3,461 research outputs found

    Laboratory requirements for in-situ and remote sensing of suspended material

    Get PDF
    Recommendations for laboratory and in-situ measurements required for remote sensing of suspended material are presented. This study investigates the properties of the suspended materials, factors influencing the upwelling radiance, and the various types of remote sensing techniques. Calibration and correlation procedures are given to obtain the accuracy necessary to quantify the suspended materials by remote sensing. In addition, the report presents a survey of the national need for sediment data, the agencies that deal with and require the data of suspended sediment, and a summary of some recent findings of sediment measurements

    Laboratory requirements for in-situ and remote sensing of suspended material

    Get PDF
    Recommendations for laboratory and in-situ measurements required for remote sensing of suspended material are presented. This study investigates the properties of the suspended materials, factors influencing the upwelling radiance, and the various types of remote sensing techniques. Calibration and correlation procedures are given to obtain the accuracy necessary to quantify the suspended materials by remote sensing. In addition, the report presents a survey of the national need for sediment data, the agencies that deal with and require the data of suspended sediment, and a summary of some recent findings of sediment measurements

    Applicability of a drift-flux model of aerosol deposition in a test tunnel and an indoor heritage environment

    Get PDF
    Near-wall turbulence associated with air flows parallel to walls can promote aerosol deposition. In indoor environments, where this kind of flow is frequently present, this results in local deposition gradients near ventilation inlets and outlets. This phenomenon is of special interest to the heritage field, which is often concerned about the spatial distribution of deposition and its links to environmental management. In this paper we investigate the capability of a drift-flux model of particulate matter deposition to describe this mechanism. This model has often been validated using decay rates of particulate matter concentration; however, in several indoor applications the interest is not in concentration but in the spatial distribution of the deposition flux. To test the model, we use untreated atmospheric aerosols in two different cases: an experimental tunnel designed to induce near-wall velocity gradients and an actual indoor room with various ventilation regimes. Both systems exhibit significantly inhomogeneous deposition distributions. While the first system is operated under controlled laboratory conditions, the second yields data collected in-situ during a six-month monitoring campaign. In either case the model reproduces the experimental values with enough accuracy to allow understanding how the environment behaves. This work confirms the usability of the drift-flux approach as an analysis tool for particle deposition in complex environments in a wide range of geometries

    Potential Role of Ultrafine Particles in Associations between Airborne Particle Mass and Cardiovascular Health

    Get PDF
    Numerous epidemiologic time-series studies have shown generally consistent associations of cardiovascular hospital admissions and mortality with outdoor air pollution, particularly mass concentrations of particulate matter (PM) ≤2.5 or ≤10 μm in diameter (PM(2.5), PM(10)). Panel studies with repeated measures have supported the time-series results showing associations between PM and risk of cardiac ischemia and arrhythmias, increased blood pressure, decreased heart rate variability, and increased circulating markers of inflammation and thrombosis. The causal components driving the PM associations remain to be identified. Epidemiologic data using pollutant gases and particle characteristics such as particle number concentration and elemental carbon have provided indirect evidence that products of fossil fuel combustion are important. Ultrafine particles < 0.1 μm (UFPs) dominate particle number concentrations and surface area and are therefore capable of carrying large concentrations of adsorbed or condensed toxic air pollutants. It is likely that redox-active components in UFPs from fossil fuel combustion reach cardiovascular target sites. High UFP exposures may lead to systemic inflammation through oxidative stress responses to reactive oxygen species and thereby promote the progression of atherosclerosis and precipitate acute cardiovascular responses ranging from increased blood pressure to myocardial infarction. The next steps in epidemiologic research are to identify more clearly the putative PM casual components and size fractions linked to their sources. To advance this, we discuss in a companion article (Sioutas C, Delfino RJ, Singh M. 2005. Environ Health Perspect 113:947–955) the need for and methods of UFP exposure assessment

    Prediction of Air-Side Particulate Fouling of HVAC&R Heat Exchangers

    Get PDF
    Air-to-refrigerant heat exchangers used in heating, ventilation, air-conditioning, and refrigeration systems routinely experience air-side fouling due to the presence of particulates in outdoor and indoor environments. The influence on the performance of the heat exchanger, in terms of heat transfer efficiency and pressure drop imposed, depends on the extent of air-side fouling. Fouling of a heat exchanger is determined by a variety of parameters such as the dimensions of the heat exchanger, physical properties of the airborne particulates, and airflow conditions over the heat exchange surfaces. A comprehensive model is developed to deterministically calculate the extent of fouling of a heat exchanger as a function of these parameters by accounting for each of the possible deposition mechanisms. The study enhances modeling approaches previously employed in the literature by accounting for time-dependent accumulation of particles as well as the effects of the streamwise distribution of accumulated dust on subsequent fouling; the calculations for the deposition due to several of the mechanisms are also refined to improve prediction accuracy. Particulate matter deposits already present on the surface are found to accelerate the process of fouling by decreasing available area for airflow; an existing deposit layer effectively decreases the distance that a particle must travel to collide with a surface and increases the surface area available for deposition. The modified model predictions are compared against extant experimental deposition fraction data; an improved agreement is observed compared to previous models in the literature

    Deposition of suspended fine particulate matter in a library

    Full text link

    Particles in the Indoor Environment

    Get PDF

    Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

    Get PDF
    The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features
    corecore