Sanayi kaynaklı hava kirliliğinde modellerin kullanımının karar verme sürecindeki rolü
The role of models in terms of decision making in industry based air pollution
Yazar: EZGİ ERDOĞAN
Danışman: PROF. DR. KADİR ALP
İstanbul Teknik Üniversitesi / Fen Bilimleri Enstitüsü / Çevre Mühendisliği Ana Bilim Dalı / Çevre Bilimleri ve Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2012
Konu: Çevre Mühendisliği = Environmental Engineering

Özet:
Ülke kalkınması, bölgesel potansiyellerin ortaya konulup en iyi şekilde değerlendirilip, bölgelerde mevcut kaynakların verimli olarak kullanılması ve bölgelerin geleceğe yönelik yatırım ve stratejilerinin oluşturulması ile sağlanır. Ülkemizde ise son yıllarda enerji ihtiyacının hızla artmakta olması sebebiyle yatırımlar, kömür ve/veya doğalgaz ile çalışan santrallere yapılmaya başlanmıştır. Enerji santrallerinin kurulumu üzerine yapılan yatırımların yoğunlaştığı bölgelerin başında ise Hatay ili gelmektedir.Bu gibi yoğun sanayileşmiş bölgelerde yeni kurulacak tesislerle mevcut tesislerin kümülatif etkilerinin ortaya konması önem kazanmakta ve bu amaçla uygun modellerin kullanımı gerekmektedir.Hava kirliliği dağılımının belirlenmesinde kullanılan matematik modeller; herhangi bir bölgede mevcut ve oluşacak olan hava kirliliğinin yönetiminde vazgeçilmez bir öneme sahiptir. Bu modeller vasıtası ile alıcı ortam hava kalitesini iyileştirmek, yasal hava kalitesi standartlarını sağlamak ve devam ettirmek için kirletici kaynak emisyonlarını kontrole yönelik kontrol teknolojileri alternatiflerinin senaryolar üzerinden geliştirilmesi mümkün olmaktadır.Bu çalışmada ülkemizde endüstriyel tesislerin, yer aldıkları bölgelerdeki hava kalitesi üzerindeki etkileri ile ilgili karar verme konumundaki mercilerin sıklıkla kullandığı bir model olan ISCT3 ve bunun daha gelişmiş bir versiyonu olan AERMOD modeli birlikte kullanılarak sonuçlarının temsil edicilik ve doğruluğu karşılaştırılmıştır. Hava kirliliği modelleme programları ile henüz faaliyete başlamamış olan Hatay Erzin Aşağı Burnaz mevkiinde kurulması planlanan 900 MWe / 882MWe kurulu güce sahip bir Doğalgaz Kombine Çevrim Santrali kaynaklı SO2, NO2 ve PM10gazları için öncelikle Sanayi Kaynaklı Hava Kirlenmesinin Kontrolü Yönetmeliği(SKHKKY)'nde öngörülen kriterlere göre belirlenen 6km x 6km'lik bir alan için çalıştırılmıştır. Bölgenin kaynak bilgileri, topografik özellikleri ve o bölgeyi en iyi temsil eden yıllık meteorolojik verileri içeren meteorolojik veri dosyası model programına eklenerek alıcı ortam noktalarındaki kirleticilerin 1 saatlik, 24 saatlik,aylık ve yıllık konsantrasyonları belirlenmiş, hava kalitesi katkı değerleri hesaplanmış, modellerin sonuçları karşılaştırılmıştır. Sonra model çıktı dosyaları analiz edilerek hava kirliliği probleminin en yoğun olabileceği noktalar ve kirliliğin oluştuğu bölgelerde problemin hangi faktörlerden etkilendiği belirlenmiştir.SKHKKY'ne göre belirlenen çalışma alanının büyütülmesinin bölgedeki hava kirliliğinin dağılımı üzerindeki etkisinin incelenmesi yasal düzenlemelerin etkinliğinin anlaşılması bakımından yararlı olacaktır. Bu amaçla kaynak merkezi aynı kalmak koşulu ile yasal durumu gösteren 6km x 6km yerine, 12km x 12km ve 24km x 24km olmak üzere iki farklı çalışma alanı için modeller çalıştırılmıştır. Bu senaryolarda mevcut baca yükseklikleri yerine farklı baca yükseklikleri ve çalışma xxalanlarında kullanılan grid edatlarının değiştirilmesinin kirleticinin yer seviyesi dağılımı üzerinde neden oldukları etkileri AERMOD ve ISCST3 modelleri kullanımı ile incelenmiştir.Modellerin bölge topoğrafyasının kırsal ve şehir olarak tanımlanmasına göre yer seviyesi kirletici konsantrasyonları üzerindeki tahminlerinin değişimi de iki model için ayrı ayrı incelenmiş ve farklılıkların sebebi irdelenmiştir

Abstract:
Development of the countries are provided with evaluated of regional potentials in the best way and efficient use of available resources in areas, the creation of strategies for the future investment. In recent years, demand for energy is increasing rapidly in our country and coal and / or natural gas-fired power plants began to be invested. Among the regions where investments are concentrated on the installation of power plants comes from the province of Hatay.The mathematical models in the dispersion of air pollution can be used to reduce any desired level of air pollution in a region and to control air pollution with the different alternatives to keep under the limit of standards, to develop scenarios and recommendations in air quality management studies.Air quality management model had been evaluated according to source receptor interaction in Hatay-Erzin zone, Natural Gas Combined Cycle Power Plant (NGCCPP). In the plant natural gas will be used.In the modeling study, PCRAMMET and AERMET, which is internationally approved and ISCST3 and AERMOD dispersion models were used. Monthly and yearly concentrations were calculated for SO2, NO2 and PM10 in the 6km x 6km, 12km x 12km and 24km x 24km areas by ISCST3 and AERMOD models.To evaluate the effect of air pollution on the distribution of growth is the work area in the region will be useful for understanding the effectiveness of the legal regulations. Models based on regional topography has been identified as groundlevel pollutant concentrations in rural and urban change on the estimates because of differences in the two models are examined and evaluated separately for each. For this purpose, instead of 6km x 6km, 12km x12km and 24km x 24km to be operated in two different models for the study area."Google Earth" program is used to find the exact coordinates for investigation area. After getting coordinates for investigation area, SRTM (Shuttle Radar Topography Mission) map selected and added to the ISCT3 and AERMOD models. In this manner AERMAP can read all of the elevations for each receptor and it shows us exact terrain profiles.Then source type, emission rate, stack height, diameter, flue gas exit velocity and temperature are added to model with the coordinates informations of source.After getting all data modules for ISCST3 atmospheric dispersion model, model run succesfully and as a result of ISC terrains that have affected from gaseous emissions, are determined. After that model run for the most different senarios and determine air pollution problem.xxiiThe ISC model (USEPA(1995), currently the ISCST3 version) has been one of the USEPA's standard regulatory models since its introduction in the early 1980's and has been modified little since that period. The ISCST3 model is applicable to receptors within about 50 km from the source. And ISCST3 is best described as gaussian steady-state plume dispersion model with a minimum one-hour timestep. It is by now probably the most widely used dispersion model of all time.AERMOD is the latest generation air dispersion model designed for short-range (up to 50 kilometers) dispersion of air emissions from stationary industrial sources. In the stable boundary layer (SBL), it assumes the concentration distribution to be Gaussian in both the vertical and horizontal. In the convective boundary layer (CBL), the horizontal distribution is also assumed to be Gaussian, but the vertical distribution is described with a bi-Gaussian probability density function (pdf). Whereas the ISCST3model uses a Gaussian vertical distribution for both convective and stable conditions.AERMOD uses a different method than ISC uses for calculating dispersion; ISC uses the Pasquill-Gifford-Turner (PGT) atmospheric stability class scheme, while AERMOD uses refined planetary boundary physics and not the PGT stability classes. Additionally , AERMOD treats the lower atmosphere's mixing height as a somewhat porous layer rather than an impenetrable ceiling. AERMOD also incorporates plume path adjustment techniques that allow plumes to partially follow terrain. This improvement significantly reduces predicted impacts in complex terrain when compared to the ISCST3 model.AERMOD makes use of two continuous stability parameters, the friction velocity and the Monin-Obukhov length to characterize the atmosphere. The friction velocity is a measure of mechanical effects alone, i.e., wind shear at ground level. The Monin-Obukhov length indicates the relative strengths of mechanical and buoyant effects on turbulence. Thus, AERMOD can account for turbulence both from wind shear and from buoyancy effects due to solar heating during the day and radiational cooling at night. To properly account for these effects, AERMOD requires three land use parameters: albedo, Bowen ratio, and surface roughness.AERMOD is actually a modeling system with three separate components: AERMOD (AERMIC Dispersion Model), AERMAP (AERMOD Terrain Preprocessor), and AERMET (AERMOD Meteorological Preprocessor) (EPA, 2003a).AERMAP uses gridded terrain data for the modeling area to calculate a representative terrain-influence height associated with each receptor location. The gridded data is supplied to AERMAP in the format of the Digital Elevation Model (DEM) data. Although the raw meteorological data used by AERMOD and ISCST3 are the same, AERMOD requires different processed data than the ISC family of models requires. As a result, processed meteorological data ready for use in the ISC family of models cannot be used in AERMOD.AERMET is a meteorological data preprocessor that accepts surface meteorological data, upper air soundings, and optionally, data from on-site instrument towers. It then calculates atmospheric parameters needed by the dispersion model, such as atmospheric turbulence characteristics, mixing heights, friction velocity, MoninObukov length and surface heat flux. Meteorological datas are divided to the two parts. One part is for surface meteorological datas and other part is high atmosphere datas. AERMOD combines to kind of datas and convert them to the one file.xxiiiThe surface and upper air stations should be selected for their meteorological representativeness of the general area being modeled. Generally, this criterion corresponds to the stations closest to the source(s) being modeled and in the same climatological regime.AERMET only creates meteorological input files for AERMOD. However, AERMET's flexibility allows for future expansion to create input files for other dispersion models requiring other algorithms and output formats.PCRAMMET is a meteorological preprocessor used for preparing National Weather Service (NWS) data for use in the Agency's short term air quality dispersion models such as ISCST3, CRSTER, RAM, MPTER, BLP, SHORTZ, and COMPLEX1. In this study PCRAMMET is used for ISCST3 model and also AERMET is used for AERMOD model. AERMET uses meteorological data and surface characteristics to calculate boundary layer parameters (e.g. mixing height, friction velocity, etc.) needed by AERMOD. This data, whether measured off-site or on-site, must be representative of the meteorology in the modeling domain.The input data requirements for PCRAMMET depend on the dispersion model andthe model options for which the data are being prepared. The minimum input data requirements to PCRAMMET are the twice-daily mixing heights and hourly surface observations of wind speed, wind direction, dry bulb temperature, opaque cloud cover, and cloud ceiling height. There are two basic types of inputs that are needed to run the ISC models. The operations performed by PCRAMMET include: calculate hourly values for atmospheric stability from meteorological surface observations; interpolate twice daily mixing heights (morning and afternoon) to hourly values.As for that comparison of AERMOD and ISCST3, urban option either on or off; no other specification available for ISCST3 model, so all sources must be modeled either rural or urban. Whereas AERMOD provides variable urban treatment as a function of city population and sources can individually be modeled rural or urban(EPA, 2004a).The ISCST3 model is run for one pollutant at a time; the AERMOD model is run for more than one pollutant at a time.The ISCST3 and AERMOD models compute an hourly concentration for each receptor. Other averaging periods, e.g., 3-hour, daily, seasonal and annual can also be aggregated The averaging period selected is based on the intended use.The basic types of printed output available with AERMOD are: summaries of high values (highest, second highest, etc.) by receptor for each averaging period and source group combination; summaries of overall maximum values (e.g., the maximum 50) for each averaging period and source group combination; and tables of concurrent values summarized by receptor for each averaging period and source group combination for each day of data processed. The tables by receptor and maximum value tables can be output for the source group values or for the individual source values, or both. In addition, when maximum values for individual sources are output, the user has the option of specifying whether the maximum source values are to be the maximum values for each source independently, or the contribution of each source to the maximum group values, or both (EPA, 2004b).xxivIt is not possible to state that AERMOD always predicts higher or lower ambient concentrations than ISCST3. Direct comparisons of the results provided by the two models have been inconclusive. AERMOD typically yields lower concentrations than ISCST3 when nearby complex terrain is present, but can yield higher concentrations in other terrain regimes. However AERMOD is clearly superior to ISCST3 in characterizing the atmosphere, and should provide more representative predictions of ambient concentrations.