Enhancing Accuracy in London's Air Quality Data Analysis: Addressing Bias through a Comprehensive Framework

In-scope research introduces a framework to address bias in air quality data analysis for London. With the rise of machine learning (ML), bias has become a significant challenge, threatening the accuracy of air quality assessments, which is crucial for policymaking. Current methodologies often overlook bias in the data processing stages, leading to inaccurate assessments and misguided policies. The research identifies a literature gap that has focused on aspects such as fairness and transparency but neglected biases in data analysis. To address this, the thesis proposes a holistic framework integrating multiple air quality datasets from London Air and UK Local Government Monitoring sites into a unified dataset for unbiased analysis. A scoring methodology assesses and mitigates bias risks throughout the data analysis life cycle, considering factors such as data source reliability, sensor inaccuracies, and confounding variables.

This framework aims to minimise bias at every stage, enhancing the validity and reliability of findings. The significance of this research lies in its potential to provide a systematic approach to ensuring unbiased air quality data analysis. Accurate data are essential for developing effective strategies to combat air pollution, a pressing concern for London and other urban areas. Furthermore, the framework serves as a valuable resource for researchers and policymakers, offering a systematic process for identifying and addressing bias in complex air quality data analysis. The research also highlights the ethical implications of biased data analysis, highlighting the need for transparency and accountability in the use of advanced data science techniques in public policy. The research findings have broad implications for both academia and policymakers, supporting the goal of achieving cleaner air and healthier environments for urban populations.