Changes in water use and wastewater generation influenced by the COVID-19 pandemic: A case study of China.

This paper examines and projects the water use and wastewater generation during and after the SARS-CoV-2 (COVID-19) in China, and discussed the water use/wastewater generation pattern changes among different sectors. Existing studies on the impact of pandemic spread-prevention measures on water consumption and wastewater treatment during the pandemic are reviewed. The water use and wastewater discharge in China through the COVID-19 period are then projected and analyzed using Multivariate Linear Regression. The projection is carried out for years 2019-2023 and covers an (estimated) full process of pre-pandemic, pandemic outbreak, and recovery phase and provides essential information for determining the complete phase impact of the COVID-19. Two scenarios, i.e. the recovery scenario and the business as usual scenario, are set to investigate the water use and wastewater generation characteristics after the pandemic. The results imply that in both scenarios, the water use in China shows a V-shaped trend from 2019 to 2023 and reached a low point in 2020 of 5,813✕108 m3. The wastewater discharge shows an increasing trend throughout the COVID period in both scenarios. The results are also compared with the water consumption and wastewater generation during the SARS-CoV-1 period. The implication for policymakers is the possible increase of water use and wastewater discharge in the post COVID period and the necessity to ensure the water supply and control of water pollution and wastewater discharge.

Introduction

The widespread SARS-CoV-2 (COVID-19) has been leading to significant changes in various aspects globally, which also caused immense challenges to human health and covers almost the whole list of the Sustainable Development Goals (SDGs) (Tortajada and Biswas, 2020). The initial sharp spreading and the continuous waves of the virus have also been posing considerable impacts to the environment and resources. For example, mobility limitations and large scale lockdowns might have a positive impact on the overall air pollution reduction and water pollution generation. On the other hand, the pandemic and related measures also have direct impacts including the increase in medical waste and municipal waste from food deliveries and online shopping packages, water (medical) pollution. In addition, as the pandemic still going on, its long-term environmental and social impacts are not yet clear. The post-pandemic challenges including the treatment of medical waste and the reuse/recycling of other solid waste, ecological challenges due to the recovery of tourism, water and food security improvement in low-income regions and countries, etc., should be carefully considered.

As one of the most critical environmental elements, water has been facing new challenges brought up by the pandemic. Water scarcity caused by the quantitative shortage and qualitative degradation has been increasingly severe in various regions globally. The influence of the pandemic has been diverse, with both positive and negative in the water sector. For example, the limitation on mobility and large-scale lockdown could reduce industrial water consumption and pollution generation, and the lifestyle change could shift the sectoral water consumption patterns (Li et al., 2021a). In addition to the direct and indirect impacts caused by changes in water consumption patterns, industrial production, international trade, and lifestyle changes would also affect the water sector in the long term.

Infectious viruses in the water and wastewater networks have been reported to cause viral disease transmissions at the community levels (Liu et al., 2021). The interaction of the water and wastewater sanitation networks with day-to-day human activities can cause the transmission of SARS-CoV-2 between buildings and communities (Wang et al., 2021). The hospitals and healthcare buildings are of particular concern for these sorts of high-risk pathogenic transmission issues (Gormley et al., 2020). This situation becomes even more dangerous where sewage directly runs into the surface waters without any pre-treatment. It is a particular risk in most low-income countries where appropriate sewage and wastewater disposal systems are often lacking (Sunkari et al., 2021), and surface water is utilized to fulfil daily water demands without any further purification and/or sanitary treatment (Anand et al., 2022).

SARS-Co-2 has been reported to survive on various surfaces for up to 21 days (Kasloff et al., 2021), while in the surface water and wastewater networks, it is estimated to survive for 25 days at 5 °C (Shutler et al., 2020). The existence of SARS-Co-2 in freshwater resources such as river water has been examined and reported (Haramoto et al., 2020). The water samples of urban rivers of the Quito region with high COVID-19 frequency in Ecuador have been reported positive for SARS-CoV-2 (Guerrero-Latorre et al., 2020). SARS-CoV-2 has also been reported in wastewater by scientists from different countries such as Australia, the USA, France, India, Spain, the Netherlands, Italy, UAE, Israel, and many others (Patel et al., 2021). The studies also described the direct correlation between the COVID-19 cases reported and SARS-CoV-2 RNA remains in the wastewater (Hasanah et al., 2020). The presence of the SARS-CoV-2 RNA's in the sewage sludge has also been testified by scientists in Turkey (Núñez-Delgado, 2020), Spain (Balboa et al., 2021), and the USA (Peccia et al., 2020). It is evident from the reported literature that wastewater-based epidemiology (WBE) can be utilized for timely detection (Zhu et al., 2021) and assessment of viral pandemic transmission and the evolution of active cases in the wastewater catchment areas (Tiwari et al., 2021).

The primary methods utilized for the inactivation of the viruses include sunlight, elevated temperature, pH range fluctuation, and commonly used disinfectants (Carraturo et al., 2020). The primary methods include secondary and tertiary treatments in wastewater treatment plants (WWTPs), implications of effective disinfectants, application of heat and radiations, and other available technologies used in the drinking water treatments (Arora et al., 2020). The inactivation of SARS-CoV-2 at high temperatures and the use of ethanol, povidone-iodine, bleach, chloroxylenol and benzyl alkyl ammonium chloride for rapid removal of SARS-CoV-2 is well recognized (Chin et al., 2020). The United States Environmental Protection Agency (USEPA) has listed common disinfectants like hydrogen peroxide, hypochlorite, monosulphates, chlorine dioxide, and quaternary ammonium salts for SARS-CoV-2 disinfection (USEPA, 2020). The studies also show that wastewater treatment at high temperatures also inactivates SARS-CoV-1 (Kariwa et al., 2006) and SARS-CoV-2 (Arora et al., 2020). The inactivation time varies depending upon the applied temperature (Wu et al., 2020). To minimize the spread of the SARS-CoV-2, 100% removal of its contamination from the surface as well as wastewater is inevitable (Panda et al., 2021).

The likely variation in the water consumption demand also affects the quality of the drinking water available in the water supply chain. The increased water encaged time in plumbing and/or water supply networks is due to a lack of flow (Asadi-Ghalhari and Aali, 2020). This water stagnation might decrease the number of disinfectant residuals, the development of the disinfectant by-products (Li et al., 2021b), corrosion escalation, nitrification, microorganisms regrowth, and formation of the biofilms (Berglund et al., 2021).

There have been also some studies investigating the impact of the COVID-19 pandemic on the water sectors. Section 2 provides an analytical review of the studies investigating the environmental (especially the water environmental) impact of the pandemic at various scales. Section 3, 4 present the evaluation, prediction and analysis method and results of the water use and wastewater discharge of China at a national level. In addition, Section 4 also provides an initial comparative analysis of the water use and wastewater discharge during the SARS-CoV-1 and the SARS-CoV-2, intending to estimate the potential water use and wastewater discharge trend, and provide insights for future water management.

Literature review

This review mainly covers analytical and review studies of the water environmental impact of the COVID-19 on various scales. Searching in Scopus with the Keywords “Water” & “COVID-19” or “SARS-CoV-2”, with the time scale from 2019 to 2022. As the review aims to investigate the environmental impact of the pandemic, the subject area of the review is limited to Environmental Science, medical subject areas such as Medicine, Immunology and Microbiology, Physics and Astronomy, Computer Science, etc. are excluded. A number of 915 peer-reviewed journal articles published in English are found applying these filters. To select the articles under the aimed scope, a screening selection was then carried out by reading the articles' titles, keywords, and abstracts and identifying their relevance to the scope. From the 863 selected papers, more than 90% of the papers are focusing on the environmental surveillance or wastewater surveillance of the SARS-CoV-2. A number of 65 studies were investigating the influence of the pandemic on the water environment, which were then analyzed in detail to identify the up-to-date research trends.

The selected reviewed studies can be divided into five groups based on their research scope, including the overall analysis of the environmental influence of the pandemic; Influence of pandemic on the water cycle; large scale water usages/demand; water and wastewater management; and pandemic related water pollution or water quality improvements.

Nazir et al. (2021) presented an overview to discuss the eco-environmental influence of the COVID-19 and discussed the environmental changes related to the pandemic. According to their study, the pandemic and related social-economic measures has to some extent reduced air pollution, alleviated water pollution, and reduced noise and light pollution. However, one of the major issues arising during the pandemic is the use and disposal of a large amount of medical waste from personal protective equipment(s) (PPE) such as masks, gloves, face shields, etc. Kumar et al. (2021) discussed the impact of COVID-19 on society, environment, economy, education as well as globalization. Specifically, the influence of the pandemic on air pollution, water, as well as wildlife is discussed. With most of the studies discussing the current short term impact of the pandemic, Irfan et al. (2021) investigated the shorter-term impacts and summarized the long-term implications. The study pointed out that although in short term the environment quality has been improved mainly due to the reduction in human activities (especially tourism), for example, such as air quality improvements, clean beaches, and the decline in environmental noise, etc., these improvements are not a sustainable way of environmental mitigation in long term with the society recovers to its full function.

Besides discussing the issue from a general perspective, there are about 50 articles discussing the influence of the pandemic on the water sector. Many studies investigate pandemic related water pollution or water quality improvements. For example, Walker (2021) pointed out the issue of marine pollution from PPE with an editorial review and reusable PPE are encouraged besides the improvement of PPE management. Chirani et al. (2021) discussed the water as well as waste pollution from the significant increase in soap consumption during the pandemic, and proposed the sustainable production of natural-ingredients based and biodegradable soap, as well as the sustainable use of soap packages. Hora et al. (2020) studied the water environmental impacts of using Quaternary Ammonium Compounds, which are active ingredients in various types of disinfectants. The study claimed that high concentration QAC would affect the wastewater treatment efficiency, increase antibiotic resistance, and is toxic to aquatic and soil microorganisms. More attention is needed to monitor the presence and concentration in the wastewater and develop remediation plans when needed. In addition, although a great effort has been made to improve the wastewater treatment performance and facilitate water reuse (Sepehri and Sarrafzadeh, 2019), medical pollution also posed a serious challenge in this potential direction.

On the other hand, there are also many studies noticing the positive impact of the pandemic on short term water quality improvements. Pons et al. (2020) investigated the effect of large scale lockdown on the wastewater characteristics of two large-scale urban areas with between 250 and 300 thousand inhabitants in France. The results showed that in the research area with more daily inward commuters, a positive correlation is observed between the lockdown and the wastewater characteristics. There was a decrease in the monthly load averages of chemical oxygen demand, biochemical oxygen demand, total Kjeldahl nitrogen, total suspended solids and total phosphorus in the wastewater. Liu et al. (2022) claimed that the lockdown improved the river water quality in China. Their results also observed the decrease in major water quality indicators such as ammonia nitrogen, chemical oxygen demand (COD), dissolved oxygen (DO), and pH, etc. The study also observed the changing pattern of different indicators. For example, the reduction of ammonia nitrogen started soon after the lockdown, COD and DO showed improvements at the earlier stage of the lockdown, while the pH increased during the later stage of the lockdown.

There are also a few studies investigating the large scale water usage/demand changes during the pandemic. Menneer et al. (2021) studied the changes in domestic water and energy consumption in UK during the lockdown in 2020. The analysis was carried out using high-resolution temporal data, and found that the gas usage increased by 20% in the week before full lockdown, and no difference was seen during full lockdown itself. Hourly water usage increased by 17% during the lockdown with a 1-h delay in peak morning usage. Kazak et al. (2021) estimated the changes in water demand patterns during the lockdown in 2020 in Krakow, Poland. The results showed that the total water consumption during the lockdown did not change significantly, but the water use in different sectors changed significantly. Compared with the previous months before the lockdown, the water use in residential buildings increased 13.2%, commercial objects decreased 17.2%, and education facilities decreased 38.1%. Antwi et al. (2021) examined the water sector responses to the COVID-19 of EU-27 from a qualitative perspective. Results showed that among the 27 EU countries, 8 countries implemented full-cost measures to temporarily suspend the water bills, three countries released direct support to pay the water bills or rescued the water bills in the form of subsidies. Another 16 countries had not implemented any water-related measures until the studied time (June 2020). Accordingly, suggestions are also proposed for water and wastewater management during the pandemic. Maal-Bared et al. (2021) pointed out the importance of the wastewater sector and the role of water utility professionals during the pandemic, and proposed that increase the safety awareness of water utility processional such as using PPE and adopting safe work practices. Poch et al. (2020) assessed the impacts of the pandemic on the urban water cycle, and discussed the water digitalization implications on the COVID-19. The study concluded that the water systems should be further developed considering the water infrastructure, digitalization, and privacy protections. A more reactive system could highly improve the monitoring and response of the system and could facilitate the control of the COVID-19.

Overall, existing studies have not covered the influence of COVID-19 on large-scale water consumption and wastewater generation. This study aims to determine the water sectors’ response to the unprecedented COVID-19 pandemic, by estimating the water consumption and wastewater generation during the pandemic and for the post-pandemic period in China. Besides the estimation and analysis, the water use and wastewater generations during the COVID-19 are also compared with the SARS-CoV-1 epidemic in 2003. The outcomes of this study shall facilitate to control and mitigation the future urban water management issues that may arise due to un-seen future pandemics.

Water use and wastewater discharge predictions

In order to simulate the predicted water use during and after the COVID-19 pandemic, the water consumption data, including total water consumption and the sectoral water use from 2000 to 2020, were collected (Detailed data is presented in Appendix Table A1) from the National Bureau of Statistics (2021a). The agricultural water use (Agr. WU) includes the water used for irrigation, farming, and other agriculture and forestry-related activities (National Bureau of Statistics, 2019). The agricultural water use is related to agriculture and forestry development and precipitation within the year. Industrial water use (Ind.WU) covers the water used for the whole industrial and manufacturing-related activities. Residential water use (Rsd.WU) includes water use in households and tertiary industries (hotels and restaurants, etc.) and is correlated with population, GDP, etc. Environmental water use (Env.WU) represents the water used for public greeneries and entertainment water facilities, etc.

Correlation analyses of total water use, sectoral water use, and the selected indicators (Population, GDP, precipitation, passenger turnover, freight turnover) are carried out to screen the more related indicators. The results of the correlation analysis are shown in Table 1 .

Table 1

Correlation analysis results of water use and wastewater discharge.

	Total WU	Agr.WU	Ind.WU	Rsd.WU	Env.WU	Wastewater discharge
Population (108)	0.725	0.307	0.040	0.975	0.813	0.985
GDP (1012 CNY)	0.660	0.302	−0.094	0.952	0.853	0.958
Precipitation (mm)	0.267	0.155	−0.212	0.466	0.498	0.440
Passenger turn-over (1012 person-km)	0.889	0.507	0.482	0.826	0.415	0.966
Freight turnover (1012 t-km)	0.835	0.449	0.193	0.953	0.695	0.995

The results showed that Total water use (Total WU) and wastewater discharge are significantly correlated with all social development indicators except precipitation. Agriculture water use correlates with Freight and passenger turnover, GDP, and population and is least correlated with precipitation. Industrial water use is relatively stable during the years and does not show an obvious correlation with the selected indicators. Residential water use is significantly correlated with all the selected indicators except precipitation. Environmental water use has a major correlation with the Population and GDP, Freight turnover, precipitation, and the least correlation with passenger turnover. As precipitation has the least correlation with both total water use and sectoral water use in China among the selected social development indicators, it is less representative and thus not considered in the following studies for the regression for projecting future water consumption. As a consequence, population, GDP, Passenger turnover, and Freight turnover are used for the projection analysis of water use and wastewater discharges.

Methods

Regression analysis is one of the most used statistical methods to examine the relationship between two or more variables in the dataset. The selected social-economic factors in the COVID- and post-COVID years (2019–2023) are projected with linear regression. As there are multiple factors identified to be significantly or highly correlated with the water use and wastewater discharge, a multivariate regression model is needed to project the water use and wastewater discharge in the targeted years. Multivariate Regression is a commonly used supervised machine learning algorithm involving multiple data variables for analysis. In this study, the multivariate linear regression is applied to project the water use in the COVID and post-COVID period based on the four selected independent variables. A regression model is applied to provincial water consumption and wastewater generation from 2001 to 2005 by sectors obtained from the statistical office. The 2002–2004 severe acute respiratory syndrome (SARS) outbreak was claimed as the first pandemic in the 21st century. The rapid outbreak of SARS in China also caused a large number of infections and mortality and had a devastating influence on economic development. Measures such as lockdowns and limitations in mobility were also introduced at both national and regional levels. The water use and social development data during the SARS and post-SARS period, i.e., from 2001 to 2005, are used as the training data for the Multivariate Linear Regression model to predict the water use throughout the COVID period, i.e., 2019–2023. From 2006 to 2019 is considered as the “normal period”.

Considering the SARS-CoV-2 is more deadly in all aspects with a much more serious impact, and combined with the current social-economic performances in China, it is estimated the social-economic indicators might be possible to recover since or after 2023. Consequently, 2023 is selected as the last year of the prediction. It is assumed that the projected water use and wastewater discharge from 2019 to 2023 is able to show a trend for the future (years after 2023).

The Multivariate Linear Regression follows:

is the estimate of component of dependent variable y, which is the total water use in year i, where there are n independent variables (number of selected social development indicators in this study, n = 4. denotes the component of the independent variable. and are the Intercept and regression coefficients for the component.

The water use data and the selected social development indicators (Population, GDP, Passenger turnover, Freight turnover) of 2001–2005 are used for the Multivariate Linear Regression training, and the data from 2019 to 2023 is used for the fitting and prediction.

Projection of the selected social development indicator

Two scenarios, i.e., the Business as usual (BAU) scenario and the Recovery curve scenario (RC), are set to project the social development indicators in 2021–2023. In this BAU scenario, it is assumed that the country successfully manages to handle the COVID situation, and the social and economic development returns to a normal level. The data during 2006–2019 (the normal period) is used for the social-economic indicators projection in the BAU scenario with linear programming. In the RC scenario, it is assumed that the social-economic indicators will follow the development pattern of the SARS epidemic period. Data from 2001 to 2005 is used for the projection with a linear programming model. The projection of each indicator is explained in detail as follows.

Population

The population in 2020 in China is 14.1 ✕ 108 according to the National Bureau of Statistics (2021b). In the BAU scenario, the population in 2021–2023 is projected with linear programming with the data from 2006 to 2019. According to the National Bureau of Statistics (2021b), the average population increase rate during 2010–2020 is 0.53% and is showing a slowing trend. The population in recent years will maintain at a stable (or minor increase) level (National Bureau of Statistics, 2021b). In this study, it is assumed that the population will keep a minor annual increase rate of 0.25% in the RC scenario. The linear programming parameters (BAU scenario only) and the projected population from 2021 to 2023 for both scenarios are listed in Table 2 .

Table 2

Linear programming parameters and projected populations in China (2021–2023).

Parameters for Linear programming in BAU scenario				Projected Population (10^8)
Regression Statistics		Coefficients		Year	BAU	RC
Multiple R	1.000	Intercept	−121.7	2021	14.2	14.2
R Square	0.999	X-Variable	0.1	2022	14.3	14.2
Adjusted R Square	0.999			2023	14.4	14.2
Standard Error	0.009
Observations	14

GDP, passenger turnover, and freight turnover

GDP is highly affected by the outbreak of the COVID-19 pandemic. According to the National Bureau of Statistics (2021c), GDP in the first half of the year 2021 is 53.2✕1012 CNY. In both scenarios, it is assumed that the GDP in the second half-year will maintain the same. Therefore the GDP in 2021 whole year will be 106.4✕1012 CNY. In the BAU scenario, the GDP in 2022 and 2023 is projected by linear programming with the data from 2006 to 2019. While in the RC scenario, the GDP is estimated with linear programming using the data from 2001 to 2005. The parameters of the linear programming and the projected GDP are listed in Table 3 (BAU scenario) and 4 (RC scenario) (see Table 4).

Table 3

Parameters and projected GDP in China (2021–2023) in the BAU scenario.

Regression Statistics		Coefficients		Projected GDP (1012 CNY)
Multiple R	0.997	Intercept	−11,656.3	Year	BAU
R Square	0.993	X Variable 1	5.8	2021	106.4
Adjusted R Square	0.993			2022	112.4
Standard Error	2.191			2023	118.2
Observations	15

Table 4

Parameters and projected GDP in China (2021–2023) in RC scenario.

Regression Statistics		Coefficients		Projected GDP (1012 CNY)
Multiple R	0.988	Intercept	−3879.2	Year	RC
R Square	0.975	Corrected Intercept	−3822.0	2021	106.4
Adjusted R Square	0.967	X Variable 1	1.9	2022	108.4
Standard Error	0.563			2023	110.3
Observations	5

Note that due to the significant difference of the GDP values between the 2001–2005 period and the post-2020s, the intercept generated from the linear programming brings up a major error when used to project the GDP value in 2022–2023 in the RC scenario. The known GDP data in 2021 (106.4✕1012 CNY) is used to calibrate the intercept, and the corrected intercept is used to project the GDP in 2022 and 2023.

The passenger turnover and Freight turnover projection follow the same procedure as the GDP. As shown in a report from the Industrial Information Net (2021), the passenger turnover in the first half-year in 2021 is 1.05✕ 012 person-km and the Freight turnover in the first half-year in 2021 is 10.45 1012 t-km. It is assumed that both indicators in the second half-year in 2021 remain the same.

Consequently, the passenger turnover and Freight turnover in 2021 are 2.1✕1012 person-km, and 20.9✕1012 t-km. The parameters and projected values are presented in Table 5 and Table 6. However, when using the data from 2006 to 2019 to construct the linear programming model, it showed that there is an obvious data gap around the year 2012 (As shown in Fig. 1 a and b).

Table 5

Projected Passenger turnover and Freight turnover in 2022 and 2023 in the two scenarios.

Year	Projected Passenger turnover (1012 person-km)		Projected Freight turnover (1012 t-km)
	BAU	RC	BAU	RC
2021	2.1	2.1	20.9	20.9
2022	2.2	2.2	22.3	21.7
2023	2.4	2.3	23.8	22.6

Overall, the data of selected social development indicators in the two scenarios are summarized in Table 6.

Table 6

Projected social-economic indicators in the BAU and RC scenarios in 2021–2023.

Year	Population (108)		GDP (1012 CNY)		Passenger turnover (1012 person-km)		Freight turnover (1012 t-km)
	BAU	RC	BAU	RC	BAU	RC	BAU	RC
2019	14.0	14.0	98.9	98.9	3.5	3.5	19.9	19.9
2020	14.1	14.1	101.6	101.6	1.9	1.9	19.7	19.7
2021	14.2	14.2	106.4	106.4	2.1	2.1	20.9	20.9
2022	14.3	14.2	112.4	108.4	2.2	2.2	22.3	21.7
2023	14.4	14.2	118.2	110.3	2.4	2.3	23.8	22.6

Note: Social&economic data in 2019 and 2020 are collected from the National Bureau of Statistics (2021), and the data in 2021–2023 are projected.

Fig. 1

The scatter diagram of passenger turn-over and Freight turn-over in China: a) passenger turn-over (2006–2019); b) Freight turn-over (2006–2019); c) passenger turn-over (2013–2019); b) Freight turn-over (2006–2012).

The reason for this data gap is not yet figured out, even with the help of other related data released by the National Bureau of Statistics (2021d). In this case, the R2 of the regressions for Passenger turn-over and Freight turn-over are 0.7982 and 0.9300, which are not ideal to be used for the projection. By carrying out linear programming for the two pieces of data set (2006–2012 and 2013–2019), it is found that the regression results are the best using the data from 2013 to 2019 for passenger turnover (R2 = 0.9983), and data from 2006 to 2012 for freight turnover (R2 = 0.9864), as shown in Fig. 1c and d. The intercepts in both linear equations have been corrected using the data in 2021 (2.1✕1012 person-km, and 20.9✕1012 t-km).

The projected Passenger turnover and Freight turnover in 2022 and 2023 in the two scenarios are shown in Table 5.

Projection results and discussions

As the social-economic factors in 2020–2023 have been projected, the water use and wastewater discharge are then projected using Multivariable Linear Regression (MLR). The training data used for the MLR is from 2003 to 2019, which is since the outbreak of the SARS and covers the whole recovery period. The regressions parameters are presented in Table 7 .

Table 7

Regression statistic and coefficients of water use and wastewater discharge (based on data from 2003 to 2019).

Water use: Regression Statistics		Coefficients
Multiple R	0.969	Intercept	−12,440
R Square	0.939	Population	1369
Adjusted R Square	0.918	GDP	−22.3
Standard Error	67.08	Passenger turn-over	122.9
Observations	17	Freight turnover	49.0
Wastewater discharge: Regression Statistics		Coefficients
Multiple R	0.995	Intercept	−294.0
R Square	0.990	Population	52.8
Adjusted R Square	0.987	GDP	−0.2
Standard Error	11.71	Passenger turn-over	−33.5
Observations	17	Freight turnover	22.2

Using the social-&economic data obtained in Table 6, the water use and wastewater discharge from 2020 to 2023 are projected and presented in Fig. 2 . It is assumed that the five years from 2019 to 2023 are the before, during, and after the COVID period, and the analysis and discussion are based on the five years’ data. Water use and wastewater data in 2019 are from the National Bureau of Statistics (2021), and data for 2020–2023 are projected by this study.

Fig. 2

Projected water use and wastewater discharge during 2019–2023.

In both BAU and RC, the total water use shows a “V” shaped trend during the pre-in-post COVID-19 period. The total water use, which is the sum of industrial, residential, agriculture, and environmental water use, reaches a low point in 2020 when the country was most seriously hit by the pandemic. In both scenarios, the water use turned to an increase after 2020, the major reason is the recovery of social-economic development and the increased human activities with the ease of preventive measures at a national level. Especially the re-opening of industrial and service sectors can significantly increase the total water use. In the BAU scenario, as it is projected that the social and economic development recovers at a normal rate since 2021, the water use quickly gets back to an increase after the low point in 2020 (5,813✕108 m3). The water use reaches a higher value than the normal level (in 2019) for the first time in 2022 (6,084 ✕108 m3), as a consequence of social and economic developments recovery. In the RC scenario, the water use shows a gradual increase after the low point in 2020, and the water use in 2023 (5,970✕108 m3) is still lower than the value in 2019 (6,021✕108 m3). Wastewater discharge represents the treated industrial and residential water released from wastewater treatment plants (WWTPs), and wastewater from agriculture and environmental irrigation are not counted in the statistical data. In both scenarios, wastewater discharge increases through the assumed pre-in-post COVID-19 period (2019–2023).

The projected water and wastewater results are compared with the historical data to investigate the issue from a broader view (Fig. 3 ). The water and wastewater data in the SARS period (2002–2006) and the assumed COVID-19 period (2019–2023) are highlighted in the dash line box.

Fig. 3

Water use and wastewater discharge from 2000 to 2023 (2020–2023 projected).

In Fig. 3, it can be seen that there is a water use increase from 2001 to 2013 and follows a decreasing trend after. Wastewater discharge shows an increasing trend in the selected years. Similar exceptions occurred during the SARS period and are projected to happen during the COVID-19 period. During both epidemics, water use reached a low point in the years of the outbreak and recovered to the normal level after 2 y (BAU scenario). In the Recovery scenario, water use increases at a mild rate. Wastewater discharge shows a general increasing trend during the two pandemics.

Conclusions

This study first provides an overview of the current research investigating the impact of the outbreak of COVID-19 on the water sector. Five research trends were identified from the existing studies, including the overall analysis of the environmental influence of the pandemic, influence of pandemic on the water cycle, large scale water usages/demand; water and wastewater management, and pandemic related water pollution or water quality improvements. Most of the studies focused on revealing the positive influence (reduces water pollution) of the lockdown, but also pointing out that these short-term changes are not sustainable. The literature review also reveals the research gap in the investigation and predictive discussion of the post-pandemic impact on the water sectors, to provide insightful information for the water managers to respond to the water issues together with the pandemic.

The second part of this study analyzes and projects the water use and wastewater discharge during the post-pandemic from 2020 to 2023. The projection is carried out using multivariate linear regression and the multilinear programming method. As another serious pandemic at a national level in China, the water use and social-economic data during the SARS period (2001–2005) are used as training data to project the water use data in the COVID and post-COVID period. The projected results showed that both water use and wastewater discharge during the COVID period shows a similar trend during the post the pandemic. The water use shows a V-shaped trend during the COVID period (2019–2023). The water use reached a low point in 2020 of 5,813✕108 m3, and is projected to increase water use after the COVID along with the recovery of social and economic activities. In the BAU scenario, the water use in 2023 will be higher than in 2019 for the first time in the post-COVID period. While in the RC scenario, the projected results show that the water use will not return to normal (i.e., 2019) level until 2023. The projected wastewater shows an increasing trend in both scenarios. The projected results implicated that the decision-makers should consider the solutions of a coming increase in water use in the recent two or three years. In addition to water use, increasing water-related energy consumption and other environmental impacts should also be considered. The volumetric increase of wastewater generation and discharge and the possible complexity in the water pollution should be also expected due to the increase of medical waste disposal.

The limitation of this study and the potential tasks for future studies, include i) Big-data based water quantity and quality analysis and simulations at a regional level, which can help predict water use and pollution data in emergencies (e.g. a pandemic); ii) It is found hardly possible to collect water quality data even at a regional/city level because the water quality monitoring has been also affected by the mobility limitations during the pandemic. It is worth investigating the possibility of digitalization of the water sectors and integrating water with the internet of things (IoT). These potential future development directions could enable the systematic monitoring of the water quality and quantity and facilitate smart water management.

Credit author statement

Xuexiu Jia: Conceptualization; Data curation; Investigation; Methodology; Visualization; Writing - original draft; Writing - review & editing; Khurram Shahzad: Writing - original draft; Writing - review & editing; Validation; Jiří Jaromír Klemeš: Writing - review & editing; Validation; Project administration; Supervision; Resources; Funding acquisition; Xiaoping Jia: Data curation; Formal analysis; Writing - review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Table A1

Total water consumption and social-economic indicators during 2000–2020 (National Bureau of Statistic, 2021)

Year	Total water consumption					Population	GDP	Precipitation	Passenger turnover	Freight turnover
		Agr.WU	Ind.WU	Rsd.WU	Env.WU
	108 m3					108	1012 CNY	Mm	1012 person-km	1012 t-km
2000	5565.0	3783.5	1139.1	574.9	67.5	12.7	9.9	633.0	1.2	4.4
2001	5639.0	3825.7	1141.8	599.9	71.6	12.8	10.9	612.0	1.3	4.8
2002	5572.6	3736.2	1142.4	618.7	75.3	12.8	12.0	660.0	1.4	5.1
2003	5320.4	3432.8	1177.2	630.9	79.5	12.9	13.7	638.0	1.4	5.4
2004	5547.8	3585.7	1228.9	651.2	82.0	13.0	16.1	601.0	1.6	6.9
2005	5633.0	3580.0	1285.2	675.1	92.7	13.1	18.6	644.3	1.7	8.0
2006	5795.0	3664.4	1343.8	693.8	93.0	13.1	21.9	610.8	1.9	8.9
2007	5818.6	3599.5	1403.0	710.4	105.7	13.2	27.1	610.0	2.2	10.1
2008	5910.1	3663.5	1397.1	729.3	120.2	13.3	32.1	654.8	2.3	11.0
2009	5965.2	3723.1	1390.9	748.2	103.0	13.3	34.8	591.1	2.5	12.2
2010	6022.0	3689.1	1447.3	765.8	119.8	13.4	41.0	695.4	2.8	14.2
2011	6107.2	3743.6	1461.8	789.9	111.9	13.5	48.3	582.3	3.1	15.9
2012	6131.2	3902.5	1380.7	739.7	108.3	13.5	53.7	688.0	3.3	17.4
2013	6183.4	3921.5	1406.4	750.1	105.4	13.6	58.8	661.9	2.8	16.8
2014	6094.9	3869.0	1356.1	766.6	103.2	13.7	64.4	622.3	2.9	18.2
2015	6103.2	3852.2	1334.8	793.5	122.7	13.7	68.6	660.8	3.0	17.8
2016	6040.2	3768.0	1308.0	821.6	142.6	13.8	74.3	730.0	3.1	18.7
2017	6043.4	3766.4	1277.0	838.1	161.9	13.9	83.1	664.8	3.3	19.7
2018	6015.5	3693.1	1261.6	859.9	200.9	14.0	91.4	682.5	3.4	20.5
2019	6021.2	3682.3	1217.6	871.7	249.6	14.0	98.9	651.3	3.5	19.9
2020	5812.9	3612.4	1030.4	863.1	307	14.1	101.6	706.5	1.9	19.7

Note: Agr.WU: Agriculture water use, Ind.WU: Industrial water use, Rsd. WU: Residential water use, Env. WU: Environmental water use.

Ref: National Bureau of Statistics, (2021). Chinese statistical yearbook 2001–2019. accessed 30.7.2021.