To analyze the effect of the state-owned capital transfer policy on the sustainability of China's urban employee basic pension insurance fund (CUEBPIF), this study develops an actuarial model for pension insurance. The results reveal the following: (i) Without policy intervention, the CUEBPIF would face a deficit in 2027 and a cumulative shortfall of RMB207.44 trillion by 2050, and the proportion of fiscal subsidies for the CUEBPIF in the total fiscal expenditure would increase to 12.86 percent in 2050. (ii) Based on a delayed retirement policy, the transfer of 10 percent of state-owned capital can delay the onset of the fund deficit by 6 years, and the accumulated shortfall in 2050 would fall to RMB39.42 trillion, and the proportion of fiscal subsidies would decrease by 11.77 percentage points. (iii) The state-owned capital transfer policy can improve the sustainability of the CUEBPIF and reduce the burden of enterprise social security contributions when the transfer ratio increases to 20 percent.

The irrational use of arable land has caused increased nutrient loss and aggravated non-point source (NPS) pollution. This natural process has a significant socioeconomic origin. However, because of the inconsistency between the simulation scale of NPS pollution mechanism models and the statistical calibre of socioeconomic factors, the quantitative expression of the relationship between socioeconomic development, arable land use, and NPS pollutant output needs further exploration. We improved the mechanism model using a flow accumulation algorithm to construct a measure of the NPS pollutant output response at the administrative region scale. The mediating effect test confirmed that socioeconomic factors such as labour transfer, farmland property rights, large-scale operations, agricultural technological progress and risk aversion were also fundamental drivers that affected the NPS pollutant output by changing the arable land use form factors. The mediating drivers included endowment, landscape morphology, management system, planting mode and production capacity. The average total phosphorus concentrations per unit area in townships with various arable land use patterns were grain and economical crop extensive operations (0.83 mg/L); grain and economical crop scale operations (0.54 mg/L); grain, oil, and cotton scale operations (0.45 mg/L); and suburban compound operations (0.44 mg/L). We found that the degree of influence and path of each fundamental and mediating driver on NPS pollutant output concentrations showed clear differences between townships with different patterns, which were the primary reasons for the significant differences in NPS pollutant output responses.

Previous studies have extensively revealed the essential impact of urban morphology on the local thermal environment, but the nonlinear relationship has not yet been fully understood, especially for the diurnal variation. In this study, the fine-scale investigation of the thermal environment is comprehensively assessed by field measurement in five representative blocks in Xi'an, China. Twenty-two potential morphological variables from spatial composition, land-use features, and surrounding environment were obtained from the geographic information system. Both the ordinary least squares (OLS) and random forest (RF) models were adopted to explore the relationship between the thermal parameters and the predictor variables. The modeling results demonstrate that the RF model, effectively capturing the nonlinear relationship, outperforms the OLS model in study areas with a higher R2, a lower MSE and MAE values. The RF-based results clearly disclose that surrounding variables, such as distance to industry area (DI) and water bodies (DW), are generally the top-3 variables with the highest contribution rate (mean SHAP value>0.1) during the different periods. Building density is also a stronger driving factor than building height. Moreover, all important variables have an obvious nonlinear relationship with the threshold effect for the diurnal thermal environment variation. The proper thresholds for DI and DW are 3200 m and 1500 m, respectively, indicating that controlling DI larger than 3200 m and DW less than 1500 m may contribute to mitigating the heat island in Xi'an. Our study provides insights into machine learning models for thermal environment assessment and quantitative recommendations for decision-makers and urban planners to develop heat resilience cities.

In this study, we investigated the relationship between scale management and non-point source (NPS) total phosphorus (TP) load in Chaohu Lake Basin. We used agricultural point of interest (POI) density to characterize scale management and employed the Soil and Water Assessment Tool (SWAT) model for simulations. Our findings in 2018 showed that there was a segmented relationship between agricultural POI density and TP load in townships. Significant negative correlations between POI density and TP load were observed in medium-density (0.076 < POI<0.172) and high-density (POI>0.172) scale management townships, but not in low-density (POI<0.076) townships. The slope coefficient for high-density townships was approximately one-tenth of that for medium-density townships, indicating the prevalence of agricultural land scale management at only a certain density can effectively inhibit NPS TP emissions, and this inhibitory effect has a marginal diminishing effect. These observations also applied to vegetable, fruit, and flower nurseries bases. Notably, low-density scale management of farms still had a significant inhibitory effect on TP load. However, scale management of forest farms and tea plantations did not inhibit TP loads due to the influence of high natural background phosphorus content, low hills with large gradients, and low POI density.

The accurate recognition of outdoor weather has a very important application value in weather prediction, disaster warning, automatic driving and other fields. Fog, rain, snow and other bad weather pose a serious threat to driving safety, which is the focus of outdoor weather recognition. At present, video surveillance system has been widely used in highway surveillance system, and fog detection based on video image has received extensive attention. This paper will study fog detection technology based on dynamic texture features.This paper uses MATLAB as the simulation platform to realize the fog detection based on optical flow method. First of all, considering that the fog area in the video image will change in shape and concentration over time, appropriate anti-interference methods including median filtering are selected to complete the preprocessing; Secondly, according to the characteristics of fog, such as diffusion, the method of feature calculation and motion analysis based on optical flow is studied; Finally, the corresponding motion rules and analysis methods are established to detect and recognize the foggy video regions. The smoke video is processed in this paper, and the results show that the fog area can be accurately detected and the detection effect is good. The experimental results show that the processing method in this paper has a good effect, and has a high application value in video fog detection.

The quality of graduate students is one of the most important aspects that affect the quality of graduate education. This research builds a system of quality evaluation for graduate students by using Pearson Correlation Coefficient. The indicators are empowered by basing on the entropy weight method, and the candidates are ranked by according to the TOPSIS and RSR model. The difference between the two models is not noticeable, and the sorting outcomes are superior, by comparing the sorting results. We apply these models to study the quality of graduate students in Changchun University, and we obtain the conclusion that the quality of students in literature, law and economics are the best among all admissions majors.

In summer, Ozone (O3) pollution and urban heat island (UHI) pose serious health risks to humans. To obtain the spatial distributions of ozone and urban heat island in Xi'an in summer and develop a simultaneous control strategy of ozone and urban heat island, the land use regression model is modified and improved using the machine learning random forest algorithm. The LUR-Kriging-RF integrated prediction model is then established. The land use regression and kriging are used to extract the feature variables, while random forest is used to establish a regression model. The spatial distribution maps of ozone and urban heat island in Xi'an are obtained by regression mapping of the prediction model, and the spatial relationships between them are analyzed. The SHapley Additive explanation (SHAP) and partial dependence plot (PDP) are adopted to explain the way feature variables act on ozone and urban heat island. Based on the spatial distribution and interaction mode, a simultaneous control strategy of ozone and urban heat island in Xi'an is put forward. For ozone, the R2 of the integrated prediction model (0.65) is higher than that of land use regression (0.4), while the RMSE (28.18) of the integrated model is lower than that of land use regression (35.66). For temperature, the R2 of the integrated model (0.93) is higher than that of land use regression (0.8), while its RMSE (0.92) is lower than that of land use regression (1.52). The performance of the LUR-Kriging-RF integrated prediction model is better than that of land use regression. This study reveals the spatial interactions between ozone and urban heat island in the central urban areas. The suitable strategies for mapping ozone pollution and urban heat island control include reducing VOCs emissions from industrial sources and agricultural sources, increasing plants with low VOCs emissions, and spray humidification. This study can be used to evaluate ozone exposure and thermal exposure, provide scientific support for environmental protection and urban heat island control policies, contribute to reducing public health threats, promote the sustainability of urban environments, and promote the practical application of machine learning in this field.

An increasing number of studies in recent years have investigated the relationship between urban morphology and the urban heat island (UHI) effect in the context of global climate change and urbanization. However, most research does not consider the spatial heterogeneity of UHI effect and its relationship to urban morphology at the block level. In this study, we used 410 management units (MUs) of Xi'an, China, as the spatial scale and qualified the relationships between UHI effect and several influencing factors of block morphology. Geographically weighted regression (GWR) models were adopted combining multi-source data such as remote sensing images and building footprints. Compared to the ordinary least squares (OLS) models, the GWR models considerably improve modeling fit by capturing the spatial heterogeneity. The results show significant spatial variations of different variables. The impervious surface ratio (ISR) and building density (BD) are the top 2 urban morphology features intensifying the UHI effect, while green ratio (GR) is a critical factor forming a cool urban island in the dense urban areas. More importantly, floor area ratio (FAR) and sky view factor (SVF) show the strong non-stationary effect on the local UHI effect. These findings suggest that morphological variables significantly impact the UHI effect, and it is necessary to consider the spatial context. This study provides useful insights to understand the UHI effect as a function of urban morphology and substantial implications for sustainable urban planning, especially in high-density urban areas.

Fine particulate matter (PM2.5) has become a significant issue of ecological environment. However, few studies have explored the vertical distribution of PM2.5 in cities. The objectives of this paper are to reveal the vertical distribution regular pattern of PM2.5 over urban underlying surfaces near the ground with a hexacopter-type unmanned aerial vehicle (UAV) in winter. Results showed that the maximum vertical gradient of PM2.5 near the ground was typically the greatest in the morning as the stable atmospheric conditions. Moreover, regression model illustrated that relative humidity had the greatest impact on the vertical profile of PM2.5 compared to air temperature and altitude as hygroscopic of PM2.5 aerosols. Curve model shown that vertical profile of PM2.5 over the surfaces of water and green space first increased slowly and then declined, besides, the highest concentration inflection of PM2.5 above the water body (23.7 m) is higher than the green space (14.3 m). Thus, suggesting residents living vertical of 10–30 m from the ground around large water bodies and green spaces should not open windows for ventilation in the morning. Therefore, this study provides insights into the vertical distributions of PM2.5 over different underlying surfaces and should be of reference value to urban planners for designing urban spaces to optimize atmosphere environment to provide a healthy living environment.

Chinese government has instated strict restrictions to halt the spread of COVID-19. Given the complete shutdown of emission-resources, like traffic, factories, restaurants, and construction sites, responses to this pandemic have wrought unintended consequences in air quality. We assessed air pollution during the COVID-19 pandemic in terms of PM2.5, PM10, and O3 in Xi'an, China, and revealed the relations between air quality and potential emission resources. We gleaned pollutant concentration data of O3, PM2.5, and PM10 from five monitoring sites and identified their trending during the observed periods. We also deployed ArcGIS to interpolate points among data detected by 130 monitoring sites and obtained spatial distribution of pollution during the observed periods. Correlation analysis helped us reveal the relations between pollutants and seven sources. The results showed that during the lockdown section, the concentration of O3 rose by 100.61 %, and those of PM2.5 and PM10 dropped by 22.4 % and 20.7 %, respectively; and during the recovering section, the concentration of PM10 increased by 12.8 %. The spatial distributions also helped us identify two high-polluted areas and two risky areas where PM10 increased sharply. The correlation analysis also implied that decreasing emission sources is the key to improve air quality. Our study also suggests that coordinated control on ozone and particles should be the focus in the future, and the two high-polluted and the two risky areas require immediate administrative interference. Our study can be a valuable reference for public propaganda on green life and governments' sustainable development strategies. The research method for Xi'an might also inspire similar studies on other cities.

Volcanic activity remains highly detrimental to populations, property and activities in the range of its products. In order to reduce the impact of volcanic processes and products, it is critically important to conduct comprehensive volcanic risk assessments on volcanically active areas. This study tests a volcanic risk assessment methodology based on numerical simulations of volcanic hazards and quantitative analysis of social vulnerability in the Spanish island of Tenerife, a well-known tourist destination. We first simulated the most likely volcanic hazards in the two eruptive scenarios using the Volcanic Risk Information System (VORIS) tool and then evaluated the vulnerability using a total of 19 socio-economic indicators within the Vulnerability Scoping Diagram (VSD) framework by combining the analytic hierarchy process (AHP) and the entropy method. Our results show good agreement with previous assessments. In two eruptive scenarios, the north and northwest of the island were more exposed to volcanic hazards, and the east registered the highest vulnerability. Overall, the northern municipalities showed the highest volcanic risk in two scenarios. Our test indicates that disaster risk varies greatly across the island, and that risk reduction strategies should be prioritized on the north areas. While refinements to the model will produce more accurate results, the outputs will still be beneficial to the local authorities when designing policies for volcanic risk reduction policies in Tenerife. This study tests a comprehensive volcanic risk assessment for Tenerife, but it also provides a framework that is applicable to other regions threatened by volcanic hazards.

PM2.5 and PM10 could increase the risk for cardiovascular and respiratory diseases in the general public and severely limit the sustainable development in urban areas. Land use regression models are effective in predicting the spatial distribution of atmospheric pollutants, and have been widely used in many cities in Europe, North America and China. To reveal the spatial distribution characteristics of PM2.5 and PM10 in Xi'an during the heating seasons, the authors established two regression prediction models using PM2.5 and PM10 concentrations from 181 monitoring stations and 87 independent variables. The model results are as follows: for PM2.5, R2 = 0.713 and RMSE = 8.355 μg/m3; for PM10, R2 = 0.681 and RMSE = 14.842 μg/m3. In addition to the traditional independent variables such as area of green space and road length, the models also include the numbers of pollutant discharging enterprises, restaurants, and bus stations. The prediction results reveal the spatial distribution characteristics of PM2.5 and PM10 in the heating seasons of Xi'an. These results also indicate that the spatial distribution of pollutants is closely related to the layout of industrial land and the location of enterprises that generate air pollution emissions. Green space can mitigate pollution, and the contribution of traffic emission is less than that of industrial emission. To our knowledge, this study is the first to apply land use regression models to the Fenwei Plain, a heavily polluted area in China. It provides a scientific foundation for urban planning, land use regulation, air pollution control, and public health policy making. It also establishes a basic model for population exposure assessment, and promotes the sustainability of urban environments.

As an effective indicator of urbanization, impervious surfaces play a significant role in urban planning and ecological protection. It is, therefore, important to characterize impervious surfaces in urban geographical studies. As a key city in East China, Xuzhou has experienced rapid urbanization in recent decades and is now becoming an environmentally friendly city. To better understand the spatiotemporal heterogeneity of Xuzhou’s urban development, we extracted its impervious surfaces from Landsat images of 1995, 2003, 2010, and 2018 by a linear spectral mixture analysis. Then, a range of complementary methods including landscape indices, profile lines, median centers, standard deviational ellipses, and spatial autocorrelation were adopted to analyze the landscape pattern and expansion of impervious surfaces on both city and district scales. Results show that (1) there was a constant impervious surface expansion, originating in downtown Xuzhou; (2) promoting ecological protection in urban areas fragmented impervious surfaces with increasing heterogeneity and diversity overall; and (3) expansion directions and rates of impervious surfaces varied with district and town, and the central urban area expanded towards east and southeast, which could be related to their own resources and governmental policies. Findings from this study provide useful insights into urban planning of this economically prospective region.

As uncontrolled urban growth has increasingly challenged the sustainable use of urban land, it is critically important to model urban growth from different perspectives. Using the SLEUTH (Slope, Land use, Exclusion, Urban, Transportation, and Hill-shade) model, the historical data of Hefei in 2000, 2005, 2010, and 2015 were collected and input to simulate urban growth from 2015 to 2040. Three different urban growth scenarios were considered, namely a historical growth scenario, an urban planning growth scenario, and a land suitability growth scenario. Prediction results show that by 2040 urban built-up land would increase to 1434 km(2) in the historical growth scenario, to 1190 km(2) in the urban planning growth scenario, and to 1217 km(2) in the land suitability growth scenario. We conclude that (1) exclusion layers without effective limits might result in unreasonable prediction of future built-up land; (2) based on the general land use map, the urban growth prediction took the governmental policies into account and could reveal the development hotspots in urban planning; and (3) the land suitability scenario prediction was the result of the trade-off between ecological land and built-up land as it used the MCR -based (minimum cumulative resistance model) land suitability assessment result. It would help to form a compact urban space and avoid excessive protection of farmland and ecological land. Findings derived from this study may provide urban planners with interesting insights on formulating urban planning strategies.

Because planning management of the leisure and tourism-oriented suburban villages lags behind, a series of problems occur: sustainable development of rural tourism is limited, and the content of planning management is unclear, and implement does not reach the designed position. In order to solve these problems, through research and analysis, we master the status quo of planning management of leisure and tourism-oriented suburban villages in China, and determine the content of planning management. The author analyze the interactive relationship between system theory and leisure and tourism-oriented suburban rural planning management and construct a planning management system based on the perspective of systems theory. The informatization design of system was achieved by information technology. The results show that the informatization system based on the perspective of system theory not only provides a solution for the planning management of the leisure and tourism-oriented suburban villages, but also lays a foundation for software development and website function design.

In this work, the differences among the vertical distribution of pollutant PM2.5 concentration of three urban land types near the ground were analyzed to get the data on pollutant PM2.5 concentration of urban water, green space and road in Xi’an. The Guang Yuntan waters of Ba River, the green space of Binhe Park in western bank of Ba River, the roadside that is 400 m away from western bank of Ba River was selected as the monitoring points among which four monitoring periods were set and pollutant PM2.5 concentration data were collected with UAV (unmanned aerial vehicle) monitoring platform between 0m to 100 m. The experimental results show that pollutant PM2.5 concentration data is different in the vertical distribution of PM2.5 near the ground. The changes of the vertical distribution of PM2.5 concentration existing in such three types of land as waters area, green space and roads are also different at the same time, especially at 8:00 am. The same monitoring point during different periods differs in a tendency of high value in the morning and low in the afternoon to analyze from the perspective of different monitoring points at the same time, there exists difference between the vertical distribution of pollutant PM2.5 near the ground impacted by different urban land types.