Guanzhong Urban Agglomeration Research Articles

Variation Characteristics and Source Analysis of Atmospheric Ozone Pollution in Guanzhong Region

Guanzhong urban agglomeration has a good development foundation and great development potential, and it has a unique strategic position in the national all-round opening up pattern. In recent years, the problem of near-surface ozone （O3） in the Guanzhong Region has become increasingly prominent, which has become a bottleneck affecting the continuous improvement of air quality. In order to effectively prevent and control O3 pollution, this study analyzed the characteristics of annual, monthly, and daily changes in O3 concentration in the Guanzhong Region based on the environmental monitoring data from 2018 to 2021. A geo-detector was used to study the driving factors of the spatial differentiation of O3 concentration, and the sources of O3 were analyzed using a backward trajectory model and emission inventory construction. The results showed that the daily and monthly variation in O3 concentration in the Guanzhong Region were unimodal. The daily maximum value appeared at 15：00, the minimum value appeared at 07：00, the peak value of the monthly average appeared in June, and the valley value appeared in December. The O3 concentration was highest in summer, followed by that in spring, and the lowest in winter. The days of O3 exceeding the standard showed mainly mild pollution, and moderate and above pollution showed a trend of decreasing first and then increasing. The O3 concentration in the Guanzhong Region was mainly closely related to precursors and meteorological factors, and the explanatory power of the interaction of each factor was significantly greater than that of any single factor. The regional transport of O3 concentration in the Guanzhong Region was mainly affected by easterly airflow, followed by the northwest direction, with the potential source areas located mainly in Henan Province and Hubei Province. The main local sources of volatile organic compounds （VOCs） were solvent use sources, process sources, and mobile sources, and the main emission sources of nitrogen oxides （NOx） were mobile sources and industrial production combustion sources. The research results have a guiding significance for O3 joint prevention and control in the Guanzhong Region.

Identifying the Coupling Coordination Relationship between Urbanization and Ecosystem Services Supply–Demand and Its Driving Forces: Case Study in Shaanxi Province, China

Exploring the relationship and driving forces between supply–demand of ecosystem services (ESs) and urbanization can help solve the environmental problems and promote regional sustainable development. This study analyzed the spatio-temporal distribution characteristics of supply–demand of ESs and comprehensive urbanization level (CUL) in Shaanxi Province from 2010 to 2019 and assessed the coupling relationship between ecosystem service supply–demand ratio (ESSDR) and CUL using the coupling coordination degree (CCD) model. Random forests and geographically weighted regression methods were utilized to characterize the contribution and spatial distribution of the drivers of CCD. The results showed that: (1) except for habitat quality, the ESSDR of the other three types of services as well as the comprehensive services showed a decreasing trend, CUL exhibited increasing trend; (2) Although CCD was generally increasing, a significant portion (78.51%) of regions still remained uncoordinated, with relatively better coordination shown around the Guanzhong urban agglomeration, which has a higher urbanization level; (3) The CCD in Shaanxi Province was primarily influenced by local financial income, the secondary industry, and temperature forces. In regions with high and increasing CCD, the tertiary industry was the decisive force. In other areas, there were significant spatial variations in the driving forces. These findings provide a coupled and coordinated perspective for urban ecological management, which can provide scientific reference and practical guidance for cities with different development modes.

Spatio-Temporal Relationship between Land Use Carbon Emissions and Ecosystem Service Value in Guanzhong, China

In the context of escalating global concerns for “carbon neutrality and peak carbon” and the urgent need for ecological conservation, deciphering the spatiotemporal interactions between carbon emissions and the ecosystem service value (ESV) in relation to land use changes becomes critically significant. Identifying areas to bolster ecosystem services and curtail carbon emissions, especially within the Guanzhong urban agglomeration, is crucial for advancing sustainable and low-carbon regional development. The study focuses on the urban agglomeration of Guanzhong, using land use and socio-economic data from three periods between 2010 and 2020. Methods such as grid analysis and bivariate spatial autocorrelation models are employed to explore the temporal and spatial evolution characteristics and interaction patterns of carbon emissions and ESV in relation to land use. The findings reveal: (1) during 2010–2020, the Guanzhong urban agglomeration experienced varied transitions in land use types, marked by a significant net decrease in arable land and net increases in grasslands and urban construction areas. (2) The ESV in the Guanzhong urban agglomeration witnessed a consistent rise, exhibiting a spatial distribution pattern with higher values in the southwest and lower in the northeast. Among the categorized ecosystem service functions, services related to hydrological and climate regulation stood out. (3) The Guanzhong urban agglomeration observed an average annual growth rate of 5.03% in carbon emissions due to land use, with a spatial trend that was higher in the center and tapered towards the periphery. Predominant carbon sources included arable lands and urban construction areas, while forests accounted for 94% of carbon sequestration. (4) A pronounced negative correlation between the ESV and carbon emissions was discerned in Guanzhong. Regions with a stronger correlation were primarily centered in Guanzhong, notably around Xi’an and Baoji. The results emphasize the pivotal role of the primary sector’s qualitative development in harmonizing the ESV and carbon emission dynamics in the Guanzhong urban agglomeration. This research provides valuable insights for optimizing land resource management, aligned with the rural revitalization strategy, streamlining carbon dynamics, bolstering ESV, augmenting carbon sequestration efficiency, and guiding ecological spatial planning.

Identification of Ecological Restoration Priority Areas Integrating Human Activity Intensity and Multi-Criteria Decision Analysis

Restoration action is critical to ensure a safe environment for humans. Reasonable planning is essential to optimize the efficiency of ecological restoration inputs and outputs when implementing restoration measures. In this study, a method that combines human activity intensity assessment and multi-criteria decision analysis to determine ecological restoration priority (ERP) areas was developed to identify priority and feasible areas for ecological restoration in Shaanxi Province in 2020. The results showed that the total area involved in restoration feasibility assessment in Shaanxi is 10.89 × 104 km2. Among them, the percentage of regions with low feasibility (less than 0.2) is 68.86%, mainly located in Qinling area. High feasibility areas (more than 0.6) accounted for 2.47%, mainly located in the Loess Plateau area of northern Shaanxi. The spatial distribution of the human activity intensity is concentrated in urban areas and extended with the distribution of roads. In total, 10.69% of the regions showed high and very high intensity of human activity, including the Guanzhong urban agglomeration region. This study identified 6078 km2 and 671 km2 of medium and high ecological restoration priority areas, which are more concentrated in the north of the study area. The need for ecological restoration work is even more urgent in northern Shaanxi. In general, the framework in this study has spatially located the priority and feasible areas for restoration, and may provide a useful reference for landscape-scale spatial conservation planning.

Climate and industrial pollution determine the seasonal and spatial mercury variations in the China's Weihe River

Natural processes and human activities impact mercury (Hg) pollution in rivers. Investigating the individual contributions and interactions of factors affecting variations in Hg concentrations, particularly under climate change, is crucial for safeguarding watershed ecosystems and human health. We collected 381 water samples from China's Weihe River Basin (WRB) during dry and wet seasons to assess the total Hg (THg) concentration. Results revealed high Hg concentrations in the WRB (0.1–2200.9 ng/L, mean 126.2 ± 335.5 ng/L), with higher levels during the wet season (wet season: 249.1 ± 453.5 ng/L, dry season: 12.7 ± 14.0 ng/L), particularly in the mainstream and southern tributaries of the Weihe River. Industrial pollution (contributing 26.2 %) and precipitation (contributing 33.5 %) drove spatial heterogeneity in THg concentrations during the dry and wet seasons, respectively. Notably, combined explanatory power increased to 47.9 % when interaction was considered, highlighting the amplifying effect of climate change, particularly precipitation, on the impact of industrial pollution. The middle and downstream of the Weihe River, especially the Guanzhong urban agglomeration, were identified as high-risk regions for Hg pollution. With ongoing climate change the risk of Hg exposure in the WRB is expected to escalate. This study lays a robust scientific foundation for the effective management of Hg pollution in analogous river systems worldwide.

Ecological Sensitivity of Urban Agglomeration in the Guanzhong Plain, China

In the past two decades, China’s urbanization has advanced rapidly. In 2018, Xi’an was successfully selected as a national central city, and the Guanzhong Plain urban agglomeration (GZPUA) is emerging rapidly due to Xi’an. This study focuses on the current ecological status of the region and how to strike a balance between economic development and ecological protection. This study uses the ecological vulnerability of the Guanzhong urban agglomeration as a starting point to investigate the changes in its spatial and temporal distribution of ecological vulnerability and the primary driving factors, as well as to investigate the interaction between the changes in ecological vulnerability and urban agglomeration development in the GZPUA region. Using the “sensitivity–elasticity–pressure (SEP)” assessment framework model, this paper selects the spatial distribution data of natural, social, and economic sources in 2000 and 2020 based on the ecological environment characteristics of GZPUA. By using spatial principal component analysis, this paper quantitatively evaluates the ecological vulnerability changes of GZPUA in two periods, 2000 and 2020, with 1000 m × 1000 m raster as the evaluation unit, classifies the ecological vulnerability of the area into levels, and conducts a sub-regional in-depth study from different administrative regions. This research helps to comprehend the change in ecological environment quality in the GZPUA and provides a basis for ecological environment management decisions in the region. The results showed that (1) the ecological vulnerability of the Guanzhong Plain urban agglomeration as a whole is moderate, with the highest ecological vulnerability index (EVI) value of 0.89 and the lowest EVI value of 0.087 in 2000, and the highest EVI value of 0.93 and the lowest EVI value of 0.082 in 2020. The percentage of areas with the highest ecological vulnerability (moderate or severe) was 5.07% in 2000 and 15.11% in 2020. (2) The variation scope of the integrated EVI in the study region is 1.78–4.96 (2000) and 1.81–4.99 (2020), among which the EVI values in Xi’an, Bei Lin, Lian Hu, and Xin Cheng are the highest and the EVI values in Tai Bai, Zhou Zhi, and Feng Xian are the lowest. (3) In the central region of GZPUA, the spatial variation of CEVI is distributed in a circle that is highly congruent with the region’s economic and population development characteristics, whereas the spatial variation of CEVI in the southern mountainous region of the study area is primarily constrained by the topography and natural conditions. This region has low CEVI because of the high mountains and thick forests, which is a crucial ecological barrier for the GZPUA. (4) The EVI, ecological sensitivity index (ESI), ecological elasticity index (EEI), and ecological pressure index (EPI) have a strong relationship with land use. Among them, farmland and built-up land showed highly significant correlations with the EVI, ESI, EEI, and EPI (p < 0.01).

Estimating PM2.5 Concentrations Using the Machine Learning RF-XGBoost Model in Guanzhong Urban Agglomeration, China

Fine particulate matter (PM2.5) is a major pollutant in Guanzhong Urban Agglomeration (GUA) during the winter, and GUA is one of China’s regions with the highest concentrations of PM2.5. Daily surface PM2.5 maps with a spatial resolution of 1 km × 1 km can aid in the control of PM2.5 pollution. Thus, the Random Forest and eXtreme Gradient Boosting (RF-XGBoost) model was proposed to fill the missing aerosol optical depth (AOD) at the station scale before accurately estimating ground-level PM2.5 using the recently released MODIS AOD product derived from Multi-Angle Implementation of Atmospheric Correction (MAIAC), high density meteorological and topographic conditions, land-use, population density, and air pollutions. The RF-XGBoost model was evaluated using an out-of-sample test, revealing excellent performance with a coefficient of determination (R2) of 0.93, root-mean-square error (RMSE) of 12.49 μg/m3, and mean absolution error (MAE) of 8.42 μg/m3. The result derived from the RF-XGBoost model indicates that the GUA had the most severe pollution in the winter of 2018 and 2019, owing to the burning of coal for heating and unfavorable meteorological circumstances. Over 90% of the GUA had an annual average PM2.5 concentrations decrease of 3 to 7 μg/m3 in 2019 compared to the previous year. Nevertheless, the air pollution situation remained grim in the winter of 2019, with more than 65% of the study area meeting the mean PM2.5 values higher than 35 μg/m3 and the maximum reaching 95.57 μg/m3. This research would be valuable for policymakers, environmentalists, and epidemiologists, especially in urban areas.

Impact of the differences in carbon footprint driving factors on carbon emission reduction of urban agglomerations given SDGs: A case study of the Guanzhong in China

• The driving mechanism of CF based on SDGs is revealed. • There is a critical point in the population agglomeration effect on CF. • The effects of energy structure and energy intensity on CF differ significantly. • The “ strong decoupling ” state of CF and economic growth is unstable. • Urban agglomerations plays an important role in carbon emission reduction. Urban agglomerations (UAs) are important actors in carbon emission reduction (CER) of China, but with the acceleration of urbanization, the increasing carbon footprint (CF) seriously hinders UAs to achieve the Sustainable Development Goals (SDGs). Taking the Guanzhong urban agglomeration (GUA) as a case, the CF are measured by IPCC emission inventory method. It is analyzed that the temporal and spatial evolution characteristics of CF. The logarithmic mean Divisia index model and Tapio decoupling model are used to explore the effects and difference changes of the CF driving factors and analyze the decoupling relationship between CF and economic growth. The results show that the CF of the GUA increased from 161.60 to 274.02 million tons of carbon dioxide equivalent in 2007-2018, which is an increase of 71.25%. Urban expansion, industrial structure and economic growth promoted the increases, and population agglomeration promoted the decreases. The effects of the energy structure and energy intensity on the CF changes are significantly different among cities. The decoupling state of CF and economic growth is unstable. The study provides the SDGs-oriented research framework on CF driving factors for developing regions and helps improve the adaptability of CER policies in UAs.

Evaluating the ability of transformed urban agglomerations to achieve Sustainable Development Goal 6 from the perspective of the water planetary boundary: Evidence from Guanzhong in China

Differences in water resource endowment and socio-economic development of transformed urban agglomerations (TUAs) lead to differences in the ability of cities to achieve Sustainable Development Goal 6 (SDG6), which inevitably underestimates the possibility of water crises. This study employs the water footprint (WF) and reduced water planetary boundary (WPB) as absolute environmental sustainability indicators and uses a drive-pressure-state-impact-response (DPSIR) framework to describe the spatio-temporal differences and evolutionary characteristics of the ability of a TUA to achieve SDG6. This study takes the Guanzhong urban agglomeration (GUA) in China as a case. The WF of GUA increased from 19.55 billion m3 to 23.44 billion m3 in 2007–2018, but it increased initially and then fell. The proportion of the ecological environment WF increased by 1.41%, which is in line with SDG6.4 and SDG6.6. However, WF is larger than WPB. The water deficit was within the interval of [6 billion m3, 23 billion m3]. The pressure subsystems in Tongchuan and Weinan were as high as 28.80%, and the response subsystems in Xi'an and Baoji had the highest weights of 33.79% and 25.84%, respectively. Thus, GUA is under severe water pressure. Causal chain relationships between the socio-economic activities of each city in the water resource system were not the same, reflecting a spatial difference in the ability to achieve SDG6. To this end, this study proposes the potential value and policy implications of reducing the differences in the ability of cities to achieve SDG6 to avoid water crises in TUAs.

Urban Sustainability Evaluation Based on the DPSIR Dynamic Model: A Case Study in Shaanxi Province, China

The sustainability of urban cities has been the focus of significant academic research in recent years and is emphasized in Goal 11 of the Sustainable Development Goals (SDGs). In this study, we adopted the Drive-Pressure-State-Impact-Response model (DPSIR) to promote a conceptual study of sustainable development index (SDI) to compare the different urban sustainable development status and try to find the factors that affect the urban sustainable development. The framework of indicators we used is mainly based on Goal 11 of the SDGs’ targets and indicators. We chose six cities in the Shaanxi Province of China and studied them from 2008 to 2018. The results show that: (1) the sustainable development of urban cities is greatly influenced by China’s national economic development plans and urban development strategies; (2) the economic growth and management level of authorities can significantly promote urban sustainability; (3) the urban sustainability of the six cities in Shaanxi Province showed a significant imbalance and this imbalance affected the overall development of the region; (4) compared with Guanzhong urban agglomeration, Shannan urban agglomeration is subject to the policy needs of environmental protection in the Qinling mountain area and its economic development is restricted; therefore, its urban sustainability is relatively low. Theoretical contributions are presented to assist in addressing these challenges and to support policies and initiatives that move these cities in China towards achieving SDG 11.

Study on conceptual model of regional pollution meteorological conditions in Guanzhong Urban Agglomeration

This paper defined regional pollution day and regional heavy pollution day, calculated the atmospheric stability by Pasqual classification and the height of mixed layer by national standard method, established a conceptual model of meteorological conditions in Guanzhong Urban Agglomeration. The results show that：(1) The more possibility of the regional pollution and regional heavy pollution is, the lower the mixing layer height is. (2)The frequency of stability increased with degree of pollution, the frequency of regional heavy pollution stability reached nearly 60% as regional heavy pollution. (3)In heavily polluted area, the frequency of ground temperature inversion is nearly 90%, the average cumulative temperature inversion thickness is 260m at assurance rate of 80%, and the maximum temperature inversion strength is 10.8℃/100m. (4) Regional pollution is easy to occur when the frequency of quiet wind is higher than 50%. At the same time, it is easy to form wind convergence area in the hinterland of Guanzhong Area, which causes the accumulation of pollutants.

Predict Coordinated Development Degree of County Eco-Environment System Using GA-SVM

This article describes how economic development has had a significant impact on the environment. County eco-environment coordinated development has contributed to regional coordinated development in China. A support vector machine (SVM) model was constructed to classify and predict coordinated development degrees of the county eco-environment system. In order to improve the discrimination precision of SVM in classification, a Genetic Algorithm (GA) was used to optimize SVM parameters in the solution space. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding coordinated development degree of county eco-environment system prediction for Guanzhong urban agglomeration. It found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient. The simulation indicates that the county slowing-down of economic development would not have positive effect on the environment sustainability. GA-SVM provides an effective measurement for region eco-environment system classification and prediction.

The Research on Air Pollution Laws in Guanzhong Urban Agglomeration Based on High Frequency AQI Data

Based on high frequency data of AQI and contaminants, this paper makes researches including general situation of air pollution, fluctuation rules of air quality in one day and associated rules of air pollution between cities by using statistical analysis methods like the hour index of AQI and building the VAR model. The conclusions are as following: firstly, it is obvious that air pollution which often manifests as pollution of particulates has a seasonal effect and a clustering property. Secondly, different seasons have different air quality fluctuation rules in one day. However, with no consideration of the phase position diversity of graphs, intraday fluctuation rules of air quality in one season are similar to those in other seasons. Thirdly, the deterioration of air condition of one city can cause the deterioration of air condition of other cities, and the peak of this influence appears in one day generally, and the influence weakens with the increase of spatial distance.

Study on Commodity Residential House Price Volatility of the Guanzhong Urban Agglomeration Using Spatial Econometric Analysis

This paper was based on the theory of spatial econometric model. It selected the panel datas of the guanzhong urban agglomeration of five core cities from 1998 to 2012, and inspected the commodity residential house price if there is a space dependency relationship between the two cities. On the basis to analyze the main factors influencing the commodity residential house price volatility and research on the influence of the housing price direction. Results show that the sample is significant spatial correlation between urban housing prices. Xi 'an have great influence on regional cities housing price. The urban population, household disposable income, land acquisition costs, sales area are the main influence factors affecting housing price volatility.

Application of GA-SVM Prediction in Region Coordinated Development of Economy-Resource-Environment System: County-Level Evidence from China Guanzhong Urban Agglomeration

Application of GA-SVM Prediction in Region Coordinated Development of Economy-Resource-Environment System: County-Level Evidence from China Guanzhong Urban Agglomeration