The Dynamic Detection of Water Quality Monitoring and Pollution Prevention and Control

With the development of society, the material living standard of our people has been significantly improved, but we sacrificed the environment in the course of development, which led to the current number of environmental problems in our country is particularly large, so that now we need to pick up the tone of protecting the environment, so now the overall tone of the country is to protect the environment, adhere to the green water green mountain is the basic strategy of Jinshan Yinshan, play a good pollution prevention and control of the three major battles, care for the environment, protect the environment. And in the environment water is the most important, it carries everything, the purpose of this paper is to study based on water quality monitoring and pollution prevention and control of dynamic detection technology. In order to conduct the experiment better, after consulting the literature on water quality monitoring and pollution prevention and control, and dynamic detection technology, we used a variety of algorithms to construct a corresponding dynamic detection technology system to monitor water quality and conduct real-time surveys of pollutants, and obtain relevant experimental data to complete the experiment. The experimental results show that the improved adaptive parameter DBSCAN clustering algorithm is better than the AdaBoost algorithm and the genetic algorithm, so we finally choose to build a dynamic detection technology system using the improved adaptive parameter DBSCAN clustering algorithm.

Air Quality Evaluation and Improvement of China’s Three Major Urban Agglomerations Based on the Modified MetaFrontier Dynamic Slack-Based Measures Model

Urban agglomeration has become a unique form of cities during the rapid development of emerging economies. With the increasing attention on global energy and environmental efficiency, air quality evaluation and pollution control have become important standards to measure the health and orderly development of such agglomerations. Based on panel data of 60 cities in the three major urban agglomerations of Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD), this study uses the Modified MetaFrontier Dynamic SBM model to evaluate their air quality over the 5-year period of 2013–2017. The results present that the development level of air pollution prevention and control in China’s three major urban agglomerations is relatively low, and YRD as the most developed area has the worst effect of air pollution prevention and control. The MetaFrontier and Group Frontier Efficiency analysis confirms the conclusion of the cluster analysis that a significant two-level differentiation exists in China’s three urban agglomerations. Moreover, China’s three major urban agglomerations are still in the stage of high energy consumption and high development. Lastly, we point out different recommendations for industrial structure and governance foci of the three major urban agglomerations. Dust prevention technology should be improved to reduce PM2.5 in BTH, desulfurization technology should be enhanced to cut industrial SO2 emissions in YRD, and better emission reduction targets and other targeted measures should be formulated in PRD.

Spatial and Seasonal Characteristics of Air Pollution Spillover in China

Air pollution spillover can cause air pollution to negatively affect neighboring regions. The structure of air pollution spillover varies with changes in season and space. Researching the spatial and seasonal characteristics of air pollution spillover is beneficial for determining air pollution prevention and control policies. First, this paper uses the GARCH-BEKK model to correlate the air pollution spillover among cities. Second, a complex network is constructed, and cities that have stronger spillover correlations are grouped into the same region. Finally, motifs are analyzed regarding the spillover relationships among regions. This paper also compares the structure of air pollution spillover during various seasons. This study determines that every season has a core region where the air pollution spillover exits the region. The core region in the spring is western East China, in the summer it is northern East China, in the autumn it is northern East China, and in the winter it is northern North China. These regions interact with most other regions. Furthermore, in spring and winter, the phenomena of air pollution spillover between regions are stronger than those in summer and autumn. We can weaken the air pollution spillover by controlling the air pollution in core regions.

Long-term trends and drivers of aerosol pH in eastern China

Aerosol acidity plays a key role in regulating the chemistry and toxicity of atmospheric aerosol particles. The trend of aerosol pH and its drivers are crucial in understanding the multiphase formation pathways of aerosols. Here, we reported the first trend analysis of aerosol pH from 2011 to 2019 in eastern China. The implementation of the Air Pollution Prevention and Control Action Plan leads to −35.8 %, −37.6 %, −9.6 %, −81.0 % and 1.2 % changes of PM2.5, SO42−, NHx, NVCs and NO3− in YRD during this period. Different from the fast changes of aerosol compositions due to the implementation of the Air Pollution Prevention and Control Action Plan, aerosol pH shows a moderate change of −0.24 unit over the 9 years. Besides the multiphase buffer effect, the opposite effects of SO42− and non-volatile cations changes play key roles in determining the moderate pH trend, contributing to a change of +0.38 and −0.35 unit, respectively. Seasonal variations in aerosol pH were mainly driven by the temperature, while the diurnal variations were driven by both temperature and relative humidity. In the future, SO2, NOx and NH3 emissions are expected to be further reduced by 86.9 %, 74.9 % and 41.7 % in 2050 according to the best health effect pollution control scenario (SSP1-26-BHE). The corresponding aerosol pH in eastern China is estimated to increase by ~0.9, resulting in 8 % more NO3− and 35 % less NH4+ partitioning/formation in the aerosol phase, which suggests a largely reduced benefit of NH3 and NOx emission control in mitigating haze pollution in eastern China.