Assessment of Groundwater Quality in Relation to Organic versus Mineral Fertilization

Covaciu (Neamțu), Diana Cătălina; Balint, Ana Claudia; Neamțu, Călin Vasile; Moșneag, Silvia Claudia; Bordea, Daniela; Dîrjan, Sorina; Odagiu, Antonia Cristina Maria

doi:10.3390/w15162895

Water

2023

DOI: 10.3390/w15162895

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Assessment of Groundwater Quality in Relation to Organic versus Mineral Fertilization

Diana Cătălina Covaciu (Neamțu),

Ana Claudia Balint,

Călin Vasile Neamțu

et al.

Abstract: Groundwater pollution is a threat to the environment and human health because it is an important source of drinking water. Groundwater is used to supply water to communities and pollution occurs when harmful substances and contaminants infiltrate into the groundwater. Through excessive use of fertilizers, agriculture is a major contributor to groundwater pollution. This study tests the impact of organic and mineral fertilization on the groundwater physiochemical parameters and explores the potential consequenc… Show more

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2024

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Cited by 2 publications

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Spatial and Temporal Variability of Soil pH, Organic Matter and Available Nutrients (N, P and K) in Southwestern China

Guo,

Zhou,

Zhan

et al. 2024

Agronomy

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Knowledge of the spatial and temporal variations in soil nutrients is crucial for designing efficient site-specific nutrient management plans, which can improve crop yields and maximize nutrient use efficiency. The present study was conducted to evaluate and compare the status and spatio-temporal distribution pattern of available nitrogen (AHN), phosphorus (AP) and potassium (AK) and some selected soil properties [soil pH and soil organic matter (SOM)] in cultivated soils of Southwestern China over a 15-year period (2007–2022). We visualized the correlations among soil properties, AHN, AP and AK and analyzed the spatial structures of these parameters. A total of 3845 topsoil (0–20 cm) samples (3331 in 2007 and 514 in 2022) were collected from the cultivated areas of Jiangjin District, Chongqing, Southwestern China. Soil pH, SOM, AHN and AK showed moderate variability with coefficient of variation (CV) values varying between 10 and 100%, except AP (CV > 100%, high variability). The mean soil pH, SOM, AP and AK in 2022 were significantly higher than those in 2007. AHN was significantly positively correlated with SOM (r = 0.531, p < 0.01 in 2007, r = 0.768, p < 0.01 in 2022) and significantly negatively correlated with soil pH (r = −0.186, p < 0.01 in 2007, r = −0.102, p < 0.05 in 2022). AP was significantly negatively correlated with soil pH (r = −0.075, p < 0.01 in 2007, r = −0.126, p < 0.01 in 2022). AK was significantly positively correlated with SOM (r = 0.164, p < 0.01 in 2007, r = 0.229, p < 0.01 in 2022), ANH (r = 0.131, p < 0.01 in 2007, r = 0.251, p < 0.01 in 2022) and AP (r = 0.145, p < 0.01 in 2007, r = 0.52, p < 0.01 in 2022). The exponential function performed best for the soil properties and all three nutrients, with higher R2 values (0.203 to 0.93 in 2007 and 0.316 to 0.796 in 2022) in both years. The nugget/sill ratios (which varied from 31.58% to 72% in 2007 and from 29.31% to 47.02% in 2022) indicated the moderate spatial dependence of all soil parameters, except AK in 2022 (nugget/sill ratio = 23.81%, strong spatial dependence). During the study period, soil pH, AP and AK increased in the central and northern areas; SOM increased in the northern and southwestern parts; AHN decreased in the central areas. The current study highlighted the change in spatial variability of soil pH, AHN, AP and AK in the study area over 15 years.

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Spatial and Temporal Variability of Soil pH, Organic Matter and Available Nutrients (N, P and K) in Southwestern China

Guo,

Zhou,

Zhan

et al. 2024

Agronomy

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Groundwater Contamination Source Recognition Based on a Two-Stage Inversion Framework with a Deep Learning Surrogate

Wang,

2024

Water

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Groundwater contamination source recognition is an important prerequisite for subsequent remediation efforts. To overcome the limitations of single inversion methods, this study proposed a two-stage inversion framework by integrating two primary inversion approaches—simulation-optimization and simulation-data assimilation—thereby enhancing inversion accuracy. In the first stage, the ensemble smoother with multiple data assimilation method (a type of simulation-data assimilation) conducted a global broad search to provide better initial values and ranges for the second stage. In the subsequent stage, a collective decision optimization algorithm (a type of simulation-optimization) was used for a refined deep search, further enhancing the final inversion accuracy. Additionally, a deep learning method, the multilayer perceptron, was utilized to establish a surrogate of the simulation model, reducing computational costs. These theories and methods were applied and validated in a hypothetical scenario for the synchronous identification of the contamination source and boundary conditions. The results demonstrated that the proposed two-stage inversion framework significantly improved search accuracy compared to single inversion methods, with a mean relative error and mean absolute error of just 4.95% and 0.1756, respectively. Moreover, the multilayer perceptron surrogate model offered greater approximation accuracy to the simulation model than the traditional shallow learning surrogate model. Specifically, the coefficient of determination, mean relative error, mean absolute error, and root mean square error were 0.9860, 9.72%, 0.1727, and 0.47, respectively, highlighting its significant advantages. The findings of this study can provide more reliable technical support for practical case applications and improve subsequent remediation efficiency.

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Assessment of Groundwater Quality in Relation to Organic versus Mineral Fertilization

Cited by 2 publications

References 39 publications

Spatial and Temporal Variability of Soil pH, Organic Matter and Available Nutrients (N, P and K) in Southwestern China

Spatial and Temporal Variability of Soil pH, Organic Matter and Available Nutrients (N, P and K) in Southwestern China

Groundwater Contamination Source Recognition Based on a Two-Stage Inversion Framework with a Deep Learning Surrogate

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