Background
The current soil environmental assessment system is inadequate in terms of the spatiotemporal distribution of heavy metal pollutants. This study employed the numerical simulation technique to predict spatiotemporal distribution patterns of heavy metals within 50 days and to assess the soil risk characteristics of heavy metal pollution near a lead–zinc mine in Hunan Province, China.
Results
The spatiotemporal distribution results indicate that the soil in the sewage plant and mining areas served as the pollution center, exhibiting a ladder-shaped pollution diffusion trend outward. When the pollution migration time reached 20 days, pollutant migration and changes tended to remain stable, high-pollution areas exhibited no drastic changes within 10 m, and low-pollution and medium-pollution areas revealed obvious changes. Moreover, the low-pollution area width approached 2 m, the depth reached 2 m, the medium-pollution area width was close to 2.5 m, and the depth approached 4 m. The percentage of areas containing lead–zinc mine soil with high to extremely high risks reached 82.88%, and extremely high-risk farmland, mining and residential areas accounted for up to 100%, 95% and 90%, respectively, of the total area. Among the pollution sources, high-risk and extremely high-risk areas in regard to heavy metal Cd accounted for 13.51 and 49.55%, respectively, of the total area.
Conclusion
This study provides new insights into the migration patterns and risk characteristics of pollutants to address soil environmental assessment system problems.
Cloud and fog droplets are essential in atmospheric chemistry since they affect the distribution and chemical transformation of pollutants. Collecting sufficient volumes using cloud/fog samplers is the premise of cloud fog chemical studies. Accurate identification of fog events and high collection efficiency are the basic principles of sampler design. Therefore, an automatic cloud/fog collector (ACFC) has been designed, fabricated, and extensively tested for collecting samples of cloud/fog water. The control box and standard sensors for air temperature, relative humidity, and instantaneous rainfall were used to ensure sampling automation. Airflow measurement was used to guarantee the stability of the airspeed on the inlet section, and the airspeed is 7.5 m s−1. Moreover, the median collection rate of ACFC was 160–220 mL h−1, which was tested via a simulation experiment. To evaluate the actual performance of the device in the field, we obtained eight samples of cloud fog water from Shanghuang Observatory in eastern China from the summer through the fall of 2022. Collection rates varied from 62 to 169 mL h−1. For a cloud/fog sampler equipped with multiple sensors, the ACFC has excellent sampling efficiency in thick fog, sufficient cloud fog water samples can be collected in weak fog, and it can precisely identify fog mingled with rain.
Dubbo is a high-performance, lightweight Java Remote Procedure Call (RPC) framework developed by Alibaba, which provides interface-oriented remote method call, intelligent fault tolerance and automatic service registration. Since Dubbo is extensively applied recently as an excellent representative RPC framework, it is of great significance to formally analyze Dubbo. In this paper, we use Communicating Sequential Processes (CSP) to model and formalize Dubbo. In order to enhance the reliability of the call, we use token authentication mechanism in the modeling process. Moreover, we put the CSP description of the established model into the model checker Process Analysis Toolkit (PAT) for simulation and verification. We verify whether the four properties are valid, including Deadlock Freedom, Connectivity, Robustness and Parallelism. Our final verification results show that the model can satisfy these properties, thus we can conclude the framework can guarantee the highly available remote call.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.