The dataset is a large-scale dataset that consists of 5-year spatiotemporal data in Seoul city, Korea, from 2015 to 2019. This dataset includes air pollutants, such as PM2.5, meteorological data, like temperature, wind speed, wind direction, rainfall,...; traffic volume of main roads; average driving speed on roads; and the air pollution from 3 areas in China (Beijing, Shanghai, and Shandong) that affects Seoul’s air quality.

In 2022, the annual air pollution level of particulate matter (PM2.5) in Seoul in South Korea stood at around 18 micrograms per cubic meter, down from about 20 micrograms per cubic meter a year ago.

In 2022, the annual air polluton level of ozone (O3) in Seoul in South Korea amounted to around 29 parts per billion. It was one point up from about 28 parts per billion that year before.

Data collected for this research provides information on mixing layer heights and in-situ formaldehyde concentrations at Olympic Park during the KORUS-AQ field campaign.

This dataset is associated with the following publication: Kim, H., J. Gil, J. Jung, A. Whitehill, J. Szykman, G. Lee, D. Kim, S. Cho, J. Ahn, J. Hong, and M. Park. Factors controlling surface ozone in the Seoul Metropolitan Area During the Korus AQ campaign. Elementa: Science of the Anthropocene. University of California Press (UC Press), Oakland, CA, USA, NA, (2020).

Air Quality Forecast: Contaminant Concentration: PM2.5: South Korea: Busan data was reported at 14.639 mcg/Cub m in 31 Mar 2025. This records an increase from the previous number of 12.297 mcg/Cub m for 30 Mar 2025. Air Quality Forecast: Contaminant Concentration: PM2.5: South Korea: Busan data is updated daily, averaging 16.322 mcg/Cub m from Oct 2019 (Median) to 31 Mar 2025, with 1986 observations. The data reached an all-time high of 125.513 mcg/Cub m in 20 Aug 2020 and a record low of 2.304 mcg/Cub m in 23 Jan 2024. Air Quality Forecast: Contaminant Concentration: PM2.5: South Korea: Busan data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s South Korea – Table CAMS.AQF: Air Quality Forecast: Contaminant Concentration: PM2.5: by Cities. [COVID-19-IMPACT]

Background

Air pollution is a major problem in South Korea. On days with high pollution, citizens are advised not to go outdoors. This is especially true for those who are elderly or have pre-existing medical conditions. Pollution levels are higher at certain times of year and can change rapidly based on meteorological effects. Being able to accurately forecast the level of pollution would allow South Koreans to plan ahead and avoid exposing themselves to the harsh pollutants.

Data

Pollution data

• date - date of measurement
• pm25 - fine particulate matter (PM2.5) (µg/m3)
• pm10 - fine particulate matter (PM10) (µg/m3)
• o3 - Ozone (O3) (µg/m3)
• no2 - Nitrogen Dioxide (NO2) (ppm)
• so2 - Sulfur Dioxide (SO2) (ppm)
• co - Carbon Monoxide (CO) (ppm)
• Lat - Latitude where measurement was taken
• Long - Longitude where measurement was taken
• City - City where measurement was taken
• District - District where measurement was taken
• Country - Country where measurement was taken

Weather Data (auxiliary)

• STATION - Station Number
• NAME - Station Name
• LATITUDE - Latitude of station
• LONGITUDE - Longitude of station
• ELEVATION - Elevation of station
• DATE - Date of observation
• LIQUID_PRECIPITATION - Liquid precipitation (AA1)
• SNOW_DEPTH - Snow depth (AJ1)
• DEW - Dew
• EXTREME_AIR_TEMP - Extreme air temperature (KA1)
• ATMOSPHERIC_PRESSURE - Atmospheric pressure (MA1)
• SEA_LEVEL_PRESSURE - Sea level pressure (SLP)
• TEMP - Temperature (TMP)
• VIS - Visibility
• WND - Wind

For more detailed information about each field, you can view the documentation here: documentation. NOTE: some field names were changed for clarity -- if so, original field names are in parenthesis.

Source

pollution data: https://www.airkorea.or.kr/

weather data: https://www.ncei.noaa.gov/

In 2022, the air pollution level of NO2 in Seoul amounted to 21 parts per billion, down from 35 parts per billion in 2009. The annual emissions of NO2 in the year decreased over the years; the value recorded in the last three years was the lowest in the past decade.

South Korea Air Pollution: Tax Revenue: % of GDP: Transport data was reported at 0.044 % in 2014. This records a decrease from the previous number of 0.046 % for 2013. South Korea Air Pollution: Tax Revenue: % of GDP: Transport data is updated yearly, averaging 0.056 % from Dec 1995 (Median) to 2014, with 20 observations. The data reached an all-time high of 0.060 % in 2006 and a record low of 0.026 % in 1995. South Korea Air Pollution: Tax Revenue: % of GDP: Transport data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s South Korea – Table KR.OECD.ESG: Environmental: Environmentally Related Tax Revenue: Environmental Protection Domains: OECD Member: Annual.

North Korea KP: PM2.5 Air Pollution: Population Exposed to Levels Exceeding WHO Interim Target-3 Value: % of Total data was reported at 100.000 % in 2016. This stayed constant from the previous number of 100.000 % for 2015. North Korea KP: PM2.5 Air Pollution: Population Exposed to Levels Exceeding WHO Interim Target-3 Value: % of Total data is updated yearly, averaging 100.000 % from Dec 1990 (Median) to 2016, with 11 observations. The data reached an all-time high of 100.000 % in 2016 and a record low of 100.000 % in 2016. North Korea KP: PM2.5 Air Pollution: Population Exposed to Levels Exceeding WHO Interim Target-3 Value: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s North Korea – Table KP.World Bank: Environment: Pollution. Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 3 (IT-3) is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 15 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed.; ; Brauer, M. et al. 2016, for the Global Burden of Disease Study 2016.; Weighted Average;

Air pollution is closely associated with the development of respiratory illness. Behavioral adaptations of people to air pollution may influence its impact, yet this has not been investigated in the literature. Our hypothesis is that people experience and learn the underlying air quality to decide their adaptation, and they have a stronger incentive to behaviorally adapt to the air quality as it deteriorates. We tested our hypothesis on a sample of approximately 25,700 individuals from South Korea from 2002 to 2013 that contained information on daily doctor’s visits due to respiratory disease. We matched individuals to the mean of the past seven-day concentration of the particulate matter of size between 2.5 and 10 micrometers (PM10) in their county of residence. We examined whether people living in counties with greater air pollution suffer less from respiratory disease when the concentration increases. For the analysis, we separated counties into quintiles based on their mean seven-day PM10, and regressed the binary indicator of a daily doctor’s visit with a resulting diagnosis of respiratory disease on the seven-day PM10 concentration of the county of residence interacted with the quintile dummies. The key findings are that a 1-standard-deviation increase in the seven-day PM10 concentration in the two lowest quintiles is associated with an increase of 0.054 percentage points in the likelihood of a doctor’s visit with a resulting diagnosis of respiratory disease, which is about 40% larger than the effect in higher quintiles, and the size of 1-standard-deviation gradually increases from 0.037 percentage points in the third quintile to 0.040 percentage points in the fifth quintile. The smaller increase in the likelihood of respiratory disease in more polluted locations can be explained by the behavioral adaptation to the environment, but the effectiveness of the adaptation seems limited among the highly polluted locations.

Korea PM2.5 Air Pollution: Population Exposed to Levels Exceeding WHO Interim Target-2 Value: % of Total data was reported at 95.973 % in 2016. This records a decrease from the previous number of 96.019 % for 2015. Korea PM2.5 Air Pollution: Population Exposed to Levels Exceeding WHO Interim Target-2 Value: % of Total data is updated yearly, averaging 76.464 % from Dec 1990 (Median) to 2016, with 11 observations. The data reached an all-time high of 98.489 % in 2014 and a record low of 59.610 % in 2011. Korea PM2.5 Air Pollution: Population Exposed to Levels Exceeding WHO Interim Target-2 Value: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Korea – Table KR.World Bank: Environment: Pollution. Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 2 (IT-2) is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 25 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed.; ; Brauer, M. et al. 2016, for the Global Burden of Disease Study 2016.; Weighted Average;

Amidst recent industrialization in South Korea, Seoul has experienced high levels of air pollution, an issue that is magnified due to a lack of effective air pollution prediction techniques. In this study, the Prophet forecasting model (PFM) was used to predict both short-term and long-term air pollution in Seoul. The air pollutants forecasted in this study were PM2.5, PM10, O3, NO2, SO2, and CO, air pollutants responsible for numerous health conditions upon long-term exposure. Current chemical models to predict air pollution require complex source lists making them difficult to use. Machine learning models have also been implemented however their requirement of meteorological parameters render the models ineffective as additional models and infrastructure need to be in place to model meteorology. To address this, a model needs to be created that can accurately predict pollution based on time. A dataset containing three years worth of hourly air quality measurements in Seoul was sourced from the Seoul Open Data Plaza. To optimize the model, PFM has the following parameters: model type, changepoints, seasonality, holidays, and error. Cross validation was performed on the 2017-18 data; then, the model predicted 2019 values. To compare the predicted and actual values and determine the accuracy of the model, the statistical indicators: mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), and coverage were used. PFM predicted PM2.5 and PM10 with a MAE value of 12.6 µg/m3 and 19.6 µg/m3, respectively. PFM also predicted SO2 and CO with a MAE value of 0.00124 ppm and 0.207 ppm, respectively. PFM's prediction of PM2.5 and PM10 had a MAE approximately 2 times and 4 times less, respectively, than comparable models. PFM's prediction of SO2and CO had a MAE approximately five times and 50 times less, respectively, than comparable models. In most cases, PFM's ability to accurately forecast the concentration of air pollutants in Seoul up to one year in advance outperformed similar models proposed in literature. This study addresses the limitations of the prior two PFM studies by expanding the modelled air pollutants from three pollutants to six pollutants while increasing the prediction time from 3 days to 1 year. This is also the first research to use PFM in Seoul, Korea. To achieve more accurate results, a larger air pollution dataset needs to be implemented with PFM. In the future, PFM should be used to predict and model air pollution in other regions, especially those without advanced infrastructure to model meteorology alongside air pollution. In Seoul, Seoul's government can use PFM to accurately predict air pollution concentrations and plan accordingly.

Korea PM2.5 Air Pollution: Population Exposed to Levels Exceeding WHO Interim Target-1 Value: % of Total data was reported at 13.227 % in 2016. This records an increase from the previous number of 13.129 % for 2015. Korea PM2.5 Air Pollution: Population Exposed to Levels Exceeding WHO Interim Target-1 Value: % of Total data is updated yearly, averaging 0.000 % from Dec 1990 (Median) to 2016, with 11 observations. The data reached an all-time high of 13.227 % in 2016 and a record low of 0.000 % in 2013. Korea PM2.5 Air Pollution: Population Exposed to Levels Exceeding WHO Interim Target-1 Value: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Korea – Table KR.World Bank: Environment: Pollution. Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 1 (IT-1) is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 35 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed.; ; Brauer, M. et al. 2016, for the Global Burden of Disease Study 2016.; Weighted Average;

In 2022, the air pollution level of ozone (O3) in South Korea amounted to around 32 parts per billion. It is up from about 23 parts per billion in 2008.

The data set used in the paper as well as figures presented in the paper are provided.

Additional file 2: Figure S1. The location of the Korean Air Quality monitoring stations (AQMS) in South Korea with highlighted box of Seoul Metropolis. Figure S2. The results of cross-validation for daily mean concentration of PM10, PM2.5, and NO2 in South Korea during 2012 and 2013. x-axis: observed values. y-axis: predicted values.

Dataset and Figures for "Long-term air pollution exposure, adaptation and respiratory illness: evidence from South Korea" by Tackseung Jun and In-sik Min

In 2022, the air pollution level from particulate matter (PM10) in South Korea amounted to approximately 31 micrograms per cubic meter. It is a decrease from 54 micrograms per cubic meter in 2008.

The Asia Pacific Air Quality Monitoring Market size was valued at USD 77.40 Million in 2023 and is projected to reach USD 105.68 Million by 2032, exhibiting a CAGR of 4.55 % during the forecasts periods. Air quality monitoring is the systematic process of measuring and assessing the concentration of pollutants in the air to ensure it meets established health and environmental standards. This process involves the use of various sensors and instruments to detect pollutants such as particulate matter (PM2.5 and PM10), ground-level ozone, nitrogen dioxide, sulfur dioxide, and carbon monoxide. These pollutants can originate from various sources, including vehicle emissions, industrial activities, and natural events like wildfires and dust storms. Air quality monitoring can be conducted at different levels, from local to global scales. Local monitoring networks provide detailed information about air quality in specific areas, while satellite-based monitoring offers a broader view of regional and global air quality trends. The collected data is often used to calculate the Air Quality Index (AQI), a standardized indicator that communicates the level of air pollution and its potential health impacts to the public. Recent developments include: Januaru 2023: The government of India launched the Technology for Air Quality Monitoring System (AI-AQMS v1.0) developed under MeitY-supported projects. The Centre for Development of Advanced Computing (C-DAC), Kolkata, in partnership with TeXMIN, ISM, Dhanbad under the ‘National program on Electronics and ICT applications in Agriculture and Environment (AgriEnIcs)’ has developed an outdoor air quality monitoring station to monitor environmental pollutants which includes parameters like PM 1.0, PM 2.5, PM 10.0, SO2, NO2, CO, O2, ambient temperature, relative humidity etc., for continuous air quality analysis of the environment., September 2022: The Asian Development Bank (ADB) launched the Asia Clean Blue Skies Program (ACBSP) to scale up ADB's investments in improving air quality in Asia and the Pacific. ADB launched the ACBSP at the Fourth Asia Pacific Clean Air Partnership joint forum in Seoul, Korea. The program supports the development and strengthening of policies and plans for ADB's developing member countries (DMCs) so that investments are stimulated in air quality projects, such as greenhouse gas reductions in energy, agriculture, transportation, industrial development, and urban development.. Key drivers for this market are: 4., Increasing Awareness and Favorable Government Policies and Non-government Initiatives for Curbing Air Pollution. Potential restraints include: 4., High Costs of Air Quality Monitoring Systems. Notable trends are: Outdoor Segment to Witness Significant Growth.