The Air Quality Dataset provides a comprehensive overview of atmospheric pollution levels across various locations in Poland from 2017 to 2023. It features extensive measurements of numerous air pollutants captured through an extensive network of air quality monitoring stations throughout the country. The dataset includes both hourly (1g) and daily (24g) averages of the recorded pollutants, offering detailed temporal resolution to study short-term peaks and long-term trends in air quality.

Pollutants Measured:

1. Gaseous Pollutants: Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Nitric Oxide (NO), Nitrogen Oxides (NOx), Sulfur Dioxide (SO2), Ozone (O3), and Benzene (C6H6).
2. Particulate Matter: PM10, PM2.5; and specific elements and compounds bound to PM10 such as Lead (Pb), Arsenic (As), Cadmium (Cd), Nickel (Ni), among others.
3. Polycyclic Aromatic Hydrocarbons (PAHs) associated with PM10: Benzo[a]anthracene (BaA), Benzo[b]fluoranthene (BbF), Benzo[j]fluoranthene (BjF), Benzo[k]fluoranthene (BkF), Benzo[a]pyrene (BaP), Indeno[1,2,3-cd]pyrene (IP), Dibenzo[a,h]anthracene (DBahA).
4. Additional Chemicals: Including various volatile organic compounds (VOCs) like ethylene, toluene, xylene variants, aldehydes, and hydrocarbons.

Features of the Dataset:

Locations: Data from numerous air quality monitoring stations distributed across various urban, suburban, and rural areas in Poland.
Time Resolution: Measurements are provided in both hourly and daily intervals, catering to different analytical needs.
Coverage Period: This dataset encompasses data from 2017 to the year, 2023, enabling analysis over multiple years to discern trends and assess the effectiveness of air quality management policies.
Deployment of Deposition Sampling: Concentrations of certain pollutants in wet and dry deposition forms, noted with 'cdepoz' (cumulative deposition), providing insights into the deposition rates of airborne pollutants.

Potential Applications:

Environmental Research: Study the impact of various pollutants on air quality, health, and the environment.
Policy Making: Assist policymakers in evaluating the effectiveness of past regulations and planning future actions to improve air quality.
Public Health: Correlate pollutant exposure levels with health outcomes, helping public health professionals to mitigate risks associated with poor air quality.

Data Format:

The dataset is structured in a tabular format with each row representing an observation time (either hourly or daily) and columns representing different pollutants and their concentrations at various monitoring stations.

This dataset is an essential resource for researchers, policymakers, environmental agencies, and health professionals who need a detailed and robust dataset to understand and combat air pollution in Poland.

Source of data: Chief Inspectorate of Environmental Protection (GIOS)

The historic weather dataset for Cracow and Warsaw

The historic weather dataset for Cracow and Warsaw with suburbs, covering daily observations from 2019 to August 2024, would encompass a range of atmospheric and meteorological data points collected over the defined time period and locations. Here’s a description of what such a dataset might include and signify: Key Characteristics:

Locations: The cities of Cracow and Warsaw, along with their suburbs. The dataset would likely specify the exact areas or measurements stations.
Time Frame: Daily records from January 1, 2019, to August, 2024, providing a comprehensive view of weather variations through different seasons and years.
Data Granularity: Daily data would allow trends such as temperature fluctuations, precipitation patterns, and weather anomalies to be studied in considerable detail.

Likely Data Fields:

Each record in the dataset might contain:

DATE_VALID_STD: Representing each day within the date range specified (from 2019-01-01 to 2024-08-20 for Cracow and Warsaw suburbs).
Temperature Fields (Min, Max, Avg): Temperature readings at specified intervals, likely in Celsius, providing insight into daily and seasonal temperature patterns and extremes.
Humidity Fields (Min, Max, Avg): Relative and specific humidity readings to assess moisture levels in the air, which have implications for weather conditions, comfort levels, and health.
Precipitation: Data on rainfall, snowfall, and total snow depth, essential for understanding water cycle dynamics, agricultural planning, and urban water management in these areas.
Wind Measurements: May include minimum, average, and maximum speeds and perhaps prevailing directions, useful in sectors like aviation, construction, and event planning.
Pressure and Tendency: Barometric pressure readings at different measurement standards to help predict weather changes.
Radiation and Cloud Cover: D...

Caeli can provide this data through an API, dashboard, real-time geo map, or via datasets(.csv). In addition, all this data is available in daily, monthly and annual formats. The data can be delivered in various spatial resolutions starting from 0.001 degrees latitude and longitude (WSG 84), which roughly converts to 100X100 meter.

The Caeli datasets are often used for creating and validating various models and for training machine learning algorithms. We’ll allow you to specify your state or country, your preferred timeframe, resolution, and pollutant. Based on this information we’ll compile a reliable dataset. The measurements in de dataset can be used in determining the air quality of a region for a specific period of time. Additionally, your composite dataset can also serve for strategy and reporting purposes, such as ESG strategy, TCDF, SFDR, and sustainable decision making. The price of the dataset is based on the size of the area, the resolution chosen, and the number of years.

Additional information about particulate matter(PM2,5 – PM10): Particulate matter (PM) refers to tiny particles suspended in the air that can be inhaled into the respiratory system. PM is classified by size, with PM2.5 and PM10 referring to particles that are 2.5 micrometers and 10 micrometers in diameter, respectively. PM2.5 particles are particularly harmful because they are small enough to pass through the respiratory system and enter the bloodstream, where they can cause a variety of health problems. PM2.5 and PM10 are often used as indicators of air quality, with higher concentrations of these particles in the air being associated with increased risk of respiratory and cardiovascular diseases.

Are you interested in the pollutant particulate matter(PM2,5 – PM10) or would you like to gather more information about our opportunities? Please, do not hesitate to contact us. www.caeli.space

Sector coverage: Financial | Energy | Government | Agricultural | Health | Shipping.

This United States Environmental Protection Agency (US EPA) feature layer represents monitoring site data, updated hourly concentrations and Air Quality Index (AQI) values for the latest hour received from monitoring sites that report to AirNow.

This layer includes contains air quality and meteorologic measurements from air monitoring stations in Michigan that is sourced from AirNow. This dataset contains only the most recent recorded values. Note that this data is preliminary and is subject to validation and changes.

Field Name

Alias

Description

OBJECTID

N/A

N/A

StationID

Station ID

The station ID assigned by EGLE

StationName

Station Name

Station name of the air monitoring station. StationType

Station TypeThe type of air monitoring station. The value 'Permanent' indicates the station is a fixed, long-term installation.

StationStatus

Station Status

Activity status of the station.

LastObservation

Last Observation

Date and time of the most recent recorded observation.

shape

shape

ESRI geometry field.

WD_DEGREES

Wind Direction

Wind direction for current observation expressed in degrees.

WS_MS

Wind Speed

Wind speed measured in meters per second.

TEMP_CTemperatureTemperature measure in degrees Celsius.

PM25_UGM3

PM 2.5

Concentration of particulate matter ≤ 2.5 micrometers (PM2.5) measured in micrograms per cubic meter (µg/m³).

OZONE_PPBOzone

Concentration of ozone (O3) measured in parts per billion (ppb).

NO2_PPB

NO2

Concentration of nitrogen dioxide (NO₂) measured in parts per billion (ppb).

SO2_PPB

SO2Concentration of sulfur dioxide (SO₂) measured in parts per billion (ppb).

CO_PPM

CO

Concentration of carbon monoxide (CO) measured in parts per million (ppm).

NO_PPB

NOConcentration of nitrogen monoxide (NO) measured in parts per billion (ppb).

PM10_UGM3

PM 10

Concentration of particulate matter ≤ 10 micrometers (PM10) measured in micrograms per cubic meter (µg/m³). NOX_PPB

NOxConcentration of nitrogen oxides (NOx) measured in parts per billion (ppb).RWD_DEGREESResultant Wind Direction The average wind direction expressed in degrees. NOY_PPB

NOy

Concentration of total reactive nitrogen (NOy) measured in parts per billion (ppb). RWS_KNOTS

Resultant Wind Speed

The average wind speed measured in knots.

If you have questions related to air quality, please reach out to Susan Kilmer (KilmerS@Michigan.gov or 517-242-2655). If you have map suggestions or functionality issues, please reach out to EGLE-Maps@Michigan.gov.From EPA AirNow:Although preliminary data quality assessments are performed, the data in AirNow are not fully verified and validated through the quality assurance procedures monitoring organizations used to officially submit and certify data on the EPA Air Quality System (AQS).This data sharing, and centralization creates a one-stop source for real-time and forecast air quality data. The benefits include quality control, national reporting consistency, access to automated mapping methods, and data distribution to the public and other data systems. The U.S. Environmental Protection Agency, National Oceanic and Atmospheric Administration, National Park Service, tribal, state, and local agencies developed the AirNow system to provide the public with easy access to national air quality information. State and local agencies report the Air Quality Index (AQI) for cities across the US and parts of Canada and Mexico. AirNow data are used only to report the AQI, not to formulate or support regulation, guidance or any other EPA decision or position.About the AQIThe Air Quality Index (AQI) is an index for reporting daily air quality. It tells you how clean or polluted your air is, and what associated health effects might be a concern for you. The AQI focuses on health effects you may experience within a few hours or days after breathing polluted air. EPA calculates the AQI for five major air pollutants regulated by the Clean Air Act: ground-level ozone, particle pollution (also known as particulate matter), carbon monoxide, sulfur dioxide, and nitrogen dioxide. For each of these pollutants, EPA has established national air quality standards to protect public health. Ground-level ozone and airborne particles (often referred to as "particulate matter") are the two pollutants that pose the greatest threat to human health in this country.A number of factors influence ozone formation, including emissions from cars, trucks, buses, power plants, and industries, along with weather conditions. Weather is especially favorable for ozone formation when it’s hot, dry and sunny, and winds are calm and light. Federal and state regulations, including regulations for power plants, vehicles and fuels, are helping reduce ozone pollution nationwide.Fine particle pollution (or "particulate matter") can be emitted directly from cars, trucks, buses, power plants and industries, along with wildfires and woodstoves. But it also forms from chemical reactions of other pollutants in the air. Particle pollution can be high at different times of year, depending on where you live. In some areas, for example, colder winters can lead to increased particle pollution emissions from woodstove use, and stagnant weather conditions with calm and light winds can trap PM2.5 pollution near emission sources. Federal and state rules are helping reduce fine particle pollution, including clean diesel rules for vehicles and fuels, and rules to reduce pollution from power plants, industries, locomotives, and marine vessels, among others.How Does the AQI Work?Think of the AQI as a yardstick that runs from 0 to 500. The higher the AQI value, the greater the level of air pollution and the greater the health concern. For example, an AQI value of 50 represents good air quality with little potential to affect public health, while an AQI value over 300 represents hazardous air quality.An AQI value of 100 generally corresponds to the national air quality standard for the pollutant, which is the level EPA has set to protect public health. AQI values below 100 are generally thought of as satisfactory. When AQI values are above 100, air quality is considered to be unhealthy-at first for certain sensitive groups of people, then for everyone as AQI values get higher.Understanding the AQIThe purpose of the AQI is to help you understand what local air quality means to your health. To make it easier to understand, the AQI is divided into six categories:Air Quality Index(AQI) ValuesLevels of Health ConcernColorsWhen the AQI is in this range:..air quality conditions are:...as symbolized by this color:0 to 50GoodGreen51 to 100ModerateYellow101 to 150Unhealthy for Sensitive GroupsOrange151 to 200UnhealthyRed201 to 300Very UnhealthyPurple301 to 500HazardousMaroonNote: Values above 500 are considered Beyond the AQI. Follow recommendations for the Hazardous category. Additional information on reducing exposure to extremely high levels of particle pollution is available here.Each category corresponds to a different level of health concern. The six levels of health concern and what they mean are:"Good" AQI is 0 to 50. Air quality is considered satisfactory, and air pollution poses little or no risk."Moderate" AQI is 51 to 100. Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people. For example, people who are unusually sensitive to ozone may experience respiratory symptoms."Unhealthy for Sensitive Groups" AQI is 101 to 150. Although general public is not likely to be affected at this AQI range, people with lung disease, older adults and children are at a greater risk from exposure to ozone, whereas persons with heart and lung disease, older adults and children are at greater risk from the presence of particles in the air."Unhealthy" AQI is 151 to 200. Everyone may begin to experience some adverse health effects, and members of the sensitive groups may experience more serious effects."Very Unhealthy" AQI is 201 to 300. This would trigger a health alert signifying that everyone may experience more serious health effects."Hazardous" AQI greater than 300. This would trigger a health warnings of emergency conditions. The entire population is more likely to be affected.AQI colorsEPA has assigned a specific color to each AQI category to make it easier for people to understand quickly whether air pollution is reaching unhealthy levels in their communities. For example, the color orange means that conditions are "unhealthy for sensitive groups," while red means that conditions may be "unhealthy for everyone," and so on.Air Quality Index Levels of Health ConcernNumericalValueMeaningGood0 to 50Air quality is considered satisfactory, and air pollution poses little or no risk.Moderate51 to 100Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.Unhealthy for Sensitive Groups101 to 150Members of sensitive groups may experience health effects. The general public is not likely to be affected.Unhealthy151 to 200Everyone may begin to experience health effects; members of sensitive groups may experience more serious health effects.Very Unhealthy201 to 300Health alert: everyone may experience more serious health effects.Hazardous301 to 500Health warnings of emergency conditions. The entire population is more likely to be affected.Note: Values above 500 are considered Beyond the AQI. Follow recommendations for the "Hazardous category." Additional information on reducing exposure to extremely high levels of particle pollution is available here. Visit Michigan.gov/EGLE for more information about air monitoring in Michigan.

Air pollution has a significant impact on human health and the economy. Air quality in Sydney is usually very good by international standards.

For more information about air quality in Sydney, how our ventilation systems work to manage air quality within and outside the tunnels, and what has contributed to improve vehicle emissions visit the Air Quality Portal.

This dataset provides standardised measures of:

• Carbon Monoxide
• Nitrogen dioxide
• Nitrogen oxides
• Ozone
• Sulfur dioxide
• Particles < 10μm diameter
• Particles < 2.5μm diameter
• BTEX
• Methane
• Non-Methane Hydrocarbons
• THC

The data captured is from 01/01/2004 - 31/12/2017 and only includes sites where RMS had access to the monitor's data. More information about the sites covered can be found in the Report and associated data files.

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/

Hourly Air Pollution Index (API) at different Air Quality Monitoring stations in Feb.2010.

The API is a simple way of describing air pollution levels. In Hong Kong, the API converts air pollution data from several types of pollutants into a value ranging from 0 to 500. Since API has been replaced by AQHI on 30 December 2013, please refer to the archive of API webpage, if you want to have the information of API.

The above external links lead to a collection of CSV files containing past hourly API data from July 1999 to December 2013.

The Air Pollution Index has been replaced by the Air Quality Health Index (AQHI) since 30 December 2013. At present, there are 3 roadside and 12 general stations reporting AQHI. Please refer to the thematic page of AQHI for more information.

Abstract

Polluted air is a major health hazard in developing countries. Improvements in pollution monitoring and statistical techniques during the last several decades have steadily enhanced the ability to measure the health effects of air pollution. Current methods can detect significant increases in the incidence of cardiopulmonary and respiratory diseases, coughing, bronchitis, and lung cancer, as well as premature deaths from these diseases resulting from elevated concentrations of ambient Particulate Matter (Holgate 1999).

Scarce public resources have limited the monitoring of atmospheric particulate matter (PM) concentrations in developing countries, despite their large potential health effects. As a result, policymakers in many developing countries remain uncertain about the exposure of their residents to PM air pollution. The Global Model of Ambient Particulates (GMAPS) is an attempt to bridge this information gap through an econometrically estimated model for predicting PM levels in world cities (Pandey et al. forthcoming).

The estimation model is based on the latest available monitored PM pollution data from the World Health Organization, supplemented by data from other reliable sources. The current model can be used to estimate PM levels in urban residential areas and non-residential pollution hotspots. The results of the model are used to project annual average ambient PM concentrations for residential and non-residential areas in 3,226 world cities with populations larger than 100,000, as well as national capitals.

The study finds wide, systematic variations in ambient PM concentrations, both across world cities and over time. PM concentrations have risen at a slower rate than total emissions. Overall emission levels have been rising, especially for poorer countries, at nearly 6 percent per year. PM concentrations have not increased by as much, due to improvements in technology and structural shifts in the world economy. Additionally, within-country variations in PM levels can diverge greatly (by a factor of 5 in some cases), because of the direct and indirect effects of geo-climatic factors.

The primary determinants of PM concentrations are the scale and composition of economic activity, population, the energy mix, the strength of local pollution regulation, and geographic and atmospheric conditions that affect pollutant dispersion in the atmosphere.

Geographic coverage

The database covers the following countries: Afghanistan Albania Algeria Andorra Angola
Antigua and Barbuda Argentina
Armenia Australia
Austria Azerbaijan
Bahamas, The
Bahrain Bangladesh
Barbados
Belarus Belgium Belize
Benin
Bhutan
Bolivia Bosnia and Herzegovina
Brazil
Brunei
Bulgaria
Burkina Faso
Burundi Cambodia
Cameroon
Canada
Cayman Islands
Central African Republic
Chad
Chile
China
Colombia
Comoros Congo, Dem. Rep.
Congo, Rep. Costa Rica
Cote d'Ivoire
Croatia Cuba
Cyprus
Czech Republic
Denmark Dominica
Dominican Republic
Ecuador Egypt, Arab Rep.
El Salvador Eritrea Estonia Ethiopia
Faeroe Islands
Fiji
Finland France
Gabon
Gambia, The Georgia Germany Ghana
Greece
Grenada Guatemala
Guinea
Guinea-Bissau
Guyana
Haiti
Honduras
Hong Kong, China
Hungary Iceland India
Indonesia
Iran, Islamic Rep.
Iraq
Ireland Israel
Italy
Jamaica Japan
Jordan
Kazakhstan
Kenya
Korea, Dem. Rep.
Korea, Rep. Kuwait
Kyrgyz Republic Lao PDR Latvia
Lebanon Lesotho Liberia Liechtenstein
Lithuania
Luxembourg
Macao, China
Macedonia, FYR
Madagascar
Malawi
Malaysia
Maldives
Mali
Mauritania
Mexico
Moldova Mongolia
Morocco Mozambique
Myanmar Namibia Nepal
Netherlands Netherlands Antilles
New Caledonia
New Zealand Nicaragua
Niger
Nigeria Norway
Oman
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines Poland
Portugal
Puerto Rico Qatar
Romania Russian Federation
Rwanda
Sao Tome and Principe
Saudi Arabia
Senegal Sierra Leone
Singapore
Slovak Republic Slovenia
Solomon Islands Somalia South Africa
Spain
Sri Lanka
St. Kitts and Nevis St. Lucia
St. Vincent and the Grenadines
Sudan
Suriname
Swaziland
Sweden
Switzerland Syrian Arab Republic
Tajikistan
Tanzania
Thailand
Togo
Trinidad and Tobago Tunisia Turkey
Turkmenistan
Uganda
Ukraine United Arab Emirates
United Kingdom
United States
Uruguay Uzbekistan
Vanuatu Venezuela, RB
Vietnam Virgin Islands (U.S.)
Yemen, Rep. Yugoslavia, FR (Serbia/Montenegro)
Zambia
Zimbabwe

Kind of data

Observation data/ratings [obs]

Mode of data collection

Other [oth]

https://www.who.int/data/gho/data/themes/air-pollution/who-air-quality-database/2022

Following text is taken from website. " This database was release in April 2022 (5th edition) and the years of measurement range mostly from 2010 to 2019.

The WHO air quality database compiles data on ground measurements of annual mean concentrations of nitrogen dioxide (NO2), particulate matter of a diameter equal or smaller than 10 μm (PM10) or equal or smaller than 2.5 μm (PM2.5) which aim at representing an average for the city or town as a whole, rather than for individual stations. Both groups of pollutants originate mainly from human activities related to fossil fuel combustion.

The 2022 update (Fifth Version) database was released in April 2022 and hosts data on air quality for over 600 human settlements in more than 100 countries.

The data compiled in this database is used as input to derive the Sustainable Development Goal Indicator 11.6.2, Air quality in cities, for which WHO is custodial agency. "

The Air Pollutant Emission Inventory (APEI) is an annual report of air pollutant emissions across Canada published by Environment and Climate Change Canada. The report details the release of air pollutants from all known of sources since 1990. The APEI serves many purposes, including: - supporting the development of and tracking progress on air quality management strategies, policies and regulations - fulfilling Canada’s domestic and international reporting obligations - informing Canadians about air pollutants emissions - providing data to support Canada’s air quality health indices Emissions data is available for the following: Criteria air contaminants (CACs): - Total particulate matter (TPM) - Particulate matter less than or equal to 10 microns (PM10) - Particulate matter less than or equal to 2.5 microns (PM2.5) - Sulphur oxides (SOx) - Nitrogen oxides (NOx) - Volatile organic compounds (VOCs) - Carbon monoxide (CO) - Ammonia (NH3) Heavy metals: - Mercury (Hg) - Lead (Pb) - Cadmium (Cd) Persistent organic pollutants (POPs): - Dioxins and furans (D/F) - Four polycyclic aromatic hydrocarbons (PAHs) compounds (Benzo[a]pyrene, Benzo[b]fluoranthene, Benzo[k]fluoranthene and Indeno[1,2,3-cd]pyrene) - Hexachlorobenzene (HCB) This data record breaks down the historical trends of reported pollutants by individual substances. To perform more customized selections of APEI data, please visit our website to use our interactive query tool. The Air Pollutant Emission Inventory is compiled from many different data sources. Emissions data reported by individual facilities to Environment and Climate Change Canada’s National Pollutant Release Inventory are supplemented with well documented, science-based estimation tools to quantify total emissions. Together these data sources provide a comprehensive overview of pollutant emissions across Canada. Supplemental Information Air Pollutants Inventory Main Page: https://Canada.ca/APEI APEI and Black Carbon Interactive Query Tool: https://pollution-waste.canada.ca/air-emission-inventory/ Canada's Black Carbon Emission Inventory: https://Canada.ca/black-carbon Supporting Projects: National Air Pollutant Emissions Trends for 1990–2023

This dataset contains synthetic estimates of ambient air pollution concentrations across England, provided as hourly averages representing typical conditions. The data cover major pollutants, including Nitrogen Dioxide (NO2), Nitric Oxide (NO), Nitrogen Oxides (NOx), Ozone (O3), Particulate Matter smaller than 10 micrometres (PM10) and smaller than 2.5 micrometres (PM2.5), and Sulphur Dioxide (SO2). Each pollutant's concentrations are predicted not only as average (mean) values but also include estimates at lower (5th percentile), median (50th percentile), and upper (95th percentile) levels to highlight typical and potential extreme pollution scenarios.

The spatial coverage of the dataset includes the entire area of England, structured as an evenly spaced grid, with each grid square covering an area of 1 square kilometre (1 km^2). Data points correspond to the centre of these grid squares. Temporally, the dataset does not represent actual hourly measurements from specific dates; instead, it provides aggregated "typical day" profiles constructed by averaging observations collected from multiple years (2014-2018) for each month, weekday, and hour. This method offers representative insights into typical air pollution patterns, avoiding the complexity of handling large-scale raw datasets.

These pollution estimates were produced using a supervised machine learning method, which is a computational approach where algorithms are trained to identify patterns in historical data and apply these learned patterns to predict new data points. The predictions incorporated various environmental factors including weather conditions (e.g., temperature, wind, precipitation), human activities (traffic patterns), satellite measurements, land-use types (urban, rural, industrial areas), and emission inventories (datasets detailing pollutants released into the atmosphere). Additionally, the dataset provides uncertainty intervals through percentile-based estimates, giving users insights into the reliability of the predictions.

The dataset was developed to facilitate easier access to high-quality air pollution information for diverse stakeholders, such as researchers, policymakers, urban planners, and health professionals. By providing clear, simplified air quality scenarios, it helps users make informed decisions in urban planning, public health, environmental management, and policy development, as well as to assess potential impacts and interventions related to air pollution.

The dataset was created by Liam J. Berrisford at the University of Exeter during his PhD studies, supported by the UK Research and Innovation (UKRI) Centre for Doctoral Training in Environmental Intelligence. Full methodological details and data validation information are available in the associated open-access scientific publication. For more information about the data, see the README.md archived alongside this dataset.

In terms of completeness, this dataset intentionally provides representative hourly pollution estimates rather than exact historical measurements or specific pollution events. While it extensively covers typical pollution scenarios across England, direct measurements from specific air quality monitoring stations are not included. Users requiring detailed historical observations or data about specific events should refer to original monitoring station datasets.

New Delhi was the most polluted city in India in 2024, based on an average air quality index (AQI) of 169. The seven most polluted cities in India in 2024 all had AQI levels above 150. An AQI between 151 and 200 is classified as unhealthy. Air pollution in India India was the third most polluted country in the world in 2023, behind only Bangladesh and Pakistan. The South Asian country recorded an average annual fine particulate matter (PM2.5) concentration of 54 micrograms per cubic meter of air (µg/m3) that year, more than 10 times above the World Health Organization’s recommended limit. Health effects of air pollution Exposure to air pollution can lead to a range of health issues, such as strokes, respiratory conditions, and cardiovascular disease. Air pollution is attributable to millions of premature deaths every year around the world, with India one of the most affected countries.

Dataset of annual average pollutant concentrations (rolling average of the last 3 years) for the nitrogen dioxide NO2, PM10 particles, PM2.5 fine particles and Ozone O3 from 2015.

Airparif monitors and analyzes on a daily basis the long-term evolution of pollution levels according to pollutants, and in particular regulated pollutants, for each year and on all Ile-de-France municipalities.

The concentrations of regulated pollutants have been decreasing for several years in Paris and Île-de-France. However, the concentrations of particles and nitrogen dioxide remain problematic in Paris, with regulatory limit values ​​and WHO health recommendation thresholds are exceeded. For PM2.5 particles, ozone (O3) the concentrations measured comply with the limit values, but still exceed the quality objectives.

Since 2023, Airparif has distinguished the average annual concentrations of traffic on the Boulevard Périphérique from that of the Intramuros for NO2 and PM10 and only on the Boulevard Périphérique for PM2.5. < /span>

This breakdown may have been calculated from 2015.

That's why you have another dataset at your disposal Quality of air - Average concentration - NO2 - PM2.5 - PM10 - O3 from 2004 to 2014.

NB:

Nitrogen oxides (NOx) include nitrogen monoxide (NO) and nitrogen dioxide (NO2). The main emitters are motorized vehicles. NO2 is a gas that irritates the bronchi.In asthmatics, it increases the frequency and severity of attacks.In children, it promotes lung infections.

The data provider is Airparif >> Annual pollution reports and maps | Airparif; Monitored pollutants | Airparif

Link Paris.fr >> Air and sound environment - City of Paris; State of air quality in Paris - City of Paris; Regulated and monitored air quality - City of Paris< /span>

ChinaHighSO₂ is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.

Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level SO₂ dataset for China from 2013 to 2018. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R²) of 0.84, a root-mean-square error (RMSE) of 10.07 µg m^-3, and a mean absolute error (MAE) of 4.68 µg m^-3 on a daily basis.

If you use the ChinaHighSO₂ dataset in your scientific research, please cite the following reference (Wei et al., ACP, 2023):

• Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. Ground-level gaseous pollutants (NO₂, SO₂, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmospheric Chemistry and Physics, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023

Note that the ChinaHighSO₂ dataset was improved to a 1 km resolution after 2019:

all (including daily) data for the years after 2019 are accessible at: https://doi.org/10.5281/zenodo.10476944

More CHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html

This dataset consists of worldwide air quality analytics. The data inputs was sourced from the official govermental website which describes the current Air Quality Index (AQI) based on daily and/or monthly reports. This dataset was generated on April 11, 2025.

Data inputs were given minor changes to avoid copyright of the original author from various website, approximately between 10-18 inputs (note that the dataset was under development due to frequent dataset updates).

Time series since 2004 of the values measured for the main air pollutants: particulate matter, nitrogen dioxide and ozone. Fine particles, called PM10 (diameter less than 10 μm), are polluting particles in the air that can be inhaled and penetrate the upper respiratory tract. The higher the concentration of fine dust in the air, the greater the harmful effect on the health of the population. The PM10 concentration thresholds, calculated on a daily and annual time basis, are established by Legislative Decree 155/2010: the annual limit value for the protection of human health of 40 μg/m3 and the daily limit value of 50 μg/m3 shall not be exceeded more than 35 times/year. Nitrogen dioxide is a toxic gas irritating the mucous membranes and responsible for specific diseases affecting the respiratory system (bronchitis, allergies, irritation), among the emission sources vehicular traffic has been identified as the one that contributes most to the increase in levels in the air. The limit value for the concentration of nitrogen dioxide for the protection of human health calculated on an annual time basis and established by Legislative Decree 155/2010 is 40 μg/m3. Ozone is a very toxic pollutant to humans, the effects of ozone range from irritation in the throat and respiratory tract to burning eyes; Higher concentrations of the pollutant may lead to changes in respiratory function and increased frequency of asthma attacks, especially in susceptible individuals. Ozone is also responsible for damage to vegetation and crops. The assessment of the status of the indicator is based on the days when the long-term target threshold for the protection of human health of 120 μg/m3 is exceeded, calculated as the daily maximum of the 8-hour moving average, and the maximum number of exceedances allowed in a year is 25. Original data from http://www.arpalombardia.it This dataset has been issued by the Municipality of Milan. The path to be used to find the original dataset on sisi.comune.milano.it is: sisi.comune.milano.it - Environment and Mobility - Environment - Air pollution

This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.

Zimbabwe ZW: PM2.5 Air Pollution: Population Exposed to Levels Exceeding WHO Interim Target-1 Value: % of Total data was reported at 12.334 % in 2016. This records an increase from the previous number of 11.255 % for 2015. Zimbabwe ZW: PM2.5 Air Pollution: Population Exposed to Levels Exceeding WHO Interim Target-1 Value: % of Total data is updated yearly, averaging 11.255 % from Dec 1990 (Median) to 2016, with 11 observations. The data reached an all-time high of 20.604 % in 1990 and a record low of 2.004 % in 2010. Zimbabwe ZW: 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 Zimbabwe – Table ZW.World Bank.WDI: 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;

Per capita carbon dioxide (CO₂) emissions in India have soared in recent decades, climbing from 0.4 metric tons per person in 1970 to a high of 2.07 metric tons per person in 2023. Total CO₂ emissions in India also reached a record high in 2023. Greenhouse gas emissions in India India is the third-largest CO₂ emitter globally, behind only China and the United States. Among the various economic sectors of the country, the power sector accounts for the largest share of greenhouse gas emissions in India, followed by agriculture. Together, these two sectors were responsible for more than half of India's total emissions in 2023. Coal emissions One of the main reasons for India's high emissions is the country's reliance on coal, the most polluting of fossil fuels. India's CO₂ emissions from coal totaled roughly two billion metric tons in 2023, a near sixfold increase from 1990 levels.