The Effects of Increasing Population Granularity in $\mathbf{P}{\mathbf{M}}_{2.5}$ Population-Weighted Exposure and Mortality Risk Assessment

Introduction

The Global Burden of Diseases, Injuries, and Risk Factors Study (Vos et al. 2020) is a global assessment of morbidity and mortality from publicly available data and information on 369 diseases and injuries. According to the Global Burden of Disease study (GBD), $4.14\text{\hspace{0.17em}million}$ people died from exposure to ambient ${\mathrm{PM}}_{2.5}$ (fine particulate matter with a diameter of $2.5\mathrm{\mu }\mathrm{m}$ or less) in 2019 (GBD 2019a). Developments in earth observation have created opportunities for more granular analysis of relative risk (RR) attributed to ${\mathrm{PM}}_{2.5}$. Our study assessed the potential value and feasibility of improved spatial and geographic resolution of population density along with more highly resolved spatial concentrations of ${\mathrm{PM}}_{2.5}$ in Russia, a country of nearly $146\text{\hspace{0.17em}million}$ (UNDESA n.d.).

Methods

Like the GBD, other global studies of ${\mathrm{PM}}_{2.5}$ attributable mortality (Hammer et al. 2020), have used global satellite ${\mathrm{PM}}_{2.5}$ data, and global population databases for exposure estimates for all countries. We used global ${\mathrm{PM}}_{2.5}$ annual average concentrations at two spatial resolutions ($0.1°\times 0.1°$ (or $10\mathrm{km}\times 10\mathrm{km}$) and $0.01°\times 0.01°$ (or $1\mathrm{km}\times 1\mathrm{km}$); from WUSTL (https://sites.wustl.edu/acag/datasets/surface-pm2-5/) databases for 2018, which was the most recent year with data at the higher resolution.

We developed a stepwise process to search for a level of population granularity to better estimate population exposure to ${\mathrm{PM}}_{2.5}$, especially because air pollution can be more concentrated in areas that may be more highly populated. Second, we wanted to explore the usefulness of the more highly spatially resolved ${\mathrm{PM}}_{2.5}$ data. Our first step was to map the population of Russia’s many large regions more precisely. Russia is an extremely large country, but its people live in a very small area of the country, heavily clustered in cities and towns. To do this mapping, we used the official Russian national population statistics from ROSSTAT (https://rosstat.gov.ru/folder/210/document/13207; https://rosstat.gov.ru/compendium/document/13282) that provide total population data for regions, subregions, and municipalities.

For the first step, we used an approach to generally replicate the GBD results by evenly distributing the population across Russia’s large regions. Next, we looked at ways to locate the population more precisely. For the second step, we used municipalities which are much smaller than regions. The population of all municipalities of a region add up to the total population of the region. Although it should be noted that Russia’s two largest cities, Moscow and St. Petersburg, are themselves classified as regions in these statistics and are separate from their surrounding regions. Then we used OpenStreetMap (OpenStreetMap Contributors n.d.) to map the population more finely through the use of buildings. We used density of buildings as a proxy for population density. Building density was estimated by using its areal footprint, without taking into account either building height or function. Finally, this analysis excluded the area north of 70°, an area without satellite coverage and only $\sim \mathrm{6,800}$ inhabitants according to ROSSTAT data (https://rosstat.gov.ru/compendium/document/13282).

To calculate population-weighted ${\mathrm{PM}}_{2.5}$ concentration we conducted the following analyses. For regions, the arithmetic average ${\mathrm{PM}}_{2.5}$ concentration of all grid cells belonging to a particular region was calculated. Then we used that region’s share of the total Russian population as the weighting coefficient. The identical approach was used for municipalities, using the arithmetic average ${\mathrm{PM}}_{2.5}$ concentration of all grid cells belonging to a particular municipality. These calculations, both for regions and municipalities, were calculated only using the lower spatial resolution of $0.1°\times 0.1°$.

For buildings, populations were assigned proportionately, based on the buildings’ areal extent. Within each municipality, for each grid cell, we divided the total footprint of buildings belonging to this grid cell by the total footprint of all buildings within the municipality. This coefficient constitutes a proportion of the population in a municipality that belongs to each grid cell. Then that coefficient was multiplied by the total population of the municipality, which gives a population for each grid cell. This could then be used with the ${\mathrm{PM}}_{2.5}$ concentration within each grid cell to estimate the population-weighted ${\mathrm{PM}}_{2.5}$ concentration. This calculation for buildings was done for each of the two spatial resolutions ($0.1°\times 0.1°$ and $0.01°\times 0.01°$).

The results of these analyses provided three levels of population granularity. The least fine-grained had the population evenly distributed within each region, and the medium granularity had a population evenly distributed within a municipality. The most fine-grained had the population distributed by buildings.

To ensure comparability with the GBD we calculated country-wide population-weighted concentration and country-wide mortality risk using GBD data and RR functions. The numbers of deaths from each of these diseases due to ${\mathrm{PM}}_{2.5}$ in Russia were downloaded from GBD (2019a) and are available in the Open Science Framework (https://osf.io/egn87/). GBD (2019b) estimates of ${\mathrm{PM}}_{2.5}$ attributable mortality are based on RR functions for six diseases: lower respiratory infections; type 2 diabetes; chronic obstructive pulmonary disease; tracheal, bronchial, and lung cancers; ischemic heart disease (IHD); and stroke. The RR is a function of the ambient ${\mathrm{PM}}_{2.5}$ concentration for each disease and for each age group as described above and was calculated using the GBD RR tables (GBD 2019a). For concentrations in the range of $5–2{0\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$, the RR could be approximated as a linear function of ${\mathrm{PM}}_{2.5}$ concentration.

The population mortality attributable to ${\mathrm{PM}}_{2.5}$ pollution was calculated using a simplified formula, not a multi-risk model. It was assumed that the risk attributable to ${\mathrm{PM}}_{2.5}$ pollution increased proportionally to the increase in the attributable fraction of the background mortality corresponded to increase in RR:

$$MN=\sum M{B}_i^j\left[\frac{RR{N}_i^j-1}{RR{N}_i^j}/\frac{RR{B}_i^j-1}{RR{B}_i^j}\right],$$

where MN denotes a new estimate of mortality attributed to ambient ${\mathrm{PM}}_{2.5}$ pollution (calculated for each of the six diseases in the GBD analysis (lower respiratory infections; type 2 diabetes; chronic obstructive pulmonary disease; tracheal, bronchial and lung cancers; IHD; and stroke). Additionally, for IHD and stroke, the same specific age groups were used ($<5$, and then in 5-y cohorts starting at age 25–29 y and ending with the cohort age 90–94 y). In addition, $M{B}_i^j$ is mortality (from disease i for age group j) attributed to ambient ${\mathrm{PM}}_{2.5}$ pollution reported in GBD. $RR{N}_i^j$ stands for the RR at recalculated population-weighted concentration (for disease i and age group j); and $RR{B}_i^j$ was calculated for the concentration estimated in GBD ($11.6{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$).

Results and Discussion

In this analysis, we were able to use satellite data to better map population densities using regions, municipalities, and buildings. The more granular population data resulted in higher population risk. The effects of these different population aggregations can be seen in Table 1. The four estimates of population-weighted ${\mathrm{PM}}_{2.5}$ concentrations for all of Russia are shown in the first column. The table shows that our estimated population-weighted ${\mathrm{PM}}_{2.5}$ concentration and mortality using the lowest granularity and spatial resolution is virtually identical to the State of the Global Air (SOGA) estimate of $11.6{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$. SOGA is an annual report that is part of the GBD and is a peer-reviewed air quality analysis (HEI 2020). Table 1 also shows that a stepwise increasing granularity led to higher estimates of ${\mathrm{PM}}_{2.5}$ exposures and thus to higher estimates of ${\mathrm{PM}}_{2.5}$ attributable mortality. The higher spatial resolution also resulted in higher estimates of ${\mathrm{PM}}_{2.5}$ exposure and attributable mortality. The single largest increase in population-weighted ${\mathrm{PM}}_{2.5}$ concentration came from the change from regions to municipalities.

Table 1.

Population-weighted ${\mathrm{PM}}_{2.5}$ concentration (micrograms per cubic meter) and attributable mortality for all of Russia at three levels of population granularity and by spatial resolution. Population data were from 2018. Population granularity and estimates of ${\mathrm{PM}}_{2.5}$ concentrations and attributable mortality were calculated as described in the “Methods” section of the text. PM, particulate matter; ${\mathrm{PM}}_{2.5}$, particulate matter with aerodynamic diameter $2.5\mathrm{\mu }\mathrm{g}$ or less.

Population unit	Population granularitya	Spatial resolution of ${\mathrm{PM}}_{2.5}$ concentrationsb	Estimated population-weighted average ${\mathrm{PM}}_{2.5}$ concentrationc	Estimated ${\mathrm{PM}}_{2.5}$ attributable mortality
Regions	Low	$0.1°\times 0.1°$	11.9	73,300
Municipalities	Medium	$0.1°\times 0.1°$	13.2	80,080
Buildings	High	$0.1°\times 0.1°$	13.5	81,620
Buildings	High	$0.01°\times 0.01°$	13.9	83,650

Note: PM, particulate matter; ${\mathrm{PM}}_{2.5}$, particulate matter with aerodynamic diameter $2.5\mathrm{\mu }\mathrm{g}$ or less.

^a Population data from ROSSTAT (https://rosstat.gov.ru/folder/210/document/13207; https://rosstat.gov.ru/compendium/document/13282 and buildings data from OpenStreetMap (https://www.openstreetmap.org/#map=5/38.007/-95.844).

^b ${\mathrm{PM}}_{2.5}$ data from WUSTL (https://sites.wustl.edu/acag/datasets/surface-pm2-5/).

^c For comparison purposes, the ${\mathrm{PM}}_{2.5}$ concentration for all of Russia from the State of the Global Air was $11.6{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$ (https://www.stateofglobalair.org/data/#/air/plot), and the estimated ${\mathrm{PM}}_{2.5}$ attributable mortality in 2018 for Russia from the Global Burden of Diseases was 71,903 (GBD 2019a). The total Russian population for 2018 was 145,734,000 (UNDESA n.d.).

Figure 1 shows that for any location, population-weighted ${\mathrm{PM}}_{2.5}$ concentrations were almost always higher in the municipality, when compared with the larger region, and similarly that for any location, population-weighted ${\mathrm{PM}}_{2.5}$ concentrations were higher when resolved by buildings as opposed to municipalities. Using regional population densities and therefore population-weighted concentrations introduced a systematic underestimation, because all but one municipality is above the reference line (starting at the origin running along the ${45}^{\circ }\text{angle}$), and many are substantially higher.

Figure 1.

Comparison of PWAC in Russia by buildings, municipalities, and regions. PWAC for 80 Russian regions were calculated according to the four different methods of aggregation as described in the methods and shown in Table 1. This figure compares precision of these population-weighted ${\mathrm{PM}}_{2.5}$ concentrations using population allocation by buildings, municipalities, and regions. PWAC of buildings in this figure were based on the calculations using the $0.01\times 0.01$ spatial resolution satellite data from Washington University in St. Louis [WUSTL (https://sites.wustl.edu/acag/datasets/surface-pm2-5/)]. These estimates for this figure are described in the “Methods” section in the main text. The black dots represent the ${\mathrm{PM}}_{2.5}$ concentrations (micrograms per cubic meter). Each dot on the graph represents PWAC calculated by two alternative methods. The left panel shows the PWAC using average population density in all Russian municipalities (horizontal axis), and the PWAC computed with population density allocated by building footprint within each of the municipalities (vertical axis). On the right panel the horizontal axis is the PWAC calculated using the average population density in each region, and the vertical axis shows PWAC using average population density of each municipality belonging to that region. The diagonal reference line runs from the origin at a ${45}^{\circ }\text{angle}$. If a given dot is on the diagonal line, then both methods provide the same estimate. If a black dot is above the diagonal line, then the more granular method provides a more precise estimation of the PWAC and therefore a higher overall precision for the exposure calculations. Virtually all of the black dots are on or above the diagonal line and many are substantially higher, showing that increased granularity leads to higher ${\mathrm{PM}}_{2.5}$ concentration estimates. Note: PM, particulate matter; ${\mathrm{PM}}_{2.5}$, particulate matter with aerodynamic diameter $2.5\mathrm{\mu }\mathrm{g}$ or less; PWAC, population-weighted average ${\mathrm{PM}}_{2.5}$ concentrations.

Use of more localized, fine-grained population data made a difference in estimations of air pollution health risk in Russia in comparison with the GBD estimates, while using the same RR functions. Russia is a country with a large proportion of its population living in relatively high urban densities. In the Khabarovsk Region, for example, the population of the two largest cities is 65% of the region’s total but only 0.09% of the area. When estimated by region the population-weighted concentration of ${\mathrm{PM}}_{2.5}$ was $6.{9\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$, but when estimated by municipalities it was $11.6{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$. Another example is the Alagir subregion of North Ossetia, an area $>\mathrm{2,000}{\mathrm{km}}^2$. However, more than half of its population is in the city of Alagir, an area of only $26.7{\mathrm{km}}^2$. The calculated population-weighted concentration in the Alagir subregion is $12.1{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$, but when calculated using the population distributed by buildings, the ${\mathrm{PM}}_{2.5}$ weighted concentration rises to $18{\mathrm{\mu }\mathrm{g}/\mathrm{m}}^3$. Also, the use of the more highly spatially resolved concentration data from the WUSTL database also increased estimates of risk but was a smaller effect than from more granular population data. This was an unanticipated finding from this study.

The remarkable aspect of the Global Burden of Diseases is its comparability across the countries of the world. Nonetheless, there may be ways to improve accuracy of assessment of the global burden of diseases. This example for Russia shows how improved mapping of population based on readily available satellite data on settlements and buildings seems to us to be a potential approach to update the GBD while maintaining a globally uniform method to estimate health risk from air pollution. The methodology for increased precision of the exposure assessment could be replicated in other countries and could result in more accurate estimates of the health risks associated with air pollution.

As more of the world’s population lives in cities, improving air pollution exposure assessment is important for improving estimates of air pollution attributable mortality and for air pollution regulation itself. These results would make the case that more stringent management of ${\mathrm{PM}}_{2.5}$ concentrations are required to reduce population-level risk. Also, using the higher ${\mathrm{PM}}_{2.5}$ spatial resolution within cities could be used to identify hotspots with higher-than-average concentrations. The contribution of this methodology is to show the potential for more accurate representation of population distribution by using readily available data from national statistics and additionally using building information available from open sources. These methods are just a first step and can clearly be improved, but even this proposed approximation can benefit air pollution health risk analyses. Mapping populations by buildings is just a start, and clearly, populations are not evenly distributed by buildings just based on footprint, nor did this approach account for height and building use. Accounting for these issues could lead to further improvements in population exposures. Nonetheless, for Russia, we showed an approach to more precise population mapping approach that led to an $\sim 17\%$ higher estimate of population-weighted ${\mathrm{PM}}_{2.5}$ concentrations in comparison with the GBD.