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. The main results are as follows: the overall ecological environment of the area exhibited a gradually improving distribution change from southwest to northeast; the proportion of ecological environment classification exhibited a gradually decreasing change pattern; the spatial differentiation of ecological environment quality exhibited a significant spatial positive correlation; from the influencing factors, an observation can be made that natural ecological factors were highly significant; the prediction accuracy verification revealed that the CA-Markov model was suitable for the prediction of the ecological environment quality in the region and had high accuracy; and the comprehensive regional ecological environment quality indexes were 5.7392, 6.1856, and 6.4366, respectively, while the forecasted value for 2026 was predicted to be 6.6285, indicating that the overall ecological environment quality of the region will improve and develop well. The present research results reveal the law of dynamic changes and future development of the ecological environment quality in the region, which can be used as a theoretical reference for the formulation of ecological environmental protection measures.

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. Local spatial autocorrelation analysis is used to further study the spatial agglomeration effect of the coupling and coordination of urbanization development in the counties. The results show the following: (1) The comprehensive urbanization level of 76 counties in Xinjiang has the characteristics of "center-periphery" development, and high-level counties are clustered on the northern slopes of the Tian Mountains. (2) Most counties are in a serious state of imbalance; notably, the degree of population-economy-society-living environment coupling and coordination in the border counties and towns is in an unsatisfactory state. (3) The county-level cities in Northern Xinjiang belong to the diffusion and spillover areas, the county-level cities in southern Xinjiang belong to the polarization benefit areas, and most other counties are in the state of no spillover effect.

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. Based on the aforementioned findings, the GA-BP neural network was introduced to predict the carbon emission trend of Xinjiang in 2020-2050. The results reveal that the peak time of the low-carbon scenario was the earliest, between 2029 and 2033. The peak time of the middle scenario was later than low-carbon scenario, between 2032 and 2037, while the peak time of the high-carbon scenario was the latest and was unlikely to reach the peak before 2050.

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. The geological landscape resource sites are widely distributed and have a ring-shaped distribution in the desert area of southern Xinjiang. The biological landscape resources are distributed in a strip along the Tianshan Mountains. The water landscape resources are concentrated in the northern Xinjiang area. The site ruins are mostly distributed in the western region of Xinjiang. The distributions of the architectural landscape and entertainment and shopping resources are highly coupled with the distribution of cities. The distributions of the six categories of tourism resource points are in the northeast-southwest direction. The centripetal force and directional nature of the resource points of the water landscape are not obvious. The remaining five categories of resource points have their own characteristics. The distribution of resources in the site ruins is relatively even, and there are many hotspot areas in the geomantic and architectural landscapes, which are mainly concentrated in Bazhou and other places. The biological landscape has many cold-spot areas, distributed in areas such as Altai in northern Xinjiang and Hotan in southern Xinjiang. The remaining four categories have cold-spot and hotspot areas with different distributions. Tourism is an important thrust for economic development. The study of the distribution of tourism resources on the spatial distribution of tourism resources has clear guidance for later tourism development, can help the tourism industry optimize the layout of resources, and can promote tourism resources to achieve maximum benefits. The government can implement effective control and governance.

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. This is mainly associated with the mining of the cities. (2) Igeo analysis showed that there was no obvious Ni, Mn, or Co pollution in street dust, while the other tested heavy metals had a range of low to high levels of pollution, in particular, seven metals among them had low to extremely strong levels of pollution (Igeo values between 0 and 7.154), and the average Igeo values were in the following order: Cd > Hg > Zn > Pb > Cu > As>Cr > Mn > Co > Ni. (3) Health risks evaluation showed that of the three exposure ways, the HQing from hand-mouth intake was the most common exposure route for both children and adults, especially for children, followed by skin absorption and respiration ways. This research showed that the HI value for children was higher than 1, indicative of no carcinogenic risks, while the HI values for both male and female were lower than 1, indicative of carcinogenic risks; calculation of carcinogenic risk through respiratory route showed that the risks of five elements were within the range 10-6-10-4, indicative of carcinogenic risk, among which Cr accounting exceeded 90% of total, which needs to be paid more attention to.