This study investigates the influence of biomass burning and the COVID-19 pandemic on atmospheric pollution across South Asia from 2019 to 2024 using satellite observations. Biomass burning across South Asia exhibits two dominant seasonal peaks during March-April and October-November, with burned area ranging from approximately 5,000 to 25,000 km². These burning events are closely associated with elevated levels of atmospheric pollutants, including CO, NO₂, and aerosols, indicating a direct linkage between fire activity and air quality degradation. Analysis of Sentinel-5 Precursor TROPOMI data for aerosols, nitrogen dioxide (NO₂), and carbon monoxide (CO) shows distinct enhancements in pollutant levels during these burning windows. Time series decomposition highlights the “Covid Dip” in 2020, when April burned area dropped to 8,690 km² compared to over 15,000 km² in 2019. Temporary decline in NO₂ and aerosol concentrations was observed because reduction was primarily driven by decreased transportation and industrial activities and biomass burning showed only partial decline during the pre-monsoon season. In contrast, CO was less affected, reflecting its longer atmospheric lifetime and continued fire emissions. Spearman correlation analysis confirms CO as the most reliable tracer of burning (r = 0.68), while aerosols (r = 0.24) and NO₂ (r = 0.20) show weaker associations with burning area due to their multiple sources and short lifetimes. City-level analysis reveals that Indo-Gangetic Plain centers such as Delhi, Ludhiana, and Amritsar experience the strongest pollution spikes during burning seasons, while coastal cities such as Mumbai and Colombo display minimal seasonal variation due to stronger atmospheric dispersion. Overall, the findings highlight biomass burning as a dominant seasonal driver of poor air quality in South Asia, emphasizing the need for sustainable residue management and regional policies to mitigate its impacts.
Atmospheric secondary organic aerosols (SOAs) play a significant role in climate change, air quality, and human health, yet their formation mechanisms and influencing factors in the field environment are not fully understood. This study conducted a one-year-long observation of specific organic tracers of SOAs derived from isoprene (SOAI), α/β-pinene (SOAP), β-caryophyllene (SOAC), and aromatics (SOAA) oxidation in PM2.5 at a tropical rainforest site in Xishuangbanna, Southwest China. All the SOA tracers presented obviously higher concentrations in dry season than in wet season. The ratio of 2-methylglyceric acid (2-MGA) to 2-methyltetrols (2-MTs) in dry season (0.49) was around 3 times higher than that in wet season (0.17), because the higher NOx concentrations (36.18 µg m−3 in dry season vs. 22.96 µg m−3 in wet seasons) enhanced the 2-MGA formation in dry season. The ratios of 3-methyl-1,2,3-butanetricarboxylic acid to the sum of cis-pinonic and pinic acids (M/P) were above 2 in both seasons, suggesting that the organic aerosols in the tropical rainforest region have undergone a significant degree of oxidation. The concentration of β-caryophyllene was approximately 18 times higher in the dry season (9.18 ± 10.23 ngm−3) than in the wet season (0.49 ± 0.35 ng m−3), which is related to the seasonal activity of biomass burning. In dry season, levoglucosan exhibited significant correlations with SOAP, SOAC, and SOAA, indicating that they were likely influenced by biomass burning. Backward trajectory analysis and potential source contribution factor analyses revealed that the sources of all the SOA tracers are likely influenced by biomass burning from the south and northwest directions in dry season, whereas in wet season they are likely affected by the transport from the southwest and northeast directions. This study highlights the important contribution of biomass burning in Southeast Asia on the SOA formation in the tropical rainforest region of Southwest China.
Black carbon (BC), a toxic climate pollutant with short residence time, poses a serious public-health risk in urban environments. This study assessed long-term BC trends in Delhi and Nagpur using MERRA-2 data (2009–2022) and quantified the associated cause-specific mortality and economic losses. Delhi showed consistently higher annual BC concentrations (3.0–3.6 µg m−3) than Nagpur (1.4–2.8 µg m−3), with pronounced peaks during winter and post-monsoon. Across all years, BC concentrations in Delhi were 1.9–2.6 times higher than in Nagpur. The relative risks (RRs) linked to BC exposure for all-cause mortality (ACM), respiratory mortality and cardiovascular mortality were 1.0489, 1.0937 and 1.060, respectively in Delhi and 1.0237, 1.0449 and 1.0290 in Nagpur. Excess ACM attributable to BC exposure ranged from 2512 to 4294 deaths annually in Delhi and from 653 to 1333 deaths in Nagpur, accounting for 4.2–5.2
The urbanisation process in sub-Saharan Africa has caused rapid increases in fine particulate matter (PM2.5) concentrations, but high-resolution diurnal data are limited. In this research, spatiotemporal and diurnal changes in three different urban centers of Benin City, Nigeria, were assessed: Ugbowo (institutional), Sapele Road (commercial/traffic), and Etete (mixed-use). Purple air sensors were used for monitoring from October to December 2024, and the health risks were quantified on the basis of the air quality index (AQI) and respiratory deposition dose (RDD) modelling. The findings revealed that the concentrations were significantly above the WHO 24-hour limit, with the highest results recorded in December. The maximum daytime mean (183.84 µg/m3) was observed in Etete. A surge was recorded at Sapele Road during the daytime, with an AQI of 202, which is considered very unhealthy, but the conditions were stable at night, with an AQI of 176–198 at all locations. RDD modelling revealed that the absolute mass of PM2.5 inhaled by adult workers was the highest (161.12 µg/h), although the normalized dose of PM2.5 in children (2.36 µg/h/kg) was more than two times greater than the adult dose (0.94 µg/h/kg). This physiological imbalance highlights a critical risk of developmental threats in paediatric populations. On the basis of this research, microenvironment-specific interventions, which include stricter waste-burning laws and low-emission transport zones, are needed to reduce these dire and immediate public health threats in Benin City.
Wastewater treatment plants (WWTPs) are recognized as a significant source of fungal aerosols, which may pose adverse health effects to workers and nearby residents. This study integrated field observations, dispersion modelling, and lung deposition modelling to investigate the characteristics, dispersion and human impact of fungal aerosols from an urban WWTP. Culturable fungal aerosol concentrations exhibited site- and diurnal-specific variations, predominantly within the 2.1–3.3 μm particle size range. Cladosporium and Nigrospora were the dominant genera in PM2.5, and pathogenic/allergenic and microbial volatile organic compounds (MVOCs) related fungi were detected, raising concerns regarding potential health risks associated with inhalation of fine particles and their dispersion around the WWTP. The direction of fungal aerosol dispersion was determined by wind direction, while dispersion range and distance were related to wind speed and their source strength. The dispersion behavior of fungal aerosols varied with particle size. These aerosols were estimated to be deposited in the extrathoracic region for on-site workers and surrounding residents, followed by the alveolar and then the tracheobronchial region. Although total deposition decreased with distance from the WWTP, small-sized fungal aerosols travelled farther and contributed disproportionately to alveolar deposition. This study provides valuable insights into the presence of fungi and the potential for exposure to fungal aerosols in urban hotspots, and identifies safe areas around the WWTP during sampling, which may inform safety-based considerations for the siting of municipal facilities in urban areas.
This study collected offline samples of fine particulate matter (PM2.5) from 2019 to 2020 at three functional sites (HQ, ZL, and XH) in a coastal city in the Yangtze River Delta (YRD). The spatiotemporal variations in PM2.5 mass concentration and major chemical components were investigated, and the Positive Matrix Factorization (PMF) model was applied to estimate source contributions. During the sampling period, PM2.5 concentrations at the three sites showed seasonal differences, with higher concentrations in autumn and lower concentrations in summer. The average PM2.5 concentrations across the three sites followed the order of ZL (39.5 µg/m3) > HQ (37.2 µg/m3) > XH (36.2 µg/m3) during the sampling period. Organic carbon, nitrate, sulfate, and ammonium were the dominant chemical components, together accounting for 50
Accurate forecasting of air quality is critical, as PM _2.5 pollution poses serious risks to environmental sustainability and public health. This study proposes a novel hybrid deep ensemble framework integrating CNN, BiLSTM, and GRU within a stacked architecture, with a Gradient Boosting Machine (GBM) serving as the meta-learner. Using daily PM _2.5 data from Delhi, India, the framework is benchmarked against classical and deep learning models. Results demonstrate that the hybrid ensemble consistently outperforms baselines, achieving MAE of 8.09, RMSE of 10.70, and R ^2 of 0.96, representing a 27.80
The study investigates the impact of an extensive open waste burning incident at the Brahmapuram (BP) Municipal Solid Waste (MSW) dumpsite in Kochi, Kerala, India. It includes a field monitoring campaign during 7–10th March, 2023, to characterise dispersion dynamics and quantification of the pollutants’ levels. The highest levels of PM10 (4266.3 µg/m3) and PM2.5 (679.7 µg/m3) were observed on 8th March during the real-time monitoring. Two downwind receptor sites, viz. Niko Hotel (NH), Kadavanthra, and Rajagiri School of Engineering and Technology (RS), Kakkanad, were also monitored to assess dispersion impact. These sites also observed PM levels far above the National Ambient Air Quality Standards (NAAQS). This spatio-temporal mapping revealed pollutant dispersion patterns consistent with the wind direction modelled by HYSPLIT, showing the impact of the burning event on downwind locations and the coastal areas. The chemical characterisation of collected samples from the burning site was also conducted, and the analysis revealed a dominance of NO3−, SO42−, NH4+, and trace metals such as Ca, Fe, and Zn. The enrichment factor study also indicates anthropogenic emission of trace metals. These findings highlight the severe air quality and health risks posed by open dumpsite fires and underline the need for urgent reforms in solid waste handling, fire prevention, and regional air quality monitoring.
Traditional medicinal fumigation is widely practiced in Ethiopia; however, quantitative information on the physicochemical characteristics and emission profiles of commonly used fumigants remains limited. This study characterized two widely used Ethiopian fumigants, Echinops kebericho (KE) and Otostegia integrifolia (TU), through elemental (CHNS), functional group (FTIR), and mineral analyses, coupled with controlled chamber combustion experiments. Differences in emission rates and emission factors between the fumigants were evaluated using the non-parametric Mann-Whitney U test. Elemental analysis indicated a higher carbon content in TU, whereas KE exhibited significantly greater combustion efficiency and oxidation completeness, likely due to its finer porous structure and higher mineral content. Consequently, KE produced significantly higher CO₂ emissions (18730.64 ± 5172.31 mg/h; p = 0.0119) than TU (8138.67 ± 3844.61 mg/h). Chemical and morphological differences also influenced size-dependent pollutant emissions. TVOC emissions were detected only in KE (15.25 ± 10.98 mg/h; p = 0.0075), reflecting its high content of volatile essential oils. Furthermore, KE generated significantly higher PM₁ emission rates (27.81 ± 7.41 mg/h; p = 0.0159) than TU (14.52 ± 7.60 mg/h). In contrast, no significant differences were observed in PM₂.₅ emissions (p = 0.0952) or formaldehyde (CH₂O) emissions (p = 0.548), likely due to the erratic smoldering behavior and compact fuel-bed geometry of TU. Both fumigants released substantial amounts of respirable particulate matter within ranges reported for solid biomass combustion. These findings provide critical baseline data on fumigant smoke chemistry and establish a scientific foundation for indoor air quality modeling and inhalation exposure assessments.
Environmental conditions exert strong influence on the formation and evolution of thermally generated aerosols, particularly those derived from semi-volatile organic compounds (SVOCs). In this study, a temperature–humidity-controlled platform was developed to investigate how ambient temperature and relative humidity individually influence aerosol dynamics across puff sequences in a heated tobacco product (HTP) system under controlled baseline conditions, used here as a representative thermally generated SVOCs aerosol source. Puff-by-puff aerosol size distributions and number median diameters (D50) were measured using a Scanning Mobility Particle Sizer (SMPS), while particulate- and gas-phase constituents were analyzed using GC–MS. The results show that low ambient temperature enhances gas–particle partitioning and condensation processes, leading to significantly higher particle number concentrations and larger contributions from condensation-driven growth. In contrast, elevated humidity was associated with shifts in aerosol number distribution and median diameter, reflecting humidity-dependent modulation of aerosol formation processes rather than hygroscopic growth during measurement. Temperature and humidity were found to independently influence aerosol formation pathways, affecting particle size evolution, number concentration, and the distribution of major aerosol constituents. These findings provide mechanistic insight into how environmental gradients modulate the physicochemical evolution of thermally generated SVOCs aerosols and offer a framework for future studies in aerosol engineering and environmental aerosol modeling.
Fine particulate matter (PM2.5) adversely affects human health and climate, yet its vertical distribution—critical for understanding transport mechanisms, radiative forcing, and altitude-resolved exposure—remains poorly characterized due to the spatial limitations of ground-based monitoring. This study develops an ensemble machine learning framework to estimate vertical profiles of PM2.5 mass concentration and chemical composition by integrating Light Detection and Ranging (LiDAR)-derived aerosol extinction coefficients with ground-based speciation data from Xiamen, a coastal urban location in southeastern China. A Mie scattering LiDAR system provided high-resolution vertical profiles of aerosol optical properties, while co-located measurement supplied hourly concentrations of PM2.5, sulfate (SO₄²⁻), nitrate (NO₃⁻), ammonium (NH₄⁺), organic carbon (OC), and elemental carbon (EC). Meteorological variables from surface stations and ERA5 reanalysis were incorporated as predictors. We systematically compared linear regression, support vector regression (SVR), artificial neural networks (ANN), XGBoost, and an ensemble model combining all three ML algorithms. The ensemble model outperformed traditional approaches, achieving a slope of 0.64 and bin-MAE of 5.56 µg m⁻³ for total PM2.5 mass on an independent test dataset, with SHAP analysis identifying the extinction coefficient as the dominant predictor. Reconstructed vertical profiles revealed distinct seasonal patterns: maximum concentrations near the surface in spring and winter (18.1 ± 8.09 µg m⁻³ at 0.4 km), suggesting the combined influence of low-level regional transport and local emissions, broadly consistent with trajectory clustering results showing predominant transport from northern regions. In contrast, elevated maxima above 1000 m in summer suggest enhanced marine-influenced regional transport aloft.
Exposure to household air pollutants has become a major environmental health issue in developing countries. This study aimed to explore the impact of indoor air pollution and its toxicological hazards, especially focusing on cooking practices in rural households. We conducted real-time monitoring of ambient particulate matter (PM2.5) and carbon monoxide (CO) levels in 61 rural kitchens and surveyed 516 households in West Bengal, India. The observed average concentrations of CO, PM2.5, relative humidity, and temperature in five different kitchen types were 11.50 ppm, 780.64 µg/m³, 56.43
Air pollution poses a serious concern to cities and towns across western Uttar Pradesh, India. Therefore, we have examined the dynamics of gaseous and particulate pollutants, as well as meteorological factors and their sources, at four Central Pollution Control Board monitoring sites in Ghaziabad and Meerut. We reported high concentrations of SO2 (36.83 µg/m³), NO2 (115.58 µg/m³), and O3 (92.38 µg/m³) during March, December, and April, respectively. These elevated levels are attributed to anthropogenic aerosols and photochemical oxidation. Conversely, pollution levels dropped significantly during the rainy season, as rainfall helped wash contaminants out of the atmosphere. High levels of PM10, PM2.5, and Black Carbon (BC) were recorded in November and December, mainly due to biomass burning, temperature inversions, and the shallow boundary layer. Loni and Indirapuram showed high monthly AQI values of 294.27 and 221.64, respectively. We also estimated the SO2/NO2 ratio to distinguish between vehicular and non-vehicular emissions. High ratios in May (4.59) and April (3.48) of 2022 indicate a significant contribution from industrial sources. Similarly, high PM2.5/PM10 ratios (0.7–0.75) imply that fine particulate matter (PM2.5) is likely from construction activities. During the Post-Monsoon and winter seasons, AQI is strongly influenced by biomass burning (BB), during which we observed elevated BC concentrations higher than 5 µg/m³. Additionally, we identified different aerosol sources using Ångström Exponent and Aerosol Optical Depth. Biomass burning aerosols were dominant throughout the study period. During the post-monsoon season, BB aerosols, along with urban and industrial emissions, were prevalent. The weak correlation between BC and wind speed indicates the contribution from local sources. In the pre-monsoon season, dust and mixed-phase aerosols were prominent, while winter featured high levels of urban and industrial emissions. This study is critical for understanding AQI trends, pollution sources, and their dynamics over the past few years across western Uttar Pradesh.
Polycyclic aromatic hydrocarbons (PAHs) are pollutants of substantial concern due to their carcinogenic and mutagenic properties. This study involved 24-h PM2.5 sampling conducted from October 14, 2021, to January 8, 2022, at a suburban site in Wuhan, central China. An in-injection port thermal desorption-gas chromatography/mass spectrometry procedure was used to determine 14 PAHs in the particulate phase. The average concentrations of total PAHs and benzo(a)pyrene carcinogenic equivalent concentration are 15.2 ± 8.2 ng m−3 and 0.87 ± 0.73 ng m−3, respectively, with a predominant presence of 4-ring PAHs (59.6
Air quality degradation has emerged as a significant urban challenge in recent decades, with fine particulate matter (PM2.5) recognized as one of the most hazardous pollutants. PM2.5 arises from both primary sources, including vehicular emissions, biomass burning, and industrial activities, and from secondary particle formation via atmospheric reactions involving precursor gases such as SO₂, NOx, NH₃, and VOCs. Exposure to these pollutants is closely associated with elevated risks of respiratory and cardiovascular diseases. This research applies machine learning (ML) algorithms to forecast PM2.5 concentrations using hourly data from Bengaluru, a major metropolitan area in southern India. Multiple models—Extra Trees, k-Nearest Neighbors, Random Forests, Support Vector Machines, CatBoost, and Decision Trees—were developed and evaluated using R2, RMSE, MAE, and MSE as performance metrics. The ExtraTreesRegressor demonstrated superior predictive performance, achieving an R2 value of 0.92 and the lowest error among the models tested. Hyperparameter optimization and Explainable AI techniques, including LIME and SHAP, were employed to improve model interpretability and robustness. The findings indicate that ensemble ML models can deliver reliable short-term forecasts of PM2.5 concentrations, thereby supporting air quality professionals and policymakers in issuing timely health advisories and implementing effective emission control measures.
Cylindrical differential mobility analyzers (DMAs) are fundamental instruments for submicron aerosol characterization; however, their sizing precision is highly susceptible to manufacturing and assembly imperfections, particularly electrode eccentricity. This study systematically investigates the coupled effects of flow rate parameters and axial eccentricity on the DMA transfer function. Building upon established theoretical frameworks developed by Alsharifi and Chen (Aerosol Science and Technology 2019a), we analyze how structural misalignment degrades classification resolution. The numerical model was rigorously validated against experimental and computational results from Chen et al. (1998) and Alsharifi and Chen (2019a, b). Our findings reveal that while the total flow rate remains invariant to eccentricity, electrode misalignment induces a detrimental shift toward a bimodal transfer function. Crucially, we demonstrate that strategic optimization of the flow rate ratio can significantly suppress this resolution degradation and mitigate the bimodal shift. This research provides essential theoretical insights and practical guidelines for tuning operational parameters, ultimately enhancing the sizing accuracy and reliability of DMAs in high-precision aerosol measurements.
Exposure to particulate matter (PM) and its adverse health effects are linked to oxidative stress processes. The oxidative potential (OP) serves as a valuable exposure metric for a more nuanced understanding of health responses to PM. This study investigates the OP of PM2.5 and PM1 and the respiratory deposition doses of PM across various settings- urban, roadside, and rural in Agra, India. The Multiple Path Dosimetry model (MPPD) was used to assess respiratory deposition doses in four different subject categories: infants, children, adults, and retirees. The study findings reveal that the OP of PM varies considerably throughout Agra, OP- DTTv (352.8 ± 95.65 pmol/min/m³) values for PM2.5 significantly higher than OP-DTTv (266 ± 79.86 pmol/min/m3) for PM1. On the other hand, OP-DTTm values for PM1 (15.22 ± 8.73 pmol/min/µg) were elevated than PM2.5 (10.29 ± 7 pmol/min/µg) indicating increased PM1 levels in roadside settings. The study findings reveal that the OP of PM varies considerably throughout Agra, with OP-DTTv values for PM2.5 were significantly higher and OP-DTTm values indicating increased PM1 levels in roadside settings. Notably, the highest deposition values of PM2.5 were found in the upper respiratory tract, particularly in the head regions (47
Atmospheric aerosols affect climate and air quality through chemical reactions that are not yet fully understood. Here, we revealed that black carbon (BC) particles can promote the heterogeneous oxidation of isoprene, a major biogenic volatile organic compound, under simulated sunlight. Using a flow reactor coupled with proton-transfer-reaction mass spectrometry and in situ infrared spectroscopy, we observed that this surface-driven process formed small carbonyl-containing oligomers derived from C1–C4 fragments, rather than the C5 products commonly associated with gas-phase isoprene oxidation. Electron spin resonance measurements further confirmed that BC can act as a photosensitized catalyst via the formation of surface reactive oxygen species (ROS). Light-activated BC generated singlet oxygen (1O2), which reacted with adsorbed water to produce surface hydroxyl radicals (OH). Additional surface bidentate sites supported continued reactant uptake and product formation. These results identified a previously underrecognized pathway for particle growth during haze episodes and suggest that aerosols can actively drive oxidant chemistry rather than simply remove oxidants from the atmosphere. Including BC-catalyzed heterogeneous reactions in atmospheric models may improve predictions of secondary organic aerosol formation in polluted environments.
