41 research outputs found
Characterization of urban pollution in Parisian region by a synergy of surface measurements and high-resolution modelling
No full textL’impact sanitaire lié à la pollution de l’air nécessite une estimation précise de celle-ci. Les réseaux de stations de mesures des agences de surveillance de la qualité de l’air (AIRPARIF en Île-de-France) ne sont pas suffisamment denses pour renseigner sur l’hétérogénéité de la pollution en ville. Et, les modèles haute résolution simulant les champs de concentration de polluants en 3D ont une large couverture spatiale mais sont limités par leurs incertitudes. Ces deux sources d’information exploitées indépendamment ne permettent pas d’évaluer finement l’exposition d’un individu. Nous proposons deux approches pour résoudre ce problème : (1) par la mesure directe des polluants avec des capteurs mobiles à bas coût et des instruments de référence. Des niveaux de pollution très variables ont été constatés entre les microenvironnements et dans une même pièce. Ces capteurs devraient être déployés en grand nombre pour palier à leurs contraintes techniques. Les instruments de référence, très coûteux et volumineux, ne peuvent être utilisés que ponctuellement. (2) en combinant les concentrations simulées par le modèle Parallel Micro-SWIFT-SPRAY (PMSS) à Paris avec une résolution horizontale de 3 mètres et les mesures des stations de surface AIRPARIF. Nous avons déterminé des « zones de représentativité » - zones géographiques où les concentrations sont très proches de celle de la station - uniquement à partir des sorties du modèle PMSS. Ensuite, nous avons développé un modèle bayésien pour propager la mesure des stations dans ces zones.The harmful effects of air pollution need a high-resolution concentration estimate. Ambient pollutant concentrations are routinely measured by surface monitoring sites of local agencies (AIRPARIF in Paris area, France). Such networks are not dense enough to represent the strong horizontal gradients of pollutant concentrations over urban areas. And, high-resolution models that simulate 3D pollutant concentration fields have a large spatial coverage but suffer from uncertainties. Those both information sources exploited independently are not able to accurately assess an individual’s exposure. We suggest two approaches to solve this problem : (1) direct pollution measurement by using low cost mobile sensors and reference instruments. A high variability across pollution levels is shown between microenvironments and also in the same room. Mobile sensors should be deployed on a large scale due to their technical constraints. Reference instruments are very expensive, cumbersome, and can only be used occasionally. (2) by combining concentration fields of the Parallel Micro-SWIFT-SPRAY (PMSS) model over Paris at a horizontal resolution of 3 meters with AIRPARIF local ground stations measurements. We determined “representativeness areas” - perimeter where concentrations are very close to the one of the station location – only from PMSS simulations. Next, we developed a Bayesian model to extend the stations measurements within these areas
Transferring the heterogeneity of surface emissions to variability in pollutant concentrations over urban areas through a chemistry-transport model
No full textInternational audienceHorizontal resolution of grid-based chemistry-transport models is limited to a few square kilometers which has been proved insufficient for assessing human exposure and health impact. We propose a general methodology, applicable on any kind of grid-based air-quality model, that combines subgrid scale information on emission and land-use data in order to disaggregate the grid-averaged emission flux into a set of source-specific components (subgrid-environments). Different subgrid concentrations are calculated inside each one of these environments providing a direct estimate of pollutant variability along with the 'standard' grid-averaged model output. The method was first validated over a controlled emissions case by comparing concentrations modeled in the subgrid-environments with concentrations modeled directly at higher model resolution and next over a real case-study, where subgrid concentrations were compared with monitor data from sites representing different types of urban environments (i.e. roads and residential blocks). It was shown that the method is capable to yield accurate estimates of small scale pollutant variability
Air quality modeling in the city of Marrakech, Morocco using a local anthropogenic emission inventory
No full textInternational audienceExposure to high levels of suspended particles, and particularly dust, has been associated with increased risk of morbidity and premature mortality. The city of Marrakech is situated at a distance of only 560 km from the Sahara desert, the major dust source in the world, leading to an atmosphere rich in particulate matter all year round. In this study, we use for the first-time local scale information on anthropogenic emissions in the city of Marrakech and conduct urban-scale chemistry-transport model simulations with the CHIMERE-WRF coupled system. We compare simulated airborne particles of diameter lower to 10 μm (PM10), NO2 and O3 concentrations against surface in-situ measurements and quantify the added value of the local inventory compared to the state-of-the-art global anthropogenic emission dataset (CAMS-GLOB_ANT). We show that correlation with measurements increases and the bias decreases in all cases (pollutants, seasons and monitor sites). The major component of summertime PM10 composition is dust particles, whereas in winter PM10 consists mainly of primary organic aerosol. Comparison between simulated and observed aerosol optical depth suggests that the model reproduces accurately most of the observed summertime dust plumes. PM10 simulated concentrations are closer to in-situ surface measurements during summer than during winter with an overestimation of 13% in summer versus an underestimation of 37% in winter. Finally, we show how the Atlas range blocks at some extent the import of dust plumes in the region and the export of local air-pollution to the surrounding area leading to ozone formation at high altitude. Modeling is an important activity to predict air-quality in Africa, where monitor networks are scarce. Our results highlight the necessity of using fine scale, local information on anthropogenic emissions to assess air-quality and in particular to (i) quantify the chemical composition of atmospheric aerosol; ii) compare the relative role of dust transport compared to locally emitted or formed suspended particles and iii) provide evidence of ozone formation at high altitude
Using a chemistry transport model to account for the spatial variability of exposure concentrations in epidemiologic air pollution studies
No full textInternational audienceEnvironmental epidemiology and more specifically time-series 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 PM2.5 (particulate matter [PM] = 10 µm 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. Copyright 2011 Air & Waste Management Association
On the spatial representativeness of NO<sub>X</sub> and PM<sub>10</sub> monitoring-sites in Paris, France
No full textInternational audienceAmbient pollutant concentrations in Paris, France are routinely measured by the local surface-monitor network of the AIRPARIF agency. Such networks, however dense, have a limited spatial representativeness around the monitoring-site and are not capable to represent the strong horizontal gradients of pollutant concentrations over urban areas. High resolution models simulate 3D pollutant concentration fields at a spatial resolution as fine as a few meters over the urban area by integrating the underlying emission sources and accounting for the effect of buildings on the dispersion patterns. These models, provide a good spatial variability over the urban area but suffer from uncertainties related to the emission inventories, meteorological fields and parametrizations of the physical and chemical processes.In this paper, simulations conducted by ARIA Technologies with the Parallel Micro-Swift-Spray (PMSS) model (http://www.aria.fr/projets/aircity) are used to assess NOX and PM10 representativeness areas around five urban background and five traffic-oriented monitoring-sites of the AIRPARIF network during ten days in March 2016. Commonly, the spatial representativeness of a monitor site is defined through homogeneity areas, namely the area around a monitoring-site where pollutant concentration is above 20% of the concentration at the location of the monitoring-site. Here, we propose a novel approach that uses similarity areas to define the spatial representation of monitor sites. Similarity areas integrate points that respect the additional condition to be highly correlated in time with the concentration at the monitor station. Thus, the criterion to select similarity areas is a combination of a high value of the correlation coefficient and a small value of the normalized root mean square error with regards to the concentration at the grid-cell corresponding to the location of the monitor. Criteria thresholds are determined through an iterative analysis and a representative area is defined through image processing that selects all the connected pixels that satisfy criteria thresholds and incorporate the grid-cell of the monitor.Daily similarity areas estimated around each monitor are compared against homogeneity areas with regards to their shape, spatial extent, and urban specific characterization. Around urban background sites they are of the same order of magnitude, whereas around traffic sites similarity areas are generally larger than homogeneity areas. PM10 representativeness areas are found to be 2.2 times larger than the NOX ones. Urban background areas are representative of the broad neighborhood around the monitoring-site, whereas traffic-oriented monitoring-sites are representative of specific urban features such as sections of roads and sidewalks along the road. Averaged over the 10 days of the study and across all monitoring-sites, representativeness areas for urban background monitoring-sites are about 8 times larger than traffic representativeness areas (0.6 km2 vs. 0.07 km2)
A statistical framework for the validation of a population exposure model based on personal exposure data
No full textInternational audienceCurrently, ambient pollutant concentrations at monitoring sites are routinely measured by local networks, such as AIRPARIF in Paris, France. Pollutant concentration fields are also simulated with regional-scale chemistry transport models such as CHIMERE (http://www.lmd.polytechnique.fr/chimere) under air-quality forecasting platforms (e.g. Prev’Air http://www.prevair.org) or research projects. These data may be combined with more or less sophisticated techniques to provide a fairly good representation of pollutant concentration spatial gradients over urban areas. Here we focus on human exposure to atmospheric contaminants. Based on census data on population dynamics and demographics, modeled outdoor concentrations and infiltration of outdoor air-pollution indoors we have developed a population exposure model for ozone and PM2.5. A critical challenge in the field of population exposure modeling is model validation since personal exposure data are expensive and therefore, rare. However, recent research has made low cost mobile sensors fairly common and therefore personal exposure data should become more and more accessible. In view of planned cohort field-campaigns where such data will be available over the Paris region, we propose in the present study a statistical framework that makes the comparison between modeled and measured exposures meaningful.Our ultimate goal is to evaluate the exposure model by comparing modeled exposures to monitor data. The scientific question we address here is how to downscale modeled data that are estimated on the county population scale at the individual scale which is appropriate to the available measurements. To assess this question we developed a Bayesian hierarchical framework that assimilates actual individual data into population statistics and updates the probability estimate
ACCEPTED: An Assessment of Changing Conditions, Environmental Policies, Time-Activities, Exposure and Disease
No full textChanges in urban design and traffic policy, demography, climate and associated adaptation, mitigation measures and environmental policies are likely to modify both outdoor and indoor air quality and therefore public health. The project aims to improve our understanding of future exposure situations and their impact on health, from an interdisciplinary approach. This will be achieved by using various state-of-the-art atmospheric models, measurements, epidemiological studies and reviews. To assess population full exposure, an integrated view accounting both for indoor and outdoor air pollution as well as for population time activity data will be developed. New dose-response functions will be estimated between health outcome, air pollution and temperature in order to better estimate the effects on the foetus and young children. Ultimately, scenarios of future urban climate and air quality will be simulated, combining future exposure scenarios, population scenarios and exposure-response functions to describe the effects of different trends and relevant policies on relative risk and burden of illness attributed to urban pollutants and their interactions with extreme temperatures. Also the mitigation strategies that can be used to reduce urbanization and climate change effects on the local urban meteorology and air quality will be assessed. With applications in several large European cities, the project will study the impact of several alternative adaptation scenarios on urban air quality and human health to a mid-century horizon (2030-2060) accounting for the effects of a changing urban climate. Scenario-based health impact assessments will combine exposure information from climate models, emission scenarios, policy evaluation studies and concentration calculations with exposure-response functions from epidemiological studies of vulnerable groups within the project and previously published functions for mortality and hospital admissions. The effects of socioeconomic and demographic trends will be discussed, the predicted health impacts and benefits associated with different interventions and policies and other urban changes will be described.ANSES; ADEME; BelSPO; UBA; Swedish EP
Air quality modelling for the mid-21th century in the greater Paris area under 2 climate scenarios
No full textThere has been an increasing interest on the impact of climate change on future air quality at both global and regional scales. The largest amount of research up to now used global-scale modelling tools to address the issue, while few recent papers use regional scale models to assess the impact of climate change on large urban agglomerations. The main issues of concern related to a regional scale set-up focusing on a city are the representativeness of the emission estimates of a regional inventory for the city as well as uncertainties in the emission projections. Regional scale projections, may be consistent with global scale climate scenarios but they are not representative of the future trend of a specific city. In this study we modelled air quality in the city of Paris, France at a mid-21st century horizon (2045-2055) under two emission and climate scenarios. The emission scenarios were developed for Europe from the Global Energy Assessment (GEA) to be consistent with the IPCCs recently developed Representative Concentration Pathways (RCPs) which incorporate only climate change actions. The emission scenarios include both climate (RCP consistent) and regional air quality policies. To cope with the aforementioned problems we combined two sources of information to project emissions for the city of Paris to the mid-century horizon. The first stems from a local agency (AIRPARIF) and includes a bottom-up high resolution emission inventory compiled for the year 2008 based on information on local activity and statistics. This inventory is projected by AIRPARIF to the year 2020 based on various air-quality policies already in place or planned for the next years. The second is a set of projection coefficients extracted from the two GEA scenarios for France and applied to the 2020 local inventory in order to obtain an emission inventory for 2050. Global scale concentrations were modelled with the coupled LMDz-INCA system and then downscaled with the regional scale air-quality model CHIMERE using two-level one-way nesting first at 0.5º (50km) grid covering Europe and then at a 4km horizontal resolution grid over the greater Paris area (Ile-de-France region). The IPSL-CM5-MR global-scale model was used to drive the WRF meteorological model for a regional domain in 50km resolution covering Europe which was subsequently downscaled to 10km resolution in order to derive meteorology for the Ile-de-France region. Two sets of simulations are performed: a continuous control run from 1995 to 2004 representing present time air-quality and a continuous run over the 2045-2054 decade representing air-quality projection to the mid-21st century. This effort aims in the development of a health impact assessment study for ozone and PM2.5 in the area and the potential differences that arise in air quality and health by using a local scale setup-up compared with a regional scale setup-up
EXPLUME v1.0: a model for personal exposure to ambient O3 and PM2.5
No full textInternational audienceThis paper presents the first version of the regional-scale personal exposure model EXPLUME (EXposure to atmospheric PolLUtion ModEling). The model uses simulated gridded data of outdoor O3 and PM2.5 concentrations and several population and building-related datasets to simulate (1) space–time activity event sequences, (2) the infiltration of atmospheric contaminants indoors, and (3) daily aggregated personal exposure. The model is applied over the greater Paris region at 2 km×2 km resolution for the entire year of 2017. Annual averaged population exposure is discussed. We show that population mobility within the region, disregarding pollutant concentrations indoors, has only a small effect on average daily exposure. By contrast, considering the infiltration of PM2.5 in buildings decreases annual average exposure by 11 % (population average). Moreover, accounting for PM2.5 exposure during transportation (in vehicle, while waiting on subway platforms, and while crossing on-road tunnels) increases average population exposure by 5 %. We show that the spatial distribution of PM2.5 and O3 exposure is similar to the concentration maps over the region, but the exposure scale is very different when accounting for indoor exposure. We model large intra-population variability in PM2.5 exposure as a function of the transportation mode, especially for the upper percentiles of the distribution. Overall, 20 % of the population using bicycles or motorcycles is exposed to annual average PM2.5 concentrations above the EU target value (25 µg m−3), compared to 0 % for people travelling by car. Finally, we develop a 2050 horizon projection of the building stock to study how changes in the buildings' characteristics to comply with the thermal regulations will affect personal exposure. We show that exposure to ozone will decrease by as much as 14 % as a result of this projection, whereas there is no significant impact on exposure to PM2.5
