Introduction

Industrialisation is an important driver of economic development and can be significant in alleviating poverty1,2. However, industrialisation also leads to water pollution, an unwanted byproduct, particularly prevalent in developing countries, which often lack control legislation and prevention infrastructure3,4,5. Water pollution discharged by industrial sectors has caused severe ecological degradation in the surface water environment and has yielded significant health problems in indigenous populations6,7,8. However, efforts to identify the impact that industrial sectors as pollution sources have on surface waters are usually conducted at coarse spatial resolutions9. Specifically, the discharge volume and location of enterprises affiliated with different industrial sectors are usually masked by aggregated information on industrial discharge10. Additionally, pollution discharge from industrial enterprises is virtually transported along complex industrial supply chains, which are largely outsourced by the final consumption beyond local boundaries through trade11. Neglecting investigations on the above information for industries would raise questions, such as which industrial enterprises or sectors have the largest contribution to water contamination and how the final consumption drives excess pollution discharge from industrial sectors? Answering these questions will help policymakers to understand and control surface water pollution from industrial sources based on both local and global perspectives.

A surface water network is a multipurpose system with varying designated functions in different sections of the network12,13. Classified as a point source, the impact that industrial pollution has on different surface water function zones is spatially heterogeneous and determined by complex pollution transportation mechanisms14. Many studies have evaluated the impact that pollution transportation has on surface water deterioration using a water quality model or monitoring approach15,16,17. However, these studies have typically accounted for pollution sources by starting from sewage outlets, thus failing to understand the origins and spatial characteristics of industrial pollution sources at the sectoral level.

Efforts have been made to further link water deterioration to anthropogenic pollution sources at regional scales. One group of studies applied statistical correlation analysis to achieve this goal18. For example, Ma et al.10 applied a generalised linear model to link the changes in the surface water quality of China to anthropogenic water pollution discharge. Another group of studies have evaluated the impact that the excess discharge of water pollution has on the deterioration of the aquatic environment, which were based on efforts to build a regional safe operating space (threshold) for local water pollution management19. Liu et al.20 developed a water pollution level indicator, i.e., the ratio between the grey water footprint and actual discharge of a river basin, to show the impact that nitrogen and phosphorus inputs have on global river basins. Yu et al.21 quantified the excess total nitrogen discharge of China to the aquatic environment using an absolute nitrogen boundary, which was set according to water quality standards in different representative river basins. However, setting a single threshold for a river basin does not reflect the variations in the water quality conditions, water quality goals, and complex pollution transportation mechanisms in different sections of a surface water network. Hence, the water pollution discharge thresholds set in different water function zones of a surface water network are more policy relevant for controlling surface water pollution.

Until recently, studies identifying the impact that water pollution from different industrial sectors has on different sections of a surface water network were scarce. This was mainly owing to a lack of understanding of the spatial layout of enterprises affiliated with these industrial sectors and the varying thresholds of different sections of water bodies for these spatially dispersed discharges. Additionally, failing to link water pollution discharge from different industrial sectors to the aquatic environment further impedes analyses of the impact that consumption has on water quality responses. Introducing a consumption perspective to environmental research is essential for addressing the impact that consumers and trade have on pollutant outsourcing22,23,24. This perspective is also key to achieving Sustainable Development Goal (SDG) 12, i.e., Sustainable Consumption and Production25,26.

In summary, existing studies linked anthropogenic pollution sourced from production to water deterioration, but did not further link the pollution to consumption, which takes place both within the production area and beyond through trade. Meanwhile, some recent studies focused on the impact of final consumption on the sectoral level pollution discharge11,27,28, but failed to link the discharge to surface water deterioration. Also, although recent studies have linked the excess use of phosphorus fertilizer on croplands, as well as excess freshwater use, to final consumption using multi-region input–output (MRIO) modelling29,30, no study has evaluated the impact that consumption has on excess pollution discharge across industrial sectors and areas.

In this study, we used spatial data to investigate excess industrial water pollution discharge to the surface water bodies in 2015 in Jiangsu Province, the most industrialised province in China (Supplementary Note 1, Supplementary Fig. 1). The chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) were selected as water quality proxies, as these are the principal pollutants causing water deterioration in China31. We divided the surface water network of Jiangsu into 1338 surface water function zones and evaluated their response to pollution discharge from 11,094 industrial enterprises affiliated with a broad range of 22 industrial sectors. We then linked excess pollution discharge from these industrial sectors to the final demand of different sectors in all Chinese provinces using the latest national MRIO table. We also proposed an indicator, i.e., the pollution exceedance rate, to examine industrial sectors with high levels of risk for excess water pollution discharge. Our research framework can help identify hotspots and industrial sectors with high levels of risk for excess discharge, thereby supporting the formulation of effective mitigation strategies for industrially derived water pollution.

Results

Territorial-based excess industrial pollution (TEIP) in Jiangsu

In 2015, 174,824 tons of COD and 12,241 tons of NH3-N were discharged by 11,094 industrial enterprises in Jiangsu. Approximately 12% of the COD discharge (20,330 tons) and 14% of the NH3-N discharge (1749 tons) occurred in water function zones where the pollutant amount exceeded the maximum allowable pollution load (MAPL). Specifically, there were 41 water function zones receiving excess COD discharge from 1099 enterprises and 45 water function zones receiving excess NH3-N discharge from 396 enterprises.

Geographically, enterprises with excess pollution discharge were mainly located in southern Jiangsu, where the river network is dense, and the economy is highly developed (Fig. 1). Enterprises with excess annual COD discharge were densely distributed in the Yangtze River Delta urban agglomeration, including the cities of Suzhou (8075 tons/yr), Yangzhou (2936 tons/yr), Nanjing (1542 tons/yr), and Nantong (1374 tons/yr) (refer to Supplementary Fig. 2 for city location and Supplementary Note 2 for detailed city results). As the most industrialised region in China, this urban agglomeration has built 174 industrial parks during the past 30 years and contributed ~13% (11.9 trillion yuan) to the industrial output of China in 2015 alone32. Hotspots covering most enterprises with excess COD include the Wusongjiang water function zone in Suzhou, the Beijing-Hangzhou Grand Canal (Yangzhou section) in Yangzhou, Qinhuai River Lishui section, and Yangtze River Dachang section water function zones in Nanjing, Sanhe Port, and Rutai Canal water function zones in Nantong (Fig. 1). These water function zones each accommodated 57–341 polluting enterprises, most of which belonged to the ‘Chemical industry’, ‘Textiles’, and ‘Paper making, printing, stationery, etc.’ sectors. Enterprises with excess NH3-N discharge were concentrated in Suzhou City (574 tons/yr), Yangzhou City (318 tons/yr), and Huai’an City (146 tons/yr). Excluding the water function zones of the Wusongjiang and Beijing-Hangzhou Grand Canal (Yangzhou section), hotspots covering most enterprises with excess NH3-N include the Baoshe River water function zone in Yangzhou and the Qing’an River water function zone in Huai’an. Over one-third of the enterprises in the Baoshe River water function zone were food processing enterprises, whereas over one-third of those in the Qing’an River water function zone were chemical enterprises.

Fig. 1: Distribution of the industrial enterprises in Jiangsu Province with excess pollution discharge (tons/yr).
figure 1

a Excess COD and b excess NH3-N. The dots in the figure show the locations of the enterprises with excess pollution discharge while the colours represent the amount of territorial-based excess industrial pollution. Three basins, where enterprises with excess pollution discharge are densely distributed, are partially enlarged, including the Yangtze River Basin, Hongze Lake Basin, and Taihu Basin. Ten hotspot water function zones, which cover most enterprises with excess pollution, are marked. The blue lines represent the main rivers.

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Eighty-six percent of the enterprises analysed in this study had an excess amount of COD of <10 tons/yr while 88% had an excess amount of NH3-N of <1 ton/yr. The number of large polluting enterprises is small, but their contributions are substantial. For example, two enterprises in the ‘Chemical industry’ sector and one in the ‘Food processing and tobacco’ sector had an excess COD of >1000 tons/yr. These three enterprises are located in the Yangtze River Delta urban agglomeration; combined, they accounted for 18% of the total excess COD in Jiangsu. The same two chemical enterprises and an enterprise in the ‘Textiles’ sector in the Hongze Lake Basin each caused an excess NH3-N of >50 tons/yr, contributing to 23% of the total excess NH3-N.

In terms of the sectoral distribution, the ‘Chemical industry’ sector had the largest TEIP (7459 tons/yr COD and 653 tons/yr NH3-N) in Jiangsu, followed by ‘Textiles’ (2943 tons/yr COD and 267 tons/yr NH3-N), and ‘Food processing and tobacco’ (2880 tons/yr COD and 337 tons/yr NH3-N). Approximately 65% of the excess COD and 72% of the excess NH3-N were related to production in these three sectors. This was followed by the ‘Paper making, printing, stationery, etc.’ which resulted in 1955 tons/yr of excess COD (Fig. 2). These sectors are widely characterised as heavy polluting sectors and are important basic industries in China33.

Fig. 2: Inter-sector flows of excess pollution discharge from the production end in Jiangsu Province to the consumption end in Jiangsu and other Chinese provinces.
figure 2

a excess COD and b excess NH3-N. Sectors with excess COD (from both production and consumption) of <100 tons/yr and sectors with an excess NH3-N of <10 tons/yr are not shown in this figure. The list of 30 Chinese provinces is shown in Supplementary Table 1.

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TEIP of Jiangsu linked to final consumption

Linking the TEIP to the final consumption, our results showed the impact of consumption on excess industrial pollution discharge in Jiangsu. We found that 56% of the excess COD discharge (11,362 tons/yr) and 55% of the excess NH3-N discharge (963 tons/yr) in Jiangsu can be linked to the final consumption within Jiangsu, whereas the remaining excess discharge was embodied in the products exported from Jiangsu to fulfil the final demand of the other 29 Chinese provinces. The contribution of the final demand from other provinces to the TEIP of Jiangsu varied, with excess COD ranging from 30 to 1006 tons/yr and excess NH3-N ranging between 3 and 90 tons/yr (Fig. 3, Supplementary Table 1).

Fig. 3: Excess pollution discharge in Jiangsu induced by final demand of Jiangsu and other Chinese provinces.
figure 3

a Excess COD and b excess NH3-N. Map colours show the amount of excess pollution embodied in the exported products of Jiangsu to 30 Chinese provinces (tons/yr). The top 10 provinces outsourcing excess pollution to Jiangsu are marked on the map.

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In terms of the sectoral distribution, the top five sectors whose final demand induced the largest excess discharge were ‘Electronic equipment’ (2328 tons/yr for COD, 190 tons/yr for NH3-N), ‘Construction’ (2039 tons/yr for COD, 175 tons/yr for NH3-N), ‘Other services’ (2015 tons/yr for COD, 154 tons/yr for NH3-N), ‘Food processing and tobacco’ (1939 tons/yr for COD, 215 tons/yr for NH3-N), and ‘Chemical industry’ (1861 tons/yr for COD, 164 tons/yr for NH3-N) (Fig. 2). The large impact from the consumption of these sectors may be attributed to their final demand for TEIP-intensive products. We found that 84% of the excess COD discharge and 89% of the excess NH3-N discharge from the ‘Food processing and tobacco’ sector in Jiangsu can be linked to the final demand in the same sector while the larger excess discharge induced by the final demand of the remaining major sectors was due to the large demand for chemical products as intermediate inputs. Apart from ‘Food processing and tobacco,’ these TEIP-intensive chemical products contributed 39–83% of the excess COD discharge and 44–82% of the excess NH3-N discharge to the top five sectors.

Pollution exceedance rate

Economic sectors with high pollution exceedance rates have a high risk level for excess pollution discharge but are not necessarily those currently producing the most pollution (Fig. 4). For example, sectors such as ‘Coal mining,’ ‘Electronic equipment,’ and ‘Instruments and meters’ which were found to have the largest exceedance rates for both COD and NH3-N, only discharged a limited amount of pollution. Their discharge amounts were lower than the provincial average (5827 tons/yr COD, 408 tons/yr NH3-N), but their COD and NH3-N exceedance rates were between 6% and 17% and 5% and 20% higher than the provincial average (12% for COD and 14% for NH3-N), respectively (refer to LH quadrant in Supplementary Fig. 3). In contrast, some economic sectors with the largest discharge amounts had relatively lower pollution exceedance rates (Fig. 4). For example, the COD exceedance rates for ‘Textiles,’ ‘Food processing and tobacco,’ and ‘Metallurgy,’ and the NH3-N exceedance rates for ‘Textiles,’ ‘Paper making, printing, stationery, etc.’ and ‘Metallurgy’ were all below the provincial average pollution exceedance rate.

Fig. 4: Comparison of the pollution discharge, pollution discharge induced by final demand, and pollution exceedance rate.
figure 4

a COD and b NH3-N. The coloured bars denote the amount of pollution discharge and pollution discharge induced by final demand while the dots represent the pollution exceedance rate.

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Having a higher pollution exceedance rate may mainly be attributed to spatial agglomerations of industry. The most common form of agglomeration of industrial activities is within industrial parks34, in which enterprises within the same sector are often co-located. Water pollution arising from industrial parks is typically combined and discharged into the same water function zone. For example, the Wusongjiang water function zone (Fig. 1), which had the most severe pollution exceedance rate, was surrounded by 56 enterprises classified in the ‘Electronic equipment’ sector and 37 enterprises in the ‘Instruments and meters’ sector. Although the amount of discharge from each enterprise was limited, the combined pollution exceeded the MAPL of the water function zone. Another example of spatial agglomeration was associated with proximity to raw materials, i.e., factories were built near these resources. As all the coal reserves of Jiangsu were located in Pei County (Xuzhou City) (Fig. 1), this county contained nine bituminous coal and anthracite coal mining enterprises; therefore, the generated water pollution was discharged to the same water function zone. On the provision that the MAPL of the water function zone was limited, the centralised discharge caused a higher pollution exceedance rate in the ‘Coal mining’ sector. Supplementary Table 2 lists the exceedance rates of the other sectors.

Economic sectors with high pollution exceedance rates (above average, HH and LH quadrants in Supplementary Fig. 3) may be linked to the final demand of different sectors. For sectors with high COD exceedance rates, the final demand of ‘Electronic equipment’ (17%), ‘Chemical industry’ (13%), ‘Other services’ (12%), and ‘Construction’ (11%) were the main contributors to their TEIP. For sectors with high NH3-N exceedance rates, the final demand of ‘Food processing and tobacco’ accounted for 16% of its TEIP, followed by ‘Electronic equipment’ (13%), ‘Chemical industry’ (12%), and ‘Construction’ (10%).

Discussion

We developed a novel framework to quantitatively analyse the spatial patterns of excess pollution discharge across a range of industrial sectors, further tracking the contributions of consumption leading to this discharge. The framework helped identify the industrial sectors with the largest contribution and risk level to water pollution in high spatial resolution. The results demonstrated that regional water pollution derived from industrial production is not only determined by the amount of pollution discharged but also by the spatial distribution of enterprises, industrial structure, and final consumption beyond the research area. Hence, our framework highlights the complexities associated with the effective control of industrial pollution sources. For emerging economies, it is necessary to incorporate industrial production, consumption, and the response of surface water quality into a unified assessing and management framework to achieve water pollution mitigation. Using Jiangsu as a case example thus provides an appropriate foundation for emerging economies experiencing significant industrial expansion and associated water pollution.

Notably, our results highlighted the sectors that had high risk levels for the discharge of large amounts of excess pollution, but for which limited concerns have been raised. Characterised by high pollution exceedance rates, these are mainly high-tech manufacturing sectors, including ‘Electronic equipment,’ ‘Instruments and meters,’ and ‘Electrical equipment.’ Although these sectors had limited amounts of pollution discharge, the excess discharge of these sectors would increase rapidly to a high level when there was an increase in the amount of the pollution discharge. Indeed, these sectors are projected to continue to expand and experience high economic growth; since 2015, these industries have experienced rapid development and an annual growth rate in their industrial output >8%35. Under such a strong development momentum, the pollution discharge from high-tech manufacturing industries may continue to increase, aggravating the excess pollution discharge. In contrast, heavy polluting sectors with large TEIP values, including the ‘Chemical industry,’ ‘Textiles,’ ‘Food processing and tobacco,’ and ‘Paper making, printing, stationery, etc.’ have received wider and well-documented concerns28,33. In 2015, the Chinese government issued an ‘Action Plan for Prevention and Control of Water Pollution (Water Ten Plan)’36, with emissions reductions for heavy polluting sectors given prominent positions. In response, 4022 heavy polluting chemical enterprises in Jiangsu were placed on a rectification list, and nine chemical industrial parks, comprising 337 enterprises, were shut down37.

We found that industrial agglomeration was the main reason for the high pollution exceedance rates of high-tech manufacturing sectors. Such gathering of industrial activities into intensive zones, such as industrial parks, is a common strategy for economic growth worldwide38. More than 2500 national-level and provincial-level industrial parks exist in China, contributing to >50% of the national industrial output34. Industrial agglomeration is especially prevalent among high-tech industries, owing to their intensive requirement for specialised technology and talent6. Our results showed that 16% of ‘Electronic equipment’ manufacturing enterprises and 23% of ‘Instruments and meters’ manufacturing enterprises in Jiangsu were located in Suzhou Industrial Park and Wujiang Industrial Park, near the Wusongjiang water function zone. However, the lack of a pollution discharge control plan may create problems with pollution agglomeration39. To decouple industrial development from pollution exceedance, the Chinese government must implement greening strategies, such as the establishment of eco-industrial parks. An eco-industrial park is a business community dedicated to enhance its environmental, economic, and social performance through collaboration40. Measures may be taken to minimise pollution, such as gathering of complementary enterprises in terms of their economy and ecology, closing material cycles, and reusing ‘waste’ materials41.

Pollution exceedance in industrial parks also exposes problems associated with current wastewater discharge standards based on concentration. For example, for the Yuanhetang water function zone, the total pollution received exceeds its MAPL. However, the concentration of pollution discharged from each enterprise to this water function zone conforms to the wastewater discharge standards (Fig. 1, Supplementary Table 3). The regulatory focus is on the concentration of the pollution discharge from individual enterprises, but it ignores the layout of different enterprises and its cumulative impact on the MAPL of the receiving water function zone42. Therefore, load standards (the MAPL values of the water function zones) established based on the assimilative pollution capacity theory can serve as more stringent standards42.

Different load standards have been developed in different countries. The United States has prepared a complete water pollution control framework based on load standards i.e., the ATTAINS. It involves setting the total maximum daily load (TMDL) of impaired water bodies, allocating the TMDL to pollution sources, and applying pollution control practices43. The EU’s Industrial Emissions Directive regulates the emission limit values for industrial enterprises based on the best available techniques associated emission levels (BAT-AELs). The BAT was applied to over 50,000 industrial enterprises comprising 31 sectors regulated by the EU in 2018. These efforts have led to the investment of at least 100 billion EUR per year44, which may not be an affordable choice for developing countries. The efforts of China to develop a load standard started in 2011, when the Chinese State Council mandated that water pollution discharge must be strictly regulated to avoid transgressing the ‘water pollution redline’ i.e., the MAPL in the water function zone unit45. However, the large amount and dense distribution of enterprises in China leads to difficulties in collecting pollution discharge data. In fact, pollution load control based on the MAPL and enterprise-level pollution discharge have not been efficiently conducted to date42. In this study, we identified the enterprises and water function zones with excess industrial pollution discharge, through comparing the enterprise-level pollution discharge with the corresponding MAPLs. This helps specify the enterprises and sectors needing pollution reduction actions, thereby, reducing the cost for a wide range of end-of-pipe investment. Our work, thus, provides a reference for emerging economies experiencing significant industrial expansion and consequent severe water pollution, such as India46, Brazil47 and South Africa48.

Our fine-scale analysis helped in identifying the hotspots of water function zones presenting excess industrial water pollution discharge. We found that the MAPL of a water function zone to pollution discharge was largely affected by its hydrodynamic condition. Plain river network is a typical surface water form in Jiangsu, which is a combination of dense rivers and flat terrain. Characterised as low flow velocity, a plain river network has relatively poor hydrodynamics, resulting in a lower MAPL for its water function zones49. As a result, the intensive industrial pollution discharge to these water bodies easily transgresses the MAPL. For example, excess pollution discharge in plain river networks was found in the Rutai Canal, Sanhe Port, and Qinhuai River Lishui section water function zones, owing to dense distribution of enterprises in these regions. In contrast, the Yangtze River has good hydrodynamic conditions characterised as large flow velocity, leading to a higher MAPL in its water function zones49. However, these water function zones also tend to accommodate heavy polluting enterprises. For example, the enterprises with the heaviest discharges among the ‘Chemical industry’ and ‘Petroleum refining, coking, etc.’ sectors were distributed around the Yangtze River Dachang section water function zone.

Consequently, the hydrological characteristics of water function zones and their MAPLs must be considered when planning the regional layout of industrial enterprises. First, the number of newly built enterprises and industrial parks should be strictly limited around the water function zones that have lower MAPLs. Second, clean technologies for pollution prevention and end-of-pipe treatment should be implemented for enterprises with heavy excess pollution discharge. Third, high polluting enterprises centred in the hotspot water function zones can be transferred to areas with large environmental carrying capacities50. For example, the Yangtze River Protection Law51 has led to the relocation of steel and petrochemical industries along the river to coastal areas with a higher carrying capacity for water pollution35. However, during the transfer, the distribution of enterprises, the scale of centralised sewage discharge, and the MAPLs of the surrounding water function zones in the undertaking areas must be considered to prevent the transferred regions from becoming ‘pollution havens’52.

From a consumption perspective, high pollution exceedance rates can be linked to the final demand for the ‘Electronic equipment,’ ‘Other services,’ ‘Food processing and tobacco,’ ‘Chemical industry,’ and ‘Construction’ sectors. The final consumption for these sectors may increase in the future. First, China is now entering a ‘new consumption’ era in which traditional consumption patterns are gradually transforming into high-tech and service-based products53,54. Hence, there is an increasing demand for products such as electronics and services55,56. Second, although the ‘Chemical industry’ and ‘Construction’ sectors face declining demands, boosting the demand for these sectors has been included in the 14th Five-Year Plan of the Chinese government57. Third, due to the growing population and the promotion of the Chinese policy for expanding domestic demand, there will be a steady increase in the final demand of China’s ‘Food processing and tobacco’ sector58. These increasing demands together may cause the TEIP of Jiangsu to increase in industrial sectors with large exceedance rates. However, the number of chemical enterprises in Jiangsu has declined by 70%59 since the release of the ‘Jiangsu Province improvement plan for safety and environmental protection of the chemical industry’ in 2019. Hence, the final consumption toward the ‘Chemical industry’ in Jiangsu may be transferred to other provinces in the future. Neighbouring provinces with high levels of industrial activity, such as Shandong, Zhejiang, and Anhui, may embrace more ‘Chemical industry’ enterprises to fulfil the production gap created by Jiangsu60. Such an activity transfer to other provinces may increase the TEIP in these provinces.

In this study, we aggregated the enterprise-level excess water pollution discharge into sectoral levels and attributed this sectoral level excess discharge to the final consumption, both within and beyond the pollution discharging area. Our framework stressed on the importance of fulfilling SDG 12, i.e., Sustainable Consumption and Production, which refers to the use of services or products in a manner that minimises the use of natural resources and emission of pollutants26. Meanwhile, mitigating water pollution from the consumption perspective is also in line with the Integrated Water Resources Management (IWRM) policies of the UN61 and the EU Water Framework Directive62. The linkage of final consumption and surface water deterioration is a necessary first step to raise consumer awareness towards reducing water pollution. Our enterprise-level results can facilitate the development of a mechanism that requires payments from consumers to offset their impacts on excess pollution generated in production areas11.

Methods

Quantifying territorial-based excess industrial pollution

Here, we propose an indicator, i.e., the territorial-based excess industrial pollution (TEIP), to quantify the transgression of pollution discharge across different industrial sectors above the water pollution discharge thresholds of different water function zones. We considered the water function zone as the minimum unit to calculate the TEIP for each industrial sector. The Ministry of Water Resources of China proposed the concept of the water function zone63, which refers to an area of a water body defined according to its dominant function, e.g., fishery, agriculture, or industrial use, and applicable to a corresponding water quality standard in the ‘Environmental quality standards for surface water (GB 3838-2002)’64 (Supplementary Table 4). Its purpose is to control water pollution based on the natural conditions of the water body, as well as socio-economic and environmental protection requirements63. With clear functional division and protection requirements between zones, the aim is to maximise the economic, social, and environmental capacities of different surface water bodies.

The MAPL of a water function zone was selected as the water pollution discharge threshold for industrial water pollution discharge. The Chinese State Council first proposed the MAPL concept in 2011, which was set as the ‘water pollution redline’ that should not be exceeded in each water function zone45. The MAPL calculates the maximum allowable load discharged by point sources derived from industrial facilities and municipal wastewater treatment plants without damaging the water quality requirements of a water function zone49,65. This concept is similar to the TMDL concept proposed by the U.S. EPA, which calculates the maximum daily amount of a pollution allowed to enter a water body under certain water quality standards43. The magnitude of the MAPL in a water function zone is related to the water quality standards, spatial distribution of sewage outlets, pollution degradation coefficients, and hydrological conditions.

We calculated the TEIP of a water function zone, l, by subtracting the MAPL of l from the total discharge received by l:

$${\mathrm {TEIP}}_{l,s}(k) = \mathop {\sum}\limits_n {R_{n,l,s}(k)} - W_{l,s}(k),$$
(1)

where \({\mathrm {TEIP}}_{l,s}(k)\) (tons/yr) is the amount of territorial-based excess pollutant k [COD or NH3-N] received by water function zone l in region s. \({\mathrm {TEIP}}_{l,s}(k)\) is set to 0 if it is <0. \(\mathop {\sum}\nolimits_n {R_{n,l,s}(k)}\) (tons/yr) is the total amount of pollutant k discharged by industrial enterprise n to water function zone l in region s. Here, we assumed that domestic water pollution discharge is fully treated, such that \(R_{n,l,s}(k) = R_{n,l,s}(k){^{\prime}} \times {\mathrm{Re}} _s(k)\) can be calculated by multiplying the actual discharge amount, \(R_{n,l,s}(k){^{\prime}}\), by the sewage treatment residual rate, \({\mathrm{Re}}_s(k)\). \({\mathrm {Re}}_s(k)\) is obtained by dividing the outlet concentration of pollutant k (mg/L) from the wastewater treatment plant by its inlet concentration to the wastewater treatment plant (mg/L), which indicates the proportion of pollutant k remaining in water body after treatment. For pollutants discharged directly into the water body without any treatment, \({\mathrm {Re}}_s(k) = 1\). \(W_{l,s}(k)\) (tons/yr) is the MAPL of pollutant k in water function zone l, whose calculation and validation procedures can be found in the Supplementary Methods, Supplementary Figs. 4–6.

Next, we adopted a grandfathering approach to allocate \({TEIP}_{l,s}(k)\) to each pollution discharging enterprise in water function zone l. This allocation was conducted according to the enterprises’ share of the total emissions in water function zone l:

$${TEIP}_{n,l,s}^i(k) = \frac{{R_{n,l,s}(k)}}{{\mathop {\sum}\nolimits_n {R_{n,l,s}(k)} }} \times {TEIP}_{l,s}(k),$$
(2)

where \({TEIP}_{n,l,s}^i(k)\) (tons/yr) is the amount of territorial-based excess pollutant k caused by industrial enterprise n that enters water function zone l in region s, where superscript i represents the economic sector of the enterprise.

Subsequently, \({TEIP}_{n,l,s}^i(k)\) was aggregated to different sectors to acquire the TEIP at the sector level, \({\mathrm {TEIP}}_s^i(k)\) (tons/yr):

$${TEIP}_s^i(k) = \mathop {\sum}\limits_l {\mathop {\sum}\limits_n {{TEIP}_{n,l}^i(k)} } .$$
(3)

The territorial-based excess industrial pollutants in region s, \({TEIP}_s(k)\) (tons/yr), were therefore summed as follows:

$${TEIP}_s(k) = \mathop {\sum}\limits_i {{TEIP}_s^i(k)} .$$
(4)

To obtain the risk level of each sector for excess pollution discharge, we proposed the pollution exceedance rate. This was calculated by dividing the excess amount of pollutant k released by sector i in region s, \({TEIP}_s^i(k)\), by its total discharge, \(R_s^i(k)\). This indicator shows that the excess pollution discharge of a certain sector increases rapidly to a high level once there is an increase in the amount of pollution discharge. Hence, the larger the pollution exceedance rate, the greater the risk level the sector exhibits for having an impact on the water quality of a water function zone.

Linking TEIP to the final consumption of Jiangsu and other provinces

The TEIP for Jiangsu Province can be linked to the final demand of different consumers, including Jiangsu and other Chinese provinces. This link can be investigated using the MRIO approach. With the MRIO model, the excess pollution discharge induced by the final consumption (EPF) at the sectoral level was calculated as follows:

$${{{\mathbf{epf}}}} = {{{\mathbf{epf}}}}_s + \mathop {\sum}\limits_{r \ne s} {{{{\mathbf{epf}}}}_r} = \left( {\mathop {\sum}\limits_i {\widehat {{{\mathbf{d}}}}_s({{{\mathbf{I}}}}{{{\mathrm{ - }}}}{{{\mathbf{A}}}}_s)^{ - 1}\widehat {{{\mathbf{y}}}}_s} } \right)^\prime + \mathop {\sum}\limits_{r \ne s} {\left( {\mathop {\sum}\limits_i {\widehat {{{\mathbf{d}}}}_s({{{\mathbf{I}}}}{{{\mathrm{ - }}}}{{{\mathbf{A}}}}_s)^{ - 1}\widehat {{{\mathbf{e}}}}_{{\mathrm {sr}}}} } \right)} ^\prime ,$$
(5)

where \({{{\mathbf{epf}}}}\) is the EPF in vector form (30 × 1), and its ith element denotes the EPF of sector i. It has two components, i.e., \({{{\mathbf{epf}}}}_s\) and \(\mathop {\sum}\nolimits_{r \ne s} {{{{\mathbf{epf}}}}_r}\).\({{{\mathbf{epf}}}}_s\) is the TEIP in Jiangsu driven by its final demand while \(\mathop {\sum}\nolimits_{r \ne s} {{{{\mathbf{epf}}}}_r}\) is the TEIP in Jiangsu driven by the final demand of the other 29 provinces. \(\widehat {{{\mathbf{d}}}}_s\) is a diagonal matrix of the excess pollution intensity in Jiangsu; \({{{\mathbf{d}}}}_s{{{\mathrm{ = }}}}{{{\mathbf{t}}}}_s{{{\mathrm{/}}}}{{{\mathbf{x}}}}_s\) is calculated by dividing the vector of the territorial-based excess industrial pollutant, \({{{\mathbf{t}}}}_s\), by the output, \({{{\mathbf{x}}}}_s\), of Jiangsu. \({{{\mathbf{L}}}}_s{{{\mathrm{ = }}}}({{{\mathbf{I}}}}\,{{{ - }}}\,{{{\mathbf{A}}}}_s)^{ - {{{\mathrm{1}}}}}\) is the Leontief inverse matrix, where \({{{\mathbf{I}}}}\) is the unit matrix and \({{{\mathbf{A}}}}_s\) is the technical coefficient matrix of Jiangsu. \(\widehat {{{\mathbf{y}}}}_s\) is a diagonal matrix of the final demand of Jiangsu and \(\widehat {{{\mathbf{e}}}}_{sr}\) is the export of final products from Jiangsu to other provinces.

It should be noted that the system boundary in our study was constrained within China. Hence, international imports were neglected during EPF accounting. Meanwhile, the TEIP driven by the final demand for international exports was allocated to provinces that export products for final demand to other countries. Such boundary settings can be observed in existing studies11,66,67. Further uncertainties and limitations concerning methods and data are shown in the Supplementary Discussion and Supplementary Table 5.

Data sources

In this study, the MAPLs of 1338 surface water function zones in Jiangsu were acquired from Fan68, whose results have been officially released as ‘Opinions of Water Resources Department of Jiangsu Province and Jiangsu Development and Reform Commission on the pollutant load capacity and the MAPL of water function zones’69. The detailed procedure of MAPL calculation is presented in the Supplementary Methods. Enterprise-level data, covering 11,094 industrial enterprises in Jiangsu, included the basic situational information, i.e., the geographical position (Supplementary Fig. 1), industrial sector, and annual discharge of principal pollutants. More importantly, the data included the drainage destinations, i.e., the water function zones of different enterprises. These data were obtained from the Chinese Environmental Statistics Database and have been officially released in Jiangsu Statistical Yearbook 201632. Established by the Ministry of Ecology and Environment (MEE), this database contains environmental information from all major industrial pollution sources nationwide70. These data are self-reported by enterprises and compiled by the MEE. County-level ecology and environment bureaus ensure data quality through environmental monitoring and unannounced field inspections71. Raw discharge data are first compiled into a report and submitted to the ecology and environment department at the municipal or provincial level. After scrutiny, the environmental information is submitted to the MEE, where it is finally compiled into the annual environmental statistical yearbook70. Owing to the strict data inspection procedures, this database is the most reliable environmental database in China72.

The COD and NH3-N were selected as water quality proxies because they are widely used for water quality assessments in China31 and are the principal pollutants responsible for the deterioration of the water quality in Jiangsu73. The inlet and outlet concentrations of the COD and NH3-N for 797 wastewater treatment plants in Jiangsu were used to calculate the sewage treatment residual rates and were sourced from the Chinese Environmental Statistics Database. As wastewater treatment plants in the same city shared similar residual rates, we used the average residual rates of all wastewater treatment plants in the same city to represent the situation in each city (Supplementary Table 6).

To calculate the EPF, we used the 2015 China MRIO table74 which covers 30 Chinese provincial-level administrative regions (abbreviated as provinces) and 30 economic sectors. To explore consumer responsibility for the TEIP within Jiangsu, we used the TEIP of Jiangsu as input and ignored the TEIP of the other 29 Chinese provinces. As our study focused on sector-level industrial pollution, we assigned the TEIP of 11,094 industrial enterprises to 22 industrial sectors in the MRIO model. As the MAPL was set as the water pollution discharge threshold for point source discharge, agriculture sector pollution discharge originating from nonpoint sources was only considered when calculating the MAPL (see Supplementary Methods for details) and was not included as an input to the MRIO model. The TEIP associated with service sectors was included with the domestic wastewater production, which was assumed to be fully treated by municipal wastewater treatment plants, as the average domestic sewage treatment rate in Jiangsu was up to 90% in 201575. The sectoral aggregation principle was based on the ‘Industrial Classification for National Economic Activities (GB/T 4754-2017)’76.