Water Quality Monitoring System for Temperature, pH, Turbidity, DO, BOD, and COD Parameters Based on Internet of Things in the Garang Watershed

Syafrudin; Sarminingsih, Anik; Juliani, Henny; Budihardjo, Mochamad Arief; Puspita, Annisa Sila; Mirhan, Shafa Auliya Arlin

doi:10.12912/27197050/174412

Artykuł - szczegóły

Tytuł artykułu

Water Quality Monitoring System for Temperature, pH, Turbidity, DO, BOD, and COD Parameters Based on Internet of Things in the Garang Watershed

Autorzy

Syafrudin , Sarminingsih Anik , Juliani Henny , Budihardjo Mochamad Arief , Puspita Annisa Sila , Mirhan Shafa Auliya Arlin

Treść / Zawartość

Pełne teksty:

Pobierz

Identyfikatory

DOI

10.12912/27197050/174412

Warianty tytułu

Języki publikacji

Abstrakty

Water has recently become a final disposal site for wastewater. Land use has evolved with the global population growth and is generally transformed into settlements and industrial areas. These land use changes could potentially increase wastewater generation from both domestic and non-domestic activities. The Garang watershed, one of the watersheds in Central Java, flows through the Semarang Regency, Kendal Regency, and Semarang City. This study analyzed the water quality conditions in the Garang watershed and designed a real-time water quality monitoring system. The methods used in this study included SWMM modeling, the national sanitation foundation water quality index (NSF-WQI), and the internet of things. Samples were collected from 10 points in the Garang Watershed, with a sampling frequency of five times at each point. The results of the data analysis demonstrated that the differences in land use resulted in varying water parameter levels. The results of the SWMM modeling demonstrated an acceptable model value (NOF between 0 and 1). On the other hand, the WQI analysis results demonstrated that the quality status at the Garang watershed is "medium" at nearly all location points. The designed real-time water quality sensor tool successfully transmitted water quality data online and in real-time, particularly for temperature, pH, turbidity, and DO. This water quality monitoring system offers a variable percentage error value, with the pH sensor ranging between 0.16% and 9.86% and the temperature sensor ranging between 0.64% and 1.72%.

Słowa kluczowe

water quality index SWMM monitoring system internet of things

Wydawca

Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej
Lublin University of Technology
WNGB Wydawnictwo Naukowe

Czasopismo

Ecological Engineering & Environmental Technology

Rocznik

2024

Tom

Vol. 25, iss. 2

Strony

1--16

Opis fizyczny

Bibliogr. 63 poz., rys., tab.

Twórcy

autor

Syafrudin

Departement of Environmental Engineering, Faculty of Engineering, Universitas Diponegoro, Jalan Prof. Soedarto, SH, Semarang, 502775, Indonesia

https://orcid.org/0000-0002-1187-6361

autor

Sarminingsih Anik

Departement of Environmental Engineering, Faculty of Engineering, Universitas Diponegoro, Jalan Prof. Soedarto, SH, Semarang, 502775, Indonesia

https://orcid.org/0000-0002-8550-5803

autor

Juliani Henny

Faculty of Law, Universitas Diponegoro, Jalan Prof. Soedarto, SH, Semarang, 502775, Indonesia

https://orcid.org/0009-0003-1319-8642

autor

Budihardjo Mochamad Arief

m.budihardjo@ft.undip.ac.id

Departement of Environmental Engineering, Faculty of Engineering, Universitas Diponegoro, Jalan Prof. Soedarto, SH, Semarang, 502775, Indonesia

https://orcid.org/0000-0002-1256-3076

autor

Puspita Annisa Sila

Environmental Sustainability Research Group, Departement of Environmental Engineering, Faculty of Engineering, Universitas Diponegoro, Indonesia

https://orcid.org/0000-0002-5207-0735

autor

Mirhan Shafa Auliya Arlin

Environmental Sustainability Research Group, Departement of Environmental Engineering, Faculty of Engineering, Universitas Diponegoro, Indonesia

https://orcid.org/0009-0004-4535-2517

Bibliografia

1. Ajith, J.B., Manimegalai, R., Ilayaraja, V. 2020. An IoT based smart water quality monitoring system using cloud. Paper presented at the 2020 International conference on emerging trends in information technology and engineering (ic-ETITE).
2. Akhtar, N., Syakir Ishak, M.I., Bhawani, S.A., Umar, K. 2021. Various natural and anthropogenic factors responsible for water quality degradation: A review. Water, 13(19), 2660.
3. Akhter, F., Siddiquei, H.R., Alahi, M.E.E., Jayasundera, K.P., Mukhopadhyay, S.C. 2021. An IoT-enabled portable water quality monitoring system with MWCNT/PDMS multifunctional sensor for agricultural applications. IEEE Internet of Things Journal, 9(16), 14307-14316.
4. Al Rasyid, M.U.H., Nadhori, I.U., Sudarsono, A., Alnovinda, Y.T. 2016. Pollution monitoring system using gas sensor based on wireless sensor network. International Journal of Engineering and Technology Innovation, 6(1), 79.
5. Andimuthu, R., Kandasamy, P., Mudgal, B., Jeganathan, A., Balu, A., Sankar, G. 2019. Performance of urban storm drainage network under changing climate scenarios: Flood mitigation in Indian coastal city. Scientific reports, 9(1), 7783.
6. Asgari, G., Shabanloo, A., Salari, M., Eslami, F. 2020. Sonophotocatalytic treatment of AB113 dye and real textile wastewater using ZnO/persulfate: Modeling by response surface methodology and artificial neural network. Environmental research, 184, 109367.
7. BPS. 2022. Kota Semarang dalam Angka 2022.
8. Brontowiyono, W., Asmara, A. A., Jana, R., Yulianto, A., Rahmawati, S. 2022. Land-use impact on water quality of the opak sub-watershed, yogyakarta, indonesia. Sustainability, 14(7), 4346.
9. Budihardjo, M.A., Arumdani, I.S., Puspita, A.S., Ambariyanto, A. 2022. Improving Water Conservation at Universitas Diponegoro, Indonesia. Journal of Sustainability Perspectives, 2, 277-284.
10. Burns, M.J., Nixon, G.J., Foy, C.A., Harris, N. 2005. Standardisation of data from real-time quantitative PCR methods–evaluation of outliers and comparison of calibration curves. BMC biotechnology, 5(1), 1-13.
11. Campisi, T., Acampa, G., Marino, G., Tesoriere, G. 2020. Cycling master plans in Italy: The I-BIM feasibility tool for cost and safety assessments. Sustainability, 12(11), 4723.
12. Cicero Fernandez, D., Expósito Camargo, J., Peña Fernandez, M., Antizar-Ladislao, B. 2019. Carex paniculata constructed wetland efficacy for stormwater, sewage and livestock wastewater treatment in rural settlements of mountain areas. Water Science and Technology, 79(7), 1338-1347.
13. Corsita, L. 2021. Pengelolaan Waduk Kaskade Citarum Untuk Pengembangan Air Baku Infrastruktur Air Minum DKI Jakarta.
14.Costabile, P., Costanzo, C., Ferraro, D., Macchione, F., Petaccia, G. 2020. Performances of the new HEC-RAS version 5 for 2-D hydrodynamic-based rainfall-runoff simulations at basin scale: Comparison with a state-of-the art model. Water, 12(9), 2326.
15. Deng, Q., Goudarzi, M., Buyya, R. 2021. Fogbus2: a lightweight and distributed container-based framework for integration of iot-enabled systems with edge and cloud computing. Paper presented at the Proceedings of the international workshop on big data in emergent distributed environments.
16. Dibaba, W.T., Demissie, T.A., Miegel, K. 2020. Drivers and implications of land use/land cover dynamics in Finchaa catchment, northwestern Ethiopia. Land, 9(4), 113.
17. Dong, T.Y., Nittrouer, J.A., McElroy, B., Il’icheva, E., Pavlov, M., Ma, H., Moreido, V.M. 2020. Predicting water and sediment partitioning in a delta channel network under varying discharge conditions. Water Resources Research, 56(11), e2020WR027199.
18. Dwi Sholehah, F., Samudro, G., Sarminingsih, A. 2019. Evaluasi kualitas air saluran pembuangan di sekitar segmen vi sungai garang menggunakan software swmm studi kasus: kecamatan gajahmungkur, kota semarang. Universitas Diponegoro.
19. Ellis, E.C. 2021. Land use and ecological change: A 12,000-year history. Annual Review of Environment and Resources, 46, 1-33.
20. Ewaid, S.H., Abed, S.A., Al-Ansari, N., Salih, R.M. 2020. Development and evaluation of a water quality index for the Iraqi rivers. Hydrology, 7(3), 67.
21. Ghazavi, R., Imani, R., Esmali Ouri, A. 2020. Development and application of a new index-overlay method to assess urban runoff vulnerability to contamination (evaluation in the Ardabil city, Iran). Arabian Journal of Geosciences, 13, 1-12.
22. Gulati, K., Boddu, R.S.K., Kapila, D., Bangare, S.L., Chandnani, N., Saravanan, G. 2022. A review paper on wireless sensor network techniques in Internet of Things (IoT). Materials Today: Proceedings, 51, 161-165.
23. Hanafi, F., Pamungkas, D. 2021. Aplikasi Model Rusle untuk Estimasi Kehilangan Tanah Bagian Hulu di Sub Das Garang, Jawa Tengah. Jurnal Geografi: Media Informasi Pengembangan dan Profesi Kegeografian, 18(1), 30-36.
24. Hardyanti, N., Susanto, H., Budihardjo, M., Purwono, P., Saputra, A. 2023. Application of response surface methodology (rsm) for optimisation of cod removal from tofu wastewater in an adsorption process using silica adsorbent. Paper presented at the IOP Conference Series: Earth and Environmental Science.
25. Huan, J., Li, H., Wu, F., Cao, W. 2020. Design of water quality monitoring system for aquaculture ponds based on NB-IoT. Aquacultural Engineering, 90, 102088.
26. Huda, A. S., Nugraha, A. L., Bashit, N. 2019. Analisis Perubahan Laju Erosi Periode Tahun 2013 Dan Tahun 2018 Berbasis Data Pengindraan Jauh Dan Sistem Informasi Geografis (Studi Kasus: Das Garang). Jurnal Geodesi Undip, 9(1), 106-114.
27. Hussain, S. N., Zwain, H. M., Nile, B. K. 2022. Modeling the effects of land-use and climate change on the performance of stormwater sewer system using SWMM simulation: case study. Journal of Water and Climate Change, 13(1), 125-138.
28. Ijaradar, J., Chatterjee, S. 2018. Real-time water quality monitoring system. International Research Journal of Engineering and Technology, 5(3), 1166-1171.
29. Kafy, A.-A., Al Rakib, A., Fattah, M.A., Rahaman, Z.A., Sattar, G.S. 2022. Impact of vegetation cover loss on surface temperature and carbon emission in a fastest-growing city, Cumilla, Bangladesh. Building and Environment, 208, 108573.
30. Khan, R., Saxena, A., Shukla, S. 2020. Evaluation of heavy metal pollution for River Gomti, in parts of Ganga Alluvial Plain, India. SN Applied Sciences, 2(8), 1451.
31. Kondo, T., Sakai, N., Yazawa, T., Shimizu, Y. 2021. Verifying the applicability of SWAT to simulate fecal contamination for watershed management of Selangor River, Malaysia. Science of the Total Environment, 774, 145075.
32. Kumar, S., Tiwari, P., Zymbler, M. 2019. Internet of Things is a revolutionary approach for future technology enhancement: a review. Journal of Big data, 6(1), 1-21.
33. Kumar Sarangi, P., Subudhi, S., Bhatia, L., Saha, K., Mudgil, D., Prasad Shadangi, K., Arya, R. K. 2023. Utilization of agricultural waste biomass and recycling toward circular bioeconomy. Environmental Science and Pollution Research, 30(4), 8526-8539. https://doi.org/10.1007/s11356-022-20669-1
34. Ma, S., Kim, K., Chun, S., Moon, S.Y., Hong, Y. 2020. Plasma-assisted advanced oxidation process by a multi-hole dielectric barrier discharge in water and its application to wastewater treatment. Chemosphere, 243, 125377.
35. Maruffi, L., Stucchi, L., Casale, F., Bocchiola, D. 2022. Soil erosion and sediment transport under climate change for Mera River, in Italian Alps of Valchiavenna. Science of the Total Environment, 806, 150651.
36. McDonnell, B.E., Ratliff, K., Tryby, M.E., Wu, J.J.X., Mullapudi, A. 2020. PySWMM: the python interface to stormwater management model (SWMM). Journal of open source software, 5(52), 1.
37. Muratoglu, A. 2020. Grey water footprint of agricultural production: An assessment based on nitrogen surplus and high-resolution leaching runoff fractions in Turkey. Science of the Total Environment, 742, 140553.
38. Pal, S.K., Masum, M.M.H., Salauddin, M., Hossen, M.A., Ruva, I.J., Akhie, A.A. 2023. Appraisal of stormwater-induced runoff quality influenced by site-specific land use patterns in the south-eastern region of Bangladesh. Environmental Science and Pollution Research, 30(13), 36112-36126.
39. Pan, C., Bao, Y., Xu, B. 2020. Seasonal variation of antibiotics in surface water of Pudong New Area of Shanghai, China and the occurrence in typical wastewater sources. Chemosphere, 239, 124816.
40. Pasika, S., Gandla, S.T. 2020. Smart water quality monitoring system with cost-effective using IoT. Heliyon, 6(7).
41. Perdana, R.H.Y., Hidayati, N., Yulianto, A.W., Firdaus, V.A.H., Sari, N.N., Suprianto, D. 2020. Jig detection using scanning method base on internet of things for smart learning factory. Paper presented at the 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS).
42. Perera, T., McGree, J., Egodawatta, P., Jinadasa, K., Goonetilleke, A. 2021. Catchment based estimation of pollutant event mean concentration (EMC) and implications for first flush assessment. Journal of Environmental Management, 279, 111737.
43. Prasetyo, V.R., Lazuardi, H., Mulyono, A.A., Lauw, C. 2021. Penerapan Aplikasi RapidMiner Untuk Prediksi Nilai Tukar Rupiah Terhadap US Dollar Dengan Metode Regresi Linier. Jurnal Nasional Teknologi dan Sistem Informasi (TEKNOSI), 7(1), 8-17.
44. Puspita, A.S., Budihardjo, M.A., Samadikun, B.P. 2023. Evaluating coconut fiber and fly ash composites for use in landfill retention layers. Global nest journal, 25(4), 1-7.
45.Ramadhan, A., Ali, A., Kareem, H. 2020. Smart water-quality monitoring system based on enabled real-time internet of things. Journal of Engineering Science and Technology, 15(6), 3514-3527.
46. Saadali, B., Khedidja, A., Mihoubi, N., Ouddah, A., Djebassi, T., Kouba, Y. 2020. Water quality assessment and organic pollution identification of Hammam-Grouz dam (Northeastern Algeria). Arabian Journal of Geosciences, 13(20), 1091.
47. Salam, M., Kabir, M., Yee, L., Khan, M. 2019. Water quality assessment of perak river, Malaysia. Pollution, 5(3), 637-648.
48. Sharma, P., Iqbal, H.M., Chandra, R. 2022. Evaluation of pollution parameters and toxic elements in wastewater of pulp and paper industries in India: A case study. Case Studies in Chemical and Environmental Engineering, 5, 100163.
49. Smys, S. 2020. A survey on internet of things (IoT) based smart systems. Journal of ISMAC, 2(04),181-189.
50. Soltaninia, S., Taghavi, L., Hosseini, S.A., Motamedvaziri, B., Eslamian, S. 2023. The effects of antecedent dry days and land use types on urban runoff quality in a semi-Arid region. International Journal of Urban Sciences, 27(2), 215-238.
51. Specht, M. 2020. Statistical distribution analysis of navigation positioning system errors—issue of the empirical sample size. Sensors, 20(24), 7144.
52. Suryawan, I.W.K., Prajati, G., Afifah, A.S., Apritama, M.R. 2021. Nh3-n and cod reduction in endek (Balinese textile) wastewater by activated sludge under different do condition with ozone pretreatment. Walailak Journal of Science and Technology (WJST), 18(6), 9111-9127.
53. Swamy, S.N., Kota, S.R. 2020. An empirical study on system level aspects of Internet of Things (IoT). IEEE Access, 8, 188082-188134.
54. Tahiru, A.A., Doke, D.A., Baatuuwie, B.N. 2020. Effect of land use and land cover changes on water quality in the Nawuni Catchment of the White Volta Basin, Northern Region, Ghana. Applied Water Science, 10(8), 1-14.
55. Tong, D., Sun, Y., Tang, J., Luo, Z., Lu, J., Liu, X. 2023. Modeling the interaction of internal and external systems of rural settlements: The case of Guangdong, China. Land Use Policy, 132, 106830.
56. Trevathan, J., Schmidtke, S., Read, W., Sharp, T., Sattar, A. 2021. An IoT general-purpose sensor board for enabling remote aquatic environmental monitoring. Internet of Things, 16, 100429.
57. Tsuji, S., Takahara, T., Doi, H., Shibata, N., Yamanaka, H. 2019. The detection of aquatic macroorganisms using environmental DNA analysis – A review of methods for collection, extraction, and detection. Environmental DNA, 1(2), 99-108.
58. Uddin, M.G., Nash, S., Olbert, A.I. 2021. A review of water quality index models and their use for assessing surface water quality. Ecological Indicators, 122, 107218.
59. Wang, Y., Liu, X., Wang, T., Zhang, X., Feng, Y., Yang, G., Zhen, W. 2021. Relating land-use/land-cover patterns to water quality in watersheds based on the structural equation modeling. Catena, 206, 105566.
60. Wiranto, U.A. 2019. Pengaruh Musim Penghujan Terhadap Produktivitas Primer dan Status Mutu Kualitas Perairan Bendungan Lahor Kabupaten Malang Provinsi Jawa Timur. ITN Malang.
61. Wu, J., Hu, K., Cheng, Y., Zhu, H., Shao, X., Wang, Y. 2020. Data-driven remaining useful life prediction via multiple sensor signals and deep long shortterm memory neural network. ISA transactions, 97, 241-250.
62. Xiang, Z., Demir, I. 2020. Distributed long-term hourly streamflow predictions using deep learning–A case study for State of Iowa. Environmental Modelling & Software, 131, 104761.
63. Yazdi, M.N., Sample, D.J., Scott, D., Wang, X., Ketabchy, M. 2021. The effects of land use characteristics on urban stormwater quality and watershed pollutant loads. Science of the Total Environment, 773, 145358.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-2c85bbc3-5674-40b3-833e-ecd3e1c06deb