Environmental hazard and ecological infrastructure construction of hilly city:a case of metropolitan area of Chongqing Municipality

This paper takes metropolitan area of Chongqing Municipality as an example, Chongqing, located at the tail area of the Three Gorges Reservoir; is a typically in-land hilly city in China. A series of serious eco-environmental hazard affecting hilly city, have appeared： the forest landscape has been fragmented; the stability of ecological system has nat been maintained： soil erosion has been serionus; geological disasters have become more and more frequented; the phenomenon of city ＂heat islands＂ is obvious： and environmental pollution is worsened with Iow physical self-puri- fication. All of the hazards above will affect ecological security of the tail area of the Three Gorges Reservoir and sustainable development of Changqing. In order to guarantee ecological security and health of Chongqing and the Three Gorges Reservoir, the eountermeasures for ecological infrastructure construction are put forward： quickly drawing up ecological infrastructure planning of Chongqing Municipality, strengthening the integration and continuation of hilly-water spatial pattern, maintaining the diversity and stability of local ecosystem, establishing the green space system in-cluding suburban.forest, farmland and green space. recovering wetland ecosystem, optimizing industrial structure and urban spatial distribution, developing recycling economy; establishing ecological infrastructure monitoring network and information forecast system, carrying out ecological management for Chongqing Municipality and so on.

Storage and Density of Soil Organic Carbon in Urban Topsoil of Hilly Cities:A Case Study of Chongqing Municipality of China

Rapid urbanization results in the conversion of natural soil to urban soil,and consequently,the storage and density of the soil carbon pools change.Taking Chongqing Municipality of China as a study case,this investigation attempts to better understand soil carbon pools in hilly cities.First,the vegetated areas in the study area were derived from QuickBird images.Then,topsoil data from 220 soil samples(0-20 cm) in the vegetated areas were collected and their soil organic carbon(SOC) densities were analyzed.Using the Kriging interpolation method,the spatial pattern of SOC was estimated.The results show that the SOC density exhibited high spatial variability in the urban topsoil of Chongqing.First,the SOC density in topsoil decreased according to slope in the order 2°-6° < 25°-90° < 0°-2° < 6°-15° < 15°-25°.Second,the newly developed areas during 2001-2010 had a lower SOC density than the areas built before 1988.Third,urban parks and gardens had a higher SOC density in topsoil,residential green land followed,and scattered street green land ranked last.For hilly cities,the variability of terrain affects the distribution of SOC.The Kriging results indicate that Kriging method combining slope with SOC density produced a high level of accuracy.The Kriging results show that the SOC density to the north of the Jialing River was higher than the south.The vegetated areas were estimated to amount to 73.5 km2 across the study area with an SOC storage of 0.192 Tg and an average density of 2.61 kg/m2.

Particle Swarm Optimization Bat Algorithm Path Automatically Planning Research for Police Drones in Hilly Cities

Mountain cities are complex asymmetric dynamic network architectures,and the flight of UAVs in this environment is subject to various constraints,while efficiency is a crucial factor in the trajectory planning of police UAVs,which need to maintain high efficiency and safe flight paths between their starting and ending points,but the traditional trajectory planning method cannot meet the requirements of rapid maneuvering of police UAVs.To achieve this,a 3D terrain map is built,an objective function is established for the flight cost in the UAV trajectory planning process,and a planning algorithm called particle swarm optimization bat algorithm(PSOBA)is proposed.PSOBA combines the characteristics of the bat algorithm(BA)and the particle swarm optimization algorithm(PSO)to improve population diversity and resolve the delayed convergence issue in the last phases of BA.Simulation results show that PSOBA is more effective than BA,with a search time for the best solution that is approximately 20.43%shorter and a convergence value of the objective function that is approximately 38%smaller.PSOBA is also able to plan a quicker,shorter,and safer flight path compared to other trail planning algorithms that enhance the bat algorithm.These findings suggest that PSOBA is a powerful algorithm with potential application value in UAV trajectory planning control in the mobile intelligence era.Contribute to the service of public social security.