The present study found that ∼80 million people in India, ∼60 million people in Pakistan, ∼70 million people in Bangladesh, and ∼3 million people in Nepal are exposed to arsenic groundwater contamination above 10 μg/L, while Sri Lanka remains moderately affected. In the case of fluoride contamination, ∼120 million in India, >2 million in Pakistan, and ∼0.5 million in Sri Lanka are exposed to the risk of fluoride above 1.5 mg/L, while Bangladesh and Nepal are mildly affected. The hazard quotient (HQ) for arsenic varied from 0 to 822 in India, 0 to 33 in Pakistan, 0 to 1,051 in Bangladesh, 0 to 582 in Nepal, and 0 to 89 in Sri Lanka. The cancer risk of arsenic varied from 0 to 1.64 × 1−1 in India, 0 to 1.07 × 10−1 in Pakistan, 0 to 2.10 × 10−1 in Bangladesh, 0 to 1.16 × 10−1 in Nepal, and 0 to 1.78 × 10−2 in Sri Lanka. In the case of fluoride, the HQ ranged from 0 to 21 in India, 0 to 33 in Pakistan, 0 to 18 in Bangladesh, 0 to 10 in Nepal, and 0 to 10 in Sri Lanka. Arsenic and fluoride have adverse effects on animals, resulting in chemical poisoning and skeletal fluorosis. Adsorption and membrane filtration have demonstrated outstanding treatment outcomes.

  • India, Pakistan, Bangladesh, and Nepal are severely affected by As contamination.

  • India, Pakistan, and Sri Lanka are severely affected by F contamination.

  • Arsenic is found to cause DNA damage and promotes diabetes and obesity.

  • Genetic background of person is found to increase or decrease the fluorosis risk

  • There is a lack of adequate information on the hydro-geochemistry of pollutants.

Water is a precious resource needed for the survival of all life forms in our planet. Groundwater is the most important source of fresh water for humans, and about one-third (>2.5 billion) of the world's population depends on groundwater for drinking purposes (Li et al. 2021). Out of the total 36 Mkm2 of estimated freshwater on the Earth, ∼22% exists as groundwater (Sarath Prasanth et al. 2012). Over-exploitation of groundwater has led to groundwater stress in many parts of the world and degradation in groundwater quality, primarily owing to human activities and geogenic pollutants (Takem et al. 2010). There is an urgent need to properly manage and protect freshwater resources, including groundwater, as freshwater resources are very limited (Asadi et al. 2019). South Asia is home to more than 24% of the world's population, having only ∼4% of the total land area. South Asia is the most densely populated and the most extensive user of groundwater resources (Mukherjee 2018). Despite having three of the largest river systems in the world (river basins of the Indus, Ganges, and Brahmaputra), the availability and accessibility of safe and sustainable groundwater are of growing concern due to the presence of pollutants of geogenic nature (Mukherjee 2018).

According to the World Bank report in 2012, India is the largest user of groundwater globally (Baboo et al. 2022). Groundwater pollution is a global issue since everyone needs adequate and affordable clean water for drinking and other activities. Rapid population growth, urbanization, and industrialization have resulted in significant groundwater contamination, creating a huge challenge worldwide. Although the geogenic origin of pollution is significant through the dissolving of minerals and ores, anthropogenic toxins from factories and other human activities have exacerbated the situation (Thambidurai et al. 2013). Ingestion of contaminated groundwater results in an adverse impact on human health and causes severe diseases such as arsenicosis, fluorosis, etc. Toxic metals are persistent in nature and tend to bioaccumulate in the food chain (He & Li 2020; Thambidurai & Singh 2020; Vazhacharickal & Thambidurai 2022). Groundwater contamination results in lesser availability of drinking water, resulting in conflicts among citizens, and may lead to socio-economic crises and even wars (Li et al. 2021). The South Asia region faces major challenges and concerns due to groundwater contamination from arsenic (As) and fluoride (F). Among other South Asian countries, India and Pakistan are highly affected countries by the As and F in groundwater contamination, and about a total of ∼125 million people are at risk of As in India and Pakistan, while ∼30 million people are at risk of F contamination in both the countries (Kumar et al. 2020). The present study aims to critically analyze and review the existing studies on groundwater contamination due to As and F and its adverse effects on human health in terms of non-carcinogenic risks, viz., average daily dose (ADD) and hazard quotient (HQ), and cancer risks (CRs). The study area is limited to the south Asian countries, viz., India, Pakistan, Bangladesh, Sri Lanka, and Nepal, while the scope of the study includes the sources of contamination, their concentration, exposure, risk, and limitations. The majority of the studies conducted in Bangladesh, Nepal, and Sri Lanka reported As and F contamination of groundwater but not the health risks. In the present study, we have identified the health risks due to consumption of As- and F-contaminated groundwater by calculating the HQ quotient and CR based on data from reported studies. The existing studies assessed the health risks focusing only on arsenicosis and skin lesions, but this review study also discussed other As-related diseases such as DNA damage. Likewise, existing studies on health risks due to F contamination mostly concentrated on dental caries and fluorosis, but this review study discussed in detail the role of genes in exposure to F risk.

The literature search and selection strategy is presented in Figure 1. Out of 67 reviewed studies on As contamination, 26 studies were from India, 22 were from Pakistan, 18 were from Bangladesh, three were from Nepal, and eight were from Sri Lanka. Out of 80 reviewed studies on F contamination, 46 studies were from India, 18 were from Pakistan, five were from Bangladesh, five were from Nepal, and six were from Sri Lanka.
Figure 1

Flowchart for literature search and selection strategy.

Figure 1

Flowchart for literature search and selection strategy.

Close modal
The health risk due to consumption of As-contaminated groundwater was calculated based on ingestion values for the ADD, HQ, and CR (Alam et al. 2016; Tirkey et al. 2017; Islam et al. 2019; Bhattacharya et al. 2020; Goswami et al. 2020; Ahmed et al. 2021; Alsubih et al. 2021; Chakraborty et al. 2022; Khan & Rai 2022; Rahman et al. 2022). In this study, the ADD, HQ, and CR values were calculated for all the reviewed studies. ADD was calculated by the following equation (US EPA 1998):
formula
(1)
where C, IR, EF, ED, BW, and AT represent the concentration of As in water (mg/L), water ingestion rate (liters per day), exposure frequency, exposure duration, body weight, and average life time, respectively.
HQ was calculated using the following formula (US EPA 1998):
formula
(2)
where HQ is the hazard quotient, which is considered to occur if HQ > 1.00, and RfD is the oral reference dose (0.0003 mg/kg/day for arsenic (US EPA 2005). The values used for calculating HQ are given in Table 1, as per US EPA (2005). The safe limit is HQ < 1. However, HQ > 1 is a possible source of concern; however, due to safety factors, HQ > 1 may not actually result in harm.
Table 1

Values used for calculating HQ

Parameters for oral ingestionUnitValues for male adultsValues for female adultsValues for children (up to 12 years)Reference
Ingestion rate (IR) L/day 2.0 2.0 1.0 Riaz et al. (2022); Rehman et al. (2022)  
Exposure frequency (EF) days/year 365 365 365 US EPA (1989)  
Exposure duration (ED) Year 70 70 US EPA (2005)  
Body weight (BW) kg 70 53 15 ICMR (2009) and Riaz et al. (2022)  
Average time (AT) (ED × 365) days 25,550 25,550 2,190 US EPA (2005)  
Parameters for oral ingestionUnitValues for male adultsValues for female adultsValues for children (up to 12 years)Reference
Ingestion rate (IR) L/day 2.0 2.0 1.0 Riaz et al. (2022); Rehman et al. (2022)  
Exposure frequency (EF) days/year 365 365 365 US EPA (1989)  
Exposure duration (ED) Year 70 70 US EPA (2005)  
Body weight (BW) kg 70 53 15 ICMR (2009) and Riaz et al. (2022)  
Average time (AT) (ED × 365) days 25,550 25,550 2,190 US EPA (2005)  
CR was calculated using the following formula (Ahmed et al. 2021):
formula
(3)
where CSF is the cancer slope factor for arsenic, which is 1.5 mg/kg/day, as specified by US EPA (2005).
The non-carcinogenic risk from drinking F-contaminated groundwater was calculated using the following equation (USEPA 2001):
formula
(4)
where C, IR, EF, ED, BW, and AT represent the concentration of F in water (mg/L), water ingestion rate (liters per day), exposure frequency, exposure duration, body weight, and average life time, respectively.
HQ was calculated using the following formula (US EPA 2001):
formula
(5)
where HQ is the hazard quotient, which is considered to occur if HQ > 1.00, and RfD is the reference dose for F in drinking water (0.06 mg/kg/day for fluoride (US EPA 2005). The values used for calculating HQ are given in Table 1. The safe limit is HQ <1 (Rehman et al. 2022).

Arsenic is a toxic and carcinogenic element found in abundance in the earth's crust at an average concentration of 5 mg/kg (Rasheed et al. 2016). The source of As can be geogenic, anthropogenic, and biogenic; however, more than 90% of As contamination is said to be geogenic in nature as it is in more than 200 different mineral species (Dhillon 2020; Shaji et al. 2021). Arsenopyrite is the most common form of As, and according to estimates, about one-third of As occurring in the environment has originated naturally, mainly through volcanic action and low temperatures (Thambidurai et al. 2014; Dhillon 2020). Arsenic is commonly found in deposits of copper, silver, cadmium, zinc, tin, mercury, iron, uranium, gold, cobalt, selenium, phosphorus, molybdenum, lead, antimony, nickel, platinum, tellurium, sulfur, bismuth, and tungsten (Shakoor et al. 2017). The major cause of As in groundwater contamination is rock–water interactions in the aquifer system. In physical and geochemical settings, aquifers tend to favor As mobilization, particularly in reducing conditions. The major sources of anthropogenic As contamination are mining, coal, and petroleum extraction. Metal smelting, burning of fossil fuels, and use of As during timber preservation are also responsible for anthropogenic As contamination worldwide. Several agricultural practices, such as pesticides, herbicides, and fertilizer, also contribute to As contamination (Thakur et al. 2013). Arsenic sources can also be biogenic, mainly through agricultural organisms or micro-aquatic biota, mainly agrarian organisms, micro-aquatic biota, and plants. The release of As into the groundwater depends on several factors, such as (i) pH, (ii) presence of organic matter in sediments, (iii) water saturation of sediment, (iv) fluctuation in the water table, (v) short supply of sulfur, (vi) flow direction of groundwater, (vii) age of groundwater, (viii) topography, and (ix) marine transgression (Shaji et al. 2021). Tropical climates are more prone to As pollution because the environment encourages the release of As from their chemical compounds. Alluvial sediments or alluvial terrane are the main source (90%) of high concentrations of As in groundwater in South Asian countries like India, Pakistan, and Bangladesh (Thambidurai et al. 2012, 2013; Ali et al. 2019a; Shaji et al. 2021), while hard rock aquifers contribute ∼10% of As groundwater contamination in Indian states such as Karnataka and Chhattisgarh.

Fluoride is an essential component of many minerals, including fluorspar, mica, rock phosphate, apatite, fluorite, cryolite, topaz, and others. Igneous rocks, mineralized veins, and sedimentary strata have the highest concentrations of F (Jha & Tripathi 2021). The earth's crust has 600–700 ppm (0.06–0.09%) of F, making it the 13th most prevalent element (Yadav et al. 2019a, 2019b). The first major source of F contamination is geogenic, occurring from weathering of F-bearing minerals such as fluorite, cryolite, sellaite, apatite, topaz, mica, and amphiboles (Ali et al. 2016; Mukherjee & Singh 2018). Fluoride contamination from a volcanic eruption is the second major natural source, while marine aerosols are the third major natural source (Mukherjee & Singh 2018). The anthropogenic sources also contribute to F contamination, and unscientific use of phosphatic fertilizers is a major anthropogenic source of F contamination in developing countries like India and Pakistan (Ali et al. 2016). Other anthropogenic sources include mining and smelting, coal combustion, manufacturing of cement and bricks, and landfilling, which contribute to F contamination in the groundwater (Mukherjee & Singh 2018; Rasool et al. 2018; Jha & Tripathi 2021).

The countries of South Asia are severely affected by As-contaminated groundwater. Approximately 188 million people are exposed to As groundwater contamination in the South Asia region (Natasha et al. 2020). The present work comprehensively reviewed the As groundwater contamination and its health effects in India, Pakistan, Bangladesh, Nepal, and Sri Lanka. The maximum As concentration found in the groundwater of various states/provinces in different countries is shown in Figure 2, and the As concentration range and the number of samples exceeding the limit of 10 μg/L specified by WHO are listed in Table 2. Most studies on As groundwater contamination in India, Bangladesh, and Nepal reported minimum and maximum As concentrations and not the mean As concentration. The present study assumed the suitable mean As concentration, which was calculated based on the number of samples within different As concentration ranges reported by the reviewed studies. In this review study, As groundwater contamination in affected states/divisions/provinces of different countries was first discussed, followed by information on monitoring of biomarkers of exposure and the diseases caused due to contaminant in brief (if any details provided in the existing literature). Finally, the adverse effect of As was discussed in detail based on calculated HQ and CR. A similar pattern was followed for presenting the F groundwater contamination. The population exposure and groundwater concentration are two different metrics and will be reported to the extent of availability.
Table 2

Arsenic groundwater contamination in South Asian countries

ReferenceStudy areaNo. of samplesArsenic range in μg/L (mean in μg/L)No. of samples >10 μg/L
India 
Goswami et al. (2020)  Majuli, Assam 20 5–386 (137) 16 (80%) 
Kanungo (2016)  Silchar, Assam 30 < 3–188 (33) 13 (43%) 
Kumar et al. (2016)  Diphu, Assam 38 N.D. to 6.3 (2.1) 
Chetia et al. (2011)  Golghat, Assam 222 N.D. to 128 (2.1) 149 (67%) 
Mahanta et al. (2008)  Bongaigaon (B) and Darrang (D), Assam 50 0–606 (B) (42) & N.D to 60 (D) (7) 33 (66%) 
Thambidurai et al. (2013)  Barak Valley, Assam 50 12–97 (33.35) 17 (34%) 
Kumar et al. (2021a, 2021bSaran, Bihar 128 < 3–244.20 (39) 78 (61%) 
Rahman et al. (2019)  Buxar, Bihar 323 < 3–1,929 (98.62) 304 (94%) 
Thakur & Gupta (2019)  Patna and Bhojpur, Bihar 388 < 10– > 500 (87.53) 297 (77%) 
Chakraborti et al. (2016b)  Shahpur, Bihar 4,704 < 3–1,805 (90) 1,896 (40%) 
Chakraborti et al. (2016c)  Patna, Bihar 1,365 < 3–1,466 (43.5) 833 (61%) 
Singh et al. (2014)  Vaishali (V) and Bhagalpur (B), Bihar 40 1–21 (9.7) & 3–143 (B) (49.8) 8 (20%) 
Alsubih et al. (2021)  Patparganj, Delhi 24 > 10–610 (262.5) 24 (100%) 
Wu et al. (2021)  Gujarat 398 < 3–26 (6.17) 24 (6%) 
Kumar et al. (2022b)  Ramgarh, Jharkhand 15 0.8–2.8 (1.07) 
Tirkey et al. (2017)  Ranchi, Jharkhand 44 0–200 (58) 37 (84%) 
Alam et al. (2016)  Sahibganj, Jharkhand 60 1–133 (45.4) 54 (90%) 
Chakraborti et al. (2008)  Manipur Valley, Manipur 628 < 3–502 (60.82) 398 (63.3%) 
Kumar & Singh (2020)  Punjab 73 < 3–256 (16.44) 18 (24.6%) 
Bhattacharya et al. (2020)  Dharmanagar, Tripura 71 < 3–30 (12) 26 (36%) 
Khan & Rai (2022)  Haridwar, Uttarakhand 208 0.1–102 (5.68) 35 (17%) 
Singh et al. (2022)  Bahraich, Uttar Pradesh (U.P.) 40 0.01–128 (14.82) 17 (42%) 
Srivastava & Sharma (2013)  Ballia and Ghazipur, U.P. 36 43.75–620.75 (329) 36 (100%) 
Chakraborti et al. (2017b)  Kolkata, West Bengal (W.B.) 4,210 < 3–825 598 (14.2%) 
Malakar et al. (2016)  Kolkata, W.B. 144 wards N.D. to 164 (36.4) 49 wards (34%) 
Chakraborti et al. (2009)  West Bengal 140,150 < 3–3,700 (79.83) 33,993 (24.25%) 
Pakistan 
Podgorski et al. (2017)  Along Indus Plain 1,184 0–500 (100) 785 (66%) 
Shahid et al. (2022)  Lahore, Punjab 55 1.98–1,555 (96.5) 47 (85%) 
Riaz et al. (2022)  Lodhran, Punjab 200 0–14.33 (3.85) 18 (9%) 
Ehsan et al. (2020)  Sheikhpura, Punjab 20 2–357 (100.3) 14 (70%) 
Tabassum et al. (2019)  Hasilpur, Punjab 61 < 5–100 (9) 20 (33%) 
Shahid et al. (2018a)  Vehari, Punjab 156 0.4–132 (37.3) 148 (95%) 
Waqas et al. (2017)  Lahore, Punjab 100 2–111 (36.5) 78 (78%) 
Rasool et al. (2016)  Sargana (S) and Mailsi (M), Punjab 44 14–787 (165.8) (S), 11–828 (145.6) (M) – 
Shakoor et al. (2015)  Chichawatni, Sahiwal (CW), Vehari (VH), and Rahim Yar Khan (RYK) Punjab 62 1.5–201 (95) (CW), 23–201 (41.5) (VH) & 1.5–144 (9.2) (RYK) 33 (52%) 
Rasool et al. (2016)  Mailsi, Punjab 52 5.9–507 (155.71) 45 (86.6%) 
Sultana et al. (2014)  Kalalanwala (KL), Manga Mandi (MM), and Shamki Bhattian (SB), Punjab 30 37.5–375 (143) (KL); 1–250 (70) (MM) 0–525 (179) (SB) 26 (87%) 
Malana & Khosa (2011)  Dera Ghazi Khan, Punjab 32 0.37–29 (4) 7 (22%) 
Farooqi et al. (2007)  Kalalanwala, Punjab 147 1–2,400 (111.3) 134 (91%) 
Kori et al. (2018)  Larkana, Sindh 110 0.01–17 (6.78) 13 (12%) 
Kandhro et al. (2016)  Nawab Shah, Sindh 68 5–200 (102.5) 7 (10%) 
Memon et al. (2016)  Dadu (Johi), Sindh 46 8–67 (24.8) 11 (24%) 
Rubab et al. (2014)  Gujjo (GJ) and Ghulamullah Thatta (GT), Sindh GJ = 23 & GT = 14 0–20 (2.39) (GJ), 0–200 (47) (GT) GJ = 1 (4%) & GT = 8 (57%) 
Brahman et al. (2013)  Tharpakar, Sindh 12 0.006–4.33 (1.14) 
Jakhrani et al. (2011)  Gambat, Sindh 334 0.01–126 (26.6) 136 (41%) 
Majidano et al. (2010)  Tando Allahayar, Sindh 175 0.04–300 (123) 82 (47%) 
Baig et al. (2009)  Jamshoro, Sindh 153 13–106 (45) 153 (100%) 
Jakhrani et al. (2009)  Khairpur, Sindh 222 0.24–316 (21.1) 77 (35%) 
Bangladesh     
Rahman et al. (2022)  Laksam, Chittagong 21 6–581 (199) 19 (90%) 
Rahman et al. (2015)  Noakhali, Chittagong 70 1.5–587.6 (297.5) 69 (99%) 
Chakraborti et al. (2010)  Noakhali, Chittagong 843 < 10–4,730 (413) 838 (99%) 
Brahmanbaria, Chittagong 47 < 10–210 (75.63) 35 (74%) 
Chandpur, Chittagong 1,165 < 10–1,318 (255.56) 1,115 (96%) 
Chittagong, Chittagong 366 < 10–275 (12.43) 47 (13%) 
Comilla, Chittagong 545 < 10–1,769 (320.77) 432 (79%) 
Feni, Chittagong 186 < 10–1,000 (66.82) 128 (69%) 
Lakshmipur, Chittagong 2,662 < 10–2,030 (293.68) 2,358 (89%) 
Ahmed et al. (2021)  Tangail, Dhaka 10 40–170 (13.43) 10 (100%) 
Chakraborti et al. (2010)  Faridpur, Dhaka 707 < 10–1,630 (120.92) 464 (66%) 
Dhaka District, Dhaka 574 < 10–352 (30.87) 125 (22%) 
Gazipur, Dhaka 3,386 < 10–533 (7.89) 74 (2%) 
Shariatpur, Dhaka 152 < 10–580 (78.52) 89 (59%) 
Gopalganj, Dhaka 384 < 10–920 (99.15) 29 (78%) 
Jamalpur, Dhaka 144 < 10–1,172 (80.69) 55 (62%) 
Madaripur, Dhaka 2,309 < 10–1,200 (135) 1,856 (80%) 
Mymensingh, Dhaka 1,825 < 10–330 (7.51) 120 (7%) 
Narayanganj, Dhaka 412 < 10–1,750 (238.9) 358 (87%) 
Narshingdi, Dhaka 336 < 10–1,000 (68.64) 84 (25%) 
Chakraborti et al. (2010)  Netrokna, Dhaka 533 < 10–580 (52.53) 332 (62%) 
Rajbari, Dhaka 174 < 10–714 (40.97) 95 (55%) 
Chakraborty et al. (2022)  Jhikargachha, Jessore, Khulna 33 13–501 (90) 32 (97%) 
Huq et al. (2019)  Kushtia district, Khulna 50 6.05–591 (58.3) 41 (82%) 
Chakraborti et al. (2010)  Bagerhat, Khulna 371 < 10–958 (161.28) 281 (76%) 
Chuadanga, Khulna 457 < 10–841 (60.57) 281 (73%) 
Jessore, Khulna 5,465 < 10–1,120 (43.44) 1,238 (23%) 
Magura, Khulna 496 < 10–1,050 (38.94) 253 (51%) 
Khulna district, Khulna 1,000 < 10–3,143 (66.1) 482 (48%) 
Meherpur, Khulna 1,024 < 10–1,230 (47.86) 498 (49%) 
Shatkhira, Khulna 532 < 10–750 (168.69) 500 (94%) 
Islam et al. (2019)  Nawabganj, Rajshahi 18 2.5–150.6 (66.16) 17 (95%) 
Chakraborti et al. (2010)  Bogra, Rajshahi 767 < 10–1,040 (15.44) 160 (21%) 
Nawabganj, Rajshahi 1,902 < 10–1,600 (69.90) 982 (52%) 
Pabna, Rajshahi 5,117 < 10–2,108 (74.41) 3,522 (69%) 
Rajshahi District, Rajshahi 2,698 < 10–524 (17.93) 501 (19%) 
Sirajganj, Rajshahi 278 < 10–216 (16.21) 29 (11%) 
Islam et al. (2019)  Rangpur District, Rangpur 47 0.5–42.8 (8.81) 13 (28%) 
Chakraborti et al. (2010)  Gaibanda, Rangpur 1,233 < 10–512 (17.11) 370 (30%) 
Rangpur District, Rangpur 464 < 10–939 (46.59) 179 (39%) 
Thakurgaon, Rangpur 461 < 10–130 (8.28) 45 (10%) 
Nepal 
Gwachha et al. (2020)  Kathmandu valley, Bagmati 20 0–640 (250) 15 (76%) 
Panthi et al. (2006)  Nawalparasi, Lumbini 3,833 < 10–571 (38.73) 2,448 (64%) 
Rupandehi, Lumbini 3,725 < 10–2,620 (12.99) 534 (14%) 
Kapilbastu, Lumbini 4,099 < 10–589 (9.33) 628 (15%) 
Banke, Lumbini 1,835 < 10–270 (8.88) 519 (28%) 
Bardiya, Lumbini 652 < 10–181 (8.92) 180 (28%) 
Kayastha & Pradhanang (2021)  Bara District, Madhesh 36 0.127–75.7 (22.11) 24 (67%) 
Panthi et al. (2006)  Saptari, Madhesh 772 < 10–98 (5.73) 103 (13%) 
Siraha, Madhesh 289 < 10–90 (12.25) 98 (34%) 
Dhanusha, Madhesh 331 < 10–140 (7.64) 64 (19%) 
Mahottari, Madhesh 202 < 10–80 (5.79) 25 (12%) 
Bara, Madhesh 2,124 < 10–254 (6.90) 341 (16%) 
Sarlahi, Madhesh 532 < 10–98 (8.35) 130 (24%) 
Rautahat, Madhesh 2,485 < 10–324 (19.91) 1,745 (70%) 
Parsa, Madhesh 2,207 < 10–456 (7.00) 312 (14%) 
Panthi et al. (2006)  Jhapa, Province No. 1 571 < 10–79 (4.56) 78 (14%) 
Morang, Province No. 1 341 < 10–70 (6.57) 77 (23%) 
Sunsari, Province No. 1 675 < 10–75 (5.12) 109 (16%) 
Sri Lanka 
Wickramarathna et al. (2017)  Wilgamuwa, Central Province 12 0.15–1.64 (0.36) 
Herath et al. (2017)  Batticaloa, Eastern Province 30 0–14 (3) 1 (3.3%) 
Amarathunga et al. (2019)  Girandurukotte, Northern Province 6.5–43.8 (25.5) 8 (100%) 
Bandara et al. (2018)  Mannar Island, Northern Province 35 0.6–34 (8.38) 8 (23%) 
Herath et al. (2017))  Mannar, Northern Province 32 0–66 (3) 6 (18.8%) 
Mullaitivu, Northern Province 23 0–13 (3) 2 (8.7%) 
Puttalam, North Western 28 0–15 (4) 2 (7.4%) 
Wickramarathna et al. (2017)  Nikawewa, North Western 0.15–0.51 (0.19) 
Chandrajith et al. (2016)  Jaffna, North Western 35 0.07–15.1 (2) 2 (6%) 
Senarathne et al. (2019)  Malala Oya basin, Southern Province 30 0.07–0.65 (0.25) 
Rajasooriyar et al. (2013)  Uda Walawe, Southern Province 105 < 10–400 (200) 42 (40%) 
Nanayakkara et al. (2019)  Girandurukotte, Uva Province 29 0.06–1.90 (0.26) 
Wickramarathna et al. (2017)  Girandurukotte, Uva Province 52 0.15–0.73 (0.23) 
ReferenceStudy areaNo. of samplesArsenic range in μg/L (mean in μg/L)No. of samples >10 μg/L
India 
Goswami et al. (2020)  Majuli, Assam 20 5–386 (137) 16 (80%) 
Kanungo (2016)  Silchar, Assam 30 < 3–188 (33) 13 (43%) 
Kumar et al. (2016)  Diphu, Assam 38 N.D. to 6.3 (2.1) 
Chetia et al. (2011)  Golghat, Assam 222 N.D. to 128 (2.1) 149 (67%) 
Mahanta et al. (2008)  Bongaigaon (B) and Darrang (D), Assam 50 0–606 (B) (42) & N.D to 60 (D) (7) 33 (66%) 
Thambidurai et al. (2013)  Barak Valley, Assam 50 12–97 (33.35) 17 (34%) 
Kumar et al. (2021a, 2021bSaran, Bihar 128 < 3–244.20 (39) 78 (61%) 
Rahman et al. (2019)  Buxar, Bihar 323 < 3–1,929 (98.62) 304 (94%) 
Thakur & Gupta (2019)  Patna and Bhojpur, Bihar 388 < 10– > 500 (87.53) 297 (77%) 
Chakraborti et al. (2016b)  Shahpur, Bihar 4,704 < 3–1,805 (90) 1,896 (40%) 
Chakraborti et al. (2016c)  Patna, Bihar 1,365 < 3–1,466 (43.5) 833 (61%) 
Singh et al. (2014)  Vaishali (V) and Bhagalpur (B), Bihar 40 1–21 (9.7) & 3–143 (B) (49.8) 8 (20%) 
Alsubih et al. (2021)  Patparganj, Delhi 24 > 10–610 (262.5) 24 (100%) 
Wu et al. (2021)  Gujarat 398 < 3–26 (6.17) 24 (6%) 
Kumar et al. (2022b)  Ramgarh, Jharkhand 15 0.8–2.8 (1.07) 
Tirkey et al. (2017)  Ranchi, Jharkhand 44 0–200 (58) 37 (84%) 
Alam et al. (2016)  Sahibganj, Jharkhand 60 1–133 (45.4) 54 (90%) 
Chakraborti et al. (2008)  Manipur Valley, Manipur 628 < 3–502 (60.82) 398 (63.3%) 
Kumar & Singh (2020)  Punjab 73 < 3–256 (16.44) 18 (24.6%) 
Bhattacharya et al. (2020)  Dharmanagar, Tripura 71 < 3–30 (12) 26 (36%) 
Khan & Rai (2022)  Haridwar, Uttarakhand 208 0.1–102 (5.68) 35 (17%) 
Singh et al. (2022)  Bahraich, Uttar Pradesh (U.P.) 40 0.01–128 (14.82) 17 (42%) 
Srivastava & Sharma (2013)  Ballia and Ghazipur, U.P. 36 43.75–620.75 (329) 36 (100%) 
Chakraborti et al. (2017b)  Kolkata, West Bengal (W.B.) 4,210 < 3–825 598 (14.2%) 
Malakar et al. (2016)  Kolkata, W.B. 144 wards N.D. to 164 (36.4) 49 wards (34%) 
Chakraborti et al. (2009)  West Bengal 140,150 < 3–3,700 (79.83) 33,993 (24.25%) 
Pakistan 
Podgorski et al. (2017)  Along Indus Plain 1,184 0–500 (100) 785 (66%) 
Shahid et al. (2022)  Lahore, Punjab 55 1.98–1,555 (96.5) 47 (85%) 
Riaz et al. (2022)  Lodhran, Punjab 200 0–14.33 (3.85) 18 (9%) 
Ehsan et al. (2020)  Sheikhpura, Punjab 20 2–357 (100.3) 14 (70%) 
Tabassum et al. (2019)  Hasilpur, Punjab 61 < 5–100 (9) 20 (33%) 
Shahid et al. (2018a)  Vehari, Punjab 156 0.4–132 (37.3) 148 (95%) 
Waqas et al. (2017)  Lahore, Punjab 100 2–111 (36.5) 78 (78%) 
Rasool et al. (2016)  Sargana (S) and Mailsi (M), Punjab 44 14–787 (165.8) (S), 11–828 (145.6) (M) – 
Shakoor et al. (2015)  Chichawatni, Sahiwal (CW), Vehari (VH), and Rahim Yar Khan (RYK) Punjab 62 1.5–201 (95) (CW), 23–201 (41.5) (VH) & 1.5–144 (9.2) (RYK) 33 (52%) 
Rasool et al. (2016)  Mailsi, Punjab 52 5.9–507 (155.71) 45 (86.6%) 
Sultana et al. (2014)  Kalalanwala (KL), Manga Mandi (MM), and Shamki Bhattian (SB), Punjab 30 37.5–375 (143) (KL); 1–250 (70) (MM) 0–525 (179) (SB) 26 (87%) 
Malana & Khosa (2011)  Dera Ghazi Khan, Punjab 32 0.37–29 (4) 7 (22%) 
Farooqi et al. (2007)  Kalalanwala, Punjab 147 1–2,400 (111.3) 134 (91%) 
Kori et al. (2018)  Larkana, Sindh 110 0.01–17 (6.78) 13 (12%) 
Kandhro et al. (2016)  Nawab Shah, Sindh 68 5–200 (102.5) 7 (10%) 
Memon et al. (2016)  Dadu (Johi), Sindh 46 8–67 (24.8) 11 (24%) 
Rubab et al. (2014)  Gujjo (GJ) and Ghulamullah Thatta (GT), Sindh GJ = 23 & GT = 14 0–20 (2.39) (GJ), 0–200 (47) (GT) GJ = 1 (4%) & GT = 8 (57%) 
Brahman et al. (2013)  Tharpakar, Sindh 12 0.006–4.33 (1.14) 
Jakhrani et al. (2011)  Gambat, Sindh 334 0.01–126 (26.6) 136 (41%) 
Majidano et al. (2010)  Tando Allahayar, Sindh 175 0.04–300 (123) 82 (47%) 
Baig et al. (2009)  Jamshoro, Sindh 153 13–106 (45) 153 (100%) 
Jakhrani et al. (2009)  Khairpur, Sindh 222 0.24–316 (21.1) 77 (35%) 
Bangladesh     
Rahman et al. (2022)  Laksam, Chittagong 21 6–581 (199) 19 (90%) 
Rahman et al. (2015)  Noakhali, Chittagong 70 1.5–587.6 (297.5) 69 (99%) 
Chakraborti et al. (2010)  Noakhali, Chittagong 843 < 10–4,730 (413) 838 (99%) 
Brahmanbaria, Chittagong 47 < 10–210 (75.63) 35 (74%) 
Chandpur, Chittagong 1,165 < 10–1,318 (255.56) 1,115 (96%) 
Chittagong, Chittagong 366 < 10–275 (12.43) 47 (13%) 
Comilla, Chittagong 545 < 10–1,769 (320.77) 432 (79%) 
Feni, Chittagong 186 < 10–1,000 (66.82) 128 (69%) 
Lakshmipur, Chittagong 2,662 < 10–2,030 (293.68) 2,358 (89%) 
Ahmed et al. (2021)  Tangail, Dhaka 10 40–170 (13.43) 10 (100%) 
Chakraborti et al. (2010)  Faridpur, Dhaka 707 < 10–1,630 (120.92) 464 (66%) 
Dhaka District, Dhaka 574 < 10–352 (30.87) 125 (22%) 
Gazipur, Dhaka 3,386 < 10–533 (7.89) 74 (2%) 
Shariatpur, Dhaka 152 < 10–580 (78.52) 89 (59%) 
Gopalganj, Dhaka 384 < 10–920 (99.15) 29 (78%) 
Jamalpur, Dhaka 144 < 10–1,172 (80.69) 55 (62%) 
Madaripur, Dhaka 2,309 < 10–1,200 (135) 1,856 (80%) 
Mymensingh, Dhaka 1,825 < 10–330 (7.51) 120 (7%) 
Narayanganj, Dhaka 412 < 10–1,750 (238.9) 358 (87%) 
Narshingdi, Dhaka 336 < 10–1,000 (68.64) 84 (25%) 
Chakraborti et al. (2010)  Netrokna, Dhaka 533 < 10–580 (52.53) 332 (62%) 
Rajbari, Dhaka 174 < 10–714 (40.97) 95 (55%) 
Chakraborty et al. (2022)  Jhikargachha, Jessore, Khulna 33 13–501 (90) 32 (97%) 
Huq et al. (2019)  Kushtia district, Khulna 50 6.05–591 (58.3) 41 (82%) 
Chakraborti et al. (2010)  Bagerhat, Khulna 371 < 10–958 (161.28) 281 (76%) 
Chuadanga, Khulna 457 < 10–841 (60.57) 281 (73%) 
Jessore, Khulna 5,465 < 10–1,120 (43.44) 1,238 (23%) 
Magura, Khulna 496 < 10–1,050 (38.94) 253 (51%) 
Khulna district, Khulna 1,000 < 10–3,143 (66.1) 482 (48%) 
Meherpur, Khulna 1,024 < 10–1,230 (47.86) 498 (49%) 
Shatkhira, Khulna 532 < 10–750 (168.69) 500 (94%) 
Islam et al. (2019)  Nawabganj, Rajshahi 18 2.5–150.6 (66.16) 17 (95%) 
Chakraborti et al. (2010)  Bogra, Rajshahi 767 < 10–1,040 (15.44) 160 (21%) 
Nawabganj, Rajshahi 1,902 < 10–1,600 (69.90) 982 (52%) 
Pabna, Rajshahi 5,117 < 10–2,108 (74.41) 3,522 (69%) 
Rajshahi District, Rajshahi 2,698 < 10–524 (17.93) 501 (19%) 
Sirajganj, Rajshahi 278 < 10–216 (16.21) 29 (11%) 
Islam et al. (2019)  Rangpur District, Rangpur 47 0.5–42.8 (8.81) 13 (28%) 
Chakraborti et al. (2010)  Gaibanda, Rangpur 1,233 < 10–512 (17.11) 370 (30%) 
Rangpur District, Rangpur 464 < 10–939 (46.59) 179 (39%) 
Thakurgaon, Rangpur 461 < 10–130 (8.28) 45 (10%) 
Nepal 
Gwachha et al. (2020)  Kathmandu valley, Bagmati 20 0–640 (250) 15 (76%) 
Panthi et al. (2006)  Nawalparasi, Lumbini 3,833 < 10–571 (38.73) 2,448 (64%) 
Rupandehi, Lumbini 3,725 < 10–2,620 (12.99) 534 (14%) 
Kapilbastu, Lumbini 4,099 < 10–589 (9.33) 628 (15%) 
Banke, Lumbini 1,835 < 10–270 (8.88) 519 (28%) 
Bardiya, Lumbini 652 < 10–181 (8.92) 180 (28%) 
Kayastha & Pradhanang (2021)  Bara District, Madhesh 36 0.127–75.7 (22.11) 24 (67%) 
Panthi et al. (2006)  Saptari, Madhesh 772 < 10–98 (5.73) 103 (13%) 
Siraha, Madhesh 289 < 10–90 (12.25) 98 (34%) 
Dhanusha, Madhesh 331 < 10–140 (7.64) 64 (19%) 
Mahottari, Madhesh 202 < 10–80 (5.79) 25 (12%) 
Bara, Madhesh 2,124 < 10–254 (6.90) 341 (16%) 
Sarlahi, Madhesh 532 < 10–98 (8.35) 130 (24%) 
Rautahat, Madhesh 2,485 < 10–324 (19.91) 1,745 (70%) 
Parsa, Madhesh 2,207 < 10–456 (7.00) 312 (14%) 
Panthi et al. (2006)  Jhapa, Province No. 1 571 < 10–79 (4.56) 78 (14%) 
Morang, Province No. 1 341 < 10–70 (6.57) 77 (23%) 
Sunsari, Province No. 1 675 < 10–75 (5.12) 109 (16%) 
Sri Lanka 
Wickramarathna et al. (2017)  Wilgamuwa, Central Province 12 0.15–1.64 (0.36) 
Herath et al. (2017)  Batticaloa, Eastern Province 30 0–14 (3) 1 (3.3%) 
Amarathunga et al. (2019)  Girandurukotte, Northern Province 6.5–43.8 (25.5) 8 (100%) 
Bandara et al. (2018)  Mannar Island, Northern Province 35 0.6–34 (8.38) 8 (23%) 
Herath et al. (2017))  Mannar, Northern Province 32 0–66 (3) 6 (18.8%) 
Mullaitivu, Northern Province 23 0–13 (3) 2 (8.7%) 
Puttalam, North Western 28 0–15 (4) 2 (7.4%) 
Wickramarathna et al. (2017)  Nikawewa, North Western 0.15–0.51 (0.19) 
Chandrajith et al. (2016)  Jaffna, North Western 35 0.07–15.1 (2) 2 (6%) 
Senarathne et al. (2019)  Malala Oya basin, Southern Province 30 0.07–0.65 (0.25) 
Rajasooriyar et al. (2013)  Uda Walawe, Southern Province 105 < 10–400 (200) 42 (40%) 
Nanayakkara et al. (2019)  Girandurukotte, Uva Province 29 0.06–1.90 (0.26) 
Wickramarathna et al. (2017)  Girandurukotte, Uva Province 52 0.15–0.73 (0.23) 
Figure 2

Arsenic distribution (μg/L) in groundwater of various South Asian countries.

Figure 2

Arsenic distribution (μg/L) in groundwater of various South Asian countries.

Close modal

India

India is one of the most severely afflicted countries in the world by arsenic groundwater contamination. Out of 28 states and eight union territories, about 20 states and four union territories are reported to be affected by As-contaminated groundwater (Shaji et al. 2021). Very high As concentrations were found in the groundwater samples of West Bengal, Uttar Pradesh, Bihar, and Assam (Kumar et al. 2021a, 2021b). Kumar & Singh (2020) documented that more than 10 Indian states have excessive levels of As in the groundwater. In India, the population of As-endemic states is approximately 359 million, and ∼70 million people are exposed to As contamination of above 10 μg/L (Chakraborti et al. 2016a). The Himalayan mountains and the Shillong Plateau are reported to be the major sources of As pollution in the Gangetic River basin and delta sediments. Other geological sources of As include the Gondwana coal region and Bihar mica belt in eastern India, the pyrite-bearing region in the Vindhya Range in Central India, the Son River Valley gold belt in the eastern area, and the sulfide regions of the eastern Himalayas (Srivastava 2020). Arsenic contamination in the groundwater extends from Punjab (India) in the north to Manipur in the north-east India. Most of the shallow aquifers were found to have high As contaminations (>10 ppb), while the deeper aquifers (>100 m) were free from As (Shaji et al. 2021). Anthropogenic sources consisting of several industrial processes such as smelting, coal mining, coal combustion, and agricultural practices also contribute to the elevated concentration of As in Indian groundwater (Shukla et al. 2020). The first As groundwater contamination in India was reported in Chandigarh in north India, and the second As contamination was reported in West Bengal (Datta & Kaul 1976; Garat et al. 1984).

Assam and north-eastern part of India

The As concentration (>50 μg/L) in Assam was first reported in the districts of Karimganj, Dhemaji, and Dhubri in 2004 (Shukla et al. 2020). Assam is among the highly affected states by As-contaminated groundwater in north-east India. Out of 23 districts, 18 reported high As concentrations (Devi et al. 2009). Assam has an area of 78,738 km2; out of this area, ∼8,822 km2 is As-affected (Chakraborti et al. 2017a). Out of a total population of 31.1 million, ∼1.2 million people are exposed to the risk of As groundwater contamination (>10 μg/L) in Assam (Chakraborti et al. 2017a). The high concentration of As in the groundwater of south Assam could be due to its location in the low-lying portions of the Barak valley, which is made of Holocene sediments. Thambidurai et al. (2013) reported the major source of As is the litho-facies of the Tipam formation and Holocene alluvial terrain due to pyrite-rich coal seams. Arsenic groundwater contamination-affected districts in Assam are presented in Table 2 and Figure S1 in supplementary files (all the figures, i.e., Figures S1–S9, show the As-affected regions only and not the whole country). The highly As-affected district is Majuli in Assam, having a mean As value of 137 μg/L. Arsenic concentration in the biological samples from the Majuli District for hair varied from 220 to 5,461 μg/kg in adults and 251 to 3,083 μg/kg in children. Arsenic concentration in nail samples varied from 426 to 11,275 μg/kg in adults and 325 to 5,853 μg/kg in children, and the arsenic concentration in urine samples varied from 43.1 to 305 μg/L in adults and 54 to 698 μg/L in children (Goswami et al. 2020). The permissible limits for arsenic toxin levels in hair, nail, and urine are <200 μg/kg, <500 μg/kg, and <100 μg/L, respectively (Chakraborti et al. 2016c). The entire biological sample examination showed that As accumulated in the human body beyond the tolerable limits, and 80% of the groups examined were exposed to the non-carcinogenic risk of As contamination in groundwater (Goswami et al. 2020). The CR of As found was 43% for >1 in 100 exposed and 80% for 1 in 1,000 exposed, exceeding the maximum safe limit for CR (1 in 10,000) of people (Mohammadi et al. 2019; Goswami et al. 2020). Very few As-related studies were conducted in Manipur, with As ranging from 3 to 502 μg/L (Table 2).

Bihar

Arsenic was first reported in Bihar from two villages in Bhojpur District in 2002 (Shaji et al. 2021). The district covers a total area of 94,163 km2, and about 21,271 km2 of groundwater area is affected by As concentration (Chakraborti et al. 2017a). The total population is 83 million, and ∼9 million people are at risk of As toxicity (>10 μg/L). The major source of As in this region is geogenic in nature from the alluvial aquifer of the Ganga River basin (Chakraborti et al. 2017a; Shaji et al. 2021). The hard rock aquifers of Bihar were not found to be responsible for As contamination in groundwater (Kumari et al. 2019). Bihar is the second most As-contaminated state in India, and out of 38 districts, 17 are highly affected, viz., Buxar, Bhojpur, Patna, Saran, Darbhanga, Vaishali, Samastipur, Begusarai, Khagaria, Katihar, Munger, Bhagalpur, Lakhisarai, Kishanganj, Supaul, Siwan, and Muzaffarpur (Table 2 and Figure S1) (Kumar et al. 2021a, 2021b). These areas were found to have an As concentration of more than 50 μg/L (ppb) (Kumar et al. 2021a, 2021b). Chakraborti et al. (2016b) reported that out of 1,422 people examined in Bhojpur, 161 people had arsenical skin lesions, and 82% of hair samples, 89% of nail samples, and 91% of urine samples had arsenic above the normal levels. Out of 102 arsenicosis patients, 49 patients were found to have arsenical neuropathy. In another study performed in Patna, out of 712 people examined, 69 people had arsenical skin lesions, 100% of biological samples had an arsenic concentration above the allowable limit, and arsenical neuropathy was observed in 15 of 37 arsenicosis patients (Chakraborti et al. 2016c). Thakur & Gupta (2019) assessed the economic loss due to As contamination in Patna and Bhojpur. The annual wage loss was estimated to be INR 2,437.92 ($45.83), the cost of treatment was INR 5,942.40 ($111.72), and the cost of illness was INR 8,380.32 ($157.55) for sample households. The Government of Bihar has launched the ‘Har Ghar Nal Ka Jal’ programme to provide clean drinking water through piped water supply to approximately 20 million households across the state to tackle the health issue of drinking As-contaminated groundwater.

Jharkhand

Arsenic was first reported in the Sahibganj District of Jharkhand by the Central Ground Water Board (CGWB) in the year 2004, and then Alam et al. (2016) reported high As concentration in groundwater (up to 133 μg/L) of the Sahibganj District (Shaji et al. 2021). More than 200,000 people are affected in Jharkhand by As toxicity (Shaji et al. 2021). The affected districts in Jharkhand due to As-contaminated groundwater are presented in Table 2 and Figure S1. Jharkhand is less affected by As contamination than Bihar and West Bengal, as only two out of 24 districts are found to have As-contaminated groundwater.

Uttar Pradesh

The first case of excess As was reported in groundwater from the Ballia District in 2003 (Mukherjee et al. 2006). The total area covers 238,000 km2, and ∼10,375 km2 area is affected by As-contaminated groundwater (Chakraborti et al. 2017a). Out of 166 million people in Uttar Pradesh, ∼3 million people are exposed to the risk of As groundwater contamination (>10 μg/L) (Chakraborti et al. 2017a). The highly affected districts are Ballia and Ghazipur, where the mean and maximum As concentrations were found to be 329 and 3,192 μg/L, respectively (Ahamed et al. 2006). In different districts of Uttar Pradesh, 989 people were examined and 154 people were diagnosed with arsenical skin lesions (Chakraborti et al. 2017a). The affected areas of the state are presented in Table 2 and Figure S1. Ahamed et al. (2006) reported that out of 989 villagers, 137 (19.8%) of the adults and 17 (5.7%) of the children had arsenical skin lesions in Ballia, Varanasi, and Ghazipur Districts of Uttar Pradesh. More than 90% of hair, nail, and urine samples contained As higher than the tolerance level. About 74 patients were examined for arsenical neuropathy, and 32 patients were found to suffer from arsenical neuropathy in the study area (Ahamed et al. 2006).

West Bengal

The first case of As groundwater contamination was reported in West Bengal in 1978. West Bengal is one of the highly affected states in India (Chakraborti et al. 2017a). Chakraborti et al. (2009) classified West Bengal into three sub-categories based on As severity (i) highly affected (As > 300 μg/L), (ii) mildly affected (As between 10 and 50 μg/L), and (iii) unaffected (As < 10 μg/L). The state covers an area of ∼88,750 km2; out of this area, ∼38,861 km2 (44%) is affected by As contamination (Chakraborti et al. 2017a). Out of 80.2 million people, ∼26 million are at risk of As exposure in West Bengal (Chakraborti et al. 2017a). West Bengal has 23 districts, out of which 15 districts are severely affected (Chakraborti et al. 2017a). The As contamination is severe on the left side of the Bhagirathi River in the direction of the flow, and West Bengal has a thick alluvial deposition of Quaternary age (Shukla et al. 2020). The mildly and highly affected districts in West Bengal due to As-contaminated groundwater are presented in Table 2 and Figure S2. The unaffected districts having As concentrations less than 10 μg/L are Bankura, Birbhum, Purulia, Medinipur East, and Medinipur West. It is estimated that about 9.5 million people are drinking water having As >10 μg/L, 4.2 million people are drinking water having As >50 μg/L, and 0.53 million people are drinking water having As >300 μg/L in West Bengal (Chakraborti et al. 2009). In West Bengal, 7,135 samples were tested for arsenic contamination in hair, 7,381 samples were tested for arsenic in nails, and 9,795 samples were tested for arsenic in urine (Chowdhury et al. 2000). In total, 57% of hair samples, 82% of nail samples, and 89% of urine samples had arsenic above the permissible limit. The arsenic concentration ranged from 180 to 20,340 μg/kg (mean = 1,480 μg/kg) in hair samples, 380 to 44,890 μg/kg (mean = 4,560 μg/kg) in nail samples, and 10 to 3,147 μg/L (mean = 180 μg/L) in urine samples (Chowdhury et al. 2000).

Pakistan

Pakistan is one of the countries in South Asia severely affected by As groundwater contamination, with As concentrations ranging from <1 to 2,580 μg/L (Ali et al. 2019b). Out of a total population of 242 million, ∼50–60 million people are exposed to the risk of As-contaminated groundwater (Podgorski et al. 2017; Fida et al. 2022). As contamination in groundwater in Pakistan is mostly confined to the Punjab and Sindh Provinces, and about 25–36% of the population of the two provinces is exposed to As-contaminated drinking water having As >10 μg/L (Shaji et al. 2021). Shahid et al. (2018b) reported that 73% of the values for mean As concentrations in the existing 43 studies exceeded the World Health Organization (WHO) limit of 10 μg/L and 41% of the values of mean As were above the permissible limit specified by Pakistan (50 μg/L). Most of the As contamination in Pakistan (Lahore, Kasur, Tharpakar, Hyderabad, Tharimirwah, Kotdigi, Sobo Dero, and Kingri) is geogenic in nature, while coal mining and geothermal sources are responsible for As in groundwater contamination in Jhelum and Chakwal located in the Punjab Province of Pakistan (Shaji et al. 2021). Shahid et al. (2018b) in their study predicted the ADD ranging from 0 to 77 μg/kg/day (mean = 4.4 μg/kg/day), the HQ varying from 0 to 256 (mean = 14.7), and the CR ranging from 0 to 0.0512 (mean = 0.0029) in Pakistan.

Punjab Province

In the Punjab Province, groundwater at most locations in the Lahore and Kasur Districts of central Punjab and Muzaffargarh in South Punjab was found to have As concentrations as high as above 10 μg/L (Shahid et al. 2018b). The As concentration in groundwater in the Punjab Province varied from 0 to 2,400 μg/L, having a mean As concentration of >10 μg/L at most of the locations; the highest concentration of As in groundwater was found to be confined in the Lahore District (Table 2 and Figure S3). The sources of As groundwater contamination in Punjab are geogenic, industrial, and agricultural (Shahid et al. 2018b). Most industries are located in Lahore, causing very high As groundwater contamination within the district. Agricultural activities are also responsible for As groundwater contamination in the Punjab Province (Qurat-ul-Ain et al. 2017; Shahid et al. 2018b). The high As concentration in the groundwater of Dera Ghazi Khan was possibly due to the leaching of the metal from mine waste, hydrous ferric oxide reduction, and microbial contamination (Malana & Khosa 2011). The As-affected districts in the Punjab Province are presented in Figures 4 and Table S1. The Hyderabad District in Sindh and the Lahore District in Punjab are the hotspots for As contamination in Pakistan. Both of these districts lie along the Indus River with Holocene fluvial sediments similar to the As-affected areas of the Ganges/Brahmaputra Rivers in India and Bangladesh.

Sindh Province

The groundwater of various districts in the Sindh Province (Tharpakar, Thatta, Khairpur, Larkana, and Jamshoro) has As concentrations above 10 μg/L, as seen in Table 2 and Figure S4 (Baig et al. 2009; Brahman et al. 2013; Shahid et al. 2018b). The As concentration in groundwater samples of various cities located in districts in Sindh varied from 0 to 315.6 μg/L (Kori et al. 2018). Fida et al. (2022) reported that out of the total samples examined during the studies performed between 2010 and 2022, 11% of samples in Sindh and 17% of samples in the Punjab Province exceeded the standard As concentration limit of 50 μg/L specified by the Pakistan National Standards for Drinking Water Quality (NSDWQ). About 16–36% of people in the Sindh Province are exposed to As-contaminated groundwater with As concentration above the permissible limit of 50 μg/L (Baig et al. 2009). The locations in Lahore along the Ravi River had very high non-carcinogenic and CRs (Shahid et al. 2022). The existing studies in Pakistan revealed that oral exposure poses a much higher risk than dermal exposure (Ehsan et al. 2020; Shahid et al. 2022).

Bangladesh

Bangladesh is another South Asian country highly affected by As groundwater contamination (Chakraborti et al. 2010). The first case of As poisoning in groundwater was reported from Chamagram village in the Chapai Nawabganj District in 1993 (Ahmad et al. 2018; Shaji et al. 2021). The As concentration in the groundwater ranged from 0.5 to 4,600 μg/L (Whaley-Martin et al. 2017). Out of 64 districts in Bangladesh, 50 districts are affected by As-contaminated groundwater having As concentration above the Bangladesh standard of 50 μg/L; moreover, 59 districts had As exceeding the WHO limit of 10 μg/L (Huq et al. 2020). About 35 million people are exposed to As concentrations above 50 μg/L, and ∼85 million people are exposed to As concentrations above 10 μg/L (Chakraborty et al. 2015; Shaji et al. 2021). The existing study reported that sedimentary lacustrine aquifers with shallow groundwater had high As concentrations, whereas deeper aquifers (>300 m) were found to have As concentrations within the limit (Shaji et al. 2021). From 1996 to 2010, about 52,202 water samples from hand tube wells were analyzed for As concentration. Out of 52,202 water samples, 14,200 had As >50 μg/L, 21,977 had As >10 μg/L, and 3,915 had As >300 μg/L (Chakraborti et al. 2010). The northern parts of Bangladesh were found to be less As-contaminated than southern Bangladesh (Huq et al. 2020). The locations of high As groundwater contamination areas in various divisions of Bangladesh are presented in Table 2 and Figures S5–S7. Out of 41 locations in Bangladesh, 26 locations had As concentration above 10 μg/L in more than 50% of samples. The highly affected district was Noakhali in the Chittagong division, having the mean and maximum As concentrations of 413 and 4,730 μg/L, respectively, and 99% of samples had As above 10 μg/L. In the Jessore District, about 1,537 people were suffering from arsenicosis, while in the Jhikargachha Subdistrict, 375 patients had arsenicosis (Khan et al. 2016).

Nepal

High arsenic concentration in groundwater is a major issue in Nepal, like in other South Asian countries (Thakur et al. 2010). Out of 77 districts, 25 have been reported for As concentration, and ∼2.29 million people are at risk due to consumption of As-contaminated groundwater (Natasha et al. 2020; Shaji et al. 2021). The concentration of As in the groundwater varied from <10 to 2,620 μg/L, and the highest concentration was found in the Rupandehi District in the Lumbini division, while the highest mean As concentration (250 μg/L) was found in Kathmandu Valley in the Bagmati division (Bhattacharya et al. 2019). The Terai region of Nepal is highly affected by As groundwater contamination as it falls in the Bengal Delta Plain (Pokhrel et al. 2009). Out of 25 affected districts, 20 are falling in the alluvial-rich region of Terai (Kayastha & Pradhanang 2021). According to the study conducted by the Nepal State Department of Water Supply and Sewage (DWSS) in 2007, out of 2,59,828 wells, about 27,529 wells (10.5%) exceeded the WHO limit and 7,232 wells (3%) exceeded the limit of 50 μg/L (Bhattacharya et al. 2019). The Terai region includes Bara, Parsa, and Nawalparasi Districts, where about 5,215 people were exposed to As contamination above 50 μg/L and about 5.1% of the population is suffering from arsenicosis (Pokhrel et al. 2009). The As-affected provinces in Nepal are presented in Table 2 and Figure S8.

Sri Lanka

Sri Lanka is the least affected by As groundwater contamination in the South Asia region. Out of 25 districts, about six districts were found to have As concentration in groundwater above 10 μg/L. The metamorphic aquifers were found to be free from As contamination. However, sandy aquifers in certain parts (Jaffna, Mannar, Puttalam, Batticaloa) had As concentration above the WHO limit (Shaji et al. 2021). The distribution of As-affected groundwater in various districts is presented in Table 2 and Figure S9. Most existing studies on As in groundwater in Sri Lanka did not estimate the human health risk. Nanayakkara et al. (2019) performed a human health risk assessment and found that As concentration in urine samples ranged from 1.49 to 259 μg/L, with a mean value of 33.2 μg/L. In hair samples, the As concentration varied from 0.001 to 0.94 μg/g, with a mean value of 0.12 μg/g.

Exposure to high levels of As increases the risk of skin diseases, several cancers (kidney, lung, bladder, liver), and cardiovascular, respiratory, and neurological diseases (Sinha & Prasad 2020). There are increased risks of skin lesions and lung and bladder cancer associated with the ingestion of arsenic-contaminated drinking water at concentrations below 50 μg/L (WHO 2022). Recent studies found that As exposure promotes the risk of diabetes and obesity and may also induce lipodystrophy (Farkhondeh et al. 2019). Generally, these diseases start showing symptoms after decades of chronic exposure. In India, more than 0.1 million deaths and 0.3 million cases of illness have been reported due to groundwater As contamination. The Ministry of Drinking Water and Sanitation, Government of India, reported that As in groundwater poses major health risks to 14.7 million people in India (India Today 2018). The intake of As-contaminated groundwater has been estimated to cause 0.3–0.6% of all cardiovascular disease mortality in India; however, this number is substantially higher (>3%) in Bihar, West Bengal, and north-eastern states (Wu et al. 2021). Kumar et al. (2021a, 2021b) reported a high occurrence of cancer in the Gangetic basin, which is prone to As contamination. As per the report, 13 out of 1,000 people could develop cancer over their lifetimes if they drink 1 L of As-contaminated water each day, having a concentration of 50 μg/L (Chakraborti et al. 2016a). In Bangladesh, about 43,000 persons die every year due to the consumption of As-contaminated groundwater (Hasan et al. 2019). Infants and children are more vulnerable to the ill effects, and among children, girls are more susceptible to cancer and non-carcinogenic risks due to As exposure (Chakraborti et al. 2017a; Rana et al. 2022). There has been growing evidence that exposure to As in utero is associated with impaired cognitive development, deoxyribonucleic acid (DNA) damage, and increased mortality in young adults (Farzan et al. 2013; Navasumrit et al. 2019; Ahmed et al. 2020). Arsenic is a recognized chromosomal-damaging agent (clastogen), and it also affects the spindle fibres, causing chromosome loss (aneugen), which could result in improper chromosome segregation and the development of micronuclei (Chakraborty & De 2009). Arsenic also has adverse effects on animals and causes oxidative stress, DNA damage, immune imbalance, and inflammation induction (Su et al. 2023). Mandal (2017) reported that acute arsenic poisoning in animals causes intense abdominal discomfort, vomiting, and diarrhea, followed by a rapid circulatory collapse in animals. Arsenic poisoning can also cause death within a few hours to a few days (Mandal 2017).

Limited studies have been performed with respect to risks to human health from consuming As-contaminated water in India, Bangladesh, Nepal, and Sri Lanka, and the present study calculated the health risks of drinking As-contaminated groundwater. The max HQ and max CR values calculated for As-affected states/provinces are listed in Table 3 for males, females, and children. The detailed health risks of drinking As-contaminated groundwater for males, females, and children are presented in Tables S1, S2, and S3, respectively. Out of 29 locations in India, mean HQ values were within the safe limits for males at six locations, for females at five locations, and for children at two locations (Tables S1, S2, and S3). In Pakistan, mean HQ values were within the safe limits for males at seven locations, for females at five locations, and for children at four locations (Table S1, S2, and S3). The mean HQ values were within the safe limits at four and two locations for males and females, respectively, in Bangladesh, while no locations had mean HQ values within the safe limits for children. The mean HQ values were within the safe limits at 12 and eight locations for males and females, respectively, in Nepal, while no locations had mean HQ values within the safe limits for children. However, mean HQ values were below 2 at the majority of locations in Nepal for all age groups (Tables S1, S2, and S3). Most of the locations in Sri Lanka had HQ values within the safe limits for all age groups in Nepal. The mean CR values were within the safe limits at two locations in India for all age groups. In Pakistan, the mean CR values were within limits at four, three, and one locations for males, females, and children, respectively. None of the locations had mean CR values within the safe limits in Bangladesh. The mean CR values were within the safe limits at two locations for males and no locations for females and children; however, the mean CR values were near-to-safe limits at most locations. The mean CR values were within the safe limits at 10 locations for males, nine locations for females, and six locations for children in Sri Lanka (Table S1, S2, and S3). People were exposed to very high non-carcinogenic and CRs from consumption of As-contaminated groundwater in India, Pakistan, and Bangladesh than those in Nepal, and people in Sri Lanka were at much lesser risk (Table 3). The adverse effects of As contamination on human health are very serious (life-threatening), and every government and concerned authorities must act to tackle this problem at utmost priority. According to the United Nations, the right to water guarantees that everyone has access to enough water for personal and domestic use that is safe, acceptable, physically accessible, and affordable. In every country, clean water must not be limited to the urban areas but also provided in rural areas covering each district in the states.

Table 3

Calculated max HQ and max CR values for arsenic contamination in males, females, and children

Mean HQ
Maximum HQ
Mean CR
Maximum CR
States/ProvincesMaleFemaleChildren (up to 12 years)MaleFemaleChildren (up to 12 years)MaleFemaleChildren (up to 12 years)MaleFemaleChildren (up to 12 years)
India 
Assam 13.05 17.23 30.44 57.71 76.23 134.67 2.61 × 10−3 3.45 × 10−3 6.09 × 10−3 1.15 × 10−2 1.52 × 10−2 2.69 × 10−2 
Bihar 9.39 12.41 21.92 183.71 242.64 428.67 1.88 × 10−3 2.48 × 10−3 4.38 × 10−3 3.67 × 10−2 4.85 × 10−2 8.57 × 10−2 
Delhi 25.00 33.02 58.33 58.10 76.73 135.56 5.00 × 10−3 6.60 × 10−3 1.17 × 10−2 1.16 × 10−2 1.53 × 10−2 2.71 × 10−2 
Gujarat 0.59 0.78 1.37 2.48 3.27 5.78 1.18 × 10−4 1.55 × 10−4 2.74 × 10−4 4.95 × 10−4 6.54 × 10−4 1.16 × 10−3 
Jharkhand 5.52 7.30 12.89 19.05 25.16 44.44 1.10 × 10−3 1.46 × 10−3 2.58 × 10−3 3.81 × 10−3 5.03 × 10−3 8.89 × 10−3 
Manipur 5.79 7.65 13.52 47.81 63.14 111.56 1.16 × 10−3 1.53 × 10−3 2.70 × 10−3 9.56 × 10−3 1.26 × 10−2 2.23 × 10−2 
Punjab 1.57 2.07 3.65 24.38 32.20 56.89 3.13 × 10−4 4.14 × 10−4 7.31 × 10−4 4.88 × 10−3 6.44 × 10−3 1.14 × 10−2 
Tripura 1.14 1.51 2.67 2.86 3.77 6.67 2.29 × 10−4 3.02 × 10−4 5.33 × 10−4 5.71 × 10−4 7.55 × 10−4 1.33 × 10−3 
Uttarakhand 0.54 0.71 1.26 9.71 12.83 22.67 1.08 × 10−4 1.43 × 10−4 2.52 × 10−4 1.94 × 10−3 2.57 × 10−3 4.53 × 10−3 
Uttar Pradesh 31.38 41.44 73.22 59.14 78.11 138.00 8.46 × 10−3 8.29 × 10−3 1.46 × 10−2 1.18 × 10−2 1.56 × 10−2 2.76 × 10−2 
West Bengal 7.60 10.04 17.74 352.38 465.41 822.22 1.52 × 10−3 2.01 × 10−3 3.55 × 10−3 7.05 × 10−2 9.31 × 10−2 1.64 × 10−1 
Pakistan 
Punjab 17.06 22.53 39.80 228.57 301.89 533.33 3.41 × 10−3 4.51 × 10−3 7.96 × 10−3 4.57 × 10−2 6.04 × 10−2 1.07 × 10−1 
Sindh 11.71 15.47 27.33 30.10 39.75 70.22 2.34 × 10−3 3.09 × 10−3 5.47 × 10−3 6.02 × 10−3 7.95 × 10−3 1.40 × 10−2 
Bangladesh 
Chittagong 39.33 51.95 91.78 450.48 594.97 1,051.11 7.87 × 10−3 1.04 × 10−2 1.84 × 10−2 9.01 × 10−2 1.19 × 10−1 2.10 × 10−1 
Dhaka 22.75 30.05 53.09 314.29 415.09 733.33 4.55 × 10−3 6.01 × 10−3 1.06 × 10−2 6.29 × 10−2 8.30 × 10−2 1.47 × 10−1 
Khulna 16.08 21.24 37.53 299.33 395.35 698.44 3.22 × 10−3 4.25 × 10−3 7.51 × 10−3 5.99 × 10−2 7.91 × 10−2 1.40 × 10−1 
Rajshahi 7.09 9.36 16.54 200.76 265.16 468.44 1.42 × 10−3 1.87 × 10−3 3.31 × 10−3 4.02 × 10−2 5.30 × 10−2 9.37 × 10−2 
Rangpur 4.44 5.86 10.35 89.43 118.11 208.67 8.87 × 10−4 1.17 × 10−3 2.07 × 10−3 1.79 × 10−2 2.36 × 10−2 4.17 × 10−2 
Nepal 
Bagmati 23.81 31.45 55.56 60.95 80.50 142.22 4.76 × 10−3 6.29 × 10−3 1.11 × 10−2 1.22 × 10−2 1.61 × 10−2 2.84 × 10−2 
Lumbini 3.69 4.87 8.61 249.52 329.56 582.22 7.38 × 10−4 9.74 × 10−4 1.72 × 10−3 4.99 × 10−2 6.59 × 10−2 1.16 × 10−1 
Madhesh 2.11 2.78 4.91 43.43 57.36 101.33 4.21 × 10−4 5.56 × 10−4 9.83 × 10−4 8.69 × 10−3 1.15 × 10−2 2.03 × 10−2 
Province No. 1 0.63 0.83 1.46 7.52 9.94 17.56 1.25 × 10−4 1.65 × 10−4 2.92 × 10−4 1.50 × 10−3 1.99 × 10−3 3.51 × 10−3 
Sri Lanka 
Central Province 0.03 0.05 0.08 0.16 0.21 0.36 6.86 × 10−6 9.06 × 10−6 1.60 × 10−5 3.12 × 10−5 4.13 × 10−5 7.29 × 10−5 
Eastern Province 0.29 0.38 0.67 1.33 1.76 3.11 5.71 × 10−5 7.55 × 10−5 1.33 × 10−4 2.67 × 10−4 3.52 × 10−4 6.22 × 10−4 
Northern Province 2.43 3.21 5.67 6.29 8.30 9.73 4.86 × 10−4 6.42 × 10−4 1.13 × 10−3 1.26 × 10−3 1.66 × 10−3 1.95 × 10−3 
North-western Province 0.38 0.50 0.89 1.44 1.90 3.36 7.62 × 10−5 1.01 × 10−4 1.78 × 10−4 2.88 × 10−4 3.80 × 10−4 6.71 × 10−4 
Southern Province 19.05 25.16 44.44 38.10 50.31 88.89 3.81 × 10−3 5.03 × 10−3 8.89 × 10−3 7.62 × 10−3 1.01 × 10−2 1.78 × 10−2 
Uva Province 0.02 0.03 0.06 0.18 0.24 0.42 4.95 × 10−6 6.54 × 10−6 1.16 × 10−5 3.62 × 10−5 4.78 × 10−5 8.44 × 10−5 
Mean HQ
Maximum HQ
Mean CR
Maximum CR
States/ProvincesMaleFemaleChildren (up to 12 years)MaleFemaleChildren (up to 12 years)MaleFemaleChildren (up to 12 years)MaleFemaleChildren (up to 12 years)
India 
Assam 13.05 17.23 30.44 57.71 76.23 134.67 2.61 × 10−3 3.45 × 10−3 6.09 × 10−3 1.15 × 10−2 1.52 × 10−2 2.69 × 10−2 
Bihar 9.39 12.41 21.92 183.71 242.64 428.67 1.88 × 10−3 2.48 × 10−3 4.38 × 10−3 3.67 × 10−2 4.85 × 10−2 8.57 × 10−2 
Delhi 25.00 33.02 58.33 58.10 76.73 135.56 5.00 × 10−3 6.60 × 10−3 1.17 × 10−2 1.16 × 10−2 1.53 × 10−2 2.71 × 10−2 
Gujarat 0.59 0.78 1.37 2.48 3.27 5.78 1.18 × 10−4 1.55 × 10−4 2.74 × 10−4 4.95 × 10−4 6.54 × 10−4 1.16 × 10−3 
Jharkhand 5.52 7.30 12.89 19.05 25.16 44.44 1.10 × 10−3 1.46 × 10−3 2.58 × 10−3 3.81 × 10−3 5.03 × 10−3 8.89 × 10−3 
Manipur 5.79 7.65 13.52 47.81 63.14 111.56 1.16 × 10−3 1.53 × 10−3 2.70 × 10−3 9.56 × 10−3 1.26 × 10−2 2.23 × 10−2 
Punjab 1.57 2.07 3.65 24.38 32.20 56.89 3.13 × 10−4 4.14 × 10−4 7.31 × 10−4 4.88 × 10−3 6.44 × 10−3 1.14 × 10−2 
Tripura 1.14 1.51 2.67 2.86 3.77 6.67 2.29 × 10−4 3.02 × 10−4 5.33 × 10−4 5.71 × 10−4 7.55 × 10−4 1.33 × 10−3 
Uttarakhand 0.54 0.71 1.26 9.71 12.83 22.67 1.08 × 10−4 1.43 × 10−4 2.52 × 10−4 1.94 × 10−3 2.57 × 10−3 4.53 × 10−3 
Uttar Pradesh 31.38 41.44 73.22 59.14 78.11 138.00 8.46 × 10−3 8.29 × 10−3 1.46 × 10−2 1.18 × 10−2 1.56 × 10−2 2.76 × 10−2 
West Bengal 7.60 10.04 17.74 352.38 465.41 822.22 1.52 × 10−3 2.01 × 10−3 3.55 × 10−3 7.05 × 10−2 9.31 × 10−2 1.64 × 10−1 
Pakistan 
Punjab 17.06 22.53 39.80 228.57 301.89 533.33 3.41 × 10−3 4.51 × 10−3 7.96 × 10−3 4.57 × 10−2 6.04 × 10−2 1.07 × 10−1 
Sindh 11.71 15.47 27.33 30.10 39.75 70.22 2.34 × 10−3 3.09 × 10−3 5.47 × 10−3 6.02 × 10−3 7.95 × 10−3 1.40 × 10−2 
Bangladesh 
Chittagong 39.33 51.95 91.78 450.48 594.97 1,051.11 7.87 × 10−3 1.04 × 10−2 1.84 × 10−2 9.01 × 10−2 1.19 × 10−1 2.10 × 10−1 
Dhaka 22.75 30.05 53.09 314.29 415.09 733.33 4.55 × 10−3 6.01 × 10−3 1.06 × 10−2 6.29 × 10−2 8.30 × 10−2 1.47 × 10−1 
Khulna 16.08 21.24 37.53 299.33 395.35 698.44 3.22 × 10−3 4.25 × 10−3 7.51 × 10−3 5.99 × 10−2 7.91 × 10−2 1.40 × 10−1 
Rajshahi 7.09 9.36 16.54 200.76 265.16 468.44 1.42 × 10−3 1.87 × 10−3 3.31 × 10−3 4.02 × 10−2 5.30 × 10−2 9.37 × 10−2 
Rangpur 4.44 5.86 10.35 89.43 118.11 208.67 8.87 × 10−4 1.17 × 10−3 2.07 × 10−3 1.79 × 10−2 2.36 × 10−2 4.17 × 10−2 
Nepal 
Bagmati 23.81 31.45 55.56 60.95 80.50 142.22 4.76 × 10−3 6.29 × 10−3 1.11 × 10−2 1.22 × 10−2 1.61 × 10−2 2.84 × 10−2 
Lumbini 3.69 4.87 8.61 249.52 329.56 582.22 7.38 × 10−4 9.74 × 10−4 1.72 × 10−3 4.99 × 10−2 6.59 × 10−2 1.16 × 10−1 
Madhesh 2.11 2.78 4.91 43.43 57.36 101.33 4.21 × 10−4 5.56 × 10−4 9.83 × 10−4 8.69 × 10−3 1.15 × 10−2 2.03 × 10−2 
Province No. 1 0.63 0.83 1.46 7.52 9.94 17.56 1.25 × 10−4 1.65 × 10−4 2.92 × 10−4 1.50 × 10−3 1.99 × 10−3 3.51 × 10−3 
Sri Lanka 
Central Province 0.03 0.05 0.08 0.16 0.21 0.36 6.86 × 10−6 9.06 × 10−6 1.60 × 10−5 3.12 × 10−5 4.13 × 10−5 7.29 × 10−5 
Eastern Province 0.29 0.38 0.67 1.33 1.76 3.11 5.71 × 10−5 7.55 × 10−5 1.33 × 10−4 2.67 × 10−4 3.52 × 10−4 6.22 × 10−4 
Northern Province 2.43 3.21 5.67 6.29 8.30 9.73 4.86 × 10−4 6.42 × 10−4 1.13 × 10−3 1.26 × 10−3 1.66 × 10−3 1.95 × 10−3 
North-western Province 0.38 0.50 0.89 1.44 1.90 3.36 7.62 × 10−5 1.01 × 10−4 1.78 × 10−4 2.88 × 10−4 3.80 × 10−4 6.71 × 10−4 
Southern Province 19.05 25.16 44.44 38.10 50.31 88.89 3.81 × 10−3 5.03 × 10−3 8.89 × 10−3 7.62 × 10−3 1.01 × 10−2 1.78 × 10−2 
Uva Province 0.02 0.03 0.06 0.18 0.24 0.42 4.95 × 10−6 6.54 × 10−6 1.16 × 10−5 3.62 × 10−5 4.78 × 10−5 8.44 × 10−5 

Fluoride is one of the main contaminants in groundwater all over the world. This section discusses the distribution of F in groundwater and their health risk in India, Pakistan, Bangladesh, Nepal, and Sri Lanka.

India

India is one of the highly affected countries by F contamination in groundwater in South Asia. Out of 85 million tonnes of F deposits on the earth's crust, about 12 million tonnes are found in India (Unde et al. 2018). Arid and semi-arid regions of India are highly affected by F groundwater contamination (Yadav et al. 2019a, 2019b). Fluoride-contaminated groundwater is found in 20 states in India, with 230 districts having F contamination levels above the WHO limit of 1.5 mg/L (Dutta et al. 2019). About 30% of the Indian population (411 million) is highly exposed to the risk of drinking F-contaminated groundwater, and more than 66 million of the population in India are suffering from skeletal and dental fluorosis (Chakraborti et al. 2016a). The first case of endemic fluorosis in humans in India was reported in the Prakasam District of Andhra Pradesh in 1937 (Shortt et al. 1937). The F concentration in the groundwater of various South Asian countries is presented in Table 4 and Figure 3. In India, highly F-contaminated groundwater states are Andhra Pradesh, Telangana, Punjab, Haryana, Rajasthan, Gujarat, Madhya Pradesh, Odisha, and Tamil Nadu (Chakraborti et al. 2016a; Mukherjee & Singh 2018; Ali et al. 2019a). The average concentration of F in India is about 2.37 mg/L, and the rural parts of the country are more affected (1.85 times higher) than the urban regions (Ali et al. 2019a). Around 97% of districts in Rajasthan and 92% of districts in Gujarat are highly affected by F-enriched groundwater (Chakraborti et al. 2016a). The highest F concentration in groundwater of 85 mg/L is reported in Haryana (Poonia et al. 2021).
Table 4

Fluoride groundwater contamination in South Asian countries

ReferenceStudy areaNo. of samplesFluoride range in mg/L (mean in mg/L)No. of samples >1.5 mg/L
Sunitha et al. (2022)  Cuddapah, Andhra Pradesh 30 0.1–3.2 12 (40%) 
Adimalla et al. (2019)  Markapur, Andhra Pradesh 123 0.4–5.8 (1.98) 54 (44%) 
Hanse et al. (2019)  Karbi Anglong, Assam 80 0.15–17.53 (1.94) 16 (20%) 
Kumar et al. (2016)  Diphu, Assam 38 N.D. to 0.7 (0.1) 
Mridha et al. (2021)  Gaya and Nawada, Bihar 192 0.38–8.56 (2.64) 130 (68%) 
Bhunia & Shit (2021)  Surguja District, Chhattisgarh 55 0–3 (0.56) 3 (5.45%) 
Yadav et al. (2020)  Rajnandgaon, Chhattisgarh 160 0.6–18.5 (3.7) – 
Kashyap et al. (2020)  Bijapur District, Chhattisgarh 33 0.1–7.1 (1.8) 17 (52%) 
Sahu et al. (2017)  Dongargaon, Chhattisgarh 30 3.3–11.3 (6.7) 30 (100%) 
Mandal et al. (2021)  Mehsana, Gujarat 74 0.3–12 (1.61) 26 (35%) 
Senthilkumar et al. (2021)  Gujarat State 6,407 0.1–9.6 (1.02) 1,217 (19%) 
Shirke et al. (2020)  Ambadongar, Gujarat 60 0.43–4.25 (1.5) 25 (41%) 
Prajapati et al. (2020)  Surat, Gujarat 82 0.17–2.10 (0.91) 29 (35%) 
Patel et al. (2019)  Bhavnagar District, Gujarat 87 0.4–7.8 (0.91) 9 (10%) 
Prajapati et al. (2017)  Mandvi, Gujarat 57 0.17–4.17 (0.98) 22 (39%) 
Kumar et al. (2017)  Patan District, Gujarat 62 0.4–4.8 (0.98) 50 (80.6%) 
Kaur et al. (2020)  Panipat, Haryana 74 0.2–6.9 (1.4) 22 (30.3%) 
Yadav et al. (2019bMahendergarh, Haryana 355 0.3–16 150 (42%) 
Gupta & Misra (2018)  Jhajjar, Haryana 20 0.3–9.3 (2.1) 12 (60%) 
Ali et al. (2018)  Siwani, Haryana 10 0.3–18.5 (5.68) 6 (60%) 
Ugran et al. (2017)  Indi Taluk, Karnataka 62 0.26–3.53 (1.22) 27 (43%) 
Raj & Shaji (2017)  Alleppey, Kerala 16 0.68–2.88 (1.65) 10 (63%) 
Mukate et al. (2022)  Bhokardan, Maharashtra 190 0.4–2 L(1.4) 91 (48%) 
Kadam et al. (2020)  Western Ghats, Maharashtra 34 0.03–1.60 (0.72) 1 (3%) 
Sahoo et al. (2022)  Balangir, Odisha 37 0.1–4.29 (1.5) 14 (38%) 
Naik et al. (2022)  Cuttack, Odisha 104 0–2.97 (0.86) 36 (34.6%) 
Naik et al. (2021)  Angul, Odisha 106 0–3.4 (1.27) 31 (28%) 
Chaudhry & Sachdeva (2022)  Rupnagar, Punjab 14 0.13–4.91 (0.78) – 
Sharma et al. (2021)  Mansa, Punjab 59 0.51–2.7 (1.62) 31 (53%) 
Ahada & Suthar (2019)  Malwa, Punjab 76 0.60–5.07 (1.62) 72 (95%) 
Jandu et al. (2021)  Jhunjhunu, Rajasthan 28 0–5.74 (1.69) 15 (54%) 
Keesari et al. (2021)  Jaipur and Dausa, Rajasthan 33 0.04–8.2 (3.6) 24 (73%) 
Tiwari et al. (2020)  Dausa, Rajasthan 34 0.48–3.64 (1.66) 14 (41%) 
Khan et al. (2021)  East coast of Tamil Nadu and Puducherry 66 0–1.78 (0.77) 3 (4.5%) 
Balamurugan et al. (2020a)  Salem, Tamil Nadu 67 0.12–2.8 (1.21) 12 (18%) 
Balamurugan et al. (2020b)  Sarabanga River, Tamil Nadu 50 0.1–1.6 (0.8) 3 (6%) 
Aravinthasamy et al. (2020)  Shanmuganadhi basin, Tamil Nadu 61 0.01–3.30 16 (26%) 
Panneer et al. (2017)  Dharmapuri, Tamil Nadu 149 0–3.58 (0.99) 52 (35%) 
Shanmugasundaram et al. (2015)  Krishnagiri, Tamil Nadu 34 0.37–3.47 (1.79) 31 (90%) 
Duvva et al. (2022)  Medchal, Telangana 56 0.35–2.56 (1.24) 25 (45%) 
Adimalla & Qian (2022)  Peddavagu, Telangana 35 0.6–3.6 (2.07) 24 (68%) 
Adimalla et al. (2018)  Nirmal, Telangana 34 0.06–4.33 (1.13) 7 (21%) 
Narsimha & Sudarshan (2017a)  Siddipet, Telangana 104 0.2–2.2 (1.1) 23(22%) 
Narsimha & Sudarshan (2017b)  Basara, Telangana 34 0.06–4.33 (1.13) 7 (20%) 
Bhattacharya et al. (2020)  Dharmanagar, Tripura 71 0.005–4.8 (1.8) 21 (30%) 
Dutta et al. (2019)  Fatehpur, Uttar Pradesh 105 0.82–7.15 72 (69%) 
Tiwari et al. (2017)  Pratapgarh, Uttar Pradesh 55 0.41–4 (1.95) 39 (70%) 
Chowdhury et al. (2022)  Purulia, West Bengal 60 1.30–7 (4.06) 50 (83%) 
Pakistan 
Ling et al. (2022)  Entire Pakistan 5,543 Max of 33.3 (1.11) 911 (16%) 
Durrani & Farooqi (2021)  Quetta, Baluchistan 87 0–20 (2.8) 55 (63%) 
Chandio et al. (2015)  Baluchistan 150 < 1–14 96 (64%) 
Noor et al. (2022)  Batkhela, Khyber 60 0.30–2.4 (1.70) 17 (28.5%) 
Ather et al. (2022)  Khyber District 61 0.01–11.2 (2.90) 9 (14.6%) 
Rashid et al. (2020)  Dargai, Khyber 75 0.5–8.7 (2.0) 38 (51%) 
Masood et al. (2022)  Salt Range, Punjab 131 0.1–2.7 (1.0) 10 (8%) 
Rehman et al. (2022)  Isa Khel, Punjab 236 0.02–5.35 (1.8) 138 (59%) 
Parvaiz et al. (2021)  Rachna Doab, Punjab 54 N.D. to 3.9 (1.2) 15 (54%) 
Hameed et al. (2021)  Vehari, Punjab 48 0.29–0.86 (0.46) 
Khan & Khan (2020)  Punjab, Pakistan 46,457 0–5.51 (0.74) 49,385 (10.7%) 
Younas et al. (2019)  Lahore and Kasur, Punjab 66 0.54–17.5 (4.7) 46 (70%) 
Raza et al. (2016)  Gujarat, Punjab 70 0.3–6.4 (2.2) 45 (64%) 
Rasool et al. (2015)  Mailsi, Punjab 52 5.5–29.6 (11.52) 52 (100%) 
Kumar et al. (2022aTharpakar, Sindh 25 0.1–5.1 (3.1) 13 (52%) 
Lanjwani et al. (2020a)  Larkana, Sindh 25 0.02–11.1 (3.65) 12 (48%) 
Lanjwani et al. (2020b)  Qamber, Sindh 21 0.39–21.8 (6.90) 17 (81%) 
Ali et al. (2019c)  Sindh and Punjab 146 0.1–10.3 40 (27.4%) 
Bangladesh 
Jannat et al. (2022)  Indo-Bangladesh coastal region 123 0–16.11 (1.32) in East & 0.1–1.95 (0.76) in West 29 (24%) 
Rakib et al. (2022)  Coastal region of Bangladesh 25 0.03–1.3 (0.25) 
Rahman et al. (2020)  Coastal districts of Bangladesh 840 0.01–16.11 138 (16%) 
Islam et al. (2018)  Panchbibi, Bangladesh 20 0.02–0.1 (0.052) 
Islam et al. (2018)  Panchbibi, Rajshahi 20 0.02–0.1 (0.052) 
Nepal 
Bhandari et al. (2021)  Bagmati River Basin 50 0.15–9.2 (2.4) 30 (60%) 
Thakur et al. (2015)  Bhaktapur, Bagmati 85 0.03–1.89 (0.47) 21 (25%) 
Mahato et al. (2018)  East Terai region 175 0.02–1.10 (0.23) 
Pant (2011)  Kathmandu Valley 87 0.06–1.92 (0.48) – 
Das et al. (2021)  Biratnagar, Province 1 110 
Sri Lanka     
Senarathne et al. (2019)  Malala Oya basin 30 0.1–3.42 (1.25) 11 (37%) 
Ranasinghe et al. (2019a)  Sri Lanka 6,107 0.02–12 1,191 (20%) 
Wickramarathna et al. (2017)  Girandurukotte, Uva Province 52 0.02–2.50 (0.76) – 
Nikawewa, North Western 0.43–3.44 (1.61) – 
Wilgamuwa 12 0.15–5.47 (1.04) – 
Rajasooriyar et al. (2013)  Uda Walawe, Southern Province 105 0.1–9.2 – 
Young et al. (2011)  Giribawa and Kakirawa 294 0.02–4.34 (0.9) – 
Chandrajith et al. (2011)  Girandurukotte 46 0.02–2.14 (0.64) – 
Huruluwewa 29 0.02–1.68 (0.72) – 
Medawachchiya 10 0.52–4.90 (1.42) – 
Nikawewa 52 0.02–5.30 (1.21) – 
Padaviya 34 0.02–1.33 (0.62) – 
Wellawaya 0.45–2.20 (1.05) – 
ReferenceStudy areaNo. of samplesFluoride range in mg/L (mean in mg/L)No. of samples >1.5 mg/L
Sunitha et al. (2022)  Cuddapah, Andhra Pradesh 30 0.1–3.2 12 (40%) 
Adimalla et al. (2019)  Markapur, Andhra Pradesh 123 0.4–5.8 (1.98) 54 (44%) 
Hanse et al. (2019)  Karbi Anglong, Assam 80 0.15–17.53 (1.94) 16 (20%) 
Kumar et al. (2016)  Diphu, Assam 38 N.D. to 0.7 (0.1) 
Mridha et al. (2021)  Gaya and Nawada, Bihar 192 0.38–8.56 (2.64) 130 (68%) 
Bhunia & Shit (2021)  Surguja District, Chhattisgarh 55 0–3 (0.56) 3 (5.45%) 
Yadav et al. (2020)  Rajnandgaon, Chhattisgarh 160 0.6–18.5 (3.7) – 
Kashyap et al. (2020)  Bijapur District, Chhattisgarh 33 0.1–7.1 (1.8) 17 (52%) 
Sahu et al. (2017)  Dongargaon, Chhattisgarh 30 3.3–11.3 (6.7) 30 (100%) 
Mandal et al. (2021)  Mehsana, Gujarat 74 0.3–12 (1.61) 26 (35%) 
Senthilkumar et al. (2021)  Gujarat State 6,407 0.1–9.6 (1.02) 1,217 (19%) 
Shirke et al. (2020)  Ambadongar, Gujarat 60 0.43–4.25 (1.5) 25 (41%) 
Prajapati et al. (2020)  Surat, Gujarat 82 0.17–2.10 (0.91) 29 (35%) 
Patel et al. (2019)  Bhavnagar District, Gujarat 87 0.4–7.8 (0.91) 9 (10%) 
Prajapati et al. (2017)  Mandvi, Gujarat 57 0.17–4.17 (0.98) 22 (39%) 
Kumar et al. (2017)  Patan District, Gujarat 62 0.4–4.8 (0.98) 50 (80.6%) 
Kaur et al. (2020)  Panipat, Haryana 74 0.2–6.9 (1.4) 22 (30.3%) 
Yadav et al. (2019bMahendergarh, Haryana 355 0.3–16 150 (42%) 
Gupta & Misra (2018)  Jhajjar, Haryana 20 0.3–9.3 (2.1) 12 (60%) 
Ali et al. (2018)  Siwani, Haryana 10 0.3–18.5 (5.68) 6 (60%) 
Ugran et al. (2017)  Indi Taluk, Karnataka 62 0.26–3.53 (1.22) 27 (43%) 
Raj & Shaji (2017)  Alleppey, Kerala 16 0.68–2.88 (1.65) 10 (63%) 
Mukate et al. (2022)  Bhokardan, Maharashtra 190 0.4–2 L(1.4) 91 (48%) 
Kadam et al. (2020)  Western Ghats, Maharashtra 34 0.03–1.60 (0.72) 1 (3%) 
Sahoo et al. (2022)  Balangir, Odisha 37 0.1–4.29 (1.5) 14 (38%) 
Naik et al. (2022)  Cuttack, Odisha 104 0–2.97 (0.86) 36 (34.6%) 
Naik et al. (2021)  Angul, Odisha 106 0–3.4 (1.27) 31 (28%) 
Chaudhry & Sachdeva (2022)  Rupnagar, Punjab 14 0.13–4.91 (0.78) – 
Sharma et al. (2021)  Mansa, Punjab 59 0.51–2.7 (1.62) 31 (53%) 
Ahada & Suthar (2019)  Malwa, Punjab 76 0.60–5.07 (1.62) 72 (95%) 
Jandu et al. (2021)  Jhunjhunu, Rajasthan 28 0–5.74 (1.69) 15 (54%) 
Keesari et al. (2021)  Jaipur and Dausa, Rajasthan 33 0.04–8.2 (3.6) 24 (73%) 
Tiwari et al. (2020)  Dausa, Rajasthan 34 0.48–3.64 (1.66) 14 (41%) 
Khan et al. (2021)  East coast of Tamil Nadu and Puducherry 66 0–1.78 (0.77) 3 (4.5%) 
Balamurugan et al. (2020a)  Salem, Tamil Nadu 67 0.12–2.8 (1.21) 12 (18%) 
Balamurugan et al. (2020b)  Sarabanga River, Tamil Nadu 50 0.1–1.6 (0.8) 3 (6%) 
Aravinthasamy et al. (2020)  Shanmuganadhi basin, Tamil Nadu 61 0.01–3.30 16 (26%) 
Panneer et al. (2017)  Dharmapuri, Tamil Nadu 149 0–3.58 (0.99) 52 (35%) 
Shanmugasundaram et al. (2015)  Krishnagiri, Tamil Nadu 34 0.37–3.47 (1.79) 31 (90%) 
Duvva et al. (2022)  Medchal, Telangana 56 0.35–2.56 (1.24) 25 (45%) 
Adimalla & Qian (2022)  Peddavagu, Telangana 35 0.6–3.6 (2.07) 24 (68%) 
Adimalla et al. (2018)  Nirmal, Telangana 34 0.06–4.33 (1.13) 7 (21%) 
Narsimha & Sudarshan (2017a)  Siddipet, Telangana 104 0.2–2.2 (1.1) 23(22%) 
Narsimha & Sudarshan (2017b)  Basara, Telangana 34 0.06–4.33 (1.13) 7 (20%) 
Bhattacharya et al. (2020)  Dharmanagar, Tripura 71 0.005–4.8 (1.8) 21 (30%) 
Dutta et al. (2019)  Fatehpur, Uttar Pradesh 105 0.82–7.15 72 (69%) 
Tiwari et al. (2017)  Pratapgarh, Uttar Pradesh 55 0.41–4 (1.95) 39 (70%) 
Chowdhury et al. (2022)  Purulia, West Bengal 60 1.30–7 (4.06) 50 (83%) 
Pakistan 
Ling et al. (2022)  Entire Pakistan 5,543 Max of 33.3 (1.11) 911 (16%) 
Durrani & Farooqi (2021)  Quetta, Baluchistan 87 0–20 (2.8) 55 (63%) 
Chandio et al. (2015)  Baluchistan 150 < 1–14 96 (64%) 
Noor et al. (2022)  Batkhela, Khyber 60 0.30–2.4 (1.70) 17 (28.5%) 
Ather et al. (2022)  Khyber District 61 0.01–11.2 (2.90) 9 (14.6%) 
Rashid et al. (2020)  Dargai, Khyber 75 0.5–8.7 (2.0) 38 (51%) 
Masood et al. (2022)  Salt Range, Punjab 131 0.1–2.7 (1.0) 10 (8%) 
Rehman et al. (2022)  Isa Khel, Punjab 236 0.02–5.35 (1.8) 138 (59%) 
Parvaiz et al. (2021)  Rachna Doab, Punjab 54 N.D. to 3.9 (1.2) 15 (54%) 
Hameed et al. (2021)  Vehari, Punjab 48 0.29–0.86 (0.46) 
Khan & Khan (2020)  Punjab, Pakistan 46,457 0–5.51 (0.74) 49,385 (10.7%) 
Younas et al. (2019)  Lahore and Kasur, Punjab 66 0.54–17.5 (4.7) 46 (70%) 
Raza et al. (2016)  Gujarat, Punjab 70 0.3–6.4 (2.2) 45 (64%) 
Rasool et al. (2015)  Mailsi, Punjab 52 5.5–29.6 (11.52) 52 (100%) 
Kumar et al. (2022aTharpakar, Sindh 25 0.1–5.1 (3.1) 13 (52%) 
Lanjwani et al. (2020a)  Larkana, Sindh 25 0.02–11.1 (3.65) 12 (48%) 
Lanjwani et al. (2020b)  Qamber, Sindh 21 0.39–21.8 (6.90) 17 (81%) 
Ali et al. (2019c)  Sindh and Punjab 146 0.1–10.3 40 (27.4%) 
Bangladesh 
Jannat et al. (2022)  Indo-Bangladesh coastal region 123 0–16.11 (1.32) in East & 0.1–1.95 (0.76) in West 29 (24%) 
Rakib et al. (2022)  Coastal region of Bangladesh 25 0.03–1.3 (0.25) 
Rahman et al. (2020)  Coastal districts of Bangladesh 840 0.01–16.11 138 (16%) 
Islam et al. (2018)  Panchbibi, Bangladesh 20 0.02–0.1 (0.052) 
Islam et al. (2018)  Panchbibi, Rajshahi 20 0.02–0.1 (0.052) 
Nepal 
Bhandari et al. (2021)  Bagmati River Basin 50 0.15–9.2 (2.4) 30 (60%) 
Thakur et al. (2015)  Bhaktapur, Bagmati 85 0.03–1.89 (0.47) 21 (25%) 
Mahato et al. (2018)  East Terai region 175 0.02–1.10 (0.23) 
Pant (2011)  Kathmandu Valley 87 0.06–1.92 (0.48) – 
Das et al. (2021)  Biratnagar, Province 1 110 
Sri Lanka     
Senarathne et al. (2019)  Malala Oya basin 30 0.1–3.42 (1.25) 11 (37%) 
Ranasinghe et al. (2019a)  Sri Lanka 6,107 0.02–12 1,191 (20%) 
Wickramarathna et al. (2017)  Girandurukotte, Uva Province 52 0.02–2.50 (0.76) – 
Nikawewa, North Western 0.43–3.44 (1.61) – 
Wilgamuwa 12 0.15–5.47 (1.04) – 
Rajasooriyar et al. (2013)  Uda Walawe, Southern Province 105 0.1–9.2 – 
Young et al. (2011)  Giribawa and Kakirawa 294 0.02–4.34 (0.9) – 
Chandrajith et al. (2011)  Girandurukotte 46 0.02–2.14 (0.64) – 
Huruluwewa 29 0.02–1.68 (0.72) – 
Medawachchiya 10 0.52–4.90 (1.42) – 
Nikawewa 52 0.02–5.30 (1.21) – 
Padaviya 34 0.02–1.33 (0.62) – 
Wellawaya 0.45–2.20 (1.05) – 
Figure 3

Fluoride distribution (mg/L) in groundwater of various South Asian countries.

Figure 3

Fluoride distribution (mg/L) in groundwater of various South Asian countries.

Close modal

Pakistan

The drier climate of Pakistan seems to be favorable for high F concentration in the groundwater due to the physio-chemical process (Kumar et al. 2020). In several places in Pakistan, F concentrations in groundwater exceeded the WHO permissible limit of 1.5 mg/L (Ling et al. 2022). Out of a total of 29 major cities, 10 cities were reported to have F concentrations in groundwater above 1.5 mg/L (Rasool et al. 2018). The highly affected districts are mostly in Punjab and Sindh Provinces of Pakistan, which consist of more than 75% of the population (Ali et al. 2018; Ling et al. 2022). Ling et al. (2022) reported that the F concentration in groundwater samples from Sindh varied from 0.1 to 33.3 mg/L, with the highest concentration of 33.3 mg/L in Mithi City in Tharpakar District. In the Punjab Province, the F concentration varied from 0.1 to 27.51 mg/L, with the highest concentration of 27.51 mg/L in Lahore (Ling et al. 2022). In the Sindh Province, elevated F contamination was mostly confined to the Thar Desert area, and in the Punjab Province, elevated F contamination was mostly confined to Sargodha, Lahore, and Kasur (Ling et al. 2022). Khan & Khan (2020) analyzed groundwater samples from various cities in the Punjab Province and reported that the highest mean value of F concentration (5.51 mg/L) was found in Narowal City in the Punjab Province of Pakistan. Approximately 13 million people (6% of the population) in Pakistan are at risk of fluorosis (Ling et al. 2022). Fluoride-contaminated groundwater in various districts of Pakistan is presented in Table 4.

Bangladesh

Bangladesh and India are among the two most affected countries (Rahman et al. 2020). Very few studies have been conducted in Bangladesh to assess the F concentration in groundwater (Islam et al. 2018; Rahman et al. 2020; Jannat et al. 2022; Rakib et al. 2022). Rakib et al. (2022) analyzed the groundwater samples from across the country, including the different districts of the coastal region and found that the F concentration was within the WHO limits (Table 4). The groundwater water of the Panchbibi Subdistrict in the Rajshahi division had an F concentration of <1.5 mg/L (Islam et al. 2018). The groundwater samples of the districts on the east coast of Bangladesh reported high F concentrations of 16.11 mg/L (dry season) and 15 mg/L (wet season) in Chittagong and Cox's Bazar, respectively (Rahman et al. 2020; Jannat et al. 2022). One of the highly affected districts by high F concentrations in groundwater were Chittagong, Cox's Bazar, and Patuakhali (Rahman et al. 2020). Fluoride-contaminated groundwater in various districts of Bangladesh is presented in Table 4.

Nepal

The existing study reported that Nepal is the least affected country among the other considered South Asian countries, but some regions have high F concentrations above 1.5 mg/L. Nepal experiences high rainfall, which is unfavorable for high F concentrations in groundwater (Sahu 2019). Bhandari et al. (2021) analyzed groundwater samples in the Bagmati River corridor and found that the F concentration ranged from 0.15 to 9.2 mg/L, with an average value of 2.4 mg/L. In Biratnagar, East Terai region, and Kathmandu valley of Nepal, the groundwater F concentration was found within the WHO limit of 1.5 mg/L (Table 4). Fluoride-contaminated groundwater in various districts of Nepal is presented in Table 4.

Sri Lanka

In Sri Lanka, the majority of the population, ∼16.1 million (82.4%), is at risk of fluoride below 0.5 mg/L, while ∼0.5 million people (2.3%) are exposed to risk of fluoride >1.5 mg/L (Ranasinghe et al. 2019b). The eastern and south-eastern parts of Sri Lanka were mostly found to be affected by high F concentration (Table 4). The eastern and south-eastern parts fall in the dry zone of Sri Lanka. The dry zone constitutes about two-thirds of the land area, receiving rainfall less than 1,000 mm per annum (Chandrajith et al. 2020). The different types of metamorphic rocks in Sri Lanka have high F contents varying from 95 to 1,440 mg/kg, and high F concentrations in groundwater have been found in dry zone metamorphic aquifers (Chandrajith et al. 2020). Sedimentary aquifers in the dry zone and metamorphic aquifers in the wet zone mostly reported F concentrations less than the WHO limit (Chandrajith et al. 2020).

In general, the intake of groundwater having a F concentration of less than 0.5 mg/L causes dental caries, whereas an F concentration above 1.5 mg/L causes dental and skeletal fluorosis and in severe cases promotes cancer (Ahada & Suthar 2019). The intake of excess F also has adverse effects on the reproductive system and the cardiovascular system, promotes kidney stones, and causes gastrointestinal effects and neurobehavioral effects (Yadav et al. 2019a, 2019b). Exposure to F-contaminated drinking water can have adverse effects on children's intelligence quotient, and children are found to be more susceptible to dental fluorosis (Pramanik & Saha 2017). Skeletal fluorosis has been found in children and adults in all of the affected districts in India (Chakraborti et al. 2016a). Severe adverse effects such as skeletal fluorosis, spinal cord compression, crippling defects, neurological defects, paralysis, and muscle wasting are caused due to F exposure levels of above 5 mg/L (Mukherjee & Singh 2020). In addition to teeth and bone problems, adults from the F-endemic regions also experience impaired fertility, which includes lower sperm count and quality, miscarriage, and birth abnormalities (Sun et al. 2017; Mukherjee & Singh 2020). Recent studies discovered that an individual's genetic background plays a crucial role in influencing fluorosis risk even when other exposure factors stay constant. Genetic variants such as ESR (estrogen receptor), COL1A2 (collagen type 1 alpha 2), MMP-2 (matrix metallopeptidase 2), MPO (myeloperoxidase), CTR (calcitonin receptor gene), and VDR (vitamin D receptor) could escalate or reduce the risk of fluorosis in the exposed individuals (Pramanik & Saha 2017). Huang et al. (2008) reported that children with the homozygous PP genotype (absence of cut site) of COL1A2 PvuII polymorphism exhibited a significantly higher risk of dental fluorosis than children with the homozygous pp genotype (presence of cut site) in an endemic region. Another study found that the individual having the gene T allele of the CTR AluI polymorphism is at higher risk of fluorosis (Jiang et al. 2015). Ba et al. (2011) found that the ESR RsaI gene was associated with the high risk of dental fluorosis, while no association was found for PvuII and XbaI polymorphisms. Children having the ‘R’ allele (absence of restriction site) of ESR RsaI had a significantly increased risk of dental fluorosis than children who retained the ‘r’ allele (presence of restriction site). Also, children bearing the ‘X’ allele (absence of restriction site) of ESR XbaI had a significantly lower risk of dental fluorosis than children harboring the ‘x’ allele (presence of restriction site). In the case of skeletal fluorosis, positive associations have been found between polymorphisms in GSTP1 (glutathione S-transferase pi 1), MMP-2, PRL (prolactin), VDR, and MPO genes and higher risks of skeletal fluorosis among affected individuals from endemic regions (Pramanik & Saha 2017). Fluoride has been found to have adverse effects on animal health, resulting in dental and skeletal fluorosis (Choubisa & Choubisa 2016).

The mean and maximum HQ values calculated for F-affected states/provinces for males, females, and children are given in Table 5. The detailed health risks from drinking F-contaminated groundwater for males, females, and children are presented in Tables S4, S5, and S6, respectively, in the supplementary files. Out of 46 locations in India, 35 locations had mean HQ and five locations had max HQ values within the safe limits for male adults. In the case of females, 23 locations had mean HQ and only one location had max HQ values within the safe limits. The mean HQ and max HQ values for children were within the safe limits at seven and one locations, respectively. Out of 16 locations in Pakistan, seven locations had mean HQ and one location had max HQ values within the safe limits for male adults. In the case of females, four locations had mean HQ and only one location had max HQ values within the safe limits. The mean HQ and max HQ values for children were within the safe limits at two and one locations, respectively. Out of five locations in Bangladesh, four locations had mean HQ and three locations had max HQ values within the safe limits for male adults. In the case of females, four locations had mean HQ and only two locations had max HQ values within the safe limits. The mean HQ and max HQ for children were within the safe limits at three and one locations, respectively. Out of five locations in Nepal, four locations had mean HQ and max HQ values within the safe limits for male adults. In the case of females, four locations had mean HQ and two locations had max HQ values within the safe limits. The mean HQ and max HQ values for children were within the safe limits at four and one locations, respectively. Out of 12 locations in Sri Lanka, 11 locations had mean HQ and two locations had max HQ values within the safe limits for male adults. In the case of females, 10 locations had mean HQ and only one location had max HQ values within the safe limits. The mean HQ values were within the safe limits at four locations, and the max HQ values were above the safe limit at all the locations.

Table 5

Calculated mean HQ and max HQ values for fluoride contamination in males, females, and children

States/ProvincesMean HQ
Maximum HQ
MaleFemaleChildren (up to 12 years)MaleFemaleChildren (up to 12 years)
India 
Andhra Pradesh 0.94 1.25 2.20 2.76 3.65 6.44 
Assam 0.92 1.22 2.16 8.35 11.03 19.48 
Bihar 1.26 1.66 2.93 4.08 5.38 9.51 
Chhattisgarh 3.19 4.21 7.44 5.38 7.11 12.56 
Gujarat 0.77 1.01 1.79 5.71 7.55 13.33 
Haryana 2.7 3.57 6.31 8.81 11.64 20.56 
Karnataka 0.58 0.77 1.36 1.68 2.22 3.92 
Kerala 0.79 1.04 1.83 1.37 1.81 3.20 
Maharashtra 0.67 0.88 1.56 0.95 1.26 2.22 
Odisha 0.71 0.94 1.67 2.04 2.70 4.77 
Punjab 0.77 1.02 1.80 2.41 3.19 5.63 
Rajasthan 1.71 2.26 4.00 3.90 5.16 9.11 
Tamil Nadu 0.85 1.13 1.99 1.70 2.25 3.98 
Telangana 0.99 1.30 2.30 2.06 2.72 4.81 
Tripura 0.86 1.13 2.00 2.29 3.02 5.33 
Uttar Pradesh 0.93 1.23 2.17 3.40 4.50 7.94 
West Bengal 1.93 2.55 4.51 3.33 4.40 7.78 
Pakistan 
Baluchistan 1.33 1.76 3.11 9.52 12.58 22.22 
Khyber Pakhtunkhwa 1.38 1.82 3.22 5.33 7.04 12.44 
Punjab 5.49 7.25 12.80 14.10 18.62 32.89 
Sindh 3.29 4.34 7.67 10.38 13.71 24.22 
Bangladesh 
East coastal region 0.63 0.83 1.47 7.67 10.83 17.90 
West coastal region 0.36 0.48 0.84 0.93 1.23 2.17 
Coastal region 0.12 0.16 0.28 0.62 0.82 1.44 
Coastal districts – – – 7.67 10.13 17.90 
Rajshahi 0.02 0.03 0.06 0.05 0.06 0.11 
Nepal 
Bagmati 1.14 1.51 2.67 4.38 5.79 10.22 
East Terai Region 0.11 0.15 0.26 0.52 0.69 1.22 
Kathmandu Valley 0.23 0.30 0.53 0.91 1.21 2.13 
Province No. 1 0.00 0.00 0.00 0.00 0.00 0.00 
Sri Lanka       
Central Province 0.50 0.65 1.16 2.60 3.44 6.08 
Northern Central Province 0.68 0.89 1.58 2.33 3.08 5.44 
North Western Province 0.58 0.76 1.34 2.52 3.33 5.89 
Southern Province – – – 4.38 5.79 10.22 
Uva Province 0.50 0.66 1.17 1.19 1.57 2.78 
States/ProvincesMean HQ
Maximum HQ
MaleFemaleChildren (up to 12 years)MaleFemaleChildren (up to 12 years)
India 
Andhra Pradesh 0.94 1.25 2.20 2.76 3.65 6.44 
Assam 0.92 1.22 2.16 8.35 11.03 19.48 
Bihar 1.26 1.66 2.93 4.08 5.38 9.51 
Chhattisgarh 3.19 4.21 7.44 5.38 7.11 12.56 
Gujarat 0.77 1.01 1.79 5.71 7.55 13.33 
Haryana 2.7 3.57 6.31 8.81 11.64 20.56 
Karnataka 0.58 0.77 1.36 1.68 2.22 3.92 
Kerala 0.79 1.04 1.83 1.37 1.81 3.20 
Maharashtra 0.67 0.88 1.56 0.95 1.26 2.22 
Odisha 0.71 0.94 1.67 2.04 2.70 4.77 
Punjab 0.77 1.02 1.80 2.41 3.19 5.63 
Rajasthan 1.71 2.26 4.00 3.90 5.16 9.11 
Tamil Nadu 0.85 1.13 1.99 1.70 2.25 3.98 
Telangana 0.99 1.30 2.30 2.06 2.72 4.81 
Tripura 0.86 1.13 2.00 2.29 3.02 5.33 
Uttar Pradesh 0.93 1.23 2.17 3.40 4.50 7.94 
West Bengal 1.93 2.55 4.51 3.33 4.40 7.78 
Pakistan 
Baluchistan 1.33 1.76 3.11 9.52 12.58 22.22 
Khyber Pakhtunkhwa 1.38 1.82 3.22 5.33 7.04 12.44 
Punjab 5.49 7.25 12.80 14.10 18.62 32.89 
Sindh 3.29 4.34 7.67 10.38 13.71 24.22 
Bangladesh 
East coastal region 0.63 0.83 1.47 7.67 10.83 17.90 
West coastal region 0.36 0.48 0.84 0.93 1.23 2.17 
Coastal region 0.12 0.16 0.28 0.62 0.82 1.44 
Coastal districts – – – 7.67 10.13 17.90 
Rajshahi 0.02 0.03 0.06 0.05 0.06 0.11 
Nepal 
Bagmati 1.14 1.51 2.67 4.38 5.79 10.22 
East Terai Region 0.11 0.15 0.26 0.52 0.69 1.22 
Kathmandu Valley 0.23 0.30 0.53 0.91 1.21 2.13 
Province No. 1 0.00 0.00 0.00 0.00 0.00 0.00 
Sri Lanka       
Central Province 0.50 0.65 1.16 2.60 3.44 6.08 
Northern Central Province 0.68 0.89 1.58 2.33 3.08 5.44 
North Western Province 0.58 0.76 1.34 2.52 3.33 5.89 
Southern Province – – – 4.38 5.79 10.22 
Uva Province 0.50 0.66 1.17 1.19 1.57 2.78 

The observation of results of the existing studies revealed that the arsenic concentration does not always decrease with an increase in depth. The deep aquifers (110–120 m) in the north-eastern states of Assam and Manipur, India and in Punjab, Pakistan had high As concentrations. However, the Gangetic plain regions such as Bihar, West Bengal in India and Bangladesh showed a decrease in As concentration with an increase in tube-well/aquifer depth. High As concentrations in deep aquifers in some states may be due to cross contamination of As from top shallow aquifers. The possibility of cross contamination will increase with the occurrence of over-exploitation of groundwater, making the deep aquifers more vulnerable to contamination. A few study results reported that high As was found in 5–25-year-old tube wells, and older tube wells have a higher possibility of As contamination. A few studies assessed the temporal variations in As groundwater contamination (Farooq et al. 2011; Chakraborti et al. 2009). These studies reported the temporal variations in As concentration (not significant) and that the concentration decrease in the wet season may be due to dilution caused by runoff. The reviewed studies showed that the As concentration was more likely reduced in the wet season in oxidative aquifers located in mountainous areas and in fractured bedrocks located in coastal, semi-arid regions. In reductive aquifers located in humid plain and delta regions, the As concentrations showed an inverse trend. The majority of the studies that considered a large number of samples (>100) reported a high range of As concentration, showing the importance of accounting for large samples for assessing As contamination. In the case of F contamination, it was observed that the majority of the studies reported the presence of high F concentrations in both shallow and deeper wells/aquifers. The presence of high F concentrations in deeper wells is attributed to the fact that F has been reported to be more dependent on rock–water interaction rather than depth.

This section discusses the trend and pattern of the existing studies in brief. Several studies are conducted on As and F contamination in India, Pakistan, and Bangladesh, but very few are conducted in Nepal and Sri Lanka. Many of these studies analyzed the correlation of As and F with other contaminants or elements, such as manganese, pH, and iron, in the groundwater using the Pearson correlation/correlation matrix (Alam et al. 2016; Tirkey et al. 2017; Goswami et al. 2020; Singh et al. 2022). A few studies used pollution indices such as heavy metal pollution index and heavy metal evaluation index to identify the extent of pollution, which provided ease in understanding as these indices are classified as low, medium, and high based on the values obtained (Chakraborty et al. 2022; Kumar et al. 2022a, 2022b; Masood et al. 2022). Thakur & Gupta (2019) in a unique study evaluated the economic loss to the household due to As contamination. Kumar et al. (2021a, 2021b) in their study assessed the As exposure in Sabalpur village, Bihar, India and also implemented mitigation measures by installing membrane-based As filters. A few studies used the Monte Carlo simulation to assess the CR associated with As and to measure the uncertainty and also performed sensitivity analysis to identify the input factor that contributes most to CR among others (Islam et al. 2019; Chakraborty et al. 2022). Very few studies presented As concentration in different age groups and identified the effect of As on antioxidant enzymes in the human body. A few studies identified the influence of salinity on F and As contamination, which is an important aspect as groundwater salinity is reported to increase across the world (Younas et al. 2019; Parvaiz et al. 2021). The studies involving clinical and experimental investigation are lacking as the majority of the studies focus on calculating the health risk.

Across the globe, the co-occurrence of As and F contaminants has been observed. Most studies revealed that Fe (hydro) oxides play a significant role in the co-occurrence as they are common hosts for both elements (Kumar et al. 2020), although few co-occurrences of both contaminants have been reported through research in India. The findings indicated that those hotspots throughout the Ganga–Brahmaputra River plain with alluvial sediment have the potential for co-occurrence (Kumar et al. 2020). In Pakistan, many studies showed the co-occurrence and positive correlation between As and F in cities such as Kalalanwala, Tharpakar, and Mailsi (Farooqi et al. 2007; Brahman et al. 2013; Rasool et al. 2015). Coal combustion and brick factories were found to be responsible for both As and F in Mailsi and Kalalanwala Districts, Pakistan (Farooqi et al. 2007; Rasool et al. 2015). Fluoride and As were shown to be stabilized and retained in groundwater by high alkalinity and salinity. The co-occurrence had been observed in the Tharpakar and Rahim Yar Khan Districts in Pakistan due to desorption as most of the correlation is found between arsenate (AsV) and F (Kumar et al. 2020). In Bangladesh, the correlation of both elements was not studied, although co-occurrence was observed mainly in districts with hot and humid climates. The co-occurrence and strong correlation were observed in other countries all over the world. In South Korea, Argentina, Mexico, and the United States, the studies reported co-occurrence and a strong positive correlation between As and F. The co-occurrence and strong positive correlation were observed due to desorption, volcanic rocks and sediments, hydrothermal systems, geothermal activities, alluvial regions, and volcanic glass (Kumar et al. 2020).

Majority of the existing studies on As and F presented the spatial distribution of both the contaminants in the groundwater, which helped identify hotspots in the states or provinces. However, very few studies characterized the As species present in the groundwater (Brahman et al. 2013). The characterization of As species should be performed in future studies as it will help develop the most suitable risk mitigation measures. The assessment of risk to human health from both contaminants revealed the severity of the ill effects and the importance of managing As and F contamination in groundwater. There is a need to evaluate the As risk to human health from different species of arsenic as toxicity varies from species to species. The observation from the results of the existing studies shows that different countries in South Asia have different contamination severity, but it is needed today to mitigate the risk at regions where the HQ and CR are above the safe limits as millions of people are affected. The HQ and CR are found to be very high, especially for As, creating an alarming situation. The existing studies provide extensive information about the risk to human health regarding the HQ and CR. However, there is a need to conduct more research and explore the risk to human health from both these contaminants, such as the role of malnutrition or a healthy diet in influencing the ill effects of drinking As- and F-contaminated water. The future studies on As and F groundwater contamination may look not just to measuring the As and F concentrations and calculating the HQ and CR but should aim to identify the key factors that can help in mitigating the groundwater contamination and reducing the risk to human health.

The correlation of As and F with other contaminants/elements was analyzed in many studies. Some studies presented the results in terms of pollution indices for easy understanding of the pollution level. The probabilistic approach used in a few studies helped to analyze the health risk more extensively. A few studies identified the economic loss due to As contamination in a household. The identification of the influence of As on antioxidant enzymes in the human body is a step ahead. This section presents the research gaps in the existing literature. Most of the reviewed studies performed in India and Pakistan analyzed the groundwater samples for As and F contamination and also assessed the risk to human health, but the majority of studies performed in Nepal, Bangladesh, and Sri Lanka did not evaluate the risk to human health. In the future, an assessment of human health risks needs to be performed in the studies reporting As and F contamination above the specified limits. Despite several studies estimating the health risk due to As and F groundwater contamination, clinical and experimental investigations discussing the As- and F-induced health concerns to those drinking As- and F-contaminated water are nearly non-existent. The existing studies on genetic influence on fluorosis had been performed on different ethnic groups of China, namely, Tibetans, Kazakhs, Mongolians, Hans, and Russians. Future studies must examine the genetic influence on fluorosis based on ethnic groups in South Asian countries. A few reviewed studies analyzed the co-occurrence but did not analyze the correlation. Several variables influence the simultaneous release of As and F in groundwater, most of which are unknown. There is a lack of adequate information on the hydro-geochemistry of pollutants, particularly in areas with high concentrations of both. Future research should study the possible interactions between the co-occurring contaminants and the other pollutants. Future studies should employ probabilistic models as much as possible to assess the health risk as the probabilistic approach was found to reveal the health risk more extensively compared to the deterministic models. The present study is not a completely systematic review but rather a comprehensive review. Future review studies could perform a complete systematic review study on health effects of As and F groundwater contamination.

The review study presented a detailed and comprehensive review of As and F contamination in groundwater of the South Asia Region and its effect on human health. The findings of this study revealed that India, Pakistan, Bangladesh, and Nepal are severely affected by As groundwater contamination, while Sri Lanka is mildly affected. In the case of F contamination, India, Pakistan, and Sri Lanka are severely affected, followed by Bangladesh and Nepal, which is mildly affected. As groundwater contamination is not limited to rural areas but also extends to urban cities such as Kolkata and Patna in India and Lahore in Pakistan. The areas of As exposure that are most prevalent in West Bengal, Bihar, and several parts of Pakistan are the low socio-economic class villages where malnutrition is widespread. As a result, there is reason to suspect that, due to inadequate nutrition, these populations may be more vulnerable to the negative health effects of As than other As-endemic regions around the world. Bangladesh was one of the most severely affected countries by As contamination in groundwater, which was spread across the country, and F contamination was less severe than As. In Nepal, As contamination was confined to the Terai region, and F contamination was found to be less severe. Sri Lanka was moderately affected by As contamination but severely affected by F contamination, mostly in dry regions. It has been observed that irrigation using As-contaminated water is contaminating soil with As in the South Asian countries.

The HQ and CR were found to be significantly higher than the normal limits, indicating a very high risk due to exposure to both contaminants. Arsenic and F contamination in groundwater is a severe issue all around the world, including South Asia, which needs to be addressed as millions of people are suffering from arsenical skin lesions, fluorosis, and other diseases due to drinking As- and F-contaminated groundwater. The government must provide alternate sources of water such as piped water supply to the household to decrease the dependency on groundwater. There is also a need to aware citizens, especially in the As-affected rural areas, about As contamination and its ill effects. The implementation of rainwater harvesting could also provide an alternative source of water in the As-affected areas. Many research studies are being performed to develop low-cost arsenic filters in developing countries including India, and in some places, these low-cost filters are being used effectively. Developing low-cost As filters is one of the most important strategies for tackling As groundwater contamination. The agencies should try to reduce the As concentration in groundwater as low as possible because exposure to low levels of As throughout the lifetime may also cause cancer and other diseases.

The authors are thankful to the Environmental Science & Engineering Department, IIT Bombay for providing the necessary facilities for research. All authors have read the manuscript and agreed to publish it.

Conceptualization, review, and supervision were performed by Anil Kumar Dikshit, Thambidurai P., and Balamurugan Panneerselvam. Writing – original draft preparation and literature review were performed by Yash Aryan and Thambidurai P. Figures and tables were prepared by Yash Aryan. Editing and review and explanation were performed by Anil Kumar Dikshit, Thambidurai P., and Balamurugan Panneerselvam. Formal analysis, editing, and explanation were performed by Thambidurai P. and Balamurugan Panneerselvam. All authors read and approved the final manuscript.

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

The authors declare there is no conflict.

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