Assessment of Water Quality Evolution in the Pearl River Estuary (South Guangzhou) from 2008 to 2017

Spatially Explicit Impact of Land Use Changes in the Bay Area on Anthropogenic Phosphorus Emissions and Freshwater Eutrophication Potential

Land use changes significantly impact anthropogenic phosphorus (P) emissions, their migration to a water environment, and the formation of freshwater eutrophication potential (FEP), yet the spatiotemporally heterogeneous relationships at the regional scale have been less explored. This study combines land use classification, P-flow modeling, spatial analysis, and cause-effect chain modeling to assess P emissions and P-induced FEP at a fine spatial resolution in Guangdong-Hong Kong-Macao Greater Bay Area and reveals their dynamic responses to land use changes. We find that land conversion from cultivated land to impervious land corresponded to an increase in P emissions of 4.1, 1.8, and 0.5 Gg during 2000-2005, 2005-2010, and 2010-2015 periods, respectively, revealing its dominant but weakening role in the intensification of P emissions especially in less-developed cities. Expansion of aquacultural land gradually became the primary contributor to the increase in both the amount and intensity of P emissions. Land conversions from cultivated land to impervious land and from natural water bodies to aquacultural land led to 35.9% and 25.3% of the increase in FEP, respectively. Our study identifies hotspots for mitigating the environmental pressure from P emissions and provides tailored land management strategies at specific regional development stages and within sensitive areas.

Spatiotemporal analysis and prediction of water quality in Pearl River, China, using multivariate statistical techniques and data-driven model

Identifying spatiotemporal variation patterns and predicting future water quality are critical for rational and effective surface water management. In this study, an exploratory analysis and forecast workflow for water quality in Pearl River, Guangzhou, China, was established based on the 4-h interval dataset selected from 10 stations for water quality monitoring from 2019 to 2021. The multiple statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), correlation analysis (CoA), and redundancy analysis (RDA), as well as data-driven model (i.e., gated recurrent unit (GRU)), were applied for assessing and predicting the water quality in the basin. The investigated sampling stations were classified into 3 categories based on differences in water quality, i.e., low, moderate, and high pollution regions. The average water quality indexes (WQI) values ranged from 38.43 to 92.63. Nitrogen was the most dominant pollutant, with high TN concentrations of 0.81-7.67 mg/L. Surface runoff, atmospheric deposition, and anthropogenic activities were the major contributors affecting the spatiotemporal variations in water quality. The decline in river water quality during the wet season was mainly attributed to increased surface runoff and extensive human activities. Furthermore, the short-term prediction of river water quality was achieved using the GRU model. The result indicated that for both DLCK and DTJ stations, the WQI for the 5-day lead time were predicted with accuracies of 0.82; for the LXH station, the WQI for the 3-day lead time was forecasted with an accuracy of 0.83. The finding of this study will shed a light on an effective reference and systematic support for spatio-seasonal variation and prediction patterns of water quality.

Spatiotemporal dynamics and anthropologically dominated drivers of chlorophyll-a, TN and TP concentrations in the Pearl River Estuary based on retrieval algorithm and random forest regression

Estimation of large-scale and high-precision water quality parameters is critical in explaining the spatiotemporal dynamics and the driving factors of water quality variability, especially in areas with environmental complexity (e.g., crisscrossing waterways, high flood risk in rainy season and seawater invasion). Thus, in this study, a retrieval algorithm was developed to predict chlorophyll-a (Chl-a), total nitrogen (TN) and total phosphorus (TP) concentrations in the Pearl River Estuary (PRE) based on a large amount of in situ measurements and Landsat 8 remote sensing images. Random Forest (RF) machine learning was conducted to identify the relationship between environmental indicators (pH, turbidity, conductivity, total dissolved solids and water temperature), Chl-a, TN and TP. The results showed that the NIR/R Binomial algorithm for Chl-a estimation presented appreciable reliability with R² of 0.7429, root mean square error (RMSE) of 1.2089 and mean absolute percent error (MAPE) of 15.33%. The water quality variation in the PRE showed a characteristic of overall improvement and regional deterioration with average concentrations of 7.28 μg/L, 1.15 mg/L and 0.12 mg/L for Chl-a, TN, and TP respectively. Turbidity and pH were identified as the most important indicators to explain Chl-a (52.86%, 39.91%), TN (52.38%, 40.57%) and TP (55.23%, 40.03%) variation. Agricultural pollution was the main pollution source due to the intensive application of fertilizer and increased field size. Besides, land use patterns (e.g., increasing farmland but decreasing forest) greatly influenced water quality from 2010 to 2020. Moreover, light limitation caused by high turbidity reduced the algae productivity and further lowered the Chl-a concentration. The driving factors for regional water quality variations were anthropologically dominated and supplemented by climate change. This study improved the monitoring accuracy of regional water environment and provided quantitative early warning of water pollution events for environmental practitioners, so as to achieve long-term monitoring, precise pollution management and efficient water resources management.

A coastal Ramsar site on transition to hypoxia and tracking pollution sources: a case study of south-west coast of India

Coastal lakes and estuaries are considered economic drivers for coastal communities by delivering invaluable economic and ecosystem services. The coastal ecosystems are facing recurrent hypoxia events (dissolved oxygen; DO < 2.0 mg L^-1) and are emerging as a major threat to ecosystem structure and functioning. The Ashtamudi Lake, (area = 56 km²), is one of the Ramsar sites in the State of Kerala and located on the SW coast of India. The waterways are extensively used for backwater tourism and for fishery activities. This paper discusses the spatio-temporal variation of water quality attributes with emphasis on hypoxia during non-monsoon and monsoon seasons. The extent of hypoxia on fishery diversity was discussed. The Southern Zone, adjacent to the urban area, shows the hypoxic condition with higher concentration of BOD, NO₃-N, and NH₄-N. The hypoxic condition is largely limited to the Southern Zone in both seasons. The occurrence of low DO in the lake is highly related to salinity and organic load in the lake system. The tracking of pollution sources in the lake system was also done through identification of pollution potential zones and found that catchments adjacent to Southern and Western Zones (urban regions) are the major source of pollution. The study suggests that hypoxia is chiefly attributed to anthropogenic interventions in the form of discharge of wastes into the lake causing overloading of nutrients and organic effluents, decrease in the freshwater supply, the absence of proper freshwater mixing or dilution, and effluent discharge from nearby urban centers.

Ecotoxicological assessment of estuarine surface waters receiving treated and untreated sanitary wastewater

Pollution from sewage discharge is one of the most critical environmental problems worldwide, e.g., in Brazil, where basic sanitation is still scarce. As pollution can affect biomes, especially estuaries where intensive ecological and human activities occur, has caused widespread concern. This work aimed to study the water quality of the Jundiaí/Potengi Estuary (JPE) in an area close to the discharge of treated and untreated wastewater for 18 months. Physicochemical and microbiological parameters were measured and integrated using the Water Quality Index of the Canadian Council of Ministers of the Environment. Ecotoxicological tests were performed with Brazilian endemic organisms to assess the impact of water pollution on biota. A generalized linear regression model was applied to understand the effects of water quality on ecotoxicological responses. Concentrations of metals, dissolved oxygen, total ammonia nitrogen, nitrate, and thermotolerant coliforms did not comply with Brazilian environmental regulations. A significant increase in the mortality rate of Mysidopsis juniae and Nitocra sp. and a significant decrease in the reproductive rate of Nitocra sp. indicated the most affected areas related to the discharge of treated and untreated wastewater. Only 10% of the samples from sites without direct wastewater impact showed a toxic response in at least one organism. Both water quality and sampling sites were statistical predictors of ecotoxicological response, describing not only the pollutant load but also the type of effluent. This study demonstrated the degradation of the environmental quality of the JPE, particularly due to the discharge of sanitary wastewater, and highlights the importance of protection and remediation measures to preserve this protected area.

Effect of aeration on water quality and sediment humus in rural black-odorous water

Black-odorous water, which exists widely in rural areas of China, affects the living environments of residents and causes a loss of ecosystem functions, and improper treatment may even cause secondary pollution. Moreover, the transformation relationships among the components of humus in the sediments of black-odorous water are largely unknown. Therefore, it is necessary to select appropriate methods for treating black-odorous water in rural areas and to understand the characteristics of humus in sediment during the treatment process. In this study, the use of aeration in treating black-odorous water and interstitial water in rural areas was studied and the effects of different treatment methods on the contents and spectral characteristics of humus components in sediments were explored. It was found that the quality of the treated water improved from severe black-odorous to nonblack-odorous, the colour of the sediment changed from black to greyish-brown to turquoise, and the odour changed from strong to weak. The removal rates of ammonia nitrogen, total nitrogen, total phosphorus, and chemical oxygen demand in the sediment aeration group reached more than 50% in the upper water, and more than 70% in the interstitial water. After treatment, the content of fulvic acid, the main black substance in sediment, decreased by 0.36-1.58 g/kg, and the molecular structure of the humus was simplified.

Water-Quality Assessment and Pollution-Risk Early-Warning System Based on Web Crawler Technology and LSTM

The openly released and measured data from automatic hydrological and water quality stations in China provide strong data support for water environmental protection management and scientific research. However, current public data on hydrology and water quality only provide real-time data through data tables in a shared page. To excavate the supporting effect of these data on water environmental protection, this paper designs a water-quality-prediction and pollution-risk early-warning system. In this system, crawler technology was used for data collection from public real-time data. Additionally, a modified long short-term memory (LSTM) was adopted to predict the water quality and provide an early warning for pollution risks. According to geographic information technology, this system can show the process of spatial and temporal variations of hydrology and water quality in China. At the same time, the current and future water quality of important monitoring sites can be quickly evaluated and predicted, together with the pollution-risk early warning. The data collected and the water-quality-prediction technique in the system can be shared and used for supporting hydrology and in water quality research and management.

Inversion and Driving Force Analysis of Nutrient Concentrations in the Ecosystem of the Shenzhen-Hong Kong Bay Area

Although satellite remote sensing technology is intensively used for the monitoring of water quality, the inversion of coastal water bodies and non-optically active parameters is still a challenging issue. Few ongoing studies use remote sensing technology to analyze the driving forces of changes in water quality from multiple aspects based on inversion results. By the use of Landsat 5/8 imagery and measured in situ data of the total nitrogen (TN) and total phosphorus (TP) in the Shenzhen-Hong Kong Bay area from 1986 to 2020, this study evaluated the modeling effects of four machine learning methods named Tree Embedding (TE), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Back-propagation Neural Network (BPNN). The results show that the BPNN creates the most reliable and robust results. The values of the obtained correlation coefficients (r) are 0.83, 0.92, 0.84, and 0.90, and that of the coefficients of determination (R2) are 0.70, 0.84, 0.70, and 0.81. The calculated mean absolute errors (MAEs) are 0.41, 0.16, 0.06, and 0.02, while the root mean square errors (RMSEs) are 0.78, 0.29, 0.12, and 0.03. The concentrations of TN and TP (CTN, CTP) in the Shenzhen Bay, the Starling Inlet, and the Tolo Harbor were relatively high, fluctuated from 1986 to 2010, and decreased significantly after 2010. The CTN and CTP in the Mirs Bay kept continuously at a low level. We found that urbanization and polluted river discharges were the main drivers of spatial and inter-annual differences of CTN and CTP. Temperature, precipitation, and wind are further factors that influenced the intra-annual changes of CTN and CTP in the Shenzhen Bay, whilethe expansion of oyster rafts and mangroves had little effect. Our research confirms that machine learning algorithms are well suited for the inversion of non-optical activity parameters of coastal water bodies, and also shows the potential of remote sensing for large-scale, long-term monitoring of water quality and the subsequent comprehensive analysis of the driving forces.

Surface Water Quality Assessment and Contamination Source Identification Using Multivariate Statistical Techniques: A Case Study of the Nanxi River in the Taihu Watershed, China

Understanding the spatiotemporal patterns of water quality is crucial because it provides essential information for water pollution control. The spatiotemporal variations in water quality for the Nanxi River in the Taihu watershed of China were evaluated by a water quality index (WQI) and multivariate statistical techniques; additionally, the potential sources of contamination were identified. The data set included 22 water quality parameters collected during the monitoring period from 2015 to 2020 for 14 monitoring stations. WQI assessment revealed that approximately 85% of monitoring stations were classified as “medium-low” water quality, and most showed continuous improvement in water quality. Cluster analysis divided the 14 monitoring stations into three clusters (low contamination, medium contamination and high contamination). Discriminant analysis identified pH, petroleum, volatile phenol, chemical oxygen demand, total phosphorus, F, S, fecal coliform, SO4, Cl, NO3-N, total hardness, NO2-N and NH3 as important parameters affecting spatial variations. Factor analysis identified four potential contamination source types: nutrient, organics, feces and oil. This study demonstrated the usefulness of multivariate statistical techniques in assessing large data sets, identifying contamination source types, and better understanding spatiotemporal variations in water quality to restore and protect water resources.

Metagenomics combined with comprehensive validation as a public health risk assessment tool for urban and agricultural run-off

Early detection of emerging and life-threatening pathogens circulating in complex environments is urgently required to combat infectious diseases. This study proposed a public health risk assessment workflow with three stages, pathogen screening, pathogen genotyping, and risk assessment. In stage one, pathogens were screened with metagenomic sequencing, microfluidic chip, and qPCR. In stage two, pathogens were isolated and genotyped with multi-locus sequence typing (MLST) or conventional PCR. Finally, virulence genes from metagenomic data were assessed for pathogenicity. Two regions (Donggang and Zhanjiang) with potential public health concerns were selected for evaluation, each of which comprised of one urban and one farming wastewater sampling location. Overall, metagenomic sequencing reflected the variation in the relative abundance of medically important bacteria. Over 90 bacterial pathogens were monitored in the metagenomic dataset, of which 56 species harbored virulence genes. In Donggang, a pathogenic Acinetobacter sp. reached high abundances in 2018 and 2020, whereas all pathogenic Vibrio spp. peaked in October 2019. In Zhanjiang, A. baumanni, and other Enterobacteriaceae species were abundantly present in 2019 and 2020, whereas Aeromonas and Vibrio spp. peaked in November-2017. Forty species were subsequently isolated and subtyped by MLST, half of which were prevalent genotypes in clinical data. Additionally, we identified the African Swine Fever Virus (ASFV) in water samples collected in 2017, ahead of the first reported ASFV outbreak in 2018 in China. RNA viruses like Hepatitis A virus (HAV) and Enterovirus 71 (EV71) were also detected, with concentrations peaking in April 2020 and April 2018, respectively. The dynamics of HAV and EV71 were consistent with local epidemic trends. Finally, based on the virulence gene profiles, our study identified the risk level in wastewater of two cities. This workflow illustrates the potential for an early warning of local epidemics, which helps to prioritize the preparedness for specific pathogens locally.

Long-Term Water Quality Patterns in an Estuarine Reservoir and the Functional Changes in Relations of Trophic State Variables Depending on the Construction of Serial Weirs in Upstream Reaches

Water quality degradation is one of the major problems with artificial lakes in estuaries. Long-term spatiotemporal patterns of water quality in a South Korean estuarine reservoir were analyzed using seasonal datasets from 2002 to 2020, and some functional changes in relations of trophic state variables due to the construction of serial weirs in the upper river were also investigated. A total of 19 water quality parameters were used for the study, including indicators of organic matter, nutrients, suspended solids, water clarity, and fecal pollution. In addition, chlorophyll-a (CHL-a) was used to assess algal biomass. An empirical regression model, trophic state index deviation (TSID), and principal component analysis (PCA) were applied. Longitudinal fluctuations in nutrients, organic matter, sestonic CHL-a, and suspended solids were found along the axis of the riverine (Rz), transition (Tz), and lacustrine zones (Lz). The degradation of water quality was seasonally caused by resuspension of sediments, monsoon input due to rainfall inflow, and intensity of Asian monsoon, and was also related to intensive anthropic activities within the catchment. The empirical model and PCA showed that light availability was directly controlled by non-algal turbidity, which was a more important regulator of CHL-a than total nitrogen (TN) and total phosphorus (TP). The TSID supported our hypothesis on the non-algal turbidity. We also found that the construction of serial upper weirs influenced nutrient regime, TSS, CHL-a level, and trophic state in the estuarine reservoir, resulting in lower TP and TN but high CHL-a and high TN/TP ratios. The proportions of both dissolved color clay particles and blue-green algae in the TSID additionally increased. Overall, the long-term patterns of nutrients, suspended solids, and algal biomass changed due to seasonal runoff, turnover time, and reservoir zones along with anthropic impacts of the upper weir constructions, resulting in changes in trophic state variables and their mutual relations in the estuarine reservoir.

Distinct interaction effects of warming and anthropogenic input on diatoms and dinoflagellates in an urbanized estuarine ecosystem

Diatoms and dinoflagellates are two major bloom-forming phytoplankton groups in coastal ecosystems and their dominances will notably affect the marine ecosystems. By analyzing an 18-year monthly monitoring dataset (2000-2017) in the Pearl River Estuary (one of the most highly urbanized and populated estuarine in the world), we observe an increasing trend of the diatom to dinoflagellate ratio (Diatom/Dino). As revealed by multiple statistical models (generalized additive mixed model, random forest, and gradient boosting algorithms), both groups are positively correlated with temperature. Diatoms are positively correlated with nitrate and negatively correlated with ammonium while dinoflagellates show an opposite pattern. The Diatom/Dino trend is explained by an altered nutrient composition caused by a decadal increase in anthropogenic input, at which nitrate increased rapidly while ammonium and phosphate were relatively constant. Regarding the interaction of warming and nutrient dynamics, we observe an additive effect of warming and nitrate enrichment that promotes the increase in diatom cell density, while the dinoflagellate cell density only increases with warming when nutrients are depleted. Our models predict that the Diatom/Dino ratio will further increase with increasing anthropogenic input and global warming in subtropical estuarine ecosystems with nitrate as the dominant inorganic nitrogen; its ecological consequences are worthy of further investigation.

Renovation of Automation System Based on Industrial Internet of Things: A Case Study of a Sewage Treatment Plant

The Industrial Internet of Things (IIoT) is of great significance to the improvement of industrial efficiency and quality, and to reduce industrial costs and resources. However, there are few openly-reported practical project applications based on the IIoT up to now. For legacy automation devices in traditional industry, it is especially challenging to realize the upgrading of industrial automation adopting the IIoT technology with less investment. Based on the practical engineering experience, this paper introduces the automation renovation of a sewage treatment plant. The legacy automation devices are upgraded by the central controller of a STM32 processor (Produced by STMicroelectronics company, located in Geneva, Switzerland), and the WeChatApplet (Developed by Tencent company, located in Shenzhen, China) is used as the extended host computer. A set of remote monitoring and control systems of sewage treatment based on the IIoT is built to realize the wide-area monitoring and control of sewage treatment. The paper describes the field hardware system, wide-area monitoring and control application program, management cloud platform and security technologies in detail. The actual operation results show that the monitoring system has the requirements of high accuracy, good real-time performance, reliable operation and low cost.