RESUMEN
This study developed innovative predictive models of groundwater pollution using in situ electrical conductivity (EC) and oxidation-reduction potential (ORP) measurements at livestock carcass burial sites. Combined electrode analysis (EC and ORP) and machine learning techniques efficiently and accurately distinguished between leachate and background groundwater. Two models-empirical and theoretical-were constructed based on a supervised classification framework. The empirical model constructs a classifier with high accuracy, sensitivity, and specificity, utilizing the comprehensive in situ EC and ORP measurements. The theoretical model with only two end members achieves comparable performance by simulating the leachate-groundwater interactions using a geochemical mixing model. Besides enhancing the early detection capabilities, our approach considerably reduces the reliance on extensive hydrochemical analyses, thus streamlining the monitoring process. Moreover, the use of field parameters was found to proactively identify potential pollution incidents, enhancing the efficiency of groundwater monitoring strategies. Our approach is applicable to various waste disposal sites, indicating its extensive potential for environmental monitoring and management.
RESUMEN
This study introduces a novel groundwater pollution index (GPI) formulated through compositional data analysis (CoDa) and robust principal component analysis (RPCA) to enhance groundwater quality assessment. Using groundwater quality monitoring data from sites impacted by the 2010-2011 foot-and-mouth disease outbreak in South Korea, CoDa uncovers critical hydrochemical differences between leachate-influenced and background groundwater. The GPI was developed by selecting key subcompositional parts (NH4+-N, Cl-, and NO3--N) using RPCA, performing the isometric log-ratio (ILR) transformation, and normalizing the results to environmental standards, thereby providing a more precise and accurate assessment of pollution. Validated against government criteria, the GPI has shown its potential as an alternative assessment tool, with its reliability confirmed by receiver operating characteristic curve analysis. This study highlights the essential role of CoDa, especially the ILR -transformation, in overcoming the limitations of traditional statistical methods that often neglect the relative nature of hydrochemical data. Our results emphasize the utility of the GPI in significantly advancing groundwater quality monitoring and management by addressing a methodological gap in the quantitative assessment of groundwater pollution.
RESUMEN
The evaluation of leachate leakage at livestock mortality burial sites is challenging, particularly when groundwater is previously contaminated by agro-livestock farming. Supervised machine learning was applied to discriminate the impacts of carcass leachate from pervasive groundwater contamination in the following order: data labeling, feature selection, synthetic data generation, and classification. Physicochemical data of 359 water samples were collected from burial pits (LC), monitoring wells near pits (MW), pre-existing shallow household wells (HW), and background wells with pervasive contamination (BG). A linear classification model was built using two representative groups (LC and BG) affected by different pollution sources as labeled data. A classifier was then applied to assess the impact of leachate leakage in MW and HW. As a result, leachate impacts were observed in 40% of MW samples, which indicates improper construction and management of some burial pits. Leachate impacts were also detected in six HW samples, up to 120 m downgradient, within one year. The quantitative decision-making tool to diagnose groundwater contamination with leachate leakage can contribute to ensuring timely responses to leakage. The proposed machine learning approach can also be used to improve the environmental impact assessment of water pollution by improper disposal of organic waste.
Asunto(s)
Agua Subterránea , Contaminantes Químicos del Agua , Animales , Monitoreo del Ambiente , Ganado , Granjas , Contaminantes Químicos del Agua/análisis , Entierro , Aprendizaje Automático SupervisadoRESUMEN
Burial is applied to dispose of livestock carcasses due to its convenience and cost efficiency despite concerns about groundwater contamination by leachate from burial pits. In particular, the burial method has caused debates about groundwater contamination sources around on-farm livestock burial sites because of pre- and coexisting contamination from livestock production and agriculture. To assess the causes of groundwater contamination around poultry burial pits that were constructed after an outbreak of avian influenza in 2010-11 in Korea, hydrochemical data of groundwater samples from monitoring wells (MWs, n = 14) and household wells (HWs, n = 30) were monitored to differentiate contamination sources. Hydrochemical data indicated that groundwater from MWs is characterized by higher enrichments of inorganic constituents including electrical conductivity (EC), NH4, Ca, Mg, K, SO4, HCO3, Fe(Total), and Mn(Total), but lower concentrations of DO than groundwater from HWs. The combined use of the principal component analysis (PCA) and K-means cluster analysis (KCA) indicated that groundwater in seven MWs was affected by leachate. The parameters such as NH4, Ca, Mg, K, SO4, HCO3, Fe(Total), and Mn(Total) are expected to be useful to identify the impact of leachate on groundwater in agricultural areas. This study suggests that (1) regional hydrochemical characteristics should be assessed to distinguish the effect of livestock burial leachate from other contamination sources and (2) the combined use of PCA and KCA is effective to identify the weakened impact of leachate leakage among overlapping multiple sources and processes of groundwater contamination.
Asunto(s)
Agua Subterránea , Contaminantes Químicos del Agua , Agricultura , Animales , Entierro , Monitoreo del Ambiente , Granjas , Ganado , Aves de Corral , República de Corea , Contaminantes Químicos del Agua/análisisRESUMEN
We investigated the impacts of leachates from a swine carcass burial site and a cow manure heap on the geochemical and microbiological properties of agricultural water samples, including leachate, groundwater from monitoring wells and background wells, and stream water. The leachate from the livestock burial site showed extremely high electrical conductivity, turbidity, and major ion concentrations, but low redox potential and dissolved oxygen levels. The groundwater in the monitoring wells adjacent to both sites showed severe contamination from the leachate, as indicated by the increases in EC, turbidity, Cl-, and SO42-. Bacteria from the phylum Firmicutes and Bacteriodetes and Archaea from the phylum Euryarchaeota were the major phyla in both the leachates and manure heap. However, the class- or genus-level components of these phyla differed markedly between the leachate and manure heap samples. The relative abundance of Firmicutes decreased from 35% to 0.3~13.9% in the monitoring wells and background wells at both sites. The Firmicutes in these wells was unlikely to have originated from the transportation of leachate to the surrounding environment because Firmicutes genera differed drastically between the leachate and monitoring wells. Meanwhile, sulfate-reducing bacteria (SRB) from the livestock carcass burial site were detected in the monitoring wells close to the leachate. This was likely because the release of carcass decomposition products, such as organic acids, to adjacent areas improved the suitability of the local environments for SRB, which were not abundant in the leachate. This study highlights the need to better understand microbial community dynamics along groundwater flow paths to evaluate bacterial transport in subsurface environments and provides new insights into the effective management of groundwater quality at both farm and regional scales.