Institute of Methodologies for Environmental Analysis

In the present work, we report daytime latent heat flux profile measurements in the convective boundary layer (CBL) obtained from the combined use of a wind lidar and a thermodynamic Raman lidar. Water vapour flux profiles and, consequently, latent heat flux profiles were obtained as the covariance between the vertical profiles of the water vapour mixing ratio and vertical wind fluctuations. Profile measurements of the water vapour mixing ratio were carried out by the thermodynamic Raman lidar CONCERNING, while simultaneous profile measurements of the vertical wind speed were carried out by a co-located Doppler wind lidar. The considered dataset was collected in the frame of the international field campaign “Water Vapor Lidar Network Assimilation” (WaLiNeAs). Three cloud-free time intervals on 31 October, 28 November, and 8 December 2022 were selected as case studies. Measurements of turbulent kinetic energy (TKE) were also carried out over the same time intervals based on the use of wind lidar data. The three selected case studies were characterised by different atmospheric stability conditions and, consequently, by a different potential for the occurrence of convective activity. More specifically, the atmospheric conditions on 31 October 2022 were very unstable, with intensive convective activity taking place in the area and ultimately leading to relatively intense thunderstorms and rainfall events. The atmospheric conditions on 28 November 2022 were moderately unstable, ultimately leading to light convective activity, with scattered rain episodes observed throughout the day but with no severe thunderstorms taking place. Stratiform precipitations were present on 8 December 2022, with weak embedded convective processes taking place within stratiform clouds and leading to moderate additional precipitation. In all three selected case studies, representative of pre-convective conditions, both latent heat flux and TKE profiles are characterised by values increasing with altitude up to approx. 500 m, while both latent heat flux and TKE are found to decrease, with a steeper negative gradient up to approx. 600 m and more gradually above this altitude, returning to zero just above the top of the CBL. In all three cases, peak values of TKE appear to be strongly correlated with corresponding peak values of the latent heat flux; the higher the maximum values of TKE and latent heat flux, the more intense the following precipitation events.

We present a multi-year study of Saharan dust intrusions on a mountainous site located in the central Mediterranean Basin regarding their aerosol optical and geometrical properties. The observations were carried out at the Consiglio Nazionale delle Ricerche-Istituto di Metodologie per l’Analisi Ambientale (CNR-IMAA) located in Potenza (40,360N, 15,440E), Italy, from March 2010 to October 2022, using ACTRIS (Aerosol Clouds and Trace Gases Research InfraStructure). A total of 101 night-time lidar measurements of dust intrusions were identified. The following properties were calculated for the periods December, January, February (DJF), March, April, May (MAM), June, July, August (JJA) and September, October, November (SON): aerosol layer center of mass altitude, particle lidar ratio at 355 and 532 nm, particle depolarization ratio at 532 nm and backscattering Ångström exponent at 532–1064 nm. Both geometrical and optical aerosol properties vary considerably with the seasons. During SON and DJF, air masses transporting dust travel at lower altitudes, and become contaminated with local continental particles. In MAM and JJA, dust is also likely to travel at higher altitudes and rarely mix with other aerosol types. As a result, aerosols are larger in size and irregular in shape during the warm months. The ratio of the lidar ratios at 355 and 532 nm is 1.11 ± 0.15 in DJF, 1.12 ± 0.07 in SON, 0.94 ± 0.12 in MAM, and 0.92 ± 0.08 in JJA. The seasonal radiative effect estimated using the Fu–Liou–Gu (FLG) radiative transfer model indicates the most significant impact during the JJA period. A negative dust radiative effect is observed both at the surface (SRF) and at the top of the atmosphere (TOA) in all the seasons, and this could be related to a minimal contribution from black carbon. Specifically, the SRF radiative effect estimation is −14.48 ± 1.32 W/m2 in DJF, −18.00 ± 0.89 W/m2 in MAM, −22.08 ± 1.36 W/m2 in JJA, and −13.47 ± 1.12 W/m2 in SON. Instead, radiative effect estimation at the TOA is −22.23 ± 2.06 W/m2 in DJF, −38.23 ± 2.16 W/m2 in MAM, −51.36 ± 3.53 W/m2 in JJA, and −22.57 ± 2.11 W/m2 in SON. The results highlight how the radiative effects of the particles depend on the complex relationship between the dust load, their altitude in the troposphere, and their optical properties. Accordingly, the knowledge of aerosols optical property profiles is of primary importance to understand the radiative impact of dust.

Climate change mitigation and adaptation along with air pollution are key challenges that require a comprehensive planning approach able to promote win-win strategies and avoid transferring environmental pressures from one sector to another. The joint approach is widely advocated at the policy level while the scientific research investigates the role of co-benefits in planning processes. A methodological framework was developed with the twofold aim of (i) assessing the current integration degree between regional Air Quality Plans and Climate Plans and (ii) providing useful hints for pursuing greater air-climate integration. Its application to the Italian case study provides useful policy implications for strengthening the role of co-benefits as drivers of planning processes. Results show that a greater air-climate integration can be achieved by enhancing the role of regional authorities in supporting the implementation of local-scale plans and actions. This is especially important in contexts where an integrated planning framework is lacking and the mainstreaming within sectoral sub-regional plans turns out to be crucial.

Abstract An adaptation gap exists when adaptation efforts fail to meet adaptation needs. Conceptualizing and operationalizing this gap is challenging, but should start with, as we argue, addressing ‘internal adaptation policy consistency’. Through a comprehensive European study of local adaptation planning and its progress, we demonstrate how consistency checks applied to adaptation plans can conceptually and methodologically advance understanding of the adaptation (implementation) gap and, most importantly, how to reduce it.

ABSTRACT Geostationary satellites have the capability to offer AOD products with a higher frequency of observation within a given period, thus improving the geographical coverage of averaged AOD products compared to polar satellites. Moreover, the averaged AOD of geostationary satellites is more reflective of the average aerosol load as compared to AOD products derived from polar orbit satellites. Despite this, there is still an absence of comprehensive research on the comparative representativeness of AOD mean products from geostationary and polar orbit satellites, and most current research only focuses on retrieval accuracy. This paper compares the geographical coverage of averaged AOD products released by the Advanced Baseline Imager (ABI) sensor on the Geostationary Operational Environmental Satellite-R (GOES-R) (09:00 UTC-23:00 UTC) with AOD products released by the Moderate-resolution Imaging Spectroradiometer (MODIS) (16:00 UTC and 19:00 UTC) on hourly, daily, semi-monthly and monthly scales. Moreover, the Aerosol Robotic Network datasets were used to evaluate the accuracy of ABI AOD and to propose usage suggestions. In terms of daily AOD products, the AOD daily mean generated by the geostationary satellite GOES-R/ABI consistently outperforms the AOD daily mean generated by MODIS AOD in terms of spatial coverage. However, on monthly scales, the difference is no longer significant. With regard to accuracy, it is proved that when the time scale of averaging is gradually expanded, root mean square error (RMSE) and mean absolute error (MAE) gradually decrease. On the season scale, ABI AOD exhibits the highest level of accuracy during the autumn season (September, October, November); on the spatial scale, ABI AOD exhibits the best accuracy in North America. Therefore, if the daily, semi-monthly or monthly averaged ABI AOD datasets are used, the recommended time is autumn, and the study area is North America.

The use of zeolites in agriculture as a soil conditioner is becoming an important field of research in crop growth. To study the effect of synthetic zeolites and deficit irrigation on sweet pepper (Capsicum annuum L.) cultivation, an experiment was conducted in a controlled environment. In particular, sweet peppers were cultivated in a glasshouse using polypropylene pots filled with sandy loam soil, to which 2% zeolite was added. The zeolite employed in the experiments was obtained using coal fly ash as a raw material. The experiment consisted of two main treatments: (a) soil with a zeolite at 2% (Z) and (b) soil without a zeolite as a control (C). Three subplot treatments consisted of (1) full irrigation at 100% of the available water content (AWC) (100); (2) deficit irrigation at 70% of the AWC (70); and (3) deficit irrigation at 50% of the AWC (50). Sweet pepper cultivation started on 24 April 2023 and lasted until 23 June 2023; during the trial, the environmental data, such as the soil humidity, air temperature, and relative humidity, and some crop parameters, such as the plant height, leaf number, and the SPAD index, were monitored. At the end of the trial, the fresh and dry plant weights, the dry matter content, and the leaf water potential were measured. The results showed that, for the plant fresh weight and dry matter content, no significant differences were observed in the treatments and their interactions, whereas, for the other parameters, the statistical analysis showed significant differences. The study suggests that the soil’s structural benefits, resulting from zeolite application, are not followed by an equal positive effect in terms of sweet pepper growth under deficit irrigation conditions.

This survey presents an in-depth analysis of machine learning techniques applied to lidar observations for the detection of aerosol and cloud optical, geometrical, and microphysical properties. Lidar technology, with its ability to probe the atmosphere at very high spatial and temporal resolution and measure backscattered signals, has become an invaluable tool for studying these atmospheric components. However, the complexity and diversity of lidar technology requires advanced data processing and analysis methods, where machine learning has emerged as a powerful approach. This survey focuses on the application of various machine learning techniques, including supervised and unsupervised learning algorithms and deep learning models, to extract meaningful information from lidar observations. These techniques enable the detection, classification, and characterization of aerosols and clouds by leveraging the rich features contained in lidar signals. In this article, an overview of the different machine learning architectures and algorithms employed in the field is provided, highlighting their strengths, limitations, and potential applications. Additionally, this survey examines the impact of machine learning techniques on improving the accuracy, efficiency, and robustness of aerosol and cloud real-time detection from lidar observations. By synthesizing the existing literature and providing critical insights, this survey serves as a valuable resource for researchers, practitioners, and students interested in the application of machine learning techniques to lidar technology. It not only summarizes current state-of-the-art methods but also identifies emerging trends, open challenges, and future research directions, with the aim of fostering advancements in this rapidly evolving field.

Seismo-electromagnetic signals are electromagnetic signals generated by the propagation of a seismic wave in a porous media containing fluids (Gao & Hu, 2010).These signals can potentially provide useful information on the poro-elastic media and the hosted fluids (Garambois & Dietrich, 2002).Thus, there has been a growing interest in the study of SES in recent years, due to their potential.Researchers are focusing both on modelling and analysis of both passive and active experiments to investigate the characteristics of these signals (e.g. Honkura et al., 2000; Matsushima et al., 2002; Warden et al., 2013; Gao et al., 2016; Balasco et al., 2014; Dzieran et al., 2019).Passive experiments involve the observation and analysis of naturally occurring SES triggered by earthquakes, while active experiments involve the controlled generation of these signals using seismic source.The aim of our work is to present the results deriving from the analysis of SES recorded with both approaches. As for the passive one, the data set consists of the time series recorded by two magnetotelluric stations in continuous monitoring, co-located with two seismic stations, in seismically active areas of Southern Italy (the Gargano promontory and the Agri valley).As for the active one, the data set derives by an active seismic experiment carried out in the caldera of the Phlegrean Fields, the Italian super-volcano.