Instruments and variables used to analyze the November biomass burning aerosols intrusion event.
\r\n\tThis cell has evolved an effective defense system to counteract the challenges as it is always in an oxygen-rich environment. The evolution of hemoglobin and deformability of erythrocyte membrane adapting to its function in circulation is especially striking. Erythrocyte aging and eryptosis strike a balance - the mixed population of cells and constant recycling every 120 days is a very distinct feature. Its metabolic shunt pathways and metabolites/enzymes alter and adapt with age and changes in the microenvironment.
\r\n\r\n\tErythrocyte and its cytoskeleton responses to various situations such as infections, hypoxia, hypothermia, intrigues researchers and biologists alike. This book aims to throw light on the significance of erythrocyte and its characteristic nature and survival in different physiological situations as it plays a very crucial role.
\r\n\r\n\tThis book hopes to bring different perspectives from various aspects and provide insights into the effective mechanisms evolved by erythrocytes, to counteract the challenges faced in its oxidation environment and the further research approaches.
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Lakshmi, Lakshmi P.S. and Sreekanth S.K.",authors:[{id:"25837",title:"Prof.",name:"Deepa",middleName:null,surname:"Tv",fullName:"Deepa Tv",slug:"deepa-tv"},{id:"38078",title:"Dr.",name:"Lakshmi",middleName:null,surname:"G",fullName:"Lakshmi G",slug:"lakshmi-g"},{id:"38079",title:"Dr.",name:"Sreekanth",middleName:null,surname:"SK",fullName:"Sreekanth SK",slug:"sreekanth-sk"}]},{id:"21992",title:"Changing to Minimal Reliance on Pesticides",slug:"changing-to-minimal-reliance-on-pesticides",signatures:"Paul A. Horne and Jessica Page",authors:[{id:"28576",title:"Dr.",name:"Paul",middleName:null,surname:"Horne",fullName:"Paul Horne",slug:"paul-horne"},{id:"28579",title:"Ms.",name:"Jessica",middleName:null,surname:"Page",fullName:"Jessica Page",slug:"jessica-page"}]},{id:"21993",title:"Increasing IPM Knowledge Through FFS in Benin",slug:"increasing-ipm-knowledge-through-ffs-in-benin",signatures:"Trine Lund and Hafizur Rahman",authors:[{id:"26976",title:"MSc.",name:"Trine",middleName:null,surname:"Lund",fullName:"Trine Lund",slug:"trine-lund"},{id:"33037",title:"Mr.",name:"Md. Hafizur",middleName:null,surname:"Rahman",fullName:"Md. Hafizur Rahman",slug:"md.-hafizur-rahman"}]},{id:"21994",title:"The Millardetian Conjunction in the Modern World",slug:"the-millardetian-conjunction-in-the-modern-world",signatures:"Marie-Pierre Rivière, Michel Ponchet and Eric Galiana",authors:[{id:"28618",title:"Dr.",name:"Eric",middleName:null,surname:"Galiana",fullName:"Eric Galiana",slug:"eric-galiana"},{id:"39499",title:"Dr.",name:"Marie-Pierre",middleName:null,surname:"Rivière",fullName:"Marie-Pierre Rivière",slug:"marie-pierre-riviere"},{id:"39500",title:"Dr.",name:"Michel",middleName:null,surname:"Ponchet",fullName:"Michel Ponchet",slug:"michel-ponchet"}]},{id:"21995",title:"Comparative Study of the Mobility of Malathion, Attamix and Thiodan as Obsolete Pesticides in Colombian Soil",slug:"comparative-study-of-the-mobility-of-malathion-attamix-and-thiodan-as-obsolete-pesticides-in-colombi",signatures:"Rosalina González Forero",authors:[{id:"14241",title:"Dr.",name:"Rosalina",middleName:null,surname:"Gonzalez Forero",fullName:"Rosalina Gonzalez Forero",slug:"rosalina-gonzalez-forero"}]},{id:"21996",title:"Biodegradation of Pesticides",slug:"biodegradation-of-pesticides",signatures:"André Luiz Meleiro Porto, Gliseida Zelayarán Melgar, Mariana Consiglio Kasemodel and Marcia Nitschke",authors:[{id:"29131",title:"Prof.",name:"André",middleName:null,surname:"LM Porto",fullName:"André LM Porto",slug:"andre-lm-porto"},{id:"29147",title:"Prof.",name:"Marcia",middleName:null,surname:"Nitschke",fullName:"Marcia Nitschke",slug:"marcia-nitschke"},{id:"80005",title:"Dr.",name:"Gliseida",middleName:null,surname:"Z Melgar",fullName:"Gliseida Z Melgar",slug:"gliseida-z-melgar"},{id:"80400",title:"BSc",name:"Mariana",middleName:null,surname:"Consiglio Kasemodel",fullName:"Mariana Consiglio Kasemodel",slug:"mariana-consiglio-kasemodel"}]},{id:"21997",title:"Pesticide-Soil Interaction",slug:"pesticide-soil-interaction",signatures:"Rita Földényi, Imre Czinkota and László Tolner",authors:[{id:"34950",title:"Dr.",name:"Rita",middleName:null,surname:"Földényi",fullName:"Rita Földényi",slug:"rita-foldenyi"},{id:"40373",title:"Dr.",name:"Laszlo",middleName:null,surname:"Tolner",fullName:"Laszlo Tolner",slug:"laszlo-tolner"},{id:"40374",title:"Dr.",name:"Imre",middleName:null,surname:"Czinkota",fullName:"Imre Czinkota",slug:"imre-czinkota"}]},{id:"21998",title:"Application of Fenton Processes for Degradation of Aniline",slug:"application-of-fenton-processes-for-degradation-of-aniline",signatures:"Chavalit Ratanatamskul, Nalinrut Masomboon and Ming-Chun Lu",authors:[{id:"27980",title:"Prof.",name:"Chavalit",middleName:"-",surname:"Ratanatamskul",fullName:"Chavalit Ratanatamskul",slug:"chavalit-ratanatamskul"},{id:"47314",title:"Ms",name:"Nalinrut",middleName:null,surname:"Masomboon",fullName:"Nalinrut Masomboon",slug:"nalinrut-masomboon"},{id:"47315",title:"Prof.",name:"Ming-Chun",middleName:null,surname:"Lu",fullName:"Ming-Chun Lu",slug:"ming-chun-lu"}]},{id:"21999",title:"Bioremediation of Hexachlorocyclohexane Contaminated Soil: Field Trials",slug:"bioremediation-of-hexachlorocyclohexane-contaminated-soil-field-trials",signatures:"H.K. Manonmani",authors:[{id:"40561",title:"Dr.",name:"Haravey",middleName:"Krishnan",surname:"Manonmani",fullName:"Haravey Manonmani",slug:"haravey-manonmani"}]},{id:"22000",title:"Setting Up of Risk Based Remediation Goal for Remediation of Persistent Organic Pesticides (Pesticide-POPs) Contaminated Sites",slug:"setting-up-of-risk-based-remediation-goal-for-remediation-of-persistent-organic-pesticides-pesticide",signatures:"Tapan Chakrabarti",authors:[{id:"40586",title:"Dr.",name:"Tapan",middleName:null,surname:"Chakrabarti",fullName:"Tapan Chakrabarti",slug:"tapan-chakrabarti"}]}]}],publishedBooks:[{type:"book",id:"923",title:"Herbicides",subtitle:"Theory and Applications",isOpenForSubmission:!1,hash:"54a8eb808c05a5fe01c676e7047d4576",slug:"herbicides-theory-and-applications",bookSignature:"Sonia Soloneski and Marcelo L. Larramendy",coverURL:"https://cdn.intechopen.com/books/images_new/923.jpg",editedByType:"Edited by",editors:[{id:"14764",title:"Dr.",name:"Marcelo L.",surname:"Larramendy",slug:"marcelo-l.-larramendy",fullName:"Marcelo L. Larramendy"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1884",title:"Fungicides",subtitle:"Beneficial and Harmful Aspects",isOpenForSubmission:!1,hash:"a3fbd872a8b6ef123bc9a23e9c28e2ee",slug:"fungicides-beneficial-and-harmful-aspects",bookSignature:"Nooruddin Thajuddin",coverURL:"https://cdn.intechopen.com/books/images_new/1884.jpg",editedByType:"Edited by",editors:[{id:"89852",title:"Dr.",name:"Nooruddin",surname:"Thajuddin",slug:"nooruddin-thajuddin",fullName:"Nooruddin Thajuddin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2105",title:"Herbicides",subtitle:"Mechanisms and Mode of Action",isOpenForSubmission:!1,hash:"18c4b83c0fe78634b60d6744919648e8",slug:"herbicides-mechanisms-and-mode-of-action",bookSignature:"Mohammed Naguib Abd El-Ghany Hasaneen",coverURL:"https://cdn.intechopen.com/books/images_new/2105.jpg",editedByType:"Edited by",editors:[{id:"121899",title:"Dr.",name:"Mohammed Nagib",surname:"Hasaneen",slug:"mohammed-nagib-hasaneen",fullName:"Mohammed Nagib Hasaneen"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2106",title:"Herbicides",subtitle:"Properties, Synthesis and Control of Weeds",isOpenForSubmission:!1,hash:"82ba2e3ac63cdbe36375b3b9d3215a1d",slug:"herbicides-properties-synthesis-and-control-of-weeds",bookSignature:"Mohammed Naguib Abd El-Ghany Hasaneen",coverURL:"https://cdn.intechopen.com/books/images_new/2106.jpg",editedByType:"Edited by",editors:[{id:"121899",title:"Dr.",name:"Mohammed Nagib",surname:"Hasaneen",slug:"mohammed-nagib-hasaneen",fullName:"Mohammed Nagib Hasaneen"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2216",title:"Pesticides",subtitle:"Advances in Chemical and Botanical Pesticides",isOpenForSubmission:!1,hash:"47f8991faa031547b7e5bdf66b6e8131",slug:"pesticides-advances-in-chemical-and-botanical-pesticides",bookSignature:"R.P. Soundararajan",coverURL:"https://cdn.intechopen.com/books/images_new/2216.jpg",editedByType:"Edited by",editors:[{id:"145081",title:"Dr.",name:"R.P.",surname:"Soundararajan",slug:"r.p.-soundararajan",fullName:"R.P. 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Part II - Troposphere",doi:"10.5772/intechopen.95451",slug:"lidar-observations-in-south-america-part-ii-troposphere",body:'In Brazil, there are four lidar systems dedicated to the study of aerosols in the troposphere. Installed at Embrapa in Manaus (Western Amazon), there is a lidar system coordinated by the Atmospheric Physics Laboratory (LFA) of the Institute of Physics at the University of São Paulo [1]. In São Paulo, there are two other systems, the SPU Lidar Station, installed at the Institute of Energy and Nuclear Research (IPEN/CNEN) and coordinated by the Laboratory of Environmental Applications of Lasers (LEAL) hosted in the Center for Lasers and Applications (CELAP) of the referred institute, and the scanning lidar system located at CEPEMA (Centre for Training and Research in Environment) in Cubatão (State of São Paulo) [2, 3, 4, 5]. Figure 1 shows an example of the aerosol profile retrieved at SPU Station.
Quick-look of the Lidar Range Corrected Signal (RCS) at 532 nm measured at SPU Lidar Station on 02 July 2019. The signal between 10 and 14 km indicates cirrus clouds.
The DUSTER Lidar system, situated at the Department of Atmospheric and Climate Sciences (DCAC) at the Federal University of Rio Grande do Norte (UFRN), can measure marine aerosols’ physical and optical properties. It can also measure aerosols (mineral dust) that cross the Atlantic Ocean and come from the desert Sahara, and aerosols originated from fires in the African continent [6, 7]. The lidar system’s design and installation in Natal result from a technical and scientific collaboration among UFRN and IPEN. The DUSTER Lidar system is a biaxial monostatic lidar with a typical spatial resolution of 7.5 m. Brazil is a country with continental dimensions where different aerosols are generated, whether by natural or anthropogenic sources. The two systems mentioned above, SPU Lidar Station in São Paulo and DUSTER Lidar system in Natal, can measure different aerosols types.
Quality assurance and quality control programs developed by the European Aerosol Research Lidar Network (EARLINET) [8] are being implemented at the LALINET stations of São Paulo, Manaus, and Natal. This implementation intends to increase the capability to provide a reliable dataset in collaboration with three EARLINET stations (Bucharest, Granada, and Munich) in the framework of the project APEL (Assessment of atmospheric optical Properties during biomass burning Events and Long-range transport of desert dust) [9, 10]. The main objective is to make the final data products from the two networks comparable and study the similarities and differences in aerosol loads, transport heights, types, and properties [11]. The evaluation will be done at the hardware and software levels. At the hardware level, the quality of the signals will be checked using the specific EARLINET procedures, and, at the software level, the LALINET data processing algorithms will be compared with the EARLINET Single Calculus Chain [12, 13, 14]. The last is a fully automatic evaluation process that can be used for virtually any lidar configuration and was validated for several EARLINET lidar stations, being a powerful tool that allows lidar stations to retrieve the aerosol backscatter and extinction profiles from the raw lidar data (Figure 2).
Particle backscatter (Mm−1 sr−1) and extinction (Mm−1) coefficients and Lidar Ratio (sr), measured at SPU Lidar Station on 14 July 2019. Smoothed retrievals, obtained at 355 nm and 532 nm, using the Single Calculus Chain Algorithm.
In Bolivia’s case, the Laboratory for Atmospheric Physics of Universidad Mayor de San Andrés (LFA for its acronym in Spanish) is carrying out some studies related to urban aerosols and pollution monitoring in the metropolitan region of La Paz and El Alto. This region is one of the fastest-growing urban settlements in South America, with the particularity of being located in very complex terrain at a high altitude over the Andes. With a total population of around 1.8 million inhabitants is the second most populous urban area in Bolivia. La Paz city is located in a stepped valley, whose height starts at 3200 m a.s.l. (southern area), going up to 4000 m a.s.l. (in the north). The metropolitan area includes El Alto city (4100 m a.s.l), adjacent to the west’s valley, and is extended over the Altiplano plateau. The valley has many basins that converge in the lower part of the city generating complex air fluxes.
An elastic lidar system was installed in the Science Campus of the Universidad Mayor de San Andrés (16.5333 S, 68.0667 W, 3420 m a.s.l.) in 2007 to study the boundary layer’s behavior in this complex terrain. The lidar system was developed by improving an old system donated by the European Space Agency to the LFA and an essential collaboration of the Raman Lidar Laboratory of NASA’s Goddard Space Flight Center. The instrument regularly worked for some years collecting data every Monday. These ancillary data were used for different short-term studies [15].
In 2011, this lidar acquired an additional relevance when a new Global Atmosphere Watch (GAW) station was set up near the metropolitan area at Mount Chacaltaya (16.3502 S, 68.1314 W, 5240 m a.s.l.). This station was set up to study aerosols’ physical and chemical properties, measure atmospheric gas concentrations, study the aerosols injected into the free troposphere, besides the effect of aerosols deposition onto the Andean glaciers. In this sense, the main task of the lidar system was to help with the study of the air fluxes that go from the metropolitan area to the Chacaltaya GAW station and vice versa and the behavior of the local atmospheric boundary layer, especially in connection with atmospheric pollution in the urban area.
Besides, in 2018 and thanks to a collaboration of the Andalusian Institute for Earth System Research, Granada, Spain, a Lufft CHM 15 k ceilometer was installed in the northern part of La Paz city, closer to the Chacaltaya GAW station than the LFA. The goal was to characterize the boundary layer height’s seasonal behavior through continuous measurements for at least one year. The Wavelet Covariance Transform (WCT) was used to estimate this behavior using both the ceilometer and the university campus’s lidar. Although we gained knowledge about the local ABL’s temporal behavior, it is clear that due to the complexity of topography in this region, extending this work’s main conclusions is not straightforward. More measurements and modeling are needed for this purpose.
The Atmospheric Boundary Layer (ABL) is the lowest section of the troposphere and is directly affected by the surface, responding to surface forcing within a one-hour or less time scale. The ABL has turbulent properties and high variability in its daily cycle, and it is a fundamental parameter to several studies, e.g., air quality, numerical weather forecasting, climate modeling, and wind energy applications [16]. These characteristics, associated with the variations in the ABL stability, enable us to subdivide it into three main layers: The Convective Boundary Layer (CBL), the Stable Boundary Layer (SBL), and the Residual Layer (RL).
The ABL height (ABLH) is obtained from the vertical profile of some tracers like a potential temperature [17], vertical wind speed [18], relative humidity [19], and aerosols [11]. The radiosondes are the more traditional method to estimate the variation of some tracers indicated above and, consequently, estimate the ABLH. Nevertheless, in most regions, the radiosondes are launched only twice a day, which does not provide a detailed observation of the ABLH behavior. In this scenario, due to the lidar systems’ high temporal and spatial resolution, the utilization of this kind of equipment to estimate the ABLH and other ABL properties had increased significantly in the last decade, mainly in South America [20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30].
Elastic lidar and ceilometers can estimate the ABLH from the characteristic reduction in the aerosol concentrations in the transition region between the Free Troposphere (FT) and ABL. Figure 3 presents an example of the ABLH and its subdivisions, both estimated from elastic lidar data. Moreira et al. estimated the Urban ABLH to the city of São Paulo (Brazil), using elastic lidar data, from a method based on the curtain-plot of the Range Corrected Signal and Wavelet Covariance Transform (WCT), respectively [31, 32]. Both algorithms were validated by radiosonde data, resulting in high correlations during convective and clear sky conditions. Also, based on WCT, Niesperuza et al. estimated the ABLH to Medellín (Colombia), demonstrating the influence of the selections of the parameters in the Haar Wavelet performance [33]. Salvador et al. performed a comparison among the ABLH estimated from elastic lidar, SODAR, and Weather Research and Forecasting Model (WRF), finding a high positive correlation during the convective period [26]. Besides, elastic lidar data can also observe aerosol plumes’ movement as the mixing level in the CBL region, from the skewness and kurtosis profiles. This method was applied in the city of São Paulo [34]. The methodology can be applied in studies about air quality providing a better observation about pollutant concentrations.
ABL and its subdivisions. WCT and Gradient Methods, applied in elastic lidar data, estimated the CBLH and RLH. The aerosol profiles were measured at SPU Lidar Station on 03 August 2020.
In comparison with elastic lidar, Doppler lidar provides more possibilities to identify the ABLH due to the capacity to obtain the wind speed profile, which can be applied as a tracer from its variance. Using Doppler Lidar data, Moreira et al. estimated the ABLH from the variance in the wind speed profile in São Paulo [24]. This method was compared with radiosonde data demonstrating a high correlation in CBL and SBL situations. The wind speed profile was used to detect low-level jets (LLJ). Then from the maximum of LLJ, the SBL height was estimated [25]. Marques et al. used the maximum variance in the Noise Ratio to estimate the ABLH. Such a result was compared with radiosonde data, reaching high correlations in stable and convective situations [28].
In Colombia (Cali), to detect the ABL altitude, lidar signals obtained from the LiDAR-CIBioFi station at Universidad del Valle are employed. The study site is georeferenced in Figure 4. The methodology employs an interplay between the Gradient [32, 33] and WCT [34] Methods, as described in detail in Ref. [27].
(a) The location of Valle del Cauca county (the gray area) and the city of Cali (white area) in Colombia (altitude values according to the color scale). (b) The light color shape represents Cali’s rural area. The blue region is linked to the city’s urban area. The red dots mark the LiDAR-CIBioFi system’s location at Universidad del Valle (UniValle) and the radiosonde station (at Cali’s international airport). (c) Political and administrative division of Cali. The blue dots place the Air Quality Monitoring System (SVCA) ground stations of the local Administrative Department of Environmental Management (DAGMA).
Statistical validation of the implemented instrumentation is performed to support the data quality by contrasting atmospheric profiles retrieved by radiosondes launched at the local international airport, a few kilometers away from our station. The maximum vertical gradient level of potential temperature is used to detect the ABL top (ABLT) by employing radiosonde profiles. A linear relationship between the daily ABLT evolution retrieved by the lidar station and the radiosonde profiles goes as follows: ABLLiDAR = 0.967 × ABLRadio - 0.022. It is statistically significant at the 95% confidence level and R2 (consider the separation between the radiosonde launching site and the lidar station, see Figure 4b).
Once the data are validated, the ABLT levels are compared against the available local Particulate Matter (PM) concentration information. The correlations between daily ABLT evolution and PM concentration data from three representative city ground-stations (Cañaveralejo, Pance, and UniValle), shown in Figure 4c, are analyzed. A strong negative relationship for the Cañaveralejo station gives R2 = 0.79, while the Pance station exhibits an unencouraging positive slope with a correlation coefficient R2 = 0.13, meaning a PM concentration increase for higher ABLT values, with PM values above the World Health Organization limits. The UniValle station, located about 100 m away from the LiDAR-CIBioFi station, reveals a low negative correlation (R2 = 0.20) for the ABLT evolution for all months, especially at the beginning of the wet season.
An innovative method for retrieving the ABL top from LiDAR signals was developed at the LiDAR-CIBioFi station. It consists of training a convolutional neural network (NN) in a supervised manner, driving it to learn how to retrieve this dynamical parameter on real, non-ideal conditions and, in a fully automated and unsupervised process [31]. The Wavelet Covariance Transform (WCT) is used as a labeling method for constructing the training data set and as a baseline method for comparison with the trained NN.
The dataset used for the model’s training and tuning is composed of 15,000 signals extracted from daytime measurements taken during December 2018 and February 2019. The signals were labeled using WCT, with a custom search threshold for each one; this was done to ensure the labels’ quality and, consequently, the neural network predictions [31].
It is expected that the corrected training of the model replicates the predictions of a signal-by-signal fine-tuned WCT but in a completely automated and non-supervised process. The convolutional neural network proposed for the ABLT detection is compared to WCT in a supervised variant (custom search threshold for all time evolution) and unsupervised WCT (full signal as input during all time evolution) [31]. WCT was chosen as the labeling and comparison method for its ease of implementation and well-known robustness and performance, as for the past two decades, it has been used to measure the ABLT in numerous case studies [31].
On 14 August (Figure 5a), clouds around 4 km height, with some formations around 2 km, were detected, with the latter being very close to the boundary layer’s height, thus posing a challenge for accurate ABLT detection. Besides, some cases of changes in density were detected after 14 h [31].
Lidar retrievals for 14 August 2019 [
The first single lidar measured profile (Figure 5b), taken at about 12:30 h, exhibits a well-mixed layer, making it easy to discriminate between ABL and free troposphere. This condition allows a straightforward evaluation of the predictions: the NN gives very similar results to the supervised WCT (about 1.8 km), while the unsupervised WCT located the ABLT in a cloud formation above 4 km height [31]. In contrast, the second profile (Figure 5c), taken at 15:40 h, gives very different results for the supervised (sup.) WCT, the unsupervised WCT, and the NN. The supervised WCT located the ABLT at 500 m, below the expected result.
The unsupervised WCT placed the result at around 4 km, in a cloud formation pattern, while the NN located the ABLT about 2.6 km, following its actual behavior [31]. The temporal evolution of the LiDAR measurement profiles of Figure 5 clarifies that the unsupervised WCT detection locates the boundary layer position at cloud formation height, erroneously placing the ABLT in most cases [31]. The supervised WCT shows ABLT detection problems, severely underestimating ABLT for cases of proximity to clouds (see, e.g., around 12 h), and in cases of residual layers (e.g., after 14 h). Despite the drawbacks of supervised WCT and unsupervised WCT, Figure 5 clearly shows that our convolutional NN estimation of ABLT is more resilient to nearby clouds than WCT [31].
The SVCA is the local Air Quality Monitoring System, a governmental policy for continuously monitoring air quality and assessing the pollutants trend. This air quality network provides information in the medium and long term to support developing strategies to attend to the air pollution effects from a holistic view. This network is composed of nine ground stations distributed, as shown in Figure 4. There are six stations to measure PM10 concentration and four stations for PM2.5 concentration measurements. These stations count with automatic analyzers technology for monitoring aerosols and gases such as ozone, carbon dioxide, and others. Employing the raw data retrieved by these stations, the local Administrative Department of Environmental Management (DAGMA) makes monthly and annual reports, which can be found [31] at http://www.cali.gov.co/dagma.
Hourly raw data from three ground base stations that belong to the SVCA network were used to analyze the daily behavior of PM10 and PM2.5. Due to its proximity to the LiDAR-CIBioFi station, Cañaveralejo, Pance, and UniValle stations have been picked. Specifically, the UniValle station is about 100 m away from the LiDAR-CIBioFi, and for a direct intercomparison with ABL altitude is a primary source of data. The data retrieved from these ground stations are significant since they are the only available aerosols data source that features Cali to support environmental public policy reinforcement; these are meant to control day-by-day vehicular fleet restrictions and industrial emissions.
One way to assess the impact of PM concentration on public health and radiative forcing is to study its response according to the ABL vertical and horizontal dynamics. We account for PM10 and PM2.5 concentrations retrieved by the automatic analyzers and the vertical atmospheric profiles from LiDAR-CIBioFi. The correlations for the PM as a function of the ABL altitude retrieved by the LiDAR system, the vertical response of PM for each station, according to the ABL daily evolution during July, August, September, and October 2018, are analyzed. The daily data are used as a first step to identify PM daily behavior within the ABL.
The hypothesis to understand the relationship between PM and ABL is that the aerosol’s mass volume has an inverse behavior to the ABLT evolution. Figure 6 shows how daily data from Cañaveralejo station (red dots) already comply with this hypothesis: PM10 concentration decreases with the ABL height; this behavior is a continuous trend for each of the four covered months, regardless of the temperature and solar radiation transition along the day. Especially in the mornings, the Cañaveralejo station reports the maximum concentration amongst the three stations, with values not above the local 24-hour limit.
(a) Linear regression between PM (μg m−3) concentration and ABLT (or PBL top-planetary boundary layer top) altitude retrieved by LiDAR-CIBioFi in July 2018. (b), (c), and (d) denote the same as in (a) except for the month of data collected (August, September, and October 2018, respectively).
Regarding Pance (green dots) and UniValle (blue crosses) stations, the regressions exhibit odd behavior, particularly during July and August 2018: Figure 6a and b show that Pance’s PM10 concentration dangerously grows with increasing ABL height, a situation of concern since this means that the population remains exposed to high concentrations within a large air volume mass in the ABL. Since we do not have additional information about the emission source, we postulate that Pance high concentrations could be attributed to tropical forests’ haze. Nevertheless, it is necessary to warn on this behavior and spot possible emission sources, and identify such aerosols’ optical properties.
The Pance and Cañaveralejo stations were chosen due to their proximity to the LiDAR-CIBioFi station, but their locations correspond to a different landscape, and the ABLT dynamics could be very different from that of UniValle’s. Since UniValle station is located about 100 m away from the LiDAR-CIBioFi station (and within the same university campus), it is a direct source for PM data comparison/analysis against ABLT dynamics; it is, however, necessary to keep in mind that UniValle station only makes measurements of fine (PM2.5) particles concentration. Even though UniValle station shows PM2.5 values well below those of PM10 Pance and Cañaveralejo stations during July, August, and September, during October 2018, Pance and UniValle registered very similar values for PM2.5 and PM10 against the ABL height; this means a high worrying indicator of fine (PM2.5) particles concentration at UniValle. The linear regression between UniValle and Pance stations and the ABL height shows similar behavior. Thus, PM2.5 concentration values are kept almost constant during the whole day and ABL height evolution. These fine particles’ size allows them to stay longer in the atmosphere, explaining the regression line trend.
All data for each station in Figure 6 and corresponding regressions were organized chronologically. Hence, regarding air quality, unquestionably, the most critical report is that for UniValle station due to the size of measured particles and the worrying linear regression balance with the ABLT daily evolution. The reported concentration levels are slightly above the local annual threshold and the World Health Organization (WHO) 24-hour PM limits.
To summarize, the ABLT results obtained at the LiDAR-CIBioFi station were compared against three PM SVCA ground-stations (Pance, Cañaveralejo, and UniValle) to analyze the behavior of the correlation with PM concentration near the LiDAR station. The Cañaveralejo station that carries out PM10 measurements shows an inversely proportional relationship with the ABLT, indicating that the population is not overexposed to PM10 concentration as higher ABLT values are reached. On the other hand, PM10 and PM2.5 concentrations retrieved by Pance and UniValle stations show a different relationship with ABLT. Unexpectedly, the linear correlation slopes for each one of these stations were quite close to zero. The slope’s mean value for Pance station was 1.67, reporting positive values for July and August, which means that PM10 concentrations increased with increasing ABLT, a word of warning due to a possible negative impact on public health. On the other hand, the slope’s mean value for UniValle (PM2.5) station was −3.85, with negative monthly mean values.
Wildfires generate large amounts of suspended particles in the atmosphere and increase the levels of carbon monoxide. The presence of these particles reduces both visibility and solar radiation reaching the Earth’s surface. Besides, they act as cloud condensation nuclei, modifying the climate and the air composition and being harmful to human health [35]. On 8 November 2019, a dense feather of smoke was detached from Australia’s coasts due to the intense fires that affected the region. These smoke layers were dragged by the winds to South America, entering Argentine territory on 14 November and remaining in suspension until the next day. In the particular case of the fires in Australia, a large amount of soot not only affected the entire surrounding region, devastating forests, and wildlife: the effects were seen around the planet, with measurements of the transport of aerosols at thousands of kilometers from the emission sources. An immediate effect of the accumulated soot from such a biomass burning was the alteration of river courses and the drinking water production in Eastern Australia [36].
The Australian fires started in September 2019 and intensified in November, given the drought conditions that affected the region. It was the second warmest summer registered, having a rainfall regime below the Australian summer average [36]. These major fires produced dense smoke plumes, detected by the Suomi NPP (National Polar-orbiting Partnership) VIIRS (Visible Infrared Imaging Radiometer Suite) satellites. The images are presented in Figures 7 and 8 for 8 November, and in Figures 9 and 10, for 9 November. The figures show Australia’s east coast, the most affected area. Figures 7 and 9 show the Earth’s surface’s natural-looking satellite images, called True Color RGB images (I1-M4-M3). Meteorological clouds can be distinguished in white and smoke layers in translucent gray tones. Figures 8 and 10 show another combination of spectral bands (M11-I2-I1), which allows observing, in shades of blue, the smoke plumes and, in reddish shades, the scars left on the surface of the Earth by fires (burned surface) [37]. These smoke plumes crossed the Pacific Ocean, reaching the American continent and Argentine territory in mid-November 2019.
Satellite image of Australia’s east coast from 8 November 2019. VIIRS - Suomi NPP sensor (combination: I1-M4-M3).
Satellite image of Australia’s east coast from 8 November 2019. VIIRS - Suomi NPP sensor (combination: M11-I2-I1).
Satellite image of Australia’s east coast from 9 November 2019. VIIRS - Suomi NPP sensor (combination: I1-M4-M3).
Satellite image of Australia’s east coast from 9 November 2019. VIIRS - Suomi NPP sensor (combination: M11-I2-I1).
Measurements from sensors onboard satellites and ground-based platforms were used to analyze the November biomass burning aerosols intrusion event from Australia. Within the satellite measurements, the data from the OMPS sensor (Ozone Mapping Profiler Suite) [38, 39] onboard the Suomi NPP satellite were analyzed for the study of the space–time variability of the Aerosol Index (AI Aerosol Index). AOD (Aerosol Optical Depth) measurements at 550 nm were retrieved from the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument onboard the TERRA satellite [40, 41]. Additionally, the total columnar CO (carbon monoxide) content and the AI were sensed by the TROPOMI (TROPOspheric Monitoring Instrument) instrument onboard the Sentinel-5P satellite [42, 43]. AI is a qualitative index that indicates aerosol’s presence at the higher layers of the atmosphere, absorbing or reflecting UV radiation. The main types of aerosols detected with this index are desert dust, biomass burning, and volcanic ash plumes. An advantage of AI is that it can be calculated for clear and (partially) cloudy ground pixels.
The ground-based aerosol optical depth (AOD) data is obtained from the AERONET/NASA sun photometer network measurements at Buenos Aires, CEILPA-BA (34.555 W, 58.506 S, 26 m), Córdoba, and Pilar (31.667 W, 63.883 S, 333 m) stations, at Level 1.5 (data where the clouds have been extracted automatically) [44, 45].
The AOD is the aerosol vertical column integrated extinction at a given wavelength. This dimensionless quantity indicates how much aerosols attenuate the solar radiation as it passes through the atmosphere. Another value, the Ångström coefficient (or exponent), shows the AOD spectral dependence, and it is related to the root mean square distribution of the aerosol radii. It is calculated as the slope of the linear fit of the spectral AOD in a particular wavelength interval in a log–log scale graph. By relating the AOD at 440 nm and the Ångström coefficient, it is possible to classify the aerosol type using the classification table of Reference [46].
Measurements from the lidar instrument installed at CITEDEF were analyzed to determine the height of the aerosol layers. The normalized aerosol backscattering coefficient was calculated at 532 nm [47, 48, 49]. This system allows measuring the atmosphere’s profiles from a few meters to several kilometers, exceeding the tropopause height up to the lower stratosphere. Table 1 summarizes the variables analyzed, the sensors, and the platform employed.
Platform | Sensor | Variable |
---|---|---|
Satellite Suomi NPP | OMPS | AI |
Satellite Sentinel 5p | TROPOMI | CO AI |
Satellite TERRA | MODIS | AOD (550 nm) Ångström coefficient |
Ground-based | LIDAR | Aerosol backscatter |
Ground-based | Sun Photometer | AOD (440 nm) Ångström coefficient |
Instruments and variables used to analyze the November biomass burning aerosols intrusion event.
Figure 11 shows the AI’s space–time evolution measured by the OMPS sensor from 8 to 13 November 2019. The images show how a high AI value (greater than 5) smoke plume emerges from Australia’s coasts on day 8 November 2019. This plume advances over the Pacific Ocean and reaches the coast of South America on 13 November 2019.
Spatial–temporal evolution of the smoke plume through the OMPS sensor analysis of the Aerosol Index (AI), from 8 to 13 November 2019.
On 14 November 2019, the Australian smoke arrived over Argentine territory for the first time, through Neuquén province and covering the country’s entire central region. Figure 12 shows the AI coverage map (OMPS), indicating aerosols’ presence throughout the area.
Aerosol Index (AI), calculated by the OMPS sensor measurements for 14 November 2019.
Figure 13 shows the TROPOMI sensor AI measurement for 14 November. The AI retrieved from OMPS and TROPOMI show similar values, around 1, in almost the entire territory and, in particular, values between 2 and 3, in the province of Entre Ríos. Both overlapping measurements are plotted in Figure 14 to compare the AI measurements coverages with the two sensors. The TROPOMI measurement is taken as the basis, and the polygon (red outline) from the OMPS AI coverage is superimposed. It can be seen that the presence of aerosols in the upper layers of the atmosphere is the same for both sensors. It is known that biomass burning is one of the primary sources of CO release to the atmosphere. Figure 15 shows the regional total columnar abundance of CO measured by the TROPOMI instrument [50]. The values observed throughout the region are not high.
Aerosol Index (AI), measured with the TROPOMI sensor for 14 November 2019.
Comparison of the Aerosol Index (AI) coverages, calculated with the TROPOMI sensor (in color palette) and the OMPS sensor (polygon in red), for 14 November 2019.
CO in the total column from TROPOMI-Sentinel-5P for 14 November 2019.
A slight increase of CO measurements was observed in the same area where AI values were high, as shown in Figure 16, where the CO measurement overlaps the AI coverage (OMPS - red polygon). CO measurements reached a maximum of 0.04 mol/m2 over the province of Entre Ríos.
CO in the total column from TROPOMI-Sentinel-5P and the coverage of the AI (OMPS - red polygon).
A slight increase of CO measurements was observed in the same area where AI values were high, as shown in Figure 16, where the CO measurement overlaps the AI coverage (OMPS - red polygon). CO measurements reached a maximum of 0.04 mol/m2 over the province of Entre Ríos.
Another interesting measurement to analyze the aerosols’ presence is the AOD, which indicates at which level the aerosols prevent the sunlight from passing through the atmosphere. Aerosols scatter and absorb sunlight, resulting in reduced visibility. An AOD of less than 0.1 is characteristic of a clean atmosphere, with a very low number of suspended particles and maximum visibility. The AOD increases due to the increase of suspended particles, and this causes visibility loss.
Figure 17 shows the AOD measurement at 550 nm from the MODIS - TERRA sensor for 14 November 2019. Maximum values of 0.6 are observed in the southern part of the province of Entre Ríos. Values about 0.3 in the vicinity of the City of Buenos Aires and 0.2 in the vicinity of the City of Córdoba are also observed.
AOD at 550 nm from MODIS-TERRA for 14 November 2019 (logarithmic scale).
This satellite measurement can be contrasted with the AERONET/NASA sun photometer measurements available at Buenos Aires and Córdoba. Figure 18 shows the AOD temporal evolution at 440 nm for 14 and 15 November 2019 for the Buenos Aires station and 15 November 2019 for the Córdoba station. At both stations, the values are higher than 0.1 along the two days.
Temporal evolution of the AOD at 440 nm during 14 and 15 November 2019 (BA: CEILAP-BA station (blue); CO: Pilar station (red)).
The Ångström coefficient calculation can be used to provide additional information related to the size of the aerosols. The higher this coefficient is, the smaller the particle size. Values less than 1 suggest the domain of coarse particles (e.g., dust), and values greater than 1 suggest the domain of fine particles (e.g., smoke). Figure 19 shows the Ångström coefficient from MODIS-TERRA product, where maximum values of 1.8 are observed in the southern part of Entre Ríos (in light blue), and 1.1 in the surroundings of Buenos Aires and south of Santa Fe (in green).
Ångström coefficient (Blue) of MODIS-TERRA for 14 November 2019.
The Ångström coefficient was calculated from the AERONET/NASA sun photometers’ AOD data at 870, 670, 500, and 440 nm. Figure 20 shows the AOD at 440 nm versus the calculated Ångström coefficient. This style of graphs allows classifying the type of aerosols suspended in the atmosphere. From such an analysis, it can be extracted that most of the particles are originated from biomass burning, therefore being of the types Biomass Burning and Contaminated Continental, according to the classification table in Ref. [46].
Ångström coefficient versus AOD (440 nm) for 14 and 15 November 2019 (BA: CEILAP-BA station (blue); CO: Pilar station (red)).
Measurements with the lidar system made it possible to determine the aerosol layers’ heights over the City of Buenos Aires. Figures 21 and 22 show the normalized aerosol backscattering spatial–temporal evolution at 532 nm. On the horizontal axis is UTC’s time (Local Time is UTC-3 h); on the vertical axis is the height in kilometers (the tropopause is about 13 km). The color palette shows the intensity of the signal. The blue color represents a clean, molecular atmosphere (without suspended particles), and the red color indicates particulate material. For both days, numerous well-defined layers of aerosols were observed above the atmospheric boundary layer at various heights and with different intensities, up to 13 km.
Spatial–temporal evolution of the normalized aerosols backscattering at 532 nm for 14 November 2019.
Spatial–temporal evolution of the normalized aerosols backscattering at 532 nm for 15 November 2019.
This work analyzed one event of the arrival of smoke plumes from Australia’s intense fires to the Argentinean territory in November 2019. The study determined that, during that period, the AOD values were about 0.25, on average, and the Ångström coefficients were about 1.2. The aerosol layers were found above the atmospheric boundary layer, between 2 km and 13 km of altitude, in the vicinity of the City of Buenos Aires. The CO values were slightly increased without presenting significant risk values for human health. The importance of conducting this type of study is to show that in such aerosols transport events, particles can be transported for hundreds of kilometers from their origin and affect the climate, air quality, and visibility of other areas very distant from the emission source. Satellite measurements, in combination with sun photometers and lidar systems measurements, have allowed an essential synergy for the detection and spatial–temporal monitoring of the smoke columns that, generated thousands of kilometers away, arrived in Argentine territory. Together, these measurements help understand wildfires’ environmental impacts in short and long time series, as they provide relevant data for climate and particle dispersion models [51].
The long-range transport of desert dust has an essential impact on the atmospheric radiative balance, with a global impact on climate, air quality, and human health [52, 53]. It is estimated that between 1 and 2 billion tons of dust are transported from Sahara each year through the atmosphere [54, 55].
Knowledge of the transport mechanisms of dust aerosols from the Sahara Desert to the South American continent can help understand its impact on the balance of nutrients in the Amazon basin [56, 57] and its nutrients deposition in the equatorial Atlantic [58]. Dust transport from the Sahara over the Atlantic Ocean can reach distances greater than 5000 km from its origin and is more intense during December, January and February at 5 N, and during June, July, and August at 20 N latitude. During the boreal winter, desert aerosols are transported across the Atlantic towards the northeast coast of South America, and in the summer, they can reach the Caribbean Sea [59].
The most favorable periods to observe the presence of Saharan dust with a lidar in the atmosphere of the city of Natal (located about 5 S) are in December, January, and February. In this period, Oliveira et al. [60] found the mineral dust predominance occurred during February, followed by December, January, and March in 2018. Research with lidar on aerosols in the Natal atmosphere focuses on the long-range transport of mineral dust and also smoke from biomass burning from the African continent, occurring under the strong influence of the trade winds, during austral summer, when the Intertropical Convergence Zone (ITCZ) is positioned further to the south [61, 62]. In a case study on 09 February 2018 (Reference [60]), Oliveira et al. show that aerosol plumes were identified below 4000 m altitude.
Landulfo et al. were the first to identify an aerosol plume with a lidar, at 3000 m altitude in Natal’s atmosphere, on 01 June 2016 [63]. Direct measurement of the Sun and radiances of the sky with a CIMEL solar photometer may also be used to identify the measured dust aerosol, as in Ref. [60]. Similar aerosol identification studies were carried out [57, 61, 62, 64, 65], showing that African dust’s transatlantic transport reaches the Caribbean. Episodes of mineral dust particles transported from Africa to the Amazon between January and April were also observed [66].
The DUSTER Lidar system started operating in 2016 with operational data collection campaigns called MOnitoring aerosol LOng-range Transportation OVer Natal (MOLOTOV) Zero (March 2016 to July 2016.), MOLOTOV I (December 2016 to mid-February 2017), MOLOTOV II (January and February 2018), and APEL (Assessment of Atmospheric Optical Properties During Biomass Burning Events and Long-Range Transport of Desert Dust) (November and December 2017), at the Federal University of Rio Grande do Norte (UFRN), in Natal.
Also, during the campaigns MOLOTOV Zero and MOLOTOV I, eighteen cirrus cloud profiles were analyzed (7 during MOLOTOV Zero and 11 from MOLOTOV I). Part of the research was conducted to calibrate the DUSTER system, applying the Δ90° method [67]. The main physical parameters of cirrus clouds obtained from lidar data were their thickness, base height, top height, and linear particle depolarization (δp) [68, 69]. The determination of parameters such as base height and top height was calculated from the analysis of the depolarization profiles obtained for each data interval. Data from radiosondes launched by the Natal airport (about 7 km from the DUSTER system) were used to obtain the average cirrus temperature. The average depolarization value for cirrus clouds during the MOLOTOV Zero campaign was δp(cirrus) = 0.43 ± 0.15 with an average base height of 14.23 km and an average top height of 15.53 km [70]. The average temperature of cirrus during this campaign was −69.23°C. The average thickness of the cirrus found was 1.30 km. For the MOLOTOV I campaign, the average value was δp(cirrus) = 0.49 ± 0.13 with the average base height equal to 12.96 km and the average top height equal to 14.44 km. The average thickness found was 1.48 km. The average cirrus temperature found for this campaign was −62.06°C [70].
Clouds play an important role in Earth’s radiation budget as they reflect incoming solar radiation and absorb outgoing thermal radiation emitted from the surface, the atmosphere, and other clouds [71]. In tropical regions, Cirrus clouds are omnipresent [72] and hence affect the radiation balance significantly because of their sizable horizontal extent (hundreds to thousands of km) and long lifetime (hours to days). Cirrus optical properties, altitude, vertical and horizontal coverage control, radiative forcing, and detailed measurements are of absolute value [73, 74].
Studies reporting cirrus properties over tropical South America used to be scarce, with most studies based on in-frequent satellite observations [72, 75]. For obtaining detailed geometrical and microphysical properties of Cirrus clouds, especially the sub-visible type, ground-based lidars are indispensable. For this reason, many lidar groups around the world have used such measurements to obtain and report these characteristics in mid-latitudes [74] and tropical regions [76]. A similar effort is being made in South America, mostly under the auspices of LALINET. Here we review studies from five LALINET stations (Cuba, Manaus, Natal, São Paulo, and Punta Arenas), and we also discuss results from Reference [77], who report on measurements in Buenos Aires. It is compiled a set of statistics for optical and geometrical properties of cirrus occurring in this side of the world, summarized in Table 2.
Loc. | Camagüey, Cuba | Manaus, Brazil | Natal, Brazil | São Paulo, Brazil | B. Aires, Argentina | P. Arenas, Chile |
---|---|---|---|---|---|---|
Ref. | [78] | [79] | [80] | [81] | [77] | [82] |
21 N | 3 S | 6 S | 23 S | 35 S | 53 S | |
1993–1998 | 2011–2012 | Jan-Feb 2017–2018 | Jun-Jul 2007 | 2001–2005 | 2016–2018 | |
532 | 355 | 532 | 532 | 532 | 532 | |
41% | 74% | 67% | 54% | — | 45% | |
14.1 ± 1.4 | 14.3 ± 1.9 | 13.4 ± 1.6 | 12.4 ± 0.6 | 11.8 | 10.8 ± 2.2 | |
11.6 ± 1.5 | 12.9 ± 2.2 | 11.5 ± 1.9 | 10.2 ± 0.7 | 9.6 | 9.0 ± 2.4 | |
0.50 ± 0.27 | 0.35 ± 0.55 | — | 0.27 ± 0.22 | — | 0.25 ± 0.32 | |
10 (fixed) | 23.6 ± 8.1 | — | 26 ± 12 | — | — |
Cirrus clouds’ properties were measured by different LALINET stations, from north to south, during the last 15 years. Columns indicate latitude (deg) of the station, period of study and wavelength, frequency of occurrence, cirrus layer average top and base altitudes, column cloud optical depth (COD), and lidar ratio (LR).
The first studies were performed in Cuba by the lidar group in Camagüey using 6-years (1993 to 1998) elastic lidar data [78]. The lidar measurements were performed for detecting aerosols in the stratosphere, and hence were conducted mostly on clear nights to the naked eye, thus introducing a bias in the cloud measurements towards subvisual cirrus clouds. Indeed, from 131 clouds measured, only 8% were thick (COD >0.3), 67% were thin (0.03 < COD <0.3) and 25% were sub-visual (COD <0.03). Sub-visual and thin cirrus have an average cloud top and base at 14.1 and 11.6 km, respectively, with thick clouds occurring at slightly lower altitudes. The authors estimated the respective optical depths to be 0.50 ± 0.27, 0.07 ± 0.05, and 0.02 ± 0.01, but these are an upper limit as they were calculated assuming a lidar ratio (LR) of 10 sr, which we now know is too low.
In a follow-up study, Barja and Antuña performed radiative transfer simulations (GFDL model, Freidenreich and Ramaswamy, 1999) of the impact of cirrus clouds on solar radiation (SW) [83]. They have found that the daily mean value of SW cirrus radiative forcing (CRFSW) has an average value of −9.1 W m−2 at the top of the atmosphere (TOA) and −5.6 W m−2 at the surface (SFC). There is a linear relation between CRFSW and COD, with a slope of - 30 W m−2/COD at TOA. The local radiative heating effect where the cirrus is found ranged from 0.35 to 1.24 K day−1, with an average of 0.63 K day−1. These results were the first to show that tropical cirrus radiative impact on Earth’s energy balance is essential.
In the Amazon region, Gouveia et al. used continuous measurements (July 2011 to June 2012) at the Manaus station [1] to retrieve the optical and geometrical properties of cirrus clouds during day and night [79]. The cirrus frequency of occurrence was about 88% during the wet season and not lower than 50% during the dry season, with a mean column optical depth of 0.25 ± 0.46. The cloud top and base heights, as well as cloud thickness and cloud optical depth, were, respectively, 14.3 ± 1.9 (std) km, 12.9 ± 2.2 km, 1.4 ± 1.1 km, and 0.25 ± 0.46, similar to the values reported in Cuba. These clouds have a significant radiative impact with such a high frequency of occurrence and altitude over the dark-pristine Amazon forest (albedo about 0.12).
Gouveia then used these measured optical depths and vertical profiles of the cirrus extinction coefficient [84], at 5-min and 200 m resolutions, and calculated the cirrus radiative forcing (CRF) with libRadtran [85]. Cirrus in the Amazon region produced a net CRF at TOA and SFC of +15.3 and − 3.7 W m−2, respectively, much more intense than predicted for 3 European sites (+0.9, +1, and + 1.7 W m−2 at TOA) [74, 86]. Optically thicker cirrus, in general, have more prominent net CRF, with instantaneous CRF that could reach extremes up (down) to +140 (−65) W m−2 for the night (day) time [85]. Together, the vertical profiles with total COD >0.3 were responsible for about 72% (62%) of the TOA (BOA) net CRF, which means that a large fraction of the CRF is generated by optically thin cirrus (COD <0.3) that are harder to detect by radars and passive instruments on board of satellites [86]. A definite daily cycle of the optical depth was found and shows a minimum about local noon and a maximum in the late afternoon (~16 h LT), associated with the diurnal precipitation cycle. This results in a mean instantaneous TOA (SFC) net CRF ranging from +1.7 (−23) W m−2 in the afternoon to +47 (+3.1) W m−2 at night [86]. The cirrus clouds produced an average in-cloud heating rate ranging between −1 K day−1 to +2 K day−1 vertical profile from 8 to 18 km (in-cloud), but with instantaneous values that can reach values higher than 10 K day−1 for portions of the cloud with high ice water content [86].
The DUSTER Lidar station at Natal, Brazil, is the most recent addition to the network. Cirrus measurements started only recently, and Santos reports on measurements during January–February (pre-rainy season) of 2017 and 2018 [80]. A total number of 35 clouds were studied and showed cirrus up to 16 km. These clouds are lower than those in Manaus and were never observed above the tropopause at 17–18 km. The reason is the less vigorous convection in this coastal site, which resembles an oceanic precipitation regime. The frequency of occurrence and average cloud top altitude was 74% (57%) and 13.9 km (12.3 km) in 2017 (2018), respectively. In situ data obtained by radiosondes (9.5 km away) for selected case studies showed an increase of the relative humidity in the layer where the lidar identified the cirrus clouds, from around 10–20% below/above the cloud to around 40–55% in the cloud altitude region.
Cirrus clouds over São Paulo, in the subtropics of South America, were studied by Larroza [81]. She analyzed 34 days, from June to July 2007, using the methodology described in Ref. [87]. The cirrus frequency of occurrence was 54%. The vertical distribution of cloud tops showed peaks at 9.6, 10.6, 12.3, and 13.9 km, with an average of 12.4 km. These cirri were optically thinner (0.27) and occurred at lower altitudes (cloud top 12.4 km) than their tropical counterpart but had a similar lidar ratio of about 26 sr. The clouds observed were either produced by the passage of cold fronts or transported from the tropics or mid-latitudes.
Going into the mid-latitudes, Lakkis et al. used data from a lidar system in Buenos Aires, Argentina, that was not part of LALINET [77]. They studied 60 diurnal cirrus clouds from 2001 to 2005. Unlike the tropics and sub-tropics, cirrus tops were only found very close to the tropopause (~380 m), with cloud tops at 11.8 ± 0.86 and bases at 9.6 ± 0.9 km. Unfortunately, the low statistics did not allow the calculation of a frequency of occurrence, nor did the authors report values of optical depth or lidar ratios. The southernmost LALINET station is at Punta Arenas, Chile (53°S, 71°W), a sub-Antarctic region. Lidar cirrus measurements there began in October 2016 and continue to the present. A preliminary result of cirrus clouds’ geometric characteristics in the region, over two years (from 2016 to 2018), shows that the cirrus’ mean base height is 9.0 ± 2.4 km and the mean top height is 10.8 ± 2.2 km. In the same site in November 2018, the Leipzig Aerosol and Cloud Remote Observations System (LACROS) [88] was deployed by the Leibniz Institute for Tropospheric Research (TROPOS) in collaboration with the University of Magallanes (UMAG) for the field experiment DACAPO - PESO (Dynamics, Aerosol, Cloud And Precipitation Observations in the Pristine Environment of the Southern Ocean). Cirrus clouds measurements are being performed with a Raman polarization lidar that will allow the calculation of LR and COD at multiple wavelengths, which will be reported in a future study.
There has been a great effort to study cirrus clouds in South America, with measurements taking place from 1993 to today, and from 21 N to 53 S. Table 3 summarizes the main characteristics, and we can see how the cirrus altitude changes from the Tropical (high, 14.3 km) to the sub-Antarctic regions (low 10.8 km). The frequency of occurrence also becomes smaller, reducing from 74% in Manaus to 45% in Punta Arenas. Deep convection, prevalent in the tropics and sub-tropics, pushes the tropopause upward and creates optically and physically thick cirrus clouds from the anvil’s detrainment. From the subtropics towards the polar regions, convection is limited by the lack of surface heating, and the mixing of tropospheric air depends on uplift by frontal systems. This does not reach high altitudes, and the primary cirrus production mechanism will be the large-scale lifting of water vapor, rendering physically and optically thinner clouds. Lidars can directly observe the optical depth, and Table 3 shows that it also becomes smaller, decreasing from 0.35 in Manaus to 0.25 in Punta Arenas. Unfortunately, there are not enough LR estimations to allow for a comparison. They were calculated only for Manaus and São Paulo, and the results are in close agreement, indicating similar crystal habits and formation mechanisms, as expected. Similar relation of the cirrus features and latitude was first reported by Cordoba-Jabonero et al. using data from the LALINET subtropical station of São Paulo and ground-based lidar located in Belgrano Antarctic Station [88].
Barja and Antuña Camagüey, Cuba | −5.6 | — | — | |
−9.1 | — | — | ||
Gouveia Manaus, Brazil | −5.0 ± 0.4 | 1.25 ± 0.04 | −3.7 ± 0.2 | |
−5.7 ± 0.5 | 21.0 ± 0.6 | 15.3 ± 0.4 |
Another aspect of this LALINET effort is the diversity in the methods and the timespan of the different studies, limiting our ability for a more in-depth comparison. The combination of the Klett and transmittance methods, as described by Larroza [87] and Gouveia et al. [79], can be applied to elastic data from any of the LALINET lidars, providing COD and LR with high temporal resolution (e.g., 5-min), during both day and night. The use of radiative transfer models to calculate the cirrus’ radiative impact could also be performed for all stations doing cirrus measurements. It would be essential to homogenize cirrus clouds’ analysis throughout the network groups, even by sharing analysis algorithms. Moreover, it should be emphasized that these critical analyses could be automatized and performed unattended on a unique central server. These facts highlight the importance of establishing systematic data sharing in the context of LALINET and GALION.
Tropospheric lidars can provide impressive results: aerosol studies related to volcanic eruptions, tropospheric systems, biomass burning, and dust transport (both into and out of the continent) were conducted over the past 15 years. Pollutant dispersion studies in large cities of South America employing PBL dynamics, conducted in different sites, and with specific conditions, greatly aid local air quality authorities. More recently, studies on clouds and their impact on radiative transfer have been carried out. Given the geographical location of the lidar sites, essential comparisons can be carried on along a wide longitudinal interval, and tropospheric-stratospheric circulation experiments can be performed, as well as their climatological interpretation.
The authors are thankful to the Brazilian Agencies National Council for Scientific and Technological Development (CNPq), Coordination for the Improvement of Higher Education Personnel (CAPES), São Paulo Research Foundation (FAPESP), Brazilian Agricultural Research Corporation (EMBRAPA), and National Institute of Amazonian Research (INPA) LBA Central Office in Manaus. The authors also thank the NASA/AERONET teams, Japan International Cooperation Agency (JICA), the Argentine Agencies National Scientific and Technical Research Council (CONICET), National Agency for the Promotion of Research, Technological Development and Innovation (ANPCyT), the Argentine National Defense University (UNDEF), UNDEFI and PID-UTN Projects, the Ministry of Defense of Argentina, and the French National Centre for Scientific Research (CNRS). Also, to all NASA’s technical personnel, the Argentine Institute of Scientific and Technical Research for Defense (CITEDEF), and the Argentine National Meteorological Service (SMN), who have kept the solar photometers in operation, and especially to Raúl D’Elia. The authors wish to acknowledge the entire NASA CALIPSO and MODIS (AQUA/TERRA) teams, the NOAA Air Resources Laboratory, for providing the HYSPLIT transport and dispersion model and the READY website, ESA/EOM projects teams, the Suomi NPP (National Polar-orbiting Partnership) Mission teams, and the Sentinel 5-P TROPOMI team. The authors also acknowledge the financial support from CIBioFi, the Colombian Science, Technology, and Innovation Fund-General Royalties System (Fondo CTeI-Sistema General de Regalías), and Gobernación del Valle del Cauca. The authors acknowledge the China-Brazil Joint Laboratory for Space Weather (CBJLSW) for Supporting this Book Chapter. Vania F. Andrioli would like to thank the CBJLSW and the National Space Science Center (NSSC) of the Chinese Academy of Sciences (CAS) for supporting her postdoctoral fellowship. The authors from the Universidad de Magallanes would like to acknowledge the financial support of the Japan Science and Technology Agency (JST) /Japan International Cooperation Agency (JICA), the Science and Technology Research Association for Sustainable Development (SATREPS) through the SAVERNet project; and the Program FONDECYT of the Chilean National Agency for Research and Development (ANID) through Project FONDECYT 11181335.
The authors declare no conflict of interest.
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. He has contributed in stochastic estimation of control area especially, in the Multiple Target Tracking and Interactive Multiple Model (IMM) research, Ball & Beam Control Problem, Robotics, Levitation Control. He has contributed in developing Algorithms for Fingerprint Matching, Computer Vision and Face Recognition. He has been supervising Pattern Recognition, Formal Languages and Distributed Processing projects for several years. He has reviewed many books on Management, Computer Science. Currently, he is an active and permanent reviewer for many international conferences and symposia and the program committee member for many international conferences.\nIn teaching he has taught the core computer science subjects like, Digital Design, Real Time Embedded System Programming, Operating Systems, Software Engineering, Data Structures, Databases, Compiler Construction. 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This should sharpen the consciousness of all health practitioners involved in blood transfusion services towards taking measures at preventing transfusion reactions right from donor selection up to the infusion of blood into the recipients.",book:{id:"6905",slug:"blood-groups",title:"Blood Groups",fullTitle:"Blood Groups"},signatures:"John Ayodele Olaniyi",authors:[{id:"202764",title:"Dr.",name:"John",middleName:null,surname:"Olaniyi",slug:"john-olaniyi",fullName:"John Olaniyi"}]},{id:"49387",title:"Thalassemia — From Genotype to Phenotype",slug:"thalassemia-from-genotype-to-phenotype",totalDownloads:4802,totalCrossrefCites:2,totalDimensionsCites:3,abstract:"Thalassemia encompasses serious diseases with complex pathophysiology that is difficult to explain since it is considered a group of defects with similar clinical effects, still not a single disorder.",book:{id:"4729",slug:"inherited-hemoglobin-disorders",title:"Inherited Hemoglobin Disorders",fullTitle:"Inherited Hemoglobin Disorders"},signatures:"Ghada Y. 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Amr",authors:[{id:"58735",title:"Prof.",name:"Ghada",middleName:null,surname:"El-Kamah",slug:"ghada-el-kamah",fullName:"Ghada El-Kamah"},{id:"176872",title:"Prof.",name:"Khalda",middleName:null,surname:"Amr",slug:"khalda-amr",fullName:"Khalda Amr"}]},{id:"51831",title:"Disorders Mimicking Myelodysplastic Syndrome and Difficulties in its Diagnosis",slug:"disorders-mimicking-myelodysplastic-syndrome-and-difficulties-in-its-diagnosis",totalDownloads:4475,totalCrossrefCites:1,totalDimensionsCites:6,abstract:"Myelodysplastic morphology of blood cells can be encountered not only in myelodysplastic syndrome (MDS) but also in nonclonal disorders like viral, bacterial, parasitic infections, juvenile rheumatoid arthritis, polyarteritis nodosa, immune thrombocytopenic purpura (ITP), iron deficiency anemia, megaloblastic anemia, dysgranulopoietic neutropenia, congenital neutropenia, cases with microdeletion 22q11.2, malignant lymphoma, after administration of granulocyte colony stimulating factor, chemotherapy, steroids, smoking, alcohol, posttransplantation, copper deficiency also, together with or without cytopenia. Absence of cytogenetic abnormality in 50–70% of cases with MDS, some overlapping morphological and/or pathophysiological features make it challenging to differentiate between MDS and other diseases/disorders like aplastic anemia, refractory ITP, copper deficiency. Transient genetic abnormalities including monosomy 7 in megaloblastic anemia; increased immature myeloid cells in bone marrow of cases with copper, vitamin B12, or folic acid deficiency in the setting of cytopenia and dysmorphism may also lead to the misdiagnosis of MDS. On the other hand, there are also cases of transient MDS. In this chapter, a literature is be presented to draw attention of the readers on the disorders that mimic MDS. Additionally, our personal experiences are also be shared. Awareness of disorders mimicking MDS may prevent over- or underdiagnosis of MDS.",book:{id:"5276",slug:"myelodysplastic-syndromes",title:"Myelodysplastic Syndromes",fullTitle:"Myelodysplastic Syndromes"},signatures:"Lale Olcay and Sevgi Yetgin",authors:[{id:"184156",title:"Prof.",name:"Lale",middleName:null,surname:"Olcay",slug:"lale-olcay",fullName:"Lale Olcay"}]},{id:"64871",title:"Diagnosis and Classification of Myelodysplastic Syndrome",slug:"diagnosis-and-classification-of-myelodysplastic-syndrome",totalDownloads:3165,totalCrossrefCites:0,totalDimensionsCites:1,abstract:"Myelodysplastic syndrome (MDS) is a clonal hematopoietic stem cell disorder characterized by morphological dysplastic changes in one or more of the major hematopoietic cell lines. MDS can present with varying degrees of single or multiple cytopenias including neutropenia, anemia and thrombocytopenia. Presentation of MDS can range from asymptomatic to life threatening. MDS diagnosis and classification present important challenges, particularly in the distinction from benign conditions. French-American-British (FAB) classification proposed a classification based on easily obtainable laboratory information and was recommended in early and as modified by guidelines of new classification of World Health Organization (WHO). The strategy of diagnostic laboratory in MDS depends on morphological changes and is based on existence of dysplastic changes in the peripheral blood and bone marrow including peripheral blood smear, bone marrow aspirate smear and bone marrow trephine biopsy. The correct morphological interpretation and the use of cytogenetics, immunophenotyping, immunohistochemistry and molecular analysis will give valuable information on diagnosis and prognosis.",book:{id:"7138",slug:"recent-developments-in-myelodysplastic-syndromes",title:"Recent Developments in Myelodysplastic Syndromes",fullTitle:"Recent Developments in Myelodysplastic Syndromes"},signatures:"Gamal Abdul Hamid, Abdul Wahab Al-Nehmi and Safa Shukry",authors:[{id:"36487",title:"Prof.",name:"Gamal",middleName:null,surname:"Abdul Hamid",slug:"gamal-abdul-hamid",fullName:"Gamal Abdul Hamid"},{id:"273724",title:"Dr.",name:"Safa",middleName:null,surname:"Shukry",slug:"safa-shukry",fullName:"Safa Shukry"},{id:"277511",title:"Dr.",name:"Abdulwahab",middleName:null,surname:"Al-Nehmi",slug:"abdulwahab-al-nehmi",fullName:"Abdulwahab Al-Nehmi"}]},{id:"70780",title:"Laboratory Diagnosis of β-Thalassemia and HbE",slug:"laboratory-diagnosis-of-thalassemia-and-hbe",totalDownloads:1338,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"β-Thalassemia and HbE, each, is a syndrome resulted from quantitative and qualitative defects of β-globin chain, respectively. In addition to history retrieve and physical examination, diagnosis of these disorders requires laboratory information. Laboratory tests that are conventionally performed to diagnose the β-thalassemia and HbE are classified into two groups, based on the purposes, including the screening tests and confirmatory tests. The screening tests are aimed to screen for carriers of the β-thalassemia and HbE, while confirmatory tests are the tests performed to definitely diagnose these disorders. This chapter will explain all of these tests, the information of which will be useful for those who are working and interested in the β-thalassemia and HbE.",book:{id:"8450",slug:"beta-thalassemia",title:"Beta Thalassemia",fullTitle:"Beta Thalassemia"},signatures:"Thanusak Tatu",authors:null}],onlineFirstChaptersFilter:{topicId:"183",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:287,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:10,numberOfPublishedChapters:103,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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Fungal infectious illness prevalence and prognosis are determined by the exposure between fungi and host, host immunological state, fungal virulence, and early and accurate diagnosis and treatment. \r\nPatients with both congenital and acquired immunodeficiency are more likely to be infected with opportunistic mycosis. Fungal infectious disease outbreaks are common during the post- disaster rebuilding era, which is characterised by high population density, migration, and poor health and medical conditions.\r\nSystemic or local fungal infection is mainly associated with the fungi directly inhaled or inoculated in the environment during the disaster. The most common fungal infection pathways are human to human (anthropophilic), animal to human (zoophilic), and environment to human (soilophile). Diseases are common as a result of widespread exposure to pathogenic fungus dispersed into the environment. \r\nFungi that are both common and emerging are intertwined. In Southeast Asia, for example, Talaromyces marneffei is an important pathogenic thermally dimorphic fungus that causes systemic mycosis. Widespread fungal infections with complicated and variable clinical manifestations, such as Candida auris infection resistant to several antifungal medicines, Covid-19 associated with Trichoderma, and terbinafine resistant dermatophytosis in India, are among the most serious disorders. \r\nInappropriate local or systemic use of glucocorticoids, as well as their immunosuppressive effects, may lead to changes in fungal infection spectrum and clinical characteristics. Hematogenous candidiasis is a worrisome issue that affects people all over the world, particularly ICU patients. CARD9 deficiency and fungal infection have been major issues in recent years. Invasive aspergillosis is associated with a significant death rate. Special attention should be given to endemic fungal infections, identification of important clinical fungal infections advanced in yeasts, filamentous fungal infections, skin mycobiome and fungal genomes, and immunity to fungal infections.\r\nIn addition, endemic fungal diseases or uncommon fungal infections caused by Mucor irregularis, dermatophytosis, Malassezia, cryptococcosis, chromoblastomycosis, coccidiosis, blastomycosis, histoplasmosis, sporotrichosis, and other fungi, should be monitored. \r\nThis topic includes the research progress on the etiology and pathogenesis of fungal infections, new methods of isolation and identification, rapid detection, drug sensitivity testing, new antifungal drugs, schemes and case series reports. It will provide significant opportunities and support for scientists, clinical doctors, mycologists, antifungal drug researchers, public health practitioners, and epidemiologists from all over the world to share new research, ideas and solutions to promote the development and progress of medical mycology.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",keywords:"Emerging Fungal Pathogens, Invasive Infections, Epidemiology, Cell Membrane, Fungal Virulence, Diagnosis, Treatment"},{id:"5",title:"Parasitic Infectious Diseases",scope:"Parasitic diseases have evolved alongside their human hosts. In many cases, these diseases have adapted so well that they have developed efficient resilience methods in the human host and can live in the host for years. Others, particularly some blood parasites, can cause very acute diseases and are responsible for millions of deaths yearly. Many parasitic diseases are classified as neglected tropical diseases because they have received minimal funding over recent years and, in many cases, are under-reported despite the critical role they play in morbidity and mortality among human and animal hosts. The current topic, Parasitic Infectious Diseases, in the Infectious Diseases Series aims to publish studies on the systematics, epidemiology, molecular biology, genomics, pathogenesis, genetics, and clinical significance of parasitic diseases from blood borne to intestinal parasites as well as zoonotic parasites. We hope to cover all aspects of parasitic diseases to provide current and relevant research data on these very important diseases. In the current atmosphere of the Coronavirus pandemic, communities around the world, particularly those in different underdeveloped areas, are faced with the growing challenges of the high burden of parasitic diseases. At the same time, they are faced with the Covid-19 pandemic leading to what some authors have called potential syndemics that might worsen the outcome of such infections. Therefore, it is important to conduct studies that examine parasitic infections in the context of the coronavirus pandemic for the benefit of all communities to help foster more informed decisions for the betterment of human and animal health.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",keywords:"Blood Borne Parasites, Intestinal Parasites, Protozoa, Helminths, Arthropods, Water Born Parasites, Epidemiology, Molecular Biology, Systematics, Genomics, Proteomics, Ecology"},{id:"6",title:"Viral Infectious Diseases",scope:"The Viral Infectious Diseases Book Series aims to provide a comprehensive overview of recent research trends and discoveries in various viral infectious diseases emerging around the globe. The emergence of any viral disease is hard to anticipate, which often contributes to death. A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. This series will focus on various crucial factors related to emerging viral infectious diseases, including epidemiology, pathogenesis, host immune response, clinical manifestations, diagnosis, treatment, and clinical recommendations for managing viral infectious diseases, highlighting the recent issues with future directions for effective therapeutic strategies.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",keywords:"Novel Viruses, Virus Transmission, Virus Evolution, Molecular Virology, Control and Prevention, Virus-host Interaction"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"May 18th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:287,numberOfPublishedBooks:27,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRqB9QAK/Profile_Picture_1626163237970",institutionString:null,institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/22504",hash:"",query:{},params:{id:"22504"},fullPath:"/profiles/22504",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()