environmental-science

name	environmental-science
description	Analyzes environmental and climate data including temperature trends, pollution monitoring, ecological modeling, carbon footprint assessment, and biodiversity metrics; trigger when users discuss climate change, ecosystems, pollutants, or sustainability assessments.

When to Trigger

Activate this skill when the user mentions:

• Climate data, temperature anomalies, CO2 levels, greenhouse gases
• Air/water quality, pollutant concentrations, EPA standards
• Ecological modeling, species distribution, biodiversity indices
• Carbon footprint, life cycle assessment (LCA), emissions inventory
• Remote sensing, satellite imagery for environmental monitoring
• Deforestation, habitat loss, conservation planning
• Ocean acidification, sea level rise, ice sheet dynamics

Step-by-Step Methodology

1. Define the environmental question - Specify the spatial scale (local, regional, global), temporal range, and environmental domain (atmosphere, hydrosphere, lithosphere, biosphere).
2. Data acquisition - Identify appropriate datasets: NOAA/NASA for climate, EPA for pollution, GBIF for biodiversity, Copernicus for satellite data. Check data quality, coverage, and temporal resolution.
3. Exploratory analysis - Visualize spatial and temporal patterns. Plot time series for trends, anomalies, and seasonal decomposition. Map spatial distributions using appropriate projections.
4. Statistical modeling - Apply trend analysis (Mann-Kendall, Sen's slope for non-parametric trends). Use regression models for exposure-response relationships. For ecological data: species distribution models (MaxEnt, random forests), diversity indices (Shannon, Simpson).
5. Impact assessment - Quantify environmental impact using standard metrics: carbon equivalent (tCO2e), air quality index (AQI), water quality index (WQI), ecological footprint. Compare against regulatory thresholds (EPA NAAQS, WHO guidelines).
6. Scenario analysis - Model future projections under different scenarios (RCP/SSP pathways for climate, land-use change scenarios). Conduct sensitivity analysis on key parameters.
7. Communication - Present findings with clear maps, time series, and comparison to baselines. Translate technical results into policy-relevant language.

Key Databases and Tools

• NOAA / NASA GISS - Climate and weather data
• EPA / EEA - Pollution and environmental monitoring
• Copernicus / MODIS - Satellite remote sensing
• GBIF - Global biodiversity occurrence records
• IPCC AR6 - Climate assessment reports and scenarios
• Our World in Data - Environmental statistics

Output Format

• Time series plots with trend lines, confidence bands, and anomaly baselines.
• Maps with proper projections, color scales, and legends (use diverging colormaps for anomalies).
• Impact metrics in standard units with regulatory threshold comparisons.
• Scenario projections clearly labeled with assumptions.

Quality Checklist

• Data source, spatial resolution, and temporal coverage documented
• Baseline period defined for anomaly calculations
• Appropriate statistical tests for trend significance
• Uncertainty quantified and communicated (confidence intervals, ensemble spread)
• Regulatory standards cited with specific thresholds
• Map projection appropriate for the geographic extent
• Seasonal and cyclical patterns separated from long-term trends
• Limitations of data coverage and model assumptions stated