Between 2015 and 2019, the estimated proportion of land degradation in Sub-Saharan Africa increased from 6.7 percent to 14.63 percent. This was the fastest rate of degradation over this period when compared to other regions. The region with the largest proportion of land degradation is Eastern Asia and South-eastern Asia, at almost 25 percent.

The graph shows the level of concern about soil pollution in Mexico as of May 2019. During a survey, 45.7 percent of the respondents with internet-connected smartphones considered soil pollution to be a severe problem in their city or municipality.

The level of soil pollution caused by TPH in South Korea has been observed to be at around 34.34 milligrams per kilogram (mg/kg) in 2022. The contagious level has decreased in particular when compared to the value of 2015.

Mexico Soil Pollution: Tax Revenue: USD: Pollution data was reported at 35.027 USD mn in 2022. This records an increase from the previous number of 34.776 USD mn for 2021. Mexico Soil Pollution: Tax Revenue: USD: Pollution data is updated yearly, averaging 0.000 USD mn from Dec 1994 (Median) to 2022, with 29 observations. The data reached an all-time high of 38.294 USD mn in 2015 and a record low of 0.000 USD mn in 2013. Mexico Soil Pollution: Tax Revenue: USD: Pollution data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Mexico – Table MX.OECD.ESG: Environmental: Environmentally Related Tax Revenue: Environmental Protection Domains: OECD Member: Annual.

United States Soil Pollution: Tax Revenue: % of GDP data was reported at 0.006 % in 2021. This records an increase from the previous number of 0.004 % for 2020. United States Soil Pollution: Tax Revenue: % of GDP data is updated yearly, averaging 0.004 % from Dec 1994 (Median) to 2021, with 28 observations. The data reached an all-time high of 0.007 % in 2011 and a record low of 0.001 % in 2002. United States Soil Pollution: Tax Revenue: % of GDP data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.ESG: Environmental: Environmentally Related Tax Revenue: Environmental Protection Domains: OECD Member: Annual.

This dataset describes the simulations at two pilot sites in the northeast from 1900-2100 for several soil and plant community responses to climate and nitrogen deposition across a number of future scenarios. This dataset is associated with the following publications: Phelan, J., S. Belyazid, C. Clark , P. Jones, and J. Cajka. Assessing the Effects of Climate Change and Air Pollution on Soil Properties and Plant Diversity in Sugar Maple-Beech-Yellow Birch Hardwood Forests in the Northeastern United States: Model Simulations from 1900-2100. WATER, AIR, & SOIL POLLUTION. Springer, New York, NY, USA, 227(3): 1-30, (2016). Belyazid, S., J. Phelan, B. Nihlgard, H. Sverdrup, C. Driscoll, I. Fernandez, J. Aherne, L.M. Teeling-Adams, S. Bailey, M. Arsenault, N. Cleavitt, B. Engstrom, R. Dennis, D. Sperduto, D. Werier, and C. Clark. Assessing the Effects of Climate Change and Air Pollution on Soil Properties and Plant Diversity in Northeastern U.S. Hardwood Forests: Model Setup and Evaluation. WATER, AIR, & SOIL POLLUTION. Springer, New York, NY, USA, 230: 106, (2019).

The data presented are dependent on two pre-existing Mendeley Data datasets: Rate, Andrew; McGrath, Gavan (2022), “Sediment and soil quality at Ashfield Flats Reserve, Western Australia”, Mendeley Data, V1, doi: 10.17632/d7m3746byk.1 https://data.mendeley.com/datasets/d7m3746byk/1 Rate, Andrew; McGrath, Gavan (2022), “Surface water quality at Ashfield Flats Reserve, Western Australia”, Mendeley Data, V1, doi: 10.17632/vphzgjshgm.1 https://data.mendeley.com/datasets/vphzgjshgm/1

Elemental (including Al, As, B, Ba, Be, Ca, Cd, Ce, Co, Cr, Cu, Fe, Ga, Gd, Ge, K, La, Mg, Mn, Mo, Na, Nb, Nd, Ni, P, Pb, S, Sc, Si, Sr, Th, Ti, V, Y, Zn, Zr) concentrations, pH, electrical conductivity, microplastics, Longitude-Latitude and UTM Zone 50 coordinates, sample material type, sampling strata, and sample identification codes for 231 samples of sediment or soil, and elemental (including Al, As, B, Ba, Ca, Co, Cr, Cu, Fe,Gd, K, La, Mg, Mn, Mo, Na, Nd, Ni, P, S, Si, Sr, V, Zn) concentrations, selected nutrient ion concentrations (nitrate+nitrite (NOx), filterable reactive phosphate (FRP)), pH, electrical conductivity, Longitude-Latitude and UTM Zone 50 coordinates, sampling strata, and sample identification codes for 172 samples of surface water. Samples were collected in 2019, 2020, and 2021 from Ashfield Flats Reserve, an urban nature reserve in Western Australia.

The objective of the supplementary data is to analyse the statistical distributions of measured variables, and assess whether these variables (or log10- or Box-Cox-power-transformed variables) are normally distributed, and whether the distributions are unimodal. Maps are presented to contextualize the data spatially and visually. The distribution statistics are a guide to which subsequent statistical analyses (e.g. parametric or non-parametric) should be used.

This supplementary data includes: » Map images showing location of the study site and location of samples; » A table of lower limits of detection for analyses; » 3 Tables of distribution statistics (Shapiro-Wilk, Hartigan Dip-test) for raw and transformed numeric variables in sediment/soil data by sampling year (2019, 2020, 2021) » A figure showing density distribution plots for chemical water quality parameters. » 3 Tables of distribution statistics (Shapiro-Wilk, Hartigan Dip-test) for raw and transformed numeric variables in surface water data by sampling year (2019, 2020, 2021)

USHAP (USHighAirPollutants) is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for the United States. It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM2.5 dataset in the United States from 2000 to 2020. Our daily PM2.5 estimates agree well with ground measurements with an average cross-validation coefficient of determination (CV-R2) of 0.82 and normalized root-mean-square error (NRMSE) of 0.40, respectively. All the data will be made public online once our paper is accepted, and if you want to use the USHighPM2.5 dataset for related scientific research, please contact us (Email: weijing_rs@163.com; weijing@umd.edu).

Wei, J., Wang, J., Li, Z., Kondragunta, S., Anenberg, S., Wang, Y., Zhang, H., Diner, D., Hand, J., Lyapustin, A., Kahn, R., Colarco, P., da Silva, A., and Ichoku, C. Long-term mortality burden trends attributed to black carbon and PM2.5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study. The Lancet Planetary Health, 2023, 7, e963–e975. https://doi.org/10.1016/S2542-5196(23)00235-8 More air quality datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html

The aerial photography inventoried by the Pilot Land Data System (PLDS) at NASA AMES Research Center has been transferred to the USGS EROS Data Center. The photos were obtained from cameras mounted on high and medium altitude aircraft based at the NASA Ames Research Center. Several cameras with varying focal lengths, lenses and film formats are used, but the Wild RC-10 camera with a focal length of 152 millimeters and a 9 by 9 inch film format is most common. The positive transparencies are typically used for ancillary ground checks in conjunctions with digital processing for the same sites. The aircraft flights, specifically requested by scientists performing approved research, often simultaneously collect data using other sensors on
board (e.g. Thematic Mapper Simulators (TMS) and Thermal Infrared Multispectral Scanners). High altitude color infrared photography is used regularly by government agencies for such applications as crop yield forecasting, timber inventory and defoliation assessment, water resource management, land use surveys, water pollution monitoring, and natural disaster assessment.

To order, specify the latitude and longitude of interest. You will then be given a list of photos available for that location. In some cases, "flight books" are available at EDC that describe the nature of the mission during which the photos were taken and other attribute information. The customer service personnel have access to these books for those photo sets for which the books exist.

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 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

Italy Soil Pollution: Tax Revenue: % of GDP: Pollution data was reported at 0.000 % in 2022. This stayed constant from the previous number of 0.000 % for 2021. Italy Soil Pollution: Tax Revenue: % of GDP: Pollution data is updated yearly, averaging 0.000 % from Dec 1994 (Median) to 2022, with 29 observations. The data reached an all-time high of 0.001 % in 2009 and a record low of 0.000 % in 2022. Italy Soil Pollution: Tax Revenue: % of GDP: Pollution data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Italy – Table IT.OECD.ESG: Environmental: Environmentally Related Tax Revenue: Environmental Protection Domains: OECD Member: Annual.

no abstract provided

In 2022, the level of soil pollution caused by lead (Pb) in South Korea stood at approximately 19.68 milligrams per kilogram (mg/kg). Considering the data since 2015, the highest lead contamination level had been recorded in 2015 at around 25.96 mg/kg.

Percentages of the data records with the pollution rates in different ranges.

The announcement data of contaminated sites for soil and groundwater (where the source of soil pollution or groundwater pollution is clear, and the concentration of soil or groundwater pollutants reaches the soil or groundwater pollution control standards, the competent authority in the area should announce it as a soil and groundwater pollution control site) (The field of soil pollution control zone (IsSoil) and groundwater pollution control zone (IsGw) indicates 1 for control, and 0 for non-control)

The variation in percentage loss for the best estimate between states results from the different levels of land-sourced pollution (solid waste, sedimentation, septic tank leakage and all other unidentified sources), 2003

Display the range of soil pollution in Taiwan according to the site, and announce the range of soil pollution control zones.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

LISTOS_Ground_Other_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) ground site data collected at a collection of ground sites during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete. The New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO. LISTOS_Ground_Other_Data are data collected at other/miscellaneous ground sites during the LISTOS campaign.