Air Quality Atmosphere and Health最新文献

{"title":"Quantifying the equivalency factor between PM-absorbance and EC mass concentration – converting the “old” exposure proxy in large health studies to the new metric for diesel soot","authors":"Harry ten Brink,&nbsp;Gerard Hoek,&nbsp;Regina Hitzenberger","doi":"10.1007/s11869-025-01741-4","DOIUrl":"10.1007/s11869-025-01741-4","url":null,"abstract":"<div><p>The light absorbance of PM-samples on PTFE filters is often used as a measure for exposure to diesel soot in large-scale health studies. Absorbance is a synonym for the optical parameter “absorption coefficient” (AbsC). The formal exposure measure for diesel soot is the mass concentration of the light absorbing species of PM, viz. elemental carbon (EC). In the central health effects study by Janssen et al. [2011] a relation between AbsC and <u>EC</u> is presented, however with an overall uncertainty of 2.5. In the present study, we started with an analysis of the measuring approach of light absorption according to ISO-9835 [ISO, 1993]. Following this procedure, absorption is probed in reflection and expressed in the ratio of the intensity of light reflected from/by a clean and a loaded filter. The AbsC is the logarithm of this ratio (which is known as optical depth) scaled to the volume of air sampled and loaded filter area. We first critically reanalysed the studies used by Janssen et al. [2011] in which the equivalency factor between absorbance and EC concentration was given. We found a good linear relationship when we selected only those data points for which the optical density (OD) was within the proper limits of 0.05 and 2.0. We then analysed which methods had been used to obtain EC data in those studies (and also more recent ones) and selected only those studies where EC had been determined with an official reference approach, i.e. the USreference method NIOSH-5400. The overall relation of EC mass concentration in µg m⁻³ and AbsC in units of 10^–5 m⁻¹ was 0.8 (R² = 0.92), or 1.0 according to the EU-reference method EUSAAR2-TOT with a factor of 1.25 between US and EU reference EC values. This highly improved estimate of equivalence factors between AbsC and EC might be used to translate the results of existing health effects studies (based on AbsC) to studies using current EC monitoring data (as prescribed in EU-guidelines for air quality) to investigate the possible health effects at a given EC level.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 6","pages":"1887 - 1893"},"PeriodicalIF":2.9,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-025-01741-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145163039","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":"Assessment of PM2.5 deposition in the human respiratory tract from a hyperlocal dataset in Bhubaneshwar, India","authors":"Sneha Mahalingam,&nbsp;Babu Priyadharshini,&nbsp;A. Asutosh,&nbsp;Monalin Mishra","doi":"10.1007/s11869-025-01743-2","DOIUrl":"10.1007/s11869-025-01743-2","url":null,"abstract":"<div><p>Air pollution is a leading environmental health risk in India, contributing to disease burden and mortality. Effective mitigation measures require high-resolution monitoring. This study leverages hyperlocal air quality data from 21 low-cost sensor (LCS) stations in Bhubaneswar (March 2023–February 2024) to assess spatiotemporal variations in PM_2.5. Additionally, the Multiple-Path Particle Dosimetry (MPPD) model was used to quantify wintertime PM_2.5 deposition in the human respiratory tract across seven age groups. PM_2.5 exhibited strong seasonal variation, peaking in winter and remaining elevated in post-monsoon. Persistent high-pollution regions, including Rasulgarh, Dumduma, Nandan Vihar, Patarapada experienced consistently high concentrations due to vehicular emissions, industrial activities, and biomass burning, emerging as persistent high-pollution regions necessitating year-round mitigation. Seasonal pollution hotspots - Kesora, Niladri Vihar, Sailashree Vihar experienced episodic PM_2.5 spikes exceeding 20% of seasonal means, requiring targeted interventions. Dosimetry analysis revealed age-specific deposition patterns: infants and toddlers retained the highest PM_2.5 in the head and tracheobronchial regions, increasing their risk of upper respiratory conditions, while children had the highest pulmonary deposition, posing long-term respiratory risks. Adults (&gt; 49 years) exhibited lower pulmonary deposition but remained vulnerable to cumulative exposure effects. Lobar analysis showed predominant PM_2.5 accumulation in the lower lung lobes across all age groups, with infants experiencing the highest deposition due to smaller airway diameters. These findings highlight the pressing need for targeted air pollution mitigation strategies in high-risk regions and among vulnerable populations to minimize long-term health impacts in the city.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 7","pages":"1931 - 1944"},"PeriodicalIF":2.9,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814486","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":"Assessing human health risks: impact of variable air quality index on asymmetric spirometry flow","authors":"Digamber Singh,&nbsp;Abdullah Y. Usmani","doi":"10.1007/s11869-025-01742-3","DOIUrl":"10.1007/s11869-025-01742-3","url":null,"abstract":"<div><p>The escalation of occupational and environmental pollutants poses a significant threat to human health, particularly exacerbating chronic respiratory diseases (CRD). Globally, CRD account for 4.0 million reported deaths (Momtazmanesh et al. EClinicalMedicine 59, 2023). This study investigates the repercussions of exposure to unfavourable Air Quality Index (AQI) levels on respiratory health, focusing on asymmetric spirometry flow during natural inspiration at a flow rate of <i>Q</i>_<i>in</i> = 10 l/min. Here, employing digital imaging techniques, we developed an in-silico human respiratory tract model, encompassing up to the 7th bifurcation of a healthy male individual. The results reveal that the dynamics of inspired airflow and particles, particularly in turbulent regions, influence particle deposition in the airways. Thus, the upper airways and bifurcations region have higher deposition efficiency of fine particles ~ 2.5 and 10 μm, consequently creating hotspots for respiratory illnesses. Moreover, to quantify the internal flow characteristics, we utilised a set turbulence model, and the trajectory of fine particles was computed by discrete phase model (DPM). The localised quantitative quantification of particle physics focuses on deposition efficiency at different time instants, <i>t</i> = 1.5 s, 2.1s and 2.5 s, complemented by insights into internal flow features, particles are depicted and quantified through regional deposition efficiency, while flow physics is presented by, surface streamlines, turbulent kinetic energy, turbulence intensity and <i>Q</i>-criterion. These findings have a significant implication in effective diagnosis and management of chronic respiratory diseases (CRD), providing valuable insights into the intricate interplay between air quality, airflow dynamics, and respiratory health.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 6","pages":"1845 - 1859"},"PeriodicalIF":2.9,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145162447","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":"Exposure patterns of PM2.5 and CO concentrations in residential and commercial buildings: factors influencing indoor air quality","authors":"Deborah Nibagwire,&nbsp;Godson R. E. E. Ana,&nbsp;Egide Kalisa,&nbsp;Gratien Twagirayezu,&nbsp;Abdou Safari Kagabo,&nbsp;Jacques Nsengiyumva","doi":"10.1007/s11869-025-01740-5","DOIUrl":"10.1007/s11869-025-01740-5","url":null,"abstract":"<div><p>Indoor air pollution is a growing concern as people spend most of their time indoors. However, information on indoor air quality and factors influencing indoor exposures remains limited, particularly in rapidly urbanizing cities like Kigali, Rwanda. To address this gap, this study examined the distribution of carbon monoxide (CO) levels and fine particulate matter (PM_2.5) in residential and commercial buildings. Indoor and outdoor PM_2.5 and CO concentrations. Along with meteorological variables such as relative humidity (RH) and temperature, were measured every two minutes over 24-h period during both dry and wet seasons. PM_2.5 measurements were conducted from March to August 2024 in residential and commercial buildings, CO measurements were conducted from June to August 2024 in 88 households across urban, suburban, and industrial zones. The study found significantly higher PM_2.5 levels during the dry season, with indoor levels averaging 40.2 ± 19.3 μg/m³ and outdoor levels 41.3 ± 17.5 μg/m³, compared to the wet season, where indoor levels were 32.8 ± 82.9 μg/m³ and outdoor levels 35 ± 15.8 μg/m³ outdoors. CO levels were consistently higher outdoors (691.3 ± 181.8 μg/m³) than indoors (478.4 ± 128.2 μg/m³). Indoor/outdoor (I/O) ratios for PM_2.5 were consistently below 1, indicating a strong influence of outdoor sources on indoor PM_2.5 levels. While, location type showed a slight correlation with PM_2.5 concentrations, building features such as age, window count, and ventilation exhibited varying but insignificant impacts. These findings highlight the critical role of outdoor pollution and meteorological factors in determining indoor air quality in Kigali and underscores the need for regulating outdoor pollution sources and indoor environments.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 6","pages":"1827 - 1843"},"PeriodicalIF":2.9,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145162070","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":"Deep learning-based pm forecasting and post-infant mortality assessment in urban areas: a case study in Bangladesh","authors":"Rashik Islam,&nbsp;Yunsoo Choi,&nbsp;Shihab Ahmad Shahriar,&nbsp;Seyedeh Reyhaneh Shams,&nbsp;Deveshwar Singh","doi":"10.1007/s11869-025-01739-y","DOIUrl":"10.1007/s11869-025-01739-y","url":null,"abstract":"<div><p>This study investigated the application of advanced deep learning models for forecasting particulate matter (PM) pollution and assessing its associated health impacts in Bangladesh. Specifically, the Temporal Fusion Transformer (TFT), Deep Autoregressive Recurrent Neural Network (DeepAR), Generative Adversarial Network (GAN), and 1D Convolutional Neural Network (1D-CNN) were employed to forecast daily PM concentrations for the next seven days across four urban areas (Chattogram, Rajshahi, Dhaka, and Sylhet) during 2013–2018. The study further quantified post-infant mortality risks attributable to PM₁₀ exposure in these regions. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Coefficient of Determination (R²), and Index of Agreement (IOA). TFT demonstrated superior capability, achieving mean RMSEs of 21.23 µg/m³ (PM_2.5) and 34.90 µg/m³ (PM₁₀), outperforming DeepAR by 39% and 40.6%, respectively, and surpassing 1D-CNN and GAN. The attention mechanism in TFT revealed distinct temporal dynamics, with PM_2.5 prediction dominated by temperature and relative humidity indicating inversion-driven accumulation, while PM₁₀ forecasting emphasized wind speed, rainfall, and relative humidity aligning with Bangladesh’s monsoon-driven dispersion and dust resuspension. Health risk analysis, conducted using the World Health Organization’s (WHO) Air Quality Health Impact Assessment Tool (AirQ+), revealed significant post-infant mortality impacts, with the highest attributable mortality per 1000 population observed in Dhaka (42.95%) and Sylhet (24.64%). The strong agreement between observed and forecasted health outcomes validated the reliability of the TFT model.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 6","pages":"1803 - 1825"},"PeriodicalIF":2.9,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161151","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":"Black carbon characterization and source apportionment over a semi-tourist site in the central Himalayan Region, India","authors":"Jagdish Chandra Kuniyal,&nbsp;Archana Bawari,&nbsp;Sheetal Chaudhary,&nbsp;Arushi Sharma,&nbsp;Bimal Pande","doi":"10.1007/s11869-025-01727-2","DOIUrl":"10.1007/s11869-025-01727-2","url":null,"abstract":"<div><p>Black carbon (BC), released due to partial burning of fuel, has serious negative impacts on climate and human health. We conducted continuous BC measurements over 5 years (January 2019–December 2023) at Katarmal (29.38˚N, 79.37˚E, 1225 m amsl) in the Central Himalayan Region. We examined BC mass concentration, temporal fluctuations, meteorological alterations, source apportionment, and the impact of COVID-19 period. BC concentrations on daily basis ranged from 0.01 to 13.7 μg m⁻³, with an annual concentration of 1.6 ± 1.3 μg m⁻³. The highest BC levels were in April 2022 (5.9 ± 2.5 µg m⁻³), March 2022 (4.7 ± 1.8 μg m⁻³) and February 2023 (4.3 ± 1.4 μg m⁻³). Planetary boundary layer height (PBLH) was shallow in winter with higher BC concentrations, and higher in the monsoon (~ 1154 m), with lower BC levels (~ 0.11 μg m⁻³). Elevated BC in the pre-monsoon persisted despite of high PBLH (~ 1905 m). Meteorological parameters like wind speed exhibited a positive correlation (<i>r</i> = 0.71), while relative humidity, air temperature, and total rainfall were negatively correlated with BC as <i>r</i> = -0.82, -0.60, and -0.79, respectively. Over a period of five years, fossil fuel sources (BC_ff) accounted for 59.4% of the total BC, while biomass burning (BC_bb) contributed 40.6%, indicating a higher biomass-burning impact compared to other Indian Himalayan areas. During the COVID-19 lockdown period, a significant decrease (~ 16%) was observed in BC due to reduced anthropogenic activities, mainly vehicular emissions. This was supported by 10% increase in the absorption Ångström exponent. Despite reduced fossil fuel emissions during the lockdown, 67.8% of BC at the site still originated from fossil fuel combustion, which is likely due to long-range transported dust of BC from outside the region.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 6","pages":"1781 - 1802"},"PeriodicalIF":2.9,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145171457","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 the impact of climate change on the mortality rates of COPD with the generalized estimating equation","authors":"Özlem Akay","doi":"10.1007/s11869-025-01734-3","DOIUrl":"10.1007/s11869-025-01734-3","url":null,"abstract":"<div><p>Chronic Obstructive Pulmonary Disease (COPD) is a major global public health concern, with mortality rates affected by various factors, including climate change. Rising global temperatures and increased temperature variability due to climate change present heightened risks for COPD patients, worsening morbidity and mortality. This study investigates the effect of climate change on COPD mortality rates using Generalized Estimating Equations (GEE), which account for repeated measures over time and incorporation covariates. The analysis uses longitudinal data from 2009, 2014, and 2019, covering six world regions: North America and the Caribbean, Central and South America, Asia, Oceania, Europe, and Africa. Independent variables include regional classification, temperature levels, and greenhouse gas emissions. The results indicate that COPD mortality rates are significantly associated with greenhouse gas levels and specific regions, particularly Oceania and Europe. However, the temperature variable does not considerably influence COPD mortality statistically. These findings underscore the need for region-specific strategies to mitigate health risks related to climate change.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 6","pages":"1771 - 1780"},"PeriodicalIF":2.9,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145171283","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":"Grilling and air pollution: how charcoal quality affects emissions","authors":"Alessio Mencarelli,&nbsp;Rosa Greco,&nbsp;Stefano Grigolato","doi":"10.1007/s11869-025-01737-0","DOIUrl":"10.1007/s11869-025-01737-0","url":null,"abstract":"<div><p>Cooking with charcoal is widely popular worldwide. Several types of lump charcoal and charcoal briquettes are available on the market, presenting distinct characteristics based on raw materials and production methods. Charcoal quality can significantly impact combustion properties and pollutant emissions. This study aims to: (1) assess the qualitative characteristics of commercial lump charcoal and charcoal briquettes, (2) evaluate the emission factors (EFs) associated with their combustion, and (3) investigate how different characteristics of charcoal affect air pollutant emissions. Significant differences in quality were observed among the charcoal samples analyzed. Lump charcoal generally exhibited superior characteristics, including a higher carbon content (&gt; 75% d.b.), fixed carbon (&gt; 75% d.b.), heating value (&gt; 29 MJ/kg), and fuel ratio (&gt; 4.5). In contrast, charcoal briquettes showed higher levels of volatile matter (&gt; 20% d.b.), ash (&gt; 15% d.b.), and moisture content (&gt; 6% w.b.). Furthermore, charcoal briquettes exhibited higher EFs for carbon monoxide (CO) (&gt; 250 g/kg), volatile organic compounds (VOC) (&gt; 1.5 g/kg), and total suspended particulates (TSP) (&gt; 7.5 g/kg) than lump charcoal. Principal component analysis (PCA) demonstrated a strong influence of qualitative characteristics on emission factors. Volatile matter, ash yield, moisture, and nitrogen content were positively correlated with higher emissions, while carbon content, fixed carbon, and fuel ratio were negatively correlated. As lump charcoal aligns more closely with the latter parameters, it releases lower emissions than briquettes. Improving charcoal production techniques and reinforcing international standards are crucial to ensure product quality, reduce emissions, and minimize environmental and health risks.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 6","pages":"1757 - 1770"},"PeriodicalIF":2.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-025-01737-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145169552","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":"Chemical characteristics and source apportionment of fine particulate matters at a typical suburban site in Nanjing, China","authors":"Ziqian Wei,&nbsp;Ruoyuan Lei,&nbsp;Meijuan Chen,&nbsp;Yunjiang Zhang,&nbsp;Xiangpeng Huang,&nbsp;Xinlei Ge","doi":"10.1007/s11869-025-01730-7","DOIUrl":"10.1007/s11869-025-01730-7","url":null,"abstract":"<div><p>Regional atmospheric pollution events still occur frequently in China despite the decline in PM_2.5 concentrations in recent years. In order to investigate the chemical composition and pollution characteristics of atmospheric fine particulate matters measured by off-line soot particle aerosol mass spectrometer in Nanjing during the cold seasons, this study presented a comprehensive analysis of PM_2.5 diurnal samples from October 10 to December 31 in the year 2020, which collected at a typical suburban site in Nanjing. Results showed that water-soluble inorganic ions dominated PM_2.5 mass, with NH₄⁺ primarily forming NH₄NO₃, (NH₄)₂SO₄ and NH₄Cl through secondary reactions. Excess NO₃^- was found in the form of Ca(NO₃)₂ and Mg(NO₃)₂. Emissions from vehicle exhaust significantly contributed to the inorganic component, and both organic and inorganic analyses indicated that biomass burning was a more significant primary source during the night. In terms of water-soluble organic components (WSOM), the average O/C, H/C, N/C, and organic matter to organic carbon ratio (OM/OC) were 0.48, 1.38, 0.03, and 1.79, respectively. Four OA factors were identified in WSOM: hydrocarbon-related OA from traffic emissions (HOA), mixed primary OA (MPOA), low oxidized OA (LO-OOA), and more oxidized OA (MO-OOA). Their daytime contributions were 10.6%, 25.6%, 35.5%, and 28.8%, while nighttime contributions were 9.7%, 33.5%, 32.9%, and 23.9%, respectively. Potential source contribution function (PSCF) analysis showed that Yangtze River Delta, North China, and Mongolia were major potential sources of PM_2.5, highlighting the importance of coordinated measures to reduce PM_2.5 in Nanjing.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 6","pages":"1745 - 1756"},"PeriodicalIF":2.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145169553","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":"Formaldehyde-to-nitrogen dioxide ratio (FNR) analysis for ozone sensitivity: a case study over bangladesh using OMI data","authors":"Md Masudur Rahman,&nbsp;Roman Shults,&nbsp;Md. Feroz Ali,&nbsp;Md Galib Hasan,&nbsp;Wang Shuo","doi":"10.1007/s11869-025-01732-5","DOIUrl":"10.1007/s11869-025-01732-5","url":null,"abstract":"<div><p>The ratio of space-based column HCHO to NO₂ (FNR) is widely used to assess the chemical sensitivity of near-surface ozone (O₃) formation in NOₓ-limited, VOC-limited, or mixed NOₓ-VOC-limited regimes. This study represents the first attempt to analyse FNR over Bangladesh using OMI (Ozone Monitoring Instrument) data. In the absence of significant emission control measures (ECMs), three natural scenarios were considered as proxies for ECMs: the 2020 Covid-19 lockdown, highly precipitated days (&gt; 20 mm), and weekends. Results show FNR serves as a reliable proxy for O₃ sensitivity. Spatially, FNR values typically range from 0–1 (VOC-limited regime), 1–2 (mixed regime), and &gt; 2 (NOₓ-limited regime). During the lockdown and highly precipitated days, most of Bangladesh exhibited an NOₓ-limited regime (FNR &gt; 2), except Dhaka, which showed a mixed regime during lockdowns. A Pearson correlation analysis shows that FNR is positively correlated with surface temperature and precipitation, with Random Forest predictions strongly matching observed FNR values (<i>r</i> = 0.96). The O₃/NO_y ratio analysis further supports the FNR-based ozone sensitivity classification. These findings suggest that ECMs significantly shifted ozone formation to a NOx-limited regime, with FNR being strongly influenced by reductions in NO₂ and increased HCHO production driven by rising surface temperatures.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 6","pages":"1879 - 1886"},"PeriodicalIF":2.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145169118","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}