Abstract. 1 Introduction

Transcription

1 Ozone simulation over the southern Taiwan area Kuang, Yun-chun & Yi-lin Chen Environmental Engineering Department (I) Sinotech Engineering Consultants, LTD. 171, Nankin E. Rd Sec. 5, Taipei 105, Taiwan, Republic of China Abstract Last autumn, ozone became the predominant air pollutant over southern part of Taiwan and the new era of air quality management began. Full scale simulations are performed to verify the object of emission reduction in this study. UAM dated and released by US EPA is used to fulfill the simulations. The wind fields and mixing height field are composed from surface and sounding data. The verification comparisons are done for stations NO% and VOC observations with correlation coefficients of 0.62 and 0.33, respectively. Local emissions from low stacks, traffic and area sources are found to be the main precursors by sensitivity tests of emission turning on/off. Plumes from elevated stacks are blown offshore and have no chance to be mixed with ground level VOC before sunrise. The results also show that the ground level VOC/NOx sources from petroleum industrial parks are the most urgent candidates for emission reduction. Their plumes are well mixed during the night time and becoming 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 gas emitted from industry and the vehicles transform to be the photochemical smog that is harmful not only to the human health but also to the crops and plants. The photochemical smog phenomenon is quite complicated and case-dependent because the meteorological environment and the emission allocation characteristic is unique for each metropolitan area.

2 564 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 of designated region. There are 4 main petroleum refineries and chemical industrial parks (denoted by CPC, Chinese Petroleum Cooperation) and 3 sets 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 international airport of Kaohsiung, the city is continuously developing to be a highly industrialized and commercialized metropolitan ^ r- 1,,, UIME(M) Figure 1. Map of southern Taiwan area. The air quality stations are denoted 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.

3 565 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 also at the inland stations[2]. The back trajectories[3] from inland stations show that the high potential precursor plumes are coming from the highly industrial and urbanized onshore area. In autumn and winter, the exceeding frequency of ambient hourly ozone standard 120 PPB is almost once every 3 days. Over 26% of a year it will violate the 8 hours average ambient standard of 60 PPB The maximum ozone concentration even 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 concentration The 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 the sounding data from Air force base that date in chance. The maximum concentrations and peak hours of NO*, NMHC and ozone at selected stations are listing in Table 1. The precursor concentrations at downwind stations are not as much as the upwind ones while the ozone is much higher. Since the air monitoring station is too sparse to reveal the progress 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 inland stations originate from the points within model domain, the boundary condition will affect the result ozone in a limited range.

4 566 Air Pollution No. A3 A4 A5 A6 A7 A8 A9 A10 All A12 A13 A14 A15 A17 A18 A19 Table 1 Peak values and Peak hours of pollutants Site description NMHC NOx Os PPDMC Hr. PPB Hr. PPB 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 Note: "-" means not monitored Table 2 The initial and boundary condition used in simulation. Name BC/IQPPB) Ref./Note Name BQIC(PPB) Ref./Note NO 2 [1] TOL 0.31 Liao, et al [6] NO2 8 [1] XYL 0.52 Liao, et al.[61 O3 40 Hsiu[5] FORM 0.4 Liao, et al[6]. ETH 0.1 Liao, et ALD Liao, et al.[6] al[6]. OLE 0.85 Liao, et ISOP 0.96 Hsiu[5],Tso al.[6] m PAR 53.9 Liao, et NMHC P] al.[6] Note [1] are obtained from the minimum observation of monitoring station, and [2] is in unit of ppbc and is the summation of all the VOC's Hr

5 The meteorological module The wind observation and wind field generation Diurnal cycle of wind direction is obvious from the surface meteorology stations. The wind is blowing southward in the night time as affected by the monsoon, and switching to the sea breeze of northwestern wind. The cycle is quite typical for any autumn and winter sunny days. The correlation between wind speed and ozone concentrations is poor, in facts that the sea breeze could dominant the field and up to 4.5 m/s during the episode. Since there is no significant obstacle mountain between the sources and high ozone stations downwind, and the number of the surface stations is sufficient in the coastal region, the objective interpolation approach is considered to get better representative of wind field over the plains. The wind speed components are interpolated by using Barnes[8] method The sounding data and mixing height generation There is no apparent diffusion break aloft. But the gradient of the potential temperature is within degree/ 1 DOOM and is more stable than usual. This will inhibit the penetration of elevated point sources through the diffusion break. The maximum surface temperature is over 30 degree, so the mixing height can be more than 1000 m as predicted by Holzworth 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. And the potential temperature in mountainous region is obtained also by interpolating postulated mountain station with same altitude. The minimum 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 is shallow. At the noon time, the surface is much warmer than the sea, then the stronger sea breeze and deeper mixing height is developed to bring the coastal emission to the inland area. 2.3 The geographic setting and discretization The type of land use is greatly related to the aerodynamic roughness, biological emission of VOC's, and also the deposition resistance. The

6 568 Air Pollution land use type of each grid is read from the vegetation map. Because of the turbulent diffusion effect, the downwind concentration will be more homogenous, and because the resolution of the VOC's both in industrial park and the urban area is not so good, finer mesh resolution will not benefit greatly but increase the computation loading. In this study, the model domain is set as 120 Km time 190 Km and 60x95x9 grids resolution, with 2 layers above the diffusion break, 6 layers under it, and a surface layer of 30 M. In sparse vertical resolution, the model will underestimates the contribution of ground source and exaggerate the UTME (M) UTME (M) 5 M/sec 6:00 12:00 Figure 2. The generated wind field and mixing height at 6:00 and 12:00. fumigation of elevated source. 2.4 The source module Overall structure of precursor emission The works of emission estimation were performed by ROC EPA. The baseline year is designated as Target years of 2001 and 2011 are also predicted from the proposal of developing projects including refineries, power plants, and other fundamental industries, and also the highway systems, as well as the economical growth predictions and population predictions. In order to keep simple, this study focus on the baseline year emission.

7 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 NO,(Ton/day) VOC(Ton/day) Mobile Area Biological Industry sources The emission rates are estimated by SCC factors[9] and activities data from inventory data bank. The data of operation hours in a year are used to break down from annual to hourly base. This approximation may be too conservative, since the power plants are usually shut down for maintenance in autumn or winter seasons. The operation hours of a day are also carefully considered, since 7.2% difference may occur between day and night time. The effective plume heights are estimated by Briggs'[10] formula and the hourly meteorological data. The penetration of plume through the diffusion break is determined by the heat flux from stack and the temperature gradient above the diffusion break suggested by McRae[ll]. Almost all of the emission from elevated sources injects into the cells above the surface layer at night. Even at noon time and above the mixing layer, the ratio is about 50%. This will keep the YOG/NO* ratio high in the mixing layer, since the wind directions diverse across the diffusion break. Ground level industry sources include the short stacks, and the fugitive source Mobile sources The 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/commercial passenger car, low duty gas/diesel engine truck, high duty truck, 2/4 strokes motorcycle. Since the air quality data are highly dependent on the hourly traffic, the daily emissions are broken down to hourly base by using traffic counting data separately from national and province

8 570 /*/ Highway Bureau. The night time traffic on the highway (24% of the average volume) is much more than the local traffic(7% of average), and this part of emissions will has much more time to react Area source The 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 individual operation patterns. Area sources are distributed by weighting of populations and in annual base without time variations Biological sources Eight kinds of different vegetation and land use types are taken into account in this study. The emission rate data are based on the historical studies and also broadly used in the BEIS model[15, 16]. Time variations of biological sources are dependent on the plants types, temperature and solar radiation. The respiration of plants will be very active in the noon time when the photosynthesis taking place most actively. The noon time emission is about 3.2 times of emission at night. Whether the biological sources contribute to the high ozone concentration must be determined by the limiting factors of sufficient reaction time and NOx concentration. 3 Evaluations of Model Performance 3.1 NO, concentration The simulation results of NOx at Kaohsiung metropolitan (A 15), refinery parks (A 12, A7) and downwind area (A 17) are compared to the observations in Figure 3. The concentration level and the variation pattern can both be well simulated by the model. While overestimation is happened at the station just beside the highway (A7). This is due to the discretization of highway emission. The lateral diffusion of highway emission is over exaggerated. The NOx downwind station (A 17) is underestimated also due to discretization process, since A17 is located in the center of Pingtung city and the local emission is larger then the grid average values. The overall correlation coefficient is The averaged NO/NOx ratio is 0.11, which is almost the same as the observed NO/NOx ofo.13.

9 I Transactions on Ecology and the Environment vol 21, 1998 WIT Press, ISSN Air Pollution # +* U4 AK A7 417 / ; "t 4.' r, 4 ; ^. 40 / ^ i s* *! v "V * < * / 200 * ii* ^4 ' * ' ** ^ ' t» - ' */ «##j%$-j** V V *A '"< ** "+ ( ) < \ \; i ZI [ J < 1- I; i D / I ii Figure 3 The comparisons of observed NOx (squares) and model results (lines) in PPB at selected stations. The station locations are in Figure NMHC concentration The match between observation and simulation NMHC at selected stations is pretty good as depicted in Figure 4. Both the concentration level 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 site A17, NHHC is lower than the observations. The reason is also due to the discretization process. The overall correlation coefficient is A7 U2 A15 \ y 10 *. _*^ 4 4 *»_ *,- **J 0 L_4~ * 4 ti* ****^-4 * 44 ^'4 '!*' ^*444 i* ' 4 *^-.j.* - ^^4'+ '--t () i1 (\ /\ Figure 4 The comparisons of observed NMHC (squares) and model results (lines) in PPDMC at selected stations. The station locations are in Figure Ozone concentration The simulation results in Figure 5 are fairly good in order of magnitude. While the simulated peak hour is about 2-3 hour latter than the observation. The results at A17 located just downwind of refinery park is significant underestimated, while other downwind station A18 is quite match. The ozone plume passes over the A17 station by its southern side as found in the contour Figure 6. This is possible due to the error of wind vector interpolating, since there is one observation at A18, but is no any data around A17. The highest concentration in the ozone plume can reach 204 PPB

10 572 Air Pollution ocfi 200 A7 \14 * yk!7 V18 1 <A < _. * 100 ^^ ff\ //,- $»* ++ % < S* \_ * "--_. 0 *#$,,*" ***, *** *«* *#.*-' ^ẏ t** *,«V i$w () [ I! [ *! /[ * I t1 *{ 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 relative error of postulated cases with respect to baseline simulation results in Figure The contribution of industry source categories Figure 6a shows that the industry sources have a very significant contribution on the ozone plume. The contribution shape is very similar as the baseline ozone plume. If shut down all industry source and remain the others, the maximum ozone concentration of the region will be down to 150 PPB. While comparing each grid point to the baseline case, the maximum relative contribution of industry source is up to 45%, which location is very near to the peak location of baseline ozone plume. Negative contribution of industry sources may occur because of too much NOx emission at specific area. While the large point sources will not affect the ground level ozone significantly, since they are above the mixing layer and blown southwestern by upper air. 4.2 The contribution of mobile source categories The contribution of mobile sources is not exactly the same phase as the industry sources in Figure 6b. The pattern looks like in highway shape and not in the plume shape. The front of the high contribution region seems moving longer than the industry sources This can be explained by the effect of diffusion break. Since the emission of mobile sources is trapped by the night time diffusion break, while the elevated industry sources must wait until the mixing layer is as high as their plume height.

11 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 with contour interval 20 PPB Figure 6a to 6c are the relative contribution of industry, mobile and area sources with interval 0.1. The maximum contribution 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 categories The maximum extent which area sources can contribute in Figure 6c is only 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 Taiwan area are well-done by this study. Although the phenomena is too complicated to resolved by data analysis only, the industry source is found to be the most important emission contribution to the high ozone episode. The mobile sources also can not be ignored, while its contribution to the ozone maximum is limited. Due to least quantity, we think the abatements on area sources will not reduce the ozone significantly. 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 Analysis and the Control Strategy Development of Urban Ozone Pollution, ROC EPA Report, (in Chinese).

12 574 Air Pollution [4] U.S. EPA, Guideline for Regulatory Application of the Urban Airshed Model, Research Triangle Park, NC, [5] Hsiu, K.R. Baseline Measurement of Taiwan Atmosphere (II) Analysis of Og, NO, NO] and PAN, National Science Council Report. NSC M , 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* Air Pollution 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 M , 1983 (in Chinese). [8] Barnes, S.L. Mesoscale Objective Map Analysis Using Weighted Time Series Observation NOAA Tech. Memo, ERL NSSL-62, National Severe Storms Laboratory, Norman, Oklahoma, pp60, [9] US EPA, AIRS Facility Subsystem, Source Classification Codes and Emission Factor Listing for Criteria Air Pollutants, EPA 450/ , [10] Briggs, G.A. Plume Rise, USAEC Critical Review Series, TID , NTIS, Springfield, Va. 81 pp., [11] McRae, G.J. Mathematical modeling of photochemical air pollution, Ph. D dissertation, California Institute of Technology, pp , [12] US EPA, User's Guide to MOBILES, EPA-AA-AQAB-94-01, [13] Energy and Environmental Analysis Inc., User's Manual for MOBILE-TAIWAN Version 2, 1996.

13 Air Pollution 575 [14] ROC EPA, The Driving Pattern and Emission Model Installation and Maintaining Program in Taipei Metropolitan, EPA , pp3-128~3-133, 1996 (in Chinese). [15] Pierce, T.E., Lamb, B.K. and Meter, A.R., Development of a Biogenic 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 , [16] Olson, P, Emerson, C and Nunsgesser, N., Geocology: A County Level Environmental Data Base for the Conterminous United States, ORNL/TM-7351, Oak Ridge National Laboratory, Oak Ridge, TN, p. 54, 1980.