Land surface thermal alteration and pattern simulation based on influencing factors of rural landscape

Quantitative assessment can scientifically determine the health status of a regional ecosystem, identify regional eco-environmental problems, and assist in promoting regional sustainable development and environmental management. Taking China’s important ecological function region, the Yellow River affected area as an example, this study constructed an extended evaluation index system based on the pressure-state-response framework, and remote sensing and GIS techniques were used to dynamically evaluate the spatial and temporal characteristics of ecosystem health in the study area. Furthermore, influencing factors on ecosystem health in the study area were extensively analyzed using the GeoDetector model. The results show that the ecosystem health level in the study area shows significant spatial heterogeneity from 1995–2015, and showed a fluctuating change process. Areas with large fluctuations in health level were mainly distributed in extreme climate areas, ecologically fragile areas, on plains and in hilly areas. Spatial differences of ecosystem health were well explained by using the biological abundance index, relief degree of land surface, soil type, annual average precipitation, elevation, annual average temperature, and population density. Influencing factors have significant interactive effects on ecosystem health.

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Rework Quantification and Influence of Rework on Duration and Cost of Equipment Development Task

Sustainability ◽

10.3390/su10103590 ◽

2018 ◽

Vol 10 (10) ◽

pp. 3590 ◽

Cited By ~ 1

Author(s):

Xilin Zhang ◽

Yuejin Tan ◽

Zhiwei Yang

Keyword(s):

Influencing Factors ◽

Design Structure Matrix ◽

Design Structure ◽

Simulation Based ◽

Task Requirements ◽

Equipment Development ◽

The Impact ◽

Mapping Models ◽

Development Task ◽

Development Tasks

Rework is a sub-task within equipment development tasks that is revised after initial completion to meet task requirements. Some sub-tasks require multiple rework iterations due to their uncertainty and complexity, or the technology and process needs of the overall task, resulting in inefficient task implementation and resource wastage. Therefore, studying the impact of rework iterations on the duration and cost of development tasks is worthwhile. This study divides rework into foreseeable and hidden types and uses several methods to express and quantify their parameters. The main influencing factors in rework iterations—the uncertainty and complexity of the development task—are quantitatively analyzed. Then, mathematical and mapping models of the dependence between sub-tasks, uncertainty, complexity, and rework parameters are established. The impacts of rework type and rework parameters on the duration and cost of equipment development tasks are analyzed via simulation based on the design structure matrix (DSM). Finally, an example is used to illustrate the influence of different rework types and rework parameters on development tasks’ duration and cost. The results show that the duration and cost of development tasks are greater, their volatility range is wider, and the distribution is more dispersed when both foreseeable and hidden rework are considered.

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Crack pattern simulation based on 3D surface cellular automata

The Visual Computer ◽

10.1007/s003710100099 ◽

2001 ◽

Vol 17 (5) ◽

pp. 287-309 ◽

Cited By ~ 34

Author(s):

Stéphane Gobron ◽

Norishige Chiba

Keyword(s):

Cellular Automata ◽

Crack Pattern ◽

3D Surface ◽

Simulation Based ◽

Pattern Simulation

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Spatiotemporal variations of land surface albedo and associated influencing factors on the Tibetan Plateau

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.150100 ◽

2021 ◽

pp. 150100

Author(s):

Guojin Pang ◽

Deliang Chen ◽

Xuejia Wang ◽

Hui-Wen Lai

Keyword(s):

Tibetan Plateau ◽

Influencing Factors ◽

Land Surface ◽

Surface Albedo ◽

The Tibetan Plateau ◽

Spatiotemporal Variations

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Influencing Factors and Quality Evaluation of Urban Thermal Environment Based on Artificial Neural Network

International Journal of Heat and Technology ◽

10.18280/ijht.390113 ◽

2021 ◽

Vol 39 (1) ◽

pp. 128-136

Author(s):

Liangwei Zhao ◽

Xiaowei Li ◽

Xuguang Chai

Keyword(s):

Neural Network ◽

Influencing Factors ◽

Bp Neural Network ◽

Land Surface ◽

Quality Evaluation ◽

Thermal Environment ◽

Island Effect ◽

Urban Heat ◽

Urban Thermal Environment ◽

Mlr Model

To alleviate the urban heat island effect that is getting increasingly serious these days, the research on the monitoring, simulation, and regulation of the thermal environment of cities has become a necessity. Aiming at figuring out the correlations between influencing factors and giving accurate quality evaluation of urban thermal environment, this study extracted 10 influencing factors of urban thermal environment and gave their influence, and then performed the Land Surface Temperature (LST) retrieval of a target city. After that, this paper constructed a Multiple Linear Regression (MLR) model and explored the law of the numerical changes of the influencing factors of urban thermal environment. At last, this paper also built a BP neural network to predict the quality evaluation of urban thermal environment and used experimental results to prove the effectiveness of the proposed algorithm and model.

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The dispersal of automotive lead from metropolitan Adelaide into adjacent rural areas

Soil Research ◽

10.1071/sr9870155 ◽

1987 ◽

Vol 25 (2) ◽

pp. 155 ◽

Cited By ~ 28

Author(s):

KG Tiller ◽

LH Smith ◽

RH Merry ◽

PM Clayton

Keyword(s):

Land Surface ◽

South Australia ◽

Rural Landscape ◽

Lead Content ◽

City Centre ◽

Atmospheric Fallout ◽

Surface Soils ◽

Study Region ◽

Lead Levels ◽

The City

About 600 surface samples and key horizons of 240 soil profiles from a 90 x 20 km study area extending from the metropolitan area of Adelaide, South Australia, to its rural hinterland, were analysed for lead. Atmospheric fallout was collected monthly at 19 locations for 2 years, and up to 3 years at fewer sites in the same area. Lead in atmospheric fallout showed little year-to-year variation and tended to be seasonally controlled with highest values in winter. Collection of lead in fallout was unaffected by the filtering action of vegetation. The lead content of surface soils and atmospheric fallout showed that part of petrol-lead emitted within Adelaide from automotive exhausts has measurably contaminated the rural landscape to about 50 km downwind of the city. The variation in lead content of surface soils in the agricultural region near Adelaide can be largely explained in terms of accessions of aerosol-lead of automotive origin. This research complements previous investigations which showed that the lead isotopic compositions of selected soils were close to the composition of the lead tetraethyl used in South Australia. An environmental budget showed that only 3% of the lead in petrol burned in Adelaide has been dispersed via the atmosphere beyond the immediate highway zone, and deposited on the land surface within 50 km of the city centre. On the assumption that no more than 35% of the lead is retained within the vehicle, and that about half of the total burned lead is deposited near the roadway, the 30% of the total lead which cannot be accounted for (about 200 t at the time of this study) has probably dispersed beyond the study region and should be viewed as a contribution to continental and global pollution. Lead levels measured in rainfall, air and soils were low in relation to the accepted standards and experience. Although the lead levels were low, this investigation indicates the likely dispersal pattern of other pollutants with similar atmospheric residence times, and thus provides guidance to planning decisions concerning placement of polluting industries, and in relation to possible industrial accidents which cause pollution.

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