Environmental Datasets (2015–2024) for a Coastal Municipal Megacity: Weather, Soil, and Urban Tree Species Data

This dataset provides three integrated environmental data resources for a coastal municipal megacity, developed to support urban climate research, soil suitability assessment, and climate-resilient urban forestry planning.

The first dataset contains monthly aggregated meteorological records for 100 georeferenced locations across the costal metropolitan city from January 2015 to December 2024. Variables include mean air temperature (°C), relative humidity (%), and wind speed (km/h). Raw hourly or daily weather data were retrieved via a publicly accessible API and aggregated to monthly averages. The dataset enables long-term climate trend analysis, seasonal variability studies, and spatial comparisons across urban zones.

The second dataset consists of soil properties for 104 georeferenced locations extracted from the SoilGrids v2 API (0–5 cm depth). Variables include organic carbon density (kg/m³), pH, clay (%), sand (%), and bulk density (g/cm³). SoilGrids raw values were converted into standard scientific units using documented scaling factors. Locations were grouped into 20 spatial clusters representing broader soil zones, and six dominant soil-type patterns were identified across the coastal metropolitan city. Soil predictions were compared with published field observations (Naz et al., 2019), showing strong agreement in pH gradients, texture distributions, and organic matter ranges, with an estimated overall reliability of ~87%.

The third dataset is a structured trait database of 30 commonly planted urban tree species in the coastal metropolitan city, containing 49 ecological, morphological, and environmental tolerance attributes. Traits include growth rate, canopy development, soil and salinity tolerance, climatic thresholds, drought resistance, pollution tolerance, and urban heat resilience. Data were compiled from horticultural literature, FAO EcoCrop database, municipal records, and field observations, and validated by botanical experts.

Together, these datasets provide an integrated foundation for urban climate modeling, soil suitability assessment, species selection analysis, machine learning applications, and green infrastructure planning in arid coastal megacities.