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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]

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]

The persistent plastic litter, originating from different sources and transported from rivers to oceans, has posed serious biological, ecological, and chemical effects on the marine ecosystem, and is considered a global issue. In the past decade, many studies have identified, monitored, and tracked marine plastic debris in coastal and open ocean areas using remote sensing technologies. Compared to traditional surveying methods, high-resolution (spatial and temporal) multispectral or hyperspectral remote sensing data have been substantially used to monitor floating marine macro litter (FMML). In this systematic review, we present an overview of remote sensing data and techniques for detecting FMML, as well as their challenges and opportunities. We reviewed the studies based on different sensors and platforms, spatial and spectral resolution, ground sampling data, plastic detection methods, and accuracy obtained in detecting marine litter. In addition, this study elaborates the usefulness of high-resolution remote sensing data in Visible (VIS), Near-infrared (NIR), and Short-Wave InfraRed (SWIR) range, along with spectral signatures of plastic, in-situ samples, and spectral indices for automatic detection of FMML. Moreover, the Thermal Infrared (TIR), Synthetic aperture radar (SAR), and Light Detection and Ranging (LiDAR) data were introduced and these were demonstrated that could be used as a supplement dataset for the identification and quantification of FMML. [Display omitted] • Assessed the currently used data and methods for the detection of FMML. • Few studies detected the ≥3–5 m aggregated floating plastic patches. • Indices like FDI, PI, and NDVI detected ≥5 m plastic targets. • In-situ, spectral signature, and high-resolution hyperspectral Remote Sensing data are vital. • Using TIR, SAR, LiDAR, and X-band Radar with optical sensors improves FMML detection accuracy. [ABSTRACT FROM AUTHOR]

As a clean and renewable resource, solar energy is increasingly being used to relieve the pressures on environmental protection and the exhaustion of conventional energy. Although photovoltaic modules have been installed in many cities, the lack of quantitative mapping of the annual solar energy potential of urban surfaces hinders the effective utilization of solar energy. Herein, we provide a solar irradiation estimation solution for three-dimensional (3D) cities to quantify annual irradiations on urban envelopes and to investigate the effect of urban morphology on the resulting solar capacity. By modelling urban surfaces as 3D point clouds, annual irradiations of the point clouds were estimated. An empirical investigation across ten cities suggests that urban areas at lower latitudes tend to have larger values of annual irradiation; moreover, an area having greater building heights consistently has the largest third quartile of irradiation compared with lower buildings in the same city. Conversely, areas with many low buildings have a larger proportion of useable areas; in this arrangement, façades can optimally utilize solar energy, meaning that large irradiations are concentrated on certain façades. The Pearson correlation coefficients between solar capacity and urban morphology indices suggest that urban morphology has an important effect on solar capacity. • Proposes a 3D solar city including multiple reflections between urban envelops. • Estimates annual solar irradiation and solar capacity on 10 cities across the world. • Reveals distribution patterns of annual solar irradiation on three partitions. • Analyzes the useable areas that are quantitatively high and spatially concentrated. • Urban morphology has an important effect on solar capacity influenced by weather. [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]

Spatial equity embeddedness in fragmented open space has long been neglected but is now becoming a pivotal topic in sustainable urban development. It is unclear whether open space fragmentation has widened existing spatial inequalities. Thus, this study proposes an integrated methodological framework of open space fragmentation and its associated spatial equity issue in towns at different stages of urbanisation development. Hong Kong's built–up area could provide a typical case to unveil this topic due to the high shortage of open space, continued urbanisation, high immigrant rate, large wealth gap and aging population. The characteristics of open space fragmentation forms in old and new towns are elaborated through landscape pattern analysis and principal components analysis. Spatial horizontal equity and spatial vertical equity based on demographic characteristics and social economic status are portrayed by means of the Theil index and spatial matching. The findings indicate that the heterogeneity of open space fragmentation is evidenced by the uneven distribution of residents' environments in the old and new towns. Statistics reveal that in addition to shape fragmentation, the mean values of use fragmentation, internal fragmentation, extensive fragmentation and location fragmentation in old towns are all larger than those in new towns. Additionally, internal fragmentation overall is embedded in more spatial horizontal inequalities, and there is a higher level of spatial horizontal inequality in old towns than in new towns. Vulnerable groups that rely more on open spaces, including children, the elder, low education groups, immigrant groups and unemployed groups, suffer more from spatial vertical inequalities in old towns than in new towns. The knowledge gained from this research could provide a valuable reference for open space planning at home and abroad. • Understanding open space fragmentation via horizontal and vertical equity lenses • Various open space fragmentation forms are characterised by spatial heterogeneity. • Old towns are embedded in more spatial horizontal inequalities than new towns do. • Vulnerable groups are exposed to more fragmented open spaces, notably in old towns. • New knowledge is gained regarding open space fragmentation and related inequalities. [ABSTRACT FROM AUTHOR]

To respond to the increasing demand for a comfortable, productive and energy efficient study environment, the application of artificial intelligence technologies in the smart control of Heating, ventilation, and air conditioning (HVAC) systems plays an increasingly important role. This research uses a classroom, equipped with a traditional central HVAC system, in Hong Kong as a case study to demonstrate an innovative approach for a more intelligent and efficient HVAC system. Through a field investigation (i.e. measurement and questionnaire) and Computational Fluid Dynamics (CFD) simulation, it is found that the number and spatial location of students have a significant impact on their thermal comfort. Applying a computer vision model (YOLOv5) detected dynamic occupant information (variations in student numbers and locations) in a classroom, the SimScale (a cloud-native simulation platform) was then used to estimate the current thermal comfort state (predicted mean vote, PMV) and change in PMV (ΔPMV) of students in the classroom. Furthermore, a fuzzy logic control system is implemented to adjust air temperature and air velocity based on the simulation results. Preliminary scenario analysis has proven the feasibility of the proposed smart HVAC system for classrooms, as well as its ability to provide better quality of thermal comfort with more robust control. This study contributes to the smart and low-carbon retrofitting of university buildings with traditional central HVAC systems, while also serving as a benchmark for the energy-efficient transformation of HVAC systems in other types of indoor spaces. • Student number and distribution impact thermal comfort in university classrooms. • The calibrated CFD simulation creditably predicts classroom thermal comfort. • YOLOv5 model excels in real-time student detection with limited computing resources. • Smart HVAC has potential for increased thermal comfort and energy savings. • Intelligent HVAC retrofit relies on cloud computing and machine learning. [ABSTRACT FROM AUTHOR]

Rapid urbanization, which is closely related to economic growth, human health, and micro-climate, has resulted in a considerable amount of anthropogenic heat emissions. The lack of estimation data on long-term anthropogenic heat emissions is a great concern in climate and urban flux research. This study estimated the annual average anthropogenic heat fluxes (AHFs) in Beijing–Tianjin–Hebei region in China between 1995 and 2015 on the basis of multisource remote sensing images and ancillary data. Anthropogenic heat emissions from different sources (e.g., industries, buildings, transportation, and human metabolism) were also estimated to analyze the composition of AHFs. The spatiotemporal dynamics of long-term AHFs with high spatial resolution (500 m) were estimated by using a refined AHF model and then analyzed using trend and standard deviation ellipse analyses. Results showed that values in the region increased significantly from 0.15 W· m−2 in 1995 to 1.46 W· m−2 in 2015. Heat emissions from industries, transportation, buildings, and human metabolism accounted for 64.1%, 17.0%, 15.5%, and 3.4% of the total anthropogenic heat emissions, respectively. Industrial energy consumption was the dominant contributor to the anthropogenic heat emissions in the region. During this period, industrial heat emissions presented an unstable variation but showed a growing trend overall. Heat emissions from buildings increased steadily. Spatial distribution was extended with an increasing tendency of the difference between the maximum and the minimum and was generally dominated by the northeast–southwest directional pattern. The spatiotemporal distribution patterns and trends of AHFs could provide vital support on management decision in city planning and environmental monitoring. Image 1 • AHF increased significantly from 0.15 W· m−2 in 1995 to 1.46 W· m−2 in 2015 in BTH region. • Industrial energy consumption was the dominant contributor to AHF, accounting for about 69%. • The spatial distribution of AHF was generally dominated by the "northeast-southwest" directional pattern. The characteristics of spatiotemporal dynamics of anthropogenic heat flux are conducive for providing holistic information related to urban heat island study and vital support on management decision in city planning and environmental monitoring for policy makers. [ABSTRACT FROM AUTHOR]

Abstract Neighborhood-based environmental vulnerability is significantly associated with long-term community health impacts. Previous studies have quantified environmental vulnerability using objective environmental datasets. However, environmental cognition among a population may influence subjective feelings of environmental vulnerability, and this can be associated with community health risk. In this study, a mixed-methods approach was applied to estimate neighborhood-based environmental vulnerability based on objective environmental measures and subjective environmental understanding from a local population. The synergistic use of both qualitative and quantitative data resulted in a "subjective environmental vulnerability" index which can demonstrate environmental deprivation across Hong Kong. The resultant maps were compared with a mortality dataset between 2007 and 2014, based on a case-series analysis. The case-series analysis indicated that using a subjective environmental vulnerability index as an approach for neighborhood mapping is able to estimate the community health risk across Hong Kong. In particular, the following types of cause-specific mortality have significant association with the subjective environmental vulnerability index: 1) mortality associated with mental and behavioral disorders, 2) cardiovascular mortality, 3) respiratory mortality, and 4) mortality associated with diseases of the digestive system. In conclusion, the use of a subjective environmental vulnerability index can be implemented within a community health planning program, especially to reduce long-term adverse impacts on population with mental impairment. Graphical abstract Unlabelled Image Highlights • Combined subjective and objective environmental measures for vulnerability index • Evaluated subjective environmental vulnerability index with mortality data • Vulnerability Index was highly associated with mental related mortality. • Cardiorespiratory and digestive deaths were associated with vulnerability index. [ABSTRACT FROM AUTHOR]

Abstract Fine particulate matter (PM 2.5) has been recognized as a key air pollutant that can influence population health risk, especially during extreme cases such as wildfires. Previous studies have applied geospatial techniques such as land use regression to map the ground-level PM 2.5 , while some recent studies have found that Aerosol Optical Depth (AOD) derived from satellite images and machine learning techniques may be two elements that can improve spatiotemporal prediction. However, there has been a lack of studies evaluating use of different machine learning techniques with AOD datasets for mapping PM 2.5 , especially in areas with high spatiotemporal variability of PM 2.5. In this study, we compared the performance of eight predictive algorithms with the use of multiple remote sensing datasets, including satellite-derived AOD data, for the prediction of ground-level PM2.5 concentration. Based on the results, Cubist, random forest and eXtreme Gradient Boosting were the algorithms with better performance, while Cubist was the best (CV-RMSE = 2.64 μg/m3, CV-R2 = 0.48). Variable importance analysis indicated that the predictors with the highest contributions in modelling were monthly AOD and elevation. In conclusion, appropriate selection of machine learning algorithms can improve ground-level PM2.5 estimation, especially for areas with nonlinear relationships between PM2.5 and predictors caused by complex terrain. Satellite-derived data such as AOD and land surface temperature (LST) can also be substitutes for traditional datasets retrieved from weather stations, especially for areas with sparse and uneven distribution of stations. Graphical abstract Image Highlights • Estimation of long-term spatially-continuous monthly PM 2.5 dataset. • Cubist outperforms other machine learning algorithms. • Several new predictors were employed to improve the estimation of PM 2.5. • PM 2.5 was estimated with a CV-RMSE of 2.64 μg/m3. [ABSTRACT FROM AUTHOR]

• A deep learning based approach for constructing 3D buildings from satellite imagery was developed. • Rooftop segmentation and building height prediction are satisfactory. • Estimated PV potentials derived from the actual and predicted buildings showed little difference. • Proposed approach can facilitate PV penetration and urban studies in various fields. Building-integrated photovoltaics are increasingly used to build low-carbon buildings and promote energy transition. However, the absence of three-dimensional (3D) building models may hinder accurate estimation of photovoltaic (PV) potential on 3D urban surfaces. This study develops a detail-oriented deep learning approach, which for the first time constructs 3D buildings from high-resolution satellite images and estimates PV potential. Specifically, two convolutional neural networks, i.e., the Rooftop Segmentation Model and Height Prediction Model, were developed by advancing the basic DeepLabv3+ architecture and integrating dedicated layers, adaptive activation functions, and hybrid losses. Next, the two models were trained and tested on a self-made dataset targeted at Shanghai and an open datasets under standard data augmentation and transfer learning strategies. Then, morphological post-processing procedures were developed to cluster and regularize individual rooftops with estimated heights. Finally, PV potentials in typical areas were estimated and compared. Accuracy assessments suggest satisfactory rooftop segmentationand building height estimation. The absolute relative error between the PV potentials derived from the actual and predicted building models showed little difference, implying the reliability of the extracted buildings. The proposed model is novel and effective for constructing 3D building models that can facilitate PV penetration and urban studies in various fields. [ABSTRACT FROM AUTHOR]

Decades of urbanization can lead to significant wind reduction in urban areas. At the King's Park meteorological station in the heart of the Kowloon Peninsula Hong Kong, a wind speed reduction of 0.6 m/s per decade was observed from 1968 to 1995, and -0.16 m/s per decade from 1996 to 2017. We obtained data on the changing three-dimensional urban morphology of Kowloon during the period of 1964–2010, and conducted computational fluid dynamics simulations on historical wind environment considering the prevailing winds. The wind speed and its loss were calculated as both intrinsic and comprehensive spatial averages within an elevation of 200 m. The results show that the overall mean wind speed in the studied urban areas gradually decreased due to the continuous urban development and elevation in building height. The total wind loss ratios at three representative locations have increased from less than 10% to greater than 20% during the study period. The total wind loss ratio may increase to about 40% by 2050 if the current weakening trend continues. The average wind speed at pedestrian level has significantly declined, and local acceleration of wind was only observed in some local areas. However, such accelerated airflow is only maintained around a few blocks of buildings. Our study demonstrates the impact of urbanization on the wind weakening in Hong Kong and reveals the importance and need of factoring in urban air ventilation into the design of urban morphology. [ABSTRACT FROM AUTHOR]

Air pollution has been shown to be significantly associated with morbidity and mortality in urban areas, but there is lack of studies focused on extreme pollution events such as extreme dust episodes in high-density Asian cities. However, such cities have had extreme climate episodes that could have adverse health implications for downwind areas. More importantly, few studies have comprehensively investigated the mortality risks of extreme dust events for socioeconomically vulnerable populations. This paper examined the association between air pollutants and mortality risk in Hong Kong from 2006 to 2010, with a case-crossover analysis, to determine the elevated risk after an extreme dust event in a high-density city. The results indicate that PM 10-2.5 dominated the all-cause mortality effect at the lag 0 day (OR: 1.074 [1.051, 1.098]). This study also found that people who were aged ≥ 65, economically inactive, or non-married had higher risks of all-cause mortality and cardiorespiratory mortality during days with extreme dust events. In addition, people who were in areas with higher air pollution had significantly higher risks of all-cause mortality and cardiorespiratory mortality. In conclusion, the results of this study can be used to target the vulnerable among a population or an area and the day(s) at risk to assist in health protocol development and emergency planning, as well as to develop early warnings for the general public in order to mitigate potential mortality risk for vulnerable population groups caused by extreme dust events. [ABSTRACT FROM AUTHOR]

Haze is an extreme weather event that can severely increase air pollution exposure, resulting in higher burdens on human health. Few studies have explored the health effects of haze, and none have investigated the spatiotemporal interaction between temperature, air quality and urban environment that may exacerbate the adverse health effects of haze. We investigated the spatiotemporal pattern of haze effects and explored the additional effects of temperature, air pollution and urban environment on the short-term mortality risk during hazy days. We applied a Poisson regression model to daily mortality data from 2007 through 2014, to analyze the short-term mortality risk during haze events in Hong Kong. We evaluated the adverse effect on five types of cause-specific mortality after four types of haze event. We also analyzed the additional effect contributed by the spatial variability of urban environment on each type of cause-specific mortality during a specific haze event. A regular hazy day (lag 0) has higher all-cause mortality risk than a day without haze (odds ratio: 1.029 [1.009, 1.049]). We have also observed high mortality risks associated with mental disorders and diseases of the nervous system during hazy days. In addition, extreme weather and air quality contributed to haze-related mortality, while cold weather and higher ground-level ozone had stronger influences on mortality risk. Areas with a high-density environment, lower vegetation, higher anthropogenic heat, and higher PM 2.5 featured stronger effects of haze on mortality than the others. A combined influence of haze, extreme weather/air quality, and urban environment can result in extremely high mortality due to mental/behavioral disorders or diseases of the nervous system. In conclusion, we developed a data-driven technique to analyze the effects of haze on mortality. Our results target the specific dates and areas with higher mortality during haze events, which can be used for development of health warning protocols/systems. [ABSTRACT FROM AUTHOR]

The high-rise/high-density environment of a compact city can influence the microclimate resulting in lower living quality. Previous studies have analyzed the relationships between high-rise/high-density environment and microclimates, by either a temporal study or a spatial approach, while a strategy for investigating the spatiotemporal relationship has yet to be developed. This study initiated a set of innovative strategies to map the historical built environment/microclimates of a compact city, with a spatiotemporal approach to analyze the relationships between building structures and urban climates, for developing a sustainable protocol for future urban planning. Three major components were reconstructed, including 1) the annually averaged Land Surface Temperature (LST) for determining the relative temperature across a compact city; 2) 3D building datasets for representing the building morphology; and 3) sets of urban morphological data derived from building datasets for analyzing microclimate and thermal distress. There are high correlations between observed and predicted LSTs (R = 0.64 to 0.89), with mean absolute error (MAE) of annually averaged LST ranging 0.49 °C–2.60 °C, and root mean square error (RMSE) ranging 0.62 °C–2.98 °C. There are low errors for reconstructing building data, in which MAEs and RMSEs of an open space are 0.41 m–1.23 m and 0.78 m - 1.46 m; and for an area with buildings are 0.81 m–3.25 m and 1.06 m - 5.92 m. The spatiotemporal estimation indicated areas with improved air ventilation through years can significantly reduce an additional 0.12 °C - 1.09 °C than the areas without improvement, while areas with an increase in shades through years have 0.6 °C–0.76 °C higher reduction of relative temperature. [ABSTRACT FROM AUTHOR]

This study proposes a method for estimating phytoplankton cell counts associated with an algal bloom, using satellite images coincident with in situ and meteorological parameters. Satellite images from Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM +), Operational Land Imager (OLI) and HJ-1 A/B Charge Couple Device (CCD) sensors were integrated with the meteorological observations to provide an estimate of phytoplankton cell counts. All images were atmospherically corrected using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) atmospheric correction method with a possible error of 1.2%, 2.6%, 1.4% and 2.3% for blue (450–520 nm), green (520–600 nm), red (630–690 nm) and near infrared (NIR 760–900 nm) wavelengths, respectively. Results showed that the developed Artificial Neural Network (ANN) model yields a correlation coefficient (R) of 0.95 with the in situ validation data with Sum of Squared Error (SSE) of 0.34 cell/ml, Mean Relative Error (MRE) of 0.154 cells/ml and a bias of − 504.87. The integration of the meteorological parameters with remote sensing observations provided a promising estimation of the algal scum as compared to previous studies. The applicability of the ANN model was tested over Hong Kong as well as over Lake Kasumigaura, Japan and Lake Okeechobee, Florida USA, where algal blooms were also reported. Further, a 40-year (1975–2014) red tide occurrence map was developed and revealed that the eastern and southern waters of Hong Kong are more vulnerable to red tides. Over the 40 years, 66% of red tide incidents were associated with the Dinoflagellates group, while the remainder were associated with the Diatom group (14%) and several other minor groups (20%). The developed technology can be applied to other similar environments in an efficient and cost-saving manner. [ABSTRACT FROM AUTHOR]

Accurate mapping of wind ventilation in an urban environment is challenging when large spatial coverage is required. This study has developed a GIS-based model for estimating the frontal area index (FAI) of buildings, infrastructure, and trees using very high resolution airborne light detection and ranging (LiDAR) data, which can also be used to investigate the “wall effect” caused by high-rise buildings at a finer spatial scale along the coasts in the Kowloon Peninsula of Hong Kong. New algorithms were created by improving previous algorithms utilizing airborne LiDAR data in raster unit, as well as considering the backward flow coefficient between windward and leeward buildings. The ventilation corridors estimated by FAI and least cost path (LCP) analysis were analyzed. The optimal ventilation corridors passing through the Kowloon peninsula were observed in the east-west and west-east directions. In addition, these ventilation paths were validated with a computer fluid dynamics (CFD) model i.e. Airflow Analysis in ESRI. The newly developed model calculates finer FAI with greater accuracy when compared with vector-based building polygons. This model further depicts buildings, infrastructure, and trees which are considered as obstacles to wind ventilation. The results can be used by environmental and planning authorities to identify ventilation corridors, and for scenario analysis in urban redevelopment. [ABSTRACT FROM AUTHOR]

Haze pollution has long been a significant research topic and challenge in China, with adverse effects on air quality, agricultural production, as well as human health. In coupling with ground-based Lidar measurements, air quality observation, meteorological data, and backward trajectories model, two typical haze events at Wuxi, China are analyzed respectively, depicting summer and winter scenarios. Results indicate that the winter haze pollution is a compound pollution process mainly affected by calm winds that induce pollution accumulation near the surface. In the summer case, with the exception of influence from PM 2.5 concentrations, ozone is the main pollutant and regional transport is also a significant influencing factor. Both events are marked by enhanced PM 2.5 concentrations, driven by anthropogenic emissions of pollutants such as vehicle exhaust and factory fumes. Meteorological factors such as wind speed/direction and relative humidity are also contributed. These results indicate how the vertical profile offered by routine regional Lidar monitoring helps aid in understanding local variability and trends, which may be adapted for developing abatement strategies that improve air quality. [ABSTRACT FROM AUTHOR]

Although visibility is a widely-used indicator to quantify the aerosol loadings, only a few studies have been analyzed the representativeness of visibility in deriving Aerosol Optical Thickness (AOT). In this paper, ground-based visibility, MODerate-resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging SpectroRadiometer (MISR) monthly AOT products between July 2002 and December 2014 were analyzed in order to extract the dominant modes of variability using the Singular Value Decomposition (SVD) method. The method has significant merit to reduce data dimension and examine both spatial and temporal variability simultaneously. Results indicated that the satellite retrieved AOTs agreed well with ground-based visibility in terms of inter-annual variability. The correlation coefficients in the first deseasonalized mode are greater than 0.65 between visibility and satellite AOT products. However, large differences were observed in the seasonal variability between ground-based visibility and AOT. In addition, Aerosol vertical distribution from LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies (LIVAS) and cloud data from ground-based meteorological station were used to investigate the seasonal variability disagreement. The AOT values derived from LIVAS extinction coefficients between 0 and 500 m above surface have a stronger relationship with visibility, than total column AOT with visibility. It also indicates that seasonal variation of aerosol vertical distribution is the main cause of the disagreement between two parameters, and the uncertainties of satellite products also contribute to the disagreement. Results in this study highlighted that the visibility observation could only be used to depict the inter-annual AOT and more ancillary information could be used for studying seasonal AOT variation. [ABSTRACT FROM AUTHOR]

This paper studies the use of Remote Sensing (RS) technologies and Geographic Information Systems (GIS) for estimation of city-wide photovoltaic (PV) potential in Hong Kong. It investigates the spatial distribution of cloud coverage through geostationary satellites from the Multi-functional Transport Satellite (MTSAT). The results indicate that a non-prominent spatial variation of cloud cover presides over a majority of Hong Kong territories. Appropriate locations for deploying solar PV panels, such as rooftops, were delineated using RS, GIS, and existing ancillary data. Extraction and filtering of pixels based on a set of criterions were used to identify optimal PV rooftops. This study shows that the summarization of PV potentials in Hong Kong is 2.66 TWh on building rooftops. The methodologies and findings from this study permits detailed spatial estimation of city-wide solar energy potential, and assists the policy-decision process on the use of renewable energy in Hong Kong. [ABSTRACT FROM AUTHOR]

This paper demonstrates the design and development of an Integrated micro-Environmental Monitoring System (IEMS), which aims to provide an effective platform for micro-environmental monitoring in construction sites. The system mainly comprises three parts: sensing device, server platform and Android application. The monitoring sensing device is equipped with an Arduino microprocessor, wireless communication modules and environmental sensors including humidity, temperature, dust intensity, UV radiation, noise index sensors. In addition, a correction algorithm and a random error detection method were developed and integrated in the processing engine running at the background server platform. Multiple sets of monitoring sensing devices were deployed in a construction site for evaluation. This system is the first ever developed and used system in construction sites to monitor micro-environmental conditions for improving safety and health assessment. This system can also be modified for other applications (e.g. in bus terminal, shopping mall, factory, as well as in household apartment). [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]

This study analyzes a large-scale navigation dataset that captures travel activities of domestic inbound visitors in Jeju, Korea in the first nine months of 2020. A collection of regression models are introduced to quantify the dynamic effects of local and national COVID-19 indicators on their travel behavior. Results suggest that behavior of inbound travelers was jointly affected by pandemic severity locally and remotely. The daily number of new cases in Jeju has a greater impact on reducing travel activities than the national-level daily new cases of COVID-19. The impacts of the pandemic did not diminish over time but produced heterogeneous effects on travels with different trip purposes. Our findings reveal the persistence of COVID-19's effects on travel behavior and the variability in travelers' responses across tourism activities with different levels of perceived health risks. The implications for crisis management and recovery strategies are also discussed. [ABSTRACT FROM AUTHOR]

Physiological Equivalent Temperature (PET) has been widely used as an indicator for impacts of climate change on thermal comfort of humans. The effects of thermal stress are often examined using longitudinal observational studies over many years. A major problem in retrospective versus prospective studies is that it is not feasible to go back in time to measure historical data not collected in the past. These data must be reconstructed for the baseline period to enable comparative analysis of change and its human impact. This paper describes a systematic method for constructing a PET map using spatial analytical procedures. The procedures involve estimating PET values (based on the RayMan model and four key parameters of temperature, relative humidity, wind velocity, and mean radiant temperature) at a spatially disaggregated level comprising of a grid of 100 m × 100 m cells. The method can be applied to other geographic locations pending availability of basic meteorological and morphological data of the locations. [ABSTRACT FROM AUTHOR]

Dust storms are known to have adverse effects on public health. Atmospheric dust loading is also one of the major uncertainties in global climatic modeling as it is known to have a significant impact on the radiation budget and atmospheric stability. This study develops an integrated model for dust storm detection and retrieval based on the combination of geostationary satellite images and forward trajectory model. The proposed model consists of three components: (i) a Neural Network (NN) model for near real-time detection of dust storms; (ii) a NN model for dust Aerosol Optical Thickness (AOT) retrieval; and (iii) the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model to analyze the transports of dust storms. These three components are combined using an event-driven active geo-processing workflow technique. The NN models were trained for the dust detection and validated using sunphotometer measurements from the AErosol RObotic NETwork (AERONET). The HYSPLIT model was applied in the regions with high probabilities of dust locations, and simulated the transport pathways of dust storms. This newly automated hybrid method can be used to give advance near real-time warning of dust storms, for both environmental authorities and public. The proposed methodology can be applied on early warning of adverse air quality conditions, and prediction of low visibility associated with dust storm events for port and airport authorities. [ABSTRACT FROM AUTHOR]