Volatile organic compounds (VOCs) are major contributors to indoor air pollution in hospitals, where disinfectants, sterilants, and pharmaceutical agents are used continuously. This study provides a multi-ward, multi-shift assessment of total VOC (TVOC) concentrations in a large university hospital using a calibrated photoionization detector (PID) with daily zero/span checks and inter-instrument consistency verification. Measurements were conducted between 10 April and 23 May 2023 (a 44-day spring field campaign) across 41 wards, a non-clinical reference building, and open air to distinguish healthcare-related emissions from background indoor sources. TVOC concentrations were compared with a 108 ppb screening threshold derived from the 500 µg/m3 TVOC reference used in LEED, WELL, and UBA frameworks, converted to PID units using a published GC–PID calibration model. Indoor TVOC levels were elevated in most clinical areas, with the highest median concentrations in operating rooms (857 parts per billion [ppb]) and the endoscopy ward (842 ppb). Mean indoor-to-outdoor (I/O) ratios exceeded 1 in all sampled wards, supporting the predominance of indoor sources. Significant spatial variability was observed across functional ward categories (Kruskal–Wallis p < 0.001), and temporal variation across work shifts was statistically significant in 26 of 41 wards (p < 0.05, Kruskal–Wallis with Dunn post-hoc comparisons), with morning shifts generally recording the highest TVOC levels. These findings demonstrate substantial VOC accumulation in procedure-intensive areas and highlight the need for improved ventilation management and targeted exposure-control strategies in healthcare environments.
Oil mist pollution in machining workshops poses risks to worker health, equipment reliability, and operational safety. This study characterizes the non-uniform spatial distribution of oil mist and proposes an optimized local ventilation strategy. Field measurements of oil mist concentration and airflow velocity were conducted in a 14 m × 12 m × 5 m workshop at a precision equipment factory in Zhuzhou, Hunan. A computational fluid dynamics (CFD) model, employing the RNG k–ε turbulence model and a bidirectional coupled discrete phase model (DPM), was developed to simulate oil mist transport and dispersion. The numerical results were validated against field data obtained using TSI-8386 hot-wire anemometers and TSI-8530 DustTrak particle monitors, with deviations within 20
Industrial development has significantly contributed to economic growth and modernization, but it has also intensified a range of environmental challenges. Among these, air pollution has emerged as a major concern because of its strong implications for ecosystem stability, climate related stress, and public health. In particular, PM2.5 has received considerable attention due to its fine particulate nature, wide spatial reach, and well documented harmful effects. Although previous studies have extensively examined the relationship between PM2.5 and industrial or anthropogenic activity, direct quantification of industrial impact on PM2.5 remains methodologically limited, especially in data constrained regional settings. The present study addresses this gap by proposing a quantile mapping based statistical framework for quantifying industrial impact on PM2.5 across Pakistan. We called the proposed framework as - Baseline-Referenced Industrial Quantification of PM2.5 (BRIQ-PM2.5). The proposed framework BRIQ-PM2.5 is based on the comparison of observed PM2.5 distributions with province specific baseline reference conditions representing comparatively lower industrial settings. By aligning the PM2.5 distributions of target locations with their corresponding baseline cities through quantile mapping, the study estimates industrial impact as the excess burden reflected in the difference between observed and corrected PM2.5 levels. The proposed framework BRIQ-PM2.5 is applied across four provinces of Pakistan using satellite derived PM2.5 data. The results showed that the quantile mapping procedure effectively aligned the corrected PM2.5 series with the selected baseline conditions, as reflected in distributional summaries, histogram comparisons, empirical cumulative distribution function(ECDF) alignment, and improved RMSE values after correction. The resulting industrial impact estimates revealed clear spatial variation across stations and provinces, indicating that industrially aligned PM2.5 burden is not uniformly distributed. Further, spatial analysis based on variogram modeling and kriging demonstrated that the estimated industrial impact follows a coherent spatial pattern across the country. The study contributes a new methodological perspective to air pollution analysis by moving beyond simple concentration comparison toward baseline referenced industrial impact quantification. From a practical point of view, the proposed framework BRIQ-PM2.5 provides a useful statistical tool for hotspot identification, regional comparison, and geographically targeted environmental planning. The findings may support policymakers and environmental agencies in identifying areas where industrial influence on PM2.5 appears comparatively stronger and where more focused monitoring and mitigation efforts may be required.
Emerging Persistent Organic Pollutants (POPs) pose a multifaceted and evolving challenge to environmental and public health worldwide. Unlike legacy POPs, these new contaminants exhibit chemical diversity, pseudo-persistence, complex exposure pathways, and subtle toxicological effects that complicate detection, risk assessment, and regulation. This perspective article highlights critical gaps in environmental epidemiology, emphasizing inadequate biomonitoring, delayed health outcomes, and disparities in exposure affecting vulnerable populations globally. It advocates for harnessing digital chemistry innovations—including AI-driven predictive modeling, high-throughput screening, and real-time sensor technologies—to improve monitoring and risk evaluation. Additionally, it calls for integrated interdisciplinary collaboration and the adoption of precautionary regulatory frameworks to incentivize green chemistry and sustainable alternatives. Centering equity and environmental justice in research and policy is essential to mitigate disproportionate burdens on marginalized communities. This article presents a roadmap for advancing scientific understanding and fostering effective, just, and proactive responses to emerging POPs.
Air quality is closely related to weather patterns. Regional factors such as wind direction, wind speed, and dispersion conditions directly affect fine particulate matter (PM2.5) concentrations. Therefore, categorizing weather patterns aids in the analysis of the corresponding contributions of pollution sources. This study employed three online monitoring instruments to conduct assessments of the chemical species in PM2.5 during the winter season of 2020 and 2021. The measured data were analyzed using a positive matrix factorization (PMF) model to identify pollution sources and their contributions in Taipei City. Additionally, weather patterns with PM2.5 concentrations exceeding 25 µg/m3 were classified using clustering techniques to investigate the relationship between weather patterns and pollution sources. The results revealed that high-concentration weather patterns could be classified into three groups, each showing distinct associations with pollution sources: (1) During periods of low wind speed and poor dispersion, traffic-related pollution was significantly higher compared to other weather patterns, reaching 7.4 µg/m3; (2) Strong northeast monsoon patterns exhibited relatively low averaged PM2.5 concentration owing to limited pollutant accumulation within the basin compared to the other two weather patterns; and (3) Weather patterns predominantly influenced by northwest winds showed significant contributions from pollution sources related to coal/fuel combustion and industry mixed with secondary aerosols. The PM2.5 concentration level in northwest wind patterns could reach as high as 56 µg/m3. Furthermore, this study represents the first instance in which source contributions obtained through individual receptor site calculations were used in conjunction with nonparametric trajectory analysis (NTA). During the three selected high-pollution events, the results demonstrated that the contributions from different pollution sources were associated with unique spatial distribution patterns. Receptor modeling coupled with weather cluster analysis and NTA can serve as a valuable reference for policymakers to formulate effective PM2.5 control strategies.
This study evaluates the field performance of three mainstream low-cost particulate matter sensors (LCS) from the networks IQAir, PurpleAir, and AirGradient in Changchun, Northeast China, a region with clean background air, frequent spring dust storms, and autumn biomass-burning smoke. Two core research questions are addressed: whether heterogeneous commercial LCS can deliver reliable PM2.5 observations across the full pollution gradient, and which sensor platform achieves optimal stability across diverse aerosol regimes. All LCS datasets are cross-validated against China National Environmental Monitoring Centre (CNEMC) regulatory reference monitors, with overall Pearson correlation coefficients of r = 0.92–0.95 across all pollution conditions. Clear platform-specific performance differentiation is evident: AirGradient achieves the most consistent agreement with reference measurements, with minimal systematic bias; PurpleAir displays a stable linear correlation yet persistent positive overestimation after factory calibration; IQAir units show prominent inter-device variability, with some monitors exhibiting time-varying concentration bias. Sensor consistency differs markedly between two typical extreme pollution episodes: all three LCS types yield highly correlated readings during wildfire and agricultural smoke events, whereas coarse mineral dust transport significantly weakens sensor-reference consistency, with PurpleAir exhibiting the most severe performance degradation. XGBoost-SHAP attribution analysis demonstrates that meteorological conditions, ambient PM2.5 loading, and aerosol particle size jointly explain approximately 30
The prevalence of pollen-induced allergic rhinitis (AR) has increased substantially due to global warming and rapid urbanization. Hohhot, a city with a high prevalence of allergic rhinitis, urgently requires epidemiological investigations into the health effects of airborne pollen exposure. In this study, we used a Generalized Additive Model (GAM) to assess the effect of pollen concentration on outpatient visits for allergic rhinitis in Hohhot from March to October in the years 2017, 2018, and 2019. Stratified analyses were conducted for nine pollen taxa across age and gender subgroups during the peak pollen periods. The mean daily pollen concentration in spring (April to May) was lower than that observed from late summer to autumn (August to September, 279.65 × 103 pollen/m2). The results demonstrated a strong positive association between pollen concentration and outpatient visits for allergic rhinitis (AR). Exposure–response analyses revealed a lower risk threshold for allergic rhinitis during August to September. Age-stratified analyses indicated that individuals aged 0 to 17 years were the most susceptible to pollen exposure and exhibited the highest risk of outpatient visits for AR throughout the study period (RR 1.017, 95
Polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) are persistent organic pollutants that can remain in the environment for a long time. This study quantified the concentrations of 17 PCDDs and PCDFs congeners in PM2.5, total suspended particles (TSP), the gaseous phase, and total ambient air (TSP + gaseous) at an urban roadside site in Kuala Lumpur, Malaysia, a densely populated tropical city influenced by traffic and other anthropogenic activities, and assessed the associated health risks. PM2.5 and TSP were collected on quartz microfibre filters using separate high-volume samplers, whereas the gaseous phase was captured on polyurethane foam. The results revealed that the total ambient concentration of Ʃ17PCDD + PCDF was 736 ± 375 fg WHO-TEQ m−3, whereas PM2.5, TSP, and gaseous phase concentrations were 223 ± 161 fg WHO-TEQ m−3, 337 ± 213 fg WHO-TEQ m−3 and 507 ± 273 fg WHO-TEQ m−3, respectively. Tetra-, penta-, and hexa-PCDDs/PCDFs contributed between 88 and 99
Organophosphate esters (OPEs) have emerged as ubiquitous environmental contaminants, yet their interfacial dynamics in subtropical urban catchments remain under-characterized. This study investigates the seasonal variability, gas–particle partitioning, and air–water exchange of 17 OPEs in a coupled river–lake system in Taoyuan, northern Taiwan, throughout 2024. Results indicate distinct spatiotemporal patterns: the urban Xinjie River exhibited significantly higher total OPE concentrations (mean dissolved: 50.90 ± 30.87 ng/L) compared to the campus-adjacent lake (mean dissolved: 11.06 ± 5.45 ng/L), driven by wastewater discharge and lower hydrological dilution during the dry winter season. Conversely, atmospheric burdens peaked in spring, suggesting a decoupling of aquatic and atmospheric drivers. Fugacity fraction analysis revealed that while deposition occurs, the urban river acts predominantly as a net source of OPEs to the atmosphere, with mean volatilization fluxes for TBEP reaching 8806 ng/m2/day. Chlorinated OPEs (TCIPP, TDCPP) dominated the atmospheric gas phase, while alkyl species (TBEP) prevailed in the aqueous phase. Positive Matrix Factorization (PMF) identified untreated wastewater effluent and traffic emissions as primary sources governing OPE burdens in both compartments. Although ecological risk quotients (RQs) and human inhalation cancer risks remained below threshold levels, the findings highlight the potential for urban water bodies to act as secondary emission sources, modulating local atmospheric chemistry. These results underscore the necessity of integrating air–water exchange fluxes into regional pollution management strategies to mitigate the cycling of semi-volatile organic contaminants.
Air pollution has been associated with respiratory and cardiovascular diseases; however, its effects on routinely measured blood indices remain incompletely understood. This study examined associations between short-term particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) and hematological and biochemical indices in middle-aged and older adults. A study involving 989 adults aged 40 years and older, recruited from the community in Taiwan from 2018 to 2022, assessed hematological (erythrocyte, leukocyte, and thrombocyte) and biochemical (electrolyte levels, renal function, glycemic control, hepatobiliary function, lipid profile, and iron status) indices. PM2.5 exposure over 1-day, 7-day, and 1-month periods was estimated to evaluate both immediate and lagged effects. Associations between PM2.5 and blood indices were explored employing linear regression models, adjusted by age, sex, body mass index, smoking status, vegetarian diet, relative humidity, temperature, work status, exercise habits, and comorbidities. Higher PM2.5 exposure was associated with changes in several hematological and biochemical indices, including lower hematocrit, mean platelet volume, sodium, calcium, magnesium, creatinine, albumin, and iron, and higher mean corpuscular hemoglobin concentration, red cell distribution width, monocytes, and total bilirubin. The magnitude of the effects increased as the exposure period lengthened. Associations were generally more frequent among participants without baseline anemia. Although these modest changes should be interpreted as subclinical biomarker alterations rather than clinical endpoints, they may help characterize early systemic responses to short-term PM2.5 exposure in community settings.
Large-scale datasets collected from sensor networks in domains such as industrial IoT, healthcare, transportation, and environmental monitoring often contain significant temporal and spatial gaps caused by sensor failures, communication losses, or maintenance outages. Such missing data can introduce bias, reduce reliability, and limit the performance of predictive models. To address this challenge, we propose DeepSIP (Deep Sensor Imputation and Prediction), a novel deep learning framework that unifies imputation and forecasting for multivariate time series data. DeepSIP employs a cluster-based training approach on fully observed sensor data to identify contextual and temporal patterns prior to imputation. Its autoencoder-based module learns latent representations from correlated sensor variables and contextual information (e.g., time and date) to reconstruct missing values. Subsequently, a deep predictive network with multiple fully connected layers is trained on the imputed datasets to model complex temporal and cross-sensor dependencies for accurate forecasting. Our experiments across various missingness scenarios demonstrate that DeepSIP consistently achieves the lowest reconstruction errors (MSE, MAE, RMSE) and the highest forecasting accuracy and R^2 scores compared to K-Nearest Neighbors (KNN), Multiple Imputation by Chained Equations (MICE), and Linear Interpolation. These results validate DeepSIP’s robustness and adaptability for sensor-driven applications, highlighting the importance of high-quality imputation in improving downstream predictive performance.
This study evaluates mid-term calibration strategies for MONICA, a compact low-cost multi-sensor device for urban air quality monitoring, in the context of the upcoming EU Air Quality Directive 2024/2881. A key objective is to optimize the trade-off between calibration duration and long-term monitoring performance, balancing statistical robustness with practical deployment constraints. This question is particularly relevant in mid-latitude regions, where seasonal variability may introduce biases if calibration and observation periods are misaligned. Over a three-and-a-half-month winter co-location with reference-grade instruments, we assessed three models—Multiple Linear Regression (MLR), Random Forest (RF), and Generalized Additive Models (GAM)—across three pollutants: PM _2.5 , PM _10 , and NO _2 . MLR emerged as the most stable model for temporal extrapolation, while RF and GAM, although accurate short-term, showed performance degradation outside the training range. A two-week calibration period was sufficient for PM, whereas NO _2 required only one week. Although sensor accuracy declines over time-especially for NO _2 —the MONICA system remains effective in tracking temporal trends and identifying regulatory exceedances. These results support the development of efficient and scalable low-cost sensor networks, offering practical insights for planning reliable air quality monitoring campaigns.
Sulfur emissions from fossil-fuel combustion and industrial flue gas remain a persistent challenge because sulfur dioxide (SO2), sulfur trioxide (SO3), and sulfuric acid mist contribute to sulfate aerosol formation, corrosion, acid deposition, and adverse health impacts. Although flue gas desulfurization (FGD) is the dominant technology for large-scale SO2 control, ceramic fiber filters (CFFs) and catalytic ceramic fiber filters (CCFFs) are increasingly considered as compact platforms for integrating high-temperature particulate filtration with reactive gas treatment. A central challenge, however, is that sulfur loading and sulfur speciation are often substantially modified by upstream FGD units or alkaline sorbent injection before the gas reaches the filter. As a result, the intrinsic contribution of CFF/CCFF materials to sulfur mitigation is difficult to isolate, and reported removal efficiencies cannot be directly compared without considering the complete flue-gas treatment train. This review critically examines sulfur sources, transformations, and environmental impacts, followed by a focused assessment of CFF/CCFF materials, fabrication routes, catalytic configurations, and sulfur-removal mechanisms. Particular attention is given to distinguishing non-catalytic filtration, catalyst-assisted gas treatment, and sorbent-assisted SO2 capture governed by alkaline sorbents, cake-layer chemistry, operating temperature, particle size, and gas residence time. The available literature shows that CFF/CCFF systems provide a promising high-temperature platform for multi-pollutant control, but sulfur-specific demonstrations remain limited and strongly dependent on upstream process conditions. Future studies should therefore prioritize mechanism-resolved evaluation, standardized reporting of sulfur speciation and sorbent-to-sulfur ratios, long-term stability testing, regeneration behavior, and end-of-life management under realistic flue-gas matrices.
The current work evaluates the potential of Machine Learning (ML)-based stubble biomass modelling and associated Greenhouse Gases (GHG) and air pollutant emissions using multi-frequency remote sensing observations. The developed model uses Sentinel-1 C-band Synthetic Aperture Radar (SAR) (VV, VH, VH/VV) and Sentinel-2 vegetation indices (NDVI, SAVI, EVI) data, co-located with stratified field observations, as an input stack. The findings reveal that Extra Trees Regressor (ETR) performed better with fused SAR-optical data, with an R2 = 0.78, RMSE = 0.12 kg/100 m2, and MAE = 0.09 kg/100 m2 at standard 70:30 train-test split and 15-fold cross-validation score. During the post-harvest period of 2023 (October and November), the worked model estimated a staggering fresh (wet) RSB of 4.76 million tonnes of which 1.88 million tonnes (39.6 percent) was open-burnt. Accounting for a standard 60
The use of low-cost sensors (LCS) for air quality monitoring has grown rapidly across a wide range of groups, including community and citizen scientists, academic researchers, environmental agencies, and the private sector. Traditional air monitoring conducted by regulatory agencies relies on expensive, regulatory-grade instruments that require frequent maintenance and rigorous quality control procedures. In contrast, the low purchase price, minimal operating costs, user-friendly design, and open data accessibility have significantly contributed to the widespread adoption of LCS. Over the past decade, hundreds of studies have proposed diverse calibration strategies to tailor LCS performance to specific project needs. This study examines the role of PM2.5 sensors in monitoring air quality across contrasting environments and highlights the importance of inter-sensor consistency. We evaluate PurpleAir (PA) PA-II sensors against regulatory-grade Federal Equivalent Method (FEM) PM2.5 instruments and develop calibration algorithms to improve data accuracy. Calibration deployments were conducted for 2–4 weeks in Raleigh, North Carolina, and Delhi, India, to assess sensor behavior under different aerosol loadings and environmental conditions. The goal of this effort is to create a robust calibration model that uses PA-measured parameters, PM2.5, temperature, and relative humidity as inputs to generate bias-corrected hourly PM2.5 values. The model relies on concurrent FEM PM2.5 measurements as the reference data during calibration development. Multiple statistical and machine-learning approaches were applied to produce a regional calibration model. Our results show that, with proper calibration, PA sensors can provide bias-corrected PM2.5 estimates within 12
As the precursor of ozone (O3), volatile organic compounds (VOCs) largely derive from biogenic sources. However, future global changes in climate and land cover may profoundly affect O3 by enhancing biogenic VOC (BVOC) emissions, posing new challenges to public health. In this study, we developed a coupled modeling framework under Shared Socioeconomic Pathways (SSP1-2.6 and SSP2-4.5), integrating chemical transport model and health impact assessment model to assess the impacts of climate and land-use-driven BVOC emissions on O3 concentrations and human health in Guangdong Province with one of the highest BVOC emissions in China. Results indicate that BVOC emissions are projected to increase by 18.8
Tire-related emissions account for a significant proportion of traffic-related emissions. However, unlike brakes, the measurement of tire wear and airborne tire wear particle emissions is not yet standardized. On the contrary, there are numerous research approaches. This paper provides an overview on test procedures to measure tire wear and airborne particle emissions from tires that are described in literature. These methods include laboratory tests such as inner and outer drums, as well as measurements on road simulators, test tracks or public roads. Since wear mechanisms may differ depending on the test setup, a short overview on wear mechanisms is given. The measurement equipment used to evaluate the particle number (PN) and the particle mass (PM2.5, PM10) is described in detail, as well as the specific test setups. Finally, a comparison is shown of values for PN and PM presented in the literature. The results show clear differences in the measurement results for PM10 and PM2.5, even when subdivided into indoor and outdoor measurements. The same applies to PN. This highlights substantial differences in controllability, representativeness, and measurement uncertainty among methods shown in literature.
As global climate governance shifts from state-centric models to polycentric systems, the agri-food sector must translate net-zero commitments into actionable pathways. This study employs ESG (Environmental, Social, and Governance) as the central analytical framework to assess bamboo’s strategic potential in the net-zero transition. From the governance dimension, we extract transferable ESG practices from leading agri-food firms to guide institutional adaptation in the bamboo sector. Building on this, environmental and social dimensions are addressed through a technical evaluation of bamboo as a Nature-Based Solution (NbS), offering 15–20
Air pollutants released from firework burning pose a potential threat to air quality and human health. To identify their emission characteristics and contributions, we conducted in-situ continuous measurements of the six criteria pollutants (i.e., NO2, SO2, O3, CO, PM2.5, and PM10) along with chemical speciation in Chengdu during the Chinese New Year period (February 8–17, 2024). Results showed mass concentrations of PM2.5, PM10, and SO2 increased by about 91
This study quantitatively evaluated the deposition of protein nanoparticle aerosols in an in vitro air–liquid interface (ALI) exposure system and compared experimental results with in silico lung deposition predictions using the Multiple-Path Particle Dosimetry (MPPD) model. A whey protein suspension was aerosolized at 3 L/min and delivered to a custom-built flow-through ALI system containing six transwells. Aerosols were sampled at impinger flow rates of 12 or 30 mL/min for 2 h using a low-flow pump. Particle size distribution and concentration were continuously monitored using a scanning mobility particle sizer. Deposited protein mass on apical and basolateral compartments was quantified by UV–Vis spectrophotometry. Deposition efficiency was calculated and compared with MPPD model predictions. Aerosol exposures remained stable within ± 20
