Research on Spatial Spillover Effects of Comprehensive Carrying Capacity of Water and Soil Resources: Evidence from the Yellow River Basin, China
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Indicator System
2.3. Research Methodology
2.3.1. Kernel Density Estimation
2.3.2. Spatial Autocorrelation
2.3.3. Space Markov Chains
2.3.4. Obstacle Model
2.3.5. Software for Data Analysis and Visualization
3. Results
3.1. Spatiotemporal Evolution Characteristics of the WSR-CCC
3.1.1. Temporal Evolution Characteristics
3.1.2. Spatial Evolution Characteristics
3.2. Spatial Spillover Effects of the WSR-CCC
3.3. Obstacle Factors of the WSR-CCC
4. Discussion
4.1. Interpretation of Spatiotemporal Evolution Characteristics
4.2. Mechanism of Spatial Spillover Effects
4.3. Analysis of Obstacle Factor Stages
4.4. Limitations and Future Research Directions
5. Conclusions
- (1)
- The basin’s carrying capacity demonstrates a significant upward trajectory, transitioning from critical overload level IV to sustainable level II and the pressure has been fundamentally alleviated, but inter-provincial disparities increased. The box plot revealed a prominent pattern of low-value clustering and high-value dispersion. The kernel density curves show a three-stage growth trend, namely gradual growth, decelerated growth, and accelerated growth, as well as peak evolution that changed from bimodal to trimodal and then to unimodal forms. This reflects a shift toward regional equilibrium, though low-value convergence remained slow and high-value dispersion increased.
- (2)
- A “west-high east-low, north-strong south-stagnant” gradient emerged: upper-reach provinces (Qinghai, Gansu, Shanxi, Shaanxi, Henan, Shandong) formed high-value zones, while Ningxia and Sichuan stayed below average. Global Moran’s I (all > 0.05) indicated no stable spatial self-organization, with weak inter-provincial agglomeration; local LISA clustering showed dynamic high-value synergy and low-value dependency, highlighting the need for zonal regulation.
- (3)
- The traditional Markov chain analysis shows that the transition of carrying capacity types exhibits club convergence characteristics. The stability levels of low and relatively low levels are lower than that of high levels, and the upward transition probability exceeds the downward one, indicating a positive development trend. The spatial Markov chain reveals a significant geographical proximity effect: transition probabilities vary substantially under different spatial lag contexts, demonstrating a Matthew effect where strong regions continue to strengthen and weak regions further deteriorate.
- (4)
- The criterion layer obstacle degree shows a three-stage dominant feature of “driving-influencing-state”: from 2012 to 2019, driving factors were the core constraint, with development pressure driven by economic growth and population expansion prevailing. During 2020–2021, impact factors became the primary obstacle, as constraints from industrial pollution and agricultural non-point source pollution intensified. In 2022–2023, state and impact factors showed joint dominance, where extreme climate and cumulative long-term development degraded the water–soil resource base, overlapping with pollution constraints to form a composite bottleneck.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
WSR-CCC | Comprehensive Carrying Capacity of Water and Soil Resources |
YB | Yellow River |
DPSIR | Driving–Pressure–State–Impact–Response |
WRCC | Water Resources Carrying Capacity |
LISA | Local Indicators of Spatial Association |
GDP | Gross Domestic Product |
Moran’s I | Global Moran Index |
References
- José, T.G. The role of soils in sustainability, climate change, and ecosystem services: Challenges and opportunities. Ecologies 2023, 4, 552–567. [Google Scholar] [CrossRef]
- Doru, B.; Vladica, S.; Kevin, C.; Sophia, B.; Sergey, A.; Ahmet, O.; Grant, M.; Snežana, S.; Angela, C.B. Freshwater as a sustainable resource and generator of secondary resources in the 21st century: Stressors, threats, risks, management and protection strategies, and conservation approaches. Int. J. Environ. Res. Public Health 2022, 19, 16570. [Google Scholar] [CrossRef]
- Lal, R.; Bouma, J.; Brevik, E. Soils and sustainable development goals of the United Nations: An International Union of Soil Sciences perspective. Geoderma Reg. 2021, 25, e00398. [Google Scholar] [CrossRef]
- Makanda, K.; Nzama, S.; Kanyerere, T. Assessing the role of water resources protection practice for sustainable water resources management: A review. Water 2022, 14, 3153. [Google Scholar] [CrossRef]
- Simane, B.; Kapwata, T.; Naidoo, N.; Cissé, G.; Wright, C.Y.; Berhane, K. Ensuring Africa’s Food Security by 2050: The role of population growth, climate-resilient strategies, and putative pathways to resilience. Foods 2025, 14, 262. [Google Scholar] [CrossRef]
- Biswas, A.; Sarkar, S.; Das, S.; Dutta, S.; Choudhury, M.R.; Giri, A.; Bera, B.; Bag, K.; Banerjee, K.; Gupta, D.; et al. Water scarcity: A global hindrance to sustainable development and agricultural production–A critical review of the impacts and adaptation strategies. Camb. Prism. Water 2025, 3, e4. [Google Scholar] [CrossRef]
- Li, J.H.; Xia, F.; Yang, D.; Hu, J.W. Comprehensive Evaluation of Water Resource Carrying Capacity in Northwest China. Water 2024, 17, 67. [Google Scholar] [CrossRef]
- Zhang, J.L.; Chen, S.H. Evaluating water conservation capacity in the Yellow River water conservation area integrating ecological model and machine learning. J. Hydrol. 2025, 663, 134202. [Google Scholar] [CrossRef]
- Fan, Y.Y.; Yu, Y.; Song, T. Evaluation of water resources carrying capacity in the Yellow River Basin: A Hu Huanyong Line perspective. J. Water Clim. Change 2025, 16, 2241–2258. [Google Scholar] [CrossRef]
- Zhu, Q.Y.; Yi, C. Research on provincial water resources carrying capacity and coordinated development in China based on combined weighting TOPSIS model. Sci. Rep. 2024, 14, 12497. [Google Scholar] [CrossRef]
- Wang, B.; Zhang, F.W.; Chen, L. Evaluation of Coupling and Coordination Efficiency of Land-water Resource and Economic System in Heilonggang Area. Bull. Soil Water Conserv. 2013, 33, 276–280+301. (In Chinese) [Google Scholar] [CrossRef]
- Cheng, K.; Fu, Q.; Chen, X.; Li, T.X.; Jiang, Q.X.; Ma, X.S.; Zhao, K. Adaptive allocation modeling for a complex system of regional water and land resources based on information entropy and its application. Water Resour. Manag. 2015, 29, 4977–4993. [Google Scholar] [CrossRef]
- Pereira, P.; Barceló, D.; Panagos, P. Soil and water threats in a changing environment. Environ. Res. 2020, 186, 109501. [Google Scholar] [CrossRef]
- Liu, G.; Yang, Z.; Tang, Y.; Ulgiati, S. Spatial correlation model of economy-energy-pollution interactions: The role of river water as a link between production sites and urban areas. Renew. Sustain. Energy Rev. 2017, 69, 1018–1028. [Google Scholar] [CrossRef]
- Ding, X.; Liu, H.; Zhang, J. Does the local government multi-objective competition intensify the transfer of polluting industries in the Yangtze River Economic Belt? Environ. Res. 2024, 245, 118074. [Google Scholar] [CrossRef]
- Wang, G.; Xiao, C.; Qi, Z.; Meng, F.; Liang, X. Development tendency analysis for the water resource carrying capacity based on system dynamics model and the improved fuzzy comprehensive evaluation method in the Changchun city, China. Ecol. Indic. 2021, 122, 107232. [Google Scholar] [CrossRef]
- Liao, S.; Wu, Y.; Wong, S.W.; Shen, L. Provincial perspective analysis on the coordination between urbanization growth and resource environment carrying capacity (RECC) in China. Sci. Total Environ. 2020, 730, 138964. [Google Scholar] [CrossRef] [PubMed]
- Sun, M.; Wang, J.; He, K. Analysis on the urban land resources carrying capacity during urbanization—A case study of Chinese YRD. Appl. Geogr. 2020, 116, 102170. [Google Scholar] [CrossRef]
- Liu, H.Y.; Xia, J.; Zou, L.; Huo, R. Comprehensive quantitative evaluation of the water resource carrying capacity in Wuhan City based on the “human–water–city” framework: Past, present and future. J. Clean. Prod. 2022, 366, 132847. [Google Scholar] [CrossRef]
- He, Y.; Wang, Z. Water-land resource carrying capacity in China: Changing trends, main driving forces, and implications. J. Clean. Prod. 2022, 331, 130003. [Google Scholar] [CrossRef]
- Li, M.; Cao, X.; Liu, D.; Fu, Q.; Li, T.; Shang, R. Sustainable management of agricultural water and land resources under changing climate and socio-economic conditions: A multi-dimensional optimization approach. Agric. Water Manag. 2022, 259, 107235. [Google Scholar] [CrossRef]
- Jiao, R.; Pei, J.; Wang, S.; Wang, M.; Zhang, Y.; Shi, J.; Li, S. Relationship between agricultural water and soil resources carrying capacity and crop yield with long-term plastic film mulching coupled with fertilization. Field Crops Res. 2025, 328, 109927. [Google Scholar] [CrossRef]
- Guo, L.; Zhu, W.; Wei, J.; Wang, L. Water demand forecasting and countermeasures across the Yellow River basin: Analysis from the perspective of water resources carrying capacity. J. Hydrol. Reg. Stud. 2022, 42, 101148. [Google Scholar] [CrossRef]
- Chen, Q.Y.; Zhu, M.T.; Zhang, C.J.; Zhou, Q. The driving effect of spatial-temporal difference of water resources carrying capacity in the Yellow River Basin. J. Clean. Prod. 2023, 388, 135709. [Google Scholar] [CrossRef]
- Zhang, Z.; Li, Y.; Lv, Y.; Feng, X.; Chen, X. Comprehensive evaluation of “Three Waters” carrying capacity and path evolution study: A case of the Yellow River Basin. Sci. Total Environ. 2024, 951, 175464. [Google Scholar] [CrossRef] [PubMed]
- Li, W.; Jiang, S.; Zhao, Y.; Li, H.; Zhu, Y.; Ling, M.; Qi, T.; He, G.; Yao, Y.; Wang, H. Comprehensive evaluation and scenario simulation of water resources carrying capacity: A case study in Xiong’an New Area, China. Ecol. Indic. 2023, 150, 110253. [Google Scholar] [CrossRef]
- Xu, C.; Wang, X.; Liu, Z.; Hu, L.; Tian, J.; Zhao, Z.; Ren, Z. Analysis of the spatial and temporal evolution of water and soil resource carrying capacity in arid region of northwest China. Water Supply 2022, 22, 8813–8834. [Google Scholar] [CrossRef]
- Chen, P.; Duan, J.; Wang, Y. Spatio-temporal evolution and driving forces of urban gravity centers and ecological security risk gravity centers in China. Ecol. Indic. 2025, 170, 113025. [Google Scholar] [CrossRef]
- Ling, M.H.; Guo, X.; Shi, X.; Han, H. Temporal and spatial evolution of drought in Haihe River Basin from 1960 to 2020. Ecol. Indic. 2022, 138, 108809. [Google Scholar] [CrossRef]
- Tan, S.K.; Qi, L.; Han, S.Y. Spatial-temporal evolution of coupling relationship between land development intensity and resources environment carrying capacity in China. J. Environ. Manag. 2022, 301, 113778. [Google Scholar] [CrossRef]
- Li, D.Z.; Liang, D.L.; Li, T.N.; Chen, S.H. Ecological-economic coordination in the Yellow River basin: Spatial and temporal evolution and driving mechanisms. Environ. Dev. Sustain. 2022, 26, 3819–3848. [Google Scholar] [CrossRef]
- Ma, T.; Hong, T.; Zhang, H. Tourism spatial spillover effects and urban economic growth. J. Bus. Res. 2015, 68, 74–80. [Google Scholar] [CrossRef]
- Feng, Y.D.; Zou, L.H.; Yuan, H.X.; Dai, L. The spatial spillover effects and impact paths of financial agglomeration on green development: Evidence from 285 prefecture-level cities in China. J. Clean. Prod. 2022, 340, 130816. [Google Scholar] [CrossRef]
- Sun, C.; Zhao, L.; Zou, W.; Zheng, D. Water resource utilization efficiency and spatial spillover effects in China. J. Geogr. Sci. 2014, 24, 771–788. [Google Scholar] [CrossRef]
- Zhang, M.M.; Tan, S.K.; Pan, Z.C.; Hao, D.Q.; Zhang, X.S.; Chen, Z.H. The spatial spillover effect and nonlinear relationship analysis between land resource misallocation and environmental pollution: Evidence from China. J. Environ. Manag. 2022, 321, 115873. [Google Scholar] [CrossRef]
- Wang, Y.X.; Wang, Y.; Su, X.L.; Qi, L.; Liu, M. Evaluation of the comprehensive carrying capacity of interprovincial water resources in China and the spatial effect. J. Hydrol. 2019, 575, 794–809. [Google Scholar] [CrossRef]
- Chen, W.; Chi, G. Urbanization and ecosystem services: The multi-scale spatial spillover effects and spatial variations. Land Use Policy 2022, 114, 105964. [Google Scholar] [CrossRef]
- Brierley, G.J.; Yu, G.A.; Li, Z.W. Geomorphic diversity of rivers in the Upper Yellow River Basin. In Landscape and Ecosystem Diversity, Dynamics and Management in the Yellow River Source Zone; Springer: Berlin/Heidelberg, Germany, 2016; pp. 59–77. [Google Scholar]
- Jia, Y.; Wang, H. Study on water resource carrying capacity of Zhengzhou city based on DPSIR model. Inter. J. Environ. Res. Public Health 2023, 20, 1394. [Google Scholar] [CrossRef]
- Peng, T.; Deng, H. Comprehensive evaluation on water resource carrying capacity based on DPESBR framework: A case study in Guiyang, southwest China. J. Clean. Prod. 2020, 268, 122235. [Google Scholar] [CrossRef]
- Carr, E.R.; Wingard, P.M.; Yorty, S.C.; Thompson, M.C.; Jensen, N.K.; Roberson, J. Applying DPSIR to sustainable development. Int. J. sustain. Dev. World Ecol. 2007, 14, 543–555. [Google Scholar] [CrossRef]
- Wang, Z.; Fu, X. Scheme simulation and predictive analysis of water environment carrying capacity in Shanxi Province based on system dynamics and DPSIR model. Ecol. Indic. 2023, 154, 110862. [Google Scholar] [CrossRef]
- Zhang, X.; Duan, X. Evaluating water resource carrying capacity in Pearl River-West River economic Belt based on portfolio weights and GRA-TOPSIS-CCDM. Ecol. Indic. 2024, 161, 111942. [Google Scholar] [CrossRef]
- Yang, Z.Y.; Song, J.X.; Cheng, D.D.; Xia, J.; Li, Q. Comprehensive evaluation and scenario simulation for the water resources carrying capacity in Xi’an city, China. J. Environ. Manag. 2019, 230, 221–233. [Google Scholar] [CrossRef]
- Yang, L.; Hao, Y.; Wang, B.; Li, X.Y.; Gao, W.F. Evaluation of the water resources carrying capacity in Shaanxi Province based on DPSIRM–TOPSIS analysis. Ecol. Indic. 2025, 173, 113369. [Google Scholar] [CrossRef]
- Lee, W.J.; Mendis, G.P.; Triebe, M.J.; Sutherland, J.W. Monitoring of a machining process using kernel principal component analysis and kernel density estimation. J. Intell. Manuf. 2020, 31, 1175–1189. [Google Scholar] [CrossRef]
- Anselin, L. Local indicators of spatial association—LISA. Geogr. Anal. 1995, 27, 93–115. [Google Scholar] [CrossRef]
- Roy, V. Convergence diagnostics for markov chain monte carlo. Annu. Rev. Stat. Its Appl. 2020, 7, 387–412. [Google Scholar] [CrossRef]
- Agovino, M.; Crociata, A.; Sacco, P.L. Proximity effects in obesity rates in the US: A Spatial Markov Chains approach. Soc. Sci. Med. 2019, 220, 301–311. [Google Scholar] [CrossRef]
- Chen, Y.; Zhu, M.K.; Lu, J.L.; Zhou, Q.; Ma, W.B. Evaluation of ecological city and analysis of obstacle factors under the background of high-quality development: Taking cities in the Yellow River Basin as examples. Ecol. Indic. 2020, 118, 106771. [Google Scholar] [CrossRef]
- Sun, X.R.; Zhou, Z.X.; Wang, Y. Water resource carrying capacity and obstacle factors in the Yellow River basin based on the RBF neural network model. Environ. Sci. Pollut. Res. 2023, 30, 22743–22759. [Google Scholar] [CrossRef]
- Qiao, R.; Li, H.M.; Han, H. Spatio-temporal coupling coordination analysis between urbanization and water resource carrying capacity of the provinces in the Yellow River Basin, China. Water 2021, 13, 376. [Google Scholar] [CrossRef]
- Zhou, Y.J.; Li, W.F.; Li, H.J.; Wang, Z.; Zhang, B.; Zhong, K.Y. Impact of water and land resources matching on agricultural sustainable economic growth: Empirical analysis with spatial spillover effects from Yellow River Basin, China. Sustainability 2022, 14, 2742. [Google Scholar] [CrossRef]
- Zhang, K.L.; Fang, B.; Zhang, Z.H.; Xia, C.H.; Liu, Q.Q.; Liu, K. Spatial optimisation based on ecosystem service spillover effect and cross-scale knowledge integration: A case study of the Yellow River Basin. J. Geogr. Sci. 2025, 35, 1080–1114. [Google Scholar] [CrossRef]
- Wang, H.L.; Huang, S.; Di, D.; Wang, Y.; Zhang, F. Study on the spatial distribution of water resource value in the agricultural system of the Yellow River Basin. Water Policy 2021, 23, 1044–1058. [Google Scholar] [CrossRef]
- Diao, Y.X.; Xia, J.; Dong, Q.J.; Zuo, Q.T.; Xie, M.Y. From resource matching to economic sustainability: A multi-stage analysis of water-land-economy interactions in the lower Yellow River Basin. J. Environ. Manag. 2025, 380, 125078. [Google Scholar] [CrossRef]
- Wang, D.; Xu, J.M.; Yu, Y.; Ding, Y. Study on spatial and temporal differences of water resource sustainable development and its influencing factors in the Yellow River Basin, China. Sustainability 2023, 15, 14316. [Google Scholar] [CrossRef]
- Pang, B.; Li, X.; Fu, Y. Coupling coordination analysis and obstacle factors of water-energy-environment-economy in the Yellow River Basin. J. Clean. Prod. 2024, 468, 143108. [Google Scholar] [CrossRef]
- Zhang, Q.; Tang, D.; Boamah, V. Exploring the role of forest resources abundance on economic development in the Yangtze River delta region: Application of spatial Durbin SDM model. Forests 2022, 13, 1605. [Google Scholar] [CrossRef]
Dimensionality | Indicator Name | Unit | Benefit |
---|---|---|---|
Driving factor | Per capita gross domestic product (D1) | yuan/person | + |
Population growth rate (D2) | % | − | |
Urbanization level (D3) | % | + | |
Per capita disposable income(D4) | yuan/person | + | |
Pressure factor | Water resources utilization ratio (P1) | % | − |
Land resources utilization ratio (P2) | % | − | |
Water consumption per unit of GDP (P3) | m3/million | − | |
Rocky desertification area ratio (P3) | % | − | |
State factor | Water resources per capita (S1) | m3/person | + |
Land resources per capita (S2) | ha/person | + | |
Water–soil matching coefficient (S3) | - | + | |
Water resources per land (S4) | m3/ha | + | |
Influence factor | Industrial wastewater discharge (I1) | tons | − |
Chemical fertilizer pesticide load (I2) | kg/ha | − | |
Industrial sulfur dioxide discharge (I3) | tons | − | |
Domestic sewage discharge (I4) | million m3 | − | |
Response factor | Water and soil conservation investment ratio (R1) | % | + |
Green land coverage (R2) | % | + | |
Sewage centralized treatment rate (R3) | % | + | |
Environmental infrastructure investment (R4) | yuan | + |
Year | ||
---|---|---|
2012 | 0.1456 | 1.0474 |
2013 | −0.0493 | 0.3198 |
2014 | −0.1755 | −0.1841 |
2015 | −0.2887 | −0.7886 |
2016 | −0.3087 | −0.7013 |
2017 | −0.3111 | −0.7928 |
2018 | −0.1777 | −0.1977 |
2019 | 0.2621 | 1.4817 |
2020 | −0.2126 | −0.3121 |
2021 | 0.0687 | 0.6767 |
2022 | −0.3186 | −0.7755 |
2023 | −0.2467 | −0.4401 |
t/t + 1 | n | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|
1 | 23 | 0.7826 | 0.1739 | 0.0435 | 0.0000 |
2 | 25 | 0.1600 | 0.4800 | 0.3200 | 0.0400 |
3 | 24 | 0.0000 | 0.2083 | 0.4583 | 0.3333 |
4 | 18 | 0.0000 | 0.1111 | 0.0000 | 0.8889 |
Domain Type | t/t + 1 | n | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|---|
1 | 1 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
2 | 11.0000 | 0.0000 | 0.4545 | 0.5455 | 0.0000 | |
3 | 7.0000 | 0.0000 | 0.1429 | 0.4286 | 0.4286 | |
4 | 4.0000 | 0.0000 | 0.5000 | 0.0000 | 0.5000 | |
2 | 1 | 12.0000 | 0.5833 | 0.3333 | 0.0833 | 0.0000 |
2 | 13.0000 | 0.3077 | 0.5385 | 0.0769 | 0.0769 | |
3 | 13.0000 | 0.0000 | 0.2308 | 0.6154 | 0.1538 | |
4 | 1.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | |
3 | 1 | 7.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 |
2 | 1.0000 | 0.0000 | 0.0000 | 1.0000 | 0.0000 | |
3 | 4.0000 | 0.0000 | 0.2500 | 0.0000 | 0.7500 | |
4 | 9.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 | |
4 | 1 | 4.0000 | 1.0000 | 0.0000 | 0.0000 | 0.0000 |
2 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | |
3 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | |
4 | 4.0000 | 0.0000 | 0.0000 | 0.0000 | 1.0000 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Dong, G.; Xiong, S.; Wang, L.; An, X.; Li, X. Research on Spatial Spillover Effects of Comprehensive Carrying Capacity of Water and Soil Resources: Evidence from the Yellow River Basin, China. Sustainability 2025, 17, 9299. https://doi.org/10.3390/su17209299
Dong G, Xiong S, Wang L, An X, Li X. Research on Spatial Spillover Effects of Comprehensive Carrying Capacity of Water and Soil Resources: Evidence from the Yellow River Basin, China. Sustainability. 2025; 17(20):9299. https://doi.org/10.3390/su17209299
Chicago/Turabian StyleDong, Guanghua, Shiya Xiong, Lunyan Wang, Xiaowei An, and Xin Li. 2025. "Research on Spatial Spillover Effects of Comprehensive Carrying Capacity of Water and Soil Resources: Evidence from the Yellow River Basin, China" Sustainability 17, no. 20: 9299. https://doi.org/10.3390/su17209299
APA StyleDong, G., Xiong, S., Wang, L., An, X., & Li, X. (2025). Research on Spatial Spillover Effects of Comprehensive Carrying Capacity of Water and Soil Resources: Evidence from the Yellow River Basin, China. Sustainability, 17(20), 9299. https://doi.org/10.3390/su17209299