Tracing CO2 sources in urban and rural areas characterized by different land-use patterns using carbon isotopes

Eui-Kuk Jeong; Youn-Young Jung; Seung-Hyun Choi; Moojin Choi; Kwang-Sik Lee; Woo-Jin Shin

doi:10.1371/journal.pone.0326306

Abstract

Air samples were collected from urban and rural areas of Korea with different land-use patterns between October 2022 and April 2023 to identify the sources of atmospheric CO₂. We analyzed representative end-members from natural and anthropogenic sources (soil, vehicle exhaust gases, and coal) to determine the CO₂ concentrations and carbon isotope compositions of CO₂ (δ¹³C-CO₂). Urban samples exhibited lower δ¹³C values and higher CO₂ concentrations than rural samples. Both urban and tunnel samples showing similar slopes and intercepts on the plot of 1000/CO₂ vs. δ¹³C-CO₂ likely shifted toward the vehicle exhaust end-member. Among the rural samples, those collected from coastal areas showed trends similar to the urban and tunnel samples, which differed from those collected from inland areas. This suggests that samples from coastal areas were affected by CO₂ emissions from coal-fired power plants in the region. In contrast, inland samples showed the highest slope and lowest intercept (i.e., estimated δ¹³C-CO₂ value of −27.0‰), suggesting that natural sources such as soil CO₂ dominate the contribution to atmospheric CO₂ in inland areas. This study demonstrates that the primary CO₂ sources in regions with different land-use patterns can be distinguished using the relationship between atmospheric CO₂ concentrations and δ¹³C-CO₂ values.

Citation: Jeong E-K, Jung Y-Y, Choi S-H, Choi M, Lee K-S, Shin W-J (2025) Tracing CO₂ sources in urban and rural areas characterized by different land-use patterns using carbon isotopes. PLoS One 20(6): e0326306. https://doi.org/10.1371/journal.pone.0326306

Editor: Lee W. Cooper, University of Maryland Center for Environmental Science, UNITED STATES OF AMERICA

Received: March 14, 2025; Accepted: May 27, 2025; Published: June 17, 2025

Copyright: © 2025 Jeong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files.

Funding: This work was supported by a KBSI grant (C525200).

Competing interests: The authors have declared that no competing interests exist.

Introduction

Carbon dioxide (CO₂) is a major greenhouse gas closely associated with global warming [1]. The CO₂ concentration on Earth has rapidly increased since the pre-industrial period, from 280 ppm in the 1700s to 414 ppm in 2020 [2]. Increased CO₂ concentrations are regarded as a major cause of climate change and extreme weather [2–4]. Thus, many countries worldwide have tried to reduce the amount of CO₂ released from fossil fuels [5]. In line with this global issue, identifying the CO₂ sources in the atmosphere has also been studied for a long time [6–9].

Atmospheric CO₂ concentration is predominantly controlled by the input of fossil fuel (gasoline, diesel, and coal) combustion and soil CO₂ contribution from photosynthesis-respiration processes [10–12]. Identifying the source of atmospheric CO₂ can be challenging because of the complicated CO₂ mixing processes that occur between the hydrosphere, pedosphere, biosphere, and atmosphere [13–16], as well as the production and consumption of CO₂ within these spheres [17–19]. For example, activities such as electricity generation, transportation, and urbanization are representative sources of anthropogenic CO₂ emissions and their contributions to atmospheric CO₂ would vary spatiotemporally depending on factors affecting CO₂ concentrations; i.e., population density, electricity production, and transportation intensity are locally different. Additionally, local land-use patterns can affect the consumption or production of both natural and anthropogenic CO₂, leading to regionally variable contributions to atmospheric CO₂ levels.

The major CO₂ sources varied in their concentrations and carbon isotopic compositions. Carbon dioxide emitted from fossil fuel combustion typically shows CO₂ concentrations from 1 to 15% and δ¹³C-CO₂ values from –40.5 to –22.8‰ [20–25]. In areas where C3 plants prevail extensively, soil CO₂ concentrations range from several thousand ppm to tens of thousands, and δ¹³C-CO₂ values ranges from –30 to –23‰ [26,27]. In specific environments such as waste landfill, CO₂ has relatively enriched ¹³C unlike the δ¹³C values of typical soil CO₂. Organic matter is naturally decomposed in the landfill and produces ¹³C-depleted biogenic methane gas. The resulting CH₄ typically has δ¹³C values from −65 to −50‰, and the residual CO₂ has relatively high δ¹³C-CO₂ value from −15.9 to 0.7‰ [28,29]. In previous studies, the δ¹³C-CO₂ values of fumarole samples ranged from −0.95 to −2‰ (average −1.48 ± 0.22‰, n = 78), having CO₂ concentrations reaching up to 36,000 ppmv [30,31]. In area with continuous degassing and seismic activity, the emitted CO₂ had a δ¹³C value of −1.67‰ [32]. Identifying the CO₂ source in the atmosphere using only one parameter or a simple correlation is difficult. Therefore, many studies have used the relationship between 1/CO₂ concentration and δ¹³C-CO₂ value, known as the Keeling plot, to trace the origin of CO₂ sources in the atmosphere. Typically, the binary mixing line generated from a Keeling plot represents the relationship between atmospheric CO₂ (i.e., tropospheric CO₂) and another CO₂ source (e.g., contaminant sources). This relationship is expressed by the equation: δ¹³C-CO₂ = a × 1/CO₂+ b. Widory and Javoy [22] reported that CO₂ gases exhibited different slopes and intercepts in polluted and unpolluted air in Paris, suggesting that fossil fuel combustion and human respiration influence the urban atmosphere. Clark-Thorne and Yapp [7] found that urban and rural samples exhibited estimated lines with different slopes and intercepts on a Keeling plot. Furthermore, seasonal variations in winter and summer were observed along the slope and intercept owing to the contribution of soil CO₂ to the atmosphere. Recently, the effects of urbanization on atmospheric CO₂ concentrations and δ¹³C values have been reported using Keeling plots [33].

This study aims to determine the primary sources of atmospheric CO₂ in South Korea. To this end, we analyzed the CO₂ concentrations and carbon isotope compositions of representative natural (soil CO₂) and anthropogenic (vehicle exhaust gases and coal combustion gas) sources. In addition, we collected atmospheric CO₂ from urban and rural areas to evaluate whether CO₂ emissions originated from different sources as urbanization increased. Rural areas were subdivided into regions with coal-fired power plants and forest-dominated areas. Keeling plots were used to identify CO₂ sources in the study area. The approach used to interpret atmospheric CO₂ sources in this study can assess the contribution of CO₂ from natural and anthropogenic sources to the atmosphere.

Study area

Urban area.

Seoul, the capital of South Korea, has an area of 605.2 km² and is a densely populated city, with approximately 20% (9 million people) of the country’s total population [34]. According to the latest statistics on land-use patterns in Seoul [35], the city consists of residential, business, and public facilities (40.9%); forests and open spaces (26.7%); transportation facilities (14.5%); and surface water (8.8%) (Table 1). The transportation facility area includes 55 vehicular tunnels throughout the city. Fifteen tunnels are over 1 km in length. Approximately 3.1 million vehicles are registered in the city, accounting for 12% of the country’s total. According to the 2022 Seoul Transportation Statistics [36], the city’s traffic volume is approximately 17 million vehicles (including public buses) daily.

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Table 1. Statistics on the land-use patterns in the administrative districts where air samples were collected in this study.

https://doi.org/10.1371/journal.pone.0326306.t001

Rural areas.

Air samples from rural areas were collected from three locations along the western coast (Dangjin, Seosan, and Taean) and an inland area (Chungju). The areas of Dangjin, Seosan, Taean, and Chungju are 705 km², 740 km², 516 km², and 984 km², respectively. The four sampling regions have significantly lower populations, corresponding to <2% of Seoul’s population; Dangjin, Seosan, Taean, and Chungju represent residents of 160,000, 170,000, 60,000, and 200,000 populations, respectively [34]. All regions have the highest proportion of forests (32–62%), followed by agricultural areas (18–41%), surface water (5–7%), and residential and business areas (3–4%) (Table 1) [35]. The number of registered vehicles in each rural area ranged from 37,000–120,000, less than 4% of that in Seoul [37]. In addition, there are 38 fishing ports and 3 harbors along the western coast [38]. In Dangjin and Taean regions, coal-fired power plants are located near these harbors and supply approximately 40% of the country’s electricity [39].

Methods

Ambient air samples

Ambient air samples were collected from the urban (Seoul) and rural (suburban) areas between October 2022 and April 2023 (Fig 1). Of the samples collected from the urban area (n = 21), 12 were collected from six tunnels with lengths of over 1 km. All tunnels, whether straight or curved, have two lanes in each direction and jet fan ventilation systems are installed overhead to circulate air inside. Except for one sample collected from the Hongjimoon Tunnel, the remaining tunnel samples were collected during rush hour to measure CO₂ concentrations and isotopic compositions in ambient air predominantly influenced by vehicle exhaust. All air samples from tunnels were collected while driving in both directions, by pumping ambient air through vehicle window into Tedlar bags. Other urban samples were mostly collected from the roadside or through vehicle windows while driving; two were collected from a building approximately 20 m high in the city. Rural samples were collected near the western coastline in Korea, where coal-fired power plants are located, and from the central Korean region, where forested areas are relatively dominant. The sampling was conducted in sparsely populated areas. The former was collected monthly from October 2022 to February 2023, whereas the latter was collected in April 2023. Inland areas were sampled twice, in February and April 2023. Additionally, ambient air samples were collected from forested areas to estimate the background CO₂ concentrations and isotopic compositions. The sampling campaign was conducted four times, on October 22, 2022, and January 7, 2023, in the middle of a mountain at 229 m above sea level. All samples, with an exception for tunnel samples, were collected before sunset to minimize the diurnal variation of CO₂ level caused by both natural (e.g., photosynthesis and respiration) and anthropogenic (e.g., vehicles) sources during the study period. All air samples were collected approximately 2 m above ground level, passed through a dust filter and moisture absorbent, and then transferred to a 10-L Tedlar bag with a stopcock (polyvinyl chloride, SL.Bag3505) connected to a pump operating at a flow rate of 200 ml/min for 20 min. The bag was rinsed with 99.999% nitrogen gas three times in the laboratory before doing sampling campaign and it was reused during this study period. The tunnel samples were collected while driving through both sides of the tunnel. For air samples in sample bags, carbon isotopic compositions (δ¹³C) and CO₂ concentrations were analyzed using a Picarro G2131-i Analyzer (Picarro, Santa Clara, CA, USA) at the Korea Basic Science Institute (KBSI). Two standard materials with different CO₂ concentrations and carbon isotope compositions (cylinder #CC707290: 411.3 ppm for CO₂ and −8.673‰ for δ¹³C; and cylinder #CC702380: 435.38 ppm for CO₂ and −10.065‰ for δ¹³C) were used to generate a calibration line with an estimated slope and offset (less than 0.5 ppm for CO₂ concentration-drift test). These standard materials were calibrated by the Stable Isotope Lab at INSTAAR (University of Colorado), in cooperation with the Global Monitoring Division of the National Oceanic and Atmospheric Administration (NOAA). δ¹³C values were expressed relative to the V-PDB standard, using delta (δ) notation: δ¹³C (‰) = [(R_sample/R_reference) – 1] × 1000, where R represents ¹³C/¹²C ratio. According to the V-PDB scale, the reference ¹³C/¹²C ratio (R_reference) is internationally accepted as 0.011802 [40], the ¹³C/¹²C ratio of the samples (R_sample) was calculated for the two standard materials, and the slope and offset were determined. The measurements were conducted for approximately 60 min per bag, and the analyzed data were averaged.

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Fig 1. Maps showing locations where air and coal samples were collected in South Korea.

The locations of coal-fired power plants are denoted by blue circles with diagonal lines (a). The black symbols in (Ⅰ), (Ⅱ), and (Ⅲ) represent the air sampling sites in Seoul, Chungju, and West coast regions, respectively, and the white triangle symbols in (Ⅱ) and (Ⅲ) indicate soil sampling sites (b).

https://doi.org/10.1371/journal.pone.0326306.g001

Vehicle exhaust gas and coal samples

Vehicle exhaust gas, a major anthropogenic source of atmospheric pollution, was collected directly from the tailpipes of several types of vehicles (two gasoline and three diesel vehicles; Table 2). The vehicles used in the experiment were driven until the temperature gauge stabilized before the exhaust gas was collected. When the vehicle was idling, a nylon tube with an inner diameter (ID) of 3.21 mm and an outer diameter (OD) of 6.35 mm was inserted into the tailpipe, and the other end of the tube was connected to a 47-mm PTFE dust filter with a pore size of 1.0 μm. Then, exhaust gas samples were collected in a 10-L Tedlar bag through a dust filter and a water trap using a pump at a flow rate of 200 ml/min for 20 min. Because the CO₂ concentration was extremely high, the exhaust gas was mixed with 99.999% nitrogen gas in a separate sampling bag to control CO₂ concentration in the 400–1000 ppm range. The carbon isotope composition of the mixed gas was determined using a Picarro G2131-i Analyzer (Picarro, Santa Clara, CA, USA) at KBSI in the same manner. Coal samples imported to South Korea from eight countries (Canada, Russia, Australia, the USA, Philippines, Indonesia, South Africa, and Colombia) were collected from major coal-fired power plants in South Korea [41]. The preparation and analysis of the samples were described in detail in Jeong et al. [41]. Briefly, the coal samples were thoroughly dried, weighed to approximately 50 μg, and enclosed in tin capsules for carbon isotope analysis. δ¹³C values were measured using a VisION mass spectrometer (Isoprime, Manchester, United Kingdom) interfaced with a Vario PyroCube elemental analyzer (Elementar, Hesse, Germany) at KBSI. Carbon isotope ratios were expressed relative to the V-PDB standard, using delta notation as shown above. δ¹³C values were normalized using international standards IAEA-600, NBS-22a, USGS-40, and IAEA-CH6 with assigned δ¹³C values of −27.8‰, −29.7‰, −26.4‰, and −10.5‰, respectively, and laboratory standard UREA with assigned δ¹³C values of −35.46‰. The precision of the δ¹³C analysis was assessed through replicate analysis of the three international standard materials. The standard deviation of δ¹³C value for all the standard materials was less than ±0.2‰ (n = 3) for each analytical batch.

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Table 2. Concentrations and δ¹³C values of CO₂ (δ¹³C-CO₂) derived from natural and anthropogenic sources.

https://doi.org/10.1371/journal.pone.0326306.t002

Soil samples

Soil samples of 1 kg (n = 10) were collected near areas where the ambient air samples were collected. Samples were carefully collected from the forests to minimize the influence of anthropogenic factors on soil carbon content. To obtain representative soil from each sampling site, soil was collected at a depth of 20 cm from four points approximately 10 m apart and one point in the center and then mixed well. The soil samples were individually packed in Ziploc (25 × 30 cm) bags and transported to the laboratory. After handpicking leaves and litter from the soil samples, they were completely dried at room temperature in the laboratory and sieved to a particle size of 2 mm. The dried samples were soaked in a 10% HCl solution to remove inorganic carbon from the soil samples, washed several times with deionized water, re-dried in an oven maintained at 40 °C, and stored in a vacuum oven until analysis was completed. Similar to the coal samples, the carbon isotope compositions of the soil samples were determined at KBSI. δ¹³C values were normalized using international standards IAEA-600, USGS-40, and IAEA-CH6, respectively, and laboratory standard urea with assigned δ¹³C values of –8.0‰.

Results and discussion

Table 2 lists CO₂ concentrations and δ¹³C-CO₂ values of ambient air in the forest, and δ¹³C values of potential anthropogenic sources (i.e., vehicle exhaust gas, coal combustion) that contribute chemically and isotopically to atmospheric CO_2. As mentioned earlier, CO₂ concentrations of these anthropogenic sources could not be directly measured due to their extremely high levels. CO₂ concentrations and isotopic compositions of atmospheric CO₂ collected from urban (including tunnels and roadside) and rural areas (inland and coastal areas) are shown in the supplementary S1–S3 Tables.

Air pollutants: CO₂ emissions from vehicle exhaust and coal

As mentioned earlier, the CO₂ concentration of vehicle exhaust gas collected using the adopted experimental method could not be estimated because of its very high concentration. Previous studies have shown that the concentrations of CO₂ from vehicle exhaust and CO₂ from coal combustion range from 1% to 15% and 3% to 15%, respectively [20,21,23,25]. Therefore, CO₂ concentrations from contaminant sources can be limited to 1%–15%. In this study, vehicle exhaust gas and coal samples showed δ¹³C values ranging from −28.2‰ to −25.3‰ (avg. −26.7 ± 1.4‰, n = 5) and from −28.1‰ to −22.8‰ (avg. −25.4 ± 1.6‰, n = 68) [41], respectively (Fig 2). The δ¹³C values were similar to results in previous studies; i.e., CO₂ emitted from gasoline engine vehicles has δ¹³C values ranging from −29.3‰ to −24.4‰ [7,22,24], and CO₂ from diesel engine vehicles ranges from −29.2‰ to −28.6‰ [22]. The δ¹³C values of global coal range from −27.4‰ to −23.5‰ [42–44]. Meanwhile, the carbon isotope composition of CO₂ derived from coal-fired power plants may include ¹³C-depleted CO₂ due to fractionation during combustion processes, which is about 1.3‰ lower than that of coal [45]. Considering the δ¹³C values of coals reported in previous studies and the isotope fractionation associated with coal combustion, CO₂ derived from vehicles and coal are characterized by similar δ¹³C-CO₂ values. Subsequently, both CO₂ sources can be used as end-members with δ¹³C-CO₂ ranging from −28.2 to −25.3‰ and −29.4 to −24.1‰, respectively.

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Fig 2. Box-Whisker diagrams showing concentrations (a) and

δ¹³C values (b) for CO₂ gas in major natural and anthropogenic sources and air samples collected in the study areas. The red background represents typical sources of atmospheric CO₂, while the green background is marked to indicate the forest. The box and central line represent the interquartile range (IQR; Q3–Q1) and the median, respectively. The upper and lower whiskers represent Q3 + 1.5 × IQR and Q1 − 1.5 × IQR, respectively.

https://doi.org/10.1371/journal.pone.0326306.g002

Natural sources: soil and atmospheric CO₂

Soil CO₂ is produced through the decay of soil organic matter (SOM) and plant respiration and is regulated by complex CO₂ interactions among the hydrosphere, atmosphere, biosphere, and pedosphere [14,46,47]. Soil respiration occurred mainly at shallow depths in the soil profile. Previous studies have shown that soil CO₂ concentrations released from soil profiles deeper than 30 cm range from 481 to 74,073 ppm with an average of 17,273 ppm [26,27]. The carbon isotope composition of soil CO₂ is closely related to the type of vegetation (e.g., C3 or C4 plants) because no significant carbon isotope fractionation occurs during SOM decomposition. Diffusion in the soil zone results in δ¹³C-CO₂ value as high as 4.4‰ compared to that of SOM [48]. The ¹³C-enriched CO₂ remaining in the soil zone then mixes with atmospheric CO₂ and reaches a steady state with δ¹³C-CO₂ value similar to atmospheric CO₂ (typically, −10 to −8‰) on the soil surface. Meanwhile, for C3 plant, considering isotopic fractionation during CO₂ diffusion through stomata (~4.4‰) and during carboxylation (~29‰), photosynthetic discrimination (Δδ¹³C) was estimated as follows [49,50]: Δδ¹³C = 4.4 + (29–4.4)*(C_c/C_a), where C_c and C_a represent chloroplast CO₂ and ambient CO₂ concentrations, respectively, and typical value of C_c/C_a is ~ 0.55 [50]. Thus, when the mean δ¹³C of the biosphere is about −26‰, δ¹³C value of atmospheric CO₂ at equilibrium would be approximately −8‰. The concentration and δ¹³C value of atmospheric CO₂ near the soil surface depend on those of soil CO₂. In this study, the δ¹³C value for soil samples ranged from −28.6 to −20.9‰ (avg. −25.5 ± 2.2‰), with values ranging from −26.9 to −24.4‰ corresponding to the 25^th–75^th percentile. According to photosynthetic discrimination in previous studies, atmospheric CO₂ near the soil surface should range from −8.9 to −6.4‰.

Air samples collected from the forests showed a narrow range of CO₂ concentrations, ranging from 427 to 445 ppm, with an average of 437 ppm. In 2022, this concentration was similar to that (415–434 ppm, avg. 424 ppm) reported at the TAP on the west coast of Taean, South Korea, a NOAA observation site. The carbon isotope composition for the samples from forests and TAP ranged from −9.3‰ to −8.5‰ (avg. −8.9‰) and from −9.5‰ to −8.3‰ (avg. −9.0‰), respectively (data available at https://gml.noaa.gov/dv/iadv/graph.php?code=TAP&program=ccgg&type=ts). Considering that the administration area including TAP has a small population (approximately 9,000 individuals), CO₂ contribution from anthropogenic sources can be negligible, and both carbon isotope compositions were similar to those estimated for the soil samples collected in this study. Thus, the forest samples used in this study were considered representative of natural sources regarding CO₂ concentration and carbon isotope composition.

Air samples

Atmospheric CO₂ concentrations in urban areas range from 455 to 1,314 ppm (avg. 625 ± 271 ppm), whereas those in rural areas range from 428 to 580 ppm (avg. 449 ± 22 ppm) (Fig 2a). Rural samples from coastal and inland areas had similar CO₂ concentrations, 450 ± 20 ppm, and 446 ± 26 ppm, respectively. The δ¹³C values of CO₂ for air samples in urban and rural areas ranged from −17.4 to −8.8‰ (avg. −12.1 ± 2.5‰) and from −13.4 to −8.9‰ (avg. −9.8 ± 0.6‰), respectively (Fig 2b). Like CO₂ concentration, samples from coastal and inland areas showed similar δ¹³C-CO₂ values of −9.8 ± 0.6‰ and −9.9 ± 0.6‰, respectively. During sampling, two samples (RC-3 and RI-3) from coastal and inland areas were polluted with vehicle exhaust gas, showing the highest CO₂ concentrations and lowest δ¹³C-CO₂ values. Except for these two samples, no temporal variations in CO₂ concentration and carbon isotope composition were observed.

Air samples collected from the tunnels exhibited relatively high CO₂ concentrations of 699–1,658 ppm (avg. 1,030 ± 299 ppm) and significantly lower δ¹³C-CO₂ values of −18.8 to −14.3‰ (avg. −16.9 ± 1.6‰) compared to air samples from urban and rural areas. For tunnel samples collected two or three times (T-1, T-2, T-3, and T-4), CO₂ concentrations and δ¹³C-CO₂ values varied significantly during the study period, with δ¹³C-CO₂ values systematically decreasing as CO₂ concentrations increased and vice versa. For example, the T-2 sample collected in January 2023 had a CO₂ concentration up to approximately 2.4 times higher than in October 2022, while δ¹³C-CO₂ value was −18.4‰ in January 2023 and −14.7‰ in October 2022.

Sources of CO₂ in air samples: using the Keeling plots

The ‘Keeling plot’ method was applied to determine the contribution and trends of various sources to atmospheric CO₂ [51]. When the reciprocal of CO₂ concentration (1/CO₂) is plotted against δ¹³C-CO₂ value, the samples are represented by a binary mixing line of δ¹³C-CO₂ = a × 1/CO₂+ b, which indicates that two major sources determine the CO₂ concentrations and δ¹³C-CO₂ values of samples. Atmospheric CO₂, which includes both natural and anthropogenic contributions, is typically selected as one of the main CO₂ sources (first endmember). For the second endmember, a pure CO₂ source, it is theoretically possible to estimate δ¹³C value of that source assuming the observed relationship arises from two-component mixing. Thus, if the mixing line is expressed with a high R-squared value, the CO₂ samples can be reliably explained by contributions from the two main sources. In contrast, a low R-squared value suggests interference from additional sources or variability in the endmembers.

In this study, the correlation between samples was determined through plots of 1/CO₂ and δ¹³C-CO₂ values (Fig 3). Major sources of atmospheric CO₂ include CO₂ from soil, vehicle exhaust gas, and coal combustion, which have significantly low δ¹³C values with greatly elevated CO₂ concentrations. Subsequently, the natural (i.e., soil CO₂) and anthropogenic sources were displayed on the lower left of the plot, while atmospheric CO₂ was characterized by a δ¹³C-CO₂ value of −8.9‰ at a CO₂ concentration of 437 ppm, located on the upper right. All air samples collected in this study varied between CO₂ derived from external sources (e.g., vehicle exhaust, coal combustion, and soil CO₂) and CO₂ derived from the atmosphere, indicating that the CO₂ in the samples was formed by mixing atmospheric CO₂ and CO₂ derived from external sources.

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Fig 3. Keeling plot of 1000/CO₂ and

δ¹³C value for CO₂ gas in air samples collected from tunnel, urban and rural areas. The samples in rural areas were collected from coastal area where coal-fired power plants are located and from inland area.

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Air samples from the tunnels shifted toward natural and anthropogenic sources in the plot shown in Fig 3, whereas air samples from urban, rural, and forested areas were generally closer to the representative atmospheric CO₂ range. Some urban samples were distant from atmospheric CO₂ end-members. To better determine the contribution of sources to ambient air samples, tunnel and urban samples (Fig 4a) and rural samples (Fig 4b) were plotted separately on the plot of 1000/CO₂ and δ¹³C value. Both the tunnel and urban samples generated regression lines with similar slopes and intercepts: δ¹³C-CO₂ = 5.2 × 1000/CO₂–22.3‰ (R² = 0.82) and δ¹³C-CO₂ = 5.4 × 1000/CO₂–21.7‰ (R² = 0.93), respectively. These results indicated that the high concentrations of ¹³C-depleted CO₂ in the urban samples were primarily due to vehicle exhaust gas. This observation suggests that vehicle exhaust gas contributes significantly to increase CO₂ in urban areas. The poor correlation for tunnel samples would result from differing CO₂ contribution from gasoline and diesel vehicles. In practice, carbon isotope compositions of CO₂ emitted from the two vehicle types showed a deviation of approximately 3‰ (Table 2). The slope and intercept of the urban samples were similar to those (δ¹³C-CO₂ = 4.8 × 1000/CO₂–22.3‰) reported for polluted CO₂ in Poland [52]. As traffic volumes increase in urban areas, CO₂ concentrations increase [53]. Furthermore, urbanization changes land use, reducing the natural spaces available to absorb CO₂ through photosynthesis and increasing CO₂ concentrations in urban atmospheres [54]. Unexpectedly, the estimated intercept from the tunnel samples did not shift toward the vehicle exhaust gas on the plot but showed higher δ¹³C-CO₂ value. This result may reflect the nature of sufficiently polluted atmospheric CO₂ and/or the small number of samples. Except for the two samples with the highest CO₂ concentrations, the δ¹³C-CO₂ value was estimated at −24.3‰.

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Fig 4. Keeling plots of 1000/CO₂ and δ¹³C-CO₂ values for urban area and tunnel samples (a), and air samples from coastal and inland areas (b).

There are several coal-fired power plants in the coastal area.

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During the study period, the wind direction in the urban area (center of Seoul) was predominant from the northwest to southeast in the morning and from the southwest to northeast in the afternoon, with wind speeds mostly at ~2m/s [55]. Based on δ¹³C-CO₂ values of the urban samples in this study, the urban CO₂ dome was mostly influenced by locally produced CO₂, regardless of the advected CO₂ from the northwest and southwest, or vertical mixing during the day. This interpretation is consistent with findings from previous studies. Idso et al. [56] reported that near-surface CO₂ concentrations in urban areas varied between pre-dawn and mid-afternoon time periods, and those in the mid-afternoon were generally reduced due to enhanced vertical mixing. The loss of CO₂ due to the vertical mixing was compensated by advection of CO₂ produced in city and CO₂ domes were preserved. In a recent study, Di Martino et al. [57] found that airborne CO₂ in the Naples metropolitan area showed ¹³C-depleted isotopic signature from −17.65‰ to −8.54‰ (average ~ −11‰) due to vehicle exhaust emissions, forming an urban CO₂ dome. The contribution of the anthropogenic CO₂ to atmosphere was also diluted by advected CO₂ from the sea.

In contrast, the coastal and inland samples from the rural areas exhibited different slopes and intercepts (Fig 4b). Samples from the coastal area had a regression line with a lower slope and higher intercept than samples from inland areas, which were similar to the regression lines from urban areas. These results suggest that CO₂ sources contributing to atmospheric CO₂ in coastal areas are different from those in inland areas. Based on the slope and intercept, coastal samples are likely to be affected by sources with carbon isotope signatures similar to the CO₂ sources contributing to the urban samples.

According to population and traffic statistics for coastal areas, the population and traffic volume are relatively small compared to urban areas such as Seoul. Coal-fired power plants have been operational for a long time since the late 1990s [58,59]. The linear regression line for the coastal samples was similar to that for the urban areas; however, the statistics and existence of apparent CO₂ suppliers suggest that CO₂ from coal, rather than CO₂ from vehicle exhaust, maybe a significant source of emissions. CO₂ concentrations and their carbon isotopic compositions would be affected by varying contributions from different sources depending on the sampling period and time. Nonetheless, the samples from urban, rural and tunnel were explained by the corresponding binary mixing lines on the keeling plot. Meanwhile, air quality in South Korea has been affected by the budget of air pollutants from China owing to westerly winds. The δ¹³C values of CO₂ gas derived from Chinese coals and coals currently used in South Korea was estimated to be in the range of −30.6 to −23.4‰ and from −29.4 to −24.1‰, respectively when considering isotope fractionation during the process of converting coal into CO₂ gas. These values were slightly lower than the estimated δ¹³C value for one of the primary sources contributing to CO₂ in coastal area samples. These results indicate that coal-fired power plants are a major source of air in coastal areas.

Samples from inland areas were represented by a linear regression line with the highest slope and lowest intercept: δ¹³C-CO₂ (‰) = 7.6 × 1000/CO₂–27.0 (R² = 0.76). The estimated δ¹³C value for one of the sources was similar to that of soil CO₂ in this study, considering carbon isotope fractionation due to diffusion of soil-respired CO₂ is 4.4‰ [60]. In addition, according to Shin et al. [61], the δ¹³C values of organic materials (e.g., leaf litters) ranged from −31.1 to −29.3‰, resulting in δ¹³C values of approximately −27‰. The estimated δ¹³C value from inland samples differed clearly from coastal samples characterized by similar population and traffic levels. In addition, the slopes of the coastal and inland samples were distinguished. Compared to the land-use patterns at other sampling sites, inland areas were collected from regions with a higher proportion of forests. This result indicated that the major CO₂ source in the inland samples was controlled by a combination of soil CO₂ and atmospheric CO₂. In previous studies, uncontaminated air samples were located between soil CO₂ and atmospheric CO₂ end-members on the Keeling plot, represented by a linear regression line with a slope of 8.3 and an intercept of −29.6‰ [22]. Note that decrease of tree-ring δ¹³C values for some decades was induced by rising levels of CO₂ mainly due to massive vehicle exhaust emissions [62]. The influence of photosynthetic CO₂ uptake on ambient CO₂ concentration and δ¹³C value would also be reflected in the inland samples.

Conclusions

A total of 151 air samples were collected from urban and rural areas and analyzed for atmospheric CO₂ concentration and carbon isotopic composition. We identified the major sources of CO₂ by considering land-use patterns. Representative CO₂ sources were collected during the sampling campaigns. The CO₂ concentrations were higher in urban areas than in rural areas. In rural areas where coal-fired power plants are located, and forests dominate, CO₂ concentrations were similar regardless of land-use patterns. The deviation between urban and rural samples and the similarity among rural samples were associated with δ¹³C values. Urban samples have ¹³C-depleted CO₂ in the range of −17.4 to −8.8‰ (avg. −12.1 ± 2.5‰), while rural samples showed relatively higher δ¹³C values of −13.4 to −8.9‰ (avg. −9.8 ± 0.6‰). Meanwhile, the urban samples showed similar CO₂ concentrations and δ¹³C-CO₂ values to the tunnel samples. On a plot of 1/CO₂ and δ¹³C-CO₂ showing representative CO₂ sources, urban and tunnel samples had similar slopes and intercepts, shifting toward vehicle exhaust gas, indicating that air quality in urban areas is mainly affected by contaminant sources such as fossil fuel emissions. Among the rural samples, those collected near coal-fired power plants had slopes and intercepts different from those of other rural samples and were quite similar to those of urban samples. These results indicate that emissions (including CO₂) from coal-fired power plants affect air quality in rural areas. In contrast, rural samples from forest-dominated areas showed the highest slope and lowest intercept, indicating that natural sources such as soil CO₂ play a major role in determining air quality in rural areas. The relationship between atmospheric CO₂ concentration and δ¹³C-CO₂ values can be used to identify specific regions and distinguish CO₂ sources. However, distinguishing sources with overlapping carbon isotope compositions requires additional information and analytical techniques.

Supporting information

S1 Table. Averages of CO₂ concentrations and δ¹³C-CO₂ values in air samples collected from roadside and tunnels in urban areas.

https://doi.org/10.1371/journal.pone.0326306.s001

(XLSX)

S2 Table. Concentrations and δ¹³C-CO₂ values of CO₂ in air samples collected from rural sites located near coastal area.

https://doi.org/10.1371/journal.pone.0326306.s002

(XLSX)

S3 Table. Averages of CO₂ concentrations and δ¹³C-CO₂ values in air samples collected from rural-inland area.

https://doi.org/10.1371/journal.pone.0326306.s003

(XLSX)

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Figures

Abstract

Introduction

Study area

Urban area.

Rural areas.

Methods

Ambient air samples

Vehicle exhaust gas and coal samples

Soil samples

Results and discussion

Air pollutants: CO2 emissions from vehicle exhaust and coal

Natural sources: soil and atmospheric CO2

Air samples

Sources of CO2 in air samples: using the Keeling plots

Conclusions

Supporting information

S1 Table. Averages of CO2 concentrations and δ13C-CO2 values in air samples collected from roadside and tunnels in urban areas.

S2 Table. Concentrations and δ13C-CO2 values of CO2 in air samples collected from rural sites located near coastal area.

S3 Table. Averages of CO2 concentrations and δ13C-CO2 values in air samples collected from rural-inland area.

References

Air pollutants: CO₂ emissions from vehicle exhaust and coal

Natural sources: soil and atmospheric CO₂

Sources of CO₂ in air samples: using the Keeling plots

S1 Table. Averages of CO₂ concentrations and δ¹³C-CO₂ values in air samples collected from roadside and tunnels in urban areas.

S2 Table. Concentrations and δ¹³C-CO₂ values of CO₂ in air samples collected from rural sites located near coastal area.

S3 Table. Averages of CO₂ concentrations and δ¹³C-CO₂ values in air samples collected from rural-inland area.