Pub Date : 2020-03-25DOI: 10.5772/intechopen.91894
H. Hachimi, Chakib El Mokhi, Badr T. Alsulami, Abderrahim Lakhouit
This paper presents three ventilation scenarios for a bronchoscopy unit using a numerical study. A Fire Dynamics Simulator (FDS) is employed for this purpose. The results obtained are visualized using Smokeview (SMV), which is a program for displaying FDS results. The numerical results are compared with experimental ones from Cheong and Phua ’ s research study. This study was chosen because it investi-gates ventilation strategies in hospital isolation rooms using a tracer gas technique. In the present work, six points of measurements are utilized to evaluate the concentrations of contaminants and air velocity. The results show that the concentrations estimated by FDS are inferior to the experimental results given by Cheong and Phua . For example, in the SP1 point of measurement, the concentrations estimated by FDS and by Cheong and Phua are 20 and 28.9 ppm, respectively, while in the SP5 point, the concentrations estimated by FDS and by Cheong and Phua are 28.6 and 32.9 ppm, respectively. The error percentages between FDS estimates and experimental measurements made by Cheong and Phua range between 1 and 32%.
{"title":"Numerical Analysis of Indoor Air Quality in Hospital Case Study: Bronchoscopy Unit","authors":"H. Hachimi, Chakib El Mokhi, Badr T. Alsulami, Abderrahim Lakhouit","doi":"10.5772/intechopen.91894","DOIUrl":"https://doi.org/10.5772/intechopen.91894","url":null,"abstract":"This paper presents three ventilation scenarios for a bronchoscopy unit using a numerical study. A Fire Dynamics Simulator (FDS) is employed for this purpose. The results obtained are visualized using Smokeview (SMV), which is a program for displaying FDS results. The numerical results are compared with experimental ones from Cheong and Phua ’ s research study. This study was chosen because it investi-gates ventilation strategies in hospital isolation rooms using a tracer gas technique. In the present work, six points of measurements are utilized to evaluate the concentrations of contaminants and air velocity. The results show that the concentrations estimated by FDS are inferior to the experimental results given by Cheong and Phua . For example, in the SP1 point of measurement, the concentrations estimated by FDS and by Cheong and Phua are 20 and 28.9 ppm, respectively, while in the SP5 point, the concentrations estimated by FDS and by Cheong and Phua are 28.6 and 32.9 ppm, respectively. The error percentages between FDS estimates and experimental measurements made by Cheong and Phua range between 1 and 32%.","PeriodicalId":331562,"journal":{"name":"Atmospheric Air Pollution and Monitoring","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127798941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-27DOI: 10.5772/INTECHOPEN.86554
Y. Aliyu, J. Botai, A. Abubakar, T. Youngu, J. O. Sule, M. Shebe, Mohammed Ahmed Bichi
In Nigeria, the rising levels of used/poorly maintained vehicles are contributing to most urban air pollution with possible repercussion on the general public health. This study evaluates the inferences of vehicular traffic surge on outdoor pollutant measurement using Zaria, northern Nigeria, as a case study. The study collected a 1-year time-series dataset for the vehicular count and the respective outdoor criteria pollutant measurements over 19 study sites. The vehicular traffic was categorized into motorcycles (2-W), tricycles (3-W), cars, buses, light-duty vehicles (LDV) and heavy-duty vehicles (HDV). The outdoor pollutants that were measured include carbon monoxide (CO), sulfur dioxide (SO 2 ) and particulate matter (PM 2.5 /PM 10 ). We utilized validated portable monitors (CW-HAT200 particulate counter and the MSA Altair 5x multigas sensor) for the outdoor measurements during December 2015 – November 2016. The observed measurements for the validation procedure were normally distributed [kurtosis (0.301); skewness ( (cid:1) 0.334)] and coefficient of determination (R2 ≥ 0.808). The time-series analysis of particulate matter (PM) measurements displayed alarming concentrations levels. Combined vehicular traffic density analysis revealed significant contribution (R ≥ 0.619) to the population exposed outdoor pollutant measurements. The 2-W (motorcycle) was found to be the vehicular category that attributed the most significant relationship with observed outdoor pollutant measurements.
{"title":"Atmospheric Air Pollution in Nigeria: A Correlation between Vehicular Traffic and Criteria Pollutant Levels","authors":"Y. Aliyu, J. Botai, A. Abubakar, T. Youngu, J. O. Sule, M. Shebe, Mohammed Ahmed Bichi","doi":"10.5772/INTECHOPEN.86554","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.86554","url":null,"abstract":"In Nigeria, the rising levels of used/poorly maintained vehicles are contributing to most urban air pollution with possible repercussion on the general public health. This study evaluates the inferences of vehicular traffic surge on outdoor pollutant measurement using Zaria, northern Nigeria, as a case study. The study collected a 1-year time-series dataset for the vehicular count and the respective outdoor criteria pollutant measurements over 19 study sites. The vehicular traffic was categorized into motorcycles (2-W), tricycles (3-W), cars, buses, light-duty vehicles (LDV) and heavy-duty vehicles (HDV). The outdoor pollutants that were measured include carbon monoxide (CO), sulfur dioxide (SO 2 ) and particulate matter (PM 2.5 /PM 10 ). We utilized validated portable monitors (CW-HAT200 particulate counter and the MSA Altair 5x multigas sensor) for the outdoor measurements during December 2015 – November 2016. The observed measurements for the validation procedure were normally distributed [kurtosis (0.301); skewness ( (cid:1) 0.334)] and coefficient of determination (R2 ≥ 0.808). The time-series analysis of particulate matter (PM) measurements displayed alarming concentrations levels. Combined vehicular traffic density analysis revealed significant contribution (R ≥ 0.619) to the population exposed outdoor pollutant measurements. The 2-W (motorcycle) was found to be the vehicular category that attributed the most significant relationship with observed outdoor pollutant measurements.","PeriodicalId":331562,"journal":{"name":"Atmospheric Air Pollution and Monitoring","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128626637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-02DOI: 10.5772/INTECHOPEN.86316
T. Mujawar, L. Deshmukh
This chapter focuses on the implementation of a smart environment monitoring system using wired and wireless sensor networks (WSN). The goal was to develop a LabVIEW based system to monitor environmental parameters that provide inaccessible, real-time monitoring. The development of portable and efficient environment monitoring system based on LabVIEW GUI that monitors various environmental parameters such as temperature, relative humidity, Air quality and light intensity was developed. This chapter targets on both wired and wireless approach for environment monitoring. The limitations of wired network were explained by flourish-ing the portable system. For proceedings with the impediment and insufficiency of wired network, Arduino augmentation ascendancy, are mingled with XBee wireless sensor network. The data from the environment was sent to the sink node wire-lessly through mote. Monitoring of the data was done in a personal computer (PC) through a graphical user interface made by LabVIEW. The pertinent sensor for each was connected to analog input of Arduino UNO and their values are displayed on front panel of LabVIEW. LabVIEW run time engine makes the system cost effective and facile. To reveal the effectiveness of the system, some measurement results are also predicted in this chapter.
{"title":"Smart Environment Monitoring System Using Wired and Wireless Network: A Comparative Study","authors":"T. Mujawar, L. Deshmukh","doi":"10.5772/INTECHOPEN.86316","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.86316","url":null,"abstract":"This chapter focuses on the implementation of a smart environment monitoring system using wired and wireless sensor networks (WSN). The goal was to develop a LabVIEW based system to monitor environmental parameters that provide inaccessible, real-time monitoring. The development of portable and efficient environment monitoring system based on LabVIEW GUI that monitors various environmental parameters such as temperature, relative humidity, Air quality and light intensity was developed. This chapter targets on both wired and wireless approach for environment monitoring. The limitations of wired network were explained by flourish-ing the portable system. For proceedings with the impediment and insufficiency of wired network, Arduino augmentation ascendancy, are mingled with XBee wireless sensor network. The data from the environment was sent to the sink node wire-lessly through mote. Monitoring of the data was done in a personal computer (PC) through a graphical user interface made by LabVIEW. The pertinent sensor for each was connected to analog input of Arduino UNO and their values are displayed on front panel of LabVIEW. LabVIEW run time engine makes the system cost effective and facile. To reveal the effectiveness of the system, some measurement results are also predicted in this chapter.","PeriodicalId":331562,"journal":{"name":"Atmospheric Air Pollution and Monitoring","volume":"341 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122543142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-27DOI: 10.5772/INTECHOPEN.87031
D. Stoyanov, I. Nedkov, V. Groudeva, Z. Cherkezova-Zheleva, I. Grigorov, G. Kolarov, M. Iliev, R. Ilieva, D. Paneva, C. Ghelev
Light detection and ranging (LIDAR) atmospheric sensing is a major tool for remote monitoring of aerosol pollution and its propagation in the atmosphere. Combining LIDAR sensing with ground-based aerosol monitoring can form the basis of integrated air-quality characterization. When present, biological atmospheric contamination is transported by aerosol particles of different size known as bioaerosol, whose monitoring is now among the basic areas of atmospheric research, especially in densely-populated large urban regions, where many bio-aerosol-emitting sources exist. Thus, promptly identifying the bioaerosol sources, including their geographical coordinates, intensities, space-time distributions, etc., becomes a major task of a city monitoring system. This chapter argues in favor of integrating a LIDAR mapping schematic with in situ sampling and characterization of the bioaerosol in the urban area. The measurements, data processing, and decision-making aimed at preventing further atmospheric contamination should be performed in a near-real-time mode, which imposes certain demands on the typical LIDAR schematics, including long-range sensing as a critical parameter, especially over large areas (10 – 100 km2). In this chapter, we describe experiments using a LIDAR schematic allowing near-real-time long-distance measurements of urban bioaerosol combined with its ground-based sampling and physicochemical and biological studies.
{"title":"Long-Distance LIDAR Mapping Schematic for Fast Monitoring of Bioaerosol Pollution over Large City Areas","authors":"D. Stoyanov, I. Nedkov, V. Groudeva, Z. Cherkezova-Zheleva, I. Grigorov, G. Kolarov, M. Iliev, R. Ilieva, D. Paneva, C. Ghelev","doi":"10.5772/INTECHOPEN.87031","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.87031","url":null,"abstract":"Light detection and ranging (LIDAR) atmospheric sensing is a major tool for remote monitoring of aerosol pollution and its propagation in the atmosphere. Combining LIDAR sensing with ground-based aerosol monitoring can form the basis of integrated air-quality characterization. When present, biological atmospheric contamination is transported by aerosol particles of different size known as bioaerosol, whose monitoring is now among the basic areas of atmospheric research, especially in densely-populated large urban regions, where many bio-aerosol-emitting sources exist. Thus, promptly identifying the bioaerosol sources, including their geographical coordinates, intensities, space-time distributions, etc., becomes a major task of a city monitoring system. This chapter argues in favor of integrating a LIDAR mapping schematic with in situ sampling and characterization of the bioaerosol in the urban area. The measurements, data processing, and decision-making aimed at preventing further atmospheric contamination should be performed in a near-real-time mode, which imposes certain demands on the typical LIDAR schematics, including long-range sensing as a critical parameter, especially over large areas (10 – 100 km2). In this chapter, we describe experiments using a LIDAR schematic allowing near-real-time long-distance measurements of urban bioaerosol combined with its ground-based sampling and physicochemical and biological studies.","PeriodicalId":331562,"journal":{"name":"Atmospheric Air Pollution and Monitoring","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116878613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-08DOI: 10.5772/INTECHOPEN.86091
S. Cryer, I. Wesenbeeck
Monitoring ambient air to assess environmental exposure and risk for volatile agricultural chemicals requires extensive resources and logistical effort. The cost and technical limitations of monitoring can be mitigated using a validated air dispersion model to simulate concentrations of volatile organic chemicals in ambient air. The SOil Fumigant Exposure Assessment (SOFEA) model was developed to explore volatile pesticide exposure and bystander risk. SOFEA assembles sources and source strengths, uses weather data from the region of interest, and executes an air dispersion model (AERMOD, ISCST3) to simulate pesticide concentrations at user defined receptors that can be used in exposure and risk assessment. This work highlights SOFEA development from inception and modifications over the last 1.5 decades, to the current delivery within the public domain. Various examples for the soil fumigant 1,3-dichloropropene are provided.
{"title":"Prediction of Agricultural Contaminant Concentrations in Ambient Air","authors":"S. Cryer, I. Wesenbeeck","doi":"10.5772/INTECHOPEN.86091","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.86091","url":null,"abstract":"Monitoring ambient air to assess environmental exposure and risk for volatile agricultural chemicals requires extensive resources and logistical effort. The cost and technical limitations of monitoring can be mitigated using a validated air dispersion model to simulate concentrations of volatile organic chemicals in ambient air. The SOil Fumigant Exposure Assessment (SOFEA) model was developed to explore volatile pesticide exposure and bystander risk. SOFEA assembles sources and source strengths, uses weather data from the region of interest, and executes an air dispersion model (AERMOD, ISCST3) to simulate pesticide concentrations at user defined receptors that can be used in exposure and risk assessment. This work highlights SOFEA development from inception and modifications over the last 1.5 decades, to the current delivery within the public domain. Various examples for the soil fumigant 1,3-dichloropropene are provided.","PeriodicalId":331562,"journal":{"name":"Atmospheric Air Pollution and Monitoring","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122928531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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