Plastic pollution is a global concern affecting water, soil, and air quality. Urgent action is needed to address this issue. This study aims to identify factors influencing the use of biodegradable plastic to reduce its negative impacts. Data were collected from 269 households-129 in Punggawan and 140 in Mojosongo, Surakarta, and analyzed using multiple regression analysis to study the determinants of WTP (Willingness to Pay) for biodegradable plastic with STATA software. The results show that the average WTP for biodegradable plastic is IDR 2,214. Most people in Surakarta are already environmentally conscious. Age, knowledge, occupation, interaction of sex and location, education, and marital status influence WTP for biodegradable plastic. It is hoped that the implications of the research will be used as a recommendation for government policies to reduce the amount of plastic waste generation, which is a danger to human beings and the environment.

This study investigates the use of thermal indexes, specifically Physiologically Equivalent Temperature (PET) and Universal Thermal Climate Index (UTCI), to determine outdoor comfort in the Bogor Botanical Gardens (KRB). This park is centrally located in Bogor city, with elevations ranging from 215-260 m above sea level. The thermal sensation was determined using seven references: PET in Europe, Taiwan, Tianjin, Tel Aviv, and UTCI in the Mediterranean, Tianjin, and general contexts. The study involved 284 visitors surveyed for their thermal comfort perceptions. Findings indicate that, based on thermal sensation criteria from the seven references, KRB is generally not within the comfort zone throughout the year, except for the PET in Taiwan, which is comfortable year-round. In-situ measurements show an average daily PET of 33.8°C and UTCI of 34.4°C. According to the Taiwan PET range, the thermal sensation is categorized as somewhat warm to warm (uncomfortable). However, 69.4% of visitors reported feeling comfortable, likely due to the environmental conditions, with 70.3% tree coverage in the 54.7 ha park area.

Agriculture has been a vital sector for the majority of people, especially in countries like India. However, the increasing need for food production has led to intensive farming practices that have resulted in the deterioration of soil quality. This deterioration in soil quality poses significant challenges to both agricultural productivity and environmental sustainability. To address these challenges, advanced soil nutrient prediction systems that utilize machine learning and deep learning techniques are being developed. These advanced soil nutrient prediction systems utilize various sources of data, such as soil parameters, plant diseases, pests, fertilizer usage, and changes in weather patterns. By mapping and analyzing these data sources, machine learning algorithms can accurately predict the distribution of soil nutrients and other properties essential for precise agricultural practices. A previous study compared machine learning algorithms like SVM and Random Forest with deep learning algorithms CNN and LSTM for predicting crop yields. The most appropriate model is a significant challenge, but several studies have evaluated recommendation system models using deep machine learning techniques. Deep learning models attain accuracy above 90%, while many ML models achieve rates between 90% and 93%. Furthermore, the research seeks to propose specific crop suggestions grounded in soil nutrients for precision agriculture to enhance crop productivity.

Sandy soils are not suitable for agriculture because they do not retain nutrients, and water drains quickly. The biochar applied to these soils provides nutrients, improves their fertility, and favors crop yields. Thus, this work aimed to evaluate the effect of the application of pine biochar and the pruning of green areas obtained by slow pyrolysis on the physicochemical attributes of sandy soil. For this purpose, a greenhouse experiment was conducted in fifteen pots randomly divided into three groups (five replicas) of treatment depending on the dose of biochar: 0% (0 g/pot, T1 control treatment), 10% (100 g/pot, T2), and 25% (250 g/pot, T3) calculated according to the volume of the soil. Likewise, 05 seeds of turnip (Brassica rapa subsp. rapa) were placed in each pot, where their germination and growth were monitored. Application of biochar reported an increase in organic matter, porosity, pH, electrical conductivity, cation exchange capacity, NO3-, K, and Mg (without significant differences) and a reduction in bulk density, P, and Ca (without significant differences). These behaviors were higher in T3, followed by T2, compared to T1. Similarly, T3 (68%, 7.5 ± 0.9 cm) showed a higher number of turnip germinations and growth compared to T2 (48%, 7 ± 0.6 cm) and T1 (28% 6 ± 0.4 cm). The biochar applied improved the attributes of the sandy soil, strengthening it against possible erosion and promoting the preservation of terrestrial ecosystems.

The present study is focused on the estimation of Land Surface Temperature (LST) and its relationship with three Land Use and Land Cover (LULC) indices--Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), and Normalised Difference Built-up Index (NDBI) in Raiganj Municipality, India. Landsat-5 TM (2001 & 2011) and Landsat-8 OLI (2021) satellite images were used, processed, and analyzed in the ArcGIS. The study observed that the values of LST and NDBI were increased by +0.9˚C and +0.71, and the values of NDVI and NDWI were decreased by -0.20 and -0.34 during 2001- 2021. The highest LST is observed over the built-up spaces and the lowest over vegetation cover and water bodies. The result indicates LST has a significant positive correlation with NDBI and a negative correlation with NDVI and NDWI. LST is increased due to dramatic changes in LULC especially in unplanned infrastructural development and losses in green and blue spaces.

Water quality prediction plays an important role in environmental monitoring and ecosystem sustainability in the Amazon. Therefore, this review focuses on determining the advances in the scientific production of artificial intelligence in water quality prediction in the Amazon, as well as the limitations and perspectives compared to water quality indexes (WQI). In this sense, Boolean operators were applied, using the following terms: “artificial intelligence”, “machine learning”, “water quality,” and “Amazonia” The databases were Scopus, web of Science, Springer, and IEEE. In this study, 14 scientific articles published during the period 2000-2024 focused on Amazonian countries were evaluated. Although in the Amazon low scientific production was evidenced and is led by Brazil, the highest scientific growth was for 2021, and 93% belongs to the Scopus database, with a compound annual rate of 12.16%. The IA is characterized by using data from governmental institutions and is only limited to parameters such as Total Suspended Solids (TSS), Total Organic Carbon, Turbidity, and Chlorophyll, using satellite imaging techniques, and the most commonly used algorithm was the Clustering Algorithms. In this context, AI applications are still very low in Amazonian countries compared to other European countries. Its limitations are in the accuracy and the limited amount of physicochemical and microbiological data used for predictions. However, AI is a tool that will replace the water quality indexes used manually.

Vegetable waste, particularly cabbage waste (CW), is a valuable raw material for various applications, including bioenergy production, owing to its high lignocellulosic content. However, the potential of lignin in biomass conversion remains largely untapped. This study is significant as it aims to optimize the pretreatment of CW biomass using different chemical reagents and concentrations (sulphuric acid, acetic acid, sodium hydroxide, potassium hydroxide, and sodium chloride) at 12 and 24 h for 50, 75, and 100°C. In this study, a novel pretreatment approach was introduced with 2% NaCl at 50°C for 12 h for CW biomass. At this optimized condition, 2% NaCl led to 28% delignification for CW biomass. The study examined the impact of pretreatment efficacy on biomass characterization using SEM, XRD, and FTIR analytical techniques. Results showed that 2% NaCl pretreatment significantly improved digestibility, increased surface area and porosity, altered the crystallinity index, and confirmed delignification through shifts in peaks and intensity changes. Furthermore, reduced hemicellulose and reduced lignin were noted in comparison to untreated biomass. This reassures us of the effectiveness of the pretreatment method. This promising result underscores the feasibility, economics, sustainability, and environmental friendliness of this pretreatment method. The method not only offers a cost-effective solution but also aligns with the principles of sustainability and environmental protection, thereby reassuring the researchers about its potential for various industrial applications.

Land Use and Land Cover (LULC) classification is critical for monitoring and managing natural resources and urban development. This study focuses on LULC classification for change detection analysis of remotely sensed data using a machine learning-based Random Forest classifier. The research aims to provide a detailed analysis of LULC changes between 2010 and 2020. The Random Forest classifier is chosen for its robustness and high accuracy in handling complex datasets. The classifier achieved a classification accuracy of 86.56% for the 2010 data and 88.42% for the 2020 data, demonstrating an improvement in classification performance over the decade. The results indicate significant LULC changes, highlighting areas of urban expansion, deforestation, and agricultural transformation. These findings highlight the importance of continuous monitoring and provide valuable insights for policymakers and environmental managers. The study demonstrates the effectiveness of using advanced machine-learning techniques for accurate LULC classification and change detection in remotely sensed data.

Paraben(s), or p-hydroxybenzoate derivatives, have been extensively used as preservatives in catalogs of products for decades. The chemical(s) of the group are well known for their water solubility, chemical stability, and low production costs. Additionally, these synthetic organics can be used as supplements in cosmetics, packaged foods, pharmaceuticals, and many other products requiring prolonged shelf lives. However, recent reports of parabenmediated endocrine disruptions, allergic responses, cancer, loss of fertility, and respiratory disorders are alarming and are the signs of growing health and environmental hazards. The unregulated disposal of packaged products supplemented with parabens and unintended uses may increase the environmental burden in the time to come. Recent studies exploring the health hazards associated with the use or consumption of compounds have provided insight into the underlying mechanisms of action. The paraben(s) are assimilated through two routes: oral administration and skin permeation. The ability to detect compounds in different environmental habitats with robust and specific techniques is important due to the unintended public health burdens of these compounds. This review presents the recent findings on the health burden of the compounds, fallacies in detection, and chronological advancements in the detection of paraben(s). This review assesses the impact of the increasing use of parabens on different cohorts, health hazards, and the need to develop more robust and accurate tools for detecting parabens in different environments.

Textile effluents are major pollutants with varied contaminants. Traditional treatment methods are costly and produce sludge, necessitating alternative, eco-friendly solutions. Biological treatment methods are receiving attention as it is proven to be cheap, environment-friendly, and highly efficient treatment methods for dye effluent on an industrial scale as compared to the other available treatment methods. The present work evaluates the bioremediation of textile effluent using a pure culture of a bacterium isolated from the soil samples contaminated with textile wastewater. The strain was identified as Achromobacter xylosoxidans KUESCCHK-6 (GenBank Accession Number: OM475749) through 16S rRNA molecular analysis. This bacterial strain was used to treat textile effluent under specific conditions: glucose as the carbon source, urea as the nitrogen source, a C/N ratio of 6:1, a temperature of 35°C, a pH of 8.5, and a static incubation period of 5 days. The results indicated that the strain effectively reduced various physiochemical parameters of the raw textile wastewater: color by 87.94%, BOD by 80.61%, COD by 80.96%, EC by 73.11%, fluoride by 81.15%, phosphate by 79.57%, sodium by 76.88%, and turbidity by 81.02%. Additionally, metal ions, including iron, were removed by 84.83%, while other metals, such as zinc, nickel, manganese, copper, lead, cadmium, total chromium, arsenic, barium, cobalt, and boron, were reduced to belowdetectable limits. Phytotoxicity tests confirmed the non-toxic nature of the treated effluent. Overall, the study concludes that Achromobacter xylosoxidans KUESCCHK-6 is a promising candidate for the bioremediation of textile industrial effluents, with potential for commercial application.