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.

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]

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

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

In 2024, Bangladesh's capital Dhaka had a pollution index score of 93.9, the highest among megacities in the Asia-Pacific region. In contrast, Japan's capital Tokyo had a pollution index score of 42.2 that year. Megacities on course for growth The United Nations defines megacities as cities with over ten million inhabitants. The population living in megacities has doubled in size in the last twenty years and is expected to rise even more until 2035. Today, the Asia-Pacific region is home to the highest number of megacities, with China and India alone accounting for around half of all megacities worldwide. At the same time, only half of the population in Asia is living in cities. This figure is also expected to rise exponentially over the next years, especially with much of the younger population migrating to larger cities. The growth of megacities and their higher population densities bring along several environmental problems. Exposure to pollution in India The most populated cities in APAC are located in Japan, China and India. As seen above, India's capital also falls among the top three most polluted megacities in the region and ranks second among the most polluted capital cities worldwide with an average PM2.5 concentration. As one of the fastest emerging economies in the world, India's rapid urbanization and industrialization have led to high pollution rates in different areas. The volume of emissions from coal-fired power plants has led to electricity and heat accounting for the largest share of greenhouse gas emissions in India. The country is also among the nations with the highest population share exposed to hazardous concentrations of air pollution worldwide.

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.

Environmental monitoring stations (EMS) were installed in Campbelltown and Liverpool's CBD in December 2020. The EMS measures weather data and pollutants data. This dataset stores pollutants related measures:nitrogendioxide (NO2 measured in ppb)carbonmonoxide (CO in ppb)ozone (O3 in ppb)particulate matter 10 (PM10 in µg/m³)particulate matter 2.5 (PM2.5 in µg/m³)Associated Air Quality Index is calculated based on a number of parameters. Data in this dataset is presented in the Quality of Place dashboard.Please note this data is indicative as sensors may from time to time provide incorrect data due to wear and tear or unforeseen circumstances.

This file describes the dataset used in Ou et al., "Air pollution control strategies directly limiting national health damages in the US."

This work used the Global Change Assessment Model (GCAM) with state-level representation of the U.S. energy system (GCAM-USA). GCAM and GCAM-USA are developed and released by the University of Maryland/Pacific Northwest National Laboratory Joint Global Change Research Center (JGCRI). For further details, see the GCAM documentation: jgcri.github.io/gcam-doc. The model source code is available at github.com/JGCRI/gcam-core.

A modified version of GCAMv4.3 was used for this analysis. Source code and input data specific for this paper are available upon request.

This dataset contains Excel spreadsheets and an R script that link to comma-separated values (CSV) files that were extracted from the model output. The spreadsheets and scripts show the data and reproduce each of the figures in the paper.

This dataset is associated with the following publication: Ou, Y., J. West, S. Smith, C. Nolte, and D. Loughlin. Air pollution control strategies directly limiting national health damages in the US.. Nature Communications. Nature Publishing Group, London, UK, 11: 957, (2020).

Caeli can provide this data through an API, dashboard, real-time geo map, or via datasets(.csv). In addition, all this data is available in daily, monthly and annual formats. The data can be delivered in various spatial resolutions starting from 0.001 degrees latitude and longitude (WSG 84), which roughly converts to 100X100 meter.

The Caeli datasets are often used for creating and validating various models and for training machine learning algorithms. We’ll allow you to specify your state or country, your preferred timeframe, resolution, and pollutant. Based on this information we’ll compile a reliable dataset. The measurements in de dataset can be used in determining the air quality of a region for a specific period of time. Additionally, your composite dataset can also serve for strategy and reporting purposes, such as ESG strategy, TCDF, SFDR, and sustainable decision making. The price of the dataset is based on the size of the area, the resolution chosen, and the number of years.

Are you interested in one of these pollutants or would you like to gather more information about our opportunities? Please, do not hesitate to contact us. www.caeli.space

Sector coverage: Financial | Energy | Government | Agricultural | Health | Shipping.

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.

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]

This database provides estimates for the amount of pollution (load) produced from a unit of production (in this case, indicated by employment) by specific industry sectors and by firms within particular size categories. The pollution intensities are for the following pollutants: particulates (PT); sulphur oxide (SOX); carbon monoxide (CO); nitrogen oxide (NOX); hydrocarbons (HC). This dataset has been produced by DECRG-IE of the World Bank in collaboration with Mexico’s Instituto Nacional de Ecologia (INE), using a database they provided, the Sistema Nacional de Informacion de Fuentes Fijas. The units of measurement for employment intensities are tons per employee. The pollution intensities are at 2 and 3-digit ISIC (version 2) levels. Additional datasets were calculated for firms of various size (small, medium, large). Small firms are defined as having employment at twenty or less. Medium firms are defined as having employment between 21 and 100. Large firms have employment over 100. The sample size for intensities by firm size was: small firms 2,346, medium firms 2,143, large firms 1,310. Preliminary analysis of results revealed an outlier problem. Therefore, the top twenty-five polluters were deleted from the overall dataset, and the top ten polluters from each plant-size category were removed, before calculation of pollution intensities. This dataset was revised on 11/17/97. The 2 and 3 digit results were changed to make them consistent with the results for the size categories by removing the top 10 polluters from each category. Please make sure that you are using data marked 11/17/97.

Mobile monitoring data generated using an instrumented electric vehicle. This dataset is associated with the following publication: Deshmukh, P., E. Kimbrough, R. Logan, S. Krabbe, V. Isakov, and R. Baldauf. Identifying Air Pollution Source Impacts in Urban Communities Using Mobile Monitoring. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 000, (2020).

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.

The dataset spans from January 1, 2014, to March 15, 2020, with measurements recorded on an hourly basis.

The environmental and pollutant data was provided by the Austrian government under the following license: CC-BY-4.0: Land Steiermark - data.steiermark.gv.at

Air quality by means of NO2, NO, NOx, PM10 and O3 was measured at five sites in Graz, Austria (Süd (eng. South) - S, Nord (eng. North) - N, West (eng. West) - W, Don Bosco – D, Ost (eng. East) – O).

Temperature, precipitation, relative humidity, pressure, and wind speed are among the weather conditions considered. To represent wind direction, the wind speed was multiplied by the sine and cosine of the wind direction.

Lags were generated using weather data, considering the last 12 data points. The mean of these 12 values was then calculated to represent an hourly metric.

The ERA5-Land data is subject to the Copernicus licence from following source https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.e2161bac?tab=overview

it includes following variables :

Snowfall - sf

Surface latent heat flux - slhf

Snowmelt - smlt

Snow cover - snowc

Windspeed - speed

Surface latent heat flux sshf

Soil temperature level 4 - stl4

Skin temperature - str

Surface thermal radiation downwards - strd

Total precipitation - tp

Temperature of snow layer - tsn

10m u-component of wind - u10

10m v-component of wind - v10

Surface net radiation - rsn

Snow depth - sd

Snow depth water equivalent - sde

2m dewpoint temperature - d2m

Forecast albedo - fal

Temporal values are also incorporated into this dataset, values such as holidays, weekdays, seasons, and months.

The dataset includes Prophet values for all pollutants, which were determined by considering various metrics such as trend, seasonality (weekly, yearly, and daily), as well as yhat lower and upper bounds.

The Air Pollutant Emission Inventory (APEI) is an annual report of air pollutant emissions across Canada published by Environment and Climate Change Canada. The report details the release of air pollutants from all known of sources since 1990. The APEI serves many purposes, including: - supporting the development of and tracking progress on air quality management strategies, policies and regulations - fulfilling Canada’s domestic and international reporting obligations - informing Canadians about air pollutants emissions - providing data to support Canada’s air quality health indices Emissions data is available for the following: Criteria air contaminants (CACs): - Total particulate matter (TPM) - Particulate matter less than or equal to 10 microns (PM10) - Particulate matter less than or equal to 2.5 microns (PM2.5) - Sulphur oxides (SOx) - Nitrogen oxides (NOx) - Volatile organic compounds (VOCs) - Carbon monoxide (CO) - Ammonia (NH3) Heavy metals: - Mercury (Hg) - Lead (Pb) - Cadmium (Cd) Persistent organic pollutants (POPs): - Dioxins and furans (D/F) - Four polycyclic aromatic hydrocarbons (PAHs) compounds (Benzo[a]pyrene, Benzo[b]fluoranthene, Benzo[k]fluoranthene and Indeno[1,2,3-cd]pyrene) - Hexachlorobenzene (HCB) This data record breaks down the historical trends of reported pollutants by individual substances. To perform more customized selections of APEI data, please visit our website to use our interactive query tool. The Air Pollutant Emission Inventory is compiled from many different data sources. Emissions data reported by individual facilities to Environment and Climate Change Canada’s National Pollutant Release Inventory are supplemented with well documented, science-based estimation tools to quantify total emissions. Together these data sources provide a comprehensive overview of pollutant emissions across Canada. Supplemental Information Air Pollutants Inventory Main Page: https://Canada.ca/APEI APEI and Black Carbon Interactive Query Tool: https://pollution-waste.canada.ca/air-emission-inventory/ Canada's Black Carbon Emission Inventory: https://Canada.ca/black-carbon Supporting Projects: National Air Pollutant Emissions Trends for 1990–2022

Some 56 percent of American worried a great deal about polluted drinking water in 2024, according to a survey of approximately 1,000 adults. Meanwhile, seven percent of respondents stated they did not worry at all about drinking water pollution. Overall, the share of American adults who worry a great deal about contaminated drinking water has fallen since 1990.

This paper is under review

Abstract:

Kampala, the political and economic capital of Uganda and one of the fastest urbanising cities in sub-Saharan Africa, is experiencing a deteriorating trend in air quality with emissions from multiple diffused local sources like transportation, domestic and outdoor cooking, and industries, and sources outside the city airshed like seasonal open fires in the region. PM2.5 (particulate matter under 2.5um size) is the key pollutant of concern in the city with monthly spatial heterogeneity of 60-100 ug/m3. Outdoor air pollution is distinctly pronounced in the global south cities and lack the necessary capacity and resources to develop integrated air quality management programmes including ambient monitoring, emissions and pollution analysis, source apportionment, and preparation of clean air action plans. This paper presents an integrated assessment of air quality in Kampala drawing from ground measurements (from a hybrid network of stations), satellite observations (from NASA’s MODIS and OMI), global reanalysis fields (from GEOS-chem and CAMS simulations), high resolution (~1km) multi-pollutant emissions inventory for the airshed, WRF-CAMx based PM2.5 pollution analysis, and a qualitative review of institutional and policy environment for air quality management in Kampala. The proposed clean air action plans aim for better air quality in the region using a combination of short-, medium-, and long-term emission control measures for all the dominate sources and institutionalize pollution tracking mechanisms (like emissions and pollution monitoring and reporting) for effective management of air pollution.

This data archive serves as a supplemenary to the journal article and with a short description of the files below:

• File: AQ-Kampala-Analysis-Summary.pptx (Caution: large 60MB)
  A composite presentation including the following
  
  • Grid summaries
  • Snapshots of airshed GIS files, emission activities
  • Summary of meteorology from WRF simulations and historical synoptics
  • Summaries of ambient monitoring data
  • CAMS reanalysis summary
  • Summaries of Emission inventory and WRF-CAMx modelling (annual and monthly)
  • Summaries of PM2.5 Source apportionment (annual and monthly)
• File: grids_kampala.rar
  Grid file (KML and ESRI shapefiles format) for the airshed spanning 0.0N to 0.6N and 32.3E to 32.9E with a spatial resolution of 0.01deg (~1km)
• File: gis_roads_from_opensteetmaps.rar
  ESRI shapefiles of primary roads and all roads, extracted from the openstreetmaps
  Raw data archive @ https://download.geofabrik.de/index.html
• File: gis-scanned2021image-quarries.kml
  KML file of quarries scanned using the imagery on Google Earth platform
• File: population_kampala_2000-2022.csv
  Gridded population data 2000 to 2022
  Raw data archive is from LANDSCAN - https://landscan.ornl.gov
• File: Monitoring-Kampala_USEmbassy_2017-2024.xlsx
  Summary of monitoring data collected at the US Embassy in Kampala
  Raw data archive is @ https://www.airnow.gov/international/us-embassies-and-consulates/#Uganda$Kampala
• File: meteo_wrf_stats.xlsm
  Summary of output of WRF simulations for the Kampala region. Have to activiate macros to summarize the results by month and update the charts. The tool can be used to other cities also by changing the input data.
• File: meteo_precip-era5-reanalysis.csv
  Summary of monthly precipitation date (mm/day) from ERA5 reanalysis fields
  Raw data archive is @ https://psl.noaa.gov/data/atmoswrit/timeseries
• File: TROPOMI_EastAfrica_NO2_Maps.zip
  Images of monthly average TROPOMI NO2 extracts covering East Africa (Uganda and Ethiopia)
  Extracted from Google Earth Engine, using 10% cloud fraction
• File: TROPOMI_EastAfrica_CSVs.zip
  CSV files of gridded monthly average NO2, SO2, HCHO, and Ozone columnar densities
  Extracted from Google Earth Engine, using 10% cloud fraction
  Read the data descriptions and applicability of the data for analysis before using (for example, negative numbers in the SO2 file).
  NO2 - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2
  SO2 - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2
  HCHO - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_HCHO
  O3 - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3
• File: composite_emisson_factors_gains.xlsx
  A composite library of emission factors for reference
• File: kampala_gridded_emissions_2018.rar
  Gridded emissions inventory for Kampala - PM25, PM10, SO2, NO, NO2, and CO
  PM25 is speciated into FPRM, BC, and OC (sum all for PM25)
  PM10 is speciated into FPRM, CPRM, BC, OC (sum all for PM10)
  All emissions in tons/year/grid
  Emissions are seggragted into sectors and fuels - included in the filenames
• File: kampala_gridded_modelled_monthavgp25.csv
  Gridded PM2.5 concentrations for 2018, from WRF-CAMx modelling system
  Monthly averages in ug/m3