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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(cè)順風(fēng)氣味和源排放率</p>

72、<p>  摘要:對(duì)CALPUFF模型ISCST3高斯擴(kuò)散模型進(jìn)行了預(yù)測(cè)順風(fēng)氣體濃度和后臺(tái)計(jì)算面源氣味排放率的評(píng)價(jià)。通過(guò)比較比較表明,CALPUFF模型能較好地預(yù)測(cè)平均順風(fēng)氣體濃度。然而,然而,ISCST3傾向于在預(yù)測(cè)順風(fēng)氣味濃度比測(cè)量。無(wú)論是CALPUFF和ISCST3模型不能預(yù)測(cè)高峰氣體濃度的平均排放率。在相同濃度的氣體順風(fēng)實(shí)地用ISC的模型測(cè)量得出氣體排放率與使用CALPUFF計(jì)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(dòng)物飼養(yǎng)場(chǎng)的異味氣體排放已越來(lái)越受到美國(guó)的關(guān)注。這種關(guān)注突出表現(xiàn)在異味氣體擴(kuò)散模型的研究。目前,大氣擴(kuò)散模型是用來(lái)作為一種工具

74、來(lái)預(yù)測(cè)下風(fēng)向污染物濃度,或到后臺(tái)順風(fēng)濃度測(cè)量計(jì)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(guó)環(huán)境保護(hù)署(EPA)批準(zhǔn),并建議分散建模程序,它是被用來(lái)在美國(guó)大多數(shù)州的空氣污染監(jiān)

75、督管理機(jī)構(gòu)(SAPRAs)估計(jì)污染物濃度順風(fēng)。ISCST3包括高斯煙羽為基礎(chǔ),可以用來(lái)預(yù)測(cè)模型集順風(fēng)濃度從點(diǎn),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(cè)順風(fēng)氣味強(qiáng)度,還有很多重要的原因。大量的研究已進(jìn)行改善順風(fēng)顆粒物(PM)的濃度預(yù)測(cè),反算顆粒物排放率ISCST3模型的準(zhǔn)確性。據(jù)報(bào)道(王:《2004年),ISCST3可用于預(yù)測(cè)平均濃度,但對(duì)氣味的下風(fēng)處濃度峰值氣味排放除外。當(dāng)風(fēng)速超6m/s時(shí)ISCST3也很難預(yù)測(cè)順風(fēng)

76、濃度。</p><p>  CALPUFF擴(kuò)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(gè)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(guó)加州空氣資源委員會(huì)。美國(guó)環(huán)保局建議用作監(jiān)管涉及遠(yuǎn)距離運(yùn)輸?shù)纳暾?qǐng),并為近場(chǎng)應(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(píng)價(jià)模

77、型對(duì)氣味的排放性能預(yù)測(cè)需要大量實(shí)地調(diào)查。本文報(bào)告了通過(guò)對(duì)逃逸氣體采用現(xiàn)場(chǎng)采樣數(shù)據(jù)來(lái)對(duì)ISCST3和CALPUFF性能進(jìn)行定量考核。這項(xiàng)研究的最終目標(biāo)是解決這兩個(gè)色散建模中的應(yīng)用系統(tǒng)建模氣味有關(guān)的問(wèn)題。</p><p>  1.1 ISCST3高斯煙羽模型</p><p>  ISCST3預(yù)測(cè)從點(diǎn)順風(fēng)氣味濃度高斯煙羽模型,線,面源可以通過(guò)下面的公式描述:</p><p&g

78、t; ?。辄c(diǎn)源) (1)</p><p>  (為線源) (2)</p><p> ?。槊嬖矗?( 3 )</p><p>  其中C是氣味順風(fēng)氣味濃度單位(OU),</p><p>  Qp為的點(diǎn)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(kuò)散的基礎(chǔ)上穩(wěn)定類參數(shù),</p><p>  ü的污染物釋放時(shí)的平均風(fēng)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(yǔ),用來(lái)描述垂直分布(m),

80、x的上風(fēng)方向(m)和Y的交叉風(fēng)向。</p><p>  1.2 CALPUFF模擬系統(tǒng)</p><p>  正如上文''用戶的對(duì)CALPUFF擴(kuò)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(píng)估的是一個(gè)粉塵在由一個(gè)“快照”的時(shí)間受體濃度的貢獻(xiàn)。每個(gè)粉塵是“凝固”微粒(采樣步驟)的時(shí)間間隔。由 “凝固”

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

82、運(yùn)輸和分散模式,以平流“粉塵”的物質(zhì)排放源為藍(lán)本,模擬沿途分散和轉(zhuǎn)化過(guò)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(gè)非穩(wěn)態(tài)拉格朗日高斯煙團(tuán)模式。對(duì)于一個(gè)粉塵貢獻(xiàn)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(qiáng)度(OUm3),</p><p>  為標(biāo)準(zhǔn)差(對(duì)在沿風(fēng)高斯分布米)的方向,</p><p>  為標(biāo)準(zhǔn)差(對(duì)在風(fēng)切變的高斯分布米)的方向,</p><p>  為標(biāo)準(zhǔn)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(fēng)受體方向,</p><p>  dc距離(從噴中心米)到在跨風(fēng)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(wèn)公司,2004年)來(lái)預(yù)測(cè)順風(fēng)氣味濃度。在本研究中,在飼養(yǎng)場(chǎng)場(chǎng)源進(jìn)行采樣,以確定CALPUFF和ISC模擬排放率Q值。在1oum/s是測(cè)量樣品的平均的面源排放,并輸入氣象數(shù)據(jù)(風(fēng)速,風(fēng)向,氣溫等)用CALPUFF和ISCST3預(yù)測(cè)歐順風(fēng)氣味濃度。用模擬結(jié)果來(lái)比較各個(gè)領(lǐng)域順風(fēng)抽樣結(jié)果的氣味。<

86、/p><p>  在建模過(guò)程中,只有飼養(yǎng)場(chǎng)排放的氣體認(rèn)為是排放源。為了比較參照順風(fēng)與采樣現(xiàn)場(chǎng)的氣味濃度,ISC的濃度進(jìn)行了調(diào)整,包括迎風(fēng)氣味濃度(氣味時(shí),從東北或東南風(fēng)大作,見圖。 1)</p><p>  圖.1德州肉牛飼養(yǎng)場(chǎng)的商業(yè)布局</p><p>  2.1 背氣味排放率計(jì)算</p><p>  CALPUFF和ISCST3也被用來(lái)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(fēng)C1的濃度為給定的氣象條件。對(duì)于一個(gè)特定領(lǐng)域順風(fēng)C2的濃度測(cè)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(dāng)于C1的初始濃度為藍(lán)本順風(fēng),(OUms

88、-1為面源),第一季度本研究作為來(lái)源抽樣結(jié)果,第二季度后備排放率計(jì)算相應(yīng)的具體的現(xiàn)場(chǎng)實(shí)測(cè)濃度C2的下風(fēng)向,C1為第一季度初始模型預(yù)測(cè)在順風(fēng)排放濃度(OU),在CALPUFF和C2樣本下建模,在C2的順風(fēng)濃度場(chǎng)采樣并用ISC模擬順風(fēng)逆風(fēng)濃度的濃度場(chǎng)。</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(chǎng)對(duì)25000頭牛排

89、放的氣體進(jìn)行氣味源的排放量抽樣。圖。 1說(shuō)明了飼養(yǎng)場(chǎng)的布局。一個(gè)動(dòng)態(tài)流過(guò)室是用于氣味源頭采樣。(Parker et al., 2003; Baek et al., 2003; and Aneja et al., 2001). 無(wú)異味的空氣環(huán)境產(chǎn)生一熱儀(地)模型(Franklin, MA) 指示零級(jí)發(fā)電機(jī)進(jìn)入在11至14升每分鐘通過(guò)聚四氟乙烯(PTFE)管。由一個(gè)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(shí)驗(yàn)袋進(jìn)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(cè)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。總的平均排放率模型,采用順風(fēng)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(chǎng)也收集了環(huán)境氣味樣本。從飼養(yǎng)場(chǎng)在地面以上1米的高度從逆風(fēng)和順風(fēng)立

92、即收集氣味樣本放在10L的實(shí)驗(yàn)袋里。為了減少空氣袋氣味,在異味氣體清除之前對(duì)所采集標(biāo)本在100攝氏度的條件下加熱24小時(shí)(Parker et al., 2003).。在West Texas A&M University對(duì)所有樣品進(jìn)行了24小時(shí)的分析檢測(cè)閾值(DT)。迎風(fēng)和順風(fēng)取樣位置,確定后,根據(jù)風(fēng)向確定在采樣時(shí)間。</p><p>  一個(gè)現(xiàn)場(chǎng)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(fēng)氣味濃度。處理了異味氣味源采樣和環(huán)境采樣的細(xì)節(jié)。</p><p><b>  3 結(jié)果和討論</b></p><p>  公式(7)用來(lái)計(jì)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(cè)整體平均氣味排放率1.19OUm /s。表2中取樣和模擬順風(fēng)氣味的濃度。由于氣象條件對(duì)預(yù)測(cè)順風(fēng)氣味的情況有重大影響的因素,表2也提

94、供了同時(shí)同步風(fēng)速和風(fēng)向數(shù)據(jù)。 </p><p> ?。ū?) 飼養(yǎng)場(chǎng)表面測(cè)量氣味排放率</p><p>  PDTa: 面板檢測(cè)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(chǎng)使用CALPUFF和ISC模式計(jì)算的氣味排放率。同時(shí)使用測(cè)量的氣象數(shù)據(jù)進(jìn)行建模,在C

95、ALPUFF建模過(guò)程中現(xiàn)場(chǎng)采樣順風(fēng)異味氣體濃度作為C2,再用公式(6)計(jì)算表面排放率。在ISC建模過(guò)程中,從飼養(yǎng)場(chǎng)抽取樣本順風(fēng)濃度數(shù)據(jù)作為C2,再用公式(6)計(jì)算飼養(yǎng)場(chǎng)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(fēng)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(guò)建模過(guò)程(后臺(tái)計(jì)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(jìn)入通量室導(dǎo)致的結(jié)果。進(jìn)一步的研究需要進(jìn)行檢驗(yàn),以驗(yàn)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(duì)濕度等)能顯著影響的氣味排放率以及順風(fēng)氣味濃度。</p><p>  綜述采樣迎風(fēng)和順風(fēng)氣味濃度(OU)的氣味與建立順風(fēng)模型,利用一個(gè)1.19OUm/s假設(shè)一致的氣味排放率隨著時(shí)間的推移(表2)。圖.二說(shuō)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(fēng)),和實(shí)地采樣順風(fēng)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(fēng)方向。結(jié)果在表2和圖2表明,在下風(fēng)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(shì)預(yù)測(cè)濃度低于實(shí)測(cè)濃度。相比抽樣表2,CALPUFF結(jié)果較好地預(yù)測(cè)該領(lǐng)域平均順風(fēng)氣味濃度。但是,無(wú)論CALPUFF還是ISC很難預(yù)測(cè)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(fēng)速的采樣時(shí)間,</p><p>  b: 風(fēng)吹的方向?yàn)轱L(fēng)速的采樣時(shí)間的方向,</p><p>  c:氣體濃度采樣為飼養(yǎng)場(chǎng)迎風(fēng)方向,</p><p>  d:氣體濃度采樣為飼養(yǎng)場(chǎng)順風(fēng)采樣,</p><p>  e: CALPUFF預(yù)計(jì)在大約順風(fēng)場(chǎng)異味氣體濃度的采樣時(shí)間,</p><p>  f

99、: ISC的預(yù)測(cè)在大約順風(fēng)場(chǎng)異味氣體濃度采樣時(shí)間,</p><p>  g: ISC的臭氣濃度順風(fēng)調(diào)整=ISC的預(yù)測(cè)順風(fēng)逆風(fēng)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(shí)不同的。</p><p>  表(3)從飼養(yǎng)場(chǎng)使用CALPUFF和ISCST3返回計(jì)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(shuō)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(fēng)),和實(shí)地采樣順風(fēng)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(fēng)方向。結(jié)果在表2和圖2表明,在下風(fēng)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(shì)預(yù)測(cè)濃度低于實(shí)測(cè)濃度。相比抽樣表2,CALPUFF結(jié)果較好地預(yù)測(cè)該領(lǐng)域平均順風(fēng)氣味濃度。但是,無(wú)論CAL

101、PUFF還是ISC很難預(yù)測(cè)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(fēng)氣味濃度</p><p><b>  采樣日期</b></p><p>  圖3 比較順風(fēng)氣體濃度在順風(fēng)的升序排序濃度</p>

102、<p><b>  4. 結(jié)論</b></p><p>  通過(guò)CALPUFF和ISCST3高斯擴(kuò)散模型用異味氣體采樣數(shù)據(jù)評(píng)估預(yù)測(cè)順風(fēng)濃度和后臺(tái)計(jì)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(yùn)用CALPUFF可以較好預(yù)測(cè)平均順風(fēng)氣味濃度,而ISCST3往往更傾向于預(yù)測(cè)氣味濃度與實(shí)地測(cè)量的比較。</p><p&g

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