Urban Climate

those are the original data used in this paper.

lst.stefan-boltzman.sh - GRASS GIS shellscript

• The datasets presented here can be useful to identify gaps of research and opportunities for further development on indoor thermal comfort in tropical regions. • The data allows statistical analysis of volume, origin, emphasis, and impact of representative documentation in this subject. • The tables provided facilitate the comparison of research from different countries and cities concerning their climate and demographics. • The reader can easily visualise the content, impact and number of citations of different studies on ITC and the presence of tendencies and data patterns. • This data can be combined with similar data from other regions for further insights into state-of-the-art research worldwide.

In this paper, we are presenting databases and results for 20 Indian cities. Due to space constraints and to avoid repetition, several figures and tables are not included in the main text. The Supplementary contains the following (a) for 20 cities - selected urban airsheds as google KML files showing 0.01º grids (b) for 20 cities - urban-rural built area maps, emission projections for PM2.5 between 2015 and 2030, gridded PM2.5 emission maps, modeled PM2.5 concentration maps, and modeled monthly variation of PM2.5 concentrations for the selected airshed (c) for 20 cities – the WRF modeled wind speed, wind direction, temperature, precipitation and mixing height for the airshed as annual summary figures, summary table showing monthly variation, and database of hourly values (d) for all India – list of number of operational ambient monitoring stations and recommendation number of monitoring stations by state (37) and by district (640) (e) for all India – daily average ambient monitoring data for the period of 2011-2015 from all the stations operational under the national ambient monitoring programme (f) for all India – summary of satellite data + global model based surface PM2.5 estimates for the period of 1998 and 2016 by state (37) and by district (640) and (g) for 20 cities – summary of WRF-CAMx modeled PM2.5 source apportionment.

Universal Thermal Climate Index (UTCI) is a physiological temperature that is widely used in biometeorological studies to assess the heat stress felt by humans. UTCI considers the shortwave and longwave radiation incident on humans from the six cubical directions as well as air temperature, humidity, wind speed and clothing. As a part of NOAA National Integrated Heat Health Information System (NIHHIS) and NASA Interdisciplinary Research in Earth Science (IDS) project, we have generated the UTCI data for Austin, Texas and surrounding peri-urban area at 2-meters spatial resolution for the year 2017. Details on data generation and methodology can be found in Kamath et al., (2023) but are summarized here. 1. Datasets and model used The solar and longwave environmental irradiance geometry (SOLWEIG) model was used to simulate shadows, mean radiant temperature (TMRT) and the UTCI (Lindberg et al., 2008). TMRT is the equivalent temperature due to exposure to absorbed shortwave and longwave radiation from all directions in a standing position. SOLWEIG was forced using near-surface ERA-5 data available at a spatial resolution of 0.25°x 0.25°. Building, vegetation heights, and digital terrain model were again derived from 3DEP LiDAR point cloud data. SOLWEIG was run using the urban multi-scale environment predictor (UMEP) (Lindberg et al., 2018) plug-in with QGIS. 2. Data availability Diurnal UTCI data were calculated for typical meteorological clear sky days corresponding to Summer and Fall. The typical clear sky day was selected using the 10-year Typical meteorological Year (TMY) for Austin, Texas (30.2672° N, 97.7431° W) provided by National Solar Radiation Database (NSRDB). More details on TMY files can be found at: https://nsrdb.nrel.gov/data-sets/tmy Additionally, data is developed for heat hazard for daytime Human Heat Health Index (H3I) calculation as defined by Kamath et al., (2023). Briefly, this heat hazard is defined as the fraction of the day when the UTCI exceeds certain threshold. The threshold used to calculate heat hazard for Summer and Fall were 35° C and 32°C, respectively that imply strong heat stress (Jendritzky et al., 2012). Note that UTCI is on a different scale compared to air temperature, and could yield different heat stress levels. 3. Data format The georeferenced UTCI and heat hazard data are available in the geoTIFF file format. The files can be readily visualized using GIS software such as QGIS and ArcGIS, as well as programing languages such as Python. 4. Companion dataset Based on the calculated UTCI here, the potential locations for tree planting were calculated to increase the shade to reduce heat vulnerability for Austin, Texas. [https://doi.org/10.5281/zenodo.6363494] References Kamath, H. G., Martilli, A., Singh, M., Brooks, T., Lanza, K., Bixler, R. P., ... & Niyogi, D. (2023). Human heat health index (H3I) for holistic assessment of heat hazard and mitigation strategies beyond urban heat islands. Urban Climate, 52, 101675. Lindberg, F., Holmer, B., & Thorsson, S. (2008). SOLWEIG 1.0–Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. International journal of biometeorology, 52, 697-713. Lindberg, F., Grimmond, C. S. B., Gabey, A., Huang, B., Kent, C. W., Sun, T., ... & Zhang, Z. (2018). Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services. Environmental modelling & software, 99, 70-87. Jendritzky, G., de Dear, R., & Havenith, G. (2012). UTCI—why another thermal index?. International journal of biometeorology, 56, 421-428. Bixler, R. P., Coudert, M., Richter, S. M., Jones, J. M., Llanes Pulido, C., Akhavan, N., ... & Niyogi, D. (2022). Reflexive co-production for urban resilience: Guiding framework and experiences from Austin, Texas. Frontiers in Sustainable Cities, 4, 1015630. Lanza, K., Jones, J., Acuña, F., Coudert, M., Bixler, R. P., Kamath, H., & Niyogi, D. (2023). Heat vulnerability of Latino and Black residents in a low-income community and their recommended adaptation strategies: A qualitative study. Urban Climate, 51, 101656.

Urban sprawl and urbanization as driving forces of land degradation have direct and indirect impacts on local climate dynamic. In this paper, the hypothesis that urban sprawl and unsustainable land use change cause local climate changes has been studied. Tehran as a megacity has been considered to show the urban sprawl and urbanization impacts on local climate. The methodology is divided into two main parts based on the primary datasets (satellite imagery and local climate data). The Landsat images and digital elevation model maps extracted from Shuttle Radar Topography Mission 1 Arc-Second Global data of Tehran acquired in every 5 years during June and July from 1975 to 2015 have been used for this study. The second dataset that has been used in this study contains daily mean temperature and precipitation (from 1990 to 2010) of eight meteorological synoptic stations in the study area. The results show that the rapid and unsustainable urban growth have significant effects on local climate. Moreover, it has been found that the urbanization and urban sprawl as well as unsustainable land use change caused significant change (P = 0.005) in evaporation rate in the study area (especially in east and center regions of the city with high population density).

The indicators (non-EPA owned, yet described in the Handbook and Manuscript) represent: 1) potential exposures due to extreme events (heat, floods, drought, and wildfire), 2) specific sources of contaminant releases (the different types of sites/waste facilities), 3) contaminant fate and transport (through water and wind), and 4) population sensitivity characteristics (demographics, socioeconomic conditions, existing health conditions) that indicate which individuals in the community may be impacted more by extreme events. The geospatial indicator data layers are at the Block Group level (U.S. Census Bureau, 2022), and each Block Group is considered to be a “community”.

The dataset includes tables of the 58 indicator datasets (dataset descriptions, data formats, links to publicly available data sources for producing GIS data layers).