Assessment of nitrogen application limits in agro-livestock farming areas using quantile regression between nitrogen loadings and groundwater nitrate levels

Kim, Ho Rim; Yu, Soonyoung; Oh, Junseop; Kim, Kyoung-Ho; Oh, Yun Yeong; Kim, Hyun Koo; Park, Sunhwa; Yun, Seong Taek

doi:10.1016/j.agee.2019.106660

Cited by 22 publications

(13 citation statements)

References 53 publications

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“…The slope ranges from 1 to 20 percent in this watershed, where 5 percent of the slope has a larger pixel area. Generally, flat slopes and flat land are mostly associated with nitrate in groundwater, but steep slopes at high altitudes have a major impact on nitrogen loss due to the large surface runoff, resulting in minute nitrate leaching into groundwater [ 65 ]. Low land and low slopes are closely linked to agricultural land, which is why this type of topography causes nitrate concentrations in groundwater.…”

Section: Resultsmentioning

confidence: 99%

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Band

Janizadeh

Pal

et al. 2020

Sensors

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Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.

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Section: Resultsmentioning

confidence: 99%

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Band

Janizadeh

Pal

et al. 2020

Sensors

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“…The area has an average stream density of about 1 km km −2 , relatively shallow groundwater, and hydromorphic riparian soils that cover about 20% of the land surface (Mourier et al, 2008;Dupas et al, 2013;Marçais et al, 2018). Land use is dominated by row crops, indoor pig and poultry husbandry, and pastureland for cows (a mean of 80% agricultural cover across the study watersheds; Table S1), making Brittany one of the highest density regions in France and Europe for animal breeding (Gascuel-Odoux et al, 2010;Poisvert et al, 2017;Kim et al, 2019). N and P concentrations in many Brittany watersheds are decreasing, attributable primarily to reduction of point sources such as wastewater and feedlot effluent (Moatar et al, 2017;Abbott et al, 2018b;Dupas et al, 2018).…”

Section: Site Description and Experimental Designmentioning

confidence: 99%

Predicting Nutrient Incontinence in the Anthropocene at Watershed Scales

Frei

Abbott

Dupas

et al. 2020

Front. Environ. Sci.

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Quantifying nutrient attenuation at watershed scales requires long-term water chemistry data, water discharge, and detailed nutrient input chronicles. Consequently, nutrient attenuation estimates are largely limited to long-term research areas or modeling studies, constraining understanding of the ecological characteristics controlling nutrient attenuation and complicating efforts to protect or restore water quality in developed and developing regions. Here, we combined long-term data and a broad suite of biogeochemical parameters from 49 watersheds in northwestern France to test how well instantaneous measurements can predict nitrogen (N) and phosphorus (P) attenuation at watershed scales. We evaluated 13 biogeochemical and 12 hydrological proxies of hydrological flowpaths, residence time, and biogeochemical transformation. Across the 49 watersheds, nutrient attenuation ranged from 88 to −2% for N and 99-96% for P. The strongest biogeochemical proxies of N attenuation were NO − 3 isotopes, rare earth elements (REEs), radon, and turbidity, together explaining 75% of observed variation. For P attenuation, REEs, NO − 3 isotopes, molecular weight of dissolved organic matter, and radon were the strongest proxies, but only explained 27% of observed variation. However, a single hydrological parameter-annual runoff-explained 91% of N attenuation and the relative abundance of schist bedrock explained 56% of P attenuation. We discuss how runoff both controls and reflects watershed hydrology, biogeochemistry, and nutrient attenuation. For example, runoff was correlated with long-term decreases in nutrient concentration, demonstrating how leakier watersheds recover more quickly from nutrient saturation. Given the immense fertilization capacity of modern society, we propose that eutrophication can only be solved by reducing nutrient inputs, though hydrochemical proxies can provide valuable information on where to carry out essential food production activities.

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“…The World Health Organization has recommended that nitrate (NO 3 ‐N) and nitrite (NO 2 ‐N) in drinking water should not exceed 11.3 and 1.0 mg L −1 , respectively. However, a high nitrate level of approximately 21.9 ± 18.3 mg L −1 was detected in rural groundwater in Yantai, China 7 and a wide nitrate range from 0.1 to 325.1 mg L −1 was observed in groundwater of agro‐livestock farming in Korea 5 …”

Section: Introductionmentioning

confidence: 96%

“…Nitrate contamination in groundwater is one of the most important environmental issues in many countries such as Thailand, India, China and Vietnam 1‐4 . This nitrate contamination results from an extreme use of chemical fertilizer in agricultural areas, an excessive application of nitrogen in agro‐livestock farming and a daily manure discharge 5,6 . The consumption of nitrate‐contaminated water leads to health hazards such as blue baby syndrome in infants and blue‐gray discoloration of the skin in adults.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of nitrate removal under limited organic carbon with hydrogen‐driven autotrophic denitrification in low‐cost electrode bio‐electrochemical reactors

Peungtim

Meesungnoen

Mahachai

et al. 2021

J of Chemical Tech & Biotech

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BACKGROUND Nitrate‐contaminated water is a concerning environmental and health problem in agricultural areas. Due to its low organic carbon content, such water cannot be purified using a conventional bioreactor with heterotrophic denitrification. Low‐cost bio‐electrochemical reactors were established with the aim of enhancing denitrification performance by cooperation of heterotrophic and hydrogenotrophic denitrification. RESULTS At the lowest C/N ratio of 1.0, the bioreactor reached only 67.4% for NO3‐N removal and 47.5% for total N removal (total N removal rate of ca 1.2 mg L−1 h−1), whereas the Cu reactor (bio‐electrochemical reactor using copper wire as cathode) achieved the best efficiencies of 73.7% and 53.8% for NO3‐N and total N removal (total N removal rate of ca 1.3 mg L−1 h−1), and followed by the SS reactor (bio‐electrochemical reactor using stainless steel wire as cathode). On the other hand, the greatest total organic carbon (TOC) removal efficiency was observed for the bioreactor and followed by the SS reactor and the Cu reactor. The low TOC consumption of 1.2–1.4 mg‐TOC (mg‐N)−1 in the Cu reactor and the SS reactor indicated the enhanced denitrification from coexisting hydrogenotrophic denitrification. In addition, the performance of hydrogenotrophic denitrification was improved by increasing the applied current from 10 to 30 mA. CONCLUSIONS The abundance of autotrophic denitrifying bacteria (identified as hydrogenotrophs) was higher than that of heterotrophic denitrifying bacteria in both the Cu reactor and the SS reactor. Archromobacter and Flavobacterium were the most dominant genera in the biofilm of the Cu reactor cathode and in the suspended sludge, respectively. Hydrogenophaga were detected in both biomass samples, but were absent from the biomass samples of the conventional bioreactor. © 2021 Society of Chemical Industry (SCI).

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Assessment of nitrogen application limits in agro-livestock farming areas using quantile regression between nitrogen loadings and groundwater nitrate levels

Cited by 22 publications

References 53 publications

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Predicting Nutrient Incontinence in the Anthropocene at Watershed Scales

Enhancement of nitrate removal under limited organic carbon with hydrogen‐driven autotrophic denitrification in low‐cost electrode bio‐electrochemical reactors

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