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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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DOE OSTI2020

Fine Root and Soil Organic Carbon Depth Distributions are Inversely Related Across Fertility and Rainfall Gradients in Lowland Tropical Forests

Humid tropical forests contain some of the largest soil organic carbon (SOC) stocks on Earth. Much of this SOC is in subsoil, yet variation in the distribution of SOC through the soil profile remains poorly characterized across tropical forests. We used a correlative approach to quantify relationships among depth distributions of SOC, fine root biomass, nutrients and texture to 1 m depths across 43 lowland tropical forests in Panama. The sites span rainfall and soil fertility gradients, and these are largely uncorrelated for these sites. We used fitted b parameters to characterize depth distributions, where b is a numerical index based on an asymptotic relationship, such that larger b values indicate greater concentrations of root biomass or SOC at depth in the profile. Root b values ranged from 0.82 to 0.95 and were best predicted by soil pH and extractable potassium (K) stocks. For example, the three most acidic (pH < 4) and Kpoor (< 20 g K m -2 ) soils contained 76 ± 5% of fine root biomass from 0 to 10 cm depth, while the three least acidic (pH > 6.0) and most K-rich (> 50 g K m -2 ) soils contained only 41 ± 9% of fine root biomass at this depth. Root b and SOC b values were inversely related, such that a large fine root biomass in surface soils corresponded to large SOC stocks in subsoils (50–100 cm). SOC b values were best predicted by soil pH and base cation stocks, with the three most base-poor soils containing 34 ± 8% of SOC from 50 to 100 cm depth, and the three most base-rich soils containing just 9 ± 2% of SOC at this depth. Nutrient depth distributions were not related to Root b or SOC b values. Here, these data show that large surface root biomass stocks are associated with large subsoil C stocks in strongly weathered tropical soils. Further studies are required to evaluate why this occurs, and whether changes in surface root biomass, as may occur with global change, could in turn influence SOC storage in tropical forest subsoils.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Thermal Conductivity of AM Fabricated Stainless Steel 316L Part

Thermal properties are an integral part of many diverse engineering applications, and additive manufacturing (AM), particularly laser powder bed fusion (LPBF) is shown to affect thermal properties due to the laser processing parameters. For 316L stainless steel, there is little prior research to determine the effects of the process on thermal properties. In this work, the temperature gradient is shown to create uniform, chess board-like distributions of grains in the build direction. These zones create dislocations which were visualized with EBSD techniques. Processing parameters cause hierarchal grain size distribution, with localized concentrations of small grains and large grains. Thermomechanical stresses in the rapid solidification increases dislocation density during grain formation. Previous research shows a higher density of dislocations decreases local thermal conductivity. Local and bulk thermal conductivity are shown in this work to have statistically lowered values to an average of 10-12 W/m-K compared to 14 W/m-K for conventional 316L.

36 MATERIALS SCIENCE↗

NASA NTRS1977

An experimental estimate of the O/+/-O resonant charge transfer cross section, collision frequency, and energy transfer rate

The resonant-charge-transfer cross section dominates the interaction between neutral and ionized atomic oxygen at temperatures greater than several hundred degrees Kelvin. This cross section is of direct importance to F-region studies of ion diffusion, ion thermal balance, and neutral atmospheric winds and pressure gradients, since it determines collision frequencies and energy and momentum transfer rates. However, the uncertainty in this cross section is roughly 25-40%. Incoherent-scatter measurements allow determination of the product of the atomic oxygen concentration multiplied by the cross section. If present-day atmospheric models are accepted as statistically representative of F-region atomic oxygen concentrations experimental (aeronomical) data presented indicate that the resonant-charge-transfer cross section is about 1.3 times greater than the value presently in general use, though in excellent agreement with the only other available (laboratory) experimental value. This revised cross section has obvious implications for thermospheric studies.

Carlson, H. C.↗

NASA NTRS1986

Influence of large-scale motion on turbulent transport for confined coaxial jets. Volume 2: Navier-Stokes calculations of swirling and nonswirling confined coaxial jets

The existence of large scale coherent structures in turbulent shear flows has been well documented. Discrepancies between experimental and computational data suggest a necessity to understand the roles they play in mass and momentum transport. Using conditional sampling and averaging on coincident two-component velocity and concentration velocity experimental data for swirling and nonswirling coaxial jets, triggers for identifying the structures were examined. Concentration fluctuation was found to be an adequate trigger or indicator for the concentration-velocity data, but no suitable detector was located for the two-component velocity data. The large scale structures are found in the region where the largest discrepancies exist between model and experiment. The traditional gradient transport model does not fit in this region as a result of these structures. The large scale motion was found to be responsible for a large percentage of the axial mass transport. The large scale structures were found to convect downstream at approximately the mean velocity of the overall flow in the axial direction. The radial mean velocity of the structures was found to be substantially greater than that of the overall flow.

Weinberg, B. C.↗

NASA NTRS1988

Color maps of X-ray globular clusters

The results of a search for optical counterparts to X-ray sources in six globular clusters, 47 Tuc, NGC 1851, NGC 6441, NGC 6624, NGC 6712, and M15, are reported. Maps of the U-B color of the central regions of the clusters were prepared. A candidate for the optical counterpart of the source in NGC 6712 was found, along with a blue region near the X-ray source in 47 Tuc. Upper limits on the colors and magnitudes of possible optical counterparts are reported for the other three clusters. The use of color maps to determine color gradients in globular clusters is explored. It is found that, while such gradients do exist and vary from cluster to cluster, they can be explained by crowding effects. Crude limits are placed on the excess populations of blue objects such as CVs, which have been postulated to be concentrated in the centers of dense clusters.

Bailyn, Charles D.↗

NASA NTRS2019

Enhanced Habitability on High Obliquity Bodies Near the Outer Edge of the Habitable Zone of Sun-Like Stars

High obliquity planets represent potentially extreme limits of terrestrial climate, as they exhibit large seasonality, a reversed annual-mean pole-to-equator gradient of stellar heating, and novel cryospheres. A suite of 3-D global climate model simulations with a dynamic ocean is performed with Earthlike atmospheres for low and high obliquity planets with various stellar fluxes, CO2 concentrations, and initial conditions to explore the propensity for high obliquity climates approaching the outer edge of the Habitable Zone to undergo global glaciation. We also simulate planets with thick CO2 or H2 atmospheres, such as those expected to develop near or beyond the outer edge of the Habitable Zone. We show that high obliquity planets are hotter than their low obliquity counterparts due to ice-albedo feedbacks for cold climates, and water vapor in warm climates. We suggest that the water vapor greenhouse trapping is greater on high obliquity bodies due to the different dynamical regimes that occur between the two states. While equatorial ice-belts are stable at high obliquity in some climate regimes, it is harder to achieve global glaciation than for a low obliquity planet. Temperate polar conditions can be present at high obliquity at forcings for which low obliquity planets would be in a hard snowball state. We suggest the conditions on high obliquity planets are likely to be more favorable for a robust biosphere to develop approaching the outer edge of the HZ. However, the influence of obliquity diminishes for dense atmospheres, in agreement with calculations from 1-D Energy Balance Models.

planets and satellites: atmospheres↗

NASA NTRSDate not supplied

A Novel Machine Learning Method for Surface PM2.5 Estimations from Geostationary Satellites

Particulate matter (PM) with a diameter of less or equal to 2.5 μm, known as PM , affects human health as it penetrates the respiratory system. The Environmental Protection Agency (EPA) measures the atmospheric concentration of PM using air quality monitors stationed throughout the Continental United States (CONUS). Such measurements are points on a spatial domain and therefore, might not be representative of the air quality at nearby areas considering that the composition of the atmosphere is highly variable from place to place. Satellite based AOD permits a spatially uniform means of estimating PM and new geostationary satellites provide high temporal and spatial resolution estimation of AOD. However, the concentration of PM is non-linearly dependent on other atmospheric parameters that include relative humidity, temperature, and height of the planetary boundary layer. This information may be estimated at similar spatial and temporal resolutions as AOD from numerical modeling such as from the National Oceanic and Atmospheric Administration’s (NOAA) High Resolution Rapid Refresh (HRRR) model which resolves near real-time atmospheric conditions over the CONUS. The estimation of PM concentration is a multi-parametric problem that considers the effect of temporal dependencies among the different parameters. Deep learning approaches are appropriate for such complex estimation problems as they intrinsically capture relations among multiple non-linear parameters. This study compares deep-learning methods to traditional regression analysis to demonstrate the capabilities of these methods in predicting PM2.5 concentrations. Additionally, a novel ensemble learning approach is employed to identify scientific processes that could further improve the estimation of PM concentration. Utilizing Long Short-Term Memory (LSTM) neural networks, which are suitable for multivariate time series estimation problems as they are capable of learning long-term dependencies, individual models are created for each EPA station and trained on the aforementioned dataset collocated over each station. Individual station models are merged if the model's performance is improved by reducing the root mean squared error (RMSE) metric. This ensemble training method ultimately reduces the RMSE value. Evaluation of these results provide insights into physical processes and related observable parameters that may contribute to PM concentrations. Identified parameters evaluated to be statistically different between the merged and unmerged models are expected to improve overall performance. These new parameters are then utilized for reevaluation of the deep learning methods with an extreme gradient boosting model with an RMSE of 5.5 providing the best results.

George Priftis↗

NASA NTRS2021

Photochemical and Microbial Degradation of Chromophoric Dissolved Organic Matter Exported from Tidal Marshes

Wetlands export chromophoric dissolved organic matter (CDOM) to estuaries, where CDOM is removed and transformed through biotic and abiotic process, subsequently impacting nutrient cycling, light availability, ecosystem metabolism, and phytoplankton activity. We examined the bioavailability and photoreactivity of CDOM exported from four Chesapeake Bay tidal marshes across three seasons and along an estuarine salinity gradient using three incubation treatments: 14-day microbial (MD), 7-day combined photochemical/microbial (PB+MD), and 7-day microbial incubation after photobleaching (MD after PB). CDOM absorption at 300 nm (aCDOM300) and dissolved organic carbon (DOC) concentrations showed strong seasonality, with minima in winter, but CDOM quality (absorption spectral slopes, fluorescence component ratios) was less variable seasonally. PB+MD over 7 days decreased aCDOM300 (–56.0%), humic-like fluorescence (–67.6%), and DOC (–17.8%), but increased the spectral slope ratio SR (=S275-295/S300-350) (+94.8%), suggesting a decrease in CDOM molecular weight. Photochemistry dominated the PB+MD treatment. Photoreactivity was greater during the winter and in marsh/watershed versus down-estuary sites, likely due to less previous light exposure. Prior photobleaching increased the bioavailability of marsh-exported CDOM, resulting in a greater loss of aCDOM300 and DOC, and a greater increase in humic-like fluorescence (–6.0%, –5.9%, and +18.4% change, respectively, over 7-day MD after PB incubations, versus –2.8%, –5.5%, and +2.6% change, respectively, over 14-day MD incubations). CDOM exported from a marsh downstream of a major wastewater treatment plant showed the greatest photoreactivity and bioavailability. This highlights the significance of human activity on estuarine CDOM quality and biogeochemical cycles.

Dissolved organic matter↗

DOE OSTI2020

Impact of Ionic Liquid on Lithium Ion Battery with a Solid Poly(Ionic Liquid) Pentablock Terpolymer as Electrolyte and Separator

In this work, a ternary blend solid polymer electrolyte (SPE) consisting of a poly(ionic liquid) (PIL) multiblock polymer, lithium salt, and ionic liquid (IL) was investigated with the goal of understanding the influence of IL concentration on ion transport mechanisms and electrochemical stability. The physical, transport, mechanical, morphological, and electrochemical properties of the ternary blend SPE were systematically investigated as a function of IL concentration (r = [IL]/[PIL] mole ratio between 0.1 and 0.7). The results indicate that with increasing IL concentration, the continuous conductive domain increases along with the polymer chain segmental mobility, as well as facilitate ionic conductivity, while the mechanical modulus exhibits a percolation threshold (from 52.17 MPa to 0.55 MPa at r = 0.2 to 0.3). Surprisingly, at higher IL concentrations, there is a reduction in the lithium cation mobility (i.e., 9.3 × 10-12 m2 s-1 versus 3.8 × 10-12 m2 s-1 for r = 0.2 versus r = 0.5, respectively) as evidenced by pulsed-field gradient nuclear magnetic resonance (PFG-NMR), which coincides with an increased overpotential (i.e., 50 mV versus 150 mV for r = 0.2 versus r = 0.5, respectively) evidenced by lithium metal stripping and plating. This work provides valuable insights into the impact of IL on lithium ion conducting PIL block polymers, which may enable the design of new SPEs with both high ionic conductivity and improved stability for the future lithium ion batteries.

Chen, Tzu-Ling↗

DOE OSTI2022

Closed-loop pressure retarded osmosis draw solutions and their regeneration processes: A review

Pressure-Retarded Osmosis (PRO) is an osmotic process that has been used to harvest energy from salinity gradients using a semi permeable membrane. A comparison between open-loop PRO (OLPRO) and closed-loop PRO (CLPRO) was made regarding their performance and costs. In CLPRO, where the diluted draw solution is re-concentrated in the regeneration system to be reutilized in the process, has recently received an intensive focus as the most viable configuration for a standalone power plant. The choice of the PRO draw solution in CLPRO is crucial to garner a high osmotic pressure as the key for the feasibility of the process. Here, in this review, the draw solutions are critically evaluated in the literature in terms of energy output as well as the method of regeneration used to recirculate them. A set of practical criteria has been suggested to appraise the adequacy of the solution for CLPRO application. It was concluded that NH 3 – CO 2 theoretically can produce 170 W/m 2 of power density. Inorganic draw solutes such as NaCl can generate high power density up to 87 W/m 2 . Organic draw solutes with their remarkably low reverse salt flux (RSF) have promising potential for future application in PRO. Similarly, the regeneration systems of the diluted draw solutions have also been reviewed and discussed. How the energy consumption of the regeneration process affects the feasibility of CLPRO is explained. For the specific case of osmotic heat engines (OHEs), when the energy of the regeneration process is supplied by heat waste, the range of applicability of the heat waste in CLPRO in terms of efficiency is defined and compared to Organic Rankine Cycle (ORC). The results showed that CLPRO has better efficiency than ORC for temperatures T < 80 °C, which makes it a promising process or low-grade heat energy recovery. In addition, a PRO-RO hybrid system coupled with solar power can reduce the net specific energy consumption (SEC) to 0.39 kWh/m 3 . The conditions that regeneration processes should operate under to make PRO viable are discussed in the last section. Overall, the study indicates the key factors for optimizing the performance of CLPRO process.

42 ENGINEERING↗

DOE OSTI2022

Evaluation of Ocean Biogeochemistry and Carbon Cycling in CMIP Earth System Models With the International Ocean Model Benchmarking (IOMB) Software System

Abstract The International Ocean Model Benchmarking (IOMB) software package is a new community resource that we use here to evaluate surface and upper ocean biogeochemical variables and integrated anthropogenic carbon uptake from earth system models (ESMs) contributing to the 5th and 6th phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). IOMB generates graphics and tables for systematically comparing model predictions against multiple datasets. Our analysis reveals some improvement in the multi‐model mean from CMIP5 to CMIP6 for most of the variables we examined. Compared to data‐constrained estimates of ocean anthropogenic carbon uptake for the 1994–2007 period, negative biases exist for many models between 30 and 50°S. Global model estimates of anthropogenic carbon uptake for the same period do not change significantly from CMIP5 to CMIP6, with the combined ensemble mean estimate of 27.8 ± 0.5 Pg C lower than a data‐constrained estimate of 33.0 ± 4.0 Pg C. At the same time, the change in the natural carbon inventory from CMIP is estimated to be a source of 0.7 ± 0.3 Pg C, which is considerably smaller in magnitude than a data‐constrained estimate of 5.0 ± 3.0 Pg C. With chlorofluorocarbon (CFC) predictions available for several models, we demonstrate that negative anthropogenic dissolved inorganic carbon biases coincide with negative biases in CFC concentration, highlighting the importance of weak exchange between the surface and interior ocean in regulating rates of anthropogenic carbon uptake. To examine the robustness of this attribution across the CMIP models, we calculate the global vertical temperature gradient between 200 and 1,000 m as a metric for global stratification and exchange between the surface and deeper waters. We find a linear relationship between the bias of the vertical temperature gradients and the bias in global anthropogenic carbon uptake, consistent with the hypothesis that model biases in anthropogenic carbon uptake are related to biases in surface‐to‐interior exchange by physical processes.

58 GEOSCIENCES↗

DOE OSTI2023

Divergent selection and climate adaptation fuel genomic differentiation between sister species of Sphagnum (peat moss)

Abstract Background and Aims New plant species can evolve through the reinforcement of reproductive isolation via local adaptation along habitat gradients. Peat mosses (Sphagnaceae) are an emerging model system for the study of evolutionary genomics and have well-documented niche differentiation among species. Recent molecular studies have demonstrated that the globally distributed species Sphagnum magellanicum is a complex of morphologically cryptic lineages that are phylogenetically and ecologically distinct. Here, we describe the architecture of genomic differentiation between two sister species in this complex known from eastern North America: the northern S. diabolicum and the largely southern S. magniae. Methods We sampled plant populations from across a latitudinal gradient in eastern North America and performed whole genome and restriction-site associated DNA sequencing. These sequencing data were then analyzed computationally. Key Results Using sliding-window population genetic analyses we find that differentiation is concentrated within ‘islands’ of the genome spanning up to 400 kb that are characterized by elevated genetic divergence, suppressed recombination, reduced nucleotide diversity and increased rates of non-synonymous substitution. Sequence variants that are significantly associated with genetic structure and bioclimatic variables occur within genes that have functional enrichment for biological processes including abiotic stress response, photoperiodism and hormone-mediated signalling. Demographic modelling demonstrates that these two species diverged no more than 225 000 generations ago with secondary contact occurring where their ranges overlap. Conclusions We suggest that this heterogeneity of genomic differentiation is a result of linked selection and reflects the role of local adaptation to contrasting climatic zones in driving speciation. This research provides insight into the process of speciation in a group of ecologically important plants and strengthens our predictive understanding of how plant populations will respond as Earth’s climate rapidly changes.

58 GEOSCIENCES↗

DOE OSTI2023

Water mass age structures the auxiliary metabolic gene content of free-living and particle-attached deep ocean viral communities

Viruses play important roles in the ocean’s biogeochemical cycles. Yet, deep ocean viruses are one of the most under-explored fractions of the global biosphere. Little is known about the environmental factors that control the composition and functioning of their communities or how they interact with their free-living or particle-attached microbial hosts. We analysed 58 viral communities associated with size-fractionated free-living (0.2–0.8 μm) and particle-attached (0.8–20 μm) cellular metagenomes from bathypelagic (2150–4018 m deep) microbiomes obtained during the Malaspina expedition. These metagenomes yielded 6631 viral sequences, 91% of which were novel, and 67 represented high-quality genomes. Taxonomic classification assigned 53% of the viral sequences to families of tailed viruses from the order Caudovirales. Computational host prediction associated 886 viral sequences to dominant members of the deep ocean microbiome, such as Alphaproteobacteria (284), Gammaproteobacteria (241), SAR324 (23), Marinisomatota (39), and Chloroflexota (61). Free-living and particle-attached viral communities had markedly distinct taxonomic composition, host prevalence, and auxiliary metabolic gene content, which led to the discovery of novel viral-encoded metabolic genes involved in the folate and nucleotide metabolisms. Water mass age emerged as an important factor driving viral community composition. We postulated this was due to changes in quality and concentration of dissolved organic matter acting on the host communities, leading to an increase of viral auxiliary metabolic genes associated with energy metabolism among older water masses. These results shed light on the mechanisms by which environmental gradients of deep ocean ecosystems structure the composition and functioning of free-living and particle-attached viral communities.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Warming Response of Deep Soil Carbon (LDRD Final Report)

The overarching objective of this LDRD project was to determine the vulnerability of deep soil organic carbon (SOC) to warming in the Sierra Nevada region. Deep soils (>30 cm) store more than 70% of global SOC, and increased SOC decomposition and CO 2 emissions caused by warming are potentially large climate change feedbacks. According to the Intergovernmental Panel on Climate Change, temperatures are expected to increase by 4°C by the year 2100, warming the land and underlying soil, and making understanding of how warming will influence deep SOC storage and persistence critical to projecting the land carbon sink. However, uncertainty remains in our process-level understanding and ability to quantify how projected warming will impact the stability of carbon in deep soils. We investigated warming effects on deep (up to 16 m) SOC stability across climate, vegetation, and soil mineralogy gradients in California. We sampled soils from the surface to bedrock (down to 16 meters) at four sites representing vastly different ecosystems across the Northern and Southern Sierra Nevada mountains. This study quantified the response of SOC concentration, distribution, and vulnerability to warming using a soil incubation experiment to warm the whole soil profile and radiocarbon and stable isotopes to assess which pools are vulnerable to loss under warming. Our results refine our understanding of the terrestrial carbon cycle by revealing the vulnerability of deep SOC to future changes in climate and how minerology may influence that vulnerability.

58 GEOSCIENCES↗

NASA NTRS1983

Numerical solution of the problem of flame propagation by the use of the random element method

A numerical, grid-free algorithm is presented for one-dimensional reaction-diffusion model of laminar flame propagation in premixed gases. It is based on the random element method we developed for the analysis of diffusional processes. The effect of combustion is taken into account by applying the principle of fractional steps to separate the process of diffusion, modeled by the random walk of computational elements, from the exothermic effects of chemical reaction, monitoring their strength. The validity of the algorithm is demonstrated by application to flame propagation problems for which exact solutions exist. The flame speed evaluated by its use oscillates around the exact value at a relatively small amplitude, while the temperature and species concentration profiles are self-correcting in their convergence to the exact solution. A satisfactory resolution is obtained by the use of quite a small number of computational elements which automatically adjust their distribution of fit sharp gradients.

Ghoniem, A. F.↗

NASA NTRS1990

Patterns in simulated turbulent channel flow

The surface stress is a key diagnostic in wall bounded turbulent flows. Large fluctuations in the stress are believed to be associated with intermittent 'bursting' events during which a large proportion of the turbulence production takes place. If this is so, then a detailed investigation of the structure of the surface stress and its spatial relationship to events within the flow could have wide application in drag reduction and other aspects of flow control. The initial phase of this project, therefore, concentrated on the surface stress field. The first objective is to carry out a statistical analysis of the instantaneous surface stress in a simulated turbulent channel flow, including comparison with multipoint experimental data from a zero pressure gradient turbulent boundary layer. The second objective is to apply a simple pattern recognition procedure to educe the characteristic spatial structure of various flow diagnostics. The final objective is to extend the pattern recognition analysis to examine the whole three dimensional structure of the flow.

Stretch, D.↗

NASA NTRS1986

Analysis of remote measurements of tropospheric carbon monoxide concentrations made during the 1979 Summer Monsoon Experiment (MONEX)

Mixing ratios of tropospheric CO as measured by an aircraft-mounted radiometer over Saudi Arabia, the Arabian Sea, and northern India during May and June 1979 are reported. During early May, exceptionally high CO levels were detected over Saudi Arabia, and strong horizontal gradients in CO mixing ratios were seen to develop over a period of several days. Over the Arabian Sea, mixing ratios of the order of 150 parts per billion by volume were observed before the monsoon onset, and a pronounced decrease in CO was detected toward the equator. Subsequent measurements after the monsoon had become established revealed a consistent decrease in CO mixing ratio across this region. Analysis of aircraft dropsonde data and constant pressure daily streamline charts lend strong support to the hypothesis that this reduction is associated with the influx of CO-poor Southern Hemisphere air in the monsoon southwesterlies.

Doherty, G. M.↗

NASA NTRS2023

Reconstructing PM 2.5 Data Record for the Kathmandu Valley Using a Machine Learning Model

This paper presents a method for reconstructing the historical hourly concentrations of particulate matter 2.5 (PM2.5) over the Kathmandu Valley from 1980 to the present. The method uses a machine learning model that is trained using PM2.5 readings from US Embassy (Phora Durbar) as a ground truth, and the meteorological data from Modern-Era Retrospective Analysis for Research and Applications v2 (MERRA2) as input. The Extreme Gradient Boosting (XGBoost) model acquires a credible 10-fold cross-validation (CV) score of ~83.4%, an r2-score of ~84%, a Root Mean Square Error (RMSE) of ~15.82 µg/m3, and a Mean Absolute Error (MAE) of ~10.27 µg/m3. Further demonstrating the model's applicability to years other than those for which truth values are unavailable, the multiple cross-test with an unseen data set offered r2-scores for 2018, 2019, and 2020 ranging from 56% to 67%. The model-predicted data agrees with true values and indicates that MERRA2 underestimates PM2.5 over the region. It strongly agrees with ground-based evidence showing substantially higher mass concentrations in the dry pre- and post-monsoon seasons than in the monsoon months. It also shows a strong anti-correlation between PM2.5 concentration and humidity. The results also demonstrate that none of the years fulfilled the annual mean air quality index (AQI) standards set by the World Health Organization (WHO).

machine learning↗