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Drones can be used for tasks such as data collection and logistics in civil engineering. Current research on autonomous drones mainly focuses on planning a safe path and avoiding obstacles in a static environment. However, navigating a drone in complex environments like urban areas involves many dynamic constraints, such as building layout, winds, and signal coverage, which are interdependent. The wind factor is the most important among these environmental factors, which may cause a drone to lose control or even crash. This paper presents a multi-objective navigation reinforcement learning algorithm (MONRL) for the drone to navigate and avoid obstacles in an unknown environment when dynamic wind zones present, with only imagery data about the building layouts. Based on a deep reinforcement learning and memory architecture, the drone develops policies to prioritize navigation decisions, optimizing the path while minimizing negative impact of winds with only sparse sensor data, in our case, camera inputs. By leveraging the advantages of the proposed method in estimating the environmental factors from previous trials, no aerodynamic force sensors are needed for the drone to develop effective strategies to navigate to target while counteracting to milder winds and dodging aways from stronger winds. The proposed method was tested in a virtual environment, and a real model of New York City. The method is expected to contribute to new automation algorithms for urban aerial logistics and future automated civil infrastructure inspection. • A multi-objective reinforcement learning method was proposed for autonomous drones to avoid dynamic winds while navigating. • By leveraging a memory RL architecture, no aerodynamic force sensors are needed to sense and react to winds. • A framework for representing complex aerodynamic forces with mathematical formulation was proposed to supportRL trials. • Experiments performed in a VR simulator with data from New York City to confirm the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

The built environment accounts for approximately 38% of global emissions. The evaluation of returns linked to investments in building decarbonization is impeded when confronted with uncertainties surrounding future energy prices, building emission regulations, and real estate market conditions. This paper develops an integrated framework that combines energy modeling with investment analysis to support the adoption of decarbonizing technologies under a context of uncertainty. The integrated framework includes projections of costs and benefits associated with the adoption of technology (i.e., changes in rental cashflows, vacancy rates, maintenance and energy costs) under a wide range of potential pathways of the energy markets and regulatory environment, and local real estate market demand for green buildings using Monte Carlo simulations. Additionally, this paper develops the approach to investigate the potential value of flexibility in designing for building electrification (i.e., the ability to fully electrify gas heating systems at a later year). We illustrate the use of this approach in a case study of a new office building in New York City, faced with design choices between natural gas and electrified heating systems. We consider 10,000 scenarios changing future greenhouse emission penalties, investments in the local electric grid, local real estate market conditions and energy prices. In approximately 60 percent of simulations, the most profitable decision for the building owner is to adopt a natural gas-powered heating system. However, adopting a building design that provides a building the flexibility to fully electrify at a later date is more profitable than a natural gas-heating building in 96 percent of scenarios. A sensitivity analysis shows that the most influential parameter in the valuation of a design option is the size of the green premiums associated with improved energy efficiency. [ABSTRACT FROM AUTHOR]

Bike-Sharing Systems (BSSs) have exploded in popularity worldwide because of their beneficial impacts on traffic, pollution levels, and public health, which has resulted in moving toward a green city. The rebalancing problem, as one of the most important operational problems of such systems, deals with planning bike distribution at different stations. Regarding conducted studies, simulation models are the most common tool for analyzing BSSs and decision-making. This popularity is based on simulation's capabilities in modeling complexities of systems and uncertainty. Despite their advantages, lack of quickness is a significant drawback of simulation-based methods, making them inefficient for real-time decision-making processes, especially in large-scale and complex systems. In this regard, this paper introduces a Supervised Learning-Based Simulation (SLBS) method as an alternative to the conventional simulation-based methods dealing with rebalancing problems. SLBS is a huge step toward developing a real-time Decision Support System (DSS) for BSSs. For developing SLBS, firstly, we have developed a simulation model based on real-world assumptions of station-based BSSs and big data analysis of CitiBike, a well-known BSS located in New York City. The simulation model is able to calculate the number of unsatisfied demands (either number of failed pick-ups (FPs) or failed drop-offs (FDs)) as a result of different rebalancing plans. Then, the developed simulation model was used to generate quality and quantity training datasets to train Machine Learning (ML) algorithms involved in SLBS. While these ML models are trained once, SLBS will be capable of predicting the number of unsatisfied demands without running highly time-intensive simulations replications. The results obtained from a wide range of conducted experiments indicate that SLBS, up to 300 times faster than simulation models, can provide predictions with over 90% of R 2 Score. • A Decision Support Tool to address the rebalancing problem in the Bike-Sharing System. • A two-stage Supervised Learning-based Simulation (SLBS) method has been developed. • Substituting ML for simulation, SLBS aims to predict two objective functions. • Ensemble ML algorithms are applied to be compared in terms of accuracy and execution time. • The results support that SLBS performs much more time-saving with remarkable. predictions. [ABSTRACT FROM AUTHOR]

Sharing economy platforms hold a promise of more sustainable uses of resources. However, their legitimacy remains "debated" as various ecosystem stakeholders hold different opinions, challenging platform's values and activities. One key contested aspect is their capacity to contribute to sustainability beyond a search for economic performance. Despite these criticisms, sharing economy platforms have managed to grow and spread worldwide. Nevertheless, little is known about how a sudden market crisis affects an online platform characterized by a debated legitimacy. Studying the relationships between Airbnb and its main stakeholders during the first wave of COVID-19 pandemic in New York City, our paper identifies three relational strategies - identity, professionalization, and selection - implemented by the platform to reinforce its legitimacy within its local ecosystem. Based on secondary and primary data analyses, we identify and describe the arbitrations and opportunistic choices made by Airbnb to manage various stakeholder groups, to maintain its legitimacy during the COVID-19 crisis, and their impact on sustainability. [ABSTRACT FROM AUTHOR]

• Cross-sectional studies of infection rates are critical to understand the drivers behind the COVID epidemic. • This study sought to identify demographic and socio-economic indicators that drive the incidence rate of COVID-19. • The paper assesses these factors at zip-code granularity. • The average household size is the single most important explanatory variable; the percentage of the population 65 or older, and that below the poverty line are also strongly positively associated. • Population density, per se, does not have a significantly positive impact on incidence rates. With an eye toward possible public policy implications, our objective is to identify the socio-economic and demographic factors that drive the large variation in COVID-19 incidence rates observed within relatively compact geographic regions, and to quantify the relative impact of each of these factors. We use international comparisons as a starting point. New York City, consisting of some 175 zip codes, is an ideal arena to pursue the above study given the large variation in case incidence rates across zip codes. We conducted systematic regression studies employing data with zip code granularity. Our model specifications are based on a well-established epidemiologic model that explains the effects of household sizes on R0. Average household size emerges as the single most important driver behind the large variation in COVID-19 incidence rates. It independently explains 62% of the variation. The percentage of the population above the age of 65 and the percentage below the poverty line are also strongly positively associated with zip code incidence rates. As to ethnic/racial characteristics, the percentages of African Americans, Hispanics and Asians within the population are significantly associated, but the magnitude of the impact is smaller. (The proportion of Asians within a zip code has a negative association.) Contrary to common belief, population density, by itself, does not have a significantly positive impact (other than when a high population is driven by large household sizes). Our findings support implemented and proposed policies to quarantine patients and separate infected individuals from families or dormitories; they also support newly revised nursing home admission policies. [ABSTRACT FROM AUTHOR]

[Display omitted] • The influence of spatio-temporal factors on human comfort was quantified. • Seasonal change is the main factor affecting physical comfort. • The artificial impermeable matrix increases the surface thermal field temperature. • Natural factors can always effectively reduce the surface temperature. • Human comfort can be improved to a certain extent through human intervention. In recent decades, urbanization and the dramatic increase in urban populations have exacerbated the urban heat island effect. At present, much attention has been paid to the causes and patterns of the urban heat island effect. However, there are few quantitative studies on the impact of the urban heat island effect. By extracting post-world climate data, DEM data, and land use data for 20 years between 2001 and 2020, this paper first studies the variation pattern and spatial distribution characteristics of urban heat islands in New York State, summarizes the seasonal distribution characteristics of temperature, and then uses Giles formula to calculate Tom's discomfort index and evaluate human thermal comfort, and evaluates the effect of heat island effect on human sensory thermal comfort. The results show that on the time scale, the surface temperature in the study area generally showed a slow upward trend over the past 20 years. For example, in July, the maximum and minimum temperatures increased by 3.2 °C and 4.1 °C, respectively. At the spatial scale, most of the heat island areas in the study area were distributed in the New York City agglomeration, especially from May to October, when the heat island effect was particularly obvious. The temperature map showed obvious high temperatures. Compared with 2001, the human discomfort index (DI) increased between June and August 2020. The land use map shows that as the city expands, people's DI index also increases, and the proportion of people who feel uncomfortable with heat increases to 50%. Except for the New York City cluster, other areas were mostly athermic-free. This result shows that excessive urban development concentration seriously affects residents' quality of life. We should pay attention to the superimposed impact of climate change and urban heat islands on the human discomfort index, and adjust the local high temperature and thermal field area through reasonable planning, strengthening greening, and using building technology to make cities more livable. [ABSTRACT FROM AUTHOR]

In the era of post-Coronavirus Disease 2019, the dedicated outdoor air system (DOAS), which provides 100% outdoor air for the building, is widely acknowledged as it can ensure acceptable indoor air quality by delivering fresh outdoor air to occupied space. The DOAS with a proper design and operation can provide sufficient ventilation and dehumidification while achieving energy efficiency. Nonetheless, there is limited guidance in determining the optimal control sequence of the DOAS for the designers and operators to implement in practice. Accordingly, in practice, a number of issues have been acknowledged in the design and control phases of DOAS, including insufficient ventilation and dehumidification, and increasing supply air dry-bulb temperature in fear of over-cooling, which might cause significant discomfort and energy waste. There have been efforts to develop high-performing DOAS controls for better energy efficiency. However, such controls are often complex, or difficult to interpret, for building designers and operators to consider in practice. In this regard, this paper explores a simulation-based framework for generating a supply air temperature control sequence of the DOAS not only to ensure improved energy-saving potential but also to guarantee the implement-ability of the control logic. The U.S. Department of Energy prototype primary school with dynamic occupancy profiles was modeled with a whole building simulation program, EnergyPlus. The model consists of a DOAS with an exhaust air energy recovery system for ventilation and fan-coil units for space cooling and heating. Then, a Genetic Algorithm was adopted to find the true optimal supply air temperature control sequence in terms of minimizing the energy cost of the heating, ventilation, and air conditioning system operation. Lastly, Decision Tree was adopted to extract rules out of the optimums to derive an implementable sequence of operation for the DOAS supply air temperature. A total of 12 week-simulation including four weeks of heating, cooling, and shoulder seasons, separately, under the weather condition of New York City was conducted for the case study. This case study identified that the optimization-informed rule extraction-based control, when compared to conventional outdoor air temperature-based reset control, could save about 13% of energy cost and 25% of energy consumption throughout the heating, cooling, and shoulder seasons. It is notable that the energy-saving was mainly achieved by reducing the heating energy consumption. Importantly, it nearly corresponds to the true optimal control result, which reduces approximately 14% of energy cost and 27% of energy consumption. From the results, it can be highlighted that the optimization-informed rule extraction can be as energy effective as the optimal control, while significantly reducing the complexity of the control. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel approach to control CO 2 and H 2 O composition in an enclosed environment. • A cyclic CO 2 adsorption process and dehumidication units are considered. • The mathematical model shows superior performance of the proposed system. • Carbon footprint of the system is discussed with scale-up study. • Influence of parametric uncertainties in the model is discussed. Rapid growth in global energy consumption has raised concern on the environmental impacts such as ozone layer depletion and climate change. Enclosed space, such as commercial buildings, accounts for about 40% of global energy consumption and the demand is constantly increasing due to increasing population, urbanization, and economic development. The energy demands in the building sector calls for strategic measures to develop energy efficient technologies. This paper presents a strategy to decrease energy demands inside buildings by proposing a ventilation system which regulates the enclosed air quality resulting in reduced air conditioning. The system consists of multiple adsorption beds with zeolite 13X monoliths for CO 2 removal, and silica gel for humidity control, inside the enclosed space. The air conditioning system results in decrease in energy requirement and improvement in economics by 55% as compared with conventional ventilation system. The model is scaled up to the size comparable with total office inventory of New York City, and the reduction in carbon emissions by introducing the air composition control system for New York City is equivalent to replacing 57 million incandescent light bulbs by LEDs. This paper concludes that the air conditioning system proposed in this study results in the improvement in performance as compared to a conventional ventilation system and could reduce energy consumption inside commercial buildings. [ABSTRACT FROM AUTHOR]

This paper seeks to address the profound power resilience inequity in New York City by means of strategic allocation of electric vehicle (EV) charging infrastructure to support the power grid operation in challenging scenarios, such as when facing high demand or during natural disasters. First, we uncover the most disproportionately affected communities in New York by developing a metric of power resilience inequity to measure the combined impact of power failure-related factors on these areas. We employ data-driven approaches to infer the statistical relationships between communities' power resilience index, their available EV charging infrastructure, and several other prominent socio-demographical variables. This inference yields the development of a machine learning model that can predict the reduction of power resilience inequity after deployment of the proposed resource allocation strategy. We further develop an optimization framework that jointly considers equity and efficiency to guide the optimized distribution of EV charging infrastructure across the city. A number of case studies are leveraged to demonstrate the capability of the devised approach in enhancing urban power resilience equity, yielding favorable results in marginalized communities. [ABSTRACT FROM AUTHOR]

Cellular automata (CA) models have increasingly been used to simulate land use/cover changes (LUCC). Metaheuristic optimization algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA) have been recently introduced into CA frameworks to generate more accurate simulations. Although Markov Chain Monte Carlo (MCMC) is simpler than PSO and GA, it is rarely used to calibrate CA models. In this article, we introduce a novel multi-chain multi-objective MCMC (mc-MO-MCMC) CA model to simulate LUCC. Unlike the classical MCMC, the proposed mc-MO-MCMC is a multiple chains method that imports crossover operation from classical evolutionary optimization algorithms. In each new chain, after the initial one, the crossover operator generates the initial solution. The selection of solutions to be crossed over are made according to their fitness score. In this paper, we chose the example of New York City (USA) to apply our model to simulate three conflicting objectives of changes from non-urban to low-, medium- or high-density urban between 2001 and 2016 using USA National Land Cover Database (NLCD). Elevation, slope, Euclidean distance to highways and local roads, population volume and average household income are used as LUCC causative factors. Furthermore, to demonstrate the efficiency of our proposed model, we compare it with the multi-objective genetic algorithm (MO-GA) and standard single-chain multi-objective MCMC (sc-MO-MCMC). Our results demonstrate that mc-MO-MCMC produces accurate simulations of land use dynamics featured by faster convergence to the Pareto frontier comparing to MO-GA and sc-MO-MCMC. The proposed multi-objective cellular automata model should efficiently help to simulate a trade-off among multiple and, possibly, conflicting land use change dynamics at once. • This paper presents a multi-objective CA land use/cover change model. • The model uses a novel multi-objective Markov chain Monte Carlo (MO-MCMC). • We compare MO-MCMC with a multi-objective genetic algorithm (MO-GA). • We apply our model to simulate the expansion of multi-density urban areas in New York. • The results show that the MO-MCMC outperformed MO-GA in terms of accuracy and speed. [ABSTRACT FROM AUTHOR]

In modern power systems, a clean database of historical electric loads is crucial for system analysis, planning studies, load forecasting, and decision making. To this end, the clean database should be free of anomalies while preserving the power consumption behavior of special events like holidays. In recent years, model-based anomaly detection methods have been successfully used for anomaly detection and correction in electrical load. In these methods, the collected load data are first compared with the predicted load obtained from a forecasting model; if the difference is greater than a specific limit, the collected load data are replaced with the predicted value. One of the disadvantages of these methods is the consideration of load profiles of the days with higher levels of difficulty in load forecasting, such as holidays, as anomalous load profiles and replacing them with predicted values, which results in fading some important characteristics of the load series. Therefore in this paper, a two-stage anomaly detection method is proposed to alleviate this problem. In the first stage, the wavelet transform is used to identify suspected load profiles. In the second stage, a robust regression method is used to further inspect the suspected load profiles and decide whether they are anomalies or not. By performing experiments on three datasets (a utility of Iran, New York City, and a North American utility) and comparing the results with four other methods, it's shown that the proposed method decreases false detection of anomalies while accurately correcting anomalous load profiles. Results of load forecasting with the Iranian dataset corrected by the proposed method showed an average of 2.28% and 1.58% error on the holidays and total days, respectively, which is less than the other four methods. [ABSTRACT FROM AUTHOR]

The effects of hydrothermal pretreatment with and without H 2 O 2 addition on the properties of the resulted biofuel pellets prepared from cotton stalk (CS) and Chinese fir wood sawdust (WS) were mainly studied in the paper. Based on the changes of three main components (hemicellulose, cellulose and lignin) of biomass during the pretreatment process, the relaxation density, compressive strength, heating values (HVs), and combustion characteristics of the resulted pellets were analyzed to evaluate the feasibility of the utilization as biofuel pellets. The results indicate that the hydrothermal pretreatments with and without H 2 O 2 addition are beneficial to the quality of the resulted biofuel pellets. Particularly, the hydrothermal pretreatment with H 2 O 2 addition, namely the hydrothermal oxidation pretreatment can significantly alleviate the pretreatment severity of the high-strength biofuel pellet preparation. H 2 O 2 addition promotes the decomposition of hemicellulose and cellulose during the pretreatment. Compared with CS, WS is more sensitive to the pretreatment, especially hydrothermal oxidation pretreatment. The crystallinity index and the cellulose content of the biomass sample both increase firstly and then decrease with the increasing pretreatment temperature. In the study, the pretreatment temperature for the biofuel pellets with the best mechanical properties is 230 °C for the hydrothermal process and 200 °C for the hydrothermal oxidation process, respectively. The HVs of the resulted biofuel pellet increases with the increasing pretreatment intensity and the suitable pretreatment can improve the combustion characteristics of the resulted biomass. The mechanical properties of the pretreated biofuel pellets were positively correlated with the crystallinity and the content of the cellulose. HVs of WS pellets is higher than CS pellets, with the better mechanical properties. The cellulose is more favorable for the improvement of the combustion properties of the biomass sample than the hemicellulose and lignin. In addition, HVs and the compressive strength of all the biofuel pellets prepared from the pretreated biomasses meet the Sweden Standard (SS18 7120) and the Ministry of Agriculture of China standard (NY/T1878–2010), respectively. [Display omitted] • Physico-chemical properties of biofuel pellets preparation by HT and HO pretreatment of CS and WS is evaluated. • The evolution of three components in WS and CS is researched. • The quality of the biofuel pellets pretreated by HT and HO pretreatment have been improved. • The mechanical properties of biofuel pellets were positively correlated with crystallinity and content of the cellulose. • HO pretreatment can obtain better quality biofuel pellets at a lower temperature than HT. [ABSTRACT FROM AUTHOR]

Over the past two decades, high school reform has been characterized by a belief that “smaller is better.” Much of the expected academic benefit from attending small schools has been credited to their better learning environments. There is little empirical support for this claim, however, and the existing research fails to provide causal evidence. Moreover, recent studies in New York City have shown that students attending newly created small schools do better academically relative to students attending both large and older established small schools. Are these differences in academic outcomes also mirrored by differences in learning environments? In this paper, we address this question by exploring the impact of attending large compared to small high schools on students’ learning environments, considering the differences between small high schools formed in two different eras with different missions and resources. We use a unique data set of school and student-level data from New York City public high school students entering 9th grade in 2008–09 and 2009–10 to examine students’ attitudes about school learning environments along three dimensions: interpersonal relationships, academic expectations and support, and social behavior and safety. While OLS results show that students attending small schools (new and old) perceive better learning environments, instrumenting for selection into these schools challenges those results. In general, it is not clear that small schools provide better learning environments than large schools. Our results challenge the conventional wisdom that the higher academic performance of students in small schools is driven by a better learning environment. [ABSTRACT FROM AUTHOR]

This paper presents results from sensitivity studies conducted using the Distributed Generation Build-out Economic Assessment Tool (DG-BEAT). The viability of meeting commercial building loads with a stationary fuel cells is studied under different conditions of electricity pricing, dispatch strategies, and complementary technologies. Key findings support the notion that fuel cells are becoming economically viable alternatives in California, New York and Connecticut at installed costs of $7000–10,000/kW. Michigan is identified as another state well suited to fuel cell development with heat recovery. Fuel cell installations reduce net carbon emissions for commercial buildings by 20–30% when compared to local, time-resolved, grid emissions. Grid sell-back, at 50% retail price, significantly improves the economics of a base load fuel cell, but has little impact for a dispatchable system. At installed costs below $5000/kW, load following capability results in significant additional cost reductions as the generating capacity is increased beyond the building's base load requirements. Complementary technologies such as chillers and thermal storage have a pronounced impact, particularly in warmer climates. Installing fuel cells paired with electric chillers and thermal storage in Florida at buildings with exceptionally high air conditioning demands can achieve the same economic benefit as a typical New York building. [ABSTRACT FROM AUTHOR]

Thermal properties have significant impact on ecological footprint and life-cycle assessment of buildings. This is even more crucial aspect for glass buildings, which have been criticised as major factors in climate change around the world. Recent debates on glass facades, which culminated in plans for a "glass building ban" in New York City highlighted the importance of innovation in glass construction. The current solutions for mitigating energy consumption for glass facades are improving insulation (U-value) and solar heat gain coefficient (SHGC) with advanced glazing or using shading devices. A fourth approach is the recent technology of water-filled glass (WFG), which utilizes water layer to improve thermal comfort and energy performance of the façade. This paper introduces Smart Water-filled Glass (SWFG) control method, which enables the change of the opacity of the façade element by colouring the fluid over a year regarding seasonal changes. The impact of changing transparency of water layer on cooling and heating energy demand is evaluated by simulating and analysing the energy performance of a reference office room with large glazing facade in different climates. The significance of this energy evaluation is that a water-filled glass with switchable transparency is simulated and assessed here for the first time. The paper analyses the performance in seven cities from all relevant major climatic regions using LBNL WINDOW and TRNSYS (v.18) As a novel approach, the simulation study makes it possible to evaluate the overall performance of two proposed operational methods (insolation-based & storage-based) for adjusting water layer settings depending on climate conditions and reusability of the captured heat in water layer. Moreover, seven different base cases are presented in this study to compare the performance of proposed system, including conventional solutions (e.g. double or triple glazing with/without shading) and a state-of-the-art switchable technology (e.g. electrochromic glazing or EC). The results verify the hypothesis that the impact of SWFG compared to WFG varies depending on the climate with 0%-5.28% for hot climates and 0.25–3.39% for climates with heating demand. The discussion includes comparison with standard glass facades, where SWFG shows 47.41%-78.01% energy savings depending on climate. The climate-based approach of SWFG and WFG offers a significant shift in façade design that sees glass buildings as an opportunity for sustainability rather than liability for climate change. [ABSTRACT FROM AUTHOR]

Access to and affordability of electric vehicle (EV) charging infrastructure are the two prominent barriers for EV adopttion. While major efforts are underway in the United States to roll-out public EV charging infrastructure, persistent social disparities in EV adoption call for interventions. In this paper, we analyze the existing EV charging infrastructure across New York City (NYC) to identify such socio-demographic and transportation features that correlate with the current distribution of EV charging stations. Our results demonstrate that population density is not correlated with the density of EV chargers, hindering New York's EV adoption and decarbonization goals. On the contrary, the distribution of EV charging stations is heavily skewed against low–income, Black–identifying, and disinvested neighborhoods in NYC, however, positively correlated to presence of highways in a zip code. The results underscore the need for policy frameworks that incorporate equity and justice in the roll-out of EV charging infrastructure. [ABSTRACT FROM AUTHOR]

Despite extensive empirical evidence of the environmental benefits of green buildings and the increasing urgency to reduce carbon emissions in cities, there has been limited widespread adoption of energy retrofit investments in existing buildings. In this paper, we empirically model financial returns to energy retrofit investments for more than 3600 multifamily and commercial buildings in New York City, using a comprehensive database of energy audits and renovation work extracted from city records using a natural language processing algorithm. Based on auditor cost and savings estimates, the median internal rate of return for adopted energy conservation measures is 21% for multifamily buildings and 25% for office properties. Logistic regression modeling demonstrates adoption rates are higher for office buildings than multifamily, and in both cases adopter buildings tend to be larger, higher value, and less energy efficient prior to retrofit implementation. The economically significant magnitudes of returns to adopted energy conservation measures raise important questions about why many property owners choose not to adopt. As such, we discuss incentive and regulatory mechanisms that can overcome financial and informational barriers to the adoption of energy efficiency measures. • A large-scale empirical study of financial returns to building energy retrofits. • A novel dataset of audit recommendations and permitted work developed using NLP. • Analysis of IRR and NPV curves for both adopter and non-adopter buildings. • Median IRRs are found to be 21% for multifamily and 25% for office buildings. • Retrofit decisions not fully explained by potential investment returns. [ABSTRACT FROM AUTHOR]

This paper introduces a probabilistic approach to spatio-temporal high resolution meso-scale modeling of near-surface temperature and applies it to regions of dimension about 150∼200 km, with 1 km grid spacing and 30-min interval. Our probabilistic approach, based on linear Gaussian models and dimensionality reduction, can accurately forecast short-term temperature fields and serve as a computationally less expensive alternative to physics-based models that necessitate high-performance computing. The probabilistic models here are calibrated from simulations of a physics-based model, the Princeton Urban Canopy Model, coupled to the Weather Research and Forecasting Model (WRF-PUCM). We assess the performance of the calibrated models to forecast short-term near-surface temperature in various cases. In the numerical campaign, our models achieve 0.97–1.13 °C root mean squared error (RMSE) for 24-hours ahead forecast; generating three days of forecast takes between 20 and 170 sec on a single processor (Intel Xeon E5-2690 v4@2.60GHz). Hence, the proposed approach provides predictions at relatively high accuracy and low computational cost. [Display omitted] • We develop probabilistic models for meso-scale near-surface urban air temperature. • We calibrate/validate the models on simulated data for New York City and Pittsburgh. • A Kalman filter/smoother updates the proposed models for adaptive forecast. • The proposed models use 3–8 % the computing resources used by a comparable model. • 24-h ahead forecasts show 0.97–1.13 °C prediction error. [ABSTRACT FROM AUTHOR]

Abstract: Optical characteristics and long-distance transport of smoke plumes are studied using satellite observations and ground-based remote sensing in New York City. Vertical distribution and column optical properties of aerosol plume are derived from a combination of multi-wavelength lidar and sunphotometer measurement. Aloft smoke plumes from two cases are investigated in this paper from U.S. western and south-western forest fires. The smoke source and transport pathway to the U.S. east coast are analyzed by MODIS and CALIOP imageries as well as HYSPLIT backward trajectory analysis. In both cases, the plumes have relatively high optical depth (as much as 1.2 at 500-nm) and Angstrom exponent of up to 1.8, but show different intrusion heights, source regions and transport process. Lidar profiling observations indicate these smoke plumes mixing downward into the planetary-boundary-layer which are coincident with increasing trends of surface PM2.5 (particulate matter, diameter < 2.5 μm) concentrations. [Copyright &y& Elsevier]

Abstract: This paper uses student level data from New York City to study the relationship between a public school losing enrollment to charter school competitors and the academic achievement of students who remain enrolled in it. Geographic measures most often used to study the effect of school choice policies on public school student achievement are not well suited for densely populated urban environments. I adopt a direct approach and measure charter school exposure as the percentage of a public school''s students who exited for a charter school at the end of the previous year. Depending on model specification, I find evidence that students in schools losing more students to charter schools either are unaffected by the competitive pressures of the choice option or benefit mildly in both math and English. [Copyright &y& Elsevier]

Abstract: In this paper we present theoretical models for microcellular communications at UHF and microwave frequencies. For rural microcells, a vectorial analysis of the two-ray model is carried out, and comparisons between the developed model and measurements reported in rural areas of New Jersey and San Francisco Bay are provided. For urban microcells, the street is three-dimensionally modeled as a deterministic multislit waveguide channel, according to its database, and vectorial analysis is also taken into account. For the 3D street model, comparisons in good agreement with measurements reported in New York City are performed. Comparisons provided with measured data show that the models proposed can be very useful tools toward increasing theoretical analysis in spite of empirical analysis in the field of prediction. [Copyright &y& Elsevier]

Abstract: Handheld GPS provides a new technology to trace people’s daily travels and has been increasingly used for household travel surveys in major cities worldwide. However, methodologies have not been developed to successfully manage the enormous amount of data generated by GPS, especially in a complex urban environment such as New York City where urban canyon effects are significant and transportation networks are complicated. We develop a GIS algorithm that automatically processes the data from GPS-based travel surveys and detects five travel modes (walk, car, bus, subway, and commuter rail) from a multimodal transportation network in New York City. The mode detection results from the GIS algorithm are checked against the travel diaries from two small handheld GPS surveys. The combined success rate is a promising 82.6% (78.9% for one survey and 86.0% for another). Challenges we encountered in the mode detection process, ways we developed to meet these challenges, as well as possible future improvement to the GPS/GIS method are discussed in the paper, in order to provide a much-needed methodology to process GPS-based travel data for other cities. [Copyright &y& Elsevier]

Abstract: Fossil fuel combustion is the primary source of anthropogenic acidity in cloud water. Since 1994 there is a measurable decrease in hydrogen, sulfate, and nitrate ion concentrations in cloud water collected at Whiteface Mountain. In this paper we assess these changes from the point of view of progress toward clean air conditions. The cleanest clouds crossing Whiteface Mountain, those with the lowest total ion concentrations, are found to have pH values in the range 5.0–5.25. Furthermore, there is a linear relationship between pH, sulfate and nitrate ion concentrations in cloud water. With this in mind, we define clean cloud water values of sulfate, nitrate and hydrogen as mean concentrations observed in cloud water samples with pH in the range 5.0–5.25. We then compare mean annual values to clean air values to determine annual ratios. In 1994, ratios for , and H+ were respectively 26.9, 13.1 and 29.9 times above the clean air value. In 2009, the , and H+ corresponding ratios were 4.2, 2.7, and 4.8 times above the clean air value. In other words, comparison of the 1994 and 2009 results suggest reductions in anthropogenic concentrations of , and H+, by 84%, 79% and 84% respectively. To verify our approach, we have calculated corresponding changes in the aerosol ratio, with clean air concentrations equal to the natural background aerosol value used for the Regional Haze Rule. These results compared favorably to our cloud water ratio. [Copyright &y& Elsevier]

Abstract: West Nile Virus is an arthropod-borne flavivirus that appeared for the first time in New York City in the summer of 1999 and then spread prolifically within birds, with over 200 species having been infected. Mammals, such as humans and horses, do not develop sufficiently high bloodstream titers to play a significant role in transmission, which is a reason for considering the mosquito–bird cycle. In this paper we propose a model to study bird involvement in transmitting West Nile Virus using a system of ordinary differential equations considering the mosquito and several avian populations. A threshold value , depending on the model’s parameters, is obtained that determines the disease level and allows us to propose possible control strategies. We determine the effects regarding the disease transmission considering the coexistence of two bird species, and then generalize this taking into account several bird species. We conclude that knowledge of the relative abundance of several bird species allows us to estimate with accuracy the risk for overall West Nile Virus transmission. Also, the establishment of the disease at an endemic level can be explained by the interaction between responsible populations of birds and mosquitoes. [ABSTRACT FROM AUTHOR]

Abstract: In 2010, it is the 25th anniversary of the first paper describing the genetic identification of human individuals by DNA fingerprint analysis. Since then DNA analysis has become a major tool to relate biological evidence to the persons involved in a crime or to determine the biological relationship among individuals. The currently used methodology is the result of major technological changes that were partly driven by criticism on previous methodologies, and partly driven by demand especially due to mass disasters such as the ‘9/11 attack’ on the World Trade Center in New York. This review will give an overview of the current methodology in genetic identification and new developments that will have a future impact on forensic identification. [ABSTRACT FROM AUTHOR]

Abstract: Problem: Subway transit is a relatively safe mode of transportation, yet compared to all other forms of mass transit in the United States (U.S.), subways have the highest fatality rate. The aim of this paper is to characterize subway-related fatalities in order to identify opportunities for risk reduction. Method: Medical examiner records for all New York City (NYC) subway-related deaths (1990-2003) were reviewed. Data were abstracted on decedents'' demographics and autopsy findings, including laboratory findings. Results: There were 668 subway-related fatalities, of these, 10 (1.5%) were homicides, 343 (51.3%) were determined to be suicides, and 315 (47.2%) were accidental. Although decedent characteristics varied between fatality categories, they were not particularly informative with regard to prevention. Conclusion: Prevention strategies that focus on structural controls are likely to be most efficacious in improving the overall safety of the NYC subway systems. Impact on industry: These findings suggest that structural rather than individual-level interventions would be most successful in preventing subway fatalities. [Copyright &y& Elsevier]

• Air taxis (urban air mobility (UAM) services) are planned to be launched in coming years. • Proposed a centralized framework to allocate and dispatch air taxis dynamically. • Developed hybrid simulation goal programming model that can make real-time decisions. • Tested the proposed approach using millions of estimated air taxi demand data. Expected to begin its operations in the coming years, air taxi aims to provide everyday transportation services to customers in metropolitan cities. These vehicles offer urban air mobility (UAM) services using the electric vertical takeoff and landing (eVTOL) technology. This research is the first to present a hybrid simulation goal programming (HSGP) approach to dispatch vehicles in a centralized air taxi network. After each customer drop-off, the model makes real-time decisions on (i) whether the air taxi must become idle or pick up customers, and (ii) the station to which the air taxi should be dispatched (if the air taxi is operational). The feasibility of the HSGP approach is tested using potential air taxi demands in New York City (NYC) provided by a prior study. The results of the experimentation suggest that the minimum number of air taxis required for efficient operation in NYC is 84, functioning with an average utilization rate of 66%. In addition, the impacts of commuter's "willingness to fly" rate, percentage of demand fulfillment, on-road travel limit, maximum customer wait time, and arrival distribution on the optimal number of air taxis, utilization rate, number of customers served, and cost incurred per customer are examined. Analyses show that the "willingness to fly" rate appears to have a linear influence on the number of air taxis and the efficiency, while on-road travel distance has an exponential impact on the performance measures. The HSGP algorithm developed in this paper can be used by any company that is interested in venturing into the air taxi market. [ABSTRACT FROM AUTHOR]

This paper examines the effects of Asian peers on non-Asian student achievement in New York City public schools. We use exogenous variation in the share of Asian students across cohorts within schools stemming from a fertility shock among the Asian population in the Chinese year of the Dragon. Results show that a 10-percentage-point increase in the share of Asian students reduces non-Asian math and ELA scores by 0.14 and 0.16 standard deviations. The reduction in achievement is associated with an increase in the share of non-Asian students who fail to demonstrate the skills expected at the grade, especially in math. [ABSTRACT FROM AUTHOR]

Observed hydroxyl (OH) and hydroperoxy (HO2) radicals, collectively called HOx, were compared with OH and HO2 calculated by a box model that used the regional atmospheric chemistry mechanism and was constrained to the ancillary measurements during the PM2.5 Technology Assessment and Characterization Study-New York (PMTACS-NY) summer 2001 intensive in New York City. The measurements are described in the companion paper, Ren et al. (HOx concentrations and OH reactivity observations in New York City during PMTACS-NY2001, Atmospheric Environment, this issue). This comparison enables an investigation of HOx chemistry in this polluted urban atmosphere. For HO2, the observed concentrations and diurnal variation were usually well reproduced by the model calculations, with an observed-to-modeled ratio of 1.24, on average, for day and night. For OH, the model was generally able to match the measured concentrations during daytime with an observed-to-modeled ratio of about 1.10, but the calculations significantly underestimated OH during nighttime. The budgets of HOx show that its production was dominated by the photolysis of HONO, accounting for ∼56% of HOx production on average, during daytime due to relatively high HONO concentrations, while nighttime HOx production was mainly from the O3 reactions with alkenes. The OH reactivity measurements agree with the calculations to within 10% for both the composite diurnal variation and individual days. Calculations indicate that the reactions of OH with NO2, hydrocarbons, CO, NO, and carbonyls accounted for about 32%, 25%, 12%, 10% and 7% of total OH loss, respectively, in this urban area. Modeled instantaneous O3 production from HO2 and RO2 reactions with NO was 150±100 ppbv day−1. O3 production rates from measured HO2(P(O3)obsHO2) was greater than modeled HO2(P(O3)calcHO2) at higher values of NO. Average daily cumulative P(O3)obsHO2 was ∼140 ppbv day−1, a factor of 1.5, greater than average daily P(O3)calcHO2. [Copyright &y& Elsevier]

Previous source apportionment analyses of New York City (NYC) PM 2.5 have found that transported pollution can account for up to half of total fine particulate matter (PM 2.5) mass. In recent years, several local and federal emissions regulations on both the transportation and energy sector have been introduced, such as the Clean Heavy-Duty Bus and Truck Rule, and policies related to the NYC Clean Heat Program. Since the implementation of these regulations, there have been limited studies on regional transport of particulate air pollution into NYC. This paper aims to quantify long-term changes in local and regional contributions to PM 2.5 in NYC. The composition of PM 2.5 in NYC over the period 2002–2018 was evaluated using data from the US Environmental Protection Agency (EPA) Chemical Speciation Network (CSN) and the New York City Community Air Survey (NYCCAS). Background site data obtained from EPA CSN was used to assess regional transport. Two estimates of transported PM 2.5 were calculated, one assuming that transported PM 2.5 consists solely of ammonium sulfate and ammonium nitrate as a lower bound and the other taking into account organic matter (OM) as an upper bound. Using this method, regional transport was estimated to account for 25–46% of total PM 2.5 measured in NYC in 2018, down from 46 to 57% in 2002. Secondary PM 2.5 output from the Community MultiScale Air Quality (CMAQ) model for the years 2008, 2011, and 2017 was used as an additional upper-bound estimate of regional transport (56%, 35%, and 30%, respectively). In addition to CMAQ model estimates of sulfate, nitrate, and primary emitted PM 2.5 , land-use regression (LUR) models for sulfur, vanadium, copper, nickel, and PM 2.5 measurements at 60 locations across NYC during the winter and summer seasons of 2008–2015 were assessed for changes in local emissions. The results indicate decreased emissions from residual oil burning, traffic, and ships. As a consequence of regional and local changes, PM 2.5 in NYC had fallen to approximately 55% of its 2002 levels by 2018. The observed temporal and spatial trends of PM 2.5 and its components point to the effect of regulations as well as economic influences on fuel usage, such as the decline of coal in favor of natural gas, in reducing air pollution over the last two decades. While other major PM 2.5 constituents have declined, OM is becoming an increasingly dominant component with potential implications for PM 2.5 toxicity. Further improvements in NYC air quality could be achieved by targeting sources of ambient OM, such as commercial cooking, traffic, and biomass burning for residential heating. • Multiple techniques were used to track PM 2.5 source trends in NYC from 2002 to 2018. • Transported PM 2.5 in NYC has decreased in response to policy and economic trends. • NYC has seen reduced local emissions of PM 2.5 from residual oil, ships and traffic. [ABSTRACT FROM AUTHOR]

• A novel simulation-based optimization framework for autonomous electric taxicab. • Incorporation of battery behaviors into the scheduling optimization problem. • Application of rolling horizon technique to capture the future ride information. • Break-down of the integrated problem to reduce the computational complexity. This paper proposes a simulation-based optimization framework for an autonomous electric taxi (AET) to achieve economic optimization by determining the optimal operations in the operating time horizon. The operating time horizon of the AET is equally divided into a set of consecutive time slots. For each time slot, there are four possible operations: driving, cruising, parking, and charging. To reduce the computational complexity, instead of solving the scheduling problem for the whole operating time horizon as a single problem, the whole problem is decomposed into a set of subproblems that are built for a one-day period. From an integrated electric vehicle simulation model, which simulates the AET operation based on the optimal schedule determined by the optimization problem, precise battery status parameters, such as the state of charge, capacity loss and battery temperature, are derived and used as the initial values for the optimization problem with rolling horizon implementation. A case study on NYC is presented, and the results show that the proposed framework can extend the battery life by 3%, and also increase the daily profit by 3% and 520%, compared to the 24hr rule-based strategy and 8hr rule-based strategy, respectively. [ABSTRACT FROM AUTHOR]