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Groundwater is an important source of freshwater. At the same time, anthropogenic activities, in particular, industrialization, urbanization, population growth, and excessive application of fertilizers, are some of the major reasons for groundwater quality deterioration. Therefore, the present study is conducted to evaluate groundwater quality by using integrated water quality indices and a geospatial approach to identify the different water quality zones and propose management strategies for the improvement of groundwater quality. Groundwater quality was evaluated through the physicochemical parameters (pH, chloride (Cl−), fluoride(F−), iron (Fe−2), nitrate (NO3−1), nitrite (NO2), arsenic (As), total hardness, bicarbonate (HCO3−), calcium (Ca+2), magnesium (Mg+2), color, taste, turbidity, total dissolved solids (TDS)) and microbiological parameters including total coliforms, fecal coliforms, and Escherichia coli of samples collected from the water and sanitation agency (WASA) and urban units. Irrigation parameters crucial to the assessment, including (electrical conductivity (EC), residual sodium carbonates (RSC), and sodium adsorption ratio (SAR)), were also collected at more than 1100 sites within the study area of upper and central Punjab. After collecting the data of physicochemical parameters, the analysis of data was initiated to compute the water quality index for groundwater quality, a four-step protocol in which the Analytical Hierarchy Process (AHP) was used to determine the weights of selected parameters by generating a pairwise matrix, on the relative importance of parameters using the Satty scale. The index was then classified into five classes for quality assessment of drinking water (excellent, good, medium, bad, and very bad) and four classes for irrigation water quality assessment (excellent, good, permissible, and unsuitable). After computing the index values for drinking as well as irrigation purposes, the values were interpolated, and various maps were developed to identify the status of groundwater quality in different zones of the study area. Mitigation strategies for water pollution involve source control, such as monitoring industrial discharge points and managing waste properly. Additionally, treating wastewater through primary, secondary, or tertiary stages significantly improves water quality, reducing contaminants like heavy metals, microbiological agents, and chemical ions, safeguarding water resources. The findings highlight significant regional variations in water quality issues, with heavy metal concerns concentrated notably in Lahore and widespread emerging microbiological contamination across all studied divisions. This suggests a systemic problem linked to untreated industrial effluents and poorly managed sewerage systems. The computed indices for the Lahore, Sargodha, and Rawalpindi divisions indicate water quality ranging from marginal to unfit, underscoring the urgency for remediation. Conversely, other divisions fall within a medium class, potentially suitable for drinking purposes. Notably, microbiological contamination at 27% poses a major challenge for water supply agencies, emphasizing the critical need for pre-disposal primary, secondary, and tertiary treatments. These treatments could potentially rehabilitate 9%, 35%, and 41% of the study area, respectively, pointing toward tangible, scalable solutions critical for safeguarding broader water resources and public health. With the current pace of water quality deterioration, access to drinking water is a major problem for the public. The government should prioritize implementing strict monitoring mechanisms for industrial effluent discharge, emphasizing proper waste management to curb groundwater contamination. Establishing comprehensive pre-disposal treatments, especially primary, secondary, and tertiary stages, is imperative to address the prevalent heavy metal and microbiological issues, potentially rehabilitating up to 41% of affected areas. Additionally, creating proactive policies and allocating resources for sustainable groundwater management are crucial steps for ensuring broader water resource security and public health in the face of deteriorating water quality. Therefore, urgent regional action is needed to address escalating anthropogenic threats to groundwater, emphasizing the crucial need for proactive measures to safeguard public health and ensure sustainable water resources.

La implementación de comercio electrónico para la mejora de la cadena de valor en el sector agrícola ha ido evolucionando con los años, después de pandemia se evidenció la importancia de las herramientas digitales en todo sector económico. El objetivo de esta investigación analizar las principales investigaciones científicas sobre el comercio electrónico y la cadena de valor en el sector agrícola en los últimos 5 años, por lo que se ha usado como fuente de información las bases de datos de Scielo, Redalyc, ScienceDirect, Scopus y Google Académico, donde los artículos fueron filtrados y clasificados a través de ecuaciones booleanas, un análisis de exclusión e inclusión y un margen de tiempo desde el año 2018 hasta la actualidad, en esta investigación hemos encontrado 46 artículos que comprendan las variables de la investigación. Al clasificar los artículos adecuados para esta revisión, obtuvimos como resultado que la mayor cantidad de artículos científicos seleccionados se realizaron en China y el año que más destacó fue el 2022, que se refleja el impacto de lo electrónico y su acogida después de las restricciones sanitarias de pandemia. Finalmente, demostró un impacto positivo de la implementación del comercio electrónico en la cadena de valor para el sector agrícola. The implementation of electronic commerce for the improvement of the value chain in the agricultural sector has been evolving over the years, after the pandemic, the importance of digital tools in every economic sector became evident. The objective of this research analyze the main scientific research on e-commerce and the value chain in the agricultural sector in the last 5 years so the Scielo, Redalyc, ScienceDirect, Scopus and Google Scholar databases have been used as a source of information, where the articles were filtered and classified through Boolean equations, an exclusion and inclusion analysis and a time range from 2018 to the present, in this research we found 46 articles comprising the variables of the research. By classifying the articles suitable for this review, we obtained as a result that the largest number of selected scientific articles were made in China and the year that stood out the most was 2022 which reflects the impact of electronic and its reception after the pandemic health restrictions. Finally, it showed a positive impact of the implementation of e-commerce in the value chain for the agricultural sector.

Extreme events are increasing in frequency and severity due to climate change, making the littoral zone even more vulnerable and requiring continuous monitoring for its optimized management. The low-lying Ebro Delta ecosystem, located in the NW Mediterranean, was subject to Storm Gloria in the winter of 2020, the most severe coastal storm registered in the area in decades and one of the most intense ever recorded in the Mediterranean. This event caused intense rainfall, severe flooding, the erosion of beaches, and the destruction of coastal infrastructures. In this study, the Landsat-8 and Sentinel-2 satellites were used to monitor the flooding impact and water quality status, including chlorophyll-a, suspended particulate matter, and turbidity, to evaluate the pre-, syn-, and post-storm scenarios. Image processing was carried out using the ACOLITE software and the on-the-cloud Google Earth Engine platform for the water quality and flood mapping, respectively, showing a consistent performance for both satellites. This cost-effective methodology allowed us to characterize the main water quality variation in the coastal environment during the storm and detect a higher flooding impact compared to the one registered three days later by the Copernicus Emergency Service for the same area. Moreover, the time series revealed how the detrimental impact on the water quality and turbidity conditions was restored two weeks after the extreme weather event. While transitional plumes of sediment discharge were formed, no phytoplankton blooms appeared during the study period in the delta. These results demonstrate that the workflow implemented is suitable for monitoring extreme coastal events using open satellite imagery at 10–30 m spatial resolution, thus providing valuable information for early warning to facilitate timely assistance and hazard impact evaluation. The integration of these tools into ecological disaster management can significantly improve current monitoring strategies, supporting decision-makers from the local to the national level in prevention, adaptation measures, and damage compensation.

Mineral oil resources are depleting rapidly, and the slower conventional oil biodegradation process results in environmental pollution. To resolve this issue, cupric oxide (CuO) nanoparticles (1% wt) were introduced into a base oil to improve the lubricating capability of castor oil. In addition, 1% wt. sodium dodecyl sulfate was also blended with the base oil in order to attain the maximum dispersion stability of CuO nanoparticles in the castor oil. Afterward, thermophysical property, atomic absorption spectroscopy, and Fourier transform infrared radiation (FTIR) testing of the lubricant oil sample were performed before and after 100 h of engine operations at 75% throttle and 2200 rpm for each lubricant sample in order to check the capability of the novel oil with mineral oil. Compared with the natural mineral oil, the behavior of the CuO-based lubricant has essentially the same physical features, as measured according to ASTM standard methods. The physicochemical properties like (KV)40 °C, (KV)100 °C, FP, ash, and TBN decrease more in the case of the synthetic oil by 1.15, 1.11, 0.46, 1.1, and 1.2% than in the conventional oil, respectively. FTIR testing shows that the maximum peaks lie in the region of 500 to 1750 cm−1, which shows the presence of C=O, C-N, and C-Br to a maximum extent in the lubricant oil sample. AAS testing shows that the synthetic oil has 21.64, 3.23, 21.44, and 1.23% higher chromium, iron, aluminum, and zinc content. However, the copper and calcium content in the synthetic oil is 14.72 and 17.68%, respectively. It can be concluded that novel bio-lubricants can be utilized as an alternative to those applications that are powered by naturally produced mineral oil after adding suitable additives that further enhance their performance.

El artículo pone en cuestión el concepto y práctica del desarrollo en zonas rurales tomando como referencia el caso de un proyecto de irrigación en una zona aimara en el altiplano del sur andino del Perú. Ejecutado éste en la década de 1990, en el cual las miradas del ámbito técnico-profesional y las de los campesinos locales se distanciaron desde el inicio de la formulación, pero convivieron durante la ejecución y puesta en marcha del proyecto, en una suerte de acuerdo no manifiesto que se derrumbó ante un hecho que causó perjuicios en los cultivos de una comunidad beneficiaria, poniendo en evidencia la existencia de brechas socio-culturales. La tecnología de la academia se impuso sobre el conocimiento, la tecnología y la práctica campesina que quedó pos-tergada, lo que conllevó a tener escasos logros con la iniciativa de desarrollo. Se concluye en la necesidad que desde las ciencias agropecuarias se haga uso de un mejor análisis de contexto, con mayor apertura y libre de prejuicios culturales aún vigentes, para lograr sostenibilidad en los proyectos desde una mirada intercultural y multidisciplinaria que implique aceptar otros saberes y prácticas, y se distancie de la colonialidad impuesta, adecuándose a las dinámicas de cada contexto.

Remote sensing involves actions to obtain information about an area located on Earth. In the Amazon region, the presence of clouds is a common occurrence, and the visualization of important terrestrial information in the image, like vegetation and temperature, can be difficult. In order to estimate land surface temperature (LST) and the normalized difference vegetation index (NDVI) from satellite images with cloud coverage, the inpainting approach will be applied to remove clouds and restore the image of the removed region. This paper proposes the use of the neural network LaMa (large mask inpainting) and the scalable model named Big LaMa for the automatic reconstruction process in satellite images. Experiments are conducted on Landsat-8 satellite images of the Amazon rainforest in the state of Acre, Brazil. To evaluate the architecture’s accuracy, the RMSE (root mean squared error), SSIM (structural similarity index) and PSNR (peak signal-to-noise ratio) metrics were used. The LST and NDVI of the reconstructed image were calculated and compared qualitatively and quantitatively, using scatter plots and the chosen metrics, respectively. The experimental results show that the Big LaMa architecture performs more effectively and robustly in restoring images in terms of visual quality. And the LaMa network shows minimal superiority for the measured metrics when addressing medium marked areas. When comparing the results achieved in NDVI and LST of the reconstructed images with real cloud coverage, great visual results were obtained with Big LaMa.

Traffic flow analysis is essential to develop smart urban mobility solutions. Although numerous tools have been proposed, they employ only a small number of parameters. To overcome this limitation, an edge computing solution is proposed based on nine traffic parameters, namely, vehicle count, direction, speed, and type, flow, peak hour factor, density, time headway, and distance headway. The proposed low-cost solution is easy to deploy and maintain. The sensor node is comprised of a Raspberry Pi 4, Pi camera, Intel Movidius Neural Compute Stick 2, Xiaomi MI Power Bank, and Zong 4G Bolt+. Pre-trained models from the OpenVINO Toolkit are employed for vehicle detection and classification, and a centroid tracking algorithm is used to estimate vehicle speed. The measured traffic parameters are transmitted to the ThingSpeak cloud platform via 4G. The proposed solution was field-tested for one week (7 h/day), with approximately 10,000 vehicles per day. The count, classification, and speed accuracies obtained were 79.8%, 93.2%, and 82.9%, respectively. The sensor node can operate for approximately 8 h with a 10,000 mAh power bank and the required data bandwidth is 1.5 MB/h. The proposed edge computing solution overcomes the limitations of existing traffic monitoring systems and can work in hostile environments.

Madre de Dios is considered an important center of biodiversity in Peru due to its extensive Amazonian forests. However, the forests are under growing pressure due to land invasion, agricultural expansion, and gold mining. This makes support for forest management very important. This study aimed to evaluate the relationship between forest loss, land cover, land-use changes, and Brazil nut (Bertholletia excelsa Humb. & Bonpl) production in forest concessions in the Peruvian Amazon (2004–2020). Remote sensing techniques were used to classify images using the random forest algorithm, which was applied to the Landsat-5 thematic mapper, Landsat-7 enhanced thematic mapper, and Landsat-8 operational land imagery. Brazil nut production data from 2004–2020 was provided by the Regional Forest and Wildlife Service of Madre de Dios. In forest concessions, the forest area decreased continuously over the whole study period (160.11 ha/year). During the same time period, the change in forest cover in the concessions from Brazil nut to other uses was 4681 ha. At the same time, the authorization and extraction of Brazil nuts varied during the study period but did not show a downward trend. We found a significant and inverse relationship between the conversion of forest to agricultural land and Brazil nut production. However, there were insignificant relationships between forest loss, the persistence of agricultural and forest areas, and Brazil nut production. Therefore, despite the forest loss in the forest concession areas, Brazil nut production has not decreased. Production may not be affected because land pressure is higher near access roads, affecting only the areas near the roads rather than the actual areas where the Brazil nut-producing trees are located. Our results showed that nut production in non-timber forest product concessions would be negatively affected by deforestation and forest degradation, but only slightly.