Detection & Monitoring the Water Pollutants Using Light Detection & Ranging

Microplastics are small fragments of plastic debris, generally defined as pieces smaller than five millimeters in size. They are present in virtually every environment on Earth, from the deepest ocean trenches to the highest mountain peaks. Microplastics have even been found in the digestive tracts of many marine organisms and sea birds, making them a significant ecological concern. The increasing awareness of microplastics' widespread impact has led to a growing demand for research on this topic, particularly in the areas of sampling, quantification, and standardization.

### The Importance of Microplastic Research

The study of microplastics is critical because these small plastic particles have become a pervasive environmental issue. Marine ecosystems are particularly affected by the accumulation of microplastics, which are often ingested by marine life, sometimes with toxic consequences. Additionally, microplastics can act as carriers for pollutants, further increasing the risk to wildlife and ecosystems. As the concern about microplastic pollution grows, so does the need for accurate and standardized methods to quantify and analyze these particles in various environments.

However, despite the growing body of research, one of the primary challenges facing scientists is the lack of a standardized protocol for sampling and quantifying microplastics. Different laboratories often use different methods depending on factors such as budget, available equipment, and specific research objectives. As a result, comparing data from different studies can be difficult, making it challenging to assess the full extent of microplastic pollution on a global scale.

### The Project's Focus: Standardization of Microplastic Analysis

To address the issue of inconsistent methodologies, this project aimed to standardize the sampling and quantification of microplastics. The project consisted of two main components: the development of a standardized laboratory protocol for isolating and quantifying microplastic debris and an interlaboratory comparison study to evaluate the comparability of existing methods used by different research groups.

The first component of the project involved creating a simple, cost-effective, and unbiased method for processing environmental samples to isolate microplastics. Researchers at the University of Washington Tacoma developed this protocol to be versatile, allowing it to be applied to different sample types, including beach sand, bed sediment, and water. The procedure involves multiple steps, including sieving, separation, removal of organic matter, drying, sorting, and weighing the microplastic debris. These steps help to ensure that the method is both effective and adaptable to various research needs.

The second component of the project involved conducting an interlaboratory comparison study. This part of the research sought to determine whether different laboratories could produce consistent and comparable results analyzing

1. 3 rd  International Conference on Optimization Techniques in the Field of Engineering (ICOFE-2024) 289- Detection & Monitoring the Water Pollutants Using Light Detection & Ranging 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 Authors Name 1. MOHAMED RIDWAN NISATH S 2. Mohammed nabeel 3. Mota harshavardhan reddy 4. N.SIVAKUMAR KSR College Of Engineering , Nammakal , Tamil Nadu, India & Debre Tabor University  Ethiopia 22 nd And 23 rd  Oct 2024 Corresponding Author Affiliation Details: MOHAMED RIDWAN NISATH S UG Student, Department of Artificial Intelligence and Data Science, Panimalar Engineering College, Chennai, 600123 Email: [email protected] 1

2. TABLE OF CONTENTS 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 2 Introduction Problem Statement Literature Review/Related Work Research Methodology System Architecture/Design Experimental Setup Results Conclusion Future Work Acknowledgments References

3. 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 3 INTRODUCTION Water pollution is a critical global environmental issue requiring efficient solutions. LiDAR technology is utilized for precise 3D surface mapping and accurate water quality assessment. Folium is integrated for advanced geospatial visualization, enabling identification of pollution hotspots. This method enables real-time monitoring and rapid evaluation of water quality, surpassing traditional methods. The research highlights the role of cutting-edge technologies in timely pollution detection and environmental conservation .

4. 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 4 PROBLEM STATEMENT Current water quality assessment methods are often slow and lack accuracy, hindering effective monitoring and management of pollution. A more efficient solution is required for real-time detection and evaluation of water pollution .

5. 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 5 LITERATURE REVIEW Title Author(s) Year Cons The role of remote sensing in the evolution of water pollution detection and monitoring Gordana Kaplan, Fatma Yalcinkaya , Esra Altıok , Andrea Pietrelli , Rosa Anna Nastro , Nicola Lovecchio 2023 Can cause information overload, complicating relevant detail extraction. Interpolation of airborne LiDAR data for archaeology Lozić, Edisa & Eichert, Stefan & Štular, Benjamin 2023 Computational intensity, data sparsity, accuracy trade-offs, and processing time. High-Density and Low-Crosstalk Multilayer Silicon Nitride Waveguide Superlattices with Air Gaps Li, Wenling & Liu, Jing- wei & Cheng, Guo-an & Zheng, Rui-ting & Wu, Xiao-ling 2023 Complex fabrication, sensitivity to variations, limited operational wavelength range.

6. 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 6 LITERATURE REVIEW Title Author(s) Year Cons Evaluating the Archaeological Efficacy of Bathymetric LiDAR across Oceanographic Contexts Cook Hale, Jessica & Davis, Dylan & Sanger, Matthew 2023 Environmental factors, high sedimentation impacts, limited underwater visibility, technology adaptation. A Simultaneous Pipe-Attribute and PIG-Pose Estimation (SPPE) Using 3-D Point Cloud in Compressible Gas Pipelines Hung, Nguyen & Park, Jae-Hyun & Jeong, Han-You. 2023 Complex optimization, dependency on sensor accuracy, requires extensive calibration. Solar Potential Uncertainty in Building Rooftops as a Function of Digital Surface Model Accuracy Polo, Jesus & García, Redlich 2023 High uncertainty in DSM accuracy, complex topography issues, variable measurement reliability.

7. 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 7 RESEARCH METHODOLOGY STEP 1: Data Collection & Sampling Techniques Microplastic Data Collection: Data on microplastics were gathered from various environmental samples, such as water and sediment, across different regions. A laboratory method was employed, involving filtering, separating, drying, and weighing samples to quantify the amount of microplastic debris present. Interlaboratory Comparison: Identical water samples containing known quantities of microplastics were distributed to different laboratories. Each lab utilized its own procedures for filtering and isolating microplastics. The resulting data were compared to evaluate the consistency and reliability of the methodologies employed by each laboratory.

8. 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 8 STEP 2: Data Analysis & Visualization Libraries and Packages: Various Python libraries were used for data analysis, such as pandas for data manipulation, seaborn and matplotlib for visualizations, folium for geospatial mapping, and scipy for scientific computations. These tools enabled an in-depth exploration of trends, anomalies, and correlations within the datasets. Data Sources: SEA_MICRO.csv : A dataset containing latitude, longitude, and microplastic density (Pieces per KM²) over time. GEO_READING.csv: Data on microplastic concentration (particles per cubic meter) from marine locations. ADVENTURE_MICRO_FROM_SCIENTIST.csv: Contributions from adventure scientists, mapping the presence of microplastics over different dates and locations. Geospatial Mapping: The use of the folium library and its plugins allowed the visualization of microplastic concentrations across different geographic locations. Heatmaps and marker clusters were created to visually represent areas with higher densities of microplastics, making it easier to identify pollution hotspots.

9. 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 9 STEP 3: Interlaboratory Data Comparison The interlaboratory comparison involved sending identical reference samples to six laboratories, allowing researchers to compare results. Differences in results (e.g., in microplastic concentrations) were then evaluated against the known values in the reference samples, assessing each lab's accuracy and consistency. STEP 4: Output & Results Visualizing Trends: Time-series plots using plotly and matplotlib provided insights into changes in microplastic concentration over time. These visualizations helped identify spikes in microplastic density, such as the highest value of 12,316,946 pieces per KM² recorded on October 16, 2012. Geospatial Heatmaps: The folium library was used to generate heatmaps, pinpointing regions with the highest microplastic concentrations, e.g., locations around 21.507712° N, 119.547692° E were found to have the highest concentration of microplastic particles per cubic meter.

10. 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 10 Methodology Summary Data Collection: Environmental samples were collected, processed, and quantified using standardized and laboratory-specific methods. Geospatial and Temporal Analysis: Microplastic densities were mapped over time and location using Python data visualization libraries. Comparative Analysis: Interlaboratory comparison results were examined for discrepancies to promote global standardization in sampling protocols.

11. 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 11 FLOWCHART/DESIGN

12. 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 12 Experimental Design Reference Sample Creation: Prepare samples with known concentrations of microplastics and organic matter. Distribution: Mail reference samples to selected laboratories (6 national and international) with established expertise in microplastic research. Data Collection: Each laboratory will use their own protocols to analyze the samples and report the results. Analysis Parameters Quantification of Microplastics: Count and weigh the isolated microplastics. Comparative Analysis: Compare the results obtained by different laboratories with the known concentrations in the reference samples. EXPERIMENTAL SETUP

13. 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 13 Data Analysis Methods Use pandas for data manipulation and analysis. Visualize results using matplotlib and plotly for graphical representation. Conduct statistical analysis to assess the variability and reliability of results. Packages and Libraries Required import pandas as pd, import matplotlib.pyplot as plt , import plotly.express as px , import folium, from folium import plugins, import seaborn as sns . Data Visualization and Interpretation Data Input: Read and process datasets (CSV files) containing microplastic concentration and geographic information. Geospatial Analysis: Create maps visualizing the distribution of microplastics using folium. Implement heatmaps to illustrate areas with higher concentrations of microplastics. Time Series Analysis : Use line plots to depict trends in microplastic concentration over time.

14. 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 14 Our research on microplastic sampling protocols revealed significant findings that enhance our understanding of microplastic distribution across various environments. By standardizing sampling techniques, we ensured the comparability of results across laboratories, ultimately facilitating a more accurate assessment of microplastic pollution. These insights are crucial for informing effective environmental policies and strategies. Additionally, our approach can significantly improve monitoring efforts and contribute to the development of sustainable practices aimed at mitigating the impacts of microplastics in ecosystems. RESULTS

15. 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 15 This study demonstrates the effectiveness of LiDAR technology in conducting detailed topographic surveys of American coastal regions. By capturing high-resolution, three-dimensional data, LiDAR facilitates the creation of Digital Elevation Models (DEMs) and other visual outputs, providing precise representations of coastal landscapes. The extracted parameters, such as elevation, slope, and aspect, offer valuable quantitative insights for coastal terrain analysis. The centimeter-level precision of the data enhances its reliability, making LiDAR an indispensable tool for coastal science. This approach significantly contributes to coastal management, environmental planning, and disaster management, enabling stakeholders to make informed decisions based on accurate terrain assessments. CONCLUSIONS

16. 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 16 Automated Sampling and Analysis Tools Robotic Sample Collection : Develop autonomous underwater vehicles (AUVs) equipped with sensors and sampling equipment to collect water samples from various depths and locations. Image Recognition for Microplastics: Implement machine learning algorithms using image recognition to automatically identify and classify microplastic types from collected samples. Standardized Database for Microplastic Data Centralized Data Repository: Create a global database where researchers can upload their microplastic data, including methods, results, and sampling conditions. This would facilitate better comparisons and analyses. Open Access: Ensure that the database is accessible to all researchers and policymakers to promote transparency and collaboration. FUTURE WORK

17. 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 17 Research Team: I would like to thank my teammates for their valuable contributions and support throughout this research project. Institution Support: A special thanks to Panimalar Engineering College for providing an encouraging environment for research and collaboration. Personal Gratitude: I appreciate the guidance and insights from my professors and mentors who helped shape this project. ACKNOWLEDGMENTS

18. 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 18 Gordana Kaplan, Fatma Yalcinkaya , Esra Altıok , Andrea Pietrelli , Rosa Anna Nastro , Nicola Lovecchio , Ioannis A. Ieropoulos , Argyro Tsipa , The role of remote sensing in the evolution of water pollution detection and monitoring: A comprehensive review, Physics and Chemistry of the Earth, Parts A/B/C, Volume 136, 2024, 103712, ISSN 1474-7065. Lozić , Edisa & Eichert , Stefan & Štular , Benjamin. (2023). Interpolation of airborne LiDAR data for archaeology. Journal of Archaeological Science: Reports. 48.103840. 10.1016/j.jasrep.2023.103840 Li, Wenling & Liu, Jing- wei & Cheng, Guo-an & Zheng, Rui-ting & Wu, Xiao-ling. (2023). High-Density and Low-Crosstalk Multilayer Silicon Nitride Waveguide Superlattices with Air Gaps. IEEE Photonics Journal. 15. 1-8. 10.1109/JPHOT.2022.3232094. Cook Hale, Jessica & Davis, Dylan & Sanger, Matthew. (2023). Evaluating the Archaeological Efficacy of Bathymetric LiDAR across Oceanographic Contexts: A Case Study from Apalachee Bay, Florida. Heritage. 6. 928-945. 10.3390/heritage6020051. REFERENCES

19. 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 19 Hung, Nguyen & Park, Jae-Hyun & Jeong, Han-You. (2023). A Simultaneous Pipe-Attribute and PIG-Pose Estimation (SPPE) Using 3-D Point Cloud in Compressible Gas Pipelines. Sensors. 23. 1196. 10.3390/s23031196. Polo, Jesus & García, Redlich. (2023). Solar Potential Uncertainty in Building Rooftops as a Function of Digital Surface Model Accuracy. Remote Sensing. 15. 567. 10.3390/rs15030567. Ze- hou Yang, Yong- ke Zhang, Yong Chen, Xiao-feng Li, Yong Jiang, Zhen- zhong Feng, Bo Deng, Chun-li Chen, Ding-fu Zhou, Simultaneous detection of multiple gaseous pollutants using multi-wavelength differential absorption LIDAR, Optics Communications, Volume 518, 2022, 128359, ISSN 0030-4018. Nina Gnann , Björn Baschek , Thomas A. Ternes, Close-range remote sensing-based detection and identification of microplastics on water assisted by artificial intelligence: A review, Water Research, Volume 222, 2022, 118902, ISSN 0043-1354. Yang, H.; Kong, J.; Hu, H.; Du, Y.; Gao, M.; Chen, F. A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges. Remote Sens. 2022, 14, 1770.

20. 22 nd And 23 rd  Oct 2024 3 rd International Conference on Optimization Techniques in the Field of Engineering ICOFE-2024 20 Nur Hanis Hayati Hairom , Chin Fhong Soon, Radin Maya Saphira Radin Mohamed, Marlia Morsin , Nurfarina Zainal, Nafarizal Nayan, Che Zalina Zulkifli , Nor Hazlyna Harun, A review of nanotechnological applications to detect and control surface water pollution, Environmental Technology & Innovation, Volume 24, 2021, 102032, ISSN 2352-1864. Georgios Zamanakos , Lazaros Tsochatzidis , Angelos Amanatiadis , Ioannis Pratikakis , A comprehensive survey of LiDAR-based 3D object detection methods with deep learning for autonomous driving, Computers & Graphics, Volume 99,PP 153-181, 2021. Yu-Cheng Fan, Chitra Meghala Yelamandala , Ting-Wei Chen, Chun-Ju Huang, "Real-Time Object Detection for LiDAR Based on LS-R-YOLOv4 Neural Network", Journal of Sensors, vol. 2021, Article ID 5576262,11 pages, 2021. Muro , S., Yoshida, I., Hashimoto, M. et al. Moving-object detection and tracking by scanning LiDAR mounted on motorcycle based on dynamic background subtraction. Artif Life Robotics 26, 412–422 (2021). [15] Prosposito , P.; Burratti , L.; Venditti , I. Silver Nanoparticles as Colorimetric Sensors for Water Pollutants. Chemosensors 2020, 8, 26.