environmental-science

## 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