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NASA NTRS2018

Top-Down NOx Emissions of European Cities Based on the Downwind Plume of Modelled and Space-Borne Tropospheric NO2 Columns

Top-down estimates of surface NOX emissions were derived for 23 European cities based on the downwind plume decay of tropospheric nitrogen dioxide (NO2) columns from the LOTOS-EUROS (Long Term Ozone Simulation-European Ozone Simulation) chemistry transport model (CTM) and from Ozone Monitoring Instrument (OMI) satellite retrievals, averaged for the summertime period (April-September) during 2013. Here we show that the top-down NOX emissions derived from LOTOS-EUROS for European urban areas agree well with the bottom-up NOX emissions from the MACC-III inventory data (R(exp 2) = 0.88) driving the CTM demonstrating the potential of this method. OMI top-down NOX emissions over the 23 European cities are generally lower compared with the MACC-III emissions and their correlation is slightly lower (R(exp 2) = 0.79). The uncertainty on the derived NO2 lifetimes and NOX emissions are on average ~55% for OMI and ~63% for LOTOS-EUROS data. The downwind NO2 plume method applied on both LOTOS-EUROS and OMI tropospheric NO2 columns allows to estimate NOX emissions from urban areas, demonstrating that this is a useful method for real-time updates of urban NOX emissions with reasonable accuracy.

NOx emissions↗

NASA NTRS2019

New York City Panel on Climate Change 2019 Report Chapter 4: Coastal Flooding

Coastal flooding from storm surge is one of the most dangerous and damaging natural hazards that societies face. It was responsible for half of all hurricane related mortalities in the United States from 1963 to 2012, far more than any other factor. Coastal extreme water levels are increasing globally, mainly driven by rises in mean sea level. Sea level rise is also causing rapid increases in the annual number of shallow “nuisance floods” for low-lying neighborhoods. The objectives of this chapter are to review the latest knowledge on New York City flood risk from storms and tides, and to evaluate how climate change will affect this risk between now and the end of the century. Methods used by NPCC (2015) for assessing storm-driven extreme floods are generally repeated here, including the use of the Federal Emergency Management Agency (FEMA, 2013) baseline flood hazards (e.g., the 100-year flood(sup a)) and the methods for adding sea level rise and mapping the resulting hazard. New advancements include an innovative analysis of monthly tidal flooding based on a dynamic model, a broadened set of sea level rise scenarios supplemented with the Antarctic Rapid Ice Melt (ARIM) scenario (see Chapter 3), and sensitivity analyses that show how differing methods would affect our results. Wind is a primary factor for coastal storm surge, and a brief review is given in Appendix 4.A, with the latest scientific knowledge on what drives extreme wind events in the New York City area and how they may change in the future.

Orton, Philip↗

NASA NTRSDate not supplied

Building a Real-Time Flood Prediction Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the local government of Howard County, Maryland, to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a statistical model capable of hindcasting the two severe flash flood events that devastated Ellicott City and transitioned to a ‘Long Short-Term Memory’ based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using Nash-Sutcliffe Efficiency. The final product, the Sequentially Trained Real-time EstimAted Model (STREAM) predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

NASA DEVELOP↗

NASA NTRSDate not supplied

Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the Howard County government in Maryland to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a prediction model capable of hindcasting the two severe flash flood events that devastated Ellicott City, and transitioned to an Long Short-Term Memory (LSTM) based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using the Nash-Sutcliffe Efficiency (NSE). The final product, called the Sequentially Trained Real-time EstimAted Model (STREAM), predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh (HRRR) model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate the OEM’s emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

Ryan Hammock↗

NASA NTRS2021

Cities Exacerbate Climate Warming

Urbanization is a complex land transformation, not explicitly resolved within large-scale climate models. Long-term timeseries of high-resolution satellite data are essential to characterize it within land surface models and assess its contribution to surface temperature changes. The potential for additional surface warming from urbanization-induced land use change is investigated and de-coupled from that due to change in climate over the continental US using a decadal timescale. We show that, aggregated over the US, the summer mean urban-induced surface temperature increased by 0.15°C, with a warming of 0.24°C in cities built in vegetated areas and a cooling of 0.25°C in cities built in non-vegetated arid areas. This temperature change is comparable in magnitude to the 0.13°C/decade global warming trend observed over the last 50 years caused by increased CO2. We also show that the effect of urban-induced change on surface temperature is felt above and beyond that of the CO2 effect. Our results suggest that climate mitigation policies must consider urbanization feedback to put a limit on the worldwide mean temperature increase.

Lahouari Bounoua↗

NASA NTRS2019

Enhanced Capabilities of TROPOMI NO2: Estimating NOX from North American Cities and Power Plants

The TROPOspheric Monitoring Instrument (TROPOMI) is used to derive top-down NOXemissions for twolarge power plants and three megacities in North America. Wefirst re-process the vertical column NO2with an improved airmass factor to correct for a known systematic low bias in the operational retrieval near urban centers. For the two power plants,top-down NOXemissions agree to within 10% of the emissions reported by the power plants. We then derive top-down NOXemissions rates for New York City, Chicago, and Toronto, and compare them to projected bottom-up emissions inventories. Inthis analysis of 2018 NOXemissions, wefind a +22% overestimate for New York City, a−21% underestimate in Toronto, andgood agreement in Chicago in the projected bottom-up inventories when compared to the top-down emissions. Top-downNOXemissions also capture intraseasonal variability, such as the weekday versus weekend effect (emissions are +45% larger onweekdays versus weekends in Chicago). Finally, we demonstrate the enhanced capabilities of TROPOMI, which allow us toderive a NOXemissions rate for Chicago using a single overpass on July 7, 2018. The large signal-to-noise ratio of TROPOMI iswell-suited for estimating NOXemissions from relatively small sources and for sub-seasonal timeframes.

TROPOspheric Monitoring Instrument (TROPOMI)↗

NASA NTRS2022

Embedding Climate Change in Urban Planning and Urban Design in New York City

Confronting the challenges of a rapidly urbanizing world threatened by climate change requires expanding the traditional influence and capabilities of urban planning and urban design, integrating climate science, natural systems and compact urban form to configure dynamic, desirable and healthy communities. Cost-effective planning and design measures that help mitigate emissions while bringing adaptive benefits should be prioritized. The chapter draws from the publication Climate Change and Cities (Cambridge University Press 2018) by the Urban Climate Change Research Network (UCCRN). The two-phase New York City case study by the Urban Design Climate Lab at the New York Institute of Technology and a team of international urban design climate experts illustrates how this emerging expertise can be replicated and implemented worldwide. Its focus on configuring people-centered public spaces that enhance energy efficiency and improve public health draws from four urban climate factors: improving efficiency of urban systems, both in energy and transportation; optimizing the form and layout of urban districts to enhance ventilation; promoting appropriate building materials with high reflectivity; and increasing green and blue urban infrastructure. The chapter highlights a set of tools and methods to measure success.

Resiliency↗

NASA NTRSDate not supplied

Assessing Environmental and Socioeconomic Factors of Urban Flood Vulnerability in Kansas City, Kansas

Pluvial flooding, over-saturated ground, and drainage systems disproportionately impact historically marginalized urban neighborhoods during extreme rainfall events. These communities are impacted by physical and socioeconomic factors that make them vulnerable to flooding events, such as high concentrations of impervious landcover, high precipitation rates, and a combined sewer system framework. Despite known vulnerability to environmental hazards, understanding potential pluvial street-level flooding events are largely unknown. Using the open-source National Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation model, NASA DEVELOP examined neighborhood scale runoff retention and potential economic damages for risk mapping throughout Kansas City, Kansas. The generated outputs aid in identifying vulnerable neighborhoods susceptible to flooding and in need of future intervention. Previous studies have applied this model framework to understand urban flood vulnerability regarding ecosystem services, urban planning, and flood mitigation strategies. We utilized the InVEST outputs to develop indices pertaining to environmental justice factors of race, socioeconomic status, social vulnerability, and health. The findings indicate that historically redlined neighborhoods in Kansas City, Kansas face disproportional impacts from flood events and are subject to greater environmental stressors. This research provides an approach to utilizing an open-source flood vulnerability model to empower neighborhood-scale environmental justice analysis, enhancing the local communities' understanding of present-day impacts of historical environmental injustices.

Hadwynne Gross↗

NASA NTRS2023

The Compound Risk of Heat and COVID-19 in New York City: Riskscapes, Physical and Social Factors, and Interventions

Climate change is disrupting the fundamental conditions of human life and exacerbating existing inequity by placing further burdens on communities that are already vulnerable. Risk exposure varies by where people live and work. In this article, we examine the spatial overlap of the compound risks of COVID-19 and extreme heat in New York City. We assess the relationship between socio-demographic and natural, built and social environmental characteristics, and the spatial correspondence of COVID-19 daily case rates across three pandemic waves. We use these data to create a compound risk index combining heat, COVID-19, density and social vulnerability. Our findings demonstrate that the compound risk of COVID-19 and heat are public health and equity challenges. Heat and COVID-19 exposure are influenced by natural, built, and social environmental factors, including access to mitigation infrastructure. Socio-demographic characteristics are significant indicators of COVID-19 and heat exposure and of where compound vulnerability exists. Using GIS mapping, we illustrate how COVID-19 risk geographies change across the three waves of the pandemic and the particular impact of vaccinations before the onset of the third wave. We, then, use our compound risk index to assess heat interventions undertaken by the City, identify neighborhoods of both adequate and inadequate coverage and provide recommendations for future interventions.

COVID-19↗

DOE OSTI2023

GSA Oklahoma City Federal Building: Smart Buildings Case Study

The purpose of this smart buildings case study is to showcase a leading example of a GEB renovation project in the federal buildings space and provide key information on the project roles, processes, costs, and benefits. The findings from this successful GEB project can be used to help pave the way for additional GEB-ready retrofits in the future. The General Services Administration’s (GSA’s) Oklahoma City (OKC) Federal Building, located in downtown Oklahoma City, Oklahoma, demonstrates that GEB-ready strategies and technologies can be realistically deployed today across buildings with minimal investment. The project team implemented nine energy conservation measures (ECMs) and/or smart building technologies, making it a leading example of a smart, sustainable, and efficient commercial building. The case study highlights the challenges and the lessons learned throughout the project design and execution in addition to some of the best practices and considerations when implementing GEB technologies.

Butrico, Mark↗

DOE OSTI2024

Privacy by Design in Distributed Edge Systems: Innovating Secure Workflows for Smart Cities

The proliferation of distributed edge systems, such as those in smart cities, healthcare, and industrial IoT, offers unprecedented opportunities for data processing closer to its source, thereby reducing latency and enhancing efficiency. However, these systems also present significant privacy challenges due to the handling of sensitive data from multiple sources. This article explores the critical need for designing privacy-preserving workflows in distributed edge systems to ensure data security while maximizing the potential of edge computing. By examining the challenges, technological advancements, and potential of privacy-by-design approaches, we highlight the importance of integrating advanced privacy-preserving techniques like federated learning, differential privacy, homomorphic encryption, secure multi-party computation, and zero-knowledge proofs. These innovations are crucial for enhancing data security, regulatory compliance, and public trust in smart city applications, ultimately leading to safer and more efficient urban environments.

Kotevska, Olivera↗

DOE OSTI2024

Clean Cities Coalitions: 2022 Activity Report

The U.S. Department of Energy's (DOE's) Vehicle Technologies Office (VTO) works with local Clean Cities coalitions across the country as part of its Technology Integration Program. These efforts help businesses and consumers make smarter and more informed transportation energy choices that can save energy, lower costs, provide resilience through fuel diversification, and reduce emissions. This report summarizes the success and impact of coalition activities based on data and information provided in their annual reports.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

CMCSCFCB—Critical Minerals in Coaly Strata of the Cherokee-Forest City Basin

This report summarizes results of CMCSCFCB—Critical Minerals in Coaly Strata of the Cherokee-Forest City Basin, a multiyear and multidisciplinary study funded through DE-FOA-0002364 as part of the Department of Energy Office of Fossil Energy and Carbon Management (FECM) Carbon Ore, Rare Earth and Critical Minerals (CORE-CM) Initiative for U.S. Basins. Our working group of state and tribal agencies collected data and made interpretations included herein. It also surveyed the regional potential for critical mineral/material production.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2023

Clean Cities Coalitions: Advancing Affordable, Efficient, and Clean Transportation Fuels and Technologies

A collaborative network of more than 75 Clean Cities coalitions boosts the country's economic vitality, energy security, and quality of life by advancing the deployment of affordable, efficient, and clean transportation fuels and technologies. Coalitions provide the technical expertise local decision makers and fleets need to understand and implement alternative and renewable fuels, electric vehicles, idle-reduction measures, fuel economy improvements, new mobility choices, and emerging transportation technologies.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Clean Cities Coalitions: Advancing Affordable, Efficient, and Clean Transportation Fuels and Technologies

This fact sheet provides an overview of the U.S. Department of Energy's (DOE's) Vehicle Technologies Office Clean Cities Coalition Network, which boosts the country's economic vitality, energy security, and quality of life by advancing the deployment of affordable, efficient, and clean transportation fuels and technologies. Coalitions provide the technical expertise local decision makers and fleets need to understand and implement alternative and renewable fuels, electric vehicles, idle-reduction measures, fuel economy improvements, new mobility choices, and emerging transportation technologies.

accomplishments↗

DOE OSTI2023

Clean Cities Coalitions: Advancing Affordable, Efficient, and Clean Transportation Fuels and Technologies

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Virtual storage capability of residential buildings for sustainable smart city via model-based predictive control

This paper evaluates the virtual storage capability of a residential air-conditioning (AC) system by utilizing the building mass as a thermal storage to enable sustainable cities through model-based predictive control (MPC). The control-oriented building model was developed with a grey-box model structure based on the experimental data to predict the indoor air temperature. Based on the model, the operation cost-saving potential of the MPC was investigated in a single house with different comfort levels, electricity prices, and prediction horizons. The cost-saving of the MPC was approximately 10.6 % compared to the conventional control. The MPC was expanded to a residential building cluster considering the peak demand charge. In an hourly shedding and load-up scenario, the performances of the MPC and the heuristic prior-based control (PBC) are promising. When shedding was enforced all day, the peak demand saving of the MPC was 36~38 %, whereas that of the PBC was 25.7 % compared to the baseline. In addition, the monthly electricity bill, including the operation and peak demand cost, was investigated with different demand charge rates and weather conditions. The total saving decreased with a low demand charge rate and hot conditions, and the operation cost saving was compromised with more aggressive demand shedding.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Complexities of urban impacts on long-term seasonal trends in a mid-sized arid city

The variability of surface air temperature is of great importance for both society and the environment, is impacted by global warming and local-scale changes. In the arid eastern part of Washington state of USA, substantial urbanization has transformed the Tri-cities into the state’s third-largest urban cluster. This study utilizes a combination of in situ observations and reanalysis datasets to investigate the influence of land use changes on the region’s 2-meter temperature, revealing local effects that compensate for the background global warming. Within the urban fabric, distinct microclimates have emerged due to varying land use, establishing unique relationships between greenness and temperature alterations caused by land use transitions. Notably, our findings demonstrate that the observed compensating signal in the temperature of farmland locations is primarily attributed to local vegetation increases. Through these observations, this research highlights the urban impacts on local climate, offering valuable insights into the complexities surrounding land use and its consequences on the environment.

54 ENVIRONMENTAL SCIENCES↗