Comparative Analysis of Air Pollution in Beijing and Seoul: Long-Term Trends and Seasonal Variations

Abstract

This study compares long-term air pollution trends and seasonal patterns in Beijing and Seoul from 2014 to 2024, focusing on PM2.5, PM10, CO, NO₂, SO₂, and O₃. Using statistical analyses including Mann–Kendall tests and generalized additive models, we found that Beijing achieved notable reductions in particulate matter, largely due to stricter industrial controls and reduced coal use, though winter pollution peaks remain. In contrast, Seoul’s improvements were slower, mainly due to persistent vehicular emissions and recurring spring dust storms from northern China. Seasonal analysis showed winter peaks in Beijing linked to coal heating, and spring peaks in Seoul driven by transboundary dust, with higher summer ozone in Seoul reflecting photochemical activity. These findings highlight the need for city-specific air quality management and regional cooperation, recommending further reductions in vehicular emissions for Seoul and continued transition from coal in Beijing to mitigate health impacts. This study identifies specific seasonal trends and pollution sources that require targeted policy interventions to improve air quality.

1. Introduction

Air pollution continues to be one of the most significant environmental challenges globally, posing serious threats to public health, ecosystems, and climate stability. The impacts of air pollution are particularly pronounced in urban areas, where high population density, rapid industrialization, and increasing transportation contribute to the concentration of harmful pollutants. Urbanization, coupled with climate change, has further intensified the severity of air pollution in these regions, complicating efforts to regulate emissions and improve air quality. In East Asia, regions experiencing rapid economic development and urban expansion, such as Beijing and Seoul, are facing some of the highest levels of air pollution globally. These cities have long struggled with elevated concentrations of pollutants, including particulate matter (PM2.5, PM10), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), and ozone (O₃), all of which have been linked to a range of severe health problems, including respiratory and cardiovascular diseases [1,2]. Additionally, these pollutants contribute to environmental degradation, including reduced visibility, ecosystem disruption, and negative effects on climate change [3].

In Beijing, the continued reliance on coal for residential heating during the winter months exacerbates pollution levels, particularly fine particulate matter (PM2.5), which tends to peak during the heating season. In addition to coal usage, Beijing’s industrial sector continues to contribute significantly to PM2.5 and PM10 emissions [4]. These pollutants have been directly linked to public health concerns, with studies showing a correlation between high pollution levels and increased rates of respiratory diseases, such as asthma and chronic bronchitis, as well as cardiovascular problems, including heart attacks and strokes [5]. Despite efforts to reduce pollution through policy changes and technological advancements, Beijing still faces challenges in mitigating these risks, especially during colder months when coal usage spikes [6]. Furthermore, the city’s rapidly expanding transportation infrastructure continues to contribute significantly to PM2.5 and NO₂ emissions, which have been exacerbated by population growth and urban expansion [7].

Seoul, South Korea, similarly faces significant air quality challenges, though its pollution sources differ somewhat from those in Beijing. While Seoul has made significant strides in reducing industrial emissions, vehicular emissions, particularly from diesel-powered vehicles, continue to be the dominant source of pollution. In addition, Seoul is heavily affected by transboundary pollution, primarily dust storms originating from northern China, which exacerbate particulate pollution during the spring [8]. Seoul is significantly affected by transboundary pollution, particularly dust storms originating from northern China. Dust storms exacerbate particulate pollution during the spring months, contributing to elevated PM2.5 concentrations. Seasonal variations further complicate air quality management, with higher concentrations of pollutants, including NO₂ and O₃, during the warmer months. The increase in ozone (O₃) concentrations in Seoul, particularly during the summer, is also influenced by photochemical reactions, which are intensified by higher traffic emissions and increased sunlight [9]. Despite measures aimed at reducing emissions from vehicles and promoting cleaner energy alternatives, air quality improvements in Seoul have been slower compared to Beijing, largely due to the city’s continued struggle with vehicular emissions and the seasonal effects of dust storms [10]. In addition, the presence of urban heat islands has further compounded the problem of ozone formation during the warmer months, which exacerbates air quality challenges [11].

Given the complexity of air pollution dynamics in both cities, understanding the factors that drive long-term pollution trends and seasonal variations is critical for effective policy development. Meteorological influences, such as temperature inversions and vertical convection, also play a significant role in pollutant concentrations, especially in Seoul, where higher ozone levels are observed during the summer months. In contrast, Beijing’s pollution levels are more significantly influenced by the continued use of coal during the winter months, exacerbated by meteorological conditions that prevent pollutant dispersion [12]. Recent studies have also pointed to the combined effects of urbanization and climate change in exacerbating the long-term trends of PM2.5 and ozone concentrations in both cities [13]. This study provides a comparative analysis of air pollution in Beijing and Seoul, focusing on the long-term trends of key pollutants, seasonal variations, and their health and environmental impacts. Additionally, the study examines the effectiveness of air quality management policies in each city and emphasizes the need for region-specific strategies that address both local and transboundary pollution sources. The findings of this study will contribute to the broader understanding of air pollution challenges in East Asia and inform the development of more effective air quality management strategies in rapidly urbanizing regions [14].

2. Data and Methods

2.1. Data Collection

The data used in this study were collected from official air quality monitoring stations in Beijing and Seoul, spanning from 2014 to 2024. These cities were selected due to their significant air pollution issues and the availability of long-term air quality data. The monitoring stations in Beijing were operated by the Beijing Municipal Environmental Monitoring Center, and those in Seoul were operated by the Korean Ministry of Environment. The data collected includes daily concentrations of the following pollutants: PM2.5, PM10, NO₂, SO₂, CO, and O₃. These pollutants were selected due to their significant impact on public health, environmental degradation, and their connections to urbanization and climate change [1,2].

Beijing, the capital city of China, is characterized by rapid urbanization, industrial growth, and high vehicle emissions. The city has struggled with severe air pollution, particularly during the winter heating season, due to the widespread use of coal for residential heating. In addition to coal burning, Beijing’s industrial sector continues to contribute significantly to PM2.5 and PM10 emissions. Recent studies have shown that meteorological factors, such as temperature inversions, significantly influence pollution levels in Beijing, particularly during colder months [3,4].

Seoul, South Korea, also faces significant air quality challenges, although the sources of pollution differ somewhat from those in Beijing. While Seoul has successfully reduced industrial emissions, vehicular emissions, particularly from diesel-powered vehicles, continue to be the dominant source of pollution. In addition, Seoul is heavily affected by seasonal dust storms originating from northern China, which exacerbate particulate pollution during the spring. Seasonal variations further complicate air quality management, with higher concentrations of pollutants, including NO₂ and O₃, during the warmer months [5,6]. The increase in ozone (O₃) concentrations, particularly in the summer, is influenced by photochemical reactions and increased traffic emissions [7].

2.2. Data Preprocessing

Data preprocessing is an essential step to ensure the reliability and comparability of the air quality data collected from different monitoring stations. The raw data from the monitoring stations included missing values and outliers that could potentially distort the analysis. Outliers were identified using the Mahalanobis Distance method, and approximately 2.5% of the data points were flagged and removed. Missing data, accounting for 3.1% of the total dataset, were handled using Multiple Imputation to ensure the integrity of the analysis. To address this issue, Multiple Imputation methods were employed to handle missing data in the time series. Multiple Imputation allows for a more robust estimation of missing values based on the assumption that the missing data are missing at random, thus improving the reliability of the analysis [8].

In addition, outliers were identified and removed using the Mahalanobis Distance method, which calculates the distance of each data point from the mean. Any data points that significantly deviate from the expected pattern were flagged and removed to avoid distorting the analysis results. This process ensures that the dataset used for further analysis is accurate and free from anomalies [9].

2.3. Statistical Analysis

The primary statistical methods used in this study include the Mann–Kendall S test and Pearson correlation coefficients. The Mann–Kendall S test is a non-parametric test widely used in environmental studies to detect trends in time series data. It is particularly useful for analyzing air quality data that exhibits irregular patterns and outliers, as it does not assume normality and can handle missing values or irregular time intervals [10]. This test was applied to identify any significant upward or downward trends in the concentrations of pollutants over the study period, particularly PM2.5, NO₂, and O₃. The Mann–Kendall test was applied to identify trends in pollutant concentrations. A significance level of 0.05 (p < 0.05) was used to determine whether trends were statistically significant.

$$S={\sum }_{i=1}^{n-1}\hspace{1em}{\sum }_{j=i+1}^{n}sign(X_j-X_i)$$

(1)

where, $X_i$ and $X_j$ are the pollutant values at times $i$ and $j$.

Additionally, Pearson correlation coefficients were calculated to evaluate the strength of relationships between different pollutants. Specifically, the correlations between PM2.5 and NO₂, as well as PM2.5 and O₃, were analyzed to investigate whether common sources of pollution, such as vehicular emissions and industrial activities, contribute to the levels of these pollutants in both cities [11].

The formula for calculating the Pearson correlation coefficient is as follows:

$$r={\displaystyle \frac{\sum _{i=1}^n(x_i-{\mu }_x\left)\right(y_i-{\mu }_y)}{\sqrt{\sum _{i=1}^n(x_i{{-\mu }_x)}^2\sum _{i=1}^n{(y_i-{\mu }_y)}^2}}}$$

(2)

where $x_i$ and $y_i$ are the pollutant concentrations at time $i$, and $\overline{x}$ and $\overline{y}$ are the means of $x$ and $y$, respectively. This method was applied to both daily and seasonal data to assess the interrelationships between PM2.5, NO₂, and O₃ concentrations [12].

2.4. Data Normalization and Standardization

As the pollutants are measured in different units (e.g., µg/m³ for particulate matter and ppm for ozone), normalization and standardization were performed to ensure comparability. Normalization and standardization were performed by converting the data into z-scores, which allows for a direct comparison of the relative changes in each pollutant over time [13]. This method ensures that the data from different pollutants are on a comparable scale, eliminating any biases caused by differences in measurement units. Normalization of the data allows for a clearer comparison of trends in pollutants like PM2.5, NO₂, and O₃ across different times and locations, providing a better understanding of how air quality changes over time in both cities.

2.5. Limitations and Considerations

While the data used in this study provides valuable insights into the trends and relationships between pollutants in Beijing and Seoul, there are certain limitations that need to be considered. The data may be subject to measurement errors or biases due to variations in monitoring equipment and environmental conditions at different monitoring stations. While the field sampling and analysis methods employed in this study are robust, there are limitations in source identification. These methods do not provide explicit quantification of source contributions, which could be addressed by applying receptor modeling or source apportionment techniques in future research. Additionally, the analysis does not account for some potential sources of pollution, such as natural events like wildfires or volcanic activity, which can temporarily elevate pollutant levels [14]. These limitations should be kept in mind when interpreting the results, and future research could aim to improve the methodology by incorporating these factors. While our study does not apply receptor modeling or source apportionment techniques, previous studies such as [2,15] have demonstrated the effectiveness of these methods in quantifying the contributions of various pollution sources in cities like Seoul and Beijing. These techniques could provide a more robust understanding of source-specific contributions to urban air quality.

3. Result

3.1. Overview of Air Pollution Trends in Beijing and Seoul

The analysis of air quality data from 2014 to 2024 revealed significant long-term trends in the concentration of key pollutants in both Beijing and Seoul. In Beijing, PM2.5 levels tend to peak in the winter months due to heating-related emissions, particularly from coal burning. [16] highlighted that coal combustion for residential heating is the primary driver of these pollution peaks. On the other hand, in Seoul, vehicular emissions, especially from diesel vehicles, have been found to contribute significantly to PM2.5 and NO₂ levels, particularly during winter months when traffic is more intense due to heating demands [17]. Figure 1 illustrates this seasonal trend, with PM2.5 levels consistently peaking in December and January, coinciding with the winter heating season. As shown in

Table 1. Mann–Kendall S Test and slope for key pollutants.

	Beijing Mann–Kendall S	Seoul Mann–Kendall S	Beijing Slope	Seoul Slope
PM2.5	−35	−19	−2.58	−0.350
PM10	−325	−29	−2.56	−1.450
CO	7	−25	+0.042	−0.010
NO2	−25	−39	−0.001	−0.002
SO2	−3	−41	−0.001	−0.001
O3	−1	33	+0.001	+0.001

, the annual average concentrations of PM2.5 in Beijing are considerably higher than the [18] recommended levels, with winter months consistently exceeding the permissible limits by a significant margin. The elevated levels are strongly correlated with the use of coal for heating, along with local industrial emissions, which exacerbate the concentrations of particulate matter (PM).

In Seoul, while the annual mean concentration of PM2.5 has gradually decreased due to stricter emission control measures, particulate pollution remains a significant concern, particularly in spring when transboundary pollution from dust storms originating in northern China contributes significantly to the PM2.5 levels. Figure 2 shows a sharp increase in PM2.5 concentrations during the spring months, highlighting the impact of external pollution sources. Despite efforts to reduce local emissions, PM2.5 levels remain higher than international standards during this period, as evidenced by the data in

Table 2. Seasonal averages of air pollutant concentrations in both cities (2014–2024).

		PM2.5 (µg/m3)	PM10 (µg/m3)	CO (ppm)	NO2 (ppm)	SO2 (ppm)	O3
Spring	Beijing	57.842	71.605	3.415	0.004	0.017	0.039
	Seoul	25.495	50.393	0.487	0.033	0.004	0.033
Summer	Beijing	50.510	57.089	3.511	0.001	0.015	0.048
	Seoul	17.970	28.399	0.387	0.023	0.003	0.033
Fall	Beijing	61.086	68.722	3.729	0.004	0.018	0.031
	Seoul	17.758	32.067	0.501	0.030	0.003	0.021
Winter	Beijing	67.045	72.082	4.562	0.003	0.021	0.028
	Seoul	27.323	46.566	0.642	0.035	0.004	0.015

. Furthermore, Ozone (O3) concentrations in Seoul exhibit a clear seasonal trend, with summer months seeing a marked increase in O3 levels, as demonstrated in Figure 3. This increase is primarily due to photochemical reactions facilitated by sunlight and increased vehicular emissions, highlighting the city’s struggle with ozone formation during warmer months.

Future studies could benefit from incorporating receptor modeling or source apportionment techniques, which would enable more robust quantification of the individual contributions of vehicular traffic and residential heating to the observed pollution levels in both cities. This approach has been successfully applied in other studies, such as those by [1,15].

3.2. Monthly and Seasonal Variability

The seasonal variation in pollutant concentrations was found to be one of the most significant factors influencing air quality in both cities. In Beijing, the highest concentrations of PM2.5 were consistently recorded during the winter months, primarily due to coal burning for residential heating. As shown in Figure 3, PM2.5 levels peaked sharply between December and January, coinciding with the winter heating season. These findings are consistent with previous studies that have identified the impact of temperature inversions on the accumulation of pollutants during colder months, as Figure 4 highlights. During this period, temperature inversions trap PM2.5 and PM10 near the surface, preventing their dispersion and worsening air quality. Table 2 further demonstrates the PM2.5 concentration in Beijing during winter is significantly higher than in other seasons, reaffirming the contribution of coal combustion and industrial emissions during the cold months. Notably, these elevated concentrations have been linked to increased rates of respiratory diseases, such as asthma and chronic bronchitis, as shown in numerous public health studies.

Seasonal trends were calculated using monthly average values of pollutant concentrations. Mann–Kendall tests were then applied to the seasonal data to identify significant trends over the study period. In Seoul, seasonal variations also played a critical role in pollutant concentrations, particularly with respect to NO₂ and O₃. As illustrated in Figure 4, NO2 levels in Seoul exhibited a clear increase during the summer months, correlating strongly with higher vehicular emissions due to increased traffic during warmer months. Similarly, O₃ levels rose significantly during the summer, as shown in Figure 4. This seasonal increase in O₃ concentrations is primarily driven by photochemical reactions, where NO₂ from vehicular emissions interacts with sunlight to produce ozone. The rise in O₃ in Seoul is compounded by high temperatures and sunlight, leading to elevated ozone formation and worsening air quality, particularly during the summer. The high correlation between NO₂ and O₃ in Seoul (Pearson coefficient of 0.85) indicates that these pollutants are largely driven by the same source—vehicular emissions, with NO₂ serving as a precursor to O₃ formation [19].

3.3. Correlations Between Pollutants

3.3.1. Correlation Between Beijing and Seoul Air Pollutants

The correlation analysis between the key pollutants, PM2.5, NO₂, and O₃, in Beijing and Seoul provides valuable insight into how these pollutants relate across both cities. Figure 3 and Table 2 present the comparison of PM2.5 and NO₂ concentrations in both cities over the study period. The correlation between PM2.5 and NO₂ was significantly strong in Seoul, with a Pearson correlation coefficient of 0.85. This suggests that both pollutants share a common source, primarily vehicular emissions, which are a major contributor to air pollution in Seoul. As illustrated in Figure 3, PM2.5 levels in Seoul tend to increase with rising NO₂ concentrations, especially during high-traffic periods in the summer months. The strong positive correlation indicates that the emissions from vehicles, particularly diesel-powered vehicles, are responsible for both NO₂ and PM2.5 levels in Seoul, where both pollutants have a clear seasonal variation driven by traffic density and climatic conditions.

In contrast, in Beijing, the correlation between PM2.5 and NO₂ was slightly weaker, with a Pearson correlation coefficient of 0.77. This lower correlation can be attributed to the fact that Beijing’s pollution is heavily influenced not only by vehicular emissions but also by using coal for residential heating and industrial emissions, which are prominent during the winter months. As shown in Figure 4, the seasonal peak of PM2.5 in Beijing during the winter does not always coincide with an increase in NO₂, as the major contributor to PM2.5 in this period is coal burning. This difference suggests that the correlation between these two pollutants is more complex in Beijing, where the role of industrial emissions and coal combustion complicates the analysis compared to Seoul, where traffic-related emissions are the dominant source of both pollutants.

3.3.2. Correlation Between Pollutants Within Each City

The analysis of correlations within each city reveals different patterns in the relationship between PM2.5, NO₂, and O₃ in Beijing and Seoul.

In Seoul, the correlation between PM2.5 and O₃ was found to be quite strong during the warmer months, with a Pearson correlation coefficient of 0.80. This positive correlation is primarily driven by the photochemical processes that occur when NO₂ emitted from vehicular traffic interacts with sunlight, leading to the formation of O₃. Figure 4 shows how PM2.5 and O₃ concentrations in Seoul follow a similar seasonal pattern, with both pollutants peaking in the summer. The presence of PM2.5 can enhance the formation of O₃ by acting as a catalyst in photochemical reactions, thus leading to higher concentrations of ozone during periods of high traffic and sunlight exposure. This relationship emphasizes the interconnected nature of particulate matter and ozone formation in Seoul, where the high levels of NO₂ from vehicle emissions are key contributors to both PM2.5 and O₃ pollution.

In Beijing, the correlation between PM2.5 and O₃ was weaker, with a Pearson correlation coefficient of 0.65. PM2.5 levels are much higher during the winter months, largely due to coal burning for heating, which does not significantly influence O₃ concentrations. The formation of O₃ in Beijing is driven more by industrial emissions and photochemical reactions, but the correlation between PM2.5 and O₃ is not as strong as in Seoul due to the differing primary sources of pollution. The weaker correlation in Beijing suggests that coal combustion is the dominant factor influencing PM2.5 concentrations, while O₃ formation is primarily driven by industrial emissions and vehicular traffic, which contribute to NO₂ levels but not necessarily to PM2.5 in the same manner. Additionally, the impact of meteorological factors, such as temperature inversions during winter, limits the interaction between PM2.5 and O₃, as the atmospheric conditions prevent the dispersion of pollutants and inhibit the photochemical processes that form O₃.

3.4. Effectiveness of Air Quality Management Policies

The analysis of air quality management policies in Beijing and Seoul reveals varying degrees of success in the implementation of interventions aimed at reducing air pollution.

In Beijing, significant improvements have been made, particularly in transitioning from coal to natural gas for energy production and encouraging the use of cleaner industrial technologies. These initiatives have contributed to a general reduction in industrial emissions, but the city still faces severe challenges, especially during the winter heating season. The reliance on coal for residential heating remains a significant factor in the high concentrations of PM2.5 during the colder months. Despite the efforts to reduce emissions from industrial sources, the PM2.5 concentration remains high during the winter months, consistently exceeding acceptable thresholds. The persistence of coal usage in the colder season means that air quality does not improve significantly during this period, indicating that further policy interventions are needed to address the issue of coal burning.

Moreover, transboundary pollution from surrounding regions, such as dust and industrial emissions from neighboring provinces or countries, contributes significantly to Beijing’s air quality issues. This external factor complicates efforts to maintain cleaner air and highlights the need for stronger regional cooperation. Policies to reduce local emissions are important, but they need to be complemented by strategies that address cross-border pollution, including agreements with neighboring regions or countries to curb pollution transport across borders.

In Seoul, significant progress has been made in reducing NO₂ levels, primarily through the implementation of stricter vehicle emission standards and the promotion of electric vehicles (EVs). The city has successfully reduced vehicular emissions over the past decade, which is evident in the steady decline of NO₂ concentrations. These policies have helped mitigate NO₂ pollution, especially in urban areas with heavy traffic. However, despite these advancements, diesel vehicles continue to be a significant source of both NO₂ and PM2.5 pollution, particularly during the winter months. Diesel engines, known for their high emissions of nitrogen oxides and particulate matter, are still widely used in Seoul, exacerbating pollution levels during the colder season when heating demands and vehicular traffic are at their peak.

Additionally, transboundary pollution from dust storms originating in northern China remains a persistent issue for Seoul. These seasonal dust storms carry large amounts of particulate matter into the city, significantly increasing PM2.5 levels during the spring months. This external factor complicates efforts to improve Seoul’s air quality, as local emission reductions alone cannot fully counter the influx of dust from neighboring regions. This highlights the need for a broader, regional approach to air quality management, where Seoul collaborates with neighboring countries to address the challenges of transboundary pollution and mitigate its impact on the city’s air quality.

4. Summary and Conclusions

This study presents a comparative analysis of air pollution trends and their associated health and environmental impacts in two major East Asian cities, Beijing and Seoul, over a period of ten years (2014–2024). By examining the long-term trends of key pollutants such as PM2.5, NO₂, and O₃, this study provides a comprehensive understanding of the sources, seasonal variations, and health implications of air pollution in these rapidly urbanizing cities.

4.1. Summary of Key Findings

The analysis revealed several key findings regarding air pollution in Beijing and Seoul. In Beijing, the primary sources of PM2.5 are coal burning for residential heating and industrial emissions, with significant peaks in PM2.5 levels observed during the winter months. The continued reliance on coal has been a major challenge in improving air quality, despite significant efforts to reduce industrial emissions and promote cleaner technologies. Furthermore, transboundary pollution from neighboring regions, particularly during the winter heating season, continues to exacerbate Beijing’s air quality problems.

In Seoul, air quality has improved over the years, with significant reductions in NO₂ levels primarily due to stricter vehicle emission standards and the promotion of electric vehicles. However, diesel vehicles remain a significant source of NO₂ and PM2.5 pollution, especially during colder months. Transboundary pollution, particularly from dust storms originating in northern China, continues to complicate air quality management efforts, as it significantly increases PM2.5 levels during the spring months.

Seasonal variations in PM2.5, NO₂, and O₃ were observed in both cities, with higher concentrations during the winter in Beijing and during the summer in Seoul, highlighting the influence of meteorological conditions and climatic factors on pollution levels. The strong positive correlations between PM2.5 and NO₂, and between PM2.5 and O₃, particularly in Seoul, suggest a shared source of pollution, primarily from vehicular emissions.

4.2. Conclusions

In conclusion, this study has provided a detailed analysis of the long-term trends and seasonal variations of air pollution in Beijing and Seoul, highlighting the primary sources, impacts, and effectiveness of air quality management policies. While progress has been made in both cities, significant challenges remain, particularly in addressing transboundary pollution and coal usage in Beijing, as well as diesel vehicle emissions in Seoul. The findings underscore the importance of region-specific air quality management strategies, continued efforts to reduce emissions, and enhanced regional cooperation to address cross-border pollution. Future research should continue to refine these strategies and explore new approaches to improving air quality in rapidly urbanizing regions.