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...     

Spatial and temporal estimation of soil loss for the sustainable management of a wet semi-arid watershed cluster

The ungauged wet semi-arid watershed cluster, Seethagondi, lies in the Adilabad district of Telangana in India and is prone to severe erosion and water scarcity. The runoff and soil loss data at watershed, catchment, and field level are necessary for planning soil and water conservation interventions. In this study, an attempt was made to develop a spatial soil loss estimation model for Seethagondi cluster using RUSLE coupled with ARCGIS and was used to estimate the soil loss spatially and temporally. The daily rainfall data of Aphrodite for the period from 1951 to 2007 was used, and the annual rainfall varied from 508 to 1351 mm with a mean annual rainfall of 950 mm and a mean erosivity of 6789 MJ mm ha^?1 h^?1 year^?1. Considerable variation in land use land cover especially in crop land and fallow land was observed during normal and drought years, and corresponding variation in the erosivity, C factor, and soil loss was also noted. The mean value of C factor derived from NDVI for crop land was 0.42 and 0.22 in normal year and drought years, respectively. The topography is undulating and major portion of the cluster has slope less than 10°, and 85.3 % of the cluster has soil loss below 20 t ha^?1 year^?1. The soil loss from crop land varied from 2.9 to 3.6 t ha^?1 year^?1 in low rainfall years to 31.8 to 34.7 t ha^?1 year^?1 in high rainfall years with a mean annual soil loss of 12.2 t ha^?1 year^?1. The soil loss from crop land was higher in the month of August with an annual soil loss of 13.1 and 2.9 t ha^?1 year^?1 in normal and drought year, respectively. Based on the soil loss in a normal year, the interventions recommended for 85.3 % of area of the watershed includes agronomic measures such as contour cultivation, graded bunds, strip cropping, mixed cropping, crop rotations, mulching, summer plowing, vegetative bunds, agri-horticultural system, and management practices such as broad bed furrow, raised sunken beds, and harvesting available water using farm ponds and percolation tanks. This methodology can be adopted for estimating the soil loss from similar ungauged watersheds with deficient data and for planning suitable soil and water conservation interventions for the sustainable management of the watersheds.     

Biochemical properties of some Salvia L. species

Antioxidant capacity, total phenol and mineral contents of aerial parts of sage belonging to some Salvia species were established. The lowest and highest antioxidant values of Salvia dichroantha Stapf and Salvia heldreichiana Boiss. ex Benth. extracts were found as 73.855 and 80.207 mg GAE/g, respectively. While the highest total phenol was established in Salvia tomentosa Mil. (13.316 mg GAE/100 ml), the lowest level was found in Salvia halophila Hedge (6.168 mg GAE/100 ml). While K contents of plants changed between 14,518 and 24,171 mg/kg, Ca contents ranged between 12,402 and 18,553 mg/kg. P and Mg contents were found low compared with K and P values of plants. Mg content was changed between 2,118 and 2,914 mg/kg; the mean was calculated as 2,496 mg/kg. P contents of plants were determined between 1,385 to 1,910 mg/kg. As a microelement, Fe was found at the highest level. Fe contents of plants were found between 179 and 782 mg/kg.     

Chemometric application in identifying sources of organic contaminants in Langat river basin

Increasing urbanization and changes in land use in Langat river basin lead to adverse impacts on the environment compartment. One of the major challenges is in identifying sources of organic contaminants. This study presented the application of selected chemometric techniques: cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) to classify the pollution sources in Langat river basin based on the analysis of water and sediment samples collected from 24 stations, monitored for 14 organic contaminants from polycyclic aromatic hydrocarbons (PAHs), sterols, and pesticides groups. The CA and DA enabled to group 24 monitoring sites into three groups of pollution source (industry and urban socioeconomic, agricultural activity, and urban/domestic sewage) with five major discriminating variables: naphthalene, pyrene, benzo[a]pyrene, coprostanol, and cholesterol. PCA analysis, applied to water data sets, resulted in four latent factors explaining 79.0% of the total variance while sediment samples gave five latent factors with 77.6% explained variance. The varifactors (VFs) obtained from PCA indicated that sterols (coprostanol, cholesterol, stigmasterol, β-sitosterol, and stigmastanol) are strongly correlated to domestic and urban sewage, PAHs (naphthalene, acenaphthene, pyrene, benzo[a]anthracene, and benzo[a]pyrene) from industrial and urban activities and chlorpyrifos correlated to samples nearby agricultural sites. The results demonstrated that chemometric techniques can be used for rapid assessment of water and sediment contaminations.     

Effect of alkalinity on the performance of a simulated landfill bioreactor digesting organic solid wastes

This study investigated the effects of alkalinity on the anaerobic treatment of the organic solid wastes collected from the kitchen of Engineering Faculty in Dokuz Eylul University, Izmir, Turkey and the leachate characteristics treated in three simulated landfill anaerobic bioreactors. All of the reactors were operated with leachate recirculation. One reactor was operated without alkalinity addition. The second reactor was operated by the addition of 3 g l-1 d-1 of NaHCO3 alkalinity to the leachate and the third reactor was operated by the addition of 6 g l-1 d-1 NaHCO3 alkalinity to the leachate. After 65 d of anaerobic incubation, it was observed that the chemical oxygen demand (COD), volatile fatty acids (VFA) concentrations, and biochemical oxygen demand to chemical oxygen demand (BOD5/COD) ratios in the leachate samples produced from the alkalinity added reactors were lower than the control reactor while the pH values were higher than the control reactor. The COD values were measured as 18900, 3800 and 2900 mg l-1 while the VFA concentrations were 6900, 1400 and 1290 mg l-1, respectively, in the leachate samples of the control, and reactors containing 3 g l-1 NaHCO3 and 6 g l-1 NaHCO3 after 65 d of anaerobic incubation. The total nitrogen (TN), total phosphorus (TP) and ammonium nitrogen (NH4-N) concentrations in organic solid waste (OSW) significantly reduced in the reactor containing 6 g l-1 NaHCO3 by d 65. The values of pH were 6.54, 7.19 and 7.31, after 65 d of anaerobic incubation, respectively, in the aforementioned reactors results in neutral environmental conditions in alkalinity added reactors. Methane percentage of the control, reactors containing 3 g l-1 NaHCO3 and 6 g l-1 NaHCO3 were 37%, 64% and 65%, respectively, after 65 d of incubation. BOD5/COD ratios of 0.27 and 0.25 were achieved in the 3 and 6 g l-1 NaHCO3 containing reactors, indicating a better OSW stabilization. Alkalinity addition reduced the waste quantity, the organic content of the solid waste and the biodegradation time.     

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,...