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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Air Quality Analysis and Tools For Policy

South Asia is home to some of the most significant air quality challenges in the world. More than 99% of the region’s population is exposed to unhealthy fine particulate matter (PM2.5) concentrations, as defined by the World Health Organization. Recent policy initiatives in South Asia show that air quality policy is a priority, given the health impacts of air pollution. At the same time, capacity and data to assess and address air pollution remains limited in many areas. The main objective of this report is to outline a set of publicly-available tools that can be used to inform policies to improve air quality in South Asia. The report primarily focuses on tools such as emission inventories and integrated modeling systems with country-level resolution and illustrates their air quality assessment capabilities through examples and brief technical summaries. The report focuses on: (a) the Community Emissions Data System (CEDS) that provides historical emissions of key pollutants for all countries in South Asia, and (b) profiles an analysis of Indian air pollution and policy scenarios, using several integrated modeling tools. Both of these examples draw on recent research and data that outlines the scale of the challenge as well as the policy options for reducing air pollution in the region. The report concludes with key recommendations for regional stakeholders working towards mitigating air pollution in South Asia.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

High-Efficiency Low-Carbon Fuel Flexible Burner for Steel Industry

GTI Energy, with Bloom Engineering, Argonne National Laboratory and Oak Ridge National Laboratory, are developing the FlexGen-H burner. This high-efficiency burner can achieve 45•100% CO2e emissions reduction using green hydrogen with natural gas. It offers NOx, CO emissions ?50 ppm (3% O2), 15•51% fuel savings, ?50% PM2.5 reduction with turndown of ?5:1. The technology can work with renewable liquid fuels, ethanol or methanol. The technology aims to significantly reduce emissions, enhance resiliency, improve process efficiency. The paper will discuss preliminary results of development, with planned laboratory validation including safety, CFD, burner system design and market for the technology.

Sperry, McKay [ORNL] (ORCID:0009000658602049)↗
DOE OSTI2025

Sizing Accuracy of Low-Cost Optical Particle Sensors Under Controlled Laboratory Conditions

Low-cost particulate matter sensors have seen increased use for monitoring at personal and local levels due to their affordability, ease of operation, and high time resolution. However, the quality of data reported by these sensors can be questionable, and a thorough evaluation of their performance is necessary. This study evaluated the particle sizing accuracy of several commonly used optical sensors, including the Alphasense optical particle counter (OPC), TSI DustTrak DRX aerosol monitor, Plantower PMS5003 sensor, and Sensirion SPS30 sensor, using laboratory-generated monodisperse particles. The OPC and DRX agreed partially with reference instruments and showed promise in detecting coarse-size particles. However, the PMS5003 and SPS30 did not correctly size fine and coarse particles. Furthermore, their reported mass distributions do not directly correspond to their number distribution. Despite these limitations, field measurements involving a dust storm period showed that the SPS30 correlated reasonably well with reference instruments for both PM2.5 and PM10, though the regression slopes differed significantly. These findings underscore the need for caution when interpreting data from low-cost optical sensors, particularly for coarse particles. Recommendations for improving the performance of these sensors are also provided.

Gautam, Prakash↗
DOE OSTI2025

Unified and consistent structure growth measurements from joint ACT, SPT and \textit{Planck} CMB lensing

We present the tightest cosmic microwave background (CMB) lensing constraints to date on the growth of structure by combining CMB lensing measurements from the Atacama Cosmology Telescope (ACT), the South Pole Telescope (SPT) and \textit{Planck}. Each of these surveys individually provides lensing measurements with similarly high statistical power, achieving signal-to-noise ratios of approximately 40. The combined lensing bandpowers represent the most precise CMB lensing power spectrum measurement to date with a signal-to-noise ratio of 61 and an amplitude of $A_\mathrm{lens}^\mathrm{recon} = 1.025 \pm 0.017$ with respect to the theory prediction from the best-fit CMB \textit{Planck}-ACT cosmology. The bandpowers from all three lensing datasets, analyzed jointly, yield a $1.6\%$ measurement of the parameter combination $S_8^\mathrm{CMBL} \equiv σ_8\,(Ω_m/0.3)^{0.25} = 0.825^{+0.015}_{-0.013}$. Including Dark Energy Spectroscopic Instrument (DESI) Baryon Acoustic Oscillation (BAO) data improves the constraint on the amplitude of matter fluctuations to $σ_8 = 0.829 \pm 0.009$ (a $1.1\%$ determination). When combining with uncalibrated supernovae from \texttt{Pantheon+}, we present a $4\%$ sound-horizon-independent estimate of $H_0=66.4\pm2.5\,\mathrm{km\,s^{-1}\,Mpc^{-1}} $. The joint lensing constraints on structure growth and present-day Hubble rate are fully consistent with a $Λ$CDM model fit to the primary CMB data from \textit{Planck} and ACT. While the precise upper limit is sensitive to the choice of data and underlying model assumptions, when varying the neutrino mass sum within the $Λ\mathrm{CDM}$ cosmological model, the combination of primary CMB, BAO and CMB lensing drives the probable upper limit for the mass sum towards lower values, comparable to the minimum mass prior required by neutrino oscillation experiments.

Cosmology and Nongalactic Astrophysics (astro-ph.C↗
DOE OSTI2020

CAPS PMssa Aerosol Extinction and SSA Data

We collected rapid (1 second time response) measurements of aerosol extinction and scattering (Mm-1) using an Aerodyne CAPS PMssa deployed during LASIC (as part of the LASIC SSA-constraint study) to measure ambient SSA at 530 nm wavelength to compare with other optical measurement methods. Measured from August 4 to September 22, 2017. Sampled from both PM1 and PM10 inlets. CAPS PMssa monitors have been tested extensively for PM2.5 and smaller particle sizes, but not for particles greater than 2,500 nm in diameter (i.e., PM10). The data were collected in order to provide an independent measurement of aerosol single scattering albedo at 530 nm wavelength to compare with ARM standard methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

TAP Absorption Coefficients

The data set of aerosol light absorption coefficients were collected by two tricolor absorption photometers (TAP; Brechtel Model 2901) in July and August, 2022 during the TRACER field campaign at the AMF1 site in La Porte, Texas. One of the TAPs at the La Porte site was an RGB model measuring aerosol light absorption at 652, 528, 467 nm, and the other was an UV model measuring absorption at the same red and green wavelengths but at an additional UV wavelength at 365 nm. Since TAP is a filter-based optical instrument using 47-mm glass fiber filters, the output absorption coefficients are corrected for filter loading and scattering effects based on a correction scheme developed in "Comment on ‘Calibration and Intercomparison of Filter-Based Measurements of Visible Light Absorption by Aerosols’" by John A. Ogren 2010. This uploaded dataset contains both the Ogren corrected absorption coefficients (column header with “ Ogren”) and the raw absorption coefficients without the correction scheme (column header with “_ Raw”). During the field campaign, the two TAPs operated side by side with a shared inlet at a flow rate of 2 lpm. In July, no inlet impactors/cyclones were installed; however, beginning in August, a PM2.5 cyclone inlet was installed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

IPN Absorption Coefficients

The dataset contains aerosol light absorption and scattering coefficients measured by three single-wavelength Integrated Photoacoustic Nephelometers (IPN). The absorption coefficients were measured at 405, 721, and 1047 nm, while the scattering coefficients were measured at 405 and 721 nm. The single scattering albedo (SSA) at 405 and 721 nm were also calculated from the IPN measurements. The three IPNs shared a common inlet without impactor/cyclone in July; in August, a PM2.5 cyclone inlet was installed to reduce measurement noise and improve data quality. The 721-nm IPN experienced laser shutdown issues, causing missing of a large fraction of the 721-nm measurements, especially in July. As described before, the 721-nm laser issue was less frequent in August after the cyclone installation, improving the data completeness and quality. The IPN raw data were noisy, making identifying patterns and trends difficult; therefore, the hourly average data are recommended over the raw data. The hourly average data were cross-checked with the filter-based TAP and PSAP measurements, which showed that their patterns, trends, and peaks matched. However, due to the differences in working mechanisms and principles of the filter-based and photoacoustic particle-phase instruments, the IPN measurements are lower than the filter-based measurements despite that they have consistent patterns, trends, and peaks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

TRACER-MAP_2022_absorption_coefficient

Absorption coefficient for PM2.5 measured by the Tri-color Absorption Photometer averaged every 2 minutes for July-August 2022. The location of the measurement is indicated by latitude and longitude coordinates; TRACER-MAP included time at the AMF site in La Porte and additional sites around Houston. Three wavelengths are reported: 365, 520 and 640 nm. The AAE is also reported using the three wavelengths. The data is reported as 2 minute averages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Environmental impact of national and subnational carbon policies in China based on a multi-regional dynamic CGE model

Carbon policies are important not only for mitigating global climate change, but also for controlling local and regional air pollution. The large regional disparities in economic development and air pollution across China calls for regionally differentiated policies. Previous studies have not systematically investigated the environmental impacts of regional carbon policies in China covering different spatial scales. This study constructs a multi-regional dynamic computable general quilibrium (CGE) model covering 30 provinces of China to assess the impacts of national and subnational carbon polices on CO2 emissions and co-emitted air pollutants from 2020 to 2050. We consider one national carbon policy which aims to achieve China’s 2030 national CO2 abatement target, as well as three regional policies with the same policy stringency as the national one but only applied to eastern China (EP), the Jiangsu-Shanghai-Zhejiang area (JSZP), and the Beijing-Tianjin-Hebei area (BTHP), respectively. We find that regional policies (EP, JSZP, and BTHP) are as effective in reducing CO2 emissions in their targeted regions as the national policy. However, they lead to an increase in CO2 emissions in untargeted regions (the so-called “emissions leakage”). The CO2 leakage rates, which depend on the policy spatial coverage, are 4%, 13%, and 65% for EP, JSZP, and BTHP, respectively, in 2050. Compared with CO2, changes in air pollutant emissions, including sulfur dioxide (SO2), nitrogen oxides (NOx), volatile organic compounds (VOC), and fine particulate matter (PM2.5), exhibit a similar pattern under all policy scenarios, but the magnitude of change is significantly smaller. Transportation, thermal power, and some energy intensive industries are the three largest contributors to CO2 and air pollutant emission reductions. Our results suggest that regional carbon policy is effective in reducing CO2 and air pollutant emissions in the targeted regions, and extending the spatial coverage or increasing policy stringency can largely inhibit emissions leakage.

Zhang, Wenwen↗
DOE OSTI2025

A Framework for Assessing Economic and Environmental Trade-offs of Internalized Emission Costs in ERCOT Grid Planning

The power grid is on the cusp of a massive transition driven by three major areas: 1) the growth in demand for electricity, 2) efforts to decarbonize the United States economy, and 3) a desire to mitigate social disparities from the impact of electricity generation on local populations. However, most studies of the electricity sector do not include equity impacts in their models. This study seeks to do so by developing a comprehensive and generalizable model tailored to the Electric Reliability Council of Texas (ERCOT) grid, designed to incorporate the equity impacts of electricity generation in a decarbonized and resilient framework. To integrate equity into our research, we incorporate environmental externalities into our capacity expansion model of ERCOT. Specifically, we factor in intermediate-level local marginal damages of precursor pollutants (NH3, NOx, primary PM2.5, SO2, and VOC) and global pollutant CO2 into the cost of generating electricity. We do this by taking into account county population, county ambient pollution concentration, and generator emission rates. Leveraging open-source modeling tools, such as PowerGenome, pyGRETA, and GenX we construct a county-level model to account for these costs. We integrate these marginal damages into the variable operations and maintenance costs of generators, for both existing and potential future builds. This study’s findings suggest that the value of a dynamic social cost of carbon (SSC) will cover criteria pollutant marginal damages within the ERCOT grid and solar and wind is expected to increase out to 2035. Key metrics evaluated within this research include fuel mix distribution across technologies, transmission and grid infrastructure costs, CO2 emissions, local pollutants marginal damages, and the variation in generation capacity built by the model. These results and framework can be used to support grid decisions that explicitly include distributional and procedural equity within a decarbonized and sustainable grid framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Evaluating county-level lung cancer incidence from environmental radiation exposure, PM 2.5 , and other exposures with regression and machine learning models

Characterizing the interplay between exposures shaping the human exposome is vital for uncovering the etiology of complex diseases. For example, cancer risk is modified by a range of multifactorial external environmental exposures. Environmental, socioeconomic, and lifestyle factors all shape lung cancer risk. However, epidemiological studies of radon aimed at identifying populations at high risk for lung cancer often fail to consider multiple exposures simultaneously. For example, moderating factors, such as PM 2.5 , may affect the transport of radon progeny to lung tissue. This ecological analysis leveraged a population-level dataset from the National Cancer Institute’s Surveillance, Epidemiology, and End-Results data (2013–17) to simultaneously investigate the effect of multiple sources of low-dose radiation (gross γ activity and indoor radon) and PM 2.5 on lung cancer incidence rates in the USA. County-level factors (environmental, sociodemographic, lifestyle) were controlled for, and Poisson regression and random forest models were used to assess the association between radon exposure and lung and bronchus cancer incidence rates. Tree-based machine learning (ML) method perform better than traditional regression: Poisson regression: 6.29/7.13 (mean absolute percentage error, MAPE), 12.70/12.77 (root mean square error, RMSE); Poisson random forest regression: 1.22/1.16 (MAPE), 8.01/8.15 (RMSE). The effect of PM 2.5 increased with the concentration of environmental radon, thereby confirming findings from previous studies that investigated the possible synergistic effect of radon and PM 2.5 on health outcomes. In summary, the results demonstrated (1) a need to consider multiple environmental exposures when assessing radon exposure’s association with lung cancer risk, thereby highlighting (1) the importance of an exposomics framework and (2) that employing ML models may capture the complex interplay between environmental exposures and health, as in the case of indoor radon exposure and lung cancer incidence.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2024

Comparing the interactions between particulate matter and cloud properties over two populated cities in Texas using WRF-Chem fine-resolution modeling

Accurate modeling of aerosol-cloud interactions is essential for reliable weather and air quality simulations, given their significant impact on precipitation patterns, cloud dynamics, and aerosol distributions. This study employed the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to examine the impact of enhanced meteorological simulations, achieved through advanced microphysics parameterization supported by data assimilation techniques, on air quality across Texas on August 19 and 20, 2022. We tested four distinct configurations: (1) the Morrison two-moment bulk microphysics scheme, (2) Morrison's with observation nudging, (3) the Spectral Bin Microphysics (SBM), and (4) SBM with observation nudging. While the SBM scheme is known for its detailed representation of aerosol-cloud interactions, our focus was on how improvements in meteorological accuracy translate to more precise air quality simulations. Our findings demonstrated a progressive improvement in simulation accuracy, starting with the Morrison's scheme and further enhanced by adopting the SBM scheme, complemented by incorporating observation nudging. Specifically, the combination of the SBM scheme and the nudging substantially enhanced the model's ability to capture convective precipitation events, as shown by better alignment with NEXRAD radar reflectivity, with R increasing from –0.21 to 0.82, IOA from 0.10 to 0.87, and NMB decreasing from 99% to 34% in Houston. The enhanced meteorology translated into more accurate PM 2.5 concentration simulations, particularly through the more accurate representation of aerosol washout during precipitation events. In Houston, the SBM scheme with nudging improved the model's PM 2.5 simulations significantly, with NMB decreasing from –20% to 5% and IOA improving from 0.43 to 0.61. In San Antonio, improvements were also notable, with NMB improved from –27% to –22%, R increased from 0.48 to 0.82, and IOA increased from 0.66 to 0.86. Furthermore, our results underscore the crucial role of accurate meteorological simulations in refining our understanding of aerosol behaviors in relation to precipitation patterns, directly enhancing the reliability and effectiveness of air quality modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Co-benefits of subnationally differentiated carbon pricing policies in China: Alleviation of heavy PM 2.5 pollution and improvement in environmental equity

Carbon policies differentiated according to regional disparities have been recognized and recommended. However, the air quality co-benefits of differentiated policies remain unclear. Here we combine a multi-regional dynamic computable general equilibrium (CGE) model and an extend response surface model (ERSM) to investigate the impact of nationally uniform and subnationally differentiated carbon pricing policies on fine particulate matter (PM 2.5 ) concentrations in China. All policies examined lead to the same accumulated national CO 2 emissions (2020–2050), with the 2030 emissions attaining China's national abatement target. We find that, when subnational policies differentiated according to provincial PM 2.5 concentrations are implemented, the higher-than-average carbon price over more polluted provinces results in twice as much reduction in CO 2 and air pollutant emissions in 2050 as the national policy. As a result, the subnational policies cause larger PM 2.5 concentration reductions in these provinces (9%–18%) than the national policy (7%–11%). Also, the subnational policies eliminate high PM 2.5 exposure of over 45 μg/m 3 which 12% people suffer from under the national policy. Overall, the subnational policies substantially reduce regional disparity in PM 2.5 pollution and hence improve environmental equity. The results suggest that subnationally differentiated carbon policies are a promising instrument to mitigate severe pollution and promote environmental equity.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Molecular Imaging Reveals Two Distinct Mixing States of PM 2.5 Particles Sampled in a Typical Beijing Winter Pollution Case

Mixing states of aerosol particles are crucial for understanding the role of aerosols in influencing air quality and climate. However, a fundamental understanding of the complex mixing states is still lacking because most traditional analysis techniques only reveal bulk chemical and physical properties with limited surface and 3-D information. In this research, 3-D molecular imaging enabled by ToF-SIMS was used to elucidate the mixing states of PM 2.5 samples obtained from a typical Beijing winter haze event. In light pollution cases, a thin organic layer covers separated inorganic particles, while in serious pollution cases, ion exchange and an organic-inorganic mixing surface on large-area particles were observed. Finally, the new results provide key 3-D molecular information of mixing states, which is highly desirable for reducing uncertainty and bias in representing aerosol-cloud interactions in current Earth System Models, and improving the understanding of aerosols on air quality and human health.

3-D molecular imaging↗