key: cord-0065234-bwfiajls authors: Khan, Ramsha; Saxena, Abhishek; Shukla, Saurabh title: Assessment of the impact of COVID‐19 lockdown on the heavy metal pollution in the River Gomti, Lucknow city, Uttar Pradesh, India date: 2021-05-07 journal: nan DOI: 10.1002/tqem.21746 sha: 1654f2d3155e8f7f094f752300da18168c21f84f doc_id: 65234 cord_uid: bwfiajls In the current study, attempts were made to analyze the effect of COVID‐19 lockdown on the heavy metal concentrations in River Gomti through comparison with pre‐COVID‐19 lockdown status. The concentration of all the six heavy metals (As, Cd, Cr, Fe, Mn, and Pb) clearly shows a significant reduction, highlighting the impact of closure of agricultural, industrial, and commercial activities. The values of heavy metal pollution index (HPI) at all sites have also decreased with the maximum improvement at Site S1 (Chandrika Devi), signifying the impact of reduced agricultural runoff into the river from nearby fields. The correlation analysis stated a strong correlation between HPI and Cd, signifying the relatively high weightage of Cd in pollution levels. Findings from the Caboi diagram suggest classification of all water samples under the “near neutral‐low metal” category. Human existence on earth is dependent on various natural resources including water as one of the primary needs. The degrading impact of rapid urbanization and industrialization on the quality of water resources has emerged as a matter of concern across the globe (Adimalla et al., 2020; Shukla & Saxena, 2020a , 2020b . The history of settlements and constant encroachment in the vicinity of rivers has resultantly created extreme pressure on surface water sources, both quantitatively and quantitatively (Kumar et al., 2021) . The demand of potable water for providing to the needs of human population specially in developing nations is constantly increasing (Mishra et al., 2018; Shukla & Saxena, 2020d; . The discharge of wastewater consequent to the pace of commercial activities, industrial activities into freshwater bodies, makes implementation of regular monitoring and prevention activities all the more important Tripathi & Shukla, 2018) . The rapid increase in the levels of heavy metals has created a hazard risk of biomagnification of these heavy metals through the entrance of noxious elements in the food chain . Moreover, the persistent, nonbiodegradable, and toxic nature of heavy metals makes them an issue for both aquatic and human population (Mishra et al., 2018) . The breakout of coronavirus disease (COVID-19) emerged as a huge challenge for all the nations across the world. During a time when the world was striving with various issues associated to human survival and growth including climate change, depleting quality of natural resources, scarcity of fresh water, etc., the novel COVID-19 outbreak affected ∼30 million people while claiming more than 1 million lives (Khan et al., 2021; . The fatality associated with the virus led to various steps and precautionary measures by governments, including lockdowns and other advisories. India, being the second most populous nation in the world (∼1.3 billion), was at a great risk of community transmission of the virus. Hence, to safe-gained momentum and researchers across the world collaborated for further assessment. The improved water quality of the River Ganga at Haridwar during lockdown was highlighted in a study by Dutta et al. (2020) . On the other hand, some reports and studies stated depletion in water quality of rivers (Ganga, Beas, Chambal, Sutlej, Svarnarekha) during the lockdown (DTE, 2020), 2021). Khan et al. (2021) also reported on depleted water quality of the River Gomti at various sites within Lucknow city during the lockdown period. However, the status of heavy metal pollution to quantify the impact of COVID-19 lockdown has not been assessed in any of the previous studies. Thus, the current study has been taken up by the authors in continuation with their pre-COVID-19 lockdown study in October 2019 , to comparatively evaluate the status of heavy metal pollution in the River Gomti, Lucknow city, post COVID-19 lockdown (June 2020). the status of heavy metal pollution to quantify the impact of COVID-19 lockdown has not been assessed in any of the previous studies River Gomti, a groundwater-fed river, traverses ∼960 km, providing for various needs of many rural and urban habitations along its path. Its water is utilized for drinking and other domestic purposes at many locations, including Lucknow city, where the abstraction rate is the highest. The depleting water quality has been mentioned in several previous studies (Dutta et al., 2018; Goel et al., 2018; Khan et al., 2020 Khan et al., , 2021 , highlighting the variation in its water quality index (WQI) along the upstream and downstream locations. Along with the issue of domestic sewage discharge without or with partial treatment from various drains, industrial effluent discharge has emerged as a matter of concern that needs urgent attention and remedial actions, considering the use of river water for drinking and other domestic purposes. The positive impact of COVID-19 lockdown on the natural resources has gathered the attention of researchers and academicians across the globe. There have been various studies highlighting the improved quality of various water resources in the lockdown period (Arif et al., 2020; Dutta et al., 2020; Yunus et al., 2020) . Hence, this study was taken up with an aim to evaluate the impact of COVID-19 lockdown on the heavy metal pollution status in the River Gomti at Lucknow city. A comparative assessment of the concentration of heavy metals recorded) in the pre-COVID-19 lockdown phase, with the values estimated in the current study is also done. This comparative assessment will help in quantifying the effects of the closure of anthropogenic activities and other enterprises. The Gomti River Basin, with an approximate area of 30,437 km 2 , receives mean annual rainfall ranging between 850 and 1,100 mm (Dutta et al., 2015) . In the present study, 30 samples (three at each sampling station) were collected from the River Gomti, across a total stretch of ∼ 61 km, in The use of HPI for the evaluation of water quality with respect to heavy metal pollution has gained wide acceptance across the globe. It can be Work methodology adopted for the current study [Color figure can be viewed at wileyonlinelibrary.com] explained as an average-weighted arithmetic tool for assessing heavy metal pollution levels such as WQI. HPI is an index for the classification of pollution levels and degree of toxicity due to heavy metals in water bodies. Pollution parameters are selected and assigned weightage through development of a rating scale with values between 0 and 1. The rating scale is developed through standard values of all parameters in inverse proportion (Mishra & Kumar, 2020) . The unit weightage, W i , is calculated using Equation (1): where W i is the unit weight of the ith heavy metal; n is the number of heavy metals in the present study; the subindex (Q i ) is evaluated through Equation (2): where M i is the monitored value of ith heavy metal; S i is the standard value of a particular contaminant; I i is the ideal value of heavy metals (µg/L) in drinking water. This study incorporates a classification system of water samples based on the obtained HPI values, i.e., "low pollution" (HPI < 15); "medium pollution" (15 < HPI < 30), "high pollution" (HPI > 30), and "critically polluted" when HPI≥100 (Edet & Offiong, 2002; Prasad & Bose, 2001 ). The classification of water samples using the method given by Ficklin et al. (1992) and modified by Caboi et al. (1999) has also been attempted. The designation of water samples depends upon the pH value of river water and the metal load (expressed in µg/L) at a particular sampling location. The Caboi diagram represents the degree of mobility of heavy metals, which is in direct proportion with any variation in pH (Singh et al., 2017) . Initially, the spatial variation of the heavy metals among the sampling sites across the studies stretch of the River Gomti was evaluated through the Kruskal-Wallis H test (Fatema et al., 2014) . The Kruskal-Wallis H test is a nonparametric test, which is used to determine whether the difference of the parameters among the sampling sites is significant or not. The Pearson correlation matrix has been generated using Origin Pro 2019b software. The matrix incorporates the value of correlation coefficient r, signifying the degree of correlation, to establish any linear relationship between two or more parameters. The extent of any positive or negative correlation is assessed based upon the values of r, which ranges between ±1. The increase in the metal loading of one metal causing increase in the other metal concentration presents a positive correlation and vice versa is applicable for negative correlation. The range of Pearson correlation coefficient r lies between ±1, with no correlation when r is zero; "strong" correlation when r is in the range between ± 0.9 and ± 1 and "good" correlation for values between 0.51 and ± 0.89. A poor correlation for values between 0 and ± 0.50 (Adimalla & Qian, 2019; Batabyal, 2018; Shukla & Saxena, 2020b) . Furthermore, the data set was also subjected to cluster analysis, which was done through "Q-mode Hierarchical Cluster Analysis" Analytical summary of the monitored heavy metals for all the sampling sites is presented in Exhibit 3. A total of eight heavy metals were analyzed, out of which six heavy metals were detected in the water samples, viz., arsenic (As), iron (Fe), cadmium (Cd), lead (Pb), manganese All concentrations are expressed in (µg/L); SD: standard deviation. 71 MLD, respectively, overflows directly into the river without any treatment . The riverfront development project in Lucknow city was supposedly a very beneficial step toward management of domestic sewage and creation of recreation spots. However, the lack of balance between ecological and construction activities along with complete execution and operational management emerges as a cause of reduced and sluggish flow, further affecting the self cleansing/healing capacity of the river. The use of fertilizers in agricultural activities is a potential cause of heavy metal pollution in the river specifically along sampling stations S1 and S2. The contribution of stabilizing pigments used in paint industries in heavy metal contamination also needs to be considered . The HPI values at none of the sampling sites were in the low pollution category as illustrated in Exhibit 6. Although the value of HPI at sampling station S1 was 15.32 marginally above the low pollution level status (HPI < 15), 40% of the stations fell in the "highly polluted" category and 60% were in "critically polluted" category. The HPI values at all sites were in the order of S1 > S9 > S2 > S8 > S10 > S4 > S3 > S7 > S6 > S5, with least pollution at station S1 and maximum at station S5. The release of wastewater from various sources, including untreated or partially treated sewage, domestic waste, and agrochemical activities, has been previously reported to be a cause of heavy metal pollution in river Gomti by . The possible entrance of heavy metals from sediments in June 2020 (monsoon season) at sites S1, S2, S3, S8, S9, and S10, which are unlined (riverfront has not been developed), cannot be ignored. Many previous studies have confirmed (Caboi et al., 1999; Ficklin et al., 1992) . The Caboi diagram for the current study illustrates the variation in relationship between pH and metal load. The various metal load categories expressed in the Caboi diagram are "near neutral-low metal," "acid-high metal," "near neutralhigh metal," "near neutral-extreme metal," and "high acid-extreme metal" (Exhibit 7). The metal load (As + Fe + Pb + Cd + Mn + Cr) was plotted against pH for categorization of water samples at all sampling sites. Water samples from the current study were found to be falling in "near neutral-low metal" category. Because the rate and degree of precipitation of metals are primarily dependent upon pH, the near neutral to alkaline range of the river water in the current study suggests that the most of the heavy metals must have precipitated as carbonates, oxides in the sediment channel . Hence, the metal load in the Caboi diagram is seen under "low metal" category. The extent of spatial variation among the sampling sites and signif- The categorization of the data set into different groups primarily depends upon the homogeneity or nonhomogeneity among the data sets. Sampling sites were grouped into four clusters based on the similarity of heavy metals and HPI at various locations of the River Gomti, which are presented through the dendrogram (Exhibit 9). Cluster I: This cluster represents the least-polluted site S1 with respect to its HPI value 15.3, marginally above the "low pollution" range (HPI < 15). The upstream location of the site and impact of the closure of the release of agricultural runoff and visits of pilgrims emerge as the potential reason for the least HPI value. Cluster II: This cluster contains sites S2 and S8. This cluster represents "high pollution," with similar values for Fe, Mn, Cd, and HPI. This cluster is also in coherence with the findings of a study by Khan et al. (2020) , in which there is similarity of pollution status between sites S2 and S8. Cluster III: This cluster represents three sites S3, S4, and S10 signifying "critical pollution" levels and one site S4 representing "high pollution." The reduced flow in Lucknow is prominently affected due to the development of riverfront in the city. Cluster IV: This cluster represents sites S5, S6, and S7 highlighting "critical pollution," which have "severely high" levels of heavy metal pollution (HPI > 200). The midstream location and sluggish flow of the river considering the riverfront development in the city merge as possible cause of such high HPI values. The findings from the study have clearly stated the positive impact of COVID-19 lockdown on the heavy metal pollution of the River Gomti. The findings from this study provide relevant insights to authorities toward the necessity of stringent regulations to prevent the release of untreated municipal and industrial wastewater into the river. The efficacy and advantages associated with implementation of temporary, and partial lockdowns through development of policies can also be suggested through this study. The authors are thankful to Dr. A. K. Singh, Vice-Chancellor, Shri Ramswaroop Memorial University for providing lab facilities for conducting this study. They would also like to acknowledge the guidance in sample testing from Dr. D. K. Saxena, Bareilly College. The support of our families during compilation of this manuscript needs to be recognized. Data are available on request due to privacy/ethical restrictions. 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