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  • 1
    In: Atmosphere, MDPI AG, Vol. 6, No. 11 ( 2015-11-20), p. 1785-1800
    Type of Medium: Online Resource
    ISSN: 2073-4433
    Language: English
    Publisher: MDPI AG
    Publication Date: 2015
    detail.hit.zdb_id: 2605928-9
    SSG: 23
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  • 2
    In: Remote Sensing, MDPI AG, Vol. 12, No. 13 ( 2020-07-07), p. 2174-
    Abstract: The vertical distribution of aerosols is important for accurate surface PM2.5 retrieval and initial modeling forecasts of air pollution, but the observation of aerosol profiles on the regional scale is usually limited. Therefore, in this study, an approach to aerosol extinction profile fitting is proposed to improve surface PM2.5 retrieval from satellite observations. Owing to the high similarity of the single-peak extinction profile in the distribution pattern, the log-normal distribution is explored for the fitting model based on a decadal dataset (3248 in total) from Micro Pulse LiDAR (MPL) measurements. The logarithmic mean, standard deviation, and the height of peak extinction near-surface (Mode) are manually derived as the references for model construction. Considering the seasonal impacts on the planetary boundary layer height (PBLH), Mode, and the height of the surface layer, the extinction profile is then constructed in terms of the planetary boundary layer height (PBLH) and the total column aerosol optical depth (AOD). A comparison between fitted profiles and in situ measurements showed a high level of consistency in terms of the correlation coefficient (0.8973) and root-mean-square error (0.0415). The satellite AOD is subsequently applied for three-dimensional aerosol extinction, and the good agreement of the extinction coefficient with the PM2.5 within the surface layer indicates the good performance of the proposed fitting approach and the potential of satellite observations for providing accurate PM2.5 data on a regional scale.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2513863-7
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  • 3
    Online Resource
    Online Resource
    MDPI AG ; 2021
    In:  Remote Sensing Vol. 13, No. 8 ( 2021-04-16), p. 1544-
    In: Remote Sensing, MDPI AG, Vol. 13, No. 8 ( 2021-04-16), p. 1544-
    Abstract: Quantifying aerosol compositions (e.g., type, loading) from remotely sensed measurements by spaceborne, suborbital and ground-based platforms is a challenging task. In this study, the first and second-order spectral derivatives of aerosol optical depth (AOD) with respect to wavelength are explored to determine the partitions of the major components of aerosols based on the spectral dependence of their particle optical size and complex refractive index. With theoretical simulations from the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) model, AOD spectral derivatives are characterized for collective models of aerosol types, such as mineral dust (DS) particles, biomass-burning (BB) aerosols and anthropogenic pollutants (AP), as well as stretching out to the mixtures among them. Based on the intrinsic values from normalized spectral derivatives, referenced as the Normalized Derivative Aerosol Index (NDAI), a unique pattern is clearly exhibited for bounding the major aerosol components; in turn, fractions of the total AOD (fAOD) for major aerosol components can be extracted. The subtlety of this NDAI method is examined by using measurements of typical aerosol cases identified carefully by the ground-based Aerosol Robotic Network (AERONET) sun–sky spectroradiometer. The results may be highly practicable for quantifying fAOD among mixed-type aerosols by means of the normalized AOD spectral derivatives.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2513863-7
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  • 4
    In: Atmosphere, MDPI AG, Vol. 12, No. 8 ( 2021-08-03), p. 1000-
    Abstract: After almost thirty years’ efforts on studying transient luminous events (TLEs), ground-based observation has confirmed the TLE family, including elves, halos, sprites, and blue jets, etc. The typical elve has the shortest emission time ( 〈 1 ms) in comparison with other TLEs. The second shortest is the halo emission. Although elves and halos are supposed to be more frequent than sprites, ground campaigns still have less probability of recording their images due to their fleeting and short emission. Additionally, the submillisecond imaging of elves, halos, and sprite halos helps us resolve their electro-optic dynamics and morphological features, but few have been reported in the literature. Our study presents the 10,000 fps imaging frames on elves, halos and sprite halos, compares their similarity and disparity, and analyzes their parent lightning properties with associated VLF and ELF data.
    Type of Medium: Online Resource
    ISSN: 2073-4433
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2605928-9
    SSG: 23
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  • 5
    Online Resource
    Online Resource
    MDPI AG ; 2023
    In:  Atmosphere Vol. 14, No. 1 ( 2023-01-05), p. 121-
    In: Atmosphere, MDPI AG, Vol. 14, No. 1 ( 2023-01-05), p. 121-
    Abstract: The Hunga Tonga–Hunga Ha’apai underwater volcano (20.57° S, 175.38° W) violently erupted on 15 January 2022. The volcanic cloud’s top height and initial evolution are delineated by using the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC)-2 radio occultation (RO) measurements. The bending angle (BA) anomaly over the Tonga volcanic plume (within 200 km of the eruption center) at 5:17 UTC on 15 January showed a prominent peak at higher stratospheric heights. The top of the BA anomaly revealed that negative to positive change occurred at ~38 km, indicating the first height where the RO line-of-sight encountered the volcanic plume. The BA anomaly further revealed an increase of ~50% at ~36.1 km, and confirmed that the volcanic plume reached above ~36 km. Furthermore, the evolution of BA perturbations within 24 h after the initial explosion is also discussed herein. From collocated RO profiles with the volcanic plume, we can find a clear descent of the peak altitude of the BA perturbation from ~36.1 km to ~29 km within 24 h after the initial eruption. The results from this study will provide some insights into advancing our understanding of volcanic cloud dynamics and their implementation in volcanic plume modeling.
    Type of Medium: Online Resource
    ISSN: 2073-4433
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2605928-9
    SSG: 23
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  • 6
    In: Remote Sensing, MDPI AG, Vol. 12, No. 17 ( 2020-08-26), p. 2769-
    Abstract: East Asia is the most complex region in the world for aerosol studies, as it encounters a lot of varieties of aerosols, and aerosol classification can be a challenge in this region. In the present study, we focused on the relationship between aerosol types and aerosol optical properties. We analyzed the long-term (2005–2012) data of vertical profiles of aerosol extinction coefficients, lidar ratio (Sp), and other aerosol optical properties obtained from a NASA Micro-Pulse Lidar Network and Aerosol Robotic Network site in northern Taiwan, which frequently receives Asian continental outflows. Based on aerosol extinction vertical profiles, the profiles were classified into two types: type 1 (single-layer structure) and type 2 (two-layer structure). Fall season (October–November) was the prevailing season for the Type 1, whereas type 2 mainly happened in spring (March–April). In type 1, air masses normally originated from three regional sectors, i.e., Asia continental (AC), Pacific Ocean (PO), and Southeast Asia (SA). The mean Sp values were 39 ± 17 sr, 30 ± 12 sr, and 38 ± 18 sr for the AC, PO, and SA sectors, respectively. The Sp results suggested that aerosols from the AC sector contained dust and anthropogenic particles, and aerosols from the PO sector were most likely sea salts. We further combined the EPA dust event database and backward trajectory analysis for type 2. Results showed that Sp was 41 ± 14 sr and 53 ± 21 sr for dust storm and biomass-burning events, respectively. The Sp for biomass-burning events in type 2 showed two peaks patterns. The first peak occurred within range of 30–50 sr corresponding to urban pollutant, and the second peak occurred within range of 60–80 sr in relation to biomass burning. Finally, our study summarized the Sp values for four major aerosol types over northern Taiwan, viz., urban (42 ± 18 sr), dust (34 ± 6 sr), biomass-burning (69 ± 12 sr), and oceanic (30 ± 12 sr). Our findings provide useful references for aerosol classification and air pollution identification over the western North Pacific.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2513863-7
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  • 7
    In: Applied Sciences, MDPI AG, Vol. 12, No. 16 ( 2022-08-11), p. 8034-
    Abstract: To overcome the challenges brought about by abnormal weather and the growing industrial water consumption in Taiwan, the Taiwanese government is transporting water from the northern to the southern part of the country to help with droughts occurring in Taoyuan and Hsinchu. In addition, the government invested NTD 2.78 billion to build the backup water pipelines necessary in Taiyuan and Hsinchu, which help ensure a stable and safe water supply required for regional economic development. The construction adheres to the four major strategic goals of “open source, throttling, dispatch, and backup”. However, the leakage rate of water pipelines remains high. To help with large-scale right-of-way applications and the timeliness of emergency repairs, establishing a system that can detect the locations of leakages is vital. This study intended to apply artificial intelligence (AI) deep learning to develop a water pipe leakage and location identification system. This research established an intelligent sound-assisted water leak identification system, developed and used a localized AI water leak diagnostic instrument to capture on-site dynamic audio, and integrated Internet of Things (IoT) technology to simultaneously identify and locate the leakage. Actual excavation verification results show that the accuracy of the convolutional neural network (CNN) after training is greater than 95%, and the average absolute error calculated between the output data and the input data of the encoder is 0.1021, confirming that the system has high reliability and can reduce the cost of excavation by 26%.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2704225-X
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