Visualizing changes in nationally averaged PM 2.5 concentrations by an alluvial diagram

Ailments related to ambient fine particulate matter (PM_2.5) cause 3.22 million deaths per year on average worldwide, which, along with high blood pressure and tobacco smoking, is defined as the leading risk factor for global burden of disease (Lim et al., 2013). The alluvial diagram displayed in Figure 1 demonstrates the temporal changes in the ranks of nationally averaged PM_2.5 concentrations of developed areas from 1998 to 2013 (Rosvall and Bergstrom, 2010). A ‘developed area’ within a country was delineated using the Defense Meteorological Satellite Program’s Operational Linescan System annual stable lights image composites with pixels’ digital number values equal to or larger than 10 (Zhao and Samson, 2012). A total of 46 countries with at least 10 identified developed areas in the 1998 stable lights image product are included in the alluvial diagram (Doll et al., 2000). Here each wide line in the alluvial diagram is called an alluvium. PM_2.5 concentrations were extracted from a satellite-derived gridded PM_2.5 datasets (van Donkelaar et al., 2016). This dataset is considered to be one of the most accurate global PM_2.5 concentration image products at the 0.01° × 0.01° (or 1 km × 1 km) spatial resolution and has been used in applied public health studies (van Donkelaar et al., 2015). OPEN IN VIEWER

Figure 1 displays differences in the width of each alluvium, which represents a country’s yearly variations in PM_2.5 concentrations. The scaled width of the alluvium corresponding to the largest national average PM_2.5 concentration (63 μg/m³) of the 46 countries, which is found in Iraq, is marked in the diagram. In the vertical direction, the countries are ranked upward by their average PM_2.5 concentrations (from lowest to highest) for each year. The left-hand axis is ordered from the lowest to highest concentrations in 1998, while the right-hand axis is for that of 2013. Thus, a country with a drastic change in PM_2.5 concentration between the time period, such as Côte d’Ivoire, will change position substantially.

In the alluvial diagram, the locations with the highest PM_2.5 concentrations tend to be consistently occupied by the countries in Middle East, North Africa, and the Asian continent. The PM_2.5 concentrations of the countries with fast economic growth, such as India and China, tend to increase from 1998 to 2013. In contrast, the PM_2.5 concentrations of the European, Oceanian, American, and South Asian island countries are low in general. Furthermore, the PM_2.5 concentrations in most European countries (e.g. the United Kingdom, Germany, and Netherlands) exhibit clear decreasing trends. Interestingly, Côte d’Ivoire and Cameroon’s PM_2.5 concentrations and variations across the years are relatively large, which need to be further investigated in the future. Overall, the alluvial diagram in Figure 1 is an effective method to communicate inter- and intra-country PM_2.5 concentrations within dense urban centers, allowing for identification of specific events and trends of interest by year.