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.

Socio-environmental conflicts in mining regions are often examined through political, economic, or social lenses, while the role of land-system transformation remains less integrated into quantitative analysis. This study examines the co-evolution of socio-environmental conflict and territorial change in Las Bambas (Apurímac, Peru) as a socio-territorial process. Annual conflict records from the Peruvian Ombudsman’s Office (2007–2024) were combined with annual land-cover data from MapBiomas. Yearly conflict influence zones were reconstructed from reported affected communities and geographic features using buffered spatial entities and concave hull polygons. Clustering methods (K-medoids, DBSCAN, and agglomerative hierarchical clustering) and FP-Growth association rule mining were applied to 23 unique conflicts consolidated from the original records and encoded with 10 root causes. The most intense conflict phases were accompanied by measurable landscape transformations, including the emergence of mining-related land cover from 2012 onward, sustained loss of high-Andean natural vegetation, expansion of agricultural mosaics, urban growth along the Apurímac–Cusco corridor, and hydrological alterations in wetlands and headwaters. Three conflict typologies were identified, with unfulfilled company commitments emerging as the most recurrent co-occurring grievance. The dynamic polygon approach offers a replicable framework for linking conflict records with land-system change in extractive regions.

Urban seismic vulnerability poses severe challenges for disaster preparedness in Lima, Peru, where a long-standing seismic gap increases risk to a metropolitan population of approximately ten million residents. This study presents an adaptive multi-objective optimization framework that dynamically adjusts shelter allocation priorities across earthquake intensity scenarios. The methodology integrates spatial data on population distribution, infrastructure vulnerability, and seismic hazard zones to optimize three competing objectives through the NSGA-III algorithm: inter-shelter spacing, population coverage, and safety. Model parameters were calibrated using controlled synthetic scenarios and subsequently validated with real-world data from Lima. Under the high-impact scenario used by the Municipality of Lima, the official set of 356 designated shelters was compared with an optimized configuration selected from 5855 potential sites under identical hazard and demand conditions. The optimized solution increased population coverage by 66.82% and reduced the average distance to critical resources by 24.55%, while reducing service gaps in peripheral districts. Scenario-adaptive optimization improved the robustness of shelter planning by producing configurations that were better aligned with operational priorities as hazard severity escalated, supporting more equitable access in a resource-constrained urban context. This research contributes an evidence-based decision-support tool for emergency management, translating multi-objective trade-offs into actionable shelter layouts for Lima.