Transition in source contributions of PM2.5 exposure and associated premature mortality in China during 2005–2015

Zheng, Haotian; Zhao, Bin; Wang, Yafei; Wang, Tong; Ding, Dian; Chang, Xing; Liu, Kaiyun; Xing, Jia; Dong, Zhaoxin; Aunan, Kristin; Liu, Tonghao; Wu, Ye; Zhang, Shaojun

doi:10.1016/j.envint.2019.105111

Cited by 117 publications

(106 citation statements)

References 34 publications

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“…Our results were rather close to the estimates of Burnett et al, (2018), but prominently higher than the previous estimates of 0.87-1.37 million during 2010-2015 (Lelieveld et al, 2015;Liu et al, 2016c;Hu et al, 2017;Zheng et al, 2017Zheng et al, , 2019, which applied the Integrated Exposure-Response (IER) function developed in the 25 Global Burden of Disease (GBD) Project (Burnett et al, 2014;Cohen et al, 2017). We conducted a sensitivity analysis by repeating the calculations with the IER risk functions used in GBD 2015, and estimated the anthropogenic PM2.5-related mortalities in China in 2015 were 0.97 million (Table 4), which were close to the estimate of 0.87 million in 2015 by Zheng et al, (2019). The sensitivity analysis shows that different PM2.5 exposure-response functions exhibit large discrepancies in the estimates of PM2.5-related mortality, which calls for more additional cohort studies in the China to improve the health risk 30 models and narrow the gaps between different studies.…”

Section: Uncertainties and Limitationssupporting

confidence: 87%

“…A decreasing trend of the contribution from the power sector during 2005-2015 was illustrated by all the studies. Large discrepancies occurred in the agricultural sector, with the contribution ranging from 0.1% (Reddington et al, 2019) to over 30% (Lelieveld et al, 2015;Zheng et al, 2019). The magnitude of the contribution from the transportation sector was relatively 30 close in all the studies.…”

Section: Comparison With Other Studies 20mentioning

confidence: 77%

“…These differences were acceptable considering the uncertainty ranges from the GEMM functions. Previous studies on long-term trends in the chemical composition of population-weighted PM2.5 (Li et al, 2017a), the attribution of source contributions to the PM2.5-related mortality to emissions changes (Lelieveld et al, 2015;Ma 15 et al, 2017;Gu et al, 2018;Zheng et al, 2019) also applied the chemical transport model simulations to estimate exposure.…”

Section: Uncertainties and Limitationsmentioning

confidence: 99%

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Decadal changes in anthropogenic source contribution of PM2.5 pollution and related health impacts in China, 1990–2015

Li¹,

Zheng²,

Geng³

et al. 2019

Preprint

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Abstract. Air quality in China has changed dramatically in response to rapid development of economy and policies. In this work, we investigate the changes of anthropogenic source contribution to ambient fine particulate matter (PM2.5) air pollution and related health impacts in China during 1990–2015 and elucidate the drivers behind the decadal transition. We estimate the contribution of five anthropogenic emitting sectors to ambient PM2.5 exposure and related premature mortality over China during 1990–2015 with 5-yr intervals, by using an integrated model framework of bottom-up emission inventory, chemical transport model, and the Global Exposure Mortality Model (GEMM). The national anthropogenic PM2.5-related premature mortality estimated with GEMM for the nonaccidental deaths due to noncommunicable diseases and lower respiratory infections rose from 1.26 million (95 % CI: 1.05, 1.46) in 1990 to 2.18 million (95 % CI: 1.84, 2.50) in 2005; then, it decreased to 2.10 million (95 % CI: 1.76, 2.42) in 2015. In 1990, the residential sector was the leading source of the PM2.5-related premature mortality [559,000 (95 % CI: 467,000, 645,900), 44 % of total] in China, followed by industry (29 %), power (13 %), agriculture (9 %) and transportation (5 %). In 2015, the industrial sector became the largest contributor of PM2.5-related premature mortality [734,000 (95 % CI: 615,500, 844,900), 35 % of total], followed by residential (25 %), agriculture (23 %), transportation (10 %) and power (6 %). The decadal changes in source contribution to PM2.5-related premature mortality in China represents a combined impact of socioeconomic development and clean air policy. For example, active control measures have successfully reduced pollution from power sector, while contribution from industrial and transportation sector continuously increased due to more prominent growth of activity rates. Transition in fuel consumption dominated the decrease of contribution from residential sector. In the meanwhile, contribution from agriculture sector continuously increased due to persistent NH3 emissions and enhanced formation of secondary inorganic aerosols under a NH3 rich environment.

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Section: Uncertainties and Limitationssupporting

confidence: 87%

Section: Comparison With Other Studies 20mentioning

confidence: 77%

Section: Uncertainties and Limitationsmentioning

confidence: 99%

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Decadal changes in anthropogenic source contribution of PM2.5 pollution and related health impacts in China, 1990–2015

Li¹,

Zheng²,

Geng³

et al. 2019

Preprint

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“…The rapid development of China has led to severe air pollution due to the ever-increasing energy demand and lax environmental legislation over the past decades, which exerts negative influences on human health, climate, agriculture, and ecosystems (Xue et al, 2019;Zheng et al, 2019). In 2013, China implemented the Air Pollution Prevention and Control Action Plan (denoted as the Action Plan) to fight against air pollution (China State Council, 2013), and a series of active clean air policies for various sectors were taken in support of the Action Plan.…”

Section: Introductionmentioning

confidence: 99%

Dynamic projection of anthropogenic emissions in China: methodology and 2015–2050 emission pathways under a range of socioeconomic, climate policy, and pollution control scenarios

Tong¹,

Cheng²,

Yang³

et al. 2020

Preprint

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Abstract. Future trends in air pollution and greenhouse gas (GHG) emissions for China are of great concern to the community. A set of global scenarios regarding future socioeconomic and climate developments, combining shared socioeconomic pathways (SSPs) with climate forcing outcomes as described by the representative concentration pathways (RCPs), were created by the Intergovernmental Panel on Climate Change (IPCC). Chinese researchers have also developed various emission scenarios by considering detailed local environmental and climate policies. However, a comprehensive scenario set connecting SSP-RCP scenarios with local policies and representing dynamic emission changes under local policies is still missing. In this work, to fill this gap, we developed a dynamic projection model, the Dynamic Projection model for Emissions in China (DPEC), to explore China&#8217;s future anthropogenic emission pathways. The DPEC model is designed to integrate the energy system model, emission inventory model, dynamic projection model, and parameterized scheme of Chinese policies. The model contains two main modules, an energy-model-driven activity rate projection module and a sector-based emission projection module. The activity rate projection module provides the standardized and unified future energy scenarios after reorganizing and refining the outputs from the energy system model. Here we use a new China-focused version of the Global Change Assessment Model (GCAM-China) to project future energy demand and supply in China under different SSP-RCP scenarios at the provincial level. The emission projection module links a bottom-up emission inventory model, the Multi-resolution Emission Inventory for China (MEIC), to GCAM-China, and accurately tracks the evolution of future combustion/production technologies and control measures under different environmental policies. We developed technology-based turnover models for several key emitting sectors (e.g., coal-fired power plants, key industries, and on-road transportation sectors), which can simulate the dynamic changes in the unit/vehicle fleet turnover process by tracking the lifespan of each unit/vehicle on an annual basis. With the integrated modelling framework, we connected five SSP scenarios (SSP1-5), five RCP scenarios (RCP 8.5, 7.0, 6.0, 4.5, and 2.6), and three pollution control scenarios (business as usual (BAU), enhanced control policy (ECP), and best health effect (BHE)) to produce six combined emission scenarios. With those scenarios, we presented a wide range of China's future emissions to 2050 under different development and policy pathways. We found that, with a combination of strong low-carbon policy and air pollution control policy (i.e., SSP1-26-BHE scenario), emissions of major air pollutants (i.e., SO2, NOx, PM2.5, and NMVOCs) in China will be reduced by 34&#8211;66&#8201;% in 2030 and 58&#8211;87&#8201;% in 2050 compared to 2015. End-of-pipe control measures are more effective for reducing air pollutant emissions before 2030, while low-carbon policy will play more important role for continuous emission reduction until 2050. On contrast, China's emissions will remain high level until 2050 under a reference scenario without active action (i.e., SSP3-70-BAU). Compared to similar scenarios set in the CMIP6 (Coupled Model Intercomparison Project Phase 6), our estimates of emission ranges are much lower than the estimates from the CMIP6 in 2020/2030, but their emission ranges become similar in the year 2050.

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“…As one of the major city clusters in China, the Pearl River Delta (PRD) region is exposed to a high level of PM 2.5 pollution [7]. The population-weighted mean PM 2.5 concentrations have been extensively used by researchers as an indicator of the population exposure level [8][9][10][11][12][13][14][15]. It is estimated by averaging the ambient PM 2.5 concentration associated with each individual within the study region.…”

Section: Introductionmentioning

confidence: 99%

Decomposing the Long-term Variation in Population Exposure to Outdoor PM2.5 in the Greater Bay Area of China Using Satellite Observations

et al. 2019

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The Greater Bay Area (GBA) of China is experiencing a high level of exposure to outdoor PM2.5 pollution. The variations in the exposure level are determined by spatiotemporal variations in the PM2.5 concentration and population. To better guide public policies that aim to reduce the population exposure level, it is essential to explicitly decompose and assess the impacts of different factors. This study took advantage of high-resolution satellite observations to characterize the long-term variations in population exposure to outdoor PM2.5 for cities in the GBA region during the three most-recent Five-Year Plan (FYP) periods (2001–2015). A new decomposition method was then used to assess the impact of PM2.5 variations and demographic changes on the exposure variation. Within the decomposition framework, an index of pollution-population-coincidence–induced PM2.5 exposure (PPCE) was introduced to characterize the interaction of PM2.5 and the population distribution. The results showed that the 15-year average PPCE levels in all cities were positive (e.g., 6 µg/m3 in Guangzhou), suggesting that unfavorable city planning had led to people dwelling in polluted areas. An analyses of the spatial differences in PM2.5 changes showed that urban areas experienced a greater decrease in PM2.5 concentration than did rural areas in most cities during the 11th (2006–2010) and 12th (2011–2015) FYP periods. These spatial differences in PM2.5 changes reduced the PPCE levels of these cities and thus reduced the exposure levels (by as much as -0.58 µg/m3/year). The population migration resulting from rapid urbanization, however, increased the PPCE and exposure levels (by as much as 0.18 µg/m3/year) in most cities during the three FYP periods considered. Dongguan was a special case in that the demographic change reduced the exposure level because of its rapid development of residential areas in cleaner regions adjacent to Shenzhen. The exposure levels in all cities remained high because of the high mean PM2.5 concentrations and their positive PPCE. To better protect public health, control efforts should target densely populated areas and city planning should locate more people in cleaner areas.

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Transition in source contributions of PM2.5 exposure and associated premature mortality in China during 2005–2015

Cited by 117 publications

References 34 publications

Decadal changes in anthropogenic source contribution of PM<sub>2.5</sub> pollution and related health impacts in China, 1990–2015

Decadal changes in anthropogenic source contribution of PM<sub>2.5</sub> pollution and related health impacts in China, 1990–2015

Dynamic projection of anthropogenic emissions in China: methodology and 2015–2050 emission pathways under a range of socioeconomic, climate policy, and pollution control scenarios

Decomposing the Long-term Variation in Population Exposure to Outdoor PM2.5 in the Greater Bay Area of China Using Satellite Observations

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Transition in source contributions of PM2.5 exposure and associated premature mortality in China during 2005–2015

Cited by 117 publications

References 34 publications

Decadal changes in anthropogenic source contribution of PM&lt;sub&gt;2.5&lt;/sub&gt; pollution and related health impacts in China, 1990–2015

Decadal changes in anthropogenic source contribution of PM&lt;sub&gt;2.5&lt;/sub&gt; pollution and related health impacts in China, 1990–2015

Dynamic projection of anthropogenic emissions in China: methodology and 2015–2050 emission pathways under a range of socioeconomic, climate policy, and pollution control scenarios

Decomposing the Long-term Variation in Population Exposure to Outdoor PM2.5 in the Greater Bay Area of China Using Satellite Observations

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Decadal changes in anthropogenic source contribution of PM<sub>2.5</sub> pollution and related health impacts in China, 1990–2015

Decadal changes in anthropogenic source contribution of PM<sub>2.5</sub> pollution and related health impacts in China, 1990–2015