A5086685560- Urban Heat Island Mitigation
- Remote Sensing and Land Use
- Building Energy and Comfort Optimization
- Urban Green Space and Health
- Land Use and Ecosystem Services
- Climate Change and Health Impacts
- Climate variability and models
- Noise Effects and Management
- Plant Water Relations and Carbon Dynamics
- Remote-Sensing Image Classification
- Impact of Light on Environment and Health
- Remote Sensing in Agriculture
- Air Quality and Health Impacts
- Child Nutrition and Water Access
- Atmospheric chemistry and aerosols
- Meteorological Phenomena and Simulations
- Wind and Air Flow Studies
- Influenza Virus Research Studies
- Human Mobility and Location-Based Analysis
- Atmospheric aerosols and clouds
- Climate change impacts on agriculture
- Atmospheric and Environmental Gas Dynamics
- Environmental and Agricultural Sciences
- Disaster Management and Resilience
- Air Quality Monitoring and Forecasting
University of Alabama in Huntsville
2017-2024
National Space Science and Technology Center
2017
NSF National Center for Atmospheric Research
2015-2016
University of Kansas
2013-2014
State Key Laboratory of Remote Sensing Science
2010-2011
There is a growing need to apply geospatial artificial intelligence analysis disparate environmental datasets find solutions that benefit frontline communities. One such critically needed solution the prediction of health-relevant ambient ground-level air pollution concentrations. However, many challenges exist surrounding size and representativeness limited ground reference stations for model development, reconciling multi-source data, interpretability deep learning models. This research...
Abstract Urban land use cover (LULC) change raises ambient temperature and modifies atmospheric moisture, which increases heat-related health risks in cities. Greenspace bluespace commonly coexist urban landscapes are nature-based heat mitigation strategies. Yet, their interactive effects on thermal environments rarely assessed it remains unclear how extreme events (EHEs) affect ability to regulate human comfort. Using multi-year observations from a dense observational network Madison, WI,...
Abstract The significant reduction in human activities during COVID‐19 lockdown is anticipated to substantially influence urban climates, especially heat islands (UHIs). However, the UHI variations periods remain be quantified. Based on MODIS daily land surface temperature and in‐situ air observations, we reveal a substantial decline both canopy UHIs over 300‐plus megacities China compared with reference periods. intensity (UHII) reduced by 0.25 (one S.D. = 0.22) K daytime 0.23 (0.20) at...
Abstract A comprehensive comparison of the trends and drivers global surface canopy urban heat islands (termed I s c trends, respectively) is critical for better designing mitigation strategies. However, such a remains largely absent. Using spatially continuous land temperatures air (2003–2020), here we find that magnitude mean trend (0.19 ± 0.006°C/decade, SE) 5,643 cities worldwide nearly six‐times corresponding (0.03 0.002°C/decade) during day, while former (0.06 0.004°C/decade) double...
Urbanization extensively modifies surface roughness and properties, impacting regional climate hydrological cycles. Urban effects on temperature precipitation have drawn considerable attention. These associated physical processes are also closely linked to clouds' formation dynamics. Cloud is one of the critical components in regulating urban hydrometeorological cycles but remains less understood urban-atmospheric systems. We analyzed satellite-derived cloud patterns spanning two decades...
Three practical sampling methods are proposed and compared
Daily commute substantially increases urban residents’ exposure to heat waves and slightly alleviates cold waves.
Abstract Urban areas are known to modify the spatial pattern of precipitation climatology. Existing observational evidence suggests that can be enhanced downwind a city. Among proposed mechanisms, thermodynamic and aerodynamic processes in urban lower atmosphere interact with meteorological conditions play key role determining resulting patterns. In addition, these influenced by form, such as impervious surface extent. This study aims unravel how different forms impact patterns climatology...
Advances in remote sensing and machine learning enable increasingly accurate, inexpensive, timely estimation of poverty malnutrition indicators to guide development humanitarian agencies’ programming. However, state the art models often rely on proprietary data and/or deep or transfer methods whose underlying mechanics may be challenging interpret. We demonstrate how interpretable random forest can produce estimates a set (potentially correlated) prevalence measures using free, open access,...