Air | Interview with Editorial Board Member—Dr. Angelo Riccio

Name: Dr. Angelo Riccio
Affiliation: Dipartimento di Scienze e Tecnologie, Parthenope University of Naples, 80133 Naples, Italy
Interests: atmospheric PM modelling; atmospheric PM characterization; ensemble modeling for air quality and meteorological applications; inverse analysis for source apportionment studies

Dr. Angelo Riccio is an atmospheric scientist at the Department of Science and Technology, Parthenope University of Naples, Italy. His research focuses on atmospheric and climate physics, particularly advanced air quality modelling and the integration of physical and data-driven approaches, including machine learning, with applications in air pollution prediction, aviation impacts, and climate scenario improvement. He is actively involved in major European and national projects, such as Horizon Europe and SESAR initiatives, leading the development of next-generation modelling frameworks and digital tools for environmental and aviation applications. In addition to his research, he serves on the Editorial Board of Air and has extensive experience in peer review and editorial processes. Through his scientific and editorial contributions, he supports the advancement of interdisciplinary research and high-quality scientific publishing in atmospheric science.

The following is a short interview with Dr. Angelo Riccio:

1. Could you briefly introduce your current research focus and any key projects you are working on?
My current research focuses on atmospheric and climate physics, with a strong emphasis on advanced air quality modelling and the integration of physical and data-driven approaches, including machine learning. I develop and apply numerical and statistical models for the prediction of air pollution, the assessment of anthropogenic impacts (e.g., aviation non-CO₂ effects), and the correction of climate scenarios. I am currently leading several European and national projects (e.g., Horizon Europe and SESAR initiatives), where I coordinate the development of next-generation modelling frameworks for environmental and climate applications, including operational tools and digital twins for sustainable aviation and air quality management.

2. How has your experience been collaborating with MDPI, and what motivated you to support our new journal Air, despite it not yet being indexed in major databases?
My experience collaborating with MDPI has been overall very positive. I have appreciated the efficiency of the editorial workflow and the structured interaction between authors, reviewers, and editors, which reflects a well-organized and transparent peer-review system.
At the same time, I see this collaboration as an opportunity to further understand and learn the mechanisms of the editorial process from the inside, including manuscript handling, reviewer coordination, and decision-making.

3. What are your expectations for the future development of Air, and what steps do you think are most important for accelerating its inclusion in major indexing databases?
I expect Air to progressively establish itself as a credible and recognized venue in atmospheric science, particularly given its focused scope on air quality, modelling, and environmental impacts. If supported by a strong editorial strategy, it has the potential to become a reference journal in this domain.
In my view, the most important step toward inclusion in major indexing databases such as Scopus or Web of Science is to ensure consistent publication of high-quality, original research, as content quality and scientific relevance remain the primary evaluation criteria. Also, regular publication frequency and stability over time with a transparent peer-review process are equally important.

4. While many traditional topics in air research are well established, which emerging research directions do you believe will gain the most attention in the coming years?
First, artificial intelligence and hybrid modelling approaches will play a central role. There is a clear shift toward combining physical models with machine learning and deep learning techniques to improve prediction accuracy and capture complex spatio-temporal dynamics of air pollutants.
Second, multi-source data integration and real-time monitoring will become increasingly important. The fusion of ground-based sensors, satellite observations, IoT networks, and meteorological data is enabling high-resolution, real-time air quality assessment and forecasting systems, with direct applications for early warning and decision support.
Another key direction is the development of health-oriented air quality modelling, where pollution data are directly linked to exposure, vulnerability, and epidemiological outcomes.
In addition, I expect growing interest in explainable and trustworthy AI, as well as uncertainty quantification, since interpretability and reliability are becoming critical for the operational use of models in policy and regulatory contexts.

5. What do you see as the biggest scientific challenges currently facing air-related research?
The main challenges in air-related research include reducing uncertainties in emissions and modelling processes, and improving the integration across spatial and temporal scales. Another key issue is the effective use of heterogeneous data sources while ensuring data quality and consistency. In addition, the growing use of AI raises challenges related to model generalization, interpretability, and uncertainty quantification.

6. As a member of the Editorial Board, how do you see your role in shaping the journal’s direction and quality?
As a member of the Editorial Board, I see my role as contributing to the scientific quality of the journal. This includes ensuring a fair peer-review process, identifying emerging research topics, and promoting high-quality submissions.

7. Based on your experience as an author or editor, which aspects of the editorial and peer-review process have had the greatest impact on your experience?
Based on my experience, the aspects that have had the greatest impact are the quality and constructiveness of reviewer feedback, the efficiency of the editorial process, and the clarity of communication from editors. Constructive and detailed reviews are essential, as they directly improve the quality of the manuscript and guide the final decision.

8. What advice would you give to early career researchers entering this field?
I would advise early career researchers to build a solid methodological foundation while remaining open to interdisciplinary approaches, especially at the interface between physics and data science. Developing strong collaborations and seeking mentorship are also key to navigating the research and publication process effectively.

9. Is there anything else you would like to share with our readers?
I would like to emphasize the importance of maintaining scientific rigour, transparency, and openness in air-related research, especially given its direct relevance for policy and society. In this context, journals like Air can play a key role in fostering high-quality, interdisciplinary contributions and supporting emerging research directions.