Brightness Index Research Articles

Mango Seed Kernel Agronomical Bio-Waste for Ecofriendly Cotton Dyeing: Optimization of Dyeing Period and Temperature

Typical synthetic colorant and their application processes include various negative eco-toxicological effect and human health risk. Intend to depose the impediments of synthetic dyeing herein mango seed kernel agronomical bio-waste was employed for sustainable dyeing of cotton fabric. 100% cotton knitted single jersey commercially scoured-bleached fabric having areal density of 160 grams per square meter was used for this study. Natural dye was extracted by alkali extraction method from raw seed kernel of Mangifera indica. Optimum dyeing temperature and duration were quantified. Colorimetric appearance of the dyed samples for different dyeing condition were evaluate by using CIE L*a*b* color space in terms of color co-ordinates, color strength (K/S), brightness index (BI) and degree of color levelness values. Dye fiber bonding stability of the dyed fabric was evaluated in terms of colorfastness to wash, water, perspiration, rubbing and light. Overall the colorfastness values were recorded as 4-5 except light fastness. Excellent colorimetric appearance and serviceability indicates future green coloration of cotton fabric through mango seed kernel as well as decent agronomical bio-waste management.

Proxy Index Derived From All Sky Imagers for Space Weather Impact on GPS

AbstractGlobal Positioning System (GPS) signals passing through the auroral ionosphere, which exhibits multiscreen electron density structuring, maybe scintillated causing observation and possibly positioning errors. It is advantageous to determine the magnitude of GPS signal scintillation associated with a given level of auroral brightness observed around the signal's ionospheric pierce point (IPP). Such information would enable the exploitation of auroral image observations in space weather monitoring and help in assessment of impact on infrastructure/services reliant on GNSS. Studies have observed a general positive correlation between auroral brightness and GPS phase scintillation but not a definite one‐to‐one relationship. In this study a correlation coefficient of 0.38 is observed between the phase scintillation and the level of auroral arc brightness around the GPS signal's raypath for a data set of 292 events in the Canadian sector. Alternatively, a new pseudo‐scintillation index, Rate of change of Brightness index, is introduced in this study which is derived from the changing auroral brightness around the satellite's IPP. This Rate of change of Brightness index is highly positively correlated (correlation coefficient: 0.75) with GPS phase scintillation. Spatial and spectral relationships between auroral brightness around the satellite's IPP and phase scintillation were also analyzed. It is observed that probability of GPS signals experiencing phase scintillation is high when the auroral brightness around the satellite's IPP has dominant fluctuations in the frequency band ~0.06 to 0.16 Hz. These results indicate that GPS signal scintillation is related to the dynamics of the brightness around the satellite's IPP as the satellite signal propagates through the aurora.

Mapping the water erosion risk in the Lakhdar river basin (central High Atlas, Morocco)

ABSTRACTThe objective of this study is to develop a methodology using the remote sensing and Geographic Information System to map soil degradation by water erosion and highlighting the various levels of soil degradation in the Lakhdar river basin (central High Atlas) during the period between 1987 and 2014. This allows producing a map of soil degradation risk, which can be used as reference document for the decision-makers. The methodology develops a geomatics approach based on the processing of satellite images, using the analysis and the interpretation of spectral indices, such as the Form Index, the Coloration Index, the Brightness Index, and the Normalized Difference Vegetation Index (NDVI). The results show that the surface of soil strongly degraded decreased about 900 ha during the period of study whereas the surface of soil weakly and moderately degraded was subject of a progressive increase for an approximate total of 2800 ha over 27 years. Moreover, the method of spectral indices allowed us to assess and locate soil quantitative loss (organic matter, mineral salts, texture, fertility, etc.) due to the water erosion and climate change. These results are decisive when it comes to establish priority zonation for the interventions of erosion control.

Factores ambientales relacionados con la cobertura de Agave angustifolia (Asparagaceae) en el matorral costero de Yucatán, México

Antecedentes y Objetivos: Agave angustifolia, o chelem, es el único maguey silvestre de la Península de Yucatán y es el ancestro de especies domesticadas de gran valor económico y cultural. Presta importantes servicios ambientales como recurso alimenticio para la fauna, fijación de suelos arenosos y captura de carbono en ambientes áridos con suelos pobres. Es un cultivo potencial de cara al aumento de las sequías predichas para la región y del empobrecimiento genético del henequén. El objetivo de este trabajo es recabar datos sobre la densidad de sus poblaciones, su patrón espacial, estructura poblacional y la relación entre su abundancia y los factores ambientales en el matorral costero de Yucatán, con el fin de evaluar su uso en la restauración ecológica.Métodos: Se muestrearon tres localidades costeras: Chuburná (Ch), San Benito (SB) y Río Lagartos (RL). En SB y RL se trazaron 36 cuadrantes de 4 m2; dentro de cada uno se estimó el porcentaje de cobertura de Agave angustifolia, el de los arbustos de más de 1 m de alto y la cobertura de mantillo. Se tomaron muestras de suelo a las que se analizó pH, conductividad eléctrica, nitrógeno total, materia orgánica e escala de gris. Se describieron la densidad poblacional, distribución espacial y estructura por tamaños de A. angustifolia en SB y Ch.Resultados clave: La cobertura de Agave angustifolia se correlaciona negativamente con la cobertura de arbustos y mantillo, y cantidad de materia orgánica. Las densidades de su población fueron inferiores a 0.1 individuos/m2, hubo una mayor proporción de plántulas y su distribución espacial es agregada.Conclusiones: Al soportar suelos pobres, altas temperaturas y radiación solar, la especie puede servir para restaurar algunas funciones ecológicas en las zonas más áridas de la costa donde la vegetación ha sido severamente dañada.

Drainage morphometric analysis using open access earth observation datasets in a drought-affected part of Bundelkhand, India

In this study, a part of Yamuna and Ken basin was selected for quantitative estimation of morphometric parameters through open access earth observation datasets. Further, spectral indices, namely Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), and Soil Brightness Index (SBI), have been calculated to know the relationship with the morphometric parameters. The area has sub-dendritic to dendritic drainage pattern and designated as fifth order stream and drainage density (0.564 km/km2). The stream order is mainly controlled by physiographic and lithological conditions of the area. Spectral indices have been employed to understand the vegetation, wetness, and soil moisture and soil characteristics in the area. The water retention curves of the area have been developed. The spectral indices and morphometric parameters (Dd) shows highly positive correlation. The result has been validated with the aid of borehole/lithology datasets. These quantitative estimates will help in achieving and developing long-term strategies for combating hydrological drought, meteorological drought, climate-related drought, agricultural drought, water mismanagement-related drought, and affected region for preparation of suitable groundwater management plans.

APPLICATION OF ROTATED FILM APPARATUS AT PRODUCTION OF MULTI-COMPONENT FRUIT PASTS

The aim of the study is to probate the improved rotated film apparatus (RFA) at producing high-quality multi-component fruit pasts. The construction of the improved RFA with an innovative solution as to the system of heating its working chamber at the expanse of using a radiating heater – FFREhRT is developed. The designed construction is resource-effective and allows to concentrate fruit puree by using saving non-inertial thermal regimes (50...60 °С) with processing duration 0,65...0,8 min to content 25...30 % of RM. At the researches there was realized the probation of the improved RFA at concentrating offered multi-component fruit purees, based on apple, cranberry and haw. The high quality of obtained multi-component fruit pasts was confirmed as a result of studying their structural-mechanical and color characteristics. Structural-mechanical parameters of studied fruit purees allowed to determine the influence of each component of the composition on the dispersed stability of an obtained structure of puree. It was established that the best disperse stable structure is inherent to the composition with percent content of apples, cranberries and haws as 60 ÷ 30 ÷ 10 with the mean radius of microcapillaries nm. As a result of concentrating fruit purees, there were determined their color characteristics by CIELab system. And the comparisons of the studied sample before and after concentration indicate their unessential change. Thus, for the recipe composition with best organoleptic indices: apple, cranberry, haw in ratio 60 ÷ 30 ÷ 10, the brightness index for puree is: Т = 37,8 %, and for the concentrated past – Т = 36,4 % respectively. All obtained compositions are characterized by the red-orange color that totally confirms preservation of their physical-chemical characteristics at thermal processing. The improved RFA construction may be implemented at enterprises of tinned production for processing and getting high-quality fruit pasts for providing needs of food industry of the country.

Digital soil mapping in a Himalayan watershed using remote sensing and terrain parameters employing artificial neural network model

Digital soil mapping relies on field observations, laboratory measurements and remote sensing data, integrated with quantitative methods to map spatial patterns of soil properties. The study was undertaken in a hilly watershed in the Indian Himalayan region of Mandi district, Himachal Pradesh for mapping soil nutrients by employing artificial neural network (ANN), a potent data mining technique. Soil samples collected from the surface layer (0–15 cm) of 75 locations in the watershed, through grid sampling approach during the fallow period of November 2015, were preprocessed and analysed for various soil nutrients like soil organic carbon (SOC), nitrogen (N) and phosphorus (P). Spectral indices like Colouration Index, Brightness Index, Hue Index and Redness Index derived from Landsat 8 satellite data and terrain parameters such as Terrain Wetness Index, Stream Power Index and slope using CartoDEM (30 m) were used. Spectral and terrain indices sensitive to different nutrients were identified using correlation analysis and thereafter used for predictive modelling of nutrients using ANN technique by employing feed-forward neural network with backpropagation network architecture and Levenberg–Marquardt training algorithm. The prediction of SOC was obtained with an R2 of 0.83 and mean squared error (MSE) of 0.05, whereas for available nitrogen, it was achieved with an R2 value of 0.62 and MSE of 0.0006. The prediction accuracy for phosphorus was low, since the phosphorus content in the area was far below the normal P values of typical Indian soils and thus the R2 value observed was only 0.511. The attempts to develop prediction models for available potassium (K) and clay (%) failed to give satisfactory results. The developed models were validated using independent data sets and used for mapping the spatial distribution of SOC and N in the watershed.

Geospatial Mapping of Soil Organic Carbon Using Regression Kriging and Remote Sensing

Geo-spatial mapping of soil organic carbon using regression kriging was performed for Lalo khala sub-watershed (a part of Solani watershed) located in western Uttar Pradesh, India. Soil organic carbon was predicted using eight predictor variables derived from the advanced space borne thermal emission and reflection radiometer satellite images and digital elevation model. The soil organic carbon was determined in 248 soil samples collected randomly within a 300 m2 grid overlaid on the study area. Out of the eight predictor variables used in simple regression, the normalized difference vegetation index has the maximum correlation with the soil organic carbon (0.64) followed by vegetation temperature condition index (0.60), brightness index (− 0.60), greenness index (0.57) and wetness index (0.51). Standardized principle components of the predictor variables were used in the prediction model so as to address the multicollinearity problem. The regression kriging predicted SOC value ranged from 0.19 to 1.93% with a mean value of 0.64 and standard deviation of 0.29. The SOC values were higher in upper piedmont with moderate forest followed by Siwalik hills while low values were found in the upper alluvial plains. The RMSE of the predicted SOC map was only 0.196 indicating the closeness of predicted values to the observed values. Regression kriging predicted SOC map can be used for spatial agriculture planning and consider as an ideal input for spatially distributed models. The higher efforts for its preparation are justified when quality, spatial distribution and accuracy are considered.

Enhancing the Spatial Variability of Soil Salinity Indicators by Remote Sensing Indices and Geo-Statistical Approach

Soil salinization is considered limiting factor for crop production and land management for dry land in Sudan, its spatial variation is affected by different factors of soil properties, vegetation and environment hence its interaction formulate the planning for successful sustainable agriculture in salt affected soils. This study aims to evolve the spatial prediction of soil salinity indicators by integrated remote sensing indices and geo-statistical cokriging model. Soil samples were collected from 476 square kilometer area in salt affected area, the samples were analyzed following standard procedures for electrical conductivity, sodium adsorption ratio, hydrogen ions and saturation percentage. Information of vegetation status identified by Normalized Difference Vegetation Index (NDVI) and soil salinization by Salinity index and brightness index were used and utilized for prediction of the soil parameters variability by cokriging model. It was found that the method was resulted in high accuracy based on RMSE and enhances the soil spatial variability assessment and provides significant interaction of different variables and indices in the landscape.

Modelling habitat and planning surveillance using Landsat imagery: a case study using Imported Red Fire ants

This paper presents a Bayesian mixture model approach for detecting areas of habitat that are suitable for S. invicta infestation, aiding the ongoing surveillance for early detection of these exotic pests. We show that Landsat imagery is an affordable and valuable tool to assist in determining an informed surveillance strategy. In this paper, we use Landsat band 3 (visible red), Landsat band 6 (mid infrared) and a soil brightness index, in various combinations, to assess the probability that the area associated with each pixel is habitable terrain, either in a multivariate analysis, or as a univariate spatial temporal model. The multivariate analysis allows researchers to create meaningful clusters that reflect the sometimes complex combinations of conditions of conditions that form suitable habitat, rather then relying on single derived indices.

Habitat suitability mapping for sand cat (Felis margarita) in Central Iran using remote sensing techniques

One of the primary reason of species extinction especially rare species with very specific requirements, is habitat destruction. To protect these species, habitat suitability evaluation plays a central role. Hence, an attempt is made in this study to evaluate the suitability of sand cat’s habitat in a sand dune-dominated landscape in Iran. Four Landsat-derived indices including Normalized Difference Vegetation Index (NDVI), Weighted Difference Vegetation Index, Brightness Index (BI) and Salinity Index were combined to characterize sand cat’s habitat requirements through a land use land cover (LULC) map. Furthermore, a set of landscape metrics were employed to explore the spatial pattern LULC classes. Sand cat’s habitat suitability map was generated by linear combination of the standardized and relatively weighted NDVI and BI indices and then categorized into five classes of most suitable, highly suitable, moderately suitable, least suitable and not suitable. The results showed that about 75% of the total area is suitable for sand cat. Although this region is rich in biodiversity, it has not yet been subject to any conservation planning and should be granted more conservation attentions.

A Feature-Based Approach of Decision Tree Classification to Map Time Series Urban Land Use and Land Cover with Landsat 5 TM and Landsat 8 OLI in a Coastal City, China

Accurate mapping of temporal changes in urban land use and land cover (LULC) is important for monitoring urban expansion and changes in LULC, urban planning, environmental management, and environmental modeling. In this study, we present a feature-based approach of the decision tree classification (FBA-DTC) method for mapping LULC based on spectral and topographic information. Landsat 5 TM and Land 8 OLI images were employed, and the technique was applied to the coastal city of Xiamen, China. The method integrates multi-spectral features such as SAVI (soil adjustment vegetation index), NDWI (normalized water index), MNDBaI (modified normalized difference barren index), BI (brightness index), and WI (wetness index), with topographic features including DEM and slope. In addition, the new approach distinguishes between fallow land and cropland, and separates high-rise buildings from beaches and water bodies. Several of the FBA-DTC parameters (or rules) from 1997 to 2015 remained constant (i.e., DEM and slope), whereas others changed slightly. WI was negatively related to percent area of beach land, and BI was negatively related to percent area of arable land. The FBA-DTC method had an average user’s accuracy (UA) of 91.36% for built-up land, an average overall accuracy (OA) of 92.13%, and a Kappa coefficient (KC) of 0.90 for the period from 2003 to 2015, representing respective increases of 15.87%, 10.17%, and 0.13, compared with values calculated using maximum likelihood classification (MLC). Over the past 12 years, built-up land increased from 23.67% to 43.17% owing to occupation of coastal reclamation, arable land, and forest land. The FBA-DTC method presented here is a valuable technique for evaluating urban growth and changes in LULC classification for coastal cities.

Mineralogical characterization and beneficiation study of kaolin from Equador (RN) and Junco do Seridó (PB) to increase the brightness index

Abstract Kaolin is a rock composed mainly of kaolinite. It is used in many industrial segments, such as paper and ceramics. However, for these uses it is necessary to submit the ore to appropriate beneficiation, which generally involves magnetic separation and chemical bleaching, aiming to remove iron oxide and hydroxide, raising brightness index. This work reports the mineralogical characterization and analysis of the beneficiation of three samples of kaolin, two from Equador (Rio Grande do Norte - RN) and the third from Junco do Seridó (Paraíba - PB). The samples were submitted to granulometric classification in sieve of 44 μm, magnetic separation in a magnetic induction of 14000 gauss, and chemical bleaching with sodium dithionite during 240 min. The processes were divided into two beneficiation routes. X-ray diffraction, chemical analysis by X-ray fluorescence and scanning electron microscopy were used for mineralogical characterization of the ore and measurement of the brightness index, while electron paramagnetic resonance was applied to study the variation of iron oxides and hydroxides during beneficiation. The results indicate that just 30 min of chemical bleaching without magnetic separation was sufficient to increase the brightness index from 78.2 to 90.2% and from 91.3 to 95.7% in the two samples from Equador (RN) and from 86.9 to 90.4% in the sample from Junco do Seridó (PB). The magnetic separation, although causing a small increase in the brightness index, was inefficient for removal of iron oxides and hydroxides. The results indicated no need for using magnetic separation in plants for beneficiation of kaolin from these two locations, providing the reduction of production costs allowing better exploitation of the ore.

Photometric Period of the Star PZ Mon

Results are presented from a search for periodic variations in the brightness and color indices of the active star PZ Mon based on many years of photometric data from 1992 to 2015. The photometric period derived from the entire set of observations is 34.16 days, but the period may vary by 1.5% within individual intervals. The color index V-R varies with the same period. These variations are indicative of reddening of the star with decreasing brightness. A correlation between the values exists over the entire observation interval. The variations in B-V occur over an interval of 26-28 days. A nonuniqueness in these variations shows up in a brightness-color index diagram: a reduction in the color index with decreasing brightness is observed in some epochs, which can be explained in terms of a spottedness model by the presence of cold, as well as hot, formations on the star’s surface.

Detection of water abundance in Baluran National Park with landsat satellite imagery analysis

Indonesia is one of the mega-biodiversity countries that have a great responsibility in maintaining the balance of the global climate and forest ecosystems. Drought causes shifting of ecosystems causing disturbances on animal life leading to death of species. Alongside fires in the savanna, drought is a recurrent problem in the park, which occurs every year. This study aims to detect the abundance of water by using satellite imagery in Baluran National Park (BNP). The research analyzed using Landsat satellite imagery ETM7 + in 1999 and 2010 and three (3) main factors that have great potential abundance of water, are: (1) plant density (GI = Greenness Index), (2) soil moisture (WI = Wetness Index), and (3) soil conditions (SBI = Soil Brightness Index). Three factors are summed and divided by three to get 5 levels of water abundance: 1) Very abundant, 2) Abundant, 3) Medium, 4) Few, and 5) Very little. The results showed that the abundance of water decreased between 1999 and 2010 for moderate conditions from 85% to 38%, if the abundance of low water (slightly) increased from 15% to 60%. The level of accuracy of the abundance of water in the field of more than 80% is exactly 91%. The extreme drought conditions will be very dangerous for the survival of flora and fauna in Baluran National Park that are in desperate need of water and potentially in danger of a fire. Construction of water reservoirs and water supply continuously using a water tank in the dry season is very necessary in the Baluran National Park.

Effects of Acid/Alkali and Cellulase Enriched Mixed Enzyme Treatments on Properties of Jute-Cotton Union Furnishing Fabric

The effects of chemical pre-treatments using dilute acids [HCl, CH3COOH, HCOOH, (CH2COOH)2C(OH)COOH] and alkali (NaOH) on raw jute and jute-cotton(75:25) union fabric and the effects of specified mixed enzyme system (different proportion of mixture of cellulase, xylanase and pectinase) pretreatment on conventional 2% H2O2 bleached jute-cotton union fabric have been comparatively evaluated. Among different organic/ mineral acid pretreatments 1% HCl treatment shows higher surface brightening action than that obtained by formic acid, acetic acid and citric acid pre-treatment. All these acid pretreatment do not show desirable softening action but show varying degree of surface leaching action associated with measurable changes in K/S value, whiteness, yellowness and brightness indices. However, 10-18 % NaOH pretreatment of raw jute-cotton union fabric show appreciable softening action but is associated with higher mass loss, appreciable reduction in tenacity and surface darkening. While, 5% dilute NaOH pretreatment show somewhat less softening action, but exhibit lower mass loss, lower surface darkening and lower reduction in tenacity which may be practically useful in imparting desirable softness in jute-cotton union fabric. Treatment with specified cellulase enriched mixed enzyme system (175 unit/ml of cellulase, 96 unit/ml of xylanase and 136 unit/ml of pectinase) on either 1 or 5% NaOH pretreated or conventional 2%H2O2 bleached jute-cotton union fabric renders a moderate degree of softening action and surface smoothening with overall good balance in important textile related properties. However, higher degree of softening action may be achieved, if the said mixed enzyme treatment is carried out after both sequential pretreatments i.e. 5% NaOH pretreatment followed by conventional H2O2 bleaching at the cost of higher weight loss and reduction in tenacity. Studies on Dyeability of such chemically treated jute-cotton union fabric reveals that NaOH pretreatment show an increasing trend for acid dye uptake and reactive dye uptake, while all the dilute acid pretreatments show an increasing trend of basic dye uptake in general.

Nutritional Composition, Whiteness Index, and Starch Granule Profile of Iodine Fortified Modified Cassava Flour (Mocaf)

Background. Modified cassava flour (Mocaf), high amylose carbohydrate source, was made from available easy to find cassava. It used as the vehicle for potassium iodide (KIO3) in iodization program for IDD alleviation. Objectives. To analyze nutritional composition at various KIO3 concentration, to evaluate brightness (L*) and whiteness index (WI) at different KIO3 and package type during storage, and to analyze starch granule profile. Method. This study is a complete randomized experiment. Mocaf was separated and packed into several types of material packages made of polyethylene (PE), polypropylene (PP), and aluminum foil (AF). Data were analyzed using ANOVA followed by Duncan test. Results. There were no significant differences among moisture, ash, protein, and fat content from various KIO3 concentrations (p>0.05), however starch and amylose showed significant differences (p<0.05). The range of sample compositions were11.57±0.05-11.73±0.09% wb moisture, 0.91±0.02-1.00±0.01% db ash, 1.13±0.008-1.16±0.02% db protein, fat content around 0.35±0.005% dB at all fortification concentrations, 88.61±0.05–91.50±0.18% db starch, and 31.91±0.25–33.76±0.15% db amylose. The level of brightness (L*) among samples packed in PE, PP, and AL was significantly different (p<0.05), at a range of 88.59±0.22-89.45±0.21%, 88.83±0.13-89.62±0.10%, and 89.36±0.44-89.97±0.01%, respectively. Whiteness index (WI) for those packed in PE, PP, and AL was also significantly different (p<0.05), at a range of 87.02±0.29 – 90.56±0.07%, 87.12±0.21% - 90.58±0.62%, and 87.42±0.45 – 91.27±0.19%, respectively. The starch granule of mocaf was round, oval, and partially truncated, with particle size ranged between 1.523 - 20.464 μm. Conclusion. KIO3 fortified mocaf had similar nutritional composition at various fortification levels and packages, with good brightness (L*) and whiteness index(WI). Recommendation. Mocaf is feasible as wheat flour substitution and fortification vehicle alternative for iodization.

Climate Change Vulnerability Analysis of Baluran National Park

Every ecosystem has a different level of susceptibility to environmental disturbances it receives, both from natural factors or anthropogenic disturbance. National Park (NP) Baluran is one national park that has a representation of a complete ecosystem that includes upland forest ecosystems, lowland forests, coastal forests, mangroves, savanna and evergreen forest. The objective of this study is to get a formula calculation of vulnerability analysis of constant and dynamic factors. Baluran NP vulnerability assessment to climate change done by looking at the dynamic and fixed factors. Vulnerability remains a vulnerability factor to the condition of the original (control), whereas vulnerability is the vulnerability of the dynamic change factors which affected the condition from the outside. Constant Vulnerability (CV) in Baluran NP dominated resistant conditions (61%), meaning that the geomorphology and other fixed factors (slope and slope direction/aspect, then the condition in Baluran NP sufficiently resilient to climate change. Dynamic Vulnerability (DV) is the vulnerability of an area or areas that change because of pressure from external factors. DV is influenced by climatic factors (WI = Wetness Index), soil (SBI = Soil Brightness Index), and vegetation (GI = Greenness Index). DV in Baluran NP from 1999 to 2010 shifted from the original category of being (84.76%) and shifted to the susceptible (59.88%). The role of remote sensing for the analysis of raster digital system, while the geographic information system to display the results of cartographic maps.

Scale Effects of the Relationships between Urban Heat Islands and Impact Factors Based on a Geographically-Weighted Regression Model

Urban heat island (UHI) effect, the side effect of rapid urbanization, has become an obstacle to the further healthy development of the city. Understanding its relationships with impact factors is important to provide useful information for climate adaptation urban planning strategies. For this purpose, the geographically-weighted regression (GWR) approach is used to explore the scale effects in a mountainous city, namely the change laws and characteristics of the relationships between land surface temperature and impact factors at different spatial resolutions (30–960 m). The impact factors include the Soil-adjusted Vegetation Index (SAVI), the Index-based Built-up Index (IBI), and the Soil Brightness Index (NDSI), which indicate the coverage of the vegetation, built-up, and bare land, respectively. For reference, the ordinary least squares (OLS) model, a global regression technique, is also employed by using the same dependent variable and explanatory variables as in the GWR model. Results from the experiment exemplified by Chongqing showed that the GWR approach had a better prediction accuracy and a better ability to describe spatial non-stationarity than the OLS approach judged by the analysis of the local coefficient of determination (R2), Corrected Akaike Information Criterion (AICc), and F-test at small spatial resolution (&lt; 240 m); however, when the spatial scale was increased to 480 m, this advantage has become relatively weak. This indicates that the GWR model becomes increasingly global, revealing the relationships with more generalized geographical patterns, and then spatial non-stationarity in the relationship will tend to be neglected with the increase of spatial resolution.

Evaluation of Enzymatic Deinking of Non-impact Ink Laser-Printed Paper Using Crude Enzyme from Penicillium rolfsii c3-2(1) IBRL

Application of microbial enzymes for paper deinking is getting tremendous attention due to the rapidly increasing of waste paper every year. This study reports the deinking efficiency of laser-printed paper by the lignocellulolytic enzyme from Penicillium rolfsii c3-2(1) IBRL strain compared to other enzyme sources as well as commercial available enzymes. High enzymatic deinking efficiency of approximately 82% on laser-printed paper was obtained by pulp treatment with crude enzyme from P. rolfsii c3-2(1) IBRL. However, this crude enzyme was found to reduce the paper strength properties of the pulp based on the results of tensile, tear and burst indices, most probably due to the cellulose degradation. This was further proven by the low viscosity of paper pulp obtained after enzymatic treatment and increasing of sugar production during the treatment. Balancing to this detrimental effect on paper pulp, high deinking efficiency was achieved within a short period of time, in which the enzymatic treatment was conducted for 30min that enabled contribution to higher brightness index obtained, thus promoting savings of time and energy consumption, therefore environmental sustainability. Extensive research should be conducted to understand the nature and mechanism of enzymatic deinking process by the crude enzyme from P. rolfsii c3-2(1) IBRL in order to improve paper strength properties.