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

Graph and download economic data for Moving 12-Month Total Vehicle Miles Traveled (M12MTVUSM227NFWA) from Dec 1970 to Apr 2025 about miles, travel, vehicles, and USA.

This dataset is a subset of data attributes selected from the (full) 1966-2019 North American Breeding Bird Survey (BBS) dataset to assist in populating the U.S. Fish and Wildlife's FWSpecies application. This subset data was used to add species occurrence information to species lists for each refuge in the Pacific Northwest (Washington, Oregon, Idaho). The full dataset can be accessed online through ScienceBase. And this metadata leaves most metadata fields untouched from the original, except to add details related to additional processing to make a refuge specific subset, for a specific purpose (populating species lists/occurrences on refuges). All questions regarding the BBS data itself should go to USGS Patuxent Wildlife Research Center. The 1966-2019 North American Breeding Bird Survey (BBS) dataset contains avian point count data for more than 700 North American bird taxa (species, races, and unidentified species groupings). These data are collected annually during the breeding season, primarily in June, along thousands of randomly established roadside survey routes in the United States and Canada. Routes are roughly 24.5 miles (39.2 km) long with counting locations placed at approximately half-mile (800-m) intervals, for a total of 50 stops. At each stop, a citizen scientist highly skilled in avian identification conducts a 3-minute point count, recording every bird seen or heard within a quarter-mile (400-m) radius. Surveys begin 30 minutes before local sunrise and take approximately 5 hours to complete. Routes are sampled once per year, with the total number of routes sampled per year growing over time; just over 500 routes were sampled in 1966, while in recent decades approximately 3000 routes have been sampled annually. In addition to avian count data, this dataset also contains survey date, survey start and end times, start and end weather conditions, a unique observer identification number, route identification information, and route location information including country, state, and BCR, as well as geographic coordinates of route start point, and an indicator of run data quality.

This dataset contains a simulated river data product to be provided by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this dataset is a subset for the North America continent. This product is derived from the measurements produced by the main SWOT instrument, the Ka-band Interferometer. They are produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. This product contains two shapefiles: 1) river reaches (approximately 10 km long) identified in the prior river database (PRD); and 2) river nodes (approximately 200 m spacing) identified in prior river database (PRD). Each river reach is divided into a number of nodes. Attributes include water surface elevation, slope, width, and uncertainty estimates. As they are derived from SWOT KaRIn measurements, each granule covers an area that is approximately 128 km wide in the cross-track direction with a 20-km nadir gap. Note that this is a simulated SWOT product and not suited for any scientific exploration.

United States US: Urban Land Area data was reported at 802,053.592 sq km in 2010. This stayed constant from the previous number of 802,053.592 sq km for 2000. United States US: Urban Land Area data is updated yearly, averaging 802,053.592 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 802,053.592 sq km in 2010 and a record low of 802,053.592 sq km in 2010. United States US: Urban Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Land Use, Protected Areas and National Wealth. Urban land area in square kilometers, based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Sum;

NOAA is responsible for depicting on its nautical charts the limits of the 12 nautical mile Territorial Sea, 24 nautical mile Contiguous Zone, and 200 nautical mile Exclusive Economic Zone (EEZ). The outer limit of each of these zones is measured from the U.S. normal baseline, which coincides with the low water line depicted on NOAA charts and includes closing lines across the entrances of lega...

The 1966-2023 North American Breeding Bird Survey (BBS) dataset contains avian point count data for more than 700 North American bird taxa (species, races, and unidentified species groupings). These data are collected annually during the breeding season, primarily in June, along thousands of randomly established roadside survey routes in the United States and Canada. Routes are roughly 24.5 miles (39.2 km) long with counting locations placed at approximately half-mile (800-m) intervals, for a total of 50 stops. At each stop, a citizen scientist highly skilled in avian identification conducts a 3-minute point count, recording all birds seen within a quarter-mile (400-m) radius and all birds heard. Surveys begin 30 minutes before local sunrise and take approximately 5 hours to complete. Routes are surveyed once per year, with the total number of routes sampled per year growing over time; just over 500 routes were sampled in 1966, while in recent decades approximately 3000 routes have been sampled annually. No data are provided for 2020. BBS field activities were cancelled in 2020 because of the coronavirus disease (COVID-19) global pandemic and observers were directed to not sample routes. In addition to avian count data, this dataset also contains survey date, survey start and end times, start and end weather conditions, a unique observer identification number, route identification information, and route location information including country, state, and BCR, as well as geographic coordinates of route start point, and an indicator of run data quality.

A digital magnetic anomaly database and map for the North American continent is the result of a joint effort by the Geological Survey of Canada (GSC), U. S. Geological Survey (USGS), and Consejo de Recursos Minerales of Mexico (CRM). The database and map represent a substantial upgrade from the previous compilation of magnetic anomaly data for North America, now over a decade old. This report presents three unique, gridded data sets used to make the magnetic anomaly map of North America. All three grids have 1-km spacing and are projected to the DNAG projection. These grids are provided in Geosoft binary grid format, with two files describing each of the grids (suffixes .grd and .gi). The first grids (NAmag_origmrg.grd and USmag_origmrg.grd) show the magnetic field at 1,000 m. above terrain. For the second grids (NAmag_hp500.grd and USmag_hp500.grd) we removed long-wavelength anomalies (500 km and greater) from the first grid. This grid was used for the published map. Although the North American merged grid represents a significant upgrade to older compilations, the existing patchwork of surveys is inherently unable to accurately represent anomalies with long (greater than roughly 150 km) wavelengths, particularly in the US and Canada (U.S. Magnetic- Anomaly Data Set Task Group, 1994). The lack of information about long wavelength anomalies is primarily related to datum shifts between merged surveys, caused by data acquisition at widely different times and by differences in merging procedures. Therefore, we removed anomalies with wavelengths greater than 500 km from the merged grid to reduce the effects caused by the spurious long wavelengths but still maintain the continuity of anomalies. The correction was accomplished by transforming the merged grid to the frequency domain, filtering the transformed data with a long-wavelength cutoff at 500 km, and subtracting the long-wavelength data grid from the merged grid. In addition to the 500-km high pass filter, an equivalent source method, based on long-wavelength characterization using satellite data (CHAMP satellite anomalies, Maus and others, 2002), was also used to correct for spurious shifts in the original magnetic anomaly grid (Ravat and others, 2002). These results are presented in the third grids (NAmag_CM.grd and USmag_CM.grd), in which the wavelengths longer than 500 km have been replaced by downward-continued satellite data.

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 Tree Ring. The data include parameters of tree ring with a geographic location of Massachusetts, United States Of America. The time period coverage is from 588 to 268 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

This data set consists of monthly long-term mean temperature data (degrees C) for the last glacial maximum (21 ka) downscaled to a 10-km grid of North America. The 10-km data were derived using simulated temperature data from 10 general circulation models (GCMs; CCSM4, CNRM-CM5, COSMOS-ASO, FGOALS-g2, GISS-E2-R, IPSL-CM5A-LR, MIROC-ESM, MPI-ESM-P-OA, MPI-ESM-P-OAC, and MRI-CGCM3) run under the PMIP3/CMIP5 (Paleoclimate Modelling Intercomparison Project phase 3 / Coupled Model Intercomparison Project phase 5) “lgm” and “piControl” experiments. The lgm and piControl data are available from the Earth System Grid - Center for Enabling Technologies (ESG-CET; https://esgf-node.llnl.gov/projects/esgf-llnl/). Additional information about the data is available from the CMIP5 (https://pcmdi.llnl.gov/mips/cmip5/) and PMIP3 (https://pmip3.lsce.ipsl.fr/) web sites. The names of the lgm and piControl files we used are listed in the “source_file” global attribute of each GCM temperature netCDF file in this data release. For each GCM, the PMIP3/CMIP5 lgm temperature data were bias corrected using long-term mean differences calculated as the lgm long-term mean minus the piControl long-term mean. These long-term mean differences were regridded to a North America 10-km Lambert azimuthal equal-area grid using the CDO (Climate Data Operators, https://code.mpimet.mpg.de/projects/cdo) bilinear interpolation function “remapbil”. We used ICE-5G (VM2) data (Peltier, 2004, https://doi.org/10.1146/annurev.earth.32.082503.144359) to identify grid cells with ice cover at 21 ka. The interpolated long-term mean differences were applied to CRU CL 2.0 (1961-1990 30-year mean) climate data (New et al., 2002, https://doi.org/10.3354/cr021001). The CRU CL 2.0 data were also regridded to the 10-km grid using local lapse-rate adjusted interpolation (Praskievicz and Bartlein, 2014, https://doi.org/10.1016/j.jhydrol.2014.06.017). The ensemble mean data were calculated using the bias corrected temperature data from each of the 10 GCM simulations.

The Harvard Forest is a collection of five properties, totaling about 1500 hectares, in Petersham, Massachusetts. Petersham is a rural town in Worcester County, Massachusetts, about 60 miles west of Boston. It is largely in the Swift River Watershed, and lies near the center of a twenty-mile wide band of hilly uplands that form the eastern edge of the Connecticut Valley. The north part of the town is rolling and the south more distinctly hilly; the lowest basins are about 200 m above sea level, the flats around 400m. Th e climate is cool temperate. Petersham, like many of the adjacent towns, was settled in the early 18th century, extensively cleared and farmed in the next hundred years, and then progressively abandoned after about 1830. Reforestation proceeded quickly, and by the time of the first Harvard Forest maps in 1909 HF was almost entirely wooded. Th e common forest types are dominated, variously, by red oak, red maple, white pine, or hemlock. Most are of low or average fertility and under 100 years old. Hemlock is now locally dominant in many stands that have been continuously forested; oaks, red maples and pines are the common dominants in stands that developed in old fields.

Summary:

Estimated stand-off distance between ADS-B equipped aircraft and obstacles. Obstacle information was sourced from the FAA Digital Obstacle File and the FHWA National Bridge Inventory. Aircraft tracks were sourced from processed data curated from the OpenSky Network. Results are presented as histograms organized by aircraft type and distance away from runways.

Description:

For many aviation safety studies, aircraft behavior is represented using encounter models, which are statistical models of how aircraft behave during close encounters. They are used to provide a realistic representation of the range of encounter flight dynamics where an aircraft collision avoidance system would be likely to alert. These models currently and have historically have been limited to interactions between aircraft; they have not represented the specific interactions between obstacles and aircraft equipped transponders. In response, we calculated the standoff distance between obstacles and ADS-B equipped manned aircraft.

For robustness, this assessment considered two different datasets of manned aircraft tracks and two datasets of obstacles. For robustness, MIT LL calculated the standoff distance using two different datasets of aircraft tracks and two datasets of obstacles. This approach aligned with the foundational research used to support the ASTM F3442/F3442M-20 well clear criteria of 2000 feet laterally and 250 feet AGL vertically.

The two datasets of processed tracks of ADS-B equipped aircraft curated from the OpenSky Network. It is likely that rotorcraft were underrepresented in these datasets. There were also no considerations for aircraft equipped only with Mode C or not equipped with any transponders. The first dataset was used to train the v1.3 uncorrelated encounter models and referred to as the “Monday” dataset. The second dataset is referred to as the “aerodrome” dataset and was used to train the v2.0 and v3.x terminal encounter model. The Monday dataset consisted of 104 Mondays across North America. The other dataset was based on observations at least 8 nautical miles within Class B, C, D aerodromes in the United States for the first 14 days of each month from January 2019 through February 2020. Prior to any processing, the datasets required 714 and 847 Gigabytes of storage. For more details on these datasets, please refer to "Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing" and “Benchmarking the Processing of Aircraft Tracks with Triples Mode and Self-Scheduling.”

Two different datasets of obstacles were also considered. First was point obstacles defined by the FAA digital obstacle file (DOF) and consisted of point obstacle structures of antenna, lighthouse, meteorological tower (met), monument, sign, silo, spire (steeple), stack (chimney; industrial smokestack), transmission line tower (t-l tower), tank (water; fuel), tramway, utility pole (telephone pole, or pole of similar height, supporting wires), windmill (wind turbine), and windsock. Each obstacle was represented by a cylinder with the height reported by the DOF and a radius based on the report horizontal accuracy. We did not consider the actual width and height of the structure itself. Additionally, we only considered obstacles at least 50 feet tall and marked as verified in the DOF.

The other obstacle dataset, termed as “bridges,” was based on the identified bridges in the FAA DOF and additional information provided by the National Bridge Inventory. Due to the potential size and extent of bridges, it would not be appropriate to model them as point obstacles; however, the FAA DOF only provides a point location and no information about the size of the bridge. In response, we correlated the FAA DOF with the National Bridge Inventory, which provides information about the length of many bridges. Instead of sizing the simulated bridge based on horizontal accuracy, like with the point obstacles, the bridges were represented as circles with a radius of the longest, nearest bridge from the NBI. A circle representation was required because neither the FAA DOF or NBI provided sufficient information about orientation to represent bridges as rectangular cuboid. Similar to the point obstacles, the height of the obstacle was based on the height reported by the FAA DOF. Accordingly, the analysis using the bridge dataset should be viewed as risk averse and conservative. It is possible that a manned aircraft was hundreds of feet away from an obstacle in actuality but the estimated standoff distance could be significantly less. Additionally, all obstacles are represented with a fixed height, the potentially flat and low level entrances of the bridge are assumed to have the same height as the tall bridge towers. The attached figure illustrates an example simulated bridge.

It would had been extremely computational inefficient to calculate the standoff distance for all possible track points. Instead, we define an encounter between an aircraft and obstacle as when an aircraft flying 3069 feet AGL or less comes within 3000 feet laterally of any obstacle in a 60 second time interval. If the criteria were satisfied, then for that 60 second track segment we calculate the standoff distance to all nearby obstacles. Vertical separation was based on the MSL altitude of the track and the maximum MSL height of an obstacle.

For each combination of aircraft track and obstacle datasets, the results were organized seven different ways. Filtering criteria were based on aircraft type and distance away from runways. Runway data was sourced from the FAA runways of the United States, Puerto Rico, and Virgin Islands open dataset. Aircraft type was identified as part of the em-processing-opensky workflow.

All: No filter, all observations that satisfied encounter conditions

nearRunway: Aircraft within or at 2 nautical miles of a runway

awayRunway: Observations more than 2 nautical miles from a runway

glider: Observations when aircraft type is a glider

fwme: Observations when aircraft type is a fixed-wing multi-engine

fwse: Observations when aircraft type is a fixed-wing single engine

rotorcraft: Observations when aircraft type is a rotorcraft

License

This dataset is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International(CC BY-NC-ND 4.0).

This license requires that reusers give credit to the creator. It allows reusers to copy and distribute the material in any medium or format in unadapted form and for noncommercial purposes only. Only noncommercial use of your work is permitted. Noncommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. Exceptions are given for the not for profit standards organizations of ASTM International and RTCA.

MIT is releasing this dataset in good faith to promote open and transparent research of the low altitude airspace. Given the limitations of the dataset and a need for more research, a more restrictive license was warranted. Namely it is based only on only observations of ADS-B equipped aircraft, which not all aircraft in the airspace are required to employ; and observations were source from a crowdsourced network whose surveillance coverage has not been robustly characterized.

As more research is conducted and the low altitude airspace is further characterized or regulated, it is expected that a future version of this dataset may have a more permissive license.

Distribution Statement

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

© 2021 Massachusetts Institute of Technology.

Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.

This material is based upon work supported by the Federal Aviation Administration under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Federal Aviation Administration.

This document is derived from work done for the FAA (and possibly others); it is not the direct product of work done for the FAA. The information provided herein may include content supplied by third parties. Although the data and information contained herein has been produced or processed from sources believed to be reliable, the Federal Aviation Administration makes no warranty, expressed or implied, regarding the accuracy, adequacy, completeness, legality, reliability or usefulness of any information, conclusions or recommendations provided herein. Distribution of the information contained herein does not constitute an endorsement or warranty of the data or information provided herein by the Federal Aviation Administration or the U.S. Department of Transportation. Neither the Federal Aviation Administration nor the U.S. Department of Transportation shall be held liable for any improper or incorrect use of the information contained herein and assumes no responsibility for anyone’s use of the information. The Federal Aviation Administration and U.S. Department of Transportation shall not be liable for any claim for any loss, harm, or other damages arising from access to or use of data or information, including without limitation any direct, indirect, incidental, exemplary, special or consequential damages, even if advised of the possibility of such damages. The Federal Aviation Administration shall not be liable to anyone for any decision made or action taken, or not taken, in reliance on the information contained

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 Tree Ring. The data include parameters of tree ring with a geographic location of Alaska, United States Of America. The time period coverage is from 38 to -51 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

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 Tree Ring. The data include parameters of tree ring with a geographic location of Massachusetts, United States Of America. The time period coverage is from 496 to 267 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

For calculating the GLOWOPT representative route network, a forecast model chain was used. The model was calibrated with 2019 flight movement data (unimpeded by COVID-19) and provided forecasted aircraft movements from the year 2019 (~2020) to 2050 in 5 years intervals.

Two formats of datasets are generated with the results of the forecast model chain, a csv file format and 4-dimensional array supported with MATLAB (.mat).

CSV Datasets

For each forecasted year a csv file is generated with the information on the origin-destination (OD) airports IATA codes, region, latitude and longitude of OD pair, representative aircraft type along with the aircraft category , the average load factor and finally, the distance between the OD pair. The airports worldwide are sub-dived into nine regions namely Africa, Asia, Caribbean, Central America, Europe, Middle East, North America, Oceania and South America. There are total of seven datasets, one for each forecasted year i.e. for years 2019 (~2020), 2025, 2030, 2035, 2040, 2045 and 2050.

Description of the data labels:

Origin- Origin airport IATA code

Origin_Region- Region of the Origin Airport

Origin_Latitude- Latitude of the Origin Airport

Origin_Longitude- Longitude of the Origin Airport

Destination- Destination airport IATA code

Destination_Region- Region of the Destination Airport

Destination_Latitude- Latitude of the Destination Airport

Destination_Longitude- Longitude of the Destination Airport

AcType- Representative aircraft type

Load_Factor- Average load factor per flight

Yearly_Frequency- Total aircraft movements per annum

RefACType- Aircraft Category based on number of seats (Category 6 represents aircraft with seats 252-301 and category 7 represents aircraft with seats greater than 302.)

Distance- Great circle distance between Origin and Destination in Km.

MATLAB Datasets

The dataset generated with MATLAB is a 4-dimensional array with the extension *.mat. The first dimension is the region of the origin airport and subsequently the second dimensions contains the region of the destination airport. The third and fourth dimension are the aircraft category based on seat numbers and the categorized great circle distances. The information received therein is a 1X1 cell with the IATA codes of the OD pairs, frequency and great circle distance in Km.

The 4D array is categorised such that the user can select the route segment specific to a region or a combination of regions. The range categorisation in combination with an aircraft category additionally offers the user the possibility to select routes depending on their great circle distances. The ranges are categorised to represent very short range (0-2000 km), short range (2000-6000 km), medium range (6000-10000 km) and long range (10000 – 15000 km).

Indexing based on the categorisation of the 4D array dataset - Refer to file 'Indexing_MAT_Dataset.PNG'

For example:

To derive the OD pairs and yearly frequency of aircraft movements for routes which originate from Europe and are destined to Asia, operated with category 6 aircraft type and are separated by distances between 10,000 to 15,000 km:

In MATLAB (Indexing based on file 'Indexing_MAT_Dataset.PNG' ):

Route_Network (5,2,1,4),

Description on Index:

5 – Europe: Origin Region

2 – Asia: Destination Region

1– Category 6: Aircraft Type

4 – 10000-15000 km: Range

The Plum Island Ecosystems (PIE) LTER research site consists of coupled watersheds and estuaries in northeastern Massachusetts, USA. The Ipswich River (400km2), Parker River (161km2), and Rowley River (39km2) basins make up the watersheds of the system. The watersheds lie within the Boston Metropolitan region. Population density is about 250 people per km2. The 25 km long (16 miles) macrotidal Plum Island Sound estuary contains salt marsh, dominated by marsh hay (Spartina patens) and smooth cordgrass (Spartina alterniflora), fresh marsh, dominated by cattail (Typha), intertidal flats, and open water tidal creeks and bays. This is the largest wetland dominated estuary in New England and it supports extremely productive commercial and recreational soft-shell clam and striped bass fisheries. We have been investigating the ecology of Plum Island Sound estuary since the late 1980s with support primarily from the National Science Foundation. (NSF) We were part of NSF Land Margin Ecosystems Research program in the early 90’s. The site became part of the NSF's Long Term Ecological Research (LTER) Network in 1998. The Plum Island project is one of only 4 LTER sites that studies the effects of human activities in watersheds on estuaries. The PIE LTER has developed an extensive database open to the public via the Internet that includes our results from long-term field observations and experiments in the Ipswich, Parker and Rowley River watersheds and the Plum Island Sound estuary.

USHighPM_2.5 is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for the United States (i.e., USHighAirPollutants, USHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.

Here is the first big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM_2.5 dataset for the United States from 2000 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R²) of 0.92 and a normalized root-mean-square error (NRMSE) of 0.4 on a daily basis.

If you use the USHighPM_2.5 dataset in your scientific research, please cite the following reference (Wei et al., LPH, 2023):

• 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 USHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html

Southern California residents were rudely awakened Sunday morning June 28, 1992 at 04:57 am (June 28 at 11:57 GMT), by an earthquake of magnitude 7.6 (Ms) followed by a smaller 6.7 (Ms) magnitude earthquake about three hours later (June 28 at 15:05 GMT). The largest shock occurred approximately 6 miles southwest of Landers, California and 110 miles east of Los Angeles. The second earthquake was entered approximately 8 miles southeast of Big Bear City in the San Bernardino Mountains near Barton Flats. A distance of 17 miles and 7,000 feet in elevation separate the two earthquake locations.

These data were estimated for use in the bioecomomic model simulation of the rainbow trout population in the Colorado River in Marble Canyon. The initial rainbow trout abundance is a vector (RBT_intN) representing the population of rainbow trout within each river segment (151 mile long sergments) along the mainstem of the Colorado River from Lees Ferry to 151 river miles downstream. The movement matrix (MMat) is a distribution that estimates the probability that a rainbow trout wil move to any one of the 151 river segments downstream of Lees Ferry.

description: This part of DS 781 presents data for faults for the geologic and geomorphic map of the Offshore of Fort Ross map area, California. The vector data file is included in "Faults_OffshoreFortRoss.zip," which is accessible from http://pubs.usgs.gov/ds/781/OffshoreFortRoss/data_catalog_OffshoreFortRoss.html. The Offshore of Fort Ross map area is cut by the northwest-trending San Andreas Fault, the right-lateral transform boundary between the North American and Pacific tectonic plates. The San Andreas extends across the inner shelf in the southern part of the map, then crosses the shoreline at Fort Ross and continues onland for about 75 km to the east flank of Point Arena (fig. 8-1). Seismic-reflection data are used to map the offshore fault trace, and reveal a relatively simple, 200- to 500-m wide zone typically characterized by one or two primary strands. About 1500 m west of the San Andreas Fault, the mid shelf (between water depths of 40 m and 70 m) in the southernmost part of the map area includes an about 5-km-wide field of elongate, shore-normal sediment lobes (unit Qmsl). Individual lobes within the field are as much as 650-m long and 200-m wide, have as much as 1.5 m (check with Steve) of relief above the surrounding smooth seafloor, and are commonly connected with upslope chutes. Given their morphology and proxmity to the San Andreas fault, we infer that these lobes result from slope failures associated with strong ground motions triggered by large San Andreas earthquakes. Movement on the San Andreas has juxtaposed different coastal bedrock blocks (Blake and others, 2002). Rocks east of the fault that occur along the coast and in the nearshore belong to the late Tertiary, Cretaceous, and Jurassic Franciscan Complex, either sandstone of the Coastal Belt or Central Belt (unit TKfs) or melange of the central terrane (unit fsr). Bedrock west of the fault are considered part of the Gualala Block (Elder, 1998) and include the Eocene and Paleocene German Rancho Formation (unit Tgr) and the Miocene sandstone and mudstone of the Fort Ross area (unit Tsm). This section of the San Andreas Fault onland has an estimated slip rate of about 17 to 25 mm/yr (Bryant and Lundberg, 2002). The devastating Great 1906 California earthquake (M 7.8) is thought to have nucleated on the San Andreas Fault about 100 kilometers south of this map area offshore of San Francisco (e.g., Bolt, 1968; Lomax, 2005), with the rupture extending northward through the Offshore of Fort Ross map area to the south flank of Cape Mendocino. Emergent marine terraces along the coast in the Offshore of Fort Ross map area record recent contractional deformation associated with the San Andreas Fault system. Prentice and Kelson (2006) report uplift rates of 0.3 to 0.6 mm/yr for a late Pleistocene terrace exposed at Fort Ross, and this recent uplift must also have affect the nearshore and inner shelf. Previously, McCulloch (1987) mapped a nearshore (within 3 to 5 km of the coast) fault zone from Point Arena to Fort Ross (Fig. 8-1) using primarily deeper industry seismic-reflection data. Subsequently, Dickinson and others (2005) named this structure the "Gualala Fault." Our mapping, also based on seismic-reflection data, reveals this structure as a steep, northeast trending fault and similarly shows the fault ending to the south in the northern part of the Offshore of Fort Ross map area. We have designated the zone of faulting and folding above this structure the "Gualala Fault deformation zone." Faults were primarily mapped by interpretation of seismic reflection profile data (see field activity S-8-09-NC). The seismic reflection profiles were collected between 2007 and 2010. References Cited Blake, M.C., Jr., Graymer, R.W., and Stamski, R.E., 2002, Geologic map and map database of western Sonoma, northernmost Marin, and southernmost Mendocino counties, California: U.S. Geological Survey Miscellaneous Field Studies Map 2402, scale 1:100,000. Bolt, B.A., 1968, The focus of the 1906 California earthquake: Bulletin of the Seismological Society of America, v. 58, p. 457-471. Bryant, W.A., and Lundberg, M.M., compilers, 2002, Fault number 1b, San Andreas fault zone, North Coast section, in Quaternary fault and fold database of the United States: U.S. Geological Survey website, accessed April 4, 2013, at http://earthquakes.usgs.gov/hazards/qfaults. Dickinson, W.R., Ducea, M., Rosenberg, L.I., Greene, H.G., Graham, S.A., Clark, J.C., Weber, G.E., Kidder, S., Ernst, W.G., and Brabb, E.E., 2005, Net dextral slip, Neogene San Gregorio-Hosgri Fault Zone, coastal California: Geologic evidence and tectonic implications: Geological Society of America Special Paper 391, 43 p. Elder, W.P., ed., 1998, Geology and tectonics of the Gualala Block, northern California: Pacific Section, Society of Economic Paleontologists and Mineralogists, Book 84, 222 p. Lomax, A., 2005, A reanalysis of the hypocentral location and related observations for the Great 1906 California earthquake: Bulletin of the Seismological Society of America, v. 95, p. 861-877. McCulloch, D.S., 1987, Regional geology and hydrocarbon potential of offshore central California, in Scholl, D.W., Grantz, A., and Vedder, J.G., eds., Geology and Resource Potential of the Continental Margin of Western North America and Adjacent Oceans -- Beaufort Sea to Baja California: Houston, Texas, Circum-Pacific Council for Energy and Mineral Resources, Earth Science Series, v. 6., p. 353-401. Prentice, C.S., and Kelson, K.I., 2006, The San Andreas fault in Sonoma and Mendocino counties, in Prentice, C.S., Scotchmoor, J.G., Moores, E.M., and Kiland, J.P., eds., 1906 San Francisco Earthquake Centennial Field Guides: Field trips associated with the 100th Anniversary Conference, 18-23 April 2006, San Francisco, California: Geological Society of America Field Guide 7, p. 127-156.; abstract: This part of DS 781 presents data for faults for the geologic and geomorphic map of the Offshore of Fort Ross map area, California. The vector data file is included in "Faults_OffshoreFortRoss.zip," which is accessible from http://pubs.usgs.gov/ds/781/OffshoreFortRoss/data_catalog_OffshoreFortRoss.html. The Offshore of Fort Ross map area is cut by the northwest-trending San Andreas Fault, the right-lateral transform boundary between the North American and Pacific tectonic plates. The San Andreas extends across the inner shelf in the southern part of the map, then crosses the shoreline at Fort Ross and continues onland for about 75 km to the east flank of Point Arena (fig. 8-1). Seismic-reflection data are used to map the offshore fault trace, and reveal a relatively simple, 200- to 500-m wide zone typically characterized by one or two primary strands. About 1500 m west of the San Andreas Fault, the mid shelf (between water depths of 40 m and 70 m) in the southernmost part of the map area includes an about 5-km-wide field of elongate, shore-normal sediment lobes (unit Qmsl). Individual lobes within the field are as much as 650-m long and 200-m wide, have as much as 1.5 m (check with Steve) of relief above the surrounding smooth seafloor, and are commonly connected with upslope chutes. Given their morphology and proxmity to the San Andreas fault, we infer that these lobes result from slope failures associated with strong ground motions triggered by large San Andreas earthquakes. Movement on the San Andreas has juxtaposed different coastal bedrock blocks (Blake and others, 2002). Rocks east of the fault that occur along the coast and in the nearshore belong to the late Tertiary, Cretaceous, and Jurassic Franciscan Complex, either sandstone of the Coastal Belt or Central Belt (unit TKfs) or melange of the central terrane (unit fsr). Bedrock west of the fault are considered part of the Gualala Block (Elder, 1998) and include the Eocene and Paleocene German Rancho Formation (unit Tgr) and the Miocene sandstone and mudstone of the Fort Ross area (unit Tsm). This section of the San Andreas Fault onland has an estimated slip rate of about 17 to 25 mm/yr (Bryant and Lundberg, 2002). The devastating Great 1906 California earthquake (M 7.8) is thought to have nucleated on the San Andreas Fault about 100 kilometers south of this map area offshore of San Francisco (e.g., Bolt, 1968; Lomax, 2005), with the rupture extending northward through the Offshore of Fort Ross map area to the south flank of Cape Mendocino. Emergent marine terraces along the coast in the Offshore of Fort Ross map area record recent contractional deformation associated with the San Andreas Fault system. Prentice and Kelson (2006) report uplift rates of 0.3 to 0.6 mm/yr for a late Pleistocene terrace exposed at Fort Ross, and this recent uplift must also have affect the nearshore and inner shelf. Previously, McCulloch (1987) mapped a nearshore (within 3 to 5 km of the coast) fault zone from Point Arena to Fort Ross (Fig. 8-1) using primarily deeper industry seismic-reflection data. Subsequently, Dickinson and others (2005) named this structure the "Gualala Fault." Our mapping, also based on seismic-reflection data, reveals this structure as a steep, northeast trending fault and similarly shows the fault ending to the south in the northern part of the Offshore of Fort Ross map area. We have designated the zone of faulting and folding above this structure the "Gualala Fault deformation zone." Faults were primarily mapped by interpretation of seismic reflection profile data (see field activity S-8-09-NC). The seismic reflection profiles were collected between 2007 and 2010. References Cited Blake, M.C., Jr., Graymer, R.W., and Stamski, R.E., 2002, Geologic map and map database of western Sonoma, northernmost Marin, and southernmost Mendocino counties, California: U.S. Geological Survey Miscellaneous Field Studies Map 2402, scale 1:100,000. Bolt, B.A., 1968, The focus of the 1906 California earthquake: Bulletin of the Seismological Society of America, v. 58, p. 457-471. Bryant, W.A.,