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Modeling atmospheric turbulence in the convective boundary layer is challenging at kilometer and subkilometer resolutions, as the horizontal grid spacing approaches the size of the most energetic turbulent eddies. In this range of resolutions, termed terra incognita or gray zone, partially resolved convective structures are grid dependent and neither traditional 1D mesoscale parameterizations nor 3D large-eddy simulations closures are theoretically appropriate. Leveraging on a new set of one-way nested, full-physics multiscale numerical experiments, we quantify the magnitude of the errors introduced at gray zone resolutions in a real-case application and we provide new perspectives on recently proposed modeling approaches. The new set of experiments is forced by real-time-varying boundary conditions, spans a wide range of scales, and includes traditional 1D schemes, 3D closures, scale-aware parameterizations, and strategies to suppress resolved convection at gray zone resolutions. The study area is Riyadh (Saudi Arabia), where deep CBLs develop owing to strong convective conditions. Detailed analyses of our experiments, including validation with radiosonde data, calculations of spectral features, and partitioning of turbulent fluxes between resolved and subgrid scales, show that (i) grid-dependent convective structures entail minor impacts on the first-order characteristics of the fully developed boundary layer due to some degree of implicit scale awareness of 1D parameterizations and (ii) 3D closures and scale-aware schemes outperform traditional 1D schemes especially in the surface layer, among other findings. The new suite of experiments provides a benchmark of real simulations that can be extended to assess how new turbulence closures perform at gray zone resolutions. Significance Statement As recent advances in high-performance computing are leading to a new era in numerical simulations, regional atmospheric models can now increase their resolution to the widely unexplored kilometer and subkilometer range. While increasing the resolution of atmospheric models is desirable to (i) have more realistic weather and air quality predictions and (ii) better represent boundary conditions for microscale models, kilometer and subkilometer grid spacings pose some theoretical challenges that need to be addressed by the atmospheric modeling community. In this work we run a set of numerical experiments for a real case study that aim to offer new perspectives on recently developed modeling strategies and identify the most promising directions that should be investigated by follow-up studies.

The impact of the emissions from a municipal Waste-to-Energy (WtE) plant in Northern Italy on local air quality was assessed using the CALMET-CALPUFF atmospheric dispersion modelling system. Model simulations were based on hourly emission rates measured by continuous stack monitoring systems and considered both air quality-regulated pollutants (nitrogen oxides, particulate matter, toxic elements, benzo(a)pyrene), and other trace pollutants typical of WtE plants (dioxins, furans, and mercury). The model results were compared to both long-term observations from the air quality monitoring network and with short-term measurements from dedicated monitoring campaigns in the vicinity of the WtE plant, in both warm and cold season conditions. Modelling and observational results showed that the estimated plant contributions are very limited. This suggests that the observed concentration levels were the result of the contribution of all the sources distributed over the area and that they were not solely driven by the activity of the plant. Estimated contributions from the plant’s emissions were usually at least two orders of magnitudes lower than the ambient levels at the nearest monitoring site and even lower at the farthest sites.

Summary Background Exposure to poor air quality leads to increased premature mortality from cardiovascular and respiratory diseases. Among the far-reaching implications of the ongoing COVID-19 pandemic, a substantial improvement in air quality was observed worldwide after the lockdowns imposed by many countries. We aimed to assess the implications of different lockdown measures on air pollution levels in Europe and China, as well as the short-term and long-term health impact. Methods For this modelling study, observations of fine particulate matter (PM2·5) concentrations from more than 2500 stations in Europe and China during 2016–20 were integrated with chemical transport model simulations to reconstruct PM2·5 fields at high spatiotemporal resolution. The health benefits, expressed as short-term and long-term avoided mortality from PM2·5 exposure associated with the interventions imposed to control the COVID-19 pandemic, were quantified on the basis of the latest epidemiological studies. To explore the long-term variability in air quality and associated premature mortality, we built different scenarios of economic recovery (immediate or gradual resumption of activities, a second outbreak in autumn, and permanent lockdown for the whole of 2020). Findings The lockdown interventions led to a reduction in population-weighted PM2·5 of 14·5 μg m−3 across China (−29·7%) and 2·2 μg m−3 across Europe (−17·1%), with unprecedented reductions of 40 μg m−3 in bimonthly mean PM2·5 in the areas most affected by COVID-19 in China. In the short term, an estimated 24 200 (95% CI 22 380–26 010) premature deaths were averted throughout China between Feb 1 and March 31, and an estimated 2190 (1960–2420) deaths were averted in Europe between Feb 21 and May 17. We also estimated a positive number of long-term avoided premature fatalities due to reduced PM2·5 concentrations, ranging from 76 400 (95% CI 62 600–86 900) to 287 000 (233 700–328 300) for China, and from 13 600 (11 900–15 300) to 29 500 (25 800–33 300) for Europe, depending on the future scenarios of economic recovery adopted. Interpretation These results indicate that lockdown interventions led to substantial reductions in PM2·5 concentrations in China and Europe. We estimated that tens of thousands of premature deaths from air pollution were avoided, although with significant differences observed in Europe and China. Our findings suggest that considerable improvements in air quality are achievable in both China and Europe when stringent emission control policies are adopted. Funding None.

This paper presents and discusses the outcome of a multi-disciplinary study aimed at evaluating ground surface movements which could be potentially induced by the future gas production of a carbonate reservoir located in central Italy. Thanks to both good quality and quantity of data (i.e. seismic surveys, well logs, petrophysical data, PVT-PressureVolumeTemperature-fluid data, geotechnical lab data) the issue was addressed via the set-up of a coupled 3D fluid flow and mechanical model based on the preliminary definition of a 3D structural and geological model. Because it is a forecast study, no model calibration was possible, consequently a set of sensitivity analyses were performed so as to assess the effect of the most critical parameters on subsidence evolution. The 3D FEM (Finite Element Method) mechanical model was set up by adopting an elasto-plastic constitutive law. The model was populated via the integration of data from different sources at different scales (i.e. lab tests, in situ acquisition, literature) and data interpretation adopting a traditional rock mechanics approach, in other words Bieniawski classification and the Barton classification. The obtained strength and deformation parameters depend on the Hoek and Brown criterion and the GSI classification application.

The representation of air quality and meteorology over Asia remains challenging for chemical transport models as a result of the complex interactions between the East Asian monsoons and the large uncertainty (in space and time) of the high anthropogenic emissions levels over the region. High spatial resolution models allow resolving small-scale features induced by the complex topography of this region. In this study, the Weather Research and Forecasting model with Chemistry (WRF-Chem) was used to simulate the spatial and seasonal variability of main physical and chemical variables over Asia for the year 2015 at 8-km horizontal resolution to enable resolving small-scale features induced by the region complex topography. The simulated atmospheric composition was evaluated against satellite retrievals (MOPITT, IASI + GOME2, MODIS and OMI) in addition to ground-based observations in China for the year 2015, while the meteorological variables were evaluated by several observational-based datasets (ERA5, CRU, MODIS, MTE). Results showed low to moderate seasonal biases for major meteorological variables, i.e. air temperature, relative humidity, precipitation, latent heat, sensible heat and snow cover fraction. Overall, WRF-Chem reproduced well the spatial and seasonal variability of lowermost tropospheric ozone content, total column carbon monoxide and aerosol optical depth, while large discrepancies were found for tropospheric nitrogen dioxide content, mainly during the warm season. In consistency with previous studies, the different biases between model-simulated and satellite-retrieved values can be mainly attributed to i) the large uncertainties in anthropogenic and natural nitrogen oxides emission estimates, as well as dust and sea salt emissions in the case of aerosol optical depth, and ii) some coarse parameterizations used to reproduce main small-scale features (e.g. meteorology, chemical processes, dry deposition to vegetation). Compared to ground-based observations, the WRF-Chem model reproduced well the mean annual cycle of surface nitrogen dioxide, ozone and fine particles concentrations in all seasons across China. Our results suggest that WRF-Chem provides reliable spatio-temporal patterns for most of the meteorological and chemical variables, adding thus confidence to its applicability in the context of air pollution risk assessment to human and ecosystems health.

Policymakers worldwide have set challenging sustainable energy targets to decarbonize their economy. Despite the ambitious pledges, several emerging countries still lack an actual progress towards the envisioned goals, often due to the scarcity of accurate data. Here, we propose a practical methodology for bridging the gap between the wind energy targets and their implementation. We illustrate our new methodology by focusing on Saudi Arabia, which endeavors to play a leading role in the renewable energy sector and pledges to install 16GW of wind capacity by 2030. We propose a blueprint for the optimal wind farms buildout, combining novel high-resolution model simulations, a unique set of observations, land-use restrictions and a thorough cost assessment. The most suitable technological option is selected among multiple turbine models for each potential site. Our findings suggest that Saudi Arabia is well positioned to become a role model for wind energy development within the Middle East, with 26% of the electricity demand that could be met by wind power. The average levelized cost of energy of the proposed buildout is 39 USD MWh−1, which confirms the competitiveness of wind resources in Saudi Arabia. We identify the area close to Gulf of Aqaba as the most cost-effective region for wind harvesting, with turbines characterized by moderate specific rating (350 W m−2) at relatively low hub height (75 m). The modelling framework proposed in this work can be adopted by other countries aiming to start or strengthen their wind energy portfolio.

This study presents a high–resolution (5 km) set of new simulations performed with CAMx v6.40 over the Po Valley area (Northern Italy), aimed to enhance organic aerosol (OA) levels prediction and to gain insight into the sensitivity of CAMx to different uncertain features of the input setup. In particular, we mainly investigated the role of (i) volatility distributions of organic emissions, (ii) parametrizations of semi– and intermediate–volatile organic compounds (S/IVOC) emissions and (iii) different aging schemes, by exploiting the latest experimental information available in the recent scientific literature. Model results were validated against two OA–specific datasets, available for both an urban site (Bologna, February 2013) and a rural one (Ispra, March 2013). We found out a remarkable performance enhancement of modelled OA levels when applying revisions in S/IVOC emission parametrizations together with the new volatility distributions, at both the validation sites. This performance enhancement is associated with a very significant improvement in secondary organic aerosol (SOA) prediction, mainly due to revised IVOC emissions. At Bologna urban site, mean fractional bias (MFB) of OA ranged from −80.1% in the worst run to −10.1% in the best one and index of agreement (IOA) from 0.52 to 0.75. Notable improvements but overall poorer metrics were found for Ispra site, where MFB ranges from −84.2% to −35% and IOA from 0.45 to 0.50. These findings indicate that organic matter in the semi– and intermediate–volatile range are most likely underestimated in official emission inventories for each main source category (i.e. biomass burning, diesel and gasoline vehicles exhaust). Finally, model results did not show a very pronounced sensitivity to aging processes, due to the low photochemical activity typically observed during winter–time. However, we give evidence that enabling aging processes for biomass burning related SOA, which is by default disabled in CAMx v6.40, can help in closing the gap between modelled and observed SOA concentrations.

This work analyzes the frequency of days characterized by daytime temperature inversion and air stagnation events in the Po valley area. The analysis is focused on both historical series and future projections under climate change. Historical sounding data from two different Italian stations are used as well as future projections data, provided by CMCC-CCLM 4-8-19 regional climate model (MED-CORDEX initiative). A new method to detect layers of temperature inversion is also presented. The developed method computes the occurrence of a temperature inversion layer for a given day at 12 UTC without a detailed knowledge of temperature vertical profile. This method was validated using sounding data and applied to the model projections, under two different emissions scenarios (RCP4.5 and RCP8.5). Under RCP4.5 intermediate emissions scenario, the occurrence of temperature inversions is projected to increase by 12 days/year (around + 10%) in the last decade of 21st century compared to 1986–2005 average. However, the increase in temperature inversions seems to be especially concentrated in the warm period. Under RCP8.5 extreme scenario, temperature inversions are still projected to increase, though to a lesser extent compared to RCP4.5 scenario (+ 6 days/year in the last decade of 21st century). A similar trend was found also for air stagnation events, which take into account the variation of precipitation pattern and wind strength. The expected increases are equal to + 13 days/year and + 11 days/year in the last decade of 21st century compared to 1986–2005 average, under RCP4.5 and RCP8.5 scenarios respectively.