technologies. Critical Reviews in Environmental Science and Technology, 44(12), 1370–
1421. https://doi.org/10.1080/10643389.2013.781936.
3. Hoskins, J. (1934). Most probable numbers for evaluation of coliaerogenes tests by
fermentation tube method. Public Health Reports, 1896–1970, 393–405.
4. Slanetz, L., Bent, D., & Bartley, C. H. (1955). Use of the membrane filter technique to
enumerate enterococci in water. Public Health Reports, 70(1), 67.
5. Bej, A. K., Steffan, R. J., DiCesare, J., Haff, L., & Atlas, R. M. (1990). Detection of coliform
bacteria in water by polymerase chain reaction and gene probes. Applied and Environment
Microbiology, 56(2), 307–314.
6. Glöckner, F. O., Babenzien, H.-D., & Amann, R. (1998). Phylogeny and identification
in situ of Nevskia ramosa. Applied and Environment Microbiology, 64(5), 1895–1901.
7. Giddings, J., Karaiskakis, G., & Caldwell, K. (1981). Concentration and analysis of dilute
colloidal samples by sedimentation field-flow fractionation. Separation Science and
Technology, 16(6), 725–744. https://doi.org/10.1080/01496398108058124.
8. Joyce, R. J., & Dhillon, H. S. (1994). Trace level determination of bromate in ozonated
drinking water using ion chromatography. Journal of Chromatography A, 671(1–2), 165–
171. https://doi.org/10.1016/0021-9673(94)80235-1.
9. Kolpin, D. W., Furlong, E. T., Meyer, M. T., Thurman, E. M., Zaugg, S. D., Barber, L. B.,
et al. (2002). Pharmaceuticals, hormones, and other organic wastewater contaminants in US
streams, 1999–2000: A national reconnaissance. Environmental Science and Technology, 36
(6), 1202–1211. https://doi.org/10.1021/es011055j.
10. Cloete, N. A., Malekian, R., & Nair, L. (2016). Design of smart sensors for real-time water
quality monitoring. IEEE Access, 4, 3975–3990. https://doi.org/10.1109/ACCESS.2016.
2592958.
11. Dong, J., Wang, G., Yan, H., Xu, J., & Zhang, X. (2015). A survey of smart water quality
monitoring system. Environmental Science and Pollution Research, 22(7), 4893–4906.
12. Geetha, S., & Gouthami, S. (2016). Internet of things enabled real time water quality
monitoring system. Smart Water, 2(1), 1. https://doi.org/10.1186/s40713-017-0005-y.
13. Rodriguez-Mozaz, S., de Alda, M. J. L., & Barcelo, D. (2006). Biosensors as useful tools for
environmental analysis and monitoring. Analytical and Bioanalytical Chemistry, 386(4),
1025–1041. https://doi.org/10.1007/s00216-006-0574-3.
14. Bisha, B., Adkins, J. A., Jokerst, J. C., Chandler, J. C., Pérez-Méndez, A., Coleman, S.M.,
et al. (2014). Colorimetric paper-based detection of Escherichia coli, Salmonella spp., and
Listeria monocytogenes from large volumes of agricultural water. JoVE (Journal of
Visualized Experiments), 88, e51414. https://doi.org/10.3791/51414.
15. Kim, J.-Y., & Yeo, M.-K. (2016). A fabricated microfluidic paper-based analytical device
(lPAD) for in situ rapid colorimetric detection of microorganisms in environmental water
samples. Molecular & Cellular Toxicology, 12(1), 101–109. https://doi.org/10.1007/s13273016-0013-2.
16. Yang, K., Peretz-Soroka, H., Liu, Y., & Lin, F. (2016). Novel developments in mobile
sensing based on the integration of microfluidic devices and smartphones. Lab on a Chip, 16
(6), 943–958.
17. Mao, X., & Huang, T. J. (2012). Microfluidic diagnostics for the developing world. Lab on a
Chip, 12(8), 1412–1416. https://doi.org/10.1039/c2lc90022j.
18. Martinez, A. W., Phillips, S. T., Butte, M. J., & Whitesides, G. M. (2007). Patterned paper as
a platform for inexpensive, low-volume, portable bioassays. Angewandte Chemie
International Edition, 46(8), 1318–1320. https://doi.org/10.1002/anie.200603817.
19. Cate, D. M., Adkins, J. A., Mettakoonpitak, J., & Henry, C. S. (2014). Recent developments
in paper-based microfluidic devices. Analytical Chemistry, 87(1), 19–41. https://doi.org/10.
1021/ac503968p.
20. Li, M., Cao, R., Nilghaz, A., Guan, L., Zhang, X., & Shen, W. (2015). “Periodic-table-style”
paper device for monitoring heavy metals in water. Analytical Chemistry, 87(5), 2555–2559.
https://doi.org/10.1021/acs.analchem.5b00040.
Emerging Techniques and Materials for Water Pollutants Detection
291
1421. https://doi.org/10.1080/10643389.2013.781936.
3. Hoskins, J. (1934). Most probable numbers for evaluation of coliaerogenes tests by
fermentation tube method. Public Health Reports, 1896–1970, 393–405.
4. Slanetz, L., Bent, D., & Bartley, C. H. (1955). Use of the membrane filter technique to
enumerate enterococci in water. Public Health Reports, 70(1), 67.
5. Bej, A. K., Steffan, R. J., DiCesare, J., Haff, L., & Atlas, R. M. (1990). Detection of coliform
bacteria in water by polymerase chain reaction and gene probes. Applied and Environment
Microbiology, 56(2), 307–314.
6. Glöckner, F. O., Babenzien, H.-D., & Amann, R. (1998). Phylogeny and identification
in situ of Nevskia ramosa. Applied and Environment Microbiology, 64(5), 1895–1901.
7. Giddings, J., Karaiskakis, G., & Caldwell, K. (1981). Concentration and analysis of dilute
colloidal samples by sedimentation field-flow fractionation. Separation Science and
Technology, 16(6), 725–744. https://doi.org/10.1080/01496398108058124.
8. Joyce, R. J., & Dhillon, H. S. (1994). Trace level determination of bromate in ozonated
drinking water using ion chromatography. Journal of Chromatography A, 671(1–2), 165–
171. https://doi.org/10.1016/0021-9673(94)80235-1.
9. Kolpin, D. W., Furlong, E. T., Meyer, M. T., Thurman, E. M., Zaugg, S. D., Barber, L. B.,
et al. (2002). Pharmaceuticals, hormones, and other organic wastewater contaminants in US
streams, 1999–2000: A national reconnaissance. Environmental Science and Technology, 36
(6), 1202–1211. https://doi.org/10.1021/es011055j.
10. Cloete, N. A., Malekian, R., & Nair, L. (2016). Design of smart sensors for real-time water
quality monitoring. IEEE Access, 4, 3975–3990. https://doi.org/10.1109/ACCESS.2016.
2592958.
11. Dong, J., Wang, G., Yan, H., Xu, J., & Zhang, X. (2015). A survey of smart water quality
monitoring system. Environmental Science and Pollution Research, 22(7), 4893–4906.
12. Geetha, S., & Gouthami, S. (2016). Internet of things enabled real time water quality
monitoring system. Smart Water, 2(1), 1. https://doi.org/10.1186/s40713-017-0005-y.
13. Rodriguez-Mozaz, S., de Alda, M. J. L., & Barcelo, D. (2006). Biosensors as useful tools for
environmental analysis and monitoring. Analytical and Bioanalytical Chemistry, 386(4),
1025–1041. https://doi.org/10.1007/s00216-006-0574-3.
14. Bisha, B., Adkins, J. A., Jokerst, J. C., Chandler, J. C., Pérez-Méndez, A., Coleman, S.M.,
et al. (2014). Colorimetric paper-based detection of Escherichia coli, Salmonella spp., and
Listeria monocytogenes from large volumes of agricultural water. JoVE (Journal of
Visualized Experiments), 88, e51414. https://doi.org/10.3791/51414.
15. Kim, J.-Y., & Yeo, M.-K. (2016). A fabricated microfluidic paper-based analytical device
(lPAD) for in situ rapid colorimetric detection of microorganisms in environmental water
samples. Molecular & Cellular Toxicology, 12(1), 101–109. https://doi.org/10.1007/s13273016-0013-2.
16. Yang, K., Peretz-Soroka, H., Liu, Y., & Lin, F. (2016). Novel developments in mobile
sensing based on the integration of microfluidic devices and smartphones. Lab on a Chip, 16
(6), 943–958.
17. Mao, X., & Huang, T. J. (2012). Microfluidic diagnostics for the developing world. Lab on a
Chip, 12(8), 1412–1416. https://doi.org/10.1039/c2lc90022j.
18. Martinez, A. W., Phillips, S. T., Butte, M. J., & Whitesides, G. M. (2007). Patterned paper as
a platform for inexpensive, low-volume, portable bioassays. Angewandte Chemie
International Edition, 46(8), 1318–1320. https://doi.org/10.1002/anie.200603817.
19. Cate, D. M., Adkins, J. A., Mettakoonpitak, J., & Henry, C. S. (2014). Recent developments
in paper-based microfluidic devices. Analytical Chemistry, 87(1), 19–41. https://doi.org/10.
1021/ac503968p.
20. Li, M., Cao, R., Nilghaz, A., Guan, L., Zhang, X., & Shen, W. (2015). “Periodic-table-style”
paper device for monitoring heavy metals in water. Analytical Chemistry, 87(5), 2555–2559.
https://doi.org/10.1021/acs.analchem.5b00040.
Emerging Techniques and Materials for Water Pollutants Detection
291