Long-term trends of impacts of global gasoline and diesel emissions on ambient PM_2.5 and O₃ pollution and the related health burden for 2000–2015

In this study, CAM6-Chem was run at a horizontal resolution of 0.95° latitude by 1.25° longitude, with model nudged to the Modern-Era Retrospective analysis for Research and Applications, Version 2. Spatially distributed anthropogenic emission inventory with monthly time resolution is from the CEDS release v2021_04_21 [32]. CEDS provides anthropogenic emissions of chemically reactive gases, carbonaceous aerosols, and fossil carbon dioxide (CO₂) on a sectoral basis from 1750 to 2019 at a resolution of 0.5° latitude by 0.5° longitude. For this project CEDS emissions were re-processed to produce separate emission grids for emissions from diesel and gasoline fuel combustion (detailed source categories are provided in table S1). To be consistent with CAM6-Chem model resolution, we re-gridded CEDS anthropogenic emissions inventory from 0.5° × 0.5° longitude–latitude grid onto a 0.9° × 1.25° grid. Further details on CAM6-Chem model configuration can be found in supplemental text S1.

We run CAM6-Chem with three model simulations: a control ('CTRL') simulation with emissions from all sectors (including gasoline and diesel sectors), and two sensitivity simulations, otherwise-identical simulations to the 'CTRL' but turned off emissions from gasoline ('GSLOFF') and diesel ('DSOFF') sectors, respectively. All three simulations were run at 5 year intervals from 2000 to 2015, with the first year discarded as spin-up and the remaining year kept for post-analysis. Comparisons of these pairs (CTRL and GSLOFF, CTRL and DSOFF) of simulations allow us to isolate the impacts of gasoline and diesel emissions on air quality, which is subsequently quantified by taking the difference between the control and corresponding sensitivity simulation. Additionally, to validate model performance in simulating surface PM_2.5 and O₃, we evaluated the modeled PM_2.5 and O₃ in control simulation against a suite of ground observations globally (supplemental text S3).

Our methodology to estimate PM_2.5- and O₃-induced premature deaths from gasoline and diesel sectors is identical to the methods used in Huang et al [26], who used the newly developed Global Exposure Mortality Model (GEMM) [11] to estimate excess deaths from exposure to ambient PM_2.5 and O₃. In GEMM, the premature deaths are calculated as a function of the gridded population density, the age-specific baseline mortality rate (BMR), and the hazard ratio (HR) associated with PM_2.5 and O₃ exposure [11]. A detailed description of PM_2.5- and O₃-induced premature deaths calculation can be found in supplemental text S2.

To estimate the number of PM_2.5-induced mortality owing to gasoline and diesel sectors' emissions, we employed attribution method that scales total PM_2.5 mortality in control simulation by the fractions of PM_2.5 exposure from the gasoline and diesel sectors, given the strong nonlinearity of the CRFs associated with PM_2.5 exposure [33]. This mortality calculation is represented by equation (1):

$$\begin{align} {M_{DS}} & = \,\frac{{P{M_{2.5,\,CTRL\,}} - \,P{M_{2.5,\,DSOFF}}}}{{P{M_{2.5,\,CTRL}}}}\, \times {M_{CTRL}} \nonumber\\[4pt] {M_{GSL}} & = \,\frac{{P{M_{2.5,\,CTRL\,}} - \,P{M_{2.5,\,GSLOFF}}}}{{P{M_{2.5,\,CTRL}}}}\, \times {M_{CTRL}}\,\,\, \end{align} \tag{ 1 }$$

where M_DS and M_GSL represent PM_2.5-induced premature deaths due to diesel and gasoline emissions, respectively; and M_CTRL is the PM_2.5-induced premature mortalities from all sectors in control simulation, which is calculated in equation (S3). PM_{2.5, CTRL}, PM_{2.5, DSOFF}, and PM_{2.5, GSLOFF} denote model simulated surface PM_2.5 concentrations in control, without diesel, and without gasoline simulations, respectively. For O₃-induced mortalities associated with gasoline and diesel emissions, we subtracted the total mortalities in two sensitivity simulations (DSOFF and GSLOFF) from the total mortalities in the control (CTRL) simulation (subtraction method), which is identical to that we used for quantifying the impacts of gasoline and diesel emissions on air quality (section 2.1).

To compare premature deaths associated with exposure to different levels of PM_2.5 and O₃ across the world, we followed our previous studies [26] to divide the geography of the world into 11 regions, including China, India, the US, Canada, Western Europe (WEurope), Eastern and Central Europe (ECEurope), Latin America (LATIN), Sub-Saharan Africa (SSA), Northern Africa and the Middle East (NAME), rest of Asia (ROA), and rest of the world (ROW). Among them, the US, Canada, WEurope, and ROW are further classified as the developed countries, while China, India, ECEruope, LATIN, SSA, ROA, and NAME are the developing countries, as classified by the International Monetary Fund [34]. We sum the PM_2.5- and O₃-induced premature deaths from each grid cells and each 5 year interval age groups to determine regional and global health burdens associated with exposure to these air pollutants.

In general, gasoline and diesel emissions in the developed countries differ significantly from those in the developing countries during 2000–2015 (text S4). The transportation sector in CEDS contributed the most to global gasoline (57.7%–98.7%) and diesel (39.4%–84.5%) emissions, followed by residential, commercial, and other, industry, energy, and shipping sectors. Interestingly, we found that the emission changes in CEDS transportation sector were likely driven by the gasoline and diesel emissions, while other CEDS sectors were less impacted by the changes of these two categories (text S5). A detailed discussion of long-term trends of regional and global emissions for gasoline and diesel sectors are presented in texts S4–S5.

Spatiotemporal distributions of annual mean surface PM_2.5 concentrations from the global gasoline and diesel emissions for the years 2000, 2005, 2010, and 2015 is shown in figure 1. In general, the global annual mean surface PM_2.5 concentrations from the diesel sector are 2.1–3.4 times higher than that from the gasoline sector, which can be explained by high emissions of reactive gases and carbonaceous aerosols in the diesel sector (text S4). Gasoline and diesel emissions lead to increases in annual mean surface PM_2.5 concentrations by up to 17.5 and 13.7 µg m⁻³, respectively. For the diesel sector, large PM_2.5 burdens simulated by CAM6-Chem were found over China, India, ROA, WEurope, and NAME. By contrast, Australia, China, eastern Europe, and the Middle East were source regions associated with increases in surface PM_2.5 concentrations attributable to the gasoline sector. Meanwhile, we found distinct temporal variabilities in surface PM_2.5 concentrations over the study period. For instance, diesel emissions in China contributed 0.29, 0.51, 0.65, and 0.54 µg m⁻³ enhancements to the annual mean surface PM_2.5 concentrations in 2000, 2005, 2010, and 2015, respectively. A similar trend was also observed for gasoline emissions in China, where annual mean surface PM_2.5 concentrations simulated by CAM6-Chem increased from 0.39 µg m⁻³ to 0.43 µg m⁻³ during 2000–2010 but decreased to 0.27 µg m⁻³ in 2015. China implemented fuel (both gasoline and diesel) standard upgrades and tightened vehicle emissions standards to control the air pollutant emissions from gasoline and diesel-powered vehicles. The China 4 emission standards for new light-duty gasoline vehicles and light-duty diesel vehicles had been phased in since July 2011 and 2013, respectively [35]. The China 4 standard for heavy-duty diesel vehicles was implemented in January 2015, which reduced the emission limits of PM and NO_x by 88% and 30%, respectively [36]. Additionally, improvement in fuel quality such as lowering maximum sulfur content in on-road diesel fuel from 350 ppm to 50 ppm had been achieved by the end of 2014 in China [36]. All 'yellow labeled' gasoline and diesel vehicles that respectively failed to meet China 1 and China 3 standards were eliminated by the end of 2017 [36]. Implementation of new emission standards for new vehicles, retiring old cars, and upgrading fuel quality together explained the decreasing annual mean PM_2.5 concentrations in China during 2010–2015 for both gasoline and diesel sectors. Similarly, European countries (both WEurope and ECEurope) showed substantial (∼50%) reduction in surface PM_2.5 concentrations, resulting from the continuously improved fuel properties specified in European standards (https://dieselnet.com/standards/eu/ld.php) coupled with tightened vehicle emissions standards. Thus, mandating more stringent fuel and vehicle emissions standards is critical for controlling PM_2.5 emissions from the gasoline and diesel sectors.

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Figure 2 shows the global spatial and temporal variations of annual mean surface O₃ attributable to the gasoline and diesel emissions during 2000–2015. The emission hot spots of surface O₃ from the diesel sector simulated by CAM6-Chem were confined to NAME, India, ROA, and China, where diesel emissions contributed annual mean surface O₃ concentrations up to 7.2, 7.2, 6.9, and 6.1 ppbv, respectively. NAME, ROA, LATIN, and China were the primary source regions of surface O₃ for the gasoline sector, with the largest contributions of 7.1, 6.7, 6.6, and 5.2 ppbv, respectively. Significant temporal variabilities in surface O₃ concentrations were also found during the period of 2000–2015, with decreasing contributions (1.13–0.72 ppbv) from the gasoline sector while increasing contributions (1.12–1.51 ppbv) from the diesel sector. This opposite trend was primarily related to the emissions of O₃ precursors from each sector (text S4). It is interesting to note that diesel sector emissions led to decreases in O₃ concentrations over China, the US, and WEurope. This finding is consistent with Huang et al [7] who revealed that diesel vehicle fleet caused up to 2.5 ppbv decreases in surface O₃ over North China Plain in 2015, likely resulting from the VOC-limited regime associated with non-linear O₃ production for these particular regions.

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The global annual total premature deaths (APDs) associated with exposure to ambient PM_2.5 and O₃ from the gasoline and diesel sectors are listed in table 1. Globally, we estimate PM_2.5- and O₃-induced APDs attributable to all anthropogenic emission sectors are 5.12 (95 CI: 4.49–5.73), 5.57 (95 CI: 4.90–6.21), 6.02 (95 CI: 5.31–6.71), and 6.81 (95 CI: 6.00–7.59) million for the years of 2000, 2005, 2010, and 2015, respectively. Gasoline and diesel sectors together lead to 363 730, 412 087, 417 037, and 448 985 premature deaths associated with PM_2.5 and O₃ exposure for the same period (figure 3).

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Specifically, we attribute gasoline (diesel)-emitted PM_2.5 and O₃ to 0.16 (0.21), 0.17 (0.24), 0.16 (0.26), 0.14 (0.31) million APDs globally for the years of 2000, 2005, 2010, and 2015, respectively, which account for 3.1% (4.0%), 3.1% (4.3%), 2.6% (4.3%), and 2.1% (4.5%) of the total baseline mortality, respectively (table 1). The fractional contribution of PM_2.5- and O₃-induced APDs from the gasoline and diesel sectors (6.9%, in 2010) relative to all sectors is slightly higher than reported contribution of 5% for the transportation sector by Lelieveld et al [29]. PM_2.5-induced premature deaths dominate global annual total PM_2.5 and O₃-induced mortalities for both gasoline and diesel sectors, as its shares in the totals range from 51.5% to 63.5% (gasoline) and 77.0%–83.7% (diesel) during 2000–2015. Globally, the diesel sector leads to 2.0–2.8 times higher PM_2.5-induced mortalities than gasoline over the study period, while the larger burdens of O₃-induced mortalities (by a factor of 1.2–1.9) are observed in the gasoline sector compared with the diesel except for the year 2015. Lower O₃-induced mortalities from the diesel sector is expected given its negative impact on surface O₃ concentrations over China, the US, and WEurope (figure 2); while higher PM_2.5-induced mortalities from the diesel sector can be attributed to the significantly higher emissions of BC and NO_x (a factor of 21 and 3, respectively) that result in 2.1–3.4 times higher concentrations of surface PM_2.5 than those from the gasoline sector. In terms of years of life lost (YLL), annual total YLL associated with PM_2.5- and O₃-induced premature deaths ranges from 2.74 million (95 CI: 2.29–3.12 million) to 3.23 million (95 CI: 2.61–3.76 million) years for gasoline and 4.56 million (95 CI: 3.97–5.11 million) to 6.52 million (95 CI: 5.66–7.30 million) years for diesel, respectively accounting for about 1.9%–2.8% and 3.9%–4.4% of the baseline YLL during 2000–2015 (table 1).

While global APDs provide a snapshot of important trends of gasoline and diesel sectors' impacts on human health, regional and country-level contributions provide information more relevant to local impacts of ambient PM_2.5 and O₃. Therefore, figure 4 additionally shows PM_2.5- and O₃-induced APDs attributable to the diesel and gasoline emission sectors for 11 regions. At the country level, the estimated APDs associated with long-term PM_2.5 and O₃ exposure from the gasoline and diesel sectors vary by several orders of magnitude across regions. For instance, nearly 32 000–73 000 and 6800–7100 premature deaths could be respectively avoided in China and the US by eliminating PM_2.5 emissions from the diesel sector during 2000–2015. Apart from local impacts of PM_2.5 pollution, large variations in total number and density of the exposed population, and differences in BMRs should be considered when comparing total mortalities across the countries [37]. Specifically, the PM_2.5-induced mortality from the diesel sector is estimated at 2.57–5.29 deaths per 100 000 population in China during 2000–2015, which is on average 1.02–2.36 times greater than that in the US (2.24–2.73 deaths per capita). The BMRs due to noncommunicable diseases (NCDs) and lower respiratory infections (LRIs) associated with PM_2.5 exposure was also 1.26–1.45 times larger in China than that in the US. In combination with much higher (1.76–3.75 fold) PM_2.5 concentrations over China, it is not surprising to observe up to an order of magnitude higher diesel-related premature deaths in China than the US.

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At the regional scale, the countries with the largest PM_2.5 mortalities due to gasoline and diesel emissions are China, India, and ROA, collectively representing 63.6%–70.3% and 52.5%–70% of the global total premature mortality for the gasoline and diesel sectors, respectively. The largest PM_2.5-induced premature deaths associated with gasoline emissions are found in China, with 37 000–47 000 APDs during 2000–2015, accounting for 42.9%–49.3% of the global total mortality for gasoline (figure 4(b)). China, India, and ROA are also the countries with the highest O₃-induced mortalities, together accounting for 76.4%–80.5% and 70.1%–84.3% of the global mortality for the gasoline and diesel sectors, respectively (figures 4(d) and (c)). Interestingly, the shares of O₃-induced mortality attributable to gasoline (diesel) emissions have shrunk significantly in China, from 61% (26%) in 2000 to 36.5% (4%) in 2015. The declining share of O₃-induced mortality in China contrasts to the growing shares in India, which increased by 100% and 55% during 2000–2015 for the diesel and gasoline sectors, respectively.

To investigate the effect of economic disparities on PM_2.5- and O₃-induced mortalities, we compare total mortalities per capita (×10⁵) in the developed countries with those in the developing countries. We found health effect disparities in both PM_2.5- and O₃-induced mortality between the developed and developing countries. On average, the PM_2.5-induced moralities are estimated at 3.8, 4.6, 4.7, and 4.6 per 100 000 population in the developing countries (table 2), and 7.0, 6.4, 5.0, and 3.9 in the developed countries for the years of 2000, 2005, 2010, and 2015, respectively. The developed countries have relatively higher PM_2.5 mortalities per capita associated with diesel and gasoline emissions than the developing countries during 2000–2010. However, this trend is reversed in 2015, as the total mortality in the developed countries decreased by 38% while the total population expanded by 11.1% from 2000 to 2015, resulting in a higher PM_2.5-induced mortalities per capita in the developing countries than developed countries in 2015. For O₃-induced mortality per capita, we found an opposite health burden disparity, with the developing countries having slightly higher mortality per capita (1.6–2.2) than the developed countries (1.1–1.4) for 2000–2010 but reversing in 2015 (table 2). Taken together, diesel and gasoline emissions create health-effect disparities between the developed and developing countries, which are likely to aggravate afterwards (figure S10).