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Fine particulate matter with aerodynamic diameters ≤2.5 µm (PM2.5) has adverse effects on human health and the atmospheric environment. The estimation of surface PM2.5 concentrations has made intensive use of satellite-derived aerosol products. However, it has been a great challenge to obtain high-quality and high-resolution PM2.5 data from both ground and satellite observations, which is essential to monitor air pollution over small-scale areas such as metropolitan regions. Here, the space–time extremely randomized trees (STET) model was enhanced by integrating updated spatiotemporal information and additional auxiliary data to improve the spatial resolution and overall accuracy of PM2.5 estimates across China. To this end, the newly released Moderate Resolution Imaging Spectroradiometer Multi-Angle Implementation of Atmospheric Correction AOD product, along with meteorological, topographical and land-use data and pollution emissions, was input to the STET model, and daily 1 km PM2.5 maps for 2018 covering mainland China were produced. The STET model performed well, with a high out-of-sample (out-of-station) cross-validation coefficient of determination (R2) of 0.89 (0.88), a low root-mean-square error of 10.33 (10.93) µg m-3, a small mean absolute error of 6.69 (7.15) µg m-3 and a small mean relative error of 21.28 % (23.69 %). In particular, the model captured well the PM2.5 concentrations at both regional and individual site scales. The North China Plain, the Sichuan Basin and Xinjiang Province always featured high PM2.5 pollution levels, especially in winter. The STET model outperformed most models presented in previous related studies, with a strong predictive power (e.g., monthly R2=0.80), which can be used to estimate historical PM2.5 records. More importantly, this study provides a new approach for obtaining high-resolution and high-quality PM2.5 dataset across mainland China (i.e., ChinaHighPM2.5), important for air pollution studies focused on urban areas.

Since flood frequency increases with the impact of climate change, the damage that is emphasized on flood-risk maps is based on actual flooded area data; therefore, flood-susceptibility maps for the Seoul metropolitan area, for which random-forest and boosted-tree models are used in a geographic information system (GIS) environment, are created for this study. For the flood-susceptibility mapping, flooded-area, topography, geology, soil and land-use datasets were collected and entered into spatial datasets. From the spatial datasets, 12 factors were calculated and extracted as the input data for the models. The flooded area of 2010 was used to train the model, and the flooded area of 2011 was used for the validation. The importance of the factors of the flood-susceptibility maps was calculated and lastly, the maps were validated. As a result, the distance from the river, geology and digital elevation model showed a high importance among the factors. The random-forest model showed validation accuracies of 78.78% and 79.18% for the regression and classification algorithms, respectively, and boosted-tree model showed validation accuracies of 77.55% and 77.26% for the regression and classification algorithms, respectively. The flood-susceptibility maps provide meaningful information for decision-makers regarding the identification of priority areas for flood-mitigation management.

Access to adequate housing is a fundamental human right, essential to human security, nutrition and health, and a core objective of the United Nations Sustainable Development Goals 1 , 2 . Globally, the housing need is most acute in Africa, where the population will more than double by 2050. However, existing data on housing quality across Africa are limited primarily to urban areas and are mostly recorded at the national level. Here we quantify changes in housing in sub-Saharan Africa from 2000 to 2015 by combining national survey data within a geostatistical framework. We show a marked transformation of housing in urban and rural sub-Saharan Africa between 2000 and 2015, with the prevalence of improved housing (with improved water and sanitation, sufficient living area and durable construction) doubling from 11% (95% confidence interval, 10–12%) to 23% (21–25%). However, 53 (50–57) million urban Africans (47% (44–50%) of the urban population analysed) were living in unimproved housing in 2015. We provide high-resolution, standardized estimates of housing conditions across sub-Saharan Africa. Our maps provide a baseline for measuring change and a mechanism to guide interventions during the era of the Sustainable Development Goals. The prevalence of improved housing (with improved drinking water and sanitation, sufficient living area and durable construction) in urban and rural sub-Saharan Africa doubled between 2000 and 2015.

This study proposes a simple method to produce a flood hazard assessment map in burned and urban areas, where primary data are scarce. The study area is a municipal unit of Nea Makri, a coastal part of the eastern Attica peninsula (central Greece), which has been strongly urbanized and suffered damage from urban fires in 2018. Six factors were considered as the parameters most controlling runoff when it overdraws the drainage system’s capacity. The analytical hierarchy process (AHP) method and a geographical information system (GIS) were utilized to create the flood hazard assessment map. The outcome revealed that the areas with highest flood hazard are distributed in the eastern and southern parts of the study area, as a result of the combination of lowlands with gentle slopes, torrential behavior of the streams, streams covered by construction, increasing urbanization and burned areas. The uncertainty and the verification analyses demonstrate a robust behavior for the model predictions, as well as reliability and accuracy of the map. Comparing the existing urban fabric and road network to the potential flood hazard areas showed that 80% of the urban areas and 50% of the road network were situated within areas prone to flood. The method may be applied to land use planning projects, flood hazard mitigation and post-fire management.

ContextLocated between urban area and rural area, urban–rural fringe is challenged with urbanization related social-ecological problems. Accurately identifying the urban–rural fringe can help to integrated urban–rural development planning, especially in metropolitan region. Among the various case studies to identify the urban–rural fringe, land use degree and impervious surface area are widely used. However, both indexes are only focused on land development size, resulting in coarse identifying results.ObjectivesIt is aimed to propose a three-dimensional approach to integrating land development size, pattern and density, in order to accurately identifying the urban–rural fringe.MethodsLandsat TM and DMSP/OLS datasets were used to establish a three-dimensional index system consisting of land development size (LDS), land development pattern (LDP) and land development density (LDD). Self-Organizing Feature Map (SOFM) was applied to identify the urban–rural fringe of Beijing City, China.ResultsFrom 2001 to 2009, the inner boundary of the urban–rural fringe had expanded to outside the fifth ring road. Likewise, the outer boundary moved from the fifth to the sixth ring road. The new urban development zone was the main area of urban expansion controlled by urban planning, where the increments of urban–rural fringe was 1273.5 km2, accounting for 75.24% of the whole city. Partial correlation analysis indicated that LDS played a leading role in SOFM clustering, but the spatial continuity of the urban–rural fringe was the best when it was integrated with LDP and LDD, especially the latter to comprehensively define and quantify land development intensity.ConclusionsThe integration of land development size, pattern and density is effective to quantify land development intensity, and thus to identify the urban–rural fringe in metropolitan regions.

The aim of this paper is to reveal, describe, explain, and map variations in diurnal population in the metropolitan area of Prague. We use an alternative data source to traditional census-based cartographic presentations and employ location data from mobile phones to identify types of daily rhythm that shape the region at different times during a typical weekday. These rhythms are influenced especially by residential and commercial suburbanization and the consequent dynamic development of new working centres, services, and leisure-time facilities within the metropolitan region. The main output consists of three maps. The first map contains a typology of the main functions - residential, work, transportation, and services - and is used as the main analytical tool for sorting settlements, resulting in classification of nine types of settlement in all. The other two maps show a plastic image of the day- and night-time populations of the metropolitan area.

In May 2024, southern Brazil experienced a severe flood that caused widespread devastation, particularly in the metropolitan area of Porto Alegre. This disaster resulted from a rare combination of atmospheric conditions: a heatwave stalled a cold front, leading to prolonged and intense rainfall. The flood claimed 183 lives, left 27 missing, and displaced many more. Notably, the flood's peak coincided with satellite observations from both SWOT and Sentinel‐2, providing a valuable snapshot of the disaster. To assess the flood's scope, and volume, we integrate these satellite data with FABDEM topography. SWOT's water height measurements, evaluated with in situ data, underscore its flood monitoring potential. The estimated floodwater volume was ∼${\\sim} $1.5 billion m3${\\mathrm{m}}^{3}$ . While primarily damaging croplands, the flood directly affected ∼${\\sim} $420,000 individuals in the study region, with ∼${\\sim} $16% identified as socially vulnerable. These findings offer insights into floodwater distribution and contribute to future flood dynamics research, mitigation strategies, and disaster preparedness. Plain Language Summary In May 2024, the state of Rio Grande do Sul in southern Brazil faced a devastating flood, with the capital city, Porto Alegre, and its metro area being particularly hard‐hit. An unusual combination of weather conditions, including a heatwave that blocked a cold front, led to record‐breaking rainfall. This resulted in a disaster that claimed 183 lives, left 27 people missing, and displaced over 600,000 individuals from their homes in the whole state. To better understand the scale of the flooding, this study used advanced satellite data and topographic maps to measure how much water was involved, how deep the floodwaters were, and how far they reached. The findings showed that croplands were heavily affected, but within the city, over 420,000 people were directly impacted, including 67,000, especially vulnerable due to social and economic factors. The total volume of floodwater was estimated to be 1.5 billion cubic meters—enough to supply New York City for more than a year. This research helps us better understand the dynamics of floods and can be used to improve disaster preparedness and response in the future. Key Points Unusual high‐temperature anomalies and an atmospheric blocking caused unprecedented rainfall in south Brazil during May of 2024 Using a novel combination of satellite data, we assessed the extent and volume of flooded areas, showing SWOT's flood monitoring potential ∼${\\sim} $16% of affected communities are socially vulnerable, emphasizing the urgent need for targeted climate disaster preparedness and mitigation

Despite being one of the wealthiest nations, disparities in adverse birth outcomes persist across racial and ethnic lines in the United States. We studied the association between historical redlining and preterm birth, low birth weight (LBW), small-for-gestational age (SGA), and perinatal mortality over a ten-year period (2006-2015) in Los Angeles, Oakland, and San Francisco, California. We used birth outcomes data from the California Office of Statewide Health Planning and Development between January 1, 2006 and December 31, 2015. Home Owners' Loan Corporation (HOLC) Security Maps developed in the 1930s assigned neighborhoods one of four grades that pertained to perceived investment risk of borrowers from that neighborhood: green (grade A) were considered \"Best\", blue (grade B) \"Still Desirable\", yellow (grade C) \"Definitely Declining\", and red (grade D, hence the term \"redlining\") \"Hazardous\". Geocoded residential addresses at the time of birth were superimposed on HOLC Security Maps to assign each birth a HOLC grade. We adjusted for potential confounders present at the time of Security Map creation by assigning HOLC polygons areal-weighted 1940s Census measures. We then employed propensity score matching methods to estimate the association of historical HOLC grades on current birth outcomes. Because tracts graded A had almost no propensity of receiving grade C or D and because grade B tracts had low propensity of receiving grade D, we examined birth outcomes in the three following comparisons: B vs. A, C vs. B, and D vs. C. The prevalence of preterm birth, SGA and mortality tended to be higher in worse HOLC grades, while the prevalence of LBW varied across grades. Overall odds of mortality and preterm birth increased as HOLC grade worsened. Propensity score matching balanced 1940s census measures across contrasting groups. Logistic regression models revealed significantly elevated odds of preterm birth (odds ratio (OR): 1.02, 95% confidence interval (CI): 1.00-1.05), and SGA (OR: 1.03, 95% CI: 1.00-1.05) in the C vs. B comparison and significantly reduced odds of preterm birth (OR: 0.93, 95% CI: 0.91-0.95), LBW (OR: 0.94-95% CI: 0.92-0.97), and SGA (OR: 0.94, 95% CI: 0.92-0.96) in the D vs. C comparison. Results differed by metropolitan area and maternal race. Similar to prior studies on redlining, we found that worsening HOLC grade was associated with adverse birth outcomes, although this relationship was less clear after propensity score matching and stratifying by metropolitan area. Higher odds of preterm birth and SGA in grade C versus grade B neighborhoods may be caused by higher-stress environments, racial segregation, and lack of access to resources, while lower odds of preterm birth, SGA, and LBW in grade D versus grade C neighborhoods may due to population shifts in those neighborhoods related to gentrification.

Flood hazard is increasing in frequency and magnitude in major South East Asian metropolitan areas due to fast urban development and changes in climate, threatening people's property and life. Typically, flood management actions are mostly focused on large-scale defences, such as river embankments or discharge channels or tunnels. However, these are difficult to implement in town centres without affecting the value of their heritage districts and might not provide sufficient mitigation. Therefore, urban heritage buildings may become vulnerable to flood events, even when they were originally designed and built with intrinsic resilient measures, based on the local knowledge of the natural environment and its threats at the time. Their aesthetic and cultural and economic values mean that they can represent a proportionally high contribution to losses in any event. Hence it is worth investigating more localized, tailored mitigation measures. Vulnerability assessment studies are essential to inform the feasibility and development of such strategies. In this study we propose a multilevel methodology to assess the flood vulnerability and risk of residential buildings in an area of Kuala Lumpur, Malaysia, characterized by traditional timber housing. The multiscale flood vulnerability model is based on a wide range of parameters, covering building-specific parameters, neighbourhood conditions and catchment area conditions. The obtained vulnerability index shows the ability to reflect different exposure by different building types and their relative locations. The vulnerability model is combined with high-resolution fluvial and pluvial flood maps providing scenario events with 0.1 % annual exceedance probability (AEP). A damage function of generic applicability is developed to compute the economic losses at individual building and sample levels. The study provides evidence that results obtained for a small district can be scaled up to the city level, to inform both generic and specific protection strategies.