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

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]

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

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