Spatial Regression and Prediction of Water Quality in a Watershed with Complex Pollution Sources

Research progress in water quality prediction based on deep learning technology: a review

Water, an invaluable and non-renewable resource, plays an indispensable role in human survival and societal development. Accurate forecasting of water quality involves early identification of future pollutant concentrations and water quality indices, enabling evidence-based decision-making and targeted environmental interventions. The emergence of advanced computational technologies, particularly deep learning, has garnered considerable interest among researchers for applications in water quality prediction because of its robust data analytics capabilities. This article comprehensively reviews the deployment of deep learning methodologies in water quality forecasting, encompassing single-model and mixed-model approaches. Additionally, we delineate optimization strategies, data fusion techniques, and other factors influencing the efficacy of deep learning-based water quality prediction models, because understanding and mastering these factors are crucial for accurate water quality prediction. Although challenges such as data scarcity, long-term prediction accuracy, and limited deployments of large-scale models persist, future research aims to address these limitations by refining prediction algorithms, leveraging high-dimensional datasets, evaluating model performance, and broadening large-scale model application. These efforts contribute to precise water resource management and environmental conservation.

Conflict between urbanization and water environmental protection: Lessons from the Xiangjiang River Basin in China

China, the largest developing country, has experienced rapid urbanization since its reform and opening-up. However, the increasing pollution load from urban areas has deteriorated urban river water quality, contradicting the concept of sustainable and green development promoted by the Chinese government. This situation elucidates governmental shortcomings in systematic environmental protection. Our study revealed that the current wastewater treatment plant (WWTP) discharge standards in urban areas are insufficient for attaining the desired urban river water quality and thus intensify the conflict between urbanization and water environmental protection. As urbanization continues, the urban population will grow, further exacerbating pollution and conflict. Our focus was the Xiangjiang River basin in Zunyi, a typical urbanized city in China. Using a validated one-dimensional mathematical model, we compared the water quality in the Xiangjiang River between current and upgraded WWTP discharge standards. The results showed that the water quality in the Xiangjiang River falls short of the standards, with more than 60 % of the river exceeding limits. However, upgrading WWTP discharge standards significantly reduces the proportion of river sections exceeding limits, with only 0.4 % exceeding standards during specific periods. This enhancement greatly improved the Xiangjiang River's water quality, aided in restoring the entire water environment in the basin, and supported water environmental protection goals. Our research findings offer crucial support for local governments in shaping comprehensive water environmental protection policies and insights for addressing similar environmental challenges caused by rapid urbanization in other developing regions.

Data Insights for Sustainable Cities: Associations between Google Street View-Derived Urban Greenspace and Google Air View-Derived Pollution Levels

Unprecedented levels of urbanization have escalated urban environmental health issues, including increased air pollution in cities globally. Strategies for mitigating air pollution, including green urban planning, are essential for sustainable and healthy cities. State-of-the-art research investigating urban greenspace and pollution metrics has accelerated through the use of vast digital data sets and new analytical tools. In this study, we examined associations between Google Street View-derived urban greenspace levels and Google Air View-derived air quality, where both have been resolved in extremely high resolution, accuracy, and scale along the entire road network of Dublin City. Particulate matter of size fraction less than 2.5 μm (PM2.5), nitrogen dioxide, nitric oxide, carbon monoxide, and carbon dioxide were quantified using 5,030,143 Google Air View measurements, and greenspace was quantified using 403,409 Google Street View images. Significant (p < 0.001) negative associations between urban greenspace and pollution were observed. For example, an interquartile range increase in the Green View Index was associated with a 7.4% [95% confidence interval: -13.1%, -1.3%] decrease in NO2 at the point location spatial resolution. We provide insights into how large-scale digital data can be harnessed to elucidate urban environmental interactions that will have important planning and policy implications for sustainable future cities.

Effect of different levels of EDTA on phytoextraction of heavy metal and growth of Brassica juncea L

Heavy metal pollution of soil is a major concern due to its non-biodegradable nature, bioaccumulation, and persistence in the environment. To explore the probable function of EDTA in ameliorating heavy metal toxicity and achieve the sustainable development goal (SDG), Brassica juncea L. seedlings were treated with different concentrations of EDTA (0, 1.0, 2.0, 3.0, and 4.0 mM Kg-1) in heavy metal-polluted soil. Plant samples were collected 60 days after sowing; photosynthetic pigments, H2O2, monoaldehyde (MDA), antioxidant enzymes, and ascorbic acid content, as well as plant biomass, were estimated in plants. Soil and plant samples were also examined for the concentrations of Cd, Cr, Pb, and Hg. Moreover, values of the phytoremediation factor were utilized to assess the accumulation capacity of heavy metals by B. juncea under EDTA treatments. In the absence of EDTA, B. juncea seedlings accrued heavy metals in their roots and shoots in a concentration-dependent manner. However, the highest biomass of plants (roots and shoots) was recorded with the application of 2 mM kg-1 EDTA. Moreover, high levels (above 3 mM kg-1) of EDTA concentration have reduced the biomass of plants (roots and shoots), photosynthetic area, and chlorophyll content. The effect of EDTA levels on photosynthetic pigments (chlorophyll a and b) revealed that with an increment in EDTA concentration, accumulation of heavy metals was also increased in the plant, subsequently decreasing the chlorophyll a and b concentration in the plant. TLF was found to be in the order Pb> Hg> Zn> and >Ni, while TF was found to be in the order Hg>Zn>Ni>Pb, and the best dose was 3 mM kg-1 EDTA for Hg and 4 mM kg-1 for Pb, Ni, and Zn. Furthermore, hyperaccumulation of heavy metals enhanced the generation of hydrogen peroxide (H2O2), superoxide anions (O2•-), and lipid peroxidation. It also interrupts mechanisms of the antioxidant defense system. Furthermore, heavy metal stress reduced plant growth, biomass, and chlorophyll (chl) content. These findings suggest that the exogenous addition of EDTA to the heavy metal-treated seedlings increases the bioavailability of heavy metals for phytoextraction and decreases heavy metal-induced oxidative injuries by restricting heavy metal uptake and components of their antioxidant defense systems.

What will the water quality of the Yangtze River be in the future?

The long-term prediction of water quality is important for water pollution control planning and water resource management, but it has received little attention. In this study, the water quality trend in the Yangtze River is found to stabilize at most monitoring stations under environmental protection activities. Based on the physical mechanism and stochastic theory, a novel river water quality prediction model combining pollution source decomposition (including local point, local nonpoint and upstream sources) and time series decomposition (including trend, seasonal and residential components) is developed. The observed water quality data from 76 monitoring stations in the Yangtze River, including permanganate index (COD_Mn) and total phosphorus (TP), are used to drive this model to make long-term water quality predictions. The results show that this model has an acceptable accuracy. In the future, the concentration of COD_Mn will meet the water quality targets at most stations in the Yangtze River, but the concentration of TP will not be able to meet the water quality target at 28.5 % of the stations. Furthermore, the prediction value of COD_Mn is 62.2 % lower than the target on average. However, the prediction value of TP is only 24.4 % lower than the target on average, and it will exceed the water target by >50 % at some stations. This model has the potential to be widely used for long-term water quality prediction in the future.

Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing

The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH4-N), nitrate-nitrogen (NO3-N), and pH) were modeled and verified. The results show that the performance R2 of the training model is above 80%, and the performance R2 of the verification model is above 70%. In the training model, the highest fitting degree is TN (R2 = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R2 = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters.

An Integrated Approach for Evaluating Water Quality between 2007–2015 in Santa Cruz Island in the Galapagos Archipelago

Water quality in Galápagos has been deteriorating by increased human impacts over the past few decades. Water quality is a key environmental component and issue in need to be evaluated in the Pelican Bay Watershed, the biggest urban and economic development of Santa Cruz Island, for better management and regulation of water resources. This study assesses coastal and ground water bodies of Pelican Bay by employing a 9-year dataset obtained during a local water quality monitoring program conducted by the Galápagos National Park. Physical-chemical and microbial parameters were evaluated with respect to national and international water quality standards. A statistical integrated approach was performed to calculate environmental background levels of water quality parameters and to explore their seasonal and spatial variation. In addition, a sensitivity analysis was conducted to evaluate the impact of changes in tourism and residents in San Cruz Island in the degradation of water sources. Results highlighted are: (a) water is not suitable for drinking and domestic use at some inland sites; (b) saline water is used for irrigation in the highlands; (c) the presence of parameters of concern at coastal sites represent a risk for human and ecosystem health; (d) background levels may serve for defining site-specific limits to control water quality, and; (e) the influence of population change on water quality conditions varied at each site with a higher effect at coastal sites relatively to inland sites. This study provided valuable information of the water quality status in Santa Cruz Island and can serve as a baseline for effective water management and control of pollution.

Rapid assessment of heavy metal pollution using ion-exchange resin sachets and micro-XRF core-scanning

Conventional pollution monitoring strategies for heavy metals are often costly and unpractical. Innovative sampling and analytical approaches are therefore needed to efficiently monitor large areas. This study presents a novel, simple, fast, and inexpensive method to monitor heavy metal pollution that uses cation-exchange resin sachets and the micro-XRF core-scanning technique (XRF-CS). The resin passive samplers act as concentrators of cationic species and can be readily deployed spatially and temporally to record pollution signals. The large number of analytical tasks are then overcome by the fast and non-destructive XRF-CS to precisely assess elemental concentrations. Quantifying element loading involves direct comparison with a set of identically prepared and scanned resin reference standards containing Ca, Ti, Cr, Mn, Ni, Cu, Zn, Pb. The results show that within the test range (from 0-1000 s mg kg^-1), the calibration lines have excellent regressions (R² ≥ 0.97), even at the shortest exposure time (1 s). A pilot field survey of a suspected polluted area in central Taiwan, where 30 resin sachets had been deployed, identified a pollution hot spot in a rapid and economical manner. Therefore, this approach has the potential to become a valuable tool in environmental monitoring and forensics.

Evaluating the applicability of MESS (matrix exponential spatial specification) model to assess water quality using GIS technique in agricultural mountain catchment (Western Carpathian)

The formation of many sources of pollution in a short period of time is due to mountain soil erosion by water. One of the major mechanisms decisive in the intensification of such erosion is the loosening of soil material on the slope. Water quality studies show the impact of diversified spatial management and allow making the right decisions in environmental management in mountain areas with high variability of use and land cover. The research undertaken as part of the paper was carried out in order to determine the dependency between total suspended solids (TSS) and the physicochemical parameters of surface waters and the amount of soil losses in the use structure within the mountain catchment. The paper focused on the frequency of phenomena in time and the possibility of stopping the surface runoff on the slope and on the soil's susceptibility to water erosion. The dependencies between multipoint sampling and the concentration of material washed off the slope due to precipitation were verified with a multivariate analysis. Sampling took place in hydrometric sections, and during small floods, in the waterbed cross section. Research shows that such sampling is the basis for the calculation of the transported load, reflecting the average variation in concentration. The variation in the volume of the load from the individual parts of the catchment was assessed by the spatial autoregressive model. It was found that the use of river basin areas affects water chemistry. Water reservoirs are an important ecological barrier for the migration of nitrate nitrogen (N-NO₃) and phosphate phosphorus (P-PO₄), which is marked by changes in the growing season. Water along the sections of the river near the quarry with a high degree of sodding showed good quality condition. Despite significant differences between measurement sampling sites, high total dissolved solid (TDS) values were found in communities adjacent to forests and meadows. However, the highest electrical conductivity (EC) and TSS concentrations were found in the interface with cultivated areas. Biogenic indices showed variation depending on the way the adjacent areas were used. GIS linked spatial variables with the formation of water pollution. The analysis of spatial autoregression pointed to the impact of arable land. Moreover, the analysis of spatial autoregression with the MESS function designated a connection between agricultural land use and nitrite nitrogen (N-NO₂), EC, TSS, and dissolved oxygen (DO). Graphical abstract ᅟ.

Simulation of the Transboundary Water Quality Transfer Effect in the Mainstream of the Yellow River

In order to not only solve the technical problems of quantifying the degree and range of the effect that is caused by the water quality of upstream on that of downstream portions of a river, and of dividing the responsibility of transboundary water pollution, but also to tackle the difficulty in adapting to dynamic changes of the traditional water quality model in terms of practical application, pollutant discharge and water consumption were taken as the main influence factors to build the transboundary water quality transfer effect model. Supported by a comprehensive integration platform, the transboundary water quality transfer effect simulation system of the Yellow River mainstream was constructed. The simulation results show that the concentration decreases exponentially along the range. Gansu, Ningxia, and Inner Mongolia had a more significant effect of exceeding standard water consumption on pollution, while Ningxia, Inner Mongolia, Shaanxi, and Shanxi had a more distinct contribution to the over standard pollution discharge effect. The proposed model and simulation system can provide new methods and instruction for quantifying the degree and range of transboundary water pollution, as well as dividing the responsibility for water environment compensation.