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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

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2018 Impact Factor: 3.883

Front. Environ. Sci. Eng.    2021, Vol. 15 Issue (5) : 88    https://doi.org/10.1007/s11783-020-1382-2
RESEARCH ARTICLE
Understand the local and regional contributions on air pollution from the view of human health impacts
Yueqi Jiang1,2, Jia Xing1,2(), Shuxiao Wang1,2, Xing Chang1,2, Shuchang Liu1,2, Aijun Shi3, Baoxian Liu1,4, Shovan Kumar Sahu1,2
1. State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
2. State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
3. Beijing Municipal Research Institute of Environment Protection, Beijing 100037, China
4. Beijing Key Laboratory of Airborne Particulate Matter Monitoring Technology, Beijing Municipal Environmental Monitoring Center, Beijing 100048, China
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Abstract

• PM2.5-related deaths were estimated to be 227 thousand in BTH & surrounding regions.

• Local emissions contribute more to PM2.5-related deaths than PM2.5 concentration.

• Local controls are underestimated if only considering its impacts on concentrations.

• Rural residents suffer larger impacts of regional transport than urban residents.

• Reducing regional transport benefits in mitigating environmental inequality.

The source-receptor matrix of PM2.5 concentration from local and regional sources in the Beijing-Tianjin-Hebei (BTH) and surrounding provinces has been created in previous studies. However, because the spatial distribution of concentration does not necessarily match with that of the population, such concentration-based source-receptor matrix may not fully reflect the importance of pollutant control effectiveness in reducing the PM2.5-related health impacts. To demonstrate that, we study the source-receptor matrix of the PM2.5-related deaths instead, with inclusion of the spatial correlations between the concentrations and the population. The advanced source apportionment numerical model combined with the integrated exposure–response functions is used for BTH and surrounding regions in 2017. We observed that the relative contribution to PM2.5-related deaths of local emissions was 0.75% to 20.77% larger than that of PM2.5 concentrations. Such results address the importance of local emissions control for reducing health impacts of PM2.5 particularly for local residents. Contribution of regional transport to PM2.5-related deaths in rural area was 22% larger than that in urban area due to the spatial pattern of regional transport which was more related to the rural population. This resulted in an environmental inequality in the sense that people staying in rural area with access to less educational resources are subjected to higher impacts from regional transport as compared with their more resourceful and knowledgeable urban compatriots. An unexpected benefit from the multi-regional joint controls is suggested for its effectiveness in reducing the regional transport of PM2.5 pollution thus mitigating the associated environmental inequality.

Keywords PM2.5      Regional transport      Local emissions      Health impact      Environmental inequality     
Corresponding Author(s): Jia Xing   
Issue Date: 03 December 2020
 Cite this article:   
Yueqi Jiang,Jia Xing,Shuxiao Wang, et al. Understand the local and regional contributions on air pollution from the view of human health impacts[J]. Front. Environ. Sci. Eng., 2021, 15(5): 88.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-020-1382-2
https://academic.hep.com.cn/fese/EN/Y2021/V15/I5/88
Region Mean observations (µg/m3) Mean predictions (µg/m3) MNB MNE MFB MFE Data fusion (µg/m3)
BJ 57.10 35.30 -0.36 0.54 -0.35 0.44 54.88
TJ 64.08 45.38 -0.22 0.53 -0.25 0.37 66.48
TS 66.74 58.95 -0.08 0.37 -0.10 0.26 69.81
LF 61.12 40.48 -0.24 0.49 -0.25 0.36 55.02
BD 84.18 41.53 -0.55 0.57 -0.48 0.49 75.18
CZ 66.28 43.94 -0.37 0.47 -0.32 0.38 64.02
SJZ 81.87 50.50 -0.37 0.47 -0.34 0.40 87.59
SHB 81.54 50.52 -0.38 0.48 -0.35 0.41 77.95
ESD 66.15 53.78 -0.18 0.39 -0.19 0.31 50.90
WSD 65.80 58.52 -0.11 0.37 -0.14 0.30 65.39
HN 71.63 53.38 -0.23 0.40 -0.22 0.32 67.73
NSX 64.27 29.12 -0.60 0.61 -0.53 0.54 51.78
SSX 63.91 36.39 -0.38 0.53 -0.26 0.43 53.13
Tab.1  Evaluation of PM2.5 for baseline simulation in CMAQ model
Fig.1  Relative contribution of regional transport of (a) PM2.5 concentration and (b) PM2.5-related deaths in 2017. The left of the dashed line are cities, and the right are regions. BJ, TJ, TS, LF, BD, CZ, SJZ and SHB are included in BTH region, while ESD, WSD, HN, NSX and SSX are surrounding regions as marked at top.
Receptor Source
BJ TJ TS LF BD CZ SJZ SHB ESD WSD HN NSX SSX Local emissions Inner regional transport Outer regional transport
BJ 50.95 4.46 0.93 3.67 4.84 0.25 0.15 2.15 1.46 0.87 1.03 0.51 0.44 50.95 20.77 28.28
TJ 3.53 41.03 2.94 3.95 2.40 4.17 0.19 3.34 4.14 2.40 1.68 0.58 0.61 41.03 29.92 29.05
TS 2.15 8.17 48.00 0.70 1.43 0.43 0.09 1.84 2.39 1.50 1.20 0.45 0.48 48.00 20.84 31.16
LF 10.61 10.22 1.33 27.11 8.32 4.66 0.23 3.76 2.53 1.71 1.43 0.60 0.57 27.11 45.97 26.92
BD 2.99 2.24 0.18 1.26 50.02 1.43 3.04 5.36 1.63 1.17 1.60 0.86 0.73 50.02 22.49 27.49
CZ 2.03 4.67 0.28 2.31 5.85 26.68 0.53 8.36 10.31 4.08 2.17 0.70 0.72 26.68 42.00 31.31
SJZ 1.24 1.26 0.10 0.17 7.93 0.40 44.49 9.45 1.35 0.97 1.83 2.69 1.13 44.49 28.50 27.01
SHB 1.11 1.26 0.09 0.22 2.62 1.23 2.65 45.26 3.22 4.41 6.85 1.18 1.92 45.26 26.77 27.97
ESD 1.10 1.54 0.17 0.22 1.07 1.18 0.17 2.95 38.74 6.14 1.98 0.61 0.69 38.74 17.81 43.45
WSD 1.10 0.88 0.11 0.11 0.83 0.17 0.16 3.29 4.82 35.73 4.20 0.52 0.72 35.73 16.92 47.35
HN 2.33 0.85 0.11 0.21 1.02 0.13 0.18 5.78 1.56 4.13 38.47 0.60 2.72 38.47 19.63 41.89
NSX 1.61 0.72 0.09 0.47 0.89 0.09 0.89 1.98 0.62 0.49 1.54 49.28 2.27 28.51 15.51 55.98
SSX 0.91 0.82 0.12 0.37 0.74 0.11 0.18 3.45 0.91 0.99 6.55 0.95 43.01 31.28 18.03 50.70
Tab.2  Source-receptor matrix of PM2.5-related deaths in the BTH and surrounding regions for the year 2017 (unit: %)
Fig.2  Relative contribution of local emissions to PM2.5 concentration and PM2.5-related deaths.
Fig.3  Ratio of contributions in rural and urban area ({rj}) for (a) regional transport and (b) local emissions. The black line equals to 1.
Fig.4  Relevance between illiteracy rate over 15 years old and proportion of (a) regional transport and (b) local emissions to PM2.5-related deaths (each point represents a county in studied regions).
Fig.5  Differences in proportion of regional transport to PM2.5-related deaths by educational attainment category (comparing less than high school or equivalent and college or higher to high school or equivalent).
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