Journal Description
Atmosphere
Atmosphere
is an international, peer-reviewed, open access journal of scientific studies related to the atmosphere published monthly online by MDPI. The Italian Aerosol Society (IAS) and Working Group of Air Quality in European Citizen Science Association (ECSA) are affiliated with Atmosphere and their members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, GEOBASE, GeoRef, Inspec, CAPlus / SciFinder, Astrophysics Data System, and other databases.
- Journal Rank: CiteScore - Q2 (Environmental Science (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.7 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our editors and authors say about the Atmosphere.
- Companion journal: Meteorology.
Impact Factor:
2.9 (2022);
5-Year Impact Factor:
3.0 (2022)
Latest Articles
Agricultural Disaster Prevention System: Insights from Taiwan’s Adaptation Strategies
Atmosphere 2024, 15(5), 526; https://doi.org/10.3390/atmos15050526 (registering DOI) - 25 Apr 2024
Abstract
In response to the adverse effects of climate change-induced frequent extreme disasters on agricultural production and supply stability, this study develops a comprehensive agricultural disaster prevention system based on current adaptation strategies for mitigating agricultural meteorological disasters. The primary goal is to enhance
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In response to the adverse effects of climate change-induced frequent extreme disasters on agricultural production and supply stability, this study develops a comprehensive agricultural disaster prevention system based on current adaptation strategies for mitigating agricultural meteorological disasters. The primary goal is to enhance disaster preparedness and recovery through three core platforms: a fine-scale weather forecast service system, a crop disaster early warning system, and an agricultural information service platform for disasters. The results show that every major agricultural production township in Taiwan now has dedicated agricultural weather stations and access to refined weather forecasts. Additionally, a disaster prevention calendar for 76 important crops is established, integrating cultivation management practices and critical disaster thresholds for different growth periods. Utilizing this calendar, the crop disaster early warning system can provide timely disaster-related information and pre-disaster prevention assistance to farmers through various information dissemination tools. As a disaster approaches, the agricultural information service platform for disasters provides updates on current crop growth conditions. This service not only pinpoints areas at higher risk of disasters and vulnerable crop types but also offers mitigation suggestions to prevent potential damage. Administrative efficiency is then improved with a response mechanism incorporating drones and image analysis for early disaster detection and rapid response. In summary, the collaborative efforts outlined in this study demonstrate a proactive approach to agricultural disaster prevention. By leveraging technological advancements and interdisciplinary cooperation, the aim is to safeguard agricultural livelihoods and ensure food security in the face of climate-induced challenges.
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(This article belongs to the Special Issue Agriculture-Climate Interactions in Tropical Regions)
Open AccessArticle
Hourly Particulate Matter (PM10) Concentration Forecast in Germany Using Extreme Gradient Boosting
by
Stefan Wallek, Marcel Langner, Sebastian Schubert, Raphael Franke and Tobias Sauter
Atmosphere 2024, 15(5), 525; https://doi.org/10.3390/atmos15050525 (registering DOI) - 25 Apr 2024
Abstract
Air pollution remains a significant issue, particularly in urban areas. This study explored the prediction of hourly point-based PM10 concentrations using the XGBoost algorithm to assimilate them into a geostatistical land use regression model for spatially and temporally high-resolution prediction maps. The
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Air pollution remains a significant issue, particularly in urban areas. This study explored the prediction of hourly point-based PM10 concentrations using the XGBoost algorithm to assimilate them into a geostatistical land use regression model for spatially and temporally high-resolution prediction maps. The model configuration and training incorporated meteorological data, station metadata, and time variables based on statistical values and expert knowledge. Hourly measurements from approximately 400 stations from 2009 to 2017 were used for training. The selected model performed with a mean absolute error (MAE) of 6.88 μg m−3, root mean squared error (RMSE) of 9.95 μg m−3, and an R² of 0.65, with variations depending on the siting type and surrounding area. The model achieved a high accuracy of 98.54% and a precision of 73.96% in predicting exceedances of the current EU-limit value for the daily mean of 50 μg m−3. Despite identified limitations, the model can effectively predict hourly values for assimilation into a geostatistical land use regression model.
Full article
(This article belongs to the Special Issue Air Pollution in Urban and Regional Level: Sources, Sinks and Transportation (2nd Edition))
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Open AccessArticle
Determination of Transport Pathways and Mutual Exchanges of Atmospheric Moisture between Source Regions of Yangtze and Yellow River Basins
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Beiming Kang, Jiahua Wei, Olusola O. Ayantobo and Haijiao Yang
Atmosphere 2024, 15(5), 524; https://doi.org/10.3390/atmos15050524 (registering DOI) - 25 Apr 2024
Abstract
Knowledge of the quantitative importance of the moisture transport pathways and mutual moisture exchange of the source regions of the Yangtze (SYZR) and Yellow (SYR) rivers’ basins, the adjacent origins of China’s two longest rivers, can provide insights into the regional atmospheric branch
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Knowledge of the quantitative importance of the moisture transport pathways and mutual moisture exchange of the source regions of the Yangtze (SYZR) and Yellow (SYR) rivers’ basins, the adjacent origins of China’s two longest rivers, can provide insights into the regional atmospheric branch of the hydrological cycle over the source regions. The method with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and a Lagrangian moisture source diagnostic to identify the major moisture transport pathways quantifies their importance to two types of daily precipitation events—daily precipitation more than 10 mm (PM) events and daily precipitation less than 10 mm (PL) events—for the two rivers’ regions during the summer (June–August, 1986–2015) and finds the characteristics of mutual moisture exchange. The results indicated that both the Bay of Bengal group pathway and the northwest China group pathway play significant roles in PM and PL events over the SYZR, contributing 41.87% and 39.12% to PM events and 41.33% and 33.16% to PL events, respectively. The SYR has five main moisture path groups; the Bay of Bengal group pathway, the northwest China group pathway, and the southeast China group pathway play significant roles in PM and PL events over the SYR, contributing 32.34%, 23.28%, and 34.36% to PM events and 34.84%, 36.18%, and 19.83% to PL events, respectively. The volume of moisture passing from the SYZR to the SYR is approximately 60 times that of the reverse, constituting about 6.9% of the total moisture released in SYR precipitation. It is worth noting that the moisture release was concentrated in the nearer west group pathway, and the main moisture uptake locations were beyond the source region of the two rivers (remote sources) in the PM events. The aggregate moisture release high-frequency moisture transport path groups are found in the southeastern parts of Zhiduo County and the southeast of Zaduo County.
Full article
(This article belongs to the Special Issue Ocean–Atmosphere–Land Interactions and Their Roles in Climate Change)
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Open AccessArticle
Satellite Time-Series Analysis for Thermal Anomaly Detection in the Naples Urban Area, Italy
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Alessia Scalabrini, Massimo Musacchio, Malvina Silvestri, Federico Rabuffi, Maria Fabrizia Buongiorno and Francesco Salvini
Atmosphere 2024, 15(5), 523; https://doi.org/10.3390/atmos15050523 (registering DOI) - 25 Apr 2024
Abstract
Naples is the most densely populated Italian city (7744 inhabitants per km2). It is located in a particular geological context: the presence of Mt Vesuvius characterizes the eastern part, and the western part is characterized by the presence of the Phlegrean
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Naples is the most densely populated Italian city (7744 inhabitants per km2). It is located in a particular geological context: the presence of Mt Vesuvius characterizes the eastern part, and the western part is characterized by the presence of the Phlegrean Fields, making Naples a high-geothermal-gradient region. This endogenous heat, combined with the anthropogenic heat due to intense urbanization, has defined Naples as an ideal location for Surface Urban Heat Island (SUHI) analysis. SUHI analysis was effectuated by acquiring the Land Surface Temperature (LST) over Naples municipality by processing Landsat 8 (L8) Thermal Infrared Sensor (TIRS) images in the 2013–2023 time series by employing Google Earth Engine (GEE). In GEE, two different approaches have been followed to analyze thermal images, starting from the Statistical Mono Window (SMW) algorithm, which computes the LST based on the brightness temperature (Tb), the emissivity value, and the atmospheric correction coefficients. The first one is used for the LST retrieval from daytime images; here, the emissivity component is derived using, firstly, the Normalized Difference Vegetation Index (NDVI) and then the Vegetation Cover Method (VCM), defining the Land Surface Emissivity (LSɛ), which considers solar radiation as the main source of energy. The second approach is used for the LST retrieval from nighttime images, where the emissivity is directly estimated from the Advance Spaceborne Thermal Emission Radiometer database (ASTER-GED), as, during nighttime without solar radiation, the main source of energy is the energy emitted by the Earth’s surface. From these two different algorithms, 123 usable daytime and nighttime LST images were downloaded from GEE and analyzed in Quantum GIS (QGIS). The results show that the SUHI is more concentrated in the eastern part, characterized by intense urbanization, as shown by the Corine Land Cover (CLC). At the same time, lower SUHI intensity is detected in the western part, defined by the Land Cover (LC) vegetated class. Also, in the analysis, we highlighted 40 spots (10 hotspots and 10 coldspots, both for daytime and nighttime collection) that present positive or negative temperature peaks for all the time series. Due to the huge amount of data, this work considered only the five representative spots that were most representative for SUHI analysis and determination of thermal anomalies in the urban environment.
Full article
(This article belongs to the Special Issue UHI Analysis and Evaluation with Remote Sensing Data)
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Open AccessCorrection
Correction: Dong et al. Computerized Ionospheric Tomography Based on the ADS-B System. Atmosphere 2023, 14, 1091
by
Xiang Dong, Zhigang Yuan, Qinglin Zhu, Haining Wang, Fang Sun, Jiawei Zhu, Yi Liu and Chen Zhou
Atmosphere 2024, 15(5), 522; https://doi.org/10.3390/atmos15050522 (registering DOI) - 25 Apr 2024
Abstract
In the original publication [...]
Full article
(This article belongs to the Special Issue New Insight into Observations of the Ionospheric Effect)
Open AccessArticle
MTS Decomposition and Recombining Significantly Improves Training Efficiency in Deep Learning: A Case Study in Air Quality Prediction over Sub-Tropical Area
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Benedito Chi Man Tam, Su-Kit Tang and Alberto Cardoso
Atmosphere 2024, 15(5), 521; https://doi.org/10.3390/atmos15050521 (registering DOI) - 25 Apr 2024
Abstract
It is crucial to speed up the training process of multivariate deep learning models for forecasting time series data in a real-time adaptive computing service with automated feature engineering. Multivariate time series decomposition and recombining (MTS-DR) is proposed for this purpose with better
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It is crucial to speed up the training process of multivariate deep learning models for forecasting time series data in a real-time adaptive computing service with automated feature engineering. Multivariate time series decomposition and recombining (MTS-DR) is proposed for this purpose with better accuracy. A proposed MTS-DR model was built to prove that not only the training time is shortened but also the error loss is slightly reduced. A case study is for demonstrating air quality forecasting in sub-tropical urban cities. Since MTS decomposition reduces complexity and makes the features to be explored easier, the speed of deep learning models as well as their accuracy are improved. The experiments show it is easier to train the trend component, and there is no need to train the seasonal component with zero MSE. All forecast results are visualized to show that the total training time has been shortened greatly and that the forecast is ideal for changing trends. The proposed method is also suitable for other time series MTS with seasonal oscillations since it was applied to the datasets of six different kinds of air pollutants individually. Thus, this proposed method has some commonality and could be applied to other datasets with obvious seasonality.
Full article
(This article belongs to the Topic Modelling and Management of Environment, Energy and Resources: Methods, Applications, and Challenges)
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Open AccessArticle
Machine Learning Forecast of Dust Storm Frequency in Saudi Arabia Using Multiple Features
by
Reem K. Alshammari, Omer Alrwais and Mehmet Sabih Aksoy
Atmosphere 2024, 15(5), 520; https://doi.org/10.3390/atmos15050520 - 24 Apr 2024
Abstract
Dust storms are significant atmospheric events that impact air quality, public health, and visibility, especially in arid Saudi Arabia. This study aimed to develop dust storm frequency predictions for Riyadh, Jeddah, and Dammam by integrating meteorological and environmental variables. Our models include multiple
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Dust storms are significant atmospheric events that impact air quality, public health, and visibility, especially in arid Saudi Arabia. This study aimed to develop dust storm frequency predictions for Riyadh, Jeddah, and Dammam by integrating meteorological and environmental variables. Our models include multiple linear regression, support vector machine, gradient boosting regression tree, long short-term memory (LSTM), and temporal convolutional network (TCN). This study highlights the effectiveness of LSTM and TCN models in capturing the complex temporal dynamics of dust storms and demonstrates that they outperform traditional methods, as evidenced by their lower mean absolute error (MAE) and root mean square error (RMSE) values and higher R2 score. In Riyadh, the TCN model demonstrates its remarkable performance, with an R2 score of 0.51, an MAE of 2.80, and an RMSE of 3.48, highlighting its precision, adaptability, and responsiveness to changes in dust storm frequency. Conversely, in Dammam, the LSTM model proved to be the most accurate, achieving an MAE of 3.02, RMSE of 3.64, and R2 score of 0.64. In Jeddah, the LSTM model also exhibited an MAE of 2.48 and an RMSE of 2.96. This research shows the potential of using deep learning models to improve the accuracy and reliability of dust storm frequency forecasts.
Full article
(This article belongs to the Special Issue New Insights in Air Quality Assessment: Forecasting and Monitoring)
Open AccessArticle
Comparison of Surface Ozone Variability in Mountainous Forest Areas and Lowland Urban Areas in Southeast China
by
Xue Jiang, Xugeng Cheng, Jane Liu, Zhixiong Chen, Hong Wang, Huiying Deng, Jun Hu, Yongcheng Jiang, Mengmiao Yang, Chende Gai and Zhiqiang Cheng
Atmosphere 2024, 15(5), 519; https://doi.org/10.3390/atmos15050519 - 24 Apr 2024
Abstract
The ozone (O3) variations in southeast China are largely different between mountainous forest areas located inland, and lowland urban areas located near the coast. Here, we selected these two kinds of areas to compare their similarities and differences in surface O
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The ozone (O3) variations in southeast China are largely different between mountainous forest areas located inland, and lowland urban areas located near the coast. Here, we selected these two kinds of areas to compare their similarities and differences in surface O3 variability from diurnal to seasonal scales. Our results show that in comparison with the lowland urban areas (coastal areas), the mountainous forest areas (inland areas) are characterized with less human activates, lower precursor emissions, wetter and colder meteorological conditions, and denser vegetation covers. This can lead to lower chemical O3 production and higher O3 deposition rates in the inland areas. The annual mean of 8-h O3 maximum concentrations (MDA8 O3) in the inland areas are ~15 μg·m−3 (i.e. ~15%) lower than that in the coastal areas. The day-to-day variation in surface O3 in the two types of the areas is rather similar, with a correlation coefficient of 0.75 between them, suggesting similar influences on large scales, such as weather patterns, regional O3 transport, and background O3. Over 2016–2020, O3 concentrations in all the areas shows a trend of “rising and then falling”, with a peak in 2017 and 2018. Daily MDA8 O3 correlates with solar radiation most in the coastal areas, while in the inland areas, it is correlated with relative humidity most. Diurnally, during the morning, O3 concentrations in the inland areas increase faster than in the coastal areas in most seasons, mainly due to a faster increase in temperature and decrease in humidity. While in the evening, O3 concentrations decrease faster in the inland areas than in the coastal areas, mostly attributable to a higher titration effect in the inland areas. Seasonally, both areas share a double-peak variation in O3 concentrations, with two peaks in spring and autumn and two valleys in summer and winter. We found that the valley in summer is related to the summer Asian monsoon that induces large-scale convections bringing local O3 upward but blocking inflow of O3 downward, while the one in winter is due to low O3 production. The coastal areas experienced more exceedance days (~30 days per year) than inland areas (~5-10 days per year), with O3 sources largely from the northeast. Overall, the similarities and differences in O3 concentrations between inland and coastal areas in southeastern China are rather unique, reflecting the collective impact of geographic-related meteorology, O3 precursor emissions, and vegetation on surface O3 concentrations.
Full article
(This article belongs to the Special Issue Characteristics and Source Apportionment of Urban Air Pollution)
Open AccessArticle
Effects of Speleotherapy on Aerobiota: A Case Study from the Sežana Hospital Cave, Slovenia
by
Rok Tomazin, Andreja Kukec, Viktor Švigelj, Janez Mulec and Tadeja Matos
Atmosphere 2024, 15(5), 518; https://doi.org/10.3390/atmos15050518 - 24 Apr 2024
Abstract
Speleotherapy is one of the non-pharmacological methods for the treatment and rehabilitation of patients with chronic respiratory diseases, especially those with chronic obstructive pulmonary disease (COPD) and asthma. On the one hand, one of the alleged main advantages of speleotherapeutic caves is the
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Speleotherapy is one of the non-pharmacological methods for the treatment and rehabilitation of patients with chronic respiratory diseases, especially those with chronic obstructive pulmonary disease (COPD) and asthma. On the one hand, one of the alleged main advantages of speleotherapeutic caves is the low microbial load in the air and the absence of other aeroallergens, but on the other hand, due to the lack of comprehensive air monitoring, there is little information on the pristine and human-influenced aerobiota in such environments. The aim of this study was to assess the anthropogenic effects of speleotherapy on the air microbiota and to investigate its potential impact on human health in Sežana Hospital Cave (Slovenia). From May 2020 to January 2023, air samples were collected in the cave before and after speleotherapeutic activities using two different volumetric air sampling methods—impaction and impingement—to isolate airborne microbiota. Along with sampling, environmental data were measured (CO2, humidity, wind, and temperature) to explore the anthropogenic effects on the aerobiota. While the presence of patients increased microbial concentrations by at least 83.3%, other parameters exhibited a lower impact or were attributed to seasonal changes. The structure and dynamics of the airborne microbiota are similar to those in show caves, indicating anthropization of the cave. Locally, concentrations of culturable microorganisms above 1000 CFU/m3 were detected, which could have negative or unpredictable effects on the autochthonous microbiota and possibly on human health. A mixture of bacteria and fungi typically associated with human microbiota was found in the air and identified by MALDI-TOF MS with a 90.9% identification success rate. Micrococcus luteus, Kocuria rosea, Staphylococcus hominis, and Staphylococcus capitis were identified as reliable indicators of cave anthropization.
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(This article belongs to the Special Issue Bioaerosol Exposure and Its Risk Assessment)
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Observation and Simulation of CO2 Fluxes in Rice Paddy Ecosystems Based on the Eddy Covariance Technique
by
Jinghan Wang, Jiayan Wang, Hui Zhao and Youfei Zheng
Atmosphere 2024, 15(5), 517; https://doi.org/10.3390/atmos15050517 - 24 Apr 2024
Abstract
As constituents of one of the vital agricultural ecosystems, paddy fields exert significant influence on the global carbon cycle. Therefore, conducting observations and simulations of CO2 flux in rice paddy is of significant importance for gaining deeper insights into the functionality of
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As constituents of one of the vital agricultural ecosystems, paddy fields exert significant influence on the global carbon cycle. Therefore, conducting observations and simulations of CO2 flux in rice paddy is of significant importance for gaining deeper insights into the functionality of agricultural ecosystems. This study utilized an eddy covariance system to observe and analyze the CO2 flux in a rice paddy field in Eastern China and also introduced and parameterized the Jarvis multiplicative model to predict the CO2 flux. Results indicate that throughout the observation period, the range of CO2 flux in the paddy field was −0.1 to −38.4 μmol/(m2·s), with a mean of −12.9 μmol/(m2·s). The highest CO2 flux occurred during the rice flowering period with peak photosynthetic activity and maximum CO2 absorption. Diurnal variation in CO2 flux exhibited a “U”-shaped curve, with flux reaching its peak absorption at 11:30. The CO2 flux was notably higher in the morning than in the afternoon. The nocturnal CO2 flux remained relatively stable, primarily originating from respiratory CO2 emissions. The rice canopy CO2 flux model was revised using boundary line analysis, elucidating that photosynthetically active radiation, temperature, vapor pressure deficit, phenological stage, time, and concentration are pivotal factors influencing CO2 flux. The simulation of CO2 flux using the parameterized model, compared with measured values, reveals the efficacy of the established parameter model in simulating rice CO2 flux. This study holds significant importance in comprehending the carbon cycling process within paddy ecosystems, furnishing scientific grounds for future climate change and environmental management endeavors.
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(This article belongs to the Special Issue Ozone Pollution and Effects in China)
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Impacts of Climate Change on Runoff in the Heihe River Basin, China
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Qin Liu, Peng Cheng, Meixia Lyu, Xinyang Yan, Qingping Xiao, Xiaoqin Li, Lei Wang and Lili Bao
Atmosphere 2024, 15(5), 516; https://doi.org/10.3390/atmos15050516 - 23 Apr 2024
Abstract
Located in the central part of the arid regions of Northwest China, the Heihe River Basin (HRB) plays an important role in wind prevention, sand fixation, and soil and water conservation as the second largest inland river basin. In the context of the
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Located in the central part of the arid regions of Northwest China, the Heihe River Basin (HRB) plays an important role in wind prevention, sand fixation, and soil and water conservation as the second largest inland river basin. In the context of the warming and wetting climate observed in Northwest China, the situation of the ecological environment in the HRB is of significant concern. Using the data from meteorological observation stations, grid fusion and hydrological monitoring, this study analyzes the multi-scale climate changes in the HRB and their impacts on runoff. In addition, predictive models for runoff in the upper and middle reaches were developed using machine learning methods. The results indicate that the climate in the HRB has experienced an overall warming and wetting trend over the past 60 years. At the same time, there are clear regional variabilities in the climate changes. Precipitation shows decreasing trends in the northwestern part of the HRB, while it shows increases at rates higher than the regional average in the southeastern part. Moreover, the temperature increases are generally smaller in the upper reaches than those in the middle and lower reaches. Over the past 60 years, there has been a remarkable increase in runoff at the Yingluo Gorge (YL) hydrological station, which exhibits a distinct “single-peak” pattern in the variation of monthly runoff. The annual runoff volume at the YL (ZY) hydrological station is significantly correlated with the precipitation in the upper (middle) reaches, indicating the precipitation is the primary influencing factor determining the annual runoff. Temperature has a significant impact only on the runoff in the upper reaches, while its impact is not significant in the middle reaches. The models trained by the support vector machines and random forest models perform best in predicting the annual runoff and monthly runoff, respectively. This study can provide a scientific basis for environmental protection and sustainable development in the HRB.
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(This article belongs to the Section Climatology)
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Open AccessReview
Drone-Assisted Particulate Matter Measurement in Air Monitoring: A Patent Review
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Eladio Altamira-Colado, Daniel Cuevas-González, Marco A. Reyna, Juan Pablo García-Vázquez, Roberto L. Avitia and Alvaro R. Osornio-Vargas
Atmosphere 2024, 15(5), 515; https://doi.org/10.3390/atmos15050515 - 23 Apr 2024
Abstract
Air pollution is caused by the presence of polluting elements. Ozone (O3), carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM) are the most controlled gasses because they
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Air pollution is caused by the presence of polluting elements. Ozone (O3), carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM) are the most controlled gasses because they can be released into the atmosphere naturally or as a result of human activity, which affects air quality and causes disease and premature death in exposed people. Depending on the substance being measured, ambient air monitors have different types of air quality sensors. In recent years, there has been a growing interest in designing drones as mobile sensors for monitoring air pollution. Therefore, the objective of this paper is to provide a comprehensive patent review to gain insight into the proprietary technologies currently used in drones used to monitor outdoor air pollution. Patent searches were conducted using three different patent search engines: Google Patents, WIPO’s Patentscope, and the United States Patent and Trademark Office (USPTO). The analysis of each patent consists of extracting data that supply information regarding the type of drone, sensor, or equipment for measuring PM, the lack or presence of a cyclone separator, and the ability to process the turbulence generated by the drone’s propellers. A total of 1473 patent documents were retrieved using the search engine. However, only 13 met the inclusion criteria, including patent documents reporting drone designs for outdoor air pollution monitoring. Therefore, was found that most patents fall under class G01N (measurement; testing) according to the International Patents Classification, where the most common sensors and devices are infrared or visible light cameras, cleaning devices, and GPS tracking devices. The most common tasks performed by drones are air pollution monitoring, assessment, and control. These categories cover different aspects of the air pollution management cycle and are essential to effectively address this environmental problem.
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(This article belongs to the Special Issue Advances in Integrated Air Quality Management: Emissions, Monitoring, Modelling (3rd Edition))
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Open AccessArticle
The Observed Changes in Climate Characteristics in the Trebinje Vineyard Area (Bosnia and Herzegovina)
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Tijana Banjanin, Zorica Ranković-Vasić, Milica Glišić and Zoran Pržić
Atmosphere 2024, 15(4), 514; https://doi.org/10.3390/atmos15040514 - 22 Apr 2024
Abstract
The productivity and quality of grapes and wine are significantly influenced by changing climate conditions in vineyard regions worldwide. This study assesses changes in temperature, precipitation, and viticultural indices between the periods of 1971–1990 and 2000–2019 in Trebinje, a vineyard area located in
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The productivity and quality of grapes and wine are significantly influenced by changing climate conditions in vineyard regions worldwide. This study assesses changes in temperature, precipitation, and viticultural indices between the periods of 1971–1990 and 2000–2019 in Trebinje, a vineyard area located in the Herzegovina region of Bosnia and Herzegovina. Between the two periods, mean annual temperature increased by 2 °C and mean vegetational temperature by 2.4 °C, while mean precipitation remained within the range of climatological variability, with annual values increasing by 6% and vegetational values decreasing by 4.6%. Warming resulted in a longer duration of the vegetation season by 23.7 days, a reduced risk of late spring frosts, and an increased risk of very high temperatures during summer. These changes led to the reclassification of Trebinje vineyards’ climate from Region III to Region V, based on the Winkler index values, from a “temperate warm” to a “warm” category, based on the Huglin heliothermic index, and from “cool nights” to “temperate nights” based on the cool nights index. The category of the dryness index remained unchanged between the two periods. The findings emphasize the necessity for a renewal of the viticultural zoning and the development of climate change-adaptation plans for this region.
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(This article belongs to the Special Issue Climate Change Impacts and Adaptation Strategies in Agriculture)
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Open AccessArticle
Monitoring Snow Cover in Typical Forested Areas Using a Multi-Spectral Feature Fusion Approach
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Yunlong Wang and Jianshun Wang
Atmosphere 2024, 15(4), 513; https://doi.org/10.3390/atmos15040513 (registering DOI) - 22 Apr 2024
Abstract
Accurate snow cover monitoring is greatly significant for research on the hydrology model and regional climate variation, especially in Northeast China where forests cover almost forty percent of the total area. However, effectively monitoring snow cover under the forest canopy is still challenging
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Accurate snow cover monitoring is greatly significant for research on the hydrology model and regional climate variation, especially in Northeast China where forests cover almost forty percent of the total area. However, effectively monitoring snow cover under the forest canopy is still challenging with either in situ or remote sensing observations. The global SNOWMAP algorithm pertinent to the fixed normalized difference snow index (NDSI) threshold is, therefore, no longer applicable in a typical forested region of Northeast China. In order to achieve the goal of improving the accuracy of monitoring snow cover in areas with forest, utilizing MOD09GA and MOD13A1 products, a new approach of snow mapping was developed in this study, and it exploits the fusion and coupling of spectral features by integrating and analyzing the normalized difference forest snow index (NDFSI), the normalized difference vegetation index (NDVI), and the NDSI index. Then, Landsat 8 OLI images of high resolution were used to evaluate snow cover mapping precision. The experimental results indicated that the NDFSI index combined with the NDVI index showed great potential for extracting the snow cover distribution in forested regions. Compared with the snow distribution obtained from the Landsat 8 images, the average bias and FAR (false alarm ratio) values of snow cover mapping obtained by this algorithm were 1.23 and 13.54%, which were reduced by 1.98 and 29.36%, respectively. The overall accuracy of 81.31% was reached, which is improved by 20.19%. Thus, the snow classification scheme combining multiple spectral features from MODIS data works effectively in improving the precision of automatic snow cover mapping in typical forested areas of Northeast China, which provides essential support and significant perspectives for the next step of establishing a runoff model and rationally regulating forest water resources.
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(This article belongs to the Special Issue Precipitation Monitoring and Databases)
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Open AccessArticle
Investigation on the Sensitivity of Precipitation Simulation to Model Parameterization and Analysis Nudging over Hebei Province, China
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Yuanhua Li, Zhiguang Tian, Xia Chen, Xiashu Su and Entao Yu
Atmosphere 2024, 15(4), 512; https://doi.org/10.3390/atmos15040512 - 22 Apr 2024
Abstract
The physical parameterizations have important influence on model performance in precipitation simulation and prediction; however, previous investigations are seldom conducted at very high resolution over Hebei Province, which is often influenced by extreme events such as droughts and floods. In this paper, the
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The physical parameterizations have important influence on model performance in precipitation simulation and prediction; however, previous investigations are seldom conducted at very high resolution over Hebei Province, which is often influenced by extreme events such as droughts and floods. In this paper, the influence of parameterization schemes and analysis nudging on precipitation simulation is investigated using the WRF (weather research and forecasting) model with many sensitivity experiments at the cumulus “gray-zone” resolution (5 km). The model performance of different sensitivity simulations is determined by a comparison with the local high-quality observational data. The results indicate that the WRF model generally reproduces the distribution of precipitation well, and the model tends to underestimate precipitation compared with the station observations. The sensitivity simulation with the Tiedtke cumulus parameterization scheme combined with the Thompson microphysics scheme shows the best model performance, with the highest temporal correlation coefficient (0.45) and lowest root mean square error (0.34 mm/day). At the same time, analysis nudging, which incorporates observational information into simulation, can improve the model performance in precipitation simulation. Further analysis indicates that the negative bias in precipitation may be associated with the negative bias in relative humidity, which in turn is associated with the positive bias in temperature and wind speed. This study highlights the role of parameterization schemes and analysis nudging in precipitation simulation and provides a valuable reference for further investigations on precipitation forecasting applications.
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(This article belongs to the Special Issue Observations and Modeling of Precipitation Extremes and Tropical Cyclones)
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Inactivation Mechanisms of Escherichia coli in Simulants of Respiratory and Environmental Aerosol Droplets
by
Mara Otero-Fernandez, Richard J. Thomas, Henry Oswin, Robert Alexander, Allen Haddrell and Jonathan P. Reid
Atmosphere 2024, 15(4), 511; https://doi.org/10.3390/atmos15040511 - 22 Apr 2024
Abstract
The airborne transmission of disease relies on the ability of microbes to survive aerosol transport and, subsequently, cause infection when interacting with a host. The length of time airborne microorganisms remain infectious in aerosol droplets is a function of numerous variables. We present
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The airborne transmission of disease relies on the ability of microbes to survive aerosol transport and, subsequently, cause infection when interacting with a host. The length of time airborne microorganisms remain infectious in aerosol droplets is a function of numerous variables. We present measurements of mass and heat transfer from liquid aerosol droplets combined with airborne survival data for Escherichia coli MRE162, an ACDP category 1 microorganism used as a model system, under a wide range of environmental conditions, droplet compositions and microbiological conditions. In tandem, these companion measurements demonstrate the importance of understanding the complex relationship between aerosol microphysics and microbe survival. Specifically, our data consist of the correlation of a wide range of physicochemical properties (e.g., evaporation rates, equilibrium water content, droplet morphology, compositional changes in droplet solute and gas phase, etc.), with airborne viability decay to infer the impact of aerosol microphysics on airborne bacterial survival. Thus, a mechanistic approach to support prediction of the survival of microorganisms in the aerosol phase as a function of biological, microphysical, environmental, and experimental (aerosol-generation and sampling) processes is presented. Specific findings include the following: surfactants do not increase bacteria stability in aerosol, while both the bacteria growth phase and bacteria concentration may affect the rate at which bacteria decay in aerosol.
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(This article belongs to the Special Issue Atmospheric Bioaerosols: Detection, Characterization and Modelling)
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The Synergistic Effect of the Filtration Area Controlled by the Electromagnetic Valve and Injection Pressure on Pulse-Jet Dust Cleaning Performance
by
Yu Fu, Juan Lǖ, Shenglong Huang, Longyuan Lin and Haiyan Chen
Atmosphere 2024, 15(4), 510; https://doi.org/10.3390/atmos15040510 - 22 Apr 2024
Abstract
In engineering pulse-jet dust collector applications, the filtration area and injection pressure are chosen mostly based on experience. The peak pressure is tested under different injection pressures and filtration areas controlled by an electromagnetic valve, and then comprehensively analyzes the effects of dust
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In engineering pulse-jet dust collector applications, the filtration area and injection pressure are chosen mostly based on experience. The peak pressure is tested under different injection pressures and filtration areas controlled by an electromagnetic valve, and then comprehensively analyzes the effects of dust intensity, uniformity, and air consumption on dust cleaning to obtain a better filtration area controlled by an electromagnetic valve and injection pressure. The results show that considering the uniformity and intensity of dust cleaning, the filtration area of 33 m2 under the injection pressure of 0.4 MPa should be preferentially selected, with a standard deviation of 0.246 and a variance of 0.061. The filtration area of 27 m2 under the injection pressure of 0.3 MPa should be preferentially selected considering the unit air consumption, uniformity, and intensity of dust cleaning, the standard deviation of 0.252, and the variance of 0.064. The paper presents a theoretical foundation for selecting the optimal injection pressure and filter area regulated by an electromagnetic valve in pulse-jet dust collector systems.
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(This article belongs to the Section Air Quality)
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Wavelet Analysis of Atmospheric Ozone and Ultraviolet Radiation on Solar Cycle-24 over Lumbini, Nepal
by
Prakash M. Shrestha, Suresh P. Gupta, Usha Joshi, Morgan Schmutzler, Rudra Aryal, Babu Ram Tiwari, Binod Adhikari, Narayan P. Chapagain, Indra B. Karki and Khem N. Poudyal
Atmosphere 2024, 15(4), 509; https://doi.org/10.3390/atmos15040509 - 21 Apr 2024
Abstract
This research aims to comprehensively examine the clearness index (KT), total ozone column (TOC), and ultraviolet A (UVA) and ultraviolet B radiation (UVB) over Lumbini, Nepal (27°28’ N, 83°16’ E, and 150 m above sea level) throughout the 11 years of
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This research aims to comprehensively examine the clearness index (KT), total ozone column (TOC), and ultraviolet A (UVA) and ultraviolet B radiation (UVB) over Lumbini, Nepal (27°28’ N, 83°16’ E, and 150 m above sea level) throughout the 11 years of solar cycle 24 (2008 to 2018). The Lumbini, a highly polluted region, is important in advancing the identification and analysis of TOC variations across regions with similar geographical and climatic attributes. Data from the Ozone Monitoring Instrument (OMI) of the EOS-AURA satellite of NASA were used to analyze the daily, monthly, seasonal, and annual trends in the clearness index (KT), ultraviolet A (UVA), ultraviolet B (UVB), and TOC from the Comprehensive Environmental Data Archive (CEDA). The study found that the yearly averages for KT, TOC, UVA, and UVB were 0.55 ± 0.13, 272 ± 14 DU, 12.61 ± 3.50 W/m2, and 0.32 ± 0.11 W/m2, respectively. These values provide insights into the long-term variations in atmospheric parameters at Lumbini. The study also applied the continuous wavelet transform (CWT) to analyze KT, TOC, UVA, and UVB temporal variations. The power density peak of 35,000 DU2 in the TOC was observed from the end of 2010 to the end of 2011, within 8.5 years, underscoring the significance of analyzing TOC dynamics over extended durations to understand atmospheric behavior comprehensively.
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(This article belongs to the Topic Accessing and Analyzing Air Quality and Atmospheric Environment)
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Quantifying Urban Daily Nitrogen Oxide Emissions from Satellite Observations
by
Tao Tang, Lili Zhang, Hao Zhu, Xiaotong Ye, Donghao Fan, Xingyu Li, Haoran Tong and Shenshen Li
Atmosphere 2024, 15(4), 508; https://doi.org/10.3390/atmos15040508 - 21 Apr 2024
Abstract
Urban areas, characterized by dense anthropogenic activities, are among the primary sources of nitrogen oxides (NOx), impacting global atmospheric conditions and human health. Satellite observations, renowned for their continuity and global coverage, have emerged as an effective means to quantify pollutant
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Urban areas, characterized by dense anthropogenic activities, are among the primary sources of nitrogen oxides (NOx), impacting global atmospheric conditions and human health. Satellite observations, renowned for their continuity and global coverage, have emerged as an effective means to quantify pollutant emissions. Previous bottom-up emission inventories exhibit considerable discrepancies and lack a comprehensive and reliable database. To develop a high-precision emission inventory for individual cities, this study utilizes high-resolution single-pass observations from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite to quantify the emission rates of NOx. The Exponentially Modified Gaussian (EMG) model is validated for estimating NOx emission strength using real plumes observed in satellite single-pass observations, demonstrating good consistency with existing inventories. Further analysis based on the results reveals the existence of a weekend effect and seasonal variations in NOx emissions for the majority of the studied cities.
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(This article belongs to the Special Issue Reactive Nitrogen and Halogen in the Atmosphere)
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Forecasting the Mitigation Potential of Greenhouse Gas Emissions in Shenzhen through Municipal Solid Waste Treatment: A Combined Weight Forecasting Model
by
Xia Zhang, Bingchun Liu and Ningbo Zhang
Atmosphere 2024, 15(4), 507; https://doi.org/10.3390/atmos15040507 - 20 Apr 2024
Abstract
As a significant source of anthropogenic greenhouse gas emissions, the municipal solid waste sector’s greenhouse gas emission mode remains unknown, hampering effective decision-making on possible greenhouse gas emission reductions. Rapid urbanization and economic growth have resulted in massive volumes of municipal solid trash.
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As a significant source of anthropogenic greenhouse gas emissions, the municipal solid waste sector’s greenhouse gas emission mode remains unknown, hampering effective decision-making on possible greenhouse gas emission reductions. Rapid urbanization and economic growth have resulted in massive volumes of municipal solid trash. As a result, identifying emission reduction routes for municipal solid waste treatment is critical. In this research, we investigate the potential of municipal solid waste treatment methods in lowering greenhouse gas (GHG) emissions in Shenzhen, a typical Chinese major city. The results showed that the combined treatment of 58% incineration, 2% landfill, and 40% anaerobic digestion (AD) had the lowest greenhouse gas emissions of about 5.91 million tons under all scenarios. The implementation of waste sorting and anaerobic digestion treatment of organic municipal solid waste after separate collection can reduce greenhouse gas emissions by simply increasing the incineration ratio.
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(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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