核電工程外文翻譯----比較calpuff和iscst3模型預(yù)測順風氣味和源排放率

1、<p><b>　　翻譯原文</b></p><p>　　Comparison of CALPUFF and ISCST3 models for predicting downwind odor and source emission rates</p><p><b>　　Abstract</b></p><p&g

2、t;　　CALPUFF model and ISCST3 Gaussian dispersion models were evaluated for predicting downwind odor concentrations and back-calculating area source odor emission rates. The comparison between the predicted and fieldsampl

3、ed downwind concentrations indicates that the CALPUFF model could fairly well predict average downwind odor concentrations. However, ISCST3 tended to under predict downwind odor concentrations as compared to the measured

4、 concentrations. Both the CALPUFF and ISCST3 models failed to pr</p><p>　　Keywords: Odor modeling; CALPUFF; ISCST3; Odor emission rate; Odor flux</p><p>　　1. Introduction</p><p>　　O

5、dorous gas emission from large confined animal feeding operations has been of increasing concern in the USA. This concern accentuates the need for additional study of odor mitigation and modeling. Currently, atmospheric

6、dispersion models are used as a tool to predict downwind pollutant concentrations, or to back-calculate average pollutant emission rates from downwind concentration measurements (Gassman, 1992; Chen, et al., 1998; Jacobs

7、on, et al.,2000; Zhu, et al., 2000; Hoff and Bundy, 2003).</p><p>　　Industrial Source Complex-Short Term, Version 3 (ISCST3) is the US Environmental Protection Agency (EPA) approved and recommended dispersio

8、n modeling program that is being used by most State Air Pollution Regulatory Agencies (SAPRAs)in the USA to estimate downwind concentrations of pollutants. ISCST3 includes a set of Gaussian plume-based models that can be

9、 used to predict downwind concentrations from point, line, and area sources. As pointed out by Smith (1993), there are a number of important </p><p>　　CALPUFF dispersion model, and other similar models and p

10、rograms were developed by Sigma Research Corporation as a generalized non-steady state air emission modeling system for regulatory use (Earth Tech and Inc., 2000). The original development of the CALPUFF system was spons

11、ored by the California Air Resources Board. The US EPA has proposed to use the CALPUFF modeling system as a guideline model for regulatory applications involving long range transport and on a case-by-case basis for near-

12、fiel</p><p>　　Evaluation of a model performance for odor emission prediction requires a large amount of fieldwork. This paper reports a quantitative examination of the performance of ISCST3 and CALPUFF for f

13、ugitive odor emission using field sampling data. The ultimate goal of this research is to address the problems associated with these two dispersion-modeling systems in application for odor modeling.</p><p>　

14、　1.1. ISCST3 Gaussian plume models</p><p>　　ISCST3 Gaussian plume models for predicting downwind odor concentrations from point, line and area sources can be described by the following equations:</p>

15、<p>　　(for point source） （1）</p><p>　　( for line source ) (2)</p><p>　　(for area source ) (3)</p><p>　　where C is the downwind odor concentration in odor

16、 units (OU), QP the point source odor emission rate(OUm3s-1), QL is line source odor emission rate (OUm2 s-1), QA the area source odor emission rate (OUms-1), , the Pasquill–Gifford plume spread parameters based on stabi

17、lity class, u the average wind speed at pollutant release height (ms-1), H the effective height above ground of emission source (m), V the vertical term used to describe vertical distribution of the plume, x the upwind d

18、irection (</p><p>　　1.2. CALPUFF modeling system</p><p>　　As described in ‘‘A User’s Guide for the CALPUFF Dispersion Model’’ (Earth Tech and Inc.,2000), Puff models represent a continuous plume

19、 as a number of discrete packets of pollutant. The puff model evaluates the contribution of a puff to the concentration at a receptor by a ‘‘snapshot “approach.Each puff is ‘‘frozen’’ at particulate time intervals (sampl

20、ing steps). The concentration due to the ‘‘frozen’’ puff at that time is computed. The puff is then allowed to move, evolving in size and streng</p><p>　　The CALPUFF modeling system includes three main compo

21、nents: CALMET, CALPUFF, and CALPOST. CALMET is a meteorological model that develops hourly wind and temperature fields on a 3-D gridded modeling domain. CALPUFF is a transport and dispersion model that advects ‘‘puffs’’

22、of material emitted from modeled sources, simulating dispersion and transformation processes along the way. CALPOST is used to process the files from CALPUFF, producing a summary of the simulation results in tabulated fo

23、rms.</p><p>　　CALPUFF is a non-steady-state Lagrangian Gaussian puff model. The basic equation for the contribution of a puff at a receptor is:</p><p><b>　　(4) </b></p><p&

24、gt;<b>　　(5)</b></p><p>　　Where C is the ground-level pollutant concentration (OU), Q the product of odor strength in the puff and the puff volume (OUm3), the standard deviation (m) of the Gaussia

25、n distribution in the along-wind direction, the standard deviation (m) of the Gaussian distribution in the cross-wind direction, the standard deviation (m) of the Gaussian distribution in the vertical direction, dc the d

26、istance (m) from the puff center to the receptor in the along-wind direction, dc the distance (m) from the puf</p><p>　　2. Methodology</p><p>　　CALPUFF and ISCST3 with the graphical interface Br

27、eezeCALPUFF and BreezeISC (Trinity Consultants Incorporated, 2004) were used to model downwind odor concentrations reported as CALPUFF or ISC predicted. In this research, field source sampling in the feedlot pens was con

28、ducted to determine emission rates Q for CALPUFF and ISC modeling. Measured average area source emission rate (from pens) in OUm/s and simultaneous meteorological data (wind speed, wind direction, air temperature, etc.)

29、were input</p><p>　　In the modeling process, only feedlot pens were considered emission source. In order to compare modeled downwind odor concentrations with those from field sampled, ISC concentrations were

30、 adjusted to include upwind odor concentrations (pond odor emission when wind blew from northeast or southeast, see Fig. 1). Even though odor strength may not be additive, upwind odor concentrations were added into model

31、 predicted concentrations to obtain adjusted model downwind odor concentrations reported as a</p><p>　　2.1. Back-calculating odor emission rates</p><p>　　CALPUFF and ISCST3 were also used to bac

32、k calculate source emission rate (Q2). To determine emission rate for the models, an initial emission rate</p><p>　　Q1 (from source sampling) was used as input to determine a CALPUFF or ISC modeled downwind

33、concentration C1 for a given meteorological condition. For a given field downwind concentration measurement C2, the corresponding emission rate Q2 was determined using the following equation:</p><p><b>

34、;　　(6) </b></p><p>　　where Q1 the model initial emission rate corresponding to initial modeled downwind concentration C1, ( OUms -1for area source). Source sampling results were used as Q1 in this rese

35、arch.Q2 the back-calculated emission rate corresponding to specific field measured downwind concentration C2, (OUms-1 for area source), C1 the initial model predicted downwind concentration (OU) at emission rate Q1, and

36、C2 the field sampled downwind concentration in CALPUFF modeling, where C2 the field sampled downwind</p><p>　　2.2. Odor source sampling</p><p>　　Odor source sampling from feedlot pen emissions w

37、as conducted in the pens on a commercial beef cattle feedlots farm in West Texas with 25,000 heads. Fig. 1 illustrates the feedlots layout. A dynamic flow-through chamber was used for the odor source sampling (Parker et

38、al., 2003; Baek etal., 2003; and Aneja et al., 2001). Odor-free air generated by a Thermo Environment Instrument (TEI) Model 111 (Franklin, MA) zero-grade generator was directed into the chamber at 11–14 L/min through po

39、lytetrafluo</p><p><b>　　(7) </b></p><p>　　where, J the odor emission rate (flux) in OUms_1 for area source, q the air flow rate introduced to chamber in m3 s_1, (C) the odor sample c

40、oncentration measured by panelist in OU, and A the surface area covered by the flux chamber in m2.</p><p>　　Odor samples were collected three times over a two-week period in January 2004. The overall average

41、 emission rate was used to model downwind odor concentrations.</p><p>　　2.3. Ambient odor sampling</p><p>　　Ambient odor samples were also collected from the same feed-lots farm. Odor samples we

42、re collected in 10-L Teldar bags at a height of 1m above the ground surface from immediately upwind and downwind from the feedlot pens. To reduce ambient bag odor, each bag was heated for 24 h at 100 1C and purged with o

43、dor free air before the odor samples were taken (Parker et al., 2003). All the samples were analyzed for detection threshold (DT) within 24 h at West Texas A&M University. Upwind and downwind sa</p><p>　

44、　3. Results and discussion</p><p>　　Eq. (7) was used to calculate emission rates from source-sampling results. The surface odor emission rates are listed in Table 1. The overall average odor emission rate wa

45、s 1.19OUm/s. This emission rate was used as input Q in the CALPUFF and ISC to predict downwind odor concentrations in OU. Table 2 summarizes the results of sampled and modeled downwind odor concentrations. Since meteorol

46、ogical conditions are significant factors that impact downwind odor predictions, simultaneous wind speed and </p><p>　　Table 3 lists back-calculated odor emission rates from feedlot pens using CALPUFF and IS

47、C models. The simultaneously measured meteorological data were used in this modeling process. In CALPUFF modeling process, field sampled downwind odor concentrations were used as C2 in Eq. (6) to back-calculate emission

48、rate from the pen surface, whereas, in ISC modeling process, subtracting sampled upwind concentrations from sampled downwind concentrations yields net downwind concentrations from feedlot pens</p><p>　　The r

49、esults listed in Table 3 also indicate significant variations in odor emissions from day to day. Ambient air condition (temperature, relative humidity, etc.) could significantly impact odor emission rate as well as downw

50、ind odor concentrations.</p><p>　　Fig. 2 illustrates the comparison of downwind odor concentrations from CALPUFF and ISC modeling results, adjusted ISC concentrations (including the upwind with the modeled r

51、esults), and field-sampled results, whereas Fig. 3 shows this comparison sorted by downwind concentration in ascending order. Results in both Table 2 and Fig. 2 indicate that CALPUFF produced higher downwind concentratio

52、ns than ISC with the same emission rate and meteorological data. ISC tended to predict concentrations lower</p><p>　　4. Conclusion</p><p>　　Field odor sampling data were used to evaluate CALPUFF

53、 and ISCST3 Gaussian dispersion models for predicting downwind concentrations and back-calculating area source odor emission rates. Results from this research indicate the following observations:</p><p>　　1.

54、 The CALPUFF could fairly well predict average downwind odor concentrations, whereas ISCST3 tended to under predict odor concentration as compared to the field measurements.</p><p>　　2. Both CALPUFF and ISCS

55、T3 models failed to predict peak odor concentrations using the constant average emission rated from field measurements.</p><p>　　3. Odor emission rates obtained by back-calculating fluxes using CALPUFF and I

56、SC models with the same field measurements of downwind odor concentrations are significantly different. It indicates that back-calculated emission rates are model specific.</p><p>　　4. The modeled emission r

57、ates tended to be higher than flux chamber source sampling results. The flux chamber protocol may under-estimate odor emission rates, further research need to be conducted to verify this conclusion.</p><p>　

58、　Acknowledgements</p><p>　　The authors would like to acknowledge the assistance of Dr. Auvermann and his group who generously provided us with GIS measurements of the sampling site. Also,a very special thank

59、s goes to Dr. Weiping Dai and Christine Otto at Trinity Consultants for helping with CALPUFF modeling.</p><p>　　References</p><p>　　Aneja, V.P., Overton, J.M., Malik, B.P., Tong, Q., Kang, D.,20

60、01. Measurement and modeling of ammonia emissions at waste treatment lagoon atmospheric interface. Journal of Water, Air and Soil Pollution 1, 177–188.</p><p>　　Baek, B.H., Koziel, A.J., Kiehl, L., Spinhirne

61、, J.P., 2003.Estimation of ammonia and hydrogen sulfide fluxes and rates from cattle feedlots in Texas. ASAE Paper No. 034111.ASAE, St. Joseph, MI.</p><p>　　Chen, Y.C., Bundy, D.S., Hoff, S., 1998. Developme

62、nt of a model of dispersion parameters for odor transmission from agricultural sources. Journal of Agriculture Engineering Research 69, 229–238.</p><p>　　Earth Tech, Inc. 2000. A user’s guide for the CALPUFF

63、 dispersion model version 5. Concord, MA.</p><p>　　Gassman, P.W., 1992. Simulation of odor transport: a review.ASAE Paper No. 924517. ASAE, St. Joseph, MI.</p><p>　　Hoff, S., Bundy, D.S., 2003.

64、Modeling odor dispersion from multiple sources to multiple receptors. In: Proceedings of the CIGR International Symposium on Gaseous and Odour Emissions from Animal Production Facilities, Horsens,Denmark, June 2003.</

65、p><p>　　Jacobson, L.D., Gauo, H., Schmidt, D., Nicolai, R.E., 2000.Calibrating INPUFF-2 model by resident panelists for longdistance odor dispersion from animal feedlots. In: Proceedings of the second Internati

66、onal Conference on Air Pollution from Agricultural Operations (held by ASAE) Des Moines,Iowa, October 9–11, pp. 278–286.</p><p>　　Parker, D.B., Rhoades, M.B., Shuster, G.L., Koziel, J.A.,Perschbacher, Z.L.,

67、2003. Odor characterization at open-lot beef cattle feedyards using triangular forced-choice olfactometry.ASAE Paper No. 034105. ASAE, St. Joseph, MI.</p><p>　　Smith, R.J., 1993. Dispersion of odours from gr

68、ound level agricultural sources. Journal of Agriculture Engineering Research 54, 187–200.</p><p>　　Trinity Consultants Incorporated, 2004. Breeze CALPUFF,Version 1.4.4 and Breeze ISC, Version 4.1.4. Dallas,

69、TX.</p><p>　　Wang, L., Parnell, C.B., Parker, D.B., Lacey, R.E., Shaw, B.W.,Wanjura, J.D., 2004. Engineering Basis for Odor Dispersion Modeling—Part I: Preliminary Evaluation of ISCST3 for Predicting Downwin

70、d Odors. Presented at the 2004 CIGR International Conference in Beijing. Paper No.30-219A,Beijing, China.</p><p>　　Zhu, J., Jacobson, L.D., Schmidt, D.R., Nicolai, R., 2000.Evaluation of INPUFF-2 model for p

71、redicting downwind odors from animal production facilities. Applied Engineering in Agriculture 16 (2), 159–164.</p><p><b>　　翻譯中文</b></p><p>　　比較CALPUFF和ISCST3模型預(yù)測順風氣味和源排放率</p>

72、<p>　　摘要：對CALPUFF模型ISCST3高斯擴散模型進行了預(yù)測順風氣體濃度和后臺計算面源氣味排放率的評價。通過比較比較表明，CALPUFF模型能較好地預(yù)測平均順風氣體濃度。然而，然而,ISCST3傾向于在預(yù)測順風氣味濃度比測量。無論是CALPUFF和ISCST3模型不能預(yù)測高峰氣體濃度的平均排放率。在相同濃度的氣體順風實地用ISC的模型測量得出氣體排放率與使用CALPUFF計算通量有顯著不同。它表明，該模型的排放率

73、往往高于高通量室抽樣源的結(jié)果。通量結(jié)果可能低于氣味排放率。</p><p>　　關(guān)鍵字：氣體模型；CALPUFF；ISCST3；氣味排放率；氣體通量</p><p><b>　　1.介紹</b></p><p>　　大型動物飼養(yǎng)場的異味氣體排放已越來越受到美國的關(guān)注。這種關(guān)注突出表現(xiàn)在異味氣體擴散模型的研究。目前，大氣擴散模型是用來作為一種工具

74、來預(yù)測下風向污染物濃度，或到后臺順風濃度測量計算出的平均污染物排放率。(Gassman,1992; Chen,et al., 1998; Jacobson, et al.,2000; Zhu, et al., 2000; Hoff and Bundy, 2003).</p><p>　　工業(yè)源復(fù)雜，短期，版本3（ISCST3）是美國環(huán)境保護署（EPA）批準，并建議分散建模程序，它是被用來在美國大多數(shù)州的空氣污染監(jiān)

75、督管理機構(gòu)（SAPRAs）估計污染物濃度順風。ISCST3包括高斯煙羽為基礎(chǔ)，可以用來預(yù)測模型集順風濃度從點，線，面源。正如史密斯指出（1993），利用高斯分布模型技術(shù)預(yù)測順風氣味強度，還有很多重要的原因。大量的研究已進行改善順風顆粒物（PM）的濃度預(yù)測，反算顆粒物排放率ISCST3模型的準確性。據(jù)報道(王:《2004年),ISCST3可用于預(yù)測平均濃度,但對氣味的下風處濃度峰值氣味排放除外。當風速超6m/s時ISCST3也很難預(yù)測順風

76、濃度。</p><p>　　CALPUFF擴散模型，以及其他類似的模型和方案是由研究開發(fā)的西格瑪公司作為一個廣義的非穩(wěn)態(tài)空氣排放的監(jiān)管使用（地球技術(shù)和公司，2000年）的建模系統(tǒng)。該CALPUFF系統(tǒng)原始開發(fā)是由美國加州空氣資源委員會。美國環(huán)保局建議用作監(jiān)管涉及遠距離運輸?shù)纳暾?，并為近場?yīng)用中的非穩(wěn)態(tài)的影響可能是重要的逐案基礎(chǔ)上的指引模式CALPUFF模型系統(tǒng)。</p><p>　　評價模

77、型對氣味的排放性能預(yù)測需要大量實地調(diào)查。本文報告了通過對逃逸氣體采用現(xiàn)場采樣數(shù)據(jù)來對ISCST3和CALPUFF性能進行定量考核。這項研究的最終目標是解決這兩個色散建模中的應(yīng)用系統(tǒng)建模氣味有關(guān)的問題。</p><p>　　1.1 ISCST3高斯煙羽模型</p><p>　　ISCST3預(yù)測從點順風氣味濃度高斯煙羽模型，線，面源可以通過下面的公式描述：</p><p&g

78、t;　?。辄c源） （1）</p><p>　?。榫€源） （2）</p><p>　?。槊嬖矗?( 3 )</p><p>　　其中C是氣味順風氣味濃度單位（OU），</p><p>　　Qp為的點源排放率的氣味（OU）,</p><p>　　QL為線源

79、氣味排放率（OUm2s-1）,</p><p>　　QA為面源排放率的氣味（ms-1）,</p><p>　　、為羽流擴散的基礎(chǔ)上穩(wěn)定類參數(shù)，</p><p>　　ü的污染物釋放時的平均風速（m/s），</p><p>　　H為地面排放源的有效高度，</p><p>　　V為垂直術(shù)語，用來描述垂直分布（m），

80、x的上風方向（m）和Y的交叉風向。</p><p>　　1.2 CALPUFF模擬系統(tǒng)</p><p>　　正如上文''用戶的對CALPUFF擴散模型指南''（地球技術(shù)和公司，2000年）一樣，代表了作為一種連續(xù)型和離散型煙羽模型的污染物。煙羽模型評估的是一個粉塵在由一個“快照”的時間受體濃度的貢獻。每個粉塵是“凝固”微粒（采樣步驟）的時間間隔。由 “凝固”

81、粉塵計算濃度，直到下一次采樣步驟，這個凝固的粉塵可以移動和發(fā)展。受體總濃度是對所有附近膨化貢獻的總和平均為基本的時間內(nèi)所有取樣步驟的一步。抽樣步驟和時間步驟可能是一兩個小時，說明只有一個“快照”的粉塵是每一小時采樣的。</p><p>　　該CALPUFF模擬系統(tǒng)包括三個主要部分組成：卡爾梅特，CALPUFF CALPOST?？柮诽厥且粋€氣象模型，開發(fā)一個三維網(wǎng)格模型域每小時平均風速和溫度場。 CALPUFF是

82、運輸和分散模式，以平流“粉塵”的物質(zhì)排放源為藍本，模擬沿途分散和轉(zhuǎn)化過程。 CALPOST用于處理從CALPUFF文件，生產(chǎn)出以列表形式的模擬結(jié)果的摘要。</p><p>　　CALPUFF是一個非穩(wěn)態(tài)拉格朗日高斯煙團模式。對于一個粉塵貢獻在1受體的基本公式為：</p><p><b>　　（4）</b></p><p><b>　　

83、（5）</b></p><p>　　其中C為地面污染物濃度（OU）,</p><p>　　Q的粉塵的氣味強度（OUm3），</p><p>　　為標準差（對在沿風高斯分布米）的方向，</p><p>　　為標準差（對在風切變的高斯分布米）的方向，</p><p>　　為標準差（在垂直的方向高斯分布米），&l

84、t;/p><p>　　da距離（米粉撲中心）在沿風受體方向,</p><p>　　dc距離（從噴中心米）到在跨風向受體,</p><p>　　g為垂直任期（1米）的高斯方程，</p><p>　　H為有效高度（米）以上的地面和吞吐中心，h的混合層高度。</p><p><b>　　2.方法論</b>&

85、lt;/p><p>　　用CALPUFF和ISCST3與圖形界面BreezeCALPUFF和BreezeISC（三位一體顧問公司，2004年）來預(yù)測順風氣味濃度。在本研究中，在飼養(yǎng)場場源進行采樣，以確定CALPUFF和ISC模擬排放率Q值。在1oum/s是測量樣品的平均的面源排放，并輸入氣象數(shù)據(jù)（風速，風向，氣溫等）用CALPUFF和ISCST3預(yù)測歐順風氣味濃度。用模擬結(jié)果來比較各個領(lǐng)域順風抽樣結(jié)果的氣味。<

86、/p><p>　　在建模過程中，只有飼養(yǎng)場排放的氣體認為是排放源。為了比較參照順風與采樣現(xiàn)場的氣味濃度，ISC的濃度進行了調(diào)整，包括迎風氣味濃度（氣味時，從東北或東南風大作，見圖。 1）</p><p>　　圖.1德州肉牛飼養(yǎng)場的商業(yè)布局</p><p>　　2.1 背氣味排放率計算</p><p>　　CALPUFF和ISCST3也被用來反算源

87、排放率（第二季）。為了確定模型排放率，廢氣排放率第一季度的初步（從源頭抽樣）作為輸入，用于確定CALPUFF ISC的模擬或順風C1的濃度為給定的氣象條件。對于一個特定領(lǐng)域順風C2的濃度測量，相應(yīng)的排放率，確定第二季度使用下列公式：</p><p><b>　　(6)</b></p><p>　　其中第一季度的模型的初始排放率相當于C1的初始濃度為藍本順風,(OUms

88、-1為面源)，第一季度本研究作為來源抽樣結(jié)果，第二季度后備排放率計算相應(yīng)的具體的現(xiàn)場實測濃度C2的下風向，C1為第一季度初始模型預(yù)測在順風排放濃度（OU），在CALPUFF和C2樣本下建模，在C2的順風濃度場采樣并用ISC模擬順風逆風濃度的濃度場。</p><p><b>　　2.2 氣味源采樣</b></p><p>　　從西得克薩斯商業(yè)肉牛飼養(yǎng)場對25000頭牛排

89、放的氣體進行氣味源的排放量抽樣。圖。 1說明了飼養(yǎng)場的布局。一個動態(tài)流過室是用于氣味源頭采樣。(Parker et al., 2003; Baek et al., 2003; and Aneja et al., 2001). 無異味的空氣環(huán)境產(chǎn)生一熱儀（地）模型(Franklin, MA) 指示零級發(fā)電機進入在11至14升每分鐘通過聚四氟乙烯（PTFE）管。由一個15升/分鐘質(zhì)量流量控制器(Aalborg, NY)控制流量，空氣樣品裝到

90、10L的實驗袋進行濃度分析。氣味排放率（或流量）由下列公式確定：</p><p>　　, (7)</p><p>　　其中，J為在面源的氣味排放率（流量）（OUms-1）,</p><p>　　Q為空氣流通量(m3s-1),</p><p&g

91、t;　　[c]為氣味測定樣品濃度（OU），</p><p>　　A的面積覆蓋通量（m2）。</p><p>　　氣味樣本收集為2004年1月為期兩周的3倍?？偟钠骄欧怕誓Ｐ?，采用順風氣味濃度。</p><p>　　2.3 環(huán)境氣味采樣</p><p>　　在相同的飼料的很多農(nóng)場也收集了環(huán)境氣味樣本。從飼養(yǎng)場在地面以上1米的高度從逆風和順風立

92、即收集氣味樣本放在10L的實驗袋里。為了減少空氣袋氣味，在異味氣體清除之前對所采集標本在100攝氏度的條件下加熱24小時(Parker et al., 2003).。在West Texas A&M University對所有樣品進行了24小時的分析檢測閾值（DT）。迎風和順風取樣位置，確定后，根據(jù)風向確定在采樣時間。</p><p>　　一個現(xiàn)場氣象站錄得的所有采樣天間隔1分鐘的氣象數(shù)據(jù)。這些氣象數(shù)據(jù)被用

93、于模型在給定的排放率順風氣味濃度。處理了異味氣味源采樣和環(huán)境采樣的細節(jié)。</p><p><b>　　3 結(jié)果和討論</b></p><p>　　公式（7）用來計算從源頭抽樣結(jié)果的排放率，表面氣味排放率列于表1。用CALPUFF和ISC預(yù)測整體平均氣味排放率1.19OUm /s。表2中取樣和模擬順風氣味的濃度。由于氣象條件對預(yù)測順風氣味的情況有重大影響的因素，表2也提

94、供了同時同步風速和風向數(shù)據(jù)。 </p><p>　?。ū?） 飼養(yǎng)場表面測量氣味排放率</p><p>　　PDTa: 面板檢測閾值（OU）</p><p>　　ERb: 表面氣味排放率(OUm/s)</p><p>　　表3列出了從飼養(yǎng)場使用CALPUFF和ISC模式計算的氣味排放率。同時使用測量的氣象數(shù)據(jù)進行建模，在C

95、ALPUFF建模過程中現(xiàn)場采樣順風異味氣體濃度作為C2，再用公式（6）計算表面排放率。在ISC建模過程中，從飼養(yǎng)場抽取樣本順風濃度數(shù)據(jù)作為C2，再用公式（6）計算飼養(yǎng)場的排放率。氣味排放量的表面狀況，如水分含量等，由于天氣條件等諸多因素作用，CALPUFF和ISCST3相同順風氣味和氣象數(shù)據(jù)的產(chǎn)生不同排放率。這表明，從不同模式得出不同排放速率。所得的平均排放率通過建模過程（后臺計算）比采樣排放率更高通量室，見表1和3。氣味的采樣往往低于

96、氣味排放率源流通量，這可能是由于高稀釋流量進入通量室導(dǎo)致的結(jié)果。進一步的研究需要進行檢驗，以驗證這一假設(shè)。表3還列出的結(jié)果表明從每一天開始顯著異味氣體排放的變化。環(huán)境空氣狀況（溫度，相對濕度等）能顯著影響的氣味排放率以及順風氣味濃度。</p><p>　　綜述采樣迎風和順風氣味濃度（OU）的氣味與建立順風模型，利用一個1.19OUm/s假設(shè)一致的氣味排放率隨著時間的推移（表2）。圖.二說明了CALPUFF和ISC

97、模擬結(jié)果, 比較ISC的調(diào)整濃度（包括與模擬結(jié)果迎風），和實地采樣順風氣味濃度的結(jié)果。而圖3顯示在升序排序比較集中的順風方向。結(jié)果在表2和圖2表明，在下風處，CALPUFF產(chǎn)生相同的排放率和氣象數(shù)據(jù)比ISC的更高濃度。 ISC的趨勢預(yù)測濃度低于實測濃度。相比抽樣表2，CALPUFF結(jié)果較好地預(yù)測該領(lǐng)域平均順風氣味濃度。但是，無論CALPUFF還是ISC很難預(yù)測高峰氣味濃度，該模型可能由于使用恒定的平均排放率。</p>&l

98、t;p>　　a: 風速的采樣時間,</p><p>　　b: 風吹的方向為風速的采樣時間的方向,</p><p>　　c：氣體濃度采樣為飼養(yǎng)場迎風方向，</p><p>　　d：氣體濃度采樣為飼養(yǎng)場順風采樣，</p><p>　　e: CALPUFF預(yù)計在大約順風場異味氣體濃度的采樣時間，</p><p>　　f

99、: ISC的預(yù)測在大約順風場異味氣體濃度采樣時間，</p><p>　　g: ISC的臭氣濃度順風調(diào)整=ISC的預(yù)測順風逆風氣味濃度+濃度的采樣,</p><p>　　h: 在程度為0.05的同一條件下數(shù)值時不同的。</p><p>　　表（3）從飼養(yǎng)場使用CALPUFF和ISCST3返回計算的氣味的排放率</p><p>　　a:在0.05

100、水平顯著不同的英文字母</p><p>　　圖。二說明了從CALPUFF和ISC模擬結(jié)果, 比較ISC的調(diào)整濃度（包括與模擬結(jié)果迎風），和實地采樣順風氣味濃度的結(jié)果。而圖3顯示在升序排序比較集中的順風方向。結(jié)果在表2和圖2表明，在下風處，CALPUFF產(chǎn)生相同的排放率和氣象數(shù)據(jù)比ISC的更高濃度。 ISC的趨勢預(yù)測濃度低于實測濃度。相比抽樣表2，CALPUFF結(jié)果較好地預(yù)測該領(lǐng)域平均順風氣味濃度。但是，無論CAL

101、PUFF還是ISC很難預(yù)測高峰氣味濃度，該模型可能由于使用恒定的平均排放率。</p><p><b>　　采樣日期</b></p><p>　　圖2 比較順風氣味濃度</p><p><b>　　采樣日期</b></p><p>　　圖3 比較順風氣體濃度在順風的升序排序濃度</p>

102、<p><b>　　4. 結(jié)論</b></p><p>　　通過CALPUFF和ISCST3高斯擴散模型用異味氣體采樣數(shù)據(jù)評估預(yù)測順風濃度和后臺計算面源氣味排放率。從本研究結(jié)果顯示以下意見：</p><p>　　1. 運用CALPUFF可以較好預(yù)測平均順風氣味濃度，而ISCST3往往更傾向于預(yù)測氣味濃度與實地測量的比較。</p><p&g