The AQS Data Mart is a database containing all of the information from AQS. It has every measured value the EPA has collected via the national ambient air monitoring program. It also includes the associated aggregate values calculated by EPA (8-hour, daily, annual, etc.). The AQS Data Mart is a copy of AQS made once per week and made accessible to the public through web-based applications. The intended users of the Data Mart are air quality data analysts in the regulatory, academic, and health research communities. It is intended for those who need to download large volumes of detailed technical data stored at EPA and does not provide any interactive analytical tools. It serves as the back-end database for several Agency interactive tools that could not fully function without it: AirData, AirCompare, The Remote Sensing Information Gateway, the Map Monitoring Sites KML page, etc.

AQS must maintain constant readiness to accept data and meet high data integrity requirements, thus is limited in the number of users and queries to which it can respond. The Data Mart, as a read only copy, can allow wider access.

The most commonly requested aggregation levels of data (and key metrics in each) are:

Sample Values (2.4 billion values back as far as 1957, national consistency begins in 1980, data for 500 substances routinely collected) The sample value converted to standard units of measure (generally 1-hour averages as reported to EPA, sometimes 24-hour averages) Local Standard Time (LST) and GMT timestamps Measurement method Measurement uncertainty, where known Any exceptional events affecting the data NAAQS Averages NAAQS average values (8-hour averages for ozone and CO, 24-hour averages for PM2.5) Daily Summary Values (each monitor has the following calculated each day) Observation count Observation per cent (of expected observations) Arithmetic mean of observations Max observation and time of max AQI (air quality index) where applicable Number of observations > Standard where applicable Annual Summary Values (each monitor has the following calculated each year) Observation count and per cent Valid days Required observation count Null observation count Exceptional values count Arithmetic Mean and Standard Deviation 1st - 4th maximum (highest) observations Percentiles (99, 98, 95, 90, 75, 50) Number of observations > Standard Site and Monitor Information FIPS State Code (the first 5 items on this list make up the AQS Monitor Identifier) FIPS County Code Site Number (unique within the county) Parameter Code (what is measured) POC (Parameter Occurrence Code) to distinguish from different samplers at the same site Latitude Longitude Measurement method information Owner / operator / data-submitter information Monitoring Network to which the monitor belongs Exemptions from regulatory requirements Operational dates City and CBSA where the monitor is located Quality Assurance Information Various data fields related to the 19 different QA assessments possible

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.epa_historical_air_quality.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

Data provided by the US Environmental Protection Agency Air Quality System Data Mart.

Provide the air quality index (AQI) for each station per hour.

The City of Detroit has installed seven Teledyne T640x air quality monitors at fixed locations across the city to measure real-time particulate matter (PM) in ambient air. The measurement and collection of public real-time and historical air pollution data provides additional information for the public, organizations, and local and state regulators to make informed air quality decisions, educate the public regarding the interaction of air quality, regulations and health, and provide necessary data to manage and regulate air quality in the region. This air quality monitoring project seeks to supplement existing monitoring efforts in Detroit from the State of Michigan Department of Environment, Great Lakes, and Energy (EGLE), Wayne County, and citizen and community group monitors, and aims to collectively provide coverage across every Detroit City Council district.This database presents the locations of the monitors, hourly averages for PM10 (PM less than 10 micrometers in aerodynamic diameter/particle size), PM2.5 (PM less than 2.5 micrometer particle size), PMcoarse (PM between particle size 2.5-10 micrometers), the air quality index (AQI) calculated for both PM10 and PM2.5, and representative meteorological data for each monitor (wind speed, direction, and temperature) from the closest Michigan Department of Environment, Great Lakes, and Energy (EGLE) meteorological station. The T640x option is an approved Federal Equivalent Method (FEM) for PM2.5, PM10 and PMcoarse, designating the method as acceptable for use in state or local air quality surveillance systems. The database will be updated daily with historical data available for download, while the live data dashboard showing current data is updated hourly.The AQI is EPA’s tool for categorizing and communicating air quality into levels of health concern, with specific information for which groups of people may be affected and preventative measures to reduce exposure.[1] Historical AQI data present an AQI for the 24-hour period prior- for example the AQI at the end of a given day will give a representative value for the air quality over the full course of the day. Real-time AQI reporting is calculated using EPA’s NowCast methodology, which uses an algorithm that relates hourly readings from air quality monitors to the AQI using a weighted average of the previous 12 hours.[2] For the historical data download, note that for past dates the AQI value should be used instead of the NowCast AQI, as noted it will give a representative value for the full day. Both real-time and downloaded data is preliminary and subject to change, and monthly QA/QC reports will be posted that will include any data corrections made for periods of calibration or maintenance. *Data Flag – Monitoring Station 1 (DPD 6th Precinct) used a replacement Teledyne T640 from 7/2/25 until 10/21/25. While the Teledyne T640 still measures PM2.5, PM10 and PMcoarse, only the PM2.5 measurments meet FEM requirements. A visualization of the Air Quality Monitor Readings is available from the open data Analytics Hub[1] U.S. EPA. Technical Assistance Document for the Reporting of Daily Air Quality- the Air Quality Index (AQI). EPA-454/B-24-002. May 2024. https://document.airnow.gov/technical-assistance-document-for-the-reporting-of-daily-air-quailty.pdf.[2] Id at 16.

Background:

Looking at Chicago's gleaming skyline today, it's surprising to remember that not so long ago many of those buildings were black with soot from coal-fired furnaces and factories all over the city. Take a look back at old photos or films, though, and that skyline isn't so pristine.

During the Industrial Age belching smokestacks were looked at as a good thing – this meant the city that works was working! Eventually, though, we learned you can have too much of a good thing. Some days, pollution turned day into night, ruining clothing, blackening buildings, sickening Chicagoans and even stopping airplanes from taking off. Today, we can see a similar situation in countries like India, Iran, Pakistan and China where coal is still widely used.

The Chicago Tribune led the crusade against Chicago’s dirty air. The newspaper began reporting on the condition of the city's air as early as the 1870s. In one report, the author Rudyard Kipling is quoted as saying simply, "the air is dirt" after a visit to Chicago.

In 1959, Chicago established the Department of Air Pollution Control to investigate and regulate emission sources. Subsequent regulations, including the federal Clean Air Act of 1970, and more recent city and state legislation have helped further mitigate city-wide emissions. Today, Chicago air pollution levels are a small fraction of their historical levels.

Standands:

The US Environmental Protection Agency (EPA) defines “moderate” air quality as air potentially unhealthy to sensitive groups including children, the elderly, and people with pre-existing cardiovascular or respiratory health conditions.

AQI ratings are calculated by weighting 6 key criteria pollutants for their risk to health. The pollutant with the highest individual AQI becomes the ‘main pollutant’ and dictates the overall air quality index. Fine particulate matter (PM2.5) and ozone represent two of the most common ‘main pollutants’ responsible for a city’s AQI due to the weight the formula ascribes to them for their potential harm and prevalence at high levels.

PM2.5 pollution is fine particle pollution with a range of chemical compositions that measures 2.5 microns in diameter or less. The US EPA recommends that annual PM2.5 exposure not exceed 12 μg/m3. The World Health Organization (WHO), meanwhile, employs a more stringent standard, recommending that exposure remain below 10 μg/m3 annually.

learn more: https://www.iqair.com/usa/illinois/chicago

In this dataset we explore the pollution levels and learn EDA techniques in the process.

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 WHO, air pollution causes over 7 million deaths each year—more than HIV, TB, and malaria combined. The estimated global cost of air pollution-related health damages is $8.1 trillion. It affects human well-being in significant ways:

-Lower cognitive performance -Mood disturbances and mental health impacts -Increased respiratory and cardiovascular risk -Loss of productivity due to illness

Ambios is building the world’s largest decentralized platform for real-time environmental insights. Our Air Quality Index Data product offers high-resolution, real-world air quality data with wide spatial and temporal coverage across the US and Europe.

-Covers AQI according to both US EPA and EU standards -Includes pollutant measurements for PM2.5, PM10, NO₂, CO, and O₃ -Captures humidity and temperature metrics -Real-time updates every 15 minutes -Historical data -Hyperlocal sensor data across urban, suburban, and rural areas

This data supports a variety of applications:

-Air quality monitoring and real-time alerts -ESG reporting and sustainability scoring -Location-based risk analysis and infrastructure planning -AI and machine learning model development -Smart city and environmental policy programs

Ambios leverages DePIN and blockchain technologies to ensure transparency, decentralization, and traceability of every data point in the network.

The Air Quality Index (AQI) for each monitoring station is provided hourly. The original data version is announced on the Air Quality Monitoring Network website https://airtw.moenv.gov.tw

Hourly provide air quality index (AQI) of each monitoring station.

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.

Ambios provides trusted, real-time, historical Air Quality Data from a decentralized network of 3,000+ outdoor sensors across 20+ countries. Our data includes key environmental variables:

-PM1, PM2.5, PM10 (particulate matter) -NO₂, O₃, CO (gaseous pollutants) -Temperature & Humidity

This high-frequency, hyperlocal dataset is used across industries for operational, regulatory, and research purposes.

Use Cases Include:

-Smart Cities: Monitor pollution hotspots, evaluate clean air zones, and drive zoning or mobility policy. -Real Estate & ESG: Support green certifications, assess site-level environmental quality, and meet reporting standards. -Logistics & Transport: Optimize routes, reduce emissions, and manage compliance in urban corridors. -Government & Regulation: Fill gaps in national monitoring networks, inform alerts, and shape environmental policy. -Research & Academia: Power climate, health, and pollution exposure studies with real-world environmental data.

Ambios data is 100% first-party, verifiable, and available in real-time or historical formats. Our system is built on a DePIN (Decentralized Physical Infrastructure Network) and ensures transparency, traceability, and global scalability.

Whether you’re building environmental models, managing urban systems, or meeting ESG goals, Ambios Air Quality Data provides the environmental intelligence you need to act.

The Air Quality Index (AQI) is a comprehensive indicator used to assess air pollution levels, based on the concentrations of six major pollutants: PM2.5, PM10, SO₂, NO₂, O₃, and CO, with threshold values determined by the GB3095-2012 national standard. The AQI classification is as follows: Excellent (AQI ≤ 50), Good (AQI ≤ 100), Light Pollution (AQI ≤ 150), Moderate Pollution (AQI ≤ 200), Heavy Pollution (AQI ≤ 300), and Severe Pollution (AQI > 300). Using real-time monitoring data from air quality stations across China from 2014 to 2024, the AQI for each city is calculated by first determining the daily average AQI based on monitoring stations within the city, then computing the annual average AQI, and finally aggregating the annual average AQI values for 367 cities, providing a comprehensive analysis of air quality trends over the decade.

Snapshot img

Air Quality Monitor Market Size 2025-2029

The air quality monitor market size is forecast to increase by USD 2.29 billion at a CAGR of 7.1% between 2024 and 2029.

The market is experiencing significant growth due to the increasing awareness and necessity of monitoring indoor air quality in both residential and commercial sectors. This trend is further fueled by the rising adoption of green buildings, which prioritize energy efficiency and occupant health. However, the high cost of deploying air quality monitoring devices remains a significant challenge for market expansion. Despite this obstacle, companies can capitalize on the growing demand for indoor air quality solutions by offering cost-effective and efficient monitoring technologies. Additionally, partnerships with real estate developers and building management companies can provide lucrative opportunities for market growth. Overall, the market presents a promising landscape for companies seeking to address the growing need for indoor air quality monitoring while navigating the challenge of affordability.

What will be the Size of the Air Quality Monitor Market during the forecast period?

Request Free SampleThe market continues to evolve, driven by growing health concerns and the need for real-time, data-driven solutions. Ambient air quality plays a significant role in public health, with health risks associated with air pollution levels. Remote monitoring through cloud-based platforms enables air quality management, allowing for proactive responses to changing conditions. Infrared sensors and machine learning algorithms are used for particle matter detection, while ultrasonic sensors measure sound levels. Energy efficiency is a key consideration, with sensor fusion and data analysis techniques improving sensor reliability and accuracy. Air filtration systems, nitrogen dioxide sensors, and mobile apps are integral components of air quality management. Laser particle counters and mass spectrometry are used for industrial emissions monitoring. Multi-sensor systems and predictive analytics enable compliance reporting and data visualization. Carbon monoxide, sulfur dioxide, and volatile organic compounds are among the gases monitored. The integration of artificial intelligence and smart cities enhances air quality management, with real-time monitoring and API integration facilitating building management and pollution control. Public awareness campaigns and occupancy monitoring further optimize ventilation systems. Regulatory standards continue to evolve, driving innovation in sensor technology and data analysis techniques. Overall, the market is a dynamic and evolving landscape, with ongoing advancements in sensor technology, data analysis, and regulatory standards shaping its future.

How is this Air Quality Monitor Industry segmented?

The air quality monitor industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ProductIndoorOutdoorWearableEnd-userGovernmentCommercial and residentialEnergy and pharmaceuticalsOthersTypeChemical pollutantsPhysical pollutantsBiological pollutantsComponentHardwareSoftwareServicesGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaRest of World (ROW)

By Product Insights

The indoor segment is estimated to witness significant growth during the forecast period.Indoor air quality monitors are essential devices for assessing and maintaining healthy and comfortable environments within homes, offices, schools, hospitals, and other buildings. These monitors employ sensors and detectors to measure various parameters, such as temperature, humidity, carbon dioxide (CO2) levels, volatile organic compounds (VOCs), and particulate matter (PM), to evaluate indoor air quality. Real-time data and insights are provided through continuous monitoring, enabling building managers and occupants to address potential issues promptly. Advancements in technology have led to the integration of remote monitoring, cloud-based platforms, and the Internet of Things (IoT) in indoor air quality management. These innovations facilitate real-time data analysis, predictive analytics, and compliance reporting. Sensor fusion, machine learning, and artificial intelligence are employed to enhance sensor reliability and accuracy, ensuring precise measurements. Indoor air quality is crucial for public health, as poor indoor air quality can lead to various health risks, including respiratory issues, headaches, and fatigue. Regulatory standards mandate specific air quality index (AQI) thresholds for various pollutants, making it essential for building managers to maintain optimal indoor air quality. Indoor air quality monitors utilize various sensors, including infrared, ultrasonic, electrochemical, metal oxide

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.

📘 Overview

This dataset provides hourly air-quality measurements for 50 major global cities over a continuous 15-day period, including pollutant concentrations, meteorological conditions, geographical metadata, and an engineered AQI index.

All values are synthetically generated using historically consistent pollutant patterns and statistical ranges, allowing researchers and ML practitioners to work with realistic air-quality trends without licensing restrictions or data-collection barriers.

This dataset is ideal for time-series modeling, forecasting, environmental analytics, and machine-learning experimentation.

🧭 Cities Included

Covers all major regions:

North America — New York, Los Angeles, Toronto

Europe — London, Paris, Berlin, Zurich

Asia — Delhi, Tokyo, Seoul, Beijing, Singapore

Middle East — Dubai, Riyadh, Doha

Africa — Lagos, Cairo, Nairobi

Oceania — Sydney, Melbourne, Auckland

South America — São Paulo, Buenos Aires

🧱 Dataset Structure

Each hourly record includes:

Air Pollutants

PM2.5 (µg/m³)

PM10 (µg/m³)

NO₂ (ppb)

SO₂ (ppb)

O₃ (ppb)

CO (ppm)

Weather Features

Temperature (°C)

Humidity (%)

Wind Speed (m/s)

Location Metadata

City

Country

Latitude

Longitude

Other

Timestamp (ISO-8601)

AQI (Computed index)

🧹 Data Quality & Formatting

No missing values — 100% complete

Numeric values rounded to 3 decimals

Clean column names (snake_case)

Consistent hourly frequency

Fully ML-ready

📊 Example Use Cases

✔ AQI forecasting (LSTM, GRU, Transformers) ✔ Multivariate time-series modeling ✔ Clustering cities by pollution patterns ✔ Environmental trend visualization ✔ Weather–pollution correlation studies ✔ Anomaly detection (peak pollution events)

Column	Description	Unit	Type
timestamp	Hourly timestamp (UTC)	—	datetime
city	City name	—	string
country	Country name	—	string
latitude	City latitude	°	float
longitude	City longitude	°	float
pm25	Fine particulate matter	µg/m³	float
pm10	Coarse particulate matter	µg/m³	float
no2	Nitrogen dioxide	ppb	float
so2	Sulfur dioxide	ppb	float
o3	Ozone	ppb	float
co	Carbon monoxide	ppm	float
temperature	Ambient temperature	°C	float
humidity	Relative humidity	%	float
wind_speed	Wind speed	m/s	float
aqi	Derived Air Quality Index	—	int

🧪 Data Generation Method (Provenance)

This dataset is synthetically generated using realistic pollutant behavior patterns based on historical studies and open-source environmental datasets.

Modeling steps included:

City-specific pollutant baseline ranges

Randomized variation using Gaussian noise

Temporal patterns using sinusoidal diurnal cycles (morning & evening peaks)

Weather-pollution correlation rules (e.g., low wind → higher PM)

AQI computed using standard US-EPA breakpoints

All numeric values standardized to 3-decimal precision

This ensures that although synthetic, the dataset follows realistic environmental dynamics.

📁 File Information

global_air_quality_50_cities.csv

Rows: 18,000+

Columns: 16

Format: UTF-8 CSV

# Air Quality Index

An** air quality index (AQI)** is used by government agencies to communicate to the public how polluted the air currently is or how polluted it is forecast to become. AQI information is obtained by averaging readings from an air quality sensor, which can increase due to vehicle traffic, forest fires, or anything that can increase air pollution. Pollutants tested include ozone, nitrogen dioxide, sulphur dioxide, among others.

Public health risks increase as the AQI rises, especially affecting children, the elderly, and individuals with respiratory or cardiovascular issues. During these times, governmental bodies generally encourage people to reduce physical activity outdoors, or even avoid going out altogether. The use of face masks such as cloth masks may also be recommended.

Different countries have their own air quality indices, corresponding to different national air quality standards.

**## Overview **

Computation of the AQI requires an air pollutant concentration over a specified averaging period, obtained from an air monitor or model. Taken together, concentration and time represent the dose of the air pollutant. Health effects corresponding to a given dose are established by epidemiological research. Air pollutants vary in potency, and the function used to convert from air pollutant concentration to AQI varies by pollutant. Its air quality index values are typically grouped into ranges. Each range is assigned a descriptor, a color code, and a standardized public health advisory.

On a day when the AQI is predicted to be elevated due to fine particle pollution, an agency or public health organization might:

• Advise sensitive groups, such as the elderly, children, and those with respiratory or cardiovascular problems, to avoid outdoor exertion.[6]
• Declare an "action day" to encourage voluntary measures to reduce air emissions, such as using public transportation.[7]
• Recommend the use of masks to keep fine particles from entering the lungs

🌍 Bangladesh Air Quality Index (AQI) Dataset (2000-2025)

Comprehensive Historical Air Pollution Data Across 103 Cities

📊 Dataset Overview

This dataset provides comprehensive air quality measurements for 103 cities across Bangladesh, spanning from 2000 to 2025. It contains over 3.19 million hourly observations of key air pollutants and environmental indicators, making it one of the most extensive air quality datasets for Bangladesh available for public use.

Key Features: - ✅ 103 cities covering all major regions of Bangladesh - ✅ 3.19+ million hourly observations (2000-2025) - ✅ 8 pollutant measurements: PM10, PM2.5, CO, CO₂, NO₂, SO₂, O₃, AQI - ✅ Precise geolocation with latitude/longitude coordinates - ✅ Standardized format with consistent column naming - ✅ Research-ready for environmental science, public health, and ML applications

📋 Dataset Summary

Property	Value
Total Records	3,193,198 rows
Number of Cities	103 cities
Time Period	2000-2025 (25 years)
Temporal Resolution	Hourly measurements
File Format	CSV (Comma-separated values)
Total Columns	13
Geographic Coverage	All major regions of Bangladesh

📅 City-wise Data Coverage

City Name	From Date	Total Rows
Dhaka	2000-01-01	227,016
Narsingdi	2020-01-01	37,991
Rangpur	2022-08-04	28,993
Sherpur	2022-08-04	28,993
Dinājpur	2022-08-04	28,993
Lākshām	2022-08-04	28,993
Comilla	2022-08-04	28,993
Thākurgaon	2022-08-04	28,993
Teknāf	2022-08-04	28,993
Tungi	2022-08-04	28,993
Sylhet	2022-08-04	28,993
Dohār	2022-08-04	28,993
Jamālpur	2022-08-04	28,993
Shibganj	2022-08-04	28,993
Sātkhira	2022-08-04	28,993
Sirājganj	2022-08-04	28,993
Netrakona	2022-08-04	28,993
Sandwīp	2022-08-04	28,993
Shāhzādpur	2022-08-04	28,993
Rāmganj	2022-08-04	28,993
Rājshāhi	2022-08-04	28,993
Purbadhala	2022-08-04	28,993
Pirojpur	2022-08-04	28,993
Panchagarh	2022-08-04	28,993
Patiya	2022-08-04	28,993
Parbatipur	2022-08-04	28,993
Nārāyanganj	2022-08-04	28,993
Nālchiti	2022-08-04	28,993
Nāgarpur	2022-08-04	28,993
Nageswari	2022-08-04	28,993
Mymensingh	2022-08-04	28,993
Muktāgācha	2022-08-04	28,993
Mirzāpur	2022-08-04	28,993
Maulavi Bāzār	2022-08-04	28,993
Morrelgonj	2022-08-04	28,993
Mehendiganj	2022-08-04	28,993
Mathba	2022-08-04	28,993
Lalmanirhat	2022-08-04	28,993
Kushtia	2022-08-04	28,993
Kālīganj	2022-08-04	28,993
Jhingergācha	2022-08-04	28,993
Joypur Hāt	2022-08-04	28,993
Ishurdi	2022-08-04	28,993
Habiganj	2022-08-04	28,993
Gaurnadi	2022-08-04	28,993
Gafargaon	2022-08-04	28,993
Feni	2022-08-04	28,993
Rāipur	2022-08-04	28,993
Sarankhola	2022-08-04	28,993
Chilmāri	2022-08-04	28,993
Chhāgalnāiya	2022-08-04	28,993
Lālmohan	2022-08-04	28,993
Khagrachhari	2022-08-04	28,993
Chhātak	2022-08-04	28,993
Bhātpāra Abhaynagar	2022-08-04	28,993
Bherāmāra	2022-08-04	28,993
Bhairab Bāzār	2022-08-04	28,993
Bāndarban	2022-08-04	28,993
Kālia	2022-08-04	28,993
Baniachang	2022-08-04	28,993
Bājitpur	2022-08-04	28,993
Badarganj	2022-08-04	28,993
Narail	2022-08-04	28,993
Tungipāra	2022-08-04	28,993
Sarishābāri	2022-08-04	28,993
Sakhipur	2022-08-04	28,993
Raojān	2022-08-04	28,993
Phultala	2022-08-04	28,993
Pālang	2022-08-04	28,993
Pār Naogaon	2022-08-04	28,993
Nabīnagar	2022-08-04	28,993
Lakshmīpur	2022-08-04	28,993
Kesabpur	2022-08-04	28,993
Jahedpur	2022-08-04	28,993
Hājīganj	2022-08-04	28,993
Farīdpur	2022-08-04	28,993
Uttar Char Fasson	2022-08-04	28,993
Chittagong	2022-08-04	28,993
Char Bhadrāsan	2022-08-04	28,993
Bera	2022-08-04	28,993
Burhānuddin	2022-08-04	28,993
Sātkania	2022-08-04	28,993
Cox's Bāzār	2022-08-04	28,993
Khulna	2022-08-04	28,993
Bhola	2022-08-04	28,993
Barisāl	2022-08-04	28,993
Jessore	2022-08-04	28,993
Pābna	2022-08-04	28,993
Tāngāil	2022-08-04	...

This dataset makes historical air quality data accessible for various parameters at Calgary monitoring stations. The data is collected by the Calgary Region Airshed Zone and submitted to Alberta Environment and Parks (AEP). The data is delivered to the airdata warehouse, a central repository for Alberta's ambient air quality data via a web service provided by AEP. In this way, AEP provides access to quality controlled historical air quality data at its air quality stations. In addition to air quality monitoring data, an Air Quality Health Index (AQHI) is calculated hourly at certain monitoring stations. The AQHI is a simple way to interpret air quality conditions: it provides a number from 1 to 10+ which indicates the relative health risk associated with local air quality. The higher the number, the greater the health risk. Further information on the AQHI calculation is available at: https://www.alberta.ca/air-quality-health-index--calculation.aspx

The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

The City of Montreal measures air quality in the form of a numerical value called the “Air Quality Index (AQI).” This data set provides access to daily AQI values, updated every hour, approximately 50 minutes after the hour. For example, the 13:00 data is available around 13:50, for all three resources. ***Note: ** the AQI systems do not change the time: they always use Eastern Standard Time (EST) and not Eastern Daylight Time (EDT). Thus, in the example above, around 13:50 EDT, the most recent data available will be from 12:00 EDT.* Historical values, updated daily, are available here. The list of stations and sectors linked to RSQA data is also available.

Overview:

The India AQI dataset provides comprehensive information on air quality across various cities and regions in India. The dataset includes measurements of different air pollutants that contribute to the overall air quality index, enabling researchers, policymakers, and the public to understand and address air quality issues.

Key Components:

Parameters Measured:

PM2.5:

Particulate matter with a diameter of 2.5 micrometers or smaller, which can penetrate the respiratory system.

PM10:

Particulate matter with a diameter of 10 micrometers or smaller.

NO2:

Nitrogen dioxide, primarily produced from vehicle emissions and industrial processes.

SO2:

Sulfur dioxide, which results from burning fossil fuels and industrial processes.

CO:

Carbon monoxide, a colorless, odorless gas produced by burning carbon-based fuels.

O3:

Ozone, which can be beneficial in the upper atmosphere but harmful at ground level.

AQI Calculation:

The dataset may include the calculated AQI values based on the concentrations of the above pollutants, categorized into different levels (e.g., Good, Moderate, Unhealthy, Hazardous). Geographical Coverage:

Information on various states and cities across India, allowing for regional comparisons and analysis. Temporal Coverage:

The dataset may provide historical data over a specific time frame (e.g., daily, weekly, monthly), enabling trend analysis.

Data Sources:

Data collected from government agencies, environmental monitoring stations, and satellite data. Use Cases:

Useful for researchers studying environmental impacts on public health. Helps policymakers in formulating regulations to improve air quality. Provides valuable information for the public to make informed decisions regarding outdoor activities based on air quality levels. Format:

The dataset may be available in formats like CSV, JSON, or Excel, facilitating ease of use in data analysis tools.

Data Accessibility:

Information on how to access the dataset, including links to online repositories or APIs for real-time data retrieval.