High-efficiency Model Research Articles

Optimizing sowing density-based management decisions with different nitrogen rates on smallholder maize farms in Northern Nigeria

AbstractIn this study, the CERES-Maize model was calibrated and evaluated using data from 60 farmers’ fields across Sudan (SS) and Northern Guinea (NGS) Savannas of Nigeria in 2016 and 2017 rainy seasons. The trials consisted of 10 maize varieties sown at three different sowing densities (2.6, 5.3, and 6.6 plants m−2) across farmers’ field with contrasting agronomic and nutrient management histories. Model predictions in both years and locations were close to observed data for both calibration and evaluation exercises as evidenced by low normalized root mean square error (RMSE) (≤15%), high modified d-index (> 0.6), and high model efficiency (>0.45) values for the phenology, growth, and yield data across all varieties and agro-ecologies. In both years and locations and for both calibration and evaluation exercises, very good agreements were found between observed and model-simulated grain yields, number of days to physiological maturity, above-ground biomass, and harvest index. Two separate scenario analyses were conducted using the long-term (26 years) weather records for Bunkure (representing the SS) and Zaria (representing the NGS). The early and extra-early varieties were used in the SS while the intermediate and late varieties were used in the NGS. The result of the scenario analyses showed that early and extra-early varieties grown in the SS responds to increased sowing density up to 8.8 plants m−2 when the recommended rate of N fertilizers (90 kg N ha−1) was applied. In the NGS, yield responses were observed up to a density of 6.6 plants m−2 with the application of 120 kg N ha−1 for the intermediate and late varieties. The highest mean monetary returns to land (US$1336.1 ha−1) were simulated for scenarios with 8.8 plants m−2 and 90 kg N ha−1, while the highest return to labor (US$957.7 ha−1) was simulated for scenarios with 6.6 plants m−2 and 90 Kg N ha−1 in the SS. In the NGS, monetary return per hectare was highest with a planting density of 6.6 plants m−2 with the application of 120 kg N, while the return to labor was highest for sowing density of 5.3 plants m−2 at the same N fertilizer application rates. The results of the long-term simulations predicted increases in yield and economic returns to land and labor by increasing sowing densities in the maize belts of Nigeria without applying N fertilizers above the recommended rates.

LoG: a locally-global model for entity disambiguation

Entity disambiguation (ED) aims to link textual mentions in a document to the correct named entities in a knowledge base (KB). Although global ED models usually outperform local models by collectively linking mentions based on the topical coherence assumption, they may still incur incorrect entity assignment when a document contains multiple topics. Therefore, we propose a Locally-Global model (LoG) for ED which extracts global features locally, i.e., among a limited number of neighboring mentions, to combine the respective superiority of both models. In particular, we derive mention neighbors according to the syntactic distance on a dependency parse tree, and propose a tree connection method CoSimTC to measure the cross-tree distance between mentions. We also recognize the importance of keywords in a document for collective entity disambiguation, which reveal the central topic information of the document. Hence, we propose a keyword extraction method Sent2Word to detect keywords of each document. Furthermore, we extend the Graph Attention Network (GAT) to integrate both local and global features to produce a discriminative representation for each candidate entity. Our experimental results on six widely-adopted public datasets demonstrate better performance compared with state-of-the-art ED approaches. The high efficiency of the LoG model further verifies its feasibility in practice.

Relating Sediment Yield Estimations to the Wet Front Term Using Rainfall Simulator Field Experiments

Depth to wet front is generally considered as the amount of water that penetrates into soil and wets the internal soil layer. This is an important variable especially in applications such as runoff generation and sediment yield estimation. This variable in some cases is used in the hydrological science, in the form of a proxy for infiltration as an important factor in soil erosion processes. This work deals with estimating event-based suspended sediment yield in relation to depth to wet front to capture the significance of the depth to wet front in Modified Universal Soil Loss Equation (MUSLE). In this field study, a rainfall simulator equipped with drip systems installed over an experimental hillslope plot was used to generate rainfall with intensities of 45, 60, and 70 mm/h over three slopes 10, 20, and 30% with three repetitions. The coefficient of determination (R2) and the Nash–Sutcliffe efficiency (ECNS) were applied as performance metrics for the model. The results revealed that storm rainfall energy or Erosivity Index (EI30) itself was not suitable for estimating the sediment yield. On the other hand, incorporating both EI30 and rainfall-runoff resulted in higher model efficiency. Upon addition of the depth to wet factor into MUSLE model, a greater efficiency was obtained. Further, the results of event-based simulated and observed sediment indicated that they closely corresponded to a straight line (푅2 = 0.94 and NSE = 0.93). Thus, the depth to wet front was a useful variable in sediment yield simulation. The Nash efficiency coefficient and correlation factor for the estimation total sediment yield were obtained (R2 = 0.93; NSE : 0.92) for both calibration and (R2 = 0.93; NSE : 0.86) validation stages. The result suggests that consideration of the depth to wet front in MUSLE model may lead to improved hydrological and sediment applications.

Evaluation of CH4MOD<sub>wetland</sub> and Terrestrial Ecosystem Model (TEM) used to estimate global CH<sub>4</sub> emissions from natural wetlands

Abstract. Wetlands are the largest and most uncertain natural sources of atmospheric methane (CH4). Several process-based models have been developed to quantify the magnitude and estimate spatial and temporal variations in CH4 emissions from global wetlands. Reliable models are required to estimate global wetland CH4 emissions. This study aimed to test two process-based models, CH4MODwetland and Terrestrial Ecosystem Model (TEM), against the CH4 flux measurements of marsh, swamp, peatland and coastal wetland sites across the world; specifically, model accuracy and generality were evaluated for different wetland types and in different continents, and then the global CH4 emissions from 2000 to 2010 were estimated. Both models showed similar high correlations with the observed seasonal/annual total CH4 emissions, and the regression of the observed versus computed total seasonal/annual CH4 emissions resulted in R2 values of 0.81 and 0.68 for CH4MODwetland and TEM, respectively. The CH4MODwetland produced accurate predictions for marshes, peatlands, swamps and coastal wetlands, with model efficiency (EF) values of 0.22, 0.52, 0.13 and 0.72, respectively. TEM produced good predictions for peatlands and swamps, with EF values of 0.69 and 0.74, respectively, but it could not accurately simulate marshes and coastal wetlands (EF <0). There was a good correlation between the simulated CH4 fluxes and the observed values on most continents. However, CH4MODwetland showed no correlation with the observed values in South America and Africa. TEM showed no correlation with the observations in Europe. The global CH4 emissions for the period 2000–2010 were estimated to be 105.31 ± 2.72 Tg yr−1 by CH4MODwetland and 134.31 ± 0.84 Tg yr−1 by TEM. Both models simulated a similar spatial distribution of CH4 emissions globally and on different continents. Marshes contribute 36 %–39 % of global CH4 emissions. Lakes/rivers and swamps are the second and third greatest contributors, respectively. Other wetland types account for only approximately 20 % of global emissions. Based on the model applicability, if we use the more accurate model, i.e., the one that performs best as evidenced by a higher model efficiency and a lower model bias, to estimate each continent and wetland type, we obtain a new assessment of 116.99–124.74 Tg yr−1 for the global CH4 emissions for the period 2000–2010. Our results imply that performance at a global scale may conceal model uncertainty. Efforts should be made to improve model accuracy for different wetland types and regions, particularly hotspot regions, to reduce the uncertainty in global assessments.

Moving extremum surrogate modeling strategy for dynamic reliability estimation of turbine blisk with multi-physics fields

To improve the dynamic reliability analysis of complex structures like turbine blisk, moving extremum surrogate modeling strategy (MESMS) is proposed in respect of multi-physics coupling with various dynamics/uncertainties. In this strategy, extremum thought is adopted to handle the dynamic process of input parameters and output response, and the importance sampling (IS) method is utilized to extract efficient samples and improve the efficiency of dynamic reliability estimation, and moving least square (MLS) method is used to select good samples from training samples with local compact support region to establish a precise surrogate model. The dynamic reliability analysis of turbine blisk radial deformation with fluid-thermo-structural interaction is performed to validate the developed MESMS in approximate precision and simulation performance by comparing to other methods. As shown in this study, (i) the reliability degree of turbine blisk is 0.9975 when the allowable value of radial deformation is 2.6856 × 10−3 m subject to 3 sigma levels; (ii) the proposed MESMS processes high modeling accuracy and efficiency due to small error; (iii) the MESMS holds high simulation performance in efficiency and accuracy owing to outstanding computational consumption and simulation. These results demonstrate that the MESMS is effective and applicable in structural reliability estimation regarding dynamics and uncertainties. The efforts of this paper provide a useful insight for performing the reliability-based design optimization of complex structures besides turbine blisk.

A Space-Interconnection Algorithm for Satellite Constellation Based on Spatial Grid Model

With the rapid development of large-scale satellite constellations and the increasing demand for rapid communication and emergency rescue using global satellite-based Internet, there have been new requirements for efficient algorithms for inter-communication between satellites. As the constellations of low-orbit satellites become larger, the complexities of real-time inter-satellite calculation and path planning are becoming more complicated and are increasing geometrically. To address the bottlenecks in large-scale node space computing, we introduced a global space grid. Based on this grid, an efficient calculation method of spatial inter-connection between satellite constellations is proposed, according to the concept of “storage for computing” and the high computational efficiency of the spatial grid model. This strategy includes the following parts: (1) the introduction of the GeoSOT-3D global grid model into aerospace and the construction of the aerospace grid indexing BigTable; (2) a set of algorithms for satellite visibility analysis according to the visible grid look-up table and the secondary grid index; and (3) planning inter-satellite routing by querying the grid’s inherent visibility. The idea at the basis of this method is to employ the “space for time” concept to convert the high-dimensional floating operations into one-dimensional matching operations by querying the inherent “visible” attribute of the grid. In our study, we simulated thousands of satellites, discretized their trajectories into grids, and pre-calculated the visibility between grid cells to plan the routing path for the ground data transmission. The theoretical analysis and experimental verification show that the algorithm is feasible and efficient, and it can significantly improve the computational efficiency of inter-satellite connection. We hope that the method can be used in emergency communications, disaster warning, and maritime rescue, and can contribute to the next generation of satellite internet and “satellite-ground” integrated networks.

Evaluating the Energy-Saving Effects of a Utility Demand-Side Management Program: A Difference-in-Difference Coarsened Exact Matching Approach

This paper seeks to estimate the energy-saving effect of a Demand-Side Management program, specifically Gainesville Regional Utility’s (GRU) high-efficiency central Air Conditioner (AC) rebate program in which GRU offers incentives to its customers to replace their old, low-efficiency AC unit with a high-efficiency model. We use a difference-in-difference coarsened exact matching approach to reduce the imbalance of pre-treatment characteristics between treated and control households. We find substantial annual energy savings of the high-efficiency AC program. We disaggregate the energy-saving effects into summer peak effects, winter peak effects, and non-peak effects. The results indicate that the summer peak effects are substantial and statistically significant while there are little or no statistically significant effects of the program on winter peak demand. Also, by following program participants over a three-year period, we find that there is no statistically significant rebound effect of the high-efficiency AC rebate program.

Determination of Adulteration of the B10 Blend of Diesel and Crambe Biodiesel Using Proton Nuclear Magnetic Resonance (1H NMR) Spectroscopy with a Data Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) Model

A methodology was developed to monitor the adulteration of the B10 blend of diesel and crambe biodiesel using proton nuclear magnetic resonance (1H NMR) spectroscopy combined with data driven soft independent modeling of class analogy (DD-SIMCA) model. The training was performed only with samples of the target class (B10) while the validation was performed with a test set consisting of new samples of the target class (B10) and samples of B10 adulterated with crambe oil, used frying oil, and residual automotive lubricating oil. The efficiency of this methodology was characterized based on the sensitivity parameters for the training set and specificity for the test set, in which a value of 1 was obtained for both parameters. This sensitivity value for the training set indicates that no target class samples were classified as extreme or outliers. The specificity for the test set shows that all samples in the test set were correctly classified into their respective classes, demonstrating the high efficiency of the DD-SIMCA model in monitoring adulterants in B10 mixture of diesel and crambe biodiesel. The DD-SIMCA model is simpler to construct than the multivariate control chart and the partial least squares discriminant analysis (PLS-DA) because its development does not require prior information about the adulterants. The excellent obtained results in the application of this model suggest that this analytical methodology is efficient, feasible and suitable for use by inspection agencies to characterize the quality of this fuel.

Spatiotemporal ozone pollution LUR models: Suitable statistical algorithms and time scales for a megacity scale

Abstract Ambient air ozone (O3), a secondary photochemical pollutant, is seriously harmful to human health. Accurate estimation of O3 exposure requires the ability to monitor O3 surface concentration with a high spatiotemporal resolution. Several spatiotemporal land use regression (LUR) models have integrated meteorological factors based on different statistical algorithms to support such epidemiological studies. From among such various existing statistical algorithms, we aim to identify a high-efficiency modeling method, as well as the most suitable lengths of the modeling period (time scale). Three types of typical spatiotemporal LUR models based on parametric, semi-parametric, and non-parametric statistic methods, respectively, are considered to predict daily ground-level O3 in the megacity of Tianjin, China. Based on monthly, seasonal (cold and warm), and annual time scales, these models include: a series of monthly hybrid LUR (Two-stage) models consisting of two sub-models based on the multiple linear regression (MLR) algorithm, general additive mixed models (GAMMs), and land use random forest (LURF) models. Leave-one-out cross-validation was performed to evaluate the temporal and spatial predictive accuracy of each model using the adjusted coefficient of determination (adjR2CV) and root mean square error (RMSECV). In the GAMMs and LURF models, models using a shorter time scale (monthly models) outperformed those using a longer one. In monthly models, the GAMMs performed the best, with the highest average adjR2CV (0.747) and the lowest average RMSECV (15.721 μg/m3), followed by the LURF models (average adjR2CV = 0.695, average RMSECV = 16.405), and the Two-stage models (average adjR2CV = 0.466, average RMSECV = 23.934). Thus, the modeling format consisting of a shorter time scale and the GAMM algorithm performs relatively well in predicting daily O3 pollution on a megacity scale. These findings can be used to select appropriate modeling methods for epidemiological research of O3 pollution.

Urban scale flood simulation based on dynamic wave

Currently based on the full 2D shallow water equations (SWEs) model has been widely used in flood simulation, but there are still two problems to be solved in urban inundation simulation: small-scale structure simulation and calculation efficiency of urban scale model. In this paper, the effective representation of small-scale structure and GPU acceleration technology are introduced into the numerical model to establish a high efficiency and high score model. The model is applied to the simulation of urban inundation in Yuhuan. The simulation results show that the model can simulate urban inundation well with a high accuracy.

2.5D self-potential forward modeling by natural-infinite element coupling method

The self-potential method is an effective method for time-lapse monitoring of underground fluxes, in particular of contaminants. High efficiency and precise forward modeling are necessary for data interpretation and inversion. The natural element method is a new meshless numerical modeling method based on Voronoi diagram, Delaunay triangulation, and natural neighborhood interpolation, where the model nodes are arranged flexibly without grid constraints. However, adding boundary conditions is cumbersome for multi-source models. The infinite element method is an effective numerical method for truncated boundary problems without loading boundary conditions. A new numerical modeling approach is proposed by the coupling of natural elements and infinite elements and applied to redox self-potential models which are multi-source models. The numerical result shows the global stiffness matrix of the new coupled method is sparse, symmetric and independent of the position of field sources, which is helpful to improve forward modeling efficiency. The new method has high precision and is suitable for multi-source models.

Wind turbine power curve modeling based on interval extreme probability density for the integration of renewable energies and electric vehicles

Accurate modeling of the wind turbine power curve (WTPC) is crucial for calculating the theoretical wind power for the integration of renewable energies and electric vehicles. However, existing WTPC modeling methods cannot simultaneously guarantee high modeling accuracy and efficiency for data samples with a large amount of accumulated abnormal data. To address this problem, this paper presents a WTPC modeling method based on interval extreme probability density, which does not require complicated and time-consuming abnormal data cleaning and can significantly improve the modeling efficiency while guaranteeing high modeling accuracy. To verify the applicability and validity of the proposed method, firstly, WTPC models were constructed using actual operation data from 12 wind turbines in a Chinese wind farm and were compared with the manufacturer’s power curve and with WTPC modeling methods based on abnormal data cleaning algorithms. Secondly, the theoretical wind power was calculated and compared with the commonly used manufacturer’s power curve. The results demonstrated that the proposed WTPC modeling method has high modeling accuracy and efficiency and can improve the calculation accuracy of the theoretical wind power effectively, providing more reliable data support for the integrated planning of renewable energies and electric vehicles in nearby regions.

Toward Self-Adaptive Selection of Kernel Functions for Support Vector Regression in IoT-Based Marine Data Prediction

Support vector machine (SVM) is a powerful machine learning (ML) technology and the distinctive generalization ability makes it one of the most popular approximation tools in the field of Internet-of-Things (IoT)-based marine data processing. However, SVM has been criticized for trial and error of parameters, especially, kernel function. How to determine a suitable kernel for SVM in a specific problem has been rather tricky. To give a systematic research of the field, we concentrate on the self-adaptive selection of kernel functions in the framework of SVM for IoT-based marine data prediction. Specifically, we adopt the optimal kernel for obtaining competitive SVM and devises a kernel selection criteria of such high-efficiency models. Experiments are conducted via IoT-based real-world marine data sets of different characteristics. The results demonstrate that our proposed self-adaptive SVM model can autonomously provide a suitable kernel for given marine environmental factor prediction, and outperform the alternative with the linear combination of multiple kernels. Besides, the superior performance is verified from the perspective of statistic analysis.

The modeling design and analysis of centrifugal compressor

Objectives As a hot topic in the field of turbomachinery, centrifugal compressor has a huge impact on gas turbine general performance. In order to achieve the new compressor design with excellent performance under relatively few test conditions, the high efficiency and low cost modeling design method can be adopted. Methods In this paper, a new centrifugal compressor based on similarity principles with pressure ratio of 4.42 and efficiency of 79.19% was designed by modeling a prototype of a high-performance centrifugal compressor. NUMECA software was used to mesh and simulate the three-dimensional flow field of the centrifugal compressor, and the new compressor's main performance parameters and those of the prototype were compared at 80%, 90%, 100% and 110% rotation speeds. Results The results showed that at rotation speed settings of under 100%, the new compressor's surge margin is 5.48% higher than the prototype, but reduced by 1% at other speed settings. The new compressor's pressure ratio and efficiency are slightly improved at low flow conditions when compared to the prototype. However, at high flow conditions, the pressure ratio and efficiency of both compressors are significantly reduced. Conclusions The numerical simulation results show that the general performance of the new compressor has reached the performance level of the prototype compressor. The research results can be used as a reference for the modeling design of a centrifugal compressor.

Prediction models for monitoring heavy-metal accumulation by wheat (Triticum aestivum L.) plants grown in sewage sludge amended soil

Prediction of heavy-metal concentration in the edible parts of economic crops, based on their concentration in soil and other environmental factors, is urgently required for human risk assessment. The present investigation aimed to develop regression models for predicting heavy-metal concentration in wheat plants via their contents in sewage sludge amended soil, organic matter (OM) content and soil pH. The concentration of heavy metals in the plant tissues reflected its concentration in the soil with high Fe followed by Al, Mn, Cr, Zn, Ni, Co, Cu, and Pb. Soil OM content had a significant positive correlation with all investigated heavy-metal concentrations in the different tissues of wheat plants, while soil pH was negatively significant with most heavy metals except spike Pb and grain Cr. The bio-concentration factor of Al, Cu, and Zn from soil to wheat root was >1, while that of shoot, spikes, and grains was <1 for all heavy metals. Significantly valid regression models were developed with fluctuated coefficient of determination (R2), high model efficiency (ME) values and low mean normalized average error (MNAE). The significant positive correlations between the concentration of some heavy metals in the soil and the same in wheat tissues indicate the potential of this plant as a biomonitor for these metals in contaminated soils. The significant correlations between heavy-metal concentrations in soil and its properties (pH and OM) with metal concentrations in wheat plants support the prediction model as an appropriate option. This study recommends the use of models with R2 greater than 50% and recommend other researchers to use our models according to their own specific conditions.

Finite Element Analysis Framework for Dynamic Vehicle-Bridge Interaction System Based on ABAQUS

This paper presents a unified framework for dynamic analysis of vehicle-bridge interaction (VBI) systems using a commercial finite element software suite (ABAQUS[Formula: see text]). This framework can provide bridge designers and engineering practitioners with a general platform to analyze the coupled system with high modeling efficiency and accuracy in modeling and outputting. Moreover, it has readily available nonlinear material/element models and nonlinear dynamic analysis functions for complex structures. This analysis framework was first validated with a classical VBI problem involving a sprung mass moving on a simply supported beam, whose closed-form solution is readily available. Validation for the application on complex structure was then presented with a typical 16-car Japanese high-speed train (Shinkansen) and a three-block bridge. The cars comprised car bodies, bogies and wheelsets, which were all modeled as rigid bodies and which were connected with springs and dashpots. The bridge was modeled with typical three-dimensional solid elements. Interaction between wheelsets and tracks was realized using the penalty method. Rail irregularity was also considered in the analysis. The consistency between calculated dynamic responses and field experiment data of certain pre-specified observation points validated the proposed method. Furthermore, ease in analyzing VBI problems involving nonlinear material properties and with high spatial resolutions was demonstrated with a classical cracked beam problem: a point mass moving on a simply supported cracked beam. Both linear and nonlinear crack models were employed. The former model assigned crack surfaces with a mechanical contact property and showed its accuracy in comparison to the reference model. The latter assigned a nonlinear material model in crack-prone zones and illustrated the potential applicability to dynamic crack propagation simulation in VBI problems. The present framework was further applied to seismic response analysis of a train-bridge interaction system involving material nonlinearity and separation between track and wheel under a strong earthquake.

Predicting the hydrological response of a forest after wildfire and soil treatments using an Artificial Neural Network

Accurate predictions of surface runoff and soil erosion after wildfire help land managers adopt the most suitable actions to mitigate post-fire land degradation and rehabilitation planning. The use of the Artificial Neural Networks (ANNs) is advisable as hydrological prediction tool, given their lower requirement of input information compared to the traditional hydrological models.This study proposes an ANN model, purposely prepared for forest areas of the semi-arid Mediterranean environments. The ANN hydrological prediction capability in non-burned, burned by wildfire, and burned and then treated soils has been verified at the plot scale in pine forests of South-Eastern Spain. Runoff and soil loss were much higher than non-burned soils (assumed as control), but mulch application was effective to control runoff and soil erosion in burned plots. Moreover, logging did not affect the hydrological response of these soils. The model gave very accurate runoff and erosion predictions in burned and non-burned soils as well as for all soil treatments (mulching and/or logging or not), with only one exception (that is, in the condition with the combination of treatments which gave the worst performance, burning, mulching and logging), as shown by the exceptionally high model efficiency and coefficients of determination. Although further experimental tests are needed to validate the ANN applicability to the burned forests of the semi-arid conditions and other ecosystems, the use of ANN can be suggested to landscape planners as decision support system for the integrated assessment and management of forests.

Biomonitoring potential of the native aquatic plant Typha domingensis by predicting trace metals accumulation in the Egyptian Lake Burullus

The ability of the native emergent macrophytes Typha domingensis for monitoring pollution with trace metals in Egyptian Lake Burullus was investigated through developing regression models for predicting their concentrations in the plant tissues. Plant samples (above-ground shoot and below-ground root and rhizome) as well as sediment samples were collected monthly during one growing season and analyzed. The association of trace metals concentration with several sediment characteristics (pH, organic matter, clay and silt) was also studied using the simple linear correlation coefficient (r). The concentration of some trace metals was significantly proportional to its values in the sediment such as Cd in the shoot, rhizome and root, Fe in the rhizome, and Ag in the root. There was positive relationship between the bioaccumulation factor (BAF) of Ag, Cd, Fe, Pb and Zn and sediment pH, organic matter and clay content. The developed regression models were significantly valid with high model efficiency and coefficient of determination, and low mean normalized average error. Trace metals were accumulated in the below-ground root and rhizome rather than in the shoot. Only Ag, Co and Ni provided bioaccumulation factor (BAF) < 1, while Ag was the only trace metal that could be transferred to some extend from the root to the rhizome and from there to the shoot [translocation factor (TF) 2.55 and 1.15, respectively]. Typha domingensis in Lake Burullus could be regarded as a bioindicator of trace metals pollution, and a good candidate as phytoremediator for Ag. The information on the phytoremediation capacity of T. domingensis certainly helps to solve contamination problems at Egyptian Lake Burullus region using this native plant.

High Efficiency of Solar Cell Model Based on Two Types of Nanoparticles

Climatology of aerosols, their trends and classification based on the long-term Moderate Resolution Imaging Spectroradiometer (MODIS) measurements (from February 2000 to July 2015) of aerosol optical depths at 550 nm (τ550) and Angstrom exponent (α470-660) using the wavelengths of 470 and 660nm in Nairobi, Skukuza and Ilorin AERONET stations were analyzed in this work. The level-2 collection-6 Deep Blue (L2 C006 DB) of the parameters listed above from the aqua- (MYD04) and terra- (MOD04) MODIS of the study area were statistically analyzed using SPSS. To be able to understand the temporal variation in the characteristics of aerosols in the three stations and during each season separately, MODIS measurements of τ, retrieved for the study area, were compared with AERONET τ. Overall, aqua-MODIS τ corroborate the AERONET measurements well in Nairobi and Ilorin stations with underestimation of 29.80 % and overestimation of 2.90 % respectively, whereas Skukuza station has terra-MODIS τ as the best representation of the AERONET measurements with underestimation of 1.90 %. ....

Публічна дипломатія Італійської Республіки

Публічна дипломатія Італійської Республіки