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At least 19 records

Machine Learning Models for Mapping Groundwater Pollution Risk: Advancing Water Security and Sustainable Development Goals in Georgia, USA

The widespread use of pesticides, such as atrazine and malathion, in agricultural systems raises significant concerns regarding the contamination of groundwater, which serves as a critical resource for drinking water. This study applies machine learning techniques to predict the concentrations of atrazine and malathion in groundwater across Georgia, USA, using 2019 data. A Random Forest classifier was employed to integrate various environmental and demographic factors, including pesticide application rates, precipitation, lithology, and population density, to predict pesticide contamination in groundwater. The models demonstrated high training accuracies of 100% and moderate average testing accuracy of 55% for atrazine and 60% for malathion across five iterations. The low test accuracy of the model, ranging from 50% to 75%, is likely due to overfitting, which can be attributed to the small dataset size and the complex nature of pesticide-contamination patterns, making it challenging for the model to generalize to unseen data. Feature importance analysis revealed that average pesticide usage emerged as the most influential factor for atrazine, while aquifer lithology and precipitation played crucial roles in both models. These results provide valuable insights into the dynamics of pesticide contamination, highlighting areas at greater risk of contamination. The findings underscore the importance of integrating environmental, geological, and agricultural variables for more effective groundwater management and sustainable agricultural practices, contributing to the protection of water resources and public health.

54 ENVIRONMENTAL SCIENCES↗

HCMM energy budget data as a model input for assessing regions of high potential groundwater pollution

The author has identified the following significant results. Day thermal data were analyzed to assess depth to groundwater in the test site. HCMM apparent temperature was corrected for atmospheric effects using lake temperature of the Oahe Reservoir in central South Dakota. Soil surface temperatures were estimated using an equation developed for ground studies. A significant relationship was found between surface soil temperature and depth to groundwater, as well as between the surface soil-maximum air temperature differential and soil water content (% of field capacity) in the 0 cm and 4 cm layer of the profile. Land use for the data points consisted of row crops, small grains, stubble, and pasture.

Moore, D. G.↗

HCMM energy budget data as a model input for assessing regions of high potential groundwater pollution

The author has identified the following significant results. The finite difference model was used to calculate the differences in surface temperature between two hypothetical sites which result from a temperature difference at 50 cm due to the presence of shallow ground water at one of the sites. Although qualitative results of the model seemed consistant with experimental results, further evaluation showed a need for taking account of differences in thermal conductivity due to different moisture profiles at the two sites considered.

Moore, D. G.↗

HCMM energy budget data as a model input for assessing regions of high potential groundwater pollution

The author has identified the following significant results. To investigate the general relationship between surface temperature and soil moisture profiles, a series of model calculations were carried out. Soil temperature profiles were calculated during a complete diurnal cycle for a variety of moisture profiles. Preliminary results indicate the surface temperature difference between two sites measured at about 1400 hours is related to the difference in soil moisture within the diurnal damping depth (about 50 cm). The model shows this temperature difference to vary considerably throughout the diurnal cycle.

Moore, D. G.↗

HCMM energy budget data as a model input for assessing regions of high potential groundwater pollution

The author has identified the following significant results. In early April 1978, heavy spring runoff from snowmelt caused significant flooding along a portion of the Big Sioux River Basin in southeastern South Dakota. The flooded area was visible from surrounding areas on a May 15 HCMM IR test image. On May 15, the flood waters had receded but an area of anomalous residual high soil moisture remained. The high soil moisture area was not visible on a HCMM day visible test image of the same scene, or on LANDSAT imagery. To evaluate the effect of water table depth on surface temperatures, thermal scanner data collected on September 5 and 6, 1978 at approximate HCMM overpass times at an altitude of 3650 m were analyzed. Apparent surface temperatures measured by the scanner included emittance contributions from soil surface and the land cover. Results indicated that the shallow water tables produced a damping of the amplitude of the diurnal surface temperature wave.

Moore, D. G.↗

Integrating density functional theory into reductive dechlorination research

Abstract Chlorinated organics have been frequently detected in groundwaters, threatening the quality of drinking water supplies worldwide. A promising method for groundwater remediation involves reductive dechlorination (RD), in which chlorine atoms are sequentially removed and substituted by hydrogen, producing less harmful byproducts. In this paper, for the first time, RD research is reviewed in light of the growing incorporation of density functional theory (DFT) as a research tool. DFT has been used to uncover a variety of reaction properties for a range of relevant groundwater pollutants, including 1,2,3‐trichloropropane, hexachlorobenzene, and various dioxins. DFT models have revealed the role of surface interactions in driving the kinetics of catalytically driven RD. Mechanisms involved with biologically mediated RD have also been elucidated with insights gleaned from DFT. Issues and challenges for future research are also discussed.

Das Schober, Jaya↗

General synthesis of high-entropy single-atom nanocages for electrosynthesis of ammonia from nitrate

Given the growing emphasis on energy efficiency, environmental sustainability, and agricultural demand, there’s a pressing need for decentralized and scalable ammonia production. Converting nitrate ions electrochemically, which are commonly found in industrial wastewater and polluted groundwater, into ammonia offers a viable approach for both wastewater treatment and ammonia production yet limited by low producibility and scalability. Here we report a versatile and scalable solution-phase synthesis of high-entropy single-atom nanocages (HESA NCs) in which Fe and other five metals-Co, Cu, Zn, Cd, and In-are isolated via cyano-bridges and coordinated with C and N, respectively. Incorporating and isolating the five metals into the matrix of Fe resulted in Fe-C5 active sites with a minimized symmetry of lattice as well as facilitated water dissociation and thus hydrogenation process. As a result, the Fe-HESA NCs exhibited a high selectivity toward NH3 from the electrocatalytic reduction of nitrate with a Faradaic efficiency of 93.4% while maintaining a high yield rate of 81.4 m h -1 mg -1 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Efficient conversion of low-concentration nitrate sources into ammonia on a Ru-dispersed Cu nanowire electrocatalyst

We report electrochemically converting nitrate ions, a widely distributed nitrogen source in industrial wastewater and polluted groundwater, into ammonia represents a sustainable route for both wastewater treatment and ammonia generation. However, it is currently hindered by low catalytic activities, especially under low nitrate concentrations. Here we report a high-performance Ru-dispersed Cu nanowire catalyst that delivers an industrial-relevant nitrate reduction current of 1 A cm –2 while maintaining a high NH 3 Faradaic efficiency of 93%. More importantly, this high nitrate-reduction catalytic activity enables over a 99% nitrate conversion into ammonia, from an industrial wastewater level of 2,000 ppm to a drinkable water level <50 ppm, while still maintaining an over 90% Faradaic efficiency. Coupling the nitrate reduction effluent stream with an air stripping process, we successfully obtained high purity solid NH 4 Cl and liquid NH 3 solution products, which suggests a practical approach to convert wastewater nitrate into valuable ammonia products. Density functional theory calculations reveal that the highly dispersed Ru atoms provide active nitrate reduction sites and the surrounding Cu sites can suppress the main side reaction, the hydrogen evolution reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Evaluating Trichloroethylene Degradation Using Differing Nano- and Micro-Scale Iron Particles

Trichioroethylene, or TCE, is a central nervous system depressant and possible carcinogen, as well as a persistent groundwater pollutant. TCE exists in the aquifer either as free product in the form of a dense non-aqueous phase liquid (DNAPL) or as a dissolved-phase constituent. It is only slightly soluble in water, so dissolution of the contaminant is a long-term process and in-situ remediation is difficult. To remedy this, NASA and the University of Central Florida developed Emulsified Zero-Valent Iron, or EZVI. The emulsion droplet contains ZVI particles and water encapsulated by an oil/surfactant membrane, and effectively penetrates to degrade DNAPL-phase TCE. To maximize the efficiency of this process, several commercially available ZVIs of radically different particle sizes and morphologies both in emulsion and as neat (unemulsified) metal were evaluated for relative effectiveness at TCE degradation.

Berger, Cristina M.↗