China’s forest ecosystem service supply has faced intense pressure during the last few decades, calling for a unified valuation on a long timescale to reveal its temporal and spatial changes. Yet, existing studies are confined to static valuations for a single year at the regional scale, while a temporal–spatial investigation for the ecosystem service value of China’s various forest types at the national scale is currently lacking. This study quantified the ecosystem service value of China’s various forest types during 1990–2020 and analyzed its temporal–spatial variations. As a supplement to emergy analysis, a unified assessment framework based on cosmic exergy as a biophysical metric has been proposed for ecosystem service valuation. Results showed that forests in southwest, southeast, and northeast China were the major providers of ecosystem services. From 1990 to 2020, although the aggregated value of China’s forest ecosystem service increased by only 4%, the service value of forests in coastal, northeast, and southwest China showed a declining trend. We also found that evergreen needleleaf forests, deciduous broadleaf forests, and evergreen broadleaf forests respectively contributed 35%, 29%, and 28% to the total service value, while in terms of ecosystem function types, erosion control, microclimate regulation, air purification, and soil purification contributed 27%, 22%, 22%, and 19% to the total, respectively. The outcome of this study may provide a new methodological framework and a benchmark reference for forest ecosystem service valuation, which is supportive of formulating forest management measures for different types of forest ecosystems.
A leaf litter fermented product (LLFP) was prepared via aerobic fermentation of leaf litter of Betula pendula by indigenous microflora. The LLFP exerted effects on the growth and cold tolerance of Begonia grandis plants cultivated outdoors under the conditions of the forest-steppe zone of West Siberia. Despite the sufficient data on the positive effects of similar products as biostimulants and boosters of stress tolerance, there is a gap in information about their active ingredients. The biggest gap is related to low-molecular-weight components. Chromatographic profiling of the LLFP in comparison with green leaves and leaf litter of B. pendula as performed by high-performance liquid chromatography revealed a significant decrease in the concentrations and number of phenolic compounds. The chromatographic profile of the LLFP showed salicin and oxalic, ascorbic and gallic acids as the main extracted organic compounds. Meanwhile, a substantial amount of indoleacetic acid (1.26–4.13 ng mL−1) and traces of abscisic acid and t-zeatin were revealed by liquid chromatography with tandem mass spectrometry. The LLFP showed dose-dependent free-radical–scavenging activity. The results indicate that phytohormones, most likely of microbial origin, are the main active ingredients of the LLFP. Phenolic compounds can enhance antioxidant properties of B. grandis plants and soil health; besides, organic acids improve assimilation of nutrients from soil. The results hold promise for a uniform approach to the assessment and prediction of the quality of plant fermented products for their wide dissemination as ecofriendly and sustainable products.
Pre-sowing seed treatment with nanoparticles has a promising role towards the improvement of seed germination and seedling growth. In the present study, silver nanoparticles (AgNPs) were synthesized from silver nitrate (AgNO3) through green route using aqueous extract of Parthenium hysterophorus L. roots. The synthesized nanoparticles were characterized using various analytical instruments such as UV–Vis spectrophotometer, TEM, SEM, EDX, XRD, and FTIR. Further, the impacts of AgNPs, AgNO3, and plant extract on germination, seedling growth, activity of hydrolytic enzymes, and ROS generation of three pulses (Cicer arietinum L., Pisum sativum L., and Vigna radiata L.) were investigated. Characterization of nanoparticles revealed that the green synthesized AgNPs were mostly spherical with an average size of 11–20 nm and crystallinity was 71.3%. The growth experiment revealed that seed germination and seedling growth were increased under AgNPs (10 and 50 mg/L) and AgNO3 (10 mg/L) treatments as compared to control for three tested pulses. Results also demonstrated the increased hydrolytic enzyme activities during early seedling establishment of three pulses under nanoparticle treatments. Meanwhile, dose dependent increase in ROS production was recorded under both AgNPs and AgNO3 treatments and it was always higher in AgNO3 as compared to AgNPs treatments. However, the growth inhibition at higher concentrations of both AgNPs and AgNO3 treatments suggested that the ROS generation at an optimum level might play an important role towards the enhancement of seed germination. Therefore, AgNPs mediated alteration of the activity of hydrolytic enzymes and generation of ROS might regulate early seedling establishment.
Most of kinetic models applied to microalgal wastewater treatment are focused on the specific contaminant removal (i.e. correlating a limiting substrate to microbial growth) or considering autotrophic characteristic that cannot be correlated to the mixotrophic/heterotrophic cultivation of these microorganisms, specially treating wastewater. However, to find an integrated kinetic approach using the simultaneous removal of organic carbon, nitrogen and phosphorus (main macronutrients) is difficult considering the different characteristics of the wastewaters, and the high number of kinetic constants associated with complex models. In this sense, this study designed a procedure to apply the kinetic modelling of microalgal growth using both the Monod model and the Silva and Cerqueira model (multiple substrates), estimating the characteristic kinetic constants and simulating the bioprocess by implementation on Python language and using Particle Swarm Optimisation (PSO). For contaminants removal, the n-th order equation proved to be more suitable, with an intermediate order between 1st and 2nd order (i.e. 1 < n < 2) and with a kinetic constant 0 < k < 0.2, obtaining an error between 15 and 28%. Using the Monod model, the algorithm was able to determine μ max and Ks, which were shown in the intervals: 0 < μ max < 4 day−1 and 0 < Ks < 50 mg L−1 with error of 15–25%. In the Silva and Cerqueira model, analysing the delimitation of m and p resulted in the following interval of convergence between 0 < p < 0.5 and 0 < m < 2, obtaining an error between 4 and 17% was obtained (significantly lower than for Monod) and using multiple substrates. The algorithm of the procedure is presented.
Improvement of thermal performance of energy storage leads to energy savings and reduction of carbon emissions. In this study, the effect of tube arrangement on the performance of thermal energy storage is examined during the melting process of a phase change material (RT50). The heat transfer and phase change modeling are based on conservation equations and lattice Boltzmann method. The combined effect of the tube's vertical position and nanoparticles are studied for various nanoparticle concentrations and Rayleigh numbers. Natural convection, melt fraction, heat storage, and storage time are examined and discussed. The results indicate that tubes placed at the horizontal centerline of the cavity is the best arrangement for reducing the charging time at low heating rates (Ra = 104), while downward displacement of one heating tube is more effective at high heating rates (Ra ≥ 105), the loading time can be reduced by up to 28%. The results of this study can guide design of efficient thermal energy storage systems.
The usage of unconventional aggregate materials in road construction is still in the experimental stage. A possible environmental risk is the continued use of conventional aggregates in the construction of roads and highways. The issue may be resolved by sensible mitigating measures and essential research on substitute aggregate materials. The present article investigates the laboratory performance of asphalt mixtures incorporating electric arc furnace slag (EAF) and basic oxygen furnace (BOF) slag as partial replacements (10%, 30%, and 50%) for conventional aggregates. Surface morphology and chemical composition of aggregates have been studied using SEM and EDAX analysis. The characteristics of the asphalt mixture’s mix design, including abrasion loss, moisture sensitivity, rutting resistance, fatigue behaviour, and resilient modulus, were investigated. Results indicate that the mix design properties of asphalt mixtures with EAF and BOF slag were within the requirements. Further, the incorporation of EAF and BOF slag in asphalt mixtures reduced abrasion loss irrespective of replacement percentage. Moisture sensitivity, rutting, fatigue, and resilient modulus properties of asphalt mixtures with 30% EAF slag were higher compared to asphalt mixtures with 30% BOF slag and conventional mixtures. However, asphalt mixtures with 50% replacement of EAF and BOF slag were lower compared to control mixtures. According to the findings, the optimum replacement percentage for EAF and BOF steel slag is 30% conventional aggregate. When compared to BOF steel slag, EAF steel slag performed better in terms of moisture sensitivity, rutting resistance, fatigue behaviour, and resilient modulus. The results of this study can be used as a reference for creating recycled asphalt mixtures with slag aggregates sourced from the steel industry.
This study examined the effect of various design parameters on the relationship between compressive strength and ultrasonic pulse velocity (UPV) of geopolymer concrete. To prepare the data set, a series of samples were made with increasing alkaline activator liquid to binder (AAL/B) ratios and varying densities. The variation in density was attained by partial replacement of normal weight fine and coarse aggregates by denser copper slag and lightweight recycled aggregates, respectively. The influence of AAL/B ratio and density on compressive strength and UPV was evaluated. After comprehensive analysis of results, a unified empirical equation is proposed to non-destructively compute the compressive strength of geopolymer concrete using UPV, AAL/B ratio and density. The findings demonstrated that the suggested equation could accurately predict the compressive strengths with a high determination coefficient (D c) of 0.97. In addition, an attempt was made to check the validity of the constructed equation by comparing it with other relevant published experimental data.