Urban Domain Research Articles

Opportunities for the Circular Economy in Smart Cities

Our current global economy is based on the linear flow of material and energy at a speed faster than earth can regenerate its services. A logical answer is reversing this into a circular economy, implemented through Circular Business Models (CBM). While cities count for the majority of current and future inhabitants, consumption and negative externalities people presume the CE should play an important role in coping with its challenges. To maintain urban livability, there is another emerging city strategy. That is to integrate technology in the urban domain and make a city “smart.” This development questions how digitization can also leverage CBM in the smart city area. However, little research is known on this topic. This article therefore studies the relationship between the circular economy and a circular smart city by exploring digital technology as a common variable. The authors first conceptualize the possibilities to enhance CBM by digital technology and then apply concept mapping to determine if and which CBM have greatest possibility to flourish in a circular smart city context.

Conceptualizing Small and Medium-Sized Smart Cities in the Mediterranean Region

Despite the remarkable interest in smart cities, noticed during the last decade, a consistent comprehension of the concept is not yet fully realized. Various definitions, ranging from exclusively technology-oriented perceptions to broader views, have been introduced, establishing a definitional polyphony and causing lack of semantic interoperability. Empirical evidence witnesses the prevalence of technology-pushed smart city initiatives as well as their failure to meet expectations in several urban domains. When planning “smart,” the relevance of ICT and their applications should be in alignment with spatial and other urban peculiarities and sub-systems' interactions, implying the need for getting deep insight into the city's ontology. The paper focuses on the extension/enrichment of an existing smart city ontology, with concepts and relationships stemming from Mediterranean small and medium-sized cities, in an attempt to outline their main key drivers and their interrelationships and fully grasp the smart city concept in the particular spatial context.

The Hestia fossil fuel CO<sub>2</sub> emissions data product for the Los Angeles megacity (Hestia-LA)

Abstract. High-resolution bottom-up estimation provides a detailed guide for city greenhouse gas mitigation options, offering details that can increase the economic efficiency of emissions reduction options and synergize with other urban policy priorities at the human scale. As a critical constraint to urban atmospheric CO2 inversion studies, bottom-up spatiotemporally explicit emissions data products are also necessary to construct comprehensive urban CO2 emission information systems useful for trend detection and emissions verification. The “Hestia Project” is an effort to provide bottom-up granular fossil fuel (FFCO2) emissions for the urban domain with building/street and hourly space–time resolution. Here, we report on the latest urban area for which a Hestia estimate has been completed – the Los Angeles megacity, encompassing five counties: Los Angeles County, Orange County, Riverside County, San Bernardino County and Ventura County. We provide a complete description of the methods used to build the Hestia FFCO2 emissions data product for the years 2010–2015. We find that the LA Basin emits 48.06 (±5.3) MtC yr−1, dominated by the on-road sector. Because of the uneven spatial distribution of emissions, 10 % of the largest-emitting grid cells account for 93.6 %, 73.4 %, 66.2 %, and 45.3 % of the industrial, commercial, on-road, and residential sector emissions, respectively. Hestia FFCO2 emissions are 10.7 % larger than the inventory estimate generated by the local metropolitan planning agency, a difference that is driven by the industrial and electricity production sectors. The detail of the Hestia-LA FFCO2 emissions data product offers the potential for highly targeted, efficient urban greenhouse gas emissions mitigation policy. The Hestia-LA v2.5 emissions data product can be downloaded from the National Institute of Standards and Technology repository (https://doi.org/10.18434/T4/1502503, Gurney et al., 2019).

Human Activity Patterns in Big Data for Healthcare Applications

Nowadays, there is a consistently growing migration of people to urban domains. Therapeutic administrations organizations are a champion among the most testing viewpoints that is massively impacted by the colossal surge of people to downtown territories. In this manner, urban zones far and wide are placing enthusiastically in cutting edge change with a ultimate objective to give progressively profitable organic network to people. In such change, a large number homes are being equipped with canny contraptions (for example splendid meters, sensors, etc.) which produce massive volumes of fine-grained and indexical data that can be penniless down to help sharp city organizations. In this paper, we propose a model that utilizations splendid home huge data as techniques for picking up and finding human development structures for restorative administrations applications. We propose the use of normal model mining, bunch examination and gauge to measure and dismember essentialness use changes begun by occupants' direct. Since people's penchants are generally recognized by standard timetables, finding these calendars empowers us to see strange activities that may exhibit people's inconveniences in taking oversee to themselves, for instance, not arranging sustenance or not using shower/shower. Our places of business the need to analyze transient imperativeness use structures at the machine level, which is clearly related to human activities. The data from sharp meters is recursively mined in the quantum/data cut of 24 hours, and the results are kept up across over dynamic mining works out. [1,2,3,4,5]

The MetVed model: development and evaluation of emissions from residential wood combustion at high spatio-temporal resolution in Norway

Abstract. We present here emissions estimated from a newly developed emission model for residential wood combustion (RWC) at high spatial and temporal resolution, which we name the MetVed model. The model estimates hourly emissions resolved on a 250 m grid resolution for several compounds, including particulate matter (PM), black carbon (BC) and polycyclic aromatic hydrocarbons (PAHs) in Norway for a 12-year period. The model uses novel input data and calculation methods that combine databases built with an unprecedented high level of detail and near-national coverage. The model establishes wood burning potential at the grid based on the dependencies between variables that influence emissions: i.e. outdoor temperature, number of and type and size of dwellings, type of available heating technologies, distribution of wood-based heating installations and their associated emission factors. RWC activity with a 1 h temporal profile was produced by combining heating degree day and hourly and weekday activity profiles reported by wood consumers in official statistics. This approach results in an improved characterisation of the spatio-temporal distribution of wood use, and subsequently of emissions, required for urban air quality assessments. Whereas most variables are calculated based on bottom-up approaches on a 250 m spatial grid, the MetVed model is set up to use official wood consumption at the county level and then distributes consumption to individual grids proportional to the physical traits of the residences within it. MetVed combines consumption with official emission factors that makes the emissions also upward scalable from the 250 m grid to the national level. The MetVed spatial distribution obtained was compared at the urban scale to other existing emissions at the same scale. The annual urban emissions, developed according to different spatial proxies, were found to have differences up to an order of magnitude. The MetVed total annual PM2.5 emissions in the urban domains compare well to emissions adjusted based on concentration measurements. In addition, hourly PM2.5 concentrations estimated by an Eulerian dispersion model using MetVed emissions were compared to measurements at air quality stations. Both hourly daily profiles and the seasonality of PM2.5 show a slight overestimation of PM2.5 levels. However, a comparison with black carbon from biomass burning and benzo(a)pyrene measurements indicates higher emissions during winter than that obtained by MetVed. The accuracy of urban emissions from RWC relies on the accuracy of the wood consumption (activity data), emission factors and the spatio-temporal distribution. While there are still knowledge gaps regarding emissions, MetVed represents a vast improvement in the spatial and temporal distribution of RWC.

Urban population exposure to NO<sub><i>x</i></sub> emissions from local shipping in three Baltic Sea harbour cities – a generic approach

Abstract. Ship emissions in ports can have a significant impact on local air quality (AQ), population exposure and therefore human health in harbour cities. We determined the impact of shipping emissions in harbours on local AQ and population exposure in the Baltic Sea harbour cities Rostock (Germany), Riga (Latvia) and the urban agglomeration of Gdańsk–Gdynia (Poland) for 2012. An urban AQ study was performed using a global-to-local chemistry transport model chain with the EPISODE-CityChem model for the urban scale. We simulated NO2, O3 and PM concentrations in 2012 with the aim of determining the impact of local shipping activities on population exposure in Baltic Sea harbour cities. Based on simulated concentrations, dynamic population exposure to outdoor NO2 concentrations for all urban domains was calculated. We developed and used a novel generic approach to model dynamic population activity in different microenvironments based on publicly available data. The results of the new approach are hourly microenvironment-specific population grids with a spatial resolution of 100 m × 100 m. We multiplied these grids with surface pollutant concentration fields of the same resolution to calculate total population exposure. We found that the local shipping impact on NO2 concentrations is significant, contributing 22 %, 11 % and 16 % to the total annually averaged grid mean concentration for Rostock, Riga and Gdańsk–Gdynia, respectively. For PM2.5, the contribution of shipping is substantially lower, at 1 %–3 %. When it comes to microenvironment-specific exposure to annual NO2, the highest exposure to NO2 from all emission sources was found in the home environment (54 %–59 %). Emissions from shipping have a high impact on NO2 exposure in the port area (50 %–80 %), while the influence in home, work and other environments is lower on average (3 %–14 %) but still has high impacts close to the port areas and downwind of them. Besides this, the newly developed generic approach allows for dynamic population-weighted outdoor exposure calculations in European cities without the necessity of individually measured data or large-scale surveys on population data.

A dataset of 30 m annual vegetation phenology indicators (1985–2015) in urban areas of the conterminous United States

Abstract. Medium-resolution satellite observations show great potential for characterizing seasonal and annual dynamics of vegetation phenology in urban domains from local to regional and global scales. However, most previous studies were conducted using coarse-resolution data, which are inadequate for characterizing the spatiotemporal dynamics of vegetation phenology in urban domains. In this study, we produced an annual vegetation phenology dataset in urban ecosystems for the conterminous United States (US), using all available Landsat images on the Google Earth Engine (GEE) platform. First, we characterized the long-term mean seasonal pattern of phenology indicators of the start of season (SOS) and the end of season (EOS), using a double logistic model. Then, we identified the annual variability of these two phenology indicators by measuring the difference of dates when the vegetation index in a specific year reaches the same magnitude as its long-term mean. The derived phenology indicators agree well with in situ observations from the PhenoCam network and Harvard Forest. Comparing with results derived from the moderate-resolution imaging spectroradiometer (MODIS) data, our Landsat-derived phenology indicators can provide more spatial details. Also, we found the temporal trends of phenology indicators (e.g., SOS) derived from Landsat and MODIS are consistent overall, but the Landsat-derived results from 1985 offer a longer temporal span compared to MODIS from 2001 to present. In general, there is a spatially explicit pattern of phenology indicators from the north to the south in cities in the conterminous US, with an overall advanced SOS in the past 3 decades. The derived phenology product in the US urban domains at the national level is of great use for urban ecology studies for its medium spatial resolution (30 m) and long temporal span (30 years). The data are available at https://doi.org/10.6084/m9.figshare.7685645.v5.

White Blood Cell Indices and Health Parameters of Workers in Industrial and Urban Areas

Background. Working far from urban domains exposes workers to harsh environmental hazards like air pollutants and harmful byproduct emissions. Site workers often are exposed to harmful agents and may suffer from dangerous health disorders. The aim of this study is to estimate the hazard level posed by these conditions through clinical examination of assays taken at the State Company of Fertilizers, Southern Region, in the Basra governorate of the city of Iraq. Methods. A study was conducted on 35 workers from both the State Company of Fertilizers (Southern Region) and Basrah University. The study involved an inquiry into the occupational history, chronic illnesses, and smoking habits of participants, along with a physical examination of arterial blood pressure, heart rate, body temperature, and oxygen saturation (SpO2). A complete blood picture (CBP) test was given to all study participants to obtain WBCs, while serum was assessed for liver (ALT) and (AST). Results. The study showed a higher incidence of hypertension and diabetes among urban workers than among industrial workers, while 14% more industrial workers underwent surgical operations and 29% had fillings of dental caries and showed a non-significant rise in body temperature when compared with urban workers. Industrial employees had a highly significant increase (P≤0.001) in systolic blood pressure. Both groups of workers had uniformly decreased monocyte levels, while total WBCs and granulocytes were non-uniformly distributed, except for lymphocytes. Data showed a highly significant decrease (p≤0.005) in the monocytes of industrial workers, while a general decrease was present in all other WBC parameters, though with no statistical significance (P≥0.05). Liver function tests showed abnormalities in 42% of the industrial workers (ALT 36 U/L), while 92.8% were suggestive of chronic hepatitis or steatosis due to a ratio of less than 1 in AST : ALT levels. Conclusion. Decreased WBCs (leukopenia) and health problems, along with markers of liver dysfunction, are potential health hazards faced by workers in the fertilizer industry. Maintenance and site equipment engineers had recurrent longterm exposure to ammonia gases and toxic environmental agents. The findings of this clinical surveillance are significant; however, studying the etiological factors and subsequent pathogenicity remains necessary. Keywords: White blood cell indices, industrial, urban

Ancient trash mounds unravel urban collapse a century before the end of Byzantine hegemony in the southern Levant

The historic event of the Late Antique Little Ice Age (LALIA) was recently identified in dozens of natural and geological climate proxies of the northern hemisphere. Although this climatic downturn was proposed as a major cause for pandemic and extensive societal upheavals in the sixth-seventh centuries CE, archaeological evidence for the magnitude of societal response to this event is sparse. This study uses ancient trash mounds as a type of proxy for identifying societal crisis in the urban domain, and employs multidisciplinary investigations to establish the terminal date of organized trash collection and high-level municipal functioning on a city-wide scale. Survey, excavation, sediment analysis, and geographic information system assessment of mound volume were conducted on a series of mounds surrounding the Byzantine urban settlement of Elusa in the Negev Desert. These reveal the massive collection and dumping of domestic and construction waste over time on the city edges. Carbon dating of charred seeds and charcoal fragments combined with ceramic analysis establish the end date of orchestrated trash removal near the mid-sixth century, coinciding closely with the beginning of the LALIA event and outbreak of the Justinian Plague in the year 541. This evidence for societal decline during the sixth century ties with other arguments for urban dysfunction across the Byzantine Levant at this time. We demonstrate the utility of trash mounds as sensitive proxies of social response and unravel the time-space dynamics of urban collapse, suggesting diminished resilience to rapid climate change in the frontier Negev region of the empire.

On the sustainability of smart and smarter cities in the era of big data: an interdisciplinary and transdisciplinary literature review

There has recently been a conscious push for cities across the globe to be smart and even smarter and thus more sustainable by developing and implementing big data technologies and their applications across various urban domains in the hopes of reaching the required level of sustainability and improving the living standard of citizens. Having gained momentum and traction as a promising response to the needed transition towards sustainability and to the challenges of urbanisation, smart and smarter cities as approaches to data-driven urbanism are increasingly adopting the advanced forms of ICT to improve their performance in line with the goals of sustainable development and the requirements of urban growth. One of such forms that has tremendous potential to enhance urban operations, functions, services, designs, strategies, and policies in this direction is big data analytics and its application. This is due to the kind of well-informed decision-making and enhanced insights enabled by big data computing in the form of applied intelligence. However, topical studies on big data technologies and their applications in the context of smart and smarter cities tend to deal largely with economic growth and the quality of life in terms of service efficiency and betterment while overlooking and barely exploring the untapped potential of such applications for advancing sustainability. In fact, smart and smarter cities raise several issues and involve significant challenges when it comes to their development and implementation in the context of sustainability. With that in regard, this paper provides a comprehensive, state-of-the-art review and synthesis of the field of smart and smarter cities in relation to sustainability and related big data analytics and its application in terms of the underlying foundations and assumptions, research issues and debates, opportunities and benefits, technological developments, emerging trends, future practices, and challenges and open issues. This study shows that smart and smarter cities are associated with misunderstanding and deficiencies as regards their incorporation of, and contribution to, sustainability. Nevertheless, as also revealed by this study, tremendous opportunities are available for utilising big data analytics and its application in smart cities of the future to improve their contribution to the goals of sustainable development by optimising and enhancing urban operations, functions, services, designs, strategies, and policies, as well as by finding answers to challenging analytical questions and thereby advancing knowledge forms. However, just as there are immense opportunities ahead to embrace and exploit, there are enormous challenges and open issues ahead to address and overcome in order to achieve a successful implementation of big data technology and its novel applications in such cities.

Increasing the Accuracy of Radiation Heat Transfer Estimation in a Lumped Parameter Urban Canopy Models

Detailed simulation of urban microclimate momentum/energy exchanges can be attempted via Computational Fluid Dynamics (CFD). Developing and simulating a comprehensive CFD model of a realistic urban domain is, however, a monumental task. Furthermore, such a model cannot be used in a decision support framework where multiple parametric simulations are required. Simplified Urban Canopy Models (UCM) have gained in popularity over the past couple of decades. Some of the most notable ones are: Town Energy Balance (TEB), Square Prism Urban Canopy (SPUC), Urban Weather Generator (UWG), etc. The complexity and computational speed of these models is significantly reduced thanks to the implementation a simplified representation of momentum transfers. Concurrently, the spatial organization of the surfaces (slopes, orientations, shape factors), and their physical characteristics (albedo, emissivity, thermal conductivity) need to be simplified as well. In particular, the models often rely on very rough estimations of short-wave (solar) and long-wave (infrared) radiation heat transfer. In this study, we assess the comparative validity of the radiation heat transfer approximations proposed by the different simplified modelling approaches in comparison to exact solutions calculated for an actual urban district. The main contribution is the assessment of the impact of the improvement of an existing thermal lumped-parameter UCM by replacing the default radiation transfer calculations with much more accurate ones determined in consideration of the detailed geometry of a real urban district.

Navigating the City and the Workplace: Migrant Female Construction Workers and Urban (Im)Mobilities

While their labour shapes the growing cityscape, migrant construction workers often remain invisible – not only to property developers and consumers but also to the state. For female workers, this is compounded by gender-based discrimination within the industry. Utilising ethnographic data, this article explores how women working in construction in Bengaluru, India, both experience and strive for mobility. It provides a multi-sited analysis to establish the ways in which intersectionality between employment conditions, the urban environment and gender norms may inhibit or facilitate urban mobility for migrant female workers. Few ethnographic studies have attended to women’s experiences of intermingled work/accommodation sites within the industry, although the practices and outcomes produced by the blurring of such boundaries provides fertile ground for analysis. While the article confirms the enduring nature of discrimination experienced by women in the construction industry, it also attends to the ways in which female workers were able to utilise spaces of exploitation. I conclude that precarious livelihoods may not at first glance yield enduring or substantive beneficial outcomes for those compelled to undertake them, but they are nevertheless productive – allowing for the maintenance and fulfilment of aspirations which may not reside within the urban domain.
 KEYWORDS circular migration; labour; gender; women; construction work

Characterizing the relationship between satellite phenology and pollen season: A case study of birch

Pollen released by vegetation in urban and surrounding rural areas is an important risk factor for respiratory allergies. Mapping the onset, duration, and severity of pollen is difficult, because of insufficient stations and/or monitoring networks at the national and international levels. To address this challenge, we evaluated the spatiotemporal relationship between satellite derived vegetation phenology and in-situ station derived information of pollen season during 2001–2015 at five stations in Canada. First, we calculated annual indicators of start of season (SOS) and start of pollen season (SPS) for birch from Landsat time series and pollen concentration data, respectively, at the five stations. Second, we explored the relationship between derived indicators of SOS and SPS and investigated the scale effect on the relationship. Our results indicate the Landsat based SOS is a good proxy for the indicator of SPS in the birch dominated area. The mean SOS and SPS in the study period are close (within 2.7 days) across the five stations. The interannual variability of SOS and SPS suggests the satellite derived phenology indicator can capture well the dynamics of pollen season in spring. Our analysis also indicates that the annual SOS at a finer resolution (30 m) is more consistent with SPS as compared to the indicators derived from coarser resolution satellite observations. This study shows the potential of using fine spatial resolution satellite data (e.g., Landsat) to derive the pollen season indicators, which is important for predicting pollen season severity and risk, and assessment of their impacts on respiratory allergies in urban domains.

Numerical simulation of different pollution sources in an urban environment

A model for representing different pollution sources in an Urban Environment is presented in this paper. An in-house incompressible flow solver called caffa3d.MBRi was used and modified in order to include the sources from chimneys, cars and houses to compute the dispersion of different pollutants simultaneously. Moreover, different urban domains with a wide variety of sources were simulated reaching promising results.

Investigating sources of variability and error in simulations of carbon dioxide in an urban region

As cities embark upon greenhouse gas (GHG) mitigation efforts, there is an increasing need for accurate quantification of urban emissions. In urban areas, transport and dispersion is particularly difficult to simulate using current mesoscale meteorological models due, in part, to added complexity from surface heterogeneity and fine spatial/temporal scales. It is generally assumed that the errors in GHG estimation methods in urban areas are dominated by errors in transport and dispersion. Other significant errors include, but are not limited to, those from assumed emissions magnitude and spatial distribution. To assess the predictability of simulated trace gas mole fractions in urban observing systems using a numerical weather prediction model, we employ an Eulerian model that combines traditional meteorological variables with multiple passive tracers of atmospheric carbon dioxide (CO2) from anthropogenic inventories and a biospheric model. The predictability of the Eulerian model is assessed by comparing simulated atmospheric CO2 mole fractions to observations from four in situ tower sites (three urban and one rural) in the Washington DC/Baltimore, MD area for February 2016. Four different gridded fossil fuel emissions inventories along with a biospheric flux model are used to create an ensemble of simulated atmospheric CO2 observations within the model. These ensembles help to evaluate whether the modeled observations are impacted more by the underlying emissions or transport. The spread of modeled observations using the four emission fields indicates the model's ability to distinguish between the different inventories under various meteorological conditions. Overall, the Eulerian model performs well; simulated and observed average CO2 mole fractions agree within 1% when averaged at the three urban sites across the month. However, there can be differences greater than 10% at any given hour, which are attributed to complex meteorological conditions rather than differences in the inventories themselves. On average, the mean absolute error of the simulated compared to actual observations is generally twice as large as the standard deviation of the modeled mole fractions across the four emission inventories. This result supports the assumption, in urban domains, that the predicted mole fraction error relative to observations is dominated by errors in model meteorology rather than errors in the underlying fluxes in winter months. As such, minimizing errors associated with atmospheric transport and dispersion may help improve the performance of GHG estimation models more so than improving flux priors in the winter months. We also find that the errors associated with atmospheric transport in urban domains are not restricted to certain times of day. This suggests that atmospheric inversions should use CO2 observations that have been filtered using meteorological observations rather than assuming that meteorological modeling is most accurate at certain times of day (such as using only mid-afternoon observations).

A path-dependence perspective on the adoption of Internet of Things: Evidence from early adopters of smart and connected sensors in the United States

As an integral part of the Internet of Things (IoT), smart and connected sensors are emerging information and communication technologies that collect and transmit real-time data from various urban domains to inform decision-making. While smart sensors and IoT technologies have great potential to transform public service provision, their adoption in the public sector seems to be slow and incremental. Using cross-sectional data of 65 large and mid-sized cities in the United States, we examine what affects local governments' adoption of smart and connected sensors. We propose a path-dependence explanation for the incremental adoption and test if a local government's sensor adoption behavior is shaped by its existing policy priorities and managerial practices in related fields. Our results show that local governments' early adoption of smart sensors is likely to stem from their needs in specific policy domains. We also find that a local government's historical paths on urban sustainability and data-driven decision-making practices can predict its trajectory of sensor deployment, in terms of the scope and the integration of smart sensors across different urban domains. Surprisingly, a local government's e-government progressiveness is not a significant predictor. Our results confirm the incremental change in smart sensor adoption and provide implications for local governments' IoT planning.

DEVELOP A GIS-BASED CONTEXT-AWARE SENSOR NETWORK DEPLOYMENT ALGORITHM TO OPTIMIZE SENSOR COVERAGE IN AN URBAN AREA

Abstract. Adequate coverage is an important issue in geosensor networks in order to fulfill the sensing applications in urban areas. GIS as well as Optimization methods are widely used to distribute geosensors in the network to achieve the desired level of coverage. Most of the algorithms applied in urban domain suffer from the lack of considering real environmental information. In this paper, the problem of placing sensors to get optimum coverage is studied by investigating the concept of urban contextual information in sensor network. Then, a local GIS-based context-aware framework of sensor network deployment optimization method is introduced. Obtained results of our algorithm under different working conditions and applications show the effectiveness of our approach.

A fully hydrodynamic urban flood modelling system representing buildings, green space and interventions

City Catchment Analysis Tool – CityCAT-is a novel software system for rapid assessment of combined pluvial and fluvial flood risk using a unique combination of: efficient software architecture throughout and especially in the numerical part; use of standard, readily available data sets; efficient algorithms for grid generation; and robust and accurate solutions of the flow equations. It is based on advanced software architecture and accurate solutions for complex free-surface flow over the terrain distinguishing between permeable and impermeable surfaces and taking into account effects of man-made features such as buildings as obstacles to flow. The software is firstly rigorously validated with demanding test cases based on analytical solutions and laboratory studies. Then the unique capability for assessment of the effectiveness of specific flood alleviation interventions across large urban domains, such as roof storage on buildings or introduction of permeable surfaces, is demonstrated.

StUPscales: An R-Package for Spatio-Temporal Uncertainty Propagation across Multiple Scales with Examples in Urban Water Modelling

Integrated environmental modelling requires coupling sub-models at different spatial and temporal scales, thus accounting for change of support procedures (aggregation and disaggregation). We introduce the R-package spatio-temporal Uncertainty Propagation across multiple scales, stUPscales, which constitutes a contribution to state-of-the-art open source tools that support uncertainty propagation analysis in temporal and spatio-temporal domains. We illustrate the tool with an uncertainty propagation example in environmental modelling, specifically in the urban water domain. The functionalities of the class setup and the methods and functions MC.setup, MC.sim, MC.analysis and Agg.t are explained, which are used for setting up, running and analysing Monte Carlo uncertainty propagation simulations, and for spatio-temporal aggregation. We also show how the package can be used to model and predict variables that vary in space and time by using a spatio-temporal variogram model and space-time ordinary kriging. stUPscales takes uncertainty characterisation and propagation a step further by including temporal and spatio-temporal auto- and cross-correlation, resulting in more realistic (spatio-)temporal series of environmental variables. Due to its modularity, the package allows the implementation of additional methods and functions for spatio-temporal disaggregation of model inputs and outputs, when linking models across multiple space-time scales.

Developing a 1 km resolution daily air temperature dataset for urban and surrounding areas in the conterminous United States

High spatiotemporal resolution air temperature (Ta) datasets are increasingly needed for assessing the impact of temperature change on people, ecosystems, and energy system, especially in the urban domains. However, such datasets are not widely available because of the large spatiotemporal heterogeneity of Ta caused by complex biophysical and socioeconomic factors such as built infrastructure and human activities. In this study, we developed a 1 km gridded dataset of daily minimum Ta (Tmin) and maximum Ta (Tmax), and the associated uncertainties, in urban and surrounding areas in the conterminous U.S. for the 2003–2016 period. Daily geographically weighted regression (GWR) models were developed and used to interpolate Ta using 1 km daily land surface temperature and elevation as explanatory variables. The leave-one-out cross-validation approach indicates that our method performs reasonably well, with root mean square errors of 2.1 °C and 1.9 °C, mean absolute errors of 1.5 °C and 1.3 °C, and R2 of 0.95 and 0.97, for Tmin and Tmax, respectively. The resulting dataset captures reasonably the spatial heterogeneity of Ta in the urban areas, and also captures effectively the urban heat island (UHI) phenomenon that Ta rises with the increase of urban development (i.e., impervious surface area). The new dataset is valuable for studying environmental impacts of urbanization such as UHI and other related effects (e.g., on building energy consumption and human health). The proposed methodology also shows a potential to build a long-term record of Ta worldwide, to fill the data gap that currently exists for studies of urban systems.