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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Survey of Technologies to Meet PAG limits for Detection of Cesium-137 in Water

The goal of this report is to evaluate the process of adding a sorbent to the sampling process to preconcentrate Cs-137 and then analyze the sorbent to provide a rapid assessment of results to guide decisions on drinking water sources and the protection of the public. Multiple unit design variables were considered: sample configuration/geometry, sorbent efficiency, sample volume. Model simulations demonstrated through these unit design efficiencies that 10 L of sample volume processed through a sorbent provides nearly two orders of magnitude improvement in sample counting time to provide results to a decision maker than the current process. Over 60 publications and reports concerning Cs-137 and sorbents were evaluated. The key attributes were readiness of sorbent to be deployed in a mobile laboratory environment, and minimum sample preparation and conditioning. Examples of sorbents not considered further included nanomaterials, which have not been evaluated at a commercial scale and are limited in availability. The recommended sorbents for further consideration include: ferrocyanide salts, zeolite, and chemically modified granular activated carbon.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Huge ensembles – Part 2: Properties of a huge ensemble of hindcasts generated with spherical Fourier neural operators

Abstract. In Part 1, we created an ensemble based on spherical Fourier neural operators. As initial condition perturbations, we used bred vectors, and as model perturbations, we used multiple checkpoints trained independently from scratch. Based on diagnostics that assess the ensemble's physical fidelity, our ensemble has comparable performance to operational weather forecasting systems. However, it requires orders-of-magnitude fewer computational resources. Here in Part 2, we generate a huge ensemble (HENS), with 7424 members initialized each day of summer 2023. We enumerate the technical requirements for running huge ensembles at this scale. HENS precisely samples the tails of the forecast distribution and presents a detailed sampling of internal variability. HENS has two primary applications: (1) as a large dataset with which to study the statistics and drivers of extreme weather and (2) as a weather forecasting system. For extreme climate statistics, HENS samples events 4σ away from the ensemble mean. At each grid cell, HENS increases the skill of the most accurate ensemble member and enhances coverage of possible future trajectories. As a weather forecasting model, HENS issues extreme weather forecasts with better uncertainty quantification. It also reduces the probability of outlier events, in which the verification value lies outside the ensemble forecast distribution.

Mahesh, Ankur↗
DOE OSTI2021

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

IMAGINE: The neutron protein crystallography beamline at the high flux isotope reactor

IMAGINE is a high intensity, quasi-Laue neutron crystallography beamline developed at the 85 MW High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL). This state-of-the-art facility for neutron-diffraction enables neutron protein structures to be determined at or near atomic resolutions from crystals with volumes of < 1 mm 3 and unit cell edges of < 150 Å. The beamline features include elliptical focusing mirrors that deliver neutrons into a 2.0 × 3.2 mm 2 focal spot at the sample position, and variable short and long wavelength cutoff optics that provide automated exchange between multiple wavelength configurations. The beamline is equipped with a single-axis goniometer, neutron-sensitive cylindrical image plate detector and room temperature and cryogenic sample environments. This article describes the beamline components, the diffractometer and the data collection and data analysis protocols that are used, and outlines the protein deuteration, crystallization and conventional crystallography capabilities that are available to users at ORNL's neutron facilities. We also present examples of the scientific questions being addressed at this beamline and highlight important findings in enzyme chemistry that have been made possible by IMAGINE.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

The Fall Speed Variability of Similarly Sized Ice Particle Aggregates

The terminal velocity ( V t ) of ice hydrometeors is of high importance to atmospheric modeling. V t is governed by the physical characteristics of a hydrometeor, including mass and projected area, as well as environmental conditions. When liquid hydrometeors coalesce to form larger hydrometeors, the resulting hydrometeor can readily be characterized by its spherical or near-spherical shape. For ice hydrometeors, it is more complicated because of the variability of ice shapes possible in the atmosphere as well as the inherent randomness in the aggregation process, which leads to highly variable characteristics. The abundance of atmospheric processes affecting ice particle dimensional characteristics creates potential for highly variable V t for ice particles that are predicted or measured to be of the “same size.” In this article we explore the variability of ice hydrometeor V t both theoretically and through the use of experimental observations. Theoretically, the variability in V t is investigated by analyzing the microphysical characteristics of randomly aggregated hexagonal shapes. The modeled dimensional characteristics are then compared to aircraft probe measurements to constrain the variability in atmospheric ice hydrometeor V t . Results show that the spread in V t can be represented with Gaussian distributions relative to a mean. Variability expressed as the full width at half maximum of the normalized Gaussian probability distribution function is around 20%, with somewhat higher values associated with larger particle sizes and warmer temperatures. Field campaigns where mostly convective clouds were sampled displayed low variability, while Arctic and midlatitude winter campaigns showed broader V t spectra.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Operando X-ray Nanocharacterization of Polycrystalline Thin Film Modules

Non-destructive measurement techniques, with high spatial resolution, capable of correlating composition and structure with device properties, are few and far between. For the case of polycrystalline and inhomogeneous materials, the added challenge is that large sampling areas are necessary in order to have a statistical representation of the specimen under study. For the study of grain cores and grain boundaries in polycrystalline solar absorbers this is of particular importance since their dissimilar behavior and variability throughout the samples makes it difficult to draw conclusions and ultimately optimize the materials and devices. Our approach to use state-of-the-art x-ray microscopy to study full module stacks under operating conditions is unique. Understanding the effects of elemental and charge migration under full encapsulation and operating conditions will allow CIGS and CdTe manufactures and in particular our partners in this proposal to modify materials, stacks, architectures, and processing steps to minimize the performance losses through time.

14 SOLAR ENERGY↗
DOE OSTI2024

Machine learning models of intermittent operation of RO wellhead water treatment for salinity reduction and nitrate removal

Machine learning models were developed for intermittent multi-mode operation of a wellhead reverse osmosis water purification and desalination system to predict salt passage, nitrate passage, and permeate flux. The models, based on long short-term memory (LSTM) recurrent neural network (RNN) architecture, included an attention mechanism to increase model performance in proximity of the regulatory limit for nitrate. Training and testing of the models for the Startup, Production, Shutdown and Flushing operational modes were based on operational data (consisting of 22 process variables per data sample) acquired every 2–5 s over a six-month period. The significant sets of model input attributes for the different operational modes were assessed via Spearman ranking correlation, Self-Organizing Map (SOM) analysis and feed forward feature selection (FFFS). Although the variability of nitrate passage, salt passage and permeate flux was significant over the four operational modes, prediction performance for the three outcomes were with R2 and Average Absolute Relative Error (AARE) of 0.78–0.95 and 2.96–6.16 %, respectively. Model updates post membrane elements replacement demonstrated similar levels of prediction accuracy. The study results suggest that there is merit in exploring the utility of multi-mode models for sensor fault detection, data imputation, and for potential use in model-predictive control.

Intermittent RO operation↗
DOE OSTI2016

Versailles Project on Advanced Materials and Standards Interlaboratory Study on Measuring the Thickness and Chemistry of Nanoparticle Coatings Using XPS and LEIS

We report the results of a VAMAS (Versailles Project on Advanced Materials and Standards) inter-laboratory study on the measurement of the shell thickness and chemistry of nanoparticle coatings. Peptide-coated gold particles were supplied to laboratories in two forms: a colloidal suspension in pure water and; particles dried onto a silicon wafer. Participants prepared and analyzed these samples using either X-ray photoelectron spectroscopy (XPS) or low energy ion scattering (LEIS). Careful data analysis revealed some significant sources of discrepancy, particularly for XPS. Degradation during transportation, storage or sample preparation resulted in a variability in thickness of 53%. The calculation method chosen by XPS participants contributed a variability of 67%. However, variability of 12% was achieved for the samples deposited using a single method and by choosing photoelectron peaks that were not adversely affected by instrumental transmission effects. The study identified a need for more consistency in instrumental transmission functions and relative sensitivity factors, since this contributed a variability of 33%. The results from the LEIS participants were more consistent, with variability of less than 10 % in thickness and this is mostly due to a common method of data analysis. The calculation was performed using a model developed for uniform, flat films and some participants employed a correction factor to account for the sample geometry, which appears warranted based upon a simulation of LEIS data from one of the participants and comparison to the XPS results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Identification of Distribution of Elastic Constants of SiC/SiC Braided Tubes

There has been considerable interest in chemical vapor infiltration (CVI) manufactured silicon carbide fiber and silicon carbide matrix (SiCf/SiCm) composite tubes due to their superior mechanical properties. There are many parameters in the manufacturing of SiCf/SiCm composite tubes such as elastic constants of constituent SiC fiber and SiC matrix, braiding angle, porosity, and others whose values vary over a range. A sensitivity analysis will be carried out to determine the dominant parameters among the abovementioned ones. We intend to determine the variability in elastic constants of SiCf/SiCm composite tubes considering the variability in dominant parameters. A chosen number of combinations of dominant variables will be sampled through Latin hypercube sampling (LHS) method. The elastic constants of composite tube namely Young's moduli in circumferential and longitudinal direction, Poisson's ratio and shear modulus will be calculated at sampled points through finite element (FE) analysis. A polynomial response surface (PRS) will be built for each of the elastic constants to be used a surrogate to FE analysis. once the predictive accuracy of PRS is verified at the validation set of data points, the PRS will be invoked 105 times in Monte Carlo simulations (MCS). The variability can be calculated based on MCS-generated data which will be presented at the conference.

42 ENGINEERING↗
DOE OSTI2019

Characterization of Covetic Materials for Process Control. Final CRADA report

GDC Industries LLC has been performing process research on covetic materials since its formation in February 2015. With assistance from DOE and the Air Force via SBIR projects, GDC focused on the enhancement of aluminum alloys to covetics to replicate a previously reported electrical conductivity of about 67% IACS (International Annealed Copper Standard). GDC has established a joint development agreement with General Cable Corporation, wherein General Cable agreed to perform numerous extrusions of as cast covetics and subsequent tensile and electrical conductivity tests on the resultant wire. Covetics conversion was proven but the significant improvement is yet to be replicated. To control the experiments towards enhanced process control, a series of designed experiments was created by an expert in process control from the automotive industry. Six process variables were identified and a designed experiment matrix requiring 18 independent conversions was created at GDC. The result of those designed experiments has now allowed GDC to focus on two process variables, namely, residence time and DC current in the conversions with the other four variable held constant. While this work is useful in replicating the earlier enhancements, GDC feels it will not be sufficient for the necessary process knowhow for scale up required by industry as GDC still does not have a direct correlation of the process variables to the actual carbon in the finished covetics. Since 2015, GDC has been aware of the capabilities existing at Argonne National Laboratory for performing carbon analysis that exceeds the precision commonly available to Industry. GDC and ANL submitted a proposal in response to the DOE-EERE – Small Business Vouchers Pliot Program announcement and the proposal was selected for funding. GDC will melt-process covetic aluminum samples following their design of experiment criteria and have the as-casted samples extruded by their partner, General Cable. Argonne will measure the conductivity, analyze carbon content, perform X-ray diffraction, and examine the microstructural features of the samples. Careful process variable measurements will be recorded by each party such that correlations can be developed indicating process parameters that yield the best carbon concentration in covetics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Impact of feed variability on cesium removal with multiple actual waste samples from the Hanford site

Here, the Tank Side Cesium Removal (TSCR) system, under development by Washington River Protection Solutions (WRPS), will send initial low-activity Hanford waste tank supernate feeds to the Hanford Waste Treatment and Immobilization Plant (WTP) Low-Activity Waste (LAW) Facility. In addition to entrained solids removal from the supernate, the primary goal of TSCR is to remove cesium-137 ( 137 Cs) by ion exchange, allowing contact handling of the liquid effluent product at WTP. Crystalline silicotitanate (CST) ion exchange media, manufactured by Honeywell UOP (product IONSIV™ R9140-B), was selected as the ion exchange media at TSCR. This lot of material was found to have superior performance in comparison to historic batches of CST. Ion exchange column and batch contact testing with supernate from Hanford tanks AP-105, AP-107 and AW-102 was performed to assess the impact of feed variability on system performance. These tests demonstrated that batch contact measurements provide a reasonable prediction of column capacities with some deviation in performance attributed to column dynamics. In addition, the variability in CST capacity for cesium in the various actual waste samples does not track with historical understanding of the competing cations, suggesting that tank waste samples contain other components that may significantly impact cesium loading.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Say where you sample: Increasing site selection transparency in urban ecology

Urban ecological studies have the potential to extend our understanding of socio-ecological systems beyond that of an individual city or region. Cross-comparative empirical work and synthesis are imperative to develop a general urban ecological theory. This can be achieved only if studies are replicable and generalizable. Transparency in methods reporting facilitates generalizability and replicability by documenting the decisions scientists make during the various steps of research design; this is particularly true for sampling design and selection because of their impact on both internal and external validity and the potential to unintentionally introduce bias. Three interdependent aspects of sample design are study sample selection (e.g., specific organisms, soils, or water), sample specification (measurement of specific variable of interest), and site selection (locations sampled). Of these, documentation of site selection—the where component of sample design—is underrepresented in the urban ecology literature. Using a stratified random sample of 158 papers from 12 major urban ecology journals, we investigated how researchers selected study sites in urban ecosystems and evaluated whether their site selection methods were transparent. We extracted data from these papers using a 50-question, theory-based questionnaire and a multiple-reviewer approach. Our sample represented almost 45 years of urban ecology research across 40 different countries. We found that more than 80% of the papers read were not transparent in their site selection methodology. We do not believe site selection methods are replicable for 70% of the papers read. Key weaknesses include incomplete descriptions of populations and sampling frames, urban gradients, sample selection methods, and property access. Low transparency in reporting the where methodology limits urban ecologists’ ability to assess the internal and external validity of studies’ findings and to replicate published studies; it also limits the generalizability of existing studies. The challenges of low transparency are particularly relevant in urban ecology, a field where standard protocols for site selection and delineation are still being developed. These limitations interfere with the fields’ ability to build theory and inform policy. We conclude by offering a set of recommendations to increase transparency, replicability, and generalizability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Application of a Geochemically Informed Leak Detection (GILD) Model to CO 2 Injection Sites on the United States Gulf Coast

The Gulf Coast region possesses great potential for CO 2 enhanced oil recovery (EOR) and CO 2 storage. A geochemically informed leak detection (GILD) model has been applied to CO 2 injection sites on the Gulf Coast with considerations of measurement variability. The Jasper aquifer in Montgomery County, Texas, was chosen to demonstrate the method. Based on background data from wells in the area, combinations of mineral and fluid compositions were used to create 23 scenarios for the geochemical model. The output from the geochemical model was used to identify sensitive monitoring species, and response functions were generated for these as a function of the CO 2 leakage concentration. The sources of measurement variability for background conditions were characterized from the Jasper aquifer background data, and then normalized using the coefficient of variation of each species across the monitoring wells. Bayesian belief network (BBN) models were constructed, and measurement variability of different levels were added to compare leak detection probabilities. Increasing measurement variability decreased the power to detect a leak of a given size. For a moderately high CO 2 concentration of 0.2 mol/kg, the probability of detecting this leakage effect using pH as the monitoring variable in an aquifer with calcite decreases from 98% (no measurement variability) to 61% (medium variability) to 33% (high variability). The loss in power of the sampling protocol with increasing measurement variability is similar in magnitude when Ca 2+ or HCO 3 - is used as the monitoring parameter, but only for aquifers with calcite.

58 GEOSCIENCES↗
DOE OSTI2025

Huge ensembles – Part 1: Design of ensemble weather forecasts using spherical Fourier neural operators

Abstract. Simulating low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems presently use up to 100 members, larger ensembles could enrich the sampling of internal variability. They may capture the long tails associated with climate hazards better than traditional ensemble sizes. Due to computational constraints, it is infeasible to generate huge ensembles (comprised of 1000–10 000 members) with traditional, physics-based numerical models. In this two-part paper, we replace traditional numerical simulations with machine learning (ML) to generate hindcasts of huge ensembles. In Part 1, we construct an ensemble weather forecasting system based on spherical Fourier neural operators (SFNOs), and we discuss important design decisions for constructing such an ensemble. The ensemble represents model uncertainty through perturbed-parameter techniques, and it represents initial condition uncertainty through bred vectors, which sample the fastest-growing modes of the forecast. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) as a baseline, we develop an evaluation pipeline composed of mean, spectral, and extreme diagnostics. With large-scale, distributed SFNOs with 1.1 billion learned parameters, we achieve calibrated probabilistic forecasts. As the trajectories of the individual members diverge, the ML ensemble mean spectra degrade with lead time, consistent with physical expectations. However, the individual ensemble members' spectra stay constant with lead time. Therefore, these members simulate realistic weather states during the rollout, and the ML ensemble passes a crucial spectral test in the literature. The IFS and ML ensembles have similar extreme forecast indices, and we show that the ML extreme weather forecasts are reliable and discriminating. These diagnostics ensure that the ensemble can reliably simulate the time evolution of the atmosphere, including low-likelihood high-impact extremes. In Part 2, we generate a huge ensemble initialized each day in summer 2023, and we characterize the simulations of extremes.

Mahesh, Ankur↗
DOE OSTI2026

Predicted aboveground biomass of Typha angustifolia in an upland brackish tidal marsh, PIE LTER, Byfield, MA (2022-2024)

This dataset contains predicted monthly aboveground Typha angustifolia biomass per sample and per square meter in a brackish tidal marsh site dominated by Typha angustifolia near the Parker River in the upper estuary of the Plum Island Sound, Massachusetts (MA) during the growing seasons (May-September) of 2022, 2023, and 2024. This site is also located within the Plum Island Ecosystems Long Term Ecological Research Station (PIE LTER). Allometric equations were developed from dry weight data and associated maximum heights collected in 2022 and 2023. The goal of this study was to investigate the difference in aboveground biomass between the site’s marsh interior (MI) and the creek bank (CB). Metadata files (Typha_biomass_predictions_dd.csv and Typha_biomass_predictions_flmd.csv) contain detailed information on variable definitions, calculations, sampling methods, and the location of the site.

DATE↗
DOE OSTI2022

Optical variability of quasars with 20-yr photometric light curves

ABSTRACT We study the optical gri photometric variability of a sample of 190 quasars within the SDSS Stripe 82 region that have long-term photometric coverage during ∼1998−2020 with SDSS, PanSTARRS-1, the Dark Energy Survey, and dedicated follow-up monitoring with Blanco 4m/DECam. With on average ∼200 nightly epochs per quasar per filter band, we improve the parameter constraints from a Damped Random Walk (DRW) model fit to the light curves over previous studies with 10–15 yr baselines and ≲ 100 epochs. We find that the average damping time-scale τDRW continues to rise with increased baseline, reaching a median value of ∼750 d (g band) in the rest frame of these quasars using the 20-yr light curves. Some quasars may have gradual, long-term trends in their light curves, suggesting that either the DRW fit requires very long baselines to converge, or that the underlying variability is more complex than a single DRW process for these quasars. Using a subset of quasars with better-constrained τDRW (less than 20 per cent of the baseline), we confirm a weak wavelength dependence of τDRW∝λ0.51 ± 0.20. We further quantify optical variability of these quasars over days to decades time-scales using structure function (SF) and power spectrum density (PSD) analyses. The SF and PSD measurements qualitatively confirm the measured (hundreds of days) damping time-scales from the DRW fits. However, the ensemble PSD is steeper than that of a DRW on time-scales less than ∼ a month for these luminous quasars, and this second break point correlates with the longer DRW damping time-scale.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Modeling Changes in Measured Conductance of Thin Boron Carbide Semiconducting Films Under Irradiation

Semiconducting, p-type, amorphous partially dehydrogenated boron carbide films (a-B 10 C 2+x :H y ) were deposited utilizing plasma enhanced chemical vapor deposition (PECVD) onto n-type silicon thus creating a heterojunction diode. A model was developed for the conductance of the device as a function of perturbation frequency (f) that incorporates changes of the electrical properties for both the a-B 10 C 2+x :H y film and the silicon substrate when irradiated. The virgin model has 3 independent variables (R1, C1, R3), and 1 dependent variable (f). These samples were then irradiated with 200 keV He + ions, and the conductance model was matched to the measured data. It was found that initial irradiation (0.1 displacements per atom (dpa) equivalent) resulted in a decrease in the parallel junction resistance parameter from 6032 Ω to 2705 Ω. Further irradiation drastically increased the parallel junction resistance parameter to 39000 Ω (0.2 dpa equivalent), 77440 Ω (0.3 dpa equivalent), and 190000 Ω (0.5 dpa equivalent). It is believed that the initial irradiation causes type inversion of the silicon substrate changing the original junction from a p-n to a p-p+ with a much lower barrier height leading to a lower junction resistance component between the a-B 10 C 2+x :H y and irradiated silicon. In addition, it was found that after irradiation, a second parallel resistor and capacitor component is required for the model, introducing 2 additional independent variables (R2, C2). This is interpreted as the junction between the irradiated and virgin silicon near ion end of range.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Metal(loid) bioaccessibility and inhalation risk assessment: A comparison between an urban and an industrial area

Highlights: • Metal(loid) bioaccessibility of PM{sub 10} was evaluated with simulated lung fluids. • Most studied metal(loids) were highly bioaccessible in artificial lysosomal fluid. • Metal(loid)s from the industrial site had the highest bioaccessibility. • Elevated non-carcinogenic risk associated with Mn was found at the studied area. • Carcinogenic risk was slightly exceeded for Cd exposure at the industrial location. The content of metal(loid)s in particulate matter (PM) is of special concern due to their contribution to overall (PM) toxicity. In this study, the bioaccessibility and human health risk of potentially toxic metal(loid)s associated with PM{sub 10} were investigated in two areas of the Cantabrian region (northern Spain) with different levels of exposure: an industrial area mainly influenced by a ferromanganese alloy plant; and an urban area consisting mainly of residential and commercial activities, but also affected, albeit to a lesser extent by the ferroalloy plant. Total content and bioaccessible fractions in simulated lung fluids (SLFs) of Fe, Mn, Zn, Ni, Cu, Sb, Mo, Cd and Pb were determined by ICP-MS. Gamble's solution and artificial lysosomal fluid (ALF) were used to mimic different conditions inside the human respiratory system. A health risk assessment was performed based on the United States Environmental Protection Agency's (USEPA) methodology. Most metal(loid)s showed moderate and high bioaccessibility in Gamble's solution and ALF, respectively. Despite the high variability between the samples, metal(loid) bioaccessibility was found to be higher on average at the industrial site, suggesting a greater hazard to human health in the proximity of the main metal(loid) sources. Based on the results of the risk assessment, the non-carcinogenic risk associated with Mn exposure was above the safe limit (HQ> 1) under all the studied scenarios at the industrial site and under some specific scenarios at the urban location. The estimated carcinogenic inhalation risk for Cd exposure at the industrial site was found to be within the range between 1.0 × 10{sup −6} to 1.0 × 10{sup −4} (uncertainty range) under some scenarios. The results obtained in this study indicate that Mn and Cd inhalation exposure occurring in the vicinities of the studied areas may pose a human health risk.

54 ENVIRONMENTAL SCIENCES↗