The Geospatial Data Gateway (GDG) is the One Stop Source for environmental and natural resources data, at anytime, from anywhere, to anyone.

Use the Environmental Dataset Gateway (EDG) to find and access EPA's environmental resources. Many options are available for easily reusing EDG content in other other applications. This allows individuals to provide direct access to EPA's metadata outside the EDG interface. The EDG CS-W Interface allows each users to query the catalog through a URL using CS-W syntax.

Use the Environmental Dataset Gateway (EDG) to find and access EPA's environmental resources. Many options are available for easily reusing EDG content in other other applications. This allows individuals to provide direct access to EPA's metadata outside the EDG interface. The EDG REST Interface allows each users to query the catalog through a URL using REST syntax. Accessing individual metadata documents through their REST URLs, or groups of documents that match specific search criteria through a REST-formatted search URL, provides powerful functionality for searching, viewing, and sharing EDG records.

The Geospatial Data Gateway (GDG) provides access to a map library of over 100 high resolution vector and raster layers in the Geospatial Data Warehouse. It is the One Stop Source for environmental and natural resources data, at any time, from anywhere, to anyone. It allows you to choose your area of interest, browse and select data, customize the format, then review and download. This service is made available through a close partnership between the three Service Center Agencies (SCA); Natural Resources Conservation Service (NRCS), Farm Service Agency (FSA) and Rural Development (RD).

description: Use the Environmental Dataset Gateway (EDG) to find and access EPA's environmental resources. Many options are available for easily reusing EDG content in other other applications. This allows individuals to provide direct access to EPA's metadata outside the EDG interface. The EDG Search Widget makes it possible to search the EDG from another web page or application. The search widget can be included on your website by simply inserting one or two lines of code. Users can type a search term or lucene search query in the search field and retrieve a pop-up list of records that match that search.; abstract: Use the Environmental Dataset Gateway (EDG) to find and access EPA's environmental resources. Many options are available for easily reusing EDG content in other other applications. This allows individuals to provide direct access to EPA's metadata outside the EDG interface. The EDG Search Widget makes it possible to search the EDG from another web page or application. The search widget can be included on your website by simply inserting one or two lines of code. Users can type a search term or lucene search query in the search field and retrieve a pop-up list of records that match that search.

This dataset is a Preliminary Assessment of the Gateway Park property located at the intersection of Bright Street and Merseles Street.View PDF

Data sets and individual XCD results used to develop the field-based benchmarks are available at the U.S. EPA Environmental Dataset Gateway (https://doi.org/10.23719/1371707) (Cormier, 2017). Data are contained in three zip files. The folder “Biological.zip” contains occurrences of benthic invertebrate genera in 24 state data sets. This paper uses only the data from West Virginia ecoregions 69 and 70. The folder “Environmental.zip” contains environmental data sorted into 24 data sets. The folder “model.zip” contains the calculated XC95 values, probability of observation plots as generalized additive models, and the cumulative frequency distribution for benthic invertebrate genera from WV69 and 70 plus data sets used to develop other models not discussed in this paper. A spreadsheet for calculating XC95 values and XCD05 is described in Cormier et al. (2018c). The tools, data sets, example calculations, and example outputs are available online at https://wiley.figshare.com/ieam and https://github.com/smcormier/Biological-Extirpation-Analysis-Tools-BEAT/releases/tag/v.1.0.2. Alternatively, calculation of XC95, GAM plots, XCD05 can be calculated using batch R-code. Similarly, the R-code and data sets are available on GitHub (https://github.com/leppott/XC95).

The Geospatial Data Gateway (GDG) provides access to a map library of over 100 high resolution vector and raster layers in the Geospatial Data Warehouse. It is the One Stop Source for environmental and natural resources data, at any time, from anywhere, to anyone. It allows you to choose your area of interest, browse and select data, customize the format, then review and download.This service is made available through a close partnership between the three Service Center Agencies (SCA); Natural Resources Conservation Service (NRCS), Farm Service Agency (FSA) and Rural Development (RD).

Meter-scale Urban Land Cover (MULC), a unique, high resolution (one meter2 per pixel) land cover dataset, has been developed for 30 US communities for the United States Environmental Protection Agency (US EPA) EnviroAtlas. MULC data categorize the landscape into these land cover classes: impervious surface, tree, grass-herbaceous, shrub, soil-barren, water, wetland and agriculture. MULC data are used to calculate approximately 100 EnviroAtlas metrics that serve as indicators of nature’s benefits (ecosystem goods and services). MULC, a dataset for which development is ongoing, is produced by multiple classification methods using aerial photo and LiDAR datasets. The mean overall fuzzy accuracy across the EnviroAtlas communities is 88% and mean Kappa coefficient is 0.84. MULC is available in EnviroAtlas via web browser, web map service (WMS) in the user’s geographic information system (GIS), and as downloadable data at EPA Environmental Data Gateway. Fact Sheets and metadata for each MULC Community are available through EnviroAtlas. Some MULC applications include mapping green and grey infrastructure, connecting land cover with socioeconomic/demographic variables, street tree planting, urban heat island analysis, mosquito habitat risk mapping and bikeway planning. This article provides practical guidance for using MULC effectively and developing similar high resolution (HR) land cover data. This dataset is associated with the following publication: Pilant, D., K. Endres, D. Rosenbaum, and G. Gundersen. US EPA EnviroAtlas Meter-Scale Urban Land Cover (MULC): 1-m Pixel Land Cover Class Definitions and Guidance. Remote Sensing. MDPI AG, Basel, SWITZERLAND, 12(12): 1909, (2020).

The Service Records Retention System (SRRS) was developed to store weather observations, summaries, forecasts, warnings, and advisories provided by the U.S. National Weather Service (NWS) for public use, and are retained by the National Centers for Environmental Information (NCEI) [formerly the National Climatic Data Center (NCDC)]. Service products issued by the NWS offices are transmitted on the Advanced Weather Interactive Processing System (AWIPS) or Federal Aviation Administration (FAA) communications to an NWS Gateway (NWSTG) server. The products are then electronically transferred to the NCEI and ingested into the Hierarchal Data Storage System (HDSS) for records retention. The three basic groupings of NWS Service Products (SP) are: 1) Observations: reports originated by NWS, Federal Aviation Administration, or Department of Defense facilities and transmitted through the NWS Telecommunication Gateway, NOAAPORT, or equivalent. These reports include, but are not limited to, surface observations (including Automated Surface Observing System and Automated Weather Observing System reports), pilot reports, upper air reports, marine reports, and automated buoy observations; 2) Forecasts: all official routine and non-routine disseminated products related to, or derived from, NWS forecast and warning programs (alphanumeric and graphic format), regardless of dissemination method; and 3) Graphics: all routine and non-routine environmental data analysis graphics, such as surface analysis, standard layer upper air analyses, weather depiction, radar summary, etc.; and all routine and non-routine graphics represented as official NWS forecasts including aviation prognostic graphics (e.g., Low Level and High Level Significant Weather Prognosis) produced by the National Centers for Environmental Prediction and other NWS facilities (e.g., Alaska Aviation Weather Unit).

This map service contains GIS data from the EPA Office of Water 305(b) Assessed Waters Program. The information supporting this service resides in the Reach Address Database (RAD) which is part of the Watershed Assessment, Tracking & Environmental Results System (WATERS).The 305(b) program system provide assessed water data and assessed water features for river segments, lakes, and estuaries designated under Section 305(b) of the Clean Water Act. 305(b) waterbodies are coded onto NHDPlus v2.1 features creating area, point and linear events representing assessed and non-assessed waters. In addition to NHDPlus reach indexed data there may also be custom events (point, line, or area) that are not associated with NHDPlus and are in an EPA standard format that is compatible with EPA's Reach Address Database. These custom events are used to represent locations of 305(b) waterbodies that are not represented well in NHDPlus. To identify the spatial extent of waters listed under the 305(b) program attributed as being assessed in the ATTAINS database, these waters can be linked to the 305(b) information stored in the EPA's Assessment and TMDL Tracking and Implementation System (ATTAINS) for query and display. Use the Source_FeatureID field and Cycle_Year field to link indexed assessed waters to the EPA's ATTAINS Database. For complete metadata, please use EPA's Environmental Data Gateway (EDG): https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B81060F20-4F5C-42E2-BBC7-CD96E442B8FA%7D.

Homeowners in coastal environments often augment their access to estuarine ecosystems by building private docks on their personal property. Despite the commonality of docks, particularly in the Southeastern United States, few works have investigated their historical development, their distribution across the landscape, or the environmental justice dimensions of this distribution. In this study, we used historic aerial photography to track the abundance and size of docks across six South Carolina counties from the 1950s to 2016. Across our roughly 60-year study period, dock abundance grew by two orders of magnitude, the mean length of newly constructed docks doubled, and the cumulative length of docks ballooned from 34 to 560 km. Additionally, we drew on census data interpolated into consistent 2010 tract boundaries to analyze the racial and economic distribution of docks in 1994, 1999, 2011, and 2016. Racial composition, measured as the percentage of a tract’s population that was White,..., Dock data was collected via historic aerial imagery of the South Carolina coast. Pre-1990 imagery was obtained from the University of South Carolina library, 1994 and 1999 imagery was obtained from the South Carolina Department of Natural Resources, and 2011 imagery was obtained from the US Department of Agriculture National Agriculture Imagery Program’s Geospatial Data Gateway (https://nrcs.app.box.com/v/gateway/folder/19350726983). Census data was obtained from the NHGIS and Historical Housing Unit and Urbanization Database at the tract level using their crosswalk files to interpolate 1990 and 2000 data to 2010 tract geographies. Docks were tracked across decades in ArcGIS Pro and statistical models were run using R. Greater methodological detail is provided in the "Historic Infrastructure Methodology" file in the "Historic_Dock_Supplemental" folder on Zenodo. All pre-1990 images therein are reproduced with permission of the University of South Carolina library., R, ArcGIS Pro Version 2.9 or greater., # Exponential growth of private coastal infrastructure influenced by geography and race in South Carolina, USA

This data set contains ArcGIS Pro files and a CSV of every structure identified in the study. The code used to run the models can be found on the associated Zenodo page in the "Historic_Dock_Code" folder. Additional methodological information and model diagnostics can be found in the "Historic_Dock_Supplemental" folder on the associated Zenodo page. If you have any questions or requests please email Jeffrey Beauvais (he/him) at beauvais.work@gmail.com

Description of the Data and file structure

Parent folder: Historic_Dock_ArcPro_Files

Contains a geodatabase (.gdb file) with final point layers used in the analysis for dock counts, lengths, and geographic boundaries. Intermediate files were redundant and excluded but available upon request. Some folders within are empty and automatically generated by ArcGIS Pro when loading the p...

Developed by the US Environmental Protection Agency (EPA), RSIG selects, downloads, assembles, and visualizes atmospheric data, and can display and store the assembled data on the user's workstation. Within RSIG, a user can select from among 15 or so variables available from the following data sources: NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data; AIRNow data via the AIRNow Gateway; Air Quality System (AQS) data via the Air Quality Data Mart; EPA's Community Multiscale Air Quality (CMAQ) model output (for 2002 only); National Environmental Satellite, Data, and Information Service (NESDIS) biomass burning data; and UVNet, among others. After retrieving the data, RSIG visualizes the data on a latitude-longitude map, automatically locating the data in the correct geographic position. Images, animations, and data can be exported to such standard formats as portable binary, ASCII, NetCDF, binary XDR, MPEG, and KMZ.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Paleoceanography. The data include parameters of paleoceanography with a geographic location of Indian Ocean. The time period coverage is from 5461000 to 2013000 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

This EPA Office of Water hosted web service contains a point layer depicting Facilities that Discharge to Water indexed to NHDPlus version 2.1. NPDES is used by EPA programs, regions, states, and water programs for monitoring water quality.This map service contains GIS data for Facilities that Discharge to Water. The information supporting this service resides in the Reach Address Database (RAD) which is part of the Watershed Assessment, Tracking & Environmental Results System (WATERS).Reach indexed NPDES locations comprise two basic types: facilities and pipes. For a given NPDES permit, the program may have locational information on the facility alone or specific pipe outfalls. When a RAD Source_FeatureID value is nine characters (the NPDES ID alone) the event is indexed against the location of the NPDES facility. This location may or may not correspond well with the location of actual outflows. When a RAD Source_FeatureID is twelve or thirteen characters (NPDES ID + PIPE SCHEDULE ID) this event is indexed against the location of the outflow as stored in NPDES. Thus a given NPDES facility id may be expressed as a single event indexed to the facility or one more events indexed to corresponding facility pipe schedules. The Source_DataDesc field provides a shorthand for this with three possible values: "FAC no PIPE" indicates the facility provided no pipe outflows to NPDES and thus the facility location is indexed; "PIPE" indicates the record is a pipe outflow and this pipe location is indexed; "FAC bad PIPE" indicates the facility has pipe outflow information in NPDES but the pipe locations cannot be indexed. In this situation the facility location is indexed instead.For complete metadata, please use EPA's Environmental Data Gateway (EDG): https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B091FC504-8762-8E7F-DCD7-513F648BC5B5%7D.

The global waterproof gateway market is experiencing robust growth, driven by increasing demand for reliable and durable connectivity solutions across diverse sectors. The market, estimated at $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $7 billion by 2033. This expansion is fueled by several key factors. The proliferation of IoT devices in industrial automation, smart agriculture, and environmental monitoring necessitates robust, weather-resistant gateways capable of seamless data transmission. Furthermore, the rising adoption of smart cities initiatives and the growing need for secure communication in harsh environments are significantly boosting market demand. The industrial sector currently dominates the application segment, followed by commercial and household applications. Geographically, North America and Europe hold substantial market share, driven by early adoption of IoT technologies and robust infrastructure. However, the Asia-Pacific region is poised for significant growth, fueled by rapid industrialization and urbanization, particularly in China and India. While the market faces certain restraints, such as high initial investment costs and potential cybersecurity concerns, ongoing technological advancements, including improved battery life and enhanced security protocols, are mitigating these challenges. The market is segmented by application (industrial, commercial, household) and type (indoor, outdoor), with the outdoor segment exhibiting faster growth due to its critical role in outdoor IoT deployments. Key players are strategically focusing on product innovation, partnerships, and geographical expansion to capitalize on the market's growth potential. The competitive landscape is characterized by a mix of established technology companies and specialized manufacturers. Companies like Advantech and Zipit Wireless leverage their existing expertise in networking and communication technologies to offer advanced waterproof gateway solutions. Meanwhile, Chinese companies such as Xiamen Four-Faith Communication Technology Co., Ltd. and Beijing Dihui Technology Co., Ltd. are playing a crucial role in supplying cost-effective solutions and catering to the growing demand in the Asia-Pacific region. The market's future trajectory hinges on continued innovation in areas such as low-power wide-area network (LPWAN) technologies, enhanced security features, and the development of more user-friendly interfaces for seamless integration with IoT ecosystems. The increasing adoption of 5G and the expansion of private LTE networks will further propel market growth by providing faster and more reliable connectivity for waterproof gateways.

Carbon Footprint API Gateway Market Outlook

According to our latest research, the global Carbon Footprint API Gateway market size is valued at USD 1.12 billion in 2024 and is expected to reach USD 7.98 billion by 2033, expanding at a robust CAGR of 24.1% during the forecast period. This remarkable growth is primarily fueled by the increasing demand for real-time, automated carbon emissions tracking and sustainability reporting across diverse sectors. Enterprises and governments are accelerating their adoption of digital solutions to meet stringent environmental regulations and achieve net-zero commitments, thereby propelling the market forward.

One of the key growth factors for the Carbon Footprint API Gateway market is the intensifying global focus on sustainability and environmental, social, and governance (ESG) compliance. Organizations worldwide are under mounting pressure to disclose their carbon emissions transparently and accurately, both to satisfy regulatory requirements and to meet the expectations of eco-conscious investors and consumers. This has led to a surge in the deployment of API gateways that enable seamless integration with various data sources and enterprise resource planning (ERP) systems, automating the collection, analysis, and reporting of carbon footprint data. The proliferation of corporate sustainability initiatives and the growing need for granular, real-time emissions data are further accelerating the adoption of these solutions across industries such as energy, manufacturing, and retail.

Furthermore, the rapid advancement of digital technologies such as cloud computing, artificial intelligence, and the Internet of Things (IoT) is significantly enhancing the capabilities of Carbon Footprint API Gateways. Modern API gateways now offer advanced analytics, machine learning-driven insights, and scalable architectures, allowing organizations to process vast volumes of environmental data with high accuracy and efficiency. The integration of carbon footprint APIs with IoT sensors, for example, enables continuous monitoring of emissions from manufacturing plants, logistics fleets, and energy systems. This technological evolution is not only improving data accuracy and operational efficiency but also lowering the barriers to entry for small and medium enterprises, thereby expanding the addressable market.

Additionally, the market is benefiting from a favorable regulatory environment and a growing ecosystem of partnerships between technology providers, environmental organizations, and government agencies. Governments across North America, Europe, and Asia Pacific are enacting stricter carbon reporting mandates and incentivizing the adoption of digital sustainability solutions. This regulatory push, combined with increasing collaboration between public and private sectors, is fostering innovation and driving the development of more robust and interoperable Carbon Footprint API Gateway solutions. As a result, the market is witnessing heightened investment activity and rapid product innovation, further solidifying its long-term growth prospects.

Regionally, North America currently holds the largest share of the Carbon Footprint API Gateway market, driven by early adoption of digital sustainability solutions, strong regulatory frameworks, and significant investments from both corporate and government sectors. Europe follows closely, propelled by ambitious climate targets and a mature digital infrastructure. The Asia Pacific region is emerging as a high-growth market, supported by rapid industrialization, urbanization, and increasing awareness of environmental issues among enterprises and governments. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as regional governments and corporations ramp up their sustainability efforts.

Component Analysis

The Carbon Footprint API Gateway market by component is segmented into Software, Hardware, and Services. The software segment dominates

The dataset is provided in a single .xlsx file named "eucalyptus_growth_environment_data_V2.xlsx" and consists of fifteen sheets:

Codebook: This sheet details the index, values, and descriptions for each field within the dataset, providing a comprehensive guide to understanding the data structure.

ALL NODES: Contains measurements from all devices, totalling 102,916 data points. This sheet aggregates the data across all nodes.

GWD1 to GWD10: These subset sheets include measurements from individual nodes, labelled according to the abbreviation “Generic Wireless Dendrometer” followed by device IDs 1 through 10. Each sheet corresponds to a specific node, representing measurements from ten trees (or nodes).

Metadata: Provides detailed metadata for each node, including species, initial diameter, location, measurement frequency, battery specifications, and irrigation status. This information is essential for identifying and differentiating the nodes and their specific attributes.

Missing Data Intervals: Details gaps in the data stream, including start and end dates and times when data was not uploaded. It includes information on the total duration of each missing interval and the number of missing data points.

Missing Intervals Distribution: Offers a summary of missing data intervals and their distribution, providing insight into data gaps and reasons for missing data.

All nodes utilize LoRaWAN for data transmission. Please note that intermittent data gaps may occur due to connectivity issues between the gateway and the nodes, as well as maintenance activities or experimental procedures.

Software considerations: The provided R code named “Simple_Dendro_Imputation_and_Analysis.R” is a comprehensive analysis workflow that processes and analyses Eucalyptus growth and environmental data from the "eucalyptus_growth_environment_data_V2.xlsx" dataset. The script begins by loading necessary libraries, setting the working directory, and reading the data from the specified Excel sheet. It then combines date and time information into a unified DateTime format and performs data type conversions for relevant columns. The analysis focuses on a specified device, allowing for the selection of neighbouring devices for imputation of missing data. A loop checks for gaps in the time series and fills in missing intervals based on a defined threshold, followed by a function that imputes missing values using the average from nearby devices. Outliers are identified and managed through linear interpolation. The code further calculates vapor pressure metrics and applies temperature corrections to the dendrometer data. Finally, it saves the cleaned and processed data into a new Excel file while conducting dendrometer analysis using the dendRoAnalyst package, which includes visualizations and calculations of daily growth metrics and correlations with environmental factors such as vapour pressure deficit (VPD).

To improve public health and the environment, the United States Environmental Protection Agency (EPA) collects information about facilities, sites, or places subject to environmental regulation or of environmental interest. Through the Geospatial Data Download Service, the public is now able to download the EPA Geodata Shapefile, Feature Class or extensible markup language (XML) file containing facility and site information from EPA's national program systems. The files are Internet accessible from the Envirofacts Web site (https://www.epa.gov/enviro). The data may be used with geospatial mapping applications. (Note: The files omit facilities without latitude/longitude coordinates.) The EPA Geospatial Data contains the name, location (latitude/longitude), and EPA program information about specific facilities and sites. In addition, the files contain a Uniform Resource Locator (URL), which allows mapping applications to present an option to users to access additional EPA data resources on a specific facility or site. i. This data was collected by Stone Environmental, Inc. for the New York State Department of State with funds provided under Title 11 of the Environmental Protection Fund. Additional Source Info: https://edg.epa.gov/data/public/OEI/FRS/FRS_Interests_Download.zipView Dataset on the Gateway

The Danish Environmental Portal: One gateway to data on nature and the environment in Denmark. This dataset is an extract of species observations from the nationwide Naturdatabasen. Data are mainly collected by municipalities and governmental organizations (the Danish Environmental Protection Agency), in nature monitoring and other administrative tasks and projects.