Background Indoor air quality (IAQ) is a well demonstrated actionable determinant of health status. Low-cost sensors (LCS) could enable patient-centred assessments, but real-world clinical utility is uncertain. Objective Evaluate the feasibility, usability and clinical indications of home IAQ monitoring with LCS. Methods We conducted a cohort study involving household continuous IAQ monitoring with LCS of 205 adults with chronic obstructive pulmonary disease, bronchiectasis or asthma. Household IAQ was profiled prospectively over a 2-month period registering concentrations of carbon dioxide, particulate matter 2.5 µm (PM 2.5 ) and formaldehyde every 10 min. For each pollutant, dwellings were classified as good, moderate or unhealthy according to the Global Open Air Quality Standards thresholds. Pulmonary exacerbations requiring unplanned hospitalisations and all-cause emergency department (ED) visits over the preceding 12 months were registered and potential relationships with household IAQ results were explored. Results Of the 205 participants, 178 were included in the final analysis after excluding dropouts and cases with insufficient monitoring data. More than half of homes (51.7%) had at least one pollutant in an at-risk category. The burden was mostly generated by PM 2.5 : 40.1% of dwellings were classified as at risk (32.8% moderate; 7.3% unhealthy). Formaldehyde exceeded the low-risk threshold in 22 homes (12.4%). Tobacco smoking, either active or passive, was significantly associated with PM 2.5 levels (p<0.001). No relationships were found between IAQ categories and hospitalisations nor with all-cause ED visits. Conclusions LCS are useful tools for short-term, targeted household IAQ screening in chronic respiratory patients. Indoor pollution is highly prevalent and largely PM 2.5 driven. Further research is needed to assess the short-term health impacts of these exposures. Trial registration number NCT06421402 .

Extreme desert dust events and chronic PM2.5/PM10 exposure: A public health risk assessment in the Taklamakan region, China

Air pollution remains a primary global health concern, with one of its most hazardous components, fine particulate matter PM2.5, being recognized as a significant contributor to premature mortality worldwide. Thus, forecasting PM2.5 concentrations is essential for policymaking and mitigating their harmful effects. Low-cost sensor (LCS) technology has advanced air quality monitoring by providing real-time PM2.5 measurements at finer spatial and temporal granularity due to their higher deployment density, enabling more comprehensive data for predictive modelling. However, these sensors can experience significant data gaps, necessitating effective imputation methods to improve the accuracy of PM2.5 predictions. In this study, we focus on reconstructing missing PM2.5 readings by evaluating the impact of different imputation strategies on PM2.5 forecasting accuracy using data from eight LCSs deployed across Edmonton, Alberta, from January 2023 to December 2024. The dataset includes 7 features: temperature, relative humidity, wind speed, wind direction, latitude, longitude, and altitude, integrated from both LCS sensors and adjacent air quality monitoring stations. We evaluate the efficacy of Kriging as a spatial imputation method for PM2.5 forecasting, comparing its performance along with mean imputation and k-nearest neighbours, against a no-imputation benchmark to assess whether imputation techniques provide meaningful improvements in forecasting accuracy. Additionally, we compare its performance across data with varying levels of missingness. Our comparative analysis examines how these imputation techniques affect various prediction models, including Random Forest, Extreme Gradient Boosting, Convolutional Neural Networks, Long Short-Term Memory (LSTM), and LSTM with an Attention mechanism. Our findings indicate that although differences among imputation methods were modest, Kriging consistently yielded the best results, particularly at 90% data availability for the deep learning models. The LSTM-AM architecture produced the best overall results in a short-horizon setting, [Formula: see text] = 0.9602, MAE = 1.7815μg/m[Formula: see text], and RMSE = 4.5208μg/m[Formula: see text], suggesting an optimal combination of Kriging imputation with LSTM-AM modeling for near-term PM2.5 forecasting applications.

Low-cost PM2.5 sensors have gained popularity due to their simplicity and low maintenance. However, calibration challenges often limit their accuracy and reliability. In South Asian countries, strong seasonal meteorological variation can require dynamic sensor calibration approaches. This study investigated sensor performance of TSI BlueSky low-cost PM2.5 sensors within a harmonized sensor network using linear regression (LR), multiple linear regression (MLR), and echo-state network (ESN) models across two distinct climatic zones in Sri Lanka. Results indicated that the harmonized sensors were generally in close agreement with one another, with 10% variability. The results of LR, MLR, and ESN indicate that all three models significantly enhance performance with increasing temporal averaging windows, especially from the simple LR model at 24-h averaging in the wet season (R2 = 0.60, MAE = 0.93 µgm-3, MAPE = 7.81%) and dry season (R2 = 0.85, MAE = 1.98 µgm-3, MAPE = 9.17%). Applying wet season calibration values to dry season data results in a significant mean absolute percentage error (26.57%). The effectiveness of calibration models developed in Colombo decreased when they were applied to PM2.5data from Kandy, suggesting that they have limited transferability across different climatic zones. Conversely, models that were specific to Kandy and incorporated temperature and relative humidity demonstrated a substantial increase in precision during both wet (R2 = 0.84, MAE = 1.78 µgm-3, MAPE = 11.71%) and dry (R2 = 0.92, MAE = 1.59 µgm-3, MAPE = 7.73%) seasons. These findings emphasize the critical need for both temporal and spatial calibration strategies to enhance PM2.5 estimation reliability for low-cost sensors.

Traffic-related air pollution is increasingly recognized as a modifier of pollen physicochemical properties and allergenic potential, yet field-based mechanistic evidence remains limited. This study investigated the effects of traffic-related pollution on the morphology, chemical composition, and Bet v 1 allergen content of Betula pendula pollen using a multi-method approach. Pollen samples were collected from 16 B. pendula trees across Ankara, Bingöl, and Tunceli during April 2025 along a traffic-exposure gradient, with air-quality data spanning 1 June 2024-31 May 2025, and sampling sites linked to Türkiye's national air-quality monitoring network (PM₁₀, PM₂.₅, NO₂, NOₓ, SO₂, CO). Pollen was analysed using light microscopy, SEM, FT-IR ATR spectroscopy, and ELISA. Polluted sites (n = 13) showed morphological and biochemical differences compared with clean reference sites (n = 3): pollen exhibited significantly increased exine wall thickness and equatorial dimensions, alongside greater particle deposition near apertures; polar axis showed weaker group-level differences. FT-IR analysis revealed altered protein-related spectral profiles, including an increased Amide I/Amide II area ratio and reduced O-H band areas, consistent with modifications in protein secondary structure. Bet v 1 concentrations did not differ significantly between polluted (95.7 ± 23.8µg/g) and clean (85.5 ± 24.6µg/g) samples; given the small reference group (n = 3), this comparison is interpreted as non-directional. These findings provide field-based evidence that traffic-related pollution is associated with modifications in the structural and biochemical characteristics of B. pendula pollen in ways consistent with enhanced allergenic potential, underscoring the need to incorporate air-quality effects into aeroallergen risk assessment.

Open-pit coal mining is a significant source of air pollution, especially in developing countries, where dust and gaseous emissions from mining operations often exceed regulated limits due to limited mitigation measures and environmental controls. This study introduces a scalable, Internet of Things (IoT)-based real-time air quality monitoring system implemented in the Jharia coalfields, India, using low-cost sensors across three zones: mine site (industrial), mine office (buffer), and IIT-ISM (sensitive). The system monitored PM1, PM2.5, PM10, CO₂, NO₂, TVOCs, temperature, and humidity over one year. ANOVA and Tukey's HSD confirmed statistically significant differences in pollutant concentrations between monitoring zones (p < 0.05), with the mine site consistently recording the highest levels. Seasonal analysis showed winter peaks due to thermal inversion, while monsoon conditions supported pollutant washout. Diurnal PM2.5 peaks were observed at 6:00 AM and 6:00 PM, coinciding with peak operational activity. High PM2.5/PM10 (0.86-0.91) and PM1/PM2.5 (0.80-0.84) ratios indicated dominance of fine and ultrafine particles. Correlation analysis showed AQI strongly correlated with PM (r ≈ 1.0), while temperature showed a moderate negative correlation in winter (r ≈ -0.65). CO correlated moderately to strongly with PM (r = 0.6-0.7), while O₃ showed weak or negative correlations, indicating photochemical influences. Time-series forecasting using machine learning models identified LightGBM with higher accuracy followed by Random Forest and XGBoost. The study contributes to sustainable mining practices by integrating meteorological data into a scalable IoT-based monitoring framework. It also delivers actionable insights for environmental compliance and health risk mitigation in mining regions.

Road dust is a major carrier of potentially toxic elements (PTEs) in urban environments. This study characterizes road dust samples collected in the vicinity of public schools (n = 17) to assess their physicochemical properties, mineralogy, chemical composition, magnetic susceptibility, particle morphology, contamination indices, and gastric bioaccessibility of some PTEs (< 250µm fraction). Samples were mainly composed of quartz, with feldspars, Σphyllosilicates, carbonates, and magnetite-maghemite, revealing geogenic and anthropogenic sources, e.g., tire wear, brake abrasion, and resuspended pavement debris. The fraction < 106µm was enriched in PM10, suggesting high potential for inhalation and ingestion. The geoaccumulation index was generally < 1, indicating low contamination, although isolated Zn hotspots reached moderate levels. Pollution Index identified Zn as the main pollutant (max. 11.6), while Pollution Load Index classified the area as slightly polluted. Bioaccessibility tests showed negligible gastric solubility for As and Cr, while Ni, Cu, and Pb exhibited moderate to high bioaccessible fractions, with increased ingestion risk despite moderate total concentrations. SEM-EDS analysis revealed carbonaceous soot, Fe-oxide brake debris, alloy fragments, Ti-rich paint particles, and occasional As-sulfide grains in respirable sizes. Road dust showed moderate contamination but included some hotspots with bioaccessible PTEs fractions relevant to children exposure, highlighting the need for targeted dust-control measures and periodic monitoring. Simultaneously, indoor Rn concentrations in classrooms were high (mean 1540 ± 921Bq/m3), posing a cancer risk by inhalation. The combined assessment of road dust and Rn exposure provided an integrated evaluation of inhalation and ingestion pathways in school environments, underscoring the importance of multi-hazard air-quality monitoring.

Formaldehyde (FA) is a well-known indoor pollutant and human carcinogen, making the development of field-deployable sensors for its monitoring crucial for public safety. Herein, we report a facile strategy to synthesize carbon nanoparticles functionalized with diethanolamine (CNPs-DEA) as FA sensors. The strategic surface functionalization dictates a specific spatial arrangement that is key to efficient and cooperative interaction with the analyte. In water solution, the probe exhibits a LOD of 46 ppb with a binding constant (log β) value of 4.31 ± 0.01. By immobilizing the CNPs on polyamide membranes, we engineered a solid-state strip test integrated with a 3D-printed smartphone readout platform. This setup delivers an ultralow detection threshold (1 ppb) for gaseous FA and, crucially, maintains robust performance under saturated humidity conditions, overcoming a major limitation of conventional carbon-based sensors. The practical utility of the device was validated by quantifying FA in commercial paint samples. Combining operational simplicity, low cost, and high sensitivity, this platform represents a robust early warning tool for air quality monitoring, bridging the gap between sophisticated laboratory instrumentation and accessible real-world applications.

Fine particulate matter (PM₂.₅) poses severe public health and environmental risks in Nepal's Tarai and Dun Valley regions, where ground-based air quality monitoring is spatially sparse and temporally inconsistent. This study integrates ground-based observations, satellite-derived Aerosol Optical Depth (AOD) from MODIS MCD19A2, TROPOMI trace gases (CO, NO₂, SO₂), and ERA5 meteorological reanalysis (temperature, relative humidity, wind components) to characterize the spatiotemporal variability of PM₂.₅ and to reconstruct long-term trends from 2000 to 2023. Daily PM₂.₅ measurements from six monitoring stations across southern Nepal were analyzed alongside collocated satellite and reanalysis data. Correlation analysis revealed strong positive associations between PM₂.₅ and both AOD (r = 0.59) and CO (r = 0.62), while temperature (r = -0.38) and relative humidity (r = -0.46) showed moderate negative correlations. A Random Forest model incorporating AOD, CO, temperature, relative humidity, and wind components achieved robust predictive performance (R2 = 0.65, RMSE = 22.6µg/m3), substantially outperforming multiple linear regression (R2 = 0.54). The strong contribution of CO performance dropped to R2 = 0.62 when excluded, highlights the dominance of combustion sources (biomass burning, vehicular emissions, forest fires) in driving PM₂.₅ pollution. Long-term reconstruction using AOD and meteorological variables (excluding CO due to limited historical data) revealed a distinct east-west gradient. Eastern stations (Jhumka, Bharatpur, Hetauda) show statistically significant increasing trends (up to + 0.41µg/m3/year, p < 0.01), while western stations (Dhangadhi, Bhimdatta, Dang) exhibit stable or slightly declining trends. Seasonal analysis showed the highest concentrations during winter and pre-monsoon (January-April, > 60µg/m3) and substantial reductions during monsoon months due to rainfall-driven washout. The results underscore the importance of integrating satellite and ground-based data with machine learning to assess historical air quality, identify pollution hotspots, and inform evidence-based mitigation strategies in data-sparse regions. This framework provides a robust basis for air quality management and public health planning in southern Nepal and across the Indo-Gangetic Plain, where transboundary cooperation is essential to reverse worsening pollution trends.

Despite extensive studies in major industrial regions, the spatiotemporal behavior of ground-level ozone (O3) in smaller cities such as Jilin City, China, remains poorly understood, particularly with respect to meteorological variability and health risks. This study examined summertime O3 formation and the associated population-level risk in Jilin City during May-July 2020 and 2023 by integrating WRF v4.4.1 and CMAQ v5.4 simulations with observations from air quality monitoring stations. Compared with 2020, the 2023 period was characterized by higher temperatures by 1.1-1.5°C, altered wind regimes, shallower boundary-layer conditions during peak episodes, and anticyclonic circulation, with station-level pressures above 950hPa, equivalent to approximately 1010-1018hPa at sea level. These conditions favored precursor accumulation, restricted vertical mixing, and enhanced photochemical O3 formation. Difference analysis showed that meteorological shifts accounted for 3.89µgm-3, or 59.1%, of the 6.59µgm-3 domain-wide MDA8 O3 increase between 2020 and 2023, although the fixed 2020 emission inventory limits complete separation of meteorological effects from post-pandemic emission changes. CMAQ reproduced the broad seasonal and interannual patterns but underestimated peak O3 concentrations, highlighting the need for improved year-specific emissions and for online representation of biogenic VOCs. Conventional AQI changed only modestly, increasing from 40.62 to 46.22, whereas city-average excess health risk increased from 2.76 to 3.16%, indicating a 14.5% deterioration in population-level risk. Observed seasonal O3 levels of 68-118µgm-3 exceeded the WHO 2021 peak-season MDA8 guideline of 60µgm-3. Persistent high-risk hotspots were identified at Hada Bay and Jiuzhan, emphasizing the need for localized, source-differentiated O3 mitigation strategies.

Airborne particles smaller than 2.5µm (PM2.5) pose significant risks to human health and influence the climate. However, the accuracy of ground- and satellite-based estimates varies widely across regions. To evaluate the potential of machine learning (ML) to improve ground PM2.5 concentration measurements, we analyzed ground-level PM2.5 concentrations in 12 cities across the Greater Middle East (GME) and Canada. We deployed three ML models to enhance daily PM2.5 estimations, using nearly a decade of combined ground-based observations and MODIS-MAIAC aerosol optical depth (AOD), along with a uniform predictor set comprising AOD and meteorological variables. To ensure comparability, each city was anchored to a single regulatory monitor in both Canada and the GME. Using tenfold cross-validation, ML improved the AOD-PM2.5 correlation from approximately 0.15 to a mean R of 0.59 in Canada and ~ 0.48 in the GME. A pooled regional model integrating all GME observations achieved high out-of-sample agreement (r ≈ 0.90), compared to an AOD-only fit (r ≈ 0.11). SHAP diagnostics revealed that PM2.5 history (lags and rolling means), AOD, its interaction with physical processes (e.g., temperature and pressure), and boundary-layer height were the dominant drivers in the GME, with more stable influences observed in Canada. PM2.5 levels in Canada rarely exceeded the WHO guideline, whereas exceedances were frequent across all GME cities. These findings demonstrate that ML, particularly when incorporating temporal context and regional pooling, can significantly enhance PM2.5 inference in data-scarce environments. Nonetheless, we emphasize the ongoing need for denser ground monitoring to support high-resolution mapping.

BackgroundAn estimated 5 to 8 million US children live with a parent who uses cannabis, and most cannabis users report smoking cannabis inside their homes, placing children at risk for cannabis secondhand smoke (cSHS) exposure. Indoor air quality (IAQ) monitoring provides real-time feedback on airborne pollutants and has shown promise in reducing in-home tobacco secondhand smoke exposure, suggesting its potential as an effective harm reduction strategy for cSHS.ObjectiveThis pilot study evaluated the feasibility, acceptability, and preliminary effectiveness of using low-cost, off-the-shelf IAQ monitors to increase caregivers’ awareness of children’s cSHS exposure risk and to change smoking behavior. Secondary aims were to assess participant engagement, perceived usefulness, and household communication regarding in-home cannabis smoking.MethodsBetween February 2025 and April 2025, 14 adults who smoked cannabis indoors and lived with at least 1 child aged younger than 16 years were recruited primarily via targeted social media advertisements and completed a 3-week trial. Participants received an Awair Element IAQ monitor, printed health education materials, and text messaging prompts for brief surveys. The IAQ monitor continuously measured PM2.5, VOCs, CO₂, temperature, and humidity. Daily surveys captured self-reported PM2.5 readings and recent cannabis use, while baseline and end-of-study assessments evaluated IAQ perceptions, cSHS risk awareness, and in-home smoking behavior. Survey results were summarized via descriptive statistics, and linear mixed-effects models were used to characterize objective IAQ trends. Six additional adult household members provided parallel end-of-study data.ResultsReported engagement was high, with 85% (11/13) of participants indicating that they reviewed the monitor at least daily. The average number of days in the previous week that a caregiver reported a child being home while cannabis was smoked declined from 4.5 (SD 2.2) at the trial start to 2.8 (SD 2.9) at the end (6/13, 46% had a reduction; 1/13, 8% reported an increase). Furthermore, 62% (8/13) of participants reported that they reduced (4/13, 31%) or thought about changing (4/13, 31%) their smoking habits. Around 62% (8/13) of participants agreed or strongly agreed that IAQ monitoring helped drive conversations about changing indoor smoking rules, while 100% (13/13) reported no IAQ-driven disagreements among household residents regarding in-home smoking rules. A linear mixed-effects model did not indicate a consistent trend in PM2.5 levels across participants over time (β=–0.28; SE 1.13; P=.81), but there was heterogeneity in trends, and those with the largest reductions in PM2.5 over the trial had the largest reduction in reported children’s cSHS exposure.ConclusionsIn-home IAQ monitoring was feasible and perceived as useful among caregivers who smoked cannabis indoors. Real-time IAQ feedback supported risk awareness, promoted family dialogue, and coincided with reductions in in-home smoking around children. These findings suggest that IAQ feedback may represent a scalable tool for reducing children’s cSHS exposure and merits further testing in larger, controlled trials.

BackgroundIdiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterised by progressive fibrosis. Environmental exposures, including fine particulate matter (PM2.5), have been implicated in the progression of IPF; however, indoor data are limited. We aimed to evaluate the impact of indoor exposure to PM2.5 on the progression of IPF.Methods and analysisThis prospective, multicentre cohort study involves 15 medical institutions across South Korea. A total of 120 patients with IPF have been recruited and will be followed for 1 year. The impact of indoor exposure to PM2.5 on clinical outcomes, including progression of the disease (defined as a relative decline in forced vital capacity ≥10% or diffusing capacity for carbon monoxide ≥15%), hospitalisation, acute exacerbations and mortality, will be assessed. Pulmonary function tests will be conducted quarterly at institutions, with home-based spirometry performed daily in a subset of 50 participants. The indoor PM2.5 level will be continuously measured using a scattering-based indoor air quality monitor (IAQ-CL; KWeather, Seoul, South Korea) and PM10, volatile organic compounds and carbon dioxide will also be measured. Biomarker analysis of Krebs von den Lungen-6 and other related markers will be performed. Statistical analyses will use linear mixed-effects models and time-to-event models to evaluate the association between exposure to PM and clinical and biomarker outcomes.Ethics and disseminationThe study has been approved by the Institutional Review Boards of all institutions, including Asan Medical Center (S2021-1136-0051). Written informed consent was obtained from all participants prior to enrolment.Trial registration numberKCT0008638.

The short-term respiratory health effects of recurrent landfill fires: A time-series analysis, Havering, UK (2018-2023).

The "green exercise" paradox: Quantifying the estimated breakpoint of acute physiological perturbation from ambient PM2.5 in urban runners using wearable biosensors.

A multi-view machine learning approach for estimating PM2.5 concentrations from smartphone photographs.

Wildfire smoke substantially degrades air quality by increasing total PM2.5 concentrations and altering their chemical speciation. Although existing research has documented PM2.5 during active fires, the spatiotemporal persistence of these pollutants across U.S. regions remains insufficiently quantified. Leveraging quasi-experimental variation arising from 2007 to 2023 wildfire smoke events, we employ difference-in-differences (DID) and event study approaches to estimate the causal effects on both PM2.5 concentrations and their chemical speciation, while assessing their duration. Our analysis reveals that smoke events increase PM2.5 concentrations by 35 percent nationally, with pronounced regional heterogeneity: Western states experience the most severe impacts (+50 percent persisting for three days), whereas northeastern and southern regions exhibit weaker and shorter lived effects. Chemical speciation analysis reveals distinct temporal dynamics: Heavy metals (chromium, manganese) and inorganic compounds exhibit transient spikes (one day), whereas lead, vanadium, and organic carbon remain elevated for three days. Our findings underscore the urgent need to integrate chemical speciation into air quality monitoring systems to better assess composition-specific health risks under climate-driven intensification of wildfires.

Seasonal dynamics of polycyclic aromatic hydrocarbons in different-sized urban areas assessed by moss transplants.

Air quality monitoring currently relies mostly on a combination of epidemiological data and classic experimental data. Our objective was to design an alternative approach for assessing air pollutant risk potential using a specialized platform capable of detecting the cumulative and indirect effects of exposure via the inhaled route. We used a bronchial airways-on-chip (BOC) that captures key physiological features of the human lung. The platform integrates our previously developed device with in vitro differentiated bronchial epithelium derived from induced pluripotent stem cells (iPSCs). This setup is capable of replicating bronchial epithelial exposure to irritants at the air-liquid interface under controlled and reproducible conditions. It comprises the first proof-of-concept design combining a BOC with iPSC-derived bronchial epithelium as an alternative approach toward potential risk assessment of inhaled pollutants. As a representative pollutant, we use benzene, a volatile organic compound (VOC). At low concentrations and short-term exposure, it is not considered acutely harmful, but long-term exposure can result in mutagenic and carcinogenic effects. As air pollutant toxicity is known to be mediated by the respiratory epithelial lining and secretion of cytokines, we demonstrate our system to be sufficiently sensitive to capture increased cytokine secretion corresponding to increasing concentrations of benzene. Of utmost relevance is our finding that a cumulative effect could be detected, only caused by prolonged exposure at low concentrations of benzene, previously shown to be nontoxic in classic short-term in vitro studies. Finally, the cumulative effect could be reversed using a commonly used asthma medication (Montelukast), further supporting the relevance of the setup.

Accurate forecasting of PM2.5 concentration is a critical focus in air quality monitoring research. Considering the nonlinearity and non-stationarity of the PM2.5 time series, this paper proposes a forecasting model that combines variational mode decomposition (VMD), deep learning methods such as temporal convolutional networks (TCN), bidirectional long short-term memory (BiLSTM), and an attention mechanism. Initially, VMD is used to decompose the original PM2.5 sequence into a set of intrinsic mode functions (IMFs) that assess distinct physical interpretations. Sample entropy (SE) is calculated to evaluate the complexity of each IMF, followed by the application of K-means clustering to group the components into high-, medium-, and low-frequency components. A TCN-BiLSTM-based forecasting architecture is then established, where separate forecasting models are trained for each frequency band. Finally, an attention mechanism is introduced to adaptively learn the importance weights of different frequency component forecasting models, and a weighted fusion approach is applied to generate the final forecasting results. Experimental evaluations establish that the proposed method achieved the best forecasting accuracy with a lowest RMSE of 16.920µg/m3, a lowest MAE of 11.134µg/m3, and a highest R2 of 0.960, thereby demonstrating improved forecasting capability.