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Article

An Analysis of the Spatiotemporal Variability of Key Water Quality Parameters in China

by
Kexin Li
1,
Qichun Yang
1,2,* and
Xia Li
3
1
Thrust of Earth, Ocean and Atmospheric Sciences, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China
2
Center for Ocean Research in Hong Kong and Macau, Department of Ocean Science, Hong Kong University of Science and Technology, Hong Kong 999077, China
3
Research and Development Center for Watershed Environmental Eco-Engineering, Beijing Normal University, Zhuhai 519087, China
*
Author to whom correspondence should be addressed.
Hydrology 2024, 11(9), 135; https://doi.org/10.3390/hydrology11090135
Submission received: 29 June 2024 / Revised: 27 July 2024 / Accepted: 21 August 2024 / Published: 26 August 2024
(This article belongs to the Special Issue Novel Approaches in Contaminant Hydrology and Groundwater Remediation)
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Abstract

:
Intensifying anthropogenic disturbances have caused water pollution in China in recent decades. China has a vast territory with diverse climate conditions, land use types, and human activities, leading to significant water quality variability. However, few studies have investigated nationwide spatiotemporal patterns of key water quality parameters. In this study, we analyze monthly water quality observations from 3647 gauge stations to understand how water quality changes over time and space in China. We group the stations by water resource regions and adopt Python and SPSS to analyze the spatiotemporal variability and intercorrelations of eight water quality parameters. Results indicate that the concentrations of biochemical oxygen demand of 5 days (BOD5), chemical oxygen demand (COD), dissolved oxygen (DO), ammonia nitrogen (NH3-N), total nitrogen (TN), and total phosphorus (TP) show similar spatial patterns, with higher concentrations in the northern parts than the southern regions of China. The concentrations of COD and TP are higher in the rainy season than in the dry season, while DO, NH3-N, and TN show the opposite seasonal patterns. Strong positive correlations were found between BOD and COD, NH3-N and TP. The annual cumulative distribution figures demonstrate that all parameters showed slightly lower concentrations in 2022 and 2023 than in 2021, except for DO and TN. The TN/TP ratios across different water resource regions in China are significantly higher than 16, indicating that phosphorus is the limiting factor of eutrophication. This investigation provides a comprehensive understanding of the spatiotemporal variability of water quality parameters across China. The results of this study are highly valuable for investigating mechanisms regulating water quality across large spatial scales, thus providing valuable implications for improving water quality and mitigating water pollution.

1. Introduction

Water pollution has been a critical environmental problem that is increasingly concerning due to intensifying anthropogenic disturbances [1,2]. According to China’s 2023 Surface Water Environmental Quality Report (https://www.mee.gov.cn/hjzl/shj/, accessed on 5 June 2024) and China’s Surface Water Environmental Quality Standard (GB3838-2002) (https://www.mee.gov.cn/ywgz/fgbz/bz/, accessed on 13 May 2024), 10.6% of streams across the country cannot reach the Class III water quality condition, 1.5% of streams are categorized as Class V, while 0.7% of the streams have water quality conditions that are worse than Class V.
Intensifying human activities are the primary reason for water pollution. Anthropogenic disturbances, such as deforestation and the expansion of agricultural and urban areas, contribute to water quality degradation. Pollutants produced by human activities mentioned above are often insufficiently treated, and then transported to rivers and lakes through runoff, resulting in water pollution [3]. According to the 2022 Ecological Environment Statistical Annual Report of China (https://www.mee.gov.cn/hjzl/sthjzk/, accessed on 5 June 2024), agricultural and domestic sewage discharge were identified as two primary pollutant sources for chemical oxygen demand (COD), ammonia nitrogen (NH3-N), total nitrogen (TN), and total phosphorus (TP) in the surface waters of China.
Excessive nutrients transported to rivers cause a series of environmental problems. Sewage water and agricultural runoff containing large amounts of nutrients are the primary reasons for the boom of phytoplankton [4,5], which leads to decreases in the concentration of dissolved oxygen (DO) and increases in the biochemical oxygen demand of 5 days (BOD5) and COD. During algae bloom, toxic substances produced by algae and the decrease in DO can kill many organisms, accelerating eutrophication and the deterioration of water quality. Degraded water quality affects drinking water supply, aquaculture, and recreation [6]. For example, in 2014, excessive nitrogen and phosphorus caused an algae bloom in Lake Erie in the United States, resulting in a shutdown of water supplies to 500,000 residents over the past several decades [7]. In China, the cyanobacteria pollution incident in Lake Taihu in 2007 caused drinking water pollution, resulting in a serious water supply shortage for millions of residents in Wuxi City for nearly a week [8].
In addition to nutrients, heavy metals are another group of pollutants causing environmental problems. Watersheds with excessive heavy metals, such as Hg [9], Cd [10], and Cr [11], are not suitable for drinking water supply or water withdrawal for crop irrigation. Agricultural and industrial sewage have been identified as the primary sources of heavy metal pollution in rivers, especially in the Yellow River Basin and Northeast China [12]. Although lots of resources have been invested to control water pollution, it remains a problem across many parts of China.
China has a vast territory with various climate zones and population densities, resulting in substantial variability of water quality parameters over time and space. These parameters demonstrate strong seasonal variations due to changes in climate conditions and human activities over time [13]. Water quality also varies with the types of land use. For example, the concentrations of nutrients show a negative correlation with forest coverage and a positive correlation with the area of agricultural and urban land [14]. Water quality demonstrated close correlations with the drainage area properties, including reservoir densities, agricultural land areas, and rainfall, across the upper, middle, and downstream regions of the Yangtze River Basin [15]. Climate change also increases the risks of water pollution. For example, decreases in runoff due to drought can result in increases in nutrient concentrations, favoring the growth of algae and plankton [16].
Over the past four decades, since the reform and opening up, water bodies in China have experienced increasingly intensive anthropogenic disturbances. At the same time, the implementation of ecological protection policies has improved aquatic environment conditions at the local scale, mitigating the adverse impacts of the disturbances on water quality [17]. How these counteracting activities have jointly affected water quality remains unclear. As a result, an analysis of the spatiotemporal variability of water quality will provide valuable implications for balancing economic development and environmental protection [17,18].
Analyzing the spatial and temporal dynamics of water quality parameters [19,20] and their relationships with watershed properties [14] is needed to support water quality management [21]. Statistical analyses were adopted to analyze the characteristics of water quality [18,22], and to infer the driving forces of aquatic nutrients [17], as well as the relationships between human activities and water pollution [23]. For example, the Mann–Kendall trend analysis [24] and time-series decomposition methods were applied to investigate long-term trends and the seasonal variability of water quality [25]. Machine learning models were recently applied to estimate the spatial and temporal changes in nutrient concentrations [26,27,28]. Temporal trends of different land use types were also explored, to identify critical pollution sources.
However, previous investigations are limited by the lack of long-term nationwide monitoring data [17]. Existing analyses have mainly focused on one or a few watersheds [29,30,31,32], while only a few studies have paid attention to large spatial scales [33]. At the same time, the number of gauge stations and water quality parameters used in these studies is often small [32], and the water quality data used in some studies are relatively short [34]. For example, based on water quality observations in 2014 from 763 gauge stations, Wang et al. identified water pollution hotspots in North China [29]. Dou et al. analyzed water quality changes in the Huaihe River based on 78 gauge stations and identified the important impacts of river discharge and temperature on water quality [30]. Li et al. explored the spatiotemporal variability of TN, TP, COD, and NH3-N in Honghu Lake and highlighted the contribution of anthropogenic activities to water pollution [31]. Wu et al. investigated the water quality of the Pearl River Basin based on observations from 16 gauge stations, and suggested that regionalized management activities could help improve water quality [32]. Using data collected from 145 gauge stations, Zhou et al. [35] analyzed the impacts of population and economic development on water quality across China.
Long-term monitoring at a large spatial scale can provide a more comprehensive analysis of water quality conditions and the long-term impact of human activities on water quality [36,37]. In addition, existing studies in China mainly use comprehensive water quality indicators, such as the water quality index (WQI) and water pollution index (WPI), to evaluate the overall water quality of one or a few watersheds [38,39,40,41]. However, the spatiotemporal variability of individual water quality parameters has not been sufficiently investigated, limiting the applications of findings from these investigations to support water quality management. In addition, systematic water quality monitoring in China at the national scale only started in recent decades, and thus just accumulated much shorter periods of observations compared with other countries, such as the United States [36]. A national-scale analysis of water quality in China is needed to improve our understanding of how human activities affect water quality and watershed biogeochemistry.
In recognition of the necessity and existing challenges, this study provides a comprehensive analysis of the spatiotemporal variability of key water quality parameters across China, filling a gap in the current research on national-scale water pollution in China. We compiled a 3-year (2021–2023) monthly water quality dataset for 3647 gauge stations across China and analyzed the spatial distribution of water quality parameter concentrations across nine water resource regions. The seasonal and annual variation patterns of water quality parameters, the spatiotemporal variability of parameters, and the correlation between water quality parameters are investigated to understand the overall water pollution conditions in China and to infer the underlying mechanisms controlling water quality. The results of this study are expected to provide valuable information to policymakers to formulate pollution mitigation strategies.
The objectives of the study are to (1) provide an overall evaluation of water quality conditions in China, (2) understand how key water parameters vary over time and across China, and (3) explore the underlying mechanisms responsible for the variability of water quality. To achieve these objectives, we analyze the statistical characteristics of water quality parameters by water resource regions and months (Section 3.1, Section 3.2 and Section 3.3). To understand complex interactions among selected water quality parameters, we also quantify the intercorrelations of the parameter, as well as ratios between TN and TP (Section 3.4). Factors affecting water quality variability are discussed in Section 4.1, Section 4.2, Section 4.3 and Section 4.4.

2. Materials and Methods

2.1. Study Area

The study area of this investigation is mainland China (excluding the Hong Kong, Macao, and Taiwan regions), which is characterized by a variety of land use types and cultivation patterns, high heterogeneity of urban development, and different levels and types of water pollution (Figure 1). Dominant land use types include forest areas in the northeastern, southwestern, and southeastern areas of China, cropland in northern China, and desert in northwestern China. In northern China, wheat and corn are the primary crop types, while rice is mainly cultivated in southern China. The majority of the population is located in the eastern part of China. The climate types include a tropical monsoon climate and a subtropical monsoon climate located in southern China, as well as a temperate monsoon climate in northern China. The climate type in western China is a typical temperate continental climate, where Northwest parts of the country are characterized by an inland dry climate. Precipitation ranges from ca. 50 mm/year in the desert areas to over 2000 mm/year in the coastal areas of southern China. The average annual temperature ranges from ca. 5 to 25.1 °C.
According to the classification for primary water resource areas, mainland China includes nine major regions, including the Haihe River Basin, the Huaihe River Basin, the Northeast Area, the Northwest Area, the Pearl River Basin, the Southeast Area, the Southwest Area, the Yangtze River Basin, and the Yellow River Basin (Figure 1). Water resource regions, such as the Northeast Area, the Northwest Area, the Pearl River Basin, the Southeast Area, and the Southwest Area, are all composed of many smaller watersheds. In this study, we compiled water quality data from 3647 gauge stations across China to investigate water quality conditions nationwide. Both point and non-point source pollution have contributed to the degraded water quality in China.

2.2. Data Collection

The water quality data of this study are from the automatic water quality monitoring platform of the China General Environmental Monitoring Station network (https://www.cnemc.cn, accessed on 28 June 2024). The dataset includes monthly concentrations of water quality parameters from 3647 gauge stations for the period 2021–2023. Water quality parameters were selected based on pollution types, such as the depletion of oxygen, eutrophication, and biochemical pollution. In this study, we primarily focus on nutrients (e.g., TN, TP, and NH3-N) in watersheds, parameters indicating the oxygen levels in water (e.g., BOD5, COD, and DO), and concentrations of heavy metals (e.g., Cr6 and Pb).
According to the environmental quality standards for surface water (GB3838-2002) in Table 1, the concentration of water quality parameters of gauge stations is categorized into five classes (Table 1). For water bodies designated as sources of drinking water, the water quality must meet at least Class III standards. Classes IV and V represent mild and moderate pollution levels, respectively. Concentrations surpassing the Class V standard indicate severe pollution, which means measures must be taken to mitigate pollution. For heavy metal elements, higher standards are required for water sources that meet drinking water standards [42].

2.3. Data Analysis

In this research, we first calculated the averages of the selected parameters to demonstrate the magnitudes of the pollution levels. The spatiotemporal analysis was conducted using Box and Whisker Plots, which demonstrate the median, upper and lower quantiles, as well as the upper and lower extremes of the parameters, enabling the comparisons of water quality by month or water resource regions. Pearson correlation was adopted to analyze the intercorrelations among the selected water quality parameters [43]. We also used the cumulative distribution function to compare water quality conditions in different years [44].
The spatial distributions of water quality parameters were illustrated using ArcGIS based on the average concentrations of each gauge station (https://www.esri.com/, accessed on 26 July 2024). The cumulative probability distribution of water quality parameters was analyzed using Python (https://www.python.org/, accessed on 26 July 2024). We also used Python to plot the ratio of total nitrogen to total phosphorus across nine water resource regions. For the correlation analysis, we used IBM SPSS Statistics 23 to process the data and visualize the results (https://www.ibm.com/spss, accessed on 25 July 2024).

3. Results

3.1. Spatial Pattern of Water Quality Parameters

The distributions of water quality concentrations across the whole of China provide details of the spatial variability of water quality (Figure 2). As depicted in Figure 2a, a few gauge stations located along the downstream region of the Haihe River and the Yellow River have recorded BOD concentrations exceeding 4 mg/L, exceeding the Class III water quality standards for BOD. In Northeast China, COD concentrations above 20 mg/L are recorded across many gauge stations, with a few stations even showing COD concentrations over 30 mg/L (Class IV water quality standard). DO concentrations are notably low at the confluence of major tributaries and lakes within the Yangtze River Basin, as well as in the estuaries of the Yangtze and Pearl Rivers, and in the southeastern coastal regions (Figure 2).
A few gauge stations in the Yellow River Basins show Cr6 concentrations higher than 0.01 mg/L (Figure 2c), especially in Shaanxi Province and Gansu Province, where the concentrations of Cr6 are particularly high, even more than 0.1 mg/L, exceeding the Class V water quality standard. Gauge stations located in Liaoning and Anhui Provinces have recorded Pb concentrations close to 0.01 mg/L, approaching the water quality standards of Class I. High concentrations of NH3-N and TP often appear simultaneously, as shown in Figure 2e,h, in the Southeast of Northeast China, the Huang Huai Hai region, upstream of the Yangtze River Basin (east of Yunnan Province), and the coastal areas of South China.
The distribution of water quality parameters across different water resource regions shows significant variability (Figure 3). Specifically, BOD and COD concentrations are higher in the Huaihe River Basin, the Haihe River Basin, and Northeast regions, while being lower in the Pearl River Basin, Southwest, and Southeast China. In the latter three southern regions, levels of BOD and COD are categorized as Class I water quality conditions according to the China Environmental Quality standard of surface water. However, the concentrations of DO tend to be lower in southern regions. As can be seen in Figure 3d, the median values of DO concentrations in the Pearl River Basin and Southeast region are below 7.5 mg/L.
For heavy metal cations, the concentrations of Cr6 and Pb in the nine water resource areas are usually lower than 0.01 mg/L, meeting the Class I water quality standard. Large amounts of gauge stations observed Cr6 concentrations of 0.002 mg/L. The concentrations of nutrients (TN and TP) are higher in the Huaihe River Basin, the Haihe River Basin, and Northeast China, while being lower in the Southwest and Southeast China. From Figure 3e, it can be seen that the NH3-N concentrations of most gauge stations are below 0.5 mg/L, meeting the Class II water quality standard. Figure 3g demonstrates that the TP concentration is generally consistent with NH3-N spatially. Except for the Huaihe River Basin and Northeast China, the majority of gauge stations have TP concentrations below 0.1 mg/L, meeting the standard of Class II. The concentrations of TN are generally higher than TP, especially in the Haihe River Basin and the Yellow River Basin. In these two regions, the median values of TN concentrations are greater than 2 mg/L, exceeding the Class V standard of TN according to the national water quality criteria (Table 1).

3.2. Temporal Variability of Water Quality Parameters

The water quality parameters also demonstrate evident temporal changes (Figure 4). The concentrations of BOD fluctuate throughout the year and are slightly higher in the first half of the year than in the latter half. The COD concentrations are higher in winter and spring, but lower in summer and autumn. The BOD and COD concentrations of most water bodies in China are below 4 and 20 over different seasons, meeting the Class III water quality standards. Figure 4d demonstrates that the level of DO declines at the beginning of the year and then rises from late summer to the end of the year, and it is slightly higher in the first half of the year than in the latter half. For most water bodies, the concentrations of DO are higher than 6 mg/L over the whole year, meeting Class II standards, and most water bodies meet Class I water quality standards in winter and spring.
Most gauge stations have Cr6 concentrations of 0.002 mg/L. Pb concentrations are higher in spring and lower in winter (Figure 4f). Similar seasonal patterns can be seen in NH3-N and TN, with higher concentrations in the dry season (winter) compared to the wet season (summer). For NH3-N, most water bodies have concentrations lower than 0.5 mg/L in the whole year, reaching the level of the Class II water quality standard. However, for TN concentrations, the median reached 2 mg/L almost every month, which is the water quality standard of Class V. Figure 4h illustrates an opposite seasonal variation pattern of TP compared to NH3-N and TN, with increasing concentrations at the beginning of the year and decreasing concentrations after July. The concentrations of TP in summer are higher than in other seasons.
We further analyze how the water quality parameters have changed over the three years (Figure 5 and Figure 6). Compared to 2022 and 2023, slightly higher concentrations of BOD, COD, Cr6, NH3-N, and TP in 2021 accounted for a higher proportion, indicating that these water parameters might have improved over time. Especially for NH3-N, as depicted in Figure 5e, the concentrations decreased in the last two years. The average annual concentrations of DO were the lowest in 2022. No significant changes can be seen in the concentrations of TN (Figure 5g), which indicates that the issue of nitrogen pollution has not improved over time. More evident differences are observed in the plot of the high concentrations (top 10% for each parameter) (Figure 6). Except for Pb and TN, all the other six parameters show reductions in high concentrations, suggesting pollution was less severe in 2002 and 2003, relative to 2021.

3.3. Spatiotemporal Variability of Water Quality Parameters

Similarities can be found in the magnitude and spatiotemporal patterns of some of the selected water quality parameters (Figure 7 and Figure 8). Figure 7 demonstrates similar magnitudes of parameters BOD, COD, and DO across nine water resource regions. The concentrations of BOD and COD in the Huaihe River Basin, the Haihe River Basin, and Northeast China exhibit significantly higher values compared to other regions, particularly in summer. The monthly median concentrations of COD in Northeast China, the Huaihe River Basin, and the Haihe River Basin consistently exceed 15 mg/L, which is the value of Class I and Class II water quality standards. In all water resource regions, DO has lower concentrations in the wet season, showing an opposite seasonal change from COD. A sharp decrease in DO concentrations is observed in the Huaihe River Basin during summer. Except for the Southwest region and the Pearl River Basin, the average concentrations of DO in other water resource regions exceed 7.5 mg/L in different months of the year.
The concentrations of Cr6 at a few gauge stations in the Yellow River Basin show values higher than 0.01 mg/L in June, surpassing the Class I water quality standard and significantly higher than other water resource regions (Figure 7), indicating potential pollution caused by this element. It is necessary to conduct a comprehensive investigation of the sources of pollution that are responsible for the abnormally high Cr6 levels in this region. The concentrations and seasonal variabilities of Pb in the Pearl River Basin are markedly lower than the other regions.
In the Huaihe River Basin and the Haihe River Basin, NH3-N concentrations decrease in spring, followed by a sudden increase in July and August. In other regions, the concentrations of NH3-N are lower during the wet season than in the dry season. Figure 8 shows that the concentrations of TN in the Haihe River Basin, the Huaihe River Basin, the Yellow River Basin, and Northeast China are higher across all months compared to other regions. In addition, the seasonal variation in TN in the northern water resource regions also exhibits a greater fluctuation than that in the southern regions. Specifically, TN concentrations in northern China tend to decrease during spring and summer, and then increase during autumn and winter, whereas the Southwest water resource region shows an opposite temporal trend. Similarly, TP concentration is generally higher in the northern water resource regions than those in the south, showing increases during spring and summer, followed by a decrease in autumn and winter. Notably, the concentrations of TP in July and August are significantly higher compared to other months.

3.4. Intercorrelations among Parameters

Some parameters demonstrate strong intercorrelations (Figure 9). A strong positive correlation is observed between BOD and COD across all water resource regions. TP shows strong positive correlations with BOD and COD, while NH3-N displays a correlation with these two parameters as well. The concentrations of TN show weaker correlations with BOD and COD, especially in the Huaihe River Basin, the Haihe River Basin, and Northeast China. However, a strong positive correlation can be seen between TN, BOD, and COD concentrations in Southwest and Southeast China. DO exhibits weak negative correlations with nutrients (TN, TP, and NH3-N). In the northern water resource regions, DO and TN show a weak positive correlation, while a weak negative correlation can be seen in the southern regions. In the Yellow River Basin, Cr6 shows a weak positive correlation with COD and a strong positive correlation with TN. NH3-N shows a strong positive correlation with TP. Weak positive correlations between NH3-N and TN, as well as TN and TP, are observed in the northern region. Conversely, in the southern water resource regions like the Yangtze River Basin, the Pearl River Basin, and Southeast and Southwest China, strong positive correlations between these nutrient parameters are identified. The correlations between heavy metal and other water quality parameters are not significant.
The ratios of TN and TP demonstrate temporal and spatial variations across different water resource regions throughout the year (Figure 10). The TN/TP ratio is significantly higher than 16, which is considered the most suitable molar ratio for plankton growth. Higher ratios are observed in the Yellow River Basin, the Huaihe River Basin, and Northwest China, whereas relatively low ratios are observed in the Yangtze River Basin, the Pearl River Basin, and Southwest and Southeast China. For the Yellow River Basin and the Huaihe River Basin, high concentrations of TN and TP are observed simultaneously. A relatively high TN/TP ratio is noted in Northwest China, possibly due to the low TP concentration in this region. The TN/TP ratio decreases in spring and summer, and then increases in autumn and winter. In the water resource regions with higher TN/TP ratios, the ratio exhibits a significant change within a year.

4. Discussion

4.1. Overall Water Quality Condition in China

The spatial distribution of water quality parameters reveals that northern China exhibits higher concentrations for most of the selected water quality parameters than those in southern China, indicating poorer water quality conditions in the Huaihe River Basin, the Haihe River Basin, the Yellow River Basin, and Northeast China. This result aligns with previous investigations [45], which utilized the water quality index to assess water quality conditions across various water resource regions.
Water quality parameters also exhibit seasonal and annual fluctuations. The concentrations of COD and TP are higher in summer than in winter, but DO, NH3-N, and TN display opposite seasonal patterns. BOD, COD, Cr6, NH3-N, and TP show lower concentrations in 2022 and 2023 compared to 2021, suggesting an overall improvement in China’s water quality over time. This temporal trend of water quality change found in this study is in line with previous studies [2,41]. Conversely, TN concentrations have not shown a clear trend over the same time period, and the average concentration of TN across all water resource regions is worse than the Grade IV water quality standard, except for Northwest and Southwest China, indicating that nitrogen pollution will remain a challenge for water quality management in China.

4.2. Factors Affecting the Variability of Water Quality Parameters

The variability of water quality parameters is significantly influenced by numerous factors. Higher temperatures can influence the physical and chemical conditions of watersheds, such as pH, salinity, solubility, viscosity, and diffusion rates, which in turn affect biochemical processes, like nitrification and denitrification [46]. This might be the reason for the strong seasonality of many parameters. Precipitation in northern China is much lower than that in the south, and the dilution effects of water may be responsible for the higher concentrations in northern areas and low concentrations in southern regions for the selected parameters [35].
In addition, the dilution effect of precipitation may also play a role in seasonal changes in concentrations of water quality parameters. In northern China, during spring and early summer, precipitation does not increase significantly, and warming temperatures lead to enhanced evapotranspiration and higher concentrations of the selected parameters [47]. Watersheds characterized by steep slopes usually experience rapid streamflow rates and soil erosion, resulting in shorter water retention times and a weaker nutrient retention capacity [17]. Gauge stations on the Yunnan‒Guizhou Plateau have observed higher concentrations of Cr6 and Pb, which are attributable to parent materials and the low soil pH in those areas [48].
Human activities have increasingly impacted water quality over recent decades. Plains and lowland areas usually have a high population density. Intensified land use and urbanization can cause an increased discharge of agricultural, domestic, and industrial sewage, thereby contributing to both point and non-point source pollution [35,49]. For example, higher concentrations of nutrients were observed in Jilin and Liaoning Provinces, as well as downstream from the Yellow River Basin. This widespread phenomenon of nutrient pollution may be explained by the dominant land use types (agricultural land) in these areas. Concentrations of nutrients are more affected by human activities compared to climatic and geographical factors [17]. Gauge stations around Taihu Lake have higher TN concentrations, which can be explained by intensive urbanization, high population density, and active human activities indicated by the high GDP in these areas [50].
Reservoir operation also affects water quality. Li et al. [45] found that the construction of the Three Gorges Dam has decreased the flow velocity in the Yangtze River, reducing river runoff and diminishing its capacity for self-purification. However, Yang et al. [51] insisted that enhanced water retention due to reservoir damming can reduce sediment transport. Additionally, frequent mining activities are a possible explanation for the high levels of heavy metals in Yunnan Province [52], emphasizing the complex interaction between human disturbances and natural processes in affecting water quality.

4.3. Intercorrelation among Water Quality Parameters

Eutrophication is typically characterized by high concentrations of BOD, COD, and nutrients, as well as low levels of DO. However, in this study, the correlations between DO and other water quality parameters are weak, with only a slightly negative correlation between DO and concentrations of NH3-N or TP. In the Pearl River Basin, the correlation coefficient between DO and TP is −0.36, consistent with the negative correlation reported in previous studies for the same water resource region [32,37]. The correlation between DO and other parameters appears weaker in northern watersheds compared to the south, which may be related to differences in temperature among these regions. In southern China, higher temperatures lead to less oxygen dissolved in the water.
The correlation between TN and other water quality parameters also exhibits spatial variations. In the Hai River Basin, the Huai River Basin, and Northeast China, the concentrations of TN show low correlations with BOD and COD; high ratios of TN/TP also appear in these water resource regions. This may be due to higher anthropogenic N input in these regions, leading to high variabilities in water quality parameters [17]. The concentration of riverine COD is influenced by the activity of aquatic organisms, atmospheric deposition, and the influx from terrestrial vegetation and soil organic matter. Temperature fluctuations can alter water’s physical and chemical properties [53], further affecting biochemical processes, and leading to the significant variability of COD. Further research on water quality analysis needs to quantify the contribution of anthropogenic and natural factors to regional water quality to unravel the underlying mechanisms responsible for the variability of water quality parameters.

4.4. Processes Leading to High Concentrations of Water Quality Parameters

The rapid development of industry, urban expansion, and changes in agricultural activities have greatly increased the discharge of pollutants, leading to widespread water pollution [54]. In Northeast China, many gauge stations have observed COD concentrations higher than 20 mg/L. This region, as an important base for grain production, has extensive agricultural land where excessive fertilizer use leads to high concentrations of BOD, COD, and nutrients, contributing to non-point source pollution [55]. The higher concentration of Cr6 in the Loess Plateau is attributable to the higher local soil background values and the recharge of groundwater with a high concentration of Cr6 [56]. In addition, the wastewater and soil erosion in the Loess Plateau also contribute to phosphorus transport [57]. Population densities and agricultural yields are relatively high in the middle and downstream of the Yellow River Basin [58,59]. Shanxi Province, located in the middle reaches of the Yellow River Basin, is an important base for coal production in China. The higher TN concentration in Shanxi Province can be explained by anthropogenic discharges, such as domestic sewage, livestock manure, nitrogen fertilizer, industrial sewage, etc. [60].
In southern China, eutrophication in the middle and downstream of the Yangtze River is closely linked to high population density and GDP. Point source pollution is the main component of water pollution in Yunnan Province, including mining activities [8], inadequate sewage treatment, and the expansion of built-up land [61]. The southern coastal areas of China are facing eutrophication challenges, driven by the development of big coastal cities and estuarine sewage discharges. Therefore, it is necessary to monitor and manage the water quality of estuaries to mitigate coastal eutrophication caused by human activities.

4.5. Implications for Pollution Control and Future Work

Our research highlights that northern water resource regions usually have worse water quality than southern regions of China. Water pollution is relatively light in Northwest and Southwest China, corresponding to the smaller population and lower impact of human activities in those regions. This spatial distribution has much to do with China’s economic development strategy. Therefore, water treatment facilities should keep up with the economic development and minimize the human impacts on water quality. Since TN concentrations exceed the Class V standard (2 mg/L) for most measuring stations across the country, local management agencies should pay more attention to the attenuation of nutrient transport to rivers.
We note that a few water quality parameters showed lower concentrations in 2022 and 2023 compared to 2021, indicating that the water quality has improved compared to the results of the previous water quality survey [43]. However, urbanization is still the main reason for pollution emissions and will continue to exert pressure on water quality [2,59]. Even in watersheds where water quality has significantly improved, the issue of excessively high pollutant concentrations still occurs frequently [8]. Therefore, further mitigation of water pollution will still be a major challenge in the coming decades.
This investigation provides an overview of China’s water quality and lays the foundation for future investigations. Further investigations on the spatiotemporal analysis of water quality and underlying mechanisms in each water resource area can be performed based on this study. At the same time, in addition to analyzing each water quality parameter, comprehensive evaluation methods and the water quality index can be used, as well as regional economic development, to make a comprehensive evaluation of water quality conditions [22]. In addition, this study can also provide a reference for the analysis of water quality with process-based water quality models.
Meanwhile, a few limitations should be noticed to better understand the results of this work. First, because of the low data availability, we primarily focused on water quality data in 2021–2023, which is a short time period for understanding the long-term trends in water quality. More efforts in data compilation are needed to extend the data to early years to show how water quality has changed over a long time period. Second, more quantitative analyses of how river basin properties have contributed to the variability of water quality are needed to derive a solid understanding of mechanisms regulating water quality. Third, linking water quantity and water quality will be needed to understand the control of the water cycle on water quality.

5. Conclusions

This study investigated the national-scale monthly water quality observation data from 2021 to 2023 and analyzed the spatiotemporal variability of key water quality parameters in China. Similarities can be found through the spatial distribution of BOD5, COD, DO, NH3-N, TN, and TP concentrations, with a lower magnitude in the south than in the north. The concentrations of heavy metal elements can mostly meet the Class I water quality standards. Nitrogen pollution should receive more attention, since it is becoming a national problem, especially during the dry season. When comparing water conditions in 2021 to the next two years, all parameters showed better conditions, except for DO and TN. The TN/TP ratios of all water resource regions were above 16, indicating that phosphorus was the limiting factor of eutrophication. The findings of this study can provide valuable information to water quality management agencies and support the formulation of water pollution control strategies. Reducing nitrogen export, particularly in dry seasons and in the northern parts of China, will be needed to improve water quality in China.

Author Contributions

Conceptualization, K.L. and Q.Y.; methodology, K.L. and Q.Y.; formal analysis, K.L.; investigation, K.L. and Q.Y.; resources, Q.Y.; data curation, Q.Y.; writing—original draft preparation, K.L.; writing—review and editing, K.L., Q.Y. and X.L; visualization, K.L.; supervision, Q.Y.; project administration, Q.Y. and X.L.; funding acquisition, Q.Y. and X.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Hongkong-Macau Center of Ocean Research (CORE) 2023 program (CORE is a joint research center for ocean research between Laoshan Laboratory and HKUST), the Guangzhou Technology Bureau and the Hong Kong University of Science and Technology (Guangzhou) 2023 joint program (2024A03J0611), and the Chinese Academy of Science Earth System simulator program (elpt_2023_000430). The work described in this paper was substantially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project Reference Number: AoE/P-601/23-N). Xia Li was supported by the GuangDong Basic and Applied Basic Research Foundation (Grant Numbers: 2021A1515110830 and the National Natural Science Foundation of China (42206170).

Data Availability Statement

Data will be available upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Locations of the water quality gauge stations and 9 water resource regions.
Figure 1. Locations of the water quality gauge stations and 9 water resource regions.
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Figure 2. Spatial distribution of water quality parameters of gauge stations. The concentrations of BOD, COD, Cr6, DO, NH3-N, Pb, TN, and TP are shown in subplots (a–h), respectively (the value of each gauge station is calculated based on the average of monthly observations from 2021 to 2023, and then classified according to the natural break method and China Surface Water Environmental Quality Standard).
Figure 2. Spatial distribution of water quality parameters of gauge stations. The concentrations of BOD, COD, Cr6, DO, NH3-N, Pb, TN, and TP are shown in subplots (a–h), respectively (the value of each gauge station is calculated based on the average of monthly observations from 2021 to 2023, and then classified according to the natural break method and China Surface Water Environmental Quality Standard).
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Figure 3. Water quality conditions across 9 water resource regions in 2021–2023. The concentrations of BOD, COD, Cr6, DO, NH3-N, Pb, TN, and TP are shown in subplots (a–h), respectively. Abbreviations on the x-axis refer to the water resource regions: the Haihe River Basin, the Huaihe River Basin, the Northeast Area, the Northwest Area, the Pearl River Basin, the Southeast Area, the Southwest Area, the Yangtze River Basin, and the Yellow River Basin.
Figure 3. Water quality conditions across 9 water resource regions in 2021–2023. The concentrations of BOD, COD, Cr6, DO, NH3-N, Pb, TN, and TP are shown in subplots (a–h), respectively. Abbreviations on the x-axis refer to the water resource regions: the Haihe River Basin, the Huaihe River Basin, the Northeast Area, the Northwest Area, the Pearl River Basin, the Southeast Area, the Southwest Area, the Yangtze River Basin, and the Yellow River Basin.
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Figure 4. Seasonal variation in water quality parameters across 9 water resource regions from 2021 to 2023. The concentrations of BOD, COD, Cr6, DO, NH3-N, Pb, TN, and TP are shown in subplots (a–h), respectively.
Figure 4. Seasonal variation in water quality parameters across 9 water resource regions from 2021 to 2023. The concentrations of BOD, COD, Cr6, DO, NH3-N, Pb, TN, and TP are shown in subplots (a–h), respectively.
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Figure 5. Annual variations in concentrations of water quality parameters from 2021 to 2023. The concentrations of BOD, COD, Cr6, DO, NH3-N, Pb, TN, and TP are shown in subplots (a–h), respectively.
Figure 5. Annual variations in concentrations of water quality parameters from 2021 to 2023. The concentrations of BOD, COD, Cr6, DO, NH3-N, Pb, TN, and TP are shown in subplots (a–h), respectively.
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Figure 6. Annual variations in top 10% highest water quality parameter concentration records from 2021 to 2023. The concentrations of BOD, COD, Cr6, DO, NH3-N, Pb, TN, and TP are shown in subplots (a–h)respectively.
Figure 6. Annual variations in top 10% highest water quality parameter concentration records from 2021 to 2023. The concentrations of BOD, COD, Cr6, DO, NH3-N, Pb, TN, and TP are shown in subplots (a–h)respectively.
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Figure 7. Spatiotemporal variability of BOD, COD, Cr6, and DO across 9 water resource regions in 2021–2023.
Figure 7. Spatiotemporal variability of BOD, COD, Cr6, and DO across 9 water resource regions in 2021–2023.
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Figure 8. Same as Figure 7, but for NH3-N, Pb, TN, and TP.
Figure 8. Same as Figure 7, but for NH3-N, Pb, TN, and TP.
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Figure 9. Inter-correlations of the water quality parameters in 9 water resource regions.
Figure 9. Inter-correlations of the water quality parameters in 9 water resource regions.
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Figure 10. The ratio of TN/TP across 9 water resource regions.
Figure 10. The ratio of TN/TP across 9 water resource regions.
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Table 1. Environmental quality standards of China for surface waters.
Table 1. Environmental quality standards of China for surface waters.
Parameters 1 Classes
IIIIIIIVV
BOD≤334610
COD≤1515203040
Cr6≤0.010.050.050.050.1
DO≥7.56532
NH3-N≤0.150.51.01.52.0
Pb≤0.010.010.050.050.1
TN≤0.20.51.01.52.0
TP≤0.020.10.20.30.4
1 The unit of water quality parameters is mg/L.
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Li, K.; Yang, Q.; Li, X. An Analysis of the Spatiotemporal Variability of Key Water Quality Parameters in China. Hydrology 2024, 11, 135. https://doi.org/10.3390/hydrology11090135

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Li K, Yang Q, Li X. An Analysis of the Spatiotemporal Variability of Key Water Quality Parameters in China. Hydrology. 2024; 11(9):135. https://doi.org/10.3390/hydrology11090135

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Li, Kexin, Qichun Yang, and Xia Li. 2024. "An Analysis of the Spatiotemporal Variability of Key Water Quality Parameters in China" Hydrology 11, no. 9: 135. https://doi.org/10.3390/hydrology11090135

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Li, K., Yang, Q., & Li, X. (2024). An Analysis of the Spatiotemporal Variability of Key Water Quality Parameters in China. Hydrology, 11(9), 135. https://doi.org/10.3390/hydrology11090135

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