Pollution and Architecture: Variation in Dispersion Conditions at the Neighborhood Scale In Cergy

Pollution and Architecture: Variation in Dispersion Conditions at the Neighborhood Scale In Cergy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pollution and Architecture: Variation in Dispersion Conditions at the Neighborhood Scale In Cergy Souad Lagmiri, Salem Dahech This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6882779/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The increasing densification of buildings in cities impacts the natural dispersion of atmospheric pollutants, thereby increasing residents' exposure to these contaminants. To better understand the effects of urban configuration on air quality, a typomorphological analysis was conducted in three distinct neighborhoods. This analysis focused on key criteria such as building density, urban porosity, street orientation, and the presence of vegetation. These architectural and spatial parameters were correlated with pollution data (NO 2 , PM 10 , PM 2.5 ) measured using nine fixed sensors, strategically distributed, along with measurements of wind speed, wind direction, and atmospheric pressure. The results underscore the decisive influence of building density and urban porosity on the capacity of urban spaces to either facilitate or hinder the dispersion of gaseous and particulate pollutants. Vegetation, depending on its location and density, also plays a significant modulating role. The orientation of streets relative to prevailing winds, along with their degree of enclosure, shapes the complex mechanisms of pollutant transport, including horizontal and vertical movements as well as the formation of air vortices. A statistical modeling approach employing multiple regression identified and validated the key spatial and environmental factors driving the variability of pollution concentrations within urban areas. Additionally, the application of the Inverse Distance Weighting (IDW) interpolation method to mobile measurement data revealed localized high-pollution zones, particularly at road intersections and in confined urban spaces. These findings provide a robust foundation for guiding urban planning strategies aimed at enhancing the natural ventilation of neighborhoods and minimizing residents' exposure to atmospheric pollutants, thereby contributing to healthier living environments. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Introduction Air pollution is now recognized as one of the main environmental risks to human health worldwide. In urban areas, where an increasing share of the global population lives, the effects of this pollution are particularly severe (WHO 2021). According to a study by the World Health Organization (WHO), nearly 99% of the global urban population breathes poor-quality air, frequently exceeding recommended thresholds for fine particulate matter (PM) and nitrogen dioxide (NO₂). Chronic exposure to these pollutants is associated with a significant increase in cardiovascular, respiratory, and neurological diseases, as well as premature mortality estimated at several million deaths annually (Ben Romdhane 2017; Landrigan et al. 2017; Zhao 2019a; Dedoussi 2020). These effects are especially pronounced in areas with heavy traffic and continuous emissions of NOₓ and fine particles. Many of the population—pedestrians, cyclists, and city dwellers—live or work close to emission sources, thereby increasing health risks (Host et al. 2012). Although emission sources such as transportation, industry, and heating have been extensively studied, identifying pollution hotspots remains complex due to the need to differentiate between pollutant emissions and their concentrations after dispersion (immissions). Immission refers to the concentration of pollutants after their dispersion, which is influenced by urban layout and meteorological conditions such as wind. Urban morphology denotes the physical and spatial configuration of a city, including the arrangement of buildings, streets, public spaces, infrastructure, and vegetation (Lévy 2005). This spatial organization results from historical, cultural, geographical, and political dynamics and varies significantly from one urban area to another, and even between neighborhoods (CERTU 2007). It influences not only the functionality and aesthetics of urban spaces but also their environmental performance, particularly regarding local climate and air quality (He et al. 2020). Urban geometry—particularly the height-to-width ratio (H/W), facade alignment, and the sky view factor (the portion of visible sky at a given point)—directly affects turbulence, ventilation, and solar radiation penetration. Indeed, the way buildings are arranged, their height, street width, and the presence of trees or water bodies determine how air circulates through streets, thus affecting pollutant dispersion or accumulation. In contemporary cities, confined spaces form that reduce air renewal, leading to local concentrations of NO₂, PM₁₀, and PM₂.₅ well above regional averages (Kastner-Klein 2004; Vardoulakis 2003). Pollution dispersion dynamics are particularly complex in city centers, where building layouts are often irregular and urban activity is intense. These areas also tend to be “pollution hotspots” of vehicle emissions, further increasing pollutant concentrations (Ilie 2023; Lagmiri and Dahech 2024). Studying these phenomena presents significant methodological challenges. Understanding the interactions between urban morphology, local meteorological conditions, and atmospheric chemistry requires a multidisciplinary and multi scale approach. Numerical models (CFD, microscale dispersion models) enable the simulation of idealized air circulation scenarios based on urban geometry, but require detailed data on materials, prevailing winds, and emissions, and must be validated by field measurements (Moussafir 2013; Ghafghazi and Hatzopoulou 2015; Mohammedi et al. 2020). Meanwhile, in situ measurement campaigns—using fixed or mobile sensors—capture the real temporal and spatial variability of pollutant concentrations. However, they are limited by spatial coverage and by the difficulty of distinguishing the effects of morphology from those of meteorological conditions or traffic. In this context, studies combining field measurements, urban geometry, and modeling emerge as the most promising way to understand the effects of urban form on air pollution. Given these findings, it is necessary to place urban morphology at the heart of air quality considerations, not only as a constraint but also as an opportunity for action. Many authors thus advocate integrating environmental performance indicators into urban planning tools, such as air permeability indices, optimal H/W ratios, or greening indices (Maignant 2007; Su 2008; Badach 2022). Urban planning could then use this knowledge to design healthier, more breathable, and more resilient neighborhoods in the face of contemporary environmental challenges. This article adopts this perspective by examining the effects of urban morphology on air quality through an analysis combining geometric, environmental, and atmospheric data. The main objective is to identify which urban configurations promote pollutant dispersion by analyzing differences between neighborhoods characterized as open, channeled, or sheltered. This analysis aims to answer two key questions: What are the most influential morphological characteristics on pollutant concentration or dilution? And how do microclimatic variations caused by urban morphology affect their spatial distribution? Studies comparing pollution differences among various pollutant types in neighborhoods sharing the same living space but featuring diverse architectural structures remain rare. Within this framework, the city center of Cergy, composed of three contiguous neighborhoods with distinct urban characteristics, was selected as the study site. The analysis focuses on three major pollutants: nitrogen dioxide (NO₂), fine particulate matter (PM₂.₅), and suspended particulate matter (PM₁₀). The spatial distribution of pollutants was explored through a series of measurements taken at different strategic points. A comparative method was developed to evaluate pollution levels according to various urban parameters, revealing distinctions between areas near parks, spaces between buildings, and roadways. The influence of wind regimes on pollution levels was also analyzed. Furthermore, temporal variations in pollutant concentrations were studied in relation to factors such as traffic and meteorological conditions. Finally, NO₂ concentration modeling was performed, providing an analytical framework to better understand pollutant dynamics in urban environments. Study area The city center of Cergy (Fig. 1 ) has developed through several phases of evolution, resulting in a mosaic of contiguous urban blocks with varied characteristics. Since 2016, the area has undergone numerous developments aimed at establishing it as the true heart of the Cergy-Pontoise agglomeration. These transformations have been accompanied by an increase in building heights and the progressive infill of interstitial spaces. The Cergy city center unfolds in a quadrilateral shape and consists of main roads encircling the center. Their widths range from 29 to 33 m on the northwest and southeast sides, with a narrower section of 14 m on the southwest side near the park. The A15 motorway, with an average width of 51 m, delineates the area to the northeast. Furthermore, the center plays a strategic role as a major transit hub, supported by an especially dense public transport network that facilitates mobility within Cergy and connections to other municipalities and regions. This transit position, combined with urban developments, raises significant concerns related to air quality while offering diverse environments and experiences. Successive interventions over different periods have shaped the neighborhoods in the city center, each with distinct morphologies often defined by major road axes (Fig. 2 ). These neighborhoods can be categorized into three main areas: • The Slab District Characterized by buildings constructed on a raised slab, beneath which lie technical zones and parking spaces. The parcel system here shows notable irregularity, with parcels of varying sizes and shapes. Some parcels are dedicated to commercial and professional activities, exhibiting rectangular or chevron configurations. Residential areas account for 19% of built-up surfaces, featuring diverse architectural forms, including U-shaped, chevron, rectangular, and circular configurations. The road network in this neighborhood is entirely pedestrianized, with narrow, interconnected streets whose Landsberg indices range from 1.8 to 3.7. Vehicular traffic is concentrated beneath the slab, except for an elevated road 15 meters wide, connecting the neighborhood to the main road bordering the city center. • The Park District Distinguished by its commitment to sustainability and environmental preservation. The parcel system is discontinuous, consisting of parcels shaped like halos, chevrons, and rectangles. The neighborhood is primarily served by the road bordering the block, with cul-de-sacs providing access to buildings. The internal road network consists exclusively of pedestrian pathways. • The Administrative District Features a more traditional layout, with large complexes built directly on the ground. It is organized with a generally uniform parcel system, consisting of U-shaped parcels that create a cohesive urban aesthetic. Roads here typically feature wide sidewalks, facilitating pedestrian movement, particularly along Oise Boulevard, the main artery traversing the neighborhood. This boulevard, 48 meters wide, includes lanes for vehicle traffic and designated bus lanes, and parking. The streets dividing parcels are of two types: two-way streets and one-way streets. Most roads follow a north-south orientation, with a slight tilt to the west or east. A two-way road, oriented east-west with a Landsberg index of 0.8, separates this neighborhood from the Elevated Slab Neighborhood. This structured layout reflects both the diverse urban forms and the complex dynamics of mobility and environmental concerns within the Cergy city center. Data and methods In this research, we conducted a typomorphological analysis to identify the typological and morphological characteristics of the three districts (the slab district, the park district, and the administrative district). In climatology, this approach typically seeks to understand how the physical configuration of an urban space influences variations in climatic parameters. According to Panerai et al (1999), a typomorphological analysis includes examining the distribution patterns of buildings, construction density, urban porosity, and street orientation. This study analyzed these criteria for each urban district while also considering other relevant characteristics, such as vegetation density. We then cross-referenced this architectural information with pollution data and climatic parameters to assess the ability of each structural entity to either disperse or accumulate pollutants. The objective is to evaluate how this spatial diversity influences air quality variations and to identify the key factors driving this variability. The pollutants studied (NO 2 , PM 10 , and PM 2.5 ) were measured using nine fixed “Ecosmart” sensors, installed on public lighting poles at a height of 3 meters. These sensors are calibrated with reference analyzers approved by the LCSQA (Central Laboratory for Air Quality Monitoring) and verified according to ISO 17025 standards. Each district has two to four measurement points, selected to allow a comparative study of air quality variations (Fig. 2 ). These points were carefully chosen to represent the different urban configurations unique to each district. The spatial characteristics associated with each measurement point are listed in Fig. 3 . The pollution sensors were installed between March 2023 and February 2024 to cover a wide range of meteorological conditions, which significantly influence the spatial and temporal variability of pollutants. However, due to a communication outage between May and September 2023, we were only able to collect 5,112 hours of data for this study, representing 58% of the deployment period. A key element in assessing pollutant concentrations is the measurement of atmospheric pressure, as well as wind speed and direction, recorded at a 30-minute time interval. In this section, data for these climatic parameters come from the Davis weather station installed on the roof of a building in the slab district. In this study, we also carried out a modeling system using an inductive method based on multiple regression applied to the administrative district. In this observation area, the sampling of NO 2 pollution was established from traveling measurements (by Ecosmart sensor). These measurements were programmed exclusively in winter (from February 3 to 5 and from February 14 to 17, 2024) in stable weather and during the morning traffic peak with a 5-minute stop at each sampling point. At the same time, information on weather conditions (T°, H% and wind) was collected along the route using a thermal probe and an anemometer type Testo 400. Over seven days of measurements, the average behavior of the parameters collected (pollutants, meteorological parameters, and car flow) was carried out for 43 observation points, spaced on average nearly 40m apart (Fig. 4 a). Explanatory parameters were collected at each measurement point (types of voices, presence of traffic lights, distance between measurement points and roads or intersections, orientation of streets relative to the prevailing wind, width of streets, height of buildings, and degree of street encasement). In addition, our approach included data on the types of screens along the arteries, whether wall-mounted, vegetal, mixed, or open. All the steps followed to carry out the statistical modeling by linear regression are detailed in Fig. 4 b. Results Exploring Building Density Building density is assessed by the ratio between the total footprint of buildings and the area of ​​the urban block on which they are located, multiplied by the average number of levels. Mathematically, this relationship is formulated as follows: $$\:Building\:density=\left(\:\frac{total\:footprint\:of\:buildings}{total\:area\:of\:the\:urban\:block\:}\right)x\:Average\:number\:of\:levels$$ Table 1 presents the results of density calculations for the three studied districts, revealing three distinct levels of density in Cergy's city center. With a built density of 3.5, the slab district shows the highest value, primarily due to the presence of a large shopping center with a significant footprint and the compact arrangement of buildings along narrow streets. In comparison, the administrative district, despite having a similar average building height, adopts a distinct urban organization. Its layout incorporates open spaces at the center of each residential block, designated for gardens or playgrounds. This configuration, allowing nine parcels to benefit from open sky exposure, results in a more moderate built density of 1.59. As for the park district, although its total area is comparable to that of the slab district, it exhibits the lowest built density, with an index of 0.55. This low density is attributed to the smaller building footprints and slightly lower average building heights. This architectural choice emphasizes the preservation of green spaces, which overwhelmingly dominate the district's landscape. Table 1 Value of the surface area and building density in the three districts studied (data calculated using ArcGis) Slab District Administrative District Park District Building footprint (hectares) 11.3 3.06 3.14 Average number of levels 6.2 6.25 3.71 Total area of ​​the urban block (hectares) 20 12.03 21.2 Building density 3.5 1.59 0.55 Exploration of vegetation density Vegetation density is expressed as the ratio between the total plant area and the total area of ​​the urban block studied: Vegetation density =( Total plant area / \(\:total\:area\:of\:the\:urban\:block\:\) ​)×100. It represents a key indicator of urban texture, influencing wind patterns and thermal exchanges between the ground and the atmosphere. The results of vegetation density assessments in the three districts are presented in Table 2 . In the park district, nearly half of the area is covered by vegetation, corresponding to a vegetation index of 48%. This high vegetation density creates a microclimatic effect, fostering the development of local park breezes and influencing the depth of the urban boundary layer. In contrast, green spaces and trees are scarce in the slab district, with a vegetation density of only 3.7%. This low value reflects urban planners' and architects' lack of attention to green spaces, prioritizing the construction of an elevated urban unit to separate traffic flows from pedestrian areas. The inherent constraints of slab architecture also make it difficult to integrate green spaces, limiting opportunities for tree planting. In the administrative district, vegetation density is calculated at 16%. This value is due to the presence of a small public garden, green spaces at the entrances of residences, and rows of street trees. Although this proportion is not yet optimal, it demonstrates a deliberate effort by developers to incorporate natural elements into the urban architecture and move away from the slab district model. Table 2 Value of the surface area and vegetation density in the three districts studied Slab District Administrative District Park District Plant surface (hectares) 0.74 1.95 10.29 Total area of ​​the urban block (hectares) 20 12.03 21.2 Vegetation Density (%) 3.7 16 48 Exploring urban porosity The calculation of urban porosity is based on the system of open spaces. The equation used for this calculation is as follows: Urban porosity =( Free or open space / \(\:total\:area\:of\:the\:urban\:block\) )×100. In the slab district, urban porosity reaches 31% (Table 3 ). Since this neighborhood is not homogeneous, it is possible to distinguish two units based on the type of development. On one hand, there is a historic unit of 3.46 hectares, characterized by a more enclosed organization with canyon-like streets. This area consists of buildings arranged in blocks, with flat façades aligned along narrow alleys. Urban porosity in this historic section is limited to 11.8%, allowing air to circulate only through ventilation corridors. On the other hand, the second unit is designed to offer a more open view of the sky, notably due to its public squares. Porosity in this zone reaches 19.2%. The layout of the administrative district, mainly composed of chevron-shaped parcels, creates a unique arrangement that generates open spaces, thereby increasing urban porosity. This configuration facilitates the formation of ventilation zones with a mesh-like effect, influencing how air penetrates through the block. Furthermore, the wide streets and main axes within this urban fabric play a key role in creating wind corridors. Urban porosity calculated for this area reaches 45%, reflecting the effectiveness of the urban design in promoting natural ventilation within a downtown neighborhood. In the park district, urban porosity is 54%. The presence of a large open space plays a crucial role in facilitating smooth airflow. The dominant vegetation in this area also contributes to wind infiltration. Besides protecting against strong winds, the trees enable gentle and consistent ventilation through their foliage. Moreover, air circulation around the neighborhood is enhanced by connecting channels that link the interior and exterior of the park. Table 3 Value of surface area and urban porosity in the three districts studied Slab District Administrative District Park District Free or open space (hectares) 6.3 5.46 11.31 Total area of ​​the urban block (hectares) 20 12.03 21.2 urban porosity 31% 45% 54% Analysis of air quality and morpho-structural dependency indicators Correlation with atmospheric pressure The objective of this analysis is to confirm the hypothesis that correlations exist between atmospheric pressure and pollutant dispersion. We examine the distribution of pollution concentrations in each district according to atmospheric pressure to identify the degree of dependency between these variables. The results of this analysis are presented in Fig. 5 . A generally positive correlation between atmospheric pressure and pollutant concentrations (PM 10 and NO 2 ) is observed in the three districts studied. The anticyclonic regime, with pressure above 1013 hPa, promotes air stability and thermal inversions, which hinder the vertical dispersion of pollutants and increase their concentrations. Conversely, low pollutant concentrations are associated with low-pressure systems, often accompanied by precipitation and ventilation, which disperse particles and cleanse the boundary layer. However, the low determination coefficients (ranging from 0.03 to 0.1) found in the three neighborhoods indicate a complex and nonlinear relationship between pollutants and atmospheric pressure. Sometimes, low atmospheric pressure coincides with high pollutant concentrations. This may be due to heating emissions and the complex interaction between wind speed and urban morphology. Correlation with wind speed As with atmospheric pressure, we studied the distribution of NO₂ concentrations as a function of wind speed. The conclusions of our analysis are illustrated in Fig. 6 . We observe a significant and negative correlation between wind speed and NO₂ concentrations in the three districts studied, as evidenced by the coefficient of determination (R²) and the regression curve. NO₂ accumulates under calm weather conditions and is more effectively removed at higher wind speeds. We also calculated the specific correlation coefficients for each district, which are 0.5, 0.58, and 0.64 for the slab, administrative, and park districts, respectively. These results are consistent with previous research, notably that of Han et al (2015) in Shinguai, who found negative correlation coefficients ranging from 0.51 to 0.61, close to our findings. The studies by Zhou et al (2020) in Beijing and Nanjing and Radović et al (2022) in Bijeljina also confirm this trend with correlation coefficients between 0.4 and 0.56. Regarding particulate pollution, the correlation with wind speed is less pronounced due to the diversity of sources influencing particle levels. This suggests that gaseous pollutants are more easily diluted by wind than particulate matter. Wind remains strongly linked to building density. Therefore, we cross-referenced our data with indicators of the urban morphology of each district. We identified two indicators that significantly influence wind patterns: porosity and building density. Street orientation could also be included in this analysis, but it would require in-depth studies on the angle of wind penetration for each location and time of day. Table 4 shows that low building density and a high percentage of porosity are associated with a stronger correlation between wind and NO₂ pollution. These indicators reflect an increase in areas open to wind flow. In districts where the density of surrounding barriers is low, such as the park district, NO₂ concentrations decrease as wind speed increases, highlighting the positive impact of ventilation on pollutant dispersion. Conversely, the denser and more enclosed the barriers, as is the case in the slab district, the less effective strong winds are in dispersing pollutants, with a reduction in efficiency of approximately 22%. Indeed, low porosity combined with high building density reduces the incident wind speed within the district, thereby preventing pollutants from dissipating. Table 4 Cross-analysis of the correlation index between wind and pollution (NO₂) with the morphological indices of the studied districts. Slab district Administrative district Park district Correlation index between wind and NO₂ -0.5 -0,58 -0.64 Porosity index 31% 45% 54% Building density index 3.5 1.59 0.55 Comparison of daily pollution variations The daily variations of NO₂ concentrations established for each district reveal low concentrations around midday and high concentrations when the mixing layer thins. Under stable weather conditions, NO₂ concentrations appear to be more influenced by microscale phenomena, where atmospheric characteristics allow urban and human factors to exert their effects. The administrative district shows the highest concentrations with two clear NO₂ peaks (one at 9 a.m. and another at 5 p.m.) (Fig. 7 ). However, around midday, this neighborhood has lower concentrations than the park district, due to convective movements transporting air to higher altitudes. These movements also occur in the park district but are less intense because of the vegetation cover. This neighborhood, lacking significant nitrogen oxide sources within it, exhibits concentration levels that tend to remain relatively stable over time. The slab district generally follows the temporal pattern observed in the administrative district, except for the 5 p.m. peak, which is shifted to 8 p.m. Although the circadian rhythm is identifiable, concentrations remain lower overall. Under disturbed weather conditions, the difference in NO₂ concentrations between the three districts narrows compared to stable conditions, especially between the park and administrative districts, where wind mixing strongly influences throughout the day. The only significant difference occurs between 4 p.m. and 8 p.m., linked to intense vehicle traffic in the administrative district during that period. Regarding particulate pollution, the peak for all three districts generally occurs between 9 a.m. and 10 a.m., regardless of weather type. Stable weather conditions are associated with higher daytime concentrations. This increase is caused by recycling particles contained within the nocturnal layer from the previous day, convective movements, and emissions from construction sites and daytime traffic. Conversely, during disturbed periods, a relative stability in particulate concentrations is observed throughout the day, with mesoscale atmospheric conditions being the main driver of this pattern. Analysis of morphological indicators and pollutant dispersion patterns in each urban fabric To deepen our analysis of the impact of architectural configurations, we examined the variations in pollution levels at each measurement point, as illustrated in Fig. 8 . We specifically selected the low-pressure period (without rain), during which wind plays a crucial role in highlighting the effect of urban geometry. During this period, the wind was on average moderate to strong, with a predominant direction from the southwest. In the administrative district, point 3, located on an avenue characterized by a Landsberg index of 0.8, shows the lowest average concentration (43 ppb) due to its NE/SW alignment parallel to the prevailing wind. In contrast, the nearby street (point 4), oriented NW/SE (perpendicular to the wind), exhibits a higher average concentration (50 ppb), despite being less trafficked and connected to the courtyard of a residence, which increases its openness to the sky. This demonstrates that when the wind direction is parallel to the street (0°), the main mode of atmospheric pollution transport within the street canyon is horizontal, parallel to the street, thereby promoting NO₂ dispersion. However, when the wind direction is perpendicular to the street (90°), vortices form within the street canyon, and NO₂ is primarily transported vertically with relatively low diffusion efficiency. Oise Boulevard (point 6), with a Landsberg index of 0.5, wider than the other streets and aligned perpendicularly to the prevailing winds, shows an average concentration (50 ppb) comparable to that of point 4. However, its first quartile is lower while its third quartile is higher. These concentrations are mainly attributable to the traffic emission patterns and accumulation due to the southwest wind direction, as the station is located near the leeward wall. At the highway station (point 5), the highest concentration was expected. However, its concentrations are quite similar to those at Oise Boulevard, due to the open sky exposure of its location and the dispersion of gaseous emissions away from the station by the southwest wind. The influence of sky exposure and street orientation relative to the wind is also evident in the slab district. The street canyon (point 1), oriented NW/SE and at a 45° angle to the prevailing winds, shows higher concentrations compared to point 2. The latter has a higher sky view factor and its courtyard axis is parallel to the prevailing wind, which favors pollutant dispersion. In the park district, the two stations inside the park (points 8 and 9) show similar NO₂ concentrations (49 ppb on average). The street oriented N/S at point 7, however, has the lowest average concentration (42 ppb). This street does not form a canyon but rather features two asymmetrical and discontinuous facades, providing multiple air passage channels between buildings. These openings limit air confinement, which can effectively reduce gaseous pollution levels in the street. Particulate pollutants interact with wind direction as significantly as gaseous pollutants. The lowest concentrations are recorded at Point 3 due to effective ventilation (Fig. 9 ). However, this site shows the highest averages (46 µg/m³ for PM 10 and 56 µg/m³ for PM 2.5 ), primarily due to extreme concentrations observed when particles are transported by wind during active construction along this axis. The site near Oise Boulevard (Point 6), close to construction zones, exhibits the lowest averages (34 µg/m³ for PM 10 and 20 µg/m³ for PM 2.5 ) due to its perpendicular orientation to the wind, which blocks the intrusion of emissions from the construction sites. The recorded concentrations mainly result from the vortex recirculation of locally emitted particles. This same mechanism is responsible for the concentrations at Point 4, although higher concentrations are observed there (averaging 39 µg/m³ for PM 10 and 25 µg/m³ for PM 2.5 ) because the station is located near an alignment of trees emitting biogenic pollutants. These biogenic emissions might also contribute to the elevated concentrations at the highway-adjacent site, in addition to traffic-related emissions (averaging 41 µg/m³ for PM 10 and 24 µg/m³ for PM 2.5 ). In the slab district, Point 1 records higher PM concentrations (51 µg/m³ for PM 10 and 33 µg/m³ for PM 2.5 on average) than Point 2 (averaging 43 µg/m³ for PM 10 and 27 µg/m³ for PM 2.5 ). This difference is again attributed to the angle of wind penetration, as observed for NO₂. In the park district, the mechanism differs. The park center (Point 8), which is well-ventilated, shows higher PM concentrations (32 µg/m³ for PM 10 and 20 µg/m³ for PM 2.5 on average) due to the predominance of vegetative substrate. Conversely, Point 9 records the lowest PM concentrations (23 µg/m³ for PM 10 and 15 µg/m³ for PM 2.5 on average), in contrast to NO₂ findings. This can be attributed to the halo-shaped buildings effectively blocking the entry of particulate pollutants, though their impact on gaseous pollutants is less pronounced. Point 7, closer to traffic, also shows lower concentrations than the park center due to the presence of ventilation channels that facilitate particle dispersion (27 µg/m³ for PM 10 and 16 µg/m³ for PM 2.5 on average). Modeling the impact of urban planning on pollution distribution Correlation between fixed and mobile measurements The objective of this analysis is to validate the correspondence between NO₂ measurements obtained through mobile monitoring and those collected via fixed monitoring. This step is essential for assessing the reliability of mobile data before incorporating it into the modeling phase. To achieve this, a comparison was made using mobile data collected near the fixed sensors. The determination coefficient obtained (R² = 0.72) indicates a significant agreement between the two datasets (Fig. 10). This result confirms that mobile measurements, conducted with a 5-minute stop at each sampling point, are generally reliable and consistent with fixed-mode measurements. The average discrepancies observed, approximately 17 ppb, can be attributed to two factors: Sensor response time : In mobile mode, the sensor is exposed to rapid variations in environmental conditions, which may affect the stabilization of measurements compared to fixed mode. Positioning difference : Mobile measurements are taken at a height of about 1.5 meters, whereas fixed measurements are conducted at 3 meters. This height difference can lead to concentration discrepancies due to varying exposure to emissions. Overall, these discrepancies align with expectations and do not significantly compromise the reliability of mobile measurements. Outputs of the multiple linear regression analysis After several attempts to optimize the regression model, a coefficient of determination (R²) of 0.68 was achieved, indicating that 68% of the data variance is explained by the model. This result aligns reasonably well with the state of the art (see Deletraz 2002, for example). The analysis of metric bases related to error and robustness confirms the model's reliability. The RMSE, DW, and PC values are 26, 2.2, and 0.4, respectively. This result suggests that for the 43 points analyzed, the estimated data is, on average, close to the actual values. This is illustrated in Fig. 11 , showing a low dispersion of simulated and actual values around a straight line. Among the tested variables, three were selected: road width, distance to intersections, and the number of vehicles. Unsurprisingly, the number of vehicles shows a strong linear correlation with NO₂ concentrations (r = 0.8), while the influence of road width and distance to intersections is less pronounced (Fig. 12 ). Indeed, morning NO₂ peaks are primarily driven by traffic, making the other two variables less significant during these periods. Nevertheless, including them in the model notably improves the accuracy of the spatial distribution. For example, urban road width indirectly indicates the air’s capacity to dilute pollutants, while distance to intersections reflects the level of exposure to high concentrations. The sign of the correlation for these two variables is somewhat counterintuitive in the model. This result aligns with other studies, notably Abdmouleh (2024), conducted in Paris. The main difference with that study lies in the meteorological variables. Our model did not capture these because the data were generally homogeneous due to stable weather conditions (sampling was conducted under the same weather type). The constant of 50.1 in the model represents a theoretical baseline level that would be observed under ideal conditions—i.e., far from intersections, on a very wide road, and with no traffic. This baseline level is lower than typical NO₂ values, such as the median and mean (73 and 69 ppb, respectively). The model suggests that the included variables are responsible for a substantial increase in NO₂ levels, around 19 to 23 ppb on average. The gap between the constant and typical values may indicate that the model effectively captures the variability in concentrations due to urban factors. However, it is also important to consider that other factors not included in the model, such as other pollution sources (industrial emissions, long-range transport), could influence NO₂ levels. Residual analysis The mean of the residuals (1.12) is relatively close to zero, which is generally desirable in a fitted model. Additionally, the average standard deviation of the residuals is 8.1, which is much lower than that of the sample (25.7). Such a marked reduction in the residuals’ standard deviation compared to that of the sample is a positive indicator of the model’s quality. However, 63% of the measurement points have residuals within ± 25.7 ppb, while 37% show residuals exceeding this range, indicating that the model performs relatively poorly for some measurements. The residual analysis reveals deviations ranging from − 33.9 to 66.3 ppb, highlighting that the model can both underestimate and overestimate values (Fig. 13 ). For example, the model overestimates concentrations in certain residential courtyards, with an average difference of 27 ppb compared to actual values (which average 15 ppb). Conversely, it underestimates concentrations in some public squares and streets, where actual concentrations average 89 ppb and differ from predicted values by an average of 46.5 ppb. To address these prediction anomalies, improvements are necessary, such as incorporating additional variables not currently included in the model and increasing the sample size. Spatial interpolation of NO₂ concentrations Spatial interpolation was performed using observed data, employing the Inverse Distance Weighting (IDW) method as the tool. This method is often preferred for mapping pollution due to its simplicity and effectiveness with unevenly distributed data. It is based on the principle that closer points have a greater influence on the interpolated values, which is relevant in the context of pollution, where geographic proximity is a key factor. IDW also allows adjusting the weight of neighboring points through an exponent parameter, providing flexibility and adaptability. Moreover, it does not require assumptions about the data distribution, thus simplifying the analysis. This interpolation enabled the identification of hotspots within the neighborhood (Fig. 14 ). It shows that intersections are problematic in terms of exposure. These locations are often prone to frequent traffic jams, as was the case during our morning peak measurements. Pollution concentrations decrease progressively with distance from these intersections. Pollution along the Oise Boulevard is mainly due to bus stops, the intersection itself, and the sheltered effect of the area. This interpolation method also highlighted that narrow streets tend to have lower concentrations, which can be explained by a lower traffic volume. It further shows that residential courtyards experience less exposure to pollution during the morning peak. Discussion This study explored the relationship between urban morphology and air quality. The objective was to better understand the various elements of urban texture that influence concentrations of NO₂, PM 10 , and PM 2.5 pollutants at a fine spatial scale. Urban texture was examined through a set of commonly used indicators describing the form and distribution of building clusters, including building density, urban porosity, and street orientation. Additionally, an indicator of vegetation texture, represented by vegetation density, was included. Although this selection of indicators is narrower than that used in some comparable studies (Özsoy 2017; Wang et al. 2024), their combination offers a synthetic and relevant view of the urban fabric’s morphology. It allows for a direct assessment of the degree and nature of resistance exerted by the urban canopy against pollution dispersion. The results highlight the importance of favoring less dense and better-ventilated urban configurations, contrasting with large-scale studies such as Clifton et al (2008), which emphasized the environmental benefits of compact and mixed-use forms. Our findings reveal that pollutant distribution patterns for NO₂, PM 10 , and PM 2.5 are influenced by the interaction between wind-driven transport and urban texture. In districts where open spaces are abundant, NO₂ concentrations decrease as wind speed increases, underscoring the beneficial effect of ventilation on pollutant dispersion. Conversely, in areas with many obstacles close together, such as the slab district, ventilation loses about 22% of its effectiveness in dispersing pollutants. Regarding particulate pollution, the correlation with wind speed is less pronounced due to the variety of sources affecting particle levels. However, a closer look at PM distribution shows that urban morphology continues to influence concentrations. In densely built and enclosed neighborhoods, the airflow’s ability to evacuate pollutants is restricted. Consequently, locally emitted particles remain trapped longer, hindering dispersion and increasing air pollution in these confined spaces. In contrast, less dense neighborhoods generally exhibit lower concentrations despite active particle emissions, attributable to better self-cleaning capacity associated with higher porosity and lower building density. These observations support findings by Yu (2023), who demonstrated that high building densities in urban centers lead to increased PM 2.5 pollution levels. The urban canopy’s resistance to pollutant dispersion was also analyzed from the perspective of street morphology. Several studies have shown that dispersion processes are closely linked to street orientation and arrangement relative to prevailing winds (Kim 2004; Lv 2021; Yang 2023). This study observed that elevated concentration levels in narrow alleys are mainly due to reduced dispersion capacity rather than increased emissions. In street canyons, vertical exchange and horizontal transport are the main modes of atmospheric pollution movement. Streets aligned parallel to the ambient wind promote pollutant dispersion through a channeling effect, while perpendicular streets generate vortices that trap pollutants and locally increase concentrations. These observations are consistent with the work of Kastner-Klein et al (1999), who demonstrated a tendency for pollution levels to rise as the angle between wind direction and street axis increases from 0° to 90°. These results highlight the importance of considering street orientation relative to prevailing winds in urban planning. Thoughtful design can improve pollutant dispersion and thus air quality in urban environments. Temporally, this study examined the effects of urban morphology on ambient air pollution on a daily scale, highlighting dynamics specific to different times of day. The results emphasized the role of local emissions in ranking urban fabrics by pollution intensity while revealing a certain consistency in the circadian rhythm of pollutant levels. PM 2.5 and PM 10 concentrations peak in the morning, primarily due to the specific dynamics of the urban boundary layer at this time and morning traffic congestion. The highest NO₂ concentrations occur during rush hours, coinciding with commuter travel times and increasing pedestrian exposure to pollutants. In light of these observations, it would be advisable for pedestrians to adopt strategies to minimize exposure during these critical periods, such as adjusting travel schedules, changing routes, or implementing protective measures, especially for vulnerable groups. Finally, while some conclusions were drawn regarding the role of urban morphology in pollution, it is clear that fixed sensors are not well suited for studying spatial variability. Our analysis thus shifted toward a modeling approach based on a panel of mobile measurements and morphological and functional indicators of urban space. The resulting model shows reasonable statistical robustness, but further improvements could enhance its predictive capacity. Its application to other neighborhoods remains uncertain unless it is first refined and validated on additional sites. These points suggest that further research is needed to improve both the precision and generalizability of the model. Moreover, data from mobile monitoring represent a particularly suitable source for mapping air quality with high spatial precision. This approach enables the spatialization of pollution, which is difficult to achieve with fixed sensors. In this study, mapping conducted under stable morning conditions in the tested neighborhood revealed zones of high concentration, identified as hotspots. These findings corroborate previous work, such as that of Peters et al (2013), Van den Bossche et al (2015), and Idir et al (2021), which highlighted the effectiveness of repeated mobile measurement campaigns in analyzing spatial variability of pollutants in various urban environments. Conclusion This study highlighted the quantitative and qualitative relationship between neighborhood morphology and the distribution of pollutant concentrations. Initially relying on an approach based on fixed sensors, it identified several crucial aspects to better understand the interactions between urban planning and pollutant levels. Physical characteristics such as built density, porosity, and vegetation cover were found to be key factors influencing pollutant dispersion. The results show that densely built areas amplify pollution, whereas open spaces promote pollutant dissipation. The study also revealed the influence of street orientation relative to the prevailing wind and the sky view factor on pollutant concentrations. Furthermore, the study emphasizes the limitations of fixed sensors for fine spatial mapping of pollution, while mobile measurements prove their relevance for high-resolution mapping. By identifying local hotspots and exploring interactions between urban structures and pollutant dynamics, this approach opens promising avenues for developing decision-support tools in urban planning and air quality management. From a methodological standpoint, the approach adopted, which combines quantitative and qualitative data with statistical and geospatial analyses, proved particularly well-suited to exploring the complex relationships between urban morphology and air quality. This methodology also has the advantage of being easily transferable to other urban contexts. By adapting it to local specificities, it could enable a better understanding of the mechanisms influencing pollutant distribution and guide public policies toward more effective and targeted solutions. Looking ahead, future research could incorporate environmental parameters such as temperature, humidity, and wind characteristics at each measurement point to further refine the understanding of the relationships between urban morphology and pollution. Such efforts would contribute not only to combating air pollution but also to improving urban microclimates, thereby offering healthier, more resilient, and sustainable environments for urban populations. Declarations Conflicts of Interest The authors declare no conflict of interest. The sponsors had no role in the design, execution, interpretation, or writing of the study. Funding This work was supported by the DIM Qi2 program of the Île-de-France Region, the technical and financial support of the Cergy-Pontoise Conurbation, and the French Ministry of Higher Education and Research via the “Social Sciences” doctoral school (ED 624) (Paris Cité University). Author Contribution Conceptualization, S.L. and S.D.; Methodology, S.L.; Software, S.L.; Validation, S.D.; Formal Analysis, S.L.; Investigation, S.L.; Resources, S.L. and S.D.; Data Curation, S.L.; Writing—Original Draft Preparation, S.L.; Writing—Review & Editing, S.L. and S.D.; Visualization, S.L and S.D; Supervision, S.D.; Project Administration, S.L.; Funding Acquisition, S.L. and S.D. All authors have read and agreed to the published version of the manuscript. Data Availability Statement The data presented in this study are available on request from the corresponding author. The data are not publicly available due to file sizes. Tables Abdmouleh M (2023). Pollution de l’air et sonore dans Paris à l’échelle intra-urbaine : répartition spatio-temporelle pendant et en dehors de la période de confinement du COVID-19 dans le XIIIème arrondissement. Thèse de doctorat, Université Paris Cité. Badach J , Szczepanski J, Bonenberg W, G˛ebicki J, Nyka L (2022). Developing the Urban Blue-Green Infrastructure as a Tool for Urban Air Quality Management. Sustainability 2022, 14, 9688. https://doi.org/ 10.3390/su14159688. Ben Romdhane S (2017). Effets du climat et de la pollution de l'air sur la santé respiratoire à Tunis. Géographie. Thèse de doctorat, Université Paris Cité. CERTU (2007). La forme urbaine et l'enjeu de sa qualité" Lavoisier éditions, 91 p. Clifton K, Ewing R, Knaap GJ, Song Y ( 2008 ).Quantitative analysis of urban form: A multidisciplinary review. J. Urban. Int. Res. Placemaking Urban Sustain, 1, pp. 17 – 45. Dedoussi I.C., Eastham S.D., Monier E. et al. (2020). Premature mortality related to United States cross-state air pollution. Nature 578, 261–265. https://doi.org/10.1038/s41586-020-1983-8. Deletraz G (2002). Géographie des risques environnementaux liés aux transports routiers en montagne: Incidences des emissions d’oxydes d’azote en vallées d’Aspe et de Biriatou (Pyrénées). PhD Thesis, Université de Pau et des Pays de l’Adour. Ghafghazi G., Hatzopoulou M (2015). Simulating the air quality impacts of traffic calming schemes in a dense urban neighborhood, Transportation Research Part D: Transport and Environment, 35, 11-22. https://doi.org/10.1016/j.trd.2014.11.014. Han L, Zhou W, Li W, Meshesha D.T, Li L, & Zheng M (2015). Meteorological and urban landscape factors on severe air pollution in Beijing. Journal of the Air & Waste Management Association, 65, 782– 87p. He B, Ding L, & Prasad D.K (2020). Relationships among local-scale urban morphology, urban ventilation, urban heat island and outdoor thermal comfort under sea breeze influence. Sustainable Cities and Society, 60, 102-289p. Host S., Chatignoux E., Leal C., Grémy I (2012). Exposition à la pollution atmosphérique de proximité liée au trafic : quelles méthodes pour quels risques sanitaires ? [Health risk assessment of traffic-related air pollution near busy roads]. Rev Epidemiol Sante Publique. 60(4):321-30. doi: 10.1016/j.respe.2012.02.007. Idir Y. M, Orfila O, Judalet V, Sagot B, & Chatellier P (2021). Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics. Sensors , 21 (14), 4717. https://doi.org/10.3390/s21144717. Ilie A, Vasilescu J, Talianu C, Iojă C, & Nemuc A (2023). Spatiotemporal Variability of Urban Air Pollution in Bucharest City. Atmosphere , 14 (12), 1759. https://doi.org/10.3390/atmos14121759. Kastner-Klein P, Plate E.J (1999). Wind-tunnel study of concentration fields in street canyons. Atmos. Environ, 33 , 3973–3979. https://doi.org/10.1016/S1352-2310(99)00139-9. Kastner-Klein P, Berkowicz R, & Britter R. (2004). The influence of street architecture on flow and dispersion in street canyons. Meteorol Atmos Phys 87, 121–131. https://doi.org/10.1007/s00703-003-0065-4. Kim J.-J (2004). A numerical study of the effects of ambient wind direction on flow and dispersion in urban street canyons using the RNG k-ε turbulence model. Atmos. Environ. 38 , 3039–3048. Landrigan P. J, Fuller R, Acosta N. J. R, Adeyi O, Arnold R, Basu N, Baldé A. B, Bertollini R, Bose-O’Reilly S, Boufford J. I, et al. (2017). The Lancet Commission on Pollution and Health. The Lancet, Volume 391, Issue 10119, 462 – 512. Lagmiri S, & Dahech S (2024). Distribution of PM 10 , PM 2.5 , and NO 2 in the Cergy-Pontoise urban area (France). Meteorological Applications , 31(4), e2223. https://doi.org/10.1002/met.2223. Lévy A (2005). Formes Urbaines et significations : revisiter la morphologie urbaine. Espace et société. No 122, 25 – 48p. Lv W, Wu Y, & Zang J (2021). A Review on the Dispersion and Distribution Characteristics of Pollutants in Street Canyons and Improvement Measures. Energies , 14 (19),6155. https://doi.org/10.3390/en14196155. Maignant G (2007). Dispersion de polluants et morphologie urbaine. L’Espace géographique, Tome 36(2), 141-154. https://doi.org/10.3917/eg.362.0141. Mohammedi B, Hanini S, Gheziel A, Mellel N (2020). Simulation de l'Effet des Obstacles sur la Dispersion Atmosphérique par un code CFD. Algerian Journal of Environmental Science and Technology March edition. Vol.6. No1. ISSN : 2437-1114. Moussafir J, Olry C, Nibart M et al. (2013). Aircity: A very high-resolution 3D atmospheric dispersion modelling system for Paris, rapport de projet, 2013. Disponible sur : http://www.aria.fr/projets/aircity/pdf/H15-184.Moussafir.AIRCITY.V4.pdf. Özsoy D (2017). Prise en compte des problématiques liées à la qualité de l’air extérieur dans les processus de conception urbaine : quels indicateurs de forme urbaine et comment les intégrer dans le processus opérationnel ? Architecture, aménagement de l’espace. ‌dumas-01502897. Panerai P, Depaule J.C, Demorgon M (1999). Analyse urbaine. Edition parenthèse, collection Eupalinos, 189p. Peters J, Theunis J, Van Poppel M, Berghmans P (2013). Monitoring PM10 and ultrafine particles in urban environments using mobile measurements. Aerosol and Air Quality Research 13, 509–522. Radović B, Ilić P, Popović Z, Vuković J.P, & Smiljanić S (2022). Air Quality in the Town of Bijeljina – Trends and Levels of So2 and No2 Concentrations. Quality of Life (Banja Luka) - APEIRON. 13(1-2):46-57p. Su J.G, Brauer M, Buzzelli M (2008). Estimating urban morphometry at the neighborhood scale for improvement in modeling long-term average air pollution concentrations. Atmospheric Environment. Volume 42, Issue 34. https://doi.org/10.1016/j.atmosenv.2008.07.023. Van den Bossche Jori, Peters Jan, Verwaeren Jan, Botteldooren Dick, Theunis Jan (2015). Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset. Atmospheric Environment, Vol 105, Pages 148-161, ISSN 1352-2310. https://doi.org/10.1016/j.atmosenv.2015.01.017. Vardoulakis Sotiris, Fisher Bernard E.A, Pericleous Koulis, Gonzalez-Flesca Norbert (2003).Modelling air quality in street canyons: a review. Atmospheric Environment. Volume 37, Issue 2. https://doi.org/10.1016/S1352-2310(02)00857-9. Wang Y, Dai X, Gong D, Zhou L, Zhang H, & Ma W (2024). Correlations between Urban Morphological Indicators and PM 2.5 Pollution at Street-Level: Implications on Urban Spatial Optimization. Atmosphere , 15 (3), 341. https://doi.org/10.3390/atmos15030341. WHO (World Health Organization) (2021). Global Air Quality Guidelines: Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide. World Health Organization. 273p. Licence: CC BY-NC-SA 3.0 IGO. Available online: https://apps.who.int/iris/handle/10665/345329. Yang S, Zhan QM, Liu W (2023). Research on air pollution characteristics and planning strategy of urban street environment. Journal of Building Design and Environment, 2(1):028063. https://doi.org/10.37155/2811-0730-0201-1. Yu R (2023). Correlation analysis of urban building form and PM2.5 pollution based on satellite and ground observations. Frontiers in Environmental Science , Volume 10. Doi : 10.3389/fenvs.2022.1111223. Zhao H, Geng G, Zhang Q, Davis S.J, Li X, Liu Y, He K (2019a). Inequality of household Consumption and air pollution-related deaths in China. Nat. Commun. 10 (1), 4337. Zhou H, Yu Y, Gu X, Wu Y, Wang M, Yue H, Gao J, Lei R, & Ge, X (2020). Characteristics of Air Pollution and Their Relationship with Meteorological Parameters: Northern Versus Southern Cities of China. Atmosphere. 11, 253p. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6882779","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":477919428,"identity":"df104744-f0fc-42de-838c-90d846511dee","order_by":0,"name":"Souad Lagmiri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYLACHgYLIMnYwJBQAaSZmRuI0SIB1XIGpIWRaC0gXW0MEL34gHx7j+GHNwwS8vJhh9s+PJxXG83fDtTyo2IbTi0GZ84YS85hkDDceDuxeUbituO5Mw4zNjD2nLmNW4tEjoE00GGMG2cnNjMkbjuW2wDUwszYhluL/Iwc499ALfYQLXOO5c4npIXhRo4ZyJbE+dIgLQ01uRsIaTE4c6zMco6BRPIGkJaEYwdyNwK1HMTnF/n25s033lTY2M6fnf6Y8UdNXe6884cPPvhRgcdhELuA6ACYdRhMHiCgHmpdA5iqI0rxKBgFo2AUjCwAAJQQWauW7zpYAAAAAElFTkSuQmCC","orcid":"","institution":"UMR 8586 (CNRS), Paris City University","correspondingAuthor":true,"prefix":"","firstName":"Souad","middleName":"","lastName":"Lagmiri","suffix":""},{"id":477919429,"identity":"bbe8f1e9-6404-460d-ac49-c259e9c96d48","order_by":1,"name":"Salem Dahech","email":"","orcid":"","institution":"UMR 8586 (CNRS), Paris City University","correspondingAuthor":false,"prefix":"","firstName":"Salem","middleName":"","lastName":"Dahech","suffix":""}],"badges":[],"createdAt":"2025-06-12 19:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6882779/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6882779/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85638485,"identity":"49c29e01-64db-4b89-b84a-0318327eb493","added_by":"auto","created_at":"2025-06-30 06:41:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":321046,"visible":true,"origin":"","legend":"\u003cp\u003eShape and geographical location of the urban fabric in Cergy's city center\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6882779/v1/aac4425fa5e8e4c41e9caacb.png"},{"id":85638490,"identity":"8bc0d65c-9e05-409a-8a1e-3735c2bfa4b1","added_by":"auto","created_at":"2025-06-30 06:41:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":245862,"visible":true,"origin":"","legend":"\u003cp\u003eParcel configuration of the three studied districts in Cergy's city center. 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sourced from Ecosmart \"NO2 and PM10\" and Davis sensor \" surface atmospheric pressure\")\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6882779/v1/9e9cce6dbda38047a6c0dedb.png"},{"id":85639299,"identity":"c1ceeac7-d787-4d1e-89c2-4b1fd3b1631e","added_by":"auto","created_at":"2025-06-30 06:57:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":78955,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the daily average concentrations of NO₂ (top) and PM₁₀ (bottom) and wind speed in the three districts studied (data from 152 days, from March to April 2023 and from October 2023 to February 2024; sourced from Ecosmart for \"NO₂ and PM₁₀\" and Davis sensor for \"wind\")\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6882779/v1/00234750d2f549e14493f83c.png"},{"id":85639556,"identity":"a335bc62-3f5f-4339-971d-8c75b708e1bb","added_by":"auto","created_at":"2025-06-30 07:05:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":105187,"visible":true,"origin":"","legend":"\u003cp\u003eHourly variations of NO₂, PM₁₀, and PM₂.₅ concentrations under stable (53 days) and disturbed (61 days) weather conditions in the three studied districts (data from March to April 2023 and October 2023 to February 2024; sourced from Ecosmart sensors)\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6882779/v1/4c641954a5435aabc35952de.png"},{"id":85638618,"identity":"8cf2bd03-4c35-4c36-b66a-1f089a881802","added_by":"auto","created_at":"2025-06-30 06:49:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":200843,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot of NO₂ concentrations for the 9 sampling points (data from March to April 2023 and October 2023 to February 2024; sourced from Ecosmart sensors)\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6882779/v1/92b76fe5473f1e95b0ede13c.png"},{"id":85638504,"identity":"5c191199-f0cc-48d2-80d5-2ebacc776c27","added_by":"auto","created_at":"2025-06-30 06:41:53","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":230257,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot of PM\u003csub\u003e10 \u003c/sub\u003eand PM\u003csub\u003e2.5 \u003c/sub\u003econcentrations for the 9 sampling points (data from March to April 2023 and October 2023 to February 2024; sourced from Ecosmart sensors)\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6882779/v1/31b766343945096bb8ff5e8b.png"},{"id":85638489,"identity":"f4b3dd02-e6cb-4b1e-9e76-04159a5d6e33","added_by":"auto","created_at":"2025-06-30 06:41:53","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":21733,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between fixed and mobile NO₂ measurements conducted by Ecosmart sensors (data from February 3–5 and February 14–17, 2024)\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6882779/v1/1c3401a29a44d51b37c4a328.png"},{"id":85638484,"identity":"7d73a30b-ddc0-42cb-b868-aa5837b1d3d8","added_by":"auto","created_at":"2025-06-30 06:41:53","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":37988,"visible":true,"origin":"","legend":"\u003cp\u003eAdjustment coefficients of the selected model on the right and correlation between observed and simulated data on the left (sample of 43 measurement points)\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6882779/v1/3553dc75e260b3a129a154e2.png"},{"id":85638627,"identity":"da97beb1-b6dc-46c4-8cbf-8e7c05cff383","added_by":"auto","created_at":"2025-06-30 06:49:54","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":15762,"visible":true,"origin":"","legend":"\u003cp\u003eRelative weights of the variables retained in the multiple linear regression (MLR) model for NO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-6882779/v1/a5ec86e4069d05aa565ff379.png"},{"id":85638509,"identity":"33c8066a-6c52-49fd-a783-be1defb6bcf7","added_by":"auto","created_at":"2025-06-30 06:41:54","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":20574,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of residuals versus observed NO₂ concentrations (left) and predicted NO₂ concentrations (right)\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-6882779/v1/4f6cb4611dfbc3bfe7aca0c4.png"},{"id":85638620,"identity":"19e3d754-e425-4aaa-9a0b-d8bd82d8787e","added_by":"auto","created_at":"2025-06-30 06:49:53","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":276396,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of NO₂ concentrations in the administrative district (data from February 3 to 5 and February 14 to 17, 2024, during the morning peak with a 5-minute time step; sourced from the Ecosmart sensor)\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-6882779/v1/1807a86b59c226c255665781.png"},{"id":86915109,"identity":"2a1c872d-7e47-4c7b-bfaa-c71109cabdfc","added_by":"auto","created_at":"2025-07-17 06:17:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3072860,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6882779/v1/220bb8b3-6a91-41f1-9761-2368da41752d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pollution and Architecture: Variation in Dispersion Conditions at the Neighborhood Scale In Cergy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAir pollution is now recognized as one of the main environmental risks to human health worldwide. In urban areas, where an increasing share of the global population lives, the effects of this pollution are particularly severe (WHO 2021). According to a study by the World Health Organization (WHO), nearly 99% of the global urban population breathes poor-quality air, frequently exceeding recommended thresholds for fine particulate matter (PM) and nitrogen dioxide (NO₂). Chronic exposure to these pollutants is associated with a significant increase in cardiovascular, respiratory, and neurological diseases, as well as premature mortality estimated at several million deaths annually (Ben Romdhane 2017; Landrigan et al. 2017; Zhao 2019a; Dedoussi 2020). These effects are especially pronounced in areas with heavy traffic and continuous emissions of NOₓ and fine particles. Many of the population\u0026mdash;pedestrians, cyclists, and city dwellers\u0026mdash;live or work close to emission sources, thereby increasing health risks (Host et al. 2012).\u003c/p\u003e \u003cp\u003eAlthough emission sources such as transportation, industry, and heating have been extensively studied, identifying pollution hotspots remains complex due to the need to differentiate between pollutant emissions and their concentrations after dispersion (immissions). Immission refers to the concentration of pollutants after their dispersion, which is influenced by urban layout and meteorological conditions such as wind. Urban morphology denotes the physical and spatial configuration of a city, including the arrangement of buildings, streets, public spaces, infrastructure, and vegetation (L\u0026eacute;vy 2005). This spatial organization results from historical, cultural, geographical, and political dynamics and varies significantly from one urban area to another, and even between neighborhoods (CERTU 2007). It influences not only the functionality and aesthetics of urban spaces but also their environmental performance, particularly regarding local climate and air quality (He et al. 2020).\u003c/p\u003e \u003cp\u003eUrban geometry\u0026mdash;particularly the height-to-width ratio (H/W), facade alignment, and the sky view factor (the portion of visible sky at a given point)\u0026mdash;directly affects turbulence, ventilation, and solar radiation penetration. Indeed, the way buildings are arranged, their height, street width, and the presence of trees or water bodies determine how air circulates through streets, thus affecting pollutant dispersion or accumulation. In contemporary cities, confined spaces form that reduce air renewal, leading to local concentrations of NO₂, PM₁₀, and PM₂.₅ well above regional averages (Kastner-Klein 2004; Vardoulakis 2003).\u003c/p\u003e \u003cp\u003ePollution dispersion dynamics are particularly complex in city centers, where building layouts are often irregular and urban activity is intense. These areas also tend to be \u0026ldquo;pollution hotspots\u0026rdquo; of vehicle emissions, further increasing pollutant concentrations (Ilie 2023; Lagmiri and Dahech 2024).\u003c/p\u003e \u003cp\u003eStudying these phenomena presents significant methodological challenges. Understanding the interactions between urban morphology, local meteorological conditions, and atmospheric chemistry requires a multidisciplinary and multi scale approach. Numerical models (CFD, microscale dispersion models) enable the simulation of idealized air circulation scenarios based on urban geometry, but require detailed data on materials, prevailing winds, and emissions, and must be validated by field measurements (Moussafir 2013; Ghafghazi and Hatzopoulou 2015; Mohammedi et al. 2020).\u003c/p\u003e \u003cp\u003eMeanwhile, in situ measurement campaigns\u0026mdash;using fixed or mobile sensors\u0026mdash;capture the real temporal and spatial variability of pollutant concentrations. However, they are limited by spatial coverage and by the difficulty of distinguishing the effects of morphology from those of meteorological conditions or traffic. In this context, studies combining field measurements, urban geometry, and modeling emerge as the most promising way to understand the effects of urban form on air pollution.\u003c/p\u003e \u003cp\u003eGiven these findings, it is necessary to place urban morphology at the heart of air quality considerations, not only as a constraint but also as an opportunity for action. Many authors thus advocate integrating environmental performance indicators into urban planning tools, such as air permeability indices, optimal H/W ratios, or greening indices (Maignant 2007; Su 2008; Badach 2022). Urban planning could then use this knowledge to design healthier, more breathable, and more resilient neighborhoods in the face of contemporary environmental challenges.\u003c/p\u003e \u003cp\u003eThis article adopts this perspective by examining the effects of urban morphology on air quality through an analysis combining geometric, environmental, and atmospheric data. The main objective is to identify which urban configurations promote pollutant dispersion by analyzing differences between neighborhoods characterized as open, channeled, or sheltered. This analysis aims to answer two key questions: What are the most influential morphological characteristics on pollutant concentration or dilution? And how do microclimatic variations caused by urban morphology affect their spatial distribution?\u003c/p\u003e \u003cp\u003eStudies comparing pollution differences among various pollutant types in neighborhoods sharing the same living space but featuring diverse architectural structures remain rare. Within this framework, the city center of Cergy, composed of three contiguous neighborhoods with distinct urban characteristics, was selected as the study site. The analysis focuses on three major pollutants: nitrogen dioxide (NO₂), fine particulate matter (PM₂.₅), and suspended particulate matter (PM₁₀). The spatial distribution of pollutants was explored through a series of measurements taken at different strategic points. A comparative method was developed to evaluate pollution levels according to various urban parameters, revealing distinctions between areas near parks, spaces between buildings, and roadways. The influence of wind regimes on pollution levels was also analyzed. Furthermore, temporal variations in pollutant concentrations were studied in relation to factors such as traffic and meteorological conditions. Finally, NO₂ concentration modeling was performed, providing an analytical framework to better understand pollutant dynamics in urban environments.\u003c/p\u003e\n\u003ch3\u003eStudy area\u003c/h3\u003e\n\u003cp\u003eThe city center of Cergy (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) has developed through several phases of evolution, resulting in a mosaic of contiguous urban blocks with varied characteristics. Since 2016, the area has undergone numerous developments aimed at establishing it as the true heart of the Cergy-Pontoise agglomeration. These transformations have been accompanied by an increase in building heights and the progressive infill of interstitial spaces.\u003c/p\u003e \u003cp\u003eThe Cergy city center unfolds in a quadrilateral shape and consists of main roads encircling the center. Their widths range from 29 to 33 m on the northwest and southeast sides, with a narrower section of 14 m on the southwest side near the park. The A15 motorway, with an average width of 51 m, delineates the area to the northeast. Furthermore, the center plays a strategic role as a major transit hub, supported by an especially dense public transport network that facilitates mobility within Cergy and connections to other municipalities and regions. This transit position, combined with urban developments, raises significant concerns related to air quality while offering diverse environments and experiences. Successive interventions over different periods have shaped the neighborhoods in the city center, each with distinct morphologies often defined by major road axes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These neighborhoods can be categorized into three main areas:\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; The Slab District\u003c/h2\u003e \u003cp\u003eCharacterized by buildings constructed on a raised slab, beneath which lie technical zones and parking spaces. The parcel system here shows notable irregularity, with parcels of varying sizes and shapes. Some parcels are dedicated to commercial and professional activities, exhibiting rectangular or chevron configurations. Residential areas account for 19% of built-up surfaces, featuring diverse architectural forms, including U-shaped, chevron, rectangular, and circular configurations. The road network in this neighborhood is entirely pedestrianized, with narrow, interconnected streets whose Landsberg indices range from 1.8 to 3.7. Vehicular traffic is concentrated beneath the slab, except for an elevated road 15 meters wide, connecting the neighborhood to the main road bordering the city center.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e• The Park District\u003c/h3\u003e\n\u003cp\u003eDistinguished by its commitment to sustainability and environmental preservation. The parcel system is discontinuous, consisting of parcels shaped like halos, chevrons, and rectangles. The neighborhood is primarily served by the road bordering the block, with cul-de-sacs providing access to buildings. The internal road network consists exclusively of pedestrian pathways.\u003c/p\u003e\n\u003ch3\u003e• The Administrative District\u003c/h3\u003e\n\u003cp\u003eFeatures a more traditional layout, with large complexes built directly on the ground. It is organized with a generally uniform parcel system, consisting of U-shaped parcels that create a cohesive urban aesthetic. Roads here typically feature wide sidewalks, facilitating pedestrian movement, particularly along Oise Boulevard, the main artery traversing the neighborhood. This boulevard, 48 meters wide, includes lanes for vehicle traffic and designated bus lanes, and parking. The streets dividing parcels are of two types: two-way streets and one-way streets. Most roads follow a north-south orientation, with a slight tilt to the west or east. A two-way road, oriented east-west with a Landsberg index of 0.8, separates this neighborhood from the Elevated Slab Neighborhood.\u003c/p\u003e \u003cp\u003eThis structured layout reflects both the diverse urban forms and the complex dynamics of mobility and environmental concerns within the Cergy city center.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Data and methods","content":"\u003cp\u003eIn this research, we conducted a typomorphological analysis to identify the typological and morphological characteristics of the three districts (the slab district, the park district, and the administrative district). In climatology, this approach typically seeks to understand how the physical configuration of an urban space influences variations in climatic parameters. According to Panerai et al (1999), a typomorphological analysis includes examining the distribution patterns of buildings, construction density, urban porosity, and street orientation. This study analyzed these criteria for each urban district while also considering other relevant characteristics, such as vegetation density.\u003c/p\u003e \u003cp\u003eWe then cross-referenced this architectural information with pollution data and climatic parameters to assess the ability of each structural entity to either disperse or accumulate pollutants. The objective is to evaluate how this spatial diversity influences air quality variations and to identify the key factors driving this variability.\u003c/p\u003e \u003cp\u003eThe pollutants studied (NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and PM\u003csub\u003e2.5\u003c/sub\u003e) were measured using nine fixed \u0026ldquo;Ecosmart\u0026rdquo; sensors, installed on public lighting poles at a height of 3 meters. These sensors are calibrated with reference analyzers approved by the LCSQA (Central Laboratory for Air Quality Monitoring) and verified according to ISO 17025 standards. Each district has two to four measurement points, selected to allow a comparative study of air quality variations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These points were carefully chosen to represent the different urban configurations unique to each district. The spatial characteristics associated with each measurement point are listed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe pollution sensors were installed between March 2023 and February 2024 to cover a wide range of meteorological conditions, which significantly influence the spatial and temporal variability of pollutants. However, due to a communication outage between May and September 2023, we were only able to collect 5,112 hours of data for this study, representing 58% of the deployment period.\u003c/p\u003e \u003cp\u003eA key element in assessing pollutant concentrations is the measurement of atmospheric pressure, as well as wind speed and direction, recorded at a 30-minute time interval. In this section, data for these climatic parameters come from the Davis weather station installed on the roof of a building in the slab district.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this study, we also carried out a modeling system using an inductive method based on multiple regression applied to the administrative district. In this observation area, the sampling of NO\u003csub\u003e2\u003c/sub\u003e pollution was established from traveling measurements (by Ecosmart sensor). These measurements were programmed exclusively in winter (from February 3 to 5 and from February 14 to 17, 2024) in stable weather and during the morning traffic peak with a 5-minute stop at each sampling point. At the same time, information on weather conditions (T\u0026deg;, H% and wind) was collected along the route using a thermal probe and an anemometer type Testo 400. Over seven days of measurements, the average behavior of the parameters collected (pollutants, meteorological parameters, and car flow) was carried out for 43 observation points, spaced on average nearly 40m apart (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Explanatory parameters were collected at each measurement point (types of voices, presence of traffic lights, distance between measurement points and roads or intersections, orientation of streets relative to the prevailing wind, width of streets, height of buildings, and degree of street encasement). In addition, our approach included data on the types of screens along the arteries, whether wall-mounted, vegetal, mixed, or open. All the steps followed to carry out the statistical modeling by linear regression are detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExploring Building Density\u003c/h2\u003e \u003cp\u003eBuilding density is assessed by the ratio between the total footprint of buildings and the area of ​​the urban block on which they are located, multiplied by the average number of levels. Mathematically, this relationship is formulated as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Building\\:density=\\left(\\:\\frac{total\\:footprint\\:of\\:buildings}{total\\:area\\:of\\:the\\:urban\\:block\\:}\\right)x\\:Average\\:number\\:of\\:levels$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the results of density calculations for the three studied districts, revealing three distinct levels of density in Cergy's city center. With a built density of 3.5, the slab district shows the highest value, primarily due to the presence of a large shopping center with a significant footprint and the compact arrangement of buildings along narrow streets.\u003c/p\u003e \u003cp\u003eIn comparison, the administrative district, despite having a similar average building height, adopts a distinct urban organization. Its layout incorporates open spaces at the center of each residential block, designated for gardens or playgrounds. This configuration, allowing nine parcels to benefit from open sky exposure, results in a more moderate built density of 1.59.\u003c/p\u003e \u003cp\u003eAs for the park district, although its total area is comparable to that of the slab district, it exhibits the lowest built density, with an index of 0.55. This low density is attributed to the smaller building footprints and slightly lower average building heights. This architectural choice emphasizes the preservation of green spaces, which overwhelmingly dominate the district's landscape.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValue of the surface area and building density in the three districts studied (data calculated using ArcGis)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlab District\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdministrative District\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePark District\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding footprint (hectares)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage number of levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal area of ​​the urban block (hectares)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBuilding density\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cb\u003eExploration of vegetation density\u003c/b\u003e\u003c/div\u003e \u003cp\u003eVegetation density is expressed as the ratio between the total plant area and the total area of ​​the urban block studied:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eVegetation density =( Total plant area /\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:total\\:area\\:of\\:the\\:urban\\:block\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e​)\u0026times;100.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIt represents a key indicator of urban texture, influencing wind patterns and thermal exchanges between the ground and the atmosphere. The results of vegetation density assessments in the three districts are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn the park district, nearly half of the area is covered by vegetation, corresponding to a vegetation index of 48%. This high vegetation density creates a microclimatic effect, fostering the development of local park breezes and influencing the depth of the urban boundary layer.\u003c/p\u003e \u003cp\u003eIn contrast, green spaces and trees are scarce in the slab district, with a vegetation density of only 3.7%. This low value reflects urban planners' and architects' lack of attention to green spaces, prioritizing the construction of an elevated urban unit to separate traffic flows from pedestrian areas. The inherent constraints of slab architecture also make it difficult to integrate green spaces, limiting opportunities for tree planting.\u003c/p\u003e \u003cp\u003eIn the administrative district, vegetation density is calculated at 16%. This value is due to the presence of a small public garden, green spaces at the entrances of residences, and rows of street trees. Although this proportion is not yet optimal, it demonstrates a deliberate effort by developers to incorporate natural elements into the urban architecture and move away from the slab district model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValue of the surface area and vegetation density in the three districts studied\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlab District\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdministrative District\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePark District\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant surface (hectares)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal area of ​​the urban block (hectares)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVegetation Density (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eExploring urban porosity\u003c/h3\u003e\n\u003cp\u003eThe calculation of urban porosity is based on the system of open spaces. The equation used for this calculation is as follows:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eUrban porosity =( Free or open space /\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:total\\:area\\:of\\:the\\:urban\\:block\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e)\u0026times;100.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the slab district, urban porosity reaches 31% (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Since this neighborhood is not homogeneous, it is possible to distinguish two units based on the type of development. On one hand, there is a historic unit of 3.46 hectares, characterized by a more enclosed organization with canyon-like streets. This area consists of buildings arranged in blocks, with flat fa\u0026ccedil;ades aligned along narrow alleys. Urban porosity in this historic section is limited to 11.8%, allowing air to circulate only through ventilation corridors. On the other hand, the second unit is designed to offer a more open view of the sky, notably due to its public squares. Porosity in this zone reaches 19.2%.\u003c/p\u003e \u003cp\u003eThe layout of the administrative district, mainly composed of chevron-shaped parcels, creates a unique arrangement that generates open spaces, thereby increasing urban porosity. This configuration facilitates the formation of ventilation zones with a mesh-like effect, influencing how air penetrates through the block. Furthermore, the wide streets and main axes within this urban fabric play a key role in creating wind corridors. Urban porosity calculated for this area reaches 45%, reflecting the effectiveness of the urban design in promoting natural ventilation within a downtown neighborhood.\u003c/p\u003e \u003cp\u003eIn the park district, urban porosity is 54%. The presence of a large open space plays a crucial role in facilitating smooth airflow. The dominant vegetation in this area also contributes to wind infiltration. Besides protecting against strong winds, the trees enable gentle and consistent ventilation through their foliage. Moreover, air circulation around the neighborhood is enhanced by connecting channels that link the interior and exterior of the park.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValue of surface area and urban porosity in the three districts studied\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlab District\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdministrative District\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePark District\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree or open space (hectares)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal area of ​​the urban block (hectares)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eurban porosity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e31%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e45%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e54%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of air quality and morpho-structural dependency indicators\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eCorrelation with atmospheric pressure\u003c/h2\u003e \u003cp\u003eThe objective of this analysis is to confirm the hypothesis that correlations exist between atmospheric pressure and pollutant dispersion. We examine the distribution of pollution concentrations in each district according to atmospheric pressure to identify the degree of dependency between these variables. The results of this analysis are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eA generally positive correlation between atmospheric pressure and pollutant concentrations (PM\u003csub\u003e10\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e) is observed in the three districts studied. The anticyclonic regime, with pressure above 1013 hPa, promotes air stability and thermal inversions, which hinder the vertical dispersion of pollutants and increase their concentrations. Conversely, low pollutant concentrations are associated with low-pressure systems, often accompanied by precipitation and ventilation, which disperse particles and cleanse the boundary layer. However, the low determination coefficients (ranging from 0.03 to 0.1) found in the three neighborhoods indicate a complex and nonlinear relationship between pollutants and atmospheric pressure. Sometimes, low atmospheric pressure coincides with high pollutant concentrations. This may be due to heating emissions and the complex interaction between wind speed and urban morphology.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation with wind speed\u003c/h2\u003e \u003cp\u003eAs with atmospheric pressure, we studied the distribution of NO₂ concentrations as a function of wind speed. The conclusions of our analysis are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. We observe a significant and negative correlation between wind speed and NO₂ concentrations in the three districts studied, as evidenced by the coefficient of determination (R\u0026sup2;) and the regression curve. NO₂ accumulates under calm weather conditions and is more effectively removed at higher wind speeds.\u003c/p\u003e \u003cp\u003eWe also calculated the specific correlation coefficients for each district, which are 0.5, 0.58, and 0.64 for the slab, administrative, and park districts, respectively. These results are consistent with previous research, notably that of Han et al (2015) in Shinguai, who found negative correlation coefficients ranging from 0.51 to 0.61, close to our findings. The studies by Zhou et al (2020) in Beijing and Nanjing and Radović et al (2022) in Bijeljina also confirm this trend with correlation coefficients between 0.4 and 0.56.\u003c/p\u003e \u003cp\u003eRegarding particulate pollution, the correlation with wind speed is less pronounced due to the diversity of sources influencing particle levels. This suggests that gaseous pollutants are more easily diluted by wind than particulate matter.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWind remains strongly linked to building density. Therefore, we cross-referenced our data with indicators of the urban morphology of each district. We identified two indicators that significantly influence wind patterns: porosity and building density. Street orientation could also be included in this analysis, but it would require in-depth studies on the angle of wind penetration for each location and time of day.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that low building density and a high percentage of porosity are associated with a stronger correlation between wind and NO₂ pollution. These indicators reflect an increase in areas open to wind flow. In districts where the density of surrounding barriers is low, such as the park district, NO₂ concentrations decrease as wind speed increases, highlighting the positive impact of ventilation on pollutant dispersion.\u003c/p\u003e \u003cp\u003eConversely, the denser and more enclosed the barriers, as is the case in the slab district, the less effective strong winds are in dispersing pollutants, with a reduction in efficiency of approximately 22%. Indeed, low porosity combined with high building density reduces the incident wind speed within the district, thereby preventing pollutants from dissipating.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCross-analysis of the correlation index between wind and pollution (NO₂) with the morphological indices of the studied districts.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlab district\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdministrative district\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePark district\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrelation index between wind and NO₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePorosity index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding density index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eComparison of daily pollution variations\u003c/h2\u003e \u003cp\u003eThe daily variations of NO₂ concentrations established for each district reveal low concentrations around midday and high concentrations when the mixing layer thins. Under stable weather conditions, NO₂ concentrations appear to be more influenced by microscale phenomena, where atmospheric characteristics allow urban and human factors to exert their effects. The administrative district shows the highest concentrations with two clear NO₂ peaks (one at 9 a.m. and another at 5 p.m.) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). However, around midday, this neighborhood has lower concentrations than the park district, due to convective movements transporting air to higher altitudes. These movements also occur in the park district but are less intense because of the vegetation cover. This neighborhood, lacking significant nitrogen oxide sources within it, exhibits concentration levels that tend to remain relatively stable over time.\u003c/p\u003e \u003cp\u003eThe slab district generally follows the temporal pattern observed in the administrative district, except for the 5 p.m. peak, which is shifted to 8 p.m. Although the circadian rhythm is identifiable, concentrations remain lower overall.\u003c/p\u003e \u003cp\u003eUnder disturbed weather conditions, the difference in NO₂ concentrations between the three districts narrows compared to stable conditions, especially between the park and administrative districts, where wind mixing strongly influences throughout the day. The only significant difference occurs between 4 p.m. and 8 p.m., linked to intense vehicle traffic in the administrative district during that period.\u003c/p\u003e \u003cp\u003eRegarding particulate pollution, the peak for all three districts generally occurs between 9 a.m. and 10 a.m., regardless of weather type. Stable weather conditions are associated with higher daytime concentrations. This increase is caused by recycling particles contained within the nocturnal layer from the previous day, convective movements, and emissions from construction sites and daytime traffic. Conversely, during disturbed periods, a relative stability in particulate concentrations is observed throughout the day, with mesoscale atmospheric conditions being the main driver of this pattern.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of morphological indicators and pollutant dispersion patterns in each urban fabric\u003c/h2\u003e \u003cp\u003eTo deepen our analysis of the impact of architectural configurations, we examined the variations in pollution levels at each measurement point, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. We specifically selected the low-pressure period (without rain), during which wind plays a crucial role in highlighting the effect of urban geometry. During this period, the wind was on average moderate to strong, with a predominant direction from the southwest.\u003c/p\u003e \u003cp\u003eIn the administrative district, point 3, located on an avenue characterized by a Landsberg index of 0.8, shows the lowest average concentration (43 ppb) due to its NE/SW alignment parallel to the prevailing wind. In contrast, the nearby street (point 4), oriented NW/SE (perpendicular to the wind), exhibits a higher average concentration (50 ppb), despite being less trafficked and connected to the courtyard of a residence, which increases its openness to the sky. This demonstrates that when the wind direction is parallel to the street (0\u0026deg;), the main mode of atmospheric pollution transport within the street canyon is horizontal, parallel to the street, thereby promoting NO₂ dispersion. However, when the wind direction is perpendicular to the street (90\u0026deg;), vortices form within the street canyon, and NO₂ is primarily transported vertically with relatively low diffusion efficiency. Oise Boulevard (point 6), with a Landsberg index of 0.5, wider than the other streets and aligned perpendicularly to the prevailing winds, shows an average concentration (50 ppb) comparable to that of point 4. However, its first quartile is lower while its third quartile is higher. These concentrations are mainly attributable to the traffic emission patterns and accumulation due to the southwest wind direction, as the station is located near the leeward wall. At the highway station (point 5), the highest concentration was expected. However, its concentrations are quite similar to those at Oise Boulevard, due to the open sky exposure of its location and the dispersion of gaseous emissions away from the station by the southwest wind.\u003c/p\u003e \u003cp\u003eThe influence of sky exposure and street orientation relative to the wind is also evident in the slab district. The street canyon (point 1), oriented NW/SE and at a 45\u0026deg; angle to the prevailing winds, shows higher concentrations compared to point 2. The latter has a higher sky view factor and its courtyard axis is parallel to the prevailing wind, which favors pollutant dispersion.\u003c/p\u003e \u003cp\u003eIn the park district, the two stations inside the park (points 8 and 9) show similar NO₂ concentrations (49 ppb on average). The street oriented N/S at point 7, however, has the lowest average concentration (42 ppb). This street does not form a canyon but rather features two asymmetrical and discontinuous facades, providing multiple air passage channels between buildings. These openings limit air confinement, which can effectively reduce gaseous pollution levels in the street.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eParticulate pollutants interact with wind direction as significantly as gaseous pollutants. The lowest concentrations are recorded at Point 3 due to effective ventilation (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). However, this site shows the highest averages (46 \u0026micro;g/m\u0026sup3; for PM\u003csub\u003e10\u003c/sub\u003e and 56 \u0026micro;g/m\u0026sup3; for PM\u003csub\u003e2.5\u003c/sub\u003e), primarily due to extreme concentrations observed when particles are transported by wind during active construction along this axis.\u003c/p\u003e \u003cp\u003eThe site near Oise Boulevard (Point 6), close to construction zones, exhibits the lowest averages (34 \u0026micro;g/m\u0026sup3; for PM\u003csub\u003e10\u003c/sub\u003e and 20 \u0026micro;g/m\u0026sup3; for PM\u003csub\u003e2.5\u003c/sub\u003e) due to its perpendicular orientation to the wind, which blocks the intrusion of emissions from the construction sites. The recorded concentrations mainly result from the vortex recirculation of locally emitted particles. This same mechanism is responsible for the concentrations at Point 4, although higher concentrations are observed there (averaging 39 \u0026micro;g/m\u0026sup3; for PM\u003csub\u003e10\u003c/sub\u003e and 25 \u0026micro;g/m\u0026sup3; for PM\u003csub\u003e2.5\u003c/sub\u003e) because the station is located near an alignment of trees emitting biogenic pollutants. These biogenic emissions might also contribute to the elevated concentrations at the highway-adjacent site, in addition to traffic-related emissions (averaging 41 \u0026micro;g/m\u0026sup3; for PM\u003csub\u003e10\u003c/sub\u003e and 24 \u0026micro;g/m\u0026sup3; for PM\u003csub\u003e2.5\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003eIn the slab district, Point 1 records higher PM concentrations (51 \u0026micro;g/m\u0026sup3; for PM\u003csub\u003e10\u003c/sub\u003e and 33 \u0026micro;g/m\u0026sup3; for PM\u003csub\u003e2.5\u003c/sub\u003e on average) than Point 2 (averaging 43 \u0026micro;g/m\u0026sup3; for PM\u003csub\u003e10\u003c/sub\u003e and 27 \u0026micro;g/m\u0026sup3; for PM\u003csub\u003e2.5\u003c/sub\u003e). This difference is again attributed to the angle of wind penetration, as observed for NO₂.\u003c/p\u003e \u003cp\u003eIn the park district, the mechanism differs. The park center (Point 8), which is well-ventilated, shows higher PM concentrations (32 \u0026micro;g/m\u0026sup3; for PM\u003csub\u003e10\u003c/sub\u003e and 20 \u0026micro;g/m\u0026sup3; for PM\u003csub\u003e2.5\u003c/sub\u003e on average) due to the predominance of vegetative substrate. Conversely, Point 9 records the lowest PM concentrations (23 \u0026micro;g/m\u0026sup3; for PM\u003csub\u003e10\u003c/sub\u003e and 15 \u0026micro;g/m\u0026sup3; for PM\u003csub\u003e2.5\u003c/sub\u003e on average), in contrast to NO₂ findings. This can be attributed to the halo-shaped buildings effectively blocking the entry of particulate pollutants, though their impact on gaseous pollutants is less pronounced. Point 7, closer to traffic, also shows lower concentrations than the park center due to the presence of ventilation channels that facilitate particle dispersion (27 \u0026micro;g/m\u0026sup3; for PM\u003csub\u003e10\u003c/sub\u003e and 16 \u0026micro;g/m\u0026sup3; for PM\u003csub\u003e2.5\u003c/sub\u003e on average).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eModeling the impact of urban planning on pollution distribution\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eCorrelation between fixed and mobile measurements\u003c/h2\u003e \u003cp\u003eThe objective of this analysis is to validate the correspondence between NO₂ measurements obtained through mobile monitoring and those collected via fixed monitoring. This step is essential for assessing the reliability of mobile data before incorporating it into the modeling phase. To achieve this, a comparison was made using mobile data collected near the fixed sensors. The determination coefficient obtained (R\u0026sup2; = 0.72) indicates a significant agreement between the two datasets (Fig.\u0026nbsp;10). This result confirms that mobile measurements, conducted with a 5-minute stop at each sampling point, are generally reliable and consistent with fixed-mode measurements.\u003c/p\u003e \u003cp\u003eThe average discrepancies observed, approximately 17 ppb, can be attributed to two factors:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSensor response time\u003c/b\u003e: In mobile mode, the sensor is exposed to rapid variations in environmental conditions, which may affect the stabilization of measurements compared to fixed mode.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePositioning difference\u003c/b\u003e: Mobile measurements are taken at a height of about 1.5 meters, whereas fixed measurements are conducted at 3 meters. This height difference can lead to concentration discrepancies due to varying exposure to emissions.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eOverall, these discrepancies align with expectations and do not significantly compromise the reliability of mobile measurements.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eOutputs of the multiple linear regression analysis\u003c/h2\u003e \u003cp\u003eAfter several attempts to optimize the regression model, a coefficient of determination (R\u0026sup2;) of 0.68 was achieved, indicating that 68% of the data variance is explained by the model. This result aligns reasonably well with the state of the art (see Deletraz 2002, for example).\u003c/p\u003e \u003cp\u003eThe analysis of metric bases related to error and robustness confirms the model's reliability. The RMSE, DW, and PC values are 26, 2.2, and 0.4, respectively. This result suggests that for the 43 points analyzed, the estimated data is, on average, close to the actual values. This is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003e, showing a low dispersion of simulated and actual values around a straight line.\u003c/p\u003e \u003cp\u003eAmong the tested variables, three were selected: road width, distance to intersections, and the number of vehicles.\u003c/p\u003e\u003cp\u003eUnsurprisingly, the number of vehicles shows a strong linear correlation with NO₂ concentrations (r\u0026thinsp;=\u0026thinsp;0.8), while the influence of road width and distance to intersections is less pronounced (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003e). Indeed, morning NO₂ peaks are primarily driven by traffic, making the other two variables less significant during these periods. Nevertheless, including them in the model notably improves the accuracy of the spatial distribution. For example, urban road width indirectly indicates the air\u0026rsquo;s capacity to dilute pollutants, while distance to intersections reflects the level of exposure to high concentrations. The sign of the correlation for these two variables is somewhat counterintuitive in the model. This result aligns with other studies, notably Abdmouleh (2024), conducted in Paris. The main difference with that study lies in the meteorological variables. Our model did not capture these because the data were generally homogeneous due to stable weather conditions (sampling was conducted under the same weather type).\u003c/p\u003e \u003cp\u003eThe constant of 50.1 in the model represents a theoretical baseline level that would be observed under ideal conditions\u0026mdash;i.e., far from intersections, on a very wide road, and with no traffic. This baseline level is lower than typical NO₂ values, such as the median and mean (73 and 69 ppb, respectively). The model suggests that the included variables are responsible for a substantial increase in NO₂ levels, around 19 to 23 ppb on average. The gap between the constant and typical values may indicate that the model effectively captures the variability in concentrations due to urban factors. However, it is also important to consider that other factors not included in the model, such as other pollution sources (industrial emissions, long-range transport), could influence NO₂ levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eResidual analysis\u003c/h2\u003e \u003cp\u003eThe mean of the residuals (1.12) is relatively close to zero, which is generally desirable in a fitted model. Additionally, the average standard deviation of the residuals is 8.1, which is much lower than that of the sample (25.7). Such a marked reduction in the residuals\u0026rsquo; standard deviation compared to that of the sample is a positive indicator of the model\u0026rsquo;s quality. However, 63% of the measurement points have residuals within \u0026plusmn;\u0026thinsp;25.7 ppb, while 37% show residuals exceeding this range, indicating that the model performs relatively poorly for some measurements.\u003c/p\u003e \u003cp\u003eThe residual analysis reveals deviations ranging from \u0026minus;\u0026thinsp;33.9 to 66.3 ppb, highlighting that the model can both underestimate and overestimate values (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003e). For example, the model overestimates concentrations in certain residential courtyards, with an average difference of 27 ppb compared to actual values (which average 15 ppb). Conversely, it underestimates concentrations in some public squares and streets, where actual concentrations average 89 ppb and differ from predicted values by an average of 46.5 ppb.\u003c/p\u003e \u003cp\u003e To address these prediction anomalies, improvements are necessary, such as incorporating additional variables not currently included in the model and increasing the sample size.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSpatial interpolation of NO₂ concentrations\u003c/h2\u003e \u003cp\u003eSpatial interpolation was performed using observed data, employing the Inverse Distance Weighting (IDW) method as the tool. This method is often preferred for mapping pollution due to its simplicity and effectiveness with unevenly distributed data. It is based on the principle that closer points have a greater influence on the interpolated values, which is relevant in the context of pollution, where geographic proximity is a key factor. IDW also allows adjusting the weight of neighboring points through an exponent parameter, providing flexibility and adaptability. Moreover, it does not require assumptions about the data distribution, thus simplifying the analysis.\u003c/p\u003e \u003cp\u003eThis interpolation enabled the identification of hotspots within the neighborhood (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e14\u003c/span\u003e). It shows that intersections are problematic in terms of exposure. These locations are often prone to frequent traffic jams, as was the case during our morning peak measurements. Pollution concentrations decrease progressively with distance from these intersections.\u003c/p\u003e \u003cp\u003ePollution along the Oise Boulevard is mainly due to bus stops, the intersection itself, and the sheltered effect of the area. This interpolation method also highlighted that narrow streets tend to have lower concentrations, which can be explained by a lower traffic volume. It further shows that residential courtyards experience less exposure to pollution during the morning peak.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study explored the relationship between urban morphology and air quality. The objective was to better understand the various elements of urban texture that influence concentrations of NO₂, PM\u003csub\u003e10\u003c/sub\u003e, and PM\u003csub\u003e2.5\u003c/sub\u003e pollutants at a fine spatial scale. Urban texture was examined through a set of commonly used indicators describing the form and distribution of building clusters, including building density, urban porosity, and street orientation. Additionally, an indicator of vegetation texture, represented by vegetation density, was included. Although this selection of indicators is narrower than that used in some comparable studies (\u0026Ouml;zsoy 2017; Wang et al. 2024), their combination offers a synthetic and relevant view of the urban fabric\u0026rsquo;s morphology. It allows for a direct assessment of the degree and nature of resistance exerted by the urban canopy against pollution dispersion.\u003c/p\u003e \u003cp\u003eThe results highlight the importance of favoring less dense and better-ventilated urban configurations, contrasting with large-scale studies such as Clifton et al (2008), which emphasized the environmental benefits of compact and mixed-use forms. Our findings reveal that pollutant distribution patterns for NO₂, PM\u003csub\u003e10\u003c/sub\u003e, and PM\u003csub\u003e2.5\u003c/sub\u003e are influenced by the interaction between wind-driven transport and urban texture. In districts where open spaces are abundant, NO₂ concentrations decrease as wind speed increases, underscoring the beneficial effect of ventilation on pollutant dispersion. Conversely, in areas with many obstacles close together, such as the slab district, ventilation loses about 22% of its effectiveness in dispersing pollutants.\u003c/p\u003e \u003cp\u003eRegarding particulate pollution, the correlation with wind speed is less pronounced due to the variety of sources affecting particle levels. However, a closer look at PM distribution shows that urban morphology continues to influence concentrations. In densely built and enclosed neighborhoods, the airflow\u0026rsquo;s ability to evacuate pollutants is restricted. Consequently, locally emitted particles remain trapped longer, hindering dispersion and increasing air pollution in these confined spaces. In contrast, less dense neighborhoods generally exhibit lower concentrations despite active particle emissions, attributable to better self-cleaning capacity associated with higher porosity and lower building density. These observations support findings by Yu (2023), who demonstrated that high building densities in urban centers lead to increased PM\u003csub\u003e2.5\u003c/sub\u003e pollution levels.\u003c/p\u003e \u003cp\u003eThe urban canopy\u0026rsquo;s resistance to pollutant dispersion was also analyzed from the perspective of street morphology. Several studies have shown that dispersion processes are closely linked to street orientation and arrangement relative to prevailing winds (Kim 2004; Lv 2021; Yang 2023). This study observed that elevated concentration levels in narrow alleys are mainly due to reduced dispersion capacity rather than increased emissions. In street canyons, vertical exchange and horizontal transport are the main modes of atmospheric pollution movement. Streets aligned parallel to the ambient wind promote pollutant dispersion through a channeling effect, while perpendicular streets generate vortices that trap pollutants and locally increase concentrations. These observations are consistent with the work of Kastner-Klein et al (1999), who demonstrated a tendency for pollution levels to rise as the angle between wind direction and street axis increases from 0\u0026deg; to 90\u0026deg;. These results highlight the importance of considering street orientation relative to prevailing winds in urban planning. Thoughtful design can improve pollutant dispersion and thus air quality in urban environments.\u003c/p\u003e \u003cp\u003eTemporally, this study examined the effects of urban morphology on ambient air pollution on a daily scale, highlighting dynamics specific to different times of day. The results emphasized the role of local emissions in ranking urban fabrics by pollution intensity while revealing a certain consistency in the circadian rhythm of pollutant levels. PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations peak in the morning, primarily due to the specific dynamics of the urban boundary layer at this time and morning traffic congestion. The highest NO₂ concentrations occur during rush hours, coinciding with commuter travel times and increasing pedestrian exposure to pollutants. In light of these observations, it would be advisable for pedestrians to adopt strategies to minimize exposure during these critical periods, such as adjusting travel schedules, changing routes, or implementing protective measures, especially for vulnerable groups.\u003c/p\u003e \u003cp\u003eFinally, while some conclusions were drawn regarding the role of urban morphology in pollution, it is clear that fixed sensors are not well suited for studying spatial variability. Our analysis thus shifted toward a modeling approach based on a panel of mobile measurements and morphological and functional indicators of urban space. The resulting model shows reasonable statistical robustness, but further improvements could enhance its predictive capacity. Its application to other neighborhoods remains uncertain unless it is first refined and validated on additional sites. These points suggest that further research is needed to improve both the precision and generalizability of the model.\u003c/p\u003e \u003cp\u003eMoreover, data from mobile monitoring represent a particularly suitable source for mapping air quality with high spatial precision. This approach enables the spatialization of pollution, which is difficult to achieve with fixed sensors. In this study, mapping conducted under stable morning conditions in the tested neighborhood revealed zones of high concentration, identified as hotspots. These findings corroborate previous work, such as that of Peters et al (2013), Van den Bossche et al (2015), and Idir et al (2021), which highlighted the effectiveness of repeated mobile measurement campaigns in analyzing spatial variability of pollutants in various urban environments.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlighted the quantitative and qualitative relationship between neighborhood morphology and the distribution of pollutant concentrations. Initially relying on an approach based on fixed sensors, it identified several crucial aspects to better understand the interactions between urban planning and pollutant levels. Physical characteristics such as built density, porosity, and vegetation cover were found to be key factors influencing pollutant dispersion. The results show that densely built areas amplify pollution, whereas open spaces promote pollutant dissipation. The study also revealed the influence of street orientation relative to the prevailing wind and the sky view factor on pollutant concentrations.\u003c/p\u003e \u003cp\u003eFurthermore, the study emphasizes the limitations of fixed sensors for fine spatial mapping of pollution, while mobile measurements prove their relevance for high-resolution mapping. By identifying local hotspots and exploring interactions between urban structures and pollutant dynamics, this approach opens promising avenues for developing decision-support tools in urban planning and air quality management.\u003c/p\u003e \u003cp\u003eFrom a methodological standpoint, the approach adopted, which combines quantitative and qualitative data with statistical and geospatial analyses, proved particularly well-suited to exploring the complex relationships between urban morphology and air quality. This methodology also has the advantage of being easily transferable to other urban contexts. By adapting it to local specificities, it could enable a better understanding of the mechanisms influencing pollutant distribution and guide public policies toward more effective and targeted solutions.\u003c/p\u003e \u003cp\u003eLooking ahead, future research could incorporate environmental parameters such as temperature, humidity, and wind characteristics at each measurement point to further refine the understanding of the relationships between urban morphology and pollution. Such efforts would contribute not only to combating air pollution but also to improving urban microclimates, thereby offering healthier, more resilient, and sustainable environments for urban populations.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest. The sponsors had no role in the design, execution, interpretation, or writing of the study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the DIM Qi2 program of the \u0026Icirc;le-de-France Region, the technical and financial support of the Cergy-Pontoise Conurbation, and the French Ministry of Higher Education and Research via the \u0026ldquo;Social Sciences\u0026rdquo; doctoral school (ED 624) (Paris Cit\u0026eacute; University).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, S.L. and S.D.; Methodology, S.L.; Software, S.L.; Validation, S.D.; Formal Analysis, S.L.; Investigation, S.L.; Resources, S.L. and S.D.; Data Curation, S.L.; Writing\u0026mdash;Original Draft Preparation, S.L.; Writing\u0026mdash;Review \u0026amp; Editing, S.L. and S.D.; Visualization, S.L and S.D; Supervision, S.D.; Project Administration, S.L.; Funding Acquisition, S.L. and S.D. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e \u003cp\u003eThe data presented in this study are available on request from the corresponding author. The data are not publicly available due to file sizes.\u003c/p\u003e"},{"header":"Tables","content":"\u003col\u003e\n\u003cli\u003e\u003cstrong\u003eAbdmouleh M\u003c/strong\u003e (2023). Pollution de l\u0026rsquo;air et sonore dans Paris \u0026agrave; l\u0026rsquo;\u0026eacute;chelle intra-urbaine : r\u0026eacute;partition spatio-temporelle pendant et en dehors de la p\u0026eacute;riode de confinement du COVID-19 dans le XIII\u0026egrave;me arrondissement. Th\u0026egrave;se de doctorat, Universit\u0026eacute; Paris Cit\u0026eacute;. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBadach\u003c/strong\u003e \u003cstrong\u003eJ\u003c/strong\u003e, Szczepanski J, Bonenberg W, G˛ebicki J, Nyka L (2022). Developing the Urban Blue-Green Infrastructure as a Tool for Urban Air Quality Management. Sustainability 2022, 14, 9688. https://doi.org/ 10.3390/su14159688.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBen\u003c/strong\u003e \u003cstrong\u003eRomdhane S\u003c/strong\u003e (2017). Effets du climat et de la pollution de l\u0026apos;air sur la sant\u0026eacute; respiratoire \u0026agrave; Tunis. G\u0026eacute;ographie. Th\u0026egrave;se de doctorat, Universit\u0026eacute; Paris Cit\u0026eacute;. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCERTU\u003c/strong\u003e (2007). La forme urbaine et l\u0026apos;enjeu de sa qualit\u0026eacute;\u0026quot; Lavoisier \u0026eacute;ditions, 91 p.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eClifton\u003c/strong\u003e K, Ewing R, Knaap GJ, Song Y ( 2008 ).Quantitative analysis of urban form: A multidisciplinary review. J. Urban. Int. Res. Placemaking Urban Sustain, 1, pp. 17 \u0026ndash; 45. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDedoussi\u003c/strong\u003e I.C., Eastham S.D., Monier E. et al. (2020). Premature mortality related to United States cross-state air pollution. Nature 578, 261\u0026ndash;265. https://doi.org/10.1038/s41586-020-1983-8.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDeletraz G\u003c/strong\u003e (2002). G\u0026eacute;ographie des risques environnementaux li\u0026eacute;s aux transports routiers en montagne: Incidences des emissions d\u0026rsquo;oxydes d\u0026rsquo;azote en vall\u0026eacute;es d\u0026rsquo;Aspe et de Biriatou (Pyr\u0026eacute;n\u0026eacute;es). PhD Thesis, Universit\u0026eacute; de Pau et des Pays de l\u0026rsquo;Adour.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eGhafghazi\u003c/strong\u003e G., Hatzopoulou M (2015). Simulating the air quality impacts of traffic calming schemes in a dense urban neighborhood, Transportation Research Part D: Transport and Environment, 35, 11-22. https://doi.org/10.1016/j.trd.2014.11.014.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHan\u003c/strong\u003e L, Zhou W, Li W, Meshesha D.T, Li L, \u0026amp; Zheng M (2015). Meteorological and urban landscape factors on severe air pollution in Beijing. Journal of the Air \u0026amp; Waste Management Association, 65, 782\u0026ndash; 87p.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHe\u003c/strong\u003e B, Ding L, \u0026amp; Prasad D.K (2020). Relationships among local-scale urban morphology, urban ventilation, urban heat island and outdoor thermal comfort under sea breeze influence. Sustainable Cities and Society, 60, 102-289p. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHost\u003c/strong\u003e S., Chatignoux E., Leal C., Gr\u0026eacute;my I (2012). Exposition \u0026agrave; la pollution atmosph\u0026eacute;rique de proximit\u0026eacute; li\u0026eacute;e au trafic : quelles m\u0026eacute;thodes pour quels risques sanitaires ? [Health risk assessment of traffic-related air pollution near busy roads]. Rev Epidemiol Sante Publique. 60(4):321-30. doi: 10.1016/j.respe.2012.02.007. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eIdir\u003c/strong\u003e Y. M, Orfila O, Judalet V, Sagot B, \u0026amp; Chatellier P (2021). Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics. \u003cem\u003eSensors\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(14), 4717. https://doi.org/10.3390/s21144717. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eIlie\u003c/strong\u003e A, Vasilescu J, Talianu C, Iojă C, \u0026amp; Nemuc A (2023). Spatiotemporal Variability of Urban Air Pollution in Bucharest City. \u003cem\u003eAtmosphere\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(12), 1759. https://doi.org/10.3390/atmos14121759. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eKastner-Klein\u003c/strong\u003e P, Plate E.J (1999). Wind-tunnel study of concentration fields in street canyons. \u003cem\u003eAtmos. \u003c/em\u003e\u003cem\u003eEnviron,\u003c/em\u003e \u003cem\u003e33\u003c/em\u003e, 3973\u0026ndash;3979. https://doi.org/10.1016/S1352-2310(99)00139-9.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eKastner-Klein\u003c/strong\u003e P, Berkowicz R, \u0026amp; Britter R. (2004). The influence of street architecture on flow and dispersion in street canyons. \u003cem\u003eMeteorol Atmos Phys\u003c/em\u003e 87, 121\u0026ndash;131. https://doi.org/10.1007/s00703-003-0065-4. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eKim\u003c/strong\u003e J.-J (2004). A numerical study of the effects of ambient wind direction on flow and dispersion in urban street canyons using the RNG k-\u0026epsilon; turbulence model. \u003cem\u003eAtmos. Environ.\u003c/em\u003e \u003cem\u003e38\u003c/em\u003e, 3039\u0026ndash;3048.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLandrigan\u003c/strong\u003e P. J, Fuller R, Acosta N. J. R, Adeyi O, Arnold R, Basu N, Bald\u0026eacute; A. B, Bertollini R, Bose-O\u0026rsquo;Reilly S, Boufford J. I, et al. (2017). The Lancet Commission on Pollution and Health. The Lancet, Volume 391, Issue 10119, 462 \u0026ndash; 512.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLagmiri\u003c/strong\u003e S, \u0026amp; Dahech S (2024). Distribution of PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, and NO\u003csub\u003e2\u003c/sub\u003e in the Cergy-Pontoise urban area (France). \u003cem\u003eMeteorological Applications\u003c/em\u003e, 31(4), e2223. https://doi.org/10.1002/met.2223.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eL\u0026eacute;vy\u003c/strong\u003e A (2005). Formes Urbaines et significations : revisiter la morphologie urbaine. Espace et soci\u0026eacute;t\u0026eacute;. No 122, 25 \u0026ndash; 48p.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLv\u003c/strong\u003e W, Wu Y, \u0026amp; Zang J (2021). A Review on the Dispersion and Distribution Characteristics of Pollutants in Street Canyons and Improvement Measures. \u003cem\u003eEnergies\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(19),6155. https://doi.org/10.3390/en14196155. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMaignant\u003c/strong\u003e G (2007). Dispersion de polluants et morphologie urbaine. L\u0026rsquo;Espace g\u0026eacute;ographique, Tome 36(2), 141-154. https://doi.org/10.3917/eg.362.0141.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMohammedi\u003c/strong\u003e B, Hanini S, Gheziel A, Mellel N (2020). Simulation de l\u0026apos;Effet des Obstacles sur la Dispersion Atmosph\u0026eacute;rique par un code CFD. Algerian Journal of Environmental Science and Technology March edition. Vol.6. No1. ISSN : 2437-1114. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMoussafir\u003c/strong\u003e J, Olry C, Nibart M et al. (2013). Aircity: A very high-resolution 3D atmospheric dispersion modelling system for Paris, rapport de projet, 2013. Disponible sur : http://www.aria.fr/projets/aircity/pdf/H15-184.Moussafir.AIRCITY.V4.pdf.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u0026Ouml;zsoy\u003c/strong\u003e D (2017). Prise en compte des probl\u0026eacute;matiques li\u0026eacute;es \u0026agrave; la qualit\u0026eacute; de l\u0026rsquo;air ext\u0026eacute;rieur dans les processus de conception urbaine : quels indicateurs de forme urbaine et comment les int\u0026eacute;grer dans le processus op\u0026eacute;rationnel ? Architecture, am\u0026eacute;nagement de l\u0026rsquo;espace. \u0026zwnj;dumas-01502897.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePanerai \u003c/strong\u003eP, Depaule J.C, Demorgon M (1999). Analyse urbaine. Edition parenth\u0026egrave;se, collection Eupalinos, 189p.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePeters\u003c/strong\u003e J, Theunis J, Van Poppel M, Berghmans P (2013). Monitoring PM10 and ultrafine particles in urban environments using mobile measurements. Aerosol and Air Quality Research 13, 509\u0026ndash;522.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eRadović\u003c/strong\u003e B, Ilić P, Popović Z, Vuković J.P, \u0026amp; Smiljanić S (2022). Air Quality in the Town of Bijeljina \u0026ndash; Trends and Levels of So2 and No2 Concentrations. Quality of Life (Banja Luka) - APEIRON. 13(1-2):46-57p. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSu\u003c/strong\u003e J.G, Brauer M, Buzzelli M (2008). Estimating urban morphometry at the neighborhood scale for improvement in modeling long-term average air pollution concentrations. Atmospheric Environment. Volume 42, Issue 34. https://doi.org/10.1016/j.atmosenv.2008.07.023.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eVan den Bossche\u003c/strong\u003e Jori, Peters Jan, Verwaeren Jan, Botteldooren Dick, Theunis Jan (2015). Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset. Atmospheric Environment, Vol 105, Pages 148-161, ISSN 1352-2310. https://doi.org/10.1016/j.atmosenv.2015.01.017.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eVardoulakis\u003c/strong\u003e Sotiris, Fisher Bernard E.A, Pericleous Koulis, Gonzalez-Flesca Norbert (2003).Modelling air quality in street canyons: a review. Atmospheric Environment. Volume 37, Issue 2. https://doi.org/10.1016/S1352-2310(02)00857-9.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWang\u003c/strong\u003e Y, Dai X, Gong D, Zhou L, Zhang H, \u0026amp; Ma W (2024). Correlations between Urban Morphological Indicators and PM\u003csub\u003e2.5\u003c/sub\u003e Pollution at Street-Level: Implications on Urban Spatial Optimization. \u003cem\u003eAtmosphere\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(3), 341. https://doi.org/10.3390/atmos15030341.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWHO\u003c/strong\u003e (World Health Organization) (2021). Global Air Quality Guidelines: Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide. World Health Organization. 273p. Licence: CC BY-NC-SA 3.0 IGO. Available online: https://apps.who.int/iris/handle/10665/345329.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eYang\u003c/strong\u003e S, Zhan QM, Liu W (2023). Research on air pollution characteristics and planning strategy of urban street environment. Journal of Building Design and Environment, 2(1):028063. https://doi.org/10.37155/2811-0730-0201-1.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eYu\u003c/strong\u003e R (2023). Correlation analysis of urban building form and PM2.5 pollution based on satellite and ground observations. \u003cem\u003eFrontiers in Environmental Science\u003c/em\u003e, Volume 10. Doi : 10.3389/fenvs.2022.1111223.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eZhao\u003c/strong\u003e H, Geng G, Zhang Q, Davis S.J, Li X, Liu Y, He K (2019a). Inequality of household Consumption and air pollution-related deaths in China. Nat. Commun. 10 (1), 4337. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eZhou \u003c/strong\u003eH, Yu Y, Gu X, Wu Y, Wang M, Yue H, Gao J, Lei R, \u0026amp; Ge, X (2020). Characteristics of Air Pollution and Their Relationship with Meteorological Parameters: Northern Versus Southern Cities of China. Atmosphere. 11, 253p.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6882779/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6882779/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe increasing densification of buildings in cities impacts the natural dispersion of atmospheric pollutants, thereby increasing residents' exposure to these contaminants. To better understand the effects of urban configuration on air quality, a typomorphological analysis was conducted in three distinct neighborhoods. This analysis focused on key criteria such as building density, urban porosity, street orientation, and the presence of vegetation. These architectural and spatial parameters were correlated with pollution data (NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e) measured using nine fixed sensors, strategically distributed, along with measurements of wind speed, wind direction, and atmospheric pressure.\u003c/p\u003e \u003cp\u003eThe results underscore the decisive influence of building density and urban porosity on the capacity of urban spaces to either facilitate or hinder the dispersion of gaseous and particulate pollutants. Vegetation, depending on its location and density, also plays a significant modulating role. The orientation of streets relative to prevailing winds, along with their degree of enclosure, shapes the complex mechanisms of pollutant transport, including horizontal and vertical movements as well as the formation of air vortices. A statistical modeling approach employing multiple regression identified and validated the key spatial and environmental factors driving the variability of pollution concentrations within urban areas. Additionally, the application of the Inverse Distance Weighting (IDW) interpolation method to mobile measurement data revealed localized high-pollution zones, particularly at road intersections and in confined urban spaces.\u003c/p\u003e \u003cp\u003eThese findings provide a robust foundation for guiding urban planning strategies aimed at enhancing the natural ventilation of neighborhoods and minimizing residents' exposure to atmospheric pollutants, thereby contributing to healthier living environments.\u003c/p\u003e","manuscriptTitle":"Pollution and Architecture: Variation in Dispersion Conditions at the Neighborhood Scale In Cergy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-30 06:41:48","doi":"10.21203/rs.3.rs-6882779/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7f6a70a5-474f-41f7-904e-009b08a0fa92","owner":[],"postedDate":"June 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-17T06:08:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-30 06:41:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6882779","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6882779","identity":"rs-6882779","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}