ZrO2 modified BiOCl0.5I0.5 composites (ZBCI), synthesized via a facile precipitation method at room temperature, were utilized to photocatalytically oxidize and adsorb arsenite from water under visible light irradiation. The composites were well characterized by using various techniques. With visible light irradiation, 5 mg L−1 of As(III) could be completely removed by ZBCI (0.25 g L−1) in 90 min. Particularly, we found that ZBCI composites not only could oxidize As(III) into As(V) with visible light irradiation, but also could effectively capture the generated As(V), leading to the negligible residual As(III) or As(V) in aqueous solutions after 90 min treatment. In the fabricated composites, ZrO2 acted as the main adsorption sites while BiOCl0.5I0.5 served as the primary photocatalysis center. Because of the heterostructure of ZBCI, e- generated by BiOCl0.5I0.5 would be transferred to ZrO2 and inhibited e–h+ recombination rate, contributing to the improved photocatalytic efficiency. ZBCI could effectively remove As(III) over a broad range of pH from 3 to 11. Chloride and nitrate did not obviously affect the photocatalytic As(III) removal, while sulfate and phosphate yet reduced the capture of As(III). Moreover, ZBCI composites exhibited high photocatalytic As(III) removal efficiency during the fourth reused cycles. The facile synthesized ZBCI could be employed to capture and oxidize As(III) from water.
Increasing urbanization in the world brings tremendous social, economic and environmental challenges. It is essential to fully analyze urban GHG emissions metabolism systems to reveal economic emissions reduction pathways and support sustainable development. In this study, a factorial-based ecologically-extended input-output (FEEIO) model is developed to facilitate urban GHG emissions metabolism analysis. A special case study of the Province in Saskatchewan, Canada, is conducted to illustrate the potential benefits of its use in urban metabolism system health diagnosis. A factorial analysis is introduced to further investigate the effects of the main factors and their interactions. It is found that an urban GHG emissions metabolism system differs from other metabolism systems in regards to its special structure. A high efficiency represents limited emissions pathways in an urban GHG emissions metabolism system, which further provides good opportunities to realize GHG emissions mitigation. In the Province of Saskatchewan, the urban GHG emissions system has high redundancy and low efficiency across twenty scenarios. The GHG emissions from other sources are much simpler than emissions from coal, which further indicates that the emissions from other sources are easier to control through technology improvements or industrial regulations for specific sectors.
Emojis have become more and more popular in text-based online communication to express emotions. This indicates a potential to utilize emojis in sentiment analysis and emotion measurements. However, many factors could affect people’s emoji usage and need to be examined. Among them, age, gender, and relationship types may result in different interpretations of the same emoji due to the ambiguity of the iconic expression. In this paper, we aim to explore how these factors may affect the frequency, type, and sentiment of people’s emoji usage in communications. After analyzing 6,821 Wechat chatting messages from 158 participants, we found people between 26–35 had lowest frequency of emoji usage; younger and elder groups showed different sentiment levels for the same emojis; people chose emoji types based on relationships. These findings shed light on how people use emojis as a communication tool.
To increase the temporal resolution and maximal imaging time of super-resolution (SR) microscopy, we have developed a deconvolution algorithm for structured illumination microscopy based on Hessian matrixes (Hessian-SIM). It uses the continuity of biological structures in multiple dimensions as a priori knowledge to guide image reconstruction and attains artifact-minimized SR images with less than 10% of the photon dose used by conventional SIM while substantially outperforming current algorithms at low signal intensities. Hessian-SIM enables rapid imaging of moving vesicles or loops in the endoplasmic reticulum without motion artifacts and with a spatiotemporal resolution of 88 nm and 188 Hz. Its high sensitivity allows the use of sub-millisecond excitation pulses followed by dark recovery times to reduce photobleaching of fluorescent proteins, enabling hour-long time-lapse SR imaging of actin filaments in live cells. Finally, we observed the structural dynamics of mitochondrial cristae and structures that, to our knowledge, have not been observed previously, such as enlarged fusion pores during vesicle exocytosis.
Particulate nitrate (pNO(3)(-)) is an important component of secondary aerosols in urban areas. Therefore, it is critical to explore its formation mechanism to assist with the planning of haze abatement strategies. Here we report vertical measurements of NOx and O-3 by in situ instruments on a movable carriage on a tower during a winter heavy-haze episode (18 to 20 December 2016) in urban Beijing, China. Based on the box model simulation at different heights, we found that pNO(3)(-) formation via N2O5 heterogeneous uptake was negligible at ground level due to N2O5 concentrations of near zero controlled by high NO emissions and NO concentration. In contrast, the contribution from N2O5 uptake was large at high altitudes (e.g., > 150 m), which was supported by the lower total oxidant (NO2 + O-3) level at high altitudes than at ground level. Modeling results show the specific case that the nighttime integrated production of pNO(3)(-) for the high-altitude air mass above urban Beijing was estimated to be 50 mu gm(-3) and enhanced the surface-layer pNO(3)(-) the next morning by 28 mu gm(-3) through vertical mixing. Sensitivity tests suggested that the nocturnal NOx loss by NO3-N2O5 chemistry was maximized once the N2O5 uptake coefficient was over 2 x 10(-3) on polluted days with S-a at 3000 mu m(2) cm(-3) in wintertime. The case study provided a chance to highlight the fact that pNO(3)(-) formation via N2O5 heterogeneous hydrolysis may be an important source of particulate nitrate in the urban airshed during wintertime.
Particulate nitrate (pNO3-) is an important component of secondary aerosols in urban areas. Therefore, it is critical to explore its formation mechanism to assist with the planning of haze abatement strategies. Here we report vertical measurement of NOx and O3 by in-situ instruments on a movable carriage on a tower during a winter heavy-haze episode (December 18 to 20, 2016) in urban Beijing, China. Based on the box model simulation at different height, we found that pNO3- formation via N2O5 heterogeneous uptake was negligible at ground level due to N2O5 concentration of near zero controlling by high NO emission and NO concentration. In contrast, the contribution from N2O5 uptake was large at high altitudes (e.g., > 150 m), which was supported by the low total oxidant (NO2 + O3) level at high altitudes than that at ground level. Modeling results show the specific case that the nighttime integrated production of pNO3- for the high-altitude air mass above urban Beijing was estimated to be 50 μg m-3 and enhanced the surface-layer pNO3- the next morning by 28 μg m-3 through vertical mixing. Sensitivity tests suggested that the nocturnal NOx loss by NO3-N2O5 chemistry was maximized once the N2O5 uptake coefficient was over 2×10-3 on polluted days with Sa was 3000 μm2 cm-3 in wintertime. The case study provided a chance to highlight that pNO3- formation via N2O5 heterogeneous hydrolysis may be an important source of the particulate nitrate in the urban airshed during wintertime.
Understanding the underlying dynamics of building energy consumption is the very first step towards energy saving in building sector; as a powerful tool for knowledge discovery, data mining is being applied to this domain more and more frequently. However, most of previous researchers focus on model development during the pipeline of data mining, with feature engineering simply being overlooked. To fill this gap, three different feature engineering approaches, namely exploratory data analysis (EDA) as a feature visualization method, random forest (RF) as a feature selection method and principal component analysis (PCA) as a feature extraction method, are investigated in the paper. These feature engineering methods are tested with a building energy consumption dataset with 124 features, which describe the building physics, weather condition, and occupant behavior. The 124 features are analyzed and ranked in this paper. It is found that although feature importance depends on specific machine learning model, yet certain features will always dominate the feature space. The outcome of this study favors the usage of effective yet computationally cheap feature engineering methods such as EDA; for other building energy data mining problems, the method proposed in this study still holds important implications since it provides a starting point where efficient feature engineering and machine learning models could be further developed.
Phosphate is commonly added to drinking water to inhibit lead release from lead service lines and lead-containing materials in premise plumbing. Phosphate addition promotes the formation of lead phosphate particles, and their aggregation behaviors may affect their transport in pipes. Here, lead phosphate formation and aggregation were studied under varied aqueous conditions typical of water supply systems. Under high aqueous PO4/Pb molar ratios (>1), phosphate adsorption made the particles more negatively charged. Therefore, enhanced stability of lead phosphate particles was observed, suggesting that although addition of excess phosphate can lower the dissolved lead concentrations in tap water, it may increase concentrations of particulate lead. Adsorption of divalent cations (Ca2+ and Mg2+) onto lead phosphate particles neutralized their negative surface charges and promoted their aggregation at pH 7, indicating that phosphate addition for lead immobilization may be more efficient in harder waters. The presence of natural organic matter (NOM, ≥ 0.05 mg C/L humic acid and ≥ 0.5 mg C/L fulvic acid) retarded particle aggregation at pH 7. Consequently, removal of organic carbon during water treatment to lower the formation of disinfection-byproducts (DBPs) may have the additional benefit of minimizing the mobility of lead-containing particles. This study provided insight into fundamental mechanisms controlling lead phosphate aggregation. Such understanding is helpful to understand the observed trends of total lead in water after phosphate addition in both field and pilot-scale lead pipe studies. Also, it can help optimize lead immobilization by better controlling the water chemistry during phosphate addition.