Remote sensing of wetland evolution in predicting shallow groundwater arsenic distribution in two typical inland basins

Gao, Zhipeng; Guo, Huaming; Li, Shanyang; Wang, Jiao; Ye, Haolin; Han, Songjun; Cao, Wengeng

doi:10.1016/j.scitotenv.2021.150496

Cited by 24 publications

(7 citation statements)

References 74 publications

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“…The migration trajectory of the gravity center for wetlands could reflect the spatial heterogeneity and bias of the change patterns of different wetland types in varying periods [42][43][44]. As shown in Figure 10, the gravity center of each wetland type in the YRD was distributed along the Yellow River Basin from 2015 to 2021.…”

Section: Gravity Center Migrations For Different Wetlandsmentioning

confidence: 99%

The Change Pattern and Its Dominant Driving Factors of Wetlands in the Yellow River Delta Based on Sentinel-2 Images

et al. 2022

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There were significant differences in the dominant driving factors of the change process of different types of wetlands in the Yellow River delta. In addition, to our knowledge, the optimal classification feature sets with the Random Forest algorithm for wetlands in the Yellow River delta were least explored. In this paper, the wetland information in the study area was extracted based on a Random Forest algorithm with de-feature variable redundancy, and then the change process of wetland and its dominant factors from 2015 to 2021 was monitored and analyzed using the Geodetector and gravity center model. The results showed that (1) the optimal variable sets composed of red edge indexes based on the Random Forest algorithm had the highest classification accuracy, with the overall accuracy and Kappa coefficient of 95.75% and 0.93. (2) During 2015–2021, a large area of natural wetland in the Yellow River delta was transformed into an artificial wetland. The wetlands showed an overall development direction of “northwest–southeast” along the Yellow River. (3) The interaction between vegetation coverage and accumulated temperature had the largest explanatory power of the change in the natural wetland area. The interaction between solar radiation and DEM had the largest explanatory power for the change in the artificial wetland area. The research results could better provide decisions for wetland protection and restoration in the Yellow River delta.

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Section: Gravity Center Migrations For Different Wetlandsmentioning

confidence: 99%

The Change Pattern and Its Dominant Driving Factors of Wetlands in the Yellow River Delta Based on Sentinel-2 Images

et al. 2022

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“…The urban population comprises 81.74% of the total population, primarily concentrated in the urban area. The transportation network shows significant spatial differences in both level and density [23,24].…”

Section: Case Study Areamentioning

confidence: 99%

“…Yingchuan City in Western China is located in a typical semi-arid region. This city has undergone extensive urban wetland constructions over a prolonged period [23,24]. Currently, it has a relatively large number of wetlands compared to other cities in the same region.…”

Section: Introductionmentioning

confidence: 99%

Low Residents’ Satisfaction with Wetland Leisure Demand in Typical Urban Areas of the Semi-Arid Region in Western China: Spatial Variations and Their Causes

Zhang,

Zeng

2024

Land

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Wetlands, as a crucial component of urban green spaces, provide important leisure services for residents. Construction of wetlands has increased with the rapid urban expansion and population growth in China over recent decades, especially in semi-arid regions with scarce water resources. However, the residents’ satisfaction with wetland leisure demand remains unclear. This study evaluated the residents’ satisfaction with wetland leisure demand by a framework of physical calculation, taking Yinchuan City, the capital city of Ningxia Hui Autonomous Region, in the semi-arid region of Western China as a case study area. Spatial variations in residents’ satisfaction and their causes were revealed by a supply–demand relationship between the population capacity of wetlands and the total population of communities under a framework of physical calculation. The results indicated that 4.22% of the study area, which covered 7.38% of the total population, was fully satisfied with wetland leisure demand. Residents’ satisfaction in the urban area as a whole is low at 0.49, with a concentric distribution pattern increasing as the distance from the central urban area increases. The high population density and scanty wetlands mainly induced a relative-low residents’ satisfaction in the central urban area, accounting for 12.02% of the area and 32.70% of the population. Meanwhile, a relative-high residents’ satisfaction in the outer ring of the central urban area accounting for 59.10% of the area and 20.63% of the population was primarily due to the adequate capacity of wetlands. Medium residents’ satisfaction was mainly attributed to the road network density, which partially transferred local wetland leisure demand to adjacent areas. Wetland constructions and internal structural optimization to improve the capacity for leisure in densely populated central urban areas could provide a feasible path to alleviate unbalanced conditions. These results could deepen the understanding of supply–demand dynamics in the wetland leisure service and provide valuable information for optimizing wetland allocation in urban construction in semi-arid regions.

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“…Machine learning predictions are suitable for mapping groundwater chemical characteristics with large spatially distributed data sets, which often have irregular or sparse source distributions and complex processes. Recent studies using machine learning methods have successfully mapped the chemical composition and characteristics of groundwater, including arsenic (Fan et al., 2024; Guo et al., 2023; Lombard et al., 2021; Tan et al., 2020), manganese (Erickson et al., 2021), nitrate (Ransom et al., 2022), salinity (Knierim et al., 2020), and pH (Stackelberg et al., 2021), using existing data sets of groundwater chemistry in diverse hydrogeologic settings. The random forests algorithm (Breiman, 2001) is a kind of comprehensive artificial intelligence learning algorithm that has developed rapidly in recent years.…”

Section: Introductionmentioning

confidence: 99%

Organic and Inorganic Carbon Sinks Reduce Long‐Term Deep Carbon Emissions in the Continental Collision Margin of the Southern Tibetan Plateau: Implications for Cenozoic Climate Cooling

Wang,

Quan,

Tang

et al. 2024

JGR Solid Earth

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This paper aims to update our understanding of the carbon cycle in the Himalayas, the most intense collisional orogeny globally, by providing new insight into its impact on Cenozoic climate cooling through the use of isotopic variations in both organic and inorganic carbon and an isotopic mass balance model. Our results from 20 selected hot springs show that the relative contributions of dissolved carbon from the mantle, metamorphic decarbonization, aqueous dissolution, and soil organic matter are approximately 2%, 82%, 6%, and 10%, respectively. Approximately 87% ± 5% of CO2 generated in the deep crust precipitates as calcite, while approximately 5.5% ± 1% of this carbon is converted to biomass through microbial chemosynthesis at depths less than 2 km. Our random forests approach yielded a metamorphic carbon flux from the entire Himalayan orogenic belt of approximately 2.7 ∼ 4.5 × 1012 mol/yr. The minor CO2 released into the atmosphere (2.5 ∼ 4.2 × 1011 mol/yr) is comparable to the carbon consumption driven by Himalayan weathering. This paper provides new insights into deep carbon cycling, notably that approximately 93% of deeply sourced carbon is trapped in the shallow crust, rendering orogenic processes carbon neutral and possibly acting as one of the major triggers of long‐term climate cooling in the Cenozoic.

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Remote sensing of wetland evolution in predicting shallow groundwater arsenic distribution in two typical inland basins

Cited by 24 publications

References 74 publications

The Change Pattern and Its Dominant Driving Factors of Wetlands in the Yellow River Delta Based on Sentinel-2 Images

The Change Pattern and Its Dominant Driving Factors of Wetlands in the Yellow River Delta Based on Sentinel-2 Images

Low Residents’ Satisfaction with Wetland Leisure Demand in Typical Urban Areas of the Semi-Arid Region in Western China: Spatial Variations and Their Causes

Organic and Inorganic Carbon Sinks Reduce Long‐Term Deep Carbon Emissions in the Continental Collision Margin of the Southern Tibetan Plateau: Implications for Cenozoic Climate Cooling

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