The heterogenous distribution of both COVID-19 incidence and mortality in Catalonia (Spain) during the firsts moths of the pandemic suggests that differences in baseline risk factors across regions might play a relevant role in modulating the outcome of the pandemic. This paper investigates the associations between both COVID-19 incidence and mortality and air pollutant concentration levels, and screens the potential effect of the type of agri-food industry and the overall land use and cover (LULC) at area level. We used a main model with demographic, socioeconomic and comorbidity covariates highlighted in previous research as important predictors. This allowed us to take a glimpse of the independent effect of the explanatory variables when controlled for the main model covariates. Our findings are aligned with previous research showing that the baseline features of the regions in terms of general health status, pollutant concentration levels (here NO₂ and PM₁₀), type of agri-food industry, and type of land use and land cover have modulated the impact of COVID-19 at a regional scale. This study is among the first to explore the associations between COVID-19 and the type of agri-food industry and LULC data using a population-based approach. The results of this paper might serve as the basis to develop new research hypotheses using a more comprehensive approach, highlighting the inequalities of regions in terms of risk factors and their response to COVID-19, as well as fostering public policies towards more resilient and safer environments.

This study examines the public's perceptions of the ecological restoration of the Congost River over the past thirty years, focusing on the period between 2010 and 2022. We conducted a survey of 112 river users across five key zones identified through a pilot study for their high pedestrian density, aiming to analyse how different sociodemographic groups perceive the river's ecological state. A structured questionnaire was distributed along both sides of the river to engage a diverse range of individuals typically utilizing the river environment. The collected data were analysed using regression models and Mann-Whitney U tests to assess differences between groups, with Bonferroni adjustments applied to control for multiple comparisons.

The use of Land use and Land cover (LULC) data is gradually becoming more widely spread in studies relating the environment to human health. However, little research has acknowledged the compositional nature of these data. The goal of the present study is to explore, for the first time, the independent effect of eight LULC categories (agricultural land, bare land, coniferous forest, broad-leaved forest, sclerophyll forest, grassland and shrubs, urban areas, and waterbodies) on three selected common health conditions: type 2 diabetes mellitus (T2DM), asthma and anxiety, using a compositional methodological approach and leveraging observational health data of Catalonia (Spain) at area level. We fixed the risk exposure scenario using three covariates (socioeconomic status, age group, and sex). Then, we assessed the independent effect of the eight LULC categories on each health condition. Our results show that each LULC category has a distinctive effect on the three health conditions and that the three covariates clearly modify this effect. This compositional approach has yielded plausible results supported by the existing literature, highlighting the relevance of environmental heterogeneity in health studies. In this sense, we argue that different types of envi-ronment possess exclusive biotic and abiotic elements affecting distinctively on human health. We believe our contribution might help researchers approach the environment in a more multidimensional man-ner integrating environmental heterogeneity in the analysis. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).