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20 pages, 9414 KiB

Open AccessArticle

Construction of Linear Models for the Normalized Vegetation Index (NDVI) for Coffee Crops in Peru Based on Historical Atmospheric Variables from the Climate Engine Platform

by Ligia García, Jaris Veneros, Manuel Oliva-Cruz, Neiro Olivares, Segundo G. Chavez and Nilton B. Rojas-Briceño

Atmosphere 2024, 15(8), 923; https://doi.org/10.3390/atmos15080923 (registering DOI) - 1 Aug 2024

Abstract

The rapid development of digital tools for crop management offers new opportunities to mitigate the effects of climate change on agriculture. This study examines the Normalized Difference Vegetation Index (NDVI) in coffee-growing areas of the province of Rodriguez de Mendoza, southern Peru, from [...] Read more.

The rapid development of digital tools for crop management offers new opportunities to mitigate the effects of climate change on agriculture. This study examines the Normalized Difference Vegetation Index (NDVI) in coffee-growing areas of the province of Rodriguez de Mendoza, southern Peru, from 2001 to 2022. The objectives were the following: (a) to analyze NDVI trends in these areas; (b) to investigate trends in climatic variables and their correlations with altitude and NDVI; and c) to develop linear models tailored to each coffee-growing area. The study identified significant differences in NDVI trends among coffee plants, with mean NDVI values ranging from about 0.6 to 0.8. These values suggest the presence of stress conditions that should be monitored to improve crop quality, particularly in Huambo. Variability in rainfall, maximum and minimum temperatures, relative humidity, and altitude was also observed, with NDVI values showing a strong negative correlation with altitude. These results are crucial for making informed strategic decisions in integrated crop management and for monitoring crop health using vegetation indices. Full article

(This article belongs to the Section Climatology)

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18 pages, 20146 KiB

Open AccessArticle

Changed Relationship between the Spring North Atlantic Tripole Sea Surface Temperature Anomalies and the Summer Meridional Shift of the Asian Westerly Jet

by Lin Chen, Gen Li and Jiaqi Duan

Atmosphere 2024, 15(8), 922; https://doi.org/10.3390/atmos15080922 (registering DOI) - 1 Aug 2024

Abstract

The summer Asian westerly jet (AWJ)’s shifting in latitudes is one important characteristic of its variability and has great impact on the East Asian summer climate. Based on the observed and reanalyzed datasets from the Hadley Center Sea Ice and Sea Surface Temperature [...] Read more.

The summer Asian westerly jet (AWJ)’s shifting in latitudes is one important characteristic of its variability and has great impact on the East Asian summer climate. Based on the observed and reanalyzed datasets from the Hadley Center Sea Ice and Sea Surface Temperature dataset (HadISST), the Japanese 55-year reanalysis (JRA-55), and the fifth generation of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5), this study investigates the relationship between the spring tripole North Atlantic SST (TNAT) anomalies and the summer meridional shift of the AWJ (MSJ) for the period of 1958–2020. Through the method of correlation analysis and regression analysis, we show that the ‘+ - +’ TNAT anomalies in spring could induce a northward shift of the AWJ in the following summer. However, such a climatic effect of the spring TNAT anomalies on the MSJ is unstable, exhibiting an evident interdecadal strengthening since the early 1990s. Further analysis reveals that this is related to a strengthened intensity of the spring TNAT anomalies in the most recent three decades. Compared to the early epoch (1958–1993), the stronger spring TNAT anomalies in the post epoch (1994–2020) could cause a stronger pan-tropical climate response until the following summer through a series of ocean–atmosphere interactions. Through Gill responses, the resultant more prominent cooling in the central Pacific in response to the ‘+ - +’ TNAT anomalies induces a pan-tropical cooling in the upper troposphere, which weakens the poleward gradient of the tropospheric temperature over subtropical Asia. As a result, the AWJ shifts northward via a thermal wind effect. By contrast, in the early epoch, the spring TNAT anomalies are relatively weaker, inducing weaker pan-tropical ocean–atmosphere interactions and thus less change in the meridional shit of the summer AWJ. Our results highlight a strengthened lagged effect of the spring TNAT anomalies on the following summer MSJ and have important implications for the seasonal climate predictability over Asia. Full article

(This article belongs to the Section Climatology)

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82 pages, 4399 KiB

Open AccessReview

Statistical Dynamics and Subgrid Modelling of Turbulence: From Isotropic to Inhomogeneous

by Jorgen S. Frederiksen, Vassili Kitsios and Terence J. O’Kane

Atmosphere 2024, 15(8), 921; https://doi.org/10.3390/atmos15080921 (registering DOI) - 31 Jul 2024

Abstract

Turbulence is the most important, ubiquitous, and difficult problem of classical physics. Feynman viewed it as essentially unsolved, without a rigorous mathematical basis to describe the statistical dynamics of this most complex of fluid motion. However, the paradigm shift came in 1959, with [...] Read more.

Turbulence is the most important, ubiquitous, and difficult problem of classical physics. Feynman viewed it as essentially unsolved, without a rigorous mathematical basis to describe the statistical dynamics of this most complex of fluid motion. However, the paradigm shift came in 1959, with the formulation of the Eulerian direct interaction approximation (DIA) closure by Kraichnan. It was based on renormalized perturbation theory, like quantum electrodynamics, and is a bare vertex theory that is manifestly realizable. Here, we review some of the subsequent exciting achievements in closure theory and subgrid modelling. We also document in some detail the progress that has been made in extending statistical dynamical turbulence theory to the real world of interactions with mean flows, waves and inhomogeneities such as topography. This includes numerically efficient inhomogeneous closures, like the realizable quasi-diagonal direct interaction approximation (QDIA), and even more efficient Markovian Inhomogeneous Closures (MICs). Recent developments include the formulation and testing of an eddy-damped Markovian anisotropic closure (EDMAC) that is realizable in interactions with transient waves but is as efficient as the eddy-damped quasi-normal Markovian (EDQNM). As a similarly efficient closure, the realizable eddy-damped Markovian inhomogeneous closure (EDMIC) has been developed. Moreover, we present subgrid models that cater to the complex interactions that occur in geophysical flows. Recent progress includes the determination of complete sets of subgrid terms for skilful large-eddy simulations of baroclinic inhomogeneous turbulent atmospheric and oceanic flows interacting with Rossby waves and topography. The success of these inhomogeneous closures has also led to further applications in data assimilation and ensemble prediction and generalization to quantum fields. Full article

(This article belongs to the Special Issue Isotropic Turbulence: Recent Advances and Current Challenges)

15 pages, 883 KiB

Open AccessArticle

Chemical Composition of PM₁₀ in a Classroom near the Copper Smelter in Bor, Serbia

by Bojan Radović, Viša Tasić, Renata Kovačević, Tatjana Apostolovski-Trujić, Dragan Manojlović, Mira Cocić and Tamara Urošević

Atmosphere 2024, 15(8), 920; https://doi.org/10.3390/atmos15080920 (registering DOI) - 31 Jul 2024

Abstract

An analysis was conducted on the influence of advancements in smelting technology at the copper smelter in Bor, Serbia, and seasonal changes on the level and chemical composition of PM₁₀ inside and outside a classroom at the Technical Faculty in Bor in [...] Read more.

An analysis was conducted on the influence of advancements in smelting technology at the copper smelter in Bor, Serbia, and seasonal changes on the level and chemical composition of PM₁₀ inside and outside a classroom at the Technical Faculty in Bor in 2015 and 2019. The results of chemical analyses revealed that the average level of arsenic in PM₁₀ within the classroom reached 11.9 ng/m³, nearly double the annual target value. In comparison, the average level of arsenic (As) in PM₁₀ in ambient air stood at 15 ng/m³. A notable enrichment factor was observed for elements present in copper concentrates (Se > Ag > Bi > Pb > Cd > As > Sb > S > Cu > Sn > Zn) in both the classroom and outdoor air, underscoring their predominantly anthropogenic origin. Higher concentrations of As in PM₁₀ were recorded during the non-heating season and the operation of the copper smelter with outdated smelting technology (2015). These findings hold significant implications for health protection for all citizens in the investigated area surrounding the Technical Faculty in Bor. The study highlights the need for additional measures to reduce As content in PM₁₀ in ambient air and prevent the infiltration of suspended particles from outdoor air into classrooms. Full article

(This article belongs to the Section Aerosols)

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21 pages, 7948 KiB

Open AccessArticle

Response of Sea Surface Temperature and Chlorophyll-a to Typhoon Lekima (2019)

by Yaowei Shi, Biyun Guo, Yuqian Niu, Venkata Subrahmanyam Mantravadi, Jushang Wang, Zhaokang Ji, Yingliang Che and Menglu Ye

Atmosphere 2024, 15(8), 919; https://doi.org/10.3390/atmos15080919 (registering DOI) - 31 Jul 2024

Abstract

Typhoon (hurricane) is the most influential process of ocean–air interaction on the synoptic scale; it has a great influence on the heat exchange, mixing and ecological processes in the upper ocean, which in turn affect sea surface temperature (SST), leading to chlorophyll-a (Chl-a) [...] Read more.

Typhoon (hurricane) is the most influential process of ocean–air interaction on the synoptic scale; it has a great influence on the heat exchange, mixing and ecological processes in the upper ocean, which in turn affect sea surface temperature (SST), leading to chlorophyll-a (Chl-a) concentration variation. SST is also an important factor affecting marine fishery resources. Chl-a is closely related to the marine ecosystem and primary productivity. In this study, we analyzed the response of SST and Chl-a to Typhoon Lekima (2019) process. The result indicates that the response of temperature to typhoon decreases from the center to the outer edge, which has a good correlation with the location, path and influence area of the typhoon center. The mean SST in the study area (14°~40° N, 116°~136° E) decreased during the typhoon’s passage, from 28.97 °C at the beginning (5 August) to 28.22 °C (15 August). The concentration of Chl-a was high in the northwest and coastal areas; its mean value in the study area decreased from 2 to 8 August (on 2 and 8 August, the concentration was 0.484 mg/m³ and 0.405 mg/m³, respectively). From 8 to 14 August, Chl-a decreased with the increase in SST, and 10 and 14 August were the two peak values of Chl-a (while SST was low). Chl-a concentration increased after the typhoon’s landfall (from 15 to 31 August); the Chl-a trend was the same as that of SST. The stronger the typhoon and the longer the residence time, the greater the contribution to the increase in Chl-a concentration at sea surface. Typhoon-induced rainfall over the ocean surface, increased evaporation of seawater, enhanced mixing within the mixed layer and upwelling of the pycnocline resulted in an increase in Chl-a quantity. This study describes the spatial response of the upper ocean to typhoons. It provides a general method for the comprehensive assessment of typhoons in marginal seas and upper open oceans, which has wide applicability and good scientific application prospects. Full article

(This article belongs to the Special Issue Ocean–Atmosphere–Land Interactions and Their Roles in Climate Change)

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28 pages, 2702 KiB

Open AccessArticle

Carbon Reduction Pathways for Chinese Provinces: Considering Carbon Intensity Convergence, Regional Development Plans, and Shared Socioeconomic Pathways

by Fanglei Zhong, Yanjie Yin, Jingwen Tian, Daiwei Jiang and Yijun Mao

Atmosphere 2024, 15(8), 918; https://doi.org/10.3390/atmos15080918 (registering DOI) - 31 Jul 2024

Abstract

The achievement of dual carbon goals varies significantly across Chinese provinces due to the differences in resource endowments and socioeconomic development levels. These variations impose distinct pressures for transitioning along different socioeconomic development paths. Exploration of orderly carbon reduction paths for each province [...] Read more.

The achievement of dual carbon goals varies significantly across Chinese provinces due to the differences in resource endowments and socioeconomic development levels. These variations impose distinct pressures for transitioning along different socioeconomic development paths. Exploration of orderly carbon reduction paths for each province is, thus, crucial, but current predictions have not adequately integrated provincial development plans and carbon emission convergence trends. This study combines the Intergovernmental Panel on Climate Change (IPCC’s) shared socioeconomic pathways (SSPs) with a dual-layer target-setting approach based on trends in carbon emissions and each province’s five-year plans. The scalable stochastic impacts by regression on population, affluence, and technology (STIRPAT) model and ridge regression method were employed to identify the factors influencing carbon emissions. Carbon peak values and timing were then predicted for provinces under different scenarios. The results indicate that total carbon emissions are primarily influenced by provincial economic level and population, with spatial and temporal variations in the driving factors. Carbon emissions are ranked from low to high in the following order: SSP1, SSP2, and SSP5. Provinces were categorized into four tiers based on their peak times: early, on-time, delayed, and significantly delayed peaking. Under SSP1, 24 provinces would achieve carbon peaking as scheduled. Under SSP2, only 17 provinces would achieve carbon peaking on time. Among the first-tier provinces, Beijing and Shanghai would achieve peak carbon five to eight years ahead of schedule; the second-tier provinces of Henan, Fujian, Guangdong, and Inner Mongolia would achieve peak carbon by 2030; the third tier would achieve carbon peak before 2035; and the fourth tier would gradually reach peak carbon before 2050. Policy implications for differentiated carbon peak paths are proposed for different regions based on these findings. Full article

(This article belongs to the Special Issue Carbon Emission and Carbon Neutrality in China)

25 pages, 14461 KiB

Open AccessArticle

Urban Climate Dynamics: Analyzing the Impact of Green Cover and Air Pollution on Land Surface Temperature—A Comparative Study Across Chicago, San Francisco, and Phoenix, USA

by Sepideh Azizi and Tahmineh Azizi

Atmosphere 2024, 15(8), 917; https://doi.org/10.3390/atmos15080917 (registering DOI) - 31 Jul 2024

Abstract

Rapid urbanization worldwide has significantly altered urban climates, creating a need to balance urban growth with thermal environmental quality for sustainable development. This study examines the relationship between land surface temperature (LST) and urban characteristics, particularly focusing on how green cover can mitigate [...] Read more.

Rapid urbanization worldwide has significantly altered urban climates, creating a need to balance urban growth with thermal environmental quality for sustainable development. This study examines the relationship between land surface temperature (LST) and urban characteristics, particularly focusing on how green cover can mitigate urban heat and how air pollution can increase temperatures. Recognizing the predictive value of LST for urban heat island (UHI) intensity, we analyzed three distinct U.S. cities—Chicago, San Francisco, and Phoenix—each characterized by unique climate and urban planning features. This study investigates the relationship between atmospheric pollutants (SO₂, NO₂, CO, O₃) and the Normalized Difference Vegetation Index (NDVI) with land surface temperature (LST) using regression and correlation analyses. The analysis aims to elucidate how changes in atmospheric pollutants and NDVI affect variations in land surface temperature. Regression analysis is employed to estimate the coefficients of independent variables and quantify their impact on LST. Correlation analysis assesses the linear relationships between variables, providing insights into their pairwise associations. The study also examines multicollinearity between independent variables to identify potential confounding factors. The results reveal significant associations between atmospheric pollutants, NDVI, and land surface temperature, contributing to our understanding of the environmental factors influencing LST dynamics and informing climate change mitigation strategies. The observed inconsistencies in correlations across cities highlight the importance of the local context in environmental studies. Understanding these variations can aid in developing tailored urban planning policies that consider unique city characteristics for more effective climate resilience. Furthermore, a positive association was consistently obtained between pollutants and LST, indicating that increased pollution levels contribute to higher surface temperatures across different urban settings. Full article

(This article belongs to the Special Issue Land Surface Temperature Retrieval Using Satellite Remote Sensing (2nd Edition))

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28 pages, 8540 KiB

Open AccessReview

Earthquake Lights Observed in Japan—Possible Underlying Mechanisms

by Yuji Enomoto

Atmosphere 2024, 15(8), 916; https://doi.org/10.3390/atmos15080916 (registering DOI) - 31 Jul 2024

Abstract

In Japan, a country prone to earthquakes, numerous damaging earthquakes have been recorded throughout history, often accompanied by descriptions of mysterious earthquake lights (EQL), which may involve various mechanisms. In this article, the possible mechanisms for different types of EQL in 11 cases [...] Read more.

In Japan, a country prone to earthquakes, numerous damaging earthquakes have been recorded throughout history, often accompanied by descriptions of mysterious earthquake lights (EQL), which may involve various mechanisms. In this article, the possible mechanisms for different types of EQL in 11 cases are reviewed among 21 selected earthquakes. These involve preseismic physicochemical variations in the geological structure of the fault in the lithosphere, which contains deep Earth gases such as radon, methane, and others, as a primary factor for EQL generation. Additionally, various seismic, atmospheric, hydrospheric, and ionospheric variations interact with each other, resulting in the visualization of characteristic anomalous phenomena, such as glowing or shining ground, mountains, offshore areas, and skies of various colors. These phenomena appear momentarily but can sometimes last for extended periods. Because EQL often appear just before an earthquake, their study might be significant for earthquake prediction. Additionally, EQL involving methane flames in the ground is an important research topic as it relates to public safety. Was what they witnessed paranormal? Full article

(This article belongs to the Special Issue Recent Advances in Lightning Research)

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16 pages, 12799 KiB

Open AccessArticle

Research of Air Purification Using Ion Energy Effect on Particulate Matter Agglomeration

by Audrius Čereška, Ina Tetsmann, Regimantas Bareikis and Raimondas Jasevičius

Atmosphere 2024, 15(8), 915; https://doi.org/10.3390/atmos15080915 (registering DOI) - 31 Jul 2024

Abstract

The article presents a special stand featuring ion generators designed and manufactured for experimental research. This study investigates the use of ion energy for air purification, specifically focusing on its effect on PM (0.3–10 μm) agglomeration. By introducing charged ions into the air, [...] Read more.

The article presents a special stand featuring ion generators designed and manufactured for experimental research. This study investigates the use of ion energy for air purification, specifically focusing on its effect on PM (0.3–10 μm) agglomeration. By introducing charged ions into the air, particulate matter can be charged, resulting in increased particle sizes through agglomeration, and subsequently improving the filtration efficiency. Experiments were conducted in a controlled chamber to evaluate the performance of an ion-based air purification system. The effects of various parameters, such as ion concentration, airflow rate, and particle size distribution, on PM agglomeration were studied. The results demonstrated that the ion-based air purification system could effectively enhance PM agglomeration, particularly for ultrafine particles. The study also revealed that the efficiency of PM agglomeration was influenced by the ion concentration and airflow rate, with an optimal range identified to maximize the system’s performance. Furthermore, the potential real-world application of this technology was discussed, considering factors such as energy consumption, maintenance requirements, and compatibility with existing air purification systems. This study contributes to the development of innovative air purification solutions and offers valuable insights into the potential of ion energy for improving indoor air quality and safeguarding human health. Full article

(This article belongs to the Special Issue Indoor Air Pollution Exposure and Health Risk Assessment)

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15 pages, 3598 KiB

Open AccessArticle

Spatiotemporal Predictive Learning for Radar-Based Precipitation Nowcasting

by Xiaoying Wang, Haixiang Zhao, Guojing Zhang, Qin Guan and Yu Zhu

Atmosphere 2024, 15(8), 914; https://doi.org/10.3390/atmos15080914 (registering DOI) - 31 Jul 2024

Abstract

Based on C-band weather radar and ground precipitation data from the Helan Mountain area in Yinchuan between 2017 to 2020, we evaluated the forecasting performances of 15 mainstream deep learning models used in recent years, including recurrent-based and recurrent-free models. The critical success [...] Read more.

Based on C-band weather radar and ground precipitation data from the Helan Mountain area in Yinchuan between 2017 to 2020, we evaluated the forecasting performances of 15 mainstream deep learning models used in recent years, including recurrent-based and recurrent-free models. The critical success index (CSI), probability of detection (POD), false alarm rate (FAR), mean square error (MSE), mean absolute error (MAE), and learned perceptual image patch similarity (LPIPS), were used to evaluate the forecasting abilities. The results showed that (1) recurrent-free models have significant parameter quantity and computing power advantages, especially the SimVP model. Among the recurrent-based models, PredRNN and PredRNN++ demonstrate good predictive capabilities for changes in echolocation and intensity, PredRNN++ performs better in predicting long sequences (1 h); (2) SimVP uses Inception to extract temporal features, which cannot capture the complex physical changes in radar echo images and fails to extract spatial–temporal correlations and accurately predict heavy rainfall areas effectively. Therefore, we constructed the SimVP-GMA model, replacing the temporal prediction module in SimVP and modifying the spatial encoder part. Compared with SimVP, the MSE and LPIPS indicators were improved by 0.55 and 0.0193, respectively. It can be seen from the forecast images that the forecast details have been significantly improved, especially in the forecasting of heavy rainfall weather. Full article

(This article belongs to the Special Issue Deep Learning Algorithms for Weather Forecasting and Climate Prediction)

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23 pages, 4832 KiB

Open AccessArticle

Influence of Short-Term Variations in Solar Activity on Total Electron Content

by Plamen Mukhtarov and Rumiana Bojilova

Atmosphere 2024, 15(8), 913; https://doi.org/10.3390/atmos15080913 (registering DOI) - 30 Jul 2024

Abstract

In the present work, the variations in Total Electron Content (TEC) induced by changes in the ionizing radiation of the Sun, which are related to the rotation period (about 27 days), were investigated. This study was based on a 30-year period. The relative [...] Read more.

In the present work, the variations in Total Electron Content (TEC) induced by changes in the ionizing radiation of the Sun, which are related to the rotation period (about 27 days), were investigated. This study was based on a 30-year period. The relative deviations in the TEC and F10.7 values were used in the data analysis. The use of this modification aimed to eliminate the stationary diurnal, seasonal, and solar course of the TEC over the course of the long-term variations in solar activity, preserving the variations within a time scale of 27 days and less. As a result, the values of the linear regression coefficient between the relative deviations in the two considered quantities from the median (quiet conditions) for one rotation period were obtained. Depending on the general level of solar activity, the season, and the latitude, this coefficient varied between 40% and 60%. The analysis showed that the minimum values were observed during high solar activity. The latitudinal distribution demonstrated an increase in the area of the Equatorial Ionization Anomaly (EIA) under the influence of the so-called “fountain effect”. As a result, there was a seasonal variation and an increase in the winter months at mid and high latitudes and a decrease in the months of the minimum zenith angle of the Sun at low latitudes. A well-pronounced asymmetry in the equinox months was also obtained. The obtained results are the novelty of this study and can be used to improve empirical models for short-term TEC forecasting. Full article

(This article belongs to the Section Upper Atmosphere)

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15 pages, 3094 KiB

Open AccessArticle

Neural Hierarchical Interpolation for Standardized Precipitation Index Forecasting

by Rafael Magallanes-Quintanar, Carlos Eric Galván-Tejada, Jorge Isaac Galván-Tejada, Hamurabi Gamboa-Rosales, Santiago de Jesús Méndez-Gallegos and Antonio García-Domínguez

Atmosphere 2024, 15(8), 912; https://doi.org/10.3390/atmos15080912 (registering DOI) - 30 Jul 2024

Abstract

In the context of climate change, studying changes in rainfall patterns is a crucial area of research, remarkably so in arid and semi-arid regions due to the susceptibility of human activities to extreme events such as droughts. Employing predictive models to calculate drought [...] Read more.

In the context of climate change, studying changes in rainfall patterns is a crucial area of research, remarkably so in arid and semi-arid regions due to the susceptibility of human activities to extreme events such as droughts. Employing predictive models to calculate drought indices can be a useful method for the effective characterization of drought conditions. This study applies two type of machine learning methods—long short-term memory (LSTM) and Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS)—to develop and deploy artificial neural network models with the aim of predicting the regional standardized precipitation index (SPI) in four regions of Zacatecas, Mexico. The predictor variables were a set of climatological time series data spanning from 1964 to 2020. The results suggest that the N-HiTS model outperforms the LSTM model in the prediction and forecasting of SPI time series for all regions in terms of performance metrics: the Mean Squared Error, Mean Absolute Error, Coefficient of Determination and ξ correlation coefficient range from 0.0455 to 0.5472, from 0.1696 to 0.6661, from 0.9162 to 0.9684 and from 0.9222 to 0.9368, respectively, for the regions under study. Consequently, the outcomes revealed the successful performance of the N-HiTS models in accurately predicting the SPI across the four examined regions. Full article

(This article belongs to the Special Issue Extreme Climate in Arid and Semi-arid Regions)

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19 pages, 9519 KiB

Open AccessArticle

Potential Health Risks of Indoor Particulate Matter Heavy Metals in Resource-Constrained Settings of South Africa

by Busisiwe Shezi, Nkosana Jafta and Rajen N Naidoo

Atmosphere 2024, 15(8), 911; https://doi.org/10.3390/atmos15080911 - 30 Jul 2024

Abstract

Evidence suggests that pollutants like particulate matter and heavy metals significantly impact health. This study investigated the determinants and sources of indoor PM_2.5 heavy metals and assessed the health risk to children in Durban. Thirty households of mothers participating in the mother [...] Read more.

Evidence suggests that pollutants like particulate matter and heavy metals significantly impact health. This study investigated the determinants and sources of indoor PM_2.5 heavy metals and assessed the health risk to children in Durban. Thirty households of mothers participating in the mother and child in the environment (MACE) birth cohort study were assessed for PM_2.5 heavy metals. Multivariate linear regression was used to identify the determinants of PM_2.5 heavy metals, while Pearson correlation and principal component analysis identified their sources. The health risk for children was assessed using measured metal concentrations. Proximity to industry, wall type, house age, and presence of windows increased some PM_2.5 heavy metals, while cross ventilation reduced indoor PM_2.5 As and Cu levels. Pearson correlation and principal component analysis indicated household, traffic, industrial, and natural sources as potential contributors. The health index was greater than 1, and cancer risk values for PM_2.5 As and Pb exceeded acceptable levels. The findings highlight the toxicity of indoor air due to heavy metals and the potential for adverse health outcomes in children. To protect vulnerable groups, it is essential to prevent exposure to high-risk metals. Full article

(This article belongs to the Special Issue Health Impacts Related to Indoor Air Pollutants)

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12 pages, 1931 KiB

Open AccessArticle

Estimation of Ammonia Emission Inventory Using Life Cycle Assessment Based on Livestock Manure Flow: A Case Study of the Manure Management Sector in Korea

by Hye-Min Lee, Kyoung-Chan Kim, Min-Wook Kim, Ju-Yong Lee and Hung-Soo Joo

Atmosphere 2024, 15(8), 910; https://doi.org/10.3390/atmos15080910 - 30 Jul 2024

Abstract

Ammonia is one of the precursor gases in the formation of particulate matter (PM) that reacts with nitrogen oxides and sulfur oxides in the atmosphere. Based on the Clean Air Policy Support System (CAPSS) of Korea, the annual ammonia emissions amounted to 261,207 [...] Read more.

Ammonia is one of the precursor gases in the formation of particulate matter (PM) that reacts with nitrogen oxides and sulfur oxides in the atmosphere. Based on the Clean Air Policy Support System (CAPSS) of Korea, the annual ammonia emissions amounted to 261,207 tons in 2020 and the agricultural source (manure management sector) contributes the highest proportion of the ammonia inventory. However, the methodology for the study of ammonia emissions in Korea has some limitations regarding the representativeness of the sites selected and the reliability of the measurement method. In this study, we aimed to recalculate the ammonia emissions from the livestock industry in Korea using the UK’s estimation method, which uses the life cycle assessment of livestock manure. Three major animal types, i.e., cattle (beef cattle and dairy cows), pigs and chickens, and three major processes based on the manure flow, i.e., housing, manure storage and treatment and land application processes, were considered. The total ammonia emissions were estimated to be approximately 33% higher than the official ammonia emissions stated by the CAPSS. For the manure flow, the ammonia emissions were the highest from land application processes. The ammonia emissions from dairy cow and poultry manure were much higher than those stated by the CAPSS, while the emissions from beef cattle and pig manure showed similar levels. The methodology used in this study can offer an alternative approach to the ammonia emission estimation of the manure management sector in the agriculture industry of Korea. Korean emission factors based on the manure flow should be developed and applied in the future. Full article

(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia)

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16 pages, 2970 KiB

Open AccessArticle

Quantifying Tornado Outbreak Intensity and Frequency Relationships with Interannual and Monthly Variability

by Andrew Mercer, Kenneth Swan and Adonte Knight

Atmosphere 2024, 15(8), 909; https://doi.org/10.3390/atmos15080909 - 30 Jul 2024

Abstract

Tornado outbreaks (TOs) are highly dangerous meteorological phenomena common in the United States and have limited known relationships with climate variability. Many of the challenges in understanding TOs result from a lack of formal TO quantification (both in definition and impact). Here, we [...] Read more.

Tornado outbreaks (TOs) are highly dangerous meteorological phenomena common in the United States and have limited known relationships with climate variability. Many of the challenges in understanding TOs result from a lack of formal TO quantification (both in definition and impact). Here, we present a TO definition based on a spatially cohesive and distinct region of tornado activity and present a TO intensity index using tornado characteristics within the TO region. In developing this index, we present a support vector regression-based detrending methodology to remove the secular trends within tornado reporting. The resulting TO definition suggests a decline in TO activity of roughly 1 TO per 4–5 years, with a similar decline in TO intensity. In addition, the relationship between this new quantification of TOs and common North American interannual and monthly climate variability indices is explored, namely the El Niño Southern Oscillation, the North Atlantic Oscillation, the Arctic Oscillation, and the Pacific North American Oscillation. In general, the links between these teleconnections and TO frequency and intensity were minimal (and sometimes in opposition when comparing TO frequency and intensity), but interesting patterns emerged that may offer an initial pathway to exploring longer-term TO predictability. Full article

(This article belongs to the Special Issue Tornado Activities in a Changing Climate)

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20 pages, 8429 KiB

Open AccessArticle

Optimization of High-Temperature CO₂ Capture by Lithium Orthosilicate-Based Sorbents Using Response Surface Methodology

by Eleonora Stefanelli, Flavio Francalanci, Sandra Vitolo and Monica Puccini

Atmosphere 2024, 15(8), 908; https://doi.org/10.3390/atmos15080908 - 30 Jul 2024

Abstract

The major challenge in the current context of the rising world energy demand is to limit the global temperature increase for mitigating climate change. This goal requires a large reduction of CO₂ emissions, mainly produced by power generation and industrial processes using [...] Read more.

The major challenge in the current context of the rising world energy demand is to limit the global temperature increase for mitigating climate change. This goal requires a large reduction of CO₂ emissions, mainly produced by power generation and industrial processes using fossil fuels. In this study, a novel methodology for K₂CO₃-doped Li₄SiO₄ sorbents production for CO₂ capture at high temperatures was adopted based on the Design of Experiments (DoE). This innovative approach systematically tested different synthesis (temperature and K₂CO₃ content) and adsorption conditions (sorption temperature and CO₂ concentration), allowing for the assessment of individual and interactive effects of process parameters. The Response Surface Methodology (RSM) was employed to obtain non-linear predictive models of CO₂ uptake and Li₄SiO₄ conversion. The results of RSM analysis evidenced a maximum adsorption capacity of 196.4 mg/g for a sorbent produced at 600 °C and with 36.9 wt% of K₂CO₃, tested at 500 °C and 4 vol% of CO₂. Whereas at 50 vol% of CO₂, the best uptake of 295.6 mg/g was obtained with a sorbent synthesized at 600 °C, containing less K₂CO₃ (17.1 wt%) and tested at a higher temperature (662 °C). These findings demonstrate that K₂CO₃-doped Li₄SiO₄ sorbents can be tailored to maximize CO₂ capture under various operating conditions, making them suitable for use in industrial processes. Full article

(This article belongs to the Special Issue Advances in CO₂ Capture and Absorption)

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21 pages, 4159 KiB

Open AccessArticle

Simulation of China’s Carbon Peak Path Based on Random Forest and Sparrow Search Algorithm—Long Short-Term Memory

by Zhoumu Yang, Xiaoying Wu, Yinan Song and Jiao Pan

Atmosphere 2024, 15(8), 907; https://doi.org/10.3390/atmos15080907 - 29 Jul 2024

Abstract

How to decouple economic growth from carbon dioxide emissions and achieve low-carbon transformation of the Chinese economy has become an urgent problem that needs to be solved. Firstly, the Tapio index is used to identify China’s carbon peak status, and then the Technology [...] Read more.

How to decouple economic growth from carbon dioxide emissions and achieve low-carbon transformation of the Chinese economy has become an urgent problem that needs to be solved. Firstly, the Tapio index is used to identify China’s carbon peak status, and then the Technology Choice Index (TCI) and economic complexity are introduced into the comprehensive factor analysis framework for carbon dioxide emissions. Key influencing factors are identified using random forest and ridge regression. On this basis, a novel sparrow search algorithm–long short-term memory (SSA-LSTM) model which has more prediction accuracy compared with past studies is constructed to predict the dynamic evolution trend of carbon dioxide emissions, and in combination with scenario analysis, the path towards the carbon peak is simulated. The following conclusions are obtained: The benchmark scenario peaks in 2031, with a peak of 12.346 billion tons, and the low-carbon scenario peaks in 2030, with a peak of 11.962 billion tons. The extensive scenario peaks in 2037, with a peak of 13.291 billion tons. Under six scenarios, it can be concluded that energy intensity is the key factor in reducing the peak. These research results provide theoretical support for decision-makers to formulate emission reduction policies and adjust the carbon peak path. Full article

(This article belongs to the Special Issue Carbon Emission and Transport: Measurement and Simulation (2nd Edition))

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16 pages, 9567 KiB

Open AccessArticle

Using the Multiple-Sensor-Based Frost Observation System (MFOS) for Image Object Analysis and Model Prediction Evaluation in an Orchard

by Su Hyun Kim, Seung-Min Lee and Seung-Jae Lee

Atmosphere 2024, 15(8), 906; https://doi.org/10.3390/atmos15080906 - 29 Jul 2024

Abstract

Accurate frost observations are crucial for developing and validating frost prediction models. In 2022, the multi-sensor-based automatic frost observation system (MFOS), including an RGB camera, a thermal infrared camera, a leaf wetness sensor (LWS), LED lighting, and three glass plates, was developed to [...] Read more.

Accurate frost observations are crucial for developing and validating frost prediction models. In 2022, the multi-sensor-based automatic frost observation system (MFOS), including an RGB camera, a thermal infrared camera, a leaf wetness sensor (LWS), LED lighting, and three glass plates, was developed to replace the naked-eye observation of frost. The MFOS, herein installed and operated in an apple orchard, provides temporally high-resolution frost observations that show the onset, end, duration, persistence, and discontinuity of frost more clearly than conventional naked-eye observations. This study introduces recent additions to the MFOS and presents the results of its application to frost weather analysis and forecast evaluation in an orchard in South Korea. The NCAM’s Weather Research and Forecasting (WRF) model was employed as a weather forecast model. The main findings of this study are as follows: (1) The newly added image-based object detection capabilities of the MFOS helped with the extraction and quantitative comparison of surface temperature data for apples, leaves, and the LWS. (2) The resolution matching of the RGB and thermal infrared images was made successful by resizing the images, matching them according to horizontal movement, and conducting apple-centered averaging. (3) When applied to evaluate the frost-point predictions of the numerical weather model at one-hour intervals, the results showed that the MFOS could be used as a much more objective tool to verify the accuracy and characteristics of frost predictions compared to the naked-eye view. (4) Higher-resolution and realistic land-cover and vegetation representation are necessary to improve frost forecasts using numerical grid models based on land–atmosphere physics. Full article

(This article belongs to the Special Issue ICT-Based High-Resolution Weather and Climate Research for an Early-Warning System for Agricultural Disasters)

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