Determinants of Declining Water Quality

Ayana, Essayas K.

doi:10.1596/33224

Cited by 6 publications

(5 citation statements)

References 139 publications

(216 reference statements)

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“…Other researchers have shown an association between soil P and TDS resulting from soil erosion due to field operations and fertilizer applications in a watershed that was dominated by grasslands and arable land (small grains and potato [ Solanum tuberosum L.]) (Halecki et al., 2018). Nutrients are a component of TDS (Ayana, 2019). Phosphorus in H 2 O samples are associated with losses of P from fertilizer applications and P in suspended solids associated with erosion and runoff resulting from management of agronomic systems (Hart et al., 2004; Sharpley et al., 2001).…”

Section: Resultsmentioning

confidence: 99%

Use of archived data to derive soil health and water quality indicators for monitoring shifts in natural resources

Fortuna¹,

Starks²,

Moriasi³

et al. 2023

J of Env Quality

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Current gaps impeding researchers from developing a soil and watershed health nexus include design of long‐term field‐scale experiments and statistical methodologies that link soil health indicators (SHI) with water quality indicators (WQI). Land cover is often used to predict WQI but may not reflect the effects of previous management such as legacy fertilizer applications, disturbance, and shifts in plant populations) and soil texture. Our research objectives were to use nonparametric Spearman rank‐order correlations to identify SHI and WQI that were related across the Fort Cobb Reservoir experimental watershed (FCREW); use the resulting rho (r) and p values (P) to explore potential drivers of SHI‐WQI relationships, specifically land use, management, and inherent properties (soil texture, aspect, elevation, slope); and interpret findings to make recommendations regarding assessment of the sustainability of land use and management. The SHI values used in the correlation matrix were weighted by soil texture and land management. The SHI that were significantly correlated with one or more WQI were available water capacity (AWC), Mehlich III soil P, and the sand to clay ratio (S:C). Mehlich III soil P was highly correlated with three WQI: total dissolved solids (TDS) (0.80; P < 0.01), electrical conductivity of water (EC‐H2O) (0.79; P < 0.01), and water nitrates (NO3‐H2O) (0.76; P < 0.01). The correlations verified that soil texture and management jointly influence water quality (WQ), but the size of the soils dataset prohibited determination of the specific processes. Adoption of conservation tillage and grasslands within the FCREW improved WQ such that water samples met the U.S. Environmental Protection Agency (EPA) drinking water standards. Future research should integrate current WQI sampling sites into an edge‐of‐field design representing all management by soil series combinations within the FCREW.

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Section: Resultsmentioning

confidence: 99%

Use of archived data to derive soil health and water quality indicators for monitoring shifts in natural resources

Fortuna¹,

Starks²,

Moriasi³

et al. 2023

J of Env Quality

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“…Biological parameters include the presence of microorganisms, like viruses, algae, and bacteria, like Escherichia coli (E. coli) and total coliforms [120]. The traditional system of water quality classification covers all forms of pollution except for toxic chemicals and microplastics pollution [121][122][123].…”

Section: Satellite Applications For Water Resources and Quality Monit...mentioning

confidence: 99%

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Mukonza,

Chiang

2023

Environments

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This review paper adopts bibliometric and meta-analysis approaches to explore the application of supervised machine learning regression models in satellite-based water quality monitoring. The consistent pattern observed across peer-reviewed research papers shows an increasing interest in the use of satellites as an innovative approach for monitoring water quality, a critical step towards addressing the challenges posed by rising anthropogenic water pollution. Traditional methods of monitoring water quality have limitations, but satellite sensors provide a potential solution to that by lowering costs and expanding temporal and spatial coverage. However, conventional statistical methods are limited when faced with the formidable challenge of conducting pattern recognition analysis for satellite geospatial big data because they are characterized by high volume and complexity. As a compelling alternative, the application of machine and deep learning techniques has emerged as an indispensable tool, with the remarkable capability to discern intricate patterns in the data that might otherwise remain elusive to traditional statistics. The study employed a targeted search strategy, utilizing specific criteria and the titles of 332 peer-reviewed journal articles indexed in Scopus, resulting in the inclusion of 165 articles for the meta-analysis. Our comprehensive bibliometric analysis provides insights into the trends, research productivity, and impact of satellite-based water quality monitoring. It highlights key journals and publishers in this domain while examining the relationship between the first author’s presentation, publication year, citation count, and journal impact factor. The major review findings highlight the widespread use of satellite sensors in water quality monitoring including the MultiSpectral Instrument (MSI), Ocean and Land Color Instrument (OLCI), Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and the practice of multi-sensor data fusion. Deep neural networks are identified as popular and high-performing algorithms, with significant competition from extreme gradient boosting (XGBoost), even though XGBoost is relatively newer in the field of machine learning. Chlorophyll-a and water clarity indicators receive special attention, and geo-location had a relationship with optical water classes. This paper contributes significantly by providing extensive examples and in-depth discussions of papers with code, as well as highlighting the critical cyber infrastructure used in this research. Advances in high-performance computing, large-scale data processing capabilities, and the availability of open-source software are facilitating the growing prominence of machine and deep learning applications in geospatial artificial intelligence for water quality monitoring, and this is positively contributing towards monitoring water pollution.

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“…Basically, water quality is easily affected by the presence of phytoplankton, suspended sediment, organic pollutants, and dissolved substances. Furthermore, many water quality parameters, such as chlorophyll-a (Chla) concentration, total suspended solids (TSS), and colored dissolved organic matters (CDOM), are often used to measure the condition of water [4], [8], [9]. These water constituent concentrations jointly modulated water turbidity.…”

Section: Introductionmentioning

confidence: 99%

“…Turbidity describes the amount of light scattered or blocked by suspended particles in water [8], [10]. It is a common physical water quality parameter for measuring the condition of water and describing the level of water clarity [11], [12], [13], [14].…”

Section: Introductionmentioning

confidence: 99%

“…It is a common physical water quality parameter for measuring the condition of water and describing the level of water clarity [11], [12], [13], [14]. In addition, due to the strong relationship between turbidity and total suspended solid, low-cost turbidity measurement can be applied to estimate total suspended solids (instead of using the gravimetric method which requires a lengthy and costly procedure) [8], [15]. However, dissolved substances (such as CDOM) that also affect water clarity may be too small to be counted in a suspended solids concentration, but they can be part of a turbidity measurement [12].…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing of Turbidity for Lakes in Northeast China Using Sentinel-2 Images With Machine Learning Algorithms

Ma¹,

Song²,

Wen³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Monitoring water quality of inland lakes and reservoirs is a great concern for the public and government in China. Water turbidity is a reliable and direct indicator that can reflect the water quality. Remote sensing has become an efficient technology for monitoring large-scale water turbidity. This study aims to search an optimal regression model to accurately predict water turbidity using remote sensing data. To achieve this goal, 187 water samples were collected from field campaigns across Northeast China in 2018, of which the samples were gathered within ±6 days of Sentinel-2 overpasses. The spectral reflectance data was used as independent variables for modeling. The simple regression (SR), partial least squares regression (PLSR), support vector regression (SVR), extreme learning machine (ELM), back-propagation neural network (BP), classification and regression tree (CART), gradient boosting decision tree (GBDT), random forest (RF), and K-nearest neighbor (KNN) were used to compare. From model validation, we identified GBDT as the best regression model (R 2 =0.88, RMSE=9.90 NTU, MAE=6.71 NTU). We applied GBDT to retrieve the water turbidity and obtained a satisfactory result. Feature selection technique from tree-based ensemble method was also tested. We selected B2, B3, B4, and B5 as the important variables because of their high ability to explain the variation of turbidity. These results demonstrated the significance of using a promising method to retrieve water turbidity using Sentinel-2 imagery at the regional scale. It is beneficial to monitor the spatial-temporal distribution of water turbidity; support water quality management and inland water environment protection.

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Determinants of Declining Water Quality

Cited by 6 publications

References 139 publications

Use of archived data to derive soil health and water quality indicators for monitoring shifts in natural resources

Use of archived data to derive soil health and water quality indicators for monitoring shifts in natural resources

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Remote Sensing of Turbidity for Lakes in Northeast China Using Sentinel-2 Images With Machine Learning Algorithms

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