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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The Gulf Nuclear Energy Infrastructure Institute: A Multidisciplinary Educational Approach for Integrated Nuclear Energy Safety Security and Safeguards in the Middle East

The Gulf Nuclear Energy Infrastructure Institute (GNEII) at Khalifa University of Science and Technology was created as a regional institute offering education, research and technical services to support nuclear energy safety, security and safeguards (3S) objectives. A mixed methods approach—using the (1) Course Evaluation, (2) GNEH Alumni Survey, (3) Capstone Project and, (4) GNEII-Related Literature data sets—was used to evaluate the effect of implementing this multidisciplinary `3S' educational program and the broader impact of the associated `3S' multidisciplinary institute on nuclear energy human resource development. Data sets (1), (2) and (3) illustrate how well GNEII implemented this novel 3S curriculum and resulted in successful knowledge transfer. Data sets (2), (3) and (4) illustrate how well GNEII's impact has positively influenced professional workplace behaviors and the institute's broader reputation to support responsible nuclear energy program education. Furthermore, GNEII demonstrates one option for successfully providing a multidisciplinary, 3S curriculum to support broader nuclear infrastructure and human resource development aims.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2019

Response Versus Scan-Angle Assessment of MODIS Reflective Solar Bands in Collection 6.1 Calibration

The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments onboard the Aqua and Terra satellites have been operated for nearly two decades, producing high-quality earth observation data sets suitable for a broad range of scientific studies regarding the earth’s land, ocean, and atmospheric processes. The high radiometric accuracy of MODIS reflective solar band (RSB) calibration has also served as benchmark measurements for on-orbit cross-calibration studies. As the two MODIS instruments have operated well beyond their design lifespan of six years, the measurements from the onboard calibrators alone become inadequate to characterize the sensor’s response at all scan angles, as evinced by long-term drifts observed at certain scan positions of the Aqua-MODIS 0.64- and 0.86-μm bands in Collection 6 (C6) data set. The latest MODIS Level 1B C6.1 data set incorporates earth-view response trending from invariant desert sites as supplemental inputs to characterize the scan-angle calibration dependencies for all RSB. This article presents a deep convective cloud (DCC)-based calibration approach for an independent evaluation of the MODIS RSB response versus scan-angle (RVS) performance in C6.1. The long-term calibration stability and RVS differences in C6.1 have been significantly improved for Aqua-MODIS RSB. The observed RVS differences of more than 2% in Aqua-MODIS C6 bands 1 and 2 have been reduced to within 1% in C6.1. Some RSBs of Terra-MODIS have suffered temporal drifts up to ~2% and calibration shifts up to 3%, particularly around 2016 when the Terra satellite entered into safe mode. The DCC approach has been found very effective in tracking the on-orbit RVS changes over time.

Clouds and the Earth’s Radiant Energy System (CERE↗
NASA NTRS1989

An interim reference model for the variability of the middle atmosphere H2O vapor distribution

Water vapor is an important minor constituent in the studies of the middle atmosphere for a variety of reasons, including its role as a source for active HO(y) chemicals and its use in analysis of transport processes. A number of in situ and remote techniques were employed in the determination of water vapor distributions. Two of the more complete data sets were used to develop an interim reference profile. First, there are the seven months of Nimbus 7 limb infrared monitor of the stratosphere (LIMS) data obtained during Nov. 1978 to May 1979 over the range 64S to 84N latitude and from about 100 to 1 mb in the mid-mesosphere at several fixed Northern Hemisphere mid-latitude sites. These two data sets were combined to give a mid-lattitude, interim reference water vapor profile for the entire vertical range of the middle atmosphere and with accuracies of better than 25 percent. The daily variability of stratospheric water vapor profiles about the monthly mean was also established from these data sets for selected months. Information is also provided on the longitudinal variability of LIMS water vapor profiles about the daily, weekly, and monthly zonal means. Generally, the interim reference water vapor profile and its variability are consistent with prevailing ideas about chemistry and transport.

Remsberg, E. E.↗
NASA NTRS2005

Comparison of Monthly Mean Cloud Fraction and Cloud Optical depth Determined from Surface Cloud Radar, TOVS, AVHRR, and MODIS over Barrow, Alaska

A one year comparison is made of mean monthly values of cloud fraction and cloud optical depth over Barrow, Alaska (71 deg 19.378 min North, 156 deg 36.934 min West) between 35 GHz radar-based retrievals, the TOVS Pathfinder Path-P product, the AVHRR APP-X product, and a MODIS based cloud retrieval product from the CERES-Team. The data sets represent largely disparate spatial and temporal scales, however, in this paper, the focus is to provide a preliminary analysis of how the mean monthly values derived from these different data sets compare, and determine how they can best be used separately, and in combination to provide reliable estimates of long-term trends of changing cloud properties. The radar and satellite data sets described here incorporate Arctic specific modifications that account for cloud detection challenges specific to the Arctic environment. The year 2000 was chosen for this initial comparison because the cloud radar data was particularly continuous and reliable that year, and all of the satellite retrievals of interest were also available for the year 2000. Cloud fraction was chosen as a comparison variable as accurate detection of cloud is the primary product that is necessary for any other cloud property retrievals. Cloud optical depth was additionally selected as it is likely the single cloud property that is most closely correlated to cloud influences on surface radiation budgets.

Uttal, Taneil↗
NASA NTRS2002

Global Precipitation Analyses at Monthly to 3-HR Time Scales

Global precipitation analysis covering the last few decades and the impact of the new TRMM precipitation observations are discussed. The 20+ year, monthly, globally complete precipitation analysis of the World Climate Research Program's (WCRP/GEWEX) Global Precipitation Climatology Project (GPCP) is used to explore global and regional variations and trends and is compared to the much shorter TRMM(Tropica1 Rainfall Measuring Mission) tropical data set. A trend pattern that is a combination of both El Nino and La Nina precipitation features is evident in the 20-year data set. This pattern is related to an increase with time in the number of combined months of El Nino and La Nina during the 20 year period. Monthly anomalies of precipitation are related to ENSO variations with clear signals extending into middle and high latitudes of both hemispheres. The GPCP daily, 1deg latitude-longitude analysis, which is available from January 1997 to the present is described and the evolution of precipitation patterns on this time scale related to El Nino and La Nina is described. Finally, a TRMM-based 3-hr analysis is described that uses TRMM to calibrate polar-orbit microwave observations from SSM/I and geosynchronous IR observations and merges the various calibrated observations into a final, 3-hr resolution map. This TRMM standard product will soon be available for the entire TRMM period (January 1998- present). A real-time version of this merged product is being produced and is available at 0.25deg latitude-longitude resolution over the latitude range from 50degN-50degS. Images from this data set can be seen at the U.S. TRMM web site (trmm.gsfc.nasa.gov). Examples will be shown, including its use in monitoring flood conditions and relating weather-scale events to climate variations.

Adler, Robert F.↗
DOE OSTI2025

Bayesian Inference for the Seismic Moment Tensor Using Regional Waveforms and Teleseismic- P Polarities with a Data-Derived Distribution of Velocity Models and Source Locations

The largest source of uncertainty in any source inversion is the velocity model used in the transfer function that relates observed ground motion to the seismic moment tensor. However, standard inverse procedure often does not quantify uncertainty in the seismic moment tensor due to error in the Green’s functions from uncertain event location and Earth structure. Here, we incorporate this uncertainty into an estimation of the seismic moment tensor using a data-derived distribution of velocity models based on complementary geophysical data sets, including thickness constraints, velocity profiles, gravity data, surface-wave group velocities, and regional body-wave travel times. The data-derived distribution of velocity models is then used as a prior distribution of Green’s functions for use in Bayesian inference of an unknown seismic moment tensor using regional and teleseismic-P waveforms. The use of multiple data sets is important for gaining resolution to different components of the moment tensor. The combined likelihood is estimated using data-specific error models and the posterior of the seismic moment tensor is estimated and interpreted in terms of the most probable source type.

58 GEOSCIENCES↗
NASA NTRS2018

Pattern Recognition Control Design

Spacecraft control algorithms must know the expected vehicle response to any command to the available control effectors, such as reaction thrusters or torque devices. Spacecraft control system design approaches have traditionally relied on the estimated vehicle mass properties to determine the desired force and moment, as well as knowledge of the effector performance to efficiently control the spacecraft. A pattern recognition approach was used to investigate the relationship between the control effector commands and spacecraft responses. Instead of supplying the approximated vehicle properties and the thruster performance characteristics, a database of information relating the thruster ring commands and the desired vehicle response was used for closed-loop control. A Monte Carlo simulation data set of the spacecraft dynamic response to effector commands was analyzed to establish the influence a command has on the behavior of the spacecraft. A tool developed at NASA Johnson Space Center to analyze flight dynamics Monte Carlo data sets through pattern recognition methods was used to perform this analysis. Once a comprehensive data set relating spacecraft responses with commands was established, it was used in place of traditional control methods and gains set. This pattern recognition approach was compared with traditional control algorithms to determine the potential benefits and uses.

Gambone, Elisabeth A.↗
DOE OSTI2024

Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals

Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting the dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surfaces (PESs) when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a predefined functional form. Machine learning (ML) models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics, and the complex nanoscale PESs, without sacrificing the bulk properties. Here, in this study, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NNs) for capturing the cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage, to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher-fidelity first-principles training data set to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled data set that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly nonequilibrium as well as learning strategies that iteratively improve the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles data set of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML-trained potentials to accelerate materials discovery and design.

36 MATERIALS SCIENCE↗
NASA NTRS1995

Reduction and analysis of seasons 15 and 16 (1991 - 1992) Pioneer Venus radio occulation data and correlative studies with observations of the near infrared emission of Venus

In this study, we sought to characterize variations in the abundance and distribution of subcloud H2SO4(g) in the Venus atmosphere by using a number of 13cm radio occultation measurements conducted with the Pioneer Venus Orbiter near the inferior conjunction of 1991. A total of ten data sets were examined and analyzed, producing vertical profiles of temperature and pressure in the neutral atmosphere, and sulfuric acid vapor abundance below the main cloud layer. Two of the vertical profiles of the abundance of H2SO4(g) were correlated with NIR images of the night side of Venus made during the same period of time by Boris Ragent (under a separate PVO Guest Investigator Grant). Initially, we had hoped that the combination of these two different types of data would make it possible to constrain or identify the composition of the large particles causing the features observed in the NIR images. However, the sparseness of the radio occultation data set, combined with the sparseness of the NIR data set (one image per day over an 8 day period) made it impossible to draw strong conclusions. Considered on their own, however, the parameters retrieved from the radio occultation experiments are valuable science products.

Jenkins, Jon M.↗
NASA NTRS2019

Global Carbon Budget 2019

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (E(FF)) are based on energy statistics and cement production data, while emissions from land use change (E(LUC)), mainly deforestation, are based on land use and land use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (G(ATM)) is computed from the annual changes in concentration. The ocean CO2 sink (S(OCEAN)) and terrestrial CO2 sink (S(LAND)) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (B(IM)), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2009–2018), E(FF) was 9.5±0.5 GtC/yr, E(LUC) 1.5±0.7 GtC/yr, G(ATM) 4.9±0.02 GtC/yr (2.3±0.01 ppm/yr), S(OCEAN) 2.5±0.6 GtC/yr, and S(LAND) 3.2±0.6 GtC/yr, with a budget imbalance B(IM) of 0.4 GtC/yr indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in E(FF) was about 2.1 % and fossil emissions increased to 10.0±0.5 GtC/yr, reaching 10 GtC/yr for the first time in history, E(LUC) was 1.5±0.7 GtC/yr, for total anthropogenic CO2 emissions of 11.5±0.9 GtC/yr (42.5±3.3 GtCO2). Also for 2018, G(ATM) was 5.1±0.2 GtC/yr (2.4±0.1 ppm/yr), S(OCEAN) was 2.6±0.6 GtC/yr, and S(LAND) was 3.5±0.7 GtC/yr, with a B(IM) of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in E(FF) of +0.6 % (range of −0.2 % to 1.5 %) based on national emissions projections for China, the USA, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. Overall, the mean and trend in the five components of the global carbon budget are consistently estimated over the period 1959–2018, but discrepancies of up to 1 GtC/yr persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations shows (1) no consensus in the mean and trend in land use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018a, b, 2016, 2015a, b, 2014, 2013).

global carbon budget 2019↗
DOE OSTI2022

EASTERN NORTH ATLANTIC (ENA) CONDENSATION PARTICLE COUNTER (CPC) with the AEROSOL MASK (ENA-AM) for the YEAR 2016

The Eastern North Atlantic (ENA) central facility is periodically affected by episodes of local aerosols. High-concentration aerosol events associated with potential local aerosol sources are sampled by the concentration particle counter (CPC) and can be observed in the high-time-resolution data. In an effort to remove aerosol data that is not regionally representative from the CPC data, an aerosol mask (ENA-AM) has been previously developed and validated during the ACE-ENA campaign (Gallo et al. 2020). The ENA-AM is a standard deviation algorithm based on the statistically different behavior of adjacent data points used to determine periods when the measurements are impacted by local aerosols. This file contains one-minute-time-resolution submicron aerosol particle concentration data sets from the CPC for the year 2016, and the corresponding ENA-AM data sets to remove data points affected by local aerosol sources.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2000

BOREAS RSS-14 Level-2 GOES-7 Shortwave and Longwave Radiation Images

The BOREAS RSS-14 team collected and processed several GOES-7 and GOES-8 image data sets that covered the BOREAS study region. This data set contains images of shortwave and longwave radiation at the surface and top of the atmosphere derived from collected GOES-7 data. The data cover the time period of 05-Feb-1994 to 20-Sep-1994. The images missing from the temporal series were zero-filled to create a consistent sequence of files. The data are stored in binary image format files. Due to the large size of the images, the level-1a GOES-7 data are not contained on the BOREAS CD-ROM set. An inventory listing file is supplied on the CD-ROM to inform users of what data were collected. The level-1a GOES-7 image data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). See sections 15 and 16 for more information. The data files are available on a CD-ROM (see document number 20010000884).

Hall, Forrest G.↗
DOE OSTI2020

Generation of Synthetic Data for a Radiation Detection Algorithm Competition

This paper details the generation of synthetic radiation data using large-scale Monte Carlo transport models to evaluate radiation search detection algorithms. Modular 3-D Monte Carlo models spanning multiple city blocks were constructed, loosely based on downtown Knoxville, TN, containing buildings composed of multiple materials (brick, granite, and concrete), sidewalks, a four-lane road, side streets, parking lots, and grassy fields. Background and simulated source detector response calculations from these models were used to create synthetic list mode data sets for a 2"×4"×16" NaI(Tl) detector moving through a city street at a constant speed. For the background simulations, major isotopes were computed individually so that background composition and variability could be computed efficiently outside Monte Carlo. The source detector response included six simulated sources placed at 15 source locations. Detector response was developed to be periodic through the city street so that a detector path could begin at one end of the model and wrap around to the other. This framework allowed for the creation of diverse data sets, each with its own unique background and simulated source detector response. Synthetic data allows for high-quality labels, which are useful in developing data-driven radiation detection algorithms. This methodology was used to create synthetic data sets which were released as part of a public data competition to spur the development of new radiation detection algorithms for radiological search applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as time series data shifts. In this research, a changepoint detection (CPD) algorithm that automatically detects data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window across the labeled data sets. Pending approval, we plan to release the labeled data sets for this research on NREL's DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package. By supplying the training sets and algorithm, we hope to encourage further development in this research space.

data shift↗
NASA NTRS2011

Sub-Scale Testing and Development of the J-2X Fuel Turbopump Inducer

In the early stages of the J-2X upper stage engine program, various inducer configurations proposed for use in the fuel turbopump (FTP) were tested in water. The primary objectives of this test effort were twofold. First, to obtain a more comprehensive data set than that which existed in the Pratt & Whitney Rocketdyne (PWR) historical archives from the original J-2S program, and second, to supplement that data set with information regarding the cavitation induced vibrations for both the historical J-2S configuration as well as those tested for the J-2X program. The J-2X FTP inducer, which actually consists of an inducer stage mechanically attached to a kicker stage, underwent 4 primary iterations utilizing sub-scaled test articles manufactured and tested in PWR's Engineering Development Laboratory (EDL). The kicker remained unchanged throughout the test series. The four inducer configurations tested retained many of the basic design features of the J-2S inducer, but also included variations on leading edge blade thickness and blade angle distribution, primarily aimed at improving suction performance at higher flow coefficients. From these data sets, the effects of the tested design variables on hydrodynamic performance and cavitation instabilities were discerned. A limited comparison of impact to the inducer efficiency was determined as well.

Sargent, Scott R.↗
NASA NTRS2002

Seasonal to Decadal-Scale Variability in Satellite Ocean Color and Sea Surface Temperature for the California Current System

Support for this project was used to develop satellite ocean color and temperature indices (SOCTI) for the California Current System (CCS) using the historic record of CZCS West Coast Time Series (WCTS), OCTS, WiFS and AVHRR SST. The ocean color satellite data have been evaluated in relation to CalCOFI data sets for chlorophyll (CZCS) and ocean spectral reflectance and chlorophyll OCTS and SeaWiFS. New algorithms for the three missions have been implemented based on in-water algorithm data sets, or in the case of CZCS, by comparing retrieved pigments with ship-based observations. New algorithms for absorption coefficients, diffuse attenuation coefficients and primary production have also been evaluated. Satellite retrievals are being evaluated based on our large data set of pigments and optics from CalCOFI.

Mitchell, B. Greg↗
DOE OSTI2021

LANL Meteorological Program: 2020 Data Completeness/Quality Report

Los Alamos National Laboratory (LANL) operates four mesa-top meteorology towers: Technical Area (TA) 06, TA-49, TA-53, and TA-54. An additional tower is located in Mortandad Canyon (TA-5 MDCN), and a rain gauge at North Community (NCOM). A description of the meteorology monitoring network is found in Dewart and Boggs (2014). Mesa-top towers are instrumented at 1.2 meters (m), 11.5 m, 23 m, and 46 m. In addition, TA-06 is instrumented at 92 m. The TA-5 MDCN tower is 10 m in height and is instrumented at 1.2 m and 10 m. Data are collected every 15 minutes. Range checking is done on each measurement every 15 minutes; data that are beyond normal ranges are eliminated from the data set and replaced by a code for missing data. In addition, data are reviewed weekly by meteorologists to identify bad data not identified by range checking. The data steward eliminates these data from the data set and replaces them with a code for missing data. The instrument technicians also review that data and schedule instrument replacement as required. Data completeness is determined by the number of total 15-minute records available versus the total number of possible measurements for the entire year. As a rule, the meteorologists do not attempt to estimate data that are eliminated as bad data. Original datalogger records, containing bad data, can be recalled from program archival storage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

LANL Meteorological Program: 2022 Data Completeness/Quality Report

Los Alamos National Laboratory (LANL) operates seven mesa-top instrumented meteorology towers: Technical Area (TA) 6, TA-49, TA-53, TA-54, TA-63, TA-54B, and TA-16B. An additional instrumented tower is located in Mortandad Canyon (TA-5 MDCN), and there is a rain gauge at North Community (NCOM), located within the town of Los Alamos. The 10 meter (m) towers at TA-63, TA- 54B, and TA-16B were installed in 2021, and will be included in the 2023 data completeness/quality report. A description of the meteorology monitoring network, prior to the installation of the TA-63, TA-54B, and TA-16B is found in Dewart and Boggs (2014). Four of the mesa-top towers (e.g., TA-6, TA-49, TA-53, and TA-54) are instrumented at the 1.2 m, 11.5 m, 23 m, and 46 m levels. In addition, the TA-6 tower is instrumented at the 92 m level. The TA-5 MDCN tower is 10 m in height and is instrumented at 1.2 m and 10 m. Data are collected and averaged every 15 minutes. Range checking is performed on each measurement every 15 minutes; data that are beyond normal ranges are eliminated from the data set and replaced by a code for missing data. In addition, data are reviewed weekly by qualified meteorologists to identify bad data not identified by the range checking technique. The data steward eliminates these data from the data set and replaces them with a code for missing data. The instrument technicians also review that data and schedule instrument replacement, as required. All instruments are calibrated at a frequency that meets the criteria identified in ANSI/ANS-3.11-2015. Data completeness is determined by the number of total 15-minute records available versus the number of possible measurements for the entire year. As a rule, the meteorologists do not attempt to estimate data that are eliminated as bad data. Original datalogger records, including bad data, can be recalled from program archival storage.

54 ENVIRONMENTAL SCIENCES↗