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Atmosphere, Volume 12, Issue 8 (August 2021) – 155 articles

Cover Story (view full-size image): Are atmospheric composition changes due to COVID-19 restrictions visible in the Alps? What pollutant emission sources are most affected by the lockdown? How large is the influence of meteorology? Based on multi-technique measurements at the surface and observations along the vertical column throughout 2020, we quantify the “lockdown effect” in an Alpine valley. Variations due to reduced emissions, partly offset by the influence of meteorology and pollutant transport, are assessed based on source apportionment techniques, machine-learning methods, and chemical transport models. View this paper.
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17 pages, 9681 KiB  
Article
Analysis of the Precipitable Water Vapor Observation in Yunnan–Guizhou Plateau during the Convective Weather System in Summer
by Heng Hu, Yunchang Cao, Chuang Shi, Yong Lei, Hao Wen, Hong Liang, Manhong Tu, Xiaomin Wan, Haishen Wang, Jingshu Liang and Panpan Zhao
Atmosphere 2021, 12(8), 1085; https://doi.org/10.3390/atmos12081085 - 23 Aug 2021
Cited by 3 | Viewed by 2597
Abstract
The ERA5 reanalysis dataset of the European Center for Medium-Range Weather Forecasts (ECMWF) in the summers from 2015 to 2020 was used to compare and analyze the features of the precipitable water vapor (PWV) observed by six ground-based Global Navigation Satellite System (GNSS) [...] Read more.
The ERA5 reanalysis dataset of the European Center for Medium-Range Weather Forecasts (ECMWF) in the summers from 2015 to 2020 was used to compare and analyze the features of the precipitable water vapor (PWV) observed by six ground-based Global Navigation Satellite System (GNSS) meteorology (GNSS/MET) stations in the Yunnan–Guizhou Plateau. The correlation coefficients of the two datasets ranged between 0.804 and 0.878, the standard deviations ranged between 4.686 and 7.338 mm, and the monthly average deviations ranged between −4.153 and 9.459 mm, which increased with the altitude of the station. Matching the quality-controlled ground precipitation data with the PWV in time and space revealed that most precipitation occurred when the PWV was between 30 and 65 mm and roughly met the normal distribution. We used the vertical integral of divergence of moisture flux (∇p) and S-band Doppler radar networking products combined with the PWV to study the convergence and divergence process and the water vapor delivery conditions during the deep convective weather process from August 24 to 26, 2020, which can be used to analyze the real-time observation capability and continuity of PWV in small-scale and mesoscale weather processes. Furthermore, the 1 h precipitation and the cloud top temperature (ctt) data at the same site were used to demonstrate the effect of PWV on the transit of convective weather systems from different time–space scales. Full article
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Figure 1
<p>Distribution of GNSS/MET stations in Yunnan–Guizhou Plateau (black dots: GNSS/MET stations; blue star: GNSS/MET stations matching the ERA5, the precipitation, and the CTT).</p>
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<p>PWV, TCWV, and altitude of 6 stations (box plot: PWV and TCWV; histogram: station altitude; green line: mean value; error bars: standard deviation).</p>
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<p>Scattered distributions and linear fitting results of TCWV and PWV of 6 stations (color scatter: scatter distribution of two kinds of data at each station; colored straight line: linear fitting of PWV (cyan: MNLA; yellow: MNZI; blue: TNCH; black: BEKM; green: BFDI; red: BFLJ).</p>
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<p>Monthly mean deviation of TCWV and PWV for 6 stations (cyan: MNLA; yellow: MNZI; blue: TNCH; black: BEKM; green: BFDI; red: BFLJ).</p>
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<p>Scattered distribution of the PWV and 1 h precipitation of 6 stations (cyan: MNLA; yellow: MNZI; blue: TNCH; black: BEKM; green: BFDI; red: BFLJ).</p>
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<p>Doppler radar network diagram in China at 12:00 on 24 August 2020 (UTC) (color scale represents reflectance factor, unit: dBz; black box: Yunnan–Guizhou Plateau).</p>
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<p>The combination of <math display="inline"><semantics> <mrow> <msub> <mo>∇</mo> <mi>p</mi> </msub> </mrow> </semantics></math> and 850 hPa wind field in Yunnan–Guizhou Plateau from 06:00 (UTC) to 12:00 on 24 August 2020 (the isosurface represents the <math display="inline"><semantics> <mrow> <msub> <mo>∇</mo> <mi>p</mi> </msub> </mrow> </semantics></math>, unit: kg m<sup>−2</sup> s<sup>−1</sup>. The direction of the arrow represents the wind direction, and the length represents the wind speed. (<b>a</b>–<b>d</b>) represent the results of 2 h interval).</p>
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<p>The distribution of PWV from 06:00 (UTC) to 12:00 on 24 August 2020 in Yunnan–Guizhou Plateau (the color scale of the contour represents the PWV, unit: mm. (<b>a</b>–<b>d</b>) represent the results of 2 h interval).</p>
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<p>Precipitation at stations in Yunnan–Guizhou Plateau from 06:00 (UTC) to 12:00 on 24 August 2020 (scattered points represent stations with precipitation greater than, and color scales represent the amount of precipitation. (<b>a</b>–<b>d</b>) represent the results of 2 h interval).</p>
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<p>The combination of ∇<span class="html-italic"><sub>p</sub></span> and 850 hPa wind field in Yunnan–Guizhou Plateau from 17:00 on 24 August 2020 (UTC) to 18:00 on 25 August 2020 (isosurface represents ∇<span class="html-italic"><sub>p</sub></span>, unit: kg m<sup>−2</sup> s<sup>−1</sup>; the direction of the arrow represents the wind direction, and the length represents the wind speed. (<b>a</b>–<b>f</b>) represent the results of 5 h interval).</p>
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<p>Distribution of PWV in Yunnan–Guizhou Plateau from 17:00 on 24 August (UTC) to 18:00 on 25 August 2020 (the contour color scale represents the PWV, unit: mm. (<b>a</b>–<b>f</b>) represent the results of 5 h interval).</p>
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<p>Precipitation at stations in Yunnan–Guizhou Plateau from 17:00 on 24 August (UTC) to 18:00 on 25 August 2020 (scattered points represent stations with precipitation greater than, and color scales represent the amount of precipitation. (<b>a</b>–<b>f</b>) represent the results of 5 h interval).</p>
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<p>The precipitation and the PWV at stations from 22 August to 28 August 2020 (blue curve: the PWV, unit: mm; green column: the precipitation, unit mm. (<b>a</b>) BFDI, (<b>b</b>) BFLJ, (<b>c</b>) MNLA, (<b>d</b>) MNZI).</p>
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<p>The ctt and the PWV of stations from 07:00 to 13:00 on 24 August 2020 (blue curve: the ctt, unit: k; red scattered points: the PWV, unit mm. (<b>a</b>) BFDI, (<b>b</b>) BFLJ, (<b>c</b>) MNLA, (<b>d</b>) MNZI).</p>
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21 pages, 6129 KiB  
Article
Assessment of Outdoor Thermal Comfort in Serbia’s Urban Environments during Different Seasons
by Milica Lukić, Dejan Filipović, Milica Pecelj, Ljiljana Crnogorac, Bogdan Lukić, Lazar Divjak, Ana Lukić and Ana Vučićević
Atmosphere 2021, 12(8), 1084; https://doi.org/10.3390/atmos12081084 - 23 Aug 2021
Cited by 20 | Viewed by 4230
Abstract
The urban microclimate is gradually changing due to climate change, extreme weather conditions, urbanization, and the heat island effect. In such an altered environment, outdoor thermal comfort can have a strong impact on public health and quality of life in urban areas. In [...] Read more.
The urban microclimate is gradually changing due to climate change, extreme weather conditions, urbanization, and the heat island effect. In such an altered environment, outdoor thermal comfort can have a strong impact on public health and quality of life in urban areas. In this study, three main urban areas in Serbia were selected: Belgrade (Central Serbia), Novi Sad (Northern Serbia), and Niš (Southern Serbia). The focus was on the temporal assessment of OTC, using the UTCI over a period of 20 years (1999–2018) during different seasons. The main aim is the general estimation of the OTC of Belgrade, Novi Sad, and Niš, in order to gain better insight into the bioclimatic condition, current trends and anomalies that have occurred. The analysis was conducted based on an hourly (7 h, 14 h, and 21 h CET) and “day by day” meteorological data set. Findings show the presence of a growing trend in seasonal UTCI anomalies, especially during summer and spring. In addition, there is a notable increase in the number of days above the defined UTCI thresholds for each season. Average annual UTCIs values also show a positive, rising trend, ranging from 0.50 °C to 1.33 °C. The most significant deviations from the average UTCI values, both seasonal and annual, were recorded in 2000, 2007, 2012, 2015, 2017, and 2018. Full article
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<p>(<b>a</b>) Locations of the studied areas: (1) Belgrade, (2) Novi Sad and (3) Niš; (<b>b</b>) Geographical location of Serbia in Europe <a href="#atmosphere-12-01084-f001" class="html-fig">Figure 1</a>a,b were created using QGIS 3.8 software by OSGeo (Beaverton, OR, USA), based on the Eurostat’ official data sets (<a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/" target="_blank">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/</a> (accessed on 21 May 2021)). Map ratio: 1:1,900,000; projection: WGS 84/UTM, Zone 34N. Altitude structure data of Serbia was obtained by classification of the Digital Elevation Model (DEM) (<a href="https://www.opendem.info/" target="_blank">https://www.opendem.info/</a> (accessed on 22 May 2021)).</p>
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<p>Frequencies of different cold and heat stress category in (<b>a</b>) Belgrade; (<b>b</b>) Novi Sad, and (<b>c</b>) Niš based on UTCI<sub>07h</sub> (first column), UTCI<sub>14h</sub> (second column), UTCI<sub>21h</sub> (third column) during spring, over a period of 20 years (1999–2018).</p>
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<p>Frequencies of different cold and heat stress category in (<b>a</b>) Belgrade; (<b>b</b>) Novi Sad; and (<b>c</b>) Niš based on UTCI<sub>07h</sub> (first column), UTCI<sub>14h</sub> (second column), UTCI<sub>21h</sub> (third column) during summer, over a period of 20 years (1999–2018).</p>
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<p>Frequencies of different cold and heat stress category in (<b>a</b>) Belgrade; (<b>b</b>) Novi Sad and (<b>c</b>) Niš based on UTCI<sub>07h</sub> (first column), UTCI<sub>14h</sub> (second column), UTCI<sub>21h</sub> (third column) during autumn, over a period of 20 years (1999–2018).</p>
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<p>Frequencies of different cold and heat stress category in (<b>a</b>) Belgrade; (<b>b</b>) Novi Sad; and (<b>c</b>) Niš based on UTCI<sub>07h</sub> (first column), UTCI<sub>14h</sub> (second column), UTCI<sub>21h</sub> (third column) during winter, over a period of 20 years (1999–2018).</p>
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<p>Average annual UTCI at 7 h, 14 h and 21 h CET during the period 1999–2018. Blue indicates UTCI values and trends for Belgrade, red is used for Novi Sad, and green for Niš.</p>
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16 pages, 6109 KiB  
Article
Propagation of a Meteotsunami from the Yellow Sea to the Korea Strait in April 2019
by Kyungman Kwon, Byoung-Ju Choi, Sung-Gwan Myoung and Han-Seul Sim
Atmosphere 2021, 12(8), 1083; https://doi.org/10.3390/atmos12081083 - 23 Aug 2021
Cited by 6 | Viewed by 2980
Abstract
A meteotsunami with a wave height of 0.1–0.9 m and a period of 60 min was observed at tide gauges along the Korea Strait on 7 April 2019, while a train of two to four atmospheric pressure disturbances with disturbance heights of 1.5–3.9 [...] Read more.
A meteotsunami with a wave height of 0.1–0.9 m and a period of 60 min was observed at tide gauges along the Korea Strait on 7 April 2019, while a train of two to four atmospheric pressure disturbances with disturbance heights of 1.5–3.9 hPa moved eastward from the Yellow Sea to the Korea Strait. Analysis of observational data indicated that isobar lines of the atmospheric pressure disturbances had angles of 75–83° counterclockwise due east and propagated with a velocity of 26.5–31.0 m/s. The generation and propagation process of the meteotsunami was investigated using the Regional Ocean Modeling System. The long ocean waves were amplified due to Proudman resonance in the southwestern Yellow Sea, where the water is deeper than 75 m; here, the long ocean waves were refracted toward the coast on the shallow coastal region of the northern Korea Strait. Refraction and reflection by offshore islands significantly affect the wave heights at the coast. To investigate the effects of an eastward-moving velocity and angle of atmospheric pressure disturbance on the height of a long ocean wave, sensitivity simulations were performed. This result will be useful for the real-time prediction system of meteotsunamis in the Korea Strait. Full article
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<p>(<b>a</b>) The red dashed rectangle represents the study area covering the Korea Strait between the Yellow Sea, East China Sea, and East Sea. (<b>b</b>) Observation stations for atmospheric pressure (red) and sea level (yellow) in the Korea Strait. CJ, SGP, GM, GH, YS, GJ, GD, MS, SSB, and HKT stand for Chujakdo, Seogwipo, Geomundo, Goheung, Yeosu, Geojedo, Gadeokdo, Masan, Sasebo, and Hakata, respectively. Contours represent the bottom topography (m). Jeju Island and Tsushima Island are located at 126.8° E, 33.4° N and 129.5° E, 34.5° N, respectively, in the Korea Strait. MS is located in Jinhae-Masan Bay (128.4–128.8° E, 34.9–35.3° N).</p>
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<p>Rainfall intensity (mm/h) estimated from rainfall radar at (<b>a</b>) 15:00, (<b>b</b>) 16:30, (<b>c</b>) 18:00, and (<b>d</b>) 19:30 on 7 April 2019.</p>
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<p>Variations of (<b>a</b>) atmospheric pressure (hPa) and (<b>b</b>) sea levels (cm), which were high-pass-filtered with a cutoff period of 180 min. (<b>c</b>) Arrival times (hour:minute) of atmospheric pressure disturbances and (<b>d</b>) the long ocean waves (meteotsunami) along the Korea Strait on 7 April 2019. The contour intervals are 30 min. The dashed green contour lines in (<b>c</b>,<b>d</b>) are the extrapolated locations of atmospheric pressure disturbance and long ocean wave, respectively, based on the patterns of nearby observation data.</p>
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<p>Wavelet spectrum of (<b>a</b>) atmospheric pressure and (<b>b</b>) sea level time series at the SGP, GH, and MS stations from 12:00 on 7 April to 12:00 on 8 April 2019.</p>
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<p>Eastward propagation of atmospheric pressure disturbances with a moving speed (<math display="inline"><semantics> <mi mathvariant="normal">U</mi> </semantics></math>) and angle (θ) at 13:00 on 7 April 2019. The angle of a moving isobar was measured counterclockwise due east. The arrow represents the moving direction of the atmospheric pressure disturbances. The waves under the arrow represent a schematic sinusoidal form of the atmospheric pressure disturbances.</p>
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<p>Comparison of (<b>a</b>) atmospheric pressure anomalies and (<b>b</b>) sea level variations from observations and numerical simulations at SGP, GH, and MS from 12:00 on 7 April to 06:00 on 8 April 2019.</p>
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<p>Spatial distribution of modeled sea level elevation (colors) in the Yellow Sea and Korea Strait every 1.5 h from 11:00 to 23:00 on 7 April 2019. Colors represent sea level anomalies. Black straight lines represent 1024 hPa isobars in atmospheric pressure that propagate eastward (<a href="#atmosphere-12-01083-f005" class="html-fig">Figure 5</a>).</p>
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<p>Spatial distributions of bottom depth (m) for (<b>a</b>) the control (CON) experiment and (<b>b</b>) no offshore islands (NOI) experiment. The contour intervals of the isobaths are 50 m.</p>
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<p>Spatial distributions of surface elevation every 15 min from 16:15 to 17:15 on 7 April 2019. The upper and lower panels are (<b>a</b>) surface elevation from the original topography (CON) and (<b>b</b>) no offshore island (NOI) models, respectively. The contour intervals of surface elevation are 5 cm.</p>
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<p>Sea level variations from the observation (black line), original topography model (CON; red line), and no offshore island model (NOI; blue dotted line) at GJ, GD, and MS from 15:00 on 7 April to 06:00 on 8 April 2019.</p>
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<p>Enlarged topographic maps of the (<b>a</b>) SGP, (<b>b</b>) GH, and (<b>c</b>) MS tide observation stations. Colors represent bottom depths in meter. The bottom topography is contoured at depths of 10, 20, 50 and 100 m.</p>
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<p>Expected amplitudes (cm) of the long ocean waves at (<b>a</b>) SGP, (<b>b</b>) GH, and (<b>c</b>) MS in the Korea Strait if an atmospheric pressure disturbance propagated eastward with various isobar angles (θ in <a href="#atmosphere-12-01083-f005" class="html-fig">Figure 5</a>) and traveling speeds (<math display="inline"><semantics> <mi mathvariant="normal">U</mi> </semantics></math>) from 20 to 40 m/s.</p>
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15 pages, 2921 KiB  
Article
Early-Warning Signals of Drought-Flood State Transition over the Dongting Lake Basin Based on the Critical Slowing Down Theory
by Hao Wu, Wei Hou, Dongdong Zuo, Pengcheng Yan and Yuxing Zeng
Atmosphere 2021, 12(8), 1082; https://doi.org/10.3390/atmos12081082 - 23 Aug 2021
Cited by 6 | Viewed by 2532
Abstract
In this study, the standardized precipitation index (SPI) data in Hunan Province from 1961 to 2020 is adopted. Based on the critical slowing down theory, the moving t-test is firstly used to determine the time of drought-flood state transition in the Dongting [...] Read more.
In this study, the standardized precipitation index (SPI) data in Hunan Province from 1961 to 2020 is adopted. Based on the critical slowing down theory, the moving t-test is firstly used to determine the time of drought-flood state transition in the Dongting Lake basin. Afterwards, by means of the variance and autocorrelation coefficient that characterize the phenomenon of critical slowing down, the early-warning signals indicating the drought-flood state in the Dongting Lake basin are explored. The results show that an obvious drought-to-flood (flood-to-drought) event occurred around 1993 (2003) in the Dongting Lake basin in recent 60 years. The critical slowing down phenomena of the increases in the variance and autocorrelation coefficient, which are detected 5–10 years in advance, can be considered as early-warning signals indicating the drought-flood state transition. Through the studies on the drought-flood state and related early-warning signals for the Dongting Lake basin, the reliabilities of the variance and autocorrelation coefficient-based early-warning signals for abrupt changes are demonstrated. It is expected that the wide application of this method could provide important scientific and technological support for disaster prevention and mitigation in the Dongting Lake basin, and even in the middle and lower reaches of the Yangtze River. Full article
(This article belongs to the Special Issue Hydrological Responses under Climate Changes)
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<p>We calculated the variance and autocorrelation coefficient by sliding the window. (<b>a</b>) We calculated variance by sliding the window, L1, L2, L3, …, Ln denote windows of the same length(ML), S1, S2, S3, …, Sn represent the variances of the corresponding windows,L is the total length of the sequence, and MT is the sliding step; (<b>b</b>) we calculated the autocorrelation coefficient by sliding the window, L1(L12), L2(L22), L3(L32), …, Ln(Ln2)represent windows of the same length, α1 denotes the autocorrelation coefficients of L1 and the L12, α2 denotes the autocorrelation coefficient of the L2, while L22, the αn refer to the autocorrelation coefficients of the Ln and Ln2, LT represents the lag time, and L, ML and MT have the same meaning as those in (<b>a</b>).</p>
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<p>Temporal variation of the SPI sequence over the Dongting Lake basin in recent 60 years. The bar represents the SPI value (red bar indicates positive SPI and blue bar indicates negative SPI), and the black curve represents the trend extracted from 51month moving average.</p>
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<p>Detection of the abrupt change in the SPI sequence over the Dongting Lake basin based on the moving <span class="html-italic">t</span>-test (MTT) method with the size of the sliding window being. (<b>a</b>) 5 years (60 months) and (<b>b</b>) 10 years (120 months), respectively.</p>
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<p>Spatial distributions of the SPI values in different decades over the Dongting Lake basin.</p>
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<p>500-hPa geopotential height field (black solid line, unit: gpm) and the composite anomaly field (shading, unit: gpm) at the stages of (<b>a</b>) 1961–1992, (<b>b</b>) 1993–2002 and (<b>c</b>) 2003–2020 over the Dongting Lake basin. The red solid line represents the climatology of 5870-gpm isoline from 1981 to 2010, and the black dots indicate the area with the values exceeding the confidence level at 95%.</p>
Full article ">Figure 5 Cont.
<p>500-hPa geopotential height field (black solid line, unit: gpm) and the composite anomaly field (shading, unit: gpm) at the stages of (<b>a</b>) 1961–1992, (<b>b</b>) 1993–2002 and (<b>c</b>) 2003–2020 over the Dongting Lake basin. The red solid line represents the climatology of 5870-gpm isoline from 1981 to 2010, and the black dots indicate the area with the values exceeding the confidence level at 95%.</p>
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<p>Same as <a href="#atmosphere-12-01082-f005" class="html-fig">Figure 5</a>, (<b>a</b>) 1961–1992, (<b>b</b>) 1993–2002 and (<b>c</b>) 2003–2020 over the Dongting Lake basin. but for 200-hPa zonal wind (contours, unit: m·s<sup>−1</sup>), where the red solid lines represent the climatology of wind speed isolines of 0 m·s<sup>−1</sup> and 30 m·s<sup>−1</sup> from 1981 to 2010.</p>
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<p>Variance signal-based detection for abrupt changes in (<b>a</b>) 1993 and (<b>b</b>) 2003 in the SPI sequence over the Dongting Lake basin.</p>
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<p>Autocorrelation coefficient signal-based detection for the abrupt changes in (<b>a</b>) 1993 and (<b>b</b>) 2003 of the SPI sequence over the Dongting Lake basin.</p>
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22 pages, 4493 KiB  
Article
A Global Empirical Model of the Ion Temperature in the Ionosphere for the International Reference Ionosphere
by Vladimír Truhlík, Dieter Bilitza, Dmytro Kotov, Maryna Shulha and Ludmila Třísková
Atmosphere 2021, 12(8), 1081; https://doi.org/10.3390/atmos12081081 - 23 Aug 2021
Cited by 10 | Viewed by 3902
Abstract
This study presents a suggestion for improvement of the ion temperature (Ti) model in the International Reference Ionosphere (IRI). We have re-examined ion temperature data (primarily available from NASA’s Space Physics Data Facility (SPDF)from older satellites and combined them with newly available data [...] Read more.
This study presents a suggestion for improvement of the ion temperature (Ti) model in the International Reference Ionosphere (IRI). We have re-examined ion temperature data (primarily available from NASA’s Space Physics Data Facility (SPDF)from older satellites and combined them with newly available data from the Defense Meteorological Satellite Program (DMSP), the Communication Navigation Outage Forecasting System (C/NOFS), and from the recently launched Ionospheric Connection Explorer (ICON). We have compiled these data into a unified database comprising in total Ti data from 18 satellites. By comparisons with long term records of ion temperature from the three incoherent scatter radars (ISRs) (Jicamarca, Arecibo, and Millstone Hill), it was found that an intercalibration is needed to achieve consistency with the ISR data and among individual satellite data sets. This database with thus corrected data has been used for the development of a new global empirical model of Ti with inclusion of solar activity variation. This solar activity dependence is represented by an additive correction term to the Ti global pattern. Due to the limited data coverage at altitudes above 1000 km, the altitude range described by the model ranges from 350 km to 850 km covering only the region where generally Ti is higher than the neutral temperature (Tn) and lower than the electron temperature (Te). This approach is consistent with the current description of Ti in the IRI model. However, instead of one anchor point at 430 km altitude as in the current IRI, our approach includes anchor points at 350, 430, 600, and 850 km. At altitudes above 850 km Ti is merged using a gradient derived from the model at 600 and 850 km, with the electron temperature described by the IRI-2016/TBT-2012 option. Comparisons with the ISR data (Jicamarca, Arecibo, Millstone Hill, and Kharkiv) for high and low solar activity and equinox show that the proposed Ti model captures local time variation of Ti at different altitudes and latitudes better than the current IRI-2016 Ti model. Full article
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<p>Year–altitude coverage of data in the database for individual satellite missions and corresponding 81-day running mean of the F10.7 index.</p>
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<p>Scatterplots of binned Ti values from ISRs vs. binned Ti values from satellites. The bins corresponding to Jicamarca are shown by red points, Arecibo by green points, and Millstone Hill by blue points. Information on the Pearson correlation coefficient (corr.) is also included. For DMSP (brown points), the scatterplot represents a comparison with already corrected satellite data (vertical axis; “other_sat” stand for DE-2, OGO-6, and C/NOFS). Additionally, shown are the least-square fits to the data (dash-dotted and dashed line) and the line marking perfect agreement: Ti_sat = Ti_isr (solid line).</p>
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<p>Histogram (number of data points in 5 km bins vs. altitude) of the ion temperature data in our database.</p>
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<p>Example of altitude profiles (case for 21 March 2016; PF10.7 = 92; Solar local time 12 h, geographical latitude = 20°; geographical longitude = 0°) from the proposed Ti model (red line; 4 red circles represent 4 fixed anchor points with altitudes of 350, 430, 600 and 850 km; 2 red triangles symbolize additional anchor points with variable altitudes-lower one a tangent point on the Tn profile (orange line) and higher one an intersection with the Te profile (black line). The corresponding Ti profile computed from the IRI-2016 model is shown in the blue color (one fixed anchor point at 430 km height-circle; two anchor points for the connection with Tn and Te-triangles).</p>
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<p>Contour plots of the Ti model for equinox and solstice for the four anchor altitudes during solar minimum (PF10.7 = 70).</p>
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<p>Contour plots of the Ti model for equinox and solstice for four altitude intervals and solar maximum (PF10.7 = 200).</p>
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<p>Local time variation of Ti for low solar activity (PF10.7 = 82.5) from the three ISRs (black circles—medians, error bars—upper/lower quartile; Jicamarca—upper row, Arecibo—middle row, Millstone Hill—lower row) and for three altitudes (350 km—left column, 430 km—middle column, 600 km—right column). Included is the proposed Ti model (red line), the IRI-2016 Ti model (blue line), and Tn from the IRI-2016/NRLMSISE-00 model (orange line).</p>
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<p>The same as <a href="#atmosphere-12-01081-f007" class="html-fig">Figure 7</a>, but for high solar activity (PF10.7 = 200).</p>
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<p>Local time variation of Kharkiv Ti data (grey points—individual measurements; black circles—medians in 1 h wide MLT bins; error bars represent upper and lower quartiles), corresponding medians and lower/upper quartiles from the proposed Ti and IRI-2016 models (red and blue, respectively). The left panel represents low solar activity (LSA) and the right panel represents medium solar activity (MSA).</p>
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26 pages, 5519 KiB  
Article
Adaptive Crop Management under Climate Uncertainty: Changing the Game for Sustainable Water Use
by Soe W. Myint, Rimjhim Aggarwal, Baojuan Zheng, Elizabeth A. Wentz, Jim Holway, Chao Fan, Nancy J. Selover, Chuyuan Wang and Heather A. Fischer
Atmosphere 2021, 12(8), 1080; https://doi.org/10.3390/atmos12081080 - 23 Aug 2021
Cited by 9 | Viewed by 4398
Abstract
Water supplies are projected to become increasingly scarce, driving farmers, energy producers, and urban dwellers towards an urgent and emerging need to improve the effectiveness and the efficiency of water use. Given that agricultural water use is the largest water consumer throughout the [...] Read more.
Water supplies are projected to become increasingly scarce, driving farmers, energy producers, and urban dwellers towards an urgent and emerging need to improve the effectiveness and the efficiency of water use. Given that agricultural water use is the largest water consumer throughout the U.S. Southwest, this study sought to answer two specific research questions: (1) How does water consumption vary by crop type on a metropolitan spatial scale? (2) What is the impact of drought on agricultural water consumption? To answer the above research questions, 92 Landsat images were acquired to generate fine-resolution daily evapotranspiration (ET) maps at 30 m spatial resolution for both dry and wet years (a total of 1095 ET maps), and major crop types were identified for the Phoenix Active Management Area. The study area has a subtropical desert climate and relies almost completely on irrigation for farming. Results suggest that there are some factors that farmers and water managers can control. During dry years, crops of all types use more water. Practices that can offset this higher water use include double or multiple cropping practice, drought tolerant crop selection, and optimizing the total farmed area. Double and multiple cropping practices result in water savings because soil moisture is retained from one planting to another. Further water savings occur when drought tolerant crop types are selected, especially in dry years. Finally, disproportionately large area coverage of high water consuming crops can be balanced and/or reduced or replaced with more water efficient crops. This study provides strong evidence that water savings can be achieved through policies that create incentives for adopting smart cropping strategies, thus providing important guidelines for sustainable agriculture management and climate adaptation to improve future food security. Full article
(This article belongs to the Special Issue Remote Sensing and GIS Applications in Urban Climate Research)
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<p>Phoenix Active Management Area (Phoenix AMA) Basin. (<b>a</b>) Phoenix AMA located in Arizona and neighboring states, (<b>b</b>) Phoenix AMA, (<b>c</b>) soil map of Phoenix AMA.</p>
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<p>ET modeling and crop identification to quantify crop water use. This diagram demonstrates how ET maps (1095 ET maps) and crop type maps (3 year crop maps) were generated using 92 Landsat scenes, daily ancillary data from weather stations, and reference crop type data to determine consumptive water use by crop.</p>
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<p>Farm units within and around several irrigation districts. The irrigation districts include ARLINGTON, BUCKEYE, ROOSID, MWD, AIWDD, SRVWUA, STJOHNS, and PENINSULA. Note that farm units can be located outside of any irrigation districts.</p>
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<p>Landsat NDVI time series data and its crop type map. In total, 29 NDVI layers in 2001 (<b>a</b>) were used to generate its crop type map (<b>b</b>) using the support vector machine (SVM) classifiers.</p>
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<p>Comparison between total annual ET and total water use. (<b>a</b>) Irrigation district level; (<b>b</b>) farm unit level.</p>
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<p>Comparison between total annual ET and total water use. The analysis was performed at the farm unit level for each irrigation district.</p>
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<p>Common planting and harvesting dates for some crops grown in the region (USDA-NASS, 2004).</p>
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<p>Water consumed by different crops across irrigation districts. (<b>a</b>) SVFIII water consumed for alfalfa was higher in a wet year than a dry year (an exception to the rest); (<b>b</b>) AIWDD water consumed for Barley was higher in a wet year than a dry year (an exception to the rest); (<b>c</b>) St. Johns water consumed for corn was higher in a wet year than a dry year (an exception to the rest); (<b>d</b>). MWD water consumed for cotton was higher in a dry year than wet year (an exception to the rest); (<b>e</b>) Water consumed for wheat was higher in dry year than wet year for all districts (i.e., SRVWUA, ROOSID, RWCD, MWD); (<b>f</b>) Water consumed for Dbl. Wheat/Cotton was higher in dry year than wet year for QCID, ROOSID, and RWD (an exception to the rest); (<b>g</b>) Water consumed for Dbl. Wheat/Sorghum was higher in dry year thanwet year for all districts (i.e., BUCKEYE, ROOSID, SRVWUA).</p>
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<p>Excessive water consumption and considerably low water consumption areas for 2001, 2002, and 2005.</p>
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19 pages, 2355 KiB  
Article
Microbiological Contamination Assessment in Higher Education Institutes
by Carla Viegas, Raquel Pimenta, Marta Dias, Bianca Gomes, Miguel Brito, Liliana Aranha Caetano, Elisabete Carolino and Anita Quintal Gomes
Atmosphere 2021, 12(8), 1079; https://doi.org/10.3390/atmos12081079 - 23 Aug 2021
Cited by 9 | Viewed by 4723
Abstract
The higher education sector represents a unique environment and it acts as a work environment, a learning environment for students, and frequently, also a home environment. The aim of this study was to determine the microbial contamination (SARS-CoV-2, fungi, and bacteria) [...] Read more.
The higher education sector represents a unique environment and it acts as a work environment, a learning environment for students, and frequently, also a home environment. The aim of this study was to determine the microbial contamination (SARS-CoV-2, fungi, and bacteria) in Higher Education Facilities (HEI) by using active and passive sampling methods and combining culture-based methods with molecular tools targeting Aspergillus section Fumigati. In addition, the resistance to azole profile was also assessed. Surface samples showed a range of total bacterial contamination between 1 × 103 to 3.1 × 106 CFU·m−2, while Gram-negative bacteria ranged from 0 to 1.9 × 104 CFU·m−2. Fungal contamination ranged from 2 × 103 to 1.8 × 105 CFU·m−2 on MEA, and from 5 × 103 to 1.7 × 105 CFU·m−2 on DG18. The most prevalent species found on both media was Cladosporium sp. (47.36% MEA; 32.33% DG18). Aspergillus genera was observed on MEA (3.21%) and DG18 (14.66%), but not in the supplemented media used for the azole screening. Aspergillus section Fumigati was detected in 2 air samples (2.22%, 2 out of 90 samples) by qPCR. When testing for SARS-CoV-2 all results were negative. The present study showed that although cleaning and disinfection procedures are done regularly due to the COVID-19 pandemic, being effective in eliminating SARS-CoV-2, surfaces were often contaminated with microorganisms other than SARS-CoV-2. This can be a result of increasing resistance to biocides, and to the wide range of environmental factors that can contribute to the dissemination of microbial contamination indoors. Full article
(This article belongs to the Special Issue Indoor Air Quality—What Is Known and What Needs to Be Done)
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<p>Sampling strategy adopted and assays applied. * Lack of extracts quantities in two samples to perform the assay.</p>
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<p>Total bacteria in surface swabs.</p>
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<p>Gram-negative bacteria in surface swabs.</p>
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<p>Fungal contamination in surface swabs on MEA.</p>
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<p>Fungal contamination in surface swabs on DG18.</p>
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<p><span class="html-italic">Aspergillus</span> sections distribution in surface swabs after inoculation onto (<b>a</b>) MEA and (<b>b</b>) DG18.</p>
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20 pages, 8210 KiB  
Article
Computer Simulations of Air Quality and Bio-Climatic Indices for the City of Sofia
by Georgi Gadzhev and Kostadin Ganev
Atmosphere 2021, 12(8), 1078; https://doi.org/10.3390/atmos12081078 - 22 Aug 2021
Cited by 5 | Viewed by 2755
Abstract
Air pollution is responsible for many adverse effects on human beings. Thermal discomfort, on the other hand, is able to overload the human body and eventually provoke health implications due to the heat imbalance. Methods: The aim of the presented work is to [...] Read more.
Air pollution is responsible for many adverse effects on human beings. Thermal discomfort, on the other hand, is able to overload the human body and eventually provoke health implications due to the heat imbalance. Methods: The aim of the presented work is to study the behavior of two bio-climatic indices and statistical characteristics of the air quality index for Sofia city—the capital of Bulgaria for the period 2008–2014. The study is based on the WRF-CMAQ model system simulations with a spatial resolution of 1 km. The air quality is estimated by the air quality index, taking into account the influence of different pollutants and the thermal conditions by two indices, respectively, for hot and cold weather. It was found that the recurrence of both the heat and cold index categories and of the air quality categories have heterogeneous space distribution and well manifested diurnal and seasonal variability. For all of the situations, only O3 and PM10 are the dominant pollutants—these which determine the AQI category. It was found that AQI1, AQI2, and AQI3, which fall in the “Low” band, have the highest recurrence during the different seasons, up to more than 70% in some places and situations. The recurrence of AQI10 (very high) is rather small—no more than 5% and concentrated in small areas, mostly in the city center. The Heat index of category “Danger” never appears, and the Heat index of category “Extreme caution” appears only in the spring and summer with the highest recurrence of less than 5% in the city center. For the Wind-chill index category, “Very High Risk” never appears, and the category “High Risk” appears with a frequency of about 1–2%. The above leads to the conclusion that both from a point of view of bioclimatic and air quality indices, the human health risks in the city of Sofia are not as high. Full article
(This article belongs to the Special Issue Urban Climate and Air Quality in Mediterranean Cities)
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<p>Domain elevation in (m) (<b>a</b>). Land cover (<b>b</b>) categories: 1—Urban and Built-up Land; 2—Dryland Cropland and Pasture; 3—Irrigated Cropland and Pasture; 5—Cropland/Grassland Mosaic; 6—Cropland/Woodland Mosaic; 11—Deciduous Broadleaf Forest; 14—Evergreen Needleleaf; 15—Mixed Forest; 16—Water Bodies. Elevation (m) of five nested domains (<b>c</b>).</p>
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<p>Annual recurrences (in %) of the AQI for the band Low, Moderate, High, and Very High for the territory of Sofia city.</p>
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<p>Average recurrence (in %) of the different indices (from 1 to 10) for the territory of the city of Sofia.</p>
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<p>Average recurrence (in %) of the different indices (from 1 to 10) for the Orlov Most (Sofia).</p>
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<p>Average recurrence (in %) of the different indices (from 1 to 10) for the Bistrica.</p>
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<p>Frequency (in %) for (<b>a</b>) spring, (<b>b</b>) summer, and (<b>c</b>) autumn of the Heat index categories in the Sofia region during the spring (first row), summer (second row), and autumn (third row).</p>
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<p>Frequency (in %) for (<b>a</b>) autumn, (<b>b</b>) winter, and (<b>c</b>) spring of the Wind chill categories in the Sofia region during the spring (first row), summer (second row), and autumn (third row).</p>
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<p>Frequency (in %) for (<b>a</b>) spring, (<b>b</b>) summer, and (<b>c</b>) autumn of the Heat index categories “Caution” (first column), “Extreme caution” (second column), and “Danger” (third column) in the Sofia region at 12 UTC during the spring (first row), summer (second row), and autumn (third row).</p>
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<p>Frequency (in %) for (<b>a</b>) autumn, (<b>b</b>) winter, and (<b>c</b>) spring of the Wind-chill index categories “Low risk” (first column), “Moderate risk” (second column), and “High risk” (third column) in the Sofia region at 06 UTC during the autumn (first row), winter (second row), and spring (third row).</p>
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<p>Frequency (in %) for (<b>a</b>) autumn, (<b>b</b>) winter, and (<b>c</b>) spring of the Wind-chill index categories “Low risk” (first column), “Moderate risk” (second column), and “High risk” (third column) in the Sofia region at 15 UTC during the autumn (first row), winter (second row), and spring (third row).</p>
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16 pages, 3133 KiB  
Article
Concentrations, Size Distribution, and Community Structure Characteristics of Culturable Airborne Antibiotic-Resistant Bacteria in Xinxiang, Central China
by Xu Yan, Jiahui Ma, Jingyuan Ren, Mengjia Cui, Xinqing Chen, Dezhi Qiu, Miao Lei, Tianning Li, Li Guo, Chun Chen and Yunping Han
Atmosphere 2021, 12(8), 1077; https://doi.org/10.3390/atmos12081077 - 22 Aug 2021
Cited by 5 | Viewed by 2817
Abstract
Antimicrobial resistance is considered an important threat to global health and has recently attracted significant attention from the public. In this study, the concentrations and size distribution characteristics of culturable airborne total bacteria (TB) and four antibiotic-resistant bacteria (tetracycline-resistant bacteria (TRB), ciprofloxacin-resistant bacteria [...] Read more.
Antimicrobial resistance is considered an important threat to global health and has recently attracted significant attention from the public. In this study, the concentrations and size distribution characteristics of culturable airborne total bacteria (TB) and four antibiotic-resistant bacteria (tetracycline-resistant bacteria (TRB), ciprofloxacin-resistant bacteria (CRB), erythromycin-resistant bacteria (ERB), and ampicillin-resistant bacteria (ARB)) were investigated for approximately one year to explore their variations under different seasons, diurnal periods, and air quality levels. The concentrations of TB and four antibiotic-resistant bacteria in winter and night were higher than during other seasons and diurnal periods. Their maximum concentrations were detected from air under moderate pollution or heavy pollution. PM2.5, PM10, SO2, and NO2 were positively related to TB and four antibiotic-resistant bacteria (p < 0.01), whereas O3 and wind speed were negatively related to them (p < 0.05). The particle size of TB and four antibiotic-resistant bacteria were mainly distributed in stage V (1.1–2.2 µm). Bacillus was the dominant genus of ARB (75.97%) and CRB (25.67%). Staphylococcus and Macrococcus were the dominant genera of TRB (46.05%) and ERB (47.67%), respectively. The opportunistic pathogens of Micrococcus, Sphingomonas, Enterococcus, Rhodococcus, and Stenotrophomonas were also identified. This study provides important references for understanding the threat of bioaerosols to human health. Full article
(This article belongs to the Special Issue Atmospheric Pollution of Agriculture-Dominated Cities)
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<p>Variation of airborne bacteria, antibiotic-resistant bacteria, and PM<sub>2.5</sub> during sampling days.</p>
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<p>Size distribution of culturable bacteria and four antibiotic-resistant bacteria under different air quality levels.</p>
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<p>Diurnal variation in size distribution and median diameter of culturable bacteria and four antibiotic-resistant bacteria (the diameter corresponding to a cumulative percentage of 50% is the median diameter (d<sub>50</sub>)).</p>
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<p>Diurnal variation in particle size distribution of the culturable bacteria and the four antibiotic-resistant bacteria.</p>
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<p>The diversity and relative abundance of the top 50 bacterial genera of four antibiotic-resistant bacteria.</p>
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<p><span class="html-italic">HQ</span> and <span class="html-italic">HI</span> of airborne bacteria under different diurnal periods, air quality levels, and seasons. (MO: morning; NO: noon; NI: night; EX: excellent; GO: good; SLP: slight pollution; MOP: moderate pollution; HEP: heavy pollution; SP: spring; SU: summer; AU: autumn; WI: winter).</p>
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16 pages, 13222 KiB  
Article
Research on Monthly Precipitation Prediction Based on the Least Square Support Vector Machine with Multi-Factor Integration
by Jingchun Lei, Quan Quan, Pingzhi Li and Denghua Yan
Atmosphere 2021, 12(8), 1076; https://doi.org/10.3390/atmos12081076 - 21 Aug 2021
Cited by 6 | Viewed by 2352
Abstract
Accurate precipitation prediction is of great significance for regional flood control and disaster mitigation. This study introduced a prediction model based on the least square support vector machine (LSSVM) optimized by the genetic algorithm (GA). The model was used to estimate the precipitation [...] Read more.
Accurate precipitation prediction is of great significance for regional flood control and disaster mitigation. This study introduced a prediction model based on the least square support vector machine (LSSVM) optimized by the genetic algorithm (GA). The model was used to estimate the precipitation of each meteorological station over the source region of the Yellow River (SRYE) in China for 12 months. The Ensemble empirical mode decomposition (EEMD) method was used to select meteorological factors and realize precipitation prediction, without dependence on historical data as a training set. The prediction results were compared with each other, according to the determination coefficient (R2), mean absolute errors (MAE), and root mean square error (RMSE). The results show that sea surface temperature (SST) in the Niño 1 + 2 region exerts the largest influence on accuracy of the prediction model for precipitation in the SRYE (RSST2= 0.856, RMSESST= 19.648, MAESST= 14.363). It is followed by the potential energy of gravity waves (Ep) and temperature (T) that have similar effects on precipitation prediction. The prediction accuracy is sensitive to altitude influences and accurate prediction results are easily obtained at high altitudes. This model provides a new and reliable research method for precipitation prediction in regions without historical data. Full article
(This article belongs to the Special Issue Hydrological Responses under Climate Changes)
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<p>Location of the SRYE and the meteorological stations.</p>
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<p>Prediction scheme for precipitation combining with meteorological factors.</p>
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<p>Boxplot of average periods of IMFs in the series of monthly precipitation in 18 meteorological stations.</p>
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<p>Prediction results of precipitation integrating SST, T, and Ep.</p>
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<p>Taylor diagram of precipitation prediction integrating meteorological factors. The blue contours represent Pearson’s correlation coefficient; the green contours indicate RMSE; the black contours denote standard deviation of simulated diagram.</p>
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<p>Parameter optimization based on GA.</p>
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<p>The relationship between prediction accuracy R<sup>2</sup> of the model integrating factors and altitude. The blue, red, and black polylines represent the factors Ep, SST, and T, respectively.</p>
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<p>Scatter diagram for the relationship of precipitation with altitude and prediction accuracy R<sup>2</sup>.</p>
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19 pages, 5954 KiB  
Article
Atmospheric Rivers and Associated Precipitation over France and Western Europe: 1980–2020 Climatology and Case Study
by Benjamin Doiteau, Meredith Dournaux, Nadège Montoux and Jean-Luc Baray
Atmosphere 2021, 12(8), 1075; https://doi.org/10.3390/atmos12081075 - 21 Aug 2021
Cited by 5 | Viewed by 3365
Abstract
Atmospheric rivers are important atmospheric features implicated in the global water vapor budget, the cloud distribution, and the associated precipitation. The ARiD (Atmospheric River Detector) code has been developed to automatically detect atmospheric rivers from water vapor flux and has been applied to [...] Read more.
Atmospheric rivers are important atmospheric features implicated in the global water vapor budget, the cloud distribution, and the associated precipitation. The ARiD (Atmospheric River Detector) code has been developed to automatically detect atmospheric rivers from water vapor flux and has been applied to the ECMWF ERA5 archive over the period 1980–2020 above the Atlantic Ocean and Europe. A case study of an atmospheric river formed in the East Atlantic on August 2014 that reached France has been detailed using ECMWF ERA5 reanalysis, ground based observation data, and satellite products such as DARDAR, AIRS, GPCP, and GOES. This atmospheric river event presents a strong interaction with an intense upper tropospheric jet stream, which induced stratosphere–troposphere exchanges by tropopause fold. A 1980–2020 climatology of atmospheric rivers over Europe has been presented. The west of France, Iberian Peninsula, and British Islands are the most impacted regions by atmospheric rivers with an occurrence of up to four days per month during the October–April period. Up to 40% of the precipitation observed on the west European coast can be linked to the presence of ARs. No significant trend in the occurrence of the phenomena was found over 1980–2020. Full article
(This article belongs to the Special Issue Atmospheric Rivers – Bridging Weather, Climate and Society)
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<p>Schematic overview of ARiD. AR1 to AR5 refer to the recently created atmospheric river intensity and impact scale [<a href="#B33-atmosphere-12-01075" class="html-bibr">33</a>].</p>
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<p>Average number of AR days each month over the period 1980–2020 from January (<b>top left</b>) to December (<b>bottom right</b>). The location of Clermont-Ferrand is marked by a pink cross.</p>
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<p>Monthly percentage of precipitation linked to the presence of AR over the period 1980–2020 from January (<b>top left</b>) to December (<b>bottom right</b>). The location of Clermont-Ferrand is marked by a pink cross.</p>
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<p><b>ECMWF ERA5</b> IVT maps during the atmospheric river event from 24 (<b>top left</b>) to 27 August 2014 (<b>bottom right</b>). The location of Clermont-Ferrand is marked by a pink cross.</p>
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<p><b>ECMWF ERA5</b> horizontal wind on the 250 hPa pressure level (<b>left</b>) and IVT maps (<b>right</b>) on 25 August 2014 at 00 UT. The dashed line represents the cross sections presented in <a href="#atmosphere-12-01075-f004" class="html-fig">Figure 4</a>. The location of Clermont-Ferrand is marked by a pink cross. An animated version of this figure covering the 24–28 August period is provided as a video in the <a href="#app1-atmosphere-12-01075" class="html-app">Supplementary Materials</a>.</p>
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<p><b>ECMWF ERA5</b> Cross sections along 20° W longitude (25 August 2014 at 00 UT) of water vapor flux (kg kg<sup>-1</sup> m s<sup>−1</sup>, <b>top left</b>), PV (PVU, <b>top right</b>), horizontal wind (m s<sup>−1</sup>, <b>bottom left</b>), and vertical wind (Pa s<sup>−1</sup>, the correspondence between sign, colors, and direction of the vertical wind is given with the color bar, <b>bottom right</b>).</p>
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<p>DARDAR simplified characterization on 25 August 2014 at 04 UT (<b>top</b>), and WVMR retrieved by AIRS at 700 hPa on 25 August from 00 to 06 UT (<b>bottom</b>). The black line represents the localization of the DARDAR cross-section.</p>
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<p>GOES mosaic in visible channels covering the different stages of the AR from 24 (<b>top left</b>) to 27 August 2014 (<b>bottom right</b>).</p>
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<p>Daily precipitation estimates from GPCP v1.3 from 24 (<b>top left</b>) to 27 August 2014 (<b>bottom right</b>). The location of Clermont-Ferrand is marked by a pink cross.</p>
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<p>IVT and IWV retrieved by ERA5 and IWV retrieved by GPS at Cézeaux (<b>top</b>); hourly precipitation accumulation from ERA5, disdrometer and rain gauge at Opme (<b>middle</b>); cloud cover and cloud base height (ECMWF ERA5, <b>bottom</b>). The interpolation location of ERA5 is 3° E, 45.75° N.</p>
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25 pages, 67320 KiB  
Article
Enhancing the Output of Climate Models: A Weather Generator for Climate Change Impact Studies
by Pietro Croce, Paolo Formichi and Filippo Landi
Atmosphere 2021, 12(8), 1074; https://doi.org/10.3390/atmos12081074 - 21 Aug 2021
Cited by 5 | Viewed by 3181
Abstract
Evaluation of effects of climate change on climate variable extremes is a key topic in civil and structural engineering, strongly affecting adaptation strategy for resilience. Appropriate procedures to assess the evolution over time of climatic actions are needed to deal with the inherent [...] Read more.
Evaluation of effects of climate change on climate variable extremes is a key topic in civil and structural engineering, strongly affecting adaptation strategy for resilience. Appropriate procedures to assess the evolution over time of climatic actions are needed to deal with the inherent uncertainty of climate projections, also in view of providing more sound and robust predictions at the local scale. In this paper, an ad hoc weather generator is presented that is able to provide a quantification of climate model inherent uncertainties. Similar to other weather generators, the proposed algorithm allows the virtualization of the climatic data projection process, overcoming the usual limitations due to the restricted number of available climate model runs, requiring huge computational time. However, differently from other weather generation procedures, this new tool directly samples from the output of Regional Climate Models (RCMs), avoiding the introduction of additional hypotheses about the stochastic properties of the distributions of climate variables. Analyzing the ensemble of so-generated series, future changes of climatic actions can be assessed, and the associated uncertainties duly estimated, as a function of considered greenhouse gases emission scenarios. The efficiency of the proposed weather generator is discussed evaluating performance metrics and referring to a relevant case study: the evaluation of extremes of minimum and maximum temperature, precipitation, and ground snow load in a central Eastern region of Italy, which is part of the Mediterranean climatic zone. Starting from the model ensemble of six RCMs, factors of change uncertainty maps for the investigated region are derived concerning extreme daily temperatures, daily precipitation, and ground snow loads, underlying the potentialities of the proposed approach. Full article
(This article belongs to the Topic Climate Change and Environmental Sustainability)
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<p>Implementation scheme of the weather generator algorithm.</p>
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<p>Monthly mean statistics of <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>Max</mi> </mrow> </msub> </mrow> </semantics></math> (<b>a</b>), Taylor diagram (<b>b</b>) considering the RCM ECEARTH-HIRHAM5 at cell 101 and the period 1981–1990. In (<b>b</b>), each red dot represents a generated series made with the investigated model, whereas the red diamond represents the reference one.</p>
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<p>Example of bivariate factor of change map for <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>Max</mi> <mo>,</mo> <mi mathvariant="normal">k</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Investigated area in the Italian Mediterranean region (<b>a</b>), illustration of the Italian topography at EUR11-grid resolution (<b>b</b>).</p>
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<p>Factors of change for <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>Max</mi> <mo>,</mo> <mi mathvariant="normal">k</mi> </mrow> </msub> </mrow> </semantics></math> at cell 101—prediction interval 25–75%.</p>
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<p>Factors of change for <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>Max</mi> <mo>,</mo> <mi mathvariant="normal">k</mi> </mrow> </msub> </mrow> </semantics></math> in the time windows 1976–2015, 1996–2035, 2016–2055, and 2036–2075 in comparison with the reference time interval 1956–1995—prediction interval (25–75%) map (Scenario RCP4.5).</p>
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<p>Factors of change for <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>Max</mi> <mo>,</mo> <mi mathvariant="normal">k</mi> </mrow> </msub> </mrow> </semantics></math> in the time windows 1976–2015, 1996–2035, 2016–2055, and 2036–2075 in comparison with the reference time interval 1956–1995—prediction interval (25–75%) map (Scenario RCP8.5).</p>
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<p>Factors of change for <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>Min</mi> <mo>,</mo> <mi mathvariant="normal">k</mi> </mrow> </msub> </mrow> </semantics></math> in the time windows 1976–2015, 1996–2035, 2016–2055, and 2036–2075 in comparison with the reference time interval 1956–1995—prediction interval (25–75%) map (Scenario RCP4.5).</p>
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<p>Factors of change for <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>Min</mi> <mo>,</mo> <mi mathvariant="normal">k</mi> </mrow> </msub> </mrow> </semantics></math> in the time windows 1976–2015, 1996–2035, 2016–2055, and 2036–2075 in comparison with the reference time interval 1956–1995—prediction interval (25–75%) map (Scenario RCP8.5).</p>
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<p>Factors of change for <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mrow> <mi mathvariant="normal">r</mi> <mo>,</mo> <mi mathvariant="normal">k</mi> </mrow> </msub> </mrow> </semantics></math> in the time windows 1976–2015, 1996–2035, 2016–2055, and 2036–2075 in comparison with the reference time interval 1956–1995—prediction interval (25–75%) map (Scenario RCP4.5).</p>
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<p>Factors of change for <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mrow> <mi mathvariant="normal">r</mi> <mo>,</mo> <mi mathvariant="normal">k</mi> </mrow> </msub> </mrow> </semantics></math> in the time windows 1976–2015, 1996–2035, 2016–2055, and 2036–2075 in comparison with the reference time interval 1956–1995—prediction interval (25–75%) map (Scenario RCP8.5).</p>
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<p>Factors of change for <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mi mathvariant="normal">k</mi> </msub> </mrow> </semantics></math> in the time windows 1976–2015, 1996–2035, 2016–2055, and 2036–2075 in comparison with the reference time interval 1956–1995—prediction interval (25–75%) map (Scenario RCP4.5).</p>
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<p>Factors of change for <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mi mathvariant="normal">k</mi> </msub> </mrow> </semantics></math> in the time windows 1976–2015, 1996–2035, 2016–2055, and 2036–2075 in comparison with the reference time interval 1956–1995—prediction interval (25–75%) map (Scenario RCP8.5).</p>
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<p>Factors of change for <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>max</mi> <mo>,</mo> <mi mathvariant="normal">k</mi> </mrow> </msub> </mrow> </semantics></math> for the time window 1981–2020 in comparison with the reference time interval 1961–2000.</p>
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<p>Factors of change for <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>min</mi> <mo>,</mo> <mi mathvariant="normal">k</mi> </mrow> </msub> </mrow> </semantics></math> for the time window 1981–2020 in comparison with the reference time interval 1961–2000.</p>
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<p>Factors of change for <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mrow> <mi mathvariant="normal">r</mi> <mo>,</mo> <mi mathvariant="normal">k</mi> </mrow> </msub> </mrow> </semantics></math> for the time window 1981–2020 in comparison with the reference time interval 1961–2000.</p>
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17 pages, 4419 KiB  
Article
Three-Year Variations in Criteria Atmospheric Pollutants and Their Relationship with Rainwater Chemistry in Karst Urban Region, Southwest China
by Jie Zeng, Xin Ge, Qixin Wu and Shitong Zhang
Atmosphere 2021, 12(8), 1073; https://doi.org/10.3390/atmos12081073 - 21 Aug 2021
Cited by 6 | Viewed by 2482
Abstract
Air pollutants have been investigated in many studies, but the variations of atmospheric pollutants and their relationship with rainwater chemistry are not well studied. In the present study, the criteria atmospheric pollutants in nine monitoring stations and rainwater chemistry were analyzed in karst [...] Read more.
Air pollutants have been investigated in many studies, but the variations of atmospheric pollutants and their relationship with rainwater chemistry are not well studied. In the present study, the criteria atmospheric pollutants in nine monitoring stations and rainwater chemistry were analyzed in karst Guiyang city, since the time when the Chinese Ambient Air Quality Standards (CAAQS, third revision) were published. Based on the three-year daily concentration dataset of SO2, NO2, CO, PM10 and PM2.5, although most of air pollutant concentrations were within the limit of CAAQS III-Grade II standard, the significant spatial variations and relatively heavy pollution were found in downtown Guiyang. Temporally, the average concentrations of almost all air pollutants (except for CO) decreased during three years at all stations. Ratios of PM2.5/PM10 in non- and episode days reflected the different contributions of fine and coarse particles on particulate matter in Guiyang, which was influenced by the potential meteorological factors and source variations. According to the individual air quality index (IAQI), the seasonal variations of air quality level were observed, that is, IAQI values of air pollutants were higher in winter (worst air quality) and lower in summer (best air quality) due to seasonal variations in emission sources. The unique IAQI variations were found during the Chinese Spring Festival. Air pollutant concentrations are also influenced by meteorological parameters, in particular, the rainfall amount. The air pollutants are well scoured by the rainfall process and can significantly affect rainwater chemistry, such as SO42−, NO3, Mg2+, and Ca2+, which further alters the acidification/alkalization trend of rainwater. The equivalent ratios of rainwater SO42−/NO3 and Mg2+/Ca2+ indicated the significant contribution of fixed emission sources (e.g., coal combustion) and carbonate weathering-influenced particulate matter on rainwater chemistry. These findings provide scientific support for air pollution management and rainwater chemistry-related environmental issues. Full article
(This article belongs to the Special Issue Outdoor Air Pollution and Human Health)
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<p>Map of study area. (<b>a</b>) The location of the Guizhou Province; (<b>b</b>) the position of Guiyang city; (<b>c</b>) land use and air quality monitoring stations in Guiyang city.</p>
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<p>(<b>a</b>) The concentration variations of atmospheric SO<sub>2</sub> and NO<sub>2</sub> at Guiyang city since 2003 and (<b>b</b>) the monthly rainfall amount in 2012, 2013, and 2014. The data source [<a href="#B27-atmosphere-12-01073" class="html-bibr">27</a>,<a href="#B31-atmosphere-12-01073" class="html-bibr">31</a>].</p>
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<p>Yearly average concentrations of criteria atmospheric pollutants in Guiyang City from 2013 to 2015.</p>
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<p>The seasonal variations of citywide daily mean concentrations of air pollutants in Guiyang city from 2013 to 2015. (<b>a</b>) SO<sub>2</sub> and NO<sub>2</sub>, (<b>b</b>) PM<sub>10</sub> and PM<sub>2.5</sub>, (<b>c</b>) CO.</p>
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<p>The PM<sub>2.5</sub>/PM<sub>10</sub> ratios in non- and episode days at all stations in Guiyang City during 2013–2015, the error bars are standard deviations. Shihuanbaozhan station (SHS), Xinhualu station (XHS), Hongbianmen station (HBS), Maanshan station (MAS), Zhongyuancun station (ZYS), Biyunwo station (BYS), Jianhulu station (JHS), Yanzichong station (YZS), Tongmuling station (TMS).</p>
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<p>Temporal and seasonal variations in IAQI of SO<sub>2</sub>, NO<sub>2</sub>, PM<sub>10</sub>, PM<sub>2.5</sub>, and CO; S = summer, W = winter. Monthly IAQI values of SO<sub>2</sub> (<b>a</b>), NO<sub>2</sub> (<b>b</b>), PM<sub>10</sub> (<b>c</b>), PM<sub>2.5</sub> (<b>d</b>), and CO (<b>e</b>); Seasonal IAQI values of all air pollutants (<b>f</b>).</p>
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<p>The monthly variations of SO<sub>4</sub><sup>2−</sup>, NO<sub>3</sub><sup>−</sup>, Mg<sup>2+</sup>, and Ca<sup>2+</sup> concentrations of rainwater and atmospheric SO<sub>2</sub>, NO<sub>2</sub>, PM<sub>10</sub> + PM<sub>2.5</sub> concentrations (<b>a</b>–<b>c</b>), the relationships between atmospheric SO<sub>2</sub>, NO<sub>2</sub>, PM<sub>10</sub> + PM<sub>2.5</sub> concentrations and rainfall amount (<b>d</b>–<b>f</b>), the equivalent ratios of rainwater SO<sub>4</sub><sup>2−</sup>/NO<sub>3</sub><sup>−</sup> (<b>g</b>) and Mg<sup>2+</sup>/Ca<sup>2+</sup> (<b>h</b>). The related data sources and reference values are from [<a href="#B27-atmosphere-12-01073" class="html-bibr">27</a>,<a href="#B61-atmosphere-12-01073" class="html-bibr">61</a>].</p>
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17 pages, 4831 KiB  
Article
30 Years of Air Quality Trends in Japan
by Akiyoshi Ito, Shinji Wakamatsu, Tazuko Morikawa and Shinji Kobayashi
Atmosphere 2021, 12(8), 1072; https://doi.org/10.3390/atmos12081072 - 21 Aug 2021
Cited by 37 | Viewed by 12264
Abstract
The aim of this paper is to obtain information that will contribute to measures and research needed to further improve the air quality in Japan. The trends and characteristics of air pollutant concentrations, especially PM2.5, ozone, and related substances, over the past [...] Read more.
The aim of this paper is to obtain information that will contribute to measures and research needed to further improve the air quality in Japan. The trends and characteristics of air pollutant concentrations, especially PM2.5, ozone, and related substances, over the past 30 years, are analyzed, and the relationships between concentrations and emissions are discussed quantitatively. We found that PM2.5 mass concentrations have decreased, with the largest reduction in elemental carbon (EC) as the PM2.5 component. The concentrations of organic carbon (OC) have not changed significantly compared to other components, suggesting that especially VOC emissions as precursors need to be reduced. In addition, the analysis of the differences in PM2.5 concentrations between the ambient and the roadside showed that further research on non-exhaust particles is needed. For NOx and SO2, there is a linear relationship between domestic anthropogenic emissions and atmospheric concentrations, indicating that emission control measures are directly effective in the reduction in concentrations. Also, recent air pollution episodes and the effect of reduced economic activity, as a consequence of COVID-19, on air pollution concentrations are summarized. Full article
(This article belongs to the Special Issue Air Pollution in Japan)
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<p>Distribution of ambient air quality monitoring stations (AAQMS) and roadside air quality monitoring stations (RsAQMS) in Japan and the eight regions (region 1 to 8) defined in this study.</p>
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<p>Annual average concentrations of air pollutants at AAQMS and RsAQMS in Japan from 1990 to 2018 (<b>top</b>: PM<sub>2.5</sub> and SPM, <b>middle</b>: O<sub>3</sub>, NOx and NMHC, <b>bottom</b>: SO<sub>2</sub> and CO).</p>
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<p>Annual average concentration of main components in PM<sub>2.5</sub> (AAQMS, RsAQMS) for 2012–2018.</p>
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<p>Annual mean concentrations of PM<sub>2.5</sub> components at AAQMS and RsAQMS by region (<b>left</b>: region 1 to 4 from the top, <b>right</b>: region 5 to 8 from the top).</p>
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<p>Annual average of 1-h daytime and annual maximum ozone concentrations at the AAQMS and RsAQMS of the nation and each region from 1990 to 2018.</p>
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<p>Annual average NOx and NMHC concentrations at the AAQMS and RsAQMS of the nation from 1990 to 2018.</p>
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<p>Annual average SO<sub>2</sub> and CO concentrations at the AAQMS and RsAQMS of the nation from 1990 to 2018.</p>
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<p>Anthropogenic emission data and annual average concentrations at the AAQMS for SO<sub>2</sub>, NOx, and PM.</p>
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<p>Distribution of ozone concentration at 15:00 (local time) from 21 May to 28 May 2019 [<a href="#B20-atmosphere-12-01072" class="html-bibr">20</a>].</p>
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<p>Time series of ozone concentrations in regions 2, 5 and 8 (left: maximum ozone concentration in the regions, right: average ozone concentration in the regions).</p>
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<p>Distribution of PM<sub>2.5</sub> concentrations at 18:00 on August 2 through to 4 August 2020.</p>
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<p>Median and 98% concentrations of PM<sub>2.5</sub> in region 8 from 1 August to 10 August 2020.</p>
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<p>Monthly average concentrations of NOx, O<sub>3</sub>, PM<sub>2.5</sub> and SO<sub>2</sub> at AAQMS and RsAQMS in the Tokyo metropolitan area from 2016 to 2020.</p>
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18 pages, 3738 KiB  
Article
Variations in the Peczely Macro-Synoptic Types (1881–2020) with Attention to Weather Extremes in the Pannonian Basin
by János Mika, Csaba Károssy and László Lakatos
Atmosphere 2021, 12(8), 1071; https://doi.org/10.3390/atmos12081071 - 20 Aug 2021
Cited by 2 | Viewed by 2013
Abstract
Daily Peczely circulation types are investigated over 140 years (1881–2020). After presenting monthly mean frequencies and durations of the 13 circulation types, two further questions are investigated: (i) How do the circulation types influence local weather extremes?; (ii) Are there significant trends in [...] Read more.
Daily Peczely circulation types are investigated over 140 years (1881–2020). After presenting monthly mean frequencies and durations of the 13 circulation types, two further questions are investigated: (i) How do the circulation types influence local weather extremes?; (ii) Are there significant trends in the frequency of the original and the grouped circulation types in the recent monotonically warming 50 year period (1971–2020)? The answers are as follows: (i) Four local weather extremes were investigated in nine grid-points of the Pannonian Basin and analyzed in the central months of the seasons. It was established that high precipitation and wind maxima occur in almost all circulation types and months, whereas for both high temperature maxima and low temperature minima, there are six circulation types, where no extremity occurred in one, two, or three investigated months. (ii) In the last 50 years, 37% of the linear seasonal frequency trends have been significant. However, these trends are rarely significant in the shorter monotonously warming (1911–1940) and cooling (1941–1970) 30-year periods. Therefore, the significant trends of the last 50 years are unlikely to be the direct consequences of the monotonous hemispherical warming. Since these hemispherical temperature trends are most likely caused by different sets of physical reasons, the reality of the presented circulation frequency trends needs to be validated by climate models. Full article
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<p>The 13 macro-circulation types. All examples are taken from the year 2020. Within the maps, A means low pressure (cyclone) center, and M means high pressure (anticyclone) center.</p>
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<p>Location of the nine grid-points in the Pannonian Basin used for extreme values. The locations of the grid-points are indicated by the points below the capital letters, A–I.</p>
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<p>Anomalies of Northern Hemisphere annual surface air temperature since 1850 according to Hadley CRUT, a cooperative effort between the Hadley Center for Climate Prediction and Research and the University of East Anglia’s Climatic Research Unit (CRU), UK. The source of information is <a href="https://crudata.uea.ac.uk/cru/data/temperature/HadCRUT5.0Analysis.pdf" target="_blank">https://crudata.uea.ac.uk/cru/data/temperature/HadCRUT5.0Analysis.pdf</a> (accessed on 15 July 2021). The link for the licensing conditions is seen in the description of [<a href="#B66-atmosphere-12-01071" class="html-bibr">66</a>], in the list of References.</p>
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<p>Monthly mean duration of the macro-synoptic types as grouped by Peczely (1957) according to the wind direction in the Pannonian Basin.</p>
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<p>Distribution of the 5% extreme days among the macro-circulation types on average of the nine investigated grid-points: (<b>a</b>) high precipitation, (<b>b</b>) high maximum temperature, (<b>c</b>) low minimum temperature, and (<b>d</b>) high wind speed.</p>
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<p>Significant linear trends in frequency of the various macro-circulation types in the 1971–2020 monotonically warming period. Winter.</p>
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<p>Significant linear trends in frequency of the various macro-circulation types in the 1971–2020 monotonically warming period. Spring.</p>
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<p>Significant linear trends in frequency of the various macro-circulation types in the 1971–2020 monotonically warming period. Spring.</p>
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<p>Significant linear trends in frequency of the various macro-circulation types in the 1971–2020 monotonically warming period. Summer.</p>
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<p>Significant linear trends in frequency of the various macro-circulation types in the 1971–2020 monotonically warming period. Autumn.</p>
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<p>Significant linear trends in frequency of the various macro-circulation types in the 1971–2020 monotonically warming period. Autumn.</p>
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19 pages, 3412 KiB  
Article
Warming Trends of the Upper Troposphere in Taiwan Observed by Radiosonde and Surface Meteorological Stations at Various Altitudes
by Chih-wen Hung and Ming-Fu Shih
Atmosphere 2021, 12(8), 1070; https://doi.org/10.3390/atmos12081070 - 20 Aug 2021
Cited by 1 | Viewed by 2374
Abstract
In recent decades, a more prominent warming trend in the upper troposphere above the tropical western Pacific has been proposed in the literature derived from model simulations, satellite-borne observations, or reanalysis datasets. Rather than applying these “indirect” approaches, this study obtains surface-based and [...] Read more.
In recent decades, a more prominent warming trend in the upper troposphere above the tropical western Pacific has been proposed in the literature derived from model simulations, satellite-borne observations, or reanalysis datasets. Rather than applying these “indirect” approaches, this study obtains surface-based and radiosonde observations in Taiwan in order to investigate long-term changes in temperature at different altitudes within the troposphere under the conditions of ongoing global warming. These surface-based observations indicate more pronounced warming in areas of high terrain, and the radiosondes reveal faster warming trends in the upper troposphere, with the maximum temperature increase between 400 hPa and 250 hPa. The upper-tropospheric warming becomes even more pronounced during boreal winter and spring; however, the intense warming does not carry over near the tropopause. Notable warming is also observed near the surface in Taipei, which may be related to the urban heat island effect caused by the rapid development of anthropic activities. Since Taiwan is located right on the edge of the tropics in the western Pacific, the upper-tropospheric warming, particularly between December and March, above the island should be contributed by the radiative and non-radiative processes, which were previously proposed by other studies. Full article
(This article belongs to the Section Climatology)
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<p>Interannual variations of the (<b>a</b>) annual mean, (<b>b</b>) summer mean, (<b>c</b>) winter mean temperatures based on the CWB conventional weather stations in various groups of altitudes (blue: the surface to 500 m; green: 501 m to 1500 m; red: above 1501 m), unit: °C. (<b>d</b>–<b>f</b>) are same as (<b>a</b>–<b>c</b>) but for the anomaly (applying five-year running mean). The 30-year period of 1961 to 1990 is averaged to produce a mean for the anomaly.</p>
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<p>Radiosonde temperature data availability for every date at each pressure layer from the CWB (<b>a</b>) Taipei and (<b>b</b>) Hualien sounding stations. Black is marked for the date when the data are available.</p>
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<p>Climatological annual cycle of vertical temperature profile over the (<b>a</b>) Taipei sounding station and (<b>b</b>) Hualien sounding station, unit: °C. (<b>c</b>,<b>d</b>) are same as (<b>a</b>,<b>b</b>) but for the anomaly relative to the annual mean. The black lines on 1 May and 1 November denote the transitions of the winter half (from November through next April) and the summer half (from May through October).</p>
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<p>Decadal trend of the (<b>a</b>,<b>b</b>) annual mean temperature at each pressure layer, (<b>c</b>) each group of pressure layers (subtracting the trend value of the first observation year) over the Taipei sounding station, unit: °C. (<b>d</b>–<b>f</b>) are the same as (<b>a</b>–<b>c</b>) but for the Hualien sounding station. The colors indicate the grouped pressure layers (black: the surface to 1000 hPa; blue: 850 hPa to 500 hPa; green: 400 hPa to 250 hPa; red: 200 hPa to 150 hPa). The thin line at each pressure layer denotes the corresponding trend of the variations.</p>
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<p>Same as <a href="#atmosphere-12-01070-f004" class="html-fig">Figure 4</a> but for the winter mean temperature from November to next April.</p>
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<p>Same as <a href="#atmosphere-12-01070-f005" class="html-fig">Figure 5</a> but for the mean temperature from December to next March (DJFM) defined by [<a href="#B17-atmosphere-12-01070" class="html-bibr">17</a>].</p>
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<p>Decadal trend of the (<b>a</b>,<b>b</b>) mean temperature from December to next March (DJFM) at each pressure layer, (<b>c</b>) each group of pressure layers (subtracting the trend value of the first observation year) through the ERA5 data, unit: °C. The colors indicate the grouped pressure layers (black: 1000 hPa; blue: 850 hPa to 500 hPa; green: 400 hPa to 250 hPa; red: 200 hPa to 150 hPa). The thin line at each pressure layer denotes the corresponding trend of the variations.</p>
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12 pages, 693 KiB  
Article
Bioaerosol Emissions during Organic Waste Treatment for Biopolymer Production: A Case Study
by Erica Pascale, Elena Franchitti, Chiara Caredda, Stefania Fornasero, Giulia Carletto, Biancamaria Pietrangeli, Francesco Valentino, Paolo Pavan, Giorgio Gilli, Elisa Anedda and Deborah Traversi
Atmosphere 2021, 12(8), 1069; https://doi.org/10.3390/atmos12081069 - 20 Aug 2021
Cited by 1 | Viewed by 2839
Abstract
Environmentally sustainable methods of waste disposal are a strategic priority. For organic waste management and innovative biological treatments present advantageous opportunities, although organic waste treatment also includes environmental drawbacks, such as bioaerosol production. This study aims to evaluate bioaerosol spread during an innovative [...] Read more.
Environmentally sustainable methods of waste disposal are a strategic priority. For organic waste management and innovative biological treatments present advantageous opportunities, although organic waste treatment also includes environmental drawbacks, such as bioaerosol production. This study aims to evaluate bioaerosol spread during an innovative experimental treatment. The process consists of two anaerobic steps: acidogenesis, which includes polyhydroxyalkanoate accumulation, followed by methanogenesis. Bioaerosol, PM10, and endotoxin concentrations were measured at three sampling points during different campaigns to evaluate: (1) the background levels, (2) the contamination produced in the pre-treatment stage, and (3) the residual contamination of the outgoing digested sludge. Environmental PM10 seemed to be generally quite contained, while the endotoxin determination was close to 90 EU/m3. Significant microbial concentrations were detected during the loading of the organic fraction of municipal solid waste (fungi > 1300 CFU/m3, Bacillus genus (≈103 CFU/m3), higher Clostridium spp. and opportunistic human pathogens such as Pseudomonas aeruginosa and Klebsiella pneumoniae), suggesting a significant contamination level. Such results are useful for hazard identification in the risk assessment of innovative processes, as they reveal contaminants potentially harmful to both workers’ health and the environment. Full article
(This article belongs to the Special Issue Occupational Exposure Biological Agents: Focus on a Growing Concern)
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<p>Three sampling locations (P0, P1, and P2) at the pilot plant. P0: Area dedicated to preparation of shredded organic fraction of municipal solid waste (OFMSW); P1: position next to entry point of OFMSW pre-treated by shredding or squeezing; P2: location of final methanogenesis.</p>
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12 pages, 563 KiB  
Review
Effects of PM2.5 on Chronic Airway Diseases: A Review of Research Progress
by Xin Li and Xiaoju Liu
Atmosphere 2021, 12(8), 1068; https://doi.org/10.3390/atmos12081068 - 20 Aug 2021
Cited by 19 | Viewed by 5433
Abstract
The adverse effects of polluted air on human health have been increasingly appreciated worldwide. It is estimated that outdoor air pollution is associated with the death of 4.2 million people globally each year. Accumulating epidemiological studies indicate that exposure to ambient fine particulate [...] Read more.
The adverse effects of polluted air on human health have been increasingly appreciated worldwide. It is estimated that outdoor air pollution is associated with the death of 4.2 million people globally each year. Accumulating epidemiological studies indicate that exposure to ambient fine particulate matter (PM2.5), one of the important air pollutants, significantly contributes to respiratory mortality and morbidity. PM2.5 causes lung damage mainly by inducing inflammatory response and oxidative stress. In this paper, we reviewed the research results of our group on the effects of PM2.5 on chronic obstructive pulmonary disease, asthma, and lung cancer. And recent research progress on epidemiological studies and potential mechanisms were also discussed. Reducing air pollution, although remaining a major challenge, is the best and most effective way to prevent the onset and progression of respiratory diseases. Full article
(This article belongs to the Special Issue Outdoor Air Pollution and Human Health)
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<p>The role of macrophages in PM<sub>2.5</sub>-related chronic airway diseases. PM<sub>2.5</sub> exposure promotes recruitment of macrophages and release of cytokines, such as IL-6, IL-1β, and TNF-α. Macrophages also release eotaxin-1 to attract eosinophil recruitment, and stimulate T cells to produce IFN-γ, IL-17, and IL-21. PM<sub>2.5</sub> induces macrophage phagocytosis dysfunction, which is related to oxidative stress. Above effects leads to chronic lung inflammation. The increased expressions of ROS and HO-1 in macrophages aggravates pulmonary oxidative stress. MMPs and TGF-β released by macrophages are involved in the process of emphysema and airway remodeling. In addition, macrophages involved in tumor angiogenesis by releasing VEGF.</p>
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12 pages, 911 KiB  
Article
Assessment of Air Pollution Tolerance and Particulate Matter Accumulation of 11 Woody Plant Species
by Huong-Thi Bui, Uuriintuya Odsuren, Kei-Jung Kwon, Sang-Yong Kim, Jong-Cheol Yang, Na-Ra Jeong and Bong-Ju Park
Atmosphere 2021, 12(8), 1067; https://doi.org/10.3390/atmos12081067 - 20 Aug 2021
Cited by 24 | Viewed by 4793
Abstract
High concentration of particulate matter (PM) threatens public health and the environment. Increasing traffic in the city is one of the main factors for increased PM in the air. Urban green spaces play an important role in reducing PM. In this study, the [...] Read more.
High concentration of particulate matter (PM) threatens public health and the environment. Increasing traffic in the city is one of the main factors for increased PM in the air. Urban green spaces play an important role in reducing PM. In this study, the leaf surface and in-wax PM (sPM and wPM) accumulation were compared for 11 plant species widely used for landscaping in South Korea. In addition, biochemical characteristics of leaves (ascorbic acid chlorophyll content, leaf pH, and relative water content) were analyzed to determine air pollution tolerance. Plant species suitable for air quality improvement were selected based on their air pollution tolerance index (APTI) and anticipated performance index (API). Results showed a significant difference according to the accumulation of sPM and wPM and the plant species. PM accumulation and APTI showed a positive correlation. Pinus strobus showed the highest PM accumulation and APTI values, while Cercis chinensis showed the lowest. In 11 plants, API was divided into five groups. Pinus densiflora was classified as the best group, while Cornus officinalis and Ligustrum obtusifolium were classified as not recommended. Full article
(This article belongs to the Section Biosphere/Hydrosphere/Land–Atmosphere Interactions)
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<p>Photos of the 11 plant leaves selected for the study.</p>
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<p>Amount of PM accumulation of 11 different plant species. (<b>A</b>) Amount of wPM accumulation, (<b>B</b>) amount of sPM accumulation.</p>
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<p>Amount of EW on leaf in 11 different plant species. The lowercase alphabets in the graph are Duncan’s multiple range test. The different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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15 pages, 2499 KiB  
Article
Copula-Based Drought Monitoring and Assessment According to Zonal and Meridional Temperature Gradients
by Abudureymjang Otkur, Dian Wu, Yin Zheng, Jong-Suk Kim and Joo-Heon Lee
Atmosphere 2021, 12(8), 1066; https://doi.org/10.3390/atmos12081066 - 20 Aug 2021
Cited by 2 | Viewed by 2159
Abstract
Drought is one of the most severe natural disasters. However, many of its characteristic variables have complex nonlinear relationships. Therefore, it is difficult to construct effective drought assessment models. In this study, we analyzed regional drought characteristics in China to identify their relationship [...] Read more.
Drought is one of the most severe natural disasters. However, many of its characteristic variables have complex nonlinear relationships. Therefore, it is difficult to construct effective drought assessment models. In this study, we analyzed regional drought characteristics in China to identify their relationship with changes in meridional and zonal temperature gradients. Drought duration and severity were extracted according to standardized precipitation evapotranspiration index (SPEI) drought grades. Trends in drought duration and severity were detected by the Mann-Kendall test for the period of 1979–2019; they showed that both parameters had been steadily increasing during that time. Nevertheless, the increasing trend in drought severity was particularly significant for northwest and southwest China. A composite analysis confirmed the relationships between drought characteristics and temperature gradients. The northwest areas were relatively less affected by temperature gradients, as they are landlocked, remote from the ocean, and only slightly influenced by the land–ocean thermal contrast (LOC) and the meridional temperature gradient (MTG). The impacts of LOC and MTG on drought duration and severity were positive in the southwest region of China but negative in the northeast. As there was a strong correlation between drought duration and severity, we constructed a 2D copula function model of these parameters. The Gaussian, HuslerReiss, and Frank copula functions were the most appropriate distributions for the northeast, northwest, and southwest regions, respectively. As drought processes are highly complex, the present study explored the internal connections between drought duration and severity and their responses to meteorological conditions. In this manner, an accurate method of predicting future drought events was developed. Full article
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<p>Study framework for copula-based drought monitoring and assessment using zonal and meridional circulations.</p>
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<p>Study area comprising the northeastern, northwestern, and southwestern parts of China.</p>
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<p>Definition of drought events used in the present study.</p>
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<p>Time series of global temperature gradients. (<b>a</b>) LOC index. (<b>b</b>) MTG index during the period from 1979 to 2019. Green dotted line represents the linear trend of the indices. Gray dashed line indicates the +1 σ and −1 σ. For each gradient, red dots indicate significant positive events while blue dots indicate significant negative events.</p>
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<p>Composite analysis of drought characteristics with temperature gradients.</p>
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<p>Long-term trend in drought components in China between 1979 and 2019. (<b>a</b>) Drought duration. (<b>b</b>) Drought severity.</p>
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<p>Relationship between drought duration and severity in the (<b>a</b>) southwest, (<b>b</b>) northeast, and (<b>c</b>) southwest.</p>
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<p>Copula distributions of drought duration and severity in positive (negative) LOC/MTG years.</p>
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18 pages, 21744 KiB  
Article
Low-Cost Air Quality Stations’ Capability to Integrate Reference Stations in Particulate Matter Dynamics Assessment
by Lorenzo Brilli, Federico Carotenuto, Bianca Patrizia Andreini, Alice Cavaliere, Andrea Esposito, Beniamino Gioli, Francesca Martelli, Marco Stefanelli, Carolina Vagnoli, Stefania Venturi, Alessandro Zaldei and Giovanni Gualtieri
Atmosphere 2021, 12(8), 1065; https://doi.org/10.3390/atmos12081065 - 19 Aug 2021
Cited by 8 | Viewed by 2735
Abstract
Low-cost air quality stations can provide useful data that can offer a complete picture of urban air quality dynamics, especially when integrated with daily measurements from reference air quality stations. However, the success of such deployment depends on the measurement accuracy and the [...] Read more.
Low-cost air quality stations can provide useful data that can offer a complete picture of urban air quality dynamics, especially when integrated with daily measurements from reference air quality stations. However, the success of such deployment depends on the measurement accuracy and the capability of resolving spatial and temporal gradients within a spatial domain. In this work, an ensemble of three low-cost stations named “AirQino” was deployed to monitor particulate matter (PM) concentrations over three different sites in an area affected by poor air quality conditions. Data of PM2.5 and PM10 concentrations were collected for about two years following a protocol based on field calibration and validation with a reference station. Results indicated that: (i) AirQino station measurements were accurate and stable during co-location periods over time (R2 = 0.5–0.83 and RMSE = 6.4–11.2 μg m−3; valid data: 87.7–95.7%), resolving current spatial and temporal gradients; (ii) spatial variability of anthropogenic emissions was mainly due to extensive use of wood for household heating; (iii) the high temporal resolution made it possible to detect time occurrence and strength of PM10 concentration peaks; (iv) the number of episodes above the 1-h threshold of 90 μg m−3 and their persistence were higher under urban and industrial sites compared to the rural area. Full article
(This article belongs to the Special Issue PM Sensors for the Measurement of Air Quality)
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<p>Integrated composition of the monitoring area showing: (<b>a</b>) location of the municipality of Capannori within the Tuscany region; (<b>b</b>) location of the three AirQino stations (S15, S16 and S19) and ARPAT station (co-located with S19); (<b>c</b>) air pollution areas identified within the study area. The pictures of each AirQino station and the area of deployment are also shown.</p>
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<p>Boxplot of daily averaged PM<sub>10</sub> and PM<sub>2.5</sub> concentrations measured by the AirQino stations as compared to ARPAT daily observations during the periods of field calibration for (<b>a</b>) PM<sub>10</sub> and (<b>c</b>) PM<sub>2.5</sub>; field validation for (<b>b</b>) PM<sub>10</sub> and (<b>d</b>) PM<sub>2.5</sub>.</p>
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<p>Annual and seasonal daily cycles observed by the S15-IND, S16-RB, and S19-UB stations across the whole study period: (<b>a</b>,<b>b</b>) yearly cycle of PM<sub>10</sub> and PM<sub>2.5</sub> concentrations; (<b>c</b>–<b>f</b>) seasonal cycle of PM<sub>10</sub> concentrations.</p>
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<p>PM<sub>10</sub> daily concentrations observed by ARPAT, S15-IND, S16-RB, and S19-UB stations during the 2018–2019 and 2019–2020 winter periods. The dashed purple line represents the PM<sub>10</sub> daily limit value (50 µg m<sup>−3</sup>). The blue shaded area denotes the co-location period of AirQino stations during the field validation (25 January–20 February 2020).</p>
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<p>Examples of (<b>a</b>) summer and (<b>b</b>) winter daily patterns at high-frequency (10-min) scale of PM<sub>10</sub> concentrations observed by S15-IND, S16-RB, and S19-UB stations as compared to those observed at low-frequency (24-h) scale by the ARPAT reference station. The dotted purple line denotes the PM<sub>10</sub> daily limit value.</p>
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<p>Detection of PM<sub>10</sub> concentration peaks at high-frequency (10-min) scale by the S15-IND, S16-RB, S19-UB stations as compared to the daily values observed by the ARPAT reference station. Examples of further insights by the AirQino stations with respect to the ARPAT station may be observed during both (<b>a</b>,<b>b</b>) no co-location period and (<b>c</b>) co-location period.</p>
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<p>Number of critical episodes and their persistence (i.e., number of consecutive 1-h PM<sub>10</sub> concentrations &gt; 90 µg m<sup>−3</sup>) for S15-IND, S16-RB, S19-UB stations for the period 28 June 2018–15 April 2020.</p>
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23 pages, 9201 KiB  
Article
A Study of Traffic Emissions Based on Floating Car Data for Urban Scale Air Quality Applications
by Felicita Russo, Maria Gabriella Villani, Ilaria D’Elia, Massimo D’Isidoro, Carlo Liberto, Antonio Piersanti, Gianni Tinarelli, Gaetano Valenti and Luisella Ciancarella
Atmosphere 2021, 12(8), 1064; https://doi.org/10.3390/atmos12081064 - 19 Aug 2021
Cited by 5 | Viewed by 2821
Abstract
Urban air quality in cities is strongly influenced by road traffic emissions. Micro-scale models have often been used to evaluate the pollutant concentrations at the scale of the order of meters for estimating citizen exposure. Nonetheless, retrieving emissions information with the required spatial [...] Read more.
Urban air quality in cities is strongly influenced by road traffic emissions. Micro-scale models have often been used to evaluate the pollutant concentrations at the scale of the order of meters for estimating citizen exposure. Nonetheless, retrieving emissions information with the required spatial and temporal details is still not an easy task. In this work, we use our modelling system PMSS (Parallel Micro Swift Spray) with an emission dataset based on Floating Car Data (FCD), containing hourly data for a large number of road links within a 1 × 1 km2 domain in the city of Rome for the month of May 2013. The procedures to obtain both the emission database and the PMSS simulations are hosted on CRESCO (Computational Centre for Research on Complex Systems)/ENEAGRID HPC facilities managed by ENEA. The possibility of using such detailed emissions, coupled with HPC performance, represents a desirable goal for microscale modeling that can allow such modeling systems to be employed in quasi-real time and nowcasting applications. We compute NOx concentrations obtained by: (i) emissions coming from prescribed hourly modulations of three types of roads, based on vehicle flux data in the FCD dataset, and (ii) emissions from the FCD dataset integrated into our modelling chain. The results of the simulations are then compared to concentrations measured at an urban traffic station. Full article
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<p>Scheme of the PMSS modelling system.</p>
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<p>Simulation domain (red square, with a resolution of 3 m) centered in the AQ station of Magna Grecia (pink point). (<b>a</b>) shows the domain within the Rome urban area, (<b>b</b>) at increased zoom level, (<b>c</b>) includes (in blue) the street segments for which the FCD based emissions are available.</p>
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<p>Street segments in proximity to the AQ Station Magna Grecia for which the time variability was studied. The labels indicate the ID number of the street segment within the database.</p>
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<p>Vehicle flow modulation profiles used in TREFIC.</p>
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<p>Maps reporting the location of urban background air quality stations. The yellow points were used for the calculation of the background NOx concentrations, while the pink point represents the urban traffic station of Magna Grecia inside the simulation domain (the area within the red square).</p>
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<p>Time series of the main statistics parameters calculated for the NO<sub>X</sub> concentrations of the urban background AQ stations surrounding the simulation domain. (<b>a</b>) Average background NO<sub>X</sub> concentration compared to median concentration. (<b>b</b>) Average background in blue with the shaded grey area between the minimum and maximum values and the traffic station observed concentration in green.</p>
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<p>Average simulated NOx concentration in the month of May.</p>
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<p>Hourly NOx concentration comparisons between observations (blue line) and simulations (orange line).</p>
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<p>Daily linear correlation coefficients and the absolute value of the daily fractional bias relative to the time series shown in <a href="#atmosphere-12-01064-f009" class="html-fig">Figure 9</a>.</p>
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<p>Regression plot of modelled vs. observed NOx hourly concentrations of the complete dataset (<b>a</b>) and of selected days 10 May (<b>b</b>), 16 May (<b>c</b>), 19 May (<b>d</b>) with a mean fractional bias lower than 20%.</p>
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<p>Q–Q plot of modelled vs. observed NOx.</p>
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<p>Histogram of the frequency of the differences of hourly modelled NOx concentrations vs. ob<span class="html-italic">Scheme 3</span>. 2. Passenger car simulations (Sim 2 and Sim 3).</p>
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<p>Comparison between Sim 2 and Sim 3 at Magna Grecia in terms of the concentration timeseries.</p>
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<p>Maps of the ensemble average of the NOx concentration (Sim 3) at 19:00 (<b>a</b>) and the ensemble average of the NOx percentage difference at 19:00 (<b>b</b>).</p>
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<p>Scheme to illustrate the steps to create the files to use as input into PSPRAY.</p>
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<p>Comparison between Sim 2 and Sim 3 in terms of percentage difference. The orange line indicates the zero.</p>
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<p>NOx concentrations scatter plot of Sim 2 vs. Sim 3.</p>
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28 pages, 7948 KiB  
Article
Fine-Resolution WRF Simulation of Stably Stratified Flows in Shallow Pre-Alpine Valleys: A Case Study of the KASCADE-2017 Campaign
by Michiel de Bode, Thierry Hedde, Pierre Roubin and Pierre Durand
Atmosphere 2021, 12(8), 1063; https://doi.org/10.3390/atmos12081063 - 19 Aug 2021
Cited by 12 | Viewed by 2791
Abstract
In an overall approach aiming at the development and qualification of various tools designed to diagnose and/or forecast the flows at the local scale in complex terrain, we qualified a numerical model based on the WRF platform and operated in a two-way nested [...] Read more.
In an overall approach aiming at the development and qualification of various tools designed to diagnose and/or forecast the flows at the local scale in complex terrain, we qualified a numerical model based on the WRF platform and operated in a two-way nested domain mode, down to a horizontal resolution of 111 m for the smallest domain. The area in question is the Cadarache valley (CV), in southeast France, which is surrounded by hills and valleys of various sizes. The CV dimensions (1 km wide and 100 m deep) favor the development of local flows greatly influenced by the diurnal cycle and are prone to thermal stratification, especially during stable conditions. This cycle was well documented due to permanent observations and dedicated field campaigns. These observations were used to evaluate the performance of the model on a specific day among the intensive observation periods carried out during the KASCADE-2017 campaign. The model reproduced the wind flow and its diurnal cycle well, notably at the local CV scale, which constitutes considerable progress with respect to the performances of previous WRF simulations conducted in this area with kilometric resolution, be it operational weather forecasts or dedicated studies conducted on specific days. The diurnal temperature range is underestimated however, together with the stratification intensity of the cold pool observed at night. Consequently, the slope drainage flows along the CV sidewalls are higher in the simulation than in the observations, and the resulting scalar fields (such as specific humidity) are less heterogeneous in the model than in the observations. Full article
(This article belongs to the Special Issue The Stable Boundary Layer: Observations and Modeling)
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<p>The locations of the different WRF nested domains used in this study (<b>a</b>), zooming in on the topography around the three inner domains (<b>b</b>) and the zoomed innermost domain with its topography (<b>c</b>) (steep slopes are shaded) and land cover (<b>d</b>) (see <a href="#app2-atmosphere-12-01063" class="html-app">Appendix B</a> for the color definition). CV, DV, and the corresponding red and blue lines in (<b>b</b>) represent the axes of the Cadarache and Durance valleys, respectively. The blue, red, and cyan lines in (<b>c</b>,<b>d</b>) mark the location of the cross-sections presented and discussed in <a href="#sec3dot6-atmosphere-12-01063" class="html-sec">Section 3.6</a>.</p>
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<p>Location of the observation sites: the permanent platforms are the 110 m GBA tower, the Metek sodar (SOD2), and the MET01 weather station. The other platforms were installed during the KASCADE 2017-campaign: SOD1 is the Remtech sodar, AS1 to AS5 are sonic anemometers, and B1 to B4, S1 to S4, and N1 to N4 are the LEMS stations installed at the bottom and on the southern and northern sidewalls of the CV, respectively. The color scale represents the topography of the terrain.</p>
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<p>Synoptic situation at the beginning of the IOP: sea level pressure chart (<b>a</b>) and geopotential height (in meters) at 500 hPa (<b>b</b>). Plots originate from the KNMI (<b>a</b>) and MeteoFrance (<b>b</b>).</p>
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<p>Observations of 20 and 21 February 2017 at the GBA and MET01 stations. The beginning and end of the IOP are indicated by dashed lines. The nighttime periods are shaded.</p>
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<p>Representation of the surface characteristics (land use on the top and topography on the bottom) according to the horizontal resolution. In (<b>a</b>,<b>d</b>), the finest available resolution is presented on an area of 5 × 5 km encompassing the Cadarache valley. In (<b>b</b>,<b>e</b>), the description is degraded to a single value for each 1 × 1 km cell, whereas in (<b>c</b>,<b>f</b>), a sub-area is zoomed in on with the corresponding values for the 111 m horizontal resolution. The locations of the stations (black dots) sometimes fall in a single cell for the 1 km resolution (their names are provided in <a href="#atmosphere-12-01063-f002" class="html-fig">Figure 2</a>), whereas at the finest resolution, all the stations lie in different cells. <a href="#app2-atmosphere-12-01063" class="html-app">Appendix B</a> contains a table with the color coding of the land use classes.</p>
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<p>Performance of the model in simulating wind speed (top) and direction (bottom) at MET01 for different resolutions of the finest domain. The blue dots are observations, whereas the red, orange, and green lines (dots) represent the simulated wind speed (direction) for the 1000, 333, and 111 m resolutions, respectively. The shaded area represents nighttime. The average bias on the wind speed (in m s<sup>−1</sup>) and the direction accuracy (DACC45, in %) are indicated on the plots for the three resolutions.</p>
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<p>Radiosonde profiles and corresponding simulated profiles of potential temperature (<b>upper left</b>), specific humidity (<b>upper right</b>), and wind speed (<b>lower left</b>) and direction (<b>lower right</b>), at 18:00 UTC (blue), 00:00 UTC (green), and 06:00 UTC (violet). Dots mark observations, and continuous lines mark simulations, except for wind direction where lines were replaced with pluses at the levels of the model. The scales on y-axes are the absolute altitude (on the <b>left</b>) and the corresponding height above the ground (on the <b>right</b>).</p>
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<p>Time–height diagram of wind direction (<b>left</b>) and speed (<b>right</b>) above the MET01 site (center of the CV), observed by the sodar (<b>top</b>) and simulated (<b>bottom</b>). Black lines in simulated plots indicate the lowest height of sodar observations.</p>
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<p>Time series of potential temperature (θ), specific humidity (q), wind speed (WS), and wind direction (WD) at the MET 01 site, observed (blue) and simulated (green).</p>
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<p>Time series of CPI and θ* parameters, observed (dots) and simulated (lines). The LEMS used to represent the top of the CV flank is either N4 (bottom), which is the highest station but located on an isolated hill, or N3 (top), which is at the top of the cross-valley instrumented axis (see <a href="#atmosphere-12-01063-f002" class="html-fig">Figure 2</a> for the location).</p>
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<p>Horizontal field of simulated specific humidity at 2 m (left; <b>a</b>,<b>d</b>) and wind direction at 10 m (central; <b>b</b>,<b>e</b>), at 15:30 UTC (top; <b>a</b>,<b>b</b>) and 18:00 UTC (middle; <b>d</b>,<b>e</b>). The topography of the surface is recalled on the right (<b>c</b>,<b>f</b>). Bottom (<b>g</b>,<b>h</b>): time series of specific humidity observed (blue dots) and simulated (green lines) at the bottom of the valley (site B3, left), and at the northern top of the cross-valley axis (right). The blue, red, and cyan lines mark the location of the cross-sections presented in <a href="#atmosphere-12-01063-f012" class="html-fig">Figure 12</a>. Note that the cross-section represented by the blue line extends beyond the border of the plot.</p>
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<p>Vertical cross-sections of wind speed (left) and wind direction (right) along the Durance valley, moving down the valley from the right to the left (<b>a</b>,<b>b</b>; blue line in <a href="#atmosphere-12-01063-f001" class="html-fig">Figure 1</a>c), along the Cadarache valley, moving down the valley from the right to the left (<b>c</b>,<b>d</b>; red line in <a href="#atmosphere-12-01063-f001" class="html-fig">Figure 1</a>c and <a href="#atmosphere-12-01063-f011" class="html-fig">Figure 11</a>), and perpendicular to the Cadarache Valley, from the SW to the NE (<b>e</b>,<b>f</b>; cyan line in <a href="#atmosphere-12-01063-f001" class="html-fig">Figure 1</a>c and <a href="#atmosphere-12-01063-f011" class="html-fig">Figure 11</a>). Red arrows indicate crossing points with the Cadarache valley cross-section, dark blue arrows indicate the crossing points with the Durance valley cross-section, and cyan shows where the cross-section perpendicular to the CV axis passes.</p>
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<p>Example of an observed spectrum at 10 m agl and the fit with the model from [<a href="#B67-atmosphere-12-01063" class="html-bibr">67</a>] (matching to the last entry of <a href="#atmosphere-12-01063-t0A1" class="html-table">Table A1</a>). The abscissa is the wavenumber (in rad m<sup>−1</sup>), and the spectrum energy is in m<sup>2</sup> s<sup>−2</sup>. The green line represents the observed spectrum (values averaged on wavenumber bins), and the red line is the fit from [<a href="#B67-atmosphere-12-01063" class="html-bibr">67</a>]. The blue line is the −2/3 power law. The dotted line indicates the wavenumber value corresponding to the size of the horizontal cell in the finest simulation domain.</p>
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<p>Classifications of all 44 Corine Land Cover classes with three hierarchical levels.</p>
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15 pages, 4958 KiB  
Article
Evaluation and Projection of Near-Surface Wind Speed over China Based on CMIP6 Models
by Hao Deng, Wei Hua and Guangzhou Fan
Atmosphere 2021, 12(8), 1062; https://doi.org/10.3390/atmos12081062 - 18 Aug 2021
Cited by 20 | Viewed by 3482
Abstract
The characteristics of near-surface wind speed (NWS) are important to the study of dust storms, evapotranspiration, heavy rainfall, air pollution, and wind energy development. This study evaluated the performance of 30 models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) through comparison [...] Read more.
The characteristics of near-surface wind speed (NWS) are important to the study of dust storms, evapotranspiration, heavy rainfall, air pollution, and wind energy development. This study evaluated the performance of 30 models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) through comparison with observational NWS data acquired in China during a historical period (1975–2014), and projected future changes in NWS under three scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) based on an optimal multi-model ensemble. Results showed that most models reproduced the spatial pattern of NWS for all seasons and the annual mean, although the models generally overestimated NWS magnitude. All models tended to underestimate the trends of decline of NWS for all seasons and the annual mean. On the basis of a comprehensive ranking index, the KIOST-ESM, CNRM-ESM2-1, HadGEM3-GC31-LL, CMCC-CM2-SR5, and KACE-1-0-G models were ranked as the five best-performing models. In the projections of future change, nationally averaged NWS for all months was weaker than in the historical period, and the trends decreased markedly under all the different scenarios except the winter time series under SSP2-4.5. Additionally, the projected NWS over most regions of China weakened in both the early period (2021–2060) and the later period (2061–2100). Full article
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<p>Generalized topography of China and distribution of the national meteorological stations considered in this study.</p>
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<p>Spatial distributions of (<b>a</b>–<b>e</b>) observed NWS (m·s<sup>−1</sup>), (<b>f</b>–<b>j</b>) AMME simulated NWS (m·s<sup>−1</sup>), and (<b>k</b>–<b>o</b>) AMME simulation biases relative to the observations (i.e., simulation minus observation/observation) for the historical period. Black solid dots represent that the absolute bias passed the 95% significance test.</p>
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<p>Taylor diagrams of (<b>a</b>) spring, (<b>b</b>) summer, (<b>c</b>) autumn, (<b>d</b>) winter, and (<b>e</b>) the annual mean for CMIP6 simulations in comparison with observations over China during 1975–2014. In the Taylor diagrams, angular axes show the spatial correlation coefficient (SCC) between the simulations and observations, radial axes show the spatial standard deviation (STD) normalized against that of the observations, and dashed arcs show the centered root mean square error (CRMSE). Numbers 1–31 represent the different CMIP6 models and AMME, and “REF” represents the observed NWS, which is at the intersection of the SCC of 1 and STD of 1. The closer the position is to REF, the better the performance of the model when not considering relative bias.</p>
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<p>Nationally averaged seasonal and annual relative bias of the CMIP6 models.</p>
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<p>Interannual variability score (<span class="html-italic">IVS</span>) of the CMIP6 models in terms of seasonal and annual NWS over China.</p>
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<p>(<b>a</b>) Heatmap of the ranking of the performance of the CMIP6 models in reproducing the seasonal and annual NWS in China (m·s<sup>−1</sup>). The grid boxes from left to right present the ranking of the Taylor diagram, <span class="html-italic">BIAS</span>, and <span class="html-italic">IVS</span>, respectively. (<b>b</b>) Histogram of the <span class="html-italic">MR</span> for each model based on the Taylor diagrams, <span class="html-italic">BIAS</span>, and <span class="html-italic">IVS</span>.</p>
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<p>Spatial distribution of (<b>a</b>–<b>c</b>) absolute bias (m·s<sup>−1</sup>) and (<b>d</b>–<b>f</b>) temporal correlation for AMME, PMME, and BMME between the observations and CMIP6 simulations of annual NWS over China.</p>
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<p>Climatological monthly distribution of NWS over China in the historical period and under different future scenarios.</p>
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<p>Time series of nationally averaged (<b>a</b>) annual, (<b>b</b>) summer, and (<b>c</b>) winter NWS over China under SSP1-2.6 (black line), SSP2-4.5 (blue line), and SSP5-8.5 (red line), with the trends passing the 99% statistical significance test.</p>
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<p>Projected changes of NWS (m·s<sup>−1</sup>) under (<b>a</b>–<b>c</b>) SSP1-2.6, (<b>d</b>–<b>f</b>) SSP2-4.5, and (<b>g</b>–<b>i</b>) SSP5-8.5 for the period 2021–2060 relative to the reference period (1975–2014). The first column (<b>a</b>,<b>d</b>,<b>g</b>) is annual, the second column (<b>b</b>,<b>e</b>,<b>h</b>) is summer, and the third column (<b>c</b>,<b>f</b>,<b>i</b>) is winter variation. Black solid dots indicate those grids with statistically significant changes at the 95% level.</p>
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<p>Same as <a href="#atmosphere-12-01062-f010" class="html-fig">Figure 10</a>, but for the later period (2061–2100).</p>
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12 pages, 4100 KiB  
Article
Effect of Mid-Latitude Jet Stream on the Intensity of Tropical Cyclones Affecting Korea: Observational Analysis and Implication from the Numerical Model Experiments of Typhoon Chaba (2016)
by Gunwoo Do and Hyeong-Seog Kim
Atmosphere 2021, 12(8), 1061; https://doi.org/10.3390/atmos12081061 - 18 Aug 2021
Cited by 2 | Viewed by 3438
Abstract
The effect of the jet stream on the changes in the intensity of tropical cyclones (TC) affecting Korea is discussed. We classified the TCs into three categories based on the decreasing rate of TC intensity in 24 h after TC passed 30° N. [...] Read more.
The effect of the jet stream on the changes in the intensity of tropical cyclones (TC) affecting Korea is discussed. We classified the TCs into three categories based on the decreasing rate of TC intensity in 24 h after TC passed 30° N. The TCs with a large intensity decrease had a more vigorous intensity when the TCs approached the mid-latitudes. The analysis of observational fields showed that the strong jet stream over Korea and Japan may intensify TCs by the secondary circulations of jet entrance but induces a large decrease in TC intensity in the mid-latitudes by the strong vertical wind shear. We also performed the numerical simulation for the effect of the jet stream on the intensity changes of Typhoon Chaba (2016). As a result, the stronger jet stream induced more low-level moisture convergence at the south of the jet stream entrance, enhancing the intensity when the TC approached Korea. Furthermore, it induced a rapid reduction in intensity when TC approached in the strong jet stream area. The results suggest that the upper-level jet stream is one of the critical factors modulating the intensity of TC affecting Korea in the vicinity of the mid-latitudes. Full article
(This article belongs to the Section Meteorology)
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<p>TC tracks analyzed in this study. The red box represents the region of 30–34° N and 122–129° E. The red, blue, green, and magenta dots indicate the average location of TCs at the −1, 0, 1, and 2 days after the timing of passing through 30° N.</p>
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<p>The box-whisker plot of increase in the central pressure of TCs in 24 h after passing 30° N. The box covers lower and upper quartiles and the whiskers are extended to the minimum and maximum values.</p>
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<p>The temporal changes in the average central pressure of TCs before/after the TCs pass 30° N for all and three cases. The zero at the x-axis indicates the time when the TCs pass 30° N and negative (positive) values represent the time before (after) the TCs pass 30° N. The light gray line represents the central pressure of Typhoon Chaba (2016) that is the representative TC in case 3 discussed in <a href="#sec3dot2-atmosphere-12-01061" class="html-sec">Section 3.2</a>.</p>
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<p>The composite maps of SST and vertical wind shear between 850 hPa and 200 hPa when TCs pass 30° N (<b>a</b>,<b>e</b>) and their anomalies for the three cases (<b>b</b>–<b>d</b>,<b>f</b>–<b>h</b>). In the anomaly maps, the statistically significant regions at the 95% confidence level are shaded.</p>
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<p>The composite maps of the 250-hPa zonal wind (<b>a</b>–<b>d</b>), divergence at the 250 hPa (<b>e</b>–<b>h</b>) and 850 hPa (<b>f</b>–<b>l</b>), and 500-hPa pressure velocity (<b>m</b>–<b>p</b>) during three days before TCs arrived at 30° N for all and three cases.</p>
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<p>Domains for WRF simulation.</p>
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<p>The average 250-hPa zonal winds during simulation time (1–5 October 2016) for observation (<b>a</b>) and each simulation (<b>b</b>–<b>e</b>).</p>
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<p>The tracks of Typhoon Chaba in observation (best-track, black line) and each simulation. The tracks are plotted for 3–6 October 2016.</p>
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<p>The wind speed averaged within 3 degrees from the TC center (<b>a</b>) and central pressure (<b>b</b>) during 3–6 October.</p>
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<p>The box-whisker plots of the hourly moisture convergences at the 850 hPa (<b>a</b>) and 700 hPa (<b>b</b>) averaged 3 degrees around the TC center for 3 October 00 UTC to 5 00 UTC.</p>
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10 pages, 1638 KiB  
Article
The Effects of Air Quality on Hospital Admissions for Chronic Respiratory Diseases in Petaling Jaya, Malaysia, 2013–2015
by Karyn Morrissey, Ivy Chung, Andrew Morse, Suhanya Parthasarath, Margaret M. Roebuck, Maw Pin Tan, Amanda Wood, Pooi-Fong Wong and Simon P. Frostick
Atmosphere 2021, 12(8), 1060; https://doi.org/10.3390/atmos12081060 - 18 Aug 2021
Cited by 7 | Viewed by 3195
Abstract
This study assesses the impact of a decrease in air quality and the risk of hospital admissions to a public hospital for chronic respiratory diseases for residents of Petaling Jaya, a city in the Greater Kuala Lumpur area in Malaysia. Data on hospital [...] Read more.
This study assesses the impact of a decrease in air quality and the risk of hospital admissions to a public hospital for chronic respiratory diseases for residents of Petaling Jaya, a city in the Greater Kuala Lumpur area in Malaysia. Data on hospital admissions for asthma, bronchitis, emphysema and other chronic obstructive pulmonary disease, weather conditions and the Malaysian Air Pollution Index, a composite indicator of air quality, were collated. An unconstrained distributed lag model to obtain risk of hospitalization for a 10 μg/m3 increase in the API. The lag cumulative effect for a 10 μg/m3 increase in the API was calculated to test for harvesting in the short term. Findings indicate that after an initial decrease in admissions (days 3 and 4), admissions increased again at day 7 and 8 and this relationship was significant. We therefore conclude that a 10 μg/m3 increase has a greater effect on admissions for respiratory health in the short term than a harvesting effect alone would suggest. These results suggest that while air quality is improving in the Greater Kuala Lumpur area, no level of air pollution can be deemed safe. Full article
(This article belongs to the Special Issue Air Pollution and Public Health Effects)
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<p>Seasonal pattern of daily admissions for chronic respiratory diseases for Petaling Jaya Residents 2013–2015.</p>
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<p>Seasonal pattern of temperature, relative humidity, and air pollution levels in Petaling Jaya (PJ), 2013–2015.</p>
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<p>Single day lag models percentage change (with 95% confidence interval) in admissions for chronic respiratory disease associated with a 10 ug/m<sup>3</sup> increases in API over 10 days.</p>
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<p>Cumulative effect associated with a 10 μg/m<sup>3</sup> increases in the API over 10 days on admissions for chronic respiratory disease.</p>
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33 pages, 5798 KiB  
Article
Pre-Seismic Irregularities during the 2020 Samos (Greece) Earthquake (M = 6.9) as Investigated from Multi-Parameter Approach by Ground and Space-Based Techniques
by Sudipta Sasmal, Swati Chowdhury, Subrata Kundu, Dimitrios Z. Politis, Stelios M. Potirakis, Georgios Balasis, Masashi Hayakawa and Sandip K. Chakrabarti
Atmosphere 2021, 12(8), 1059; https://doi.org/10.3390/atmos12081059 - 18 Aug 2021
Cited by 47 | Viewed by 4796
Abstract
We present a comprehensive analysis of pre-seismic anomalies as computed from the ground and space-based techniques during the recent Samos earthquake in Greece on 30 October 2020, with a magnitude M = 6.9. We proceed with a multi-parametric approach where pre-seismic irregularities are [...] Read more.
We present a comprehensive analysis of pre-seismic anomalies as computed from the ground and space-based techniques during the recent Samos earthquake in Greece on 30 October 2020, with a magnitude M = 6.9. We proceed with a multi-parametric approach where pre-seismic irregularities are investigated in the stratosphere, ionosphere, and magnetosphere. We use the convenient methods of acoustics and electromagnetic channels of the Lithosphere–Atmosphere–Ionosphere-Coupling (LAIC) mechanism by investigating the Atmospheric Gravity Wave (AGW), magnetic field, electron density, Total Electron Content (TEC), and the energetic particle precipitation in the inner radiation belt. We incorporate two ground-based IGS GPS stations DYNG (Greece) and IZMI (Turkey) for computing the TEC and observed a significant enhancement in daily TEC variation around one week before the earthquake. For the space-based observation, we use multiple parameters as recorded from Low Earth Orbit (LEO) satellites. For the AGW, we use the SABER/TIMED satellite data and compute the potential energy of stratospheric AGW by using the atmospheric temperature profile. It is found that the maximum potential energy of such AGW is observed around six days before the earthquake. Similar AGW is also observed by the method of wavelet analysis in the fluctuation in TEC values. We observe significant energetic particle precipitation in the inner radiation belt over the earthquake epicenter due to the conventional concept of an ionospheric-magnetospheric coupling mechanism by using an NOAA satellite. We first eliminate the particle count rate (CR) due to possible geomagnetic storms and South Atlantic Anomaly (SAA) by the proper choice of magnetic field B values. After the removal of the statistical background CRs, we observe a significant enhancement of CR four and ten days before the mainshock. We use Swarm satellite outcomes to check the magnetic field and electron density profile over a region of earthquake preparation. We observe a significant enhancement in electron density one day before the earthquake. The parameters studied here show an overall pre-seismic anomaly from a duration of ten days to one day before the earthquake. Full article
(This article belongs to the Special Issue Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) Models (Vol. 2))
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<p>The locations of GPS-IGS stations DYNG and IZMI (blue squares), the EQ epicenter (red disk) and the EPZ (blue circle), CZ (red circle), and Swarm satellite track (black line).</p>
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<p>Daily variation of (<b>a</b>) Dst; (<b>b</b>) Kp; (<b>c</b>) 3-hour average Ap; (<b>d</b>) daily average Ap; (<b>e</b>) SID; (<b>f</b>) IMF-Bz from the day of the year 288 to 314. Solar active days are shown by a red bar, whereas solar quiet days are indicated by a black bar (for (<b>c</b>–<b>e</b>)). The dashed red line denotes the EQ day. The blue dashed line represents the three-hour average A<sub><span class="html-italic">p</span></sub> value of 25, and the magenta dashed line represents the daily average A<span class="html-italic"><sub>p</sub></span> value of 16.</p>
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<p>(<b>a</b>) The diurnal variation of TEC (black line) from 17 October to 4 November, the 2020 Samos EQ along with the upper (red line) and lower (green) bound (upper panel). (<b>b</b>) The lower panel shows the fluctuations in TEC. The day of the EQ is marked with an arrow.</p>
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<p>Same (<b>a</b>,<b>b</b>) as <a href="#atmosphere-12-01059-f003" class="html-fig">Figure 3</a>, but for IZMI station.</p>
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<p>Potential Energy (<math display="inline"><semantics> <msub> <mi>E</mi> <mi>p</mi> </msub> </semantics></math>) variation associated with with AGW from 17 October to 4 November 2020. Along the <span class="html-italic">x</span>- and <span class="html-italic">y</span>-axis, we present the date and altitude in km, respectively. The colorbar indicates the <math display="inline"><semantics> <msub> <mi>E</mi> <mi>p</mi> </msub> </semantics></math> values in J/kg. The black dashed line indicates the EQ day.</p>
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<p>Spatial variation of potential energy at an altitude of 47 km from 17–31 October 2020. Along the <span class="html-italic">x</span>-axis and <span class="html-italic">y</span>-axis, we present the longitude and latitude, respectively. The magenta diamond represents the epicenter of the EQ, and the white lines indicate the country border.</p>
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<p>Diurnal variation of dVTEC (black curve) with the un-perturbed levels (red lines) from 17 to 31 November 2020 for DYNG station.</p>
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<p>Scalogram from 17 to 31 October 2020 as computed from dVTEC variation for DYNG station. Along the <span class="html-italic">x</span>- and <span class="html-italic">y</span>-axis, the time (in hours) and wavelet period (in minutes) are plotted, respectively. The colorbar represents the wavelet power. The white line represents the cone of influence (COI).</p>
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<p>Diurnal variation of (<b>a</b>) VTEC (top), (<b>b</b>) dVTEC (middle), and (<b>c</b>) scalogram (bottom) for 19 October 2020. In the top panel, the black solid curve and the red dotted curve represent the observed and fitted TEC profiles, respectively.</p>
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<p>Same as <a href="#atmosphere-12-01059-f007" class="html-fig">Figure 7</a>, but for IZMI station.</p>
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<p>Same as <a href="#atmosphere-12-01059-f008" class="html-fig">Figure 8</a>, but for IZMI station.</p>
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<p>Same (<b>a</b>–<b>c</b>) as <a href="#atmosphere-12-01059-f009" class="html-fig">Figure 9</a>, but for IZMI station.</p>
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<p>Count rate (CR) daily average data availability in (L, <math display="inline"><semantics> <mi>α</mi> </semantics></math>, B) cell on 30 October 2020. The colorbar denotes the number of times the satellite passes through the same cell. The satellite passes the maximum number through the geomagnetic field value B = 22.0–25.0 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>T.</p>
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<p>Distribution of averaged 8 s counts on 30 October 2020, for 0<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> telescope electrons with energy between 30 keV and 100 keV and with a B value ranges from 22 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>T &lt; B &lt; 25 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>T. The <span class="html-italic">x</span>-axis indicates the averaged counts in 8 s, and the <span class="html-italic">y</span>-axis denotes the daily count number. Here, the vertical dashed lines denote the 4<math display="inline"><semantics> <mi>σ</mi> </semantics></math> level. The red histograms which are over 4<math display="inline"><semantics> <mi>σ</mi> </semantics></math> level are treated as PB, whereas the black histograms are the normal statistical fluctuations.</p>
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<p>Diurnal variation of particle bursts from 15 October to 9 November 2020. (<b>a</b>) The black histograms are for quite solar days, and the red histograms are contaminated due to solar activity. The dashed horizontal line indicates the average PB. (<b>b</b>) The lower panel shows the enhancement of PB on ten and four days before the EQ.</p>
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<p>Detection of the anomalous track (track number 15) the magnetic field of the Swarm Satellite C by MASS algorithm on 29 October 2020. From (<b>a</b>–<b>d</b>) the first panel to the fourth panel, the <span class="html-italic">x</span>-axis indicates the time derivative of X, Y, Z, and scalar components of the magnetic field, respectively. The <span class="html-italic">y</span>-axis indicates the geomagnetic latitude.</p>
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<p>Comparison of magnetic filed anomaly from MASS with the electron density profile as obtained from NeLOG algorithm in the track number 15 of SAT-C. Geomagnetic latitude variation of rate of change of magnetic field in (<b>a</b>) X-component, (<b>b</b>) Y-component, (<b>c</b>) Z-component, (<b>d</b>) scaler component (F). (<b>e</b>) In the fifth panel, the black line shows the original Log<math display="inline"><semantics> <msub> <mrow/> <mn>10</mn> </msub> </semantics></math> (N<math display="inline"><semantics> <msub> <mrow/> <mi>e</mi> </msub> </semantics></math>) variation, and the red line indicates the polynomial fitted curve.</p>
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<p>Detection of anomalous track identified by NeLOG algorithm in electron density. Latitudinal (<b>a</b>) electron density variation in lograrithmic scale, (<b>b</b>) rate of change of electron density variation, (<b>c</b>) residual of rate of change of electron density, (<b>d</b>) electron temperature (black curve) and potential (red curve).</p>
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<p>NeSTAD analysis on a track recorded for Swarm (SAT-C) on 29 October 2020. (<b>a</b>) The left panel shows the electron density variation on the sat-C track number 15 as a function of latitude, (<b>b</b>) the right panel shows the anomaly in the 5<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> N to 30<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> N latitude range.</p>
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<p>Temporal evolution of TEC-AGW (violet histogram), TEC (red histogram), PB (magenta histogram), swarm magnetic field (in nT) (green histogram), swarm plasma density (in m<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </semantics></math>) (orange histogram), SABER-AGW (black histogram) activities from multiparametric analysis. <span class="html-italic">y</span>-axis has no scale or units, it only indicates dates of multiparametric activity. The differences in the <span class="html-italic">y</span>-axis dimension only serve as a means of simple visual identification of the activity for the various parameters, especially in overlapping cases.</p>
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21 pages, 11480 KiB  
Article
Experimental Analysis of the Influence of Urban Morphological Indices on the Urban Thermal Environment of Zhengzhou, China
by Xuefan Zhou and Hong Chen
Atmosphere 2021, 12(8), 1058; https://doi.org/10.3390/atmos12081058 - 18 Aug 2021
Cited by 7 | Viewed by 2850
Abstract
Summer extreme high-temperatures occur frequently in large cities; urban spatial form is the primary factor affecting the urban thermal environment. Thus, planning and arranging urban spaces is a key approach to regulating urban microclimates. Studies into how urban spatial forms influence the formation [...] Read more.
Summer extreme high-temperatures occur frequently in large cities; urban spatial form is the primary factor affecting the urban thermal environment. Thus, planning and arranging urban spaces is a key approach to regulating urban microclimates. Studies into how urban spatial forms influence the formation of urban microclimates have been carried out for multiple cities in warm and hot regions; however, few studies of this kind have been carried out for cities in cold regions. In this study, we analyze Zhengzhou, a city located in a cold region of China, using summer 2017 measurement data to determine why high temperatures develop in cold areas. We investigated how temperature and humidity vary during the morning, at noon, and in the evening given different land use properties (commercial and residential) and different spatial forms (building height, building density, green coverage rate, and plot ratio); we then studied the correlation between urban spatial form and the urban thermal environment. Our research results indicate that the commercial district’s thermal microclimate was related to PR and BH in the afternoon and GCR in the morning and at night. In the residential district, the key urban morphology factors related to its thermal microclimates were BD, PR, and GCR during almost the whole day. Full article
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<p>Locations of Zhengzhou city, sampling commercial district (CD), residential district (RD), and reference point (RP).</p>
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<p>Average annual maximum air temperature and minimum air temperature of Zhengzhou (data source from <a href="http://www.weather.com.cn/forecast/history.shtml?areaid=101180101&amp;month" target="_blank">http://www.weather.com.cn/forecast/history.shtml?areaid=101180101&amp;month</a>, accessed on 21 June 2021).</p>
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<p>Locations of the Er-qi commercial district and 27 sampling circle areas (<b>A</b>), locations of the Yanzhuang residential district and 23 sampling circle areas (<b>B</b>).</p>
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<p>Instruments’ installations of the reference point (<b>A</b>) and for mobile measurements (<b>B</b>).</p>
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<p>Four-day average (<b>A</b>) air temperatures and (<b>B</b>) relative humidity for six of the mobile measurement routes and for the reference point.</p>
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<p>Distributions of four-day average air temperature ratio and relative humidity ratio of the Er-qi commercial district.</p>
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<p>Distributions of four-day average air temperature ratio and relative humidity ratio of the Yanzhuang residential district.</p>
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<p>Relationships between Urban morphology index (<span class="html-italic">x</span>-axis) and air temperature ratio from the mobile and reference point (<span class="html-italic">y</span>-axis) measurements in the commercial district.</p>
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<p>Relationships between urban morphology index (<span class="html-italic">x</span>-axis) and relative humidity ratio from the mobile and reference point (<span class="html-italic">y</span>-axis) measurements in the commercial district.</p>
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<p>Relationships between urban morphology index (<span class="html-italic">x</span>-axis) and air temperature ratio from the mobile and reference point (<span class="html-italic">y</span>-axis) measurements in the residential district.</p>
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<p>Relationships between the urban morphology index (<span class="html-italic">x</span>-axis) and relative humidity ratio from the mobile and reference point (<span class="html-italic">y</span>-axis) measurements in the commercial district.</p>
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<p>Infrared thermography of the Er-qi commercial district (<b>A</b>,<b>B</b>) and the Yanzhuang residential district (<b>C</b>,<b>D</b>).</p>
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8 pages, 731 KiB  
Article
222Rn Exhalation Rates from Some Granite and Marble Used in Korea: Preliminary Study
by Hyewon Lee, Jungsub Lee, Sungwon Yoon and Cheolmin Lee
Atmosphere 2021, 12(8), 1057; https://doi.org/10.3390/atmos12081057 - 18 Aug 2021
Cited by 4 | Viewed by 2738
Abstract
The objective of this study was to establish a test method for assessing radon exhalation rates from building materials considering radon related environmental policy and research in Korea. This method was established in consideration of cost-effectiveness based on the International Standards Organization (ISO) [...] Read more.
The objective of this study was to establish a test method for assessing radon exhalation rates from building materials considering radon related environmental policy and research in Korea. This method was established in consideration of cost-effectiveness based on the International Standards Organization (ISO) method and the closed chamber method, which is an evaluation method for the emission of hazardous chemical substances from building materials in Korea. The assessment of radon exhalation rates from five types each of granite and marble used in the construction industry in Korea gave mean radon exhalation rates of 0.497 ± 0.467 Bq/m2∙h from granite and 0.193 ± 0.113 Bq/m2∙h from marble, indicating higher radon exhalation rates from granite. These results are consistent with those of a previous study, indicating that granites are more likely to show higher radon exhalation rates than marbles. Full article
(This article belongs to the Special Issue Atmospheric Radon Measurements, Control, Mitigation and Management)
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<p>The experiment system used to assess <sup>222</sup>Rn exhalation rates from building materials.</p>
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24 pages, 1900 KiB  
Article
Forest Management and Adaptation Strategies in Response to Climate Change by the Taiwanese Public
by Wan-Yu Liu, Chien-Chen Wu and Shih-Yu Simon Wang
Atmosphere 2021, 12(8), 1056; https://doi.org/10.3390/atmos12081056 - 17 Aug 2021
Cited by 5 | Viewed by 3166
Abstract
Forests account for 60% of lands in Taiwan. Climate change impacts forests in many aspects and is increasingly likely to undermine the ability of forests to provide basic ecosystem services. To help reduce the impact of climate change on Taiwan’s forests, people must [...] Read more.
Forests account for 60% of lands in Taiwan. Climate change impacts forests in many aspects and is increasingly likely to undermine the ability of forests to provide basic ecosystem services. To help reduce the impact of climate change on Taiwan’s forests, people must be made aware of the relationship between climate change and forests. Based on questionnaires collected from 17 cities in Taiwan, this study applied spatial analysis to assess the respondents’ understanding of climate change and adaptation strategies for forest management. A total of 650 questionnaires were distributed and 488 valid ones were collected. The results show that (1) Most respondents believe that climate change is true and more than half of the respondents have experienced extreme weather events, especially extreme rainfall; (2) Most respondents believe that climate change will affect Taiwan’s forests with the majority recognizing the increasing impact of extreme events being the primary cause, followed by changes in the composition of tree species and the deterioration of forest adaptability due to climate change; (3) Most respondents expressed that forest management should be adjusted for climate change and called for measures being taken to establish mixed forests as well as monitoring forest damage; (4) In order to address the difficulties faced by forest owners on the impact of climate change, the majority of respondents felt that the government should raise forest owners’ understanding on climate change and adaptation policies, while the subsidy incentives must also be adjusted. The results of this study show that the respondents do realize the importance of climate change and forest management so much so their awareness in this matter led to their support for forest adaptation measures and policies. Full article
(This article belongs to the Special Issue Climate Change and Forest Environment)
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<p>Study Site.</p>
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<p>Supporting ratio of adaptation policies in response to climate change in each county/city in Taiwan (question 3).</p>
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<p>Supporting ratio of adaptation policies in response to climate change when the respondents imagined that they were forest owners (question 9).</p>
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<p>Venn diagram. Note 1: A Venn diagram is a diagram used to represent sets or classes (in a slightly loose sense) in the mathematical field of set theory (or theory of classes). This diagram is used to demonstrate the mathematical or logical connections between collections of objects or sets. This diagram is particularly suitable for presenting approximate relationships between sets or classes, or for being used to conduct derivation (or understanding the derivation of) the rules of set or class operations. Note 2: The size of the Venn diagram is positively related to the sample size of each county or city; the area of the circle is positively related to the number of samples. Note 3: * means the Venn diagram was finely adjusted for clarity and is not a true representation of the percentage of samples.</p>
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