Enhanced NTU Measurement by Modified Turbidity Sensor: A Cost-Effective Approach for Household Water Quality Monitoring

Enhanced NTU Measurement by Modified Turbidity Sensor: A Cost-Effective Approach for Household Water Quality Monitoring | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhanced NTU Measurement by Modified Turbidity Sensor: A Cost-Effective Approach for Household Water Quality Monitoring Rajendrra Yashavant Lelle¹, Nitin Madhukar Kulkarni, Arvind Digamber Shaligram, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8979025/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Water turbidity monitoring is necessary for water quality check in household applications. Commercial nephelometric sensors measuring turbidity using multiple angle scattered light are too expensive for domestic appliances, while standard low-cost sensors measure only direct light transmission, neglecting scattered light. This paper presents a simple but effective modification to existing standard sensors by adding a phototransistor at 90° to capture scattered light. The work focuses on the data acquisition and fusion methodology that combines transmitted and scattered light signals to produce an enhanced turbidity reading. The modified sensor demonstrates enhanced capability, showing readings up to 5.0 NTU compared to 3.6 NTU maximum for unmodified sensors—a 38.9% improvement. Experimental validation over 24-hour monitoring confirms enhanced sensitivity and reliability, with statistical analysis revealing a strong linear correlation (r = 0.89) between sensor outputs . The scattered light contribution analysis shows it provides 30–60% of the total signal, with increasing importance at low turbidity levels. The modification adds less than ₹55 to sensor cost, bridging the gap between expensive commercial and basic household turbidity sensors. This data-driven approach enables smarter water usage in appliances and potentially reduces water and energy consumption. Water Turbidity NTU Data Acquisition Sensor Fusion Environmental Monitoring Low-Cost Sensor Scattered Light Detection Household Appliances Water Conservation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Water quality monitoring is crucial all over, with turbidity serving as a primary indicator of water clarity and contamination [ 1 ]. Turbidity measurement relies on light interaction with suspended particles, with Nephelometric Turbidity Units (NTU) based on 90° scattered light detection according to ISO 7027 standards [ 2 ], [ 3 ]. The current market presents a division: commercial sensors (₹50,000–5,00,000) use multiple detectors at various angles, while household sensors (₹200–800) use only 180° transmission measurement [ 4 ]. This research addresses this gap by proposing a minimal-cost modification that incorporates 90° scattered light detection into standard sensors, enhancing performance while maintaining affordability for household applications like washing machines and dishwashers. From an informatics perspective, turbidity measurement is fundamentally a data acquisition and signal processing problem . The raw optical signals must be converted, calibrated, and fused to produce meaningful NTU values. This study contributes to the field of environmental data science by: (1) presenting a dual-channel data acquisition architecture that captures both transmitted and scattered light signals; (2) developing a data fusion model (NTU_enhanced = α × f_t(V_t) + β × f_s(V_s)) that optimally combines these signals; (3) providing statistical validation of the enhanced measurement through regression analysis and coefficient of variation studies; and (4) demonstrating the temporal dynamics of turbidity through 24-hour continuous monitoring. These contributions align with the core themes of computational Earth science and the application of formal methods to environmental data. Related Work Wang et al. (2018) demonstrated 90° scattering provides optimal sensitivity for 0-40 NTU ranges [5]. Rocher et al. (2023) validated simple 90° configurations for water quality monitoring [6]. Farmanullah et al. (2021) reviewed IoT-based water monitoring systems using low-cost sensors [7]. However, limited research exists on enhancing existing low-cost sensor designs specifically for household applications. Our preliminary design and initial findings were presented in [10], and this manuscript provides a significant extension with complete experimental validation, 24-hour monitoring data, and detailed statistical analysis. Sensor Modification Design Base Sensor Specifications The standard sensor (Fig. 1) uses an IR LED (850-940 nm) and phototransistor at 180° opposition with 12-15 mm gap. It measures only transmitted light: I t = I 0 × e − μ × l , missing scattered light information. Modification Implementation We integrated an MOC7811 phototransistor (₹35) at 90° (Fig. 2 showing the enclosed modified sensor). Figure 3 shows the photo transistor side encircled in MOC7811 while Figure 4 shows the scattering of the light towards the added photo transistor at 90 0 . Figure 5 shows actual modified turbidity sensor showing additional phototransistor installed at 90 0 . The modification involves: 1. Creating housing opening at 90° position 2. Mounting phototransistor with precise alignment 3. Adding separate signal processing circuit 4. Resealing for water resistance The enhanced sensor measures both transmitted (V t ) and scattered (V s ) signals: NTU enhanced = α × f t ( V t ) + β × f s ( V s ) Experimental Methodology Setup Configuration Both sensors interfaced with Arduino Uno (10-bit ADC) for simultaneous measurement in identical turbid water samples. Water samples prepared with clay particles to simulate household conditions. Calibration Calibrated using reference samples: clear water (0.5-1.0 NTU), slightly turbid (2-3 NTU), and moderately turbid (4-5 NTU). Measurement Protocol 24-hour monitoring of natural particle settlement with hourly measurements, averaging 10 readings per measurement point. Sample Readings (Selected Time Intervals) The early phase readings (Hours 0-8) showed significant differences between the sensors (see Table 1). Table 1: Early Phase Readings (Hours 0-8) Time (hrs) Unmodified Sensor (NTU) Modified Sensor (NTU) Difference (ΔNTU) Improvement (%) Water Condition 0 - - - - Initial setup 2 2.0 5.0 3.0 150.0 Highly turbid 4 3.3 5.0 1.7 51.5 Turbid 6 3.4 5.0 1.6 47.1 Moderately turbid 8 3.4 5.0 1.6 47.1 Moderately turbid The settlement pattern continued through the mid-phase (see Table 2). Table 2: Mid Phase Readings (Hours 10-16) Time (hrs) Unmodified Sensor (NTU) Modified Sensor (NTU) Difference (ΔNTU) Improvement (%) Water Condition 10 3.4 5.0 1.6 47.1 Moderately turbid 12 3.4 5.0 1.6 47.1 Moderately turbid 14 3.0 4.6 1.6 53.3 Settling 16 3.0 4.8 1.8 60.0 Settling The late-phase observations revealed significant differences between sensors (see Table 3). Table 3: Late Phase Readings (Hours 18-24) Time (hrs) Unmodified Sensor (NTU) Modified Sensor (NTU) Difference (ΔNTU) Improvement (%) Water Condition 18 2.6 4.0 1.4 53.8 Clearer water 20 1.0 3.4 2.4 240.0 Much clearer 22 0.0 1.0 1.0 ∞ Near-clear 24 0.0 2.0 2.0 ∞ Near-clear 24 hours turbidity profiles The following graph (Fig. 6) shows the comparison of performance between modified and un-modified sensors. Results and Analysis Performance Comparison The key performance metrics comparing both sensors are presented in Table 4. Table 4: Key Performance Metrics Sr. Parameter Standard Sensor Modified Sensor Improvement 1. Max. NTU 3.6 5.0 +38.9% 2. Min. NTU 0.0 1.0 No false zeros 3. Zero readings 4/24 (16.7%) 0/24 (0%) Complete elimination 4. Average CV* 2.3% 2.0% Comparable stability *Coefficient of Variation. CV =(Standard Deviation/ Mean)×100% Temporal Response 24-hour turbidity profiles shows modified sensor consistently reads higher and never reports 0.0 NTU, while standard sensor shows false zeros at hours 21, 22, 24. During high turbidity (hours 1-12): - Modified sensor: 4.8-5.0 NTU - Standard sensor: 3.0-3.6 NTU - Enhancement factor: 1.45× to 1.83× Enhancement factor analysis The following graph (Fig. 7) shows the enhancement achieved by modifying the standard turbidity sensor. Statistical Validation Linear regression shows strong correlation (r = 0.89): NTU modified = 1.35 × NTU standard +1.12 ( R 2 = 0.79) Scattered light contributes 1.0-2.0 NTU (30-60% of total signal), increasing in importance at low turbidity levels. Data Fusion and Signal Processing Framework The enhanced sensor implements a simple but effective data fusion architecture. The raw voltage signals from both detectors are acquired simultaneously using a 10-bit ADC (Arduino Uno), sampled 10 times per measurement point to reduce noise. The data processing pipeline consists of: Preprocessing : Moving average filtering to remove high-frequency noise Calibration : Linear mapping of voltage to NTU using reference standards Fusion : Weighted combination of transmitted and scattered signals Validation : Statistical comparison with standard sensor output The scattered light signal (V_s) shows particular sensitivity in the 0-2 NTU range, where transmitted light measurements become unreliable. This demonstrates how multi-sensor data fusion can extend the effective measurement range of low-cost sensors, a principle applicable to other environmental monitoring contexts. Linear regression analysis The following graph (Fig. 8) shows the linear regression of modified turbidity sensor against un-modified standard turbidity sensor. Scattered light contribution analysis The following graph (Fig. 9) shows the role and significance of scattered light in NTU over time. Discussion Practical Applications Washing Machine Optimization: - Prevents premature cycle termination (0 vs 2 NTU detection) - Enables accurate soil level assessment - Potential 5-10% water/energy savings per household Cost-Benefit Analysis: - Additional cost: < ₹55 (< $0.70) - ROI: < 3 months with one prevented rewash cycle monthly - National impact: 120 billion liters water saved annually for 50M Indian households Commercial Comparison A comparative analysis with existing sensor technologies is provided in Table 5. Table 5: Sensor Comparison Feature Commercial Sensor Modified Sensor Standard Sensor Cost ₹50,000-5,00,000 ₹255 ₹200-300 Angles Multiple 90° + 180° 180° only Range 0.01-4000 NTU 0.5-10 NTU 0-5 NTU Application Lab/Industrial Household Basic household Modified sensor delivers 40-50% of commercial performance at 0.05-0.5% of cost. Limitations 1. Effective range limited to ~10 NTU 2. No temperature compensation 3. Requires two-channel calibration 4. Particle size dependent (optimized for 1-10 μm) These are acceptable for household applications (0-5 NTU target range). Conclusion This research demonstrates a cost-effective sensor modification that enhances standard turbidity measurements by incorporating 90° scattered light detection. From an informatics perspective, the key contributions are: A dual-channel data acquisition system that captures complementary optical signals A data fusion methodology combining transmitted and scattered light for enhanced measurement Statistical validation (r = 0.89 correlation, R² = 0.79) confirming the reliability of the approach Temporal analysis of turbidity dynamics over 24 hours The modified sensor shows 38.9% improvement in measurement range, eliminates false zero readings, and provides more realistic turbidity assessment at minimal additional cost (< ₹55). The scattered light contribution analysis reveals it provides 30-60% of total signal, with increasing importance at low turbidity levels. This work bridges the gap between expensive commercial sensors and basic household sensors by applying principles of environmental data science to a practical monitoring challenge. The approach enables smarter water usage in appliances with potential significant resource conservation. Future work includes implementing machine learning algorithms for predictive maintenance, developing IoT connectivity for smart home integration, and exploring multi-angle detection for extended range applications. Declarations Acknowledgments We acknowledge Tikaram Jagannath College, Khadki and Fergusson College, Pune for laboratory facilities and support. Funding This research received no external funding. Ethics, Consent to Participate, and Consent to Publish Declaration Not applicable. Data Availability All data generated or analyzed during this study are included in this published article. The 24-hour monitoring data is presented in Tables 1-3, and all experimental results are shown in Figures 6-9. Conflicts of Interest The authors declare no conflicts of interest. Author Contributions Rajendrra Yashavant Lelle: Conceptualization, Methodology, Software, Investigation, Formal Analysis, Data Curation, Writing - Original Draft, Visualization Dr. Nitin Madhukar Kulkarni: Methodology, Validation, Resources, Writing - Review & Editing, Supervision Dr. Arvind Digamber Shaligram: Conceptualization, Methodology, Validation, Resources, Writing - Review & Editing, Supervision, Project Administration Dr. Kailash Baliram Sapnar: Resources, Writing - Review & Editing, Validation, Supervision All authors have read and agreed to the published version of the manuscript. References Farmanullah et al. (2021). IoT Based Smart Water Quality Monitoring: Recent Techniques, Trends and Challenges for Domestic Applications. Water from https://www.mdpi.com/2073-4441/13/13/1729. D. Gillett, A. Marchiori (2019). A Low-Cost Continuous Turbidity Monitor. Sensors from https://www.mdpi.com/1424-8220/19/14/3039. ISO 7027. (1999). Water Quality—Determination of Turbidity, from https://www.iso.org/obp/ui/#iso:std:iso:7027:ed-3:v1:en ISO 7027-1. (2016). Water Quality—Determination of Turbidity, from https://www.iso.org/obp/ui/en/#iso:std:iso:7027:-1:ed-1:v1:en MPCB (2023). Water Quality Status Report of Maharashtra 2023-24, from https://mpcb.gov.in/sites/default/files/Establishment%20of%20MPCB/Seniority%20list/2014/WQR_2023-24.pdf J. Rocher et al. (2023). Low-Cost Turbidity Sensor for Eutrophication. Sensors , from https://www.mdpi.com/1424-8220/23/8/3913. R. Sanchez et al. (2023). Development of a Frugal, In Situ Sensor Implementing a Ratiometric Method for Continuous Monitoring of Turbidity in Natural Waters. Sensors from https://www.mdpi.com/1424-8220/23/4/1897. Y. Wang et al. (2018). Low-Cost Turbidity Sensor. IEEE Sensors Journal . WHO (2017). Guidelines for Drinking-water Quality, Retrieved January 3, 2026, from https://www.who.int/publications/i/item/9789241549950 R. Lelle, N. M. Kulkarni and A. D. Shaligram, "Enhanced NTU by Modified Turbidity Sensor," 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA) , Pune, India, 2024, pp. 1-4, doi: 10.1109/ICCUBEA61740.2024.10775295. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8979025","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611549336,"identity":"6dbc1ebd-3f9d-41a7-9ef9-2c37cd3034c0","order_by":0,"name":"Rajendrra Yashavant 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8","display":"","copyAsset":false,"role":"figure","size":61134,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinear regression analysis: Modified sensor NTU vs. unmodified standard sensor NTU\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8979025/v1/24b2afd228d6d7af78c2fd7a.png"},{"id":105401963,"identity":"2d4eed5e-17d1-4397-8f10-da163b6d88c0","added_by":"auto","created_at":"2026-03-25 15:29:35","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":150893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScattered light contribution to total NTU measurement over 24-hour monitoring period\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8979025/v1/9713d84b1e6f797d9c1e0a10.png"},{"id":108567390,"identity":"aec944d7-f862-4d22-9ccf-8280d314eb4b","added_by":"auto","created_at":"2026-05-06 05:10:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2148388,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8979025/v1/da5f21cc-52b7-4517-8694-195a991395ff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhanced NTU Measurement by Modified Turbidity Sensor: A Cost-Effective Approach for Household Water Quality Monitoring","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWater quality monitoring is crucial all over, with turbidity serving as a primary indicator of water clarity and contamination [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Turbidity measurement relies on light interaction with suspended particles, with Nephelometric Turbidity Units (NTU) based on 90\u0026deg; scattered light detection according to ISO 7027 standards [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current market presents a division: commercial sensors (₹50,000\u0026ndash;5,00,000) use multiple detectors at various angles, while household sensors (₹200\u0026ndash;800) use only 180\u0026deg; transmission measurement [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This research addresses this gap by proposing a minimal-cost modification that incorporates 90\u0026deg; scattered light detection into standard sensors, enhancing performance while maintaining affordability for household applications like washing machines and dishwashers.\u003c/p\u003e \u003cp\u003eFrom an informatics perspective, turbidity measurement is fundamentally a \u003cb\u003edata acquisition and signal processing problem\u003c/b\u003e. The raw optical signals must be converted, calibrated, and fused to produce meaningful NTU values. This study contributes to the field of \u003cb\u003eenvironmental data science\u003c/b\u003e by: (1) presenting a \u003cb\u003edual-channel data acquisition architecture\u003c/b\u003e that captures both transmitted and scattered light signals; (2) developing a \u003cb\u003edata fusion model\u003c/b\u003e (NTU_enhanced\u0026thinsp;=\u0026thinsp;α\u0026thinsp;\u0026times;\u0026thinsp;f_t(V_t) + β\u0026thinsp;\u0026times;\u0026thinsp;f_s(V_s)) that optimally combines these signals; (3) providing \u003cb\u003estatistical validation\u003c/b\u003e of the enhanced measurement through regression analysis and coefficient of variation studies; and (4) demonstrating the \u003cb\u003etemporal dynamics\u003c/b\u003e of turbidity through 24-hour continuous monitoring. These contributions align with the core themes of computational Earth science and the application of formal methods to environmental data.\u003c/p\u003e"},{"header":"Related Work","content":"\u003cp\u003eWang et al. (2018) demonstrated 90\u0026deg; scattering provides optimal sensitivity for 0-40 NTU ranges [5]. Rocher et al. (2023) validated simple 90\u0026deg; configurations for water quality monitoring [6]. Farmanullah et al. (2021) reviewed IoT-based water monitoring systems using low-cost sensors [7]. However, limited research exists on enhancing existing low-cost sensor designs specifically for household applications.\u003c/p\u003e\n\u003cp\u003eOur preliminary design and initial findings were presented in [10], and this manuscript provides a significant extension with complete experimental validation, 24-hour monitoring data, and detailed statistical analysis.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eSensor Modification Design\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eBase Sensor Specifications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe standard sensor (Fig. 1) uses an IR LED (850-940 nm) and phototransistor at 180\u0026deg; opposition with 12-15 mm gap. It measures only transmitted light: \u003cem\u003eI\u003c/em\u003e\u003csub\u003et\u003c/sub\u003e=\u003cem\u003eI\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u0026times;\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u0026minus;\u003cem\u003e\u0026mu;\u003c/em\u003e\u0026times;\u003cem\u003el\u003c/em\u003e\u003c/sup\u003e, missing scattered light information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModification Implementation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe integrated an MOC7811 phototransistor (₹35) at 90\u0026deg; (Fig. 2 showing the enclosed modified sensor). Figure 3 shows the photo transistor side encircled in MOC7811 while Figure 4 shows the scattering of the light towards the added photo transistor at 90\u003csup\u003e0\u003c/sup\u003e. Figure 5 shows actual modified turbidity sensor showing additional phototransistor installed at 90\u003csup\u003e0\u0026nbsp;\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe modification involves:\u003c/p\u003e\n\u003cp\u003e1. Creating housing opening at 90\u0026deg; position\u003c/p\u003e\n\u003cp\u003e2. Mounting phototransistor with precise alignment\u003c/p\u003e\n\u003cp\u003e3. Adding separate signal processing circuit\u003c/p\u003e\n\u003cp\u003e4. Resealing for water resistance\u003c/p\u003e\n\u003cp\u003eThe enhanced sensor measures both transmitted (V\u003csub\u003et\u003c/sub\u003e) and scattered (V\u003csub\u003es\u003c/sub\u003e) signals:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNTU\u003csub\u003eenhanced\u003c/sub\u003e\u003c/em\u003e = \u003cem\u003e\u0026alpha;\u0026nbsp;\u003c/em\u003e\u0026times; \u003cem\u003ef\u003csub\u003et\u003c/sub\u003e\u003c/em\u003e(\u003cem\u003eV\u003csub\u003et\u003c/sub\u003e\u003c/em\u003e) + \u003cem\u003e\u0026beta;\u0026nbsp;\u003c/em\u003e\u0026times; \u003cem\u003ef\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e(\u003cem\u003eV\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Experimental Methodology","content":"\u003cp\u003e\u003cstrong\u003eSetup Configuration\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth sensors interfaced with Arduino Uno (10-bit ADC) for simultaneous measurement in identical turbid water samples. Water samples prepared with clay particles to simulate household conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCalibration\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCalibrated using reference samples: clear water (0.5-1.0 NTU), slightly turbid (2-3 NTU), and moderately turbid (4-5 NTU).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMeasurement Protocol\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e24-hour monitoring of natural particle settlement with hourly measurements, averaging 10 readings per measurement point.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSample Readings (Selected Time Intervals)\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe early phase readings (Hours 0-8) showed significant differences between the sensors (see Table 1).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Early Phase Readings (Hours 0-8)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTime (hrs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUnmodified Sensor (NTU)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eModified Sensor (NTU)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDifference (\u0026Delta;NTU)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eImprovement (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWater Condition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInitial setup\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e150.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHighly turbid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTurbid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModerately turbid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModerately turbid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe settlement pattern continued through the mid-phase (see Table 2).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Mid Phase Readings (Hours 10-16)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTime (hrs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUnmodified Sensor (NTU)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eModified Sensor (NTU)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDifference (\u0026Delta;NTU)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eImprovement (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWater Condition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e3.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e5.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e47.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eModerately turbid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e3.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e5.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e47.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eModerately turbid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e3.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e4.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e53.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSettling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e3.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e4.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e60.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSettling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe late-phase observations revealed significant differences between sensors (see Table 3).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Late Phase Readings (Hours 18-24)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTime (hrs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUnmodified Sensor (NTU)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eModified Sensor (NTU)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDifference (\u0026Delta;NTU)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eImprovement (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWater Condition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eClearer water\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e240.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMuch clearer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNear-clear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNear-clear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e24 hours turbidity profiles\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following graph (Fig. 6) shows the comparison of performance between modified and un-modified sensors.\u003c/p\u003e"},{"header":"Results and Analysis","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePerformance Comparison\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe key performance metrics comparing both sensors are presented in Table 4.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Key Performance Metrics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSr.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Sensor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModified Sensor\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImprovement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eMax. NTU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e+38.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eMin. NTU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eNo false zeros\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eZero readings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e4/24 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0/24 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eComplete elimination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eAverage CV*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e2.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eComparable stability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Coefficient of Variation. \u003cem\u003eCV\u003c/em\u003e=(Standard\u0026nbsp;Deviation/ Mean)\u0026times;100%\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTemporal Response\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e24-hour turbidity profiles shows modified sensor consistently reads higher and never reports 0.0 NTU, while standard sensor shows false zeros at hours 21, 22, 24.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDuring high turbidity (hours 1-12):\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Modified sensor: 4.8-5.0 NTU\u003c/p\u003e\n\u003cp\u003e- Standard sensor: 3.0-3.6 NTU\u003c/p\u003e\n\u003cp\u003e- Enhancement factor: 1.45\u0026times; to 1.83\u0026times;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEnhancement factor analysis\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following graph (Fig. 7) shows the enhancement achieved by modifying the standard turbidity sensor. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical Validation\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLinear regression shows strong correlation (r = 0.89):\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNTU\u003csub\u003emodified\u003c/sub\u003e\u003c/em\u003e = 1.35 \u0026times; \u003cem\u003eNTU\u003csub\u003estandard\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e+1.12 (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= 0.79)\u003c/p\u003e\n\u003cp\u003eScattered light contributes 1.0-2.0 NTU (30-60% of total signal), increasing in importance at low turbidity levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Fusion and Signal Processing Framework\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe enhanced sensor implements a simple but effective data fusion architecture. The raw voltage signals from both detectors are acquired simultaneously using a 10-bit ADC (Arduino Uno), sampled 10 times per measurement point to reduce noise. The data processing pipeline consists of:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003ePreprocessing\u003c/strong\u003e: Moving average filtering to remove high-frequency noise\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCalibration\u003c/strong\u003e: Linear mapping of voltage to NTU using reference standards\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFusion\u003c/strong\u003e: Weighted combination of transmitted and scattered signals\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eValidation\u003c/strong\u003e: Statistical comparison with standard sensor output\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe scattered light signal (V_s) shows particular sensitivity in the 0-2 NTU range, where transmitted light measurements become unreliable. This demonstrates how \u003cstrong\u003emulti-sensor data fusion\u003c/strong\u003e can extend the effective measurement range of low-cost sensors, a principle applicable to other environmental monitoring contexts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLinear regression analysis\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe following graph (Fig. 8) shows the linear regression of modified turbidity sensor against un-modified standard turbidity sensor. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eScattered light contribution analysis\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following graph (Fig. 9) shows the role and significance of scattered light in NTU over time.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePractical Applications\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWashing Machine Optimization:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e- Prevents premature cycle termination (0 vs 2 NTU detection)\u003c/p\u003e\n\u003cp\u003e- Enables accurate soil level assessment\u003c/p\u003e\n\u003cp\u003e- Potential 5-10% water/energy savings per household\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCost-Benefit Analysis:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e- Additional cost: \u0026lt; ₹55 (\u0026lt; $0.70)\u003c/p\u003e\n\u003cp\u003e- ROI: \u0026lt; 3 months with one prevented rewash cycle monthly\u003c/p\u003e\n\u003cp\u003e- National impact: 120 billion liters water saved annually for 50M Indian households\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCommercial Comparison\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA comparative analysis with existing sensor technologies is provided in Table 5.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Sensor Comparison\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeature\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommercial Sensor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModified Sensor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Sensor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCost\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e₹50,000-5,00,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e₹255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e₹200-300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAngles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e90\u0026deg; + 180\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e180\u0026deg; only\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.01-4000 NTU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.5-10 NTU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0-5 NTU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eApplication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eLab/Industrial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHousehold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eBasic household\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModified sensor delivers 40-50% of commercial performance at 0.05-0.5% of cost.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003e1. Effective range limited to ~10 NTU\u003c/p\u003e\n\u003cp\u003e2. No temperature compensation\u003c/p\u003e\n\u003cp\u003e3. Requires two-channel calibration\u003c/p\u003e\n\u003cp\u003e4. Particle size dependent (optimized for 1-10 \u0026mu;m)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese are acceptable for household applications (0-5 NTU target range).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research demonstrates a cost-effective sensor modification that enhances standard turbidity measurements by incorporating 90\u0026deg; scattered light detection. From an\u0026nbsp;informatics perspective, the key contributions are:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eA\u0026nbsp;dual-channel data acquisition system\u0026nbsp;that captures complementary optical signals\u003c/li\u003e\n \u003cli\u003eA\u0026nbsp;data fusion methodology\u0026nbsp;combining transmitted and scattered light for enhanced measurement\u003c/li\u003e\n \u003cli\u003eStatistical validation\u0026nbsp;(r = 0.89 correlation, R\u0026sup2; = 0.79) confirming the reliability of the approach\u003c/li\u003e\n \u003cli\u003eTemporal analysis\u0026nbsp;of turbidity dynamics over 24 hours\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe modified sensor shows 38.9% improvement in measurement range, eliminates false zero readings, and provides more realistic turbidity assessment at minimal additional cost (\u0026lt; ₹55). The scattered light contribution analysis reveals it provides 30-60% of total signal, with increasing importance at low turbidity levels.\u003c/p\u003e\n\u003cp\u003eThis work bridges the gap between expensive commercial sensors and basic household sensors by applying principles of environmental data science to a practical monitoring challenge. The approach enables smarter water usage in appliances with potential significant resource conservation. Future work includes implementing machine learning algorithms for predictive maintenance, developing IoT connectivity for smart home integration, and exploring multi-angle detection for extended range applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eWe acknowledge Tikaram Jagannath College, Khadki and Fergusson College, Pune for laboratory facilities and support.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish Declaration\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article. The 24-hour monitoring data is presented in Tables 1-3, and all experimental results are shown in Figures 6-9.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRajendrra Yashavant Lelle: Conceptualization, Methodology, Software, Investigation, Formal Analysis, Data Curation, Writing - Original Draft, Visualization\u003c/p\u003e\n\u003cp\u003eDr. Nitin Madhukar Kulkarni: Methodology, Validation, Resources, Writing - Review \u0026amp; Editing, Supervision\u003c/p\u003e\n\u003cp\u003eDr. Arvind Digamber Shaligram: Conceptualization, Methodology, Validation, Resources, Writing - Review \u0026amp; Editing, Supervision, Project Administration\u003c/p\u003e\n\u003cp\u003eDr. Kailash Baliram Sapnar: Resources, Writing - Review \u0026amp; Editing, Validation, Supervision\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFarmanullah et al. (2021). IoT Based Smart Water Quality Monitoring: Recent Techniques, Trends and Challenges for Domestic Applications. \u003cem\u003eWater\u003c/em\u003e from https://www.mdpi.com/2073-4441/13/13/1729.\u003c/li\u003e\n\u003cli\u003eD. Gillett, A. Marchiori (2019). A Low-Cost Continuous Turbidity Monitor. \u003cem\u003eSensors\u003c/em\u003e from https://www.mdpi.com/1424-8220/19/14/3039.\u003c/li\u003e\n\u003cli\u003eISO 7027. (1999). Water Quality\u0026mdash;Determination of Turbidity, from https://www.iso.org/obp/ui/#iso:std:iso:7027:ed-3:v1:en\u003c/li\u003e\n\u003cli\u003eISO 7027-1. (2016). Water Quality\u0026mdash;Determination of Turbidity, from https://www.iso.org/obp/ui/en/#iso:std:iso:7027:-1:ed-1:v1:en\u003c/li\u003e\n\u003cli\u003eMPCB (2023). Water Quality Status Report of Maharashtra 2023-24, from https://mpcb.gov.in/sites/default/files/Establishment%20of%20MPCB/Seniority%20list/2014/WQR_2023-24.pdf\u003c/li\u003e\n\u003cli\u003eJ. Rocher et al. (2023). Low-Cost Turbidity Sensor for Eutrophication. \u003cem\u003eSensors\u003c/em\u003e, from https://www.mdpi.com/1424-8220/23/8/3913.\u003c/li\u003e\n\u003cli\u003eR. Sanchez et al. (2023). Development of a Frugal, In Situ Sensor Implementing a Ratiometric Method for Continuous Monitoring of Turbidity in Natural Waters. \u003cem\u003eSensors\u003c/em\u003e from https://www.mdpi.com/1424-8220/23/4/1897.\u003c/li\u003e\n\u003cli\u003eY. Wang et al. (2018). Low-Cost Turbidity Sensor. \u003cem\u003eIEEE Sensors Journal\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eWHO (2017). Guidelines for Drinking-water Quality, Retrieved January 3, 2026, from https://www.who.int/publications/i/item/9789241549950\u003c/li\u003e\n\u003cli\u003eR. Lelle, N. M. Kulkarni and A. D. Shaligram, \u0026quot;Enhanced NTU by Modified Turbidity Sensor,\u0026quot; \u003cem\u003e2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA)\u003c/em\u003e, Pune, India, 2024, pp. 1-4, doi: 10.1109/ICCUBEA61740.2024.10775295.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Water Turbidity, NTU, Data Acquisition, Sensor Fusion, Environmental Monitoring, Low-Cost Sensor, Scattered Light Detection, Household Appliances, Water Conservation","lastPublishedDoi":"10.21203/rs.3.rs-8979025/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8979025/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWater turbidity monitoring is necessary for water quality check in household applications. Commercial nephelometric sensors measuring turbidity using multiple angle scattered light are too expensive for domestic appliances, while standard low-cost sensors measure only direct light transmission, neglecting scattered light. This paper presents a simple but effective modification to existing standard sensors by adding a phototransistor at 90\u0026deg; to capture scattered light. The work focuses on the \u003cb\u003edata acquisition and fusion methodology\u003c/b\u003e that combines transmitted and scattered light signals to produce an enhanced turbidity reading. The modified sensor demonstrates enhanced capability, showing readings up to 5.0 NTU compared to 3.6 NTU maximum for unmodified sensors\u0026mdash;a 38.9% improvement. Experimental validation over 24-hour monitoring confirms enhanced sensitivity and reliability, with \u003cb\u003estatistical analysis revealing a strong linear correlation (r\u0026thinsp;=\u0026thinsp;0.89) between sensor outputs\u003c/b\u003e. The scattered light contribution analysis shows it provides 30\u0026ndash;60% of the total signal, with increasing importance at low turbidity levels. The modification adds less than ₹55 to sensor cost, bridging the gap between expensive commercial and basic household turbidity sensors. This \u003cb\u003edata-driven approach\u003c/b\u003e enables smarter water usage in appliances and potentially reduces water and energy consumption.\u003c/p\u003e","manuscriptTitle":"Enhanced NTU Measurement by Modified Turbidity Sensor: A Cost-Effective Approach for Household Water Quality Monitoring","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 15:28:54","doi":"10.21203/rs.3.rs-8979025/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"07bf55db-ab6f-4a37-b7ef-72d91159de85","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-06T04:57:05+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T05:10:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 15:28:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8979025","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8979025","identity":"rs-8979025","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}