The existence of a coal mining company in the vicinity of the community is something to be feared related to environmental damage due to coal mining. On the other hand, coal mining can have a positive impact on the economy of communities around the mine through corporate social responsibility programs. The problem in this research is that MSMEs need help to improve their performance. Therefore, this research aims to examine how the role of mining companies through corporate social responsibility (CSR) programs can contribute to the development of MSMEs in communities around mining areas. The company provides promotional assistance, funding, and capacity building. This research conducted surveys and interviews with respondents, namely MSMEs, around mining locations. The findings show that corporate social responsibility programs in coal mining companies have a positive impact on empowering MSMEs in communities around the mine. By providing training and promotion facilities to MSMEs, mining companies can also improve MSME performance compared to providing access to financial assistance programs. The company not only takes advantage of mining and focuses on its environmental impact but also the company’s role in empowering MSMEs.

The project aims to remove reactive blue dye from the effluent of textile industries by utilizing coal-associated soil as an adsorbent, as it possesses effective physical properties and distinguishing characteristics. In comparison to other separation techniques, the adsorption method is the most effective, cost-effective, and straightforward. A batch adsorption investigation was carried out to examine the various adsorption-influencing factors, including solution pH, adsorbent dosage, contact time, temperature, and dye concentration. Contact time of 30 min, an adsorbent dosage of 10g.100 mL-1, a solution pH of 7, a temperature of 30°C, and an initial dye concentration of 100 mg.L-1 were found to be optimal for dye adsorption. Using two distinct kinetic models, the evaluation of kinetic studies revealed that the pseudo-second-order provided the greatest fit, with a higher R2 value than the pseudo-first-order. The thermodynamic parameters Gibbs free energy (ΔG°), entropy (ΔS°), and enthalpy (ΔH°) indicated that the current adsorption system was exothermic and spontaneous. Further study of the adsorption isotherm revealed that the Langmuir isotherm model provided the best fit, with an R2 value of 0.977%.

A high concentration of hydrogen sulfide (H2S) released from pig farming is one of the major environmental problems affecting surrounding communities. In modern pig farms, the bioscrubber is used to eliminate H2S, which is found to be driven mainly by the sulfur-oxidizing bacteria (SOB) community. Therefore, in this study, molecular biology techniques such as next-generation sequencing (NGS) and DNA microarray are proposed to study the linkage between enzyme activity and the abundance of the SOB community. The starting sludge (SFP1) and recirculating sludge (SFP2) samples were collected from the bioscrubber reactor in the pig farm. The abundance of microbial populations between the two sampling sites was considered together with the gene expression results of both soxABXYZ and fccAB. Based on the NGS analysis, the members of phylum Proteobacteria such as Halothiobacillus, Acidithiobacillus, Thiothrix, Novosphingobium, Sulfuricurvum, Sulfurovum, Sulfurimonas, Acinetobacter, Thiobacillus, Magnetospirillum, Arcobacter, and Paracoccus were predominantly found in SFP2. The presence of Cyanobacteria in SFP pig farms is associated with increased biogas yields. The microarray results showed that the expression of soxAXBYZ and fccAB genes involved in the oxidation of sulfide to sulfate was increased in Halothiobacillus, Paracoccus, Acidithiobacillus, Magnetospirillum, Sphingobium, Thiobacillus, Sulfuricurvum, Sulfuricurvum, Arcobacter, and Thiothrix. Both NGS and DNA microarray data supported the functional roles of SOB in odor elimination and the oxidation of H2S through the function of soxABXYZ and fccAB. The results also identified the key microbes for H2S odor treatment, which can be utilized to monitor the stability of biological treatment systems and the toxicity of sulfide minerals by oxidation.

Biofuels are the cheapest source of energy, and the continuous decline of traditional sources of energy with the increasing population leads to looking for alternatives to reduce the consumption of traditional sources of energy. Bioethanol production from lignocellulosic wastes and cellulosic wastes is not a new approach for fuel production but a cheap and accessible way for the production of fuel. Bacillus is one of the major species that can act as a source of diversified enzymes. In this study, it was emphasized on screening and isolation of a novel, characterization, and best catalytic action on both celluloses and proteins in the presence of different carbon and nitrogen sources. It was observed the effective catalytic breakdown of cellulose with the crude enzyme to glucose allowed fur for fermentation with Saccharomyces, ultimately leading to the generation of alcohol. The study aims to isolate the microbes that can produce cellulases and enzymes and could be used for biodegradation to produce ethanol in the reaction. The maximum enzyme activity was achieved at 3.112 UI with optimized pH and temperature, and the maximum conversion of sugars into alcohol was about 70% in the newspaper, cartons, colored paper, and disposable paper cups. An essential observation was the decolorization of the origami craft paper within 24 hours. The study was involved in enhancing the maximum Enzyme activity of cellulases from different cellulosic raw materials. Hence, it was achieved by JCB strain, optimization of pH, temperature, and acids for the biodegradation. The presence of peaks at 3200 and 2900 was a confirmation of ethanol bonds in the biodegradation reaction mixtures.

The increased use of metallic nanoparticles has led to concern for environmental contamination and disruption in water quality. Therefore, effective screening of metallic nanoparticles is important for detecting metallic nanoparticles in aquatic environments. Biosensors offer several advantages, including high sensitivity to pollutants, short response time, energy efficiency, and low waste generation. In this study, a whole-cell biosensor was developed using microalga Chlorella vulgaris as a recognition element, and its fluorescence response was used as a measuring parameter for detecting the presence of titanium dioxide (TiO2) and silver (Ag) nanoparticles in water. The responses of C. vulgaris at the lag, exponential, and stationary phases to different concentrations of TiO2 and Ag nanoparticles were studied. The results showed that in TiO2 and Ag nanoparticles exposures, the highest fluorescence change (50-150%) was observed at the lag phase, whereas the lowest fluorescence change (40-75%) was observed at the stationary phase. A significant fluorescence change was observed in 15 min. The immobilized C. vulgaris under TiO2 and Ag nanoparticles exposures showed 30-180% higher fluorescence change than the negative control, indicating the potential of C. vulgaris as a biosensor for rapid detection of TiO2 and Ag nanoparticles in water. The mathematical modeling of the responses of C. vulgaris to TiO2 and Ag nanoparticles at 15 min of exposure with high R2 indicated that this biosensor is sensitive to the concentration tested (0.010–10.000 mg.L-1). Taken together, these results reveal that, for the first time, it is possible to detect TiO2 and Ag nanoparticles in water within a very short time using a microalgae-based biosensor. Moreover, no genetic engineering requirement makes this biosensor simple, economical, and free from the restriction on genetically modified microorganisms for environmental applications.

Sawdust, a product of the forest industry is mostly left untreated as solid waste. This phenomenon is well observed along the Lagos Lagoon in Nigeria where hundreds of trees are cut daily by sawmills to deliver wood for mainly the furniture industry. Different types of trees are utilized in this manner and the massive amounts of sawdust produced as a result of these activities are polluting the environment causing health risks for humans and animals. Cellulose, a glucose bio-polymer is a major structural component of sawdust and could be developed as a renewable energy resource should the cellulose be degraded into glucose, a fermentable sugar. This saccharification was done with Aspergillus niger cellulase and to make the cellulose more susceptible for cellulase action the sawdust was delignified with hydrogen peroxide. Both delignified and non-delignified sawdust were treated with the cellulase enzyme at incubation temperatures of 30°C, 40°C, 50°C, and 60°C. Delignification proved to be effective as an increased amount of sugar was released from all delignified sawdust materials relative to the non-delignified materials when saccharified with A. niger cellulase. Most of the materials were degraded at an incubation temperature of 40°C and 50°C and the highest percentage saccharification of 58% was obtained during the degradation of delignifed cellulose from the tree, Ricindendron heudelotti

The study presented aims to find the most appropriate climate dataset for the data-scarce Jalaur River Basin (JRB), Iloilo, Philippines, by evaluating the statistical performance of five rainfall datasets (APHRODITE, CPC NOAA, ERA5, SA-OBS, and PGF-V3) with resolutions of 0.25° and 0.5° having a time domain of 1981 to 2005. Bilinear interpolation implemented through Climate Data Operator (CDO) was used to extract and process grid climate datasets with Linear scaling as bias correction to minimize product simulation uncertainties. The datasets were compared to the lone meteorological station nearest to JRB investigated at monthly and annual timescales using six statistical metrics, namely, Pearson’s correlation coefficient (r), coefficient of determination (R2), modified index of agreement (d1), Kling-Gupta efficiency, Nash-Sutcliffe efficiency (NSE), and RMSE-observations standard deviation ratio (RSR). The results indicate a strong positive correlation with the observed data for both rainfall and temperature (r > 0.8; R2, d1 > 0.80). Although graphical observation shows an underestimation of rainfall, goodness-of-fit values indicate very good model performance (NSE, KGE > 0.75; RSR < 0.50). In terms of temperature, variable responses are observed with significant overestimation for maximum temperature and underestimation for minimum temperature. SA-OBS proved to be the best-performing dataset, followed by ERA5 and PGF-V3. These key findings supply useful information in deciding the most appropriate gridded climate dataset for hydrometeorological investigation in the JRB and could enhance the regional representation of global datasets.

Aging oil is a common pollutant in petrochemical enterprises due to its severe emulsification and flocculation, poor settling performance, low oil recovery rate, and high difficulty in treatment. This article adopts the method of mechanical, ultrasonic, and chemical coupling demulsification to treat aging oil, with the water content and oil recovery rate of the treated aging oil as the inspection indicators. The experiment shows that when the oil-water ratio is 1:4, the heating temperature is 50℃, the stirring speed is 180rpm, the ultrasonic frequency is 25kHz, the power is 40W, the ultrasonic time is 25min, and the pH is adjusted to 3-4. The additional amount of FeSO4 is 160mg/L, the additional amount of H2O2 is 0.11%, and the heating stirring reaction is 40min. When the dosage of cationic PAM with an ion degree of 50 is 35mg/L, the centrifugation speed is 3200rpm. The centrifugation time is 20 min, the crude oil recovery rate after aging oil treatment can reach over 94.6%, and the water content of the treated crude oil is less than 0.5%, meeting the standards for crude oil gathering and transportation in China. The oil content in the water generated after aging oil treatment is about 150 mg.L-1, the suspended solids content is 200 mg.L-1, the oil content in the residue is 6%, and the water content is 53%. By analyzing the appearance of aging oil before and after treatment, it was found that when using this process to treat aging oil, the original spatial cross-linking network structure of the aging oil was broken, allowing the water droplets wrapped in the oil to be released, thereby significantly reducing the water content in the recovered oil and improving the oil recovery rate.

The in-lab incubation experiments were conducted to identify the passivation effect of corn vinasse biochar, which was prepared at different temperatures, on heavy metal Pb in paddy soil of the Pb-Zn mining area. The results showed that after 30 days of biochar amended to the soil, the soil pH and organic carbon content increased by 2.72%-8.47% and 27.79%-65.26%, respectively. The CO32- and OH- contained in corn vinasse biochar could react with Pb and generate carbonate and hydroxide of Pb. In comparison with the treatment control, the bioavailable fractions of Pb were reduced by 26.6%, 23.30%, 26.95%, and 35.33%, respectively, in biochar-amended treatments. Exchangeable fractions of Pb decreased by 21.50%, 21.33%, 22.58%, and 22.58% for the treatment 3% (300°C), 6% (300°C), 3% (600°C), and 6% (300°C) corn vinasse biochar, respectively, compared with the treatment control. As a whole, corn vinasse biochar could effectively promote the transformation of Pb in soil from the exchangeable fractions into the Fe-Mn oxide-bound fractions and residue fractions, with a significant passivation effect for Pb in soil and more effective passivation by high-temperature preparation and increased dosage of biochar.

The spread of the Coronavirus disease 2019 (COVID-19) has impacted human life severely since November 2019. The urban centers in the world, especially, were highly affected by the diseases. Several socioeconomic and environmental factors probably enhanced the spread of the pandemic and consequent mortality. Many studies examining environmental factors, such as air quality, in urban centers indicate the roles of those factors in the spread of diseases and consequent mortality. However, other socioeconomic factors that directly or indirectly elevate the mass death of people are seldom studied. The present study explores the socioeconomic factors and air quality influencing COVID-19 deaths in urban India. We randomly selected 19 Indian cities and collected each city’s socioeconomic and air quality data from reliable and open sources. The data were analyzed using multivariate data analysis techniques using R statistics. The results showed significant positive relationships, population, and total area of the urban centers.