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DOE OSTI2019

Evaluation of China’s Environmental Pressures Based on Satellite NO 2 Observation and the Extended STIRPAT Model

China’s rapid urbanization and industrialization have affected the spatiotemporal patterns of nitrogen dioxide (NO 2 ) pollution, which has caused greater environmental pressures. In order to mitigate the environmental pressures because of NO 2 pollution, it is of vital importance to investigate the influencing factors. We first obtained data for NO 2 pollution at the city level using satellite observation techniques and analyzed its spatial distribution. Next, we introduced a theoretical framework, an extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model, to quantify the relationship between NO 2 pollution and its contributing natural and socio-economic factors. The results are as follows. Cities with high NO 2 pollution are mainly concentrated in the North China Plain. On the contrary, southwestern cities are characterized by low NO 2 pollution. In addition, we find that population, per capita gross domestic product, the share of the secondary industry, ambient air pressures, total nighttime light data, and urban road area have a positive impact on NO 2 pollution. In contrast, increases in the normalized difference vegetation index (NDVI), relative humidity, temperature, and wind speed may reduce NO 2 pollution. These empirical results should help the government to effectively and efficiently implement further emission reductions and energy saving policies in Chinese cities in a bid to mitigate the environmental pressures.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Airport Analyses Informing New Mobility Shifts: Opportunities to Adapt Energy-Efficient Mobility Services and Infrastructure: Preprint

An airport is one of the most important assets for a region's economic development and connectivity with the rest of the nation and world. Key aspects for investigation of energy efficient mobility at airports is ground transportation including factors ranging from the infrastructure, mobility services, and associated revenues. Data is critical to understand the maturity of new mobility services that can inform both cities and airports on how to respond, approach, manage, and adapt to the challenges, opportunities, and uncertainties associated with shifts in new mobility that influence human behavior, energy-efficiency and sustainability strategies. One key question identified in this article is how quickly we are adapting to new mobility options - such as app-based ride-hailing and 'pooling' services - that may provide an opportunity to influence energy efficiency of ground transportation to and from airports. By starting with airports in the regions of four smart city finalists in the U.S. DOT Smart City Challenge, this paper focuses on key observability aspects of new modes and the rate of shifts in mobility patterns across San Francisco, Portland, Denver, and Kansas City. With the emerging megatrend of rising urbanization and rising air travel demand (a predicted doubling in demand by 2035), airports are expected to increasingly be on the front lines of adaptation to new transportation technology and services in terms of infrastructure investments, policies, and revenues. As airports have demonstrated the most potential and capability of any public institution to implement fees for new ride-hailing services, they are also a prime resource for collecting important data to help understand smart mobility transitions. Results focused on the shifts in revenues for ground transportation at airports offer one vantage point into the pace of transitions and adaptations in the new emerging mobility landscape, and present an opportunity to analyze how future adaptations could support more energy-efficient scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Drivers of Change Analysis for Local GHG Emissions: Data for Better Decisions

The City of Bellevue, Washington, and ICLEI Local Governments for Sustainability with funding from the Energy Departments Cities Leading through Energy Analysis and planning (Cities-LEAP) program, developed a toolkit to give cities more insight into what is driving changes in local greenhouse gas (GHG) emissions from year to year.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2018

Piloting the Use of Energy Policy Data to Drive Market Action

Information is only useful when put to use. Across the U.S., an ever-growing number of state and local jurisdictions are implementing building performance reporting laws regarding building energy and water use in the commercial and multifamily sectors. This wealth of performance information is not yet being deployed to its full potential to drive smarter business decisions and increase energy savings. Recognizing that this information can help advance energy efficiency in cities, in 2015 the Institute for Market Transformation (IMT) partnered with New York City and the District of Columbia and local groups, NYCEEC and the District of Columbia Sustainable Energy Utility, on a three-year project to understand and document how, exactly, this data can and should be integrated to reap an array of benefits for cities, energy efficiency service providers, utilities, and building owners. This paper examines how local governments, utilities, and program implementers can replicate the success of DC and NYC. It gathers the findings from the Cities’ and their partners’ efforts to fully understand the datasets, overcome integration challenges, and apply the data to visual engagement, strategic outreach, evidence-based policymaking, and retrofit project identification and deployment. The paper goes beyond theoretical discussion by outlining practical application of data using specific examples from NYC and DC, summarizing challenges and findings, and highlighting next steps for other jurisdictions to learn from and build off the work done in those jurisdictions.

Cities↗

DOE OSTI2017

Climate Resilience and the Design of Smart Buildings

How will buildings of the future achieve increased climate resilience while operating at low energy utilization indices? Could smart buildings be networked and designed to foresee energy consumption variations and change their performance in real-time? This chapter will attempt to answer these questions by a) reviewing existing literature on expected climate change impacts to cities and buildings, b) reviewing of existing climate change action plans and initiatives for cities, c) discussing current efforts in designing smart, climate resilient buildings and d) discussing building design strategies for optimizing energy use and addressing future climate change in a smart city network. The objective of this chapter is to provide readers with state of the art knowledge and actionable approaches to addressing climate change, in the context of future city developments, and its impact on building energy consumption and resilience.

smart buildings, climate change, greenhouse gas em↗

DOE OSTI2018

Optimizing Travel Time to Outpatient Interventional Radiology Procedures in a Multi-Site Hospital System Using a Google Maps Application

The purpose of this study is to determine whether a custom Google Maps application can optimize site selection when scheduling outpatient interventional radiology (IR) procedures within a multi-site hospital system. The Google Maps for Business Application Programming Interface (API) was used to develop an internal web application that uses real-time traffic data to determine estimated travel time (ETT; minutes) and estimated travel distance (ETD; miles) from a patient’s home to each a nearby IR facility in our hospital system. Hypothetical patient home addresses based on the 33 cities comprising our institution’s catchment area were used to determine the optimal IR site for hypothetical patients traveling from each city based on real-time traffic conditions. For 10/33 (30%) cities, there was discordance between the optimal IR site based on ETT and the optimal IR site based on ETD at non-rush hour time or rush hour time. By choosing to travel to an IR site based on ETT rather than ETD, patients from discordant cities were predicted to save an average of 7.29 min during non-rush hour (p = 0.03), and 28.80 min during rush hour (p < 0.001). Using a custom Google Maps application to schedule outpatients for IR procedures can effectively reduce patient travel time when more than one location providing IR procedures is available within the same hospital system.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2023

DownScaleBench for developing and applying a deep learning based urban climate downscaling- first results for high-resolution urban precipitation climatology over Austin, Texas

Abstract Cities need climate information to develop resilient infrastructure and for adaptation decisions. The information desired is at the order of magnitudes finer scales relative to what is typically available from climate analysis and future projections. Urban downscaling refers to developing such climate information at the city (order of 1 – 10 km) and neighborhood (order of 0.1 – 1 km) resolutions from coarser climate products. Developing these higher resolution (finer grid spacing) data needed for assessments typically covering multiyear climatology of past data and future projections is complex and computationally expensive for traditional physics-based dynamical models. In this study, we develop and adopt a novel approach for urban downscaling by generating a general-purpose operator using deep learning. This ‘DownScaleBench’ tool can aid the process of downscaling to any location. The DownScaleBench has been generalized for both in situ (ground- based) and satellite or reanalysis gridded data. The algorithm employs an iterative super-resolution convolutional neural network (Iterative SRCNN) over the city. We apply this for the development of a high-resolution gridded precipitation product (300 m) from a relatively coarse (10 km) satellite-based product (JAXA GsMAP). The high-resolution gridded precipitation datasets is compared against insitu observations for past heavy rain events over Austin, Texas, and shows marked improvement relative to the coarser datasets relative to cubic interpolation as a baseline. The creation of this Downscaling Bench has implications for generating high-resolution gridded urban meteorological datasets and aiding the planning process for climate-ready cities.

Singh, Manmeet (ORCID:0000000233747149)↗

DOE OSTI2021

Urban cells: Extending the energy hub concept to facilitate sector and spatial coupling

The rapid growth of urban areas and concerns over climate change make it vital to improve the energy sustainability of cities. Understanding the complex interactions within different sectors (sectoral) and localities (spatial) of cities plays a crucial role in improving efficiency and sustainability, which is extremely challenging due to the complex urban morphology. State-of-the-art energy concepts do not facilitate a detailed consideration of both sectoral and spatial coupling that energy infrastructure maintains at the urban scale. This has become a significant challenge when designing interconnected urban energy infrastructure. The Urban Cell concept is introduced to address this bottleneck. A novel computational model using a modular approach is introduced to create an interconnected urban infrastructure, including the energy, building, and transportation sectors. Optimal sizing of the distributed energy system (including renewables, energy storage, and dispatchable sources) and optimal urban morphology is determined within a modular unit. A game-theoretic approach is used to model the interactions between urban cells (modular units). The study revealed that the urban cell concept can reduce the net present value of the interconnected energy infrastructure by 37% while increasing the installed renewable energy capacity by 25%. This demonstrates the benefit potential of urban cells and the importance of considering interactions between different sectors and different parts within a city. The Urban Cell concept can be used to present the complex interactions maintained within a city.

Perera, ATD↗

DOE OSTI2019

Ten questions on urban building energy modeling

Buildings in cities consume up to 70% of all primary energy. To achieve cities’ energy and climate goals, it is necessary to reduce energy use and associated greenhouse gas emissions in buildings through energy conservation and efficiency improvements. Computational tools empowered with rich urban datasets can model performance of buildings at the urban scale to provide quantitative insights for stakeholders and inform their decision making on urban energy planning, as well as building energy retrofits at scale, to achieve efficiency, sustainability, and resilience of urban buildings. Designing and operating urban buildings as a group (from a city block to a district to an entire city) rather than as single individuals requires simulation and optimization to account for interactions among buildings and between buildings and their surrounding urban environment, and for district energy systems serving multiple buildings with diverse thermal loads across space and time. When hundreds or more buildings are involved in typical urban building energy modeling (UBEM) to estimate annual energy demand, evaluate design or retrofit options, and quantify impacts of extreme weather events or climate change, it is crucial to integrate urban datasets and UBEM tools in a seamless automatic workflow with cloud or high-performance computing for users including urban planners, designers and researchers. This paper presents ten questions that highlight significant UBEM research and applications. The proposed answers in this work aim to stimulate discussion and provide insights into the current and future research on UBEM, and more importantly, to inspire new and important questions from young researchers in the field.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Ten questions on building stock modeling to inform energy efficiency and sustainability

To enhance economic competitiveness and ensure energy efficiency, resilience, and security, cities and governments are adopting technologies and strategies to improve their existing building stocks. This approach aims to reduce energy use, improve energy affordability, and ensure a reliable power supply while safeguarding occupants during extreme weather events that may disrupt energy services. The effectiveness of these solutions will depend on building stock characteristics, use patterns, weather conditions, evolving technologies and their markets, and a city’s socio-economic conditions. This paper presents ten questions and answers that highlight the most important issues regarding the use of building stock modeling as a powerful tool to provide insights for informing stakeholders’ actions and decision-making on energy efficiency, costs reduction, and resilience of buildings in cities. Building stock modeling should build upon the fit-for-purpose framework, balancing the use case accuracy requirements, level of complexity, and needed resources (expertise, compute). The advancements in Artificial Intelligence (AI), the increasingly available open dataset of building stock in cities, and the more affordable powerful computing will accelerate the adoption of building stock modeling across scales by researchers and practitioners to inform decision making on sustainability and efficiency.

AI↗

DOE OSTI2021

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

A comparison between residential relocation timing of Sydney and Chicago residents: A Bayesian survival analysis

We report that understanding households' behaviour in residential relocation timing is of great importance in the field of transport engineering and economics. This research aims to develop a residential relocation model by considering the potential dynamic impacts of other households' decisions and variables, including economic and demographic attributes, housing features, intra-household decision-making structures, travel mode choice, and other life-course attributes. A multivariate parametric survival model with both fixed and time-varying covariates is developed. To the best of the authors' knowledge, this study is the first paper in the literature of residential relocation timing to propose the use of a Bayesian model in contrast to the widely used classic frequentist approach and have conducted a discussion on its advantages. An emerging residential relocation dataset collected for two cities in Australia and the USA (Sydney and Chicago cities) has been used, which covers residence, vehicle ownership, occupation, education, economic and demographic attributes of respondents. A comprehensive comparison between the results of two cities and a comparison between two Bayesian and frequentist approaches are made. This study confirms the impact of life-course variables, intra-household decision-making behaviours, and sociodemographic attributes on home mobility. According to the model outputs, the accelerating or decelerating impact of explanatory variables on the relocation timing has been almost the same in the two cities. The Bayesian model was confirmed to have some advantages over the frequentist model, including being straightforward to interpret, availability of making inferences on the results, and ease of handling complex models, and optimisation convergence complexities.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2015

Sustainability on the urban scale: Proposal of a structure of indicators for the Spanish context

Some efforts to assess sustainability on the urban scale have been made and different tools for measuring the impact on and caused by cities have emerged. However, the sustainability concept varies from region to region, and indicators to measure it should be suitable for the context-specific conditions of the region under study. After doing a comprehensive review of the indicators included in 13 tools developed to assess urban sustainability of cities, this article proposes a new structure of indicators adapted to a Mediterranean city in Spain. The proposed structure is based on a two-level scheme that consists in 14 categories and 63 subcategories, which agglutinate urban sustainability indicators according to their purpose. This structure suggests a set of comprehensible qualitative and quantitative indicators that are easily applicable on neighbourhood or city scales. Given the similar features of Mediterranean countries in terms of environmental and socio-economic aspects, the proposed structure could be extrapolated to other countries with climatic and cultural similarities. Otherwise, the system is a useful tool in the decision-making process to help the different stakeholders involved in new urban developments and regeneration projects in existing neighbourhoods, such as developers, urban planners and public administrations. - Highlights: • Comprehensive review of 13 urban sustainability assessment tools • Proposal of a two-level structure to cluster urban sustainability indicators • Inclusion of sustainability criteria for urban planning projects and interventions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Assessing the urban carbon footprint: An overview

All cities present environmental sustainability issues, above all regarding greenhouse gas (GHG) emissions, and specifically carbon dioxide (CO{sub 2}), that directly affect climate change. Consequently, it is very important to quantify and report their Carbon Footprint (CF) for implementing national and international policies/strategies aimed at mitigating and adapting these concerns. The Urban Carbon Footprint (UCF), indeed, has been recognized as the more valuable choice to inform, specifically, decision makers about city environmental sustainability. Several accounting systems and inventory methods have been taken into account to perform UCF, highlighting the complexity of the topic and generating very often confusion among users. In this context, the authors aim to summarize what has been done and what is going on with UCFs, trying to classify them according to some principal dimensions. Thus, they divide UFCs in two main categories namely: “spatial” or “direct”, with a limited amount of data requested, and “economic” or “life cycle based”, more or less data inclusive according to the accounting systems considered. Furthermore, they observe that there is not a “global agreed-upon protocol” yet, neither is there a specific model shared among researchers, even if some steps have been made towards this direction (Relative Carbon Footprint - RCF, Publicly Available Specification – PAS 2070 and Global Protocol for Community scale - GPC). Consequently, it is necessary to complete and standardize, in the short term, the accounting and reporting frameworks, in order to compare different UCFs for adopting shared climate strategies and actions at global level. - Highlights: •Cities present environmental sustainability issues. •GHG emissions - specifically carbon dioxide - directly affect climate change. •UCF is a tool to support policy makers towards city environmental sustainability. •There is not a “global agreed-upon protocol” to perform UFC. •There are still missing points to be considered to provide a sharing model.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

Modeling urban building energy use: A review of modeling approaches and procedures

With rapid urbanization and economic development, the world has been experiencing an unprecedented increase in energy consumption and greenhouse gas (GHG) emissions. While reducing energy consumption and GHG emissions is a common interest shared by major developed and developing countries, actions to enable these global reductions are generally implemented at the city scale. This is because baseline information from individual cities plays an important role in identifying economical options for improving building energy efficiency and reducing GHG emissions. Numerous approaches have been proposed for modeling urban building energy use in the past decades. This paper aims to provide an up-to-date review of the broad categories of energy models for urban buildings and describes the basic workflow of physics-based, bottom-up models and their applications in simulating urban-scale building energy use. Because there are significant differences across models with varied potential for application, strengths and weaknesses of the reviewed models are also presented. This is followed by a discussion of challenging issues associated with model preparation and calibration.

Bottom-up approaches↗

DOE OSTI2023

Using deep transfer learning and satellite imagery to estimate urban air quality in data-poor regions

Urban air pollution is a critical public health challenge in low-and-middle-income countries (LMICs). At the same time, LMICs tend to be data-poor, lacking adequate infrastructure to monitor air quality (AQ). As LMICs undergo rapid urbanization, the socio-economic burden of poor AQ will be immense. Here we present a globally scalable two-step deep learning (DL) based approach for AQ estimation in LMIC cities that mitigates the need for extensive AQ infrastructure on the ground. We train a DL model that can map satellite imagery to AQ in high-income countries (HICs) with sufficient ground data, and then adapt the model to learn meaningful AQ estimates in LMIC cities using transfer learning. The trained model can explain up to 54% of the variation in the AQ distribution of the target LMIC city without the need for target labels. The approach is demonstrated for Accra in Ghana, Africa, with AQ patterns learned and adapted from two HIC cities, specifically Los Angeles and New York.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Ecosystem services: Urban parks under a magnifying glass

Highlights: • We mapped ecosystem services in an urban park with high spatial detail. • Ecosystem services varied widely with vegetation type. • Native forest provided the most ecosystem services. • Urban parks should not be seen homogenous units regarding ecosystem services. • Nature-based solutions can help managers to optimize ecosystem services provision. Urban areas’ population has grown during the last century and it is expected that over 60% of the world population will live in cities by 2050. Urban parks provide several ecosystem services that are valuable to the well-being of city-dwellers and they are also considered a nature-based solution to tackle multiple environmental problems in cities. However, the type and amount of ecosystem services provided will vary with each park vegetation type, even within same the park. Our main goal was to quantify the trade-offs in ecosystem services associated to different vegetation types, using a spatially detailed approach. Rather than relying solely on general vegetation typologies, we took a more ecologically oriented approach, by explicitly considering different units of vegetation structure and composition. This was demonstrated in a large park (44 ha) located in the city of Almada (Lisbon metropolitan area, Portugal), where six vegetation units were mapped in detail and six ecosystem services were evaluated: carbon sequestration, seed dispersal, erosion prevention, water purification, air purification and habitat quality. The results showed that, when looking at the park in detail, some ecosystem services varied greatly with vegetation type. Carbon sequestration was positively influenced by tree density, independently of species composition. Seed dispersal potential was higher in lawns, and mixed forest provided the highest amount of habitat quality. Air purification service was slightly higher in mixed forest, but was high in all vegetation types, probably due to low background pollution, and both water purification and erosion prevention were high in all vegetation types. Knowing the type, location, and amount of ecosystem services provided by each vegetation type can help to improve management options based on ecosystem services trade-offs and looking for win-win situations. The trade-offs are, for example, very clear for carbon: tree planting will boost carbon sequestration regardless of species, but may not be enough to increase habitat quality. Moreover, it may also negatively influence seed dispersal service. Informed practitioners can use this ecological knowledge to promote the role of urban parks as a nature-based solution to provide multiple ecosystem services, and ultimately improve the design and management of the green infrastructure. This will also improve the science of Ecosystem Services, acknowledging that the type of vegetation matters for the provision of ecosystem services and trade-offs analysis.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

A new approach to modeling temperature-related mortality: Non-linear autoregressive models with exogenous input

Highlights: • NARX models are established in other environmental research, but are unused in temperature-related mortality research. • NARX models do not assume any particular shape to the temperature-mortality association. • NARX models are cross-validated and are easily modified to take on real-time, multi-step-ahead predictions and hindcasts. • Year-round, daily mortality in 41 different US cities was modeled using an ensemble of NARX models in each location. • Models show skill everywhere. Results vary spatially and seasonally, improving upon a persistence model by 17% points. • When compared with distributed lag non-linear models, the output time series are largely similar Temperature-mortality relationships are nonlinear, time-lagged, and can vary depending on the time of year and geographic location, all of which limits the applicability of simple regression models in describing these associations. This research demonstrates the utility of an alternative method for modeling such complex relationships that has gained recent traction in other environmental fields: nonlinear autoregressive models with exogenous input (NARX models). All-cause mortality data and multiple temperature-based data sets were gathered from 41 different US cities, for the period 1975–2010, and subjected to ensemble NARX modeling. Models generally performed better in larger cities and during the winter season. Across the US, median absolute percentage errors were 10% (ranging from 4% to 15% in various cities), the average improvement in the r-squared over that of a simple persistence model was 17% (6–24%), and the hit rate for modeling spike days in mortality (>80th percentile) was 54% (34–71%). Mortality responded acutely to hot summer days, peaking at 0–2 days of lag before dropping precipitously, and there was an extended mortality response to cold winter days, peaking at 2–4 days of lag and dropping slowly and continuing for multiple weeks. Spring and autumn showed both of the aforementioned temperature-mortality relationships, but generally to a lesser magnitude than what was seen in summer or winter. When compared to distributed lag nonlinear models, NARX model output was nearly identical. These results highlight the applicability of NARX models for use in modeling complex and time-dependent relationships for various applications in epidemiology and environmental sciences.

54 ENVIRONMENTAL SCIENCES↗