Search arXivSearch

arXiv · 2605.28672

Population size and centrality effects on NO2 air pollution across and within European cities

Abstract

Understanding how nitrogen dioxide (NO2) varies both within and across cities is essential for assessing urban health inequalities, yet the joint influence of city size and internal structure remains poorly quantified. While it is expected that agglomeration size increases NO2 concentrations and that distance from major urban activities reduces them, the magnitude and form of their combined effect have not been established. Our objective is to move beyond city-specific local context effects and to characterize the general structural form of NO2 distributions, understood as their systematic variation with distance from the center across cities of different sizes. Using both ground monitoring stations and satellite data for 378 European Functional Urban Areas, we estimate parallel models for each measurement type and show that the same structural relationships hold in both cases. We find that NO2 concentrations increase with population size as a power law (with an exponent between 0.14 and 0.22) and decrease with distance from the city center (with an exponent between -0.12 and -0.18), with consistent results across measurement types and robust to local and regional controls. These effects combine into a scalable radial profile, where the total air pollution over a city is proportional to (N/r)^0.16, which generalizes the spatial distribution of NO2 in European cities. This formulation clarifies how total and per-capita pollution depend on the extent to which pollution is integrated and provides a simple framework for evaluating the health implications of urban growth.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yufei Wei, Rémi Lemoy, Geoffrey Caruso. 2026-05-27. Population size and centrality effects on NO2 air pollution across and within European cities. https://doi.org/10.1177/239980832614464

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Human-Agent Interaction in Synthetic Social Networks: A Framework for Studying Online Polarization

Online social networks have dramatically altered the landscape of public discourse, creating both opportunities for enhanced civic participation and risks of deepening social divisions. Prevalent approaches to studying online polarization have been limited by a methodological disconnect: mathematical models excel at formal analysis but lack linguistic realism, while language model-based simulations capture natural discourse but often sacrifice analytical precision. This paper introduces an innovative computational framework that synthesizes these approaches by embedding formal opinion dynamics principles within LLM-based artificial agents, enabling both rigorous mathematical analysis and naturalistic social interactions. We validate our framework through comprehensive offline testing and experimental evaluation with 122 human participants engaging in a controlled social network environment. The results demonstrate our ability to systematically investigate polarization mechanisms while preserving ecological validity. Our findings reveal how polarized environments shape user perceptions and behavior: participants exposed to polarized discussions showed markedly increased sensitivity to emotional content and group affiliations, while perceiving reduced uncertainty in the agents' positions. By combining mathematical precision with natural language capabilities, our framework opens new avenues for investigating social media phenomena through controlled experimentation. This methodological advancement allows researchers to bridge the gap between theoretical models and empirical observations, offering unprecedented opportunities to study the causal mechanisms underlying online opinion dynamics.

physics.soc-ph

How a shared state is described determines whether AI agents synchronize

Language-model agents increasingly act in populations, where the outcome that matters is collective: whether they align, split or fail to coordinate. Each acts not on the world but on a text description of it, a choice usually fixed in software. Using synchronization, the canonical probe of how interaction rules produce collective order, we show that this choice can decide the outcome. Agents on a circle chose to advance, stay or move back after reading the others' relative positions, in 507,112 valid responses across matched populations, controlled inputs and three model families. In GPT, numerical summaries aligned every matched population at both positive couplings, whereas histograms aligned none; Claude showed the reverse at the stronger coupling. Re-describing identical states shifted action probabilities in all three families, even between histograms carrying the same information. No single directional coefficient explained the outcome: state descriptions are part of the interaction rule that turns individual responses into collective order.

physics.soc-ph

Twenty-Five Years of Air Quality in Bangladesh: Trends, Seasonality, and Spatial Pollution Regimes

Air pollution is one of Bangladesh's most pressing environmental problems, yet few studies have examined it at a national scale over a long time span. To describe trends, seasonality, geographic patterns, and pollution regimes, this study examines 25 years of hourly air quality data (2000-2025), covering over 3.19 million records, eight pollutants, and 103 cities monitored from 2022 onward following expansion from a single Dhaka station. The composite AQI series showed no significant long-term trend, an artifact of this network expansion rather than a real flattening of pollution, since Dhaka's own AQI rose steadily before the network grew. Seasonality was strong and highly significant, as AQI peaked in January (162.26) and fell to its lowest in July (61.47), tracking the dry and monsoon seasons. Geographically, southeastern coastal cities, including Teknaf and Bandarban, consistently recorded the lowest AQI levels. Dhaka remained the most polluted city, and air quality improved progressively with distance from the capital. K-means clustering of city-level AQI trajectories identified four distinct pollution regimes, ranging from cleaner coastal regions to a rapidly deteriorating Dhaka-Narsingdi cluster with an average increase of 1.84 AQI per year. Correlation analysis showed that PM2.5 and PM10 were the dominant contributors to AQI variation, and so combustion-related emissions are the primary target for air quality management and policy intervention.

physics.soc-ph