The study estimated the risk due to Cryptosporidium, Giardia, and Ascaris, associated with non-potable water reuse in the city of Jaipur, India. The study first determined the exposure dose of Cryptosporidium, Giardia, and Ascaris based on various wastewater treatment technologies for various scenarios of reuse for six wastewater treatment plants (WWTPs) in the city. The exposure scenarios considered were (1) garden irrigation; (2) working and lounging in the garden; and (3) consumption of crops irrigated with recycled water. The estimated annual risk of infection varied between 8.57 × 10−7 and 1.0 for protozoa and helminths, respectively. The order of treatment processes, in decreasing order of annual risk of infection, was found to be: moving-bed bioreactor (MBBR) technology > activated sludge process (ASP) technology > sequencing batch reactor (SBR) technology. The estimated annual risk was found to be in this order: Ascaris > Giardia > Cryptosporidium. The study also estimated the maximum allowable concentration (Cmax) of pathogen in the effluent for a benchmark value of annual infection of risk equal to 1:10,000, the acceptable level of risk used for drinking water. The estimated Cmax values were found to be 6.54 × 10−5, 1.37 × 10−5, and 2.89 × 10−6 (oo) cysts/mL for Cryptosporidium, Giardia, and Ascaris, respectively.

  • Use of the Sketcher tool for modelling concentrations of Cryptosporidium, Giardia, and Ascaris in treated wastewater.

  • Estimation of annual risk of infection due to Cryptosporidium, Giardia, and Ascaris during reuse of treated wastewater.

  • Estimation of concentrations of Cryptosporidium, Giardia, and Ascaris corresponding to annual risk of infection value (i.e., 1:10,000).

Water reuse is an option for adapting to diminishing water supplies and achieving sustainable development goals, specifically in water-stressed regions. Nevertheless, treated wastewater may contain pathogenic microorganisms, as some of these organisms including Norovirus, Adenovirus, Rotavirus, Cryptosporidium, and Giardia have demonstrated persistence in tertiary treated wastewater (Quintero-Betancourt et al. 2003; Hewitt et al. 2011; Schmitz et al. 2016).

The removal of pathogens in municipal wastewater treatment plants (WWTPs) depends on hydraulic retention time, influent characteristics, and treatment design (Hajare et al. 2021a). In underdeveloped regions of the world, where there is a lack of reliable wastewater treatment processes, municipal effluents that carry harmful pathogens might be released into surface waters. These pathogens include Escherichia coli, Salmonella spp., Shigella spp., Vibrio spp., Entamoeba hystolitica, and helminths such as Ascaris. The World Health Organization (WHO) recommends the monitoring of indicator microorganisms (i.e., coliform bacteria, E. coli, faecal streptococci, Clostridium perfringens, and enterococci) in wastewater before discharge (Al-Gheethi et al. 2013). Likewise, regulatory guidelines in different countries include the monitoring of indicator microorganisms in treated water before discharge or reuse (Al-Gheethi et al. 2013). Additional bacterial pathogens with documented environmental persistence, such as E. coli O157:H7, Streptococcus faecalis, Salmonella spp. (S. typhi, S. typhimurium), Shigella spp., K. pneumonia, E. aerogenes, have been reported in treated wastewater (Al-Gheethi et al. 2013). Extensive literature is available on the health concerns associated with these waterborne pathogens (Al-Gheethi et al. 2013; Hajare et al. 2021a, 2021b; Chowdhari et al. 2022). As a result, improperly treated reclaimed water may expose people to pathogenic microorganisms that may result in the risk of infection (Ueki et al. 2005; Ito et al. 2016, 2017; Sano et al. 2016; Gerba et al. 2017, 2018; Khalid et al. 2018; Ofori et al. 2021).

Quantitative microbial risk assessment (QMRA) is an established framework for estimating the probability of infection/illness from exposure to pathogens in multiple environmental scenarios. In general, risk assessment is described as the process of determining the likelihood of an event occurring as well as the likely scale of its negative consequences – economic, health/safety-related, or ecological – over a certain time period (Gerba 2000; Quintero-Betancourt et al. 2003; Ueki et al. 2005; Blatchley et al. 2007; Ito et al. 2016, 2017; Sano et al. 2016; Chaudhry et al. 2017; Gerba et al. 2017, 2018; Nappier et al. 2018; Soller et al. 2018).

For non-potable reuse options, deterministic QMRA has been used to estimate the risk of infection/illness from crop irrigation, toilet flushing, and recreational use (Jolis et al. 1999; Quintero-Betancourt et al. 2003; Ueki et al. 2005; Ryu et al. 2007; Amha et al. 2015; Ito et al. 2016; Sano et al. 2016; Chaudhry et al. 2017; Moazeni et al. 2017; Ronco et al. 2017; Gerba et al. 2018; Soller et al. 2018; Rock et al. 2019; Ezzat 2020; Alegbeleye & Sant'Ana 2021; Emilse et al. 2021). The estimation of the risk of infection or illness due to waterborne pathogens is often under-reported due to the limited data on the concentrations of pathogenic bacteria and protozoa in the treated effluent. The under-reporting may further subvert the efforts to prevent infections, especially in developing countries. The challenge is to identify correct exposure routes, analyse microbial loads accurately and define appropriate model constants to avoid over- or under-estimating the risks associated with water reuse. These factors may have significant temporal and geographical variations (Hajare et al. 2021b).

In India, the trend of water reuse has been increasing, and therefore understanding the risk of microbial infection during water reuse has become increasingly important. This study investigates the microbial risks associated with current practices of non-potable water reuse in Jaipur city (India). Jaipur is the capital city of the state of Rajasthan in India and the home to approximately 3 million people as per Census 2011. It has a semi-arid climate with limited and highly exploited water resources. Moreover, being a UNESCO world heritage site, the city has a very high influx of tourists every year.

For non-potable reuse, the risks associated with bacteria and viruses have been estimated (Moazeni et al. 2017; Marques et al. 2021). However, the risks from protozoa and helminths have received less attention (Soller et al. 2018; Hajare et al. 2021b). Furthermore, there is a lack of comprehensive characterization regarding how the risks of infection may vary based on different wastewater treatment technologies. In this study, the risk of infection is calculated for protozoa and helminths using estimated removal efficiencies for six WWTPs with different treatment technologies, i.e., activated sludge process (ASP), moving-bed bioreactor (MBBR) and sequencing batch reactor (SBR) (see Figure 1 for locations).

The present study was undertaken in order to fulfil the following objectives: (i) to determine the exposure concentration of selected pathogens for various water reuse scenarios under different wastewater treatment technologies; (ii) to estimate value of annual risk of infection for each scenario; (iii) to calculate the permissible pathogen concentration corresponding to typical risk targets for water reuse applications. The findings of this work are expected to be useful for policymakers formulating regulatory guidelines regarding wastewater reuse in India; for wastewater operators to understand the need to upgrade their treatment systems and, for the general public to understand exposure scenarios when visiting public parks for leisure.
Figure 1

Wastewater treatment plant locations in Jaipur (source: Google maps). (SBR-1: sequencing batch reactor of 100 (million litres per day) MLD municipal WWTP at RIICO industrial area; SBR-2: 15 MLD municipal WWTP at Shipra path; SBR-3: 20 MLD municipal WWTP at Bassi; DLS-ASP: 62.5 MLD activated sludge process municipal WWTP at Delawas; MNT: 1 MLD moving-bed bioreactor institutional WWTP at Malaviya National Institute of Technology; JWC: 1 MLD moving-bed bioreactor municipal WWTP at Jawahar circle).

Figure 1

Wastewater treatment plant locations in Jaipur (source: Google maps). (SBR-1: sequencing batch reactor of 100 (million litres per day) MLD municipal WWTP at RIICO industrial area; SBR-2: 15 MLD municipal WWTP at Shipra path; SBR-3: 20 MLD municipal WWTP at Bassi; DLS-ASP: 62.5 MLD activated sludge process municipal WWTP at Delawas; MNT: 1 MLD moving-bed bioreactor institutional WWTP at Malaviya National Institute of Technology; JWC: 1 MLD moving-bed bioreactor municipal WWTP at Jawahar circle).

Close modal

This study relies on the QMRA framework to identify public health risks associated with water reuse applications from six municipal WWTPs in operation in the city of Jaipur, India (Table 1). The selected treatment plants employ three different wastewater treatment technologies including ASP, MBBR, and SBR.

Table 1

Characteristic of selected WWTPs in Jaipur city

S. No.WWTP location (short name)TechnologyTreatment schemeCapacity (MLD)Average daily flow (MLD)HRT (h)SRT (days)Chlorine dosageEffluent reuse (as obtained by personal communication with WWTP officials)
SBR-1 SBR + Cl2 Screening – Settling -SBR; Sludge Drying beds; Chlorination 100 60 13 90–150 CT Treated effluent is discharged into Dravyavati river 
SBR-2 SBR + Cl2 Screening – Settling-SBR; Sludge Drying beds; Chlorination 15 13 13 60 CT Treated effluent is discharged into Dravyavati river 
DLS-ASP ASP Screening-Settling-ASP; Sludge Drying beds 62.5 62.5 NA Treated effluent is discharged into Dravyavati river 
MNT MBBR + Cl2 Screening – MBBR-Settling; Sludge holding tanks; Chlorination 4.3  150–180 CT Plantation, Irrigation 
JWC MBBR Screening – MBBR -Settling; Chlorination  NA Plantation 
SBR-3 SBR + Cl2 Screening – Settling-SBR; Sludge Drying beds; Chlorination 20 15 13 90 CT Treated effluent is discharged into Dravyavati river 
S. No.WWTP location (short name)TechnologyTreatment schemeCapacity (MLD)Average daily flow (MLD)HRT (h)SRT (days)Chlorine dosageEffluent reuse (as obtained by personal communication with WWTP officials)
SBR-1 SBR + Cl2 Screening – Settling -SBR; Sludge Drying beds; Chlorination 100 60 13 90–150 CT Treated effluent is discharged into Dravyavati river 
SBR-2 SBR + Cl2 Screening – Settling-SBR; Sludge Drying beds; Chlorination 15 13 13 60 CT Treated effluent is discharged into Dravyavati river 
DLS-ASP ASP Screening-Settling-ASP; Sludge Drying beds 62.5 62.5 NA Treated effluent is discharged into Dravyavati river 
MNT MBBR + Cl2 Screening – MBBR-Settling; Sludge holding tanks; Chlorination 4.3  150–180 CT Plantation, Irrigation 
JWC MBBR Screening – MBBR -Settling; Chlorination  NA Plantation 
SBR-3 SBR + Cl2 Screening – Settling-SBR; Sludge Drying beds; Chlorination 20 15 13 90 CT Treated effluent is discharged into Dravyavati river 

SBR-1, sequencing batch reactor; DLS-ASP, Delawas activated sludge process; MNT, Malaviya Institute of technology; JWC Jawahar circle; SBR + Cl2; sequencing batch reactor with chlorination for disinfection; MBBR + Cl2;, moving-bed bio reactor with chlorination for disinfection; HRT, hydraulic retention time; SRT, solid retention time.

The reuse of treated wastewater is practiced for irrigation in two ways: either onsite (before dilution) or offsite (after dilution) irrigation. In case of onsite irrigation, the treated wastewater is sent directly from the WWTP to irrigate the surrounding landscaping. However, for offsite irrigation, the treated wastewater is first discharged into a surface water body, i.e., Dravyavati River in Jaipur and then only utilized for irrigation purposes. The risk of infection was estimated for two protozoan parasites (Cryptosporidium and Giardia) and helminths (Ascaris). These pathogens were selected by the following criteria: (a) the pathogens should not be a part of routine monitoring and, (b) the pathogens have been found to be resistant to chlorine or combined forms (e.g., chloramines). The hazard characterization and exposure assessment steps were carried out for the selected pathogens taking into consideration the concentration values from the literature; their removal in WWTPs and the reuse conditions for the treated wastewater. Each of the QMRA steps are described below in detail.

Hazard identification

For the risk evaluation, the most common protozoan parasites associated with gastrointestinal illness (i.e., Giardia and Cryptosporidium) and the most common intestinal worm associated with helminth infection (i.e., Ascaris) were included (Westrell et al. 2004). Ascaris ova can persist in harsh environments for months to years, making it a good candidate for QMRAs in developing countries (Seidu et al. 2008).

Protozoa go through a number of stages in their life cycle, each with unique activities and structure. Oocysts are a thick-walled, environmentally resistant pathogenic stage that coccidian parasites, such as Cryptosporidium generate during their life cycle. Cryptosporidium can cause gastrointestinal infections that are potentially lethal in infants and in immune-compromised individuals. Cysts, which are dormant phases of Giardia, have a protective membrane or thicker wall that allows them to live in unfavourable environmental conditions (Jain et al. 2019). Giardia intestinalis, which is also referred to as G. duodenalis or G. lambia, is the most common species infecting humans. Giardiasis develops after ingestion of the cyst that is spread through contaminated food, water, or human-to-human contact (faecal–oral pathway). It causes symptoms similar to diarrhoea in 90% of symptomatic patients, which often resolve within 2–6 weeks in healthy people (Jain et al. 2019).

Exposure assessment

The treated wastewater from the selected treatment plants is mainly used for irrigation (onsite and offsite) as per the information obtained in personal communication with WWTP officials (Table 1). Accordingly, three scenarios were considered: (i) Scenario 1 considers direct ingestion of treated wastewater and ingestion of aerosols; (ii) Scenario 2 considers exposure during working and lounging in a garden irrigated with treated wastewater and is mainly concerned with the general public visiting for leisure; (iii) Scenario 3 is related to the consumption of produce from a field irrigated with treated wastewater (Chhipi-Shrestha et al. 2017; Busgang et al. 2018; Hajare et al. 2021a, 2021b). In each of these scenarios, there may be two cases: (i) onsite irrigation and (ii) offsite irrigation (Table 2). The exposed population consists of mainly workers in the gardens or on the farms, and children visiting the garden for leisure.

Table 2

Ingestion volume of treated wastewater per event for different scenarios

Scenario numberScenario nameIngestion volume (mL)Reference
Scenario 1 Garden irrigation 0.1 Natural Resources Management Ministerial Council (2006)  
Scenario 2 Garden lounging Busgang et al. (2018)  
Scenario 3 Food crop consumption (ingestion of crops) Chhipi-Shrestha et al. (2017)  
Scenario numberScenario nameIngestion volume (mL)Reference
Scenario 1 Garden irrigation 0.1 Natural Resources Management Ministerial Council (2006)  
Scenario 2 Garden lounging Busgang et al. (2018)  
Scenario 3 Food crop consumption (ingestion of crops) Chhipi-Shrestha et al. (2017)  

Pathogen concentration in raw wastewater

A compilation of the influent concentrations of the selected pathogens in municipal wastewater from studies all over the world are shown in the supplementary information (Table S1). The concentration of protozoan parasites (i.e., Cryptosporidium and Giardia) in raw wastewater has been taken from a number of studies spanning various countries including Sweden, Ireland, USA, UK, Spain, Malaysia, and China (Ottoson et al. 2006; Cheng et al. 2009; Fu et al. 2010). For helminths (i.e., Ascaris), the concentration in raw wastewater has been taken from France, Germany, Great Britain, the United States, and other countries (Navarro & Jiménez 2011) (Table S1). As for India, there are limited studies on the concentration of these pathogens in treated effluent (Hajare et al. 2021b) and none in raw wastewater. The maximum values of the pathogen's concentrations, obtained from the literature, have been taken to derive a conservative or health protective risk estimate. (Table 3).

Table 3

Concentrations of selected pathogens in raw wastewater from literature and the values assumed for Jaipur city for all WWTPs

Pathogen typeConcentrationReference
Cryptosporidium (oocysts/l) 1,000 Fu et al. (2010)  
Giardia (cysts/l) 13,600 Fu et al. (2010)  
Ascaris (eggs/l) 3,000 Navarro & Jiménez (2011)  
Pathogen typeConcentrationReference
Cryptosporidium (oocysts/l) 1,000 Fu et al. (2010)  
Giardia (cysts/l) 13,600 Fu et al. (2010)  
Ascaris (eggs/l) 3,000 Navarro & Jiménez (2011)  

Estimation of pathogens concentrations after treatment, and after dilution in surface water

The log10 reduction values (LRV) for pathogens in WWTPs vary from 0.44 (Cheng et al. 2009) to 3.60 (Sidhu et al. 2017). The organism-specific LRV vary between 0.44 and 2.15 for Cryptosporidium and between 1.70 and 2.61 for Giardia (Ottoson et al. 2006; Cheng et al. 2009) (Table 4). One study in India, Hajare et al. (2021b) reported the presence of protozoan parasites in treated effluent; however, this study did not determine the protozoan removal efficiency of the treatment process. The study considered 13 treatment plants in Delhi having the following treatment train: grid chamber and screens followed by equalization tanks, pre-chlorination tanks, tube settlers, and dual media and activated carbon filters. For Jaipur city, there has been one study which considered the removal of E.coliO157:H7, Salmonella spp, and Pseudomonas spp in two municipal WWTPs, i.e., MNT and DLS-ASP (Bhatt et al. 2020).

Table 4

Pathogen removal information in terms of log reduction value (LRV) in WWTPs from literature

Location (remark)WWTP name (if identified)PathogenRaw influentLRVReference
Different cities in Sweden (average values of four WWTPs analysed from different parts of Sweden) WWTP Cryptosporidium (oocysts/L) 20 1.18 Ottoson et al. (2006)  
WWTP Giardia cysts (cysts/L) 2,042 2.61 
North-western Ireland Plant A Cryptosporidium (oocysts/L) 592 2.15 Cheng et al. (2009)  
Plant A Giardia (cysts/L) 320 2.52 
Plant B Cryptosporidium (oocysts/L) 280 1.60 
Plant B Giardia (cysts/L) 123 1.70 
Plant C Cryptosporidium (oocysts/L) 11 0.44 
Location (remark)WWTP name (if identified)PathogenRaw influentLRVReference
Different cities in Sweden (average values of four WWTPs analysed from different parts of Sweden) WWTP Cryptosporidium (oocysts/L) 20 1.18 Ottoson et al. (2006)  
WWTP Giardia cysts (cysts/L) 2,042 2.61 
North-western Ireland Plant A Cryptosporidium (oocysts/L) 592 2.15 Cheng et al. (2009)  
Plant A Giardia (cysts/L) 320 2.52 
Plant B Cryptosporidium (oocysts/L) 280 1.60 
Plant B Giardia (cysts/L) 123 1.70 
Plant C Cryptosporidium (oocysts/L) 11 0.44 

Although these studies discuss the removal of pathogens in WWTPs (Ottoson et al. 2006; Cheng et al. 2009), the technologies used in the treatment plants were not discussed. There are some other studies also on pathogen detection and wastewater treatment (Tan 1993; Kobayashi et al. 2017; Moazeni et al. 2017; Saidulu et al. 2021; Gupta et al. 2022). Howe studies (Tan 1993; Kobayashi et al. 2017) did not study the removal of protozoa and helminth in full-scale treatment plants. Moazeni et al. (2017) studied only the influent concentrations of Enterovirus, fecal coliform, and total coliform and did not determine the pathogen removal. Although Gupta et al. (2022) and Saidulu et al. (2021) reviewed MBBR technology for removal of parameters such as chemical oxygen demand (COD), total nitrogen, phosphorus, and emerging contaminants, they did not consider any pathogens. In addition, researchers have also investigated the inactivation of these pathogens by using different kinds of disinfectants (Campbell et al. 1995; Betancourt & Rose 2004; Craun et al. 2010; Esther et al. 2019). Cryptosporidium oocysts and Giardia cysts are well known to be resistant to chlorination. As the effect of specific processes on the pathgoen removal was not in the scope of the study, it was not investigated further.

In the present study, the pathogen removal in WWTPs was determined using the Sketcher tool (Musaazi 2020; Tumwebaze et al. 2021) available at https://www.waterpathogens.org/tools/treatment-plant-sketcher-tool. The Sketcher tool can predict the proportion of pathogens attenuated by a treatment system and allows users to view the fraction of pathogens ending up in the liquid effluent using statistical models based on data from scientific publications. It can be used to build a customized ‘sketch’ of a treatment system, including information regarding treatment reactors. It predicts pathogen removal by group (like viruses, protozoa, bacteria) so all the pathogens belonging to a specific group are modelled as one pathogen. For the modelling of treatment technologies, the only secondary treatment processes available in the model are ASP, trickling filter and waste stabilization pond. For the WWTPs based on SBR and MBBR technologies, this study modelled them as both, i.e., ASP and trickling filter and, the log-reduction values on the conservative side were employed for dose calculations.

Water ingestion concentration (Nie) of Cryptosporidium, Giardia, and Ascaris in the effluent of WWTP was calculated by the following equation:
formula
(1)
where Nio denotes the concentration of specific pathogen (Cryptosporidium, Giardia, and Ascaris) in the raw wastewater; fi2 and fid are the fractions of microorganisms removed during combined primary and secondary treatment, and during disinfection, respectively (in the absence of disinfection, fid is taken as 0). The log-reduction values obtained from the Sketcher tool ranged from 0.92 to 1.24 log10 for both protozoa and helminths (Table 5). For the helminths, the Sketcher tool modelled the log reduction values near a single value of 1. Based on these log-reduction values, the values of fi2 were determined by converting LRV value to percentage value (Table 5). As the Sketcher tool models all the protozoa in a similar way, the log-reduction values for both Cryptosporidium and Giardia are the same.
Table 5

Log- reduction values calculated for selected pathogens with the help of the Sketcher tool and pathogens concentration in wastewater effluent

PathogenWWTPCapacity (MLD)Modelling in Sketcher toolLRV obtained from the Sketcher toolCalculated fi2Ceffluent
Cryptosporidium (oocysts/mL)Giardia (cysts/mL)
Protozoa SBR-1 100 As ASP 1.22 0.94 6 × 10−02 8.16 × 10−01 
SBR-2 15 As ASP 1.21 0.94 6 × 10−02 8.16 × 10−01 
DLS-ASP 62.5 As ASP 1.02 0.9 0.1 1.36 
MNT As Trickling filter 0.94 0.89 0.11 1.5 
As ASP 1.24 –   
JWC As Trickling filter 0.92 0.88 0.12 1.63 
As ASP 1.21 –   
SBR-3 20 As Tickling filter 1.21 0.94 6 × 10−02 8.61 × 10−01 
Helminth  Ascaris (eggs/mL) 
SBR-1 100 As ASP 0.9 3 × 10−01  
SBR-2 15 As ASP 1.01 0.9 3 × 10−01  
DLS-ASP 62.5 As ASP 0.9 3 × 10−01  
MNT As Trickling filter 1.01 0.9 3 × 10−01  
As ASP 1.01 –   
JWC As Trickling filter 1.01 0.9 3 × 10−01  
As ASP 1.01 –   
SBR-3 20 As ASP 1.01 0.9 3 × 10−01  
PathogenWWTPCapacity (MLD)Modelling in Sketcher toolLRV obtained from the Sketcher toolCalculated fi2Ceffluent
Cryptosporidium (oocysts/mL)Giardia (cysts/mL)
Protozoa SBR-1 100 As ASP 1.22 0.94 6 × 10−02 8.16 × 10−01 
SBR-2 15 As ASP 1.21 0.94 6 × 10−02 8.16 × 10−01 
DLS-ASP 62.5 As ASP 1.02 0.9 0.1 1.36 
MNT As Trickling filter 0.94 0.89 0.11 1.5 
As ASP 1.24 –   
JWC As Trickling filter 0.92 0.88 0.12 1.63 
As ASP 1.21 –   
SBR-3 20 As Tickling filter 1.21 0.94 6 × 10−02 8.61 × 10−01 
Helminth  Ascaris (eggs/mL) 
SBR-1 100 As ASP 0.9 3 × 10−01  
SBR-2 15 As ASP 1.01 0.9 3 × 10−01  
DLS-ASP 62.5 As ASP 0.9 3 × 10−01  
MNT As Trickling filter 1.01 0.9 3 × 10−01  
As ASP 1.01 –   
JWC As Trickling filter 1.01 0.9 3 × 10−01  
As ASP 1.01 –   
SBR-3 20 As ASP 1.01 0.9 3 × 10−01  

SBR, sequencing batch reactor; DLS-ASP; Delawas activated sludge process; MNT, Malaviya national institute of technology; JWC, Jawahar circle.

The reduction of the microbes in the surface water body occurs by decay and dilution. Following assumptions were made based on the (Haas 1983) study: (i) uniform, plug-flow conditions at a steady state in the river, (ii) same value of decay constant (ki = 0.69/day) in the river length considered and (iii) instant dilution of the WWTP effluent in the river.

The pathogen concentration after dilution in surface water is estimated by Equation (2) (Haas 1983; Tyagi et al. 2022). The attenuation of microbes and the resulting concentration during travel in a surface water body, for time ‘t’ from the discharge was calculated using the following equation:
formula
(2)
Where Nie is pathogen concentration in WWTP (oocysts/ml), Nii is pathogen concentration in downstream river after discharge from WWTP (oocysts/ml), k is the decay coefficient (=0.69/day), t (=2 days) is the travel time, and D is the dilution factor (100:1) (Tyagi et al. 2022).
Aerosol ingestion in scenario 1, i.e., the number of organisms ingested per exposure (N) was calculated using the following equation:
formula
(3)
where ec is the pathogen concentration in treated wastewater before or after dilution, pc is the partitioning coefficient (=1.07 × 10−5 L/m3), br (=0.61 m3/h) is the breathing rate, t (=8 h) is the time of exposure and ag is the aerosol ingestion rate (=0.1) (Brooks et al. 2005; Dungan 2014; Chattopadhyay et al. 2017).

Dose–response assessment

To calculate the probability of infection from Ascaris, beta-possion dose–response modelled (Mara & Sleigh 2010) as per the following equation.
formula
(4)

In Equation (4), P (N) is the risk of infection due to ingestion of Ascaris eggs on one occasion; N50 is the Ascaris median infective dose; and α is an Ascaris ‘infectivity constant’. The values of N50 and α are 859 and 0.104, respectively.

Similarly, an exponential model was used for estimating the risk of infection due to exposures to Cryptosporidium and Giardia (Gerba 2000; Chhipi-Shrestha et al. 2017)
formula
(5)
where ‘r’ is the dose–response parameter whose values are taken as 0.004191 and 0.02 for Cryptosporidium and Giardia, respectively (Gerba 2000).
For estimating annual risk of infection values, the following equation was used (Rose et al. 1990; Gerba 2000; Haas et al. 2014):
formula
(6)
where n is the number of days (260) per year for workers.

Risk management

After the estimation of risk from treated wastewater for various scenarios, it becomes important to understand the concentration of pathogens in treated wastewater above which the risk becomes higher than the widely used threshold of 1:10,000. As the value for acceptable risk is not available for non-potable applications, the benchmark level of risk for drinking (i.e., 1:10,000) has been used for this study (U.S. Environmental Protection Agency 1989; Regli et al. 1991; Gerba 2000). As the estimated annual risk values would be increasing from scenarios 1 to 3 on the account of an increase in ingestion volume, the maximum allowable concentration (Cmax) values of different pathogens may be calculated for scenario 3 by equating annual risk of infection values to 1:10,000 as per the following equation.
formula
(7)
In Equation (7), the risk of infection (P) value was taken from the Equation (6). For calculating Cmax for helminths, the following equation is used:
formula
(8)
where the risk of infection (P) value was taken from Equation (4).

The final effluent concentrations of Cryptosporidium and Giardia ranged between 1.33 × 10−2 and 2.65 × 10−2 cysts/mL. For the helminths, there was no change in final effluent concentration for different WWTPs, yielding 3.00 × 10−1 eggs/mL of wastewater (Table 5).

The estimated annual risk of infection from selected pathogens in treated wastewater (Table 6) was found to exceed the target value of 1:10,000 (being represented as 10−4) in most of the scenarios. In terms of the technology employed by WWTPs, the annual risk of infection values from pathogens in treated wastewater have been found to be following this order: SBR-based WWTPs < ASP-based WWTP < MBBR-based WWTPs. The minimum values of annual risk of infection for all three pathogens have been found in case of WWTPs based on SBR technology, i.e., SBR-1, 2, and 3. On the other hand, the maximum value of the annual risk of infection is always found for JWC, a WWTP based on MBBR technology. The risks posed by the effluent from the WWTP based on ASP fall in the middle for all three scenarios.

Table 6

Estimated annual risk of infection for general population for all the WWTPs

PathogenScenariosSample typeSBR 1,2,3DLS-ASPMNTJWC
fi2 = 0.94fi2 = 0.90fi2 = 0.89fi2 = 0.88
Cryptosporidium Scenario 1 Aerosol ingestion Treated wastewater 3.41 × 10−04 5.69 × 10−04 6.24 × 10−04 6.83 × 10−04 
Water after dilution 8.50×10−07 1.42×10−06 1.56×10−06 1.70×10−06 
Routine ingestion Treated wastewater 6.52 × 10−03 1.08 × 10−02 1.19 × 10−02 1.30 × 10−02 
Water after dilution 1.63×10−05 2.71×10−05 2.99×10−05 3.26×10−05 
Scenario 2 Garden work and lounging Treated wastewater 6.33 × 10−02 1.03 × 10−01 1.13 × 10−01 1.23 × 10−01 
Water after dilution 1.63 × 10−04 2.71 × 10−04 2.99 × 10−04 3.26 × 10−04 
Scenario 3 Food crop consumption Treated wastewater 2.79 × 10−01 4.20 × 10−01 4.51 × 10−01 4.80 × 10−01 
Water after dilution 8.14 × 10−04 1.36 × 10−03 1.49 × 10−03 1.63 × 10−03 
Giardia Scenario 1 Aerosol ingestion Treated wastewater 2.19 × 10−02 3.63 × 10−02 3.98 × 10−02 4.33 × 10−02 
Water after dilution 5.52×10−05 9.20×10−05 1.01 × 10−04 1.10 × 10−04 
Routine ingestion Treated wastewater 3.46 × 10−01 5.07 × 10−01 5.41 × 10−01 5.72 × 10−01 
Water after dilution 1.06 × 10−03 1.76 × 10−03 1.94 × 10−03 2.11 × 10−03 
Scenario 2 Garden work and lounging Treated wastewater 9.86 × 10−01 9.99 × 10−01 
Water after dilution 1.05 × 10−02 1.75 × 10−02 1.92 × 10−02 2.09 × 10−02 
Scenario 3 Food crop consumption Treated wastewater 
Water after dilution 5.15 × 10−02 8.43 × 10−02 9.23 × 10−02 1.00 × 10−01 
Ascaris Scenario 1 Aerosol ingestion Treated wastewater 3.79 × 10−02 3.79 × 10−02 3.79 × 10−02 3.79 × 10−02 
Water after dilution 9.62×10−05 9.62×10−05 9.62×10−05 9.62×10−05 
Routine ingestion Treated wastewater 5.18 × 10−01 5.18 × 10−01 5.18 × 10−01 5.18 × 10−01 
Water after dilution 1.84 × 10−03 1.84 × 10−03 1.84 × 10−03 1.84 × 10−03 
Scenario 2 Garden work and lounging Treated wastewater 9.99 × 10−01 9.99 × 10−01 9.99 × 10−01 9.99 × 10−01 
Water after dilution 1.83 × 10−02 1.83 × 10−02 1.83 × 10−02 1.83 × 10−02 
Scenario 3 Food crop consumption Treated wastewater 
Water after dilution 8.79 × 10−02 8.79 × 10−02 8.79 × 10−02 8.79 × 10−02 
PathogenScenariosSample typeSBR 1,2,3DLS-ASPMNTJWC
fi2 = 0.94fi2 = 0.90fi2 = 0.89fi2 = 0.88
Cryptosporidium Scenario 1 Aerosol ingestion Treated wastewater 3.41 × 10−04 5.69 × 10−04 6.24 × 10−04 6.83 × 10−04 
Water after dilution 8.50×10−07 1.42×10−06 1.56×10−06 1.70×10−06 
Routine ingestion Treated wastewater 6.52 × 10−03 1.08 × 10−02 1.19 × 10−02 1.30 × 10−02 
Water after dilution 1.63×10−05 2.71×10−05 2.99×10−05 3.26×10−05 
Scenario 2 Garden work and lounging Treated wastewater 6.33 × 10−02 1.03 × 10−01 1.13 × 10−01 1.23 × 10−01 
Water after dilution 1.63 × 10−04 2.71 × 10−04 2.99 × 10−04 3.26 × 10−04 
Scenario 3 Food crop consumption Treated wastewater 2.79 × 10−01 4.20 × 10−01 4.51 × 10−01 4.80 × 10−01 
Water after dilution 8.14 × 10−04 1.36 × 10−03 1.49 × 10−03 1.63 × 10−03 
Giardia Scenario 1 Aerosol ingestion Treated wastewater 2.19 × 10−02 3.63 × 10−02 3.98 × 10−02 4.33 × 10−02 
Water after dilution 5.52×10−05 9.20×10−05 1.01 × 10−04 1.10 × 10−04 
Routine ingestion Treated wastewater 3.46 × 10−01 5.07 × 10−01 5.41 × 10−01 5.72 × 10−01 
Water after dilution 1.06 × 10−03 1.76 × 10−03 1.94 × 10−03 2.11 × 10−03 
Scenario 2 Garden work and lounging Treated wastewater 9.86 × 10−01 9.99 × 10−01 
Water after dilution 1.05 × 10−02 1.75 × 10−02 1.92 × 10−02 2.09 × 10−02 
Scenario 3 Food crop consumption Treated wastewater 
Water after dilution 5.15 × 10−02 8.43 × 10−02 9.23 × 10−02 1.00 × 10−01 
Ascaris Scenario 1 Aerosol ingestion Treated wastewater 3.79 × 10−02 3.79 × 10−02 3.79 × 10−02 3.79 × 10−02 
Water after dilution 9.62×10−05 9.62×10−05 9.62×10−05 9.62×10−05 
Routine ingestion Treated wastewater 5.18 × 10−01 5.18 × 10−01 5.18 × 10−01 5.18 × 10−01 
Water after dilution 1.84 × 10−03 1.84 × 10−03 1.84 × 10−03 1.84 × 10−03 
Scenario 2 Garden work and lounging Treated wastewater 9.99 × 10−01 9.99 × 10−01 9.99 × 10−01 9.99 × 10−01 
Water after dilution 1.83 × 10−02 1.83 × 10−02 1.83 × 10−02 1.83 × 10−02 
Scenario 3 Food crop consumption Treated wastewater 
Water after dilution 8.79 × 10−02 8.79 × 10−02 8.79 × 10−02 8.79 × 10−02 

Notes: Number in italics represents risk values less than the target value of 1:10,000 (equivalent to 10−4).

For the scenarios, the values for estimated annual risk of infection were increased from scenario 1 to scenario 3. The estimated annual risk of infection value for scenario 3 (i.e., food crop consumption involving irrigation by treated wastewater) was found to be the highest. For all the organisms, treatment technologies, and dilution cases, the annual risk of infection value was found to be higher than the target value for scenarios 2 and 3. However, for scenario 1, the annual risk of infection was not found to be always higher than the target value. For Cryptosporidium, the annual risk of infection was found to be below the benchmark while irrigating with treated wastewater after dilution resulted in the value of the annual risk of infection ranging between 8.50 × 10−7 and 3.26 × 10−5 (aerosol/routine ingestion). Similar trends were observed for Giardia and Ascaris in the case of scenario 1 with dilution.

For pathogens in treated wastewater before dilution, the scenario involving ingestion of aerosols poses minimum risk. The minimum values of risk for Cryptosporidium, Giardia, and Ascaris are obtained in this scenario for WWTPs based on SBR technology. However, within scenario 1, the risk from routine ingestion is always higher than the threshold value for the case of treatment plants considered except for the case of Cryptosporidium. In the case of scenario 2 again, all the estimated risk values are found to be above the threshold value. The maximum values of 1.00 × 100 are observed in this scenario for Giardia and Ascaris with treated wastewater before dilution. Again, the estimated risk in scenario 3 for Giardia and Ascaris (for treated wastewater before dilution) has the same maximum value of 1.00 × 100 for all the six WWTPs. Furthermore, all the other estimated values for both the cases (i.e., treated wastewater before and after dilution) in scenario 3 are found to be higher than the threshold value (Kandiah 1991; Blumenthal et al. 2000).

For Cryptosporidium, the values of the estimated annual risk of infection vary from 8.50 × 10−7 to 4.80 × 10−1. For Giardia, these values vary from 5.52 × 10−5 to 1.00 × 100. When comparing the scenario-wise annual risk of infection values from Cryptosporidium and Giardia, the risk from Giardia was always found to be higher. As far as Ascaris is concerned, the estimated risk is in the range of 9.62 × 10−5 and 1.00 × 100. As there was no difference in the removal of Ascaris for all the treatment plants, the estimated risk values only differ among the scenarios. In the case of Ascaris, the only estimated values that are below the 1:10,000 threshold belong to Scenario 1 (aerosol ingestion and treated wastewater after dilution). For all the other scenarios and cases, the risk posed is more than 10−4, the benchmark used for drinking water.

Maximum allowable concentration of pathogen per technology

This section discusses the value of maximum allowable concentration of a pathogen in the treated wastewater (Cmax) for an estimated annual risk of infection equal to 1:10,000. An annual risk of infection of 1:10,000 means that in a population of 10,000 people, one person is expected to contract the infection in question each year (Regli et al. 1991) (Table 7). These concentrations (Cmax) were calculated to be in the range of 8.15 × 10−7 to 1.84 × 10−5 which is lower than the calculated concentrations of various pathogens in treated wastewater using the Sketcher tool. The value of Cmax was found to be lowest for Ascaris while the maximum value was obtained for Cryptosporidium. This emphasises the need for enhanced removal of pathogens in WWTPs. The additional treatment measures should achieve higher removal of helminths in particular. The additional removals required for all the pathogens with different types of technologies are shown in Table 7. For protozoa, i.e., Cryptosporidium and Giardia, maximum additional removal is required in the case of JWC, an MBBR-based WWTP, whereas the minimum additional removal is required in the case of SBR based WWTPs. For Ascaris, additional removal required is 5-log removal and this value is the same for all the WWTPs.

Table 7

Maximum permissible concentration of pathogens in treated wastewater

WWTPCmax (maximum permissible concentration of pathogens in treated wastewater)
Additional removal required (LRV) at WWTP to meet Cmax limit
Cryptosporidium (oocysts/mL)Giardia (cysts/mL)Ascaris (eggs/mL)CryptosporidiumGiardiaAscaris
SBR-1 6.54 × 10−05 1.37 × 10−05 2.89 × 10−06 2.96 4.77 5.02 
SBR-2 6.54 × 10−05 1.37 × 10−05 2.89 × 10−06 2.96 4.77 5.02 
DLS-ASP 6.54 × 10−05 1.37 × 10−05 2.89 × 10−06 3.18 5.00 5.02 
MNT 6.54 × 10−05 1.37 × 10−05 2.89 × 10−06 3.23 5.04 5.02 
JWC 6.54 × 10−05 1.37 × 10−05 2.89 × 10−06 3.26 5.08 5.02 
SBR-3 6.54 × 10−05 1.37 × 10−05 2.89 × 10−06 2.96 4.77 5.02 
WWTPCmax (maximum permissible concentration of pathogens in treated wastewater)
Additional removal required (LRV) at WWTP to meet Cmax limit
Cryptosporidium (oocysts/mL)Giardia (cysts/mL)Ascaris (eggs/mL)CryptosporidiumGiardiaAscaris
SBR-1 6.54 × 10−05 1.37 × 10−05 2.89 × 10−06 2.96 4.77 5.02 
SBR-2 6.54 × 10−05 1.37 × 10−05 2.89 × 10−06 2.96 4.77 5.02 
DLS-ASP 6.54 × 10−05 1.37 × 10−05 2.89 × 10−06 3.18 5.00 5.02 
MNT 6.54 × 10−05 1.37 × 10−05 2.89 × 10−06 3.23 5.04 5.02 
JWC 6.54 × 10−05 1.37 × 10−05 2.89 × 10−06 3.26 5.08 5.02 
SBR-3 6.54 × 10−05 1.37 × 10−05 2.89 × 10−06 2.96 4.77 5.02 

SBR, sequencing batch reactor; DLS-ASP, Delawas activated sludge process; MNT, Malaviya national institute of technology; JWC, Jawahar circle WWTP.

Discussion

This study performs theoretical risk characterization for exposure of pathogens during water reuse associated with WWTPs. In India, there is one other study (Hajare et al. 2021b) which has estimated the risk of infection with the reuse of treated wastewater of 11 effluent treatment plants in Delhi, considering four representative pathogens (pathogenic Escherichia coli spp., Salmonella spp., Cryptosporidium spp., and Giardia spp.) In Delhi, the estimated annual risk of infection ranged between 2.00 × 10−4 and 5.74 × 10−4 and between 4.63 × 10−7 and 1.22 × 10−6 for Cryptosporidium and Giardia, respectively. In the present study, these values are 8.50 × 10−7 to 4.80 × 10−1 and 5.52 × 10−5 to 1.00 × 100 for Cryptosporidium and Giardia, respectively. The study in Delhi did not estimate risk from Ascaris. In terms of parameters, the present study assumes exposure of 270 days per year while the study in Delhi considered exposure for fewer days per year.

The present study also estimates the maximum permissible concentration (Cmax). The Cmax values corresponded to 6.54 × 10−5 (oocysts/mL), 1.37 × 10−5 (cysts/mL), and 2.89 × 10−6 (eggs/mL) for Cryptosporidium, Giardia, and Ascaris, respectively. The Cmax value for treated effluent depends on the framework employed for determining the estimated annual risk of infection and, does not depend on the treatment technology. However, the additional removal of pathogens required to match the pathogen concentrations in treated wastewater with that of Cmax depends on the technology employed in a treatment plant.

As pathogen removal is a function of the technology being used in a WWTP, the estimated annual risk of infection to the workers and general public varies with the technology being employed in a WWTP. This fact also introduces uncertainty in our results as the pathogen removal was modelled by a statistics-based tool, known as the Sketcher tool. Presently, the Sketcher tool is able to estimate the pathogen removal only for a limited number of secondary treatment technologies, i.e., ASP, trickling filter and waste stabilization pond. LRVs calculated by the Sketcher tool for Cryptosporidium and Giardia are the same as the tool that gives LRV for pathogen groups like viruses, protozoa, bacteria, etc.

The study may assist in selecting the process which should be installed for treating wastewater in Jaipur, as the results show that the risk of infection is lowest for SBR WWTP. Another study (Hajare et al. 2021a) estimated the probability of infection for effluent treatment plants (ETPs) but did not focus on the technology of the treatment plants.

The main limitation of the study lies in modelling of pathogen removal in WWTPs. The pathogen removal in MBBR-based WWTPs was calculated by modelling these units as ASP and trickling filter technologies. The results obtained for the trickling filter were employed finally in the risk assessment model in order to keep the values on the conservative side. This study also did not consider the removal of pathogens resulting from specific processes such as sedimentation or disinfection in the treatment plants being studied. Further work is necessary in this area, especially for accurate estimation of the removal of different types of pathogens in WWTPs. This study also does not estimate the cumulative risk of infection and the Disability Adjusted Life Year approach (DALYS), which may be dealt with in future studies. Also, the study has compared the estimated theoretical risk with the threshold of 1:10,000, the allowable risk value for drinking water. Hence these estimated values can again be revisited when an acceptable risk value for non-potable applications is available in the literature.

Overall, risk estimation for the case of protozoa and helminths has been done for the first time in India, as per the authors' knowledge. It also provided an approach for selecting wastewater treatment technologies capable of producing water safe for reuse applications which had not been addressed before. These aspects are important for effective design decision-making on predicting pathogen concentration at exposure points during reuse practices.

This study estimated the theoretical risk of the reuse of treated wastewater from six WWTPs in Jaipur, India. The important conclusions of the study are as follows:

  • 1.

    In most of the scenarios being considered in the study, the estimated annual risk of infection from selected pathogens in treated wastewater was found to exceed the target value of 1:10,000. When comparing the technology employed by WWTPs, the estimated risks associated with the pathogens followed this order: SBR based WWTPs < ASP-based WWTP < MBBR-based WWTPs. The minimum values of estimated annual risk for all three pathogens have been found in the case of pathogens in treated wastewater from WWTPs based on SBR technology, i.e., SBR-1, 2 and 3. On the other hand, the maximum value of the estimated annual risk is always posed by pathogens in treated wastewater from JWC, a WWTP based on MBBR technology. The estimated risks posed by the effluent from the ASP-based WWTP fall in the middle for all three scenarios.

  • 2.

    For the scenarios evaluated, the estimated annual risk of infection increased from scenario 1 to scenario 3. For scenarios 2 and 3, the estimated annual risk of infection was always higher than the benchmark (i.e., 10−4) irrespective of pathogen types, treatment technologies and dilution cases. However, for scenario 1, the estimated annual risk of infection was not always higher. For Cryptosporidium, the infection risk was found to be below the benchmark while irrigating with treated wastewater after dilution. Similar trends were observed for Giardia and Ascaris in the case of scenario 1 with dilution.

  • 3.

    For Cryptosporidium, the values of the estimated annual risk of infection vary from 8.50 × 10−7 to 4.80 × 10−1. For Giardia, the values of the estimated annual risk vary from 5.52 × 10−5 to 1.00 × 100, higher than that for the case of Cryptosporidium. As far as Ascaris is concerned, the estimated risk is in the range of 9.62 × 10−5 and 1.00 × 100.

  • 4.

    At the selected benchmark level of annual risk of infection (i.e., 10−4), the acceptable concentrations of pathogens in the treated wastewater ranged from 2.89 × 10−6 to 6.54 × 10−5. The lowest value was observed for Ascaris, while the highest value was found for Cryptosporidium. These findings highlight the necessity for improved removal of helminths from wastewater prior to its reuse.

A number of studies have performed risk estimation from water reuse, but results have not been clearly compared by the biological treatment method and the complete wastewater treatment scheme. This study presents this information which can be used by wastewater treatment plant designers in selecting appropriate treatment schemes for achieving the desired water quality. The stakeholders in developing countries and locations where treatment plants are being upgraded may use this information for making corrective measures if needed.

This research was supported through PARTNERSHIP2020 program, a collaborative agreement between University of Nebraska at Omaha (UNO) and US Department of State (DOS) with Centre for Strategic and International Studies (CSIS) playing a key advisory role. This project was funded through an agreement between the University of Nebraska, Omaha and Drexel University, Philadelphia under Federal Award# SIN65018CA0034.

Amit Kumar, Arun Kumar, Walter Batencourt, Rajveer Singh and Patrick L. Gurian contributed to the study conception and design. Material preparation and data collection were performed by Ayushi Chaudhary, Shubham Rana and Amit Kumar. Ayushi Chaudhary, Arun Kumar and Amit Kumar performed the data analysis. The first draft of the manuscript was written by Ayushi Chaudhary, Arun Kumar and Amit Kumar and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

The research did not involve human or animal subjects.

The research did not involve human subjects.

The manuscript does not contain any individual person's data in any form.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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