Reliable source term prediction for hazardous pollutant puffs in urban microenvironments is challenging, especially for risk management under strict time constraints. Puff movement is highly stochastic due to atmospheric turbulence, intensified by complex urban canopies. This complexity, combined with time limitations, makes advanced computational modeling impractical. A more efficient approach is leveraging past and present data using Machine Learning (ML) techniques. This study proposes an ML-based method, enriched with simplified physical modeling, for source term estimation of unforeseen hazardous air releases in monitored urban areas. The Random Forest Regression, commonly used in meteorology and air quality studies, has been selected. A novel variable selection method is introduced, including the following: (a) a model-derived Exposure Burden Index (EBI) reflecting plume–morphology interactions; (b) a plume travel time indicator; (c) the standard deviation of input variables capturing stochastic behavior; and (d) the total dosage-to-mass released ratio at sensor locations as the target variable. The case study examines JU2003 field experiments involving SF6 puffs released at street level in Oklahoma City’s urban core, a challenging scenario due to the limited number of sensors and historical data. Results demonstrate the approach’s effectiveness, offering a promising, realistic alternative to traditional computationally intensive methods.

The estimation of an airborne hazardous release and its associated uncertainty from a point source is a necessity for assessing the associated risk and taking possible protective actions. The problem here is to be able to make such an estimation, based on the availability of a rather limited number of sensors measuring pollutant/agent concentrations with a given time resolution. The challenge is to not only provide reliable outcome but also timely results even in operation level. A radically new concept is under development by (a) making full use of the real detailed inflow data and sensors concentration signals, (b) performing in the customisation – pre-emergency – phase only, a quite limited number of steady-state flow and dispersion simulations under reference inflow and release conditions and (c) projection of the steady state/reference results to real atmospheric and release conditions via appropriate novel scaling approaches based on experimental evidence and theory and (d) generating a low computational time demand tool, that can be handled by the operator on duty, even in the extreme cases of very complex topographies. In this study first experimental evidence is provided from a wind-tunnel flow and dispersion experiment in a typical urban area supporting the validity of the whole approach. In addition, the present approach is simultaneously assessing each sensor's signal suitability to be used for source quantification.

In the framework of the Forum for Air Quality Modelling in Europe (FAIRMODE), a modelling intercomparison exercise for computing NO2 long-term average concentrations in urban districts with a very high spatial resolution was carried out. This exercise was undertaken for a district of Antwerp (Belgium). Air quality data includes data recorded in air quality monitoring stations and 73 passive samplers deployed during one-month period in 2016. The modelling domain was 800 x 800 m2. Nine modelling teams participated in this exercise providing results from fifteen different modelling applications based on different kinds of model approaches (CFD - Computational Fluid Dynamics-, Lagrangian, Gaussian, and Artificial Intelligence). Some approaches consisted of models running the complete one-month period on an hourly basis, but most others used a scenario approach, which relies on simulations of scenarios representative of wind conditions combined with post-processing to retrieve a one-month average of NO2 concentrations. The objective of this study is to evaluate what type of modelling system is better suited to get a good estimate of long-term averages in complex urban districts. This is very important for air quality assessment under the European ambient air quality directives. The time evolution of NO2 hourly concentrations during a day of relative high pollution was rather well estimated by all models. Relative to high resolution spatial distribution of one-month NO2 averaged concentrations, Gaussian models were not able to give detailed information, unless they include building data and street-canyon parameterizations. The models that account for complex urban geometries (i.e. CFD, Lagrangian, and AI models) appear to provide better estimates of the spatial distribution of one-month NO2 averages concentrations in the urban canopy. Approaches based on steady CFD-RANS (Reynolds Averaged Navier Stokes) model simulations of meteorological scenarios seem to provide good results with similar quality to those obtained with an unsteady one-month period CFD-RANS simulations.

This study investigated the indoor environment quality (IEQ) of eight office buildings of interest due to: (a) their location at the region of Western Macedonia, Greece, which is an area characterized by aggravated air quality and is currently in a transition phase because of changes in the energy production strategy to reduce the use of lignite as an energy fuel; and (b) the survey's timing, characterized by new working conditions implemented during the COVID-19 pandemic period. In-site measurements were performed to identify the indoor air pollutants to which the occupants were exposed, while questionnaires were collected regarding the participants' perception of the working environment conditions, indoor air quality, and health symptoms. The statistical analysis results showed that the most-reported health symptoms were headache, dry eyes, and sneezing. The acceptance of new working conditions showed a significant correlation with their overall comfort and health perception. Occupants in offices with higher pollutant concentrations, such as NO2, benzene and toluene, were more likely to report health symptoms. The evaluation of the plausible health risks for the occupants of the buildings with carcinogenic and no-cancer models showed that health problems could exist despite low pollutant concentration levels.

Phthalates can be found in personal care products as solvents and plasticizers in various polymers, especially PVC, wall coverings, certain paints, vinyl floor coverings, electronic devices, medical devices, food packages, toys, cables and other products. Humans are ingesting food products that contain phthalates, or they have dermal contact with phthalate-containing material, such as clothes, PVC gloves, personal care products or house dust. In this study, samples of dust from several houses in Kozani city, Greece, were collected and analyzed for phthalate concentration, and the potential association with building characteristics was examined utilizing detailed checklists. Samples were taken from the vacuum cleaner of the houses and extracted with ethyl acetate, and then analyzed with GC-MS in the SIM mode. The levels of phthalate ranged from 10.57 to 221.19 μg/g for Di-iso-butyl phthalate (DiBP), 4.03 to 264.91 μg/g for Di-n-butyl phthalate (DBP), 0.72 to 20.22 μg/g for benzyl-butyl phthalate (BBP) and 62.73 to 1233.54 μg/g for Di- (2-ethylhexyl) phthalate (DEHP), with detection limits of 4.5, 3.3, 11.6 and 13.1 ng/g, respectively. Using the Kruskal–Wallis statistical test, several associations were found between the measured phthalate and occupant activities (duration of ventilation and location of temporary garbage storage) and building characteristics (plastic or synthetic materials inside the houses).

This study aims to examine the potential impact of air quality improvement measures on environment and human health, and evaluate them economically to assess whether they constitute "win-win" solutions for carbon mitigation in the short and medium teen (2020 - 2030) at the urban level in the city of Milan (Italy). To this end, for five selected policies, the following effects have been evaluated: (a) changes in emissions of major air pollutants; (b) changes in emissions of greenhouse gases (GHGs); (c) changes in ambient concentration of air pollutants; (d) changes in the exposure to major air pollutants; (e) changes in the associated impacts on human health; and (f) cost-benefit analysis and cost-effectiveness analysis. The impacts of the five selected policy options were assessed under the assumption of the RCP4.5 scenario for climate change. An Air Pollution - Health Impact Analysis (AP-HIA), can help to answer specific policy questions. It has been observed that European countries are rapidly adopting this methodology as part of the decision-making process for new programmes, projects, regulations, and policies aimed at improving air quality or that may affect it as a co-benefit.

Future climatic change is expected to have a significant impact on local scale air quality. The quantification of such an impact is not a straightforward task if one takes into consideration the inherent uncertainties due to lack of accurate enough input data. It is more reliable to look for trends rather than absolute values on the relevant parameterization.In the frame of the European Project ICARUS, the present study aims at providing heat-wave and concentrations trends for the period 2001-2050, following the moderate Representative-Concentration-Pathway (RCP4.5), on major air pollutants (PM10,PM2.5,NO2,O3) in Europe, focusing on nine cities: Athens, Basel, Brno, Copenhagen/Roskilde, Ljubljana, Madrid, Milan, Stuttgart and Thessaloniki. A novel approach, based on weather clustering is inaugurated to study climate change by inducing air quality trends, allowing to introduce proper trend indicators and focus on targeted weather and air quality local simulations. The adopted clustering approach has been applied utilizing daily weather data of 50-year period (2001-2050).The detailed weather data were obtained from the Coordinated-Regional-Climate-Downscaling-Experiment (CORDEX).The Regional-Climate-Model INERIS-WRF331F data have been selected, using the EUR11 (10km resolution) horizontal domain projection. Representative days have been identified per cluster, per five years period, where a detailed (2x2km) atmospheric modeling has been performed using WRFChem model. Concerning emissions input, the USTUTT High-Resolution (1kmx1km) data produced within ICARUS, have been postprocessed.The study has provided interesting city dependent results, revealing among others the correlation between weather patterns with higher heat-wave events and elevated O3 concentrations strengthening the hypothesis that the greenhouse effect leads to intensification of the atmospheric photochemical activity.

A sensor campaign was conducted in Milan as part of the H2020 project ICARUS (Integrated Climate forcing and Air pollution Reduction in Urban Systems) in order to characterize the exposure to major air pollutants of the urban population. The campaigns involved more than 100 participants from over 30 households. Utilizing spatio-temporal data on air pollution and activity data collected from individuals, we were able to identify individual exposure profiles and summarize the information according to specific microenvironments and activities. All participants received personal exposure reports. The paper summarizes the general experience with conducting sampling campaigns in Milan, focusing on the following aspects: sensor selection and evaluation, study design, data harmonization, establishment of the supporting information technology infrastructure, and overall feasibility assessment, including participant feedback.

Pollution levels in an urban street-canyon area are determined numerically as part of the European research project OSCAR using the ADREA-HF code. Aim of the modeling is twofold: (i) to investigate the flow-field and carbon monoxide (CO) concentrations in the area and (ii) compare with measurements. The latter is achieved due to the availability of measurements, contrary to previous street-canyon-simulation studies where ADREA-HF was used and where no measurements were available. Results show a tendency of overprediction of CO concentration by the model that is attributed mostly to the uncertainty of the meteorological data and emission levels within the studied time frames. The concentration distribution and flow field within the canyon are shown to be highly correlated whereas the in-canyon induced vortex plays a prominent role in the concentration dispersion.

In any health risk related event such as an air hazardous release, a key question that needs to be addressed is, to what extent an individual is exposed to a hazardous pollutant/agent for a specific time interval at a specific location downstream the release. Any systematic and reliable approach on this problem especially at the application/operation level, requires the knowledge not only of the exposure itself, but also its associated uncertainty quantified using probability density functions. A radically new approach is proposed (a) by making full use of the real detailed inlet flow and release rate signals, (b) by performing a limited number of flow and dispersion simulations in comparison to straightforward approaches, dealing only with steady state and reference inflow and release conditions and (c) by projection of the steady state/reference results to real conditions via appropriate novel scaling approaches based on experimental evidence and theory. A validation exercise has been performed with remarkable results using the well-studied University of Hamburg S2 Michelstadt Wind Tunnel Experiment with building structures representing distinct characteristics of typical central European cities. The message is for an attractive approach that needs further validation with the help of carefully designed experiments combined with dispersion model adjustments and improvements.

Abstract. This review provides a community’s perspective on air quality research focussing mainly on developments over the past decade. The article provides perspectives on current and future challenges as well as research needs for selected key topics. While this paper is not an exhaustive review of all research areas in the field of air quality, we have selected key topics that we feel are important from air quality research and policy perspectives. After providing a short historical overview, this review focuses on improvements in characterising sources and emissions of air pollution, new air quality observations and instrumentation, advances in air quality prediction and forecasting, understanding interactions of air quality with meteorology and climate, exposure and health assessment, and air quality management and policy. In conducting the review, specific objectives were (i) to address current developments that push the boundaries of air quality research forward, (ii) to highlight the emerging prominent gaps of knowledge in air quality research and (iii) and to make recommendations to guide the direction for future research within the wider community. This review also identifies areas of particular importance for air quality policy. The original concept of this review was borne at the International Conference on Air Quality 2020 (held online due to the COVID 19 restrictions during 18–26 May 2020), but the article incorporates a wider landscape of research literature within the field of air quality science. On air pollution emissions the review highlights, in particular, the need to reduce uncertainties in emissions from diffuse sources, particulate matter chemical components, shipping emissions and the importance of considering both indoor and outdoor sources. There is a growing need to have integrated air pollution and related observations from both ground based and remote sensing instruments, including especially those on satellites. The research should also capitalize on the growing area of lower cost sensors, while ensuring a quality of the measurements which are regulated by guidelines. Connecting various physical scales in air quality modelling is still a continual issue, with cities being affected by air pollution gradients at local scales and by long range transport. At the same time, one should allow for the impacts from climate change on a longer timescale. Earth system modelling offers considerable potential by providing a consistent framework for treating scales and processes, especially where there are significant feedbacks, such as those related to aerosols, chemistry and meteorology. Assessment of exposure to air pollution should consider both the impacts of indoor and outdoor emissions, as well as apply more sophisticated, dynamic modelling approaches. With particulate matter being one of the most important pollutants for health, research is indicating the urgent need to understand, in particular, the role of particle number and chemical components in terms of health impact, which in turn requires improved emission inventories and models for predicting high resolution distributions of these metrics over cities. The review also examines, how air pollution management needs to adapt to the above-mentioned new challenges and briefly considers the implications from the COVID-19 pandemic for air quality. Finally, we provide recommendations for air quality research and support for policy.

When considering accidental or/and deliberate releases of airborne hazardous substances the release duration is often short and in most cases not precisely known. The downstream exposure in those cases is stochastic due to ambient turbulence and strongly dependent on the release duration. Depending on the adopted modelling approach, a relatively large number of dispersion simulations may be required to assess exposure and its statistical behaviour. The present study introduces a novel approach aiming to replace the large number of the abovementioned simulation scenarios by only one simulation of a corresponding continuous release scenario and to derive the exposure-related quantities for each finite-duration release scenario by simple relationships. The present analysis was concentrated on dosages and peak concentrations as the primary parameters of concern for human health. The experimental and theoretical analysis supports the hypothesis that the dosage statistics for short releases can be correlated with the corresponding continuous release concentration statistics. The analysis shows also that the peak concentration statistics for short-duration releases in terms of ensemble average and standard deviation are well correlated with the corresponding dosage statistics. However, for more reliable quantification of the associated correlation coefficients further experimental and theoretical research is needed. The probability/cumulative density function for dosage and peak concentration can be approximated by the beta function proposed in an earlier work by the authors for continuous releases.

Despite that commuters spend only 5.5% of their time in cabin vehicles, their exposure to harmful air pollutants, originated from the vehicle itself, and traffic emission is considered significant. In this study, two passenger cars with different type of fuels were investigated in terms of air quality and thermal comfort of their cabin. Investigation was performed in the city of Kozani, Northern Greece. Moreover, air samples near the exhausts were taken, in order to compare concentration of compounds found indoors. Twelve volatile organic compounds and CO2 were measured inside the cabin when the cars were stopped, when idle and when they were cruising in medium and heavy traffic roads, under various ventilated conditions. Thermal comfort was investigated while driving the cars through the city traffic. Results showed that the air around the diesel exhaust is less affected by emissions from the engine compared to LPG fuel. This is reflected to the TVOC measured into the cabin. Results also revealed that the air quality of a diesel fuel moving car with open windows is only affected by the traffic emissions from neighbouring vehicles, while for the car with LPG fuel, the self-pollution from its own exhaust might contribute together with the outdoor air.

The differences between LH2, LNG and pressurized NH3 dispersion are investigated using a new adiabatic mixing model which is based on Raoult's law and PH-flashing techniques for ideal mixtures and accounts for phase changes of the released substance and all ambient air components. Model validation is performed first against recent LH2 experiments and older experiments involving LNG and NH3 releases with reasonable agreement. The intercomparison analysis performed next assumes the given substance released at its boiling point temperature either as liquid or vapour. Ambient air is assumed composed of N2, O2 and H2O with relative humidity either zero or 100%. The performed comparative assessment addresses the temperature versus molar fraction dependence, the condensed phase appearance conditions, the variation of the amount of the condensed phase in the mixture and its composition and finally important safety related parameters like the O2/N2 and fuel/(fuel + O2) ratios. It is found that for all three substances considered the temperature-molar fraction relation is significantly affected by source vapour quality and to less but still important extent by ambient air relative humidity. It is also found that for H2 releases the stoichiometric ratio can be reached in the condensed phase (due to O2 enrichment) and that this is not possible for CH4 and NH3 releases. The present model can be used as an engineering tool for adiabatic mixing calculations.

The aim of this study was to identify determinants of aldehyde and volatile organic compound (VOC) indoor air concentrations in a sample of more than 140 office rooms, in the framework of the European OFFICAIR research project. A large field campaign was performed, which included (a) the air sampling of aldehydes and VOCs in 37 newly built or recently retrofitted office buildings across 8 European countries in summer and winter and (b) the collection of information on building and offices' characteristics using checklists. Linear mixed models for repeated measurements were applied to identify the main factors affecting the measured concentrations of selected indoor air pollutants (IAPs). Several associations between aldehydes and VOCs concentrations and buildings' structural characteristic or occupants' activity patterns were identified. The aldehyde and VOC determinants in office buildings include building and furnishing materials, indoor climate characteristics (room temperature and relative humidity), the use of consumer products (eg, cleaning and personal care products, office equipment), as well as the presence of outdoor sources in the proximity of the buildings (ie, vehicular traffic). Results also showed that determinants of indoor air concentrations varied considerably among different type of pollutants.

The aim of this study was to explore the association between the building related occupants' reported health symptoms and the indoor pollutant concentrations in a sample of 148 office rooms, within the framework of the European OFFICAIR research project. A large field campaign was performed in 37 office buildings among eight countries, which included (a) 5-day air sampling of volatile organic compounds (VOCs), aldehydes, ozone and NO2 (b) collection of information from 1299 participants regarding their personal characteristics and health perception at workplace using on-line questionnaires. Step wise and multilevel logistic regressions were applied to investigate associations between health symptoms and pollutant concentrations considering personal characteristics as confounders. Occupants of offices with higher pollutant concentrations were more likely to report health symptoms. Among the studied VOCs, xylenes were associated with general (such as headache and tiredness) and skin symptoms, ethylbenzene with eye irritation and respiratory symptoms, a-pinene with respiratory and heart symptoms, d-limonene with general symptoms and styrene with skin symptoms. Among aldehydes, formaldehyde was associated with respiratory and general symptoms, acrolein with respiratory symptoms, propionaldehyde with respiratory, general and heart symptoms and hexanal with general SBS. Ozone was associated with almost all symptom groups.

The identification of PM sources and their contribution to measured PM concentrations is crucial for the environmental policy making since the findings be able to conduce to the development of relevant legislation in order to achieve effective air quality manage. In this study, the sources of PM10 at three receptors with different characteristics, within the Western Macedonia (WM) in NW Greece, were investigated: S1 in the center of Kozani, a medium sized city located at the southern edge of the industrial axis of WM, where urban activities and traffic density occur. S2 in the city of Ptolemaida, a medium sized city located in the centre of industrial zone, and S3 in the village of Eratyra, a rural residential district outside of the industrial area. For this purpose, the multivariate Positive Matrix Factorization (EPA PMF 5.0) receptor model was applied on elemental data. Specifically, PM10 samples, obtained by filtration during 1-year sampling campaign, were analyzed by ICP-MS instrument. Twenty-five elements were detected at quantifiable concentrations in the investigated PM samples. For the particle samples obtained in areas within and proximal from mining and power station operations, a six-factor model gave source profiles that attributed to be vehicle exhaust, road dust, soil dust, coal combustion, oil combustion and biomass burning. Furthermore, at the background site, the major contributors were biomass burning, soil dust and oil burning while no distant transport from industrial axis was recorded.

Potential chemical and biological (CB) attacks in indoor spaces pose specific challenges for prevention and preparedness. This paper summarises the conclusions obtained from the work conducted in the framework of the ERNCIP1. A thorough review of the existing and emerging technologies for CB detection, coupled with simulations of CB airborne dispersion in (critical) infrastructures to investigate the implementation of such sensors is presented. The conclusions include: (a) limitations of current sensor technology, (b) the effect of spatial variability of contamination within a building envelop on the effectiveness of such technology (c) the importance of early identification of both CB compounds for post-event mitigation.

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Personal control over various indoor environment parameters, especially in the last decades, appear to have a significant role on occupants' comfort, health and productivity. To reveal this complex relationship, 7441 occupants of 167 recently built or retrofitted office buildings in eight European countries participated in an online survey about personal/health/work data as well as physical/psycho-social information. The relationship between the types of control available over indoor environments and the perceived personal control of the occupants was examined, as well as the combined effect of the control parameters on the perceived comfort using multilevel statistical models. The results indicated that most of the occupants have no or low control on noise. Half of the occupants declared no or low control on ventilation and temperature conditions. Almost one-third of them remarked that they do not have satisfactory levels of control for lighting and shading from sun conditions. The presence of operable windows was shown to influence occupants' control perception over temperature, ventilation, light and noise. General building characteristics, such as floor number and floor area, office type, etc., helped occupants associate freedom positively with control perception. Combined controlling parameters seem to have a strong relation with overall comfort, as well as with perception regarding amount of privacy, office layout and decoration satisfaction. The results also indicated that occupants with more personal control may have less building-related symptoms. Noise control parameter had the highest impact on the occupants' overall comfort.