Search

Abstract The non-coordination between the socio-economic systems and ecosystems of a region is a crucial obstacle to sustainable development. To reveal the relationships between complex urban systems and achieve the goal of sustainable and coordinated urban development, we constructed a coupling coordination degree model (CCDM) and coupling angle model (CAM) and analyzed the degree of coupling coordination and evolution process among the population, water resources, economy, and ecology (PWEE) system of the Tuha region for 2005–2020. The results indicated that: (1) During 2005–2020, the comprehensive development index (CDI) of the population, water resources and economy subsystems was 0.21–0.65, with the three subsystems portraying an overall increase; the average values of the RSEI at five-year intervals were 0.29, 0.28, 0.28, and 0.26, indicating a downward trend in the environmental quality. (2) The coupling coordination effect of the PWEE system portrayed a low level; the coupling coordination degree (CCD) values were 0.28–0.58, portraying a fluctuating upward trend. The level of CCD increased from low disorder to marginal coordination. (3) The PWEE system’s scissor difference reflects large evolutionary characteristics. The ecological support capacity was not observed until the late stage. We conclude that the PWEE composite system of the region is in a stage of disordered development. These findings significantly bolster the theoretical underpinnings of sustainable development studies, offering essential scientific theories and methodological frameworks for crafting sustainable development policies tailored to urban systems in the Tuha region.

In recent years, increasing urbanization has profoundly impacted the quality of regional habitats, presenting a severe risk to the ability of a region to develop in a high-quality manner. Therefore, the scientific assessment of the features of habitat quality (HQ) evolution over time and space and the prediction of future trends in changes in the HQ are of great significance for the formulation of effective ecological protection policies. Based on five periods of land use and land cover (LULC) data from 2000 to 2020, InVEST model was used to estimate both geographical and chronological trends in the HQ in the Tuha region, China. Spatial autocorrelation analysis methods were used to assess HQ and spatial aggregation of habitat degradation, and ecological zoning was delineated in conjunction with the Human Footprint Index (HFI). Based on the results of ecological zoning, the study predicted changes in habitat quality (HQ) in 2040 under three scenarios: natural development (ND), ecological preservation (EP), and urban development (UD) by applying the Patch-Generating Land Use Simulation (PLUS) model. The results demonstrated that (1) from 2000 to 2020, the habitat quality in the Tuha region exhibited a downward trend, with the proportion of low HQ increasing from 83.63% to 84.24%. Spatially, high habitat quality (HQ) is mainly concentrated in the Tianshan Mountains. From 2000 to 2020, the Moran index for habitat quality (HQ) decreased from 0.967 to 0.959, while the Moran index for habitat degradation declined from 0.805 to 0.780. The habitat quality (HQ) and degradation exhibit significant spatial aggregation, and the degree of degradation has increased incrementally. (2) From 2000 to 2020, human activities in the Tuha area increased continuously and were mainly concentrated in Tuha district and counties. The proportion of high Human Footprint Index (HFI) increased from 0.66% to 1.32%, while the proportion of medium HFI increased from 3.13% to 7.46%. (3) The expansion of urbanized land has exacerbated habitat degradation. The proportion of high HQ in the EP scenario is higher than that in ND and UD scenario. The results show that the ecological protection scenario is more conducive to the sustainable development of habitat quality in the Tuha region. The results can provide a scientific basis for ecological management and protection in the Tuha area.

This study investigates, for the first time, the urbanization effect (UE) on local extreme climate events in Urumqi, China, based on 22 indices of climate extremes, which are calculated with daily observation data from 1976 to 2018. These analyses reveal a pronounced nocturnal urban heat island (UHI) effect and a daytime urban cold island (UCI) effect. Due to Urumqi’s arid climate background, the UCI effect is considered a unique feature of the UE, which significantly differs from those in eastern and northern China. The UE on the TR20 index (number of days with minimum daily temperature exceeding 20 °C) reached 5.22 d/10a, indicating that urbanization has led to a fast increase in the number of hot nights in Urumqi. The absolute averaged UE on the indices measuring the frequency of warm events is about twice as large as that on the indices measuring the frequency of cold events, while that on the indices measuring the intensity of warm events is about one third of that on the indices measuring the intensity of cold events. The highest averaged urbanization contributions (UCs) to the extreme warm and cold events are represented by the frequency indices and the intensity indices, respectively, while those contributing to the extreme precipitation events are represented by the duration indices. Moreover, urbanization probably exacerbates the degree of wetting in the overall “warming and wetting” climate trend of the region. These findings can be seen as new evidence to provide scientific basis for further investigation of the UE on climate changes in arid regions.

Rapid urbanization threatens the ecological environment and quality of life by significantly altering land use and land cover (LULC) and heat distribution. One of the most significant environmental consequences of urbanization is the urban heat island effect (UHI). This study investigated the spatiotemporal characteristics of the SUHI and its relationship with land use types from 2000 to 2020 in Urumqi City, located in an arid and semi-arid region of northwestern China. Additionally, the ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to quantify the relationship between the land surface temperature (LST) and influencing factors. The results showed that the area of the lower surface temperature classes has decreased significantly. In comparison, the area of the higher surface temperature classes has experienced a steady rise over the last two decades. From 2000 to 2020, the share of the area occupied by the temperature range 2 and adjusted R2 of the GWR were 0.75 and 0.73, obviously larger than the 0.58 of the OLS models. The GWR model’s higher R2 and adjusted R2 compared to the OLS model indicates that the relationship between LST and the influencing factors underlying the model may exhibit spatial non-stationarity, and the GWR model performs better than the OLS model. The results of both OLS and GWR models show that the normalized difference vegetation index (NDVI) and slope were negatively correlated with LST, while the urban index (UI) and normalized difference built-up index (NDBI) were positively correlated with LST. The findings of the study indicate that increasing green spaces and limiting the unplanned expansion of urban areas are effective measures to mitigate the UHIs in the study area. The results of the study may provide valuable insights into the spatiotemporal characteristics of the UHI and its drivers. Understanding the spatiotemporal characteristics of the UHI can help urban planners, policymakers, and scientists develop more effective urban cooling strategies and improve the urban thermal environment.

Water pricing is regarded as the most important and simplest economic instrument to encourage more efficient use of irrigation water in crop production. In the extremely water-scarce Tarim River basin in northwest China, improving water use efficiency has high relevance for research and policy. A Bayesian network modeling approach was applied, which is especially suitable under data-scarce conditions and the complex geo-hydrological, socioeconomic, and institutional settings of the study region, as it allows the integration of data from various types of sources. The transdisciplinary approach aimed at understanding the actual water pricing practices, the shortcomings of the current system, and possible ways of improvement. In an iterative procedure of expert interviews and group workshops, the key factors related to water pricing and water use efficiency were identified. The interactions among specific factors were defined by the respective experts, generating a causal network, which describes all relevant aspects of the investigated system. This network was finally populated with probabilistic relationships through a second round of expert interviews and group discussions. The Bayesian modeling exercise was then conducted using Netica software. The modeling results show that the mere increase of water price does not lead to significant increases in water use efficiency in crop production. Additionally, the model suggests a shift to volumetric water pricing, subsidization of water saving irrigation technology, and advancing agricultural extension to enable the farmer to efficiently react to increased costs for water. The applied participatory modeling approach helped to stimulate communication among relevant stakeholders from different domains in the region, which is necessary to create mutual understanding and joint targeted action. Finally, the challenges related to the applied transdisciplinary Bayesian modeling approach are discussed in the Chinese context.

In order to understand the occurrence of PAHs in soil and crops, the enrichment capacity of different crops for PAHs, and the distribution characteristics of PAHs in different parts of crops, the crops and soil planted in the farmland around Urumqi were studied as examples. Samples were collected in the farmland gathering area in the suburb of Urumqi in July 2021. A total of 100 crop samples were collected, including 21 crop species and 45 surface soil samples. The results showed that 16 types of PAHs were detected in the soil and crops. The total concentration of PAHs in farmland soil ranged from 19.06 to 1870.86 μg·kg -1 , and the average concentration was 127.40 μg·kg -1 . Seven carcinogenic PAHs accounted for 42.85%-79.20% of the 16 types of PAHs, among which BaP was the main pollutant in the soil. Through the characteristic ratio method, it was found that the main sources of PAHs in the soil were biomass and coal combustion. Total PAHs in crops ranged from 1.86 μg·kg -1 to 974.05 μg·kg -1 , with an average of 303.30 μg·kg -1 . Different crops had different enrichment capacities for PAHs. Among the 21 crops sampled, the accumulative content of PAHs in pumpkin was the highest (431.75 μg·kg -1 ). In leaf vegetable crops, the content of PAHs in leaves was higher than that in roots and fruits. In fruit and vegetable crops, the PAH content in fruit was higher than that in the root or leaf. There was a significant correlation between high cyclic PAHs in soil and PAHs in plant leaves. The health risk assessment of PAHs in crops showed that dietary intake had potential carcinogenic risk and even had high carcinogenic risk in adult male and female groups, which requires further attention.