جستجوی مقالات مرتبط با کلیدواژه "عمق نوری هواویزها" در نشریات گروه "محیط زیست"

Atmospheric aerosols, including solid and liquid particles suspended in the atmosphere, are a mixture of particles in the air, of different sizes, shapes, compositions, and chemical, physical, and thermodynamic properties. They affect the earth’s radiative budget and climate directly by absorbing and scattering the radiation, and indirectly by acting as cloud condensation nuclei. Aerosols have both direct and indirect effects on the climate by scattering and absorbing solar and terrestrial radiation as well as modifying the distribution of clouds and their radiative properties. They have been concerned in health effects and visibility reduction mostly in urban and regional areas. Aerosol types which contribute to the scattering include organic particles, water-soluble inorganic species and dust. In urban areas, the principle particle species that absorbs radiation is black carbon, that is produced from incomplete combustion processes mainly from diesel engines. Natural aerosols are generally larger in size than the secondary aerosols produced from gaseous precursors and combustion, and their chemical composition depends on their sources. However, aerosols produced from natural and antropogenic sources are mixed together and thereby each aerosol particle is a composite of different chemical constituents. Atmospheric aerosol optical and physical properties are two of the major uncertainties in global climate change which are also responsible for many impressive atmospheric effects. Therefore, retrieval of the aerosol optical parameters is an important issue for the atmospheric research communities. Investigations of aerosol characteristics and their optical properties will lead to a better understanding of both the regional and local behavior of aerosols over a region. Aerosol optical indices such as aerosols optical depth, Angstrom exponent, single scaterring albedo, asymmetry parameter are the most important characteristics of aerosols that are influenced by the physical properties and concentration of particles. These properties also play an important role in the Earth’s climate and radiation budget. Aerosols optical depth is a key factor to measure the degree of atmospheric pollution and to study the climate response to aerosol radiative forcing. Its value shows the aerosol density, while Angstrom exponent is an intensive parameter that depends on the aerosol size distribution and increases with decreasing particle size. In other words, Angstrom exponent is the slope of the logarithm of aerosol optical depth versus the logarithm of wavelength. It is commonly used to characterize the wavelength dependence of aerosols optical depth and provides some nformation on the aerosols size distribution. When scattering is dominated by fine particles, Angstrom exponent has large values(i.e., around 2); it approaches to 0 when scattering is dominated by coarse particles. Remote sensing of aerosols from satellite-based sensors turn into an important instrument to monitor and quantify the aerosol optical properties over the globe. Study of aerosol optical properties provides a detailed knowledge of both the regional and local behavior of aerosols as well as their influence on the Earth’s climate, radiative forcing, visibility and photochemistry. Although considerable development has been taken in understanding aerosol properties, they are poorly quantified because of the lack of adequate information on temporal and spatial variability of aerosols. In this paper, using the satellite data from the Ozone Monitoring Instrument (OMI) aerosols optical depth and Angstrom exponent are investigated over two megacities in Iran, Tehran and Mashhad, during the period from January 2010 to December 2013. OMI was launched in July 2004 on NASA’s EOS-Aura satellite, also part of the A-train constellation. The reasons of choosing these urban areas are mainly the existence of a large number of populations and substantial sources of emissions from natural and anthropogenic emissions. Previous studies show that the increasing emissions of aerosols during the past decades in these two area have affected their local climate. Here daily, monthly and seasonal variations of aerosol properties in terms of optical depth and Angstrom exponent are analyzed to provide a detailed insight into the variation of aerosols loading and their possible causes. Results concerning the seasonal frequency distribution of aerosols optical depth (AOD) at 500 nm indicate that values of this index in Tehran are higher than Mashhad in all seasons. It shows the existence of higher aerosol density causing the higher atmospheric turbidity over Tehran than Mashhad. During the study period, the daily amount of AOD over Tehran is ranged from 0.2 to 1.6, while over Mashhad the daily AOD is ranged from 0.1 to 0.9. High values of aerosol optical depth are obtained during the spring and summer seasons, respectively, and low values are seen during the winter in the both cities. There are also significant variations of Angstrom exponent over the two cities. Based on the results, the dominant mode of aerosols over Tehran is a mixture of fine and coarse particles, but fine particles are dominant over Mashhad. Therefore, it can be deduced that turbidity in Tehran is subject to a complex mixture of aerosol types, including anthropogenic aerosols and dust, while anthropogenic aerosols are dominant over Mashhad. To further understand the seasonal variations of aerosols, AOD was studied at different wavelengths. Results show the seasonal dependency of AOD values that are mainly related to various emission sources. In order to investigate the origins of aerosols and transports of the air masses toward the understudy regions, back trajectory analyses based on the NOAA HYSPLIT (National Oceanic and Atmospheric Administration Hybrid Single Particle Langrangian Integrated Trajectory) model, was performed. For six days, as the representatives of polluted and clean days, air mass back trajectories were computed using HYSPLIT model. Results indicate the existence of different patterns of particles transport over the two cities. It is seen that the sources of aerosols over Tehran are both from local emissions and from the long range dust transport, while aerosols over Mashhad are more likely from local sources.

  Atmospheric aerosols have different sources that we can refer to volcanic activities, dust, salt particles in the seas and oceans, or they due to human activities that we can refer to activities that such as industrial activities, transportation, fuel costs and …. aerosols have very important role in transitive radiation and chemical process that they are the earth’s climate controller. Among the internationally-conducted works in this area can refer to the Olcese et al. , 2015 which have been done based on the use of the artificial neural network model (MLP). They used previous values of the AOD at two stations as input of artificial neural network model to estimate the AOD under cloudy conditions and in situations where little data is available. This method was used to predict the values of AOD on nine stations with 440nm wavelengths on the east coast of the United States during the 1999 to 2012. The calculated R2=0. 85 between the observed and predicted AOD indicate a good performance of this model. To date, there is no research to estimate AOD by using different models such as Multiple linear Regression, Principal Component Regression, Artificial Neural Networks and Autoregressive integrated moving average model in Iran. Therefor in this research estimation AOD examined in two cases including estimate for areas with no Pyranometer stations and long- lasting estimation in stations with solar radiation detector for the future under.
Material and In this study related data to Pyranometer were collected for understudied are though the Meteorology office in center of Kurdistan province ranged 2005/01/01 until 2016/12/31. Thus, the total number of available data for the mentioned time period was 4382 data in the study area, and since there was no solar radiation for some days of the year, the total number of data used for Sanandaj city was reduced to 3956.
Study area:Sanandaj is the capital of Kurdistan province. About geographic location this city is located in within limits 35 degree and 20 minutes north latitude and 47 degree east longitude from Greenwich Hour circle and in the 1373/4 meters height above sea level.
Multiple linear Regression Model:In the Multiple linear Regression turn to check the relation between a dependent variable and several independent by earned relationship for them in the SPSS software, in the Multiple linear Regression the measure of AOD serve as dependent variable and meteorology numeral quantity such as temperature, relative humidity, wind speed and also altitude atmosphere were considered as independent variable. The general formula for the MLR model is as follows:Y=β_0+β_1 x_1+⋯+β_n x_n+ε
In this case, y is dependent variable. X1, …, Xn denote the independent variables, and also nβ0, …, β report the fixed constants. Ԑ also indicates the remaining values.
Principal Component Regression Model:Principal Component Regression Model is a combination of Principal Component Analysis (PCA) and Multiple Linear Regression (MLR). These calculations are as follows:Y=φβ_PCR+e
Where φ is the matrix of base components, which is obtained as n * k, and βPCR represents the first of the components of the K score. The vector of e is a random error which defined as n٭1. Mark and scores for the components are based on the original version of the OLS method as follows:β_PCR= (φ^' 〖φ) 〗^ (-1) φ^' y= (L^2) ^ (-1) φ^' y
In this case, L2 is the amount of slice of the matrix, which is based on the Kth parameter, which also indicates the slip of the parameter k⅄. Finally, the following equation was reached. β_PCR=∑_ (K=1) ^K▒ (υ_k u_k^') /d_k y, K<min⁡ (n, p) in this model primary variable changed to new components and Independent from each, that both of the two components have Zero correlation coefficient, finally these used as primary variables.
Autoregressive Integrated Moving Average Model:Autoregressive integrated moving average model is one of the important method in anticipation time series which presented by Box and Jenkins in 1970. ARIMA model is a Data- driven model, it means the mentioned model use of the structure of data and this model facet. Limitation if data have any meaningful nonlinearities relationships. ARIMA model is able in this way present the forecasts related to the time series. This model is a forecasting method with Statistical theory and because of having advantages such as high attention and strong adaptability ability is able to have a good usage in many bases.
Artificial Neural Networks Model:Multilayer perceptron (MLP) is the most well know and mostly the most used among different kind of neural networks and in most cases act as signals that transfer input to output in the network. In these kind of multilayer network layers are joined as outputs of first layer act as second layer inputs, and output from second layer are the third layer inputs and it will be continued till last layer output, that they are the main outputs and the certain and real answer.
Discussion of Results & The first model, Multiple linear Regression according to the made result for this model, the measure of the AOD in understudied city has a direct connection with temperature and wind speed parameters out the level 850 hectopascal, but also this have an opposite connection with relative moisture and atmospheric layer altitude also the measure of got determination factor by this model allocated itself less numerical value and it is used because of linear structure in the data. The equation presented for it is as follows:AOD=458/0+039/0T_850-127/0〖RH〗_850+021/0〖Speed〗_850-064/0 BLH
The R^2=0. 071, RMSE=0. 1698 and MAE=0. 1498 were obtained for training phase and R^2=0. 096, RMSE=0. 1703 and MAE=0. 1494 were acquired for testing phase. The results of the training and testing phases of the MLR model indicate the low accuracy of this model in predicting the AOD in Sanandaj city. The second used model in this research was Principal Component Regression model. In this model AOD have direct connection with temperature and wind speed but it has a negative connection with the other parameters such as relative moisture and atmospheric layer altitude. The extracted equation for PCR model as follows:AOD=457/0+041/0T_850-126/0〖RH〗_850+021/0〖Speed〗_850-065/0BLH
In this section, the R^2=0. 071, RMSE=0. 1699 and MAE=0. 15 were obtained for training phase and R^2=0. 069, RMSE=0. 1694 and MAE=0. 1484 were acquired for testing phase. According to the result, got out puts by MLR and PCR models have a close result to estimate the AOD for stations with no Pyranometer. Autoregressive Integrated Moving Average Model was the third used model. This model had the best function to estimate AOD in the station with no Pyranometer. The obtained equation for ARIMA model as follows:AOD=0061/0+7084/0y_ (t-1) +0572/0 y_ (t-2) +2189/0y_ (t-3)
In this section, the R^2=0. 91, RMSE=0. 0501 and MAE=0. 033 were obtained for training phase and R^2=0. 89, RMSE=0. 086 and MAE=0. 0374 were acquired for testing phase. Artificial Neural Networks model was the fourth used model. In the research two hidden layers were used in this model. The number of optimized neurons for the understudied area was different with available data. The number of optimized neurons determined for Sanandaj city were 24 and 33 neurons to estimate the AOD in the long time (a year) in the station with no Pyranometer. In this section, the R^2=0. 75, RMSE=0. 1162 and MAE=0. 0921 were obtained for training phase and R^2=0. 63, RMSE=0. 14 and MAE=0. 113 were acquired for testing phase. It can be concluded that for estimate AOD in the area with Pyranometer instrument is better using the autoregressive stage instead follows the training and testing phases of the different models. Because, as it has been showed, the data required for the autoregressive stage is only the data of the AOD at the station. In general, the results of this research showed that use of different and efficient models can be a suitable solution for estimating AOD for regions with Pyranometer, as well as the area without a Pyranometer.