Startseite Factors impacting spatial distribution of black and odorous water bodies in Hebei
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Factors impacting spatial distribution of black and odorous water bodies in Hebei

  • Qiliang Li , Huixiong Lu EMAIL logo , Bing Wang , En Zhang , Qing Xue , Yongbin Sun , Shen Wang und Shaofei Han
Veröffentlicht/Copyright: 14. Juli 2025
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Aus der Zeitschrift Open Geosciences Band 17 Heft 1

Abstract

Black and odorous water bodies in Hebei Province, China, are continuously attracting governmental attention. These water bodies exhibit a complex spatial distribution with multiple contributing factors; however, existing studies have not fully revealed its inherent patterns. Using publicly available data, this research employed various spatial analysis methods to thoroughly analyze the spatial distribution characteristics of black and odorous water bodies in Hebei Province and the coupling mechanism of multiple factors. The goal was to provide management suggestions for eliminating these water bodies. The study found the following: (1) black and odorous water bodies in this region exhibited a notable spatial clustering pattern, with kernel density results indicating concentration in the Cangzhou–Baoding–Langfang triangle. (2) The primary natural factors influencing the spatial distribution of these water bodies included the number of water bodies and temperature; social factors such as population density, ammonia nitrogen emissions, drainage pipeline density, and waste treatment capacity also played important roles. (3) There were deep-rooted causes for the spatial distribution of black and odorous water bodies, suggesting that effective management of these water bodies should focus on key areas and implementing comprehensive strategies that involve policies and regulations, infrastructure development, and ecological restoration.

Keywords: water pollution; black and odorous water bodies; ammonia nitrogen emissions; drainage infrastructure; spatial distribution; influencing factors; geodetector

1 Introduction

In recent years, Hebei Province, China, has experienced continued economic growth and accelerated urbanization, with its gross domestic product (GDP) being 4,394.41 billion yuan and urbanization rate being 62.77% of the resident population in 2023 [1]. However, despite these positive developments, challenges of atmospheric, solid-waste, and surface water pollution persist. Black and odorous water bodies – water bodies that exhibit unpleasant colors and/or emit foul odors – represent an extreme form of water pollution [2] that degrades the landscape and poses serious threats to the ecological environment and human health. To solve these problems, multiple departments in Hebei Province jointly issued the Hebei Province Urban Black and Odorous Waters Bodies Control Tackling Action Program in 2022 [3], leading to substantial improvements in the water environment. However, as urbanization and industrialization continue to advance, the growing imbalance between population density and infrastructure development has become increasingly evident. With increased environmental regulations, small and less visible water bodies have emerged as the most vulnerable to pollution.

Domestic waste, domestic sewage, livestock and poultry waste, and industrial effluent are mainly responsible for forming black and odorous water bodies in Hebei Province, China [4]. The study of water pollution in China began in the 1980s, a period marked by rapid industrialization and urbanization. During this time, industrial discharge is the primary cause of river pollution in major waterways and industrial hubs [5,6,7,8]. Extensive industrial production coupled with inadequate wastewater treatment for cooling, washing, and process reactions leads to the direct release of pollutants into rivers [9,10]. In the twenty-first century, the rapid growth of the livestock and poultry industry in China, following the promulgation of the Animal Husbandry Law, introduced another significant pollutant. To reduce treatment costs, wastewater from family and large-scale farms was often directly discharged into water bodies, resulting in eutrophication and serious environmental damage [11,12]. With the strengthening of environmental protection and the standardization of industries, including livestock and poultry farming, the proportion of domestic sewage and waste in overall wastewater discharge is increasing [13], particularly in rural areas, where lagging infrastructure contributes to persistent water pollution challenges [14].

Since the promulgation of the Ten Rules on Water policy in 2015, which demonstrates the Chinese government’s determination to combat the growing problem of water pollution, research on black malodorous water bodies has gradually increased and various remote-sensing identification models have been proposed. Among the existing techniques are knowledge-driven remote-sensing interpretation methods [4], band ratio methods [15,16,17], and deep learning models [18,19,20]. From 2017 to 2024, scholars proposed more than a dozen methods based on the differences in band ratios between black and odorous water bodies and clean water bodies [21], advancing the field of remote sensing identification of black and odorous water bodies. Digital technologies such as remote sensing also play an increasingly important role in the identification of black and odorous water bodies [22].

Currently, black and odorous water bodies are primarily treated using methods such as interception, aeration, chemical oxidation, and sediment remediation [23]. Interception projects necessitate the construction of new sewage pipelines or the modification of existing ones [24]. Although aeration can increase the dissolved oxygen (DO) content in water, its effectiveness is limited in severely polluted waters [25]. Chemical oxidation methods can quickly remove black and odorous substances, but they risk secondary pollution [23]. Sediment remediation removes sewage sediment, purifies it, and then landfills it off-site [26]. However, some small black and odorous water bodies are directly landfilled because of insufficient treatment funds. Ecological restoration methods employing algae or aquatic plants yield slow results [27].

Black and odorous bodies of water pose a serious public health concern and present a direct threat to surrounding populations. The closer people live to these polluted waters, and the longer they are exposed, the higher their health risk, primarily due to bacterial and fungal contamination. For short-term exposure to black and odorous water bodies, children have the highest health risk, followed by adult females, with adult males being the least affected [28]. Untreated pollution sources that directly enter water bodies can deteriorate the water quality and produce malodorous gases. Such water conditions tend to harbor pathogenic anaerobic bacteria, increasing the risk of spreading waterborne diseases such as dysentery and cholera [29].

Despite the increasing number of remote-sensing identification methods for black and odorous water bodies, identification studies have been mainly limited to small-scale areas [15,16,17,18,19,20]. Few studies have considered the distribution of black and odorous water bodies over large-scale areas. The Chinese government is committed to eliminating black and odorous water bodies in the counties of Hebei Province by 2025. To assist the government in meeting this goal, the present study provides timely information on the current distribution status of black and odorous water bodies in Hebei Province and recommendations for their management. Chen et al. [30] conducted a China-wide study of the spatial distribution and imaging factors of state-regulated black and odorous water bodies in 2017. However, a field survey identified a considerable number of black and odorous water bodies that are not officially published but are instead regulated by provincial, prefectural, and county governments. Moreover, black and odorous water bodies that suddenly appear within the regulatory lag period, those affected by factors beyond the scope of jurisdiction, and very large or small water bodies, may be unpublished. Therefore, a single data source will likely bias the results. The present authors conducted a similar study in the Yangtze River Delta region of China. Liu and Li conducted a remote-sensing identification and spatial-temporal distribution study of black and odorous water bodies in Guangzhou City, which resides in the Pearl River Delta region of China [31]. This study completes the research on black and odorous water bodies in the three major economic circles of China, filling gaps in the existing spatial distributions of such water bodies in the Beijing–Tianjin–Hebei region. It also improves the regional specificity of the study area, encompassing a new natural area in China, namely, the Xiong’an New Area, within Hebei Province.

Therefore, this research employs various spatial analysis methods to study black and odorous water bodies in Hebei Province using high-fraction remote-sensing images and data related to these water bodes to identify the natural and social factors influencing the spatial distribution of the water bodies. The aim of this study is to provide valuable suggestions for the management of black and odorous water bodies in Hebei Province. The proposed knowledge-driven identification method for black and odorous water bodies and its field validation provide a more comprehensive and real-time evaluation than existing approaches.

2 Methods

2.1 Overview of the study area

The study area is Hebei Province, located in North China (113°27′–119°50′E, 36°05′–42°40′N), covering an area of 188,800 km2 (Figure 1). It is bordered by the Bohai Sea to the east, Henan Province to the south, Shanxi Province to the west, and Beijing and Tianjin to the north. The regionʼs terrain is characterized by a high northwest and low southeast, with a complex and diverse topography, and the southeastern plain forms an important part of the North China Plain. Hebei has a warm temperate continental monsoon climate, with cold, dry winters, and hot, rainy summers. Precipitation ranges from 400 to 800 mm, decreasing from southeast to northwest. The province is rich in rivers and large lakes, such as the Baiyangdian and Hengshui Lakes, and the southeastern plains have well-developed river networks and are home to numerous rivers, ditches, and pits.

Figure 1 Map showing the extent of the study area.
Figure 1

Map showing the extent of the study area.

As an important economic province in China, Hebei boasts a strong industrial base, with industries such as those associated with iron and steel, building materials, and chemicals playing pivotal roles in the province. The long-standing rough development model has brought some environmental legacy problems to Hebei Province. In recent years, industrial restructuring and environmental protection supervision have been strengthened; in addition, environmental quality has been improved. However, the problem of small and less visible black and odorous water bodies still exists in rural areas, urban villages, and near industrial zones.

2.2 Data sources

2.2.1 High-resolution image data

GF7-BWD (rear-view 0.65 m panchromatic and 2.6 m multispectral) and GF2-PMS (0.8 m panchromatic and 3.2 m multispectral) remote-sensing image data were collected, which needed to be clear with no or few clouds. A total of 240 views of Gaofen satellite data were collected from January to October 2023, of which 166 views were from Gaofen-2 and 74 views were from Gaofen-7. The imagery covered 96.5% of the involved plains area in Hebei Province and mountainous urban areas.

A goal module based on Python completed the batch preprocessing of the remote-sensing image data, which primarily included radiometric correction, atmospheric correction, orthometric correction, image fusion, and geometric fine correction. First, the images were radiometrically corrected using the “Domestic Land Observation Satellite In-Orbit Absolute Radiation Calibration Coefficients” updated by the China Center For Resources Satellite Data and Application on April 11, 2024. Batch atmospheric correction was conducted using the 6 s model, whereas orthorectification was conducted in the multispectral and panchromatic bands using the Shuttle Radar Topography Mission 30 m digital elevation model (DEM), respectively. The Gram–Schmidt algorithm was used for image fusion. Finally, the automatic geoalignment tool of ArcGIS was employed to fine-tune the images into existing offline maps.

2.2.2 Data on black and odorous water bodies

Using the 2023–2024 high-fraction satellite data, the identification and field verification of suspected black and odorous water bodies were conducted in Hebei Province using a knowledge-driven black and odorous water body identification method. Then, a list of the distribution of suspected black and odorous water bodies was provided to the government. The data source for this study comprised 518 black and odorous water bodies publicly identified on official government websites (Figure 2) [32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47] at the provincial, municipal, district, and county levels, and the data attributes included latitude and longitude locations, water body type, pollution level, and major pollution issues. These black and odorous water bodies are primarily distributed in rural and plain areas. The main type of such water bodies is ponds, and their pollution sources are mainly domestic waste and pollution degree is primarily mild. The public black and odorous water bodies at the municipal level account for more. The publicly available documents showed that all of these black and odorous water bodies had been treated.

Figure 2 Spatial distribution of black odor water points in Hebei Province.
Figure 2

Spatial distribution of black odor water points in Hebei Province.

2.2.3 Data on social and economic development

Elevation data were selected from 30 m Shuttle Radar Topography Mission elevation data (https://www.earthdata.nasa.gov/data/instruments/srtm). Precipitation and temperature data were obtained from the China 1 km resolution month-to-month precipitation dataset [38] of the National Tibetan Plateau Data Center (https://www.tpdc.ac.cn/zh-hans/data/faae7605-a0f2-4d18-b28f-5cee413766a2). Water body quantity data were obtained from the point data of pits, ponds, and ditches of water bodies in Hebei Province, which were formed during the process of comprehensive remote-sensing identification of black and odorous water bodies.

GDP per capita and drainage density data were obtained from the 2023 Statistical Yearbook of 11 municipalities in Hebei Province. Population density data were obtained from the global population density raster data produced by WorldPop in 2020 [39] (https://hub.worldpop.org/project/categories?id=18). Ammonia nitrogen emissions and waste treatment capacity data were obtained from the open platform for pollution source enforcement monitoring and automatic monitoring data of domestic waste incineration power plants in the data center of the Hebei Provincial Department of Ecology and Environment (https://hbepb.hebei.gov.cn/hbhjt/sjzx/index.html). Functional zoning data were obtained from the Beijing Municipal People’s Government website in the Beijing Urban Master Plan (2016–2035). Cross-section water quality data were obtained from the Hebei Province Statistical Yearbook 2023 (Table 1 and Figure 3).

Table 1

List of geodetector influencing factors

Detection factor Detection indicator Unit Factor interpretation
×1 DEM M Average elevation
×2 Precipitation Mm Average annual precipitation
×3 Temperature °C Average annual temperature
×4 Number of water bodies / Total number of ponds and ditches
×5 Population density Persons/km2 Average population density
×6 GDP per capita 10,000 Chinese Yuans Ratio of total GDP to resident population
×7 Ammonia emission mg/L Annual sum of ammonia nitrogen emissions from enforcement monitoring data published by the Hebei Ecological and Environmental Monitoring Department
×8 Drainage pipe density km/km2 Sum of the length of drainage pipes per square kilometer, including sewage and stormwater pipes
×9 Waste disposal capacity Persons/tons/day Ratio of the total population to the daily treatment capacity of domestic waste incineration power plants
×10 Functional zoning Category Strategic positioning of regions in Hebei Province in the Beijing Urban Master Plan (2016–2035)
×11 Cross-sectional water quality % Proportion of national examination section meeting or better than Class III sections
Figure 3 Distribution map of geodetector influencing factors: (×1) DEM, (×2) precipitation, (×3) temperature, (×4) number of water bodies, (×5) population density, (×6) GDP per capita, (×7) ammonia emission, (×8) drainage pipe density, (×9) waste disposal capacity, (×10) functional zoning, and (×11) cross-sectional water quality.
Figure 3

Distribution map of geodetector influencing factors: (×1) DEM, (×2) precipitation, (×3) temperature, (×4) number of water bodies, (×5) population density, (×6) GDP per capita, (×7) ammonia emission, (×8) drainage pipe density, (×9) waste disposal capacity, (×10) functional zoning, and (×11) cross-sectional water quality.

2.3 Knowledge-Driven remote-sensing identification method for suspected black and odorous water bodies

The knowledge-driven identification method is specifically a process of analyzing the comprehensive knowledge of the distribution location, formation causes, and image features of black and odorous water bodies. It includes three processes: spatial distribution pattern identification, pollution source identification, and remote-sensing feature identification (Figure 4).

  1. Spatial distribution pattern identification involves understanding the relation between potential pollution sources and water bodies to spatially locate susceptible locations and susceptible types of black and odorous water bodies.

  2. Pollution source identification further analyzes the attributes of pollution sources and uses points of interest to trace back to the source to analyze whether it belongs to the type of key sewage enterprises.

  3. Remote-sensing feature identification involves remotely identifying black and odorous water bodies themselves, which are classified according to four categories: domestic wastewater, domestic waste, farming manure, and industrial wastewater categories. Each category of black and odorous water body has different remote-sensing image features.

Figure 4 Flowchart of the knowledge-driven remote-sensing-integrated identification methodology.
Figure 4

Flowchart of the knowledge-driven remote-sensing-integrated identification methodology.

If a water body is judged to be a suspected black and odorous water body in the spatial distribution pattern identification, the pollution source is further identified. If there is no such identification, then it is judged to be a suspected general water body. If the water body is judged to be a suspected black and odorous water body in the identification of the pollution source, the high scores of remote-sensing features are further identified. If there is no such identification, then there is no clear source of pollution in the vicinity of the water body and it is judged to be a suspected general water body. If the water body is judged to be a suspected black and odorous water body in the identification of high scores of remote-sensing features, then it is a suspected black and odorous water body. If there is no such identification, then it is considered a water body of key concern, which requires further judgment using multiperiod remote-sensing images. Through the three steps, the distribution range of suspected black and odorous water bodies is reduced step by step, and black and odorous water bodies are comprehensively identified using remote sensing.

As shown in the remote-sensing image presented in Figure 4, the first water body is relatively independent of the spatial distribution pattern, far away from human life and production activities. Moreover, the pollution path is far away, so it is judged to be a suspected general water body. The second water body is a park water body, and there is no obvious source of pollution in the vicinity; thus, it is judged to be a suspected general water body. Meanwhile, according to the high-resolution remote-sensing image, the third water body is dark green and there is no obvious pollution. The third water body is dark green from the high-bit remote-sensing image features and has no obvious pollution features. However, according to the nearby points of interest, the eastern part of this water body is adjacent to a food factory and a breeding factory, which belongs to key sewage enterprises. Meanwhile, the western part is adjacent to a residential area, which has clear pollution sources and pollution paths. Thus, this water body is considered a water body of key concern. According to the high-bit remote-sensing features, the water body is classified and identified per the characteristics of four categories: domestic wastewater, domestic waste, breeding wastewater, and industrial wastewater. Thus, it is judged as a suspected black and odorous water body. Water bodies with black odor characteristics are judged as suspected black and odorous water bodies, and field verifications were simultaneously conducted to confirm the final list of black and odorous water bodies.

2.4 Field verification

We conducted simultaneous field verifications of the list of suspected black and odorous water bodies. During the investigation, we employed the following methods:

  1. Water bodies with poor environmental conditions and very obvious fishy and irritating odors were identified as black and odorous water bodies.

  2. Water bodies with difficulties in human judgment were tested for water quality, and the ammonia nitrogen content in the water bodies was measured using the nanoreagent spectrophotometric method. The measurement process strictly adhered to the operating specifications of the ammonia nitrogen detector (LH-M900), and the water bodies were evaluated using this intelligent portable detector. The DO and oxidation–reduction potential content in the water bodies were measured using the intelligent portable detector, and the measurement process strictly adhered to the operation specification of LH-T600.

Drone photographs were taken of the water bodies that were difficult to reach or sample, and a comprehensive determination was made based on the status of the water environment.

2.5 Nearest-neighbor index

The nearest-neighbor index was derived by calculating the average distance between each black and odorous water body point and its nearest-neighbor point and then by determining the spatial distribution pattern of black and odorous water bodies in Hebei Province in general for each type and class.

(1) R = r ̅ 1 r ̅ E ,

where R is the nearest-neighbor index of the black-smelling water body points, r ̅ 1 is the actual average distance between each black-smelling water body point and its nearest-neighbor points, and r ̅ E is the expected average distance between the black-smelling water body points in the stochastic pattern. If the nearest-neighbor index is <1, the exhibited pattern is clustering; if the index is >1, the exhibited pattern tends to be diffusion.

2.6 Kernel density estimation

Kernel density estimation is a nonparametric method used to estimate the probability density function to explore the location of the main distributions of black odorous water bodies in Hebei Province in general, by type and class, by calculating the density of each black odorous water body point in its surrounding neighborhood:

(2) f ( x ) = 1 Nk ∑ i = 1 N H X i − x k ,

where x represents the black and odorous water body points to be estimated, k is the threshold of the search radius distance, N is the number of black and odorous water body points in the threshold range, and H is the kernel density function. The Gaussian kernel function is selected herein.

2.7 Geodetector method

Geodetector is a statistical tool for the spatial analysis of data, which can detect the spatial heterogeneity of the distribution of black and odorous water bodies in Hebei Province and reveal the driving mechanisms behind them. It primarily comprises four modules: factor detection, interactive detection, risk detection, and ecological detection [40]. The model involving optimal parameter-based geographical detectors [41] is an improvement to the geodetector model, where a parameter optimization module is added based on the traditional method. This makes up the parameter optimization part of the abovementioned improved model and can determine the optimal spatial scale by comparing the q-values of multiple spatial unit sizes according to the characteristics of the independent and dependent variables and adopting natural breaks, equal breaks, and quartiles classification methods to determine the optimal spatial scale.

Herein, natural breaks, equal breaks, quantile breaks, geometric breaks, standard deviation breaks, and manual breaks were employed to discretize the continuous data of the independent variables and compare the explanatory power of the q-values of the six discrete methods in different partitions to determine the optimal spatial scale of the explanatory variables.

3 Results

3.1 Spatial characteristics

The nearest-neighbor indices for all black and odorous water body points; for ditch, pit, and pond-type black and odorous water body points; and for the mild and severe black and odorous water body points in Hebei Province were all below 1. This observation indicated significant spatial clustering distribution patterns, implying that black and odorous water bodies were concentrated in specific areas. The mean observed distance ( r ̅ 1 ) was considerably smaller than the expected mean distance ( r ̅ E ), and the z-scores and p-values further confirmed the significance of the clustered distribution pattern (Table 2). The p-values represent a probability, and the z-scores represent standard deviations. The z-score and p-value are the measures of statistical significance that determine whether to reject the null hypothesis.

Table 2

Table of nearest-neighbor indices for black and odorous water bodies

Type R z-score p-value r ̅ 1 ( km ) r ̅ E ( km )
All 0.3774 −21.7027 0 4,498 11,920
Gully 0.4140 −12.0216 0 8,385 20,254
Ponds 0.3778 −17.5358 0 5,570 14,745
Mild 0.4206 −15.8326 0 6,396 15,207
Severe 0.4110 −12.7479 0 7,891 19,198

3.2 Spatial density characteristics

The estimated nuclear densities of all the black and odorous water bodies, ditches, and pits, and the mild and severe black and odorous water bodies in Hebei Province are presented in Figure 5. Four kernel nuclear density centers for all black and odorous water bodies in Hebei Province were identified: one in Baoding City, another in Hetao and Suning County in the western part of Cangzhou City, a third in Botou and Nanpi County in the central part of Cangzhou City, and the fourth around Mengcun and Yanshan Counties in the southeastern part of the city. These centers aligned with a general northwestern–southeastern orientation, with kernel density values ranging from 259 to 446 water bodies per 104 km2. In addition, two subcenters were located in Langfang City, including the counties of Dacheng and Wen’an in the south.

Figure 5 Spatial distribution of kernel density estimates for black and odorous waters: (a) all, (b) gully, (c) ponds, (d) mild, and (e) severe.
Figure 5

Spatial distribution of kernel density estimates for black and odorous waters: (a) all, (b) gully, (c) ponds, (d) mild, and (e) severe.

Different types and classes of black and odorous water bodies exhibited different distribution characteristics. For ditch-type black and odorous water bodies, a nuclear density center was located near Cangzhou City, with densities ranging from 168 to 297 water bodies per 104 km2. Additionally, there were three subcenters located in Baoding City, Renqiu City (Cangzhou City), and Dacheng and Wen’an Counties (south of Langfang City).

For puddle and pond-type black and odorous water bodies, two nuclear density centers were identified: one in Baoding City and the other in Hechang City and Suning County in the western part of Cangzhou City. The nuclear densities for centers ranged from 232 to 381 water bodies per 104 km2, with two subcenters in Botou City, Nanpi County, Mengcun County, and Yanshan County areas in the southern part of Cangzhou City.

For mildly black and odorous water bodies, two nuclear density centers were located in Baoding City and Hechang City and Suning County (western part of Cangzhou City), with nuclear center densities ranging between 182 and 346 water bodies per 104 km2. Seven subcenters were scattered across Tangshan City, Langfang City, and the southern, eastern, and western parts of Cangzhou City, Shijiazhuang City, and Nangong City in Xingtai.

For severely black and odorous water bodies, three nuclear density centers were identified in the west, central, and southeastern parts of Cangzhou City, with densities ranging from 197 to 316 water bodies per 104 km2.

3.3 Factors influencing spatial characteristics

3.3.1 Parameter optimization

Herein, the GD package in R was employed to grade the indicator factors, with discrete classes set between two and eight. Six discretization methods – the use of equal breaks, natural breaks, quantile breaks, geometric breaks, standard deviation breaks, and manual breaks – were used to detect the optimal interval range for the explanatory variables (Table 3). The results indicated significant differences in the combinations of discretization methods and the number of discretizations for various explanatory variables.

Table 3

Optimization table of the discretization method and number of discretizations for each explanatory variable based on optimal parameter-based geographical detectors

Type ×1 ×2 ×3 ×4 ×5 ×6 ×7 ×8 ×9 ×10 ×11
All Quantile 5 Quantile 8 Quantile 5 Sd 5 Quantile 8 Natural 8 Geometric 4 Quantile 8 Equal 7 Manual 4 Manual 6
Gully Quantile 5 Quantile 8 Quantile 5 Natural 6 Quantile 8 Natural 8 Geometric 4 Quantile 8 Equal 7 Manual 4 Manual 6
Ponds Quantile 7 Quantile 8 Quantile 8 Sd 5 Quantile 5 Quantile 8 Sd 7 Quantile 8 Equal 7 Manual 4 Manual 6
Mild Quantile 5 Quantile 8 Quantile 8 Sd 5 Quantile 5 Quantile 8 Sd 7 Quantile 8 Sd 8 Manual 4 Manual 6
Severe Quantile 7 Quantile 8 Quantile 5 Sd 7 Quantile 8 Natural 8 Geometric 4 Sd 8 Equal 7 Manual 4 Manual 6

3.3.2 Factor detection

To assess the contribution of each driving factor to the number of black and odorous water bodies, the factor detector was employed to calculate the q-value, which indicates the explanatory power of each indicator factor on the change in the number of black and odorous water bodies (Figure 6).

Figure 6 Driver q-values influencing the distribution of black and odorous water bodies by type.
Figure 6

Driver q-values influencing the distribution of black and odorous water bodies by type.

The influence of each factor on the total number of black and odorous water bodies in Hebei Province was ranked as follows: number of water bodies > population density > elevation > cross-section water quality > ammonia nitrogen emission > temperature > functional zoning > GDP per capita > precipitation > waste treatment capacity > drain density. The significance level for the first seven factors was <0.05. For the number of ditch-type black and odorous water bodies, the top five drives were as follows: the number of water bodies > ammonia nitrogen emission > elevation > section water quality > population density. The driving factors for pit pond-type black and odorous water bodies followed the same order as those influencing the total number of black and odorous water bodies.

For mildly black and odorous water bodies, the top five factors were as follows: number of water bodies > population density > elevation > temperature > precipitation. Finally, for severely black and odorous water bodies, the top five drivers were as follows: number of water bodies > ammonia nitrogen discharge > water quality of section > elevation > population density. All of these factors had a significance level of < 0.05.

The study results showed that the number of water bodies (all: q = 0.598) was the primary factor influencing the number of black and odorous water bodies in Hebei Province, across all types and categories. For ditch-type black and odorous water bodies, ammonia nitrogen emission (ditches: q = 0.500) was the most contributing factor, suggesting that the spatial distribution of black and odorous water bodies identified herein aligned with governmental monitoring data. Natural factors were found to be the main factors influencing the number of mildly black and odorous water bodies in Hebei Province, indicating that they played a greater role than social factors in causing mildly black and odorous conditions. In addition, ammonia nitrogen emission (heavy: q = 0.366) influenced the number of severely black and odorous water bodies to a moderate extent.

3.3.3 Risk detection

Risk detection was employed to identify which classification interval of each driver factor had the most significant effect on the number of black and odorous water bodies. A higher q-value for a driver factor’s stratification indicates the largest mean value of the number of black and odorous water bodies (at a 95% confidence level). Through risk detection using geodetectors, the mean values of the number of black and odorous water bodies across different factor stratification intervals were determined, allowing the identification of high-risk areas within the province where black and odorous water bodies were frequently observed.

There were significant differences in the explanatory power (q-values) for the number of black and odorous water bodies across different stratification intervals of the driving factors (Table 4). Among the natural factors, the highest mean number of black and odorous water bodies in Hebei Province when the number of water bodies in Hebei Province was high, with elevation ranging from 4 to 15 m, average annual temperature being between 14.4 and 14.8°C, and average annual precipitation being between 5,070 and 5,330 mm. These conditions had the most significant effect on the number of black and odorous water bodies. Among the social factors, the influence on the number of black and odorous water bodies gradually became significant with increases in population density, GDP per capita, and ammonia nitrogen emissions. The influence on the number of black and odorous water bodies was the most significant when the waste treatment capacity was low and the proportion of the national examination sections meeting or exceeding Class III standards was also low. The density of drainage pipes differed from the other social factors: there was an inflection point in the influence on the number of black and odorous water bodies at a density range of 8.66–10.1 km/km2, indicating that areas within this range had the highest risk of black and odorous water bodies. In addition, the region of Hebei Province strategically positioned as the central core functional area in the Beijing Urban Master Plan is a high-risk area for black and odorous water bodies.

Table 4

Distribution of high-risk areas and their mean values for different black and odorous water body types (nos)

Factor Name All Gully Ponds Mild Severe
×1 DEM (m) 4–15 4–15 4–12 4–15 4–12
×2 Precipitation (mm) 507–533 533–565 507–533 507–533 533–565
×3 Temperature (°C) 14.4–14.8 14.4–14.8 14.4–14.8 14.4–14.8 14.4–14.8
×4 Number of water bodies 50–70 94–189 50–70 50–70 80–100
×5 Population density (persons/km2) 2,410–3,050 2,410–3,050 1,570–2,550 1,570–2,550 2,410–3,050
×6 GDP per capita (10,000 Chinese Yuans) 12.9–16.4 12.9–16.4 3.53–3.9 5.84–6.87 12.9–16.4
×7 Ammonia emission (mg/L) 39.3–255 39.3–255 102–125 102–125 39.3–255
×8 Drainage pipe density (km/km2) 8.66–10.1 8.66–10.1 8.66–10.1 8.66–10.1 14.3–16.2
×9 Waste disposal capacity (persons/tons/day) 466–997 466–997 466–997 2,040–2,510 466–997
×10 Functional zoning Central core functional area Central core functional area Central core functional area Central core functional area Central core functional area
×11 Cross-sectional water quality (%) 50% 50% 50% 50% 50%

3.3.4 Interaction detection

The degree of explanatory power between any two driving factors simultaneously acting on the quantity of each type of black and odorous water body was investigated by quantitatively analyzing factor interactions (Figure 7). The results showed that any interactions between any two factors were primarily dominated by nonlinear enhancement and two-factor enhancement, where the combination of factors enhanced the effect of individual factors on the number of black odorous water bodies.

Figure 7 Interaction detection results of the influencing factors for each type of black and odorous water body.
Figure 7

Interaction detection results of the influencing factors for each type of black and odorous water body.

The driving interaction factor for the effect on the number of all black and odorous water bodies was number of water bodies ∩ drainage pipe density (q = 0.777), indicating that districts and counties with higher numbers of water bodies but average densities of drainage pipes had a higher number of black and odorous water bodies. A low density of drainage pipes meant that urbanization was low and there were fewer sources of pollution; thus, the number of black and odorous water bodies was low or the drainage system was insufficient, but the pollutant emission itself was not high. Thus, the problem of black and odorous water bodies was not clear. In constrast, areas with high drainage pipe density, indicating that higher urbanization and an efficient drainage system, experienced fewer number of black and odorous waters bodies as the system was better at collecting and treating sewage.

The primary interaction factor influencing the number of all black and odorous water bodies was population density ∩ waste treatment capacity (q = 0.730), indicating that the number of black and odorous water bodies was higher in areas with high population density and low waste treatment capacity. The driving interaction factor for the effect on the number of black and odorous water bodies in the ditch category was number of water bodies ∩ drain density (q = 0.772) > number of water bodies ∩ population density (q = 0.750).

The driving interaction factor for the effect on the number of black and odorous water bodies in the pit and pond categories was number of water bodies ∩ population density (q = 0.880) > temperature ∩ number of water bodies (q = 0.833). Meanwhile, the main interaction factor for the effect on the number of mildly odorous water bodies was number of water bodies ∩ population density (q = 0.736), and the main factor was temperature ∩ number of water bodies (q = 0.672).

The driving interaction factors for the effect on the number of heavy black and odorous water bodies were air temperature ∩ number of water bodies (q = 0.809) > precipitation ∩ number of water bodies (q = 0.800) > population density ∩ waste disposal capacity (q = 0.757), and the main interaction factors were elevation ∩ population density (q = 0.709) > number of water bodies ∩ waste disposal capacity (q = 0.704).

The water quality problems of heavy black and odorous water bodies are more serious and complex, and the driving factors affecting their quantity are more diverse. The combined effects of multiple factors superimposed on each other make the number of factors greater than other types.

4 Discussion

Unlike similar studies on the spatial distribution of black and odorous water bodies, our study covers numerous field surveys that support the government’s black and odorous water body identification service, providing the government with a publicly available list of black and odorous water bodies. The results reveal the underlying causes – drainage problems, waste treatment capacity, ammonia nitrogen emission, water volume, and temperature – of distributions of black odorous water bodies. Our study also explains the in-depth mechanisms by which these causes lead to black odorous water.

4.1 Causes

Remote-sensing images of black and odorous water body identification features, along with field verification of environmental observations and analyses, reveal that domestic waste, farming manure litter, domestic sewage, industrial sewage direct discharge, and other pollution sources are often present near these water bodies. These sources constitute the immediate causes of water body pollution. Government regulations and governance efforts primarily address surface-level issues, such as pit, pond, landfill, and slope waste cleanup, and substrate remediation. However, these measures often treat symptoms rather than the root causes of the problem. Pollution persists because of its inertia, and water bodies often experience recurring issues involving black and odorous contamination.

The primary factors influencing odorous water bodies identified by geodetector analysis revealed deep-rooted pollution causes.

  1. Drainage pipe issues: With the steady advancement of urbanization and industrialization, drainage infrastructure in densely populated areas has not been accordingly upgraded. Thus, there is a mismatch between the treatment capacity of drainage systems and the actual pollution loads, leading to increases in the number of black odorous water bodies. In addition, the irrational layout of drainage pipes, insufficient maintenance, incomplete sewage treatment facilities, and issues like mixed or misconnected pipelines worsen the problem, further increasing the number of black and odorous water bodies. Similar findings were reported by Chen et al. [30]. However, they identified the length of urban drainage pipes as the sole important factor, neglecting pipe layout, mixed connections, and misconnections. For example, water pollution can arise from incorrect connections of sewage pipes to stormwater pipes by individual enterprises. In Renqiu City, ongoing renovation and repair projects are attempting to correct misconnected and mixed pipelines through public bidding.

  2. Uneven waste treatment capacity: Densely populated areas typically generate more waste and domestic sewage. The increase in pollution sources, coupled with insufficient waste treatment capacity, can result in waste and sewage being discharged into nearby pits and ponds. Currently, Hebei Province has shut down all its domestic waste landfills [42], and domestic waste is now processed through incineration power plants. According to data from the Hebei Provincial Department of Ecology and Environment, 72 of these plants are currently in operation, with pollutant emissions disclosed daily, and a preliminary model for waste management, involving village collection, township transfer, and county-level treatment, has been established. Interaction detection results show that areas with a limited number of waste treatment plants have average waste treatment capacity, highlighting the need for supplementary construction or increased treatment capacity to match population density.

  3. Excessive ammonia nitrogen emissions: Wastewater from each unit converges into the main sewage pipeline through its respective wastewater outfalls. Herein, the ammonia nitrogen emission concentrations from the law enforcement monitoring data of each outfall were summarized, and it was found that ammonia nitrogen emission was the main factor influencing the number of black and odorous water bodies. Because of inadequate supervision, sewage pipes are often incorrectly connected to stormwater pipes, resulting in the direct discharge of sewage into rivers and ditches. The current wastewater discharge standards in China stipulate an ammonia nitrogen discharge limit of 15 mg/L [43], and the US usually specifies an ammonia nitrogen discharge limit of 5 mg/L in municipal wastewater.

  4. The number of water bodies serves the basis for forming black and odorous water bodies. The greater the number of water bodies polluted, the greater the possibility of black and odorous water bodies. In addition, the larger number of water bodies complicates management and maintenance. Inadequate supervision and improper control of pollution sources can worsen the problem, leading to an increase in the number of black and odorous water bodies.

  5. Temperature: In the eastern plains of Hebei Province, moderate temperatures and many water bodies interact, increasing the number of black and odorous water bodies. Moderate temperatures promote microbial activity, triggering eutrophication of the water body and consequent massive growth of duckweed. When pollutants enter the water body, the development of black and odorous water is exacerbated by gradual lowering of the DO concentration. As the air temperature decreases in the cooler seasons, the duckweed gradually dies, the DO concentration gradually rises, and the black odor phenomenon is weakened. Therefore, black and odorous water presents significant seasonal variations [44]. In addition, a larger number of water bodies increase the potential for pollution and biochemical reactions. Pits and ponds, which have poorer mobility, are more prone to oxygen depletion owing to increased temperatures, making them more likely to contribute to water pollution than ditches and canals. The combination of temperature and the number of water bodies acts as a driving factor for these classes of black and odorous water bodies. The premountain plains in the western and southern parts of Hebei Province are part of the region’s high-temperature zone. Although higher temperatures can promote biochemical reactions in water bodies, the relatively low number of water bodies in these areas means that the problem of black and odorous water bodies is less severe than in the eastern plains. In the mountainous areas of the western and northern parts of Hebei Province, where temperatures are lower and the number of water bodies is smaller, the problem of black and odorous water is even less prominent.

4.2 Recommendations

Through spatial analysis and factor detection, the deep-rooed reasons behind the formation of black and odorous water bodies have been revealed herein. Based on these findings, the following governance recommendations are proposed, which are discussed in terms of policies and regulations, infrastructure, and ecological restoration.

4.2.1 Policies and regulations

The spatial distribution of black and odorous water bodies in Hebei Province exhibits clear clustering characteristics, with several prominent nuclear density centers. Targeted management measures should be implemented with a focus on key areas. Special attention should be given to monitoring and managing the region formed by Cangzhou–Baoding–Langfang, which constitutes a triangle of high concern. A cross-regional joint prevention and control mechanism should be established, and relevant departments and management bodies between cities and municipalities must coordinate through multiagent cooperation innovation [45] to collectively control pollution sources and restore the water ecosystem.

In addition, as this region is a part of the central core functional area, urban construction, economic development, and the relocation of Beijing’s noncapital functions are key priorities. However, environmental protection and governance should not be overlooked during this process. Environmental protection funds should be allocated with a focus on this triangle, promoting more efficient governance and ensuring optimal resource distribution, which will help balance environmental quality improvement with social development, ultimately fostering a green and sustainable path for the region’s social development.

Moreover, existing environmental protection laws and regulations should be revised and improved to meet the stricter environmental requirements of the new era. From a policy perspective, the legal discharge standards for various sewage indicators – such as chemical oxygen demand, ammonia nitrogen, total nitrogen, and total phosphorus – should be strengthened. Increased supervision is also necessary to ensure effective enforcement of laws and regulations. Green manufacturing relies on the systematic innovation of green technology; meanwhile, the green transformation of the manufacturing industry requires stricter environmental requirements and systematic innovations of emission technology [46]. The imposition of severe penalties will promote the adoption of environmentally friendly manufacturing and emission reduction [22].

4.2.2 Infrastructure

Existing sewage treatment facilities must be expanded and upgraded to enhance the drainage system’s treatment capacity, thereby allowing it to handle the increased pollution load caused by population concentration. Further, the layout of drainage pipes must be optimized, and the issues of pipe mixing and misconnection must be promptly rectified to ensure that domestic sewage flows directly into sewage treatment facilities. Furthermore, intelligent diversion wells must be constructed to control pollution from pits, ponds, and ditches, especially during initial rainfall runoff. Intelligent diversion wells use integrated water-quality monitors, level gauges, and Internet-of-Things technology to smartly divert sewage, early rainwater, and subsequent rainwater. These wells are already effectively deployed in cities such as Wuhan [47], Taiyuan [48], Nanchang [49], and Wuji County, Hebei Province [50]. Given their success, China should implement a unified technical standard to support the application of this technology [48].

Existing domestic waste incineration power plants must be expanded and upgraded, and their treatment capacities must be increased to match the population density. Moreover, temporary storage sites for domestic waste must be constructed in areas with high population density, and environmental protection education programs must be conducted to reduce the effect of waste on water bodies. Furthermore, efforts to improve rural waste collection and transfer mechanisms should be strengthened.

4.2.3 Ecological restoration

Water bodies must be ecologically restored and the DO content of water bodies must be improved. This can be achieved by installing additional aeration devices in key areas to actively boost oxygen levels of water bodies, especially in pits and ponds affected by rising temperatures. These measures will help comprehensively improve the overall enviromental quality. Due to the simple structure and low cost of aeration and aeration equipment, it has become the main treatment method of mild black and odorous water bodies in Hebei Province [23].

4.3 Limitations and future research

This research used the publicly available data on black and odorous water bodies at all governmental levels. The field survey identified blackness/odor regression and seasonal changes in some black and odorous water bodies. Such sudden and seasonal changes will introduce bias to the study results and change the spatial distributions and influencing factors of black and odorous water bodies. The present investigation in Hebei Province has proceeded over 2 years and is currently extending into the third year. In future research, black malodorous water bodies will be identified in multiple periods using deep-learning-based methods, and their spatial and temporal variations will be examined after many years of data collection on such bodies in Hebei Province.

5 Conclusion

Herein, we analyzed 518 publicly available datasets on black and odorous water bodies in Hebei Province and examined their spatial distribution characteristics using the nearest-neighbor index, kernel density estimation, and geodetector methods. The natural and social factors influencing the spatial distribution of these water bodies in the region were identified. The key conclusions of our analysis are as follows:

  1. All black and odorous water body points, ditch-, pit-, and pond-type black and odorous water body points, and lightly and heavily black and odorous water body points in Hebei Province showed notable clustering distribution patterns in space. These bodies were primarily distributed in the Cangzhou–Baoding–Langfang triangle.

  2. Single-factor detection results showed that the number of water bodies was the main factor influencing the distribution of all types of black and odorous water bodies in Hebei Province. Meanwhile, ammonia nitrogen emissions had a significant effect on ditch-type and heavily black and odorous waters bodies in Hebei Province. The interaction between any two factors was dominated by nonlinear enhancement and two-factor enhancement, and their interactions strengthened the effect of individual factors on the number of black and odorous waters bodies. Key factors, such as the number of water bodies, drainage density, population density, waste disposal capacity, and temperature, were identified as important contributors to the occurrence of black and odorous water bodies in Hebei Province.

  3. The findings revealed several deep-rooted causes of the distribution of black and odorous water bodies, including drainage problems, waste treatment capacity, ammonia nitrogen emissions, water volume, and temperature. The mechanisms by which these causes lead to black and odorous water were investigated in depth, and a comprehensive approach for managing these water bodies in Hebei Province was suggested. Management strategies should go beyond superficial solutions like covering or filling and should include comprehensive reforms in policies and regulations, improvements in infrastructure, and focused efforts on ecological restoration.

Acknowledgments

The authors appreciate the constructive suggestions provided by the anonymous reviewers, which have helped considerably improve the quality of this article.

  1. Funding information: This study received financial support from the Key Laboratory of Airborne Survey and Remote Sensing Technology, which also contributed to the study design, data collection, analysis, interpretation, manuscript preparation, and the decision to submit for publication.

  2. Author contributions: L.Q.L. prepared the manuscript with contributions from all coauthors; L.H.X. and W.B. designed the experiments; Z.E. controlled the quality; and X.Q., S.Y.B., W.S., and H.S.F. collected and collated the data.

  3. Conflict of interest: The authors state no conflict of interest.

  4. Data availability statement: We utilized publicly accessible data on black and odorous water bodies regulated at the provincial, prefectural, and county levels. More detailed attribute information of these water bodies has not been released by the government. We conducted on - site verification of these water bodies, but certain limitations prevent the disclosure of all such information to the public.

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Received: 2025-02-06
Revised: 2025-06-06
Accepted: 2025-06-06
Published Online: 2025-07-14

© 2025 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  47. Pore, fracture characteristics and diagenetic evolution of medium-maturity marine shales from the Silurian Longmaxi Formation, NE Sichuan Basin, China
  48. Study of the earthquakes source parameters, site response, and path attenuation using P and S-waves spectral inversion, Aswan region, south Egypt
  49. Source of contamination and assessment of potential health risks of potentially toxic metal(loid)s in agricultural soil from Al Lith, Saudi Arabia
  50. Regional spatiotemporal evolution and influencing factors of rural construction areas in the Nanxi River Basin via GIS
  51. An efficient network for object detection in scale-imbalanced remote sensing images
  52. Effect of microscopic pore–throat structure heterogeneity on waterflooding seepage characteristics of tight sandstone reservoirs
  53. Environmental health risk assessment of Zn, Cd, Pb, Fe, and Co in coastal sediments of the southeastern Gulf of Aqaba
  54. A modified Hoek–Brown model considering softening effects and its applications
  55. Evaluation of engineering properties of soil for sustainable urban development
  56. The spatio-temporal characteristics and influencing factors of sustainable development in China’s provincial areas
  57. Application of a mixed additive and multiplicative random error model to generate DTM products from LiDAR data
  58. Gold vein mineralogy and oxygen isotopes of Wadi Abu Khusheiba, Jordan
  59. Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms
  60. 2D–3D Geological features collaborative identification of surrounding rock structural planes in hydraulic adit based on OC-AINet
  61. Spatiotemporal patterns and drivers of Chl-a in Chinese lakes between 1986 and 2023
  62. Land use classification through fusion of remote sensing images and multi-source data
  63. Nexus between renewable energy, technological innovation, and carbon dioxide emissions in Saudi Arabia
  64. Analysis of the spillover effects of green organic transformation on sustainable development in ethnic regions’ agriculture and animal husbandry
  65. Factors impacting spatial distribution of black and odorous water bodies in Hebei
  66. Large-scale shaking table tests on the liquefaction and deformation responses of an ultra-deep overburden
  67. Impacts of climate change and sea-level rise on the coastal geological environment of Quang Nam province, Vietnam
  68. Reservoir characterization and exploration potential of shale reservoir near denudation area: A case study of Ordovician–Silurian marine shale, China
  69. Seismic prediction of Permian volcanic rock reservoirs in Southwest Sichuan Basin
  70. Application of CBERS-04 IRS data to land surface temperature inversion: A case study based on Minqin arid area
  71. Geological characteristics and prospecting direction of Sanjiaoding gold mine in Saishiteng area
  72. Research on the deformation prediction model of surrounding rock based on SSA-VMD-GRU
  73. Geochronology, geochemical characteristics, and tectonic significance of the granites, Menghewula, Southern Great Xing’an range
  74. Hazard classification of active faults in Yunnan base on probabilistic seismic hazard assessment
  75. Characteristics analysis of hydrate reservoirs with different geological structures developed by vertical well depressurization
  76. Estimating the travel distance of channelized rock avalanches using genetic programming method
  77. Landscape preferences of hikers in Three Parallel Rivers Region and its adjacent regions by content analysis of user-generated photography
  78. New age constraints of the LGM onset in the Bohemian Forest – Central Europe
  79. Characteristics of geological evolution based on the multifractal singularity theory: A case study of Heyu granite and Mesozoic tectonics
  80. Soil water content and longitudinal microbiota distribution in disturbed areas of tower foundations of power transmission and transformation projects
  81. Oil accumulation process of the Kongdian reservoir in the deep subsag zone of the Cangdong Sag, Bohai Bay Basin, China
  82. Investigation of velocity profile in rock–ice avalanche by particle image velocimetry measurement
  83. Optimizing 3D seismic survey geometries using ray tracing and illumination modeling: A case study from Penobscot field
  84. Sedimentology of the Phra That and Pha Daeng Formations: A preliminary evaluation of geological CO2 storage potential in the Lampang Basin, Thailand
  85. Improved classification algorithm for hyperspectral remote sensing images based on the hybrid spectral network model
  86. Map analysis of soil erodibility rates and gully erosion sites in Anambra State, South Eastern Nigeria
  87. Identification and driving mechanism of land use conflict in China’s South-North transition zone: A case study of Huaihe River Basin
  88. Evaluation of the impact of land-use change on earthquake risk distribution in different periods: An empirical analysis from Sichuan Province
  89. A test site case study on the long-term behavior of geotextile tubes
  90. An experimental investigation into carbon dioxide flooding and rock dissolution in low-permeability reservoirs of the South China Sea
  91. Detection and semi-quantitative analysis of naphthenic acids in coal and gangue from mining areas in China
  92. Comparative effects of olivine and sand on KOH-treated clayey soil
  93. YOLO-MC: An algorithm for early forest fire recognition based on drone image
  94. Earthquake building damage classification based on full suite of Sentinel-1 features
  95. Potential landslide detection and influencing factors analysis in the upper Yellow River based on SBAS-InSAR technology
  96. Assessing green area changes in Najran City, Saudi Arabia (2013–2022) using hybrid deep learning techniques
  97. An advanced approach integrating methods to estimate hydraulic conductivity of different soil types supported by a machine learning model
  98. Hybrid methods for land use and land cover classification using remote sensing and combined spectral feature extraction: A case study of Najran City, KSA
  99. Streamlining digital elevation model construction from historical aerial photographs: The impact of reference elevation data on spatial accuracy
  100. Analysis of urban expansion patterns in the Yangtze River Delta based on the fusion impervious surfaces dataset
  101. A metaverse-based visual analysis approach for 3D reservoir models
  102. Late Quaternary record of 100 ka depositional cycles on the Larache shelf (NW Morocco)
  103. Integrated well-seismic analysis of sedimentary facies distribution: A case study from the Mesoproterozoic, Ordos Basin, China
  104. Study on the spatial equilibrium of cultural and tourism resources in Macao, China
  105. Urban road surface condition detecting and integrating based on the mobile sensing framework with multi-modal sensors
  106. Application of improved sine cosine algorithm with chaotic mapping and novel updating methods for joint inversion of resistivity and surface wave data
  107. The synergistic use of AHP and GIS to assess factors driving forest fire potential in a peat swamp forest in Thailand
  108. Dynamic response analysis and comprehensive evaluation of cement-improved aeolian sand roadbed
  109. Rock control on evolution of Khorat Cuesta, Khorat UNESCO Geopark, Northeastern Thailand
  110. Gradient response mechanism of carbon storage: Spatiotemporal analysis of economic-ecological dimensions based on hybrid machine learning
  111. Comparison of several seismic active earth pressure calculation methods for retaining structures
  112. Mantle dynamics and petrogenesis of Gomer basalts in the Northwestern Ethiopia: A geochemical perspective
  113. Study on ground deformation monitoring in Xiong’an New Area from 2021 to 2023 based on DS-InSAR
  114. Paleoenvironmental characteristics of continental shale and its significance to organic matter enrichment: Taking the fifth member of Xujiahe Formation in Tianfu area of Sichuan Basin as an example
  115. Equipping the integral approach with generalized least squares to reconstruct relict channel profile and its usage in the Shanxi Rift, northern China
  116. InSAR-driven landslide hazard assessment along highways in hilly regions: A case-based validation approach
  117. Attribution analysis of multi-temporal scale surface streamflow changes in the Ganjiang River based on a multi-temporal Budyko framework
  118. Maps analysis of Najran City, Saudi Arabia to enhance agricultural development using hybrid system of ANN and multi-CNN models
  119. Hybrid deep learning with a random forest system for sustainable agricultural land cover classification using DEM in Najran, Saudi Arabia
  120. Long-term evolution patterns of groundwater depth and lagged response to precipitation in a complex aquifer system: Insights from Huaibei Region, China
  121. Remote sensing and machine learning for lithology and mineral detection in NW, Pakistan
  122. Spatial–temporal variations of NO2 pollution in Shandong Province based on Sentinel-5P satellite data and influencing factors
  123. 10.1515/geo-2025-0901
  124. Numerical modeling of geothermal energy piles with sensitivity and parameter variation analysis of a case study
  125. Review Articles
  126. Humic substances influence on the distribution of dissolved iron in seawater: A review of electrochemical methods and other techniques
  127. Applications of physics-informed neural networks in geosciences: From basic seismology to comprehensive environmental studies
  128. Ore-controlling structures of granite-related uranium deposits in South China: A review
  129. Shallow geological structure features in Balikpapan Bay East Kalimantan Province – Indonesia
  130. A review on the tectonic affinity of microcontinents and evolution of the Proto-Tethys Ocean in Northeastern Tibet
  131. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part II
  132. Depopulation in the Visok micro-region: Toward demographic and economic revitalization
  133. Special Issue: Geospatial and Environmental Dynamics - Part II
  134. Advancing urban sustainability: Applying GIS technologies to assess SDG indicators – a case study of Podgorica (Montenegro)
  135. Spatiotemporal and trend analysis of common cancers in men in Central Serbia (1999–2021)
  136. Minerals for the green agenda, implications, stalemates, and alternatives
  137. Spatiotemporal water quality analysis of Vrana Lake, Croatia
  138. Functional transformation of settlements in coal exploitation zones: A case study of the municipality of Stanari in Republic of Srpska (Bosnia and Herzegovina)
  139. Hypertension in AP Vojvodina (Northern Serbia): A spatio-temporal analysis of patients at the Institute for Cardiovascular Diseases of Vojvodina
  140. Regional patterns in cause-specific mortality in Montenegro, 1991–2019
  141. Spatio-temporal analysis of flood events using GIS and remote sensing-based approach in the Ukrina River Basin, Bosnia and Herzegovina
  142. Flash flood susceptibility mapping using LiDAR-Derived DEM and machine learning algorithms: Ljuboviđa case study, Serbia
  143. Geocultural heritage as a basis for geotourism development: Banjska Monastery, Zvečan (Serbia)
  144. Assessment of groundwater potential zones using GIS and AHP techniques – A case study of the zone of influence of Kolubara Mining Basin
  145. Impact of the agri-geographical transformation of rural settlements on the geospatial dynamics of soil erosion intensity in municipalities of Central Serbia
  146. Where faith meets geomorphology: The cultural and religious significance of geodiversity explored through geospatial technologies
  147. Applications of local climate zone classification in European cities: A review of in situ and mobile monitoring methods in urban climate studies
  148. Complex multivariate water quality impact assessment on Krivaja River
  149. Ionization hotspots near waterfalls in Eastern Serbia’s Stara Planina Mountain
  150. Shift in landscape use strategies during the transition from the Bronze age to Iron age in Northwest Serbia
  151. Assessing the geotourism potential of glacial lakes in Plav, Montenegro: A multi-criteria assessment by using the M-GAM model
  152. Flash flood potential index at national scale: Susceptibility assessment within catchments
https://doi.org/10.1515/geo-2025-0827
Schlagwörter für diesen Artikel
water pollution; black and odorous water bodies; ammonia nitrogen emissions; drainage infrastructure; spatial distribution; influencing factors; geodetector
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Artikel in diesem Heft

  1. Research Articles
  2. Seismic response and damage model analysis of rocky slopes with weak interlayers
  3. Multi-scenario simulation and eco-environmental effect analysis of “Production–Living–Ecological space” based on PLUS model: A case study of Anyang City
  4. Remote sensing estimation of chlorophyll content in rape leaves in Weibei dryland region of China
  5. GIS-based frequency ratio and Shannon entropy modeling for landslide susceptibility mapping: A case study in Kundah Taluk, Nilgiris District, India
  6. Natural gas origin and accumulation of the Changxing–Feixianguan Formation in the Puguang area, China
  7. Spatial variations of shear-wave velocity anomaly derived from Love wave ambient noise seismic tomography along Lembang Fault (West Java, Indonesia)
  8. Evaluation of cumulative rainfall and rainfall event–duration threshold based on triggering and non-triggering rainfalls: Northern Thailand case
  9. Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan
  10. The use of radar-optical remote sensing data and geographic information system–analytical hierarchy process–multicriteria decision analysis techniques for revealing groundwater recharge prospective zones in arid-semi arid lands
  11. Effect of pore throats on the reservoir quality of tight sandstone: A case study of the Yanchang Formation in the Zhidan area, Ordos Basin
  12. Hydroelectric simulation of the phreatic water response of mining cracked soil based on microbial solidification
  13. Spatial-temporal evolution of habitat quality in tropical monsoon climate region based on “pattern–process–quality” – a case study of Cambodia
  14. Early Permian to Middle Triassic Formation petroleum potentials of Sydney Basin, Australia: A geochemical analysis
  15. Micro-mechanism analysis of Zhongchuan loess liquefaction disaster induced by Jishishan M6.2 earthquake in 2023
  16. Prediction method of S-wave velocities in tight sandstone reservoirs – a case study of CO2 geological storage area in Ordos Basin
  17. Ecological restoration in valley area of semiarid region damaged by shallow buried coal seam mining
  18. Hydrocarbon-generating characteristics of Xujiahe coal-bearing source rocks in the continuous sedimentary environment of the Southwest Sichuan
  19. Hazard analysis of future surface displacements on active faults based on the recurrence interval of strong earthquakes
  20. Structural characterization of the Zalm district, West Saudi Arabia, using aeromagnetic data: An approach for gold mineral exploration
  21. Research on the variation in the Shields curve of silt initiation
  22. Reuse of agricultural drainage water and wastewater for crop irrigation in southeastern Algeria
  23. Assessing the effectiveness of utilizing low-cost inertial measurement unit sensors for producing as-built plans
  24. Analysis of the formation process of a natural fertilizer in the loess area
  25. Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco)
  26. Chemical dissolution and the source of salt efflorescence in weathering of sandstone cultural relics
  27. Molecular simulation of methane adsorption capacity in transitional shale – a case study of Longtan Formation shale in Southern Sichuan Basin, SW China
  28. Evolution characteristics of extreme maximum temperature events in Central China and adaptation strategies under different future warming scenarios
  29. Estimating Bowen ratio in local environment based on satellite imagery
  30. 3D fusion modeling of multi-scale geological structures based on subdivision-NURBS surfaces and stratigraphic sequence formalization
  31. Comparative analysis of machine learning algorithms in Google Earth Engine for urban land use dynamics in rapidly urbanizing South Asian cities
  32. Study on the mechanism of plant root influence on soil properties in expansive soil areas
  33. Simulation of seismic hazard parameters and earthquakes source mechanisms along the Red Sea rift, western Saudi Arabia
  34. Tectonics vs sedimentation in foredeep basins: A tale from the Oligo-Miocene Monte Falterona Formation (Northern Apennines, Italy)
  35. Investigation of landslide areas in Tokat-Almus road between Bakımlı-Almus by the PS-InSAR method (Türkiye)
  36. Predicting coastal variations in non-storm conditions with machine learning
  37. Cross-dimensional adaptivity research on a 3D earth observation data cube model
  38. Geochronology and geochemistry of late Paleozoic volcanic rocks in eastern Inner Mongolia and their geological significance
  39. Spatial and temporal evolution of land use and habitat quality in arid regions – a case of Northwest China
  40. Ground-penetrating radar imaging of subsurface karst features controlling water leakage across Wadi Namar dam, south Riyadh, Saudi Arabia
  41. Rayleigh wave dispersion inversion via modified sine cosine algorithm: Application to Hangzhou, China passive surface wave data
  42. Fractal insights into permeability control by pore structure in tight sandstone reservoirs, Heshui area, Ordos Basin
  43. Debris flow hazard characteristic and mitigation in Yusitong Gully, Hengduan Mountainous Region
  44. Research on community characteristics of vegetation restoration in hilly power engineering based on multi temporal remote sensing technology
  45. Identification of radial drainage networks based on topographic and geometric features
  46. Trace elements and melt inclusion in zircon within the Qunji porphyry Cu deposit: Application to the metallogenic potential of the reduced magma-hydrothermal system
  47. Pore, fracture characteristics and diagenetic evolution of medium-maturity marine shales from the Silurian Longmaxi Formation, NE Sichuan Basin, China
  48. Study of the earthquakes source parameters, site response, and path attenuation using P and S-waves spectral inversion, Aswan region, south Egypt
  49. Source of contamination and assessment of potential health risks of potentially toxic metal(loid)s in agricultural soil from Al Lith, Saudi Arabia
  50. Regional spatiotemporal evolution and influencing factors of rural construction areas in the Nanxi River Basin via GIS
  51. An efficient network for object detection in scale-imbalanced remote sensing images
  52. Effect of microscopic pore–throat structure heterogeneity on waterflooding seepage characteristics of tight sandstone reservoirs
  53. Environmental health risk assessment of Zn, Cd, Pb, Fe, and Co in coastal sediments of the southeastern Gulf of Aqaba
  54. A modified Hoek–Brown model considering softening effects and its applications
  55. Evaluation of engineering properties of soil for sustainable urban development
  56. The spatio-temporal characteristics and influencing factors of sustainable development in China’s provincial areas
  57. Application of a mixed additive and multiplicative random error model to generate DTM products from LiDAR data
  58. Gold vein mineralogy and oxygen isotopes of Wadi Abu Khusheiba, Jordan
  59. Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms
  60. 2D–3D Geological features collaborative identification of surrounding rock structural planes in hydraulic adit based on OC-AINet
  61. Spatiotemporal patterns and drivers of Chl-a in Chinese lakes between 1986 and 2023
  62. Land use classification through fusion of remote sensing images and multi-source data
  63. Nexus between renewable energy, technological innovation, and carbon dioxide emissions in Saudi Arabia
  64. Analysis of the spillover effects of green organic transformation on sustainable development in ethnic regions’ agriculture and animal husbandry
  65. Factors impacting spatial distribution of black and odorous water bodies in Hebei
  66. Large-scale shaking table tests on the liquefaction and deformation responses of an ultra-deep overburden
  67. Impacts of climate change and sea-level rise on the coastal geological environment of Quang Nam province, Vietnam
  68. Reservoir characterization and exploration potential of shale reservoir near denudation area: A case study of Ordovician–Silurian marine shale, China
  69. Seismic prediction of Permian volcanic rock reservoirs in Southwest Sichuan Basin
  70. Application of CBERS-04 IRS data to land surface temperature inversion: A case study based on Minqin arid area
  71. Geological characteristics and prospecting direction of Sanjiaoding gold mine in Saishiteng area
  72. Research on the deformation prediction model of surrounding rock based on SSA-VMD-GRU
  73. Geochronology, geochemical characteristics, and tectonic significance of the granites, Menghewula, Southern Great Xing’an range
  74. Hazard classification of active faults in Yunnan base on probabilistic seismic hazard assessment
  75. Characteristics analysis of hydrate reservoirs with different geological structures developed by vertical well depressurization
  76. Estimating the travel distance of channelized rock avalanches using genetic programming method
  77. Landscape preferences of hikers in Three Parallel Rivers Region and its adjacent regions by content analysis of user-generated photography
  78. New age constraints of the LGM onset in the Bohemian Forest – Central Europe
  79. Characteristics of geological evolution based on the multifractal singularity theory: A case study of Heyu granite and Mesozoic tectonics
  80. Soil water content and longitudinal microbiota distribution in disturbed areas of tower foundations of power transmission and transformation projects
  81. Oil accumulation process of the Kongdian reservoir in the deep subsag zone of the Cangdong Sag, Bohai Bay Basin, China
  82. Investigation of velocity profile in rock–ice avalanche by particle image velocimetry measurement
  83. Optimizing 3D seismic survey geometries using ray tracing and illumination modeling: A case study from Penobscot field
  84. Sedimentology of the Phra That and Pha Daeng Formations: A preliminary evaluation of geological CO2 storage potential in the Lampang Basin, Thailand
  85. Improved classification algorithm for hyperspectral remote sensing images based on the hybrid spectral network model
  86. Map analysis of soil erodibility rates and gully erosion sites in Anambra State, South Eastern Nigeria
  87. Identification and driving mechanism of land use conflict in China’s South-North transition zone: A case study of Huaihe River Basin
  88. Evaluation of the impact of land-use change on earthquake risk distribution in different periods: An empirical analysis from Sichuan Province
  89. A test site case study on the long-term behavior of geotextile tubes
  90. An experimental investigation into carbon dioxide flooding and rock dissolution in low-permeability reservoirs of the South China Sea
  91. Detection and semi-quantitative analysis of naphthenic acids in coal and gangue from mining areas in China
  92. Comparative effects of olivine and sand on KOH-treated clayey soil
  93. YOLO-MC: An algorithm for early forest fire recognition based on drone image
  94. Earthquake building damage classification based on full suite of Sentinel-1 features
  95. Potential landslide detection and influencing factors analysis in the upper Yellow River based on SBAS-InSAR technology
  96. Assessing green area changes in Najran City, Saudi Arabia (2013–2022) using hybrid deep learning techniques
  97. An advanced approach integrating methods to estimate hydraulic conductivity of different soil types supported by a machine learning model
  98. Hybrid methods for land use and land cover classification using remote sensing and combined spectral feature extraction: A case study of Najran City, KSA
  99. Streamlining digital elevation model construction from historical aerial photographs: The impact of reference elevation data on spatial accuracy
  100. Analysis of urban expansion patterns in the Yangtze River Delta based on the fusion impervious surfaces dataset
  101. A metaverse-based visual analysis approach for 3D reservoir models
  102. Late Quaternary record of 100 ka depositional cycles on the Larache shelf (NW Morocco)
  103. Integrated well-seismic analysis of sedimentary facies distribution: A case study from the Mesoproterozoic, Ordos Basin, China
  104. Study on the spatial equilibrium of cultural and tourism resources in Macao, China
  105. Urban road surface condition detecting and integrating based on the mobile sensing framework with multi-modal sensors
  106. Application of improved sine cosine algorithm with chaotic mapping and novel updating methods for joint inversion of resistivity and surface wave data
  107. The synergistic use of AHP and GIS to assess factors driving forest fire potential in a peat swamp forest in Thailand
  108. Dynamic response analysis and comprehensive evaluation of cement-improved aeolian sand roadbed
  109. Rock control on evolution of Khorat Cuesta, Khorat UNESCO Geopark, Northeastern Thailand
  110. Gradient response mechanism of carbon storage: Spatiotemporal analysis of economic-ecological dimensions based on hybrid machine learning
  111. Comparison of several seismic active earth pressure calculation methods for retaining structures
  112. Mantle dynamics and petrogenesis of Gomer basalts in the Northwestern Ethiopia: A geochemical perspective
  113. Study on ground deformation monitoring in Xiong’an New Area from 2021 to 2023 based on DS-InSAR
  114. Paleoenvironmental characteristics of continental shale and its significance to organic matter enrichment: Taking the fifth member of Xujiahe Formation in Tianfu area of Sichuan Basin as an example
  115. Equipping the integral approach with generalized least squares to reconstruct relict channel profile and its usage in the Shanxi Rift, northern China
  116. InSAR-driven landslide hazard assessment along highways in hilly regions: A case-based validation approach
  117. Attribution analysis of multi-temporal scale surface streamflow changes in the Ganjiang River based on a multi-temporal Budyko framework
  118. Maps analysis of Najran City, Saudi Arabia to enhance agricultural development using hybrid system of ANN and multi-CNN models
  119. Hybrid deep learning with a random forest system for sustainable agricultural land cover classification using DEM in Najran, Saudi Arabia
  120. Long-term evolution patterns of groundwater depth and lagged response to precipitation in a complex aquifer system: Insights from Huaibei Region, China
  121. Remote sensing and machine learning for lithology and mineral detection in NW, Pakistan
  122. Spatial–temporal variations of NO2 pollution in Shandong Province based on Sentinel-5P satellite data and influencing factors
  123. 10.1515/geo-2025-0901
  124. Numerical modeling of geothermal energy piles with sensitivity and parameter variation analysis of a case study
  125. Review Articles
  126. Humic substances influence on the distribution of dissolved iron in seawater: A review of electrochemical methods and other techniques
  127. Applications of physics-informed neural networks in geosciences: From basic seismology to comprehensive environmental studies
  128. Ore-controlling structures of granite-related uranium deposits in South China: A review
  129. Shallow geological structure features in Balikpapan Bay East Kalimantan Province – Indonesia
  130. A review on the tectonic affinity of microcontinents and evolution of the Proto-Tethys Ocean in Northeastern Tibet
  131. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part II
  132. Depopulation in the Visok micro-region: Toward demographic and economic revitalization
  133. Special Issue: Geospatial and Environmental Dynamics - Part II
  134. Advancing urban sustainability: Applying GIS technologies to assess SDG indicators – a case study of Podgorica (Montenegro)
  135. Spatiotemporal and trend analysis of common cancers in men in Central Serbia (1999–2021)
  136. Minerals for the green agenda, implications, stalemates, and alternatives
  137. Spatiotemporal water quality analysis of Vrana Lake, Croatia
  138. Functional transformation of settlements in coal exploitation zones: A case study of the municipality of Stanari in Republic of Srpska (Bosnia and Herzegovina)
  139. Hypertension in AP Vojvodina (Northern Serbia): A spatio-temporal analysis of patients at the Institute for Cardiovascular Diseases of Vojvodina
  140. Regional patterns in cause-specific mortality in Montenegro, 1991–2019
  141. Spatio-temporal analysis of flood events using GIS and remote sensing-based approach in the Ukrina River Basin, Bosnia and Herzegovina
  142. Flash flood susceptibility mapping using LiDAR-Derived DEM and machine learning algorithms: Ljuboviđa case study, Serbia
  143. Geocultural heritage as a basis for geotourism development: Banjska Monastery, Zvečan (Serbia)
  144. Assessment of groundwater potential zones using GIS and AHP techniques – A case study of the zone of influence of Kolubara Mining Basin
  145. Impact of the agri-geographical transformation of rural settlements on the geospatial dynamics of soil erosion intensity in municipalities of Central Serbia
  146. Where faith meets geomorphology: The cultural and religious significance of geodiversity explored through geospatial technologies
  147. Applications of local climate zone classification in European cities: A review of in situ and mobile monitoring methods in urban climate studies
  148. Complex multivariate water quality impact assessment on Krivaja River
  149. Ionization hotspots near waterfalls in Eastern Serbia’s Stara Planina Mountain
  150. Shift in landscape use strategies during the transition from the Bronze age to Iron age in Northwest Serbia
  151. Assessing the geotourism potential of glacial lakes in Plav, Montenegro: A multi-criteria assessment by using the M-GAM model
  152. Flash flood potential index at national scale: Susceptibility assessment within catchments
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