Science of the Total Environment 518–519 (2015) 595–604

Contents lists available at ScienceDirect

Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Long term trends of methane, non methane hydrocarbons, and carbon monoxide in urban atmosphere Ezaz Ahmed a, Ki-Hyun Kim a,⁎, Eui-Chan Jeon b, Richard J.C. Brown c a b c

Department of Civil & Environmental Engineering, Hanyang University, 222 Wangsimni-Ro, Seoul 133-791, Republic of Korea Department of Environment & Energy, Sejong University, Seoul 143-747, Republic of Korea Analytical Science Division, National Physical Laboratory, Hampton Rd., Teddington, TW11 0LW UK

H I G H L I G H T S • Despite the abundance, the population of airborne carbon species is poorly known except CO2. • The factors governing the distributions of diverse carbon species in air have been explored. • Over a long-term they are distinguished between increase (THC and CH4) vs. decrease (NMHC and CO).

a r t i c l e

i n f o

Article history: Received 3 October 2014 Received in revised form 5 February 2015 Accepted 16 February 2015 Available online xxxx Editor: P. Kassomenos Keywords: Carbon Total hydrocarbon Methane Non-methane hydrocarbon Urban Long-term

a b s t r a c t The concentrations of methane (CH4), non-methane hydrocarbons (NMHC), and carbon monoxide (CO) were measured at two urban locations (Guro (GR) and Nowon (NW)) in Seoul, Korea between 2004 and 2013. The mean amount fractions of CH4, NMHC, and CO, measured at GR over this period were 2.06 ± 0.02, 0.32 ± 0.03, and 0.61 ± 0.05 ppm, respectively, while at NW they were 2.08 ± 0.06, 0.33 ± 0.05, and 0.54 ± 0.06 ppm, respectively. The ratio of CH4 to the total hydrocarbon amount fraction remained constant across the study years: 0.82 to 0.90 at GR and 0.81 to 0.89 at NW. Similarly, stable ratios were also observed between NMHC and THC at the two sites. In contrast, the annual mean ratios for CH4/NMHC showed a larger variation: between 4.55 to 8.67 at GR and 4.25 to 8.45 at NW. The seasonality of CO was characterized by wintertime maxima, while for CH4 and NMHC the highest amount fractions were found in fall. The analysis of their long-term trends based on Mann– Kendall and Sen's methods showed an overall increase of THC and CH4, whereas a decreasing trend was observed for NMHC and CO. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Carbon is the fourth most dominant component in the universe by mass after hydrogen, helium, and oxygen, and there are approximately 1500–2000 Pg C in various organic forms up to a soil depth of 1 m (Pan et al., 2010). It is also assumed to be present significantly in various forms beyond such soil level (Amundson, 2001). Terrestrial ecosystems contain about 500 Pg C in plant biomass and 2000 Pg C in soil organic matter. The atmosphere (containing about 785 Pg C) allows the cycling of carbon between different environmental reservoirs (Janzen, 2004). However, many compounds containing carbon, if present in air, are of concern for human health (e.g., CO and non-methane hydrocarbons) and environmental sustainability (e.g., major greenhouse gas components like CO2 and CH4). As such, airborne carbon species have become ⁎ Corresponding author. E-mail addresses: [email protected], [email protected] (K.-H. Kim).

http://dx.doi.org/10.1016/j.scitotenv.2015.02.058 0048-9697/© 2015 Elsevier B.V. All rights reserved.

one of the biggest scientific and political challenges to humankind in the current century (Bardgett et al., 2008; McMichael et al., 2006). In this research, we investigate the behavior of airborne carbon species (except CO2) as a means to assess the long-term changes of air quality in an urban area. Hydrocarbons are a large family of organic molecules composed of hydrogen atoms bonded to a chain of carbon atoms often with oxygen, nitrogen, and halogens. Total hydrocarbons (THCs) in air can be operationally defined as the sum of methane (CH4) and nonmethane hydrocarbons (NMHCs) both of which can act as greenhouse gases either directly or indirectly (Bäckstrand et al., 2008; Chen et al., 2014). As THCs constitute a significant proportion of the carbon cycle, a better understanding of their behavior is important for climate change studies (Christensen et al., 2007). CH4, generally produced by methanogenic bacteria during anaerobic decomposition of organic matter, is usually the most prominent component of THC flux from organic soils and peat (Beckmann and Lloyd, 2001; Bradley et al., 2010). It is also known to be emitted from non-

596

E. Ahmed et al. / Science of the Total Environment 518–519 (2015) 595–604

regulated landfills containing municipal solid wastes (Bogner et al., 2008; Shin et al., 2005). Being a potent greenhouse gas with a global warming potential far greater than that of CO2, its atmospheric concentration is estimated to have nearly tripled since pre-industrial times (Shindell et al., 2009). However, the extent of its presence in air is determined by the balance between surface emissions and photochemical destruction (Bousquet et al., 2006; Themelis and Ulloa, 2007). NMHCs, referred to as light hydrocarbons containing 2–12 carbon atoms, are a significant class of air pollutants in the atmosphere, as many of them are known to possess toxic and carcinogenic properties (Borbon et al., 2002). In addition, photochemical oxidation of NMHCs can lead to the formation of ozone and ultimately influence the lifetime of other trace species in the troposphere (Platt et al., 2013; Poisson et al., 2000). As NMHCs are also the key precursors of other secondary pollutants, they play a very important role in tropospheric chemistry (Sauvage et al., 2009; Streets et al., 2007). As such, the enhanced level of NMHCs can directly contribute to the deterioration of the air quality to adversely affect human health (Logue et al., 2010; Wang et al., 2010). Carbon monoxide (CO) is a product of incomplete combustion of carbon containing compounds (Von Burg, 1999). CO has significant toxic effects on humans and animals. The presence of CO even at low amount fractions (70 ppm) can cause cardiovascular and neurological disorders, leading to more acute effects at higher concentrations (Raub et al., 2000). In order to learn more about the environmental behavior of these pollutants in ambient air, the concentration data of major carbon species in air (THC, CH4, NMHC, and CO) were investigated from two urban stations (Guro and Nowon) in Seoul, the capital city of Korea during a ten year period from 2004 to 2013. Concentrations at the two locations were examined for temporal patterns on different timescales. In addition, relationships between major carbon species, other pollutant species (nitrogen oxide (NO), nitrogen dioxide (NO2), oxides of nitrogen (NOx), sulfur dioxide (SO2), ozone (O3), and mercury (Hg)) and meteorological parameters (wind speed (WS), temperature (TEMP), humidity (HUM), ultraviolet radiation (UV), and solar radiation (SR)) were investigated to assess the factors controlling their interactions. 2. Materials and methods 2.1. Site characteristics of the study area Fig. 1S depicts the geographical locations of the two selected study sites: Guro (GR) and Nowon (NW). General information concerning the two study sites is also described in Table 1S. GR is a southwestern district of Seoul. It is an important transport hub, consisting of residential (7.08 km2; 35.2%), commercial (0.42 km2; 2.1%), industrial (6.89 km2; 34.3%), and green belt (5.72 km2; 28.4%) areas. The Guro Industrial Complex (GIC) is a special feature of this district which has rapidly changed from a manufacturing industrial zone into a futuristic industrial hub. The main activities of GIC since 2000 have been research and development activities, advanced information processing, and other knowledge industries. NW is located in the northeastern part of the city (area of 35.44 km2). It has the highest population in Seoul with 587,248 people. It is mainly a residential district, currently accommodating a number of big educational institutions (e.g., Sahmyook University, the Korea Military Academy, Sejong Science High School, Seoul National University of Technology, Induk University, and the Seoul Women's University). 2.2. Data processing The details of data acquisition and processing of pollutants and meteorological data are provided in the Supplementary material (SM) along with information of instrumental setup (Table 2S). Concentration data for CH4 and NMHC were measured hourly using a hydrocarbon analyzer (Model HA771, KIMOTO ELECTRIC CO., LTD., Japan) based on a

hydrogen flame ionizing detection (FID) method complying with JIS B 7956. Carbon monoxide (CO) was measured using non-dispersive infrared absorption (NDIR) method using double beam single light source (ZRF, Fuji Electric Instruments Co., Ltd., Japan). Robust least squares best fit techniques were used to assess trends in measured concentrations over time. Given the scatter in the input data an uncertainty in the resulting slopes may be calculated and this can therefore lead to an assessment of whether or not the slope is statistically different from zero at a given confidence level. The robust approach taken meant that the slopes determined were much less sensitive to outliers than would have been the case if standard least squares techniques would have been used. Further, principal component analysis has been used to look for correlations where multivariate data have been collected. The outputs of the analysis are components associated with one or more species to account for the largest proportions of variation within the dataset. For such datasets this provides the quickest and most efficient method for demonstrating these corrections and gives better information than one can obtain by simple linear regression alone. The analysis has been carried out with the ten years of data that were available to the study. We recognize that it is only a starting point and that the confidence in conclusions drawn about trends in concentrations will improve with the collection of more data to add to this dataset. 3. Results and discussion 3.1. Basic features of major airborne carbonic pollutants and their inter-city comparison A statistical summary of THC, CH4, NMHC, and CO for both sites between 2004 and 2013 is presented in Table 1. (Note that the results of THC are also provided as the sum of CH4 and NMHC.) The annual mean concentrations for three targets (CH4, NMHC, and CO) for the entire study period maintained similar levels such as: 2.06 ± 0.02, 0.32 ± 0.03, and 0.61 ± 0.05 at GR and 2.08 ± 0.06, 0.33 ± 0.05, and 0.54 ± 0.06 ppm at NW, respectively (Fig. 1). The analysis of statistical compatibility between the two sites, if compared by a Z statistics test, indicates that their differences were generally statistically significant (p b 0.001) (Table 3S). The maximum annual means for THC were 2.49 ± 0.28 (2011: GR) and 2.54 ± 0.16 (2013: NW). In contrast to CH4 and THC, CO, and NMHC were decreasing during the study period. The annual mean values of NMHC showed gradual reductions from 0.43 ± 0.22 (2004) to 0.25 ± 0.12 (2013) at GR and from 0.47 ± 0.20 (2005) to 0.25 ± 0.07 ppm (2012) at NW. Their daily concentrations indicate differences in temporal patterns over the decadal period at both sites (Fig. 2). Note that CO maintained the most repeatable trends across the study period. For each carbon species, the frequency distribution was also examined using the daily data. As shown in Fig. 2S, log-normal distribution shapes are similar between all pollutants. The dominant fraction of THC (35.4%) at NW was observed at 2.2–2.4 ppm, while its counterpart at GR decreased slightly (31.2%). NMHC showed predominance in 0.2–0.4 ppm range (65.5 and 45.4%, respectively). In contrast, the pattern between two sites was reversed in the case of CH4 with 41.8% (GR) vs. 39.1% (NW) at 2.0–2.2 ppm. This pattern was maintained for CO (40.3% (GR) at 0.4–0.6 ppm vs. 36.8% (NW) at 0.2–0.4 ppm). Table 2 presents the comparison of concentration levels of airborne carbon species between this work and those measured from other places around the world. If the results are compared between different cities, we can easily observe the existence of spatial gradients to a certain degree due probably to several factors (sampling location, meteorological conditions, site topography, etc.) (Guo et al., 2007). Note that there are differences in CH4 level in 1] Karachi and Mexico City (2.60–6.30 ppm) compared to other cities, 2] Santiago, Guangzhou, and Seoul (2.08–2.20 ppm), and 3] Hong Kong, Ahmedabad, Khaldiya,

(A) Guro (GR) a. Pollutant species 2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

All period

2.40 ± 0.34a (2.31)b 1.78–3.51c (353)d 1.97 ± 0.18 (1.93) 1.57–2.54 (353) 0.43 ± 0.22 (0.40) 0.03–1.27 (353) 0.69 ± 0.32 (0.60) 0.20–2.00 (362)

2.35 ± 0.28 (2.30) 1.82–3.36 (342) 1.99 ± 0.14 (1.99) 1.66–2.60 (342) 0.36 ± 0.21 (0.32) 0.05–1.14 (342) 0.53 ± 0.28 (0.40) 0.20–1.80 (357)

2.36 ± 0.28 (2.34) 1.79–3.30 (356) 2.02 ± 0.17 (2.00) 1.68–2.59 (356) 0.34 ± 0.17 (0.31) 0.04–0.85 (356) 0.65 ± 0.35 (0.60) 0.10–2.10 (343)

2.36 ± 0.30 (2.31)

2.38 ± 0.28 (2.32)

2.35 ± 0.26 (2.28)

2.40 ± 0.29 (2.34)

2.49 ± 0.28 (2.42)

2.38 ± 0.26 (2.34)

2.40 ± 0.24 (2.35)

2.39 ± 0.03 (2.33)

1.84–4.23 (337)

1.93–3.44 (353)

1.89–3.12 (363)

1.98–3.79 (360)

2.04–3.77 (362)

1.83–3.36 (361)

1.97–3.30 (362)

1.99 ± 0.15 (1.95)

2.04 ± 0.14 (2.02)

2.09 ± 0.13 (2.06)

2.07 ± 0.16 (2.04)

2.21 ± 0.16 (2.17)

2.11 ± 0.16 (2.09)

2.15 ± 0.13 (2.14)

1.64–2.56 (337)

1.76–2.70 (353)

1.85–2.49 (363)

1.82–2.99 (360)

1.90–2.85 (362)

1.77–2.80 (361)

1.86–2.59 (362)

0.37 ± 0.19 (0.34)

0.34 ± 0.17 (0.31)

0.26 ± 0.15 (0.23)

0.32 ± 0.17 (0.30)

0.28–0.16 (0.24)

0.27 ± 0.14 (0.24)

0.25 ± 0.12 (0.22)

0.06–1.68 (337)

0.07–1.02 (353)

0.02–0.76 (361)

0.03–1.32 (360)

0.05–1.00 (362)

0.05–1.00 (361)

0.06–0.80 (362)

0.67 ± 0.29 (0.60)

0.62 ± 0.23 (0.60)

0.64 ± 0.26 (0.57)

0.64 ± 0.24 (0.58)

0.57–0.22 (0.52)

0.55 ± 0.19 (0.50)

0.57 ± 0.24 (0.51)

0.20–1.70 (343)

0.30–1.90 (361)

0.29–1.80 (361)

0.28–2.09 (360)

0.24–1.64 (363)

0.20–1.39 (360)

0.22–1.84 (363)

b. Meteorological parametersf WS (m/s) 1.66 ± 0.66 (1.60) 0.20–4.10 (366) TEMP (°C) 15.5 ± 8.56 (16.7) −8.10–30.2 (366) HUM (%) 64.8 ± 16.1 (64.0) 29.0–98.0 (366) UV (mW/cm2) 0.25 ± 0.20 (0.22) 0.01–0.93 (361)

1.63 ± 0.66 (1.50) 0.20–4.10 (363) 14.6 ± 10.1 (16.9) −8.70–30.2 (355) 52.0 ± 12.2 (53.0) 18.0–92.0 (363) 0.28 ± 0.15 (0.24) 0.03–0.75 (342)

1.38 ± 0.71 (1.20) 0.20–4.20 (354) 11.6 ± 8.83 (11.0) −11.1–27.7 (307) 57.3 ± 14.4 (57.0) 23.0–93.0 (310) 0.31 ± 0.19 (0.25) 0.04–0.87 (318)

1.93 ± 0.67 (1.90)

1.82 ± 0.54 (1.70)

1.70 ± 0.57 (1.57)

1.57 ± 0.57 (1.42)

1.43 ± 0.58 (1.36)

1.89 ± 0.57 (1.76)

1.84 ± 0.49 (1.75)

0.20–5.40 (343)

0.90–3.40 (366)

0.78–4.31 (365)

0.65–4.15 (365)

0.45–3.19 (365)

0.89–4.99 (362)

0.99–3.78 (365)

14.7 ± 9.18 (15.5)

13.8 ± 10.1 (15.3)

13.6 ± 10.1 (16.2)

13.2 ± 11.0 (13.5)

12.9 ± 10.6 (14.6)

13.4 ± 11.5 (15.8)

13.6 ± 11.1 (13.6)

13.7 ± 0.98 (15.4)

−5.30–30.3 (343)

−8.00–31.3 (366)

−8.99–29.8 (365)

−11.5–30.6 (365)

−13.0–30.4 (365)

−11.9–33.5 (362)

−11.6–30.6 (365)

−13.0–33.5 (3559)

60.0 ± 13.1 (60.0)

56.1 ± 13.7 (57.0)

55.4 ± 13.5 (56.2)

62.3 ± 15.6 (62.6)

59.2 ± 17.1 (56.2)

51.3 ± 13.0 (51.4)

54.1 ± 12.5 (53.0)

57.2 ± 1.66 (56.6)

26.0–89.0 (328)

22.0–88.0 (366)

24.9–86.3 (365)

26.1–94.3 (365)

25.1–95.7 (365)

20.5–81.3 (362)

24.3–82.1 (365)

0.32 ± 0.22 (0.26)

0.30 ± 0.21 (0.26)

0.30 ± 0.20 (0.25)

0.28 ± 0.19 (0.23)

0.28 ± 0.20 (0.23)

0.31 ± 0.19 (0.27)

0.29 ± 0.19 (0.25)

0.03–0.91 (343)

0.01–0.93 (366)

0.00–0.83 (365)

0.02–0.82 (365)

0.01–1.03 (365)

0.01–0.81 (362)

0.01–0.79 (365)

THC

CH4

(ppm)

(ppm)

NMHC (ppm)

CO

(ppm)

1.78–4.23 (3549)e 2.06 ± 0.02 (2.03) 1.57–2.99 (3549) 0.32 ± 0.03 (0.31) 0.02–1.68 (3549) 0.61 ± 0.05 (0.57) 0.10–2.10 (3573)

E. Ahmed et al. / Science of the Total Environment 518–519 (2015) 595–604

Table 1 Summary of annual mean amount fractions of THC, CH4, NMHC, and CO (ppm) at (A) Guro and (B) Nowon, Korea between 2004 and 2013: computation made using their daily mean values.

1.69 ± 0.07 (1.58) 0.20–5.40 (3614)

18.0–98.0 (3555) 0.29 ± 0.02 (0.25) 0.00–1.03 (3552) 597

(continued on next page)

598

Table 1 (continued) (A) Guro (GR) a. Pollutant species 2004 SR

2

(W/m )

g

–

2005

2006

–

–

2007 135 ± 73.1 (124) 9.00–321 (306)

2008 133 ± 67.6 (126) 9.00–287 (366)

2009 139 ± 75.4 (129) 2.50–302 (365)

2010

2011

135 ± 74.5 (120) 8.88–325 (365)

133 ± 77 (119) 6.13–300 (365)

2012 140 ± 71.6 (136) 6.21–300 (362)

2013 134 ± 69.5 (127) 6.67–295 (365)

All period 136 ± 3.36 (126) 2.50–325 (2494)

(B) Nowon (NW) a. Pollutant species 2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

All period

2.23 ± 0.36a (2.19)b 1.62–3.56c (293)d 1.96 ± 0.29 (1.89) 1.52–3.14 (293) 0.27 ± 0.10 (0.25) 0.07–0.68 (293) 0.68 ± 0.42 (0.60) 0.10–2.00 (337)

2.47 ± 0.25 (2.44) 1.94–3.36 (312) 2.00 ± 0.13 (2.01) 1.64–2.26 (312) 0.47 ± 0.20 (0.45) 0.11–1.24 (312) 0.57 ± 0.26 (0.50) 0.20–1.60 (323)

2.45 ± 0.24 (2.41) 2.02–3.02 (360) 2.02 ± 0.14 (2.01) 1.65–2.44 (360) 0.43 ± 0.18 (0.38) 0.19–1.02 (360) 0.58 ± 0.27 (0.50) 0.10–1.40 (361)

2.46 ± 0.19 (2.44)

2.33 ± 0.16 (2.33)

2.29 ± 0.15 (2.27)

2.51 ± 0.28 (2.42)

2.49 ± 0.21 (2.45)

2.36 ± 0.17 (2.35)

2.54 ± 0.16 (2.53)

2.41 ± 0.07 (2.42)

2.03–3.55 (355)

1.98–2.83 (345)

2.00–2.95 (349)

1.89–3.45 (350)

2.07–3.15 (349)

1.97–3.01 (360)

2.16–2.96 (352)

2.05 ± 0.14 (2.03)

2.00 ± 0.11 (1.98)

2.02 ± 0.11 (2.01)

2.17 ± 0.26 (2.09)

2.19 ± 0.17 (2.18)

2.11 ± 0.15 (2.09)

2.27 ± 0.15 (2.30)

1.71–2.71 (355)

1.78–2.45 (345)

1.76–2.36 (349)

1.50–2.91 (350)

1.56–2.69 (349)

1.76–2.62 (360)

1.94–2.67 (352)

0.42 ± 0.10 (0.43)

0.33 ± 0.09 (0.34)

0.26 ± 0.09 (0.24)

0.34 ± 0.07 (0.32)

0.30 ± 0.07 (0.28)

0.25 ± 0.07 (0.24)

0.27 ± 0.08 (0.24)

0.12–0.85 (355)

0.15–0.63 (345)

0.14–0.64 (349)

0.22–0.70 (350)

0.18–0.71 (349)

0.06–0.61 (360)

0.18–0.64 (352)

0.64 ± 0.30 (0.60)

0.54 ± 0.24 (0.50)

0.52 ± 0.26 (0.44)

0.46 ± 0.25 (0.40)

0.48 ± 0.26 (0.43)

0.46 ± 0.22 (0.40)

0.48 ± 0.24 (0.41)

0.10–1.70 (359)

0.20–1.90 (360)

0.19–1.56 (359)

0.13–2.33 (363)

0.11–1.65 (359)

0.11–1.25 (364)

0.18–1.45 (359)

b. Meteorological parametersf WS (m/s) 1.74 ± 0.47 (1.70) 1.00–3.60 (361) TEMP (°C) 13.6 ± 9.94 (15.2) −13.7–30.7 (361) HUM (%) 65.9 ± 18.9 (66.0) 22.0–98.0 (357) UV (mW/cm2) 0.19 ± 0.09 (0.18) 0.03–0.42 (361) 2 SR (W/m ) 81.3 ± 29.2 (83.0) 11.0–146 (61)

1.64 ± 0.45 (1.50) 0.70–3.60 (323) 14.3 ± 10.5 (15.8) −10.5–30.5 (323) 58.9 ± 15.5 (61.0) 21.0–92.0 (323) 0.72 ± 0.38 (0.70) 0.02–1.55 (323) 60.4 ± 32.2 (59.0) 3.00–145 (306)

1.57 ± 0.47 (1.50) 0.70–4.30 (365) 12.9 ± 9.97 (14.6) −11.0–29.8 (365) 58.9 ± 14.5 (59.0) 21.0–94.0 (365) 0.45 ± 0.22 (0.43) 0.08–1.04 (365) 118 ± 83.1 (107) 8.00–323 (365)

1.50 ± 0.49 (1.40)

1.40 ± 0.47 (1.30)

1.32 ± 0.49 (1.22)

1.30 ± 0.51 (1.20)

1.27 ± 0.48 (1.18)

1.27 ± 0.52 (1.19)

1.36 ± 0.48 (1.31)

0.50–3.70 (364)

0.40–3.10 (364)

0.53–3.21 (365)

0.36–3.50 (365)

0.43–2.67 (364)

0.36–3.95 (366)

0.26–3.26 (365)

12.8 ± 9.49 (13.4)

12.7 ± 10.1 (14.1)

13.6 ± 10.1 (15.8)

13.1 ± 11.0 (13.4)

12.8 ± 10.6 (14.5)

13.0 ± 11.3 (15.0)

13.0 ± 10.6 (12.8)

13.2 ± 0.55 (14.5)

−6.90–27.8 (364)

−8.00–29.8 (362)

−9.11–29.7 (365)

−11.6–29.6 (365)

−13.0–29.4 (364)

−12.5–31.9 (366)

−12.1–29.5 (365)

−13.7–31.9 (3600)

60.9 ± 13.5 (61.0)

57.4 ± 14.6 (58.0)

55.6 ± 14.3 (56.6)

58.1 ± 14.7 (59.2)

56.0 ± 16.7 (52.7)

53.7 ± 14.9 (52.8)

57.5 ± 14.2 (56.9)

58.3 ± 1.55 (58.5)

26.0–89.0 (364)

22.0–94.0 (362)

22.6–85.8 (365)

23.2–89.7 (365)

22.1–91.0 (364)

18.7–87.9 (366)

22.8–89.3 (365)

0.38 ± 0.22 (0.31)

0.39 ± 0.22 (0.33)

0.39 ± 0.23 (0.34)

0.36 ± 0.23 (0.29)

0.20 ± 0.19 (0.12)

0.40 ± 0.26 (0.34)

0.24 ± 0.12 (0.20)

0.06–1.08 (364)

0.02–1.01 (364)

0.02–1.05 (365)

0.01–1.07 (365)

0.02–0.86 (364)

0.02–1.07 (361)

0.05–0.63 (325)

129 ± 69.1 (118)

143 ± 70.9 (135)

143 ± 75.2 (132)

136 ± 74.4 (122)

140 ± 77.7 (129)

153 ± 73.3 (146)

152 ± 78.4 (151)

13.0–300 (364)

12.0–298 (364)

4.50–302 (365)

9.83–331 (365)

4.17–303 (364)

8.08–313 (366)

7.67–315 (365)

THC

CH4

(ppm)

(ppm)

CO

a b c d e f g

(ppm)

Mean ± standard deviation (SD). Numbers in parentheses denote median value. Range = minimum–maximum. N = number of daily data. N = number of total data (from 2004 to 2013). Acronym used for meteorological parameters denote: WS = wind speed, TEMP = temperature, HUM = humidity, UV = ultraviolet, SR = solar radiation. No available data.

2.08 ± 0.06 (2.02) 1.50–3.14 (3425) 0.33 ± 0.05 (0.30) 0.06–1.24 (3425) 0.54 ± 0.06 (0.47) 0.10–2.33 (3544)

1.44 ± 0.02 (1.30) 0.26–4.30 (3602)

18.7–98.0 (3596) 0.37 ± 0.08 (0.32) 0.01–1.55 (3557) 126 ± 19.2 (126) 3.00–331 (3285)

E. Ahmed et al. / Science of the Total Environment 518–519 (2015) 595–604

NMHC (ppm)

1.62–3.56 (3425)e

04-01-2005

09-01-2005

09-01-2005

09-01-2005

02-01-2006

02-01-2006

02-01-2006

07-01-2006

07-01-2006

07-01-2006

07-01-2006

12-01-2006

12-01-2006

12-01-2006

12-01-2006

05-01-2007

05-01-2007

05-01-2007

05-01-2007

11-01-2009

01-01-2009 06-01-2009 11-01-2009

08-01-2008 01-01-2009

06-01-2009 11-01-2009

10-01-2007

03-01-2008 08-01-2008 01-01-2009 06-01-2009 11-01-2009

09-01-2010

09-01-2010

09-01-2010

02-01-2011

02-01-2011

02-01-2011

02-01-2011

07-01-2011

07-01-2011

07-01-2011

07-01-2011

12-01-2011

12-01-2011

12-01-2011

12-01-2011

05-01-2012

05-01-2012

05-01-2012

05-01-2012

10-01-2012

10-01-2012

10-01-2012

03-01-2013

03-01-2013

03-01-2013

08-01-2013

08-01-2013

08-01-2013

10-01-2012 03-01-2013 08-01-2013

GR

04-01-2010

09-01-2010

NW

04-01-2010

CO

04-01-2010

NMHC

04-01-2010

Pollutant species

06-01-2009

08-01-2008

03-01-2008

CH4

01-01-2009

03-01-2008

10-01-2007

Time (Month-Day-Year)

03-01-2008

08-01-2008

10-01-2007

Time (Month-Day-Year)

10-01-2007

Time (Month-Day-Year)

09-01-2005

02-01-2006

E. Ahmed et al. / Science of the Total Environment 518–519 (2015) 595–604

04-01-2005

3.0

04-01-2005

2.5

11-01-2004

04-01-2005

2.0

11-01-2004

1.5

06-01-2004

11-01-2004

1.0

06-01-2004

0.5

01-01-2004

06-01-2004

0.0

01-01-2004

Concentration (ppm)

THC

01-01-2004 06-01-2004

599

Fig. 1. Comparison of the mean concentrations of THC, CH4, NMHC, and CO (ppm) measured from Guro (GR) and Nowon (NW) using their daily data between 2004 and 2013 (error bars denote SD).

4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0

3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0

2.0

1.5

1.0

0.5

0.0

2.5 2.0 1.5 1.0 0.5 0.0

01-01-2004

(A) Guro (GR)

THC (ppm)

CH4 (ppm)

NMHC (ppm)

11-01-2004

Time (Month-Day-Year)

Fig. 2. Plot of daily concentrations of THC, CH4, NMHC, and CO measured from (A) Guro (GR) and (B) Nowon (NW) from 2004 to 2013.

CO (ppm)

600

E. Ahmed et al. / Science of the Total Environment 518–519 (2015) 595–604

02-01-2011

07-01-2011

12-01-2011

05-01-2012

10-01-2012

07-01-2011

12-01-2011

05-01-2012

10-01-2012

07-01-2011

12-01-2011

05-01-2012

10-01-2012

03-01-2013

08-01-2013

02-01-2011

07-01-2011

12-01-2011

05-01-2012

10-01-2012

03-01-2013

08-01-2013

08-01-2013

09-01-2010

02-01-2011 02-01-2011

09-01-2010

03-01-2013

04-01-2010

09-01-2010

09-01-2010

11-01-2009

04-01-2010

06-01-2009

01-01-2009

08-01-2008

03-01-2008

10-01-2007

05-01-2007

12-01-2006

07-01-2006

02-01-2006

09-01-2005

04-01-2005

11-01-2004

06-01-2004

4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0

01-01-2004

THC (ppm)

(B) Nowon (NW)

08-01-2013

03-01-2013

11-01-2009

06-01-2009

01-01-2009

08-01-2008

03-01-2008

10-01-2007

05-01-2007

12-01-2006

07-01-2006

02-01-2006

09-01-2005

04-01-2005

11-01-2004

06-01-2004

3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 01-01-2004

CH4 (ppm)

Time (Month-Day-Year)

04-01-2010

11-01-2009

06-01-2009

01-01-2009

08-01-2008

03-01-2008

10-01-2007

05-01-2007

12-01-2006

07-01-2006

02-01-2006

09-01-2005

04-01-2005

11-01-2004

06-01-2004

1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0

01-01-2004

NMHC (ppm)

Time (Month-Day-Year)

04-01-2010

11-01-2009

06-01-2009

01-01-2009

08-01-2008

03-01-2008

10-01-2007

05-01-2007

12-01-2006

07-01-2006

02-01-2006

09-01-2005

04-01-2005

11-01-2004

06-01-2004

2.5 2.0 1.5 1.0 0.5 0.0

01-01-2004

CO (ppm)

Time (Month-Day-Year)

Time (Month-Day-Year) Fig. 2 (continued).

and American cities (1.86–1.98 ppm). In the case of NMHC, many Asian cities showed high concentration levels (0.27–0.33 ppm), while its values were much lower in American and European cities (0.004–0.02 ppm). In the case of CO, our results from Seoul were moderately high (2.06–2.14 ppm) compared to other cities. 3.2. Seasonal trends of carbon containing airborne pollutants The concentrations were compared after dividing the data into monthly and seasonal intervals. The classification of each season was as follows: spring (March to May), summer (June to August), fall (September to November), and winter (December to February) (Table 4S). Unlike CH4, the seasonal variations were distinctive for NMHC and CO. The amplitude of the seasonal variation, if computed

as [(seasonal maximum − seasonal minimum) / seasonal average)], was small for CH4: 5% (GR) and 2% (NW), in contrast to those of NMHC (25 and 9%) and CO (55 and 76%, respectively). CO exhibited highly strong seasonality with winter dominance, regardless of site. Such seasonality of CO was commonly observed, as Yonagunijima and Okinawa Island in Japan (Suthawaree et al., 2008; Tsutsumi et al., 2006). The results suggest the relative dominance of biomass and biofuel burning activities in winter (and fall) (Guo et al., 2004). The relative ordering of seasonal CH4 was also consistent: winter N fall N summer N spring. In the case of NMHC, the seasonal trends between sites were slightly different with the dominance of fall (GR) or winter (NW) (Fig. 3S). As a means to test the statistical significance of seasonality, a t-test was conducted between the maximum and the 2nd highest mean

E. Ahmed et al. / Science of the Total Environment 518–519 (2015) 595–604

601

Table 2 Comparison of hydrocarbons and carbon monoxide levels measured in Seoul and in other cities. Order

Study area

Period

CH4

NMHC

CO

Reference

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Mexico City, Mexico Santiago, Chile Khaldiya, Kuwait Karachi, Pakistan Guangzhou, China Ahmedabad, India Hong Kong, China Washington, DC, USA New York City, USA Salt Lake City, USA London, UK Hopenpeissenberg, Germany Guro, Seoul, Korea Guro, Seoul, Korea Nowon, Seoul, Korea

February, 1993 June, 1996 June, 1997 December, 1998–January, 1999 2000 2002 September, 2002–August, 2003 2003 2003 2004 2008 2008 2010–2011 2004–2013 2004–2013

2.60 2.20 1.74 6.30 2.10 1.88 1.88 1.98 1.92 1.86 – – 2.14 2.06 2.08

0.34 0.29 0.62 0.27 0.06 0.02 0.01 0.009 0.009 0.01 0.02 0.004 0.30 0.32 0.33

–a – 2.76 1.60 1.40 0.39 0.31 0.38 0.26 0.29 0.53 0.16 0.61 0.61 0.54

Blake and Rowland (1995) Chen et al. (2001) Abdul-Wahab and Al-Alawi (2002) Barletta et al. (2002) Chan et al. (2006) Sahu and Lal (2006) Guo et al. (2007) Baker et al. (2008) Same Same von Schneidemesser et al. (2010) Same Kim et al. (2013) This study This study

a

Denotes not detected/found.

values for each compound. In Table 5S, the observed t values were statistically significant (p b 0.001) for CH4 and NMHC at GR only. In the case of CO, its seasonality was evident at both sites. The noticeable decrease in CO values during summer is explained by reductions in anthropogenic sources such as biomass burning, accounting for 5–8% of the total Asian contribution (Jaeglé et al., 2003). In addition, maximum CO values in winter (300 ppb) are found to be associated with latitudinal band, reflecting the major outflow pathway from Europe to Asia (Duncan and Bey, 2004). 3.3. Long-term trends of airborne carbonic pollutants Long-term analysis is important to understand trends of airborne pollutants. The Mann–Kendall (hereafter MK) non-parametric statistical method is an efficient tool for long-term analysis (Yue et al., 2002). For the MK test, a monotonic trend of increase or decrease is evaluated along with the non-parametric Sen's method for estimating the slope of a linear trend (Gilbert, 1987; Hamed, 2009). In Table 3 (also in Fig. 3), constant reductions took place at GR in NMHC and CO levels with negative slopes of −0.018 and −0.013 ppm yr−1, respectively (p = 0.003

and 0.105). Likewise, their slope values at NW were − 0.023 and − 0.020 ppm yr−1, respectively (p = 0.059 and 0.007). In the case of THC and CH4, constantly increasing trends were apparent with positive slope values of 0.005 and 0.022 ppm yr−1 (p = 0.198 and 0.002) at GR and 0.010 and 0.030 ppm yr−1 (p = 0.216 and 0.004) at NW, respectively. A constantly increasing pattern for THC was also reported during 1990 to 2008 in southern California (Bradley et al., 2010). The analysis of temporal trends for long term measurement and source apportionment of NMHC in three French sites indicated similar decreasing trends (Sauvage et al., 2009). The seasonal MK trends (Fig. 3) for THC showed a slightly increasing trend (slope = 5.310E− 4 ppm yr−1 (p = 0.245: GR) and slope = 0.001 ppm yr−1 (p = 0.115: NW)). It is interesting that CH4 and NMHC displayed almost identical slopes (0.002 and −0.001 ppm yr−1, respectively) at both sites (p b 0.001). CO also showed similar decreasing seasonal trends at both sites (slope values − 8.069E− 4 and − 0.002 ppm yr−1 with p = 0.003 and b 0.0001, respectively), which is similar to the annual MK trend for CO. In fact, a similar decreasing trend using MK was also seen for CO in Kolkata, India, on a seasonal scale, from 2002 to 2011 (Chaudhuri and Dutta, 2014).

Table 3 Summary of Mann–Kendall (MK) statistics for concentrations of airborne carbon compounds and relevant meteorological parameters measured at Guro (GR) and Nowon (NW) between 2004 and 2013 (using (A) annual and (B) monthly mean concentrations (for seasonal trend)). Parameters

GR Mean

A. Annual trend THC 2.39 2.06 CH4 NMHC 0.32 CO 0.61 WS 1.69 TEMP 13.7 HUM 57.3 UV 0.29 SR 136 B. Seasonal trend THC 2.39 2.06 CH4 NMHC 0.32 CO 0.62 WS 1.68 TEMP 13.6 HUM 57.2 UV 0.29 SR 136

NW SD

MK coefficient (tau)

Sen's slope (Q)

Probability (p)

Mean

SD

MK coefficient (tau)

Sen's slope (Q)

0.04 0.08 0.06 0.05 0.19 1.08 4.37 0.02 2.82

0.358 0.809 −0.764 −0.432 0.111 −0.405 −0.289 0.094 0.050

0.005 0.020 −0.018 −0.013 0.010 −0.250 0.291 0.000 0.000

0.198a 0.002 0.003 0.105 0.727 0.127 −0.60 0.784 1.000

2.41 2.08 0.33 0.54 1.44 13.2 58.3 0.37 126

0.10 0.10 0.08 0.08 0.17 0.50 3.34 0.15 31.0

0.333 0.750 −0.494 −0.705 −0.809 −0.230 −0.584 −0.225 0.719

0.010 0.030 −0.023 −0.020 −0.059 −0.067 −0.75 −0.023 7.000

0.216 0.004 0.059 0.007 0.002 0.415 0.025 0.419 0.005

0.13 0.12 0.10 0.16 0.34 9.74 9.32 0.14 42.1

0.085 0.507 −0.433 −0.219 0.089 −0.032 −0.153 −0.089 0.156

5.310E−4 0.002 −0.001 −8.069E−4 5.923E−4 −0.002 −0.037 1.046E−4 −0.094

0.245 b0.0001 b0.0001 0.003 0.225 0.696 0.050 0.225 0.143

2.41 2.08 0.33 0.54 1.44 13.0 58.2 0.37 131

0.19 0.16 0.11 0.20 0.26 9.93 9.67 0.22 49.2

0.115 0.393 −0.274 −0.467 −0.727 0.116 −0.356 −0.125 0.458

0.001 0.002 −0.001 −0.002 −0.005 0.006 −0.045 −0.001 0.586

0.115 b0.0001 0.000 b0.0001 b0.0001 0.140 b0.0001 0.111 b0.0001

Acronyms used for meteorological parameters denote: WS = wind speed, TEMP = temperature, HUM = humidity, UV = ultraviolet, and SR = solar radiation. a Bolded p values denote not significant at 0.05 levels in the slope values.

Probability (p)

602

E. Ahmed et al. / Science of the Total Environment 518–519 (2015) 595–604

(A) MK test for Annual trend GR

NW 3.0

2.5

2.0

Q (THC) = 0.005

2.5

THC Q (CH4) = 0.022

CH4

1.5 1.0

NMHC Q (CO) = -0.013

CO

0.5

Concentration (ppm)

Concentration (ppm)

3.0

Q (NMHC) = -0.018 0.0

2.0

THC

Q (CH4) = 0.03

1.5 1.0

CH4

NMHC

Q (CO) = -0.02

CO

0.5

0.0

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Time (Year)

Q (THC) = 0.01

Q (NMHC) = -0.023 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Time (Year)

(B) MK test for seasonality

2.5 2.0

THC

1.5

CH4

1.0

NMHC

0.5 0.0

CO

Time (Month-Year)

3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0

Q (THC) = 0.001

Q (CH4) = 0.002

Q (NMHC) = -0.001

Q (CO) = -0.002

THC CH4 NMHC

01-2004 08-2004 03-2005 10-2005 05-2006 12-2006 07-2007 02-2008 09-2008 04-2009 11-2009 06-2010 01-2011 08-2011 03-2012 10-2012 05-2013 12-2013

3.0

Q (CH4) = 0.002 Q (CO) = -8.069E-4

Concentration (ppm)

NW Q (THC) = 5.310E-4 Q (NMHC) = -0.001

01-2004 07-2004 01-2005 07-2005 01-2006 07-2006 01-2007 07-2007 01-2008 07-2008 01-2009 07-2009 01-2010 07-2010 01-2011 07-2011 01-2012 07-2012 01-2013 07-2013

Concentration (ppm)

GR

CO

Time (Month-Year)

Fig. 3. Long-term trends of airborne carbon compounds in Guro (GR) and Nowon (NW) (2004–2013) showing (A) MK test for Annual trend and (B) MK test for seasonality.

3.4. Factors affecting the source identification of carbon containing airborne pollutants Natural emissions are responsible for a major portion of airborne carbon compounds, although human activities gradually intervened in their emissions characteristics (Guenther et al., 2000; Song et al., 2007). Hence, long-term variations in CH4 emissions have been affected by both natural and anthropogenic emissions (Wang et al., 2004; Manning et al., 2005; Chen and Prinn, 2006). According to comparative measurement of NMHCs in the Pearl River Delta area, China, industrial emissions had a dominant influence on the ambient levels of NMHCs (Chan et al., 2006). From the study at a clean, remote site on Hainan Island, south China, the major sources of hydrocarbons were identified as long-range transport from the upwind source regions (Tang et al., 2007). However, uncertainties remain in emission inventories due to the diversity and complicated nature of airborne carbon pollutant sources. Hence, an accurate evaluation of emissions is still important and critical to assess their source processes (Gaimoz et al., 2011; Urbanski et al., 2011). To learn more about the factors controlling the behavior of airborne carbon species, a correlation analysis was carried out using the daily data sets between carbon species and relevant parameters. In Table 6S, there are highly significant correlations between different carbon (C) species (at p b 0.01). The carbonic pollutants also revealed strong correlations (p b 0.01) with NO, NO2, and NOx in both study sites, indicating similar urban emission sources. Most C species were moderately inversely correlated with WS at both sites as one might expect for an area source profile containing a few point source hotspots. However, there was no obvious correlation with other meteorological parameters (TEMP, HUM, UV, and SR). It is noted that CO showed an inverse correlation with TEMP: their relationship was more evident at NW (r = − 0.454, p b 0.01) than at GR (r = − 0.341, p b 0.01). An inverse relationship between C species (CH4, NMHC, and CO) and some meteorological parameters (e.g., WS, TEMP, HUM, UV, and SR) has commonly been reported previously: the urban area of Prague, Czech Republic (Thimmaiah et al., 2009), in Umea, Sweden (Pettersson et al., 2010), and at Khaldiya residential area in Kuwait (Abdul-Wahab et al., 2005).

The annual mean values of carbon species and their related ratios for the two sites (GR and NW) are summarized in Table 7S. It is well known that numerous factors (e.g., nature of sampling sites) can affect the mixing ratios of hydrocarbons (methane and non-methane hydrocarbon) (Hsieh and Tsai, 2003). Hence, this comparison may be useful to explain the general status of air quality at both sites. In fact, higher CH4/NMHC ratios may reflect the increased effects of global warming (Shindell et al., 2009). The spatial compatibility between the two sites can also be checked using the concentration data (THC, CH4, NMHC, and CO) from each site. The results of a correlation analysis between two sites indicate strong correlations in their monthly data (Fig. 4S). CO showed a strong relationship between the two sites with R2 = 0.714 and p = 7.29E− 07. Among all C-species, NMHC exhibited the lowest correlations (R2 = 0.052, and p = 0.305) between the two sites. The strong correlations of CH4, NMHC, and CO with THC are explained by the common phenomenon of rapid economic development and increasing fossil fuel use, as commonly observed in east Asia, particularly China and Korea (Turnbull et al., 2011). As another means to assess the source characteristics, we carried out principal component analysis (PCA). Accordingly, six factors were identified at each site that accounted for more than 80% of the total variability (Table 4). In the case of GR, the first (F1) and the second factor (F2) accounted for 34 and 15% of the total variance, respectively, while the third (F3), fourth (F4), fifth (F5), and sixth (F6) were responsible for 14, 10, 8, and 6%, respectively. In the case of NW, all six factors were corresponding to 36, 15, 11, 8, 7, and 6%, respectively. Most of the airborne pollutants showed a strong correlation from factor F1 at both sites. Environmental parameters are dominant for F2 at GR, while PM is the dominant projection along F2 at NW. CH4 and NMHCs are emitted from traffic related sources and solvent use, natural gas leaks and LPG, industrial burning and/or biomass and biofuel burning (Cofala et al., 2007; Saito et al., 2009). A study in 43 Chinese cities indicated that vehicular emissions were the major source of NMHCs in 10 cities, while coal and biofuel combustions were the major sources in 15 other cities (Barletta et al., 2005). In contrast, CO from incomplete fossil fuel combustion, is also likely to represent biomass and biofuel burning (Prockop and Chichkova, 2007). Taken together, these findings suggest

E. Ahmed et al. / Science of the Total Environment 518–519 (2015) 595–604

603

Table 4 Results of PCA showing the factors loading for THC, CH4, NMHC, and CO collected from (A) Guro (GR) and (B) Nowon (NW) (using daily data). Parameters

THC CH4 NMHC CO PM2.5 PM10 TSP NO NO2 NOx SO2 O3 Hg WS TEMP HUM UV SR Initial eigenvalue % of variability Cumulative %

(A) GR

(B) NW

F1

F2

F3

F4

F5

F6

F1

F2

F3

F4

F5

F6

0.787 0.652 0.591 0.721 0.488 0.459 0.388 0.822 0.868 0.866 0.710 −0.492 0.102 −0.349 −0.435 −0.287 −0.433 −0.213 6.074 33.742 33.742

0.048 −0.112 0.198 0.042 0.496 0.385 0.222 −0.007 0.140 0.045 0.126 0.462 0.672 −0.227 0.668 0.152 0.723 0.632 2.644 14.690 48.432

−0.443 −0.405 −0.292 −0.168 0.424 0.342 0.703 0.001 0.042 0.015 0.271 −0.331 −0.352 0.660 −0.290 −0.461 0.127 0.338 2.436 13.531 61.963

0.170 −0.193 0.485 0.520 0.337 0.402 0.176 −0.431 −0.358 −0.420 −0.321 0.073 0.174 0.187 −0.210 0.097 −0.316 −0.248 1.757 9.761 71.724

−0.302 −0.190 −0.290 −0.161 0.262 0.209 0.187 0.129 0.077 0.115 0.206 0.003 0.015 −0.063 0.299 0.721 −0.291 −0.572 1.488 8.267 79.991

0.084 0.451 −0.344 −0.148 −0.073 0.405 0.130 −0.207 −0.089 −0.173 0.244 0.526 −0.147 −0.214 −0.201 −0.112 −0.126 0.015 1.090 6.057 86.047

0.591 0.459 0.349 0.922 0.665 0.551 0.458 0.855 0.853 0.920 0.739 −0.563 0.424 −0.426 −0.477 0.067 −0.358 −0.395 6.531 36.286 36.286

−0.132 −0.271 0.142 0.061 0.559 0.709 0.739 −0.246 0.086 −0.128 0.074 0.587 0.361 0.350 0.203 −0.189 0.564 0.421 2.734 15.191 51.477

0.440 0.213 0.428 −0.091 0.097 −0.033 −0.084 −0.077 −0.085 −0.086 −0.358 −0.046 0.466 −0.349 0.709 0.719 0.204 −0.111 2.011 11.172 62.648

0.401 0.543 −0.066 0.003 −0.108 −0.196 −0.201 0.083 0.166 0.123 0.019 0.235 −0.114 −0.267 0.132 −0.497 0.326 0.642 1.522 8.455 71.104

0.376 −0.109 0.746 0.063 −0.075 −0.171 −0.148 −0.017 −0.126 −0.064 0.129 0.062 −0.138 0.448 −0.208 −0.188 0.222 −0.211 1.204 6.689 77.792

0.367 0.565 −0.149 −0.136 0.113 0.236 0.256 −0.243 −0.205 −0.246 −0.030 0.079 −0.216 0.224 −0.092 0.045 −0.392 −0.139 1.078 5.989 83.781

Factor loadings ≥ 0.5 bolded. Extraction method: principal component analysis. Only factors with eigenvalue ≥ 1 shown. Acronyms used for meteorological parameters denote: WS = wind speed, TEMP = temperature, HUM = humidity, UV = ultraviolet, and SR = solar radiation.

that both vehicle exhaust and biomass or biofuel burning along with local industrial emissions contribute to F1 (Guo et al., 2006). Considering the daily emission patterns of carbon compounds, it is more likely that vehicle and industrial emissions have a greater influence on the ambient levels of airborne carbonic pollutants relative to biofuel burning (Guo et al., 2004; Chan et al., 2006). 4. Conclusion In this study, we present a ten-year record of airborne carbon species (CH4, NMHC, and CO) amount fractions at two urban areas in Seoul, Korea. The concentration data, when analyzed at various temporal scales, displayed very different seasonality between CO and others. Although CO exhibited a clear seasonality with the wintertime maximum, it was not the case for other airborne carbon species. Analysis of the decadal data shows highly significant correlations between different C species. The results of principal component analysis indicated factor (F1) with the highest factor loading to show strong correlations with the airborne pollutant concentrations at both sites. The long-term analysis indicated a consistently decreasing trend for NMHC and CO throughout the decadal period, whereas CH4 demonstrated statistically significant increasing pattern. The ratio of CH4 to the total hydrocarbon amount fraction remained constant across the study years, indicating similarity in their emission profiles over the study period and the strong role of methane in the overall emission profile. This is corroborated by the larger variation shown in the annual mean ratios for CH4 to nonmethane hydrocarbons. Acknowledgments This study was supported by a National Research Foundation of Korea (NRF) grant funded by the Ministry of Education, Science and Technology (MEST) (No. 2009-0093848). Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2015.02.058.

References Abdul-Wahab, S.A., Al-Alawi, S.M., 2002. Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks. Environ. Model Softw. 17, 219–228. Abdul-Wahab, S.A., Bakheit, C.S., Al-Alawi, S.M., 2005. Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations. Environ. Model Softw. 20, 1263–1271. Amundson, R., 2001. The carbon budget in soils. Annu. Rev. Earth Planet. Sci. 29, 535–562. Bäckstrand, K., Crill, P.M., Mastepanov, M., Christensen, T.R., Bastviken, D., 2008. Total hydrocarbon flux dynamics at a subarctic mire in northern Sweden. J. Geophys. Res. Biogeosci. 2005–2012, 113. Baker, A.K., Beyersdorf, A.J., Doezema, L.A., Katzenstein, A., Meinardi, S., Simpson, I.J., Blake, D.R., Rowland, F.S., 2008. Measurements of nonmethane hydrocarbons in 28 United States cities. Atmos. Environ. 42, 170–182. Bardgett, R.D., Freeman, C., Ostle, N.J., 2008. Microbial contributions to climate change through carbon cycle feedbacks. ISME J. 2, 805–814. Barletta, B., Meinardi, S., Simpson, I.J., Khwaja, H.A., Blake, D.R., Rowland, F.S., 2002. Mixing ratios of volatile organic compounds (VOCs) in the atmosphere of Karachi, Pakistan. Atmos. Environ. 36, 3429–3443. Barletta, B., Meinardi, S., Sherwood Rowland, F., Chan, C.-Y., Wang, X., Zou, S., Yin Chan, L., Blake, D.R., 2005. Volatile organic compounds in 43 Chinese cities. Atmos. Environ. 39, 5979–5990. Beckmann, M., Lloyd, D., 2001. Extraction and identification of volatile organic substances (VOS) from Scottish peat cores. Atmos. Environ. 35, 79–86. Blake, D.R., Rowland, F.S., 1995. Urban leakage of liquefied petroleum gas and its impact on Mexico City air quality. Science 269, 953–956. Bogner, J., Pipatti, R., Hashimoto, S., Diaz, C., Mareckova, K., Diaz, L., Kjeldsen, P., Monni, S., Faaij, A., Gao, Q., 2008. Mitigation of global greenhouse gas emissions from waste: conclusions and strategies from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report. Working Group III (Mitigation). Waste Manag. Res. 26, 11–32. Borbon, A., Locoge, N., Veillerot, M., Galloo, J., Guillermo, R., 2002. Characterisation of NMHCs in a French urban atmosphere: overview of the main sources. Sci. Total Environ. 292, 177–191. Bousquet, P., Ciais, P., Miller, J., Dlugokencky, E., Hauglustaine, D., Prigent, C., Van der Werf, G., Peylin, P., Brunke, E.-G., Carouge, C., 2006. Contribution of anthropogenic and natural sources to atmospheric methane variability. Nature 443, 439–443. Bradley, E., Leifer, I., Roberts, D., 2010. Long-term monitoring of a marine geologic hydrocarbon source by a coastal air pollution station in southern California. Atmos. Environ. 44, 4973–4981. Chan, L.Y., Chu, K.W., Zou, S.C., Chan, C.Y., Wang, X.M., Barletta, B., Blake, D.R., Guo, H., Tsai, W.Y., 2006. Characteristics of nonmethane hydrocarbons (NMHCs) in industrial, industrial‐urban, and industrial‐suburban atmospheres of the Pearl River Delta (PRD) region of south China. J. Geophys. Res. Atmos. 1984–2012, 111. Chaudhuri, S., Dutta, D., 2014. Mann–Kendall trend of pollutants, temperature and humidity over an urban station of India with forecast verification using different ARIMA models. Environ. Monit. Assess. 1–24. Chen, Y.H., Prinn, R.G., 2006. Estimation of atmospheric methane emissions between 1996 and 2001 using a three‐dimensional global chemical transport model. J. Geophys. Res. Atmos. 1984–2012, 111.

604

E. Ahmed et al. / Science of the Total Environment 518–519 (2015) 595–604

Chen, T.Y., Simpson, I.J., Blake, D.R., Rowland, F.S., 2001. Impact of the leakage of liquefied petroleum gas (LPG) on Santiago air quality. Geophys. Res. Lett. 28, 2193–2196. Chen, S.-P., Liao, W.-C., Chang, C.-C., Su, Y.-C., Tong, Y.-H., Chang, J.S., Wang, J.-L., 2014. Network monitoring of speciated vs. total non-methane hydrocarbon measurements. Atmos. Environ. 90, 33–42. Christensen, T.R., Johansson, T., Olsrud, M., Ström, L., Lindroth, A., Mastepanov, M., Malmer, N., Friborg, T., Crill, P., Callaghan, T.V., 2007. A catchment-scale carbon and greenhouse gas budget of a subarctic landscape. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 365, 1643–1656. Cofala, J., Amann, M., Klimont, Z., Kupiainen, K., Höglund-Isaksson, L., 2007. Scenarios of global anthropogenic emissions of air pollutants and methane until 2030. Atmos. Environ. 41, 8486–8499. Duncan, B., Bey, I., 2004. A modeling study of the export pathways of pollution from Europe: seasonal and interannual variations (1987–1997). J. Geophys. Res. Atmos. 1984–2012, 109. Gaimoz, C., Sauvage, S., Gros, V., Herrmann, F., Williams, J., Locoge, N., Perrussel, O., Bonsang, B., d'Argouges, O., Sarda-Estève, R., 2011. Volatile organic compounds sources in Paris in spring 2007. Part II: source apportionment using positive matrix factorisation. Environ. Chem. 8, 91–103. Gilbert, R.O., 1987. Statistical Methods for Environmental Pollution Monitoring. John Wiley & Sons. Guenther, A., Geron, C., Pierce, T., Lamb, B., Harley, P., Fall, R., 2000. Natural emissions of non-methane volatile organic compounds, carbon monoxide, and oxides of nitrogen from North America. Atmos. Environ. 34, 2205–2230. Guo, H., Wang, T., Simpson, I., Blake, D., Yu, X., Kwok, Y., Li, Y., 2004. Source contributions to ambient VOCs and CO at a rural site in eastern China. Atmos. Environ. 38, 4551–4560. Guo, H., Wang, T., Blake, D., Simpson, I., Kwok, Y., Li, Y., 2006. Regional and local contributions to ambient non-methane volatile organic compounds at a polluted rural/coastal site in Pearl River Delta, China. Atmos. Environ. 40, 2345–2359. Guo, H., So, K., Simpson, I., Barletta, B., Meinardi, S., Blake, D., 2007. C1–C8 volatile organic compounds in the atmosphere of Hong Kong: overview of atmospheric processing and source apportionment. Atmos. Environ. 41, 1456–1472. Hamed, K., 2009. Exact distribution of the Mann–Kendall trend test statistic for persistent data. J. Hydrol. 365, 86–94. Hsieh, C.-C., Tsai, J.-H., 2003. VOC concentration characteristics in southern Taiwan. Chemosphere 50, 545–556. Jaeglé, L., Jaffe, D.A., Price, H.U., Weiss‐Penzias, P., Palmer, P.I., Evans, M.J., Jacob, D.J., Bey, I., 2003. Sources and budgets for CO and O3 in the northeastern Pacific during the spring of 2001: results from the PHOBEA‐II experiment. J. Geophys. Res. Atmos. 1984–2012, 108. Janzen, H., 2004. Carbon cycling in earth systems—a soil science perspective. Agric. Ecosyst. Environ. 104, 399–417. Kim, K.-H., Yoon, H.-O., Brown, R.J., Jeon, E.-C., Sohn, J.-R., Jung, K., Park, C.-G., Kim, I.-S., 2013. Simultaneous monitoring of total gaseous mercury at four urban monitoring stations in Seoul, Korea. Atmos. Res. 132, 199–208. Logue, J.M., Small, M.J., Stern, D., Maranche, J., Robinson, A.L., 2010. Spatial variation in ambient air toxics concentrations and health risks between industrial-influenced, urban, and rural sites. J. Air Waste Manage. Assoc. 60, 271–286. Manning, M.R., Lowe, D.C., Moss, R.C., Bodeker, G.E., Allan, W., 2005. Short-term variations in the oxidizing power of the atmosphere. Nature 436, 1001–1004. McMichael, A.J., Woodruff, R.E., Hales, S., 2006. Climate change and human health: present and future risks. Lancet 367, 859–869. Pan, Y., Wang, Y., Xin, J., Tang, G., Song, T., Wang, Y., Li, X., Wu, F., 2010. Study on dissolved organic carbon in precipitation in Northern China. Atmos. Environ. 44, 2350–2357. Pettersson, E.R., Lindmark, F., Ohman, M., Nordin, A., Westerholm, R., Boman, C., 2010. Design changes in a fixed-bed pellet combustion device: effects of temperature and residence time on emission performance. Energy Fuel 24, 1333–1340. Platt, S., Haddad, I.E., Zardini, A., Clairotte, M., Astorga, C., Wolf, R., Slowik, J., TemimeRoussel, B., Marchand, N., Ježek, I., 2013. Secondary organic aerosol formation from

gasoline vehicle emissions in a new mobile environmental reaction chamber. Atmos. Chem. Phys. 13, 9141–9158. Poisson, N., Kanakidou, M., Crutzen, P.J., 2000. Impact of non-methane hydrocarbons on tropospheric chemistry and the oxidizing power of the global troposphere: 3dimensional modelling results. J. Atmos. Chem. 36, 157–230. Prockop, L.D., Chichkova, R.I., 2007. Carbon monoxide intoxication: an updated review. J. Neurol. Sci. 262, 122–130. Raub, J.A., Mathieu-Nolf, M., Hampson, N.B., Thom, S.R., 2000. Carbon monoxide poisoning—a public health perspective. Toxicology 145, 1–14. Sahu, L., Lal, S., 2006. Distributions of C2–C5 NMHCs and related trace gases at a tropical urban site in India. Atmos. Environ. 40, 880–891. Saito, S., Nagao, I., Kanzawa, H., 2009. Characteristics of ambient C2–C11 non-methane hydrocarbons in metropolitan Nagoya, Japan. Atmos. Environ. 43, 4384–4395. Sauvage, S., Plaisance, H., Locoge, N., Wroblewski, A., Coddeville, P., Galloo, J., 2009. Long term measurement and source apportionment of non-methane hydrocarbons in three French rural areas. Atmos. Environ. 43, 2430–2441. Shin, H.-C., Park, J.-W., Kim, H.-S., Shin, E.-S., 2005. Environmental and economic assessment of landfill gas electricity generation in Korea using LEAP model. Energy Policy 33, 1261–1270. Shindell, D.T., Faluvegi, G., Koch, D.M., Schmidt, G.A., Unger, N., Bauer, S.E., 2009. Improved attribution of climate forcing to emissions. Science 326, 716–718. Song, Y., Shao, M., Liu, Y., Lu, S., Kuster, W., Goldan, P., Xie, S., 2007. Source apportionment of ambient volatile organic compounds in Beijing. Environ. Sci. Technol. 41, 4348–4353. Streets, D.G., Fu, J.S., Jang, C.J., Hao, J., He, K., Tang, X., Zhang, Y., Wang, Z., Li, Z., Zhang, Q., 2007. Air quality during the 2008 Beijing Olympic games. Atmos. Environ. 41, 480–492. Suthawaree, J., Kato, S., Takami, A., Kadena, H., Toguchi, M., Yogi, K., Hatakeyama, S., Kajii, Y., 2008. Observation of ozone and carbon monoxide at Cape Hedo, Japan: seasonal variation and influence of long-range transport. Atmos. Environ. 42, 2971–2981. Tang, J.H., Chan, L.Y., Chan, C.Y., Li, Y.S., Chang, C.C., Liu, S.C., Li, Y.D., 2007. Nonmethane hydrocarbons in the transported and local air masses at a clean remote site on Hainan Island, south China. J. Geophys. Res. Atmos. 1984–2012, 112. Themelis, N.J., Ulloa, P.A., 2007. Methane generation in landfills. Renew. Energy 32, 1243–1257. Thimmaiah, D., Hovorka, J., Hopke, P.K., 2009. Source apportionment of winter submicron prague aerosols from combined particle number size distribution and gaseous composition data. Aerosol Air Qual. Res. 9, 209–236. Tsutsumi, Y., Mori, K., Ikegami, M., Tashiro, T., Tsuboi, K., 2006. Long-term trends of greenhouse gases in regional and background events observed during 1998–2004 at Yonagunijima located to the east of the Asian continent. Atmos. Environ. 40, 5868–5879. Turnbull, J.C., Tans, P.P., Lehman, S.J., Baker, D., Conway, T.J., Chung, Y., Gregg, J., Miller, J.B., Southon, J.R., Zhou, L.X., 2011. Atmospheric observations of carbon monoxide and fossil fuel CO2 emissions from east Asia. J. Geophys. Res. Atmos. 1984–2012, 116. Urbanski, S., Hao, W., Nordgren, B., 2011. The wildland fire emission inventory: western United States emission estimates and an evaluation of uncertainty. Atmos. Chem. Phys. 11, 12973–13000. Von Burg, R., 1999. Carbon monoxide. J. Appl. Toxicol. 19, 379–386. von Schneidemesser, E., Monks, P.S., Plass-Duelmer, C., 2010. Global comparison of VOC and CO observations in urban areas. Atmos. Environ. 44, 5053–5064. Wang, J.S., Logan, J.A., McElroy, M.B., Duncan, B.N., Megretskaia, I.A., Yantosca, R.M., 2004. A 3‐d model analysis of the slowdown and interannual variability in the methane growth rate from 1988 to 1997. Glob. Biogeochem. Cycles 18. Wang, T., Nie, W., Gao, J., Xue, L., Gao, X., Wang, X., Qiu, J., Poon, C., Meinardi, S., Blake, D., 2010. Air quality during the 2008 Beijing Olympics: secondary pollutants and regional impact. Atmos. Chem. Phys. 10, 7603–7615. Yue, S., Pilon, P., Cavadias, G., 2002. Power of the Mann–Kendall and Spearman's rho tests for detecting monotonic trends in hydrological series. J. Hydrol. 259, 254–271.