Autism is a heterogeneous condition, and functional magnetic resonance imaging-based studies have advanced understanding of neurobiological correlates of autistic features. Little work has focused on the optimal brain states to reveal brain-phenotype relationships. Here, using connectome-based predictive modeling, we interrogated four datasets to determine scanning conditions that boost prediction of clinically relevant phenotypes and assess generalizability. In dataset one, a sample of youth with autism and neurotypical participants (n = 63), we found that a sustained attention task resulted in high prediction performance of autistic traits compared with a free-viewing social attention task and a resting-state condition. In dataset two (n = 25), we observed the predictive network model of autistic traits generated from the sustained attention task generalized to predict measures of attention in neurotypical adults. In datasets three and four, we determined the same predictive network model further generalized to predict measures of social responsiveness in the Autism Brain Imaging Data Exchange (n = 229) and the Healthy Brain Network (n = 643). Our data suggest an in-scanner sustained attention challenge can help delineate robust markers of autistic traits.

Respiratory viral outbreaks are a major cause of nursing home (NH) resident morbidity and mortality. These outbreaks are often seeded by infected staff who have minimal or no symptoms at the beginning of their illness. We piloted air monitoring for viral nucleic acids to evaluate whether it can predict healthcare worker infections caused by common respiratory viruses before they become clinically apparent.Figure 1.Comparison of air sample tested using on-site (Cepheid Xpert) and laboratory-based (CDC) real-time reverse transcription PCR (rRT-PCR) assays.The 2x2 table on the left demonstrates moderate correlation between both methods when looking at all results. The graph on the right demonstrates a significant correlation between the positive results obtained on both assays (n = 57).Figure 2.Comparison of positive air sample results collected in parallel at CLC entryway and nursing station. Positive air samples (solid diamond), whether a laboratory-confirmed CLC staff illness was (solid circle) or was not (open circle) identified in the subsequent five days and between a positive air sample and staff result (line connecting diamonds and circles) denoted in left figure. Sensitivity and specificity for prediction of staff illnesses displayed in the 2x2 tables on the right. Air sampling units were placed in the entryway and nursing station of a single Midwest Veterans Administration Community Living Center (CLC). Air samples were collected daily (Su-Th) July 2024-Jan 2025. A commercial point-of-care reverse transcription PCR (RT-PCR) platform (Cepheid GenXpert) was used to test samples on-site, and its validity was assessed through parallel testing using a laboratory-based qRT-PCR assay. Research staff identified cases of acute respiratory illness (ARI) among unit staff and compared these data with air sample results. Analyses of 127 paired air samples showed moderate levels of agreement (Kappa = 0.46) between the on-site and laboratory-based PCR assays (Figure 1). Six CLC staff developed an ARI during the study period. Entryway air samples were while highly sensitive (100.0%) for subsequent identification of a staff ARI but lacked specificity (68.3%; Figure 2). In contrast, nursing station air samples demonstrated good sensitivity (83.3%) as well as excellent specificity (93%; Figure 2). The average time interval between a positive air sample and a staff ARI was 2.8 days (range: 1–5 days). On-site point-of-care testing of environmental air samples reliably detected the presence of respiratory viruses in the CLC environment. Results of air samples collected in/near nursing stations appear to be more actionable than those collected at unit entryways. Environmental air samples were frequently positive several days before a staff ARI became clinically apparent suggesting this surveillance approach may help prevent or reduce the size of respiratory viral outbreaks in NHs. Further studies in different long-term care environments are needed to demonstrate the feasibility and benefits of air monitoring in NHs. Sally Jolles, MA, MS, Merck: Grant/Research Support Christopher J. Crnich, MD, PhD, Merck: Grant/Research Support

Particulate matter (PM2.5) is a critical indicator of air quality and has significant health implications. This study presents the development and evaluation of a custom-built PM2.5 device, named the P-Tracker, designed to offer an accessible alternative to commercially available air quality monitors. This paper presents the design framework used to address the requirements of a low-cost, accessible device which meets the performance of existing commercial systems. Step-by step build instructions are provided for hardware and software development and connection to the P-tracker open access website which displays the data and interactive map. To demonstrate the performance, the P-Tracker was compared against leading consumer devices, including the AtmoTube Pro by AtmoTech Inc., Flow by Plume Labs, View Plus by Airthings, and the Smart Citizen Kit 2.1 by Fab Lab Barcelona, across four controlled tests. The tests included: (1) a controlled paper combustion test in which all devices were exposed to combustion aerosols in a sealed environment alongside the DustTrak 8530 (TSI Incorporated, Shoreview, MN, USA), used as the gold standard reference, where the P-Tracker achieved a Pearson correlation of 0.99 with DustTrak over the final measurement period; (2) an outdoor test comparing readings with a stationary reference sensor, Osiris (Turnkey Instruments Ltd., Rudheath, UK), where the P-Tracker recorded a mean PM2.5 concentration of 3.08 µg/m3, closely aligning with the Osiris measurement of 3.53 µg/m3 and achieving a Pearson correlation of 0.77; (3) a controlled indoor air quality assessment, where the P-Tracker displayed stable readings with a standard deviation of 0.11 µg/m3, comparable to the AtmoTube Pro; and (4) a real-world kitchen environment test, where the P-Tracker effectively captured fluctuations in PM2.5 levels due to cooking activities, maintaining a consistent response with the DustTrak reference. The results indicate varied degrees of agreement across devices in different conditions, with the P-Tracker demonstrating strong correlation and low error margins in high-pollution and controlled scenarios. This research underscores the potential of open-source, low-cost, custom-built air quality sensors which may be developed and deployed by communities to provide hyperlocal measurements of air pollution.

Conceptually, brain states reflect some combination of the internal mental processes of a person, and the influence of their external environment. Importantly, for neuroimaging, brain states may impact brain-based modeling of a person's traits, which should be independent of moment-to-moment changes in behavior. Investigation of brain states, and modeling of traits or behaviors are both often done using fMRI-based functional connectivity. Brain states can fluctuate in time periods shorter than a typical fMRI scan, and an array of methods called dynamic functional connectivity analyses has been developed to measure them. It has previously been shown that brain state can be manipulated through the use of continuous performance tasks that put the brain in a particular configuration while the task is performed. Here, we focus on moment-to-moment changes in brain state and test the hypothesis that there are particular brain-states that maximize brain-trait modeling performance. We use a regression-based framework, Connectome-based Predictive Modelling, allied to a resample aggregating approach, to identify behavior and trait-related brain states, as represented by dynamic functional connectivity maps. We find that there is not a particular brain state that is optimal for trait-based prediction, and combining data from distinct brain states across the scan is better. We also find that this is not the case for in-scanner behavioral prediction where more isolated and temporally specific parts of the scan session are better for building predictive models of behavior. The resample aggregated dynamic functional connectivity models of behavior replicated in sample using unseen left-out data. The modeling framework also showed success in estimating variance in behavior in a separate dataset. The method detailed here may prove useful for both the study of behaviorally related brain states, and for short-time predictive modeling.

Primary Biological Aerosol Particles (PBAPs) range in size from a few nanometres to hundreds of micrometres and include particles such as pollen and fungal spores, which are significant due to their prevalence and potential to induce allergic reactions and other health-related issues. This study employs real-time fluorescence spectroscopy devices, the Wideband Integrated Bioaerosol Sensor (WIBS-NEO) and IBAC-2, to monitor PBAPs in the urban environment of Dublin, Ireland, during a six-week campaign in the summer of 2019. The WIBS-NEO demonstrated moderate correlations (R2 = 0.5–0.7) in comparison with Hirst volumetric air sampler data for pollen and fungal spore concentrations following appropriate data filtering. In contrast, the IBAC-2 showed poor correlation with ambient fungal spore concentrations and lacked the necessary particle size ranges for reliable pollen measurement. Comparative analysis between WIBS-NEO and IBAC-2 revealed moderate correlation (R2 ≈ 0.6) for small fluorescent particles (fluorescent particles between 0.7 and 1.5 μm optical diameter) but lower correlation for larger particles and specific bioaerosol types. The findings suggest that while the WIBS-NEO is effective for real-time bioaerosol monitoring, further refinement is needed for IBAC-2. This study underscores the potential of using advanced real-time monitoring tools in urban environments and highlights the need for continued development to address current limitations.

INTRODUCTION: Fetal ventriculomegaly, the most common antenatally-diagnosed brain abnormality, is the defining feature of congenital hydrocephalus (CH). Fetal ventriculomegaly is also an overlooked associated finding in neuropsychiatric disorders, including autism spectrum disorder (ASD), which is diagnosed at a 10-fold higher rate in CH patients than in the general population. METHODS: We subjected 2,978 parent-trio probands with primary ventriculomegaly, including shunted, sporadic CH, to whole exome sequencing (WES). Using mouse molecular genetics, we generated a novel CH mutant mouse model via prenatal, genetic deletion of a WES-identified CH gene. MRI, measurement of CSF secretion, and cortex-wide, mesoscopic Ca2+ imaging were performed in mice. RESULTS: We identify phosphatase and tensin homolog (PTEN) to be the most frequently mutated gene in primary human ventriculomegaly. Integrative analysis of the human fetal brain revealed PTEN was most highly expressed in NKX2.1+ neuroprogenitor cells (NPCs) and their post-natal interneuron descendants. Pten mutant mice with Nkx2.1-specific Pten deletion exhibited neonatal-onset obstructive hydrocephalus, resulting from aqueductal stenosis due to mTor-activated hyperproliferation of NPCs, and CSF hypersecretion due to inflammation-driven choroid plexus hyperplasia. Hydrocephalic Pten mutants also exhibit autism-like hypersynchronization of the somatosensory cortices due to impaired activity of interneurons. Strikingly, genetic or pharmacologic mTORC1 inhibition (everolimus) corrects ventriculomegaly and rescues cortical pathology of Pten mutants. CONCLUSIONS: Our data demonstrate that PTEN, a commonly mutated ASD gene, is also the most frequently mutated gene in primary ventriculomegaly. To attenuate the pathologically entangled enlargement of the ventricular system and intrinsic neuronal deficits within the surrounding cortical mantle, the use of rapamycin analogs has high translational potential as an adjunct therapy to neurosurgical CSF diversion in ventriculomegalic patients harboring PTEN mutations. Ventriculomegaly may also be a useful radiographic biomarker for early referral for exome sequencing and formal neurodevelopmental assessments.

Graphical AbstractOpen in new tabDownload slideDysregulation of the prefrontal cortex and amygdala relationship by chronic stress has direct effects on the cardiovascular system, via the hypothalamic-pituitary-adrenal (HPA) axis. These effects, which include include vasoconstriction, increased vascular resistance, increased heart rate, and decreased heart rate variability (HRV), are potentiated by SNS hyperactivation, which promotes the release of norepinephrine into the systemic circulation. Activation of the HPA axis results in the release of cortisol from the adrenal cortex, which promotes cardiometabolic disease in the form of hypertension, increased adiposity, and insulin resistance. Subsequent vascular remodelling can lead to hypoperfusion, and potentially further dysregulation of the relationship between the prefrontal cortex and amygdala.

Climate change, coupled with the rise in pollen concentrations and extended pollen seasons, poses significant challenges to public health, particularly for individuals with pollen allergies and/or respiratory diseases. In response to these challenges, the work here's objective is to pioneer an automated trainable system for the recognition and counting of pollen grains. Such a system would enhance efficiency, allowing for quicker and more accurate assessments of airborne pollen concentrations, ultimately aiding in the mitigation of allergy symptoms associated with changing environmental conditions and the spread of allergenic species. This approach utilizes image processing tools to segment pollens on digitalized slides and several deep learning tools to recognize them among 17 different allergenic species in total. The system has been designed to avoid the time spent by palynologists on the microscope and to considerably increase the number of observation sites above the current European standards in allergenic pollen concentrations evaluation. In turn reducing associated errors with such analysis.

This study examines the influence of meteorological factors and air pollutants on the performance of automatic pollen monitoring devices, as part of the EUMETNET Autopollen COST ADOPT-intercomparison campaign held in Munich, Germany, during the 2021 pollen season. The campaign offered a unique opportunity to compare all automatic monitors available at the time, a Plair Rapid-E, a Hund-Wetzlar BAA500, an OPC Alphasense, a KH-3000 Yamatronics, three Swisens Polenos, a PollenSense APS, a FLIR IBAC2, a DMT WIBS-5, an Aerotape Sextant, to the average of four manual Hirst traps, under the same environmental conditions. The investigation aimed to elucidate how meteorological factors and air pollution impact particle capture and identification efficiency.The analysis showed coherent results for most devices regarding the correlation between environmental conditions and pollen concentrations. This reflects on one hand, a significant correlation between weather and airborne pollen concentration, and on the other hand the capability of devices to provide meaningful data under the conditions under which measurements were taken. However, correlation strength varied among devices, reflecting differences in design, algorithms, or sensors used. Additionally, it was observed that different algorithms applied to the same dataset resulted in different concentration outputs, highlighting the role of algorithm design in these systems (monitor + algorithm).Notably, no significant influence from air pollutants on the pollen concentrations was observed, suggesting that any potential difference in effect on the systems might require higher air pollution concentrations or more complex interactions. However, results from some monitors were affected to a minor degree by specific weather variables.Our findings suggest that the application of real-time devices in urban environments should focus on the associated algorithm that classifies pollen taxa. The impact of air pollution, although not to be excluded, is of secondary concern as long as the pollution levels are similar to a large European city like Munich.

Mobile monitoring provides high-resolution observation on temporal and spatial scales compared to traditional fixed-site measurement. This study demonstrates the use of high spatio-temporal resolution of air pollution data collected by Google Air View vehicles to identify hotspots and assess compliance with WHO Air Quality Guidelines (AQGs) in Dublin City. The mobile monitoring was conducted during weekdays, typically from 7:00 to 19:00, between 6 May 2021 and 6 May 2022. One-second data were aggregated to 377,113 8 s road segments, and 8 s rolling medians were aggregated to hourly and daily levels for further analysis. We assessed the temporal variability of fine particulate matter (PM2.5), nitrogen monoxide (NO), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and carbon dioxide (CO2) concentrations at hyperlocal levels. The average daytime median concentrations of NO2 (28.4 ± 15.7 µg/m3) and PM2.5 (7.6 ± 4.7 µg/m3) exceeded the WHO twenty-four hours (24 h) Air Quality Guidelines in 49.4

Skin volatile emissions offer a noninvasive insight into metabolic activity within the body as well as the skin microbiome and specific volatile compounds have been shown to correlate with age, albeit only in a few small studies. Building on this, here skin volatiles were collected and analyzed in a healthy participant study (n = 60) using a robust headspace-solid phase microextraction (HS-SPME) gas chromatography-mass spectrometry (GC-MS) workflow. Following processing, 18 identified compounds were deemed suitable for this study. These were classified according to gender influences and their correlations with age were investigated. Finally, 6 volatiles (of both endogenous and exogenous origin) were identified as significantly changing in abundance with participant age (p < 0.1). The potential origins of these dysregulations are discussed. Multiple linear regression (MLR) analysis was employed to model age based on these significant volatiles as independent variables, along with gender. Our analysis shows that skin volatiles show a strong predictive ability for age (explained variance of 68%), stronger than other biochemical measures collected in this study (skin surface pH, water content) which are understood to vary with chronological age. Overall, this work provides new insights into the impact of aging on the skin volatile profiles which comprises both endogenously and exogenously derived volatile compounds. It goes toward demonstrating the biological significance of skin volatiles and will help pave the way for more rigorous consideration of the healthy "baseline" skin volatile profile in volatilomics-based health diagnostics development going forward.

The advent of automatic pollen and fungal spore monitoring over the past few years has brought about a paradigm change. The provision of real-time information at high temporal resolution opens the door to a wide range of improvements in terms of the products and services made available to a widening range of end-users and stakeholders. As technology and methods mature, it is essential to properly quantify the impact automatic monitoring has on the different end-user domains to better understand the real long-term benefits to society. In this paper, we focus the main domains where such impacts are expected, using Europe as a basis to provide qualitative estimates and to describe research needs to better quantify impacts in future. This will, in part, also serve to justify further investment and help to expand monitoring networks.

Background and Aims Chronic stress associates with cardiovascular disease, but mechanisms remain incompletely defined. Advanced imaging was used to identify stress-related neural imaging phenotypes associated with atherosclerosis.Methods Twenty-seven individuals with post-traumatic stress disorder (PTSD), 45 trauma-exposed controls without PTSD, and 22 healthy controls underwent 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MRI). Atherosclerotic inflammation and burden were assessed using 18F-FDG PET (as maximal target-to-background ratio, TBR max) and MRI, respectively. Inflammation was assessed using high-sensitivity C-reactive protein (hsCRP) and leucopoietic imaging (18F-FDG PET uptake in spleen and bone marrow). Stress-associated neural network activity (SNA) was assessed on 18F-FDG PET as amygdala relative to ventromedial prefrontal cortex (vmPFC) activity. MRI diffusion tensor imaging assessed the axonal integrity (AI) of the uncinate fasciculus (major white matter tract connecting vmPFC and amygdala).Results Median age was 37 years old and 54% of participants were female. There were no significant differences in atherosclerotic inflammation between participants with PTSD and controls; adjusted mean difference in TBR max (95% confidence interval) of the aorta 0.020 (-0.098, 0.138), and of the carotids 0.014 (-0.091, 0.119). Participants with PTSD had higher hsCRP, spleen activity, and aorta atherosclerotic burden (normalized wall index). Participants with PTSD also had higher SNA and lower AI. Across the cohort, carotid atherosclerotic burden (standard deviation of wall thickness) associated positively with SNA and negatively with AI independent of Framingham risk score.Conclusions In this study of limited size, participants with PTSD did not have higher atherosclerotic inflammation than controls. Notably, impaired cortico-limbic interactions (higher amygdala relative to vmPFC activity or disruption of their intercommunication) associated with carotid atherosclerotic burden. Larger studies are needed to refine these findings. Structured Graphical Abstract Multimodal imaging of the brain-heart connection. Individuals with PTSD, individuals exposed to trauma but who did not develop PTSD (trauma controls), and individuals with neither trauma exposure nor a history of psychiatric disorders (healthy controls) were recruited. Participants underwent 18F-FDG PET and MRI (including DTI MRI of the brain) to assess the function and structure of stress-associated neural tissues, leucopoietic organs (bone marrow and spleen), and the arteries. The lower panel proposes a pathway by which neural tissues may potentiate atherosclerosis. A higher metabolic activity in the amygdala relative to the vmPFC (on 18F-FDG PET), which is also linked to a disruption in their interconnection (the uncinate fasciculus) on DTI MRI, was associated with carotid atherosclerotic burden. Heightened systemic inflammation and leucopoiesis may be involved in this brain-heart connection. DTI, diffusion tensor imaging; 18F-FDG PET, 18F-fluorodeoxyglucose positron emission tomography; MRI, magnetic resonance imaging; PTSD, post-traumatic stress disorder; SNA, stress-associated neural network activity; vmPFC, ventromedial prefrontal cortex.

The prevalence in allergic diseases has increased considerably in the past decades. An important trigger of the symptoms of allergic rhinitis (hay fever) is the pollen of wind-pollinating plants. This pollen is developed by plants and is released into the air where it gets exposed to environmental influences and air pollution. We investigated the chemical changes to pollen that occur after release from the flower in a rural (Veluwe) and an urban (Amsterdam) site in the Netherlands using Fourier Transform Infrared (FTIR) spectroscopy. During the spring/summer of 2020 (during the COVID pandemic) the pollen of nine taxa (Alnus, Betula, Fagus, Fraxinus, Pinus, Plantago, Poaceae, Quercus and Salix) were collected directly from flowers and the air (using a mobile sampler). FTIR spectra were obtained for multiple individual pollen grains for each taxa. The spectra obtained from airborne pollen collected at the rural vs. urban sites did not show any statistical difference. This is possibly a result of a reduced difference in pollutant concentrations between the two sites due to the COVID-19-lockdown measures were in place. However, consistent differences in the FTIR spectra recovered from airborne vs. flower pollen were recorded for all pollen taxa. After the release from the flower the chemical composition of the pollen changed: (i) polysaccharides are converted to monosaccharides; (ii) protein concentration and/or nitration/oxidation level is altered; (iii) lipids are modified and/or reduced in concentration. These changes may alter the allergenicity of the pollen and suggest that further work on the allergenic nature of airborne pollen is required.

Bioaerosol is composed of different particles, originating from organisms, or their fragments with different origin, shape, and size. Sampling, analysing, identification and describing this airborne diversity has been carried out for over 100 years, and more recently the use of molecular genetic tools has been implemented. However, up to now there are no established protocols or standards for detecting airborne diversity of bacteria, fungi, viruses, pollen, and plant particles. In this review we evaluated commonalities of methods used in molecular genetic based studies in the last 23 years, to give an overview of applicable methods as well as knowledge gaps in diversity assessment. Various sampling techniques show different levels of effectiveness in detecting airborne particles based on their DNA. The storage and processing of samples, as well as DNA processing, influences the outcome of sampling campaigns. Moreover, the decisions on barcode selection, method of analysis, reference database as well as negative and positive controls may severely impact the results obtained. To date, the chain of decisions, methodological biases and error propagation have hindered DNA based molecular sequencing from offering a holistic picture of the airborne biodiversity. Reviewing the available studies, revealed a great diversity in used methodology and many publications didn't state all used methods in detail, making comparisons with other studies difficult or impossible. To overcome these limitations and ensure genuine comparability across studies, it is crucial to standardize protocols. Publications need to include all necessary information to enable comparison among different studies and to evaluate how methodological choices can impacts the results. Besides standardization, implementing of automatic tools and combining of different analytical techniques, such as real-time evaluation combined with sampling and molecular genetic analysis, could assist in achieving the goal of accurately assessing the actual airborne biodiversity.

This research builds upon a previous study that explored the potential of the modified WIBS-4+ to selectively differentiate and detect different bioaerosol classes. The current work evaluates the influence of meteorological and air quality parameters on bioaerosol concentrations, specifically pollen and fungal spore dynamics.Temperature was found to be the most influential parameter in terms of pollen production and release, showing a strong positive correlation. Wind data analysis provided insights into the potential geographic origins of pollen and fungal spore concentrations. Fungal spores were primarily shown to originate from a westerly direction, corresponding to agricultural land use, whereas pollen largely originated from a North-easterly direction, corresponding to several forests.The influence of air quality was also analysed to understand its potential impact on the WIBS fluorescent parameters investigated. Most parameters had a negative association with fungal spore concentrations, whereas several anthropogenic influences showed notable positive correlations with daily pollen concentrations. This is attributed to similar driving forces (meteorological parameters) and geographical origins. In addition, the WIBS showed a significant correlation with anthropogenic pollutants originating from combustion sources, suggesting the potential for such modified spectroscopic instruments to be utilized as air quality monitors.By combining all meteorological and pollution data along with WIBS-4+ channel data, a set of Multiple Linear Regression (MLR) analyses were completed. Successful results with R2 values ranging from 0.6 to 0.8 were recorded. The inclusion of meteorological parameters was dependent on the spore or pollen type being examined.

Air quality monitoring in Ireland is under the jurisdiction of the Environmental Protection Agency in compliance with the Gothenburg Protocol, EU/national legislation, and the National Clean Air Strategy. Secondary inorganic aerosols (SIAS) have been acknowledged as a key atmospheric pollutant, with serious public health impacts and no safe exposure threshold in place to date. Ammonia (NH3) emissions are linked to the secondary production of aerosols through atmospheric reactions occurring with acidic atmospheric components such as sulfuric, nitric, and hydrochloric acid. These reactions result in the formation of ammonium sulfate, ammonium nitrate and ammonium chloride, among others. Approximately 98% of NH3 emissions occurring in Ireland arise from agriculture, with minor contributions from transport and natural sources. A better understanding of NH3 emissions and SIA formation can be achieved through monitoring emissions at the source level. Additionally, mitigation strategies with a more thorough understanding of NH3 dynamics at the source level and consequential SIA formation allow for more efficient action. This project monitored ambient NH3 and SIA on two selected arable agricultural sites and a control site in a rural site close to Dublin on the east coast of Ireland to establish emission levels. Meteorological factors affecting emissions and SIA formation were also measured and cross-correlated to determine micro-meteorological effects. Monitoring at the agricultural sites observed ambient NH3 concentrations ranging from 0.52 µg m−3 to 1.70 µg m−3, with an average of 1.45 µg m−3. At the control site, ambient NH3 measured concentrations ranged from 0.05 µg m−3 to 1.76 µg m−3 with an average of 0.516 µg m−3. Aerosol NH4+ ranged from 0.03 µg m−3 to 1.05 µg m−3 with an average concentration of 0.27 µg m−3 at the agricultural site. The potential effects of meteorological conditions and the implications for the effects of these emissions are discussed, with recommendations to aid compliance with the National Emissions Ceiling and the National Clean Air Strategy (Directive 2001/81/EC).

The rapid expansion of 3D printing technologies has led to increased utilization in various industries and has also become pervasive in the home environment. Although the benefits are well acknowledged, concerns have arisen regarding potential health and safety hazards associated with emissions of volatile organic compounds (VOCs) and particulates during the 3D printing process. The home environment is particularly hazardous given the lack of health and safety awareness of the typical home user. This study aims to assess the safety aspects of 3D printing of PLA and ABS filaments by investigating emissions of VOCs and particulates, characterizing their chemical and physical profiles, and evaluating potential health risks. Gas chromatography-mass spectrometry (GC-MS) was employed to profile VOC emissions, while a particle analyzer (WIBS) was used to quantify and characterize particulate emissions. Our research highlights that 3D printing processes release a wide range of VOCs, including straight and branched alkanes, benzenes, and aldehydes. Emission profiles depend on filament type but also, importantly, the brand of filament. The size, shape, and fluorescent characteristics of particle emissions were characterized for PLA-based printing emissions and found to vary depending on the filament employed. This is the first 3D printing study employing WIBS for particulate characterization, and distinct sizes and shape profiles that differ from other ambient WIBS studies were observed. The findings emphasize the importance of implementing safety measures in all 3D printing environments, including the home, such as improved ventilation, thermoplastic material, and brand selection. Additionally, our research highlights the need for further regulatory guidelines to ensure the safe use of 3D printing technologies, particularly in the home setting.

Aeroallergens or inhalant allergens, are proteins dispersed through the air and have the potential to induce allergic conditions such as rhinitis, conjunctivitis, and asthma. Outdoor aeroallergens are found predominantly in pollen grains and fungal spores, which are allergen carriers. Aeroallergens from pollen and fungi have seasonal emission patterns that correlate with plant pollination and fungal sporulation and are strongly associated with atmospheric weather conditions. They are released when allergen carriers come in contact with the respiratory system, e.g. the nasal mucosa. In addition, due to the rupture of allergen carriers, airborne allergen molecules may be released directly into the air in the form of micronic and submicronic particles (cytoplasmic debris, cell wall fragments, droplets etc.) or adhered onto other airborne particulate matter. Therefore, aeroallergen detection strategies must consider, in addition to the allergen carriers, the allergen molecules themselves. This review article aims to present the current knowledge on inhalant allergens in the outdoor environment, their structure, localization, and factors affecting their production, transformation, release or degradation. In addition, methods for collecting and quantifying aeroallergens are listed and thoroughly discussed. Finally, the knowledge gaps, challenges and implications associated with aeroallergen analysis are described.

COVID-19 lockdowns in early 2020 reduced human mobility, providing an opportunity to disentangle its effects on animals from those of landscape modifications. Using GPS data, we compared movements and road avoidance of 2300 terrestrial mammals (43 species) during the lockdowns to the same period in 2019. Individual responses were variable with no change in average movements or road avoidance behavior, likely due to variable lockdown conditions. However, under strict lockdowns 10-day 95th percentile displacements increased by 73%, suggesting increased landscape permeability. Animals' 1-hour 95th percentile displacements declined by 12% and animals were 36% closer to roads in areas of high human footprint, indicating reduced avoidance during lockdowns. Overall, lockdowns rapidly altered some spatial behaviors, highlighting variable but substantial impacts of human mobility on wildlife worldwide.