50 research outputs found

    Urban air quality and meteorology on opposite sides of the Alps: The Lyon and Torino case studies

    Get PDF
    Several European urban areas are characterised by low air quality due to high local emission per unit surface. A further key feature can be related to the pollutant load due to adverse local meteo-climatic conditions. This study aims to compare the two urban agglomerations of Torino and Lyon – located on opposite sides of the Alps and characterised by similar size and population – to enlighten the role of meteorology on local pollutant dispersion. The assessment of air quality has been developed by monitoring network data, emissions analysis and the SIRANE urban dispersion model. Although the two agglomerations have similar NOX and PM10 emissions, the simulation results show higher ground level concentrations in Torino. To quantify the effect of meteorology on this excess of concentrations, we run simulations in Torino imposing the meteorological conditions of Lyon and vice versa. This implies an overall reduction of ground level concentrations in the city centre of Torino between 20% and 40% (analogously, Lyon concentrations increase by a similar amount). These results show the peculiar difficulties faced by Po valley's cities in maintaining pollution levels below regulatory thresholds and highlight the need of systemic policies and site-specific mitigation to reduce air pollution health risks

    On the Regression and Assimilation for Air Quality Mapping Using Dense Low-Cost WSN

    Get PDF
    International audienceThe use of low-cost Wireless Sensor Networks (WSNs) for air quality monitoring has recently attracted a great deal of interest. Indeed, the cost-effectiveness of emerging sensors and their small size allow for dense deployments and hence improve the spatial granularity. However, these sensors offer a low accuracy and their measurement errors may be significant due to the underlying sensing technologies. The main aim of this work is to reconsider and compare some regression approaches to assimilation ones while taking into account the intrinsic characteristics of dense deployment of low cost WSN for air quality monitoring (high density, numerical model errors and sensing errors). For that, we propose a general framework that allows the comparison of different strategies based on numerical simulations and an adequate estimation of the simulation error covariances as well as the sensing errors covariances. While considering the case of Lyon city and a widely used numerical model, we characterize the simulation errors, conduct extensive simulations and compare several regression and assimilation approaches. The results show that from a given sensing error threshold, regression methods present an optimal sensor density from which the mapping quality decreases. Results also show that the Random Forest method is often the best regression approach but still less efficient than the BLUE assimilation approach when using adequate correction parameters
    corecore