RESUMEN
Environmental heat is a growing public health concern in cities. Urbanization and global climate change threaten to exacerbate heat as an already significant environmental cause of human morbidity and mortality. Despite increasing risk, very little is known regarding determinants of outdoor urban heat exposure. To provide additional evidence for building community and national-scale resilience to extreme heat, we assess how US outdoor urban heat exposure varies by city, demography, and activity. We estimate outdoor urban heat exposure by pairing individual-level data from the American Time Use Survey (2004-2015) with corresponding meteorological data for 50 of the largest metropolitan statistical areas in the US. We also assess the intersection of activity intensity and heat exposure by pairing metabolic intensities with individual-level time-use data. We model an empirical relationship between demographic indicators and daily heat exposure with controls for spatiotemporal factors. We find higher outdoor heat exposure among the elderly and low-income individuals, and lower outdoor heat exposure in females, young adults, and those identifying as Black race. Traveling, lawn and garden care, and recreation are the most common outdoor activities to contribute to heat exposure. We also find individuals in cities with the most extreme temperatures do not necessarily have the highest outdoor heat exposure. The findings reveal large contrasts in outdoor heat exposure between different cities, demographic groups, and activities. Resolving the interplay between exposure, sensitivity, adaptive capacity, and behavior as determinants of heat-health risk will require advances in observational and modeling tools, especially at the individual scale.
Asunto(s)
Demografía , Exposición a Riesgos Ambientales/efectos adversos , Calor/efectos adversos , Recreación , Adolescente , Adulto , Anciano , Ciudades/estadística & datos numéricos , Cambio Climático , Femenino , Humanos , Masculino , Persona de Mediana Edad , Modelos Estadísticos , Encuestas y Cuestionarios , Estados Unidos , Adulto JovenRESUMEN
As local governments plan to expand airport infrastructure and build air service, monetized estimates of damages from air pollution are important for balancing environmental impacts. While it is well-known that aircraft emissions near airports directly affect nearby populations, it is less clear how the airport-specific aircraft operations and impacts result in monetized damages to human health and the environment. We model aircraft and ground support equipment emissions at major U.S. airports and estimate the monetized human health and environmental damages of near airport (within 60 miles) emissions. County-specific unit damage costs for PM, SOx, NOx, and VOCs and damage valuations for CO and CO2 are used along with aircraft emissions estimations at airports to determine impacts. We find that near-airport emissions at major U.S. airports caused a total of $1.9 billion in damages in 2013, with airports contributing between $720 thousand and $190 million each. These damages vary by airport from $1 to $9 per seat per one-way flight and costs per passenger are often greater than airport charges levied on airlines for infrastructure use. As the U.S. aviation system grows, it is possible to minimize human and environmental costs by shifting aircraft technologies and expanding service into airports where fewer impacts are likely to occur.
Asunto(s)
Contaminación del Aire/análisis , Contaminación del Aire/economía , Aeropuertos , Contaminantes Atmosféricos/análisis , Contaminantes Atmosféricos/economía , Aeronaves , Aeropuertos/economía , Aviación/economía , Dióxido de Carbono/análisis , Dióxido de Carbono/economía , Monóxido de Carbono/análisis , Monóxido de Carbono/economía , Humanos , Modelos Teóricos , Óxidos de Nitrógeno/análisis , Óxidos de Nitrógeno/economía , Salud Pública , Estados Unidos , Emisiones de Vehículos/análisis , Compuestos Orgánicos Volátiles/análisis , Compuestos Orgánicos Volátiles/economíaRESUMEN
Metropolitan greenhouse gas and air emissions inventories can better account for the variability in vehicle movement, fleet composition, and infrastructure that exists within and between regions, to develop more accurate information for environmental goals. With emerging access to high quality data, new methods are needed for informing transportation emissions assessment practitioners of the relevant vehicle and infrastructure characteristics that should be prioritized in modeling to improve the accuracy of inventories. The sensitivity of light and heavy-duty vehicle greenhouse gas (GHG) and conventional air pollutant (CAP) emissions to speed, weight, age, and roadway gradient are examined with second-by-second velocity profiles on freeway and arterial roads under free-flow and congestion scenarios. By creating upper and lower bounds for each factor, the potential variability which could exist in transportation emissions assessments is estimated. When comparing the effects of changes in these characteristics across U.S. cities against average characteristics of the U.S. fleet and infrastructure, significant variability in emissions is found to exist. GHGs from light-duty vehicles could vary by -2%-11% and CAP by -47%-228% when compared to the baseline. For heavy-duty vehicles, the variability is -21%-55% and -32%-174%, respectively. The results show that cities should more aggressively pursue the integration of emerging big data into regional transportation emissions modeling, and the integration of these data is likely to impact GHG and CAP inventories and how aggressively policies should be implemented to meet reductions. A web-tool is developed to aide cities in improving emissions uncertainty.
Asunto(s)
Contaminación del Aire , Vehículos a Motor , Emisiones de Vehículos , Ciudades , Clima , Efecto Invernadero , Humanos , Material Particulado/análisis , Transportes , Incertidumbre , Estados UnidosRESUMEN
The environmental outcomes of urban form changes should couple life-cycle and behavioral assessment methods to better understand urban sustainability policy outcomes. Using Phoenix, Arizona light rail as a case study, an integrated transportation and land use life-cycle assessment (ITLU-LCA) framework is developed to assess the changes to energy consumption and air emissions from transit-oriented neighborhood designs. Residential travel, commercial travel, and building energy use are included and the framework integrates household behavior change assessment to explore the environmental and economic outcomes of policies that affect infrastructure. The results show that upfront environmental and economic investments are needed (through more energy-intense building materials for high-density structures) to produce long run benefits in reduced building energy use and automobile travel. The annualized life-cycle benefits of transit-oriented developments in Phoenix can range from 1.7 to 230 Gg CO2e depending on the aggressiveness of residential density. Midpoint impact stressors for respiratory effects and photochemical smog formation are also assessed and can be reduced by 1.2-170 Mg PM10e and 41-5200 Mg O3e annually. These benefits will come at an additional construction cost of up to $410 million resulting in a cost of avoided CO2e at $16-29 and household cost savings.
Asunto(s)
Ciudades , Ambiente , Transportes/economía , Arizona , Automóviles , Dióxido de Carbono , Materiales de Construcción/economía , Efecto Invernadero , Vivienda/economía , Humanos , Técnicas de Planificación , Densidad de Población , Transportes/métodos , ViajeRESUMEN
We present an approach to the problems of weak plume detection and sub-pixel target detection in hyperspectral imagery that operates in a two-dimensional space. In this space, one axis is a matched-filter projection of the data and the other axis is the magnitude of the residual after matched-filter subtraction. Although it is only two-dimensional, this space is rich enough to include several well-known signal detection algorithms, including the adaptive matched filter, the adaptive coherence estimator, and the finite-target matched filter. Because this space is only two-dimensional, adaptive machine learning methods can produce new plume detectors without being stymied by the curse of dimensionality. We investigate, in particular, the utility of the support vector machine for learning boundaries in this matched-filter-residual space, and compare the performance of the resulting nonlinearly adaptive detector to well-known alternatives.
Asunto(s)
Algoritmos , Inteligencia Artificial , Gases/análisis , Interpretación de Imagen Asistida por Computador/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Análisis Espectral/métodosRESUMEN
Highly penetrating cosmic ray muons constantly shower the earth at a rate of about 1 muon per cm2 per minute. We have developed a technique which exploits the multiple Coulomb scattering of these particles to perform nondestructive inspection without the use of artificial radiation. In prior work [1]-[3], we have described heuristic methods for processing muon data to create reconstructed images. In this paper, we present a maximum likelihood/expectation maximization tomographic reconstruction algorithm designed for the technique. This algorithm borrows much from techniques used in medical imaging, particularly emission tomography, but the statistics of muon scattering dictates differences. We describe the statistical model for multiple scattering, derive the reconstruction algorithm, and present simulated examples. We also propose methods to improve the robustness of the algorithm to experimental errors and events departing from the statistical model.