Children are vulnerable to indoor contamination by semi-volatile organic compounds (SVOC) due to their physiology, behaviour, and time spent indoors. Among these, organophosphate esters (OPEs), phthalate esters (PEs), and alternative plasticizers (APs) are of increasing concern, yet their occurrence across microenvironments and seasons remains insufficiently characterized. This study included 38 such compounds in indoor and outdoor air and settled dust from homes and schools in Barcelona, assessing children’s exposure, including seasonal and site-specific patterns in an urban Mediterranean setting. PEs were the most abundant group, with diethyl phthalate (DEP) and di(2-ethylhexyl)phthalate (DEHP) reaching 271 and 314 ng/m³ in air, and DEHP and diisononyl phthalate (DiNP) reaching 267 and 126 µg/g in dust. APs were the second most prevalent group, including dibutyl adipate (DBA), bis(ethylhexyl) adipate (DEHA), and diisononyl 1,2-cyclohexanedicarboxylic acid (DINCH), while OPEs occurred at lower levels. Seasonal patterns showed higher PE, AP, and total concentrations in home air during summer, whereas total concentrations in schools decreased, suggesting effects of temperature-driven volatilization and ventilation. Outdoor samples showed lower APs (homes) and higher OPEs (schools), indicating contributions from ambient sources. Dust in schools contained higher median OPEs, PEs, and total concentrations than in homes. Children’s total airborne estimated daily intake (EDI) median was 69.4 ng/(kg bw·day), with group EDIs ranging from 0.245 to 2773 ng/(kg bw·day). Cancer (CR) and non-cancer risks (Non-CR) estimates remained within screening benchmarks. Overall, plasticizers and flame retardants were widespread across environments, with seasonal variability playing a key role in children’s exposure.

Polycyclic aromatic hydrocarbons (PAHs) are compounds with well-established negative health effects. In this study, the levels of the 16 Environmental Protection Agency (EPA) - PAHs were determined in air and deposited dust in different indoor environments, including schools, homes and sports halls in six metropolitan areas across Europe (Athens, Barcelona, Colchester, Copenhagen, Helsinki and Lisbon). However, naphthalene, acenaphthylene, anthracene, and dibenz[ah]anthracene were excluded from the final dataset due to analytical limitations. Results showed considerable spatial variability, suggesting that the highest median PAH concentrations (>50,000 pg/m3) in both schools and homes are linked to emissions from intensive local residential wood burning and poor ventilation, while the lowest PAH concentrations were observed in south-western Europe (<10,000 pg/m3). The levels of high-molecular weight (HMW) PAHs were generally higher in schools compared to homes likely due to greater occupancy and accumulation of outdoor-derived particles. Low molecular weight (LMW) PAHs, on the other hand, were more prominent in homes and associated with household practices (e.g., cleaning and ventilation frequency). In addition to active air sampling, indoor air concentrations were also estimated based on concentrations in the deposited dust samples. However, this methodology underestimated HMW-PAHs. The estimated Excess Cancer Risk for a 5-year primary education exposure period (ECR5) across all monitored sites ranged from 10-8 to 10-5, remaining below the unacceptable risk value of 10-4, which was used as a contextual reference within the USEPA cancer risk assessment framework. Nevertheless, these findings highlight the need for continuous proactive strategies to reduce PAH exposure and improve indoor air quality, particularly in environments occupied by children.

Abstract Aerosol hygroscopicity is a critical parameter for predicting radiative forcing and climate sensitivity, particularly under sub-saturated regimes where it drives complex aerosol–water interactions. Here, we show that externally mixed aerosols exert a stronger influence on direct radiative forcing than is currently represented in models. Incorporating our findings into radiative forcing calculations indicates a stronger aerosol cooling effect, especially at suburban sites, highlighting the importance of representing regional differences in mixing state. The conventional bulk-chemistry approach, which assumes volume-based mixing with limited spatial variability, exhibits low predictive performance for aerosol hygroscopicity (R² ≈ 0.61) at urban and suburban sites. Using an interpretable machine learning framework trained on geographically diverse, region-specific datasets can capture this variability with higher accuracy (R² ≈ 0.97), identifying key chemical compositional and mixing-state drivers.

Indoor air quality at schools and homes is a concern for children's health. This study assessed the concentrations of polycyclic aromatic hydrocarbons (PAHs) and equivalent black carbon (eBC) in indoor and outdoor environments and investigated their possible sources. Samplings were conducted at five primary schools and seven homes in urban Barcelona (Spain) during cold and warm seasons, along with a rural school located 130 km from the city. Air gas and particles were collected in SPE cartridges connected to low-volume samplers for PAH analysis by GC-MS/MS. PM2.5 filter samples were also collected for organic carbon and organic molecular tracer analysis, and continuous eBC concentrations were monitored with portable aethalometers. Time integrated samples showed higher total PAH levels indoors (12.8 +/- 7.9 ng/m3) than outdoors (6.9 +/- 2.9 ng/m3), largely due to volatile PAHs from indoor activities at homes. In contrast, the less volatile and more hazardous PAHs were strongly correlated with eBC. The average eBC indoor-outdoor concentration ratio of 1.1 +/- 0.4 indicated traffic as dominant contributor to indoor eBC and less volatile PAHs in the urban areas, whereas biomass burning was a major source at the rural site. These sources contributed largely to PAHs, eBC and organic matter identified in indoor samples, followed by dust inputs and cooking activities. The PAH and eBC levels observed in this study are lower than those reported in Barcelona a decade ago, suggesting a positive impact of the city's traffic-related air pollution mitigation measures, which may have improved outdoor air and benefited indoor environments.

low-cost air quality sensors were installed on the front roof area of three buses to enable real-time monitoring of air quality across the city of Valladolid (Spain) over a seven-month period, capturing variability in meteorological conditions and emission sources. Prior to deployment, the sensors were placed at a reference station in Barcelona (Spain) for validation and calibration. Measurements deviating by more than 30% from reference values were discarded, and correlation coefficients (R-2) were calculated. After the monitoring campaign, the calibration procedure was repeated. The results (R-2 = 0.85) suggest that bus-mounted sensors can effectively support real-time detection of urban air quality changes and contribute to detailed air quality mapping. Integrating data from reference air quality monitoring networks (AQMNs) with low-cost sensor (LCS) systems can strengthen evidence-based policymaking and help refine regulatory frameworks aimed at reducing urban air pollution.

Rotary dry cutting and rectifying of ceramic tiles are sources of fine particulate matter (PM2.5) and nanoparticles (NPs). These activities are typically carried out inside industrial facilities during the manufacturing process, as well as outdoors and in residential indoor spaces during the installation phase, where mitigation measures are seldom implemented. This work aimed to understand the particle formation and release mechanisms, as well as particle properties (physical, chemical, and toxicological) and potential impacts on human health and the environment, for particles generated during ceramic tile rotary dry cutting operations. Aerosols were characterised in terms of particle number and mass concentrations, chemical composition, morphology and in vitro cytotoxicity. Two types of commercially available and representative tiles were tested in controlled chamber experiments: porous and non-porous ceramic body tiles (referred to in this work as A and B types, respectively). Results evidenced the release of fine particles and NPs during dry cutting of both materials, in comparable concentrations (20.000-45.000/cm3, 1-min average). However, the particle size distribution was significantly finer from A tiles (70% of the particle number concentration was nanosized (<100 nm)) in comparison to B tiles (<20%). While airborne particle chemical profiles were similar for both types of materials in the coarser size fractions (>0.6 mu m), in the smaller size fractions (<0.6 mu m) larger differences were observed. The chemical composition of airborne aerosols was consistent with that of the deposited dust. In vitro cytotoxicity responses evidenced statistically significant differences between exposure to aerosols from both types of tiles: cell viability was lower after exposure to aerosols from A tiles (50% at the original concentration) compared to those from B tiles, which exhibited high cell viability regardless of the aerosol concentration. Overall, results evidenced NP formation and release during rotary dry cutting of ceramic tiles, varying physical-chemical and cytotoxic profiles as a function of the material being processed, and highlight this activity as a potential health hazard in scenarios where prevention and mitigation measures are not implemented.

Wildland fires, including both wildfires and prescribed burns, emit large quantities of smoke containing hazardous air pollutants such as polycyclic aromatic hydrocarbons (PAHs). Understanding PAHs concentrations in these smoke-filled environments is key to developing effective mitigation strategies to protect public health and safety. However, conventional measurement strategies are not always feasible in the highly dynamic and logistically complex settings of wildfire events. As a result, alternative approaches are needed, such as the use of silicone wristbands (SWBs) as passive air samplers. In this study, PAHs were analyzed in SWBs worn by firefighters during prescribed burns and wildfires. After deployment, extraction, and GC-MS/MS analysis, PAH air concentrations were calculated using a compound-specific kinetic uptake model. Personal exposure to PAHs was task-specific: wildfire operators and torchers, who ignite the fires, experienced the highest concentrations (mean sum of PAHs approximate to 5000 ng/m(3)), followed by liners, who manage fire boundaries (approximate to 2000 ng/m(3)), while truck drivers exhibited the lowest exposure concentrations (approximate to 100 ng/m(3)), likely due to their roles keeping them farther from the smoke-dense areas. Additionally, to evaluate the SWBs' ability to capture particle-bound PAHs, PAH air concentrations measured in SWBs were compared with those obtained from PM2.5 filter sampling. The findings highlight the utility of passive air sampling with SWBs in detecting PAHs and underscore their potential for monitoring exposure concentrations in complex atmospheric environments.

In industrial scenarios, nanoparticles are incidentally generated in high concentrations during diverse material transformation processes, presenting potential health hazards for exposed workers. Consequently, as an indoor air quality management measure, their concentration is commonly reduced through localized forced ventilation. However, the control of these systems usually relies on traditional rule-based algorithms, which cannot deploy efficient control strategies such as model predictive control. To solve this issue, we propose a novel grey-box reduced order model method, never used before for industrial indoor nanoparticles. This approach can be deployed in model predictive control algorithms in buildings and does not present the data-reliance and transferability issues of black-box modeling. To test this model, a data collection campaign was conducted under real-world operating conditions in an industrial-scale thermal spraying booth, aiming to test the method’s viability for model calibration and validation of indoor total nanoparticle concentration through the maximum likelihood method, statistical validation tests, and physical viability assessment. Results for three different lumped sum models illustrate the effectiveness of grey-box modeling in industrial scenarios with confined processes and forced ventilation systems, handling observations’ noise and background concentration fluctuations, and allowing a performance comparison between models. Further research could be conducted to study the viability of indoor total nanoparticle concentration reduced order models with higher spatial resolution, non-confined sources, and natural airflows.

Prescribed burns are valuable tools for landscape management, which reduce wildfire risk in fire-prone ecosystems. However, they generate smoke emissions containing hazardous pollutants such as polycyclic aromatic hydrocarbons (PAHs) and black carbon (BC). Here, PAHs were analyzed in atmospheric particulate matter (PM2.5) filters in firefighter's personal real-time BC monitors during prescribed burns and wildfires between 2022 and 2024 in Catalonia (NE Spain). The innovative analytical method using gas-chromatography coupled to Q-exactive Orbitrap mass spectrometry (GC-Orbitrap-MS) allows the determination of personal exposure levels of toxic particle-phase PAHs in firefighters across a broad concentration range, and reveals task-specific exposure levels and profiles, with torchers, who ignite the prescribed burns, facing the highest concentrations (mean BC: 69 μg/m3, mean sum of PAHs: 394 ng/m3, mean benzo[a]pyrene (BaP): 56 ng/m3) during shifts, followed by liners, who manage the fire lines, and truck drivers exhibited the lowest exposure concentrations due to their roles being farther from the hotspot (mean BC < 4 μg/m3). A strong correlation between BC and individual PAHs highlights BC's potential as a proxy for PAH exposure. Although exposure times to elevated smoke particles were normally under 4 h during a shift, risk assessment estimations show that torchers' excess lifetime cancer risk (ELCR) safety thresholds. Findings emphasize the need for improved respiratory protection, task-specific safety protocols, and safer ignition methods to reduce health risks. This study highlights potential risks associated with prescribed burns and wildfires, providing critical data to inform firefighter safety in wildfire-prone regions.

This study presents a methodology for sampling, preserving, and analyzing 15 organophosphate esters (OPEs), 11 phthalate esters (PEs), and 6 alternative plasticizers (APs) in indoor air. Accurate quantification is essential, given their widespread use in household items and adverse health effects associated with exposure. Solid-phase extraction (SPE) cartridges, collecting both gaseous and particulate phases, connected to a low-volume pump were used for the collection of air samples. Stability tests confirm that storing samples at 4 °C is most optimal to maintain analyte integrity for at least 2 weeks. The analytes were extracted and then purified online using turbulent flow chromatography, before analysis via liquid chromatography-tandem mass spectrometry (TFC-LC–MS/MS). Quantification was carried out by applying the isotopic dilution method with labelled standards. Recoveries ranged from 50 to 136

Research on nanoparticle (NP) release and potential exposure can be assessed through experimental field campaigns, laboratory simulations, and prediction models. However, risk assessment models are typically designed for manufactured NP (MNP) and have not been adapted for incidental NP (INP) properties. A notable research gap is identifying NP sources and their chemical, physical, and toxicological properties, especially in real-world settings. This work aims to provide insights into the release and physico-chemical properties of INP while contributing to improving models for INP release. INP release was evaluated through a case study in a ceramic tile firing facility, where aerosol (10 nm - 10 μm) properties were determined. The Control Banding (CB) Nanotool model was applied to test outputs based on provided input parameters. RESULTS: demonstrate the constant generation and release of INP during tile firing, with NP concentrations up to 68711/cm³ and mean diameters of 37 nm, with 95% smaller than 100 nm. Particle morphology was mostly spherical, suggesting nucleation from precursor gases as the main formation mechanism. INP chemical composition was driven by primary ceramic components, while trace elements like Ni and Ti exhibited size-dependent patterns. In vitro cell viability tests indicated low to medium cytotoxicity of PM2 aerosols, decreasing human alveolar epithelial cell viability in a concentration-dependent manner. Applying the risk model with varying input parameters revealed that the risk level (RL) based on severity scores decreased when aerosol size distribution data were used, illustrating the model's sensitivity to input variables. We conclude on the need for comprehensive experimental datasets to support risk assessment models and achieve effective risk management strategies in real-world scenarios.

Industry 5.0 focuses on human well-being, sustainability, and system resilience, with an emphasis on optimizing energy resources and reducing pollution in built environments. Achieving these goals requires integrated models that link indoor air quality (IAQ), ventilation systems, energy use, and production planning. This study addresses these goals with an integrated multidomain model that bridges environmental and energy management. Unlike current models that consider these aspects in isolation, the proposed approach incorporates energy generation and dynamic electricity prices into production planning, particularly in scenarios involving photovoltaic energy generation and fluctuating electricity prices. Consequently, this model enables tools to align ventilation operations with energy availability or cost. The model also simulates incidental nanoparticle (INP) dynamics in industrial environments, where processes generate INPs that pose health risks and productivity losses. Modeling interactions between INP dynamics, ventilation, and energy systems enables optimization of IAQ while maximizing solar energy self-consumption and minimizing energy costs. The model was validated using real operational data from three booths of a thermal spraying workshop. Integration of photovoltaic systems with and without storage with ventilation demonstrated the model's capacity to align energy generation with extraction processes, enhancing energy and IAQ management flexibility. Results include a 24.9 % increase in solar selfconsumption and a 6.8 % reduction in energy exportation, alongside reduced costs and improved operational efficiency. This work contributes to Industry 5.0 by advancing sustainable industrial production planning. The model provides a framework for tool design integrating renewable energy, real-time data, and energy optimization strategies, offering new pathways for workshop management and aligning with smart grid interactions. Future work, including energy storage systems and the study of other emissions processes, will further increase its adaptability, making it a valuable solution for sustainable industrial operations.

Estimating nanoparticle emission rates from industrial activities is essential for developing quantitative risk assessment tools and prediction models for indoor air quality and occupational exposure. However, determining them is challenging, particularly for incidentally generated nanoparticles (INPs), due to their calculation from concentration measurements in complex environments with polluted backgrounds. This study addresses the challenges of defining INP emission rates by proposing a reduced-order grey-box modeling approach. The method was tested in three industrial scenarios with different thermal spraying activities, evaluating 78 models based on mass-balance aerosol concentration equations. Convergence tests, statistical analyses, and physical feasibility studies revealed that 33 % of the models met all criteria. The simplest models, incorporating forced ventilation and particle generation while excluding natural diffusion, aggregation, and deposition, demonstrated the best performance and robustness, with two models reaching a 100 % successful performance on six applied datasets. Emission rates for the monitored processes were of similar magnitude, with minor variations around 4 x 1015 particles/min attributed to the materials and component morphology. Estimated ventilation airflow rates also aligned with the expected slight underperformance of the extraction systems between 1 and 22 x 107cm3/ min depending on the monitored booth and the ventilation configuration, showing air change per hour rates within the 39-105 h-1 range. The findings highlight that grey-box modeling combined with model reduction through lumped sum parameters provides a systematic and reliable approach to estimating INP emissions. This method could inform new standard procedures. Future research should apply this approach to diverse industrial activities and exposure applications.

Changes in climate and land-use have significantly increased both the frequency and intensity of wildland fires globally, exacerbating the potential for hazardous impacts on human health. A better understanding of particle exposure concentrations and scenarios is crucial for developing mitigation strategies to reduce the health risks. Here, PM2.5 and black carbon (BC) concentrations were monitored during wildland fires between 2022 and 2024, in fire-prone areas in Catalonia (NE Spain), by means of personal monitors (AirBeam2 and Micro-aethalometers AE51 and MA200). Results revealed that exposures to combustion aerosols (PM2.5 and BC) were significant and comparable during wildfires and prescribed burns (mean PM2.5 during wildfires = 152 mu g/m3 vs. 110-145 mu g/m3 for prescribed burns). Overall, BC/PM2.5 ratios showed a large variability as a function of the monitoring scenario, indicating varying contributions from mineral aerosols to the emissions mix originating from fire management and extinction tasks. Specifically, mop-up tasks (final extinction tasks involving stirring top soil using handheld tools) were identified as a significant contributor to PM2.5 exposures, with 1-min PM2.5 peak concentrations reaching up to 1190 mu g/m3. These results may be especially valuable for emissions modelling. Source apportionment of multi-wavelength BC datasets provided deeper insights into emissions and their impact on exposure profiles: line operators (who control the fire perimeter) were predominantly exposed to biomass burning smoke BCbb (61%) when compared to BC from fossil-fuel combustion (BCff = 39%), while torchers (in charge of initiating technical fires using fossil-fuel drip-torches) were predominantly exposed to BCff (77% vs. 23% BCbb). These findings highlight the value of portable monitors in the assessment of wildfire emissions and impacts on human exposure and environment. The combination of these tools, reporting data in real-time and with high time-resolution, is key to the design and implementation of effective mitigation strategies for environmental and health concerns related to wildland fires.

The urban particulate matter (PM) carbonaceous and water-soluble ions were investigated in Amman, Jordan during May 2018-March 2019. The PM2.5 total carbon (TC) annual mean was 7.6 f 3.6 mu g/m3 (organic carbon (OC) 5.9 f 2.8 mu g/m3 and elemental carbon (EC) 1.7 f 1.1 mu g/m3), which was about 16.3% of the PM2.5. The PM10 TC annual mean was 8.4 f 3.9 mu g/m3 (OC 6.5 f 3.1 mu g/m3 and elemental carbon (EC) 1.9 f 1.1 mu g/m3), about 13.3% of the PM10. The PM2.5 total water-soluble ions annual mean was 7.9 f 1.9 mu g/m3 (about 16.9%), and that of the PM10 was 10.1 f 2.8 mu g/m3 (about 16.0%). The minor ions (F-, NO2- , Br- , and PO43- ) constituted less than 1% in the PM fractions. The significant fraction was for SO42- (PM2.5 4.7 f 1.6 mu g/m3 (10.0%) and PM10 5.3 f 1.9 mu g/m3 (8.3%)). The NH4+ had higher amounts of PM2.5 (1.3 f 0.6 mu g/m3; 2.7%) than that PM10 (0.9 f 0.4 mu g/m3; 1.4%). During sand and dust storm (SDS) events, TC, Cl-, and NO3 -were doubled in PM, SO42- did not increase significantly, and NH4+ slightly decreased. Regression analysis revealed: (1) carbonaceous aerosols come equally from primary and secondary sources, (2) about 50% of the OC came from non-combustion sources, (3) traffic emissions dominate the PM, (4) agricultural sources have a negligible effect, (5) SO42- is completely neutralized by NH4+ in the PM2.5 but there could be additional reactions involved in the PM10, and (6) (NH4)2SO4, was the major species formed by SO42- and NH4+ instead of NH4HSO4. It is recommended to perform long-term sampling and chemical speciation for the urban atmosphere in Jordan.

The origin of atmospheric particulate matter (PM) events in an industrialized mega-city of Central China (Wuhan) was investigated. Wuhan constitutes an ideal case scenario for the study of atmospheric pollution episodes, given that it is representative of densely populated and industrialized Chinese cities. Levels of PM10, NOx and SO2 were evaluated and automatic PM levels were corrected following EU-guidelines, aiming to achieve comparability with results from outside China (Europe and US). This correction evidenced that PM10 levels were underestimated with the automatic instrumentation by 26–38%. This correction is thus essential for the potential use of ambient PM data in epidemiological and climate studies within China and abroad. Several types of peak PM10 events were identified: winter pollution events (daily mean PM10 = 200–350 µg/m3), Asian desert dust events (PM10 increments of 60–70 µg/m3 on the daily means) and biomass burning episodes (scarce during the study period). Natural contributions are thus a significant PM source to be taken into account for the evaluation of air quality in the area, the application of daily or annual limit values and the potential assessment of the data from an epidemiological point of view. In addition, the influence of the Wuhan pollution plume was detected in the Mulanhu regional site, which is 40 km far from the city, underlining the large area of influence of atmospheric pollution from Wuhan on the regional-scale. Finally, the results from this work evidence that a detailed knowledge of PM episodes and sources at a given site may be obtained by means of relatively straightforward and economic routine measurements (PM10, NO2, SO2).

Sectional physics-based aerosol models imply a computational effort that hinders their use in building digital twins, real-time predictive control, and computer-based iterative optimization versus black-box approaches. The innovation of this paper lies in the proposal of a novel systematic methodology to optimize the number of size bins in sectional reduced-order models for particle concentration simulations. This allows its application in indoor air quality management and overcomes generalizability and data-dependency issues of black-box models. This method, based on k-means clustering, aims to ensure precision when tackling relatively fast fine and ultrafine particle simulations targeting the reduction of time and resource consumption from experimentally-determined aerosol size distribution time series. Consequently, three tests were carried out using combustion aerosols inside a custom-designed emission chamber to simulate emission hotspots in non-commercial and occupational settings. 2-, 3-, 4-, and 5-cluster classifications were evaluated for data coming from 13 particle size bins through silhouette analysis and the study of their temporal profiles. Results show that the 4-cluster classification summarizes the behavior of data in the 10-420 nm range, ensuing up to a 77% improvement in the model’s computational demand. Moreover, this method allows an accurate definition of the necessary size ranges to calculate nanoparticle concentrations inside the chamber and facilitates the interpretation of aerosol behavior and processes through the resulting clusters’ temporal profiles.

In some regions of the world, cooking with solid biomass fuels in open fires constitutes the largest source of elemental and organic carbon emissions. However, cooking-related carbonaceous aerosols are still poorly characterized. This paper presents an innovative characterization of elemental and organic carbon (EC and OC) emissions from cookstoves in West Africa. Four stove types (three-stone fire, rocket stove, basic ceramic stove, and gasifier) using two wood species (dimb and filao) were analyzed on a laboratory scale. The EC and OC emission factors based on fuel energy (EFs) when burning dimb were higher for all stoves, highlighting the need to account for the fuel type when reporting cookstove EFs. The highest EC EF was found for the rocket stove (0.18 ± 0.06 g MJ−1 and 0.06 ± 0.01 g MJ−1 for dimb and filao, respectively). The other tested stoves exhibited the same EC EF when burning dimb (0.09 ± 0.02 g MJ−1) and EC EFs ranging between 0.04 ± 0.01 and 0.05 ± 0.01 g MJ−1 when burning filao. The OC EF was highest, on average, for the gasifier (0.08 ± 0.01 g MJ−1), followed by those for the three-stone fire (0.18 ± 0.03 g MJ−1) and the basic ceramic stove (0.21 ± 0.08 g MJ−1). However, the results from testing the rocket stove and the three-stone fire under real cooking conditions using dimb wood indicate that the laboratory-scale tests overestimate the actual EC EFs. Also, the rocket stove did not show a reduction in wood use compared to the three-stone fire, suggesting that the carbonaceous aerosol emissions from the former produce more warming than those from the latter. Therefore, the total EC and OC stove emissions, in addition to the EFs, must be reported. As the impacts of carbonaceous aerosol highly depend on the location of emission, this study contributes valuable data to emission inventories and climate prediction models at national and regional levels.

Air pollution in urban areas poses a significant and pressing challenge for modern society. Unfortunately, the existing network of pollution detectors in many cities is limited in scope and fails to adequately cover the entire geographical area. Consequently, the implementation of spatial prediction algorithms becomes essential to generate high-resolution data. In this paper, we introduce two significant contributions: 1) We formalize the air pollution prediction problem as a Maximum A Posteriori (MAP) estimate within the framework of a Markov Random Field and 2) we propose a message-passing algorithm, which stands out as an efficient solution that surpasses the current state of the art. The experimental procedure has been carried out using the case study of the city of Barcelona, based on a dataset extracted from the BCN Open Data portal.

Nanoparticles cause several adverse effects on human health. Moreover, their concentration can reach values of millions per cubic centimetre in industrial scenarios where multiple industrial activities generate them at a very high rate. However, current models and tools for simulating indoor nanoparticle concentration in the industry have to tackle two main problems: 1) they are based on deterministic approaches that cannot manage system randomness and uncertainties, so prevalent in these scenarios; 2) these tools are not user-friendly for non-experts and cannot be used in control systems to deal with optimisation problems in cyber-physical systems. In this context, this paper introduces a comprehensive framework for modelling indoor nanoparticles as emerging pollutants transport and control in the industry. To undertake the first point, a novel stochastic approach has been proposed to parametrise and validate nanoparticle total number concentration reduced-order models with data collected from a field campaign in a precision mechanics company with forced extraction. In this case study, generation, extraction, and diffusion processes are significant. Adversely, deposition and aggregation are not significant, probably due to their minor effect on the concentration or their slower dynamics. For the second point, a Modelica library has been developed to simulate nanoparticle concentrations in industrial layouts. This library, named INP, facilitates its integration in cyber-physical systems control and fast multi-domain simulations. Finally, examples from the case study illustrate the library’s use in risk management measures assessment to improve indoor air quality in industrial scenarios.