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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, a stochastic microscopic traffic flow simulation model (VISSIM), an existing speed-based emission database (MODEM), and a Gaussian dispersion model are combined.
Abstract: Road traffic is a major source of air pollution, and substantial effort is currently being devoted to the development of both technological and transport policy measures to reduce the impacts. It is well established that the emission of certain pollutants is closely related to both traffic speed and fluctuations in traffic speed. However, conventional transport emission models are largely based on average traffic conditions, and thus they cannot properly represent the effects of policy measures, such as automatic speed control or traffic calming, that directly affect the speed dynamics of the traffic stream. Given the prevalence of such policies, there has been considerable effort to develop improved emissions modeling capabilities. A new approach to the microscopic modeling of air pollution from road traffic is described. This approach can represent detailed speed fluctuations in the flow of traffic, and it is applied to a local network in Maidstone, Kent, in the United Kingdom. A stochastic microscopic traffic flow simulation model (VISSIM), an existing speed-based emission database (MODEM), and a Gaussian dispersion model are combined. Simulated results are compared with a macroscopic model of air pollutant concentrations (DMRB method) and roadside pollutant measurements. Results are encouraging and show a good comparison with the DMRB method and, with some exceptions, good comparisons with trends in measured pollutant concentrations. Statistical differences in the methods, however, suggest that either measurement error or other inaccuracies are present.

42 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper developed an Artificial Neural Network (ANN) model using a combination of numerical model derived meteorological variables and variables indicating emission and circulation type variations for estimating daily SO2, NO2, and PM10 concentrations over urban Lanzhou, Northwestern China.
Abstract: Knowledge of the relationship between air quality and impact factors is very important for air pollution control and urban environment management. Relationships between winter air pollutant concentrations and local meteorological parameters, synoptic-scale circulations and precipitation were investigated based on observed pollutant concentrations, high-resolution meteorological data from the Weather Research and Forecast model and gridded reanalysis data. Artificial neural network (ANN) model was developed using a combination of numerical model derived meteorological variables and variables indicating emission and circulation type variations for estimating daily SO2, NO2, and PM10 concentrations over urban Lanzhou, Northwestern China. Results indicated that the developed ANN model can satisfactorily reproduce the pollution level and their day-to-day variations, with correlation coefficients between the modeled and the observed daily SO2, NO2, and PM10 ranging from 0.71 to 0.83. The effect of four factors, i.e., synoptic-scale circulation type, local meteorological condition, pollutant emission variation, and wet removal process, on the day-to-day variations of SO2, NO2, and PM10 was quantified for winters of 2002–2007. Overall, local meteorological condition is the main factor causing the day-to-day variations of pollutant concentrations, followed by synoptic-scale circulation type, emission variation, and wet removal process. With limited data, this work provides a simple and effective method to identify the main factors causing air pollution, which could be widely used in other urban areas and regions for urban planning or air quality management purposes.

42 citations

Journal ArticleDOI
TL;DR: In this paper, a cross-correlation analysis was performed with air temperature, solar radiation, wind direction and velocity, highlighting a strong coupling for the most of cases except for particular matter.

41 citations

Journal ArticleDOI
21 Aug 2015-PLOS ONE
TL;DR: The London LEZ has not significantly improved air quality within the city, or the respiratory health of the resident population in its first three years of operation, and highlights the need for more robust measures to reduce traffic emissions.
Abstract: The adverse effects of traffic-related air pollution on children’s respiratory health have been widely reported, but few studies have evaluated the impact of traffic-control policies designed to reduce urban air pollution. We assessed associations between traffic-related air pollutants and respiratory/allergic symptoms amongst 8–9 year-old schoolchildren living within the London Low Emission Zone (LEZ). Information on respiratory/allergic symptoms was obtained using a parent-completed questionnaire and linked to modelled annual air pollutant concentrations based on the residential address of each child, using a multivariable mixed effects logistic regression analysis. Exposure to traffic-related air pollutants was associated with current rhinitis: NOx (OR 1.01, 95% CI 1.00–1.02), NO2 (1.03, 1.00–1.06), PM10 (1.16, 1.04–1.28) and PM2.5 (1.38, 1.08–1.78), all per μg/m3 of pollutant, but not with other respiratory/allergic symptoms. The LEZ did not reduce ambient air pollution levels, or affect the prevalence of respiratory/allergic symptoms over the period studied. These data confirm the previous association between traffic-related air pollutant exposures and symptoms of current rhinitis. Importantly, the London LEZ has not significantly improved air quality within the city, or the respiratory health of the resident population in its first three years of operation. This highlights the need for more robust measures to reduce traffic emissions.

41 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202316
20229
2021100
202084
201972
201852