## 4 Conclusions  
The developed methodology offers a comprehensive approach to quantifying greenhouse gas (GHG) emissions and other pollutant emissions affecting air quality per trip utilizing open data geostatistics. By generating detailed routes for each origin-destination (OD) trip, the methodology enables nuanced emission analyses, including identifying specific neighborhoods influencing each other's pollution levels thanks to the origin-destination matrix. Moreover, its scalability allows for studies at various geographic levels, from municipalities to specific industrial areas like the port of Valencia. Application of this methodology allows for its replication across the globe, as it is adaptable to diverse datasets and local conditions. By integrating data on emissions chemistry, GIS-based route analysis, and transportation activity, this approach can be tailored to the specific characteristics of any city or region. Whether working with detailed emissions inventories, transportation data, or broader environmental metrics, the methodology provides a flexible framework for scaling from individual routes to entire urban zones. This adaptability ensures that stakeholders worldwide, from policymakers to urban planners, can leverage it to accurately assess emissions and implement targeted mitigation strategies, fostering more sustainable and data-driven urban management practices. Application of this methodology in the Valencia case study pinpointed the most polluted neighborhoods, notably the city's main entrances and areas surrounding public transport infrastructure. By precisely quantifying emissions geographically, the methodology lends objectivity to pollution assessments. The comparison of results reveals a notable discrepancy between the Origin-Destination Matrix (ODM) methodology and the data reported by the PACES program for the entire city of Valencia. The ODM-based methodology estimates a total of 910,310 tons of CO\({}_{2}\) emissions, while the PACES program reports a higher value of 1,121,456 tons for  
Figure 5: Percentage of emissions by means of transport. (**a**) CO\({}_{2}\), (**b**) NOx, (**c**) PM. the year 2019. This difference could be due to several factors. It is possible that the ODM methodology did not fully capture all sources of emissions, such as private company transport trucks entering and leaving the port of Valencia on a daily basis. These unpublished data were not included in the analysis and may contribute significantly to the city's total emissions. The lack of comprehensive data on private freight transport limits the accuracy of the methodology applied and could affect the full assessment of urban emissions. In order to effectively manage mobility and reduce emissions, comprehensive and detailed data on all sources of pollution is crucial. Additional and specific data on freight transport should be integrated into the methodology to improve emissions estimates at the neighborhood level. Advanced technologies and analytical tools could also be employed to better capture transport dynamics and their environmental impacts. The methodology equips public decision-makers with actionable insights to efficiently allocate resources for emission mitigation. With results presented objectively and with spatial specificity, private transport companies and government authorities can make informed decisions to combat emissions and safeguard the city's environmental health. Additionally, it facilitates targeted improvements in public transportation infrastructure, addressing disparities in private versus public transport usage across different neighborhoods. This targeted approach ensures resource allocation is strategic, optimizing impact and avoiding unnecessary dispersion of resources. Finally, the developed methodology not only offers a means to quantify emissions but also serves as a valuable tool for strategic urban planning, environmental policy-making, and sustainable development efforts aimed at mitigating climate change and ensuring the well-being of urban populations.