École des Ponts ParisTech

Traditionally, philosophy views the temporal relationship between technology and life as one where an unnatural clock of the first disrupts and alienates the natural clock of the second. However, when viewed as an ecological force, technology’s relationship with time can be reframed inside a context of adaptation. This paper lays out a framework for understanding the ecology of temporal relationships between actors and their environments via two different notions of time: (1) chronoception (the way an actor perceives time), and (2) characteristic time (the amount of change that appears in an environment). In the latter, computation acts as the means through which actors can expand their chronoception to further timescales of characteristic time. This expansion, however, is not neutral. Through their computational expansion, actors do not merely perceive but also affect the characteristic time of their environments, which are constructed by nested and interwoven temporalities, creating an evolutionary force of temporal misalignment. This framework enables us to rethink cybernetic governance, not as a problem of control but as a problem of temporal mediation, where computation serves not to impose its own temporality but to mediate and coordinate actors across entangled temporalities.

Power reactor noise refers to the small periodical variations of the neutron flux in nuclear reactor cores, induced by various perturbations. A prominent example is the vibration of fuel assemblies due to fluid-structure interactions. Although noise is generally an unwanted phenomenon, its analysis is useful for core monitoring. In this respect, several state-of-the-art deterministic or Monte Carlo solvers have been developed to solve the equations describing neutron noise. In this paper, we investigate the convergence properties of Monte Carlo methods for neutron noise analysis, within the orthodox linearization approximation. For this purpose, we establish a theoretical framework for complex-weighted Monte Carlo games and we show that convergence of such algorithms can be assessed using two sets of eigenvalues. In order to substantiate our findings, we examine a few relevant benchmark configurations for noise problems.

Urban trees are often not considered in air-quality models although they can significantly impact the concentrations of pollutants. Gas and particles can deposit on leaf surfaces, lowering their concentrations, but the tree crown aerodynamic effect is antagonist, limiting the dispersion of pollutants in streets. Furthermore, trees emit Biogenic Volatile Organic Compounds (BVOCs) that react with other compounds to form ozone and secondary organic aerosols. This study aims to quantify the impacts of these three tree effects (dry deposition, aerodynamic effect and BVOC emissions) on air quality from the regional to the street scale over Paris city. Each tree effect is added in the model chain CHIMERE/MUNICH/SSH-aerosol. The tree location and characteristics are determined using the Paris tree inventory, combined with allometric equations. The air-quality simulations are performed over June and July 2022. The results show that the aerodynamic tree effect increases the concentrations of gas and particles emitted in streets, such as NOx (+4.6 % on average in streets with trees and up to +37 % for NO2). This effect increases with the tree Leaf Area Index and it is more important in streets with high traffic, suggesting to limit the planting of trees with large crowns on high-traffic streets. The effect of dry deposition of gas and particles on leaves is very limited, reducing the concentrations of O3 concentrations by −0.6 % on average and at most −2.5 %. Tree biogenic emissions largely increase the isoprene and monoterpene concentrations, bringing the simulated concentrations closer to observations. Over the two-week sensitivity analysis, biogenic emissions induce an increase of O3, organic particles and PM2.5 street concentrations by respectively +1.1, +2.4 and + 0.5 % on average over all streets. This concentration increase may reach locally +3.5, +12.3 and + 2.9 % respectively for O3, organic particles and PM2.5, suggesting to prefer the plantation of low-emitting VOC species in cities.

Abstract Accepted by: Konstantinos Nikolopoulos This paper develops a new stochastic volatility model for the average daily temperature. It is a natural extension of a Gaussian model in which the temperature returns to a seasonal trend with a deterministic time-dependent volatility. The new model allows to be more conservative regarding extreme events while keeping tractability. We give a method based on conditional least squares to estimate the parameters on daily data and estimate our model on eight major European cities. We then show how to calculate efficiently the average payoff of weather derivatives both by Monte-Carlo and Fourier transform techniques. This new model allows to better assess the risk related to temperature volatility.

It is common that strategic investment decisions are made at a slow time-scale, whereas operational decisions are made at a fast time-scale. Hence, the total number of decision stages may be huge. In this paper, we consider multistage stochastic optimization problems with two time-scales, and we propose a time block decomposition scheme to address them numerically. More precisely, i) we write recursive Bellman-like equations at the slow time-scale and ii), under a suitable monotonicity assumption, we propose computable upper and lower bounds -relying respectively on primal and dual decomposition -for the corresponding slow time-scale Bellman functions. With these functions, we are able to design policies. We assess the methods tractability and validate their efficiency by solving a battery management problem where the fast time-scale operational decisions have an impact on the storage current capacity, hence on the strategic decisions to renew the battery at the slow time-scale.

Abstract. The quantification of emissions of greenhouse gases and air pollutants through the inversion of plumes in satellite images remains a complex problem that current methods can only assess with significant uncertainties. The anticipated launch of the CO2M (Copernicus Anthropogenic Carbon Dioxide Monitoring) satellite constellation in 2026 is expected to provide high-resolution images of CO2 (carbon dioxide) column-averaged mole fractions (XCO2), opening up new possibilities. However, the inversion of future CO2 plumes from CO2M will encounter various obstacles. A challenge is the low CO2 plume signal-to-noise ratio due to the variability in the background and instrumental errors in satellite measurements. Moreover, uncertainties in the transport and dispersion processes further complicate the inversion task. To address these challenges, deep learning techniques, such as neural networks, offer promising solutions for retrieving emissions from plumes in XCO2 images. Deep learning models can be trained to identify emissions from plume dynamics simulated using a transport model. It then becomes possible to extract relevant information from new plumes and predict their emissions. In this paper, we develop a strategy employing convolutional neural networks (CNNs) to estimate the emission fluxes from a plume in a pseudo-XCO2 image. Our dataset used to train and test such methods includes pseudo-images based on simulations of hourly XCO2, NO2 (nitrogen dioxide), and wind fields near various power plants in eastern Germany, tracing plumes from anthropogenic and biogenic sources. CNN models are trained to predict emissions from three power plants that exhibit diverse characteristics. The power plants used to assess the deep learning model's performance are not used to train the model. We find that the CNN model outperforms state-of-the-art plume inversion approaches, achieving highly accurate results with an absolute error about half of that of the cross-sectional flux method and an absolute relative error of ∼ 20 % when only the XCO2 and wind fields are used as inputs. Furthermore, we show that our estimations are only slightly affected by the absence of NO2 fields or a detection mechanism as additional information. Finally, interpretability techniques applied to our models confirm that the CNN automatically learns to identify the XCO2 plume and to assess emissions from the plume concentrations. These promising results suggest a high potential of CNNs in estimating local CO2 emissions from satellite images.

Abstract. The Low-Level Jet (LLJ) and the Urban Heat Island (UHI) are common nocturnal phenomena in the atmospheric boundary layer. While the canopy layer UHI has been studied extensively, interactions of the LLJ and the urban atmosphere in general (and the UHI in particular) have received less attention. In the framework of the PANAME initiative in the Paris region, continuous measurements of wind speed and vertical velocity profiles were recorded with two Doppler Wind Lidars (DWL) – for the first time allowing for a detailed investigation of the summertime LLJ characteristics in the region. Jets are detected for 70 % of the examined nights, often simultaneously at an urban and a suburban site highlighting the LLJ regional spatial extent. Emerging at around sunset, the mean LLJ duration is ∼10 h, the mean wind speed is ∼9 ms−1, and the average core height is 400 m above the city. For many jets, results show signatures in the temporal evolution that indicate the inertial oscillation mechanism plays a role in the jet development: a clockwise veering of the wind direction and a rapid acceleration followed by a slower deceleration. The LLJ core induces variance in the vertical velocity (σ2w) above the urban canopy layer. It is shown that σ2w is a powerful indicator for the air temperature spatial contrasts as UHI intensity decreases exponentially with increasing σ2w and strong values only occur when σ2w is very weak. This study demonstrates how DWL observations in cities provide valuable insights into near-surface processes relevant to human and environmental health.