Bino Maiheu

Improving local air quality in cities: To tree or not to tree?

Vegetation is often quoted as an effective measure to mitigate urban air quality problems. In this work we demonstrate by the use of computer models that the air quality effect of urban vegetation is more complex than implied by such general assumptions. By modelling a variety of real-life examples we show that roadside urban vegetation rather leads to increased pollutant concentrations than it improves the air quality, at least locally. This can be explained by the fact that trees and other types of vegetation reduce the ventilation that is responsible for diluting the traffic emitted pollutants. This aerodynamic effect is shown to be much stronger than the pollutant removal capacity of vegetation. Although the modelling results may be subject to a certain level of uncertainty, our results strongly indicate that the use of urban vegetation for alleviating a local air pollution hotspot is not expected to be a viable solution.

Impact of trees on pollutant dispersion in street canyons: A numerical study of the annual average effects in Antwerp, Belgium

Effects of vegetation on pollutant dispersion receive increased attention in attempts to reduce air pollutant concentration levels in the urban environment. In this study, we examine the influence of vegetation on the concentrations of traffic pollutants in urban street canyons using numerical simulations with the CFD code OpenFOAM. This CFD approach is validated against literature wind tunnel data of traffic pollutant dispersion in street canyons. The impact of trees is simulated for a variety of vegetation types and the full range of approaching wind directions at 15° interval. All these results are combined using meteo statistics, including effects of seasonal leaf loss, to determine the annual average effect of trees in street canyons. This analysis is performed for two pollutants, elemental carbon (EC) and PM10, using background concentrations and emission strengths for the city of Antwerp, Belgium. The results show that due to the presence of trees the annual average pollutant concentrations increase with about 8% (range of 1% to 13%) for EC and with about 1.4% (range of 0.2 to 2.6%) for PM10. The study indicates that this annual effect is considerably smaller than earlier estimates which are generally based on a specific set of governing conditions (1 wind direction, full leafed trees and peak hour traffic emissions).

Heat wave hazard classification and risk assessment using artificial intelligence fuzzy logic

The average summer temperatures as well as the frequency and intensity of hot days and heat waves are expected to increase due to climate change. Motivated by this consequence, we propose a methodology to evaluate the monthly heat wave hazard and risk and its spatial distribution within large cities. A simple urban climate model with assimilated satellite-derived land surface temperature images was used to generate a historic database of urban air temperature fields. Heat wave hazard was then estimated from the analysis of these hourly air temperatures distributed at a 1-km grid over Athens, Greece, by identifying the areas that are more likely to suffer higher temperatures in the case of a heat wave event. Innovation lies in the artificial intelligence fuzzy logic model that was used to classify the heat waves from mild to extreme by taking into consideration their duration, intensity and time of occurrence. The monthly hazard was subsequently estimated as the cumulative effect from the individual heat waves that occurred at each grid cell during a month. Finally, monthly heat wave risk maps were produced integrating geospatial information on the population vulnerability to heat waves calculated from socio-economic variables.

Evaluation of satellite-derived products for the characterization of the urban thermal environment

Abstract. Knowledge of the air and land surface temperature and their temporal and spatial variations within a city environment is of prime importance to the study of urban climate and human–environment interactions and to monitoring environmental changes due to urbanization. We present a number of air and land surface temperature products that have been produced, archived, evaluated, and analyzed for 10 European cities within the framework of the European Space Agency–funded “Urban Heat Islands and Urban Thermography” project. We evaluate in what way these products are suited to explore the urban thermal dynamics and how products with different temporal and spatial resolution can provide a complementary view, both for thermal patterns as well as heat waves. Level of confidence was evaluated through quantitative, qualitative, and user-based analyses.

A study of the hourly variability of the urban heat island effect in the Greater Athens Area during summer.

Measurements of air temperature and humidity in the urban canopy layer during July 2009 in 26 sites in Athens, Greece, allowed for the mapping of the hourly spatiotemporal evolution of the urban heat island (UHI) effect. City districts neighboring to the mountains to the east were the hottest during the afternoon, while being among the coolest during the early morning hours. While during the early morning some coastal sites were the hottest, the warm air plume slowly moved to the densely urbanized center of the city until 14:00-15:00, moving then further west, to the Elefsis industrial area in the afternoon. Results from the UrbClim model agree fairly well with the observations. Satellite-derived land surface temperature (LST) data from AATSR, ASTER, AVHRR and MODIS, for pixels corresponding to ground stations measuring Tair, showed that LST can be up to 5K lower than the respective Tair during nighttime, while it can be up to 15K higher during the rest of the day. Generally, LST during late afternoon as acquired from AATSR is very near to Tair for all stations and all days, i.e., the AATSR LST afternoon retrieval can be used as a very good approximation of Tair. The hourly evolution of the spatial Tair distribution was almost the same during days with NE Etesian flow as in days with sea breeze circulation, indicating that the mean wind flow was not the main factor controlling the diurnal UHI evolution, although it influenced the temperatures attained. No unambiguous observation of the urban moisture excess (UME) phenomenon could be made.

The Role of Vegetation in Local and Urban Air Quality

We present the outcome of the international conference ‘Local Air Quality and its Interactions with Vegetation’, which took place in Antwerp, Belgium on January 21-22, 2010. Results of international CFD studies, measurement campaigns and experimental studies show that vegetation can have an important effect on dispersion patterns determining local air quality. However, there are many parameters involved (vegetation structure, local meteorology, urban canopy characteristics, mechanical turbulence properties) and the results show that the complexity of the mechanisms of vegetation affecting local air quality are often underestimated.

Assessing Climate Change in Cities Using UrbClim

The urban heat island effect, in which air temperatures tend to be higher in urban environments than in rural areas, is known to exacerbate the heat impact on population health. We introduce a new urban climate model, further referred to as UrbClim, designed to study the urban heat island effect at a spatial resolution of a few hundred metres. Despite its simplicity, UrbClim is found to be of the same level of accuracy as more sophisticated models, while also being much faster than high-resolution mesoscale climate models. Because of that, the model is well suited for long time integrations, in particular for applications in urban climate projections. In this contribution, we present temperature maps for London, including an assessment of the present-day climate, and projections for the future (2081–2100).

Development of a Screening Tool for Quick Environmental Assessment of Mobility Scenarios

Large mobility projects, for example, construction of new highways, have attracted an increased interest from the public at large. Pressure groups and environmental activists often propose their own alternatives for the planned projects. The vast amount of scenarios that are proposed in this way lead to an enormous increase of work related to the environmental assessment. Therefore, a new screening tool is proposed that can serve as a first phase in the environmental assessment procedure for air pollution. The tool enables users to quickly estimate the impact of a scenario on local air quality. Only scenarios which show promising results in this screening tool are then to be discussed in the normal environmental assessment procedure which is much more time-consuming. There are several requirements in order to have a successful approach: the screening tool needs to be fast, it needs to provide results which are close to the detailed environmental assessment procedure and it needs to enable calculations for the major pollutants (NO2, PM10, EC/BC, …) including fast ozone chemistry. Such a screening tool is presented here. It determines annual average concentrations of the major pollutants (NO2, PM10, PM2.5, EC, C6H6) in a short calculation time of only some 10 min on 1 CPU, compared to calculations of 2 days on 24 CPUs for the detailed assessment procedure. The speed-up is obtained by using lookup tables of pre-simulated situations, which are then combined into the large-scale scenario. For instance, the effect of a 100 m road segment from north to south with a unit emission strength is calculated beforehand. If such a segment occurs in the input, this result is then used with the emissions scaled according to need. The tool does not calculate absolute concentrations (therefore, a standard model is still used) but only differences between scenarios. For some scenarios both the screening tool and the regular assessment procedure have been applied and results are compared. Comparing for both methods the screening tool with the full model yields small biases (−0.0022–0.0075 µg/m3), a small RMSE (0.02–0.21 µg/m3), a high R2 of (0.75–0.87) and a slope of the regression curve close to 1 (1.01–1.18), showing that for screening purposes the tool is well capable of making the cut between good and bad scenarios.

Is the Recent Decrease in Belgian Air Pollution Concentration Levels Due to Meteorology or to Emission Reductions

Recent years have shown significant decrease in concentrations levels of particulate matter (PM10) and nitrogen dioxide (NO2) in Belgium. For ozone (O3), no such trend is found. Recent years, however, did not feature many periods with unfavourable meteorological dispersion conditions, casting some ambiguity on the underlying reasons for the decrease. This study tries to separate the impact of weather effects from emission reductions in the long-term trend. We build a statistical model explaining the daily averaged concentrations based on 32 meteorological parameters, the day of the week, the month of the year and the year itself, for the period 2004–2014. The 32 meteorological parameters are those considered to train the neural network prediction model OVL. Many of these meteo variables have only a small predictability and are intercorrelated with each other. Therefore, only those meteo parameters are used that have a significant impact on concentration levels. This procedure is applied for the complete time series and for each air quality monitoring location separately. In order to avoid overfitting, the same analysis is done, restricted to the data of even-numbered years, and the regression is then applied to the odd-numbered years. It is shown that the statistical parameters remain reasonably constant, which proves that the amount of overfitting is not significant. The results show, on average over all measurement locations, a range of yearly meteorological effects of 1.9 µg/m3 for NO2, 3.1 µg/m3 for PM10 and 2.7 µg/m3 for O3. Meteorology combined with the residuals of the statistical fit show a range of 1.2 µg/m3 for NO2, 2.9 µg/m3 for PM10 and 4.4 µg/m3 for O3. Finally, the long-term trend shows a range of 5.3 µg/m3 for NO2, 11.1 µg/m3 for PM10 and 2.3 µg/m3 for O3, with clearly decreasing trends for NO2 and PM10, and an oscillating trend for O3. Differences between rural, urban background, urban and industrial stations exist but are rather small. We can conclude that the major trend in air pollution (Belgium) is a long-term trend, linked to emission changes, and it can be expected that the concentration decreases of the last years will not suddenly disappear in the near future given unchanged policy. Furthermore, it can be concluded that emission reductions at the local, regional, European and worldwide scale are the dominant factors explaining the improvement of air quality.

Comparing Different Modeling Approaches in Obtaining Regional Scale Concentration Maps

We studied and compared different operational modeling techniques that are used to generate regional scale concentration maps for PM10, PM2,5, NO2 and O3 over Belgium. The various techniques and resulting maps were analyzed, validated and compared aiming at identifying the best possible regional scale concentration map for each pollutant. A distinction was made between a temporal and a spatial validation. The temporal analysis revealed that an intelligent interpolation technique based on land use characteristics in general performs best in capturing the temporal aspects of air quality in Belgium for the investigated pollutants. For PM10 and PM2.5 this technique also performs best in generating the spatial pattern of the observed annually averaged concentrations. A deterministic model combined with a corrective ‘Optimal Interpolation’ data assimilation technique performs best in reproducing the spatial pattern of O3. For NO2 the interpolation technique manages best in explaining the spatial pattern of the observed annually averaged concentrations in Belgium, but when restricted to the region of Flanders, it competes with a thoroughly calibrated Lagrangian type of modeling.