Citation :

Akshata Shagoti, Urmila R. Kawade, Vidyashree J C, Satish D. Kene, Megha Dabas, Brijbhushan S, Prashant Sunagar, "Environmental Impact Assessment of Urban Demolition Sites: A Tri-Point Monitoring Framework with Three-Stage Pollutant Quantification and Artificial Neural Network Predictive Modelling," International Journal of Civil Engineering, vol. 13, no. 8, pp. 66-85, 2026. Crossref, https://doi.org/10.14445/23488352/IJCE-V13I8P104

Abstract

Urban densification in rapidly expanding Indian cities has led to an increase in the construction and demolition of residential buildings, resulting in the release of various pollutants into the ambient atmosphere that adversely impact the health of the workers at the demolition sites and the residents near these sites. An investigation into the emissions from four demolition sites of varying sizes in Bengaluru, India, employed a three-point, three-stage monitoring approach to measure the levels of PM10 and PM2.5, SO2, NO2, CO, and noise pollution for a total period of 108 working hours at each site. The results indicate that the levels of PM10 at two of the four demolition sites exceeded the NAAQS limit of 100 μg/m³ by up to 29.97%, while the noise pollution at three of the four sites exceeded the permissible limit of 75 dB(A) for residential areas. Furthermore, a novel Composite Pollution Index was developed as a means of providing a single metric to represent the pollution levels at each demolition site. An Artificial Neural Network model for estimating the levels of PM10 at demolition sites achieved a coefficient of determination of 0.964 on the test partition of the data set, outperforming other machine learning models for the same estimation task. Additionally, a SHAP analysis of the ANN model revealed that the area of the construction site plan and the phase of demolition were the two most important variables that influenced the levels of PM10 released during demolition activities.

Keywords

Environmental Impact Assessment, Demolition sites, Particulate matter PM10 PM2.5, Noise pollution, Artificial Neural Network, SHAP analysis, CPCB, NAAQS, Bengaluru, Construction dust management.

References

1. World Health Organization, Air Quality Guidelines Global Update 2005 Particulate Matter, Ozone, Nitrogen Dioxide and Sulfur Dioxide, World Health Organization, pp. 1-1496, 2006.
   [Google Scholar] [Publisher Link]
2. Ingrid P. S. Araújo, Dayana B. Costa, and Rita J. B. de Moraes, “Identification and Characterisation of Construction Site Particulate Matter Concentrations,” Sustainability, vol. 6, no. 11, pp. 7666-7688, 2014.
   [CrossRef] [Google Scholar] [Publisher Link]
3. Farhad Azarmi, and Prashant Kumar, “Ambient Exposure to Coarse and Fine Particle Emissions from Building Demolition,” Atmospheric Environment, vol. 137, pp. 62-79, 2016.
   [CrossRef] [Google Scholar] [Publisher Link]
4. Farhad Azarmi et al., “Assessment of the Long-Term Impacts of PM₁₀ and PM_2.5 Particles from Construction works on Surrounding Areas,” Environmental Science: Processes & Impacts, vol. 18, no. 2, pp. 208-221, 2016.
   [CrossRef] [Google Scholar] [Publisher Link]
5. Christopher M. Beck et al., “The Impact of a Building Implosion on Airborne Particulate Matter in an Urban Community,” Journal of the Air & Waste Management Association, vol. 53, no. 10, pp. 1256-1264, 2003.
   [CrossRef] [Google Scholar] [Publisher Link]
6. Ily Hanisah Mohd Fauzey et al., “Emergent Occupational Safety & Health and Environmental Issues of Demolition Work: Towards Public Environment,” Procedia - Social and Behavioural Sciences, vol. 168, pp. 41-51, 2015.
   [CrossRef] [Google Scholar] [Publisher Link]
7. “IS 5182 (Part 2): 2001 Reaffirmed 2012. Methods for Measurement of Air Pollution, Part 2: Sulphur Dioxide,” Bureau of Indian Standards, pp. 1-15, 2001.
   [Google Scholar] [Publisher Link]
8. “IS 5182-23 (2006): Methods for Measurement for Air Pollution, Part 23: Respirable Suspended Particulate Matter (PM₁₀), Cyclonic Flow Technique, Cyclonic Flow Technique,” Bureau of Indian Standards, pp. 1-12, 2006.
   [Publisher Link]
9. “IS 5182-6 (2006): Methods for Measurement of Air Pollution, Part 6: Oxides of Nitrogen,” Bureau of Indian Standards, pp. 1-12, 2006.
   [Publisher Link]
10. Jungang Chen et al., “The Concentrations and Reduction of Airborne Particulate Matter (PM₁₀, PM_2.5, PM₁) at Shelterbelt Site in Beijing,” Atmosphere, vol. 6, no. 5, pp. 650-676, 2015.
    [CrossRef] [Google Scholar] [Publisher Link]
11. National Ambient Air Quality Standards Notification, Central Pollution Control Board, 2009. [Online]. Available: https://scclmines.com/env/docs/naaqs-2009.pdf
12. “Guidelines for the Measurement of Ambient Air Pollutants,” Guidelines for Manual Sampling & Analyses, Central Pollution Control Board, pp. 1-74, 2011.
    [Publisher Link]
13. Scott M Lundberg, and Su-In Lee, “A Unified Approach to Interpreting Model Predictions,” 31^st Conference on Advances in Neural Information Processing Systems, Long Beach, CA, USA, vol. 30, pp. 1-10, 2017.
    [Google Scholar] [Publisher Link]
14. G. Cybenko, “Approximation by Superpositions of a Sigmoidal Function, Mathematics of Control,” Mathematics of Control, Signals, and Systems Publishing Model, vol. 2, no. 4, pp. 303-314, 1989.
    [CrossRef] [Google Scholar] [Publisher Link]