The idea of reusing municipal incinerator bottom ash (MIBA), the residue from incinerating municipal solid wastes, fits nicely in a circular economy scheme, which leads to an avoided impact of landfill disposal, and at the same time reduces the demand of natural resources. Past studies have attempted to add 20 to 60% MIBA for ceramic production, and resulted in some inspiring success. Focused on delivering quality interior and exterior floor tiles meeting industrial standards, this study investigated the operative conditions and the optimum amount of MIBA in the mix. In this study, only the kaolinite clay and MIBA were used. Before making specimens, raw materials of clay and MIBA underwent SEM, EDS, and TCLP tests to determine their chemical contents. Six sets of specimens with different replacement levels of MIBA (0%, 5%, 10%, 15%, 20%, and 30%) were then prepared. These specimens were fired at 1000°C, 1050°C,1100°C, and 1150°C and the products underwent a series of mechanical tests to verify their performance. NMR (nuclear magnetic resonance spectroscopy) were also used to determining the organic compound structure after each specimens’ crystallization. Research results showed that proper mix of MIBA up to 20% could result in quality tiles complying with specifications for interior and exterior flooring applications at certain kiln temperatures, while the specimens with 30% MIBA failed to meet either bending strength or size shrinkage requirement at all four kiln temperatures, and could not deliver a satisfactory result.

In order to study the pollution characteristics and main sources of fine particulate matter in the atmosphere of the city of Changchun, PM₂.₅ samples were collected during the four seasons in 2014, and representative months for each season are January, April, July, and October. Sample collection was carried out on 10 auto-monitoring stations in Changchun, and PM₂.₅ mass concentration and its chemical components (including inorganic elements, organic carbon, elemental carbon, and water-soluble ions) were measured. The results show that the annual average mass concentration of PM₂.₅ in Changchun in 2014 was about 66.77 μg/m³. Organic matter was the highest component in PM₂.₅, followed by secondary inorganic ions (SNA), mineral dust (MIN), elemental carbon (EC), and trace elements (TE). Positive matrix factorization (PMF) results gave seven factors, namely, industrial, biomass and coal burning, industrial and soil dust, motor vehicle, soil and secondary ion, light industrial, and hybrid automotive and industrial sources in PM₂.₅, with contributing values of 18.9%, 24.2%, 5.7%, 23.0%, 11.5%, 13.0%, and 3.6%, respectively.

The Pan-Third Pole (PTP) region, which encompasses the Eurasian highlands and their surroundings, has experienced unprecedented, accelerated warming during the past decades. This study evaluates the performance of historical simulation runs of the Coupled Model Intercomparison Project (CMIP6) in capturing spatial patterns and temporal variations observed over the PTP region for mean and extreme temperatures. In addition, projected changes in temperatures under four Shared Socioeconomic Pathway (SSP) scenarios (SSP1‐2.6, SSP2‐4.5, SSP3-7.0, and SSP5‐8.5) are also reported. Four indices were used to characterize changes in temperature extremes: the annual maximum value of daily maximum temperature (TXx), the annual minimum value of daily minimum temperature (TNn), and indices for the percentage of warm days (TX90p) and warm nights (TN90p). Results indicate that most CMIP6 models generally capture the characteristics of the observed mean and extreme temperatures over the PTP region, but there still are slight cold biases in the Tibetan Plateau. Future changes of mean and extreme temperatures demonstrate that a strong increase will occur for the entire PTP region during the twenty-first century under all four SSP scenarios. Between 2015 and 2099, ensemble area-averaged annual mean temperatures are projected to increase by 1.24 °C/100 year, 3.28 °C/100 year, 5.57 °C/100 year, and 7.40 °C/100 year for the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. For TXx and TNn, the most intense warming is projected in Central Asia. The greatest number of projected TX90p and TN90p will occur in the Southeast Asia and Tibetan Plateau, respectively.

Irrigation water scheduling is crucial to make the most efficient use of ever-decreasing water. As excessive irrigation decreases yield, while imprecise application also causes various environmental issues. Therefore, efficient management of irrigation frequency and irrigation level is necessary to sustain productivity under limited water conditions. The objective of the current study is to assess the water productivity at various irrigation regimes during peanut crop growing seasons (2014 and 2015) in Eastern Mediterranean, Turkey. The field experiments were conducted with treatments consisting of three irrigation frequencies (IF) (IF₁: 25 mm; IF₂: 50 mm; and IF₃: 75 mm of cumulative pan evaporation (CPE)), and four irrigation water levels (WL₁ = 0.50, WL₂ = 0.75, WL₃ = 1.0, and WL₄ = 1.25). WL₁, WL₂, WL₃, and WL₄ treatments received 50, 75, 100, and 125 of cumulative pan evaporation. The CSM-CROPGRO-Peanut model was calibrated with experimental data in 2014 and evaluated with second-year experimental data (2015). The model simulated seed yield and final biomass (dry matter) reasonably well with low normalized root mean square error (RMSEₙ) in various irrigation intervals. The model simulated reasonably well for days to anthesis (RMSE = 2.53, d-stat = 0.96, and r² = 0.90), days to physiological maturity (RMSE = 2.55), seed yield (RMSE = 1504), and tops biomass dry weight at maturity (RMSE = 3716). Simulation results indicated good agreement between measured and simulated soil water content (SWC) with low RMSEₙ values (4.0 to 16.8% in 2014 and 4.3 to 18.2% in 2015). Further results showed that IF₂I₁₂₅ irrigation regime produced the highest seed yield. Generally, model evaluation performed reasonably well for all studied parameters with both years’ experimental data. Results also showed that the crop model would be a precision agriculture tool for the extrapolation of the allocation of irrigation water resources and decision management under current and future climate.

The study proposes a novel and sustainable method to appropriately utilize wastes from granite as well as glass industries in brick manufacturing. An ecofriendly and low-cost manufacturing process of ash-based bricks pertaining to the Indian standard codal provisions that can be adopted on the commercial scale is deliberated. The research also recommends the method for predicting the strength of the ash-based bricks using machine learning algorithms like random forests and decision trees. For positive synergy in the performance, both the granite tailings and glass waste must be used together. Using the granite tailings and glass waste together led to a significant reduction of 75% in the fly ash requirement without compromising the brick’s performance. The addition of the granite tailings and glass waste in the mix could increase the strength of the brick by 90.5% and 37.7%, respectively. Beyond 30% dosage of granite, tailings are not recommended as they may lead to the poor gradation of particles and weak bonding in the microstructure. The glass waste in the mixture should not be more than 15% as it causes the dilution of pozzolanic reactions thereby forming fewer hydrated compounds. Brick’s durability is known after exposing the specimens for 1 year to sewers and biogenic corrosion environment, marine environment, and saline soil environment, respectively. The inclusion of the industrial wastes significantly reduced the specimen damage in the extreme environmental conditions along with the least absorption rates. The dosage of ash, granite tailings, and glass waste has to be maintained around 15%, 30%, and 15%, respectively for attaining the optimum performance. Out of the generated machine learning algorithms, only random forests could be able to predict the values accurately with R² values at 0.90 and with comparatively lesser errors.

COVID-19 pandemic has had a devastating effect on the global food production system. Large-scale food producing countries restricted exports for food sovereignty, while small and import-dependent countries are at risk. After COVID-19 pandemic, integrated and planned action is necessary to overcome this global stalemate. In this review paper, we have tried to show the damage caused to global agriculture as well as in Bangladesh due to COVID-19 pandemic. At the same time, the prospects and tasks of Bangladesh in the post-pandemic global economy have also been discussed. Due to the middlemen-based marketing system in Bangladesh, farmers have been deprived of their profits from the early period. The government should move forward with a long-term planning to find alternative food market such as processing and export alongside the one-way marketing system. On the other hand, it will benefit the farmers’ community of Bangladesh, as well as ensure global food security after this pandemic.

Forest ecosystem carbon (C) storage primarily includes vegetation layers C storage, litter C storage, and soil C storage. The precise assessment of forest ecosystem C storage is a major concern that has drawn widespread attention in global climate change worldwide. This study explored the C storage of different layers of the forest ecosystem and the nutrient enrichment capacity of the vegetation layer to the soil in the Castanopsis eyeri natural forest ecosystem (CEF) present in the northeastern Hunan province, central China. The direct field measurements were used for the estimations. Results illustrate that trunk biomass distribution was 48.42% and 62.32% in younger and over-mature trees, respectively. The combined biomass of the understory shrub, herb, and litter layers was 10.46 t·hm⁻², accounting for only 2.72% of the total forest biomass. On average, C content increased with the tree age increment. The C content of tree, shrub, and herb layers was 45.68%, 43.08%, and 35.76%, respectively. Litter C content was higher in the undecomposed litter (44.07 %). Soil C content continually decreased as the soil depth increased, and almost half of soil C was stored in the upper soil layer. Total C stored in CEF was 329.70 t·hm⁻² and it follows the order: tree layer > soil layer > litter layer > shrub layer > herb layer, with C storage distribution of 51.07%, 47.80%, 0.78%, 0.25%, and 0.10%, respectively. Macronutrient enrichment capacity from vegetation layers to soil was highest in the herb layer and lowest in the tree layer, whereas no consistent patterns were observed for trace elements. This study will help understand the production mechanism and ecological process of the C. eyeri natural forest ecosystem and provide the basics for future research on climate mitigation, nutrient cycling, and energy exchange in developing and utilizing sub-tropical vegetation.

This study aims to explore the relationships between electricity consumption and per capita GDP, urbanization rate, and the proportion of tertiary industry in China during 2005–2018. The results demonstrate that electricity consumption and its determinants are cointegrated. Then fully modified ordinary least square (FMOLS) and fixed effect with varying coefficients and intercept techniques are used to explore the relationships. The empirical results show that all three factors positively affect electricity consumption, per capita GDP has exerted positive impacts on electricity consumption in the 28 provinces, urbanization rate also contributes a positive influence on electricity consumption in 29 provinces, while the positive effect of the proportion of tertiary industry emerges only in 10 provinces. Meanwhile, the urbanization rate makes the most considerable contribution to electricity consumption, while the proportion of the tertiary industry is the least essential determinant. The panel causality results reveal that per capita GDP and urbanization rate cause electricity consumption in the short run and per capita GDP and electricity consumption Granger cause each other in the long run. Finally, we propose policy implications including enhancing the security and reliability of power supply and optimizing the structure of electricity consumption in different industries in China.

The occurrence of cyanobacteria in freshwater systems can negatively impact drinking water resources by affecting esthetics, taste, creating odor, and/or causing health concerns due to the release of toxins. To understand and predict cyanobacteria growth, monitoring programs for drinking water reservoirs include the measurement of physical and chemical water quality parameters known to influence cyanobacteria growth. This study was authored to elucidate the factors governing cyanobacteria growth for data captured from a monitoring program implemented from 2011 to 2016 in a raw water reservoir in Waterloo, Ontario, Canada. Total phosphorus (TP), dissolved inorganic nitrogen (DIN), phycocyanin levels (a proxy for cyanobacteria cell counts), and turbidity are the key parameters assessed. Phycocyanin relative fluorescence units (PC- RFU ≥ 1.5) were used as an indicator of elevated growth periods to help predict the onset of elevated growth. The TP concentration during baseline growth (0.029 mg/L, ρ < 0.05) was significantly different from that observed during elevated growth (0.074 mg/L), suggesting TP was involved in promoting growth. Pearson’s correlation coefficient analysis indicated that PC-RFU had a statistically significant, positive and moderately strong relationship with TP: r(84) = 0.56, ρ < 0.01, and turbidity: r(84) = 0.60, ρ < 0.01 suggesting a link between nutrients and elevated growth. This information is useful for implementing future cyanobacteria growth mitigation strategies in raw water reservoirs.

This paper aimed to explore the correlation between PM₂.₅ concentration and hospitalization rate of esophageal cancer in Fujian province, and tried to find out the accurate lag effect between PM₂.₅ and hospitalization rate in 70 counties from the linear and nonlinear aspects. We extracted inpatients data of esophageal cancer from the New Rural Cooperative Medical Scheme (NRCMS) database and air pollutant PM₂.₅ grid data published by the atmospheric composition analysis group. The study showed that the hospitalization rate of esophageal cancer presented spatial aggregation in 70 counties of Fujian province. Southeast urban agglomerations had high hospitalization rates, while central and western regions had low hospitalization rates. The study found that the spatial distribution of the hospitalization rate of esophageal cancer in 2016 was not consistent with that of the PM₂.₅ concentration in the same year. The concentration of PM₂.₅ in 2003 and 2004 had the strongest correlation with the hospitalization rate of esophageal cancer in 2016, with Pearson correlation coefficient r value of − 0.365 and Geodetector q-statistic value of 0.148 (p < 0.05). Our findings showed that there existed a 13-year lag period of air pollutant PM₂.₅ on the esophageal cancer hospitalization rate, which can provide helpful guidance in the early screening strategy of esophageal cancer in Fujian. The research progress of this paper will help to understand the lag period of the impact of air pollutants on the hospitalization rate of esophageal cancer, provide valuable information for the prevention and treatment strategy of esophageal cancer in Fujian province, and provide relevant experience for alike regions.