A pilot study was carried out to explore the application of carbon dioxide for pH depression in a bubble column and its ability to
inhibit bromate formation for water with a low alkalinity. Results showed that in the absence of ammonia, CO2 was capable of reducing
bromate 38.0%–65.4% with one-unit pH depression. CO2 caused a slightly lower bromate reduction (4.2%) than did H2SO4 when the
pH was depressed to 7.4, and a more a pronounced lower reduction (8.8%) when the pH was depressed to 6.9. In the presence of 0.20
mg/L-N ammonia, bromate was largely inhibited with 73.9% reduction. When the pH was depressed to 7.4, CO2 and H2SO4 showed an
11.3% and 23.5% bromate reduction respectively, demonstrating that the joint use of CO2 and ammonia might be a plausible strategy of
blocking all three bromate formation pathways. CO2 could be applied through the aeration diffuser together with ozone gas, resulting
in a similar bromate reduction compared with the premixing method through Venturi mixer. 相似文献
The increasing quantities of polluted waters are calling for advanced purification methods. Flocculation is an essential component of the water purification process, yet flocculation is commonly not optimal due to our poor understanding of the flocculation process. In particular, there is little knowledge on the mechanisms ruling the migration of pollutants during treatment. Here we have created the first tensor diagram, a mathematical framework for the flocculation process, analyzed its properties with a deep learning model, and developed a classification scheme for its relationship with pollutants. The tensor was constructed by combining pixel matrices from a variety of floc images, each with a particular flocculation period. Changing the factors used to make flocs images, such as coagulant dose and pH, resulted in tensors, which were used to generate matrices, that is the tensor diagram. Our deep learning algorithm employed a tensor diagram to identify pollution levels. Results show tensor map attributes with over 98% of sample images correctly classified. This approach offers potential to reduce the time delay of feedback from the flocculation process with deep learning categorization based on its clustering capabilities. The advantage of the tensor data from the flocculation process improves the efficiency and speed of response for commercial water treatment.
Antu County in the Changbai Mountains is an important source of mineral water, but there is a lack of research on the source of groundwater characteristic components, affecting the protection of water resources. This study obtained hydrochemical and isotopic data (28 groups in total, April and September in 2019) by summarizing research and sampling data in order to identify the formation process of characteristics. The formation mechanism of the characteristic components was revealed using geostatistical, isotopic, and hydrogeochemical inversion simulations. The results show that the metasilicic acid is a common component of groundwater water chemistry in the study area. The water body primarily receives stable recharge from low-mineralized precipitation with ages ranging from 27.7 to 38.4 years and recharge elevations ranging from 1160 to 2393 m, providing ample time for water–rock interaction. The dissolution of olivine, pyroxene, albite, and other siliceous minerals is the source of characteristic components, and deep faults and deep basalt heat flow are the key conditions for the formation of metasilicic acid. When low-mineralized precipitation recharges the underground aquifer, it dissolves the silica-aluminate and silicon-containing minerals in the surrounding rocks through the water–rock action under the effect of CO2, causing a large amount of metasilic acid to dissolve into the groundwater and forming metasilic acid-type mineral water.
