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Asian Journal of Atmospheric Environment - Vol. 11 , No. 1

[ Research Article ]
Asian Journal of Atmospheric Environment - Vol. 11, No. 1
Abbreviation: Asian J. Atmos. Environ
ISSN: 1976-6912 (Print) 2287-1160 (Online)
Print publication date 31 Mar 2017
Received 11 Dec 2016 Revised 31 Dec 2016 Accepted 31 Dec 2016

Performance of Air Fresher System for the Removal of Various Odorants Released from Foodstuffs
Ki-Hyun Kim1), * ; Adedeji A. Adelodun2) ; Akash Deep1), 3), * ; Eilhann E. Kwon4) ; Eui-Chan Jeon4) ; Yong-Hyun Kim1) ; Sang-Hee Jo1) ; Min-Hee Lee1) ; Sung-Back Cho5) ; Ok-Hwa Hwang5)
1)Department of Civil and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seoul 04763, Republic of Korea
2)Department of Marine Science and Technology, School of Earth and Mineral Science, The Federal University of Technology, P.M.B. 704, Akure, Nigeria
3)Central Scientific Instruments Organisation (CSIR-CSIO), Sector 30 C, Chandigarh, 160030, India
4)Department of Environment and Energy, Sejong University, Seoul 05006, Republic of Korea
5)National Institute of Animal Science, Rural Development Administration, Wanju-gun 55365, Republic of Korea

Correspondence to : * Tel: +91-172-2657811-452, E-mail: dr.akashdeep@csio.res.inTel: +82-2-2220-2325, E-mail:

Funding Information ▼


The effectiveness of four air fresher (AF) systems was evaluated with respect to their removal efficiencies against offensive odorants. For this purpose, malodorous species were generated by exposing freshly cooked foods emitting odorants with levels moderately above their respective threshold values in a confined room. The deodorization efficiency of the four AF systems was then tested for a period of 30 min by estimating the extent of reduction in odorant levels after the operation of each AF. The removal efficiency of the four AF units against each odorant was evaluated as follows: (1) between AF products from different manufacturers, (2) between odorants and ultrafine particulate matter (PM2.5), and (3) between operation and natural degassing. The average sorptive removal of odorants was generally <80% and considered less effective or non-effective relative to PM2.5. Further examination of odor reduction, if evaluated in terms of odor indices like odor intensity (OI) and odor activity value (OAV), recorded a mean of 33% and 87%, respectively. The overall results of this study confirmed that all tested AF units were not effective to resolve odor problems created under our testing conditions.

Keywords: Air fresher, Food odor, Hydrogen sulfide, Ammonia, Fresh food odor

1. Introduction

The demand for clean air is rapidly increasing. In our living environments, various odorants are released from diverse sources or activities including cooking, use of spray products and cosmetics, or painting (Kim and Kim, 2014; Kim et al., 2013; Lee et al., 2012; Du et al., 2011; Burbank and Qian, 2008; Frauendorfer and Schieberle, 2008; Ahn et al., 2007; Lee et al., 2006; Wettasinghe et al., 2001). Odorants are very diverse and include various volatile species such as reduced sulfur compounds (RSCs), carbonyl compounds (CCs), volatile fatty acids (VFAs), nitrogenous compounds, and volatile organic compounds (VOCs). In fact, several VOCs (e.g., benzene, toluene, ethylbenzene, and the xylene isomers (BTEX)) represent the most commonly cited contaminants. However, there are a variety of VOCs with diverse odor profiles that contribute to odor pollution under various conditions in diverse settings.

The aftermath of active anthropogenic activities generally leads to the significant emission of volatile and odorous pollutants. Because many pollutants are carcinogens and/or allergens, they should be adequately treated to prevent potential health-related problems in many situations. In light of the significant problems associated with indoor pollution, the use of air fresher (AF) systems has become a common trend for the maintenance of air quality in confined environments. The efficiency of AF systems has often been recognized in the control of particulate matter (PM) (Huss et al., 2010); in addition, AFs have been advertised for the removal of odorant substances. However, the reliability of AF systems with respect to odorant species has been poorly validated (Kim et al., 2010; Choi et al., 2002). So far, the most commonly employed means of mitigating odor pollution is decomposition by photocatalysis (Huang et al., 2015; Yu et al., 2013; Mo et al., 2009; Geng et al., 2008; Hodgson et al., 2007; Sakai et al., 2006; Ao and Lee, 2005; Kataoka et al., 2005; Oh, 2003; Zhao and Yang, 2003). However, in light of the inherent shortcomings of AF systems (such as high cost, low availability, lack of portability, eye irritation etc.), many efforts have been put forward to develop efficient AF systems to diminish such issues (Kim, 2006).

In order to learn more about the performance of AFs in odor problem treatment, we investigated the deodorization capacity of four popular AF products commercially available on the Korean market. To examine their deodorization efficiency, we operated the different AF systems under strong malodorous atmospheres using a number of freshly cooked foodstuffs with intrinsic strong odorant properties. The analysis of diverse odorants was conducted both before and after the operation of each AF system and in reference to natural degassing conditions. The results of this study provide valuable insight into the removal characteristics of various odorants by the selected AF systems.

2. Materials and methods

In this work, four types of AF instruments (A, B, C, and D) were procured to test their performance against odor pollution originating from diverse foodstuffs and in reference to PM2.5 (Huss et al., 2010; Zhao et al., 2007). Table 1S provides descriptive information on the AFs and sampling of the odorants. The chosen food samples are known to exhibit pungent smells (Lee et al., 2012; Burbank and Qian, 2008; Wettasinghe et al., 2001). We focused on a list of popular VOCs that are key offensive odorants such as acetaldehyde, hydrogen sulfide, and trimethylamine (KMOE, 2007). The basic physicochemical properties (e.g., chemical formula, structural formula, molecular weight, CAS number, etc.) of all target compounds (n=24) that are designated as offensive odorants by the KMOE (2007) are summarized in Table 1. The detailed information of analytical procedures used for each air component is described in the supporting information (Table 2S) along with operation conditions for all instrumental systems. The procedures for their quantitation have been described in our recent study (Jo et al., 2013)

Table 1. 
List of the major odorants and volatile organic compounds investigated in this study.
Order Group Full name Short
CAS No. Chemical
weight (g/mol)
analysis methodc
1 Aldehyde Formaldehyde FA 50-00-0 CH2O 30.0 18,882 0.9956 0.004 0.86 DNPH-cartridge/
2 Acetaldehyde AA 75-07-0 C2H4O 44.1 14,647 0.9965 0.01 0.27
3 Propionaldehyde PA 123-38-6 C3H6O 58.1 11,244 0.9968 0.01 0.06
4 Butyraldehyde BA 123-72-8 C4H8O 72.1 8,906 0.9966 0.01 1.27
5 Isovaleraldehyde IA 590-86-3 C5H10O 86.1 7,427 0.9966 0.01 1.52
6 Valeraldehyde VA 110-62-3 C5H10O 86.1 7,162 0.9970 0.01 1.15
7 Ketone Acrolein Acrolein 107-02-8 C3H4O 56.1 13,511 0.9960 0.01 0.71
8 Acetone Acetone 67-64-1 C3H6O 58.1 9,408 0.9979 0.01 0.29
9 Aldehyde Crotonaldehyde CA 4170-30-3 C4H6O 70.1 9,519 0.9961 0.01 1.89
10 Benzaldehyde BZA 100-52-7 C7H6O 106 6,211 0.9959 0.01 2.05
11 Volatile fatty
Propionic acid PPA 79-09-4 C3H6O2 74.1 10,047 0.9949 0.27 0.60 ST/TD/GC/Q-MS
12 n-Butyric acid BTA 107-92-6 C4H8O2 88.1 8,604 0.9969 0.16 1.14
13 i-Valeric acid IVA 503-74-2 C5H10O2 102 33,172 0.9987 0.09 0.41
14 n-Valeric acid VLA 109-52-4 C5H10O2 102 28,028 0.9995 0.10 0.49
15 i-Butyric acid IBA 79-31-2 C4H8O2 88.1 39,131 0.9994 0.09 2.72
16 Hexanoic acid HXA 142-62-1 C6H12O2 116 39,684 0.9985 0.09 0.22
17 Heptanoic acid HPA 111-14-8 C7H14O2 130 32,166 0.9991 0.08 0.50
18 Nitrogenous Trimethylamine TMA 75-50-3 C3H9N 59.1 46,196 0.9984 0.05 0.25
19 Sulfurous Hydrogen sulfide H2S 7783-06-04 H2S 34.1 53.9 0.9992 0.15 3.94 AS/TD/GC/SCD
20 Methanethiol CH3SH 74-93-1 CH4S 48.1 38.0 0.9981 0.22 3.46
21 Dimethyl sulfide DMS 75-18-3 C2H6S 62.1 34.5 0.9961 0.24 1.76
22 Dimethyl disulfide DMDS 624-92-0 C2H6S2 94.2 37.9 0.9961 0.22 2.19
23 Nitrogenous Ammonia NH3 7664-41-7 NH3 17.0 18.3 0.9999 0.59 0.41 Sensor
24 Ultrafine dust
Particulate Matter
Less than 2.5 μm
PM2.5 - - - - - - - 90° light-scattering
laser photometer
aMethod detection limit (MDL): The product of the standard deviation of 7 replicate analyses multiplied by the Student’s t-test at the 99.9% confidence level (6 df, t=3.14).
bRelative standard error (RSE): Triplicate analyses of the standards at one calibration point.
cHPLC=high-performance liquid chromatography, UV=ultraviolet, ST=sorbent tube, TD=thermal desorber, GC=gas chromatography, Q-MS=quadruple mass spectrometer, AS=air server, SCD=sulfur chemiluminescence detector.

2. 1 Sample Collection

For the purpose of this study, four types of foodstuffs (soy bean soup, steamed egg, kimchi, and fermented skate fish) were purchased from a local market on the day of the AF test. Sampling was conducted in a room (capacity of≈20 m3) at a mean temperature of 22±1℃. All four foodstuffs were placed on a table in the closed room for 10 min to ensure the generation of sufficient odorant levels before air samples were collected using both sorbent tubes and bag samplers. Simultaneously, PM2.5 was also quantified using a PM sensor. Each AF system was repeatedly operated for 30 min after removing the food samples from the room. Lastly, air samples were again collected and analyzed using each analytical instrument.

2. 2 Instrumental Set-up for Analysis

In this study, a total of 17 odorous compounds (including two reference compounds) were measured by three different analytical methods: (1) air server (AS)/thermal desorber (TD)/gas chromatography (GC)/pulsed flame photometric detector (PFPD) for five sulfur compounds (hydrogen sulfide, methanethiol, dimethyl sulfide, and dimethyl disulfide), (2) TD/GC/mass spectrometry (MS) for 13 odorants (aldehyde, ketone, ester, alcohol, carboxylic groups, and trimethylamine), and (3) ultraviolet (UV)/visible (Vis) spectrophotometry for ammonia analysis based on the indophenol blue method (Kim and Kim, 2014; Kim and Kim, 2013, 2012). In addition to GC and spectrophotometry-based analysis, air dilution sensory (ADS) tests were performed based on the olfactometry threshold method by diluting odor samples with odorless pure air to determine the dilution-to-threshold ratio (D/T ratio) values (Kim and Park, 2008). Detailed information of these analytical procedures regarding the following targets is available in our previous work (Jo et al., 2013) as well as in the supporting information of the present work (Table 2S).

2. 3 Data Analysis

Apart from the preliminary quantitation of the differences between the input and output odorant concentrations, three analytical parameters, including percent sorptive removal (%SR), odor intensity (OI; (Nagata, 2003)) and odor activity value (OAV; (Qian and Wang, 2005)) were assessed in order to estimate the performance of the different AF units toward all different target odorants. Generally, the SR of an odorant is an indication of adsorption efficiency of the air purifier system. The SR was estimated as the difference in the concentration data for each odorant as given in equation 1:


where X1 and X2 represent the concentrations of odorant before (input) and after (output) exposure to the AF system, respectively. Following this simple estimation, the use of Student t-tests was employed to statistically investigate the significant differences between the efficiencies of each pair of treatment conditions (i.e., paired combination among different AF units (A, B, C and D) as well as the natural degassing (E) condition). In the case of ultrafine dust removal (DR), %DR was also estimated.

The concept of OI and OAV pair can be used to numerically quantify or describe the strength of known odorants emitted from identifiable sources. The full details of these concepts have already been described in our recent work (Adelodun et al., 2015). Therein, we stated that by the use of odorant concentration, the odor intensity (OI, measured in OU m-3) of all target compounds is calculated according to Stevens (1960), where psychophysical phenomena (stimuli) such as odor strength follows a power law (Stevens, 1960). The law is expressed as:


where X is the odorant concentration in mg m-3, k is the stimulus-dependent constant (Dravnieks et al., 1986), and n is the characteristic constant of each odorant. The relationship between perceived psychological intensity and odorant concentration was later modeled by the Weber-Fechner law (modified from the Steven’s law) as:

OI=a log X+b(3) 

where a is the Weber-Fechner coefficient and b is the intercept constant (Jiang et al., 2006). By the calculation of OI, the extent of reduction in odor intensity due to the purifiers could also be assessed in addition to the pattern for their concentration data. OAV (Qian and Wang, 2005), which is a dimensionless expression of odor strength, was obtained by the ratio of the odorant concentration after exposure (to a purifier, X2) to the odorant threshold value (Nagata, 2003; Nagata and Takeuchi, 2003). Regardless of the extent by which the odor concentration is reduced after treatment, the odorant removal system is deemed “not environmentally efficient” if OAV>1, and vice versa. Hence, lower OAV values indicate a more efficient air purifier system in lowering odorant (and dust pollution) menace in the environment. Along with sorptive removal (%SR), these odor indices (OI and OAV) were used to judge the performance of the four commercially available air purifiers. Similarly, we also estimated percent reduction and we made comparisons with data presented in the open literature.

3. Results and discussion
3. 1 Removal of Odorant and Ultrafine Dust Particles by Air Purifiers

Using the analytical procedures described in Table 2S, the reduction in target odorant concentration (in logarithm form) by the four AFs (A to D) and natural degassing (E) was monitored and recorded (Fig. 1S). The concentrations (in ppb) before (- 1) and after (- 2) use of each AF are listed in Table 2. We observed that all AFs did not show any significant response to five odorants (PA, IA, VA, CA, and BZA). All the odorants had an unchanged concentration value between initial and final measurements under our experimental conditions (1.59, 0.90, 0.75, 0.94, and 0.97 ppb, respectively) between blanks and samples. Apart from these five targets, ammonia was the only odorant that did not undergo any observable changes from natural degassing conditions (i.e. E1=E2=1,000 ppb). As one of the most influential odorants most commonly encountered, a significant reduction in the concentration levels of ammonia would be an appreciable challenge for AF. However, as there was no detectable change in the concentration of ammonia in natural degassing, the performance of each AF cannot be directly compared relative to such conditions, i.e. below detection limit (BDL) as written in the footnote of Table 2. As the odorants that were BDL were not evaluated further, the total number of targets was reduced to 18 odorants for further statistical analyses.

Table 2. 
Comparison of concentration levels of odorants and VOC measured from air samples.
Order Group Short name Unit Concentration of test samples Blank sample
A-1 A-2 B-1 B-2 C-1 C-2 D-1 D-2 E1 E2
1 Aldehyde FA ppb 207 109 97.3 68.6 80.2 74.1 88.7 88.9 146 123
2 AA ppb 86.0 68.9 32.1 36.5 32.9 24.9 36.0 42.8 77.9 72.6
3 PA ppb 1.59 1.59 1.59 1.59 1.59 1.59 1.59 1.59 1.59a 1.59
4 BA ppb 3.99 2.87 2.96 3.59 2.76 3.31 2.95 2.77 4.03 3.34
5 IA ppb 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90
6 VA ppb 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75
7 Ketone Acrolein ppb 9.80 5.90 2.65 1.99 1.94 1.61 2.65 1.82 7.39 5.53
8 Acetone ppb 32.6 19.2 15.7 18.8 12.1 12.5 14.4 16.9 50.4 46.9
9 Aldehyde CA ppb 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94
10 BZA ppb 0.97 0.97 0.97 0.97 0.97 0.97 0.97 0.97 0.97 0.97
11 Volatile fatty
PPA ppb 2.31 1.54 3.28 1.90 2.40 1.71 2.48 2.22 2.74 2.56
12 BTA ppb 0.38 0.12 1.01 0.22 0.46 0.23 0.74 0.27 0.41 0.28
13 IVA ppb 1.28 0.48 3.56 0.87 1.30 0.51 2.91 0.77 1.40 0.70
14 VLA ppb 0.41 0.35 0.37 0.30 0.28 0.26 0.35 0.33 0.49 0.45
15 IBA ppb 9.21 1.53 28.0 5.26 9.81 2.35 21.0 4.02 9.94 3.02
16 HXA ppb 0.20 0.10 0.18 0.10 0.14 0.13 0.17 0.16 0.23 0.18
17 HPA ppb 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.07 0.06
18 Nitrogenous TMA ppb 87.6 30.6 73.2 20.9 38.5 21.4 80.9 18.6 84.0 47.6
19 Sulfurous H2S ppb 14.4 2.15 15.3 5.11 17.8 7.56 16.2 3.90 27.9 20.1
20 CH3SH ppb 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 1.34 1.53
21 DMS ppb 0.88 0.87 0.53 0.71 0.67 0.54 0.91 0.91 1.27 1.48
22 DMDS ppb 1.18 1.75 0.75 0.68 0.85 0.71 0.85 0.58 2.11 2.21
23 Nitrogenous NH3 ppb 13,000 7,000 8,333 4,333 9,667 3,833 10,333 4,857 1,000 1,000
24 Ultrafine
particles dust
PM2.5 mg/m3 3.13 0.09 1.66 0.03 1.29 0.06 0.59 0.03 0.62 0.59
aThe underlined values imply results that were below the detection limit (BDL) of each sample.

The %SR values were calculated (equation 1) to estimate the extent of reduction in the target odorant levels due to the AF operation (or degassing). The obtained results are provided in Fig. 1 (Table 3S). Because adsorption is known to be influenced by both surface chemical groups and atomic or molecular weights, comparisons among the odorants were made and depicted according to (a) chemical functionalities and (b) decreasing order of molecular weights. It was obvious from the haphazard distribution of %SR that the extent of sorptive removal was neither directly nor solely dependent on such criteria. In certain cases, the AFs lowered the level of odorants relative to those reduced by natural degassing. As such, the results suggest that the effectiveness of AF with respect to odorants is not satisfactory relative to PM. Exceptions to this affirmation were observed where one or two AFs tended to either increase the levels of specific odorants (by emission) or were inefficient (slower scavenging rate) when compared to natural degassing. According to Fig. 1(b), such cases included those of HPA (all AFs), HXA (C & D), DMDS (A), BA (B & C), DMS (B), acetone (B & D) and AA (C & D). Here again, since no AF showed superior preference toward a specific chemical group of odorants, a conclusive remark regarding discrepancies in efficiency and specificity among the AFs toward the various odorant targets could not be drawn. However, when considering differences in their chemical properties (Fig. 1(a)), it was found that all AFs performed very well (between 60% and 80%) against VFAs, TMA, H2S, and NH3. However, in the case of AA, BA, acetone, DMS, and DMDS, a negative %SR was commonly seen. A negative %SR value in this work signifies an occurrence of self-emission of the specific odorant(s) by the AF or a situation whereby the rate of desorption was faster than the adsorption rate. The AF unit B demonstrated the worst performance in this regard. On average (~44% SR), the A product exhibited the highest removal efficiency amongst the different products. Apart from this rough estimation, the other AFs performed similarly well. By general comparison of their %SR, the removal of IBA was moderate (~78% SR) while that of DMS was worst (~23% SR).

Fig. 1. 
Comparison of odor removal by natural degassing and air freshers with respect to (a) chemical group and (b) molecular weight.

Based on random removal efficiencies of the air purifying systems against various target odorants, Student’s paired t-tests were used (on the %SR) to determine if significant differences were present between the treatment pairs, i.e. between five sets of data (resulting in 10 pairs: A-B, A-C, A-D, B-C, B-D, C-D, E-A, E-B, E-C, and E-D) by examining the removal efficiency of the odorants after treatment. The results derived from this test on a population or a number of the 18 target compounds (df=17 and confidence limit of 95%) are listed in Table 4S. Accordingly, significant differences in the performance between each pair (tcal) were greater than the tabulated values (ttab) (i.e., tcal>ttab) in all cases. Performance-wise, among the AFs (based on the difference between tcal and ttab as well as between each pair of sorption efficiency), those for B and C products were the most closely related, while the highest discrepancy existed between the A and C products. It was also confirmed from this analysis that their correlation with natural degassing (E) was almost non-existent, as in most cases (especially for the E-A pair), tcal>>ttab. Judging from the large differences in the two terms between the pairs of natural degassing and the AFs (relative to the differences between each AF pair), the following conclusions can be drawn. Most importantly, although the odor removal ability of the AFs was higher than that of the natural scavenging process, they were yet ineffective in completely removing or significantly ameliorating the odor that originated from the selected food sources.

The response of the air purifier systems toward ultrafine particulates was also evaluated in reference to odorant data. From the actual concentration values provided in Table 2, we estimated percent dust removal (%DR) as shown in Fig. 2, and the values were compared along with the concentration values (before and after removal) to assess the relationship between the two conditions. When the extent of reduction was compared in terms of concentration levels amongst the AFs, purifier A was found to show the best removal ability toward ultrafine dust particles compared to others as it lowered the concentration by ~3.0 mg/m3. Note that product D exhibited the worst performance in this respect at an ~0.5 mg/m3 reduction of concentration. Despite such differences, product D exhibited approximately the same %DR (~95%; range of ~90-96 %) as products A, B and C. As such, most of the AFs effectively treated ultrafine dust relative to the odorants tested in this work. In the absence of AFs, the removal of PM2.5 by natural means was found to be insignificant. We suggest that these particles are extremely fine and lightweight, and therefore tend to exhibit long residence time in the air by remaining suspended unless treated by the AF system.

Fig. 2. 
Comparison of PM2.5 concentrations (before and after treatment) and removal efficiency (%DR) by each air fresher.

3. 2 Evaluation of Odor Intensity and Odor Activity Value

The paired indices of odor strength (OI and OAV) have often been studied in the field of odor chemistry to control and assess the actual effect of odor pollution instead of evaluation based on concentration values (Jiang et al., 2006; Dravnieks et al., 1986; Stevens, 1960b). These calculations enable the quantification of olfactory stimuli strength for easy evaluation of intensity as well as comparison with other odorants.

In this study, OI values were estimated both before and after treatment using equation 3 and the detailed results are described in Table 3. Again, some odorants measured at or below the detection limit were not assessed here. In addition, CH3SH and NH3 were the only undetected odorants when natural degassing and the AF treatments were applied. Since the odorants were initially present at varying concentrations, calculating the percent reduction in terms of OI also quantitated the effect of the AF systems on each odorant. The results are depicted in Fig. 3(a). The odorants are arranged in descending order of %OI reduction to simulate the decreasing order of human tolerance. This comparison was also made along with those derived from natural degassing conditions. By virtue of OI values, the effect of the AF on various odorants was found to be random, as no AF showed either a particular preference or determent toward/against a particular or group of odorants. The after-treatment intensities (OIinitial-OIfinal) of IBA, along with the two commonly encountered inorganic odorants (H2S and NH3), were found to be the highest while those of sulfur-containing compounds (DMDS, CH3SH, and DMS) were the lowest (Table 3 and Fig. 3(a)). However, the order of OI reduction was IBA>H2S>TMA>NH3>PPA, and was found as the all-positive reduction results from the exercise. The increase in OI after treatment was random; consequently, we could infer that the effect of odor reduction in terms of OI-based assessment is not effective to rank the performance between the different AFs. The performance of all the AF units in lowering OIs of target odorants were nonetheless better than those of natural degassing. Such affirmation specifically holds true for IBA, H2S, TM, NH3 and PPA. Also, by comparing the ability of AFs to lower OI values, it was found that on average, purifier A was regarded as the product with the highest performance as it recorded superior values in OI reduction over the other products (Fig. 3(a)).

Table 3. 
Odor intensity (OI) values of main odorants emitted from gaseous samples.
Order Group Short
OI formula Odor intensity (OI)
A-1 A-2 B-1 B-2 C-1 C-2 D-1 D-2 E-1 E-2
1 Aldehyde FA Y=1.530 logX+1.59 0.54 0.12 0.04 - - - - - 0.31 0.20
2 AA Y=1.010 logX+3.85 2.77 2.68 2.34 2.40 2.35 2.23 2.39 2.47 2.73 2.70
3 PA Y=1.010 logX+3.86 - - - - - - - - - -
4 BA Y=1.030 logX+4.61 2.14 1.99 2.01 2.09 1.97 2.06 2.00 1.98 2.14 2.06
5 IA Y=1.350 logX+6.01 - - - - - - - - - -
6 VA Y=1.360 logX+5.28 - - - - - - - - - -
7 Ketone Acrolein Y=1.510 logX+3.30 0.27 - - - - - - - 0.08 -
8 Acetone Y=1.790 logX-1.64
11 Volatile fatty
PPA Y=1.580 logX+7.29 3.12 2.84 3.36 2.99 3.15 2.92 3.17 3.10 3.24 3.20
12 BTA Y=1.400 logX+1.05 - - - - - - - - - -
13 IVA Y=1.570 logX+2.44 - - - - - - - - - -
14 VLA Y=1.420 logX+3.10 - - - - - - - - - -
15 IBA Y=1.430 logX+5.08 2.17 1.05 2.86 1.82 2.21 1.32 2.68 1.65 2.22 1.48
18 Nitrogenous TMA Y=0.901 logX+4.56 3.61 3.20 3.54 3.05 3.29 3.06 3.58 3.00 3.59 3.37
19 Sulfur H2S Y=0.950 logX+4.14 2.39 1.61 2.42 1.96 2.48 2.12 2.44 1.85 2.66 2.53
20 CH3SH Y=1.250 logX+5.99 - - - - - - - - 2.40 2.47
21 DMS Y=0.784 logX+4.06 1.66 1.66 1.49 1.59 1.57 1.50 1.68 1.68 1.79 1.84
22 DMDS Y=0.985 logX+4.51 1.62 1.79 1.43 1.39 1.49 1.41 1.49 1.32 1.87 1.89
23 Nitrogenous NH3 Y=1.670 logX+2.38 4.24 3.79 3.92 3.44 4.03 3.35 4.07 3.53 - -

Fig. 3. 
Comparison of % reduction of odor intensity values of target odorants from (a) the current work with (b) similar results in the literature (McGinley and McGinley, 2006) (Note: Situations where the calculated % reduction in OI value is negative are not indicated in this figure).

In a similar work, three air purifiers (A, B, and C) and one fragrance (D) were tested against odors emitted from food samples (McGinley and McGinley, 2006). The effect of the treatment was monitored with respect to the extent of the odorant reduction over a period of 2 h and the reduction in %OI was further computed and plotted (Fig. 3(b)). In the current work, we estimated the percent reduction in OI odors between the four AFs used as well as that of natural degassing. In order to ensure quantitative comparison of our work with the reference (see dotted arrow in Fig. 3(b)), the percent reduction in odor after 20 min (as the period of experimental exposure used in current work) was compared. It was reported that the three (A, B, C) AFs showed an average reduction in OI of 4.6% (range ~3.1%-6.7%) while D exhibited a distinct value of 37.3%. Despite the fact that we selected multiple target compounds for comparison, the AFs used in our study showed on average a comparatively similar percent reduction of OI.

The OAV values were also computed to assess how effectively odor was reduced after treatment (Table 4). More importantly, investigation into how significantly the AFs faired in elevating the sanity of the test atmosphere was conducted. Similar to the assessment based on OI, the results were also compared based on percent reduction in OAV as provided in Fig. 2S. Here, PA, IA, VA, and CH3SH were the only inestimable odorants due to aforementioned reasons (under sorptive removal and OI analyses). Unlike the findings based on OI analysis, the OAV results showed a more appreciable response of the AFs towards the odorants. A percent reduction as high as 87% was exhibited by most AFs in terms of OAV, and if the results are compared in terms of relative ordering, the most significant reduction in OAV after the treatment was seen from IBA, H2S, IVA, BTA, TMA, and NH3. At the other extreme, odorants such as DMS, AA, acetone, and BA (in no specific order) demonstrated the least reduction in OAV values, and remain as difficult targets to treat based on this criterion.

Table 4. 
Odor activity values (OAV) for the main odorants emitted from gaseous samples.
Order Group Short
Odor threshold
Odor activity values (OAV)
A-1 A-2 B-1 B-2 C-1 C-2 D-1 D-2 E-1 E-2
1 Aldehyde FA 500 0.41 0.22 0.19 0.14 0.16 0.15 0.18 0.18 0.29 0.25
2 AA 1.50 57.3 45.9 21.4 24.4 21.9 16.6 24.0 28.5 51.9 48.4
3 PA 1.00 - - - - - - - - - -
4 BA 0.67 5.95 4.29 4.42 5.35 4.12 4.95 4.40 4.13 6.02 4.98
5 IA 0.1 - - - - - - - - - -
6 VA 0.41 - - - - - - - - - -
7 Ketone Acrolein 3.60 2.72 1.64 0.74 0.55 0.54 0.45 0.74 0.51 2.05 1.54
8 Acetone 42,000 0.001 0.0005 0.0004 0.0004 0.0003 0.0003 0.0003 0.0004 0.001 0.001
11 Volatile fatty
PPA 5.70 0.41 0.27 0.57 0.33 0.42 0.30 0.44 0.39 0.48 0.45
12 BTA 0.19 2.00 0.61 5.31 1.17 2.44 1.20 3.91 1.42 2.16 1.50
13 IVA 0.08 16.4 6.18 45.7 11.1 16.7 6.57 37.3 9.87 17.9 9.03
14 VLA 0.04 11.0 9.59 9.97 8.07 7.65 6.93 9.52 8.87 13.3 12.1
15 IBA 1.50 6.14 1.02 18.6 3.51 6.54 1.57 14.01 2.68 6.62 2.01
18 Nitrogenous TMA 0.032 2,737 956 2,286 654 1,202 670 2,528 580 2,625 1,488
19 Sulfur H2S 0.41 35.2 5.24 37.3 12.5 43.5 18.4 39.5 9.52 68.1 49.0
20 CH3SH 0.07 - - - - - - - - 19.1 21.9
21 DMS 3.00 0.29 0.29 0.18 0.24 0.22 0.18 0.30 0.30 0.42 0.49
22 DMDS 2.20 0.53 0.80 0.34 0.31 0.39 0.32 0.39 0.26 0.96 1.01
23 Nitrogenous NH3 1,500 8.67 4.67 5.56 2.89 6.44 2.56 6.89 3.24 - -

4. Conclusion

An experimental survey to assess the effectiveness of four commercially available air fresheners against a list of offensive odorants (and PM2.5) commonly emitted from cooking activities was conducted. In each case, performance of the AF units before and after operation was compared with a natural odor scavenging process. With regard to the %SR values, the AFs were helpful to lower the levels of the odorants. All AFs performed moderately (between 60% and 80% SR values) against certain odorants like VFAs, TMA, H2S, and NH3. In contrast, the AFs showed no reduction or a slight increase in AA, BA, acetone, DMS, and DMDS odors.

The effectiveness of AF operation was also assessed in terms of OAV. All AFs showed positive percent reduction values in OAV against 9 odorants, in descending order: IBA, H2S, IVA, BTA, TMA, NH3, acrolein, PPA, and VLA. In contrast, some odorants like DMS, acetone, and BA were seen to be the least affected by the operation of AFs. The estimation of OAV is believed to better indicate odor remediation with respect to the usefulness of AFs than that of OI. This assumption is based on the direct relation of the former to the threshold values of human permissible limits. In this case, we suggest that OAV serves as a better indicator of human tolerance levels than OI, a more or less simple chemical index. Consequently, over natural means of degassing emitted odors from selected food materials, the AF systems used in this study were found ineffective in reducing the unpleasant discomfort that arises due to exposure to pungent, strong odors.


This study was supported by a National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT & Future Planning (No. 2016R1E1A1A01940995). We also acknowledge the support made from the Cooperative Research Program for Agriculture Science & Technology Development (Project title: Study on model development to control odor from hog barn, Project No. PJ010521), Rural Development Administration, Republic of Korea.

1. Adelodun, A.A., Vellingiri, K., Jeon, B.-H., Oh, J.-M., Kim, K.-H., (2015), A test of sorptive removal of offensive odorants by zeolite, Manuscript under review.
2. Ahn, J., Oh, K., Park, S., Lee, K., Kim, Y., Kim, K., (2007), Characterization of odorous emissions from decaying food samples: A case study on trimethylamine and ammonia, Korean Journal of Odor and Indoor Environment, 6, p172-182.
3. Ao, C., Lee, S., (2005), Indoor air purification by photocatalyst TiO2 immobilized on an activated carbon filter installed in an air cleaner, Chemical Engineering Science, 60, p103-109.
4. Burbank, H., Qian, M.C., (2008), Development of volatile sulfur compounds in heat-shocked and pasteurized milk cheese, International Dairy Journal, 18, p811-818.
5. Choi, B.-C., Kim, J., Lim, H., (2002), A Study on the Automotive Air Purifier for a Cabin using Photo-catalysts, KSAE 2002 spring conference proceedings (Kwangju, Honam), p53-58.
6. Dravnieks, A., Schmidtsdorff, W., Meilgaard, M., (1986), Odor thresholds by forced-choice dynamic triangle olfactometry: reproducibility and methods of calculation, Journal of the Air Pollution Control Association, 36, p900-905.
7. Du, X., Plotto, A., Baldwin, E., Rouseff, R., (2011), Evaluation of volatiles from two subtropical strawberry cultivars using GC-olfactometry, GC-MS odor activity values, and sensory analysis, Journal of Agricultural and Food Chemistry, 59, p12569-12577.
8. Frauendorfer, F., Schieberle, P., (2008), Changes in key aroma compounds of Criollo cocoa beans during roasting, Journal of Agricultural and Food Chemistry, 56, p10244-10251.
9. Geng, Q., Guo, Q., Cao, C., Zhang, Y., Wang, L., (2008), Investigation into photocatalytic degradation of gaseous ammonia in CPCR, Industrial & Engineering Chemistry Research, 47, p4363-4368.
10. Hodgson, A., Destaillats, H., Sullivan, D., Fisk, W., (2007), Performance of ultraviolet photocatalytic oxidation for indoor air cleaning applications, Indoor Air, 17, p305-316.
11. Huang, D., Fu, X., Long, J., Jiang, X., Chang, L., Meng, S., Chen, S., (2015), Hydrothermal synthesis of MSn (OH) 6 (M= Co, Cu, Fe, Mg, Mn, Zn) and their photocatalytic activity for the destruction of gaseous benzene, Chemical Engineering Journal, 269, p168-179.
12. Huss, A., Kooijman, C., Breuer, M., Böhler, P., Zünd, T., Wenk, S., Röösli, M., (2010), Fine particulate matter measurements in Swiss restaurants, cafés and bars: What is the effect of spatial separation between smoking and non-smoking areas?, Indoor Air, 20, p52-60.
13. Jiang, J., Coffey, P., Toohey, B., (2006), Improvement of odor intensity measurement using dynamic olfactometry, Journal of the Air and Waste Management Association, 56, p675-683.
14. Jo, S.-H., Kim, K.-H., Kim, Y.-H., Lee, M.-H., Ahn, J.-H., Szulejko, J. E., et al., (2013), Study of odor from boiled eggs over time using gas chromatography, Microchemical Journal, 110, p517-529.
15. Kataoka, S., Lee, E., Tejedor-Tejedor, M.I., Anderson, M.A., (2005), Photocatalytic degradation of hydrogen sulfide and in situ FT-IR analysis of reaction products on surface of TiO2, Applied Catalysis B: Environmental, 61, p159-163.
16. Kim, J.-B., Hwang, C.-W., Kim, Y.-S., Choi, K.-C., (2010), Removal of odor of sewage sludge of sulfur compounds by photocatalytic air purifier, Meeting Abstracts in Korean Journal Atmospheric Environment, p361-361.
17. Kim, J.-C., (2006), Trends and control technologies of volatile organic compound, Journal of Korean Society for Atmospheric Environment, 22, p743-757.
18. Kim, K.-H., Kim, Y.-H., (2014), Composition of key offensive odorants released from fresh food materials, Atmospheric Environment, 89, p443-452.
19. Kim, K.-H., Park, S.-Y., (2008), A comparative analysis of malodor samples between direct (olfactometry) and indirect (instrumental) methods, Atmospheric Environment, 42, p5061-5070.
20. Kim, Y.-H., Kim, K.-H., (2012), Novel approach to test the relative recovery of liquid-phase standard in sorbenttube analysis of gaseous volatile organic compounds, Analytical Chemistry, 84, p4126-4139.
21. Kim, Y.-H., Kim, K.-H., (2013), An accurate and reliable analysis of trimethylamine using thermal desorption and gas chromatography-time of flight mass spectrometry, Analytica Chimica Acta, 780, p46-54.
22. Kim, Y.-H., Kim, K.-H., Szulejko, J.E., Parker, D., (2013), Quantitative analysis of fragrance and odorants released from fresh and decaying strawberries, Sensors, 13, p7939-7978.
23. KMOE, (2007), Odor analysis method in South Korea, volume 2007-17, Korean Ministry of Environment.
24. Lee, M.-H., Kim, K.-H., Kim, Y.-H., Jo, S.-H., (2012), Emission characteristics of odor from salted food materials using Spam, Analytical Science and Technology, 25, p447-459.
25. Lee, S., Xu, Q., Booth, M., Townsend, T.G., Chadik, P., Bitton, G., (2006), Reduced sulfur compounds in gas from construction and demolition debris landfills, Waste Management, 26, p526-533.
26. McGinley, M., McGinley, C., (2006), Performance Verification of Air Freshener Products and other Odour Control Devices for Indoor Air Quality Malodours, St. Croix Sensory Inc., Lake Elmo, MN, USA.
27. Mo, J., Zhang, Y., Xu, Q., Lamson, J.J., Zhao, R., (2009), Photocatalytic purification of volatile organic compounds in indoor air: a literature review, Atmospheric Environment, 43, p2229-2246.
28. Nagata, Y., (2003), Odor intensity and odor threshold value, Journal of Japan Air Cleaning Association, 41, p17-25.
29. Nagata, Y., Takeuchi, N., (2003), Measurement of odor threshold by triangle odor bag method, Odor Measurement Review, Office of Odor, Noise and Vibration Environmental Management Bureau, Ministry of the Environment, Government of Japan, Tokyo, Japan, p118-127.
30. Oh, S.-M., (2003), TiO2 photocatalyst as a VOC remover, Korean Journal of Environmental Administration, 9, p407-412.
31. Qian, M.C., Wang, Y., (2005), Seasonal variation of volatile composition and odor activity value of ‘Marion’ (Rubus spp. hyb) and ‘Thornless Evergreen’ (R. laciniatus L.) blackberries, Journal of Food Science, 70, pC13-C20.
32. Sakai, S., Hiraoka, K., Yamashita, R., Matsumoto, Y., (2006), Effect of TiO2 photocatalyst on odorous gases using glass fiber filter for repeatable use, Japanese Journal of Applied Physics, 45, L343.
33. Stevens, S.S., (1960), The psychophysics of sensory function, American Scientist, 48, p226-253.
34. Wettasinghe, M., Vasanthan, T., Temelli, F., Swallow, K., (2001), Volatile flavour composition of cooked by-product blends of chicken, beef and pork: a quantitative GC-MS investigation, Food Research International, 34, p149-158.
35. Yu, Y., Zhang, T., Zheng, L., Yu, J., (2013), Photocatalytic degradation of hydrogen sulfide using TiO2 film under microwave electrodeless discharge lamp irradiation, Chemical Engineering Journal, 225, p9-15.
36. Zhao, J., Yang, X., (2003), Photocatalytic oxidation for indoor air purification: a literature review, Building and Environment, 38, p645-654.
37. Zhao, Y., Hu, M., Slanina, S., Zhang, Y., (2007), Chemical compositions of fine particulate organic matter emitted from Chinese cooking, Environmental Science & Technology, 41, p99-105.

Table 1S. 
Basic information of gaseous samples used for the performance evaluation of air purifiers with respect to the removal efficiency of odorants+PM2.5.
Order Sample
Manufacturer Model Area for
use (m3)
Filter typea Sampling pointb Date
1 A-1 WINIX (A) WACU150 20.0 HEPA filter, Prefilter,
Antivirus filter
Before operating
air purifier
2015-06-23, 24
2 A-2 Carbon filter,
Allergy filter
After operating
air purifier
3 B-1 LG (B) LA-V079SE 23.0 Prefilter,
Deodorization filter
Before operating
air purifier
2015-06-23, 24
4 B-2 After operating
air purifier
5 C-1 Samsung (C) AX20H5000NDD 22.0 HEPA filter, Prefilter,
Deodorization filter
Before operating
air purifier
2015-06-23, 24
6 C-2 After operating
air purifier
7 D-1 Coway (D) AP-0712FH
21.5 Microfiber net-Prefilter,
Deodorization filter
Before operating
air purifier
2015-06-23, 24
8 D-2 Antivirus-HEPA filter After operating
air purifier
9 E-1 - - - - Before removing
10 E-2 - - - - After removing
aHEPA filter: High efficiency particulate air filter.
b“naturally” means the foodstuff was left in closed room conditions in the absence of an AF.

Table 2S. 
Information of the main instrumental setups (HPLC, ST/TD/GC/Q-MS, AS/TD/GC/SCD, sensor and TSI system) used for the analysis of odorants and VOC.
(A) Laboratory condition
Sample ID Tempa mVol (1 atm) L·mole-1
Water-bath 25 24.5
aWater bath-controlled temperature for collection with the sampling tube (using the ST/TD/GC/Q-MS system).

(B) Operational conditions of HPLC system (CC)
Pump (SPECTRA SYSTEM P4000, Thermo Scientific)
Column: Acclaim120 C18 (Dionex)
Mobile phase 70 : 30 (Acetonitrile: H2O)
Injection volume: 20 μL
Flow rate: 1.5 mL min-1
Detector (SPECTRA SYSTEM UV2000, Thermo Scientific)
Wavelength: 360 nm
Analysis time: 20 min

(C) Operational conditions of ST/TD/GC/MS system (VOC)
Column: CP Wax (length: 30 m, diameter: 0.25 mm, film thickness: 2.5 μm)
1) Oven setting 2) Detector setting 3) Carrier gas setting
Temp. (hold time): 40℃ (5 min) Ionization mode: EI (70 eV) Gas type: He (>99.999%)
Heating rate: 24℃ min-1 Ion source temp: 230℃ Initial gas flow: 1.85 mL min-1
Max. temp (hold): 220℃ (18 min) Interface temp: 230℃ Constant gas pressure: 25 psi
Total time: 32 min TIC scan range: 35-600 m/z
Scan speed: 1250 sec-1
Thermal desorber (UNITY II, Markes International, Ltd., UK)
Cold trap sorbent: Quartz wool+Carbopack C+Carbopack B (volume ratio=1 : 1 : 1)
Split ratio: 0.185 Adsorption temp: 5℃
Split flow: 10 mL min-1 Desorption temp: 330℃
Trap hold time: 5 min Flow path temp: 180℃
Sampling (sorbent) tube
Absorbent: Carbopack C+Carbopack B+Carbopack X (70, 50, and 50 mg)
Desorption time: 7 min Desorption temp: 320℃
Desorption flow: 100 mL min-1

(D) Operational conditions of AS/TD/GC/SCD system (RSC)
GC (7890A, Agilent Technologies, USA)
SCD (355 Sulfur Chemiluminescence Detector, Agilent Technologies, USA)
Column: HP-1 (length: 60 m, diameter: 0.32 mm, film thickness: 5 μm)
1) Oven setting 2) Detector setting
Temp. (hold time): 80℃ (5 min) Plasma temp: 800℃
Heating rate: 20℃ min-1 Detector temp: 250℃
Max temp. (hold): 200℃ (5 min) H2 flow: 40 mL min-1
Total time: 16 min Air flow: 60 mL min-1
Gas type: N2(>99.999%)
Air server/Thermal desorber (UNITY I, Markes International, Ltd., UK)
Cold trap sorbent: Carbopack B+Silica gel (volume ratio=1.5 : 2.5)
Split ratio: 7 : 1 Adsorption temp: - 15℃
Split flow: 10 mL min-1 Desorption temp: 275℃
Trap hold time: 5 min Flow path temp: 80℃

(E) Operational conditions of NH3 sensor system (NH3)
Ibrid MX6 (USA)
1) Sampling (Direct measurement)
Flow rate: 333 mL min-1 Interval time 5 min

(F) Operational conditions of the TSI system
TSI (Desktop DRX, 8533, USA)
Detector: 90° light-scattering laser photometer

Table 3S. 
Comparison of the percent sorptive removal of target compounds for each air purifier.
Order Group Short
Sorptive removal (%)a
1 Aldehyde FA 47.4 29.5 7.63 - 0.26 15.7
2 AA 19.9 - 13.9 24.2 - 18.9 6.84
3 BA 28.0 - 21.1 - 20.1 5.96 17.3
4 Ketone Acrolein 39.8 24.8 17.2 31.4 25.2
5 Acetone 41.1 - 20.2 - 3.23 - 17.8 6.94
6 Volatile fatty
PPA 33.5 41.9 28.7 10.6 6.42
7 BTA 69.4 77.9 50.6 63.8 30.7
8 IVA 62.4 75.6 60.5 73.5 49.7
9 VLA 12.9 19.0 9.32 6.83 9.54
10 IBA 83.4 81.2 76.0 80.9 69.6
11 HXA 50.2 44.4 9.14 6.06 20.1
12 HPA - - - - 17.5
13 Nitrogenous TMA 65.1 71.4 44.3 77.1 43.3
14 Sulfur H2S 85.1 66.5 57.6 75.9 28.0
15 CH3SH - - - - - 14.9
16 DMS 0.47 - 34.7 18.9 - 0.21 - 15.8
17 DMDS - 48.8 10.2 16.5 32.0 - 4.70
18 Nitrogenous NH3 46.2 48.0 60.3 53.0 -
19 Ultrafine particles PM2.5 97.1 98.2 95.3 94.9 4.84
aSorptive efficiency (%)=[(Conc. (Sample 1)- Conc. (Sample 2)) / Conc. (Sample 1)]*100.

Table 4S. 
Statistical analysis using paired Student t-tests between pairs of odorant removal efficiencies.
Paired data set A-B A-C A-D A-E B-C B-D B-E C-D C-E D-E
Parameters 1.1766 1.5580 1.2218 -3.6531 0.4819 0.2854 -2.1272 -0.2882 -1.9550 -2.0330
atcal 0.2566 0.1428 0.2402 0.00214 0.6371 0.7790 0.0493 0.7769 0.0683 0.0590
bttab -6.342 –3.825 -.848 -38.98 -9.190 -8.819 -33.47 -11.06 -29.32 -31.44
~22.16 ~25.04 ~25.41 ~10.36 ~14.58 ~11.56 ~0.0577 ~8.419 ~1.1869 ~0.6586
95% cCI 7.910 10.61 9.282 -24.68 2.696 1.372 -16.77 -1.325 -14.06 -15.39
Conclusion (dsd)
at-cal=calculated student t-value.
bt-tab=theoretical/tabulated student t-value.
cCI=confidence interval.
dsd=significant difference. If TRUE, it means it does EXIST; if FALSE, it means it does NOT EXIST.

Fig. 1S. 
Comparison of measured concentrations of odorants between the before and after operation of each air fresher.

Fig. 2S 
Comparison of calculated percent reduction of odor activity values after exposure to air fresher systems.