Other Impacts🔗

Air Quality–PM2.5🔗

The air quality sector simulates annual global emissions of PM2.5. En-ROADS estimates annual global emissions from three sources: energy generation (electricity), energy generation (non electricity), and other sources (including agriculture and open fires).

Ambient PM2.5 is considered the leading environmental health risk factor globally and is a top 10 risk factor in countries across the economic development spectrum. PM2.5 is fine particulate matter as defined by the mass per cubic meter of air of particles with a diameter of <=2.5 micrometers (µm).

The components of PM2.5 are solid and liquid particles small enough to remain airborne and are defined as two forms:

1. Solids/liquid particles directly emitted to the atmosphere (primary PM).
2. Solids/liquid particles formed from gaseous precursors (secondary PM).

Components of PM2.5 may include (some of) the following:

• Carbons
• Sulfates
• Nitrates
• Chlorides
• Iron
• Calcium
• Other Organics (solid/liquid)

Sources of PM2.5 in En-ROADS – Overview🔗

PM2.5 is generated from multiple sources. The chart was from research Global Sources of Fine Particulate Matter: Interpretation of PM2.5 Chemical Composition Observed by SPARTAN using a Global Chemical Transport Model (Weagle et al 2018).

En-ROADS aggregates these sources into the following sources:

1. Energy generation a. Electricity production b. Energy (non electricity) production
2. Non-energy generation a. agriculture, b. open fires, c. other sources.

PM2.5 from Energy Generation🔗

En-ROADS calculates energy generated PM2.5 emissions by applying an emissions factor (EF) (in million metric tons (Mtons) emitted per exajoule (EJ)) for each fuel source to the annual rate of energy produced (in EJ/year).

$$ Emission Rate[Fuel] = EF[Fuel] \times Electricity Production[Fuel] $$

EFs for fuel sources are calculated in several input-output models. En-ROADS applies EFs estimated from analysis by the International Institute for Applied Systems Analysis (IIASA). The EFs for coal, oil, and gas were calculated using the GAINS model (IIASA) to estimate emissions/year from G20 countries/regions and then averaged. Countries included the United States, several EU countries, India (2 regions) and China (3 different regions). The EF for bio was calculated from the RAINS model (IIASA).

Estimates for EFs were not significantly different between electricity and non electricity (which includes industry). En-ROADS applies the same EFs to electricity and non electricity. Users can vary the EF assumption across a range (by source), with a range of 50% to 150% of the base EF (shown in the table below).

PM2.5 from Non Energy Sources🔗

Non-energy sources of PM2.5 are estimated by applying a per capita EF (Mtons/year/billion people) to global population (billion people). The per capita EF is set at the start of the scenario year.

In 2015, non-energy sources of PM2.5 accounted for 35% total PM2.5 emissions. En-ROADs uses that 35% as an estimate of the non-energy contribution to total prior to 2015.

The per capita PM2.5, is calculated in 2015 (Scenario Year) by dividing global non-energy PM2.5 (Mtons/year) by global population in billions (2015). For 2015 and remaining simulated years, non-energy PM2.5 (Mtons/year) is calculated by multiplying global population (billions) by the 2015 emissions factor.

pH🔗

The pH sector of En-ROADS reflects the empirical function presented by Bernie et al. (2010). As the atmospheric concentration in the atmosphere increases, the pH of the ocean decreases by a third order response.

Other Impacts from Temperature Change🔗

The continuous increase in the global temperature is expected to cause a variety of impacts on ecology and human activities – in addition to sea level rise, increased ocean acidity and the loss in global GDP discussed in previous sections. More frequent and intense extreme weather events, major reduction in global crop yield and biodiversity loss are some examples of the other anticipated impacts of climate change. En-ROADS simulates five categories of such climate impact metrics (some categories containing more than one metric):

• Population Exposed to Sea Level Rise
• Probability of Ice-free Arctic Summer
• Decrease in Crop Yield from Temperature
• Species Losing More than 50% of Climatic Range
• Additional Deaths from Extreme Heat

Building on the findings of five peer-reviewed climate studies, we formulated the relationship between global mean temperature (as well as sea level rise) and these metrics (primarily through interpolation and extrapolation).

Model Equations🔗