Real-time monitoring of water quality to identify pollution pathways in small and middle scale rivers

Meyer, Angelika M.; Klein, Christina; Fünfrocken, Elisabeth; Kautenburger, Ralf; Beck, H. P.

doi:10.1016/j.scitotenv.2018.10.069

Cited by 100 publications

(64 citation statements)

References 31 publications

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“…However, the importance of industrialisation in the achievement of global and national economic growth, wealth and development notwithstanding, it can also be harmful to the environment. Rapid industrialisation is believed to be one of the main contributors to the pollution of environmental resources around the world [1], and South Africa is no exception [2,3]. While high-quality water is essential for most industrial processes, many of these processes generate large effluents of contaminated wastewater, whose safe disposal into receiving water bodies is of great environmental and health concern worldwide.…”

Section: Introductionmentioning

confidence: 99%

Investigating Industrial Effluent Impact on Municipal Wastewater Treatment Plant in Vaal, South Africa

Iloms

Ololade

Ogola

et al. 2020

IJERPH

162

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Industrial effluents with high concentrations of toxic heavy metals are of great concern because of their persistence and non-degradability. However, poor operation and maintenance of wastewater treatment infrastructure is a great concern in South Africa. In this study, physico-chemical parameters and heavy metals (HMs) concentration of wastewater from five different industries, Leeuwkuil wastewater treatment plant (WWTP) inflow and effluent, and Vaal River water samples were monitored between January and September 2017, to investigate the correlation between heavy metal pollution and the location of industries and ascertain the effectiveness of the municipal WWTP. Physico-chemical variables such as pH, biological oxygen demand (BOD), dissolved oxygen (DO), chemical oxygen demand (COD), total dissolved solids (TDS) and electrical conductivity (EC) exhibited both temporal and spatial variations with the values significantly higher in the industrial samples. Inductively coupled plasma optical emission spectrometry (ICP-OES) results also showed that aluminium (Al), copper (Cu), lead (Pb) and zinc (Zn) were significantly higher in industrial effluents (p < 0.05), with only Zn and Al exhibiting significant seasonal variability. Statistical correlation analysis revealed a poor correlation between physicochemical parameters and the HMs compositional quality of wastewater. However, toxic HMs (Zn, Cu and Pb) concentrations in treated wastewater from WWTP were above the permissible limits. Although the WWTP was effective in maintaining most of the wastewater parameters within South African Green drop Standards, the higher Cu, Zn, Pb and COD in its final effluent is a concern in terms of Vaal river health and biological diversity. Therefore, we recommend continuous monitoring and maintenance of the WWTPs infrastructure in the study area.

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Section: Introductionmentioning

confidence: 99%

Investigating Industrial Effluent Impact on Municipal Wastewater Treatment Plant in Vaal, South Africa

Iloms

Ololade

Ogola

et al. 2020

IJERPH

162

View full text Add to dashboard Cite

show abstract

“…Luckily, at present, more data are available and data-driven approaches and statistical (or numerical) models are now playing an increasingly important role in water management, so that environmental decision support systems (EDSSs) are more reliable and are capable of coping with real-world environmental systems [9][10][11]. Numerous researchers have analyzed real-time data to support the management of urban water and water supplies in developed countries [12][13][14][15], but this approach has not often been used to manage large rural watersheds or wastewater [16,17].…”

Section: Introductionmentioning

confidence: 99%

Using Real-Time Data and Unsupervised Machine Learning Techniques to Study Large-Scale Spatio–Temporal Characteristics of Wastewater Discharges and their Influence on Surface Water Quality in the Yangtze River Basin

Miao

Guo

et al. 2019

Water

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Most worldwide industrial wastewater, including in China, is still directly discharged to aquatic environments without adequate treatment. Because of a lack of data and few methods, the relationships between pollutants discharged in wastewater and those in surface water have not been fully revealed and unsupervised machine learning techniques, such as clustering algorithms, have been neglected in related research fields. In this study, real-time monitoring data for chemical oxygen demand (COD), ammonia nitrogen (NH3-N), pH, and dissolved oxygen in the wastewater discharged from 2213 factories and in the surface water at 18 monitoring sections (sites) in 7 administrative regions in the Yangtze River Basin from 2016 to 2017 were collected and analyzed by the partitioning around medoids (PAM) and expectation–maximization (EM) clustering algorithms, Welch t-test, Wilcoxon test, and Spearman correlation. The results showed that compared with the spatial cluster comprising unpolluted sites, the spatial cluster comprised heavily polluted sites where more wastewater was discharged had relatively high COD (>100 mg L−1) and NH3-N (>6 mg L−1) concentrations and relatively low pH (<6) from 15 industrial classes that respected the different discharge limits outlined in the pollutant discharge standards. The results also showed that the economic activities generating wastewater and the geographical distribution of the heavily polluted wastewater changed from 2016 to 2017, such that the concentration ranges of pollutants in discharges widened and the contributions from some emerging enterprises became more important. The correlations between the quality of the wastewater and the surface water strengthened as the whole-year data sets were reduced to the heavily polluted periods by the EM clustering and water quality evaluation. This study demonstrates how unsupervised machine learning algorithms play an objective and effective role in data mining real-time monitoring information and highlighting spatio–temporal relationships between pollutants in wastewater discharges and surface water to support scientific water resource management.

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“…Statistical and numerical models enable environmental decision support systems (EDSS) to be more reliable and powerful in coping with real-world environmental systems [15]. Real-time data are widely used in urban water management and by water utilities in developed countries [16][17][18][19][20], but rarely in rural watershed management, especially in large watershed management [21,22]. In China, the rapidly growing economy and population is generating widely distributed polluted surface water throughout the country.…”

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confidence: 99%

Water Quality Evaluation of the Yangtze River in China Using Machine Learning Techniques and Data Monitoring on Different Time Scales

Miao

Guo

2019

Water

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Unlike developed countries, China has a nationally unified water environment standard and a specific watershed protection bureau to perform water quality evaluation. It is a major challenge to assess the water quality of a large watershed at a wide spatial scale and to make decisions in a scientific way. In 2016, weekly and real-time data for four monitoring indicators (pH, dissolved oxygen, permanganate index, and ammonia nitrogen) were collected at 21 surface water sections (sites) of the Yangtze River Basin, China. Results showed that one site had a relatively low Site Water Quality Index and was polluted for 12 weeks meanwhile. By using expectation-maximization clustering and hierarchical clustering algorithms, the 21 sites were classified. Variable spatiotemporal distribution characteristics for water quality and pollutants were found; some sites exhibited similar water quality variations on the weekly scale, but had different yearly grades. The results revealed polluted water quality for short periods and abrupt anomalies, which imply potential pollution sources and negative effects on water ecosystems. Potential spatio-temporal water quality characteristics, explored by machine learning methods and evidenced by time series and statistical models, could be applied in environmental decision support systems to make watershed management more objective, reliable, and powerful. and to evaluate alternative hypotheses regarding important contaminant sources and watershed properties that control transport over large spatial scales. However, there is no unified model available for the Chinese government to make decisions regarding large watershed management [12]. Local governments have a variety of environmental models that they can choose from, and may select diverse models that do not allow meaningful comparisons with the results of models chosen in other areas [13]. Even in the same area, different departments of the same local government use different models with diverse data to assess the water quality of the same river basin, resulting in a huge amount of variability in the water quality evaluation reports, which often fail to reach the same conclusion [3,4].Thanks to the increased collection and use of data, data-driven approaches have been playing an increasingly important role in water management [14]. Statistical and numerical models enable environmental decision support systems (EDSS) to be more reliable and powerful in coping with real-world environmental systems [15]. Real-time data are widely used in urban water management and by water utilities in developed countries [16][17][18][19][20], but rarely in rural watershed management, especially in large watershed management [21,22]. In China, the rapidly growing economy and population is generating widely distributed polluted surface water throughout the country. Thus, there is an increasing need for online data for large watershed management to meet the objectives of early warning monitoring of surface water quality, and for monitoring and control of tot...

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Real-time monitoring of water quality to identify pollution pathways in small and middle scale rivers

Cited by 100 publications

References 31 publications

Investigating Industrial Effluent Impact on Municipal Wastewater Treatment Plant in Vaal, South Africa

Investigating Industrial Effluent Impact on Municipal Wastewater Treatment Plant in Vaal, South Africa

Using Real-Time Data and Unsupervised Machine Learning Techniques to Study Large-Scale Spatio–Temporal Characteristics of Wastewater Discharges and their Influence on Surface Water Quality in the Yangtze River Basin

Water Quality Evaluation of the Yangtze River in China Using Machine Learning Techniques and Data Monitoring on Different Time Scales

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