Development of a vehicle emission inventory with high temporal–spatial  resolution based on NRT traffic data and its impact on air pollution in  Beijing – Part 1: Development and evaluation of vehicle emission inventory

Jing, Boyu; Wu, Lin; Mao, Hongjun; Gong, Sunning; He, Jianjun; Zou, Chao; Song, Guohua; Li, Xiaoyu; Wu, Zhong

doi:10.5194/acp-16-3161-2016

Articles | Volume 16, issue 5

https://doi.org/10.5194/acp-16-3161-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue:

Haze-fog forecasts and near real time (NRT) data application...

https://doi.org/10.5194/acp-16-3161-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 16, issue 5

Research article

|

10 Mar 2016

Research article |

| 10 Mar 2016

Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 1: Development and evaluation of vehicle emission inventory

Boyu Jing, Lin Wu, Hongjun Mao, Sunning Gong, Jianjun He, Chao Zou, Guohua Song, Xiaoyu Li, and Zhong Wu

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Jan 2016	38	18	3	59
Feb 2016	18	12	2	32
Mar 2016	189	93	1	283
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May 2016	25	17	0	42
Jun 2016	20	13	2	35
Jul 2016	12	5	4	21
Aug 2016	16	13	1	30
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Oct 2016	24	14	2	40
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Jan 2017	26	13	1	40
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Apr 2017	40	32	3	75
May 2017	43	42	2	87
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Jul 2017	31	21	0	52
Aug 2017	9	17	0	26
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Oct 2017	21	23	0	44
Nov 2017	48	25	2	75
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Jan 2018	18	20	0	38
Feb 2018	24	17	1	42
Mar 2018	31	34	1	66
Apr 2018	22	29	0	51
May 2018	20	14	2	36
Jun 2018	18	11	1	30
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Apr 2019	20	24	12	56
May 2019	14	22	15	51
Jun 2019	17	15	1	33
Jul 2019	17	19	4	40
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Jun 2020	36	12	5	53
Jul 2020	38	24	7	69
Aug 2020	25	16	3	44
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Oct 2020	23	13	0	36
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Apr 2021	55	21	4	80
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Feb 2022	22	21	4	47
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May 2022	37	18	3	58
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Nov 2022	37	23	1	61
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Jan 2023	26	19	2	47
Feb 2023	29	21	3	53
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Sep 2024	36	19	0	55
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Jan 2025	25	20	0	45
Feb 2025	41	13	2	56
Mar 2025	44	15	2	61
Apr 2025	27	18	0	45
May 2025	26	13	1	40
Jun 2025	62	25	2	89
Jul 2025	28	22	1	51
Aug 2025	41	22	1	64
Sep 2025	48	17	1	66
Oct 2025	36	35	0	71
Nov 2025	12	10	4	26

Cumulative views and downloads (calculated since 05 Oct 2015)

Month	HTML	PDF	XML	Total
Oct 2015	95	69	14	178
Nov 2015	17	23	9	49
Dec 2015	7	11	4	22
Jan 2016	38	18	3	59
Feb 2016	18	12	2	32
Mar 2016	189	93	1	283
Apr 2016	40	31	0	71
May 2016	25	17	0	42
Jun 2016	20	13	2	35
Jul 2016	12	5	4	21
Aug 2016	16	13	1	30
Sep 2016	19	17	2	38
Oct 2016	24	14	2	40
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Dec 2016	8	5	0	13
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Feb 2017	20	13	2	35
Mar 2017	41	28	2	71
Apr 2017	40	32	3	75
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Jul 2017	31	21	0	52
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Oct 2017	21	23	0	44
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Feb 2019	6	8	1	15
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Nov 2020	25	11	3	39
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Dec 2022	32	11	2	45
Jan 2023	26	19	2	47
Feb 2023	29	21	3	53
Mar 2023	62	37	4	103
Apr 2023	33	31	1	65
May 2023	47	19	1	67
Jun 2023	73	19	3	95
Jul 2023	41	6	2	49
Aug 2023	47	16	5	68
Sep 2023	48	12	2	62
Oct 2023	61	18	3	82
Nov 2023	29	4	0	33
Dec 2023	63	9	1	73
Jan 2024	53	12	3	68
Feb 2024	49	22	1	72
Mar 2024	59	25	4	88
Apr 2024	56	25	7	88
May 2024	41	25	3	69
Jun 2024	34	13	2	49
Jul 2024	32	19	3	54
Aug 2024	42	34	2	78
Sep 2024	36	19	0	55
Oct 2024	39	16	0	55
Nov 2024	37	19	2	58
Dec 2024	36	12	0	48
Jan 2025	25	20	0	45
Feb 2025	41	13	2	56
Mar 2025	44	15	2	61
Apr 2025	27	18	0	45
May 2025	26	13	1	40
Jun 2025	62	25	2	89
Jul 2025	28	22	1	51
Aug 2025	41	22	1	64
Sep 2025	48	17	1	66
Oct 2025	36	35	0	71
Nov 2025	12	10	4	26

Latest update: 08 Nov 2025

Short summary

This paper presents a bottom-up methodology based on the local emission factors, complemented with the widely used emission factors of COPERT model and near-real-time (NRT) traffic data on road segments to develop a high temporal/spatial resolution vehicle emission inventory (HTSVE) for the urban Beijing area. The results show that the vehicle emissions of NO_x, CO, HC and PM were 10.54 × 10⁴, 42.51 × 10⁴ and 2.13 × 10⁴ and 0.41 × 10⁴ Mg respectively.