Search

In this article, the side-entry agitator commonly used in the wet desulfurization absorption tower of a 300 MW thermal power unit was taken as the research object, and the gas–liquid two-phase flow field in the stirring tank was numerically simulated by using the two-fluid model based on Euler–Euler method. The vertical and horizontal velocity field distribution and gas phase distribution in the stirring tank were studied. The results showed that the side-entry agitator formed a “fountain” circulating flow in the stirring tank. The high-speed region of the liquid flow was mainly concentrated near the mixing blade and the bottom banded region. The range of large changes in the fluid velocity was mainly concentrated from the bottom of the mixing tank to 2–3 m from the bottom. The distribution of the oxidizing air was concentrated in the middle of the stirring tank. The mixing effect of two phases in the stirred tank was good, and the oxidizing air was fully diffused. The numerical study in this article has reference significance for in-depth understanding of the flow field and mixing effect of the side-entry agitator.

Changes in aerosol loadings and properties are important for the understanding of atmospheric environment and climate change. This study investigates the characteristics and long-term trends of aerosols of different sizes and types in Asia from 2000 to 2020 by considering multi-source aerosol data and novel analysis methods and perspectives, all of which provides the groundwork for promoting the acquisition of new discoveries that are different from the past. The geometric mean aggregation method is applied, and serial autocorrelation is considered to avoid the overestimation of trend significance. Among regions in Asia, high values of aerosol optical depth (AOD) are mainly concentrated in East Asia (EA) and South Asia (SA) and are closely related to the population density. The AOD in EA showed the most significant negative trend, with a value of -5.28×10-4 per year, mainly owing to decreases in organic carbon (OC), black carbon (BC), and dust aerosols. It is also worth noting that this observed large-scale decrease in OC and BC is a unique and significant phenomenon to region of EA and mainly around China. In contrast, the aerosol concentrations in SA generally show a positive trend, with an increased value of AOD of 1.25 × 10 -3 per year. This increase is mainly due to large emissions of fine-mode aerosols, such as OC and sulfate aerosol. Additionally, the high aerosol loading in northern SA has a lower AOD variability when compared with that of East China Plain, revealing a relatively more persistent air pollution situation. Over the whole region of Asia, the characteristics of the percentage changes in different types of AOD are seen in the increases in BC (6.23 %) and OC (17.09 %) AOD, together with a decrease in dust (-5.51 %), sulfate (-3.07 %), and sea salt (-9.80 %) AOD. Except for anthropogenic emissions, the large increase in the percentage of OC is also due to wildfires found in northern Asia in the summer, whereas the differently sized AOD only shows slight changes in Asia in that small-sized AOD decreases (-3.34 %) and that the total AOD did not show a significant change. This suggests that, from a trend perspective, decreases in aerosol in recent years have mostly been offsetting earlier increases in the anthropogenic emissions over Asia. To summarize, the above findings analyse the comprehensive characteristics of aerosol distributions and reappraise the long-term trends of different aerosol parameters, which will greatly enhance the understanding of regional and global aerosol environment and climatology, in addition to filling the gaps and breaking through the limitations of past knowledge. [ABSTRACT FROM AUTHOR]

Particle matter (PM) mass concentrations have an important influence on human and environmental health. Lidar plays an important role in the monitoring of PM concentrations. However, the accuracy of PM concentrations retrieved via lidar depends on the performance of the conversion model from the aerosol extinction coefficient (EC) to PM concentration. Therefore, surface PM concentrations, aerosol EC and five meteorological factors are used to build the conversion model that can also be applicable to lidar for retrieving PM concentrations. In this study, the traditional linear model (LM), random forest (RF) and artificial neural network (ANN) algorithms are used to estimate the mass concentrations of PM with aerodynamic diameters < 1 µm (PM1), 2.5 µm (PM2.5) and 10 µm (PM10). The influence of meteorological factors on the conversion model is analyzed. The results show that the meteorological parameters play a non-ignorable role in the model of PM retrieval based on EC, especially when retrieving PM10. Moreover, the performance of three models is investigated by comparing with the surface measurements. The results indicate that the RF and ANN models are more suitable to estimate PM than the LM model. The diurnal variations in mean relative error (MRE) from the three models are then analyzed. There is a diurnal pattern in MRE values, meaning that the maximum values occur in the afternoon and the minimum values occur at night. In addition, there are subtle differences in performance between two machine learning (ML) models. After analysis, it is found that for PM10, the RF method is superior to the ANN when the EC value is small, while the ANN method is superior to the RF when the EC value is relatively high, and the EC threshold is set to 0.6 km−1. For PM1 and PM2.5 estimation, the ANN is the most appropriate model. Finally, accurate diurnal variations in PM1 and PM2.5 based on the ANN model and PM10 based on the combined model of RF and ANN (named RA) are investigated. The results exhibit that the daily maximum values of PM1, PM2.5 and PM10 in the Wuhan area all occur at approximately 08:00–10:00 local time (LT), which is mainly due to the impact of commuter vehicle emissions and the impact of secondary photochemistry response aggravated by sufficient illumination and temperature rises after sunrise. These research results provide an important basis for particulate matter monitoring. [ABSTRACT FROM AUTHOR]