Addressing the issue of exposure to primary pollution in urban areas: Application to Greater Paris

Etuman, Arthur Elessa; Coll, Isabelle; Makni, Inès; Benoussaïd, Taos

doi:10.1016/j.atmosenv.2020.117661

Cited by 7 publications

(5 citation statements)

References 28 publications

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“…As for multi-scale modelling, the main research efforts associated with these numerical approaches are directed towards the downscaling of simulated pollutant concentration fields in urban areas, the improvement of CTM forecast using additional observation data, and a refined representation of individual exposure at the street scale (Berrocal et al, 2020;Elessa Etuman et al, 2020). Gariazzo et al (2020) used a random forest model to enhance CTM results and produce improved population exposure estimates at 200 m resolution, in a multi-pollutant, multi-city, and multi-year study conducted over Italy.…”

Section: Use Of Advanced Numerical Approaches and Statistical Modelsmentioning

confidence: 99%

“…P. Wang et al, 2015;Zhan et al, 2017;Just et al, 2020;Alimissis et al, 2018). CTMs have also been developed to improve spatial resolution, for example, through downscaling approaches for predicting air quality in urban areas, forecast-ing air quality, and simulation of exposure at the street scale (Berrocal et al, 2020;Elessa Etuman et al, 2020;. Ensemble simulations have proven to be successful to provide more reliable air quality prediction and forecasting (e.g.…”

Section: Air Quality Modellingmentioning

confidence: 99%

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Advances in air quality research – current and emerging challenges

et al. 2022

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Abstract. This review provides a community's perspective on air quality research focusing mainly on developments over the past decade. The article provides perspectives on current and future challenges as well as research needs for selected key topics. While this paper is not an exhaustive review of all research areas in the field of air quality, we have selected key topics that we feel are important from air quality research and policy perspectives. After providing a short historical overview, this review focuses on improvements in characterizing sources and emissions of air pollution, new air quality observations and instrumentation, advances in air quality prediction and forecasting, understanding interactions of air quality with meteorology and climate, exposure and health assessment, and air quality management and policy. In conducting the review, specific objectives were (i) to address current developments that push the boundaries of air quality research forward, (ii) to highlight the emerging prominent gaps of knowledge in air quality research, and (iii) to make recommendations to guide the direction for future research within the wider community. This review also identifies areas of particular importance for air quality policy. The original concept of this review was borne at the International Conference on Air Quality 2020 (held online due to the COVID 19 restrictions during 18–26 May 2020), but the article incorporates a wider landscape of research literature within the field of air quality science. On air pollution emissions the review highlights, in particular, the need to reduce uncertainties in emissions from diffuse sources, particulate matter chemical components, shipping emissions, and the importance of considering both indoor and outdoor sources. There is a growing need to have integrated air pollution and related observations from both ground-based and remote sensing instruments, including in particular those on satellites. The research should also capitalize on the growing area of low-cost sensors, while ensuring a quality of the measurements which are regulated by guidelines. Connecting various physical scales in air quality modelling is still a continual issue, with cities being affected by air pollution gradients at local scales and by long-range transport. At the same time, one should allow for the impacts from climate change on a longer timescale. Earth system modelling offers considerable potential by providing a consistent framework for treating scales and processes, especially where there are significant feedbacks, such as those related to aerosols, chemistry, and meteorology. Assessment of exposure to air pollution should consider the impacts of both indoor and outdoor emissions, as well as application of more sophisticated, dynamic modelling approaches to predict concentrations of air pollutants in both environments. With particulate matter being one of the most important pollutants for health, research is indicating the urgent need to understand, in particular, the role of particle number and chemical components in terms of health impact, which in turn requires improved emission inventories and models for predicting high-resolution distributions of these metrics over cities. The review also examines how air pollution management needs to adapt to the above-mentioned new challenges and briefly considers the implications from the COVID-19 pandemic for air quality. Finally, we provide recommendations for air quality research and support for policy.

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Section: Use Of Advanced Numerical Approaches and Statistical Modelsmentioning

confidence: 99%

Section: Air Quality Modellingmentioning

confidence: 99%

Advances in air quality research – current and emerging challenges

et al. 2022

View full text Add to dashboard Cite

show abstract

“…As for multi-scale modelling, the main research efforts associated with these numerical approaches are directed towards the downscaling of simulated pollutant concentration fields in urban areas, the improvement of CTM forecast using additional observation data, and a refined representation of individual exposure at the street scale (Berrocal et al, 2020, Elessa Etuman et al, 2020. Gariazzo et al (2020) used a random forest model to enhance CTM results and produce improved population exposure estimates at 200m resolution, in a multi-pollutant, multi-city and multi-year study conducted over Italy.…”

Section: Use Of Advanced Numerical Approaches and Statistical Modelsmentioning

confidence: 99%

“…New approaches of artificial neural network models and machine learning have shown a more detailed representation of air quality in complex built-up areas (e.g., Wang et al, 2015b, Zhan et al, 2017, Just et al, 2020, Alimissis et al, 2018. CTMs have also been developed to improve spatial resolution, for example, through downscaling approaches for predicting air quality in urban areas, forecasting air quality and simulation of exposure at the street scale (Berrocal et al, 2020, Elessa Etuman et al, 2020. Ensemble simulations have proven to be successful to provide more reliable air quality prediction and forecasting (e.g., Galmarini et al, 2012 and complementary hybrid approaches have been explored for multi-scale applications (Galmarini et al, 2018).…”

Section: Air Quality Modellingmentioning

confidence: 99%

Advances in Air Quality Research – Current and Emerging Challenges

Sokhi

Moussiopoulos

Baklanov

et al. 2021

Preprint

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Abstract. This review provides a community’s perspective on air quality research focussing mainly on developments over the past decade. The article provides perspectives on current and future challenges as well as research needs for selected key topics. While this paper is not an exhaustive review of all research areas in the field of air quality, we have selected key topics that we feel are important from air quality research and policy perspectives. After providing a short historical overview, this review focuses on improvements in characterising sources and emissions of air pollution, new air quality observations and instrumentation, advances in air quality prediction and forecasting, understanding interactions of air quality with meteorology and climate, exposure and health assessment, and air quality management and policy. In conducting the review, specific objectives were (i) to address current developments that push the boundaries of air quality research forward, (ii) to highlight the emerging prominent gaps of knowledge in air quality research and (iii) and to make recommendations to guide the direction for future research within the wider community. This review also identifies areas of particular importance for air quality policy. The original concept of this review was borne at the International Conference on Air Quality 2020 (held online due to the COVID 19 restrictions during 18–26 May 2020), but the article incorporates a wider landscape of research literature within the field of air quality science. On air pollution emissions the review highlights, in particular, the need to reduce uncertainties in emissions from diffuse sources, particulate matter chemical components, shipping emissions and the importance of considering both indoor and outdoor sources. There is a growing need to have integrated air pollution and related observations from both ground based and remote sensing instruments, including especially those on satellites. The research should also capitalize on the growing area of lower cost sensors, while ensuring a quality of the measurements which are regulated by guidelines. Connecting various physical scales in air quality modelling is still a continual issue, with cities being affected by air pollution gradients at local scales and by long range transport. At the same time, one should allow for the impacts from climate change on a longer timescale. Earth system modelling offers considerable potential by providing a consistent framework for treating scales and processes, especially where there are significant feedbacks, such as those related to aerosols, chemistry and meteorology. Assessment of exposure to air pollution should consider both the impacts of indoor and outdoor emissions, as well as apply more sophisticated, dynamic modelling approaches. With particulate matter being one of the most important pollutants for health, research is indicating the urgent need to understand, in particular, the role of particle number and chemical components in terms of health impact, which in turn requires improved emission inventories and models for predicting high resolution distributions of these metrics over cities. The review also examines, how air pollution management needs to adapt to the above-mentioned new challenges and briefly considers the implications from the COVID-19 pandemic for air quality. Finally, we provide recommendations for air quality research and support for policy.

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“…Dhondt et al(48) obtained more sharply contrasted results, with an average dynamic exposure value higher than the static value for NO 2(21.6 versus 20:98 mg=m 3 ) yet lower for ozone (79.7 versus 81:2 mg=m 3 ).Hatzopoulou and Miller (43) showed that, in the case of Toronto, the distribution of cumulative NO x concentrations throughout the day by individuals presents a larger range of values than that obtained with a static approach. Similarly, the work of Elessa Etuman et al(51) suggested that, in the case of Greater Paris (Grand Paris), the distribution of individual exposure to NO…”

mentioning

confidence: 97%

Modeling Exposure to Mobility-Related Pollution: Review and Key Challenges

Gastineau,

Can,

Yaméogo

et al. 2023

Transportation Research Record: Journal of the Transportation R

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The health and equity issues related to urban pollution, coupled with the need to assess the consequences of implementing transport policies or new mobility solutions, make it imperative to provide decision-makers with a relevant modeling framework. The coupling of activity-based travel models with environmental models presents a promising approach to thoroughly investigate the interconnected issues related to the impact of road traffic on individual exposure to noise and air pollution. Integrated modeling chains provide a robust framework to comprehensively assess traffic scenarios on a city scale and address issues related to environmental inequalities. This article reviews the recent work in this field to identify both the main contributions and key challenges still to be addressed. It illustrates how the use of a dynamic approach, which considers the intraday dynamics of individual mobilities and pollution concentrations, can contribute to more accurate estimates of the exposure of individuals to pollutants. This modeling approach also has the potential to investigate how the level of exposure depends on the characteristics of the individuals (socioeconomic factors, commuting patterns, location of residence, and main activities). Nonetheless, there are still important challenges to overcome to further enhance the accuracy and applicability of these models: taking into account all types of traffic and transport modes; refining the modeling of exposure to pollution during travel; taking better consideration of microenvironments; and so forth.

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Addressing the issue of exposure to primary pollution in urban areas: Application to Greater Paris

Cited by 7 publications

References 28 publications

Advances in air quality research – current and emerging challenges

Advances in air quality research – current and emerging challenges

Advances in Air Quality Research – Current and Emerging Challenges

Modeling Exposure to Mobility-Related Pollution: Review and Key Challenges

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