Asian Journal of atmospheric environment
Asian Journal of atmospheric environment Asian Journal of atmospheric environment
Asian Journal of atmospheric environment

Current Issue

Asian Journal of Atmospheric Environment - Vol. 12 , No. 3

[ Technical Information ]
Asian Journal of Atmospheric Environment - Vol. 12, No. 3, pp.274-288
Abbreviation: Asian J. Atmos. Environ
ISSN: 1976-6912 (Print) 2287-1160 (Online)
Print publication date 30 Sep 2018
Received 29 Mar 2018 Revised 01 Jun 2018 Accepted 27 Jun 2018
DOI: https://doi.org/10.5572/ajae.2018.12.3.274

Measurements of 50 Non-polar Organic Compounds Including Polycyclic Aromatic Hydrocarbons, n-Alkanes and Phthalate Esters in Fine Particulate Matter (PM2.5) in an Industrial Area of Chiba Prefecture, Japan
Yujiro Ichikawa* ; Takehisa Watanabe ; Yasuhide Horimoto ; Katsumi Ishii ; Suekazu Naito
Chiba Prefectural Environmental Research Center, 1-8-8 Iwasaki Nishi, Ichihara, Chiba 290-0046, Japan

Correspondence to : *Tel: +81-436-21-6371 E-mail: y.ichkw17@pref.chiba.lg.jp
Current affiliation Community Affairs Department Air Quality Division, 1-1 Ichiba-cho, Chuo-ku, Chiba, Chiba 260-8667, Japan Tel: +81-43-223-3804


Copyright ⓒ 2018 by Asian Journal of Atmospheric Environment
This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Quantitative data of 50 non-polar organic compounds constituting PM2.5 were continuously collected and analyzed from June 2016 to October 2017 (approximately 17 months) at Ichihara, one of the largest industrial areas in Japan. Target nonpolar organic compounds including 21 species of polycyclic aromatic hydrocarbons (PAHs), 24 species of n-alkanes and 5 species of phthalate esters (PAEs) were simultaneously measured by gas chromatography/mass spectrometry. Basically, the average concentrations of the total PAHs, n-alkanes and PAEs in each season remained nearly level, and seasonal variations were little throughout the study period. These results suggest that the emission sources, which are not influenced by the seasons, are the dominant inputs for the target organic compounds. Diagnostic ratios of PAHs, assessment of n-alkane homologue distributions, carbon preference index, and the contribution of wax n-alkanes from plants were used to estimate source apportionments. These results indicate that anthropogenic sources were the main contributor for most PAHs and n-alkanes throughout the study period. The concentrations of PAEs selected in this study were low because emission amounts of these chemicals were little within the source areas of the sampling site. To our knowledge, this study is the first attempt to simultaneously measure a high number of non-polar organic compounds in PM2.5 collected from the ambient air of Japan, and the resultant data will provide valuable data and information for environmental researchers.


Keywords: PM2.5, Polycyclic aromatic hydrocarbons, n-Alkanes, Phthalate esters, Organic compounds, Japan

1. INTRODUCTION

Particulate matter (PM) is an atmospheric pollutant that has received widespread concern as it causes public health problems and plays important roles in regulating regional and global climate (Fuzzi, et al., 2015; IPCC, 2013; WHO, 2013; Jacob, 1999). Among the PM, particle sizes with aerodynamic diameter less than 2.5 μm (PM2.5) are particularly hazardous to public health because of their ability to penetrate into the alveoli of lungs and they are therefore more likely to increase the incidence of respiratory and cardiovascular diseases (Ueda, 2011; Schwartz and Neas, 2000).

According to the information provided by the Ministry of the Environment Japan, 74.5% of stations monitoring general ambient air, and 58.4% of stations monitoring auto-exhaust emissions met environmental quality standards for PM2.5 during the 2015 fiscal year (from April to March of the following year), and further countermeasures to reduce the level of PM2.5 are required (http://www.env.go.jp/press/103858.html, in Japanese, Accessed on June 1, 2018). For an effective reduction of PM2.5 levels, it is important to identify their emission sources and describe their chemical properties in the ambient air. Long time series of quantitative data of the chemical compositions that constitute PM2.5 are necessary to provide the required information to achieve PM2.5 reduction.

Along with chemical compounds that are utilized for the assessment of PM2.5 emission sources (e.g., trace metals), some organic compounds have definitive chemical structures that can be correlated to emission sources and are known as molecular markers. They can originate from many sources, including combustion of fossil fuels, biogenic origin, biomass burning, motor vehicles, waste incineration, steel/plastic manufacturing, cooking, and road dust (Hayakawa et al., 2016; Kawamura and Bikkina, 2016; Wang et al., 2016; Zhang et al., 2013; Alves et al., 2009; Alves, 2008; Brandenberger et al., 2005; He et al., 2004; Simoneit, 2002; Oros et al., 1999; Khalili et al., 1995; Rogge et al., 1993a, 1993b, 1993c; Simoneit et al., 1991).

A wealth of publications have reported that the organic fraction of PM2.5 consists of various kinds of organic compounds with different abundances in different areas of the world (Ichikawa et al., 2017; Mikuška et al., 2017; Ahmed et al., 2016; Fang et al., 2016; Li et al., 2016; Alves et al., 2015; Yadav et al., 2013; Park et al., 2006; Simoneit et al., 2004). Moreover, increasing attention has been paid to the study of the organic compounds in PM2.5 to improve understanding of the emission sources and source apportionment. However, in Japan, most PM2.5 studies have focused on metallic elements, carbonaceous components, and water-soluble inorganic components, and studies into organic compounds remain deficient.

Therefore, we intensively measured 50 different nonpolar organic compounds including 21 species of polycyclic aromatic hydrocarbons (PAHs), 24 species of n-alkanes and 5 species of phthalate esters (PAEs) in PM2.5 in a long time series at Ichihara, a major industrial area in Japan. Selected non-polar organic compounds were simultaneously measured employing GC/MS. To the best of our knowledge, this paper is the first attempt to simultaneously measure as many non-polar organic compounds in PM2.5 collected for a long time series (approximately 17 months) from the ambient atmosphere of Japan. Our results aim to provide useful insights and data for use in improving understanding of the target organic compounds in PM2.5 and thereby contribute to better air quality management strategies.


2. MATERIALS AND METHODS
2. 1 Sampling Site Description

Sampling was conducted on the roof (12 m height above the ground) of the Chiba Prefectural Environmental Research Center (CERC). The CERC is located between the industrial zone of Tokyo Bay (a large industrial base including gas power plants, heavy petrochemical industries, and many light industries within ca. 2 km radius), and the residential zone of Ichihara, Chiba Prefecture, Japan (Fig. 1). The site is located approximately 200 m southeast of the Japanese National Route 16 (average weekday 24-hour traffic of 42,007 vehicles, of which 29.1% are large-sized vehicles). Ichihara is about 40 km southeast of central Tokyo, and the estimated population is nearly 277,000.


Fig. 1. 
Map indicating the location of Chiba Prefectural Environmental Research Center (CERC).

2. 2 Sample Collection

PM2.5 samples were collected on 47 mm diameter quartz filter (Pall Corp., 2500QAT-UP, Q-filter) from 13 June 2016 to 25 October 2017. The flow rate of the low volume air sampler (Rupprecht and Patashnick Co., Inc., Partisol-FRM Model 2000 Air Sampler) was 16.7 L/min with a constant sampling rate (accumulating to ca. 24 m3/d). From our previous study, daily air volumes per sample (24 m3/d) were too low to quantify some of the organic compounds present in relatively small amounts in the ambient atmosphere, so the samples in this study were sampled on a weekly basis (Ichikawa et al., 2017). Park et al. (2006) also pointed out the difficulty of detailed analysis of organic compounds by gas chromatography/mass spectrometry (GC/MS) from daily sampling, supporting the decision to analyze samples on a weekly basis. Some amount of organic compounds may be decomposed or evaporated from the Q-filter during the sampling. However, due to the reason mentioned above those were not taken into consideration in this study.

The sampling periods are described in Table 1. Based on the definition used by the Japanese Meteorological Agency, the four seasons were assigned as follows: March to May for spring, June to August for summer, September to November for autumn and December to February for winter.

Table 1. 
Details of sampling period in each season.
Year Season*1) Sampling period n
2016 Summer Jun. 13-Jul. 20
Jul. 4-Jul. 11
Jun. 20-Jun. 27
Jul. 11-Jul. 18
Jun. 28-Jul. 4
Aug. 22-Aug. 29
6
Autumn Aug. 29-Sep. 5
Sep. 20-Sep. 27
Oct. 17-Oct. 25
Nov. 8-Nov. 15
Sep. 5-Sep. 12
Oct. 3-Oct. 11
Oct. 25-Nov. 1
Nov. 15-Nov. 22
Sep. 12-Sep. 20
Oct. 11-Oct. 17
Nov. 1-Nov. 8
Nov. 29-Dec. 2
12
Winter Dec. 2-Dec. 7
Dec. 21-Dec. 28
Jan. 11-Jan. 18
Jan. 31-Feb. 8
Feb. 22-Mar. 1
Dec. 7-Dec. 14
Dec. 28-Jan. 4
Jan. 18-Jan. 25
Feb. 8-Feb. 15
Dec. 14-Dec. 21
Jan. 5-Jan. 10
Jan. 25-Jan. 31
Feb. 15-Feb. 22
13
2017 Spring Mar. 1-Mar. 8
Mar. 22-Mar. 29
Apr. 12-Apr. 19
May 2-May 10
May 24-May 31
Mar. 8-Mar. 15
Mar. 29-Apr. 5
Apr. 19-Apr. 26
May 10-May 17
Mar. 15-Mar. 22
Apr. 5-Apr. 12
Apr. 26-May 2
May 17-May 24
13
Summer May 31-Jun. 7
Jun. 21-Jun. 28
Jul. 12-Jul. 19
Aug. 2-Aug. 8
Aug. 23-Aug. 30
Jun. 7-Jun. 14
Jun. 28-Jul. 5
Jul. 19-Jul. 26
Aug. 8-Aug. 16
Jun. 14-Jun. 21
Jul. 5-Jul. 12
Jul. 26-Aug. 2
Aug. 16-Aug. 23
13
Autumn Aug. 30-Sep. 4
Sep. 20-Sep. 27
Oct. 11-Oct. 17
Sep. 4-Sep. 12
Sep. 27-Oct. 4
Oct. 17-Oct. 25
Sep. 12-Sep. 20
Oct. 4-Oct. 11
8
*1)The four seasons were assigned based on the definition used by the Japanese Meteorological Agency.

Typically, Q-filters are combusted in a furnace with high temperatures to remove the existing organic carbon (OC) prior to sampling. However, combustion could activate the Q-filter, resulting in positive artifacts due to the absorption of gas phase organic materials onto the filter. As described in Section 2.3, gravimetric measurements of PM2.5 collected in Q-filters were designed in this study and to eliminate the possible positive artifacts during transport, sampling and storage, Q-filters were not combusted.

Travel blank filters were prepared and placed in the same container as the samples and were transported and treated in the same way (e.g., making contact with the sampling devices, and being exposed to the conditions of the sampling site, storage, and all analytical procedures). Sample results reported in this paper were corrected based on the travel blank filters.

Sample filters were placed in petri dishes and enclosed in a sealed bag. All samples were stored in a freezer at - 30°C until chemical analysis to prevent evaporation and degradation of components constituting PM2.5. A total of 68 effective samples were collected in this study, which included 3 blank samples.

2. 3 PM2.5 Mass Concentration

Both PM2.5 sample filters and travel blank filters were weighed using an electronic microbalance (A&D Company Ltd., BM-20) with a reading precision of 1 μg. Prior to weighing, all Q-filters were kept under equilibrated conditions of constant temperature (21.5±1.5°C) and relative humidity (35±5%) for at least 24 hours. An ionizing blower was used to eliminate the effects of static electricity on the weighing process. Obtained PM2.5 mass (μg) was calculated by subtracting pre-weight from post-weight of the Q-filters. The PM2.5 mass concentrations (μg/m3) were calculated by PM2.5 mass per total volume of suction.

For the accuracy testing of gravimetric measurement, PM2.5 mass concentration was measured in parallel using an automatic instrument (Horiba Ltd., APDA-3750A) employing a beta-ray absorption technique. Automatic instrument was installed at the monitoring station of the CERC, at a height of 7 m above the ground. PM2.5 mass concentrations observed by two different methodologies showed good positive correlation (n=64, R=0.84).

2. 4 OC Measurement

The OC was measured by the thermal/optical reflectance carbon analysis system (Sunset Laboratory Inc., Lab OC-EC Aerosol Analyzer). The analytical conditions of the IMPROVE method (Chow et al., 1993) were used with the thermal optical reflection protocol (Chow et al., 2001). A total of four fractions (OC1, OC2, OC3, and OC4) and the optical pyrolysis correction of OC (PyC) were determined. The OC was calculated as OC1+OC2+OC3+OC4+PyC.

2. 5 Organic Compounds Measurement

The measured non-polar organic compounds in this study were 21 species of PAHs, 24 species of n-alkanes and 5 species of PAEs, producing a total of 50 species. Details of these compounds, including individual abbreviations, are presented in Tables 2 and 3.

Table 2. 
Temperature, limit of detection of PAHs (ng/m3), recovery percentages of PAHs (%), concentrations of PM2.5 (μg/m3), OC (μg/m3), and PAHs (ng/m3); ratios of PAHs accounting for PM2.5 and OC; and diagnostic ratios between PAHs.
Compounds and
ratios
Abbreviation Unit Recovery (%)
(n=4)
LOD Range Average
Summer
2016
(n=6)
Autumn
2016
(n=12)
Winter
2016
(n=13)
Spring
2017
(n=13)
Summer
2017
(n=13)
Autumn
2017
(n=8)
Total
(n=65)
Summer
2016
Autumn
2016
Winter
2016
Spring
2017
Summer
2017
Autumn
2017
Total
Temperature °C 17.5-36.6 −0.4-32.4 −3.5-21.3 −0.8-29.4 14.7-36.4 10.5-32.2 −3.5-36.6 25.4 18.8 7.9 14.4 25.3 20.7 18.5
PM2.5 μg/m3 4.4-11 5.3-15 7.0-16 9.4-16 6.2-14 4.9-9.4 4.4-16 7.5 10 11 12 9.0 7.4 9.9
OC μg/m3 0.81-1.3 0.80-3.3 1.3-3.7 1.1-2.4 0.66-3.1 1.0-1.9 0.66-3.7 0.96 1.9 2.3 1.8 1.3 1.4 1.7
OC/PM2.5 % 9.4-18 12-28 17-26 11-19 11-22 14-22 9.4-28 13 19 20 15 14 19 17
PAHs
Fluorene Fl ng/m3 88 0.013 N.D.*1)-N.D. N.D.-N.D. N.D.-0.070 0.020-0.067 N.D.-0.066 N.D.-0.033 N.D.-0.070 N.D. N.D. 0.035 0.033 0.020 0.023 0.022
Phenanthrene Phe ng/m3 96 0.0096 N.D.-0.024 N.D.-0.021 0.045-0.30 0.016-0.24 N.D.-0.066 N.D.-0.043 N.D.-0.30 0.0080 0.0069 0.16 0.11 0.021 0.022 0.063
Anthracene Ant ng/m3 97 0.016 N.D.-0.034 N.D.-N.D. N.D.-0.22 N.D.-0.016 N.D.-0.094 N.D.-N.D. N.D.-0.22 0.012 N.D. 0.044 0.0084 0.021 N.D. 0.018
Fluoranthene Fluor ng/m3 97 0.0077 0.14-0.56 0.092-0.53 0.22-0.96 0.16-0.69 0.094-0.57 0.11-0.24 0.092-0.96 0.30 0.22 0.50 0.45 0.23 0.17 0.33
Pyrene Py ng/m3 98 0.009 0.12-0.54 0.082-0.43 0.18-0.82 0.14-0.61 0.084-0.55 0.097-0.21 0.082-0.82 0.28 0.19 0.40 0.39 0.21 0.15 0.28
Benzo(c)phenanthrene BcP ng/m3 90 0.063 N.D.-N.D. N.D.-N.D. N.D.-N.D. N.D.-N.D. N.D.-N.D. N.D.-N.D. N.D.-N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D.
Benz(a)anthracene BaA ng/m3 99 0.032 0.067-0.18 0.052-0.16 0.038-0.14 0.037-0.12 N.D.-0.16 N.D.-0.043 N.D.-0.18 0.11 0.083 0.082 0.074 0.057 0.026 0.071
Chrysene Chry ng/m3 88 0.057 N.D.-0.35 N.D.-0.13 N.D.-0.35 0.059-0.27 N.D.-0.34 N.D.-N.D. N.D.-0.35 0.14 0.066 0.17 0.16 0.098 N.D. 0.11
Benzo(b)fluoranthene BbF ng/m3 95 0.037 0.17-0.84 0.10-0.46 0.14-0.89 0.17-0.47 N.D.-0.75 0.11-0.20 N.D.-0.89 0.37 0.27 0.45 0.33 0.24 0.16 0.31
7,12-Dimethylbenz
(a)anthracene
7,12DaA ng/m3 90 0.038 N.D.-N.D. N.D.-N.D. N.D.-N.D. N.D.-N.D. N.D.-N.D. N.D.-N.D. N.D.-N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D.
Benzo(j)fluoranthene BjF ng/m3 89 0.051 0.082-0.33 0.065-0.19 0.087-0.38 0.089-0.23 0.054-0.40 0.065-0.15 0.054-0.40 0.16 0.12 0.22 0.16 0.14 0.10 0.15
Benzo(k)fluoranthene BkF ng/m3 90 0.040 0.088-0.35 0.072-0.23 0.08-0.31 0.080-0.21 N.D.-0.32 0.045-0.097 N.D.-0.35 0.17 0.13 0.18 0.15 0.10 0.069 0.13
Benzo(e)pyrene BeP ng/m3 92 0.019 0.13-0.70 0.090-0.35 0.11-0.61 0.12-0.37 0.094-0.75 0.096-0.18 0.09-0.75 0.30 0.21 0.31 0.25 0.24 0.14 0.24
Benzo(a)pyrene BaP ng/m3 96 0.024 0.17-0.81 0.15-0.46 0.12-0.68 0.13-0.45 0.13-0.86 0.13-0.24 0.12-0.86 0.36 0.26 0.30 0.28 0.30 0.18 0.28
3-Methylcholanthrene 3MC ng/m3 100 0.046 N.D.-N.D. N.D.-N.D. N.D.-N.D. N.D.-N.D. N.D.-N.D. N.D.-N.D. N.D.-N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D.
Indeno(1,2,3-cd)pyrene IP ng/m3 100 0.026 0.13-0.94 N.D.-0.45 0.13-0.85 0.15-0.46 0.12-1.1 0.12-0.25 N.D.-1.1 0.37 0.29 0.41 0.30 0.33 0.20 0.32
Dibenz(a,h)anthracene DBahA ng/m3 100 0.019 0.054-0.19 0.043-0.14 N.D.-0.13 N.D.-0.072 N.D.-0.15 N.D.-0.040 N.D.-0.19 0.097 0.088 0.06 0.046 0.047 0.030 0.060
Benzo(g,h,i)perylene BghiP ng/m3 96 0.013 0.11-0.82 0.078-0.39 0.14-0.80 0.14-0.41 0.11-0.88 0.12-0.24 0.078-0.88 0.32 0.27 0.40 0.29 0.29 0.19 0.30
Dibenzo(a,l)pyrene DBalP ng/m3 100 0.036 0.049-0.26 0.054-0.17 N.D.-0.21 N.D.-0.098 N.D.-0.24 N.D.-0.046 N.D.-0.26 0.11 0.11 0.098 0.063 0.060 0.032 0.079
Dibenzo(a,i)pyrene DBaiP ng/m3 110 0.056 N.D.-0.19 0.12-0.31 N.D.-N.D. N.D.-N.D. N.D.-0.070 N.D.-N.D. N.D.-0.31 0.13 0.15 N.D. N.D. 0.031 N.D. 0.061
Dibenzo(a,h)pyrene DBahP ng/m3 100 0.059 N.D.-0.11 N.D.-0.11 N.D.-N.D. N.D.-N.D. N.D.-N.D. N.D.-N.D. N.D.-0.11 0.075 0.047 N.D. N.D. N.D. N.D. 0.037
ΣPAHs*2) ng/m3 1.6-7.3 1.3-4.5 1.7-7.6 1.7-4.7 1.1-7.4 1.1-2.1 1.1-7.6 3.4 2.6 4.0 3.2 2.5 1.6 2.9
ΣPAHs/PM2.5 % 0.026-0.073 0.018-0.041 0.024-0.057 0.011-0.040 0.013-0.053 0.015-0.032 0.011-0.073 0.043 0.027 0.035 0.027 0.027 0.023 0.030
ΣPAHs/OC % 0.20-0.55 0.062-0.35 0.097-0.25 0.074-0.33 0.065-0.37 0.089-0.16 0.062-0.55 0.33 0.16 0.18 0.19 0.20 0.12 0.19
Σ3-rings*3) ng/m3 0.019-0.065 0.019-0.035 0.093-0.43 0.052-0.29 0.019-0.18 0.019-0.074 0.019-0.43 0.027 0.021 0.24 0.15 0.062 0.053 0.10
Σ4-rings*4) ng/m3 0.41-1.7 0.31-1.3 0.52-2.3 0.45-1.7 0.27-1.7 0.30-0.57 0.27-2.3 0.88 0.62 1.2 1.1 0.65 0.42 0.84
Σ5-rings*5) ng/m3 0.72-3.2 0.54-1.9 0.57-3.0 0.62-1.8 0.44-3.3 0.48-0.91 0.44-3.3 1.5 1.1 1.5 1.3 1.1 0.70 1.2
Σ6-rings*6) ng/m3 0.35-2.3 0.38-1.3 0.35-1.9 0.37-1.0 0.32-2.3 0.32-0.57 0.32-2.3 1.0 0.88 0.96 0.71 0.75 0.48 0.80
BeP/(BeP+BaP) 0.42-0.46 0.38-0.54 0.47-0.58 0.44-0.53 0.41-0.47 0.40-0.46 0.38-0.58 0.44 0.46 0.51 0.48 0.44 0.44 0.46
BaA/(BaA+Chr) 0.34-0.70 0.48-0.71 0.29-0.57 0.29-0.41 0.30-0.65 0.36-0.60 0.29-0.71 0.52 0.58 0.35 0.33 0.42 0.46 0.43
IP/(IP+BghiP) 0.52-0.56 0.042-0.56 0.48-0.52 0.49-0.53 0.48-0.56 0.46-0.56 0.042-0.56 0.54 0.49 0.51 0.50 0.52 0.51 0.51
Fluor/(Flour+Py) 0.51-0.55 0.50-0.56 0.52-0.57 0.52-0.57 0.50-0.54 0.53-0.55 0.50-0.57 0.53 0.54 0.55 0.54 0.52 0.54 0.54
BghiP/BaP 0.65-1.0 0.52-1.6 1.2-1.7 0.91-1.5 0.76-1.3 0.92-1.2 0.52-1.7 0.81 1.1 1.3 1.1 0.99 1.1 1.1
Except for the temperature, all of the measured values have been rounded to no more than three significant figures.
*1) N.D. : Not detected (below LOD)
*2) ΣPAHs=Fl+Phe+Ant+Fluor+Py+BcP+BaA+Chry+BbF+7,12DaA+BjF+BkF+BeP+BaP+3MC+IP+DBahA+BghiP+DBalP+DBaiP+DBahP
*3) Σ3-rings=Fl+Phe+Ant
*4) Σ4-rings=Fluor+Py+BcP+BaA+Chry+7,12DaA
*5) Σ5-rings=BbF+BjF+BkF+BeP+BaP+3MC+DBahA
*6) Σ6-rings=IP+BghiP+DBalP+DBaiP+DBahP

Table 3. 
LOD of n-alkanes and PAEs (ng/m3), recovery percentages of n-alkanes and PAEs (%), concentrations of n-alkanes and PAEs (ng/m3) and ratios of n-alkanes and PAEs accounting for PM2.5 and OC.
Compounds,
ratios and index
Abbreviation Unit Recovery (%)
(n=4)
LOD Range Average
Summer 2016
(n=6)
Autumn 2016
(n=12)
Winter 2016
(n=13)
Spring 2017
(n=13)
Summer 2017
(n=13)
Autumn 2017
(n=8)
Total
(n=65)
Summer
2016
Autumn
2016
Winter
2016
Spring
2017
Summer
2017
Autumn
2017
Total
n-Alkanes
n-Heptadecane C17 ng/m3 87 0.0095 0.025-0.076 0.014-0.15 0.052-0.19 0.028-0.15 N.D.*1)-0.050 N.D.-0.067 N.D.-0.19 0.044 0.050 0.11 0.065 0.027 0.017 0.056
n-Octadecane C18 ng/m3 78 0.0072 0.016-0.071 0.014-0.060 0.027-0.12 0.018-0.094 N.D.-0.27 N.D.-0.058 N.D.-0.27 0.037 0.030 0.066 0.046 0.080 0.023 0.050
n-Nonadecane C19 ng/m3 84 0.0042 0.016-0.060 0.014-0.096 0.054-0.16 0.018-0.095 0.0051-0.11 N.D.-0.050 N.D.-0.16 0.035 0.041 0.09 0.047 0.026 0.024 0.046
n-Eicosane C20 ng/m3 89 0.0062 0.021-0.085 0.014-0.15 0.067-0.23 0.0071-0.12 N.D.-0.14 0.0080-0.069 N.D.-0.23 0.048 0.061 0.13 0.052 0.027 0.029 0.061
n-Heneicosane C21 ng/m3 96 0.0055 0.014-0.13 0.021-0.30 0.10-0.47 0.0096-0.21 0.0089-0.29 0.013-0.063 0.0089-0.47 0.068 0.099 0.21 0.086 0.050 0.034 0.098
n-Docosane C22 ng/m3 110 0.0076 0.016-0.24 0.029-0.72 0.22-0.78 N.D.-0.41 N.D.-0.70 0.0098-0.22 N.D.-0.78 0.12 0.20 0.46 0.17 0.088 0.063 0.20
n-Tricosane C23 ng/m3 120 0.0059 0.054-0.41 0.094-1.0 0.44-1.9 0.056-0.73 0.041-1.7 0.063-0.25 0.041-1.9 0.25 0.31 0.84 0.32 0.23 0.13 0.37
n-Tetracosane C24 ng/m3 130 0.0071 0.085-0.66 0.011-1.7 0.75-2.5 0.042-1.5 N.D.-3.7 0.068-0.62 N.D.-3.7 0.37 0.50 1.5 0.55 0.46 0.33 0.66
n-Pentacosane C25 ng/m3 120 0.010 0.35-1.0 0.13-2.1 1.1-3.2 0.46-1.8 0.21-6.2 0.30-1.2 0.13-6.2 0.68 1.0 2.0 1.0 0.96 0.64 1.1
n-Hexacosane C26 ng/m3 110 0.0097 0.41-1.2 0.29-2.3 0.9-3.2 0.62-2.0 0.33-6.4 0.61-1.4 0.29-6.4 0.81 1.2 1.8 1.0 1.1 0.92 1.2
n-Heptacosane C27 ng/m3 110 0.0061 0.40-2.1 0.37-2.8 0.63-3.7 0.90-1.9 0.70-8.1 0.95-2.1 0.37-8.1 1.2 1.5 1.9 1.3 1.7 1.2 1.5
n-Octacosane C28 ng/m3 100 0.015 0.42-3.5 0.40-2.2 0.61-3.0 0.55-1.7 0.44-6.9 0.42-2.5 0.40-6.9 2.3 1.2 1.7 0.91 1.5 1.2 1.4
n-Nonacosane C29 ng/m3 100 0.010 0.51-2.4 0.57-4.8 0.73-5.9 1.2-3.2 0.60-6.9 0.83-2.4 0.51-6.9 1.4 2.3 3.0 1.7 2.0 1.6 2.1
n-Triacontane C30 ng/m3 100 0.010 0.33-1.4 0.34-2.6 0.40-2.5 0.47-1.2 0.24-4.8 0.47-1.4 0.24-4.8 0.82 1.3 1.3 0.67 1.1 0.85 1.0
n-Hentriacontane C31 ng/m3 97 0.013 0.68-1.8 0.74-6.5 0.56-5.5 0.76-1.9 0.57-4.6 0.90-2.5 0.56-6.5 1.1 2.7 2.6 1.2 1.7 1.7 1.9
n-Dotriacontane C32 ng/m3 93 0.021 0.29-0.64 0.27-1.8 0.21-1.5 0.21-0.58 0.13-2.2 0.22-0.73 0.13-2.2 0.43 0.80 0.74 0.36 0.53 0.48 0.57
n-Tritriacontane C33 ng/m3 92 0.029 0.31-0.62 0.45-3.5 0.24-2.2 0.26-0.85 0.23-2.2 0.36-1.3 0.23-3.5 0.43 1.3 1.0 0.47 0.69 0.83 0.82
n-Tetratriacontane C34 ng/m3 92 0.036 0.15-0.23 0.14-0.86 0.084-0.61 0.079-0.26 0.045-1.2 0.11-0.31 0.045-1.2 0.19 0.38 0.33 0.17 0.24 0.21 0.26
n-Pentatriacontane C35 ng/m3 92 0.050 0.12-0.22 0.15-1.2 0.081-0.82 0.082-0.24 0.057-1.1 0.13-0.41 0.057-1.2 0.17 0.44 0.40 0.15 0.26 0.26 0.29
n-Hexatriacontane C36 ng/m3 97 0.058 0.096-0.19 0.13-0.56 0.080-0.48 0.073-0.19 0.065-0.85 0.086-0.25 0.065-0.85 0.14 0.28 0.24 0.13 0.21 0.18 0.20
n-Heptatriacontane C37 ng/m3 96 0.072 0.075-0.15 0.085-0.52 N.D.-0.37 N.D.-0.15 N.D.-0.67 N.D.-0.20 N.D.-0.67 0.11 0.21 0.19 0.093 0.14 0.12 0.15
n-Octatriacontane C38 ng/m3 96 0.097 N.D.-0.13 N.D.-0.41 N.D.-0.32 N.D.-0.13 N.D.-0.59 N.D.-0.15 N.D.-0.59 0.062 0.16 0.16 0.063 0.11 0.086 0.11
n-Nonatriacontane C39 ng/m3 100 0.096 N.D.-0.12 N.D.-0.37 N.D.-0.29 N.D.-0.096 N.D.-0.52 N.D.-0.12 N.D.-0.52 0.060 0.14 0.14 0.052 0.092 0.065 0.096
n-Tetracontane C40 ng/m3 100 0.10 N.D.-0.12 N.D.-0.30 N.D.-0.30 N.D.-0.12 N.D.-0.45 N.D.-0.11 N.D.-0.45 0.063 0.13 0.14 0.062 0.083 0.059 0.096
Σn-Alkanes*2) ng/m3 4.8-16 5.5-32 8.0-39 7.0-18 4.3-60 8.4-17 4.3-60 11 16 21 11 13 11 14
Σn-Alkanes/PM2.5 % 0.084-0.21 0.080-0.29 0.10-0.26 0.064-0.11 0.049-0.43 0.10-0.19 0.049-0.43 0.15 0.15 0.18 0.089 0.14 0.15 0.14
Σn-Alkanes/OC % 0.59-1.6 0.57-1.2 0.60-1.1 0.37-0.91 0.34-1.9 0.58-1.1 0.34-1.9 1.1 0.83 0.88 0.61 0.93 0.80 0.84
ΣCodd*3) ng/m3 2.8-8.6 3.2-21 4.1-24 4.6-9.6 2.7-32 4.6-10 2.7-32 5.5 10 12 6.5 7.8 6.6 8.6
ΣCeven*4) ng/m3 2.0-7.3 2.2-11 3.8-15 2.4-8.2 1.6-28 3.4-6.7 1.6-28 5.4 6.2 8.5 4.2 5.5 4.4 5.8
PAEs
Di-n-propyl phthalate DIPP ng/m3 91 0.0037 N.D.-0.030 N.D.-0.073 N.D.-0.11 N.D.-0.11 N.D.-0.098 N.D.-0.0048 N.D.-0.11 0.010 0.020 0.034 0.049 0.0098 0.0023 0.024
Diisobutyl phthalate DIBP ng/m3 77 0.0075 0.027-0.21 N.D.-0.32 0.038-0.40 N.D.-0.22 N.D.-0.083 N.D.-0.032 N.D.-0.40 0.12 0.076 0.26 0.068 0.022 0.012 0.097
Di-n-pentyl phthalate DPP ng/m3 85 0.0072 N.D.-0.083 N.D.-0.020 N.D.-0.018 N.D.-0.047 0.0076-0.24 0.0084-0.025 N.D.-0.24 0.017 0.0072 0.0092 0.0076 0.038 0.013 0.015
Di-n-hexyl phthalate DHP ng/m3 79 0.024 N.D.-N.D. N.D.-N.D. N.D.-0.082 N.D.-0.058 N.D.-0.26 N.D.-N.D. N.D.-0.26 N.D. N.D. 0.039 0.020 0.073 N.D. 0.031
Butyl benzyl phthalate BBP ng/m3 73 0.021 0.059-0.21 0.10-0.91 0.035-0.25 0.035-0.36 0.022-1.5 0.022-0.19 0.022-1.5 0.10 0.30 0.10 0.13 0.37 0.050 0.19
ΣPAEs*5) ng/m3 0.12-0.36 0.12-0.97 0.23-0.62 0.084-0.52 0.070-1.8 0.049-0.24 0.049-1.8 0.26 0.41 0.44 0.27 0.51 0.090 0.36
ΣPAEs/PM2.5 % 0.0016-0.0082 0.0011-0.0088 0.0020-0.0073 0.00065-0.0047 0.00071-0.024 0.00067-0.0041 0.00065-0.024 0.0040 0.0044 0.0043 0.0024 0.0062 0.0013 0.0039
ΣPAEs/OC % 0.013-0.044 0.0071-0.055 0.0087-0.038 0.0049-0.039 0.0059-0.20 0.0038-0.020 0.0038-0.20 0.028 0.025 0.023 0.016 0.047 0.0068 0.025
All measured values have been rounded to no more than three significant figures.
*1) N.D. : Not detected (below LOD)
*2) Σn-Alkanes=C17+C18+C19+C20+C21+C22+C23+C24+C25+C26+C27+C28+C29+C30+C31+C32+C33+C34+C35+C36+C37+C38+C39+C40
*3) ΣCodd=C17+C19+C21+C23+C25+C27+C29+C31+C33+C35+C37+C39
*4) ΣCeven=C18+C20+C22+C24+C26+C28+C30+C32+C34+C36+C38+C40
*5) ΣPAEs=DIPP+DIBP+DPP+DHP+BBP

2. 5. 1 Pretreatment Procedure

A Q-filter was placed in a 10 mL glass flask with stopper and spiked with defined amounts of the internal standard (IS) substances. The IS substances employed in this study were as follows. For PAHs: anthracene-d10 (Ant-d10), pyrene-d10 (Py-d10), benzo(a)pyrene-d12 (BaP-d12), and benzo(g,h,i)perylene-d12 (BghiP-d12). For n-alkanes: n-tetracosane-d50 (C24-d50). For PAEs: diethyl phthalate-d4 (DEP-d4), di-n-butyl phthalate-d4 (DBP-d4), bis(2-ethylhexyl)adipate-d8 (DEHA-d8), and benzyl butyl phthalate-d4 (BBP-d4). Samples were extracted in 5 mL dichloromethane solution twice using an ultrasonic bath for 15 min, followed by filtration through a hydrophilic PTFE syringe filter (ADVANTEC Co. Ltd., DISMIC, pore size of 0.22 μm) to remove insoluble particles and quartz fibers. The extracts were evaporated to dryness under a gentle stream of high purity nitrogen gas and re-dissolved in 100 μL of toluene for GC/MS analysis.

2. 5. 2 GC/MS Analysis

Analysis of the target organic compounds in the PM2.5 samples was carried out by GC/MS (6890 GC/5973 MSD, Agilent Technology) under the following conditions: capillary column DB-5MS (length; 60 m, internal diameter; 0.25 mm, film thickness; 0.25 μm film thickness, Agilent J&W) was used for separation of the compounds; high-purity helium was used as the carrier gas with flow rate of 1.0 mL/min; sample injection amount was 1 μL in splitless mode; the GC oven temperature was initially held at 100°C for 1 min, increasing 20°C/min until 270°C and held for 17 min, increasing 15°C/min until 320°C and then held for 32 min; the injection temperature was set to 280°C and the MS source and quadruple temperature to 230°C and 150°C, respectively; and mass spectrum peaks were obtained by electron ionization mode at electron energy of 70 eV with a selected ion monitoring (SIM) mode. Monitored target and qualifier ions, and internal standards used in SIM mode is listed in Table 4.

Table 4. 
Monitored target and qualifier ions, and internal standards used in SIM mode of GC/MS analysis.
Compounds Target ion Qualifier ion Internal standard Compounds Target ion Qualifier ion Internal standard
PAHs n-Alkanes
Fl 166 165 Ant-d10 C17 57 71 C24-d50
Phe 178 176 Ant-d10 C18 57 71 C24-d50
Ant 178 176 Ant-d10 C19 57 71 C24-d50
Fluor 202 200 Py-d10 C20 57 71 C24-d50
Py 202 200 Py-d10 C21 57 71 C24-d50
BcP 228 227 BaP-d12 C22 57 71 C24-d50
BaA 228 226 BaP-d12 C23 57 71 C24-d50
Chry 228 226 BaP-d12 C24 57 71 C24-d50
BbF 252 250 BaP-d12 C25 57 71 C24-d50
7,12DaA 256 241 BaP-d12 C26 57 71 C24-d50
BjF 252 250 BaP-d12 C27 57 71 C24-d50
BkF 252 250 BaP-d12 C28 57 71 C24-d50
BeP 252 250 BaP-d12 C29 57 71 C24-d50
BaP 252 250 BaP-d12 C30 57 71 C24-d50
3MC 268 267 BaP-d12 C31 57 71 C24-d50
IP 276 277 BghiP-d12 C32 57 71 C24-d50
DBahA 278 276 BghiP-d12 C33 57 71 C24-d50
BghiP 276 277 BghiP-d12 C34 71 85 C24-d50
DBalP 302 300 BghiP-d12 C35 57 71 C24-d50
DBaiP 302 303 BghiP-d12 C36 57 71 C24-d50
DBahP 302 303 BghiP-d12 C37 57 71 C24-d50
C38 57 71 C24-d50
C39 57 71 C24-d50
C40 57 71 C24-d50
PAEs
DIPP 149 209 DEP-d4
DIBP 149 223 DEP-d4
DPP 149 237 DBP-d4
DHP 149 251 DEHA-d8
BBP 149 206 BBP-d4

The individual organic compounds and IS substances were determined by comparing retention times and mass spectrum peaks with those of authentic standards. The five-point calibration curves were plotted based on the IS method for individual compounds and all of the compounds showed good linearity with the Pearson’s correlation coefficients of R>0.95.

A recovery test (n=4) was performed to validate the pretreatment method. Known amounts of individual organic compounds were spiked to the blank Q-filter and then prepared and handled by exactly the same procedures as those used for the PM2.5 samples. The average recovery for PAHs, n-alkanes and PAEs were 88-110%, 78-130% and 73-91%, respectively (Tables 2 and 3). In addition to the organic compounds described in Tables 2 and 3, we examined naphthalene (NaP), acenaphthene (Ace) and acenaphthylene (Acy) in PAHs, n-dodecane (C12), n-tridecane (C13), n-tetradecane (C14), n-pentadecane (C15), and n-hexadecane (C16) in n-alkanes and diethyl phthalate (DEP), dibutyl phthalate (DBP) and dicyclohexyl phthalate (DCHP) in PAEs; however, the recovery percentages of those compounds were not acceptable. Therefore, these were eliminated from the analyte candidates in this study. The unacceptable recovery percentages for these compounds may have been due to vaporization loss during the evaporation process (NaP, Ace, Acy, and C12-C16) and a high level of background contaminations (DEP, DBP, and DCHP).

The limit of detection (LOD) for the individual organic compounds is provided in Tables 2 and 3. It was calculated using the standard deviation of standard solution values (n=7) multiplied by 3. Measurement data below the LOD were replaced with the value of LOD/2 for the calculations in this study.


3. RESULTS AND DISCUSSION
3. 1 Temporal Variations of PM2.5 Mass Concentration and OC

A summary of average concentrations of PM2.5, OC and OC/PM2.5 and their ranges in each season are shown in Table 2. These observations are also plotted in Fig. 2 to reveal the temporal variation of PM2.5 and OC results. During the study period, PM2.5 and OC had higher concentrations from October 2016 (autumn) to May 2017 (spring) compared with other periods. This implies that PM2.5 and OC vary seasonally. PM2.5 and OC showed similar variation patterns over time, and their Pearson’s correlation coefficient (R) was 0.80. The overall average value of OC/PM2.5 was 17%, which is in good agreement with the value of 19.7% from our previous publication (n=373) based on data obtained at the same study site (Ichikawa et al., 2015). These results indicate that OC is one of the major chemical components that contributes to PM2.5 mass concentration. Identifying the different proportions of organic compounds could help to identify OC source.


Fig. 2. 
Temporal variations of PM2.5 and OC mass concentrations.

3. 2 Temporal Variations of Non-Polar Organic Compounds
3. 2. 1 PAHs

PAHs are derived exclusively from the incomplete combustion and pyrolysis of organic materials contained in fossil fuels, coal fuels, lubricant oils, biomass, etc., and are ubiquitous environmental contaminants (Alves, 2008; Park et al., 2006; Simoneit, 2002). There are studies indicating or suggesting the linkage between PAH properties and human health problems such as cancer (IARC, 2013, 2010).

Measurement values of individual PAHs in each season are shown in Table 2. The summed concentration of 21 species of PAHs is expressed as Σ PAHs. Fig. 3 shows temporal variations of Σ PAHs obtained through the long time series analysis. Winter 2016 showed the highest average Σ PAH concentration (4.0 ng/m3) during the study period and other studies conducted in Japan support our results (Suzuki et al., 2015; Kume et al., 2007; Kakimoto et al., 2002). There were a few samples in early summer (July) and early winter (December-January) showing an elevated level of ΣPAHs; however, most of the samples were below 4 ng/m3 during the study period and the seasonal variation was little. Kume et al. (2007) have analyzed 21 species of PAHs in PM2.5 from February 2001 to January 2002 in Shizuoka, Japan, and their result did not show strong seasonal variations, implying similarity to our result. This result suggests that the emission sources, which are not influenced by the seasons, are the dominant inputs for Σ PAHs within the source areas of the sampling site. The overall mean concentration of Σ PAHs was 2.9 ng/m3, which accounts for 0.030% and 0.19% of PM2.5 and OC, respectively. Also, PM2.5 and OC have positive correlation with Σ PAHs with the Pearson correlation coefficient (R) of 0.57 and 0.42, respectively. Our result is approximately one to two orders of magnitude lower than those observed in China (Wang et al., 2016, 2015). Although the individual amounts of organic compounds and their percentages accounting for PM2.5 and OC are small, they could be used as molecular markers for source apportionment, which will be discussed later.


Fig. 3. 
Temporal variation of ΣPAHs concentration.

As is evident from Table 2, the lighter weight PAHs (Σ 3-rings=Fl+Phe+Ant) were much lower than that of the heavier weight PAHs (Σ 4-rings=Fluor+Py+BcP+BaA+Chry+7,12DaA; Σ 5-rings=BbF+BjF+BkF+BeP+BaP+3MC+DBahA; Σ 6-rings =IP+BghiP+DBalP+DBaiP+DBahP) throughout the year. This is probably because the 3-ring PAHs have relatively higher vapor pressures compared with the higher weight PAHs and tend to partition in the gas phase. The 4-ring PAHs can exist in both gas and particulate phases, whereas the 5-ring and 6-ring PAHs are mainly condensed/adsorbed onto particle phases (Zhang et al., 2016). Among the 4, 5, and 6-ring PAHs, 5-ring PAHs were found to be dominant throughout the year. In summer and autumn, average values of 6-ring PAHs were higher than those of 4-ring PAHs. However, the opposite results were obtained in spring and winter. These results could be attributed to the atmospheric temperature (summer>autumn>spring>winter, Table 2), since 4-ring PAHs have lower vapor pressures than 6-ring PAHs.

The highest average concentrations measured in each season among all the PAHs were as follows; BbF and IP in summer 2016, IP in autumn 2016, Fluor in winter 2016, Fluor in spring 2017, IP in summer 2017, and IP in autumn 2017. Although WHO has not drafted a guideline for BaP, it is classified as carcinogenic to humans (Group 1) by the IARC (2012), and is probably the most studied PAH. BaP concentrations were above the LOD for all the samples, but no BaP values exceeded 1 ng/m3 which is an annual mean value for the protection of human health as set out in the Directive 2004/107/EC (EU, 2004).

3. 2. 2 n-Alkanes

In an urban environment, n-alkanes are emitted from a large variety of sources, including the incomplete combustion of fossil fuels, lubricant oils, biomass burning, and biogenic sources such as wind erosion of the vascular plant waxes and direct suspension of pollen microorganisms (Yadav et al., 2013; Alves et al., 2009; Alves, 2008; Simoneit, 2002; Oros et al., 1999; Rogge et al., 1993a, 1993b, 1993c).

Measurement values of n-alkane homologues in each season are shown in Table 3. The sum of C17-C40 n-alkane concentrations is expressed as Σ n-alkanes. Fig. 4 describes the temporal variation of Σ n-alkanes during the study period and the relative abundances of individual n-alkanes normalized by C29, which is one of the most abundant n-alkanes, are presented in Fig. 5 to visually confirm the distribution profile in each season. Winter 2016 showed the higher average Σ n-alkanes concentration (21 ng/m3) than the other seasons. A few samples in late autumn of 2016 (October-November) and early winter of 2016 (December-January) showed relatively elevated Σ n-alkanes concentration; however, most of the samples had concentrations below 20 ng/m3 (overall average of 14 ng/m3) during the study period and the seasonal variation was little. This result suggests that the emission sources, which are not influenced by the seasons, are the dominant inputs for Σ n-alkanes within the source areas of the sampling site. The highest Σ n-alkanes concentration was observed in late summer of 2017, when all of the n-alkane homologues appeared at high concentrations. The overall mean concentration of Σ n-alkanes accounted for 0.14% and 0.84% of PM2.5 and OC, respectively. Also, PM2.5 and OC have positive correlation with Σ n-alkanes with the Pearson correlation coefficient (R) of 0.57 and 0.80, respectively.


Fig. 4. 
Temporal variation of Σ n-alkanes concentration.


Fig. 5. 
Relative abundances of individual n-alkanes normalized by C29 in each season.

3. 2. 3 PAEs

PAEs are widely manufactured as plasticizers to make plastic materials flexible, and also in building materials, food, drinking water, soil, medical devices, cosmetics, children’s toys, and other products (Wang et al., 2017; Yao et al., 2016; Chou and Wright, 2006). Several epidemiological studies have demonstrated that human exposure to PAEs can induce adverse effects, and PAEs are suspected to be endocrine disruptors, which has stimulated public concern (Okamoto et al., 2011; Chou and Wright, 2006; Duty et al., 2005, 2003). To our knowledge, there is no previous publication involving the measurement of PAEs in PM2.5 collected from the ambient atmosphere of Japan.

Measurement values of PAEs in each season are shown in Table 3. The summed concentration of 5 species of PAEs is expressed as Σ PAEs. Fig. 6 presents the temporal variation of Σ PAEs during the study period. Average Σ PAEs was found to be higher (0.51 ng/m3) in summer 2017 than in other seasons; however, average Σ PAEs in summer 2016 (0.26 ng/m3) was about half the summer 2017 value, so there was no clear seasonal pattern. One sample in late summer of 2017 showed a relatively high level of Σ PAEs (1.8 ng/m3); however, most of the obtained data were below 1 ng/m3 during the study period and thus no clear seasonal variation was observed. The reason is uncertain but the Σ PAEs were extremely low in autumn 2017. The overall mean concentration of Σ PAEs was 0.36 ng/m3, which was one and two orders of magnitude lower than Σ PAHs and Σ n-alkanes, respectively. Also, PM2.5 and OC did not indicate correlation with Σ PAEs. Therefore, the target PAEs were not substantially emitted within the source area of the sampling site; however, PAEs those were not measured in this study might be contained in high concentrations in PM2.5, so further research should target these nondetected compounds.


Fig. 6. 
Temporal variation of ΣPAEs concentration.

3. 3 Identification of Possible Sources
3. 3. 1 Diagnostic Ratios of PAHs

The diagnostic ratios of PAHs are a useful tool to identify possible sources and have been developed and applied by many environmental researchers (Wang et al., 2017, 2016; Hayakawa et al., 2016; Zhang et al., 2016; MikuŠka et al., 2015; Alves et al., 2009; Alves, 2008; Park et al., 2006; Brandenberger et al., 2005; Yunker et al., 2002; Simcik et al., 1999; Khalili et al., 1995; Li and Kamens, 1993; Rogge et al., 1993a, 1993b). The compounds involved in each ratio have the same or similar molecular weight, so it may be assumed that they have similar physicochemical properties. Diagnostic ratios vary during different stages of phase transfer and under environmental degradation. In this study, we calculated diagnostic ratios of BeP/(BeP+BaP), BaA/(BaA+Chr), IP/(IP+BghiP), Fluor/(Fluor+Py), and BghiP/BaP from the measured PAHs shown in Table 2.

Most fresh exhaust emissions typically contain almost the same amount of BeP and BaP. However, BaP more easily degraded than BeP by photochemical reactions in the ambient atmosphere. The atmospheric lifetime of BaP is estimated as ten times shorter than that of BeP (Kalberer et al., 2002). Therefore, the increment of BeP/(BeP+BaP) can be used as in indicator of the degree of photochemical degradation (i.e., aging) of compounds in the atmospheric environment (Wang et al., 2015; Shen et al., 2013; Alves et al., 2009). The seasonal variation of calculated BeP/(BeP+BaP) in this study ranged from 0.38-0.58. Freshly released BeP/(BeP+BaP) should have a ratio close to 0.50 (MikuŠka et al., 2015; Park et al., 2006), suggesting that most of the PM2.5 containing BaP and BeP was emitted from local or nearby regional sources throughout the study period.

Table 5 presents five different types of developed diagnostic ratios from previous studies and includes BeP/(BeP+BaP), BaA/(BaA+Chr), IP/(IP+ BghiP), Fluor/(Fluor+Py), and BghiP/BaP (Hayakawa et al., 2016; Wang et al., 2015; Shen et al., 2013; Alves et al., 2009; Yunker et al., 2002; Simcik et al., 1999; Khalili et al., 1995; Li and Kamens, 1993; Rogge et al., 1993a, 1993b) and those obtained in this study. From the comparison of the diagnostic ratios shown in Table 5, most of the diagnostic ratios calculated were within the range of gasoline, diesel, coal and wood burning emissions. The emissions from gasoline and diesel emissions from vehicles, and coal emissions from industrial sources are not influenced by the seasons, which supports the assumption mentioned above in Section 3.2.1. Therefore, influences of vehicle and industrial emission sources nearby sampling site could be the possible anthropogenic sources of measured PAHs in this study. Kume et al. (2007) have analyzed 21 species of PAHs in suspended particles at urban sites of Shizuoka, Japan, within the area of vehicle and industrial emission sources nearby. Their annual means ratios of IP/(IP+BghiP) and BghiP/BaP were 0.51 and 1.28, respectively, which were in good agreement with those obtained in this study.

Table 5. 
Comparison of PAHs diagnostic ratios from this study with those reported in previous studies.
Emission
sources
Diagnostic ratios References
BeP/(BeP+BaP) BaA/(BaA+Chr) IP/(IP+BghiP) Fluor/(Flour+Py) BghiP/BaP
Local emission Index of the aging of particles.
Value around 0.5 indicates
freshly emitted particles.
Wang et al., 2015; Shen et al., 2013;
Alves et al., 2009
Gasoline 0.17-0.38 0.042-0.22 0.37-0.63 0.34-3.3 Rogge et al., 1993a; Simick et al., 1999;
Khalili et al., 1995; Li and Kamens, 1993
Diesel 0.45-0.64 0.19-0.70 0.20-0.93 0.36-2.2 Yunker et al., 2002; Simick et al., 1999;
Khalili et al., 1995; Rogge et al., 1993a;
Li and Kamens, 1993
Coal 0.35-0.62 0.48-0.85 0.15-1.1 Yunker et al., 2002; Simick et al., 1999
Wood burning 0.36-0.43 0.49-0.77 0.41-0.67 0.64 Yunker et al., 2002; Khalili et al., 1995;
Li and Kamens, 1993
Coke oven 0.34-0.41 0.61 0.90 0.13-0.20 Hayakawa et al., 2016; Simick et al., 1999;
Khalili et al., 1995
Incinerators 1.7-7.1 Simick et al., 1999
Road dust 0.51 0.42 0.91 Yunker et al., 2002; Rogge et al., 1993b
Tire ware 0.17 Rogge et al., 1993b
Brake 0.39 3.5 Rogge et al., 1993b
Range (Average)
This study 0.38-0.58 (0.46) 0.29-0.71
(0.43)
0.042-0.56
(0.51)
0.50-0.57
(0.54)
0.52-1.7
(1.1)

Moreover, careful consideration of PAHs usage data should be taken in to account when attempting to identify anthropogenic PAHs sources because PAHs can be emitted from various source types. Therefore, other molecular markers and/or tracers should be gathered for more accurate source apportionment.

3. 3. 2 Historical Distribution, CPI and Cwax of n-Alkanes

Table 3 and Fig. 5 indicate that C23-C33 were the main homologues contributing to the Σ n-alkanes. Previous research has shown that carbon number with a strong predominance of odd carbon number indicates a significant contribution from biogenic contributors (Rogge et al., 1993c), whose abundances of odd carbon number higher than 24 are approximately one magnitude higher than those of even carbon number. Whereas n-alkanes released from fossil fuel sources do not show such predominant tendency (Alves et al., 2009; Simoneit et al., 1991). Fig. 5 shows that oddnumbered carbon homologues were not dominant during the study period. Therefore, contributions arising from anthropogenic inputs might have been higher than biogenic inputs at the study site.

For the further interpretation of obtained data associated with n-alkanes, we used the carbon preference index (CPI) and contribution of plant wax to n-alkanes (Cwax) to assess the variability of sources, and they are listed in Table 6. The CPI can be used to distinguish biogenic and anthropogenic inputs. It is defined as the sum of the concentrations of the odd carbon number n-alkanes (ΣCodd) divided by the sum of the concentrations of the even carbon number n-alkanes (ΣCeven), which was calculated as follows:

Table 6. 
CPI and Cwax obtained from the measurement results of individual n-alkanes.
Year Season n CPI Cwax (%)
Range Average Range Average
2016 Summer 6 0.73-1.4 1.1 4.3-17 9.0
Autumn 12 1.1-2.0 1.6 12-34 23
Winter 13 1.1-1.6 1.4 9.7-26 19
2017 Spring 13 0.94-3.2 1.7 8.4-53 25
Summer 13 1.2-1.9 1.6 8.9-32 24
Autumn 8 0.95-2.0 1.5 7.7-35 22
Total 65 0.73-3.2 1.5 4.3-53 22

CPI=CoddCeven=C17toC39C18toC40(1) 

Plant wax n-alkanes exhibit a strong odd carbon number predominance and thus increase the CPI value (Park et al., 2006). Conversely, anthropogenic inputs such as fossil fuels reduce the CPI value. The CPI typical values in the urban environment range from 1.1 to 2.0, while a CPI higher than 2.0 has a stronger biogenic influence (Alves et al., 2009). In the present study, the CPI values during the study period were in the range 0.73-3.2 with an average of 1.5, as shown in Table 6. A CPI value above 2.0 was observed just in one sample in spring 2017 (CPI=3.2, for 2-10 May), suggesting that most of the measured n-alkanes during the study period were anthropogenic in origin.

Another methodology to analyze the contribution of plant wax sources to n-alkanes can be expressed through Cwax (Wang et al., 2015; Yadav et al., 2013; Alves, 2008; Park et al., 2006; Simoneit et al., 1991). Cwax gives the percentage concentration input of wax n-alkanes from biogenic sources in sample. The Cwax is calculated as follows:

Cwax=Cn-Cn-1+Cn+12nAlkanes×100(2) 

where n is the odd number of analyzed n-alkanes. Measured Cn values below the LOD were replaced with the value of LOD/2, and the negative values of {Cn -(Cn-1+Cn+1)/2} were determined as zero. Table 6 shows that the percentages of Cwax were in the range of 4.3-53% with an average of 22%. The higher percentage values indicate greater contributions from biogenic sources (Wang et al., 2015). The highest Cwax value of 53% was calculated from the same sample as CPI= 3.2, which could possibly mean that the contribution of biogenic sources was larger than sample during the period of 2-10 May 2017. Comprehensive evaluation including the historical distributions of n-alkanes homologues, CPI, and Cwax, indicates that the observed n-alkanes may largely originate from anthropogenic sources for most of the samples in this study. Further identification of the emission sources of n-alkanes in PM2.5 will be conducted in future research.


4. SUMMARY AND CONCLUSIONS

A long time series (approximately 17 months) of quantitative data of non-polar organic compounds constituting PM2.5 was created from samples collected at Ichihara, one of the largest industrial areas in Japan. Sample collection and analysis took place continuously from June 2016 to October 2017. The target non-polar organic compounds were 21 species of PAHs, 24 species of n-alkanes, and 5 species of PAHs, and these were simultaneously measured using GC/MS. The results indicate that anthropogenic sources were the dominant inputs for the most PAHs and n-alkanes throughout the study period. Identification of the emission sources of individual organic compounds in PM2.5 will be conducted in future research. To our knowledge, this paper describes the first attempt to measure such a large number of different nonpolar organic compounds in PM2.5 from the ambient atmosphere of Japan to create a long time series. The findings will provide valuable data and information to environmental researchers. The main findings are as follows:

(1) The average concentrations of Σ PAHs, Σ n-alkanes, and Σ PAEs in each season remained nearly level (except for a few high Σ PAHs in early winter and Σ n-alkanes in late autumn and early winter), and no strong seasonal variations were observed throughout the study period. These results suggest that the emission sources, which are not influenced by the seasons, are

(2) The contributions of Σ PAHs, Σ n-alkanes, and Σ PAEs to the PM2.5 mass concentration were each less than 1%.

(3) The target PAEs were not substantially emitted within the source area of the sampling site.

(4) All of the calculated BeP/(BeP+BaP) ratios were close to 0.50, which indicates that PM2.5 containing BaP and BeP were emitted from local or nearby regional anthropogenic sources throughout the study period.

(5) From the analysis of diagnostic ratios, gasoline, diesel and coal emissions could be possible anthropogenic sources of measured PAHs in this study.

(6) The distributions of n-alkane homologues and calculated results of CPI and Cwax, indicate that n-alkanes at the study site may largely originate from anthropogenic sources. Further identification of the emission sources of n-alkanes in PM2.5 will be conducted in future research.


References
1. Ahmed, M., Guo, X., Zhao, X.M., (2016), Determination and analysis of trace metals and surfactant in air particulate matter during biomass burning haze episode in Malaysia, Atmospheric Environment, 141, p219-229.
2. Alves, C.A., (2008), Characterisation of solvent extractable organic constituents in atmospheric particulate matter: an overview, Anais da Academia Brasileira de Ciências, 80, p21-82.
3. Alves, C.A., Vicente, A., Evtyugina, M., Pio, C.A., Hoffer, A., Kiss, G., Decesari, S., Hillamo, R., Swietlicki, E., (2009), Characterisation of Hydrocarbons in Atmospheric Aerosols from Different European Sites, International Journal of Environmental and Ecological Engineering, 3, p263-269.
4. Alves, N.O., Brito, J., Caumo, S., Arana, A., Hacon, S.S., Artaxo, P., Hillamo, P., Teinilä, K., Medeiros, S.R.B., Vasconcellos, P.C., (2015), Biomass burning in the Amazon region: Aerosol source apportionment and associated health risk assessment, Atmospheric Environment, 120, p277-285.
5. Brandenberger, S., Mohr, M., Grob, K., Neukomb, H.P., (2005), Contribution of unburned lubricating oil and diesel fuel to particulate emission from passenger cars, Atmospheric Environment, 39, p6985-6994.
6. Chou, K., Wright, R.O., (2006), Phthalates in food and medical devices, Journal of Medical Toxicology, 2, p126-135.
7. Chow, J.C., Watson, J.G., Pritchett, L.C., Pierson, W.R., Frazier, C.A., Purcell, R.G., (1993), The DRI thermal/optical reflectance carbon analysis system: description, evaluation and applications in U.S. air quality studies, Atmospheric Environment, 27, p1185-1201.
8. Chow, J.C., Watson, J.G., Crow, D., Lowenthal, D.H., Merrifield, T.Y.A., (2001), Comparison of IMPROVE and NIOSH carbon measurements, Aerosol Science and Technology, 34, p23-34.
9. Duty, S.M., Silva, M.J., Barr, D.B., Brock, J.W., Ryan, L., Chen, Z., Herrick, R.F., Christiani, D.C., Hauser, R., (2003), Phthalate exposure and human semen parameters, Epidemiology, 14, p269-277.
10. Duty, S.M., Calafat, A.M., Silva, M.J., Ryan, L., Hauser, R., (2005), Phthalate exposure and reproductive hormones in adult men, Human Reproduction, 20, p604-610.
11. EU, (2004), Directive 2004/107/EC of the European parliament and of the council of 15 December 2004 relating to arsenic, cadmium, mercury, nickel and polycyclic aromatic hydrocarbons in ambient air, http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2005:023:0003:0016:EN:PDF Accessed on February 5 , 2018.
12. Fuzzi, S., Baltensperger, U., Carslaw, K., Decesari, S., Gon, H.D., Facchini, M.C., Fowler, D., Koren, I., Langford, B., Lohmann, U., Nemitz, E., Pandis, S., Riipinen, I., Rudich, Y., Schaap, M., Slowik, J.G., Spracklen, D.V., Vignati, E., Wild, M., Williams, M., Gilardoni, S., (2015), Particulate matter, air quality and climate: lessons learned and future needs, Atmospheric Chemistry and Physics, 15, p8217-8299.
13. Hayakawa, K., Tang, N., Morisaki, H., Toriba, A., Akutagawa, T., Sakai, S., (2016), Atmospheric polycyclic and nitropolycyclic aromatic hydrocarbons in an iron-manufacturing city, Asian Journal of Atmospheric Environment, 10, p90-98.
14. He, L.Y., Hu, M., Huang, X.F., Yu, B.D., Zhang, Y.H., Liu, D.Q., (2004), Measurement of emissions of fine particulate organic matter from Chinese cooking, Atmospheric Environment, 38, p6557-6564.
15. IARC, (2010), IARC monographs on the evaluation of carcinogenic risks to humans, 92, Some non-heterocyclic polycyclic aromatic hydrocarbons and some related exposures.
16. IARC, (2012), monographs on evaluation of carcinogenic risks to humans. Chemical agents and related occupations, 100F, A review of human carcinogens.
17. Ichikawa, Y., Watanabe, T., Horimoto, Y., Ishii, K., Naito, S., (2017), Organic components of PM2.5 observed in Chiba Prefecture during the fiscal years 2014-2016, Journal of Environmental Laboratories Association, 42, p60-67, (written in Japanese with English abstract, figures and tables).
18. Ichikawa, Y., Naito, S., Ishii, K., Oohashi, H., (2015), Seasonal variation of PM2.5 components observed in an industrial area of Chiba Prefecture, Japan, Asian Journal of Atmospheric Environment, 9, p66-77.
19. IPCC, (2013), Climate change 2013. The physical science basis. Summary for policymakers, technical summary and frequently asked questions. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, Cambridge University Press, United Kingdom and New York.
20. Jacob, D.J., (1999), Introduction to atmospheric chemistry, Princeton University Press, New Jersey, p148-154.
21. Kakimoto, H., Matsumoto, Y., Sakai, S., Kanoh, F., Arashidani, K., Tang, N., Akutsu, K., Nakajima, A., Awata, Y., Toriba, A., Kizu, R., Hayakawa, K., (2002), Comparison of atmospheric polycyclic aromatic hydrocarbons and nitropolycyclic aromatic hydrocarbons in an industrialized cityKitakyushu) and two commercial citiesSapporo and Tokyo), Journal of Health Science, 48, p370-375.
22. Kalberer, M., Henne, S., Prevot, A.S.H., Steinbacher, M., (2004), Vertical transport and degradation of polycyclic aromatic hydrocarbons in an Alpine Valley, Atmospheric Environment, 38, p6447-6456.
23. Kawamura, K., Bikkina, S., (2016), A review of dicarboxylic acids and related compounds in atmospheric aerosols: molecular distributions, sources and transformation, Atmospheric Research, 170, p140-160.
24. Khalili, N.R., Scheff, P.A., Holsen, T.M., (1995), PAH source fingerprints for coke ovens, diesel and, gasoline engines, highway tunnels, and wood combustion emissions, Atmospheric Environment, 29, p533-542.
25. Kume, K., Ohura, T., Noda, T., Amagai, T., Fusaya, M., (2007), Seasonal and spatial trends of suspended-particle associated polycyclic aromatic hydrocarbons in urban Shizuoka, Japan, Journal of Hazardous Materials, 144, p513-521.
26. Li, C.K., Kamens, R.M., (1993), The use of polycyclic aromatic hydrocarbons as source signatures in receptor modeling, Atmospheric Environment, 27A, p523-532.
27. Li, X., Chen, M., Le, H.P., Wang, F., Guo, Z., Iinuma, Y., Chen, J., Herrmann, H., (2016), Atmospheric outflow of PM2.5 saccharides from megacity Shanghai to East China Sea: Impact of biological and biomass burning sources, Atmospheric Environment, 143, p1-14.
28. Mikuška, P., Křůmal, K., Večeřa, Z., (2015), Characterization of organic compounds in the PMF aerosols in winter in an industrial urban area, Atmospheric Environment, 105, p97-108.
29. Mikuška, P., Kubátková, N., Křůmal, K., Večeřa, Z., (2017), Seasonal variability of monosaccharide anhydrides, resin acids, methoxyphenols and saccharides in PM2.5 in Brno, the Czech Republic, Atmospheric Pollution Research, 8, p576-586.
30. Okamoto, Y., Ueda, K., Kojima, N., (2011), Potential risks of phthalate esters: acquisition of endocrine-disrupting activity during environmental and metabolic processing, Journal of Health Science, 57, p497-503.
31. Oros, D.R., Standley, L.J., Chen, X., Simoneit, B.R.T., (1999), Epicuticular wax compositions of predominant conifers of Western North America, Zeitschrift für Naturforschung C, 54, p17-24.
32. Park, S.S., Bae, M.S., Schauer, J.J., Kim, Y.J., Cho, S.Y., Kim, S.J., (2006), Molecular composition of PM2.5 organic aerosol measured at an urban site of Korea during the ACE-Asia campaign, Atmospheric Environment, 40, p4182-4198.
33. Rogge, W.F., Hildemann, L.M., Mazurek, M.A., Cass, G.R., Simoneit, B.R.T., (1993a), Sources of fine organic aerosol. 2. Noncatalyst and catalyst-equipped automobiles and heavyduty diesel trucks, Environmental Science and Technology, 27, p636-651.
34. Rogge, W.F., Hildemann, L.M., Mazurek, M.A., Cass, G.R., Simoneit, B.R.T., (1993b), Sources of fine organic aerosol. 3. Road dust, tire debris, and organometallic brake lining dust: roads as sources and sinks, Environmental Science and Technology, 27, p1892-1904.
35. Rogge, W.F., Hildemann, L.M., Mazurek, M.A., Cass, G.R., Simoneit, B.R.T., (1993c), Sources of fine organic aerosol. 4. Particulate abrasion products from leaf surfaces of urban plants, Environmental Science and Technology, 27, p2700-2711.
36. Schwartz, J., Neas, L.M., (2000), Fine particles are more strongly associated than coarse particles with acute respiratory health effects in schoolchildren, Epidemiology, 11, p6-10.
37. Shen, G., Tao, S., Wei, S., Zhang, Y., Wang, R., Wang, B., Li, W., Shen, H., Huang, Y., Chen, Y., Chen, H., Yang, Y., Wang, W., Wang, X., Liu, W., Staci, L.M., Simonich, S.L.M., (2012), Emissions of Parent, Nitro, and Oxygenated Polycyclic Aromatic Hydrocarbons from Residential Wood Combustion in Rural China, Environmental Science and Technology, 46, p8123-8130.
38. Simcik, M.F., Eisenreich, S.J., Lioy, P.J., (1999), Source apportionment and source/sink relationships of PAHs in the coastal atmosphere of Chicago and Lake Michigan, Atmospheric Environment, 33, p5071-5078.
39. Simoneit, B.R.T., Sheng, G., Chen, X., Fu, J., Zhang, J., Xu, Y., (1991), Molecular marker study of extractable organic matter in aerosols from urban areas of China, Atmospheric Environment, 25A, p2111-2129.
40. Simoneit, B.R.T., Kobayashi, M., Mochida, M., Kawamura, K., Lee, M., Lim, H.J., Turpin, B.J., Komazaki, Y., (2004), Composition and major sources of organic compounds of aerosol particulate matter sampled during the ACE-Asia campaign, Journal of Geophysical Research, 109, pD19S10.
41. Simoneit, B.R.T., (2002), Biomass burning - a review of organic tracers for smoke from incomplete combustion, Applied Geochemistry, 17, p129-162.
42. Simoneit, B.R.T., Sheng, G., Chen, X., Fu, J., Zhang, J., Xu, Y., (1991), Molecular marker study of extractable organic matter in aerosols from urban areas of China, Atmospheric Environment 25A, p2111-2129.
43. Suzuki, G., Morikawa, T., Kashiwakura, K., Tang, N., Toriba, A., Hayakawa, K., (2015), Variation of polycyclic aromatic hydrocarbons and nitropolycyclic aromatic hydrocarbons in airborne particulates collected in Japanese capital area, Journal of Japan Society for Atmospheric Environment, 50, p117-122, (written in Japanese with English abstract, figures and tables).
44. Ueda, K., (2011), The health effects of fine particulate matter: Evidence among Japanese populations and trends in epidemiological studies, Journal of Japan Society for Atmospheric Environment, 46, pA7-A13, (in Japanese).
45. Wang, J., Ho, S.S., Cao, J., Huang, R., Zhou, J., Zhao, Y., Xu, H., Liu, S., Wang, G., Shen, Z., Han, Y., (2015), Characteristics and major sources of carbonaceous aerosols in PM2.5 from Sanya, China, Science of The Total Environment , p530-531, 110-119.
46. Wang, J., Ho, S.S., Ma, S., Cao, J., Dai, W., Liu, S., Shen, Z., Huang, R., Wang, G., Han, Y., (2016), Characterization of PM2.5 in Guangzhou, China: uses of organic markers for supporting source apportionment, Science of The Total Environment, 550, p961-971.
47. Wang, J., Guinot, B., Dong, Z., Li, X., Xu, H., Xiao, S., Ho, S.S.H., Liu, S., Cao, J., (2017), PM2.5-bound polycyclic aromatic hydrocarbonsPAHs), oxygenated-PAHs and phthalate estersPAEs) inside and outside middle school classrooms in Xi'an, China: concentration, characteristics and health risk assessment, Aerosol and Air Quality Research, 17, p1811-1824.
48. WHO, (2013), Review of evidence on health aspects of air pollution - RVIHAAP Project, World Health Organization regional office for Europe, Copenhagen..
49. Yao, H.Y., Han, Y., Gao, H., Huang, K., Ge, X., Xu, Y.Y., Xu, Y.Q., Jin, Z.X., Sheng, J., Yan, S.Q., Zhu, P., Hao, J.H., Tao, F.B., (2016), Maternal phthalate exposure during the first trimester and serum thyroid hormones in pregnant women and their newborns, Chemosphere, 157, p42-48.
50. Yadav, S., Tandon, A., Attri, A.K., (2013), Monthly and seasonal variations in aerosol associated n-alkane profiles in relation to meteorological parameters in New Delhi, India, Aerosol and Air Quality Research, 13, p287-300.
51. Yang, F., Kawamura, K., Chen, J., Ho, K., Lee, S., Gao, Y., Cui, L., Wang, T., Fu, P., (2016), Anthropogenic and biogenic organic compounds in summertime fine aerosols (PM2.5) in Beijing, China, Atmospheric Environment, 124, p166-175.
52. Yunkera, M.B., Macdonald, R.W., Vingarzan, R., Mitchell, R.H., Goyette, D., Sylvestre, S., (2002), PAHs in the Fraser River basin: a critical appraisal of PAH ratios as indicators of PAH source and composition, Organic Geochemistry, 33, p489-515.
53. Zhang, M., Xie, J., Wang, Z., Zhao, L., Zhang, H., Li, M., (2016), Determination and source identification of priority polycyclic aromatic hydrocarbons in PM2.5 in Taiyuan, China, Atmospheric Research, p178-179, 401-414.
54. Zhang, Y., Obrist, D., Zielinska, B., Gertler, A., (2013), Particulate emissions from different types of biomass burning, Atmospheric Environment, 72, p27-35.