Air quality in the mid-21st century for the city of Paris under two climate scenarios; from the regional to local scale

Ozone and PM_2.5 concentrations over the city of Paris are modeled with the CHIMERE air-quality model at 4 km × 4 km horizontal resolution for two future emission scenarios. A high-resolution (1 km × 1 km) emission projection until 2020 for the greater Paris region is developed by local experts (AIRPARIF) and is further extended to year 2050 based on regional-scale emission projections developed by the Global Energy Assessment. Model evaluation is performed based on a 10-year control simulation. Ozone is in very good agreement with measurements while PM_2.5 is underestimated by 20% over the urban area mainly due to a large wet bias in wintertime precipitation. A significant increase of maximum ozone relative to present-day levels over Paris is modeled under the "business-as-usual" scenario (+7 ppb) while a more optimistic "mitigation" scenario leads to a moderate ozone decrease (−3.5 ppb) in year 2050. These results are substantially different to previous regional-scale projections where 2050 ozone is found to decrease under both future scenarios. A sensitivity analysis showed that this difference is due to the fact that ozone formation over Paris at the current urban-scale study is driven by volatile organic compound (VOC)-limited chemistry, whereas at the regional-scale ozone formation occurs under NO_x-sensitive conditions. This explains why the sharp NO_x reductions implemented in the future scenarios have a different effect on ozone projections at different scales. In rural areas, projections at both scales yield similar results showing that the longer timescale processes of emission transport and ozone formation are less sensitive to model resolution. PM_2.5 concentrations decrease by 78% and 89% under business-as-usual and mitigation scenarios, respectively, compared to the present-day period. The reduction is much more prominent over the urban part of the domain due to the effective reductions of road transport and residential emissions resulting in the smoothing of the large urban increment modeled in the control simulation

Climate-forced air-quality modeling at the urban scale: sensitivity to model resolution, emissions and meteorology

While previous research helped to identify and prioritize the sources of error in air-quality modeling due to anthropogenic emissions and spatial scale effects, our knowledge is limited on how these uncertainties affect climate-forced air-quality assessments. Using as reference a 10-year model simulation over the greater Paris (France) area at 4 km resolution and anthropogenic emissions from a 1 km resolution bottom-up inventory, through several tests we estimate the sensitivity of modeled ozone and PM2.5 concentrations to different potentially influential factors with a particular interest over the urban areas. These factors include the model horizontal and vertical resolution, the meteorological input from a climate model and its resolution, the use of a top-down emission inventory, the resolution of the emissions input and the post-processing coefficients used to derive the temporal, vertical and chemical split of emissions. We show that urban ozone displays moderate sensitivity to the resolution of emissions (~ 8 %), the post-processing method (6.5 %) and the horizontal resolution of the air-quality model (~ 5 %), while annual PM2.5 levels are particularly sensitive to changes in their primary emissions (~ 32 %) and the resolution of the emission inventory (~ 24 %). The air-quality model horizontal and vertical resolution have little effect on model predictions for the specific study domain. In the case of modeled ozone concentrations, the implementation of refined input data results in a consistent decrease (from 2.5 up to 8.3 %), mainly due to inhibition of the titration rate by nitrogen oxides. Such consistency is not observed for PM2.5. In contrast this consistency is not observed for PM2.5. In addition we use the results of these sensitivities to explain and quantify the discrepancy between a coarse (~ 50 km) and a fine (4 km) resolution simulation over the urban area. We show that the ozone bias of the coarse run (+9 ppb) is reduced by ~ 40 % by adopting a higher resolution emission inventory, by 25 % by using a post-processing technique based on the local inventory (same improvement is obtained by increasing model horizontal resolution) and by 10 % by adopting the annual emission totals of the local inventory. The bias of PM2.5 concentrations follows a more complex pattern, with the positive values associated with the coarse run (+3.6 μg m−3), increasing or decreasing depending on the type of the refinement. We conclude that in the case of fine particles, the coarse simulation cannot selectively incorporate local-scale features in order to reduce its error

Alterations in human papillomavirus-related biomarkers after treatment of cervical intraepithelial neoplasia

Objective: This study aims to assess the alterations in various HPV-related biomarkers 6 months post-treatment and how these relate to various risk factors and individual characteristics; their role for the prediction of treatment failure was also evaluated. Material and methods: Design: Prospective observational study. Population: Women planning to undergo treatment for cervical intraepithelial neoplasia. Intervention: A liquid-based cytology sample was taken pre-operatively. This was tested for HPV genotyping, Nucleic Acid Sequence Based Amplification, flow cytometric evaluation and p16 immunostaining. A repeat LBC sample was obtained 6 months post-treatment and was tested for the same biomarkers. Outcomes: The alterations of the biomarkers 6 months post-treatment were recorded. Their relation to individual characteristics and risk factors (age, smoking, sexual history, use of condom, CIN grade, excision margin status, crypt involvement) as well as their role for the prediction of residual/recurrent disease were assessed. Analysis: The accuracy parameters (sensitivity, specificity, positive and negative predictive value and the likelihood ratios) of each biomarker for the prediction of recurrent/residual CIN were calculated. Results: A total of 190 women were recruited. All biomarkers had significantly higher negativity rates post-treatment compared to pre-treatment ones. Multivariate analysis demonstrated that consistent condom use post-treatment significantly reduces the high-risk HPV positivity rates in comparison to no use (OR = 0.18; 95% CI: 0.09-0.38). Sensitivity and specificity for all high risk HPV DNA testing were 0.5/0.62, respectively; the relevant values for only type 16 or 18 DNA typing were 0.5/0.92, for NASBA 0.5/0.94, for flow 0.5/0.85 and for p16 0.25/0.93. Conclusion: CIN treatment reduces positivity for all HPV-related biomarkers. Consistent condom use significantly reduces high-risk HPV positivity rates. More cases of treatment failures are required in order to specify whether different combinations of HPV-related biomarkers could enhance the accuracy of follow up, possibly in the form of a Scoring System that could allow tailored post-treatment surveillance

Evaluation of regional climate simulations for air quality modelling purposes

In order to evaluate the future potential benefits of emission regulation on regional air quality, while taking into account the effects of climate change, off-line air quality projection simulations are driven using weather forcing taken from regional climate models. These regional models are themselves driven by simulations carried out using global climate models (GCM) and economical scenarios. Uncertainties and biases in climate models introduce an additional “climate modeling” source of uncertainty that is to be added to all other types of uncertainties in air quality modeling for policy evaluation. In this article we evaluate the changes in air quality-related weather variables induced by replacing reanalyses-forced by GCM-forced regional climate simulations. As an example we use GCM simulations carried out in the framework of the ERA-interim programme and of the CMIP5 project using the Institut Pierre-Simon Laplace climate model (IPSLcm), driving regional simulations performed in the framework of the EURO-CORDEX programme. In summer, we found compensating deficiencies acting on photochemistry: an overestimation by GCM-driven weather due to a positive bias in short-wave radiation, a negative bias in wind speed, too many stagnant episodes, and a negative temperature bias. In winter, air quality is mostly driven by dispersion, and we could not identify significant differences in either wind or planetary boundary layer height statistics between GCM-driven and reanalyses-driven regional simulations. However, precipitation appears largely overestimated in GCM-driven simulations, which could significantly affect the simulation of aerosol concentrations. The identification of these biases will help interpreting results of future air quality simulations using these data. Despite these, we conclude that the identified differences should not lead to major difficulties in using GCM-driven regional climate simulations for air quality projections