Aquifer Storage and Recovery: Can an Updated Inventory Predict Future System Success?

Bloetscher, Frederick; Sham, Chi Ho; Ratick, Samuel J.

doi:10.1002/awwa.1594

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…Miszczak et al conduct a general survey of results of pollution sources and analysis of pollution sources in a city and put forward pollution prevention and control measures [7]. Bloetscher et al identified beverage manufacturing, papermaking and paper products, food manufacturing, and agricultural and sideline food processing industries as typical industries to reduce oxygen demand pollution through the summary, analysis, and research on the census data of pollution sources and focused on the generation, emission, and treatment of five-day biochemical oxygen demand in four industries [8]. Liu et al analyzed the production, emission characteristics, and pollution status of pollutants in Jingjiang City, Jiangsu Province, by using the census data of pollution sources and social statistical data and pointed out that while industrial pollution is paid attention to, domestic pollution and agricultural pollution cannot be ignored [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

Liu

Sun

Jing

et al. 2022

Wireless Communications and Mobile Computing

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In order to strengthen the supervision and management of environmental pollution and timely understand and record the basic information of potential environmental pollution of enterprises and institutions, this paper proposes a general survey and inventory method of pollution sources based on big data technology under machine learning. Firstly, this paper evaluates and screens the data provided by government departments, constructs a machine learning classification model, and uses a variety of classification algorithms to compare and analyze according to the basic idea of machine learning to deal with practical problems. Then, the calibration data set constructed is used as the training set to predict and classify the national industrial and commercial data, provincial industrial and commercial data, and municipal industrial and commercial data provided by government departments. The experimental results show that the naive Bayesian classification algorithm is the best algorithm, and the F1 values of each data set are increased by 32.92%, 21.42%, and 14.91%, respectively. The classified prediction of the screened Internet data shows that the accuracy of the final Internet supplementary data is 17.26%, which is similar to the industrial and commercial data of the city. The availability of the machine learning model established in this paper is proven.

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Section: Literature Reviewmentioning

confidence: 99%

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

Liu

Sun

Jing

et al. 2022

Wireless Communications and Mobile Computing

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“…For example, some ASR wells in California, USA, have not been operational for several years due to limited water availability for recharge. 4,61 Periods of inactivity between recharge operations can alter the geochemical response induced by future MAR operations as observed in Hillsboro, Florida, USA, where As concentrations were declining with successive ASR cycles but sharply rebounded after injection resumed following five years of inactivity. 122 The chemical composition of the recharge water is a key factor determining geochemical impacts to the receiving aquifer.…”

Section: Water Quality Considerations For Thementioning

confidence: 99%

“…First, recharge water physically displaces native groundwater (Figure ), while, in most cases, migrating radially away from the injection or infiltration area. Reported injection rates at ASR wells in the United States vary from approximately 757 to 8705 m 3 day –1 with typical injection and withdrawal rates of approximately 3785 m 3 day –1 (1 million gallons per day). , Near the recharge location, the groundwater flow field is controlled by MAR-induced fluxes and is increasingly impacted by the regional-scale flow patterns at farther distances. Idealized conceptual models represent the recharge-impacted zone as a homogeneous “bubble” surrounding the injection well.…”

Section: Overview Of General Mar Processesmentioning

confidence: 99%

“…Understanding long-term, future impacts of MAR to aquifer geochemistry and water quality requires knowledge of how MAR sites will be operated in the future including anticipated changes in recharge water availability and variations in source water quality. For example, some ASR wells in California, USA, have not been operational for several years due to limited water availability for recharge. , Periods of inactivity between recharge operations can alter the geochemical response induced by future MAR operations as observed in Hillsboro, Florida, USA, where As concentrations were declining with successive ASR cycles but sharply rebounded after injection resumed following five years of inactivity…”

Section: Water Quality Considerations For the Future Of Marmentioning

confidence: 99%

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Mobilization of Arsenic and Other Naturally Occurring Contaminants during Managed Aquifer Recharge: A Critical Review

Fakhreddine

Prommer

Scanlon

et al. 2021

Environ. Sci. Technol.

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Population growth and climate variability highlight the need to enhance freshwater security and diversify water supplies. Subsurface storage of water in depleted aquifers is increasingly used globally to alleviate disparities in water supply and demand often caused by climate extremes including floods and droughts. Managed aquifer recharge (MAR) stores excess water supplies during wet periods via infiltration into shallow underlying aquifers or direct injection into deep aquifers for recovery during dry seasons. Additionally, MAR can be designed to improve recharge water quality, particularly in the case of soil aquifer treatment and riverbank filtration. While there are many potential benefits to MAR, introduction of recharge water can alter the native geochemical and hydrological conditions in the receiving aquifer, potentially mobilizing toxic, naturally occurring (geogenic) contaminants from sediments into groundwater where they pose a much larger threat to human and ecosystem health. On the basis of the present literature, arsenic poses the most widespread challenge at MAR sites due to its ubiquity in subsurface sediments and toxicity at trace concentrations. Other geogenic contaminants of concern include fluoride, molybdenum, manganese, and iron. Water quality degradation threatens the viability of some MAR projects with several sites abandoning operations due to arsenic or other contaminant mobilization. Here, we provide a critical review of studies that have uncovered the geochemical and hydrological mechanisms controlling mobilization of arsenic and other geogenic contaminants at MAR sites worldwide, including both infiltration and injection sites. These mechanisms were evaluated based on site-specific characteristics, including hydrological setting, native aquifer geochemistry, and operational site parameters (e.g., source of recharge water and recharge/recovery cycling). Observed mechanisms of geogenic contaminant mobilization during MAR via injection include shifting redox conditions and, to a lesser extent, pH-promoted desorption, mineral solubility, and competitive ligand exchange. The relative importance of these mechanisms depends on various site-specific, operational parameters, including pretreatment of injection water and duration of injection, storage, and recovery phases. This critical review synthesizes findings across case studies in various geochemical, hydrological, and operational settings to better understand controls on arsenic and other geogenic contaminant mobilization and inform the planning and design of future MAR projects to protect groundwater quality. This critical review concludes with an evaluation of proposed management strategies for geogenic contaminants and identification of knowledge gaps regarding fate and transport of geogenic contaminants during MAR.

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“…However, these high conductive zones are very small (less than 8 m) for the medium-to large-scale ASR (injection rate >100 m 3 /day), which may not be suitable for a large-scale injection in the agricultural fields (excess water from low-lying areas in that area). Like in the ASR wells of the United States, where the injection rates were reported from 757 to 8,705 m 3 /day with an approximate recovery rate of 3,785 m 3 /day (Bloetscher, 2015;Bloetscher et al, 2020). The previous ASR guidelines suggest that the large-scale ASR should have high transmissivities with relatively large aquifer thickness (more than 8 m) so that a large amount of water can be injected efficiently and economically (Stuyfzand et al, 2002;Pavelic et al, 2006;Izbicki et al, 2010;Page et al, 2014;Sultana et al, 2015).…”

Section: Nalanda University Campus Wellmentioning

confidence: 99%

Aquifer Storage and Recovery Feasibility Study With Flowing Fluid Electrical Conductivity Logging in Shallow Aquifers of South Bihar, India

Verma¹,

Sharma²

2022

Front. Water

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Growing dependence on groundwater to fulfill the water demands has led to continuous depletion of groundwater levels and, consequently, poses the maintenance of optimum groundwater and management challenge. The region of South Bihar faces regular drought and flood situations, and due to the excessive pumping, the groundwater resources are declining. Rainwater harvesting has been recommended for the region; however, there are no hydrogeological studies concerning groundwater recharge. Aquifer storage and recovery (ASR) is a managed aquifer recharge technique to store excess water in the aquifer through borewells to meet the high-water demand in the dry season. Therefore, this paper presents the hydrogeological feasibility for possible ASR installations in shallow aquifers of South Bihar with the help of flowing fluid electrical conductivity (FFEC) logging. For modeling, the well logging data of two shallow borewells (16- and 47-m depth) at Rajgir, Nalanda, were used to obtain the transmissivity and thickness of the aquifers. The estimated transmissivities were 804 m2/day with an aquifer thickness of 5 m (in between 11 and 16 m) at Ajatshatru Residential Hall (ARH) well. They were 353 and 1,154 m2/day with the aquifer thicknesses of 6 m (in between 16 and 22 m) and 2 m (in between 45 and 47 m), respectively, at Nalanda University Campus (NUC) well. Despite the acceptable transmissivities at these sites, those aquifers may not be fruitful for the medium- to large-scale (more than 100-m3/day injection rate) ASR as the thickness of the aquifers is relatively small and may not efficiently store and withdraw a large amount of water. However, these aquifers can be adequate for small (up to 20-m3/day injection rate) ASR, for example, groundwater recharge using rooftop water. For medium- to large-scale ASR, deeper aquifers need to be further explored on these sites or aquifers with similar characteristics.

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Aquifer Storage and Recovery: Can an Updated Inventory Predict Future System Success?

Cited by 5 publications

References 3 publications

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

Mobilization of Arsenic and Other Naturally Occurring Contaminants during Managed Aquifer Recharge: A Critical Review

Aquifer Storage and Recovery Feasibility Study With Flowing Fluid Electrical Conductivity Logging in Shallow Aquifers of South Bihar, India

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