Abstract
Increased human activity threatens inland water quality in China. Major efforts have been made to alleviate water pollution since 2001. Understanding how water quality responds to these forces can help to guide future efforts to maintain water security and sustainability. We here analyzed the nationwide variability of inland water quality across China from 2003 to 2017 and its responses to anthropogenic discharges. We show that water quality has been improved markedly or was maintained at favorable levels over the country because of reduced discharges in the industrial, rural, and urban residential sectors. However, growing discharges from the agricultural sector threaten these gains. Moreover, the present status of water pollution is relatively severe in north and northeast China. Our findings suggest that China’s water quality would further benefit from more flexible strategies for mitigation measures, which respond to regional differences in the factors that influence water pollution levels in specific regions.
INTRODUCTION
Over the past several decades, China’s rapid economic development has taken place at the expense of the environment (1–8). The widespread deterioration of surface water quality in inland water bodies represents one of the most serious environmental threats to human health and ecosystem services (9–21). Large investments have been made in wastewater discharge standards and pollution control strategies to address the water pollution crisis and to promote eco-environment restoration (22–25), especially since 2001 [as summarized in (25)], the start of the 10th National Five-Year Plan.
China’s inland surface water quality presently faces both escalating anthropogenic disturbance, particularly from urban domestic wastewater discharge and agricultural non-point source pollution, and growing environmental efforts, mainly concentrating on the treatment of point source pollution and the provision of urban environmental infrastructure (fig. S1). Several assessments have documented declines in pollutant concentrations at monitoring sites in inland lakes, individual rivers, and local basins due to these national policies (25–29). The impacts of different factors including socioeconomic activities, environmental processes, and land-use change on inland water pollution have also been elucidated by numerous studies (30–35), reports, and statistical bulletins (36–38). These investigations, however, have usually focused on temporal changes in pollutant concentrations for specified water bodies or local regions and lack of direct linkages to anthropogenic pollution sources. They therefore might not reflect the comprehensive relationship between inland water quality and the driving forces across the whole country over time, which plays a key role for understanding the nationwide dynamics in water pollution with regionally varying conditions and diverse human activities.
The work to investigate the effectiveness of past efforts and current regional variations in China’s inland water quality is crucially important in helping to identify pollution control and water quality improvement policies and practices that would improve water security and ecosystem sustainability across all of China’s diverse regions. Here, we report the most nationally representative investigations to date of how three typical water quality parameters—the chemical oxygen demand (COD; the permanganate index, an indicative measure of pollutant loading), ammonium nitrogen (
We compiled time series of monthly mean concentrations of COD,
Different colors overlaid on the sampling sites (dots, n = 2630) indicate regionally varying quality levels of annual mean concentrations of water quality parameters in 2017. (A) COD. (B)
RESULTS AND DISCUSSION
Trends in COD, NH 4 + -N, and DO concentrations
Our results show that the mean monthly concentrations of site-level COD and
(A to C) Country-level trends in five time series of COD,
Most of the observed time series of COD (38 of 50 samples),
Responses of water quality to changed human pressures
We used the generalized linear model (GLM) to examine the quantitative response of observed inland surface water quality (here for both Qa and Qm time series of COD and
(A) Pollution discharges measured by COD from different sectors. (B)
Changes in the spatial pattern of anthropogenic pollution discharges reveal the nationwide prevalence and regional differences in the reductions in human pressures on inland water quality (Fig. 4). On the national scale, our estimates indicate that the annual discharge of pollution per 100 km2 has been reduced by 0.34 Gg (~27% for 2003, n = 51773) and 0.02 Gg (~23% for 2003, n = 59731) for measurements of COD and
(A) Pollution discharge measured by COD in 2015. (B) Changes in pollution discharge measured by COD between 2003 and 2015. (C)
Regional-level alterations and interregional variations in human pressures from various sectors can be jointly connected to observed dynamics and regional differences in surface water quality (Fig. 1 and figs. S2, A and B, and S4). Except for west (NwR and SwR) and south (PeR and SeR) China, which generally showed favorable water quality throughout the observation period, the associated changes in anthropogenic pollution discharges from diverse sectors can explain most of the annual variation in both COD and
The effects of environmental investment on pollution discharges
To mitigate inland water pollution, several national standards for surface water quality and sector-specific discharges of point source water pollutants have been issued by the central government since 2002. Large investments have thus been made to urban environmental infrastructure [a total of approximately 4 trillion renminbi (RMB) from 2003 to 2015] and industrial wastewater treatment (0.18 trillion RMB) (table S1 and fig. S5). Reduced pollution emissions from both the industrial and urban residential sectors generally have resulted in the marked declines in both COD and
At the country level, yearly pollution discharges from the urban residential sector are estimated to have substantially declined by 52.73% (COD) and 31.22% (
Although there are notable interregional variations in anthropogenic pollution discharges and underlying driving factors, most of the basins exhibit similar trends toward decreased discharge levels (defined as the volume of pollution emissions per unit population for the urban residential sector and per unit IGDP for the industrial sector) between 2003 and 2015 (fig. S6). For the urban residential sector spanning 10 major basins, the discharge level is estimated to have declined by 19.63 to 29.21 (min-max) and 1.69 to 2.83 metric tons per 104 urban residents for COD and
Regional variations in current water quality
Although the abovementioned findings jointly suggest notable declines in COD and
(A) COD. (B)
Apart from the immediate effects of pollution discharge loadings, the aforementioned regional differences in water pollution can be further explained by the regional heterogeneity of multiple general driving factors, and thus allow us to obtain a more comprehensive understanding of regional differences in water quality in present-day China. We next performed a cluster analysis among 10 major river basins with six representative and putative variables, which can have potentially significant impacts on inland surface water quality. As presented in Fig. 6, hierarchical cluster analyses of impact variables (see the caption of Fig. 6 for definitions) and among regions explicitly reveal both the similarity of impacts on regional water quality between drivers and how drivers affect similarity in water pollution conditions between basins. Across various drivers (Fig. 6A and table S5), we can therefore categorize all impact variables into three groups forming distinct clusters of water quality drivers at the regional level: human pressure (POP and GDP), environmental effort (INV), and environmental capability (WAT, PRE, and GRE). Across different regions, we can also find that basins with similar drivers have the most similar water pollution conditions (Fig. 6B and table S5). Furthermore, the ridge regression results quantify the effects of these drivers on the interregional variations in current inland water quality (as summarized in table S5). In particular, WAT and INV commonly have a marked negative effect on both regional-level COD (P = 0.009 and P = 0.141 for WAT and INV, respectively) and
(A) Cluster dendrogram of impact factors of water quality based on the distance metrics over 10 basins. (B) Hierarchical clustering of 10 basins based on the distance metrics among multiple impact factors. WAT, surface water resource per unit area; PRE, average annual precipitation; GRE, the percent of green cover (including forest land and grassland with >20% of cover degree); POP, population density per unit of surface water resource; GDP, gross domestic product density per unit of surface water resource; INV, the proportion of environmental investment accounting for GDP. All data for clustering analysis are listed in table S5.
The results derived from clustering and regression analyses can be jointly used to further explain regional variations in inland water pollution under anthropogenic pressures, water quality improvement efforts, and different environmental conditions across China. The left and the right subclusters in Fig. 6B explicitly depict the difference in driving factors, resulting in regionally distinct water quality, between north and south China (see Fig. 1). In general, owing to the limited environmental conditions, inland surface water quality in north basins is likely less tolerant and more susceptible to human disturbance and its fluctuation. Hence, the impact of high-density demographic and socioeconomic activities with respect to environmental conditions in most of the northern areas is not yet completely offset by roughly similar environmental investment (fig. S5), especially in HaR with the highest GDP and POP, and relatively much lower WAT (table S5).
Implications for future environmental efforts
While previous research has shown that improved water quality might occur across specified water bodies (25) and diverse regions (35), there is still lack of evidence for direct linkage between inland water quality and anthropogenic pollution sources over the whole country, which is very important to future water quality improvement efforts. This study, to the best of our knowledge, provides the first comprehensive assessment of how China’s inland surface water quality responds to multiple driving forces over space and time based on nationwide observations of water quality indicators and multiple impact factors. Our findings confirm the effectiveness of massive environmental efforts aimed at pollution discharge control and water quality improvement over the past 15 years, notwithstanding growing pressures from human activity. Taken as a whole, reduced pollution discharges from the industrial and both the urban and rural domestic sectors primarily contributed to a notable improvement in surface water quality in most of China’s inland water bodies. On the other hand, our estimates could provide potential insights into water management strategies for further reducing pollution loadings and improving water quality in accordance with implementations of two important acts including the most stringent water resources management system (known as the “Three Redlines”) (39) and the water pollution control action plan (known as the “Water Ten Plan”) (40) issued by the central government for controlling water use and pollution discharge and improving water use efficiency and water quality at both regional and national levels for 2020 and 2030.
First, we note that increasing pollution discharges from the livestock sector constitute the largest for COD and a major pollution source for
Second, although having a significant trend toward decreased pollution discharges, the urban sector is still the major pollution source and faces escalating pressure resulting from sustained rural-urban migration coupled with rapid urbanization (fig. S1F and table S1). This means that continuously increasing investment for environmental facilities and sanitization will be required for all urbanized and urbanizing areas. Although having contributed to declines in COD and
Third, given the regional differences in driving factors and pollution status across the whole country, more flexible measures and strategies for pollution prevention and control would likely yield further improvement of inland surface water quality in China. For instance, higher standards of sewage discharge in terms of pollutant concentration controls are certainly desirable for the northern basins (HaR, HuR, YeR, SoR, and LiR) owing to lower environmental capability and high-density human development (Fig. 6 and tables S1 and S5). The limited growth of human pressures, especially in terms of land-use conversion, is important for the NwR and SwR basins with less anthropogenic disturbance and favorable status of water quality. Moreover, enhancement in green cover and restoration of ecosystem services in relatively highly polluted northern regions can also be potentially beneficial for local inland water quality (35).
Fourth, note that the current situation of water quality in the HaR basin, in comparison with other basins, shows an unexpected level notwithstanding marked decreases in pollution discharges from all major anthropogenic sources and the highest proportional environmental investment over the past decade (Fig. 5, fig. S4, and table S5). In this water-poor region with high-density human activity, further aquatic pollution alleviation remains a big challenge in the next decade.
In addition to the management of sector-specific forces and region-specific driving factors, regional differences in current surface water quality should be taken into account in assessing the geographic mismatch between surface freshwater demand and availability (i.e., water scarcity) by combining sectoral water quality requirements in conjunction with local water quality (45). The quality-included measure of water scarcity would be beneficial to a more comprehensive view for alleviation of water stress by physical and virtual water transfers across regions (46). Interregional variation in water quality will certainly aggravate the regional inequality of water stress in China (47) and particularly worsen water scarcity for northern basins. In these regions, aquatic environmental restoration, improvement in water use efficiency, control of pollution discharges, interbasin water transfers, and reduction in outsourcing of pollution-intensive goods from other regions (48) probably play pivotal roles in the alleviation of water stress to meet local freshwater demands in terms of both quantity and quality.
In conclusion, we believe that China’s inland surface waters will achieve good ecological status in the near future if the current trends hold. Further improvements are both possible and desirable, and the implementation of measures and strategies for water pollution mitigation and control should be guided by the relationships between water quality dynamics and the diverse driving factors and their regional variations noted in this article.
MATERIALS AND METHODS
Water quality monitoring data
From the National Environmental Monitoring Network (NEMN), we assembled monthly observations of COD,
Trend analysis for water quality data
We calculated quartiles of monthly mean COD,
We next used a seasonal-trend decomposition procedure based on locally weighted scatterplot smoothing [STL; (51)] to eliminate irregular components and streamflow and other influences caused by seasonal fluctuations in original time series of concentration data. The STL method decomposes monthly observation data into three components: trend, seasonal, and remainder. We finally obtain the five time series (i.e., Q1, Q2, Q3, Qa, and Qm) of annual mean concentrations for COD,
Anthropogenic discharge data and impact variables
Various human actions can significantly impact the dynamics of observed COD and
Our analyses were carried out for the nation and the major groundwater basins. We used a land-use area-weighted method to transform administrative boundary-based statistical and census data to basin-level estimates of various anthropogenic driving factors of observed COD and
Basin-level GDP data were obtained through weighted proportional allocation, as addressed above, using satellite-derived nighttime light data because of the well-documented significant quantitative relationship between GDP and anthropogenic nocturnal brightness at the regional level. Detailed information regarding data processing and analysis of nighttime lights can be found in (52). Moreover, the regional averaging method was used to obtain precipitation information and the rate of access to sanitary toilets for rural residents for target basins, based on site-level observations and province-level statistical data, respectively. Statistical data for 2016 and 2017 are not yet available and were estimated by linear interpolation based on the last 2 years of data. The abovementioned strategy, i.e., a proportional sharing method based on the area of different land-use types, was also used in mapping the spatial distributions of anthropogenic discharges of COD and
Statistical analysis for the connection between water quality dynamics and driving factors
We used a GLM (here using Gaussian family with log link) to investigate the quantitative response of observed inland water quality dynamics to anthropogenic pollution discharges from various sectors. A notable advantage of GLM is that response variables (i.e., the observed COD and
To eliminate the effect of multicollinearity among explanatory variables in the GLM analysis, we applied the PCA to convert sector-specific discharge into a set of linear combinations. All of the principal components were used in the GLM analysis. The first principal component, generally having the maximum contribution (except COD for Qm in NwR, SwR, and PeR; COD for Qa in SeR and PeR;
Furthermore, it should be pointed out that the anthropogenic non-point source loadings of pollution for inland water bodies were determined not only by the volume of pollution discharges but also by the geophysical conditions, such as geomorphology, surface runoff, land cover, and river network. In this study, we mainly focused on quantifying the impacts of changes in various human activities on the water quality dynamics, while geophysical conditions were assumed to be unchanged within basins over the observational period for a large-scale investigation.
In addition, the hierarchical cluster method was applied for analyzing similarities between six general driving factors (Fig. 6A) and 10 major river basins (Fig. 6B) based on distance metrics (Euclidean distance after scaling) over basins and among multiple impact factors, respectively. To reduce the impact of collinearity, we used linear ridge regression to quantify the relationships between general driving factors and
SUPPLEMENTARY MATERIALS
Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/6/1/eaau3798/DC1
Supplementary Text
Fig. S1. National-level changes in multiple driving factors of inland water quality in China during the period 2003–2015.
Fig. S2. Observed changes in water quality measures for five time series across 10 basins during the period 2003–2017.
Fig. S3. Interannual variability in percentages of five water quality levels limited by site-level records of water quality measures for 10 basins during the period 2003–2017.
Fig. S4. Changes in anthropogenic pollution discharges from different sectors across 10 basins during the period 2003–2017.
Fig. S5. Changes in the volume of environmental investment and the rate of access to sanitary toilets for rural residents across 10 basins during the period 2003–2015.
Fig. S6. The relationships between environmental investments and declining pollution emissions and changes in the discharge levels between 2003 and 2015 across 10 basins.
Table S1. Summary of the distinguishing characteristics of 10 basins delineated in this study.
Table S2. Summary of analysis results of trends in water quality indicators during the period 2003–2017 across China and 10 basins.
Table S3. Summary of analysis results of trends in the proportion of water quality levels I + II + III for water quality indicators for the period 2003–2017 across China and 10 basins.
Table S4. Coefficients of the first component derived from PCA of loading sources and used for the explained variances estimated by type II ANOVA in both the Qm and Qa time series of COD and
Table S5. Summary of impact factors of local inland surface water quality across 10 basins.
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REFERENCES AND NOTES
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