Air pollutant concentrations

About: Air pollutant concentrations is a research topic. Over the lifetime, 1652 publications have been published within this topic receiving 36138 citations.

Air pollution and the aggravation of the symptoms of COPD in children

Abstract: Studies conducted in Zagreb reported a short-term relation between air pollutant concentrations and emergency hospital visits due to the aggravation of symptoms of the chronic obstructive pulmonary disease (COPD). Air pollution in Zagreb is generally below the limit, and usually within the values recommended by the World Health Organization. Daily variations in the number of emergency room visits due to COPD were modeled in relation to daily variations in air pollution for the period 1995-1997. A generalized additive model for Poisson distribution was used, controlling for possible confounders, seasonality, trend, and autoregressive patterns. The effect of NO/sub 2/ was linear and significant with relative risk associated with a 50 /spl mu/gm/sup -3/ increase in NO/sub 2/ concentrations equal 1.297 (95% confidence limits 1.026-1.639). The effect of SO/sub 2/ was also linear, but not statistically significant, and that of black smoke was significantly nonlinear and of an intriguing U shape. The COPD emergency visits increased when black smoke concentrations decreased below 36 /spl mu/gm/sup -3/ (the 75th percentile) and increased above 48 /spl mu/gm/sup -3/ (the 90th percentile). This finding is attributed to possible confounding with ozone.

Ecological predictive maintenance in urban fleets

Abstract: Motor vehicles are one of the largest sources of air pollutants worldwide. Several studies had concluded that particulate matter (PM) are responsible for some respiratory, cardiovascular, lung diseases, increasing in death from heart and may cause lung cancer. Others studies refer that PM are one of the most important pollutants of the Diesel vehicles. Another important pollutant from the vehicles emission gases are the nitrogen oxides (NOx) that are produced from the burning of engine fuel. In developed countries, road transports are responsible for about 50% of all NOx emissions that also have an important contribution for the formation of acid rain. At same time, these pollutants combined with hydrocarbons contribute to form low level ozone pollution. Other pollutant is the carbon monoxide (CO) that is generated from the incomplete burning of engine fuel. The road traffic produces about 90% of all CO emissions in developed countries. When it is inhaled it reduces the oxygen carrying capacity of the blood and can cause headaches, fatigue, stress, respiratory problems and, at high levels, may cause death. At last, it arises the hydrocarbons (HC) that are compounds of hydrogen and carbon and are present in petrol and diesel. In developed countries, road traffic is responsible for about 35% of all HC emissions, which react with nitrogen oxides to produce a number of pollutants, including ozone, which can affect human health and also causes plant damage. Benzene itself can cause some forms of cancer. On-road remote sensing can address the problem of inter-vehicle differences by quickly and cheaply measure the emissions of large numbers of vehicles. These measurements can be used to define the contribution of total vehicle emissions to the air pollution in a specific area and to identify groups of vehicles, even specific vehicles and operating conditions that result in very high emissions. These data provide important information for the design of mitigation strategies. These are the subjects that are treated in the paper and specifically the related to urban transports. It also is analysed the effect of Vehicle Specific Power (VSP) on emissions detected, that represents the effect of engine load on emissions. Another subject that is treated, with value-added in innovation, is the use of Hidden Markov Models (HMM) to determine the vehicle state, where the models incorporate a double stochastic process, with a non visible stochastic process, because it is not seen but can be observed through another stochastic process that produces the sequence of observations that, in this case, are pollutant emissions. The time series of collected data corresponds to inputs to an HMM for dysfunction classification.