Vehicle emissions and PM(2.5) mass concentrations in six Brazilian cities

In Brazil, the principal source of air pollution is the combustion of fuels (ethanol, gasohol, and diesel). In this study, we quantify the contributions that vehicle emissions make to the urban fine particulate matter (PM(2.5)) mass in six state capitals in Brazil, collecting data for use in a larger project evaluating the impact of air pollution on human health. From winter 2007 to winter 2008, we collected 24-h PM(2.5) samples, employing gravimetry to determine PM(2.5) mass concentrations; reflectance to quantify black carbon concentrations; X-ray fluorescence to characterize elemental composition; and ion chromatography to determine the composition and concentrations of anions and cations. Mean PM(2.5) concentrations in the cities of São Paulo, Rio de Janeiro, Belo Horizonte, Curitiba, Porto Alegre, and Recife were 28, 17.2, 14.7, 14.4, 13.4, and 7.3 μg/m(3), respectively. In São Paulo and Rio de Janeiro, black carbon explained approximately 30% of the PM(2.5) mass. We used receptor models to identify distinct source-related PM(2.5) fractions and correlate those fractions with daily mortality rates. Using specific rotation factor analysis, we identified the following principal contributing factors: soil and crustal material; vehicle emissions and biomass burning (black carbon factor); and fuel oil combustion in industries (sulfur factor). In all six cities, vehicle emissions explained at least 40% of the PM(2.5) mass. Elemental composition determination with receptor modeling proved an adequate strategy to identify air pollution sources and to evaluate their short- and long-term effects on human health. Our data could inform decisions regarding environmental policies vis-à-vis health care costs.

G protein-coupled receptors in arthropod vectors: omics and pharmacological approaches to elucidate ligand-receptor interactions and novel organismal functions

Regulation of many physiological processes in animals, certainly those controlled by neuropeptide hormones, involves G protein-coupled receptors (GPCRs). Our work focusing on endocrine regulation of diuresis and water balance in mosquitoes and ticks started in 1997 with the kinin receptor, at the dawn of the omics era. After the genomic revolution, we began work on the endocrinology of reproduction in the red imported fire ant. We will use the template of this comparative work to summarize key points about GPCRs and signaling, and emphasize the most recent developments in the pharmacology of arthropod neuropeptide GPCRs. We will discuss omics' contributions to the advancement of this field, and its influence on peptidomimetic design while emphasizing work on blood feeding arthropods.

Exploration of the composition and sources of urban fine particulate matter associated with same-day cardiovascular health effects in Dearborn, Michigan

The objective was to explore associations of chemical components and source factors of ambient fine particulate matter (aerodynamic diameter ≤ 2.5 μm; PM2.5) with cardiovascular (CV) changes following same-day exposure to ambient PM2.5. Twenty-five healthy adults living in rural Michigan were exposed to ambient air in an urban/industrial community for 4 to 5 h daily for five consecutive days. CV health outcomes were measured 1-2 h post exposure. Contributing emission sources were identified via positive matrix factorization. We examined associations between PM2.5 mass, composition and source factors, and same-day changes in CV outcomes using mixed-model analyses. PM2.5 mass (10.8 ± 6.8 μg/m(3)), even at low ambient levels, was significantly associated with increased heart rate (HR). Trace elements as well as secondary aerosol, diesel/urban dust and iron/steel manufacturing factors potentially explained the HR changes. However, trace element analysis demonstrated additional associations with other CV responses including changes in blood pressure (BP), arterial compliance, autonomic balance and trends toward reductions in endothelial function. Two factors were related to BP changes (diesel/urban dust, motor vehicle) and trends toward impaired endothelial function (diesel/urban dust). This study indicates composition of PM2.5 and its sources may contribute to CV health effects independently of PM2.5 mass.

Factorization methods applied to characterize the sources of volatile organic compounds in Montreal, Quebec

OBJECTIVES

The study objective was to assemble emission characteristics of the sources of the ambient volatile organic compounds (VOCs) and to elaborate methods of organizing them into the sources' chemical profiles.

MATERIAL AND METHODS

The UNMIX--sensor modeling method from the U.S. Environment Protection Agency (EPA)--was used to process the VOC concentration data acquired over the years 2000-2009 for 175 VOC species in 4 air quality monitoring stations in Montreal, Quebec.

RESULTS

The method enabled to assess VOC emissions from the typically distributed sources existing in urban environment and VOC occurrences characterizing the local, or point-like, sources. The distributed sources were inextricably associated with hydrocarbons from exhaust, heavier hydrocarbons from contaminated urban soil, fugitive evaporations of gasoline and liquefied petroleum gases (LPG), leakage from the industrial and commercial use of solvents, and the inert, ozone depleting gases permeating urban atmosphere. The sources' profiles were charted involving 60-120 VOC species per source. Spatial distribution of the sources was examined.

CONCLUSIONS

The UNMIX application and the source profiling methods, by building robust chemical profiles of VOC sources, provided information that can be used to assign the measured VOC emissions to physical sources. This, in turn, provides means of assessing the impact of environmental policies, on one hand, and of industrial activities on the other hand on VOC air pollution.

UNMIX Methods Applied to Characterize Sources of Volatile Organic Compounds in Toronto, Ontario

UNMIX, a sensor modeling routine from the U.S. Environmental Protection Agency (EPA), was used to model volatile organic compound (VOC) receptors in four urban sites in Toronto, Ontario. VOC ambient concentration data acquired in 2000-2009 for 175 VOC species in four air quality monitoring stations were analyzed. UNMIX, by performing multiple modeling attempts upon varying VOC menus-while rejecting the results that were not reliable-allowed for discriminating sources by their most consistent chemical characteristics. The method assessed occurrences of VOCs in sources typical of the urban environment (traffic, evaporative emissions of fuels, banks of fugitive inert gases), industrial point sources (plastic-, polymer-, and metalworking manufactures), and in secondary sources (releases from water, sediments, and contaminated urban soil). The remote sensing and robust modeling used here produces chemical profiles of putative VOC sources that, if combined with known environmental fates of VOCs, can be used to assign physical sources' shares of VOCs emissions into the atmosphere. This in turn provides a means of assessing the impact of environmental policies on one hand, and industrial activities on the other hand, on VOC air pollution.

More than meets the AI: The possibilities and limits of machine learning in olfaction

Can machine learning crack the code in the nose? Over the past decade, studies tried to solve the relation between chemical structure and sensory quality with Big Data. These studies advanced computational models of the olfactory stimulus, utilizing artificial intelligence to mine for clear correlations between chemistry and psychophysics. Computational perspectives promised to solve the mystery of olfaction with more data and better data processing tools. None of them succeeded, however, and it matters as to why this is the case. This article argues that we should be deeply skeptical about the trend to black-box the sensory system's biology in our theories of perception. Instead, we need to ground both stimulus models and psychophysical data on real causal-mechanistic explanations of the olfactory system. The central question is: Would knowledge of biology lead to a better understanding of the stimulus in odor coding than the one utilized in current machine learning models? That is indeed the case. Recent studies about receptor behavior have revealed that the olfactory system operates by principles not captured in current stimulus-response models. This may require a fundamental revision of computational approaches to olfaction, including its psychological effects. To analyze the different research programs in olfaction, we draw on Lloyd's "Logic of Research Questions," a philosophical framework which assists scientists in explicating the reasoning, conceptual commitments, and problems of a modeling approach in question.