nasa-gesdisc/es-publications-researchareas huggingface dataset:
Urban emissions of water vapor in winter (2017)

Elevated water vapor (H<SUB>2</SUB>O<SUB>v</SUB>) mole fractions were occasionally observed downwind of Indianapolis, IN, and the Washington, D.C.-Baltimore, MD, area during airborne mass balance experiments conducted during winter months between 2012 and 2015. On days when an urban H<SUB>2</SUB>O<SUB>v</SUB> excess signal was observed, H<SUB>2</SUB>O<SUB>v</SUB> emission estimates range between 1.6 × 10<SUP>4</SUP> and 1.7 × 10<SUP>5</SUP> kg s<SUP>-1</SUP> and account for up to 8.4% of the total (background + urban excess) advected flow of atmospheric boundary layer H<SUB>2</SUB>O<SUB>v</SUB> from the urban study sites. Estimates of H<SUB>2</SUB>O<SUB>v</SUB> emissions from combustion sources and electricity generation facility cooling towers are 1-2 orders of magnitude smaller than the urban H<SUB>2</SUB>O<SUB>v</SUB> emission rates estimated from observations. Instances of urban H<SUB>2</SUB>O<SUB>v</SUB> enhancement could be a result of differences in snowmelt and evaporation rates within the urban area, due in part to larger wintertime anthropogenic heat flux and land cover differences, relative to surrounding rural areas. More study is needed to understand why the urban H<SUB>2</SUB>O<SUB>v</SUB> excess signal is observed on some days, and not others. Radiative transfer modeling indicates that the observed urban enhancements in H<SUB>2</SUB>O<SUB>v</SUB> and other greenhouse gas mole fractions contribute only 0.1°C d<SUP>-1</SUP> to the urban heat island at the surface. This integrated warming through the boundary layer is offset by longwave cooling by H<SUB>2</SUB>O<SUB>v</SUB> at the top of the boundary layer. While the radiative impacts of urban H<SUB>2</SUB>O<SUB>v</SUB> emissions do not meaningfully influence urban heat island intensity, urban H<SUB>2</SUB>O<SUB>v</SUB> emissions may have the potential to alter downwind aerosol and cloud properties.

Research areas: Habitat Conversion/Fragmentation
Fine-Scale Urban Heat Patterns in New York City Measured by ASTER (2021)

Urban areas have very complex spatial structures. These spatial structures are primarily composed of a complex network of built environments, which evolve rapidly as the cities expand to meet the growing populations demand and economic development. Therefore, studying the impact of spatial structures on urban heat patterns is extremely important for sustainable urban planning and growth. We investigated the relationship between surface temperature obtained by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, at 90 m spatial resolution) and different urban components based on high-resolution QuickBird satellite imagery classification. We further investigated the relationships between ASTER-derived surface temperature and building footprint and land use information acquired by the New York City (NYC) Department of City Planning. The ASTER image reveals fine-scale urban heat patterns in the NYC metropolitan region. The impervious-medium and dark surfaces, along with bright covers, generate higher surface temperatures. Even with highly reflective urban surfaces, the presence of impervious materials leads to an increased surface temperature. At the same time, trees and shadows cast by buildings effectively reduce urban heat; on the contrary, grassland does not reduce or amplify urban heat. The data aggregated to the census tract reveals high-temperature hotspots in Queens, Brooklyn, and the Bronx region of NYC. These clusters are associated with industrial and manufacturing areas and multi-family walk-up buildings as dominant land use. The census tracts with more trees and higher building height variability showed cooling effects, consistent with shadows cast by high-rise buildings and trees. The results of this study can be valuable for urban heat island modeling on the impact of shadow generated by building heights variability and trees on small-scale surface temperature patterns since recent image reveals similar hotspot locations. This study further helps identify the risk areas to protect public health.

Research areas: Heat, Habitat Conversion/Fragmentation
Urban Warming of the Two Most Populated Cities in the Canadian Province (2022)

Continuous urban expansion transforms the natural land cover into impervious surfaces across the world. It increases the citys thermal intensity that impacts the local climate, thus, warming the urban environment. Surface urban heat island (SUHI) is an indicator of quantifying such local urban warming. In this study, we quantified SUHI for the two most populated cities in Alberta, Canada, i.e., the city of Calgary and the city of Edmonton. We used the moderate resolution imaging spectroradiometer (MODIS) acquired land surface temperature (LST) to estimate the day and nighttime SUHI and its trends during 20012020. We also performed a correlation analysis between SUHI and selected seven influencing factors, such as urban expansion, population, precipitation, and four large-scale atmospheric oscillations, i.e., Sea Surface Temperature (SST), Pacific North America (PNA), Pacific Decadal Oscillation (PDO), and Arctic Oscillation (AO). Our results indicated a continuous increase in the annual day and nighttime SUHI values from 2001 to 2020 in both cities, with a higher magnitude found for Calgary. Moreover, the highest value of daytime SUHI was observed in July for both cities. While significant warming trends of SUHI were noticed in the annual daytime for the cities, only Calgary showed it in the annual nighttime. The monthly significant warming trends of SUHI showed an increasing pattern during daytime in June, July, August, and September in Calgary, and March and September in Edmonton. Here, only Calgary showed the nighttime significant warming trends in March, May, and August. Further, our correlation analysis indicated that population and built-up expansion were the main factors that influenced the SUHI in the cities during the study period. Moreover, SST indicated an acceptable relationship with SUHI in Edmonton only, while PDO, PNA, and AO did not show any relation in either of the two cities. We conclude that population, built-up size, and landscape pattern could better explain the variations of the SUHI intensity and trends. These findings may help to develop the adaptation and mitigating strategies in fighting the impact of SUHI and ensure a sustainable city environment.

Research areas: Heat, Habitat Conversion/Fragmentation
Effects of heat waves on urban warming across different urban morphologies and climate zones (2022)

Heat wave (HW) periods, which are characterized by unusual high temperatures and heat stress, are known as one of the global issues that negatively affect the land and atmosphere, especially in urban areas. Higher urbanization rates in cities may result in high temperature warming, which is known as the Urban Heat Island (UHI). Recently, researchers have revealed pronounced effects based on HW and UHI interactions; however, studies that linked their effects with surface energy fluxes in different urban landscape and climate are still inadequate. This study utilized the Community Land Model (CLM) to assess the effect of HW towards UHI, while considering energy fluxes variations in Seoul, Tokyo, Kuala Lumpur, and Melbourne, with different climate influences and urban morphologies. Generally, Seoul and Tokyo showed intense HW magnitude from 2000 to 2018 based on the HW indices. The surface energy flux components demonstrated a distinct pattern especially from 11:00 to 17:00, with the exception of sensible heat in Melbourne. Further energy-flux partitioning revealed that changes in the heat storage ratio were most significant in Seoul, with 1.39 partitioning due to its larger impervious area. A smaller height-to-width (H/W) canyon ratio of open mid-rise building has enhanced urban warming in Melbourne, whereas smaller impervious and larger pervious road fraction could mitigate urban warming effects in Kuala Lumpur. Seoul showed the highest changes in daytime UHI under HW at 2.01 C, due to the low convection efficiency in compact high-rise building, while nighttime UHI was highly influenced by heat storage and anthropogenic heat release.

Research areas: Heat, Habitat Conversion/Fragmentation
Reexamining the relationship between surface urban heat island intensity and annual precipitation: Effects of reference rural land cover (2021)

Previous studies have shown that surface urban heat island intensity (SUHII) across cities is significantly positively correlated with mean annual precipitation (MAP), including linear and nonlinear correlations. Different explanations for these SUHIIMAP relations have been suggested, yet a systematic examination of the impact of variability in reference rural land cover on geographic variations of daytime SUHII and the relation with MAP is still lacking. In this study, the previously proposed SUHIIMAP relations are reexamined by investigating 60 cities across North America and 346 cities across the globe, respectively. The focus of the study is on the role of variability of reference land cover types in the SUHIIMAP relations. The 10-yr time series of satellite-observed land surface temperature, precipitation and solar radiation data, in conjunction with global land cover data, are used for analysis. Our results confirm the reproducibility of both the linear and nonlinear SUHIIMAP relations; nevertheless, the significant positive correlations between the daytime SUHII and the MAP are found to be dependent mainly on the variability of the reference land cover types associated with MAP levels (i.e., varying from desert to tree cover). In contrast, when a constant land cover type is taken as the reference, no significant correlations between the daytime SUHII and the MAP are observed. The finding highlights that respecting SUHI intensity as a relative measure and accounting for variability of reference land cover is of crucial importance for proper interpretation and understanding of SUHI.

Research areas: Heat, Habitat Conversion/Fragmentation
Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data (2020)

Urbanisation generates greater population densities and an increase in anthropogenic heat generation. These factors elevate the urbanrural air temperature (Ta) difference, thus generating the Urban Heat Island (UHI) phenomenon. Ta is used in the fields of public health and epidemiology to quantify deaths attributable to heat in cities around the world: the presence of UHI can exacerbate exposure to high temperatures during summer periods, thereby increasing the risk of heat-related mortality. Measuring and monitoring the spatial patterns of Ta in urban contexts is challenging due to the lack of a good network of weather stations. This study aims to produce a parsimonious model to retrieve maximum Ta (Tmax) at high spatio-temporal resolution using Earth Observation (EO) satellite data. The novelty of this work is twofold: (i) it will produce daily estimations of Tmax for London at 1 km2 during the summertime between 2006 and 2017 using advanced statistical techniques and satellite-derived predictors, and (ii) it will investigate for the first time the predictive power of the gradient boosting algorithm to estimate Tmax for an urban area. In this work, 6 regression models were calibrated with 6 satellite products, 3 geospatial features, and 29 meteorological stations. Stepwise linear regression was applied to create 9 groups of predictors, which were trained and tested on each regression method. This study demonstrates the potential of machine learning algorithms to predict Tmax: the gradient boosting model with a group of five predictors (land surface temperature, Julian day, normalised difference vegetation index, digital elevation model, solar zenith angle) was the regression model with the best performance (R2 = 0.68, MAE = 1.60 C, and RMSE = 2.03 C). This methodological approach is capable of being replicated in other UK cities, benefiting national heat-related mortality assessments since the data (provided by NASA and the UK Met Office) and programming languages (Python) sources are free and open. This study provides a framework to produce a high spatio-temporal resolution of Tmax, assisting public health researchers to improve the estimation of mortality attributable to high temperatures. In addition, the research contributes to practice and policy-making by enhancing the understanding of the locations where mortality rates may increase due to heat. Therefore, it enables a more informed decision-making process towards the prioritisation of actions to mitigate heat-related mortality amongst the vulnerable population.

Research areas: Heat, Public Health
Anthropogenic forcing exacerbating the urban heat islands in India (2020)

Urban heat island (UHI) phenomena is among the major consequences of the alteration of earth's surface due to human activities. The relatively warmer temperatures in urban areas compared to suburban areas (i.e. UHI) has potential health hazards, such as mortality due to high temperatures and heat waves. In addition, UHI situation demands more energy (e.g. fans and air-conditioners) that would trigger greenhouse gas emissions. Studies on UHI intensity help to assess its impact on urban population, city planning, and urban health planning. This is particularly important for a country like India, where 32% people (~7% of total world population), live in urban areas. We conducted a detailed study on surface UHI intensity (SUHII), which is the difference between urban and surrounding rural land surface temperatures, across all seasons in 44 major cities of India, which shows that mean daytime SUHII is positive (up to 2 C) for most cities, as analysed from satellite temperature measurements for the period 20002017, in contrast to previous studies. However, although statistically insignificant, most cities show a positive trend in SUHII for monsoon and post-monsoon periods, but negative for winter and summer seasons. The increasing night-time SUHII in all seasons for most cities suggest increasing trend in temperature in cities due to the impact of the rapid urbanisation, and thus, suggesting the influence of anthropogenic forcing on SUHII. This is also supported by the analysis of aerosols, night lights, precipitation and vegetation in the study regions. Therefore, this study shall aid planning and management of urban areas by giving insights about the effects of nature and intensity of development, land cover and land use mix and the structure of cities on SUHII.

Research areas: Heat, Habitat Conversion/Fragmentation
Distinctive roles of two-and three-dimensional urban structures in surface urban heat islands over the conterminous United States (2022)

In this study, we investigated how seasonal and diurnal surface urban heat islands (SUHIs) vary with two- and three-dimensional (2D and 3D) urban structure parameters (USPs) and climate settings. Additionally, whether and how 2D and 3D USPs influenced the seasonal hysteresis between SUHIs and background temperatures were explored using remote sensing for 203 cities over the conterminous US. Results show that urban areas with high impervious surface coverage (70%) were colder in summer than surrounding rural areas in dry climates. Moreover, the cooling efficiency of vegetation in dry climates was about two times higher than that in wet climates. A hump-shaped relation between sky view factor (SVF) and SUHIs was observed; in regions with low SVF (e.g., SVF 

Research areas: Heat
Estimation of anthropogenic heat emissions in urban Taiwan and their spatial patterns (2016)

High energy consumption in the urban environment impacts the urban surface energy budget and causes the emission of anthropogenic heat fluxes (AHFs) into the atmosphere. AHFs vary over time and space. Thus, a reliable estimation of AHF is needed for mesoscale meteorological modeling. This study used a statistical regression method to estimate the annual mean gridded AHF with high spatial (1-km) resolution. Compared with current methods for AHF estimation, the statistical regression method is straightforward and can be easily incorporated with meteorological modeling. AHF of the highly populated urban areas in Taiwan were estimated using data from the anthropogenic pollutant emission inventory of CO and NOx for year 2010. Over 40% of the total AHF values in Taiwan main island fell within the range of 1040 Wm2. When the study domain was confined to urban land, the percentage contributions from AHF values were increased, with over 68% of the total AHF values within the range of 1040 Wm2. AHF values > 40 Wm2 were more abundant in the Southern region, followed by the Central and Northern regions. An assessment of the heat emissions by the large scale urban consumption of energy (LUCY) model revealed that the mean AHFs are reasonably close to those produced while the maximum AHFs are underestimated. The results obtained evidence the impact of spatial distribution of land use types, particularly population densities, main highways and industries on AHF generation in Taiwan.

Research areas: Heat, Habitat Conversion/Fragmentation
Effects of COVID-19 Restriction Policies on Urban Heat Islands in Some European Cities: Berlin, London, Paris, Madrid, and Frankfurt (2022)

The present study investigates the effects of policies restricting human activities during the COVID-19 epidemic on the characteristics of Night Land Surface Temperature (NLST) and Night Urban Heat Islands (NUHI) in five major European cities. In fact, the focus of this study was to explore the role of anthropogenic factors in the formation and intensity of NUHI. The effect of such factors was uncontrollable before the COVID-19 outbreak on the global scale and in a real non-laboratory environment. In this study, two indices, the concentration of Nitrogen dioxide (NO2) and Nighttime Lights (NL), were used as indicators of the number of anthropogenic activities. The data were collected before the COVID-19 outbreak and after its prevalence in 20192020. A Paired samples t-test and a Pearson correlation were used to examine the differences or significant relationships between the variables and indicators studied throughout the two periods. The results of the study confirmed a direct and significant relationship between NO2 and NL indices and the NUHI and NLST variables; however, using strict restrictions during the COVID-19 pandemic, the NO2 and NL indices decreased seriously, leading to significant changes in the characteristics of the NUHI and NLST in the five cities. This study has some implications for urban planners and politicians, e.g., the environmental impacts of changing the nature and level of anthropogenic activities can greatly affect the pattern and intensity of the Urban Heat Islands (UHIs) (as a serious environmental challenge).

Research areas: Heat
Modeling Impacts of Urbanization and Urban Heat Island Mitigation on Boundary Layer Meteorology and Air Quality in Beijing Under Different Weather Conditions (2018)

Beijing has experienced a rapid urbanization in the last few decades and has been suffering from serious air pollution during recent years. The Weather Research and Forecasting-Chem model is used to quantify the effects of urbanization on regional climate and air quality and those of urban heat island (UHI) mitigation strategy on urban air quality in Beijing, with a special focus on the impacts under different weather conditions (heat waves in summer and polluted days in winter). The modification of rural land use into urban impervious surface significantly increases 2-m temperature (T<SUB>2</SUB>) and planetary boundary layer height but decreases 2-m relative humidity (RH<SUB>2</SUB>) and 10-m wind speed (WS<SUB>10</SUB>) in urban Beijing, which further leads to the increases in surface-layer O<SUB>3</SUB> concentrations of 9.5 ppbv in summer and 1.8 ppbv in winter and the decreases in PM<SUB>2.5</SUB> concentrations of 16.6 μg m<SUP>-3</SUP> in summer and 26.2 μg m<SUP>-3</SUP> in winter. Compared with normal days (clean days), the UHI intensity is enhanced by 11.1% during heat waves in summer (by 16.7% during polluted days in winter). Although increasing urban albedo is an effective mitigation strategy to decrease UHI intensity, it worsens the urban air quality. When the urban albedo increases from 0.2 to 0.85, the daily average PM<SUB>2.5</SUB> concentrations are increased by 10.2 (6.1) μg m<SUP>-3</SUP> in summer (in winter), and the daily maximum O<SUB>3</SUB> concentrations are increased by 12.8 ppbv under heat waves in summer.

Research areas: Heat, Habitat Conversion/Fragmentation
Long-Term Spatiotemporal Patterns and Evolution of Regional Heat Islands in the Beijing–Tianjin–Hebei Urban Agglomeration (2022)

With the continuous development of urbanization, the urban heat island (UHI) phenomenon is becoming increasingly prominent. Especially with the development of various large urban agglomerations and the shrinking distance between cities, the regional thermal environment has attracted extensive attention. Therefore, we used Modis land surface temperature (LST) data and employed least squares, standard deviation and spatial autocorrelation analysis methods to analyze the spatiotemporal patterns and characteristics of summer daytime regional urban heat islands (RHI) in the BeijingTianjinHebei (BTH) urban agglomeration. Our results indicated that the relative land surface temperature (RLST) in the southeastern part of BTH with a relatively high level of urbanization showed a significant and continuous upward trend. With the continuous development of the level of urbanization in the southeast, the center of gravity (GC) of RHI gradually moved to the southeast, and the development direction of RHI changed from northwestsoutheast to northeastsouthwest. The area transfer of RHI was concentrated in no change and little change, indicating that the evolution trend of RHI was relatively stable. The high-high aggregation areas were mainly located in the more developed areas in the southeast. In addition, the methods and results of this study can provide reasonable and effective insights into the future development and planning of the BTH.

Research areas: Heat, Habitat Conversion/Fragmentation
Crowdsourced air temperatures contrast satellite measures of the urban (2021)

Satellites overestimate urban heat islands.
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            The ubiquitous nature of satellite data has led to an explosion of studies on the surface urban heat island (SUHI). Relatively few have simultaneously used air temperature measurements to compare SUHI with the canopy UHI (CUHI), which is more relevant to public health. Using crowdsourced citizen weather stations (>50,000) and satellite data over Europe, we estimate the CUHI and SUHI intensity in 342 urban clusters during the 2019 heat wave. Satellites produce a sixfold overestimate of UHI relative to station measurements (mean SUHI 1.45C; CUHI 0.26C), with SUHI exceeding CUHI in 96% of cities during daytime and in 80% at night. Using empirical evidence, we confirm the control of aerodynamic roughness on UHI intensity, but find evaporative cooling to have a stronger overall impact during this time period. Our results support urban greening as an effective UHI mitigation strategy and caution against relying on satellite data for urban heat risk assessments.

Research areas: Heat, Habitat Conversion/Fragmentation