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As the capital of South Korea, Seoul has a carbon footprint of 1.59 metric tones per person (Sovacool and Brown, 2010). The Seoul metropolitan area has 10 million inhabitants, while occupying only 0.6 percent of the country’s land area. Nonetheless, the city with 25 districts produces 21 percent of its GDP, mainly in business and financial sectors with technology firms and banking giants such as Samsung, Hyundai, Kia, and LG (Sovacool and Brown, 2010). More than 80% of the total energy used in Seoul comes from fossil fuels, mostly coal, petroleum, and natural gas (Sovacool and Brown, 2010). The city also maintains a large number of water and waste treatment plants along with landfills that can release fairly large quantities of greenhouse gases (Jo et al., 2008; Kaneko and Dhakal, 2008; Jo, 2002).

In this study, the distribution pattern of carbon dioxide was investigated using the data sets collected from an air quality monitoring station on the top of Mt. GA (37°26′44′′N, and 126°57′49′′E), a relatively small mountain located at the southern district of Seoul, South Korea with the total area of 19,226,942m² (Fig. 1). Mt. GA is situated to cover 4 districts in terms of the administrative zones, (1) 11,412,035m² (59.4%) in Gwanak-gu, (2) 2,120,595m² (11%) in Geumcheon-gu, and (3) 5,694,312m² (29.6%) in both Gwacheon City and Anyang City of Gyeonggi-do. Our target study area of Mt. GA belongs to a temperate climate zone with the mean temperature of 9.53±10.4°C (seasonal means of -3.2 (winter) to 20.3°C (summer)). Likewise, the UV radiation also exhibited the seasonal mean value (mWcm^-2) of 0.07 (winter) to 0.45 (summer). Relative humidity was significantly lower in spring (58.6%) than other seasons. Examination of the wind rose pattern indicated that during most of the time, winds were blown from WWN followed by westerlies. It is steep topographically with a ravine developed in all directions. Only a few types of needle-leaf trees (e.g., pine trees) are grown wildly while a variety of falling broadleaved trees (e.g., black oaks) can be found frequently (Gwanak-Gu, 2010).

Fig. 1. 
Geographical location of Mt. Gwan-ak (GA) in Seoul, Korea. (a) Location of Seoul; (b) Location of Mt. Gwan-ak (GA); and (c) Topography of Mt. Gwan-ak (source: Google map).

From the air quality monitoring site, the hourly CO₂ data were collected continuously to cover a whole year from January 1^st to December 31^st 2009 using a CO₂/CH₄/H₂O analyzer (Picarro G1301, US). An automated air pollution monitoring system (Thermo, USA) is located at a height of 620 meters above mean sea level. The vertical height of sampling inlet from the monitoring station is 14 m, while the station itself is aloft 9m above the soil. A list of the criteria pollutants (CH₄, NO_x, O₃, SO₂, and particulate matters) were also monitored concurrently along with the meteorological parameters (air temperature, UV, humidity, wind speed, etc). The analysis of CO₂ data and all the relevant parameters can help us properly evaluate the influence of various factors and processes affecting the behavior of CO₂ in the study area.

The instrument used to measure concentration of CO₂ is Picarro G1301 analyzer (Picarro, US). The analyzer is based on Picarro’s unique Wavelength-Scanned Cavity Ring Down Spectroscopy (WS-CRDS), a time based measurement technique based on a near infrared laser. This CRDS technique is a highly precise method allowing to measure a spectral signature of the target molecule (Picarro, 2010). It is a real time, trace gas monitor capable of measuring gases at parts-per-billion (ppbv) sensitivity. The instrument is capable of measuring CO₂ in the range 0-1,000 ppmv and CH₄ in the range 0-20 ppmv. By following the procedure of Busch and Busch (1997), the measurement precision was assessed by taking a spectral scan at every 5 min with the 380 ppm CO₂ standard at room temperature. The relative standard deviation was then estimated as 0.04%.

In this study, CO₂ concentration data were collected from an air quality monitoring station located on the top of Mt. GA, in the southern district of Seoul, South Korea for a one year period (January to December 2009). To explore the overall trend of CO₂, its hourly data were at first plotted as a function of time (Julian day) (Fig. 2(a)). The mean hourly concentration of CO₂ for the entire study period was 405±12.1 ppm (median=403 ppm) with a range of 344 to 508 ppm (N=8,548). The maximum hourly concentrations of CO₂ (508 ppm) was determined at 1 am on December 3^rd, while its minimum value (344 ppm) at 7 am on March 8^th. This observed annual concentration of CO₂ in Mt. GA was 4.55% higher than the average value of Mauna Loa (387.35 ppm) for the year 2009. The trend in CO₂ at Mauna Loa can be one of the most representative sites to predict the global trend.

Fig. 2. 
Plot of daily mean concentration of CO₂ in Mt. GA, Seoul, Korea in 2009. (a) Temporal changes of CO₂ at Mt. GA using hourly data collected throughout 2009; (b) Comparison of daily parameters of CO₂ data in 2007.

As shown in Table 2, the CO₂ data divided into each season show the maximum value in winter (410±13.4 ppm) followed by spring (407±9.56 ppm), summer (402±11.3 ppm) and fall (399±11 ppm). To accurately describe the temporal trend of CO₂ over a one year period, some parameters derived on daily basis (average, minimum, and maximum) were also plotted in Fig. 2(b). It is found that differences in CO₂ levels between winter (the highest) and spring (the next) are statistically significant (P<0.05). The relative enhancement in wintertime CO₂ levels can be basically sought from the combined effect of such factors as the increasing consumption rate of fossil fuel (e.g., house heating), reduced photosynthesis, and more stable atmospheric conditions (Henninger and Kuttler, 2010). Our findings of relative enhancement in CO₂ during the wintertime comply well with many previous studies conducted in the background as well as in the urban region in the northern hemisphere (Henninger and Kuttler, 2010; Miyaoka et al., 2007; Pataki et al., 2003; Aikawa et al., 1995; Woodwell et al., 1978; Bolin and Keeling, 1963). It is however interesting to note that the lowest seasonal mean took place in fall. This pattern is quite unique if one considers the fact that the minimum CO₂ values were typically seen during summer in most of the previous studies (Pataki et al., 2003; Woodwell et al., 1978; Bolin and Keeling, 1963). Summer minimum of CO₂ concentration was also observed in urban area of Nagoya and Sapporo in the neighboring country Japan as opposed to our finding of minimum concentration in fall (Miyaoka et al., 2007; Aikawa et al., 1995). Henninger and Kuttler (2010) also found the lowest concentration of CO₂ in summer in Essan, a typical urban conurbation city of Germany. In compliance with seasonal trend, comparison of monthly mean values of CO₂ shows it’s minimum and maximum in October and February, respectively. In contrast, minimum and maximum values in most of the previous studies were found most commonly in August and April, respectively (e.g., Nakazawa et al., 1992).

To understand the short-term variability of CO₂, the mean hourly CO₂ values were examined over a diurnal cycle for both seasonal group and a whole year period (Fig. 3). For all data groups, a clear cycle is observed with the maximum occurring in the late morning (around 11 am), while the minimum in the early morning (between 5 and 8 am). In addition, the presence of three shoulders throughout the year is also found along with the clear diurnal cycle of CO₂. This should be considered to reflect a dynamic nature of its temporal variabilities. One shoulder near midnight (around 12 am) is likely to be caused by the combined effects of respiration from soils and/or by the living organism. The other two shoulders observed earlier (9 am to 2 pm) and in the evening (6 to 10 pm) should be associated with traffic activities. The timing for these late peaks matches with the peak traffic hours in Seoul. These diurnal patterns of the CO₂ data thus clearly indicate the significance of anthropogenic activities (especially emission from automobiles) to a large extent; even at a mountainous site that is little distant from the highly urbanized sector of Seoul, one of the most populated megacities. It was also found that CO₂ concentration is immensely dependent on traffic activity in Denmark and France, respectively (Soegaard and Moller-Jensen, 2003; Widory and Javoy, 2003). In Mexico, Velasco et al. (2005) found the CO₂ concentration to be directly related to vehicular traffic because the transportation sector accounts for approximately 60% of emission burden.

Fig. 3. 
Diurnal variability of CO₂: all data vs. each individual season.

In this study, the analysis of diurnal trend generally indicates that minimum and maximum values of CO₂ concentrations are in early morning and afternoon, respectively (Fig. 3). However, this pattern is completely opposite to what was found both in background as well as in urban areas in a number of previous studies (Anthwal et al., 2010; Henninger and Kuttler, 2010; Miyaoka et al., 2007; Idso et al., 2002; Aikawa et al., 1995). The authors commonly found its maximum in early morning and minimum in daytime. The unique diurnal trend in this study characterized by the least values in the early morning should be accounted for by the combined effect of several factors (e.g., the nighttime respiration of living organisms and soil layer emission). In contrast, a daytime minimum is suspected to be caused by the photosynthetic activities (Nasrallah et al., 2003; Baez et al., 1988; Spittlehouse and Ripley, 1977) and the expansion of the mixing height (Aikawa et al., 1995). In addition, the appearance of CO₂ peaks near busy traffic hours, as seen in this study, should comply with those in other urban areas under strong anthropogenic activities (such as vehicular emission, and burning of fossil fuels) (Gratani and Varone, 2005; Idso et al., 2002; Takahashi et al., 2002; Aikawa et al., 1995). Many previous studies of CO₂ reported that its concentrations in many urban areas (including Phoenix (Arizona, USA), Essen (Germany), Kuwait city (Kuwait), Taiwan, and Rome) are controlled by vehicular emission to a degree (Henninger and Kuttler, 2010; Lu et al., 2007; Gratani and Varone, 2005; Nasrallah et al., 2003; Idso et al., 2002). As such, the observed diurnal trend of CO₂ in the study area should be accounted for by the typical activities in urban areas.

To examine the factors controlling the distribution of CO₂ in the studied mountain area, Pearson correlation analysis was conducted between CO₂ and the environmental parameters determined concurrently (CH₄, PM₁, PM_2.5, PM₁₀, NO, SO₂, and O₃) (Table 3a). As one of the most simplified approaches, the daily mean data for all variables were derived initially and used to assess the possible relationship between different parameters. It is now perceived that heating for industry, private automobiles, and landfill can play big roles in carbon emission in Seoul (Sovacool and Brown, 2010). As expected, a strong correlation is observed between major pollutants (like NO_x (NO₂ and NO) and SO₂). In addition, the relationship between CO₂ and CH₄ data is also evident with a correlation coefficient of 0.37 (p=3.71E-13, N=361). The particulate matter (PM) concentration also exhibited a good correlation with the CO₂ data with similar correlation coefficient (r) values between different particles of 0.441 (PM₁), 0.491 (PM_2.5), and 0.393 (PM₁₀). In contrast, CO₂ data maintained an inverse correlation with that of O₃ without meaningful significance (p=0.46). The highly strong correlation between CO₂ and other air quality indices suggest that there should be a close relation between the primarily measured trace elements.

All meteorological parameters (e.g., UV radiation, humidity, temperature, and wind speed) showed in verse correlations with the CO₂ data (Table 3b). Except UV radiation, all of those meteorological parameters are not statistically significant (at p=0.05 level). However, the CO₂ data are also found to be little affected by such factors as wind speed and wind direction (Figs. 4 and 5). This observation thus signifies the trend that the increases in many meteorological variables are associated with the reduction of CO₂ levels.

Fig. 4. 
A plot of CO₂ data in relation to wind direction in Mt. GA.

Fig. 5. 
Hourly wind rose pattern at Mt. GA for the year 2009.

The distribution of carbon dioxide in the study area can be affected not only by local sources but also by long range transport from the distant source areas. It was demonstrated previously that the distributions of relatively long lived species (e.g., atmospheric Hg and particulate matters) are influenced by long range transport from surrounding areas both in and out of Korea (Nguyen et al., 2010; Nguyen et al., 2009). It was also found that concentrations of NO and SO₂ in Korea are largely dependent on long range transport between Korea and East China (Shim and Park, 2004; Park and Cho, 1998). Although the main source of energy is petroleum in Japan and Korea, it is still coal for China (more than 75% of total energy source) (Hayakawa, 2009). As such, there is high possibility that the CO₂ concentration in the study area can be affected by long range transport from neighboring China. Future studies may be able to collect more direct evidence of such possibility.

To estimate the status of CO₂ pollution in our study area, our data were first compared with those measured previously from other sites on the Korean peninsula. This comparison was extended further to cover some mountainous sites around the world and some background areas. In case of the Korean peninsula, one may refer to the continuous measurements made in Gosan, Jeju (1990-2000) (Oh et al., 2001) or in An-Myeon Island (Climate Change Information Center, 2008). The site at Gosan, Jeju is on a small hill (71.2 m) above sea level near the ocean coast (33°17′N and 126°10′E). As seen in Fig. 6a, comparison of our data with those of two previous studies showed large differences. The average CO₂ value in Jeju (1990-2000) appears to be fairly low (363.8 ppm), whereas that for An-Myeon Island (2008) was 391.4 ppm. Because the data are taken between different years, some uncertainties in CO₂ data are expected. However, if we predict the value of Jeju by following its rate of increase (1990-2000), the value for the year 2009 is 392.7 ppm which is comparable with those of An Myon Island and Mt. GA.

Fig. 6. 
Comparison of CO₂ levels between the present and previous studies. Comparison of CO₂ concentration data measured in various locations on the Korean peninsula.

The average annual CO₂ data at our study site (405 ppm) can also be compared with those values taken from World Meteorological Organization (WMO) sites (Fig. 6b). For this comparison, the 2007 annual data from WMO global atmosphere watch (World Data Centre for Greenhouse Gases (WDCGG)) were used. The CO₂ data at Mt. GA are 2.86 to 6.26% higher than all the available WMO data from various mountainous sites, eg, Sonnblick, Austria (381.1 ppm), Deuselbach, Germany (386.1 ppm), Sary Taukum, Kazakhstan (385.3 ppm), Ullan Uul, Mongolia (383.6 ppm), Mt. Kenya, Kenya (379.63 ppm), Srinagar-Garhwal, India (393.4 ppm: Anthwal et al., 2010). We cannot directly balance the difference due to the time gap between our and previous studies. However, as most of these sites are distant from cities, the concentration levels of CO₂ in those sites are lower than our data measured in the urban area. Because Mt. GA is surrounded by a densely populated urban area, its CO₂ level may reflect the man-made activities, especially traffic activities surrounding the mountainous site.

Our CO₂ data were compared further with those derived from many urban areas around the world. It is observed that our data were comparable to those in other urban areas like Rome, Essan (Germany), and Phoenix (AZ, USA). The urban area of Essen, Germany exhibited a wintertime mean value of 415 ppm which is slightly higher than our wintertime data (410 ppm). Likewise, the minimum CO₂ value in our study (mean=399 ppm) was slightly higher than that of Essan, in the summer of 2004 (mean=393 ppm) (Henninger and Kuttler, 2010). In contrast, our yearly mean concentration (405±12.1 ppm) was much lower than those of urban areas in Rome, Italy where strong correlation was formed between traffic density and CO₂ (mean yearly value=477±30 ppm) (Gratani and Varone, 2005). Unlike other urban areas, the CO₂ data measured from Phoenix, AZ, USA was moderately lower (390.2±0.2 ppm) than other areas. Thus, we can see that the CO₂ concentrations in Mt. GA are generally comparable with those obtained from the common urban areas rather than the mountainous (or background) sites around the world.