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).

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.

Time series data for the statistic Mortality rate attributable to ambient air pollution (deaths per 100 000 population) and country United States. Indicator Definition:

Time series data for the statistic Mortality rate attributable to ambient air pollution (deaths per 100 000 population) and country Costa Rica. Indicator Definition:

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. 🚀🌍💨

The datasets contains date- and state-wise historically compiled data on air quality (by pollution level) in rural and urban areas of India from the year 2015 , as measured by Central Pollution Board (CPCB) through its daily (24 hourly measurements, taken at 4 PM everyday) Air Quality Index (AQI) reports.

The CPCB measures air quality by continuous online monitoring of various pollutants such as Particulate Matter10 (PM10), Particulate Matter2.5 (PM2.5), Sulphur Dioxide (SO2), Nitrogen Oxide or Oxides of Nitrogen (NO2), Ozone (O3), Carbon Monoxide (CO), Ammonic (NH3) and Lead (Pb) and calculating their level of pollution in the ambient air. Based on the each pollutant load in the air and their associated health impacts, the CPCB calculates the overall Air Pollution in Air Quality Index (AQI) value and publishes the data. This AQI data is then used by CPCB to report the air quality status i.e good, satisfactory, moderate, poor, very poor and severe, etc. of a particular location and their related health impacts because of air pollution.

According to the monitoring data from the Embassy of the United States, there was on average 39 micrograms of PM2.5 particles per cubic meter to be found in the air in Beijing during 2023. The air quality has improved considerably since 2013.

Reasons for air pollution in Beijing

China’s capital city Beijing is one of the most populous cities in China with over 20 million inhabitants. Over the past 20 years, Beijing’s GDP has increased tenfold. With the significant growth of vehicles and energy consumption in the country, Beijing’s air quality is under great pressure from the economic development. In the past, the city had a high level of coal consumption. Especially in winter, in which coal consumption increased due to heating, the air quality could get extremely bad on the days without wind. In spring, the wind from the north would bring sand from Mongolian deserts, resulting in severe sandstorms in Beijing. The bad air quality also affected the air visibility and threatened people’s health. On days with very bad air quality, people wearing masks for protection can be seen on the streets in the city.

Methods to improve air quality in Beijing

Over the past years, the government has implemented various methods to improve the air quality in Northern China. Sandstorms, which were quite common 15 years ago, are now rarely seen in Beijing’s spring thanks to afforestation projects on China’s northern borders. The license-plate lottery system was introduced in Beijing to restrict the growth of private vehicles. Large trucks were not allowed to enter certain areas in Beijing. Above all, the coal consumption in Beijing has been restricted by shutting down industrial sites and improving heating systems. Beijing’s efforts to improve air quality has also been highly praised by the UN as a successful model for other cities. However, there is also criticism pointing out that the improvement of Beijing’s air quality is based on the sacrifice of surrounding provinces (including Hebei), as many factories were moved from Beijing to other regions. Besides air pollution, there are other environmental problems like water pollution that China is facing. The industrial transformation is the key to China’s environmental improvement.

Time series data for the statistic Mortality rate attributable to ambient air pollution (deaths per 100 000 population) and country Trinidad and Tobago. Indicator Definition:

Time series data for the statistic Mortality rate attributable to ambient air pollution (deaths per 100 000 population) and country Korea, Rep.. Indicator Definition:

Over the last decade, China has been trying to tackle worsening air quality from urbanization and industrialization. In 2024, the average concentration of ozone was around *** micrograms per cubic meter in *** cities in China. Environmental degradation Becoming the global manufacturing hub of goods brought not only rapid economic development to China, but also deteriorating air quality in cities across the country. Among other types of environmental issues, air pollution was the most concerning issue for almost half of Chinese survey respondents. Since 2001, carbon dioxide emissions in China have tripled to over ** gigatons in 2023, with emissions increasing quickly again after dipping in 2016. Environmental protection The Chinese government saw environmental degradation primarily as a public health issue for Chinese citizens, and therefore started contributing more and more resources to protecting the environment. In 2024, public expenditure on energy conservation and environmental protection in China had amounted to nearly *** billion yuan, almost double the amount of ten years ago. Citizens have also begun to change their habits due to climate change. For example, around half of Chinese citizens have changed their commuting and water use habits to help fight climate change.

IntroductionFacing Mount Tai in the south and the Yellow River in the north, Zibo District is an important petrochemical base in China. The effect of air pollution on cardiovascular diseases (CVDs) in Zibo was unclear.MethodsDaily outpatient visits of common CVDs including coronary heart disease (CHD), stroke, and arrhythmia were obtained from 2019 to 2022 in Zibo. Air pollutants contained fine particulate matter (PM2.5), inhalable particulate matter (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO). Distributed lag non-linear models (DLNM) including single-pollutant model in single-day (lag0-lag7) and cumulative-days (lag01-lag07), concentration-response curve, subgroup analysis, and double-pollutant model were utilized to examine the relationships of daily air pollutants on CHD, stroke, and arrhythmia. Meteorological factors were incorporated to control confounding.ResultsIn single-pollutant model, NO2 was positively associated with CHD, stroke and arrhythmia, with the strongest excess risks (ERs) of 4.97% (lag07), 4.71% (lag07) and 2.16% (lag02), respectively. The highest ERs of PM2.5 on CHD, stroke and arrhythmia were 0.85% (lag01), 0.59% (lag0) and 0.84% (lag01), and for PM10, the ERs were 0.37% (lag01), 0.35% (lag0) and 0.39% (lag01). SO2 on CHD was 0.92% (lag6), O3 on stroke was 0.16% (lag6), and CO on CHD, stroke, and arrhythmia were 8.77% (lag07), 5.38% (lag01), 4.30% (lag0). No threshold was found between air pollutants and CVDs. The effects of ambient pollutants on CVDs (NO2&CVDs, PM2.5&stroke, PM10&stroke, CO&stroke, CO&arrhythmia) were greater in cold season than warm season. In double-pollutant model, NO2 was positively associated with CHD and stroke, and CO was also positively related with CHD.ConclusionAmbient pollutants, especially NO2 and CO were associated with CVDs in Zibo, China. And there were strong relationships between NO2, PM2.5, PM10, CO and CVDs in cold season.

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 Air quality indicators track ambient concentrations of fine particulate matter, ground-level ozone, sulphur dioxide, nitrogen dioxide, and volatile organic compounds at the national, regional and urban levels and at local monitoring stations. The national and regional indicators are presented with their corresponding Canadian Ambient Air Quality Standard when available. Canadians are exposed to air pollutants on a daily basis, and this exposure can cause adverse health and environmental effects. 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

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Time series data for the statistic Mortality rate attributable to ambient air pollution (deaths per 100 000 population) and country Jordan. Indicator Definition:

Accurately predicting air pollutant concentrations can reduce health risks and provide crucial references for environmental governance. In pollution prediction tasks, three key factors are essential: (1) dynamic dependencies among global monitoring stations should be considered in spatial feature extraction due to the diffusion properties of air pollutants; (2) precise temporal correlation modeling is critical because pollutant concentrations change dynamically and periodically; (3) it is vital to avoid propagation of long-term prediction errors across spatiotemporal dimensions. To address these challenges, we propose STGATN, a novel spatiotemporal graph attention network with an encoder-decoder architecture. Both the encoder and decoder incorporate a spatiotemporal embedding mechanism, a spatiotemporal graph attention block, a gated temporal convolutional network, and a fusion gate. Specifically, the spatiotemporal graph attention module is designed to use temporal and graph attention networks to extract dynamic spatiotemporal correlations. The gated temporal convolutional network is constructed to capture the long-term temporal causal relationships. The fusion gate adaptively fuses the spatiotemporal correlations and temporal causal relationships. In addition, a spatiotemporal embedding mechanism, including positional and temporal information, is added to account for pollutants’ periodicity and station-specific properties. Moreover, this paper proposes a transformer attention that establishes direct dependencies between future and historical time steps to avoid prediction error accumulation in the dynamic decoding process. The experimental results show that the proposed prediction model significantly outperforms the latest baseline methods on the air pollution dataset from actual monitoring stations in Beijing City.

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

In air pollution studies, the correlation analysis of environmental variables has usually been challenged by parametric diversity. Such variable variations are not only from the extrinsic meteorological conditions and industrial activities but also from the interactive influences between the multiple parameters. A promising solution has been motivated by the recent development of visibility graph (VG) on multi-variable data analysis, especially for the characterization of pollutants’ correlation in the temporal domain, the multiple visibility graph (MVG) for nonlinear multivariate time series analysis has been verified effectively in different realistic scenarios. To comprehensively study the correlation between pollutant data and season, in this work, we propose a multi-layer complex network with a community division strategy based on the joint analysis of the atmospheric pollutants. Compared to the single-layer-based complex networks, our proposed method can integrate multiple different atmospheric pollutants for analysis, and combine them with multivariate time series data to obtain higher temporary community division for ground air pollutants interpretation. Substantial experiments have shown that this method effectively utilizes air pollution data from multiple representative indicators. By mining community information in the data, it successfully achieves reasonable and strong interpretive analysis of air pollution data.

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.