Search

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]

Urban functional fragmentation plays an important role in assessing Nitrogen Dioxide (NO2) emissions and variations. While the mediated impact of anthropogenic-emission restriction has not been comprehensively discussed, the lockdown response to the novel coronavirus disease 2019 (COVID-19) provides an unprecedented opportunity to meet this goal. This study proposes a new idea to explore the effects of urban functional fragmentation on NO2 variation with anthropogenic-emission restriction in China. First, NO2 variations are quantified by an Autoregressive Integrated Moving Average with external variables-Dynamic Time Warping (SARIMAX-DTW)-based model. Then, urban functional fragmentation indices including industrial/public Edge Density (ED) and Landscape Shape Index (LSI), urban functional Aggregation Index (AI) and Number of Patches (NP) are developed. Finally, the mediated impacts of anthropogenic-emission restriction are assessed by evaluating the fragmentation-NO2 variation association before and during the lockdown during COVID-19. The findings reveal negative effects of industrial ED, public LSI, urban functional AI and NP and positive effects of public ED and industrial LSI on NO2 variation based on the restricted anthropogenic emissions. By comparing the association analysis before and during lockdown, the mediated impact of anthropogenic-emission restriction is revealed to partially increase the effect of industrial ED, industrial LSI, public LSI, urban functional AI and NP and decrease the effect of public ED on NO2 variation. This study provides scientific findings for redesigning the urban environment in related to the urban functional configuration to mitigating the air pollution, ultimately developing sustainable societies. [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]

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]

Background: Global warming has reduced the adaptability of the people living in subtropical regions to cope up with cold stress due to lengthening of hot days and shortening of transition period from hot to cold weather. However, existing studies on measuring cold stress are based on biometeorological indices designed for temperate regions. This may overestimate the impact of wind chill on mortality risk in subtropical cities.Methods: This study developed an Adjusted Wind Chill Equivalent Temperature (AWCET) index. A spatially-controlled time-stratified approach was applied to evaluate the ability of AWCET for estimating cold mortality in subtropical cities, based on a mortality dataset (2008-2012) in Hong Kong.Results: The use of AWCET could indicate increase in all-cause, cardiovascular, respiratory, and cancer-related mortality risk during the days with average temperature < = 1st [11.0 °C], <= 3rd [12.6 °C] and < = 5th [13.4 °C] percentiles. The results were stable and consistent based on both log-linear and curve-linear relationships between AWCET and mortality risk. AWCET was also compared with the New Wind Chill Equivalent Temperature (NWCET) designed for temperate regions, and has found that higher magnitude of mortality risk would be found when using AWCET for assessing all-cause and cause-specific mortality in Hong Kong, for days with average temperature < = 1st, <= 3rd and < = 5th percentiles.Conclusions: AWCET is validated to be effective to access cold mortality in the context of subtropical cities. The use of AWCET may enhance the cold weather warning system in subtropical cities, as a supplementary tool to help demonstrating small administrative-level perceived temperature with volunteered geographic information. [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]

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]

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]

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 study aims to evaluate the effects of urban geometry on retrieval of emissivity and surface temperature in urban areas. An improved urban emissivity model based on sky view factor (IUEM-SVF) was further enhanced to consider all radiance contributions leaving the urban canopy, including (i) emission by all facets within an instantaneous field of view (IFOV); (ii) reflection by all facets of emission from surrounding facets; and (iii) propagation of emitted and reflected radiation with multiple reflections (scattering) within a complex 3D array of urban objects. The effective emissivity derived from IUEM-SVF was evaluated with a microscale radiative transfer and energy balance model: Temperatures of Urban Facets in 3-D (TUF-3D). IUEM-SVF performs well when urban facets have uniform emissivity and temperature; e.g., root mean square deviations (RMSD) are less than 0.005 when material emissivity is larger than 0.80 (ɛ ⩾ 0.80). However, when material emissivities are variable within the observed target, differences of effective emissivity between IUEM-SVF and TUF-3D become larger, e.g., RMSD of 0.010. When the effect of geometry is not considered and a mixed pixel emissivity is defined, the difference is even much larger (i.e. 0.02) and this difference increases with the decrease of sky view factor. Thus, the geometry effect should be considered in the determination of effective emissivity. Effective emissivity derived from IUEM-SVF was used to retrieve urban surface temperature from a nighttime ASTER thermal infrared image. Promising results were achieved in comparison with standard LST products retrieved with the Temperature and Emissivity Separation (TES) algorithm. IUEM-SVF shows promise as a means to improve the accuracy of urban surface temperature retrieval. The effect of thermal heterogeneity on the effective emissivity was also evaluated by TUF-3D, and results show that the thermal heterogeneity cannot be neglected since the RMSD between the effective emissivity based on TUF-3D and IUEM-SVF is relatively large. [ABSTRACT FROM AUTHOR]

ABSTRACT Aerosol trends and rates of change were analysed between 2003 and 2013, over both land and ocean, using MODerate resolution Imaging Spectroradiometer (MODIS) monthly aerosol optical thickness ( AOT) products ( MYD08). Unlike previous research, the ensemble empirical mode decomposition ( EEMD) was implemented in this study. Results show that sustained positive or negative trends are globally observed in most areas during the study period. However, increasing rates were decelerated and even became downward trends over western North America, central South America, East China Sea and southeastern China. Comparing EEMD results with linear regression, it is evident that the increasing and decreasing rates from the EEMD method are much stronger. Zonally averaged trends clearly indicate an opposite trend between the southern and northern hemispheres. In addition, this study demonstrates that linear regression may not fit trends statistically in some areas, such as central South America and part of the Indian Ocean. Around 32.74% (12 816) of pixels exhibit low correlation ( r2 = 0.5) between linear and nonlinear trends from EEMD. Approximately 12.46% (4877), 6.56% (2567) and 1.85% (724) of pixels experience significant variations against the F-test, autoregressive process of the first-order for EEMD and linear regression, respectively. The rates of change observed in this study can be used in analysing the long-term effects of aerosols on climate change and earth's radiative budget. [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]

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]

This study presents a Single Channel Method using Urban Exitance Model (UEM-SCM) to retrieve land surface temperature (LST) from satellite data in an urbanized city, and evaluates the geometry effect on land surface temperature retrieval using single channel method and split-window algorithm. The UEM-SCM incorporates the effect of urban geometry and considers both reflection caused by the target pixel and its neighboring pixels. In order to evaluate the geometry effect, the retrieved LSTs with and without geometry effect were studied. Results show that the LSTs without geometry effect are generally higher than the LSTs with geometry effect. The temperature difference occurs because the material emissivity is always lower than the effective emissivity caused by multiple scattering and reflection in urban areas (cavity effect). The LST without geometry effect also cannot fully capture the variability and complexity of urban thermal patterns. The temperature difference between with and without the geometry effect can reach 2 K in built-up areas. A comparison was also conducted between LST retrieved by split-window algorithm with and without geometry effect. Results show that the LST retrieved by split-window algorithm without geometry effect has generally higher values than the one with the geometry effect, e.g. 1.1 K on average and 1.5–2 K in built-up areas. The geometry effect will be removed and mis-deemed as atmospheric effect when the split-window algorithm without geometry effect is applied in urban areas. The split-window algorithm with the geometry effect can be used to distinguish between geometry and atmospheric effect in further study. [ABSTRACT FROM AUTHOR]

ABSTRACT Water vapour, an important greenhouse gas in the atmosphere, is crucial for hydrological, atmospheric, and meteorological processes. This study first compared five precipitable water vapour ( PWV) products from multi-sensors including radiosonde, AERosol RObotic NETwork ( AERONET) sunphotometer, Global Positioning System ( GPS) and MODerate resolution Imaging Spectroradiometer ( MODIS), and then characterized the spatial and temporal trends of PWV in mainland China. Intercomparison results indicate good agreements among PWV products with correlation coefficients ranging from 0.775 to 0.937. As for spatial analysis, 13 years of MODIS MOD05 products were investigated and the spatial distribution of PWV is closely correlated with the topography, e.g. latitude and altitude, in mainland China. A monotonically increasing annual trend was detected in some radiosonde sites in China between 1976 and 1999, but a decreasing trend was observed between 2000 and 2012. Analysis of the differences in PWV between weekdays and weekends indicated a marked weekend effect, suggesting the influence of anthropogenic activities. Diurnal variations in PWV were also studied from 1999 to 2009 using GPS data. A pronounced diurnal cycle of PWV was observed in most of the sites during summer and spring seasons. [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]

Surface emissivity is a critical parameter for studying city-, meso-, and micro-scale climate and energy balance. The emissivity of complex surfaces e.g. a forest or an urban canopy is an effective surface property since it depends on both surface materials and geometry. This study presents a novel methodology for estimating effective emissivity using sky view factor retrieved from airborne Lidar data, building GIS data, and land use and land cover classification data. First, a high correlation between the effective emissivity retrieved from ASTER TIR bands 10–14 and the sky view factor was observed ( r 2 = 0.93, 0.99, 0.99, 0.97, 0.97). When the sky view factor decreases, the effective emissivity tends to increase, which is mainly due to multiple scattering (cavity effect), thus increases the effective emissivity. A simplified model which assumes that reflection and scattering only occurs within a single pixel was developed. Results showed that the correlations between the modeled and the spectral (band) emissivity retrieved from the ASTER multispectral TIR data (five spectral bands) are high ( r 2 = 0.93, 0.99, 0.98, 0.93, 0.97), and with low RMSE (0.019, 0.016, 0.012, 0.003 and 0.004 from band 10–14 respectively). The emissivity derived from this simplified model, however, tends to be overestimated in band 10–12. Thus, a refined urban emissivity model based on sky view factor (UEM-SVF) which considers the scattering and reflection from adjacent pixels was developed in this study. Results show a good agreement with ASTER spectral (band) emissivity: r 2 = 0.90, 0.98, 0.96, 0.94 and 0.96, and very low RMSE (0.006, 0.003, 0.004, 0.002 and 0.004). This study illustrates that the UEM-SVF can be useful for estimation of land surface emissivity of complex surfaces, and can further be used for accurate land surface temperature retrieval. [ABSTRACT FROM AUTHOR]

Dust storms are known to have adverse effects on human health and significant impact on weather, air quality, hydrological cycle, and ecosystem. Atmospheric dust loading is also one of the large uncertainties in global climate modeling, due to its significant impact on the radiation budget and atmospheric stability. Observations of dust storms in humid tropical south China (e.g. Hong Kong), are challenging due to high industrial pollution from the nearby Pearl River Delta region. This study develops a method for dust storm detection by combining ground station observations (PM 10 concentration, AERONET data), geostationary satellite images (MTSAT), and numerical weather and climatic forecasting products (WRF/Chem). The method is based on a hybrid neural network (NN) retrieval model for two scales: (i) a NN model for near real-time detection of dust storms at broader regional scale; (ii) a NN model for detailed dust storm mapping for Hong Kong and Taiwan. A feed-forward multilayer perceptron (MLP) NN, trained using back propagation (BP) algorithm, was developed and validated by the k-fold cross validation approach. The accuracy of the near real-time detection MLP-BP network is 96.6%, and the accuracies for the detailed MLP-BP neural network for Hong Kong and Taiwan is 74.8%. This newly automated multi-scale hybrid method can be used to give advance near real-time mapping of dust storms for environmental authorities and the public. It is also beneficial for identifying spatial locations of adverse air quality conditions, and estimates of low visibility associated with dust events for port and airport authorities. [ABSTRACT FROM AUTHOR]

Aerosol retrieval using MODerate resolution Imaging Spectroradiometer (MODIS) has been well researched over the past decade. However, the application is limited to global- and regional-scale studies, which may not be applicable for urban areas due to its low spatial resolution. To overcome the limitation, this paper proposed an improved aerosol retrieval algorithm for Landsat images (ImAero-Landsat) at spatial resolution of 30 m. This ImAero-Landsat algorithm has been improved in the following two aspects: (i) it does not require a comprehensive look up table and thus it is more efficient in AOT retrieval; and (ii) it can be operated in both bright and dark surfaces. The derived aerosol optical thickness (AOT) images were validated with AErosol RObotic NETwork (AERONET) measurements as well as MODIS MOD04 AOT products. Small root mean square errors (RMSEs) of 0.11 and 0.14 and mean absolute difference (MAD) of 0.07 and 0.11 between ImAero-Landsat AOT, with MODIS MOD04 and AERONET products were observed. By correlating with ground based PM 10 concentrations, the ImAero-Landsat method outperforms (r 2 = 0.32) than MOD04 AOT products (r 2 = 0.23). In addition, the accuracy of estimating PM 10 can be improved to r 2 = 0.55 when the derived AOT was integrated with meteorological parameters. The accuracy is similar to the results derived from AERONET AOT (r 2 = 0.62). This study offers a simple and accurate method to investigate aerosol optical thickness at detailed city-scale. Environmental authorities may use the derived methods for deriving aerosol distribution maps and pinpointing the sources of pollutants in urban areas. [ABSTRACT FROM AUTHOR]

Abstract: This study investigates the application of satellite data to the evaluation of aerosol extinction and visibility, for a better understanding of the spatio-temporal variation of local air quality in urban areas. A new analytical model based on aerosol microphysics and non-linear exponential fitting was applied to the analysis of aerosol extinction using aerosol optical thickness (AOT, τ a ) data retrieved from the MODerate-resolution Imaging Spectroradiometer (MODIS), over one of the largest global megacities: Seoul, Korea. The relationship between the MODIS τ a and ground-based aerosol measurements of particulate matter mass concentration, extinction, and surface visibility was assessed over Seoul, Korea, during eight intensive observation periods in 2007–2009. The results clearly demonstrate that satellite-derived τ a is a good surrogate for monitoring aerosol extinction and visibility over this study area. In particular, it is found that MODIS-estimated visibility values have a closer relation to the observed values than traditionally assumed Koschemeider's relation. The analytical results shown in this study can provide a better understanding of aerosol extinction and visibility in megacity, as well as for routine visibility monitoring. [Copyright &y& Elsevier]

Abstract: A noble volcanic ash (VA) detection method based on a hybrid reverse absorption technique was successfully applied in the analysis of major volcanic eruptions that occurred in Russia, Iceland, Chile, Italy, and Japan by using the MODerate-resolution Imaging Spectroradiometer (MODIS) observation data. Sensitivity studies using radiative-transfer simulations by using various environmental parameters such as ash loadings, sizes, layer heights, and surface emissions, revealed that VA effects on brightness temperatures (BT) can reach up to 40K. The advantage of the hybrid algorithm is its ability to detect distinct VA pixels during the day and night from satellite observations. The results showed that the hybrid algorithm can minimize the false detection of VA pixels, while well-known reverse absorption methods show abundant false VA pixels over bright surfaces and cloud formations. Further, the time-and-space distribution of the VA pixels is in good agreement with the data pertaining to operational aerosol products obtained from the scanning imaging absorption spectrometer for atmospheric cartography (SCIAMACHY) instrument on board ESA's Envisat and the cloud-aerosol Lidar and infrared pathfinder satellite observations (CALIPSO). This novel algorithm is expected to provide a fine spatial and temporal resolution of VA monitoring from high spectral or geostationary satellite observation data. [Copyright &y& Elsevier]

Abstract: This paper presents a geographic information systems (GIS) model to relate biogenic volatile organic compounds (BVOCs) isoprene emissions to ecosystem type, as well as environmental drivers such as light intensity, temperature, landscape factor and foliar density. Data and techniques have recently become available which can permit new improved estimates of isoprene emissions over Hong Kong. The techniques are based on Guenther et al.’s (1993, 1999) model. The spatially detailed mapping of isoprene emissions over Hong Kong at a resolution of 100m and a database has been constructed for retrieval of the isoprene maps from February 2007 to January 2008. This approach assigns emission rates directly to ecosystem types not to individual species, since unlike in temperate regions where one or two single species may dominate over large regions, Hong Kong''s vegetation is extremely diverse with up to 300 different species in 1ha. Field measurements of emissions by canister sampling obtained a range of ambient emissions according to different climatic conditions for Hong Kong''s main ecosystem types in both urban and rural areas, and these were used for model validation. Results show the model-derived isoprene flux to have high to moderate correlations with field observations (i.e. r 2 =0.77, r 2 =0.63, r 2 =0.37 for all 24 field measurements, subset for summer, and winter data, respectively) which indicate the robustness of the approach when applied to tropical forests at detailed level, as well as the promising role of remote sensing in isoprene mapping. The GIS model and raster database provide a simple and low cost estimation of the BVOC isoprene in Hong Kong at detailed level. City planners and environmental authorities may use the derived models for estimating isoprene transportation, and its interaction with anthropogenic pollutants in urban areas. [Copyright &y& Elsevier]

Aerosol observations are essential for understanding the Earth's radiation budget and the complexities of climate change, as they are involved in the backscattering of solar radiation and the formation of cloud condensation nuclei. In Hong Kong, the most direct effect is on air quality. Atmospheric haze caused by the emission of aerosols from industrial and vehicular sources creates visibility lower than 8 km for approximately 20% of the time, having risen at 6% per decade since 1980, but regional emissions are at least as influential as local ones. The 179,000 km2 covered by Hong Kong and neighbouring Guangdong Province cannot be adequately covered by the 76 monitoring stations set up by the two governments, and satellite images offer the only potential source of regional air quality data. However, the current satellite-based aerosol optical thickness (AOT) products are intended for global air quality monitoring, and may contain errors over a humid coastal city such as Hong Kong and its surrounding industrialized regions. This research compares the AOT retrieved from several AOT operational products, namely the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD04 product, the MODIS 500 m product, the Multiangle Imaging Spectroradiometer (MISR) product, the Ozone Monitoring Instrument (OMI) multiwavelength aerosol product, and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) product, with ground-based AOT from sunphotometers in Hong Kong. These sunphotometers include two AERONET stations, and are deployed in Hong Kong over urban, suburban, and coastal areas. The rigorous correlations, root mean square errors, and mean absolute differences available from the multilocational field data within one city region provide a strong base for validating the AOT products from different sensors and at different spatial scales over different land surface types. The results suggest that the AOT products, especially those from MODIS 10 km, provide reliable and accurate observations for daily air quality monitoring over a variety of land-cover types, as well as for identifying emission sources for coordinated actions by the governments of Hong Kong and the Chinese mainland. [ABSTRACT FROM PUBLISHER]

In densely urbanized regions, the local climate is greatly influenced by the urban morphology, including interactions between buildings, space, and human activities. The Kowloon Peninsula in Hong Kong, with some of the greatest urban population densities in the world, represents an extreme case of the influence of the built environment on climate, with high-rise buildings, narrow street canyons, and little green space. In this study, the building frontal area index (FAI), a parameter for estimating aerodynamic resistance of the urban surface as a predictor of wind ventilation, was derived from a three-dimensional building database. The relationship between FAI and urban heat island intensity (UHII) from an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite image was calculated at different scales. The highest correlation (r = 0.574, n = 4900) was obtained at 100 m resolution, which suggests that the optimum operational scale of FAI is 100 m resolution for the study area, i.e. the scale and size at which FAI impacts on the urban climate. The presence/trend of any higher correlation at different resolutions was tested using the nonparametric Mann–Kendall test, and the results show that the statistic Z-value generated from the test is smaller than the hypothesis significance levels of 90%, 95%, and 99%; thus, the hypothesis of having a higher correlation at any scale other than 100 m resolution is rejected. Planning authorities may use the FAI generated at 100 m resolution for designing wind ventilation corridors across Hong Kong at this scale, especially when temperature and air quality in the inner city are of major concern. However, for applications to other cities with different standard morphologies, the FAI–UHII relationship should be re-evaluated. [ABSTRACT FROM PUBLISHER]

Hong Kong, located adjacent to the rapidly growing urban-industrial region of south China, provides a case of mixed aerosol types (urban, industrial, marine, and long-distance, including dust) from diverse activities and has suffered many serious air pollution episodes over the last decade. However, the sources and transport pathways of aerosols measured and recorded in Hong Kong have not been well researched due to the lack of air quality monitoring stations in east Asia. Here, an integrated method combining Aerosol Robotic Network (AERONET) data, backward trajectories, and Potential Source Contribution Function (PSCF) modelling is used to identify probable transport pathways and magnitudes of source contributions for four characteristic aerosol types. These types, which are dominant in Hong Kong during defined climatic and environmental conditions, are urban fine aerosols, urban mixed aerosols, dust, and heavy pollution. They were defined by clustering a total of 730 AERONET data sets between 2005 and 2008. Results show that aerosol types 1 and 2 (urban fine and urban mixed) are associated with regional fine particulate urban emissions and predominantly local urban emissions, respectively, suggesting that mitigating measures taken within Hong Kong itself would be partially effective. Heavy pollution and dust (types 4 and 3) are more associated with short- and long-distance sources, notably heavy industries in nearby southern Guangdong and the Pearl River Delta region, and desert dust from arid regions in north China. The PSCF map representing dust aerosol type shows a wide range of eastward and southeastward trajectories from northwest China to Hong Kong. Although the contribution of dust sources is small compared to anthropogenic aerosols, a serious recent dust outbreak observed in Hong Kong was associated with an elevation of the air pollution index to 500, compared with 50–100 on normal days. The combined use of clustered AERONET, backward trajectories, and PSCF model can help to resolve long-standing issues about source regions and characteristics of pollutants carried to Hong Kong. [ABSTRACT FROM PUBLISHER]

Water vapour is one of green house gases (GHG) and a key parameter affecting weather forecasting. Situated on the edge of the South China Sea and in the path of the wet Asian monsoon, Hong Kong experiences more rainstorms than most other cities. An accurate observation of water vapour has a special significance in severe weather prediction for Hong Kong, one of the cities with the highest density of population in the world. In this paper, water vapour observations over the last 6 years from an AErosol RObotic NETwork (AERONET) sunphotometer, 9 years from the MODerate resolution Imaging Spectroradiometer (MODIS) TERRA satellite images and 38 years from radiosonde were analysed and cross-validated. The operational MODIS water vapour products, namely MOD05 and MOD07, with a spatial resolution of 1 and 5 km, respectively, were compared with both radiosonde and AERONET data. The correlation coefficients between MODIS water vapour products and radiosonde data are r = 0.878 and 0.876 for MOD05 and MOD07 products, and the correlations of those with AERONET data are r = 0.822, r = 0.976, respectively. The results also indicate that radiosonde and AERONET water vapour observations have a good agreement, with a correlation of r = 0.988 and a small mean absolute difference (MAD), and root mean square error (RMS) of 0.197 and 0.289 cm, respectively. Although the satellite data (with a frequency of once per day) represent water vapour coverage of all the territories of Hong Kong, they do not meet short-term weather prediction demand due to their low temporal resolution. Radiosonde observations with a frequency of twice per day are also temporally inadequate. This study demonstrates that the AERONET sunphotometer can provide accurate and high temporal resolution water vapour data which can be used for short-term weather prediction and long-term climate change research. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This study investigates the relationship between aerosol optical thickness (AOT) derived from MODerate resolution Imaging Spectroradiometer (MODIS) satellite and in situ particulate matter (PM2.5) from Hong Kong air-quality monitoring stations. The relationship was analysed for three different AOT products, namely, MODIS collection 5 AOT data, MODIS collection 5 fine-mode fraction AOT data, both at 10 km resolution, and MODIS AOT data at 500 m resolution. In view of the predicted low accuracies obtainable for MODIS AOT products for the south China region, these AOT products were first validated against AOT measurements from an AErosol RObotic NETwork (AERONET) station near the centre of Hong Kong. Strong relationships of R 2 = 0.78 and R 2 = 0.77 for the 10 km and 500 m AOT data, respectively, were obtained, thus providing a robust AOT image database at both coarse and fine spatial resolution for comparison with PM2.5 concentrations. When a whole year (2007) of AOT images was compared with PM2.5 concentrations recorded at five ground stations, correlations of R 2 = 0.31, R 2 = 0.10 and R 2 = 0.67 were obtained for collection 5, fine-mode fraction of collection 5 (both at 10 km resolution) and 500 m AOT, respectively. Strong correlations between MODIS 500 m AOT and PM2.5 concentration were also observed for individual stations (R 2 = 0.66, 0.74, 0.76, 0.56 and 0.62, for Central, Tung Chung, Tseun Wan, Yuen Long and Tap Mun stations, respectively). The study suggests that fine particle distributions at a high level of detail over whole cities may be obtained from satellite images. Since the model has potential for further refinement, monitoring of detailed PM2.5 concentrations on a routine basis from satellite images will provide a highly useful tool for urban environmental authorities. [ABSTRACT FROM AUTHOR]

Hong Kong is one of the world's mountainous international cities, and landslides are a constant threat to human life and property. Monitoring landslides in Hong Kong is important and this is always done by field surveying. However, although conventional survey techniques provide accurate landslide information, they are limited to small areas and physical contact with the slope may be dangerous. Remote sensing techniques can provide an alternative for collecting information about landslide causes and occurrences, and they may assist in the prediction of future landslide occurrences. This article demonstrates the use of monoscopic and stereoscopic aerial photographs, along with satellite images from the IKONOS very high resolution (VHR) sensor for detailed landslide hazard assessment over Hong Kong. For monoscopic aerial photographs, a fusion technique for generating pseudo true colour images from false colour aerial photographs was demonstrated. The pseudo true colour image is useful for better visual analysis in the photogrammetric model. For monoscopic IKONOS image, a set of image fusion techniques was applied in order to improve landslide interpretation, and the results were examined visually and statistically. The Pan-sharpening method among all the image fusion techniques has been demonstrated to have superior performance for identifying both landslide trails and crowns. Stereoscopic viewing using a stereoscopic pair of aerial photographs and stereoscopic IKONOS images was employed for more detailed landslide investigation such as landscape positional relationships (e.g. streams and ridges). Digital elevation models (DEM) were generated from aerial photographs and IKONOS stereoscopic images, and they were compared with digital contour data with 2 m contour interval. The DEMs generated from digital photogrammetric model and IKONOS stereoscopic images are consistently more accurate than an existing DEM, and are sensitive to micro-scale terrain features. The Hong Kong Civil Engineering Department may use the derived monoscopic fused images, stereoscopic images, DEM and anaglyph as objective measures for a detailed landslide study within Hong Kong. [ABSTRACT FROM AUTHOR]

Abstract: The contribution of Biogenic Volatile Organic Compounds (BVOCs) to local air quality modelling is often ignored due to the difficulty of obtaining accurate spatial estimates of emissions. Yet their role in the formation of secondary aerosols and photochemical smog is thought to be significant, especially in hot tropical cities such as Hong Kong, which are situated downwind from dense forests. This paper evaluates Guenther et al.’s [Guenther, A., Hewitt, C.N., Erickson, D., Fall, R., Geron, C., Graedel, T.E., Harley, P., Klinger, L., Lerdau, M., McKay, W.A., Pierce, T., Scholes, B., Steinbrecher, R., Tallamraju, R., Taylor, J., Zimmerman, P., 1995. A global model of natural volatile organic compound emissions. Journal of Geophysical Research 100, 8873–8892] global model of BVOC emissions, for application at a spatially detailed level to Hong Kong’s tropical forested landscape using high resolution remote sensing and ground data. The emission estimates are based on a landscape approach which assigns emission rates directly to ecosystem types not to individual species, since unlike in temperate regions where one or two single species may dominate over large regions, Hong Kong’s vegetation is extremely diverse with up to 300 different species in one hectare. The resulting BVOC emission maps are suitable for direct input to regional and local air quality models giving 10m raster output on an hourly basis over the whole of the Hong Kong territory, an area of 1100km2. Due to the spatially detailed mapping of isoprene emissions over the study area, it was possible to validate the model output using field data collected at a precise time and place by replicating those conditions in the model. The field measurement of emissions used for validating the model was based on a canister sampling technique, undertaken under different climatic conditions for Hong Kong’s main ecosystem types in both urban and rural areas. The model-derived BVOC flux distributions appeared to be consistent with the field observations, indicating the robustness of the landscape modelling approach when applied to tropical forests at detailed level, as well as the promising role of remote sensing in BVOC mapping. [Copyright &y& Elsevier]

Abstract: Aerosol retrieval algorithms for the MODerate Resolution Imaging Spectroradiometer (MODIS) have been developed to estimate aerosol and microphysical properties of the atmosphere, which help to address aerosol climatic issues at global scale. However, higher spatial resolution aerosol products for urban areas have not been well-researched mainly due to the difficulty of differentiating aerosols from bright surfaces in urban areas. Here, an aerosol retrieval algorithm using the MODIS 500-m resolution bands is described, to retrieve aerosol properties over Hong Kong and the Pearl River Delta region. The rationale of our technique is to first estimate the aerosol reflectances by decomposing the top-of-atmosphere reflectances from surface reflectances and Rayleigh path reflectances. For the determination of surface reflectances, a Minimum Reflectance Technique (MRT) is used, and MRT images are computed for different seasons. For conversion of aerosol reflectance to aerosol optical thickness (AOT), comprehensive Look Up Tables specific to the local region are constructed, which consider aerosol properties and sun-viewing geometry in the radiative transfer calculations. Four local aerosol types, namely coastal urban, polluted urban, dust, and heavy pollution, were derived using cluster analysis on 3years of AERONET measurements in Hong Kong. The resulting 500m AOT images were found to be highly correlated with ground measurements from the AERONET (r 2 =0.767) and Microtops II sunphotometers (r 2 =0.760) in Hong Kong. This study further demonstrates the application of the fine resolution AOT images for monitoring inter-urban and intra-urban aerosol distributions and the influence of trans-boundary flows. These applications include characterization of spatial patterns of AOT within the city, and detection of regional biomass burning sources. [Copyright &y& Elsevier]

Abstract: Currently, the depiction of urban air quality at boundary layer scale uses modelled climatic and land cover data. However, such models are difficult to verify, and only low to moderate accuracy may be achieved due to the complexity of the input data required and the reliance on assumptions about dispersion patterns. The provision of comprehensive air quality data to urban residents in city districts, at a level of detail commensurate with other Location-Based Services (LBS) which are time- and place-sensitive, has therefore not been possible. A method for urban air quality monitoring over cities at boundary layer scale, other than by the use of air quality models is presented here. The system presented uses empirical Aerosol Optical Thickness (AOT) data in near-real time, combining AOT data from AERONET with aerosol vertical profiles computed from twice-daily MODIS satellite images at 500m resolution, to give three dimensional (3D) air quality data over the urban landscape. There has been no previous attempt to project the horizontal spatial distribution of aerosols from satellite image pixels into a vertical dimension to give a spatially comprehensive three dimensional record of air quality. The paper describes the sources and accuracy of the AOT data input to the system as well as its storage and retrieval on a Geographic Information System (GIS) platform, to provide air quality and visibility information according to user query at any 3D geographical location, including individual buildings or building floor. [Copyright &y& Elsevier]

Observations from the AErosol RObotic NETwork (AERONET) sunphotometers, MODerate resolution Imaging Spectroradiometer (MODIS) satellite images, back-trajectory modelling and 'in-situ' PM10 measurements in Hong Kong confirmed that two dust storms on 16-17 April 2006 and 27-30 April 2009, with source areas in northwest China, affected the city. The impacts of the dust on the air quality of Hong Kong were quantified using aerosol optical properties from AERONET data and local PM10 (particle size less than 10 μm) concentrations. Combined analysis of back trajectories and the microphysical properties of the dust aerosols from AERONET inversion data suggest that the dust particulates are sometimes associated with industrial chemicals on arrival in Hong Kong. This is the first remote-sensing study to observe the presence and characteristics of Asian dust carried into the humid tropical region of south China. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease 2019 (COVID-19) has caused significantly changes in worldwide environmental and socioeconomics, especially in the early stage. Previous research has found that air pollution is potentially affected by these unprecedented changes and it affects COVID-19 infections. This study aims to explore the non-linear association between yearly and daily global air pollution and the confirmed cases of COVID-19. The concentrations of tropospheric air pollution (CO, NO2, O3, and SO2) and the daily confirmed cases between 23 January 2020 and 31 May 2020 were collected at the global scale. The yearly discrepancies of air pollutions and daily air pollution are associated with total and daily confirmed cases, respectively, based on the generalized additive model. We observed that there are significant spatially and temporally non-stationary variations between air pollution and confirmed cases of COVID-19. For the yearly assessment, the number of confirmed cases is associated with the positive fluctuation of CO, O3, and SO2 discrepancies, while the increasing NO2 discrepancies leads to the significant peak of confirmed cases variation. For the daily assessment, among the selected countries, positive linear or non-linear relationships are found between CO and SO2 concentrations and the daily confirmed cases, whereas NO2 concentrations are negatively correlated with the daily confirmed cases; variations in the ascending/declining associations are identified from the relationship of the O3-confirmed cases. The findings indicate that the non-linear relationships between global air pollution and the confirmed cases of COVID-19 are varied, which implicates the needs as well as the incorporation of our findings in the risk monitoring of public health on local, regional, and global scales. [ABSTRACT FROM AUTHOR]

The World Health Organization considered the wide spread of COVID-19 over the world as a pandemic. There is still a lack of understanding of its origin, transmission, and treatment methods. Understanding the influencing factors of COVID-19 can help mitigate its spread, but little research on the spatial factors has been conducted. Therefore, this study explores the effects of urban geometry and socio-demographic factors on the COVID-19 cases in Hong Kong. For each patient, the places they visited during the incubation period before going to hospital were identified, and matched with corresponding attributes of urban geometry (i.e., building geometry, road network and greenspace) and socio-demographic factors (i.e., demographic, educational, economic, household and housing characteristics) based on the coordinates. The local cases were then compared with the imported cases using stepwise logistic regression, logistic regression with case-control of time, and least absolute shrinkage and selection operator regression to identify factors influencing local disease transmission. Results show that the building geometry, road network and certain socio-economic characteristics are significantly associated with COVID-19 cases. In addition, the results indicate that urban geometry is playing a more important role than socio-demographic characteristics in affecting COVID-19 incidence. These findings provide a useful reference to the government and the general public as to the spatial vulnerability of COVID-19 transmission and to take appropriate preventive measures in high-risk areas. Unlabelled Image • Relationships between COVID-19 cases and urban geometry and socio-demography • Significant factors are building geometry, road network and economic characteristics. • Importance of urban geometry over socio-demographic factors [ABSTRACT FROM AUTHOR]

Satellite remote sensing has been extensively utilized for monitoring dust storms in space and time. Dust storm detection using satellite observations is important to analyze the dust storm trajectories and sources. This paper reviews the algorithms for dust storm detection used in multispectral satellite sensors, spanning visible to thermal wavelengths. Four categories of dust detection algorithms are summarized, namely, dust spectral index algorithms, temporal anomalous detection algorithms, spatial coherence tested algorithms (physical-based algorithms) and machine learning-based algorithms. Following discussions of dust storm detection algorithms, the dust presence validation methods are also reviewed. Future developments for dust storm detection are focused upon three aspects: detection of dust storms at nighttime; development of more efficient machine learning methods for retrieval; and integrating physical and machine learning methods for satellite images. • Reviewing of algorithms for dust storm detection for multispectral sensors • Physical-based algorithms and Machine-learning algorithms for dust storm detection are summarized. • Commonly used dust detection validation methods are discussed. • Limitations of current algorithms are pointed out and future developments of dust detection algorithms are suggested. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease (COVID-19) has become a public health problem at a global scale because of its high infection and mortality rate. It has affected most countries in the world, and the number of confirmed cases and death toll is still growing rapidly. Susceptibility studies have been conducted in specific countries, where COVID-19 infection and mortality rates were highly related to demographics and air pollution, especially PM2.5, but there are few studies on a global scale. This paper is an exploratory study of the relationship between confirmed COVID-19 cases and death toll per million population, population density, and PM2.5 concentration on a worldwide basis. A multivariate linear regression based on Moran eigenvector spatial filtering model and Geographically weighted regression model were undertaken to analyze the relationship between population density, PM2.5 concentration, and COVID-19 infection and mortality rate, and a geostatistical method with bivariate local spatial association analysis was adopted to explore their spatial correlations. The results show that there is a statistically significant positive relationship between COVID-19 confirmed cases and death toll per million population, population density, and PM2.5 concentration, but the relationship displays obvious spatial heterogeneity. While some adjacent countries are likely to have similar characteristics, it suggests that the countries with close contacts/sharing borders and similar spatial pattern of population density and PM2.5 concentration tend to have similar patterns of COVID-19 risk. The analysis provides an interpretation of the statistical and spatial association of COVID-19 with population density and PM2.5 concentration, which has implications for the control and abatement of COVID-19 in terms of both infection and mortality. [ABSTRACT FROM AUTHOR]

Ocean color sensors, typically installed on polar-orbiting satellites, have been used to monitor oceanic processes for last three decades. However, their temporal resolution is not considered to be adequate for monitoring highly dynamic oceanic processes, especially when considering data gaps due to cloud contamination. The Advanced Himawari Imager (AHI) onboard the Himawari-8, a geostationary satellite operated by the Japan Meteorological Agency (JMA), acquires imagery every 10 min at 500 m to 2000 m spatial resolution. The AHI sensor with three visible, one near-infrared (NIR), and two shortwave-infrared (SWIR) bands displays good potential in monitoring oceanic processes at high temporal resolution. This study investigated and identified an appropriate atmospheric correction method for AHI data; developed a model for Total Suspended Solids (TSS) concentrations estimation using hyperspectral data and in-situ measurements of TSS; validated the model; and assessed its potential to capture diurnal changes using AHI imagery. Two image-based atmospheric correction methods, the NIR-SWIR method and the SWIR method were tested for correcting the AHI data. Then, the new model was applied to the atmospherically corrected AHI data to map TSS and its diurnal changes in the Pearl River Estuary (PRE) and neighboring coastal areas. The results indicated that the SWIR method outperformed the NIR-SWIR method, when compared to in-situ water-leaving reflectance data. The results showed a good agreement between the AHI-derived TSS and in-situ measured data with a coefficient of determination (R²) of 0.85, mean absolute error (MAE) of 3.1 mg/L, a root mean square error (RMSE) of 3.9 mg/L, and average percentage difference (APD) of 30% (TSS range 1–40 mg/L). Moreover, the diurnal variation in the turbidity front, using the Normalized Suspended Material Index (NSMI), showed the capability of AHI data to track diurnal variation in turbidity fronts, due to high TSS concentrations at high temporal frequency. The present study indicates that AHI data with high image capturing frequency can be used to map surface TSS concentrations. These TSS measurements at high frequency are not only important for monitoring the sensitive coastal areas but also for scientific understanding of the spatial and temporal variation of TSS. [ABSTRACT FROM AUTHOR]