Jian-feng Li; Yuan-jing Zhang; Ya-ci Liu; Qi-chen Hao; Chun-lei Liu; Sheng-wei Cao; Zheng-hong Li

doi:10.26599/JGSE.2026.9280069

doi: 10.26599/JGSE.2026.9280069

^{1, 2},
^{1, 2},
^{1, 2},
^{1, 2},
^{1, 2},
^{1, 2},
^{1, 2, ,}

计量
- 文章访问数: 16
- HTML全文浏览量: 7
- PDF下载量: 1
- 被引次数: 0
出版历程
- 收稿日期: 2025-04-27
- 录用日期: 2025-10-09
- 网络出版日期: 2025-11-18
- 刊出日期: 2026-03-15

Application of the DITAPH model coupling human activities and groundwater dynamics for nitrate vulnerability assessment: A case study in Quanzhou, China

Jian-feng Li^{1, 2},
Yuan-jing Zhang^{1, 2},
Ya-ci Liu^{1, 2},
Qi-chen Hao^{1, 2},
Chun-lei Liu^{1, 2},
Sheng-wei Cao^{1, 2},
Zheng-hong Li^{1, 2
, ,}

1.
Fujian Provincial Key Laboratory of Water Cycling and Eco-Geological Processes, Xiamen 361000, Fujian Province, China
2.
Institute of Hydrogeology and Environmental Geology, CAGS, Shijiazhuang 050061, China

More Information

Corresponding author: lizhenghong@mail.cgs.gov.cn

摘要

- /
- /
- /
Abstract: To address the deficiencies in comprehensive surface contamination prevention strategies within China's nitrate-affected regions, this research innovatively proposes the DITAPH model - a systematic framework integrating groundwater nitrate vulnerability assessment and Nitrate Vulnerable Zones (NVZs) delineation through optimization of hydrogeological parameters. Based on detailed hydrogeological and hydrochemical investigations, the DITAPH model was applied in the plain areas of Quanzhou to evaluate its applicability. The model selected hydrogeological parameters (depth of groundwater, lithology of the vadose zone, topographic slope, aquifer water yield property), one climatic parameter (precipitation), and two anthropogenic parameters (land use type and population density) as assessment indicators. The results of the groundwater nitrate vulnerability assessment showed that the low, relatively low, relatively high, and high groundwater nitrate vulnerability zones in the study area accounted for 5.96%, 35.44%, 53.74% and 4.86% of the total area, respectively. Groundwater nitrate vulnerability was most strongly influenced by human activities, followed by groundwater depth and topographic slope. The high vulnerability zone is mainly affected by domestic and industrial wastewater, whereas the relatively high groundwater nitrate vulnerability zone is primarily influenced by agricultural activities. Validation of the DITAPH model revealed a significant positive correlation between the DITAPH index (DI) and nitrate concentration (ρ(NO₃⁻)). The results of the NVZs delineated by the DITAPH model are reliable and can serve as a tool for water resource management planning, guiding the development of targeted measures in the NVZs to prevent groundwater contamination.
- Nitrate contamination /
- NVZs delineation /
- Human activity coupling /
- Pollution risk management

HTML全文

Figure 1. Location of the research area

下载: 全尺寸图片幻灯片

Figure 2. Classes and ratings for DITAPH parameters

下载: 全尺寸图片幻灯片

Figure 3. Thematic maps of assessment parameters in the DITAPH model: (a) Depth of groundwater (D); (b) Vadose zone lithology (I); (c) Topographic slope (T); (d) Aquifer water yield property (A); (e) Precipitation (P); (f) Human activity (H)

下载: 全尺寸图片幻灯片

Figure 4. Thematic map of human activity parameters in the DITAPH model: (a) Land use map (LU); (b) nighttime lighting grading map (NTL)

下载: 全尺寸图片幻灯片

Figure 5. Comparison between theoretical weight and effective weight of DITAPH model

下载: 全尺寸图片幻灯片

Figure 6. Spatial Distribution of NVZs Delineated by the DITAPH Model

下载: 全尺寸图片幻灯片

Figure 7. Spatial distribution of groundwater samples across the NVZs delineated using the DITAPH Model

下载: 全尺寸图片幻灯片

Figure 8. Linear relationship between DITAPH Index (DI) and groundwater nitrate concentration (NO₃⁻)

下载: 全尺寸图片幻灯片

Figure 9. Spatial distribution characteristics of groundwater samples within NVZs delineated by the DRASTIC model

下载: 全尺寸图片幻灯片

Table 1. Data Sources for the DITAPH model

Data type		Sources
Depth of groundwater (D)		Manual measurement
Impact of vadose zone lithology (I)		Geological map of the southern coastal area of Fujian
Topographic slope (T)		NASA DEM - National Aeronautics and Space Administration
Aquifer thickness (A)		Hydrogeologic map of the southern coastal area of Fujian
Precipitation (P)		China 1 km Resolution Monthly Precipitation Dataset (1901–2023)
Human Activity (H)	Land use	China multi-period land use land cover data set (CNLUCC)
Human Activity (H)	Population density	Nighttime Lights Data - National Aeronautics and Space Administration

下载: 导出CSV

Table 2. Theoretical weights assigned to DITAPH parameters

Parameter	D	I	T	A	P	H
Theoretical weight	2.5	0.5	1.5	2	0.5	5
Note: The weight values were dynamically calibrated through sensitivity analysis. Each of the six assessment parameters of the DITAPH model was divided into ranges or categories, and then rating scores were assigned to each range/category according to the magnitude of the impact of the different ranges/categories on groundwater vulnerability (Fig. 2), with reference to the DRASTIC index scoring method proposed by Aller (Aller et al. 1987) and the Modified DRASTIC index scoring method proposed by Zhong (Zhong, 2005). Each of the assessment parameters is rated on a scale of 1 to 10, with higher scores indicating greater impact of the parameter on groundwater vulnerability.

下载: 导出CSV

Table 3. Characteristic parameters of NVZs

Vulnerability class		LZs	RLZs	RHZs	HZs
Index value		DI<55	55≤DI<75	75≤DI<95	DI≥95
Area (km²)		80.85	480.98	729.42	66.03
Area percentage (%)		5.96	35.44	53.74	4.86
Hydrogeological parameters	D	4–8 m: 72% of area2–4 m: 12% of area	4–8 m: 59% of area2–4 m: 28% of area	4–8 m: 53% of area2–4 m: 34% of area	4–8 m: 24% of area2–4 m: 63% of area
	I	Mainly granite, tuff lava, followed by clay, clayey sand, sandy clay	Mainly clay, clayey sand, sandy clay, silty clay, followed by granite, tuff lava	Mainly clays, clayey sands, sandy clays and silt and fine sands	Mainly fine sand and clay
	T	> 20%: 79% of area	4–10%: 38% of area≤4%: 47% of area	≤4%: 80% of area	≤4%: 97% of area
	A	100–1,000 m³/d: 69% of area	10–100 m³/d: 78% of area	10–100 m³/d: 94% of area	10–100 m³/d: 99% of area
	P	>1,000 mm: 86% of area	900–1,000: 79% of area	900–1,000: 85% of area	1,000–1,100 mm: 65% of area900–1,000 mm: 35% of area
Climatic parameter	P	>1,000 mm: 86% of area	900–1,000: 79% of area	900–1,000: 85% of area	1,000–1,100 mm: 65% of area900–1,000 mm: 35% of area
Human activity parameters	H	unfragmented woodland: 43% of areawoodland: 36% of area	Unfragmented woodland: 49% of areadry cropland: 26% of area	wet cropland: 43% of areadry cropland: 34% of area	Mainly townland, with a population density of >50 persons/km²: 62% of area

下载: 导出CSV

Table 4. Distribution of nitrate concentration in groundwater

Vulnerability Class	Samples	Proportion of each groundwater quality class
Vulnerability Class	Samples	Ⅰ	Ⅱ	Ⅳ	Ⅳ	Ⅴ
HZs	6	16.67%	0.00%	33.33%	33.33%	16.67%
RHZs	85	0.00%	0.00%	70.59%	22.35%	7.06%
RLZs	48	18.75%	27.08%	52.08%	2.08%	0.00%

下载: 导出CSV

Table 5. Comparison of groundwater quality distribution within NVZs as delineated by the DRASTIC and DITAPH models

Classification method	Vulnerability class	Samples	Groundwater quality class/%					Compliant water	Contaminated water	Proportion of contaminated water
Classification method	Vulnerability class	Samples	Ⅰ	Ⅱ	Ⅳ	Ⅳ	Ⅴ	Class (I - III)	Class (IV - V)	Proportion of contaminated water
DRASTIC	HZs	35	5	6	18	5	1	29	6	17.14%
	RHZs	19	2	2	14	0	1	18	1	5.26%
	RLZs	76	3	5	47	16	5	55	21	27.63%
	LZs	9	0	0	8	1	0	8	1	11.11%
DITAPH	HZs	6	1	0	2	2	1	3	3	50.00%
	RHZs	85	0	0	60	19	6	60	25	29.41%
	RLZs	48	9	13	25	1	0	47	1	2.08%
	LZs	0	0	0	0	0	0	0	0	-

下载: 导出CSV

参考文献(45)

Abascal E, Gómez-Coma L, Ortiz I, et al. 2022. Global diagnosis of nitrate pollution in groundwater and review of removal technologies. Science of The Total Environment, 810: 152233. DOI: 10.1016/j.scitotenv.2021.152233.

Ahada CPS, Suthar S. 2018. A GIS based DRASTIC model for assessing aquifer vulnerability in Southern Punjab, India. Modeling Earth Systems and Environment, 4: 635−645. DOI: 10.1007/s40808-018-0449-6.

Ahmad W, Iqbal J, Nasir MJ, et al. 2021. Impact of land use/land cover changes on water quality and human health in district Peshawar Pakistan. Scientific Reports, 11: 16526. DOI: 10.1038/s41598-021-96075-3.

Aller L, Bennett T, Lehr J, et al. 1987. DRASTIC: Standardized system for evaluating groundwater pollution potencial using hydrogeologic settings. Journal of the Geological Society of India: 29. DOI: 10.17491/jgsi/1987/290112

Arauzo M. 2017. Vulnerability of groundwater resources to nitrate pollution: A simple and effective procedure for delimiting Nitrate Vulnerable Zones. Science of The Total Environment, 575: 799−812. DOI: 10.1016/j.scitotenv.2016.09.139.

Arauzo M, Martínez-Bastida JJ. 2015. Environmental factors affecting diffuse nitrate pollution in the major aquifers of central Spain: Groundwater vulnerability vs. groundwater pollution. Environmental Earth Sciences, 73: 8271−8286. DOI: 10.1007/s12665-014-3989-8.

Arauzo M, Valladolid M, Andries DM. 2022. Would delineation of nitrate vulnerable zones be improved by introducing a new parameter representing the risk associated with soil permeability in the Land Use–Intrinsic Vulnerability Procedure? Science of The Total Environment, 840: 156654. DOI: 10.1016/j.scitotenv.2022.156654

Arora B, Dwivedi D, Faybishenko B, et al. 2019. Understanding and predicting vadose zone processes. Reviews in Mineralogy and Geochemistry, 85: 303−328. DOI: 10.2138/rmg.2019.85.10.

Babiker I, Mohamed M, Hiyama T, et al. 2005. A GIS-based DRASTIC model for assessing aquifer vulnerability in Kakamigahara Heights, Gifu Prefecture, central Japan. Science of The Total Environment, 345: 127−140. DOI: 10.1016/j.scitotenv.2004.11.005.

Chakraborty B, Roy S, Bera A, et al. 2022. Groundwater vulnerability assessment using GIS-based DRASTIC model in the upper catchment of Dwarakeshwar river basin, West Bengal, India. Environmental Earth Sciences, 81: 2. DOI: 10.1007/s12665-021-10002-3.

Chilaule SM, Vélez-Nicolás M, Ruiz-Ortiz V, et al. 2023. Assessment of intrinsic vulnerability using DRASTIC vs. Actual nitrate pollution: The case of a detrital aquifer impacted by intensive agriculture in Cádiz (Southern Spain). Agriculture, 13: 1082. DOI: 10.3390/agriculture13051082.

Deka D, Ravi K, Nair AM. 2025. Impact of urbanisation on groundwater vulnerability in shallow aquifer system of Assam: A DRASTIC approach. Urban Climate, 59: 102299. DOI: 10.1016/j.uclim.2025.102299.

Deng D, Lai S, Deng Y. 2002. 1: 250, 000 geological map of the coastal area of southern Fujian.

Falkenmark M. 1986. Fresh water. Time for a modified approach - Eau douce. Le moment d'un changement d'approche. Ambio, 15(4): 192−200.

Fu JJ, Le XC. 2025. Improving groundwater vulnerability assessment using machine learning. Journal of Environmental Sciences, 153: 6−9. DOI: 10.1016/j.jes.2024.12.024.

Fu SM, Wang KF. 2023. Quanzhou Statistical Yearbook 2023. China Statistics Press, Beijing. https://tjj.quanzhou.gov.cn/

George NJ, Agbasi OE, Umoh AJ, et al. 2025. Enhanced contamination risk assessment for aquifer management using the geo-resistivity and DRASTIC model in alluvial settings. Cleaner Water, 3: 100060. DOI: 10.1016/j.clwat.2024.100060.

Gutiérrez M, Biagioni RN, Alarcón-Herrera MT, et al. 2018. An overview of nitrate sources and operating processes in arid and semiarid aquifer systems. Science of the Total Environment, 624: 1513−1522. DOI: 10.1016/j.scitotenv.2017.12.252.

Hamza SM, Ahsan A, Imteaz MA, et al. 2015. Accomplishment and subjectivity of GIS-based DRASTIC groundwater vulnerability assessment method: A review. Environmental Earth Sciences, 73: 3063−3076. DOI: 10.1007/s12665-014-3601-2.

Iqbal MA, Salam MA, Nur-E-Alam M, et al. 2024. Monitoring groundwater vulnerability for sustainable water resource management: A DRASTIC-based comparative assessment in a newly township area of Bangladesh. Groundwater for Sustainable Development, 27: 01373. DOI: 10.1016/j.gsd.2024.101373.

Javadi S, Kavehkar N, Mohammadi K, et al. 2011. Calibrating DRASTIC using field measurements, sensitivity analysis and statistical methods to assess groundwater vulnerability. Water International, 36: 719−732. DOI: 10.1080/02508060.2011.610921.

Khosravi K, Sartaj M, Tsai FT-C, et al. 2018. A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment. Science of The Total Environment, 642: 1032−1049. DOI: 10.1016/j.scitotenv.2018.06.130.

Kong XK, Zhang ZX, Wang P, et al. 2022. Transformation of ammonium nitrogen and response characteristics of nitrifying functional genes in tannery sludge contaminated soil. Journal of Groundwater Science and Engineering, 10(3): 223−232. DOI: 10.19637/j.cnki.2305-7068.2022.03.002.

Kumar A, Pramod Krishna A. 2020. Groundwater vulnerability and contamination risk assessment using GIS-based modified DRASTIC -LU model in hard rock aquifer system in India. Geocarto International, 35: 1149−1178. DOI: 10.1080/10106049.2018.1557259.

Levin N, Kyba CCM, Zhang QL, et al. 2020. Remote sensing of night lights: A review and an outlook for the future. Remote Sensing of Environment, 237: 111443. DOI: 10.1016/j.rse.2019.111443.

Liu YC, Fei YH, Li YS, et al. 2024. Pollution source identification methods and remediation technologies of groundwater: A review. China Geology, 7(1): 125−137. DOI: 10.31035/cg2022080.

Lubianetzky TA, Dickson SE, Guo Y. 2015. Proposed method: incorporation of fractured rock in aquifer vulnerability assessments. Environmental Earth Sciences, 74: 4813−4825. DOI: 10.1007/s12665-015-4471-y.

Ma L, Lu J, Zhao H, et al. 2018. Nitrate Vulnerable Zones and strategies of non-point pollution mitigation in China. Journal of Agro-Environment Science, 37: 2387−2391. DOI: 10.11654/jaes.2018-1369.

Martínez-Bastida JJ, Arauzo M, Valladolid M. 2010. Intrinsic and specific vulnerability of groundwater in central Spain: the risk of nitrate pollution. Hydrogeology Journal, 18: 681−698. DOI: 10.1007/s10040-009-0549-5.

Mekonnen MM, Hoekstra AY. 2016. Four billion people facing severe water scarcity. Science Advances, 2: e1500323. DOI: 10.1126/sciadv.1500323.

Monteny GJ. 2001. The EU Nitrates Directive: A European approach to combat water pollution from agriculture. The Scientific World Journal, 1: 927−935. DOI: 10.1100/tsw.2001.377.

Nahin KTK, Basak R, Alam R. 2020. Groundwater vulnerability assessment with DRASTIC index method in the salinity-affected southwest coastal region of Bangladesh: A case study in Bagerhat Sadar, Fakirhat and Rampal. Earth Systems and Environment, 4: 183−195. DOI: 10.1007/s41748-019-00144-7.

Orellana-Macías JM, Merchán D, Causapé J. 2020. Evolution and assessment of a nitrate vulnerable zone over 20 years: Gallocanta groundwater body (Spain). Hydrogeology Journal, 28: 2207−2221. DOI: 10.1007/s10040-020-02184-0.

Peng SZ, Ding YX, Liu WZ, et al. 2019. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth System Science Data: 1931–1946. DOI: 10.5194/essd-11-1931-2019

QMEEB (Quanzhou Municipal Ecological Environment Bureau). 2021. Water ecology of key river Basins in Quanzhou City during the 14th Five-Year Plan period Environmental protection planning. http://sthjj.quanzhou.gov.cn

Sidibe AM, Lin X. 2018. Heavy metals and nitrate to validate groundwater sensibility assessment based on DRASTIC models and GIS: Case of the upper Niger and the Bani basin in Mali. Journal of African Earth Sciences, 147: 199−210. DOI: 10.1016/j.jafrearsci.2018.06.019.

Smail RQS, Dişli E. 2023. Assessment and validation of groundwater vulnerability to nitrate and TDS using based on a modified DRASTIC model: A case study in the Erbil Central Sub-Basin, Iraq. Environmental Monitoring Assessment, 195: 567. DOI: 10.1007/s10661-023-11165-1.

Stigter TY, Ribeiro L, Dill AMMC. 2006. Evaluation of an intrinsic and a specific vulnerability assessment method in comparison with groundwater salinisation and nitrate contamination levels in two agricultural regions in the south of Portugal. Hydrogeology Journal, 14: 79−99. DOI: 10.1007/s10040-004-0396-3.

Sun XB, Guo CL, Zhang J, et al. 2023. Spatial-temporal difference between nitrate in groundwater and nitrogen in soil based on geostatistical analysis. Journal of Groundwater Science and Engineering, 11: 37−46. DOI: 10.26599/JGSE.2023.9280004.

Tao M, Lai S, Deng Y. 2002. 1: 250, 000 hydrogeologic map of the southern coastal area of Fujian.

Verma A, Sharma A, Kumar R, et al. 2023. Nitrate contamination in groundwater and associated health risk assessment for Indo-Gangetic Plain, India. Groundwater for Sustainable Development, 23: 100978. DOI: 10.1016/j.gsd.2023.100978.

Xu XL, Liu JY, Zhang SW, et al. 2018. China's Multi-Period Land Use Land Cover Remote Sensing Monitoring Dataset (CNLUCC). DOI: 10.12078/2018070201.

Yankey RK, Anornu GK, Osae SK, et al. 2021. Drastic model application to groundwater vulnerability elucidation for decision making: the case of south western coastal basin, Ghana. Modeling Earth Systems and Environment, 7: 2197−2213. DOI: 10.1007/s40808-020-01031-1.

Zenebe GB, Hussien A, Girmay A, et al. 2020. Spatial analysis of groundwater vulnerability to contamination and human activity impact using a modified DRASTIC model in Elalla-Aynalem Catchment, Northern Ethiopia. Sustainable Water Resources Management, 6: 51. DOI: 10.1007/s40899-020-00406-7.

Zhong ZX, 2005. A discussion of groundwater vulnerability assessment method. Earth Science Frontiers, 12: 3–013.

[1]	Stephen Pitchaimani V, Narayanan MSS, Abishek RS, Aswin SK, Jerin Joe RJ2024: Delineation of groundwater potential zones using remote sensing and Geographic Information Systems (GIS) in Kadaladi region, Southern India, Journal of Groundwater Science and Engineering, 12, 147-160. doi: 10.26599/JGSE.2024.9280012
[2]	Fu-ning Lan, Yi Zhao, Jun Li, Xiu-qun Zhu2024: Health risk assessment of heavy metal pollution in groundwater of a karst basin, SW China, Journal of Groundwater Science and Engineering, 12, 49-61. doi: 10.26599/JGSE.2024.9280005
[3]	Xiu-bo Sun, Chang-lai Guo, Jing Zhang, Jia-quan Sun, Jian Cui, Mao-hua Liu2023: Spatial-temporal difference between nitrate in groundwater and nitrogen in soil based on geostatistical analysis, Journal of Groundwater Science and Engineering, 11, 37-46. doi: 10.26599/JGSE.2023.9280004
[4]	Tanzeel Khan, Muhammad Akhtar Malik, Gohram Malghani, Rabia Akhtar2022: Comparative analysis of bacterial contamination in tap and groundwater: A case study on water quality of Quetta City, an arid zone in Pakistan, Journal of Groundwater Science and Engineering, 10, 153-165. doi: 10.19637/j.cnki.2305-7068.2022.02.005
[5]	Cherif Kessar, Yamina Benkesmia, Bilal Blissag, Lahsen Wahib Kébir2021: Delineation of groundwater potential zones in Wadi Saida Watershed of NW-Algeria using remote sensing, geographic information system-based AHP techniques and geostatistical analysis, Journal of Groundwater Science and Engineering, 9, 45-64. doi: 10.19637/j.cnki.2305-7068.2021.01.005
[6]	GUI Chun-lei, WANG Zhen-xing, MA Rong, ZUO Xue-feng2021: Aquifer hydraulic conductivity prediction via coupling model of MCMC-ANN, Journal of Groundwater Science and Engineering, 9, 1-11. doi: 10.19637/j.cnki.2305-7068.2021.01.001
[7]	Liu Ya-ci, Zhang Zhao-ji, Zhao Xin-yi, Wen Meng-tuo, Cao Sheng-wei, Li Ya-song2021: Arsenic contamination caused by roxarsone transformation with spatiotemporal variation of microbial community structure in a column experiment, Journal of Groundwater Science and Engineering, 9, 304-316. doi: 10.19637/j.cnki.2305-7068.2021.04.004
[8]	ZHONG Hua-ping, WU Yong-xiang2020: State of seawater intrusion and its adaptive management countermeasures in Longkou City of China, Journal of Groundwater Science and Engineering, 8, 30-42. doi: 10.19637/j.cnki.2305-7068.2020.01.004
[9]	Qaisar Mehmood, Muhammad Arshad, Muhammad Rizwan, Shanawar Hamid, Waqas Mehmood, Muhammad Ansir Muneer, Muhammad Irfan, Lubna Anjum2020: Integration of geoelectric and hydrochemical approaches for delineation of groundwater potential zones in alluvial aquifer, Journal of Groundwater Science and Engineering, 8, 366-380. doi: 10.19637/j.cnki.2305-7068.2020.04.007
[10]	Dinagarapandi Pandi, Saravanan Kothandaraman, Mohan Kuppusamy2020: Delineation of potential groundwater zones based on multicriteria decision making technique, Journal of Groundwater Science and Engineering, 8, 180-194. doi: 10.19637/j.cnki.2305-7068.2020.02.009
[11]	Abdelhakim LAHJOUJ, Abdellah EL HMAIDI, Karima BOUHAFA2020: Spatial and statistical assessment of nitrate contamination in groundwater: Case of Sais Basin, Morocco, Journal of Groundwater Science and Engineering, 8, 143-157. doi: 10.19637/j.cnki.2305-7068.2020.02.006
[12]	LI Yang, KANG Feng-Xin, ZOU An-de2019: Isotope analysis of nitrate pollution sources in groundwater of Dong’e geohydrological unit, Journal of Groundwater Science and Engineering, 7, 145-154. doi: 10.19637/j.cnki.2305-7068.2019.02.005
[13]	LIU Shu-yuan, WANG Hong-qi2016: Dynamic assessment of pollution risk of groundwater source area in Northern China, Journal of Groundwater Science and Engineering, 4, 333-343.
[14]	DAI Wen-Bin, ZHANG Wei-Jun, COWEN Taha2015: An analysis of River Derwent pollution and its impacts, Journal of Groundwater Science and Engineering, 3, 39-44.
[15]	ZHANG Chuan-mian, GUO Xiao-niu, Richard Henry, James Dendy2015: Groundwater modelling to help diagnose contamination problems, Journal of Groundwater Science and Engineering, 3, 285-294.
[16]	SHI Jian-sheng, LIU Chang-li, DONG Hua, YAN Zhen-peng, WANG Yan-jun, LIU Xin-hao, GUO Xiu-yan, JIAO Hong-jun, YIN Mi-ying, HOU Huai-ren2014: Stability assessment and risk analysis of aboveground river in lower Yellow River, Journal of Groundwater Science and Engineering, 2, 1-18.
[17]	Chang-li LIU, Chao SONG, Hong-bing HOU, Xiu-yan WANG, Yun ZHANG, Jun-kun WANG, Jian-mei JIANG, Li-xin PEI, Bo SONG2014: The Impact of Human Activities on CO2 Intake by Carbonate Weathering: A Case Study of Conglin Karst Ridge-trough at Fuling Town, Chongqing, China, Journal of Groundwater Science and Engineering, 2, 29-38.
[18]	Zhao Wang, Jiansheng Shi, Zhaoji Zhang, Yuhong Fei2013: Organic Contamination of Soil and Goundwater in the Piedimont Plain of the Taihang Mountains, Journal of Groundwater Science and Engineering, 1, 74-81.
[19]	Aizhong Ding, Lirong Cheng, Steve Thornton, Wei Huang, David Lerner2013: Groundwater quality Management in China, Journal of Groundwater Science and Engineering, 1, 54-59.
[20]	Zhao-xian Zheng, Xiao-si Su2013: Risk Assessment on Organic Contamination of Shallow Groundwater of an Oilfield in Northeast China, Journal of Groundwater Science and Engineering, 1, 75-82.