T. Mashalane, S. Moja, O. Novhe, M. Kwata, K. Masindi
Abandoned asbestos mine dumps continue to have an effect on human health notwithstanding the banning of asbestos mining in South Africa in 2002. Asbestos mine dumps, especially non-rehabilitated dumps, contribute to dust pollution. Dust particles from these mine dumps find their way to nearby human settlements and cause health risks. Respiratory health issues such as lung diseases are a result of inhaled asbestos fibres/dust suspended in the air. For the purpose of environmental remediation, rehabilitated and non-rehabilitated sites around Kuruman and Prieska in the Northern Cape Province were selected to compare the presence of asbestos and the effectiveness of rehabilitation. Dustfall within a radius of 5.0 km from the asbestos mine dump to the nearest human settlement was measured and monitored. Characterisation was done for both trapped dust and dustfall samples. Dustfall samples were collected and measured using a 2.0 m stand with a single open bucket half-filled with deionised or distilled water. Trapped dust samples were collected indoors and outdoors from photo frames, window frames, old furniture and roof tops using sticky tape. The mineralogical composition of both indoor and outdoor samples was determined by X-ray diffraction (XRD). Mineralogical and morphological characterisation was further validated using scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS). The XRD results show significant amounts of the amphibole [Ca2(Fe,Mg)5Si8O22(OH)2] asbestos mineral group. Trace amounts of the serpentine [Mg3SiO5(OH)4] asbestos mineral group were detected by XRD but could not be confirmed by SEM-EDS. However, both XRD and SEM-EDS results confirmed the presence of the amphibole asbestos mineral group. Other silicate minerals detected include quartz, talc, mica, plagioclase and feldspar. Detected nonsilicate minerals include calcite, smectite and traces of haematite. Exposure of asbestos minerals within human settlements continues to be a major health concern and sample characterisation substantiates the amount or levels and composition of the minerals.
{"title":"A STUDY OF TRAPPED DUST AND DUSTFALL SAMPLES FROM HUMAN SETTLEMENTS NEAR REHABILITATED AND NON-REHABILITATED ABANDONED ASBESTOS MINE DUMPS IN THE NORTHERN CAPE PROVINCE, SOUTH AFRICA","authors":"T. Mashalane, S. Moja, O. Novhe, M. Kwata, K. Masindi","doi":"10.2495/AIR180331","DOIUrl":"https://doi.org/10.2495/AIR180331","url":null,"abstract":"Abandoned asbestos mine dumps continue to have an effect on human health notwithstanding the banning of asbestos mining in South Africa in 2002. Asbestos mine dumps, especially non-rehabilitated dumps, contribute to dust pollution. Dust particles from these mine dumps find their way to nearby human settlements and cause health risks. Respiratory health issues such as lung diseases are a result of inhaled asbestos fibres/dust suspended in the air. For the purpose of environmental remediation, rehabilitated and non-rehabilitated sites around Kuruman and Prieska in the Northern Cape Province were selected to compare the presence of asbestos and the effectiveness of rehabilitation. Dustfall within a radius of 5.0 km from the asbestos mine dump to the nearest human settlement was measured and monitored. Characterisation was done for both trapped dust and dustfall samples. Dustfall samples were collected and measured using a 2.0 m stand with a single open bucket half-filled with deionised or distilled water. Trapped dust samples were collected indoors and outdoors from photo frames, window frames, old furniture and roof tops using sticky tape. The mineralogical composition of both indoor and outdoor samples was determined by X-ray diffraction (XRD). Mineralogical and morphological characterisation was further validated using scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS). The XRD results show significant amounts of the amphibole [Ca2(Fe,Mg)5Si8O22(OH)2] asbestos mineral group. Trace amounts of the serpentine [Mg3SiO5(OH)4] asbestos mineral group were detected by XRD but could not be confirmed by SEM-EDS. However, both XRD and SEM-EDS results confirmed the presence of the amphibole asbestos mineral group. Other silicate minerals detected include quartz, talc, mica, plagioclase and feldspar. Detected nonsilicate minerals include calcite, smectite and traces of haematite. Exposure of asbestos minerals within human settlements continues to be a major health concern and sample characterisation substantiates the amount or levels and composition of the minerals.","PeriodicalId":165416,"journal":{"name":"Air Pollution XXVI","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129518537","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}
The port of Naples, with about 1 million of cruise passengers corresponding to about 400 calls and 5000 hours at berth per year, is one the most important in the Mediterranean Sea for cruise ships traffic. Therefore, cruise ship emissions can have an important impact on air pollution in Naples. Moreover, cruise ships terminal is very near to the center of the town, with some residential and commercial buildings at only about 200 m from cruise ships docks. The height of these buildings is very close to that of cruise ship funnels. Therefore, the impact of cruise ship emissions on the façades of these buildings may be very high, with negative consequences for indoor air quality and health of people living or working in these buildings. For this reason, a CFD model has been developed with the aim to assess the impact of atmospheric pollutants emitted by cruise ships at hoteling on the façades of the nearest buildings. A calculation domain of about 7 km2 and 1 km height with 10 million cells has been created. Unsteady CFD simulations have been carried out adopting the Scale Adaptive Simulation (SAS) hybrid model that allows a satisfactory accuracy in the calculation of the turbulence. Most critical emissive scenarios have been identified based on cruise ships traffic assuming wind flowing from cruise ships at berth toward the buildings. Emission rates of each pollutant and each cruise ship, during the hoteling phase, have been evaluated. These data were used as input for CFD simulations. As a result, contour maps of SO2 on the ground and on the buildings’ façade were obtained. Results of CFD model are compared with results of simulations with CALPUFF.
{"title":"USING A CFD MODEL TO ASSESS THE IMPACT OF CRUISE SHIP EMISSIONS ON THE FAÇADES OF WATERFRONT BUILDINGS IN NAPLES, ITALY","authors":"F. Murena, B. Mele, D. Toscano","doi":"10.2495/AIR180171","DOIUrl":"https://doi.org/10.2495/AIR180171","url":null,"abstract":"The port of Naples, with about 1 million of cruise passengers corresponding to about 400 calls and 5000 hours at berth per year, is one the most important in the Mediterranean Sea for cruise ships traffic. Therefore, cruise ship emissions can have an important impact on air pollution in Naples. Moreover, cruise ships terminal is very near to the center of the town, with some residential and commercial buildings at only about 200 m from cruise ships docks. The height of these buildings is very close to that of cruise ship funnels. Therefore, the impact of cruise ship emissions on the façades of these buildings may be very high, with negative consequences for indoor air quality and health of people living or working in these buildings. For this reason, a CFD model has been developed with the aim to assess the impact of atmospheric pollutants emitted by cruise ships at hoteling on the façades of the nearest buildings. A calculation domain of about 7 km2 and 1 km height with 10 million cells has been created. Unsteady CFD simulations have been carried out adopting the Scale Adaptive Simulation (SAS) hybrid model that allows a satisfactory accuracy in the calculation of the turbulence. Most critical emissive scenarios have been identified based on cruise ships traffic assuming wind flowing from cruise ships at berth toward the buildings. Emission rates of each pollutant and each cruise ship, during the hoteling phase, have been evaluated. These data were used as input for CFD simulations. As a result, contour maps of SO2 on the ground and on the buildings’ façade were obtained. Results of CFD model are compared with results of simulations with CALPUFF.","PeriodicalId":165416,"journal":{"name":"Air Pollution XXVI","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128159611","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}
Motor vehicles emit gaseous air pollutants including sulphur dioxide, carbon monoxide (CO), carbon dioxide (CO2) nitrogen oxides (NOx), volatile organic compounds (VOC), and particulate matter (PM). Vehicle exhausts are confirmed as a main contributor to air pollution. The purpose of the study is intended to understanding emission rates of gases vehicle’s emission in Jordan and to reveal the impact of driving conditions such as road grade, vehicle speed, number of vehicles and vehicle types in urban areas. In this study, gaseous emissions of NO2 were measured as an indicator for other vehicle gaseous pollutant. The maximum measured emission rate for NO2 was 0.15 ppm and the mean average emission rate for NO2 was 0.077 ppm. Overall, our finding highlights a significant influence of road grade, speed, type and number of vehicle with the rate of NO2 emission. The observed trends indicate there is a need for further investigation to includes other gaseous pollutant.
{"title":"EFFECT OF ROAD GRADE, VEHICLE SPEED AND VEHICLE TYPE ON NO2 EMISSIONS ON URBAN ROADS IN JORDAN","authors":"Jawad Al-Rifai","doi":"10.2495/AIR180501","DOIUrl":"https://doi.org/10.2495/AIR180501","url":null,"abstract":"Motor vehicles emit gaseous air pollutants including sulphur dioxide, carbon monoxide (CO), carbon dioxide (CO2) nitrogen oxides (NOx), volatile organic compounds (VOC), and particulate matter (PM). Vehicle exhausts are confirmed as a main contributor to air pollution. The purpose of the study is intended to understanding emission rates of gases vehicle’s emission in Jordan and to reveal the impact of driving conditions such as road grade, vehicle speed, number of vehicles and vehicle types in urban areas. In this study, gaseous emissions of NO2 were measured as an indicator for other vehicle gaseous pollutant. The maximum measured emission rate for NO2 was 0.15 ppm and the mean average emission rate for NO2 was 0.077 ppm. Overall, our finding highlights a significant influence of road grade, speed, type and number of vehicle with the rate of NO2 emission. The observed trends indicate there is a need for further investigation to includes other gaseous pollutant.","PeriodicalId":165416,"journal":{"name":"Air Pollution XXVI","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126505750","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}
Ben Williams, E. Hayes, Z. Nasir, C. Rolph, S. Jackson, S. Khera, A. Bennett, T. Gladding, G. Drew, J. Longhurst, S. Tyrrel
Bioaerosols, comprised of bacteria, fungi and viruses are ubiquitous in ambient air. Known to adversely affect human health, the impact of bioaerosols on a population often manifests as outbreaks of illnesses such as Legionnaires Disease and Q fever, although the concentrations and environmental conditions in which these impacts occur are not well understood. Bioaerosol concentrations vary from source to source, but specific industrialised human activities such as water treatment, intensive agriculture and open windrow composting facilitate the generation of bioaerosol concentrations many times higher than natural background levels. Bioaerosol sampling is currently undertaken according to the requirements of the Environment Agency’s regulatory framework, in which the collection of bioaerosols and not its long term measurement is of most importance. As a consequence, sampling devices are often moved around site according to changing wind direction and sampling intervals are invariably short-term. The dispersion modelling of bioaerosols from composting facilities typically relies on proxy pollutant parameters. In addition, the use of short term emission data gathering strategies in which monitors are moved frequently with wind direction, do not provide a robust reliable and repeatable dataset by which to validate any modelling or to verify its performance. New sampling methods such as the Spectral Intensity Bioaerosol Sensor (SIBS) provide an opportunity to address several gaps in bioaerosol model validation and verification. In the context of model validation, this paper sets out the current weaknesses in bioaerosol monitoring from the perspective of robust modelling requirements
{"title":"TOWARDS IMPROVED BIOAEROSOL MODEL VALIDATION AND VERIFICATION","authors":"Ben Williams, E. Hayes, Z. Nasir, C. Rolph, S. Jackson, S. Khera, A. Bennett, T. Gladding, G. Drew, J. Longhurst, S. Tyrrel","doi":"10.2495/AIR180041","DOIUrl":"https://doi.org/10.2495/AIR180041","url":null,"abstract":"Bioaerosols, comprised of bacteria, fungi and viruses are ubiquitous in ambient air. Known to adversely affect human health, the impact of bioaerosols on a population often manifests as outbreaks of illnesses such as Legionnaires Disease and Q fever, although the concentrations and environmental conditions in which these impacts occur are not well understood. Bioaerosol concentrations vary from source to source, but specific industrialised human activities such as water treatment, intensive agriculture and open windrow composting facilitate the generation of bioaerosol concentrations many times higher than natural background levels. Bioaerosol sampling is currently undertaken according to the requirements of the Environment Agency’s regulatory framework, in which the collection of bioaerosols and not its long term measurement is of most importance. As a consequence, sampling devices are often moved around site according to changing wind direction and sampling intervals are invariably short-term. The dispersion modelling of bioaerosols from composting facilities typically relies on proxy pollutant parameters. In addition, the use of short term emission data gathering strategies in which monitors are moved frequently with wind direction, do not provide a robust reliable and repeatable dataset by which to validate any modelling or to verify its performance. New sampling methods such as the Spectral Intensity Bioaerosol Sensor (SIBS) provide an opportunity to address several gaps in bioaerosol model validation and verification. In the context of model validation, this paper sets out the current weaknesses in bioaerosol monitoring from the perspective of robust modelling requirements","PeriodicalId":165416,"journal":{"name":"Air Pollution XXVI","volume":"9 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123539831","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}
H. Relvas, J. Ferreira, D. Lopes, S. Rafael, S. Almeida, A. Miranda
In 2015 up to 30% of Europeans were living in cities with air pollutant levels exceeding European Union (EU) air quality standards, and around 95% were exposed to high concentrations, namely particulate matter (PM), deemed damaging to health accordingly to the World Health Organization (WHO) Air Quality Guidelines. In order to reduce air pollution effects, particularly in cities where the majority of the population lives, it is important to define effective planning strategies for air quality improvement. For this purpose, the ongoing project LIFE Index-Air aims to develop an innovative and versatile decision support tool for policy makers, based on an integrated modelling approach, from emissions to health effects, which will help to identify measures to improve air quality, reducing PM levels, and quantitatively assess their impact on the health and well-being of the populations. Five European urban areas will be considered, Lisbon (Portugal), Porto (Portugal), Athens (Greece), Kuopio (Finland) and Treviso (Italy) at high spatial and temporal resolution, covering PM10, PM2.5 and metal elements regulated by EU legislation. For now, the WRF-CAMx air quality modelling system was applied to the Portuguese domains with a spatial resolution of 0.01° (~ 1 km) for 2015. The EMEP emission inventory for 2015 with a spatial resolution of 0.1° and including metal species was considered. For the finest resolution domains (urban) the EMEP emissions were disaggregated to 1x1 km2, based on spatial proxies and emission sources locations. This paper shows the preliminary air quality modelling results, and presents the methodology, based on Artificial Neural Networks (ANN), which will allow to quickly test different measures to improve air quality and to reduce air pollution effects.
{"title":"IMPROVING AIR QUALITY AND HUMAN HEALTH: AN APPROACH BASED ON ARTIFICIAL NEURAL NETWORKS","authors":"H. Relvas, J. Ferreira, D. Lopes, S. Rafael, S. Almeida, A. Miranda","doi":"10.2495/AIR180191","DOIUrl":"https://doi.org/10.2495/AIR180191","url":null,"abstract":"In 2015 up to 30% of Europeans were living in cities with air pollutant levels exceeding European Union (EU) air quality standards, and around 95% were exposed to high concentrations, namely particulate matter (PM), deemed damaging to health accordingly to the World Health Organization (WHO) Air Quality Guidelines. In order to reduce air pollution effects, particularly in cities where the majority of the population lives, it is important to define effective planning strategies for air quality improvement. For this purpose, the ongoing project LIFE Index-Air aims to develop an innovative and versatile decision support tool for policy makers, based on an integrated modelling approach, from emissions to health effects, which will help to identify measures to improve air quality, reducing PM levels, and quantitatively assess their impact on the health and well-being of the populations. Five European urban areas will be considered, Lisbon (Portugal), Porto (Portugal), Athens (Greece), Kuopio (Finland) and Treviso (Italy) at high spatial and temporal resolution, covering PM10, PM2.5 and metal elements regulated by EU legislation. For now, the WRF-CAMx air quality modelling system was applied to the Portuguese domains with a spatial resolution of 0.01° (~ 1 km) for 2015. The EMEP emission inventory for 2015 with a spatial resolution of 0.1° and including metal species was considered. For the finest resolution domains (urban) the EMEP emissions were disaggregated to 1x1 km2, based on spatial proxies and emission sources locations. This paper shows the preliminary air quality modelling results, and presents the methodology, based on Artificial Neural Networks (ANN), which will allow to quickly test different measures to improve air quality and to reduce air pollution effects.","PeriodicalId":165416,"journal":{"name":"Air Pollution XXVI","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128054759","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}
Air pollution is a very serious problem in Poland and elsewhere, and it is a factor that significantly affects the quality of human life. However, people are not fully aware of the terrible air quality due to the insufficient number of monitoring stations. This means they have no access to information about the quality of the air they breathe. The aim of this paper is to present and compare some linear procedures for PM10 and PM2.5 forecasting. Herein, the simulations concerning investigated prediction algorithms are based on real data originating from the Airly company network of pollution measurement stations. Related data, including measurements, were gathered every hour for a period of about one year, moreover, for forecasting purposes, weather data from the Dark Sky portal was additionally used. In this study, several Machine Learning predictive methods are considered. Among these, the results of three are presented. These are: Multiple Linear Regression, Multiple Linear Regression with Regularisation and, finally, Linear Neural Networks. The task for each predictive algorithm was to predict the concentration of PMx dust in the following hours of the next day. As a measure of the prediction task evaluation, several types of error were considered, while, during the research, machine learning group algorithms were utilized as learning models. Via these advanced, efficient and convenient algorithms, a detailed air quality forecast for the next 24 hours is obtainable. The presented algorithms will be implemented into the common air condition prediction system.
{"title":"THE COMPARISON OF LINEAR MODELS FOR PM10 AND PM2.5 FORECASTING","authors":"Piotr A. Kowalski, Wiktor Warchałowski","doi":"10.2495/AIR180161","DOIUrl":"https://doi.org/10.2495/AIR180161","url":null,"abstract":"Air pollution is a very serious problem in Poland and elsewhere, and it is a factor that significantly affects the quality of human life. However, people are not fully aware of the terrible air quality due to the insufficient number of monitoring stations. This means they have no access to information about the quality of the air they breathe. The aim of this paper is to present and compare some linear procedures for PM10 and PM2.5 forecasting. Herein, the simulations concerning investigated prediction algorithms are based on real data originating from the Airly company network of pollution measurement stations. Related data, including measurements, were gathered every hour for a period of about one year, moreover, for forecasting purposes, weather data from the Dark Sky portal was additionally used. In this study, several Machine Learning predictive methods are considered. Among these, the results of three are presented. These are: Multiple Linear Regression, Multiple Linear Regression with Regularisation and, finally, Linear Neural Networks. The task for each predictive algorithm was to predict the concentration of PMx dust in the following hours of the next day. As a measure of the prediction task evaluation, several types of error were considered, while, during the research, machine learning group algorithms were utilized as learning models. Via these advanced, efficient and convenient algorithms, a detailed air quality forecast for the next 24 hours is obtainable. The presented algorithms will be implemented into the common air condition prediction system.","PeriodicalId":165416,"journal":{"name":"Air Pollution XXVI","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131556287","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}
With the rapid of climate change, development of industrialization and urbanization in the world, the level of ozone (O3) concentration in the atmosphere has become a serious problem causes adverse human health effects. Background O3 is defined as the fraction of the O3 present in a given area that is not attributed to anthropogenic sources of local origin. As such, background O3 has several welldocumented sources, both natural and anthropogenic. These include: (a) downward transport of stratospheric O3 through the free troposphere to near the ground level, (b) in situ O3 production from methane emitted from swamps and wetlands reacting with natural NOx (from soils, lightning strikes and downward transport of NO from the stratosphere), (c) in situ production of O3 from reactions of biogenic VOCs with natural NOx and (d) long-range transport of O3 from distant pollutant sources. In this study, we collected the O3 data from air quality monitoring stations for ten years in the state of Kuwait. The O3 status and trends were analysis by using the Open-Air Package. The background level of O3 assessed using the Kolmogorov–Zurbenko (KZ) filter technique. The result shows that the observed levels of O3 background concentrations were high in the urban site. The diurnal variations of O3 at different locations showed a similar trend.
{"title":"OZONE BACKGROUND LEVELS AND TRENDS IN THE STATE OF KUWAIT","authors":"M. Yassin, M. J. Malek, M. Al-Rashidi","doi":"10.2495/AIR180351","DOIUrl":"https://doi.org/10.2495/AIR180351","url":null,"abstract":"With the rapid of climate change, development of industrialization and urbanization in the world, the level of ozone (O3) concentration in the atmosphere has become a serious problem causes adverse human health effects. Background O3 is defined as the fraction of the O3 present in a given area that is not attributed to anthropogenic sources of local origin. As such, background O3 has several welldocumented sources, both natural and anthropogenic. These include: (a) downward transport of stratospheric O3 through the free troposphere to near the ground level, (b) in situ O3 production from methane emitted from swamps and wetlands reacting with natural NOx (from soils, lightning strikes and downward transport of NO from the stratosphere), (c) in situ production of O3 from reactions of biogenic VOCs with natural NOx and (d) long-range transport of O3 from distant pollutant sources. In this study, we collected the O3 data from air quality monitoring stations for ten years in the state of Kuwait. The O3 status and trends were analysis by using the Open-Air Package. The background level of O3 assessed using the Kolmogorov–Zurbenko (KZ) filter technique. The result shows that the observed levels of O3 background concentrations were high in the urban site. The diurnal variations of O3 at different locations showed a similar trend.","PeriodicalId":165416,"journal":{"name":"Air Pollution XXVI","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121435007","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}
Cvitković Ante, I. Igor, B. Andreja, Capak Krunoslav, Vidić Sonja, Vađić Vedran, Ćosić Vesna, Miškić Blaženka, Valjetic´ Marijana
{"title":"DESCRIPTIVE ANALYSIS OF URGENT MEDICAL INTERVENTIONS BASED ON AIR POLLUTION IN SLAVONSKI BROD, CROATIA","authors":"Cvitković Ante, I. Igor, B. Andreja, Capak Krunoslav, Vidić Sonja, Vađić Vedran, Ćosić Vesna, Miškić Blaženka, Valjetic´ Marijana","doi":"10.2495/AIR180511","DOIUrl":"https://doi.org/10.2495/AIR180511","url":null,"abstract":"","PeriodicalId":165416,"journal":{"name":"Air Pollution XXVI","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129360151","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}
Aimable Kalume, Z. Gong, Chuji Wang, J. Santarpia, Yong-le Pan
{"title":"DETECTION AND CHARACTERIZATION OF CHEMICAL AND BIOLOGICAL AEROSOLS USING LASER-TRAPPING SINGLE-PARTICLE RAMAN SPECTROSCOPY","authors":"Aimable Kalume, Z. Gong, Chuji Wang, J. Santarpia, Yong-le Pan","doi":"10.2495/AIR180301","DOIUrl":"https://doi.org/10.2495/AIR180301","url":null,"abstract":"","PeriodicalId":165416,"journal":{"name":"Air Pollution XXVI","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115489648","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}
K. Lach, Šárka Bernatíková, Lenka Frišhansová, K. Klouda, V. Mička
The study of air contamination by heavy metals and dust emissions was conducted on indoor (ISR) and outdoor shooting ranges (OSR) using various firearms – gun, shotgun and submachine gun. Dust particles were collected using a Nano-ID® Select fractioning sampler for both chemical analysis and a scanning electron microscope (SEM) analysis. The total emission of ultrafine particles (UFP) was evaluated including the size distribution during shooting by various equipment (FMPS, SMPS, OPS, Aerotrak). According to expectations, the air was particularly contaminated with lead and partly by antimony, tin, and copper. Observations with SEM show that lead occurs as primary spherical particles, but more often in agglomerates that contain in addition lead, zinc, antimony, and barium. Within the study, air contamination was compared on police shooting ranges by using both traditional ammunition and so-called “green ammunition” not containing lead or antimony in the igniter. When shooting with a handgun in the ISR, the total values of the concentration of lead when using traditional ammunition was about 80 μg/m3, and with the use of “green ammunition” the value of only 2 μg/m3 was measured. The total average concentration of lead at ISR after series of firings with shotgun, submachine gun and pistol reached 1,795 μg/m3. The study has shown that during shooting, especially in indoor shooting ranges with insufficient ventilation, there is a short-term but high burden of heavy metals especially by lead on the persons present. From the long-term view, there is a health risk of lead intoxication especially for trainers and supervisors on shooting ranges. It is important to note that, in this study, health effects on staff and employees of firing ranges were not studied.
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