evaltools

The Python package `evaltools` is designed to assess surface atmosphere composition prediction models against in-situ observations. This package provides several tools to compute model scores and plot them. It is used for evaluation of air quality models from the Copernicus Atmosphere Monitoring Service (CAMS).The concept of `evaltools` is to compare observations (measured over time at fixed lat/lon locations) with simulations (wich may have a forecast horizon of several days) computed over a period of several days. Therefore, it can be suitable for other data types such as AERONET data, but will not handle data with a vertical component

Air Control Toolbox (ACT_v1.0): a flexible surrogate model to explore mitigation scenarios in air quality forecasts

International audienceWe introduce the first toolbox that allows exploring the benefit of air pollution mitigation scenarios in the every-day air quality forecasts through a web interface. Chemistry-transport models (CTMs) are required to forecast air pollution episodes and assess the benefit that shall be expected from mitigation strategies. However, their complexity prohibits offering a high level of flexibility in the tested emission reductions. The Air Control Toolbox (ACT) introduces an innovative automated calibration method to cope with this limitation. It consists of a surrogate model trained on a limited set of sensitivity scenarios to allow exploring any combination of mitigation measures. As such, we take the best of the physical and chemical complexity of CTMs, operated on high-performance computers for the every-day forecast, but we approximate a simplified response function that can be operated through a website to emulate the sensitivity of the atmospheric system to anthropogenic emission changes for a given day and location. The numerical experimental plan to design the structure of the surrogate model is detailed by increasing level of complexity. The structure of the surrogate model ultimately selected is a quadrivariate polynomial of first order for residential heating emissions and second order for agriculture, industry and traffic emissions with three interaction terms. It is calibrated against 12 sensitivity CTM simulations, at each grid point and every day for PM10, PM2.5, O3 (both as daily mean and daily maximum) and NO2. The validation study demonstrates that we can keep relative errors below 2 % at 95 % of the grid points and days for all pollutants. The selected approach makes ACT the first air quality surrogate model capable to capture non-linearities in atmospheric chemistry response. Existing air quality surrogate models generally rely on a linearity assumption over a given range of emission reductions, which often limits their applicability to annual indicators. Such a structure makes ACT especially relevant to understand the main drivers of air pollution episode analysis. This feature is a strong asset of this innovative tool which makes it also relevant for source apportionment and chemical regime analysis. This breakthrough was only possible by assuming uniform and constant emission reductions for the four targeted activity sectors. This version of the tool is therefore not suited to investigate short-term mitigation measures or spatially varying emission reductions

Impact des nouvelles lignes directrices oms pour la qualité de l’air

International audienc

Impact des nouvelles lignes directrices oms pour la qualité de l’air

International audienc

Historical reconstruction of background air pollution over France for 2000–2015

International audienceThis paper describes a 16-year dataset of air pollution concentrations and air quality indicators over France. Using a kriging method that combines background air quality measurements and modeling with the CHIMERE chemistry transport model, hourly concentrations of NO2, O3, PM10 and PM2.5 are produced with a spatial resolution of about 4 km. Regulatory indicators (annual average, SOMO35 (sum of ozone means over 35 ppb), AOT40 (accumulated ozone exposure over a threshold of 40 ppb), etc.) are also calculated from these hourly data. The NO2 and O3 datasets cover the period 2000–2015, as well as the annual PM10 data. Hourly PM10 concentrations are not available from 2000 to 2007 due to known artifacts in PM10 measurements. PM2.5 data are only available from 2009 onwards due to the limited number of measuring stations available before this date. The overall dataset was evaluated over all years by a cross-validation process against background stations (rural, sub-urban and urban) to take into account the data fusion between measurement and models in the method. The results are very good for PM10, PM2.5 and O3. They show an overestimation of NO2 concentrations in rural areas, while NO2 background values in urban areas are well represented. Maps of the main indicators are presented over several years, and trends are calculated. Finally, exposure and trends are calculated for the three main health-related indicators: annual averages of PM2.5, NO2 and SOMO35. The DOI link for the dataset is https://doi.org/10.5281/zenodo.5043645 (Real et al., 2021). We hope that the publication of this open dataset will facilitate further studies on the impacts of air pollution

Differential impact of government lockdown policies on reducing air pollution levels and related mortality in Europe

Previous studies have reported a decrease in air pollution levels following the enforcement of lockdown measures during the first wave of the COVID-19 pandemic. However, these investigations were mostly based on simple pre-post comparisons using past years as a reference and did not assess the role of different policy interventions. This study contributes to knowledge by quantifying the association between specific lockdown measures and the decrease in ­NO2, ­O3, ­PM2.5, and ­PM10 levels across 47 European cities. It also estimated the number of avoided deaths during the period. This paper used new modelled data from the Copernicus Atmosphere Monitoring Service (CAMS) to define business-as-usual and lockdown scenarios of daily air pollution trends. This study applies a spatio-temporal Bayesian non-linear mixed effect model to quantify the changes in pollutant concentrations associated with the stringency indices of individual policy measures. The results indicated non-linear associations with a stronger decrease in ­NO2 compared to ­PM2.5 and ­PM10 concentrations at very strict policy levels. Differences across interventions were also identified, specifically the strong effects of actions linked to school/workplace closure, limitations on gatherings, and stay-at-home requirements. Finally, the observed decrease in pollution potentially resulted in hundreds of avoided deaths across Europe

Impact of the COVID-19 lockdown policies on reducing air pollution levels and related deaths in Europe

International audienceBACKGROUND AND AIM: Previous studies have reported a decrease in air pollution following the enforcement of lockdown measures during the first wave of the COVID-19 pandemic. However, these investigations were mostly based on simple pre-post comparisons using past years as a reference, and did not assess the role of different policy interventions. In this contribution, we quantitatively evaluated the association between various lockdown measures and the decrease in NO2, O3, PM2.5, and PM10 levels across 47 European cities and the associated short-term mortality in the period of February-July 2020. METHODS: We used data from several chemical transport models developed by the Copernicus Atmosphere Monitoring Service (CAMS) to define trends in air pollution under business-as-usual and lockdown scenarios, thus removing differences due to weather conditions and other differences affecting pre-post comparisons. We then applied an advanced spatio-temporal Bayesian non-linear mixed effect model to determine the association with stringency indices of individual policy measures, allowing non-linear relationships and geographical correlations. RESULTS: The findings indicate evidence of non-linear relationships, with a stronger decrease in NO2 and to a lesser extent PMs under very strict lockdown regimes. The effects of lockdown measures vary geographically, with a stronger decline in pollution in Southern and Central Europe. The comparative analysis of separate lockdown policies suggests important differences across interventions. Specifically, actions linked to school/workplace closure, limitations on gatherings, and stay-at-home requirements had strong effects, while restrictions on internal movement and international travels showed little impact. The observed decrease in pollution potentially resulted in hundreds of avoided deaths across the European cities. CONCLUSIONS: This study provides important evidence on the differential impacts of various policies implemented during the COVID-19 pandemic in decreasing the level of pollutants in urban areas across Europe

Impact of the COVID-19 lockdown policies on reducing air pollution levels and related deaths in Europe

International audienceBACKGROUND AND AIM: Previous studies have reported a decrease in air pollution following the enforcement of lockdown measures during the first wave of the COVID-19 pandemic. However, these investigations were mostly based on simple pre-post comparisons using past years as a reference, and did not assess the role of different policy interventions. In this contribution, we quantitatively evaluated the association between various lockdown measures and the decrease in NO2, O3, PM2.5, and PM10 levels across 47 European cities and the associated short-term mortality in the period of February-July 2020. METHODS: We used data from several chemical transport models developed by the Copernicus Atmosphere Monitoring Service (CAMS) to define trends in air pollution under business-as-usual and lockdown scenarios, thus removing differences due to weather conditions and other differences affecting pre-post comparisons. We then applied an advanced spatio-temporal Bayesian non-linear mixed effect model to determine the association with stringency indices of individual policy measures, allowing non-linear relationships and geographical correlations. RESULTS: The findings indicate evidence of non-linear relationships, with a stronger decrease in NO2 and to a lesser extent PMs under very strict lockdown regimes. The effects of lockdown measures vary geographically, with a stronger decline in pollution in Southern and Central Europe. The comparative analysis of separate lockdown policies suggests important differences across interventions. Specifically, actions linked to school/workplace closure, limitations on gatherings, and stay-at-home requirements had strong effects, while restrictions on internal movement and international travels showed little impact. The observed decrease in pollution potentially resulted in hundreds of avoided deaths across the European cities. CONCLUSIONS: This study provides important evidence on the differential impacts of various policies implemented during the COVID-19 pandemic in decreasing the level of pollutants in urban areas across Europe

Brief report: 11p15 imprinting center region 1 loss of methylation is a common and specific cause of typical Russell-Silver syndrome: Clinical scoring system and epigenetic-phenotypic correlations

Context: Russell-Silver syndrome (RSS), characterized by intrauterine and postnatal growth retardation, dysmorphic features, and frequent body asymmetry, spares cranial growth. Maternal uniparental disomy for chromosome 7 (mUPD7) is found in 5-10% of cases. We identified loss of methylation (LOM) of 11p15 Imprinting Center Region 1 (ICR1) domain (including IGF-II) as a mechanism leading to RSS. Objective: The aim was to screen for 11p15 epimutation and mUPD7 in RSS and non-RSS small-for-gestational-age (SGA) patients and identify epigenetic-phenotypic correlations. Studied Population and Methods: A total of 127 SGA patients were analyzed. Clinical diagnosis of RSS was established when the criterion of being SGA was associated with at least three of five criteria: postnatal growth retardation, relative macrocephaly, prominent forehead, body asymmetry, and feeding difficulties. Serum IGF-II was evaluated for 82 patients. Results: Of the 127 SGA patients, 58 were diagnosed with RSS; 37 of these (63.8%) displayed partial LOM of the 11p15 ICR1 domain, and three (5.2%) had mUPD7. No molecular abnormalities were found in the non-RSS SGA group (n = 69). Birth weight, birth length, and postnatal body mass index (BMI) were lower in the abnormal 11p15 RSS group (ab-ICR1-RSS) than in the normal 11p15 RSS group [-3.4 vs. -2.6 SD score (SDS), -4.4 vs. -3.4 SDS, and -2.5 vs. -1.6 SDS, respectively; P < 0.05]. Among RSS patients, prominent forehead, relative macrocephaly, body asymmetry, and low BMI were significantly associated with ICR1 LOM. All ab-ICR1-RSS patients had at least four of five criteria of the scoring system. Postnatal IGF-II levels were within normal values. Conclusion: The 11p15 ICR1 epimutation is a major, specific cause of RSS exhibiting failure to thrive. We propose a clinical scoring system (including a BMI < -2 SDS), highly predictive of 11p15 ICR1 LOM, for the diagnosis of RSS. Copyright © 2007 by The Endocrine Society.SCOPUS: ar.jinfo:eu-repo/semantics/publishe