Massimo D'Isidoro

Bio: Massimo D'Isidoro is an academic researcher from ENEA. The author has contributed to research in topics: Air quality index & CMAQ. The author has an hindex of 20, co-authored 65 publications receiving 1294 citations. Previous affiliations of Massimo D'Isidoro include University of Ferrara & National Research Council.

Air quality trends in Europe over the past decade: a first multi-model assessment

Abstract: We discuss the capability of current state-of-the-art chemistry and transport models to reproduce air quality trends and inter annual variability. Documenting these strengths and weaknesses on the basis of historical simulations is essential before the models are used to investigate future air quality projections. To achieve this, a coordinated modelling exercise was performed in the framework of the CityZEN European Project. It involved six regional and global chemistry-transport models (Bolchem, Chimere, Emep, Eurad, OsloCTM2 and Mozart) simulating air quality over the past decade in the Western European anthropogenic emissions hotspots. Comparisons between models and observations allow assessing the skills of the models to capture the trends in basic atmospheric constituents (NO2, O3, and PM10). We find that the trends of primary constituents are well reproduced (except in some countries - owing to their sensitivity to the emission inventory) although capturing the more moderate trends of secondary species such as O3 is more challenging. Apart from the long term trend, the modelled monthly variability is consistent with the observations but the year-to-year variability is generally underestimated. A comparison of simulations where anthropogenic emissions are kept constant is also investigated. We find that the magnitude of the emission-driven trend exceeds the natural variability for primary compounds. We can thus conclude that emission management strategies have had a significant impact over the past 10 yr, hence supporting further emission reductions strategies.

Comparison of OMI NO2 tropospheric columns with an ensemble of global and European regional air quality models

Abstract: . We present a comparison of tropospheric NO2 from OMI measurements to the median of an ensemble of Regional Air Quality (RAQ) models, and an intercomparison of the contributing RAQ models and two global models for the period July 2008–June 2009 over Europe. The model forecasts were produced routinely on a daily basis in the context of the European GEMS ("Global and regional Earth-system (atmosphere) Monitoring using Satellite and in-situ data") project. The tropospheric vertical column of the RAQ ensemble median shows a spatial distribution which agrees well with the OMI NO2 observations, with a correlation r=0.8. This is higher than the correlations from any one of the individual RAQ models, which supports the use of a model ensemble approach for regional air pollution forecasting. The global models show high correlations compared to OMI, but with significantly less spatial detail, due to their coarser resolution. Deviations in the tropospheric NO2 columns of individual RAQ models from the mean were in the range of 20–34% in winter and 40–62% in summer, suggesting that the RAQ ensemble prediction is relatively more uncertain in the summer months. The ensemble median shows a stronger seasonal cycle of NO2 columns than OMI, and the ensemble is on average 50% below the OMI observations in summer, whereas in winter the bias is small. On the other hand the ensemble median shows a somewhat weaker seasonal cycle than NO2 surface observations from the Dutch Air Quality Network, and on average a negative bias of 14%. Full profile information was available for two RAQ models and for the global models. For these models the retrieval averaging kernel was applied. Minor differences are found for area-averaged model columns with and without applying the kernel, which shows that the impact of replacing the a priori profiles by the RAQ model profiles is on average small. However, the contrast between major hotspots and rural areas is stronger for the direct modeled vertical columns than the columns where the averaging kernels are applied, related to a larger relative contribution of the free troposphere and the coarse horizontal resolution in the a priori profiles compared to the RAQ models. In line with validation results reported in the literature, summertime concentrations in the lowermost boundary layer in the a priori profiles from the DOMINO product are significantly larger than the RAQ model concentrations and surface observations over the Netherlands. This affects the profile shape, and contributes to a high bias in OMI tropospheric columns over polluted regions. The global models indicate that the upper troposphere may contribute significantly to the total column and it is important to account for this in comparisons with RAQ models. A combination of upper troposphere model biases, the a priori profile effects and DOMINO product retrieval issues could explain the discrepancy observed between the OMI observations and the ensemble median in summer.

Presentation of the EURODELTA III intercomparison exercise-evaluation of the chemistry transport models' performance on criteria pollutants and joint analysis with meteorology

Abstract: . The EURODELTA III exercise has facilitated a comprehensive intercomparison and evaluation of chemistry transport model performances. Participating models performed calculations for four 1-month periods in different seasons in the years 2006 to 2009, allowing the influence of different meteorological conditions on model performances to be evaluated. The exercise was performed with strict requirements for the input data, with few exceptions. As a consequence, most of differences in the outputs will be attributed to the differences in model formulations of chemical and physical processes. The models were evaluated mainly for background rural stations in Europe. The performance was assessed in terms of bias, root mean square error and correlation with respect to the concentrations of air pollutants (NO2, O3, SO2, PM10 and PM2.5), as well as key meteorological variables. Though most of meteorological parameters were prescribed, some variables like the planetary boundary layer (PBL) height and the vertical diffusion coefficient were derived in the model preprocessors and can partly explain the spread in model results. In general, the daytime PBL height is underestimated by all models. The largest variability of predicted PBL is observed over the ocean and seas. For ozone, this study shows the importance of proper boundary conditions for accurate model calculations and then on the regime of the gas and particle chemistry. The models show similar and quite good performance for nitrogen dioxide, whereas they struggle to accurately reproduce measured sulfur dioxide concentrations (for which the agreement with observations is the poorest). In general, the models provide a close-to-observations map of particulate matter (PM2.5 and PM10) concentrations over Europe rather with correlations in the range 0.4–0.7 and a systematic underestimation reaching −10 µg m−3 for PM10. The highest concentrations are much more underestimated, particularly in wintertime. Further evaluation of the mean diurnal cycles of PM reveals a general model tendency to overestimate the effect of the PBL height rise on PM levels in the morning, while the intensity of afternoon chemistry leads formation of secondary species to be underestimated. This results in larger modelled PM diurnal variations than the observations for all seasons. The models tend to be too sensitive to the daily variation of the PBL. All in all, in most cases model performances are more influenced by the model setup than the season. The good representation of temporal evolution of wind speed is the most responsible for models' skillfulness in reproducing the daily variability of pollutant concentrations (e.g. the development of peak episodes), while the reconstruction of the PBL diurnal cycle seems to play a larger role in driving the corresponding pollutant diurnal cycle and hence determines the presence of systematic positive and negative biases detectable on daily basis.

Four‐dimensional variational assimilation in the unstable subspace and the optimal subspace dimension

Abstract: Key a priori information used in 4D-Var is the knowledge of the system's evolution equations. In this article we propose a method for taking full advantage of the knowledge of the system's dynamical instabilities in order to improve the quality of the analysis. We present an algorithm for four-dimensional variational assimilation in the unstable subspace (4D-Var - AUS), which consists of confining in this subspace the increment of the control variable. The existence of an optimal subspace dimension for this confinement is hypothesized. Theoretical arguments in favour of the present approach are supported by numerical experiments in a simple perfect nonlinear model scenario. It is found that the RMS analysis error is a function of the dimension N of the subspace where the analysis is confined and is a minimum for N approximately equal to the dimension of the unstable and neutral manifold. For all assimilation windows, from 1 to 5 d, 4D-Var - AUS performs better than standard 4D-Var. In the presence of observational noise, the 4D-Var solution, while being closer to the observations, is farther away from the truth. The implementation of 4D-Var - AUS does not require the adjoint integration. Copyright © 2010 Royal Meteorological Society.

Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model [presentation]

Abstract: Abstract A two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea. The domain includes a representation of part of Borneo as well as the sea so that the model can simulate the initiation of convection. Also included in the model are parameterizations of mesoscale ice phase and moisture processes and longwave and shortwave radiation with a diurnal cycle. This allows use of the model to test the relative importance of various heating mechanisms to the stratiform cloud deck, which typically occupies several hundred kilometers of the domain. Frank and Cohen's cumulus parameterization scheme is employed to represent vital unresolved vertical transports in the convective area. The major conclusions are: Ice phase processes are important in determining the level of maximum large-scale heating and vertical motion because there is a strong anvil componen...

Tropospheric ozone and its precursors from the urban to the global scale from air quality to short-lived climate forcer

Abstract: Ozone holds a certain fascination in atmospheric science. It is ubiquitous in the atmosphere, central to tropospheric oxidation chemistry, yet harmful to human and ecosystem health as well as being an important greenhouse gas. It is not emitted into the atmosphere but is a by-product of the very oxidation chemistry it largely initiates. Much effort is focussed on the reduction of surface levels of ozone owing to its health impacts but recent efforts to achieve reductions in exposure at a country scale have proved difficult to achieve due to increases in background ozone at the zonal hemispheric scale. There is also a growing realisation that the role of ozone as a short-lived climate pollutant could be important in integrated air quality climate-change mitigation. This review examines current understanding of the processes regulating tropospheric ozone at global to local scales from both measurements and models. It takes the view that knowledge across the scales is important for dealing with air quality and climate change in a synergistic manner.

Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China

Abstract: Observations of surface ozone available from ∼1,000 sites across China for the past 5 years (2013–2017) show severe summertime pollution and regionally variable trends. We resolve the effect of meteorological variability on the ozone trends by using a multiple linear regression model. The residual of this regression shows increasing ozone trends of 1–3 ppbv a−1 in megacity clusters of eastern China that we attribute to changes in anthropogenic emissions. By contrast, ozone decreased in some areas of southern China. Anthropogenic NOx emissions in China are estimated to have decreased by 21% during 2013–2017, whereas volatile organic compounds (VOCs) emissions changed little. Decreasing NOx would increase ozone under the VOC-limited conditions thought to prevail in urban China while decreasing ozone under rural NOx-limited conditions. However, simulations with the Goddard Earth Observing System Chemical Transport Model (GEOS-Chem) indicate that a more important factor for ozone trends in the North China Plain is the ∼40% decrease of fine particulate matter (PM2.5) over the 2013–2017 period, slowing down the aerosol sink of hydroperoxy (HO2) radicals and thus stimulating ozone production.

Statistical Methods For Environmental Pollution Monitoring

Abstract: If you really want to be smarter, reading can be one of the lots ways to evoke and realize. Many people who like reading will have more knowledge and experiences. Reading can be a way to gain information from economics, politics, science, fiction, literature, religion, and many others. As one of the part of book categories, statistical methods for environmental pollution monitoring always becomes the most wanted book. Many people are absolutely searching for this book. It means that many love to read this kind of book.