Nicolás A Mazzeo

Urban‐rural temperature differences in Buenos Aires

The hourly temperature differences between Buenos Aires City and Ezeiza Airport were calculated using 3 years of data. This paper describes statistical results on the Buenos Aires urban heat island and how it varies with days of the week, seasons, cloud cover, direction and speed of wind. The average value of the maximal heat island fell in winter from 4.6°C with light winds and little sky coverage to 3.6°C with windy and cloudy conditions. Strong winds from the city toward rural areas and winds from the river over the city facilitated the occurrence of an inverse heat island (rural area warmer than city). The inverse heat island occurred 20% of the time over a total of 22 000 hours studied. Another important factor seems to be the great activity of the city; the average value of the maximal heat island fell between weekdays and weekends 1°C with weak winds and little sky coverage conditions. The hourly average values were analyzed depending on the days of the week.

An Evaluation of Daumod Model in Estimating Urban Background Concentrations

This article presents an evaluation of the performance of the urban atmospheric dispersion model (DAUMOD) in estimating nitrogen oxides (NOx) background concentrations in Copenhagen. Estimations of hourly average (averaged over a year), mean daily and mean monthly concentrations of NOx are compared with observed values for two years of data. The model slightly underestimates low hourly average values and overestimates high values. The cumulative frequency distribution of mean daily concentration obtained from model estimations is in good agreement with the obtained from observed data. We performed a statistical analysis to determine the agreement between estimated and observed concentration values. The results show that 95.8% of hourly average estimations, 86.8% of mean daily and 100% of monthly average concentrations are within a factor of two of the observed values. The normalised mean square error of predictions is +0.13 for hourly average estimations, +0.22 for mean daily values and +0.02 for monthly mean concentrations. The fractional bias values are: –0.049 for hourly mean estimations, –0.047 for mean daily values and –0.053 for monthly average estimations. The values of the statistical parameters allow us to consider that though estimations are lightly larger than the observed values, the model performance is acceptable.

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

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.

Some aspects of air pollution in Buenos Aires city

This paper presents three aspects of air pollution in the city of Buenos Aires (Argentina). First, we describe the main features of air pollution climatology in the city: the characteristics of wind flow, atmospheric stability and mixing heights. Then, we present the results of the application of DAUMOD and ISCST3 atmospheric dispersion models to calculate spatial and temporal distributions of carbon monoxide and nitrogen oxides background concentrations. Finally, we present the main features of carbon monoxide concentrations observed in a narrow street canyon located downtown.

Estimation of Cumulative Frequency Distribution for Carbon Monoxide Concentration from Wind-Speed Data, in Buenos Aires (Argentina)

In this article we apply and test a methodology to estimate cumulative frequency distribution for air pollutant concentration from wind-speed data. We use the inverse relationship after Simpson et al. (Atmospheric Environment, 19, 75–82, 1985) between the opposing percentile values in the statistical distributions for air pollutant concentrations and wind-speed data. This relationship is valid, irrespective of the statistical distributions of both variables, if an inverse relationship between them is also applicable. The available data are five years of 8-h average carbon monoxide concentration and 8-h mean wind-speed, observed in Buenos Aires (Argentina). The performance of the obtained empirical expressions in estimating cumulative frequency distributions for 8-h CO is statistically evaluated. The results show that it is possible to obtain an acceptable cumulative frequency distribution for 8-h CO concentration at the site if the cumulative frequency distribution for wind-speed is known. Q–Q plots show a good agreement between estimated and observed values. From our data, the mean relative error of the estimations was found to be as much as 8.0%.

Evaluation of an Emission Inventory and Air Pollution in the Metropolitan Area of Buenos Aires

Air is a vital resource, so its quality must fall within a tightly bound range. This quality is the level needed to protect public health. In addition, the quality must be able to support other life, notably diverse and sustainable ecosystems. The atmosphere is an extremely complex system in which numerous physical and chemical processes occur simultaneously. Ambient measurements give us only a snapshot of atmospheric conditions at a particular time and location. Such measurements are often difficult to interpret without a clear conceptual model of atmospheric processes. Moreover, measurements alone cannot be used directly by policymakers to establish an effective strategy for solving air quality problems. An understanding of individual atmospheric processes (chemistry, transport, removal, etc.) does not imply an understanding of the system as a whole. Mathematical models provide the necessary framework for integration of our understanding of individual atmospheric processes and study of their interactions. A combination of state-of-the-science measurements with state-of-the-science models is the best approach for making real progress toward understanding the atmospheric environment. Over the past four decades, there has been a significant increase in the number of locations where air quality data have been obtained. Also, there has been a substantial improvement in the technique for modelling the different physical and chemical processes occurring in the atmosphere. Despite this progress, currently available observations are still spatially and temporally sparse and the predictions of current generation of air quality models are still uncertain. Consequently, observations and model outputs should be combined to create highresolution spatial-temporal maps of air quality. However, at present air quality observations and model results are generally used separately. Urban air pollution is still on rise at many cities worldwide, or has experienced only small improvements. Some causes of urban air pollution problems are the amount and density of air pollutant sources, particularly vehicles, residences and industries. Because of the complexity of urban systems, air quality management in these areas is still a serious problem. Emission inventories are important tools to describe the emission situation and eventually to manage air quality. An emission inventory is a list of the amount of pollutants from

Application of atmospheric dispersion models to evaluate population exposure to NO2 concentration in Buenos Aires

Chronic or repeated exposure to low concentrations of NO2 may increase the incidence of respiratory infections especially in the most sensitive persons. Air pollution monitoring data obtained in Buenos Aires City reveal that sometimes NO2 Air Quality Guidelines (AQGs) recommended by the World Health Organization (WHO) is exceeded. We estimate hourly background concentrations (Ch) of NOx (expressed as NO2) using atmospheric dispersion models and obtain a first evaluation of the possible human exposure to NO2 in the city. This study reveals that 30% of the population (900,000 inhabitants) can be exposed to Ch > AQG during 20–30% of the year and 4% of children and 5.1% of those older than 65 years old can be exposed to this condition for more than 36 consecutive hours.

A Dispersion Model for Ground-Level and Elevated Releases from Continuous Point Sources

The application of a dispersion model of pollutants released from near surface and elevated continuous point sources is presented. The model is based on the bidimensional semiempirical equation, with vertical profiles of wind and eddy diffusivity for the atmospheric boundary layer. The suggested model makes use of a continuous description of the dispersion processes in the different regimes of the atmospheric boundary layer and needs meteorological input parameters that can be estimated from routine measurements1. The model is used to simulate the dispersion of non-buoyant, non-depositing releases from several source heights in a variety of atmospheric stability and surface roughness conditions. The observational data were obtained in tracer experiments carried out at Copenhagen (Denmark)2. Lillestrom (Norway)3, Hanford (USA)4 and Cabauw (The Netherlands)5. The predicted cross-wind integrated concentrations were compared with the observed ones. All concentrations were normalized by source strength. Current quantitative measurements and techniques of model evaluation were obtained and applied6,7.

Air Pollutant Diffusion-Deposition from a Continuous Point Source

Atmospheric diffusion models are used to estimate air pollutant’s concentrations. Those considering dry deposition can be used as an alternative procedure to calculate deposition velocities and fluxes. Experimental studies show variations in a range of several orders of magnitude. The application of a diffusion-deposition model of pollutants released from a near surface continuous point source in the atmospheric boundary layer is presented. Numerical simulations of experiments carried out at Hanford (USA)1, were done. Tracer deposition velocities are estimated from the comparison of proposed values with observational data.