Mexico City (MC) is the home of 21.2M people, 19% of the country's population. The MC urban area has intense emissions of pollutants and greenhouse gases, which accumulate in the overlying air-shed due to the location of the city in a high-altitude basin surrounded by mountains. Local and national authorities have engaged into aggressive emission reduction strategies, e.g. relocation of heavy industry and vehicle-free days. The purpose of MERCI-CO2 is to develop the research needed to set up atmospheric CO2 measurements that will enable to verify the effectiveness of CO2 emission reductions taken by the city authorities, represented in the project by the Ministry of Environment of MC’s government (SEDEMA). To do so, we propose to measure atmospheric CO2 concentrations gradients within and around the megacity, attribute them to CO2 emissions from the city, and combine them with a 3D atmospheric transport model and emission inventories to reduce the uncertainty on CO2 emissions in support of reduction policies taken by regional authorities. This research project will do the design and lay the foundation of a future city scale CO2 monitoring network to be operated by the regional air-quality network, partner of the project. Given the huge extent of MC, sampling the atmosphere to best capture its CO2 emissions is a scientific challenge, which requires several stations. The proposed solution to overcome the costs of high-precision CO2 instruments is to deploy an array of 10 novel low-cost medium precision (LCMP) CO2 sensors identified and already tested at LSCE, with two existing high-precision CO2 instruments. The performances of LCMP will be tested in real-conditions against the precision instruments. The locations of the LCMP will be selected after analysis of existing air quality observations and gridded CO2 emissions fields based on energy use statistics available from the MC authorities. The new CO2 stations will be co-located with existing air quality stations measuring CO and NOx concentrations, two pollutants co-emitted with CO2 by the combustion of fuels, with different ratios depending on the emitting sector considered. Combining CO2, CO and NOx observations will help us to identify the contribution of different emission sectors (traffic, residential, industry) to total CO2 emissions. CO2 concentrations measured in the boundary layer above the city with in-situ sensors will be complemented by total air column CO2 observations made with ground-based remote sensing spectrometers. These measurements will characterize large-scale CO2 gradients between up and downwind of MC, bringing an independent check on whole city emissions, whereas the in-situ sensors provide information of spatial/sectorial details within MC area. Both measurements will be compared to simulations from a high-resolution 3D model prescribed with city-scale CO2 emission maps. First, comparison of modeled CO2 with observed data will help to identify key mesoscale transport processes and possible emission hot-spots. This will allow us to understand under which conditions the transport model performs well enough to be used in an inverse model, i.e. to retrieve CO2 emissions from measured CO2 gradients. Secondly, the inversion of selected CO2 gradients in an analytical Bayesian inversion framework will allow an independent estimate of MC emissions that will be compared to the existing official inventory based on energy use and fuel statistics. The differential CO2 column measurements of instruments located up and downwind of the city will provide a top-down verification of the overall sources at city scale. The research proposed in MERCI-CO2 will deliver a reduction of uncertainties on the MC CO2 emissions that will be taken to operations by Mexican partners after the lifetime of the project. This project benefits from the existing pollutant measurement infrastructure in Mexico City and from the strong interest and involvement of Mexican partners.

The urban-industrial territories have a great and diversified economic activity, essential to the economy of the territory, but likely to be the source of nuisances, in particular odor annoyance. Several industrial incidents were memorable because they have been associated with large-scale odor pollution. On September 26, 2019, the accident at the Lubrizol site in Rouen, resulted in pollution of air, water and soil. The odor pollution event was unprecedented in both intensity and duration. The first feedback from involved persons on field showed the need to develop new operational tools to specifically respond to industrial or accidental issues. In order to limit the incidence of future episodes of odor pollution, it is necessary to better understand emission phenomena of plume pollution following an industrial accident and to know its dispersion and evolution with time. These issues are at the origin of the DISCERNEZ project carried out by a consortium gathering public, private and associative entities with multidisciplinary skills. It includes (i) two research laboratories specializing in olfactory analysis and physico-chemical pollution in urban areas: CERI EE of IMT Lille Douai and URCOM of Le Havre Normandie University; (ii) Atmo Normandie, Normandy monitoring network approved by the French Ministry in charge of Environment and actively involved in supporting public and local authorities, and industrials, especially in case of accidents; (iii) as well as two companies: Osmanthe which carried out olfactory analyses following the Lubrizol accident and which also participates in the training and development of "Le Langage des Nez®" method and ARIA Technologies, a company specializing in the development of numerical models for air quality. With this project, the consortium aims to bring new scientific knowledge and develop tools for managing odor annoyance in dense urban areas. This involves improving an existing model of atmospheric dispersion of pollutants. The aim is to have a tool for establishing a predictive map of the odorous impact of emissions according to different scenarios (major accident, incident but also on a daily basis), depending on weather conditions , topographic and evolution of emissions within an industrial area. This assessment, mediation and decision support tool will be useful for both the emitting industries and communities. It will serve the integration of industrial zones within urban areas and will thus allow the development of the attractiveness of the territories while preserving the quality of life of the neighboring populations.

Air pollution in East Asia and especially in China is an outstanding issue. The rapid economic development and urbanization during the last decades resulted in rising pollutant emissions leading to the largest pollutant concentrations in the world, largely exceeding the recommended outdoor air pollutant thresholds from the World Health Organization (WHO) for the major pollutants (ozone, PM2.5, and PM10). This pollution causes severe health problems increasing cardiovascular and respiratory diseases and premature mortality, and reduces population welfare (e.g. closure of transportation infrastructure due to lack of visibility). Robust monitoring and forecasting systems associated with downstream services providing comprehensive risk indicators are highly needed for public authorities to establish efficient pollution mitigation strategies and for populations to take preventive actions to preserve their health and welfare. In addition, a precise evaluation of the present and future impacts of Chinese pollutant emissions is of importance to quantify: first, the consequences of pollutants export on atmospheric composition and air quality all over the globe; second, the additional radiative forcing induced by the emitted and produced short-lived climate forcers (ozone and aerosols); third, the long-term health consequences of pollution exposure. To achieve this, a detailed understanding of sources of East Asian pollution is necessary. Building on a ambitious synergistic approach combining observations and modeling to bridge the scales from local to global, the PolEASIA project aims at a better documentation and quantification of the sources and the distribution of the major pollutants (such as ozone and aerosols), and of their past, present and future evolution. Inverse modeling coupling satellite observations and regional modeling will be applied to derive highly resolved (monthly, ¼ of degree) optimized inventories of NOx and NMHC (non methane hydrocarbons) emissions over China for a decade. A multi-scale approach coupling innovative satellite observations, in situ measurements and chemical transport model simulations will be developed to characterize the spatial distribution, the interannual to daily variability and the long-term trends of the major pollutants (ozone and aerosols) over East Asia. Specific studies will be conducted to quantify the role of the different processes (emissions, transport, chemical transformation) explaining the observed pollutant distributions. A particular attention will be paid to assess the natural and anthropogenic contributions to Eastern Asian pollution. The major impacts of Chinese pollution will be assessed. The consequences of the long-range transport of pollutants on regional and global atmospheric composition will be determined using both satellite observations and model simulations. Health Impact Assessment (HIA) methods coupled with model simulations will be used to estimate the long-term impacts of exposure to pollutants (PM2.5 and ozone) on cardiovascular and respiratory mortality. In addition, the radiative forcing of short-lived species (such as ozone and aerosols) will be derived from present-day simulations and simulations under future emission scenarios. Progress made with the understanding of pollutant sources, especially in terms of modeling of pollution over East Asia and advanced numerical approaches such as inverse modeling will serve the development of an efficient and marketable forecasting system for regional outdoor air pollution. Additional modules will be coupled with the forecasting system including end-user friendly applications to provide comprehensive indices for health and visibility risks to public authorities and populations. The performances of this upgraded forecasting system will be evaluated and promoted to ensure a good visibility of the French technology.

The SECIF project consists in the extension of a first preliminary attempt led in partnership with IDDRI (“Institut for sustainable development and international relations) and related to climate change vulnerability issues for companies of industrial and services sectors. This collaboration enabled to identify precise needs into a couple of companies in terms of climate products and climate expertise. Moreover, feasibility of such a partnership has been validated using some concrete studies. They have been performed for two given sectors that are strongly sensitive to the climate adaptation issue: water and energy sectors. Several discussions with other companies and sectors (transport, all sort of services, building, urban planning …) have also shown that awareness of this different actors is actually going. A lot of work is still necessary to make companies more aware and more mature on their vulnerability and to be able to express a clear and concrete demand. In addition to this “consulting” work, research institutes must organise themselves and improve coordination in order to be able, if necessary, to respond to numerous requests and provide suitable information (data, various products and/or analysis methods). Several initiatives concerning model data distribution (regional or global scale data; raw or elaborated data) and their expertise have been launched (Drias and PRODIGUER projects for example). In the other side, several industrial requests on these vulnerability topics are often related to knowledge in the scope of basic research. Answering needs to provide an additional fundamental research and integrate multidisciplinarity aspects. Currently, at a national scale, an interface cell is missing to answer these various requests. The work that we propose in SECIF is then an exploratory step towards the implementation of climate services for industrial community. They will allow to better integrate climate data and knowledge in industrial adaptation strategies.

Climate extremes such as heat waves or tropical storms have huge social and economic impact. The forecasting of such extreme events at the sub-seasonal time scale (from 10 days to 3 months) is challenging. Since the atmosphere and the ocean are coupled systems of enormous complexity, in order to advance sub-seasonal predictability of extreme events, it is crucial to train a new kind of interdisciplinary top-level researchers. CAFE research is structured in three WP: Atmospheric and oceanic processes, Extreme events and Tools for predictability, and brings together an interdisciplinary team of scientists. Objectives: Study of the relation between RWPs and the large scale environment, and the resulting limit of predictability; Statistical characterization of MJO events, dependence on climatic factors, and simple modelling to evaluate predictability; Development of diagnosis tools for identification and tracking of the MJO, blocking, waves and oceanic structures; Analysis of climatic changes in weather patterns and their relation with new climatic phenomena and extreme events in Europe; Estimation of probabilities for severe damages due to extreme events associated to ENSO; Validation of the hypothesis of cascades of extreme events and effects of a non-stationary climate; Estimation of exceedance probabilities for intensity of severe atmospheric events, including windstorms and hurricanes; Assessment of the response of extreme weather events for different levels of stabilized global warming and comparison with their response to internal modes of climate variability; Development of a procedure to improve the predictability of the onset of monsoon; Advanced statistical analysis of dynamic associations between SSS and extreme precipitation events; Study of predictability of large-scale atmospheric flow patterns over the Mediterranean connected to extreme weather; Systematic quantification of the predictability potential of a SWG of analogues of atmospheric circulation.