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DOE OSTI2021

Evaluation of regional transport of PM2.5 during severe atmospheric pollution episodes in the western Yangtze River Delta, China

During winter 2018, the 16 prefecture-level cities in Anhui Province, Western Yangtze River Delta region, China had very high PM2.5 concentrations and prolonged pollution days. The impact of regional transport in the formation, accumulation, as well as dispersion of fine particulate matter (PM2.5) in Anhui Province was very significant. This study quantified and analyzed the vertical transport of PM2.5 in three major cities (Hefei, Fuyang, and Suzhou) of Anhui Province in January and July 2018 using the Weather Research and Forecasting (WRF) model coupled with the Community Multiscale Air Quality (CMAQ) model. The results of the inter-regional transport of PM2.5 revealed the dominant transport pathways for the three cities. The flux mainly flowed into Fuyang from Henan (2.23 and 1.42 kt/day in January and July, respectively) and Bozhou (1.96 and 1.21 kt/day in January and July, respectively), while the main flux from Fuyang flowed into Henan (-2.15 kt/day) and Lu'an (-1.91 kt/day) in January and Henan (-0.34 kt/day) and Bozhou (-0.29 kt/day) in July. In addition, the dominant transport pathways and the heights at which they occurred were identified: the northwest-southeast and northeast-south pathways in both winter and summer at both lower (?300 m) and higher (=300 m) levels for Fuyang; the northwest-south and northeast-southwest pathways in winter (at both lower and upper levels) and northwest-east and northeast-southwest pathways in summer at lower and upper levels for Hefei; and the northwest-southeast and northeast-south pathways in both winter (from 50 m up to the top level) and summer (between 100 and 300 m) for Suzhou. Furthermore, the intensities of daily PM2.5 transport fluxes in Fuyang during the atmospheric pollution episode (APE1) were stronger than the monthly average. These results show that joint emission controls across multiple cities along the identified pathways are urgently needed to reduce winter episodes.

Sulaymon, Ishaq D.↗

DOE OSTI2019

Spatial heterogeneity analysis of the effects of urbanization on airport CO2 emissions in China - A geographically weighted approach using nighttime light data

Regional disparities in carbon dioxide (CO2) emissions from airports at the city level are of increasing importance for low-carbon development of the civil aviation sector. However, CO2 emissions from airport operations have rarely been estimated and discussed. We investigate the main driving forces of airport CO2 emissions by using Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) models, separately, to investigate whether urbanization drives airport CO2 emissions and to investigate spatial heterogeneity at the city level. Nighttime light (NTL) data from satellite observations are adopted as a proxy for urbanization. We obtained energy consumption data by end-use purpose for 70 airports in China and calculated the CO2 emissions from on-ground airport operations. The median CO2 emissions of the 70 sample airports are estimated to be 15.9 million tonnes for 2015. Results from the GWR model indicate that airport CO2 emissions are affected by five main factors: urbanization, foreign direct investment, the share of tertiary industry in gross domestic output, passenger turnover of civil aviation and passenger turnover of railways. The elasticity of urbanization shows an increasing trend from the east of China to the west. The spatial heterogeneity of the CO2 emissions of the five airport clusters that are located in five urban agglomerations is discussed. In order to achieve effective reductions of CO2 emissions from airports, policy-makers should consider the spatial heterogeneity of the major driving factors of carbon emissions in different regions to avoid carbon lock-in.

Airport CO2 emissions↗

DOE OSTI2023

Global mapping of urban thermal anisotropy reveals substantial potential biases for remotely sensed urban climates

Urban thermal anisotropy (UTA) drastically biases satellite-derived urban surface temperatures and fluxes, and consequently inhibits a more comprehensive understanding of global urban climates. However, UTA patterns and associated biases in observed urban climate variables have not been investigated across an adequate number of global cities with diverse contexts; nor is it known whether there are globally measurable factors that are closely related to the UTA intensity (UTAI, quantified as the maximum difference between the nadir and off-nadir urban thermal radiation). Here we investigate the UTAI over more than 5500 cities worldwide using the moderate resolution imaging spectroradiometer (MODIS) multi-angle land surface temperature (LST) observations from 2003 to 2021. The results show that the global mean UTAI can reach 5.1, 2.7, 2.4, and 1.7 K during summer daytime, winter daytime, summer nighttime, and winter nighttime, respectively. Using nadir LST observations as a reference, our analysis reveals that UTA can lead to an underestimation of satellite-based urban surface sensible heat fluxes ( H ) by 45.4% and surface urban heat island intensity ( I s ) by 43.0% when using LST observations obtained from sensor viewing zenith angles (VZAs) of ±60°. However, practitioners can limit the biases of H and I s within ±10% by using LSTs from sensor VZAs within ±30°. We find that UTAI is closely related to urban impervious surface percentage and surface air temperature across global cities. In conclusion, these findings have implications for angular normalization of satellite-retrieved instantaneous LST observations across cities worldwide.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Comparing the cooling effectiveness of operationalisable urban surface combination scenarios for summer heat mitigation

Extreme summer heat in cities exacerbates the vulnerability of urban communities to heatwaves. Vegetative and reflective urban surfaces can help reduce urban heat. This study investigated the impacts of urban trees, green roofs and cool roofs on heat mitigation during average and extreme summer conditions in temperate oceanic Melbourne, Australia. We simulated the city climate using ‘The Air Pollution Model’ (TAPM) at a 1 km spatial resolution over 10 years, which according to our review of the literature, was the most prolonged period for simulation in Melbourne. During a widespread heatwave event, some of the tested scenarios with combined surface parameters could reduce the extreme values of the energy budget components- sensible heat, latent heat, and storage heat fluxes up to seasonal averages compared to the existing situation for Melbourne (control). The scenario with the highest (reasonable maximum) ground-level vegetation, green roofs, and cool roofs could reduce air temperatures up to 2.4 °C. The simulations suggest that a combined strategy with vegetative and high-albedo surfaces will deliver higher effectiveness with maximum cooling benefits and cost-effectiveness than individual strategies in cities. These results suggest the importance of collaborative strategic planning of urban surfaces to make cities healthier, sustainable, and liveable.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Hyper‐Local Temperature Prediction Using Detailed Urban Climate Informatics

The accurate modeling of urban microclimate is a challenging task given the high surface heterogeneity of urban land cover and the vertical structure of street morphology. Recent years have witnessed significant efforts in numerical modeling and data collection of the urban environment. Nonetheless, it is difficult for the physical‐based models to fully utilize the high‐resolution data under the constraints of computing resources. The advancement in machine learning (ML) techniques offers the computational strength to handle the massive volume of data. In this study, we proposed a modeling framework that uses ML approach to estimate point‐scale street‐level air temperature from the urban‐resolving meso‐scale climate model and a suite of hyper‐resolution urban geospatial data sets, including three‐dimensional urban morphology, parcel‐level land use inventory, and weather observations from a sensor network. We implemented this approach in the City of Chicago as a case study to demonstrate the capability of the framework. The proposed approach vastly improves the resolution of temperature predictions in cities, which will help the city with walkability, drivability, and heat‐related behavioral studies. Moreover, we tested the model's reliability on out‐of‐sample locations to investigate the modeling uncertainties and the application potentials to the other areas. This study aims to gain insights into next‐gen urban climate modeling and guide the observation efforts in cities to build the strength for the holistic understanding of urban microclimate dynamics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Urban Land Surface Effects on Summertime Clouds and Moist Convection in Houston Under Different Synoptic Conditions

Urban landscapes modify cloud formation and convection through complex thermodynamic and aerodynamic processes; however, their influence under different synoptic regimes remains poorly understood. This study investigates the impact of the Houston metropolitan area on summertime cloud cover and convective cell characteristics using a combination of satellite observations, radar data, and high-resolution process-based modeling. We isolate urban effects by comparing model simulations with realistic urban land cover against hypothetical scenarios where all urban areas are replaced by rural vegetation. Results reveal that Houston's land cover consistently enhances cloud fraction and convective activity relative to surrounding rural areas, altered by large-scale meteorological forcing. Under weakly forced conditions, enhanced surface heat flux primarily contributes to driving low-level convergence and vertical ascent, leading to over 8% cloud fraction increase between 2 and 6 km over the city. Under strongly forced conditions, urban influences manifest differently depending on cloud type. For non-convective clouds, the city acts as a barrier that decelerates and lifts moist southerly inflow, increasing low cloud cover over the urban core, while decreasing it downwind the city. For convective clouds, both synoptic ascent and urbanization modulate moisture redistribution and cloud structure, producing modest cloud enhancement over the city and slight suppression over the downwind area. Urbanization exerts small changes in the intensity of the convective cells; however, it significantly decreases their duration and traveling distance. This work highlights the importance of accounting for land surface heterogeneity in modeling clouds and precipitation and demonstrates that urban impacts on clouds are highly regime-dependent.

Liu, Ye [Pacific Northwest National Laboratory (PN↗

DOE OSTI2016

An estimation of impact of anthropogenic aerosols in atmosphere of Tirana on solar insolation. Part II: Modification of solar energy potential

Change of irradiative properties of the atmosphere during clear days is an indicator, among others, of existence of atmospheric aerosols and can be used as an indicator for assessment both air pollution and local modifications of solar energy potentials. The main objective of this study is estimation of influence of anthropogenic aerosols on solar energy falling in a horizontal surface during a cloudless day. We have analyzed and quantified the effect of aerosols on reducing the amount of solar energy that falls on the horizontal ground surface in cloudless sky conditions, estimating temporal evolution, both in daily and hour scale, considering also, side effects caused by relative humidity of the air wind speed and geometric factor. As an indicator of concentration of aerosols in atmosphere, we agreed to use the attenuation of solar radiation after the last rainy day. All data were corrected by factors such as, variations of relative humidity, wind speed and daily change of incident angle of solar radiation. We studied the change of solar insolation in three sites with different traffic intensity, one in city of Shkodra and two in city of Tirana. Fifteen days after last rainy day, approximate time needed to achieve saturation, the insolation drops only 3.1% in the city of Shkodra, while in two sites in city of Tirana are 8.5 % and 18.4%. These data show that reduction of solar insolation is closely related with anthropogenic activity, mainly traffic around the site of the meteorological station. The day to day difference tends to decrease with increasing of number of days passed from the last rainy day, which is an evidence of a trend toward a dynamic equilibrium between decantation process of aerosols during the night and their generation during the day.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Large-Scale Classification of Urban Structural Units From Remote Sensing Imagery

Remote sensing in combination with deep learning has become instrumental for efficiently and accurately classifying land-use and land-cover across large geographic areas. These technologies have also been successful in characterizing urban environments in terms of their structural units, structure types, or morphological regions. In these approaches, an urban area is partitioned into regions that exhibit homogeneous physical characteristics. However, existing approaches are typically limited to a single city, use inconsistent typologies, and lack scalability and generalization capacity. In this article, we propose an urban structural units categorization scheme and demonstrate its utility by applying it to 13 cities. Inspired by the lack of scalability and generalization capacity in urban structural units mapping, we extend the reach of deep learning and conduct a set of classification experiments in all 13 cities. These experiments offer insights into the strengths and limitations of deep neural networks for classifying urban structural units over diverse geographic regions and on heterogeneous collections of satellite imagery. The efficacy of the proposed deep learning approach is compared to a baseline method of multiscale image features and support vector machines. Our validation on five cities shows that better performance is achieved with deep neural networks. Additionally, we evaluate the impact of input size, model depth, and spatial pyramid pooling to assess the generalization capacity of deep neural networks.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Secure and Privacy Aware Data Sharing Approach for Smart Electric Vehicles

The integration of smart electric vehicles (SEVs) into smart cities marks a significant step toward creating efficient, sustainable, and connected urban spaces. However, secure and private data sharing is a major challenge as SEVs connect with smart city systems. The interaction between SEVs and consumer electronic devices (CEDs) raises serious concerns about data security and privacy. Here, to address these challenges, this article presents how blockchain technology and federated learning (FL) can address these issues. The proposed approach provides a secure and privacy-aware framework for data exchange between SEVs and CEDs in smart cities. The experiment results demonstrate the effectiveness of the proposed framework for secure data sharing and maintaining system reliability in smart city environments. It also enables trust and promotes the widespread adoption of interconnected urban technologies.

Das, Debashis [Meharry Medical College, Nashville,↗

DOE OSTI2017

Change in Brooklyn and Queens: How New York?s Reforming the Energy Vision Program and Con Edison Are Reshaping Electric Distribution Planning

The New York City boroughs of Brooklyn and Queens are undergoing a period of gentrification, infrastructure rebuilding, new construction, and load growth not experienced in decades. Significant numbers of residents are moving in, and structures that had been abandoned or were in disrepair are being refurbished and modernized to accommodate the burgeoning population. Homes, businesses, and industries are reviving areas long in decline, and Brooklyn's growth has made it the nation's fourth most populous city, according to the U.S. Census Bureau.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

The Green Ocean Amazon Experiment (GoAmazon2014/5) Observes Pollution Affecting Gases, Aerosols, Clouds, and Rainfall over the Rain Forest

The Observations and Modeling of the Green Ocean Amazon 2014–2015 (GoAmazon2014/5) experiment took place around the urban region of Manaus in central Amazonia across 2 years. The urban pollution plume was used to study the susceptibility of gases, aerosols, clouds, and rainfall to human activities in a tropical environment. Many aspects of air quality, weather, terrestrial ecosystems, and climate work differently in the tropics than in the more thoroughly studied temperate regions of Earth. GoAmazon2014/5, a cooperative project of Brazil, Germany, and the United States, employed an unparalleled suite of measurements at nine ground sites and on board two aircraft to investigate the flow of background air into Manaus, the emissions into the air over the city, and the advection of the pollution downwind of the city. Here in this paper, to visualize this train of processes and its effects, observations aboard a low-flying aircraft are presented. Comparative measurements within and adjacent to the plume followed the emissions of biogenic volatile organic carbon compounds (BVOCs) from the tropical forest, their transformations by the atmospheric oxidant cycle, alterations of this cycle by the influence of the pollutants, transformations of the chemical products into aerosol particles, the relationship of these particles to cloud condensation nuclei (CCN) activity, and the differences in cloud properties and rainfall for background compared to polluted conditions. The observations of the GoAmazon2014/5 experiment illustrate how the hydrologic cycle, radiation balance, and carbon recycling may be affected by present-day as well as future economic development and pollution over the Amazonian tropical forest.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Optimal reblocking as a practical tool for neighborhood development

Fast urbanization is a common feature of many developing human societies. In many cases, past and present, explosive population growth in cities outstrips the rate of provision of housing and urban services and leads to the formation of informal settlements or slums. Slums are extremely varied in terms of their histories, infrastructure, and rates of change, but they share certain common features: informal land use, lack of physical accesses, and nonexistent or poor quality urban services. Currently, about 1 billion people worldwide live in slums, a number that could triple by 2050 if no practical solutions are enacted to reverse this trend. Underlying most problems of slums is the issue of lack of physical accesses to places of work and residence. This prevents residents and businesses from having an address, obtaining basic services such as water and sanitation, and being helped in times of emergency. In this paper, we show how the physical layout of any neighborhood can be classified quantitatively in terms of its access topology in a way that is independent of its geometry. Topological indices capturing levels of access to structures within a city block can then be used to define a constrained optimization problem, whose solution generates an access network that makes each structure in the settlement accessible to services with minimal disruption and cost. We discuss the general applicability of these techniques to several informal settlements in developing cities and demonstrate various technical aspects of our solutions. In conclusion, we discuss how these techniques could be used on a large scale to speed up human development processes in cities throughout the world while respecting their local identity and history.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

The Association of Radiation Exposure with Stable Chromosome Aberrations in Atomic Bomb Survivors Based on DS02R1 Dosimetry and FISH Methods

The frequency of stable chromosome aberrations (sCA) in lymphocytes is a recognized radiation biological dosimeter. Its analysis can provide insights into factors that affect individual susceptibility as well as into the adequacy of radiation dose estimates used in studies of atomic bomb survivors. We analyzed the relationship between atomic bomb radiation exposure using the most recent DS02R1 dose estimates and the frequency of sCA as determined by FISH in 1,868 atomic bomb survivors. We investigated factors that may affect the background sCA rate and the shape and magnitude of the dose response. As in previous analyses of sCA in atomic bomb survivors that were based on Giemsa staining methods and used older DS86 dose estimates, the relationship between radiation dose and sCA rate was significant (P < 0.0001) with a linear-quadratic relationship at lower doses that did not persist at higher doses. As before, age at the time of the bombing and type of radiation shielding were significant dose-effect modifiers (P < 0.0001), but in contrast the difference in dose response by city was not so pronounced (P = 0.026) with a city effect not evident at doses below 1.25Gy. Background sCA rate increased with age at the time of examination (P < 0.0001), but neither sex, city, nor smoking was significantly associated with background rate. Based on FISH methods and recent dosimetry, the relationship between radiation dose and sCA frequency is largely consistent with previous findings, although the lesser importance of city as an effect modifier may reflect better dosimetry as well as more reproducible scoring of sCA. The persisting difference in sCA dose response by shielding category points to remaining problems with the accuracy or precision of radiation dose estimates in some A-bomb survivors.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2018

3-D Semantic Information Inference from Airborne Video. Final Report

We developed a full 3D modeling pipeline focusing on an important application scenario: city-scale 3D reconstruction from aerial imagery. We propose an approach to solve camera pose estimation and dense reconstruction from Wide Area Aerial Surveillance (WAAS) videos captured by an airborne platform. Our approach solves them in an online fashion: it incrementally updates a sparse 3D map and estimates the camera pose as each new frame arrives; depth maps of selected key frames are computed using a variational method and integrated to produce a full 3D model via volumetric reconstruction. In practice, aerial imagery is usually captured using a multicamera system. We propose an approach for camera pose estimation of multi-camera aerial imagery which is parallelized on multiple GPUs for efficiency. The approach is also extended for progressive 3D model acquisition with a hand-held camera. We also developed an offline approach since online approach is not a necessity and accuracy has higher priority over efficiency in many scenarios. We presented MeshRecon, a mesh-based offline system composed of three modules: a dense point cloud is generated using multi-resolution plane sweep method; an initial mesh model is extracted from the point cloud via global optimization considering visibility information of all images; the mesh model is then iteratively refined to capture structural details by optimizing the photometric consistency and spatial regularization. The major processes are parallelized on GPU for efficiency. For the aerial imagery case, we evaluate our system on several real-world multicamera aerial imagery datasets, each covering an urban scenario of several square kilometers. Quantitative result shows that the reconstructed geometric 3D model is highly accurate with error smaller than 1 meter over the entire city. Besides aerial imagery, we also evaluate its performance on general geometric 3D model acquisition of real-world objects. Result shows that the system is robust and flexible for various types of objects at different scales in both indoor and outdoor environments. Based on city 3D models reconstructed at different times, we present a system for city-scale geometry change detection by performing comparisons at the 3D geometry level. Our system is able to detect geometry changes at different scales, ranging from a building cluster to small-scale vegetation changes, with high accuracy.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Charting a Path for Reliable, Resilient and Affordable Clean Energy: A Roadmap for Three Communities in Utah

The roadmap identifies strategies to align deployment of distributed energy resources with the continued growth of utility-scale solar. The strategies outlined in the roadmap have been developed through collaborative discussions with project partners, but may not represent the opinions of, or positions of, all partner organizations and parties involved in the "Renewable Energy Impacts and Solutions in Utah" project. Rather, the strategies outlined in this Roadmap represent a suite of tools and actions that Salt Lake City, Park City, and the City of Moab can consider as each city works towards their community renewable electricity goals. While this Roadmap was developed to inform three communities as they progress towards their specific community energy goals, much of the analysis presented in the roadmap was conducted for Utah as a whole. Other communities in Utah can use the scenarios and strategies outlined in the roadmap to leverage the full potential of distributed energy resources to provide benefits to the community.

100% renewable↗

DOE OSTI2024

Equitable Strategies for Residential Building Energy Efficiency and Electrification in San José, California

Hotter summers, heat waves, wildfires, and droughts are already affecting San Jose residents, with historically marginalized communities impacted first and worst. The City of San Jose has ambitious goals to make the city climate-neutral by 2030 and serve as an example for cities to accelerate climate action around the world. Electrifying and decarbonizing residential buildings are important priorities to achieve San Jose's plans to reach carbon neutrality while enabling residents to live the "Good Life 2.0" - a vision that aligns with community-identified priorities of safety, health, freedom, community, and positive experiences. This technical assistance built upon the Electrify San Jose: Framework for Existing Building Electrification to identify actionable strategies to make single- and multi-family buildings both energy-efficient and all-electric. This report is intended to provide the San Jose Climate Smart team and their partners with relevant data, insights, and actionable analysis to aid in-depth and nuanced engagements with key stakeholders. In particular, the findings in this report highlight opportunities and challenges for San Jose City Council, residents, building owners, developers, financial institutions, and utility partners instrumental in building support and earning buy-in for energy efficiency and electrification upgrades in existing buildings.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Equitable Strategies for Residential Building Energy Efficiency and Electrification in San José, California [Slides]

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Desert-Urban SysTem IntegratEd AtmospherIc Monsoon (DUSTIEAIM) in the Southwestern United States

Phoenix, Arizona, is the fifth-largest city in the United States with about 1.6 million residents. It is also considered to be the hottest major city in the country, having set a record of 31 consecutive days with temperatures above 110 F (43 C) in summer 2023. Phoenix depends on the Colorado River for over a third of its water supply, but there are questions about how much water will eventually be available to serve the rapidly growing metropolitan area, especially in such extreme heat. Current earth system models struggle to accurately capture precipitation in the Southwestern United States, especially in relation to the summertime North American Monsoon.

54 ENVIRONMENTAL SCIENCES↗