Determination of pesticides in apples,grapes and apple juice from a Kosovo market by gradient liquid chromatography with tandem mass spectrometry

Apple, grape, and apple juice were analyzed for pesticides using a multireaction mode (MRM) method in samples taken from a Kosovo market. With this method it was possible to analyze about 100 pesticides in the ESI mode. In these samples some types of pesticides were found, but most of them did not exceed the maximum tolerance levels. In apple samples, six pesticides were detected but only three of them were in sufficient quantitites. Dimethoat was below the limit, while acephate and imazalil exceeded the limit. In grape and apple juice samples, all of pesticides which were found did not exceed the maximum tolerable limits.     

Antimicrobial Activity of the Chitosan Extracted from Metapenaeus stebbingi Shell Wastes

In the study, chitosan chemically extracted from Metapenaeus stebbingi shells obtained from shrimp processing factories and commercial chitosan were used as antimicrobial materials. Antimicrobial activities of the chitosans dissolved in acetic, lactic, formic and hydrochloric acid at different concentrations (1.00, 0.50, 0.25, 0.10 and 0.05%) were tested in vitro by using the disk diffusion method with standard microorganisms (Pseudomonas putida, Pseudomonas fluorescens, Vibrio parahaemolyticus, Staphylococcus epidermidis, Staphylococcus aureus, Escherichia coli 1, Escherichia coli 2, Listeria monocytogenes, Enterecoccus faecalis, Aeromonas caviae). On the other hand, gentamicin was used as positive control. Antimicrobial test results indicated that chitosan demonstrated different effects depending on the chitosan concentration, solvent material and bacteria type. In the present study, both the extracted and commercial chitosans were observed to have antimicrobial effects on nearly all types of the bacteria.     

Cost,environmental impact,and resilience of renewable energy under a changing climate: a review

Energy derived from fossil fuels contributes significantly to global climate change, accounting for more than 75% of global greenhouse gas emissions and approximately 90% of all carbon dioxide emissions. Alternative energy from renewable sources must be utilized to decarbonize the energy sector. However, the adverse effects of climate change, such as increasing temperatures, extreme winds, rising sea levels, and decreased precipitation, may impact renewable energies. Here we review renewable energies with a focus on costs, the impact of climate on renewable energies, the impact of renewable energies on the environment, economy, and on decarbonization in different countries. We focus on solar, wind, biomass, hydropower, and geothermal energy. We observe that the price of solar photovoltaic energy has declined from $0.417 in 2010 to $0.048/kilowatt-hour in 2021. Similarly, prices have declined by 68% for onshore wind, 60% for offshore wind, 68% for concentrated solar power, and 14% for biomass energy. Wind energy and hydropower production could decrease by as much as 40% in some regions due to climate change, whereas solar energy appears the least impacted energy source. Climate change can also modify biomass productivity, growth, chemical composition, and soil microbial communities. Hydroelectric power plants are the most damaging to the environment; and solar photovoltaics must be carefully installed to reduce their impact. Wind turbines and biomass power plants have a minimal environmental impact; therefore, they should be implemented extensively. Renewable energy sources could decarbonize 90% of the electricity industry by 2050, drastically reducing carbon emissions, and contributing to climate change mitigation. By establishing the zero carbon emission decarbonization concept, the future of renewable energy is promising, with the potential to replace fossil fuel-derived energy and limit global temperature rise to 1.5 °C by 2050.

     

Optimizing biomass pathways to bioenergy and biochar application in electricity generation,biodiesel production,and biohydrogen production

The current energy crisis, depletion of fossil fuels, and global climate change have made it imperative to find alternative sources of energy that are both economically sustainable and environmentally friendly. Here we review various pathways for converting biomass into bioenergy and biochar and their applications in producing electricity, biodiesel, and biohydrogen. Biomass can be converted into biofuels using different methods, including biochemical and thermochemical conversion methods. Determining which approach is best relies on the type of biomass involved, the desired final product, and whether or not it is economically sustainable. Biochemical conversion methods are currently the most widely used for producing biofuels from biomass, accounting for approximately 80% of all biofuels produced worldwide. Ethanol and biodiesel are the most prevalent biofuels produced via biochemical conversion processes. Thermochemical conversion is less used than biochemical conversion, accounting for approximately 20% of biofuels produced worldwide. Bio-oil and syngas, commonly manufactured from wood chips, agricultural waste, and municipal solid waste, are the major biofuels produced by thermochemical conversion. Biofuels produced from biomass have the potential to displace up to 27% of the world's transportation fuel by 2050, which could result in a reduction in greenhouse gas emissions by up to 3.7 billion metric tons per year. Biochar from biomass can yield high biodiesel, ranging from 32.8% to 97.75%, and can also serve as an anode, cathode, and catalyst in microbial fuel cells with a maximum power density of 4346 mW/m². Biochar also plays a role in catalytic methane decomposition and dry methane reforming, with hydrogen conversion rates ranging from 13.4% to 95.7%. Biochar can also increase hydrogen yield by up to 220.3%.

     

Artificial intelligence-based solutions for climate change: a review

Environmental Chemistry Letters - Climate change is a major threat already causing system damage to urban and natural systems, and inducing global economic losses of over $500 billion. These issues...     

Bioenergy production from chicken manure: a review

Environmental Chemistry Letters - Adopting waste-to-wealth strategies and circular economy models can help reduce biowaste and add value. For instance, poultry farming is an essential source of...     

Efficient photocatalytic degradation of Rhodamine B dye using ZnO/graphitic C<Subscript>3</Subscript>N<Subscript>4</Subscript> nanocomposites synthesized by microwave

Semiconductor photocatalysis is a solution to issues of environmental pollution and energy shortage because photocatalysis can use solar energy to degrade pollutants. The photocatalytic activity can be improved by using composites of ZnO and other semiconductors. Here, composites of ZnO and polymeric graphite-like C₃N₄ (g-C₃N₄) with high photocatalytic activities were prepared by microwave synthesis. Products were characterized by X-ray diffraction, transmission electron microscopy, ultraviolet–visible and Fourier transform infrared spectroscopy. The photocatalytic degradation of Rhodamine B was tested under irradiation from a Xe lamp. Results show that adding graphite-like C₃N₄ promotes the photocatalytic activity of ZnO. Composites with 1.0 wt% g-C₃N₄ showed the best photodegradation efficiency, and the reaction average energy was approximately 33.71 kJ mol^?1.     

The effects of drainage basin geomorphometry on minimum low flow discharge: the study of small watershed in Kelang River Valley in Peninsular Malaysia

This study investigate the relationships between genomorphometric properties and the minimum low flow dischare of undisturbed drainage basins in the Taman BukitCahaya Seri Alam Forest Reserve,Peninsular Malaysia.The drainage basins selected were third-order basins so as to facilitate a common base for sampling and performing an unbiased statistical analyses.Three levels of relationships were observed in the study.Significant relationships exised between the genomorphometric properties as shown by the correlation network analysis;secondly,individual geomorphometric properties were observed to influence minimum flow discharge;and finally,the multiple regression model set up showed that minimum flow discharge(Qnub)was dependent of basin area(AU),stream length(LS),maximum relief(Hmax),average relief (HAV) and stream frequency(SF).These findings further enforced other studies of this nature that drainage basins were dynamic and functional entities whose operations were governed by complex interrelationships occurring within the basins.Changes to any of the geomorphometric properties would influence their role as basin regulators thus influencing a change in basin response.In the case of the basin‘s minimum low flow,a change in any of the properties considered in the regression model influenced the “time to peak”of flow.A shorter time period would mean higher discharge,which is generally considered the prerquisite to flooding.This research also conclude that the role of geomorphometric properties to control the water supply within the stream through out the year even though during the drought and less precipitations months .Drainage basins are sensitive entities and any deterioratons involve will generate reciprocals and response to water supply as well as the habitat within the areas.     

Modeling of SO<Subscript>2</Subscript> distribution in Istanbul using artificial neural networks

Urban air pollution is a growing problem in developing countries. Some compounds especially sulphur dioxide (SO₂) is considered as typical indicators of the urban air quality. Air pollution modeling and prediction have great importance in preventing the occurrence of air pollution episodes and provide sufficient time to take the necessary precautions. Recently, various stochastic image-processing algorithms such as Artificial Neural Network (ANN) are applied to environmental engineering. ANN structure employs input, hidden and output layers. Due to the complexity of the problem, as the number of input–output parameters differs, ANN model settings such as the number of neurons of these layers changes. The ability of ANN models to learn, particularly capability of handling large amounts (or sets) of data simultaneously as well as their fast response time, are invariably the characteristics desired for predictive and forecasting purposes. In this paper, ANN models have been used to predict air pollutant parameter in meteorological considerations. We have especially focused on modeling of SO₂ distribution and predicting its future concentration in Istanbul, Turkey. We have obtained data sets including meteorological variables and SO₂ concentrations from Istanbul-Florya meteorological station and Istanbul-Yenibosna air pollution station. We have preferred three-layer perceptron type of ANN which consists of 10, 22 and 1 neurons for input, hidden and output layers, respectively. All considered parameters are measured as daily mean. The input parameters are: SO₂ concentration, pressure, temperature, humidity, wind direction, wind speed, strength of sunshine, sunshine, cloudy, rainfall and output parameter is the future prediction of SO₂. To evaluate the performance of ANN model, our results are compared to classical nonlinear regression methods. The over all system finds an optimum correlation between input–output variables. Here, the correlation parameter, r is 0.999 and 0.528 for training and test data. Thus in our model, the trend of SO₂ is well estimated and seasonal effects are well represented. As a result, we conclude that ANN is one of the compromising methods in estimation of environmental complex air pollution problems.     

Natural nutraceuticals for enhancing yogurt properties: a review

Environmental Chemistry Letters - Yogurt is a major fermented milk product providing probiotics, lactic acid bacteria, vitamins, calcium, and proteins, yet health-beneficial phenolics, flavonoids,...