Full course evolution characteristics of DPF active regeneration under different inlet HC concentrations

In this paper, based on electrical power consumption (EPC) data extracted from DMSP/OLS night light data, we select three national-level urban agglomerations in China’s Yangtze River Economic Belt(YREB), includes Yangtze River Delta urban agglomerations(YRDUA), urban agglomeration in the middle reaches of the Yangtze River(UAMRYR), and Chengdu-Chongqing urban agglomeration(CCUA) as the research objects. In addition, the coefficient of variation (CV), kernel density analysis, cold hot spot analysis, trend analysis, standard deviation ellipse and Moran’s I Index were used to analyze the Spatio-temporal Dynamic Evolution Characteristics of EPC in the three urban agglomerations of the YREB. In addition, we also use geographically weighted regression (GWR) model and random forest algorithm to analyze the influencing factors of EPC in the three major urban agglomerations in YREB. The results of this study show that from 1992 to 2013, the CV of the EPC in the three urban agglomerations of YREB has been declining at the overall level. At the same time, the highest EPC value is in YRDUA, followed by UAMRYR and CCUA. In addition, with the increase of time, the high-value areas of EPC hot spots are basically distributed in YRDUA. The standard deviation ellipses of the EPC of the three urban agglomerations of YREB clearly show the characteristics of “east-west” spatial distribution. With the increase of time, the correlations and the agglomeration of the EPC in the three urban agglomerations of the YREB were both become more and more obvious. In terms of influencing factor analysis, by using GWR model, we found that the five influencing factors we selected basically have a positive impact on the EPC of the YREB. By using the Random forest algorithm, we found that the three main influencing factors of EPC in the three major urban agglomerations in the YREB are the proportion of secondary industry in GDP, Per capita disposable income of urban residents, and Urbanization rate.

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Evolution characteristics and drivers of the water level at an identical discharge in the Jingjiang reaches of the Yangtze River

Journal of Geographical Sciences ◽

10.1007/s11442-020-1804-x ◽

2020 ◽

Vol 30 (10) ◽

pp. 1633-1648

Author(s):

Yuanfang Chai ◽

Yunping Yang ◽

Jinyun Deng ◽

Zhaohua Sun ◽

Yitian Li ◽

...

Keyword(s):

Water Level ◽

Yangtze River ◽

The Yangtze River ◽

Evolution Characteristics

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Molecular structure and evolution characteristics of dissolved organic matter in groundwater near landfill: Implications of the identification of leachate leakage

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.147649 ◽

2021 ◽

pp. 147649

Author(s):

Yan Ma ◽

Zhen-Hai Liu ◽

Bei-Dou Xi ◽

Wen-Tao Li ◽

Yan-Qiu Xu ◽

...

Keyword(s):

Molecular Structure ◽

Organic Matter ◽

Dissolved Organic Matter ◽

Evolution Characteristics

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Quantitative Recognition and Characteristic Analysis of Production-Living-Ecological Space Evolution for Five Resource-Based Cities: Zululand, Xuzhou, Lota, Surf Coast and Ruhr

Remote Sensing ◽

10.3390/rs13081563 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1563

Author(s):

Yuanyuan Tao ◽

Qianxin Wang

Keyword(s):

Remote Sensing ◽

Urban Areas ◽

Large Scale ◽

Remote Sensing Image ◽

Data Availability ◽

Function Evaluation ◽

Characteristic Analysis ◽

Accurate Identification ◽

Living Space ◽

Evolution Characteristics

The accurate identification of PLES changes and the discovery of their evolution characteristics is a key issue to improve the ability of the sustainable development for resource-based urban areas. However, the current methods are unsuitable for the long-term and large-scale PLES investigation. In this study, a modified method of PLES recognition is proposed based on the remote sensing image classification and land function evaluation technology. A multi-dimensional index system is constructed, which can provide a comprehensive evaluation for PLES evolution characteristics. For validation of the proposed methods, the remote sensing image, geographic information, and socio-economic data of five resource-based urbans (Zululand in South Africa, Xuzhou in China, Lota in Chile, Surf Coast in Australia, and Ruhr in Germany) from 1975 to 2020 are collected and tested. The results show that the data availability and calculation efficiency are significantly improved by the proposed method, and the recognition precision is better than 87% (Kappa coefficient). Furthermore, the PLES evolution characteristics show obvious differences at the different urban development stages. The expansions of production, living, and ecological space are fastest at the mining, the initial, and the middle ecological restoration stages, respectively. However, the expansion of living space is always increasing at any stage, and the disorder expansion of living space has led to the decrease of integration of production and ecological spaces. Therefore, the active polices should be formulated to guide the transformation of the living space expansion from jumping-type and spreading-type to filling-type, and the renovation of abandoned industrial and mining lands should be encouraged.

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