Coupling coordination analysis of population, economy and grain in major grain-producing counties

This study discusses the coupling and coordination relationship among population, economy, and grain production in the central primary grain-producing counties. It aims to find a dynamic balance between the responsibility of the grain-producing areas in ensuring food security and the development of the economy and population. This study focuses on the main grain-producing provinces of Jilin and Jiangsu in China. Based on county-level data on population, economy, and grain production, it constructs an index system for the population, economy, and grain systems. The study employs the entropy weighting method and coupling coordination model to analyze the coupling coordination degree and coordinated development of the three systems in Jilin and Jiangsu provinces from 2000 to 2020, covering a span of 21 years. The coupling coordination degree and coordinated development of the three systems in the main grain-producing areas have gradually moved towards high-quality coordination. In the economically underdeveloped province of Jilin, factors such as geographical environment, population size, and industrial structure impose constraints on system coordination. In the economically developed region of Jiangsu, there is a high labour force and better development of the secondary and tertiary industries, but relatively less investment in agriculture, which affects overall coordination. It is necessary to promote regions' development with high-quality coordination by leveraging their advantages in economic foundations, and further advance the construction of the main grain-producing areas. Additionally, efforts should be made to strengthen policy support for underdeveloped regions, clearly define the industrial types and positioning of counties, and focus on industrial transformation and upgrading.

Health risk assessment of PM2.5 heavy metals in county units of northern China based on Monte Carlo simulation and APCS-MLR

The key areas of China's urbanization process have gradually shifted from urban areas to county-level units. Correspondingly, air pollution in county towns may be heavier than in urban areas, which has led to a lack of understanding of the pollution situation in such areas. In view of this, 236 PM_2.5 filter samples were collected in Pingyao, north of the Fen-Wei Plain, one of the most polluted areas in China. Monte Carlo simulation was used to solve the serious uncertainties of traditional HRA, and the coupling technology of absolute principal component score-multiple linear regression (APCS-MLR) and health risk assessment (HRA) is used to quantitatively analyze the health risks of pollution sources. The results showed that PM_2.5 concentration was highest in autumn, 3.73 times the 24 h guideline recommended by the World Health Organization (WHO). Children were more susceptible to heavy metals in the county-level unit, with high hazard quotient (HQ) values of Pb being the dominant factor leading to an increased non-carcinogenic risk. A significant carcinogenic risk was observed for all groups in autumn in Pingyao, with exposure to Ni in the outdoor environment being the main cause. Vehicle emissions and coal combustion were identified as two major sources of health threats. In short, China's county-level population, about one-tenth of the world's population, faces far more health risks than expected.