at SciVerse ScienceDirect

Atmospheric Environment 45 (2011) 7235e7239

Contents lists available

Atmospheric Environment

journal homepage: www.elsevier .com/locate/atmosenv

Modeling indoor odoreodorant concentrations and the relative humidity effecton odor perception at a water reclamation plant

Tingting Wang, Chakkrid Sattayatewa, Dhesikan Venkatesan, Kenneth E. Noll, Krishna R. Pagilla,Demetrios J. Moschandreas*

Civil, Architecture and Environmental Engineering, Illinois Institute of Technology, 3201 S Dearborn St., Chicago, IL 60616, USA

a r t i c l e i n f o

Article history:Received 5 July 2011Received in revised form22 August 2011Accepted 30 August 2011

Keywords:Model formulationValidationIndoor odorsOdorantsRelative humidityBiosolids

* Corresponding author. Tel.: þ1 312 567 3532; faxE-mail address: moschandreas@iit.edu (D.J. Mosch

1352-2310/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.atmosenv.2011.08.073

a b s t r a c t

Models formulated to associate odors and odorants in many industrial and agricultural fields ignore thepotential effect of relative humidity on odor perception, and are not validated. This study addressesliterature limitations by formulating a model that includes relative humidity and by validating the model.The model employs measured paired values, n ¼ 102, of indoor odors and odorants from freshlydewatered biosolids in a post-digestion dewatering building of a Water Reclamation Plant (WRP). Arandom sub-sample of n ¼ 32 is used to validate the model by associating predicted vs. measured values(R2 ¼ 0.90). The model is validated again with a smaller independent database from a second WRP(R2 ¼ 0.85). Moreover this study asserts that reduction of hydrogen sulfide concentrations, conven-tionally used as a surrogate of sewage odors, to acceptable levels does not assure acceptable odor levels.It is concluded that: (1) The addition of relative humidity results in a stronger association between odorsand odorants than the use of H2S alone; (2) the two step model validation indicates that the model is notsimply site-specific but can be applied to similar facilities; and (3) the model is a promising tool fordesigning odor and odorant control strategies, the ultimate goal of engineering studies.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Many facilities and processes in a WRP are housed indoors andcould be potential sources of odor and odorants. Biosolids pro-cessing is among the worst odor generating areas in a WRP (Adamset al., 2003). Notwithstanding the widely recognized benefits ofbiosolids cake for land application purposes, concerns are emergingregarding the effects of occupational exposure to odors associatedwith biosolids, especially indoors in post-digestion dewateringbuildings (Witherspoon, 2006; Higgins et al., 2006).

A model associating odor perception with odorant concentra-tion is a necessary tool to assess, predict and control odors effi-ciently and effectively. However, odor perception is usually notassociated with only one odorant but a mixture. The relationshipbetween air pollutants and perceived odor is less clear. Numerousstudies have investigated odorous mixtures, and conclusionsreached vary considerably: effects of odorants present in mixturesmay be simply additive (Patterson et al., 1993), synergistic (Laskaand Hudson, 1991), partially additive or suppressive (Laing et al.,

: þ1 312 567 8874.andreas).

All rights reserved.

1994; Hobbs et al., 2001; Frederick et al., 2009). Berglund et al.(1976) presented a vector odor interaction model that relatesodor intensity and quality of mixtures to perception type; thismodel can accommodate addition, subtraction and synergism. It isalso noteworthy that in addition to odorous compounds, subjectivephysical factors such as temperature and relative humidity mayinfluence odor as well in an indoor environment (Cain et al., 1983;Reinikainen et al., 1992; Reinikainen and Jaakkola, 2003).

Reinikainen et al. (1992) investigated the effect of air humidi-fication on perception of indoor air quality in-office workers, andfound that the perception of stuffy air increased as humidificationincreased from 30% to 40%. Reinikainen and Jaakkola (2003)expanded on their earlier study to assess the impact of humidityand temperature on worker’s skin, upper airway symptoms andodor perception in the office environment. Assessor individualswere exposed to indoor office conditions for at least 2 h withtemperatures between 18 �C and 26 �C, and relative humidity in therange of 17e40%. Reinikainen and associates conclude that odorperception increased with humidity, but did not correlate withtemperature. Fang et al. (1998) employed climate chambers toinvestigate the impact of temperature and humidity on humanperception of indoor air quality. Assessors were exposed toconstant and elevated air pollution (a mixture of polyvinyl chloride

Table 1Measured variable and instrument manufacturers.

Measured Instruments Instrument Manufacturers

Air Sample CollectionTedlar Bag (1 L and 10 L) Jensen Inert ProductsAir Sampling Vacuum Chamber

Includes Integral Air PumpSt. Croix Sensory, Inc.

Field TestsField Odor Level (D/T) Nasal Ranger (St. Croix Sensory, Inc.)Hydrogen Sulfide (ppmv) Jerome 631-X (Arizona Instrument LLC)Carbon dioxide (ppmv) Carbon

monoxide (ppmv)Temperature (�F)Relative Humidity (%)

TSI IAQ-Calc IAQ Meter, Model 7545(TSI Instruments, Ltd.)

Odor and Odorant Analyses (in the IIT lab)Odor Threshold (D/T or ED50) Triangular Forced-Choice Olfactometer

(RK & Associates)Dimethyl Sulfide (ppbv) zNose Model 4200

(Electronic Sensor Technology)Dimethyl Disulfide (ppbv)Dimethyl Trisulfide (ppbv)

T. Wang et al. / Atmospheric Environment 45 (2011) 7235e72397236

and acrylic sealant) during a 20 min exposure following a step-change of air temperature and humidity. Fang et al. found that attemperature in the ranges 18e28 �C and 30e70% relative humidity,the perception of odor was independent of temperature andhumidity. The Cain et al. study (1983), which like Fang’s studyinvolves chamber exposures, indicates an impact of relativehumidity on odors. Results of these studies appear to be conflict.Three differences may account for the conflicting outcomes: (1) theReinikainen study describes the exposed population as is, but theFang and Cain studies deliberately perturb or change exposureconditions; (2) the chamber studies investigate short term,20e120 min long, exposures while the exposures of the Reinikai-nen study are much longer at least 120 min long; and (3) thechamber studies reflect uniform in-chamber agent concentrations,while the Reinikainen study may not be uniform in-office indoorconcentrations. The conflicting conclusions from these studiespartially motivated the work presented in this paper.

Relationships between odor perception and pertinent odorantconcentrations have been investigated in different fields such asliving stocks, food waste composting plants, and wastewatertreatment plants (Adams et al., 2003; Hobbs et al., 2001; Tsai et al.,2008; Blanes-Vidal et al., 2009). Studies focusing on odoreodorantrelationships in wastewater treatment systems were mostly per-formed for outdoor facilities (Koe, 1985; Gostelow and Parsons,2000; Dincer and Muezzinoglu, 2007). Hydrogen sulfide (H2S) iscommonly used as a surrogate of sewage gases. A power law rela-tionship between the H2S concentration and odor perception hasbeen established in these studies:

CðouÞ ¼ k CnðH2SÞ or log CðouÞ ¼ log kþ n log CðH2SÞ (1)

WhereC(ou) is the concentration of odor mixture expressed as dilutions

to threshold (D/T), and C(H2S) is H2S concentration (ppm), k and nare constants.

A Water Environmental Research Foundation (WERF) odorproject was performed in 11 wastewater treatment plants acrossNorth America to find correlations among wastewater character-istics, plant operations and biosolids odor characteristics (Adamset al., 2003). This study established a multiple linear relationshipbetween odor and volatile sulfur compounds for incubated biosolidsin headspace bottles for six-days, but not for freshly dewateredbiosolids.

This literature assessment reveals that

1. model validation was not carried out or even discussed in anyof these modeling efforts;

2. pertinent studies associate perceived odor from sewageairborne mixtures with only H2S, but other odorouscompounds are also present and contribute to odors;

3. none of the models evaluated includes physical factors asindependent variables;

4. models associating odors, odorants and pertinent physicalfactors for freshly dewatered biosolids indoors have not beenformulated, yet the literature has established that exposuresfrom this source are of importance in the occupational envi-ronment of WRP

The investigation presented in this paper is an element ofa comprehensive odor and odorant monitoring and control designstudy performed indoors at a post-digestion dewatering building ofa WRP. The scientific hypothesis of this paper explores the formu-lation and validation of a multiple regression empirical model thatassociates indoor odors emitted by dewatered biosolids withpertinent odorants and indoor air quality variables. Specific

objectives of this paper are to: (1) formulate a model associatingindoor odors with indoor odorants, and other air quality variables,and (2) validate the model.

2. Materials and methods

2.1. Sampling design and data collection

A preliminary air sampling (survey stage) in the post-digestiondewatering building was performed to identify potential sourcesof odor and hot spots with elevated concentrations of odors andodorants. Approximately, sixty locations were randomly selectedthroughout the dewatering building. Field odor and H2S concen-trations were measured. Carbon dioxide (CO2), carbon monoxide(CO), room temperature and relative humidity at each samplinglocation were also monitored. Field instruments used in this studyare listed in Table 1.

Based on results obtained from the survey sampling stage,sources of odor and odorants were identified indoors anda comprehensive air sampling task was carried out to characterizeodor and odorant concentrations. Eighteen sites within the dew-atering building were chosen as sampling locations, includinglocations near and far away from biosolids conveyors, centrifugesand exhausts on the roof. Ambient samples were collected asbackground for comparisons of indoor and outdoor odors. A total of124 indoor and outdoor air samples were collected and analyzedduring the two sampling campaigns.

2.2. Odor measurements

2.2.1. Sensory measurements (triangle forced-choice olfactometry)Odor concentrations in the collected odorous air samples

were determined in the laboratory using forced-choice triangleolfactometer with eight panelists, following the standard ASTME679-04 (ASTM, 2004). The olfactometer has six dilution levels,ranging from 7 to 1700 D/T. Carbon filtered air was used asdilution gas, which was set at a constant flow rate of500 ml min�1. The flow of odorous gas ranged from 0.3 to77 ml min�1. Only individuals with normal olfactory acuity werecandidates for the odor panel; anosmic and supersensitive toodor individuals as well as smokers and individuals with chronicallergies or asthma were excluded from becoming a candidate ofthe odor panel.

Table 2bMeasurements of NH3 and CO2 concentrations, room temperature and relativehumidity at different locations in the post-digestion dewatering building.

Sampling location NH3

(ppm)CO2

(ppm)RH(%)

T(�F)

Basement <5 485e689 56.2e70.4 68.2e76.3Biosolids collection floor 0e7 493e2511 27.0e73.9 52.4e88.4Centrifuge floor <5 456e991 26.6e64.4 67.7e78.3Primary biosolids conveyors 30e45 1333e>5000 59.5e82.7 66.6e88.9Secondary biosolids conveyors 0e9 508e1977 28e74.5 68.0e83.5Inclined biosolids conveyors <5 475e839 37.1e73.4 64.2e78.2

The lower and higher bounds of each range represent the minimum and maximummeasured values of the corresponding variable during the entire sampling period.

T. Wang et al. / Atmospheric Environment 45 (2011) 7235e7239 7237

2.2.2. Analytical measurementsH2S, DMS and DMDS were identified as major odorants in the

post-digestion dewatering building. Their concentrations areanalyzed using Jerome 631-X H2S Analyzer (Arizona Instruments,Phoenix, AZ) and zNose 4200 Ultra fast GC (Electronic SensorTechnology, Newbury Park, CA), see Table 1. All instruments werecalibrated at appropriate time intervals in the laboratory and inaccordance to the manufacturer’s requirement. On ConventionalQuality Control/Quality Assurance (QA/QC) protocols were imple-mented for sampling, analyses in the laboratory, and data coding.

3. Results and discussion

3.1. Characterizations of odors in the post-digestion dewateringbuilding

The range of odor and odorant concentrations measuredthroughout the post-digestion dewatering building is shown inTable 2a. Table 2b shows NH3 and CO2 concentrations, relativehumidity (RH) and room temperature (T) at each sampling area.The lower and upper bounds of each range represent the minimumand maximum measured values for the corresponding variableduring the entire sampling period. Data are summarized from thedatabase formed the survey and comprehensive samplingcampaigns.

Table 3 lists odor threshold values of three sulfur compoundsand ammonia. Since odor threshold values of these chemicals arevariable, ranges of odor thresholds and commonly used valuesformulated from the indicated references are listed. The 8-h totalweight average (TWA) permissible exposure limits (PEL) estab-lished by OSHA (Occupational Safety and Health Administration),and threshold limit values (TLV) by ACGIH (American Conference ofGovernmental Industrial Hygienists) for each chemical are alsolisted. The limit values specified by OSHA and ACGIH are based onhuman health effects; they identify the odorant concentration thatmust be exceeded to elicit a health response. Consequently expo-sure of humans to concentrations at lower than these levels doesnot cause adverse health effects. However, the limits are muchhigher than the odor detection threshold values. This signifies thatthe health limits for odorous chemicals are necessary for protectingthe health of exposed workers but not sufficient guidelines forprotecting the same workers from potential effects caused byexposures to odors such as complaints of discomfort, stress ordisease (Kilbum and Warshaw, 1995; Schiffman et al., 1995; Daltonand Jaen, 2010).

From Table 2a, the observed maximum H2S concentrationdetected at most places in the dewatering building is lower thanH2S exposure limits but higher than its odor threshold value. Note

Table 2aMeasurements of odor and odorant concentrations at each sampling location in thepost-digestion dewatering building.

Sampling location Odor (D/T) H2S (ppm) DMS (ppm) DMDS (ppm)

Basement 6e27 0.019e0.06 ND NDBiosolids collection

floor13e245 0.023e0.14 NDe0.088 NDe0.004

Centrifuge floor 9e78 0.005e0.92 ND NDPrimary biosolids

conveyors219e790 1.3e13 0.145e0.29 0.004e0.016

Secondary biosolidsconveyors

20e198 0.091e0.75 NDe0.096 NDe0.004

Inclined biosolidsconveyors

14e150 0.018e0.32 ND ND

ND e Not detected.The lower and higher bounds of each range represent the minimum and maximummeasured values of the corresponding variable during the entire sampling period.

that at the primary biosolids conveyors the maximum H2Sconcentration is higher than OSHA PEL and ACGIH TLV values.However, primary conveyors are source areas and workers usuallyspend short periods of time in the vicinity of the conveyors;consequently workers are mostly exposed to diluted and lowerthan the above standard levels. Concentrations of the two organicsulfur compounds, DMS and DMDS, do not exceed their exposurelimits, but are higher than their odor detection thresholds.

Correlations between odor concentration and pertinentparameters are checked with Spearman’s rho test, and results arelisted in Table 4. Room temperature is the only variable that is notsignificantly correlated with odors. NH3 is significantly correlatedwith odor, however at most places in the dewatering building NH3concentrations were lower than the NH3 odor detection threshold.Hence the NH3 contribution to the perceived odor is less than thecontribution of sulfur compounds in the building. Three sulfurcompounds, relative humidity (RH) and CO2 concentrations aresignificantly correlated with odors. Although RH does not affectsource emissions, increasing relative humidity may enhancehuman perception of odors.

3.2. Model formulation of odor and odorant relationship

Based on the database generated by the original comprehensiveodor study, data measured from outdoor samples were excludedbecause they are not pertinent to the environment of the presentstudy, which focuses on odor and odorant conditions inside thedewatering building. A portion of the database (n ¼ 102) was usedfor odor-odorantmodel formulation; 70 out of 102 data points wererandomly selected to construct a statistical multiple regressionmodel. The statistical model is constructed using the SPSS (SPSS Inc.Version 17.0) and the stepwise regression method:

log ðCouÞ ¼ 1:588þ 0:525� log�CH2S þ CDMS þ CDMDS

�

þ 0:01� RH (2)

R square: 0.81; where Cou is odor concentration (D/T), CH2S, CDMSand CDMDS are concentrations of the three sulfur compounds(ppmv), and RH is the field relative humidity (%).

CO2 is employed as ametric of building air ventilation. Since CO2

is odorless, it does not contribute to the odoreodorant association.NH3 concentrations were not included as input to the modelbecause the vast majority of its measured level was below the NH3odor detection threshold. The coefficient of determination R squareis 0.81 and the adjusted R square is 0.80, which indicate that about81% of the variation of odor concentrations is explained by themodel. The format of this model is similar to the power law formatshown in Eq. (1).

The model relates odor concentrations with concentrations ofall three sulfur compounds and relative humidity (RH). If only H2Sand RH were taken into consideration and the other two sulfur

Table 3Odor thresholds and permissible exposure limits for sulfur and nitrogen compounds.

Compound Range of odor thresholdvalues (ppmv)

Generally usedvaluea (ppmv)

References OSHA PEL TWA (ppmv) ACGIH TLV TWA (ppmv)(ACGIH, 2011)

H2S 0.0005e0.13 0.0081 Amoore and Hautala, 1983 10 (CFR, 2006a) 1DMS 0.0003e0.02 0.001 Buttery et al., 1990 Not established 10DMDS 0.00016e0.012 0.008 Fors, 1988 Not established 0.5NH3 0.043e53 17 AIHA Press, 1993 50 (CFR, 2006b) 25

a “Generally used values” by indicated references.

T. Wang et al. / Atmospheric Environment 45 (2011) 7235e72397238

compounds were excluded, the R square value of the reducedmodel would change to 0.80 (adjusted R square is 0.79) indicatingthat adding DMS and DMDS to the regressionmodel increases the Rsquare value by only 0.01. Therefore the addition of DMS and DMDScontributes minimally to the variance explanation of the depen-dent variable. However if RH were excluded from the model, Rsquare of the modified model would reduce to 0.72 (adjusted Rsquare is 0.72). Inclusion of the RH term is a unique feature of thismodel and it increases the coefficient of determination from 0.72 to0.81, which indicates that adding RH term improves the model.Increasing RH may not change the source emissions of odor andodorant, but this model supports the notion that increased relativehumidity levels enhance human perception of odors.

The regression model formulated in this study is based onseveral assumptions. To assure that the analysis, model formula-tion, is valid the assumptions were verified by appropriate regres-sion diagnostic analysis, which confirmed that the assumptions aresatisfied by the database and that the model is not constrained byviolation of assumptions.

Fig. 1. Plot of measured log(Cou) versus modeled log(Cou) with data from the studiedWRP.

3.3. Model validation

The coefficient of determination, R square, frequently employedas a validation index, is not a sufficient assessment of a model’spredictive ability or its accuracy. In addition to this coefficient thereare several other approaches that may be used for model validation.In this paper, the split-sample validation approach was applied(Snee, 1977). The model discussed in this paper was validated witha random sub-sample (n¼ 32) collected from the studiedWRP. Themeasured and the modeled predicted odor concentration valueswere compared. Fig. 1 shows the linear relationship between themeasured log(Cou) and model log(Cou) values along with the 95%confidence interval of the regression line. It is seen that the modelfits the measured values very well (R square ¼ 0.90). In addition,the two sample KolmogoroveSmirnov test, which serves asa goodness of fittest, was also employed to test the hypothesis thatthe model predicted values andmeasured values are from the sameprobability distribution. Statistical results show that the p-value forthe KolmogoroveSmirnov test is 0.088 (>0.05), which indicatesthat the null hypothesis is not rejected. Therefore, the measuredand model predicted odor concentration values are statisticallyfrom the same distribution.

In addition to validating the model with on-site data, a smallerindependent databases generated from a similar post-digestiondewatering building in another WRP was used to validate themodel. Fig. 2 shows the plot of measured and model predicted

Table 4Results of correlation test between odor concentration and pertinent parameters(n ¼ 102).

Spearman’s rho H2S DMS DMDS NH3 Temp RH CO2

Odor concentrationcorrelation coefficient

0.873 0.569 0.363 0.583 0.124 0.623 0.691

p-values 0.000 0.000 0.005 0.000 0.367 0.000 0.000 Fig. 2. Plot of measured log(Cou) versus modeled log(Cou) with data from a similarpost-digestion dewatering building in a different WRP.

T. Wang et al. / Atmospheric Environment 45 (2011) 7235e7239 7239

log(Cou) values, and the R square of fitting the regression model is0.85. Not surprisingly, this R square is slightly lower, than the Rsquare value of the validation effort with data from the originalplant; this is attributed to variance among plants. Clearly, an Rsquare value of 0.85 represents a model that predicts wellmeasured values. The KolmogoroveSmirnov test was also used tocompare the measured and model predicted values, and the p-value is 0.627. Similar to the result for the original WRP, themeasured observations and model predicted odor concentrationsare from the same probability distribution. This result indicates thatthe model formulated in this paper can be applied to similarfacilities in other WRPs.

4. Conclusions

It is established that the post-digestion dewatering building isone of the most odorous indoor environments in Water Reclama-tion Plants. All, but one, model studies for sewage gases relatingodor and odorant concentrations use databases formed in outdoorenvironments; the only model that links indoor odor perceptionwith odorant concentrations used a database from incubatedbiosolids for six-days. The study reported in this paper developedand validated a model associating indoor odor and odorantconcentrations emitted from freshly dewater biosolids cakes. Thisempirical model relates odor and combined concentrations ofsulfur compounds following the power law, which has been used inoutdoor environments. Importantly and uniquely, the modeldeveloped in this study adds relative humidity as a second inde-pendent variable leading to better explanation of the odorperception variance than that of models that include only one H2S,which is conventionally used in similar models.

Results of this study show that in a post-digestion dewateringbuilding, total sulfur concentration is a slightly better indicator ofthe association between odors and odorants. The addition of rela-tive humidity as an independent variable significantly enhances theability of investigators to associate odors with odorants because itaffects the odor acuity of the exposed individuals.

The literature employs hydrogen sulfide as a surrogate metricfor odors, yet this study shows that the presence of H2S atconcentrations below its occupational exposure limit (OEL) doesnot assure an odor-free environment which is not the intentionwith OELs, which intend protection against “threat to health”effects. In addition, reduction of H2S by dilution does not lead toa same proportion of odor reduction. This difference on the impactof dilution between odorants and odors suggests that the mostefficient and effective means of controlling odors is source controlinstead of increasing indoor ventilation.

Like all regression models the model generated by this work isan empirical model; however by validating with data froma different WRP, this model can be applied to similar facilities. Thisregression model can be used as a tool for assessing control strat-egies that reduce both odorants and odors to desirable levels.

References

ACGIH, 2011. 2011 Editions of TLVs and BEIs and Guide to Occupational ExposureValues. American Conference of Governmental Industrial Hygienists, Cincinnati,OH, USA. http://www.acgih.org/store/ProductDetail.cfm?id¼2147,http://www.acgih.org/store/ProductDetail.cfm?id¼2148 (accessed 08.15.11.).

Adams, G.A., Witherspoon, J., Card, T., Erdal, Z., Frobes, B., McEwen, D.,Geselbracht, J., Glindermann, D., Hargreaves, R., Hentz, L., Higgins, M.,Murthy, S., 2003. Identifying and Controlling Odor in the Municipal Wastewater

Environment Phase II: Impacts of In-Plant Parameters on Biosolids OdorQuality. Rep. no. 00-HHE-5T. Water Environment Research Foundation.

AIHA Press, 1993. Odor Thresholds for Chemicals with Established OccupationalHealth Standards. American Industrial Hygiene Association, Fairfax, VA.

Amoore, J.E., Hautala, E., 1983. Odor as an aid to chemical safety: odor thresholdcompared with threshold limit values and volatilities for 214 industrialchemicals in air and water dilution. Journal of Applied Toxicology 36 (6),272e290.

ASTM E679-04, 2004. Standard Practice for Determination of Odor and TasteThreshold by a Forced-Choice Ascending Concentration Series Method ofLimits. ASTM International, Philadelphia, PA, USA.

Berglund, B., Berglund, U., Thomas, L., 1976. Psychological processing of odormixtures. Psychological Review 83 (6), 432e441.

Blanes-Vidal, V., Hansen, M.N., Adamsen, A.P.S., Feilberg, A., Petersen, S.O.,Jensen, B.B., 2009. Characterization of odor released during handling of swineslurry: part I. relationship between odorants and perceived odor concentra-tions. Atmospheric Environment 43 (18), 2997e3005.

Buttery, R.G., Teranishi, R., Ling, L.C., Turnbaugh, J.G., 1990. Quantitative and sensorystudies on tomato paste volatiles. Journal of Agricultural and Food Chemistry38, 336e340.

Cain,W.S., Leaderer, B.P., Isseroff, R., Berglund, L.G., Huey, R.J., Lipsitt, E.D., Perlman, D.,1983. Ventilation requirements in buildings e I. control of occupancy odor andtobacco smoke odor. Atmospheric Environment 17 (6), 1183e1197.

CFR, 2006a. 29 CFR 1926.55 Appendix A (last updated 2006). http://www.osha.gov/pls/oshaweb/owadisp.show_document?p_table¼STANDARDS&p_id¼10629(accessed 8.15.11.).

CFR, 2006b. 29 CFR 1910.1000 Z-1 Table (last updated 2006). http://www.osha.gov/pls/oshaweb/owadisp.show_document?p_table¼STANDARDS&p_id¼9992&p_text_version¼FALSE (accessed 8.15.11.).

Dalton, P.H., Jaen, C., 2010. Responses to odors in occupational environments.Current Opinion in Allergy and Clinical Immunology 10 (2), 127e132.

Dincer, F., Muezzinoglu, A., 2007. Odor determination at wastewater collectionsystems: olfactometry versus H2S analyses. Clean 35 (6), 565e570.

Fang, L., Clausen, G., Fanger, P.O., 1998. Impact of temperature and humidity onperception of indoor air quality during immediate and longer whole-bodyexposures. Indoor Air 8, 276e284.

Fors, S., 1988. Sensory properties of volatile maillard reaction products and relatedcompounds e the maillard reaction in foods and nutrition. In: Waller, G.R.,Feather, M.S. (Eds.), ACS Symposium Series 215. ACS, Washington, pp. 185e286.Chapter 12.

Frederick, D.E., Barlas, L., Ievins, A., Key, L.M., 2009. A critical test of the overlaphypothesis forodormixtureperception. BehavioralNeuroscience123 (2),430e437.

Gostelow, P., Parsons, S.A., 2000. Sewage treatment works odor measurement.Water Science and Technology 41 (6), 33e40.

Higgins, M.J., Chen, Y.C., Yarosz, D.P., Murthy, S.N., Maas, N.A., Glindemann, D.,Novak, J.T., 2006. Cycling of volatile organic sulfur compounds in anaerobicallydigested biosolids and its implications for odors. Water EnvironmentalResearch 78 (3), 243e252.

Hobbs, P.J., Misselbrook, T.H., Dhanoa, M.S., Persaud, K.C., 2001. Development ofa relationship between olfactory response and major odorants from organicwastes. Journal of the Science of Food and Agriculture 81, 188e193.

Kilbum, K.H., Warshaw, R.H., 1995. Hydrogen sulfide and reduced-sulfur gasesadversely effect neurophysiological functions. Toxicology and Industrial Health11 (2), 185e197.

Koe, L.C.C., 1985. Hydrogen sulphide odor in sewage atmospheres. Water Air SoilPollution 24, 297e306.

Laing, D.G., Eddy, A., Best, D.J., 1994. Perceptual characteristics of binery, terinary,and quaternary odor mixtures consisting of unpleasant constitutes. Physiology& Behavior 56 (1), 81e93.

Laska, M., Hudson, R., 1991. A comparison of the detection thresholds of odourmixtures and their components. Chemical Senses 16, 651e662.

Patterson, M.Q., Stevens, J.C., Cain, W.S., Commeto-Muniz, J.E., 1993. Detectionthresholds for and olfactory mixture and its three constitutes compounds.Chemical Senses 18, 723e734.

Reinikainen, L.M., Jaakkola, J.J.K., 2003. Significance of humidity and temperature onskin and upper airway symptoms. Indoor Air 13, 344e352.

Reinikainen, L.M., Jaakkola, J.J.K., Seppanen, O., 1992. The effect of air humidificationon symptoms and perception of indoor air quality in office workers, a six-periodcross-over trial. Arch. Environ. Health 47, 8e15.

Schiffman, S.S., Miller, E.A.S., Suggs, M.S., Graham, B.G., 1995. The effect of envi-ronmental odors emanating from commercial swine operations on the mood ofnearby residents. Brain Research Bulletin 37, 369e375.

Snee, R.D., 1977. Validation of regression models: methods and examples. Techno-metrics 19, 415e428.

Tsai, C.J., Chen, M.L., Ye, A.D., Chou, M.S., Shen, S.H., Mao, I.F., 2008. The relationshipof odor concentration and the critical components emitted from food wastecomposting plants. Atmospheric Environment 42 (35), 8246e8251.

Witherspoon, J.R., 2006. Biosolids Processing Modifications for Cake Odor Reduc-tion, WERF 03-CTS-9T (Water Environment Research Foundation Program AreaMeeting).