Air pollutant retention within a complex of urban street canyons (original) (raw)
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Air pollution dispersion within urban street canyons
2009
A semi-empirical mathematical model, Urban Street Model (USM), is proposed to efficiently estimate the dispersion of vehicular air pollution in cities. This model describes urban building arrangements by combining building density, building heights and the permeability of building arrangements relative to wind flow. To estimate the level of air pollution in the city of Krasnoyarsk (in Eastern Siberia), the spatial distribution of pollutant concentrations off roadways is calculated using Markov's processes in USM. The USM-predicted numerical results were compared with field measurements and with results obtained from other frequently used models, CALINE-4 and OSPM. USM consistently yielded the best results. OSPM usually overestimated pollutant concentration values. CALINE-4 consistently underestimated these values. For OSPM, the maximum differences were 160% and for CALINE-4 about 400%. Permeability and building density are necessary parameters for accurately modeling urban air pollution and influencing regulatory requirements for building planning.
Journal of the Air & Waste Management Association, 2011
Environmental epidemiology and more specifically timeseries analysis have traditionally used area-averaged pollutant concentrations measured at central monitors as exposure surrogates to associate health outcomes with air pollution. However, spatial aggregation has been shown to contribute to the overall bias in the estimation of the exposure-response functions. This paper presents the benefit of adding features of the spatial variability of exposure by using concentration fields modeled with a chemistry transport model instead of monitor data and accounting for human activity patterns. On the basis of county-level census data for the city of Paris, France, and a Monte Carlo simulation, a simple activity model was developed accounting for the temporal variability between working and evening hours as well as during transit. By combining activity data with modeled concentrations, the downtown, suburban, and rural spatial patterns in exposure to nitrogen dioxide, ozone, and PM 2.5 (particulate matter [PM] Յ 10 m in aerodynamic diameter) were captured and parametrized. Exposures predicted with this model were used in a time-series study of the short-term effect of air pollution on total nonaccidental mortality for the 4-yr period from 2001 to 2004. It was shown that the time series of the exposure surrogates developed here are less correlated across co-pollutants than in the case of the area-averaged monitor data. This led to less biased exposure-response functions when all three co-pollutants were inserted simultaneously in the same regression model. This finding yields insight into pollutant-specific health effects that are otherwise masked by the high correlation among co-pollutants.
Modeling Spatial Patterns of Traffic-Related Air Pollutants in Complex Urban Terrain
Environmental Health Perspectives, 2011
The relationship between traffic emissions and mobile-source air pollutant concentrations is highly variable over space and time and therefore difficult to model accurately, especially in urban settings with complex terrain. Regression-based approaches using continuous real-time mobile measurements may be able to characterize spatiotemporal variability in trafficrelated pollutant concentrations but require methods to incorporate temporally varying meteorology and source strength in a physically interpretable fashion. oBjective: We developed a statistical model to assess the joint impact of both meteorology and traffic on measured concentrations of mobile-source air pollutants over space and time. Methods: In this study, traffic-related air pollutants were continuously measured in the Williamsburg neighborhood of Brooklyn, New York (USA), which is affected by traffic on a large bridge and major highway. One-minute average concentrations of ultrafine particulate matter (UFP), fine particulate matter [≤ 2.5 μm in aerodynamic diameter (PM 2.5 )], and particle-bound polycyclic aromatic hydrocarbons were measured using a mobile-monitoring protocol. Regression modeling approaches to quantify the influence of meteorology, traffic volume, and proximity to major roadways on pollutant concentrations were used. These models incorporated techniques to capture spatial variability, long-and short-term temporal trends, and multiple sources. results: We observed spatial heterogeneity of both UFP and PM 2.5 concentrations. A variety of statistical methods consistently found a 15-20% decrease in UFP concentrations within the first 100 m from each of the two major roadways. For PM 2.5 , temporal variability dominated spatial variability, but we observed a consistent linear decrease in concentrations from the roadways. conclusions: The combination of mobile monitoring and regression analysis was able to quantify local source contributions relative to background while accounting for physically interpretable parameters. Our results provide insight into urban exposure gradients.
Sustainable Cities and Society, 2019
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MODELLING THE CONCENTRATION FLUCTUATION AND INDIVIDUAL EXPOSURE IN COMPLEX URBAN ENVIRONMENTS
Abstract: The concentrations fluctuations of a dispersing hazardous gaseous pollutant in the atmospheric boundary layer, and the hazard associated with short-term concentration levels, demonstrate the necessity of estimating the magnitude of these fluctuations using predicting models. Moreover the computation of concentration fluctuations and individual exposure in case of dispersion in realistic situations, such as built-up areas or street canyons, is of special practical interest for hazard assessment purposes. In order to predict or/and estimate the maximum expected dosage and the exposure time within which the dosage exceeds certain health limits, the knowledge of the behaviour of concentration fluctuations at the point under consideration is needed. In this study the whole effort is based on the ‘Mock Urban Setting Test – MUST’, an extensive field test carried out on a test site of the US Army in the Great Basin Desert in 2001 (Biltoft, 2001; Yee, 2004). The experimental data t...
Spatial and temporal variability in urban fine particulate matter concentrations
Environmental Pollution, 2011
Identification of hot spots for urban fine particulate matter (PM 2.5 ) concentrations is complicated by the significant contributions from regional atmospheric transport and the dependence of spatial and temporal variability on averaging time. We focus on PM 2.5 patterns in New York City, which includes significant local sources, street canyons, and upwind contributions to concentrations. A literature synthesis demonstrates that long-term (e.g., one-year) average PM 2.5 concentrations at a small number of widely-distributed monitoring sites would not show substantial variability, whereas short-term (e.g., 1-h) average measurements with high spatial density would show significant variability. Statistical analyses of ambient monitoring data as a function of wind speed and direction reinforce the significance of regional transport but show evidence of local contributions. We conclude that current monitor siting may not adequately capture PM 2.5 variability in an urban area, especially in a mega-city, reinforcing the necessity of dispersion modeling and methods for analyzing high-resolution monitoring observations.
Variability of physical meteorology in urban areas at different scales: implications for air quality
Faraday Discussions, 2021
Air quality in cities is influenced not only by emissions and chemical transformations but also by the physical state of the atmosphere which varies both temporally and spatially. Increasingly, tall buildings (TB) are common features of the urban landscape, yet their impact on urban air flow and dispersion is not well understood, and their effects are not appropriately captured in parameterisation schemes. Here, hardware models of areas within two global mega-cities (London and Beijing) are used to analyse the impact of TB on flow and transport in isolated and cluster settings. Results show that TB generate strong updrafts and downdrafts that affect street-level flow fields. Velocity differences do not decay monotonically with distance from the TB, especially in the near-wake region where the flow is characterised by recirculating winds and jets. Lateral distance from an isolated TB centreline is crucial, and flow is still strongly impacted at longitudinal distances of several TB heights. Evaluation of a wake-flow scheme (ADMS-Build) in the isolated TB case indicates important characteristics are not captured. There is better agreement for a slender, shorter TB than a taller non-cuboidal TB. Better prediction of flow occurs horizontally further away and vertically further from the surface. TB clusters modify the shape of pollutant plumes. Strong updrafts generated by the overlapping wakes of TB clusters lift pollutants out of the canopy, causing a much deeper tracer plume in the lee of the cluster, and an elevated plume centreline with maximum concentrations around the TB mean height. Enhanced vertical spread of the pollutants in the near-wake of the cluster results in overall lower maximum concentrations, but higher concentrations above the mean TB height. These results have important implications for interpreting observations in areas with TB. Using real world ceilometer observations in two mega-cities (Beijing and Paris), we assess the