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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Air, surface, and wastewater surveillance of SARS-CoV-2; a multimodal evaluation of COVID-19 detection in a built environment

Environmental surveillance of infectious organisms holds tremendous promise to reduce human-to-human transmission in indoor spaces through early detection. In this study we determined the applicability and limitations of wastewater, indoor high-touch surfaces, in-room air, and rooftop exhaust air sampling methods for detecting SARS-CoV-2 in a real world building occupied by residents recently diagnosed with COVID-19. We concurrently examined the results of three 24-hour environmental surveillance techniques, indoor surface sampling, exhaust air sampling and wastewater surveillance, to the known daily census fluctuations in a COVID-19 isolation dormitory. Additionally, we assessed the ability of aerosol samplers placed in the large volume lobby to detect SARS-CoV-2 multiple times per day. Our research reveals an increase in the number of individuals confirmed positive with COVID-19 as well as their estimated human viral load to be associated with statistically significant increases in viral loads detected in rooftop exhaust aerosol samples (p = 0.0413), wastewater samples (p = 0.0323,), and indoor high-touch surfaces (p < 0.001)). We also report that the viral load detected in lobby aerosol samples was statistically higher in samples collected during presence of occupants whose COVID-19 diagnostic tests were confirmed positive via qPCR compared to periods when the lobby was occupied by either contact-traced (suspected positive) individuals or during unoccupied periods (p = 0.0314 and <2e–16). We conclude that each daily (24h) surveillance method, rooftop exhaust air, indoor high-touch surfaces, and wastewater, provide useful detection signals for building owner/operator(s). Furthermore, we demonstrate that exhaust air sampling can provide spatially resolved signals based upon ventilation exhaust zones. Additionally, we find that indoor lobby air sampling can provide temporally resolved signals useful during short duration sampling periods (e.g., 2-4 hours) even with intermittent occupancy by occupants diagnosed with COVID-19.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A catalog of the diversity and ubiquity of bacterial microcompartments

Abstract Bacterial microcompartments (BMCs) are organelles that segregate segments of metabolic pathways which are incompatible with surrounding metabolism. BMCs consist of a selectively permeable shell, composed of three types of structurally conserved proteins, together with sequestered enzymes that vary among functionally distinct BMCs. Genes encoding shell proteins are typically clustered with those for the encapsulated enzymes. Here, we report that the number of identifiable BMC loci has increased twenty-fold since the last comprehensive census of 2014, and the number of distinct BMC types has doubled. The new BMC types expand the range of compartmentalized catalysis and suggest that there is more BMC biochemistry yet to be discovered. Our comprehensive catalog of BMCs provides a framework for their identification, correlation with bacterial niche adaptation, experimental characterization, and development of BMC-based nanoarchitectures for biomedical and bioengineering applications.

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

Social inequalities in climate change-attributed impacts of Hurricane Harvey

Climate change is already increasing the severity of extreme weather events such as with rainfall during hurricanes. But little research to date investigates if, and to what extent, there are social inequalities in climate change-attributed extreme weather event impacts. Here, we use climate change attribution science paired with hydrological flood models to estimate climate change-attributed flood depths and damages during Hurricane Harvey in Harris County, Texas. Using detailed land-parcel and census tract socio-economic data, we then describe the socio-spatial characteristics associated with these climate change-induced impacts. We show that 30 to 50% of the flooded properties would not have flooded without climate change. Climate change-attributed impacts were particularly felt in Latina/x/o neighborhoods, and especially so in Latina/x/o neighborhoods that were low-income and among those located outside of FEMA’s 100-year floodplain. Our focus is thus on climate justice challenges that not only concern future climate change-induced risks, but are already affecting vulnerable populations disproportionately now.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Spatial and Temporal Characterization of Activity in Public Space, 2019–2020

The data reported here characterize spatial and temporal variation in the ratio of short-to-long-duration visits in public places (i.e., points of interest) in the United States for each week between January 2019 and December 2020. The underlying data on anonymized and aggregated foot traffic to public places is curated by SafeGraph, a geospatial data provider. In this work, we report the estimated number and duration of “short” (i.e., <4 hours) and “long” (i.e., >4 hours) visits to public places at the US census block group level. Long visits are shown to be a good proxy for workers based on formal economic data. We propose that short visits are more likely to represent nonobligate activities: people visiting a public place for leisure, shopping, entertainment, or civic or cultural engagement. Our work constructs a ratio of short to long visits, which can be used to inform population estimates for nonworker use of public space. These data may be useful for understanding how people’s use of public space has changed during the COVID-19 pandemic and, more generally, for understanding activity patterns in public.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Revised monthly energy generation estimates for 1,500 hydroelectric power plants in the United States

Abstract The U.S. Energy Information Administration (EIA) conducts a regular survey (form EIA-923) to collect annual and monthly net generation for more than ten thousand U.S. power plants. Approximately 90% of the ~1,500 hydroelectric plants included in this data release are surveyed at annual resolution only and thus lack actual observations of monthly generation. For each of these plants, EIA imputes monthly generation values using the combined monthly generating pattern of other hydropower plants within the corresponding census division. The imputation method neglects local hydrology and reservoir operations, rendering the monthly data unsuitable for various research applications. Here we present an alternative approach to disaggregate each unobserved plant’s reported annual generation using proxies of monthly generation—namely historical monthly reservoir releases and average river discharge rates recorded downstream of each dam. Evaluation of the new dataset demonstrates substantial and robust improvement over the current imputation method, particularly if reservoir release data are available. The new dataset—named RectifHyd—provides an alternative to EIA-923 for U.S. scale, plant-level, monthly hydropower net generation (2001–2020). RectifHyd may be used to support power system studies or analyze within-year hydropower generation behavior at various spatial scales.

13 HYDRO ENERGY↗
DOE OSTI2024

Projected income data under different shared socioeconomic pathways for Washington state

Abstract High-resolution income projections under different Shared Socioeconomic Pathways (SSPs) are essential for the climate change research communities to devise climate change adaptation and mitigation strategies. To generate income projections for Washington state, we obtain state-level GDP per capita projections and convert them into projected annual household income. The resulting state-level income projections are subsequently downscaled to the census block-level based on the Longitudinal Origin-Destination Employment Statistics (LODES) dataset. For accuracy assessment, we downscale historical income data from state- level to block- and block group-level and compare the downscaled results against the actual income data from LODES. County-level accuracy assessment is also conducted based on American Community Survey. The results demonstrate a good agreement (Average R 2 of 0.67, 0.8, and 0.99 for block-, block group-, and county-level, respectively) between the downscaled income data and the reference data, thereby validating the methodology employed. Our approach is applicable to other states for income projections, which can be utilized by a broader audience, including those involved in demographic analysis, economic research, and urban planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

LandScan Global 30 Arcsecond Annual Global Gridded Population Datasets from 2000 to 2022

Abstract Oak Ridge National Laboratory (ORNL) annually develops the LandScan Global (LSG) dataset, a 30 arcsecond global gridded population dataset representing global ambient human population distribution. This multivariable dasymetric model disaggregates census counts within administrative boundaries using ancillary data. Each country’s distribution reflects cultural and socioeconomic patterns; manual validations yield a unique global dataset for assessing populations at risk. For over two decades, LSG has been a standard for estimating populations at risk, aiding U.S. federal government, academia and humanitarian organizations. During disasters such as the 2004 Indian Ocean tsunami and the 2010 Haiti earthquake and geopolitical crises such as the Syrian civil war and the 2022 Russian invasion of Ukraine, LSG supported scientific and operational communities in emergency response and recovery. In 2022, LSG datasets from 2000 onward were made publicly available through ORNL’s LandScan Portal. This data descriptor details our methodology and the application of geospatial science and machine learning to geographic and demographic data, highlighting uses in urban resiliency, emergency management, disaster response, and human health and security.

Science & Technology - Other Topics↗
DOE OSTI2026

Heterogeneous estimations of non-pharmaceutical mitigation behavior during the COVID-19 pandemic

The COVID-19 pandemic highlighted the importance of human behavior in mitigating the spread of disease. Nonetheless, human behavior is often overlooked in models of disease spread, particularly by underutilizing real-world data. We address this by estimating probabilities that individuals engage in behaviors that influence SARS-CoV-2 transmission risk during the COVID-19 pandemic, between September 2020 and June 2022. These behaviors include wearing a mask, using public transportation, spending time with others, avoiding contact with others, and going to work. Our estimates account for the age and sex of individuals and are generated for every county in the United States. We utilized multiple open-source datasets and United States Census data to produce these estimates. Multiple datasets were used for validation, showing our estimates demonstrated comparable accuracy and robustness. Our estimates aid in understanding human behavior dynamics during the COVID-19 pandemic and could be used to inform monthly or longer-term behavior in simulations of COVID-19. Moreover, the methods presented can be applied to other behaviors and features for future simulations of infectious disease.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Current inequality and future potential of US urban tree cover for reducing heat-related health impacts

Excessive heat is a major and growing risk for urban residents. Here, we estimate the inequality in summertime heat-related mortality, morbidity, and electricity consumption across 5723 US municipalities and other places, housing 180 million people during the 2020 census. On average, trees in majority non-Hispanic white neighborhoods cool the air by 0.19 ± 0.05 °C more than in POC neighborhoods, leading annually to trees in white neighborhoods helping prevent 190 ± 139 more deaths, 30,131 ± 10,406 more doctors’ visits, and 1.4 ± 0.5 terawatt-hours (TWhr) more electricity consumption than in POC neighborhoods. We estimate that an ambitious reforestation program would require 1.2 billion trees and reduce population-weighted average summer temperatures by an additional 0.38 ± 0.01 °C. This temperature reduction would reduce annual heat-related mortality by an additional 464 ± 89 people, annual heat-related morbidity by 80,785 ± 6110 cases, and annual electricity consumption by 4.3 ± 0.2 TWhr, while increasing annual carbon sequestration in trees by 23.7 ± 1.2 MtCO 2 e yr -1 and decreasing annual electricity-related GHG emissions by 2.1 ± 0.2 MtCO 2 e yr -1 . The total economic value of these benefits, including the value of carbon sequestration and avoided emissions, would be USD 9.6 ± 0.5 billion, although in many neighborhoods the cost of planting and maintaining trees to achieve this increased tree cover would exceed these benefits. The exception is areas that currently have less tree cover, often the majority POC, which tend to have a relatively high return on investment from tree planting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The eROSITA Final Equatorial-Depth Survey (eFEDS)

Here, we investigate the physical properties – such as the stellar mass (M * ), star-formation rate, infrared (IR) luminosity (L IR ), X-ray luminosity (L X ), and hydrogen column density (N H ) – of mid-IR (MIR) galaxies and active galactic nuclei (AGN) at z < 4 in the 140 deg 2 field observed by eROSITA on SRG using the Performance-and-Verification-Phase program named the eROSITA Final Equatorial Depth Survey (eFEDS). By cross-matching the WISE 22 μm (W4)-detected sample and the eFEDS X-ray point-source catalog, we find that 692 extragalactic objects are detected by eROSITA. We have compiled a multiwavelength dataset extending from X-ray to far-IR wavelengths. We have also performed (i) an X-ray spectral analysis, (ii) spectral-energy-distribution fitting using X-CIGALE, (iii) 2D image-decomposition analysis using Subaru Hyper Suprime-Cam images, and (iv) optical spectral fitting with QSFit to investigate the AGN and host-galaxy properties. For 7088 WISE 22 μm objects that are undetected by eROSITA, we have performed an X-ray stacking analysis to examine the typical physical properties of these X-ray faint and probably obscured objects. We find that (i) 82% of the eFEDS–W4 sources are classified as X-ray AGN with log L X > 42 erg s –1 ; (ii) 67 and 24% of the objects have log(L IR /L ⊙ ) > 12 and 13, respectively; (iii) the relationship between L X and the 6 μm luminosity is consistent with that reported in previous works; and (iv) the relationship between the Eddington ratio and N H for the eFEDS–W4 sample and a comparison with a model prediction from a galaxy-merger simulation indicates that approximately 5.0% of the eFEDS–W4 sources in our sample are likely to be in an AGN-feedback phase, in which strong radiation pressure from the AGN blows out the surrounding material from the nuclear region. Thanks to the wide area coverage of eFEDS, we have been able to constrain the ranges of the physical properties of the WISE 22 μm-selected sample of AGNs at z < 4, providing a benchmark for forthcoming studies on a complete census of MIR galaxies selected from the full-depth eROSITA all-sky survey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The eROSITA Final Equatorial-Depth Survey (eFEDS): Identification and characterization of the counterparts to point-like sources

In November 2019, eROSITA on board of the Spektrum-Roentgen-Gamma (SRG) observatory started to map the entire sky in X-rays. After the four-year survey program, it will reach a flux limit that is about 25 times deeper than ROSAT. During the SRG performance verification phase, eROSITA observed a contiguous 140 deg 2 area of the sky down to the final depth of the eROSITA all-sky survey (eROSITA Final Equatorial-Depth Survey; eFEDS), with the goal of obtaining a census of the X-ray emitting populations (stars, compact objects, galaxies, clusters of galaxies, and active galactic nuclei) that will be discovered over the entire sky. This paper presents the identification of the counterparts to the point sources detected in eFEDS in the main and hard samples and their multi-wavelength properties, including redshift. To identify the counterparts, we combined the results from two independent methods (NWAY and ASTROMATCH), trained on the multi-wavelength properties of a sample of 23k XMM-Newton sources detected in the DESI Legacy Imaging Survey DR8. Then spectroscopic redshifts and photometry from ancillary surveys were collated to compute photometric redshifts. Of the eFEDS sources, 24 774 of 27 369 have reliable counterparts (90.5%) in the main sample and 231 of 246 sources (93.9%) have counterparts in the hard sample, including 2514 (3) sources for which a second counterpart is equally likely. By means of reliable spectra, Gaia parallaxes, and/or multi-wavelength properties, we have classified the reliable counterparts in both samples into Galactic (2695) and extragalactic sources (22 079). For about 340 of the extragalactic sources, we cannot rule out the possibility that they are unresolved clusters or belong to clusters. Inspection of the distributions of the X-ray sources in various optical/IR colour-magnitude spaces reveal a rich variety of diverse classes of objects. The photometric redshifts are most reliable within the KiDS/VIKING area, where deep near-infrared data are also available. This paper accompanies the eROSITA early data release of all the observations performed during the performance and verification phase. Together with the catalogues of primary and secondary counterparts to the main and hard samples of the eFEDS survey, this paper releases their multi-wavelength properties and redshifts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The miniJPAS survey: A search for extreme emission-line galaxies

Context. Galaxies with extreme emission lines (EELGs) may play a key role in the evolution of the Universe, as well as in our understanding of the star formation process itself. For this reason an accurate determination of their spatial density and fundamental properties in different epochs of the Universe will constitute a unique perspective towards a comprehensive picture of the interplay between star formation and mass assembly in galaxies. In addition to this, EELGs are also interesting in order to explain the reionization of the Universe, since their interstellar medium (ISM) could be leaking ionizing photons, and thus they could be low z, analogous of extreme galaxies at high z . Aims. This paper presents a method to obtain a census of EELGs over a large area of the sky by detecting galaxies with rest-frame equivalent widths ≥300 Å in the emission lines [O II]λλ3727,3729Å, [O III]λ5007Å, and Hα. For this, we aim to use the J-PAS survey, which will image an area of ≈8000 deg 2 with 56 narrow band filters in the optical. As a pilot study, we present a methodology designed to select EELGs on the miniJPAS images, which use the same filter dataset as J-PAS, and thus will be exportable to this larger survey. Methods. We make use of the miniJPAS survey data, conceived as a proof of concept of J-PAS, and covering an area of ≈1 deg 2 . Objects were detected in the r SDSS images and selected by imposing a condition on the flux in a given narrow-band J-PAS filter with respect to the contiguous ones, which is analogous to requiring an observed equivalent width larger than 300 Å in a certain emission line within the filter bandwidth. The selected sources were then classified as galaxies or quasi-stellar objects (QSOs) after a comparison of their miniJPAS fluxes with those of a spectral database of objects known to present strong emission lines. This comparison also provided a redshift for each source, which turned out to be consistent with the spectroscopic redshifts when available (|Δz/(1 + z spec )| ≤ 0.01). Results. The selected candidates were found to show a compact appearance in the optical images, some of them even being classified as point-like sources according to their stellarity index. After discarding sources classified as QSOs, a total of 17 sources turned out to exhibit EW 0 ≥ 300 Å in at least one emission line, thus constituting our final list of EELGs. Our counts are fairly consistent with those of other samples of EELGs in the literature, although there are some differences, which were expected due to biases resulting from different selection criteria.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A complete spectroscopic catalogue of local galaxies in the northern spring sky: Gas properties and nuclear activity in different environments

With the aim of providing the complete demography of galaxies in the local Universe, including their nuclear properties, we present SPRING, a complete census of local galaxies limited to the spring quarter of the northern sky (10h < RA < 16h; 0° < Dec < 65°). The SPRING catalogue is a flux- and volume-limited sample (r < 17.7 mag, cz < 10 000 km s -1 ) of 30 597 galaxies, including the Virgo, Coma, and A1367 clusters. Images and spectra were individually examined to clear the sample from unwanted entries. To inspect possible secular and environmental dependencies of the various nuclear excitation properties (star-forming versus active nuclei), we performed a multi-dimensional analysis by dividing the total sample according to: (i) their position in the (NUV - i) versus M star diagram, (ii) the local galaxy density, (iii) the stellar mass, (iv) the halo mass of the group to which galaxies belong, and (v) the neutral hydrogen content. We present a new calibration of the optical diameter-based H I-deficiency parameter H I def , employing a reference sample of isolated galaxies extracted from SPRING. At intermediate distances between Virgo and Coma, we identify a ring-like structure of galaxies constituted by three large filaments, each with a length of approximately 20h -1 Mpc, mostly composed of blue-cloud galaxies with stellar masses M star ≲ 10 10 M ⊙ . The fraction of H I-deficient galaxies within the filament (~30%) suggests that filaments are a transitioning environment between lower- and higher-overdensity environments in terms of H I content, as we find a clear progression from field galaxies to the filament and cluster galaxies for an increasing H I def parameter. We classify the nuclear spectra according to the four-line Baldwin-Phillips-Terlevich (BPT) and the two-line EWHα versus [NII]/Hα (WHAN) diagnostic diagrams, and investigate the variation in the fraction of active nuclei hosts with stellar mass, as well as their colours and environments. We observe that the fraction of low-ionisation nuclear emitting regions (LINERs) is a steep function of stellar mass, for example, it is consistent with zero up to M star ≲ 10 9.5 M ⊙ and becomes ~40% for M star ≳ 101 0.5 M ⊙ , whereas, for M star ≲ 10 9 - 9.5 M ⊙ , almost the entire spectroscopic sample is constituted of galaxies with star-forming nuclei. We investigate whether the nuclear-excitation fractions depend predominantly on the stellar mass or, conversely, on the galaxy environment. In general, we observe that the mass dependence of the fraction of Seyfert nuclei is not very sensitive to the galaxy environment, whereas the fraction of star-forming nuclei is a steeper function of stellar mass in lower-density environments and in blue-cloud galaxies. We find that the fraction of LINERs depends on galaxy colour and, for M star ≳ 10 9.5 - 10 M ⊙ , increases in galaxies belonging to the green valley.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Modeling Electric Vehicle Charging Load Using Origin-Destination Data

The accelerating adoption of electric vehicles (EVs) poses challenges to the power grid, necessitating precise representation of mobility patterns for effective infrastructure upgrades. Traditional simulation-based charging demand estimation faces limitations in generating trip chains reflective of actual travel patterns without complex network modeling. Hence, an innovative agent-based trip chain generation model is introduced to overcome these challenges. Drawing from the National Household Travel Survey (NHTS) and the NextGen NHTS origin-destination add-on data for Clarke County, Georgia, this study proposes a simulation method capturing both temporal and spatial mobility patterns without relying on extensive network topology data. The resulting trip chains predict EV charging load at the Census Block Group level, validated with a 1.03 correlation to actual trip counts, affirming their reflective accuracy. Two charging scenarios, residential-only and charging-everywhere, reveal distinct demand profiles. The charging-everywhere scenario aligns closely with the trip profile, while the residential-only scenario exhibits an afternoon peak slightly surpassing the former. This study contributes a data-driven charging demand estimation methodology, offering critical insights for grid resiliency planning amid the evolving landscape of EV adoption.

Pan, Melrose↗
DOE OSTI2024

A Spatial-Temporal Analysis of Travel Time Gap and Inequality between Public Transportation and Personal Vehicles

The increased use of personal vehicles presents environmental challenges, prompting the exploration of public transportation as an affordable, eco-friendly alternative. However, obstacles like fixed schedules, limited routes, and extended travel times impede widespread adoption. This study investigates the temporal evolution of spatial inequality in the travel time gap between public transportation and personal vehicles, reflecting disparities across states and time periods. Analyzing Census Transportation Planning Program data for six northeastern states in 2010 and 2016 reveals no significant increase in the travel time gap, but notable growth in inequality in a few urban and disadvantaged communities. Comprehending these trends is vital for fostering equitable advancements in transportation infrastructure and enhancing public transportation competitiveness.

Pan, Melrose↗
DOE OSTI2025

Work from Home Patterns across COVID-19 Waves: Implications for Future Transportation

The unprecedented rise in work from home (WFH) during COVID-19 poses challenges for the transportation engineers and planners with the travel demand forecasting. If WFH persists post-pandemic, it could influence traffic patterns, reducing peak-hour congestion. However, the evolution of WFH decisions across pandemic phases and varying socio-economic contexts remains unclear. This study examines factors influencing WFH choices using data from the US Census Bureau’s Household Pulse Survey. Findings reveal a decline in WFH participation from 60% during the first wave to 38% by the third wave of the COVID-19 pandemic. A Geographically Weighted Regression model highlights the influence of socio-economic, household, and COVID-19-related variables, with notable spatial variability. Results show that younger individuals, females, and households with children are consistently more likely to WFH, while non-white and higher income individuals have an increasing likelihood for WFH as the pandemic progresses. These insights inform future transportation planning, emphasizing equity and decentralization strategies for post-pandemic commuting.

Patwary, Latif [ORNL] (ORCID:0000000189174928)↗
DOE OSTI2020

A population of free-living highland wild dogs in Indonesian Papua

A 10-day rapid assessment has confirmed a small population of indigenous wild-living dogs in the highlands of Papua Province, Indonesia. The goal of the assessment was to observe, census, assess health and reproductive status, and collect biological samples or other data for this understudied canid. Trail cameras and DNA hair traps were deployed and baited with scent attractants, and game calls were used to elicit vocal responses or lure the dogs within observation range. Adults and pups of both sexes as well as a den were documented in 149 photographs. DNA testing of scat samples confirmed a canid origin with a close relationship to other oceanic canids including NGSD (New Guinea Singing Dog) and dingoes.

Zoology↗
DOE OSTI2022

Infrastructure inequality is a characteristic of urbanization

Urbanization can challenge sustainable development if it produces unequal outcomes. Infrastructure is an important urbanization dimension, providing services to support diverse urban activities. However, it can lock in unequal outcomes due to its durable nature. This paper studies inequalities in infrastructure distributions to derive insights into the structure and characteristics of unequal outcomes associated with urbanization. We analyzed infrastructure inequalities in two emerging economies in the Global South: India and South Africa. We developed and applied an inequality measure to understand the structure of inequality in infrastructure provisioning (based on census data) and infrastructure availability (based on satellite nighttime lights [NTLs] data). Consistent with differences in economic inequality, results show greater inequalities in South Africa than in India and greater urban inequalities than rural inequalities. Nevertheless, inequalities in urban infrastructure provisioning and infrastructure availability increase from finer to coarser spatial scales. NTL-based inequality measurements additionally show that inequalities are more concentrated at coarse spatial scales in India than in South Africa. Finally, results show that urban inequalities in infrastructure provisioning covary with urbanization levels conceptualized as a multidimensional phenomenon, including demographic, economic, and infrastructural dimensions. Similarly, inequalities in urban infrastructure availability increase monotonically with infrastructure development levels and urban population size. Together, these findings underscore infrastructure inequalities as a feature of urbanization and suggest that understanding urban inequalities requires applying an inequality lens to urbanization.

54 ENVIRONMENTAL SCIENCES↗