Effects of Urbanization on Rural Drinking Water Quality in Beijing, China

Research on the coupling coordination and driving role of urbanization and ecological resilience in the middle and lower reaches of the Yangtze River

Background

The growth of urbanization in the 20th and 21st centuries has resulted in unprecedented ecological security issues. The imbalance between urban development and internal ecological security is a growing concern.

Methods

Based on the urban development process and the characteristics of ecosystem resilience, the corresponding urbanization evaluation system ("scale-structure-benefit") and ecosystem resilience assessment model ("resistance-adaptability-restoring") were constructed to explore the changes in each dimension as well as to analyze the spatial and temporal changes and driving effects of the coupled coordination level of urbanization and ecological resilience using the coupled coordination degree (CCD) model and geographically and temporally weighted regression (GTWR).

Results

(1) From 2005 to 2020, urbanization levels increased (from 0.204 to 0.264, respectively), whereas the level of ecological resilience gradually decreased (from 0.435 to 0.421, respectively). The spatial distribution of urbanization is rather steady, with a "high-northeast low-southwest" pattern of regional distribution; however, the spatial distribution pattern of ecological resilience is essentially the reverse. (2) During the study period, there was an improvement in the level of coordination between urbanization and ecological resilience, with an increase from 0.524 to 0.540. However, the main coordination type remained the same, with over 46% being at the basic coordination stage. The relative development type was dominated by the lagging urbanization stage, with the lagging ecological resilience and synchronous development stages accounting for a smaller proportion, and the space was distributed in a block-like cluster. (3) The running results of the GTWR show that the core dimensions of the whole region are scale, benefit, and structure, and the impact of each dimension shows obvious spatial heterogeneity. Cities with different levels of relative development also have different central dimensions. This research will provide support for the coordination of urban development in areas where economic construction and ecological resilience are not coordinated, and will contribute to the sustainable development of urban areas.

Anthropogenic factors affecting the Moskva River water quality: levels and sources of nutrients and potentially toxic elements in Moscow metropolitan area

This study aims to identify the main patterns of distribution and sources of pollutants in the Moskva River and their influence on river water quality under different levels of anthropogenic stress caused by the largest megacity in Europe - Moscow. For this study, we determined concentrations of 18 trace elements, nutrient elements and major ions, chemical and biochemical oxygen demand, and physical parameters of water at 45 stations on the Moskva River and 20 stations on its tributaries during spring flood and low water of 2019 and 2020 to identify the extent and mechanisms of urban impact on its water chemistry. Chemical elements concentrations have been determined using ICP-MS and ICP-AES methods. Mn, Al, Cu, Sr, Zn, B, Mo, and inorganic nitrogen were outlined as key pollutants according to various drinking water and environmental guidelines. Using correlation and factor analysis, five groups of elements were identified, corresponding to different drivers controlling their unequal distribution within the watershed: mineral sources (Sr, Li, B, Mo, Ca), sewage and road runoff (TN, TP, Sb, Ni, N-NO₂, BOD₅, COD, V, Zn), impact of acidic wetlands (Al, COD, Zr, Bi), groundwater and landfills leachate (V, As, Pb, U, Sb), and industrial activities (Zn, Cu). Water quality in the Moskva Basin on the whole is good according to the CCME Water Quality Index. Local deterioration of water quality to marginal and even fair (depending on the reference water quality guideline) is confined to the Moskva River part downstream from the Kuryanovo aeration station to the Moskva mouth and to the mouths of several heavily contaminated tributaries.

Satellite-Based Monitoring of Coastal Wetlands in Yancheng, Jiangsu Province, China

The dynamic process of the wetland can reflect its impact on the environment, and finding a balance point supporting harmonious coexistence between man and nature has become an issue of increasing concern. On the basis of previous studies that have focused on local coastal wetlands, the temporal and spatial changes and driving forces of wetlands in the Yancheng coastal area from 1991 to 2021 were analyzed over a larger area. According to the study findings: (1) The results of the study of the Yancheng coastal wetland with a larger scope differed significantly from findings resulting from a study of coastal wetland only. This difference was mainly reflected in the relatively stable situation of wetland ecology as a whole, while the changes in local surface features were more significant. (2) Natural wetlands were transformed into artificial wetlands and non-wetland types, and artificial wetlands were transformed into non-wetland types; additionally, reverse transformations and internal transformations of surface features also took place. For instance, the saltpan was transformed into mudflats (86.26 km2), and some mudflats into herbaceous vegetation (193.47 km2). (3) When analyzing the impact intensity of human activities on the Yancheng wetland, it was found that this factor has experienced a process of first rising and then falling. The index was 0.650, 0.653, 0.664, 0.661, and 0.641 in 1991, 2000, 2008, 2016, and 2021, respectively. (4) Lastly, an analysis of factors driving wetland change revealed that human factors were the most critical reasons for wetland landscape change. Our work can play a reference and inspiration role in the monitoring and protection of similar coastal wetlands.

Monitoring Spatiotemporal Changes of Impervious Surfaces in Beijing City Using Random Forest Algorithm and Textural Features

As the capital city of China, Beijing has experienced unprecedented economic and population growth and dramatic impervious surface changes during the last few decades. An application of the classification method combining the spectral and textural features based on Random Forest was conducted to monitor the spatial and temporal changes of Beijing’s impervious surfaces. This classification strategy achieved excellent performance in the impervious surface extraction in complex urban areas, as the Kappa coefficient reached 0.850. Based on this strategy, the impervious surfaces inside Beijing’s sixth ring road in 1997, 2002, 2007, 2013, and 2017 were extracted. As the development of Beijing has a special regional feature, the changes of impervious surfaces within the sixth ring road were assessed. The findings are as follows: (1) the textural features can significantly improve the classification accuracy of land cover in urban areas, especially for the impervious surface with high albedo. (2) Impervious surfaces within the sixth ring road expanded dramatically from 1997 to 2017, had three expanding periods: 1997–2002, 2002–2007, and 2013–2017, and only shrank in 2007–2013. There are different possible major driving factors for each period. (3) The region between the fifth and sixth ring roads in Beijing underwent the most significant changes in the two decades. (4) The inner three regions are relatively highly urbanized areas compared to the outer two regions. Urbanization processes in the interior regions tend to be completed compared to the exterior regions.

Predicting water quality during urbanization based on a causality-based input variable selection method modified back-propagation neural network

Rapid urbanization has been recognized as the primary cause of deteriorating water quality. Thus, it is crucial to take into account urbanization in water quality forecasting. The present study aims at finding the causal relationship between urbanization and water quality, and then predicting water quality based on this causality. For this purpose, nine urbanization indicators and 12 water quality parameters from 2006 to 2018 in Nanjing were collected as urbanization and water quality indices. Correlation and path analyses were firstly used to identify causal relationships between urbanization and water quality indices. Based on these causal relationships, comprehensive water quality indicators and their correlated urbanization parameters were input into a back-propagation neural network (BPNN) to predict water quality. In the improved BPNN, the R² of the training sets were all greater than 0.99, and those of the test sets were all greater than 0.76, demonstrating that the optimized model is able to predict the water quality with reasonable accuracy. It also showed that the overall water quality in Nanjing will remain good from 2019 to 2028, which means that, when undergoing future urbanization process, water quality is not necessarily negatively affected. The transfer of industrial structure can have a positive influence on water quality. After 2028, the biological water environment index remained in a good state but the volatile phenol index continued to increase, making it a potential threat to future water quality. Industrial wastewater and fertilizer usage, as the primary sources of volatile phenols, should be prioritized for continued governmental control and monitoring into the future. This study provides new insight into the relationship between urbanization and water quality, and the presented models can assist in future-proofing water management strategies.

Comprehensive assessment of the coupling coordination degree between urbanization and ecological environment in the Siberian and Far East Federal Districts, Russia from 2005 to 2017

The urbanization growth in the 20th and 21st centuries has led to a series of unprecedented problems in the ecological environment. Based on constructing an integrated urbanization-ecological environment index system, this article conducts a comprehensive evaluation of the coupling coordination degree between urbanization and the ecological environment and uncovers its spatiotemporal variation characteristics in the Siberian and Far East Federal Districts, Russia from 2005 to 2017. The coupling coordination of urbanization and the ecological environment in the Siberian and Far East Federal Districts improve from slightly unbalanced development stage to barely balanced development stage from 2005 to 2017. In 2017, more than half regions achieved the barely balanced development of urbanization and the ecological environment. However, the most desirable development stage, the superior balanced development stage, is never achieved in the Siberian and Far East Federal Districts during the study period. The spatial pattern of the coupling coordination degree of urbanization and the ecological environment in the Siberian and Far East Federal District gradually changes from “dumbbell” to “high-north low-south”. The south part of the Siberian and Far East Federal Districts should be paid more attention in the future urban development process. This research will provide support in the future coordination of urban development in the Siberian and Far East Federal Districts.

Urbanization Impacts the Physicochemical Characteristics and Abundance of Fecal Markers and Bacterial Pathogens in Surface Water

Urbanization is increasing worldwide and is happening at a rapid rate in China in line with economic development. Urbanization can lead to major changes in freshwater environments through multiple chemical and microbial contaminants. We assessed the impact of urbanization on physicochemical characteristics and microbial loading in canals in Suzhou, a city that has experienced rapid urbanization in recent decades. Nine sampling locations covering three urban intensity classes (high, medium and low) in Suzhou were selected for field studies and three locations in Huangshan (natural reserve) were included as pristine control locations. Water samples were collected for physicochemical, microbiological and molecular analyses. Compared to medium and low urbanization sites, there were statistically significant higher levels of nutrients and total and thermotolerant coliforms (or fecal coliforms) in highly urbanized locations. The effect of urbanization was also apparent in the abundances of human-associated fecal markers and bacterial pathogens in water samples from highly urbanized locations. These results correlated well with land use types and anthropogenic activities at the sampling sites. The overall results indicate that urbanization negatively impacts water quality, providing high levels of nutrients and a microbial load that includes fecal markers and pathogens.

The Impacts of Socioeconomic Development on Rural Drinking Water Safety in China: A Provincial-Level Comparative Analysis

In China, achieving rural drinking water safety—meaning access to a safe, affordable, sufficient, and sustainable drinking water supply—remains a key challenge for government agencies and researchers. Using cross-sectional data at the provincial level, in this paper we examine the impacts of socioeconomic development on drinking water safety in rural China. Using a theoretical framework called Pressure-State-Response (PSR), existing data were organized into state and pressure indicators. Canonical Correlation Analysis was then used to analyze provincial-level relationships between the indicators. Significant drinking-water-safety-related differences were found across provinces. Our analyses suggest that, overall, China’s recent and rapid socioeconomic development yielded substantial benefits for China’s rural drinking water safety. However, this same development also negatively impacted rural drinking water safety via increased groundwater over-abstraction, reductions in water supply, and environmental contamination. The paper closes with a discussion of implications and options for improving drinking water policy, management, and regulation in rural China.