Statistical Appraisal of the Global Air Pollution Dataset: A Review and Empirical Re-analysis of City-Level Air Quality Indices

Abstract

Ambient air pollution is a leading environmental risk factor for premature mortality, and city-level air quality
indices (AQI) are widely used to communicate exposure. The Global Air Pollution Dataset records the overall
AQI and four pollutant sub-indices (PM2.5, ozone, nitrogen dioxide and carbon monoxide) for 23,463 cities
in 175 countries, yet its statistical structure and the validity of analyses built on it are rarely examined
together. This review combines a narrative synthesis of the literature with a full statistical re-analysis of
the supplied file. We audit data quality and internal consistency, describe distributions, quantify pollutant
dominance, correlation and relative importance, measure between-country heterogeneity, and evaluate
predictive models under two validation designs. Our synthesis finds that (i) the overall AQI equals the
maximum sub-index in 99.4% of records, and the 141 exceptions all occur at extreme PM2.5 levels; (ii) PM2.5
is the highest sub-index in 78.9% of cities and in every city rated Hazardous, and alone explains 96.9% of
AQI variance; (iii) country of location explains about half of the variation in log AQI (intraclass correlation
0.54; Kruskal-Wallis ϵ
2 = 0.49), with median AQI ranging from 20 (Bolivia) to 169 (Pakistan); and (iv) when
country information is allowed to leak across cross-validation folds, the AUC for predicting unhealthy air
from non-PM2.5 pollutants rises from 0.84 to 0.97, an artefact of the validation design. We propose a short
reporting checklist and six priority research directions.