Work

Diurnal variation mapping of urban NO2 concentrations at high spatial resolution using mobile phone signaling data

Year: 2025

Type: article

Abstract: Atmospheric NO 2 remains one of the primary pollutant gases in urban areas. However, current techniques such as ground-based measurements, remote sensing, and atmospheric chemistry models have persistently faced challenges in monitoring sub-daily NO 2 concentrations at high spatial resolution. Mobile phone signaling data holds significant potential for characterizing human activity intensity, offering new opportunities to track near-surface NO 2 concentrations with refined spatiotemporal precision. This study designed a simple algorithm to extract population mobility patterns from mobile phone signaling data and developed a novel deep learning model tailored for environmental variable modeling, marking the first implementation of continuous NO 2 concentration estimation at a 250-meter/1-hour spatiotemporal resolution driven by mobile phone signaling data. Application results in Ningbo City demonstrated that the derived dynamic population mobility information effectively captured local traffic activity patterns, including morning/evening rush hours and nighttime variations. The proposed deep learning model exhibited excellent robustness and spatial generalizability, with a 5-fold cross-validation R 2 of 0.815 and a spatial prediction validation R 2 of 0.774, significantly outperformed traditional machine learning models (e.g., RF, XGBoost, and DNN). Interpretability analysis revealed the dominant role of dynamic traffic emissions in NO 2 concentration estimation (22.59%), along with auxiliary influences from wind speed (16.45%), boundary layer height (9.07%), and rainfall (7.24%). The high-resolution NO 2 concentrations output by the model showed strong spatial consistency with Tropospheric Monitoring Instrument (TROPOMI) observations during satellite overpass times, while their diurnal cycle distributions displayed significant variability across agricultural, urban, and suburban areas. This research highlights the potential of leveraging mobile phone signaling data to advance high-resolution air pollutant modeling and tracking.

Source: Environment International

DOI: 10.1016/j.envint.2025.109758

Authors: Sicong He, Yanbin Yuan, Lijie Yang, Lan Luo, Heng Dong

Institutions: Wuhan University of Technology, Zhejiang Environmental Monitoring Center

Language: English

Topic: Air Quality Monitoring and Forecasting

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $4,750