The study area is Gangwon Province, South Korea, located between 37°2′–38°′ N latitude and 127°5–128°22′ E longitude (Figure 1). Gangwon Province has a total area of 16,875 km², accounting for approximately 17% of South Korea’s land area. Of this, about 81% consists of mountainous terrain, while 9% is agricultural land. The province’s major mountain ranges extend from north to south, resulting in distinct climatic differences between the western and eastern regions. The eastern region, which borders the sea, is influenced by a maritime climate but can experience relatively dry conditions due to the foehn wind from the mountains. In contrast, the western region, adjacent to the capital area, is characterized as a leeward zone, where air pollutant concentrations tend to be higher. Additionally, the eastern part of Gangwon Province has a high concentration of power plants and industrial complexes, leading to relatively higher emissions of air pollutants.

Air pollutant data were obtained from Air Korea’s finalized dataset and monthly statistical reports, including PM₁₀, PM₂.₅, and O₃ concentration data. The data were sourced from urban air quality monitoring stations in Gangwon Province. Particulate matter concentrations were measured using the beta-ray attenuation method and the gravimetric method, while O₃ concentrations were measured using the ultraviolet absorption method. Additionally, all data underwent quality assurance and quality control (QA/QC) procedures in accordance with South Korea’s official air pollution testing standards. The data period spans a total of 5 years, from 2019, when PM₂.₅ urban air quality monitoring stations were established across all cities and counties of Gangwon Province, to 2023. The air pollutant data can be accessed as open data on the Air Korea website (accessed on 1 August 2024, operating agency: Korea Environment Corporation, Incheon, Republic of Korea¹).

South Korea’s Clean Air Policy Support System (CAPSS) is a system for estimating pollutant emissions in the Air Pollutants Emission Inventory. Emissions are calculated annually and reported for nine air pollutants: total suspended particles (TSP), PM₂.₅, PM₁₀, sulfur oxides (SOx), nitrogen oxides (NOx), volatile organic compounds (VOCs), ammonia (NH₃), carbon monoxide (CO), and black carbon (BC). The system provides emission data categorized by pollutant type, emission source, and region. Fuel combustion sources are classified into 13 categories: Energy production, Non-industry, Manufacturing industry, Industrial process, Energy transport and storage, Solvent use, Road transport, Non-road transport, Waste disposal, Agriculture, Other surface-pollutant source, Fugitive dust, and Biomass burning. The Emissions data can be accessed as open data on the National Air Emission Inventory and Research Center website (accessed on 1 August 2024, operating agency: National Air Emission Inventory and Research Center, Cheongju, Republic of Korea²).

The Normalized Difference Vegetation Index (NDVI) is an index used to assess vegetation vitality and density. NDVI is calculated using red and near-infrared wavelengths. It is widely utilized in agriculture and forestry and enables vegetation monitoring in specific areas using remote sensors such as satellites or drones. NDVI values typically range from −1 to 1, where values below 0.1 indicate barren areas such as deserts and snow-covered regions, values between 0.2 and 0.3 correspond to shrublands and grasslands with low vegetation density, and values between 0.6 and 0.8 represent healthy forests (Xie et al., 2010). The dataset used in this study is the MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid v006. The NDVI data can be accessed as open data on the EARTHDATA SEARCH website (accessed on 1 August 2024, operating agency: NASA, Washington, DC, Unired States³).

While air pollutant concentration data and NDVI data used for the period from 2019 to 2023, mission data only available up to 2021, as no more responsible data exist (Table 1). Therefore, emissions can be compared for the 3 years from 2019 to 2021. Additionally, in the cases of Hongcheon, Hwacheon, Inje, and Taebaek, the establishment of urban air quality monitoring stations was delayed, resulting in fewer data points compared to other regions.

The concentrations of air pollutants (PM and O₃) in Gangwon Province exhibited regional variations (Figure 2 and Table 2), which are presumed to be influenced by factors such as topographical characteristics, atmospheric stagnation, and industrial and traffic emissions. Figure 2 illustrates the arithmetic average values of the four variables—PM₁₀, PM_2.5, O₃, and NDVI—calculated across the 5 years period. The 5 years (2019–2023) arithmetic mean concentration of PM₁₀ ranged from 26 to 34 μg/m³, with relatively high concentrations observed in inland areas such as Chuncheon (34.4 ± 29.4 μg/m³), Cheorwon (34.4 ± 28.4 μg/m³), and Hongcheon (34 ± 27.2 μg/m³). In contrast, coastal areas of Gangwon Province, such as Donghae (28.8 ± 26.1 μg/m³) and Samcheok (28.5 ± 20.9 μg/m³), exhibited slightly lower concentrations, likely due to favorable atmospheric dispersion conditions or relatively fewer emission sources compared to the western regions.

Regarding O₃ concentrations, higher levels were observed in coastal and mountainous areas such as Gangneung (0.037 ± 0.016 ppm) and Goseong (0.035 ± 0.016 ppm). This trend is associated with the topographical characteristics of Gangwon Province, where atmospheric stagnation and meteorological conditions conducive to photochemical reactions contribute to increased air pollutant levels (Allabakash et al., 2022). In particular, in coastal regions like Gangneung, the circulation of sea and land breezes is likely to influence O₃ concentrations, necessitating air quality management measures that take this factor into account.

PM₂.₅ concentration in Gangwon Province ranged from 12 to 18 μg/m³, with relatively high concentrations measured in Hongcheon (19.3 ± 16.6 μg/m³) and Wonju (19.3 ± 16.6 μg/m³). These areas have a relatively high density of industrial facilities and traffic, suggesting a significant influence of local emission sources. In contrast, some coastal areas, such as Sokcho (12.6 ± 11.1 μg/m³) and Samcheok (13.1 ± 10 μg/m³), showed lower PM₂.₅ concentrations, likely due to marine influences or efficient atmospheric circulation (Martilli, 2003). NDVI was highest in Samcheok, indicating that this area had the densest vegetation coverage in Gangwon Province (Figure 2). NDVI showed an inverse relationship with PM concentrations, whereas no significant correlation was observed between NDVI and O₃ concentrations across different regions.

South Korea’s Clean Air Policy Support System (CAPSS) is an integrated emissions information system that provides air pollutant emissions data. Using emissions data from CAPSS, it is possible to assess the emissions of PM and O₃ precursors in Gangwon Province. visualized VOCs emissions by city from 2019 to 2021 (Figure 3).

In 2019, PM₁₀ and PM₂.₅ emissions were relatively high in Wonju, Chuncheon, Hongcheon, Pyeongchang, and Gangneung, likely due to various emission sources such as industrial activities, traffic, and heating emissions. Notably, Wonju and Hongcheon consistently exhibited high emissions, indicating that these areas should be prioritized for air quality management.

In 2020, PM₁₀ and PM₂.₅ emissions generally showed a decreasing trend, which is presumed to be influenced by reduced industrial and traffic activities due to the COVID-19 pandemic, as well as the effects of air quality improvement policies. However, emissions remained high in some areas, including Wonju, Hongcheon, Pyeongchang, and Gangneung, necessitating further analysis of emission sources and additional mitigation measures.

In 2021, emissions showed an increasing trend again, with PM₁₀ and PM₂.₅ emissions in major urban areas such as Wonju, Chuncheon, and Hongcheon recovering to levels similar to those in 2019. This rebound is likely due to the resumption of economic activities and increases in industrial and traffic emissions. Although emissions remained relatively low in coastal areas, PM₂.₅ emissions remained high in Pyeongchang and Gangneung, requiring closer monitoring and management of air pollution contributions in the eastern coastal region.

In 2019, NOx emissions were high in urban and industrial areas such as Wonju, Chuncheon, Gangneung, and Donghae. This is likely due to the characteristics of NOx emissions from vehicle exhaust and industrial processes. While NOx emissions showed a slight decrease in 2020, they increased again in 2021. In particular, NOx emissions remained consistently high in Wonju, Chuncheon, and Gangneung, which is presumed to be closely related to increased traffic volumes and industrial facility operations in these areas. Since NOx serves as a key precursor for O₃ and PM₂.₅ formation through photochemical reactions, strengthening emission reduction measures in these regions is essential.

In 2019, VOCs emissions were relatively high in Wonju, Chuncheon, Gangneung, and Hongcheon. VOCs emissions are influenced by various factors, including fossil fuel combustion, solvent use, industrial processes, and biogenic emissions (Wang et al., 2013; Piccot et al., 1992). While VOCs emissions showed a slight decrease in 2020, they increased again in Wonju and Gangneung in 2021. Since VOCs play a critical role in the formation of O₃ and secondary organic aerosols (SOA), effective VOC management is essential for reducing both O₃ and fine particulate matter concentrations.

Analysis of PM₂.₅ emission sources in major cities in Gangwon Province showed distinct characteristics(Figure 4). In Chuncheon, biomass burning accounted for the highest proportion (38%), followed by fugitive dust (30%) and non-road transport (12%). This indicates that heating fuel use and urban vehicle movement significantly impact air quality, highlighting the need for improvements in fuel combustion methods and measures to reduce emissions from on-road sources.

In Wonju, non-road transport was the dominant source (30%), suggesting that dust emissions from roads and construction sites require stricter management. Additionally, fugitive dust (19%) and biomass burning (22%) also had significant shares, necessitating mitigation strategies for traffic and heating emissions.

In Gangneung, biomass burning accounted for a notably high proportion (58%), followed by fugitive dust (23%) and Non-road transport (9%). This suggests that fuel combustion in agricultural and forested areas is a major emission source, emphasizing the importance of controlling wood combustion and promoting alternative fuels.

In Donghae, reflecting its status as an industrial hub, industrial activities were the dominant emission sources, with manufacturing industry (47%) and non-road transport (25%) contributing the highest shares. energy production (11%) and biomass burning (7%) also had notable impacts, indicating the need for emissions reduction in the industrial, transportation, and energy sectors.

These findings suggest that each city in Gangwon Province has distinct emission characteristics, necessitating tailored air pollution mitigation policies. Chuncheon and Wonju should focus on managing traffic and fugitive dust, Gangneung requires measures to reduce biomass burning, and Donghae needs targeted strategies for industrial emissions control. Establishing a systematic management plan based on regional emission characteristics is essential for implementing effective air quality improvement strategies.

According to the time series graph analyzing air pollutant concentration changes in Gangwon Province from 2019 to 2023 (Figure 5), PM₁₀, PM₂.₅, and O₃ all exhibit seasonal variability, with concentrations tending to increase during specific periods.

For PM₁₀, the moving monthly average (red line) shows higher values during spring and winter, which is likely related to factors such as yellow dust events, atmospheric stagnation, and increased use of heating fuels. Additionally, sudden spikes in concentration were observed at certain points, which may have been influenced by external inflows, large-scale fires, or changes in meteorological conditions.

Compared to PM₁₀, PM₂.₅ exhibits relatively stable concentration variations, suggesting that while larger PM₁₀ particles fluctuate due to multiple factors, PM₂.₅ concentrations are less affected by changes in weather conditions and external inflows on an annual basis. A slight decrease in PM₂.₅ concentrations was observed in 2020–2021, which appears to be linked to the economic downturn and rising costs during the COVID-19 pandemic, leading to a temporary decline in industrial activities and, consequently, lower pollutant concentrations.

PM₂.₅ also shows a tendency to peak in spring and winter, similar to PM₁₀, with relatively high variability in concentration. Notably, short-term high-concentration events were observed in 2021 and 2023, likely caused by a combination of factors such as external inflows and changes in meteorological conditions.

The high-concentration fine particulate matter events that occurred from May 7 to 9, 2021, and April 11 to 15, 2023, were attributed to yellow dust originating from the Gobi Desert, leading to elevated particulate matter levels nationwide. Yellow dust from the Gobi Desert is known as a major contributor to high PM concentrations in South Korea during spring (Pi et al., 2009; Lee and Chung, 2012). Furthermore, global climate change has resulted in rising temperatures near the Gobi Desert, causing rapid snowmelt and creating an environment conducive to more frequent yellow dust events, which further contribute to high particulate matter concentrations (Hao et al., 2024; Park et al., 2024).

O₃ concentrations also exhibit seasonal variation, showing higher levels in summer and lower levels in winter. This trend aligns with the typical pattern in which O₃ formation increases during summer due to intensified photochemical reactions driven by strong solar radiation, whereas lower solar radiation and decreased temperatures in winter lead to reduced O₃ concentrations.

The concentrations of air pollutants in Gangwon Province are influenced by seasonal and environmental factors, reflecting the characteristic air pollution patterns in South Korea, where high PM concentrations occur in spring and winter, while O₃ levels increase in summer. Consequently, season-specific air quality management strategies are required to improve air quality in Gangwon Province. Strengthening domestic and international measures to reduce fine particulate matter during spring and winter, along with the continuous implementation of O₃ control policies in summer, is essential for effective air quality management.

Table 3 presents the correlation coefficients between regional air pollutants and NDVI for the year 2019. NDVI showed a negative correlation with particulate matter in all regions at the 95% confidence level, and the correlation was significant at the 99% confidence level, except for the correlation between PM₁₀ and NDVI in Sokcho. The highest negative correlations between PM₁₀ and NDVI were observed in Hongcheon (r = −0.745, p < 0.01), Pyeongchang (r = −0.715, p < 0.01), and Hoengseong (r = −0.680, p < 0.01). For PM₂.₅ and NDVI, the strongest negative correlations were found in Hwacheon (r = −0.802, p < 0.01), Chuncheon (r = −0.784, p < 0.01), and Hongcheon (r = −0.784, p < 0.01). Overall, particulate matter concentrations tended to decrease as vegetation cover increased.

The inverse relationship between monthly vegetation changes and particulate matter concentrations was evident. The negative correlation between PM concentrations and NDVI was stronger for PM₂.₅ than for PM₁₀, suggesting that smaller particulate matter is more sensitive to vegetation changes.

For O₃ and NDVI, correlation coefficients were not statistically significant at the 95% confidence level in many regions. However, in cities located in the the western part of Gangwon Province, including Cheorwon, Chuncheon, Hoengseong, Hongcheon, and Wonju, a positive correlation was observed at the 99% confidence level. This positive correlation indicates that O₃ and NDVI exhibit similar variation trends in these areas. These cities share the characteristic of being adjacent to the Seoul metropolitan area. Additionally, Cheorwon, Chuncheon, and Wonju have relatively low NDVI values compared to other regions in Gangwon Province.

Among other regions, particularly in the eastern part of Gangwon Province, the correlation between O₃ and NDVI was not significant at the 95% confidence level, with correlation values close to zero, indicating no clear relationship.

In general, both vegetation and O₃ levels are influenced by solar radiation, leading to similar trends. However, in South Korea, the summer monsoon season plays a significant meteorological role, allowing vegetation to remain stable while O₃ concentrations tend to decrease from spring to early summer, resulting in low correlation coefficients between the two variables.

Figure 6 illustrates the monthly variations of air pollutants and NDVI in Gangwon Province. The NDVI employed the vegetation index data provided by MODIS/Terra. NDVI in Gangwon Province fluctuated in response to South Korea’s seasonal changes. The decrease in NDVI observed in July–August of 2019 and 2021 could indicate actual forest degradation, but the primary cause is likely noise in satellite observation data due to cloud cover during the monsoon season. Although this summer-induced noise makes it difficult to accurately track vegetation changes in Gangwon Province, the NDVI remained around 0.8 during summer over the 5 years period, suggesting that the province maintains healthy vegetation.

For PM₁₀, concentrations were relatively high in 2019, 2021, and 2023, whereas they were lower in 2020 and 2022. Following the COVID-19 pandemic in 2020, overall fine particulate matter concentrations decreased, but PM₁₀ levels rebounded in April 2023, reaching an average of 60 μg/m³, which was higher than the March 2019 average of 57 μg/m³. PM₂.₅ concentrations showed a pattern similar to PM₁₀, but while PM₁₀ peaked in March–April, PM₂.₅ peaked in January–February, indicating a difference in seasonal concentration trends. PM₁₀ concentrations in Gangwon Province have returned to pre-pandemic levels, while PM₂.₅ concentrations remain below the 2019 peak of 37 μg/m³.

Air pollutant concentrations decreased in June and July, coinciding with the peak NDVI values. O₃ concentrations were highest in April–May, with a monthly average of 56–42 ppb, and then began to decline. From September to October, O₃ remained at lower levels (20–30 ppb), before rising again to 40–60 ppb around February of the following year. PM₁₀ and PM₂.₅ concentrations were highest from January to April, then decreased in May–June, showing a pattern similar to O₃, before increasing again around November.

In South Korea, monsoon rains inhibit photochemical reactions, preventing O₃ from reaching high concentrations in July–August. In contrast, in Northern Hemisphere regions with little or no monsoon influence, O₃ concentrations typically peak in July–August (Logan, 1985). If South Korea experiences less summer rainfall or a shorter monsoon period due to climate change, O₃ concentrations are expected to rise sharply, potentially following a pattern similar to NDVI fluctuations.

Furthermore, increased O₃ levels may threaten vegetation in Gangwon Province. O₃ can penetrate plant cells through cell membranes and cell walls, causing cellular damage and accelerating leaf aging (Pell et al., 1997). While current summer O₃ levels in Gangwon Province remain relatively low, limiting its impact on vegetation, future increases in O₃ concentrations could pose a significant risk to plant health.