Volume 15 Issue 3
Aug.  2022
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Azadeh Atabati, Hamed Adab, Ghasem Zolfaghari, Mahdi Nasrabadi. 2022: Modeling groundwater nitrate concentrations using spatial and non-spatial regression models in a semi-arid environment. Water Science and Engineering, 15(3): 218-227. doi: 10.1016/j.wse.2022.05.002
Citation: Azadeh Atabati, Hamed Adab, Ghasem Zolfaghari, Mahdi Nasrabadi. 2022: Modeling groundwater nitrate concentrations using spatial and non-spatial regression models in a semi-arid environment. Water Science and Engineering, 15(3): 218-227. doi: 10.1016/j.wse.2022.05.002

Modeling groundwater nitrate concentrations using spatial and non-spatial regression models in a semi-arid environment

doi: 10.1016/j.wse.2022.05.002
  • Received Date: 2021-08-02
  • Accepted Date: 2022-01-07
  • Rev Recd Date: 2022-01-07
  • Available Online: 2022-08-24
  • Nitrate nitrogen (NO3--N) from agricultural activities and in industrial wastewater has become the main source of groundwater pollution, which has raised widespread concerns, particularly in arid and semi-arid river basins with little water that meets relevant standards. This study aimed to investigate the performance of spatial and non-spatial regression models in modeling nitrate pollution in a semi-intensive farming region of Iran. To perform the modeling of the groundwater's NO3--N concentration, both natural and anthropogenic factors affecting groundwater NO3--N were selected. The results of Moran's I test showed that groundwater nitrate concentration had a significant spatial dependence on the density of wells, distance from streams, total annual precipitation, and distance from roads in the study area. This study provided a way to estimate nitrate pollution using both natural and anthropogenic factors in arid and semi-arid areas where only a few factors are available. Spatial regression methods with spatial correlation structures are effective tools to support spatial decision-making in water pollution control.

     

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  • [1]
    Abdalla, F., Khalil, R., 2018. Potential effects of groundwater and surface water contamination in an urban area, Qus City, Upper Egypt. Journal of African Earth Sciences 141, 164-178. https://doi.org/10.1016/j.jafrearsci.2018.02.016
    [2]
    Alexopoulos, E. 2010. Introduction to multivariate regression analysis. Hippokratia 14(s1), 23-28
    [3]
    Amini, M., Abbaspour, K.C., Berg, M., Winkel, L., Hug, S.J., Hoehn, E., Yang, H., Johnson, C.A., 2008. Statistical modeling of global geogenic arsenic contamination in groundwater. Environmental Science & Technology 42, 3669-3675. https://doi.org/10.1021/es702859e
    [4]
    Anselin, L., 1999. SpaceStat, A Software Package for the Analysis of Spatial Data, Version 1.90. BioMedware, Ann Arbor
    [5]
    Ashraf, B., Yazdani, R., Mousavi-Baygi, M., Bannayan, M., 2014. Investigation of temporal and spatial climate variability and aridity of Iran. Theoretical and Applied Climatology 118, 35-46. https://doi.org/10.1007/s00704-013-1040-8
    [6]
    Auchincloss, A.H., Gebreab, S.Y., Mair, C., Diez Roux, A.V., 2012. A review of spatial methods in epidemiology, 2000-2010. Annual Review of Public Health 33, 107-122. https://doi.org/10.1146/annurev-publhealth-031811-124655
    [7]
    Badee Nezhad, A., Emamjomeh, M.M., Farzadkia, M., Jonidi Jafari, A., Sayadi, M., Davoudian Talab, A.H., 2017. Nitrite and nitrate concentrations in the drinking groundwater of Shiraz City, South-central Iran by statistical models. Iran. J. Public Health 46, 1275-1284
    [8]
    Bailey, T.C., 2001. Spatial statistical methods in health. Cadernos de Saude Publica 17, 1083-1098. https://doi.org/10.1590/S0102-311X2001000500011
    [9]
    Banerjee, P., Singh, V., Chatttopadhyay, K., Chandra, P., Singh, B., 2011. Artificial neural network model as a potential alternative for groundwater salinity forecasting. Journal of Hydrology 398, 212-220. https://doi.org/10.1016/j.jhydrol.2010.12.016
    [10]
    Baumont, C., Ertur, C., Gallo, J., 2004. Spatial analysis of employment and population density: The case of the agglomeration of Dijon 1999. Geographical Analysis 36, 146-176. https://doi.org/10.1111/j.1538-4632.2004.tb01130.x
    [11]
    Benson, V.S., VanLeeuwen, J.A., Sanchez, J., Dohoo, I.R., Somers, G.H., 2006. Spatial analysis of land use impact on ground water nitrate concentrations. Journal of Environmental Quality 35, 421-432. https://doi.org/10.2134/jeq2005.0115
    [12]
    Bini, L.M., Diniz-Filho, J.A.F., Rangel, T.F.L.V.B., Akre, T.S.B., Albaladejo, R.G., Albuquerque, F.S., Aparicio, A., Araújo, M. B., Baselga, A., Beck, J., et al., 2009. Coefficient shifts in geographical ecology: An empirical evaluation of spatial and non-spatial regression. Ecography 32, 193-204. https://doi.org/10.1111/j.1600-0587.2009.05717.x
    [13]
    Bohdziewicz, J., Bodzek, M., Wąsik, E., 1999. The application of reverse osmosis and nanofiltration to the removal of nitrates from groundwater. Desalination 121, 139-147. https://doi.org/10.1016/S0011-9164(99)00015-6
    [14]
    Boulos, M.N.K., 2004. Towards evidence-based, GIS-driven national spatial health information infrastructure and surveillance services in the United Kingdom. International Journal of Health Geographics 3, 1. https://dx.doi.org/10.1186%2F1476-072X-3-1
    [15]
    Boy-Roura, M., Nolan, B.T., Mencio, A., Mas-Pla, J., 2013. Regression model for aquifer vulnerability assessment of nitrate pollution in the Osona region (NE Spain). Journal of Hydrology 505, 150-162. https://doi.org/10.1016/j.jhydrol.2013.09.048
    [16]
    Buczko, U., Kuchenbuch, R.O., Lennartz, B., 2010. Assessment of the predictive quality of simple indicator approaches for nitrate leaching from agricultural fields. Journal of Environmental Management 91, 1305-1315. https://doi.org/10.1016/j.jenvman.2010.02.007
    [17]
    Cannavo, P., Recous, S., Parnaudeau, V., Reau, R., 2008. Modeling N dynamics to assess environmental impacts of cropped soils. Advances in Agronomy 97, 131-174. https://doi.org/10.1016/S0065-2113(07)00004-1
    [18]
    Cho, K.H., Sthiannopkao, S., Pachepsky, Y.A., Kim, K.W., Kim, J.H., 2011. Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network. Water Research 45, 5535-5544. https://doi.org/10.1016/j.watres.2011.08.010
    [19]
    Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., Bohner, J., 2015. System for automated geoscientific analyses (SAGA) v. 2.1.4. Geoscientific Model Development Discussions 8, 2271-2312. https://doi.org/10.5194/gmd-8-1991-2015
    [20]
    Cook, P.L., Aldridge, K.T., Lamontagne, S., Brookes, J., 2010. Retention of nitrogen, phosphorus and silicon in a large semi-arid riverine lake system. Biogeochemistry 99, 49-63. https://doi.org/10.1007/s10533-009-9389-6
    [21]
    Costa, J.L., Massone, H., Martinez, D., Suero, E.E., Vidal, C.M., Bedmar, F., 2002. Nitrate contamination of a rural aquifer and accumulation in the unsaturated zone. Agricultural Water Management 57, 33-47. https://doi.org/10.1016/S0378-3774(02)00036-7
    [22]
    Creed, I.F., Band, L.E., 1998. Export of nitrogen from catchments within a temperate forest: Evidence for a unifying mechanism regulated by variable source area dynamics. Water Resources Research 34, 3105-3120. https://doi.org/10.1029/98WR01924
    [23]
    Cundill, A.P., Chapman, P.J., Adamson, J.K., 2007. Spatial variation in concentrations of dissolved nitrogen species in an upland blanket peat catchment. Science of the Total Environment 373, 166-177. https://doi.org/10.1016/j.scitotenv.2006.10.021
    [24]
    Diodato, N., Esposito, L., Bellocchi, G., Vernacchia, L., Fiorillo, F., Guadagno, F.M., 2013. Assessment of the spatial uncertainty of nitrates in the aquifers of the Campania Plain (Italy). American Journal of Climate Change 2(2), 128-137. https://doi.org/10.4236/ajcc.2013.22013
    [25]
    DiRienzo, C., Fackler, P., Goodwin, B.K., 2000. Modeling spatial dependence and spatial heterogeneity in county yield forecasting models. In: Proceedings of the American Agricultural Economics Association Annual Meeting. AAEA, Tampa
    [26]
    Doane, T.A., Horwath, W.R., 2003. Spectrophotometric determination of nitrate with a single reagent. Analytical Letters 36, 2713-2722. https://doi.org/10.1081/AL-120024647
    [27]
    Eckhardt, D.A.V., Stackelberg, P.E., 1995. Relation of ground-water quality to land use on Long Island, New York. Groundwater 33, 1019-1033. https://doi.org/10.1111/j.1745-6584.1995.tb00047.x
    [28]
    Elmi, A.A., Madramootoo, C., Egeh, M., Liu, A., Hamel, C., 2002. Environmental and agronomic implications of water table and nitrogen fertilization management. Journal of Environmental Quality 31, 1858-1867. https://doi.org/10.2134/jeq2002.1858
    [29]
    Fewtrell, L., 2004. Drinking-water nitrate, methemoglobinemia, and global burden of disease: A discussion. Environmental Health Perspectives 112, 1371-1374. https://doi.org/10.1289%2Fehp.7216
    [30]
    Fick, S.E., Hijmans, R.J., 2017. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37, 4302-4305. https://doi.org/10.1002/joc.5086
    [31]
    Fouache, É., Francfort, H.P., Bendezu-Sarmiento, J., Vahdati, A.A., Lhuillier, J., 2010. The horst of Sabzevar and regional water resources from the Bronze Age to the present day (Northeastern Iran). Geodinamica Acta 23, 287-294. https://doi.org/10.3166/ga.23.287-294
    [32]
    Frans, L.M., 2008. Estimating the Probability of Elevated Nitrate Concentrations in Ground Water in Washington State: U.S. Geological Survey Scientific Investigations Report 2008-5025. U.S. Geological Survey, Reston
    [33]
    Gates, J.B., Bohlke, J.K., Edmunds, W.M., 2008. Ecohydrological factors affecting nitrate concentrations in a phreatic desert aquifer in northwestern China. Environmental Science & Technology 42, 3531-3537. https://doi.org/10.1021/es702478d
    [34]
    Ghazavi, R., Ebrahimi, Z., 2015. Assessing groundwater vulnerability to contamination in an arid environment using DRASTIC and GOD models. International Journal of Environmental Science and Technology 12, 2909-2918. https://doi.org/10.1007/s13762-015-0813-2
    [35]
    Hsu, D., 2015. Identifying key variables and interactions in statistical models of building energy consumption using regularization. Energy 83, 144-155. https://doi.org/10.1016/j.energy.2015.02.008
    [36]
    Huang, Y., Lan, Y., Ge, Y., Hoffmann, W., Thomson, G., 2010. Spatial modeling and variability analysis for modeling and prediction of soil and crop canopy coverage using multispectral imagery from an airborne remote sensing system. Transactions of the ASABE 53, 1321-1329. https://doi.org/10.13031/2013.32582
    [37]
    Iqbal, J., Gorai, A., Katpatal, Y., Pathak, G., 2015. Development of GIS-based fuzzy pattern recognition model (modified DRASTIC model) for groundwater vulnerability to pollution assessment. International Journal of Environmental Science and Technology 12, 3161-3174. https://doi.org/10.1007/s13762-014-0693-x
    [38]
    Ismail, A., Zourbakhsh, M., Eftekhar, H., Shokri, F., Altef, A.N., Rahmat, R.A.O., 2014. An overview of interchanges and ramps in case of Sabzevar. Research Journal of Applied Sciences, Engineering and Technology 7, 1163-1166. https://doi.org/10.19026/rjaset.7.375
    [39]
    Jacquez, G.M., Kaufmann, A., Meliker, J., Goovaerts, P., AvRuskin, G., Nriagu, J., 2005. Global, local and focused geographic clustering for case-control data with residential histories. Environmental Health 4, 4. https://doi.org/10.1186/1476-069X-4-4
    [40]
    Jiang, R., Woli, K.P., Kuramochi, K., Hayakawa, A., Shimizu, M., Hatano, R., 2012. Coupled control of land use and topography on nitrate-nitrogen dynamics in three adjacent watersheds. CATENA 97, 1-11. https://doi.org/10.1016/j.catena.2012.04.015
    [41]
    Johnson, L., Richards, C., Host, G., Arthur, J., 2003. Landscape influences on water chemistry in Midwestern stream ecosystems. Freshwater Biology 37, 193-208. https://doi.org/10.1046/j.1365-2427.1997.d01-539.x
    [42]
    Jung, Y.Y., Koh, D.C., Park, W.B., Ha, K., 2016. Evaluation of multiple regression models using spatial variables to predict nitrate concentrations in volcanic aquifers. Hydrological Processes 30, 663-675. https://doi.org/10.1002/hyp.10633
    [43]
    Khalil, A., Almasri, M.N., McKee, M., Kaluarachchi, J.J., 2005. Applicability of statistical learning algorithms in groundwater quality modeling. Water Resources Research 41, W05010. https://doi.org/10.1029/2004WR003608
    [44]
    Khatri, N., Tyagi, S., 2015. Influences of natural and anthropogenic factors on surface and groundwater quality in rural and urban areas. Frontiers in Life Science 8, 23-39. https://doi.org/10.1080/21553769.2014.933716
    [45]
    Legendre, P., Fortin, M.J., 1989. Spatial pattern and ecological analysis. Vegetatio 80, 107-138. https://doi.org/10.1007/BF00048036
    [46]
    LeSage, J.P., Parent, O., 2007. Bayesian model averaging for spatial econometric models. Geographical Analysis 39, 241-267. https://doi.org/10.1111/j.1538-4632.2007.00703.x
    [47]
    Lord, E., Anthony, S., Goodlass, G., 2002. Agricultural nitrogen balance and water quality in the UK. Soil Use and Management 18, 363-369. https://doi.org/10.1111/j.1475-2743.2002.tb00253.x
    [48]
    Madani, K., 2014. Water management in Iran: What is causing the looming crisis? Journal of Environmental Studies and Sciences 4, 315-328. https://doi.org/10.1007/s13412-014-0182-z
    [49]
    Marques, C., Ferreira, J., Rocha, A., Castanheira, J., Melo-Gonçalves, P., Vaz, N., Dias, J.M., 2006. Singular spectrum analysis and forecasting of hydrological time series. Physics and Chemistry of the Earth, Parts A/B/C 31, 1172-1179. https://doi.org/10.1016/j.pce.2006.02.061
    [50]
    Mathes, S.E., Rasmussen, T.C., 2006. Combining multivariate statistical analysis with geographic information systems mapping: A tool for delineating groundwater contamination. Hydrogeology Journal 14, 1493-1507. https://doi.org/10.1007/s10040-006-0041-4
    [51]
    McCarty, J., Kaza, N., 2015. Urban form and air quality in the United States. Landscape and Urban Planning 139, 168-179. https://doi.org/10.1016/j.landurbplan.2015.03.008
    [52]
    McLay, C.D.A., Dragten, R., Sparling, G., Selvarajah, N., 2001. Predicting groundwater nitrate concentrations in a region of mixed agricultural land use: A comparison of three approaches. Environmental Pollution 115, 191-204. https://doi.org/10.1016/S0269-7491(01)00111-7
    [53]
    Mfumu Kihumba, A., Ndembo Longo, J., Vanclooster, M., 2016. Modelling nitrate pollution pressure using a multivariate statistical approach: The case of Kinshasa groundwater body, Democratic Republic of Congo. Hydrogeology Journal 24, 425-437. https://doi.org/10.1007/s10040-015-1337-z
    [54]
    Mihaescu, O., vom Hofe, R., 2013. Using spatial regression to estimate property tax discounts from proximity to brownfields: A tool for local policy-making. Journal of Environmental Assessment Policy and Management 15, 1350008. https://doi.org/10.1142/S1464333213500087
    [55]
    Miller, H.J., 1999. Potential contributions of spatial analysis to geographic information systems for transportation (GIS-T). Geographical Analysis 31, 373-399. https://doi.org/10.1111/j.1538-4632.1999.tb00991.x
    [56]
    Miranda, M.L., Edwards, S.E., 2011. Use of spatial analysis to support environmental health research and practice. North Carolina Medical Journal 72, 132-135. https://doi.org/10.1037/006690
    [57]
    Nolan, B.T., Gronberg, J.M., Faunt, C.C., Eberts, S.M., Belitz, K., 2014. Modeling nitrate at domestic and public-supply well depths in the Central Valley, California. Environmental Science & Technology 48, 5643-5651. https://doi.org/10.1021/es405452q
    [58]
    Nolan, B.T., Fienen, M.N., Lorenz, D.L., 2015. A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA. Journal of Hydrology 531, 902-911. https://doi.org/10.1016/j.jhydrol.2015.10.025
    [59]
    Oenema, O., Boers, P.C.M., van Eerdt, M.M., Fraters, B., van der Meer, H.G., Roest, C.W.J., Schrodera, J.J., Willemsd, W.J., 1998. Leaching of nitrate from agriculture to groundwater: The effect of policies and measures in the Netherlands. Environmental Pollution 102, 471-478. https://doi.org/10.1016/S0269-7491(98)80071-7
    [60]
    Ouedraogo, I., Vanclooster, M., 2016. A meta-analysis and statistical modelling of nitrates in groundwater at the African scale. Hydrol. Earth Syst. Sci. 20, 2353-2381. https://doi.org/10.5194/hess-20-2353-2016
    [61]
    Ouedraogo, I., Defourny, P., Vanclooster, M., 2019. Application of random forest regression and comparison of its performance to multiple linear regression in modeling groundwater nitrate concentration at the African continent scale. Hydrogeology Journal 27, 1081-1098. https://doi.org/10.1007/s10040-018-1900-5
    [62]
    Pabich, W.J., Valiela, I., Hemond, H.F., 2001. Relationship between DOC concentration and vadose zone thickness and depth below water table in groundwater of Cape Cod, USA. Biogeochemistry 55, 247-268. https://doi.org/10.1023%2FA%3A1011842918260
    [63]
    Rahmati, O., Choubin, B., Fathabadi, A., Coulon, F., Soltani, E., Shahabi, H., Mollaefarh, E., Tiefenbacher, J., Cipullo S., Ahmad, B.B., et al., 2019. Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods. Science of the Total Environment 688, 855-866. https://doi.org/10.1016/j.scitotenv.2019.06.320
    [64]
    Ransom, K.M., Nolan, B.T., Traum, J.A., Faunt, C.C., Bell, A.M., Gronberg, J.A.M., Wheeler, D.C., Rosecrans, C.Z., Jurgens, B., Schwarz, G.E., et al., 2017. A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA. Science of the Total Environment 601-602, 1160-1172. https://doi.org/10.1016/j.scitotenv.2017.05.192
    [65]
    Rodriguez-Galiano, V., Mendes, M.P., Garcia-Soldado, M.J., Chica-Olmo, M., Ribeiro, L., 2014. Predictive modeling of groundwater nitrate pollution using random forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain). Science of the Total Environment 476, 189-206. https://doi.org/10.1016/j.scitotenv.2014.01.001
    [66]
    Sahoo, P.K., Kim, K., Powell, M.A., 2016. Managing groundwater nitrate contamination from livestock farms: Implication for nitrate management guidelines. Current Pollution Reports 2, 178-187. https://doi.org/10.1007/s40726-016-0033-5
    [67]
    Salo, T., Turtola, E., 2006. Nitrogen balance as an indicator of nitrogen leaching in Finland. Agriculture, Ecosystems & Environment 113, 98-107. https://doi.org/10.1016/j.agee.2005.09.002
    [68]
    Sannigrahi, S., Pilla, F., Basu, B., Basu, A.S., Molter, A., 2020. Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach. Sustainable Cities and Society 62, 102418. https://doi.org/10.1016/j.scs.2020.102418
    [69]
    Shiode, N., Morita, M., Shiode, S., Okunuki, K., 2014. Urban and rural geographies of aging: A local spatial correlation analysis of aging population measures. Urban Geography 35, 608-628. https://doi.org/10.1080/02723638.2014.905256
    [70]
    Shiri, J., Kisi, O., Yoon, H., Lee, K.K., Nazemi, A.H., 2013. Predicting groundwater level fluctuations with meteorological effect implications: A comparative study among soft computing techniques. Computers & Geosciences 56, 32-44. https://doi.org/10.1016/j.cageo.2013.01.007
    [71]
    Shrestha, A., Luo, W., 2017. Analysis of groundwater nitrate contamination in the Central Valley: Comparison of the geodetector method, principal component analysis and geographically weighted regression. ISPRS International Journal of Geo-Information 6, 297. https://doi.org/10.3390/ijgi6100297
    [72]
    Shrestha, A., Luo, W., 2018. Assessment of groundwater nitrate pollution potential in Central Valley aquifer using geodetector-based frequency ratio (GFR) and optimized-DRASTIC methods. International Journal of Geo-Information 7, 211. https://doi.org/10.3390/ijgi7060211
    [73]
    Skevas, T., Skevas, I., Swinton, S.M., 2018. Does spatial dependence affect the intention to make land available for bioenergy crops? Journal of Agricultural Economics 69, 393-412. https://doi.org/10.1111/1477-9552.12233
    [74]
    Solomatine, D., Ostfeld, A., 2008. Data-driven modelling: Some past experiences and new approaches. Hydroinformatics 10, 3-22. https://doi.org/10.2166/hydro.2008.015
    [75]
    Solomatine, D., See, L.M., Abrahart, R.J., 2008. Data-driven modelling: Concepts, approaches and experiences. In: Abrahart, R.J., See, L.M., Solomatine, D.P., eds., Practical Hydroinformatics: Computational Intelligence and Technological Developments in Water Applications. Springer, Berlin, pp. 17-30
    [76]
    Song, J., Du, S., Feng, X., Guo, L., 2014. The relationships between landscape compositions and land surface temperature: Quantifying their resolution sensitivity with spatial regression models. Landscape and Urban Planning 123, 145-157. https://doi.org/10.1016/j.landurbplan.2013.11.014
    [77]
    Spalding, R.F., Gormly, J.R., Curtiss, B.H., Exner, M.E., 1978. Nonpoint nitrate contamination of ground water in Merrick County, Nebraskaa. Groundwater 16, 86-95. https://doi.org/10.1111/j.1745-6584.1978.tb03207.x
    [78]
    ter Braak, C.J.F., Juggins, S., 1993. Weighted averaging partial least squares regression (WA-PLS): An improved method for reconstructing environmental variables from species assemblages. Hydrobiologia 269, 485-502. https://doi.org/10.1007/BF00028046
    [79]
    Tong, S.T.Y., Chen, W., 2002. Modeling the relationship between land use and surface water quality. Journal of Environmental Management 66, 377-393. https://doi.org/10.1006/jema.2002.0593
    [80]
    Wang, J., Zhou, L., Yang, X., 2009. Geographic information systems and spatial analysis for coastal ecosystem research and management. In: Yang, X., ed., Remote Sensing and Geospatial Technologies for Coastal Ecosystem Assessment and Management. Springer, Berlin, pp. 45-66
    [81]
    Wheeler, D.C., Nolan, B.T., Flory, A.R., DellaValle, C.T., Ward, M.H., 2015. Modeling groundwater nitrate concentrations in private wells in Iowa. Science of the Total Environment 536, 481-488. https://doi.org/10.1016/j.scitotenv.2015.07.080
    [82]
    Wick, K., Heumesser, C., Schmid, E., 2012. Groundwater nitrate contamination: Factors and indicators. Journal of Environmental Management 111, 178-186. https://doi.org/10.1016/j.jenvman.2012.06.030
    [83]
    Wu, Z., Chen, Y., Han, Y., Ke, T., Liu, Y., 2020. Identifying the influencing factors controlling the spatial variation of heavy metals in suburban soil using spatial regression models. Science of the Total Environment 717, 137212. https://doi.org/10.1016/j.scitotenv.2020.137212
    [84]
    Yang, X., Jin., W., 2010. GIS-based spatial regression and prediction of water quality in river networks: A case study in Iowa. Journal of Environmental Management 91, 1943-1951. https://doi.org/10.1016/j.jenvman.2010.04.011
    [85]
    Yang, X., Liu, Q., Luo, X., Zheng, Z., 2017. Spatial regression and prediction of water quality in a watershed with complex pollution sources. Scientific Reports 7, 8318. https://doi.org/10.1038/s41598-017-08254-w
    [86]
    Zevenbergen, L.W., Thorne, C.R., 1987. Quantitative analysis of land surface topography. Earth Surface Processes and Landforms 12, 47-56. https://doi.org/10.1002/esp.3290120107
    [87]
    Zhang, Y., Li, F., Zhang, Q., Li, J., Liu, Q., 2014. Tracing nitrate pollution sources and transformation in surface- and ground-waters using environmental isotopes. Science of the Total Environment 490, 213-222. https://doi.org/10.1016/j.scitotenv.2014.05.004
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