Antonio Giganti

Antonio Giganti, Sara Mandelli, Paolo Bestagini et al.

Biogenic Volatile Organic Compounds (BVOCs) play a critical role in biosphere-atmosphere interactions, being a key factor in the physical and chemical properties of the atmosphere and climate. Acquiring large and fine-grained BVOC emission maps is expensive and time-consuming, so most available BVOC data are obtained on a loose and sparse sampling grid or on small regions. However, high-resolution BVOC data are desirable in many applications, such as air quality, atmospheric chemistry, and climate monitoring. In this work, we investigate the possibility of enhancing BVOC acquisitions, further explaining the relationships between the environment and these compounds. We do so by comparing the performances of several state-of-the-art neural networks proposed for image Super-Resolution (SR), adapting them to overcome the challenges posed by the large dynamic range of the emission and reduce the impact of outliers in the prediction. Moreover, we also consider realistic scenarios, considering both temporal and geographical constraints. Finally, we present possible future developments regarding SR generalization, considering the scale-invariance property and super-resolving emissions from unseen compounds.

Antonio Giganti, Sara Mandelli, Paolo Bestagini et al.

Enhancing the resolution of Biogenic Volatile Organic Compound (BVOC) emission maps is a critical task in remote sensing. Recently, some Super-Resolution (SR) methods based on Deep Learning (DL) have been proposed, leveraging data from numerical simulations for their training process. However, when dealing with data derived from satellite observations, the reconstruction is particularly challenging due to the scarcity of measurements to train SR algorithms with. In our work, we aim at super-resolving low resolution emission maps derived from satellite observations by leveraging the information of emission maps obtained through numerical simulations. To do this, we combine a SR method based on DL with Domain Adaptation (DA) techniques, harmonizing the different aggregation strategies and spatial information used in simulated and observed domains to ensure compatibility. We investigate the effectiveness of DA strategies at different stages by systematically varying the number of simulated and observed emissions used, exploring the implications of data scarcity on the adaptation strategies. To the best of our knowledge, there are no prior investigations of DA in satellite-derived BVOC maps enhancement. Our work represents a first step toward the development of robust strategies for the reconstruction of observed BVOC emissions.

Antonio Giganti, Sara Mandelli, Paolo Bestagini et al.

Due to the latest environmental concerns in keeping at bay contaminants emissions in urban areas, air pollution forecasting has been rising the forefront of all researchers around the world. When predicting pollutant concentrations, it is common to include the effects of environmental factors that influence these concentrations within an extended period, like traffic, meteorological conditions and geographical information. Most of the existing approaches exploit this information as past covariates, i.e., past exogenous variables that affected the pollutant but were not affected by it. In this paper, we present a novel forecasting methodology to predict NO$_2$ concentration via both past and future covariates. Future covariates are represented by weather forecasts and future calendar events, which are already known at prediction time. In particular, we deal with air quality observations in a city-wide network of ground monitoring stations, modeling the data structure and estimating the predictions with a Spatiotemporal Graph Neural Network (STGNN). We propose a conditioning block that embeds past and future covariates into the current observations. After extracting meaningful spatiotemporal representations, these are fused together and projected into the forecasting horizon to generate the final prediction. To the best of our knowledge, it is the first time that future covariates are included in time series predictions in a structured way. Remarkably, we find that conditioning on future weather information has a greater impact than considering past traffic conditions. We release our code implementation at https://github.com/polimi-ispl/MAGCRN.

Christopher Ummerle, Antonio Giganti, Sara Mandelli et al.

Remote sensing plays a crucial role in monitoring Earth's ecosystems, yet satellite-derived data often suffer from limited spatial resolution, restricting their applicability in atmospheric modeling and climate research. In this work, we propose a deep learning-based Super-Resolution (SR) framework that leverages land cover information to enhance the spatial accuracy of Biogenic Volatile Organic Compounds (BVOCs) emissions, with a particular focus on isoprene. Our approach integrates land cover priors as emission drivers, capturing spatial patterns more effectively than traditional methods. We evaluate the model's performance across various climate conditions and analyze statistical correlations between isoprene emissions and key environmental information such as cropland and tree cover data. Additionally, we assess the generalization capabilities of our SR model by applying it to unseen climate zones and geographical regions. Experimental results demonstrate that incorporating land cover data significantly improves emission SR accuracy, particularly in heterogeneous landscapes. This study contributes to atmospheric chemistry and climate modeling by providing a cost-effective, data-driven approach to refining BVOC emission maps. The proposed method enhances the usability of satellite-based emissions data, supporting applications in air quality forecasting, climate impact assessments, and environmental studies.

Antonio Giganti, Sara Mandelli, Paolo Bestagini et al.

Biogenic Volatile Organic Compounds (BVOCs) emitted from the terrestrial ecosystem into the Earth's atmosphere are an important component of atmospheric chemistry. Due to the scarcity of measurement, a reliable enhancement of BVOCs emission maps can aid in providing denser data for atmospheric chemical, climate, and air quality models. In this work, we propose a strategy to super-resolve coarse BVOC emission maps by simultaneously exploiting the contributions of different compounds. To this purpose, we first accurately investigate the spatial inter-connections between several BVOC species. Then, we exploit the found similarities to build a Multi-Image Super-Resolution (MISR) system, in which a number of emission maps associated with diverse compounds are aggregated to boost Super-Resolution (SR) performance. We compare different configurations regarding the species and the number of joined BVOCs. Our experimental results show that incorporating BVOCs' relationship into the process can substantially improve the accuracy of the super-resolved maps. Interestingly, the best results are achieved when we aggregate the emission maps of strongly uncorrelated compounds. This peculiarity seems to confirm what was already guessed for other data-domains, i.e., joined uncorrelated information are more helpful than correlated ones to boost MISR performance. Nonetheless, the proposed work represents the first attempt in SR of BVOC emissions through the fusion of multiple different compounds.