Uncertainty analysis of greenhouse gas emissions of monorail transit during the construction.

This paper examines the uncertainty of greenhouse gas (GHG) emissions during monorail construction. Firstly, a deterministic analysis is conducted. Subsequently, the obtained data are evaluated using the data quality indicator (DQI), and a Markov chain Monte Carlo (MCMC) simulation method is employed to assume different parameter distributions. The results of the deterministic calculation indicate that the calculated emissions per unit area of the station amount to 1.97 ton CO2e/m2, while the calculated emissions per unit section length reach 7.55 ton CO2e/m2. To simulate parameter distribution, we utilize a Beta distribution with good shape applicability. Furthermore, we establish scenarios involving system boundary reduction, low-emission factors, and reduced material and energy inputs in order to analyze scenario uncertainties. Regarding model uncertainty, this paper assumes that the material and energy quantity data conform to the normal, log-normal, uniform, and triangular distributions, respectively, subsequently analyzing the uncertainty distributions. This paper analyzes the GHG emission uncertainty evaluation of 16 monorail stations and sections during the construction period, which is divided into parameter, scenario, and model uncertainty. We provide a concrete framework for studying uncertainties related to GHG emissions at stations and sections during the monorail construction period. The scenario analysis results will help to make decisions about the choice of parameters, system boundaries, and other settings. It provides new guidance for emission reduction policies, such as reducing the use of steel-related products or using alternative environmentally friendly materials, considering emission reduction factors more comprehensively and setting emission reduction factors according to uniform distribution principle as far as possible.

Predicting and assessing greenhouse gas emissions during the construction of monorail systems using artificial intelligence.

Based on partial data, this paper uses BP neural network optimised by particle swarm optimisation algorithm to predict the total greenhouse gas (GHG) emissions of the line in the construction phase. The GHG emission efficiency is analysed by SBM (Slacks-Based Measure) super efficiency method. Finally, the grey relational analysis (GRA) is applied to sort the GHG emission correlation factors. Based on the existing design and quota document data of 16 stations and 16 sections of the Wuhu Monorail Line 1, we have employed a neural network optimized by particle swarm optimization algorithm to predict the total emissions of greenhouse gases during the construction phase of the entire line consisting of 25 stations and 24 sections. The GHG emissions of all stations and sections are 29,300 tons and 21,000 tons. The technical efficiency, pure technical efficiency, and scale efficiency of the stations and sections were high. As for stations, the order of influence degree is metal material consumption (0.9731) > cost (0.9486) > electric energy consumption (0.9481) > station area (0.9109) > concrete and cement consumption (0.9032) > other material consumption (0.8831) > gasoline and diesel consumption (0.7258). For the section, the order of influence degree is cost (0.9766) > concrete (0.9581) > steel reinforcement (0.9483) > other steels (0.874) > section length (0.8337) > power energy consumption (0.7169) > wood consumption (0.6684).

Promoted elimination of metronidazole in ferrous ions activated peroxydisulfate process by gallic acid complexation.

We applied gallic acid (GA) as the complexing agent to stabilizing the regeneration of Fe2+ during the Fe2+/peroxydisulfate (PDS) Fenton-like reaction for promoting the removal of metronidazole (MTZ). This research evaluated the elimination of MTZ by optimizing the dose of GA and Fe2+ and pH condition. MTZ removal reached 83% at the GA: Fe2+ molar ratio of 1:1 (30 µM) and initial pH 5 and 6.2 after 120 min, and the kinetics showed two degradation phases (kobs1 = 0.09636 for the rapid stage and kobs2 = 0.01056 for the slow stage). The Fe2+ and GA complexes could expand the range of pH applicability and effectively stabilize the regeneration of Fe2+, which ultimately promoted the decontamination of MTZ. Sulfate radical (SO4.-), hydroxyl radicals, and singlet oxygen were proved to exist in this ternary system and contribute to MTZ removal, and SO4.- played the dominant role. Furthermore, the possible pathways and mechanisms for MTZ degradation were proposed, and the simulation result indicated that the toxicity of degradation intermediates of MTZ were declined. The GA assisted Fe2+/PDS system provided an improved promising advanced oxidation process for organic wastewater disposal.

Quantifying Carbon Emissions Generated by Monorail Transits: A Life Cycle Assessment Approach.

The use of rail transits results in the generation of a large amount of carbon emissions. Throughout the life cycle of a rail transit system, huge amounts of carbon are emitted, which contributes to the threat posed by carbon emission on the city ecosystem. Despite the many methods previously proposed to quantify carbon emissions from rail transit systems, a method that can be applied to measure carbon emissions of monorail systems is yet to be developed. We have used the life cycle assessment (LCA) method to propose a method that can be used to quantify carbon emissions from monorail transits. The life cycle of a monorail transit system was divided into four stages (production, construction, use, and end-of-life). A monorail transit line segment in Chongqing, China, was selected for a case study. The results show that the "use" stage of the monorail transit line system significantly increases (93.2%) carbon emissions, while the "end-of-life" stage does not contribute significantly to the total carbon emitted. The processes of generation of steal, concrete, and cement are the three leading processes that contribute to the emission of carbon dioxide. The percentages of carbon emitted during these processes are 32%, 29.6%, and 13.3%, respectively. Prestressed concrete activity accounts for the largest proportion (91.1%) of the total carbon emissions. The results presented herein can potentially help in realizing sustainable development and developing green transportation.

Influence of Meso-Scale Pore Structure on Mechanical Behavior of Concrete under Uniaxial Compression Based on Parametric Modeling.

Existing concrete random aggregate modeling methods (CRAMMs) have deficiencies in in the parameterization of the mesoscale pore structure. A novel CRAMM is proposed, whose pore structure is determined by the pore gradation, total porosity, sub-porosity, and pore size of each pore gradation segment. To study the influence of pore structure on the mechanical properties of concrete, 25 mesoscopic concrete specimens with the same aggregate structure but different meso-scale pore structures are constructed and subjected to uniaxial compression tests. For the first time, the influence of sub-porosity of each pore gradation segment, average pore radius (APR), pore specific surface area (PSSA), and total porosity on concrete failure process, compressive strength, peak strain, and elastic modulus were quantitatively and qualitatively analyzed. Results indicate that the pore structure makes the germination and propagation of the damage in cement mortar show obvious locality and affects the formation and expansion of macroscopic cracks. However, it does not accelerate the propagation of the damage in cement mortar from the periphery to the center of the specimen, nor does it change the phenomenon that the ITZ is more damaged than other meso-components of concrete before peak stress. Macroscopic cracks occur in the descending section of the stress−strain curve, and the sudden drops in the descending section of the stress−strain curve are often accompanied by the generation and expansion of macroscopic cracks. The quadratic polynomial, exponential, and power functions can well fit the relationship between total porosity and compressive strength and the relationship between PSSA and compressive strength. The linear, exponential, and power functions can well reflect the relationship between total porosity and compressive modulus and the relationship between compressive modulus and PSSA. For concrete specimens with the same total porosity, the elastic modulus and strength show randomness with the increase in the sub-porosity of macropores and are basically not affected by the APR. Based on the grey relational analysis, the effects of pore structure parameters on the elastic modulus and compressive strength are in the same order: total porosity > T [k1,k2] > T [k2,k3] > T [k3,k4] > T [k4,k5] > AVR > PSSA. The order of influence of the pore structure parameters on the peak strain is: T [k2,k3] > T [k1,k2] > T [k3,k4] > T [k4,k5] > APR > PSSA > total porosity.