Abstract
Cryptosporidium and Giardia are globally recognized protozoa that directly cause human diarrhea. Their transmission route mainly involves drinking contaminated water, thus needing proper water treatment to avoid human infection. At present, there is a lack of review on the infection status and control measures of the two protozoa. Hence, this article summarizes and compares the infection status and the role of drinking water in transmitting the Cryptosporidium and Giardia in some key countries in Asia, Africa, Europe, and the Americas. With collected data, this review offers recommendations for sanitary control and provides theoretical support for the application of drinking water treatment projects.
HIGHLIGHTS
Need to strengthen preventive measures of Giardia and Cryptosporidium in animals.
Improve sanitation facilities, pay attention to non-core areas, and avoid concentrated outbreaks.
Reasonable selection of testing methods according to the needs and conditions of each country.
The water treatment process parameters need to be reasonably selected according to the natural water quality conditions.
INTRODUCTION
Giardia lamblia and Cryptosporidium are two parasitic protozoa that directly cause human diarrhea (Smith et al. 2006). Giardiasis and cryptosporidiosis are common non-viral infectious diseases, ranking highest among diarrhea-causing parasites (Cai 2005). Giardia and Cryptosporidium are commonly found in natural water bodies, especially in areas polluted with agriculture and animal husbandry wastes (Bukhari et al. 1997). Although the global distribution of water resources is uneven, the common feature is that natural water bodies are used as the main source of domestic water. Therefore, the water treatment industry should emphasize biosafety risks caused by these two protozoa (Cui et al. 2006; Xiao et al. 2013; Zhang et al. 2010; Ma et al. 2014; Sun et al. 2014).
Giardia is a genus of the anaerobic flagellated protozoa and a common parasite that cause human intestinal infections. The tetranuclear cyst is the infective stage following a fecal-oral transmission. Once humans and animals ingest the infective cyst, it decapsulates through the action of the digestive juice and then develops into a trophozoite. Trophozoites parasitize the duodenum or the front end of the small intestine and multiply by longitudinal binary fission. Encystation occurs as the trophozoites move to the colon and then form the cysts excreted through the feces (Kofoid & Christiansen 1915). The Giardia cysts measure (8–12) μm × (7–10) μm and become infective after two weeks at 25 °C or 11 weeks at 4 °C (Graczyk et al. 2008; Silva & Sabogal-Paz 2020; Singer et al. 2020).
Cryptosporidium is one of the apicomplexan parasitic alveolates, causing a zoonotic disease called cryptosporidiosis manifested clinically by watery diarrhea. Cryptosporidiosis is one of the six common diseases that cause diarrhea in the world (Cheng 2015). The life history of Cryptosporidium involves five developmental stages: the trophozoite, schizont, gametocyte, zygote, and oocyst. Oocyst is the infective stage of Cryptosporidium. Oocysts are round or elliptical with a diameter of 4–6 μm. The wall is smooth and colorless. A mature oocyst contains four crescent-shaped sporozoites and one residual body. After humans and animals swallow the oocysts, the sporozoites escape through the digestive juice and invade the intestinal epithelial cells. Sporozoites develop into trophozoites and type I schizonts. After three nuclear divisions, type I schizonts will have eight merozoites. After being released, the merozoites invade the intestinal epithelial cells and develop into second-generation trophozoites, which develop into type II schizonts after two nuclear divisions. Mature type II merozoites contain 4 merozoites. After the merozoites are released, they invade epithelial cells and develop into female and male gametocytes, further developing into female and male gametes. After fertilization of gametes, it forms the zygote that develops into oocysts. Mature oocysts contain four naked sporozoites. There are two types of oocysts: thin-walled and thick-walled oocysts. Sporozoites of the thin-walled oocysts (∼20%) invade the intestinal epithelial cells directly after escaping and proliferate to form an autologous infection. Meanwhile, thick-walled oocysts (∼80%) are formed in the intestinal epithelial cells or intestinal cavity and are excreted with the host's feces. It takes 5–11 days to complete the entire life cycle of Cryptosporidium (DuPont et al. 1995; Xue 2006; Wang & Luo 2016).
HARM AND CONTAMINATION STATUS OF GIARDIA AND CRYPTOSPORIDIUM
Hazards and transmission of Giardia and Cryptosporidium
Giardia and Cryptosporidium have a high biosecurity risk due to their wide-spreading routes and difficulty inactivating their infective stages. These intestinal pathogenic microorganisms grow and reproduce in the host body and are finally excreted in the feces in the form of oocysts and cysts, respectively. They can infect other hosts through contaminated water or food (Figure 1).
The transmission routes of Giardia and Cryptosporidium are complex and diverse, including contact transmission, water transmission, food transmission, and respiratory transmission (Thompson 2008). Among them, water transmission is the most important route. Interestingly, a few studies have shown that unclean sex is also a transmission route (Escobedo et al. 2014). Even developed and developing countries with comprehensive water treatment technologies can also be vulnerable to Giardia and Cryptosporidium environmental contamination. Some of these reasons are described below.
First, Giardia and Cryptosporidium have a wide transmission route. Although countries with comprehensive water treatment technology can guarantee drinking water quality, other ways such as vegetable pollution and contact with livestock and pets still pose greater risks (Fayer et al. 2000; Chen et al. 2002; Nahhas & Aboualchamat 2020). Some studies have shown that raw vegetables play a role in spreading parasitic food-borne diseases (Blackburn & McClure 2002; Eraky et al. 2014). Several studies have also shown that people are more susceptible to intestinal worms and protozoa by eating fruits or vegetables (Al-Shawa & Mwafy 2007; Adanir & Tasci 2013; Ismail 2016). Researches have revealed that the reason for this phenomenon may be that vegetables are polluted by wastewater during irrigation or directly contaminated by animals and humans during harvesting, packaging, transportation, processing, distribution, and sales (Amoah et al. 2007; Gabre & Shakir 2016). The increase in the number of pets raised has also increased the infection rate of humans with Giardia and Cryptosporidium. In particular, common pets in most areas of China are likely to be infected with Giardia and Cryptosporidium, which increases the risk of infection by human contact. A summary of reports on pet infections in China and some foreign countries is shown in Table 1.
Parasite category . | Country/region . | Species . | Infection rate . | Human insect species can be infected in positive samples . | References . | |
---|---|---|---|---|---|---|
Cryptosporidium | China | Henan | Totoro | 10.00% | C. parvum | Qi et al. (2015) |
Birds | 8.10% | C. meleagridis | Qi et al. (2011) | |||
Hubei | Birds | 20.20% | C. meleagridis | Liao (2019) | ||
Guangzhou | Dogs | 3.20–6.90% | C. parvum | Liao et al. (2020), Zheng et al. (2019) | ||
Cats | 6.20% | C. felis, C. parvum | Zheng et al. (2019) | |||
Sichuan | Dogs | 4.30% | C. canis | Hu (2011) | ||
Heilongjiang | Dogs | 2.20% | C. canis | Yang (2015) | ||
Cats | 3.80% | C. felis, C. parvum | ||||
Anhui | Dogs | 1.50% | C. canis | Gu et al. (2015) | ||
Zhejiang | Dogs | 1.50% | C. canis | |||
Shanghai | Dogs | 8.00% | C. canis | Xu (2016) | ||
Cats | 3.80% | C. felis | ||||
Xinjiang | Dogs | 6.80% | C. canis, C. parvum | Zhang et al. (2017) | ||
Ethiopia | Cattle | 7.80% | – | Wegayehu et al. (2013) | ||
Giardia | China | Guangdong | Dogs | 3.10–9.40% | G. lamblia(Aggregate A) | Xiao et al. (2013), Zheng et al. (2019) |
Cats | 3.60% | G. lamblia(Aggregate A) | Zheng et al. (2019) | |||
Henan | Totoro | 37.50% | – | Lu (2009) | ||
Heilongjiang | Cats | 1.90% | – | Yang (2015) | ||
Dogs | 4.50% | G. lamblia(Aggregate C) | ||||
Anhui | Dogs | 3.20% | G. lamblia(Aggregate B & D) | Gu et al. (2015) | ||
Zhejiang | Dogs | 3.20% | G. lamblia(Aggregate B & D) | |||
Shanghai | Dogs | 6.00% | G. lamblia(Aggregate A & B) | Xu (2016) | ||
Cats | 5.60% | G. lamblia(Aggregate A & B) | ||||
Russia | Dogs | 4.60% | – | Kurnosova et al. (2019) | ||
Cats | 9.80% | – | ||||
Totoro | 47.40% | – | ||||
Colombia | Dogs | 47.00% | – | Hernández et al. (2021) | ||
Ethiopia | Cattle | 2.30% | – | Wegayehu et al. (2013) |
Parasite category . | Country/region . | Species . | Infection rate . | Human insect species can be infected in positive samples . | References . | |
---|---|---|---|---|---|---|
Cryptosporidium | China | Henan | Totoro | 10.00% | C. parvum | Qi et al. (2015) |
Birds | 8.10% | C. meleagridis | Qi et al. (2011) | |||
Hubei | Birds | 20.20% | C. meleagridis | Liao (2019) | ||
Guangzhou | Dogs | 3.20–6.90% | C. parvum | Liao et al. (2020), Zheng et al. (2019) | ||
Cats | 6.20% | C. felis, C. parvum | Zheng et al. (2019) | |||
Sichuan | Dogs | 4.30% | C. canis | Hu (2011) | ||
Heilongjiang | Dogs | 2.20% | C. canis | Yang (2015) | ||
Cats | 3.80% | C. felis, C. parvum | ||||
Anhui | Dogs | 1.50% | C. canis | Gu et al. (2015) | ||
Zhejiang | Dogs | 1.50% | C. canis | |||
Shanghai | Dogs | 8.00% | C. canis | Xu (2016) | ||
Cats | 3.80% | C. felis | ||||
Xinjiang | Dogs | 6.80% | C. canis, C. parvum | Zhang et al. (2017) | ||
Ethiopia | Cattle | 7.80% | – | Wegayehu et al. (2013) | ||
Giardia | China | Guangdong | Dogs | 3.10–9.40% | G. lamblia(Aggregate A) | Xiao et al. (2013), Zheng et al. (2019) |
Cats | 3.60% | G. lamblia(Aggregate A) | Zheng et al. (2019) | |||
Henan | Totoro | 37.50% | – | Lu (2009) | ||
Heilongjiang | Cats | 1.90% | – | Yang (2015) | ||
Dogs | 4.50% | G. lamblia(Aggregate C) | ||||
Anhui | Dogs | 3.20% | G. lamblia(Aggregate B & D) | Gu et al. (2015) | ||
Zhejiang | Dogs | 3.20% | G. lamblia(Aggregate B & D) | |||
Shanghai | Dogs | 6.00% | G. lamblia(Aggregate A & B) | Xu (2016) | ||
Cats | 5.60% | G. lamblia(Aggregate A & B) | ||||
Russia | Dogs | 4.60% | – | Kurnosova et al. (2019) | ||
Cats | 9.80% | – | ||||
Totoro | 47.40% | – | ||||
Colombia | Dogs | 47.00% | – | Hernández et al. (2021) | ||
Ethiopia | Cattle | 2.30% | – | Wegayehu et al. (2013) |
Second, Giardia and Cryptosporidium are highly contagious. A study by Hunter et al. (2009) has shown that when there is a problem with water safety, individuals will substitute emergency water (untreated or improperly treated water), which are of great risks to Giardia cysts and Cryptosporidium oocysts contaminations. Many studies have pointed out that the pathogenic doses of Giardia cysts and Cryptosporidium oocysts to humans are 10–100 live cysts and 1–10 live oocysts, respectively (Xu & Hu 2007). Under Giardia infection, patients can excrete as many as 1×1010 cysts per day, thus increasing the spread of the parasite in the environment and the risk of transmission.
Third, humans are highly susceptible to Giardia and Cryptosporidium infections. Except for children, the high-risk group includes the elderly, immunodeficient individuals, hospitalized individuals, travelers, gay men, people in post-disaster areas, and homeless people in the outbreak area (Escobedo et al. 2010).
Fourth, giardiasis and cryptosporidiosis are hard to cure. At present, there is no specific medicine for giardiasis and cryptosporidiosis (Chen et al. 2018). In immunodeficient patients, the death rate after protozoan infection is extremely high (Kotloff et al. 2013; Utami et al. 2020). A few studies have shown that children infected with cryptosporidiosis will have a short-term developmental delay with varying degrees of cognitive development impairment (Bushen et al. 2007).
The contamination status of Giardia and Cryptosporidium
Contamination status of Giardia and Cryptosporidium in China
China first detected a case of Cryptosporidium in Nanjing in 1987. After that, Giardia and Cryptosporidium infections investigations have been carried out in many areas, specifically in Xuzhou, Anhui, Inner Mongolia, Fujian, Shandong, Hunan, and other provinces and cities (Cai 1995; Fu et al. 1998; Gao 2008). In 2005, an epidemiological survey of Cryptosporidium was conducted for the first time in Chinese rural populations in Jiangshan, Zhejiang Province. The results showed that the infection rate was as high as 56.72% (Shan 2007). In 2010, the survey of the contamination of Giardia and Cryptosporidium in drinking water and environmental water in Shanghai showed that oocysts were not detected in 156 water samples collected from 16 districts in Shanghai, including factory water, pipe network water, and community drinking water. Among the 70 water samples collected from the water supply plant, water source, Huangpu river, animal feed farm surrounding a river, sewage treatment plant effluent, and domestic sewage collected in 5 districts, the total detection rate of Cryptosporidium oocysts was 17.1%. The total detection rate of Giardia cysts was 20.0% (Zhang et al. 2010). In 2014, survey results showed that Cryptosporidium oocysts and Giardia cysts in the water source of the centralized water supply source in rural areas in southern China had a prevalence of 23.33% and 33.33%, respectively (Sun et al. 2014). In the same year, the survey results of Giardia and Cryptosporidium contamination in source and drinking water in typical areas of Jiangsu Province showed that 7 out of 222 samples were positive, of which the detection rates of Cryptosporidium and Giardia were 0.5% and 2.7%, respectively (Zheng et al. 2014). In 2019, Wang Lu et al. summarized Cryptosporidium studies and analyzed the map of Cryptosporidium infection in China using GIS technology. Results showed that infected people were concentrated in Jiangsu, Anhui, Shandong, Henan, and other places (Wang et al. 2019a, 2019b). In addition, a large survey indicated that the water environment of some provinces in China (Liaoning, Northeast, Qinghai, Shaanxi, and Inner Mongolia) is seriously contaminated by Giardia cysts and Cryptosporidium oocysts. Even the water environment of first-tier cities and key projects (Shanghai, Three Gorges Reservoir) had exceeded the standard contamination rate of the two protozoa (Hou et al. 2011; Liu et al. 2011; Wang et al. 2012, 2019a, 2019b; Wang 2013; Xiao et al. 2013).
Contamination status of Giardia and Cryptosporidium in other countries
Most of the research on Giardia and Cryptosporidium infections in countries other than China is concentrated in Asia and Africa. The backward health system of most countries in Asia and Africa is the main reason for the serious situation of Giardia and Cryptosporidium infections. Several studies have shown that children under five years of age in developing countries with poor basic health conditions account for cryptosporidiosis to 30–50% of overall child mortality (Checkley et al. 2015; Platts-Mills et al. 2015). Because children and immunodeficient patients are high-risk and susceptible groups, most of the researches centered on these two groups. A summary of related studies on children and immunodeficient patients in Asian and African countries infected with Giardia and Cryptosporidium is shown in Table 2.
Country . | Infected people . | Infection rate . | Infected species . | References . | |
---|---|---|---|---|---|
Southeast Asia | Cambodia | Child | 7.40% | Cryptosporidium (C. hominis, C. parvum) | Arthur et al. (1992) |
AIDS patient | 45% | Cryptosporidium (C. hominis, C. parvum) | Chhin et al. (2006) | ||
Malaysia | Child | 0.40–10.60%/2.60% | Cryptosporidium/Giardia (uncategorized) | Lai (1992), Mahmoudi et al. (2017), Lim et al. (2008) | |
Child(CWD) | 4.60% | Cryptosporidium(uncategorized) | Latif & Rossle (2015) | ||
AIDS patient | 3–64% | Cryptosporidium(uncategorized) | Lim et al. (2005), Zaidah et al. (2008), Lim et al. (2011) | ||
Myanmar | Child | 3.40% | Cryptosporidium(uncategorized) | Aye et al. (1994) | |
Philippines | Child | 2.50–2.90% | Cryptosporidium(uncategorized) | Mahmoudi et al. (2017) | |
Cancer patient | 28.30% | Cryptosporidium(C. hominis, C. parvum) | Rivera et al. (2005) | ||
Thailand | Child | 15%/0.80–10.20% | Cryptosporidium/Giardia (uncategorized) | Chokephaibulkit et al. (2001), Sagnuankiat et al. (2014), Assavapongpaiboon et al. (2018), Sanprasert et al. (2016) | |
Child(HIV) | 33% | Cryptosporidium(uncategorized) | Chokephaibulkit et al. (2001) | ||
AIDS patient | 11.50% | Cryptosporidium(C. hominis, C. meleagridis, C. parvum, C. felis, C. canis) | Pinlaor et al. (2005) | ||
Laos | AIDS patient | 13.90% | Cryptosporidium(uncategorized) | Paboriboune et al. (2014) | |
Indonesia | AIDS patient | 4.90% | Cryptosporidium(uncategorized) | Kurniawan et al. (2009) | |
South Asia | India | Child | 1.30–27.40% | Cryptosporidium(C. hominis, C. parvum) | Mahmoudi et al. (2017) |
AIDS patient | 2–77% | Cryptosporidium(C. parvum) | |||
Sri Lanka | Child(CWD) | 5.70% | Cryptosporidium(uncategorized) | Sirisena et al. (2014) | |
Bangladesh | Child | 44–64%/40% | Cryptosporidium(C. hominis, C. parvum, C. meleagridis)/Giardia (uncategorized) | Steiner et al. (2018), Berendes et al. (2020) | |
AIDS patient | 47.10% | Cryptosporidium(uncategorized) | Mahmoudi et al. (2017) | ||
Nepal | Child | 1–16% | Cryptosporidium(uncategorized) | Mahmoudi et al. (2017), Paudyal et al. (2013) | |
AIDS patient | 11–35.70% | Cryptosporidium(uncategorized) | |||
Pakistan | Cancer, Diabetes, Dialysis | 40% | Cryptosporidium(uncategorized) | Baqai et al. (2005) | |
Child | 3.30–10.30% | Cryptosporidium(uncategorized) | Mahmoudi et al. (2017) | ||
West Asia | Iran | Child | 2–7% | Cryptosporidium(C. hominis, C. parvum) | |
AIDS patient | 1.50–7% | Cryptosporidium(C. hominis, C. parvum) | Meamar et al. (2006) | ||
Iraq | Child(CWD) | 8.56% | Cryptosporidium(uncategorized) | Latif & Rossle (2015) | |
Child | 6–33.83% | Cryptosporidium(C. parvum) | Azeez & Alsakee (2017), Rahi et al. (2013) | ||
Israel | Child | 1.30–31.90% | Cryptosporidium(uncategorized) | Mahmoudi et al. (2017) | |
Jordan | Child | 42.60% | Giardia (uncategorized) | Ammoura (2010) | |
Child(CWD) | 37.30% | Cryptosporidium(uncategorized) | Latif & Rossle (2015) | ||
AIDS patient | 6% | Cryptosporidium(uncategorized) | |||
Hemodialysis patients | 11% | Cryptosporidium(uncategorized) | Zueter et al. (2019) | ||
Kuwait | Child | 3.40–94% | Cryptosporidium(C. hominis, C. parvum) | Ahmed & Karanis (2020) | |
Lebanon | Child | 10.40% | Cryptosporidium(uncategorized) | Osman et al. (2018) | |
Palestine | Child | 11.60% | Cryptosporidium(uncategorized) | ||
Child(CWD) | 1% | Giardia (uncategorized) | Abu-Elamreen et al. (2008) | ||
Yemen | Child | 43.70% | Cryptosporidium(uncategorized) | Mahmoudi et al. (2017) | |
Cancer patient | 30.1%/18% | Cryptosporidium/Giardia (uncategorized) | Al-Qobati et al. (2012) | ||
Saudi Arabia | Child | 1.70–11% | Cryptosporidium(C. hominis, C. parvum) | El-Malky et al. (2018), Shalaby et al. (2014) | |
AIDS patient | 8.10–69.70% | Cryptosporidium (C. hominis, C. parvum, C. meleagridis, C. muris) | Al-Megrin 2010, Al-Brikan et al. (2008) | ||
Africa | Nigeria | Child | 19.40% | Cryptosporidium(C. hominis, C. parvum) | Molloy et al. (2010) |
Egypt | Child | 35% | Cryptosporidium(C. hominis, C. parvum) | Gharieb et al. (2018) | |
Ethiopia | Child | 4.60%/55% | Cryptosporidium/Giardia (uncategorized) | Wegayehu et al. (2016) | |
Child(HIV) | 9.60% | Cryptosporidium(uncategorized) | Gebre et al. (2019) | ||
Botswana | Child | 10%/7% | Cryptosporidium/Giardia (uncategorized) | Alexander et al. (2012) | |
Kenya | Child | 45.20% | Cryptosporidium(uncategorized) | Mutai et al. (2020) | |
Tanzania | Child | 6% | Cryptosporidium(uncategorized) | Korpe et al. (2018) | |
Gabon | Child | 13.30%/15.60% | Cryptosporidium/Giardia (uncategorized) | Bouyou-Akotet et al. (2015) |
Country . | Infected people . | Infection rate . | Infected species . | References . | |
---|---|---|---|---|---|
Southeast Asia | Cambodia | Child | 7.40% | Cryptosporidium (C. hominis, C. parvum) | Arthur et al. (1992) |
AIDS patient | 45% | Cryptosporidium (C. hominis, C. parvum) | Chhin et al. (2006) | ||
Malaysia | Child | 0.40–10.60%/2.60% | Cryptosporidium/Giardia (uncategorized) | Lai (1992), Mahmoudi et al. (2017), Lim et al. (2008) | |
Child(CWD) | 4.60% | Cryptosporidium(uncategorized) | Latif & Rossle (2015) | ||
AIDS patient | 3–64% | Cryptosporidium(uncategorized) | Lim et al. (2005), Zaidah et al. (2008), Lim et al. (2011) | ||
Myanmar | Child | 3.40% | Cryptosporidium(uncategorized) | Aye et al. (1994) | |
Philippines | Child | 2.50–2.90% | Cryptosporidium(uncategorized) | Mahmoudi et al. (2017) | |
Cancer patient | 28.30% | Cryptosporidium(C. hominis, C. parvum) | Rivera et al. (2005) | ||
Thailand | Child | 15%/0.80–10.20% | Cryptosporidium/Giardia (uncategorized) | Chokephaibulkit et al. (2001), Sagnuankiat et al. (2014), Assavapongpaiboon et al. (2018), Sanprasert et al. (2016) | |
Child(HIV) | 33% | Cryptosporidium(uncategorized) | Chokephaibulkit et al. (2001) | ||
AIDS patient | 11.50% | Cryptosporidium(C. hominis, C. meleagridis, C. parvum, C. felis, C. canis) | Pinlaor et al. (2005) | ||
Laos | AIDS patient | 13.90% | Cryptosporidium(uncategorized) | Paboriboune et al. (2014) | |
Indonesia | AIDS patient | 4.90% | Cryptosporidium(uncategorized) | Kurniawan et al. (2009) | |
South Asia | India | Child | 1.30–27.40% | Cryptosporidium(C. hominis, C. parvum) | Mahmoudi et al. (2017) |
AIDS patient | 2–77% | Cryptosporidium(C. parvum) | |||
Sri Lanka | Child(CWD) | 5.70% | Cryptosporidium(uncategorized) | Sirisena et al. (2014) | |
Bangladesh | Child | 44–64%/40% | Cryptosporidium(C. hominis, C. parvum, C. meleagridis)/Giardia (uncategorized) | Steiner et al. (2018), Berendes et al. (2020) | |
AIDS patient | 47.10% | Cryptosporidium(uncategorized) | Mahmoudi et al. (2017) | ||
Nepal | Child | 1–16% | Cryptosporidium(uncategorized) | Mahmoudi et al. (2017), Paudyal et al. (2013) | |
AIDS patient | 11–35.70% | Cryptosporidium(uncategorized) | |||
Pakistan | Cancer, Diabetes, Dialysis | 40% | Cryptosporidium(uncategorized) | Baqai et al. (2005) | |
Child | 3.30–10.30% | Cryptosporidium(uncategorized) | Mahmoudi et al. (2017) | ||
West Asia | Iran | Child | 2–7% | Cryptosporidium(C. hominis, C. parvum) | |
AIDS patient | 1.50–7% | Cryptosporidium(C. hominis, C. parvum) | Meamar et al. (2006) | ||
Iraq | Child(CWD) | 8.56% | Cryptosporidium(uncategorized) | Latif & Rossle (2015) | |
Child | 6–33.83% | Cryptosporidium(C. parvum) | Azeez & Alsakee (2017), Rahi et al. (2013) | ||
Israel | Child | 1.30–31.90% | Cryptosporidium(uncategorized) | Mahmoudi et al. (2017) | |
Jordan | Child | 42.60% | Giardia (uncategorized) | Ammoura (2010) | |
Child(CWD) | 37.30% | Cryptosporidium(uncategorized) | Latif & Rossle (2015) | ||
AIDS patient | 6% | Cryptosporidium(uncategorized) | |||
Hemodialysis patients | 11% | Cryptosporidium(uncategorized) | Zueter et al. (2019) | ||
Kuwait | Child | 3.40–94% | Cryptosporidium(C. hominis, C. parvum) | Ahmed & Karanis (2020) | |
Lebanon | Child | 10.40% | Cryptosporidium(uncategorized) | Osman et al. (2018) | |
Palestine | Child | 11.60% | Cryptosporidium(uncategorized) | ||
Child(CWD) | 1% | Giardia (uncategorized) | Abu-Elamreen et al. (2008) | ||
Yemen | Child | 43.70% | Cryptosporidium(uncategorized) | Mahmoudi et al. (2017) | |
Cancer patient | 30.1%/18% | Cryptosporidium/Giardia (uncategorized) | Al-Qobati et al. (2012) | ||
Saudi Arabia | Child | 1.70–11% | Cryptosporidium(C. hominis, C. parvum) | El-Malky et al. (2018), Shalaby et al. (2014) | |
AIDS patient | 8.10–69.70% | Cryptosporidium (C. hominis, C. parvum, C. meleagridis, C. muris) | Al-Megrin 2010, Al-Brikan et al. (2008) | ||
Africa | Nigeria | Child | 19.40% | Cryptosporidium(C. hominis, C. parvum) | Molloy et al. (2010) |
Egypt | Child | 35% | Cryptosporidium(C. hominis, C. parvum) | Gharieb et al. (2018) | |
Ethiopia | Child | 4.60%/55% | Cryptosporidium/Giardia (uncategorized) | Wegayehu et al. (2016) | |
Child(HIV) | 9.60% | Cryptosporidium(uncategorized) | Gebre et al. (2019) | ||
Botswana | Child | 10%/7% | Cryptosporidium/Giardia (uncategorized) | Alexander et al. (2012) | |
Kenya | Child | 45.20% | Cryptosporidium(uncategorized) | Mutai et al. (2020) | |
Tanzania | Child | 6% | Cryptosporidium(uncategorized) | Korpe et al. (2018) | |
Gabon | Child | 13.30%/15.60% | Cryptosporidium/Giardia (uncategorized) | Bouyou-Akotet et al. (2015) |
Note: Child(CWD): Children with diarrhea; Child(HIV): Children with AIDS.
Infections with Giardia and Cryptosporidium are common in children in Southeast Asia. For instance, Thailand's large water systems provide a good environment for transmission. Recent research reports showed that the infection rate of Cryptosporidium in children in Thailand was as high as 15% (Chokephaibulkit et al. 2001), while the infection rate of Giardia was as high as 10.2% (Sagnuankiat et al. 2014). At the same time, the study also pointed out that the infection rate of Cryptosporidium in children with AIDS was 33%, twice that of normal children and 11.5% higher than that of normal AIDS patients. Children in Cambodia, Malaysia, Myanmar, the Philippines, Laos, and Indonesia are also at risk of being infected by the two parasitic protozoa. In South Asia, India and Bangladesh have the most serious infections in children, especially in terms of Cryptosporidium infection in which the prevalence was recorded at 27.4 and 64%, respectively (Mahmoudi et al. 2017; Steiner et al. 2018). In Bangladesh, the reported Giardia infection rate was as high as 40% (Berendes et al. 2020). West Asia is the hardest-hit area in Asia, with most infections occurring in children. For example, in a study in Kuwait, the infection rate among the surveyed groups was as high as 94%.
In Africa, the child infection rates in Ethiopia, Kenya, and Egypt were higher than 20%, far higher than that of other countries, with both ordinary AIDS and child AIDS patients showing the highest infection rates. However, there was no obvious difference in infection rate between the two AIDS patient types, indicating that these patients could be both infected by Giardia and Cryptosporidium. It is worth noting that the infection rate of Giardia and Cryptosporidium is closely related to the sanitary conditions and water environment of these countries.
Compared with Asian and African regions, other countries have fewer studies on the infection status of Giardia and Cryptosporidium. Results of the epidemiological data of some European and American countries are shown in Table 3. There are little data on human infections, and most of the relevant research directions are concentrated in agriculture, with research focusing mainly on animal or livestock scenario. In the case of human infection, the infection rate of Giardia and Cryptosporidium in European and American countries is extremely low. In Slovakia, the relatively higher infection rate (6.3%) can be explained by the survey participants in poor areas in Northeast Slovakia. This place lacks water supply and sewage treatment systems and has low sanitation, education, and health awareness.
Country . | Infected people . | Infection rate . | Infected species . | References . |
---|---|---|---|---|
United States | Patients with diarrhea | 2.99/105 | Cryptosporidium (uncategorized) | Alleyne et al. (2020) |
Croatia | Food industry personnel | 0.07% | Giardia (uncategorized) | Plutzer et al. (2018) |
Symptoms of bowel disease | 0.24% | Giardia (uncategorized) | ||
Czech Republic | Symptoms of bowel disease | 0.52% | Giardia (uncategorized) | |
Estonia | Patients with diarrhea | (0.05/105)/(18.28/105) | Cryptosporidium/Giardia (uncategorized) | |
Hungary | – | 0.03%/1.2% | Cryptosporidium/Giardia (uncategorized) | |
Latvia | Patients with diarrhea | (0.29/105)/ (2.48/105) | Cryptosporidium/Giardia (uncategorized) | |
Poland | – | (0.006/105)/(5.43/105) | Cryptosporidium/Giardia (uncategorized) | |
Romania | – | (0.01/105) | Cryptosporidium (uncategorized) | |
Slovenia | Patients with diarrhea | 1.53% | Cryptosporidium (uncategorized) | |
Bosnia and Herzegovina | Symptoms of bowel disease | 0.96% | Giardia (uncategorized) | |
Patients with diarrhea | 9.09% | Giardia (uncategorized) | ||
Serbia | Food industry personnel | 0.28% | Giardia (uncategorized) | |
Slovakia | Child | 6.30% | Giardia (Aggregate A II, B, F) | Pipiková et al. (2018) |
Austria | Child | 1.50% | Cryptosporidium (uncategorized) | Joachim (2004) |
Italy | Child(CWD) | 7.20% | Cryptosporidium (uncategorized) | |
Switzerland | Symptoms of bowel disease | 0.20% | Cryptosporidium (uncategorized) | |
Child(CWD) | 5.50% | Cryptosporidium (uncategorized) | ||
New Zealand | – | 12.90/105 | Cryptosporidium (C.hominis, C.parvum) | Pipiková et al. (2018) |
Brazil | Child | 13.70–18% | Giardia (uncategorized) | Prado et al. (2003), Teixeira et al. (2007) |
Country . | Infected people . | Infection rate . | Infected species . | References . |
---|---|---|---|---|
United States | Patients with diarrhea | 2.99/105 | Cryptosporidium (uncategorized) | Alleyne et al. (2020) |
Croatia | Food industry personnel | 0.07% | Giardia (uncategorized) | Plutzer et al. (2018) |
Symptoms of bowel disease | 0.24% | Giardia (uncategorized) | ||
Czech Republic | Symptoms of bowel disease | 0.52% | Giardia (uncategorized) | |
Estonia | Patients with diarrhea | (0.05/105)/(18.28/105) | Cryptosporidium/Giardia (uncategorized) | |
Hungary | – | 0.03%/1.2% | Cryptosporidium/Giardia (uncategorized) | |
Latvia | Patients with diarrhea | (0.29/105)/ (2.48/105) | Cryptosporidium/Giardia (uncategorized) | |
Poland | – | (0.006/105)/(5.43/105) | Cryptosporidium/Giardia (uncategorized) | |
Romania | – | (0.01/105) | Cryptosporidium (uncategorized) | |
Slovenia | Patients with diarrhea | 1.53% | Cryptosporidium (uncategorized) | |
Bosnia and Herzegovina | Symptoms of bowel disease | 0.96% | Giardia (uncategorized) | |
Patients with diarrhea | 9.09% | Giardia (uncategorized) | ||
Serbia | Food industry personnel | 0.28% | Giardia (uncategorized) | |
Slovakia | Child | 6.30% | Giardia (Aggregate A II, B, F) | Pipiková et al. (2018) |
Austria | Child | 1.50% | Cryptosporidium (uncategorized) | Joachim (2004) |
Italy | Child(CWD) | 7.20% | Cryptosporidium (uncategorized) | |
Switzerland | Symptoms of bowel disease | 0.20% | Cryptosporidium (uncategorized) | |
Child(CWD) | 5.50% | Cryptosporidium (uncategorized) | ||
New Zealand | – | 12.90/105 | Cryptosporidium (C.hominis, C.parvum) | Pipiková et al. (2018) |
Brazil | Child | 13.70–18% | Giardia (uncategorized) | Prado et al. (2003), Teixeira et al. (2007) |
Note: Child(CWD): Children with diarrhea; Child(HIV): Children with AIDS;-: The survey population is not clearly indicated.
Detection method . | Introduction . | Advantage . | Disadvantage . |
---|---|---|---|
ICR | The earliest method used to monitor Giardia and Cryptosporidium in the water environment | Qualitatively analysis |
|
EPA1623 | The globally recognized detection method for Giardia and Cryptosporidium, which has confirmed the existence of oocysts and spore cysts, and has now been widely used |
|
|
ELISA | An immunological technique widely used in epidemiological testing |
|
|
PCR | The first molecular biology detection method applied to the detection of Giardia and Cryptosporidium | Detectable genotype |
|
Nested PCR | PCR-derived detection method is one of the most effective molecular biology detections |
|
|
qRT-PCR | A detection method for adding fluorescent chemicals in DNA amplification to monitor the total amount of products in each PCR process |
|
|
RT-PCR | MRNA-based detection method |
|
|
mutiplex PCR | One of the molecular biology detection methods in which multiple primers can be amplified in the same reaction system |
|
|
LAMP | The detection technology proposed by Japanese scholars has been applied to the detection of SARS, avian influenza, HIV and other diseases |
|
|
PCR-RFLP | A method that can accurately identify the genotypes and species of Giardia and Cryptosporidium |
|
|
Detection method . | Introduction . | Advantage . | Disadvantage . |
---|---|---|---|
ICR | The earliest method used to monitor Giardia and Cryptosporidium in the water environment | Qualitatively analysis |
|
EPA1623 | The globally recognized detection method for Giardia and Cryptosporidium, which has confirmed the existence of oocysts and spore cysts, and has now been widely used |
|
|
ELISA | An immunological technique widely used in epidemiological testing |
|
|
PCR | The first molecular biology detection method applied to the detection of Giardia and Cryptosporidium | Detectable genotype |
|
Nested PCR | PCR-derived detection method is one of the most effective molecular biology detections |
|
|
qRT-PCR | A detection method for adding fluorescent chemicals in DNA amplification to monitor the total amount of products in each PCR process |
|
|
RT-PCR | MRNA-based detection method |
|
|
mutiplex PCR | One of the molecular biology detection methods in which multiple primers can be amplified in the same reaction system |
|
|
LAMP | The detection technology proposed by Japanese scholars has been applied to the detection of SARS, avian influenza, HIV and other diseases |
|
|
PCR-RFLP | A method that can accurately identify the genotypes and species of Giardia and Cryptosporidium |
|
|
In South America, Brazil has the highest infection rate (13.7–18%). The main reason is that the survey was performed in 29 slum areas in the city of Zfífola in southeastern Brazil. Secondly, Brazil has rich water resources, with freshwater resources accounting for about 13% of the world's total. In addition, the number of rivers entering the sea accounts for about 20% of the total rivers in the world. Thus, the large water system provides unique environmental conditions for the spread of Giardia and Cryptosporidium.
Tables 2 and 3 show that research data from Asia and Africa are relatively rich. Data suggest that Giardia and Cryptosporidium contamination improvements have been slow across time, and there has been a sharp increase in recent years. In some regions, such as India, Bangladesh, Iraq, and Pakistan, the infection rate in children is relatively high, directly related to the country's health system conditions. Although few relevant studies in Europe and America, the infection rate is far lower than that of Asia and Africa. However, the data of Europe and America have not shown a steady decline. In New Zealand, human Cryptosporidium infection rates in 2001 and 2002 revealed a total of 33.2 cases per 100,000 and 26.1 cases per 100,000, respectively. In addition, infection rates in 2013 and 2014 were 30.3 cases per 100,000 and 12.9 cases per 100,000, respectively (Garcia-R et al. 2020). In Stockholm, Sweden, there were more than 300 cases of cryptosporidiosis between October and November 2019. In France, the water supply system was polluted by heavy rain, causing 92 cases of cryptosporidiosis in October 2019. Therefore, European and American countries maintain a low infection rate while steadily reducing the infection rate.
DETECTION METHOD OF GIARDIA AND CRYPTOSPORIDIUM IN WATER
The earliest monitoring method for Giardia and Cryptosporidium was the Information Collection Rule (ICR) protozoan method proposed by U.S. Environmental Protection Agency (USEPA). However, this method is prone to sample loss in the elution, concentration, and purification stages. Also, it needs experienced staff to operate the fluorescence microscope, which could lead to relatively large subjective detection results. Moreover, this method cannot evaluate the activity of Giardia and Cryptosporidium and cannot further distinguish the protozoan species (Zong et al. 2005).
Since 1996, USEPA began to use immunomagnetic separation technology to optimize the detection methods of Giardia and Cryptosporidium. The first release of the EPA1622 method for the detection of Cryptosporidium was in 1999. Then, the EPA1623 method that can detect two types of protozoa was released. This method benefits from filter cartridge filtration, immunomagnetic bead separation, and immunofluorescence microscopy detection, improving this method's recovery rate and accuracy. In addition, with the help of DAPI staining and differential interference microscope, the internal structure can now be observed to confirm the presence of oocysts and cysts (USEPA, Method 1623 2001; Sun 2007). However, this method has several drawbacks. First, the binding site of the immunofluorescence antibody is located on the outer surface of oocysts and cysts, and the cyst wall after decapsulation will still be detected, which may increase the detection rate. Second, chlorine disinfection, increasing age of parasites, and environmental changes are likely to cause inactivation of binding sites and reduce the detection rate. Third, the method still cannot clearly distinguish between activity and protozoan species. However, some researches showing the optimization of the EPA1623 process could improve the system. For example, Ye et al. optimized the immunomagnetic separation stage, which replaced the Filta-Max Xpress filter element with a filter membrane for rapid filtration. It also replaced the air compressor with an ultrasonic elution filter membrane to quickly elute the filter element. After optimization, the recovery rate of Giardia and Cryptosporidium standard addition is much higher than the recovery rate obtained by operating following the national standard (Ye et al. 2017).
Enzyme-linked immunosorbent assay (ELISA) is an immunological detection technique. Because of its good specificity and sensitivity, ELISA has been used often in epidemiological studies for Giardia and Cryptosporidium detection (Li 2015). Although it can be qualitatively or quantitatively measured, the operation is complicated in quantitative measurement, and many factors affect the reaction. Moreover, each group of experiments needs to measure multiple concentrations to draw a positive standard curve.
Due to the limitations of immunological technology, molecular technology has been rapidly developed and showed great potential in detecting pathogenic protozoa. Polymerase chain reaction (PCR) is a molecular biology detection method developed in the mid-1980s (Lu 2016). However, this method has strict requirements on the test environment and is often sensitive to DNA contamination. Therefore, it can be combined with immunomagnetic separation technology (IMS-PCR) to improve PCR specificity (Giovanni et al. 1999; Rimhanen-Finne et al. 2002). Other molecular biology techniques include the Nested PCR, reverse transcription-polymerase chain reaction (RT-PCR), Quantitative Real-time PCR (qRT-PCR), reverse transcription PCR (RT-PCR), multiplex fluorescent PCR, loop-mediated isothermal amplification (LAMP), and PCR-restriction fragment polymorphism analysis (PCR-PFLP).
The Nested PCR uses two pairs of PCR primers based on traditional PCR, making it highly specific and sensitive. This technique is widely used in the detection of pathogenic microorganisms (Wang et al. 2014). For example, Meng et al. (2011) used nested PCR to detect water-borne Cryptosporidium in Xinjiang, China. Li et al. (2010) also established a nested PCR method to detect cryptosporidiosis.
Quantitative Real-time PCR (qRT-PCR) is the real-time monitoring of the entire PCR process through fluorescent signals in the traditional PCR amplification process. Kumar et al. (2016) used Real-time PCR to detect Cryptosporidium in the water environment in Malaysia, the Philippines, Thailand, and Vietnam in Southeast Asia. Results showed that Real-time PCR is sensitive and specific in the quantitative detection of Cryptosporidium. But this method still cannot distinguish and detect the activity of live oocysts. Reverse transcription PCR (RT-PCR) is based on mRNA for detection. The above PCR-derived methods are based on DNA for detection. DNA can still be stored intact for a long time after the oocysts or cysts die, so the detection rate is relatively high. Only living cells can produce mRNA. This method overcomes the shortcomings of other DNA-based molecular biology detection methods.
Traditional PCR technology can only detect one kind of pathogenic microorganism in each PCR reaction tube, which has low efficiency and high cost. To address such shortcomings, Chamberlain et al. (1988) first proposed the concept of multiplex PCR (multiplex-PCR) in their research in 1988. This method can detect multiple pathogenic microorganisms at the same time, saving workforce and material resources. For example, Moniot et al. (2020) established a simultaneous detection method for intestinal microsporidia and Cryptosporidium using multiplex PCR technology. PCR-restriction fragment polymorphism analysis (PCR-RFLP) is a technique that combines PCR amplification and restriction enzyme digestion to detect polymorphisms. It uses a specific restriction enzyme to cut the amplified product and run on gel electrophoresis. The technique is simple and can analyze samples in a short time. Therefore, it is suitable for biotyping and identifying multiple protozoan species, such as Cryptosporidium and Giardia. Rafiei et al. (2014) used PCR-RFLP technology to identify the types of Cryptosporidium infecting Iranians. The study identified three genotypes C. parvum, C. hominis, and C. meleagridis, of which C. meleagridis was the first case in Iran.
Loop-mediated isothermal amplification technology (LAMP) is a relatively new technology. In 2000, a Japanese scholar Notomi proposed a constant temperature accounting amplification technology suitable for genetic diagnosis. It uses strand displacement DNA polymerase to amplify a large amount in a short time under a constant temperature environment, and the product produces a large amount of magnesium pyrophosphate white precipitate. As a result, the presence of the target gene can be observed by naked eyes. Karanis et al. (2007) applied this technology to detect Cryptosporidium for the first time due to its high specificity, simple operation, and low requirements for equipment (PCR requires expensive equipment, LAMP only needs a water bath or incubator). Thus, this method is of great significance at the practical application level.
The above technology is the basic detection technology for Giardia and Cryptosporidium (as shown in Table 4). According to the characteristics of each detection method, combined with the actual conditions and requirements of the test, researchers from various countries often use a combination of multiple detection methods, thus proposing a variety of effective combinations. For example, Hallier-Soulier & Guillot (2000) combined immunomagnetic separation and PCR technology to detect the number of Cryptosporidium oocysts in rivers. Fontaine & Guillot (2003) used immunomagnetic separation technology combined with real-time PCR technology to detect Cryptosporidium oocysts in tap water and Seine water in France. Liao et al. (2014) combined real-time fluorescent PCR and multiplex PCR to establish a dual real-time fluorescent PCR detection method for Cryptosporidium and Giardia, creating a much faster and more accurate system.
CONTROL OF GIARDIA AND CRYPTOSPORIDIUM BY DRINKING WATER TREATMENT PROCESS
Compared with Giardia cysts, Cryptosporidium oocysts are smaller, have a lower pathogenic dose, and are more resistant to disinfectants (Betancourt & Rose 2004). Researchers believe that when Cryptosporidium oocysts are removed from the water, it could also lead to the removal of Giardia cysts. Hence, Cryptosporidium oocysts are generally used as control targets, which is why there are more studies on Cryptosporidium than Giardia. The removal rate of Giardia and Cryptosporidium is often expressed in logarithm. For example, the logarithmic removal rate of 2.0 log corresponds to an inactivation rate of 99%. These two types of protozoa are ubiquitous in the natural water environment. Thus, certain control indicators need to be reached in each stage of water treatment to ensure that subsequent processes can run well and ensure that the final effluent reaches the standard.
Coagulation-sedimentation-filtration
The outer surfaces of Cryptosporidium oocysts and Giardia cysts are negatively charged, similar to other low-density negatively charged colloids. As a result, a coagulation-sedimentation process can remove them to a certain extent. However, at the same time, because the impurity particles have a protective effect on pathogenic microorganisms, it affects the mechanism of disinfectants and the removal of pathogens (Yan & Chen 2004). Therefore, what kind of coagulant to choose, what type of filtration method, and how much effluent turbidity is controlled have become the main research directions.
The USEPA promulgated the ‘Interim Enhanced Surface Water Treatment Rule’ (IESWTR) on December 6, 1998. The regulation's core is that the maximum pollutant level indicator (MCLG) of Cryptosporidium must be set to zero. Furthermore, it is required to use only the filtration process without disinfection process, and the removal rate of Cryptosporidium oocysts needs to reach 2.0 log. When the content of Cryptosporidium oocysts in the raw water (C(Cry)) is greater than 0.075 oocysts per litter (oocysts/L), the oocyst removal rate must reach 3.0 log. Meanwhile, when C(Cry) is greater than one oocysts/L, the removal rate must reach 4.0 log. When C(Cry) is greater than three oocysts/L, the removal rate needs to reach 4.5 log (USEPA IESWTR).
States et al. (2002) pointed out that the three coagulants of ferric chloride, alum, and polyaluminum chloride can effectively remove Cryptosporidium oocysts based on the effect of pH on the removal of Cryptosporidium oocysts and Total Organic Carbon (TOC). Here, the removal rate is up to 4.3 log, and pH does not affect the removal of Cryptosporidium. Alum is the most commonly used water treatment coagulant in Australia, with a few studies showing its good removal effect on oocysts. The removal rate of Cryptosporidium oocysts is greater than 1.0 log when alum dosage is at 40–100 mg/L (Keegan et al. 2008). Cornwell et al. (2003) and Logsdon & Johnson (2010) showed that the use of the lime softening method in water treatment could greatly reduce the content of Giardia and Cryptosporidium in water (2.5–3.5 log). Ongerth & Pecoraro (1995) used a designed filter (anthracite + silica sand + garnet) to explore the ability of the probability pool to treat the two protozoa. They found that when the influent turbidity is 0.38 NTU and the effluent turbidity is 0.03 NTU at room temperature, the removal rates of Cryptosporidium and Giardia can reach 3.1 log and 3.6 log, respectively.
Based on many studies, the three conventional water treatment processes of coagulation-sedimentation-filtration can effectively remove Giardia and Cryptosporidium. In contrast to the disinfection method, these two types of protozoa are removed as suspended particles. However, filtered water effluent needs that the removal rate reaches at least 2.0 log. Therefore, as the concentration of oocysts and cysts in the raw water increases, the process needs to be changed to achieve a higher removal rate.
Disinfection
Disinfection is the most critical link in the water treatment process. Choosing the appropriate disinfectant and working conditions is key to ensuring the safety of the effluent. Giardia and Cryptosporidium, as a special type of protozoa in the water environment, are important indices to evaluate the disinfection effect and the quality of the effluent. Researchers have carried out many experimental studies on removing Giardia and Cryptosporidium to provide theoretical support for engineering practice, particularly on the impact of different disinfection methods.
Chlorine disinfection
In 1981, Rice et al. (1982) first applied chlorine disinfection to the inactivation of Giardia cysts. The research object was the cysts excreted by Giardia cyst carriers, and the chlorine (Cl2) concentration used was 2.5 mg/L. Although this is medical research, it is a milestone for applying disinfection technology to inactivate Giardia and Cryptosporidium. The Cl2 has a slight killing effect on Giardia and Cryptosporidium. The concentration of Cl2 added to the water plant cannot completely kill the protozoa cysts and oocysts. If the inactivation rate reaches 99%, the CT value needs to be 7,200 mg.min/L. Moreover, because Cl2 is used for drinking water disinfection, there are many hidden health and safety hazards, so there is less research on inactivating Giardia and Cryptosporidium using Cl2 disinfectant. Compared to Cl2, chlorine dioxide (ClO2) has a stronger killing effect. Ran et al. (2011) explored the influencing factors of ClO2 in the inactivation of Cryptosporidium and found that the best disinfection effect (inactivation rate is greater than 99%) is pH = 7, T = 25 °C, NTU = 1, C(ClO2) = 3 mg/L, and t (contact time) = 120 min. Moreover, turbidity was revealed as the main influencing factor, of which the higher the turbidity, the worse the inactivation effect. Clark et al. (2003) optimized the CT value equation for ClO2 inactivation of Cryptosporidium and proposed a CT value equation to ensure drinking water safety effectively. The data given by the current specifications are also instructive for engineering practice. The USEPA LT2ESWTR: Toolbox Guidance Manual clearly shows the relationship between the inactivation rate of Cryptosporidium, temperature, and the CT value of ClO2, as shown in Table 5.
Inactivation rate Log . | Water temperature, (°C) . | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
<=0.5 . | 1 . | 2 . | 3 . | 5 . | 7 . | 10 . | 15 . | 20 . | 25 . | 30 . | |
0.25 | 159 | 153 | 140 | 128 | 107 | 90 | 69 | 45 | 29 | 19 | 12 |
0.5 | 319 | 305 | 279 | 256 | 214 | 180 | 138 | 89 | 58 | 38 | 24 |
1.0 | 637 | 610 | 558 | 511 | 429 | 360 | 277 | 179 | 116 | 75 | 49 |
1.5 | 956 | 915 | 838 | 767 | 643 | 539 | 415 | 268 | 174 | 113 | 73 |
2.0 | 1,275 | 1,220 | 1,117 | 1,023 | 858 | 719 | 553 | 357 | 232 | 150 | 98 |
2.5 | 1,594 | 1,525 | 1,396 | 1,278 | 1,072 | 899 | 691 | 447 | 289 | 188 | 122 |
3.0 | 1,912 | 1,830 | 1,675 | 1,534 | 1,286 | 1,079 | 830 | 536 | 347 | 226 | 147 |
Inactivation rate Log . | Water temperature, (°C) . | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
<=0.5 . | 1 . | 2 . | 3 . | 5 . | 7 . | 10 . | 15 . | 20 . | 25 . | 30 . | |
0.25 | 159 | 153 | 140 | 128 | 107 | 90 | 69 | 45 | 29 | 19 | 12 |
0.5 | 319 | 305 | 279 | 256 | 214 | 180 | 138 | 89 | 58 | 38 | 24 |
1.0 | 637 | 610 | 558 | 511 | 429 | 360 | 277 | 179 | 116 | 75 | 49 |
1.5 | 956 | 915 | 838 | 767 | 643 | 539 | 415 | 268 | 174 | 113 | 73 |
2.0 | 1,275 | 1,220 | 1,117 | 1,023 | 858 | 719 | 553 | 357 | 232 | 150 | 98 |
2.5 | 1,594 | 1,525 | 1,396 | 1,278 | 1,072 | 899 | 691 | 447 | 289 | 188 | 122 |
3.0 | 1,912 | 1,830 | 1,675 | 1,534 | 1,286 | 1,079 | 830 | 536 | 347 | 226 | 147 |
Ozone disinfection
Inactivation rate Log . | Water temperature, (°C) . | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
<=0.5 . | 1 . | 2 . | 3 . | 5 . | 7 . | 10 . | 15 . | 20 . | 25 . | >30 . | |
0.25 | 6.0 | 5.8 | 5.2 | 4.8 | 4.0 | 3.3 | 2.5 | 1.6 | 1.0 | 0.6 | 0.39 |
0.5 | 12 | 12 | 10 | 9.5 | 7.9 | 6.5 | 4.9 | 3.1 | 2.0 | 1.2 | 0.78 |
1.0 | 24 | 23 | 21 | 19 | 16 | 13 | 9.9 | 6.2 | 3.9 | 2.5 | 1.6 |
1.5 | 36 | 35 | 31 | 29 | 24 | 20 | 15 | 9.3 | 5.9 | 3.7 | 24 |
2.0 | 48 | 46 | 42 | 38 | 32 | 26 | 20 | 12 | 7.8 | 4.9 | 3.1 |
2.5 | 60 | 58 | 52 | 48 | 40 | 33 | 25 | 16 | 9.8 | 6.2 | 3.9 |
3.0 | 72 | 69 | 63 | 57 | 47 | 39 | 30 | 19 | 12 | 7.4 | 4.7 |
Inactivation rate Log . | Water temperature, (°C) . | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
<=0.5 . | 1 . | 2 . | 3 . | 5 . | 7 . | 10 . | 15 . | 20 . | 25 . | >30 . | |
0.25 | 6.0 | 5.8 | 5.2 | 4.8 | 4.0 | 3.3 | 2.5 | 1.6 | 1.0 | 0.6 | 0.39 |
0.5 | 12 | 12 | 10 | 9.5 | 7.9 | 6.5 | 4.9 | 3.1 | 2.0 | 1.2 | 0.78 |
1.0 | 24 | 23 | 21 | 19 | 16 | 13 | 9.9 | 6.2 | 3.9 | 2.5 | 1.6 |
1.5 | 36 | 35 | 31 | 29 | 24 | 20 | 15 | 9.3 | 5.9 | 3.7 | 24 |
2.0 | 48 | 46 | 42 | 38 | 32 | 26 | 20 | 12 | 7.8 | 4.9 | 3.1 |
2.5 | 60 | 58 | 52 | 48 | 40 | 33 | 25 | 16 | 9.8 | 6.2 | 3.9 |
3.0 | 72 | 69 | 63 | 57 | 47 | 39 | 30 | 19 | 12 | 7.4 | 4.7 |
UV disinfection
The chemical reagents used in the disinfection stage of water treatment often produce disinfection by-products and bring potential harm to human health. Ultraviolet disinfection is gradually applied to the water treatment industry because of its broad-spectrum antibacterial properties and no by-product formation. Many scholars began to study the effect of ultraviolet rays on the removal of Giardia and Cryptosporidium. King et al. (2008) studied the impact of tap water and environmental water on C. parvum after being irradiated by the sun. Results showed that solar radiation could reduce the infectivity of C. parvum oocysts. Soliman et al. (2018) studied the inactivation of Cryptosporidium oocysts by solar ultraviolet and artificial ultraviolet radiation. The experimental results indicate that Cryptosporidium does not have the ability to infect mice again after 4 hours of artificial ultraviolet radiation of 10 mJ/cm2 or natural sunlight of 8 hours ultraviolet radiation. This conclusion provides a simple, convenient, and economical method to inactivate Giardia cysts and Cryptosporidium oocysts. Entrala et al. (2007) studied the effect of ultraviolet disinfection on the removal of Cryptosporidium oocysts by designing a medium-pressure ultraviolet reactor and a low-pressure reactor. Results showed that the flow rates in the two types of reactors are 15 m3/h and 42 m3/h, respectively. The inactivation rate of Cryptosporidium exposed to an effective UV dose of 400 J/m2 can reach 4.92 log. The U.S. and German guidelines for UV disinfection are widely recognized internationally (Ru & Pan 2011). The U.S. Environmental Protection Agency's UV Disinfection Manual (USEPA-UVDGM 2006) provides the corresponding relationship between the inactivation rate of Giardia, Cryptosporidium, and viruses with the irradiation measurement, as shown in Table 7. Ru & Pan (2011) combined USEPA-UVDGM and German standard DVGM to apply UV disinfection to a water plant in Shanghai. The designed UV reactor has a maximum water volume of 6,627 m3/h and a minimum water volume of 3,313 m3/h. The ultraviolet dose in the early stage of the operation is 270 J/m2. The long-term ultraviolet measurement is 400 J/m2, making the removal rates of Giardia and Cryptosporidium reach 2.5–3.0 log. However, the data provided by USEPA-UVDGM suggests that, although ultraviolet rays can effectively kill the Giardia cysts and Cryptosporidium oocysts, the amount of exposure required for viruses in the water is extremely large.
Inactivation rate/log . | 0.5 . | 1 . | 1.5 . | 2 . | 2.5 . | 3 . | 3.5 . | 4 . |
---|---|---|---|---|---|---|---|---|
Giardia/J/m2 | 16 | 25 | 39 | 58 | 85 | 120 | 150 | 220 |
Cryptosporidium/J/m2 | 15 | 21 | 30 | 52 | 77 | 110 | 150 | 220 |
Virus/J/m2 | 390 | 580 | 790 | 1,000 | 1,210 | 1,430 | 1,630 | 1,860 |
Inactivation rate/log . | 0.5 . | 1 . | 1.5 . | 2 . | 2.5 . | 3 . | 3.5 . | 4 . |
---|---|---|---|---|---|---|---|---|
Giardia/J/m2 | 16 | 25 | 39 | 58 | 85 | 120 | 150 | 220 |
Cryptosporidium/J/m2 | 15 | 21 | 30 | 52 | 77 | 110 | 150 | 220 |
Virus/J/m2 | 390 | 580 | 790 | 1,000 | 1,210 | 1,430 | 1,630 | 1,860 |
CONCLUSIONS
- 1.
As intestinal pathogenic microorganisms, Giardia and Cryptosporidium have a wide range of transmission routes and high biosafety risks. Studies have found that pets are commonly infected with Giardia and Cryptosporidium. Therefore, the health department should initiate investigations and prevention of pets infected with zoonotic protozoa. At the same time, water source transmission is their main way of transmission, and attention should be paid to their removal and monitoring in drinking water treatment. The pollution of Giardia and Cryptosporidium is more serious in the water environment of many places in China, and there are more reports of infections in humans. The number of reports on Giardia and Cryptosporidium in other countries is uneven. Countries in Asia and Africa have more serious pollution problems. Thailand, India, Bangladesh, Jordan, and Egypt have high infection rates among children and immunodeficient patients. The high infection rate is closely related to the country's sanitary conditions and water environment. Therefore, it is necessary to improve the construction of sanitation facilities and close attention to high-risk susceptible groups.
- 2.
The detection technology for Giardia and Cryptosporidium has matured through decades of diagnostic developments. The detection methods are abundant, and many research contents are combining multiple detection methods. Researchers should choose suitable detection methods according to their experimental conditions. International organizations should promote the LAMP method in Asia and Africa as the core detection method for such high-infection poverty areas and apply it to water environment detection and medical emergency detection in poor areas. All stages of drinking water treatment have a certain removal effect on Giardia and Cryptosporidium. The coagulation-sedimentation-filtration method is to remove them as suspended particles. The removal rate is related to the selection of coagulant, filtration method, turbidity control, among other factors. Commonly used coagulants, such as ferric chloride, alum, and polyaluminum chloride, can effectively remove Giardia and Cryptosporidium and are not affected by environmental pH. It is recommended to use the combination of anthracite + silica sand + garnet to remove the cysts and oocysts of the two protozoa. The minimum limit of the removal rate of Giardia and Cryptosporidium in the filtered water is 2.0 log, taking into account that the removal rate needs to be increased according to the content of the raw water. The different disinfection processes have different killing intensities for Giardia and Cryptosporidium. Compared with Cl2, ClO2 has a stronger killing effect, while ozone and ultraviolet disinfection have better killing effects among them. However, it is not recommended to use a single disinfectant. Moreover, the operating parameters should be implemented according to the USEPA standard.
- 3.
Improved drinking water treatment process is a key link to prevent Giardia and Cryptosporidium from spreading through water sources and causing harm to human health. However, this method requires strict treatment management and close monitoring. In the absence of a water treatment facility, protozoa infections still occur frequently in some regions of various countries. As an emerging water treatment technology, membrane separation technology can achieve excellent control for Giardia and Cryptosporidium. The most widely used microfiltration membrane can reach a removal rate of up to 6.0 log. Therefore, it is recommended that the conventional water treatment methods for Giardia and Cryptosporidium involve an appropriate amount of ozone or ultraviolet based on controlling the effluent of the filtered water. It should also use a microfiltration membrane to intercept the protozoa cysts and oocysts to ensure safety. In the emergency water source treatment, the treatment method of ultraviolet + microfiltration can be directly adapted to provide a higher water treatment per unit time under the premise of ensuring water quality safety.
ACKNOWLEDGEMENTS
This research is funded by National Natural Science Foundation of China (51678026) and Beijing University of Civil Engineering Postgraduate Innovation Project (PG2021047). We would like to express our gratitude to EditSprings (https://www.editsprings.com/) for the expert linguistic services provided.
CONFLICT OF INTEREST
We declare that we have no financial and personal conflicts of interest to this work.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.