New insights into the pollutant composition of stormwater treating wetlands

Yang, Fujia; Gato-Trinidad, Shirley; Hossain, Iqbal

doi:10.1016/j.scitotenv.2022.154229

Cited by 10 publications

(4 citation statements)

References 71 publications

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“…The volume of sewage and other pollutants produced by human activity can quickly surpass the capacity of existing wastewater treatment facilities, leading to overflow and runoff into freshwater sources [102]. Climatechange-induced extreme rainfall events, such as floods, compound these issues by further overwhelming wastewater treatment infrastructure and causing an increase in wastewater treatment plant (WWTP) runoff and overflow [103]. The negative impact of urbanization on water resources manifests in several ways, including increased flooding, sediment erosion, and stormwater overflow.…”

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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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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“…Seasonal changes affect metal removal functions in constructed wetland systems and the primary biogeochemical cycles and multiple natural processes occurring in these systems. Previous studies have linked the effect of seasons, and subsequently temperature and/or rainfall, on the sulfur cycle (Singh & Chakraborty, 2022; Xu & Mills, 2018; Yang et al, 2022). Spatial changes across depth were investigated to understand potential zones of accumulation of trace metals.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Changes in Trace Metal Bioavailability in the Sediment Pore water of a Constructed Wetland Using Passive Pore water Samplers

Elhaj Baddar,

Xu,

Spencer

2023

Enviro Toxic and Chemistry

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Sediments in aquatic systems often act as a major sink for contaminants. Diffusive gradient in thin films (DGTs) and in situ equilibrium dialysis samplers (peepers) are two major in situ porewater sampling devices that overcome the problems associated with conventional porewater sampling methods. In this study, DGTs and peepers were used to study the spatial and seasonal effects (cool months: October‐February, warm months: May‐September) on metal bioavailability in the H‐02 constructed wetland, and the sink vs. source role of the sediments by calculating the metal resupply capacity (R). Data showed similar seasonal trends in metal concentrations using both passive samplers, peepers and DGTs. Pooled Cu and Zn concentrations measured using DGTs were lower in warm months (1.67 ± 1.50 and 2.62 ± 0.68 μg.L‐1, respectively, p < 0.001) vs. (2.12 ± 0.65 and 5.58 ± 1.33 μg.L‐1, respectively, p < 0.001) in cool months (mean ± 95% Confidence Intervals). Sulfate (SO42‐) concentrations were significantly (p = 0.0139) lower in warm months (averaged at 0.22 ± 0.05 mg.L‐1) compared to (0.16 ± 0.05 mg.L‐1) in cool months. The increase in SO42‐ concentration is an indicator of the lower activity of sulfate reducing bacteria (SRB) which need SO42‐ during the anaerobic respiration, in which SO42‐ is reduced to sulfide (S2‐) which forms insoluble salts with Cu and Zn, which could partially explain higher bioavailability of these metals in the cool season. Metal resupply capacity of the sediments was mostly < 0.2 for Cu and Zn. Taken together, the H0‐2 wetland sediments mostly acted as a sink to both Cu and Zn over the course of this study.

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“…However, the concentration of St in this study is more significant as there are only 0.3 to 0.4 mg/L of St under wet weather conditions (EPA, 2013). Although Sr does not pose threat to aquatic species, the average concentration of Sr in the freshwater environment is 0.06 mg/L, and the amount of Sr presented in the stormwater runoff would bring risk to the general public (Yang, Gato-Trinidad, & Hossain, 2022). Regarding Fe, the amount of total iron in the sampled CWs remained lower than 1 mg/L, which is considerably lower than the values reported in various sources (EPA, 2013;Hamlyn-Harris et al, 2019).…”

Section: Resultsmentioning

confidence: 65%

“…Besides the well-known pollutants in stormwater runoff, heavy metals are found in many stormwater catchments in industrialized areas. However, the treatment of metallic pollutants could be inadequate with the traditionally implemented stormwater treatment devices which are constructed to treat the typical pollutants (Yang, Gato-Trinidad, & Hossain, 2022). Moreover, as stormwater volume control has been a significant constraint in the current stormwater management, a substantial amount of stormwater will be bypassed and flowing directly into the streams without treatment (Hart, Francey, Chesterfield, Blackham, & McCarthy, 2022).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the water quality of stormwater runoffs in urban area

Yang,

Gato-Trinidad,

Hossain

Global NEST International Conference on Environmental Science &Amp; Technology

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This paper presents the results of stormwater quality monitoring from four constructed wetlands located in residential and industrial catchments. Preliminary results revealed that pollutants’ types and concentrations vary depending on the catchments’ characteristics. It is found that the residential catchments often hold higher total phosphorus and suspended solids, while the industrial areas are found with high concentrations of Iron and Strontium. Nonetheless, high metal concentrations are observed in both catchments, raising concerns about the effectiveness of stormwater treatment devices and the current guidelines adopted in the design and construction of these systems. While it is found that all catchments displayed exceptional levels of pollutants that are not safe for the freshwater environment, the pollutant concentrations remained below the average concentrations in the Melbourne area. Less commonly discussed metals such as Barium and Strontium were found in all water samples with concentrations greater than the values discussed in the literature. During the investigation, challenges were faced due to the voluntary nature of the guidelines and the lack of data on trace metals. It is, therefore, recommended that further monitoring is required to confirm the changes in stormwater runoff quality and to assess the appropriateness of the current guidelines for improved management.

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New insights into the pollutant composition of stormwater treating wetlands

Cited by 10 publications

References 71 publications

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

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

Spatiotemporal Changes in Trace Metal Bioavailability in the Sediment Pore water of a Constructed Wetland Using Passive Pore water Samplers

Evaluating the water quality of stormwater runoffs in urban area

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