The rapid atmospheric monitoring system of the Pierre Auger Observatory

The Pierre Auger Observatory is a facility built to detect air showers produced by cosmic rays above 10(17) eV. During clear nights with a low illuminated moon fraction, the UV fluorescence light produced by air showers is recorded by optical telescopes at the Observatory. To correct the observations for variations in atmospheric conditions, atmospheric monitoring is performed at regular intervals ranging from several minutes (for cloud identification) to several hours (for aerosol conditions) to several days (for vertical profiles of temperature, pressure, and humidity). In 2009, the monitoring program was upgraded to allow for additional targeted measurements of atmospheric conditions shortly after the detection of air showers of special interest, e. g., showers produced by very high-energy cosmic rays or showers with atypical longitudinal profiles. The former events are of particular importance for the determination of the energy scale of the Observatory, and the latter are characteristic of unusual air shower physics or exotic primary particle types. The purpose of targeted (or "rapid") monitoring is to improve the resolution of the atmospheric measurements for such events. In this paper, we report on the implementation of the rapid monitoring program and its current status. The rapid monitoring data have been analyzed and applied to the reconstruction of air showers of high interest, and indicate that the air fluorescence measurements affected by clouds and aerosols are effectively corrected using measurements from the regular atmospheric monitoring program. We find that the rapid monitoring program has potential for supporting dedicated physics analyses beyond the standard event reconstruction.

Description of atmospheric conditions at the Pierre Auger Observatory using the Global Data Assimilation System (GDAS)

Atmospheric conditions at the site of a cosmic ray observatory must be known for reconstructing observed extensive air showers. The Global Data Assimilation System (GDAS) is a global atmospheric model predicated on meteorological measurements and numerical weather predictions. GDAS provides altitude-dependent profiles of the main state variables of the atmosphere like temperature, pressure, and humidity. The original data and their application to the air shower reconstruction of the Pierre Auger Observatory are described. By comparisons with radiosonde and weather station measurements obtained on-site in Malargue and averaged monthly models, the utility of the GDAS data is shown. (C) 2012 Elsevier B.V. All rights reserved.

Atmospheric concentrations of organic pollutants in the Arctic between 1993-2006

Continuous and comparable atmospheric monitoring programs to study the transport and occurrence of persistent organic pollutants (POPs) in the atmosphere of remote regions is essential to better understand the global movement of these chemicals and to evaluate the effectiveness of international control measures. Key results from four main Arctic research stations, Alert (Canada), Pallas (Finland), Storhofdi (Iceland) and Zeppelin (Svalbard/Norway), where long-term monitoring have been carried out since the early 1990s, are summarized. We have also included a discussion of main results from various Arctic satellite stations in Canada, Russia, US (Alaska) and Greenland which have been operational for shorter time periods. Using the Digital Filtration temporal trend development technique, it was found that while some POPs showed more or less consistent declines during the 1990s, this reduction is less apparent in recent years at some sites. In contrast, polybrominated diphenyl ethers (PBDEs) were still found to be increasing by 2005 at Alert with doubling times of 3.5 years in the case of deca-BDE. Levels and patterns of most POPs in Arctic air are also showing spatial variability, which is typically explained by differences in proximity to suspected key source regions and long-range atmospheric transport potentials. Furthermore, increase in worldwide usage of certain pesticides, e.g. chlorothalonil and quintozene, which are contaminated with hexachlorobenzene (HCB), may result in an increase in Arctic air concentration of HCB. The results combined also indicate that both temporal and spatial patterns of POPs in Arctic air may be affected by various processes driven by climate change, such as reduced ice cover, increasing seawater temperatures and an increase in biomass burning in boreal regions as exemplified by the data from the Zeppelin and Alert stations. Further research and continued air monitoring are needed to better understand these processes and its future impact on the Arctic environment.

Estudo do transporte atmosférico de Mp10 e SO2 com os Modelos WRF/CMAQ em regiões costeiras urbanas

O objetivo principal deste trabalho foi o estudo do transporte atmosférico de PM10 e SO2 em regiões costeiras urbanas usando modelos WRF/CMAQ. Duas regiões foram contempladas neste estudo. Uma é a Região da Grande Vitória (RGV), no estado do Espírito Santo, Brasil; a outra é a Região da Grande Dunkerque (RGD), no Departamento Nord Pas-de-Calais, França. A RGV é cercada por uma cadeia de montanhas paralela à costa, resultando num topografia complexa e acidentada. Já a RGD possui uma topografia muito mais suave. As entradas de dados para os modelos WRF/CMAQ englobaram o inventário de emissões de poluentes atmosféricos do IEMA-ES para a RGV, e o inventário de emissões no nível do solo de Nord Pas-de-Calais denominado ―Cadastre_totaux_3km_A2008 _M2010_V2_SNAPN2‖ para a RGD. Ambos os inventários apresentaram restrições, todavia. O inventário da RGV apresentou valores de ressuspensão em vias de tráfego elevados, em comparação com diversos estudos, e teve esses dados modificados. Os dados no nível do solo e a grande área de das células da grade (9 km2) do inventário da RGD não permitiram resultados satisfatórios de modelagem. A validação dos modelos foi realizada por comparação com resultados obtidos em duas campanhas experimentais: uma na cidade de Dunkerque, no norte da França, em setembro de 2009; a outra na cidade de Vitória, no sudeste do Brasil, em julho de 2012. Esses dados foram obtidos pelo uso de sistemas de Light Detection and Ranging (LIDAR) e Sonic Detection and Ranging (SODAR), bem como de Estações Meteorológicas de Superfície (EMS) e de monitoramento atmosférico. Os resultados deste trabalho mostraram que: a) existe uma necessidade de melhorias contínuas nos inventários regionais de emissões, adaptando-os para condições locais específicas e focando na obtenção de parâmetros necessários para modelagem fotoquímica; b) os valores de módulo e direção das velocidades obtidas na modelagem meteorológica influenciam fortemente os resultados da modelagem de concentração de poluentes; c) a qualidade do ar tanto na RGV quanto na RGD merece atenção, sobretudo no que diz respeito às concentrações de MP10. De acordo com os dados das estações de monitoramento, a situação parece mais crítica na RGD; d) a modelagem da RGV apresentou resultados mais satisfatórios do que a da RGD, de acordo com os resultados das validações; e) a entrada da brisa do mar provocou alterações significativas na concentração dos poluentes, o que pôde ser observado na análise da dinâmica da dispersão de MP10 e SO2. Esse fenômeno foi mais marcante na RGV, onde a entrada da brisa marítima provocou um movimento oscilatório na pluma de poluição, levando-a para os bairros mais densamente povoados do conglomerado urbano. Na RGD, a entrada da brisa não foi cotidiana e, no dia em que ela aconteceu, houve uma alteração de quase 180º na direção do movimento da pluma de poluição. Além do aumento da turbulência vertical, o qual já foi estudado por diversos autores, este estudo focou também na influência brisa do mar na dinâmica da pluma de dispersão de poluentes atmosféricos em regiões costeiras.

Online analysis of H2S and SO2 via advanced mid-Infrared gas sensors

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Identifying clouds over the Pierre Auger Observatory using infrared satellite data

We describe a new method of identifying night-time clouds over the Pierre Auger Observatory using infrared data from the Imager instruments on the GOES-12 and GOES-13 satellites. We compare cloud identifications resulting from our method to those obtained by the Central Laser Facility of the Auger Observatory. Using our new method we can now develop cloud probability maps for the 3000 km2 of the Pierre Auger Observatory twice per hour with a spatial resolution of ∼2.4 km by ∼5.5 km. Our method could also be applied to monitor cloud cover for other ground-based observatories and for space-based observatories.

Enhanced seasonal CO_2 exchange caused by amplified plant productivity in northern ecosystems, link to model results

Atmospheric monitoring of high northern latitudes (> 40°N) has shown an enhanced seasonal cycle of carbon dioxide (CO2) since the 1960s but the underlying mechanisms are not yet fully understood. The much stronger increase in high latitudes compared to low ones suggests that northern ecosystems are experiencing large changes in vegetation and carbon cycle dynamics. Here we show that the latitudinal gradient of the increasing CO2 amplitude is mainly driven by positive trends in photosynthetic carbon uptake caused by recent climate change and mediated by changing vegetation cover in northern ecosystems. Our results emphasize the importance of climate-vegetation-carbon cycle feedbacks at high latitudes, and indicate that during the last decades photosynthetic carbon uptake has reacted much more strongly to warming than carbon release processes.

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.

Table 4. Trace gas concentrations from samples taken at Ginninderra

We assess the performance of an inverse Lagrangian dispersion technique for its suitability to quantify leakages from geological storage of CO2. We find the technique is accurate ((QbLS/Q)=0.99, sigma=0.29) when strict meteorological filtering is applied to ensure that Monin-Obukhov Similarity Theory is valid for the periods analysed and when downwind enrichments in tracer gas concentration are 1% or more above background concentration. Because of their respective baseline atmospheric concentrations, this enrichment criterion is less onerous for CH4 than for CO2. Therefore for geologically sequestered gas reservoirs with a significant CH4 component, monitoring CH4 as a surrogate for CO2 leakage could be as much as 10 times more sensitive than monitoring CO2 alone. Additional recommendations for designing a robust atmospheric monitoring strategy for geosequestration include: continuous concentration data; exact inter-calibration of up- and downwind concentration measurements; use of an array of point concentration sensors to maximise the use of spatial information about the leakage plume; and precise isotope ratio measurement to confirm the source of any concentration elevations detected.

Atmospheric pollutants monitoring by analysis of epiphytic lichens

The Canoparmelia texana epiphytic lichenized fungi was used to monitor atmospheric pollution in the Sao Paulo metropolitan region, SP, Brazil. The cluster analysis applied to the element concentration values confirmed the site groups of different levels of pollution due to industrial and vehicular emissions. In the distribution maps of element concentrations, higher concentrations of Ba and Mn were observed in the vicinity of industries and of a petrochemical complex. The highest concentration of Co found in lichens from the Sao Miguel Paulista site is due to the emissions from a metallurgical processing plant that produces this element. For Br and Zn, the highest concentrations could be associated both to vehicular and industrial emissions. Exploratory analyses revealed that the accumulation of toxic elements in C. texana may be of use in evaluating the human risk of cardiopulmonary mortality due to prolonged exposure to ambient levels of air pollution. (c) 2007 Elsevier Ltd. All rights reserved.