Air pollution and mortality in Madrid, Spain: a time-series analysis

Odriozola, Juan Carlos; Jiménez, Julio Díaz; Rubio, Jesús; Mirón, I.J.; Ortiz, Melcor; Rodrigues, Priya

doi:10.1007/s004200050321

Cited by 31 publications

(2 citation statements)

References 27 publications

(42 reference statements)

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“…The Institute of Medicine report on burn pits also suggests high levels of particulate matter were in ambient air [1]. Particulate matter air pollution in and of itself is associated with both cardiovascular and respiratory disease [7-14]. …”

Section: Introductionmentioning

confidence: 99%

Occupational Lung Diseases among Soldiers Deployed to Iraq and Afghanistan

Szema

2013

Occup Med Health Aff

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Military personnel deployed to Iraq and Afghanistan, from 2004 to the present, has served in a setting of unique environmental conditions. Among these are exposures to burning trash in open air “burn pits” lit on fire with jet fuel JP-8. Depending on trash burned--water bottles, styrofoam trays, medical waste, unexploded munitions, and computers--toxins may be released such as dioxins and n-hexane and benzene. Particulate matter air pollution culminates from these fires and fumes. Additional environmental exposures entail sandstorms (Haboob, Shamal, and Sharqi) which differ in direction and relationship to rain. These wars saw the first use of improvised explosive devices (roadside phosphate bombs),as well as vehicle improvised explosive devices (car bombs), which not only potentially aerosolize metals, but also create shock waves to induce lung injury via blast overpressure. Conventional mortar rounds are also used by Al Qaeda in both Iraq and Afghanistan. Outdoor aeroallergens from date palm trees are prevalent in southern Iraq by the Tigris and Euphrates rivers, while indoor aeroallergen aspergillus predominates during the rainy season. High altitude lung disease may also compound the problem, particularly in Kandahar, Afghanistan. Clinically, soldiers may present with new-onset asthma or fixed airway obstruction. Some have constrictive bronchiolitis and vascular remodeling on open lung biopsy - despite having normal spirometry and chest xrays and CT scans of the chest. Others have been found to have titanium and other metals in the lung (rare in nature). Still others have fulminant biopsy-proven sarcoidiosis. We found DNA probe–positive Mycobacterium Avium Complex in lung from a soldier who had pneumonia, while serving near stagnant water and camels and goats outside Abu Gharib. This review highlights potential exposures, clinical syndromes, and the Denver Working Group recommendations on post-deployment health.

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Section: Introductionmentioning

confidence: 99%

Occupational Lung Diseases among Soldiers Deployed to Iraq and Afghanistan

Szema

2013

Occup Med Health Aff

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“…Air quality and public health in urban areas are two aspects of a single problem. Scientific literature showing the relationship between both aspects is quite extensive (Alberdi Odriozola et al, 1998; Díaz et al, 1999; Linares, Díaz, Tobías, Miguel, and Otero, 2006; Nel, 2005). For this reason monitoring systems have been deployed in cities and their outskirts, while at the same time numerous laws and protocols have been implemented to reduce the level of certain chemical components: NO 2 , O 3 , CO, particulate matter, benzene and others.…”

Section: Introductionmentioning

confidence: 99%

Detection of anomalous episodes in urban Ozone maps

Cárdenas‐Montes

2020

Expert Systems

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In addition to classification and regression, outlier detection has emerged as a relevant activity in deep learning. In comparison with previous approaches where the original features of the examples were used for separating the examples with high dissimilarity from the rest of the examples, deep learning can automatically extract useful features from raw data, thus removing the need for most of the feature engineering efforts usually required with classical machine learning approaches. This requires training the deep learning algorithm with labels identifying the examples or with numerical values. Although outlier detection in deep learning has been usually undertaken by training the algorithm with categorical labels-classifier-, it can also be performed by using the algorithm as regressor. Nowadays numerous urban areas have deployed a network of sensors for monitoring multiple variables about air quality. The measurements of these sensors can be treated individually-as time series-or collectively. Collectively, a variable monitored by a network of sensors can be transformed into a map. Maps can be used as images in machine learning algorithms-including computer vision algorithms-for outlier detection. The identification of anomalous episodes in air quality monitoring networks allows later processing this time period with finer-grained scientific packages involving fluid dynamic and chemical evolution software, or the identification of malfunction stations. In this work, a Convolutional Neural Network is trained-as a regressor-using as input Ozone-urban images generated from the Air Quality Monitoring Network of Madrid (Spain). The learned features are processed by Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for identifying anomalous maps. Comparisons with other deep learning architectures are undertaken, for instance, autoencoders-undercomplete and denoizing-for learning salient features of the maps and later to use as input of DBSCAN. The proposed approach is able efficiently find maps with local anomalies compared to other approaches based on raw images or latent features extracted with autoencoders architectures with DBSCAN.

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Bayesian analysis of dynamic factor models: an application to air pollution and mortality in São Paulo, Brazil

Sáfadi¹,

Peña²

2007

Environmetrics

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SUMMARYThe Bayesian estimation of a dynamic factor model where the factors follow a multivariate autoregressive model is presented. We derive the posterior distributions for the parameters and the factors and use Monte Carlo methods to compute them. The model is applied to study the association between air pollution and mortality in the city of São Paulo, Brazil. Statistical analysis was performed through a Bayesian analysis of a dynamic factor model. The series considered were minimal temperature, relative humidity, air pollutant of PM 10 and CO, mortality circulatory disease and mortality respiratory disease. We found a strong association between air pollutant (PM 10 ), Humidity and mortality respiratory disease for the city of São Paulo.

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Air pollution and mortality in Madrid, Spain: a time-series analysis

Abstract: These results support the hypothesis of an association between pollution levels and mortality between 1986-1992 in Madrid. Additional measures designed to reduce pollution levels without compromising thermal comfort should be implemented.

Cited by 31 publications

References 27 publications

Occupational Lung Diseases among Soldiers Deployed to Iraq and Afghanistan

Occupational Lung Diseases among Soldiers Deployed to Iraq and Afghanistan

Detection of anomalous episodes in urban Ozone maps

Bayesian analysis of dynamic factor models: an application to air pollution and mortality in São Paulo, Brazil

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