Home › JCARM › Vol 1, Issue 5 › Article
Review Article

AI-based Surveillance for Environment Management at Civil Construction Sites: A Case Study of Delhi State

Prabhat Verma

Click an author's name to view their details

Prabhat Verma Corresponding Author

Research Scholar, Guru Gobind Singh Indraprastha University, Delhi, India

ORCID: 0009-0006-3342-5816

Correspondence: prabhatknl@gmail.com

Received
30 Jun 2026
Published
10 Jul 2026

Abstract

The rapid pace of urban development and infrastructure projects in Delhi has highlighted the environmental concerns associated with civil construction activities, including air pollution, noise pollution, waste mismanagement and unsafe construction practices. The existing construction site environment monitoring methods are generally inefficient, due to manual supervision and non-real-time reporting. This paper proposes a new surveillance system for environmental management in Civil Construction Sites in Delhi using an Artificial Intelligence (AI) based system. The research is mixed methods; this study relied on secondary data from governmental environmental reports and published academic literature, regulatory documents and existing case studies on AI technologies and construction site management. This research analyzes the application of Artificial Intelligence (AI) technologies such as machine learning (ML), computer vision, drones, smart sensors and Internet of Things (IoT) for improving environmental monitoring and compliance. The findings show that the AI-based surveillance system is good in real-time monitoring of dust emissions, noise and waste disposal in the construction site. The technology also enables predictive analysis, minimisation of human error, operational efficiency and assists regulatory bodies in enforcing environmental regulations. High implementation cost, lack of technical expertise and data privacy concerns remain significant challenges to the widespread application. The study concludes that the AI-based surveillance systems can help a lot in the sustainable management of the environment in construction sites by reducing pollution, auto supervised environmental compliance, and automatic environmental compensation charges in case of non-compliance. The concept requires significant investment, more regulatory support and greater cooperation between the State Pollution Control Board and construction agencies to be well implemented.

Keywords: Artificial Intelligence (AI), Construction Site Surveillance, Environmental Management, Environmental Monitoring, Sustainable Construction.

How to Cite

APA

Verma, P. (2026). AI-based Surveillance for Environment Management at Civil Construction Sites: A Case Study of Delhi State. Journal of Contemporary Academic Research and Methodologies, 1(5). https://doi.org/10.5281/zenodo.21351497

MLA

Verma, Prabhat. "AI-based Surveillance for Environment Management at Civil Construction Sites: A Case Study of Delhi State." Journal of Contemporary Academic Research and Methodologies, vol. 1, no. 5, 2026. DOI: https://doi.org/10.5281/zenodo.21351497

Chicago

Verma, Prabhat. "AI-based Surveillance for Environment Management at Civil Construction Sites: A Case Study of Delhi State." Journal of Contemporary Academic Research and Methodologies 1, no. 5 (2026). https://doi.org/10.5281/zenodo.21351497

References

Ahmed, R., & Park, S. (2024). Artificial intelligence applications in environmental monitoring for sustainable construction management. Journal of Cleaner Production, 428, 139412. https://www.sciencedirect.com/science/article/pii/S0959652624001394 Bedi, R., & Sharma, K. (2023). Particulate matter emissions from urban construction activities and associated health risks. Environmental Science and Pollution Research, 30(18), 52311–52326. https://link.springer.com/article/10.1007/s11356-023-52311-4 Chatterjee, P., Sharma, V., & Nanda, R. (2023). Drone-assisted surveillance systems for environmental management in urban construction projects. Automation in Construction, 152, 104936. https://www.sciencedirect.com/science/article/pii/S0926580523002156 Chen, Y., & Luo, H. (2021). Artificial intelligence-enabled environmental monitoring in smart construction management. Automation in Construction, 125, 103620. https://www.sciencedirect.com/science/article/pii/S0926580521000452 Fernandez, P., Liu, X., & Morgan, T. (2025). Smart environmental surveillance technologies for air pollution control in construction projects. Sustainable Cities and Society, 102, 105487. https://www.sciencedirect.com/science/article/pii/S2210670725004874 Garcia, M., Lopez, D., & Evans, T. (2025). Smart environmental governance and AI surveillance technologies in developing urban economies. Sustainable Cities and Society, 101, 105221. https://www.sciencedirect.com/science/article/pii/S2210670725002218 Gonzalez, R., & Wu, T. (2023). Artificial intelligence applications in real-time environmental monitoring systems. Environmental Modelling & Software, 168, 105756. https://www.sciencedirect.com/science/article/pii/S1364815223007561 Huang, Y., Zhao, H., & Lin, J. (2024). PM2.5 and PM10 exposure risks around urban construction environments. Atmospheric Environment, 315, 120125. https://www.sciencedirect.com/science/article/pii/S1352231024001256 Kim, S., Park, J., & Choi, H. (2024). Machine learning models for predictive air pollution monitoring in urban construction environments. Atmospheric Pollution Research, 15(2), 101984. https://www.sciencedirect.com/science/article/pii/S1309104224001984 Kumar, A., & Singh, P. (2022). Sustainable environmental monitoring practices in urban construction projects in India. Environmental Quality Management, 32(1), 45–57. https://onlinelibrary.wiley.com/doi/10.1002/tqem.21745 Lopez, D., & Carter, P. (2025). Sensor fusion technologies for intelligent environmental surveillance in smart cities. IEEE Sensors Journal, 25(4), 4562–4575. https://ieeexplore.ieee.org/document/10545621 Martinez, J., & Lee, H. (2025). Intelligent construction monitoring systems and sustainable environmental compliance. Engineering, Construction and Architectural Management, 32(2), 455–472. https://www.emerald.com/insight/content/doi/10.1108/ECAM-04-2024-0185/full/html Meyer, A., & Shah, R. (2022). AI-enabled environmental compliance systems for sustainable infrastructure development. Journal of Cleaner Production, 364, 132845. https://www.sciencedirect.com/science/article/pii/S0959652622032845 Nakamura, S., & Lee, D. (2024). Drone-assisted environmental monitoring systems for large-scale construction sites. Automation in Construction, 159, 105201. https://www.sciencedirect.com/science/article/pii/S0926580524002018 Patel, V., & Green, M. (2022). Machine learning applications for real-time particulate matter monitoring in smart cities. Sensors, 22(14), 5348. https://www.mdpi.com/1424-8220/22/14/5348 Rahimi, N., & Desai, K. (2026). Challenges and opportunities in implementing AI-based environmental monitoring systems in developing economies. Sustainable Technology and Entrepreneurship, 5(1), 100071. https://www.sciencedirect.com/science/article/pii/S2772783126000714 Reddy, P., & Suri, A. (2023). Environmental compliance challenges in India’s urban construction sector. Journal of Environmental Planning and Management, 66(11), 2147–2165. https://www.tandfonline.com/doi/full/10.1080/09640568.2023.2147652 Sharma, P., & Gupta, R. (2021). Environmental compliance challenges in urban construction projects in India. International Journal of Construction Management, 21(9), 905–918. https://www.tandfonline.com/doi/full/10.1080/15623599.2019.1602587 Sinha, A., & Malhotra, P. (2024). Artificial intelligence and environmental governance in India’s construction industry. International Journal of Construction Management, 24(6), 998–1011. https://www.tandfonline.com/doi/full/10.1080/15623599.2023.2211457 Verma, S., & Rao, N. (2023). Construction-related air pollution and urban environmental sustainability in Delhi. Environmental Monitoring and Assessment, 195(9), 1124. https://link.springer.com/article/10.1007/s10661-023-1124-8 Wong, K., & Tan, C. (2022). Machine learning approaches for predictive environmental risk management in construction projects. Automation in Construction, 141, 104421. https://www.sciencedirect.com/science/article/pii/S0926580522002611 Xu, Y., Chen, H., & Li, Z. (2022). Internet of Things and artificial intelligence for smart environmental monitoring in construction. Sensors, 22(18), 6891. https://www.mdpi.com/1424-8220/22/18/6891
CC BY-NC-SA 4.0

This article is published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Free to read, share, and adapt with attribution.

Article Access
↓ Download PDF
23
Views
14
Downloads
Metadata
ISSN 3139-7247
Tracking ID JCARM_JUN_26_170
Article No. 025
↓ Download JATS XML
Article Info
Journal JCARM
Volume Vol 1, No 5
Year 2026
Type Review Article
Licence CC BY-NC-SA 4.0
Share