Decline of groundwater table in Beijing and recognition of seismic precursory information

Yang, Ming; Yuehu, Kang; Zhang, Qing

doi:10.1007/s11589-009-0301-1

Cited by 8 publications

(4 citation statements)

References 1 publication

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“…Therefore, the groundwater level in SBP did not decrease as in the NBP and CBP. Yang et al (2009) also have confirmed that the SBP area showed less groundwater overdraft than the NBP and CBP areas [44]. This illustrates the effect of local impact factor on shaping the groundwater system.…”

Section: Spatial Variation Of Groundwater Level In Beijing Plainmentioning

confidence: 55%

Evaluating Spatiotemporal Variation of Groundwater Depth/Level in Beijing Plain, a Groundwater-Fed Area from 2001 to 2010

Zhou

Xiao

Wang

et al. 2016

Advances in Meteorology

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Groundwater has always been a valuable resource in Beijing, facing a great decline of groundwater level during the past decades. However, few previous researches have revealed the spatial variation of groundwater level within Beijing Plain. In this study, spatiotemporal variation of groundwater level from 2001 to 2010 in Beijing Plain has been investigated. Factor analysis has been conducted to identify the primary influencing factor. Results showed that the groundwater level decreased by 8.41 m from 2001 to 2010, with a linear decreasing rate of 0.954 m per year averagely. Significant spatial variation characteristics have been detected. The north area suffered more groundwater depletion than the south part in general. The lowest groundwater level has been identified downstream Miyun Reservoir, central part of the Plain. Nevertheless, the most of the south part witnessed a slight revival between 2001 and 2010. This may be due to the differences of socioeconomic circumstances in the Plain. Three influencing factors, that is, “demand factor,” “supply factor,” and “loss factor,” have been identified in the water balance model. Eigenvalues of these factors are 3.563, 2.910, and 1.632, respectively, indicating that these factors influenced the groundwater system to various extents, with the demand factor being the primary one.

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Section: Spatial Variation Of Groundwater Level In Beijing Plainmentioning

confidence: 55%

Evaluating Spatiotemporal Variation of Groundwater Depth/Level in Beijing Plain, a Groundwater-Fed Area from 2001 to 2010

Zhou

Xiao

Wang

et al. 2016

Advances in Meteorology

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“…The texture of the two farmland soils belongs to light loamy. The groundwater level in TZ ozone is 19.26 m (Yang et al 2009). All the groundwater samples collected were from the shallow layer of the groundwater.…”

Section: Study Sitementioning

confidence: 99%

Migration of natural estrogens around a concentrated dairy-feeding operation

Han

Yang

et al. 2011

Environ Monit Assess

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Concentrated animal feeding operations have been recognized as one of the most important contributors of natural estrogens which show significant endocrine-disrupting properties in aquatic environments. In this study, the concentrations of 17α-estradiol (17α-E2), 17β-estradiol (17β-E2), estrone (E1), and estriol (E3) in several matrices, including soils (surface and deep), sediments (surface and deep), and groundwaters, around a typical dairy farm were surveyed using gas chromatography/mass spectrometry. Of the two farmlands, surface and subsurface sediments in waste lagoon and along effluent drainage drench, the concentrations of 17α-E2, 17β-E2, and E1 ranged from below detection limit to the highest level of 6.60 μg/kg, except that E3 was not detectable. Three estrogens of 17α-E2, 17β-E2, and E1 with the concentrations of 3.18-31.61 ng/L were observed in two groundwater samples. The results clearly demonstrated the vertical migration and horizontal transport of estrogens in the investigated area. Within 750-m distance, it was observed the attenuation of 17α-E2, 17β-E2, and E1 along the effluent route and the horizontal migration of estrogens was less than 1,350 m in this survey.

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“…These systems provide realtime monitoring, recording, and storage of the surrounding environment of instrument rooms, entrances and exits, as well as indoor and outdoor equipment. This enables centralized video monitoring and operation management of the seismic station network, enhancing the operation maintenance of seismic instruments, verification of abnormal observation data, and security protection level of instrument rooms [2].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Recognition of Seismic Station Environmental Interference Based on YOLOv5

et al. 2023

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In recent years, human interference in seismic-station environments has posed challenges to the quality and accuracy of seismic signals, making data processing difficult. To accurately identify interference caused by personnel and ensure the reliability of seismic-network instrument detection data, it is necessary to track the detected targets across consecutive frames. Deep neural networks have made significant progress in this field. Therefore, an intelligent identification solution for environmental interference at seismic stations is proposed, which combines deep learning with multi-object tracking techniques. A centroid-matching tracking algorithm based on Kalman filtering is introduced to identify the entry/exit timestamps, alongside motion trajectories of interfering individuals, thereby marking the anomalous data caused by the presence of interfering personnel in seismic time-series data. Experimental results demonstrate that this research provides an effective solution for intelligent identification of environmental interference in seismic station environments.

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Decline of groundwater table in Beijing and recognition of seismic precursory information

Cited by 8 publications

References 1 publication

Evaluating Spatiotemporal Variation of Groundwater Depth/Level in Beijing Plain, a Groundwater-Fed Area from 2001 to 2010

Evaluating Spatiotemporal Variation of Groundwater Depth/Level in Beijing Plain, a Groundwater-Fed Area from 2001 to 2010

Migration of natural estrogens around a concentrated dairy-feeding operation

Intelligent Recognition of Seismic Station Environmental Interference Based on YOLOv5

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