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.
To evaluate pore structures of the Bakken Shale, which is one of the most important factors that affect petrophysical properties, high-pressure mercury intrusion was employed in this study. Pore structures such as pore-throat size, pore-throat ratio, and fractal attributes are investigated in this major shale play. Pore-throat size from 3.6 to 200 um is widely distributed in these shale samples. Accordingly, pore-throat size distributions demonstrate the multimodal behavior within the samples. The whole pore-throat network can be divided into four clusters: one set of large pores, two transitional/intermediate pore groups, and one set of smaller pores. The fractal analysis revealed that fractal dimensions decrease as the pore-throat size decreases. The multifractal analysis demonstrated that as the maturity of the shale samples increases, pore-throat size distributions would become more uniform and pore structures tend to become more homogeneous. The results are compared to our previous results obtained from nitrogen gas adsorption for further verifications of fractal behavior. Finally, although fractal analysis of mercury intrusion and nitrogen gas adsorption were comparable, the results of multifractal analysis from these two methods were not identical.
The natural draft dry cooling tower (NDDCT) has been increasingly used in power generation for its merits of excellent water-saving, high energy saving, simple maintenance and long life service. To study the performance of a newly installed 660 MW NDDCT under crosswind condition, a model with scale of 1:200 was built according to the scaling law of geometric similarity. The experiments were set up in self-similar region with high Reynolds to meet momentum similarity, while meeting the scaling law of Froude and Euler numbers. A first order law radiator resistance model is also proposed and verified by a systematic test. The exponent law profile of wind velocity above the ground was built and verified by experimental data. On the ground of a constant heating rate bases, the flow field inside the NDDCT and the ventilation rate were investigated at the crosswind range of 0–20 m/s.