Physical Landscape and Environmental Change

Ambient particles raise worldwide concerns due to their impediments on human health, and important roles in the Earth’s weather and climate system via altering radiation and clouds. Particles with diameter less than 2.5 micrometers (PM2.5) are small enough to enter deeply into human lungs, posing the greatest short-term and long-term risks to human health. Accordingly, sources of PM2.5 and particle precursors are highly regulated in most industrialized countries. PM2.5 can linger in the atmosphere for days and exhibit substantial spatiotemporal variations. An accurate depiction of the dynamic evolution of PM2.5 remains a challenge, but urgently needed for better regulation of air quality and health risk assessment. Commonly, ground-based monitoring networks are established to characterize the PM2.5 concentrations in highly populated regions and protected areas such as national parks, but large gaps exist in spatial coverage. Satellite-derived aerosol optical properties serve to complement the missing spatial information of monitoring networks. However, such attempts are hampered under cloudy/hazy conditions or during nighttime. In this study, we aim to overcome the long-standing restriction that surface PM2.5 cannot be constrained with satellite remote sensing under cloudy/hazy conditions or during nighttime. We propose to build a deep learning-based model to fill these observational gaps with data from the newly launched world’s first geostationary air pollution satellite GEMS (Geostationary Environment Monitoring Spectrometer, launched on February 18, 2020 in South Korea). Cloud droplet number retrievals will be also used to constrain below-cloud PM2.5 concentrations, and this approach would provide ground-level PM2.5 concentrations with high spatial resolution and 24-hour temporal coverage. Better constrained spatiotemporal distributions of PM2.5 concentrations will help improve health effects studies, atmospheric emission estimates, and predictions of air quality.

時間分辨率對極端降水特徵推算有重要影響。氣候暖化下，日尺度極端降水的增加率通常被認為遵循Clausius-Clapeyron （CC）關係，也就是當氣溫上升1 oC，大氣持水能力上升約6.5%。而小時尺度極端降水強度增加得更快，也被稱為超CC（super-CC）關係。然而，小時尺度仍難以準確反映歷時小於1小時的暴雨特徵。在全球暖化下，精細時間尺度（如分鐘到小時）極端降水及山洪的演化特徵（如長期趨勢和變化）是防洪設施設計和城市規劃（例如水塘和公路）的重要參數，尤其是對於香港等山多及人口密集地區。因為缺乏足夠時間長度和空間範圍的高質量日內尺度降水資料，人們對日內尺度極端降水（強度、歷時和總降水量等）和山洪對氣溫上升響應的認識仍較有限。另外，具有高度空間差異性的地形以及複雜的地表水文過程使得山洪的發生和過程具有不確定性。過往研究通常只關注日內尺度極端降水強度與山洪的關係，而較少考慮陸面水文特徵及流域過程的影響。 香港是世界人口第四密集的城市，其已發展地區毗鄰陡峭與崎嶇的地形。山多的地形以及濕潤的亞熱帶季風氣候使得該國際金融中心對山洪非常脆弱。本課題將基於香港超過130年的小時尺度降水記錄及超過30年的5分鐘尺度降水觀測數據，研究日內尺度極端降水事件強度、歷時和總量的時間演化及其與氣溫的關係，並使用耦合的水文大氣模式系統WRF/WRF-Hydro考慮陸面水文過程並模擬日內尺度極端降水變化對山洪的影響。本課題有助於提升人們對氣候暖化下日內尺度極端降水變化以及其對山洪潛在影響的認識。本課題研究結果將對香港及其他城市提升氣候變化應變能力，特別是防洪管理，具有實際意義。

本研究擬發展同時同化地表PM2.5，臭氧和二氧化氮觀測數據的技術，以獲得更好的PM2.5和臭氧濃度時空分佈特徵，以減小污染健康暴露評估和空氣質量預測的不確定度。近年來有一些基於氣象-化學耦合模型的氣溶膠資料同化研究，而針對臭氧污染的同化研究仍然較少。然而，臭氧是華南區域非常嚴峻的污染問題，對於人類健康和作物生長都有不利影響。評價空氣污染的健康、氣候和生態效應也需要準確的污染物時空分佈特徵。本研究基於之前氣溶膠資料同化基礎，旨在開發同時同化PM2.5,臭氧和二氧化氮觀測數據的技術。開發的系統將應用於華南區域獲得PM2.5和臭氧時空分佈特徵，以及評估其對華南區域空氣污染暴露風險和空氣質量預測的改善效果。

This study examines rock and sediment outcrops ranging in age from the Miocene to the present (~15–0 million years) in two areas of the northern Kenya Rift Valley.

This study will examine rock and sediment outcrops ranging in age from the Miocene to the present (~15–0 million years) in two areas of the northern Kenya Rift Valley. These include the arid Suguta Valley and, to the southwest, the eroded Tugen Hills. Both areas contain well-exposed deposits laid down in a variety of terrestrial and aquatic environments. This investigation will focus on those deposits formed in spring systems and fresh to saline lakes. The research will pursue four major lines of investigation: 1) a study of modern hot and cold springs and their deposits in the Suguta Valley; 2) characterising modern sedimentation in permanent and ephemeral lakes in the Suguta Valley, as well as changes in water chemistry between spring and river sources and modern saline lakes of the Suguta; 3) An investigation of past environments and sediments formed in ancient fresh to saline lakes that date back several million years in both the Suguta Valley and the Tugen Hills; and 4) integrating information on ancient and modern lakes in the northern Kenya Rift in order to determine if their are any systematic changes that can be related to rifting and volcanism in an evolving rift valley. The project will explore these major aims through field studies and systematic analyses of water (modern lake waters, rivers, springs and groundwater inflows) and sediment (outcrops, short cores) samples. We will provide field descriptions of sediment sequences and return samples for geochemical, sedimentological and diatom analyses. The results of this work will allow us to characterise the modern environments and to decipher the past depositional settings in which ancient sediments accumulated. In previous studies of the southern Kenya Rift, two members of the research team have detected many changes in the types of sediment that have accumulated at different times. These changes in deposition partly reflect varying past climates, but also mirror changes in the stage of development of the rift valley. This study will allow the research team to explore if their are similar, or different, long-term variations in deposition that may reflect changes in the evolving northern Kenya Rift and its tectonic setting and volcanic environments. We will then combine models from our earlier studies in the southern Kenya Rift with new models from this investigation in order to develop a broader-based understanding of how sedimentation changes with time in an evolving rift system.