[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (23)

Search Parameters:
Keywords = plain city river network

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
21 pages, 6948 KiB  
Article
Causes and Transmission Characteristics of the Regional PM2.5 Heavy Pollution Process in the Urban Agglomerations of the Central Taihang Mountains
by Luoqi Yang, Guangjie Wang, Yegui Wang, Yongjing Ma and Xi Zhang
Atmosphere 2025, 16(2), 205; https://doi.org/10.3390/atmos16020205 - 11 Feb 2025
Viewed by 301
Abstract
The Taihang Mountains serve as a critical geographical barrier in northern China, delineating two major 2.5-micrometer particulate matter (PM2.5) pollution hotspots in the Beijing–Tianjin–Hebei region and the Fenwei Plain. This study examines the underlying mechanisms and interregional dynamic transport pathways of [...] Read more.
The Taihang Mountains serve as a critical geographical barrier in northern China, delineating two major 2.5-micrometer particulate matter (PM2.5) pollution hotspots in the Beijing–Tianjin–Hebei region and the Fenwei Plain. This study examines the underlying mechanisms and interregional dynamic transport pathways of a severe PM2.5 pollution event that occurred in the urban agglomerations of the Central Taihang Mountains (CTHM) from 8–13 December 2021. The WRF-HYSPLIT simulation was employed to analyze a broader range of potential pollution sources and transport pathways. Additionally, a new river network analysis module was developed and integrated with the Atmospheric Pollutant Transport Quantification Model (APTQM). This module is capable of identifying localized, small-scale (interplot) pollution transport processes, thereby enabling more accurate identification of potential source areas and transport routes. The findings indicate that the persistence of low temperatures, high humidity, and stagnant atmospheric conditions facilitated both the local accumulation and cross-regional transport of PM2.5. The eastern urban agglomerations, such as Shijiazhuang and Xingtai, were predominantly influenced by northwesterly air masses originating from Inner Mongolia and Shanxi, with pollution levels intensified due to topographic blocking and subsidence effects east of the Taihang Mountains. In contrast, western urban centers, including Taiyuan and Yangquan, experienced pollution primarily from short-range transport within the Fen River Basin, central Inner Mongolia, and Shaanxi, compounded by basin-induced stagnation. Three principal transport pathways were identified: (1) a northwestern pathway from Inner Mongolia to Hebei, (2) a southwestern pathway following the Fen River Basin, and (3) a southward inflow from Henan. The trajectory analysis revealed that approximately 68% of PM2.5 in eastern receptor cities was transported through topographic channels within the Taihang Transverse Valleys, whereas 43% of pollution in the western regions originated from intra-basin emissions and basin-capture circulation. Furthermore, APTQM-PM2.5 identified major pollution source regions, including Ordos and Chifeng in Inner Mongolia, as well as Taiyuan and the Fen River Basin. This study underscores the synergistic effects of basin topography, regional circulation, and anthropogenic emissions in shaping pollution distribution patterns. The findings provide a scientific basis for formulating targeted, regionally coordinated air pollution mitigation strategies in complex terrain areas. Full article
Show Figures

Figure 1

Figure 1
<p>Overview of the study area. (<b>a</b>) shows the extent of the study area; (<b>b</b>,<b>c</b>) correspond to the distribution of cities in the CTHM and the elevation map; (<b>d</b>) is the distribution of the “Eight Passes of the Taihang Mountains”, where the red lines are valley transmission channels, the blue are rivers, and the black dots are neighboring cities and counties (belonging to the province).</p>
Full article ">Figure 2
<p>Nested regions of the WRF grid (where d01 is 27 km, d02 is 9 km, and d03 is 3 km).</p>
Full article ">Figure 3
<p>APTQM-PM<sub>2.5</sub> modeling step analysis. Step 1: (<b>a</b>,<b>b</b>) area gridding and boundary processing where (<b>a</b>) is the target study area and (<b>b</b>) is the gridding, where the shaded grid is the matching blocks of the study area. Step 2: (<b>c</b>,<b>d</b>) gradient analysis and airflow direction classification, where (<b>c</b>) is the air pressure gradient schematic, where the blue color is the relatively high-value cell and the red color is the relatively low-value cell, and (<b>d</b>) is the air gradient flow schematic, and the black vector arrow is the airflow direction. Step 3: (<b>e</b>,<b>f</b>) river network classification and flow intensity analysis, where (<b>e</b>) is the river network classification of the study area, direction of air flow; (<b>e</b>) shows the results of river network classification in the study area, where the yellow grid (level 1) is the outflow cell, i.e., upstream grid, and the green grid (level 2) is the inflow cell, i.e., downstream grid; and (<b>f</b>) is the schematic illustration of the flow intensity, where the yellow color is the main stream flow, and the blue color is the secondary flow.</p>
Full article ">Figure 4
<p>Hourly variations of PM<sub>2.5</sub> mass concentration and comparative variations of related meteorological factors in the urban agglomerations of CTHM during the period of heavy pollution. (<b>a</b>) mean surface wind speed (wspd, m/s) and temperature (temp, °C) in December 2021 for both sides of the urban agglomerations; (<b>b</b>) relationship and dynamics between visibility (vis, km) and PM<sub>2.5</sub> mass concentration (PM<sub>2.5</sub> Conc, μg); (<b>c</b>) relationship and dynamics between relative humidity (RH, %) and PM<sub>2.5</sub> mass concentration.</p>
Full article ">Figure 5
<p>Visualization of the Taylor coefficients between the simulated and observed meteorological fields.</p>
Full article ">Figure 6
<p>Backward trajectory analysis of major cities during periods of heavy pollution based on WRF-HYSPLIT. (<b>a</b>) Eastern urban agglomeration, Shijiazhuang, Hebei Province. (<b>b</b>) Eastern urban agglomeration, Handan, Hebei Province. (<b>c</b>) Eastern urban agglomeration, Xingtai, Hebei Province. (<b>d</b>) Western urban agglomeration, Taiyuan, Shanxi Province. (<b>e</b>) Western urban agglomeration, Yangquan, Shanxi Province. (<b>f</b>) Western urban agglomeration, Jinzhong, Shanxi Province.</p>
Full article ">Figure 7
<p>Mean geopotential height field and wind field from 9–13 December 2021, at time 0. Where (<b>a</b>) is the mean geopotential height field and wind field at 500 hpa at time 0 on the 9th, (<b>b</b>) is on 500 hpa at time 0 on the 11th, (<b>c</b>) is on 500 hpa at time 0 on the 13th; (<b>d</b>–<b>f</b>) is on 750 hpa, and the (<b>g</b>–<b>i</b>) is 850 hpa, where the gray filled areas are terrain obstructions.</p>
Full article ">Figure 8
<p>The longitudinal vertical circulation, mean hourly wind field, and potential pseudo-equivalent temperature (θse) variations in the study area during 9–13 December 2021, at time 0. (<b>a</b>) For 9th, (<b>b</b>) for 11th, and (<b>c</b>) for 13th (where the left side of the y-axis is labeled as the height (km), the right side is the pseudo-equivalent temperature (K) for the corresponding height, and the bottom black fill is the terrain).</p>
Full article ">Figure 9
<p>Average migration and regional transmission distribution per unit grid (3 km × 3 km) during heavy pollution periods. (The color-filled part of the figure shows the gradient level.)</p>
Full article ">
22 pages, 7195 KiB  
Article
The Optimization of River Network Water Pollution Control Based on Hydrological Connectivity Measures
by Jiuhe Bu, Chunhui Li, Tian Xu, Tao Wang, Jinrong Da, Xiaoyun Li, Hao Chen, Weixin Song and Mengjia Sun
Water 2025, 17(2), 197; https://doi.org/10.3390/w17020197 - 13 Jan 2025
Viewed by 688
Abstract
Urbanization, driven by socio-economic development, has significantly impacted river ecosystems, particularly in plain city regions, leading to disruptions in river network structure and function. These changes have exacerbated hydrological fluctuations and ecological degradation. This study focuses on the central urban area of Changzhou [...] Read more.
Urbanization, driven by socio-economic development, has significantly impacted river ecosystems, particularly in plain city regions, leading to disruptions in river network structure and function. These changes have exacerbated hydrological fluctuations and ecological degradation. This study focuses on the central urban area of Changzhou using a MIKE11 model to assess the effects of four hydrological connectivity strategies—water diversion scheduling, river connectivity, river dredging, and sluice connectivity—across 13 different scenarios. The results show that water diversion, river dredging, and sluice connectivity scenarios provide the greatest improvements in water environmental capacity, with maximum increases of 54.76%, 41.97%, and 25.62%, respectively. The spatial distribution of improvements reveals significant regional variation, with some areas, particularly in Tianning and Zhonglou districts, experiencing declines in environmental capacity under sluice diversion and river-connectivity scenarios. In addition, the Lao Zaogang River is identified as crucial for improving the overall water quality in the network. Based on a multi-objective evaluation, combining environmental and economic factors, the study recommends optimizing water diversion scheduling at sluices (Weicun, Zaogang, and Xiaohe) with flow rates between 20–40 m3/s, enhancing connectivity at key river hubs, and focusing management efforts on the Lao Zaogang and Xinmeng rivers to strengthen hydrological and water quality linkages within the network. Full article
Show Figures

Figure 1

Figure 1
<p>Geographical location of Changzhou city.</p>
Full article ">Figure 2
<p>Flow chart.</p>
Full article ">Figure 3
<p>Route of hydrological connectivity engineering. Note: the green segments represent the river connectivity channels in the engineering plan.</p>
Full article ">Figure 4
<p>Extension of dredging project in Xinmeng River. Note: the green segments represent the river connectivity channels in the engineering plan.</p>
Full article ">Figure 5
<p>Connecting points of sluices.</p>
Full article ">Figure 6
<p>Average relative error of river network water quality in the study area.</p>
Full article ">Figure 7
<p>Water environmental capacity of river network under different scenarios in Changzhou city (t/a).</p>
Full article ">Figure 8
<p>Spatial distribution of river environmental capacity under different scenarios (green rivers indicate an improvement in water environmental capacity, while black rivers indicate a decrease in water environmental capacity).</p>
Full article ">Figure 9
<p>Environment, economy, and objective function values under different scenarios.</p>
Full article ">
18 pages, 5452 KiB  
Article
Understanding the Water Quality Changes of the Typical Plain River Network Area Using Comprehensive Assessment Methods
by Haizhen Hu, Jia Wang, Gang Zhou, Sichen Tong, Weifu Wang and Tingting Hu
Sustainability 2024, 16(20), 8766; https://doi.org/10.3390/su16208766 - 10 Oct 2024
Cited by 1 | Viewed by 1146
Abstract
Water quality assessment is an important method for understanding the spatial-temporal variation characteristics of water quality. Therefore, the present study has been performed to evaluate the water quality for a typical plain river network area in Changzhou City, Jiangsu, China, where the river [...] Read more.
Water quality assessment is an important method for understanding the spatial-temporal variation characteristics of water quality. Therefore, the present study has been performed to evaluate the water quality for a typical plain river network area in Changzhou City, Jiangsu, China, where the river system is characterized by reciprocal flow and diverse pollution sources. The water quality samples from 2017 to 2021 were comprehensively assessed using comprehensive methods that combine the single-factor pollution index (SFPI) method with multivariate statistical analysis. Initially, statistical analyses were conducted to evaluate water quality exceedances and correlations and the SFPI method was applied to classify water quality categories. Furthermore, principal component analysis (PCA) and cluster analysis (CA) were employed to reduce the dimensionality of water quality indicators and group monitoring sections with similar characteristics. The results indicate that the overall water quality in Changzhou City is lightly polluted with a trend of improvement. The primary pollutants identified are total phosphorus (TP) and ammonia nitrogen (NH3-N). This study highlights that organic pollution, self-purification capacity, and eutrophication of river water bodies are the most significant factors affecting water quality. The sampling sites were classified into three groups (good, moderate, and poor). The water quality assessment results of this study provide a theoretical reference for water environment management and ecological protection in plain river network areas. Full article
Show Figures

Figure 1

Figure 1
<p>Location and river networks.</p>
Full article ">Figure 2
<p>Pearson correlation calculations.</p>
Full article ">Figure 3
<p>The spatial-temporal variation characteristics of water quality indicators: (<b>a</b>) pH; (<b>b</b>) DO; (<b>c</b>) COD<sub>Mn</sub>; (<b>d</b>) NH<sub>3</sub>-N; (<b>e</b>) TN; (<b>f</b>) TP.</p>
Full article ">Figure 4
<p>The spatial-temporal distribution characteristics of water quality categories and multiples of exceedances: (<b>a</b>) 2017; (<b>b</b>) 2018; (<b>c</b>) 2019; (<b>d</b>) 2020; (<b>e</b>) 2021; (<b>f</b>) 2017–2021.</p>
Full article ">Figure 5
<p>The results of PCA: (<b>a</b>) scree plot; (<b>b</b>) initial factor loading plot.</p>
Full article ">Figure 6
<p>Principal component scores: (<b>a</b>) inter-annual changes of <span class="html-italic">G</span><sub>1</sub>; (<b>b</b>) inter-annual changes of <span class="html-italic">G</span><sub>2</sub>; (<b>c</b>) inter-annual changes of <span class="html-italic">G</span>; (<b>d</b>) spatial characteristics of <span class="html-italic">G</span>.</p>
Full article ">Figure 7
<p>Characterization of spatial clustering: (<b>a</b>) dendrogram using average linkage (between groups); (<b>b</b>) spatial characterization of principal component scores.</p>
Full article ">
24 pages, 8453 KiB  
Article
Simulation and Analysis of Water Quality Improvement Measures for Plain River Networks Based on Infoworks ICM Model: Case Study of Baoying County, China
by Qiande Zhu, Kaibin Fang, Dexun Zhu, Xinran Li, Xiaoyu Chen, Song Han, Feng Chen, Chuang Gao, Jun Sun, RongJie Tang, Yu Chen and Siyuan Yin
Water 2024, 16(18), 2698; https://doi.org/10.3390/w16182698 - 23 Sep 2024
Cited by 4 | Viewed by 1572
Abstract
The water environment of plain river networks can be self-cleaning to a certain extent, but if the wastewater load exceeds a certain threshold, it can disturb the natural balance and cause water pollution. This underlines the importance of water pollution control measures. However, [...] Read more.
The water environment of plain river networks can be self-cleaning to a certain extent, but if the wastewater load exceeds a certain threshold, it can disturb the natural balance and cause water pollution. This underlines the importance of water pollution control measures. However, the development of water pollution control measures requires a large number of hydrological and hydrodynamic parameters and the establishment of corresponding relationships through modelling. Therefore, this study mainly used the Infoworks ICM model to construct a detailed hydrological–hydrodynamic water environment analysis model for the Yundong area of Baoying County, Yangzhou City, China, screened the main pollution source areas and pollution time periods of the typical rivers in the study area, and proposed effective improvement measures according to the actual situation of the study area. The results show that after the synergistic effect of multiple measures, the water quality can reach the Class III standard (GB3838-2002). This study can provide a reference for the water environment management and improvement of the plain river network and has good application prospects. Full article
(This article belongs to the Section Water Quality and Contamination)
Show Figures

Figure 1

Figure 1
<p>Region of the study.</p>
Full article ">Figure 2
<p>Distribution of water quality monitoring sections.</p>
Full article ">Figure 3
<p>Results of the calibration: (<b>a</b>) COD, (<b>b</b>) NH<sub>3</sub>-N, (<b>c</b>) TP.</p>
Full article ">Figure 4
<p>10-year changes in water quality at controlled sections.</p>
Full article ">Figure 5
<p>Zhangshi Bridge, Huangtugou cross section trend of indicators in 2014–2023.</p>
Full article ">Figure 6
<p>Changes in water quality at controlled sections in 2014–2023: (<b>a</b>) Zhangshi Bridge, (<b>b</b>) Huangtugou.</p>
Full article ">Figure 7
<p>Changes in water quality at controlled sections in January–December 2023: (<b>a</b>) Zhangshi Bridge, (<b>b</b>) Huangtugou.</p>
Full article ">Figure 8
<p>Changes in water quality at controlled sections in July 2014–2023: (<b>a</b>) Zhangshi Bridge, (<b>b</b>) Huangtugou.</p>
Full article ">Figure 9
<p>In-stream loading map of different types of pollutants in the Baoshe River.</p>
Full article ">Figure 10
<p>A 6 h COD mass flow diagram through the Huangtugou section: (<b>a</b>) non-rainfall, (<b>b</b>) rainfall.</p>
Full article ">Figure 11
<p>LID simulation results: (<b>a</b>) COD, (<b>b</b>) NH<sub>3</sub>-N.</p>
Full article ">Figure 12
<p>Reducing fertiliser application: (<b>a</b>) COD, (<b>b</b>) NH<sub>3</sub>-N.</p>
Full article ">Figure 13
<p>Simulation results of reduction in direct rural domestic sewage discharge: (<b>a</b>) COD concentration in rainy days, (<b>b</b>) COD concentration in non-rainfall conditions, (<b>c</b>) NH<sub>3</sub>-N concentration in non-rainfall conditions.</p>
Full article ">Figure 14
<p>Simulation results of ecological floating islands and artificial aeration: (<b>a</b>) COD, (<b>b</b>) NH<sub>3</sub>-N.</p>
Full article ">Figure 15
<p>Simulation results of total measures (<b>a</b>) COD, (<b>b</b>) NH<sub>3</sub>-N.</p>
Full article ">
23 pages, 8229 KiB  
Article
Identifying Temporal Change in Urban Water Bodies Using OpenStreetMap and Landsat Imagery: A Study of Hangzhou City
by Mingfei Wu, Xiaoyu Zhang, Linze Bai, Ran Bi, Jie Lin, Cheng Su and Ran Liao
Remote Sens. 2024, 16(14), 2579; https://doi.org/10.3390/rs16142579 - 14 Jul 2024
Viewed by 1101
Abstract
As one of the most important ecosystems, the water body is losing water during the rapid development of the city. To understand the impacts on water body change during the rapid urbanization period, this study combines data from the OpenStreetMap platform with Landsat [...] Read more.
As one of the most important ecosystems, the water body is losing water during the rapid development of the city. To understand the impacts on water body change during the rapid urbanization period, this study combines data from the OpenStreetMap platform with Landsat 5/Thematic Mapper images to effectively and accurately identify small urban water bodies. The findings indicate that the trained U-net convolutional neural network (U-Net) water body extraction model and loss function combining Focal Loss and Dice Loss adopted in this study demonstrate high precision in identifying water bodies within the main urban area of Hangzhou, with an accuracy rate of 94.3%. Trends of decrease in water areas with a continuous increase in landscape fragmentation, particularly for the plain river network, were observed from 1985 to 2010, indicating a weaker connection between water bodies resulting from rapid urbanization. Large patches of water bodies, such as natural lakes and big rivers, located at divisions at the edge of the city are susceptible to disappearing during the rapid outward expansion. However, due to the limitations and strict control of development, water bodies, referring to as wetland, slender canals, and plain river networks, in the traditional center division of the city, are preserved well. Combined with the random forest classification method and the U-Net water body extraction model, land use changes from 1985 to 2010 are calculated. Reclamation along the Qiantang River accounts for the largest conversion area between water bodies and cultivated land, constituting more than 90% of the total land use change area, followed by the conversion of water bodies into construction land, particularly in the northeast of Xixi Wetland. Notably, the conversion of various land use types within Xixi Wetland into construction land plays a significant role in the rise of the carbon footprint. Full article
(This article belongs to the Topic Aquatic Environment Research for Sustainable Development)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Location of Hangzhou City (indicated by a red dot ●) on a digital elevation map including Anhui, Jiangsu, and Zhejiang Provinces and Shanghai City within the Yangtze River Delta; the map comes from the US National Aeronautics and Space Administration’s Shuttle Radar Topography Mission and has a 90 m resolution. (<b>b</b>) For administrative divisions of Hangzhou in 2021, data is sourced from the National Geographic Information Resources Directory Service.</p>
Full article ">Figure 2
<p><b>The</b> technical roadmap of this study. Note: MNDWI, modified normalized difference water index; AWEI, automated water body extraction index (AWEI); SVM, support vector machine; and OSM, OpenStreetMap software.</p>
Full article ">Figure 3
<p>Labelling data of the water body for Hangzhou City that aligns with the satellite images: (<b>a</b>) original Landsat 5/Thematic Mapper image of Hangzhou City in 2010; (<b>b</b>) labeling data of the water body processed by ArcGIS.</p>
Full article ">Figure 4
<p>Training process of water body extraction algorithm based on the U-Net model. Note: OSM, OpenStreetMap platform.</p>
Full article ">Figure 5
<p>Comparison of water body extraction results by different methods: (<b>a</b>) the original Land-sat5/Thematic Mapper image acquired in 2010; (<b>b</b>) modified normalized difference water index; (<b>c</b>) automated water body extraction index; (<b>d</b>) SVM; and (<b>e</b>) U-Net.</p>
Full article ">Figure 6
<p>Water body extraction results of the U-Net model every 2 years from 1986 to 2010 are shown in (<b>a</b>–<b>m</b>), respectively.</p>
Full article ">Figure 7
<p>Biennial changes of water area from 1986 to 2010 in the main urban area of Hangzhou, Zhejiang, China.</p>
Full article ">Figure 8
<p>Water body extraction from Xixi Wetland every 2 years from 1990 to 2000 is shown in (<b>a</b>–<b>f</b>), respectively.</p>
Full article ">Figure 9
<p>Reduction of water area of Xixi Wetland every 2 years from 1990 to 2000.</p>
Full article ">Figure 10
<p>Tailored Landsat5/Thematic Mapper image where the Qiantang River meanders through Zhejiang Province. The red square represents part of the main urban area of Hangzhou, where the Qiantang River flows. The blue shading represents the section of the Qiantang River with dramatic change.</p>
Full article ">Figure 11
<p>Significant variation in the water area in the eastern side of the Qiantang River in the main urban area of Hangzhou in (<b>a</b>) 1986–1988, (<b>b</b>) 1992–1996, (<b>c</b>) 2004–2008, and (<b>d</b>) Graph of the biennial changes in the water area of the Qiantang River in the main urban area of Hangzhou City from 1986 to 2010.</p>
Full article ">Figure 12
<p>Graph of the biennial changes in the number of water body patches and landscape fragmentation from 1986 to 2010.</p>
Full article ">Figure 13
<p>The variation of both the water area and fragmentation with different sizes.</p>
Full article ">Figure 14
<p>The variation of both the water area and fragmentation in different divisions.</p>
Full article ">Figure 15
<p>Land use classification results every 5 years from 1985 to 2020 are shown in (<b>a</b>–<b>f</b>), respectively.</p>
Full article ">Figure 16
<p>Biennial changes in (<b>a</b>) landscape fragmentation and (<b>b</b>) carbon footprint from 1985 to 2010.</p>
Full article ">
17 pages, 5936 KiB  
Article
Evaluation of the Habitat Suitability for Zhuji Torreya Based on Machine Learning Algorithms
by Liangjun Wu, Lihui Yang, Yabin Li, Jian Shi, Xiaochen Zhu and Yan Zeng
Agriculture 2024, 14(7), 1077; https://doi.org/10.3390/agriculture14071077 - 4 Jul 2024
Viewed by 1290
Abstract
Torreya, with its dual roles in both food and medicine, has faced multiple challenges in its cultivation in Zhuji city due to frequent global climate disasters in recent years. Therefore, conducting a study on suitable zoning for Torreya habitats based on climatic, topographic, [...] Read more.
Torreya, with its dual roles in both food and medicine, has faced multiple challenges in its cultivation in Zhuji city due to frequent global climate disasters in recent years. Therefore, conducting a study on suitable zoning for Torreya habitats based on climatic, topographic, and soil factors is highly important. In this study, we utilized the latitude and longitude coordinates of Torreya distribution points and ecological factor raster data. We thoroughly analyzed the ecological environmental characteristics of the climate, topography, and soil at Torreya distribution points via both physical modeling and machine learning methods. Zhuji city was classified into suitable, moderately suitable, and unsuitable zones to determine regions conducive to Torreya growth. The results indicate that suitable zones for Torreya cultivation in Zhuji city are distributed mainly in mountainous and hilly areas, while unsuitable zones are found predominantly in central basins and northern river plain networks. Moderately suitable zones are located in transitional areas between suitable and unsuitable zones. Compared to climatic factors, soil and topographic factors more significantly restrict Torreya cultivation. Machine learning algorithms can also achieve suitability zoning with a more concise and efficient classification process. In this study, the random forest (RF) algorithm demonstrated greater predictive accuracy than the support vector machine (SVM) and naive Bayes (NB) algorithms, achieving the best classification results. Full article
Show Figures

Figure 1

Figure 1
<p>Study area map.</p>
Full article ">Figure 2
<p>Torreya sample distribution map.</p>
Full article ">Figure 3
<p>Technical process chart.</p>
Full article ">Figure 4
<p>Climate suitability zoning map.</p>
Full article ">Figure 5
<p>Habitat suitability zoning map.</p>
Full article ">Figure 6
<p>Feature importance ranking map.</p>
Full article ">Figure 7
<p>Test set accuracy line chart (<b>left</b>: test set accuracy with feature numbers 1–16; <b>right</b>: partial amplification of test set accuracy).</p>
Full article ">Figure 8
<p>Climate suitability zone (RF) classification diagram.</p>
Full article ">Figure 9
<p>Habitat suitability zone classification diagram ((<b>a</b>): SVM; (<b>b</b>): RF).</p>
Full article ">Figure 10
<p>Verification analysis diagram ((<b>a</b>): physical model habitat suitability zoning; (<b>b</b>): RF model habitat suitability zoning; (<b>c</b>): altitude; (<b>d</b>): slope).</p>
Full article ">
15 pages, 10305 KiB  
Article
Storage Scale Assessment of a Low-Impact Development System in a Sponge City
by Mingkun Xie, Dongxu He, Zengchuan Dong and Yuning Cheng
Water 2024, 16(10), 1427; https://doi.org/10.3390/w16101427 - 17 May 2024
Cited by 2 | Viewed by 1559
Abstract
A sponge city is an established urban stormwater management approach that effectively reduces urban runoff and pollutant discharges. In order to plan and design, estimate costs, and evaluate the performance of urban sponge city systems, it is essential to calculate the storage scale. [...] Read more.
A sponge city is an established urban stormwater management approach that effectively reduces urban runoff and pollutant discharges. In order to plan and design, estimate costs, and evaluate the performance of urban sponge city systems, it is essential to calculate the storage scale. In this context, a sponge city storage scale and calculation method based on a multifactor spatial overlay was designed, utilising the starting area of the Dafeng Hi-tech Development Zone in Yancheng City, China, as an illustrative example. The indicators for assessing the impact of sponge city systems on river plain networks are constructed based on four aspects: land planning, building density, water surface rate and green space rate. The relative importance of each indicator was determined based on the necessity of controlling runoff from land parcels and the appropriateness of facility construction. The annual runoff control rate of the 39 low-impact development control units in the study area was calculated using ArcGIS through multifactor spatial overlay mapping and weighting. The results showed that (1) the Geographic Information System (GIS)overlay technology can effectively assist in the decomposition of LID scales; (2) data can be derived, including the design storage volume and other basic control scale indicators for each unit. The study results are expected to serve as a reference for the preparation of special low-impact development plans in the river plain network area of China and the promotion of the construction of a sustainable blue–green system in the city. Full article
(This article belongs to the Special Issue Urban Stormwater Harvesting, and Wastewater Treatment and Reuse)
Show Figures

Figure 1

Figure 1
<p>Satellite image of the current situation in the starting area of Dafeng High-tech Development Zone in Yancheng City.</p>
Full article ">Figure 2
<p>Yancheng Dafeng High-Tech Zone urban detailed land use plan.</p>
Full article ">Figure 3
<p>Low-impact development control unit division in the starting area of Yancheng Dafeng High-tech Zone.</p>
Full article ">Figure 4
<p>Relationship between annual runoff control rate and design rainfall in Dafeng.</p>
Full article ">Figure 5
<p>Results of the impact factor evaluation for the scale decomposition of low-impact development in the control unit of the study area, representing the (<b>a</b>) runoff coefficient, (<b>b</b>) building density, (<b>c</b>) green space rate and (<b>d</b>) water surface rate, respectively.</p>
Full article ">Figure 6
<p>Scoring the necessity and adaptability of sponge cities in the control units of the study area.</p>
Full article ">Figure 7
<p>Results of the decomposition of the annual runoff control for each control unit in the study area.</p>
Full article ">
17 pages, 1789 KiB  
Article
Analysis of Intercity Transportation Network Efficiency Using Flow-Weighted Time Circuity: A Case Study of Seven Major City Clusters in China
by Minqing Zhu, Peng Yuan and Hongjun Cui
Appl. Sci. 2024, 14(9), 3834; https://doi.org/10.3390/app14093834 - 30 Apr 2024
Cited by 1 | Viewed by 1397
Abstract
Enhancing the efficiency of intercity transportation networks is crucial for sustainable regional transport development, significantly impacting travel behaviors and energy consumption. The transportation infrastructure within the city cluster is rapidly developing to accommodate the increasing traffic demand, necessitating substantial investments. It is imperative [...] Read more.
Enhancing the efficiency of intercity transportation networks is crucial for sustainable regional transport development, significantly impacting travel behaviors and energy consumption. The transportation infrastructure within the city cluster is rapidly developing to accommodate the increasing traffic demand, necessitating substantial investments. It is imperative to investigate the effectiveness of intercity traffic within urban clusters, to evaluate the influence of transportation infrastructure enhancements on regional traffic efficiency. Circuity is a conventional metric used to assess the efficiency of transportation networks, primarily emphasizing distance, while overlooking factors such as travel time and traffic flow. In this study, the concept of circuity has been redefined in terms of travel time and has been referred to as the transportation network travel speed. Subsequently, the amalgamation of travel speed within the transportation network and traffic flow culminates in the proposition of Flow-Weighted Time Circuity (FWTC). Real-time intercity navigation data, offering accurate travel time estimations, are utilized to analyze the spatial distribution of intercity transport efficiency in the seven major city clusters of China, via both automobile and train modes of transportation. The results indicate that (1) as the travel distance extends, the speed of transportation within the network typically increases, albeit with increasing fluctuations, especially in the case of intercity train travel; (2) concerning the efficiency of intercity automobile travel, most city clusters demonstrate satisfactory performance, with the exception of the Guanzhong Plain. The Yangtze River Delta and Beijing–Tianjin–Heibei regions stand out for their superior performance. In terms of intercity train efficiency, the Yangtze River Delta, Beijing–Tianjin–Heibei, and Mid-Yangtze River regions exhibit higher levels of efficiency in intercity train transportation, while the Guanzhong Plain city cluster falls behind in this aspect. On the whole, the efficiency of intercity travel using automobiles surpasses that of train travel, indicating a pressing need for improvement in the latter. Full article
(This article belongs to the Special Issue Transportation Planning, Management and Optimization)
Show Figures

Figure 1

Figure 1
<p>Schematic diagram for calculating intercity travel time.</p>
Full article ">Figure 2
<p>Scatter diagram of transportation network travel speed and the Euclidean distance between the cities of origin and destination.</p>
Full article ">Figure 3
<p>Scatter diagram of transportation network travel speed and the Euclidean distance between the cities of origin and destination in different city clusters. (<b>a</b>–<b>g</b>) represent different city cluters, respectively.</p>
Full article ">Figure 4
<p>Comparison of intercity efficiency of city clusters.</p>
Full article ">
14 pages, 2887 KiB  
Article
Study on River Protection and Improvement Based on a Comprehensive Statistical Model in a Coastal Plain River Network
by Junmin Wang, Lei Fu, Cheng Lu, Shiwu Wang, Yongshu Zhu, Zeqi Xu and Zihan Gui
Sustainability 2024, 16(9), 3518; https://doi.org/10.3390/su16093518 - 23 Apr 2024
Viewed by 1147
Abstract
When considering the contradictions between river management and protection in a typical plain river network, it is always confirmed that the river area has usually been encroached upon due to the development of human society. Based on the analysis of multiple attributes of [...] Read more.
When considering the contradictions between river management and protection in a typical plain river network, it is always confirmed that the river area has usually been encroached upon due to the development of human society. Based on the analysis of multiple attributes of the river network, a statistical model has been proposed in this study in order to determine the river network protection indices such as river area ratio, storage capacity and flux. In this study, a numerical method is proposed to improve the structure and connectivity of the river network by calculating the occupation and supplement balance. According to the principle of water area dynamic balance, the river network structure and its connectivity are improved through water area adjustment in a typical coastal city. As the simulation results show, the water surface ratio equals 8.17%, the storage capacity equals 112.6 million m3 and the water flux equals to 656.06 m3/s in the selected study area. The flood drainage capacity is introduced as the priority function, other functions are also improved due to river management and protection. The harmonious and sustainable coexistence between human society and the river network is then promoted. This comprehensive statistical model proved to be a good tool for the coastal area to enhance the comprehensive attributes of the coastal plain river network and the sustainable development of the local area in the future. Full article
(This article belongs to the Special Issue Advances in Sustainable Hydraulic and Water Resource Engineering)
Show Figures

Figure 1

Figure 1
<p>Coastal plain river network in Pinghu City.</p>
Full article ">Figure 2
<p>Typical rivers in Pinghu City.</p>
Full article ">Figure 3
<p>Flowchart of river network protection and function improvement.</p>
Full article ">Figure 4
<p>Different zones of the river network of Pinghu City.</p>
Full article ">Figure 5
<p>Variation in the water area and the storage capacity in different zones.</p>
Full article ">Figure 6
<p>Comparison of the water surface ratios in different zones.</p>
Full article ">Figure 7
<p>Comparison of river length and number density in different zones.</p>
Full article ">Figure 8
<p>Variation in water quality from 2015 to 2020 (averaged value).</p>
Full article ">
17 pages, 1869 KiB  
Article
Joint Optimization of Urban Water Quantity and Quality Allocation in the Plain River Network Area
by Jun Zhao, Guohua Fang, Xue Wang and Huayu Zhong
Sustainability 2024, 16(4), 1368; https://doi.org/10.3390/su16041368 - 6 Feb 2024
Cited by 4 | Viewed by 1159
Abstract
Cities located in the plain river network area possess abundant water resources. However, due to urbanization and industrialization, there is a severe water shortage problem caused by poor water quality. To overcome this issue, a multi-objective optimal allocation model of water quantity and [...] Read more.
Cities located in the plain river network area possess abundant water resources. However, due to urbanization and industrialization, there is a severe water shortage problem caused by poor water quality. To overcome this issue, a multi-objective optimal allocation model of water quantity and quality is proposed. The model considers regional water resources, economic, social, and environmental requirements and uses the NSGA-II genetic algorithm for model solution. Furthermore, to evaluate and analyze the degree of spatial equilibrium of regional water resources and how it relates to economic factors, the study uses the spatial equilibrium theory of water resources and the Gini coefficient of water resources. Jingjiang, a city in Jiangsu Province characterized by a typical plain river network area, was selected as the study area. The results of the optimal allocation of water resources in Jingjiang City show that: (1) total water consumption and chemical oxygen demand (COD) emissions for the current planning period are within their respective limits. In addition, the implementation of the water conservation program has resulted in a 5% reduction in total water shortages and a reduction of COD emissions by 1276 tons, (2) the structure of the water supply in Jingjiang City has been optimized; more than 90% of Ⅳ~V surface water is used for agriculture, and the domestic water supply is mainly from transit water, which effectively ensures that high-quality water is used in the domestic water supply, (3) the spatial equilibrium coefficient of water resources per sub-area is between 0.33 and 0.74, indicating an unbalanced or almost unbalanced level. The application of a water conservation program has resulted in the improvement of the spatial equilibrium level of water resources in each sub-area, with an overall spatial equilibrium of 0.64, indicating a more balanced level; the degree of matching of water resources with population, GDP, and land area is at the matching level, (4) according to the Gini coefficient of the distribution of water resources, the plains river network area displays a better match between water resources and economic and social factors of each water receiving area, thanks to its unique geographical location and natural conditions. This study can serve as a decision-making reference for addressing the urban water quality water shortage problem in the plain river network area. Full article
(This article belongs to the Section Sustainable Water Management)
Show Figures

Figure 1

Figure 1
<p>Water resources zoning in Jingjiang City.</p>
Full article ">Figure 2
<p>Structure of water supply and water use of Jingjiang City in the planning year.</p>
Full article ">Figure 3
<p>Distribution of water use equalization coefficients for various industries under different scenarios in the planning year.</p>
Full article ">
28 pages, 10082 KiB  
Article
Spatiotemporal Pattern of Carbon Compensation Potential and Network Association in Urban Agglomerations in the Yellow River Basin
by Haihong Song, Yifan Li, Liyuan Gu, Jingnan Tang and Xin Zhang
ISPRS Int. J. Geo-Inf. 2023, 12(10), 435; https://doi.org/10.3390/ijgi12100435 - 23 Oct 2023
Cited by 5 | Viewed by 1982
Abstract
The Yellow River Basin is an important energy base and economic belt in China, but its water resources are scarce, its ecology is fragile, and the task of achieving the goal of carbon peak and carbon neutrality is arduous. Carbon compensation potential can [...] Read more.
The Yellow River Basin is an important energy base and economic belt in China, but its water resources are scarce, its ecology is fragile, and the task of achieving the goal of carbon peak and carbon neutrality is arduous. Carbon compensation potential can also be used to study the path to achieving carbon neutrality, which can clarify the potential of one region’s carbon sink surplus to be compensated to the other areas. Still, there needs to be more research on the carbon compensation potential of the Yellow River Basin. Therefore, this study calculated the carbon compensation potential using the β convergence test and parameter comparison method. With the help of spatial measurement tools such as GIS, GeoDa, Stata, and social network analysis methods, the spatiotemporal pattern and network structure of the carbon compensation potential in the Yellow River Basin were studied from the perspective of urban agglomeration. The results demonstrate the following: (1) The overall carbon compensation rate of the YRB showed a downward trend from 2005 to 2019, falling by 0.94, and the specific pattern was “high in the northwest and low in the southeast”. The spatial distribution is roughly spread along the east–west axis, and the distribution axis and the center of gravity keep shifting to the northwest. It also showed a weak divergence and a bifurcation trend. (2) The carbon compensation rate in the YRB passed the spatial correlation and β convergence tests, demonstrating the existence of spatial correlation and a “catch-up effect” among cities. (3) The overall distribution pattern of the carbon compensation potential in the YRB is a “low in the west and high in the east” pattern, and its value increased by 8.86% during the sampled period. (4) The network correlation of carbon compensation potential in the YRB has been significantly enhanced, with the downstream region being more connected than the upstream region. (5) The Shandong Peninsula Urban Agglomeration has the largest network center, followed by the Central Plains Urban Agglomeration, and the Ningxia along the Yellow River Urban Agglomeration has the fewest linked conduction paths. According to the research results, accurate and efficient planning and development suggestions are proposed for urban agglomeration in the Yellow River Basin. Full article
Show Figures

Figure 1

Figure 1
<p>Study area.</p>
Full article ">Figure 2
<p>Carbon compensation rate of the YRB urban agglomeration.</p>
Full article ">Figure 3
<p>Spatial distribution of carbon compensation rate in the YRB urban agglomeration: (<b>a</b>) in 2005; (<b>b</b>) in 2009; (<b>c</b>) in 2014; (<b>d</b>) in 2019.</p>
Full article ">Figure 4
<p>Evolutionary characteristics of the spatial distribution of carbon compensation rate in the YRB.</p>
Full article ">Figure 5
<p>Carbon compensation rate evolution characteristics with time series for urban clusters in the YRB.</p>
Full article ">Figure 6
<p>LISA agglomeration map of carbon compensation rate in YRB: (<b>a</b>) in 2005; (<b>b</b>) in 2009; (<b>c</b>) in 2014; (<b>d</b>) in 2019.</p>
Full article ">Figure 7
<p>Carbon compensation potential of urban agglomerations in the YRB.</p>
Full article ">Figure 8
<p>Spatial distribution of carbon compensation potential of urban agglomerations in the YRB: (<b>a</b>) in 2005; (<b>b</b>) in 2009; (<b>c</b>) in 2014; (<b>d</b>) in 2019.</p>
Full article ">Figure 9
<p>Overall network linkage of carbon compensation potential of urban agglomerations in the YRB: (<b>a</b>) in 2005; (<b>b</b>) in 2009; (<b>c</b>) in 2014; (<b>d</b>) in 2019.</p>
Full article ">Figure 10
<p>Block-type results for three urban agglomerations and related index line graphs: (<b>a</b>) Shangdong Peninsula Urban Agglomeration; (<b>b</b>) Central Plains Urban Agglomeration; (<b>c</b>) Guanzhong Urban Agglomeration; (<b>d</b>) Network Density; (<b>e</b>) Network Connection; (<b>f</b>) network hierarchy; (<b>g</b>) network efficiency.</p>
Full article ">
11 pages, 1267 KiB  
Article
Study and Application of Urban Aquatic Ecosystem Health Evaluation Index System in River Network Plain Area
by Rui Ding, Kai Yu, Ziwu Fan and Jiaying Liu
Int. J. Environ. Res. Public Health 2022, 19(24), 16545; https://doi.org/10.3390/ijerph192416545 - 9 Dec 2022
Cited by 7 | Viewed by 1828
Abstract
The evaluation index system of urban aquatic ecosystem health is of great significance for the assessment and management of urban river networks, and for urban development planning. In this paper, the concept of urban aquatic ecosystem health was analyzed by the relationship between [...] Read more.
The evaluation index system of urban aquatic ecosystem health is of great significance for the assessment and management of urban river networks, and for urban development planning. In this paper, the concept of urban aquatic ecosystem health was analyzed by the relationship between human, city and aquatic ecosystem, and its evaluation index system was established from environmental conditions, ecological construction, and social service. In addition, the weight value of each index was calculated by the analytic hierarchy process, and the grading standard of each index was set. Jiading New City, a typical city of the river network plain area in Yangtze River delta, was selected as the aquatic ecosystem health evaluation sample. The fuzzy comprehensive method was used to evaluate the aquatic ecosystem health of Jiading New City. The results indicated that the water ecosystem health of Jiading New City reached the “good” level. For the criterion level, environmental conditions and ecological construction reached the “good” level, and social services reached the “excellent” level. For the indicator level, most indicators reached “good” and “excellent” levels, but the river complexity and benthic macroinvertebrate diversity are still in the “poor” state, which indicates that the aquatic environment has greatly improved, but the aquatic ecosystem has not been fully restored. Results suggested that river complexity and biodiversity should be increased in urban construction planning. The evaluation index system established in this paper can be used to reflect the urban aquatic ecosystem health conditions in river network plain areas. Full article
Show Figures

Figure 1

Figure 1
<p>Water system diagram of Jiading New City.</p>
Full article ">Figure 2
<p>Relationship among human, city, and the aquatic ecosystem.</p>
Full article ">Figure 3
<p>Index evaluation grade membership degree.</p>
Full article ">
17 pages, 21535 KiB  
Article
The Evaluation of Groundwater Carrying Capacity in Xi’an
by Jing Gao, Weibo Zhou, Shuwu Li, Changhu Li and Haiyun Chen
Water 2022, 14(22), 3756; https://doi.org/10.3390/w14223756 - 18 Nov 2022
Viewed by 1845
Abstract
With the development of the economy and society, the importance of water as a necessary resource has increased. The resource attribute capacity of groundwater is limited, and excessive consumption depletes groundwater resources. The extremely serious and highly integrated groundwater problem necessitates the determination [...] Read more.
With the development of the economy and society, the importance of water as a necessary resource has increased. The resource attribute capacity of groundwater is limited, and excessive consumption depletes groundwater resources. The extremely serious and highly integrated groundwater problem necessitates the determination of the carrying capacity of groundwater resources. Based on the research findings of the carrying capacity of groundwater resources in China and other parts of the world, in this study, we proposed a new method to determine the carrying capacity of groundwater resources. We evaluated the carrying capacity of groundwater resources in Xi’an by using the probabilistic neural network method based on the ‘W–F extension law’. The results showed that the extremely low and low bearing capacity areas of groundwater in Xi’an are located in the southern plain area of Zhouzhi county and Huyi district, the southern suburbs of Xi’an city, and the loess platform source area. Due to the constant supply from the riverside water source, the groundwater associated with the Bahe river, Fengzaohe river, and Weibin water sources have a higher bearing capacity than other evaluation areas. Compared to the traditional evaluation method, in this study, we redefined the evaluation index standard of the carrying capacity of groundwater. The groundwater carrying capacity is only related to groundwater and its storage medium. The pressure index of groundwater carrying, such as the population, economy, and environment in the traditional evaluation method, is considered overexploitation. The interaction between surface water and groundwater can be distinguished, and the level limit of the evaluation index can be determined more accurately. Additionally, the probabilistic neural network method of the ‘W–F extension law’ does not allocate weights but calculates the clustering center. Thus, to avoid subjectivity, parameter weighting is not required. This method does not have regional restrictions and can reflect the non-linear relationship of the groundwater system. It can reflect the sensitivity and recovery ability of groundwater under the same future exploitation load. The evaluation results of this method were consistent with the evaluation results of the third groundwater resources survey in Xi’an in 2019, and the evaluation results were very similar to the actual situation. The accuracy and practicability of the evaluation method were verified. Full article
(This article belongs to the Section Hydrogeology)
Show Figures

Figure 1

Figure 1
<p>A distribution map of Xi’an shows the distribution of the landform, administrative division, and groundwater monitoring well.</p>
Full article ">Figure 2
<p>A hydrogeological map of Xi’an.</p>
Full article ">Figure 3
<p>The distribution of representative points of groundwater overload assessment zones in the plain area of Xi’an city.</p>
Full article ">Figure 4
<p>Evaluation results of groundwater carrying capacity.</p>
Full article ">
15 pages, 4653 KiB  
Article
Online Storage Technology of the Separate Sewage System: Demonstration Study in a Typical Plain River Network City
by Xiaohu Dai, Guozhong Xu, Yongwei Ding, Siyu Zeng, Lan You, Jianjun Jiang and Hao Zhang
Water 2022, 14(20), 3194; https://doi.org/10.3390/w14203194 - 11 Oct 2022
Cited by 1 | Viewed by 2135
Abstract
Due to the high underground water level, frequent rainfall, and large amounts of infiltration and inflow (I/I) into the sewage system, a city in the plain river network region had to face a series of problems caused by the high water-level operation of [...] Read more.
Due to the high underground water level, frequent rainfall, and large amounts of infiltration and inflow (I/I) into the sewage system, a city in the plain river network region had to face a series of problems caused by the high water-level operation of the drainage system. Suzhou, a city in the Yangtze River Delta region of China, can be a representative of cities in plain river networks, where this research was carried out. The amount of I/I into the sewage system was evaluated, and the storm water management model (SWMM) was used to further calculate the sewer water storage capacity under dry and wet weather with multi-year average rainfall. Based on the offline model calculation and artificial experiences, the rule-based online regulation and storage real-time control strategy (RTC) is verified, and the online regulation and storage intelligent scheduling demonstration is carried out in the central-city district of Suzhou. The results showed that the infiltration in dry weather accounted for about 20–25% of the total collected wastewater; in wet weather (36 mm precipitation), the extraneous water induced by I/I peaked at 73.64%. The collaborative control of regional multi-stage pumping stations through RTC of the sewage system can effectively avoid the high water-level operation caused by peak sewage flows on dry days. In combination with rainfall forecasting, the coordinated control of plants and pumping stations to pre-empty the sewer pipelines prior to rainfall can, to some extent (up to 35 mm of rainfall in this study), cope with the increase in I/I induced by rainfall. Full article
(This article belongs to the Special Issue Sustainable Governance for Resilient Water and Sanitation Service)
Show Figures

Figure 1

Figure 1
<p>A typical picture of “small bridges, flowing water and homes” in central district of Suzhou.</p>
Full article ">Figure 2
<p>The three sewage systems in the study area and the and the distribution of multi-pump stations in the service area.</p>
Full article ">Figure 3
<p>Schematic diagram of the scheduling logic for sewage pumping stations in the study area. (<b>a</b>) Ancient-city district and eastern part of the city; (<b>b</b>) Western part of the city.</p>
Full article ">Figure 4
<p>Sampling locations, represented as stars labeled from S1 to S7.</p>
Full article ">Figure 5
<p>Flow variation line on typical dry days.</p>
Full article ">Figure 6
<p>Schematic diagram of the control logic of a step-pumping station. (<b>a</b>) Schematic diagram of the multinomial tree model for a stepped pumping station. (<b>b</b>) Schematic diagram of the regional linkage control of three pumping stations in two grades.</p>
Full article ">Figure 7
<p>Analysis result of minutely I/I rates of sewage network at a typical site (S1) during rainfall periods. (<b>a</b>) 17 August 2018 (36 mm precipitation). (<b>b</b>) 22 August 2018 (7 mm precipitation).</p>
Full article ">Figure 8
<p>SWMM model used for evaluation of storage capacity.</p>
Full article ">Figure 9
<p>Calibration results. (<b>a</b>) Water level at the outlet of the area. (<b>b</b>) Inflow of the sewage treatment plant.</p>
Full article ">Figure 10
<p>The storage capacity evaluation result (unit: m<sup>3</sup>). (<b>a</b>) Storage capacity of each pumping-station service area on a dry weather day. (<b>b</b>) Storage capacity of each pumping station under multi-year average precipitation.</p>
Full article ">Figure 11
<p>The effect of implementing the control model for typical two-stage three pumping stations.</p>
Full article ">
17 pages, 4488 KiB  
Article
Exploring the Cumulative Selectivity of Polycyclic Aromatic Hydrocarbons in Phytoplankton, Water, and Sediment in Typical Urban Water Bodies
by Liling Xia, Zhenhua Zhao, Zihan Lang, Zhirui Qin and Yuelong Zhu
Water 2022, 14(19), 3145; https://doi.org/10.3390/w14193145 - 6 Oct 2022
Cited by 10 | Viewed by 2214
Abstract
To understand the interactions among eutrophication, algal bloom, and POPs (persistent organic pollutants) in freshwater ecosystems, the cumulative selectivity of PAHs (polycyclic aromatic hydrocarbons) in phytoplankton, water, and sediment with different eutrophication level waters were identified in a typical plain river network region [...] Read more.
To understand the interactions among eutrophication, algal bloom, and POPs (persistent organic pollutants) in freshwater ecosystems, the cumulative selectivity of PAHs (polycyclic aromatic hydrocarbons) in phytoplankton, water, and sediment with different eutrophication level waters were identified in a typical plain river network region located in Nanjing City. Results showed that a total of 33 algal species belonging to 27 genera and 4 phyla were identified in 15 sites of urban water bodies, and most of them belonged to the type Cyanobacteria–Bacillariophyta. The eutrophication level of these rivers and lakes led to the sample site specificity of algal composition and abundance. The planktonic algae mainly accumulated the 2-ring and 3-ring PAHs, and the sediment mainly enriched the high-ring PAHs. However, the enrichment capacity of planktonic algae on PAHs was much higher than that of sediment. Cyanophyta and some species of Bacillariophyta and Chlorophyta in mesotrophic (βm) and meso-eutrophic water bodies (ßαm) preferentially accumulated lower-ring PAHs (naphthalene, acenaphthylene, and phenanthrene). Some other specific algae species, such as Euglenophyta, some species of Bacillariophyta, and most Chlorophyta in mesotrophic and moderate eutrophic water bodies, had strong capacities to enrich high-ring PAHs subsuming benzo[a]anthracene, chrysene, and anthracene. The eutrophication level of water bodies affected the cumulative selectivity of PAHs by shaping the site specificity of phytoplankton composition, which may be related to water quality, sediment characteristics, phytoplankton composition, and the algal cell walls. Full article
(This article belongs to the Special Issue Emerging Contaminants in Riverine and Marine Ecosystems)
Show Figures

Figure 1

Figure 1
<p>Sampling sites (<b>A</b>) and the corresponding physicochemical indexes of water samples (<b>B</b>) in the study area in Nanjing City, China. Note: (1) The codes for each sampling site are as follows: 1#: Zhenzhu Bridge, 2#:Yixian Bridge, 3#: Wenjin Bridge, 4#: Wenzheng Bridge, 5#: Xiafu Bridge, 6#: Saihong Bridge, 7#: Yunliang River, 8#: Wetland Park, 9#: External Qinhuai River, 10#: Fengtai Bridge, 11#: Hanzhongmen Bridge, 12#: Yuhua Bridge, 13#: Inside the Sancha Estuary, 14#: Outside the Sancha Estuary: 15#: Xuanwu Lake. (2) TLI(∑): comprehensive nutrition state index. (3) os: Oligotrophication refers to a water body that lacks nutrients and contains a large number of species but a small number of aquatic organisms; βm: mesotrophic; βαm: meso-eutrophic; αm: moderate eutrophication.</p>
Full article ">Figure 2
<p>The composition characteristics of algae abundance and biomass in different sampling sites. Note: The codes for each sampling site are shown in <a href="#water-14-03145-f001" class="html-fig">Figure 1</a>.</p>
Full article ">Figure 3
<p>The residual characteristics of PAHs in water, sediment, and algae (<b>A</b>) and the correlation of PAHs concentration among algae, water, and sediment (<b>B</b>). Note: naphthalene (Nap), phenanthrene (Phe), anthracene (Ant), fluorene (Flu), fluoranthene (Fla), pyrene (Pyr), acenaphthylene (Ace), chrysene (Chr), benzo[a]anthracene (BaA), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), benzo[a]pyrene (BaP), dibenzo[a,h]anthracene (DahA), and benzo[g,h,i]perylene (BghiP). *: linear correlation is significant at the 0.0175 level.</p>
Full article ">Figure 4
<p>The Pearson correlation analysis between PAHs concentration and algae biomass. Note: (1) **: Correlation is significant at the 0.01 level (2-tailed); *: correlation is significant at the 0.05 level (2-tailed). (2) Naphthalene (Nap), acenaphthylene (Ace), phenanthrene (Phe), anthracene (Ant), benzo[a]anthracene (BaA), chrysene (Chr). (3) The codes of phytoplankton are listed in <a href="#water-14-03145-t001" class="html-table">Table 1</a>.</p>
Full article ">Figure 5
<p>Redundancy analysis (RDA) ordination tri-plot of algae species, sampling sites, and PAHs residues in algae. (<b>A</b>) Ordination results when the algae abundance acts as the species matrix and residual PAHs concentrations represent the environmental matrix. The data matrices in (<b>B</b>) are opposite those in (<b>A</b>). Circles symbol is the sample sites; the arrows are PAHs in (<b>A</b>) or the species of algae in (<b>B</b>). The length of the arrow corresponds to the importance of the variable.</p>
Full article ">Figure 6
<p>The bioconcentration factors (BCF) of PAHs from water to phytoplankton and the linear equation between logBCF and log<span class="html-italic">Kow</span> of PAHs. Note: (1) * and the red text shows the log<span class="html-italic">Kow</span> of different PAHs. (2) Naphthalene (Nap), acenaphthylene (Ace), phenanthrene (Phe), anthracene (Ant), benzo[a]anthracene (BaA), and chrysene (Chr).</p>
Full article ">
Back to TopTop