Urbanization and agriculture intensification jointly enlarge the spatial inequality of river water quality

Landsat monitoring reveals the history of river organic pollution across China during 1984-2023

River organic pollution exhibits pronounced spatiotemporal dynamics in response to environmental changes. However, the traditional method of tracking chemical oxygen demand (COD) and/or other organic pollution indicators at fixed locations over expansive regions is labor-intensive, time-consuming, and inadequate for achieving full spatial coverage. To address this limitation, here we developed a Random Forest algorithm using Landsat satellite data in conjunction with sub-daily (every 4 h) COD data at 1,997 sites across China. The proposed model achieved high accuracy, with a root mean square error of 0.52 mg/L and a mean absolute percent difference of 13.01 %. Additionally, the model was robust across clear, algae-laden, turbid, and black-smelling waters. Then, the algorithm was applied to investigate the spatiotemporal variations of COD concentration in Chinese rivers during 1984-2023. Across China, high river COD concentrations were observed in the eastern Songliao (3.56 ± 1.11 mg/L), Haihe (3.00 ± 0.89 mg/L), and Huaihe (3.57 ± 0.67 mg/L) basins. Anthropogenic activities could explain 79.39 % of the spatial variability in COD concentrations, and the cropland distribution had a significant impact. During 1984-2023, 73.58 % of China's rivers exhibited significant changes in COD concentrations (p < 0.05). With respect to the 800 mm isoprecipitation line, 56.62 % of the southeastern rivers showed decreasing trends; in contrast, 84.25 % of the northwestern rivers displayed increasing trends in COD concentrations. The temporal variations in COD concentrations were driven by the combined effects of factors including rainfall, vegetation coverage, and human activities; their relative contributions were 0.02 - 42.45 %, 0.07 - 68.76 %, and 0.06 - 90.31 % for COD changes in different provinces. This study underscores the feasibilities of using long-term Landsat data to efficiently and dynamically monitor organic pollution in rivers on a large scale, providing crucial implications for spatiotemporal monitoring of other water quality indicators.

Spatiotemporal evolution and driving forces of landscape structure and habitat quality in river corridors with ceased flow: A case study of the Yongding River corridor in Beijing, China

Flow cessation leads to severe degradation of river corridor landscape structure, habitat quality, and ecological functions. This study focuses on the representative river with ceased flow in northern China, the Yongding River plain section. Utilizing long-term, high-resolution satellite remote sensing imagery and the InVEST model, we analyzed the spatiotemporal evolution of landscape structure and habitat quality (HQ) before and after river corridor flow cessation over the past 50 years. The study further employs partial least squares regression (PLSR) to explore the impact of landscape structural changes on HQ and uses generalized additive models (GAMs) and geographical detector (GeoDetector) to quantitatively identify key factors affecting habitat degradation and their interactive effects. Results indicate that from 1967 to 2018, mid-channel bar, floodplain, and waterbody decreased sharply from 37.4% to 3.8%. The mean HQ value dropped from 0.58 to 0.34 after flow cessation. Although HQ slightly recovered post-2004, high-quality habitat areas remain absent. Different landscape structures significantly influence HQ, with increased size and area of the waterbody and forest patches positively contributing, while cultivated land, barren land, and built-up land generally have negative impacts. PLAND, LPI, MPS, and AWMPFD are key metrics for optimizing landscape structure and implementing habitat restoration in river management. Anthropogenic activities emerged as the primary driver of river corridor habitat degradation post-flow cessation. Different drivers exhibit complex linear and nonlinear effects on HQ. Based on these findings, we propose ecological management strategies for river corridors with ceased flow. This study is essential for a deeper understanding of river corridors' structural dynamics and degradation mechanisms, providing a scientific basis for effective ecological restoration and management.

Multi-evidences investigation into spatiotemporal variety, sources tracing, and health risk assessment of surface water nitrogen contamination in China

A comprehensive understanding of nitrogen pollution status, especially the identification of sources and fate of nitrate is essential for effective water quality management at the local scale. However, the nitrogen contamination of surface water across China was poorly understood at the national scale. A dataset related to nitrogen was established based on 111 pieces of literature from 2000 to 2020 in this study. The spatiotemporal variability, source tracing, health risk assessment, and drivers of China's surface water nitrogen pollution were analyzed by integrating multiple methods. These results revealed a significant spatiotemporal heterogeneity in the nitrogen concentration of surface water across China. Spatially, the Haihe River Basin and Yellow River Basin were the basins where surface water was seriously contaminated by nitrogen in China, while the surface water of Southwest Basin was less affected. Temporally, significant differences were observed in the nitrogen content of surface water in the Songhua and Liaohe River Basin, Pearl River Basin, Southeast Basin, and Yellow River Basin. There were 1%, 1%, 12%, and 46% probability exceeding the unacceptable risk level (HI＞1) for children in the Songhua and Liaohe River Basin, Pearl River Basin, Haihe River Basin, and Yellow River Basin, respectively. The primary sources of surface water nitrate in China were found to be domestic sewage and manure (37.7%), soil nitrogen (31.7%), and chemical fertilizer (26.9%), with a limited contribution from atmospheric precipitation (3.7%). Human activities determined the current spatiotemporal distribution of nitrogen contamination in China as well as the future development trend. This research could provide scientifically reasonable recommendations for the containment of surface water nitrogen contamination in China and even globally.

Landscape fragmentation of built-up land significantly impact on water quality in the Yellow River Basin

Urbanization development often leads to significant changes in the extent in area and fragmentation of built-up land landscape pattern (BLLP) in river basins, which greatly impact the processes of rainfall runoff and pollutant migration. Understanding the spatial scale effects and driving mechanisms of BLLP changes on water quality in large river basins is a challenging research topic and an international frontier in the interdisciplinary fields of geography and environment. This study analyzes the spatial variations of BLLP and water quality throughout the Yellow River Basin (YRB) during the rainy seasons from 2019 to 2021 (4 h scale). Utilized the random forest model to quantitatively separates the contributions of rainfall processes to surface runoff and water pollution, revealing the scale effects and non-linear driving mechanisms of BLLP impacts on water environment changes. The results indicate that: 1) The YRB exhibits great spatial heterogeneity in terms of both BLLP and water quality, with places with lower water quality displaying bigger areas and higher degrees of BLLP fragmentation. 2) The patch density and built-up land area (PD.B and CA.B) have a major impact on changes in water quality in the YRB, with notable impacts noted in circular buffer zones with radii of 20 km and 5 km, respectively. 3) PD.B is sensitive to water quality in the YRB, explaining 39.1%-49.5% of the variance under different rainfall conditions, and exhibits a significant non-linear relationship, with an impact threshold of 0.38 (n/100 ha). The study suggests that for large-scale regions like the YRB, the degree of BLLP fragmentation is more likely to lead to degradation of water environmental quality compared to its area. BLLP fragmentation due to higher PD.B and CA.B disrupts the original ecosystem and hydrological connectivity, resulting in poorer retention and filtration of pollutants carried by rainfall runoff, while increasing the export of other pollutants. However, once urbanization surpasses a certain threshold, the BLLP fragmentation can enhance water quality by reducing the impermeable surface connectivity, as they are no longer impacted by expanding areas. To achieve ecologically sustainable development, it is necessary to apply rational landscape management and water resource management policies that consider the dual process of how BLLP fragmentation affects the water environment.

Multidimensional aspects of riverine biodiversity can vary in response to nutrient pollution and environmental dynamics across climatic watersheds

The concurrent impact of anthropogenic and bioclimatic factors on biodiversity is a key focus in macroecological and biogeographical considerations in conservation programs within riverine ecosystems. However, there is still a lack of understanding about how multidimensional alpha and beta diversity measures respond to anthropogenic and bioclimatic drivers. Here, we assess the variations in taxonomic, phylogenetic, and functional alpha and beta diversity of riverine macroinvertebrate communities across different watersheds in China. Our results show significant declines in most facets of alpha diversity across watersheds with low environmental heterogeneity, reflecting the loss of species with unique traits and evolutionary legacies. Both functional and phylogenetic beta-diversity values reveal a decreasing pattern along low heterogeneity environments, whereas taxonomic beta-diversity shows a contrasting pattern, which highlights the influence of microhabitat variation. Moreover, our findings identify nutrient levels, organic matter, water quality indicators, climatic variation, and geographic and habitat characteristics as key determinants of diversity patterns that are indicative of broader water pollution challenges. These factors jointly influence functional and phylogenetic alpha diversity and contribute to spatial homogenization, which is reflected in decreased functional and phylogenetic beta diversity. These trends highlight the complex interactions of chemical and physical factors in shaping biodiversity characteristics across watersheds. Based on the null model, macroinvertebrate communities primarily show random patterns, whereas clustering and overdispersion were sporadically observed in specific communities. We propose that conservation and restoration efforts should be aimed at enhancing aquatic biomes by managing extreme environmental conditions and amplifying spatial spillover, thereby supporting the intrinsic dynamics within natural metasystems and thus preserving the multidimensional aspects of biodiversity.

Assessing the relationship between river water pollution and the LULC composition of a basin in the Isthmus of Tehuantepec in Oaxaca, Mexico

Water pollution originating from land use and land cover (LULC) can disrupt river ecosystems, posing a threat to public health, safety, and socioeconomic sustainability. Although the interactions between terrestrial and aquatic systems have been investigated for decades, the scale at which land use practices, whether in the entire basin or separately in parts, significantly impact water quality still needs to be determined. In this research, we used multitemporal data (field measurements, Sentinel 2 images, and elevation data) to investigate how the LULC composition in the catchment area (CA) of each water pollution measurement station located in the river course of the Los Perros Basin affects water pollution indicators (WPIs). We examined whether the CAs form a sequential runoff aggregation system for certain pollutants from the highest to the lowest part of the basin. Our research applied statistical (correlation, time series analysis, and canonical correspondence analysis) and geo-visual analyses to identify relationships at the CA level between satellite-based LULC composition and WPI concentrations. We observed that pollutants such as nitrogen, phosphorus, coliforms, and water temperature form a sequential runoff aggregation system from the highest to the lowest part of the basin. We concluded that the observed decrease in natural cover and increase in built-up and agricultural cover in the upper CAs of the study basin between the study period (2016 to 2020) are related to elevated WPI values for suspended solids and coliforms, which exceeded the allowed limits on all CAs and measured dates.

Eco-friendly hydrogel based on locust bean gum for water retaining in sandy soil

Sandy soils are suffering from water loss and desertification, which severely restrict the development of local agriculture. In this work, an eco-friendly hydrogel composed of borax and locust bean gum was synthesized to enhance the water retention capacity of sandy soil and support agricultural development in arid regions. Locust bean gum/borax hydrogel with a 3D network structure exhibited great water-absorbing capacity (130.29 g/g) within 30 min. After mixing 0.9 wt% hydrogel with sandy soil, the maximum soil water content, water retention time, soil porosity and soil organic matter were increased by 32.03 %, 14 days, 38.9 % and 8.64 g/kg respectively. Little effect on soil microorganisms revealed barely toxicity. Furthermore, the hydrogel was confirmed to be biodegradable at 43.47 % after 4 weeks. According to the study, locust bean gum/borax hydrogel possesses good water absorbing capacity, soil water retention ability, soil optimization ability and low adverse environmental impact. Together, it is inferred that the hydrogel can improve the water retention capacity of sandy soil in arid areas, promoting plant growth in arid areas.

Total nitrogen and phosphorus loads in surface runoff from urban land use (city of Lublin) under climate change

An expansion of impervious surfaces in urban areas leads to increases of nutrient loads discharged with the surface runoff to receivers. A study of a different density of urban development impact on total nitrogen (TN) and phosphorus (TP) loads from the city of Lublin (eastern Poland) with the use of the SWAT (Soil & Water Assessment Tool) model was performed. To distinguish between areas with high and low density of urban development (UHD and ULD), a special analysis of hydrological parameters has been proposed. Moreover, to investigate the impact of climate change, four variant scenarios were taken into account, combining the RCP (representative concentration pathway) 4.5 and 8.5 forecasts and the adopted time horizons (2026-2035 and 2046-2055). The results showed a much higher share of TN and TP from UHD compared to ULD (86%-32 022 kg/year and 89%-2574 kg/year, respectively). In addition, the variant scenarios showed that the forecasted increase in precipitation and temperature will result in increased loads of nutrients from UHD and ULD up to 30%. Furthermore, the current increase of inhabitant number, due to the Ukrainian war migration and the common tendency to convert agricultural land to residential areas, could contribute to further expansion of UHD and ULD areas and an additional increase of nutrient loads.

Effect of urbanization and urban forests on water quality improvement in the Yangtze River Delta: A case study in Hangzhou, China

In the context of rapid global urbanization, the sustainable development of ecosystems should be considered. Accordingly, the Planetary Boundaries theory posits that reducing the amount of nitrogen and phosphorus pollutants entering bodies of water is necessary as excess levels may harm the aquatic environment and reduce in water quality. Thus, based on the long-term monitoring data of representative urban rivers in the Yangtze River Delta region, we evaluated the nitrogen and phosphorus pollution of water bodies in different urbanization stages and further quantified the effect of urban forests on water quality improvement. The results showed that, with the continuous progression of urbanization, the proportion of impervious surface area increased, along with the levels of nitrogen and phosphorus pollution in water bodies. The critical period of water quality deterioration in urban rivers occurred during the medium urbanization level when the proportion of impervious surface area reached 55-65 %, and the probability of an abrupt increase in total nitrogen (TN) and total phosphorus (TP) concentration exceeded 95 %. However, increasing the area of urban forests during this period reduced TN pollution by 36.64 % and TP pollution by 49.03 %. The results of this study support the expansion of urban forests during the medium urbanization stage to improve water quality. Furthermore, our results provide a reference and theoretical basis for urban forest construction as a key aspect of the sustainable development of the urban ecosystem in the Yangtze River Delta and similar regions around world.

Empirical analysis of the relationship between carbon trading price and stock price of high carbon emitting firms based on VAR model - evidence from Chinese listed companies

At the United Nations General Assembly, the Chinese government promised to reach the peak of carbon dioxide emissions by 2030 and achieve a relative balance between carbon dioxide production and offsetting by 2060. The research aims to explore the relationship between carbon trading prices and stock prices in various high emission industries in China and analyze the attitudes and behaviors of enterprises towards carbon trading. From a market perspective, this topic has important theoretical significance and practical value for promoting energy transformation, encouraging enterprises to reduce emissions, and controlling greenhouse gas emissions. In addition, understanding the behavior and attitude of enterprises in the carbon market is also of great significance for formulating policies and measures to develop the carbon trading market. The study used the VAR model to empirically analyze the relationship between carbon trading prices and stock prices. Granger causality test, impulse response analysis, and variance decomposition analysis are used for analysis. The research results indicate that the contribution rate of carbon trading prices in the first phase is 100%, which gradually decreases to the lowest value of 98.27% in the eighth phase. The stock price contribution rates of the water, electricity, and gas supply industries reach a peak of 0.74% in the second period and gradually decrease to 5.2% in the eighth period. The contribution rate of stock prices in other industries has gradually increased. At present, the carbon trading market in China is still in the development stage, and it is necessary for the government to adopt various policies to regulate it, including encouraging enterprises in high emission industries to increase investment in emission reduction, strengthening the supervision of the carbon trading market, providing technical support and green innovation incentives, strengthening enterprise emission reduction awareness and information disclosure.

Application of ArcGIS 3D modeling technology in the study of land use policy decision making in China

In this paper, a land use management information system based on ArcGIS 3D modeling technology is constructed to process land use policy decisions through ArcSDE spatial data engine and Oracle relational database to realize a land use planning management information system. Using genetic algorithm in order to use for regional land use optimization allocation, the introduction of multi-intelligent body system in this algorithm will be able to enhance the optimization search ability of the algorithm and make the genetic algorithm to obtain land use planning supported. The behavior of the main body of the integrated land use planning decision maker will guide the development of the quantitative structure of land use in terms of spatial layout toward sustainability. The experimental results prove that the target is better than the other three types of scenarios under the integrated benefit model, then it is reduced by 18.67%, 15.98% and 16.61%, and the number of spatially contiguous areas is increased by 9.4%, 13.8% and 0.8%, respectively. The proposed model can reasonably configure the regional land use quantitative results and spatial layout, and coordinate the needs of different land use decision makers.

Predicting water quality through daily concentration of dissolved oxygen using improved artificial intelligence

As an important hydrological parameter, dissolved oxygen (DO) concentration is a well-accepted indicator of water quality. This study deals with introducing and evaluating four novel integrative methods for the prediction of DO. To this end, teaching-learning-based optimization (TLBO), sine cosine algorithm, water cycle algorithm (WCA), and electromagnetic field optimization (EFO) are appointed to train a commonly-used predictive system, namely multi-layer perceptron neural network (MLPNN). The records of a USGS station called Klamath River (Klamath County, Oregon) are used. First, the networks are fed by the data between October 01, 2014, and September 30, 2018. Later, their competency is assessed using the data belonging to the subsequent year (i.e., from October 01, 2018 to September 30, 2019). The reliability of all four models, as well as the superiority of the WCA-MLPNN, was revealed by mean absolute errors (MAEs of 0.9800, 1.1113, 0.9624, and 0.9783) in the training phase. The calculated Pearson correlation coefficients (RPs of 0.8785, 0.8587, 0.8762, and 0.8815) plus root mean square errors (RMSEs of 1.2980, 1.4493, 1.3096, and 1.2903) showed that the EFO-MLPNN and TLBO-MLPNN perform slightly better than WCA-MLPNN in the testing phase. Besides, analyzing the complexity and the optimization time pointed out the EFO-MLPNN as the most efficient tool for predicting the DO. In the end, a comparison with relevant previous literature indicated that the suggested models of this study provide accuracy improvement in machine learning-based DO modeling.

Seasonal variation of the quality of groundwater resources for human consumption and industrial purposes in the central plain zone of Punjab, India

Due to environmental pollution, climate change, and anthropogenic activities, the judicious use and regular assessment of the quality of groundwater for industrial, agricultural, and drinking purposes had gained a lot of attention across the globe. To assess the seasonal suitability of groundwater based on hydrochemistry and different quality indices, groundwater samples were collected and analyzed for different physicochemical parameters. Our findings indicated that the pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), and calcium ion (Ca2+) content of groundwater were within acceptable limits of WHO and Bureau of Indian Standards (BIS) guidelines for drinking water. However, chloride content exceeded the acceptable levels, accounting for about 29.1% during the pre-monsoon and 15.3% during the post-monsoon period. Based on the water quality index (WQI), none of the water samples were deemed unsuitable for drinking purposes. However, when considering the synthetic pollution index (SPI), 100% of the samples were categorized as moderately polluted during both the pre-monsoon and post-monsoon periods. For industrial purpose suitability, 39.8 and 30.6% of the water samples had high corrosion tendency for pre-monsoon and post-monsoon seasons, respectively. Additionally, 77.5-93.4% of the total water samples were slightly affected by salinization on the basis of Revelle index. Generally, the groundwater quality for drinking purposes meets the WHO and BIS guidelines, with high corrosion potential for industrial use and slight salinization concerns in the area.

Distinct mechanisms shape prokaryotic community assembly across different land-use intensification

Changes in land-use intensity can have a far-reaching impact on river water quality and prokaryotic community composition. While research has been conducted to investigate the assembly mechanism of prokaryotic communities, the contributions of neutral theory and niche theory to prokaryotic community assembly under different land-use intensities remain unknown. In this study, a total of 251 sampling sites were set up in the Yangtze River basin to explore the assembly mechanism under different land-use intensities. Briefly, a "source" landscape can generate pollution, whereas a "sink" landscape can prevent pollution. Firstly, our result showed that higher land-use intensity might disturb the balance between the "source" and "sink" landscape patterns, resulting in water quality deterioration. Then the prokaryotic community assembly was classified into five ecological processes, namely homogeneous selection, homogenizing dispersal, undominated processes, dispersal limitation, and variable selection. The higher land-use intensity was found to strengthen the homogeneous selection, leading to the homogenization of the community at the whole basin scale. Finally, our findings demonstrated that the Yangtze River Basin's prokaryotic community displayed a distance-decay pattern when land-use intensity was low, with a greater contribution from neutral theory to its assembly. On the other hand, with a higher land-use intensity, the degradation of the aquatic environment increased the impacts of environmental filtering on the prokaryotic community, and niche theory played a stronger role in its assembly. Our findings show how land-use intensity influence the formation of prokaryotic communities, which will be an invaluable guide for managing land use and understanding the prokaryotic community assembly mechanisms in the Yangtze River Basin.