Topic
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.
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TL;DR: In this paper, air pollutant concentrations have been analyzed statistically with meteorological factors in the city of Elazig, which is located in the east Anatolia region of Turkey, for the months of September, October, November, December, January, February, and April during the years 2003 and 2004.
Abstract: In the present study, air pollutant concentrations have been analyzed statistically with meteorological factors in the city of Elazig, which is located in the east Anatolia region of Turkey, for the months of September, October, November, December, January, February, March, and April during the years 2003 and 2004. SPSS code was used for statistical analyses. The relationship between monitored air pollutant concentrations, such as SO2 and the total suspended particles (TSP) data, and meteorological factors such as wind speed, temperature, relative humidity and pressure was investigated. According to the results of linear and non-linear regression analysis, it was found that there is a moderate and weak level of relation between the air pollutant concentrations and the meteorological factors in Elazig. The correlation between the previous day's SO2, TSP concentrations and actual concentrations of these pollutants on that day was investigated and the coefficient of determination R was found to be 0.80 and 0...
6 citations
TL;DR: In this article, the authors presented a comprehensive literature on univariate time series modelling for predicting air pollution levels, including Artificial Neural Network (ANN) and Principal Component Regression (PCR).
Abstract: In the attempt to increase the production of the industrial sector to accommodate human needs; motor vehicles and power plants have led to the decline of air quality The tremendous decline of air pollution levels can adversely affect human health, especially children, those elderly, as well as patients suffering from asthma and respiratory problems As such, the air pollution modelling appears to be an im-portant tool to help the local authorities in giving early warning, apart from functioning as a guide to develop policies in near future Hence, in order to predict the concentration of air pollutants that involves multiple parameters, both artificial neural network (ANN) and principal component regression (PCR) have been widely used, in comparison to classical multivariate time series Besides, this paper also presents comprehensive literature on univariate time series modelling Overall, the classical multivariate time series modelling has to be further investigated so as to overcome the limitations of ANN and PCR, including univariate time series methods in short-term predic-tion of air pollutant concentrations
6 citations
TL;DR: In this article , the authors suggest the current AQI should be revised in a way that new air pollution indicators would be considered so that it would better represent the health effects caused by local combustion processes from traffic and residential burning.
6 citations
6 citations
TL;DR: These initiatives create a measurement infrastructure that will facilitate two important research thrusts during the next decade: the development of guidance to the public on minimizing exposure to particles, and the incorporation of refined exposure metrics into existing cross-sectional studies.
Abstract: Epidemiologic studies of various designs have found persuasive associations between morbidity or mortality rates and ambient particle concentrations. Most of these studies rest on particle measurements that provide little or no information on diurnal variability and chemical composition. Concentrations are monitored at a fixed location by sampling ambient air at a constant rate over 24-h periods. The sample is drawn first through an inlet to remove particles with aerodynamic diameters greater than 2.5 μm or 10 μm, then through a clean filter to collect the remaining smaller particles. This filter is weighed before and after the exposure period to determine the gain attributable to collected particulate matter, which is identified as PM2.5 or PM10. The findings from such studies have prompted the U.S. Environmental Protection Agency to add PM2.5 measurements to its national ambient air monitoring network. The core of this effort involves standardized collection and gravimetric analysis of 24-h filter samples to monitor compliance with a new regulatory standard. Unlike previous regulatory monitoring programs, however, this compliance network is being augmented by networks of more advanced measurements, of sparser but still national coverage (U.S. Environmental Protection Agency, 2002). Two auxiliary networks of particular interest are directed toward continuous monitoring, to inform the public in real time, and chemical speciation, to guide strategies for the management of emissions. These initiatives create a measurement infrastructure that will facilitate two important research thrusts during the next decade: (1) the development of guidance to the public on minimizing exposure to particles, and (2) the incorporation of refined exposure metrics into existing cross-sectional studies.
6 citations