Airparif

This study quantifies the impact of residential wood heating on winter air quality in France, including street-level analysis in Paris. It applies a multi-scale model to simulate regulated and emerging pollutants, such as organic matter (OM), black carbon (BC), and ultrafine particles (UFP), associated with health risks. Wood-burning emissions in Île-de-France and Paris were estimated using detailed local surveys, a new classification of appliances and emission factors accounting for condensable compounds. Over France, emissions were quantified from the EMEP top-down emission inventory, with post-estimated condensables. Wood burning is a major contributor to particulate pollution: in France, it accounts for 39.9% of PM2.5, 72.4% of BC, and 76.7% of OM. In Paris, contributions are similar, except for BC (27.2% at street level), influenced by other sources, as road traffic. Contributions to UFP are lower, ranging from 7% in Parisian streets to 15.5% over France. Wood burning significantly contributes to outdoor population exposure in Paris (33% for PM2.5, 20% for BC, and 70% for OM), with heating emissions mostly from auxiliary and comfort use (98%). Two 2030 scenarios were evaluated: business-as-usual (BAU) and a national emission-reduction objective. Under BAU, PM2.5 emissions and concentrations decline by 32.6% and 13.4% over France, and by 18.1% and 14.2% in Paris. Concentration reductions are smaller than emission reductions because some PM2.5 components (e.g. inorganics) are unaffected by wood-burning controls. The national objective scenario achieves larger impacts, typically 50%-70% greater than BAU, reducing PM2.5 concentrations of about 22%-24% in urban and street environments. Environmental Implications Health risks of fine particles depend on their composition and size, with black carbon, organics, and ultrafine particles emerging as key indicators. Our results indicate that reducing residential wood heating is an effective policy lever to mitigate wintertime particulate pollution in urban areas. In Paris, a large share of emissions arises from auxiliary and comfort heating, suggesting that targeted measures addressing non-essential wood use could deliver substantial air-quality benefits. The transition to newer heating technologies should be carefully evaluated to ensure that improvements in mass-based air quality are not offset by increased ultrafine particle emissions, which are not covered by current regulations but may have important health implications.

BACKGROUND:Major depressive disorders pose a substantial public health burden, with emerging evidence linking these disorders to air pollution. OBJECTIVE:To investigate the association between short-term exposure to air pollution and depressive symptoms, focusing on particulate matter with a diameter ≤ 2.5 μm (PM2.5) and nitrogen dioxide (NO2). METHODS:Data from the enrollment phase of the French CONSTANCES cohort (2012-2016) were analyzed cross sectionally. For each participant, we assigned the median concentrations of PM2.5 and NO2, estimated by the CHIMERE model, over the seven days preceding completion of the Centre of Epidemiologic Studies Depression (CES-D) scale to their residential addresses. We assessed depressive symptoms using the CES-D and defined clinically significant depressive symptoms (binary variable) with the validated threshold of 19. We used negative binomial regression models (yielding Incidence Rate Ratio (IRR) and 95% confidence interval (CI)) and logistic regression models (yielding Odds Ratio (OR) and 95% CI), for an interquartile range (IQR) increase in exposures, adjusted for two sets of confounders. RESULTS:105,976 participants (mean age, 47.3 ± 13.7 years; 53% women) were included. The 7-day median PM2.5 and NO2 exposures were 8.4 µg/m3 (IQR: 5.9-12.2) and 2.2 µg/m3 (IQR: 1.3-3.5), respectively. Short-term exposures to PM2.5 and NO2 were positively associated with a higher CES-D score (IRR = 1.009 (95% CI: 1.003-1.014) and IRR = 1.009 (95% CI: 1.004-1.014), respectively) and with higher rates of clinically-significant depressive symptoms (OR = 1.027 (1.008-1.046) and OR = 1.031 (1.012-1.051), respectively). These results were consistent across sensitivity analyses. There was an interaction between BMI and exposure to NO2, with a significant effect only among subjects with obesity. CONCLUSIONS:Our results suggest that air pollution may affect mental health, even at a subclinical level, warranting further research especially during pollution peaks. However, this study could not establish causality.

Road dust resuspension is widely recognized as a major contributor to traffic-related particulate matter (PM) in urban environments. Nevertheless, reported emission factors exhibit substantial variability. These discrepancies stem not only from the intrinsic complexity of the resuspension process but also from limitations in measurement techniques, which often fail to adequately control or characterize the influencing parameters. As a result, the contribution of each parameter remains difficult to isolate, leading to inconsistencies across studies. This study presents an experimental protocol developed to quantify PM10 and PM2.5 emission factors associated with vehicle-induced road dust resuspension. Experiments were conducted on a dedicated test track seeded with alumina particles of controlled mass and size distribution to simulate road dust. A network of microsensors was strategically deployed at multiple upwind and downwind locations to continuously monitor particle concentration variations during vehicle passages. Emission factors were derived through time integration of the mass flow rate of resuspended dust measured by the sensor network. The estimated PM10 emission factor showed excellent agreement, within 2.5%, with predictions from a literature-based formulation, thereby validating the accuracy and external relevance of the proposed protocol. In contrast, comparisons with U.S. EPA formulas and other empirical equations revealed substantially larger discrepancies, particularly for PM2.5, highlighting the persistent limitations of current modeling approaches.

Abstract The Paris 2024 Olympics Research Demonstration Project lasted 5 years and was endorsed by the World Weather Research Programme within the World Meteorological Organization (WMO) to improve urban weather forecasts, using the metropolitan area of Paris as a case study. Meteorological institutes and universities from 10 countries participated. The project had three objectives to increase knowledge of urban summer meteorological hazards, to improve hectometric-scale numerical weather prediction models in cities, and to facilitate the coproduction of weather information for large sporting events. Increased convective activity downwind of Paris was highlighted by a new radar- and lightning-based climatology, and numerical experiments identified its potential driving processes. Collaborative analyses of past heat waves improved air quality models for the Olympics and simulations of thermal comfort variability. Advances in urban-scale modeling enabled real-time intercomparison of seven hectometric- or kilometric-scale atmospheric models that provided daily forecasts throughout the Olympics and Paralympics. Hectometric models showed similarities in convective precipitation characteristics, tending to have an excessive number of small showers and grid-length dependence. Model intercomparisons also showed variability in urban heat island intensity and unexpected differences in urban heat plume extent. A sociological study highlighted the differences in viewpoints between forecasters and sport managers and the importance of transparency in communicating uncertainties. Finally, a decision-making procedure to manage extreme heat contingencies for the “Marathon for All” Olympics public event was developed based on a 100-m grid-length model in collaboration with the weather forecasters’ team. All these insights open new scientific questions in urban climate research and ways forward for future hectometric numerical weather prediction. Significance Statement Forecasting in an urban environment during a global event held in one of the world’s most densely populated cities is a unique meteorological challenge. The real-time intercomparison of high-resolution models conducted during the Olympics, as presented here, demonstrates the potential of hectometric forecasting while also highlighting new scientific questions and operational challenges, such as those associated with event-specific forecasts like the Marathon for All. The findings will appeal to both the numerical weather prediction and general meteorological communities interested in urban meteorology advances and real-time forecasting improvements for major events.

An inter-laboratory comparison (ILC) involving optical particle size spectrometers (OPSSs) was organised at the French national level. The aim of this study was to make an inventory of the metrological capabilities of particle number size distribution (PNSD) measurements using OPSSs. This laboratory study was conducted over an 18-month period and involved 16 partners and 35 OPSSs. This large number of instruments provides strong statistical weight to the dataset, offering robust insight into the overall instrumental capabilities of accurately and reliably measuring PNSD. For that, each partner applied the same pre-defined experimental protocol to the OPSS(s) to be tested, operated together with a common control OPSS. Three different test aerosols were involved, and their PNSDs were measured: (1) a monodisperse amorphous silica sample, (2) glass beads, and (3) a green cornstarch powder. This article presents the measured PNSD using the 35 OPSSs associated with the description of the experimental set-up, sample preparation protocol, and comparison with scanning electron microscopy measurements.