The available data are fetched from http://pollution.gov.np by crawling and extracting web data. The activities of the fetching, cleaning, and publishing are done by the automated software bot and depend completely upon the quality of data published on the government websites, OKN does not guarantee the quality & validity of the data.

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

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.

There are eight downloadable files for this data; for the years 2013 to 2022 from the resource links.

The Pollution Inventory (PI) has been developed to collate information on annual mass releases of specified substances to air controlled waters and sewers as well as quantities of waste transferred off site from large industrial sites regulated by the Agency.

This replaced the Chemical Release Inventory (CRI) in 1998 and is sometimes referred to as the Inventory of Sources and Releases (ISR).

The reporting requirements for the PI encompass emissions from the whole of the authorisation license or permit. This includes non-point sources and fugitive emissions (e.g. leaks) along with the specific release points (point sources e.g. chimneys).

All authorisations for large industrial sites (i.e. those under IPPC and now EPR) have a condition requiring the annual reporting of releases of a core (reportable) list of substances and groups of substances. These are substances considered to be important in relation to environmental protection. Attribution statement: © Environment Agency copyright and/or database right 2024. All rights reserved.

Here are a few use cases for this project:

1. Urban Planning and Maintenance: City planning authorities can use the "Visual Pollution" model to monitor and evaluate road conditions, identify potholes, evaluate sidewalks, and observe garbage levels. This data can then inform maintenance schedules and urban improvement efforts.

2. Environmental Conservation: Environmental agencies could use this model to analyze visual pollution levels in various regions. It could help quantify the impact of littering and pollution on the environment, aiding in advocacy and conservation efforts.

3. Traffic and Safety Management: Transport authorities could use this model to identify defective speed bumps and construction road, which would enable more efficient planning of road repairs and enhancing road safety.

4. Smart Cities Development: As part of a broader smart city initiative, the model could be used alongside other technology to create a live, up-to-date database of city conditions, aiding with everything from waste management to infrastructure improvements.

5. Real Estate and Neighborhood Assessments: Real estate agencies and homebuyers could use this model to evaluate neighborhood cleanliness and infrastructure quality, which can influence property values and relocation decisions.

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Context

Air is what keeps humans alive. Since industrialization, there has been an increasing concern about environmental pollution. As mentioned in the WHO report 7 million premature deaths annually linked to air pollution, air pollution is the world's largest single environmental risk. Moreover, as reported in the NY Times article, India’s Air Pollution Rivals China’s as World’s Deadliest it has been found that India's air pollution is deadlier than even China's.

Monitoring it and understanding its quality is of immense importance to our well-being. Using this dataset one can explore India's air pollution levels at a more granular scale.

Content

The dataset contains hourly air quality data (PM 2.5) of India. More specifically, it contains 36.192 observations of the hourly PM2.5 measure in India.

Attribute Information

1. Timestamp: Timestamp in the format YYYY-MM-DD HH:MM:SS
2. Year: Year of the measure
3. Month: Month of the measure
4. Day: Day of the measure
5. Hour: Hour of the measure
6. PM2.5: Fine particulate matter air pollutant level in air

Citation Request

If you want to cite this data:

fedesoriano. (June 2022). Air Quality Data in India (2017 - 2022). Retrieved [Date Retrieved] from https://www.kaggle.com/datasets/fedesoriano/air-quality-data-in-india

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 2023, the cities of Jakarta and Mumbai had both average PM2.5 concentrations of 43.8 micrograms per cubic meter (μg/m³). By comparison, PM2.5 levels in Los Angeles and London were less than 10 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 five μg/m³. There are many sources of air pollution, such as energy production, transportation, and agricultural activities.

Annual emissions of various air pollutants in the United States have experienced dramatic reductions over the past half a century. As of 2023, emissions of nitrogen oxides (NOx) had reduced by more than 70 percent since 1970 to 6.8 million tons. Sulfur dioxide (SO₂) emissions have also fallen dramatically in recent decades, dropping from 23 million tons to 1.6 million tons between 1990 and 2023. Air pollutants can pose serious health hazards to humans, with the number of air pollution related deaths in the U.S. averaging 60,000 a year.

This dataset provides a summary of annual air pollution statistics from 1995 to the current available year for six air pollutants:

• Carbon Monoxide
• Oxides of Nitrogen (NO, NO2, NOx)
• Ozone
• Fine Particulate Matter (PM2.5)
• Sulphur Dioxide
• Total Reduced Sulphur

The annual statistics include percentiles, mean, maximums and also indicate the number of times an air monitoring station exceeded an Ontario annual ambient air quality criteria, where applicable. This information is also available in the annual Air Quality in Ontario Reports. The hourly air pollutant concentration data is posted in near real time on the Air Quality Ontario website: http://www.airqualityontario.com/

This csv file provides air pollution data information for Florida and Districts for 2017, 2018, 2019 and 2020. Through the FDOT Source Book Special Edition 2020 report, users can drill down the air pollution data at the statewide and District level. The report's link is: https://sourcebook-2020-se-fdot.hub.arcgis.com/Florida remains within acceptable EPA standards for ozone concentration and fine particulate matter (PM 2.5).Data source: Environmental Protection Agency (EPA) Air Data. For any additional information, please contact the Forecasting and Trends Office (FTO) at 850-414-5396.

Air Pollution and Mental Health

Identifying Short-Term Human Impacts of Air Pollution

By [source]

About this dataset

This dataset from the CitieS-Health project provides a unique insight into the impact of air pollution on humans. It is comprised of data collected in Barcelona, Spain, and examines various environmental variables, such as air pollution levels, and their effects on mental health and wellbeing. In addition to environmental factors, this dataset also captures self-reported survey data on mental health, physical activity, diet habits, and more. From performance in a Stroop test to information on total noise exposure at 55 dB - this comprehensive dataset will give you everything you need to understand the link between air pollution and human health so that we can begin finding better solutions for a cleaner future

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How to use the dataset

This dataset captures information on air pollution levels and variables related to mental health, such as performance in a Stroop test and self-reported surveys on mental health, physical activity, diet, and other factors. It can be used to answer the question “how does short-term exposure to air pollution affect human mental health?”

To use this dataset, start by understanding the variables you are interested in exploring. Look for correlations between environmental conditions (i.e., air pollution levels) and measures of wellbeing (i.e., performance in a Stroop test). Additionally pay special attention to any factors that may be associated with different levels of exposure (like access to green/blue spaces within 300m buffer).

Next you should examine any relevant self-reported surveys questions related to mental health or wellbeing (such as bienestar or sueno). For example consider looking at how responses vary based on age or gender; it is possible that some demographic groups are more sensitive than others when exposed to air pollutants. Finally consider incorporating information from other external sources like local noise levels or traffic patterns into your analysis – these will help contextualise your findings even further.

Using this dataset you can begin uncovering the impact of short-term exposure to air pollution on humans – by combining different variables together understanding what correlations exist between environment conditions and measures of wellbeing can help people make better decisions about their lifestyle choices like where they choose live, work or play!

Research Ideas

• Analyzing the differences in response time in Stroop tests by age and gender. By looking at the accurate response time when it comes to completing a Stroop test from participants of different genders and ages, conclusions can be drawn about how our responses are affected by environmental factors like air pollution levels and noise exposure

• Correlating green-space access with mental health outcomes over a period of time. This dataset can be used to analyze if access to green spaces has an impact on overall mental wellbeing indices like stress levels or perceived mood over a certain study period - allowing us to inform policies that leverage locations of urban green-spaces for better outcomes especially in heavily polluted cities

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: CitieSHealth_BCN_DATA_PanelStudy_20220414.csv | Column name | Description | |:------------------------------------|:---------------------------------------------------------------------------------------| | date_all | Date of the survey. (Date) | | year | Year of the survey. (Integer) | | month | Month of the survey. (Integer) | | day | Day of the survey. (Integer) | | dayoftheweek | Day of the week of the survey. (String) | | hour | Hour of the survey. (Integer) | | mentalhealth_survey | Self-reported survey responses regarding mental health. (String) | | occurrence_mental | Occurrence of mental health issues. (Integer) | | bienestar | Self-reported survey responses regarding wellbeing. (String) | | energia | Self-reported survey responses regarding energy levels. (String) | | estres | Self-reported survey responses regarding stress levels. (String) | | sueno | Self-reported survey responses regarding sleep quality. (String) | | horasfuera | Self-reported survey responses regarding time spent outdoors. (String) | | ordenador | Self-reported survey responses regarding computer use. (String) | | dieta | Self-reported survey responses regarding diet. (String) | | alcohol | Self-reported survey responses regarding alcohol consumption. (String) | | drogas | Self-reported survey responses regarding drug use. (String) | | enfermo | Self-reported survey responses regarding illness. (String) | | otrofactor | Self-reported survey responses regarding other factors. (String) | | stroop_test | Performance in a Stroop test. (Integer) | | occurrence_stroop | Occurrence of Stroop test. (Integer) | | mean_incongruent | Mean of incongruent responses in the Stroop test. (Float) | | correct | Number of correct responses in the Stroop test. (Integer) | | response_duration_ms | Response duration in milliseconds in the Stroop test. (Integer) | | performance | Performance in the Stroop test. (Float) | | mean_congruent | Mean of congruent responses in the Stroop test. (Float) | | inhib_control | Inhibition control in the Stroop test. (Float) | | z_performance | Z-score of performance in the Stroop test. (Float) | | z_mean_incongruent | Z-score of mean incongruent responses in the Stroop test. (Float) | | z_inhib_control | Z-score of inhibition control in the Stroop test. (Float) | | no2bcn_24h | Nitrogen dioxide (NO2) levels in Barcelona over 24 hours. (Float) | | no2bcn_12h | Nitrogen dioxide (NO2) levels in Barcelona over 12 hours. (Float) | | no2gps_24h | Nitrogen dioxide (NO2) levels in GPS locations over 24 hours. (Float) | | no2gps_12h | Nitrogen dioxide (NO2) levels in GPS locations over 12 hours. (Float) | | no2bcn_12h_x30 | Nitrogen dioxide (NO2) levels in Barcelona over 12 hours multiplied by 30. (Float) | | no2bcn_24h_x30 | Nitrogen dioxide (NO2) levels in Barcelona over 24 hours multiplied by 30. (Float) | | no2gps_12h_x30 | Nitrogen dioxide (NO2) levels in GPS locations over 12 hours multiplied by 30. (Float) | | no2gps_24h_x30 | Nitrogen dioxide (NO2) levels in GPS locations over 24 hours multiplied by 30. (Float) | | min_gps | Minimum GPS location. (Float) | | district | District of Barcelona where the survey was conducted. (String) | | education | Educational level of the participant. (String) | | maxwindspeed_12h | Maximum wind speed over 12 hours. (Float) | | noise_total_LDEN_55 | Total noise level in decibels (dB) over 55 minutes. (Float) | | access_greenbluespaces_300mbuff | Access to green and blue spaces within

CDP challenge - WHO Global Ambient Air Quality Database

Pollutant and meteorological data for the prediction of air pollutants in Mexico City, from 2010-12-31 to 2019-03-31. This data was used for the work: Minutti-Martinez C., Arellano-Vázquez M., Zamora-Machado M. (2021). A Hybrid Model for the Prediction of Air Pollutants Concentration, Based on Statistical and Machine Learning Techniques. Lecture Notes in Computer Science, vol 13068. Springer, Cham. https://doi.org/10.1007/978-3-030-89820-5_21

Air Quality Monitoring Data Dublin City Council measures ambient air quality in Dublin in accordance with Air Quality standards. 'This dataset contains Air Quality Monitoring Data from January to March 2011, consisting five spreadsheets taken from five air monitoring sites around Dublin City that show hourly results for the pollutants Sulphur Dioxide( SO2) , Nitrogen Dioxide (NO2), Carbon Monoxide ( CO) and Particulate Matter (PM2.5 & PM10). The regulations are set by the Clean Air for Europe Directive 2008 (2008/50); from January 1st, 2010 the directive also requires PM2.5 monitoring. There is no real time data for PM10 or PM25'Black smoke monitoring is also carried out as a form of background monitoring using the benchmark of EU Directive 80/779/EEC as a guide however this has been scaled down since the 1990s following the introduction of the coal ban.'Multi-pollutant sites are:'Winetavern Street PM10, NO2, CO, SO2'Coleraine Street- PM2.5, NO2, CO, SO2'Ballyfermot PM10, NO2, SO2'PM10 only sites include:'Phoenix Park'Rathmines'PM2.5 only:'Marino'Black Smoke:'Ringsend'Crumlin'Finglas'Cabra''Annual report published http://www.dublincity.ie/WaterWasteEnvironment/AirQualityMonitoringandNoiseControl/AirPollution/Documents/Annual_report_2009.pdf

The standardised database of quality assured air pollution monitor data from Australian state and territory governments. Access is available to researchers on request to car.data@sydney.edu.au.

EPA Positive Matrix Factorization (PMF) source profile results for fine and coarse particulate matter. Inorganic fine and coarse particulate matter concentration data used in PMF models. This dataset is associated with the following publication: Khatri, S.B., C. Newman, J.P. Hammel, T. Dey, J.J. Van Laere, K.A. Ross, T. Anderson, S. Mukerjee, L. Smith, M. Landis, A. Holstein, and G. Norris. Associations of Air Pollution and Pediatric Asthma in Cleveland, Ohio. The Scientific World Journal. Hindawi Publishing Corporation, New York, NY, USA, 2021: 8881390, (2021).

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.