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.

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/

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.

The Air Quality Dataset provides a comprehensive overview of atmospheric pollution levels across various locations in Poland from 2017 to 2023. It features extensive measurements of numerous air pollutants captured through an extensive network of air quality monitoring stations throughout the country. The dataset includes both hourly (1g) and daily (24g) averages of the recorded pollutants, offering detailed temporal resolution to study short-term peaks and long-term trends in air quality.

Pollutants Measured:

1. Gaseous Pollutants: Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Nitric Oxide (NO), Nitrogen Oxides (NOx), Sulfur Dioxide (SO2), Ozone (O3), and Benzene (C6H6).
2. Particulate Matter: PM10, PM2.5; and specific elements and compounds bound to PM10 such as Lead (Pb), Arsenic (As), Cadmium (Cd), Nickel (Ni), among others.
3. Polycyclic Aromatic Hydrocarbons (PAHs) associated with PM10: Benzo[a]anthracene (BaA), Benzo[b]fluoranthene (BbF), Benzo[j]fluoranthene (BjF), Benzo[k]fluoranthene (BkF), Benzo[a]pyrene (BaP), Indeno[1,2,3-cd]pyrene (IP), Dibenzo[a,h]anthracene (DBahA).
4. Additional Chemicals: Including various volatile organic compounds (VOCs) like ethylene, toluene, xylene variants, aldehydes, and hydrocarbons.

Features of the Dataset:

Locations: Data from numerous air quality monitoring stations distributed across various urban, suburban, and rural areas in Poland.
Time Resolution: Measurements are provided in both hourly and daily intervals, catering to different analytical needs.
Coverage Period: This dataset encompasses data from 2017 to the year, 2023, enabling analysis over multiple years to discern trends and assess the effectiveness of air quality management policies.
Deployment of Deposition Sampling: Concentrations of certain pollutants in wet and dry deposition forms, noted with 'cdepoz' (cumulative deposition), providing insights into the deposition rates of airborne pollutants.

Potential Applications:

Environmental Research: Study the impact of various pollutants on air quality, health, and the environment.
Policy Making: Assist policymakers in evaluating the effectiveness of past regulations and planning future actions to improve air quality.
Public Health: Correlate pollutant exposure levels with health outcomes, helping public health professionals to mitigate risks associated with poor air quality.

Data Format:

The dataset is structured in a tabular format with each row representing an observation time (either hourly or daily) and columns representing different pollutants and their concentrations at various monitoring stations.

This dataset is an essential resource for researchers, policymakers, environmental agencies, and health professionals who need a detailed and robust dataset to understand and combat air pollution in Poland.

Source of data: Chief Inspectorate of Environmental Protection (GIOS)

The historic weather dataset for Cracow and Warsaw

The historic weather dataset for Cracow and Warsaw with suburbs, covering daily observations from 2019 to August 2024, would encompass a range of atmospheric and meteorological data points collected over the defined time period and locations. Here’s a description of what such a dataset might include and signify: Key Characteristics:

Locations: The cities of Cracow and Warsaw, along with their suburbs. The dataset would likely specify the exact areas or measurements stations.
Time Frame: Daily records from January 1, 2019, to August, 2024, providing a comprehensive view of weather variations through different seasons and years.
Data Granularity: Daily data would allow trends such as temperature fluctuations, precipitation patterns, and weather anomalies to be studied in considerable detail.

Likely Data Fields:

Each record in the dataset might contain:

DATE_VALID_STD: Representing each day within the date range specified (from 2019-01-01 to 2024-08-20 for Cracow and Warsaw suburbs).
Temperature Fields (Min, Max, Avg): Temperature readings at specified intervals, likely in Celsius, providing insight into daily and seasonal temperature patterns and extremes.
Humidity Fields (Min, Max, Avg): Relative and specific humidity readings to assess moisture levels in the air, which have implications for weather conditions, comfort levels, and health.
Precipitation: Data on rainfall, snowfall, and total snow depth, essential for understanding water cycle dynamics, agricultural planning, and urban water management in these areas.
Wind Measurements: May include minimum, average, and maximum speeds and perhaps prevailing directions, useful in sectors like aviation, construction, and event planning.
Pressure and Tendency: Barometric pressure readings at different measurement standards to help predict weather changes.
Radiation and Cloud Cover: D...

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 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.

The Air Quality System (AQS) database contains measurements of air pollutant concentrations from throughout the United States and its territories. The measurements include both criteria air pollutants and hazardous air pollutants.

AQI: Alaska: Anchorage: SO2 data was reported at 0.000 Index in 05 Dec 1984. This stayed constant from the previous number of 0.000 Index for 04 Dec 1984. AQI: Alaska: Anchorage: SO2 data is updated daily, averaging 0.000 Index from Dec 1980 (Median) to 05 Dec 1984, with 881 observations. The data reached an all-time high of 41.000 Index in 07 Aug 1984 and a record low of 0.000 Index in 05 Dec 1984. AQI: Alaska: Anchorage: SO2 data remains active status in CEIC and is reported by United States Environmental Protection Agency. The data is categorized under Global Database’s United States – Table US.ESG.E001: Air Quality Index and Air Pollutants.

Over the last decade, China has been trying to tackle worsening air quality from urbanization and industrialization. In 2023, the average concentration of ozone was around 144 micrograms per cubic meter in 339 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 11 gigatons in 2022, with emissions increasing quickly again after dipping in 2016.

Environmental protection

The Chinese government saw environmental degradation primarily as a public health issue to Chinese citizens and therefore started contributing more and more resources into protecting the environment. In 2023, public expenditure on energy conservation and environmental protection in China had amounted to nearly 564 billion yuan, almoust 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 have made changes to their commuting and water use habits in order to help fight climate change.

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 1980-2023, 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 2023, the number of days rated to have air quality worse than moderate was the highest in the 21st century at 13. This 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 1980-2023. 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. (1980-2023). Air Quality Index Reports. (Accessed 4 June 2024).

[2] Ibid.

Source: Environmental Protection Agency. (1980-2023). Air Quality Index Reports. https://www.epa.gov/outdoor-air-quality-data/air-quality-index-report. (Accessed 4 June 2024).

AQI: Arizona: Phoenix-Mesa-Scottsdale: Ozone data was reported at 54.000 Index in 24 Mar 2025. This records a decrease from the previous number of 84.000 Index for 23 Mar 2025. AQI: Arizona: Phoenix-Mesa-Scottsdale: Ozone data is updated daily, averaging 58.000 Index from Jan 1980 (Median) to 24 Mar 2025, with 16472 observations. The data reached an all-time high of 264.000 Index in 01 Jun 2022 and a record low of 19.000 Index in 04 Dec 2022. AQI: Arizona: Phoenix-Mesa-Scottsdale: Ozone data remains active status in CEIC and is reported by United States Environmental Protection Agency. The data is categorized under Global Database’s United States – Table US.ESG.E001: Air Quality Index and Air Pollutants. [COVID-19-IMPACT]

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.

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 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.

Multiple linkages connect air quality and climate change. Many air pollutant sources also emit carbon dioxide (CO2), the dominant anthropogenic greenhouse gas (GHG). The two main contributors to non-attainment of U.S. ambient air quality standards, ozone (O3) and particulate matter (PM), interact with radiation, forcing climate change. PM warms by absorbing sunlight (e.g., black carbon) or cools by scattering sunlight (e.g., sulfates) and interacts with clouds; these radiative and microphysical interactions can induce changes in precipitation and regional circulation patterns. Climate change is expected to degrade air quality in many polluted regions by changing air pollution meteorology (ventilation and dilution), precipitation and other removal processes, and by triggering some amplifying responses in atmospheric chemistry and in anthropogenic and natural sources. Together, these processes shape distributions and extreme episodes of O3 and PM. Global modeling indicates that as air pollution programs reduce SO2 to meet health and other air quality goals, near-term warming accelerates due to “unmasking” of warming induced by rising CO2. Air pollutant controls on CH4, a potent GHG and precursor to global O3 levels, and on sources with high black carbon (BC) to organic carbon (OC) ratios could offset near-term warming induced by SO2 emission reductions, while reducing global background O3 and regionally high levels of PM. Lowering peak warming requires decreasing atmospheric CO2, which for some source categories would also reduce co-emitted air pollutants or their precursors. Model projections for alternative climate and air quality scenarios indicate a wide range for U.S. surface O3 and fine PM, although regional projections may be confounded by interannual to decadal natural climate variability. Continued implementation of U.S. NOx emission controls guards against rising pollution levels triggered either by climate change or by global emission growth. Improved accuracy and trends in emission inventories are critical for accountability analyses of historical and projected air pollution and climate mitigation policies.

Implications: The expansion of U.S. air pollution policy to protect climate provides an opportunity for joint mitigation, with CH4 a prime target. BC reductions in developing nations would lower the global health burden, and for BC-rich sources (e.g., diesel) may lessen warming. Controls on these emissions could offset near-term warming induced by health-motivated reductions of sulfate (cooling). Wildfires, dust, and other natural PM and O3 sources may increase with climate warming, posing challenges to implementing and attaining air quality standards. Accountability analyses for recent and projected air pollution and climate control strategies should underpin estimated benefits and trade-offs of future policies.

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 41.7 and 39.9 micrograms per cubic meter (μg/m³) respectively. By comparison, PM2.5 levels in London and New York were less than eight 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.

Abstract

Polluted air is a major health hazard in developing countries. Improvements in pollution monitoring and statistical techniques during the last several decades have steadily enhanced the ability to measure the health effects of air pollution. Current methods can detect significant increases in the incidence of cardiopulmonary and respiratory diseases, coughing, bronchitis, and lung cancer, as well as premature deaths from these diseases resulting from elevated concentrations of ambient Particulate Matter (Holgate 1999).

Scarce public resources have limited the monitoring of atmospheric particulate matter (PM) concentrations in developing countries, despite their large potential health effects. As a result, policymakers in many developing countries remain uncertain about the exposure of their residents to PM air pollution. The Global Model of Ambient Particulates (GMAPS) is an attempt to bridge this information gap through an econometrically estimated model for predicting PM levels in world cities (Pandey et al. forthcoming).

The estimation model is based on the latest available monitored PM pollution data from the World Health Organization, supplemented by data from other reliable sources. The current model can be used to estimate PM levels in urban residential areas and non-residential pollution hotspots. The results of the model are used to project annual average ambient PM concentrations for residential and non-residential areas in 3,226 world cities with populations larger than 100,000, as well as national capitals.

The study finds wide, systematic variations in ambient PM concentrations, both across world cities and over time. PM concentrations have risen at a slower rate than total emissions. Overall emission levels have been rising, especially for poorer countries, at nearly 6 percent per year. PM concentrations have not increased by as much, due to improvements in technology and structural shifts in the world economy. Additionally, within-country variations in PM levels can diverge greatly (by a factor of 5 in some cases), because of the direct and indirect effects of geo-climatic factors.

The primary determinants of PM concentrations are the scale and composition of economic activity, population, the energy mix, the strength of local pollution regulation, and geographic and atmospheric conditions that affect pollutant dispersion in the atmosphere.

Geographic coverage

The database covers the following countries: Afghanistan Albania Algeria Andorra Angola
Antigua and Barbuda Argentina
Armenia Australia
Austria Azerbaijan
Bahamas, The
Bahrain Bangladesh
Barbados
Belarus Belgium Belize
Benin
Bhutan
Bolivia Bosnia and Herzegovina
Brazil
Brunei
Bulgaria
Burkina Faso
Burundi Cambodia
Cameroon
Canada
Cayman Islands
Central African Republic
Chad
Chile
China
Colombia
Comoros Congo, Dem. Rep.
Congo, Rep. Costa Rica
Cote d'Ivoire
Croatia Cuba
Cyprus
Czech Republic
Denmark Dominica
Dominican Republic
Ecuador Egypt, Arab Rep.
El Salvador Eritrea Estonia Ethiopia
Faeroe Islands
Fiji
Finland France
Gabon
Gambia, The Georgia Germany Ghana
Greece
Grenada Guatemala
Guinea
Guinea-Bissau
Guyana
Haiti
Honduras
Hong Kong, China
Hungary Iceland India
Indonesia
Iran, Islamic Rep.
Iraq
Ireland Israel
Italy
Jamaica Japan
Jordan
Kazakhstan
Kenya
Korea, Dem. Rep.
Korea, Rep. Kuwait
Kyrgyz Republic Lao PDR Latvia
Lebanon Lesotho Liberia Liechtenstein
Lithuania
Luxembourg
Macao, China
Macedonia, FYR
Madagascar
Malawi
Malaysia
Maldives
Mali
Mauritania
Mexico
Moldova Mongolia
Morocco Mozambique
Myanmar Namibia Nepal
Netherlands Netherlands Antilles
New Caledonia
New Zealand Nicaragua
Niger
Nigeria Norway
Oman
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines Poland
Portugal
Puerto Rico Qatar
Romania Russian Federation
Rwanda
Sao Tome and Principe
Saudi Arabia
Senegal Sierra Leone
Singapore
Slovak Republic Slovenia
Solomon Islands Somalia South Africa
Spain
Sri Lanka
St. Kitts and Nevis St. Lucia
St. Vincent and the Grenadines
Sudan
Suriname
Swaziland
Sweden
Switzerland Syrian Arab Republic
Tajikistan
Tanzania
Thailand
Togo
Trinidad and Tobago Tunisia Turkey
Turkmenistan
Uganda
Ukraine United Arab Emirates
United Kingdom
United States
Uruguay Uzbekistan
Vanuatu Venezuela, RB
Vietnam Virgin Islands (U.S.)
Yemen, Rep. Yugoslavia, FR (Serbia/Montenegro)
Zambia
Zimbabwe

Kind of data

Observation data/ratings [obs]

Mode of data collection

Other [oth]