Air pollution scenario over China during COVID-19

The unprecedented slowdown in China during the COVID-19 period of November 2019 to April 2020 should have reduced pollution in smog-laden cities. However, moderate resolution imaging spectrometer (MODIS) satellite retrievals of aerosol optical depth (AOD) show a marked increase in aerosols over the Beijing–Tianjin–Hebei (BHT) region and most of Northeast and Central China, compared with the previous winter. Fine particulate (PM2.5) data from ground monitoring stations show an increase of 19.5% in Beijing during January and February 2020, and no reduction for Tianjin. In March and April 2020, a different spatial pattern emerges, with very high AOD levels observed over 50% of the Chinese mainland, and including peripheral regions in the northwest and southwest. At the same time, ozone monitoring instrument (OMI) satellite-derived NO2 concentrations fell drastically across China. The increase in PM2.5 while NO2 decreased in BTH and across China is likely due to enhanced production of secondary particulates. These are formed when reductions in NOx result in increased ozone formation, thus increasing the oxidizing capacity of the atmosphere. Support for this explanation is provided by ground level air quality data showing increased volume of fine mode aerosols throughout February and March 2020, and increased levels of PM2.5, relative humidity (RH), and ozone during haze episodes in the COVID-19 lockdown period. Backward trajectories show the origin of air masses affecting industrial centers of North and East China to be local. Other contributors to increased atmospheric particulates may include inflated industrial production in peripheral regions to compensate loss in the main population and industrial centers, and low wind speeds. Satellite monitoring of the extraordinary atmospheric conditions resulting from the COVID-19 shutdown could enhance understanding of smog formation and attempts to control it

A spatio-temporal analysis of rainfall and drought monitoring in the Tharparkar region of Pakistan

The Tharpakar desert region of Pakistan supports a population approaching two million, dependent on rain-fed agriculture as the main livelihood. The almost doubling of population in the last two decades, coupled with low and variable rainfall, makes this one of the world’s most food-insecure regions. This paper examines satellite-based rainfall estimates and biomass data as a means to supplement sparsely distributed rainfall stations and to provide timely estimates of seasonal growth indicators in farmlands. Satellite dekadal and monthly rainfall estimates gave good correlations with ground station data, ranging from R = 0.75 to R = 0.97 over a 19-year period, with tendency for overestimation from the Tropical Rainfall Monitoring Mission (TRMM) and underestimation from Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) datasets. CHIRPS was selected for further modeling, as overestimation from TRMM implies the risk of under-predicting drought. The use of satellite rainfall products from CHIRPS was also essential for derivation of spatial estimates of phenological variables and rainfall criteria for comparison with normalized difference vegetation index (NDVI)-based biomass productivity. This is because, in this arid region where drought is common and rainfall unpredictable, determination of phenological thresholds based on vegetation indices proved unreliable. Mapped rainfall distributions across Tharparkar were found to differ substantially from those of maximum biomass (NDVImax), often showing low NDVImax in zones of higher annual rainfall, and vice versa. This mismatch occurs in both wet and dry years. Maps of rainfall intensity suggest that low yields often occur in areas with intense rain causing damage to ripening crops, and that total rainfall in a season is less important than sustained water supply. Correlations between rainfall variables and NDVImax indicate the difficulty of predicting drought early in the growing season in this region of extreme climatic variability. Mapped rainfall and biomass distributions can be used to recommend settlement in areas of more consistent rainfall

Mass balance of the Greenland ice sheet from GRACE and surface mass balance modelling

The Greenland Ice Sheet (GrIS) is losing mass at a rate that represents a major contribution to global sea-level rise in recent decades. In this study, we used GRACE data to retrieve the time series variations of the GrIS from April 2002 to June 2017. We also estimate the mass balance from the RACMO2.3 and ice discharge data in order to obtain a comparative analysis and cross-validation. A detailed analysis of long-term trend, seasonal and inter-annual changes of the GrIS is implemented by GRACE and surface mass balance (SMB) modeling. The results indicate a decrease of -267.77±8.68 Gt/yr of the GrIS over the 16-year period. There is a rapid decline from 2002-2008, which even accelerated from 2009 to 2012, before declining relatively slowly from 2013 to 2017. The mass change inland is significantly smaller than that detected along coastal regions, especially in southeastern, southwestern, and northwestern regions. The mass balance estimates from GRACE and SMB-D are highly consistent. The ice discharge manifests itself mostly as a long-term trend, whereas seasonal mass variations are largely attributed to surface mass processes. The GrIS mass changes are mostly attributed to mass loss during summer. Summer mass changes are highly correlated with climate changes

Policy Document on Earth Observation for Urban Planning and Management: State of the Art and Recommendations for Application of Earth Observation in Urban Planning

A policy document on earth observation for urban planning and management resulting from a workshop held in Hong Kong in November 2006 is presented. The aim of the workshop was to provide a forum for researchers and scientists specializing in earth observation to interact with practitioners working in different aspects of city planning, in a complex and dynamic city, Hong Kong. A summary of the current state of the art, limitations, and recommendations for the use of earth observation in urban areas is presented here as a policy document

A disease-specific measure of health-related quality of life for use in adults with immune thrombocytopenic purpura: Its development and validation

BACKGROUND: No validated disease-specific measures are available to assess health-related quality of life (HRQoL) in adult subjects with immune thrombocytopenic purpura (ITP). Therefore, we sought to develop and validate the ITP-Patient Assessment Questionnaire (ITP-PAQ) for adult subjects with ITP. METHODS: Information from literature reviews, focus groups with subjects, and clinicians were used to develop 50 ITP-PAQ items. Factor analyses were conducted to develop the scale structure and reduce the number of items. The final 44-item ITP-PAQ, which includes ten scales [Symptoms (S), Bother-Physical Health (B), Fatigue/Sleep (FT), Activity (A), Fear (FR), Psychological Health (PH), Work (W), Social Activity (SA), Women's Reproductive Health (RH), and Overall (QoL)], was self-administered to adult ITP subjects at baseline and 7–10 days later. Test-retest reliability, internal consistency reliability, construct and known groups validity of the final ITP-PAQ were evaluated. RESULTS: Seventy-three subjects with ITP completed the questionnaire twice. Test-retest reliability, as measured by the intra-class correlation, ranged from 0.52–0.90. Internal consistency reliability was demonstrated with Cronbach's alpha for all scales above the acceptable level of 0.70 (range: 0.71–0.92), except for RH (0.66). Construct validity, assessed by correlating ITP-PAQ scales with established measures (Short Form-36 v.1, SF-36 and Center for Epidemiologic Studies Depression Scale, CES-D), was demonstrated through moderate correlations between the ITP-PAQ SA and SF-36 Social Function scales (r = 0.67), and between ITP-PAQ PH and SF-36 Mental Health Scales (r = 0.63). Moderate to strong inter-scale correlations were reported between ITP-PAQ scales and the CES-D, except for the RH scale. Known groups validity was evaluated by comparing mean scores for groups that differed clinically. Statistically significant differences (p < 0.01) were observed when subjects were categorized by treatment status [S, FT, B, A, PH, and QoL, perceived effectiveness of ITP treatment [S], and time elapsed since ITP diagnosis [PH]. CONCLUSION: Results provide preliminary evidence of the reliability and validity of the ITP-PAQ in adult subjects with ITP. Further work should be conducted to assess the responsiveness and to estimate the minimal clinical important difference of the ITP-PAQ to more fully understand the impact of ITP and its treatments on HRQoL

Modeling of Aerosol Vertical Profiles Using GIS and Remote Sensing

The use of Geographic Information Systems (GIS) and Remote Sensing (RS) by climatologists, environmentalists and urban planners for three dimensional modeling and visualization of the landscape is well established. However no previous study has implemented these techniques for 3D modeling of atmospheric aerosols because air quality data is traditionally measured at ground points, or from satellite images, with no vertical dimension. This study presents a prototype for modeling and visualizing aerosol vertical profiles over a 3D urban landscape in Hong Kong. The method uses a newly developed technique for the derivation of aerosol vertical profiles from AERONET sunphotometer measurements and surface visibility data, and links these to a 3D urban model. This permits automated modeling and visualization of aerosol concentrations at different atmospheric levels over the urban landscape in near-real time. Since the GIS platform permits presentation of the aerosol vertical distribution in 3D, it can be related to the built environment of the city. Examples are given of the applications of the model, including diagnosis of the relative contribution of vehicle emissions to pollution levels in the city, based on increased near-surface concentrations around weekday rush-hour times. The ability to model changes in air quality and visibility from ground level to the top of tall buildings is also demonstrated, and this has implications for energy use and environmental policies for the tall mega-cities of the future

The accumulation of species and recovery of species composition along a 70 year successional gradient in a tropical secondary forest

The majority of global forests are secondary and are at different stages of succession. Assessing the dynamics of species richness and similarity, and quantifying the importance of environmental filtering, dispersal limitation and other stochastic processes are essential to understanding the mechanisms of succession for forest restoration. In this article, we explored the accumulation of species, the relative importance of spatial distance, environmental factors and stand age in determining species composition of a tropical secondary forest succession in Hong Kong. Twenty-eight plots with median age of 7, 20, 39, 61 and over 70 years in the secondary forest were established and surveyed, and the indicator species for each age class were identified. Species composition shows large variation both within and between age classes, while species richness in the old growth forest (>70 yr) was significantly lower than in the mid-age classes. Rarefied species richness showed a rapid accumulation during early succession, but species richness levelled off from 20 yrs onwards. Variation partitioning indicated that spatial distance alone explained 33% of the variation in species composition, followed by environmental distance (8%) and stand age (1%). The results of Nonmetric Multidimensional Scaling suggested that idiosyncratic successional pathways and alternative stable states might be prevalent. Our results suggested dispersal limitation was the main limiting factor in explaining the turnover of species during forest succession, while environmental filtering played a lesser role in shaping species distributions. Our results highlight the importance of active restoration in overcoming the barriers of succession in secondary vegetation in the tropics

First experiences with the Landsat-8 aquatic reflectance product: evaluation of the regional and ocean color algorithms in a coastal environment

Since the launch of the Landsat-8 (L8) Operational Land Imager (OLI) on February 11, 2013, there has been a continuous effort to produce reliable ocean color products by taking the advantages of its medium spatial resolution (30 m) and higher Signal to Noise Ratio (SNR). A Provisional Aquatic Reflectance product for the L8 OLI (L8PAR) has been recently released to the public to explore its potential for ocean color applications. This study used a six-year data record of L8 for development of a regionally tuned algorithm (RTA20) for estimating Chlorophyll-a (Chl-a) concentrations around the complex coastal environment of Hong Kong, and is the first to report the usability of the L8PAR product for coastal areas. Furthermore, this study validated three previously developed algorithms, namely RTA16, RTA17 and RTA19, and two ocean color algorithms (OC2 and OC3) modified for L8 OLI by NASA’s Ocean Color group. Results indicate that the newly released L8PAR product has a high potential for estimating the coastal water Chl-a concentrations with higher detail and higher accuracy than previously. The RTA20 algorithm developed in this study outperformed the previous algorithms (RTA16, RTA17, RTA19, OC2 and OC3), e.g., with lower values for Root Mean Square Error (RMSE; 0.92 mg/m3), bias (−0.26 mg/m3) and mean ratio (1.29). Although inferior to the RTA20, the OC2 algorithm also performed well in terms of Pearson’s correlation coefficient (r; 0.84), slope (6.87) and intercept (−8.44) while for RTA20 the values for r, slope and intercept were 0.96, 0.77 and 0.27, respectively. This preliminary evaluation reveals that the OC2 algorithm can be used as an operational algorithm for L8 Chl-a product generation for global coastal areas while RTA20 can be used as a regional algorithm for the routine monitoring of Chl-a concentrations around the coastal areas of Hong Kong or for coastal areas with similar water quality elsewhere in the world

A new simplified and robust Surface Reflectance Estimation Method (SREM) for use over diverse land surfaces using multi-sensor data

Surface reflectance (SR) estimation is the most critical pre-processing step for deriving geophysical parameters in multi-sensor remote sensing. Most state-of-the-art SR estimation methods, such as the vector version of the Second Simulation of the Satellite Signal in the Solar Spectrum (6SV) Radiative Transfer (RT) model, depend on accurate information on aerosol and atmospheric gases. In this study, a Simplified and Robust Surface Reflectance Estimation Method (SREM) based on the equations from 6SV RT model, without integrating information of aerosol particles and atmospheric gasses, is proposed and tested using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper plus (ETM+), and Landsat 8 Operational Land Imager (OLI) data from 2000 to 2018. For evaluation purposes, (i) the SREM SR retrievals are validated against in-situ SR measurements collected by Analytical Spectral Devices (ASD) for the South Dakota State University (SDSU) site, USA (ii) cross-comparison between the SREM and Landsat spectral SR products, i.e., Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) and Landsat 8 Surface Reflectance Code (LaSRC), are conducted over 11 urban (2013-2018), 13 vegetated (2013-2018), and 11 desert/arid (2000 to 2018) sites located over different climatic zones at global scale, (iii) the performance of the SREM spectral SR retrievals for low to high aerosol loadings is evaluated, (iv) spatio-temporal cross-comparison is conducted for six Landsat paths/rows located in Asia, Africa, Europe, and the USA from 2013 to 2018 to consider a large variety of land surfaces and atmospheric conditions, (v) cross-comparison is also performed for the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Soil Adjusted Vegetation Index (SAVI) calculated from both the SREM and Landsat SR data, (vi) the SREM is also applied to the Sentinel-2A and Moderate Resolution Imaging Spectrometer (MODIS) data to explore its applicability, and (vii) errors in the SR retrievals are reported using the Mean Bias Error (MBE), Root Mean Squared Deviation (RMSD) and Mean Systematic Error (MSE). Results depict significant and strong positive Pearson’s correlation (r), small MBE, RMSD, and MSE for each spectral band against in-situ ASD data and Landsat (LEDAPS and LaSRC) SR products. Consistency in SREM performance against Sentinel-2A (r = 0.994, MBE = - 0.009, and RMSD = 0.014) and MODIS (r = 0.925, MBE = 0.007, and RMSD = 0.014) data suggests that SREM can be applied to other multispectral satellites data. Overall, the findings demonstrate the potential and promise of SREM for use over diverse surfaces and under varying atmospheric conditions using multi-sensor data on a global scale

Detection and Monitoring of Marine Pollution Using Remote Sensing Technologies

Recently, the marine habitat has been under pollution threat, which impacts many human activities as well as human life. Increasing concerns about pollution levels in the oceans and coastal regions have led to multiple approaches for measuring and mitigating marine pollution, in order to achieve sustainable marine water quality. Satellite remote sensing, covering large and remote areas, is considered useful for detecting and monitoring marine pollution. Recent developments in sensor technologies have transformed remote sensing into an effective means of monitoring marine areas. Different remote sensing platforms and sensors have their own capabilities for mapping and monitoring water pollution of different types, characteristics, and concentrations. This chapter will discuss and elaborate the merits and limitations of these remote sensing techniques for mapping oil pollutants, suspended solid concentrations, algal blooms, and floating plastic waste in marine waters