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.

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.

Heavy metal pollution assessment, source identification, and health risk evaluation in Aibi Lake of northwest China

This study sought to analyze heavy metal (Pb, Zn, Cu, Ni, Mn, and Fe) pollution status in the waters of Aibi Lake in northwest China through the use of an applied comprehensive pollution index, health risk model, and multivariate statistical analyses in combination with the lake's land use types. Results showed that (1) the maximum (average) values of the heavy metals Pb, Zn, Cu, Ni, Mn, and Fe were 0.0644 (0.0123), 0.0006 (0.0002), 0.0009 (0.0032), 0.1235 (0.0242), 0.0061 (0.0025), and 0.0222 (0.0080) μg/L, respectively. Among these, in all the samples, Pb and Ni exceeded the standard and acceptable values put forth by the World Health Organization by 21.13 and 25.67%, respectively. Ni also exceeded (30.16%) the third grade of the Environmental Quality Standards for Surface Water of China. The levels of the six heavy metals were all within the fishery and irrigation water quality standard ranges in China. (2) The average values for single pollution index of heavy metals Pb, Zn, Cu, Ni, Mn, and Fe were 1.000, 0.0006, 0.0009, 3.000, 0.060, and 0.070, respectively, among which Ni levels indicated moderate to significant pollution, while others indicated healthy levels. (3) Health risk evaluation showed that the R_n values for Pb, Zn, Cu, Mn, and Fe were 1.8 × 10^-4, 5.33 × 10^-9, 4.80 × 10^-7, 1.08 × 10^-6, and 2.51 × 10^-7 a^-1, respectively, of which, in all samples, Pb and Ni contents all exceeded the maximum acceptable risk levels according to the International Commission on Radiological Protection (ICRP) as well as the U.S. Environment Protection Agency. (4) Combining with multivariate statistical analyses along with the land use distribution within the lake basin, Pb, Zn, Cu, Ni, and Mn were mainly influenced by the agriculture production and emission from urban lives and traffics, and Fe mainly originated from the natural environment. The results of this research can provide reference values for heavy metal pollution prevention in Aibi Lake as well as for environmental protection of rump lakes in the arid regions of northwest China and Central Asia.