Diseases caused by bacteria, fungi, and viruses are a problem for many crops. Farmers have challenges when trying to evaluate their crops daily by manual inspection across all forms of agriculture. Also, it is difficult to assess the crops since they are affected by various environmental factors and predators. These challenges can be addressed by employing crop disease detection approaches using artificial intelligence-based machine learning and deep learning techniques. This paper provides a comprehensive survey of various techniques utilized for crop disease prediction based on machine learning and deep learning approaches. This literature review summarises the contributions of a wide range of research works to the field of crop disease prediction, highlighting their commonalities and differences, parameters, and performance indicators. Further, to evaluate, a case study has been presented on how the paradigm shift will lead us to the design of an efficient learning model for crop disease prediction. It also identifies the gaps in knowledge that are supposed to be addressed to forge a path forward in research. From the survey conducted, it is apparent that the deep learning technique shows high efficiency over the machine learning approaches, thereby preventing crop loss.

Dual concerns involving the rise in airborne pollutant levels and bulging need to protect-preserve human health have propelled the search for innovative means for air quality monitoring to aid in evidence-based decision-making (pollution prevention-mitigation). In this regard, moss bags have gathered a great deal of attention as active biomonitors. In this reflective discourse, we systematically review the world literature to present a bird’s eye view of moss bag applications and advances while highlighting potential concerns. We begin with a brief note on mosses as biomonitors, highlighting the advantages of moss bags over the passive technique (native moss), other living organisms (lichens, vascular plants), and instrument-based measurements. A major strand of moss bag research involves urban ecosystem sustainability studies (e.g., street tunnels and canyons, parks), while others include event-specific monitoring and change detection (e.g., SARS-CoV-2 Lockdown), indoor-outdoor air quality assessment, and change detection in land use patterns. Recent advances include biomagnetic studies, radioisotopic investigations, and mobile applications. Efforts are currently underway to couple moss bag results with a suite of indicators [e.g., relative accumulation factor (RAF), contamination factor (CF), pollution load index (PLI), enrichment factor (EF)] and spatially map the results for holistic appraisal of environmental quality (hot spot detection). However, while moss bag innovations and applications continue to grow over time, we point to fundamental concerns/uncertainties (e.g., lack of concordance in operational procedures and parameterization, ideal species selection, moss vitality) that still need to be addressed by targeted case studies, before the moss results could be considered in regulatory interventions.

This article discusses the major challenges related to greenhouse gas (GHG) emissions in the electricity sector and their impact on global climate change. The electricity sector is responsible for about a quarter of total global GHG emissions. To address these challenges, Life Cycle Assessment (LCA) is used to measure the environmental impact of different energy sources and electricity generation and distribution processes. The circular economy is presented as a promising approach to reducing the carbon footprint of the electricity sector. By optimizing the use and value of materials throughout their life cycle, this approach contributes to waste minimization and resource efficiency. Morocco is committed to reducing its GHG emissions and has adopted policies and regulatory frameworks to combat climate change. This study aims to calculate the climate change impacts of electricity distribution phases by applying a life-cycle approach to the case of an electricity distribution company in Morocco. This assessment makes it possible to identify significant contributors from each area. In the context of this company, it is a question of demonstrating how the application of the principles of the circular economy concepts contributes to the reduction of greenhouse gas emissions, in particular, that of scope 3. This study may be useful for other similar companies.

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.

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.

With the rapid advancement of Remote Sensing Technology, monitoring the agricultural land has become a facile task. To surveil the growth of paddy crops and provide detailed information regarding monitoring soil, drought, crop type, crop growth, crop health, crop yield, irrigation, and fertilizers, different types of remote sensing satellites are used like Landsat 8, Sentinel 2, and MODIS satellite. The main aim of Landsat 8, Sentinel 2 and MODIS satellites is to monitor the land and vegetation area and to provide data regarding agricultural activities. Each of these satellites possesses a different spectral band, resolution, and revisit period. By using the remote sensing spectral indices, different types of vegetation indices are calculated. This survey paper provides comprehensive about Remote Sensing and the major parameters that influence for growth of paddy crops, like soil and water, and the future scope of agriculture and its demand in research is discussed.

This study focuses on atmospheric aerosols, especially aerosol optical depth (AOD), over Dire Dawa, Ethiopia, from 2009 to 2020. At first, a correlation between the four satellite sensors and AERONET was made for validation purposes and to determine the sensor that best represents Dire Dawa. Intercomparisons were also made among the four satellite sensors. After all statistical tests, annual, seasonal, and decadal trend analyses were made. The validation results indicated that the AOD of MODIS-terra showed the best correlation with AERONET with R2 (0.78), RMSE (0.03), and MBE of 0.02 and represented the area better than the rest. The inter-comparison of AOD retrieved from multi-spectral satellite sensors showed a positive and satisfactory correlation between MODIS-Terra and OMI. Only MODIS-Aqua showed a linearly increasing mean annual AOD with R2 = 0.43. In three seasons (summer, autumn, and spring), AOD showed linear increments over the 12 years, with R2 ranging between 0.3 and 0.5. The three seasons also had nearly identical AODs of 0.23-0.28. However, winter had the lowest value of 0.2. MODIS-terra, out of the four sensors, exhibited increasing decadal tendency over the 2009-2020 period. Monthly analysis revealed that August had the highest AOD (0.265), and January had the lowest (0.14). The value of AOD obtained from this study over Dire Dawa shows a higher value during all seasons except during winter. Thus, this study gives a glimpse into the use of multi-spectral satellite sensors to monitor air quality over a semi-arid urban region.

Petroleum hydrocarbon is one of the dangerous substances in the process of petroleum development, refining, processing, transportation, and production. In the related activities of the petroleum industry, the output is large, and improper treatment will cause pollution to the surrounding environment. It is an urgent problem to conduct harmless and resource treatment of petroleum hydrocarbon polluted soil. Plant enrichment, as an environmentally friendly and pollution-free technical means, has the advantages of low cost and small change to the soil environment and effectively solves the problems of excessive heavy metals in petroleum hydrocarbons through plant enrichment. In this paper, the development process of plant enrichment, remediation methods, and plant enrichment of typical heavy metals (Cd, Hg, Zn) in petroleum hydrocarbon-polluted soil were systematically introduced. Through investigation, the mechanism and influencing factors of plant enrichment of heavy metals in the presence of petroleum hydrocarbons were summarized and analyzed, and the possible development direction of plant enrichment technology in the future was prospected.

In recent years, Electric Vehicles (EVs) are contributing a major share in Thailand and benefit the environment. Most of the EV charging stations are sourced from solar energy as it becomes a carbon-free source of energy production. Secondly, Thailand is rich in solar irradiance, and higher irradiance leads to higher power production. On the other hand, in tropical conditions, solar Photovoltaic (PV) module temperature increases following the solar irradiance due to high ambient temperature, resulting negative impact on the efficiency and lifespan of photovoltaic (PV) modules. Further, to increase PV power production, in this study, different rates of cooling strategies are proposed. The study found that reducing the temperature by 5% to 25% resulted in increased average power outputs of 5947.94W, 6021.43W, 6094.92W, 6168.41W, and 6241W, respectively. Notably, 25% of the cooling rate achieved higher production. However, it is lower than the nominal power production. Following that, economic analysis and environmental impacts are analyzed for Thailand’s EV charging station using a different cooling rate of PV module. Overall, it is concluded that, depending on the economic viability of the EV charging station, cooling technology can be applied, and it will benefit the EV charging station both economically and environmentally. To further enhance the solar PV power production approach for EV charging stations in Thailand, it is imperative to prioritize future endeavors towards optimizing cooling technology, integrating energy storage, and implementing supportive policies.