Future Urbanization Research Articles

Urban Integration of Green Roofs: Current Challenges and Perspectives

Green roofs (GRs) are a sustainable alternative to conventional roofs that provide multiple ecosystem services. Integrating GRs into urban areas is highly relevant considering the rapidly increasing built-up in cities. Therefore, this paper systematically and comprehensively reviews the recent literature from 2011 to 2019 on GRs to identify the challenges and perspectives related to the urban integration of GRs. The review suggests that the effectiveness of GRs in delivering ecosystem services is largely dependent on context-specific parameters such as weather conditions and existing construction or design-related parameters. Integrating GRs into urban areas can be challenging given the diversity of actors, functions, and conditions characterizing these areas. Although significant research has already been conducted on GRs, research covering more geographical locations and contexts is needed. The review points out the need to include future urbanization scenarios, such as tall buildings while analyzing the impact of GRs on ecological networks. Additionally, the review emphasizes the inclusion of urban morphological parameters alongside an analysis of the impact of GRs on microclimate regulation and air quality. In terms of social acceptance, this review points out the need to consider the temporal cycles of vegetation for noting users’ perspectives. Additionally, further research is required on the social impact of GRs, considering their influence on property prices. Lastly, the review stresses the need for more city-scale studies on the impact of GRs on ecosystem services.

Anthropogenic emissions and urbanization increase risk of compound hot extremes in cities

Urban areas are experiencing strongly increasing hot temperature extremes. However, these urban heat events have seldom been the focus of traditional detection and attribution analysis designed for regional to global changes. Here we show that compound (day–night sustained) hot extremes are more dangerous than solely daytime or nighttime heat, especially to female and older urban residents. Urban compound hot extremes across eastern China have increased by 1.76 days per decade from 1961 to 2014 with fingerprints of urban expansion and anthropogenic emissions detected by a stepwise detection and attribution method. Their attributable fractions are estimated as 0.51 (urbanization), 1.63 (greenhouse gases) and −0.54 (other anthropogenic forcings) days per decade. Future emissions and urbanization would make these compound events two to five times more frequent (2090s versus 2010s), leading to a threefold-to-sixfold growth in urban population exposure. Our findings call for tailored adaptation planning against rapidly growing health threats from compound heat in cities.

Downscaling SSP-consistent global spatial urban land projections from 1/8-degree to 1-km resolution 2000\u20132100

Long-term, spatial urban land projections that simultaneously offer global coverage and local-scale empirical accuracy are rare. Recently a set of such projections was produced using data-science-based simulations and the Shared Socioeconomic Pathways (SSPs). These projections update at decadal time intervals from 2000 to 2100 with a spatial resolution of 1/8 degree, while many socio-environmental studies customarily run their analysis and modelling at finer spatial resolutions, e.g. 1-km. Here we develop and validate an algorithm to downscale the 1/8-degree spatial urban land projections to the 1-km resolution. The algorithm uses an iterative process to allocate the decadal amount of urban land expansion originally projected for each 1/8-degree grid to its constituent 1-km grids. The results are a set of global maps showing urban land fractions at the 1-km resolution, updated at decadal intervals from 2000 to 2100, under five different urban land expansion scenarios consistent with the SSPs. The data can support studies of potential interactions between future urbanization and environmental changes across spatial and temporal scales.

Projected impact of sea-level rise and urbanization on mottled duck (Anas fulvigula) habitat along the Gulf Coast of Louisiana and Texas through 2100

Coastal wetlands along the Gulf of Mexico support a wide diversity of wildlife, are important nurseries for sport and commercial fisheries, provide erosion and flood control, and serve many other ecological functions and services. These marshes have been declining in area and degrading at alarming rates since the 1930s. Effective conservation planning is vital to protect these ecosystems, but decision makers often lack knowledge of expected future conditions to strategically target conservation actions. To address this issue, we focus on a species of conservation concern, the mottled duck (Anas fulvigula), that resides year-round in the coastal marshes of the Gulf of Mexico. We used location data collected from radiomarked hen mottled ducks from 2006 to 2011 to create an ensemble model of habitat selection for 2010. We then projected future habitat states using models of sea-level rise and human development. By combining future predictions with our ensemble model, we predict future habitat for mottled ducks through 2100, in 20-year time steps beginning with 2020. Sea-level rise models predicted reductions in coastal marsh habitats and our ensemble model predict corresponding declines in overall habitat quantity and quality for mottled ducks, with the largest rate of habitat loss predicted within the Chenier Plain of Louisiana, USA at 71%. In some areas, particularly the Texas Mid-Coast, USA, future urbanization and human development is expected to reduce the ability of wetland habitat to migrate inland with rising sea-levels. Our results also highlight areas of coastal marsh particularly vulnerable to sea-level rise; and conversely, identify areas most likely to persist into the future that could be targeted for habitat conservation to help mottled ducks persist on the landscape.

Constructing a smart framework for supplying the biogas energy in green buildings using an integration of response surface methodology, artificial intelligence and petri net modelling

Nowadays, energy crisis is considered an essential active issue for future urbanization in megacities. While the rate of population growth increases, the volume of municipal solid waste production increases significantly. This highlights the need of Sustainable Development Goals (SDGs) for both developed and developing countries. This paper constructs a novel smart framework for supplying biogas energy. Our study is applicable for fields of waste management and energy supply in green buildings. The proposed framework integrates the Response Surface Methodology (RSM), Artificial Intelligence (AI), and Petri net modeling. In this regard, the AI techniques including the Random Tree (RT), Random Forest (RF), Artificial Neural Network (ANN) and, Adaptive-Network-based Fuzzy Inference System (ANFIS) are employed. In addition, for creating the optimum condition, a dynamic control system using the Petri Net modeling is applied. Among all machine learning methods, ANFIS with 0.99 correlation coefficient had the best accuracy for Accumulated Biogas Production (ABP) based on effective factors. Finally, the main findings of this paper are to introduce a novel framework for addressing different scientific issues such as supplying the clean energy in green buildings, the development of a smart and sustainable biogas production control system, integration of solid waste management with the SDGs in green buildings.

Geographical Information System (GIS) Based Multi-Criteria Decision Analysis for Categorization of the Villages: In the Case of Kabul New City Villages

Kabul is experiencing the most powerful wave of urbanization in its history. With the rapid increase of population, typical urban problems such as lowering of the groundwater table, an influx of returnees, traffic jams, deterioration of sanitation, etc., are becoming critical. In 2006, to decrease the pressure from existing Kabul and take measurements for future urbanization process, an independent board was established for the development of a new city adjacent to the existing Kabul by the name of Kabul New City (KNC). There were already 54 villages that existed there. Due to smoothly implementing the master plan and supporting the existing villages, it was necessary to prepare village development plans. As each village has its own characteristics from the urban planning perspective, therefore this research explains a method for categorization of villages through multi-criteria decision analysis; seven potential criteria were identified and rated in collaboration with experts, and Super Decision software was used as an Analytic Hierarchy Process (AHP) tool to weight the criteria. The final output is a map showing different categories of villages. It will assist the government and urban planners in choosing a clear strategy and platform for developing each individual village to be sustainable and applicable.

Eco-District Eco-Life. Geopolitics of the resources and general considerations about future urbanization

The discussion will touch the Eco-District settlement for the future of urbanization. The presentation intends to summarize few key aspects of the topic on the fundamental element of the Eco-District strategy. The author will present three important study cases: Hammarby Lake City and Växjö in Sweden and Aarhus in Denmark. The study will explain the theory, the best practice cases, and the necessary culture and management based on social participation. The paper will touch the Multi-Scale Approach, the Multi-Layer Planning, and Smart-Node.

Development of an Urban Heat Mitigation Plan for the Greater Sacramento Valley, California, a Csa Koppen Climate Type

An urban atmospheric modeling study was undertaken with the goal of informing the development of a heat-mitigation plan for the greater Sacramento Valley, California. Realistic levels of mitigation measures were characterized and ranked in terms of their effectiveness in producing urban cooling under current conditions and future climate and land use. An urban heat-island index was computed for current and future climates based on each location’s time-varying upwind temperature reference points and its hourly temperatures per coincident wind direction. For instance, the UHII for the period 16–31 July 2015, for all-hours averaged temperature equivalent (i.e., °C · h hr−1), ranged from 1.5 to 4.7 °C across the urban areas in the region. The changes in local microclimates corresponding to future conditions were then quantified by applying a modified high-resolution urban meteorology model in dynamically downscaling a climate model along with future urbanization and land use change projections for each area. It was found that the effects of urbanization were of the same magnitude as that of the local climate change. Considering the urban areas in the region and the selected emissions scenarios, the all-hours temperature equivalent of the UHII (°C · h hr−1) increased by between 0.24 and 0.80 °C, representing an increase of between 17% and 13% of their respective values in the current climate. Locally, instantaneous (e.g., hourly) temperatures could increase by up to ~3 °C because of climate effects and up to ~5 °C because of both climate and urbanization changes. The efficacies of urban heat mitigation measures were ranked both at the county level and at local project scales. It was found that urban cooling measures could help decrease or offset exceedances in the National Weather Service heat index (NWS HI) above several warning thresholds and reduce the number of heatwave or excessive heat event days. For example, measures that combine increased albedo and urban forests can reduce the exceedances above NWS HI Danger level by between 50% and 100% and the exceedances above Extreme Caution level by between 18% and 36%. UHII offsets from each mitigation measure were quantified for two situations: (1) a scenario where a community implements cooling measures and no other nearby communities take any action and (2) a scenario where both the community and its upwind neighbors implement cooling measures. In this second situation, the community benefits from cooler air transported from upwind areas in addition to the local cooling resulting from implementation of its own heat mitigation strategies. The modeling of future climates showed that except for a number of instances, the ranking of measures in each respective urban area remains unchanged into the future.

Modelling rotavirus concentrations in rivers: Assessing Uganda's present and future microbial water quality

Faecal pathogens can be introduced into surface water through open defecation, illegal disposal and inadequate treatment of faecal sludge and wastewater. Despite sanitation improvements, poor countries are progressing slowly towards the United Nation's Sustainable Development Goal 6 by 2030. Sanitation-associated pathogenic contamination of surface waters impacted by future population growth, urbanization and climate change receive limited attention. Therefore, a model simulating human rotavirus river inputs and concentrations was developed combining population density, sanitation coverage, rotavirus incidence, wastewater treatment and environmental survival data, and applied to Uganda. Complementary surface runoff and river discharge data were used to produce spatially explicit rotavirus outputs for the year 2015 and for two scenarios in 2050. Urban open defecation contributed 87%, sewers 9% and illegal faecal sludge disposal 3% to the annual 15.6 log10 rotavirus river inputs in 2015. Monthly concentrations fell between -3.7 (Q5) and 2.6 (Q95) log10 particles per litre, with 1.0 and 2.0 median and mean log10 particles per litre, respectively. Spatially explicit outputs on 0.0833 × 0.0833° grids revealed hotspots as densely populated urban areas. Future population growth, urbanization and poor sanitation were stronger drivers of rotavirus concentrations in rivers than climate change. The model and scenario analysis can be applied to other locations.

Urban expansion quantification from remote sensing data for sustainable land-use planning in Mangaluru, India

Urban growth is indispensable in developing countries, particularly in the context of rapid economic development and growing urban population. The idea of managing urban growth is often restricted exclusively to large metropolis; ignoring small and emerging cities from the requirement of having sustainable urban growth strategies. India, experiencing a rapid pace of urban growth is expected to host its future urban population in its small and medium-sized cities. In this context, the dynamics of urban growth in numerous small and medium-sized cities must be studied and urban policies must be reoriented to ensure sustainable urbanisation in future. However, the nonavailability of geospatial data for generating information database and models to support decision making processes on a local scale pose challenges in developing countries. The remote sensing technology offers cost-effective, timely, and efficient technology to collect urban spatial data, to generate urban physical and socioeconomic information using various GIS and statistical models. In this study, the spatial pattern, dynamics and goodness of urban growth in Mangaluru city were studied in the context of its implications for future sustainable development using the techniques of remote sensing, GIS and statistical models. The urban land is delineated from the Landsat series of satellites then, the statistical models were applied to study the trend of urban growth. The built-up land in Mangaluru has increased significantly by more than 400 % during 1972–2018. Further, subtleties of urban growth were studied by extracting land-use expansion modes, UEI Index, UED Index, Pearson's Chi-square and Shannon's entropy. Finally, the goodness of urban growth was measured to identify the sustainability of urban growth. The findings suggest that the urban expansion mode was dominated by leapfrogging and edge expansion at the peri-urban area during initial periods which eventually gave way to infilling and edge-expansion. Also, a rapid expansion in urban land was expected in the southern and south-eastern part of the city. The study confirms the prevalence of the highly unsustainable nature of urban growth in the study area and therefore advocates participatory urban planning in managing and for achieving a sustainable urban growth.

Context sensitivity of surface urban heat island at the local and regional scales

In this study we analysed the multi-annual (2002–2011) average summer surface urban heat island (SUHI) intensity of the 5000 largest urban clusters in Europe. We investigated its relationship with a proposed Gravitational Urban Morphology (GUM) index that can capture the local context sensitivity of SUHI. The GUM index was found to be an effective predictor of SUHI intensity. Together with other urban factors we built different multivariate linear regression models and a climate space based geographically weighted regression (GWR) model that can better predict SUHI intensity. As the GWR model captures the variation of influence from different urban factors on SUHI, it considerably outperformed linear models in predicting SUHI intensity in terms of R2 and other statistical criteria. By investigating the variation of GWR coefficients against background climate factors, we further built a nonlinear regression model that takes into account the sensitivity of SUHI to regional climate context. The nonlinear model showed comparable performance to that of the GWR model and it prevailed against all the linear models. Our work underlines the potential of SUHI reduction through optimising urban morphology, as well as the importance of integrating future urbanisation and climate change into the implementation of urban heat mitigation strategies.

Delineating the spatial-temporal variation of air pollution with urbanization in the Belt and Road Initiative area

The rapid urbanization in the Belt and Road Initiative (BRI) area has aggravated the cross-regional pollution of PM2.5 and aroused concern about the conflicts between urban development and air quality. This study aims to examine the spatial-temporal PM2.5 variations in the BRI region, in which area many countries are undergoing rapid urbanization and the main field of future urbanization, to delineate the driving mechanism of PM2.5 accumulation or dissipation. Previous studies have analyzed the PM2.5 distribution at the national level, providing limited information regarding regional heterogeneity in urbanization and PM2.5 concentrations within each country. Additionally, the regional differences in the driving mechanisms of urbanization factors on PM2.5 concentrations have not been thoroughly investigated within the BRI areas. In this study, remote sensing raster data was combined with geographic grid units to examine variations in urbanization and PM2.5 within the BRI region, identifying “typical regions” where urbanization could enhance PM2.5 accumulation. The main results are as follows: i) The spatial autocorrelation of urbanization and PM2.5 concentrations has gradually strengthened, showing consistent high-value distributions in the North China Plain, Ganges Plain and indicating a synergistic growth among emerging developing regions such as China, India, and the Persian Gulf Coast. ii) The correlation between urbanization and PM2.5 concentrations exhibited a distinct trend of differentiation within the BRI regions. The influence of urbanization on PM2.5 changed from agglomeration to dispersion, forming a “typical region” category composed of ten countries, including China, India, and Morocco. iii) The three main urbanization-related factors for PM2.5 accumulation in the “typical regions” for 2005–2016 were energy pollution emission, economic activities, and human activities. By 2023, the effects of energy pollution and economic activities are expected to converge in some “typical region” countries. Targeted urban strategies and governance actions based on the different driving-types of “typical regions” in BRI have been proposed to coordinate relationship between urban construction and atmospheric environmental protection.

Taxonomic and functional homogenization of farmland birds along an urbanization gradient in a tropical megacity.

Urbanization is a major driver of land use change and biodiversity decline. While most of the ongoing and future urbanization hotspots are located in the Global South, the impact of urban expansion on agricultural biodiversity and associated functions and services in these regions has widely been neglected. Additionally, most studies assess biodiversity responses at local scale (α-diversity), however, ecosystem functioning is strongly determined by compositional and functional turnover of communities (β-diversity) at regional scales. We investigated taxonomic and functional β-diversity of farmland birds across three seasons on 36 vegetable farms spread along a continuous urbanization gradient in Bangalore, a South Indian megacity. Increasing amount of grey area in the farm surroundings was the dominant driver affecting β-diversity and resulting in taxonomic and functional homogenization of farmland bird communities. Functional diversity losses were higher than expected from species declines (i.e., urbanization acts as an environmental filter), with particular losses of functionally important groups such as insectivores of crop pests. Moreover, urbanization reduced functional redundancy of bird communities, which may further weaken ecosystems resilience to future perturbations. Our study underscores urbanization as a major driver of taxonomic and functional homogenization of species communities in agricultural systems, potentially threatening crucial ecosystem services for food production.

An “Othered” Land Reclamation: Decolonization in Anticipation of Another Great Flood

This paper is a series of tales of sea level rise on the coastal floodplain of Surrey, British Columbia. It begins with the Indigenous tales of the Great Flood that inform Coast Salish collective identity. This collective identity becomes permanently altered as illustrated in tales about how colonial tools of maps and surveys dispossessed Indigenous peoples of their lands while inflicting ecosystem damage. The paper concludes with tales that contemplate an expanded definition to “land reclamation”—one that provides a framework for flood adaptation and ecosystem restoration, while making space for a decolonized future urbanism on the floodplain.

How urban growth changes the heat island effect and human thermal sensations over the last 100 years and towards the future in a European city?

AbstractUrban development exacerbates urban heat island effects of cities and thermal discomfort of the residents worldwide. However, such urban effects vary with the geographical and climatic conditions of the respective cities. This study is the first to quantitatively estimate the impact of past (1878), present (2012), and future (2050) urbanization on surface air temperatures and human thermal comfort in Sofia, the largest city of Bulgaria. We used the state‐of‐the‐art Weather Research and Forecasting (WRF) climate model, with a 1‐km horizontal resolution for three cases: LU1878, LU2012, and LU2050. We first validated the results of the control simulation (case LU2012) against actual observations. The model satisfactorily reproduced the diurnal variation of the observed monthly mean surface air temperatures for July (2011–2013) with a mean bias of ±0.6°C. We then estimated the potential impacts of past and future urbanization on surface temperature. Our results suggest that because of urbanization of Sofia, the surface air temperature at 0600 Local Standard Time (LST) has increased approximately 4.0°C in the past (1878–2012) and approximately 0.4°C in future (2012–2050). We also evaluated the impact of the urban thermal environment on human comfort levels. Considering the LU2050 case in the central part of Sofia, the temperature–humidity index during mid‐afternoon (1400–1700 LST) is estimated to be 24–26, indicating that approximately 50% of the residents could feel perceptibly uncomfortable.

HYBRID GIS-BIM APPROACH FOR THE TORINO DIGITAL-TWIN: THE IMPLEMENTATION OF A FLOOR-LEVEL 3D CITY GEODATABASE

Abstract. The research tries to present a preliminary work into geo-spatial management of public administration assets thanks to interoperability of BIM-GIS models, related to urban scale scenarios. The strategy proposed tries to deepen the management, conversion and integration of databases related to public assets and particularly schools building, and related them into city-related geo-databases. The methodology, based on the real scenario of Torino Municipality and their needs addressed in recent studies in collaboration with FULL – Future Urban Legacy Lab from Politecnico di Torino, take advantage from the availability of two test dataset at different scale, with different potential and bottlenecks. The idea of developing a 3D digital twin of Torino actually stop long before the 3D city modelling only, but rather we have to deal with the integration and harmonization of existing databases. These data collections are often coming from different updating and based on non-homogeneous languages and data models. The data are often in table format and managed by different offices and as many management systems. Moreover, recently public administrations as the one of Torino, have increase availability of BIM models, especially for public assets, which need to be known, archived, and localized in a geographic dimension in order to benefit from the real strategic potential of a spatial-enabled facility management platform as Digital Twin. Combining the use of parametric modeler software (Revit) and visual programming language (Dynamo), the proposed methodology tries to elaborate rules on a set of shared language parameters (characterizing the buildings as attributes in both datasets: ID; address; construction; floors; rooms dimensions, use, floor; height; glass surfaces). This is tested as conversion workflow between the Municipality DB and the BIM model. This solution firstly allows the interaction and query between models, and then it proposes open issues once the enriched BIM model is imported into the geographical dimension of the Torino 3D city model Digital Twin (ArcGIS Pro platform), according to LOD standards, and enriched with semantic components from municipality DB.

Drivers and projections of global surface temperature anomalies at the local scale

More than half of the world’s population now lives in urban areas, and trends in rural-to-urban migration are expected to continue through the end of the century. Although cities create efficiencies that drive innovation and economic growth, they also alter the local surface energy balance, resulting in urban temperatures that can differ dramatically from surrounding areas. Here we introduce a global 1 km resolution data set of seasonal and diurnal anomalies in urban surface temperatures relative to their rural surroundings. We then use satellite-observable parameters in a simple model informed by the surface energy balance to understand the dominant drivers of present urban heating, the heat-related impacts of projected future urbanization, and the potential for policies to mitigate those damages. At present, urban populations live in areas with daytime surface summer temperatures that are 3.21 ∘C (−3.97, 9.24, 5th–95th percentiles) warmer than surrounding rural areas. If the structure of cities remains largely unchanged, city growth is projected to result in additional daytime summer surface temperature heat anomalies of 0.19 ∘C (−0.01, 0.47) in 2100—in addition to warming due to climate change. This is projected to raise the urban population living under extreme surface temperatures by approximately 20% compared to current distributions. However we also find a significant potential for mitigation: 82% of all urban areas have below average vegetation and/or surface albedo. Optimizing these would reduce urban daytime summer surface temperatures for the affected populations by an average of −0.81 ∘C (−2.55, −0.05).

Energy-Cement-Carbon Emission Nexus and its Implications for Future Urbanization in China

Energy-Cement-Carbon Emission Nexus and its Implications for Future Urbanization in China

Water Quality Dynamics in Response to Rainfall along an Estuarine Ecocline

Stormwater runoff is an important nonpoint source of fecal pollution in coastal environments. In anticipation of future coastal urbanization and altered weather patterns, it is increasingly important to understand the impact of rainfall; therefore, the purpose of this study was to determine how rainfall impacts water quality along an estuarine ecocline in the northwestern Gulf of Mexico (GOM). Decreasing levels of enterococci were detected along the ecocline from land to sea, with the highest concentrations measured immediately downstream of a wastewater treatment plant (WWTP). Human-, canine-, and gull-associated markers were detected consistently throughout the study, but they were not correlated with enterococci and they did not experience a significant trend along the ecocline. Antecedent rainfall was associated with significant changes in bacterial community structure and increased human marker concentrations. These results question the utility of enterococci as a fecal pollution indicator in the northwestern GOM and suggest that increased rainfall frequency or intensity could negatively impact water quality in this region. Taken together, these results clearly demonstrate the need for a data-based approach to water quality management that incorporates modern water quality indicators such as host-associated markers and microbial community composition.

Physically consistent conceptual rainfall–runoff model for urbanized catchments

Hydrological models should be tested and evaluated for a wide variety of levels of urbanization before they are used to predict the impact of urbanization on catchment behavior. In this study, we illustrate a top–down approach of modifying step by step an hourly conceptual model structure (GR4H) to account for urbanization features. Modifying the original model structure included accounting explicitly for runoff from impervious surfaces by bypassing the soil moisture reservoir and varying the partitioning between quick flow and slow flow. These adaptations were chosen based on the reported specificities of urbanized catchments, namely, decreasing infiltration, increasing runoff, and fast runoff dynamics. Using a split-sample test, the relevance of each modification with regard to the reproduction of catchment response (i.e., observed streamflow) was assessed for a large sample of 273 urbanized catchments, located in France and the United States, for which mean total impervious area (TIA) varied between 0.05 and 0.59. Six continuous and three event-based criteria were used, and two statistical tests were applied to assess the significance of improvements. Results showed the following: (i) Tested modifications improved the ability of the model to reproduce the catchment response, especially high flows and observed streamflow amid dry conditions. (ii) Event-based evaluation using more than 45,000 events showed an improvement in predicting the event peak flow and event runoff volume, whereas no significant improvements were obtained in predicting the timing of peak flow. (iii) Newly added parameters were moderately to highly correlated with TIA, especially the calibrated proportion of impervious surfaces, which is promising as a hydrological validation of estimated urbanization measures from land cover. The tested modifications improved both the representation of urbanization processes and the reproduction of the observed streamflow, yielding a simple and credible model for predicting the impact of future urbanization scenarios on catchment response.