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    Applying the multi-dimensional damage assessment (MDDA) methodology to the Cumbre Vieja volcanic eruption in La Palma (Spain)
    (Springer Science and Business Media B.V., 2024-12-01)
    Volcanic events with an important affectation of urban areas and other land areas with important human activity have been rare in Europe in the past century. This has led to a lack of comprehensive analysis of the social, economic and environmental damages that these types of events can cause on specific human communities. In the present study, we apply an industrial ecology approach to calculate the damage linked to the Cumbre Vieja volcanic eruption in the Canary Islands in September 2021. Therefore, the main objective was to apply the multi-dimensional damage assessment (MDDA) methodology to quantify the degree of damage that has been exerted by the eruption in the island of La Palma (Spain) through the inclusion of environmental damage endpoints with other sustainable development variables (i.e., social and economic dimensions). Data were obtained from different sources, including the cadastre of La Palma, local data on derived health, as well as data obtained from the global ecosystem dynamics investigation of NASA, among other sources. Thereafter, damage endpoints were all converted to disability-adjusted life years (DALYs). Results show that direct gaseous emissions from the volcano were responsible for a significant amount of total DALYs, above 90% in all scenarios, followed by damage linked to economic losses, as well as social losses related to morbidity. Other environmental damages played a minor part in the total damage exerted by the volcano. The results demonstrate the importance of air quality indicators in the aftermath of an eruption in densely populated areas; in contrast, the impact associated with infrastructure loss played a minor role in total damage. Although challenges remain when providing a holistic quantification of total damage linked to volcanic disasters, the MDDA method constitutes a promising systematic standardized and transparent damage quantification tool that allows computing a deterministic damage evaluation that can aid in natural hazard risk assessment. In fact, it is considered that the method has the potential to be used as a holistic decision tool to aid in mitigating disaster risk.
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    Smart Multi-Sensor Calibration of Low-Cost Particulate Matter Monitors
    (MDPI, 2023-04-01)
    A variety of low-cost sensors have recently appeared to measure air quality, making it feasible to face the challenge of monitoring the air of large urban conglomerates at high spatial resolution. However, these sensors require a careful calibration process to ensure the quality of the data they provide, which frequently involves expensive and time-consuming field data collection campaigns with high-end instruments. In this paper, we propose machine-learning-based approaches to generate calibration models for new Particulate Matter (PM) sensors, leveraging available field data and models from existing sensors to facilitate rapid incorporation of the candidate sensor into the network and ensure the quality of its data. In a series of experiments with two sets of well-known PM sensor manufacturers, we found that one of our approaches can produce calibration models for new candidate PM sensors with as few as four days of field data, but with a performance close to the best calibration model adjusted with field data from periods ten times longer.
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    Relationship between forest fires and air quality: a study of particulate matter in localities distant from the main emission source
    (Instituto Internacional de Ecologia (Brazil), 2025-01-01)
    Wildfires represent a growing threat to air quality in fire-prone regions; however, the spatiotemporal dynamics of pollutant dispersion to distant localities remain inadequately characterized. This study quantifies particulate matter concentrations (PM1.0, PM2.5, PM10) in localities distant from the February 2024 Valparaíso wildfire, evaluating their spatiotemporal variation and association with fire intensity and meteorological parameters using a network of low-cost sensors. Four PurpleAir sensors distributed along the Valparaíso-Santiago corridor (70-120 km) were integrated with meteorological stations and satellite-derived Fire Radiative Power (FRP) data. Analysis of variance revealed significant differences in PM concentrations between pre-fire, during-fire, and post-fire phases, with the proximal station (S1) showing the strongest response. Pearson correlations with False Discovery Rate adjustment demonstrated immediate associations between FRP and PM at S1 (r = 0.71-0.72, p < 0.05), while distal stations (S2-S4) exhibited significant correlations only after applying 1-2 day temporal lags (r = 0.44-0.49, p < 0.05), providing quantitative evidence of regional-scale pollutant transport. These findings establish an empirical foundation for developing early warning systems in fire-prone regions and underscore the necessity of considering temporal lags in air quality management strategies during wildfire events.
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