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The urbanization processes with growing vehicle numbers cause heavy metal pollution in street dust, and high populations in metropolitan cities are exposed to pollutants. This paper aims to monitor the spatial distribution of heavy metals and evaluate the concentrations via health risk assessment of HMs (Cu, Ni, Cd, Co, Pb, and Zn) that expose the inhabitants to health hazards. According to the results of the current study, sixty street dust samples were applied to the acid digestion technique and quantification by inductively coupled plasma-mass spectrometry (ICP-MS). The spatial distribution of the selected heavy metals in the street dust was investigated using the spatial analysis tool in ArcGIS 10.0 according to population density and land use. In the present study, we used hazard index and cancer risk methods to estimate the public health risk of the pollutants exposed to street dust in Ankara. The concentrations range of the elements in street dust over the study area ranged from 3.34-4.50, 31.69-42.87, 16.09-21.54, 42.85-57.55, 0.00-3.51, and 23.03-30.79, respectively. The overall decreasing order of mean concentration of metals was observed as follows: Pb > Cu > Ni > Co > Cd > Zn. Vehicle traffic and industrial activities seem to be the most critical anthropogenic sources responsible for dust pollution in the study area. The risk assessment of Pb and Ni exposure was the highest, and the hazard index values were 2.42E + 00 and 2.28E + 00 mg/kg/day for children. However, the effect on adults was 2.62E-01 and 2.37E-02 mg/kg/day, followed by inhalation and dermal contact with street dust was almost negligible. The decreasing concentration is modeled spatially along the western development corridor of the city. The risk to public health is high in areas with high densities close to the city center and the main artery., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

In recent decades, cities have been experiencing accelerated population growth, associated with an increase in the scales of production and consumption. This fact, combined with deficient management of resources and waste, has led to the loss of biological diversity, compromising the generation of ecosystem services, with disastrous consequences for human health and well-being, but also for the economic system. In the field of civil engineering, the predominance of artificialisation and impermeabilization of cities (called "grey engineering") is being questioned to be replaced or complemented with new types of infrastructures that represent a transformative change to achieving more sustainable cities. Through system dynamics applied to the economic modelling of the city of Santander (Spain), the aim of this study is to analyse the profitability of investment in ecosystem restoration and in both green and blue infrastructure, and of the implementation of environmental policies based on the relationships of affection established in the model, which represent the interactions between the main actors in urban dynamics. As a main conclusion, it is found that investing in green infrastructures and ecosystem restoration, and environmental policies is highly profitable: EUR 1 spent can produce up to EUR 100 as a benefit.

We propose a non-equilibrium framework for modelling the evolution of cities, which describes intra-urban migration as an irreversible diffusive process. We validate this framework using the actual migration data for the Australian capital cities. With respect to the residential relocation, the population is shown to be composed of two distinct groups, exhibiting different relocation frequencies. In the context of the developed framework, these groups can be interpreted as two components of a binary fluid mixture, each with its own diffusive relaxation time. Using this approach, we obtain long-term predictions of the cities' spatial structures, which define their equilibrium population distribution.

There is an increasing availability of geospatial data describing patterns of human settlement and population such as various global remote-sensing based built-up land layers, fine-grained census-based population estimates, and publicly available cadastral and building footprint data. This development constitutes new integrative modelling opportunities to characterize the continuum of urban, peri-urban, and rural settlements and populations. However, little research has been done regarding the agreement between such data products in measuring human presence which is measured by different proxy variables (i.e., presence of built-up structures derived from different remote sensors, census-derived population counts, or cadastral land parcels). In this work, we quantitatively evaluate and cross-compare the ability of such data to model the urban continuum, using a unique, integrated validation database of cadastral and building footprint data, U.S. census data, and three different versions of the Global Human Settlement Layer (GHSL) derived from remotely sensed data. We identify advantages and shortcomings of these data types across different geographic settings in the U.S., which will inform future data users on implications of data accuracy and suitability for a given application, even in data-poor regions of the world.

Urban transformations within large and growing metropolitan areas often generate critical dynamics affecting social interactions, transport connectivity and income flow distribution. We develop a statistical-mechanical model of urban transformations, exemplified for Greater Sydney, and derive a thermodynamic description highlighting critical regimes. We consider urban dynamics at two time scales: fast dynamics for the distribution of population and income, modelled via the maximum entropy principle, and slower dynamics evolving the urban structure under spatially distributed competition. We identify phase transitions between dispersed and polycentric phases, induced by varying the social disposition -a factor balancing the suburbs' attractiveness-in contrast with the travel impedance . Using the Fisher information, we identify critical thresholds and quantify the thermodynamic cost of urban transformation, as the minimal work required to vary the underlying parameter. Finally, we introduce the notion of thermodynamic efficiency of urban transformation , as the ratio of the order gained during a change to the amount of required work, showing that this measure is maximized at criticality., Competing Interests: We declare we have no competing interests.

The building of mathematical and computer models of cities has a long history. The core elements are models of flows (spatial interaction) and the dynamics of structural evolution. In this article, we develop a stochastic model of urban structure to formally account for uncertainty arising from less predictable events. Standard practice has been to calibrate the spatial interaction models independently and to explore the dynamics through simulation. We present two significant results that will be transformative for both elements. First, we represent the structural variables through a single potential function and develop stochastic differential equations to model the evolution. Second, we show that the parameters of the spatial interaction model can be estimated from the structure alone, independently of flow data, using the Bayesian inferential framework. The posterior distribution is doubly intractable and poses significant computational challenges that we overcome using Markov chain Monte Carlo methods. We demonstrate our methodology with a case study on the London, UK, retail system., Competing Interests: We declare that we have no competing interests.