Air Quality Atmosphere and Health最新文献

{"title":"When seasons matter more than trends: Urban air quality anomalies and pollution extremes across major urban centres of Northeast India (2022–2024)","authors":"Arghadeep Bose,&nbsp;Tushar Sarkar","doi":"10.1007/s11869-026-02080-8","DOIUrl":"10.1007/s11869-026-02080-8","url":null,"abstract":"<div><p>Urban air quality dynamics in secondary and emerging cities are as yet underexplored despite the growing exposure risks. This research focuses on seasonal anomalies, persistence behaviour and spatial inequality in air quality of eight major urban centres of the North-east India using daily average Air Quality Index (AQI) over a period from 2022 to 2024. In this study, the analysis incorporates descriptive statistics, autocorrelation diagnostics, modified Mann- Kendall trend detection, Seasonal Anomaly Index (SAI), inter-urban inequality assessment, and hierarchical clustering in order to identify temporal and spatial pollution regimes. Results show a strong heterogeneity of AQI burden across NE India, where Agartala, Guwahati and Imphal show significantly higher levels of pollution when compared to hill cities such as Aizawl, Gangtok, and Shillong. Exposure analysis shows that more than 40–50% of days have AQI around 100 or more in major valley cities, and more than 20% of days have AQI around 200 or more in Agartala and Guwahati. Autocorrelation diagnostics indicate strong atmospheric memory effects in all cities, while trend detection does not allow to find statistically significant monotonic change on the short period of observations. SAI of a systematic winter amplification and monsoon suppression of the pollution that reflects the control of boundary layer stability and rain wet scavenging. Multi-year ranking and heatmaps reveal Agartala, Guwahati and Imphal as persistent high risk pollution centres, which reflect structural interactions between emission intensity and valley confined topography. Seasonal clustering also indicates a compression of spatial inequality by monsoon conditions while the stability of the winter atmosphere increases the spatial inequality, creating a unique high-AQI regime in valley cities.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148782214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of emission factors for pollutants from dominant fuelwood species in Northern South Africa","authors":"Khuthadzo Manyatsha,&nbsp;Burgert B Hattingh,&nbsp;Stuart Piketh,&nbsp;Frederik H Conradie,&nbsp;Joshua Edokpayi","doi":"10.1007/s11869-026-02069-3","DOIUrl":"10.1007/s11869-026-02069-3","url":null,"abstract":"<div><p>Residential biomass combustion in rural communities is recognised as a major contributor to air pollution, representing the second-largest source of gaseous emissions and the primary source of atmospheric particulate matter. In many African countries, including South Africa, residential emission inventories and air quality assessments rely largely on international emission factors (EFs), introducing considerable uncertainty due to regional differences in fuel characteristics, combustion technologies, and household energy-use practices. Generating locally derived EFs is therefore essential for improving the accuracy of emission inventories and supporting effective air quality management and climate policy development. This study quantified the EFs of CO₂, CO, NO, SO₂, and PM₁₀ from the combustion of the 12 predominantly preferred fuelwood species used for household energy in rural Limpopo Province, South Africa. Controlled combustion experiments were conducted using a traditional three-legged cookstove in a simulated village kitchen under laboratory conditions. The mean EFs determined for CO₂, CO, NO, SO₂, and PM₁₀ were 1389 ± 149, 131.38 ± 91.4, 1.22 ± 0.63, 5.16 ± 4.9, and 64.7 ± 76.3 g kg⁻¹, respectively. Modified combustion efficiency (MCE) exhibited a statistically significant negative association with the EFs of CO and SO₂, indicating that lower combustion efficiency was associated with higher emissions of these pollutants. In contrast, fuel moisture content was not significantly associated with the EFs of CO₂, NO, or PM₁₀, while no statistically significant relationship was observed between fuel nitrogen (N) content and NO EFs. Furthermore, no statistically significant differences were detected among the investigated fuelwood species in the individual EFs of CO₂, CO, NO, SO₂, and PM₁₀. This study provides the first laboratory-derived, species-specific EF dataset for the predominantly preferred fuelwood species used in rural Limpopo Province. The findings reduce uncertainties associated with the application of international default EFs and provide a robust basis for improving residential biomass emission inventories, air quality modelling, and the development of evidence-based air pollution mitigation and climate management strategies in South Africa and other regions with similar household energy-use patterns.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-026-02069-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Atmospheric deposition of microparticles on the seasonally-touristed island of Nantucket, Massachusetts, US","authors":"Nicholas Warner,&nbsp;Juanita Urban-Rich","doi":"10.1007/s11869-026-02064-8","DOIUrl":"10.1007/s11869-026-02064-8","url":null,"abstract":"<div><p>Major sources of atmospheric microplastic originate from human activities (e.g. construction, driving, laundry, and agriculture dust), along with sea-spray. Due to their small size (&lt; 5 mm), they are easily transported long distances, being found in both urban and remote environments. This research examines the atmospheric deposition of microparticles on a seasonally-touristed island. Nantucket Island, USA, is a unique study site due to its proximity to ocean-atmospheric exchanges and its high influx of seasonal visitors. Atmospheric microparticles were collected at two contrasting sites and analyzed for amount and type from October 2021 to September 2022. Samples were collected each month and processed, counted, and measured. Microparticle flux ranged from 4.6 to 62.2 mp/m²/d and were composed mostly of fibers, &gt; 75 μm. The Hatchery site had an average and s.d. of 28.2 ± 13.3 mp/m²/d and the Station site had an average and s.d. of 15.7 ± 8.2 mp/m²/d. Concentrations during the tourist months were 1.5 times greater than the non-tourist months.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-world measurement of vehicular PM2.5, BC, and BrC in a tunnel with high proportion of diesel vehicle","authors":"Tong-Hyok Choe,&nbsp;Chung Song Ho,&nbsp;Chol-Ryok Im,&nbsp;Jong-Jun Jo,&nbsp;Song-Chol Pak,&nbsp;Nam-Chol O","doi":"10.1007/s11869-026-02070-w","DOIUrl":"10.1007/s11869-026-02070-w","url":null,"abstract":"<div><p>Vehicular emission is an important source of particulate matter (PM_2.5), black Carbon (BC) and brown Carbon (BrC) in urban areas, but the annual trends of vehicular emissions of PM_2.5, BC and BrC pollutants have not been clearly identified in some urban areas. This study evaluates emission factors of vehicles-related PM_2.5, BC and BrC via measurements of a road tunnel in Pyongyang, DPR Korea, in 2019 and 2024, respectively. The proportion of diesel vehicles in the tunnel is much higher than that in other urban road tunnels, with 10.2% in 2019 and 22.4% in 2024. In 2024, the emission factors of PM_2.5, BC and BrC were 7.51 ± 3.38 mg km^− 1 veh^− 1, 0.778 ± 0.302 mg km^− 1 veh^− 1 and 0.168 ± 0.066 mg km^− 1 veh^− 1 for the mixed fleet, 2.86 ± 0.66 mg km^− 1 veh^− 1, 0.444 ± 0.066 mg km^− 1 veh^− 1 and 0.105 ± 0.015 mg km^− 1 veh^− 1 for the gasoline vehicles, 27.3 ± 2.10 mg km^− 1 veh^− 1, 2.27 ± 0.208 mg km^− 1 veh^− 1 and 0.481 ± 0.047 mg km^− 1 veh^− 1 for the diesel vehicles, respectively. The emission factors of PM_2.5, BC and BC-equivalent BrC were significantly reduced in 2024, with regard to both the pollutants and vehicle types compared to those in 2019, accounting for a reduction range of 59.7–81.9%, 78.3–86.0%, and 71.6–82.6%, respectively.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying PM2.5 flexion points for environmental diseases in susceptible and vulnerable groups: A nationwide cohort in South Korea","authors":"You Hyun Park,&nbsp;Se Hwa Hong,&nbsp;Yeonjae Park,&nbsp;Yong Whi Jeong,&nbsp;Ji Ye Jung,&nbsp;Yong Jin Lee,&nbsp;Dae Ryong Kang","doi":"10.1007/s11869-026-02056-8","DOIUrl":"10.1007/s11869-026-02056-8","url":null,"abstract":"<div><p>To evaluate the health impacts of PM_2.5 exposure on sensitive and vulnerable populations in South Korea and to identify flexion points—concentration levels at which the risk of environmental diseases increases significantly. A retrospective cohort study using nationwide health insurance claims data linked with environmental monitoring data. Statistical analysis included Cox proportional hazards regression and piecewise linear regression to estimate disease risks and identify flexion points. South Korea, using data from the National Health Insurance Service–National Sample Cohort (NHIS-NSC) and national air quality monitoring stations. A total of 1,031,517 individuals aged 0–79 years who were covered by the national health insurance system between 2017 and 2019 and had no prior diagnosis of the target diseases during the 2016 washout period. Primary outcomes were the incidence and exacerbation of four environmental diseases: chronic obstructive pulmonary disease (COPD), asthma, stroke, and heart failure. Exacerbation was defined as hospitalization or emergency department visits due to these diseases. Higher PM_2.5 exposure was significantly associated with increased incidence and exacerbation of all four diseases. Flexion points were identified at 30 µg/m³ for incidence and between 24 and 26 µg/m³ for exacerbation. Subgroup analyses showed lower flexion points among elderly participants. The findings suggest that current PM_2.5 standards in South Korea may not sufficiently protect vulnerable populations. Operational flexion points identified in this study may serve as evidence-based thresholds to guide targeted public health interventions and air quality regulations.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-026-02056-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding urban PM2.5 dynamics and air pollution-health relationships using machine learning: Evidence from Northern Thailand","authors":"Pattarakun Khammisawang,&nbsp;Phakphum Paluang,&nbsp;Masami Furuuchi,&nbsp;Worradorn Phairuang","doi":"10.1007/s11869-026-02074-6","DOIUrl":"10.1007/s11869-026-02074-6","url":null,"abstract":"<div><p>This work developed and tested Random Forest Regression (RFR) and Long Short-Term Memory (LSTM)models to predict PM_2.5 concentrations and respiratory illness cases in Phrae Province, northern Thailand. In 2020–2023, daily atmospheric pollutants and meteorological parameters were analyzed alongside monthly hospital data on asthma, bronchitis, COPD, and lung cancer to examine the relationship between air pollution and respiratory health and to evaluate machine learning (ML) models. PM_2.5 correlated positively with nitrogen dioxide (NO₂) and ozone (O₃) and negatively with relative humidity, especially during the rainy season. PM_2.5 was strongly linked to bronchitis and asthma, especially during the dry season, when biomass-burning activities led to the highest PM_2.5 concentrations. The best predictive model for PM_2.5 concentrations and respiratory illness cases was RFR, which found no statistically significant short-term link between PM_2.5 and lung cancer. This suggests that long-term cumulative exposure and other risk factors are more strongly linked to lung cancer than short-term variations in air pollution. LSTM detected episodic PM_2.5 peaks. However, the limited monthly dataset (48 observations) and considerable temporal variability of patient records hindered LSTM’s respiratory illness prediction. So, consider these data exploratory rather than conclusive. Results suggest a hierarchy of decision support: RFR for normal PM_2.5 forecasting, LSTM for peak event early warning when longer, continuous time-series data are available, and dry-season public health interventions for bronchitis surveillance and prevention. Local air quality management and respiratory health planning can be integrated with ML-based environmental forecasts using the framework to assist evidence-based pollution reduction and public health preparedness decisions.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental and numerical analysis of outdoor PM2.5 in indoor microenvironments using the indoor air quality and inhalation exposure (IAQX) model","authors":"Saeed Shojaee Barjoee","doi":"10.1007/s11869-026-02004-6","DOIUrl":"10.1007/s11869-026-02004-6","url":null,"abstract":"<div><p>In urban environments, almost 90% of the population is exposed to indoor levels of fine particulate matter (PM_2.5) that surpass the World Health Organization’s (WHO) recommended annual air quality guideline (AQG) of 5 µg/m³. Accurately estimating PM_2.5 levels in indoor microenvironments and identifying the contribution of outdoor sources are critical for assessing human exposure and informing mitigation strategies. This study evaluated indoor and outdoor PM_2.5 concentrations in a student residence housing approximately 1,500 individuals in Saint Petersburg, Russia, during the summer of 2023. Indoor PM_2.5 concentrations were measured directly using a portable sensor, while outdoor PM_2.5 concentrations were obtained from the Ventusky platform based on the system for integrated modeling of atmospheric composition (SILAM) Eulerian chemical transport model and temporally aligned with indoor measurements. The Indoor Air Quality and Inhalation Exposure (IAQX) model was employed to simulate the contribution of outdoor PM_2.5 to indoor concentrations. The modeled data were used to estimate PM_2.5 burdens across microenvironments and to examine the influence of five key input parameters—zone volume, airflow rate, sink surface area, filtration efficiency, and outdoor PM_2.5 concentration—using a polynomial regression-based sensitivity analysis. The average simulated and measured indoor PM_2.5 concentrations were 12 µg/m³ and 38.20 µg/m³, respectively, while the mean outdoor concentration during the monitoring period was 7.08 µg/m³. Indoor zones with direct exposure to the outdoor environment exhibited higher infiltration rates, indicating a stronger influence of ambient PM_2.5 sources. Sensitivity analysis revealed that airflow rate was the most significant factor affecting modeled indoor PM_2.5 concentrations (<i>p</i> &lt; 0.0001). Notable secondary interactions included: outdoor PM_2.5 concentration and zone volume (<i>p</i> = 0.0010), outdoor PM_2.5 concentration and airflow rate (<i>p</i> = 0.0001), and zone volume and airflow rate (<i>p</i> = 0.0005). These results highlight the critical role of ventilation dynamics and building configuration in shaping indoor air quality in high-occupancy urban residences.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human health risk assessment of heavy metal pollution in airborne dust from Zakho City, Iraq","authors":"Berivan Hadi Mahdi,&nbsp;Dilsouz Dakhil Hassan,&nbsp;Myasar Kh. Ibrahim,&nbsp;Dalshad Azeez Darwesh,&nbsp;Jasim M. Rajab,&nbsp;Ali M. Al-Salihi,&nbsp;Hwee San Lim","doi":"10.1007/s11869-026-02075-5","DOIUrl":"10.1007/s11869-026-02075-5","url":null,"abstract":"<div><p>Airborne dust is a significant source of toxic metals in urban settings, but there is a lack of integrated evaluations of the level of pollution, ecological risk, and human health risk in semi-arid urban settings. This study examined Cd, Pb, Cu, Cr, and Zn concentrations in airborne dust collected from four locations in Zakho City during the wet and dry seasons. The sampling represented seasonally accumulated deposited/fallout airborne dust rather than event-specific dust-storm samples. The measured metal concentrations generally followed the order Zn &gt; Cu &gt; Pb &gt; Cr &gt; Cd. Due to the limited unreplicated sampling design, the observed spatial and seasonal variations should be interpreted as preliminary descriptive trends rather than definitive statistical patterns. The pollution indices (CF, EF, I_geo, and PLI) showed that the anthropogenic enrichment of Zn (CF up to 4.7; EF &gt; 3) and Cd (CF up to 2.9) was localized, whereas Pb, Cu, and Cr were in low contamination ranges. The pollution load index values at each site were less than 1, indicating a low total multi-metal pollution burden. Within the limitations of this screening-level assessment, Cd showed the highest contribution to the ecological risk indices among the analyzed metals, while the potential ecological risk index remained below 150 at all locations. However, the inhalation-only screening assessment showed HQ and HI values many-fold lower than threshold levels, indicating no apparent non-carcinogenic risk through the inhalation pathway under the applied assumptions.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating PM2.5 transport pathways and potential source contributions in severely polluted hotspots of Beijing-Tianjin-Hebei region during three key policy years","authors":"Aifang Gao,&nbsp;Yitu Liu,&nbsp;Yansen Yang,&nbsp;Xi You,&nbsp;Chenglong Liao,&nbsp;Aibin Kang,&nbsp;Hongliang Zhang","doi":"10.1007/s11869-026-02061-x","DOIUrl":"10.1007/s11869-026-02061-x","url":null,"abstract":"<div><p>To explore the transport pathways and potential source regions of fine particulate matter (PM_2.5) in the severe polluted hotspots of Beijing-Tianjin-Hebei region (Handan and Xingtai), 72-hour backward trajectories were simulated for 2017, 2020, and 2023 using Meteoinfo software. Trajectory cluster analysis, weighted potential source contribution function (WPSCF) and weighted concentration-weighted trajectory (WCWT) analysis were applied. Analysis of the vertical distribution of air pollution transport pathways in Handan showed that near-surface transport was predominantly short-range. Transport pathway analysis indicated that during winter, the trajectory with the highest PM_2.5 concentration for Handan (113.5 µg/m³) originated from Shijiazhuang, while that for Xingtai (113.6 µg/m³) originated from Cangzhou, both passing through the Hebei-Shandong border area. WPSCF and WCWT analysis revealed the spatial extent of major PM_2.5 source regions was largest in 2017, decreased in 2020, but expanded again in 2023. The WPSCF/WCWT results of PM_2.5 demonstrated that the largest potential areas were identified in winter and were mainly distributed at the junction of Hebei, Henan, and Shandong Provinces, and a larger area covering multiple provinces (Hebei, Henan, Shandong, and Shanxi Provinces) for Xingtai. The PM_2.5 pollution in Handan and Xingtai exhibited high homogeneity and mutual influence. These findings underscore the necessity of implementing regional joint prevention and control measures to mitigate PM_2.5 pollution in Handan and Xingtai.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network study of particulate matter distribution within the urban canopy layer using magnetic biomonitoring – assessing of CAR-FMI model performance and representativeness of a monitoring station","authors":"Jukka Limo,&nbsp;Mari Kauhaniemi,&nbsp;Leena Kangas,&nbsp;Petriina Paturi,&nbsp;Joni Mäkinen,&nbsp;Ari Karppinen","doi":"10.1007/s11869-026-02059-5","DOIUrl":"10.1007/s11869-026-02059-5","url":null,"abstract":"<div><p>The distribution of air pollution in urban environments is greatly influenced by its source and the complexity of the urban environment, which determine the pollution concentrations in individual locations. Traffic is one of the main pollution sources in cities and, especially particulate matter (PM) poses a serious risk for public health. This study applies magnetic biomonitoring using moss bags to establish an extensive urban canopy layer monitoring network (<i>n</i> = 52) to study the distributions and concentrations of magnetic PM. Moss bags were placed at street and roof levels within the grid plan area of Turku, Finland, for 62 days in late autumn. Samples were analysed for mass-specific magnetic susceptibility (χ), hysteresis parameters and elemental components. At street level, traffic was the primary source of magnetic PM and related elements. At higher altitudes, these concentrations were reduced and mainly replaced by crustal elements. Magnetic measurements were also applied to test for equivalence with the CAR-FMI dispersion model and to assess the representativeness of an air quality monitoring station in the city centre. Inconsistencies were observed between the model and magnetic PM concentrations at street level. At rooftop level, the model estimates were higher than indicated by the magnetic PM. The magnetic measurements complemented measurements derived from supplementary monitoring sensors and Turku monitoring station. The representativeness of the monitoring station should be re-evaluated, and recategorizing should be considered. Magnetic biomonitoring using moss bags is an unparalleled tool for monitoring airborne PM pollution and a reliable tool for evaluating monitoring stations and dispersion model performance.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"19 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-026-02059-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148750937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}