Advances in Urban Mobility Analysis, Air Quality Modelling and Spatiotemporal Data Science for Urban Pollution, Health and Social Disparities

A special issue of Urban Science (ISSN 2413-8851). This special issue belongs to the section "Urban Environment and Sustainability".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 321

Special Issue Editors


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Guest Editor
Department of Mathematics, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China
Interests: air quality modeling; health co-benefits; air pollution forecasting; climate mitigation

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Guest Editor
Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Interests: remote sensing; aerosols; air pollutions; public health

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Guest Editor
Department of Geography and Planning, University of Liverpool, Liverpool L69 7ZB, UK
Interests: human mobility; geographic data science; network analysis; mathematical modelling

Special Issue Information

Dear Colleagues,

Urbanisation, industrial activity, and rising energy demand have intensified the complex interactions that shape urban environments, with urban mobility emerging as a central driver of both economic activity and environmental change. The flows of people and goods influence patterns of energy consumption, vehicle emissions, and, ultimately, urban air quality. Mobility-driven emissions contribute to elevated concentrations of particulate matter (PM), ozone (O3), and other pollutants, posing risks to respiratory and cardiovascular health, contributing to premature mortality, and diminishing quality of life. The impacts of mobility-related air pollution are often disproportionately borne by vulnerable and disadvantaged communities, exacerbating social and health inequities.

Addressing these challenges requires innovative, data-driven, and multidisciplinary approaches. Advances in mobility analysis, spatiotemporal data science, satellite remote sensing, and machine learning allow us to better simulate, monitor, and forecast how changes in urban movement patterns affect air quality. New forms of digital trace data (e.g., mobility phone, GPS, and transport smart card data) can capture mobility dynamics at fine spatial and temporal scales, enabling the integration of transport behavior into urban air quality models.

This Special Issue welcomes original research articles and reviews that advance the understanding of how urban mobility interacts with air quality, health impacts, and social disparities. We encourage studies that integrate multiple data sources, explore scenario-based modelling of mobility interventions, and link their findings to urban planning, environmental policy, and public health strategies.

Topics of interest include, but are not limited to, the following:

  • Modelling the effects of urban mobility patterns on air quality;
  • Health exposure assessment and social disparity analysis related to mobility-driven pollution;
  • Integration of mobile phone, GPS, and other digital trace data and satellite data into air quality modelling;
  • Machine learning for improved spatiotemporal air pollution mapping of mobility and pollutant exposure;
  • Big data analytics and multi-source data fusion in urban computing;
  • Air quality modeling refinement and emission projection in the context of sustainable mobility and carbon neutrality;
  • Satellite remote sensing and emission inversion modelling for transport-related studies.

We look forward to receiving your contributions.

Dr. Xuguo Zhang
Dr. Changqing Lin
Dr. Carmen Cabrera
Guest Editors

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Keywords

  • urban mobility
  • air quality
  • sustainably cities
  • urban health
  • vehicle emissions
  • particulate matter (PM)
  • ozone (O3)
  • remote sensing
  • spatiotemporal analysis
  • machine learning
  • social/health disparities
  • big data analysis
  • climate mitigation

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Published Papers (1 paper)

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Research

22 pages, 11988 KB  
Article
Heavy Metal Pollution and Health Risk Assessments of Urban Dust in Downtown Murcia, Spain
by Ángeles Gallegos, Francisco Bautista, Pura Marín-Sanleandro, Elvira Díaz-Pereira, Antonio Sánchez-Navarro, María José Delgado-Iniesta, Miriam Romero, María-Felicidad Bógalo and Avto Goguitchaichvili
Urban Sci. 2026, 10(1), 46; https://doi.org/10.3390/urbansci10010046 - 12 Jan 2026
Abstract
Around eight million people—mainly in cities—die prematurely from pollution-related diseases; thus, studies of urban dust have become increasingly relevant over the last two decades. In this study, an assessment of heavy metal and metalloid contamination in urban dust was conducted in downtown Murcia, [...] Read more.
Around eight million people—mainly in cities—die prematurely from pollution-related diseases; thus, studies of urban dust have become increasingly relevant over the last two decades. In this study, an assessment of heavy metal and metalloid contamination in urban dust was conducted in downtown Murcia, Spain. The objectives were to evaluate the level of contamination and the associated health risks, both with a spatially explicit focus. One hundred and twenty-eight urban dust samples were collected, each from a 1-square-meter area, using plastic tools to prevent contamination. The dust was dried and weighed, then acid-digested before analysis via inductively coupled plasma mass spectrometry. Corresponding maps were then generated using a geographic information system. The elements analyzed in the urban dust (with their median concentrations, given in mg/kg) were As (2.14), Bi (14.06), Cd (0.38), Co (1.88), Cr (71.17), Cu (142.60), Fe (13,752), Mn (316.64), Mo (3.90), Ni (21.94), Pb (106.27), Sb (6.54), Se (4.34), Sr (488.08), V (28.05), and Zn (357.33). The sequence of median concentrations for the analyzed elements was Fe > Sr > Zn > Mn > Cu > Pb > Cr > V > Ni > Bi > Sb > Se > Mo > As > Co > Cd. The pollution assessment reveals that the city is moderately polluted. Using local background levels, the elements with median values exceeding the threshold for considerable contamination were As, Cu, Pb, Sb, Se, and Zn. Using the global background level, the elements with median values exceeding the threshold for considerable contamination were Bi, Cu, Mo, Pb, Sb, Se, and Zn. The median value of the sum of the hazard index (1.82) indicates a risk to children’s health. The hazard index revealed that 43% of the sites pose a relative risk to children. In contrast to previous global studies, the present research provides a multi-scale assessment of urban pollution and health risks. Pollution is evaluated by metal, city, zone, and site, while health risks are assessed by metal, city, and site. We recommend a strategy for both local authorities and residents. Full article
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