Air Quality in Cities: What do Sparse Sensor Networks Miss?

This repository contains the data and analysis code supporting the study “Air Quality in Cities: What Do Sparse Sensor Networks Miss?”. The dataset includes PM2.5, temperature, and relative humidity measurements collected during simultaneous stationary and mobile monitoring campaigns conducted across three urban areas in Boston, MA, during two seasonal field experiments (spring and summer 2025). Measurements were obtained using one stationary reference monitor paired with three mobile sensor platforms deployed within a 250 m radius, enabling evaluation of spatial representativeness at pedestrian scales. The repository provides: Geo information of experimental areas Cross-calibration test results of all PM2.5 sensors Raw PM2.5 sensor data from stationary and mobile measurements Measurements of environmental conditions (temperature and relative humidity) Python scripts used for data preprocessing, calibration, statistical analysis, and figure generation The accompanying Python code enables reproducibility of the analytical workflow, including data cleaning, temporal alignment of paired observations, uncertainty quantification, and visualization of spatial variability and exposure discrepancies.