Skip to main content
SpringerLink
Log in

Emerging Techniques and Materials for Water Pollutants Detection

  • Chapter
  • First Online:
Sensors in Water Pollutants Monitoring: Role of Material

Part of the book series: Advanced Functional Materials and Sensors ((AFMS))

Abstract

Rapid industrialization and population boom have led to deterioration of water quality, rising health issues and environmental damage, which made it imperative to monitor and regulate the water pollutants. Earlier, water samples were monitored using grab sampling method and then analysis was performed in the laboratory. This process is arduous and time consuming; also, there are poor chances of detecting a periodic pollution. For a proactive response sensor technology is popular these days for pollutant monitoring. A variety of sensing techniques and materials are available. But recently nanomaterials have absorbed attention for fabricating sensors owing to their high surface to volume ratio, ease of functionalization which enable them to have high specificity and sensitivity. This chapter intends to review the emerging materials used for making water pollutant sensors and gives an insight into the emerging techniques like microfluidic sensors, biosensors, wireless sensor network and smart sensors.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Vikesland, P. J. (2018). Nanosensors for water quality monitoring. Nature Nanotechnology, 13(8), 651.

    Article  CAS  Google Scholar 

  2. Banna, M. H., Imran, S., Francisque, A., Najjaran, H., Sadiq, R., Rodriguez, M., et al. (2014). Online drinking water quality monitoring: Review on available and emerging technologies. Critical Reviews in Environmental Science and Technology, 44(12), 1370–1421. https://doi.org/10.1080/10643389.2013.781936.

    Article  CAS  Google Scholar 

  3. Hoskins, J. (1934). Most probable numbers for evaluation of coliaerogenes tests by fermentation tube method. Public Health Reports, 1896–1970, 393–405.

    Article  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    CAS  Google Scholar 

  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.

    Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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 (μPAD) for in situ rapid colorimetric detection of microorganisms in environmental water samples. Molecular & Cellular Toxicology, 12(1), 101–109. https://doi.org/10.1007/s13273-016-0013-2.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  21. Almeida, M. I. G., Jayawardane, B. M., Kolev, S. D., & McKelvie, I. D. (2018). Developments of microfluidic paper-based analytical devices (μPADs) for water analysis: A review. Talanta, 177, 176–190. https://doi.org/10.1016/j.talanta.2017.08.072.

    Article  CAS  Google Scholar 

  22. Xu, Y., Liu, M., Kong, N., & Liu, J. (2016). Lab-on-paper micro-and nano-analytical devices: fabrication, modification, detection and emerging applications. Microchimica Acta, 183(5), 1521–1542.

    Article  CAS  Google Scholar 

  23. Liu, W., Guo, Y., Luo, J., Kou, J., Zheng, H., Li, B., et al. (2015). A molecularly imprinted polymer based a lab-on-paper chemiluminescence device for the detection of dichlorvos. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 141, 51–57. https://doi.org/10.1016/j.saa.2015.01.020.

    Article  CAS  Google Scholar 

  24. Nouanthavong, S., Nacapricha, D., Henry, C. S., & Sameenoi, Y. (2016). Pesticide analysis using nanoceria-coated paper-based devices as a detection platform. Analyst, 141(5), 1837–1846. https://doi.org/10.1039/C5AN02403J.

    Article  CAS  Google Scholar 

  25. Burnham, S., Hu, J., Anany, H., Brovko, L., Deiss, F., Derda, R., et al. (2014). Towards rapid on-site phage-mediated detection of generic Escherichia coli in water using luminescent and visual readout. Analytical and Bioanalytical Chemistry, 406(23), 5685–5693. https://doi.org/10.1007/s00216-014-7985-3.

    Article  CAS  Google Scholar 

  26. Badawy, M. E., & El-Aswad, A. F. (2014). Bioactive paper sensor based on the acetylcholinesterase for the rapid detection of organophosphate and carbamate pesticides. International Journal of Analytical Chemistry. https://doi.org/10.1155/2014/536823.

  27. Cardoso, T. M., Garcia, P. T., & Coltro, W. K. (2015). Colorimetric determination of nitrite in clinical, food and environmental samples using microfluidic devices stamped in paper platforms. Analytical Methods, 7(17), 7311–7317.

    Article  CAS  Google Scholar 

  28. Asano, H., & Shiraishi, Y. (2015). Development of paper-based microfluidic analytical device for iron assay using photomask printed with 3D printer for fabrication of hydrophilic and hydrophobic zones on paper by photolithography. Analytica Chimica Acta, 883, 55–60. https://doi.org/10.1016/j.aca.2015.04.014.

    Article  CAS  Google Scholar 

  29. Jiang, X., & Fan, Z. H. (2016). Fabrication and operation of paper-based analytical devices. Annual Review of Analytical Chemistry, 9, 203–222. https://doi.org/10.1146/annurev-anchem-071015-041714.

    Article  Google Scholar 

  30. Khanna, M., Roy, S., Kumar, R., Wadhwa, S., Mathur, A., & Dubey, A. K. (2018). MnO2 based Bisphenol-A electrochemical sensor using micro-fluidic platform. IEEE Sensors Journal, 18(6), 2206–2210. https://doi.org/10.1109/JSEN.2018.2792476.

    Article  CAS  Google Scholar 

  31. Meelapsom, R., Jarujamrus, P., Amatatongchai, M., Chairam, S., Kulsing, C., & Shen, W. (2016). Chromatic analysis by monitoring unmodified silver nanoparticles reduction on double layer microfluidic paper-based analytical devices for selective and sensitive determination of mercury (II). Talanta, 155, 193–201. https://doi.org/10.1016/j.talanta.2016.04.037.

    Article  CAS  Google Scholar 

  32. Ortiz-Gomez, I., Ortega-Muñoz, M., Salinas-Castillo, A., Álvarez-Bermejo, J. A., Ariza-Avidad, M., de Orbe-Payá, I., et al. (2016). Tetrazine-based chemistry for nitrite determination in a paper microfluidic device. Talanta, 160, 721–728.

    Article  CAS  Google Scholar 

  33. Sicard, C., Glen, C., Aubie, B., Wallace, D., Jahanshahi-Anbuhi, S., Pennings, K., et al. (2015). Tools for water quality monitoring and mapping using paper-based sensors and cell phones. Water Research, 70, 360–369. https://doi.org/10.1016/j.watres.2014.12.005.

    Article  CAS  Google Scholar 

  34. Santhiago, M., Henry, C. S., & Kubota, L. T. (2014). Low cost, simple three dimensional electrochemical paper-based analytical device for determination of p-nitrophenol. Electrochimica Acta, 130, 771–777. https://doi.org/10.1016/j.electacta.2014.03.109.

    Article  CAS  Google Scholar 

  35. Martinez, A. W., Phillips, S. T., Carrilho, E., Thomas, S. W., III, Sindi, H., & Whitesides, G. M. (2008). Simple telemedicine for developing regions: Camera phones and paper-based microfluidic devices for real-time, off-site diagnosis. Analytical Chemistry, 80(10), 3699–3707.

    Article  CAS  Google Scholar 

  36. Loh, L. J., Bandara, G. C., Weber, G. L., & Remcho, V. T. (2015). Detection of water contamination from hydraulic fracturing wastewater: A μPAD for bromide analysis in natural waters. Analyst, 140(16), 5501–5507. https://doi.org/10.1039/C5AN00807G.

    Article  CAS  Google Scholar 

  37. Jayawardane, B. M., McKelvie, I. D., & Kolev, S. D. (2015). Development of a gas-diffusion microfluidic paper-based analytical device (μPAD) for the determination of ammonia in wastewater samples. Analytical Chemistry, 87(9), 4621–4626.

    Article  CAS  Google Scholar 

  38. Lopez-Ruiz, N., Curto, V. F., Erenas, M. M., Benito-Lopez, F., Diamond, D., Palma, A. J., et al. (2014). Smartphone-based simultaneous pH and nitrite colorimetric determination for paper microfluidic devices. Analytical Chemistry, 86(19), 9554–9562. https://doi.org/10.1021/ac5019205.

    Article  CAS  Google Scholar 

  39. Turner, A. P. (2000). Biosensors–sense and sensitivity. Science, 290(5495), 1315–1317.

    Article  CAS  Google Scholar 

  40. Ivars-Barceló, F., Zuliani, A., Fallah, M., Mashkour, M., Rahimnejad, M., & Luque, R. (2018). Novel applications of microbial fuel cells in sensors and biosensors. Applied Sciences, 8(7), 1184. https://doi.org/10.3390/app8071184.

    Article  CAS  Google Scholar 

  41. Zeng, L., Gong, J., Rong, P., Liu, C., & Chen, J. (2019). A portable and quantitative biosensor for cadmium detection using glucometer as the point-of-use device. Talanta, 198, 412–416. https://doi.org/10.1016/j.talanta.2019.02.045.

    Article  CAS  Google Scholar 

  42. Wee, Y., Park, S., Kwon, Y. H., Ju, Y., Yeon, K. M., & Kim, J. (2019). Tyrosinase-immobilized CNT based biosensor for highly-sensitive detection of phenolic compounds. Biosensors & Bioelectronics. https://doi.org/10.1016/j.bios.2019.03.008.

    Article  Google Scholar 

  43. Yu, Y., Yu, C., Gao, R., Chen, J., Zhong, H., Wen, Y., et al. (2019). Dandelion-like CuO microspheres decorated with Au nanoparticle modified biosensor for Hg2+ detection using a T-Hg2+-T triggered hybridization chain reaction amplification strategy. Biosensors & Bioelectronics, 131, 207–213. https://doi.org/10.1016/j.bios.2019.01.063.

    Article  CAS  Google Scholar 

  44. Atar, N., Eren, T., Yola, M. L., & Wang, S. (2015). A sensitive molecular imprinted surface plasmon resonance nanosensor for selective determination of trace triclosan in wastewater. Sensors and Actuators B: Chemical, 216, 638–644. https://doi.org/10.1016/j.snb.2015.04.076.

    Article  CAS  Google Scholar 

  45. Nomngongo, P. N., Ngila, J. C., Msagati, T. A., Gumbi, B. P., & Iwuoha, E. I. (2012). Determination of selected persistent organic pollutants in wastewater from landfill leachates, using an amperometric biosensor. Physics and Chemistry of the Earth, Parts A/B/C, 50, 252–261. https://doi.org/10.1016/j.pce.2012.08.001.

    Article  Google Scholar 

  46. Yamashita, T., Ookawa, N., Ishida, M., Kanamori, H., Sasaki, H., Katayose, Y., et al. (2016). A novel open-type biosensor for the in-situ monitoring of biochemical oxygen demand in an aerobic environment. Scientific Reports, 6, 38552.

    Article  CAS  Google Scholar 

  47. Biswas, P., Karn, A. K., Balasubramanian, P., & Kale, P. G. (2017). Biosensor for detection of dissolved chromium in potable water: A review. Biosensors & Bioelectronics, 94, 589–604. https://doi.org/10.1016/j.bios.2017.03.043.

    Article  CAS  Google Scholar 

  48. Verma, N., Sharma, R., & Kumar, S. (2016). Advancement towards microfluidic approach to develop economical disposable optical biosensor for lead detection. Austin Journal of Biosensors & Bioelectronics, 2(2), 1021.

    Google Scholar 

  49. Qiu, G., Ng, S. P., & Wu, C.-M. L. (2018). Bimetallic Au-Ag alloy nanoislands for highly sensitive localized surface plasmon resonance biosensing. Sensors and Actuators B: Chemical, 265, 459–467. https://doi.org/10.1016/j.snb.2018.03.066.

    Article  CAS  Google Scholar 

  50. Guo, B., Wen, B., Cheng, W., Zhou, X., Duan, X., Zhao, M., et al. (2018). An enzyme-free and label-free surface plasmon resonance biosensor for ultrasensitive detection of fusion gene based on DNA self-assembly hydrogel with streptavidin encapsulation. Biosensors & Bioelectronics, 112, 120–126. https://doi.org/10.1016/j.bios.2018.04.027.

    Article  CAS  Google Scholar 

  51. Kassal, P., Steinberg, M. D., & Steinberg, I. M. (2018). Wireless chemical sensors and biosensors: A review. Sensors and Actuators B: Chemical, 266, 228–245. https://doi.org/10.1016/j.snb.2018.03.074.

    Article  CAS  Google Scholar 

  52. Yildirim, N., Long, F., & Gu, A. Z. (2014). Aptamer based E-coli detection in waste waters by portable optical biosensor system. In: 2014 40th Annual Northeast Bioengineering Conference (NEBEC) 2014 (pp. 1–3). IEEE.

    Google Scholar 

  53. Chouler, J., & Di Lorenzo, M. (2015). Water quality monitoring in developing countries; Can microbial fuel cells be the answer? Biosensors, 5(3), 450–470. https://doi.org/10.3390/bios5030450.

    Article  CAS  Google Scholar 

  54. Jiang, Y., Yang, X., Liang, P., Liu, P., & Huang, X. (2018). Microbial fuel cell sensors for water quality early warning systems: Fundamentals, signal resolution, optimization and future challenges. Renewable and Sustainable Energy Reviews, 81, 292–305. https://doi.org/10.1016/j.rser.2017.06.099.

    Article  CAS  Google Scholar 

  55. Modin, O., & Aulenta, F. (2017). Three promising applications of microbial electrochemistry for the water sector. Environmental Science: Water Research & Technology, 3(3), 391–402. https://doi.org/10.1039/C6EW00325G.

    Article  CAS  Google Scholar 

  56. Di Lorenzo, M., Thomson, A. R., Schneider, K., Cameron, P. J., & Ieropoulos, I. (2014). A small-scale air-cathode microbial fuel cell for on-line monitoring of water quality. Biosensors & Bioelectronics, 62, 182–188. https://doi.org/10.1016/j.bios.2014.06.050.

    Article  CAS  Google Scholar 

  57. Zhou, T., Han, H., Liu, P., Xiong, J., Tian, F., & Li, X. (2017). Microbial fuels cell-based biosensor for toxicity detection: A review. Sensors, 17(10), 2230. https://doi.org/10.3390/s17102230.

    Article  CAS  Google Scholar 

  58. ElMekawy, A., Hegab, H., Pant, D., & Saint, C. (2018). Bio-analytical applications of microbial fuel cell-based biosensors for onsite water quality monitoring. Journal of Applied Microbiology, 124(1), 302–313. https://doi.org/10.1111/jam.13631.

    Article  CAS  Google Scholar 

  59. Zhao, S., Liu, P., Niu, Y., Chen, Z., Khan, A., Zhang, P., et al. (2018). A novel early warning system based on a sediment microbial fuel cell for in situ and real time hexavalent chromium detection in industrial wastewater. Sensors, 18(2), 642. https://doi.org/10.3390/s18020642.

    Article  CAS  Google Scholar 

  60. Chouler, J., Cruz-Izquierdo, Á., Rengaraj, S., Scott, J. L., & Di Lorenzo, M. (2018). A screen-printed paper microbial fuel cell biosensor for detection of toxic compounds in water. Biosensors & Bioelectronics, 102, 49–56.

    Article  CAS  Google Scholar 

  61. Chen, Y., & Han, D. (2018). Water quality monitoring in smart city: A pilot project. Automation in Construction, 89, 307–316. https://doi.org/10.1016/j.autcon.2018.02.008.

    Article  Google Scholar 

  62. Myint, C. Z., Gopal, L., & Aung, Y. L. (2017). Reconfigurable smart water quality monitoring system in IoT environment. In: 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) 2017 (pp. 435–440). IEEE.

    Google Scholar 

  63. Bröring, A., Echterhoff, J., Jirka, S., Simonis, I., Everding, T., Stasch, C., et al. (2011). New generation sensor web enablement. Sensors, 11(3), 2652–2699. https://doi.org/10.3390/s110302652.

    Article  Google Scholar 

  64. Zhu, X., Li, D., He, D., Wang, J., Ma, D., & Li, F. (2010). A remote wireless system for water quality online monitoring in intensive fish culture. Computers and Electronics in Agriculture, 71, S3–S9. https://doi.org/10.1016/j.compag.2009.10.004.

    Article  Google Scholar 

  65. O’Flynn, B., Regan, F., Lawlor, A., Wallace, J., Torres, J., & O’Mathuna, C. (2010). Experiences and recommendations in deploying a real-time, water quality monitoring system. Measurement Science & Technology, 21(12), 124004.

    Article  Google Scholar 

  66. Gallah, N., Bahri, O., Lazreg, N., Chaouch, A., & Besbes, K. (2017). Water quality monitoring based on small satellite technology. International Journal of Advanced Computer Science and Applications, 8(3), 357–362.

    Article  Google Scholar 

  67. Dunbabin, M., & Marques, L. (2012). Robots for environmental monitoring: Significant advancements and applications. IEEE Robotics and Automation Magazine, 19(1), 24–39. https://doi.org/10.1109/MRA.2011.2181683.

    Article  Google Scholar 

  68. Ma, J., Meng, F., Zhou, Y., Wang, Y., & Shi, P. (2018). Distributed water pollution source localization with mobile UV-visible spectrometer probes in wireless sensor networks. Sensors, 18(2), 606. https://doi.org/10.3390/s18020606.

    Article  CAS  Google Scholar 

  69. Eichhorn, M., Taubert, R., Ament, C., Jacobi, M., & Pfuetzenreuter, T. (2013). Modular AUV system for sea water quality monitoring and management. In: 2013 MTS/IEEE OCEANS-Bergen 2013 (pp. 1–7). IEEE.

    Google Scholar 

  70. Vasilijević, A., Nađ, Đ., Mandić, F., Mišković, N., & Vukić, Z. (2017). Coordinated navigation of surface and underwater marine robotic vehicles for ocean sampling and environmental monitoring. IEEE/ASME Transactions on Mechatronics, 22(3), 1174–1184. https://doi.org/10.1109/TMECH.2017.2684423.

    Article  Google Scholar 

  71. Jadaliha, M., & Choi, J. (2013). Environmental monitoring using autonomous aquatic robots: Sampling algorithms and experiments. IEEE Transactions on Control Systems Technology, 21(3), 899–905. https://doi.org/10.1109/TCST.2012.2190070.

    Article  Google Scholar 

  72. Ferri, G., Manzi, A., Fornai, F., Ciuchi, F., & Laschi, C. (2015). The HydroNet ASV, a small-sized autonomous catamaran for real-time monitoring of water quality: From design to missions at sea. IEEE Journal of Oceanic Engineering, 40(3), 710–726. https://doi.org/10.1109/JOE.2014.2359361.

    Article  Google Scholar 

  73. Liu, J., Wu, Z., & Yu, J. (2016). Design and implementation of a robotic dolphin for water quality monitoring. In: 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO) 2016 (pp. 835–840). IEEE.

    Google Scholar 

  74. Wu, Z., Liu, J., Yu, J., & Fang, H. (2017). Development of a novel robotic dolphin and its application to water quality monitoring. IEEE/ASME Transactions on Mechatronics, 22(5), 2130–2140. https://doi.org/10.1109/TMECH.2017.2722009.

    Article  Google Scholar 

  75. Ravalli, A., Rossi, C., & Marrazza, G. (2017). Bio-inspired fish robot based on chemical sensors. Sensors and Actuators B: Chemical, 239, 325–329. https://doi.org/10.1016/j.snb.2016.08.030.

    Article  CAS  Google Scholar 

  76. Zhang, F., Ennasr, O., Litchman, E., & Tan, X. (2016). Autonomous sampling of water columns using gliding robotic fish: Algorithms and harmful-algae-sampling experiments. IEEE Systems Journal, 10(3), 1271–1281. https://doi.org/10.1109/JSYST.2015.2458173.

    Article  Google Scholar 

  77. Felemban, E., Shaikh, F. K., Qureshi, U. M., Sheikh, A. A., & Qaisar, S. B. (2015). Underwater sensor network applications: A comprehensive survey. International Journal of Distributed Sensor Networks, 11(11), 896832. https://doi.org/10.1155/2015/896832.

    Article  Google Scholar 

  78. Charef, A., Ghauch, A., Baussand, P., & Martin-Bouyer, M. (2000). Water quality monitoring using a smart sensing system. Measurement, 28(3), 219–224. https://doi.org/10.1016/S0263-2241(00)00015-4.

    Article  Google Scholar 

  79. Yifan, K., & Peng, J. (2008). Development of data video base station in water environment monitoring oriented wireless sensor networks. In: 2008 International Conference on Embedded Software and Systems Symposia 2008 (pp. 281–286). IEEE.

    Google Scholar 

  80. Jiang, P., Xia, H., He, Z., & Wang, Z. (2009). Design of a water environment monitoring system based on wireless sensor networks. Sensors, 9(8), 6411–6434. https://doi.org/10.3390/s90806411.

    Article  CAS  Google Scholar 

  81. Parra, L., Rocher, J., Escrivá, J., & Lloret, J. (2018). Design and development of low cost smart turbidity sensor for water quality monitoring in fish farms. Aquacultural Engineering, 81, 10–18. https://doi.org/10.1016/j.aquaeng.2018.01.004.

    Article  Google Scholar 

  82. Dunkels, A., Osterlind, F., Tsiftes, N., & He, Z. (2007). Software-based on-line energy estimation for sensor nodes. In: Proceedings of the 4th Workshop on Embedded Networked Sensors 2007 (pp. 28–32). ACM.

    Google Scholar 

  83. Wu, R., Salim, W. W., Malhotra, S., Brovont, A., Park, J., Pekarek, S., et al. (2013). Self-powered mobile sensor for in-pipe potable water quality monitoring. In: Proceedings of the 17th International Conference on Miniaturized Systems for Chemistry and Life Sciences (pp. 14–16).

    Google Scholar 

  84. Mao, S., Chang, J., Zhou, G., & Chen, J. (2015). Nanomaterial-enabled rapid detection of water contaminants. Small (Weinheim an der Bergstrasse, Germany), 11(40), 5336–5359. https://doi.org/10.1002/smll.201500831.

    Article  CAS  Google Scholar 

  85. Willner, M. R., & Vikesland, P. J. (2018). Nanomaterial enabled sensors for environmental contaminants. Journal of Nanobiotechnology, 16(1), 95.

    Article  CAS  Google Scholar 

  86. Grieshaber, D., MacKenzie, R., Voeroes, J., & Reimhult, E. (2008). Electrochemical biosensors-sensor principles and architectures. Sensors, 8(3), 1400–1458. https://doi.org/10.3390/s80314000.

    Article  CAS  Google Scholar 

  87. Link, S., & El-Sayed, M. A. (1999). Spectral properties and relaxation dynamics of surface plasmon electronic oscillations in gold and silver nanodots and nanorods. ACS Publications.

    Google Scholar 

  88. Saha, K., Agasti, S. S., Kim, C., Li, X., & Rotello, V. M. (2012). Gold nanoparticles in chemical and biological sensing. Chemical Reviews, 112(5), 2739–2779. https://doi.org/10.1021/cr2001178.

    Article  CAS  Google Scholar 

  89. Hnaiein, M., Hassen, W., Abdelghani, A., Fournier-Wirth, C., Coste, J., Bessueille, F., et al. (2008). A conductometric immunosensor based on functionalized magnetite nanoparticles for E. coli detection. Electrochemistry Communications, 10(8), 1152–1154. https://doi.org/10.1016/j.elecom.2008.04.009.

  90. Yang, W., Ratinac, K. R., Ringer, S. P., Thordarson, P., Gooding, J. J., & Braet, F. (2010). Carbon nanomaterials in biosensors: Should you use nanotubes or graphene? Angewandte Chemie International Edition, 49(12), 2114–2138. https://doi.org/10.1002/anie.200903463.

    Article  CAS  Google Scholar 

  91. Zhan, B., Li, C., Yang, J., Jenkins, G., Huang, W., & Dong, X. (2014). Graphene field-effect transistor and its application for electronic sensing. Small (Weinheim an der Bergstrasse, Germany), 10(20), 4042–4065. https://doi.org/10.1002/smll.201400463.

    Article  CAS  Google Scholar 

  92. Xie, Y., Tu, X., Ma, X., Fang, Q., Lu, L., Yu, Y., et al. (2019). High-performance voltammetric sensor for dichlorophenol based on β-cyclodextrin functionalized boron-doped graphene composite aerogels. Nanotechnology.

    Google Scholar 

  93. Liu, G., Jia, H., Li, N., Li, X., Yu, Z., Wang, J., et al. (2019). High-fluorescent carbon dots (CDs) originated from China grass carp scales (CGCS) for effective detection of Hg (II) ions. Microchemical Journal, 145, 718–728. https://doi.org/10.1016/j.microc.2018.11.044.

    Article  CAS  Google Scholar 

  94. Li, X., Zheng, Y., Tang, Y., Chen, Q., Gao, J., Luo, Q., et al. (2019). Efficient and visual monitoring of cerium (III) ions by green-fluorescent carbon dots and paper-based sensing. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 206, 240–245. https://doi.org/10.1016/j.saa.2018.08.021.

    Article  CAS  Google Scholar 

  95. Ramírez, M. L., Tettamanti, C. S., Gutierrez, F. A., Gonzalez-Domínguez, J. M., Ansón-Casaos, A., Hernández-Ferrer, J., et al. (2018). Cysteine functionalized bio-nanomaterial for the affinity sensing of Pb (II) as an indicator of environmental damage. Microchemical Journal, 141, 271–278. https://doi.org/10.1016/j.microc.2018.05.007.

    Article  CAS  Google Scholar 

  96. Dong, Y., Li, G., Zhou, N., Wang, R., Chi, Y., & Chen, G. (2012). Graphene quantum dot as a green and facile sensor for free chlorine in drinking water. Analytical Chemistry, 84(19), 8378–8382. https://doi.org/10.1021/ac301945z.

    Article  CAS  Google Scholar 

  97. Gong, X., Liu, Y., Yang, Z., Shuang, S., Zhang, Z., & Dong, C. (2017). An “on-off-on” fluorescent nanoprobe for recognition of chromium (VI) and ascorbic acid based on phosphorus/nitrogen dual-doped carbon quantum dot. Analytica Chimica Acta, 968, 85–96. https://doi.org/10.1016/j.aca.2017.02.038.

    Article  CAS  Google Scholar 

  98. Liu, W., Wang, X., Wang, Y., Li, J., Shen, D., Kang, Q., et al. (2018). Ratiometric fluorescence sensor based on dithiothreitol modified carbon dots-gold nanoclusters for the sensitive detection of mercury ions in water samples. Sensors and Actuators B: Chemical, 262, 810–817. https://doi.org/10.1016/j.snb.2018.01.222.

    Article  CAS  Google Scholar 

  99. Tang, S., Tong, P., Li, H., Tang, J., & Zhang, L. (2013). Ultrasensitive electrochemical detection of Pb2+ based on rolling circle amplification and quantum dotstagging. Biosensors & Bioelectronics, 42, 608–611. https://doi.org/10.1016/j.bios.2012.10.073.

    Article  CAS  Google Scholar 

  100. Yang, Y., Asiri, A. M., Du, D., & Lin, Y. (2014). Acetylcholinesterase biosensor based on a gold nanoparticle–polypyrrole–reduced graphene oxide nanocomposite modified electrode for the amperometric detection of organophosphorus pesticides. Analyst, 139(12), 3055–3060. https://doi.org/10.1039/C4AN00068D.

    Article  CAS  Google Scholar 

  101. Cui, H.-F., Wu, W.-W., Li, M.-M., Song, X., Lv, Y., & Zhang, T.-T. (2018). A highly stable acetylcholinesterase biosensor based on chitosan-TiO2-graphene nanocomposites for detection of organophosphate pesticides. Biosensors & Bioelectronics, 99, 223–229. https://doi.org/10.1016/j.bios.2017.07.068.

    Article  CAS  Google Scholar 

  102. Nie, Y., Teng, Y., Li, P., Liu, W., Shi, Q., & Zhang, Y. (2018). Label-free aptamer-based sensor for specific detection of malathion residues by surface-enhanced Raman scattering. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 191, 271–276. https://doi.org/10.1016/j.saa.2017.10.030.

    Article  CAS  Google Scholar 

  103. Yang, X., Feng, L., & Qin, X. (2018). Preparation of the Cf-GQDs-Escherichia coli O157: H7 bioprobe and its application in optical imaging and sensing of Escherichia coli O157: H7. Food Analytical Methods, 11(8), 2280–2286.

    Article  Google Scholar 

  104. Parimaladevi, R., Sathe, V., & Mahalingam, U. (2019). Graphene boosted silver nanoparticles as surface enhanced Raman spectroscopic sensors and photocatalysts for removal of standard and industrial dye contaminants. Sensors and Actuators B: Chemical, 281, 679–688. https://doi.org/10.1016/j.snb.2018.11.007.

    Article  CAS  Google Scholar 

  105. Parvathi, V. P., Parimaladevi, R., Sathe, V., & Umadevi, M. (2019). Application of G-SERS for the efficient detection of toxic dye contaminants in textile effluents using gold/graphene oxide substrates. Journal of Molecular Liquids, 273, 203–214. https://doi.org/10.1016/j.molliq.2018.10.027.

    Article  CAS  Google Scholar 

  106. Chen, C., Chen, Y.-C., Hong, Y.-T., Lee, T.-W., & Huang, J.-F. (2018). Facile fabrication of ascorbic acid reduced graphene oxide-modified electrodes toward electroanalytical determination of sulfamethoxazole in aqueous environments. Chemical Engineering Journal, 352, 188–197. https://doi.org/10.1016/j.cej.2018.06.110.

    Article  CAS  Google Scholar 

  107. Wong, A., Santos, A. M., Baccarin, M., Cavalheiro, É. T. G., & Fatibello-Filho, O. (2019). Simultaneous determination of environmental contaminants using a graphite oxide–Polyurethane composite electrode modified with cyclodextrin. Materials Science and Engineering C, 99, 1415–1423. https://doi.org/10.1016/j.msec.2019.02.093.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Technology, Mandi, 175005, Himachal Pradesh, India

    Richa Soni, Mahesh Soni & Dericks Praise Shukla

Authors
  1. Richa Soni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mahesh Soni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dericks Praise Shukla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Richa Soni .

Editor information

Editors and Affiliations

  1. Central Scientific Instruments Organisation, Chandigarh, India

    D. Pooja

  2. School of Material Science, Indian Association for Cultivation of Science, Kolkata, West Bengal, India

    Praveen Kumar

  3. P.G.D.A.V. College, University of Delhi, New Delhi, Delhi, India

    Pardeep Singh

  4. Indian Institute of Technology Kanpur, Kalyanpur, Uttar Pradesh, India

    Sandip Patil

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Soni, R., Soni, M., Shukla, D.P. (2020). Emerging Techniques and Materials for Water Pollutants Detection. In: Pooja, D., Kumar, P., Singh, P., Patil, S. (eds) Sensors in Water Pollutants Monitoring: Role of Material. Advanced Functional Materials and Sensors. Springer, Singapore. https://doi.org/10.1007/978-981-15-0671-0_15

Download citation

Publish with us

Policies and ethics

Search

Navigation