Dynamic evaluation and prediction of the ecological environment quality of the urban agglomeration on the northern slope of Tianshan Mountains.

In order to timely determine the dynamic changes of the ecological environment quality and future development laws of the urban agglomeration on the northern slope of the Tianshan Mountains, combined with the actual situation of the urban agglomeration, 11 indicators were selected from the three aspects of natural ecology, social ecology, and economic ecology. To reduce the dimensions of the indicators, principal component analysis, coefficient of variation, and analytic hierarchy process were used based on RS and GIS technology methods, and the ecological environmental quality (EQI) from 2000 to 2018 was dynamically evaluated. Further, the CA-Markov model was introduced to simulate the development status in 2026 for predictive purposes.

Study of coordinated development of county urbanization in arid areas of China: The case of Xinjiang.

Urbanization is a comprehensive process of mutual influence among the population, economy, society and living environment, and it depends on the synergy of a series of factors. This paper uses the statistical data of 76 counties in Xinjiang from 1996 to 2018 to construct a comprehensive urbanization evaluation system. Based on the entropy method, comprehensive evaluation model and coupling coordination model, from the scales of time and space, this paper discusses the current situation of the coordinated development of population, economy, society and living environment factors in counties in Xinjiang in the process of urbanization.

Spatial Pattern Analysis of Xinjiang Tourism Resources Based on Electronic Map Points of Interest.

This study considers the Point of Interest data of tourism resources in Xinjiang and studies their spatial distribution by combining geospatial analysis methods, such as the average nearest neighbor index, standard deviation ellipse, kernel density analysis, and hotspot analysis, to explore their spatial distribution characteristics. Based on the analysis results, the following conclusions are made. Different categories of tourism resource sites have different spatial distributions, and all categories of tourism resources in Xinjiang are clustered in Urumqi city.

Carbon emissions index decomposition and carbon emissions prediction in Xinjiang from the perspective of population-related factors, based on the combination of STIRPAT model and neural network.

In the present study, the STIRPAT model was adopted to examine the impacts of several factors on dioxide emissions using the time series data from 2000 to 2019 in Xinjiang. The said factors included population aging, urbanization, household size, per capita GDP, number of vehicles, per capita mutton consumption, education level, and household direct energy consumption structure. Findings were made that the positive effects of urbanization, per capita GDP, per capita mutton consumption and education on carbon emissions were obvious; the number of vehicles had the biggest positive impact on carbon dioxide emissions; and household size and household direct energy consumption structure had a significantly negative impact on carbon emissions.

Pollution assessment and health risks evaluation of (metalloid) heavy metals in urban street dust of 58 cities in China.

In this research, we conducted a statistical analysis of ten (metalloid) heavy metals, including Cu, Hg, Cd, Zn, Pb, As, Ni, Cr, Co, and Mn in urban dust of 58 cities in China from 2000 to 2018, and then we analyzed the statistic characters, pollution statue, and health risks of ten heavy metals. Results showed that (1) the maximum (average) values of ten (metalloid) heavy metals in the street dust of 58 Chinese cities all exceeded Chinese background values, and there were obvious differences in contents of heavy metals of Hg, Zn, Co, Cr, and As between industry cities and common cities. A provincial spatial distribution analysis revealed large variations of distributions of heavy metals Cu, Zn, Pb, and Cr, which distributed in cities mainly located in southern, central, and eastern China, ranging from relatively low to high levels, while Ni, Co, and Mn mainly distributed in southern and central China.