绿色呼吸：利用大规模空气质量数据最大化主动交通使用者的健康与环境效益
Breathing Green: Maximising Health and Environmental Benefits for Active Transportation Users Leveraging Large Scale Air Quality Data
AI总结：
本研究利用Google空气质量大数据，为都柏林主动交通使用者设计绿色出行路线优化策略，显著降低污染物摄入量17.87%，兼顾健康与环境效益。
摘要：
城市地区的污染会对公民的健康和福祉产生显著的不利影响，其中交通相关空气污染是许多城市面临的主要问题。车辆排放的污染物，如氮氧化物、一氧化碳和颗粒物，可能导致呼吸系统和心血管问题，尤其是对行人和骑自行车者等弱势道路使用者。此外，最近的研究表明，居住在污染更严重地区的个体患哮喘、过敏和癌症等慢性疾病的风险更大。解决这些问题对于保护公众健康和最大化环境效益至关重要。在本项目中，我们探索通过利用大数据分析和数据驱动的方法来应对这一挑战的可行性。具体而言，我们研究了最近发布的Google空气质量数据集，并设计了一种优化策略，为都柏林不同类型的主动交通使用者建议绿色出行路线。为了展示我们的成果，我们开发了一个原型，并表明使用我们模型规划户外活动的公民可以显著受益，污染物摄入量平均减少17.87%，这得益于其提供的环境优势。
英文摘要：
Pollution in urban areas can have significant adverse effects on the health and well-being of citizens, with traffic-related air pollution being a major concern in many cities. Pollutants emitted by vehicles, such as nitrogen oxides, carbon monoxide, and particulate matter, can cause respiratory and cardiovascular problems, particularly for vulnerable road users like pedestrians and cyclists. Furthermore, recent research has indicated that individuals living in more polluted areas are at a greater risk of developing chronic illnesses such as asthma, allergies, and cancer. Addressing these problems is crucial to protecting public health and maximising environmental benefits. In this project, we explore the feasibility of tackling this challenge by leveraging big data analysis and data-driven methods. Specifically, we investigate the recently released Google Air Quality dataset and devise an optimisation strategy to suggest green travel routes for different types of active transportation users in Dublin. To demonstrate our achievement, we have developed a prototype and have shown that citizens who use our model to plan their outdoor activities can benefit notably, with a significant decrease of 17.87% on average in pollutant intake, from the environmental advantages it offers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
