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For the first time, mobile campaign and building monitoring experiments were conducted in downtown Seoul, Korea during the 2018/2019 winter period to assess the possibility of the co-benefit of CO2 emission reduction and air quality improvement. Given that CO2 and air pollutants in cities both originate from fossil fuel combustion, monitoring urban CO2 concentrations using a mobile vehicle and from the top of tall buildings can be a potentially effective approach for diagnosing the air quality. The local enhancement values (△species) related to urban emission sources were estimated by eliminating baseline concentrations. The results obtained showed high correlation coefficients between △CO2 and △air pollutants (i.e., 0.753, 0.505, and 0.525 for △CO, △NOX, and △NO2), confirming the applicability of CO2 as an indicator of air pollutant sources in downtown Seoul. The evaluation of the air quality in the study area using air pollutant ratios, including the ratios of CO and NOX/NO2, relative to CO2 showed that the ratios of △CO, △NOX, and △NO2 relative to △CO2 were 5.76, 1.89, and 0.21 ppb ppm−1. The estimated △NO2/△NOX ratio (11%) and a comparison of the △NOX/△CO2 ratios obtained with those reported for other cities in different countries suggested the possibility of a relationship between the current air quality in downtown Seoul and the use of diesel vehicles. Results suggest that the development of municipal policies aimed at mitigating climate change should consider the co-benefit of air quality improvement so as to ensure the effective reduction of CO2 and air pollutant emissions.

Indoor air quality was investigated in homes and daycares located in areas with heavy traffic in Seoul, South Korea from November 2013 to January 2014. Indoor and outdoor air quality measurements were collected for 48 hours in four children’s homes and daycare centers. The I/O ratio (Indoor to outdoor ratio) for each major air pollutant (NO2, black carbon, PM10, and PM2.5) was calculated, and NO2 and PM10 concentration profiles were analyzed based on indoor activity diaries recorded during the 48 hours. Most I/O ratios for NO2, black carbon, PM10, and PM2.5 at daycare centers were less than one. At homes, I/O ratios for black carbon, PM10, and PM2.5 were less than one; however, most I/O ratios for NO2 were greater than one due to the usage of gas stoves. The children’s exposure to indoor air pollutants was calculated using a time-weighted average exposure method, and the daily intake level for each pollutant was determined.

This study measured indoor and outdoor levels of hydrocarbon volatile organic compounds (VOCs), such as benzene, toluene, ethylbenzene, and xylene isomers (collectively referred to as BTEX), as well as 13 carbonyl compounds, at 20 homes in Seoul, South Korea. Along with the sampling of BTEX and carbonyls, indoor concentrations of the air pollutants nitrogen oxide (NO) and carbon dioxide (CO2) were also measured at each home. These measurements were used to understand the characteristics of BTEX and carbonyls by calculating the various ratios and correlation coefficients between measured contaminant levels. We found that carbonyls were mostly originated from indoor sources, while BTEX were originated from both indoor and outdoor sources. A high correlation between indoor levels of NO and BTEX indicated that traffic emissions were also an important sources of BTEX.

Knowledge of the levels of pollutants to which the occupants of a residential home are exposed is important, because of the potential adverse effect of the pollutants on the occupants' health. This study presents the first indoor-to-outdoor ratio (IOR) prediction models for CO2, NO2, and the lung-deposited nanoparticles (defined as particulate matters of 10–1000 nm in diameter). The IORs of CO2, NO2, and the lung-deposited nanoparticles (LDN) were measured in 20 residential homes in Seoul, South Korea, and meteorological information, building characteristics, and information relating to occupants' indoor activities were collected as predictor candidates. A correlation analysis and a multistep multivariate linear regression analysis were performed to develop the IOR models. Based on the analysis, the selected predictors of the IOR for a home are the number of occupants, the type of stove, and the number of cooking activities for CO2, the temperature and number of cooking activities for NO2, and the number of occupants and floor level on which the home is located for LDN. The accuracies of the models were examined in terms of R2, and were found to be 0.70, 0.54, and 0.79 for CO2, NO2, and LDN models, respectively.

The large emission of volatile organic compounds (VOCs) is an important environmental issue in Seoul, the capital of South Korea. However, the spatial distribution of VOC emissions has not yet been clearly revealed. In this study, passive air samplers (PASs) were seasonally deployed to measure 50 VOCs at 26 urban, 4 road, and 6 background sites in Seoul. Among the target compounds, toluene (summer: 8.72 μg/m3; winter: 5.47 μg/m3), ethyl acetate (summer: 2.36 μg/m3; winter: 3.51 μg/m3), and ethylbenzene (summer: 1.72 μg/m3; winter: 0.88 μg/m3) exhibited higher mean concentrations than other compounds. Printing offices and industrial facilities seem to have strong influences on the VOC levels in the center and southwest of Seoul, respectively. Diagnostic ratios indicated that the main sources of VOCs were related to local volatilization in summer and vehicular exhaust and transport from other areas in winter. Positive matrix factorization (PMF) suggested four major sources of VOCs: residential/commercial solvents for printing and painting (Factor 1); transport from other areas (Factor 2); industrial solvents for painting, manufacturing, and cleaning (Factor 3); and gasoline/diesel vehicular exhausts (Factor 4). Factors 1 and 3 were dominant in summer, and Factors 2 and 4 were dominant in winter. In conclusion, the seasonal meteorological conditions (e.g., temperature and wind patterns) were found to play an important role in the spatial distribution of VOCs in Seoul, and solvent use was a predominant source.

Seoul, the capital of South Korea, is one of the largest megacities in northeast Asia. Previous studies have monitored atmospheric polycyclic aromatic hydrocarbons (PAHs) in Seoul, but few have produced highly spatially resolved data. In this study, a total of 144 polyurethane foam-passive air samplers (PUF-PASs) were deployed at 36 sites (4 road, 26 urban, and 6 background sites) in Seoul during the summer of 2016 and the winter of 2017. The concentrations of Σ13 PAHs fell within a range of 2.6–49.9 ng/m3 in summer and 15.5–77.8 ng/m3 in winter. Low-molecular-weight PAHs with 3–4 rings were dominant (accounting for about 97% of all PAHs) at all sampling sites in both seasons compared to high-molecular-weight PAHs with 5–6 rings. Phenanthrene was the most abundant individual compound, accounting for 35–54% and 42–52% of the total PAHs in summer and winter, respectively. The spatial distribution of PAHs was determined by traffic, domestic heating, topography, transport, and meteorological conditions. The concentration of PAHs at the road sites was higher than at the urban and background sites, indicating that vehicular exhaust was a major source in both seasons. In addition, diagnostic ratios and principal component analysis revealed the influence of various sources, such as gasoline and diesel emissions and coal and biomass combustion. Backward air trajectory analysis highlighted the influence of long-range transport from northern China and North Korea during the winter, whereas the influence of local emissions and satellite cities appeared to dominate during the summer. In conclusion, passive air sampling was successfully employed to identify major sources of PAHs in an Asian megacity, indicating that it could produce data with a high spatial resolution for use in human risk assessment and air monitoring networks.

The unintended influence of exhaust plumes emitted from a vehicle ahead to on-road air quality surveying data measured with a mobile laboratory (ML) at 20–40 km h −1 in dense traffic areas was investigated by experiment and life-sized computational fluidic dynamics (CFD) simulation. The ML equipped with variable sampling inlets of five columns by four rows was used to measure the spatial distribution of CO 2 and NO x concentrations when following 5–20 m behind a sport utility vehicle (SUV) as an emitter vehicle equipped with a portable emission monitoring system (PEMS). The PEMS measured exhaust gases at the tailpipe for input data of the CFD simulations. After the CFD method was verified with experimental results of the SUV, dispersion of exhaust plumes emitted from a bus and a sedan was numerically analyzed. More dilution of the exhaust plume was observed at higher vehicle speeds, probably because of eddy diffusion that was proportional to turbulent kinetic energy and vehicle speed. The CO 2 and NO x concentrations behind the emitter vehicle showed less overestimation as both the distance between the two vehicles and their background concentrations increased. If the height of the ML inlet is lower than 2 m and the ML travels within 20 m behind a SUV and a sedan ahead at 20 km h −1 , the overestimation should be considered by as much as 200 ppb in NO x and 80 ppm in CO 2 . Following a bus should be avoided if possible, because effect of exhaust plumes from a bus ahead could not be negligible even when the distance between the bus and the ML with the inlet height of 2 m, was more than 40 m. Recommendations are provided to avoid the unintended influence of exhaust plumes from vehicles ahead of the ML during on-road measurement in urban dense traffic conditions.