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At least 55 records · Page 3

Electric Vehicle Charging Demand in the Chicago Metropolitan Area through 2030

This report outlines the collaborative efforts between Argonne National Laboratory and Exelon in advancing the Agent-Based Transportation Energy Analysis Model (ATEAM). Aligning with ComEd’s beneficial electrification plan, this study developed eight scenarios to access the temporal and spatial distribution of charging load and demand stemming from the widespread adoption of battery electric vehicles (BEV) adoption, augmented public charging infrastructure deployment, and increased multi-unit dwelling (MUD) charging availability. Enhancements to the ATEAM model encompassed the simulation of multiple days of travel behavior, estimation of total public charging infrastructure needs, user interface refinements, and output tracking at both vehicle and charging station levels. The total electricity consumption for residential and public charging to support over 800,000 BEVs in Chicago in 2029 is projected at approximately 10.2 GWh. Enhanced MUD home charging accessibility (70%) amplifies the home charging load in the study area by 1.5% compared to the baseline scenario (10%). The widespread adoption of BEVs reduces peak charging loads, owing to their inclusion across households with diverse income levels, thus fostering a more dispersed charging activity pattern. However, widespread BEV adoption increases the peak home charging load in areas with lower median household incomes, reflecting a higher BEV concentration in these locales and, subsequently, heightened peak charging demands. In the Widespread BEV adoption scenario, fewer census tracts exhibit elevated peak loads for combined home and public charging, indicating a more even distribution of charging demand across the study area. Predominantly, peak loads for combined charging—both home and public— occur between 2 p.m. and 10 p.m. across all scenarios, encompassing the majority of census tracts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Urban Land Surface Temperature Downscaling in Chicago: Addressing Ethnic Inequality and Gentrification

In this study, we developed a XGBoost-based algorithm to downscale 2 km-resolution land surface temperature (LST) data from the GOES satellite to a finer 70 m resolution, using ancillary variables including NDVI, NDBI, and DEM. This method demonstrated a superior performance over the conventional TsHARP technique, achieving a reduced RMSE of 1.90 °C, compared to 2.51 °C with TsHARP. Our approach utilizes the geostationary GOES satellite data alongside high-resolution ECOSTRESS data, enabling hourly LST downscaling to 70 m—a significant advancement over previous methodologies that typically measure LST only once daily. Applying these high-resolution LST data, we examined the hottest days in Chicago and their correlation with ethnic inequality. Our analysis indicated that Hispanic/Latino communities endure the highest LSTs, with a maximum LST that is 1.5 °C higher in blocks predominantly inhabited by Hispanic/Latino residents compared to those predominantly occupied by White residents. This study highlights the intersection of urban development, ethnic inequality, and environmental inequities, emphasizing the need for targeted urban planning to mitigate these disparities. The enhanced spatial and temporal resolution of our LST data provides deeper insights into diurnal temperature variations, crucial for understanding and addressing the urban heat distribution and its impact on vulnerable communities.

Lee, Jangho (ORCID:0000000289421092)↗

Quantifying Movement Motivations, Demand, and Inflow-Outflow Dynamics in Four Cities (New York, Chicago, Austin, and San Diego) During COVID-19: Preprint

COVID-19 has impacted human activities ranging from food delivery habits to major moving and travel decisions. It’s clear that multiple pandemic-related factors have influenced millions of relocation decisions by Americans (e.g. health risk, financial pressures, more space, employment). There are positive economic and social outcomes of this influence (e.g. remote work and education) enabling more affordable living and opportunity. This paper addresses COVID-19 impacts on mobility, especially the mobility that involves permanent relocation from place to place. Survey design and data analysis with U-Haul targeted customers in Austin, New York, San Diego, and Chicago to understand mobility, new moving dynamics, and motivations.

ADVANCED PROPULSION SYSTEMS↗

Empirical Validation of UBEM: An Assessment of Bias in Urban Building Energy Modeling for Chicago

Residential and commercial buildings currently account for 30% of total global final energy consumption. Urban-scale building energy modeling (UBEM) can enable scalable investments and unlock building improvements by quantifying energy, demand, emissions, and cost reductions of specific measures or packages for building-specific technologies in large geographic regions. While the sophistication of UBEM data sources and technologies have increased dramatically in the past decade, there remains a knowledge gap for empirical validation and sources of bias between building-specific energy models and measured data at varying geographic scales.As UBEM continues to develop, systemic analysis of accuracy, bias, and limitations of the resulting models is necessary to inform best practices and move toward standardization. These are characterized for the Automatic Building Energy Modeling (AutoBEM) software suite with an initial case study involving metered electricity consumption data from 247,188 buildings in Chicago, Illinois, USA - averaged across years 2019-2021 - compared to the following datasets: (1) the AutoBEM-generated nation-scale Model America version 2 (MAv2) data for 596,064 buildings, (2) tax assessor data for 579,829 buildings, (3) tax assessor data filled with MAv2, and (4) 102 representative dynamic archetypes. The accuracy is reported for every building type and vintage combination, along with multiple sources of bias for unique building descriptors. The AutoBEM simulation workflow produced energy consumption estimates that closely match aggregated metered electricity consumption data for different types of buildings constructed during various time periods at the city scale - with initial normalized mean bias error of 10.9%, and 1.1% after removing outliers. Contribution of statistically significant factors including building type, land use, age, and size to variance in UBEM bias is quantified.

Garg, Ankur↗

The Chicago Carnegie Hubble Program: Improving the Calibration of Type Ia Supernovae with JWST Measurements of the Tip of the Red Giant Branch

We present distances to 10 supernova (SN) host galaxies determined via the tip of the red giant branch using JWST/NIRCam and the F115W, F356W, and F444W bandpasses. The majority of the analysis was conducted on photometric catalogs that had their absolute zero-points randomized to mask information on distance. The new F115W TRGB distances, anchored by the geometric maser distance to NGC 4258, agree well with our previously derived Hubble Space Telescope (HST) TRGB distances, differing by only 1% on average and 4% on a per-galaxy basis. The color-corrected F115W TRGB is therefore equally precise a method of distance measurement as, and offers unique advantages over, its color-insensitive, I-band counterpart. We use these distances to update four published H 0 calibrations and evaluate how different SN analyses, both within and across independent groups, yield different H 0 values. For our JWST sample of 11 SNe, we find consistent values of H 0 ≃ 69 km s −1 Mpc −1 , with a dispersion of just 0.6 km s −1 Mpc −1 across the updated calibrations. When we expand the sample to 24 by combining with HST TRGB measurements, the results from different SN analyses begin to diverge, with the H 0 based on Pantheon+ and the Carnegie Supernova Project II (CSP-II), respectively, increasing by +2.0 km s −1 Mpc −1 (3.1σ significance) and +0.8 km s −1 Mpc −1 (1.4σ significance). More independent analyses of low-redshift SNe and JWST observations of the TRGB are needed to improve our understanding of systematics in distance ladder determinations of H 0 .

Hoyt, Taylor J. [Lawrence Berkeley National Labora↗

Understanding Commercial Building Energy Use in the Chicago to Green Bay and Madison Area: Building Stock Segmentation for Retrofit Planning

This report is part of the second phase of a publication series focusing on approximately 100 different local geographies, or "clusters." Each report provides characteristic features and energy data for commercial buildings in a specific area to help policy makers at the city, county, and state level better understand building energy use and emissions. This report breaks down the energy consumption and emissions of the building stock in the counties shown in Figure 2 by building type, building size, end use, energy consumption, emissions, and segment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

ARIC 2019 workshop report: The 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities: Chicago, IL, USA November 5, 2019

The advancements in sensor technology and ubiquity of connected devices has enabled the generation of large volume of disparate, dynamic and geographically distributed data both by scientific communities and citizens. With astonishing technological innovations and convergence, there have been major changes in peoples daily activities and social interaction. The socio-technological innovations motivate the concept of smart and connected cities. A smart city, however, is subjected to the same challenges as a conventional city, such as environmental damages, hazard impacts, access to services and resources, due to continuous population and economic growth. Therefore, it is imperative to improve our understanding of Resilient and Intelligent Cities in order to leverage technologies and artificial intelligence to tackle the challenges cities face, which range from climate change, public health, traffic congestion, economic growth, to digital divide, social equity, political movements, and cultural conflicts, among others.

Kar, Bandana↗

The Chicago Social Interaction Model (ChiSIM)

ChiSIM is a framework for implementing agent-based models that simulate the mixing of a synthetic population. In a ChiSIM based model, each agent, that is, each person in the simulated population, resides in a place (a household, dormitory or retirement home/long term care facility, for example), and moves among other places such as workplaces, homes, clinics, and community resources. Agents typically move between places according to their domain specific activity profile, such that each agent has a profile that determines at what times throughout the day they occupy a particular location. Once in a place, an agent mixes with other agents in some model or domain-specific way. For example, an agent may expose other agents to a disease in an epidemiological model.

Ozik, Jonathan↗

Chicago microclimate and building energy use data

These data comprise three elements: - High resolution, 90 m simulated weather data for 1 year at 15 min. intervals (with known gaps toward the end of each month). These files are in .csv format. - A mapping of individual buildings with individual IDs, their latitude/longitude location, and height. (Excel file) - Energy simulation output of these individual buildings, at 15 min. intervals for a whole year. (.json and other files)

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Chicago microclimate and building energy use data

These data comprise three elements: - High resolution, 90 m simulated weather data for 1 year at 15 min. intervals (with known gaps toward the end of each month). These files are in .csv format. - A mapping of individual buildings with individual IDs, their latitude/longitude location, and height. (Excel file) - Energy simulation output of these individual buildings, at 15 min. intervals for a whole year. (.json and other files)

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Carbonyl sulfide fluxes and concentrations from midwestern US (Bondville, FermiLab and Chicago)

This data package includes measurements of carbonyl sulfide (OCS) fluxes and concentrations from flux towers in the midwestern US. The measurements were made at AmeriFlux towers and thus the data package also includes a subset of the standard eddy covariance and meteorological data (e.g. VPD and NEE) that are also available from the AmeriFlux portal. The measurements presented here were made to provide an independent constraint on whole ecosystem stomatal conductance from these ecosystems with the goal of testing how the relationship between gross primary production and stomatal conductance varies through the growing season. Furthermore, we derived whole canopy nighttime stomatal conductance estimates to test how stomatal conductance varies between native prairie and maize ecosystems. All data presented here were discussed in the publication "Seasonal evolution of canopy stomatal conductance for a prairie and maize field in the midwestern US from continuous carbonyl sulfide fluxes" in Geophysical Research Letters (doi: 10.1029/2019GL085652).

54 ENVIRONMENTAL SCIENCES↗

CROCUS Air Quality Data at Chicago State University Prairie Site

The Air Quality Transmitter (AQT) (Vaisala AQT530) instrument provides observations on meteorological conditions, including particulate matter (PM2.5, PM10), gas species concentrations (Nitric Oxide (NO), Nitrogen Dioxide (NO2), Ozone (O3)), and environment temperature and moisture. These measurements are critical for understanding air quality. These measurements are useful for understanding changes in aerosol properties, air quality research, and comparing to model experiments especially in urban environments.This dataset is from the Department of Energy Office of Science funded project, Community Research on Urban and Climate Science (CROCUS) (https://crocus-urban.org/). Datasets are stored in the netCDF data format, and we we encourage users to make use the associated toolkits available from Unidata (https://www.unidata.ucar.edu/software/netcdf/), Project Pythia (https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html), and our “Instrument Cookbooks” (https://crocus-urban.github.io/instrument-cookbooks) for more information on how to process the metadata-rich datasets.

54 ENVIRONMENTAL SCIENCES↗