The prediction of maximum air pollution concentrations for tsp and co using Larsen's model and the ATDL model

Simpson, R.W.; Daly, N J; Jakeman, Anthony J.

doi:10.1016/0004-6981(83)90075-6

Cited by 19 publications

(5 citation statements)

References 9 publications

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“…Following Simpson et al (1985) we studied the relationship between opposing percentiles of the distributions of 8-h CO concentration and mean wind speed. Previous studies Steele, 1975, 1976;Simpson at al., 1983) have found that a simple relationship of the form C = K (Um) -1 would appear to exist because of the high correlation between percentiles of mean wind speed (Um) and air pollution concentrations (C) at several sites. Simpson et al (1983) have shown that C value corresponding to p-percentile (C p ) and mean wind speed value corresponding to (100 -p)-percentile (Um (100-p) ) are related by: K=[C p .Um (100-p) ].…”

Section: Co Concentration and Mean Wind Speedmentioning

confidence: 93%

“…Previous studies Steele, 1975, 1976;Simpson at al., 1983) have found that a simple relationship of the form C = K (Um) -1 would appear to exist because of the high correlation between percentiles of mean wind speed (Um) and air pollution concentrations (C) at several sites. Simpson et al (1983) have shown that C value corresponding to p-percentile (C p ) and mean wind speed value corresponding to (100 -p)-percentile (Um (100-p) ) are related by: K=[C p .Um (100-p) ]. The values of K calculated from the cumulative distributions for 8-h CO data and mean wind speed data are shown in Figure 5.…”

Section: Co Concentration and Mean Wind Speedmentioning

confidence: 93%

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Carbon Monoxide Concentration in a Street Canyon of Buenos Aires City (Argentina)

Venegas

Mazzeo

2000

Urban Air Quality: Measurement, Modelling and Management

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The analysis of three years of 8-h CO concentration values registered in a deep street canyon downtown shows high frequency of values that exceed WHO health protection guidelines. An inverse relationship between opposing percentiles of the distributions of CO concentrations and mean wind speed could be found. Data also showed a variation of mean CO values with prevailing wind direction. The averaged concentration value obtained when the sampler probe is on the leeward side is lower than the obtained when it is on the windward wall. A preliminary explanation of this feature may be related to the advection of polluted air from a high traffic density area nearby.

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Section: Co Concentration and Mean Wind Speedmentioning

confidence: 93%

Section: Co Concentration and Mean Wind Speedmentioning

confidence: 93%

Carbon Monoxide Concentration in a Street Canyon of Buenos Aires City (Argentina)

Venegas

Mazzeo

2000

Urban Air Quality: Measurement, Modelling and Management

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“…Simpson et al (1983) have also shown that irrespective of the statistical distributions of air pollution data and wind speed the cumulative frequency distributions F(χ) and G(u) of air pollution and wind speed are related, then it would appear that if χ=K/u is applicable to all the data, the K value can be calculated from F(χ) =1-G(u) where F(χ)=prob(X≥ χ) anf G(u)=prob(U≥u). This in essence implies that the χ value corresponding to the p-percentile χp in F(χ) and the u value corresponding to the (100-p) percentile u100-p are related by K= χp* u100-p (equation (1)).…”

Section: Figurementioning

confidence: 99%

Air pollution from traffic, ships and industry in an Italian port

Fava

Ruello

2008

WIT Transactions on Ecology and the Environment

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In the present study, the levels of NO x , primary and secondary formed particulate matter (PM10, PM2.5) originating from the intensive commercial and industrial activities within the port zone of Ancona (Italy) were analysed. The data have been evaluated, calculating by a Weibull distribution approach, the hourly, or using a recoded time, statistics of the pollutants concentrations and meteorological parameters measured in an area situated in a zone of influence of the harbour area. This gave the opportunity to study the dependence of the processes on the local scale as well as the impact of the harbour area on the urban scale. The highest concentrations of PM generally occurred in the harbour area whereas the NO 2 was found to be higher in the urban area. The pollutant transport from the harbour and related to the local dispersive ventilation capacity can become important in episodic situations even in the facing urbanised area.

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“…It has been shown (e.g., [17]) that. if the wind speed data are log-normally distributed such an assumption implies that the air pollution concentration data are also log-normally distributed with the same geometric standard deviation (Sg).…”

Section: Hour Of Daymentioning

confidence: 99%

“…The application of the ATDL model in conjunction with the Larsen model has successfully been employed to predict both maximum total suspended particulate concentrations and maximum CO concentrations [17]. It has more recently been used to obtain estimates of maximum sulphur logarithm of wind speed against z (a log-normal probability graph); in this paper nand i refer to the 1st and 50th percentile points.…”

Section: The Larsen Modelmentioning

confidence: 99%

A simulation approach to assess air pollution from road transport

Jakeman

Simpson

Taylor

1984

IEEE Trans. Syst., Man, Cybern.

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A hybrid deterministic/statistical modeling approach is used to predict air pollution levels generated from motor vehicle emissions. The inclusion of the effects of source emissions and meteorology on ambient concentrations are allowed for by the deterministic models, but are applied only within their range of greatest reliability. Two deterministic models are used in separate simulation exercises: the ATDL model to obtain an areal indication of pollution levels and the GM model to provide information on concentration reduction with distance from the roadway. Extrapolation of the reliable output data obtained from the deterministic models is allowed by the use of the statistical model to predict the maximum levels of pollution concentration. Monte Carlo simulation is included to show the effects of system uncertainty with respect to emissions. Variability in meteorology is allowed by running the model separately with a time-series input of wind speed for different calendar years. The methodology is demonstrated with an example in an Australian town that is being subjected to increasing heavy vehicle traffic due to mining operations in the area.

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The prediction of maximum air pollution concentrations for tsp and co using Larsen's model and the ATDL model

Cited by 19 publications

References 9 publications

Carbon Monoxide Concentration in a Street Canyon of Buenos Aires City (Argentina)

Carbon Monoxide Concentration in a Street Canyon of Buenos Aires City (Argentina)

Air pollution from traffic, ships and industry in an Italian port

A simulation approach to assess air pollution from road transport

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