Atmosphere

21 pages, 1863 KiB

Open AccessArticle

Characterization and Concentration Prediction of Dust Pollution in Open-Pit Coal Mines

by Guilin Wang, Wei Zhou, Zhiming Wang, Xiang Lu and Yirong Zhang

Atmosphere 2024, 15(12), 1408; https://doi.org/10.3390/atmos15121408 - 22 Nov 2024

Dust pollution is a major problem formed caused by opencast coal mining, and its prevention is a key prerequisite for the realization of green and climate-friendly mining in open-pit coal mines. In this paper, we conducted the real-time monitoring of dust concentration and [...] Read more.

Dust pollution is a major problem formed caused by opencast coal mining, and its prevention is a key prerequisite for the realization of green and climate-friendly mining in open-pit coal mines. In this paper, we conducted the real-time monitoring of dust concentration and meteorological parameter data in different areas of a large-scale open-pit coal mine in China and used multivariate statistical analysis to study the characteristics of the variation in dust concentration and its influencing factors in operating and non-operating areas. The results showed that there was a significant correlation between TSP, PM10, and PM2.5 in the same area. There was a significant difference in the percentage of PM2.5/PM10 between the operation area and the non-operation area, with particles in the range of 2.5–10 μm dominating close to the operation area and particles in the range of 0–2.5 μm dominating away from the operation area. The production intensity had a greater effect on dust concentration in the operation area, and there was no significant relationship with dust concentration away from the operation area. Wind speed—wind force—wind direction, temperature, and humidity are all correlated with particulate matter. The LSTM model is more suitable for predicting the dust concentration in open-pit coal mines. The results of this study can provide a reference for dust prevention and control in open-pit coal mines. Full article

(This article belongs to the Special Issue Atmospheric Pollution in Mining Areas)

25 pages, 4564 KiB

Open AccessArticle

Harnessing Deep Learning and Snow Cover Data for Enhanced Runoff Prediction in Snow-Dominated Watersheds

by Rana Muhammad Adnan, Wang Mo, Ozgur Kisi, Salim Heddam, Ahmed Mohammed Sami Al-Janabi and Mohammad Zounemat-Kermani

Atmosphere 2024, 15(12), 1407; https://doi.org/10.3390/atmos15121407 - 22 Nov 2024

Abstract

Predicting streamflow is essential for managing water resources, especially in basins and watersheds where snowmelt plays a major role in river discharge. This study evaluates the advanced deep learning models for accurate monthly and peak streamflow forecasting in the Gilgit River Basin. The [...] Read more.

Predicting streamflow is essential for managing water resources, especially in basins and watersheds where snowmelt plays a major role in river discharge. This study evaluates the advanced deep learning models for accurate monthly and peak streamflow forecasting in the Gilgit River Basin. The models utilized were LSTM, BiLSTM, GRU, CNN, and their hybrid combinations (CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU). Our research measured the model’s accuracy through root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and the coefficient of determination (R²). The findings indicated that the hybrid models, especially CNN-BiGRU and CNN-BiLSTM, achieved much better performance than traditional models like LSTM and GRU. For instance, CNN-BiGRU achieved the lowest RMSE (71.6 in training and 95.7 in testing) and the highest R² (0.962 in training and 0.929 in testing). A novel aspect of this research was the integration of MODIS-derived snow-covered area (SCA) data, which enhanced model accuracy substantially. When SCA data were included, the CNN-BiLSTM model’s RMSE improved from 83.6 to 71.6 during training and from 108.6 to 95.7 during testing. In peak streamflow prediction, CNN-BiGRU outperformed other models with the lowest absolute error (108.4), followed by CNN-BiLSTM (144.1). This study’s results reinforce the notion that combining CNN’s spatial feature extraction capabilities with the temporal dependencies captured by LSTM or GRU significantly enhances model accuracy. The demonstrated improvements in prediction accuracy, especially for extreme events, highlight the potential for these models to support more informed decision-making in flood risk management and water allocation. Full article

(This article belongs to the Special Issue Novel Algorithms and Advanced Computing Methods Application in Atmosphere)

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28 pages, 26490 KiB

Open AccessArticle

Vertical Accelerations and Convection Initiation in an Extreme Precipitation Event in the Western Arid Areas of Southern Xinjiang

by Na Li, Lingkun Ran, Daoyong Yang, Baofeng Jiao, Cha Yang, Wenhao Hu, Qilong Sun and Peng Tang

Atmosphere 2024, 15(12), 1406; https://doi.org/10.3390/atmos15121406 - 22 Nov 2024

Abstract

A simulation of an extreme precipitation event in southern Xinjiang, which is the driest area in China, seizes the whole initiation process of the intense convective cell responsible for the high hourly rainfall amount. Considering the inner connection between convection and vertical motions, [...] Read more.

A simulation of an extreme precipitation event in southern Xinjiang, which is the driest area in China, seizes the whole initiation process of the intense convective cell responsible for the high hourly rainfall amount. Considering the inner connection between convection and vertical motions, the characteristics and mechanisms of the vertical accelerations during this initial development of the deep convection are studied. It is shown that three key accelerations are responsible for the development from the nascent cumuli to a precipitating deep cumulonimbus, including sub-cloud boundary-layer acceleration, in-cloud deceleration, and cloud-top acceleration. By analyzing the right-hand terms of the vertical velocity equation in the framework of the WRF model, together with a diagnosed relation of perturbation pressure to perturbation potential temperature, perturbation-specific volume (or density), and moisture, the physical processes associated with the corresponding accelerations are revealed. It is found that sub-cloud acceleration is associated with three-dimensional divergence, indicating that the amount of upward transported air must be larger than that of horizontally convergent air. This is favorable for the persistent accumulation of water vapor into the accelerated area. In-cloud deceleration is caused by the intrusion or entrainment of mid-level cold air, which cools down the developing cloud and delays the deep convection formation. Cloud-top acceleration is responsible for the rapid upward extension of the cloud top, which is highly correlated with the convergence and upward transport of moisture. Full article

(This article belongs to the Section Meteorology)

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13 pages, 876 KiB

Open AccessArticle

Impact of Air Pollution on the Long-Term Decline of Non-Idiopathic Pulmonary Fibrosis Interstitial Lung Disease

by Pablo Mariscal-Aguilar, Luis Gómez-Carrera, Gema Bonilla, Carlos Carpio, Ester Zamarrón, María Fernández-Velilla, Mariana Díaz-Almirón, Francisco Gayá, Elena Villamañán, Concepción Prados and Rodolfo Álvarez-Sala

Atmosphere 2024, 15(12), 1405; https://doi.org/10.3390/atmos15121405 - 22 Nov 2024

Abstract

Objective: This study examines the association between major urban pollutants and the long-term decline of non-idiopathic pulmonary fibrosis interstitial lung disease [non-IPF ILD]. Materials and methods: A total of 41 patients with non-IPF ILD were analyzed from 2011 to 2020, correlating disease long-term [...] Read more.

Objective: This study examines the association between major urban pollutants and the long-term decline of non-idiopathic pulmonary fibrosis interstitial lung disease [non-IPF ILD]. Materials and methods: A total of 41 patients with non-IPF ILD were analyzed from 2011 to 2020, correlating disease long-term decline with concentrations of key pollutants [SO₂, CO, NO₂, O₃, PM_2.5, and PM₁₀] in Madrid. The likelihood of meeting severity criteria was assessed using a generalized linear model, considering the average pollutant levels during severe episodes. Results: At diagnosis, the average age of patients was 62.95 ± 13.13 years, with 47.6% women. The study found no significant association between pollution levels and the probability of meeting severity criteria for non-IPF ILD. The odds ratios were as follows: OR SO₂ = 0.92 [0.82–1.03], p = 0.16; OR CO = 0.99 [0.97–1.05], p = 0.70; OR NO₂ = 0.97 [0.92–1.03], p = 0.38; OR PM_2.5 = 0.79 [0.54–1.17], p = 0.24; OR PM₁₀ = 1.1 [0.94–1.28], p = 0.21; OR O₃ = 0.97 [0.92–1.01], p = 0.20. Conclusions: Our study suggests that, within the cohort of 41 patients with non-IPF ILD enrolled in this study, urban air pollutants in Madrid are not significantly linked to increased long-term decline of non-IPF ILD. This is one of the first studies to explore the impact of various urban pollutants on a diverse cohort of non-IPF ILD patients, including rare conditions like LAM and histiocytosis X. Further research with larger sample sizes and comprehensive exposure assessments is recommended. Full article

(This article belongs to the Special Issue Air Pollution Exposure and Health Impact Assessment (2nd Edition))

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11 pages, 2501 KiB

Open AccessArticle

Impact of Crop Type and Soil Characteristics on Greenhouse Gas Emissions in Latvian Agricultural Systems

by Karlis Memgaudis, Jovita Pilecka-Ulcugaceva and Kristine Valujeva

Atmosphere 2024, 15(12), 1404; https://doi.org/10.3390/atmos15121404 - 22 Nov 2024

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Abstract

This study investigates the impact of crop type and soil characteristics on greenhouse gas (GHG) emissions in Latvian agriculture, offering insights directly relevant to policymakers and practitioners focused on sustainable land management. From 2020 to 2023, emissions were monitored across four agricultural sites [...] Read more.

This study investigates the impact of crop type and soil characteristics on greenhouse gas (GHG) emissions in Latvian agriculture, offering insights directly relevant to policymakers and practitioners focused on sustainable land management. From 2020 to 2023, emissions were monitored across four agricultural sites featuring different crop rotations: blueberry monoculture, continuous maize cropping, winter barley–winter rapeseed rotation, and spring barley–bean–winter wheat–fallow rotation. Results indicate that GHG emissions vary widely depending on crop and soil type. CO₂ emissions varied significantly based on both crop and soil type, with organic soils under maize cultivation in Mārupe averaging 184.91 kg CO₂ ha⁻¹ day⁻¹, while mineral soils in Bērze under spring barley emitted 60.98 kg CO₂ ha⁻¹ day⁻¹. Methane absorption was highest in well-aerated mineral soils, reaching 6.11 g CH₄ ha⁻¹ day⁻¹ in spring barley fields in Auce. Maize cultivation contributed the highest N₂O emissions, reaching 33.15 g N₂O ha⁻¹ day⁻¹. These findings underscore that targeted practices, like optimized crop rotation and fertilizer use, can substantially reduce GHG emissions. Climate variability across locations affects soil moisture and temperature, but these factors were statistically controlled to isolate the impacts of crop type and soil characteristics on emissions. This study provides valuable data to inform sustainable agricultural policies and help achieve EU climate goals. Full article

(This article belongs to the Special Issue Gas Emissions from Soil)

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17 pages, 871 KiB

Open AccessSystematic Review

The Impact of Outdoor Environmental Factors on Indoor Air Quality in Education Settings: A Systematic Review

by Jan Rožanec, An Galičič and Andreja Kukec

Atmosphere 2024, 15(12), 1403; https://doi.org/10.3390/atmos15121403 - 22 Nov 2024

Abstract

Poor indoor air quality (IAQ) in schools is associated with pupils’ health and their learning performance. This study aims to provide an overview of the outdoor factors that affect the IAQ in educational settings in order to develop public health measures. We conducted [...] Read more.

Poor indoor air quality (IAQ) in schools is associated with pupils’ health and their learning performance. This study aims to provide an overview of the outdoor factors that affect the IAQ in educational settings in order to develop public health measures. We conducted a systematic literature review to investigate the outdoor factors that affect IAQ in educational settings. The selection of articles included 17,082 search string hits from the ScienceDirect database published between 2010 and 2023, with 92 relevant studies selected based on the inclusion and exclusion criteria. Based on a systematic review of the literature, we identified the following outdoor factors: proximity to busy roads, commercial and industrial establishments, meteorological conditions, compounds from the natural environment, emissions from heating buildings, atmospheric reactions and secondary pollutants, unpaved school playgrounds, and smoking. This study provides key information on the mentioned outdoor factors and gives recommendations on measures to reduce classroom pollutant concentrations while highlighting educational settings that require special attention. Our study shows that classroom IAQ is affected by many outdoor pollutant sources, the prevalence of which depends on the educational setting’s micro location. Therefore, it is essential to develop an appropriate classroom ventilation strategy for each educational setting. Full article

(This article belongs to the Section Air Quality)

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14 pages, 1503 KiB

Open AccessArticle

Determination of Polycyclic Aromatic Hydrocarbons from Atmospheric Deposition in Malva sylvestris Leaves Using Gas Chromatography with Mass Spectrometry (GC-MS)

by Giuseppe Ianiri, Alessandra Fratianni, Pasquale Avino and Gianfranco Panfili

Atmosphere 2024, 15(12), 1402; https://doi.org/10.3390/atmos15121402 - 22 Nov 2024

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Abstract

Plant leaves can be used to determine the atmospheric deposition of organic contaminants, including polycyclic aromatic hydrocarbons (PAHs), to assess the contamination status of an area. The purpose of this study was to develop an analytical method for the determination of PAHs deriving [...] Read more.

Plant leaves can be used to determine the atmospheric deposition of organic contaminants, including polycyclic aromatic hydrocarbons (PAHs), to assess the contamination status of an area. The purpose of this study was to develop an analytical method for the determination of PAHs deriving from atmospheric deposition using Malva sylvestris leaves. Analytes were recovered from the leaves of the plant using cyclohexane as an organic solvent and subsequent sonication. The percentage recoveries (R%) were good (from 65.8 ± 3.2 to 104.2 ± 16.9), together with the instrumental analytical parameters, including correlation coefficients (r) ≥ 0.995 for all PAHs. The instrumental analysis was carried out using GC-MS in total ion current and single ion monitoring at the same time. Real samples taken from urban environments have shown that they are not always the most contaminated. At the Palermo site, leaves were observed to have high amounts of PAHs due to the deposition of dust generated by combustion processes that occurred near the sampling site. Further studies are recommended to compare the use of plants and classical sampling systems for monitoring the atmospheric deposition of key contaminants toxic to human health. Full article

(This article belongs to the Special Issue From Traditional to Emerging Air Pollutants: Tools and Health Risk Assessment)

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25 pages, 5612 KiB

Open AccessArticle

Innovative Approaches to Industrial Odour Monitoring: From Chemical Analysis to Predictive Models

by Claudia Franchina, Amedeo Manuel Cefalì, Martina Gianotti, Alessandro Frugis, Corrado Corradi, Giulio De Prosperis, Dario Ronzio, Luca Ferrero, Ezio Bolzacchini and Domenico Cipriano

Atmosphere 2024, 15(12), 1401; https://doi.org/10.3390/atmos15121401 - 21 Nov 2024

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Abstract

This study evaluated the reliability of an electronic nose in monitoring odour concentration near a wastewater treatment plant and examined the correlation between four sensor readings and odour intensity. The electronic nose chemical sensors are related to the concentration of the following chemical [...] Read more.

This study evaluated the reliability of an electronic nose in monitoring odour concentration near a wastewater treatment plant and examined the correlation between four sensor readings and odour intensity. The electronic nose chemical sensors are related to the concentration of the following chemical species: two values for the concentration of VOCs recorded via the PID sensor (VPID) and the EC sensor (VEC), and concentrations of sulfuric acid (VH₂S) and benzene (VC₆H₆). Using Random Forest and least squares regression analysis, the study identifies VH₂S and VC₆H₆ as key contributors to odour concentration (C^c_OD). Three Random Forest models (RF0, RF1, RF2), with different characteristics for splitting between the test set and the training set, were tested, with RF1 showing superior predictive performance due to its training approach. All models highlighted VH₂S and VC₆H₆ as significant predictors, while VPID and VEC had less influence. A significant correlation between odour concentration and specific chemical sensor readings was found, particularly for VH₂S and VC₆H₆. However, predicting odour concentrations below 1000 ou_E/m³ proved challenging. Linear regression further confirmed the importance of VH₂S and VC₆H₆, with a moderate R-squared value of 0.70, explaining 70% of the variability in odour concentration. The study demonstrated the effectiveness of combining Random Forest and least squares regression for robust and interpretable results. Future research should focus on expanding the dataset and incorporating additional variables to enhance model accuracy. The findings underscore the necessity of specific sensor training and standardised procedures for accurate odour monitoring and characterisation. Full article

(This article belongs to the Special Issue Environmental Odour (2nd Edition))

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13 pages, 3613 KiB

Open AccessArticle

Impact of Large-Scale Circulations on Ground-Level Ozone Variability over Eastern China

by Jinlan Li and Ying Li

Atmosphere 2024, 15(12), 1400; https://doi.org/10.3390/atmos15121400 - 21 Nov 2024

Viewed by 229

Abstract

The seasonal and interannual variations in ground-level ozone across eastern China from 2014 to 2022 were strongly influenced by meteorological conditions and large-scale atmospheric circulations. We applied empirical orthogonal function (EOF) and singular value decomposition (SVD) analyses to explore these relationships. The EOF [...] Read more.

The seasonal and interannual variations in ground-level ozone across eastern China from 2014 to 2022 were strongly influenced by meteorological conditions and large-scale atmospheric circulations. We applied empirical orthogonal function (EOF) and singular value decomposition (SVD) analyses to explore these relationships. The EOF analysis identified three primary patterns of ozone variability: a dominant seasonal cycle over most of mainland China, an anti-correlation between northern and southern China during transitional seasons, and elevated springtime ozone concentrations in coastal regions. The SVD results further demonstrated that seasonal ozone variability was primarily driven by the annual radiation cycle across much of China. In contrast, the East Asian summer monsoon (EASM) was linked to the relatively low summer ozone levels observed in southern China. The anti-correlation between northern and southern China was associated with western Pacific subtropical high (WPSH) movement, which promoted sunny weather conditions and was conducive to ozone formation. Additionally, high springtime ozone levels in northern coastal regions were influenced by pollutant transport from continental cold high (CCH) events, while the cloud-free conditions and intense solar radiation in southern China contributed to elevated ozone concentrations. Full article

(This article belongs to the Section Air Quality)

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Atmosphere, Volume 15, Issue 12 (December 2024) – 9 articles

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