By Ahmad Zaiour, Axel André Schmidt and Michael Palocz-Andresen
Microplastics, tiny plastic particles from primary and secondary sources, pose a significant threat to aquatic ecosystems and human health. The particles spread unnoticed in the environment, are absorbed by organisms and cause physical and toxic damage. Advances in sensor and IoT technologies are enabling more precise real-time monitoring that supports the analysis of pollution patterns and the development of effective countermeasures. These innovations are critical to controlling the spread of microplastics and minimizing long-term risks.
Introduction
Microplastics are a pervasive environmental problem that threatens both ecosystems and human health. These tiny plastic particles enter the environment through various sources such as cosmetic products, textiles or the decomposition of larger plastic waste. They spread unnoticed in aquatic habitats, are ingested by organisms and have harmful physical and chemical effects. At the same time, their small size makes them difficult to detect and monitor, which makes combating microplastic pollution a major challenge [1].
However, with technological innovations such as real-time sensors and IoT-based monitoring systems, more effective control is within reach. These developments open up new opportunities to analyze the spread of microplastics, assess their dangers and initiate targeted countermeasures. The following analysis highlights the sources, impacts and innovative approaches to monitoring microplastics and shows how technological advances can make a significant contribution to solving this global problem.
Background Information on Microplastics
Microplastics are plastic particles smaller than 5 mm that occur as fibers, fragments or pellets. They are created by the decomposition of larger plastic waste or are produced directly in small sizes [1]. Due to their size, they spread unnoticed in the environment and are difficult to remove, which poses specific risks to the environment and health. A distinction is made between primary microplastics, which are intentionally produced for industrial and cosmetic purposes, and secondary microplastics, which are produced by the decomposition of larger plastic parts [2].
Primary microplastics are found in cosmetic products and cleaning agents, while secondary microplastics are created by the fragmentation of plastic bags, bottles or fishing nets. Both types pose challenges for environmental monitoring, as they easily enter bodies of water and spread deep into the ecosystem.
Sources of Microplastics
Microplastics come from many sources, including cosmetic products containing microbeads, which are used as abrasives in scrubs and toothpastes and are released into the environment via wastewater [2]. Synthetic textiles such as polyester release microfibers every time they are washed, polluting rivers and oceans. Plastic waste such as bottles and bags break down into smaller fragments due to sunlight and waves, which constantly increases the amount of microplastics. Other sources are tire abrasion caused by vehicle use and industrial processes in which plastics release microplastics [3].
Fig.1 shows the image of microplastics after different time interval.
- 0 hours: The particles have a relatively smooth and regular shape.
- 800 hours: After 800 hours of sun exposure and mechanical stress, a clear fragmentation and erosion of the particles can be seen. Some particles already show cracks and irregularities.
- 2,000 hours: After 2,000 hours, the particles have disintegrated even further and the surface structure is greatly altered. The particles have become significantly smaller and more irregular.
This shows the influence of environmental conditions on the degradation of plastics such as polystyrene over longer periods of time. The decomposed particles remain in the environment in the form of microplastics and can, for example, remain in bodies of water over the long term, where they are difficult to break down and cause ecological damage.
Effects of Microplastics on Water Bodies and Ecosystems
The ecological impact of microplastics on aquatic habitats is severe. Microplastics are ingested by fish, mussels and other organisms as they resemble natural food sources and cause physical damage such as digestive organ injury and blockages. The particles can also accumulate in the animals’ tissues, leading to reduced growth, lower food intake and increased mortality.
Microplastics also adsorb pollutants that can have toxic effects via the food chain. This chemical pollution affects the health and behaviour of affected animals and could significantly reduce biodiversity in water bodies.
Impact on Human Health and the Food Chain
Research is increasingly focusing on the potential impact of microplastics on human health. Microplastics enter the human body through the food chain, mainly through the consumption of seafood and fish, as well as through drinking water, salt and other foods such as honey. This exposure carries the risk of inflammatory reactions that could affect the immune system, as well as the uptake of toxic chemicals that can cause long-term health damage.
There is initial evidence that microplastic particles may cause cell damage, but the exact mechanisms and consequences are not yet fully understood. The ubiquitous presence of microplastics in the environment and food highlights the urgency of taking measures to reduce exposure. At the same time, there is a significant need for research to better understand how microplastics affect the human body and what long-term risks could result.
Technological Approaches to Monitoring Microplastics
The use of sensors and IoT devices to monitor microplastics in water bodies has gained considerable importance in recent years. These technologies enable the continuous and precise collection of data that is essential for analyzing the distribution of microplastics. For example, sensors have been developed that are able to detect microplastic particles in real time and measure their concentration. This offers the advantage that researchers and environmentalists can react quickly to pollution events [7].
The integration of IoT devices into microplastic monitoring has also made significant progress. These devices can wirelessly transmit data to central servers where it can be analyzed in real time. This reduces the need for manual sampling and minimizes the risk of contamination during the transportation of samples. In addition, the collected data can be used immediately for further analysis and decision-making processes.
Another advantage of using IoT devices is the ability to integrate them into a network of monitoring stations. This enables comprehensive monitoring of large geographical areas and the identification of pollution sources. For example, sensors can be installed along rivers to monitor microplastic inputs and track their origin. This not only facilitates monitoring, but also the enforcement of environmental regulations [8].
The use of IoT technologies thus represents a promising solution for tackling microplastic pollution, as it not only enables data to be collected and analyzed quickly, but can also effectively monitor the spread and origin of microplastic particles, see Fig. 2.
Figure 2: Illustration of the possible IoT technology
Data Analysis and Processing
The application of big data analytics in microplastics monitoring offers numerous advantages, especially when dealing with the enormous amounts of data generated by modern monitoring technologies such as sensors and IoT devices. These analyses make it possible to efficiently process large amounts of data from various sources, such as rivers, wastewater plants and industrial discharges, identifying complex patterns and trends that would be difficult to detect using traditional methods. Big data can be used in a variety of ways to gain a deeper understanding of microplastic distribution, which is particularly crucial for the development of effective countermeasures.
An outstanding advantage of big data analytics is the ability to process and analyze data in real time. This means that environmental protection agencies and researchers can respond immediately to pollution events by using automatically generated alert systems that trigger notifications as soon as certain thresholds of microplastic concentration are exceeded. This real-time data allows decisions to be made more quickly and preventative measures to be implemented more effectively. Such algorithms are particularly important in areas of high environmental relevance, as they enable quick and targeted actions, such as diverting water flows or initiating clean-up measures.
Another field of application of big data analysis is the identification and quantification of the main sources of microplastic pollution. By comprehensively analyzing data collected from different geographical and industrial sources, researchers can locate and quantify the hotspots of microplastic emissions. These findings are invaluable not only for developing general strategies to combat microplastics, but also for taking targeted action at the places where pollution originates, such as industrial wastewater or agricultural drainage systems [9].
In addition to identifying sources of pollution, big data also offers the opportunity to develop predictive models that show future microplastic distribution trends. Using machine learning algorithms and other advanced analytical techniques, researchers can create forecasts based on historical data and current measurements that make it possible to identify potential problem areas in advance. These predictive models not only help to monitor the current situation, but also provide a valuable basis for the long-term planning and implementation of environmental protection measures. By combining big data and predictive models, decision-makers can respond to future challenges in a more targeted and resource-efficient manner.
Overall, the use of big data analytics is revolutionizing the way environmental agencies and researchers collect, process and apply data for microplastics monitoring. The ability to analyze data in real time and make predictions supports not only monitoring, but also preventative planning and implementation of pollution mitigation strategies [10].
Artificial Intelligence for Pattern Recognition
The use of artificial intelligence (AI) to recognize patterns in collected environmental data opens up new possibilities and significant advantages for monitoring microplastics in water bodies. AI systems are able to process large amounts of data quickly and efficiently, which is particularly important in complex and dynamic environments such as rivers, lakes and coastal areas. One of the main strengths of AI is that it is able to recognize data patterns and anomalies that would be difficult to identify using conventional methods.
By using AI algorithms that can operate in real time, it is possible to analyze large data sets, identifying patterns that indicate pollution sources or movements. Especially in environments where human observation and manual analysis reach their limits, AI offers a more precise and faster solution. Another important feature of AI is its ability to learn and continuously improve from the analyzed data. This means that AI models developed to monitor microplastics are constantly trained on new data and can increase their accuracy over time. This enables them to make increasingly precise predictions and continuously improve the efficiency of monitoring.
An outstanding example of the application of AI is the ability to distinguish between different types of particles. Using specially trained models, AI systems can distinguish microplastics from other natural particles in water, such as organic debris. This ability reduces the error rate in detection and ensures that the data collected for monitoring microplastics is much more accurate. This improves the quality of environmental monitoring as it is easier to quantify the actual amount of microplastics in a particular body of water.
Another major advantage of AI is the development of predictive models that make it possible to forecast future pollution trends and distribution patterns of microplastics. By analyzing historical data in combination with advanced analytical techniques, these models can help to proactively identify pollution events before they escalate. This is particularly invaluable for long-term ecosystem monitoring and protection, as decision-makers can take early action to minimize the impact of microplastic pollution [11].
However, the advantages of AI go beyond mere pattern recognition. AI-supported systems also offer a significant increase in efficiency in data processing. By automating analytical methods, researchers and environmentalists are able to analyze large amounts of data without human intervention. This not only saves time and resources, but also increases the accuracy and reliability of the results. This is particularly relevant in large-scale monitoring projects where it is important to process continuous data flows to obtain a comprehensive picture of environmental pollution.
Overall, the use of artificial intelligence for pattern recognition and predictive analysis is revolutionizing the way data is processed and used to monitor microplastics. It not only enables more precise and faster analysis of the pollution situation, but also provides the basis for strategic decisions and long-term planning to protect water bodies. By combining real-time analysis, predictive models and automated pattern recognition, AI offers a holistic solution that significantly improves the efficiency and effectiveness of monitoring measures.
Microplastic Sensor in the Industry
The RAMP-10 sensor offers an innovative solution for real-time monitoring of microplastics in water. Using high-speed Raman spectroscopy, it analyzes flowing particles directly on site and precisely determines their material and size distribution. Thanks to machine learning, the data can be processed without delay, enabling continuous online monitoring.
In contrast to conventional methods, the RAMP-10 does not require any physical modification of the sample or chemical additives. The particles remain in their natural state, allowing for unbiased analysis. This efficiency, combined with the ability to adapt to changes in concentration within microseconds, makes the sensor a powerful alternative to traditional laboratory methods.
The RAMP-10 is versatile and can be used in research facilities, laboratories or industrial quality assurance. Its flexible adaptation allows the specific classification and identification of different polymers, making it a valuable tool in microplastics monitoring, see Fig. 3 .
Figure 3: Photo of the switched-on device with user interface [11]
Potentials and Limits of IT-supported Monitoring
Technological innovations offer significant opportunities for monitoring microplastics in water bodies. Key benefits include improved accuracy, real-time monitoring and automation of data collection processes. These advances promise not only greater efficiency in the detection of microplastic particles, but also a more precise analysis of the distribution and movement of these particles in different water bodies.
The continuous development of sensors has led to significant improvements in the identification of microplastics. Modern sensor systems can detect particles in different layers of water and even determine differences in material composition. Spectral analyses and optical methods, such as Fourier transform infrared spectroscopy (FTIR), make it possible to precisely identify microplastic particles and analyze their chemical composition. In addition, laser systems are used that can detect the position and size of microplastics in real time [12].
Real-time monitoring is another significant advance in the IT-based monitoring of microplastics. Previously, monitoring processes relied heavily on manual sampling and time-consuming laboratory analysis, which often only provided sporadic data. Today, advanced sensors connected to IoT (Internet of Things) technologies enable continuous monitoring of microplastics in real time. These systems can be installed at strategic points in rivers or oceans and provide continuous data that can be analyzed immediately.
One example of this is the use of satellites and drones to detect microplastics in large bodies of water. Satellite images can detect microplastics on the water surface using special filter techniques. These images are used in combination with weather data and ocean current models to predict and analyze the movement of microplastics. This offers the advantage that pollution hotspots can be identified more quickly and containment measures can be initiated [13].
Automation is another area that greatly increases the potential of IT-based monitoring systems. Traditionally, the analysis of microplastic samples has been a manual and lengthy process, requiring researchers to take samples from water bodies, analyze them in the lab and then evaluate the results. However, the automation of data collection and analysis tools can greatly speed up this process.
Modern monitoring systems are able to process large amounts of data in a short space of time and automatically generate reports. For example, these systems can continuously monitor water quality and raise the alarm when certain thresholds are reached. With the introduction of cloud technologies, this data can be made accessible in real time across large distances. This not only facilitates monitoring, but also collaboration between international research teams and environmental protection organizations [14].
IT-supported monitoring systems also enable better integration of interdisciplinary approaches. For example, geographic information systems (GIS) can be combined with sensor data to create maps of pollution zones. These maps help to identify the sources of microplastics and take measures to combat pollution at its source. This is particularly beneficial in large river systems and oceans, where the sources of microplastics are often difficult to pinpoint. By combining technologies such as satellite imagery, weather models and real-time sensor data, more accurate predictions can be made about the spread of microplastics [15].
The global availability of IT-based monitoring systems allows not only national governments but also international organizations to monitor microplastic pollution on a global scale. This enables better coordination in the fight against plastic pollution, especially in developing countries where access to traditional monitoring methods is often limited. The use of drones and low-cost sensors in remote areas can help collect data that is crucial for environmental protection. The technologies also enable long-term sustainability strategies as they significantly reduce the resources required for sampling and laboratory testing while providing more comprehensive data.
Challenges and Limitations of Current Technologies
Monitoring microplastics in water poses a number of technical challenges. One key difficulty is the sensitivity of the sensors. Many of the sensors used today are not able to reliably detect the smallest microplastic particles, which can lead to incomplete data sets. In addition, the accuracy of the measurements is highly dependent on environmental conditions such as the turbidity of the water and the presence of organic particles. Such factors can distort the measurement results, as the sensors have difficulty distinguishing microplastics from other particles [6].
Another technical problem concerns the standardization of measurement methods. To date, there is no standardized procedure for monitoring microplastics, which is why studies often use different technologies and protocols. This makes it considerably more difficult to compare the results from different regions and studies. There is an urgent need for internationally recognized standards and protocols to increase the comparability and reliability of the collected data.
Data processing is also a key issue. As continuous monitoring generates immense amounts of data, powerful data processing systems and efficient algorithms are necessary to analyze this data in a meaningful way. Techniques such as big data analytics and artificial intelligence can help improve pattern recognition and data evaluation, but require significant technical resources and specialized expertise.
Long-term surveillance systems face additional challenges. Many current monitoring devices are not designed for continuous operation in harsh environments. Regular maintenance and calibration are necessary to keep the sensors functional, which increases operating costs and can affect monitoring efficiency [5].
Outlook and Future Developments
The future of microplastic monitoring in bodies of water will be shaped by technological advances in sensor technology and data analysis. New sensors with improved materials and optical technologies enable more precise detection of even the smallest particles that were previously difficult to detect. These innovations could significantly increase data quality and provide detailed insights into the composition of microplastic pollution.
An important trend is the miniaturization of sensors and their integration into IoT devices. These mini-sensors could create area-wide monitoring networks that enable almost seamless data collection over large geographical areas. Continuous real-time monitoring not only reduces costs, but also generates valuable data for analyzing pollution patterns [7].
The automation of sampling and data analysis also plays a central role. Automated systems that operate without human intervention ensure continuous monitoring of large volumes of water, increase data accuracy and minimize errors. These systems can operate around the clock while reducing operating costs.
In the future, hybrid technologies that combine optical sensors with chemical analysis methods could provide more comprehensive data. Such systems not only record the physical properties of microplastic particles, but also their chemical composition and origin. This enables the development of precise prediction models that can map current pollution and forecast future trends [8].
Overall, these technological advances promise more precise, cost-effective and comprehensive microplastic monitoring, which will make a significant contribution to better understanding pollution and developing effective countermeasures.
Conclusion: Future Prospects and the Role of IT-based Microplastic Monitoring
Microplastics are a serious environmental threat that not only endangers ecosystems but also human health. It originates from primary sources such as cosmetic products and synthetic textiles, as well as from the decomposition of larger plastic waste. The small particles spread unnoticed in aquatic habitats, where they are ingested by organisms and can cause physical and toxic damage. These effects not only affect the species concerned, but also extend through the entire food chain to humans, which underlines the urgency of countermeasures.
Technological advances in microplastic monitoring offer hope for more effective solutions. Sensors such as the RAMP-10 enable precise real-time measurements that allow an immediate response to pollution. The combination of sensor technology with IoT devices has the potential to create a comprehensive monitoring network that provides high quality data without manual intervention. Automated sampling and analysis methods minimize errors and costs, while hybrid technologies can provide more comprehensive information on the physical and chemical nature of microplastics.
Future developments in this area will be characterized by the continuous improvement of sensors, integration into IoT networks and the automation of processes. These advances are crucial not only to better understand the distribution and impact of microplastics, but also to develop effective measures to reduce pollution. Only through a combination of technological innovation, scientific research and decisive action can the environmental and health risks of microplastics be minimized in the long term.
About the Authors
Ahmad Zaiour is currently pursuing his studies in Business Informatics and Social Media & Information Systems at Leuphana University in Lüneburg, where he began his academic journey in 2023. With a strong interest in innovative technologies and their intersection with human interaction, he is expanding his expertise in systems that drive digital transformation and social connectivity. Ahmad is dedicated to understanding and leveraging the potential of information systems to create impactful solutions in a rapidly evolving technological landscape.
Dipl.-Phys. Axel André Schmidt is a graduate of Applied Physics from University of Hamburg, 1994/95 he developed a sophisticated online-oil-spill-in-water-monitor and joined DECKMA Hamburg GmbH, a well successful company on manufacturing oil-in-water measuring equipment for marine and industrial applications, respectively. Since 1996 he is head of research and development and generates not only the whole fleet of oil-in-water measuring instruments but also turbidity-meter and the first online micro plastics monitor based on Raman-scattering, worldwide. He is member of NEL´s Environmental Club (UK) since 2001, supports the club by several presentation, regularly. Moreover, he gives guest lectures on sustainable mobility at Leuphana University Lüneburg, since 2022.
Prof. Dr.-Ing. habil. Michael Palocz-Andresen is a full professor at the BUAP in Puebla. He has been working as a full professor for Sustainable Mobility since 2018, supported by the DAAD at the TEC Instituto Tecnológico y de Estudios Superiores in Mexico. He became a full professor at the University West Hungary till 2017. Currently, he is a guest professor at the TU Budapest, the Leuphana University Lüneburg, and at the Shanghai Jiao Tong University. He is a Humboldt scientist and instructor of the SAE International in the USA.
References
1. de Souza Machado, A. A., Kloas, W., Zarfl, C., Hempel, S., & Rillig, M. C. (2018). Microplastics as an emerging threat to terrestrial ecosystems. Global Change Biology, 24(4), 1405–1416. https://doi.org/10.1111/gcb.14020
2. Wang, J., Zheng, L., & Li, J. (2018). A critical review on the sources and instruments of marine microplastics and prospects on the relevant management in China. Waste Management & Research: The Journal of the International Solid Wastes and Public Cleansing Association, ISWA, 36(10), 898–911. https://doi.org/10.1177/0734242×18793504
3. van Raamsdonk, L. W. D., van der Zande, M., Koelmans, A. A., Hoogenboom, R. L. A. P., Peters, R. J. B., Groot, M. J., Peijnenburg, A. A. C. M., & Weesepoel, Y. J. A. (2020). Current insights into monitoring, bioaccumulation, and potential health effects of microplastics present in the food chain. Foods (Basel, Switzerland), 9(1), 72. https://doi.org/10.3390/foods9010072
4. Wißler, C. (2021). Wie sich Mikroplastik durch Sonne und Wellen vermehrt. LABORPRAXIS. https://www.laborpraxis.vogel.de/wie-sich-mikroplastik-durch-sonne-und-wellen-vermehrt-a-671aeb5766cb447670c93b757e712fe6/
5. Lusher, A. L., Welden, N. A., Sobral, P., & Cole, M. (2017). Sampling, isolating and identifying microplastics ingested by fish and invertebrates. Analytical Methods: Advancing Methods and Applications, 9(9), 1346–1360. https://doi.org/10.1039/c6ay02415g
6. Singh, H. (2024). Innovative approaches for microplastic pollution detection and remediation in aquatic ecosystems. Journal for Research in Applied Sciences and Biotechnology, 3(4), 14–21. https://doi.org/10.55544/jrasb.3.4.3
7. Kniggendorf, A.-K., Wetzel, C., & Roth, B. (2019). Microplastics detection in streaming tap water with Raman spectroscopy. Sensors (Basel, Switzerland), 19(8), 1839. https://doi.org/10.3390/s19081839
8. Dris, R., Imhof, H., Sanchez, W., Gasperi, J., Galgani, F., Tassin, B., & Laforsch, C. (2015). Beyond the ocean: contamination of freshwater ecosystems with (micro-)plastic particles. Environmental Chemistry (Collingwood, Vic.), 12(5), 539. https://doi.org/10.1071/en14172
9. Tagg, A. S., Sapp, M., Harrison, J. P., Sinclair, C. J., Bradley, E., Ju-Nam, Y., & Ojeda, J. J. (2020). Microplastic monitoring at different stages in a wastewater treatment plant using reflectance micro-FTIR imaging Frontiers in environmental science, 8. https://doi.org/10.3389/fenvs.2020.00145
10. Montoto-Martínez, T., Hernández-Brito, J. J., & Gelado-Caballero, M. D. (2020). Pump-underway ship intake: An unexploited opportunity for Marine Strategy Framework Directive (MSFD) microplastic monitoring needs on coastal and oceanic waters. PloS One, 15(5), e0232744. https://doi.org/10.1371/journal.pone.0232744
11. Deckma Hamburg GmbH (Dipl.-Phys. Axel André Schmidt & M.Sc.Timo Nieder)
12. Cirri, E., & Pohnert, G. (2019). Algae−bacteria interactions that balance the planktonic microbiome. The New Phytologist, 223(1), 100– 106.https://doi.org/10.1111/nph.15765
13. da Silva, V. H., Murphy, F., Amigo, J. M., Stedmon, C., & Strand, J. (2020). Classification and quantification of microplastics (<100 μm) using a focal plane array–Fourier transform infrared imaging system and machine learning. Analytical Chemistry, 92(20), 13724–13733. https://doi.org/10.1021/acs.analchem.0c01324
14. Ma, H., Chao, L., Wan, H., & Zhu, Q. (2024). Microplastic pollution in water systems: Characteristics and control methods. Diversity, 16(1), 70. https://doi.org/10.3390/d16010070
15. Campanale, C., Massarelli, C., Savino, I., Locaputo, V., & Uricchio, V. F. (2020). A detailed review study on potential effects of microplastics and additives of concern on human health. International Journal of Environmental Research and Public Health, 17(4), 1212. https://doi.org/10.3390/ijerph17041212
