Outdoor air pollution is an environmental health hazard. The Global Burden of Disease study estimates that outdoor air pollution was responsible for 6% (3.4 million) of global deaths in 2017 (Cohen et al., 2017). Outdoor air pollution is a combination of multiple air pollutants of concern, such as fine particulate matter, black carbon, ozone, organic compounds, and nitrogen dioxide (NO₂). NO₂ is a criteria air pollutant strongly associated with traffic-related air pollution and is often used in health studies as a marker of overall tailpipe emissions (Beckerman et al., 2008). Studies suggest both acute and chronic exposure to ambient NO₂ is associated with adverse health outcomes. Acute ambient NO₂ exposures are associated with child asthma hospital visits (Khreis et al., 2017) and adult ischemic stroke (Wang et al., 2020), while chronic NO₂ exposure is associated with increased odds of adult and childhood asthma incidence (Rice et al., 2013) and lung cancer (Hamra et al., 2015). Based on epidemiological and animal evidence, in 2021 the World Health Organization (WHO) revised its health guidelines for NO₂, reducing the annual mean NO₂ level to 10 μg/m³ (5.3 ppb) and the 24-h mean to 25 μg/m³ (13.3 ppb).

NO₂ air pollution is a global concern, and recent years have seen significant progress in advancing global NO₂ estimates through satellite measurements. Remote sensing columnar tropospheric NO₂ measurements from the TROPOspheric Monitoring Instrument (TROPOMI) are available daily at 7 × 3.5 km² resolution starting 30 April 2018 through 5 August 2019 and 5.5 × 3.5 km² thereafter (VanGeffen et al., 2021). The Ozone Monitoring Instrument (OMI) is the predecessor instrument to TROPOMI, launched in July 2004 and is still active (Levelt et al., 2018). While OMI reports data at a coarser resolution (24 × 13 km²) than TROPOMI, the measurements are over a multi-decadal timeframe, which makes it advantageous for performing retrospective long-term trend studies (Duncan et al., 2016; Krotkov et al., 2016; Jamali et al., 2020), such as this one. While satellite NO₂ measurements can be reported at finer spatial resolution (∼1 × 1 km²) when aggregated to monthly, seasonal or annual timescales using a process called oversampling (Sun et al., 2018; Goldberg et al., 2021), they still do not capture fine-scale (e.g., 100 m) spatial gradients in NO₂ concentrations around major emission sources, such as roads.

Additional modelling methods are needed to capture fine-scale spatial patterns of NO₂ air pollution. Land use regression (LUR) is a specialized application of regression modeling in which environmental features (land use) are used as independent or “predictor” variables to estimate air pollutant concentrations across large geographical extents. Developing LUR models often includes a variable selection step, in which a large collection of land use characteristics averaged across multiple spatial extents are reduced to a small subset of predictors used in the final regression model. Global LUR models for annual NO₂ are available at high spatial resolutions (100 m) for single snapshots in time (Larkin et al., 2017). Daytime and nighttime 2017 average global LUR models are also available (Lu et al., 2020), and deterministic global models adjusting OMI and TROPOMI measurements with the Geos-chem chemical transport model exist at moderate spatial resolutions (∼2.8 km²) (Cooper et al., 2020). However, there are no global NO₂ models available with spatial resolutions < 1 km and temporal resolutions < annual averages. Given that NO₂ gradients near major roads and highways rapidly decrease within 500 m (Patton et al., 2014; Richmond-Bryant et al., 2017), that traffic-related NO₂ concentrations are dependent on seasonal variations in traffic and meteorology (Patton et al., 2014; Amini et al., 2016; Richmond-Bryant et al., 2017), and that NO₂ emissions and meteorological conditions can rapidly change on a daily and even hourly basis (Patton et al., 2014; Richmond-Bryant et al., 2017), there is a need for new global LUR models that capture both fine spatial and temporal resolutions.

We developed a daily global NO₂ LUR model with 50 × 50 m² spatial resolution and coverage from 2005 to 2019. The model was trained using 5.7 million daily averages of air monitor records collected from 8,250 air monitor stations. We included a range of important datasets for prediction, including remote sensing measurements of tropospheric column NO₂ from the OMI, road networks, built up environments, and meteorological variables. This model can improve retrospective global risk estimates of NO₂ exposure and associated health burden, provide standardized NO₂ estimates for international health studies, and refine NO₂ estimates for health studies in developing countries where city- or country-specific measurements or retrospective models do not exist.

We collected 5.7 million daily averages from 8,250 air monitors (approximately 691 daily averages per monitor) and developed a daily global NO₂ model at 50 m resolution. The model captured 47% of daily, 59% of monthly, and 63% of annual global NO₂ variation. Predictor variables for the model are available from 2005 to the present, which allows for retrospective exposure estimates for global burden of disease studies as well as in long running epidemiological cohorts, particularly in developing countries where NO₂ data and models are limited or not available.

The model structure consists of variables with a range of spatial and temporal resolutions that correspond to NO₂ emission sources and patterns. Road networks make up variables from 50 to 200 m in resolution. Population density and built up environment variables capture moderate spatial resolutions (2.5 and 3 km, respectively), while OMI, meteorological variables, and protective land use characteristics such as water and trees capture more regional NO₂ distributions (10 km–31 km). While OMI and meteorological variables might have coarse spatial resolutions, these variables have daily temporal resolution and thus are responsible for the model’s ability to capture day to day variation in NO₂ concentrations.

Several model variables contributed little to global variation but were highly significant to capturing regional NO₂ variation. For example, built environments explained 11.9% of NO₂ variation in Africa (specifically, South Africa), but only 1.6% of the global NO₂ variation. This highlights one of the challenges of developing large scale LUR models, in which associations between predictors and outcomes may differ when stratified by sub-regions compared to examining unstratified associations. This trade-off has been highlighted in other studies examining global NO₂ modelling (Larkin et al., 2017). Other variables such as road networks may have strong associations across all subregions, but the magnitudes of those associations may differ due to regional factors such as fleet composition, traffic levels and congestion, and emission standards. We included Community Emissions Data System (CEDS) (McDuffie et al., 2020) Sector Specific NO_x Emissions, including surface transportation emissions, in our list of candidate variables (Table 1). However, none of the CEDS estimates were selected by the Lasso algorithm. Future models may benefit from emission inventories with higher spatial resolution.

Air monitor records are disproportionately greater in North America, Europe, and Asia. To mitigate, we weighted air monitor records to adjust for disproportionate spatial and temporal representation. Still, confidence in model predictions is greatest in these regions with greater coverage. The spatial extent of air monitors in Africa, South America, and Australia represents a small percentage of these continental regions and may therefore have limited generalizability across all respective countries or territories. Regression models were fitted to minimize mean square error (MSE), the square of RMSE, and daily RMSE of continental regions with large numbers of daily records (6.4–7.3 ppb) is surprisingly greater than RMSE of regions with small numbers of daily records (5.7–6.7 ppb). This may be due to the higher absolute concentrations in areas where there are many monitors. In non-polluted areas with lower absolute concentrations, RMSE of ∼6 ppb can still represent percent errors exceeding 100%. Despite this, for global studies which aim to standardize RMSE as equally as possible across multiple continents, the weighted modeling approach implemented in this model appears to work well. However, while RMSE is evenly distributed across regions, MB is noticeably higher and Adj. R ² (0.10) is noticeably lower for Oceania than other regions. Poor MB and Adj. R ² performance in Oceania is in partly attributable to the inclusion of a small set of NO₂ monitoring data from Australia that was available in OpenAQ. The mean and standard deviation of daily concentrations in Oceania is low (4 and 4 ppb, respectively) and well below global values (9 and 11 ppb). The smaller daily averages lead to larger MB when RMSE is the same. For example, an RMSE of 2 ppb with an air monitor record of 2 ppb is a 100% MB, while an RMSE of 2 ppb with an actual concentration of 20 ppb is 10% MB. In a larger and more geographically diverse external validation dataset of annual Oceania air monitor measurements (Larkin et al., 2017), model performance in Oceania is noticeably better and similar to North America and Europe (Table 7).

Compared to monthly or annual models, daily NO₂ estimates may be more susceptible to meteorological confounding, in which both OMI coverage and NO₂ concentrations are associated with cloudy weather. Our model does not make predictions for days with missing OMI values, and therefore has the same spatial-temporal coverage as columnar OMI. In our sensitivity analysis we used predictor variables selected by Lasso to develop new monthly and annual NO₂ models and compared these model cross-validation performances to monthly and annual averages of the daily model predictions. Differences between models were within 2%, which is within the random variation observed between bootstrap cross-validation instances. The geographic variables included in the monthly and annual model (Supplementary Table S4) were similar to the daily model, suggesting these are consistently the most important predictors of geographical NO₂ patterns. These comparisons suggest using the daily model for deriving monthly and annual exposures does not increase model error. Users should evaluate the percent missing coverage for their date(s) and region(s) of interest and might consider using statistical methods such as moving averages (Figure 4) to fill in missing days. However, it also suggests the additional computational costs of deriving daily results is not needed unless health studies can leverage daily exposure estimates to refine their health analyses. For acute studies such as hospital admissions following extreme exposures, daily estimates can be more useful than monthly or annual estimates.

Sensitivity models suggest model performance differs between urban and rural areas. Model predictions for rural locations have greater error than their respective urban counterparts, which is not surprising given the limited number of rural air pollution monitors available. For studies with rural participants, we recommend either using annual rather than daily or monthly model predictions or restricting analyses to urban and suburban participants.

Our NO₂ model has several limitations that should be considered when applying the model. First, model predictions are dependent on valid daily OMI measurements. We did not evaluate or exclude air monitor records based on air monitor sampling quality as most records from OpenAQ are semi-real time and do not include air monitor sampling details. In addition to meteorological limitations such as cloud cover, the number of valid daily pixel measurements from the OMI sensor on the Aura satellite has gradually been decreasing over time due to an instrument obstruction first noticed in 2007 (Schenkeveld et al., 2017). From 2018 onwards, measurements are available from the TROPOMI instrument, with substantially higher spatial resolution compared with OMI. Future studies would benefit from an adaptation of this model using TROPOMI rather than OMI as the satellite-based measure of columnar NO₂. Second, this model relies on road networks as a secondary indicator for vehicle emissions. Travel patterns have significantly changed since the onset of the COVID-19 pandemic and studies have demonstrated significant declines in NO₂ during lock-down periods (Hoang et al., 2021). We therefore restricted our model training and performance analysis to the years 2005–2019. Future research should examine potential long-term changes in travel behavior and how this impacts NO₂ levels and patterns.

We created a daily global NO₂ model with 50 m spatial resolution, with coverage from 2005–2019. In bootstrap cross-validation, the model captured 47%, 59%, and 63% of daily, monthly, and annual variation in NO₂ concentrations. We will make these NO₂ model estimates available, which can be used to retrospectively estimate acute and chronic exposures for risk assessments (e.g., global burden of disease studies), for multi-national health studies, where measurements are ideally standardized across regions, and for studies in developing countries were NO₂ monitoring data or detailed models are not available. Interested epidemiologists should consider the strengths and limitations of the documented model, particularly with respect to the spatial and temporal extent and resolution of their cohort data (Yin et al., 2020).