This indicator shows how many days per year were assessed to have air quality that was worse than “moderate” in Champaign County, according to the U.S. Environmental Protection Agency’s (U.S. EPA) Air Quality Index Reports. The period of analysis is 1980-2024, and the U.S. EPA’s air quality ratings analyzed here are as follows, from best to worst: “good,” “moderate,” “unhealthy for sensitive groups,” “unhealthy,” “very unhealthy,” and "hazardous."[1]

In 2024, the number of days rated to have air quality worse than moderate was 0. This is a significant decrease from the 13 days in 2023 in the same category, the highest in the 21st century. That figure is likely due to the air pollution created by the unprecedented Canadian wildfire smoke in Summer 2023.

While there has been no consistent year-to-year trend in the number of days per year rated to have air quality worse than moderate, the number of days in peak years had decreased from 2000 through 2022. Where peak years before 2000 had between one and two dozen days with air quality worse than moderate (e.g., 1983, 18 days; 1988, 23 days; 1994, 17 days; 1999, 24 days), the year with the greatest number of days with air quality worse than moderate from 2000-2022 was 2002, with 10 days. There were several years between 2006 and 2022 that had no days with air quality worse than moderate.

This data is sourced from the U.S. EPA’s Air Quality Index Reports. The reports are released annually, and our period of analysis is 1980-2024. The Air Quality Index Report websites does caution that "[a]ir pollution levels measured at a particular monitoring site are not necessarily representative of the air quality for an entire county or urban area," and recommends that data users do not compare air quality between different locations[2].

[1] Environmental Protection Agency. (1980-2024). Air Quality Index Reports. (Accessed 13 June 2025).

[2] Ibid.

Source: Environmental Protection Agency. (1980-2024). Air Quality Index Reports. https://www.epa.gov/outdoor-air-quality-data/air-quality-index-report. (Accessed 13 June 2025).

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Topsoil arsenic (As) contamination threatens the ecological environment and human health. However, traditional methods for As identification rely on on-site sampling and chemical analysis, which are cumbersome, time-consuming, and costly. Here we developed a method combining visible near infrared spectra and deep learning to predict topsoil As pollution. We showed that the optimum fully connected neural network model had high robustness and generalization (R-Square values of 0.688 and 0.692 on the validation and testing sets). Using the model, the relative As content at regional and global scales were estimated and the human populations that might potentially be affected were determined. We found that China, Brazil, and California are topsoil As-contamination hotspots. Other areas, e.g., Gabon, although also at great risk, are rarely documented, making them potential hotspots. Our results provided guidance for regions that require more detailed detection or timely soil remediation and can assist in alleviating global topsoil-As contamination.

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.

Data on India's Air Quality - state-wise air pollution due to PM 2.5, ozone and solid fuels, deaths due to pollution, and comparison with global peers.

This pie chart displays news per publication time using the aggregation count. The data is filtered where the keywords includes Air-Pollution-Government policy.

This bar chart displays news by news title using the aggregation count. The data is filtered where the keywords includes Air-Pollution-Government policy.

This dataset contains trends in days violating air quality standards by date for the Philadelphia-Camden-Wilmington Core-Based Statistical Area (CBSA).In order to convey the health impacts of air pollution to the general public, the US EPA has created a color-coded scale to identify pollutant levels in simple terms. This scale is referred to as the Air Quality Index (AQI). AQI levels are directly related to the federal air quality standards and pollutant concentrations in the air. The AQI reports pollutant levels for six different categories based on AQI: Good or green (0 to 50), Moderate or yellow (51 to 100), Unhealthy for Sensitive Groups or orange (101 to 150), Unhealthy or red (151 to 200), Very Unhealthy or purple (201 to 300), and Hazardous (301 to 500). Note that no day in 2000 or subsequent years has qualified as hazardous, so it is not present in the charts. Sensitive groups are defined as children, older adults, and those with breathing impairments. When the AQI reaches Code Orange or higher for any of the pollutants, an air quality standard violation has occurred.

Air quality standards have been revised a number of times since 1997, and the data in these charts is normalized to the current standard.

OpenAQ is an open-source project to surface live, real-time air quality data from around the world. Their “mission is to enable previously impossible science, impact policy and empower the public to fight air pollution.” The data includes air quality measurements from 5490 locations in 47 countries.

Scientists, researchers, developers, and citizens can use this data to understand the quality of air near them currently. The dataset only includes the most current measurement available for the location (no historical data).

Update Frequency: Weekly

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.openaq.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

Dataset Source: openaq.org

Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source and is provided "AS IS" without any warranty, express or implied.

The graph shows the level of concern about air pollution in Mexico as of May 2019. During a survey, **** percent of the respondents with internet-connected smartphones considered air pollution to be a severe problem in their city or municipality.

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.

The Canadian Environmental Sustainability Indicators (CESI) program provides data and information to track Canada's performance on key environmental sustainability issues. The International comparison: air pollutant emissions in selected countries indicators provide emissions and emissions intensity for member countries of the Organisation for Economic Co-operation and Development. The emissions of 5 pollutants are reported: sulphur oxides, nitrogen oxides, carbon monoxide, volatile organic compounds and fine particulate matter. These indicators help to inform Canadians about how Canada's emissions compare to those of other countries. The indicators report on key air pollutants that contribute to smog and acid rain and help the government to identify priorities, track progress, and develop strategies and policies for reducing or controlling air pollution. Information is provided to Canadians in a number of formats including: static and interactive maps, charts and graphs, HTML and CSV data tables and downloadable reports. See the supplementary documentation for the data sources and details on how the data were collected and how the indicator was calculated. Supplemental Information Canadian Environmental Sustainability Indicators - Home page: https://www.canada.ca/environmental-indicators

Time series data for the statistic PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-1 value (% of total) and country Guyana. Indicator Definition: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.

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Philippines's Percentage of population exceeding standard values for PM2.5 air pollution is 96.37% which is the 150th highest in the world ranking. Transition graphs on Percentage of population exceeding standard values for PM2.5 air pollution in Philippines and comparison bar charts (USA vs. China vs. Japan vs. Philippines), (Ethiopia vs. Egypt vs. Philippines) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

Madagascar's Percentage of population exceeding standard values for PM2.5 air pollution is 100% which is the 1st highest in the world ranking. Transition graphs on Percentage of population exceeding standard values for PM2.5 air pollution in Madagascar and comparison bar charts (USA vs. China vs. Japan vs. Madagascar), (Nepal vs. Korea, Democratic People's Republic of vs. Madagascar) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

Time series data for the statistic PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-1 value (% of total) and country Malta. Indicator Definition: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.

Time series data for the statistic PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-1 value (% of total) and country Sierra Leone. Indicator Definition: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.