GEOS-Chem Simulated Air Quality and Meteorology for the US and China, 2011-2017

GEOS-Chem simulations provide daily PM2.5 and O3 concentrations alongside MERRA-2 meteorological features for the United States (2011-2017) and China (2013-2017). The data, created by Minghao Qiu at MIT, supports replication of a 2022 study on evaluating air quality trends under changing meteorological conditions. It includes observational, counterfactual, and constant-emission scenarios to isolate the effects of meteorology and emissions.

Use Cases

• Evaluating trends in air pollutants like PM2.5 and O3 based on simulated concentration data.
• Isolating the impact of changing meteorology versus emissions using counterfactual scenarios.
• Training machine learning models for meteorological correction, as suggested by the included R scripts for methods like Random Forest (RF-regional).
• Analyzing regional air quality patterns using meteorological features aggregated to a 4x5 degree grid.

Strengths

• Includes data for two major regions: the United States (2011-2017) and China (2013-2017).
• Provides multiple experimental scenarios (observational, counterfactual, constant-emission) to disentangle confounding factors.
• Accompanied by R scripts implementing specific statistical and machine learning correction methods described in the associated paper.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale modeling.
• Last update date is unknown; freshness unverified.

Provenance

Source

Massachusetts Institute of Technology (author: Minghao Qiu)

Collection Method

Output from GEOS-Chem atmospheric model simulations, post-processed with R scripts.

Time Range

United States: 2011-2017; China: 2013-2017

Geography

United States and China