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This study is the first to use a response surface method to optimize the main parameters of ultrasonic-assisted recovery of oil from oily sludge. The solvent with the highest oil recovery (xylene) and most suitable process (Ultrasonic Assisted Extraction) were first selected based on the highest oil recovery rate. Then, single-factor experiments were performed to determine the best parameter range of solvent sludge ratio, frequency, duration, and temperature. Box-Behnken response surface methodology was used to study the influence of each variable on oil recovery. Solvent sludge ratio, frequency, and duration were found to have significant effects. The model optimization results show that the best conditions for Ultrasonic Assisted Extraction are as follows: a solvent to sludge ratio of 7.56:1, frequency of 31.48 kHz, duration of 23.67 min, and temperature of 45.87 °C. Under optimum conditions, the oil recovery predicted by the model and the actual oil recovery were 87.21 and 83.77%, respectively. The relative error between the predicted and actual values was 3.44%. These results indicate that the model results are reliable. Finally, the characteristics of the recovered oil and residue were analyzed. This research provides the possibility to utilize difficult-to-handle crude oil tank bottom sludge. [ABSTRACT FROM AUTHOR]

The Earth Observation (EO) data with their advantages in spectral, spatial and temporal resolutions have demonstrated their great value in providing information about many of the components that comprise environmental systems and ecosystems for decades that are crucial to the understating of public health issues. This literature review shows that in conjunction within situdata collection, EO data have been used to observe, monitor, measure and model many environmental variables that are associated with disease vectors. Furthermore, satellite derived aerosol optical depth has been increasingly employed to estimate ground-level PM2.5concentrations, which have been found to associate with various health outcomes such as cardiovascular and respiratory diseases. It is suggested that Landsat-like imagery data may provide important data sources to analyse and understand contagious and infectious diseases at the local and regional scales, which are tied to urbanisation and associated impacts on the environment. There is also a great need of data products from coarse resolution imagery, such as those from moderate resolution imaging spectrometer, multiangle imaging spectroradiometer and geostationary operational environmental satellite , to model and characterise infectious diseases at the continental and global scales. The infectious diseases at greater geographical scales have become unprecedentedly significant as global climate change and the process of globalisation intensify. The relationship between infectious diseases and environmental characteristic have been explored by using statistical, geostatistical and physical models, with recent emphasis on the use of machine-learning techniques such as artificial neural networks. Lastly, we suggest that the planned HyspIRI mission is crucial for observing, measuring and modelling environmental variables impacting various diseases as it will improve both spectral resolution and revisit time, thus contributing to better prediction of occurrence of infectious diseases, target intervention and tracking of epidemic events. [ABSTRACT FROM PUBLISHER]

This article illustrates two techniques for merging daily aerosol optical depth (AOD) measurements from satellite and ground-based data sources to achieve optimal data quality and spatial coverage. The first technique is a traditional Universal Kriging (UK) approach employed to predict AOD from multi-sensor aerosol products that are aggregated on a reference grid with AERONET as ground truth. The second technique is spatial statistical data fusion (SSDF); a method designed for massive satellite data interpolation. Traditional kriging has computational complexity O(N3), making it impractical for large datasets. Our version of UK accommodates massive data inputs by performing kriging locally, while SSDF accommodates massive data inputs by modelling their covariance structure with a low-rank linear model. In this study, we use aerosol data products from two satellite instruments: the moderate resolution imaging spectrometer and the geostationary operational environmental satellite, covering the Continental United States. [ABSTRACT FROM PUBLISHER]

Impervious surfaces are important environmental indicators and are related to many environmental issues, such as water quality, stream health and the urban heat island effect. Therefore, detailed impervious surface information is crucial for urban planning and environment management. To extract impervious surfaces from remote sensing imagery, many algorithms and techniques have been developed. However, there are still debates over the strengths and limitations of linear versus nonlinear algorithms in handling mixed pixels in the urban landscapes. In the meantime, although many previous studies have compared various techniques, few comparisons were made between linear and nonlinear techniques. The objective of this study is to compare the performance between nonlinear and linear methods for impervious surface extraction from medium spatial resolution imagery. A linear spectral mixture analysis (LSMA) and a fuzzy classifier were applied to three Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images acquired on 5 April 2004, 16 June 2001 and 3 October 2000, which covered Marion County, Indiana, United States. An aerial photo of Marion County with a spatial resolution of 0.14 m was used for validation of estimation results. Six impervious surface maps were yielded, and an accuracy assessment was performed. The root mean square error (RMSE), the mean average error (MAE), and the coefficient of determination (R2) were calculated to indicate the accuracy of impervious surface maps. The results show that the fuzzy classification outperformed LSMA in impervious surface estimation in all seasons. For the June image, LSMA yielded a result with an RMSE of 13.2%, while the fuzzy classifier yielded an RMSE of 12.4%. For the April image, LSMA yielded an accuracy of 21.1% and the fuzzy classifier yielded 17.0%. For the October image, LSMA yielded a result with an RMSE of 19.8%, but the fuzzy classifier yielded an RMSE of 17.5%. Moreover, a subset image of the commercial, high-density and low-density residential areas was selected in order to compare the effectiveness of the developed algorithms for estimating impervious surfaces of different land use types. The result shows that the fuzzy classification was more effective than LSMA in both high-density and low-density residential areas. These areas prevailed with mixed pixels in the medium resolution imagery, such as ASTER. The results from the tested commercial area had a very high RMSE value due to the prevalence of shade in the area. It is suggested that the fuzzy classifier based on the nonlinear assumption can handle mixed pixels more effectively than LSMA. [ABSTRACT FROM AUTHOR]

Impervious surface is a key indicator of urban environmental quality and degree of urbanization. Therefore, estimation and mapping of impervious surfaces by using remote sensing digital images has attracted increasing attention recently. For mid-latitude cities, seasonal vegetation phenology has a significant effect on the spectral response of terrestrial features, and image analysis must take into account this environmental characteristic. In this paper, three Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, acquired on 3 October 2000, 16 June 2001 and 5 April 2004, respectively, were used to test the seasonal sensitivity of impervious surface estimation. The study area was the city of Indianapolis (Marion County), Indiana, USA. Linear spectral mixture analysis (LSMA) was applied to generate high-albedo, low-albedo, vegetation and soil fraction images (endmembers), and impervious surfaces were then estimated by adding high- and low-albedo fraction images. In addition, land use/land cover (LULC) and land surface temperature (LST) maps were generated and used to create image masks to remove non-impervious pixels. The accuracy of the impervious surface maps was checked against Digital Orthophoto Quarter Quadrangle (DOQQ) images. Three accuracy indicators, the root mean square error (RMSE), mean average error (MAE) and correlation coefficient (R2), were calculated and compared to analyse the seasonal sensitivity of impervious surface estimation. Our results indicate that vegetation phenology has a fundamental impact on impervious surface estimation. The summer (June) image was better for impervious surface estimation than the spring (April) and autumn (October) images. The LULC and LST image masks can significantly increase the accuracy of impervious surface estimation. The mean LST was found appropriate to be set as the threshold for the various image masks. A summer image was most appropriate because there was full growth of vegetation, and mapping of impervious surfaces was more effective with a contrasting spectral response from green vegetation. The mixing space, based on the four endmembers, was perfectly three-dimensional. By contrast, there was significant amount of bare soil and ground and non-photosynthetic vegetation in the spring and autumn images. Plant phenology caused changes in the variance partitioning and impacted the mixing space characterization, leading to a less accurate estimation of the impervious surfaces. [ABSTRACT FROM AUTHOR]

Remote sensing estimation of impervious surfaces is significant in monitoring urban development and determining the overall environmental health of a watershed, and has therefore recently attracted increasing interest. The main objective of this study was to develop a general approach to estimating and mapping impervious surfaces by using medium spatial resolution satellite imagery. We have applied spectral mixture analysis (SMA) to Earth Observing 1 (EO-1) Advanced Land Imager (ALI) (multispectral) and Hyperion (hyperspectral) imagery in Marion County, Indiana, USA, to calculate the fraction images of vegetation, soil, high albedo and low albedo. The effectiveness of the two images was compared according to three criteria: (1) high-quality fraction images for the urban landscape, (2) relatively low error, and (3) the distinction among typical land use and land cover (LULC) types in the study area. The fraction images were further used to estimate and map impervious surfaces. The accuracy of the estimated impervious surface was checked against Digital Orthophoto Quarter Quadrangle (DOQQ) images. The results indicate that both ALI and Hyperion sensors were effective in deriving the fraction images with SMA and in computing impervious surfaces. The SMA results for both ALI and Hyperion images using four endmembers were excellent, with a mean root mean square error (RMSE) less than 0.04 in both cases. The ALI-derived impervious surface image yielded an RMSE of 15.3%, and the Hyperion-derived impervious surface image yielded an RMSE of 17.5%. However, the Hyperion image was more powerful in discerning low-albedo surface materials, which has been a major obstacle for impervious surface estimation with medium resolution multispectral images. A sensitivity analysis of the mapping of impervious surfaces using different scenarios of Hyperion band combinations suggests that the improvement in mapping accuracy in general and the better ability in discriminating low-albedo surfaces came mainly from additional bands in the mid-infrared region. [ABSTRACT FROM AUTHOR]