An Urban System Optimization Model Based on CO2 Sequestration Index: A Big Data Analytics Approach

Abstract

Urban development in recent decades has had a significant impact on climate change. Cities have implemented traffic monitoring systems to sustain the new building code in meeting the target for environmental indicators. Timisoara is the second city in Romania and manages over 60% of the development and pollution of Timis County. The analysis of large volumes of data provided by local sensors and databases requires big data analytics. In this research, for the first time, we simultaneously developed two parallel scenario-based decision-making support models to assess a CO2 sequestration index. The model is based on a tree inventory for traffic area and car flow using Roegenian processes with borders. The first scenario (dt1) analyzes the real O2-pollution car flows for process streets as receivers of pollution. The second scenario (dt2) analyzes O2-pollution flows for the same streets from the perspective of streets that garage the cars. We modeled the parallel integration of actual O2 production and pollution flows for 160 main streets that account for over 50% of the urban mileage of Timisoara city. The carbon sequestration indexes of the streets are in the range of 0.0000043–0.437 (dt1) and 0.0000092–11.78 (dt2). The results can be used to support local decision making regarding the environment CO2-O2 balances by optimizing the local fiscal policies. The research could be extended to secondary streets and separately for the pollution of heating-cooling devices for residential building areas.

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