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1 |
Predicted direct solar radiation (ECMWF) for optimized operational strategies of linear focus parabolic-trough systems
تابش مستقیم خورشیدی پیش بینی شده (ECMWF) برای استراتژی های عملیاتی بهینه شده سیستم های سهموی-تمرکز خطی -2020 Day-ahead forecasts of direct normal irradiance (DNI) from the Integrated Forecasting System (IFS), the
global model of the European Centre for Medium-Range Weather Forecasts (ECMWF), are used to
simulate a concentrating solar power (CSP) plant through the System Advisor Model (SAM) to assess the
potential value of the IFS in the electricity market. Although DNI forecasting from the IFS still demands
advances towards cloud and aerosol representation, present results show substantial improvements with
the new operational radiative scheme ecRad (cycle 43R3). A relative difference of approximately 0.12% for
the total annual energy availability is found between forecasts and local measurements, while approximately
10.6% is obtained for the previous version. Results of electric energy injection to the grid from a
simulated linear focus parabolic-trough system shows correlations coefficients of approximately 0.87
between hourly values of electric energy based on forecasted and measured DNI, while 0.92 are obtained
for the daily values. In the context of control strategy, four operational strategies are given for different
weather scenarios to handle the energy management of a CSP plant, including the effect of thermal
energy storage capacity. Charge and discharge operational strategies are applied accordingly to the
predicted energy availability. Keywords: Short-term forecasts | ECMWF | Direct normal irradiance | Concentrating solar power | System advisor model | Operational strategies |
مقاله انگلیسی |
2 |
Study on the motion law of aerosols produced by human respiration under the action of thermal plume of different intensities
مطالعه در مورد قانون حرکات ذرات معلق در هوا که توسط تنفس انسان تحت اثر ستون های حرارتی با شدت های مختلف تولید می شود-2020 Predicting influence of human thermal plume on the diffusion of respiration-produced particles is an important
issue for improving indoor air quality through eliminating infectious microbes efficiently. In this study, the Large
Eddy Simulation was utilized to predict the effects of thermal plume of different intensities on particle diffusion.
Three postures of the human body model and three room temperatures were considered. The results show that
the convective heat transfer coefficient on the surface of the human body varies greatly with different postures.
The coefficient is the largest when the model is in sitting posture, leading to the greatest heat transfer rate.
Meanwhile, the thermal plume generated by bending the thigh increases the size of the facial thermal plume in
horizon direction. The increase of the difference between indoor temperature and skin temperature causes an
increase of the convective heat transfer of the manikin, leading to stronger airflow in front of the face. The
thicker and faster the human thermal plume is, the more difficult it is penetrated by aerosols produced by nasal
breathing, finally resulting in most particles distributed within 0.2m thick under the roof. Keywords: Thermal plume | Large eddy simulation| Aerosol | Nasal breathing | Computational fluid dynamics |
مقاله انگلیسی |
3 |
Research on BP network for retrieving extinction coefficient from Mie scattering signal of lidar
تحقیقات بر روی شبکه BP برای بازیابی ضریب خاموشی از سیگنال پراکندگی میای LIDAR-2020 Mie lidar is a powerful tool for detecting the optical properties of atmospheric aerosols. However, there
are two unknown parameters in the Mie lidar equation: the extinction coefficient and the backscattering
coefficient. In the common methods for solving the equation, it is necessary to make assumptions about
the relationship between the two unknown parameters. These assumptions will reduce the detection
precision of extinction coefficient. In view of this, the back propagation (BP) neural network is used to
retrieve extinction coefficient from the Mie scattering signal of lidar. Firstly, the structure and main
parameters of the BP network are designed according to the practical application. In order to improve
the convergence speed and prevent falling into local minima, the initial weights and thresholds of BP network
are optimized by genetic algorithm (GA). Then the GA-BP network is trained with Mie scattering
signal and the extinction coefficient retrieved by Raman method. Thus the mathematical relationship
between Mie scattering signal and the extinction coefficient is stored in the BP network. The trained
GA-BP network is then used to retrieve the extinction coefficient from Mie scattering signal in different
conditions and the applicability of the GA-BP network is researched. The research will promote the development
of Mie lidar retrieving algorithm. Keywords: Aerosol | Mie scattering | Lidar | Extinction coefficient | BP network | Genetic algorithm |
مقاله انگلیسی |
4 |
A stochastic model of particulate matters with AI-enabled technique-based IoT gas detectors for air quality assessment
مدل تصادفی ذرات معلق با ردیاب های گاز IoT مبتنی بر تکنیک هوش مصنوعی برای ارزیابی کیفیت هوا-2020 Monitoring air quality in urban and industrial environments and estimating exposure to particulate matter (PM)
pollution concentrations are critical issues that affect human health. Because of aerosols (suspended particles),
PM is mostly observed near the surface and thus can be inhaled. To predict the modeling of micro-to-nano-sized
particle suspensions, this study presents a stochastic model in environmental dynamics with internet of things
(IoT) gas detectors based on an artificial intelligence (AI)-enabled technique; the model can determine floating
fine PM dispersion in a city to assess and monitor air quality. The factors that influence the prediction are
weather- and air pollution-related data, such as humidity, temperature, wind, PM2.5, and PM10. In this study,
these factors have been considered at 7 measuring stations across the urban region in Taipei City, Taiwan, from
2013 to 2018. A nonlinear autoregressive network with exogenous inputs model is constructed using estimated
states to investigate approaches for identifying PM; the model can be a state–space self-tuning stochastic model
for predicting unknown nonlinear sampled data. The results indicate that a satisfactory agreement was obtained
using a normalized root mean square deviation, with small values of 0.0504 and 0.0802 for PM2.5 and PM10,
respectively. Accordingly, this study presents that the time-domain causality between PM and the atmospheric
environment can be constructed using discrete-time models that can be satisfactorily implemented in developing
different air quality monitoring systems for the long-term prediction of air pollution. Keywords: Particulate matter | Micro-to-nano-sized particle suspensions | Modeling | Micropollutants | Artificial intelligence | Atmospheric environment |
مقاله انگلیسی |
5 |
Advancing the prediction accuracy of satellite-based PM2:5 concentration mapping: A perspective of data mining through in situ PM2:5 measurements
پیشبرد دقت پیش بینی نقشه برداری غلظت PM2:5 ماهواره ای مبتنی بر ماهواره: چشم انداز کاوی داده از طریق اندازه گیری PM2:5 درجا-2019 Ground-measured PM2.5 concentration data are oftentimes used as a response variable in various
satellite-based PM2.5 mapping practices, yet few studies have attempted to incorporate groundmeasured
PM2.5 data collected from nearby stations or previous days as a priori information to
improve the accuracy of gridded PM2.5 mapping. In this study, Gaussian kernel-based interpolators were
developed to estimate prior PM2.5 information at each grid using neighboring PM2.5 observations in
space and time. The estimated prior PM2.5 information and other factors such as aerosol optical depth
(AOD) and meteorological conditions were incorporated into random forest regression models as
essential predictor variables for more accurate PM2.5 mapping. The results of our case study in eastern
China indicate that the inclusion of ground-based PM2.5 neighborhood information can significantly
improve PM2.5 concentration mapping accuracy, yielding an increase of out-of-sample cross validation R2
by 0.23 (from 0.63 to 0.86) and a reduction of RMSE by 7.72 (from 19.63 to 11.91) mg/m3. In terms of the
estimated relative importance of predictors, the PM2.5 neighborhood information played a more critical
role than AOD in PM2.5 predictions. Compared with the temporal PM2.5 neighborhood term, the spatially
neighboring PM2.5 term has an even larger potential to improve the final PM2.5 prediction accuracy.
Additionally, a more robust and straightforward PM2.5 predictive framework was established by
screening and removing the least important predictor stepwise from each modeling trial toward the final
optimization. Overall, our results fully confirmed the positive effects of ground-based PM2.5 information
over spatiotemporally neighboring space on the holistic PM2.5 mapping accuracy. Keywords: PM2.5 | Aerosol optical depth | Spatiotemporal interpolation | Random forest | Air quality |
مقاله انگلیسی |
6 |
Predicting ground-level PM2:5 concentrations in the Beijing-Tianjin- Hebei region: A hybrid remote sensing and machine learning approach
پیش بینی غلظت PM2:5 در سطح زمین در منطقه پکن، Beijing-Tianjin- هبی: یک روش سنجش از دور و یادگیری ماشین هیبریدی-2019 An accurate estimation of PM2.5 (fine particulate matters with diameters 2.5 mm) concentration is
critical for health risk assessment and generating air pollution control strategies. In this study, a hybrid
remote sensing and machine learning approach, named RSRF model is proposed to estimate daily
ground-level PM2.5 concentrations, which integrates Random Forest (RF), one of machine learning (ML)
models, and aerosol optical depth (AOD), one of remote sensing (RS) products. The proposed RSRF model
provides an opportunity for an adequate characterization of real-time spatiotemporal PM2.5 distributions
at uninhabited places and complex surfaces. It also offers advantages in handling complicated non-linear
relationships among a large number of meteorological, environmental and air pollutant factors, as well as
ever-increasing environmental data sets. The applicability of the proposed RSRF model is tested in the
Beijing-Tianjin-Hebei region (BTH region) during 2015e2017. Deep Blue (DB) AOD from Aqua-retrieved
Collection 6.1 (C_61) aerosol products of Moderate Resolution Imaging Spectroradiometer (MODIS) is
validated with Aerosol Robotic Network. The validation results indicate C_61 DB AOD has a high correlation
with ground based AOD in the BTH region. The proposed RSRF model performed well in characterizing
spatiotemporal variations of annual and seasonal PM2.5 concentrations. It not only is useful to
quantify the relationships between PM2.5 and relevant factors such as DB AOD, meteorological and air
pollutant variables, but also can provide decision support for air pollution control at a regional environment
during haze periods. Keywords: Remote sensing | Aerosol optical depth | Machine learning | PM2.5 | Random forest |
مقاله انگلیسی |
7 |
Satellite-based PM2:5 estimation directly from reflectance at the top of the atmosphere using a machine learning algorithm
تخمین PM2:5 مبتنی بر ماهواره مستقیم از بازتاب در بالای جو با استفاده از یک الگوریتم یادگیری ماشین-2019 Atmospheric particulate matter (PM) that have particle diameter less than 2.5 μm (PM2.5) are hazardous to
public health whose concentration has been either measured on the ground or inferred from satellite-retrieved
aerosol optical depth (AOD). The latter is subject to numerous sources of errors, making the satellite retrievals of
PM2.5 highly uncertain. This study developed an ensemble machine-learning (ML) algorithm for estimating
PM2.5 concentration directly from Advanced Himawari Imager satellite measured top-of-the-atmosphere (TOA)
reflectances in 2016 integrated with meteorological parameters. The algorithm is demonstrated to perform well
across China with high accuracies at different temporal scales. The model has an overall cross-validation
coefficient of determination (R2) of 0.86 and a root-mean-square error (RMSE) of 17.3 μgm−3 for hourly PM2.5
concentration estimation. Such accuracies of the estimation on PM2.5 concentration by using TOA reflectance
directly are comparable with those of the common methods on estimating PM2.5 concentration by using satellitederived
AODs, but the former has a relatively stronger predictive power relating to spatial-temporal coverages
than the latter. Annual and seasonal variations of PM2.5 concentration over three major the developed regions in
China are estimated using the model and analyzed. The relatively stronger predictive ability of developed model
in this study may help provide information about the diurnal cycle of PM2.5 concentrations as well as aid in
monitoring the processes of regional pollution episodes and the evolution of PM2.5 concentration. Keywords: PM2.5 concentration | TOA reflectances | Machine learning |
مقاله انگلیسی |
8 |
The HITRAN2016 Molecular Spectroscopic Database
پایگاه داده طیف سنجی مولکولی HITRAN2016-2017 This paper describes the contents of the 2016 edition of the HITRAN molecular spectroscopic
compilation. The new edition replaces the previous HITRAN edition of 2012 and its updates
during the intervening years. The HITRAN molecular absorption compilation is composed of
five major components: the traditional line-by-line spectroscopic parameters required for high
resolution radiative-transfer codes, infrared absorption cross-sections for molecules not yet
amenable to representation in a line-by-line form, collision-induced absorption data, aerosol
indices of refraction, and general tables such as partition sums that apply globally to the data.
The new HITRAN is greatly extended in terms of accuracy, spectral coverage, additional
absorption phenomena, added line-shape formalisms, and validity. Moreover, molecules,
isotopologues, and perturbing gases have been added that address the issues of atmospheres
beyond the Earth. Of considerable note, experimental IR cross-sections for almost 300 additional
molecules important in different areas of atmospheric science have been added to the database.
The compilation can be accessed through www.hitran.org. Most of the HITRAN data have
now been cast into an underlying relational database structure that offers many advantages over
the long-standing sequential text-based structure. The new structure empowers the user in many
ways. It enables the incorporation of an extended set of fundamental parameters per transition,
sophisticated line-shape formalisms, easy user-defined output formats, and very convenient
searching, filtering, and plotting of data. A powerful application programming interface making
use of structured query language (SQL) features for higher-level applications of HITRAN is also
provided.
Keywords: HITRAN | Spectroscopic database | Molecular spectroscopy | Molecular absorption Spectroscopic line parameters | Absorption cross-sections | Collision-induced Absorption | Aerosols |
مقاله انگلیسی |
9 |
Estimating urban PM10 and PM2:5 concentrations, based on synergistic MERIS/AATSR aerosol observations, land cover and morphology data
برآورد غلظت PM10 و PM2:5 شهری، بر اساس هم افزایی مشاهدات آئروسل MERIS/ AATSR ، پوشش زمین و داده های مورفولوژی-2016 This study evaluates alternative spatio-temporal approaches for quantitative estimation of daily mean Particulate Matter (PM) concentrations. Both fine (PM2.5) and coarse (PM10) concentrations were estimated over the area of London (UK) for the 2002–2012 time period, using Aerosol Optical Thickness (AOT) derived from MERIS (Medium Resolution Imaging Spectrometer)/AATSR (Advanced Along-Track Scanning Radiometer) synergistic observations at 1 km × 1 km resolution. Relative humidity, temperature and the K-Index obtained from MODIS (Moderate Resolution Imaging Spectroradiometer) sensor were used as additional predictors. High- resolution (100 m × 100 m) local urban land cover and morphology datasets were incorporated in the analysis in order to capture the effects of local scale emissions and sequestration. Spatial (2-D) and spatio-temporal (3-D) kriging were applied to in situ urban PM measurements to investigate their association with satellite- derived AOT while accounting for differences in spatial support. Linear mixed-effects models with day-specific and site-specific random intercepts and slopes were estimated to associate satellite-derived products with kriged PM concentration and their predictive performance was evaluated.© 2015 Elsevier Inc. All rights reserved.
Keywords: Particulate matter | Aerosol optical thickness | MERIS/AATSR synergy | Block kriging | Change of support problem | Mixed-effects models |
مقاله انگلیسی |