MILAGRO (Megacity Initiative: Local And Global Research Observations) is an international collaborative project to examine the behavior and the export of atmospheric emissions from a megacity. The Mexico City Metropolitan Area (MCMA) – one of the world's largest megacities and North America's most populous city – was selected as the case study to characterize the sources, concentrations, transport, and transformation processes of the gases and fine particles emitted to the MCMA atmosphere and to evaluate the regional and global impacts of these emissions. The findings of this study are relevant to the evolution and impacts of pollution from many other megacities. The measurement phase consisted of a month-long series of carefully coordinated observations of the chemistry and physics of the atmosphere in and near Mexico City during March 2006, using a wide range of instruments at ground sites, on aircraft and satellites, and enlisting over 450 scientists from 150 institutions in 30 countries. Three ground supersites were set up to examine the evolution of the primary emitted gases and fine particles. Additional platforms in or near Mexico City included mobile vans containing scientific laboratories and mobile and stationary upward-looking lidars. Seven instrumented research aircraft provided information about the atmosphere over a large region and at various altitudes. Satellite-based instruments peered down into the atmosphere, providing even larger geographical coverage. The overall campaign was complemented by meteorological forecasting and numerical simulations, satellite observations and surface networks. Together, these research observations have provided the most comprehensive characterization of the MCMA's urban and regional atmospheric composition and chemistry that will take years to analyze and evaluate fully. In this paper we review over 120 papers resulting from the MILAGRO/INTEX-B Campaign that have been published or submitted, as well as relevant papers from the earlier MCMA-2003 Campaign, with the aim of providing a road map for the scientific community interested in understanding the emissions from a megacity such as the MCMA and their impacts on air quality and climate. This paper describes the measurements performed during MILAGRO and the results obtained on MCMA's atmospheric meteorology and dynamics, emissions of gases and fine particles, sources and concentrations of volatile organic compounds, urban and regional photochemistry, ambient particulate matter, aerosol radiative properties, urban plume characterization, and health studies. A summary of key findings from the field study is presented.

Urban air pollution is a serious worldwide problem due to its impact on human health. In the past sixty years, growing evidence established a correlation between exposure to air pollutants and the developing of severe respiratory diseases. Recently Particulate matter (PM) is drawing more public attention to various aspects including historical backgrounds, physicochemical characteristics and its pathological role. Therefore, this review is focused on these aspects. The most famous air pollution disaster happened in London on December 1952; it has been calculated that more than 4000 deaths occurred during this event. Air pollution is a complex mix of gases and particles. Gaseous pollutants disseminate deeply into the alveoli, allowing its diffusion through the blood-air barrier to several organs. Meanwhile, PM is a mix of solid or liquid particles suspended in the air. PM is deposited at different levels of the respiratory tract, depending on its size: Coarse particles (PM10) in upper airways and fine particles (PM2.5) can be accumulated in the lung parenchyma, inducing several respiratory diseases. Additionally to size, the composition of particulate matter has been associated with different toxicological outcomes on clinical, epidemiological, as well as in vivo and in vitro animal and human studies. PM can be constituted by organic, inorganic and biological compounds. All these compounds are capable of modifying several biological activities including alterations in cytokine production, coagulation factors balance, pulmonary function, respiratory symptoms, and cardiac function. It can also generate different modifications during its passage through the airways, like inflammatory cells recruitment, with the release of cytokines and reactive oxygen species (ROS). These inflammatory mediators can activate different pathways such as MAP-kinases, NF-B, and stat-1, or induce DNA adducts. All these alterations can mediate obstructive or restrictive respiratory diseases like asthma, COPD, pulmonary fibrosis and even, cancer. In 2013, outdoor air pollution was classified as Group 1 by IARC based on all research studies data about air pollution effects. Therefore it is important to understand how PM composition can generate several pulmonary pathologies.

Data measuring airborne pollutants, public health and environmental factors are increasingly being stored and merged. These big datasets offer great potential, but also challenge traditional epidemiological methods. This has motivated the exploration of alternative methods to make predictions, find patterns and extract information. To this end, data mining and machine learning algorithms are increasingly being applied to air pollution epidemiology. We conducted a systematic literature review on the application of data mining and machine learning methods in air pollution epidemiology. We carried out our search process in PubMed, the MEDLINE database and Google Scholar. Research articles applying data mining and machine learning methods to air pollution epidemiology were queried and reviewed. Our search queries resulted in 400 research articles. Our fine-grained analysis employed our inclusion/exclusion criteria to reduce the results to 47 articles, which we separate into three primary areas of interest: 1) source apportionment; 2) forecasting/prediction of air pollution/quality or exposure; and 3) generating hypotheses. Early applications had a preference for artificial neural networks. In more recent work, decision trees, support vector machines, k-means clustering and the APRIORI algorithm have been widely applied. Our survey shows that the majority of the research has been conducted in Europe, China and the USA, and that data mining is becoming an increasingly common tool in environmental health. For potential new directions, we have identified that deep learning and geo-spacial pattern mining are two burgeoning areas of data mining that have good potential for future applications in air pollution epidemiology. We carried out a systematic review identifying the current trends, challenges and new directions to explore in the application of data mining methods to air pollution epidemiology. This work shows that data mining is increasingly being applied in air pollution epidemiology. The potential to support air pollution epidemiology continues to grow with advancements in data mining related to temporal and geo-spacial mining, and deep learning. This is further supported by new sensors and storage mediums that enable larger, better quality data. This suggests that many more fruitful applications can be expected in the future.

Environmental Health Perspectives is an Open Access journal published by the National Institute of Environmental Health Sciences. Environmental Health Perspectives is an Open Access journal published by the National Institute of Environmental Health Sciences.

The health impacts of air pollution have received much attention and have recently been subject to extensive study. Exposure to air pollutants such as particulate matter (PM) has been linked to lung and cardiovascular disease and increases in both hospital admissions and mortality. However, little attention has been given to the effects of air pollution on the intestine. The recent discovery of genes linked to susceptibility to inflammatory bowel diseases (IBD) explains only a fraction of the hereditary variance for these diseases. This, together with evidence of increases in incidence of IBD in the past few decades of enhanced industrialization, suggests that environmental factors could contribute to disease pathogenesis. Despite this, little research has examined the potential contribution of air pollution and its components to intestinal disease. Exposure of the bowel to air pollutants occurs via mucociliary clearance of PM from the lungs as well as ingestion via food and water sources. Gaseous pollutants may also induce systemic effects. Plausible mechanisms mediating the effects of air pollutants on the bowel could include direct effects on epithelial cells, systemic inflammation and immune activation, and modulation of the intestinal microbiota. Although there is limited epidemiologic evidence to confirm this, we suggest that a link between air pollution and intestinal disease exists and warrants further study. This link may explain, at least in part, how environmental factors impact on IBD epidemiology and disease pathogenesis.