Shanghai, as an international metropolis, has an ever-growing population and ongoing economic development, so the pressure on the natural resources and the environment is continually increased. How to ease the tension among economy, resources and the environment? The sustainable green development of Shanghai has been the focus of the public and the government. Urban carrying capacity involves complex interactions among population, the economy and the environment. Understanding the balance between these elements is an important scientific issue for sustainable green development in Shanghai. For this purpose, the balance between urban development and ecological resources was emphasized, and population carrying capacity, GDP (Gross Domestic Product), green ecological index and added value of secondary industry were investigated to measure urban carrying capacity. The dynamic changes of the carrying population, GDP, green ecological index and the added value of the secondary industry in Shanghai during 2018–2035 were simulated using a system dynamics model including three subsystems and 66 variables from a macroscopic perspective. Five development scenarios were employed during the simulation, namely a status-quo scenario, an economic-centric scenario, a high-tech-centric scenario, an environment-centric scenario and a coordinated equilibrium scenario. The simulation results indicated that the potential of carrying population will decline by 2035, and the economic and ecological indicators will also be at a low level under the status-quo scenario, which is an inferior option, while the under coordinated equilibrium scenario, the ecological environment, population growth and economic development will all perform excellently, which is the best option. Therefore, the urban carrying capacity of population, economy and resources in Shanghai may be improved by increasing investment in scientific research, increasing the expenditure on environmental protection and improving the recycling efficiency of waste solid and water. The results provide insights into the urban carrying capacity of Shanghai city.
Taihu Lake, one of the five largest freshwater lakes in China, is located in the south of the Yangtze River Delta, with a water area of 2338.1km2. In recent years, with the deterioration of water quality, the water pollution in Taihu Lake has become increasingly serious. In 2007, a large-scale outbreak of cyanobacteria caused a water crisis for nearly 2 million people in Wuxi. The main performance of water pollution is that the content of nitrogen and ammonia in water exceeds the standard, which leads to eutrophication of water and excessive proliferation of algae. Therefore, this paper uses Landsat-5 TM, Landsat-7 ETM and Landsat-8 OLI remote sensing data to study the information extraction methods of eutrophic polluted water bodies in Taihu region. By comparing with the health status report on Taihu Lake, it is found that the calculation results of remote sensing data can well reflect the distribution characteristics of ammonia nitrogen pollution in this area, and provide a basis for monitoring and controlling ammonia nitrogen pollution in this area by using remote sensing technology.
Bridge engineering construction is a kind of engineering construction with high risk. Only by building scientific and reasonable safety assessment and monitoring measures can the construction safety be improved and the risk be controlled. Based on the construction of a large cantilever steel truss traffic bridge project, this paper introduces the application of three-dimensional laser scanning technology in the monitoring of bridge transverse construction from the aspects of measuring point layout, monitoring scheme, scanning measurement and data analysis and processing. This paper introduces the bridge construction safety monitoring technology, as well as the content and steps of construction safety monitoring, and summarizes the measures of bridge construction safety assessment monitoring.
Compared to traditional multi-spectral information, nighttime light data has more intuitive features and is more sensitive to feedback on human-acquired trends such as urban sprawl and industrial development. Therefore, this paper uses Shanghai as the study area and combines the 30m resolution land use raster data with the monthly NPP-VIIRS data to determine the threshold value using the spatial comparison method of higher resolution image data. The threshold dichotomy was used to extract the urban built-up areas from the nighttime light images, and the error was only 0.85 when compared with the statistical data.
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.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.