Showing papers in "Atmosphere in 2022"

Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives

Abstract: In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in the field of climate and numerical weather prediction using the Google Scholar search engine. The most common topics of interest in the abstracts were identified, and some of them examined in detail: in numerical weather prediction research—photovoltaic and wind energy, atmospheric physics and processes; in climate research—parametrizations, extreme events, and climate change. With the created database, it was also possible to extract the most commonly examined meteorological fields (wind, precipitation, temperature, pressure, and radiation), methods (Deep Learning, Random Forest, Artificial Neural Networks, Support Vector Machine, and XGBoost), and countries (China, USA, Australia, India, and Germany) in these topics. Performing critical reviews of the literature, authors are trying to predict the future research direction of these fields, with the main conclusion being that machine learning methods will be a key feature in future weather forecasting.

Evaluation of Empirical Atmospheric Models Using Swarm-C Satellite Data

Abstract: Swarm-C satellite, a new instrument for atmospheric study, has been the focus of many studies to evaluate its usage and accuracy. This paper takes the Swarm-C satellite as a research object to verify the Swarm-C accelerometer’s inversion results. This paper uses the two-row orbital elements density inversion to verify the atmospheric density accuracy results of the Swarm-C satellite accelerometer. After the accuracy of the satellite data is verified, this paper conducts comparative verification and empirical atmospheric model evaluation experiments based on the Swarm-C accelerometer’s inversion results. After comparing with the inversion results of the Swarm-C semi-major axis attenuation method, it is found that the atmospheric density obtained by inversion using the Swarm-C accelerometer is more dynamic and real-time. It shows that with more available data, the Swarm-C satellite could be a new high-quality instrument for related studies along with the well-established satellites. After evaluating the performance of the JB2008 and NRLMSISE-00 empirical atmospheric models using the Swarm-C accelerometer inversion results, it is found that the accuracy and real-time performance of the JB2008 model at the altitude where the Swarm-C satellite is located are better than the NRLMSISE-00 model.

Climate Change and Livestock Production: A Literature Review

Abstract: Globally, the climate is changing, and this has implications for livestock. Climate affects livestock growth rates, milk and egg production, reproductive performance, morbidity, and mortality, along with feed supply. Simultaneously, livestock is a climate change driver, generating 14.5% of total anthropogenic Greenhouse Gas (GHG) emissions. Herein, we review the literature addressing climate change and livestock, covering impacts, emissions, adaptation possibilities, and mitigation strategies. While the existing literature principally focuses on ruminants, we extended the scope to include non-ruminants. We found that livestock are affected by climate change and do enhance climate change through emissions but that there are adaptation and mitigation actions that can limit the effects of climate change. We also suggest some research directions and especially find the need for work in developing country settings. In the context of climate change, adaptation measures are pivotal to sustaining the growing demand for livestock products, but often their relevance depends on local conditions. Furthermore, mitigation is key to limiting the future extent of climate change and there are a number of possible strategies.

Application and Development of Selective Catalytic Reduction Technology for Marine Low-Speed Diesel Engine: Trade-Off among High Sulfur Fuel, High Thermal Efficiency, and Low Pollution Emission

Abstract: In recent years, the International Maritime Organization (IMO), Europe, and the United States and other countries have set up different emission control areas (ECA) for ship exhaust pollutants to enforce more stringent pollutant emission regulations. In order to meet the current IMO Tier III emission regulations, an after-treatment device must be installed in the exhaust system of the ship power plant to reduce the ship NOx emissions. At present, selective catalytic reduction technology (SCR) is one of the main technical routes to resolve excess NOx emissions of marine diesel engines, and is the only NOx emission reduction technology recognized by the IMO that can be used for various ship engines. Compared with the conventional low-pressure SCR system, the high-pressure SCR system can be applied to low-speed marine diesel engines that burn inferior fuels, but its working conditions are relatively harsh, and it can be susceptible to operational problems such as sulfuric acid corrosion, salt blockage, and switching delay during the actual ship tests and ship applications. Therefore, it is necessary to improve the design method and matching strategy of the high-pressure SCR system to achieve a more efficient and reliable operation. This article summarizes the technical characteristics and application problems of marine diesel engine SCR systems in detail, tracks the development trend of the catalytic reaction mechanism, engine tuning, and control strategy under high sulfur exhaust gas conditions. Results showed that low temperature is an important reason for the formation of ammonium nitrate, ammonium sulfate, and other deposits. Additionally, the formed deposits will directly affect the working performance of the SCR systems. The development of SCR technology for marine low-speed engines should be the compromise solution under the requirements of high sulfur fuel, high thermal efficiency, and low pollution emissions. Under the dual restrictions of high sulfur fuel and low exhaust temperature, the low-speed diesel engine SCR systems will inevitably sacrifice part of the engine economy to obtain higher denitrification efficiency and operational reliability.

Spatiotemporal Variations and Climatological Trends in Precipitation Indices in Shaanxi Province, China

Abstract: Precipitation, as an important part of the hydrological cycle, is often related to flood and drought. In this study, we collected daily rainfall data from 21 rainfall stations in Shaanxi Province from 1961 to 2017, and calculated eight extreme climate indices. Annual and seasonal concentration indices (CI) were also calculated. The trends in the changes in precipitation were calculated using the M–K test and Sen’s slope. The results show that the precipitation correlation index and CI (concentration index) in Shaanxi Province are higher in the south and lower in the north. For the annual scale, the CI value ranges from 0.6369 to 0.6820, indicating that Shaanxi Province has a high precipitation concentration and an uneven distribution of annual precipitation. The eight extreme precipitation indices of most rainfall stations showed a downward trend during the study period, and more than half of the stations passed the 0.05 confidence interval test. Among them, the Z value of PRCPTOT (annual total precipitation in wet days) at Huashan station reached −6.5270. The lowest slope of PRCPTOT reached −14.3395. This shows that annual rainfall in Shaanxi Province has been decreasing in recent decades. These findings could be used to make decisions about water resources and drought risk management in Shaanxi Province, China.

Haze Grading Using the Convolutional Neural Networks

Abstract: As an air pollution phenomenon, haze has become one of the focuses of social discussion. Research into the causes and concentration prediction of haze is significant, forming the basis of haze prevention. The inversion of Aerosol Optical Depth (AOD) based on remote sensing satellite imagery can provide a reference for the concentration of major pollutants in a haze, such as PM2.5 concentration and PM10 concentration. This paper used satellite imagery to study haze problems and chose PM2.5, one of the primary haze pollutants, as the research object. First, we used conventional methods to perform the inversion of AOD on remote sensing images, verifying the correlation between AOD and PM2.5. Subsequently, to simplify the parameter complexity of the traditional inversion method, we proposed using the convolutional neural network instead of the traditional inversion method and constructing a haze level prediction model. Compared with traditional aerosol depth inversion, we found that convolutional neural networks can provide a higher correlation between PM2.5 concentration and satellite imagery through a more simplified satellite image processing process. Thus, it offers the possibility of researching and managing haze problems based on neural networks.

A Quantitative Analysis of the Influence of Temperature Change on the Extreme Precipitation

Abstract: As an essential part of the hydrological cycle, precipitation is usually associated with floods and droughts and is increasingly being paid attention to in the context of global warming. Analyzing the change trends and correlation of temperature and extreme precipitation indicators can effectively identify natural disasters. This study aimed to detect the correlation and change trends of temperature and extreme precipitation indicators in Inner Mongolia from 1960 to 2019. Panel vector autoregression (PVAR) models based on Stata software were used to detect the correlation between temperature and extreme precipitation indicators at 35 climatological stations throughout Inner Mongolia. The temperature and extreme precipitation indicator trends were analyzed using the Mann–Kendall test and Sen’s slope method. The spatial distribution characteristics of the annual precipitation and rainfall intensity were more significant in the southeast and more minor in the northwest, while an increase in the annual wet days was noticeable to the northeast. The Granger cause tests of the temperature and the extreme precipitation indicators showed a correlation between each indicator and temperature at the significance level of 1%. The temperature positively correlated with only the rainfall intensity while negatively correlating with the remaining indicators. There is no doubt that trend analysis showed significant increasing trends in rainfall intensity at all stations, which means increased risk in extreme precipitation events. By contrast, the annual precipitation and annual wet days showed significant decreasing trends, which means that the precipitation is concentrated, and it is easier to form extreme precipitation events. The study can provide a basis for decision-making in water resources and drought/flood risk management in Inner Mongolia, China.

Carbon Dioxide Capture through Physical and Chemical Adsorption Using Porous Carbon Materials: A Review

Abstract: Due to rapid industrialization and urban development across the globe, the emission of carbon dioxide (CO2) has been significantly increased, resulting in adverse effects on the climate and ecosystems. In this regard, carbon capture and storage (CCS) is considered to be a promising technology in reducing atmospheric CO2 concentration. Among the CO2 capture technologies, adsorption has grabbed significant attention owing to its advantageous characteristics discovered in recent years. Porous carbon-based materials have emerged as one of the most versatile CO2 adsorbents. Numerous research activities have been conducted by synthesizing carbon-based adsorbents using different precursors to investigate their performances towards CCS. Additionally, amine-functionalized carbon-based adsorbents have exhibited remarkable potential for selective capturing of CO2 in the presence of other gases and humidity conditions. The present review describes the CO2 emission sources, health, and environmental impacts of CO2 towards the human beings, options for CCS, and different CO2 separation technologies. Apart from the above, different synthesis routes of carbon-based adsorbents using various precursors have been elucidated. The CO2 adsorption selectivity, capacity, and reusability of the current and applied carbon materials have also been summarized. Furthermore, the critical factors controlling the adsorption performance (e.g., the effect of textural and functional properties) are comprehensively discussed. Finally, the current challenges and future research directions have also been summarized.

Monitoring the Dynamic Changes in Vegetation Cover Using Spatio-Temporal Remote Sensing Data from 1984 to 2020

Abstract: Anthropogenic activities and natural climate changes are the central driving forces of global ecosystems and agriculture changes. Climate changes, such as rainfall and temperature changes, have had the greatest impact on different types of plant production around the world. In the present study, we investigated the spatiotemporal variation of major crops (cotton, rice, wheat, and sugarcane) in the District Vehari, Pakistan, from 1984 to 2020 using remote sensing (RS) technology. The crop identification was pre-processed in ArcGIS software based on Landsat images. After pre-processing, supervised classification was used, which explains the maximum likelihood classification (MLC) to identify the vegetation changes. Our results showed that in the study area cultivated areas under wheat and cotton decreased by almost 5.4% and 9.1% from 1984 to 2020, respectively. Vegetated areas have maximum values of NDVI (> 0.4), and built-up areas showed fewer NDVI values (0 to 0.2) in the District Vehari. During the Rabi season, the temperature was increased from 19.93 °C to 21.17 °C. The average temperature was calculated at 34.28 °C to 35.54 °C during the Kharif season in the District Vehari. Our results showed that temperature negatively affects sugarcane, rice, and cotton crops during the Rabi season, and precipitation positively affects sugarcane, rice, and cotton crops during the Kharif season in the study area. Accurate and timely assessment of crop estimation and relation to climate change can give very useful information for decision-makers, governments, and planners in formulating policies regarding crop management and improving agriculture yields.

Revisiting Total Particle Number Measurements for Vehicle Exhaust Regulations

Abstract: Road transport significantly contributes to air pollution in cities. Emission regulations have led to significantly reduced emissions in modern vehicles. Particle emissions are controlled by a particulate matter (PM) mass and a solid particle number (SPN) limit. There are concerns that the SPN limit does not effectively control all relevant particulate species and there are instances of semi-volatile particle emissions that are order of magnitudes higher than the SPN emission levels. This overview discusses whether a new metric (total particles, i.e., solids and volatiles) should be introduced for the effective regulation of vehicle emissions. Initially, it summarizes recent findings on the contribution of road transport to particle number concentration levels in cities. Then, both solid and total particle emission levels from modern vehicles are presented and the adverse health effects of solid and volatile particles are briefly discussed. Finally, the open issues regarding an appropriate methodology (sampling and instrumentation) in order to achieve representative and reproducible results are summarized. The main finding of this overview is that, even though total particle sampling and quantification is feasible, details for its realization in a regulatory context are lacking. It is important to define the methodology details (sampling and dilution, measurement instrumentation, relevant sizes, etc.) and conduct inter-laboratory exercises to determine the reproducibility of a proposed method. It is also necessary to monitor the vehicle emissions according to the new method to understand current and possible future levels. With better understanding of the instances of formation of nucleation mode particles it will be possible to identify its culprits (e.g., fuel, lubricant, combustion, or aftertreatment operation). Then the appropriate solutions can be enforced and the right decisions can be taken on the need for new regulatory initiatives, for example the addition of total particles in the tailpipe, decrease of specific organic precursors, better control of inorganic precursors (e.g., NH3, SOx), or revision of fuel and lubricant specifications.

Meteorological Analysis of the 2021 Extreme Wildfires in Greece: Lessons Learned and Implications for Early Warning of the Potential for Pyroconvection

Abstract: The 2021 fire season in Greece was the worst of the past 13 years, resulting in more than 130,000 ha of burnt area, with about 70% consumed by five wildfires that ignited and spread in early August. Common to these wildfires was the occurrence of violent pyroconvection. This work presents a meteorological analysis of this outbreak of extreme pyroconvective wildfires. Our analysis shows that dry and warm antecedent weather preconditioned fuels in the fire-affected areas, creating a fire environment that alone could effectively support intense wildfire activity. Analysis of surface conditions revealed that the ignition and the most active spread of all wildfires coincided with the most adverse fire weather since the beginning of the fire season. Further, the atmospheric environment was conducive to violent pyroconvection, as atmospheric instability gradually increased amid the breakdown of an upper-air ridge ahead of an approaching long-wave trough. In summary, we highlight that the severity and extent of the 2021 Greek wildfires were not surprising considering the fire weather potential for the period when they ignited. Continuous monitoring of the large- and local-scale conditions that promote extreme fire behavior is imperative for improving Greece’s capacity for managing extreme wildfires.

Large Amounts of Water Vapor Were Injected into the Stratosphere by the Hunga Tonga–Hunga Ha’apai Volcano Eruption

Abstract: The Hunga Tonga–Hunga Ha’apai (Tonga) injected only small amount of SO2 into the stratosphere, while our analyses of the Microwave Limb Sounder (MLS) measurements show that a massive amount of water vapor was directly injected into the stratosphere by the Tonga eruption, which is probably due to its submarine volcanic activity. The Tonga eruption injected a total amount of 139 ± 8 Tg of water vapor into the stratosphere and resulted in an increase of 8.9 ± 0.5% in the global stratospheric water vapor. Analyses also show that the uppermost altitude impacted by Tonga reached the 1 hPa level (~47.6 km). Additionally, the maximum hydration region for increased water vapor is at 38–17 hPa (~22.2–27 km), where the water vapor mixing ratio increased by 6–8 ppmv during the three months after the Tonga eruption. The enhanced stratospheric water vapor has great potential to influence the global radiation budget as well as ozone loss.

Global Monitoring of Ionospheric Weather by GIRO and GNSS Data Fusion

Abstract: Prompt and accurate imaging of the ionosphere is essential to space weather services, given a broad spectrum of applications that rely on ionospherically propagating radio signals. As the 3D spatial extent of the ionosphere is vast and covered only fragmentarily, data fusion is a strong candidate for solving imaging tasks. Data fusion has been used to blend models and observations for the integrated and consistent views of geosystems. In space weather scenarios, low latency of the sensor data availability is one of the strongest requirements that limits the selection of potential datasets for fusion. Since remote plasma sensing instrumentation for ionospheric weather is complex, scarce, and prone to unavoidable data noise, conventional 3D-var assimilative schemas are not optimal. We describe a novel substantially 4D data fusion service based on near-real-time data feeds from Global Ionosphere Radio Observatory (GIRO) and Global Navigation Satellite System (GNSS) called GAMBIT (Global Assimilative Model of the Bottomside Ionosphere with Topside estimate). GAMBIT operates with a few-minute latency, and it releases, among other data products, the anomaly maps of the effective slab thickness (EST) obtained by fusing GIRO and GNSS data. The anomaly EST mapping aids understanding of the vertical plasma restructuring during disturbed conditions.

A Review on the Observed Climate Change in Europe and Its Impacts on Viticulture

Abstract: The European climate is changing displaying profound on agriculture, thus strongly reaching the scientific community’s attention. In this review, the compilation of selected scientific research on the agroclimatic conditions’ changes and their impact on the productivity parameters (phenology timing, product quality and quantity) of grapevines and on the spatiotemporal characteristics of the viticultural areas are attempted for the first time. For this purpose, a thorough investigation through multiple search queries was conducted for the period (2005–2021). Overall, increasing (decreasing) trends in critical temperature (precipitation) parameters are the reality of the recent past with visible impacts on viticulture. The observed climate warming already enforces emerging phenomena related to the modification of the developmental rate (earlier phenological events, shortening of phenological intervals, lengthening of the growing season, earlier harvest), the alteration of product quality, the heterogeneous effects on grapevine yield and the emergence of new cool-climate viticulture areas highlighting the cultivation’s rebirth in the northern and central parts of the continent. The vulnerability of the wine-growing ecosystem urges the integration of innovative and sustainable solutions for confronting the impacts of climate change and safeguarding the production (quantity and quality) capacity of viticultural systems in Europe under a continuously changing environment.

Drought: A Common Environmental Disaster

Abstract: Droughts have been identified as an environmental hazard by environmentalists, ecologists, hydrologists, meteorologists, geologists, and agricultural experts. Droughts are characterised by a decrease in precipitation over a lengthy period, such as a season or a year, and can occur in virtually all climatic zones, including both high and low rainfall locations. This study reviewed drought-related impacts on the environment and other components particularly, in South Africa. Several attempts have been made using innovative technology such as earth observation and climate information as recorded in studies. Findings show that the country is naturally water deficient, which adds to the climate fluctuation with the average annual rainfall in South Africa being far below the global average of 860 mm per year. Drought in South Africa’s Western Cape Province, for example, has resulted in employment losses in the province’s agriculture sector. According to the third quarterly labor force survey from 2017, the agricultural industry lost almost 25,000 jobs across the country. In the Western Cape province, about 20,000 of these were lost which has a direct impact on income generation. Many of these impacts were linked to drought events.

Road Dust in Urban and Industrial Environments: Sources, Pollutants, Impacts, and Management

Abstract: Road dust (RD) is one of the most important sources of particles in the atmosphere, especially in industrial areas and cities. In this special issue, we collected 16 original articles that describe field, experimental, and modeling studies related to RD and its various size fractions as a key issue in understanding the relationships between several urban and industrial environments and in the identification of pollution sources. Articles in the special issue focus primarily on the following main topics: (1) study of the chemical composition and speciation of RD and its source attribution; (2) assessment of RD and aerosol pollution levels (including express technique), environmental hazards and public health risks; (3) distribution of stable and radioactive isotopes in RD; (4) determination of factors affecting the level of dust accumulation on roads and the intensity of its pollution; and (5) study of the effect of RD on the atmosphere and other environments. Based on the results presented in this special issue, but not limited to, some of the current challenges in studying RD are formulated, including the need for further geographically wider and analytically deeper work on various aspects of the formation, transport pathways, and accumulation of RD in urban, industrial and other areas.

Comparison between Air Temperature and Land Surface Temperature for the City of São Paulo, Brazil

Abstract: This study aims to identify the relationship between changes in temperature regarding urbanization processes and seasonality in the city of São Paulo, located in the Tropic of Capricorn. The land surface temperature (LST) results were compared to official weather stations measurements, identifying in the spring–summer period 65.5% to 86.2% accuracy, while in the autumn–winter period, the results ranged from 58.6% to 93.1% accuracy, when considering the standard deviation and the temperature probe error. The mean MAE and mean RMSE range from 1.2 to 1.9 °C, with 83.0% of the values being ≤2.7 °C, and the coefficient of determination values are R = 0.81 in spring–summer and R = 0.82 in autumn–winter. Great thermal amplitude was estimated in the spring–summer season, with a difference in LST of the built-up space and rural area ranging from 5.8 and 11.5 °C, while in the autumn–winter season, the LST is more distributed through the city, with differences ranging from 4.4 to 8.5 °C. In addition, the current study suggests remote sensing as a reliable, cheap, and practical methodology to assist climate in order to support public policies and decision-making actions regarding environmental and urban planning.

Circulation and Climate Variability in the Czech Republic between 1961 and 2020: A Comparison of Changes for Two “Normal” Periods

Abstract: Thirty-year periods are treated in climatology as spans with relatively representative and stable climatic patterns, which can be used for calculating climate normals. Annual and seasonal series of circulation types were used to compare two 30-year sub-periods, 1961–1990 and 1991–2020, the second one being strongly influenced by recent global warming. This analysis was conducted according to the objective classification of circulation types and the climatic characteristics of sunshine duration, temperature, humidity, precipitation, and wind speed as calculated for the territory of the Czech Republic during the 1961–2020 period. For both sub-periods, their statistical characteristics were calculated, and the statistical significance of differences between them was evaluated. There was a statistically significant increase in the annual frequencies of anticyclonic circulation types and a significant decrease in cyclonic circulation types during 1991–2020 compared with 1961–1990. Generally, in both 30-year periods, significant differences in means, variability, characteristics of distribution, density functions, and linear trends appear for all climatic variables analysed except precipitation. This indicates that the recent 30-year “normal” period of 1991–2020, known to be influenced more by recent climate change, is by its climatic characteristics unrepresentative of the stable climatic patterns of previous 30-year periods.

A Review of Field Measurement Studies on Thermal Comfort, Indoor Air Quality and Virus Risk

Abstract: People spend up to 90% of their time indoors where they continuously interact with the indoor environment. Indoor Environmental Quality (IEQ), and in particular thermal comfort, Indoor Air Quality (IAQ), and acoustic and visual comfort, have proven to be significant factors that influence the occupants’ health, comfort, productivity and general well-being. The ongoing COVID-19 pandemic has also highlighted the need for real-life experimental data acquired through field measurement studies to help us understand and potentially control the impact of IEQ on the occupants’ health. In this context, there was a significant increase over the past two decades of field measurement studies conducted all over the world that analyse the IEQ in various indoor environments. In this study, an overview of the most important factors that influence the IAQ, thermal comfort, and the risk of virus transmission is first presented, followed by a comprehensive review of selected field measurement studies from the last 20 years. The main objective is to provide a broad overview of the current status of field measurement studies, to identify key characteristics, common outcomes, correlations, insights, as well as gaps, and to serve as the starting point for conducting future field measurement studies.

Does Agricultural Credit Mitigate the Effect of Climate Change on Cereal Production? Evidence from Sichuan Province, China

Abstract: This study attempts to investigate the effects of global climate change (via temperature and rainfall) on cereal production in Sichuan over the 1978–2018 period, whether agricultural credit combining with technical progress (i.e., mechanical farming rate) mitigate the effect of climate change. The present study empirically analyzed the short-term and long-term interrelation among all the considered variables by using the autoregressive distributed lag (ARDL) model. The results of the ARDL bounds testing revealed that there is a long-term cointegration relationship between the variables. The findings showed that temperature significantly negatively affected cereal production, while rainfall significantly contributed to cereal production in the context of Sichuan province, China. Agricultural credit, especially in the long run, significantly improved cereal production, implying that agricultural credit is used to invest in climate mitigation technologies in cereal production. Findings further indicated that the mechanical farming rate significantly enhanced cereal production, indicating that technical progress has been playing a vital role. This study suggests that the policymakers should formulate more comprehensive agricultural policies to meet the financial needs of the agricultural sector and increase support for production technology.

In-Depth Analysis of Physicochemical Properties of Particulate Matter (PM10, PM2.5 and PM1) and Its Characterization through FTIR, XRD and SEM–EDX Techniques in the Foothills of the Hindu Kush Region of Northern Pakistan

Abstract: The current study investigates the variation and physicochemical properties of ambient particulate matter (PM) in the very important location which lies in the foothills of the Hindu Kush ranges in northern Pakistan. This work investigates the mass concentration, mineral content, elemental composition and morphology of PM in three size fractions, i.e., PM1, PM2.5 and PM10, during the year of 2019. The collected samples were characterized by microscopic and spectroscopic techniques like Fourier transform infrared spectroscopy, X-ray diffraction spectroscopy and scanning electron microscopy (SEM) coupled with energy-dispersive X-ray (EDX) spectroscopy. During the study period, the average temperature, relative humidity, rainfall and wind speed were found to be 17.9 °C, 65.83%, 73.75 mm and 0.23 m/s, respectively. The results showed that the 24 h average mass concentration of PM10, PM2.5 and PM1 were 64 µgm−3, 43.9 µgm−3 and 22.4 µgm−3, respectively. The 24 h concentration of both PM10 and PM2.5 were 1.42 and 2.92 times greater, respectively, than the WHO limits. This study confirms the presence of minerals such as wollastonite, ammonium sulphate, wustite, illite, kaolinite, augite, crocidolite, calcite, calcium aluminosilicate, hematite, copper sulphate, dolomite, quartz, vaterite, calcium iron oxide, muscovite, gypsum and vermiculite. On the basis of FESEM-EDX analysis, 14 elements (O, C, Al, Si, Mg, Na, K, Ca, Fe, N, Mo, B, S and Cl) and six groups of PM (carbonaceous (45%), sulfate (13%), bioaerosols (8%), aluminosilicates (19%), quartz (10%) and nitrate (3%)) were identified.

Spatiotemporal Rainfall Variability and Drought Assessment during Past Five Decades in South Korea Using SPI and SPEI

Abstract: About 41% of the earth is drought-affected, which has impacted nearly 2 billion people, and it is expected that more than 90% of terrestrial areas will be degraded by 2050. To evade and mitigate the harmful impacts of drought, it is necessary to study the rainfall variability and assess the drought trend at a global and regional level. This study utilized 70 meteorological stations in South Korea to evaluate the rainfall variability, drought, and its trend during the past five decades using the standardized precipitation evapotranspiration index (SPEI) and the standardized precipitation index (SPI). Rainfall data normality was assessed with mean, standard deviation, skewness, and kurtosis. The highest amount of rainfall was observed in the months of June, July, and August. The SPI and SPEI 12-month results revealed that 1982, 1988, 2008, 2015, and 2017 were dry years throughout the country, while from 2013 to 2017 mixed drought events were observed for the 6-month time series. The Mann-Kendall trend test was applied to the 1- and 12-month time series, and the results revealed that the months of January, March, April, May, June, and August had a significant negative trend, which means drought is increasing in these months, while the months of September, October, and December had a significant positive trend, which means wetter conditions prevailed in these months during the study period. It was observed in the 12-month time series that only two met stations had a significant negative trend, while only one had a significant positive trend. It was found that January and March were the driest months, and October was the wettest month. The detected drought events in this research are consistent with ENSO events. We have observed differences in drought characteristics (duration and frequency) for both indices. Climatic data revealed that South Korea has faced drought conditions (rainfall deficit) due to a shortened monsoon season. This study can provide guidance on water management strategies under the changing pattern of drought in South Korea.

Airborne Fungal Spore Review, New Advances and Automatisation

Abstract: Fungal spores make up a significant portion of Primary Biological Aerosol Particles (PBAPs) with large quantities of such particles noted in the air. Fungal particles are of interest because of their potential to affect the health of both plants and humans. They are omnipresent in the atmosphere year-round, with concentrations varying due to meteorological parameters and location. Equally, differences between indoor and outdoor fungal spore concentrations and dispersal play an important role in occupational health. This review attempts to summarise the different spore sampling methods, identify the most important spore types in terms of negative effects on crops and the public, the factors affecting their growth/dispersal, and different methods of predicting fungal spore concentrations currently in use.

Evaluating the Degradation of Natural Resources in the Mediterranean Environment Using the Water and Land Resources Degradation Index, the Case of Crete Island

Abstract: Natural resources degradation poses multiple challenges particularly to environmental and economic processes. It is usually difficult to identify the degree of degradation and the critical vulnerability values in the affected systems. Thus, among other tools, indices (composite indicators) may also describe these complex systems or phenomena. In this approach, the Water and Land Resources Degradation Index was applied to the fifth largest Mediterranean island, Crete, for the 1999–2014 period. The Water and Land Resources Degradation Index uses 11 water and soil resources related indicators: Aridity Index, Water Demand, Drought Impacts, Drought Resistance Water Resources Infrastructure, Land Use Intensity, Soil Parent Material, Plant Cover, Rainfall, Slope, and Soil Texture. The aim is to identify the sensitive areas to degradation due to anthropogenic interventions and natural processes, as well as their vulnerability status. The results for Crete Island indicate that prolonged water resources shortages due to low average precipitation values or high water demand (especially in the agricultural sector), may significantly affect Water and Land degradation processes. Hence, Water and Land Resources Degradation Index could serve as an extra tool to assist policymakers to improve their decisions to combat Natural Resources degradation.

Life Cycle Assessment of Battery Electric and Internal Combustion Engine Vehicles Considering the Impact of Electricity Generation Mix: A Case Study in China

Abstract: Battery Electric Vehicles (BEVs) are considered to have higher energy efficiency and advantages to better control CO2 emissions compared to Internal Combustion Engine Vehicles (ICEVs). However, in the context that a large amount of thermal power is still used in developing countries, the CO2 emission reduction effectiveness of BEVs can be weakened or even counterproductive. To reveal the impact of the electricity generation mix on carbon emissions from vehicles, this paper compares the life cycle carbon emissions of BEVs with ICEVs considering the regional disparity of electricity generation mix in China. According to Life Cycle Assessment (LCA) analysis and regional electricity carbon intensity, this study demonstrates that BEVs in the region with high penetration of thermal power produce more CO2 emissions, while BEVs in the region with higher penetration of renewable energy have better environmental performance in carbon emission reduction. For instance, in the region with over 50%penetration of renewable energy, a BEV can reduce more CO2 (18.32 t) compared to an ICEV. Therefore, the regions with high carbon emissions from vehicles need to increase the proportion of renewable generation as a priority rather than promoting BEVs.

Impact of Climate and Land-Use Change on Groundwater Resources, Study of Faisalabad District, Pakistan

Abstract: Groundwater depletion has become a major concern all over the world. Recently, the rapid population growth and need for water and food have placed a massive strain on land and water resources. In this study, groundwater depletion resulting from land-use and climate change was investigated in the Faisalabad district, Pakistan, from 2000 to 2015. A Pearson correlation analysis between climatic parameters and land-use indices with groundwater was conducted to explore the major influencing factors. Interpolation maps of groundwater were generated using the inverse distance weighting interpolation (IDW) method. The Normalized Difference Built-up Index (NDBI) of five-year intervals demonstrated a strong increasing trend, whereas the Normalized Difference Vegetation Index (NDVI) presented a declining trend. The results also indicated a significant declining trend in groundwater levels in the region, with the annual average groundwater level decreasing at a rate of approximately 0.11 m/year. Climatic parameters (i.e., precipitation and temperature) further reveal an insignificant increasing trend estimated using the Mann–Kendall test and Sens’s slope. Overall, spatial analysis results showed a statistically significant positive trend in the groundwater level of the Faisalabad district, where the NDBI ratio is high and the NDVI is low, owing to the extensive extraction of groundwater for domestic and industrial use. These findings may be useful for a better understanding of groundwater depletion in densely populated areas and could also aid in devising safety procedures for sustainable groundwater management.

Using Machine Learning Methods to Forecast Air Quality: A Case Study in Macao

Abstract: Despite the levels of air pollution in Macao continuing to improve over recent years, there are still days with high-pollution episodes that cause great health concerns to the local community. Therefore, it is very important to accurately forecast air quality in Macao. Machine learning methods such as random forest (RF), gradient boosting (GB), support vector regression (SVR), and multiple linear regression (MLR) were applied to predict the levels of particulate matter (PM10 and PM2.5) concentrations in Macao. The forecast models were built and trained using the meteorological and air quality data from 2013 to 2018, and the air quality data from 2019 to 2021 were used for validation. Our results show that there is no significant difference between the performance of the four methods in predicting the air quality data for 2019 (before the COVID-19 pandemic) and 2021 (the new normal period). However, RF performed significantly better than the other methods for 2020 (amid the pandemic) with a higher coefficient of determination (R2) and lower RMSE, MAE, and BIAS. The reduced performance of the statistical MLR and other ML models was presumably due to the unprecedented low levels of PM10 and PM2.5 concentrations in 2020. Therefore, this study suggests that RF is the most reliable prediction method for pollutant concentrations, especially in the event of drastic air quality changes due to unexpected circumstances, such as a lockdown caused by a widespread infectious disease.

Climate Trends and Extremes in the Indus River Basin, Pakistan: Implications for Agricultural Production

Abstract: Historical and future projected changes in climatic patterns over the largest irrigated basin in the world, the Indus River Basin (IRB), threaten agricultural production and food security in Pakistan, in particular for vulnerable farming communities. To build a more detailed understanding of the impacts of climate change on agriculture s in the IRB, the present study analyzes (1) observed trends in average temperature, precipitation and related extreme indicators, as well as seasonal shifts over a recent historical period (1997–2016); and (2) statistically downscaled future projections (up to 2100) from a set of climate models in conjunction with crop-specific information for the four main crops of the IRB: wheat, cotton, rice and sugarcane. Key findings show an increasing trend of about over 0.1 °C/year in observed minimum temperature across the study area over the historical period, but no significant trend in maximum temperature. Historical precipitation shows a positive annual increase driven mainly by changes in August and September. Future projections highlight continued warming resulting in critical heat thresholds for the four crops analyzed being increasingly exceeded into the future, in particular in the Kharif season. Concurrently, inter-annual rainfall variability is projected to increase up to 10–20% by the end of the 21st century, augmenting uncertainty of water availability in the basin. These findings provide insight into the nature of recent climatic shifts in the IRB and emphasize the importance of using climate impact assessments to develop targeted investments and efficient adaptation measures to ensure resilience of agriculture in Pakistan into the future.

Investigation of the Upper Respiratory Tract of a Male Smoker with Laryngeal Cancer by Inhaling Air Associated with Various Physical Activity Levels

Abstract: Smokers are at a higher risk of laryngeal cancer, which is a type of head and neck cancer in which cancer cells proliferate and can metastasize to other tissues after a tumor has formed. Cigarette smoke greatly reduces the inhaled air quality and can also lead to laryngeal cancer. In this study, the upper airway of a 70-year-old smoker with laryngeal cancer was reconstructed by taking a CT scan using Mimics software. To solve the governing equations, computational fluid dynamics (CFD) with a pressure base approach was used with the help of Ansys 2021 R1 software. As a result, the maximum turbulence intensity occurred in the larynx. At 13 L/min, 55 L/min, and 100 L/min, the maximum turbulence intensity was 1.1, 3.5, and 6.1, respectively. The turbulence intensity in the respiratory system is crucial because it demonstrates the ability to transfer energy. The maximum wall shear stress (WSS) also occurred in the larynx. At 13 L/min, 55 L/min, and 100 L/min, the maximum WSS was 0.62 Pa, 5.4 Pa, and 12.4 Pa, respectively. The WSS index cannot be calculated in vivo and should be calculated in vitro. Excessive WSS in the epiglottis is inappropriate and can lead to an airway obstruction. Furthermore, real mathematical modeling outcomes provide an approach for future prevention, treatment, and management planning by forecasting the zones prone to an acceleration of disease progression. In this regard, accurate computational modeling leads to pre-visualization in surgical planning to define the best reformative techniques to determine the most probable patient condition consequences.

Vehicle Emissions and Urban Air Quality: 60 Years of Progress

Abstract: The past 60 years have seen large reductions in vehicle emissions of particulate matter (PM), nitrogen oxides (NOx), carbon monoxide (CO), hydrocarbons (HCs), sulfur dioxide (SO2), and lead (Pb). Advanced emission after-treatment technologies have been developed for gasoline and diesel vehicles to meet increasingly stringent regulations, yielding absolute emission reductions from the on-road fleet despite increased vehicle miles traveled. As a result of reduced emissions from vehicles and other sources, the air quality in cities across the U.S. and Europe has improved greatly. Turn-over of the on-road fleet, increasingly stringent emission regulations (such as Tier 3 in the U.S., LEV III in California, Euro 6 in Europe, and upcoming rules in these same regions), and the large-scale introduction of electric vehicles will lead to even lower vehicle emissions and further improvements in air quality. We review historical vehicle emissions and air quality trends and discuss the future outlook.