" and "A" respectively. The

locations of monitoring stations are denoted by stars.

4.3 The contribution of area source categoriesThe maximum extent which area sources can contribute in Figure 6c isonly 24% of ozone concentration. The region of its contribution is not

significantly overlapped with the ozone plume. In other words, if we

reduce the area sources, nothing will happen to the ozone maximum.

5 Conclusion

The simulation structure to solve the ozone problem in Southern Taiwanarea are well-done by this study. Although the phenomena is toocomplicated to resolved by data analysis only, the industry source isfound to be the most important emission contribution to the high ozone

episode. The mobile sources also can not be ignored, while itscontribution to the ozone maximum is limited. Due to least quantity, wethink the abatements on area sources will not reduce the ozonesignificantly. More studies on how to perform the industry emission

reduction are certainly needed.

6 References

[1] ROC EPA The Annual Report of Air Quality 1997 (in Chinese).

[2] CTCI, The SIP for Kaohsiung city, Kaohsiung City Report 1994 (in

Chinese).

[3] Chang, L.F., Chang, J., Tsai, J.H. and Wu, Y.L. The Trend Analysisand the Control Strategy Development of Urban Ozone Pollution,

ROC EPA Report, 1996. (in Chinese).


574 Air Pollution

[4] U.S. EPA, Guideline for Regulatory Application of the UrbanAirshed Model, Research Triangle Park, NC, 1991.

[5] Hsiu, K.R. Baseline Measurement of Taiwan Atmosphere (II)Analysis of Og, NO, NO] and PAN, National Science CouncilReport. NSC-82-0202-M-002-070, 1983 (in Chinese).

[6] Liao, S.R., Tso, T.L. and Lo, J.K. Analysis of Refinery Ambient

NMHC Composition and Fingerprint by Cannist Method, 11* AirPollution Control Technology Conference, Taichung Chunghsin

University, 1994 (in Chinese).

[7] Tso, T.L Baseline Measurement of Taiwan Atmosphere (VIII)Analysis of C2-C5, NMHC, CO and Greenhouse Gases, National

Science Council Report. NSC-82-0202-M-007-102, 1983 (in

Chinese).

[8] Barnes, S.L. Mesoscale Objective Map Analysis Using WeightedTime Series Observation NOAA Tech. Memo, ERL NSSL-62,

National Severe Storms Laboratory, Norman, Oklahoma, pp60,

1973.

[9] US EPA, AIRS Facility Subsystem, Source Classification Codesand Emission Factor Listing for Criteria Air Pollutants, EPA 450/4-

90-003, 1990.

[10] Briggs, G.A. Plume Rise, USAEC Critical Review Series, TID-25075, NTIS, Springfield, Va. 81 pp., 1969.

[11] McRae, G.J. Mathematical modeling of photochemical airpollution, Ph. D dissertation, California Institute of Technology,

pp246-251, 1981.

[12] US EPA, User's Guide to MOBILES, EPA-AA-AQAB-94-01,

1994.

[13] Energy and Environmental Analysis Inc., User's Manual forMOBILE-TAIWAN Version 2, 1996.


Air Pollution 575

[14] ROC EPA, The Driving Pattern and Emission Model Installation

and Maintaining Program in Taipei Metropolitan, EPA024850396,

pp3-128~3-133, 1996 (in Chinese).

[15] Pierce, T.E., Lamb, B.K. and Meter, A.R., Development of aBiogenic Emissions Inventory System for Regional Scale Air

Pollution Models, the 83"* Air and Waste Management Association

Annual Meeting, June 24-29, Pittsburgh, Pennsylvania, Paper No.

90-94.3, 1990.

[16] Olson, P, Emerson, C and Nunsgesser, N., Geocology: A CountyLevel Environmental Data Base for the Conterminous UnitedStates, ORNL/TM-7351, Oak Ridge National Laboratory, Oak

Ridge, TN, p. 54, 1980.


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