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New Perspectives in Air Pollution, Climate, and Public Health
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New Perspectives in Air Pollution, Climate, and Public Health

A special issue of Climate (ISSN 2225-1154). This special issue belongs to the section "Climate and Environment".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 1540

Special Issue Editors

Prof. Dr. Roberto Ariel Abeldaño Zuñiga

E-Mail Website
Guest Editor
Helsinki Institute of Urban and Regional Studies (URBARIA), Centre for Social Data Science, Faculty of Social Sciences. University of Helsinki, 00150 Helsinki, Finland
Interests: public health care science; environmental and occupational health; social and economic geography; global development studies; climate change
Dr. Gabriela Narcizo de Lima

E-Mail Website
Guest Editor
1. Department of Geography, Faculty of Arts and Humanities, Porto University, Via Panorâmica, Campo Alegre, 4150-564 Porto, Portugal
2. Centre of Studies in Geography and Spatial Planning, Porto University, 4150-564 Porto, Portugal
Interests: climate and hydrometeorological risks; climatology; territorial planning; integrated water resources management

Special Issue Information

Dear Colleagues,

We are pleased to invite submissions to this Special Issue of the MDPI journal Climate, titled "New Perspectives in Air Pollution, Climate, and Public Health", dedicated to exploring innovative research in the intersection of air pollution, climate change, and public health. Our primary objective is to enhance understanding of how atmospheric contaminants interact with climatic variables to impact human health and well-being. This Special Issue will gather cutting-edge studies that offer new insights into the mechanisms through which air pollution and climate variability influence public health outcomes.

We aim to address a broad range of topics, including, but not limited to, the analysis of short- and long-term health effects linked to air pollution exposure, strategies for mitigating the health risks associated with climate change, and the development of sustainable policies and technologies to improve air quality and health resilience. Papers that investigate the socio-economic and institutional dimensions of air quality management, particularly in urban, coastal, and agricultural settings, are particularly desirable.

Contributions may include empirical research, comprehensive review articles, and case studies that assess the effectiveness of interventions and policies aimed at reducing the health impacts of air pollution and climate change. By integrating scientific research into health policy and practice, this Special Issue aims to inform and shape public health strategies that effectively respond to the challenges posed by the changing climate and deteriorating air quality.

This Special Issue welcomes submissions that not only highlight problems but also focus on proactive solutions and interventions at both the local and global levels to enhance public health outcomes in the face of air pollution and climate change.

Prof. Dr. Roberto Ariel Abeldaño Zuñiga
Dr. Gabriela Narcizo de Lima
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Climate is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • air pollution
  • climate change
  • climate risks
  • public health
  • environmental policy
  • resilience strategies

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

15 pages, 939 KiB  
Article
A Pilot Study with Low-Cost Sensors: Seasonal Variation of Particulate Matter Ratios and Their Relationship with Meteorological Conditions in Rio Grande, Brazil
by Gustavo de Oliveira Silveira, Gabriella Mello Gomes Vieira de Azevedo, Ronan Adler Tavella, Paula Florencio Ramires, Rodrigo de Lima Brum, Alicia da Silva Bonifácio, Ricardo Arend Machado, Letícia Willrich Brum, Romina Buffarini, Diana Francisca Adamatti and Flavio Manoel Rodrigues da Silva Júnior
Climate 2025, 13(4), 71; https://doi.org/10.3390/cli13040071 - 30 Mar 2025
Viewed by 63
Abstract
(1) Background: This study investigated seasonal variations in particulate matter (PM) ratios (PM1/PM2.5, PM2.5/PM10, and PM1/PM10) and their relationship with the meteorological conditions in Rio Grande, Brazil. (2) Methods: PM1 [...] Read more.
(1) Background: This study investigated seasonal variations in particulate matter (PM) ratios (PM1/PM2.5, PM2.5/PM10, and PM1/PM10) and their relationship with the meteorological conditions in Rio Grande, Brazil. (2) Methods: PM1, PM2.5, and PM10 levels were collected using low-cost Gaia Air Quality Monitors, which measured PM concentrations at high temporal resolution. Meteorological variables, including atmospheric pressure, temperature, relative humidity, wind speed, and precipitation, were obtained from the National Institute of Meteorology (INMET). The data were analyzed through multiple linear regression to assess the influence of meteorological factors on PM ratios. (3) Results: The results show that the highest PM ratios occurred in winter, indicating a predominance of fine and ultrafine particles, while the lowest ratios were observed in spring and summer. Multiple linear regression analysis identified atmospheric pressure, wind speed, and maximum temperature as the key drivers of PM distribution. (4) Conclusions: This study highlights the importance of continuous monitoring of PM ratios, particularly PM1, which remains underexplored in Brazil. The findings underscore the need for targeted air quality policies emphasizing seasonal mitigation strategies and improved pollution control to minimize the health risks associated with fine and ultrafine PM exposure. Full article
(This article belongs to the Special Issue New Perspectives in Air Pollution, Climate, and Public Health)
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21 pages, 2371 KiB  
Article
Predicting Asthma Hospitalizations from Climate and Air Pollution Data: A Machine Learning-Based Approach
by Jean Souza dos Reis, Rafaela Lisboa Costa, Fabricio Daniel dos Santos Silva, Ediclê Duarte Fernandes de Souza, Taisa Rodrigues Cortes, Rachel Helena Coelho, Sofia Rafaela Maito Velasco, Danielson Jorge Delgado Neves, José Firmino Sousa Filho, Cairo Eduardo Carvalho Barreto, Jório Bezerra Cabral Júnior, Herald Souza dos Reis, Keila Rêgo Mendes, Mayara Christine Correia Lins, Thomás Rocha Ferreira, Mário Henrique Guilherme dos Santos Vanderlei, Marcelo Felix Alonso, Glauber Lopes Mariano, Heliofábio Barros Gomes and Helber Barros Gomes
Climate 2025, 13(2), 23; https://doi.org/10.3390/cli13020023 - 24 Jan 2025
Viewed by 802
Abstract
This study explores the predictability of monthly asthma notifications using models built from different machine learning techniques in Maceió, a municipality with a tropical climate located in the northeast of Brazil. Two sets of predictors were combined and tested, the first containing meteorological [...] Read more.
This study explores the predictability of monthly asthma notifications using models built from different machine learning techniques in Maceió, a municipality with a tropical climate located in the northeast of Brazil. Two sets of predictors were combined and tested, the first containing meteorological variables and pollutants, called exp1, and the second only meteorological variables, called exp2. For both experiments, tests were also carried out incorporating lagged information from the time series of asthma records. The models were trained on 80% of the data and validated on the remaining 20%. Among the five methods evaluated—random forest (RF), eXtreme Gradient Boosting (XGBoost), Multiple Linear Regression (MLR), support vector machine (SVM), and K-nearest neighbors (KNN)—the RF models showed superior performance, notably those of exp1 when incorporating lagged asthma notifications as an additional predictor. Minimum temperature and sulfur dioxide emerged as key variables, probably due to their associations with respiratory health and pollution levels, emphasizing their role in asthma exacerbation. The autocorrelation of the residuals was assessed due to the inclusion of lagged variables in some experiments. The results highlight the importance of pollutant and meteorological factors in predicting asthma cases, with implications for public health monitoring. Despite the limitations presented and discussed, this study demonstrates that forecast accuracy improves when a wider range of lagged variables are used, and indicates the suitability of RF for health datasets with complex time series. Full article
(This article belongs to the Special Issue New Perspectives in Air Pollution, Climate, and Public Health)
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