Half the world’s population are exposed to increasing air pollution

In 2016, the WHO estimated that 4.2 million deaths annually could be attributed to ambient (outdoor) fine particulate matter air pollution, or PM2.5 (particles smaller than 2.5 µm in diameter). PM2.5 comes from a wide range of sources, including energy production, households, industry, transport, waste agriculture, desert dust and forest fires and particles can travel in the atmosphere for hundreds of kilometres and their chemical and physical characteristics may vary greatly over time and space. The WHO developed Air Quality Guidelines in 2006 for reducing the health impacts of air pollution. The first edition, the WHO AQG for Europe, was published in 1987 with a global update in 2005 reflecting the increased scientific evidence of the health risks of air pollution worldwide and the growing appreciation of the global scale of the problem. The current WHO AQG states that annual mean concentration should not exceed 10 µg/m3.

The adoption and implementation of refined policies have proved to be effective in improving air quality. There are at least three examples of enforcement of long-term policies that have reduced concentration of air pollutants in Europe and North America: (i) the Clean Air Act in 1963 and its subsequent amendments in the UK; (ii) the Convention on Long-range Transboundary Air Pollution (LRTAP) with protocols enforced since the beginning of the 1980s in Europe and North America; and (iii) the European emission standards passed in the European Union in the early 1990’s. However, between 1960 and 2009 concentrations of PM2.5 globally increased by 38%, due in large part to increases in China and India, with deaths attributable to air pollution increasing by 124% between 1960 and 2009.

The momentum behind the air pollution and climate change agendas, and the synergies between them, together with the Sustainable Development Goals (SDGs) provide an opportunity to address air pollution that relate to disease. Here, trends in global air quality between 2010 and 2016 are examined in the context of attempts to reduce air pollution, both through long-term policies and more recent attempts to reduce levels of air pollution. Particular focus is given to providing comprehensive coverage of estimated concentrations and obtaining (national-level) distributions of population exposures for health impact assessment. Traditionally, the primary source of information has been measurements from ground monitoring networks but, although coverage is increasing, there remain regions in which monitoring is sparse, or even non-existent. The Data Integration Model for Air Quality (DIMAQ) has been developed by the WHO Data Integration Task Force to respond to the need for improved estimates of exposures to PM2.5 at high spatial resolution (0.1° × 0.1°) globally. DIMAQ calibrates ground monitoring data with information from satellite retrievals of aerosol optical depth, their associated transport models and other sources to provide yearly air quality profiles for individual countries, regions and globally. Estimates of PM2.5 concentrations compared with previous studies show good quantitative agreement in the direction and magnitude of trends, especially in data-rich settings (North America, Western Europe and China).

The study integrates multiple data sources to estimate annual PM2.5 concentrations globally and derive population-weighted exposure metrics for 2010–2016:

• Ground monitoring: Data from approximately 6,990 ground monitor (GM) locations worldwide were compiled from the WHO cities database for 2010–2016. Coverage is uneven, with sparse data in many low-income settings and potential spatial and temporal biases even in well-established networks.
• Data Integration Model for Air Quality (DIMAQ): DIMAQ combines GM measurements with satellite-derived aerosol optical depth (AOD) estimates and chemical transport model outputs to generate high-resolution PM2.5 fields. Satellite-based estimates are informed by the GEOS-Chem aerosol transport model, incorporating species such as sulfate, nitrate, ammonium, organic carbon, and mineral dust. The version used here extends earlier work by modeling multiple data sources simultaneously and allowing the calibration relationship between GM and satellite-based estimates to vary smoothly over time.
• Resolution and coverage: The model produces comprehensive 10 km × 10 km PM2.5 estimates for each year 2010–2016 for every country.
• Population-weighted concentrations: Gridded Population of the World (GPW v4) data were spatially aligned with DIMAQ PM2.5 estimates to compute population-weighted concentrations. The Global Human Settlement Layer (GHSL) was used to classify grid cells as urban/sub-urban or rural (based on satellite imagery and population density). Sub-urban cells were allocated to urban or rural to best match country-level urban–rural population shares from United Nations estimates.
• Consistency with burden estimates: Estimates differ slightly from those used in WHO’s global burden of disease assessments and SDG indicators due to recent database updates and additional quality assurance.
• Data availability: PM2.5 estimates supporting this work are available at https://www.who.int/airpollution/data/en/.

• Global exposure trends: 55.3% of the world’s population experienced increased PM2.5 levels between 2010 and 2016, with substantial regional heterogeneity.
• Regional contrasts: In North America and Europe, annual average population-weighted PM2.5 concentrations decreased from 12.4 to 9.8 µg/m3, consistent with long-standing regulatory actions. In Central and Southern Asia, population-weighted concentrations rose from 5.8 to 6.15 µg/m3 over the same period.
• Eastern and South-Eastern Asia: Concentrations increased from 2010 to 2013 and then declined through 2016, associated with China’s Air Pollution Prevention and Control Action Plan and a transition toward cleaner energy; population-weighted declines were more pronounced, indicating substantial people-level benefits.
• Western Asia and Northern Africa: Population-weighted concentrations increased from 42.0 to 43.1 µg/m3 while area-average concentrations increased from 50.7 to 52.9 µg/m3, reflecting an inverse correlation between concentrations and population density in this region.
• Desert dust influence: Elevated concentrations across parts of the Middle East, Asia, and Sub-Saharan Africa are strongly associated with desert dust, notably from the Sahara; observed increases are consistent with predictions of rising dust under climate change.
• Share of population above WHO AQG: The global share of people living with PM2.5 above the WHO AQG (10 µg/m3 annual mean) declined from 94.2% in 2010 to 91.6% in 2016, driven largely by reductions in North America and Europe (from 71.0% to 68.6%).
• Urban vs rural: In most regions, both urban and rural populations are exposed to levels above the AQG. In 2016, rural population-weighted PM2.5 was 55.5 µg/m3 in Central and Southern Asia, 39.1 µg/m3 in Sub-Saharan Africa, 42.7 µg/m3 in Western Asia and Northern Africa, and 34.3 µg/m3 in Eastern and South-Eastern Asia. Rural exposure in Central and Southern Asia rose by ~11% from 49.8 to 55.5 µg/m3 between 2010 and 2016.

Long-term air quality policies in Europe and the United States have been effective, reflected in declining PM2.5 levels and population-weighted exposures. Nonetheless, large populations worldwide, including in high-income countries, remain exposed to levels exceeding WHO guidelines. Quantifying the precise impacts of specific policy actions remains challenging, but evidence supports the value of sustained regulatory and technological interventions. Advances in exposure modeling (e.g., DIMAQ) and the integration of diverse data sources enhance the ability to measure, monitor, and attribute health impacts of air pollution, supporting tracking of SDG-related indicators (SDG 3.9, 7.12, and 11.6.2). Cooperation across sectors and scales (urban, regional, national, international) and investments in clean energy, cleaner transport and power generation, and energy-efficient housing are crucial. Addressing air pollution offers co-benefits for climate mitigation and substantial public health gains. Despite some recent improvements, especially in North America, Europe, and parts of East Asia, exposures remain high in many regions, including rural areas, underscoring the need for comprehensive, sustained action.

Global PM2.5 exposure remains a major public health threat, with over half of the world’s population experiencing increases between 2010 and 2016 and the vast majority living above WHO guideline levels. Robust decreases in North America and Europe demonstrate the effectiveness of long-term policy frameworks and regulatory actions, while trends in Eastern and South-Eastern Asia highlight the impact of targeted interventions. However, exposures remain high in regions such as Central and Southern Asia, Western Asia and Northern Africa, and Sub-Saharan Africa, with desert dust contributing significantly in some areas. Continued development of integrated exposure models, comprehensive monitoring, and policy tools is essential to assess interventions and guide future actions. Priorities include cross-sector cooperation, expanding access to clean energy, cleaner transport and power generation, energy-efficient housing, and addressing both urban and rural exposures. Future research should improve modeling of transboundary pollution and desert dust and strengthen the evidence base to evaluate and forecast policy impacts.

• Monitoring gaps and biases: Ground monitoring networks are sparse or absent in many low-income regions, and even well-established networks may have spatial coverage biases and missing temporal data, potentially affecting estimates and trend assessments.
• Attribution challenges: Precisely quantifying the outcomes of specific policy interventions is difficult due to confounding factors, varying implementation contexts, and limitations in observational data.
• Model assumptions and data integration: DIMAQ relies on the calibration between ground monitors, satellite AOD, and chemical transport models; uncertainties in these inputs, species composition (e.g., dust), and time-varying relationships introduce uncertainty. Estimates used here differ slightly from WHO burden assessments due to database updates and QA procedures.
• Source-specific complexities: Natural sources such as desert dust vary spatiotemporally and can be transported long distances, complicating source attribution and trend interpretation.