Strengths and weaknesses of the WHO urban air pollutant database

Dietrich H. Schwela*, Gary Haq

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The 2018 World Health Organization (WHO) global ambient air quality database is an impressive compilation that includes PM10 (particulate matter [PM] with an aerodynamic diameter ≤ 10 µm) monitoring data for 3,570 cities in 97 countries and PM2.5 (PM with an aerodynamic diameter ≤ 2.5 µm) data for 2,628 cities in 81 countries. The database collects measurements and estimates of these fractions, which contain pollutants such as sulphates, nitrates, and black carbon, from established public air quality monitoring systems. These pollutants can penetrate deep into the lungs and the cardiovascular system, posing the greatest risk to human health. Unsurprisingly, the WHO database reports relatively low levels of urban PM pollution in high-income (HI) countries in Western Europe, the Americas, the Western Pacific, and Oceania but high levels in low-and middle-income (LMI) countries in Africa, Southeast Asia, and Latin America—where lack of funding and inadequate staffing are key barriers to effectively reducing the air pollution—and even in high-income countries in the third region. Unfortunately, politicians, organizations, and the media have used the database to draw inaccurate and misleading conclusions based on comparisons between cities, such as occurred with the 2016 version. In this paper, we investigate the strengths and weaknesses of the 2018 database with respect to several criteria (e.g., the selection of pollutants, completeness, spatial and temporal representativeness, and quality assurance and quality control) and offer recommendations for improvement.

Original languageEnglish
Pages (from-to)1026-1037
Number of pages12
JournalAerosol and Air Quality Research
Issue number5
Publication statusPublished - May 2020

Bibliographical note

© Taiwan Association for Aerosol Research.


  • Air pollutants
  • Comparability
  • Completeness
  • Data coverage
  • Representativeness

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