Component temperature and emissivity are crucial for understanding plant physiology and urban thermal dynamics. However, existing thermal infrared unmixing methods face challenges in simultaneous retrieval and multicomponent analysis. We propose Thermal Remote sensing Unmixing for Subpixel Temperature and emissivity with the Discrete Anisotropic Radiative Transfer model (TRUST-DART), a gradient-based multi-pixel physical method that simultaneously separates component temperature and emissivity from non-isothermal mixed pixels over urban areas. TRUST-DART utilizes the DART model and requires inputs including at-surface radiance imagery, downwelling sky irradiance, a 3D mock-up with component classification, and standard DART parameters (e.g., spatial resolution and skylight ratio). This method produces maps of component emissivity and temperature. The accuracy of TRUST-DART is evaluated using both vegetation and urban scenes, employing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images and DART-simulated pseudo-ASTER images. Results show a residual radiance error is approximately 0.05 W/(m2 & sdot;sr). In absence of the co-registration and sensor noise errors, the median residual error of emissivity is approximately 0.02, and the median residual error of temperature is within 1 K. This novel approach significantly advances our ability to analyze thermal properties of urban areas, offering potential breakthroughs in urban environmental monitoring and planning. The source code of TRUSTDART is distributed together with DART (https://dart.omp.eu).

Leakages from damaged or deteriorated buried pipes in urban water distribution networks may cause significant socio-economic and environmental impacts, such as depletion of water resources and sinkhole events. Sinkholes are often caused by internal erosion and fluidization of the soil surrounding leaking pipes, with the formation of soil cavities that may eventually collapse. This in turn causes road disruption and building foundation damage, with possible victims. While the loss of precious water resources is a well-known problem, less attention has been paid to anthropogenic sinkhole events generated by leakages in water distribution systems. With a view to improving urban smart resilience and sustainability of urban areas, this study introduces an innovative framework to localize leakages based on a Machine learning model (for the training and evaluation of candidate sets of pressure sensors) and a Genetic algorithm (for the optimal sensor set positioning) with the goal of detecting and mitigating potential hydrogeological urban disruption due to water leakage in the most sensitive/critical locations. The application of the methodology on a synthetic case study from literature and a real-world case scenario shows that the methodology also contributes to reducing the depletion of water resources.

Wind-driven rain, resulting from the combination of rainfall and wind, can cause several issues to buildings. These issues range from occupant discomfort and wall soiling to electrical equipment failure and structural damage caused by water infiltration, frost, or dirt accumulation. This paper introduces a methodology devised to assess the exposure of urban structures to wind-driven rain across extended periods, encompassing a range of temporal scales from annual to seasonal time frames. For this purpose, a set of numerical tools has been developed, reducing the need for multiple raindrop transport simulations. Specifically, the method relies on meteorological data derived from the meso-scale WRF-ARW model, which are carefully selected to conduct the transport simulations. Techniques of model reduction and interpolation are also used to effectively analyze the simulation data. The robustness of the method is tested across different scales, extending from an individual building to an entire neighborhood in Paris. Potential biases are identified, and solutions are proposed to reduce errors that may arise during the simplification process. Finally, a practical case study validates the applicability of the methodology for engineering applications.

The study focusses to investigate the variations in aerosol characteristics, concentrations and radiative properties due to the burning of firecrackers during Diwali festival event followed with New year festival celebrations over a representative urban environment. A six day's long intensive in situ measurements of Black Carbon, Particulate Matter and Aerosol Optical Depth were collected to capture pre to post-Diwali and New Year festival celebrations marked with massive fireworks. We observed an increase of 286%, 89.5%, and 60.5%, in BC, PM10, and PM2.5 concentrations, respectively on festival night as compared to pre-event days. An increase in in situ measured AOD is comparable with concurrent satellite derived AOD. Angstrom exponent, alpha > 1.0 along with high turbidity coefficient; beta estimated for the festival period clearly implies the abundance of fine-mode particles, probably the smoke aerosols loading from fireworks. The Mie-scattered return signals received by the ground based Raman LiDAR at 532 nm showed an increased concentration of 'anthropogenic aerosols', attributed to the increased crackers activity. Space based CALIPSO LiDAR observations also validate the presence of 'polluted dust' and 'smoke' types aerosols at the near surface to 5 km altitude over the study area. A sharp increase in gaseous air pollutants like SO2 and NOx concentrations exceeding the National Ambient Air Quality Standards is also observed. The COART model run simulations in SWIR region showed an increased 'cooling' at the surface (-125 Wm(-2) to -185 Wm(-2)) as compared to 'warming' in the atmosphere during the event period. A maximum heating rate (1.9 Kday(-1)) due to total aerosol radiative forcing is also estimated. These investigations provide useful insights into the impact of burning firecrackers on urban air quality besides radiative impacts at a regional scale. Such celebration induced air pollution events may lead to severe health impacts; particularly respiratory and cardiovascular ailments in the resident population.

There is growing evidence that the earth's climate is changing and will likely continue to change in the future. It is still debated whether these changes are due to natural variability of the climate system or a result of increases in the concentration of greenhouse gases in the atmosphere. Black carbon (BC) has become the subject of interest for a variety of reasons. BC aerosol may cause environmental as well as harmful health effects in densely inhabited regions. BC is a strong absorber of radiation in the visible and near-infrared part of the spectrum, where most of the solar energy is distributed. Black carbon is emitted into the atmosphere as a byproduct of all combustion processes, viz., vegetation burning, industrial effluents and motor vehicle exhausts, etc. In this paper, we present results from our measurements on black carbon aerosols, total aerosol mass concentration and aerosol optical depth over an urban environment namely Hyderabad during January to May, 2003. Diurnal variations of BC indicate high BC concentrations during 6:00-9:00 and 19:00-23:00 h. Weekday variations of BC concentrations increase gradually from Monday to Wednesday and gradually decrease from Thursday to Sunday. Analysis of traffic density along with meteorological parameters suggests that the primary determinant for BC concentration levels and patterns is traffic density. Seasonal variations of BC suggest that the BC concentrations are high during dry season compared to rainy season due to the scavenging by air. The fraction of BC to total mass concentration has been observed to be 7% during January to May. BC showed positive correlation with total mass concentration and aerosol optical depth at 500 nm. Radiative transfer calculations suggests that during January to May, diurnal averaged aerosol forcing at the surface is -33 Wm(2) and at the top of the atmosphere (TOA) above 100 km it is observed to be +9 Wm(-2). The results have been discussed in detail in the paper. (c) 2005 COSPAR. Published by Elsevier Ltd. All rights reserved.