پیوندهای مفید
آمار
| تعداد نشریات | 31 |
| تعداد شمارهها | 484 |
| تعداد مقالات | 4,286 |
| تعداد مشاهده مقاله | 7,298,708 |
| تعداد دریافت فایل اصل مقاله | 4,896,213 |
Digital transformation in environmental parameter measurement and monitoring: transitioning from traditional methods | ||
| مدل سازی و مدیریت آب و خاک | ||
| مقاله 20، دوره 5، شماره 4، 1404، صفحه 349-364 اصل مقاله (475.92 K) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22098/mmws.2025.17790.1623 | ||
| نویسندگان | ||
| Abdulqadous Abdullah1؛ Suzan Mohammed Jawad Alkazraji2؛ Ayah Ahmed Jasim3؛ Aseel Ibraheem Muhsin4؛ Ola Janan5؛ Somaye Allahvaisi* 6 | ||
| 1Al-Turath University, Baghdad 10013, Iraq, | ||
| 2Al-Mansour University College, Baghdad 10067, Iraq | ||
| 3Al-Mamoon University College, Baghdad 10012, Iraq | ||
| 4Al-Rafidain University College Baghdad 10064, Iraq, | ||
| 5Madenat Alelem University College, Baghdad 10006, Iraq, | ||
| 6Kurdistan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, AREEO, Sanandaj, Iran | ||
| چکیده | ||
| Traditional environmental monitoring, which relies on manual sampling and laboratory analysis, often suffers from slow response times, high operational costs, and limited spatial or temporal resolution. These constraints hinder timely and informed decision-making, particularly in the face of accelerating environmental change. This study investigates the potential of digital technologies—primarily Internet of Things (IoT) sensors and Artificial Intelligence (AI)—to modernize environmental monitoring systems focused on air quality, water, and soil. A comparative design was employed to evaluate traditional methods against digital systems, incorporating IoT-enabled data collection and AI-driven analytics, supported by big data infrastructure. Key environmental indicators included PM2.5 concentration, soil moisture, water pH, temperature, and carbon emissions. The results showed significant improvements: measurement accuracy increased by approximately 20%, response time was reduced by 97.9%, and data processing speed surged by more than 19,900%, effectively reducing processing durations from several hours to near real-time. Operational costs decreased by over 50%. Additionally, predictive models powered by AI allowed for early warnings, while real-time data acquisition through IoT improved responsiveness to environmental threats. Although blockchain was not used directly for measurement or analysis, it played a critical role in ensuring data integrity, transparency, and traceability—factors essential to building trust in digital monitoring frameworks. Despite ongoing challenges such as scalability, energy consumption, and connectivity in rural regions, the findings highlight the potential of integrated digital tools to create more adaptive, efficient, and sustainable environmental management systems. These smart technologies present a path toward proactive governance and resilient ecosystem stewardship. | ||
| کلیدواژهها | ||
| AI؛ Environmental Monitoring؛ Precision Agriculture؛ Environmental Governance؛ Blockchain | ||
| مراجع | ||
|
References Abdelhalim, A. M., Ibrahim, N., & Alomair, M. (2023). The moderating role of digital environmental management accounting in the relationship between eco-efficiency and corporate sustainability. Sustainability, 15(9). doi: 10.3390/su15094682 Ahamed, A., Foye, J., Poudel, S., & Trieschman, E. (2023). Machine learning in environmental analytics: A cost-efficiency perspective. Forests, 14(10). Ahmadpari, H., & Khaustov, V. (2025). Agricultural drought monitoring using meteorological indices in Darreh Dozdan Basin, Iran. Advances in Civil Engineering and Environmental Science, 2(2), 72–84. doi: 10.22034/acees.2025.512324.1022 Ahmed, K., & Zhao, M. (2023). COVID-19 and the acceleration of environmental digitalization. Global Environmental Health Review, 6(4), 203–221. Alotaibi, E., & Nassif, N. (2024). Artificial intelligence in environmental monitoring: In depth analysis. Discover Artificial Intelligence, 4, 84. Amini, A., Ali, T. M., Ghazali, A. H. B., & Huat, B. K. (2009). Adjustment of peak streamflows of a tropical river for urbanization. American Journal of Environmental Sciences, 5(4), 285–294. doi: 10.3844/ajessp.2009.285.29 Arowolo, M., Aaron, W., Kugbiyi, A., Eteng, U., Iloh, D., Aguma, C., & Olagunju, A. (2024). Integrating AI enhanced remote sensing technologies with IoT networks for precision environmental monitoring and predicative ecosystem management. World Journal of Advanced Research and Reviews, 23(02), 2156–2166. doi: 10.30574/wjarr.2024.23.2.2573 Audu, A., Umana, A., & Garba, B. (2024). The role of digital tools in enhancing environmental monitoring and business efficiency. International Journal of Multidisciplinary Research Updates, 8, 039–048. doi: 10.53430/ijmru.2024.8.2.0052 Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: Toward a next generation of insights. MIS Quarterly, 37(2), 471–482. Brasoveanu, F. (2024). Transparency and public participation in EU environmental decisionmaking: Strengthening global governance and regional cooperation. Ovidius University Annals Economic Sciences Series, XXIII(2), 28–35. doi: 10.61801/OUAESS.2023.2.04 Bwambale, E., Abagale, F. K., & Anornu, G. K. (2022). Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review. Agricultural Water Management, 260, 107324. Choudhury, M., & Haque, C. E. (2024). Disaster management policy changes in Bangladesh: Drivers and factors of a shift from reactive to proactive approach. Environmental Policy and Governance, 34(5), 445–462. doi: 10.1002/eet.1982 Feroz, A., Zo, H., & Chiravuri, A. (2021). Digital transformation and environmental sustainability: A review and research agenda. Sustainability. doi: 10.3390/su131810815 Fried, H. S., Hamilton, M., & Berardo, R. (2022). Closing integrative gaps in complex environmental governance systems. Ecology and Society, 27(1). doi: 10.5751/ES-12914-270101 Gharibreza, M., Nasrollahi, A., Afshar, A., Amini, A., & Eisaei, H. (2018). Evolutionary trend of the Gorgan Bay (southeastern Caspian Sea) during and post the last Caspian Sea level rise. Catena, 163, 213–222. doi: 10.1016/j.catena.2018.04.016 He, X., & Chen, W. (2024). Digital transformation and environmental, social, and governance performance from a human capital perspective. Sustainability, 16(11). doi: 10.3390/su16011526 Huang, S., Zhang, X., Chen, N., Ma, H., Fu, P., Dong, J., et al. (2022). A novel fusion method for generating surface soil moisture data with high accuracy, high spatial resolution, and high spatio-temporal continuity. Water Resources Research, 58(5), e2021WR030827. doi: 10.1029/2021WR030827 Jabrane, M., Touiouine, A., Bouabdli, A., Chakiri, S., Mohsine, I., Valles, V., et al. (2023). Data conditioning modes for the study of groundwater resource quality using a large physico-chemical and bacteriological database, Occitanie Region, France. Water, 15(1). doi: 10.3390/w15010083 Kargas, A., Gkika, E. C., & Sepetis, A. (2024). Exploring digital transformation intensity and its relationship with sustainability: Greek managers’ perspectives. Sustainability, 16(14). Kumar, S., & Singh, R. (2023). Reactive vs. predictive environmental governance: A comparative review. Environmental Monitoring Journal, 12(4), 301–315. doi: 10.3390/su16145678 Lakhiar, I. A., Yan, H., Zhang, C., Wang, G., He, B., Hao, B., ... & Rakibuzzaman, M. (2024). A review of precision irrigation water-saving technology under changing climate for enhancing water use efficiency, crop yield, and environmental footprints. Agriculture, 14(7), 1141. Li, C., Wang, L., & Zhou, Y. (2024). Cybersecurity in cloud-based environmental systems: A risk assessment. Journal of Environmental Informatics, 35(3), 213–228. Mahajan, S., Chen, L.-J., & Tsai, T.-C. (2018). Short-term PM2.5 forecasting using exponential smoothing method: A comparative analysis. Sensors, 18(10), 3223. doi: 10.3390/s18103223 Martinez, R., & Johnson, D. (2024). AI-driven interventions for sustainable land management. Environmental Innovation and Societal Transitions, 20(1), 64–79. Martínez-Peláez, R., Ochoa-Brust, A., Rivera, S., Félix, V. G., Ostos, R., Brito, H., et al. (2023). Role of digital transformation for achieving sustainability: Mediated role of stakeholders, key capabilities, and technology. Sustainability, 15(14). doi: 10.3390/su151410698 Nguyen, T., & Patel, J. (2025). Smart governance for ecological resilience: Global adoption trends. International Journal of Sustainability Studies, 13(1), 99–117. Othman, A. A., Al-Saady, Y., Obaid, A., & Gloaguen, R. (2012). Environmental change detection in the central part of Iraq using remote sensing data and GIS. Arabian Journal of Geosciences, 7(3). doi: 10.1007/s12517-013-0870-0 Popescu, S. M., Mansoor, S., Wani, O. A., Kumar, S. S., Sharma, V., Sharma, A., et al. (2024). Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management. Frontiers in Environmental Science, 12. doi: 10.3389/fenvs.2024.00052 Rawashdeh, A., Abdallah, A. B., Alfawaeer, M., Al Dweiri, M., & Al-Jaghbeer, F. (2024). The impact of strategic agility on environmental sustainability: The mediating role of digital transformation. Sustainability, 16(3). doi: 10.3390/su16030811 Shahid, I., Shahzad, M. I., Tutsak, E., Mahfouz, M. M. K., Al Adba, M. S., Abbasi, S. A., et al. (2024). Carbon-based sensors for air quality monitoring networks; Middle East perspective. Frontiers in Chemistry, 12. doi: 10.3389/fchem.2024.00045 Shao, X., Ahmad, M., & Javed, F. (2024). Firm-level digitalization for sustainability performance: Evidence from Ningbo City of China. Sustainability, 16(20). doi: 10.3390/su16208444 Shen, A., & Wang, R. (2023). Digital transformation and green development research: Microscopic evidence from China’s listed construction companies. Sustainability, 15(16). doi: 10.3390/su15169192 Shen, Y., Yang, Z., & Zhang, X. (2023). Impact of digital technology on carbon emissions: Evidence from Chinese cities. Frontiers in Ecology and Evolution, 11. doi: 10.3389/fevo.2023.00058 Singh, A., Patel, H., & Ouyang, X. (2024). Real-time AI algorithms for environmental data analytics. Journal of Smart Environmental Systems, 5(2), 77–91. Soussi, A., Zero, E., Sacile, R., Trinchero, D., & Fossa, M. (2024). Smart sensors and smart data for precision agriculture: a review. Sensors, 24(8), 2647. Su, X., Wang, S., & Li, F. (2023). The impact of digital transformation on ESG performance based on the mediating effect of dynamic capabilities. Sustainability, 15(18). doi: 10.3390/su151810239 Talebian, S. , Golkarieh, A. , Eshraghi, S. , Naseri, M. and Naseri, S. (2025). Artificial Intelligence Impacts on Architecture and Smart Built Environments: A Comprehensive Review. Advances in Civil Engineering and Environmental Science, 2(1), 45-56. doi: 10.22034/acees.2025.488106.1013 Xia, J., Wu, Z., & Chen, B. (2022). How digital transformation improves corporate environmental management: A review and research agenda. Frontiers in Environmental Science, 10. doi: 10.3389/fenvs.2022.927718 Zhang, Y. (2024). A study on the correlation between the digital economy and resource allocation efficiency in the context of factor mobility. Applied Mathematics and Nonlinear Sciences, 9(1). Zhong, Y., Zhao, H., & Yin, T. (2023). Resource bundling: How does enterprise digital transformation affect enterprise ESG development? Sustainability, 15(2). doi: 10.3390/su15020604 | ||
|
آمار تعداد مشاهده مقاله: 1,102 تعداد دریافت فایل اصل مقاله: 928 |
||