India is an agro-based country and in the year 2020, about 1082 lakh hectares of agricultural land have been planted, in turn, consuming 3161 lakh tonnes of fertilizer annually. However, excessive fertilizer usage has a negative impact on the soil quality and is evident by the fact that the crop response ratio has decreased from 12.1 in the 1960s to 5.0 in the 2010s. Hence, organic compost can be used as an alternative to fertilizer, reducing its negative impact on soil quality. The present study is a part of a larger investigation and its scope is confined to the utilization of bio-composts for the growth of tomato crops. Additionally, a comparison of these bio-composts was carried out with commercial composts through assessment of yield and number of fruits. Seeds were sown on prepared land, germination was observed on the 7th day, and saplings were produced on the 30th day. The dosage of each compost was fixed at 60ml per plant and was applied on the 31st and 51st days of the study period. Commercial composts produced yields of 673 ± 32.01, 830 ± 32.19 and 1560 ± 77.62 g respectively, with 54 ± 4.16, 81 ± 11.50, and 104 ± 13.50 fruits. All the bio-composts outperformed control and commercial composts in terms of yield. With 140 ± 11.50 fruits, BC5 produced a maximum yield of 2938 ± 93.72 g, which is 723 % higher than the control. Hence, it is concluded that the use of bio-compost is more effective than commercial composts and provides a much higher yield. Further studies need to be conducted to evaluate the quality of yield obtained by various bioassay tests.

In today’s context, there is a tremendous potential for the design of smart nanomaterials of carbon origin for multi-dimensional applications. The role-play of precursor is significant in the design of nanometric carbon materials apart from other experimental parameters. Correlation of the synthetic methodology, the microstructure of the product helps to tune and widen the applications aspect. The present study aimed to tune the simple ketose (reducing monosaccharide) of fructose to functionalize (with O, N, and S atoms) carbon layers, microspheres of carbon, to optimize the experimental conditions, and to establish the mechanism involved in the process. The study further explored the catalytic ability of the carbon samples in the degradation of thiazine and xanthene-based textile dyes and the sensing of heavy metal ions of chromium and copper. A simple hydrothermal process, fructose as a precursor, alkaline pH, and appropriate calcination temperature provided micro and nanostructures of carbon viz. carbon microspheres (CMs), graphene oxide (GO), sulfur doped graphene oxide (S-GO), and nitrogen-doped reduced graphene oxide (N-rGO). In this study a simple mechanism for the conversion process is suggested. Further, the results of the preliminary screening study on the catalytic ability of the sulfur and nitrogen-doped graphene samples in the presence of UV and Visible light upon the degradation of methylene blue (MB), methyl orange (MO), Rhodamine-B (Rh-B) dyes were satisfactory. The adsorbent and the ion exchange capacity of the carbon microspheres were found to be excellent. The results of the study will contribute positively to the treatment and management of industrial wastewater.

The production of Land Use and Land Cover thematic maps using remote sensing data is one of the things that must be dealt with carefully to obtain accurate results, data is obtained from sensors of different characteristics. It is not possible to obtain high spatial and spectral accuracy in one image, so we used a fusion image (multispectral image with a low spatial resolution with a panchromatic image with high spatial resolution), which achieved high efficiency in improving the methods of producing Land Use and Land Cover maps. In this study, we used Landsat-8 multispectral and panchromatic images. The study aims to investigate the effectiveness of panchromatic images in improving the methods of producing Land Use and Land Cover maps for the city of Karbala, Iraq. The Support Vector Machine was used to classify the fusion images using the Brovey method and Gram-Schmidt sharpening algorithms. The appropriate methodology for producing Land Use and Land Cover maps was suggested by comparing classifying results and the classification accuracy was evaluated through the confusion matrix. Where the results showed that the method of classifying the fused image by Gram-Schmidt and classified by Support Vector Machine is the best way to produce Land use and Land cover maps for the study area and achieved the highest results for overall accuracy and kappa coefficient of 97.81% and 0.95, respectively.

The Super-SBM model was used first to assess the energy efficiency of 30 Chinese provinces from 2012 to 2017. After that, an energy efficiency spatial correlation test was conducted, and finally, the influencing elements of energy efficiency were analyzed using a geographic panel model. The findings show that the amount of regional economic development has a substantial positive impact on energy efficiency, whereas the level of regional urbanization and the severity of environmental restrictions have a considerable negative impact on energy efficiency in China’s provinces. Other regions’ energy structure and technical innovation have a substantial positive spillover effect on the region’s energy efficiency, whereas other regions’ economic development and foreign direct investment have a significant negative spillover effect on the region’s energy efficiency.

The wind velocity reduces by encountering vegetation; thus, a shelter zone is generated at downstream of vegetation. Hence, planting vegetation, mainly shrubs, has widely been used to control sand transportation. However, plant shrubs in a large area of the desert are practically unsustainable and uneconomical. In this study, Computational Fluid Dynamic (CFD) and wind tunnel experiments were carried out to optimize the planting method of shrubs that could decrease the number of shrubs and increase wind erosion controlling efficiency in desert regions. The effects of shrub height, porosity, the number of shrub rows, and space between shrub rows on wind erosion control were studied. Based on the present study results, the downstream of shrubs was divided into three different zones: first erosion zone, sedimentation zone, and second erosion zone. Moreover, with the increase of shrub porosity, the first erosion zone’s length increased. In contrast, the sedimentation zone’s length decreased, whereas the length of the first erosion and sedimentation zones increased with shrub height. Hence, to make a better shelter zone, it is recommended to plant denser shrubs rows at the upstream and sparsely shrub rows at the far downstream.

The sludge produced in the treatment process depends on the type of coagulant and other chemicals used and the suspended particles present in raw water. Discarding this sludge in the landfills poses pollution of both ground and surface water, disturbing the lives in the water and the water quality. The primary potable water provider in Sri Lanka is the National Water Supply and Drainage Board. It focuses on finding ways of disposal, sustainable practices, and possible applications of the water treatment sludge. This research aims to identify the aluminum level in the potable water treatment sludge of the Konduwattuvana water treatment plant in Ampara and to utilize that sludge as an alternative raw material in burnt clay brick manufacturing. The national standards and limitations of the sludge content and the standard brick manufacturing process were followed. To reach the aim, a sequence of tests was conducted, and the brick characteristics are subjected to test for different sludge ratios according to the Sri Lankan Standard of 36:1978 for burnt clay bricks. Experimental results show that the aluminum content in liquid sludge and sludge cake was found to be 231.6 mg.L-1 and 54.9 mg.L-1, respectively, which implies that the sludge contains aluminum. The optimum sludge ratio to produce burnt clay bricks was found to be 10% of the total weight of the brick.

This study investigated the responses of Panax notoginseng in a high-altitude area to regulated deficit irrigation at different growth stages (seedling stage, vegetative growth stage, flowering stage, and root weight gain stage) by observing indicators such as plant growth, soil nutrients, and morbidity. Conventional irrigation (70%-80% FC) was applied at the seedling stage and the root weight gain stage. Three regulated deficit irrigation levels (50%-60% FC, 40%-50% FC, and 30%-40% FC) were applied during the vegetative growth period, and three regulated deficit irrigation levels (70%-80% FC, 50%-60% FC, and 40%-50% FC) were used in the flowering period. Conventional irrigation was also applied throughout the growth stage as a control (CK). The results showed that the content of available phosphorus, available potassium, and nitrate-nitrogen in the soil was the lowest under the T4 treatment, and the cutting+main root length, rib length, root surface area, root volume, and main root diameter all reached their maximum values under this treatment. Under the T4 treatment, the total saponin content and total dry weight were the highest, the irrigation water use efficiency was the highest, and the P. notoginseng morbidity rate was the lowest. Morbidity was reduced by 53.42 percent in individuals who received the CK therapy, whereas total saponin content increased by 8.65 percent. The T4 therapy had the highest score of all the treatments in principal component analysis. As a result, planting P. notoginseng under the T4 treatment can effectively reduce irrigation water usage, enhance production and quality, and minimize the incidence of sickness in P. notoginseng.

Rice quality and yield traits response to fertilizers under varying field conditions were obtained in our previous study. A better understanding of the intrinsic mechanism of fertilization in regulating rice quality and yield supports field operations and recommendations. This study investigated the potential role of humic acid and bean cake fertilizers as opposed to traditional nitrogen fertilizer (urea) in regulating rice quality and yield traits under water-saving conditions by identifying the quality and yield indicators. Results demonstrated that the application of humic acid and bean cake fertilizers affected the quality and yield compared with the traditional nitrogen fertilizer (urea), which alleviated the deterioration of rice quality and yield caused by excessive fertilizer amounts. The conclusions are that the addition of humic acid and bean cake fertilizers can improve grain quality.

Effluent from the textile industry is a major source of water pollution. Textile effluents contain a high amount of color, turbidity, BOD, and COD, which are highly toxic and affect aquatic organisms as well as human beings. Physical and chemical treatments of these effluents are difficult and costly. Adsorption is an effective method to treat textile industry effluent. In the present study, chitosan is selected as an adsorbent, derived from the exoskeleton of marine crustaceans like shrimp and crab by chemical processing. The color was removed using chitosan from shrimp and crab shells separately. On the sixth day of treatment, shrimp shell chitosan removed 100% of color from textile industry effluent and proved to be a better adsorbent.

Urbanization and Industrialization during the last few decades have increased air pollution causing harm to human health. Air pollution in metro cities turns out to be a serious environmental problem, especially in developing countries like India. The major environmental challenge is, to predict accurate air quality from pollutants. Envisaging air quality from pollutants like PM2.5, using the latest deep learning technique (LSTM timer series) has turned out to be a significant research area. The primary goal of this research paper is to forecast near-time pollution using the LSTM time series multivariate regression technique. The air quality data from Central Pollution Control Board over Hyderabad station has been used for the present study. All the processing is done in real-time and the system is found to be functionally very stable and works under all conditions. The Root Mean Square Error (RMSE) and R2 have been used as evaluation criteria for this regression technique. Further, the time series regression has been used to find the best fit model in terms of processing time to get the lowest error rate. The statistical model based on machine learning established a relevant prediction of PM2.5 concentrations from meteorological data.