Associations between ambient levels of combustion pollutants and small for gestational age infants in Texas

There is scant evidence regarding the associations between ambient levels of combustion pollutants and small for gestational age (SGA) infants. No studies of this type have been completed in the Southern United States. The main objective of the project presented was to determine associations between combustion pollutants and SGA infants in Texas using three different exposure assessments. ^ Birth certificate data that contained information on maternal and infant characteristics were obtained from the Texas Department of State Health Services (TX DSHS). Exposure assessment data for the three aims came from: (1) U.S. Environmental Protection Agency (EPA) National Air Toxics Assessment (NATA), (2) U.S. EPA Air Quality System (AQS), and (3) TX Department of Transportation (DOT), respectively. Multiple logistic regression models were used to determine the associations between combustion pollutants and SGA. ^ For the first study looked at annual estimates of four air toxics at the census tract level in the Greater Houston Area. After controlling for maternal race, maternal education, tobacco use, maternal age, number of prenatal visits, marital status, maternal weight gain, and median census tract income level, adjusted ORs and 95% confidence intervals (CI) for exposure to PAHs (per 10 ng/m3), naphthalene (per 10 ng/m3), benzene (per 1 µg/m3), and diesel engine emissions (per 10 µg/m3) were 1.01 (0.97–1.05), 1.00 (0.99–1.01), 1.01 (0.97–1.05), and 1.08 (0.95–1.23) respectively. For the second study looking at Hispanics in El Paso County, AORs and 95% confidence intervals (CI) for increases of 5 ng/m3 for the sum of carcinogenic PAHs (Σ c-PAHs), 1 ng/m3 of benzo[a]pyrene, and 100 ng/m3 in naphthalene during the third trimester of pregnancy were 1.02 (0.97–1.07), 1.03 (0.96–1.11), and 1.01 (0.97–1.06), respectively. For the third study using maternal proximity to major roadways as the exposure metric, there was a negative association with increasing distance from a maternal residence to the nearest major roadway (Odds Ratio (OR) = 0.96; 95% CI = 0.94–0.97) per 1000 m); however, once adjusted for covariates this effect was no longer significant (AOR = 0.98; 95% CI = 0.96–1.00). There was no association with distance weighted traffic density (DWTD). ^ This project is the first to look at SGA and combustion pollutants in the Southern United States with three different exposure metrics. Although there was no evidence of associations found between SGA and the air pollutants mentioned in these studies, the results contribute to the body of literature assessing maternal exposure to ambient air pollution and adverse birth outcomes. ^

Prevalence and risk factors of asthma and work -related asthma among U.S. adults: Data from the Third National Health and Nutrition Examination Survey, 1988--1994

This cross-sectional analysis of the data from the Third National Health and Nutrition Examination Survey was conducted to determine the prevalence and determinants of asthma and wheezing among US adults, and to identify the occupations and industries at high risk of developing work-related asthma and work-related wheezing. Separate logistic models were developed for physician-diagnosed asthma (MD asthma), wheezing in the previous 12 months (wheezing), work-related asthma and work-related wheezing. Major risk factors including demographic, socioeconomic, indoor air quality, allergy, and other characteristics were analyzed. The prevalence of lifetime MD asthma was 7.7% and the prevalence of wheezing was 17.2%. Mexican-Americans exhibited the lowest prevalence of MD asthma (4.8%; 95% confidence interval (CI): 4.2, 5.4) when compared to other race-ethnic groups. The prevalence of MD asthma or wheezing did not vary by gender. Multiple logistic regression analysis showed that Mexican-Americans were less likely to develop MD asthma (adjusted odds ratio (ORa) = 0.64, 95%CI: 0.45, 0.90) and wheezing (ORa = 0.55, 95%CI: 0.44, 0.69) when compared to non-Hispanic whites. Low education level, current and past smoking status, pet ownership, lifetime diagnosis of physician-diagnosed hay fever and obesity were all significantly associated with MD asthma and wheezing. No significant effect of indoor air pollutants on asthma and wheezing was observed in this study. The prevalence of work-related asthma was 3.70% (95%CI: 2.88, 4.52) and the prevalence of work-related wheezing was 11.46% (95%CI: 9.87, 13.05). The major occupations identified at risk of developing work-related asthma and wheezing were cleaners; farm and agriculture related occupations; entertainment related occupations; protective service occupations; construction; mechanics and repairers; textile; fabricators and assemblers; other transportation and material moving occupations; freight, stock and material movers; motor vehicle operators; and equipment cleaners. The population attributable risk for work-related asthma and wheeze were 26% and 27% respectively. The major industries identified at risk of work-related asthma and wheeze include entertainment related industry; agriculture, forestry and fishing; construction; electrical machinery; repair services; and lodging places. The population attributable risk for work-related asthma was 36.5% and work-related wheezing was 28.5% for industries. Asthma remains an important public health issue in the US and in the other regions of the world. ^

Sediment and pore fluid chemistry for IODP Sites 311-U1325 and 311-U1329

Analytical challenges in obtaining high quality measurements of rare earth elements (REEs) from small pore fluid volumes have limited the application of REEs as deep fluid geochemical tracers. Using a recently developed analytical technique, we analyzed REEs from pore fluids collected from Sites U1325 and U1329, drilled on the northern Cascadia margin during the Integrated Ocean Drilling Program (IODP) Expedition 311, to investigate the REE behavior during diagenesis and their utility as tracers of deep fluid migration. These sites were selected because they represent contrasting settings on an accretionary margin: a ponded basin at the toe of the margin, and the landward Tofino Basin near the shelf's edge. REE concentrations of pore fluid in the methanogenic zone at Sites U1325 and U1329 correlate positively with concentrations of dissolved organic carbon (DOC) and alkalinity. Fractionations across the REE series are driven by preferential complexation of the heavy REEs. Simultaneous enrichment of diagenetic indicators (DOC and alkalinity) and of REEs (in particular the heavy elements Ho to Lu), suggests that the heavy REEs are released during particulate organic carbon (POC) degradation and are subsequently chelated by DOC. REE concentrations are greater at Site U1325, a site where shorter residence times of POC in sulfate-bearing redox zones may enhance REE burial efficiency within sulfidic and methanogenic sediment zones where REE release ensues. Cross-plots of La concentrations versus Cl, Li and Sr delineate a distinct field for the deep fluids (z > 75 mbsf) at Site U1329, and indicate the presence of a fluid not observed at the other sites drilled on the Cascadia margin. Changes in REE patterns, the presence of a positive Eu anomaly, and other available geochemical data for this site suggest a complex hydrology and possible interaction with the igneous Crescent Terrane, located east of the drilled transect.

Land Surface Temperature continuous time series from SSM/I passive microwave radiometers, over northern latitudes (Latitudes > 45°N), during summer snow free period (2000-2011), with links to NetCDF files

Remote sensing instruments are key players to map land surface temperature (LST) at large temporal and spatial scales. In this paper, we present how we combine passive microwave and thermal infrared data to estimate LST during summer snow-free periods over northern high latitudes. The methodology is based on the SSM/I-SSMIS 37 GHz measurements at both vertical and horizontal polarizations on a 25 km × 25 km grid size. LST is retrieved from brightness temperatures introducing an empirical linear relationship between emissivities at both polarizations as described in Royer and Poirier (2010). This relationship is calibrated at pixel scale, using cloud-free independent LST data from MODIS instruments. The SSM/I-SSMIS and MODIS data are synchronized by fitting a diurnal cycle model built on skin temperature reanalysis provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). The resulting temperature dataset is provided at 25 km scale and at an hourly time step during the ten-year analysis period (2000-2011). This new product was locally evaluated at five experimental sites of the EU-PAGE21 project against air temperature measurements and meteorological model reanalysis, and compared to the MODIS LST product at both local and circumpolar scale. The results giving a mean RMSE of the order of 2.2 K demonstrate the usefulness of the microwave product, which is unaffected by clouds as opposed to thermal infrared products and offers a better resolution compared to model reanalysis.

Aplicación de técnicas de inteligencia artificial a la predicción de contaminantes atmosféricos

Abstract Air pollution is a big threat and a phenomenon that has a specific impact on human health, in addition, changes that occur in the chemical composition of the atmosphere can change the weather and cause acid rain or ozone destruction. Those are phenomena of global importance. The World Health Organization (WHO) considerates air pollution as one of the most important global priorities. Salamanca, Gto., Mexico has been ranked as one of the most polluted cities in this country. The industry of the area led to a major economic development and rapid population growth in the second half of the twentieth century. The impact in the air quality is important and significant efforts have been made to measure the concentrations of pollutants. The main pollution sources are locally based plants in the chemical and power generation sectors. The registered concerning pollutants are Sulphur Dioxide (SO2) and particles on the order of ∼10 micrometers or less (PM10). The prediction in the concentration of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. In this PhD thesis we propose a model to predict concentrations of pollutants SO2 and PM10 for each monitoring booth in the Atmospheric Monitoring Network Salamanca (REDMAS - for its spanish acronym). The proposed models consider the use of meteorological variables as factors influencing the concentration of pollutants. The information used along this work is the current real data from REDMAS. In the proposed model, Artificial Neural Networks (ANN) combined with clustering algorithms are used. The type of ANN used is the Multilayer Perceptron with a hidden layer, using separate structures for the prediction of each pollutant. The meteorological variables used for prediction were: Wind Direction (WD), wind speed (WS), Temperature (T) and relative humidity (RH). Clustering algorithms, K-means and Fuzzy C-means, are used to find relationships between air pollutants and weather variables under consideration, which are added as input of the RNA. Those relationships provide information to the ANN in order to obtain the prediction of the pollutants. The results of the model proposed in this work are compared with the results of a multivariate linear regression and multilayer perceptron neural network. The evaluation of the prediction is calculated with the mean absolute error, the root mean square error, the correlation coefficient and the index of agreement. The results show the importance of meteorological variables in the prediction of the concentration of the pollutants SO2 and PM10 in the city of Salamanca, Gto., Mexico. The results show that the proposed model perform better than multivariate linear regression and multilayer perceptron neural network. The models implemented for each monitoring booth have the ability to make predictions of air quality that can be used in a system of real-time forecasting and human health impact analysis. Among the main results of the development of this thesis we can cite: A model based on artificial neural network combined with clustering algorithms for prediction with a hour ahead of the concentration of each pollutant (SO2 and PM10) is proposed. A different model was designed for each pollutant and for each of the three monitoring booths of the REDMAS. A model to predict the average of pollutant concentration in the next 24 hours of pollutants SO2 and PM10 is proposed, based on artificial neural network combined with clustering algorithms. Model was designed for each booth of the REDMAS and each pollutant separately. Resumen La contaminación atmosférica es una amenaza aguda, constituye un fenómeno que tiene particular incidencia sobre la salud del hombre. Los cambios que se producen en la composición química de la atmósfera pueden cambiar el clima, producir lluvia ácida o destruir el ozono, fenómenos todos ellos de una gran importancia global. La Organización Mundial de la Salud (OMS) considera la contaminación atmosférica como una de las más importantes prioridades mundiales. Salamanca, Gto., México; ha sido catalogada como una de las ciudades más contaminadas en este país. La industria de la zona propició un importante desarrollo económico y un crecimiento acelerado de la población en la segunda mitad del siglo XX. Las afectaciones en el aire son graves y se han hecho importantes esfuerzos por medir las concentraciones de los contaminantes. Las principales fuentes de contaminación son fuentes fijas como industrias químicas y de generación eléctrica. Los contaminantes que se han registrado como preocupantes son el Bióxido de Azufre (SO2) y las Partículas Menores a 10 micrómetros (PM10). La predicción de las concentraciones de estos contaminantes puede ser una potente herramienta que permita tomar medidas preventivas como reducción de emisiones a la atmósfera y alertar a la población afectada. En la presente tesis doctoral se propone un modelo de predicción de concentraci ón de los contaminantes más críticos SO2 y PM10 para cada caseta de monitorización de la Red de Monitorización Atmosférica de Salamanca (REDMAS). Los modelos propuestos plantean el uso de las variables meteorol ógicas como factores que influyen en la concentración de los contaminantes. La información utilizada durante el desarrollo de este trabajo corresponde a datos reales obtenidos de la REDMAS. En el Modelo Propuesto (MP) se aplican Redes Neuronales Artificiales (RNA) combinadas con algoritmos de agrupamiento. La RNA utilizada es el Perceptrón Multicapa con una capa oculta, utilizando estructuras independientes para la predicción de cada contaminante. Las variables meteorológicas disponibles para realizar la predicción fueron: Dirección de Viento (DV), Velocidad de Viento (VV), Temperatura (T) y Humedad Relativa (HR). Los algoritmos de agrupamiento K-means y Fuzzy C-means son utilizados para encontrar relaciones existentes entre los contaminantes atmosféricos en estudio y las variables meteorológicas. Dichas relaciones aportan información a las RNA para obtener la predicción de los contaminantes, la cual es agregada como entrada de las RNA. Los resultados del modelo propuesto en este trabajo son comparados con los resultados de una Regresión Lineal Multivariable (RLM) y un Perceptrón Multicapa (MLP). La evaluación de la predicción se realiza con el Error Medio Absoluto, la Raíz del Error Cuadrático Medio, el coeficiente de correlación y el índice de acuerdo. Los resultados obtenidos muestran la importancia de las variables meteorológicas en la predicción de la concentración de los contaminantes SO2 y PM10 en la ciudad de Salamanca, Gto., México. Los resultados muestran que el MP predice mejor la concentración de los contaminantes SO2 y PM10 que los modelos RLM y MLP. Los modelos implementados para cada caseta de monitorizaci ón tienen la capacidad para realizar predicciones de calidad del aire, estos modelos pueden ser implementados en un sistema que permita realizar la predicción en tiempo real y analizar el impacto en la salud de la población. Entre los principales resultados obtenidos del desarrollo de esta tesis podemos citar: Se propone un modelo basado en una red neuronal artificial combinado con algoritmos de agrupamiento para la predicción con una hora de anticipaci ón de la concentración de cada contaminante (SO2 y PM10). Se diseñó un modelo diferente para cada contaminante y para cada una de las tres casetas de monitorización de la REDMAS. Se propone un modelo de predicción del promedio de la concentración de las próximas 24 horas de los contaminantes SO2 y PM10, basado en una red neuronal artificial combinado con algoritmos de agrupamiento. Se diseñó un modelo para cada caseta de monitorización de la REDMAS y para cada contaminante por separado.

Comparison and Assessment of Two Emission inventories for the Madrid Region

Emission inventories are databases that aim to describe the polluting activities that occur across a certain geographic domain. According to the spatial scale, the availability of information will vary as well as the applied assumptions, which will strongly influence its quality, accuracy and representativeness. This study compared and contrasted two emission inventories describing the Greater Madrid Region (GMR) under an air quality simulation approach. The chosen inventories were the National Emissions Inventory (NEI) and the Regional Emissions Inventory of the Greater Madrid Region (REI). Both of them were used to feed air quality simulations with the CMAQ modelling system, and the results were compared with observations from the air quality monitoring network in the modelled domain. Through the application of statistical tools, the analysis of emissions at cell level and cell – expansion procedures, it was observed that the National Inventory showed better results for describing on – road traffic activities and agriculture, SNAP07 and SNAP10. The accurate description of activities, the good characterization of the vehicle fleet and the correct use of traffic emission factors were the main causes of such a good correlation. On the other hand, the Regional Inventory showed better descriptions for non – industrial combustion (SNAP02) and industrial activities (SNAP03). It incorporated realistic emission factors, a reasonable fuel mix and it drew upon local information sources to describe these activities, while NEI relied on surrogation and national datasets which leaded to a poorer representation. Off – road transportation (SNAP08) was similarly described by both inventories, while the rest of the SNAP activities showed a marginal contribution to the overall emissions.

A New Fast Motion Estimation and Mode Decision algorithm for H.264 Depth Maps encoding in Free Viewpoint TV

In this paper, we consider a scenario where 3D scenes are modeled through a View+Depth representation. This representation is to be used at the rendering side to generate synthetic views for free viewpoint video. The encoding of both type of data (view and depth) is carried out using two H.264/AVC encoders. In this scenario we address the reduction of the encoding complexity of depth data. Firstly, an analysis of the Mode Decision and Motion Estimation processes has been conducted for both view and depth sequences, in order to capture the correlation between them. Taking advantage of this correlation, we propose a fast mode decision and motion estimation algorithm for the depth encoding. Results show that the proposed algorithm reduces the computational burden with a negligible loss in terms of quality of the rendered synthetic views. Quality measurements have been conducted using the Video Quality Metric.

Modelado de redes SMATV mediante técnicas de medida TDA

En este Proyecto se pretende establecer la forma de realizar un análisis correcto y ajustado de las redes SMATV (Satellite Master Antenna Television), incluidas dentro de las ICT (Infraestructura Común de Telecomunicaciones), mediante el método de análisis TDA (Time Domain Analysis). Para ello, en primer lugar se procederá a hacer un estudio teórico sobre las ICT’s y sobre las bases en las que se sustenta el método de análisis TDA que sirva como puente introductorio al tema principal de este proyecto. Este tema es el de, mediante el programa de simulación AWR, caracterizar la señal más adecuada para realizar medidas de calidad en las redes SMATV mediante la técnica del TDA y ser capaz de realizar un estudio conciso de estas. Esto se pretende conseguir mediante la definición más correcta de los parámetros de la señal de entrada que se introduciría en la red en futuras medidas de prueba. Una vez conseguida una señal "tipo", se caracterizarán diferentes dispositivos o elementos que forman las redes SMATV para comprobar que la medida realizada con el método del TDA es igual de válida que realizada con el método de análisis vectorial de redes (VNA). ABSTRACT This project aims to establish how to perform a proper analysis and set of SMATV networks (Satellite Master Antenna Television), included within the ICT (Common Telecommunications Infrastructure) by the method of analysis TDA (Time Domain Analysis). To do this, first it will proceed to make a theoretical study on the ICT's and the basis on which the method of analysis TDA is based, introduction that serve as a bridge to the main issue of this project. This issue is about characterizing the most appropriate signal quality measurements in SMATV networks using the technique of AD through the AWR simulation program, and be able to make a concise study of these. This is intended to achieve through the proper definition of the parameters of the input signal, that would be introduced into the network in future test measures. Once achieved a signal "type", will be characterized different devices or elements forming SMATV networks to check that the measure on the TDA method is as valid as on the method of vector network analysis (VNA) .

Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico

Salamanca is cataloged as one of the most polluted cities in Mexico. In order to observe the behavior and clarify the influence of wind parameters on the Sulphur Dioxide (SO2) concentrations a Self-Organizing Maps (SOM) Neural Network have been implemented at three monitoring locations for the period from January 1 to December 31, 2006. The maximum and minimum daily values of SO2 concentrations measured during the year of 2006 were correlated with the wind parameters of the same period. The main advantages of the SOM Neural Network is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. For each monitoring location, SOM classifications were evaluated with respect to pollution levels established by Health Authorities. The classification system can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.

Modelling Saharan dust transport into the Mediterranean basin with CMAQ

The need for a better quantification of the influence of Saharan dust transport processes on the air quality modelling in the Mediterranean basin led to the formulation of a dust emission module (DEM) integrated into the Air Quality Risk Assessment System for the Iberian Peninsula (SERCA). This paper is focused on the formulation of DEM based on the GOCART aerosol model, along with its integration and execution into the air quality model. It also addresses the testing of the module and its evaluation by contrasting results against satellite products such as MODIS and CALIPSO and ground-level observations of aerosol optical thickness (AOT) and concentration levels of PM10 for different periods in July 2007. DEM was found capable of reproducing the spatial (horizontal and vertical) and temporal profiles of Saharan dust outbreaks into the Mediterranean basin and the Atlantic coast of Africa. Moreover, it was observed that its combination with CMAQ increased the correlation degree between observed and modelled PM10 concentrations at the selected monitoring locations. DEM also enhanced CMAQ capabilities to reproduce observed AOT, although significant underestimations remain. The implementation of CMAQ + DEM succeeded in capturing Saharan dust transport into the Iberian Peninsula, with contributions up to 25 and 14 μg m−3 in 1 h and 24 h average PM10 respectively. The general improvement of total PM10 predictions in Spain are however moderate. The analysis of model performance for the main PM components points out that remaining PM10 underestimation is due to dust local sources missing in the inventories and misrepresentation of organic aerosol processes, which constitutes the main areas for future improvement of CMAQ capabilities to simulate particulate matter within SERCA.

Comparasion of road traffic emission models in Madrid (Spain)

Many cities in Europe have difficulties to meet the air quality standards set by the European legislation, most particularly the annual mean Limit Value for NO2. Road transport is often the main source of air pollution in urban areas and therefore, there is an increasing need to estimate current and future traffic emissions as accurately as possible. As a consequence, a number of specific emission models and emission factors databases have been developed recently. They present important methodological differences and may result in largely diverging emission figures and thus may lead to alternative policy recommendations. This study compares two approaches to estimate road traffic emissions in Madrid (Spain): the COmputer Programme to calculate Emissions from Road Transport (COPERT4 v.8.1) and the Handbook Emission Factors for Road Transport (HBEFA v.3.1), representative of the ‘average-speed’ and ‘traffic situation’ model types respectively. The input information (e.g. fleet composition, vehicle kilometres travelled, traffic intensity, road type, etc.) was provided by the traffic model developed by the Madrid City Council along with observations from field campaigns. Hourly emissions were computed for nearly 15 000 road segments distributed in 9 management areas covering the Madrid city and surroundings. Total annual NOX emissions predicted by HBEFA were a 21% higher than those of COPERT. The discrepancies for NO2 were lower (13%) since resulting average NO2/NOX ratios are lower for HBEFA. The larger differences are related to diesel vehicle emissions under “stop & go” traffic conditions, very common in distributor/secondary roads of the Madrid metropolitan area. In order to understand the representativeness of these results, the resulting emissions were integrated in an urban scale inventory used to drive mesoscale air quality simulations with the Community Multiscale Air Quality (CMAQ) modelling system (1 km2 resolution). Modelled NO2 concentrations were compared with observations through a series of statistics. Although there are no remarkable differences between both model runs, the results suggest that HBEFA may overestimate traffic emissions. However, the results are strongly influenced by methodological issues and limitations of the traffic model. This study was useful to provide a first alternative estimate to the official emission inventory in Madrid and to identify the main features of the traffic model that should be improved to support the application of an emission system based on “real world” emission factors.

Levels, sources and seasonality of coarse particles (PM10-PM2.5) in three European capitals e implications for particulate pollution control

Coarse particles of aerodynamic diameter between 2.5 and 10 mm (PMc) are produced by a range of natural (windblown dust and sea sprays) and anthropogenic processes (non-exhaust vehicle emissions, industrial, agriculture, construction and quarrying activities). Although current ambient air quality regulations focus on PM2.5 and PM10, coarse particles are of interest from a public health point of view as they have been associated with certain mortality and morbidity outcomes. In this paper, an analysis of coarse particle levels in three European capitals (London, Madrid and Athens) is presented and discussed. For all three cities we analysed data from both traffic and urban background monitoring sites. The results showed that the levels of coarse particles present significant seasonal, weekly and daily variability. Their wind driven and non-wind driven resuspension as well as their roadside increment due to traffic were estimated. Both the local meteorological conditions and the air mass history indicating long-range atmospheric transport of particles of natural origin are significant parameters that influence the levels of coarse particles in the three cities especially during episodic events.

Pollutant concentrations and Meteorological data classification by Neural Networks

This paper present an environmental contingency forecasting tool based on Neural Networks (NN). Forecasting tool analyzes every hour and daily Sulphur Dioxide (SO2) concentrations and Meteorological data time series. Pollutant concentrations and meteorological variables are self-organized applying a Self-organizing Map (SOM) NN in different classes. Classes are used in training phase of a General Regression Neural Network (GRNN) classifier to provide an air quality forecast. In this case a time series set obtained from Environmental Monitoring Network (EMN) of the city of Salamanca, Guanajuato, México is used. Results verify the potential of this method versus other statistical classification methods and also variables correlation is solved.