Water Quality Grade Research Articles

Impacts of Land Use on Surface Water Quality Using Self-Organizing Map in Middle Region of the Yellow River Basin, China.

The Yellow River is one of the most important water sources in China, and its surrounding land use affected by human activities is an important factor in water quality pollution. To understand the impact of land use types on water quality in the Sanmenxia section of the Yellow River, the water quality index (WQI) was used to evaluate the water quality. A self-organizing map (SOM) was used for clustering analysis of water quality indicators, and the relationship between surface water quality and land use types was further analyzed by redundancy analysis (RDA). The results showed that WQI values ranged from 82.60 to 507.27, and the highest value was the sampling site S3, whose water quality grade was “Likely not suitable for drinking”, mainly polluted by agricultural non-point sources ammonia nitrogen pollution. SOM clustered the sampling sites into 4 groups according to the water quality indicators, the main influencing factors for different groups were analyzed and explored in more depth in relation to land use types, suggesting that surface water quality was significantly connected with the proportion of land use types at the watershed scale in the interpretation of water quality change. The negative impact of cropland on surface water quality was greater than that of other land use types, and vegetation showed a greater positive impact on surface water quality than other land uses. The results provide evidence for water environment conservation based on land use in the watershed.

Water quality index assessment methods for surface water: A case study of the Citarum River in Indonesia

Water quality index (WQI) can express overall water quality status in a single term. As such, the application of daily WQI assessment should help the general public be more aware of the condition of the surface water around them. As the longest and biggest river in the West Java Province, the Citarum River plays an important role in the life of the community and ecosystem around it. Therefore, this research evaluated which WQI assessment method was best suited for determining the Citarum River's water quality. We utilized West Java Province monitoring data collected from four monitoring stations along the Upstream Citarum. The WQI was calculated using the National Sanitation Foundation WQI (NSF WQI), Canadian Council of Ministers of the Environment WQI (CCME WQI), and Oregon Water Quality Index (OWQI) assessment methods. Nine years of monitoring data were grouped and analyzed according to wet vs. dry months, wet vs. dry years, monitoring station, and year. Using the NSF WQI assessment method, the Citarum River obtained a ‘Fair’ and ‘Bad’ water quality grade with WQI ranging between 38.212 and 60.903 during dry months, 49.089 and 62.348 during wet months, 42.935 and 65.696 during dry years, and 39.002 and 58.898 during wet years. The data ranged from 41.458 and 61.206 from each monitoring station, and between 35.920 and 58.713 for the data from each monitoring year. The CCME WQI assessment method showed that the Citarum River had ‘Fair’, ‘Marginal’, and ‘Bad’ water quality with WQI ranging between 12.683 and 31.503 during dry months, 21.231 and 33.127 during wet months, 12.683 and 31.503 during dry years, 12.134 and 28.748 during wet years, 13.621 and 30.569 for the data from each monitoring station, and 13.219 and 68.808 for the data from each monitoring year. The OWQI assessment method gave the Citarum River a ‘Very Bad’ water quality rating with WQI ranging between 11.528 and 18.827 during dry months, 13.898 and 24.563 during wet months, 11.528 and 25.782 during dry years, 11.528 and 15.997 during wet years, 11.528 and 18.842 for each monitoring station, and 11.523 and 16.528 for the data from each monitoring year. Based on these results and the collated advantages and disadvantages of each method, the NSF WQI assessment method was deemed to be the best for determining the Citarum River's water quality.

Development of customized water quality index for medium- and long-term evaluation of urban river water quality

Abstract In this study, a customized WQI (Seoul water quality index, S-WQI) for urban rivers that can ultimately reflect their characteristics was developed by modifying and supplementing the existing Bascarόn WQI calculation method through linkage with statistical methods such as factor analysis. We used the water quality data generated monthly at 17 water quality monitoring networks (WQMNs) in Seoul for 18 years, from 2002 to 2019. Results of the research show that the monthly S-WQI showed an average 70 out of 100, ‘good (II)’ grades, whereas the average water quality grade according to the environmental standards was ‘slightly good (II),’ with an R2 value of 0.8298. The annual S-WQI was found to be 39 (bad) to 97 (very good), with an average of 72 (good). Through this study, S-WQI, a customized WQI for urban rivers, was judged to be a reasonable index that can represent the characteristics of urban river water quality. This is because it is easy to apply and is a calculation method that uses relatively fewer water quality items than the WQI calculated in the past, and it is highly likely to be linked to the currently implemented water quality grade system. In addition, to extend the application of WQI to various water quality survey points, based on the calculation methodology performed to derive the indices in this study, such as modified S-WQI (MS-WQI), by adding new water quality items and changing some items, it is also possible to develop an advanced customized WQI for urban rivers considering watershed characteristics and measurement items.

Assessment of karst water quality and analysis of pollution sources with a projection pursuit algorithm in Jinan spring area, China

Abstract Karst water is one of the main drinking water sources in North China. The single factor method and projection pursuit algorithm (PPA) are employed to assess the karst water quality of the Baotu spring area in Jinan. The water quality distribution pattern, its causes, and the main groundwater pollution sources are analyzed. The water quality evaluation results of the PPA model are more reliable than those of the single factor method because the PPA model comprehensively considers the weight and correlation of various factors. The water quality of the study area is generally excellent, but the NO3− index content is high. In recent years, the water quality grades have been mainly class II ∼ class IV. The driving factors of water quality evolution are not only human activities, including artificial recharge, but also natural factors, such as carbonate mineral dissolution. These factors control both the distribution and evolution trend of water quality. Urban nonpoint sources have a significant impact on groundwater quality. Based on the current water quality situation, it is urgent to strengthen protection of the ecological environment in the southern recharge area of the spring region and the water quality control in the western region.

Groundwater hydrogeochemical characterization and quality assessment based on integrated weight matter-element extension analysis in Ningxia, upper Yellow River, northwest China

• Groundwater chemistry and quality were analyzed in the Ningxia region. • NO 3 − , NH 4 + and NO 2 − are related to human pollution. • Water quality was assessed using an integrated weight matter-element extension model. • High health risks of F − , NO 3 − , NH 4 + , and NO 2 − were found, especially for children. Assessing groundwater quality and identifying pollutant risks are essential for managing groundwater resources in arid and semi-arid regions. An analysis of the hydrochemical characteristics, controlling factors, and pollutant characteristics of the groundwater in the upper Yellow River region is presented to understand the overall groundwater quality and identify the risk to human health in this region. Traditional hydrochemical analysis methods, integrated weight matter-element extension analysis, and health risk models were used in this study. A total of 210 groundwater samples were collected from the wells for physical and chemical analyses. Results showed that groundwater in the study area was weakly alkaline, with the predominant hydrochemical types of groundwater being SO 4 ·Cl-Ca·Mg, SO 4 ·Cl-Na, and HCO 3 -Ca·Mg. The hydrochemical composition of the groundwater is controlled by rock dominance, evaporation dominance, and cation exchange. Nitrogen pollution of groundwater is caused by industrial activities and fertilizers; fluorine pollution is caused by fluorite dissolution. To reasonably evaluate the groundwater quality, the groundwater evaluation method based on an integrated weight matter-element extension analysis was optimized. The method effectively avoids defects of G1 and entropy weight methods in determining weights and thoroughly considers the fuzziness and uncertainty of the water quality grade. The overall quality groundwater assessment results showed that 96.19% of the overall water quality in the study area was good and could be used in many ways. Land irrigated with groundwater is mainly exposed to salinity hazards. Health risk assessment results showed that 57% and 21% of the samples posed unacceptable health risks to children and adults, respectively. Therefore, more attention should be paid to real-time, continual monitoring of pollutants in groundwater management. The results help decision makers make wise decisions on identifying groundwater purposes to avoid further water quality deterioration and increase in pollutants, ensuring the cleanliness of groundwater resources in the upper Yellow River..

Research on application of ReliefF and improved RVM in water quality grade evaluation.

ReliefF algorithm was used to analyze the weight of each water quality evaluation factor, and then based on the Relevance Vector Machine (RVM), Particle Swarm Optimization (PSO) was used to optimize the kernel width factor and hyperparameters of RVM to build a water quality evaluation model, and the experimental results of RVM, PSO-RVM, ReliefF-RVM and PSO-ReliefF-RVM were compared. The results show that ReliefF algorithm, combined with threshold value, selects 5 evaluation factors with significant weight from eight evaluation factors, which reduces the amount of data used in the model, CSI index is used to calculate the separability of each evaluation factor combination. The results show that the overall separability of the combination is best when the evaluation factor with significant weight is reserved. When different water quality evaluation factors were included, the evaluation accuracy of PSO-ReliefF-RVM model reached 95.74%, 14.23% higher than that of RVM model, which verified the effectiveness of PSO algorithm and ReliefF algorithm, and had a higher guiding significance for the study of water quality grade evaluation. It has good practical application value.

Research on Danjiang Water Quality Prediction Based on Improved Artificial Bee Colony Algorithm and Optimized BP Neural Network

In order to ensure “a river of clear water is supplied to Beijing and Tianjin” and improve the water quality prediction accuracy of the Danjiang water source, while avoiding the local optimum and premature maturity of the artificial bee colony algorithm, an improved artificial bee colony algorithm (ABC algorithm) is proposed to optimize the Danjiang water quality prediction model of BP neural network is proposed. This method improves the local and global search capabilities of the ABC algorithm by adding adaptive local search factors and mutation factors, improves the performance of local search, and avoids local optimal conditions. The improved ABC algorithm is used to optimize the weights and thresholds of the BP neural network to establish a water quality grade prediction model. Taking the water quality monitoring data of Danjiang source (Shangzhou section) from 2015 to 2019 as the research object, it is compared with GA-BP, PSO-BP, ABC-BP, and BP models. The research results show that the improved ABC-BP algorithm has the highest prediction accuracy, faster convergence speed, stronger stability, and robustness.

Water quality assessment and pollution source analysis of Yaojiang River Basin: a case study of inland rivers in Yuyao City, China

Abstract To fully understand the water quality status of the Yuyao section in the Yaojiang River Basin, we utilized the single factor and comprehensive water quality identification index methods, as well as factor analysis in this study. The single factor water quality assessment results showed that the common parameters exceeding the standard in both the wet and dry seasons included dissolved oxygen and total nitrogen, which were inferior to the target water quality grade III by 1–6 grades. The comprehensive water quality order reaching the target grade III was Hutang River (87.5%), Changling River (50.0%), Yaojiang River (43.8%), and Linzhou River (37.5%). The comprehensive water quality in the dry season (90.0% standard-achieving rate) was better than that in the wet season (25.0% standard-achieving rate). These results suggested that the surface runoff might considerably impact water quality. The factor analysis results showed that the basin surveyed in 2018 was mainly polluted by urban sewage and agricultural sources, while the primary pollution source in 2019 was agricultural pollution. Integrated with comprehensive water quality assessment results, in this study, we demonstrated that the series of water treatment projects carried out in Yuyao City positively impacted municipal sewage treatment.

Spatial-Temporal Change of Land Use and Its Impact on Water Quality of East-Liao River Basin from 2000 to 2020

Land use change is an important driving force factor affecting the river water environment and directly affecting water quality. To analyze the impact of land use change on water quality change, this study first analyzed the land use change index of the study area. Then, the study area was divided into three subzones based on surface runoff. The relationship between the characteristics of land use change and the water quality grade was obtained by grey correlation analysis. The results showed that the land use types changed significantly in the study area since 2000, and water body and forest land were the two land types with the most significant changes. The transfer rate is cultivated field > forest land > construction land > grassland > unused land > water body. The entropy value of land use information is represented as Area I > Area III > Area II. The shift range of gravity center is forest land > grassland > water body > unused land > construction land > cultivated field. There is a strong correlation between land use change index and water quality, which can be improved and managed by changing the land use type. It is necessary to establish ecological protection areas or functional areas in Area I, artificial lawns or plantations shall be built in the river around the water body to intercept pollutants from non-point source pollution in Area II, and scientific and rational farming in the lower reaches of rivers can reduce non-point source pollution caused by farming.

Water quality analysis of the rivers Topolnitsa and Luda Yana, Bulgaria using different indices

This study focuses on the analysis of some of the worldwide use of surface water index estimates. The catchment areas of the two model basins of bulgarian rivers (Topolnitsa and Luda Yana) are subject to active anthropogenic load. There are numerous metallurgical plants, combines for non-ferrous metals, mines and tailing ponds have been operating in the area for many years. At the end the heavy metals being the final result of their production activity. In the same time the waters of the rivers have various applications - industrial, household and agricultural. To obtain an assessment of the water quality status of the Topolnitsa and Luda Yana rivers in Bulgaria, two different indices are used - Water Pollution Index (WPI) and the Bavarian Surface Quality Index - (CJ). In the first one the conditions are regulated by the creators of the algorithm (reference values, set of physicochemical indicators, requirements for the type of data), the rest are selected by the user according to criteria determined by him. The index is calculated by a number of physicochemical parameters as obligatory include “dissolved oxygen” and “BOD5”. This is due to the fact that these indicators are particularly sensitive to the anthropogenic impact. The CJ index presents a summary of water quality in a given sample according to a set of physicochemical indices and is expressed as a non-dimensional quantity from 0 to 100 (0 is the worst and 100 is the best water quality) grade of reference values are defined. In this index all conditions are set in advance by the creator, including the set of indicators, reference values, weight of individual indicators, etc. The dataset consists of analytical results from a 6-yrs survey conducted in selected points of the river systems. The main aim of this article is to test the sensitivity of two indices different in their construction, algorithm and content for complex assessment of river water quality in Bulgarian conditions.

RETRACTED ARTICLE: Risk management system for sudden public water pollution accidents based on data information mining

The traditional method ignores the division of risk level, leading to the increase of treatment cost and the decrease of water resource utilization rate. Therefore, this paper designs a risk management method for sudden public water pollution accident based on data information mining. The risk management system architecture was designed, which was composed of operation module, map operation module, data maintenance module, risk assessment module of sudden public water pollution accident, and emergency response module. According to the system, the evaluation results of main pollution factors and water quality grade of each river reach were determined. Bayesian theory and data mining technology were combined to establish a water pollution accident risk assessment model based on fuzzy Bayesian network. Specific control measures are obtained via the risk assessment results of sudden public water pollution accidents. The results show that the system designed in this paper has low treatment cost and high utilization rate of water resources.

Integrated Hydraulic Modelling, Water Quality Modelling and Habitat Assessment for Sustainable Water Management: A Case Study of the Anyang-Cheon Stream, Korea

Recent ecological stream restoration projects have focused on expanding the water-friendly space of streams, promoting the health of aquatic ecosystems, and restoring various habitats, which raise the need for relevant research. Applying integrated environmental analysis, this study quantifies the change in hydraulic characteristics before and after the restoration projects through physical habitat simulation and links the results of physical impacts to estimate benefits of increase in water quality and aquatic ecosystem health due to the implementation of the project. For this, the study area is a 3.3 km long reach of the Anyang-cheon Stream, Korea. Field monitoring revealed that five fish species are dominant and sub-dominant, and account for 76% of the total fish community. To assess the change of before and after ecological stream restoration project, the River2D and Coastal Modelling System (CMS)-Flow 2D models were used for hydraulic and water quality simulations, respectively. For the habitat simulation, the HSI (Habitat Suitability Index) model was used. In addition, the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) was used to calculate changes in water quality index and to examine changes in habitat areas with an integrated quantitative index, the methodology of Zingraff-Hamed et al. was adopted. It was found that the ecological stream restoration project significantly increased for the eco-friendly area. In addition, the changes in water quality and habitat suitability grades before the ecological river restoration project were improved to two stages and one stage, respectively. This study applied the integrated analytical framework as a policy/project assessment tool and the results of this study will be useful for the integrated water management policy.

Economic Evaluation of Water Resource Management in South Korea Based on Benefit–Cost Analysis

The economic evaluation of the government policy is useful to identify the effectiveness of its financial management and the achievement of its goal. Based on valuation studies of water quality improvement from the Environmental Valuation Information System database provided by Korea Environment Institute, this study employed a meta-regression to measure the benefits of water quality improvement in three major river basins in South Korea. The empirical model is specified to capture the differentiated impact of water quality improvement by each river basin and the regression results showed that the increase in water quality grade has an impact on willingness-to-pay values. The total benefits provided by Han, Nakdong, and Geum River are estimated to be about US$490 million, about US$173 million, and about US$50 million per year, respectively. The estimated benefits were then compared with the costs, namely river management funds (RMFs) which are financial resources to support a variety of projects for water quality improvement. Based on benefit–cost comparison, this study explores the economic evaluation of water resource management in South Korea. This study also provides policy options including the equity of imposing water use charges, operational efficiency of RMFs, and stakeholder engagement for inclusive water governance that are helpful to maintain the sustainability of water resource in the long run.

Application of Interval Information Comprehensive Ranking Model Based on Entropy Weight in River Water Quality Evaluation

Wang, Y.; Wu, Y., and Jiang, L., 2020. Application of interval information comprehensive ranking model based on entropy weight in river water quality evaluation. In: Hu, C. and Cai, M. (eds.), Geo-informatics and Oceanography. Journal of Coastal Research, Special Issue No. 105, pp. 137–140. Coconut Creek (Florida), ISSN 0749-0208.In this article, an interval information comprehensive ranking model based on entropy weight is established and applied to river water quality evaluation. In this model, the monitoring interval value of water quality index of river section is weighted with the interval data of standard grade. The weight of each index is calculated by using information entropy technology, and the water quality grade of each section is determined by comprehensive weighted ranking of each section and evaluation grade. The results show that the concept of the model is clear, the calculation is simple, and the evaluation results are objective and reasonable.

Water Quality Detection Based on FCN and Embedded System

Yan, Y.; Han, Y.; Zhang, P.H., and Wang, H.F., 2020. Water quality detection based on FCN and embedded system. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 72–76. Coconut Creek (Florida), ISSN 0749-0208.In this paper, the convolutional neural network (CNN) based on deep learning is applied to embedded systems. After summarizing the shortcomings of traditional algorithms, the image processing technology of full convolutional neural network (FCN) is adopted to study a method for online water quality measurement, which solves the key problem of image recognition. In view of the complex nonlinear characteristics of water quality related data, the full convolutional neural network is used to train water quality data, determine the mapping relationship between input and output, and input the obtained relationship into the embedded system to form a water quality detection system, so as to quickly and efficiently detect water pollution. The experimental results show that the accuracy of the predicted grade and the measured grade of water quality in the test area can be as high as 90%.

Applicability of Benthic Diatom Indices Combined with Water Quality Valuation for Dish Lake from Nanjishan Nature Reserve, Lake Poyang

Benthic diatom indices developed in the Europe Union have been widely accepted as indicators of the trophic state and water quality in freshwater ecosystems. In China, most of the benthic diatom-based indices have not been widely tested or evaluated before. For this purpose, the water quality parameters and benthic diatoms community structures at 20 sample sites in the dish lake of Nanjishan Nature Reserve in Poyang Lake were investigated in this study and 18 widely-applied diatom indices were established. The statistical results indicated that most water quality parameters including Total Nitrogen (TN), Total Phosphorus (TP), ammonia nitrogen (NH4+), Chemical Oxygen Demand (COD) and Chlorophyll (Chl) were highly correlated with each other at a confidence level of 0.05. Water quality sampling sites from 8 lakes could be classified into 4 groups based on the integrated comprehensive scores using principal components analysis (PCA). Monitoring sites could also be divided into 4 groups based on clustering analysis with hierarchical methods for diatom dominant species in 20 sampling sites. According to the status of water ecological health recognition and box plot analysis in different water quality groups and diatom dominant species groups, only 3 diatom indices including the Biological Diatom Index (IBD), Specific Pollution Sensitivity Index (IPS) and Louis Leclercq Diatom Index (IDSE) demonstrated good evaluation suitability and good correlation within different water quality grades at the final stage. The above results revealed that IBD, IPS and IDSE were the most suitable diatom indices for the water quality evaluation of the dish lake in the Nanjishan Nature Reserve, Lake Poyang.

INFLUENCE OF NANO–SILICA ON WHEAT PLANTS GROWN IN SALT–AFFECTED SOIL

A pot experiment was carried out at the Faculty of Technology and Development greenhouse farm in the winter agricultural season 2018–2019. The experiment was planned to study the role of nano–SiO2 on improving the wheat growth and productivity under salinity conditions. Six treatments (two grades of water quality are: tap water and saline water; three additional methods for silica nanoparticles are; 0, Si–soil, foliar) in three replicates. The soil was fertilized by nano–SiO2 before sowing at a rate of 80 mg kg–1and NPK recommended was added uniformly for all treatments. Wheat plants were sprayed five times by nano–silica after the month of sowing every ten days by 600 mg Si L–1 (10 mL pot-1). The wheat plant was irrigated with tap water (0.4 dS m–1) and saline–water (8 dS m–1). The nano–SiO2 was analyzed by some analysis as Fourier Transform Infrared (FTIR), the scanning electron Microscopy Coupled with Energy Dispersive X–Ray analysis (SEM–EDAX), the Transmission Electron Microscopy (TEM), particle size, Specific Surface Area (SSA), Brunauer–Emmett–Teller (BET), X–Ray Diffractometer (XRD). The obtained results indicated a significant clear increase in wheat yield under salinity stress conditions compared with the check treatment. Nano–silica use led to the improvement of nutrients absorption e.g., N, P, K and Si contents under salinity stress conditions. In contrast, Na was reduced with Si increasing in plant tissues. Conclusively, nano–SiO2 improves wheat plants on the growth and tolerance of salt stress up to 80 mg Kg-1 for soil addition and 600 mg L–1 for foliar spray. Keywords:

Understanding the risks from diffuse pollution on wetland eco-systems: The effectiveness of water quality classification schemes

Eco-environmental safety and health is an important concept for the protection of flora and fauna in the wetland eco-system. Identifying impacts of human activities on wetland ecology and risk factors of environmental decline have become significant components of habitat protection. In this study, the Qixing River wetland, one of the Ramsar Sites, was taken as the research area. Seven water quality indices, including dissolved oxygen (DO), chemical oxygen demand (COD), ammonia nitrogen (NH3−N), total phosphorus (TP), total nitrogen (TN), nitrates and nitrites were monitored at fifteen sampling locations. Entropy weight-set pair analysis, combined with discriminant analysis, was used to identify the most sensitive indicators for changes in water quality. Average concentrations for the indicators measured were: DO 7.24 (4.64–9.98) mg/L; COD 21.67 (5–50) mg/L; NH3-N 1.09 (0.41–2.15) mg/L; TP 0.18 (0.10–0.73) mg/L; TN 1.38 (0.76–2.17) mg/L, nitrite 0.016 (ND ~ 0.030) mg/L and nitrate concentration was 0.420 (0.315–0.613) mg/L. Water quality was found to be poorest at locations closer to sources of diffuse pollution such as agricultural land. In contrast, the more remote buffer zone and core zone of the wetland was characterized by improved indicators of water quality. The results of entropy weight-set pair analysis showed that the overall water quality grade of Qixing River wetland conforms to Class III of the “Environmental Quality Standards for Surface Water” of China (GB3838-2002). Discriminant analysis indicated that TP was the main contributory factor to reduced water quality classification while COD, ammonia nitrogen and total nitrogen were the key drivers of improving water quality grade and responsible for the Class III award. The prominence of these indicators is indicative of long-term use of fertilizers and pesticides in agricultural production. The more remote core area of the wetland was less affected by human activities, reflected in improved water quality results. The results showed that entropy weight-set pair analysis coupled with discriminant analysis adds value to widely used water environment quality indices and helps identify the main environmental factors driving water quality classification. In the case of the Qixing River wetland, there is clear potential to protect wetland eco-system safety by improving the water quality classification and reforming agronomic and land management practices.

Application of T-S Fuzzy-Neural Network Model in Water Quality Comprehensive Evaluation

Abstract In order to comprehensively evaluate the water environment quality of Liuxi river Irrigation District in Guangzhou more effectively, the fuzzy neural network based on T-S model was used to evaluate and analyze the water quality characteristics of Liuxi river Irrigation District. Six indexes, including dissolved oxygen, chemical oxygen demand, ammonia nitrogen, permanganate index, total phosphorus and total nitrogen, which have important effects on water quality, were selected to establish an applicable comprehensive evaluation model for water quality. Water quality of two water sampling sections, the upstream Da’ao monitoring section and the downstream Liyuan monitoring section, were sampled and analyzed. The results show that the water quality evaluation results of the upstream Da’ao monitoring section are better than that of the downstream Liyuan monitoring section. The variation trend of water quality grade evaluated by the model is consistent with that of the real index data.

Spatiotemporal assessment of marine environmental monitoring programme based on DIN concentration in the Yangtze River estuary and its adjacent sea

The marine environment is rigorously protected in the Yangtze River Estuary (YRE) and its adjacent sea, and routine monitoring is constantly upgraded. Therefore, scientific and efficient monitoring programmes are needed. Nitrogen is one of the most serious pollutants in the YRE. Obtaining the precise pollution areas of water quality grades (WQGs) are a scientific and management issue that requires optimization of monitoring programmes and interpolation methods. Based on spatiotemporal regression point means of surface with non-homogeneity (STR-PMSN), dissolved inorganic nitrogen (DIN) concentrations were estimated in a stratified heterogeneous estuary. The annual average areas of DIN Grades I and II were classified by interpolating the concentrations; the values were 3145 km2, 1626 km2, 2320 km2 and 3758 km2 for February, May, August and November, respectively. This means that November had the best water condition, and May had the worst. Meanwhile, DIN area changes showed that the water condition changed due to removal of data much more in August and May than in February and November. The descending order of importance was August, May, February and November. Every month represented different runoff periods. Monitoring frequency should not be reduced. Removal of sampling data for the third stratum had a significant effect on the area. When the sampling data for outer boundary meshes of the third stratum were removed, the water condition became worse. However, when the sampling data for inner boundary meshes were removed, the water condition improved. New sites should be added to the outer boundary region to avoid interpolation instability and reduce the sensitivity of the existing sites. This study assesses the spatiotemporal effect of the marine environmental monitoring programmes on pollutant distribution by STR-PMSN, and it offers guidance for more precise data acquisition and processing methods in the YRE and its adjacent sea.