Estimation of PM2.5 concentrations in China using a spatial back propagation neural network

Methods for estimating the spatial distribution of PM2.5 concentrations have been developed but have not yet been able to effectively include spatial correlation. We report on the development of a spatial back-propagation neural network (S-BPNN) model designed specifically to make such correlations implicit by incorporating a spatial lag variable (SLV) as a virtual input variable. The S-BPNN fits the nonlinear relationship between ground-based air quality monitoring station measurements of PM2.5, satellite observations of aerosol optical depth, meteorological synoptic conditions data and emissions data that include auxiliary geographical parameters such as land use, normalized difference vegetation index, elevation, and population density. We trained and validated the S-BPNN for both yearly and seasonal mean PM2.5 concentrations. In addition, principal components analysis was employed to reduce the dimensionality of the data and a grid of neural network models was run to optimize the model design. The S-BPNN was cross-validated against an analogous but SLV-free BPNN model using the coefficient of determination (R2) and root mean squared error (RMSE) as statistical measures of goodness of fit. The inclusion of the SLV led to demonstrably superior performance of the S-BPNN over the BPNN with R2 values increasing from 0.80 to 0.89 and with the RMSE decreasing from 8.1 to 5.8 μg/m3. The yearly mean PM2.5 concentration in China during the study period was found to be 41.8 μg/m3 and the model estimated spatial distribution was found to exceed Level 2 of the China Ambient Air Quality Standards (CAAQS) enacted in 2012 (>35 μg/m3) in more than 70% of the Chinese territory. The inclusion of spatial correlation upgrades the performance of conventional BPNN models and provides a more accurate estimation of PM2.5 concentrations for air quality monitoring

Applying Trait-Based Modeling to Achieve Functional Targets during the Ecological Restoration of an Arid Mine Area

(1) Background: Because ecosystem degradation has become a global phenomenon which seriously affects the health of natural ecosystems and human well-being, restoration of degraded ecosystems has attracted increasing attention. However, many of the methods used in current ecological restoration work have rarely combined ecological restoration practices with the quantitative goal of restoring ecosystem function. (2) Methods: In this study, based on the conceptual framework of response-effect traits and Community Assembly by Trait Selection model (CATS model), a restoration strategy for a degraded abandoned mine in Wuhai City, China has been provided. This restoration strategy connected the ecosystem function targets to the appropriate recovery species and their required abundances. (3) Results: The results showed that a relative abundance ratio of 8:2 for S. grandis to B. dasyphylla was best for a shady slope, while a 6:4 ratio of K. tragus to B. dasyphylla was best for repair on a sunny slope of the degraded mine area. (4) Conclusion: This study provides a typical example of applying ecological theory in practice that will be useful for current and future studies and applications. This approach will ensure that governance efforts to restore degraded ecosystems are effective and efficient

Pore Structure Evolution and Its Effect on Strength Development of Sulfate-Containing Cemented Paste Backfill

In this study, the effects of the initial sulfate content on the properties of cemented paste backfill (CPB) made from coarse tailings has been investigated via mercury intrusion porosimetry. The combined effects of the sulfate content and curing time on the total porosity, pore size distribution, and unconfined compressive strength of the produced material were discussed. It was found that the specimens with an initial sulfate content of 5000 and 35,000 ppm exhibited higher unconfined compressive strength, while the resulting fine porous structures characterized by pore radii of 10–400 and 1–10 μm significantly improved the mechanical properties of the CPB. In addition, an increase in the curing time decreased the overall pore volume in the radius range of 1–400 μm but increased the pore volume at pore radii less than 1 μm

Incorporating Effect Factors into the Relationship between Biodiversity and Ecosystem Functioning (BEF)

Generally, the high levels of biodiversity found in natural ecosystems have positive effects on ecosystem functions (EFs), though the intensity and direction of such effects can vary. This is associated with the impacts of other EF-driving factors. In this study, the factors that affect biodiversity-ecosystem functioning (BEF) are reviewed and summarized, and current gaps in the research on the effects of these factors on BEF are discussed. Moreover, a new conceptual model, the generating-presentation model, accounting for links between effect factors and EFs, is built to provide a systematic means of understanding how different factors affect BEF. The model shows that the correlation between biodiversity and EFs can be described as involving a cascade process, while the separation of biodiversity and EFs from ecosystems without considering integrated features is not appropriate for BEF-related research. The generating-presentation model can comprehensively reflect the effects of different factors on EFs and thus has major theoretical and applied implications

Facile Synthesis of Anatase–Rutile Diphase N-doped TiO2 Nanoparticles with Excellent Visible Light Photocatalytic Activity

To further boost the charge separation and photocatalytic activity of TiO2 under visible light, the anatase–rutile mixed-phase nitrogen-doped (N-doped) TiO2 nanoparticles were successfully synthesized through a facile one-step calcining procedure using TiN as raw materials. The crystal phases, morphologies, chemical compositions, textural structures, and optical properties of as-obtained N-doped TiO2 were characterized by the corresponding analytical techniques. The photocatalytic activities of as-fabricated samples were evaluated by degrading 4-chlorophenol (4-CP) and methylene blue (MB) aqueous solution under visible light irradiation. The results revealed that the ratio of rutile to anatase increased with the improvement of sintering temperature, and the sample prepared at 500 °C had the best photocatalytic activity. This might be because it possessed the most appropriate rutile/anatase proportion as well as the nitrogen doping. The transient photocurrent responses, photoluminescence spectra (PL) measurements, and active species trapping experiments were implemented to disclose the photocatalytic mechanism. This work will provide a further insight into the synthesis of highly efficient N-doped TiO2 photocatalysts for organic contaminant removal

An Improved Method and the Theoretical Equations for River Regulation Lines

The regulation of wandering rivers is a universal problem that attracts significant attention. To effectively control the dynamic state of river course, it is necessary to adjust and construct river training works, which can be regarded as inseparable parts of the planning of river regulation lines. In this study, by comprehensively analyzing the water and sediment discharge in the wandering river of the Yellow River over the period 1952–2020, the large change in water and sediment conditions will inevitably affect the change in river regimes. By analyzing the river regime evolution process from 1990 to 2020 and calculating the river change index, it is found that the wandering channel of the Yellow River has gradually been stabilized, and there is no longer a large channel change, but a small amplitude swing still occurs frequently since 2010. Therefore, these phenomena highlight an urgent need for improving the planning of river regulation lines. According to the properties of parabola, circular arcs, elliptical arcs and curvature arcs, these curves are used to describe the flow path of the river. The theoretical equations of river regulation line with four curve forms are developed based on the latest river regimes and the location of the existing training works as the basis. Four groups of theoretical equations were verified by selecting typical river bends or reaches. The innovative practices from this study may assist in providing technical references, which control the frequent changes that occurred in river regime, as well as guaranteeing the healthy and sustainable development of rivers

PM-profiler: a high-resolution and fast tool for taxonomy annotation of amplicon-based microbiome

ABSTRACT Amplicon sequencing stands as a cornerstone in microbiome profiling, yet concerns persist regarding its resolution and accuracy. The enhancement of reference databases and annotations marks a new era for 16S rRNA-based profiling. Capitalizing on this potential, we introduce PM-profiler, a novel tool for profiling amplicon short reads. PM-profiler is implemented by C++-based advanced algorithms, such as pre-allocated hash for reference construction, hybrid and dynamic short-read matching, big-data-guided dual-mode hierarchical taxonomy annotation strategy, and full-procedure parallel computing. This tool delivers species-level resolution and ultrafast speed for large-scale microbiomes, surpassing alignment-based approaches and the Naïve-Bayesian model. Furthermore, recognizing the global uneven distribution of microbes, we delineate optimal annotation strategies for each sampling habitat based on microbial patterns over 270,000 microbiomes. Integrated with the established workflow of Parallel-Meta Suite and the latest curated reference databases, this endeavor offers a swift and dependable solution for high-precision microbiome surveys.IMPORTANCEOur study introduces PM-profiler, a new tool that deciphers the complexity of microbial communities. With advanced algorithms, flexible annotation strategies, and well-organized big-data, PM-profiler provides a faster and more accurate way to study on microbiomes, paving the way for discoveries that could improve our understanding of microbiomes and their impact on the world

Research on Utilizable Calcium from Calcium Carbide Slag with Different Extractors and Its Effect on CO₂ Mineralization

With the increasing accumulation of alkaline industrial solid waste, the mineralization of CO2 using alkaline industrial solid waste has broad application prospects. Carbide slag is highly alkaline and contains a large amount of calcium elements, making it an excellent material for CO2 mineralization. Our idea was to acquire qualified products and fast kinetics by integrating carbide slag utilization and carbon reduction. The reaction route was divided into two steps: calcium extraction and carbonization. In order to achieve efficient extraction of utilizable calcium, we selected NH4Ac as the extraction agent, which has the advantage of buffer protection and environmental friendliness due to being an acetate radical. The extraction efficiency of utilizable calcium exceeded 90% under the conditions of L/S 20:1 and NH4+/Ca2+ 2:1. In the carbonization process, the crystal forms of CaCO3 synthesized by direct carbonation, acid extraction, and ammonium salt were characterized. The formation mechanism of vaterite in ammonium solution and the influence of impurities (Al3+, Mg2+) on the crystal transformation were revealed. This study provides technical support for using alkaline industrial waste to prepare high-purity vaterite. Therefore, alkaline industrial waste can be efficiently and sustainably utilized through CO2 mineralization

Synthesis and thermodynamic properties of arsenate and sulfate-arsenate ettringite structure phases

<div><p>Arsenic is a toxic and carcinogenic contaminant of potential concern. Ettringite [Ca₆Al₂(SO₄)₃(OH)₁₂·26H₂O] has the ability to incorporate oxyanions as a solid solution with SO₄²⁻, which could lower the soluble oxyanion concentrations. Therefore, ettringite containing SO₄²⁻ and AsO₄³⁻ has been synthesized. Results indicated that AsO₄³⁻ could substitute for SO₄²⁻ inside the channels of ettringite in the form of HAsO₄²⁻, and a linear correlation existed between <i>X</i>_{initial solution} and <i>X</i>_solid. The thermodynamic characterization of the solid samples was investigated by means of Visual MINTEQ, a freeware chemical equilibrium model, and the solubility product log<i>K</i> of -48.4 ± 0.4 was calculated for HAsO₄–ettringite at 25°C. The Lippmann phase diagram and <i>X</i>_HAsO4–<i>X</i>_HAsO4,aq plot showed that the solid solution series containing arsenate has HAsO₄-poor aqueous solutions in equilibrium. These findings can be helpful to arsenate solidification and arsenate leaching modeling projects.</p></div

Combustion Characteristics and Slagging during Co-Combustion of Rice Husk and Sewage Sludge Blends

In this work, the thermal behavior of rice husk, sewage sludge, and their blends during combustion processes was investigated by means of thermogravimetric analysis (TGA), and the slagging characteristics were studied through X-ray fluorescence (XRF) and melting temperature. The effects of the proportion of rice husk and sewage sludge blends on the combustion process, ignition and burnout characteristics were also studied. The blends had rice husk percentages of 30, 50, 70 and 100%. The results indicate that there are four main stages of the material burning processes: dehydration, volatile oxidation, and decomposition/oxidation. The reactivity of the blends improved with increasing amounts of rice husk and the results suggest synergistic interactions between rice husk and sewage sludge during the co-combustion process. All co-combustion ashes showed a lower slagging potential owing to their high amorphous SiO2 content. On the basis of combustion properties and slagging characteristics of ash, the ratio of sewage sludge in the blends should not exceed 30%