932 resultados para PARTICULATE AIR-POLLUTION


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Human exposures in transportation microenvironments are poorly represented by ambient stationary monitoring. A number of on-road studies using vehicle-based mobile monitoring have been conducted to address this. Most previous studies were conducted on urban roads in developed countries where the primary emission source was vehicles. Few studies have examined on-road pollution in developing countries in urban settings. Currently, no study has been conducted for roadways in rural environments where a substantial proportion of the population live. This study aimed to characterize on-road air quality on the East-West Highway (EWH) in Bhutan and identify its principal sources. We conducted six mobile measurements of PM10, particle number (PN) count and CO along the entire 570 km length of the EWH. We divided the EWH into five segments, R1-R5, taking the road length between two district towns as a single road segment. The pollutant concentrations varied widely along the different road segments, with the highest concentrations for R5 compared with other road segments (PM10 = 149 µg/m3, PN = 5.74 × 104 particles/cm-3, CO = 0.19 ppm), which is the final segment of the road to the capital. Apart from vehicle emissions, the dominant sources were road works, unpaved roads and roadside combustion activities. Overall, the highest contributions above the background levels were made by unpaved roads for PM10 (6 times background), and vehicle emissions for PN and CO (5 and 15 times background, respectively). Notwithstanding the differences in instrumentation used and particle size range measured, the current study showed lower PN concentrations compared with similar on-road studies. However, concentrations were still high enough that commuters, road maintenance workers and residents living along the EWH, were potentially exposed to elevated pollutant concentrations from combustion and non-combustion sources. Future studies should focus on assessing the dispersion patterns of roadway pollutants and defining the short- and long-term health impacts of exposure in Bhutan, as well as in other developing countries with similar characteristics.

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Limited studies have examined the associations between air pollutants [particles with diameters of 10um or less (PM10), sulfur dioxide (SO2), and nitrogen dioxide (NO2)] and fasting blood glucose (FBG). We collected data for 27,685 participants who were followed during 2006 and 2008. Generalized Estimating Equation models were used to examine the effects of air pollutants on FBG while controlling for potential confounders. We found that increased exposure to NO2, SO2 and PM10 was significantly associated with increased FBG levels in single pollutant models (p<0.001). For exposure to 4 days’ average of concentrations, a 100 µg/m3 increase in SO2, NO2, and PM10 was associated with 0.17 mmol/L (95%CI: 0.15–0.19), 0.53 mmol/L (95%CI: 0.42–0.65), and 0.11 mmol/L (95%CI: 0.07–0.15) increase in FBG, respectively. In the multi-pollutant models, the effects of SO2 were enhanced, while the effects of NO2 and PM10 were alleviated. The effects of air pollutants on FBG were stronger in female, elderly, and overweight people than in male, young and underweight people. In conclusion, the findings suggest that air pollution increases the levels of FBG. Vulnerable people should pay more attention on highly polluted days to prevent air pollution-related health issues.

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Exposure to ambient air pollution is a major risk factor for global disease. Assessment of the impacts of air pollution on population health and the evaluation of trends relative to other major risk factors requires regularly updated, accurate, spatially resolved exposure estimates. We combined satellite-based estimates, chemical transport model (CTM) simulations and ground measurements from 79 different countries to produce new global estimates of annual average fine particle (PM2.5) and ozone concentrations at 0.1° × 0.1° spatial resolution for five-year intervals from 1990-2010 and the year 2013. These estimates were then applied to assess population-weighted mean concentrations for 1990 – 2013 for each of 188 countries. In 2013, 87% of the world’s population lived in areas exceeding the World Health Organization (WHO) Air Quality Guideline of 10 μg/m3 PM2.5 (annual average). Between 1990 and 2013, decreases in population-weighted mean concentrations of PM2.5 were evident in most high income countries, in contrast to increases estimated in South Asia, throughout much of Southeast Asia, and in China. Population-weighted mean concentrations of ozone increased in most countries from 1990 - 2013, with modest decreases in North America, parts of Europe, and several countries in Southeast Asia.

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Most major cities around the world experience periods of elevated air pollution levels, which exceed international health-based air quality standards (Kumar et al., 2013). Although it is a global problem, some of the highest air pollution levels are found in rapidly expanding cities in India and China. The sources, emissions, transformations and broad effects of meteorology on air pollution are reasonably well accounted in air quality control strategies in many developed cities; however these key factors remain poorly constrained in the growing cities of countries with emerging economies. We focus here on Delhi, one of the largest global population centres, which faces particular air pollution challenges, now and in the future.

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The air we breathe is being polluted by activities such as vehicles; burning coal, oil, and other fossil fuels; and manufacturing chemicals. Air pollution can even come from smaller, everyday activities such as cooking, space heating, and degreasing and painting operations. These activities add gases and particles to the air we breathe. When these gases and particles accumulate in the air in high enough concentrations, they can harm us and our environment. The module on Air Pollution deals with the various sources of air pollution and the associated environmental and health impacts. It also discusses the appropriate measures to control/prevent the same.

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The Bangalore Metropolitan Transport Corporation (BMTC) took an initiative to check the overall benefits of introducing electric buses as a suitable replacement for the diesel buses to tackle the burgeoning pollution in the city of Bengaluru, India. For a trial run of three months, an electric bus was procured from a Chinese company `Build Your Dreams' (BYD). Data were collected by BMTC on the operation and maintenance of the bus. This new initiative, if rightly guided, could have a direct impact on the lives of those in the city. An economic analysis of the running as well as maintenance of the electric buses within the city limits was performed. For comparison, the same analysis was performed for the data from the existing diesel bus operating on the same route. On the basis of the study, it can be concluded that the introduction of electric buses as a means of public transport in the city would be beneficial both economically as well as environmentally. The electric bus also makes much less noise, thereby helping reduce noise pollution and makes less vibration when compared to the diesel bus. This results in a more comfortable journey for the passengers.

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An economic air pollution control model, which determines the least cost of reaching various air quality levels, is formulated. The model takes the form of a general, nonlinear, mathematical programming problem. Primary contaminant emission levels are the independent variables. The objective function is the cost of attaining various emission levels and is to be minimized subject to constraints that given air quality levels be attained.

The model is applied to a simplified statement of the photochemical smog problem in Los Angeles County in 1975 with emissions specified by a two-dimensional vector, total reactive hydrocarbon, (RHC), and nitrogen oxide, (NOx), emissions. Air quality, also two-dimensional, is measured by the expected number of days per year that nitrogen dioxide, (NO2), and mid-day ozone, (O3), exceed standards in Central Los Angeles.

The minimum cost of reaching various emission levels is found by a linear programming model. The base or "uncontrolled" emission levels are those that will exist in 1975 with the present new car control program and with the degree of stationary source control existing in 1971. Controls, basically "add-on devices", are considered here for used cars, aircraft, and existing stationary sources. It is found that with these added controls, Los Angeles County emission levels [(1300 tons/day RHC, 1000 tons /day NOx) in 1969] and [(670 tons/day RHC, 790 tons/day NOx) at the base 1975 level], can be reduced to 260 tons/day RHC (minimum RHC program) and 460 tons/day NOx (minimum NOx program).

"Phenomenological" or statistical air quality models provide the relationship between air quality and emissions. These models estimate the relationship by using atmospheric monitoring data taken at one (yearly) emission level and by using certain simple physical assumptions, (e. g., that emissions are reduced proportionately at all points in space and time). For NO2, (concentrations assumed proportional to NOx emissions), it is found that standard violations in Central Los Angeles, (55 in 1969), can be reduced to 25, 5, and 0 days per year by controlling emissions to 800, 550, and 300 tons /day, respectively. A probabilistic model reveals that RHC control is much more effective than NOx control in reducing Central Los Angeles ozone. The 150 days per year ozone violations in 1969 can be reduced to 75, 30, 10, and 0 days per year by abating RHC emissions to 700, 450, 300, and 150 tons/day, respectively, (at the 1969 NOx emission level).

The control cost-emission level and air quality-emission level relationships are combined in a graphical solution of the complete model to find the cost of various air quality levels. Best possible air quality levels with the controls considered here are 8 O3 and 10 NO2 violations per year (minimum ozone program) or 25 O3 and 3 NO2 violations per year (minimum NO2 program) with an annualized cost of $230,000,000 (above the estimated $150,000,000 per year for the new car control program for Los Angeles County motor vehicles in 1975).