Search Results (277)

To address the limited constraints of ground-based lidar with few channels in retrieving aerosol microphysical parameters in urban atmospheres, this study developed a method to retrieve aerosol volume size distribution and effective radius from a 355/532/1064 nm triple-wavelength elastic-scattering, single-polarization lidar system. The method uses 3β + 2α optical quantities as input constraints, applies Mie scattering theory as the forward model, parameterizes the volume size distribution with B-spline functions, and achieves stable solutions through Tikhonov regularization and cross-validation. To reduce uncertainties in prior parameters, including the complex refractive index, particle size range, and lidar ratio, an optimization strategy based on parameter search, retrieval reconstruction, and error minimization was introduced. Numerical simulations showed that the method reproduced the main features of a bimodal lognormal aerosol volume size distribution with good feasibility and stability. Two case studies further showed fine-mode dominance and decreasing extinction coefficient, depolarization ratio, and effective radius with height under good air quality conditions, but enhanced coarse-mode contribution and effective radius in the upper cloud-influenced layer under lightly polluted conditions, as inferred from the combined variations in RSCS, extinction coefficient, depolarization ratio, and effective radius. Full article

This study examined the chemical composition and quantitative source contributions of coarse (PM_10–2.5) and fine (PM_2.5) particles in ship-based PM₁₀ and PM_2.5 filter samples from 2020 to 2024 across the Yellow Sea. The observations were primarily conducted during the spring season, when the influence of continental air masses from East Asia is pronounced, and detailed analyses of water-soluble ions and elemental species were performed. In coarse particles, sea salt components (e.g., Na⁺ and Cl⁻) and soil-derived species (e.g., nss-Ca²⁺ and CO₃²⁻) were predominant, whereas fine particles were dominated by secondary inorganic species such as nss-SO₄²⁻, NO₃₋, and NH₄⁺. Source contributions were estimated using Dispersion Normalized Positive Matrix Factorization (DN-PMF), and eight common factors were identified, including sea salt, soil, secondary nitrate, secondary sulfate, oil combustion, biomass burning, marine biogenic emissions, and plant growth. Additionally, an industry factor was uniquely resolved in coarse particles, whereas a mobile source factor was identified in fine particles. In coarse particles, sea salt (30.9%) and soil (15.1%) were the major contributing sources, whereas fine particles were dominated by secondary nitrate (48.6%) and secondary sulfate (15.6%). Potential Source Contribution Function (PSCF) analysis indicated that the sea salt and oil combustion factors in coarse particles were associated with coastal regions of the Yellow Sea and the East China Sea, while the soil factor corresponded spatially with inland regions of northern China. In contrast, the secondary nitrate, secondary sulfate, and biomass burning factors in fine particles showed strong associations with inland regions of eastern China. Using size-resolved DN-PMF and five years of repeated observations over the same marine region, this study provides the first quantitative source apportionment analysis of interannual atmospheric composition variability and long-range transport affecting air quality over the Yellow Sea. Full article

Xinjiang experiences frequent dust storms, posing significant challenges to regional ecological security and public health. Based on the China High-resolution and High-quality Near-surface Air Pollutants (CHAP) dataset and ground monitoring data, this paper adopts the Potential Source Contribution Function (PSCF) to analyze the spatiotemporal characteristics of atmospheric particulate matter across Xinjiang and typical cities and to identify potential source regions and contribution intensities. The results show that (1) PM_2.5 and PM₁₀ concentrations are elevated in southern Xinjiang but reduced in the north, and particulate pollution in most areas has generally decreased. (2) Northern Xinjiang cities have high PM_2.5 in winter, while southern Xinjiang cities keep persistently high PM₁₀ levels. (3) The PM_2.5/PM₁₀ ratio is above 0.35 in northern cities, where pollution is dominated by fine particles affected mainly by human activities; southern Xinjiang is dominated by coarse particles from natural sources. (4) Particulate matter in Urumqi mainly comes from the northern Tianshan Mountains, with winter WPSCF over 0.9. Pollutants in Kashgar originate from both long-distance cross-border dust transmission and local emissions. These findings are of great significance for the sustainable development of Xinjiang and urban agglomerations along the Belt and Road. Full article

We present a novel algorithm for the retrieval of non-spherical particle microphysical parameters (PMP) from 3β + 2α + 3δ optical data taken with multiwavelength lidar. The 3β + 2α + 3δ optical datasets describe particle backscatter coefficients (β) at three wavelengths, λ = 355, 532, and 1064 nm, particle extinction coefficients (α) at two wavelengths, λ = 355 and 532 nm, and particle linear depolarization ratios (PLDR, δ) at three wavelengths, λ = 355, 532, and 1064 nm. The algorithm can be used for retrieving bimodal particle size distributions (PSDs). The PSDs can comprise mixtures of spheres and spheroids (SS). One or both modes can comprise spheroid-shaped particles or spherically shaped particles. The spheroids are used for approximating an arbitrary ensemble of non-spherical particles. The algorithm works on the basis of a combination of direct and analytical inversion methods. The algorithm uses the spheroid reference look-up table (RLUT) we developed and presented in part 1 of our research work. The algorithm uses constraints regarding the particle complex refractive index (CRI) and information on relative humidity (RH) in the atmosphere (in the case of aerosol lidar observation) for suppressing retrieval uncertainties. We carried out a numerical simulation study to evaluate the algorithm’s performance. In these numerical simulations, we considered perturbed synthetic 3β + 2α + 3δ optical data that mimic different organic carbon (OC)–dust (D) mixtures. Such mixtures are suitable examples for describing bimodal PSDs that consist of a fine mode of spherical particles and a coarse mode of non-spherical particles. The results of the numerical simulation show that (1) the PMPs of each mode of these particle mixtures can be found separately, (2) the mean retrieval errors of the effective radius, number, surface-area, and volume concentrations of these mixtures are 25%, 52%, 9%, and 28%, respectively, and (3) the mean retrieval error of single-scattering albedo (SSA) at 355 nm of these mixtures is as low as ±0.02. SSA retrieval accuracies at 532 and 1064 nm degrade because the complex refractive index (CRI) of OC and D particles depends on the measurement wavelength. In future studies, we will upgrade the algorithm such that it takes into account a spectrally dependent CRI. We also compare the results of our novel algorithm with our TiARA2.1 algorithm. The errors obtained from the TiARA2.1 algorithm are approximately three times larger compared to the errors we obtain with our novel ATLAS algorithm for the case of the OC-D mixtures considered in the present study. We explain the higher accuracy of the PMP retrievals by the use of three PLDRs and the extra constraints placed on CRI and RH. Full article

Concrete contributes significantly to global CO₂ emissions due to high energy demand for cement production. This research integrates multiple advanced ensemble ML-based prediction models by combining experimental evaluation, explainable framework, and life cycle sustainability analysis for SCM (supplementary cementitious materials)-incorporated concrete mixtures. A comprehensive experimental program was conducted to evaluate the compressive and tensile strength of concrete revealing that the hybrid mix of GF4 with a 40% replacement level of cement with fly ash (FA) and ground granulated blast furnace slag (GGBFS) exhibited optimum synergistic performance due to balanced hydration kinetics and improved microstructure characteristics. For computational model development, a k-fold cross validation technique was deployed to evaluate robustness across multiple data partitions and to control overfitting in models. Model performance was assessed through multiple metrics including R², RMSE, and MAE with particular emphasis on the gap between training and testing performance. The best performing model was optimized using Particle Swarm Optimization (PSO) and Bayesian Optimization (BO) techniques providing an additional safeguard against overfitting. Shapley Additive Explanation (SHAP) interpretation revealed w/b ratio and curing age as key parameters for compressive strength, while fine aggregate content and curing age influenced tensile strength. For compressive strength, XGBoost model performed well with an R² value of 0.879 which was increased to 0.918 with the PSO optimization technique. For tensile strength, the Gradient Boosting model was selected with an R² value of 0.840 which was optimized to 0.879 after the PSO optimization technique. Moreover, life cycle assessment was performed to evaluate the environmental impacts in terms of embodied carbon and energy associated with concrete mixes. The hybrid GF4 mix demonstrated a 36% reduction in embodied carbon compared to the control mix, indicating strong potential for low carbon concrete applications. This integrated research contributes to the advancement of green construction practices and supports global efforts to reduce atmospheric impacts through the circular use of industrial byproducts. Full article

Biomass burning is a major source of atmospheric pollution. However, rapid and quantitative assessment of particulate matter in smoke plumes remains challenging, owing to the physical uncertainties, limited coverage, and labor-intensive quality control of conventional monitoring approaches. Existing image-based deep learning methods typically address either smoke detection or air quality assessment separately. To address this gap, we develop a Unified Smoke Detection and Aerosol Estimation Framework (SDAF), a three-stage deep learning approach evaluated using a smoke-rich airborne dataset. The framework integrates smoke localization with PM₁ estimation by combining a YOLOv11-based detector with an optimized convolutional neural network. The model achieves high accuracy under in-plume conditions (R² of 0.985). However, its performance degrades under out-of-plume conditions due to substantial differences in visual features between the two domains. Consequently, direct across-domain transfer performs poorly, whereas region of interest (ROI)-level fine-tuning substantially improves performance for out-of-plume images (R² of 0.621). Despite these promising results, fundamental limitations remain. Image-based PM₁ estimation is intrinsically ill-posed due to the non-unique mapping between visual observations and particle mass. Overall, the framework enables an integrated workflow from smoke localization to quantitative PM₁ estimation using image data alone, offering a scalable solution for biomass burning monitoring and air quality assessment while highlighting the fundamentally indirect nature of image-based PM₁ inference relative to spatially resolved retrievals. Full article

Atmospheric arsenic (As) poses significant health threats in heavily polluted urban environments. However, the size-resolved distribution of water-soluble arsenic (WSAs) in atmospheric particulate matter, as well as the size-dependent variation in As concentration and solubility under contrasting haze and non-haze conditions, remains insufficiently characterized. This study investigated the concentration, size distribution, water solubility, sources, and health risks of particulate-bound As and WSAs in Beijing from April 2014 to February 2015. The annual mean PM_0.1–18 concentration was 136.96 ± 54.21 μg·m⁻³, with significantly higher levels observed during haze episodes (179.61 ± 41.71 μg·m⁻³) compared to non-haze periods (118.00 ± 49.42 μg·m⁻³). The annual mean concentration of As was 6.42 ± 3.69 ng·m⁻³, exceeding both WHO guidelines and Chinese standards during haze periods, while WSAs averaged 4.54 ± 2.50 ng·m⁻³. Distinct size distribution patterns were observed: As displayed, a unimodal fine-mode peak (0.32–0.56 μm) was observed during haze periods and a bimodal distribution during non-haze conditions, whereas WSAs followed comparable size-dependent behavior, reflecting shifts in dominant emission sources and atmospheric processes. The average WSAs/As ratio (0.72 ± 0.07) indicated high As solubility and strong associations with secondary species and anthropogenic emissions. Size-resolved analysis revealed that As was preferentially enriched in fine particles, particularly during haze episodes, whereas coarse particles became more prominent under non-haze conditions, especially in spring, likely driven by regional dust transport and its interactions with anthropogenic emissions. Deposition modeling based on the ICRP framework showed that As and WSAs were primarily deposited in the headway (HA: 0.68 and 0.32 ng·h⁻¹, respectively), followed by the alveolar region (AR: 0.29 and 0.20 ng·h⁻¹, respectively). Fine particles enhanced deposition in deeper lung regions during haze episodes, whereas coarse particles contributed more to upper airway deposition under non-haze conditions. Although inhalation carcinogenic risks remained within acceptable limits (10⁻⁶–10⁻⁴), risks were 1.60 times higher during haze periods, with adults bearing the greatest exposure burden. These findings demonstrate that haze conditions substantially alter the size distribution, solubility, and health risks of atmospheric arsenic, and provide a scientific basis for developing size-resolved and haze-targeted heavy metal monitoring strategies in urban environments subject to significant anthropogenic pollution. Full article

Fly ash (FA) from Zhundong coal combustion features high alkali/calcium content and a low Si/Al ratio, limiting its potential for conventional utilization. To enable its high-value application, six size-fractionated samples (FA1–FA6) were characterized via laser particle sizing, SEM-EDS, XRF, XRD, FT-IR, and TGA, to elucidate particle-size-dependent physicochemical and thermal properties. The results show that the size distribution centered at 48–150 μm (~71%). With decreasing size, the morphology shifted from irregular aggregates to smooth vitreous spheres. The chemical composition exhibits significant elemental segregation; the SiO₂ content decreases with decreasing particle size, while active components such as CaO, MgO, and Fe₂O₃ are significantly enriched in fine particles. The thermal conversion behavior is regulated by particle size: The combustion reaction under an air atmosphere conforms to the second-order kinetic model, with the activation energy decreasing from 192.73 kJ·mol⁻¹ for coarse particles (>150 μm) to 63.53 kJ·mol⁻¹ for fine particles (<43 μm); under a nitrogen atmosphere, the weight loss originates from the removal of structural water and the decomposition of carbonates, and fine particles exhibit a higher pyrolysis activation energy (504.15 kJ·mol⁻¹) in the high-temperature stage (850–940 °C) due to being rich in high-crystallinity carbonates. The results of this study elucidate the structure–activity relationship of “particle size-composition-activity” for Zhundong coal fly ash and propose a graded utilization scheme where coarse fractions are suitable for low-grade building fillers, while fine fractions can be used as feedstocks for coal pyrolysis catalysts and functional adsorbents, providing a theoretical basis for its targeted resource utilization based on particle size fractionation. Full article

This study presents an optimized numerical and empirical modeling framework for ionic wind-driven electrostatic precipitators designed for atmospheric particulate matter (PM) removal. While traditional particle tracing models in long ducts often suffer from transient evaluation errors (the “flight time paradox”), this work introduces a Fate-based Steady-state Evaluation (FSE) method. By coupling Electrostatics, Laminar Flow, and Particle Tracing in a high-fidelity 2D axisymmetric model, we achieved a baseline validation with a Mean Absolute Error (MAE) of 5.3% compared to experimental data (20 kV, 0.5 m/s). Furthermore, a non-linear regression engine based on a physical-exponential decay function was developed to provide real-time performance predictions. The resulting hybrid model demonstrates a high scientific reliability (R² = 0.98), establishing it as a robust tool for the design and optimization of air purification systems targeting fine atmospheric aerosols (0.1–3.0 μm). In addition, the proposed Fate-based Steady-state Evaluation (FSE) method eliminates transient bias commonly observed in long-duct Lagrangian particle simulations. This methodological improvement enables statistically consistent efficiency estimation for electrohydrodynamic filtration systems and can be applied to a broad class of Computational Fluid Dynamics (CFD)-based particulate capture studies. The developed framework enables rapid design optimization of compact electrohydrodynamic filtration systems and provides a practical alternative to computationally expensive full-scale Computational Fluid Dynamics (CFD) simulations. Full article

Air pollution remains a major global environmental risk, and exposure to fine particulate matter (PM_2.5) is associated with adverse health outcomes even at low concentrations. Meteorological conditions influence PM_2.5 variability, and precipitation is often expected to reduce particle loads through wet removal. However, humid and wet conditions may coincide with elevated PM_2.5 under specific atmospheric and compositional conditions. Here, we investigate long-term relationships between precipitation regimes and PM_2.5 concentrations in the Metropolitan Region of Belém (Eastern Amazonia) over the period 1980–2024. We combined PM_2.5 from the MERRA-2 reanalysis (including a bias-corrected product) with in situ precipitation records, and classified precipitation conditions using the Standardized Precipitation Index (SPI). We find statistically significant positive long-term tendencies in both precipitation and PM_2.5. Stratified analyses show that PM_2.5 concentrations are significantly higher under wet conditions, with a weak but significant positive relationship between SPI and PM_2.5 (r = 0.23 for the full period; r = 0.24 for the wet class, p-value < 0.01). These findings indicate that increased precipitation in a strong humid tropical urban environment does not necessarily lead to improved air quality. Instead, wet conditions may favor processes such as hygroscopic growth and secondary aerosol formation, contributing to higher PM_2.5 concentrations on a monthly scale. Overall, this study highlights the importance of considering precipitation regimes and associated atmospheric processes when assessing air quality in tropical urban environments. Full article

Ammonia (NH₃) emissions from concentrated animal feeding operations (CAFOs) are recognized contributors to secondary fine particulate matter (PM_2.5) formation, yet empirically derived secondary PM_2.5 emission factors applicable to livestock operations remain limited. This study investigated NH₃-derived secondary PM_2.5 formation under controlled laboratory conditions using a PTFE flow reactor in which NH₃ was reacted with sulfur dioxide (SO₂) across ammonia-rich NH₃:SO₂ ratios, with and without zero air. The resulting aerosols were characterized using gravimetric analysis, elemental analysis, Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM/EDS), and particle size distribution (PSD) measurements. The recovered particles were dominated by inorganic ammonium–sulfur species, with FTIR and elemental trends indicating sulfite-related intermediates under no-zero-air conditions and more oxidized ammonium–sulfur products under oxygenated conditions. Accounting for both filter-collected and wall-deposited particles, unit particulate emission factors normalized to ammonia input were derived. Size-based apportionment using PSD data indicated that approximately 76.6% of the recovered particulate mass was within the PM_2.5 size range. Scaling the experimentally derived unit emission factors using literature-based ammonia emission rates yielded an estimated secondary PM_2.5 emission factor of 0.351 ± 0.084 g PM_2.5 per animal head per day for cattle feedlots, corresponding to approximately 3–4% of reported total PM_2.5 emissions. Because the experimental system isolates NH₃–SO₂ interactions under idealized conditions and does not represent full atmospheric chemistry, the derived values should be interpreted as screening-level estimates of NH₃-derived secondary PM_2.5 formation potential intended to support comparative air quality assessments of CAFOs rather than direct predictions of ambient PM_2.5 concentrations. Full article

The hydrocarbon composition of liquefied PM1 aerosol samples collected using the particle into liquid system (PILS) at the Atmospheric Radiation Measurement (ARM) site of the Southern Great Plains (SGP) of the USA was analyzed in terms of organic compound composition. The results indicate that anthropogenic aliphatic compounds contributed significantly to the organic pool of PM1 fine aerosols in the ambient air of the rural area of the Southern Great Plains, with a broad range of aliphatic hydrocarbons (HCs) being the dominant organic component. The molecular markers of hopanes and steranes were generally absent or present in trace amounts in most samples, but a significant number of low-abundance hopanes and steranes were detected in only two samples, while the aromatic compounds were generally insignificant and comprised mainly low molecular weight naphthalene and its methylated derivatives. The overall composition of organic compounds and the back trajectories analysis for the sampling days suggest that the local petroleum refinery and vehicular emissions are the two major sources of the aliphatic and aromatic compounds in the fine aerosols, while plant wax may occasionally contribute a minor portion of organic matter. Furthermore, it was found that the organic composition of PM1 fine aerosol was clearly related to the ambient air temperature and suggests that the temperature is a controlling factor of organic aerosol formation. Full article

To investigate the temporal and spatial variations in smoking activities in Shanghai, atmospheric fine particles (aerodynamic diameter ≤ 2.5 μm) were collected at four sites in different functional zones, a central urban site (XJH), an urban site (PD), a suburban site (BS), and a rural site (QP), between 2012 and 2020 with the concentration of nicotine measured by GC-MS. The results showed that smoking activities in Shanghai decreased significantly from 2012 to 2020. The average concentration of nicotine in fine particles at XJH (2012–2013) was 13.86 ng m⁻³, while it was 3.39 ng m⁻³ at BS (2017–2018), and 1.13 ng m⁻³ and 0.58 ng m⁻³ at PD and QP during 2018–2020. Nicotine concentration in Shanghai showed strong spatial variability but generally followed a seasonal trend of high in winter and low in summer. At XJH and BS, where higher nicotine concentrations were detected, positive correlations between nicotine and organic carbon in fine particles were observed, but not at PD and QP. A negative correlation between nicotine and ozone was found at QP, suggesting the influence of transported nicotine at the rural site. In general, the concentration of nicotine in atmospheric fine particles is primarily governed by local smoking activities, but is also influenced by meteorological conditions. Full article

Crop residue open burning is a major source of atmospheric pollutants that degrade regional air quality, enhance climate forcing, and threaten public health through emissions of particulate matter, greenhouse gases, and toxic species. In southern China, satellite-based emission estimates are often underestimated because frequent cloud cover and limited spatiotemporal resolution hinder the detection of agricultural fires. In this study, crop residue open burning emissions in Guangxi province from 2017 to 2023 were quantified using a statistical approach. The open burning proportion (OBP) was updated on an annual basis using the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG), and recently reported emission factors (EF_S) were adopted to enhance estimation accuracy. Annual emissions of pollutants were then spatially distributed to 0.05° × 0.05° grid cells based on satellite-detected fire counts and land cover information. The results indicated the total emissions of black carbon (BC), organic carbon (OC), sulfur dioxide (SO₂), nitric oxide (NO_X), carbon monoxide (CO), carbon dioxide (CO₂), fine particles (PM_2.5), coarse particles (PM₁₀), ammonia (NH₃), methane (CH₄) and non-methane volatile organic compound (NMVOC) in Guangxi province during 2017–2023 were 58.90, 230.48, 37.90, 213.95, 4234.41, 108,775.48, 583.09, 667.70, 46.36, 322.74 and 710.20 Gg, respectively. Sugarcane residue burning was identified as the dominant contributor, accounting for 41.26–64.38% of total emissions, followed by rice (20.66–43.06%), corn (5.11–17.25%), and cassava (4.33–6.45%). Emissions exhibited clear interannual variability, declining from 2017 to 2020 under strict control measures and increasing again from 2021 to 2023 as enforcement weakened. Incorporating annually updated VIIRS-derived OBP_S into the statistical inventory improves the temporal representation and reliability of multi-year emission estimates for agricultural burning. Full article

This study presents the engineering design and system-level modeling of a high-frequency power supply architecture for electrostatic precipitators intended to improve particulate removal efficiency and operational stability. Atmospheric air pollution by fine particulate matter (PM2.5) remains one of the most critical challenges in environmental protection and public health. Although electrostatic precipitators (ESPs) are widely used for industrial gas cleaning, the efficiency and stability of conventional 50 Hz power supplies are limited under conditions of strongly nonlinear corona discharge and high-resistivity dust. This paper presents the development and investigation of an advanced high-frequency power supply system for electrostatic precipitators based on a coupled electrical–electrophysical mathematical model. The work follows an engineering design methodology that integrates converter topology selection, electrophysical modeling of corona discharge, and control-oriented system optimization. The proposed model provides a unified description of electric field formation, space charge accumulation, ion transport, and particle motion in the corona discharge region. The simulation results show that in the operating voltage range of 10–100 kV, the electric field strength reaches (2–5)·10⁶ V/m, the ion concentration stabilizes in the range of 10¹³–10¹⁵ m⁻³, and the particle drift velocity increases from approximately 0.05 to 0.3 m/s, leading to an increase in collection efficiency from about 55% to 93%. It is demonstrated that the proposed system ensures stable output voltage regulation within ±2.5–5% even under strongly nonlinear load conditions. The use of an LC output filter (C = 1–10 nF, L = 10–100 mH) reduces the voltage ripple from about 14% to 1.4–4.8% and significantly improves the transient response. In addition, adaptive adjustment of the pulse repetition frequency in the range of 10–200 kHz makes it possible to reduce energy consumption by 12–18% while simultaneously increasing the collection efficiency by 8–15%. The obtained results confirm that the proposed high-frequency power supply architecture provides a physically well-founded and energy-efficient solution for improving the environmental performance and operational stability of electrostatic precipitators. Full article