Nonlinear relationships between air pollutant emissions and PM2.5-related health impacts in the Beijing-Tianjin-Hebei region

Abstract

A direct and quantitative linkage of air pollution-related health effects to emissions from different sources is critically important for decision-making. While a number of studies have attributed the PM_2.5-related health impacts to emission sources, they have seldom examined the complicated nonlinear relationships between them. Here we investigate the nonlinear relationships between PM_2.5-related premature mortality in the Beijing-Tianjin-Hebei (BTH) region, one of the most polluted regions in the world, and emissions of different pollutants from multiple sectors and regions, through a combination of chemical transport model (CTM), extended response surface model (ERSM), and concentration-response functions (CRFs). The mortalities due to both long-term and short-term exposures to PM_2.5 are most sensitive to the emission reductions of primary PM_2.5, followed by NH₃, nonmethane volatile organic compounds and intermediate volatility organic compounds (NMVOC+IVOC). The sensitivities of long-term mortality to emissions of primary organic aerosol (POA), NMVOC+IVOC and SO₂ do not change much with reduction ratio, whereas the sensitivities to primary inorganic PM_2.5 (defined as all chemical components of primary PM_2.5 other than POA), NH₃ and NO_x increase significantly with the increase of reduction ratio. The emissions of primary PM_2.5, especially those from the residential and commercial sectors, contribute a larger fraction of mortality in winter (57-70%) than in other seasons (28-42%). When emissions of multiple pollutants or those from both local and regional emissions are controlled simultaneously, the overall sensitivity of long-term mortality is much larger than the arithmetic sum of the sensitivities to emissions of individual pollutants or from individual regions. This implies that a multi-pollutant, multi-sector and regional joint control strategy should be implemented to maximize the marginal health benefits. For NO_x emissions, we suggest a nationwide control strategy which significantly enhances the effectiveness for reducing mortality by avoiding possible side effects when only the emissions within the BTH region are reduced.

Keywords: CMAQ/2D-VBS; China; PM(2.5); extended response surface model (ERSM); premature mortality.

Fig. 1

The simulation domain in this study (left) and the spatial distribution of deaths from stroke due to long-term exposure to PM_2.5 pollution in the BTH region (right).

Fig. 2

Premature mortality due to PM_2.5 pollution in the BTH region. The two sets of colored bars for each city refer to mortality due to long-term and short-term exposures, respectively. Only median values of the mortality estimates are displayed in this figure.

Fig. 3

Sensitivity of annual mean mortality due to long-term exposure to PM_2.5 to stepped control of individual air pollutants (a) and individual pollutant-sector combinations (b). The X-axis shows the reduction ratio (= 1 – emission ratio). The Y-axis shows the sensitivity of mortality, as defined by Eq. 3. The colored bars denote the sensitivity of mortality when a particular emission source is controlled while the others stay the same as the base case; the black dotted line denotes the sensitivity of mortality when all emission sources are controlled simultaneously.

Fig. 3

Sensitivity of annual mean mortality due to long-term exposure to PM_2.5 to stepped control of individual air pollutants (a) and individual pollutant-sector combinations (b). The X-axis shows the reduction ratio (= 1 – emission ratio). The Y-axis shows the sensitivity of mortality, as defined by Eq. 3. The colored bars denote the sensitivity of mortality when a particular emission source is controlled while the others stay the same as the base case; the black dotted line denotes the sensitivity of mortality when all emission sources are controlled simultaneously.

Fig. 4

Sensitivity of annual mean (a) and monthly mean (b) short-term PM_2.5-related mortality to stepped control of individual pollutant-sector combinations. The meanings of X-axis, Y-axis, colored bars, and black dotted lines are the same as Fig. 3.

Fig. 4

Sensitivity of annual mean (a) and monthly mean (b) short-term PM_2.5-related mortality to stepped control of individual pollutant-sector combinations. The meanings of X-axis, Y-axis, colored bars, and black dotted lines are the same as Fig. 3.

Fig. 5

Sensitivity of annual mean mortality caused by long-term exposure to PM_2.5 in Beijing to stepped control of individual/all air pollutants from various regions. The meanings of X-axis, Y-axis, colored bars, and black dotted lines are the same as Fig. 3. Hollow triangle dotted lines denote the sensitivity of mortality when all emission sources from regions except Beijing are controlled simultaneously.