Air pollution is a critical issue, particularly in urban areas, making the monitoring and understanding of the  air pollutant’s sources essential. Urban regions are often very heterogeneous due to the complexity of buildings, roadways, vegetation, and various emission sources. Governments prioritize these areas to implement intervention measures aimed at addressing air pollution, as urban regions are both major sources of pollution and densely populated. Therefore, simulating the dispersion of air pollutants at high spatial and temporal resolution is crucial for understanding pollution sources and evaluating different intervention measures.

In this context, we have set up the Large-Eddy Simulation (LES)-based air quality model, PALM-4U (Parallelized Large-Eddy Simulation Model for Urban), over Munich, Germany. The goal is to simulate meteorology and concentrations of air quality relevant species at high spatial (10 meters) and temporal (10 minutes) resolution across the city. The model uses high-resolution static parameters (e.g., building height, vegetation height), dynamic meteorological variables from the WRF model (with a 400-meter resolution) for boundary conditions, and a high-resolution emission inventory (100 meters). The boundary conditions for air quality species were obtained from CAMS model ensemble outputs. 

We compared the simulated meteorology and air pollutant concentrations with data from an extensive measurement campaign conducted in August 2023 for selected days. The results showed good agreement for wind speed (Mean Bias (MB) = 0.23 m/s, Pearson correlation coefficient (R) = 0.86) and wind direction (MB = 35°, R = 0.83). The PALM-4U model overestimated NO₂ concentrations by 0.82 ppb (+15.5 %), underestimated O₃ concentrations by 4.5 ppb (-13.7 %) and overestimated PM₁₀ concentrations by 1.4 µg m^-3 (+11.7 %), although it accurately captured the diurnal variations. Sensitivity analysis revealed that the boundary conditions from the mesoscale model have a significant impact on the modeled air quality concentrations.

This model setup will be further utilized to evaluate the effectiveness of various measures, such as low-speed zones, low-emission zones, and the extent of electric vehicle adoption required to achieve safe air pollution levels within Munich.

How to cite: Balamurugan, V., Chen, J., Saathoff, H., Claus Holst, C., Wenzel, A., Abu-Hani, A., Li, Y., Li, Y., Abou-Rizk, S., and N Keutsch, F.: High-Resolution LES-Based Air Quality Modeling Over Munich: Evaluation of Model Performance, and Pollution Drivers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16731, https://doi.org/10.5194/egusphere-egu25-16731, 2025.

In France, 97% of cities exceed the WHO guide values for PM2.5 (2022 data). The transport sector is responsible for about 10% of PM2.5 emissions. The development of green infrastructure along roads, among other ecosystem services, could enable air pollution removal by deposition and needs to be better quantified and monitored.

We studied the chemical composition of particle deposits and the temporal evolution of the quantity of deposits on tree leaves over a 9-month period in Toulouse (~500,000 inhabitants), France. Leaf samples were taken from 9 deciduous trees and 2 evergreen trees at three roadside sites. We analyzed the deposition of elemental carbon (EC) and magnetic minerals on the leaf surface of different tree species. The EC deposit was extracted from the leaves using double-deionized water for on-surface deposition and chloroform for in-wax deposition. EC deposit was then estimated using thermo-optical techniques. Total magnetic mineral deposition was analyzed directly on total leaves by acquiring saturation isothermal remanent magnetization (SIRM) using a JR6 magnetometer.

Our results show that the average on-surface EC and total magnetic minerals deposit were respectively 4 and 12 times higher for evergreen than for deciduous trees. On-surface EC for evergreen trees was 2 times lower than in-wax EC. In contrast, on-surface EC for deciduous trees was found 11 times higher than in-wax EC. The analyses enabled the interpretation of changes in particle deposition over time as a function of meteorological conditions. Our results highlighted the potential of leaves to be used as biosensors to monitor ambient air pollution.

How to cite: Delville, L., Léon, J.-F., Macouin, M., Dias Alves, M., Lenoir, O., and Drigo, L.: Particulate matter deposition on roadside trees in urban area: Evaluation of Elemental carbon and magnetic minerals., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11091, https://doi.org/10.5194/egusphere-egu25-11091, 2025.

Tropospheric ozone (O₃) is a major air pollutant that negatively affects human health and vegetation, while also playing a central role in atmospheric chemistry and climate change. Dry deposition, a process by which gases are deposited on a surface by air turbulence and gravity, accounts for about 20–25% of tropospheric O₃ removal. However, the mechanisms controlling the O₃ dry-deposition velocity (Vd,O₃) in urban areas are poorly understood, largely due to the scarcity of measurements in such environments.

We hypothesized that: (i) Combining direct O₃ flux measurements with source apportionment of factors controlling ozone levels (e.g., NO and volatile organic compounds [VOCs]) based on comprehensive field measurements in an urban environment is essential for disentangling the simultaneous effects of emission sources and meteorological conditions on Vd,O₃. (ii) Integrating atmospheric chemistry model simulations with flux measurements can elucidate how emissions and environmental conditions influence O₃ formation and removal, providing critical insights for air-quality assessment and urban planning.

Accordingly, we conducted direct eddy covariance measurements of O₃, VOCs (via Vocus PTR-TOF-MS), and NOx ([NO] + [NO₂]) fluxes at a height of 102 m on a meteorological tower in Beijing between April 28 and June 26, 2023. Vertical profiles of meteorological parameters were measured at 15 levels along the tower. Source apportionment analysis of VOCs and NOx was conducted using the positive matrix factorization (PMF) model. Additionally, the Weather Research and Forecasting model with Chemistry (WRF-Chem) was applied to evaluate the contributions of anthropogenic and biogenic emissions to O₃ formation. WRF-Chem model simulations were validated against data from nearby air-quality monitoring stations.

Our results show that Vd,O₃ in the urban environment was primarily controlled by chemical reactions, including O₃ titration by NO and contributions from anthropogenic VOCs. Surface wetness was identified as a key factor influencing Vd,O₃, consistent with our findings from measurements in vegetated environments [1,2]. Comparing urban and vegetated settings highlighted the influence of air turbulence, relative humidity, and chemical interactions on Vd,O₃. These findings provide valuable insights for improving urban O₃ simulations and for air-quality management and urban planning strategies.

1. Li, Qian, et al. "Measurement-based investigation of ozone deposition to vegetation under the effects of coastal and photochemical air pollution in the Eastern Mediterranean." Science of the Total Environment 645 (2018): 1579-1597.‏

2. Li, Qian, et al. "Investigation of ozone deposition to vegetation under warm and dry conditions near the Eastern Mediterranean coast." Science of the Total Environment 658 (2019): 1316-1333.‏

How to cite: Tas, E., Choi, D., Fredj, E., Yibo, H., Yuan, B., and Liu, H.: Ozone Formation and Dry Deposition in Urban Environments: Insights from WRF-Chem Modeling and Eddy Covariance Flux Measurements in Beijing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4060, https://doi.org/10.5194/egusphere-egu25-4060, 2025.

Globally, about 14% of households have no option but to burn their waste.  Open waste burning is a significant source of black carbon (BC) emissions, yet the exposure of those engaged in this practice has not been interrogated. This study provides the first quantification of personal exposure to BC emissions from open waste burning, revealing critical insights into the potential health risks faced by individuals engaged in this practice. Between November–December 2023, we conducted a comprehensive field study in Blantyre, Malawi, monitoring BC exposure among 46 volunteers from 23 households over approximately 20 hours on the day waste was burned at their household. Within each household, one individual responsible for burning waste and one non-burner wore MicroAeth MA200 monitors to capture personal exposure data. To summarize exposure, the average BC concentration was calculated for each a) monitoring period, b) for burning times, and c) for non-burning times. The median of these averages was then used to characterize exposure levels. Results showed that waste burners experienced significantly higher BC exposure than non-burners during both burning periods and the overall monitoring period (Wilcoxon signed rank test, p = 0.04). During burning, the median BC exposure for burners was 12.8 μg/m³, over four times higher than the median exposure of non-burners at 2.9 μg/m³. The median BC exposure for burners during the 20-hour monitoring period was 5.1 μg/m³, compared to 3.0 μg/m³ for non-burners. Notably, BC exposure levels during non-burning periods were statistically indistinguishable between burners and non-burners (Wilcoxon signed rank test, p = 0.44), with median exposures of 3.6 μg/m³ and 2.6 μg/m³, respectively. This study highlights the extreme BC exposure faced by individuals actively burning waste, and underscores the health risks associated with this practice and the need for interventions to mitigate exposure.

How to cite: Vijay, S., Khonje, L., Chatha, M. L., and Tilley, E.: Open waste burning leads to significantly high black carbon exposure amongst burners, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7939, https://doi.org/10.5194/egusphere-egu25-7939, 2025.

Equivalent black carbon (eBC) is generated from the partial combustion of fossil fuels and biomass. The scientific interest in eBC is large because its contribution to the PM_2.5 fraction is high, especially in urban areas. It should be noted that combustion-related aerosols (including eBC) have been linked to adverse health effects and are considered more harmful than other aerosol components. In addition, eBC is considered the second most important component of global warming in terms of direct forcing. Until now. there is a lack of information on eBC concentrations in Poland, mostly due to lack of relevant instrumentation. The position of Poland in the center of Europe, as well as the presence of multiple combustion sources, make this type of measurements and data very relevant to the scientific community, both for health impact assessment and climate change studies. In the present study, the MABI (Multi-Wavelength Absorption Black Carbon Instrument), a new instrument measuring light transmission of particles collected on filters, was assessed.  MABI was developed by the Australian Nuclear Science and Technology Organization (www.ansto.gov.au). The instrument consists of an optical assembly and electronic case. The instrument optics includes, among others, the multi-wavelength light source (7 LEDs), sampler holder, and photodetector. In the instrument, opaque glass is used to scatter the scattered light back through the filter to the detector. MABI offers the advantage of off-line measurements of aerosol light transmission, at seven fixed wavelengths (from 405 nm to 1050 nm). The performance of the instrument was assessed for different types of filters (Teflon and quartz fibre) collected at two distinct atmospheric environments, an urban background site in Krakow, Poland and an urban background site in Athens, Greece. Mass absorption coefficients provided by the manufacturer were used in order to calculate eBC from light transmission data (Ryś and Samek, Atmosphere, 2022). In addition, thermo-optical analysis (Lab OC-EC Aerosol Analyzer, Sunset Laboratory Inc.) was performed on the quartz fibre samples for the determination of elemental carbon (EC) concentrations. EC data were then used in order to calculate site-specific mass absorption cross section (MAC) values for the MABI. Finally, an aethalometer (AE33, Aerosol Magee Scientific) was used in parallel to PM sampling in Athens, in order to provide a reference eBC value, against which the performance of MABI was assessed.

Acknowledgments: This research project was supported by the program “Excellence initiative—research university” for the University of Science and Technology and ATMO ACCESS TNA project

How to cite: Samek, L., Diapouli, E., Ryś, A., Papagiannis, S., Vassilatou, V., Jakhar, R., and Eleftheriadis, K.: Determination of equivalent Black Carbon Concentrations by MABI (Multi-Wavelength Absorption Black Carbon Instrument) and of the respective mass absorption cross section (MAC): Case study for Krakow, Poland and Athens, Greece., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12815, https://doi.org/10.5194/egusphere-egu25-12815, 2025.

Over the past two decades, Bengaluru, a major city in South India, has undergone rapid urbanization and population growth, with significant increase in vehicular traffic, construction, and industrial activities. These changes can enhance local contributions from anthropogenic biomass burning and vehicular emissions, in addition to long-range transported pollutants, adversely impacting air quality, and reducing visibility. We investigated the chemical composition and sources of particulate matter (PM_2.5) in Bengaluru using an Aerodyne Time-of-Flight Aerosol Chemical Speciation Monitor (ToF-ACSM) and two Aethalometers (AethLabs microAeth MA300 and Magee Scientific AE33). This study marks the first in-situ and high time resolution source apportionment of non-refractory PM_2.5 (NR- PM_2.5) in the city using the ToF-ACSM, commencing from post-monsoon sampling period (Aug 2024). Results thus far indicate that organic aerosols (OA) are the dominant (63%) NR-PM_2.5 species, followed by sulphate (~20%) and ammonium (~9%). Positive Matrix Factorization (PMF) analysis identified two primary organic aerosol types namely hydrocarbon-like organic aerosols (HOA) and biomass-burning organic aerosols (BBOA), and two secondary organic aerosols namely less-oxidised oxygenated organic aerosols (LO-OOA) and more-oxidised oxygenated organic aerosols (MO-OOA). The air masses from the northeast (0-90°) direction were found to be associated with elevated levels of MO-OOA, which also correlated well with increased sulphate fraction. These findings highlight the role of local sources like vehicular emissions and waste burning, as well as regional sources including thermal power plant emissions, and oxidised and aged OA. Furthermore, the study includes Diwali and Kannada Rajyotsava celebrations, periods with extensive firework activity. Despite restrictions on conventional fireworks and recommended usage of green crackers, the city witnessed significant particle pollution, with hourly PM_2.5 concentrations exceeding 100 µg/m³ and spikes up to 800 µg/m³. While NR-PM_2.5 and black carbon (BC) increased during the firework periods, the rise in PM_2.5 mass loading was much greater, resulting in incomplete mass closure (only ~16% from NR-PM_2.5 + BC) in contrast to normal periods (~80% from NR-PM_2.5 + BC). Further results and detailed analyses will be presented, including seasonal changes in PM_2.5 composition and sources as we transition from the post-monsoon to the more polluted winter season.

How to cite: Narayana Kalkura, K., Chakraborty, M., Shekar, V., Varghese, E., and Ramachandran, S.: Chemical characterisation and source apportionment of PM2.5 in the cosmopolitan city of Bengaluru, India , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-787, https://doi.org/10.5194/egusphere-egu25-787, 2025.

Air pollutants (PM2.5, PM10, O3, NO2, SO2, and CO) have become a significant environmental concern, particularly in Asian metropolises. The Indian metropolis of Delhi serves as a prime example. A key challenge in addressing urban atmospheric pollution is the significant variation in pollution levels across short distances. Various factors, including nearby industries, vehicle traffic, and population density influence this heterogeneity. Thus, to accurately assess the urban pollution situation, installing multiple pollution monitoring stations that comprehensively cover an entire city, from its center to its periphery is essential. However, the number of stations monitoring entire urban areas increases gradually. For instance, the World Air Quality Historical Database currently lists only four stations in Delhi that have been operational since 2014. The number of stations in Delhi has gradually increased, and in 2024, the same database recorded 45 operational stations, providing more comprehensive coverage of the city. However, the time covered by the available pollution records varies, and, within these periods, there are numerous missing data points. The inconsistencies between stations introduce statistical artifacts when local pollution data are averaged to produce decade-long records that might be representative of the whole city area, making it difficult to assess the actual urban air quality and the effects of policies aimed at reducing urban air pollution. In this study, we propose statistical reconstructions of six daily atmospheric pollution concentrations for all 45 stations in the city of Delhi from 2014 to the present. These reconstructions aim to produce a more consistent database that could better represent the entire city area over 11 years (from January 1, 2014, to January 1, 2025). This reconstructed network is then used to evaluate an ensemble average record that could more realistically represent the daily evolution of air pollution concentration in the city of Delhi since 2014. To accomplish such network reconstruction, we apply the Regression Learner tool in MATLAB to assess 35 machine learning (ML) regression techniques. We select and use only those that perform better in modeling the available records to estimate the missing data. The ML regression models that demonstrated superior performance include: the Fine Tree (Regression Trees family), the Bagged Trees (Ensembles of Trees family), the Optimizable Ensemble (Ensembles of Trees family), the Fine Gaussian SVM (Support Vector Machine family), the Rational Quadratic (Gaussian Process Regressions family), and the Exponential (Gaussian Process Regressions family). In contrast, our analysis revealed that the commonly used multi-linear regression model underperforms compared to 20 other ML regression models. Generally, the proposed methodology can apply to all situations typically addressed in the literature using the multi-linear regression model only because its algorithm is readily available. However, the physical relationships between a given observable and its potential constructors are often nonlinear, rendering the multi-linear regression model suboptimal for such tasks. In the case of the city of Delhi, we demonstrate that the proposed analysis methodology corrects significant biases in the decadal trend for all six network pollution records, and show that from 2014 to 2024, air pollution quality has slightly improved.

How to cite: Scafetta, N. and Shafi, S.: Optimal reconstruction of incomplete urban pollution records with machine learning regression models: a case study for Delhi, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5078, https://doi.org/10.5194/egusphere-egu25-5078, 2025.

Being Germany’s 3^rd largest city, Munich has the nation’s highest number of daily commuters leading to high traffic volumes and adding up to the urban air pollution through emissions from combustion and tire and brake wear. Although urban air quality is generally improving over the past years, all measurement stations by the Bavarian State Office for Environment (LfU) in Munich still exceed the critical NO₂ level of 10 µg/m³ as suggested in the updated WHO guidelines 2021.

For quantifying air quality at high spatiotemporal resolution in the city, we developed a self-sufficient low-cost sensor system equipped with electrochemical cells (ECs) for measuring NO₂, NO, CO and O₃ as well as an optical particle counter.

In summer 2023, we started setting up a dense sensor network of 25 sites in the inner city and since then, we have gathered 22 million data points. Prior to installation at their final locations, we mounted each sensor system for several weeks at an automated measurement station operated by the LfU in order to acquire high-accuracy reference data for calibrating our system. During operation of the sensor network, several nodes are occasionally returned to the reference site and three sensor nodes have been mounted there continuously ever since. In total, 8 million data points have been gathered during these co-location measurements. The combination of frequent rotation of sensor nodes between network locations and reference site as well as long-term co-location nodes yields a unique dataset for a novel calibration approach. Here, we analyze the calibration performance of NO₂; other pollutants will follow.

Firstly, we analyzed the correlation of the EC’s raw hourly signal to the reference station by assessing their coefficient of determination (R²). Remarkably, highest R² values occurred in fall and winter time with temperatures in the range of -5 to +15 °C. Lowest and even negative R² values occurred during summer and during long-term co-locations facing seasonal changes.

Secondly, for assessing a real-world scenario, we analyzed the performance of one node with long-term co-location at the reference station. The raw EC data yields an R² of 0.35 over 27 weeks. By applying a Random Forest Regressor using the first 30 % of the data points for training and including temperature, humidity and NO as features, R² could be increased to 0.7. Currently, we are developing a novel calibration strategy that leverages this extensive co-location data set with advanced machine learning methods to increase the calibration performance and to map air quality within our sensor network at high resolution and accuracy.

How to cite: Wenzel, A., Chen, J., Klama, T., Böhm, F., Angleitner, M., and Lobmaier, R.: Towards high-resolution air pollutants sensing through dense low-cost sensor networks – a case study in Munich, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16784, https://doi.org/10.5194/egusphere-egu25-16784, 2025.

In the last decades, the EU has observed an increase in the use of biomass for energy, and for space heating in particular. Climate change mitigation strategies have favoured the use of wood-based biomass for heat and power supply, to reduce dependency on fossil fuels. However, it can pose risks for ambient air quality and public health, especially when used in small-scale building heating systems in cities. To leverage synergies and avoid trade-offs between climate change mitigation and ambient air quality, policymaking should be supported by quantitative analyses and robust evidence.

This paper presents a framework to assess potential life-cycle greenhouse gas (GHG) emissions and fine particulate matter (PM2.5) impacts on health of wood-based biomass use in small-scale residential heating systems. The framework draws on a life-cycle assessment (LCA) model, and it is applied to 87 EU cities in 23 countries (i) to quantify current GHG and PM_2.5 impacts associated wood-based biomass heating, and (ii) to two scenario analyses that evaluate potential trade-offs and co-benefits of climate change and air pollution mitigation actions. A complementary analysis is also performed to provide insight on the robustness of PM_2.5 results, comparing health effects associated with PM_2.5 using the LCA framework and city-specific emission and effect factors.

Results confirm strong correlation between GHG and PM2.5 impacts. Scenarios for GHG mitigation increased PM2.5 impacts of small-scale biomass heating per capita up to 14%, while those for PM2.5 mitigation reduced PM impacts up to 63%, with GHG mitigation co-benefits (reduction up to 50%) or trade-offs (increase up to 125%). The framework is widely applicable and provides robust results; and the scenario analyses demonstrate the importance of context-specific assessments to inform policymaking.

How to cite: Zauli Sajani, S., Bastos, J., Giuntoli, J., and Pisoni, E.: Assessing synergies and trade-offs between climate change mitigation and air quality effects of small-scale biomass heating in the residential sector, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10545, https://doi.org/10.5194/egusphere-egu25-10545, 2025.

Volatile organic compounds (VOCs) released into the atmosphere by natural and anthropogenic sources play a key role in atmospheric processes. They can react with atmospheric oxidants leading to secondary organic aerosols and tropospheric ozone, with effects on air pollution, human health and climate.

In Europe, the improvement of air quality policies in the last decades has caused some pollutants’ concentrations to decrease. This is the case for nitrogen dioxide and particulate matter concentrations that decreased between 30-50% during 2000-2010, leading to a decreasing trend of the associated health effects on people exposed to them. Yet, 70% of EU citizens lives in urban areas, and 99-97% of this population is exposed to concentrations of ozone and fine particulate matter above the guidelines recommended by WHO in 2021 for protecting public health (EEA, 2021). The Po Valley, located in the North of Italy is one of the areas in Europe suffering the worst air pollution, with several air quality threshold exceedances throughout the years. A recent example was recorded in the city of Milan in winter 2024, when, during several days of high atmospheric pressure conditions, PM 2.5 concentration was above 100 μg/m³, while the WHO recommended threshold is 15 μg/m³ on a 24-hour averaging time.

Within the EU-funded H2020 project RI-URBANS and ACTRIS, we conducted two field campaigns at two urban areas of different size, 200 km apart in the Italian Po Valley: Milano and Bologna. In both cases, VOCs were measured with a Vocus CI-ToF (chemical ionization time of flight) 2R mass spectrometer (Tofwerk, Switzerland) that was deployed first in Milan from January 2023 during one year, then in Bologna in September 2024 for one month. The analysis of our study focuses on sixteen VOCs common to both measuring sites, identified and quantified with a certified VOC mixture and covering the measured mass range 42-371 amu. The absolute concentration, the diel and seasonal variability of the VOC measured in Milan and Bologna are discussed and compared. In particular, we determined the effect of atmospheric dilution and atmospheric reactivity on the measured concentrations. We also discuss the results in terms of potential formation of ozone and secondary organic aerosols.   

How to cite: Marinoni, A., Zannoni, N., Cristofanelli, P., Paglione, M., Rapuano, M., Perfetti, C., Bracci, A., Bellini, A., Barnaba, F., Colombi, C., and Rinaldi, M.: Volatile organic compounds at two urban areas in the Italian Po Valley: Milan and Bologna, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19635, https://doi.org/10.5194/egusphere-egu25-19635, 2025.

Nitrogen dioxide (NO₂) and volatile organic compounds (VOCs) are of concern in urban environments as primary pollutants and as main precursors of tropospheric O₃, which has adverse effects on both human health and vegetation. Furthermore, as a secondary pollutant, its complex nature due to its non-linear chemistry makes it difficult to reduce with the reduction in the precursors.

This study is carried out to spatially characterise the urban air pollution, using NO₂ and VOCs, and to evaluate the recent temporal trends of these compounds, which are closely related to the O₃ formation in the metropolitan area of Valencia. Being the third largest Spanish city, with one of the Mediterranean's largest ports, the complex emission sources contribute to high ozone levels in the surrounding areas.

A total of 97 passive samplers for NO₂ were used in the study area, covering an area of 11 km x 10 km including the urban centre with a resolution of about 1 km x 1 km. The temporal resolution of the measurement covers the winter and summer seasons (one exposure week every February and July, respectively) for the last 8 years, from 2017 to 2024. Meanwhile, the passive samplers for VOCs were installed at 10 selected points through the urban centre among the 97 points, following the main wind direction of the region. The measurement period is two years shorter than those for NO₂ from 2019 to 2024 but covers the same seasons. The measured NO₂ levels were determined using ultraviolet-visible (UV-VIS) spectroscopy, while the VOCs levels were analysed through gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS).

To characterise the spatial patterns of the city, the k-mean clustering is used to group all points where NO₂ levels are measured. As a result, the city is divided into overall two clusters. Cluster 1 is close to road traffic emissions and follows the prevailing wind direction, resulting in relatively high levels of NO₂. Cluster 2 represents the rest of the points, which have lower levels. As for VOCs, the analysis is performed at two specific points, where NO₂ has the highest and the lowest among the given 10 points, in the city centre and in the harbour area, representing Cluster 1 and Cluster 2, respectively. In the city centre, the aromatic hydrocarbons are more abundant, while in the harbour area, the contribution of the aldehydes is greater.

The Theil-Sen method is used for the temporal analysis of each cluster. NO₂ shows a decreasing trend in both clusters. The reduction is more pronounced in Cluster 1 where the levels tend to be greater than the other cluster, especially in winter. However, total VOCs levels seem to be increasing overall. In particular, there is a tendency to increase in winter, while VOCs decrease slightly in summer.

This result shows that the ozone formation regime of this area has been changing as NO₂ levels are decreasing while VOCs are generally increasing. Therefore, ozone levels may be locally increasing.

How to cite: Jung, D., Mantilla, E., Borrás, E., Vera, T., Gómez, T., and Muñoz, A.:  Spatial-temporal analysis of recent trends in nitrogen dioxide (NO2) and volatile organic compounds (VOCs) in the Mediterranean metropolitan area of Valencia city, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18113, https://doi.org/10.5194/egusphere-egu25-18113, 2025.

The influence of traffic regulations on urban air quality has been discussed for years, especially during the COVID-19 lockdowns, when significant shifts in urban air quality were observed, particularly in the concentration of nitrogen dioxide (NO₂), a key pollutant linked to vehicular emissions and industrial activities. This study provides a long-term analyzes the variation of NO₂ levels in an urban environment, and an investigation of the interplay of various influencing factors during the lockdown periods in Munich, Germany, including traffic volume, wind speed, radiation, boundary layer height, humidity, and precipitation.

Using a combination of ground-based stationary and mobile NO₂ measurements in Munich coupled with traffic flow records, we apply statistical and machine learning techniques to identify the primary drivers of NO₂ concentration variability. The analysis reveals the extent to which reductions in traffic during the lockdown contributed to NO₂ declines, while highlighting the modulating effects of meteorological conditions such as wind dispersion and atmospheric stability.

Our findings provide insights into the complex dynamics of urban air pollution and its sensitivity to human activity and weather patterns. By comparing pre-lockdown, lockdown, and post-lockdown scenarios, the study underscores the potential for targeted interventions to achieve sustained improvements in air quality and offers valuable guidance in designing evidence-based strategies to mitigate urban air pollution and its health impacts.

How to cite: Wenig, M. and Ye, S.: Long-Term analysis of the impact of traffic volume and other influencing factors on urban NO2 levels for interpreting the COVID-19 lockdown effects in Munich, Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9698, https://doi.org/10.5194/egusphere-egu25-9698, 2025.

Exposure to air pollution contributes to chronic cardiovascular and respiratory diseases as well as mortality, particularly in urban areas. For assessing the health impacts of air pollution, integrated mobility – emissions – air quality – exposure modelling chains have been developed in recent years (Gurram, Stuart, et Pinjari 2019). Numerous studies have highlighted the importance of considering daily mobility when assessing air pollution exposure (Dias et Tchepel 2018). However, the question of uncertainties associated with these modelling chains remains little studied, in particular uncertainties related to models’ spatiotemporal resolution. This work aims to perform a sensitivity analysis of individual exposure to ambient air pollution with an agent-based mobility model coupled with emission, air quality and exposure models.

This study is based on a modelling chain to assess individuals’ exposure with an agent-based approach. Individuals’ daily mobility and car traffic within the region are simulated with MATSim. As urban air quality is both affected by long-range pollution transport and local pollution sources within the urban canyon layer, spatial resolution of air quality was addressed. To this end, we developed novel methodology to generate a disaggregated car fleet attributing car types (i.e. fuel and Euro norm) to households depending on their socioeconomic characteristics, instead of the state-of-the-art average emitting car. This car fleet model aims to better represent spatial heterogeneities in car traffic emissions. Moreover, air quality is simulated at the street scale with the MUNICH street-network model (Kim et al. 2022) while urban background concentrations are simulated with the Polair3D Eulerian chemical transport model (CTM). The exposure model, at last, combines individual travel patterns and street-level pollution concentrations to assess individuals’ exposure, taking into account ambient air pollution infiltration and exposure in transportation.

To study the modelling chain sensitivity, three scenarios comparisons will be performed to assess the impact of the spatiotemporal resolution of car emissions, air quality and individual activities. First, we compare individuals’ exposure when implementing emissions based on a disaggregated car fleet versus a homogenous car fleet composed of an average emitting car. Secondly, we explore the impact of air quality spatial representation on exposure by comparing the use of the background CTM model alone (Polair3D) and the combined CTM and street air quality model. The third test will compare an exposure model incorporating mobility with a traditional static approach, where individuals stay at home.

References

Dias, Daniela, et Oxana Tchepel. 2018. « Spatial and Temporal Dynamics in Air Pollution Exposure Assessment ». International Journal of Environmental Research and Public Health 15 (3): 558. https://doi.org/10.3390/ijerph15030558.

Gurram, Sashikanth, Amy Lynette Stuart, et Abdul Rawoof Pinjari. 2019. « Agent-Based Modeling to Estimate Exposures to Urban Air Pollution from Transportation: Exposure Disparities and Impacts of High-Resolution Data ». Computers, Environment and Urban Systems 75 (mai):22‑34. https://doi.org/10.1016/j.compenvurbsys.2019.01.002.

Kim, Youngseob, Lya Lugon, Alice Maison, Thibaud Sarica, Yelva Roustan, Myrto Valari, Yang Zhang, Michel André, et Karine Sartelet. 2022. « MUNICH v2.0: A Street-Network Model Coupled with SSH-Aerosol (v1.2) for Multi-Pollutant Modelling ». Geoscientific Model Development 15 (19): 7371‑96. https://doi.org/10.5194/gmd-15-7371-2022.

How to cite: Lannes, M., Roustan, Y., and Coulombel, N.: Sensitivity analysis of a mobility – emissions – air quality – exposure modelling chain to assess individuals’ exposure in metropolitan areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20635, https://doi.org/10.5194/egusphere-egu25-20635, 2025.

Urban air quality monitoring presents unique challenges due to complex emission patterns and operational difficulties. Our work and research combine multiple approaches to address these challenges, focusing particularly on three areas: improving quality of life for vulnerable population groups in cities, managing a sensor network on a construction-site, and leakage monitoring of fugitive greenhouse gas emissions.

Through the DivAirCity project, implemented across five European cities, we developed an integrated monitoring framework that combines quantitative air quality measurements with social equity considerations, specifically addressing the disproportionate impact of pollution on vulnerable communities. Our monitoring framework combines quantitative air quality measurements with social equity considerations, requiring careful attention to sensor placement, data quality assurance, and long-term reliability.

As part of the Green Construction Site of the Future project, we gained valuable experience in deploying and maintaining sensor networks in challenging and dynamic construction environments. Over a two-year period, we successfully operated a continuous monitoring system, investigating dust mitigation strategies and PM2.5 dispersion patterns from source points. This implementation provided valuable insights into the practical challenges of maintaining long-term sensor networks in harsh urban environments.

For our environmental monitoring project MONICO, we demonstrate and evaluate the integration of low-cost sensors, satellite observations, inverse modelling, and drone measurements for quantifying fugitive emissions in the CO₂ infrastructure. Through controlled release experiments, we validated and tested methodologies for selecting and deploying sensor networks around point sources, while addressing challenges in weather influence and data reliability.

Together, these projects contribute to the practical aspects of operating sensor networks across different contexts, contributing to environmentally conscious cities and establishing best practices for effective monitoring strategies.

How to cite: Stoltenberg, M., Ottosen, T.-B., Koust, S., Cappelluti, F., Møller, S., Jørgensen, S., Cox, S., Villadsen, N., Overgaard, L., Simaitis, G., Karoff, C., Vara-Vela, A., Eikeland, A., Knudsen, J., Alberti, R., and Engedal, A. S.: Field Applications with Air Quality Monitoring: From Social Equity to CO2 Infrastructure, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15945, https://doi.org/10.5194/egusphere-egu25-15945, 2025.

Despite recent advancements in technology and purification techniques, industrial pollution continues to pose significant challenges in terms of human exposure and monitoring of air quality close to sources. Optimizing air quality networks and integrating them with advanced spatiotemporal statistical methods is thus essential for effective monitoring of atmospheric contamination.

This study addresses the problem of optimizing the placement of sensors for measuring air pollution at urban and regional scales. Several global optimization techniques, including the GlobalSearch Algorithm, Genetic Algorithm, and Particle Swarm Optimization, are applied to this problem.

Two interpolation methods are used to estimate contamination levels at control points: the standard triangulation-based Natural Neighbour interpolation method for scattered data and Gaussian Process Regression (GPR), which employs covariances derived from a dynamic pollution transfer model. The GPR technique is particularly suitable for simulating smoke-like, narrowly directed industrial pollution at distances of less than a few tens of kilometres from the source.

Numerical experiments were conducted using two pollution datasets: aerosol (PM10) concentrations simulated by the ADMS model for the Dunkirk region in northern France and sulphur dioxide (SO2) concentrations simulated by the CALPUFF model for the Dnipropetrovsk region in Ukraine. The first dataset involves diffuse pollution from multiple anthropogenic and natural sources, while the second involves emissions from industrial point sources.

Optimal sensor placements are identified, and estimation errors are evaluated for the interpolation methods and datasets. The described method could allow the construction of effective air quality networks for different types of atmospheric pollution and provide a means to estimate their effectiveness.

How to cite: Sokolov, A., Delbarre, H., and Karroum, K.: Optimizing Air Quality Sensor Networks Using Gaussian Process Regression and Global Optimization Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11481, https://doi.org/10.5194/egusphere-egu25-11481, 2025.

In recent years, the impact of air quality on health has drawn significant attention. The World Health Organization has highlighted that prolonged exposure to high concentrations of PM_2.5 can increase the incidence and mortality rates of cardiovascular and respiratory diseases, as well as elevate the risk of premature death, particularly among highly sensitive populations. Therefore, reducing air pollution levels and developing air quality policies require collaborative efforts from both the government and the public.

This study uses the WRF-CMAQ model to evaluate the inter-regional interactions of PM_2.5 and to assess the emission reduction benefits of the State Implementation Plan (SIP) implemented by Taiwan's six major cities. Taipei City, New Taipei City, Taoyuan City, Taichung City, Tainan City, and Kaohsiung City are Taiwan's primary urban centers, and their air pollution control efforts are essential for improving environmental quality nationwide.

The SIP measures in the six metropolitan focus on three major sources of pollution. For stationary sources, the policies include stricter standards for large emitters such as factories and coal-fired power plants, along with the promotion of low-pollution technologies. For mobile sources, the measures involve phasing out high-polluting vehicles, increasing the adoption of electric vehicles, and improving public transportation systems. For non-stationary sources, efforts are directed at strengthening the monitoring and control of construction dust and agricultural burning.

The policy's effectiveness is reflected in significant emission reductions: Taipei reduced PM_2.5 emissions by 283 tons; New Taipei, by 72 tons; Taoyuan, by 599.4 tons; Taichung, by 73.3 tons; Tainan, by 2,150 tons; and Kaohsiung, by 2,043 tons. For instance, in Taichung, the latest SIP measures are expected to reduce PM2.5 concentrations from 16.8 μg/m³ to 15.7 μg/m³, a 7% improvement.

Despite these achievements, inter-regional pollutant transport continues to affect the six metropolitan, particularly in southern Taiwan. Future policies must balance regional pollutant transport, climate change, and economic development needs. Moreover, collaboration with neighboring countries will be essential to reduce transboundary pollution.

Overall, the SIP policies implemented in Taiwan's six metropolitan areas have successfully improved air quality and offer valuable insights for reducing air pollution. However, continuous adjustments and strategic refinements will be necessary to address emerging challenges and ensure the long-term effectiveness of these policies.

How to cite: Chang, H.-L., Lai, H.-C., Hsiao, M.-C., and Lai, L.-W.: The impact of metropolitan's Air Quality Policies on air pollution improvement in Taiwan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14898, https://doi.org/10.5194/egusphere-egu25-14898, 2025.

Vehicle emissions are a significant source of greenhouse gases and air quality pollutants in urban areas, yet current city-scale CO and CO₂ emission inventories may not accurately reflect real-world conditions. In Toronto, Canada, traffic emissions contribute to approximately one third of the city’s total CO₂ emissions. As a part of the Toronto Atmospheric Monitoring of Emissions (TAME) project, the goal of this work is to investigate the emission signatures of the vehicle fleet in the Greater Toronto Area (GTA) under different engine operating conditions and seasons. Specifically, we are interested in the role of ‘cold-start’ emissions (i.e. emissions during the engine and catalyst warm-up period) on the distribution of CO emissions in the GTA. As exhaust aftertreatment technologies improve for gasoline engines, air quality emissions that occur before the catalytic converter has warmed are expected to contribute an increasing, and possibly dominant, proportion of non-CO₂ vehicle emissions. Simultaneous measurements of CO and CO₂ were collected by deploying calibrated low-cost sensors in parking garages at the University of Toronto over several months. To calculate the CO and CO₂ enhancements from each vehicle emission plume, a peak-finding algorithm was developed. The ΔCO/ΔCO₂ ratio of the enhancements is used as an emission signature for each vehicle. The results from this study are compared to mobile and stationary measurement campaigns conducted by Environment and Climate Change Canada using a high precision analyzer (cavity ring-down spectrometer (CRDS)).  We assess the impact of choices made about data collection, peak-finding, and cold-start definitions. Using one approach, the range of ΔCO/ΔCO₂ ratios observed is 0.1 to 779 ppb CO/ ppm CO₂, with a median of 14 ppb CO/ ppm CO₂. On average, the cold-start emission ratios are observed to be at least 2 times greater than those of warm vehicles. These results can be used to update CO and CO₂ emission inventories to more accurately capture activity patterns and independently verify emission reductions as urban vehicle fleets transition to electric.

How to cite: Corapi, A., Murphy, J., Panas, M., Ward, E., Wunch, D., Ars, S., and Vogel, F.: Investigating the impact of cold-starts on the distribution of vehicle emissions in the Greater Toronto Area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12094, https://doi.org/10.5194/egusphere-egu25-12094, 2025.

Urban air pollution and greenhouse gas (GHG) emissions stem from diverse sources, including transportation, heating, buildings, waste management, industrial and agricultural activities, and natural events like forest fires. Simultaneous monitoring of air pollutants and GHGs with high selectivity and sensitivity is crucial for identifying and evaluating their sources and sinks, as well as understanding their interactions. Accurate measurements across various spatial and temporal scales are essential for modeling and validating emission inventories or satellite observations.

Traditionally, solutions for monitoring air pollutants or GHGs with high precision and temporal resolution have been "one-gas-one-instrument," resulting in large, stationary setups with high energy consumption. MIRO Analytical’s compact laser absorption spectrometer that integrates multiple mid-IR lasers enables simultaneous high-precision measurements of greenhouse gases (CO2, N2O, H2O, CH4), pollutants (CO, NO, NO2, O3, SO2, NH3), and trace gases (OCS, HONO, CH2O) within a single instrument. With a time-resolution of up to 10Hz, it is well-suited for detecting the relationships between co-emitted pollutants and GHGs as well as eddy-covariance flux studies.

In our presentation, we will showcase examples of our instrument's applications for mobile monitoring of 10 GHGs and air pollutants in urban areas, as well as airborne measurements using airships or planes. Additionally, we will present results from parallel monitoring with our instrument and conventional gas analyzers used for GHG and air pollutant measurements. This demonstrates our instrument's capability to serve as an all-in-one solution, replacing up to seven standard gas analyzers and enabling a wide range of new mobile multi-compound gas monitoring applications, such as in airplanes or cars.

How to cite: Hundt, M., Bruckhuisen, J., and Aseev, O.: Advanced Mobile Monitoring of Greenhouse Gases and Air Pollutants Using a Compact Spectrometer, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17440, https://doi.org/10.5194/egusphere-egu25-17440, 2025.

The NOAA Chemical Sciences Laboratory has led a series of recent airborne campaigns in the U.S. to investigate urban air quality, emissions and greenhouse gases.  The Atmospheric Emissions and Reactivity Observed from Megacities to Marine Areas (AEROMMA) flew the three largest U.S. cities (New York, Chicago and Los Angeles) in 2023 on the NASA DC-8 with a comprehensive suite of trace gases, aerosols, radiation and meteorology.  The Airborne and Remote Sensing Methane and Air Pollutant Surveys (AiRMAPS) is a series of campaigns sampling urban areas and oil and gas basins over 3 years.  Airborne measurements during AEROMMA probed nonlinear O₃ photochemistry in New York, Chicago and Los Angeles. The mean ozone production efficiency (OPE), the ratio of O_x (O₃ + NO₂) to NO_x oxidation product enhancements, were 9 ± 4 (1 s), 6 ± 3 and 6 ± 3 ppbv ppbv^-1, respectively. OPE exhibited a nonlinear, inverse dependence on total reactive nitrogen (NO_y, a proxy for initial NO_x) and a positive correlation with ∆VOC/∆NO_y. A zero-dimensional photochemical model supports these observed OPE dependences on NO_x and VOCs and shows that OPE is a distinct metric from total O₃ production that may be informative to the development of O₃ pollution control strategies.  AEROMMA flights also quantified the magnitude and sources of urban methane (CH₄) emission from in-situ measurements of CH₄, CO­­₂, CO, and C₂-C₅ alkanes in Los Angeles. Using the CA Air Resources Board CO emissions inventory alongside CH4/CO enhancement ratios, the analysis determines summertime CH4 and C2–C5 alkanes emissions.  Roughly half of Los Angeles CH₄emissions are from natural gas sources and half from sources such as landfills and dairies.   Comparison to historical aircraft campaigns from 2010-2023 shows declining CH₄ but increasing ethane emissions, with the latter due to changes in pipeline natural gas ethane content.

How to cite: Brown, S., Chace, W., Woamck, C., Schafer, N., and Peischl, J. and the The AEROMMA and AiRMAPS Teams: Urban Air Quality and Greenhouse Gases from Recent Airborne Field Campaigns in the United States, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14289, https://doi.org/10.5194/egusphere-egu25-14289, 2025.

As Asian urban areas continue to expand, so will their contribution to global greenhouse gas (GHG) emissions, driven primarily by increases in fossil fuel combustion. Left unchecked, these emissions will negatively impact air quality and climate, therefore it is imperative that emission sources are properly identified and accounted for in emission inventories. Enhancement ratios of GHGs have been used to characterize regional emissions as either dominated by fossil fuel combustion or biomass burning. In particular, airborne assessment of short-term continuous emission ratios has proven useful to quantify relative contributions of fossil fuel and biomass burning to GHG emissions. The 2024 Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign, flying over the Philippines, Korea, Thailand, and Taiwan, sampled a variety of emissions including significant biomass burning, local urban pollution, and transport events. This work will explore the impact of GHG emissions on the distinct pollution of cities including Manila, Bangkok, Chiang Mai, Seoul, and Kaohsiung. The incorporation of missed approaches within urban areas allowed us to sample boundary layer pollution within these cities.  As airborne GHG measurements over Southeast Asia are scarce if not nonexistent, this work provides a crucial link between established ground-based measurements and state-of-the-art satellite observations such as those from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS). Analysis of GHG enhancement ratios in Asia will lead to more accurate emission inventories which can be used to implement more effective GHG control measures leading to improved air quality and minimizing the effect on climate.

How to cite: Miech, J., DiGangi, J., Diskin, G., Choi, Y., Moore, R., Ziemba, L., Gallo, F., Jordan, C., Shook, M., Winstead, E., Wiggins, E., Roy, S., and Gatebe, C.: Using airborne greenhouse gas enhancement ratios for source apportionment in Asia during ASIA-AQ, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3684, https://doi.org/10.5194/egusphere-egu25-3684, 2025.

Continuous measurements of carbon dioxide and black carbon emissions in Kwabia Ghana are presented.  The community is discrete and dominated by wood burning.  Here a mass balance array is deployed to measure upstream and downstream concentrations of black carbon, PM2.5, and carbon dioxide.  A report on the 2022 deployment demonstrates that top-down flux measurements using a plume superposition modeling technique is innovative and comprehensive. Accurate measurements of carbon dioxide and black carbon provides a combined quantification of the dynamics of green house gas emissions, particle/air-quality emissions, and the devastating impact of deforestation.  Results show that morning and evening cooking time introduce dangerous levels on air-quality and the subsequent emission of carbon dioxide to the atmosphere. 

How to cite: McGillis, W., Apraku, C., Chillrud, S., and Culligan, P.: In situ measurements of emissions from Kwabia Ghana, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14294, https://doi.org/10.5194/egusphere-egu25-14294, 2025.

The ICOS-Cities project aims at evaluating different observational approaches to determine CO2 emissions from large cities, such as Paris (France). A developing approach consists in building a network of three (half-)autonomous FTIR spectrometers used for CO2 total column retrieval: two EM27/SUN placed up and down the prevailing wind axis (Gonesse and Saclay) and one IFS 125 HR placed in Paris Center (Jussieu). These measurements are complementary to the surface measurements carried out in Paris for a decade in the framework of the ICOS European research infrastructure. 

When the wind blows in the instrument location axis, the spatial gradient between stations is considered to be representative of the enhancement due to the surface fluxes in the area in between. It is therefore the signal assimilated by inverse modelling for surface fluxes calculations. The columns are driven not only by the geophysical variations, but also by measurement-specific effects such as solar zenith angle) and measurement noise. Part of our work consists in characterizing these effects so as to be able to make the best use of gradients in calculating fluxes. The expected XCO2 gradients (Saclay-Gonesse) are derived from WRF-GHG modelling that accounts for prior inventory of the Paris area emissions, large scale transport of CO2 constrained by a global inversion, and atmospheric transport constrained by ECMWF meteorological parameters.  The ratio of the measured and modelled gradients is used to estimate a correction to the prior inventory leading to new estimates of the Paris area emissions.  The aim is to analyse the potential contribution of total column estimates, with respect to surface measurements, to estimate urban emissions.

How to cite: Doc, J., Ramonet, M., Lopez, M., Bréon, F.-M., Maquart, B., Cassagne, M., Leuridan, H., Jeseck, P., and Té, Y.: Greenhouse gases total column measurements from ground-based FTIR spectrometers in the Paris' region for emission inventory optimization., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5924, https://doi.org/10.5194/egusphere-egu25-5924, 2025.

Accurate and timely monitoring of urban CO₂ emissions is essential for tracking progress toward climate goals and enabling effective policy interventions. In the Netherlands (NL), emissions arise from industrial, agricultural, and transportation sources. Traditionally, emission reporting in Europe has relied on annual bottom-up inventories based on activity data and emission factors, aggregating emissions from sectors such as energy, transport, industry, and agriculture. While these methods have contributed to reducing anthropogenic emissions, they lack the granularity and timeliness required for real-time decision-making or for tracking progress toward more immediate climate targets. This highlights the urgent need for enhanced spatial and temporal resolution of urban CO₂ emissions data.

This study seeks to address this gap by leveraging a novel approach: using tropospheric NO₂ columns from TROPOMI as a proxy for urban CO₂ emissions. In recent years, a general consensus has been reached that satellite-derived NO₂ from instruments like OMI and TROPOMI are indicative of surface NO₂ concentrations and can be used to estimate top-down NOx emissions. We hypothesize that combining TROPOMI tropospheric NO₂ data with advanced deep learning (DL) methods will enable near real-time estimation of urban CO₂ emissions, offering a high-resolution, dynamic approach to emission monitoring.

Our research focused on the Netherlands, covering the period from 2018 to 2023 for model training and 2024 for validation. Various DL architectures to process TROPOMI data and predict local emissions were evaluated, incorporating ground-based emission inventories and additional metadata. Our goal is to identify the most effective DL models for improving emission estimation accuracy, reducing uncertainty, and enhancing the timeliness of reporting.

Although the initial focus is on the Netherlands, with its well-established monitoring systems (the "brownfields" effect), our methodology has broader applicability for regions with limited emissions data, such as those in developing areas (the "greenfields" effect). 

A key aspect of this research is the development of trustworthy AI, ensuring the deep learning models used are transparent, reliable, and interpretable. By combining cutting-edge AI techniques with Earth observation data and validating the results against ground-based inventories, we created a robust framework for scaling emissions monitoring, especially in regions with limited infrastructure.

How to cite: Curier, R. L., Ham, S., Stoorvogel, J. J., and Bromuri, S.: Bridging the Gap in Emission Reporting: Synergistic Use of AI and Earth Observation for Real-Time Insight on Urban CO₂ Emissions., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9108, https://doi.org/10.5194/egusphere-egu25-9108, 2025.

This study presents a comprehensive analysis of CO₂ emissions in Glasgow, utilizing a dense network of Berkeley Environmental Air quality and CO2 Network (BEACON) CO2 sensors for the year 2022. The research employs a sophisticated model setup, integrating high-resolution meteorological data from the Weather Research and Forecasting (WRF) model with a Lagrangian Particle Dispersion model for footprint modeling. A Bayesian inversion framework  developed by University of California, Berkeley refines a prior emission inventory using observed CO₂ concentrations and sensitivity footprints. The analysis reveals a 23% increase in overall mean anthropogenic emission for the year 2022 compared to available prior inventory estimate with significant seasonal variations. Winter fluxes  were  70%  higher than prior estimates, driven by increased heating demands and diminished biospheric uptake. Summer showed a 29% reduction, a combined impact of less energy demand for domestic heating and CO₂ uptake . A moderate negative correlation (R² = 0.58) between winter emission episodes and minimum daily temperatures was observed, highlighting the impact of domestic heating on CO₂ emissions. The study also found a 9.8% increase in total posterior emissions on weekends compared to weekdays, a smaller gap than the 21.8% difference in prior values. Our inverse model actively adjusts the emission values based on the real time CO2 measurement from sensors and high-resolution meteorology driven transport model at finer temporal scale, which is very valuable in making adjustments and validating local authority inventory data. Spatial analysis revealed that the most substantial emission changes were concentrated in areas corresponding to the 95th percentile of the posterior-prior emission difference. These regions, consistently exhibiting higher emissions throughout the year, reflect the combined impact of transport and heating sources in the city. These results highlight the urgent necessity for both enhancing building energy efficiency and  targeted strategies to reduce street level vehicular emission. Furthermore, the results point out the importance of continuous, sensor-based measurements for achieving a more precise representation of urban emission sources. This study also examines the impact of Glasgow's Low Emission Zone (LEZ) implemented in June 2023. A comparative analysis of CO₂ emissions and concentrations before and after the LEZ implementation provides insights into its effectiveness in reducing urban emissions. The findings underscore the importance of seasonal variability in emission patterns and the need to account for both anthropogenic activities and natural processes when analysing CO₂ fluxes at finer temporal and spatial scales.

How to cite: Kodoli, S., Michie, C., Davison, C., Tachtatzis, C., Asimow, N., and Cohen, R.: High-Resolution Urban Emission Mapping: Sensor-Driven CO2  Inverse Modeling in Glasgow, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9568, https://doi.org/10.5194/egusphere-egu25-9568, 2025.

Cities are hot spots on greenhouse gas (GHG) emissions, yet green infrastructure (GI) such as green spaces and parks provides potential solution for reducing urban carbon footprints through photosynthetic uptake and carbon sequestration. Studies have shown that the offset of urban vegetation uptake on local anthropogenic CO₂ emissions varies between 2% and 100%, underscoring the complexity associated with this solution. Quantifying CO₂ capture by GI is challenging due to the interplay of photosynthetic uptake and respiration, seasonal variability, the heterogeneous distribution of GI, urban climate, and soil conditions. While biosphere models have been used to quantify carbon exchange processes, they are often employed at the ecosystem level and at coarse spatial resolutions(10-100km), making them insufficient for capturing biospheric signals at the urban scale(10m-1km). Therefore, high-resolution quantification of biogenic CO₂ fluxes is essential for understanding their role on urban GHG budget.

This study estimates biogenic CO₂ fluxes for 2023 in the Metropolitan Area of Barcelona (AMB) at a 10 m resolution using the Vegetation Photosynthesis and Respiration Model (VPRM). Our approach integrates vegetation indices derived from Sentinel-2, a detailed vegetation land cover dataset constructed by merging local land cover and tree maps, and meteorological inputs (temperature and shortwave radiation) from the Weather Research and Forecasting (WRF) model coupled with an urban canopy scheme that better represents atmosphere exchanges inside the urban canyons. A sensitivity analysis is conducted comparing different VPRM configurations including flux parameterization, input satellite-derived vegetation indices and modifications to land cover map. To constrain the modelled biogenic CO₂ emissions and determine their uncertainties, the estimated biogenic fluxes are evaluated with atmospheric CO₂ mixing ratios observations from the AMB GHG monitoring network using an atmospheric transport model (WRF-Chem) in a passive tracer approach. This research presents an improved method to estimate the urban biogenic CO₂ fluxes and provides guidance for improving and creating more robust ways of accounting for the contribution of urban green to aid policy and urban planners in the design and implementation of GI.

How to cite: Luo, Q., Segura-Barrero, R., Badia, A., Lauvaux, T., Li, J., Chen, J., and Villalba, G.: High-Resolution Quantification of Biogenic CO2 Fluxes over a Metropolitan Area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11711, https://doi.org/10.5194/egusphere-egu25-11711, 2025.

Urbanisation and climate change are global megatrends. Currently, more than half of the world's population lives in urban areas and this number is expected to increase in the future. Urban areas are vulnerable to climate change-induced extreme weather events due to urban characteristics such as the urban heat island (UHI), but are also large sources of anthropogenic greenhouse gas emissions. Urban green spaces are increasingly being explored as a solution to offset these emissions and adapt to climate change in cities. Our knowledge of urban carbon sequestration is mainly derived from research in natural ecosystems and is lacking in the urban context. Urban environments are characterized by a high degree of complexity due to their fragmented and heterogeneous nature and intensive anthropogenic management and modification. More knowledge on carbon sequestration in different urban vegetation types and the influence of management is needed to guide the planning of resilient and climate-smart urban green spaces.

Here, the JSBACH, a process-based land surface model, was used to understand how carbon sequestration in different Nordic urban vegetation types responds to possible future climates in Finnish cities. JSBACH, previously tested for urban conditions in Helsinki, was used to simulate seven urban vegetation types in 20 Finnish cities between the years 2006 and 2100. The urban vegetation types used were urban lawn, park site with Tilia cordata, urban birch-dominated forest, mesic meadow and dry meadow. In addition, irrigated versions of urban lawn and park with Tilia cordata were also simulated. JSBACH was driven by daily EURO-CORDEX data from global models CanESM2, MIROC5 and CNMR-CM5 using  RCP4.5 and RCP8.5 emission pathways downscaled to the EUR-44 domain.

Based on these simulations, urban ecosystems with trees were more consistent carbon sinks and less sensitive to future weather conditions than vegetation types dominated by grasses. Drought decreased primary production in some vegetation types during summertime, but on an annual scale, productivity was mainly driven by the length of the growing season.  In these simulations irrigation caused a decrease in Net Ecosystem Production (NEP) compared to their non-irrigated counterparts, highlighting the role of moisture as a driver of respiration.

How to cite: Koiso-Kanttila, A., Backman, L., and Kulmala, L.: Carbon Sequestration across Urban Vegetation Types in Changing Climate in Finnish Cities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14947, https://doi.org/10.5194/egusphere-egu25-14947, 2025.

Emissions of greenhouse gases (GHGs) are known drivers of climate change and related effects; however, they continue to increase every year despite current reduction efforts. Rising populations worldwide as well as changes in land use and in anthropogenic activities contribute significantly to this observed, unmitigated increase in GHG emissions. Cities are clear hotspots for anthropogenic sources of GHGs, and a regional or national emission reduction plan is not enough to effectively target their complex and unique source compositions or relative contributions. To push local or city-level action plans forward, GHG flux towers in three pilot cities (Zurich, Munich, and Paris) were established for long-term eddy-covariance measurements as part of the H2020 ICOS Cities/PAUL project (https://www.icos-cp.eu/projects/icos-cities). The cities were chosen to offer insight into how city size, topography, and source mixture affect GHG and trace gas emissions.

We present results from emission source attribution of carbon dioxide (CO₂), carbon monoxide (CO), and methane (CH₄) using a combination of flux measurements, footprint modelling, and local emission inventories. Turbulence measurements observed at or derived from information at each tower were implemented in the Flux Footprint Prediction (FFP) model (Kljun et al., 2015) to develop highly spatially- and temporally-resolved flux footprints for each site. These footprints were subsequently combined with (1) annual emission inventories at high spatial resolution and (2) emission sector-specific hourly temporal profiles for the aforementioned trace gases to estimate the relative contributions of emission sectors to the flux signal at each tower. We also incorporate outputs of the Vegetation Photosynthesis and Respiration Model (VPRM; Mahadevan et al., 2008) to quantify biogenic contributions to the CO₂ flux signal. The presented results highlight seasonal and diurnal trends as well as spatial hotspots within the flux footprint of sector-separated CO₂, CH₄ and CO fluxes in cities with diverse characteristics, all of which is valuable for source attribution and for supporting localized and targeted emission reduction plans.

How to cite: Molinier, B., Kljun, N., Aigner, P., Brunner, D., Chen, J., Christen, A., Constantin, L., Denier van der Gon, H., Hilland, R., Holst, C., Kühbacher, D., Li, J., Maiwald, R., Stagakis, S., Super, I., and Vardag, S.: Identifying Greenhouse Gas Emission Trends and Validating Hotspot Locations via Flux Measurements and Footprints in Three Pilot Cities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2888, https://doi.org/10.5194/egusphere-egu25-2888, 2025.

Accurate quantification and monitoring of urban fossil-fuel CO₂ (FFCO₂) emissions at improved spatial granularity are critical to emission control and climate change mitigation policies. In this work, we use a top-down Bayesian inversion method and Eulerian transport modeling to constrain FFCO₂ emissions from the Xiamen-Zhangzhou-Quanzhou metropolitan area, China, based on high-resolution areal snapshots of total column CO₂ (XCO₂) from OCO-3 Snapshot Area Maps (SAMs) during September 2019 to July 2023. The emissions from point sources and different areas are constrianed simultaneously, including the area sources Xiamen, local power plants in Xiamen, and other adjacent urban areas. Observed XCO₂ enhancements range from 0.70±0.53 ppm to 2.29±1.16 ppm, indicating potential capability of OCO-3 SAMs on detecting emission signatures.  We show the mean posterior emission from Xiamen of about 7.79 ± 0.92 MtC/yr, being within the spread of different inventories and 34 % higher than their average. Several challenges hampering the inversion performance are revealed, including the spatial displacements of the modeled and observed enhancements, the limited representation of local power plants, and data availability. The results provide insights on inversely disentangling imprints of emission sources based on dense space-borne observations, facilitating applications of future missions with improved XCO₂ mapping coverage and frequency. 

How to cite: Ye, X., Li, W., Lauvaux, T., Lin, S., Zhang, Z., Lin, Y., Hua, J., and Lin, J.: Constraining Anthropogenic CO2 Emissions using XCO2 Observations from OCO-3 over Xiamen-Zhangzhou-Quanzhou Metropolitan Area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15751, https://doi.org/10.5194/egusphere-egu25-15751, 2025.

The Munich Urban Carbon Column network (MUCCnet) consists of five solar-tracking Fourier Transform spectrometers (EM27/SUN) measuring column-averaged mole fractions of carbon dioxide (XCO₂), methane (XCH₄), and carbon monoxide (XCO). They are strategically placed in the center of Munich and in every cardinal direction. Starting with one instrument in 2015, MUCCnet has been collecting data continuously with five instruments since 2020, allowing a detailed analysis of Munich's urban greenhouse gas emissions based on inverse methods. To this end, we use the Bayesian inversion approach: We compute the observed enhancements in dependence of the wind direction using one network site as background (observed background) and derive spatially resolved emissions. The forward model uses a Munich-specific inventory (100×100 m² resolution) for anthropogenic fluxes and the Vegetation Photosynthesis and Respiration Model (VPRM) for biogenic fluxes. The transport is modeled with the Lagrangian particle dispersion model STILT.

A critical aspect of our analysis is the estimation of uncertainties within the inversion framework. Balancing the confidence in transport and measurements against prior information (inventories) is of great importance. Furthermore, we investigate the number and spatial distribution of state vector parameters based on the available degrees of freedom for signal. The choice of an appropriate background is crucial, since the urban enhancements for Munich are typically below 1 ppm, which is small (< 1%) compared to the background XCO₂ concentrations (> 400 ppm). In addition to the observed background approach, we investigate background approaches derived from models (e.g. ICON-ART), and fitted, a posteriori backgrounds from the inversion approach.

Our inversion results represent spatially resolved, long-term, top-down CO₂ emission estimates for Munich. Over a comprehensive measurement period of five years, we highlight seasonal and annual trends, providing valuable insights into Munich's CO₂ emissions.

How to cite: Stauber, J., Chen, J., Klappenbach, F., Li, J., Luther, A., Makowski, M., Tang, H., Ponomarev, N., and Brunner, D.: Assessment of Munich’s CO2 emissions via Bayesian inversion using MUCCnet data from 2020-2025, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17841, https://doi.org/10.5194/egusphere-egu25-17841, 2025.

Northern Italy is one of the most polluted and densely populated areas in Europe. The diversity of land use in the Po basin makes this region an important contributor to greenhouse gas emissions from different sources. Medium and large cities, as well as industrialised areas, contribute significantly to emissions from industrial processes, combustion, waste management and natural gas distribution.

As part of the H2020 RI-URBANS project (https://riurbans.eu/) and in synergy with PNRR “ITINERIS” Project, a pilot study has been carried out in the Milan urban area with the aim of supporting the local authorities to evaluate the effectiveness of air quality policies and the effects of pollutants on human health. A one-year long measurement campaign was carried out using a mobile platform in the premises of the CNR facility (AdRMi1, 45°28’47"N 9°13’54"E; 120 m a.s.l.), located in in the urban area of Milan, in the University district. The mobile platform has been equipped with a suite of instruments (ACSM, Vocus Chemical Ionization TOF-MS, Aethalometer, NOx CLD) to provide near real time (NRT) information on aerosol source partitioning and to characterise nanoparticle contributions from urban hot spots. From July 2023 to March 2024, the mobile platform has been equipped with a Cavity Ring Down Analyser for continuous observations of carbon dioxide (CO₂) and methane (CH₄).

In this work we will provide a first characterization of the diel cycles and seasonal variations (from late summer to early spring) of atmospheric CO₂ and CH₄ in the urban area of Milan. Thanks to the combined analysis of other atmospheric tracers, first insights about the influence by atmospheric processes (i.e. PBL dynamics) and emission sources (fossil fuel and biomass burning combustion, biogenic, waste management) will be discussed. Finally, we provided a preliminary “top-down” estimate of CH₄ emission for the Milan urban area by using the interspecies correlation approach: the obtained values were higher (but still within the range of uncertainties) than emissions provided by statistical “bottom-up” inventories (e.g. INEMAR).

Acknowledgments:  RI-URBANS t has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101036245. This reserach was partially supported by the European Unione- Next Generation EU, Missione 4 Componente 2 - CUP B53C22002150006 -  IR0000032 – ITINERIS, Italian Integrated Environmental Research Infrastructures System

How to cite: Cristofanelli, P., Zannoni, N., Apadula, F., Barnaba, F., Bracci, A., Bellini, A., Calzolari, F., Diliberto, L., Manca, G., Mardonez, V., Magnani, C., Montaguti, S., Renzi, L., Zazzeri, G., and Marinoni, A.: Urban CO2 and CH4 atmospheric measurements in the Milan city area (northern Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17963, https://doi.org/10.5194/egusphere-egu25-17963, 2025.

Quantifying road traffic CO2 emissions is critical for urban climate and sustainability studies. However, detailed modeling often requires high-resolution input data that is unavailable in many regions. To address this gap, we present a simplified regression-based model that quantifies traffic-related CO2 emissions within Local Climate Zones (LCZs) using readily available data such as building surface area, asphalt surface area, population, traffic lights, and road type. This approach minimizes computational requirements and circumvents the need for traffic data, offering a practical alternative for regions with limited resources.
Our results show that road type and asphalt surface area are the most influential variables in describing CO2 emissions. Median CO2 emissions from built LCZs are 1.8 times higher than those from land cover LCZs. The generalized model can explain up to 69% of the emissions for some LCZ. Based on this model, we introduce a look-up table for LCZ-specific traffic CO2 emissions, providing a user-friendly tool to estimate emissions in data-scarce regions. This simplified methodology emphasizes accessibility and efficiency while maintaining robust results, making it an invaluable resource for urban emission studies.

How to cite: Al-Jaghbeer, O., Järvi, L., Fung, P. L., and Paunu, V.-V.: Mapping and Modeling CO2 traffic emissions within local climate zones in Helsinki, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1581, https://doi.org/10.5194/egusphere-egu25-1581, 2025.

Cities around the globe are aiming to reduce their carbon dioxide emissions, but monitoring and validating urban CO₂ emissions is a major challenge. This motivates the ICOS cities PAUL project to use a combination of different measurement and modelling techniques to provide observation-based emission estimates in three pilot cities: Zurich, Paris, and Munich. The challenge comes due to large variations in emissions and concentration gradients, high uncertainties in prior estimates, and inherent modeling errors. Here we present the results of an inverse modeling study for the city of Paris, which builds on the insights gained from similar simulations conducted for Zurich. Our approach employs the state-of-the-art atmospheric mesoscale model ICON-ART, which we ran in conjunction with an ensemble Kalman smoother to optimize CO₂ fluxes based on simulated and measured concentration differences.

Paris offers advantages for mesoscale model simulations due to its flat terrain and large size, unlike Zurich, where simulations were challenged by the city's complex topography. Furthermore, the CO₂ measurements in Paris, which were collected from a network of 2 tall towers inside and 7 towers outside the city, were easier to represent by the model due to their larger spatial representativeness compared to the more locally influenced rooftops measurements in Zurich.

The ICON-ART model simulations were performed for two offline nested model domains. The outer domain covers Central Europe with a spatial resolution of 6.5 km and was chosen large enough to serve as initial and boundary conditions for the simulations over both Zurich and Paris. The inner, high-resolution domain is centered on the Île-de-France region with a spatial resolution of 1 km. According to our previous experience with Zurich simulations, the atmospheric transport is well simulated by ICON-ART in most weather situations with the exception of low wind conditions, where relative errors in wind speeds and the corresponding dilution of CO₂ emitted from the city are the largest. The prior anthropogenic CO₂ fluxes were based on the anthropogenic inventory data prepared by AIRPARIF for the Île-de-France area at 0.5 km spatial resolution and on TNOGHGco 2018 data (1 km) for the rest of Europe. Biogenic fluxes were computed online using the Vegetation Photosynthesis and Respiration Model (VPRM), integrated online into ICON-ART.

In this presentation, we analyze the performance of ICON-ART model against meteorological and CO₂ observations in and around Paris, and demonstrate initial results from emission inversion experiments. Furthermore, we contrast the results with those obtained for Zurich to emphasize the different challenges and modelling capabilities in the two cities.

How to cite: Ponomarev, N., Rubli, P., Stuart, G., Ramonet, M., David, L., Emmenegger, L., and Brunner, D.: Estimation of carbon dioxide fluxes in the city of Paris using the ICON-ART-CTDAS model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8467, https://doi.org/10.5194/egusphere-egu25-8467, 2025.

Cities are one of the main sources of greenhouse gases, accounting for over 70% of global CO2 emissions. Accurate quantification of these emissions through direct observations is crucial for developing and assessing the effectiveness of adopted mitigation strategies.

As part of the European project ICOS Cities (https://www.icos-cp.eu/projects/icos-cities), three eddy covariance towers were installed in the Paris area to capture the variability of surface-atmosphere CO₂ fluxes as a function of an urbanization gradient. Specifically, the selected sites were chosen to be representative of a highly urbanised and densely built-up area (Jussieu), an urban forest (Vincennes), and a heterogeneous area combining highly urbanised areas with areas of vegetation (Romainville). The observations from the urban sites were also integrated with the EC flux measurements conducted on the ICOS atmosphere tower of Saclay and the observations from the ecosystem sites of Fontainebleau (FR-FON, forest) and Grignon (FR-GRI, crop).

Long-term measurements of CO₂ fluxes (2 years for the sites of Romainville and Jussieu and 1 year for the site of Vincennes) showed seasonal dynamics that reflected their respective degrees of urbanisation. The Jussieu site, in the city center, was constantly dominated by anthropogenic CO2 emissions, with maximum emission (up to  15 µmol m^-2 s^-1) during the winter months (November-February) and low absorptions (up to  -2.5 µmol m^-2 s^-1) during the summer (July-August) in the central hours of the day. On the other hand, the mixed urban forest of Vincennes showed a strong biogenic signature, characterized by a predominant CO₂ absorption in the central hours of the day (up to -10 µmol m^-2 s^-1 in the months of May, June and July). The 100 m-tall tower of Romainville  showed instead the coexistence of anthropogenic and biogenic fluxes, each contributing its own seasonal and daily variations to the measured flux. A comparison between our observations and the emissions of the City of Paris will be included in the presentation.

How to cite: Laura, B., Depuydt, J., Herig-Coimbra, P.-H., Fortineau, A., Feron, A., Stella, P., Buysse, P., Kalalian, C., Nief, G., Ramonet, M., and Loubet, B.: Eddy covariance measurements of CO2 fluxes along an urban-to-rural gradient in the Paris area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18076, https://doi.org/10.5194/egusphere-egu25-18076, 2025.

Within the research project DE-CIST (“Developing Energy Communities with Intelligent and Sustainable Technologies”) a novel, AI-based Energy Demand Simulator has been developed by our project partners. The Energy Demand Simulator determines the energy efficiency and energy saving potential of the residential building stock in Rotterdam (The Netherlands). By adding socio-economic features to the Energy Demand Simulator, the tool will guide policy makers in developing building renovation strategies on different scales, allowing to design household-level renovation packages which consider social, financial, and physical requirements.  

From such building renovation scenarios, we calculated greenhouse gas emissions and the emission reduction potential of the renovation actions. This insight will allow policymakers to optimize the renovation strategies also for climate change mitigation. We conducted a series of high-resolution (100 m²) simulations of CO₂ emissions using the Dutch Large Eddy Simulation (DALES) model for the city of Rotterdam. DALES is particularly advantageous due to its explicit simulation of boundary-layer turbulence, enhancing the accuracy of atmospheric transport and dispersion of chemical species in the urban environment. The emission inputs for DALES were refined from the Dutch national emission inventory by incorporating statistical data on household gas consumption specific to Rotterdam. This involves adjusting the emission estimates to reflect high-resolution local consumption patterns and spatial distribution, which improves the spatial accuracy of the modeled emissions. This approach will be extended to reactive gases to gain insights into the exposure of citizens to air pollution in combination with other socio-economic conditions.

How to cite: Los, A. and Doyennel, A.: Greenhouse gas emission reduction from residential building renovations in Rotterdam, The Netherlands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10364, https://doi.org/10.5194/egusphere-egu25-10364, 2025.