Size-resolved carbonaceous aerosols at near surface level and the hilltop in a typical valley city, China

Zhao, Suping; Yu, Ye; Du, Zheng; Yin, Daiying; Yang, Jingyu; Dong, Lijun; Li, Ping

doi:10.1016/j.apr.2019.09.022

Cited by 10 publications

(5 citation statements)

References 71 publications

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“…Lanzhou (Figure 1) (103 • 40 E, 36 • 03 N) is located in the Northwest of China. It is one of the typical river valley basin-type cities in semi-arid areas [39]. As of July 2020, the city covers an area of approximate 1.31 × 10 4 km 2 and has a population of over 4.13 × 10 6 .…”

Section: Study Areamentioning

confidence: 99%

Simulation of Dynamic Urban Expansion under Ecological Constraints Using a Long Short Term Memory Network Model and Cellular Automata

Liu

Xiao

et al. 2021

Remote Sensing

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Rapid urban expansion has seriously threatened ecological security and the natural environment on a global scale, thus, the simulation of dynamic urban expansion is a hot topic in current research. Existing urban expansion simulation models focus on the mining of spatial neighborhood features among driving factors, however, they ignore the over-fitting, gradient explosion, and vanishing problems caused by the long-term dependence of time series data, which results in limited model accuracy. In this study, we proposed a new dynamic urban expansion simulation model. Considering the long-time dependence issue, long short term memory (LSTM) was employed to automatically extract the transformation rules through memory units and provide the optimal attribute features for cellular automata (CA). This study selected Lanzhou, which is a semi-arid region in Northwest China, as an example to confirm the validity of the model performance using data from 2000 to 2020. The results revealed that the overall accuracy of the model was 91.01%, which was higher than that of the traditional artificial neural network (ANN)-CA and recurrent neural network (RNN)-CA models. The LSTM-CA framework resolved existing problems with the traditional algorithm, while it significantly reduced complexity and improved simulation accuracy. In addition, we predicted urban expansion to 2030 based on natural expansion (NE) and ecological constraint (EC) scenarios, and found that EC was an effective control strategy. This study provides a certain theoretical basis and reference value toward the realization of new urbanization and ecologically sound civil construction, in the context of territorial spatial planning and healthy/sustainable urban development.

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Section: Study Areamentioning

confidence: 99%

Simulation of Dynamic Urban Expansion under Ecological Constraints Using a Long Short Term Memory Network Model and Cellular Automata

Liu

Xiao

et al. 2021

Remote Sensing

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“…Lanzhou (Figure 1) (103 • 40 E, 36 • 03 N) is located in the central part of Gansu Province, China. It is one of the typical river valley basin-type cities in a semi-arid area [37]. The terrain of the study area is high in the southwest and low in the northeast, with an average altitude of 1520 m. The Yellow River flows from southwest to northeast across the entire territory and traverses the mountains, forming a beaded valley with alternate canyons and basins, which runs~35 km from east to west and 2 km to 8 km from north to south [38].…”

Section: Study Areamentioning

confidence: 99%

Coupling Fuzzy Multi-Criteria Decision-Making and Clustering Algorithm for MSW Landfill Site Selection (Case Study: Lanzhou, China)

Liu

Xiao

et al. 2021

IJGI

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The siting of Municipal Solid Waste (MSW) landfills is a complex decision process. Existing siting methods utilize expert scores to determine criteria weights, however, they ignore the uncertainty of data and criterion weights and the efficacy of results. In this study, a coupled fuzzy Multi-Criteria Decision-Making (MCDM) approach was employed to site landfills in Lanzhou, a semi-arid valley basin city in China, to enhance the spatial decision-making process. Primarily, 21 criteria were identified in five groups through the Delphi method at 30 m resolution, then criteria weights were obtained by DEMATEL and ANP, and the optimal fuzzy membership function was determined for each evaluation criterion. Combined with GIS spatial analysis and the clustering algorithm, candidate sites that satisfied the landfill conditions were identified, and the spatial distribution characteristics were analyzed. These sites were subsequently ranked utilizing the MOORA, WASPAS, COPRAS, and TOPSIS methods to verify the reliability of the results by conducting sensitivity analysis. This study is different from the previous research that applied the MCDM approach in that fuzzy MCDM for weighting criteria is more reliable compared to the other common methods.

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“…Lanzhou (Fig.1) (103°40′E, 36°03′N) is located in the central part of Gansu Province, China. It is one of the typical river valley basin-type cities in semi-arid areas (Zhao et al, 2020). The terrain of the study area is high in the southwest and low in the northeast, with an average altitude of 1520m.…”

Section: Study Areamentioning

confidence: 99%

Siting MSW landfills via the integration of DEMATEL-ANP and clustering algorithm in a fuzzy logic environment (Case Study: Lanzhou, China)

Liu¹,

Li²,

Xiao³

et al. 2021

Preprint

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The siting of Municipal Solid Waste (MSW) landfills is a complex decision based process that involves multiple hydrogeological, morphological, environmental, climatic, and socio-economic criteria. In a fuzzy logic environment, DEMATEL and ANP methods were employed to comprehensively consider uncertainty, fuzziness of data and the subjective scoring and stability of results to enhance the spatial decision-making process. Primarily, 21 criteria were identified in five groups through the Delphi method at 30m resolution, criteria weights were determined via the integration of DEMATEL and ANP, and seven sets of membership functions were simulated to obtain the best fuzzy logic environment. Combining GIS spatial analysis and the three clustering algorithms (DBSCAN, HDBSCAN, and OPTICS), candidate sites that satisfied the landfill conditions were identified, and the spatial distribution characteristics and reachability were analyzed. These sites were subsequently ranked utilizing the MOORA, WASPAS, COPRAS, and TOPSIS methods to verify the reliability of the results by conducting sensitivity analysis. This paper focuses on a flexible and novel framework for the selection of MSW landfill sites for Lanzhou, which is a semi-arid valley basin city in China. In contrast to common techniques, this model not only made the best recommendation scientifically and efficiently but could also provide accurate assessment data for decision makers in landfill construction and high-quality urban development.

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Size-resolved carbonaceous aerosols at near surface level and the hilltop in a typical valley city, China

Cited by 10 publications

References 71 publications

Simulation of Dynamic Urban Expansion under Ecological Constraints Using a Long Short Term Memory Network Model and Cellular Automata

Simulation of Dynamic Urban Expansion under Ecological Constraints Using a Long Short Term Memory Network Model and Cellular Automata

Coupling Fuzzy Multi-Criteria Decision-Making and Clustering Algorithm for MSW Landfill Site Selection (Case Study: Lanzhou, China)

Siting MSW landfills via the integration of DEMATEL-ANP and clustering algorithm in a fuzzy logic environment (Case Study: Lanzhou, China)

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