Context

This dataset, spanning 170 countries and 300+ cities, provides a holistic view of global air quality dynamics. Focused on crucial pollutants like Carbon Monoxide, Ozone, Nitrogen Dioxide, and Particulate Matter (PM2.5), it serves as a valuable resource for environmental scientists, policymakers, and researchers. The insights derived from this dataset empower users to analyze air quality trends, formulate effective policies, and contribute to fostering a healthier planet.

Content

Featuring essential columns such as country name, city name, overall Air Quality Index (AQI) values, and concentrations of specific pollutants, this dataset supports in-depth analyses and correlation studies. Researchers can uncover patterns and trends in air quality by exploring the relationships between pollutants and overall AQI values. With its comprehensive scope, this dataset is an indispensable tool for those interested in understanding air quality dynamics and actively participating in collective efforts toward a cleaner and healthier atmosphere.

Dataset Structure:

The dataset (global_air_pollution_data.csv) covers the year of 2024 and includes the following columns:

Acknowledgment

The primary dataset was sourced from eLichens, and I extend sincere gratitude to the team for providing the core data used in this dataset.

© Image credit: Freepik

Citywide raster files of annual average predicted surface for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and nitric oxide (NO); summer average for ozone (O3) and winter average for sulfure dioxide (SO2). Description: Annual average predicted surface for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and nitric oxide (NO); summer average for ozone (O3) and winter average for sulfure dioxide (SO2). File type is ESRI grid raster files at 300 m resolution, NAD83 New York Long Island State Plane FIPS, feet projection. Prediction surface generated from Land Use Regression modeling of December 2008- December 2019 (years 1-11) New York Community Air Survey monitoring data.As these are estimated annual average levels produced by a statistical model, they are not comparable to short term localized monitoring or monitoring done for regulatory purposes. For description of NYCCAS design and Land Use Regression Modeling process see: nyc-ehs.net/nyccas

This file describes the dataset used in Ou et al., "Air pollution control strategies directly limiting national health damages in the US." This work used the Global Change Assessment Model (GCAM) with state-level representation of the U.S. energy system (GCAM-USA). GCAM and GCAM-USA are developed and released by the University of Maryland/Pacific Northwest National Laboratory Joint Global Change Research Center (JGCRI). For further details, see the GCAM documentation: jgcri.github.io/gcam-doc. The model source code is available at github.com/JGCRI/gcam-core. A modified version of GCAMv4.3 was used for this analysis. Source code and input data specific for this paper are available upon request. This dataset contains Excel spreadsheets and an R script that link to comma-separated values (CSV) files that were extracted from the model output. The spreadsheets and scripts show the data and reproduce each of the figures in the paper. This dataset is associated with the following publication: Ou, Y., J. West, S. Smith, C. Nolte, and D. Loughlin. Air pollution control strategies directly limiting national health damages in the US.. Nature Communications. Nature Publishing Group, London, UK, 11: 957, (2020).

This dataset contains synthetic but realistic air pollution data from five major cities in China: Beijing, Shanghai, Guangzhou, Chengdu, and Shenzhen. It spans from 2015 to 2025, providing valuable information on air quality, meteorological conditions, and pollution levels. The dataset is structured with 3,000 rows and 24 columns, covering various air pollutants, weather conditions, and geographical details.

The dataset is designed for data analysis, machine learning models, and air quality forecasting applications.

The National Air Pollution Surveillance (NAPS) program is the main source of ambient air quality data in Canada. The NAPS program, which began in 1969, is now comprised of nearly 260 stations in 150 rural and urban communities reporting to the Canada-Wide Air Quality Database (CWAQD). Managed by Environment and Climate Change Canada (ECCC) in collaboration with provincial, territorial, and regional government networks, the NAPS program forms an integral component of various diverse initiatives; including the Air Quality Health Index (AQHI), Canadian Environmental Sustainability Indicators (CESI), and the US-Canada Air Quality Agreement. Once per year, typically autumn, the Continuous data set for the previous year is reported on ECCC Data Mart. Beginning in March of 2020 the impact of the COVID-19 pandemic on NAPS Operations has resulted in reduced data availability for some sites and parameters. For additional information on NAPS data products contact the NAPS inquiry centre at RNSPA-NAPSINFO@ec.gc.ca Last updated March 2023. Supplemental Information Monitoring Program Overview The NAPS program is comprised of both continuous and (time-) integrated measurements of key air pollutants. Continuous data are collected using gas and particulate monitors, with data reported every hour of the year, and are available as hourly concentrations or annual averages. Integrated samples, collected at select sites, are analyzed at the NAPS laboratory in Ottawa for additional pollutants, and are typically collected for a 24 hour period once every six days, on various sampling media such as filters, canisters, and cartridges. Continuous Monitoring Air pollutants monitored continuously include the following chemical species: • carbon monoxide (CO) • nitrogen dioxide (NO2) • nitric oxide (NO) • nitrogen oxides (NOX) • ozone (O3) • sulphur dioxide (SO2) • particulate matter less than or equal to 2.5 (PM2.5) and 10 micrometres (PM10) Each provincial, territorial, and regional government monitoring network is responsible for collecting continuous data within their jurisdiction and ensuring that the data are quality-assured as specified in the Ambient Air Monitoring and Quality Assurance/Quality Control Guidelines. The hourly air pollutant concentrations are reported as hour-ending averages in local standard time with no adjustment for daylight savings time. These datasets are posted on an annual basis. Integrated Monitoring Categories of chemical species sampled on a time-integrated basis include: • fine (PM2.5) and coarse (PM10-2.5) particulate composition (e.g., metals, ions), and additional detailed chemistry provided through a subset of sites by the NAPS PM2.5 speciation program; • semi-volatile organic compounds (e.g., polycyclic aromatic hydrocarbons such as benzo[a]pyrene); • volatile organic compounds (e. g., benzene) The 24-hour air pollutant samples are collected from midnight to midnight. These datasets are generally posted on a quarterly basis. Data Disclaimer NAPS data products are subject to change on an ongoing basis, and reflect the most up-to-date and accurate information available. New versions of files will replace older ones, while retaining the same location and filename. The ‘Data-Donnees’ directory contains continuous and integrated data sorted by sampling year and then measurement. Pollutants measured, sampling duration and sampling frequency may vary by site location. Additional program details can be found at ‘ProgramInformation-InformationProgramme’ also in the data resources section. Citations National Air Pollution Surveillance Program, (year accessed). Available from the Government of Canada Open Data Portal at open.canada.ca.

Dataset contains information on New York City air quality surveillance data. Air pollution is one of the most important environmental threats to urban populations and while all people are exposed, pollutant emissions, levels of exposure, and population vulnerability vary across neighborhoods. Exposures to common air pollutants have been linked to respiratory and cardiovascular diseases, cancers, and premature deaths. These indicators provide a perspective across time and NYC geographies to better characterize air quality and health in NYC. Data can also be explored online at the Environment and Health Data Portal: http://nyc.gov/health/environmentdata.

Air pollution levels in cities vary greatly around the world, though they are typically higher in developing regions. In 2024, the cities of Jakarta and Cairo had an average PM2.5 concentrations of **** and **** micrograms per cubic meter (μg/m³) respectively. By comparison, PM2.5 levels in London and New York were less than ***** μg/m³. Nevertheless, pollution levels in these four major cities are all higher than the World Health Organization's healthy limit, which are set at an annual average of less than **** μg/m³. There are many sources of air pollution, such as energy production, transportation, and agricultural activities.

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.Map and forecast data are collected using federal reference or equivalent monitoring techniques or techniques approved by the state, local or tribal monitoring agencies. To maintain "real-time" maps, the data are displayed after the end of each hour. 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.

Environmental monitoring stations (EMS) were installed in Campbelltown and Liverpool's CBD in December 2020. The EMS measures weather data and pollutants data. This dataset stores pollutants related measures:nitrogendioxide (NO2 measured in ppb)carbonmonoxide (CO in ppb)ozone (O3 in ppb)particulate matter 10 (PM10 in µg/m³)particulate matter 2.5 (PM2.5 in µg/m³)Associated Air Quality Index is calculated based on a number of parameters. Data in this dataset is presented in the Quality of Place dashboard.Please note this data is indicative as sensors may from time to time provide incorrect data due to wear and tear or unforeseen circumstances.

This image visually represents air quality data, including key pollutants such as CO, NOx, NO₂, O₃, SO₂, PM2.5, and PM10. It features an Air Quality Index (AQI) gauge, indicating the pollution level from good (green) to hazardous (red). The background showcases an urban cityscape, highlighting the impact of air pollution. Additional graphical elements like bar charts, clouds, and weather indicators (temperature, humidity, and wind speed) make this an informative and data-driven visualization for environmental monitoring and analysis.

Ideal for use as a header image in reports, dashboards, or presentations related to air quality prediction, pollution control, and environmental research. 🚀🌍💨

This dataset consists of all contributions made by Social AQI (SAQI) project. The description of dataset is as below -

Local Sensor Data (hyperlocal-air-quality-sensor-data) - contains all sensors values recorded through local neighbourhood sensors throught the length of the project
Locations for all these sensors are as below - In Najafgarh, Delhi, India : Jharoda Kalan, Nangli Dairy and DTC Bus terminal.
In Okhla : Sanjay Colony, Tekhand, Shaheen Bagh.

Data from Central Pollution Control Board (central-air-quality-sensor-data) - Najafgarh_CPCB.csv, Okhla_CPCB.csv : Contains data provided by CPCB from Najafgarh,Delhi and Oklha, Delhi

PollutionODP.owl : Ontology Design Pattern for pollution - http://ontologydesignpatterns.org/wiki/Submissions:Pollution.

Ontology : SAQI ontology as triples (ttl), xml (rdf) and json-ld (json) serialization format
Ontology documentation : ontology/diagram contains figures describing ontology, ontology/documentation/saqi.html contains LODE documentation for the ontology

ethnographic-survey-data - anonymized survey responses for initial pollution perception and literacy survey as well as SAQI app feedback survey.

SHACL-shapes - for validating against SAQI ontology.

sparql-queries - sample queries to run on our ontology.

setup-rdf-store-script - script to setup rdf store with given data using rml mapper.

Air quality data is collected from the Allegheny County Health Department monitors throughout the county. This data must be verified by qualified individuals before it can be considered official. The following data is unverified. This means that any electrical disruption or equipment malfunction can report erroneous monitored data.

For more information about the Health Department's Air Quality Program or to view a live version of the dashboard, please visit the ACHD website: https://alleghenycounty.us/Health-Department/Programs/Air-Quality/Air-Quality.aspx

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_8f5fa022cbda68494fa70e788600505b/view

The Daily and Annual PM2.5, O3, and NO2 Concentrations at ZIP Codes for the Contiguous U.S., 2000-2016, v1.0 data set contains daily and annual concentration predictions for Fine Particulate Matter (PM2.5), Ozone (O3), and Nitrogen Dioxide (NO2) pollutants at ZIP Code-level for the years 2000 to 2016. Ensemble predictions of three machine-learning models were implemented (Random Forest, Gradient Boosting, and Neural Network) to estimate the daily PM2.5, O3, and NO2 at the centroids of 1km x 1km grid cells across the contiguous U.S. for 2000 to 2016. The predictors included air monitoring data, satellite aerosol optical depth, meteorological conditions, chemical transport model simulations, and land-use variables. The ensemble models demonstrated excellent predictive performance with 10-fold cross-validated R-squared values of 0.86 for PM2.5, 0.86 for O3, and 0.79 for NO2. These high-resolution, well-validated predictions allow for estimates of ZIP Code-level pollution concentrations with a high degree of accuracy. For general ZIP Codes with polygon representations, pollution levels were estimated by averaging the predictions of grid cells whose centroids lie inside the polygon of that ZIP Code; for other ZIP Codes such as Post Offices or large volume single customers, they were treated as a single point and predicted their pollution levels by assigning the predictions using the nearest grid cell. The polygon shapes and points with latitudes and longitudes for ZIP Codes were obtained from Esri and the U.S. ZIP Code Database and were updated annually. The data include about 31,000 general ZIP Codes with polygon representations, and about 10,000 ZIP Codes as single points. The aggregated ZIP Code-level, daily predictions are applicable in research such as epidemiology, public health, and political science, by linking with ZIP Code-level demographic and medical data sets, including national inpatient care records, medical claims data, census data, and U.S. Census Bureau American Community Survey (ACS). The data are particularly useful for studies on rural populations who may lack air monitoring sites. Compared with the 1km grid data, the ZIP Code-level predictions are much smaller in size and are manageable in personal computing environments. This greatly improves the inclusion of scientists in different fields by making it easier to use these data in air pollution research. The Units are ug/m^3 for PM2.5 and ppb for O3 and NO2.

Hourly Air Pollution Index (API) at different Air Quality Monitoring stations from July 1999 to December 2013 (English Version)

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

Air pollution presents a significant environmental risk, impacting human health, accelerating climate change, and disrupting ecosystems. The main aim of air pollution research is to pinpoint the most harmful pollutants identified in previous studies and to map regions exposed to high pollution levels. This study introduces a large-scale, high-quality dataset to advance the analysis of PM2.5 pollution and reveal hidden patterns through pattern mining techniques. The dataset covers five years of hourly PM2.5 measurements collected from approximately 1,900 sensors across Japan, sourced from the Ministry of the Environment's Soramame platform. This platform offers hourly pollutant records, downloadable as monthly raw data files. The unorganised raw data files are systematically organised and stored in database tables using an Entity-Relationship (ER) schema. The primary objective of this dataset is to aid in developing and validating pattern mining models, enabling the accurate detection of..., The air pollution data was collected from Japan’s Soramame platform, which provides hourly updates on pollutant levels nationwide. The data files were collected from January 1, 2018, 01:00:00, to April 25, 2023, 22:00:00, covering records from approximately 1,900 sensors stationed in various locations across Japan. These files are initially unorganised in CSV format and require systematic organisation by year, month, time, sensor, and pollutant type. To maintain data integrity, we structured the dataset using an Entity-Relationship (ER) schema within a PostgreSQL database, comprising two main tables: the Sensor table (storing sensor name, ID, address, and location) and the Observations table (recording pollutant types and their values). A detailed step-by-step process is provided in the README, and this organization created a consolidated CSV file containing PM2.5 levels, timestamps, and sensor details., , # AEROS PM2.5 Dataset

Overview

The AEROS PM2.5 Dataset provides a comprehensive collection of hourly PM2.5 measurements recorded over a period of five years from sensors located across Japan. This dataset is a valuable resource for studying air quality trends, pollution patterns, and environmental health impacts.

Dataset Description

File Information

• File Name: FINAL_DATASET.csv
• Content: Hourly PM2.5 measurements collected from sensors located in Japan over five years.

Structure

The dataset includes the following columns:

1. Timestamps: The date and time when the measurement was recorded.
2. Sensor Location IDs: Unique identifiers for the sensor locations.
3. PM2.5 Values (µg/m³): The recorded PM2.5 concentration at a specific timestamp and location.

Units

• PM2.5 Values: Measured in micrograms per cubic meter (µg/m³).

Notes on Data

• Empty Cells: Represent instances where no PM2.5 data was recorded by the s...

Annual emissions of various air pollutants in the United States have experienced dramatic reductions over the past half a century. As of 2024, emissions of nitrogen oxides (NOx) had reduced by more than ** percent since 1970 to *** million tons. Sulfur dioxide (SO₂) emissions have also fallen dramatically in recent decades, dropping from ** million tons to *** million tons between 1990 and 2024. Air pollutants can pose serious health hazards to humans, with the number of air pollution related deaths in the U.S. averaging ****** a year.