Computer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm

Computer-aided diagnosis(CAD)models exploit artificial intelligence(AI)for chest X-ray(CXR)examination to identify the presence of tuberculosis(TB)and can improve the feasibility and performance of CXR for TB screening and triage.At the same time,CXR interpretation is a time-consuming and subjective process.Furthermore,high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis.Therefore,computer-aided diagnosis(CAD)models using machine learning(ML)and deep learning(DL)can be designed for screening TB accurately.With this motivation,this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification(WSODTL-TBC)model on Chest X-rays(CXR).The presented WSODTL-TBC model aims to detect and classify TB on CXR images.Primarily,the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image segmentation.Besides,a pre-trained residual network with a two-dimensional convolutional neural network(2D-CNN)model is applied to extract feature vectors.In addition,the WSO algorithm with long short-term memory(LSTM)model was employed for identifying and classifying TB,where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology,showing the novelty of the work.The performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset,and the outcomes were investigated in many aspects.The experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.

Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs.Chest X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and portability.In radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer variability.Using lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is advantageous.The current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on optimization.The data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s edges.Then,the OSDL model is applied to classify the CXRs under different severity levels based on CXR data.The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work.OSDL model,applied in this study,was validated using the COVID-19 dataset.The experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

An analysis of vehicular exhaust derived nanoparticles and historical Belgium fortress building interfaces

Air pollution monitoring is one of the most important features in contamination risk management.This is because many of the compounds contained within air pollution present a serious risk both for the preservation of open air cultural heritage and for human health.New particle formation is a major contributor to urban pollution,but how it occurs in cities is often puzzling.As more and more people enjoy an increased quality of life through outdoor activity,managing outdoor air quality is vital.This study presents the application of a low-cost system for monitoring the current level of road traffic passengers’exposure to particulate air contamination.The global rise in tourism also leads to apprehension about its probable destructive influence on various aspects of global preservation.One of the major risks encountered by tourists,stemming from modes of transport,are nanoparticles(NPs)(<100 nm)and ultra-fine particles(UFPs)(100-1000 nm)consisting of potentially hazardous elements(PHEs).This study examines Steen Castle,a medieval fortress located in Antwerp,Belgium.Significant NPs with PHEs,were found in the air sampled in this area.The self-made passive sampler(LSPS)described in this study,consisting of retainers specially designed for advanced microscopic analysis,is used for the first time as a simple way to characterize the surrounding atmospheric contamination caused by NPs and UFPs,without the need of other commonly employed more expensive particulate focused active samplers such as cascade impactors.This study aims to assess the result of the utilization of a low-cost,LSPS,to determine outdoor NPs and UFPs in a Belgian urban(Steen Castle)and rural area(Fort van Schoten).This work is the first to detail the usefulness of LSPS for the evaluation of Belgium’s outdoor air for NPs and UFPs,which contain PHEs.

Source identification and global implications of black carbon

Black carbon(BC)is one of the short-lived air pollutants that contributes significantly to aerosol radiative forcing and global climate change.It is emitted by the incomplete combustion of fossil fuels,biofuels,and biomass.Urban environments are quite complex and thus,the use of mobile jointly with fixed monitoring provides a better understanding of the dynamics of BC distribution in such areas.The present study addresses the measurement of BC concentration using real-time mobile and ambient monitoring in Barranquilla,an industrialized urban area of the Colombian Caribbean.A microaethalometer(MA200)and an aethalometer(AE33)were used for measuring the BC concentration.The absorption Ångström exponent(AAE)values were determined for the study area,for identifying the BC emission sources.The results of the ambient sampling show that vehicle traffic emissions prevail;however,the influence of biomass burning was also observed.The mean ambient BC concentration was found to be 1.04±1.03μg/m^(3) and varied between 0.5 and 4.0μg/m^(3).From the mobile measurements obtained in real traffic conditions on the road,a much higher average value of 16.1±16.5μg/m^(3) was measured.Many parts of the city showed BC concentrations higher than 20μg/m^(3).The spatial distribution of BC concentration shows that vehicle emissions and traffic jams,a consequence of road and transport infrastructure,are the factors that most affect the BC concentration.A comparison of results obtained from two aethalometers indicates that the concentrations measured by MA200 are 9%lower than those measured by AE33.The AAE obtained was found to vary between 1.1 and 1.6,indicating vehicular emissions as the most crucial source.In addition,it was observed that the BC concentration on working days was 2.5 times higher than on the weekends in the case of mobile monitoring and 1.5 times higher in the case of ambient monitoring.

Deposition of nanoparticles on school eyeglasses in urban and rural areas:A methodology for a more real assessment of the possible impacts

Because incomplete confirmation is available concerning the influential role of atmosphere contamination on conjunctivitis,myopia,asthma,and allergic rhinitis in Brazil,the focus of the present work is to explore the possible relations among atmosphere contamination and eye problems.Rather that a case study on eye diseases,by way of questionnaires supplemented by the investigation of nanoparticles(NPs)on eyeglasses,the study examines the mechanisms in which NPs and ultra-fine particles are deposited on the glasses of children up to 10 years of age in urban and rural area.The important connection between atmosphere contaminants and individual protection equipment justifies improving indoor school properties in order could protect children’s eyes,particularly in high-pollution/high-particulate areas.

Geo-environmental parametric 3D models of SARS-CoV-2 virus circulation in hospital ventilation systems

The novel coronavirus, SARS-CoV-2, has the potential to cause natural ventilation systems in hospital environments to be rendered inadequate, not only for workers but also for people who transit through these environments even for a limited duration. Studies in of the fields of geosciences and engineering,when combined with appropriate technologies, allow for the possibility of reducing the impacts of the SARS-CoV-2 virus in the environment, including those of hospitals which are critical centers for healthcare. In this work, we build parametric 3D models to assess the possible circulation of the SARS-CoV-2 virus in the natural ventilation system of a hospital built to care infected patients during the COVID-19 pandemic. Building Information Modeling(BIM) was performed, generating 3D models of hospital environments utilizing Revit software for Autodesk CFD 2021. The evaluation considered dimensional analyses of 0°, 45°, 90° and 180°. The analysis of natural ventilation patterns on both internal and external surfaces and the distribution of windows in relation to the displacement dynamics of the SARS-CoV-2 virus through the air were considered. The results showed that in the external area of the hospital, the wind speed reached velocities up to 2.1 m/s when entering the building through open windows. In contact with the furniture, this value decreased to 0.78 m/s. In some internal isolation wards that house patients with COVID-19, areas that should be equipped with negative room pressure, air velocity was null. Our study provides insights into the possibility of SARS-CoV-2 contamination in internal hospital environments as well as external areas surrounding hospitals, both of which encounter high pedestrian traffic in cities worldwide.

Performance-Associated Factors of Elderly Patients with a Low Education Level, with Acquired Language Alterations in Tests to Explore Executive Functions

Introduction. Secondary alterations of executive functions occur in brain injuries together with the primary neuropsychological symptoms, irrespective of the location of the damage and the affected neural networks. Such secondary alterations of executive functions in the presence of language alterations, which is the most frequent primary neuropsychological alteration, in addition to exacerbating the linguistic processing deficit, may be associated to multiple factors inherent to the brain injury or the injured patient. Purpose. To describe the secondary neuropsychological alterations of executive functions in elderly patients with low education levels with acquired language disorders and determine general factors of the injury and of the injured patient (etiology, location, time of recovery from the injury, age, education level), associated to such secondary alterations of the Attentional Control System. Patients and Methods. The study was conducted on 68 elderly patients with a low education level with language acquired disorders, of both sexes, ranging between 60 and 80 years of age. The executive functions explored included cognitive flexibility, impulsivity control and inhibition of irrelevant automatisms, with the Trail Making Test, the Porteus Maze Test and series of loops. Statistical processing involved a Distribution of Frequencies and Multiple Ordinal Regression. Results and Conclusions. The statistical analysis found secondary neuropsychological alterations of the executive functioning in the elderly patients with language disorders of the study that are associated to the location and the time of recovery from the injury and are irrespective of age, education level and etiology of the injury.

Environmental assessment and nano-mineralogical characterization of coal,overburden and sediment from Indian coal mining acid drainage

The deterioration of environmental conditions is the major contributory factor to poor health and quality of life that hinders sustainable development in any region.Coal mining is one of the major industries that contribute to the economy of a country but it also impacts the environment.The chemical parameters of the coal,overburden,soil and sediments along with the coal mine drainage(CMD)were investigated in order to understand the overall environmental impact from high sulphur coal mining at northeastern coalfield(India).It was found that the total sulphur content of the coal is noticeably high compared to the overburden(OB)and soil.The volatile matter of the coal is sufficiently high against the high ash content of the soil and overburden.The water samples have a High Electrical Conductivity(EC)and high Total Dissolve Solid(TDS).Lower values of pH,indicate the dissolution of minerals present in the coal as well as other minerals in the mine rejects/overburden.The chemical and nano-mineralogical composition of coal,soil and overburden samples was studied using a High Resolution-Transmission Electron Microscopy(HR-TEM),Energy Dispersive Spectroscopy(EDS),Selected-Area Diffraction(SAED),Field Emission-Scanning Electron Microscopy(FE-SEM)/EDS,X-ray diffraction(XRD),Fourier Transform Infrared Spectroscopy(FTIR),Raman and Ion-Chromatographic analysis,and Mossbauer spectroscopy.From different geochemical analysis it has been found that the mine water sample from Ledo colliery has the lowest pH value of 3.30,Tirap colliery samples have the highest electrical conductivity value of5.40 ms cm^(-1)Both Ledo and Tirap coals have total sulphur contents within the range 3-3.50%.The coal mine water from Tirap colliery(TW-15 B)has high values of Mg^(2+)(450 ppm),and Br^-(227.17 ppm).XRD analysis revealed the presence of minerals including quartz and hematite in the coals.Mineral analysis of coal mine overburden(OB)indicates the presence both of pyrite and marcasite which was also confirmed in XRD and Mossbauer spectral analysis.The presented data of the minerals and ultra/nano-particles present shows their ability to control the mobility of hazardous elements,suggesting possible use in environmental management technology,including restoration of the delicate Indian coal mine areas.

Automated Deep Learning Empowered Breast Cancer Diagnosis UsingBiomedical Mammogram Images

Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process.At the same time,breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques.Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate.But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives.For resolving the issues of false positives of breast cancer diagnosis,this paper presents an automated deep learning based breast cancer diagnosis(ADL-BCD)model using digital mammograms.The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms.The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation.In addition,Deep Convolutional Neural Network based Residual Network(ResNet 34)is applied for feature extraction purposes.Specifically,a hyper parameter tuning process using chimp optimization algorithm(COA)is applied to tune the parameters involved in ResNet 34 model.The wavelet neural network(WNN)is used for the classification of digital mammograms for the detection of breast cancer.The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures.The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.

A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation

Diabetic retinopathy (DR) is a complication of diabetesmellitus thatappears in the retina. Clinitians use retina images to detect DR pathologicalsigns related to the occlusion of tiny blood vessels. Such occlusion brings adegenerative cycle between the breaking off and the new generation of thinnerand weaker blood vessels. This research aims to develop a suitable retinalvasculature segmentation method for improving retinal screening proceduresby means of computer-aided diagnosis systems. The blood vessel segmentationmethodology relies on an effective feature selection based on SequentialForward Selection, using the error rate of a decision tree classifier in theevaluation function. Subsequently, the classification process is performed bythree alternative approaches: artificial neural networks, decision trees andsupport vector machines. The proposed methodology is validated on threepublicly accessible datasets and a private one provided by Hospital Sant Joanof Reus. In all cases we obtain an average accuracy above 96% with a sensitivityof 72% in the blood vessel segmentation process. Compared with the state-ofthe-art, our approach achieves the same performance as other methods thatneed more computational power.Our method significantly reduces the numberof features used in the segmentation process from 20 to 5 dimensions. Theimplementation of the three classifiers confirmed that the five selected featureshave a good effectiveness, independently of the classification algorithm.

Spatiotemporal assessment of particulate matter(PM_(10) and PM_(2.5)) and ozone in a Caribbean urban coastal city

Air pollution has become a critical issue in urban areas,so a broad understanding of its spatiotemporal characteristics is important to develop public policies.This study analyzes the spatiotemporal variation of atmospheric particulate matter(PM_(10) and PM_(2.5))and ozone(O_(3))in Barranquilla,Colombia from March 2018 to June 2019 in three monitoring stations.The average concentrations observed for the Móvil,Policía,and Tres Avemarías stations,respectively,for PM_(10):46.4,51.4,and 39.7μg/m^(3);for PM_(2.5):16.1,18.1,and 15.1μg/m^(3) and for O_(3):35.0,26.6,and 33.6μg/m^(3).The results indicated spatial and temporal variations between the stations and the pollutants evaluated.The highest PM concentrations were observed in the southern part of the city,while for ozone,higher concentrations were observed in the north.These variations are mainly associated with the influence of local sources in the environment of each site evaluated as well as the meteorological conditions and transport patterns of the study area.This study also verified the existence of differences in the concentrations of the studied pollutants between the dry and rainy seasons and the contribution of local sources as biomass burnings from the Isla Salamanca Natural Park and long-range transport of dust particles from the Sahara Desert.This study provides a scientific baseline for understanding air quality in the city,which enables policy makers to adopt efficient measures that jointly prevent and control pollution.

Possibilities of using silicate rock powder:An overview

This study evaluates the on use of crushed rocks(remineralizers)to increase soil fertility levels and which contributed to increase agricultural productivity,recovery of degraded areas,decontamination of water,and carbon sequestration.The use of these geological materials is part of the assumptions of rock technology and,indirectly,facilitates the achievement of sustainable development goals related to soil management,climate change,and the preservation of water resources.Research over the past 50 years on silicate rocks focused on soil fertility management and agricultural productivity.More recently,the combined use with microorganisms and organic correctives have shown positive results to mitigate soil degradation;to expand carbon sequestration and storage;and to contribute to the adsorption of contaminants from water and soil.In this article we show results obtained in several countries and we show that this technology can contribute to the sustainability of agriculture,as well as to reverse global warming.Although mineral nutrients are released more slowly from these types of inputs,they remain in the soil for a longer time,stimulating the soil biota.In addition,they are a technology to soluble synthetic fertilizers replace,since the few nutrients derived from such inputs not consumed by plants are lost by leaching,contaminating groundwater and water resources.In addition,conventional methods rely heavily on chemical pesticides which cause damage to soil’s microfauna(responsible for the decomposition of organic matter and nutrient cycling)and the loss of organic carbon(in the form of dioxide),which is quickly dispersed in the atmosphere.Silicate rock powders are applied in natura,have long-lasting residual effects and reduce greenhouse gas emissions.

Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare sector.Internet of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare systems.These entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor devices.The advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making process.Skin cancer is the deadliest disease which affects people across the globe.Automatic skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin lesions.The current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare environments.Primarily,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further diagnosis.Besides,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image segmentation.Followed by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature extractor.In addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic images.An extensive set of simulations was conducted to validate the performance of the presented method using benchmark dataset.The experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.

The role of airborne particles and environmental considerations in the transmission of SARS-CoV-2

Corona Virus Disease 2019(COVID-19)caused by the novel coronavirus,results in an acute respiratory condition coronavirus 2(SARS-CoV-2)and is highly infectious.The recent spread of this virus has caused a global pandemic.Currently,the transmission routes of SARS-CoV-2 are being established,especially the role of environmental transmission.Here we review the environmental transmission routes and persistence of SARS-CoV-2.Recent studies have established that the transmission of this virus may occur,amongst others,in the air,water,soil,cold-chain,biota,and surface contact.It has also been found that the survival potential of the SARS-CoV-2 virus is dependent on different environmental conditions and pollution.Potentially important pathways include aerosol and fecal matter.Particulate matter may also be a carrier for SARS-CoV-2.Since microscopic particles can be easily absorbed by humans,more attention must be focused on the dissemination of these particles.These considerations are required to evolve a theoretical platform for epidemic control and to minimize the global threat from future epidemics.

A geomorphological model of susceptibility to the effect of human interventions for environmental licensing determination(SHIELD)

Almost every country requires some form of environmental licensing prior to the inception of development projects that may affect the integrity of the environment and its social context.We developed a new conceptual and methodological model to instruct the assessment of the potential impacts posed by proposed projects.Susceptibility to Human Interventions for Environmental Licensing Determination(SHIELD)includes a novel geomorphological interpretation of the Environmental Impact Assessment(EIA).It considers the impact of human interventions on geomorphological processes and landscape functioning in the context of the entire ecosystem,going further than the classical concept of vulnerability.Estimated susceptibility of the site informs the screening stage,allowing local conditions to help define the criteria used in the process.Similarly,the level of detail of the environmental baseline is scoped by considering the degree of disturbance of natural processes posed by human intervention.Testing this geomorphological susceptibility model on different kinds of environments would allow shifting the environmental licensing practices from the prevailing anthropocentric and static conception of the environment towards an Ecosystem Approach.SHIELD addresses the need to improve the screening and scoping stages that form the basis of the rest of any EIA.SHIELD introduces several innovations to EIA including the incorporation of fuzzy logic,a preassembled database of contributions form experts,and a shifting of emphasis from the type of proposed intervention to the type of environment and its relative susceptibility.

Particulate matter geochemistry of a highly industrialized region in the Caribbean:Basis for future toxicological studies

Air pollution has become an important issue,especially in Caribbean urban areas,and,particulate matter(PM)emitted by different natural and anthropogenic sources causes environmental and health issues.In this work,we studied the concentrations of PM_(10) and PM_(2.5) sources in an industrial and port urban area in the Caribbean region of Colombia.PM samples were collected within 48-h periods between April and October 2018 by using a Partisol 2000 i-D sampler.Elemental geochemical characterization was performed by X-ray fluorescence(XRF)analysis.Further,ionic species and black carbon(BC)were quantified by ion chromatography and reflectance spectroscopy,respectively.Using the Positive Matrix Factorization(PMF)receptor model,the contributions of PM sources were quantified.The average concentration of PM_(10) was 46.6±16.2μg/m^(3),with high concentrations of Cl and Ca.For PM_(2.5),the average concentration was 12.0±3.2μg/m^(3),and the most abundant components were BC,S,and Cl.The receptor model identified five sources for PM_(10) and PM_(2.5).For both fractions,the contributions of marine sea spray,re-suspended soil,and vehicular traffic were observed.In addition,PM_(2.5) included two mixed sources were found to be fuel oil combustion with fertilizer industry emissions,and secondary aerosol sources with building construction emissions.Further,PM_(10) was found to also include building construction emissions with re-suspended soil,and metallurgical industry emissions.These obtained geochemical atmospheric results are important for the implementation of strategies for the continuous improvement of the air quality of the Caribbean region.

Geochemical study of submicron particulate matter(PM1)in a metropolitan area

Air pollution has become a major problem in urban areas due to increasing industrialization and urbanization.In this study ambient concentrations of PM1 and metal concentrations as well as source contributions were identified and quantified by using Positive Matrix Factorization(PMF)in receptor modeling in the Metropolitan Area of Porto Alegre,Brazil.The PM1 samples were collected on PTFE filters from December 2012 to December 2014 in two sampling sites.Major ion and trace element concentrations were assessed.The average concentrations were 12.8 and 15.2μg/m^(3) for Canoas and Sapucaia do Sul sites,respectively.Major ion contributions of PM1 were secondary pollutants such as sulfate and nitrate.Trace elements,especially Cu,Pb,Zn,Cd,and Ni also made important contributions which are directly associated with anthropogenic contributions.Our results show significantly higher levels in winter than in summer.Most of the PM1 and the analyzed PM species and elements originated from anthropogenic sources,especially road traffic,combustion processes and industrial activities,which are grouped in 7 major contributing sources.A back-trajectory analysis showed that the long-range transport of pollutants was not relevant in relation to the contribution to PM1 and metal concentrations.This work highlights the importance of urban planning to reduce human health exposure to traffic and industrial emissions,combined with awareness-raising actions for citizens concerning the impact of indoor sources.

Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and others.Besides,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several diseases.Ovarian Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality rate.The OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate OC.In this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart healthcare.The primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and accurately.Besides,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the data.In addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature subsets.Moreover,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification performance.The design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s novelty.The experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.

Metal-enriched nanoparticles and black carbon:A perspective from the Brazil railway system air pollution

Having a better understanding of air pollutants in railway systems is crucial to ensure a clean public transport.This study measured,for the first time in Brazil,nanoparticles(NPs)and black carbon(BC)on two groundlevel platforms and inside trains of the Metropolitan Area of Porto Alegre(MAPA).An intense sampling campaign during thirteen consecutive months was carried out and the chemical composition of NPs was examined by advanced microscopy techniques.The results showed that highest concentrations of the pollutants occur in colder seasons and influenced by variables such as frequency of the trains and passenger densities.Also,internal and external sources of pollution at the stations were identified.The predominance of NPs enriched with metals that increase oxidative stress like Cd,Fe,Pb,Cr,Zn,Ni,V,Hg,Sn,and Ba both on the platforms and inside trains,including Fe-minerals as hematite and magnetite,represents a critical risk to the health of passengers and employees of the system.This interdisciplinary and multi-analytical study aims to provide an improved understanding of reported adverse health effects induced by railway system aerosols.