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T cells are an essential part of the immune system with crucial roles in adaptive response and the maintenance of tissue homeostasis. Depending on their microenvironment, T cells can be differentiated into multiple states with distinct functions. This myriad of cellular activities have prompted the development of numerous smart probes, ranging from small molecule fluorophores to nanoconstructs with variable molecular architectures and fluorescence emission mechanisms. In this Tutorial Review, we summarize recent efforts in the design, synthesis and application of smart probes for imaging T cells in tumors and inflammation sites by targeting metabolic and enzymatic biomarkers as well as specific surface receptors. Finally, we briefly review current strategies for how smart probes are employed to monitor the response of T cells to anti-cancer immunotherapies. We hope that this Review may help chemists, biologists and immunologists to design the next generation of molecular imaging probes for T cells and anti-cancer immunotherapies. [ABSTRACT FROM AUTHOR]

Distributed solar photovoltaic (PV) harvesting is an effective way to collect solar energy in existing metropolitan cities. To facilitate the installation of PV modules at solar abundant locations, an accurate estimation of solar PV spatial potential is indispensable. Solar energy could be reflected on high-albedo building surfaces inside the urban canyon. However, using conventional ways to construct albedo datasets for different building surfaces is extremely labor-intense. In this work, we proposed to use semantic segmentation to identify façade materials from street view images. Due to the distinguishable features between materials in terms of the subtle texture and patterns rather than just their shapes and colors, identification requires more details from images, which makes multi-scale inference structure a promising solution. Compared with existing methods combining scales features at pixel-level, we proposed a novel Multi-Scale Contextual Attention Network (MSCA), using a Multi-Scale Object-Contextual Representation (OCR) block to exploit and combine contextual information from different scales in high dimensional layers. The experimental results show that the proposed model significantly outperforms the existing models, achieving a mean Intersection over Union (mIOU) of 70.23%. The results indicate that the MSCA can effectively obtain the materials information from street views and can be a reliable solution to providing urban albedo information for solar estimation. [ABSTRACT FROM AUTHOR]

Urban tree species identification performs a significant role in tree management and the sustainable development of cities. Conventionally, multispectral or hyperspectral remote sensing images are applied to identify species. However, spectral profiles of trees on images are easily affected by surroundings and illuminations, resulting in urban trees of different species possibly having similar spectral features. The handheld laser scanning (HLS) technique can capture 3D structural information of trees and be confirmed to be effective in reducing the problem of spectral similarity through tree structural properties (TSP). TSP usually varies in different leaf conditions, especially TSP of tropical tree species. In this study, we investigated the effects of leaves on urban tropical tree species identification using HLS. A total of 89 metrics that characterized the TSP were evaluated, including 19 branches, 12 stems, 45 crowns, and 13 entire tree metrics. All metrics were derived under different leaf conditions. The correlation and importance of these metrics were further evaluated. Our results demonstrated that crown metrics perform the most important role in urban species identification in leaf-on and leaf-off conditions and that the combination of metrics derived in different leaf conditions can improve the identification accuracy. Furthermore, we discovered 9 robust metrics that perform well in all leaf conditions, including 3 crowns, 2 branches, 2 stems, and 2 entire tree metrics. These metrics give a deep understanding of numerous structural properties and provide a significant reference for the relevant structure-based classification of other tropical species. This study also illustrated that HLS could help to overcome the spectrum-related limitations and improve the efficiency of species identification and sustainable forest management. [ABSTRACT FROM AUTHOR]

As the prerequisite of non-destructively measuring structural parameters and leaf distribution, wood-leaf separation plays an essential role in forest inventories. However, fewer studies focus on the separation and measurement of large tropical trees with heavy crowns and complex branch structures. This study proposed a novel method to automatically separate the leaf and wood points of large tropical trees based on geometric features. Instead of identifying all wood points using the same rules, we used different methods to separate small and large wood components, respectively. The identification of small wood components was implemented mainly by the differences in point density and linear distribution pattern between leaf and wood points, while the identification of large wood components was implemented through the comprehensive analysis of verticality, linearity, anisotropy, as well as point density. To improve the separation accuracy and implementation effectiveness, the segment-wise and point-wise methods were combined in this study. The robustness and generalization of the proposed method were tested using two datasets, i.e. twenty large tropical trees with heavy crowns and twenty-four general tropical trees without heavy crowns. The separation results verified that the proposed method could achieve good separation of wood and leaf points of large crown-heavy tropical trees with the accuracy of up to 91.5%. The highest separation accuracy of general tropical trees was about 95.03%. The examination of the tropical trees without heavy crowns demonstrated that the proposed method has promising robustness and generalization ability. In addition, to fill the gap in the large tropical tree point clouds, an open-source dataset library was built for the wood-leaf separation research, including manually labelled 20 large crown-heavy tropical trees with different types of branch structures and basic structural parameters of each tree (tree height, crown spread, and diameter at the breast height (DBH)). [ABSTRACT FROM AUTHOR]

COVID-19 can be considered as the largest public health incident since 2019, posing disturbances to the medical, economic, and social systems. Understanding different levels of city resilience to the impact of COVID-19 on sustainable urban development is therefore essential. In this paper, we analyzed the evolution of the pandemic, the recovery of urban activities and major drivers of urban resilience to COVID-19 based on urban activity patterns derived from NO 2 monitoring stations from 217 cities in China. It is observed that nearly all urban activities have been affected by the epidemic with a reduced NO 2 emission, indicating a significant decline in the intensity of urban activity. The recovery patterns of human activity among different cities show that: (1) northern cities where low-resilience cities agglomerated have been severely affected. As of April 31, 2020, 12 northern cities have not recovered to pre-epidemic levels; (2) about three-quarters of the cities went through multiple stages to return to the pre-pandemic level, which related to both pandemic stress and prevention measures; (3) about 46.04% of the cities experienced increasing activity patterns that exceeded the pre-epidemic activity level. It is also observed that urban resilience can behave quite differently driven by variations in green coverage, economic aggregate, employment security, and medical system. Our study highlights the potential of an elevated urban resilience by optimizing the urban layout, improving the quantity and quality of green space, and enhancing the medical system. [Display omitted] • Most of the southern cities of China show higher urban activity than pre-pandemic. • Low/high-resilient urban agglomerations located in the north-northeast/south China. • Most cities undergo two stages of recovery at the beginning and end of March 2020. • Urban greenery is positively correlated to urban resilience. • Hospital capacity, instead of number, is more crucial to urban resilience. [ABSTRACT FROM AUTHOR]

The synergy of fine-to-moderate-resolutin (i.e., 10–60 m) satellite data of the Landsat-8 Operational Land Imager (OLI) and the Sentinel-2 Multispectral Instrument (MSI) provides a possibility to monitor the dynamics of sensitive aquatic systems. However, it is imperative to assess the spectral consistency of both sensors before developing new algorithms for their combined use. This study evaluates spectral consistency between OLI and MSI-A/B, mainly in terms of the top-of-atmosphere reflectance ( ρ t) , Rayleigh-corrected reflectance ( ρ r c) ,   and   remote-sensing reflectance ( R r s) . To check the spectral consistency under various atmospheric and aquatic conditions, near-simultaneous same-day overpass images of OLI and MSI-A/B were selected over diverse coastal and inland areas across Mainland China and Hong Kong. The results showed that spectral data obtained from OLI and MSI-A/B were consistent. The difference in the mean absolute percentage error (MAPE) of the OLI and MSI-A products was ~8% in ρ t and ~10% in both ρ r c   and R r s for all the matching bands, whereas the MAPE for OLI and MSI-B was ~3.7% in ρ t , ~5.7% in ρ r c , and ~7.5%   in   R r s for all visible bands except the ultra-blue band. Overall, the green band was the most consistent, with the lowest MAPE of ≤ 4.6% in all the products. The linear regression model suggested that product difference decreased significantly after band adjustment with the highest reduction rate in R r s (NIR band) and R r s (red band) for the OLI–MSI-A and OLI–MSI-B comparison, respectively. Further, this study discussed the combined use of OLI and MSI-A/B data for (i) time series of the total suspended solid concentrations (TSS) over coastal and inland waters; (ii) floating algae area comparison; and (iii) tracking changes in coastal floating algae (FA). Time series analysis of the TSS showed that seasonal variation was well-captured by the combined use of sensors. The analysis of the floating algae bloom area revealed that the algae area was consistent, however, the difference increases as the time difference between the same-day overpasses increases. Furthermore, tracking changes in coastal FA over two months showed that thin algal slicks (width < 500 m) can be detected with an adequate spatial resolution of the OLI and the MSI. [ABSTRACT FROM AUTHOR]

Information about tree species plays a pivotal role in sustainable forest management. Light detection and ranging (LiDAR) technology has demonstrated its potential to obtain species information using the structural features of trees. Several studies have explored the structural properties of boreal or temperate trees from terrestrial laser scanning (TLS) data and applied them to species classification, but the study of structural properties of tropical trees for species classification is rare. Compared to conventional static TLS, handheld laser scanning (HLS) is able to effectively capture point clouds of an individual tree with flexible movability. Therefore, in this study, we characterized the structural features of tropical species from HLS data as 23 LiDAR structural parameters, involving 6 branch, 11 crown and 6 entire tree parameters, and used these parameters to classify the species via 5 machine-learning (ML) models, respectively. The performance of each parameter was further evaluated and compared. Classification results showed that the employed parameters can achieve a classification accuracy of 84.09% using the support vector machine with a polynomial kernel. The evaluation of parameters indicated that it is insufficient to classify four species with only one and two parameters, but ten parameters were recommended in order to achieve satisfactory accuracy. The combination of different types of parameters, such as branch and crown parameters, can significantly improve classification accuracy. Finally, five sets of optimal parameters were suggested according to their importance and performance. This study also showed that the time- and cost-efficient HLS instrument could be a promising tool for tree-structure-related studies, such as structural parameter estimation, species classification, forest inventory, as well as sustainable tree management. [ABSTRACT FROM AUTHOR]

An intense dust storm from 8 April to 11 April 2023 originated from the Gobi deserts bordering Mongolia and Inner Mongolia of China and affected most parts of northern, central, and eastern China, the Korean Peninsula, and Japan. It has a significant impact on air quality in these regions and alters the optical and microphysical properties of the aerosols. In this work, a comprehensive analysis of the storm was carried out by using reanalysis data, model simulation, satellite, and ground-based observations. The geopotential height along with the wind flow reveals that the development, transportation, and dissipation of the dust storms were closely related to the movement of the Mongolia cyclones caused by Mongolia low and its surrounding highs. The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data showed a two-layer dust vertical distribution, with the bottom one below 5 km and the upper one stretching from 5 to 10 km over the Japan Sea. The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) backward trajectory uncovered Central Mongolia being the origin of the dust storm that arrived over the Japan Sea on April 9, 2022, and the forward trajectory revealed that the dust aerosols would be further transported to the Pacific Ocean. Air quality analysis demonstrated that the regions of inner Mongolia (IMG) and Jing-Jin-Ji (JJJ) suffered the most from the dust storm events, followed by the Yangtze River Delta (YRD), while the Pearl River Delta (PRD) was hardly affected, as indicated by the regional-averaged PM 10 and PM 2.5. It was also found that the dust storm event has almost no significant influence on the other air quality indicators including CO, NO 2 , O 3 , and SO 2. Historical record tracking revealed that the springtime averaged PM 10 and PM 2.5 of the year 2023 are comparable to those in 2021 which has the record-breaking dust storm case. AERONET data indicated dramatic increments of aerosol optical depth at 550 nm (AOD550) and effective radius of total size (ReffT) in regions of Gobi and JJJ, accompanied by low Angstrom Exponent (AE), and high SSA870 values, while in the regions of Korea and Japan, minor increments of AOD550 and ReffT were found. This study provides a holistic scientific view of the dust storm which could aid in the implementation of protective measures against dust storms. • Mongolian low contributed to the formation of dust storms. • PM 2.5 and PM 10 increased dramatically during the dust storms in northern China. • Both AOD and SSA increased considerably over regions of Gobi and Jing-Jin-Ji. [ABSTRACT FROM AUTHOR]

Geostationary satellite observations are essential on analyzing the effects of dust on the terrestrial and solar radiation budget. However, unified dust aerosol products for both solar and terrestrial spectra are currently under-researched. To fill this gap, two sets of algorithms were developed. Firstly, the dust aerosol retrieval algorithm at visible to near-infrared bands (DARV) was developed to estimate dust aerosol optical thickness (AOT) at 0.55 μm (τ 0.55). The performance of DARV under heavy aerosol loadings was greatly improved by using a near-infrared band and spectral sensitivity factors with Advanced Himawari Imager (AHI) AOT products. Secondly, the dust aerosol retrieval algorithm at thermal-infrared bands (DART) was developed to retrieve AOT at 10.8 μm (τ 10.8) and effective radius at coarse mode (r eff) simultaneously. The DART outperforms other algorithms by (i) considering an emissivity ratio that advances the derivation of spectral surface brightness temperature, and (ii) including a spectral angle mapper that greatly constrains the retrieval uncertainties. Validation against the AERONET AOT shows a correlation coefficient (ρ), root mean square error (RMSE), and bias of 0.9, 0.26, and 0.06, respectively for the DARV algorithm at dust-dominated cases, and a ρ of 0.69 for the DART algorithm. Inter-comparisons among four officially released aerosol products and DARV AOT on five dust storm cases reveals that DARV is similar to VIIRS Deep Blue (DB) AOT with the highest ρ (ranging from 0.60 to 0.91) and lowest RMSE (ranging from 0.40 to 0.88). MODIS Deep Blue (DB) and Multi-angle Implementation of Atmospheric Correction (MAIAC) AOT are similar to each other and they are lower than the DARV and VIIRS AOT. The Japan Aerospace Exploration Agency (JAXA) AOT data are generally higher than the others. In addition, time series analysis of the three retrievals aided by the PM 2.5 , PM 10 , and wind field data verifies the trend of AOT VIR , AOT TIR and r eff for a dust storm case throughout the daytime. The results and operational algorithms from this work could be further used for facilitating accurate estimation of dust radiative forcing and other relevant atmospheric research. • A DARV algorithm was developed for dust AOT retrieval at the solar spectrum. • A DART algorithm was developed for dust AOT and r eff retrieval at the terrestrial spectrum. • Unified and consistent dust aerosol properties at both solar and terrestrial spectra were derived. • Near-real-time dust aerosol products could be generated. [ABSTRACT FROM AUTHOR]

The ubiquitous missing values in the satellite-derived aerosol optical depth (AOD) products have always been a challenge for spatial and temporal analysis. To address this concern, we propose a novel data-driven model to attain the full-coverage daily AOD dataset with 0.01° spatial resolution in the Guangdong-Hong Kong-Macao Greater Bay Area (hereafter GBA) from 2010 to 2021. Firstly, the missing values of top-of-atmosphere (TOA) reflectance and surface reflectance of Moderate Resolution Imaging Spectroradiometer (MODIS) caused by cloud contamination, were reconstructed using the Data Interpolating Empirical Orthogonal Functions (DINEOF). Subsequently, a new model was developed for the estimation of AOD which integrates the geographical and temporal encodings into the Light Gradient Boosting Machine (LightGBM) with the inputs of reconstructed TOA/surface reflectance and other influencing variables like meteorological and geographical factors. Results showed that the derived gap-free AOD dataset outperforms the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD product and agrees well with the ground-based observations, achieving an index of agreement (IOA) of 0.88, R of 0.84, root mean square error (RMSE) of 0.19 and mean absolute error (MAE) of 0.14. Moreover, the derived AOD dataset presents consistent temporal patterns with in-situ measurements, but with more spatial details than other gapless AOD datasets, i.e., Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Overall, this study has developed a promising meteorological framework for the estimation of full-coverage AOD, which can also be applied over other regions. The derived long-term full-coverage daily AOD dataset can also be used for other applications related to climate change, air quality and ecosystem assessment. • We proposed a promising methodological framework for full-coverage AOD retrieval. • Daily dataset AOD with seamless coverage in the GBA during 2010–2021 was generated. • The derived gap-free AOD dataset is highly consistent with in-situ measurements. • The estimated results outperform the MODIS AOD product with more spatial details. [ABSTRACT FROM AUTHOR]

Dust detection from satellite images has been explored with both physical dust index method and machine learning method. However, both methods have their own limitations. The dust index method is threshold dependent and uses limited satellite observations, whilst the machine learning method requires a large quantity of training data and is generally physically uninterpretable. Besides, previous studies give definite detection results, i.e., 1 and 0 representing dust and non-dust. In actual situations, dusts could be mixed with clouds, and thin dust plumes can be regarded as a mixture of dusts and land surfaces. To tackle these problems, this study proposes a new method that integrates physical, machine learning, and analytical methods, namely Physical Index and Self-Organizing Mapping (SOM) integrated detection method (PISOM). It first uses physical indices to extract all dust-like pixels. Then the preliminary detection results were refined with a well-trained SOM model. Finally, it calculated the particle-type ratios for each dust-like pixel with an analytical method. The PISOM has been testified for dust aerosol detection over Northern China using Himawari-8 AHI satellite images. It was cross-compared with the two classical physical dust indices of brightness temperature difference (BTD) and normalized difference dust index (NDDI) on 5732 samples of heavy dust (HD), thin dust (TD), desert surface (DS), and thin cloud (TIC), which were manually extracted from historical dust storm cases. The results show that PISOM has the highest accuracy score (0.930) than the BTD (0.890) and NDDI (0.700). The confusion matrix of PISOM for the four types reveals that the PISOM misclassifies 29 and 325 samples of HD and TD as TIC. An in-depth examination reveals that these misclassified samples are mixtures of HD and TD with TIC, which indicates the effectiveness of PISOM in interpreting the mixture cases. Moreover, application on typical dust storm cases demonstrates that the PISOM performs well over different regions and under various illumination conditions. Importantly, the quantified dust detection results make the PISOM application-oriented, i.e., the results can be used to estimate dust-affected areas or quantify the probability of dust ratio over particular pixel(s). • Integrating physical index and self-organizing mapping for aerosol dust detection (PISOM) was developed. • The developed method overcomes the problem of threshold dependency in the physical index method. • The developed method can well depict mixed cases, such as dust mixed with clouds. • The dust detection algorithms are application-oriented. [ABSTRACT FROM AUTHOR]

PM 2.5 as a primary air pollutant has adverse effects on the environment and public health. The air quality monitoring stations are distributed sparsely and unevenly, making it difficult to provide continuous and precise regional measurements, which can be supplemented by satellite observations. However, most satellite-based approaches for air pollution estimation are difficult to extract the spatio-temporal dependencies effectively, leading to lower accuracy in long-term prediction and assessment of episodic changes. To fill this gap, a hierarchical geospatial long short-term memory method (HG-LSTM) by considering the geospatial autocorrelation was proposed for hourly PM 2.5 estimation with 2-km spatial resolution in Yangtze River Delta (YRD) urban agglomeration. The superior accuracy of the HG-LSTM is compared with other models via the site-based and year-based cross-validation (CV) tests, indicating geospatial autocorrelation exerts non-negligible impacts on the PM 2.5 estimation. The estimations are consistent with the in-situ observations with site-based CV R2 of 0.88. The deviations less than 10 μ g/m3 account for over 80%. The PM 2.5 spatiotemporal characteristics in the YRD reveal that PM 2.5 concentrations are higher in the morning and decline significantly in the afternoon. As well, elevated PM 2.5 values are accumulated in the northern regions of the study area. Although the prediction accuracy decreases as the augment of prediction timesteps, the results can still be useful to detect air pollution changes in the near future. Overall, the HG-LSTM model can estimate hourly PM 2.5 concentrations accurately and seamlessly, which is beneficial for air pollution monitoring and environmental protection strategy formation. [Display omitted] • HG-LSTM was formed by integrating spatial autocorrelation with encoder-decoder LSTM. • HG-LSTM performs better than other models in year-based and site-based CV tests. • Estimated results are consistent with in-situ measurements, with an R2 of 0.88. • Hourly PM 2.5 values were generated with 2-km resolution seamlessly and accurately. [ABSTRACT FROM AUTHOR]

Monitoring dust aerosol properties is critical for the studies of radiative transfer budget, climate change, and air quality. Aerosol optical thickness (AOT) and effective radius (R eff) are two main parameters describing the optical and microphysical properties of airborne dust aerosol. Satellite remote sensing provides an opportunity for estimating the two parameters in spatial coverage and continuously. To take the merits of machine learning algorithms and also utilize the physical knowledge discovered in the conventional retrieval algorithms, a physical-based machine learning method was proposed and applied on the Himawari-8 geostationary satellite for robust retrieval of dust aerosol properties. The main concepts of this study comprise i) constructing the model input data by extracting highly informative features from the Himawari observations according to physical knowledge and ii) exploiting the utility of six state-of-the-art machine learning algorithms in dust aerosol retrieval. The algorithms include artificial neural network (ANN), extreme boost gradient tree (XGBoost), extra tree (ET), random forest (RF), support vector regression (SVR), and kernel Ridge regression (Ridge). The ground-truth AOT and R eff data from AERONET stations were supplied as output labels. The cross-validation technique was adopted for model training and the results show that the ANN model is superior to the other machine learning models for both AOT and R eff estimation, which exhibits the lowest mean absolute error (MAE = 0.0292 and 0.0981) and the highest correlation coefficient (r = 0.98 and 0.84). When validated on an independent dataset, the ANN model achieved the lowest MAE (0.0334 and 0.1487), and the highest r (0.94 and 0.63). More importantly, when compared against representative physical-based algorithms, the developed ANN model still retains the best performance. Furthermore, the ANN model shows an overall better performance than other machine learning models and also the JAXA Himawari-8 Level-2 AOT product, with examples exhibited in three dust storm events and for continuous monitoring of one of the dust storm events. Additionally, feature importance analysis implies that the important features of dust aerosol identified by the ANN model are consistent with that in physical model-based algorithms. In summary, this study shows great potential for generating near-real-time products of dust aerosol properties from Himawari satellite data. These products can provide a scientific basis for climate and meteorological study regarding severe dust storms. • Dust optical thickness and effective radius are retrieved simultaneously from Himawari-8 data. • Physical knowledge about dust aerosols is integrated with the machine learning models. • The ANN outperforms the XGBoost, extra tree, random forest, SVR, and Ridge regression models. • The ANN model is superior to the official aerosol retrieval algorithm of Himawari-8. • Near-real-time estimation of dust aerosol properties is achievable. [ABSTRACT FROM AUTHOR]

With large-scale urbanization globally, the world is facing environmental problems brought about by high-density cities. To achieve sustainable development in Hong Kong, especially for the heavily loaded Cross Harbour Tunnel district, The Hong Kong Polytechnic University proposes a Green Deck over the Cross Harbour Tunnel to enhance the neighboring environments. Green belts, buildings, and a new exhibition center are planned for this deck. This will require an increase in the guest capacity of nearby hotels in the Tsim Sha Tsui East (TSTE) area. This study, therefore, investigated the development potential of the 23 existing buildings in TSTE and the environmental impact brought about by the increase of plot ration (PR)/building height (BH) in that area. Spatial analyzes of solar exposure, wind ventilation, and air temperature were conducted based on four different scenarios with various PR/BH; human thermal comfort brought about by PR/BH changes were also measured. A comparison of different scenarios revealed no significant changes in the surrounding environment. The study concludes that there is potential for PR/BH relaxations in TSTE to increase spaces to meet increased population flow. [ABSTRACT FROM AUTHOR]

• Distinguish urban tree species with terrestrial hyperspectral camera (SPECIM IQ). • 450 bi-monthly images were acquired for 75 urban trees belonging to 19 tree species. • Hyperspectral phenology models were developed to explore seasonalities of trees. • A Deep Neural Network is used to identify tree species from hyperspectral imagery. Urban trees exhibit a wide range of ecosystem services that have long been unveiled and increasingly reported. The ability to map tree species and analyze tree health conditions would become vividly essential. Remote sensing techniques, especially hyperspectral imaging, are being evolved for species identification and vegetation monitoring from spectral reponse patterns. In this study, a hyperspectral library for urban tree species in Hong Kong was established comprising 75 urban trees belonging to 19 species. 450 bi-monthly images were acquired by a terrestrial hyperspectral camera (SPECIM-IQ) from November 2018 to October 2019. A Deep Neural Network classification model was developed to identify tree species from the hyperspectral imagery with an overall accuracy ranging from 85% to 96% among different seasons. Representative spectral reflectance curves of healthy and unhealthy conditions for each species were extracted and analyzed. The hyperspectral phenology models were developed to achieve high accuracy and optimization of data acquisition. The bi-monthly canopy signatures and vegetation indices revealed different seasonality patterns of evergreen and deciduous species in Hong Kong. We explored the utility of terrestrial hyperspectral remote sensing and Deep Neural Network for urban tree species identification and characterizing. This provides a unique baseline to understand hyperspectral characteristics and seasonality of urban tree species in Hong Kong that can also contribute to hyperspectral imaging and database development elsewhere in the world. [ABSTRACT FROM AUTHOR]

The impact of socio-economic restriction measures in the United States showed a significant reduction in nitrogen dioxide (NO 2) emissions due to the changes in commuting patterns, travel restrictions, and reduced social and economic activities. The data from 60 observation stations across different states representing of different urban settings were used to assess the impacts of dynamic changes in human mobility and static emission sources on NO 2 concentrations. Two predictive models, Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX) and Long Short-Term Memory (LSTM), were used to forecast NO 2 concentrations under a counterfactual scenario in the assumption of no lockdown was implemented. The predicted concentrations were then compared to the observed concentrations to evaluate the difference between the two scenarios, during lockdown and after lockdown by using the last five-years (2015–2019) average as baseline. Results showed that reduction in NO 2 varied across stations due to a combination of factors such as human mobility, population density, income, local climate, and stationary sources of NO 2 emissions in the neighbourhood. The stations located in high-income regions with higher population density experienced a greater reduction in mobility, resulting in a greater variation in NO 2 levels during both periods. In contrast, stations in densely populated low-income regions experienced lower variation in mobility and NO 2 levels during the lockdown period but showed recovery during the post-lockdown period. Stations located outside the city with very low population density experienced no significant reduction in NO 2 emissions. The study highlights the disparities in NO 2 reduction and income inequality exposed by the pandemic, which may have scientific impacts on air quality and health disparities. • Dynamic human mobility and static emissions sources found major NO 2 contributors. • NO 2 has disproportionate variation depending on the income and population density. • Populous low-income areas showed less mobility reduction & faster NO 2 recovery. • Populous high-income areas showed greater human mobility and NO 2 reductions. • Marine and mixed-humid climate zones showed a greater reduction in NO 2. [ABSTRACT FROM AUTHOR]

People's exposure to green space is a critical link between urban green space and urban residents' health. Since green space may affect human health through multiple pathways regarding diverse human health outcomes, the measurement of people's exposure to green space must be tailored to concrete study contexts and research questions. In this scoping review, we systematically categorized the available green space representations and metrics in the last two decades that can be used to derive people's exposure to green space regarding different research topics. A three-phase systematic review was conducted after a generalized search of relevant research articles from the three most-used publication databases, namely Scopus, the Web of Science, and PubMed. We identified 260 research articles that particularly discuss green space representations and metrics. We further developed a multi-pathway framework to articulate the complicated context issues in green space studies. We categorized the most relevant green space representations and metrics into five groups, including green space indices, the delineation, inventory, and usage of green space, the spatiotemporal evolution of green space, the attributes and components of green space, and the green space landscape and fragmentation. Finally, we discussed the inter-conversion between different green space representations and metrics, the "mobility-turn" in green space studies and how it may affect the derivation of people's exposure to green space, and other potential methodological issues in measuring people's exposure to green space. Our scoping review provides the most comprehensive framework and categories for deriving people's exposure to green space to date, which may strongly support a broad range of studies that concern green space's health effects. • We systematically categorized green space representations and metrics in the last two decades. • A three-phase systematic review was conducted after a generalized search of relevant articles. • We developed a multi-pathway framework to articulate the complicated context issues in green space studies. • We categorized the most relevant green space representations and metrics into 5 groups. • We discussed how the mobility turn may affect the derivation of people's exposure to green space. [ABSTRACT FROM AUTHOR]

Harvesting solar energy on rooftops can be a promising solution for providing affordable energy. This requires accurately estimating spatio-temporal solar photovoltaic (PV) potential on urban surfaces. However, it is still a challenge to obtain a fast and accurate estimation of rooftop solar PV potential over large urban built-up areas. Thus, this study proposes a parametric-based method to estimate annual rooftop solar irradiation at a fine spatial resolution. Specifically, seven parameters (Digital Surface Model, Sky View Factor, shadow from buildings, shadow from terrain, building volume to façade ratio, slope, and aspect) are determined that having great importance in modeling rooftop solar irradiation. Three machine learning methods (Random Forest (RF), Gradient Boost Regression Tree (GBRT), and AdaBoost) trained by the selected parameters are cross-compared based on R 2 , Mean Absolute Error (MAE), and computation time. As a case study in Hong Kong, China, the RF outperformed GBRT and AdaBoost, with R 2 = 0. 77 and M A E = 22. 83 kWh/m 2 / year. The time for training and prediction of rooftop solar irradiation is within 13 h, achieving a 99.32% reduction in time compared to the physical-based hemispherical viewshed algorithm. These results suggest that the proposed method can provide an accurate and fast estimation of rooftop solar irradiation for large datasets. • The parametric assessment of rooftop solar irradiation was conducted. • Random Forest, Gradient Boost Regression Tree, and AdaBoost models were compared. • The RF model showed superior accuracy (R 2 = 0.77) and fast computation. • A data and model dual-driven approach was used. [ABSTRACT FROM AUTHOR]

The urban heat island effect poses a growing threat to human society, especially in densely populated and developed megacities. With the introduction of the Local Climate Zones (LCZ) framework, new perspectives and findings have been brought to urban heat island studies. This study investigated the cooling effect of vegetation and albedo on the surface urban heat island (SUHI) in the classification system of LCZ during different seasons, using three Chinese megacities as case study areas. Single-factor linear regression and Pearson's correlation coefficient were applied to analyze the seasonal cooling effect of both albedo and the NDVI on the SUHI within different LCZs. The results show that (1) the variability of the SUHI is reflected in its dominance and intensity within certain LCZs in different cities and in the efficiency of cooling factors; (2) the cooling effect of vegetation is dominant in each season, and the cooling effect produced by albedo within specific seasons can be differentiated by LCZs. This study provides valuable information for the mitigation of the SUHI magnitude in specific regions and at specific times of the year. [ABSTRACT FROM AUTHOR]

This study evaluates the association between antivirals (Molnupiravir and Nirmatrelvir-Ritonavir) and all-cause and respiratory mortality and organ dysfunction among high-risk COVID-19 patients during an Omicron outbreak. Two cohorts, Nirmatrelvir-Ritonavir versus control and Molnupiravir versus control, were constructed with inverse probability treatment weighting to balance baseline characteristics. Cox proportional hazards models evaluated the association of their use with all-cause mortality, respiratory mortality, and all-cause sepsis (a composite of circulatory shock, respiratory failure, acute liver injury, coagulopathy, and acute liver impairment). Patients recruited were hospitalized and diagnosed with the COVID-19 Omicron variant between February 22, 2022 and April 15, 2022, and followed up until May 15, 2022. The study included 17,704 patients. There were 4.67 and 22.7 total mortalities per 1000 person-days in the Nirmatrelvir-Ritonavir and control groups respectively before adjustment (weighted incidence rate ratio, − 18.1 [95% CI − 23.0 to − 13.2]; hazard ratio, 0.18 [95% CI, 0.11–0.29]). There were 6.64 and 25.9 total mortalities per 1000 person-days in the Molnupiravir and control groups respectively before adjustment (weighted incidence rate ratio per 1000 person-days, − 19.3 [95% CI − 22.6 to − 15.9]; hazard ratio, 0.23 [95% CI 0.18–0.30]). In all-cause sepsis, there were 13.7 and 35.4 organ dysfunction events per 1000 person-days in the Nirmatrelvir-Ritonavir and control groups respectively before adjustment (weighted incidence rate ratio per 1000 person-days, − 21.7 [95% CI − 26.3 to − 17.1]; hazard ratio, 0.44 [95% CI 0.38–0.52]). There were 23.7 and 40.8 organ dysfunction events in the Molnupiravir and control groups respectively before adjustment (weighted incidence ratio per 1000 person-days, − 17.1 [95% CI, − 20.6 to − 13.6]; hazard ratio, 0.63 [95% CI 0.58–0.69]). Among COVID-19 hospitalized patients, use of either Nirmatrelvir-Ritonavir or Molnupiravir compared with no antiviral use was associated with a significantly lower incidence of 28-days all-cause and respiratory mortality and sepsis. [ABSTRACT FROM AUTHOR]

The need to protect forests and enhance the capacity of mountain ecosystems is highlighted in the U.N.'s Sustainable Development Goal (SDG) 15. The worst-hit areas of the 2008 Wenchuan Earthquake in southwest China were mountainous regions with high biodiversity and the impacted area is typical of other montane regions, with the need for detecting vegetation changes following the impacts of catastrophes. While the widely used remotely sensed vegetation indicator NDVI is available from various satellite data sources, these satellites are available for different monitoring periods and durations. Combining these datasets proved challenging to make a continuous characterization of vegetation change over an extended time period. In this study, compared with linear regression, multiple linear regression, and random forest, Convolutional Neural Networks (CNNs) performed best with an average R2 of 0.819 (leave-one-out cross-validation). Thus, the CNNs model was selected to establish the map of the overlapping periods of two remote-sensing products: SPOT-VGT NDVI and PROBA-V NDVI, to reconstruct a SPOT-VGT NDVI for the period from June 2014 to December 2018 in the worst-hit areas of the Wenchuan earthquake. We analyzed the original and reconstructed SPOT-VGT NDVI in the hard-hit areas of the Wenchuan earthquake from 1999 to 2018, and we concluded that NDVI showed an overall upward trend throughout the study period, but experienced a sharp decline in 2008 and reached its lowest value a year later (2009). Vegetation recovery was rapid from 2009 until 2011 after which, it returned to a pattern of slower natural growth (2012–2018). The Longmenshan fault zone experienced the greatest vegetation damage and initiation of recovery there has caused the overall regional average recovery to lag by 1–2 years. In areas where the land was denuded of vegetation (i.e., effectively all vegetation was stripped from the surface) after the earthquake, the damage exceeded what was experienced anywhere else in the entire study area, and by 2018 it remained unrestored. In the 15 years since the earthquake, the areas that were denuded were expected to recover to the level of restoration equivalent with the NDVI of 2007, as was the case in other earthquake-damaged regions. In addition to the earthquake and the immediate loss of vegetation, the Chinese government's Grain for Green Policy, the elevation ranges within the region, the forest's phenological conditions, and human activities all had an impact on vegetation recovery and restoration. The reconstructed NDVI provides a long-term continuous record, which contributes to the identifying changes that are improving predictive forest recovery models and to better vegetation management following catastrophic disturbances, such as earthquakes. [ABSTRACT FROM AUTHOR]

This study analyzes seasonal characteristics and long-term variations in aerosol optical parameters in Hong Kong from 2006 to 2021 using AERONET data and satellite-based observations based on the extreme-point symmetric mode decomposition (ESMD) model. The dominant aerosol types in Hong Kong are mixed aerosols and urban/industrial aerosols with fine-mode sizes, and slightly absorbing or non-absorbing properties. Aerosol optical depth (AOD), Angstrom exponent (AE) and single scattering albedo (SSA) varied seasonally with a lower AOD but higher AE and SSA in summer, and elevated AOD but lower AE and SSA in spring and winter. The long-term variations show the year 2012 to be a turning point, with an upward trend in AOD and AE before 2012 and then downwards after 2012. However, for SSA, a rising trend was exhibited in both pre- and post-2012 periods, but with a larger gradient in the first period. The ESMD analysis shows shorter-term, non-linear fluctuations in aerosol optical parameters, with alternating increasing and declining trends. The examination of the relationships between AOD and meteorological factors based on the extreme gradient boosting (XGBoost) method shows that the effects of weather conditions on AOD are complex and non-monotonic. A lower relative humidity, higher wind speed in southwest directions and lower temperature are beneficial to the abatement of aerosol loads in Hong Kong. In conclusion, the findings of this study enhance the understanding of aerosol properties and the interactions between aerosol loading and meteorological factors. [ABSTRACT FROM AUTHOR]

Harmonization of satellite imagery provides a good opportunity for studying land surface temperature (LST) as well as the urban heat island effect. However, it is challenging to use the harmonized data for the study of LST due to the systematic bias between the LSTs from different satellites, which is highly influenced by sensor differences and the compatibility of LST retrieval algorithms. To fill this research gap, this study proposes the comparison of different LST images retrieved from various satellites that focus on Hong Kong, China, by applying diverse retrieval algorithms. LST images generated from Landsat-8 using the mono-window algorithm (MWAL8) and split-window algorithm (SWAL8) would be compared with the LST estimations from Sentinel-3 SLSTR and Himawari-8 using the split-window algorithm (SWAS3 and SWAH8). Intercomparison will also be performed through segregated groups of different land use classes both during the daytime and nighttime. Results indicate that there is a significant difference among the quantitative distribution of the LST data generated from these three satellites, with average bias of up to −1.80 K when SWAH8 was compared with MWAL8, despite having similar spatial patterns of the LST images. The findings also suggest that retrieval algorithms and the dominant land use class in the study area would affect the accuracy of image-fusion techniques. The results from the day and nighttime comparisons revealed that there is a significant difference between day and nighttime LSTs, with nighttime LSTs from different satellite sensors more consistent than the daytime LSTs. This emphasizes the need to incorporate as much night-time LST data as available when predicting or optimizing fine-scale LSTs in the nighttime, so as to minimize the bias. The framework designed by this study will serve as a guideline towards efficient spatial optimization and harmonized use of LSTs when utilizing different satellite images associated with an array of land covers and at different times of the day. [ABSTRACT FROM AUTHOR]

High-frequency aerosol observation from a new-generation geostationary meteorological satellite is capable to capture and monitor the spatiotemporal dynamic variation of aerosols, which is of vital significance to environmental research and climate studies. Due to the diversity and complexity of land cover, it is a challenge to retrieve aerosol properties with high accuracy over land, especially over heterogeneous land surfaces. In this study, a geometry-discrete minimum reflectance aerosol retrieval algorithm (GeoMRA) has been proposed to retrieve 10-min high temporal resolution aerosol optical depth (AOD) datasets for geostationary Himawari-8 Advanced Himawari Imager (AHI) sensor, aiming at providing universal bidirectional reflectance distribution function (BRDF) descriptions for different land surfaces with different heterogeneous extent. The AOD retrievals from GeoMRA demonstrate good consistency against the ground-based AERONET measurements in East Asia from 2015 to 2020, with a correlation coefficient (${R}$) of 0.883 and approximately 65.6% of matchups falling within the expected error envelope of ±(0.05% +15%). Intercomparison between the GeoMRA retrieved AOD and other operational AOD products shows that the GeoMRA AOD retrievals, which generally possess similar spatial distribution and accuracy as Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products, have better performances than the Japan Aerospace Exploration Agency (JAXA) AOD products by providing more accurate AOD retrievals with higher spatial coverage. Moreover, the AOD bias analyses further demonstrate the robustness of GeoMRA algorithm, and an extreme haze event shows that the continuous GeoMRA AOD images illustrate smoother temporal variations than JAXA AOD products, demonstrating its efficacy and reliability in capturing the process of haze transport and monitoring the continuous spatiotemporal variation of aerosol. The above results suggest the considerable accuracy of GeoMRA algorithm for scientific application requirement and demonstrate the robustness of the proposed BRDF scheme in describing heterogeneous surfaces with diverse reflectance distribution. [ABSTRACT FROM AUTHOR]

Accurately quantifying carbon mitigation of operational photovoltaics (PVs) influenced by dynamic geo-environment is crucial for developing suitable initiatives on renewable energy transition. However, previous studies made strong assumptions to avoid modelling spatial heterogeneity of carbon footprints or ignore weather and shadowing effects on PV electricity generation, making the estimated results unreliable and even causing false policymaking. To tackle this challenge, we developed a novel model coupling multi-sourced data modelling and life-cycle assessment to estimate spatially heterogenous carbon mitigation of all the operational rooftop PVs in an entire city. It is built by three hierarchal modules: (i) segmenting PV areas from high-resolution satellite imagery, by using Deep Solar PV Refiner, an advanced semantic segmentation network; (ii) estimating electricity generation in the segmented PV areas, by using a well-developed 3D solar irradiation model that considers the effects of land surface solar irradiation influenced by weather and shadowing effects produced by 3D buildings; (iii) quantifying carbon mitigation potential of PVs, by developing a spatial-aware life-cycle model to track the life-cycle carbon footprints of PVs from production, transportation, operation, to decommission. Investigating Singapore by 2020, we reveal that industrial, airport, and residential areas have the largest rooftop PV installation. We also suggest a carbon emission rate of 13.20 g-CO 2 /kWh, a carbon payback time of 0.81 years, and an energy payback time of 0.94 years, showing an improved carbon mitigation capability compared to the past years. This study contributes to GIS data modelling and helps understand the geospatial characteristics of urban-scale PV carbon mitigation. • Developed life-cycle-assessment integrated multi-sourced geospatial data model. • Segmented rooftop PV areas using an advanced semantic segmentation model. • Estimated installed PV electricity output using 3D solar estimation model. • Suggested significant carbon mitigation potential from rooftop PVs in Singapore. • Contributes to renewable energy transition and Information Geography development. [ABSTRACT FROM AUTHOR]

This study compares the space‐time patterns and characteristics of high‐risk areas of COVID‐19 transmission in Hong Kong between January 23 and April 14 (the first and second waves) and between July 6 and August 29 (the third wave). Using space‐time scan statistics and the contact tracing data of individual confirmed cases, we detect the clusters of residences of, and places visited by, both imported and local cases. We also identify the built‐environment and demographic characteristics of the high‐risk areas during different waves of COVID‐19. We find considerable differences in the space‐time patterns and characteristics of high‐risk residential areas between waves. However, venues and buildings visited by the confirmed cases in different waves have similar characteristics. The results can inform policymakers to target mitigation measures in high‐risk areas and at vulnerable groups, and provide guidance to the public to avoid visiting and conducting activities at high‐risk places. [ABSTRACT FROM AUTHOR]

Automatically extracting buildings from remote sensing images with deep learning is of great significance to urban planning, disaster prevention, change detection, and other applications. Various deep learning models have been proposed to extract building information, showing both strengths and weaknesses in capturing the complex spectral and spatial characteristics of buildings in remote sensing images. To integrate the strengths of individual models and obtain fine-scale spatial and spectral building information, this study proposed a stacking ensemble deep learning model. First, an optimization method for the prediction results of the basic model is proposed based on fully connected conditional random fields (CRFs). On this basis, a stacking ensemble model (SENet) based on a sparse autoencoder integrating U-NET, SegNet, and FCN-8s models is proposed to combine the features of the optimized basic model prediction results. Utilizing several cities in Hebei Province, China as a case study, a building dataset containing attribute labels is established to assess the performance of the proposed model. The proposed SENet is compared with three individual models (U-NET, SegNet and FCN-8s), and the results show that the accuracy of SENet is 0.954, approximately 6.7%, 6.1%, and 9.8% higher than U-NET, SegNet, and FCN-8s models, respectively. The identification of building features, including colors, sizes, shapes, and shadows, is also evaluated, showing that the accuracy, recall, F1 score, and intersection over union (IoU) of the SENet model are higher than those of the three individual models. This suggests that the proposed ensemble model can effectively depict the different features of buildings and provides an alternative approach to building extraction with higher accuracy. [ABSTRACT FROM AUTHOR]

Background Nosocomial outbreaks with superspreading of coronavirus disease 2019 due to a possible airborne transmission have not been reported. Methods Epidemiological analysis, environmental samplings, and whole-genome sequencing (WGS) were performed for a hospital outbreak. Results A superspreading event that involved 12 patients and 9 healthcare workers (HCWs) occurred within 9 days in 3 of 6 cubicles at an old-fashioned general ward with no air exhaust built within the cubicles. The environmental contamination by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was significantly higher in air grilles (>2 m from patients' heads and not within reach) than on high-touch clinical surfaces (36.4%, 8 of 22 vs 3.4%, 1 of 29, P  = .003). Six (66.7%) of 9 contaminated air exhaust grilles were located outside patient cubicles. The clinical attack rate of patients was significantly higher than of HCWs (15.4%, 12 of 78 exposed patients vs 4.6%, 9 of 195 exposed HCWs, P  = .005). Moreover, the clinical attack rate of ward-based HCWs was significantly higher than of nonward-based HCWs (8.1%, 7 of 68 vs 1.8%, 2 of 109, P  = .045). The episodes (mean ± standard deviation) of patient-care duty assignment in the cubicles was significantly higher among infected ward-based HCWs than among noninfected ward-based HCWs (6.0 ± 2.4 vs 3.0 ± 2.9, P  = .012) during the outbreak period. The outbreak strains belong to SARS-CoV-2 lineage B.1.36.27 (GISAID clade GH) with the unique S -T470N mutation on WGS. Conclusions This nosocomial point source superspreading event due to possible airborne transmission demonstrates the need for stringent SARS-CoV-2 screening at admission to healthcare facilities and better architectural design of ventilation systems to prevent such outbreaks. Portable high-efficiency particulate filters were installed in each cubicle to improve ventilation before resumption of clinical service. [ABSTRACT FROM AUTHOR]

Building-integrated photovoltaics (BIPV) can produce power while occupying little urban space. Photovoltaic-integrated shading devices (PVSDs) are a key component of BIPV that can generate electricity while blocking excess daylight. However, previous studies have lacked a systematic design of PVSDs that accurately estimates the trade-offs between indoor sunshade duration and electricity generation. This study proposes a multi-objective optimization framework for maximizing PV potential, minimizing PV area, and enabling proper sunshade duration in complex urban surfaces. A GIS-based spatiotemporal analysis and optimization approach was applied to three PVSD planning scenarios: (i) parallel to the horizontal land surface, (ii) inclined at an angle equal to the local latitude, and (iii) rotated in real-time to keep the PV surface perpendicular to the solar radiation. Different PV widths are determined under different scenarios considering power generation and solar shading duration. In the real-time rotating scenario, the optimized 0.7-m-wide PVSDs can generate 0.861 GWh of electricity annually, with a competitive average power generation efficiency of 0.811 kWh/m2/day and a solar shading duration of 6.61 h/day. Flexible installation scenarios are suggested to account for shading from other upper PVSDs and surrounding buildings. This study can facilitate solar farming in global cities and contribute to renewable energy penetration. [ABSTRACT FROM AUTHOR]

Urban fragmentation is generally regarded as a strong urban structural polarisation that is closely related to sustainability, yet a comprehensive understanding of the management of different fragmentation scenarios and their causes is still lacking. The Wuhan metropolitan area, as a rapidly urbanising region, shows clear evidence of different forms of urban fragmentation due to rapid urbanisation and spatial differentiation in pedestrian network structure, neighbourhood residential pattern and land use structure. In this study, an integrated framework for examining the fragmentation of dynamic and complex metropolitan areas, is proposed. The results indicate that the 49 highly fragmented sub-districts are distributed with large gated communities, industrial parks, green spaces and rivers/lakes. The fragmentation forms of the Wuhan metropolitan area include morphological, connectional and functional (variety and conflict) fragmentation, which constitute six scenarios. The geographical detector model manifests that urban fragmentation is the result of natural, socio–economic and construction factors. The increase in spatial debris driven by housing prices (q = 0.334), gross domestic product (q = 0.282), population density (q = 0.359), constructive index (q = 0.292) and road density (q = 0.314) has dramatically affected spatial organisation co–evolution. Additionally, there are synergistic enhancement effects between each pair of driving factors, i.e., bivariate or nonlinear interaction strengthens the impact of each other factor on the index of urban fragmentation. The strongest bivariate interaction is between housing prices and constructive index, with a value of 0.503. The largest nonlinear interaction is between constructive index and industrial index, with a value of 0.487. These interactions all further exacerbate urban fragmentation. These findings not only contribute to an integrated methodological framework, but also provide scientific implications for the purpose of sustainable urban development and cleaner production planning. • Using fine–scale land use information to examine fragmentation in a metropolitan. • Fragmentation is quantified and characterised by morphology, connection and function. • Population density is found to have the greatest impact on urban fragmentation. • Bivariate/nonlinear interactions among drivers enhance the impact on fragmentation. • This empirical research highlights the sustainable urban form and anti–fragmentation. [ABSTRACT FROM AUTHOR]

Promoting the use of solar photovoltaic (PV) systems in global cities can be an effective way to cope with severe environmental problems caused by the consuming of fossil fuels. However, a complex urban environment challenges the effective use of PV systems for practical applications. Essentially, this is a spatial optimization problem, where the goal is maximizing the harvesting of solar energy while minimizing occupied urban surfaces. To address this problem, this paper proposes three hierarchical optimizations. First, computational optimization provides a parallel architecture for an established 3D solar estimation model to achieve spatially scalable computation with high spatio-temporal resolution. Second, priority optimization determines the use of different urban partitions considering various constraints. Third, capacity optimization analyzes the spatial and quantitative distribution of solar potential, constrained by the smallest solar irradiation and the minimum surface area to be used. The overall optimization framework is then set to obtain the minimum PV installation capacity required to meet the real demand with the identification of urban surfaces to be equipped with PV modules. By using smart meter data with high temporal resolution in the city of Bologna, Italy, our analysis not only provides executable plans to meet the real demand but also reveals that rebalance and storage capacity are needed to achieve a real-time self-supportive architecture. The proposed analytic and optimization framework can promote distributed PV systems in urban areas and facilitate energy transition adapted to a variety of applications. • High-speed computation of solar PV potential on 3D urban envelopes. • Determine use of different urban partitions considering various constraints. • Determine the smallest solar irradiation and the minimum surface area to be used. • Identify space to equip with PV modules using real-time smart meter data. • Reveals rebalance and storage capacity for real-time self-supportive architecture. [ABSTRACT FROM AUTHOR]

Scooter-sharing has been introduced as a new transportation mode. However, e-scooters have a limited battery capacity and require frequent charging, which causes the operational cost significantly high and hinders the viability of the service. To tackle this problem, this study proposes a solar charging solution with the creation of a real-time shareability network that maximizes the scooter-sharing capability and minimizes the total trip distance, constrained by e-scooters with real-time battery levels. Specifically, hourly solar potential is simulated based on a three-dimensional solar irradiation model so that photovoltaic (PV) electricity generation can be estimated when PV modules are installed at the parking stations, which enables solar charging when the origin–destination matrix of scooter-sharing trips is clustered and associated to the charging stations. As a case study in Singapore, the proposed solar charging system only needs 1–3 m 2 PV modules at each station and 24%–67% of the total number of e-scooters to support almost all the real trips with a 98% reduction of trips used for charging. The system also supports 90% of on-demand mobility for at least three consecutive days without solar charging, which suggests the resilience of the system and inspires us to promote the proposed solar charging in the other global cities. • Developed a solar charging system to charge e-scooters at the parking stations. • Developed a battery-level-aware real-time shareability network for scooter-sharing. • Estimated spatio-temporal distribution of solar irradiation on 3D urban surfaces. • Only needs 24%–27% of the fleet size to obtain 98% reduction of charging trips. • Only needs 1–3 m2 PV module at each station to save the charging cost dramatically. [ABSTRACT FROM AUTHOR]

• Leaf traits mediate large leaf-to-air temperature difference (Δ T) variability. • Leaf width, V cmax , and stomatal slope are the dominant traits. • The trait's relative importance in regulating Δ T is largely dynamic within a day. • Model-derived trait-Δ T relationships agree well with field observations. Leaf thermoregulation and consequent leaf-to-air temperature difference (Δ T) are tightly linked to plant metabolic rates and health. Current knowledge mainly focus on the regulation of environmental conditions on Δ T , while an accurate assessment of biotic regulations with field data remains lacking. Here, we used a trait-based model that integrates a coupled photosynthesis-stomatal conductance model with a leaf energy balance model to explore how six leaf traits (i.e. leaf width, emissivity, visible and near-infrared light absorptance, photosynthetic capacity— V c,max25 , and stomatal slope— g 1) regulate Δ T variability across the diel timescale. We evaluated the model with field observations collected from temperate to tropical forests. Our results show that: (1) leaf traits mediate large Δ T variability, with the noon-time trait-mediated Δ T variability reaching c. 15.0 °C; (2) leaf width, V c,max25 , and g 1 are the three most important traits and their relative importance in Δ T regulation varies strongly across the diel timescale; and (3) model-derived trait-Δ T relationships match field observations that were collected close to either midday or midnight. These findings advance our understanding of biotic controls of leaf-level Δ T variability, highlighting a trait-based representation of leaf energy balance that can improve simulations of diverse leaf thermoregulation strategies across species and physiological responses to climate change. [ABSTRACT FROM AUTHOR]