This thesis is a collection of studies focusing on absorption characteristics of aerosol particles using satellite and ground data as well as model simulations at an urban environment in Karachi. First of all, identification of absorbing aerosols through various aerosol optical properties was performed. After that, a study of temporal variation of absorbing Black Carbon (BC) aerosol and their impact on meteorological parameters and surface reflectance was carried out. Finally, the radiative effect of absorbing BC aerosols was investigated. In order to seasonally characterize the absorbing aerosols into different types, the optical properties of aerosol retrieved from AErosol RObotic NETwork (AERONET) and Ozone Monitoring Instrument (OMI) were utilized. Firstly, the OMI Absorption Aerosol Optical Depth (AODabs) was validated with AERONET AODabs and was found to have a high degree of correlation. Then, based on this validation, characterization was conducted by analyzing aerosol Fine Mode Fraction (FMF), Angstrom Exponent (AE), Absorption Angstrom Exponent (AAE), Single Scattering Albedo (SSA) and Aerosol Index (AI) and their mutual correlation, to identify the absorbing aerosol types and also to examine the variability in seasonal distribution. The absorbing aerosols were characterized into Mostly Black Carbon (BC), Mostly Dust and Mixed BC & Dust. The results revealed that Mostly BC aerosols contributed dominantly during winter and postmonsoon whereas, Mostly Dust was dominant during summer and premonsoon. These types of absorbing aerosol were also confirmed with the MODerate resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations. Furthermore, BC mass concentration was measured continuously for every five-minute interval with ground-based Aethalometer at an urban site in Karachi. In this study, the temporal (diurnal, monthly and seasonal) variations of BC and its relationship with meteorological variables were analyzed. Monthly averaged concentrations of BC ranged from 2.2 to 12.5 μg/m3, with maximum in the month of January, 2007 and minimum in the month of June, 2006. BC showed higher concentrations during the months of January, February and November while lower during the months of May, June, July and August throughout the years. It also displayed comparatively high concentrations during winter and postmonsoon, while moderate during premonsoon and low during summer. Diurnal analysis of BC concentration showed sharp peaks between 07:00 and 09:00 LST and again around 22:00 during all the months. Moreover, the relationship between BC concentration and meteorological variables such as Temperature (Temp), Relative Humidity (RH), Wind Speed (WS), Wind Direction (WD), VISibility (VIS) and RainFall (RF) was found and it was observed that BC concentration showed an inverse relationship with all these meteorological variables. The results revealed that BC aerosol concentration showed significant inverse relationship with surface reflectance with correlation coefficient (R) of -0.77 which implies that rise in BC aerosol concentration strongly reduces the surface reflectance. In contrast, the lower the BC aerosol concentration, the higher the surface reflectance. In addition, a significant negative correlation was recorded during all seasons showing that increase in BC aerosol concentration was accompanied with reduction in surface reflectance. The opposite relation between BC aerosol concentration and surface reflectance is relatively higher during the premonsoon and winter followed by postmonsoon, while it is lower during summer. With observations of BC aerosol concentrations, optical and radiative properties were obtained over the urban city of Karachi during the period of March 2006-December 2008. BC concentrations were continuously measured using an Aethalometer, whereas optical and radiative properties were estimated through the Optical Properties of Aerosols and Clouds (OPAC) and Santa Barbra DISORT Atmospheric Radiative Transfer (SBDART) models, respectively. The measured BC concentrations were higher during January, February and November, while these were found to be lower during May, June, July and August throughout the duration of the study. A maximum peak value was observed during January 2007 while the minimum value was observed during June 2006. The Short Wave (SW) BC Aerosol Radiative Forcing (ARF) at Top of the Atmosphere (ToA) and within the ATMOSphere (ATMOS) were positive during all the months, whereas negative BC ARF values were found at the SurFaCe (SFC). Overall, all three RF components at SW indicated that the heating by absorption of BC aerosols is higher during January, February and November mostly due to their enhanced presence, higher BC Aerosol Optical Depth (AOD) in combination with low SSA, though the surface reflectance was low. While relatively lower values of ARF were found during May, June, July and August, these were attributed to lower BC concentrations and BC AOD coupled with higher SSA even though surface reflectance was much higher in these months as compared to other months. Conversely, the Long Wave (LW) BC ARF at ToA remained positive, and BC ARF at SFC was positive whereas, BC ARF in ATMOS shifted towards positive values (heating effect) during June-August when an increase in water vapor content was found. Finally, the net (SW + LW) BC ARF at ToA and in ATMOS were found to be positive while BC ARF at SFC were found to be negative. It should be noted that a systematic increase in Atmospheric Heating Rate (AHR) was found during October to January underlining the significant effect of absorbing BC aerosols. Moreover, we found the highest correlation between AODabs and BC ARF in ATMOS followed by correlation with SFC and then with ToA. Similar to BC ARF, the values of all the three BC Absorption Forcing Efficiency (AFE) were found to be at a minimum during June 2006 while these were maximum during January 2007. On an average, the contribution of BC to the total ARF was found to greater than 80% for the whole observational period and contribute up to 99 % during January 2007.
جدائی کے نیزے پر دل تڑپتا ہے پچھلے پہر کی ساعتوں میں! خوشبو کے ساتھ۔۔۔ہوائیں بھیگی آنکھیں چومتی ہیں روح البیان کی شرطوں میں۔۔۔! خوابوں کے سوگ میں سوسن نسترن! ثنویت کی آہٹوں میں غنا سطیت کی سانسیں سمو رہی ہیں زرتشت، گوتم، عیسیٰؑ صدیوں پہلے۔۔۔! دائم ’’فارقلیط‘‘ کا صحیفہ پڑھ کر سنا گئے پھر بھی آبنائے محبت میں تیرتے بجروں کے بادباں پر۔۔۔! اک پرندہ۔۔۔! معصوم موسموں کے صحیفوں کی آہٹیں بولتا رہتا ہے ہجر کے صحرا میں دل تڑپتے ہوئے! روغن چشم سے چراغ روشن کرتے ہوئے! معتبر علامتوں کو جدائی کی میزان پر تولتا رہتا ہے
Education has been considered the core value of human life. Religion and education are interrelated. The preaching of religion depends upon the education and training. Our holy Prophet (SAW) was an ideal educationist not only of his times but for the future generations as well. The article deals with the importance of education in the light of prophetic model. Prophet Mohammad (SAW) has emphasized on education and guidance of Muslims. He has established many educational policies for the Muslims which include Treaty of Madina, brotherhood of Muslims, construction of masajid, establishment of Suffa. These initiatives provided a ground work for future generations in shaping their educational syllabus and policies.
Contribution of adaptive filters in the evolution of modern communications is remarkable. The logic of adaptive filtering establishes a significant part in the tool-set of statistical signal processing. For the problem of adaptive estimation, computational cost, convergence rate, steady-state error, stability and generalization are considered to be the main challenges. In this work several novel adaptive algorithms are developed to address these challenges. This work focus on two different types of environments, namely single-agent environments and multi-agent environments. In singleagent environments, data arrives at a single node for the estimation of a parameter of interest. For such environments we propose a novel idea of q-calculus based adaptive analysis. The qgradient is an extension of the classical gradient vector based on the concept of Jackson’s derivative. The q-derivative takes larger steps in the search direction as it evaluates the secant of the cost function rather than the tangent (as in the case of a conventional derivative). Motivated by this, in this work we developed several algorithms/ideas in which we addressed the short comings of the standard least mean square (LMS) and its variants by using q-calculus, such as, the q-least mean square (q-LMS) algorithm, in which we minimize the LMS cost function by employing the concept of q-derivative instead of the conventional derivative, the q-normalized least mean square (qNLMS), the two-dimensional q-least mean square (2D-qLMS) algorithm and the q-LMS for tracking a non-stationary channel. Consequently, several new explicit closed-form expressions for the meansquare-error (MSE) behavior are derived for the transient and steady-state analysis. On the other hand multi-agent signal processing has attract a number of researchers owing to better statistical inference in wireless networks and is therefore effectively utilized in many applications such as wireless sensor networks, smart grids etc. In multi-agent environments, data arrives at multiple nodes that are distributed over a geographical area and have a common task of estimation of a desired parameter. There are two major estimation techniques used for distributed environments: (1) centralized estimation, and (2) decentralized estimation. In centralized estimation, all the estimations take place at a single processor by sharing data from each node at a centralized unit. Such an estimation requires powerful processor along with massive amount of communication and power. To overcome these problems a decentralized estimation solution is proposed in literature, in which each node has its local estimate which is shared with the neighbors in an explicit manner (such as incremental, diffusion etc). Decentralized estimation techniques suffer from: (1) link failure problem, (2) instability issues, and (3) computational cost (particularly in the context of state-space estimation models). To deal with these problems, a number of innovative methodologies are proposed. Firstly a convex combination-based incremental least mean square (LMS) algorithm is proposed to overcome the problems of link failure between the nodes and instability in case local divergence in incremental mode of cooperation. The proposed algorithm is developed by employing convex combination of two filters. The adaptation of one filter is based on the estimate of adjacent node (incremental type), while the adaptation of the other is based on the estimate of the current local node at the previous time instant. These two filters are then fused together by using a suitable mixing parameter. Secondly to minimize the steady-state error, an optimum error non-linearity based incremental mode of cooperation is proposed. Thirdly, to reduce the computational cost for state-space estimation in distributed environments, a state-space least mean square algorithm for diffusion mode of cooperation is proposed. The proposed algorithm minimizes the computational complexity at each node which intern provides a significant advantage in terms of computational cost of the overall network and hence, can improve the response time of the network. Both the convergence in the mean and the mean square analysis of the proposed algorithm are performed and the transient and steady-state behavior of the proposed algorithm is analyzed.