Atmosphere

19 pages, 2383 KiB

Open AccessEditor’s ChoiceArticle

Potential Human and Plant Pathogenic Species in Airborne PM10 Samples and Relationships with Chemical Components and Meteorological Parameters

by Salvatore Romano, Mattia Fragola, Pietro Alifano, Maria Rita Perrone and Adelfia Talà

Atmosphere 2021, 12(5), 654; https://doi.org/10.3390/atmos12050654 - 20 May 2021

Cited by 6 | Viewed by 3597

Abstract

A preliminary local database of potential (opportunistic) airborne human and plant pathogenic and non-pathogenic species detected in PM10 samples collected in winter and spring is provided, in addition to their seasonal dependence and relationships with meteorological parameters and PM10 chemical species. The PM10 [...] Read more.

A preliminary local database of potential (opportunistic) airborne human and plant pathogenic and non-pathogenic species detected in PM10 samples collected in winter and spring is provided, in addition to their seasonal dependence and relationships with meteorological parameters and PM10 chemical species. The PM10 samples, collected at a Central Mediterranean coastal site, were analyzed by the 16S rRNA gene metabarcoding approach, and Spearman correlation coefficients and redundancy discriminant analysis tri-plots were used to investigate the main relationships. The screening of 1187 detected species allowed for the detection of 76 and 27 potential (opportunistic) human and plant pathogens, respectively. The bacterial structure of both pathogenic and non-pathogenic species varied from winter to spring and, consequently, the inter-species relationships among potential human pathogens, plant pathogens, and non-pathogenic species varied from winter to spring. Few non-pathogenic species and even fewer potential human pathogens were significantly correlated with meteorological parameters, according to the Spearman correlation coefficients. Conversely, several potential plant pathogens were strongly and positively correlated with temperature and wind speed and direction both in winter and in spring. The number of strong relationships between presumptive (human and plant) pathogens and non-pathogens, and meteorological parameters slightly increased from winter to spring. The sample chemical composition also varied from winter to spring. Some potential human and plant pathogens were correlated with chemicals mainly associated with marine aerosol and/or with soil dust, likely because terrestrial and aquatic environments were the main habitats of the detected bacterial species. The carrier role on the species seasonal variability was also investigated. Full article

(This article belongs to the Special Issue Bioaerosols: Composition, Meteorological Impact, and Transport)

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Relative percentage contribution of the overall amount of the 27 potential plant pathogenic species, 76 potential human pathogenic species, 1084 potential non-pathogenic species, and unclassified species in (a) winter (S1–S10) and (b) spring (S11–S20) samples. Bray–Curtis dissimilarity dendrograms highlighting the relatedness of the species-level bacterial communities in (a) winter and (b) spring samples have also been reported. Full article ">Figure 2
Mean percentage contribution (on a logarithmic scale) of the 10 most abundant potential and pervasive human pathogenic species in (a) winter and (b) spring: Sphingobacterium multivorum (S. multivorum), Johnsonella ignava (J. ignava), Streptococcus bovis (S. bovis), Staphylococcus aureus (S. aureus), Clostridium cadaveris (C. cadaveris), Peptococcus niger (P. niger), Propionibacterium avidum (P. avidum), Propionibacterium acnes (P. acnes), Providencia rettgeri (P. rettgeri), Acinetobacter lwoffii (A. lwoffii), Acinetobacter ursingii (A. ursingii), Acinetobacter johnsonii (A. johnsonii), Enterobacter aerogenes (E. aerogenes), Enterobacter amnigenus (E. amnigenus), and Enterobacter hormaechei (E. hormaechei). Error bars represent the standard error of the mean. Phyla related to each species are also reported on the left (Prot.: Proteobacteria, Actin.: Actinobacteria, Firm.: Firmicutes, Bact.: Bacteroidetes). Full article ">Figure 3
Mean percentage contribution (on a logarithmic scale) of the most abundant potential and pervasive plant pathogenic species in (a) winter and (b) spring: Bacillus megaterium (B. megaterium), Rathayibacter tritici (R. tritici), Clavibacter michiganensis (C. michiganensis), Curtobacterium flaccumfaciens (C. flaccumfaciens), Agrobacterium larrymoorei (A. larrymoorei), Erwinia mallotivora (E. mallotivora), Janthinobacterium agaricidamnosum (J. agaricidamnosum), Sphingomonas melonis (S. melonis), Pseudomonas viridiflava (P. viridiflava), and Enterobacter cloacae (E. cloacae). Error bars represent the standard error of the mean. Phyla related to each species are also reported on the left (Prot.: Proteobacteria, Actin.: Actinobacteria, Firm.: Firmicutes). Full article ">

5 pages, 203 KiB

Open AccessEditorial

Climate Change, Aquatic Ecosystems and Human Infectious Diseases in a Globalised World

by Arturo Sousa, Mónica Aguilar-Alba and Leoncio García-Barrón

Atmosphere 2021, 12(5), 653; https://doi.org/10.3390/atmos12050653 - 20 May 2021

Cited by 1 | Viewed by 2263

Abstract

One of the greatest challenges that human society currently faces is the hazard of climate change with respect to human health [...] Full article

(This article belongs to the Special Issue Impacts of Climate Change on Human Infectious Diseases Related to Water Ecosystems)

20 pages, 6499 KiB

Open AccessArticle

Dry Spells in Croatia: Observed Climate Change and Climate Projections

by Ivana Marinović, Ksenija Cindrić Kalin, Ivan Güttler and Zoran Pasarić

Atmosphere 2021, 12(5), 652; https://doi.org/10.3390/atmos12050652 - 20 May 2021

Cited by 19 | Viewed by 4003

Abstract

This study performs a systematic analysis of the recent and future changes of dry spells (DS) in Croatia. DS are defined as consecutive sequences of days with daily precipitation less than 5 mm of the precipitation-per-day threshold (DS5). Daily precipitation data come from [...] Read more.

This study performs a systematic analysis of the recent and future changes of dry spells (DS) in Croatia. DS are defined as consecutive sequences of days with daily precipitation less than 5 mm of the precipitation-per-day threshold (DS5). Daily precipitation data come from a dense national rain gauge network (covering seven regions) and span the period 1961–2015. The spatial and temporal changes of the observed mean (MDS5) and maximum (MxDS5) seasonal and annual dry spells were analysed by means of the Kendall tau method and the partial trend method. Future changes of DS5 were assessed by employing the three regional climate models (RegCM4, CCLM4 and RCA4) covering the EURO-CORDEX domain with a 12.5-km horizontal resolution, resulting in a realistic orography and land–sea border over Croatia. The models were forced at their boundaries by the four CMIP5 global climate models. For the reference period 1971–2000, the observed, as well as modelled, DS5 were analysed, and the systematic model errors were assessed. Finally, the projections and future changes of the DS5 statistics based on simulations under the high and medium greenhouse gases concentration scenarios (i.e., RCP8.5 and RCP4.5) with a focus on the climate change signal between 1971–2000 and two future periods, 2011–2040 and 2041–2070, were examined. A prevailing increasing trend of MDS5 was found in the warm part of the year, being significant in the mountainous littoral and North Adriatic coastal region. An increasing trend of MxDS5 was also found in the warm part of the year (both the spring and summer), and it was particularly pronounced along the Adriatic coast, while a coherent negative trend pattern was found in the autumn. By applying the partial trend methodology, an increase was found in the very long DS5 (above the 90th percentile) in the recent half of the analysed 55-year period in all seasons, except in the autumn when shortening in the DS5 was detected. The climate change signal during the two analysed future periods was positive for the summer in all regions, weakly negative for the winter and not conclusive for the spring, autumn and year. It was found that no RCM-GCM combination is the best in all cases, since the most successful model combinations depend on the season and location. Full article

(This article belongs to the Special Issue Climate Extremes in the Pannonian Basin: Current Approaches and Challenges)

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Locations of the meteorological stations used in this study and the regions listed in <a href="#atmosphere-12-00652-t001" class="html-table">Table 1</a>. The nine stations selected for the regional climate model validations and climate change projection analysis are highlighted with black diamonds. Full article ">Figure 2
Seasonal (upper four panels) and annual (bottom panel) mean dry spell durations in days (brown bars) for the 5-mm precipitation-per-day thresholds. Red dots indicate the corresponding median values, while the interquartile range (25th and 75th percentiles) for each station is depicted in grey. The seven regions are distinguished by bold dashed vertical lines. Full article ">Figure 3
Seasonal (upper four panels) and annual (bottom panel) maximum dry spell durations (MxDS5, in days) for the 5-mm precipitation-per-day thresholds. The seven regions are distinguished by bold dashed vertical lines. Full article ">Figure 4
The trends in the seasonal and annual MDS5 (in days/10 years). Dots refer to trend values (Sen’s slope) for each station in the associated region. Blue dots present nonsignificant trends, whereas significant trends are highlighted by red dots. Full article ">Figure 5
The same as <a href="#atmosphere-12-00652-f004" class="html-fig">Figure 4</a> but for MxDS5. Full article ">Figure 6
The partial trend scatter diagram for DS5 for nine selected stations in Croatia. Seasons are distinguished by different colours. The 95th percentile values from each period (1961–1987 and 1988–2014) are denoted by circles. Full article ">Figure 7
Average MDS5 from observations (O) and the median of 30-year average MDS5 from the models (M) for seven stations on the annual and seasonal time scale. Seasons are distinguished by different colours. Full article ">Figure 8
The median difference in MDS5 duration between two future periods and historical period for RCP4.5 and RCP8.5 scenarios, for seven stations. The circles refer to the period P1 (2011–2040) vs. P0 (1971–2000) and triangles refer to the second period P2 (2041–2070) vs. P0. Seasons are distinguished by different colours. For each station, left symbols present results for RCP4.5, while right symbols present results for RCP8.5 scenario. The corresponding differences in MDS5 (in days) are given on the vertical axis. Full article ">

17 pages, 4597 KiB

Open AccessArticle

A Short-Term Wind Speed Forecasting Model Based on a Multi-Variable Long Short-Term Memory Network

by Anqi Xie, Hao Yang, Jing Chen, Li Sheng and Qian Zhang

Atmosphere 2021, 12(5), 651; https://doi.org/10.3390/atmos12050651 - 19 May 2021

Cited by 37 | Viewed by 4595

Abstract

Accurately forecasting wind speed on a short-term scale has become essential in the field of wind power energy. In this paper, a multi-variable long short-term memory network model (MV-LSTM) based on Pearson correlation coefficient feature selection is proposed to predict the short-term wind [...] Read more.

Accurately forecasting wind speed on a short-term scale has become essential in the field of wind power energy. In this paper, a multi-variable long short-term memory network model (MV-LSTM) based on Pearson correlation coefficient feature selection is proposed to predict the short-term wind speed. The proposed method utilizes multiple historical meteorological variables, such as wind speed, temperature, humidity, and air pressure, to predict the wind speed in the next hour. Hourly data collected from two ground observation stations in Yanqing and Zhaitang in Beijing were divided into training and test sets. The training sets were used to train the model, and the test sets were used to evaluate the model with the root-mean-square error (RMSE), mean absolute error (MAE), mean bias error (MBE), and mean absolute percentage error (MAPE) metrics. The proposed method is compared with two other forecasting methods (the autoregressive moving average model (ARMA) method and the single-variable long short-term memory network (LSTM) method, which inputs only historical wind speed data) based on the same dataset. The experimental results prove the feasibility of the MV-LSTM method for short-term wind speed forecasting and its superiority to the ARMA method and the single-variable LSTM method. Full article

(This article belongs to the Special Issue Recent Advances and Future Prospects of Machine Learning in Predictive Modeling of Atmospheric Sciences)

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36 pages, 57857 KiB

Open AccessEditor’s ChoiceReview

Recent Advances in Our Understanding of Tropical Cyclone Intensity Change Processes from Airborne Observations

by Robert F. Rogers

Atmosphere 2021, 12(5), 650; https://doi.org/10.3390/atmos12050650 - 19 May 2021

Cited by 19 | Viewed by 6294

Abstract

Recent (past ~15 years) advances in our understanding of tropical cyclone (TC) intensity change processes using aircraft data are summarized here. The focus covers a variety of spatiotemporal scales, regions of the TC inner core, and stages of the TC lifecycle, from preformation [...] Read more.

Recent (past ~15 years) advances in our understanding of tropical cyclone (TC) intensity change processes using aircraft data are summarized here. The focus covers a variety of spatiotemporal scales, regions of the TC inner core, and stages of the TC lifecycle, from preformation to major hurricane status. Topics covered include (1) characterizing TC structure and its relationship to intensity change; (2) TC intensification in vertical shear; (3) planetary boundary layer (PBL) processes and air–sea interaction; (4) upper-level warm core structure and evolution; (5) genesis and development of weak TCs; and (6) secondary eyewall formation/eyewall replacement cycles (SEF/ERC). Gaps in our airborne observational capabilities are discussed, as are new observing technologies to address these gaps and future directions for airborne TC intensity change research. Full article

(This article belongs to the Special Issue Rapid Intensity Changes of Tropical Cyclones)

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27 pages, 20853 KiB

Open AccessArticle

High NO₂ Concentrations Measured by Passive Samplers in Czech Cities: Unresolved Aftermath of Dieselgate?

by Michal Vojtisek-Lom, Miroslav Suta, Jitka Sikorova and Radim J. Sram

Atmosphere 2021, 12(5), 649; https://doi.org/10.3390/atmos12050649 - 19 May 2021

Cited by 5 | Viewed by 4371

Abstract

This work examines the effects of two problematic trends in diesel passenger car emissions—increasing NO₂/NO_x ratio by conversion of NO into NO₂ in catalysts and a disparity between the emission limit and the actual emissions in everyday driving—on ambient [...] Read more.

This work examines the effects of two problematic trends in diesel passenger car emissions—increasing NO₂/NO_x ratio by conversion of NO into NO₂ in catalysts and a disparity between the emission limit and the actual emissions in everyday driving—on ambient air quality in Prague. NO₂ concentrations were measured by 104 membrane-closed Palmes passive samplers at 65 locations in Prague in March–April and September–October of 2019. NO₂ concentrations measured by city stations during those periods were comparable with the average values during 2016–2019. The average measured NO₂ concentrations at the selected locations, after correcting for the 18.5% positive bias of samplers co-located with a monitoring station, were 36 µg/m³ (range 16–69 µg/m³, median 35 µg/m³), with the EU annual limit of 40 µg/m³ exceeded at 32% of locations. The NO₂ concentrations have correlated well (R² = 0.76) with the 2019 average daily vehicle counts, corrected for additional emissions due to uphill travel and intersections. In addition to expected “hot-spots” at busy intersections in the city center, new ones were identified, i.e., along a six-lane road V Holešovičkách. Comparison of data from six monitoring stations during 15 March–30 April 2020 travel restrictions with the same period in 2016–2019 revealed an overall reduction of NO₂ and even a larger reduction of NO. The spatial analysis of data from passive samplers and time analysis of data during the travel restrictions both demonstrate a consistent positive correlation between traffic intensity and NO₂ concentrations along/near the travel path. The slow pace of NO₂ reductions in Prague suggests that stricter vehicle NO_x emission limits, introduced in the last decade or two, have so far failed to sufficiently reduce the ambient NO₂ concentrations, and there is no clear sign of remedy of Dieselgate NO_x excess emissions. Full article

(This article belongs to the Special Issue Ambient Air Quality in the Czech Republic)

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Locations of the passive samplers and air quality monitoring stations used for comparison in this study. Photo of a sampler is shown in the upper right corner. (Map source: <a href="http://www.mapy.cz" target="_blank">www.mapy.cz</a> (accessed on 18 May 2021), © Seznam.cz, a.s., used with permission). Full article ">Figure 2
Comparison of passive sampler reported NO2 concentrations to the corresponding average values from corresponding monitoring stations. Larger points circled in red denote the colocation of the sampler at the monitoring station. Full article ">Figure 3
Comparison of monitoring station NO2 averages during sampling periods with four-year average. Full article ">Figure 4
Comparison of spring and fall NO2 concentrations. Full article ">Figure 5
Relationship between traffic intensity and NO2 concentrations measured by passive samplers in spring and fall of 2019 and by the national monitoring network (average of 2016–2019). Full article ">Figure 6
Relationship between traffic intensity and NO2 concentrations measured by passive samplers (average of all measurement periods) and by the national monitoring network (average of 2016–2019). Full article ">Figure 7
Relationship between adjusted traffic intensity (traffic count × (1 + fraction of vehicles travelling uphill + 3 × fraction of vehicles stopping at an intersection)) and NO2 concentrations measured by passive samplers (average of all measurement periods) and by the national monitoring network (average of 2016–2019). Full article ">Figure 8
Relationship between adjusted traffic intensity (traffic count × (1 + fraction of vehicles travelling uphill + 3 × fraction of vehicles stopping at an intersection)) and NO2 concentrations measured by passive samplers (average of all measurement periods) and by the national monitoring network (average of 2016–2019). EU annual limit of 40 µg/m3 NO2 shown as a red line, Swiss federal limit of 30 µg/m3 NO2 shown as a dotted green line. Full article ">

18 pages, 4334 KiB

Open AccessArticle

Improving the Retrieval of Cloudy Atmospheric Profiles from Brightness Temperatures Observed with a Ground-Based Microwave Radiometer

by Qing Li, Ming Wei, Zhenhui Wang, Sulin Jiang and Yanli Chu

Atmosphere 2021, 12(5), 648; https://doi.org/10.3390/atmos12050648 - 19 May 2021

Cited by 5 | Viewed by 2656

Abstract

Atmospheric temperature and humidity retrievals from ground-based microwave remote sensing are useful in a variety of meteorological and environmental applications. Though the influence of clouds is usually considered in current retrieval algorithms, the resulting temperature and humidity estimates are still biased high in [...] Read more.

Atmospheric temperature and humidity retrievals from ground-based microwave remote sensing are useful in a variety of meteorological and environmental applications. Though the influence of clouds is usually considered in current retrieval algorithms, the resulting temperature and humidity estimates are still biased high in overcast conditions compared to radiosonde observations. Therefore, there is a need to improve the quality of retrievals in cloudy conditions. This paper presents an approach to make brightness temperature (TB) correction for cloud influence before the data can be used in the inversion of vertical profiles of atmospheric temperature and humidity. A three-channel method is proposed to make cloud parameter estimation, i.e., of the total 22 channels of the ground-based radiometer, three are adopted to set up a relationship between cloud parameters and brightness temperatures, so that the observations from the three channels can be used to estimate cloud thickness and water content and complete the cloud correction for the rest of the channels used in the retrieval. Based on two years of data from the atmosphere in Beijing, a comparison of the retrievals with radiosonde observations (RAOB) shows: (1) the temperature retrievals from this study have a higher correlation with RAOB and are notably better than in the vendor-provided LV2. The bias of the temperature retrievals from this study is close to zero at all heights, and the RMSE is greatly reduced from >5 °C to <2 °C in the layer, from about 1.5 km up to 5 km. The temperature retrievals from this study have higher correlation with RAOB data compared to the vendor-provided LV2, especially at and above a 2 km height. (2) The bias of the water vapor density profile from this study is near to zero, while the LV2 has a positive bias as large as 4 g/m³. The RMSE of the water vapor density profile from this study is <2 g/m³, while the RMSE for LV2 is as large as 10 g/m³. That is, both the bias and RMSE from this study are evidently less than the LV2, with a greater improvement in the lower troposphere below 5 km. Correlation with RAOB is improved even more for the water vapor density. The correlation of the retrievals from this study increases to one within the boundary layer, but the correlation of LV2 with RAOB is only 0.8 at 0.5 km height, 0.7 at 1 km, and even less than 0.5 at 2 km. (3) A parameter named the Cloud Impact index, determined by cloud water concentration and cloud thickness, together with the cloud base height, has been defined to show that both BIAS and RMSE of “high-CI subsample” are larger than those of the “low-CI subsample”, indicating that high-CI cloud has a higher impact on the retrievals and the correction for cloud influence is more necessary. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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Scatter plots showing the dependence of cloud radiation contribution (K) on (left panels) cloud water content (g/m3) and (right panels) cloud thickness (km) for channels 2, 7 and 10 (sequentially from top to bottom) of the microwave radiometer. Full article ">Figure 1 Cont.
Scatter plots showing the dependence of cloud radiation contribution (K) on (left panels) cloud water content (g/m3) and (right panels) cloud thickness (km) for channels 2, 7 and 10 (sequentially from top to bottom) of the microwave radiometer. Full article ">Figure 2
The technical flow chart for this study. (a) Flow-chart for the “3-channel cloud parameter estimation” and cloud impact identification. (b) Flow-chart for quantitative estimation of the cloud brightness temperature contribution and its correction in front of the inversion procedure for atmospheric temperature and water vapor density profiles. CI is equal or close to zero in the case when cloud impact can be neglected and TB correction for cloud is not necessary, as shown by the dotted box in <a href="#atmosphere-12-00648-f002" class="html-fig">Figure 2</a>a, which leads a “goto” circled A shown in <a href="#atmosphere-12-00648-f002" class="html-fig">Figure 2</a>b. Full article ">Figure 3
Comparison of bias and RMSE of (a) temperature and (b) humidity profiles constructed from <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>f</mi> <mo>⇀</mo> </mover> <mo>−</mo> </msub> <msub> <mi>T</mi> <mrow> <mi>B</mi> <mi>O</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> retrievals (red lines) with those from LV2 products (green lines). (c) Comparison of correlation coefficients between temperature and water vapor density profiles and radiosonde. Sample size = 109. Full article ">Figure 4
Comparison of atmospheric temperature (left panels) and humidity (right panels) profiles from this study (red curves) and LV2 products (green lines) with RAOB (blue lines) for the four typical cases. (a) Case 1 at 00:00 UTC on 17 November 2010; (b) Case 2 at 00:00 UTC on 18 November 2010; (c) Case 3 at 00:00 UTC on 30 July 2010; (d) Case 4 at 00:00 UTC on 17 November 2011. Full article ">Figure 4 Cont.
Comparison of atmospheric temperature (left panels) and humidity (right panels) profiles from this study (red curves) and LV2 products (green lines) with RAOB (blue lines) for the four typical cases. (a) Case 1 at 00:00 UTC on 17 November 2010; (b) Case 2 at 00:00 UTC on 18 November 2010; (c) Case 3 at 00:00 UTC on 30 July 2010; (d) Case 4 at 00:00 UTC on 17 November 2011. Full article ">Figure 5
The performance of the retrieved atmospheric temperature and humidity profiles for the two kinds of cloud types in terms of Cloud Impact index. (a,b) BIAS and RMS of the vendor-provided LV2 products, (c,d) BIAS and RMS of the retrievals from <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>B</mi> <mi>O</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> by this study. Full article ">Figure 5 Cont.
The performance of the retrieved atmospheric temperature and humidity profiles for the two kinds of cloud types in terms of Cloud Impact index. (a,b) BIAS and RMS of the vendor-provided LV2 products, (c,d) BIAS and RMS of the retrievals from <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>B</mi> <mi>O</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> by this study. Full article ">

22 pages, 13192 KiB

Open AccessArticle

Field Study of Metal Oxide Semiconductor Gas Sensors in Temperature Cycled Operation for Selective VOC Monitoring in Indoor Air

by Tobias Baur, Johannes Amann, Caroline Schultealbert and Andreas Schütze

Atmosphere 2021, 12(5), 647; https://doi.org/10.3390/atmos12050647 - 19 May 2021

Cited by 35 | Viewed by 5641

Abstract

More and more metal oxide semiconductor (MOS) gas sensors with digital interfaces are entering the market for indoor air quality (IAQ) monitoring. These sensors are intended to measure volatile organic compounds (VOCs) in indoor air, an important air quality factor. However, their standard [...] Read more.

More and more metal oxide semiconductor (MOS) gas sensors with digital interfaces are entering the market for indoor air quality (IAQ) monitoring. These sensors are intended to measure volatile organic compounds (VOCs) in indoor air, an important air quality factor. However, their standard operating mode often does not make full use of their true capabilities. More sophisticated operation modes, extensive calibration and advanced data evaluation can significantly improve VOC measurements and, furthermore, achieve selective measurements of single gases or at least types of VOCs. This study provides an overview of the potential and limits of MOS gas sensors for IAQ monitoring using temperature cycled operation (TCO), calibration with randomized exposure and data-based models trained with advanced machine learning. After lab calibration, a commercial digital gas sensor with four different gas-sensitive layers was tested in the field over several weeks. In addition to monitoring normal ambient air, release tests were performed with compounds that were included in the lab calibration, but also with additional VOCs. The tests were accompanied by different analytical systems (GC-MS with Tenax sampling, mobile GC-PID and GC-RCP). The results show quantitative agreement between analytical systems and the MOS gas sensor system. The study shows that MOS sensors are highly suitable for determining the overall VOC concentrations with high temporal resolution and, with some restrictions, also for selective measurements of individual components. Full article

(This article belongs to the Special Issue The Future of Air Quality Monitoring)

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18 pages, 1297 KiB

Open AccessArticle

Electric Field Multifractal Features in the High-Latitude Ionosphere: CSES-01 Observations

by Giuseppe Consolini, Virgilio Quattrociocchi, Giulia D’Angelo, Tommaso Alberti, Mirko Piersanti, Maria Federica Marcucci and Paola De Michelis

Atmosphere 2021, 12(5), 646; https://doi.org/10.3390/atmos12050646 - 19 May 2021

Cited by 36 | Viewed by 2662

Abstract

In the polar ionosphere, the electric field is characterized by broadband and power law spectral densities at small/short spatio-temporal scales, which support a possible turbulent nature of the electric field fluctuations. Here, we investigate the multifractal character of the full three-dimensional electric field [...] Read more.

In the polar ionosphere, the electric field is characterized by broadband and power law spectral densities at small/short spatio-temporal scales, which support a possible turbulent nature of the electric field fluctuations. Here, we investigate the multifractal character of the full three-dimensional electric field in the polar ionosphere as recorded on board the first Chinese Seismo-Electromagnetic Satellite (CSES-01). The results of our analysis prove a clear different degree of multifractality of the electric field fluctuations approaching either the polar cap trailing edge or the auroral region. The observed differences in the multifractal character are interpreted in terms of the different natures of the particle precipitation in the polar cap and in the auroral region. A possible link between the multifractal nature of electric field fluctuations, parallel to the geomagnetic field, and filamentation of field aligned currents (FACs) is established. Full article

(This article belongs to the Special Issue Modeling Multiscale Dynamics by Statistical Mechanics in Heliophysics and Geophysics)

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23 pages, 2565 KiB

Open AccessArticle

Improving Air Pollutant Metal Oxide Sensor Quantification Practices through: An Exploration of Sensor Signal Normalization, Multi-Sensor and Universal Calibration Model Generation, and Physical Factors Such as Co-Location Duration and Sensor Age

by Kristen Okorn and Michael Hannigan

Atmosphere 2021, 12(5), 645; https://doi.org/10.3390/atmos12050645 - 19 May 2021

Cited by 11 | Viewed by 3574

Abstract

As low-cost sensors have become ubiquitous in air quality measurements, there is a need for more efficient calibration and quantification practices. Here, we deploy stationary low-cost monitors in Colorado and Southern California near oil and gas facilities, focusing our analysis on methane and [...] Read more.

As low-cost sensors have become ubiquitous in air quality measurements, there is a need for more efficient calibration and quantification practices. Here, we deploy stationary low-cost monitors in Colorado and Southern California near oil and gas facilities, focusing our analysis on methane and ozone concentration measurement using metal oxide sensors. In comparing different sensor signal normalization techniques, we propose a z-scoring standardization approach to normalize all sensor signals, making our calibration results more easily transferable among sensor packages. We also attempt several different physical co-location schemes, and explore several calibration models in which only one sensor system needs to be co-located with a reference instrument, and can be used to calibrate the rest of the fleet of sensor systems. This approach greatly reduces the time and effort involved in field normalization without compromising goodness of fit of the calibration model to a significant extent. We also explore other factors affecting the performance of the sensor system quantification method, including the use of different reference instruments, duration of co-location, time averaging, transferability between different physical environments, and the age of metal oxide sensors. Our focus on methane and stationary monitors, in addition to the z-scoring standardization approach, has broad applications in low-cost sensor calibration and utility. Full article

(This article belongs to the Special Issue Atmospheric Trace Gas Source Detection and Quantification)

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13 pages, 1580 KiB

Open AccessArticle

Wind Shear Prediction from Light Detection and Ranging Data Using Machine Learning Methods

by Jingyan Huang, Michael Kwok Po Ng and Pak Wai Chan

Atmosphere 2021, 12(5), 644; https://doi.org/10.3390/atmos12050644 - 18 May 2021

Cited by 11 | Viewed by 3504

Abstract

The main aim of this paper is to propose a statistical indicator for wind shear prediction from Light Detection and Ranging (LIDAR) observational data. Accurate warning signal of wind shear is particularly important for aviation safety. The main challenges are that wind shear [...] Read more.

The main aim of this paper is to propose a statistical indicator for wind shear prediction from Light Detection and Ranging (LIDAR) observational data. Accurate warning signal of wind shear is particularly important for aviation safety. The main challenges are that wind shear may result from a sustained change of the headwind and the possible velocity of wind shear may have a wide range. Traditionally, aviation models based on terrain-induced setting are used to detect wind shear phenomena. Different from traditional methods, we study a statistical indicator which is used to measure the variation of headwinds from multiple headwind profiles. Because the indicator value is nonnegative, a decision rule based on one-side normal distribution is employed to distinguish wind shear cases and non-wind shear cases. Experimental results based on real data sets obtained at Hong Kong International Airport runway are presented to demonstrate that the proposed indicator is quite effective. The prediction performance of the proposed method is better than that by the supervised learning methods (LDA, KNN, SVM, and logistic regression). This model would also provide more accurate warnings of wind shear for pilots and improve the performance of Wind shear and Turbulence Warning System. Full article

(This article belongs to the Special Issue Low Level Windshear and Turbulence for Aviation Safety)

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17 pages, 5206 KiB

Open AccessArticle

Using Copernicus Atmosphere Monitoring Service (CAMS) Products to Assess Illuminances at Ground Level under Cloudless Conditions

by William Wandji Nyamsi, Philippe Blanc, Dominique Dumortier, Ruben Mouangue, Antti Arola and Lucien Wald

Atmosphere 2021, 12(5), 643; https://doi.org/10.3390/atmos12050643 - 18 May 2021

Cited by 5 | Viewed by 2671

Abstract

Natural daylight is recognized as an important variable in the energy performance of buildings. A method that estimates the global illuminance received on a horizontal surface at ground level and its direct component at normal incidence under cloudless conditions is presented. The method [...] Read more.

Natural daylight is recognized as an important variable in the energy performance of buildings. A method that estimates the global illuminance received on a horizontal surface at ground level and its direct component at normal incidence under cloudless conditions is presented. The method uses the k-distribution method and the correlated-k approximation to compute a set of clearness indices integrated over 13 spectral bands covering the range 380–780 nm. A spectral resampling technique, including a spectral disaggregation and a spectral linear interpolation, is applied to these indices for providing a detailed set of solar irradiances at 1 nm in spectral resolution over the whole range. Then, these are weighted by the standardized CIE action spectrum for human eye for assessing the illuminance. Inputs to the method include the total column contents of ozone and water vapor as well as aerosol optical properties produced by the Copernicus Atmosphere Monitoring Service. Estimates of illuminance were compared to high-quality 1 min measurements of illuminance that were collected from two experimental sites located in two different climatic zones. A slight overestimation is observed for the global illuminance: the bias is between +1 klx and +3 klx, i.e., between +1% and +4% in relative value. The root mean square error varies between 5 klx (8%) and 6 klx (9%). The squared correlation coefficient ranges between 0.95 and 0.97. At the site providing the direct illuminance at normal incidence, the performance of the method is lower compared to global illuminance with a lower squared correlation coefficient of 0.53. The bias, relative bias, RMSE, and rRMSE are +7 klx, +9%, 12 klx, and 15%, respectively. The uncertainty of the method is of the order of the uncertainty of the measurements. The method offers accurate estimates of illuminance in cloudless conditions at high spatial and temporal resolutions useful for construction industries and operators as well as thermal simulation tools for optimal building design strategies. Full article

(This article belongs to the Section Meteorology)

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Figure 1
Illustration of the spectral resampling technique. The clearness indices obtained between 363 and 791 nm by two runs of libRadtran are shown in green and brown respectively for detailed calculations at 1 nm resolution and for the Kato et al. [<a href="#B12-atmosphere-12-00643" class="html-bibr">12</a>] scheme. The selected fine bands FB are shown by crosses in magenta. The linear interpolation provides the clearness indices every 1 nm drawn in blue. Full article ">Figure 2
Two-dimensional (2D) histogram between measurements and estimates of global illuminance on horizontal surface at Golden. The color bar depicts the number of points in the area of 1.5 klx × 1.5 klx. The two thin magenta lines delimit the area of relative errors of ±8% from the measurements. Full article ">Figure 3
Box plots of the ratio (top) of the estimated (Esti) to the measured (Meas) global illuminance on horizontal surface and difference (bottom) between estimated and measured global illuminance on horizontal surface with CAMS input data at Golden. The mean of the box is marked with a red dot. The 1st, 2nd, and 3rd quartiles are marked with a blue line. The notch in the box shows the 95% confidence interval around the median. The number of data for a given range are reported in pink number. Full article ">Figure 3 Cont.
Box plots of the ratio (top) of the estimated (Esti) to the measured (Meas) global illuminance on horizontal surface and difference (bottom) between estimated and measured global illuminance on horizontal surface with CAMS input data at Golden. The mean of the box is marked with a red dot. The 1st, 2nd, and 3rd quartiles are marked with a blue line. The notch in the box shows the 95% confidence interval around the median. The number of data for a given range are reported in pink number. Full article ">Figure 4
Diurnal variability of AOD at 550 nm from CAMS and measurements at Golden on 15 April 2017. SCI stands for selected cloudless instant. Full article ">Figure 5
Two-dimensional (2D) histogram between measurements and estimates of global illuminance at Vaulx-en-Velin. The color bar depicts the number of points in the area of 1.5 klx × 1.5 klx. The two thin magenta lines delimit the area of relative errors of ±5 % from the measurements. Full article ">Figure 6
Two-dimensional (2D) histogram between measurements and estimates of direct illuminance at normal incidence at Vaulx-en-Velin. The colorbar depicts the number of points in the area of 1.5 klx × 1.5 klx. The two thin magenta lines delimit the area of relative errors of ±5% from the measurements. Full article ">

14 pages, 5059 KiB

Open AccessArticle

Aerosol Optical Properties around the East China Seas Based on AERONET Measurements

by Qianguang Tu, Zengzhou Hao, Yunwei Yan, Bangyi Tao, Chuyong Chung and Sumin Kim

Atmosphere 2021, 12(5), 642; https://doi.org/10.3390/atmos12050642 - 18 May 2021

Cited by 6 | Viewed by 2737

Abstract

Understanding aerosols optical properties over the oceans is vital for enhancing our knowledge of aerosol effects on climate and pollutant transport between continents. In this study, the characteristics of aerosol optical thickness (AOT) at 500 nm (

τ_{500 nm}

), Ångström exponent [...] Read more.

Understanding aerosols optical properties over the oceans is vital for enhancing our knowledge of aerosol effects on climate and pollutant transport between continents. In this study, the characteristics of aerosol optical thickness (AOT) at 500 nm (

τ_{500 nm}

), Ångström exponent for the wavelength pair 440–870 nm (α) and volume size distribution (VSD), are presented and analyzed over the East China seas based on the observations at four AERONET sites during 1999–2019. The main results are: (1) the mean

τ_{500 nm}

(α) value ranged from 0.31 to 0.36 (1.17–1.31); (2) the distribution of

τ_{500 nm}

(α) is similar to a log-normal distribution with a right-skewed long tail larger than 0.5 (closer to the normal distribution); (3) large AOT (

τ_{500 nm} > 0.6

) was frequently observed in summer (June and July) and spring (March to May), followed by autumn and winter; (4) all aerosol types were observed, and urban/industrial aerosols and mixed types were dominant throughout the period. The atmospheric column aerosol was characterized by a bimodal lognormal size distribution with a fine mode at effective radius, Reff = 0.16 ± 0.01 μm, and coarse mode at Reff = 2.05 ± 0.1 μm. Full article

(This article belongs to the Special Issue Atmospheric Aerosol Optical Properties)

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14 pages, 1996 KiB

Open AccessArticle

Geochemical Characterization and Heavy Metal Sources in PM₁₀ in Arequipa, Peru

by Jianghanyang Li, Greg Michalski, Elizabeth Joy Olson, Lisa R. Welp, Adriana E. Larrea Valdivia, Juan Reyes Larico, Francisco Alejo Zapata and Lino Morales Paredes

Atmosphere 2021, 12(5), 641; https://doi.org/10.3390/atmos12050641 - 18 May 2021

Cited by 5 | Viewed by 3424

Abstract

Particulate matter smaller than 10 μm (PM₁₀) is an important air pollutant that adversely affects human health by increasing the risk of respiratory and cardiovascular diseases. Recent studies reported multiple extreme PM₁₀ levels at high altitude Peruvian cities, which resulted [...] Read more.

Particulate matter smaller than 10 μm (PM₁₀) is an important air pollutant that adversely affects human health by increasing the risk of respiratory and cardiovascular diseases. Recent studies reported multiple extreme PM₁₀ levels at high altitude Peruvian cities, which resulted from a combination of high emissions and limited atmospheric circulation at high altitude. However, the emission sources of the PM₁₀ still remain unclear. In this study, we collected PM₁₀ samples from four sites (one industrial site, one urban site, and two rural sites) at the city of Arequipa, Peru, during the period of February 2018 to December 2018. To identify the origins of PM₁₀ at each site and the spatial distribution of PM₁₀ emission sources, we analyzed major and trace element concentrations of the PM₁₀. Of the observed daily PM₁₀ concentrations at Arequipa during our sampling period, 91% exceeded the World Health Organization (WHO) 24-h mean PM₁₀ guideline value, suggesting the elevated PM₁₀ strongly affected the air quality at Arequipa. The concentrations of major elements, Na, K, Mg, Ca, Fe, and Al, were high and showed little variation, suggesting that mineral dust was a major component of the PM₁₀ at all the sites. Some trace elements, such as Mn and Mo, originated from the mineral dust, while other trace elements, including Pb, Sr, Cu, Ba, Ni, As and V, were from additional anthropogenic sources. The industrial activities at Rio Seco, the industrial site, contributed to significant Pb, Cu, and possibly Sr emissions. At two rural sites, Tingo Grande and Yarabamba, strong Cu emissions were observed, which were likely associated with mining activities. Ni, V, and As were attributed to fossil fuel combustion emissions, which were strongest at the Avenida Independencia urban site. Elevated Ba and Cu concentrations were also observed at the urban site, which were likely caused by heavy traffic in the city and vehicle brake wear emissions. Full article

(This article belongs to the Topic Air Pollution – An Interdisciplinary Approach to the Problem of Air Pollution and Improvement of Air Quality)

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15 pages, 7600 KiB

Open AccessEditor’s ChoiceArticle

Accelerated Time and High-Resolution 3D Modeling of the Flow and Dispersion of Noxious Substances over a Gigantic Urban Area—The EMERGENCIES Project

by Olivier Oldrini, Patrick Armand, Christophe Duchenne, Sylvie Perdriel and Maxime Nibart

Atmosphere 2021, 12(5), 640; https://doi.org/10.3390/atmos12050640 - 18 May 2021

Cited by 5 | Viewed by 2724

Abstract

Accidental or malicious releases in the atmosphere are more likely to occur in built-up areas, where flow and dispersion are complex. The EMERGENCIES project aims to demonstrate the operational feasibility of three-dimensional simulation as a support tool for emergency teams and first responders. [...] Read more.

Accidental or malicious releases in the atmosphere are more likely to occur in built-up areas, where flow and dispersion are complex. The EMERGENCIES project aims to demonstrate the operational feasibility of three-dimensional simulation as a support tool for emergency teams and first responders. The simulation domain covers a gigantic urban area around Paris, France, and uses high-resolution metric grids. It relies on the PMSS modeling system to model the flow and dispersion over this gigantic domain and on the Code_Saturne model to simulate both the close vicinity and the inside of several buildings of interest. The accelerated time is achieved through the parallel algorithms of the models. Calculations rely on a two-step approach: the flow is computed in advance using meteorological forecasts, and then on-demand release scenarios are performed. Results obtained with actual meteorological mesoscale data and realistic releases occurring both inside and outside of buildings are presented and discussed. They prove the feasibility of operational use by emergency teams in cases of atmospheric release of hazardous materials. Full article

(This article belongs to the Special Issue High Performance Computing Serving Atmospheric Transport & Dispersion Modelling)

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15 pages, 1337 KiB

Open AccessArticle

Impact of Industrial Air Pollution on Agricultural Production

by Wei Wei and Zanxin Wang

Atmosphere 2021, 12(5), 639; https://doi.org/10.3390/atmos12050639 - 18 May 2021

Cited by 20 | Viewed by 6336

Abstract

This paper aimed to study how industrial air pollution impacts crop yield by investigating the relationship between output and changes in factors. A translog production function was estimated in the context of stochastic frontier analysis using data collected from a field survey in [...] Read more.

This paper aimed to study how industrial air pollution impacts crop yield by investigating the relationship between output and changes in factors. A translog production function was estimated in the context of stochastic frontier analysis using data collected from a field survey in the case of corn. The interaction between the factors as well as the impact of industrial air pollution on the relationship between factors was analyzed using numerical simulation, followed by the estimation of economic losses of corn yield in the polluted area. Results show that industrial air pollution causes a decrease in crop yield for two reasons. First, industrial air pollution changes the output elasticities of production factors and reduces its absolute amount. Second, industrial air pollution causes the relationship between labor and capital, labor and chemicals, capital and seeds to change from substitutable to complementary; it also resulted in an opposite result for the relationship between capital and chemicals. The paper presents a new explanation of how industrial air pollution affects agricultural production from an economic perspective. Full article

(This article belongs to the Special Issue Air Quality and Climate Effects of Traditional and Emerging Pollutants)

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15 pages, 7732 KiB

Open AccessArticle

Evaluation of a Flexible Single Ice Microphysics and a Gaussian Probability-Density-Function Macrophysics Scheme in a Single Column Model

by Jiabo Li, Xindong Peng, Xiaohan Li, Yanluan Lin and Wenchao Chu

Atmosphere 2021, 12(5), 638; https://doi.org/10.3390/atmos12050638 - 17 May 2021

Cited by 1 | Viewed by 2369

Abstract

Scale-aware parameterizations of subgrid scale physics are essentials for multiscale atmospheric modeling. A single-ice (SI) microphysics scheme and Gaussian probability-density-function (Gauss-PDF) macrophysics scheme were implemented in the single-column Global-to-Regional Integrated forecast System model (SGRIST) and they were tested using the Tropical Warm Pool-International [...] Read more.

Scale-aware parameterizations of subgrid scale physics are essentials for multiscale atmospheric modeling. A single-ice (SI) microphysics scheme and Gaussian probability-density-function (Gauss-PDF) macrophysics scheme were implemented in the single-column Global-to-Regional Integrated forecast System model (SGRIST) and they were tested using the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) and the Atmospheric Radiation Measurement Southern Great Plains Experiment in 1997 (ARM97). Their performance was evaluated against observations and other reference schemes. The new schemes simulated reasonable precipitation with proper fluctuations and peaks, ice, and liquid water contents, especially in lower levels below 650 hPa during the wet period in the TWP-ICE. The root mean square error (RMSE) of the simulated cloud fraction was below 200 hPa was 0.10/0.08 in the wet/dry period, which showed an obvious improvement when compared to that, i.e., 0.11/0.11 of original scheme. Accumulated ice water content below the melting level decreased by 21.57% in the SI. The well-matched average liquid water content displayed between the new scheme and observations, which was two times larger than those with the referencing scheme. In the ARM97 simulations, the SI scheme produced considerable ice water content, especially when convection was active. Low-level cloud fraction and precipitation extremes were improved using the Gauss-PDF scheme, which displayed the RMSE of cloud fraction of 0.02, being only half of the original schemes. The study indicates that the SI and Gauss-PDF schemes are promising approaches to simplify the microphysics process and improve the low-level cloud modeling. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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Temporal-vertical plot of cloud fraction (left), ice water content (middle column) and liquid water content (right) in addition to the atmospheric temperature (solid, °C) in the MG-PBR (d–f), SI-PBR (g–i), MG-GPDF (j–l), and SI-GPDF (m–o) simulation of the TWP-ICE in comparison with the observations and retrievals (a–c). The melting level is marked with bold lines in the middle panels. Full article ">Figure 2
Vertical profiles of cloud fraction and the deviation from the observation (a,d), ice water content (b,e) and liquid water content (c,f) averaged in the wet (18–25 January, top) and dry periods (26–30 January, bottom) in the four tests, MG-PBR (red), SI-PBR (cyan), MG-GPDF (orange), and SI-GPDF (brown) in comparison with the observations (black). Full article ">Figure 3
Vertical profiles of microphysics processes for ice in the MG–PBR and SI–PBR experiments, sublimation (red), melting (cyan), deposition (orange), and auto-conversion of cloud ice to snow (brown), respectively, averaged in the wet period. Full article ">Figure 4
(a) Total precipitation rate, (b) large-scale precipitation rate, (c) convective precipitation rate and (d) liquid cloud water path in the simulations MG-PBR (red), SI-PBR (cyan), MG-GPDF (orange), and SI-GPDF (brown) compared to the observation (black). Full article ">Figure 5
Temporal-vertical plot of cloud fraction (left), ice water content (middle column) and liquid water content (right) in addition to the atmospheric temperature (solid, °C) in the MG-PBR (d–f), SI-PBR (g–i), MG-GPDF (j–l), and SI-GPDF (m,n,o) simulation of the ARM97 in comparison with the observations and retrievals (a–c). The melting level is marked with bold lines in the middle panels. Full article ">Figure 6
Vertical profiles of cloud fraction and the deviation from the observation (a), ice water content (b) and liquid water content (c) averaged during 19 June and 17 July in the four tests, MG-PBR (red), SI-PBR (cyan), MG-GPDF (orange), and SI-GPDF (brown) in comparison with the observations (black). Full article ">Figure 7
Temporal-vertical plot of mass-weighted ice falling speed in (a) the SI-PBR and (b) the SI-GPDF. Full article ">Figure 8
(a) Total precipitation rate, (b) large-scale precipitation rate, (c) convective precipitation rate and (d) liquid cloud water path in the simulations MG-PBR (red), SI-PBR (cyan), MG-GPDF (orange), and SI-GPDF (brown) compared to the observation (black). Full article ">

11 pages, 2533 KiB

Open AccessArticle

Emissions of Volatile Organic Compounds (VOCs) from an Open-Circuit Dry Cleaning Machine Using a Petroleum-Based Organic Solvent: Implications for Impacts on Air Quality

by Hyeonji Lee, Kyunghoon Kim, Yelim Choi and Daekeun Kim

Atmosphere 2021, 12(5), 637; https://doi.org/10.3390/atmos12050637 - 17 May 2021

Cited by 10 | Viewed by 3985

Abstract

Volatile organic compounds (VOCs) are known to play an important role in tropospheric chemistry, contributing to ozone and secondary organic aerosol (SOA) generation. Laundry facilities, using petroleum-based organic solvents, are one of the sources of VOCs emissions. However, little is known about the [...] Read more.

Volatile organic compounds (VOCs) are known to play an important role in tropospheric chemistry, contributing to ozone and secondary organic aerosol (SOA) generation. Laundry facilities, using petroleum-based organic solvents, are one of the sources of VOCs emissions. However, little is known about the significance of VOCs, emitted from laundry facilities, in the ozone and SOA generation. In this study, we characterized VOCs emission from a dry-cleaning process using petroleum-based organic solvents. We also assessed the impact of the VOCs on air quality by using photochemical ozone creation potential and secondary organic aerosol potential. Among 94 targeted compounds including toxic organic air pollutants and ozone precursors, 36 compounds were identified in the exhaust gas from a drying machine. The mass emitted from one cycle of drying operation (40 min) was the highest in decane (2.04 g/dry cleaning). Decane, nonane, and n-undecane were the three main contributors to ozone generation (more than 70% of the total generation). N-undecane, decane, and n-dodecane were the three main contributors to the SOA generation (more than 80% of the total generation). These results help to understand VOCs emission from laundry facilities and impacts on air quality. Full article

(This article belongs to the Section Air Quality)

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24 pages, 6660 KiB

Open AccessArticle

“Military Parade Blue Skies” in Beijing: Decisive Influence of Meteorological Factors on Transport Channel and Atmospheric Pollutant Concentration Level

by Shujian Yang, Yang Zhang, Jing Shang, Zhengqiang Li, Benjamin de Foy, James Jay Schauer and Yuanxun Zhang

Atmosphere 2021, 12(5), 636; https://doi.org/10.3390/atmos12050636 - 17 May 2021

Cited by 1 | Viewed by 2493

Abstract

The severity of high atmospheric pollution has been a major social problem in northern China. To improve the air quality in the Beijing–Tianjin–Hebei (BTH) region and guarantee a suitable environment during the military parade and other celebrating activities for the 70th anniversary of [...] Read more.

The severity of high atmospheric pollution has been a major social problem in northern China. To improve the air quality in the Beijing–Tianjin–Hebei (BTH) region and guarantee a suitable environment during the military parade and other celebrating activities for the 70th anniversary of the victory for anti-Fascist Warcraft in the year 2015, a series of strict air quality control policies were carried out. To analyze the reduction extents of PM_2.5 and organic matter components during the control period and to examine the meteorological conditions in this region and their decisive influence on the air quality, PM_2.5 samples were collected and the Lagrangian particle dispersion model FLEXPART was performed to calculate potential source locations within the BTH region. PM_2.5, organic carbon (OC), elementary carbon (EC), and three species types were specifically analyzed. Although the results showed that PM_2.5, OC, and EC reduced by 64.55%, 48.74%, and 60.75% during the control period, the air mass transport patterns showed great difference at certain periods, which altered the dominant transport direction of air mass and the potential source region of pollutants and organic matters. This alteration completely changed major atmospheric pollutants sources contribution and caused huge concentration changes. Parallel cases also showed that meteorological conditions could avoid massive atmospheric transported from a major emission source region to a receptor site. The meteorological conditions changed the main contribution source region in control and non-control periods and proved the air quality control measures were less necessary in some southern Hebei cities during special events periods. Full article

(This article belongs to the Special Issue Aerosol Pollution in Asia)

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16 pages, 1993 KiB

Open AccessReview

The Research Progress of the Influence of Agricultural Activities on Atmospheric Environment in Recent Ten Years: A Review

by Pengxiang Ge, Mindong Chen, Yan Cui and Dongyang Nie

Atmosphere 2021, 12(5), 635; https://doi.org/10.3390/atmos12050635 - 17 May 2021

Cited by 11 | Viewed by 4932

Abstract

In recent years, the industrial emission of air pollution has been reduced via a series of measures. However, with the rapid development of modern agriculture, air pollution caused by agricultural activities is becoming more and more serious. Agricultural activities can generate a large [...] Read more.

In recent years, the industrial emission of air pollution has been reduced via a series of measures. However, with the rapid development of modern agriculture, air pollution caused by agricultural activities is becoming more and more serious. Agricultural activities can generate a large amount of air pollutants, such as ammonia, methane, nitrogen oxides, volatile organic compounds, and persistent organic pollutants, the sources of which mainly include farmland fertilization, livestock breeding, pesticide use, agricultural residue burning, agricultural machinery, and agricultural irrigation. Greenhouse gases emitted by agricultural activities can affect regional climate change, while atmospheric particulates and persistent organic pollutants can even seriously harm the health of surrounding residents. With the increasing threat of agricultural air pollution, more and more relevant studies have been carried out, as well as some recommendations for reducing emissions. The emissions of ammonia and greenhouse gases can be significantly reduced by adopting reasonable fertilization methods, scientific soil management, and advanced manure treatment systems. Regarding pesticide use and agricultural residues burning, emission reduction are more dependent on the restriction and support of government regulations, such as banning certain pesticides, prohibiting open burning of straw, and supporting the recycling and reuse of residues. This review, summarizing the relevant research in the past decade, discusses the current situation, health effects, and emission reduction measures of agricultural air pollutants from different sources, in order to provide some help for follow-up research. Full article

(This article belongs to the Special Issue Agricultural Pollutants in the Atmosphere)

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26 pages, 38633 KiB

Open AccessArticle

The 3D Neural Network for Improving Radar-Rainfall Estimation in Monsoon Climate

by Nurulhani Roslan, Mohd Nadzri Md Reba, Syarawi M. H. Sharoni and Mohammad Shawkat Hossain

Atmosphere 2021, 12(5), 634; https://doi.org/10.3390/atmos12050634 - 17 May 2021

Cited by 4 | Viewed by 3056

Abstract

The reflectivity (Z)—rain rate (R) model has not been tested on single polarization radar for estimating monsoon rainfall in Southeast Asia, despite its widespread use for estimating heterogeneous rainfall. The artificial neural network (ANN) regression has been applied to the radar [...] Read more.

The reflectivity (Z)—rain rate (R) model has not been tested on single polarization radar for estimating monsoon rainfall in Southeast Asia, despite its widespread use for estimating heterogeneous rainfall. The artificial neural network (ANN) regression has been applied to the radar reflectivity data to estimate monsoon rainfall using parametric Z-R models. The 10-min reflectivity data recorded in Kota Bahru radar station (in Malaysia) and hourly rain record in nearby 58 gauge stations during 2013–2015 were used. The three-dimensional nearest neighbor interpolation with altitude correction was applied for pixel matching. The non-linear Levenberg Marquardt (LM) regression, integrated with ANN regression minimized the spatiotemporal variability of the proposed Z-R model. Results showed an improvement in the statistical indicator, when LM and ANN overestimated (6.6%) and underestimated (4.4%), respectively, the mean total rainfall. For all rainfall categories, the ANN model has a positive efficiency ratio of >0.2. Full article

(This article belongs to the Special Issue Weather Radar in Rainfall Estimation)

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23 pages, 93677 KiB

Open AccessEditor’s ChoiceArticle

Exposure Assessment of Climate Extremes over the Europe–Mediterranean Region

by Mehmet Barış Kelebek, Fulden Batibeniz and Barış Önol

Atmosphere 2021, 12(5), 633; https://doi.org/10.3390/atmos12050633 - 17 May 2021

Cited by 19 | Viewed by 4914

Abstract

The use of a compact set of climate change indexes enhances our understanding of the combined impacts of extreme climatic conditions. In this study, we developed the modified Climate Extremes Index (mCEI) to obtain unified information about different types of extremes. For this [...] Read more.

The use of a compact set of climate change indexes enhances our understanding of the combined impacts of extreme climatic conditions. In this study, we developed the modified Climate Extremes Index (mCEI) to obtain unified information about different types of extremes. For this purpose, we calculated 10 different climate change indexes considering the temperature extremes, extreme precipitation, and moisture surplus and drought over the Europe–Mediterranean (EURO–MED) region for the 1979–2016 period. As a holistic approach, mCEI provides spatiotemporal information, and the high-resolution grid-based data allow us to accomplish detailed country-based and city-based analyses. The analyses indicate that warm temperature extremes rise significantly over the EURO–MED region at a rate of 1.9% decade⁻¹, whereas the cold temperature extremes decrease. Extreme drought has a significant increasing trend of 3.8% decade⁻¹. Although there are regional differences, extreme precipitation indexes have a significant increasing tendency. According to the mCEI, the major hotspots for the combined extremes are the Mediterranean coasts, the Balkan countries, Eastern Europe, Iceland, western Russia, western Turkey, and western Iraq. The decadal changes of mCEI for these regions are in the range of 3–5% decade⁻¹. The city-scale analysis based on urbanized locations reveals that Fes (Morocco), Izmir (Turkey), Marseille and Aix-en-Provence (France), and Tel Aviv (Israel) have the highest increasing trend of mCEI, which is greater than 3.5% decade⁻¹. Full article

(This article belongs to the Special Issue Extreme Weather Events and Geo-Climatic Hazards Under a Changing Climate)

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14 pages, 4195 KiB

Open AccessArticle

Characteristics of Summer Hourly Extreme Precipitation Events and Its Local Environmental Influencing Factors in Beijing under Urbanization Background

by Zuofang Zheng, Guirong Xu and Hua Gao

Atmosphere 2021, 12(5), 632; https://doi.org/10.3390/atmos12050632 - 16 May 2021

Cited by 10 | Viewed by 2634

Abstract

Studies on urban extreme precipitation and its influencing factors are significant for prevention and reduction of meteorological disasters; however, few studies focus on hourly extreme precipitation (HEP) events due to the limitation of observation. By using the summer hourly precipitation data in Beijing [...] Read more.

Studies on urban extreme precipitation and its influencing factors are significant for prevention and reduction of meteorological disasters; however, few studies focus on hourly extreme precipitation (HEP) events due to the limitation of observation. By using the summer hourly precipitation data in Beijing from 1980 to 2020, the spatial distribution and temporal variation of HEP as well as its local environmental influencing factors are investigated. It is found that both summer precipitation amount and frequency of HEP are affected by topography, with high values in windward slope area. The summer precipitation amount of HEP is 160–200 mm, accounting for 42–47% of the annual summer precipitation amount, while the frequency proportion of HEP is only 5.5–6.0%. Although the summer precipitation amount and frequency in Beijing both decrease in the past 41 years, those for HEP present an opposite trend mainly due to the increasing HEP since 2003 and this is a phenomenon worthy of attention. A similar bimodal pattern in diurnal variation is found for the summer precipitation amount and frequency of HEP, with two peaks in 19–22 LT and 01–05 LT, respectively, indicating that HEPs are more concentrated in the evening and early morning especially in urban area. Moreover, the urbanization process of Beijing is consistent with the change trend of HEP, implying that the stronger the urban heat island intensity (UHI), the higher the probability of HEP. Furthermore, the convergence lines of terrain are also conducive to local heavy rainfall, and lower tropospheric stability (LTS) and convective available potential energy (CAPE) as well as aerosols may also affect HEP in Beijing. Full article

(This article belongs to the Special Issue Hazards, Urbanization, and Climate Change)

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14 pages, 1177 KiB

Open AccessArticle

Pesticide Inhalation Exposure of Applicators and Bystanders Using Conventional and Innovative Cropping Systems in the Valencian Region, Spain

by Esther Fuentes, Antonio López, María Ibáñez, Vicent Yusà, Amalia Muñoz, Teresa Vera, Esther Borrás, Héctor Calvete-Sogo and Clara Coscollà

Atmosphere 2021, 12(5), 631; https://doi.org/10.3390/atmos12050631 - 15 May 2021

Cited by 4 | Viewed by 2934

Abstract

This paper provides scientific results from a European LIFE project carried out in the Valencian region of Spain during the 2017 to 2018 time frame. In 2018, more than 60,000 tons of pesticides were commercialized in Spain, with approximately 15% destined for Valencian [...] Read more.

This paper provides scientific results from a European LIFE project carried out in the Valencian region of Spain during the 2017 to 2018 time frame. In 2018, more than 60,000 tons of pesticides were commercialized in Spain, with approximately 15% destined for Valencian crops. In order to improve the air quality in the agricultural areas of this region, an innovative cropping system based on irrigation was developed and compared to conventional treatments based on hand-spray and turbo application. After applying conventional treatments to five types of crops (citrus, persimmon, nectarine, watermelon, and other stone fruits), a total of 13 active substances were detected in the air. The same active substances were applied to crops using the novel irrigation system, and no pesticide was detected in the air. Moreover, applicator and bystander populations in the region were assessed for their risk of inhalation exposure to pesticides, and no risk was found when either of the techniques, the innovative and the conventional agricultural one, were applied. Full article

(This article belongs to the Special Issue Agricultural Pollutants in the Atmosphere)

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18 pages, 7310 KiB

Open AccessArticle

Longer Time-Scale Variability of Atmospheric Vertical Motion over the Tibetan Plateau and North Pacific and the Climate in East Asia

by Rongxiang Tian, Yaoming Ma, Weiqiang Ma, Xiuyi Zhao and Duo Zha

Atmosphere 2021, 12(5), 630; https://doi.org/10.3390/atmos12050630 - 15 May 2021

Cited by 2 | Viewed by 2589

Abstract

The vertical motion of air is closely related to the amount of precipitation that falls in a particular region. The Tibetan Plateau and the North Pacific are important determinants of the East Asian climate. We use climate diagnosis and statistical analysis to study [...] Read more.

The vertical motion of air is closely related to the amount of precipitation that falls in a particular region. The Tibetan Plateau and the North Pacific are important determinants of the East Asian climate. We use climate diagnosis and statistical analysis to study the vertical motion of the air over the North Pacific and Tibetan Plateau and the relationship between the vertical motion of air over them and the climate in East Asia. Here we show that there is a downward movement of air over the Tibetan Plateau during the winter, with a maximum velocity of downward movement at 500 hPa, whereas there is an upward movement of air with a maximum velocity of upward movement at 600 hPa during the summer. Precipitation in East Asia has a significant negative correlation (The correlation coefficient exceeds −0.463 and confidence level is greater than 99%) with the vertical motion of air over the Tibetan Plateau and the North Pacific during both the winter and summer. There is also a negative correlation of precipitation in the region south of the Yangtze River with the vertical motion of air over the Tibetan Plateau in winter, whereas the area of negative correlation to the vertical motion of air over the North Pacific in winter is located to the east of the Tibetan Plateau and the Yangtze–Huaihe river basin. The research results provide a climatic framework for the vertical motion of air over both the Tibetan Plateau and the North Pacific. Full article

(This article belongs to the Section Climatology)

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Figure 1
Distribution of the vertical velocity of air over the Tibetan Plateau. (a) Vertical velocity at 500 hPa in spring (March–May), summer (June–August), autumn (September–November) and winter (December–February). (b) Profiles of vertical velocity centers in summer (33° N, 97° E) and winter (100° E, 30° N). (c) Spatial distribution of the primary EOF-analyzed mode for vertical velocities at 200 and 500 hPa in summer and winter. (d) Temporal variation of the primary mode of the vertical velocities at 200 and 500 hPa in summer and winter. PC denotes principal component and gray areas denote large fluctuations. (e) Wavelet power spectrum of temporal coefficients of the primary mode of the vertical velocity at 500 hPa in both summer and winter. The red line delineates the cone of influence and the yellow areas show confidence levels >95%. Full article ">Figure 2
Monthly means of vertical velocity over the Tibetan Plateau at 500 hPa from 1981 to 2010. Full article ">Figure 3
Distribution of the vertical velocity of air over the North Pacific. (a) Vertical velocity at 500 hPa in spring (March–May), summer (June–August), autumn (September–November) and winter (December–February). (b) Primary EOF-analyzed mode of vertical velocities at 500 and 850 hPa in summer and winter. (c) Temporal variation in the primary mode of the vertical velocities at 500 and 850 hPa in summer and winter. PC denotes principal component and gray areas denote large fluctuations. (d) Wavelet power spectrum of the temporal coefficients of the primary mode of vertical velocity at 500 hPa in both summer and winter. The red line delineates the cone of influence and the yellow areas show confidence levels >95%. Full article ">Figure 4
Correlation analysis between the surface meteorological variables and the vertical motion of air for (a) the Tibetan Plateau and (b) the North Pacific. Red shades denote a positive correlation and blue shades denote a negative correlation. Note: the South China Sea has not been marked due to layout reasons. Full article ">Figure 5
Wind velocity fields. (a) Mean meridional circulation at 90° E in January. (b) Vertical velocity field at 500 hPa in January. (c) Mean zonal circulation at 30° N in January. The grey area in part (a) represents the Tibetan Plateau area, and the white rectangle (b) represents the downdraft area and the red triangle (c) represents the intersection of the updraft and the downdraft. Full article ">

22 pages, 65908 KiB

Open AccessArticle

Novel Integrated and Optimal Control of Indoor Environmental Devices for Thermal Comfort Using Double Deep Q-Network

by Sun-Ho Kim, Young-Ran Yoon, Jeong-Won Kim and Hyeun-Jun Moon

Atmosphere 2021, 12(5), 629; https://doi.org/10.3390/atmos12050629 - 14 May 2021

Cited by 7 | Viewed by 2994

Abstract

Maintaining a pleasant indoor environment with low energy consumption is important for healthy and comfortable living in buildings. In previous studies, we proposed the integrated comfort control (ICC) algorithm, which integrates several indoor environmental control devices, including an air conditioner, a ventilation system, [...] Read more.

Maintaining a pleasant indoor environment with low energy consumption is important for healthy and comfortable living in buildings. In previous studies, we proposed the integrated comfort control (ICC) algorithm, which integrates several indoor environmental control devices, including an air conditioner, a ventilation system, and a humidifier. The ICC algorithm is operated by simple on/off control to maintain indoor temperature and relative humidity within a defined comfort range. This simple control method can cause inefficient building operation because it does not reflect the changes in indoor–outdoor environmental conditions and the status of the control devices. To overcome this limitation, we suggest the artificial intelligence integrated comfort control (AI2CC) algorithm using a double deep Q-network(DDQN), which uses a data-driven approach to find the optimal control of several environmental control devices to maintain thermal comfort with low energy consumption. The suggested AI2CC showed a good ability to learn how to operate devices optimally to improve indoor thermal comfort while reducing energy consumption. Compared to the previous approach (ICC), the AI2CC reduced energy consumption by 14.8%, increased the comfort ratio by 6.4%, and decreased the time to reach the comfort zone by 54.1 min. Full article

(This article belongs to the Special Issue Zero Energy Building and Indoor Thermal)

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19 pages, 3506 KiB

Open AccessArticle

Overview of Low-Level Wind Shear Characteristics over Chinese Mainland

by Caiyan Lin, Kaijun Zhang, Xintao Chen, Sheng Liang, Junjie Wu and Wei Zhang

Atmosphere 2021, 12(5), 628; https://doi.org/10.3390/atmos12050628 - 14 May 2021

Cited by 14 | Viewed by 3649

Abstract

The characteristics of low-level wind shear (LLWS) over the Chinese mainland were investigated using reports from pilots, air traffic controllers and the number of arriving/departing flights from 2016 to 2020. A preliminary analysis of the impact of the flights on the LLWS reports [...] Read more.

The characteristics of low-level wind shear (LLWS) over the Chinese mainland were investigated using reports from pilots, air traffic controllers and the number of arriving/departing flights from 2016 to 2020. A preliminary analysis of the impact of the flights on the LLWS reports was carried out, and the cause of LLWS was also investigated. LLWS reports from most airports indicate that LLWS is most likely to occur within 600 m AGL with a higher density below 300 m, but with some exceptions, as wind shear is reported at higher altitudes at some airports. Airports with a high frequency of LLWS reports are almost all located in or around regions with complex topography and in regions with prevailing weather conditions favorable to LLWS. The variation in overall LLWS reports displays a steady increase from 2016 to 2019 and a decrease from 2019 to 2020, consistent with the trend in the number of flights, but with no evidently similar trends for individual airports. Seasonal variations in LLWS reports are observed and demonstrate no notable impact caused by the number of flights, implying that the main cause is the monthly variation of weather conditions. Diurnal variation is also evident and largely associated with the variation in number of flights during the busy period in addition to weather conditions, such as common strong winds, in the afternoon. Full article

(This article belongs to the Special Issue Weather and Aviation Safety)

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19 pages, 4125 KiB

Open AccessArticle

Spatial Characteristics of Precipitation in the Greater Sydney Metropolitan Area as Revealed by the Daily Precipitation Concentration Index

by Kevin K. W. Cheung, Aliakbar A. Rasuly, Fei Ji and Lisa T.-C. Chang

Atmosphere 2021, 12(5), 627; https://doi.org/10.3390/atmos12050627 - 13 May 2021

Cited by 5 | Viewed by 3461

Abstract

In this study; the spatial distribution of the Daily Precipitation Concentration Index (DPCI) has been analyzed inside the Greater Sydney Metropolitan Area (GSMA). Accordingly, the rainfall database from the Australian Bureau of Meteorology archive was utilized after comprehensive quality control. The compiled data [...] Read more.

In this study; the spatial distribution of the Daily Precipitation Concentration Index (DPCI) has been analyzed inside the Greater Sydney Metropolitan Area (GSMA). Accordingly, the rainfall database from the Australian Bureau of Meteorology archive was utilized after comprehensive quality control. The compiled data contains a set of 41 rainfall stations indicating consistent daily precipitation series from 1950 to 2015. In the analysis of the DPCI across GSMA the techniques of Moran’s Spatial Autocorrelation has been applied. In addition, a cross-covariance method was applied to assess the spatial interdependency between vector-based datasets after performing an Ordinary Kriging interpolation. The results identify four well-recognized intense rainfall development zones: the south coast and topographic areas of the Illawarra district characterized by Tasman Sea coastal regions with DPCI values ranging from 0.61 to 0.63, the western highlands of the Blue Mountains, with values between 0.60 and 0.62, the inland regions, with lowest rainfall concentrations between 0.55 and 0.59, and lastly the districts located inside the GSMA with DPCI ranging 0.60 to 0.61. Such spatial distribution has revealed the rainstorm and severe thunderstorm activity in the area. This study applies the present models to identify the nature and mechanisms underlying the distribution of torrential rains over space within the metropolis of Sydney, and to monitor any changes in the spatial pattern under the warming climate. Full article

(This article belongs to the Special Issue Emerging Hydro-Climatic Patterns, Teleconnections and Extreme Events in Changing World at Different Timescales)

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13 pages, 4031 KiB

Open AccessArticle

Impact of Black Carbon on Surface Ozone in the Yangtze River Delta from 2015 to 2018

by Yue Tan, Delong Zhao, Honglei Wang, Bin Zhu, Dongping Bai, Ankang Liu, Shuangshuang Shi and Qihang Dai

Atmosphere 2021, 12(5), 626; https://doi.org/10.3390/atmos12050626 - 13 May 2021

Cited by 5 | Viewed by 2696

Abstract

Despite the yearly decline in PM_2.5 in China, surface ozone has been rapidly increasing recently, which makes it imperative to coordinate and control both PM_2.5 and ozone in the atmosphere. This study utilized the data of pollutant concentrations and meteorological elements [...] Read more.

Despite the yearly decline in PM_2.5 in China, surface ozone has been rapidly increasing recently, which makes it imperative to coordinate and control both PM_2.5 and ozone in the atmosphere. This study utilized the data of pollutant concentrations and meteorological elements during 2015 to 2018 in Nanjing, China to analyze the daily correlation between black carbon and ozone (CBO), and the distribution of the pollutant concentrations under different levels of CBO. Besides, the diurnal variations of pollutant concentrations and meteorological elements under high positive and negative CBO were discussed and compared. The results show that the percentage of positive CBO had been increasing at the average rate of 7.1%/year, and it was 38.7% in summer on average, nearly twice of that in other seasons (19.2%). The average black carbon (BC), PM_2.5 and NO₂ under positive CBO was lower than those under negative CBO. It is noticeable that the surface ozone began to ascend when CBO was up to 0.2, with PM_2.5 and NO₂ decreasing and BC remaining steady. Under negative CBO, pollutant concentrations and meteorological elements showed obvious diurnal variations: BC showed a double-peak pattern and surface ozone, PM_2.5, SO₂ and CO showed single-peak patterns and NO₂ showed a trough from 10:00 to 19:00. Wind speed and visibility showed a single-peak pattern with little seasonal difference. Relative humidity rose first, then it lowered and then it rose. Under positive CBO, the patterns of diurnal variations became less obvious, and some of them even showed no patterns, but just fluctuated at a certain level. Full article

(This article belongs to the Special Issue Black Carbon in Atmosphere: Instrumentation, Chemical–Physical Behavior, Human Health Implications)

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22 pages, 1751 KiB

Open AccessArticle

The Diurnal Variation in Stratospheric Ozone from MACC Reanalysis, ERA-Interim, WACCM, and Earth Observation Data: Characteristics and Intercomparison

by Ansgar Schanz, Klemens Hocke, Niklaus Kämpfer, Simon Chabrillat, Antje Inness, Mathias Palm, Justus Notholt, Ian Boyd, Alan Parrish and Yasuko Kasai

Atmosphere 2021, 12(5), 625; https://doi.org/10.3390/atmos12050625 - 13 May 2021

Cited by 5 | Viewed by 3637

Abstract

In this study, we compare the diurnal variation in stratospheric ozone of the MACC (Monitoring Atmospheric Composition and Climate) reanalysis, ECMWF Reanalysis Interim (ERA-Interim), and the free-running WACCM (Whole Atmosphere Community Climate Model). The diurnal variation of stratospheric ozone results from photochemical and [...] Read more.

In this study, we compare the diurnal variation in stratospheric ozone of the MACC (Monitoring Atmospheric Composition and Climate) reanalysis, ECMWF Reanalysis Interim (ERA-Interim), and the free-running WACCM (Whole Atmosphere Community Climate Model). The diurnal variation of stratospheric ozone results from photochemical and dynamical processes depending on altitude, latitude, and season. MACC reanalysis and WACCM use similar chemistry modules and calculate a similar diurnal cycle in ozone when it is caused by a photochemical variation. The results of the two model systems are confirmed by observations of the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES) experiment and three selected sites of the Network for Detection of Atmospheric Composition Change (NDACC) at Mauna Loa, Hawaii (tropics), Bern, Switzerland (midlatitudes), and Ny-Ålesund, Svalbard (high latitudes). On the other hand, the ozone product of ERA-Interim shows considerably less diurnal variation due to photochemical variations. The global maxima of diurnal variation occur at high latitudes in summer, e.g., near the Arctic NDACC site at Ny-Ålesund, Svalbard. The local OZORAM radiometer observes this effect in good agreement with MACC reanalysis and WACCM. The sensed diurnal variation at Ny-Ålesund is up to 8% (0.4 ppmv) due to photochemical variations in summer and negligible during the dynamically dominated winter. However, when dynamics play a major role for the diurnal ozone variation as in the lower stratosphere (100–20 hPa), the reanalysis models ERA-Interim and MACC which assimilate data from radiosondes and satellites outperform the free-running WACCM. Such a domain is the Antarctic polar winter where a surprising novel feature of diurnal variation is indicated by MACC reanalysis and ERA-Interim at the edge of the polar vortex. This effect accounts for up to 8% (0.4 ppmv) in both model systems. In summary, MACC reanalysis provides a global description of the diurnal variation of stratospheric ozone caused by dynamics and photochemical variations. This is of high interest for ozone trend analysis and other research which is based on merged satellite data or measurements at different local time. Full article

(This article belongs to the Section Air Quality)

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