GIS-Based Spatial Analysis and Python Transcript for Prediction Traffic Noise Pollution Over Kirkuk City, Iraq

Abstract

As traffic increases in Kirkuk, Iraq, noise pollution has become a pressing issue, particularly on the major road, Baghdad Road in Kirkuk city, which connects densely populated areas. This study aims to assess traffic noise levels along Baghdad Road, addressing challenges such as time-intensive measurements, selecting appropriate mapping methods and integrating geographic and noise data. It also presents an innovative combination of GIS and Python scripting to model and predict traffic noise pollution in Kirkuk City, Iraq, offering a replicable method for urban areas with limited noise monitoring. Noise levels were measured using a digital sound level meter, with sampling points spaced 500–750 meters apart along entry and exit roads. Maximum noise levels (Max) were recorded during peak traffic hours (8:00–10:00 AM and 5:00–7:00 PM). The highest recorded noise level was 92.15 dB at site K-B.R4 in the morning while the lowest was 74.2 dB at site K-B.R13 in the evening. Temporal variations were notable, particularly on Fridays a local holiday with noise levels ranging from 92.1 dB to 75.5 dB Spatial analysis revealed that K-B.R4 was the most congested area, while K-B.R13 was the least  The RLS 90 model implemented in Python, was used to create noise maps through spatial analysis and interpolation in ArcGIS Pro3.0.2. The model’s spatial approach provided higher accuracy and classification precision compared to other methods.  These findings emphasize the importance of considering temporal and spatial factors in noise studies, offering valuable insights for environmental monitoring, urban planning, and noise management.