Healysuhr1791

Diffuse nutrient loadings dominated the export flux into the rivers, whereas leaching and surface runoff constituted the major fractions for N and P, respectively. Discharge of agricultural irrigation water into the rivers was the major cause of the increases in nutrients and salinity. Given that the conditions in Central Asia are highly susceptible to climate change, our findings call for more efforts to establish holistic management of water quality.Quantitative estimation of soil organic carbon (SOC) is essential for the study of the C cycle and global C storage. Soil spectroscopic technology provides a cost-effective and time-efficient method for SOC quantification and has been successfully used to determine SOC storage. However, the SOC estimation accuracy remains limited by other soil properties, particularly soil water. In this study, we proposed a new deep learning algorithm named the Water Absorption Trough Dewatering Machine (WATDM) to improve estimations of SOC from soil reflectance spectra and reduce the effect of soil water. Soil water and reflectance spectral data of soil samples were measured using spectrometry. Based on the soil water contents derived from the water absorption troughs around 1900 nm, the optimal WATDM model was obtained and treated as the final model of the WATDM method, which performed better than a multiple linear regression model based on moist soil samples. The findings of this study indicate that the WATDM method can improve the estimation accuracy of SOC content by reducing the effect of soil water and can be used as a valuable new methodology within the spectroscopic estimation of soil properties.During winter 2018, the 16 prefecture-level cities in Anhui Province, Western Yangtze River Delta region, China had very high PM2.5 concentrations and prolonged pollution days. The impact of regional transport in the formation, accumulation, as well as dispersion of fine particulate matter (PM2.5) in Anhui Province was very significant. This study quantified and analyzed the vertical transport of PM2.5 in three major cities (Hefei, Fuyang, and Suzhou) of Anhui Province in January and July 2018 using the Weather Research and Forecasting (WRF) model coupled with the Community Multiscale Air Quality (CMAQ) model. The results of the inter-regional transport of PM2.5 revealed the dominant transport pathways for the three cities. The flux mainly flowed into Fuyang from Henan (2.23 and 1.42 kt/day in January and July, respectively) and Bozhou (1.96 and 1.21 kt/day in January and July, respectively), while the main flux from Fuyang flowed into Henan (-2.15 kt/day) and Lu'an (-1.91 kt/day) in January and Henan (-0.34 kt/day) and Bozhou (-0.29 kt/day) in July. In addition, the dominant transport pathways and the heights at which they occurred were identified the northwest-southeast and northeast-south pathways in both winter and summer at both lower (˂300 m) and higher (≥300 m) levels for Fuyang; the northwest-south and northeast-southwest pathways in winter (at both lower and upper levels) and northwest-east and northeast-southwest pathways in summer at lower and upper levels for Hefei; and the northwest-southeast and northeast-south pathways in both winter (from 50 m up to the top level) and summer (between 100 and 300 m) for Suzhou. Furthermore, the intensities of daily PM2.5 transport fluxes in Fuyang during the atmospheric pollution episode (APE1) were stronger than the monthly average. These results show that joint emission controls across multiple cities along the identified pathways are urgently needed to reduce winter episodes.The present work was developed to study the metal removal performance of unicellular algae isolated from the Reconquista River and to evaluate the effect of the presence of more than one metal in the removal process. Thus, native species of unicellular algae were isolated from the highly contaminated Reconquista River. All of the isolates were classified, at genus level, based on their morphological appearance. Nine isolates were screened for their Zn(II) removal capacities. Chlorella sp. RR5 and Desmodesmus sp. RR7 were selected based on their removal performance, and their potential in the remediation of multiple metals was analyzed. Therefore, zinc (Zn(II)), copper (Cu(II)), and chromium (Cr(VI)) removal was evaluated in mono- and multi-metallic solutions. Biosorption capacities were high (0.8-1.8 mmol g-1) for Zn(II) and Cu(II) in mono-metallic solutions. Removal capacities decreased up to 48% in multi-metallic solutions. Interestingly, when multi-metallic systems were considered, each strain showed a metal preference. Chlorella sp. removed better Cu(II) meanwhile Desmodesmus sp. showed a preference for Zn(II). Thus, a metal-binding selectivity in each strain was determined. Chromium (VI) remediation was almost null in the conditions analyzed in this work. Fourier transformation infrared spectroscopy (FT-IR) analysis showed that polysaccharides were the main functional group involved in metal adsorption and, in some cases, also the carboxylates played an important role. Overall, we were able to analyze a new source of algal diversity and perform a metal removal characterization of them, leading to the identification of a metal selectivity based on the characteristics of the tested algal strains.Ecological environmental issues are global focus problems. Achieving carbon neutrality is a fundamental measure to protect the ecological environment. Government environmental governance plays a key role in the process of moving towards a carbon neutral vision since the externality of the environment. Therefore, it is of great significance for achieving the goal of carbon neutrality to evaluate the governmental ecological environment performance and propose improved suggestions based on the evaluation results. However, most existing evaluation methods have the disadvantages that it is difficult to avoid information distortion and loss during the evaluation process. To overcome these problems, an evaluation model based on a DPSIR-improved matter-element extension cloud model is proposed in this study, which combines the Drivers-Pressures-State-Impact-Response (DPSIR) model, the entropy weight method, the cloud model, matter element extension theory, and the cloud entropy optimization algorithm. The ecological environmental performance of China in 2019 was evaluated based on the proposed model, exampling as Jiangsu province. The results showed that the cloud digital eigenvalues of ecological environmental performance was (2.1852, 0.2956, 0.1), indicating that the ecological environmental performance was good level. GSK2334470 However, the ambient air quality needed to be improved. To achieve carbon neutrality, suggestions including strengthening propagating, increasing investment, optimizing the industrial structure, and building a modern environmental governance system are proposed.Anaerobic digestion processes create biogases that can be useful sources of energy. The development of data-driven models of anaerobic digestion processes via operating parameters can lead to increased biogas production rates, resulting in greater energy production, through process modification and optimization. This study assessed processed and unprocessed input operating parameter variables for the development of regression models with transparent structures ('white-box' models) to (1) estimate biogas production rates from municipal wastewater treatment plant (MWTP) anaerobic digestors; (2) compare their performances to artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) models with opaque structures ('black-box' models) using Monte Carlo Simulation for uncertainty analysis; and (3) integrate the models with a genetic algorithm (GA) to optimize operating parameters for maximization of MWTP biogas production rates. The input variables were anaerobic digestion operating parameters from a MWTP including volatile fatty acids, total/fixed/volatile solids, pH, and inflow rate, which were processed via correlation tests and principal component analysis. Overall, the results indicated that the processed data did not improve regression model performances. Additionally, the developed non-linear regression model with the unprocessed inputs had the best performance based on values including R = 0.81, RMSE = 0.95, and IA = 0.89. However, this model was less accurate, but interestingly had less uncertainty, as compared to ANN and ANFIS models which indicates the compromise between model accuracy and uncertainty. Thus, all three models were coupled with GA optimization with maximum biogas production rate estimates of 22.0, 23.1, and 28.6 m3/min for ANN, ANFIS, and non-linear regression models, respectively.Copper slag is a waste obtained from copper production and it has a limited use, being mainly accumulated in landfills on a massive scale. This material presents a high hardness and it has hydrophobic properties, so it can be used as aggregate replacement in the production of asphalt mixtures. However, each size of copper slag behaves differently when used in asphalt mixes, especially under changing conditions of moisture or temperature. Precisely these climatic factors directly affect the service life of asphalt pavements. In this research, semi-dense graded asphalt mixtures were produced with copper slag as replacement of aggregates, varying the particle sizes used in the range from 2.5 to 0.08 mm to determine the size of copper slag with the best performance. Indirect tensile strength tests were used to analyze samples subjected to different moisture and temperature conditions and ageing degrees. The results show that copper slag can be used as aggregate replacement in asphalt mixes when the proper size is selected. The strength of the asphalt mixture increased as the size of the copper slag increased, especially under variable moisture and ageing conditions. Superior behaviour compared to a reference mixture was obtained when replacing the size of aggregate No. 8 with copper slag, increasing its indirect tensile strength and retained strength, reducing its stiffness under all the ageing periods, and being equally effective at the different temperatures, which results in mixtures with improved durability and delayed cracking. Furthermore, it would help to reduce between 15 and 20% of the virgin aggregate needed to produce asphalt mixes and it would also allow reducing the accumulated volume of this waste, decreasing the environmental impact of both industries.Energy intensive traditional cereals based monoculture often lead to high greenhouse gas emissions and degradation of land and environmental quality. Present study aimed at evaluating the energy and carbon budget of diversified groundnut (Arachis hypogea L) based cropping system with over existing traditional practice towards the development of a sustainable production technology through restoration of soil and environmental quality and enhancement of farming resiliency by stabilizing farmers' income. The trials comprised of three introduced groundnut based systems viz. groundnut- pea (Pisum sativum), groundnut-lentil (Lens esculenta) and groundnut-toria (Brasssica campestris var. Toria) replacing three existing systems viz. maize (Zea mays L) - fallow, maize - toria, and rice (Oryza sativa L)-fallow systems. Four years study revealed that adoption of groundnut based systems reduced non-renewable energy input use (fertilizers, chemical, machinery and fossil fuels) by 25.5%, consequently that reduced the cost of production.