Introduction

Water plays an important role in human health, but has been adversely affected due to the contaminants added from minerals of geological origin and/or from industrial and agricultural activities (Nollet 2000). In most of the countries, higher incidences of diseases and mortality due to unsafe drinking water are the foremost concerns (WHO 2011). Demand for fresh water due to increasing population and developmental activities in various sectors such as agriculture, domestic and industrial is continuously increasing (Raju et al. 2011). Due to the inadequate supply of fresh surface water, people from semi-arid regions depend on groundwater for their needs (Shankar and Sanjeev 2008; Gleeson et al. 2010) while in India, 85% of rural population exclusively depend on it (Kinzelbach et al. 2003).

Groundwater, the largest storage of fresh water is a vital constituent of hydrological cycle (Todd 1959). In India, groundwater quality has been deteriorating and constantly been questioned from health point of view (Garg et al. 2009; Alam 2014). The rivers carrying chemical effluents and toxic elements from various point and non-point sources degrade the quality of groundwater which is further degraded by the industrial wastes, unorganized use of pesticides and fertilizers, (Beg and Ali 2008). Many researchers have agreed that improper agricultural practices will lead to groundwater pollution (Mikayilov and Acar 1998; Ahmad and Khurshid 2019; Narsimha et al. 2013) including the groundwater salinity (Krishan 2019) which is primarily due to the high concentrations of salts and other elements including trace metals contributed from industrial and agricultural wastes.

Agriculture is widely recognized as an important source of water contamination (FAO and IWMI 2018). These include unsustainable cropping systems, waste from livestock and aquaculture as well as misuse of fertilizers (including manure applications), pesticides, bacteria, microorganisms and antimicrobial residues via leaching, surface and subsurface runoff from croplands and grazing systems pollute water resources (FAO 2020; Hallema et al. 2020).

Punjab, an agrarian state of the India is facing problems of groundwater depletion and contamination happening naturally and/or through agricultural inputs and industrial wastes (Lapworth et al. 2015, Lapworth et al. 2017; MacDonald et al. 2016). Groundwater salinity issues are on the rise in southwestern districts of Punjab (Krishan et al. 2020c, 2021) and there is an apprehension that it might extend to central Punjab.

There is a notable environmental recovery due to the direct and indirect impacts of COVID-19 pandemic. Satellite data are showing significant reductions of NO2 (nitrogen dioxide) over major cities in China, Europe, USA, and India (NASA Earth Observatory 2020); in Venice, Italy, canal water appeared visibly clearer (Link 2020). Impact of COVID-19 on water resources has been reported by Krishan (2020) and its impact during and post-COVID-19 by Krishan and Kulshrestha (2020) where emphasis is laid on hydro-geoethical and holistic approaches to achieve sustainable governance working on the experiences of experts from different disciplinary domains (Marone and Bohle 2020).

COVID-19 is an unprecedented challenge for India; its large population and the economy’s dependence on informal labor make lockdowns and other social distancing measures hugely disruptive. Even though the government has announced lockdown with the relaxations in farming, implementation was disrupted due to labor shortage.

The effects of reduced point source (Industrial sites) inputs on surface water quality might become evident within a matter of weeks while the effect of reduced non-point source (agriculture) inputs in groundwater could take much longer to measure. This encourages the need for this study which has been conducted in three main industrial districts of Punjab occupying the central position in the state to assess the impact of lockdown due to COVID-19 pandemic on groundwater salinity.

Study area

The study is mainly focused on the three main industry dominant districts of Punjab: Ludhiana, Jalandhar and Moga have 12, 11 and 5 administrative blocks, respectively. The study area lies in the central region of Punjab state in India, comprising an area of about 8559 km2. The geographical co-ordinates are; 75° 15′ to 76° 27′ east Longitude and 30° 33′ to 31° 37′ north Latitude and the study area surrounded by Kapurthala, Hoshiarpur and SBS Nagar districts in the north and bounded by Firozpur, Faridkot, Bathinda, Barnala, Sangrur, Fatehgarh sahib and Rupnagar districts in south.

Geologically, the study area forms a part of Indo-Gangetic alluvium of Quatenary period due to the deposits from Indus river system (Bonsor et al. 2017). The Sutlej River flows across the study area from east to west dividing the Bist-Doab in the north and Malwa region in the south. The map representing the study area is shown in the Fig. 1.

Fig. 1
figure 1

Study area showing river and canal network

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Topology

The slope of the surface influences the runoff conditions. The hilly regions are intrinsic of high slope terrain, which accounts in increased runoff rate thus reducing the infiltration. On the other hand, plain terrains are intrinsic of low slope and accounts in reduced runoff rate, significantly increasing the rate of infiltration. The steeper slope indicates the minimum groundwater recharge and the gentle slope offers maximum groundwater recharge (Duan et al. 2016; Rajaveni et al. 2015).

As the slope greatly depends on the elevation of the area, the topography should be known to determine this characteristic. The topology map was extracted from the Shuttle Radar Topography Mission (SRTM)—DEM of 1-arc second (Fig. 2). From the elevation map of the study area, it is clear that the variation of slope is gentle due to the non-occurrence of hilly terrain. Meanwhile, surface elevation varies along the flow of the river Sutlej, higher elevation in the east to lower elevation in the west of the study area.

Fig. 2
figure 2

Surface elevation map of study area

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Geomorphology

The external geological processes influence the geometry of the landform. The different geological processes such as chemical reactions, repeated freezing and thawing, seismic effects and weathering due to wind and water, dominates the shape of the surface (Rajaveni et al. 2015; Ganesh et al. 2017).

The geomorphology of the study area has been downloaded from the Geological Survey of India website (Bhukosh-GSI) and mapped with the help of ArcGIS 10.5. Four major landforms were mapped in the geomorphology map (Fig. 3). It is observed that the general landform of the study area is a alluvial plain and the flood plains were present in the sides of the Sutlej river. Additionally, Aeolian dunes were found only in the western part Jalandhar district.

Fig. 3
figure 3

Geomorphology map of study area

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Hydrogeology

The study of groundwater conditions relies on the geological mapping and type of lithology present beneath. The different geological units which affect the groundwater flow and the quality should be paid attention (Ramaiah et al. 2012). Study area forms Indus alluvial plains having multi aquifer systems upto depth of 300 m with alternate layers of sand and clay as shown in aquifer geometry map (Fig. 4) (CGWB 2017). The porosity of the rock decides the storage capacity of the rock and also the groundwater flow inside the aquifer depends upon the hydraulic conductivity or permeability of the rocks (Sedhuraman et al. 2014).

Fig. 4
figure 4

Geology map (a) and aquifer geometry (b) of study area

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The lithology of the study area has been downloaded from the Geological Survey of India website (Bhukosh-GSI). Generally, the area is covered with the oxidized silt–clay with kankar. Moreover, the yellowish brown loose sands were found distributed all along the study area in small patches but mostly in the Jalandhar district. The Sutlej River was classified as Grey sand, slit and clay and the flood plains around the river were classified with the Grey Micaceous sand, silt and clay. The finals maps were prepared in ArcGIS 10.5 software.

Rainfall

Groundwater recharge of the shallow aquifer purely depends on the rainfall which infiltrates through the porous soil. The water table fluctuation highly correlates with the amount of precipitation (Halder et al. 2020). The amount of rainfall recorded during the study period is necessary to correlate with the salinity changes in the shallow aquifer (Krishan et al. 2020a, 2020b). The data from Indian Meteorological Department (IMD) were downloaded from India-WRIS website.

Materials and methods

Groundwater availability in an area depends on various factors such as topology of the land surface, geological formation, geomorphology, rainfall conditions, soil type, etc. while the quality of groundwater depends upon both the natural and anthropogenic sources. The causes of salinity fluctuation can be predicted only with the elevation, geology, geomorphology and rainfall data.

Collection of water samples

The sampling was carried out in the study area with defined intervals between the sample points. Grids were prepared using GIS at the latitude interval of 5′ N and longitude interval of 5′E, such as it makes 10 km × 10 km sampling. Totally 48 sampling locations were selected widely across the study area and effective sampling was done August, 2019 for monsoon season and in November December, 2019 for post-monsoon season. These two samplings were carried out before lockdown period. After lockdown, sampling was carried out in the month of June, 2020 (pre-monsoon season). 13 groundwater samples from shallow depth upto 50 m and 35 samples from depth range 50–200 m were collected from the bore wells/ hand pump or open wells as per the availability. The sampled locations were shown in the gridded map (Fig. 5). Groundwater samples were collected after thoroughly flushing the water for at least 15 min or until temperature measurements indicated that all onsite storage was purged and water originated from the well bore or aquifer. The samples were collected in acid washed LDPE (low-density polyethylene) Tarson bottles.

Fig. 5
figure 5

Sampling locations with grid

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Analysis of pH and TDS

The pH of an aqueous solution is the measure acidic or basic capacity. The pH of a solution can be determined using the concentration of hydronium ion concentration in the solute. Initially the probe and glassware was rinsed with distilled or de ionized water and then probe (hand held portable Eutech pH meter) calibrated against pH buffer solution 4, 7 and 10 at 25 °C. pH measured insitu by immersing the probe in groundwater samples (Al-Qawati et al. 2018).

Conductivity is a measure of water’s capability to pass electrical flow. This ability is directly related to the concentration of ions in the water. Conductivity probe (multiparametric Eutech’s PCSTester 35, error ± 10 µS/cm i.e. ~ 1%) is rinsed with distilled water and cleaned with tissue paper and calibrated by standard solutions. After calibrating probe was once again rinsed with distilled water and cleaned with tissue paper. Then the probe was immersed in samples for EC measurements. All measurements were made in situ. Later, Electrical Conductivity (EC) converted into Total dissolved solids (TDS) by an empirical formula (Eq. 1) (Rusydi 2018; Ali et al. 2012).

$${\text{Total dissolved soilds}} = 0.65 \times {\text{electrical conductivity}}{.}$$
(1)

Mapping of groundwater salinity

Geographical information system (GIS) is a most effective tool to analyse large amount of data from different sources with greater speed and accuracy (Ojo et al. 2014; Akintorinwa and Okora 2019). Geographical information system (GIS) is considered as a significant and key tool to integrate the information with the location and temporal tags (Tiwari et al. 2017; Ferchichi et al. 2018). Interpolation is known as one of the geo-statistical technique which gives the values for unsampled locations. The mostly used interpolation techniques were inverse distance weighting (IDW) and kriging (Goyal et al. 2010; Jat et al. 2009; Shanmugasundharam et al. 2015; Elumalai et al. 2017).

Even though kriging was suggested by most of the researchers, IDW gives best results for the densely distributed sample points. Estimates of IDW (Eq. 2) interpolation are the weighted averaged values of surrounding sample points. Weights are computed by the inverse of the distance from an observation to an estimate (Burrough and McDonnell 1998; Setianto and Triandini 2013; Honarbakhsh et al. 2019; Selvam et al. 2014; Pande and Moharir 2018).

$$V_{i} = \frac{{\mathop \sum \nolimits_{j = 1}^{n} \frac{{V_{j} }}{{d_{ij}^{p} }}}}{{\mathop \sum \nolimits_{j = 1}^{n} \frac{1}{{d_{ij}^{p} }}}},$$
(2)

where

Vi = ith unknown value

n = number of points taken to obtain the unknown value

Vj = jth known value

dij = distance between the ith unknown value and the jth known value

p = power

Results and discussion

Depending on the availability of data and resources, this study concentrates on the use of surface elevation, geomorphology, geology, rainfall and sample analysis results.

Rainfall

The IMD rainfall data of the study period from November, 2018 to June, 2020 were analysed. The monthly average rainfall was found 94 mm/month for the study period where the average annual rainfall of the Punjab state is more than 640 mm. Monthly cumulative rainfall of the study area was plotted in a bar chart (Fig. 6). From the bar chart, it is also observed that, study area has a highest rainfall in the month on March, 2020 with 282.91 mm and lowest rainfall 5.08 mm was recorded in the month of Dec, 2018.

Fig. 6
figure 6

Monthly cumulative rainfall (Nov 2018–June 20)

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The rainfall behavior patterns of three states were plotted (Fig. 7), it is obvious that it follows a similar pattern in monthly fluctuations. But when comparing the cumulative rainfall between the districts, Jalandhar ranks first with 358 mm followed by Ludhiana with 284 mm and at last by Moga with 274 mm. The well-defined rainy period of the study area is during the southwest monsoon (July to September), 90% of the rainfall occurs during this period (CGWB 2017). There is not much rainfall during the study period.

Fig. 7
figure 7

District-wise monthly rainfall variations

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pH

According to Indian standards specifications (IS 10500: 2012), the pH value of drinking water should be in the range of 6.5–8.5. The pH value of the water samples for shallow aquifers varies from 6.9 to 8.1 with a mean of 7.6 in monsoon season; 7.2–8.2 with a mean of 7.7 in post-monsoon season and from 7.1 to 8.3 with the mean of 7.7 pre-monsoon season while for deep aquifers pH varies from 7.1 to 8.9 with a mean of 7.7 in monsoon season; 7.2–8.6 with a mean of 7.8 in post-monsoon season and from 7.1 to 8.9 with the mean of 7.7 pre-monsoon season (Table 1). In all three seasons, only 1% of the samples were found to be above the acceptable range of 6.5–8.5.

Table 1 Statistical computation of pH, TDS in shallow and deep aquifers
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Total dissolved solids (TDS)

The total dissolved solid (TDS) concentration is directly proportional to the salinity content of the water, assuming all the dissolved solids are of metal salts only. Many researchers have proved that TDS is an effective indicator of water quality.

According to Bureau of Indian Standards (BIS) (IS 10500: 2012), the acceptable limit of TDS for drinking purpose is 500 mg/l. The statistical parameters of TDS from the sample analysis were tabulated in the Table 1. From the mean and maximum values, it is evident that the maximum salinity 743 mg/l was found in the monsoon season in the shallow aquifers. Out of 48 samples collected, ~ 60% of samples were found to have TDS values more than acceptable limit (500 mg/l) before lockdown (post-monsoon season of 2019) period and after or during lockdown period (June, 2020) number of samples more than the acceptable limit (500 mg/l) reduced to 45%. There was also slight reduction in percentage of samples (6%) with TDS values exceeding 1000 mg/l after lockdown as compared to the period before lockdown.

Spatio-temporal distribution of salinity

The analysed samples were interpolated by the IDW technique and spatio-temporal variability maps were prepared using ArcGIS 10.5 software. The salinity distribution before and after lockdown for both shallow and deep aquifers were mapped separately and shown in Figs. 8 and 9 respectively (Li et al. 2015).

Fig. 8
figure 8

Salinity variation in deep aquifers a June, 2019 b November–December, 2019 and c June, 2020

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Fig. 9
figure 9

Salinity variation in shallow aquifers a June, 2019 b November–December, 2019 and c June, 2020

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The TDS variation in maps have been shown by separating TDS values into 6 classes, as, < 250 mg/l, 250–500 mg/l, 500–1000 mg/l, 1000–1500 mg/l, 1500–2000 mg/l and > 2000 mg/l. The area occupied by each class was calculated. In deep aquifer only minor changes were found in TDS values due to the lockdown and TDS variation in deep aquifer were classified into three classes (Fig. 8) since there was no sample found with TDS value > 1500 mg/l. The area of classes was found to be equal in second and third classes. The TDS range of 1000–1500 mg/l (fourth class) was found to be spread around 61.10 km2 before lockdown and after lockdown it reduced to 1.57 km2.

Shallow aquifers are influenced greatly by the external factors such as rainfall, pesticides, industrial wastes, etc. From the TDS values of the shallow aquifer (Fig. 9), it is found that salinity was high before lockdown and it reduced after lockdown period. Before lockdown, the TDS variation was higher as 11.64 km2 of area was having TDS > 2000 mg/l, 30.44 km2 of area was having TDS > 1500 mg/l and 273.73 km2 of area was having TDS > 1000 mg/l. There is a drastic change after lockdown in shallow aquifers with no samples was found with TDS > 1500 mg/l TDS and only 46.11 km2 of area was having TDS more than 1000 mg/l which was 315.81 km2 before lockdown. The TDS values after lockdown reduced < 500 mg/l in 3183.56 km2 area, with > 100% increase in area from 1488.38 km2 before lockdown.

In general, the change in the groundwater salinity in aquifers due to the external factors like geology or rainfall can be ruled out as the lithology of the study area does not contribute to dilution of groundwater and it is also evident that the rainfall was not the cause for the dilution of salinity since the observations were recorded during pre-monsoon period of 2020. Ludhiana and Jalandhar districts are well known for industrial activities and it is found that due to the non-functioning of these industries and other related activities probably there may be minimum contamination from industrial effluents into groundwater.

Conclusion

  • It is concluded that there is less influence of geology, geomorphology and rainfall in the shallow aquifer of study area for the period studied.

  • It is evident from that there was no industrial functioning during the lockdown.

  • From the TDS maps, it is found that the salinity in the shallow aquifers reduced due to non-working of industrial activities.

  • There is a need of efficient and proper handling of agricultural inputs like pesticides and fertilizers and by regulating the disposal of industrial wastes for saving groundwater from contaminations using the holistic and hydro-geoethical approach.