Wildland–urban interface (WUI) regions are particularly vulnerable to wildfires due to their proximity to both nature and urban developments, posing significant risks to lives and property. To enhance our understanding of the risk profiles in WUI areas, we analysed seven fire case studies in central Chile. We developed a mixed-method approach for conducting local-scale analyses, which involved field surveys, remote-sensing through satellite and drone imagery, and GIS-based analysis of the collected data. The methodology led to the generation of a georeferenced dataset of damaged and undamaged dwellings, including 16 variables representing their physical characteristics, spatial arrangement, and the availability of fire suppression resources. A binary classification model was then used to assess the relative importance of these attributes as indicators of vulnerability. The analysis revealed that spatial arrangement factors have a greater impact on damage prediction than the structural conditions and fire preparedness of individual units. Specifically, factors such as dwelling proximity to neighbours, distance to vegetation, proximity to the border of dwelling groups, and distance from the origin of the fire substantially contribute to the prediction of fire damage. Other structural attributes associated with less affluent homes may also increase the likelihood of damage, although further data are required for confirmation. This study provides insights for the design, planning, and governance of WUI areas in Chile, aiding the development of risk mitigation strategies for both built structures and the broader territorial area.

We present new Herschel observations of four massive, Sunyaev–Zel’dovich effect–selected clusters at , two of which have also been observed with the Atacama Large Millimeter/submillimeter Array (ALMA). We detect 19 Herschel/Photoconductor Array Camera and Spectrometer (PACS) counterparts to spectroscopically confirmed cluster members, five of which have redshifts determined via CO (4–3) and [C i] ( ) lines. The mean [C i]/CO line ratio is 0.19 ± 0.07 in brightness temperature units, consistent with previous results for field samples. We do not detect significant stacked ALMA dust continuum or spectral-line emission, implying upper limits on mean interstellar medium (H₂ + H i) and molecular gas masses. An apparent anticorrelation of with clustercentric radius is driven by the tight relation between star formation rate and stellar mass. We find an average specific star formation rate of log(sSFR/yr⁻¹) = −10.36, which is below the correlation measured for field galaxies at similar redshifts. The fraction of infrared-bright galaxies (IRBGs; ) per cluster and average sSFR rise significantly with redshift. For CO detections, we find , comparable to those of field galaxies, and gas depletion timescales of about 2 Gyr. We use radio observations to distinguish active galactic nuclei (AGNs) from star-forming galaxies. At least four of our 19 Herschel cluster members have , implying an AGN fraction for our PACS-selected sample.

We present a multiwavelength analysis of 48 submillimeter galaxies (SMGs) detected in the Large APEX Bolometer Camera/Atacama Cosmology Telescope (ACT) Survey of Clusters at All Redshifts, LASCAR, which acquired new 870 μm and Australia Telescope Compact Array 2.1 GHz observations of 10 galaxy clusters detected through their Sunyaev–Zel’dovich effect (SZE) signal by the ACT. Far-infrared observations were also conducted with the Photodetector Array Camera and Spectrometer (100/160 μm) and SPIRE (250/350/500 μm) instruments on Herschel for sample subsets of five and six clusters. LASCAR 870 μm maps were reduced using a multiscale iterative pipeline that removes the SZE increment signal, yielding point-source sensitivities of σ ∼ 2 mJy beam⁻¹. We detect in total 49 sources at the 4σ level and conduct a detailed multiwavelength analysis considering our new radio and far-IR observations plus existing near-IR and optical data. One source is identified as a foreground galaxy, 28 SMGs are matched to single radio sources, four have double radio counterparts, and 16 are undetected at 2.1 GHz but tentatively associated in some cases to near-IR/optical sources. We estimate photometric redshifts for 34 sources with secure (25) and tentative (9) matches at different wavelengths, obtaining a median . Compared to previous results for single-dish surveys, our redshift distribution has a comparatively larger fraction of sources at z > 3, and the high-redshift tail is more extended. This is consistent with millimeter spectroscopic confirmation of a growing number of high-z SMGs and relevant for testing of cosmological models. Analytical lens modeling is applied to estimate magnification factors for 42 SMGs at clustercentric radii >1.′2; with the demagnified flux densities and source-plane areas, we obtain integral number counts that agree with previous submillimeter surveys.

Recovery of damaged water supply systems after severe earthquakes is one of the priorities to return to normal conditions. Water supply systems are intrinsically interdependent with other important lifelines such as transportation, energy, health care, and industrial sectors. These interdependencies need to be better understood by means of empirical data and analytical models. This paper is primarily of archival nature and describes empirical impact data of large earthquakes in Chile (Maule 2010), New Zealand (Christchurch 2010–2011), and Japan (Tohoku 2011) on their respective drinking water systems, and summarizes damage observations, emergency actions, and restoration processes within a resilience framework focused on metrics of robustness and rapidity. The archival nature of this article is justified by the paramount importance of systematic data collection to improve network resilience for future analytical models that aim to predict the response and recovery of water networks. Moreover, based on the collected data, the effectiveness of response actions and implemented countermeasures are evaluated relative to the observed earthquake performance of the system components. Important observations are derived to understand the main factors causing water supply system outages, the effectiveness of strategies used, and their capacity to restore the service.