Health Risk Assessment Posed by the Mobile Source Air Toxics on an Urban to Regional Area

Air toxics are important health concern. The purpose of this research was to develop a protocol to predict exposure concentrations of air toxics and inhalation cancer and noncancer risk that come from different gasoline and diesel-fueled sources. The protocol was developed by linking the U.S. EPA’s Models-3/CMAQ model as the exposure model and toxicological and epidemiological evidence functions. The NEI version 3 for the year 1999 was used in this analysis for point, area, and non-road sources, whereas NMIM was used to create the on-road emissions. The year 2003 was used for meteorological data and as reference to compare the monitored concentrations to model performance. The modeling domain consisted of a 36 km domain. To demonstrate the system’s effectiveness, this study was performed on priority mobile sources air toxics (1, 3-butadiene, benzene, formaldehyde, acetaldehyde, acrolein, and DPM), and was applied to Nashville, Tennessee using available air toxics monitored data. Ten emissions scenarios were selected in this study to compare the main results. This research on air toxics emission scenarios was based on relative analyses and estimates of absolute exposure concentrations and health risk values. The proposed protocol was demonstrated and can be used for decision makers in the quantitative assessment of new policies that will affect the public health and the air quality by air toxics. Eliminating emission source categories is clearly not a policy option, but rather helps gain a better understanding of the total magnitude of the health effects associated with these major sources of air toxics, principally of DPM. Higher formaldehyde and acetaldehyde exposure concentrations occurred in the summer season, while benzene and 1,3-butadiene occurred in winter. DPM did not show a strong seasonality exposure during the year 2003 in Nashville. DPM generated the higher lifetime cancer risk excess among the other air toxics in Nashville, posing a cancer risk that was 4.2 times higher than the combined total cancer risk from all other air toxics. Those high cancer risk levels were due mainly to non-road sources (57.9%). For the on-road diesel fueled sources (DFS), the principal reductions were due to the DPM contributions generated by HDDVs rather than LDDVs. An evident positive synergism in the cancer risk reduction occurred when reducing diesel on-road and non-road source emissions simultaneously. The main cancer risk reductions from acetaldehyde, benzene, 1,3-butadiene, and formaldehyde (4HAPs) were due to the contribution of biogenic sources with 32.2%. This condition was followed for the scenario that did not consider on-road sources with a 27.5% of reduction. For non-road sources, the main reductions were due to the air toxics contributions generated by gasoline LDVs, principally benzene and 1,3-butadiene. The scenario 2020 showed a DPM and 4HAPs health effect reductions of approximately 32.8 and 19.4 %, respectively in Nashville. Higher cancer and non-cancer risks occurred on Southeastern urban areas due to long-term exposure to DPM, principally in Atlanta, GA, followed by Nashville, TN, Birmingham, AL, Raleigh, NC, and Memphis, TN. This research provided strong evidence that reducing ambient DPM concentrations will lead to improvement in human health more than other air toxics in Nashville, indicating that better technologies and regulations must be applied to mobile diesel engines, principally, over non-road diesel sources

Modeling and source apportionment of diesel particulate matter

The fine and ultra fine sizes of diesel particulate matter (DPM) are of greatest health concern. The composition of these primary and secondary fine and ultra fine particles is principally elemental carbon (EC) with adsorbed organic compounds, sulfate, nitrate, ammonia, metals, and other trace elements. The purpose of this study was to use an advanced air quality modeling technique to predict and analyze the emissions and the primary and secondary aerosols concentrations that come from diesel-fueled sources (DFS). The National Emissions Inventory for 1999 and a severe southeast ozone episode that occurred between August and September 1999 were used as reference. Five urban areas and one rural area in the Southeastern US were selected to compare the main results. For urban emissions, results showed that DFS contributed (77.9% ± 8.0) of EC, (16.8% ± 8.2) of organic aerosols, (14.3% ± 6.2) of nitrate, and (8.3% ± 6.6) of sulfate during the selected episodes. For the rural site, these contributions were lower. The highest DFS contribution on EC emissions was allocated in Memphis, due mainly to diesel non-road sources (60.9%). For ambient concentrations, DFS contributed (69.5% ± 6.5) of EC and (10.8% ± 2.4) of primary anthropogenic organic aerosols, where the highest DFS contributions on EC were allocated in Nashville and Memphis on that episode. The DFS contributed (8.3% ± 1.2) of the total ambient PM2.5 at the analyzed sites. The maximum primary DPM concentration occurred in Atlanta (1.44 μg/m3), which was 3.8 times higher than that from the rural site. Non-linearity issues were encountered and recommendations were made for further research. The results indicated significant geographic variability in the EC contribution from DFS, and the main DPM sources in the Southeastern U.S. were the non-road DFS. The results of this work will be helpful in addressing policy issues targeted at designing control strategies on DFS in the Southeastern U.S

Health risk assessment posed by primary diesel particulate matter and vapor air toxics in Southeastern US

Air toxics concentrations and health effects that come from different sources emission scenarios by linking Models-3/CMAQ and cancer risk assessment were predicted. The year 1999 was used to emissions inventory and the year 2003 for meteorological data and modeling performance. To demonstrate the system's effectiveness, this study was performed on priority mobile sources air toxics; benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and diesel particulate matter (DPM). The analysis was applied mainly to Nashville in the Southeastern US. Ten emissions scenarios were selected to compare the principal results. DPM posed a cancer risk that was 4.2 times higher than the combined total cancer risk from all other four air toxics. Those high cancer risk levels were due mainly to non-road sources (57.9%). For the on-road diesel fueled sources, the principal reductions were due to the DPM generated by heavy duty diesel vehicles. The main on-road reductions were due to the air toxics generated by gasoline light duty vehicles, principally benzene and 1,3-butadiene. Reducing ambient DPM concentrations would lead to improvement in human health more than other air toxics, indicating that better technologies and regulations must be applied to the mobile diesel engines, principally, over non-road diesel sources. This is an abstract of a paper presented at the AWMA's 99th Annual Conference and Exhibition (New Orleans, LA 6/20-23/2006)

Generation and Dispersion Model of Gaseous Emissions from Sanitary Landfills

A mathematical model was developed to quantify the environmental impact produced by the gases emission from sanitary landfills. The stages of gas generation and diffusion were modeled using waste and cover materials placed in a landfill over an isotropic porous medium, while the dispersion stage was modeled for the atmosphere using a Gaussian model. The United States Environmental Protection Agency (USEPA) criteria were adopted for the estimation of greenhouse gases emissions. The MATLAB computer program was used to prepare simulations of a proposed sanitary landfill to serve the municipalities of Temuco and Padre Las Casas in Chile, considering a lifetime of 20 years. The simulated results show that the conditions of confinement have a greater incidence on the rate of gas emission than does the quantity of waste disposed. It was also concluded that the level of environmental impact varies considerably according to the evaluation scenario and the project design

Emission Scenarios and the Health Risks Posed by Priority Mobile Air Toxics in an Urban to Regional Area: An Application in Nashville, Tennessee

Toxic air pollutants, also known as hazardous air pollutants, are those that are known or suspected to cause cancer or other serious health effects, such as birth defects or adverse environmental outcomes. The aim of this research was to predict air toxics related health risks due to different emission scenarios by linking Models-3/CMAQ and cancer risk assessments. To demonstrate the effectiveness of this approach, this study was performed on the priority mobile source air toxics (PMSAT) of benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and diesel particulate matter (DPM), based on data from 2003. The analysis was carried out in the eastern US, and mainly in Nashville, TN. Ten emissions scenarios were examined, including a 2020 scenario with the effects of on-road mobile source regulations. The results show that DPM poses a cancer risk that is 4.2 times higher than the combined total cancer risk from all of four other PMSAT. These high cancer risk levels are mainly due to non-road sources (57.9%). The main cancer risk from acetaldehyde, benzene, formaldehyde, and 1,3-butadiene (4HAPs) is due to biogenic sources, which account for 32.2% of this risk, although these cannot be controlled. Excluding DPM, the main on-road cancer risk contribution was due to the air toxics generated by gasoline light duty vehicles (LDVs), principally benzene and 1,3-butadiene. The scenario for 2020 showed reductions in the adverse health effects related to DPM and 4HAPs of 32.8 and 19.4%, respectively. This research provides strong evidence that reducing ambient DPM concentrations will lead to greater improvements in human health than other air toxics, indicating that better technologies and regulations must be applied to mobile diesel engines, as these have more significant adverse health effects than non-road diesel sources

A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile

Air quality time series consists of complex linear and non-linear patterns and are difficult to forecast. Box-Jenkins Time Series (ARIMA) and multilinear regression (MLR) models have been applied to air quality forecasting in urban areas, but they have limited accuracy owing to their inability to predict extreme events. Artificial neural networks (ANN) can recognize non-linear patterns that include extremes. A novel hybrid model combining ARIMA and ANN to improve forecast accuracy for an area with limited air quality and meteorological data was applied to Temuco, Chile, where residential wood burning is a major pollution source during cold winters, using surface meteorological and PM10 measurements. Experimental results indicated that the hybrid model can be an effective tool to improve the PM10 forecasting accuracy obtained by either of the models used separately, and compared with a deterministic MLR. The hybrid model was able to capture 100% and 80% of alert and pre-emergency episodes, respectively. This approach demonstrates the potential to be applied to air quality forecasting in other cities and countries

The effect of switching mobile sources to natural gas on the ozone in the great smoky mountains national park

Mobile sources are among the largest contributors of NOx in the Great Smoky Mountains National Park region (GSMNP). In 2001, these sources contributed 45% of NOx emissions. From 1990 to 2001, the growth of vehicle miles traveled (VMT) increased 60% and 55% in neighboring Sevier and Blount counties respectively. These emissions combined with the high volatile organic compounds (VOC) emissions in the Southeast area have caused the ozone ground level concentration to be as high as some major metropolitan areas in the summer season. In 2001, the maximum 8-hr ozone concentration inside the park was 103 parts per billion. In response to high ozone levels in other areas, federal, state, and local governments are promoting the use of alternative, clean, and reformulated fuel vehicles as a means to improve local air pollution. One of these fuels is compressed natural gas (CNG). The purpose of this project was to use USEPA's CMAQ system in order to model the air quality and compare the ozone ground level formation in the GSMNP from light duty vehicles (LDVs) operating with 100% CNG within 100 miles around GSMNP. A severe southeast ozone episode between August and September 1999 was used as a reference and 2004 was used as a future case. Results showed that LDVs fueled with 100% CNG in the domain could reduce ozone level by 10% and 8% for 1-hr and 8-hr ozone formation respectively in the GSMNP on the modeled time period. Scavenging occurred around the GSMNP in the morning time during the selected episode

Estudio para determinar las causas de deserción de los alumnos de Administración de Empresas a nivel de Bachillerato y Licenciatura en el Tecnológico de Costa Rica

Proyecto de graduación (Licenciatura en Administración de Empresas. Enfasis en Mercadeo) Instituto Tecnológico de Costa Rica, Escuela de Administración de Empresas, 2015.Estudio de mercado para determinar las situaciones que conducen a la deserción en los estudiantes de Administración de Empresas del Instituto Tecnológico de Costa Rica a nivel del programa de estudios de Bachillerato y Licenciatura en todas sus sedes; el estudio analiza las causas de deserción de los últimos seis años, periodo comprendido entre (2008-2014). Efectúa una revisión de la teoría de administración, mercadeo y práctica de gestión CRM para desarrollar un mejor abordaje al tema investigado. El tipo de investigación desarrollado es exploratorio-descriptivo debido a que a nivel institucional y nacional no ha sido abordado desde esta perspectiva y descriptivo debido a la necesidad de medición y evaluación de los aspectos a desarrollar por la investigación. La fuente de información consistió en la utilización de la base de datos de los estudiantes del TEC comprendida en el periodo de años 2008-2014, suministrada por el departamento de Registro de la institución. La población en estudio la constituyo todos los estudiantes que ingresaron al TEC durante el periodo del 2008 al 2014, en las sedes de San José, Cartago y San Carlos y que abandonaron la carrera durante algún momento en ese mismo lapso de tiempo. Utiliza como unidad de estudio al estudiante que ingreso en el periodo comprendido entre 2008 al 2014 en las sedes de Cartago, San José y San Carlos y que abandona la carrera durante algún momento en ese mismo lapso de tiempo. Emplea el método del censo en la población total del estudio con la finalidad de conocer sus gustos y preferencias, para la realización del censo se aplicaron entrevistas telefónicas a la totalidad de la población. El método de recolección de datos utilizada fue el cuestionario que se aplicó mediante entrevista telefónica, dicho cuestionario se configuro con preguntas cerradas y abiertas; en este proceso elabora un plan piloto, para mejorar las perspectivas abordadas y efectuar un mejor trabajo de campo. Seguidamente codifica, digita, analiza y describe los datos a través de tablas y gráficos. El estudio finaliza con el aporte de dos propuestas el plan de fidelización y retención de estudiantes y el plan para el reingreso de estudiantes desertores.Instituto Tecnológico de Costa Rica. Escuela de Administración de Empresa

An artificial-vision- and statistical-learning-based method for studying the biodegradation of type I collagen scaffolds in bone regeneration systems

[Abstract] This work proposes a method based on image analysis and machine and statistical learning to model and estimate osteocyte growth (in type I collagen scaffolds for bone regeneration systems) and the collagen degradation degree due to cellular growth. To achieve these aims, the mass of collagen -subjected to the action of osteocyte growth and differentiation from stem cells- was measured on 3 days during each of 2 months, under conditions simulating a tissue in the human body. In addition, optical microscopy was applied to obtain information about cellular growth, cellular differentiation, and collagen degradation. Our first contribution consists of the application of a supervised classification random forest algorithm to image texture features (the structure tensor and entropy) for estimating the different regions of interest in an image obtained by optical microscopy: the extracellular matrix, collagen, and image background, and nuclei. Then, extracellular-matrix and collagen regions of interest were determined by the extraction of features related to the progression of the cellular growth and collagen degradation (e.g., mean area of objects and the mode of an intensity histogram). Finally, these critical features were statistically modeled depending on time via nonparametric and parametric linear and nonlinear models such as those based on logistic functions. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity by estimating the corresponding proportion of mass loss. The relation between osteocyte growth and differentiation from stem cells, on the one hand, and collagen degradation, on the other hand, was determined too and modeled through analysis of image objects’ circularity and area, in addition to collagen mass loss. This set of imaging techniques, machine learning procedures, and statistical tools allowed us to characterize and parameterize type I collagen biodegradation when collagen acts as a scaffold in bone regeneration tasks. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity and thus to estimate the corresponding proportion of mass loss. Moreover, the proposed methodology can help to estimate the degradation degree of scaffolds from the information obtained by optical microscopy.Ministerio de Asuntos Económicos y Transformación Digital; MTM2014-52876-RMinisterio de Asuntos Económicos y Transformación Digital; MTM2017-82724-RXunta de Galicia; ED431C-2016-015Xunta de Galicia; ED431G/0

An expert system based on computer vision and statistical modelling to support the analysis of collagen degradation

[Abstract] The poly(DL-lactide-co-glycolide) (PDLGA) copolymers have been specifically designed and performed as biomaterials, taking into account their biodegradability and biocompatibility properties. One of the applications of statistical degradation models in material engineering is the estimation of the materials degradation level and reliability. In some reliability studies, as the present case, it is possible to measure physical degradation (mass loss, water absorbance, pH) depending on time. To this aim, we propose an expert system able to provide support in collagen degradation analysis through computer vision methods and statistical modelling techniques. On this base, the researchers can determine which statistical model describes in a better way the biomaterial behaviour. The expert system was trained and evaluated with a corpus of 63 images (2D photographs obtained by electron microscopy) of human mesenchymal stem cells (CMMh-3A6) cultivated in a laboratory experiment lasting 44 days. The collagen type-1 sponges were arranged in 3 groups of 21 samples (each image was obtained in intervals of 72 hours)