Carbon Monoxide and Nitrogen Oxide Emissions Analysis: Clustering-Based Approach

Carbon monoxide (CO) and nitrogen oxide (NOx) emissions from different sources present substantial environmental and health risks. Comprehending the spatial and temporal patterns of these emissions is crucial for the efficient management of the environment and the development of policies. This research uses clustering analytic approaches to identify patterns that influence emission characteristics. We employ clustering methods to find homogenous groups of emission sources with similar emission characteristics. We use a complete dataset from gas turbine located in Turkey’s northwestern region. Using unsupervised learning techniques, specifically K-means and DBSCAN clustering, we can identify geographical groups that reflect regions with similar emission levels and temporal clusters that illustrate periods of heightened emission activity. This study highlights the effectiveness of utilizing data-driven methodologies to analyze intricate emission datasets. It establishes a basis for formulating customized methods to tackle carbon monoxide (CO) and nitrogen oxide (NOx) emissions. Consequently, this contributes to the advancement of environmental sustainability objectives and the promotion of healthier communities. This study represents the preliminary iteration of emission prediction obtained by regression algorithms. Clustering techniques demonstrate that the prediction stage can be utilized to enhance the prediction capability across several clusters.