Occurrence and formation of high fluoride groundwater in the Hengshui area of the North China Plain

Liu, Haiyan; Guo, Huaming; Yang, Lijin; Wu, Lihan; Fu-lan, LI; Li, Shanyang; Ni, Pei; Liang, Xing

doi:10.1007/s12665-015-4225-x

Cited by 44 publications

(23 citation statements)

References 27 publications

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“…Hebei Province is located in the North China Plain and it is one of Chinese main cropping provinces. Hengshui City is situated between 115°10′–116°34′E and 37°03′–38°23′N, and occupies an area of 8.12 × 10 5 ha, of which farmland occupies 5.71 × 10 5 ha (approximately 70.3%) ( Liu et al, 2015 ). The study region has a warm temperate continental monsoon climate that is characterized by hot, rainy summers and cold, dry winters.…”

Section: Study Area and Data Setsmentioning

confidence: 99%

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Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data

Hao

Tang

Chen

et al. 2018

PeerJ

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Substantial efforts have been made to identify crop types by region, but few studies have been able to classify crops in early season, particularly in regions with heterogeneous cropping patterns. This is because image time series with both high spatial and temporal resolution contain a number of irregular time series, which cannot be identified by most existing classifiers. In this study, we firstly proposed an improved artificial immune network (IAIN), and tried to identify major crops in Hengshui, China at early season using IAIN classifier and short image time series. A time series of 15-day composited images was generated from 10 m spatial resolution Sentinel-1 and Sentinel-2 data. Near-infrared (NIR) band and normalized difference vegetation index (NDVI) were selected as optimal bands by pair-wise Jeffries–Matusita distances and Gini importance scores calculated from the random forest algorithm. When using IAIN to identify irregular time series, overall accuracy of winter wheat and summer crops were 99% and 98.55%, respectively. We then used the IAIN classifier and NIR and NDVI time series to identify major crops in the study region. Results showed that winter wheat could be identified 20 days before harvest, as both the producer’s accuracy (PA) and user’s accuracy (UA) values were higher than 95% when an April 1–May 15 time series was used. The PA and UA of cotton and spring maize were higher than 95% with image time series longer than April 1–August 15. As spring maize and cotton mature in late August and September–October, respectively, these two crops can be accurately mapped 4–6 weeks before harvest. In addition, summer maize could be accurately identified after August 15, more than one month before harvest. This study shows the potential of IAIN classifier for dealing with irregular time series and Sentinel-1 and Sentinel-2 image time series at early-season crop type mapping, which is useful for crop management.

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Section: Study Area and Data Setsmentioning

confidence: 99%

“…2 ), which have a high potential for producing mixed pixels on Landsat images. In addition, shortages of water further affect crop production ( Liu et al, 2015 ; Xie, Cheng & Lv, 2017 ). Therefore, early season crop type maps are urgently needed to improve crop growth monitoring and harvest management.…”

Section: Study Area and Data Setsmentioning

confidence: 99%

Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data

Hao

Tang

Chen

et al. 2018

PeerJ

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“…This region has a warm temperate continental monsoon climate that is characterized by hot, rainy summers and cold, dry winters. Cropland is the dominant land cover/use type of this area [33]. The major crop types in the study region are winter wheat, summer maize, spring maize, and cotton fields; some of these vegetable fields have been widely mulched using white plastic film.…”

Section: Study Regionsmentioning

confidence: 99%

New Workflow of Plastic-Mulched Farmland Mapping using Multi-Temporal Sentinel-2 data

et al. 2019

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Using plastic film mulch on cropland improves crop yield in water-deficient areas, but the use of plastic film on cropland leads to soil pollution. The accurate mapping of plastic-mulched land (PML) is valuable for monitoring the environmental problems caused by the use of plastic film. The drawback of PML mapping is that the detectable period of PML changes among the fields, which causes uncertainty when supervised classification methods are used to identify PML. In this study, a new workflow which merging PML of multiple temporal phases (MTPML) is proposed. For each temporal phase, the “possible PML” is firstly generated, these “temporal possible PML” layers are then combined to generate the “possible PML” layer. Finally, the maximum normalized difference vegetation index (NDVI) of the growing season is used to remove the non-cropland pixels from the “possible PML layer,” and then generate PML images. When generating “temporal possible PML layers,” three new PML indices (PMLI with near-infrared bands known as PMLI_NIR, PMLI with shortwave infrared bands known as PMLI_SWIR, and Normalized Difference PMLI known as PMLI_ND) are proposed to separate PML from bare land at plastic film cover stage; and the “temporal possible PML layer” are identified by the threshold based method. To estimate the performance of the three PML indices, two other approaches, PMLI threshold and Random Forest (RF) are used to generate “temporal possible PML layer.” Finally, PML images generated from the five MTPML approaches are compared with the image time series supervised classification (SUPML) result. Two study regions, Hengshui (HS) and Guyuan (GY), are used in this study. PML identification models are generated using training samples in HS and the models are used for PML mapping in both study regions. The results showed that MTPML workflow outperformed SUPML with 3%–5% higher classification accuracy. The three proposed PML indices had higher separability and importance score for bare land and PML discrimination. Among the five approaches used to generate the “temporal possible PML layer,” PMLI_SWIR is the recommended approach because the PMLI_SWIR threshold approach is easy to implement and the accuracy is only slightly lower than the RF approach. It is notable that no training sample was used in GY and the accuracy of the MTPML approach was higher than 85%, which indicated that the rules proposed in this study are suitable for other study regions.

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“…Although it is rare for confined groundwater affected by external factors on its hydrochemical quality, the potential poor quality as a result of the internal factors cannot be evaded [48]. Geogenic poor quality of groundwater has been one of the greatest threats to the water supply for human society and is widely reported in many regions across the world [48][49][50][51][52][53][54].…”

Section: Introductionmentioning

confidence: 99%

Hydrogeochemical Features and Genesis of Confined Groundwater and Health Perspectives for Sustainable Development in Urban Hengshui, North China Plain

Xiao

Liu

Hao

et al. 2021

Journal of Chemistry

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Groundwater in confined aquifers is the preferred water resource worldwide, and its hydrochemical quality is the premise for sustainable development. A systematic hydrogeochemical research was conducted to get insight into the hydrochemical characteristics, genesis, and potential health threats of confined groundwater, based on analytical data of 45 groundwater samples collected from the urban area of Hengshui, Central North China Plain (NCP). The results showed most groundwater had desirable hydrochemical quality with a nearly neutral to slightly alkaline nature and dominantly soft-fresh Cl-Na face. Solute chemistry was governed by rock-water interaction including minerals dissolution and ion exchange, but out of the anthropogenic influences. All nitrogen pollutants and Zn were within the desirable limit, while F−, Mn, and Fe were beyond the desirable limit recommended by WHO in 28.9%, 15.6%, and 68.9% of samples. Overall chronic health risk from these toxic elements was identified in terms of various populations and mainly contributed by F−. Infants were more prone to the health risks of aqueous pollutants. Differential water supplies based on hydrochemical quality are recommended, and water improvement measures are suggested to be conducted aiming at the harmful fluoride in confined groundwater. The present research could provide valuable references for the health sustainability of confined groundwater utilization in sedimentary plains like NCP worldwide.

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Occurrence and formation of high fluoride groundwater in the Hengshui area of the North China Plain

Cited by 44 publications

References 27 publications

Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data

Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data

New Workflow of Plastic-Mulched Farmland Mapping using Multi-Temporal Sentinel-2 data

Hydrogeochemical Features and Genesis of Confined Groundwater and Health Perspectives for Sustainable Development in Urban Hengshui, North China Plain

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