An on-line analysis of 7 odorous volatile organic compounds in the ambient air surrounding a large industrial complex

Atmospheric Environment 44 (2010) 3492e3502

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An on-line analysis of 7 odorous volatile organic compounds in the ambient air surrounding a large industrial complex Ehsanul Kabir, Ki-Hyun Kim* Department of Environment and Energy, Sejong University, Seoul, 143-747, South Korea

a r t i c l e i n f o

a b s t r a c t

Article history: Received 25 March 2010 Received in revised form 6 May 2010 Accepted 7 June 2010

The concentrations of seven odorous volatile organic compounds (VOCs) including styrene (S), toluene (T), xylene (X), methyl ethyl ketone (MEK), isobutyl alcohol (i-BuAl), methyl isobutyl ketone (MIBK), and butyl acetate (BuAc) were measured continuously at hourly intervals from an on-line odor monitoring station in Ansan city, Korea (August 2005 to December 2007). Their concentration data (ppb) exhibited a narrow range of mean values despite large variabilities: 1.33
8.81, 16.1
96.6, 3.32
11.5, 7.45
10.3, 20.4
2.38, 1.31
1.16, and 2.43
3.02, respectively. However, unlike aromatics, the distribution of other VOCs was characterized by infrequent occurrences, e.g., as large as 97.5% of i-BuAl data below detection limit. Comparison of temporal patterns indicates that aromatic VOCs are the highest in summer, while others tend to peak during fall (or summer). If the relative compositions of these VOCs were compared in terms of odor intensity, their contribution in the study area is unlikely significant as the malodor components. Evaluation of the data suggests that the distribution of the target VOCs should be affected more sensitively by local traffic activities rather than industrial processes in the surrounding area. Nonetheless, the potent roles of these volatile components should not be underestimated with respect to human health. Ó 2010 Elsevier Ltd. All rights reserved.

Keywords: On-line analysis VOC Malodor Ambient air Seasonal variation

1. Introduction Odor pollution is considered one of the major issues in urban air quality along with noise pollution (Bundy, 1992). The side effects of odor pollution are diverse enough to include environmental nuisances, unpleasantness, and potential health risks to human beings (Bundy, 1992; Pate, 1998; Sweeten, 1998; Schiffman et al., 2000). The dramatic increases in complaint frequency against odor pollution demonstrate people’s recognition on its significance and the basic need to conduct odor impact assessments and to suppress (or prevent) their occurrence in urban communities (Brocco et al., 1997). Odor is configured by sensing one or more odorants (chemicals with a recognizable odor) through the sensory receptors in the nasal cavity (Pate, 1998). Odorants are made up of various chemical components of which source processes have been identified to be diverse enough (Nahm, 2002; Whitehead and Cotta, 2004). Because certain odorants can be perceived at considerably small threshold levels, they can exert significant mental and psychological impact even at low concentrations (Mackie et al., 1998). If accumulated beyond the certain concentration range, some of

* Corresponding author. Tel.: þ82 2 499 9151, þ82 2 499 2354; fax: þ82 2 3408 4320. E-mail address: [email protected] (K.-H. Kim). 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.06.021

these compounds can cause considerable damages on human beings (Liang and Liao, 2007). Being potential airborne pollutants, volatile organic compounds (VOCs) have long been designated as the major odorant group. Chemical, agricultural, food processing, and waste production processes are typically assigned as the major sources of their emissions along with traffic activities (Bundy, 1992). It is well known that a number of VOCs (e.g. styrene, toluene, xylene, etc.) are capable of causing odor problems accompanied by the adverse health effects (Peng et al., 2009). Many of them have commonly been selected as the target of the odor research (Kuran and Sojak, 1996). In this study, continuous measurements of volatile odorants were made from an on-line monitoring station as part of routine odor pollution management task in Ansan city, Korea. The present study aims to characterize the distribution of hazardous volatile compounds as the potential components of malodor problems under the influence of known source activities in the city. The station routinely measures the concentrations of two odorant groups consisting of (1) seven VOCs (i.e., toluene, xylene, styrene, methyl ethyl ketone (MEK), butyl acetate (BuAc), methyl isobutyl ketone (MIBK), and isobutyl alcohol (i-BuAl)) and (2) four reduced sulfur compounds (RSCs) (i.e., H2S, CH3SH, DMS, and DMDS). For reader’s reference, all of these compounds officially belong to the major offensive odorants in Korea that were designated to help regulate the malodor problems (Korean Ministry of Environment

E. Kabir, K.-H. Kim / Atmospheric Environment 44 (2010) 3492e3502

(KMOE), 2008). The present study focuses on the analysis of VOC data collected from this monitoring station for the period of August 2005 to December 2007. The results of RSC measurements made in the same station have also been presented elsewhere (Susaya et al., in preparation). Based on these continuous measurements, we attempted to assess the environmental behavior of these VOCs and their potential roles as major odorants in an urban residential area.

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2. Methodology 2.1. Site characteristics An odor monitoring station has been built in the city of Ansan that lies near the central west coast of the Korean peninsula (Fig. 1). The city is well-known for the largest industrial complex that was built in 1975 on its western side. Upon its establishment, the industrial complexes have been actively operated to produce fabricated metals, petrochemicals, electronics, basic metals, and printing. Soon after, a large residential area consisting of high-altitude apartment buildings was built in the eastern side of the city in the 1990s. Because the industrial complex faces the western coastline of Korea, it can act as the potential source of air pollution in the downwind residential area. A number of citizen groups and academic organizations, concerned about the air pollution and the malodor issue, have participated in routine monitoring of air quality as well as developing a variety of control tactics against malodor. The basic physicochemical properties (e.g., chemical formula, molecular weight, functional group, and CAS number) of all seven target compounds are briefly summarized in Table 1. As shown in Table 1, these target compounds consist of 3 aromatic and 4 other VOCs. For the sake of simplicity, we occasionally distinguished them as aromatic and non-aromatic VOCs, respectively. To help explain the environmental behavior of these odorants, the data sets of relevant environmental parameters (including temperature, relative humidity (RH), dew point (DP), pressure, wind speed, rainfall, radiation, and total radiation) were measured concurrently during the study period (Table 2). Comparison of RH values indicated relatively large variations in the seasonal mean values as in the following: 68.2% (spring), 81.2% (summer), 74.2% (fall), and 69.7% (winter). Likewise, the rainfall was seen to vary between 0.318 mm (summer) and 0.035 mm (winter). However, differences in the mean wind speed and pressure were rather trivial between seasons (1.12e1.75 m s1 and 1007 to 1025 hPa, respectively). The analysis of wind rose pattern indicates that winds dominantly occurred from the south (12%) and northwest (10%) during the study period. In contrast, winds from the NNE and NE are found less frequently to cover 2% of data. Comparison of the wind data across seasons confirms that winds used to come from S and SW in most seasons, while they mainly blew from the NW during winter. 2.2. Measurements of odorous VOCs

Fig. 1. Geographical map of the study site in Ansan city, Korea.

To facilitate continuous acquisition of the concentration data of the VOCs in ambient air, gas chromatography (GC) was operated in on-line mode. This GC system (model CP 3800 GC, Varian, USA) with flame ionization detector (FID) was interfaced with the combination of air server (AS) (for the delivery of outdoor air) and thermal desorber (TD) unit with Peltier cooling system (Unity Air Server (Markes Ltd., UK)). This combined application of AS/TD has been actively employed in the preconcentration and cryofocusing of volatile samples. Air was drawn into TD through the regulation of AS unit. The cold trap was prepared by packing two adsorbents in a 1:1 (mass basis) ratio of Carbopack CTM (60/80 mesh) and Carbopack BTM (60/ 80 mesh) and used in the focusing stage of TD (Kim and Park, 2008). The operation of this GC system was conducted at the following temperature settings: holding the temperature at 55  C for 10 min, ramping to 200  C at the rate of 5  C min1, and holding the temperature for 10 min. Each target compound was separated by CP-Sil column (60 m length, 0.32 mm ID, 5 mm film thickness, Varian Inc., CA, USA) at a column flow rate of 1.7 mL min1 (N2 carrier gas). Gas flows into GC were maintained at 28 (N2), 30 (H2), and 300 mL min1(air). Upon cryofocusing VOC in the cold trap unit at 5  C, target VOCs were desorbed at a temperature of 300  C. The

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E. Kabir, K.-H. Kim / Atmospheric Environment 44 (2010) 3492e3502

Table 1 The basic physicochemical properties of the target volatile compounds investigated in this study. Order

Group

Compound name

CAS No.

Functional group

Acronym

Chemical formula

Molecular weight (g mol1)

Odor Threshold (ppb)a

Exceedance Criteria (ppb)b

1 2 3 4 5 6 7

Aromatic VOC

Styrene Toluene Xylene Methyl ethyl ketone Isobutyl alcohol Methyl isobutyl ketone Butyl acetate

100-42-5 108-88-3 106-42-3 78-93-3 78-83-1 108-10-1 123-86-4

Aromatic Aromatic Aromatic Ketone Alcohol Ketone Ester

S T X MEK i-BuAl MIBK BuAc

C6H5CH]CH2 (CH3)C6H5 (CH3)2C6H4 CH3COC2H5 CH3(CH2)3OH CH3COCH2CH(CH3)2 CH3COO(CH2)3CH3

104 92.1 106 72.1 74.1 100 116

35 330 58 440 11 170 16

400 10,000 1000 13,000 900 1000 1000

a b

Non-aromatic VOC

Source of threshold values: Odor thresholds measured by the triangle odor bag method (Nagata 2003a). According to malodor prevention law in Korea (KMOE, 2008).

detector temperature was maintained as 250  C. For the acquisition of hourly VOC data, the system was operated continuously by transferring air samples at a fixed flow rate of 25 mL min1 for a total duration of 40 min. In order to derive the quantitative data of VOC, the system was calibrated by a fixed standard concentration (FSC) method (Kim, 2006). To this end, the VOC standards of equimolar concentrations (20 ppb for aromatic VOCs and 50 ppb for non-aromatic VOCs) were supplied at a fixed flow rate of 50 mL min1 at durations of 5, 10, and 20 min. Calibration curves derived by the combination of the three loading volumes (250, 500, and 1000 mL) then yielded good linearity (R2 > 0.99). The detection limit (DL) values of the target VOCs, if expressed in terms of absolute mass (ng), were 0.13 (styrene), 0.08 (toluene), 0.06 (xylene), 1.2 (MEK), 0.97 (BuAc), 1.0 (MIBK), and 0.66 ng (i-BuAl). If the actual operation conditions for sample collection (1000 mL) are considered, their concentrations in molar ratios (ppb) correspond to 0.03 (styrene), 0.02 (toluene), 0.013 (xylene), 0.41 (MEK), 0.32 (BuAc), 0.25 (MIBK), and 0.35 (i-BuAl). Because the proportion of the data measured above DL differs greatly between compounds, the concepts of effective data (ED) are defined to specifically allocate the proportion of the data above DL. According to this definition, the ED values were computed as the following: 63.2% (styrene), 98% (toluene), 97.8% (xylene), 35.6% (MEK), 27.1% (BuAc), 16.8% (MIBK), and 2.52% (i-BuAl). Although most aromatic VOCs showed relatively large ED

values (e.g., 63e98%), others were quite limited in that respect, especially with the least ED value of i-BuAl (2.5%). 3. Results and discussion 3.1. General pattern of seasonal VOC distribution and the environmental conditions All VOC concentration data measured at hourly intervals for the entire study period are summarized in Table 3. Because of relatively large proportions of below detection limit (BDL) data, the statistical parameters of all compounds were derived after removing those with extraordinarily low concentration data. Although the proportion of effective data differs greatly between compounds, their mean (
SD: ppb) values during the entire study period fell in a relatively narrow range: 16.1
96.6 (toluene), 3.32
11.5 (xylene), 1.33
8.81 (styrene), 7.45
10.3 (MEK), 2.43
3.02 (BuAc), 2.04
2.38 (i-BuAl), and 1.31
1.16 (MIBK) (Table 3). In terms of the magnitude, aromatic and non-aromatic VOCs were dominated by toluene and MEK, respectively. Moreover, if maximum hourly concentration data are taken into consideration, toluene (4634 ppb) was almost ten times higher than the other aromatic compounds as for example, xylene (460) and styrene (256 ppb). In previous studies, toluene was also found as the most abundant VOC in ambient air without any exceptions (Table 4). The maximum hourly

Table 2 Statistical summary of basic meteorological parameters measured concurrently during the entire study period (Aug. ’05 to Dec. ’07)a. Parameterb

All data

Spring

Summer

Fall

Winter

Temp ( C)

14.0
10.7 (14.9)c 15.4e38.0 (16236)d

12.5
7.11 (12.5) 7.60e33.3 (4021)

25.4
4.46 (25.2) 12.3e38.0 (4166)

16.2
7.64 (16.9) 5.10e33.9 (4180)

0.944
5.30 (0.900) 15.4
16.9 (3869)

RH (%)

73.5
20.1 (77.0) 16.0e100 (16236)

68.3
20.6 (70.0) 16.0e100 (4021)

81.2
16.9 (88.0) 19.0e100 (4166)

74.2
20.1 (78.0) 17.0e100 (4180)

69.7
19.9 (71.0) 19.0e100 (3869)

DP ( C)

8.72
11.3 (9.60) 23.0e31.9 (16236)

5.99
7.16 (6.40) 16.0e20.5 (4021)

21.5
3.72 (21.9) 4.20e29.0 (4166)

10.9
8.03 (12.9) 10.7e31.9 (4180)

4.56
5.60 (3.90) 23.0e9.20 (3869)

Pressure (hPa)

1016
8.32 (1016) 990e1040 (16236)

1013
6.37 (1014) 990e1028 (4021)

1007
4.36 (1008) 993e1017 (4166)

1018
5.07 (1018) 1018e1033 (4180)

1025
4.94 (1025) 1008e1040 (3869)

W-Speed (ms1)

1.38
1.15 (1.10) 0e10.5 (16236)

1.75
1.38 (1.40) 0e9.40 (4021)

1.26
0.821 (1.10) 0e6.90 (4166)

1.12
0.921 (0.90) 0e8.60 (4180)

1.34
1.29 (1.20) 0e10.5 (3869)

Rainfall (mm)e

0.135
1.05 (0.50) 0.30e31.0 (1429/16236)f

0.094
0.645 (1.00) 0.33e10.9 (363/4421)

0.318
1.78 (0.90) 0.34e31.0 (384/4166)

0.083
0.770 (1.00) 0.31e30.7 (357/4180)

0.035
0.283 (1.00) 0.30e5.30 (351/3869)

Radiation (W/m2)

94.8
145 (74.0) 22.0e728 (8138/16236)

119
168 (190) 24.0e690 (2195/4421)

110
162 (56.0) 24.0e728 (2481/4166)

86.4
132 (36.0) 22.0e554 (2185/4180)

62.0
103 (31.0) 22.0e424 (2163/3869)

Total Radiation (J/m2)

346500
745529 (2640) 1320-7Eþ07 (9320/16236)

428593
595347 (30840) 1328-2Eþ06 (2504/4421)

399991
561644 (60050) 1320-2Eþ06 (2823/4166)

328045
1165725 (59055) 1332-7Eþ07 (2486/4180)

223522
362088 (59560) 1320-1Eþ06 (2460/3869)

a b c d e f

Measurements were made at every hourly interval. Acronyms: Temperature ¼ Temp, Relative humidity ¼ RH, Dew point ¼ DP, and Wind speed ¼ W-Speed. Mean
SD (median). MinimumeMaximum (no. of data). Median values are calculated without considering zero values. MinimumeMaximum (no. of data: without considering zero values (left)/all data (right)).

E. Kabir, K.-H. Kim / Atmospheric Environment 44 (2010) 3492e3502

3495

Table 3 Statistical summary of odorous VOC concentration data (ppb) and odor intensity (OI) measured during the entire study period (Aug. 2005 to Dec. 2007). Grouping

Compound

All dataa

Spring

Summer

Fall

Winter

Aromatic VOCs

Styrene

1.33
8.81 (0.193)b 0.037e256 (11399/18049)c 16.1
96.6 (5.68) 0.024e4634 (17695/18049) 3.32
11.5 (1.08) 0.015e460 (17660/18049)

0.968
1.44 (0.513) 0.137e16.7 (2821/3886) 14.5
26.0 (6.12) 0.024e338 (3837/3886) 4.86
10.9 (1.36) 0.022e226 (3851/3886)

4.20
20.2 (0.318) 0.037e256 (2095/4080) 31.8
199 (5.28) 0.087e4634 (4033/4080) 4.93
21.1 (0.825) 0.015e460 (3974/4080)

0.540
0.788 (0.322) 0.037e18.6 (3601/5800) 9.78
12.9 (5.29) 0.095e174 (5693/5800) 2.12
3.17 (1.05) 0.019e55.1 (5676/5800)

0.580
0.731 (0.372) 0.037e18.2 (2881/4283) 11.1
13.5 (6.52) 0.031e150 (4132/4283) 1.99
2.22 (1.35) 0.025e42.0 (4159/4283)

7.45
10.3 (4.22) 0.476e244 (6463/18130) 2.04
2.38 (1.21) 0.396e20.8 (457/18130) 1.31
1.16 (0.89) 0.292e19.7 (3041/18130) 2.43
3.02 (1.31) 0.329e29.3 (4916/18130)

6.99
9.40 (3.75) 0.757e94.1 (1656/3897) 1.86
2.22 (1.19) 0.640e15.3 (145/3897) 1.19
0.850 (0.922) 0.322e7.41 (624/3897) 2.29
2.64 (1.37) 0.353e29.3 (1091/3897)

9.26
13.2 (4.23) 0.815e119 (547/3606) 2.63
3.16 (1.37) 0.425e20.8 (148/3606) 1.62
1.49 (1.16) 0.300e19.7 (531/3606) 2.91
3.23 (1.61) 0.329e27.4 (734/3606)

9.69
13.0 (5.48) 0.476e244 (1334/6095) 2.71
1.90 (2.21) 0.520e7.81 (28/6095) 1.82
1.39 (1.42) 0.339e11.0 (614/6095) 3.63
3.86 (2.28) 0.331e28.0 (1395/6095)

6.47
8.53 (3.94) 0.482e144 (2926/4532) 1.47
1.15 (0.884) 0.396e6.59 (136/4532) 0.993
0.871 (0.702) 0.292e11.2 (1272/4532) 1.32
1.63 (0.776) 0.330e20.6 (1696/4532)

Toluene Xylene Non-aromatic VOCs

MEK i-BuAl MIBK BuAc

a b c

Measurements were made at every hourly interval, and the basic statistical parameters were computed without BDL data. Mean
SD (median). Minimumemaximum (no. of data: without below detection limit (left)/all data including BDL (right)).

concentrations of non-aromatics were much lower than those of aromatics. The maximum value of MEK (244 ppb) was almost ten times higher than those of BuAc (29.3), i-BuAl (20.8), and MIBK (19.7 ppb). According to the malodor prevention law in Korea, the regulation guidance levels of these odorants have been established. In the case of sources other than major industrial facilities, such criteria (ppb) are set at the following: 400 (styrene), 10,000 (toluene), 1000 (xylene), 13,000 (MEK), 11 (i-BuAl), 170 (MIBK), and 16 (BuAc) (KMOE, 2008). Hence, if the maximum hourly data collected in this study are compared in such respect, none of them have exceeded those guidelines during the study period. In Fig. 2, all concentration data of 7 VOCs including BDL values are examined to describe their relative occurrence patterns. Because the occurrences of these VOCs are generally confined to the significantly low concentration range, those data falling in such range were grouped at very narrow intervals. As shown in Fig. 2, the maximum frequency of individual VOCs tend to center at very low concentration range with their upper-bound intervals ending at 0.1 (styrene), 0.05 (toluene), 0.03 (xylene), 4 (MEK), and 2 ppb (i-BuAl, MIBK, BuAc). Comparison of these frequency data thus consistently indicates that the dominant portion of the aromatic VOC data

(around 98%) exists at or below 0.5 ppb, while almost 95% of nonaromatic VOCs data fell in much higher boundary of 10 ppb. For instance, the predominant portion of styrene data (82%) fell between the ranges of 0.04e0.1 ppb with its peak occurring at a boundary of 0.05e0.1 ppb range. Likewise, 86% of toluene and 87% of xylene data fell below 0.1 ppb. As such, the range of maximum frequency intervals of the aromatic VOCs are significantly smaller than those of non-aromatic compounds. However, as the aromatics tend to maintain a certain proportion of data at exceptionally high concentration range, the mean concentrations of each individual compound are not so different between aromatics and non aromatics. Nevertheless, the seasonal patterns of different VOC groups are highly comparable to each other with a few exceptions. As another criteria for assessing the relative intensity of odorants measured in this study, the concept of threshold can be employed. The threshold odor concentration (TOC) of a pure compound in air is defined as the least concentration perceivable by 50% of the tested population (e.g., Verschueren, 1996). Hence, the hourly measurement data for all the target VOCs can also be compared in terms of TOC criteria (ppb) such as styrene (35), toluene (330), xylene (58), MEK (440), i-BuAl (11), MIBK (170), and BuAc (16) (Nagata, 2003a).

Table 4 Comparison of odorous VOC concentration (ppb) data measured at different areas around the world. Land use type

Study site

Styrene

Toluene

Xylene

MEK

i-BuAl

MIBK

BuAc

Reference

Industrial (ambient)b

Ansan, Korea Kwai Chung, Hong Kong Kwun Tong, Hong Kong Ren Wu, Taiwan Kaohsiung, Taiwan Ulsan, Korea Ulsan, Korea Shizuoka, Japan Bombay, India Rayong, Thailand Brisbane, Australia Alberta, Canada Dunkenque, France Valencia, Spain

1.33 ea e e 9.53 0.29 e e e 0.17 4.82 e 0.04 e

16.1 37.0 17.1 15.0 10.2 3.85 3.82 3.14 5.20 9.83 10.6 1.21 1.06 2.26

3.32 6.43 0.53 5.25 5.45 2.31 2.70 0.49 0.50 0.99 4.89 0.07 0.53 1.78

7.45 e e e e e e e e e e e e 0.16

2.04 e e e e e e e e e e e e 0.71

1.31 e e e e e e e e e e e e 0.031

2.43 e e e e e e e e e e e e 0.29

This study Lee et al., 2002 Ho et al., 2004 Hsieh et al., 2006 Liu et al., 2008 Kim et al., 1998 Na et al., 2001 Ohura et al., 2006 Rao et al., 1997 Thepanondh et al., 2003 Hawas et al., 2002 Cheng et al., 1997 Badol et al., 2008 Ribes et al., 2007

Urban (ambient)

Hopewell, USA Banff, Canada Belgrade, Sarbia

e 0.01 0.02

0.26 0.74 0.51

0.17 0.42 0.30

0.60 0.55

e e e

e 0.03 e

e e e

Turner et al., 2009 Alberta Environment, 2003 Stojic et al., 2009

Rural (ambient)

Charles City, Virginia, USA

0.11

0.15

0.11

0.21

e

0.05

e

Turner et al., 2009

Road side (ambient)

Mong Kok, Hong Kong

e

7.29

0.44

0.36

e

0.08

e

RCETM, 2005

a b

Data not available. Measurements made in ambient air not directly affected by eminent source processes.

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E. Kabir, K.-H. Kim / Atmospheric Environment 44 (2010) 3492e3502

a

5000

b 8000

80% No of occurences

No of occurences

80%

Relative freq. (%)

Relat ive freq. (%)

Frequency

3000 40% 2000 20%

F requency

Relative frequency

60%

1000

0

6000

60%

4000

40%

2000

20%

0%

0

0.04 0.05 0.10 0.20 0.50 1.00 2.00 5.00 5.00<

0% 0.03 0.40 0.05 0.10 0.20 0.50 1.00 2.00 5.00 5.00<

ppb

ppb

Styrene

Toluene

c 4000

Relative frequency

4000

d

80% No of occurences

1500

80% No of occurences

Relat ive freq. (%)

Relative freq. (%)

1200

0

900 40% 600 20% 300

0

0%

0% 0.5 1 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 30<

0.02 0.03 0.04 0.05 0.10 0.20 0.50 1.00 2.00 5.00 5.00<

ppb

ppb

Xylene

MEK

f

1200

No of occurences

80% 160

No of occurences

Relative freq. (%)

1000

Relative freq. (%)

Threshold (11 ppb)

20%

800

Frequency

Relative frequency

600

0.4

400 0.2 200

0

0

0%

0 0.5 1 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3030<

0.5 1 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3030<

ppb

ppb

i-BuAl

MIBK

g

80%

1600 No of occurences

1400

Relative freq. (%)

60%

F requency

1200 1000

40%

800 600

Threshold (16 ppb)

400 200 0

20%

Relative frequency

Frequency

40%

80

40

0.6

60%

120

0.8

Relative frequency

e

Relative frequency

20%

1000

Frequency

40%

2000

Relative frequency

3000

Frequency

60%

60%

0% 0.5 1 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3030<

ppb

BuAc Fig. 2. Frequency distribution of aromatic and non-aromatic VOCs. (Threshold values are shown for compounds, if certain proportions of them are detected above such criteria). (a) Styrene, (b) Toluene, (c) Xylene, (d) MEK, (e) i-BuAl, (f) MIBK, and (g) BuAc.

E. Kabir, K.-H. Kim / Atmospheric Environment 44 (2010) 3492e3502

According to this comparison, the frequency of hourly TOC exceedance is also quite scarce with 72 (styrene), 64 (toluene), 45 (xylene), 9 (i-BuAl), and 38 cases (BuAc). 3.2. Temporal variability of odorous VOC To evaluate the effect of temporal factors on the behavior of VOC, all the data are compared after having been grouped into 4 seasons: spring (MarcheMay), summer (JuneeAugust), fall (SeptembereNovember), and winter (DecembereFebruary). If the variabilities of VOC data are examined by this seasonal criterion, there are noticeable differences between aromatic and nonaromatic VOCs. The patterns of aromatic VOCs tend to exhibit the maximum during summer and the minimum during fall (and winter). The relative enhancement of aromatic compounds in summer may be ascribable to increased evaporation with rising temperatures. According to a previous study made at a roadside area in Korea, aromatic VOCs (i.e., toluene, xylene, etc.) showed their peak occurrences during the summer, while reaching the minimum during fall or winter (Na and Kim, 2001). Similar to such finding, the concentrations of most VOCs (i.e., benzene, toluene, ethyl benzene, xylene, etc.) measured near a main roadside in Hong Kong were higher in summer than in winter (Ho et al., 2004). A line of evidence thus suggests that many aromatic VOCs can exhibit enhanced concentrations during warmer seasons probably due to their elevated evaporation capacities in the summer. To check for the statistical significance in concentration differences between seasons, a Z statistics test was conducted between the largest mean concentration (summer) and the one next to it (spring). The difference in mean values between the two seasons was significant for styrene (P ¼ 2.7E-13) and toluene (P ¼ 4.5E-8), while it was not for xylene (P ¼ 0.85) (here, the P value denotes the probability of no correlation). As the distribution of VOC can be affected by changes in meteorological conditions, the seasonal trend of these VOCs cannot be explained without considering such factors. Cetin et al. (2003) found that the concentrations of VOCs in ambient air generally increased with temperature and wind speed around a petrochemical complex and an oil refinery in Izmir, Turkey. These authors reported that the concentrations of individual VOC are tightly bound with meteorological parameters (temperature and wind speed), as can be seen from the multiple linear regression analysis:

C ¼ a0 þ a1 T þ a2 WS where C is the VOC concentration (mg m3), T is the temperature ( C), WS is the wind speed (m s1), and three ‘a’s (a0, a1 and a2) represent the fitting parameters. Hence, the assignment of positive values for a1 and a2 implies that VOC concentrations can increase with temperature and wind speed. Unlike the summertime dominance of aromatic compounds, the peak concentrations of non-aromatics were generally seen during fall which is then followed by summer. If the results are compared in a manner similar to those of aromatics, such maximum was not statistically significant in certain cases. The differences between the two seasons were no longer statically significant for MEK (P ¼ 0.52) and i-BuAl (P ¼ 0.86), while it was significant in the case of MIBK (P ¼ 0.02) and BuAc (P ¼ 5.1E-6). If the VOC concentration data are compared over 24 h scale, the results generally exhibited moderate changes over diurnal scale. Thus, it is reasonable to assume that the diurnal variation of VOCs should be affected sensitively by such factors as the operational conditions of factories and/or the driving pattern of automobiles along with the life style of the people. Examination of the data in such respect indicates that toluene, xylene, and MEK tend to peak during the early evening (Fig. 3). Although their absolute values can vary across seasons, their

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relative trends tend to remain highly consistently across seasons. The results of styrene are however slightly distinguished from other aromatics, as its summer time pattern is characterized by morning time peaks. Similarly to this finding, non-aromatic VOCs (e.g., i-BuAl, MIBK, and BuAc) exhibited higher concentrations at early morning, especially during fall. This morning time peak pattern may also be considered as emissions-driven morning peak (McCarthy et al., 2007). The results also indicate that aromatic VOCs generally recorded most noticeable patterns in summer, while non-aromatic VOCs showed relative enhancement in fall (Fig. 3). In another study made at six different industrial parks in southern Taiwan, aromatic VOCs (e.g., toluene and xylene) also displayed relative dominance during evening in most of the sampling locations (Hsieh et al., 2006). The occurrence of maximum concentrations in early evening (around 6 PM) suggests the likely important role of traffic activities. Earlier studies indicated that VOC concentration at industrial sites can be reflected by the combined source signatures of traffic and industrial activities (Altshuller, 1993; Ferrari et al., 1998; Hsieh et al., 2006). According to Liu et al. (2000), the total VOC levels in a northeast city of China were much lower in one industrial area than in the heavy traffic sites. This in turn supports the possibility that the effect of industrial sources may appear less distinctively than traffic activities under certain circumstances (Liu et al., 2000). 3.3. Comparison with previous studies In order to diagnose the status of VOC pollution in the study area, ambient VOC data measured in this study are compared with those taken from various locations around the world (Table 4). To facilitate the evaluation of these data, results are sorted into 4 different land uses: industrial, urban, rural, and road side. As most of the previous studies focused on the distribution of aromatic VOCs, the data for non-aromatic VOCs were in general scarcely available, e.g., only in few studies. If the magnitude of VOC data is compared by their mean concentration values, the results obtained at roadside area or under the industrial influence exhibit the highest levels (e.g., 0.04e9.53 (styrene), 1.06e37.0 (toluene), and 0.07e6.43 (xylene)). These VOC concentration data are easily differentiated from those of urban and rural land areas (e.g. 0.01e0.11 (styrene), 0.15e0.74 (toluene), and 0.11e0.42 (xylene)) (Turner et al., 2009; Stojic et al., 2009). The combined effects between different levels of source activities and the surrounding environmental conditions should be considered simultaneously to explain differences in the VOC distribution in each study site. If the compiled VOC data are compared between regions, the concentrations of most VOCs in industrial areas are generally higher in Asia including Australia than in America and European countries. In fact, air quality in most western countries have improved dramatically over the past decades as a consequence of more rigorous legal regulations and the adoption of less-polluting and environment friendly technologies. Comparison of toluene (and xylene) values shows that the results of Ansan city area are quite similar to those of several cities in Asia, particularly with the Ren-Wu industrial area at Taiwan (Hsieh et al., 2006). However, the results of Ansan city are noticeably large compared to other Asian industrial areas like Japan, India, and Thailand as well as other cities in Korea such as Ulsan. It is thus speculated that the effect of fugitive emissions should be prominent in the study area, as its VOC pollution should be governed by a number of complicated factors and processes. In terms of the magnitude of concentration data, toluene was observed as the most dominant among all target species in this study. For example, the average concentration of toluene (16.1 ppb) was higher by approximately 5 times than the next highest mean of xylene (3.32 ppb). Although the release of toluene generally occurs via its common use as solvents (Brocco et al., 1997), another reason

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E. Kabir, K.-H. Kim / Atmospheric Environment 44 (2010) 3492e3502

a

10

Spring Fall

Summer Winter

b

80

Spring Fall

70

Summer Winter

Conc entr ation ( ppb)

Conc entr ation ( ppb)

8

6

4

2

60 50 40 30 20 10

0

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

12

Conc entr ation ( ppb)

10

Spring Fall

Hour

Styrene

Toluene

d

Summer Winter

30

Spring Fall

25

Conc entr ation ( ppb)

c

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

8 6 4 2

Summer Winter

20 15 10 5

0

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1 2 3 4 5 6 7 8 9 10 1112 1314 15 1617 1819 2021 22 2324

Hour

Hour

Xylene

e

12

Spring Fall

Summer Winter

f

8 6 4

3.0

Spring Fall

2.5

Conc entr ation ( ppb)

10

Conc entr ation ( ppb)

MEK Summer Winter

2.0 1.5 1.0 0.5

2 0

0.0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1819 20 21 22 23 24

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

Hour

i-BuAl

MIBK

g

7

Conc entration (ppb)

6

Spring Fall

Summer Winter

5 4 3 2 1 0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

BuAc Fig. 3. Comparison of hourly distribution patterns of VOCs (error bar ¼
SE). (a) Styrene, (b) Toluene, (c) Xylene, (d) MEK, (e) i-BuAl, (f) MIBK, and (g) BuAc.

E. Kabir, K.-H. Kim / Atmospheric Environment 44 (2010) 3492e3502

for its strong abundance should also be sought. For instance, the lifetime of toluene in the atmosphere (relative to other odorous VOCs) is long enough due to the low reactivity or high stability in the atmosphere. The calculated half-life and lifetime of toluene due to reaction with the hydroxyl radical are 1.7 days and 2.4 days, respectively (Atkinson, 1994). Among the odorous VOCs, xylene is also found to be one of the most prominent compounds next to styrene. In contrast, only a few data were available for nonaromatic VOCs in ambient environment. Although our VOC data can be compiled or compared with others, their concentration levels are unlikely to cause strong odor phenomenon.

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3.4. Correlation analysis between odorous VOC and environmental parameters In order to learn more about the environmental behavior of all VOCs, a correlation analysis was conducted using the VOC data and the relevant environmental parameters (Table 5). The results indicate that the cases of strong correlations are seen fairly abundantly between most VOCs (except i-BuAl). In addition, all VOCs (except styrene) exhibited highly significant correlations with RSC (e.g., H2S: P < 0.01). These VOCs also showed good correlations with meteorological parameters, particularly temperature, dew point,

Table 5 Results of correlation analysis between all odorant and meteorological data. Section

Compound/parameter

(a) with inter-VOC

Toluene

Xylene

Styrene

MEK

i-BuAl

MIBK

BuAc

(b) with RSC

H2S

CH3SH

DMS

DMDS

(c) with MET data

Temperature

Relative humidity

Dew Point

Pressure

Wind Speed

Rainfall

Radiation

Total radiation

Toluene r p N r p N r p N r p N r p N r p N r p N

17,695 0.858** 0 17,536 0.929** 0 11,279 0.615** 0 6009 0.08265 0.0934 413 0.339** 4.5E-77 2820 0.239** 6.5E-59 4461

r p N r p N r p N r p N

0.085** 5.8E-15 8404 0.016 0.780 292 0.011 0.731 1042 0.024 0.712 230

r p N r p N r p N r p N r p N r p N r p N r p N

0.042** 2.4E-07 14,897 0.054** 6.2E-11 14,897 0.058** 9.2E-13 14,897 0.057** 3.6E-12 14,897 0.014* 0.013 14,897 0.129** 5.9E-56 14,897 0.023** 0.005 14,897 0.014 0.085 14,897

Xylene

Styrene

MEK

i-BuAl

MIBK

BuAc

1

1 17,660 0.830** 0 11,354 0.371** 4.5E-196 6031 0.108* 0.027 418 0.184** 4.5E-23 2831 0.244** 1.1E-61 4462

1 11399 0.234** 6.7E-60 4729 0.031 0.557 369 0.129** 6.1E-11 2568 0.108** 2.4E-11 3823

6463 0.046 0.447 275 0.582** 3.5E-226 2490 0.596** 0 3487

457 0.021 0.744 246 0.071 0.218 298

0.111** 2.1E-24 8427 0.019 0.749 296 0.0115 0.711 1051 0.009 0.894 233

0.0006 0.963 5958 0.051 0.503 173 0.010 0.760 940 0.034 0.647 185

0.202** 3.2E-29 3012 0.052 0.601 103 0.054 0.160 687 0.138 0.152 110

0.176** 0.003 288 0.028 0.911 18 0.048 0.770 39 0.199 0.607 9

0.145** 2.6E-09 1673 0.106 0.497 43 0.044 0.428 331 0.061 0.658 55

0.252** 1.1E-39 2656 0.014 0.913 64 0.044 0.370 419 0.076 0.558 62

0.058** 9.7E-13 14,912 0.027** 8.3E-4 14,912 0.064** 7.1E-15 14,912 0.069** 2.3E-17 14,912 0.008 0.317 14,912 0.114** 3.5E-44 14,912 0.024** 0.003 14,912 0.014 0.093 14,912

0.066** 1.9E-11 10,184 0.100** 3.3E-24 10,184 0.101** 1.4E-24 10,184 0.115** 3.6E-31 10,184 0.023* 0.019 10,184 0.182** 2.0E-76 10,184 0.043** 1.5E-05 10,184 0.045** 6.6E-06 10,184

0.191** 1.1E-46 5523 0.096** 1.1E-12 5523 0.148** 2.0E-28 5523 0.094** 2.3E-12 5523 0.059** 1.2E-05 5523 0.036** 0.007 5523 0.026 0.054 5523 0.008 0.561 5523

0.337** 2.9E-12 408 0.048 0.336 408 0.338** 2.3E-12 408 0.169** 6.3E-4 408 0.028 0.569 408 0.070 0.161 408 0.011 0.819 408 0.017 0.729 408

0.263** 3.9E-44 2709 0.024 0.208 2709 0.255** 1.6E-41 2709 0.180** 4.5E-21 2709 0.029 0.132 2709 0.041* 0.031 2709 0.016 0.391 2709 0.008 0.681 2709

0.340** 3.5E-119 4391 0.013 0.406 4391 0.333** 2.5E-114 4391 0.176** 4.7E-32 4391 0.006 0.705 4391 0.075** 6.9E-07 4391 0.030* 0.044 4391 0.057** 1.7E-4 4391

*Correlation is significant at the 0.05 level (2-tailed); **Correlation is significant at the 0.01 level (2-tailed).

1

1

1 3041 0.616** 4.1E-285 2733

1 4916

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E. Kabir, K.-H. Kim / Atmospheric Environment 44 (2010) 3492e3502

Table 6 Empirical formula to derive the odor intensity (OI) of individual VOC investigated in this study. Group

Compound name

Functional formula (X.ppm)a

Aromatic VOC

Styrene Toluene Xylene MEK i-BuAl MIBK BuAc

Y Y Y Y Y Y Y

Non-aromatic VOC

a

¼ ¼ ¼ ¼ ¼ ¼ ¼

1.140logX 1.850logX 1.650logX 0.790logX 1.400logX 1.420logX 1.530logX

þ þ þ þ þ þ þ

2.34 0.149 2.27 2.53 1.05 3.10 2.44

Nagata (2003b): odor intensity (Y) and odorant concentration (X).

pressure, and rainfall (P < 0.1). The correlations between aromatics and meteorological parameters measured in this study appear to be more prominent than non-aromatic VOCs. Note that the total VOC concentrations in a petrochemical complex and an oil refinery generally increased with temperature, as the temperature can exert direct influences on their evaporative emissions (Cetin et al., 2003). In our study, all the odorous VOCs also tend to display high correlations with temperature (P < 107). Likewise, in another study conducted inside public buses in Pamplona, Northern Spain, VOCs exhibited strong correlations with temperature (P < 0.05) (Parra et al., 2008). Stojic et al. (2009) also found that the majority of VOCs were significantly correlated with temperature (positive) and wind speed (inverse) in Belgrade, the capital and largest city of Serbia. As high wind speeds generally promote dispersion of atmospheric contaminants, a decrease in VOC concentrations may take place with increasing wind speeds (Cetin et al., 2003). In this study, however, the existence of strong inverse correlations with wind speed was scarcely observed with toluene, styrene, or MEK. 3.5. Potential impact of odorous VOCs as the source of odor pollution People generally exhibit wide detection capacities for odors which can vary with the level of odorant concentrations such as from the minimum detection range to spanning orders of magnitude. Because of the inherent subjectivity associated with measuring and defining acceptable odor levels, the controlling efforts on the odor problem are often regarded as one of the hardest tasks to manage. In this respect, the use of odor intensity (OI) concept is meaningful, as it provides one simple means to compare concentration levels

in terms of the overall strength of the perceived odorants. As the strength of odor cannot be assessed directly from concentration data, this OI concept can be used to assign their relative importance on the parallel basis (ASTM, 2004). For this purpose, the concentration data of each VOC measured in this study were converted into the OI with the varying index numbers with the aid of empirical equations developed by Nagata (2003b). As shown in Table 6, the OI scaling of 0 through 6 can be allocated as follows: 0 (no odor), 1 (very weak), 2 (weak), 3 (distinct), 4 (strong), 5 (very strong), and 6 (intolerable) (ASTM, 2004). However, as the OI values of less abundant VOC data are occasionally converted into negative range, such values were disregarded in our analysis for simplicity. In order to allow comparisons of odor strengths between VOCs, statistical summary is also provided for each VOC in Table 7. According to this convention, the number of hourly aromatic data falling into positive OI ranges are fairly limited to 109 (styrene), 27 (toluene), and 129 (xylene). The hourly means for these OI values, if converted, are found in a fairly narrow range of 0.77 (styrene), 0.71 (toluene), and 0.43 (xylene). In contrast, the maximum OI values of these compounds are in a moderately enhanced range of 1.67 (styrene), 1.38 (toluene), and 1.71 (xylene). As can be expected from the concentration data of aromatic VOCs, their OI values tend to peak mostly during the warmer seasons (e.g., summer and spring). If we compute the OI rating for non-aromatic VOCs, the largest mean is observed from MEK (0.68) followed by MIBK (0.16) and BuAc (0.05). Although MEK generally records the highest OI among the non-aromatic VOCs, none of i-BuAl data fell in the positive OI range. The overall patterns of OI conversions (and frequencies) derived for non-aromatics suggest that MEK should take the major possibility, if odor problem is observed. To assess the overall contribution of all target compounds as a group, the OI values of each individual compound were put together to derive the total odor strength in terms of the sum of odor intensity (SOI). For the derivation of the SOI term, the following equations were employed in this study (Kim and Park, 2008):


X SOI ¼ log 10OIðithÞ 
¼ Log 10OIðithÞ1 þ 10OIðithÞ2 þ 10OIðithÞ3 þ .. þ 10OIðithÞn where SOI ¼ sum of odor intensity, OI ¼ odor intensity, OI(ith) ¼ log 10OI(ith), i ¼ 1,2,3, .....n. The combined odor strengths of these odorous VOCs yield a value of 1.32, which is relatively weak odor

Table 7 Statistical summary of odor intensity (OI) and the sum of odor intensity (SOI). Group

Name

All data

Spring

Summer

Fall

Winter

Aromatic VOC

Styrene

0.768
0.509 (0.797)a 0.001e1.67 (109)c 0.711
0.343 (0.701) 0.111e1.38 (27) 0.432
0.436 (0.220) 0.011e1.71 (129)

0.099
0.079 (0.065) 0.001e0.313 (19) ed

0.368b

0.356 (1)

e

e

0.286
0.298 (0.178) 0.011e1.20 (52)

0.922
0.442 (1.02) 0.027e1.67 (88) 0.711
0.343 (0.701) 0.111e1.38 (27) 0.542
0.487 (0.317) 0.011e1.71 (75)

0.104
0.125 (0.104) 0.0156e0.123 (2)

e

0.677
0.329 (0.656) 0.007e2.05 (6439) e

0.647
0.334 (0.614) 0.064e1.719 (1656) e

0.722
0.377 (0.655) 0.090e1.80 (547) e

0.769
0.340 (0.745) 0.018e2.05 (1327) e

0.645
0.316 (0.633) 0.007e1.86 (2909) e

0.162
0.166 (0.098) 0.002e0.679 (18) 0.049
0.032 (0.051) 0.012e0.094 (5) 1.32

0.075

0.162
0.291 (0.038) 0.002e0.679 (5) 0.051

0.151
0.109 (0.148) 0.005e0.320 (8) 0.038
0.037 (0.038) 0.012e0.065 (2) 1.11

0.205
0.107 (0.209) 0.071e0.330 (4) e

Toluene Xylene Non-aromatic VOC

MEK i-BuAl MIBK BuAc

SOI a b c d

(VOC)

0.060
0.047 (0.060) 0.027e0.094 (2) 1.04

Mean
SD (median). Number of data is 1. MinimumeMaximum (Number of data: negative OI values are not considered). Data is not available.

1.39

1.05

E. Kabir, K.-H. Kim / Atmospheric Environment 44 (2010) 3492e3502

intensity. If the results are compared between seasons, the highest SOI value was obtained in summer (1.39) followed by fall (1.11), winter (1.05), and spring (1.04). However, examination of their occurrence patterns and the magnitude of their concentration data suggest that the relative contribution of selected VOCs should not be significant as the cause of odor problems in the study area. As such, it is difficult to conclude that most target compounds made significant contributions to the odor pollution, if the problem occurred during the study period. In order to have an idea about the odor strength derivable by the 7 VOCs in the study area, their intensity scales can also be compared with the SOI concept between different source types using previous measurement data. For instance, at an alumina refinery facility in Australia, SOI value was determined around 3.8 by a digital olfactometer (John et al., 2006). Knudsen et al. (1999) also investigated odor emissions from five commonly used building products with the aid of a sensory panel on the basis of line scale method in Denmark. They found SOI values of 4.2 for waterborne wall paint on gypsum board, 3.4 for floor varnish (on beech wood parquet, nylon carpet, and an acrylic sealant), and 3.3 for PVC. From a swine farm, Nimmermark et al. (2005) measured SOI of 3.5 by feedlots setback estimation tool in USA. Kim et al. (2008) determined 3.4 from a pig building by a sensory panel on the basis of air dilution sensory (ADS) method in Korea. Our work confirms that the odor strength expressed by some VOCs measured in the study area was considerably low compared to the results of all other cases (e.g., alumina refinery, swine farm, and even from some indoor sources). Although our odor strengths are mainly derived by several VOCs, they are unlikely to represent all odorants released simultaneously in the study area. Instead, the results do suggest that the contribution of VOC measured in the target area cannot be significant, as those observed from other strong source environments. 4. Conclusions In this study, the concentrations of 7 VOCs in ambient air were measured routinely at hourly intervals from an on-line monitoring station established in Ansan, Korea from August 2005 to December 2007. Evaluation of our data offered a unique opportunity to assess long-term trend of odorous VOCs in an urban area in the vicinity of a large industrial complex. According to this study, the highest concentrations of these VOCs tend to occur during warm seasons like summer (and fall). When the data were analyzed over diurnal scale, most VOCs exhibited relatively enhanced concentrations during the early evening (or early morning). The results of our analyses were also evaluated in terms of their potential as odorants by assigning the OI values. Although the maximum concentration of certain VOCs, e.g., toluene reached near or above ppm level, their strengths as odor were relatively insignificant under most circumstances. It is also recognized that their highest values were seen among all data such as 2.05 (MEK). In terms of the malodor problem, we can say that our study area is unlikely affected significantly by odorants, at least a number of odorant VOCs investigated in this study. The results thus raise the question whether the monitoring site is tightly affected by the major (industrial) source processes in the surrounding area or not. The results in fact indicate that VOC pollution in the area might have been affected more sensitively by other sources like traffic activities. Note that similar conclusions have also been drawn based on the monitoring results of other odorants (like RSCs) measured concurrently. Bear in mind that ambient air in the community holds a mixture of chemicals from the everyday activities of its citizens and the commercial enterprises that can make up modern society today. As a series of odor episodes tend to take place in a given area, information concerning the effect of the controlling factors (the frequency of these episodes, the duration of

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each odor episode, the intensity of the odors, and the character of the odor) can be used to determine the level of the nuisance prevailing in the area. Hence, to provide a more practical tactics to resolve odor problem, one needs to develop a more effective tool to detect the key odorants with the aid of direct (olfactory) methods. In this respect, the use of information concerning individual odor incidents assisted by continuous monitoring efforts will be highly valuable in the characterization of the malodor problems in the study area. Acknowledgement This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education, Science and Technology (MEST) (No. 2009-0093848). References ASTM (American Society for Testing and Materials), 2004. Standard practice for determination of odor and taste thresholds by a forced-choice ascending concentration series method of limits ASTM Standard E679-04, Annual Book of ASTM Standards, Philadelphia, pp. 105e106. Alberta Environment, 2003. Air Quality Monitoring Town of Banff, ISBN No. 0-77852464-7. Altshuller, A.P., 1993. Production of aldehydes as primary emissions and from secondary atmospheric reactions of alkenes and alkanes during the night and early morning hours. Atmospheric Environment 27, 21e32. Atkinson, R., 1994. Gas phase tropospheric chemistry of organic compounds. Journal of Physical Chemistry 98, 110e116. Badol, C., Locoge, N., Léonardis, T., Galloo, J.C., 2008. Using a sourceereceptor approach to characterise VOC behaviour in a French urban area influenced by industrial emissions part I: study area description, data set acquisition and qualitative data analysis of the data set. Science of the Total Environment 389, 441e452. Brocco, D., Fratarcangeli, R., Lepore, L., Petricca, M., Ventrone, I., 1997. Determination of aromatic hydrocarbons in urban air of Rome. Atmospheric Environment 31 (4), 557e566. Bundy, D.S., 1992. Odor issues with wastes. In: National Livestock Poultry and Aquaculture Waste Management. American Society of Agricultural Engineers (ASAE) Publication, St. Joseph, Michigan, pp. 288e292. Cetin, E., Odabasi, M., Seyfioglu, R., 2003. Ambient volatile organic compound (VOC) concentrations around a petrochemical complex and a petroleum refinery. Science of the Total Environment 312, 103e112. Cheng, L., Fu, L., Angle, R.P., Sandhu, H.S., 1997. Seasonal variations of volatile organic compounds in Edmonton, Alberta. Atmospheric Environment 31 (2), 239e246. Ferrari, C.P., Kaluzny, P., Roche, A., 1998. Aromatic hydrocarbons and aldehydes in the atmosphere of Grenoble France. Chemosphere 37, 1587e1601. Hawas, O., Hawker, D., Chan, A., Cohen, D., Christensen, E., 2002. Characterisation and identification of sources of volatile organic compounds in an industrial area in Brisbane, 16th Int. Clean Air Conf., Christchurch, New Zealand. Ho, K.F., Lee, S.C., Guo, H., Tsai, W.Y., 2004. Seasonal and diurnal variations of volatile organic compounds (VOCs) in the atmosphere of Hong Kong. Science of the Total Environment 322, 155e166. Hsieh, L.T., Yang, H.H., Chen, H.W., 2006. Ambient BTEX and MTBE in the neighborhoods of different industrial parks in Southern Taiwan. Journal of Hazardous Material 128, 106e115. John, J., Patrick, C., Brendan, T., 2006. Improvement of odor intensity measurement using dynamic olfactometry. Journal of the Air and Waste Management Association 56 (5), 675e683. Kim, K.-H., 2006. The properties of calibration errors in the analysis of reduced sulfur compounds by the combination of a loop injection system and the GC/ PFPD method. Analytica Chimica Acta 566 (1), 75e80. Kim, K.-H., Park, S.-Y., 2008. A comparative analysis of malodor samples between direct (olfactometry) and indirect (instrumental) methods. Atmospheric Environment 42 (20), 5061e5070. Kim, K.Y., Ko, H.J., Kim, H.T., Kim, Y.S., Roh, Y.M., Lee, C.M., 2008. Odor reduction rate in the confinement pig building by spraying various additives. Bioresource Technology 99 (17), 8464e8469. Kim, Y.P., Na, K., Moon, K.C., 1998. Air pollutant levels at Ulsan, an industrial area, Korea. Journal of Aerosol Science 29, 237e238. KMOE, 2008. Annual Report of Ambient Air Quality in Korea, 2008. Korean Ministry of Environment (KMOE). Knudsen, H.N., Kjaer, U.D., Nielsen, P.A., Wolko, P., 1999. Sensory and chemical characterization of VOC emissions from building products: impact of concentration and air velocity. Atmospheric Environment 33, 1217e1230. Kuran, P., Sojak, L.,1996. Environmental analysis of volatile organic compounds in water and sediment by gas chromatography. Journal of Chromatography A 733, 119e141. Lee, S.C., Chiu, M.Y., Ho, K.F., Zou, S.C., Wang, X.M., 2002. Volatile organic compounds (VOCs) in urban atmosphere of Hong Kong. Chemosphere 48, 375e382. Liang, H.M., Liao, C.M., 2007. Modeling VOC-odor exposure risk in livestock buildings. Chemosphere 68, 781e789.

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