Geodetector Method Research Articles

Effectiveness Trade-Off Between Green Spaces and Built-Up Land: Evaluating Trade-Off Efficiency and Its Drivers in an Expanding City

Urban expansion encroaches on green spaces and weakens ecosystem services, potentially leading to a trade-off between ecological conditions and socio-economic growth. Effectively coordinating the two elements is essential for achieving sustainable development goals at the urban scale. However, few studies have measured urban–ecological linkage in terms of trade-off. In this study, we propose a framework by linking the degraded ecological conditions and urban land use efficiency from a return on investment perspective. Taking a rapidly expanding city as a case study, we comprehensively quantified urban–ecological conditions in four aspects: urban heat island, flood regulating service, habitat quality, and carbon sequestration. These conditions were assessed on 1 km2 grids, along with urban land use efficiency at the same spatial scale. We employed the slack-based measure model to evaluate trade-off efficiency and applied the geo-detector method to identify its driving factors. Our findings reveal that while urban–ecological conditions in Zhengzhou’s periphery degraded over the past two decades, the inner city showed improvement in urban heat island and carbon sequestration. Trade-off efficiency exhibited an overall upward trend during 2000–2020, despite initial declines in some inner city areas. Interaction detection demonstrates significant synergistic effects between pairs of drivers, such as the Normalized Difference Vegetation Index and building height, and the number of patches of green spaces and the patch cohesion index of built-up land, with q-values of 0.298 and 0.137, respectively. In light of the spatiotemporal trend of trade-off efficiency and its drivers, we propose adaptive management strategies. The framework could serve as guidance to assist decision-makers and urban planners in monitoring urban–ecological conditions in the context of urban expansion.

Exploring the spatiotemporal evolution and risk factors for total factor energy productivity in Guangdong Province, China

The promotion of low-carbon and green development is emphasized in the report of the Twentieth National Congress of the Communist Party of China, indicating that the deepening concept of green transformation is significant to Guangdong's modernization and development of environmental protection industries. The purpose of this study is to effectively enhance the green total factor energy productivity (TFEP) and narrow its spatial differences to achieve the coherent development of environmental protection. The GML and FGNZ decomposition methodologies are adopted to examine the TFEP in Guangdong Province. Furthermore, ArcGIS's natural discontinuity, spatial trend surface analysis, and standard deviation ellipse methods are applied to characterize the spatial and temporal evolution of TFEP. Finally, the Geodetector method is used to study the spatiotemporal evolution of the TFEP in Guangdong Province according to risk factors. Overall, the TFEP in Guangdong Province has exhibited a growing trend. The general trends in TFEP are high. Nevertheless, there are large regional differences. Economic development, financial development, the industrial structure, and political reform are the core risk factors. This study is conducive to proposing effective strategies for realizing the goals of sustainable carbon reduction.

Monitoring and Analysis of the Driving Forces Behind Ecological and Environmental Quality at the County Scale Based on Remote Sensing Data

Chaohu Lake, as an important freshwater lake in China, mainly relies on surface runoff for water replenishment, and the environmental quality of the surrounding towns directly impacts the environment of Chaohu Lake. Given the characteristics of rich water resources and extensive river networks in the lake area, this paper utilizes the GEE platform and selects Landsat data from 1992 to 2022, taking Feidong County, one of the lake’s inlets, as the study area. We used the water benefit-based ecological index (WBEI) to monitor and evaluate the ecological quality of the study area and employ the Sen+MK trend analysis method to analyze the spatial-temporal characteristics of ecological quality changes. To explore the driving forces behind the spatial-temporal changes in the WBEI, this study selects land use type, elevation, slope, aspect, potential evapotranspiration, annual average precipitation, annual average temperature, and five characteristic factors used in the construction of the WBEI as influencing factors. Using the geo-detector method, the study analyzes the driving forces behind the spatial-temporal changes in the WBEI in the study area. Results show that the WBEI, considering water efficiency, integrates waterbody information into regional environmental quality assessments, comprehensively reflecting the ecological environment of lakeside cities. From 1992 to 2022, the WBEI of the study region shows an increasing trend, with an improved area accounting for 1110.42 km2, or 51.21% of the total area. Among these, the significantly improved area covers 372.9789 km2 or 17.2% of the total area, while the slightly improved area covers 737.4411 km2, corresponding to 34.01% of the total area. Interaction types of influencing factors include bivariate enhancement and nonlinear enhancement, with the primary interactive factors affecting the ecological environment quality change in Feidong County being CLCD∩RVI; changes in land use and vegetation cover are the main driving forces behind the changes in ecological and environmental quality in Feidong County. From 1992 to 2022, the main land type transformations in the study area were from arable land to other land types, with a significant conversion of arable land to construction land, which is the main reason for the degradation of local ecological and environmental quality. The results of this study can provide practical references and theoretical support for ecological environment assessment, governance, and improvement in areas with abundant water resources.

Detection of Factors Affecting the Spatial Distribution of Ozone in Summer over China Based on Geodetector

To study the factors affecting the spatial distribution of ozone concentration in different regions of China, based on the summer ozone monitoring data of 2019 and three influencing factors, namely, main meteorological, direct emission, and socio-economic factors, the influence degree of various factors on the spatial differentiation of ozone was quantified by using the Geodetector method and the comparison between regions was conducted. The results showed that the spatial differentiation of ozone concentration in summer 2019 was most affected by the main meteorological factors and the average degree of influence reached 0.277. Relative humidity was the dominant meteorological factor and the influence degree reached 0.415. The degree of influence of meteorological factors increased from northeast and western regions to central regions. The influence of social and economic factors increased from north to south, except for the low plateau climate area. The analysis on the interaction of two factors revealed that the average wind speed of meteorological factors had the greatest impact on other factors and large regional differences were present. Among socio-economic factors, population density had the greatest impact on other factors and was the key factor driving the regional differentiation of other factors. These results are helpful to understand the dominant factors of spatial distribution of ozone concentration in different regions of China and have guiding importance for the prevention and control of ozone pollution.

Vegetation Cover Changes and Environmental Drivers in Typical Desert Fringe Areas of Northern China

ABSTRACTChina is among the nations experiencing severe desertification, and several policies have been enacted to mitigate this issue. A thorough understanding of how natural processes and socioeconomic factors interact to shape the distribution of local vegetation is crucial to effectively address the widespread problem of desertification. This study investigates the dynamics of vegetation and environmental influences at the margins of deserts by utilizing MODIS data products. Through the application of the maximum synthesis method, trend analysis, and the geodetector method, this study offers a comprehensive exploration of vegetation changes and the underlying environmental factors shaping these changes. These findings indicate a discernible trend of increasing vegetation cover and notable spatiotemporal heterogeneity in desertification management in the study areas over the last 21 years. The Ta region (desert fringe of the Taklamakan Desert) presented the lowest vegetation growth rate (0.294%), followed by the Te region (desert fringe of the Tengger Desert) (0.505%), whereas the Mu region (desert fringe of the Mu Us sandy land) presented the highest growth rate (1.055%), indicating a gradient of recovery from west to east. The findings of this research indicate that the spatial pattern of vegetation distribution is determined by natural factors. However, socioeconomic factors have greater explanatory power than natural factors in explaining the local vegetation distribution. Additionally, the study underscores that the interaction between socioeconomic and natural factors holds greater significance than the influence of any single factor. These results offer valuable insights with practical implications for addressing desertification in China.

Source apportionment and driving factor identification for typical watersheds soil heavy metals of Tibetan Plateau based on receptor models and geodetector

The identification and quantification of soil heavy metal (HM) pollution sources and the identification of driving factors is a prerequisite of soil pollution control. In this paper, the Sabaochaqu Basin of the Tuotuo River, located in the Tibetan Plateau and the headwater of the Yangtze River, was selected as the study area. The soil pollution was evaluated using geochemical baseline, and the source apportionment of soil HMs was performed using absolute principal component score-multiple linear regression (APCS-MLR), edge analysis (UNMIX) and positive matrix decomposition (PMF). The driver of the source factor was identified with the geodetector method (GDM). The results of pollution evaluation showed that the HM pollution of soil in the study area was relatively light. By comparison, UNMIX model was considered to be the preferred model for soil HMs quantitative distribution in this study area, followed by PMF model. The UNMIX model results show that source 1 (U-S1) was dominated by As, with a contribution rate of 53.31%; source 2 (U-S2) was dominated by Cd and Zn, whose contribution rates are 50.35% and 46.60% respectively; source 3 (U-S3) was dominated by Pb, with a contribution rate of 45.58%; source 4 (U-S4) was dominated by Cr, Cu, Hg and Ni, with contribution rates of 60.58%, 60.07%, 51.58% and 56.45%, respectively. The GDM results showed that the main driving factors of U-S1 were distance from lake (explanatory power q = 0.328) and distance from wind channel (q = 0.168), which were defined as long-distance migration sources. The main driving factors of U-S2 were parent material type (q = 0.269) and distance from Tuotuo river (q = 0.213), which were defined as freeze-thaw sources. The main driving factors of U-S3 were distance from town (q = 0.255) and distance from county road (Yanya Line) (q = 0.221), which were defined as human activity sources. The main drivers of U-S4 were V (q = 0.346) and Sc (q = 0.323), which were defined as natural sources. The GDM results of the 3 models were generally consistent with the analytical results of similar types of sources, especially the results of PMF model and Unmix model can basically verify each other. The research results can provide important theoretical reference for the analysis of HM sources in the soil of high-cold and high-altitude regions.

Projected dryness/wetness pattern and influence factors in China under the CMIP6 scenarios for 2021–2100

Analyzing spatiotemporal patterns of dryness/wetness is important for measures and strategy development for the resulting disasters. This research explores projected dryness/wetness patterns and influence factors in China under two Shared Socioeconomic Pathway (SSP)-based scenarios (SSP245 and SSP585). The dryness/wetness is evaluated by a non-parameter Standardized Precipitation Evapotranspiration Index, and the influence is analyzed by the GeoDetector method. The result shows that under SSP245, dryness is more likely to increase in autumn. It is primarily located in North-China, South-China, and Middle-lower Yangtze in summer and autumn and in South-China and Huang-Huai-Hai in other seasons. Wetness is more likely to be enhanced in winter, and simultaneous precipitation has more influence, especially in semiarid regions. Under SSP585, the dryness is enhanced throughout China except in winter, and the wetness is enhanced except in autumn. The enhanced wetness in spring and winter is located in North-China and in summer in Southwest China. Temperature has a greater influence in spring and autumn, and precipitation has a greater influence in winter on dryness. The interaction influence is enhanced in almost all regions and seasons under two scenarios. The results could be useful for land managers to develop strategies and mitigate the effects of climate change.

Spatiotemporal evolution and driving factors of ecosystem service value in the Upper Minjiang River of China.

The stability of ecosystems in high mountain canyon areas is poor, and the interaction between humans and the land is complex, making these ecosystems more vulnerable to destruction. Quantitatively assessing the ecosystem service value (ESV) in high mountain canyon areas and revealing its spatiotemporal evolution patterns and driving factors play a crucial role in the construction of regional ecological barriers and the assurance of ecological security. This study focuses on the Upper Minjiang River as the research area, using the InVEST model and the Equivalent Factor Method to estimate ESV. This combination aims to address the inadequacy of the Equivalent Factor Method in reflecting the variability of ESV across different regions, and the sensitivity of the InVEST model to data changes that results in insufficient accuracy of ESV assessments. By harnessing spatial au-tocorrelation and the geodetector method, we unravel the spatiotemporal evolution characteristics and driving factors of ESV. The results show that: (1) From 2000 to 2020, the ESVs estimated by the two estimations increased by 31.28% and 22.47%, respectively, both indicating that the eco-environment quality of the upper Minjiang River has been continuously improved. (2) When Moran's I was greater than 0.5 (p < 0.05), the spatial clustering of "High-High" and "Low-Low" ESV was obvious. It is clear that the ESV varies geographically. High values are primarily found in the study area's center and southern regions, as well as on both banks of the Minjiang River, whereas low values are more common in the region's northern region. (3) Slope and human activity intensity (HAI) are the principal contributors to the spatial differentiation of the ESV, more than 60% of the interaction types between the two factors were classified as dual-factor enhancement. The synergistic reinforcing effects of HAI, slope, elevation, and temperature collectively shape the shifts in ESV spatial distribution. This study offers a novel evaluative lens on the ESV of the Upper Minjiang River area, supplying a sturdy data support for crafting specific ecological preservation and rejuvenation strategies in the coming years.

Assessment of grassland carrying capacity drivers and evaluation of pasture-livestock balance: A case study of Xinjiang, China

Evaluating the spatiotemporal variations and driving mechanisms of carrying capacity (CC) is critical for optimizing grassland resource utilization and promoting sustainable development in grassland animal husbandry. This study focuses on Xinjiang, an arid and semi-arid region significantly impacted by environmental changes and human activities. To capture the dynamics of grassland CC, we employed Theil-Sen trend analysis and the Mann-Kendall test to assess spatiotemporal trends. The innovative use of the Geodetector method allowed for a detailed quantification of the influence of various environmental and human-related factors on grassland CC. Additionally, we incorporated livestock data to evaluate the overgrazing conditions of the grassland ecosystem. Our findings demonstrate that: (1) Grassland CC has shown a pronounced overall upward trajectory over the last twenty years, with notable inter-annual fluctuations and significant spatial variations, particularly between the northern and southern regions. (2) The spatial distribution of grassland CC is primarily influenced by precipitation patterns and population density, with key determining factors varying across different types of grasslands. Except for alpine steppe and alpine desert, the spatial distribution of grassland CC was primarily influenced by two-factor interactions, surpassing the impacts of single-factor effects. (3) The overgrazing rate has generally declined, peaking in 2014 and showing the mildest condition in 2017. In comparison, the overgrazing situation in Northern Xinjiang is relatively more favorable, whereas the southern and eastern regions necessitates more immediate and comprehensive ecological restoration and management measures. In summary, this study provides important scientific evidence for prioritizing grassland protection and planning for the sustainable animal husbandry development in arid and semi-arid regions.

Coupling and coordination of food security and agricultural carbon emission efficiency: Changing trends, influencing factors, and different government priority scenarios

Seeking a balance between food security and carbon mitigation is key to achieving sustainable agricultural development. This study evaluates the coupling coordination degree (CCD) between the food security index (FSI) and agricultural carbon emission efficiency (ACEE) in China from 2010 to 2021 using the coupled coordination model. By adjusting the model coefficients, different government priority scenarios are simulated to explore their impact on CCD. The Geodetector method is employed to identify the influencing factors of CCD, investigate their interactions, and assess the differences in these factors across various government priority settings. The average CCD between FSI and ACEE exhibits a notable upward trend, rising from 0.4583 in 2010 to 0.6595 in 2021. Furthermore, regional disparities are widening, particularly in the major production areas. Catch-up effects exist within regions. Policy simulations showed staged interactions between food security and agricultural carbon efficiency, shifting from food security to balanced production and ecology, then to prioritizing low-carbon production for food security. Adjusting policy priorities can effectively improve coupling coordination in the short term, with increasing impact as priority shifts. CCD is influenced by policy, technology, economy, and society, varying with policy priorities. In the baseline scenario, key factors for CCD include the urban-rural income gap, technological advancement, urbanization, and farmers' education level. When the government prioritizes food security, the impact of narrowing income gaps and agricultural industry agglomeration becomes more pronounced. Conversely, emphasizing carbon emission efficiency enhances the influence of technological advancements and urbanization on CCD. Tailoring agricultural production strategies to local conditions and emphasizing interactive effects among factors is crucial for achieving environmentally friendly and high-quality agricultural development goals.

The Analysis of NPP Changes under Different Climatic Zones and under Different Land Use Types in Henan Province, 2001–2020

Net Primary Productivity (NPP) is a crucial indicator of ecological environment quality. To better understand the carbon absorption and carbon cycling capabilities of Henan Province, this study investigates the trends and driving factors of NPP across different climatic zones and land use types. The Theil–Sen Median trend analysis method and the Mann–Kendall trend test are employed to monitor NPP changes from 2001 to 2020. The average annual NPP in Henan Province during this period was 414.61 gC·m−2·year−1, showing a significant increasing trend with a growth rate of 3.73 gC·m−2·year−1. Spatially, both the annual average NPP and its increase rate were higher in the western part of Henan compared to the eastern part, and NPP variability was more stable in the southern region than in the northern region. By classifying climatic zones and using the Geodetector method to assess NPP sensitivity to natural factors, the results show that climate and vegetation factors jointly influence NPP variations, with annual precipitation being the primary natural factor affecting NPP trends in Henan Province from 2001 to 2020. By analyzing the NPP gain and loss matrix, the impact of land use changes on NPP was evaluated. Forests had the highest average annual NPP at 483.52 gC·m−2·year−1, and the conversion of arable land to urban areas was identified as the primary land change type leading to NPP reductions. In the subtropical zone of Henan, forests, croplands, and grasslands exhibited higher NPP values and increase rates compared to those in the warm belt. This study provides new insights into the spatial variation of NPP caused by changes in climatic zones and land use types.

Dissecting the characteristics and driver factors of potential vegetation water use efficiency in China

While large-scale vegetation greening in China has substantially influenced global vegetation dynamics, the specific impact of this restoration on water use efficiency (WUE) remained inadequately understood. This study employed both the Geodetector and structural equation modeling (SEM) methods, utilizing the Lund-Potsdam-Jena (LPJ) Global Dynamic Vegetation Model, to explore the contributions of various driving factors to China's potential vegetation WUE from 1982 to 2019. The results indicated: (1) there existed considerable further potential for vegetation recovery nationwide. Among them, the Loess Plateau, Inner Mongolia Plateau, and northern Xinjiang had relatively high potential for vegetation recovery. This potential was further amplified by the significant prospects for enhancing WUE in these areas; (2) The application of the Geodetector method revealed that the normalized difference vegetation index (NDVI) explained over 40 % of the variation in potential vegetation WUE in China, exerting a greater influence than climatic factors. In arid/semi-arid regions, precipitation (PRE), NDVI, and vapor pressure deficit (VPD) significantly influenced WUE. Temperature (TEM) was the dominant factor affecting WUE in humid and humid/semi-humid regions; (3) Utilizing the SEM analysis method, it was evident that NDVI exerted the most substantial direct positive influence on potential vegetation WUE in China, whereas VPD and PRE had notable negative impacts. In arid/semi-arid regions, PRE emerged as the primary determinant of WUE. Conversely, in regions where water resources were not limiting, TEM and VPD exerted a more pronounced influence on potential vegetation WUE. This indicated that while vegetation restoration generally enhanced potential vegetation WUE, other factors such as PRE, TEM, and VPD played critical roles in different climatic zones, shaping the regional variations in WUE.

Alterations in Suitable Cultivation Area for Scutellaria baicalensis under Future Climatic Scenarios in China: Geodetector-Based Prediction

The dried roots of Scutellaria baicalensis (S. baicalensis) have been widely used as a traditional medicine. Recently, climate change and human activities have caused the degeneration of its wildlife habitat. However, there is rare knowledge on the effect and interactive effect of different variables on the spatial heterogeneity of S. baicalensis and how the pattern of suitable cultivation area in China would shift in response to climate change. Based on the Geodetector and Habitat Suitability Index (HSI) method, we proposed an assessment model to identify the critical environmental variable(s) affecting the distribution of suitable cultivation area for S. baicalensis in China and to project its shift under climate change. The results showed that soil and mean annual temperature are two determining variables in its spatial heterogeneity in China. Compared to 1981–2010, future climate change may result in a decrease in its suitable area, and yet may result in an increase in the extremely suitable area (about 1.00–1.35 million km2). S. baicalensis in the southern and northwestern part of its current distribution and the southwestern part and small area of northern China may experience expansion during the 21st century, while S. baicalensis in southern China, the Huang-Huai-Hai plain, and the midwest of northwestern China may experience contractions. Meanwhile, climate warming is expected to shift its distribution northwest through an expansion at the northern (at least 84 km) and western (at least 62 km) boundary and contraction at the southern (at least 529 km) boundary, respectively. These results could provide valuable information to policy-makers for the conservation and scientific introduction of S. baicalensis.

Spatial and Temporal Changes in Habitat Quality and Driving Forces in Typical Loess Hill and Gully Areas：A Case Study of the Zuli River Basin

Loess hills and gully areas are one of the important ecological barriers in China, and the study of the spatial and temporal changes of its habitat quality and its driving force is of great significance to guaranteeing the ecological security of China and safeguarding the national ecological rights and interests. Taking the Zuli River Basin as an example, the spatiotemporal distribution of the remote-sensing ecological index （RSEI） from 2000 to 2020 was systematically investigated using the Google Earth Engine platform and Landsat remote-sensing data. Combined with the coefficient of variation CV, the Theil-Sen Median slope estimation, the Mann-Kendall test of significance, and the Hurst index, the spatial and temporal changes of habitat quality in the study area were analyzed over a period of 20 years, and the effects of six major driving factors on the spatial distribution of RSEI were investigated using the geodetector method. The results of the study showed that： ① From 2000 to 2020, the value of the RSEI showed a downward and then upward trend, with an average annual increase of 0.084 5·（10 a）-1. ② During the 20-year period, the habitat quality improvement area accounted for 92.06%, of which the significant improvement area accounted for 28.49%, and the improvement area was mainly in Huining County, whereas the habitat degradation area only accounted for 7.82%. The trend of future ecological conditions showed that 74.98% of the areas would show a trend of continuous improvement or future improvement, but there would still be a potential risk of ecological degradation in 23.48% of the areas in the future. ③ Climate factors such as precipitation were the key factors affecting the habitat quality in the Zu Li River Basin； the interaction between factors had a higher explanatory power than that of any single factor on the habitat quality, among which the interaction between the precipitation factor and the elevation factor had the strongest explanatory power. The interaction between the terracing factor and the environmental factor significantly increased the explanatory power of the spatial variance, which indicated that terracing played an important role in improving habitat quality. The results of this study can provide a scientific basis for the management and sustainable development of the ecological environment in the loess hills and gullies.

Analysis of the epidemiological characteristics and influencing factors of tuberculosis among students in a large province of China, 2008–2018

This study examines tuberculosis (TB) incidence among students in Jilin Province, China, focusing on spatial, temporal, and demographic dynamics in areas of social inequality. Variation in incidence rate of TB was analyzed using the joinpoint regression method. Spatial analyses techniques included the global and local Moran indices and Getis-Ord Gi* analysis. Demographic changes in new cases were analyzed descriptively, and the Geodetector method measured the influence of risk factors on student TB incidence. The analysis revealed a declining trend in TB cases, particularly among male students. TB incidence showed geographical heterogeneity, with lower rates in underdeveloped rural areas compared to urban regions. Significant spatial correlations were observed, with high-high clusters forming in central Jilin Province. Hotspots of student TB transmission were primarily concentrated in the southwestern and central regions from 2008 to 2018. Socio-economic factors exhibited nonlinear enhancement effects on incidence rates, with a dominant bifactor effect. High-risk zones were predominantly located in urban centers, with university and high school students showing higher incidences than other educational stages. The study revealed economic determinants as being especially important in affecting TB incidence among students, with these factors having nonlinear interacting effects on student TB incidence.

Spatial analysis of the impact of urban built environment on cardiovascular diseases: a case study in Xixiangtang, China

BackgroundThe built environment, as a critical factor influencing residents' cardiovascular health, has a significant potential impact on the incidence of cardiovascular diseases (CVDs).MethodsTaking Xixiangtang District in Nanning City, Guangxi Zhuang Autonomous Region of China as a case study, we utilized the geographic location information of CVD patients, detailed road network data, and urban points of interest (POI) data. Kernel density estimation (KDE) and spatial autocorrelation analysis were specifically employed to identify the spatial distribution patterns, spatial clustering, and spatial correlations of built environment elements and diseases. The GeoDetector method (GDM) was used to assess the impact of environmental factors on diseases, and geographically weighted regression (GWR) analysis was adopted to reveal the spatial heterogeneity effect of environmental factors on CVD risk.ResultsThe results indicate that the built environment elements and CVDs samples exhibit significant clustering characteristics in their spatial distribution, with a positive correlation between the distribution density of environmental elements and the incidence of CVDs (Moran’s I > 0, p < 0.01). Further factor detection revealed that the distribution of healthcare facilities had the most significant impact on CVDs (q = 0.532, p < 0.01), followed by shopping and consumption (q = 0.493, p < 0.01), dining (q = 0.433, p < 0.01), and transportation facilities (q = 0.423, p < 0.01), while the impact of parks and squares (q = 0.174, p < 0.01) and road networks (q = 0.159, p < 0.01) was relatively smaller. Additionally, the interaction between different built environment elements exhibited a bi-factor enhancement effect on CVDs. In the local analysis, the spatial heterogeneity of different built environment elements on CVDs further revealed the regional differences and complexities.ConclusionsThe spatial distribution of built environment elements is significantly correlated with CVDs to varying degrees and impacts differently across regions, underscoring the importance of the built environment on cardiovascular health. When planning and improving urban environments, elements and areas that have a more significant impact on CVDs should be given priority consideration.

Does self-containment of spatial scale and land use function contribute to mitigate urban heat island effects? Lessons from new towns in Shanghai

The concept of self-containment in new towns has been widely discussed from social and economic perspectives. However, localized interpretations within the context of China's development, particularly regarding climate adaptability and urban heat island (UHI) mitigation, are scarce. To fill this gap, our research analyzed self-containment from the perspectives of urban spatial scale and land use function. Focusing on Shanghai’s five new towns, we empirically demonstrated how self-containment influenced the UHI effects from 2005 to 2020, employing the Geodetector method. The findings reveal that during the daytime, the intensity of UHI in new towns decreased, serving as vital connectivity nodes of UHI within the region. Conversely, during the nighttime, both the intensity and area of UHI showed an increasing trend. The research confirmed that expanding the urban scale and functional diversity are effective strategies for mitigating the UHI. Based on these findings, we offer practical suggestions for the development of new towns: Increase population size while ensuring coordination with development scale; enhance mixed-use functions in large-scale development projects like university towns and industrial parks; and be vigilant of potential functional decline in central areas and increasing thermal impact due to new town development. Overall, this study enriches our understanding of self-containment in Chinese new towns and provides valuable insights for mitigating UHI in other similar contexts.

Dynamic Spatiotemporal Evolution and Driving Mechanisms of Vegetation in the Lower Reaches of the Tarim River, China

Analyzing the changes in vegetation under different factors is crucial for ecological protection in arid areas. The spatial-temporal variations of vegetation in the lower reaches of the Tarim River (LRTR) from 2000 to 2020, were analyzed using the Theil-Sen estimator and the Mann-Kendall test. The future trends of NDVI are projected to use the Hurst exponent method. The driving mechanisms of vegetation changes were analyzed using the GeoDetector method and multivariate residual analysis. The NDVI values in the LRTR significantly increased during the study period, indicating good vegetation recovery. The overall vegetation level remains poor and was primarily concentrated around the riverine areas. There is still a risk of vegetation degradation in most areas of the future LRTR. Compared to climate change, vegetation was more affected by human activities. Human activities have helped restore the riparian vegetation and prevented the degradation of vegetation far from the river. Therefore, distance from river channels is the strongest explanatory factor (q = 0.078) for vegetation changes, followed by precipitation, and temperature, while changes in slope have minimal impact on vegetation. Statistics have found that when two factors are combined, their impact on vegetation change is stronger. These findings are beneficial for identifying vegetation evolution patterns in LRTR and providing theoretical support for the government to carry out ecological restoration.

Characteristics of groundwater drought and its propagation dynamics with meteorological drought in the Sanjiang Plain, China: Irrigated versus nonirrigated areas

Study regionSanjiang Plain, China Study focusThe characterization of groundwater drought is inadequate, particularly in terms of the differences in the drought propagation dynamics between cropland and other regions. In this study, we identified groundwater drought events and characteristics in the Sanjiang Plain, using the standardized groundwater drought index (SGDI); we also quantified the mechanism of dynamic factors influencing drought propagation characteristics based on the geodetector method and wavelet coherence analysis and discussed the correlation between the NDVI and SGDI.New Hydrological Insights for the Region: The drought propagation time was shorter during summer and longer during spring and autumn in irrigated areas than in nonirrigated areas. The groundwater system exhibited greater resistance to drought during summer, and spatially nonirrigated area was more vulnerable to meteorological drought. The factor that best explained the variation in propagation time and threshold differed across seasons. A significant correlation time-frequency domain existed between all the factors and SGDI in nonirrigated area, with the SGDI exhibiting the fastest response to soil moisture. The relationship between the SGDI and factors was characterized by a discontinuous response of less than 8 months and a continuous response of more than 30 months. The correlations between the SGDI and NDVI in irrigated and nonirrigated area exhibit different changes due to groundwater exploitation and differences in plant physiology between artificial crops and natural vegetation.

Improvements in the InVEST water purification model for detecting spatio-temporal changesintotal nitrogen and total phosphorus discharges in the Huai river watershed, China

Improving water quality to provide freshwater is an urgent requirement for regional and even global social development. More accurate simulation of non-point sources pollution, monitored mainly by total nitrogen (TN) and total phosphorus (TP), has always been a challenge for InVEST water purification model, particularly in agricultural areas. This can be attributed to the fact that there is no reference data for TN and TP to rectify the outcomes modelled by this model. This paper provided these data to rectify simulation results of TN and TP to ensure their accuracy. The Huai River watershed (HRW) is an important grain production area with slow economic development, and non-point source pollution has exceeded point-source pollution. There is an urgent need for water management authorities to obtain complete spatio-temporal data on TN and TP loads and their exports to improve water quality. The reference data onloads and exports of TN and TP were estimated for the entire watershed and its sub-watersheds through an investigation-evaluation technique during 1980–2018. TN and TP loads generated from the agricultural sector were the major pollution sources in the HRW and had similar time trends during the same period. The spatial distribution of TN and TP exports was modelled byusingthe InVEST water purification model, and it was found that the temporal trends for the final exports of TN and TP into river systems were similar to those for TN and TP loads in the HRW for 1980–2018. Key driving factors were detected using the Geo-detector method to quantify the contribution rates of factors to the spatiotemporal exports of TN and TP. Our results showed that individual factors, such as precipitation and land use/cover, were the most important factors driving spatio-temporal variations in TN and TP exports in the HRW from 1980 to 2018. Meanwhile, the contribution rates of interactions between land use/cover and other factors were consistently highest in this watershed during the same period. In this study, we estimated the loads and exports of TN and TP, and modelled their spatial patterns in this watershed from 1980 to 2018, providing important information on TN and TP for water-related management authorities. We also provide a method for other river systems to calibrate the parameters in the biophysical table of InVEST water purification model based on final exports of TN and TP.