Air Pollution in the Urban Built Environment: A Comprehensive Evaluation

Journal Pre-proof Air Pollution in the Urban Built Environment: A Comprehensive Evaluation Elisavet Tsekeri, Aikaterini Lilli, Mihalis Lazaridis, Dionysia Kolokotsa PII: S1309-1042(25)00399-X DOI: https://doi.org/10.1016/j.apr.2025.102797 Reference: APR 102797 To appear in: Atmospheric Pollution Research Received Date: 14 March 2025 Revised Date: 20 October 2025 Accepted Date: 21 October 2025 Please cite this article as: Tsekeri, E., Lilli, A., Lazaridis, M., Kolokotsa, D., Air Pollution in the Urban Built Environment: A Comprehensive Evaluation, Atmospheric Pollution Research, https:// doi.org/10.1016/j.apr.2025.102797. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. 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Air Pollution in the Urban Built Environment: A Comprehensive 1 Evaluation 2 Elisavet Tsekeri*1, Aikaterini Lilli1, Mihalis Lazaridis1, Dionysia Kolokotsa1 3 4 1 School of Chemical and Environmental Engineering, Technical University of Crete Kounoupidiana, GR 5 73100 Chania, Crete, Greece 6 *Corresponding author: [email protected] 7 Abstract 8 This study assesses air pollution levels in the city of Chania, Greece, utilizing a combination of bike-mounted sensors 9 and stationary monitoring stations to analyze the spatial and temporal variability of microclimate conditions and key 10 pollutants, including PM2.5, PM10, SO2, CO, and NO2. The data analysis reveals significant seasonal variations in air 11 pollution levels, with concentrations peaking during winter, primarily due to increased emissions from heating-related 12 combustion and reduced atmospheric dispersion. In contrast, summer months exhibit lower pollution levels, as 13 favorable meteorological conditions enhance pollutant dispersion. In spring, periodic dust episodes contribute to 14 elevated PM concentrations, further influencing seasonal air quality patterns. Weekday pollution levels are generally 15 higher than those on weekends, primarily due to traffic emissions and daily commuting patterns. However, in spring 16 and summer, this trend becomes less consistent, as increased leisure activities and tourism-related transport led to 17 elevated pollutant concentrations on certain weekends. Spatially, the highest pollution concentrations are observed in 18 the city center, where dense traffic and urban structures contribute to pollutant accumulation. Conversely, coastal 19 areas record lower pollution levels, benefiting from natural ventilation and reduced vehicular activity. These findings 20 underscore the need for integrated air quality assessments in urban planning and policy development. Strengthening 21 public transportation networks, enforcing emission control measures, expanding urban green infrastructure, and 22 enhancing real-time air quality monitoring are recommended strategies to mitigate air pollution. By implementing 23 these measures, cities can enhance air quality, public health, and environmental resilience, fostering more sustainable 24 and equitable urban development. 25 26 Keywords: Air pollution, sustainable urban development, environmental resilience, public health, quality of life 27 1. Introduction 28 Air pollution has become a critical global challenge, driven by rapid urbanization, industrialization, and 29 development activities (Zhang et al., 2016). It refers to the presence of harmful substances, such as chemical, 30 particulate, or radiation, in the atmosphere at concentrations, durations, or quantities exceeding natural levels. These 31 pollutants can potentially negatively impact human health, ecosystems, and materials, both in the short and long term 32 (Boogaard et al., 2024; Wickham et al., 2019). Air pollution includes any natural or human made chemical compounds, 33 whether in gaseous, liquid, or solid form, that enter the atmosphere in amounts that pose a risk to living organisms 34 and the environment. (European Court of Auditors, 2018). 35 The degradation of air quality has been exacerbated by unplanned and unregulated urban growth, excessive fossil 36 fuel consumption, and inadequate national and international legislation. Over 90% of air pollution-related deaths occur 37 in lowand middle-income countries (Gurjar et al., 2016). According to the World Health Organization (WHO), air 38 pollution is responsible for an estimated seven million premature deaths annually, due to cardiovascular diseases, 39 stroke, pneumonia, chronic obstructive pulmonary disease (COPD), and cancer (Gurjar et al., 2008; Li et al., 2020). 40 Furthermore, studies highlight the severe health implications of air pollution, with (Katsouyanni et al., 1997) reporting 41 a clear link between particulate matter exposure and increased pneumonia incidence in children. It is estimated that 42 nine out of ten individuals worldwide breathe air with pollutant levels exceeding WHO guidelines that identifies 43 particulate matter (PM), ozone (O3), sulfur dioxide (SO2), and nitrogen dioxide (NO2) as the most harmful air 44 pollutants to human health (Geneva: World Health Organization, 2021). It is a fact that in the European Union, data 45 Journal Pre-proof from the European Environment Agency (EEA) attributes approximately 400,000, 75,000, and 13,600 premature 46 deaths to PM2.5, NO2, and O3 exposure, respectively (Papamitsou et al., 2020). The "polluter pays" principle forms the 47 foundation of the European Union's environmental strategy, with the prevention and remediation of environmental 48 damage as key guiding principles. Since the mid-1970s, the EU has implemented environmental action programs that 49 establish frameworks and objectives for future actions across all sectors of environmental policy. In 2018, an estimated 50 96% of EU urban residents were exposed to air pollutant levels deemed harmful by the WHO (European Environment 51 Agency (EEA), 2018). Urban populations are particularly vulnerable to air pollution due to the high concentration of 52 emissions from sources such as road transport and the limited pollutant dispersion in cities compared to rural areas 53 (European Union and Statistical Office of the European Communities, 2018). 54 A substantial amount of air quality monitoring data often fails to provide the scientific community, policymakers, 55 regulators, and the public with a clear and easily interpretable picture of air quality conditions. To address this gap, 56 environmental authorities have adopted the Air Quality Index (AQI) as a tool for data interpretation and 57 communication, especially in light of the significant health risks posed by air pollution. The AQI, a composite multi58 pollutant index, was developed to simplify the assessment of air quality by condensing complex data from various 59 pollutants into a single metric. This metric allows for easier communication and public understanding of air quality 60 and the associated health risks (Geneva: World Health Organization, 2021; Gurjar et al., 2008; Hime et al., 2018; 61 Sadeghi et al., 2015). The AQI categorizes air quality into six classifications: Good, Satisfactory, Moderately Polluted, 62 Poor, Very Poor, and Severe. Each category reflects the potential health impact of the prevailing pollutant levels. The 63 AQI is calculated based on the concentrations of key pollutants, such as PM10 and PM2.5, NO2, SO2, CO, O3 (Odekanle 64 et al., 2020). These pollutants are selected due to their widespread presence and significant impact on public health. 65 Moreover, national ambient air quality standards (NAAQS) vary between countries, leading to differences in AQI 66 thresholds and levels across nations. Despite these differences, the AQI remains a crucial tool for conveying air quality 67 information to diverse stakeholders, providing an accessible means to assess and mitigate the risks of air pollution on 68 a global scale. 69 Recent studies across Europe have extensively examined air pollution, focusing on its sources, concentrations, and 70 health impacts on urban populations (Jaume et al., 2022; Sicard et al., 2021). European Union policies have 71 successfully reduced certain pollutants, such as SO2 and Pb, through stringent regulations and mitigation strategies. 72 However, urban areas continue to grapple with elevated levels of PM10, PM2.5 and NO2, with disparities persisting 73 across socioeconomic and geographic contexts. Lower-income and densely populated regions are disproportionately 74 exposed to higher pollution levels, exacerbating public health risks. For instance, many European capitals exemplify 75 these issues: London reports persistently high NO2 concentrations driven by traffic, despite the implementation of the 76 Ultra Low Emission Zone; Paris experiences recurrent PM spikes from vehicle emissions, prompting temporary traffic 77 restrictions; and Madrid, while benefiting from regulatory measures and public awareness campaigns, continues to 78 face elevated NO2 and PM levels in high-traffic areas (Font et al., 2019; Salas et al., 2021). In Eastern Europe, 79 Bucharest faces significant air quality challenges driven by industrial activities and vehicle emissions. In addition, 80 meteorological factors, including temperature, wind patterns, humidity, and atmospheric stability, critically influence 81 pollutant dispersion and accumulation, exacerbating air quality issues in urban settings. This interplay has garnered 82 substantial research attention, particularly in emerging economies where rapid urbanization intensifies pollution 83 concerns (Zhu et al., 2021). 84 The Mediterranean basin faces distinct air quality challenges due to the complex interplay of anthropogenic 85 emissions, natural sources, marine influences, and Saharan dust intrusions, which complicate the analysis of PM 86 dynamics (Chatoutsidou et al., 2025). In Greece, elevated levels of NO2, PM10, PM2.5, and O3 are particularly 87 pronounced in the Athens metropolitan area, where winter heating and meteorological conditions exacerbate pollution. 88 While Athens faces severe air quality challenges, Ioannina records the poorest conditions in Greece, due to extensive 89 wood burning, while other cities such as Thessaloniki and Patras also experience elevated pollutant levels (Dimitriou 90 and Mihalopoulos, 2024). A comparative analysis of air quality standards in Eastern Mediterranean countries revealed 91 that national PM2.5 limits can exceed WHO guidelines by up to tenfold, highlighting the urgent need for stricter 92 regulatory alignment to reduce disease burden and premature mortality (Faridi et al., 2023). Health impact studies 93 further underscore this urgency, with (Psistaki et al., 2023) reporting that a 10 µg/m3 increase in PM2.5 is associated 94 with a 1.10% rise in cardiovascular mortality in Thessaloniki and a 3.07% increase in mortality in Limassol on the 95 same day. In Sicily, Italy, most PM10 exceedances between 2013 and 2015 were attributed to Saharan dust intrusions, 96 identified through three independent modeling approaches (Cuspilici et al., 2017). In Chania, Greece, PM10 and PM2.5 97 Journal Pre-proof concentrations generally remain below WHO limits but exhibit significant seasonal variability, driven by African dust, 98 residential heating, and biomass burning, with mineral dust contributing over 30% to PM levels, followed by sulfates, 99 traffic, sea salt, and road dust (Chatoutsidou and Lazaridis, 2022). A multi-city study of Venice, Marseille, 100 Thessaloniki, and Barcelona further revealed distinct seasonal patterns in fine particulate matter: winter peaks in 101 Venice and Marseille, autumn maxima in Thessaloniki, and spring elevations in Barcelona, denoting the combined 102 influence of meteorology and local emission sources on air quality across Mediterranean cities (Salameh et al., 2015). 103 Despite progress in air quality monitoring, stationary networks limit spatial resolution and fail to capture 104 microenvironmental variations. To overcome these limitations, this study introduces a hybrid monitoring approach in 105 Chania, Greece, combining fixed stations with mobile bicycle-mounted sensors. This method enables high-resolution 106 mapping of pollution hotspots and one year analysis of pollutant dynamics, offering a scalable model for other mid107 sized Mediterranean cities. 108 2. Materials and Methods 109 2.1 Case study 110 Chania city, located on the island of Crete and it is the largest Greek island and the fifth largest in the Mediterranean 111 which serves as an ideal location for monitoring air pollution due to its unique socio-economic and environmental 112 conditions (Figure 1). This Mediterranean city enjoys a subtropical climate characterized by sunny, dry summers and 113 mild, rainy winters, with its coastal location influencing temperature patterns compared to other Greek cities without 114 coastal access (Kolokotsa et al., 2009; Tsekeri et al., 2025). 115 116 Figure 1 Mediterranean Sea Map -Chania, Greece case study 117 According to the latest census by (European Union and Statistical Office of the European Communities, 2018; 118 Population, 2023), is the second most populous city on Crete, with a total population of 155,443 across its seven 119 municipalities, of which the municipality of Chania accounts for 110,646 residents. Demographically, Chania has a 120 birth rate of 8.96 per 1,000 inhabitants and a death rate of 8.55 per 1,000 inhabitants. Rapid urbanization and increased 121 tourism have led to heightened vehicular traffic, energy consumption, construction activities, and key sources of air 122 pollution. Furthermore, Chania's historical architecture and narrow streets contribute to congestion and localized 123 pollution, compounded by a lack of public transportation options. In addition, it has limited public open spaces, 124 averaging just 2 m2 per resident and a total green area of 0.28 km2, equivalent to 5.26 m2 per person (Dimelli, 2020). 125 The city’s unique socio-economic profile and its challenges related to urban sprawl and tourism pressure make it an 126 exemplary case for comprehensive air quality monitoring using tools like AQI. Regular monitoring will also provide 127 National Geographic, Esri, Garmin, HERE, UNEP-WCMC, USGS, NASA, ESA, METI, NRCAN, GEBCO, NOAA, increment P Corp. Journal Pre-proof critical insights into the health impacts of air pollution (Chalvatzaki and Lazaridis, 2010), help identify specific 128 pollution sources, and guide local policies on transportation, urban planning, and public health. 129 2.2 Data Collection 130 This study employs a hybrid monitoring system integrating stationary sensor kits at Chania’s bike-sharing stations 131 with mobile sensors mounted on bicycles (Figure 2): Specifically: 132 a) Stationary Monitoring System: Each of Chania’s two bike-sharing stations is equipped with a custom sensor kit 133 powered directly by the docking station. These kits measure SO2, NO2, CO, O3, PM2.5, PM10, air temperature, 134 relative humidity, and GPS coordinates (latitude and longitude) at one-minute intervals. Station 1 (S1) is situated 135 in a parking area near the coastal side (Defkalionos Street), offering a distinct environment characterized by 136 proximity to the sea and lower traffic congestion. In contrast, Station 2 (S2) is located in the city center 137 (Markopoulou Street), near the Old Chania Market, a densely populated and highly trafficked urban area with 138 both vehicle and pedestrian activity. This site includes a small parking lot and tree cover, reflecting a high139 exposure pollution setting. 140 b) Mobile Monitoring System: Twenty-eight bicycles are fitted with low-power sensor kits designed for mobile 141 deployment. Each kit records PM2.5, PM10, air temperature, relative humidity, and GPS coordinates. Data 142 collection operates in two modes: (i) Docked Mode, where parked bicycles recorded hourly data, and (ii) Mobile 143 Mode, where rented bicycles collected minute-by-minute data across the city. 144 All sensors communicated via Wi-Fi to a digital platform for real-time transmission, visualization, and secure 145 storage. Although factory-calibrated, all sensors underwent additional calibration against high-precision reference 146 stations measuring the same pollutants over an extended period to ensure data reliability and comparability. A detailed 147 description of the methodology that followed for assessing the air pollution in Chania city is provided in Section 2.3. 148 149 150 151 152 153 154 155 156 157 158 159 160 161 Figure 2 (Left) S1 and S2 Bike station in the city, (Right) Bicycles in the bike stations equipped with sensors 162 2.3 Methodology 163 The methodology outlined below (Figure 3) uses geospatial and statistical tools to map pollutant distributions, 164 identify hotspots, and assess the AQI. Specifically for the monitoring period December 2023-November 2024: 165 • Data Management: Data from stationery and mobile sensors are processed in MATLAB, where GPS coordinates 166 (Longitude and Latitude) are converted to XY coordinates ensuring compatibility with geospatial analysis tools. 167 • Spatial Statistics and Clustering: Processed data are imported into ArcGIS, and Spatial Statistics tools are used to 168 conduct grouping analysis. This mapping identifies pollution clusters based solely on-air quality similarities, free 169 from spatial or administrative constraints, ensuring clusters reflect zones with only consistent pollutant patterns. 170 • Optimal Cluster Determination: The optimal number of clusters is established using pseudo-F-statistic plots in 171 ArcGIS, to assess cluster quality, where a higher pseudo-F value indicates more cohesive and well-separated clusters. 172 This ensures that the final clustering structure is statistically robust. 173 • Geographical Distribution Analysis: The mean center of each cluster is computed in ArcGIS to map pollution 174 hotspots across Chania’s urban zones. 175 © OpenStreetMap (and) contributors, CC-BY-SA S1 S2 Journal Pre-proof • Raster Interpolation: Inverse Distance Weighting is applied to create heatmaps, interpolating continuous pollutants 176 and highlight high-risk areas with elevated pollutant levels. 177 • Seasonal Analysis: Once the optimal clustering structure is established, results are analyzed across seasons to assess 178 seasonal pollution across the year. The data are analyzed and presented according to seasonal variations, specifically 179 categorized into Winter (December to February), Spring (March to May), Summer (June to August), and Autumn 180 (September to November). This methodology has been effectively applied in various research studies (US 181 Environmental Protection Agency, 2015). 182 • AQI Calculation: Using only the stationary sensor data, computations were based on daily mean concentrations of 183 SO2, PM10, and PM2.5, the maximum hourly NO2 concentration, and the maximum 8-hour CO concentration. 184 Breakpoints for these pollutants follow the Environmental Protection Agency (EPA) scale. The AQI for each 185 pollutant is derived using a standardized formula as presented in (eq.1): 186 187 𝐴𝑄𝑖=𝐴𝑄𝐼𝑚𝑎𝑥 − 𝐴𝑄𝐼𝑚𝑖𝑛 𝐶𝑖𝑚𝑎𝑥 − 𝐶𝑖𝑚𝑖𝑛 (𝐶𝑝− 𝐶𝑖𝑚𝑖𝑛) + 𝐴𝑄𝐼𝑚𝑖𝑛 𝑒𝑞.(1) 188 189 Where: AQi: The sub-index for pollutant i (where i=1,2,N i = 1, 2, and N is the total number of pollutants). 190 Cp: The observed pollutant concentration measured by the sensors. 191 Cimin: The lower bound of the concentration range for pollutant i, as defined by regulatory standards 192 Cimax: The upper bound of the concentration range for pollutant i, as defined by regulatory standards 193 AQImin :The AQI value corresponding to Cimin 194 AQImax :The AQI value corresponding to Cimax 195 Table 1 is created based on key pollutants, which provides AQI values and corresponding pollutant concentration 196 ranges. The maximum calculated sub-index is taken as the overall AQI, which is classified on a scale of 0 to 500 into 197 six quality categories: good (0-50), moderate (51-100), unhealthy for sensitive groups (101-150), unhealthy (151198 200), very unhealthy (201-300), and hazardous (301-500). 199 200 Table 1 AQI limits are based on each pollutant 201 AQI AQI PM2.5(μg/m3) (Mean daily) PM10(μg/m3) (Mean daily) NO2(μg/m3) (Mean hourly) SO2(μg/m3) (Mean daily) CO(μg/m3) (Mean 8h) Good 0-50 0<Ci<12 0<Ci<54 0<Ci<53 0<Ci<35 0<Ci<4.4 Moderate 51-100 12.1<Ci<35.4 55<Ci<154 54<Ci<100 36<Ci<75 4.5<Ci<9.4 Unhealthy for Sensitive Groups 101-150 35.5<Ci<55.4 155<Ci<254 101<Ci<360 76<Ci<185 9.5<Ci<12.4 Unhealthy 151-200 55.5<Ci<150.4 255<Ci<354 361<Ci<649 186<Ci<304 12.5<Ci<15.4 Very Unhealthy 201-300 150.5<Ci<250.4 355<Ci<424 650<Ci<1249 305<Ci<604 15.5<Ci<30.4 Hazardous 301-500 250.5<Ci<500.4 425<Ci<604 1250<Ci<2049 605<Ci<1004 30.5<Ci<50.4 Journal Pre-proof 202 Figure 3 Methodology Flow chart 203 3. Results 204 3.1 Bike stations 205 The comparative analysis of the S1 (Figure S1, Figure S2) and S2 (Figure S5, Figure S6) monitoring stations in 206 Chania highlight distinct environmental dynamics driven by their unique geographical locations and seasonal 207 variations. Specifically, S1, positioned in the coastal area, demonstrates greater variability in air temperature and 208 relative humidity, influenced by natural factors such as sea breezes and lower urban activity levels. The temperature 209 at S1 fluctuates significantly, ranging from approximately 10 °C to 35 °C, with higher humidity levels attributable to 210 the moisture-laden environment near the sea. Seasonally, S1 exhibits a gradual temperature increase from winter to 211 summer, peaking between June and August, followed by a decline in autumn. This seasonal trend aligns with typical 212 coastal climatology, where the proximity to the sea moderates temperature extremes. Conversely, S2, located in the 213 densely populated and heavily trafficked city center, exhibits more stable temperature patterns. S2 records higher 214 temperature peaks during summer and milder lows during winter, a characteristic of urban environments where the 215 built infrastructure retains heat and suppresses natural variability. Humidity levels at S2 are lower and more consistent, 216 reflecting the dominance of anthropogenic influences over natural climatic factors. 217 An analysis of PM2.5 (Figure S3, Figure S7) and PM10 (Figure S4, Figure S8) concentrations at the S1 and S2 218 monitoring stations reveal notable differences shaped by their urban contexts. At S1, located near the coastal area with 219 less traffic congestion, PM2.5 and PM10 levels exhibit greater variability. Lower quartile values suggest cleaner air 220 during periods of minimal human activity, while occasional spikes highlight localized pollution events. In contrast, 221 S2, situated in the dense city centre, records consistently higher median PM levels. The more stable distribution at S2 222 suggests persistent urban pollution influenced by vehicle emissions and limited natural dispersion. 223 The seasonal air quality analysis at the S1 and S2 monitoring stations in Chania in Figure 4 reveals distinct spatial 224 and temporal variations influenced by the stations' unique environmental settings and anthropogenic factors. The 225 European AQI, as defined by the EEA, provides a standardized framework for interpreting pollutant levels across 226 categories ranging from "Good" (light green) to "Unhealthy for Sensitive Groups" (orange) and beyond. At S1, located 227 in a coastal parking area, PM2.5 and PM10 concentrations remain within moderate levels. Conversely, S2, situated in 228 the city center, consistently exhibits elevated PM2.5 and PM10 concentrations, as well as NO2 and SO2 levels are notably 229 higher at S2, reflecting the intensified impact of urban emissions during winter. Furthermore, seasonal temperature 230 inversions and limited atmospheric dispersion exacerbate pollutant accumulation at S2, whereas S1 benefits from its 231 Journal Pre-proof coastal location and favorable meteorological conditions, maintaining relatively lower pollution levels. CO levels, 232 however, remain negligible across both stations. It is also important to consider the composition of PM, as air pollution 233 index levels shown in Figure 4 are largely driven by PM2.5 and PM10. At S1, natural sources such as sea salt aerosol 234 contribute substantially to variability, whereas at S2, anthropogenic emissions from traffic, residential heating, and 235 construction activities dominate. Seasonal trends further highlight variations in air quality between the two stations. 236 During the spring months, PM2.5 and PM10 levels at S1 are moderate, primarily influenced by natural particulate 237 sources such as sea salt and dust, while NO2, SO2, and CO concentrations remain low due to the limited presence of 238 urban and industrial emissions. In contrast, S2 experiences significantly higher PM2.5 and PM10 levels, attributable to 239 urban activities and increased vehicular emissions. The summer season sees meteorological conditions amplifying the 240 divergence in air quality, with S2 exhibiting elevated PM2.5 and PM10 levels due to urban heat island effects, while S1 241 maintains stable pollutant levels. However, NO₂ concentrations at S1 increase during this period, likely due to 242 emissions from the adjacent parking lot, where vehicles contribute to localized pollution. In autumn, atmospheric 243 changes drive increases in PM2.5 and PM10 at both stations, although S2 remains more affected due to urban density 244 and cooler temperatures trapping pollutants near the ground. NO2 and SO2 levels peak at S2 during this period, further 245 highlighting the influence of anthropogenic activity. Despite these variations, CO levels remain negligible across all 246 seasons. 247 248 249 Figure 4 Monthly Variations in Air Pollutant Index Levels 250 It is also important to consider the composition of PM, as air pollution index levels shown in Figure 4 are largely 251 driven by PM2.5 and PM10. We believe that these contrasting source profiles are largely explained by the geographical 252 setting of the two sites: S1, located near the coast, is more exposed to marine air masses and long-range transport 253 events, while S2, situated in the dense urban core, is strongly influenced by traffic congestion, heating demand, and 254 limited dispersion within narrow streets. Seasonal trends further highlight variations in air quality between the two 255 stations. During the spring months, PM2.5 and PM10 levels at S1 are moderate, primarily influenced by natural 256 particulate sources such as sea salt and dust, while NO2, SO2, and CO concentrations remain low due to the limited 257 presence of urban and industrial emissions. In contrast, S2 experiences significantly higher PM2.5 and PM10 levels, 258 attributable to urban activities and increased vehicular emissions. The summer season sees meteorological conditions 259 amplifying the divergence in air quality, with S2 exhibiting elevated PM2.5 and PM10 levels due to urban heat island 260 effects, while S1 maintains stable pollutant levels. However, NO₂ concentrations at S1 increase during this period, 261 Journal Pre-proof likely due to emissions from the adjacent parking lot, where vehicles contribute to localized pollution. In autumn, 262 atmospheric changes drive increases in PM2.5 and PM10 at both stations, although S2 remains more affected due to 263 urban density and cooler temperatures trapping pollutants near the ground. NO2 and SO2 levels peak at S2 during this 264 period, further highlighting the influence of anthropogenic activity. Despite these variations, CO levels remain 265 negligible across all seasons. 266 In parallel, Figure 5 presents the monthly average AQI values, derived from PM2.5 concentrations, measured at 267 both monitoring stations. Elevated AQI levels during the winter months (December, January, and February) are 268 primarily attributed to increased emissions from residential heating and vehicular traffic, together with reduced 269 atmospheric dispersion caused by temperature inversions. Based on the European AQI health categories, the AQI 270 values during winter predominantly fall within the "Moderate" (51-100) range, which may pose potential health risks 271 for sensitive groups, such as individuals with respiratory or cardiovascular conditions. In contrast, spring and early 272 summer exhibit improved air quality, with AQI values generally falling within the "Good" (0-50) category due to 273 enhanced atmospheric mixing and reduced emissions, making outdoor activities safer for the general population. 274 However, during July and August, coinciding with Chania’s peak tourist season, AQI levels increase noticeably. The 275 influx of visitors significantly amplifies anthropogenic activities, particularly transportation, resulting in higher PM2.5 276 concentrations. In autumn, particularly in October, AQI levels rise again, likely due to resuspended particulates and 277 cooler temperatures that limit pollutant dispersion. While autumn AQI levels primarily remain in the "Moderate" 278 range, they approach levels that could pose risks to sensitive groups. 279 280 Figure 5 Monthly Air Quality Index Variation Across Chania 281 Given that the AQI is primarily driven by PM2.5 concentrations, followed by PM10, a supplementary analysis is 282 performed to investigate the 24-hour variations in the mass concentrations of PM2.5 and PM10 across each month from 283 December 2023 to November 2024. Specifically, 284 Figure 6 presents a detailed analysis of the hourly variations in PM2.5 and PM10 mass concentrations across weekdays 285 and weekends from December 2023 to November 2024. A consistent distinction is evident between weekday and 286 weekend pollutant levels, with weekdays showing higher concentrations due to increased commuting, industrial 287 activity, and urban emissions. Peaks in PM concentrations typically occur during the morning (6–8 AM) and evening 288 (4–8 PM), corresponding to rush hours and elevated vehicular activity. These patterns are more pronounced at S2, 289 situated in the city center, where urban density and traffic emissions significantly contribute to elevated pollution 290 levels. Conversely, weekends consistently exhibit lower pollutant levels, highlighting the reduction in human activities 291 during these periods. At S1, located in a coastal area, pollutant levels are generally lower than at S2, benefiting from 292 enhanced dispersion and reduced urban influence. In addition, wind direction also plays a key role in shaping air 293 quality, as onshore flows from the sea typically carry cleaner air masses and further promote dispersion. In the present 294 study, the absence of a busy transportation area reinforces this effect, since incoming marine air is less influenced by 295 local emissions. During winter, weekday PM2.5 and PM10 concentrations are notably higher due to increased emissions 296 from residential heating and reduced atmospheric dispersion caused by temperature inversions. However, December 297 Journal Pre-proof PM10 rising to 58.04 µg/m3. In contrast, coastal areas (Cluster 1) exhibited comparatively lower concentrations, while 371 suburban zones (Clusters 2 and 3) showed intermediate values. In spring, a new fifth cluster (Cluster 5) emerged due 372 to the increased cycling activity. The urban core (Cluster 2) remained the most polluted, with PM2.5 at 17.10 µg/m³, 373 while suburban expansion areas (Cluster 5) showed elevated PM2.5 (14.27 µg/m3) and PM10 (19.25 µg/m3). During 374 summer, PM2.5 (19.26 µg/m3) and PM10 (37.70 µg/m3) concentrations were highest in the urban core (Cluster 2), while 375 coastal and suburban clusters (Clusters 1, 3, and 4) recorded lower levels. Last, autumn represented the transitional 376 period, with PM2.5 levels peaking at 17.56 µg/m3 in the transitional zone (Cluster 3), and concentrations across other 377 clusters showing an increase compared to summer. 378 4. Discussion 379 The aim of this study is to assess the air pollution in Chania. Winter months recorded the highest AQI values, with 380 PM2.5 and PM10 levels exceeding WHO limits, particularly in the city center. These findings are consistent with 381 previous studies, showing that heating emissions and limited atmospheric dispersion contribute to elevated pollution 382 during colder months (Borge et al., 2019; Datta, 2023; Feng et al., 2020; Nigam et al., 2015). In contrast, summer 383 exhibited the lowest concentrations, with PM2.5 often below 10 µg/m3 and PM10 under 20 µg/m3, where these 384 improvements reflect enhanced atmospheric mixing, reduced heating-related emissions, and natural ventilation from 385 sea-breeze circulation. Compared to many European cities, Chania’s air quality remains relatively favorable. For 386 example, Athens, Paris, and Madrid frequently record substantially higher annual PM and NO2 levels (Dimitriou and 387 Mihalopoulos, 2024; Papamitsou et al., 2020; Sicard et al., 2021). Despite the implementation of mitigation policies, 388 London and Paris continue to struggle with elevated NO2 and PM concentrations linked to heavy traffic (Sicard et al., 389 2021) while Athens frequently exceeds EU pollution thresholds in winter (EEA, 2019). By contrast, Chania’s AQI 390 generally falls within the “Good” to “Moderate” range, with exceedances largely confined to winter peaks and 391 localized summer increases associated with tourism-related traffic. 392 In this study, weekday concentrations in Chania were generally higher than those on weekends, reflecting traffic393 related and human activity, though this pattern was less pronounced during the summer tourist season (Lonati et al., 394 2006; Vandeninden et al., 2024). In particular, this result was further confirmed in Birmingham, where weekday PM2.5 395 concentrations were reported to be 20% higher than weekend levels, driven by traffic activity (Jones et al., 2010). The 396 intra-urban differences between the city center and the coastal area similarly highlight how urban density and traffic 397 exacerbate pollution exposure in central districts, while natural ventilation mitigates concentrations in coastal areas. 398 Similar intra-urban variation was reported in Athens, where monitoring stations in dense traffic and industrial areas in 399 Greece, such as Athens Centre and Piraeus consistently recorded higher PM2.5 concentrations during morning rush 400 hours compared to suburban areas (Pateraki et al., 2013). The observed seasonal contrasts in Chania are consistent 401 with documented meteorological influences on air quality in Mediterranean cities. Winter peaks in PM2.5 and PM10 402 coincide with temperature inversions and reduced atmospheric mixing, which favor pollutant accumulation (Borge et 403 al., 2019; Feng et al., 2020). In addition, the seasonal contrasts observed in Chania mirror those documented in prior 404 local studies where authors in (Chatoutsidou et al., 2023) reported the contribution of residential wood burning as a 405 major contributor to high particulate matter concentrations during the colder months. In summer, enhanced sea-breeze 406 circulation and stronger winds facilitate dispersion, while spring reductions in PM10 are partly attributable to rainfall 407 events that remove particles from the atmosphere (Chatoutsidou and Lazaridis, 2022; Zhu et al., 2021). 408 When compared with other Greek cities, Chania’s annual averages remain lower, but its winter pollution burden is 409 still substantial. Chania’s winter PM2.5 peaks (~30 µg/m3) were 25–35% lower than those reported in Thessaloniki 410 (35–45 µg/m3) and nearly 40–50% lower than Athens (40–55 µg/m3) (Dimitriou and Mihalopoulos, 2024). Ioannina, 411 however, consistently reports the highest winter PM2.5 levels in Greece, due to residential biomass burning and 412 topographic constraints. Winter mean PM2.5 concentrations in Ioannina reached ~57 µg/m3, nearly double those 413 observed in Chania. Even in spring, Ioannina’s average (~13.4 µg/m3) remained higher than many of Chania’s summer 414 values (<10 µg/m3) (Kakouri et al., 2025; Soupiadou et al., 2023; Stavroulas et al., 2020). Within Chania itself, strong 415 intra-urban contrasts were also evident, as winter PM10 concentrations in the city center reached 58 µg/m3, almost 2.5 416 times higher than the coastal background in Akrotiri, a peri-urban area of the city (Chatoutsidou et al., 2025). At the 417 Mediterranean scale, Chania’s winter levels closely matched those reported for Marseille and Venice (28–32 µg/m3) 418 (Salameh et al., 2015). In spring, its PM2.5 concentrations (10–15 µg/m3) were about 40% lower than those observed 419 Journal Pre-proof in Barcelona (20–25 µg/m3), while autumn values showed a transitional rise similar to Thessaloniki (Dimitriou and 420 Mihalopoulos, 2024; Jaume et al., 2022). Beyond urban emissions, Saharan dust intrusions play a critical role in 421 seasonal exceedances across the Mediterranean. In Sicily, for example, they accounted for most PM10 exceedances, a 422 pattern echoed in Chania where mineral dust contributes more than 30% of PM mass (Chatoutsidou and Lazaridis, 423 2022; Cuspilici et al., 2017). In addition to particulate matters, microenvironmental conditions and other pollutants 424 play an important role in shaping exposure risks. Lazaridis highlighted that human exposure to PM can fluctuate 425 significantly across different urban locations (Lazaridis, 2025) supporting the present study's observation of strong 426 intra-urban contrasts in Chania. Similarly, research on photochemical contributions to air pollution emphasizes the 427 influence of regional and local emissions on gaseous pollutants such as NO, NO2, O3, CO, and SO2 (Chatoutsidou and 428 Lazaridis, 2024) which also exhibit trends consistent with other Mediterranean cities. Studies in Greece further 429 confirm persistent seasonal patterns, with Athens, Thessaloniki, and Patras all recording winter peaks in PM and NO₂ 430 due to dense traffic, industrial activities, and unfavorable meteorology (Dimitriou and Mihalopoulos, 2024). 431 The cluster analysis of this study further confirmed the spatial heterogeneity of air quality in Chania. The urban 432 core consistently exhibited the poorest conditions, while coastal areas showed lower concentrations due to natural 433 ventilation. This spatial disparity mirrors patterns reported in other European cities such as Brussels and Milan, where 434 dense traffic and residential activity in central districts result in systematically higher exposure than in surrounding 435 areas (Lonati et al., 2006). These spatial disparities align with recent quality-of-life assessments in Chania, where 436 residents in central neighborhoods reported lower satisfaction with nearby green spaces and with their overall 437 residential environment compared to those in less dense areas. This suggests that the populations most exposed to 438 seasonal air pollution peaks are also those with limited access to environmental amenities, compounding risks to health 439 and well-being (Tsekeri et al., 2025). These findings support the need for localized air quality assessments, particularly 440 in urban environments where pollution hotspots vary due to emission sources and meteorological conditions. Such 441 findings underscore the importance of localized assessments, as also highlighted in EU-wide clustering analyses 442 (Kuzminski and Wojtaszek, 2020), which identified regions requiring targeted interventions like the urban, suburban, 443 and coastal contrasts observed in Chania. 444 4.1 Strengths & Limitations 445 Strengths 446 Mobile sensors on bicycles enable high-resolution data collection, capturing air quality variations at one-minute 447 intervals across Chania’s urban routes and coastal side, amassing over 760,000 data points. This approach provides 448 detailed spatial and temporal insights into pollution hotspots, surpassing the limitations of traditional fixed-station 449 networks, which typically offer lower spatial resolution. For instance, it allowed robust mapping of winter peaks in 450 the urban core (e.g., PM2.5 at ~30 µg/m3). Secondly, the use of ArcGIS Spatial Statistics tools, employing grouping 451 analysis with pseudo-F-statistic plots, ensures robust identification of pollution clusters based solely on-air quality 452 similarities, independent of spatial or administrative constraints, enhancing the accuracy of hotspot mapping. IDW 453 interpolation generates precise heatmaps, effectively visualizing continuous pollutant distributions across Chania’s 454 urban landscape. In addition, the seasonal analysis, structured into Spring, Summer and Autumn, a comprehensive 455 understanding of temporal pollution dynamics, grounded in established methodologies. Last, the AQI calculation, 456 based on EPA standards, delivers a standardized, reliable metric for assessing air quality and communicating health 457 risks to diverse stakeholders (US EPA, 2018). 458 Limitations 459 Despite its strengths, several methodological limitations should be considered. Firstly, the use of only two 460 stationary monitoring stations, one on the coastal side and one in the city center, restricts spatial coverage, potentially 461 missing critical data from industrial zones or restricted areas. Similarly, bicycle-mounted sensors are limited to cycling 462 paths, excluding significant pollution sources in non-cycled areas such as industrial or pedestrian zones, which may 463 underestimate overall PM. Secondly, seasonal data imbalances may compromise the robustness of clustering results, 464 particularly for summer, potentially skewing seasonal comparisons of pollution patterns and introducing uncertainties 465 in temporal trends. Finally, the absence of measurements for key meteorological parameters and pollutants such as 466 Journal Pre-proof wind speed and O3, due to sensor accuracy limitations, restricts the analysis of dispersion mechanisms, particularly in 467 summer when O3 peaks are obvious in Mediterranean climates. 468 469 5. Conclusion 470 This study assessed air quality in Chania, Greece, through a hybrid monitoring approach that combined stationary 471 sensors with mobile, bicycle-mounted devices. In total, over 760,000 data points were collected across the city, which 472 allowed the mapping of air pollution patterns and thus overcame the spatial limitations of conventional fixed-station 473 networks. The analysis showed that there were clear seasonal and spatial contrasts: winter peaks in PM2.5 and PM10 474 concentrations were largely driven by heating emissions and reduced atmospheric dispersion, whereas lower summer 475 levels were supported by sea-breeze circulation. Moreover, intra-urban differences were observed, with the city center 476 consistently more polluted than coastal areas. In addition, weekday concentrations were generally higher than weekend 477 levels, although this pattern was less pronounced during the summer tourist season. To further support these findings, 478 cluster analysis confirmed the urban core as the primary hotspot, aligning with patterns observed in other 479 Mediterranean cities. Overall, the study demonstrates the value of integrating mobile sensing technologies into air 480 quality monitoring frameworks. While the analysis focused on Chania, the methodology is scalable and adaptable to 481 other mid-sized Mediterranean cities facing similar urban challenges. 482 483 Acknowledgments 484 This work was conducted as part of the VISIONARY NATURE BASED ACTIONS FOR HEALTH, WELLBEING 485 & RESILIENCE IN CITIES (VARCITIES) has received funding from the European Union's Horizon 2020 research 486 and innovation programme under grant agreement No 869505. 487 488 Conflicts of interest 489 The authors declare that they have no competing interests. 490 Disclosure instructions 491 Statement: During the preparation of this work the authors used the AI tool, ChatGPT in order to improve readability 492 and language. After using this tool, the authors reviewed and edited the content as needed and took full responsibility 493 for the content of the publication. 494 495 References 496 Boogaard, H., Crouse, D.L., Tanner, E., Mantus, E., van Erp, A.M., Vedal, S., Samet, J., 2024. 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Dev. 618 Sustain. 23, 3994–4010. https://doi.org/10.1007/s10668-020-00753-7 619 620 621 Journal Pre-proof • Air pollution in Chania is assessed using bike sensors and monitoring stations • Seasonal and spatial variability of PM2.5, PM10, SO2, CO, and NO2 are analyzed • Winter pollution peaks due to heating emissions and low atmospheric dispersion • Weekday pollution is higher, but tourism influences weekend levels during summer • The city center records higher air pollution compared to the coastal areas Journal Pre-proof Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐ The author is an Editorial Board Member/Editor-in-Chief/Associate Editor/Guest Editor for [Journal name] and was not involved in the editorial review or the decision to publish this article. ☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Journal Pre-proof