Impact of COVID-19 lockdown on water quality in China during 2020 and 2022: two case surges

Abstract

The COVID-19 severely affected the world in 2020. Taking the two outbreaks in China in 2020 and 2022 as examples, the spatiotemporal changes in surface water quality levels and COD_Mn and NH₃-N concentrations were analyzed, and the relationships between the variations in the two pollutants and environmental and social factors were evaluated. The results showed that during the two lockdowns, due to the total water consumption (including industrial, agricultural, and domestic water) decreased, the proportion of good water quality increased by 6.22% and 4.58%, and the proportion of polluted water decreased by 6.00% and 3.98%, the quality of water environment has been improved significantly. However, the proportion of excellent water quality decreased by 6.19% after entering the unlocking period. Before the second lockdown period, the average COD_Mn concentration exhibited a “falling, rising, and falling” trend, while the average NH₃-N concentration changed in the opposite direction. The correlation analysis revealed that the increasing trend of pollutant concentrations was positively correlated with longitude and latitude, and weakly correlated with DEM and precipitation. A slight decrease trend in NH₃-N concentration was negatively correlated with the population density variation and positively correlated with the temperature variation. The relationship between the change in the number of confirmed cases in provincial regions and the change in pollutant concentrations was uncertain, with positive and negative correlations. This study demonstrates the impact of lockdowns on water quality and the possibility of improving water quality through artificial regulation, which can provide a reference basis for water environmental management.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11356-023-27962-7.

Introduction

On December 31, 2019, the Chinese government reported the first case of COVID-19 in Wuhan, the capital city of Hubei Province (Kang et al. 2020). The disease has substantially affected human society, including healthcare, economy, and social relationships, and has caused widespread panic worldwide (Ebisu and Ebisu 2020). To curb the spread of the virus among humans, Wuhan City, Hubei Province implemented a preventive lockdown on January 23, 2020, and implemented a series of strict prevention and control measures, such as self-quarantine, social distancing measures, traffic restrictions, and community containment (Deng and Peng 2020). Other major cities in China and other countries followed this (Le et al. 2020). In April 2020, Wuhan, as the last “reopened” city in China, lifted the lockdown (Qi et al. 2022). Since then, only a few provinces and cities have experienced sporadic cases. In March 2022, China experienced another wave of the pandemic that lasted approximately 3 months and was effectively controlled by the end of May 2022.

With the halting of factories and industries, closing of commercial establishments, and transportation systems almost at a standstill, the most evident and direct results of the lockdown include global decline in the use of fossil fuels, reduction in carbon emissions, and improvement in air quality (Wang et al. 2020; Yunus et al. 2020). Similar to the improvement in air quality, surface water quality also improved (Tokatlı and Varol 2021). For example, signs of rejuvenation and significant improvement were observed in the Ganga River following the nationwide COVID-19 lockdown (Dutta et al. 2020). Similarly, the heavy metal pollution index and heavy metal evaluation index values of Meriç-Ergene River Basin showed a significant improvement in water quality of almost all stations during the lockdown period (Tokatlı and Varol 2021).

In the past few decades, factors such as China’s rapid economic growth, industrialization, urbanization, insufficient infrastructure investment, and energy-intensive development have led to severe pollution of surface water and groundwater by industrial and urban wastewater, agricultural activities, and household waste (Chen et al. 2019). In 2012, China discharged 68.5 billion tons of wastewater, with chemical oxygen demand (COD) emissions of 24.2 million tons and NH₃-N emissions of 2.5 million tons (Jin et al. 2014). Addressing river water quality and freshwater security issues in China is widely considered an emerging imperative in the twenty-first century (Huang et al. 2021).

Based on water quality data from June 2019 to May 2022, this study used nine major river basins in China as the study area and analyzed the impact of two outbreaks of COVID-19 on water quality in China. The novelty and content of this study are follows: (1) under the background of the special period of control measures during the epidemic, there are little research on the changes of surface water levels and water quality parameters (WQPs), especially in the national and different basin scales; (2) The time (monthly and seasonal) change rules of the two typical pollutants, COD_Mn and NH₃-N, are studied numerically, and the change types are spatialized, so as to visualize the change of the water quality of the stations in different basins during the COVID-19 lockdown period; (3) Select different environmental and social factors to explore the possible relationship between the above factors and the change of pollutants. In the context of the repeated occurrence of COVID-19, the above contents will aid in further understanding the potential impact of lockdown measures on surface water quality in China, provide useful guidance for those engaged in water treatment, and provide a reference basis for water resource planning during the period of epidemic prevention and control.

Materials and methods

Study area

China is located in Eastern Asia, with its eastern boundary on the Pacific Ocean. Monsoons dominate the climate in China, and precipitation in winter and summer considerably differs. In addition, because of the country’s vast size and complex terrain, precipitation varies from region to region and generally decreases from southeast to northwest.

As shown in Fig. 1, China is divided into nine major river basins, with I–IX representing the Songliao River Basin (SlRB), Inland river basin (InRB), Haihe River Basin (HaRB), Yellow River Basin (YeRB), Huaihe River Basin (HuRB), Yangtze River Basin (YaRB), Southwest River Basin (SwRB), Southeast River Basin (SeRB), and Pearl River Basin (PeRB). The total volume of river runoff in the five northern river basins, SlRB, HaRB, HuRB, YeRB, and InRB, with a total catchment area of 2.27 million km², accounts for less than 20% of the national total. However, the total volume of the four southern river basins, YaRB, PeRB, SeRB, and SwRB, with a total catchment area of 2.86 million km², accounts for more than 80% of the national total (Zhang et al. 2011). In addition, the distribution of water quality monitoring stations in China is not uniform, with more stations in the east and fewer in the west.

Fig. 1.

Distribution of monitoring stations and zoning map of nine major river basins in China. Owing to a lack of data, Hong Kong, Macao, and Taiwan were not included in the study

Data collection

Water quality data

Water quality data were sourced from the surface water quality monthly report of China (Ministry of Ecology and Environment 2019–2022) and the national surface water quality automatic monitoring real-time data publishing system (https://szzdjc.cnemc.cn:8070/GJZ/Business/Publish/Main.html). The data included water quality levels and typical WQPs concentration of nine basins (1940 stations before January 2021 and 3641 stations after January 2021). According to China’s Surface Water Environmental Quality Standard (Standardization Administration 2002), the water quality levels are determined by 21 WQPs including COD_Mn and NH₃-N. The specific concentration standards are shown in Appendix Table S1. The concentrations of two typical pollutants, COD_Mn and NH₃-N, were obtained from 151 stations (28 in the SlRB, 1 in HaRB, 9 in HuRB, 31 in YeRB, 55 in YaRB, and 27 in PeRB), because these two WQPs can well represent the pollution of surface water quality in China. Taking the national surface water quality situation in the 2019 China Ecological Environment Status Bulletin as an example (Ministry of Ecology and Environment 2020), the main pollution indicators include COD_Mn and NH₃-N. The automatic monitoring stations measure water quality six times a day, so the data from these stations were averaged to obtain daily and monthly water quality values. The monthly average water quality concentration is shown in Fig. 2. Based on GB3838-2002, the concentration standards of COD_Mn and NH₃-N selected in this study are presented in Table 1.

Fig. 2.

Monthly average WQPs concentration in each river basin

Table 1.

Baseline limit of surface water environmental quality standard (mg/l)

WQPs	Level I	Level II	Level III	Level IV	Level V	Level inferior V
CODMn	0–2	2–4	4–6	6–10	10–15	≥ 15
NH3-N	0–0.15	0.15–0.5	0.5–1	1–1.5	1.5–2	≥ 2

COVID-19 case data

Data related to the epidemic were obtained from the daily update of the National Health Commission of the People’s Republic of China (http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml). The specific numbers of cases and recoveries are shown in Fig. 3. The research period was divided into five components based on the time of the two severe outbreaks.

• Pre-lockdown period: June 2019 to January 2020, 8 months;

• Lockdown period (first epidemic): February to April 2020, 3 months;

• Unlocking period 1: May to December 2020, 8 months;

• Unlocking period 2: January 2021 to February 2022, 14 months;

• Semi-lockdown period (second epidemic): March 2022 to May 2022, 3 months.

Fig. 3.

Confirmed and cured cases of COVID-19. Cases do not include those from Hong Kong, Macau, and Taiwan. Because the National Health Commission of the People’s Republic of China first released the epidemic infection statistics on January 21, 2020, the January 2020 data include that from the January 21 to 31

Because the number of monitoring stations changed in January 2021, the unlocking period was divided into two periods to facilitate the comparison of water quality levels. The second epidemic only involved some provinces and cities, and China did not implement large-scale country-wide lockdown measures; therefore, this period was called the semi-lockdown period.

Environmental and social data

Meteorological station data including precipitation and temperature from 2019 to 2021 near the water quality monitoring stations were obtained from the National Climatic Data Center (NCDC, ftp://ftp.ncdc.noaa.gov/pub/data/noaa/isd-lite/).

Population density data (1 km × 1 km) from 2019 to 2021 were obtained from the LandScan Program (https://landscan.ornl.gov/).

The nighttime light dataset of China (1 km × 1 km) for 2020 was obtained from the National Tibetan Plateau Data Center (TPDC, http://data.tpdc.ac.cn).

Land use data (1 km × 1 km) for 2020 were obtained from the Resource and Environment Science and Data Center (https://www.resdc.cn/).

The national boundary, river basin boundary, digital elevation model (DEM), and water system data were obtained from the Resource and Environment Science and Data Center (https://www.resdc.cn/).

Water quality parameter concentrations change trend detection

We conducted linear regression based on the least squares method to detect the trend for monthly average water quality concentrations, which is the slope of the linear regression equation (Mao et al. 2012). The calculation formula is as follows:

$${\theta }_{\mathit{slope}}=\frac{n\times {\sum }_{i=1}^{n}i\times W{Q}_i-{\sum }_{i=1}^{n}i{\sum }_{i=1}^{n}W{Q}_i}{n\times {\sum }_{i=1}^n{i}^2-{({\sum }_{i=1}^{n}i)}^2}$$ 1

where n represents the time span, and i represents the water quality in the i-th month. A θ_slope < 0 indicates water quality parameter concentrations decreased, and a θ_slope > 0 indicates water quality parameter concentrations increased.

The F-test was used to examine the significance of these trends (Yin et al. 2020). A confidence level of 0.05 was selected in the F-test to evaluate the significance of the change in water quality parameter concentrations. Statistical significance was set at p ≤ 0.05, and insignificance was set at p > 0.05. The combination of the trends and p-values can divide the water quality parameter concentrations trend into four categories: significant decrease, slight decrease, significant increase, and slight increase (Table 2).

Table 2.

Significance classification criteria

θslope	p	Significance test classification
θslope < 0	p ≤ 0.05	Significant decrease
	p > 0.05	Slight decrease
θslope > 0	p ≤ 0.05	Significant increase
	p > 0.05	Slight increase

Correlation analysis of water quality parameter changes with environmental and social factors

On the basis of four different water quality parameter change types, six natural and social data including longitude, latitude, DEM, temperature, rainfall, and population were selected. Spearman correlation analysis was selected to explore the correlation between the change slope of WQPs and longitude, latitude, and DEM, and select partial correlation analysis to explore the correlation between the slope of WQPs and temperature, rainfall, and population.

Results

National monthly changes in water quality levels

The monthly surface water quality reports classify water quality levels I and II as excellent; III as good; and IV, V, and inferior V as polluted (Table S2).

Figure 4 shows the water quality ratio from June 2019 to May 2022. It can be seen from the figure that the water quality substantially improved during the lockdown period, and the proportion of excellent and polluted water quality during the lockdown period increased and decreased by 6.22% and 6.00% compared with that during the pre-lockdown period. After entering the unlocking 1 period, the water quality status deteriorated, and the proportion of excellent water quality decreased by 6.19%, almost returning to the level of pre-lockdown. The average water quality during unlocking 2 period was different from that during the unlocking period 1, owing to the addition of monitoring stations in the unlocking 2 period, and the overall water quality improved. Upon entering the semi-lockdown period, the water quality is also improved compared with that during unlocking period 2; however, the change trend was not as high as that in the lockdown period, and the proportion of excellent and polluted water quality increased and decreased by 4.58% and 3.98%.

Fig. 4.

Water quality proportions after classification from June 2019 to May 2022

Changes in water quality levels in nine major river basins at different periods

The proportion of change in water quality levels during different periods in the nine major river basins is shown in Fig. 5.

Fig. 5.

Proportion of water quality level changes in different periods of the nine major river basins. Difference between the a lockdown period and pre-lockdown period, b unlocking period 1 and lockdown period, c unlocking periods 1 and 2, and d semi-lockdown period and unlocking period 2

Figure 5(a) shows that after entering the lockdown period, the water quality of the nine major river basins greatly improved. The proportion of level I in the InRB increased 14.85%, almost all of which was transformed from level II. The changes in the water quality levels of the remaining eight basins all showed an increase in the proportion of levels I and II, and a decrease in the proportion of Level III to inferior V.

Figure 5(b) shows that during unlocking period 1, the water quality of most basins deteriorated. Only the proportion of level I in the InRB and SeRB increased, and the proportions of levels I and II in the remaining seven basins decreased.

The difference between unlocking periods 1 and 2 (Fig. 5(c)) indicates that after adding more monitoring stations, the proportions of levels III and IV in the SlRB, levels III to V in YeRB, levels I and III in the InRB, and levels II and IV in the PeRB decreased. The remaining five river basins exhibited an increase in the proportions of levels I and II and a decrease in the proportion of the sum of levels IV to inferior V.

Figure 5(d) shows that after entering the semi-lockdown period, the water quality of the remaining eight river basins has also improved in addition to InRB (level I and II decreased by 4.83%). Levels I and II of SlRB, YaRB, and SeRB increased by 0.85% and 3.97% on average, respectively. The YeRB, HuRB, and PeRB exhibited increased proportion of levels I to III by an average of 1.78%. Levels I and II in the SwRB increased and decreased by 1.20% and 1.83%, respectively.

In summary, although the proportion of water quality changes in the nine major river basins varied, except for some river basins with abnormal changes in particular periods, the overall trend of the river basins was relatively consistent with the national change trend.

Monthly and seasonal variations in typical pollutant concentrations

Monthly variations in COD_Mn and NH₃-N concentrations

Figure 6 shows the monthly variations in COD_Mn and NH₃-N concentrations. Before the semi-lockdown period, the average concentration change trends of the two pollutants were almost the opposite, and after the semi-lockdown period, both COD_Mn and NH₃-N concentrations exhibited a downward trend. Overall, the average concentrations of COD_Mn and NH₃-N during the semi-lockdown period decreased compared with the pre-lockdown period of the study.

Fig. 6.

Monthly variations in COD_Mn and NH₃-N concentrations

The variations in COD_Mn concentration were substantially affected by the COVID-19 lockdown measures, with the percentage change of the average concentration in the two adjacent period being − 9.70%, 8.58%, − 4.88%, and − 4.34%, respectively. The COD_Mn concentration was always at level II, and the average concentrations in the last two periods (unlocking period 2 and semi-locking period) decreased. However, the monthly COD_Mn concentration increased during the semi-locking period.

Changes in NH₃-N concentrations were also affected by the lockdown measures. Although the average concentration of NH₃-N during the lockdown period is higher than pre-lockdown, the monthly concentration has been decreasing. Similarly, although the average concentration during the unblocking period (0.1140 mg/L) was lower than the average concentration during the blockade period (0.1652 mg/L), it was slightly higher than the concentration in the last month of the blockade period (0.1136 mg/L). The percentage change in the average concentration of NH₃-N in the two adjacent period was 17.12%, − 32.04%, 30.13%, and − 40.19%, respectively, and consistently fluctuated at levels I and II.

Seasonal variations in COD_Mn and NH₃-N concentrations

Figure 7 shows the seasonal variations in the COD_Mn and NH₃-N concentrations. Even when affected by the lockdown measures, the COD_Mn concentration varied seasonally, with the highest concentration in summer, followed by autumn, spring, and winter. The COD_Mn concentration continued to decrease in the winter of 2019, 2020, and 2021. In summer, a rising trend was evident with the increase of the year, with a trend of “falling–rising” observed in spring and autumn, and the concentration during the spring of 2022 increased compared with that in 2021.

Fig. 7.

Seasonal variations in COD_Mn and NH₃-N concentrations. Spring: March, April, and May; summer: June, July, and August; autumn: September, October, and November; and winter: December, January, and February

The annual variations in NH₃-N concentration in different seasons were not as significant as those of COD_Mn. The order of NH₃-N concentrations from high to low was as follows: winter, summer, and autumn in 2019; winter, spring, summer, and autumn in 2020; and autumn, winter, summer, and spring in 2021. The annual variation in NH₃-N concentrations during the same season was relatively consistent. The seasons from 2019 to 2021 generally exhibited a “falling–rising” trend, and the concentration during the spring of 2022 decreased compared with that during the same period in preceding years.

Spatiotemporal variations and trend of typical pollutant concentrations in different periods

Spatiotemporal variations in COD_Mn concentrations

The spatiotemporal variations in COD_Mn concentrations in different periods are shown in Fig. 8.

Fig. 8.

Spatial variations in COD_Mn concentrations in different periods. Difference between the a lockdown period and pre-lockdown period, b unlocking period 1 and lockdown period, c unlocking periods 1 and 2, and d semi-lockdown period and unlocking period 2

The station in the HaRB improved during the lockdown period, unlocking period 2, and semi-locking period and deteriorated during unlocking period 1. In the remaining five river basins, during the lockdown period, the number of stations with decreased COD_Mn concentrations was more than the that with increased concentrations (except in the PeRB). During unlocking period 1, the number of stations with decreased COD_Mn concentrations was less than that with increased concentrations (except in the PeRB). During unlocking period 2, the number of stations with decreased COD_Mn concentrations was more than that with increased concentrations (except in the SlRB). At the beginning of the semi-lockdown period, the basins where the number of stations with decreasing concentrations exceeding the number of stations with increasing concentrations were the YeRB (70.97% and 29.03%, respectively), HuRB (66.67% and 33.33%, respectively), and YaRB (76.36% and 23.64%, respectively). The basins where the number of concentration decreases was less than the number of increases concentration during semi-lockdown period were the SlRB (42.86% and 57.14%, respectively) and PeRB (37.04% and 62.96%, respectively).

Spatiotemporal variations in NH₃-N concentrations

The variations in NH₃-N concentrations at each station during different periods are shown in Fig. 9.

Fig. 9.

Spatial variations in NH₃-N concentrations in different periods. Difference between the a lockdown period and pre-lockdown period, b unlocking period 1 and lockdown period, c unlocking periods 1 and 2, and d semi-lockdown period and unlocking period 2

The water quality at stations in the HaRB improved during the lockdown period, unlocking period 1, and semi-locking period and deteriorated during unlocking period 2. In the remaining five river basins, during the lockdown period, the number of stations with decreased NH₃-N concentrations was less than that with increased concentrations (except in the YaRB). During unlocking period 1, the number of stations with decreased NH₃-N concentrations was greater than that with increased concentrations (except in the YeRB). During unlocking period 2, the number of stations with reduced NH₃-N concentrations was greater than that with increased NH₃-N concentrations in the five river basins. Entering the semi-lockdown period, the number of stations with reduced NH₃-N concentrations was less than that with increased NH₃-N concentrations in the five river basins.

Spatial characteristics of water quality trends

The change slopes and corresponding significance values of the two WQPs from June 2019 to May 2022 were obtained and classified (Fig. 10). For COD_Mn, 41 stations passed the significance test: five in the SlRB, eight in the YeRB, six in the HuRB, and 11 each in the YaRB and PeRB. Specifically, the COD_Mn concentrations significantly decreased at 38 stations, with an average slope of -0.080. The COD_Mn concentrations significantly increased at three stations, with an average slope of 0.150. Several stations failed the significance test, of which 72 stations experienced a slight decrease in COD_Mn concentrations with an average slope of − 0.041, and 38 stations experienced a slight increase with an average slope of 0.046 (Table 3).

Fig. 10.

Long-term change trend of a COD_Mn and b NH₃-N concentrations

Table 3.

Number of stations with variations in COD_Mn and NH₃-N concentrations in different river basins

	Type of change	SlRB	HaRB	YeRB	HuRB	YaRB	PeRB	Total
CODMn	Significant decrease	3	–	7	6	11	11	38
	Significant increase	2	–	1	–	–	–	3
	Slight decrease	12	1	12	3	34	10	72
	Slight increase	11	–	11	–	10	6	38
	Total	28	1	31	9	55	27	151
NH3-N	Significant decrease	2	–	–	2	11	4	19
	Significant increase	2	–	7	–	4	–	13
	Slight decrease	14	1	10	3	25	15	68
	Slight increase	10	–	14	4	15	8	51
	Total	28	1	31	9	55	27	151

For NH₃-N, 32 stations passed the significance test: four in the SlRB, seven in the YeRB, two in the HuRB, 15 in the YaRB, and four in the PeRB. Specifically, the NH₃-N concentrations significantly decreased at 19 stations, with an average slope of − 0.015. However, the NH₃-N concentrations significantly increased at 13 stations, with an average slope of 0.017. Several stations failed the significance test, of which 68 experienced a slight decrease in NH₃-N concentrations with an average slope of − 0.006, and 51 experienced a slight increase with an average slope of 0.005 (Table 3).

Relationship between the changes in typical pollutant concentrations and environmental and social factors

Population density, precipitation, and temperature data were used to obtain the percentage variations in population density from 2019 to 2021 and the slope change of precipitation and temperature from 2019 to 2021. The IBM Statistical Product and Service Solutions (SPSS) 24.0 software was used to calculate the relationship between the slope of water quality change and environmental and social factors (Table 4).

Table 4.

Correlation coefficients between the variations in two WQPs and percentages of variations in environmental and social factors. Correlation analysis was not conducted for significant COD_Mn increase because there were extremely few samples

Water quality parameters	Type of change		Longitude	Latitude	DEM		Temperature variation	Precipitation variation	Population density variation
CODMn	Significant decrease	Spearman’s correlation	− 0.349*	− 0.129	0.098	Partial correlation	0.200	− 0.251	− 0.248
	Significant increase		–	–	–		–	–	–
	Slight decrease		− 0.010	− 0.318*	0.035		− 0.176	0.027	0.057
	Slight increase		0.013	0.504**	0.227		− 0.149	0.264	0.299
NH3-N	Significant decrease		− 0.268	− 0.147	− 0.006		0.629*	− 0.401	0.352
	Significant increase		0.209	0.648*	0.132		0.064	0.540	− 0.431
	Slight decrease		− 0.394**	− 0.525**	0.084		0.337**	− 0.149	− 0.435**
	Slight increase		0.306*	0.309*	− 0.023		− 0.284	− 0.011	− 0.071

*p < 0.05

**p < 0.01

The variations in COD_Mn and NH₃-N concentrations were correlated with longitude and latitude. Specifically, the significant decrease in COD_Mn concentration was negatively (r =  − 0.349, p < 0.05) related to longitude, the slight decrease and slight increase in COD_Mn concentration were negatively (r =  − 0.318, p < 0.05) and positively (r = 0.504, p < 0.01) related to latitude. For NH₃-N, significant increase was positively (r = 0.648, p < 0.05) correlated with the latitude; slight decrease was negatively correlated with the longitude (r =  − 0.394, p < 0.01) and latitude (r =  − 0.525, p < 0.01); slight increase was positively correlated with the longitude (r = 0.306, p < 0.05) and latitude (r = 0.309, p < 0.05).

The correlation coefficient between water quality changes and temperature, rainfall and population variation showed that the relationship between COD_Mn concentration change and three factors is weak, and the relationship between NH₃-N and precipitation is weak. For NH₃-N, significant decrease (r = 0.629, p < 0.05) and slight decrease (r = 0.337, p < 0.01) were positively correlated with temperature, and slight decrease was negatively (r =  − 0.435, p < 0.01) correlated with population change.

Discussion

Analysis of changes in water quality levels and WQPs concentration changes

Changes in water quality levels

Industrial wastewater had historically been the major culprit of water pollution in China (Hu and Cheng 2013). Due to the COVID-19 lockdown, the total water consumption in 2020 has decreased by about 4.41% compared with 2019 (26.57 billion m³_, excluding artificial ecological environment water supplement), among which, industrial water consumption decreased 18.72 billion m³, about 70.46% of the total decrease, agricultural water consumption decreased by 26.31%, and domestic water consumption decreased by 3.23% (Ministry of Water Resources 2020–2021). It can be seen that due to the COVID-19 lockdown measures, human activities have significantly decreased, industrial water consumption has declined the most, domestic water and agricultural water have also declined to varying degrees, and China’s surface water quality has been effectively improved (Fig. 11).

Fig. 11.

Percentage changes in water quality classification levels

In the water quality changes in the river basin as shown in Fig. 5, the InRB was barely affected by the lockdown measures. During the lockdown period, the water quality improved (Fig. 5(a)). When entering unlocking period 1, the water quality continued improving (Fig. 5(b)) but deteriorated during the semi-lockdown period (Fig. 5(c)). Figure 12 shows the proportion of water quality level in the InRB for each month. The proportion of level I in May and June was lower than that of level II throughout the year, and the average sum of the two was 92.31%. Because the InRB is located in the western region, where human activity is low, the change in anthropogenic pressure may have a considerably low effect, resulting in relatively stable surface water quality (Ma et al. 2020).

Fig. 12.

Proportions of monthly water quality levels in the InRB

Changes in WQPs concentration

Wastewater from agricultural and domestic sources is the main source of COD and NH₃-N (Ministry of Ecology and Environment 2020). The ministry of ecology and environment disclosed that in 2020, the discharge of COD from agricultural sources accounted for 62.12% of the total discharge of COD in wastewater nationwide, and domestic sources accounted for 35.83%; the NH₃-N emissions from agricultural sources account for 25.81% of the NH₃-N emissions from national wastewater, and the domestic sources account for 71.85%.

Due to the lockdown measures, delays in spring planting, lower sales of crop production materials (seeds, fertilizers, pesticides, and other agricultural inputs) and a shortage of agricultural labor have created major obstacles to agricultural production (Pan et al. 2020), the COD_Mn concentration decreased accordingly. For NH₃-N, although the domestic water consumption in 2020 has only decreased by 3.23% of the total reduction, 71.85% of NH₃-N emissions come from domestic water, so the reduction in domestic water consumption also affects NH₃-N concentration. In addition, the COD_Mn concentrations were high in summer and low in spring, autumn, and winter, whereas the NH₃-N concentrations were high in winter and low in spring, summer, and autumn in this study. The seasonal differences are similar to the research results on the changes in WQPs of 145 water quality monitoring stations in China (Fig. 13).

Fig. 13.

Variations in monthly average COD_Mn, and NH₃-N concentrations (Zhou et al. 2017)

The seasonal differences in COD_Mn concentrations are primarily due to the following reasons: with the increase in rainfall, land surface erosion intensifies, and the non-point source inflow into the river increases. However, the point source inflow into the river changes little with rainfall, and the total amount of pollutants increases (Jin et al. 2019). NH₃-N in the river is primarily derived from the point source, which provides a relatively constant input to the river throughout the year, and the concentration reciprocally decreases with an increasing flow rate in summer owing to the dilution effect (Edwards and Withers 2008). Simultaneously, aquatic plants absorb and use a large amount of NH₃-N in the water as a nitrogen source during their growth and reproduction in summer, which also partly reduces NH₃-N content in the water (Chen et al. 2009).

Relationship between the spatial variations in the WQPs and environmental and social factors

Relationship between the change percentages of confirmed cases and temporal variations in WQPs

We selected the time from February to April 2020 and March to May 2022, when the epidemic was relatively severe, and calculated the change percentage of the number of confirmed cases and variations in water quality parameter concentrations in the adjacent months of each provincial-level administrative region to obtain the correlation coefficient (Fig. 14).

Fig. 14.

Correlation coefficients between monthly change percentages of COVID-19 confirmed cases and monthly variations in a COD_Mn and b NH₃-N concentrations in different provinces of China. Correlation analysis was not conducted in Xinjiang, Tibet, Beijing, Hebei, Zhejiang, Fujian, Hainan, Hong Kong, Macao, and Taiwan because of the lack of water quality data

Only the correlation coefficient (0.902) between the number of confirmed cases and NH₃-N concentration in Shaanxi was significant at 0.05 level. Regardless of the significance level, the correlation between the changes in the number of confirmed cases and variations in the two WQPs was primarily negative. For example, the correlation coefficients in Shanghai, Inner Mongolia, and Shaanxi for COD_Mn were − 0.70, − 0.62, and − 0.58, respectively, whereas those in Heilongjiang, Shanghai, and Gansu for NH₃-N were − 0.63, − 0.53, and − 0.51, respectively. This is consistent with the pattern that water quality improves when the epidemic is severe and deteriorates when it slows down. However, the positive correlation coefficients in some provinces are contrary to this pattern, particularly the correlation coefficients of Jiangxi (0.46) and Henan (0.37) for COD_Mn and Shaanxi (0.90) and Shanxi (0.70) for NH₃-N.

Several studies have revealed a correlation between confirmed epidemic cases and environmental factors, such as a significant negative correlation with air quality (Bashir et al. 2020). Furthermore, when the temperature continues to increase to a particular level, the negative impact of confirmed cases on NO₂ and PM_2.5 pollution is significantly enhanced, indicating a threshold effect (Wang and Wang 2021). However, the conclusions from different regions and research scales may not be consistent (Wang and Wang 2021; Zhou et al. 2021). In this study, the correlation coefficients of different provinces were also inconsistent, which may be related to the study scale and environmental and social factors within the study area.

Relationship between the environmental and social factors and the spatial variations in WQPs

As presented in Table 4, the variations in COD_Mn and NH₃-N concentrations are correlated with latitude and longitude at varying degrees. The decreased in COD_Mn and NH₃-N concentrations was negatively correlated with the increasing trend of longitude and latitude, and the increased in concentrations was positively correlated with the increasing trend of longitude and latitude. In short, from the southwest to the northeast of China, the deterioration trend of water quality is increasing, which are consistent with the spatial variations in the two WQPs in Fig. 10.

Figures 15 and 16 show the monthly average concentrations of COD_Mn and NH₃-N during 2019–2022. Compared with those in the rest of China, COD_Mn and NH₃-N concentrations in the North China Plain and the Northeast China Plain are significantly higher, and the water quality index is correspondingly worse. This can be primarily explained by large extent of developed areas (urban and farmland) and high population density (Fig. 17(a) and (b)), which also explain the relationship between changes in water quality parameter concentrations and latitude and longitude, and the negative correlation between NH₃-N decrease and population. Tong et al. (2015) found that the monthly average NH₃-N concentration in four northern river basins (Liaohe, Yellow, Haihe, and Huaihe rivers) was significantly higher than that in the five southern rivers (Yangtze, Qiantang, Minjiang, and Pearl rivers), which is consistent with Fig. 15 and Fig. 16.

Fig. 15.

Monthly variations in COD_Mn concentration in the river basins

Fig. 16.

Monthly variations in NH₃-N concentration in the river basins

Fig. 17.

a Digital number (DN) value of nighttime light in China in 2020 and b land use in China in 2020

Research limitations and prospects

Water pollution is a highly complex problem in China, and important scientific challenges must be urgently explored. In this study, although two typical pollution parameters, COD_Mn and NH₃-N, were selected, the spatiotemporal variations in other pollutants, such as DO and BOD, and other parameters in the environmental quality standards of surface water, are also crucial (Dai et al. 2017). In addition to lake eutrophication, river water deterioration, and urban water function degradation, many surface water and groundwater resources are polluted by pesticides, arsenic, fluorine, and other toxins, which further threaten the safety of the drinking water supply (Qu and Fan 2010). In the later study, a basin can be taken as the research area to build a multi-coupled water environment model of basin hydrology, hydrodynamics and water quality, and analyze the spatial distribution and transport law of pollution load through the whole process simulation of land and water. At the same time, the health and environmental effects of improved or worsened water quality deserve further study.

Different areas are affected by varying climate, geographical conditions, cultural habits, and other factors. The discharge of domestic sewage greatly varies, and in areas with more developed industrial clusters, the amount of industrial wastewater is relatively high (Wang et al. 2022). In addition, water quality in cities, suburbs, and rural areas exhibits different patterns (Mei et al. 2014; Yu et al. 2017). Therefore, future research should also consider studying the differences in water quality patterns at different scales, for example, provincial vs. municipal, urban vs. rural, and multi-spatial scales.

In addition, considering water pollution and rapid urbanization in China, comprehensive and regular water monitoring is necessary. The rapid development of remote sensing technology, particularly the launch of super-resolution satellites, has enabled remote sensing data to be applied to large-scale and long-term water quality monitoring (Wang and Yang 2019). Surface water environment information can be obtained quickly and accurately by retrieving indicators with optical characteristics and evaluating indicators with non-optical characteristics.

Timely and strict COVID-19 control measures have gradually reduced the impact of the epidemic in China. In the post-epidemic context, how to learn from the water quality improvement incidents during the two lockdown periods, and how water quality can be improved and maintained in the future in China is also important.

Conclusions

Our research shows that the COVID-19 lockdowns in China have reduced the total water consumption including industry, agriculture, and domestic water, and thus improved the water quality to varying degrees. The proportion of good water quality increased by 6.22% and 4.58% during the two lockdowns, and the proportion of good and polluted water quality decreased by 6.00% and 3.98%. Specifically, the decrease in COD_Mn concentration was due to the restricted agricultural development in the lockdown, and the decrease in NH₃-N concentration was affected by the decline in domestic water consumption. However, one of the unlocking periods during the study observed a return of water quality to almost that of pre-lockdown levels. Although the proportions of changes in water quality levels in the nine major river basins were different, except for abnormal changes in particular periods, the overall trend was consistent with the national trend. Before the second COVID-19 outbreak, the variations in the mean concentrations of the two WQPs differed, but both had a trend of decreasing concentration, and the mean concentrations simultaneously decreased during the semi-lockdown period. The seasonal variations in COD_Mn concentration were substantial and the opposite was observed for NH₃-N, but both were affected by COVID-19 lockdown. The correlation analysis revealed that the increasing trend of pollutant concentrations was positively correlated with longitude and latitude. and weakly correlated with DEM and precipitation. A slight decrease in NH₃-N con-centration was negatively correlated with the population density variation and positively correlated with the temperature variation. The relationship between the change in the number of confirmed cases in provincial administrative regions and the variations in pollutant concentrations was uncertain, exhibiting positive and negative correlations. The present results establish that water quality in China was affected by the varying intensity of anthropogenic activities in the years affected by COVID-19; however, water quality improvement cannot rely on accidental events, thereby warranting planning and supervision.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contribution

Conceptualization, H.M. and J.Z.; methodology, H.M.; software, H.M.; validation, J.Z.; formal analysis, H.M.; investigation, H.M and J Z.; resources, J.Z.; data curation, H.M.; writing—original draft preparation, H.M.; writing—review and editing, H.M. and J. Z.; visualization, H. M. and J.Z.; supervision, J.Z.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (Grant No. 41271004).

Data availability

The data presented in this study are available on request from the corresponding author.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.