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Ozone variation, excluding meteorological effects, is very important to assess the effects of air pollution control policies. In this study, the Kolmogorov-Zurbenko (KZ) filter method and multiple linear stepwise regression are combined to study the impact of meteorological parameters on ozone concentration over the past 5 years (2016–2020) in a petrochemical industrial city in northern China. Monte Carlo simulations were used to evaluate the reliability for the potential quasi quantitative prediction of the baseline component. The average level of the city and the details of five stations in the city were studied. The results show that the short-term, seasonal, and long-term component variances of maximum daily running average 8 h (MDA8) ozone in Zibo city (City) decomposed by the KZ filter account for 32.06%, 61.67% and 1.15% of the total variance, for a specific station, the values were 32.37%–34.90%, 56.64%–62.00%, and .35%–3.14%, respectively. The average long-term component increase rate is 3.19 μg m −3 yr −1 on average for the city, while it is 1.52–5.95 μg m −3 yr −1 for a specific station. The overall meteorological impact was not stable and fluctuated between −2.60 μg m −3 and +3.77 μg m −3 . This difference in trends between the city and specific stations implied that the O 3 precursor’s mitigation strategy should be more precise to improve its practical effects.

In this study, we developed an approach that integrated multiple patterns of timescale for box modeling (MCMv3.3.1) to better understand the O3–precursor relationship at multiple sites and through continuous observations. A 5-month field campaign was conducted in the summer of 2019 to investigate the ozone formation chemistry at three sites in a major prefecture-level city (Zibo) in Shandong Province of northern China. It was found that the relative incremental reactivity (RIR) of major precursor groups (e.g., anthropogenic volatile organic compounds (AVOCs), NOx) was overall consistent in terms of timescales changed from wider to narrower (four patterns: 5-month, monthly, weekly, and daily) at each site, though the magnitudes of RIR varied at different sites. The time series of the photochemical regime (using RIRNOx / RIRAVOC as an indicator) in weekly or daily patterns further showed a synchronous temporal trend among the three sites, while the magnitude of RIRNOx / RIRAVOC was site-to-site dependent. The derived RIR ranking (top 10) of individual AVOC species showed consistency between three patterns (i.e., 5-month, monthly, and weekly). It was further found that the campaign-averaging photochemical regimes showed overall consistency in the sign but non-negligible variability among the four patterns of timescale, which was mainly due to the embedded uncertainty in the model input dataset when averaging individual daily patterns into different timescales. This implies that utilizing narrower timescales (i.e., daily pattern) is useful for deriving reliable and robust O3–precursor relationships. Our results highlight the importance of quantifying the impact of different timescales to constrain the photochemical regime, which can formulate more accurate policy-relevant guidance for O3 pollution control.

In this study, we developed an approach that integrated multiple patterns of timescale for box modeling (MCMv3.3.1) to better understand the O.sub.3 -precursor relationship at multiple sites and through continuous observations. A 5-month field campaign was conducted in the summer of 2019 to investigate the ozone formation chemistry at three sites in a major prefecture-level city (Zibo) in Shandong Province of northern China. It was found that the relative incremental reactivity (RIR) of major precursor groups (e.g., anthropogenic volatile organic compounds (AVOCs), NO.sub.x) was overall consistent in terms of timescales changed from wider to narrower (four patterns: 5-month, monthly, weekly, and daily) at each site, though the magnitudes of RIR varied at different sites. The time series of the photochemical regime (using RIRNOx / RIR.sub.AVOC as an indicator) in weekly or daily patterns further showed a synchronous temporal trend among the three sites, while the magnitude of RIRNOx / RIR.sub.AVOC was site-to-site dependent. The derived RIR ranking (top 10) of individual AVOC species showed consistency between three patterns (i.e., 5-month, monthly, and weekly). It was further found that the campaign-averaging photochemical regimes showed overall consistency in the sign but non-negligible variability among the four patterns of timescale, which was mainly due to the embedded uncertainty in the model input dataset when averaging individual daily patterns into different timescales. This implies that utilizing narrower timescales (i.e., daily pattern) is useful for deriving reliable and robust O.sub.3 -precursor relationships. Our results highlight the importance of quantifying the impact of different timescales to constrain the photochemical regime, which can formulate more accurate policy-relevant guidance for O.sub.3 pollution control.

In this study, we developed an approach that integrated multiple patterns of timescale for box modeling (MCMv3.3.1) to better understand the O3–precursor relationship at multiple sites and through continuous observations. A 5-month field campaign was conducted in the summer of 2019 to investigate the ozone formation chemistry at three sites in a major prefecture-level city (Zibo) in Shandong Province of northern China. It was found that the relative incremental reactivity (RIR) of major precursor groups (e.g., anthropogenic volatile organic compounds (AVOCs), NOx) was overall consistent in terms of timescales changed from wider to narrower (four patterns: 5-month, monthly, weekly, and daily) at each site, though the magnitudes of RIR varied at different sites. The time series of the photochemical regime (using RIRNOx / RIRAVOC as an indicator) in weekly or daily patterns further showed a synchronous temporal trend among the three sites, while the magnitude of RIRNOx / RIRAVOC was site-to-site dependent. The derived RIR ranking (top 10) of individual AVOC species showed consistency between three patterns (i.e., 5-month, monthly, and weekly). It was further found that the campaign-averaging photochemical regimes showed overall consistency in the sign but non-negligible variability among the four patterns of timescale, which was mainly due to the embedded uncertainty in the model input dataset when averaging individual daily patterns into different timescales. This implies that utilizing narrower timescales (i.e., daily pattern) is useful for deriving reliable and robust O3–precursor relationships. Our results highlight the importance of quantifying the impact of different timescales to constrain the photochemical regime, which can formulate more accurate policy-relevant guidance for O3 pollution control.

Isoprene (C 5 H 8 ) is the non-methane hydrocarbon with the highest emissions to the atmosphere. It is mainly produced by vegetation, especially broad-leaved trees, and efficiently transported to the upper troposphere in deep convective clouds, where it is mixed with lightning NO x . Isoprene oxidation products drive rapid formation and growth of new particles in the tropical upper troposphere. However, isoprene oxidation pathways at low temperatures are not well understood. Here, in experiments at the CERN CLOUD chamber at 223 K and 243 K, we find that isoprene oxygenated organic molecules (IP-OOM) all involve two successive OH ∙ oxidations. However, depending on the ambient concentrations of the termination radicals ( HO 2 ∙ , NO ∙ , and NO 2 ∙ ), vastly-different IP-OOM emerge, comprising compounds with zero, one or two nitrogen atoms. Our findings indicate high IP-OOM production rates for the tropical upper troposphere, mainly resulting in nitrate IP-OOM but with an increasing non-nitrate fraction around midday, in close agreement with aircraft observations. Experiments under upper-tropospheric conditions map the chemical formation of isoprene oxygenated organic molecules (important molecules for new particle formation) and reveal that relative radical ratios control their composition

During summer, ammonia emissions in Southeast Asia influence air pollution and cloud formation. Convective transport by the South Asian monsoon carries these pollutant air masses into the upper troposphere and lower stratosphere (UTLS), where they accumulate under anticyclonic flow conditions. This air mass accumulation is thought to contribute to particle formation and the development of the Asian Tropopause Aerosol Layer (ATAL). Despite the known influence of ammonia and particulate ammonium on air pollution, a comprehensive understanding of the ATAL is lacking. In this modelling study, the influence of ammonia on particle formation is assessed with emphasis on the ATAL. We use the EMAC chemistry-climate model, incorporating new particle formation parameterisations derived from experiments at the CERN CLOUD chamber. Our diurnal cycle analysis confirms that new particle formation mainly occurs during daylight, with a 10-fold enhancement in rate. This increase is prominent in the South Asian monsoon UTLS, where deep convection introduces high ammonia levels from the boundary layer, compared to a baseline scenario without ammonia. Our model simulations reveal that this ammonia-driven particle formation and growth contributes to an increase of up to 80% in cloud condensation nuclei (CCN) concentrations at cloud-forming heights in the South Asian monsoon region. We find that ammonia profoundly influences the aerosol mass and composition in the ATAL through particle growth, as indicated by an order of magnitude increase in nitrate levels linked to ammonia emissions. However, the effect of ammonia-driven new particle formation on aerosol mass in the ATAL is relatively small. Ammonia emissions enhance the regional aerosol optical depth (AOD) for shortwave solar radiation by up to 70%. We conclude that ammonia has a pronounced effect on the ATAL development, composition, the regional AOD, and CCN concentrations.

To address the challenge of user behavior prediction on artificial intelligence (AI)-based online education platforms, this study proposes a novel ensemble model. The model combines the strengths of Convolutional Neural Network (CNN), Multi-Head Attention Mechanism (MHAM), Weighted Random Forest (WRF), and Back Propagation Neural Network (BPNN), forming an integrated architecture that enhances WRF and BPNN with CNN and MHAM. Experimental results demonstrate that the improved BPNN model, when combined with WRF, outperforms individual models in predicting user behavior. Specifically, the integrated model achieves a prediction accuracy of 92.3% on the test dataset—approximately 5% higher than that of the traditional BPNN. For imbalanced datasets, it attains a recall rate of 89.7%, significantly surpassing the unweighted random forest’s 82.4%. The model also achieves an F1-score of 90.8%, reflecting strong overall performance in terms of both precision and recall. Overall, the proposed method effectively leverages the classification capabilities of WRF and the nonlinear fitting power of BPNN, substantially enhancing the accuracy and reliability of user behavior prediction, and offering valuable support for optimizing AI-driven online education platforms.

Cerebrovascular hemodynamics are believed to play an important role in the development of ischemic stroke (IS). However, the relationships between hemodynamics and prognosis are not fully understood. Four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) enables comprehensive characteristics of cerebrovascular hemodynamics. This study aims to investigate the associations of the different hemodynamics derived from 4D flow CMR with IS functional outcomes. Ninety-one patients (median age 64 years, 62 males) with unilateral IS in middle cerebral artery (MCA) territory were included. All subjects underwent a CMR scan, including 4D flow, three-dimensional (3D) time-of-flight magnetic resonance angiography, and 3D whole brain black-blood high-resolution vessel wall imaging of the MCA. Six hemodynamic parameters, including flow rate, velocity, pulsatility index, time-averaged wall shear stress (TAWSS), oscillatory shear index, and relative residence time (RRT), were calculated for the lesion site, pre-bifurcation M1 (pM1) segment, and the distal M1 and/or first branches of M2 (dM1/M2) segments. Vessel characteristics, such as lumen area, vessel area, wall area, maximum wall thickness, and the degree of stenosis, were calculated at the most stenotic lesion site. The modified Rankin Scale (mRS) scores were assessed at 90 days and 1 year, and an mRS >2 was considered as a poor functional outcome. Lower segment-level TAWSS (odds ratio [OR]: 0.24, P = 0.006 and OR: 0.29, P = 0.014), higher RRT (OR: 2.74, P = 0.007 and OR: 2.40, P = 0.011) of dM1/M2 segments, and lower segment- and lesion-level velocity (OR: 0.40, P = 0.019 and OR: 0.41, P = 0.025; OR: 0.41, P = 0.030 and OR: 0.42, P = 0.040) of pM1 segment were observed to be associated with poor functional outcome at both 90 days and 1 year. Using the cut-off value of 3.58 Pa and 0.29, respectively, TAWSS and RRT of dM1/M2 segments showed moderate performance in distinguishing poor functional outcome from favorable outcome (area under the curve ranging from 0.642–0.687) both at 90 days and 1 year. Distal segmental TAWSS and RRT of dM1/M2 segments were associated with poor functional outcomes. Such alterations in hemodynamics might help in the identification of patients with potentially unfavorable prognosis. [Display omitted]

We demonstrate a triple-wavelength thulium-doped fiber random laser using a 10 cm long random fiber grating to provide random distributed feedback and a superimposed fiber Bragg grating as the wavelength-selective mirror. The random fiber grating inscribed in single-mode fibers using a femtosecond laser provides strong random distributed feedback that avoids the use of long distance fibers and leads to a relatively low threshold power. Triple-wavelength random laser output at wavelengths of 1943.6, 1945.0 and 1946.3 nm was achieved with a relatively low threshold power of 2.01 W, a slope efficiency of 7.86% and a maximum output power of 151.8 mW when it was pumped using a 793 nm laser diode. The 3 dB linewidth was less than 0.1 nm and the optical signal-to-noise ratio was up to 45.6 dB. Good wavelength stability was achieved, which was attributed to the narrow band and stable reflection of the superimposed fiber Bragg grating. The time-domain characteristics of the laser output were also measured and analyzed, and some random self-pulsing caused by relaxation oscillations were observed.

Aiming at the issues of heavy weight and insufficient structural performance of optical instrument supporting structures in extremely large telescopes, the Wide-Field Optical Spectrograph (WFOS) of the Thirty Meter Telescope (TMT) was taken as a case to study. In order to develop lightweight structures which satisfies the design requirements for mass and stiffness, a design scheme of cylindrical composite shells supporting structure was proposed and their finite element models were developed. A size optimisation and a ply sequence optimisation of the composite structure were carried out. The structures before and after optimisation were evaluated from the aspects of mass, displacement, failure index and fundamental frequency. After the optimised design, the mass of the optimised WFOS cylindrical composite shell structure is reduced to approximately 50%, but its maximum displacement (0.513mm) and fundamental frequency (8.275 Hz) are nearly unchanged. The study indicates that a cylindrical composite shell structure is an efficient structural form for large optical instruments.