Ivan Haščič
Sessions
Air pollution, particularly fine particulate matter (PM2.5), ground-level ozone (O3), and nitrogen dioxide (NO2), poses a significant global health risk, contributing to early mortality. Measuring population exposure is crucial for understanding and mitigating these health impacts. This paper leverages recent advancements in air pollution data to review various global air pollution datasets based on a criteria set. The framework facilitates comparisons between various hybrid datasets (combining ground-based and satellite measurements) and offers a methodology for constructing air pollution exposure indicators for PM2.5, O3, and NO2. It uses the Global Burden of Disease data to update the indicator set on the national and subnational levels across the 1990-2020 period. Results reveal that most OECD countries fall short of the World Health Organization's (WHO) 2021 air quality guidelines for PM2.5, O3, and NO2. Countries such as Chile, Korea, Poland, and Türkiye exhibit PM2.5 concentrations (population weighted) exceeding safe levels by a factor of four. Similarly, several OECD countries such as Korea, Italy, and Slovenia experienced severe O3 exposure in 2020, while non-OECD countries such as India displayed even higher population weighted O3 concentrations, exceeding safe levels by more than double. A sensitivity analysis further indicates that despite similar trends observed across different air pollution datasets, considerable differences are found between global datasets and national statistics. This highlights the need to further examine the accuracy of the various data sources and help guide policy analysis at the national and subnational levels. Given the widespread failure to meet safe air quality standards, our findings emphasize the urgent need for global policy actions to reduce population exposure to air pollution and safeguard public health.
Biodiversity loss is a critical environmental concern, with habitat destruction and degradation identified as key drivers. Recent advancements in computational methods and the ever-growing availability of Earth Observation (EO) data enable detailed analyses of land cover changes at unprecedented spatial and temporal scales. This paper develops a set of indicators of land cover and land cover conversions to assess potential pressures on terrestrial biodiversity and ecosystems. Key land cover conversions include deforestation/reforestation, cropland expansion/contraction, and urban/infrastructure development. We leverage two high-resolution datasets (i.e. the Copernicus Climate Change Initiative Land Cover [CCI-LC] and the Global Human Settlement Layer [GHSL] built-up area) to develop national and subnational indicators for all countries globally, spanning 2000-2020 for CCI-LC and 1975-2030 for GHSL. The analysis reveals a continued decline in natural and semi-natural vegetation cover in many OECD countries and partner countries since the 2000 baseline. For example, Brazil experienced a substantial loss of tree cover (200,000 km²) between 2000 and 2020, equivalent to an area exceeding Switzerland's landmass by a factor of six. Meanwhile, most OECD countries exhibited a net gain in tree cover during the same period. Urban development is another key reason for the observed decline in natural and semi-natural vegetated land where countries such as China and India displayed a significantly higher increase in artificial surfaces compared to OECD countries over the past two decades. Results currently only account for the ecosystem extent and do not account for the ecosystem condition. For instance, some grassland land cover may have been significantly modified by long-term grazing and is in fact intensively managed grassland (wild prairies vs grassland pastures). Therefore, these results should be considered alongside complementary data sources to provide a more comprehensive picture of biodiversity pressures and highlight that current global land monitoring EO products do not adequately meet the needs of policy analysts who require data at the interface of land cover and land use.