Longitudinal assessment of plasma biomarkers for early detection of cognitive changes in subjective cognitive decline.

Background: Individuals experiencing subjective cognitive decline (SCD) are at an increased risk of developing mild cognitive impairment and dementia. Early identification of SCD and neurodegenerative diseases using biomarkers may help clinical decision-making and improve prognosis. However, few cross-sectional and longitudinal studies have explored plasma biomarkers in individuals with SCD using immunomagnetic reduction. Objective: To identify plasma biomarkers for SCD. Methods: Fifty-two participants [38 with SCD, 14 healthy controls (HCs)] underwent baseline assessments, including measurements of plasma Aß42, Aß40, t-tau, p-tau, and α-synuclein using immunomagnetic reduction (IMR) assays, cognitive tests and the Mini-Mental State Examination (MMSE). Following initial cross-sectional analysis, 39 individuals (29 with SCD, 10 HCs) entered a longitudinal phase for reassessment of these biomarkers and the MMSE. Biomarker outcomes across different individual categories were primarily assessed using the area under the receiver operating characteristic (ROC) curve. The SCD subgroup with an MMSE decline over one point was compared to those without such a decline. Results: Higher baseline plasma Aß1-42 levels significantly discriminated participants with SCD from HCs, with an acceptable area under the ROC curve (AUC) of 67.5% [95% confidence interval (CI), 52.7-80.0%]. However, follow-up and changes in MMSE and IMR data did not significantly differ between the SCD and HC groups (p > 0.05). Furthermore, lower baseline plasma Aß1-42 levels were able to discriminate SCD subgroups with and without cognitive decline with a satisfied performance (AUC, 75.0%; 95% CI, 55.6-89.1%). At last, the changes in t-tau and Aß42 × t-tau could differentiate between the two SCD subgroups (p < 0.05). Conclusion: Baseline plasma Aß42 may help identify people with SCD and predict SCD progression. The role of plasma Aß42 levels as well as their upward trends from baseline in cases of SCD that progress to mild cognitive impairment and Alzheimer's disease require further investigation.

PLCß1 by-passes early growth response -1 to induce the differentiation of neuronal cells.

The Gαq/phospholipase C-ß (PLCß) signaling system mediates calcium responses to a variety of hormones and neurotransmitters. Recent studies suggest that PLCß1 expression plays a role in the differentiation of two types of cultured neuronal cells (PC12 and SK-N-SH) through a mechanism independent of Gαq. Here, we show that, similar to that observed in PC12 and SK-N-SH cells, PLCß1 expression increases when human NT2 cells are induced to differentiate either through cytosine-ß-D-arabinofuranoside or retinoic acid. Preventing this increase, abolishes differentiation, and down-regulating PLCß1 in rat primary astrocytes causes cells to adapt an undifferentiated morphology. Surprisingly, transfecting PLCß1 into undifferentiated PC12 or NT2 cells induces differentiation without the need for differentiating agents. Studies to uncover the underlying mechanism focused on the transcription factor early growth response 1 (Egr-1) which mediates PLCß1 expression early in differentiation. Over-expressing PLCß1 in HEK293 cells enhances Egr-1 expression and induces morphological changes. We show that increased levels of cytosolic PLCß1 in undifferentiated PC12 cells disrupts the association between Egr-1 and its cytosolic binding partner (Tar RNA binding protein), promoting relocalization of Egr-1 to the nucleus, which promotes transcription of proteins needed for differentiation. These studies show a novel mechanism through which differentiation can be modulated.

Analysis of the impact of temperature on the spectral shift in ultraviolet hyperspectral imaging spectrometers.

The imaging spectrometer's high performance in practical applications may be compromised by environmental factors, particularly temperature variations, posing a challenge to its stability. Temperature fluctuations can induce spectral shift, directly impacting the accuracy of spectral measurements, subsequently influencing the precision of radiometric measurements. To address this issue, this study investigates a dual-channel UV imaging spectrometer. This instrument boasts a wavelength calibration accuracy of 0.01 nm. This paper conducts an in-depth analysis of the various mechanisms through which temperature changes influence the spectral line offset in the imaging spectrometer, integrating actual orbital temperature data to discuss the instrument's temperature load settings. The impact of temperature on spectral shift is examined using finite element analysis and optical design software. Estimations of spectral shift were made based on temperature variations. Simulation results indicated that the maximum deviation of spectral shift is estimated at 0.018 nm under a temperature condition of 16 ± 1°C. Under a more controlled orbital temperature condition (16 ± 0.3°C), the maximum deviation of spectral shift decreased to 0.01 nm. Experimental data revealed that at 16 ± 1°C, the maximum deviation of spectral shift did not exceed 0.01 nm. This effectively corroborates our theoretical analysis. The relationship between temperature and spectral shift offers a crucial theoretical foundation for calibrating spectral measurements and managing the thermal conditions of the instrument.

Near-Infrared Dual Greenhouse Gas Sensor Based on Hollow-Core Photonic Crystal Fiber for Gas-Cell In-Situ Applications.

A greenhouse gas sensor has been developed to simultaneously detect multiple gas species within a hollow-core photonic bandgap fiber (HC-PBF) structure entirely composed of fibers. To enhance sensitivity, the gas cell consists of HC-PBF enclosed between two single-mode fibers fused with a reflective end surface to double the absorption length. The incorporation of side holes for gas diffusion allows for analysis of the relationship between gas diffusion speed, number of drilled side holes, and energy loss. As the number of drilled holes increases, the response time decreases to less than 3 min at the expense of energy loss. Gas experiments demonstrated detection limits of 0.1 ppm for methane and 2 ppm for carbon dioxide, with an average time of 50 s. In-situ testing conducted in rice fields validates the effectiveness of the developed gas detection system using HC-PBF cells, establishing all-fiber sensors with high sensitivity and rapid response.

The role of calcium in neuronal membrane tension and synaptic plasticity.

Calcium is a primary second messenger that plays a role in cellular functions including growth, movement and responses to drugs. The role that calcium plays in mediating communication between neurons by synaptic vesicle release is well established. This review focuses on the dependence of the physical properties of neuronal plasma membranes on calcium levels. After describing the key features of synaptic plasticity, we summarize the general role of calcium in cell function and the signaling pathways responsible for intracellular increase in calcium levels. We then present findings showing that increases in intracellular calcium levels cause neurites to contract and break synaptic connections by changes in membrane tension.

Impact assessment of spatial-temporal distribution of riverine dust on air quality using remote sensing data and numerical modeling.

Soil erosion is a severe problem in Taiwan due to the steep terrain, fragile geology, and extreme climatic events resulting from global warming. Due to the rapidly changing hydrological conditions affecting the locations and the amount of transported sand and fine particles, timely impact evaluation and riverine dust control are difficult, particularly when resources are limited. To comprehend the impact of desertification in estuarine areas on the variation of air pollutant concentrations, this study utilized remote sensing technology coupled with an air pollutant dispersion model to determine the unit contribution of potential pollution sources and quantify the effect of riverine dust on air quality. The images of the downstream area of the Beinan River basin captured by Formosat-2 in May 2006 were used to analyze land use and land cover (LULC) composition. Subsequently, the diffusion model ISCST-3 based on Gaussian distribution was utilized to simulate the transport of PM across the study area. Finally, a mixed-integer programming model was developed to optimize resource allocation for dust control. Results reveal that sand deposition in specific river sections significantly influences regional air quality, owing to the unique local topography and wind field conditions. The present optimal plan model for regional air quality control further showed that after implementing engineering measures including water cover, revegetation, armouring cover, and revegetation, total PM concentrations would be reduced by 51%. The contribution equivalent calculation, using the air pollution diffusion model, was effectively integrated into the optimization model to formulate a plan for reducing riverine dust with limited resources based on air quality requirements.

Photochemical oxidation of VOCs and their source impact assessment on ozone under de-weather conditions in Western Taiwan.

The application of statistical models has excellent potential to provide crucial information for mitigating the challenging issue of ozone (O3) pollution by capturing its associations with explanatory variables, including reactive precursors (VOCs and NOX) and meteorology. Considering the large contribution of O3 in degrading the air quality of western Taiwan, three-year (2019-2021) hourly concentration data of VOC, NOX and O3 from 4 monitoring stations of western Taiwan: Tucheng (TC), Zhongming (ZM), Taixi (TX) and Xiaogang (XG), was evaluated to identify the effect of anthropogenic emissions on O3 formation. Owing to the high-ambient reactivity of VOCs on the underestimation of sources, photochemical oxidation was assessed to calculate the consumed VOC (VOCcons) which was followed by the source identification of their initial concentrations. VOCcons was observed to be highest in the summer season (16.7 and 22.7 ppbC) at north (TC and ZM) and in the autumn season (17.8 and 11.4 ppbC) in southward-located stations (TX and XG, respectively). Results showed that VOCs from solvents (25-27%) were the major source at northward stations whereas VOCs-industrial emissions (30%) dominated in south. Furthermore, machine learning (ML): eXtreme Gradient Boost (XGBoost) model based de-weather analysis identified that meteorological factors favor to reduce ambient O3 levels at TC, ZM and XG stations (-67%, -47% and -21%, respectively) but they have a major role in accumulating the O3 (+38%) at the TX station which is primarily transported from the upwind region of south-central Taiwan. Crucial insights using ML outputs showed that the finding of the study can be utilized for region-specific data-driven control of emission from VOCs-sources and prioritized to limit the O3-pollution at the study location-ns as well as their accumulation in distant regions.

Characterization of spatial-temporal distribution and microenvironment source contribution of PM2.5 concentrations using a low-cost sensor network with artificial neural network/kriging techniques.

Low-cost sensors (LCS) network is widely used to improve the resolution of spatial-temporal distribution of air pollutant concentrations in urban areas. However, studies on air pollution sources contribution to the microenvironment, especially in industrial and mix-used housing areas, still need to be completed. This study investigated the spatial-temporal distribution and source contributions of PM2.5 in the urban area based on 6-month of the LCS network datasets. The Artificial Neural Network (ANN) was used to calibrate the measured PM2.5 by the LCS network. The calibrated PM2.5 were shown to agree with reference PM2.5 measured by the BAM-1020 with R2 of 0.85, MNE of 30.91%, and RMSE of 3.73 µg/m3, which meet the criteria for hotspot identification and personal exposure study purposes. The Kriging method was further used to establish the spatial-temporal distribution of PM2.5 concentrations in the urban area. Results showed that the highest average PM2.5 concentration occurred during autumn and winter due to monsoon and topographic effects. From a diurnal perspective, the highest level of PM2.5 concentration was observed during the daytime due to heavy traffic emissions and industrial production. Based on the present ANN-based microenvironment source contribution assessment model, temples, fried chicken shops, traffic emissions in shopping and residential zones, and industrial activities such as the mechanical manufacturing and precision metal machining were identified as the sources of PM2.5. The numerical algorithm coupled with the LCS network presented in this study is a practical framework for PM2.5 hotspots and source identification, aiding decision-makers in reducing atmospheric PM2.5 concentrations and formulating regional air pollution control strategies.

Quantitative analysis of SO2, NO2 and NO mixed gases based on ultraviolet absorption spectrum.

SO2, NO2 and NO are the main atmospheric pollutants produced by the combustion of fossil fuel. Detecting these gases is of great significance for atmospheric protection and the online concentration detection of pollutants. In this study, the concentration retrieval methods of NO, NO2 and SO2 and their mutual effects were studied in the wavelength range of 192.3-254.4 nm. In this band, NO, NO2 and SO2 have large absorption cross-sections; however, their spectrum superpositions were serious. A novel method was proposed to separate the superposed absorption spectra of NO and SO2 or NO2. The advantage of this method is that it can remove the influence of SO2 and NO2 on NO concentration retrieval. The fast Fourier transform (FFT) amplitude method was used to calculate the concentrations of SO2 and NO2, and the direct absorption spectroscopy method was used to calculate NO concentration. Via these methods, the gas concentrations of SO2, NO2 and NO can be calculated in ternary-gas mixtures. The experimental results show that these methods can effectively remove the mutual interferences between the concentration retrieval of NO, NO2 and SO2. The maximum absolute values of the relative deviations for the concentration retrieval of SO2, NO2 and NO in ternary-gas mixtures are 3.868%, 4.740% and 5.008%, respectively. These methods have high detection precision and good adaptability and are suitable for online flue detection equipment.

Design method for a small F-number two-material uniform dispersion immersion grating imaging spectrometer.

Immersion gratings have high dispersion efficiency and have important application value in miniaturized imaging spectrometers, but its serious dispersion nonlinearity causes difficulties in calibration and image processing, which limits its application range. To solve this, this paper presents a design method for a two-material linear dispersion immersion grating device design method, and a compact small F-number immersion grating spectrometer based on it. First the vector form dispersion equation of the two-material immersion grating is derived and the linear spectral dispersion immersion grating design process is given, then a compact small F-number uniform dispersion imaging spectrometer is given as a design example using the proposed method. The results show that when the operating band of the system is 1590-1675 nm, the spectral resolution is better than 0.25 nm, and F-number can achieve better than 2. Compared with traditional single-material immersion grating imaging spectrometer, the designed imaging spectrometer dispersion linearity is significantly improved. Finally, the influence of prism materials, structure parameters and grating parameters on dispersion nonlinearity is analyzed. Design and analysis results show that the proposed two-material immersion grating device has much better spectral dispersion nonlinearity correction ability, and its design method can provide reference to the compact spectrometer design based on immersion gratings.

A Spectral Encoding Simulator for Broadband Active Illumination and Reconstruction-Based Spectral Measurement.

Spectral reflectance or transmittance measurements provide intrinsic information on the material of an object and are widely used in remote sensing, agriculture, diagnostic medicine, etc. Most reconstruction-based spectral reflectance or transmittance measurement methods based on broadband active illumination use narrow-band LEDs or lamps combined with specific filters as spectral encoding light sources. These light sources cannot achieve the designed spectral encoding with a high resolution and accuracy due to their low degree of freedom for adjustment, leading to inaccurate spectral measurements. To address this issue, we designed a spectral encoding simulator for active illumination. The simulator is composed of a prismatic spectral imaging system and a digital micromirror device. The spectral wavelengths and intensity are adjusted by switching the micromirrors. We used it to simulate spectral encodings according to the spectral distribution on micromirrors and solved the DMD patterns corresponding to the spectral encodings with a convex optimization algorithm. To verify the applicability of the simulator for spectral measurements based on active illumination, we used it to numerically simulate existing spectral encodings. We also numerically simulated a high-resolution Gaussian random measurement encoding for compressed sensing and measured the spectral reflectance of one vegetation type and two minerals through numerical simulations. We reconstructed the spectral transmittance of a calibrated filter through an experiment. The results show that the simulator can measure the spectral reflectance or transmittance with a high resolution and accuracy.

Links between oral microbiome and insulin resistance: Involvement of MAP kinase signaling pathway.

Oral dysbiosis contributes to periodontitis and has implications for systemic diseases. Diabetes mellitus is a common metabolic disorder characterized by impaired glucose regulation. AMP-activated protein kinase (AMPK) plays a vital role in regulating glucose uptake and glycogenesis in the liver. This study aimed to investigate the association between periodontal bacteria and diabetes mellitus. A clinical trial was conducted to explore the association between oral bacteria and hyperglycemia. Additionally, we elucidated the molecular mechanisms by which periodontal bacteria cause insulin resistance. In the clinical trial, we discovered significant alterations in the expression levels of Fusobacterium nucleatum (Fn) and Tannerella forsythia (Tf) in patients with diabetes compared with healthy controls. Furthermore, Fn and Tf levels positively correlated with fasting blood glucose and glycated hemoglobin (HbA1C) levels. Moreover, we explored and elucidated the molecular mechanism by which Fusobacterium nucleatum culture filtrate (FNCF) induces cytokine release via the Toll-like receptor 2 (TLR2) signaling pathway in human gingival epithelial Smulow-Glickman (S-G) cells. This study investigated the effects of cytokines on insulin resistance pathways in liver cells. The use of an extracellular signal-regulated kinase (ERK) inhibitor (U0126) demonstrated that FNCF regulates the insulin receptor substrate 1 and protein kinase B (IRS1/AKT) signaling pathway, which affects key proteins involved in hepatic glycogen synthesis, including glycogen synthase kinase-3 beta (GSK3ß) and glycogen synthase (GS), ultimately leading to insulin resistance. These findings suggest that ERK plays a crucial role in hepatocyte insulin resistance.

Shared alterations in hippocampal structural covariance in subjective cognitive decline and migraine.

Introduction: Subjective cognitive decline (SCD) and migraine are often comorbid. Hippocampal structural abnormalities have been observed in individuals with both SCD and migraine. Given the known structural and functional heterogeneity along the long axis (anterior to posterior) of the hippocampus, we aimed to identify altered patterns of structural covariance within hippocampal subdivisions associated with SCD and migraine comorbidities. Methods: A seed-based structural covariance network analysis was applied to examine large-scale anatomical network changes of the anterior and posterior hippocampus in individuals with SCD, migraine and healthy controls. Conjunction analyses were used to identify shared network-level alterations in the hippocampal subdivisions in individuals with both SCD and migraine. Results: Altered structural covariance integrity of the anterior and posterior hippocampus was observed in the temporal, frontal, occipital, cingulate, precentral, and postcentral areas in individuals with SCD and migraine compared with healthy controls. Conjunction analysis revealed that, in both SCD and migraine, altered structural covariance integrity was shared between the anterior hippocampus and inferior temporal gyri and between the posterior hippocampus and precentral gyrus. Additionally, the structural covariance integrity of the posterior hippocampus-cerebellum axis was associated with the duration of SCD. Conclusion: This study highlighted the specific role of hippocampal subdivisions and specific structural covariance alterations within these subdivisions in the pathophysiology of SCD and migraine. These network-level changes in structural covariance may serve as potential imaging signatures for individuals who have both SCD and migraine.

Source-apportionment and spatial distribution analysis of VOCs and their role in ozone formation using machine learning in central-west Taiwan.

This study assessed the machine learning based sensitivity analysis coupled with source-apportionment of volatile organic carbons (VOCs) to look into new insights of O3 pollution in Yunlin County located in central-west region of Taiwan. One-year (Jan 1 to Dec 31, 2021) hourly mass concentrations data of 54 VOCs, NOX, and O3 from 10 photochemical assessment monitoring stations (PAMs) in and around the Yunlin County were analyzed. The novelty of the study lies in the utilization of artificial neural network (ANN) to evaluate the contribution of VOCs sources in O3 pollution in the region. Firstly, the station specific source-apportionment of VOCs were carried out using positive matrix factorization (PMF)-resolving six sources viz. AAM: aged air mass, CM: chemical manufacturing, IC: Industrial combustion, PP: petrochemical plants, SU: solvent use and VE: vehicular emissions. AAM, SU, and VE constituted cumulatively more than 65% of the total emission of VOCs across all 10 PAMs. Diurnal and spatial variability of source-segregated VOCs showed large variations across 10 PAMs, suggesting for distinctly different impact of contributing sources, photo-chemical reactivity, and/or dispersion due to land-sea breezes at the monitoring stations. Secondly, to understand the contribution of controllable factors governing the O3 pollution, the output of VOCs source-contributions from PMF model along with mass concentrations of NOX were standardized and first time used as input variables to ANN, a supervised machine learning algorithm. ANN analysis revealed following order of sensitivity in factors governing the O3 pollution: VOCs from IC > AAM > VE ≈ CM ≈ SU > PP ≈ NOX. The results indicated that VOCs associated with IC (VOCs-IC) being the most sensitive factor which need to be regulated more efficiently to quickly mitigate the O3 pollution across the Yunlin County.

Design of a Prism-Grating Wide Spectral Range Transmittance Imaging Spectrometer.

As spectroscopic detection technology rapidly advances, back-illuminated InGaAs detectors with a wider spectral range have emerged. Compared to traditional detectors such as HgCdTe, CCD, and CMOS, InGaAs detectors offer a working range of 400-1800 nm and exhibit a quantum efficiency of over 60% in both the visible and near-infrared bands. This is leading to the demand for innovative designs of imaging spectrometers with wider spectral ranges. However, the widening of the spectral range has led to the presence of significant axial chromatic aberration and secondary spectrum in imaging spectrometers. Additionally, there is difficulty in aligning the system optical axis perpendicular to the detector image plane, resulting in increased challenges during post-installation adjustment. Based on chromatic aberration correction theory, this paper presents the design of a wide spectral range transmission prism-grating imaging spectrometer with a working range of 400-1750 nm using Code V. The spectral range of this spectrometer covers both the visible and near-infrared regions, which is beyond the capability of traditional PG spectrometers. In the past, the working spectral range of transmission-type PG imaging spectrometers has been limited to 400-1000 nm. This study's proposed chromatic aberration correction process involves selecting optical glass materials that match the design requirements and correcting the axial chromatic aberration and secondary spectrum, ensuring that the system axis is perpendicular to the detector plane and easy to adjust during installation. The results show that the spectrometer has a spectral resolution of 5 nm, a root-mean-square spot diagram less than 8 µm over the full field of view, and an optical transfer function MTF greater than 0.6 at a Nyquist frequency of 30 lp/mm. The system size is less than 90 mm. Spherical lenses are employed in the system design to reduce manufacturing costs and complexity while meeting the requirements of wide spectral range, miniaturization, and easy installation.

Study on the concentration retrieval of SO2 and NO2 in mixed gases based on the improved DOAS method.

NO2 and SO2 are important components of air pollutants, and their absorption spectra are superimposed at 193-253 nm. The superposed spectra affect the gas concentration retrieval based on the ultraviolet differential optical absorption spectroscopy (DOAS) method. In this study, a suitable wavelength band was chosen for concentration retrieval, moreover, the characteristics of the quasi-periodic variation of absorption cross-section with wavelength was given sufficient attention and then the superposed spectra were separated by the Fast Fourier transform (FFT) method. The concentration of the gases to be measured was calculated according to the relationship between the amplitude of absorbance after FFT and the gas concentration. The experimental results prove that by using an absorption cell with a 700 mm optical path, the relative deviation absolute value of the retrieval concentration of SO2 in a NO2 and SO2 gas mixture is less than 1.471%, and that of NO2 in a NO2 and SO2 gas mixture is less than 7.207%. The method has good adaptability, high detection precision, whether single SO2, NO2 or a mixture of both, and important reference value for the development of DOAS and future research on the high-precision detection of more types of mixed gases in the ultraviolet band, such as gas mixtures of NO2, SO2 and NO in flue gas.

Development of a metabolomics-based data analysis approach for identifying drug metabolites based on high-resolution mass spectrometry.

A metabolomics-based approach to data analysis is required for drug metabolites to be identified quickly. This study developed such an approach based on high-resolution mass spectrometry. Our approach is a two-stage one that combines a time-course experiment with stable isotope tracing. Pioglitazone (PIO) was used to improve glycemic management for type 2 diabetes mellitus. Consequently, PIO was taken as a model drug for identifying metabolites. During Stage I of data analysis, 704 out of 26626 ions exhibited a positive relationship between ion abundance ratio and incubation time in a time-course experiment. During Stage II, 25 isotope pairs were identified among the 704 ions. Among these 25 ions, 18 exhibited a dose-response relationship. Finally, 14 of the 18 ions were verified to be PIO structure-related metabolite ions. Otherwise, orthogonal partial least squares-discriminant analysis (OPLS-DA) was adopted to mine PIO metabolite ions, and 10 PIO structure-related metabolite ions were identified. However, only four ions were identified by both our developed approach and OPLS-DA, indicating that differences in the designs of metabolomics-based approaches to data analysis can result in differences in which metabolites are identified. A total of 20 PIO structure-related metabolites were identified by our developed approach and OPLS-DA, and six metabolites were novel. The results demonstrated that our developed two-stage data analysis approach can be used to effectively mine data on PIO metabolite ions from a relatively complex matrix.

Solid Particle Swarm Measurement in Jet Fuel Based on Mie Scattering Theory and Extinction Method.

To overcome the disadvantages of small and random samples in static detection, this paper presents a study on dynamic measurements of solid particles in jet fuel using large samples. In this paper, the Mie scattering theory and Lambert-Beer law are used to analyze the scattering characteristics of copper particles in jet fuel. We have presented a prototype for multi-angle scattered and transmitted light intensity measurements of particle swarms in jet fuel which is used to test the scattering characteristics of the jet fuel mixture with 0.5-10 µm particle sizes and 0-1 mg/L concentrations of copper particles. The vortex flow rate was converted to an equivalent pipe flow rate using the equivalent flow method. Tests were conducted at equivalent flow rates of 187, 250 and 310 L/min. Through numerical calculations and experiments, it has been discovered that the intensity of the scattering signal decreases as the scattering angle increases. Meanwhile, both the scattered light intensity and transmitted light intensity would vary with the particle size and mass concentration. Finally, the relationship equation between light intensity and particle parameters has also been summarized in the prototype based on the experimental results, which proves its detection capability.

Chang impact analysis of level 3 COVID-19 alert on air pollution indicators using artificial neural network.

In this study, mean monthly and diurnal variations in fine particulate matters (PM2.5), nitrate, sulfate, and gaseous precursors were investigated during the Level 3 COVID-19 alert from May 19 to July 27 in 2021. For comparison, the historical data during the identical period in 2019 and 2020 were also provided to determine the effect of the Level 3 COVID-19 alert on aerosols and gaseous pollutants concentrations in Taichung City. A machine learning model using the artificial neural network technique coupled with a kinetic model was applied to predict NOx, O3, nitrate (NO3 -), and sulfate (SO4 2-) to investigate potential emission sources and chemical reaction mechanism. D during the Level 3 COVID-19 alert, a decrease in NOx concentration due to a decrease in traffic flow under the NOx-saturated regime was observed to enhance the secondary NO3 - and O3 formation. The present models were shown to predict 80.1, 77.0, 72.6, and 67.2% concentrations of NOx, O3, NO3 -, and SO4 2-, respectively, which could help decision-makers for pollutant emissions reduction policies development and air pollution control strategies. It is recommended that more long-term datasets, including water soluble inorganic salts (WIS), precursors including OH radicals, NH3, HNO3, and H2SO4, be provided by regulatory air quality monitoring stations to further improve the prediction model accuracy.

Analysis and Correction of Polarization Response Calibration Error of Limb Atmosphere Ultraviolet Hyperspectral Detector.

A UV hyperspectral instrument was designed with a polarization measurement channel for real-time in-orbit polarization correction to reduce the influence of polarization on the detection accuracy of atmospheric radiation. One of the prerequisites for in-orbit polarization calibration is accurately calibrating the instrument's polarization properties in the laboratory. This study first introduces the calibration method and measuring device of the polarization characteristics of the ultraviolet (UV) hyperspectral detector and conducts a polarization calibration test of the instrument. The two main error sources introduced by the calibration device were emphatically analyzed, and the correction method of the error sources was deduced theoretically. Finally, the polarization calibration results of the UV hyperspectral detector were corrected, and the uncertainty analysis of the corrected calibration results was about 1.4%, which provides effective ground polarization calibration data for the on-orbit polarization correction of the instrument.