A comparison of PM exposure related to emission hotspots in a hot and humid urban environment: Concentrations, compositions, respiratory deposition, and potential health risks

Particle number concentration, particle size distribution, and size-dependent chemical compositions were measured at a bus stop, alongside a high way, and at an industrial site in a tropical city. It was found that the industry case had 4.93 × 107–7.23 × 107 and 3.44 × 104–3.69 × 104 #/m3 higher concentration of particles than the bus stop and highway cases in the range of 0.25–0.65 μm and 2.5–32 μm, respectively, while the highway case had 6.01 × 105 and 1.86 × 103 #/m3 higher concentration of particles than the bus stop case in the range of 0.5–1.0 μm and 5.0–32 μm, respectively. Al, Fe, Na, and Zn were the most abundant particulate inorganic elements for the traffic-related cases, while Zn, Mn, Fe, and Pb were abundant for the industry case. Existing respiratory deposition models were employed to analyze particle and element deposition distributions in the human respiratory system with respect to some potential exposure scenarios related to bus stop, highway, and industry, respectively. It was shown that particles of 0–0.25 μm and 2.5–10.0 μm accounted for around 74%, 74%, and 70% of the particles penetrating into the lung region, respectively. The respiratory deposition rates of Cr and Ni were 170 and 220 ng/day, and 55 and 140 ng/day for the highway and industry scenarios, respectively. Health risk assessment was conducted following the US EPA supplemented guidance to estimate the risk of inhalation exposure to the selected elements (i.e. Cr, Mn, Ni, Pb, Se, and Zn) for the three scenarios. It was suggested that Cr poses a potential carcinogenic risk with the excess lifetime cancer risk (ELCR) of 2.1–98 × 10− 5 for the scenarios. Mn poses a potential non-carcinogenic risk in the industry scenario with the hazard quotient (HQ) of 0.98. Both Ni and Mn may pose potential non-carcinogenic risk for people who are involved with all the three exposure scenarios

Biomass gasification for syngas and biochar co-production: Energy application and economic evaluation

Syngas and biochar are two main products from biomass gasification. To facilitate the optimization of the energy efficiency and economic viability of gasification systems, a comprehensive fixed-bed gasification model has been developed to predict the product rate and quality of both biochar and syngas. A coupled transient representative particle and fix-bed model was developed to describe the entire fixed-bed in the flow direction of primary air. A three-region approach has been incorporated into the model, which divided the reactor into three regions in terms of different fluid velocity profiles, i.e. natural convection region, mixed convection region, and forced convection region, respectively. The model could provide accurate predictions against experimental data with a deviation generally smaller than 10%. The model is applicable for efficient analysis of fixed-bed biomass gasification under variable operating conditions, such as equivalence ratio, moisture content of feedstock, and air inlet location. The optimal equivalence ratio was found to be 0.25 for maximizing the economic benefits of the gasification process

Particulate emission from the gasification and pyrolysis of biomass: concentration, size distributions, respiratory deposition-based control measure evaluation

Gasification and pyrolysis technologies have been widely employed to produce fuels and chemicals from solid wastes. Rare studies have been conducted to compare the particulate emissions from gasification and pyrolysis, and relevant inhalation exposure assessment is still lacking. In this work, we characterized the particles emitted from the gasification and pyrolysis experiments under different temperatures (500, 600, and 700 °C). The collection efficiencies of existing cyclones were compared based on particle respiratory deposition. Sensitivity analysis was conducted to identify the most effective design parameters. The particles emitted from both gasification and pyrolysis process are mainly in the size range 0.25–1.0 μm and 1.0–2.5 μm. Particle respiratory deposition modelling showed that most particles penetrate deeply into the last stage of the respiratory system. At the nasal breathing mode, particles with sizes ranging from 0.25 to 1.0 μm account for around 91%, 74%, 76%, 90%, 84%, and 79% of the total number of particles that deposit onto the last stage in the cases of 500 °C gasification, 600 °C gasification, 700 °C gasification, 500 °C pyrolysis, 600 °C pyrolysis, and 700 °C pyrolysis, respectively. At the oral breathing mode, particles with sizes ranging from 0.25 to 1.0 μm account for around 92%, 77%, 79%, 91%, 86%, and 81% of the total number of particles that deposit onto the last stage in the six cases, respectively. Sensitivity analysis showed that the particle removal efficiency was found to be most sensitive to the cyclone vortex finder diameter (D0). This work could potentially serve as the basis for proposing health protective measures against the particulate pollution from gasification and pyrolysis technologies

Optimal design of negative emission hybrid renewable energy systems with biochar production

To tackle the increasing global energy demand the climate change problem, the integration of renewable energy and negative emission technologies is a promising solution. In this work, a novel concept called “negative emission hybrid renewable energy system” is proposed for the first time. It is a hybrid solar-wind-biomass renewable energy system with biochar production, which could potentially provide energy generation, carbon sequestration, and waste treatment services within one system. The optimization and the conflicting economic and environmental trade-off of such system has not yet been fully investigated in the literature. To fill the research gap, this paper aims to propose a stochastic multi-objective decision-support framework to identify optimal design of the energy mix and discuss the economic and environmental feasibilities of a negative emission hybrid renewable energy system. This approach maximizes energy output and minimizes greenhouse gas emissions by the optimal sizing of the solar, wind, combustion, gasification, pyrolysis, and energy storage components in the system. A case study on Carabao Island in the Philippines, which is representative of an island-mode energy system, is conducted based on the aim of achieving net-zero emission for the whole island. For the island with a population of 10,881 people and an area of 22.05 km2, the proposed optimal system have significant negative emission capability and promising profitability with a carbon sequestration potential of 2795 kg CO2-eq/day and a predicted daily profit of 455 US$/day

MonoHair: High-Fidelity Hair Modeling from a Monocular Video

Undoubtedly, high-fidelity 3D hair is crucial for achieving realism, artistic expression, and immersion in computer graphics. While existing 3D hair modeling methods have achieved impressive performance, the challenge of achieving high-quality hair reconstruction persists: they either require strict capture conditions, making practical applications difficult, or heavily rely on learned prior data, obscuring fine-grained details in images. To address these challenges, we propose MonoHair,a generic framework to achieve high-fidelity hair reconstruction from a monocular video, without specific requirements for environments. Our approach bifurcates the hair modeling process into two main stages: precise exterior reconstruction and interior structure inference. The exterior is meticulously crafted using our Patch-based Multi-View Optimization (PMVO). This method strategically collects and integrates hair information from multiple views, independent of prior data, to produce a high-fidelity exterior 3D line map. This map not only captures intricate details but also facilitates the inference of the hair's inner structure. For the interior, we employ a data-driven, multi-view 3D hair reconstruction method. This method utilizes 2D structural renderings derived from the reconstructed exterior, mirroring the synthetic 2D inputs used during training. This alignment effectively bridges the domain gap between our training data and real-world data, thereby enhancing the accuracy and reliability of our interior structure inference. Lastly, we generate a strand model and resolve the directional ambiguity by our hair growth algorithm. Our experiments demonstrate that our method exhibits robustness across diverse hairstyles and achieves state-of-the-art performance. For more results, please refer to our project page https://keyuwu-cs.github.io/MonoHair/.Comment: Accepted by IEEE CVPR 202

Process optimization of biomass gasification with a Monte Carlo approach and random forest algorithm

Gasification technologies have been extensively studied for their potential to convert biomass feedstocks into syngas (a mixture of CH4, H2, and CO mainly) that can be further turned into heat or electricity upon combustion. It is crucial to understand optimal gasification process parameters for practical design and operation for maximizing the potential. This study combined the Monte Carlo simulation approach, gasification kinetic modeling, and the random forest algorithm to predict the optimal gasification process parameters (i.e. water content, particle size, porosity, thermal conductivity, emissivity, shape, and reaction temperature) towards a maximum syngas yield. The Monte Carlo approach randomly generated a data pool of the process parameters following either a normal or uniform distribution, which was then fed into a validated kinetic model to create 2,000 datasets (process parameters and syngas yields). For the random forest model, the mean decrease accuracy and mean decrease Gini were used to assess the importance of the process parameters on syngas yields. The accuracy of the optimization method was evaluated using the coefficient of determination (R2), the root means square error (RMSE), and the mean absolute error (MAE). Generally, the predictions for the normal distribution case were closer to the experimental data obtained from existing literature than that for the uniform distribution case. The model was used to predict the optimal syngas yield and process parameters of wood gasification and it was shown that the predictions were generally in good agreement (<12% difference for the case of normal distribution) with existing experimental results. The method serves as a useful tool for determining optimal gasification process parameters for process and operation design

Resibufogenin Targets the ATP1A1 Signaling Cascade to Induce G2/M Phase Arrest and Inhibit Invasion in Glioma

Resibufogenin (RB) is a major active ingredient in the traditional Chinese medicine Chansu and has garnered considerable attention for its efficacy in the treatment of cancer. However, the anticancer effects and underlying mechanisms of RB on glioblastoma (GBM) remain unknown. Here, we found that RB induced G2/M phase arrest and inhibited invasion in a primary GBM cell line, P3#GBM, and two GBM cell lines, U251 and A172. Subsequently, we demonstrated that RB-induced G2/M phase arrest occurred through downregulation of CDC25C and upregulation of p21, which was caused by activation of the MAPK/ERK pathway, and that RB inhibited GBM invasion by elevating intercellular Ca2+ to suppress the Src/FAK/Paxillin focal adhesion pathway. Intriguingly, we confirmed that upon RB binding to ATP1A1, Na+-K+-ATPase was activated as a receptor and then triggered the intracellular MAPK/ERK pathway and Ca2+-mediated Src/FAK/Paxillin focal adhesion pathway, which led to G2/M phase arrest and inhibited the invasion of GBM cells. Taken together, our findings reveal the antitumor mechanism of RB by targeting the ATP1A1 signaling cascade and two key signaling pathways and highlight the potential of RB as a new class of promising anticancer agents.publishedVersio