Analysis of spatial distribution and variation characteristics of cyclists' heart rates in non-motorized lane

doi:10.16265/j.cnki.issn1003-3033.2025.02.0116

Abstract

Abstract:

To guide healthy travel for cyclists in hot weather, a cycling measurement platform using micro-sensors was developed to collect high-resolution data on cyclists' heart rates, as well as atmospheric particulate matter (i.e., PM_2.5, PM₁₀, and BC(Black Carbon) on the non-motorized lanes of the Western Third-Ring Expressway in Fuzhou. Statistical analyses were conducted to characterize and interpret variations in cyclists' heart rates. Results indicate that cyclists' average heart rates near residential zones are higher than that of riverside segments, showing strong and sustained associations with all measured particulates. The number of diesel vehicles, ambient temperature, and atmospheric pressure are found to significantly influence the heart rate changes across sections of the entire route, riverside, and residential sections, respectively. Cyclists' heart rates also fluctuate due to varying environmental and topographical conditions along the route. Immediate effects of PM_2.5 and BC on heart rate are observed, while PM₁₀ effects are delayed. Therefore, implementing road-segment-specific control measures for motor vehicles, particularly strict regulation of those emitting pollutants with immediate effects on cyclists, and guiding the selection of routes with better roadside ventilation and higher greenery coverage, can effectively enhance the travel quality for cyclists.

Key words: non-motorized lane, cyclists' heart rate (HR), spatial distribution, detrended cross-correlation analysis (DCCA), particulate matter, relative risk(RR)

CLC Number:

X968高低温防护

LIN Xinyuan, LIU Kaixuan, AN Xing, HU Xisheng, XU Jinqiang, WANG Zhanyong. Analysis of spatial distribution and variation characteristics of cyclists' heart rates in non-motorized lane[J]. China Safety Science Journal, 2025, 35(2): 186-195.

Figures/Tables 8

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 1

Fig.6

Fig.7

References 25

[1]	XIE Yuquan, BO Liang, JIANG Shuo, et al. Individual PM_2.5 exposure is associated with the impairment of cardiac autonomic modulation in general residents[J]. Environmental Science and Pollution Research, 2016, 23(10): 10 255-10 261.
[2]	IBALD-MULLI A, TIMONEN K L, PETERS A, et al. Effects of particulate air pollution on blood pressure and heart rate in subjects with cardiovascular disease: a multicenter approach[J]. Environmental Health Perspectives, 2004, 112(3): 369-377.
[3]	WILLIAMS R, BROOK R, BARD R, et al. Impact of personal and ambient-level exposures to nitrogen dioxide and particulate matter on cardiovascular function[J]. International Journal of Environmental Health Research, 2012, 22(1): 71-91. doi: 10.1080/09603123.2011.588437 pmid: 21711166
[4]	RIZZA V, STABILE L, VISTOCCO D, et al. Effects of the exposure to ultrafine particles on heart rate in a healthy population[J]. Science of the Total Environment, 2019, 650: 2403-2410.
[5]	CAKMAK S, DALES R, LEECH J, et al. The influence of air pollution on cardiovascular and pulmonary function and exercise capacity: canadian health measures survey (CHMS)[J]. Environmental Research, 2011, 111(8): 1309-1312. doi: 10.1016/j.envres.2011.09.016 pmid: 22000598
[6]	LIN L, LIN C, LIN Y, et al. The effects of indoor particles on blood pressure and heart rate among young adults in Taipei, Taiwan: indoor particles, blood pressure and heart rate[J]. Indoor Air, 2009, 19(6): 482-488. doi: 10.1111/j.1600-0668.2009.00612.x pmid: 19682103
[7]	CAO Ruhui, LUO Binru, LIU Kaixuan, et al. Characterizing and interpreting the spatial variation of traffic pollution in urban non-motorized lanes using mobile measurements[J]. Air Quality, Atmosphere & Health, 2023, 16: 1907-1929.
[8]	LUO Binru, CAO Ruhui, YANG Wenbin. Analysing and predicting the fine-scale distribution of traffic particulate matter in urban nonmotorized lanes by using wavelet transform and random forest methods[J]. Stochastic Environmental Research and Risk Assessment, 2023, 37(7): 1-20.
[9]	AL-SAREJI O J, GRMASHA R A, HASHIM K S, et al. Personal exposure and inhalation doses to PM1 and PM_2.5 pollution in Iraq: an examination of four transport modes[J]. Building and Environment, 2022, 212: DOI: 10.1016/j.buildenv.2022.108847.
[10]	HAGLER G S W, YELVERTON T L B, VEDANTHAM R, et al. Post-processing method to reduce noise while preserving high time resolution in aethalometer real-time black carbon data[J]. Aerosol and Air Quality Research, 2011, 11(5): 539-546.
[11]	ZHOU Huoyan, FU Liyong, SHARMA R P, et al. A hybrid approach of combining random forest with texture analysis and VDVI for desert vegetation mapping based on UAV RGB data[J]. Remote Sensing, 2021, 13(10): DOI: 10.3390/rs13101891.
[12]	PODOBNIK B, STANLEY H E. Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series[J]. Physical Review Letters, 2008, 100(8): DOI: 10.1103/PhysRevLett.100.084102.
[13]	ENEVOLDSEN J, SIMPSON G L, VISTISEN S T. Using generalized additive models to decompose time series and waveforms, and dissect heart-lung interaction physiology[J]. Journal of Clinical Monitoring and Computing, 2023, 37(1): 165-177.
[14]	DEHGHAN A, KHANJANI N, BAHRAMPOUR A, et al. The relation between air pollution and respiratory deaths in Tehran, Iran- using generalized additive models[J]. BMC Pulmonary Medicine, 2018, 18(1): DOI: 10.1186/s12890-018-0613-9.
[15]	KHOLOD N, EVANS M. Reducing black carbon emissions from diesel vehicles in Russia: an assessment and policy recommendations[J]. Environmental Science & Policy, 2016, 56: 1-8.
[16]	GLADWELL V F, BROWN D K, WOOD C, et al. The great outdoors: how a green exercise environment can benefit all[J]. Extreme Physiology & Medicine, 2013, 2(1): DOI: 10.1186/2046-7648-2-3.
[17]	KIRSCHEN G W, SINGER D D, THODE H C, et al. Relationship between body temperature and heart rate in adults and children: a local and national study[J]. The American Journal of Emergency Medicine, 2020, 38(5): 929-933.
[18]	FITCH D T, SHARPNACK J, HANDY S L. Psychological stress of bicycling with traffic: examining heart rate variability of bicyclists in natural urban environments[J]. Transportation Research Part F, 2020, 70: 81-97.
[19]	REED R, SCARF P, JOBSON S A, et al. Determining optimal cadence for an individual road cyclist from field data[J]. European Journal of Sport Science, 2016, 16(8): 903-911. doi: 10.1080/17461391.2016.1146336 pmid: 26902667
[20]	YANG Weixin, LI Lingguang. Efficiency evaluation of industrial waste gas control in China: a study based on data envelopment analysis (DEA) model[J]. Journal of Cleaner Production, 2018, 179: 1-11.
[21]	SUN Xiaoke, CHEN Hong, LIU Zhizhen, et al. Research on PM_2.5 concentration based on dissipative structure theory: a case study of Xi'an, China[J]. Scientific Reports, 2020, 10(1): DOI: 10.1038/s41598-020-73598-9.
[22]	RICHARDS D, FUNG T, BELCHER R, et al. Differential air temperature cooling performance of urban vegetation types in the tropics[J]. Urban Forestry & Urban Greening, 2020, 50: DOI: 10.1016/j.ufug.2020.126651.
[23]	TRAN B, CHANG C, HSU C, et al. Threshold effects of PM_2.5 exposure on particle-related mortality in China[J]. International Journal of Environmental Research and Public Health, 2019, 16(19): DOI: 10.3390/ijerph16193549.
[24]	YAN Xiaodan, WANG Qiming, TIE Cai, et al. Polydatin protects the respiratory system from PM_2.5 exposure[J]. Scientific Reports, 2017, 7(1): DOI: 10.1038/srep40030.
[25]	BECKERS F, VERHEYDEN B, AUBERT A E. Aging and nonlinear heart rate control in a healthy population[J]. American Journal of Physiology-Heart and Circulatory Physiology, 2006, 290(6): H2560-H2570.

协变量	整条试验路线			A₁路段			B₁路段			B₂路段			A₂路段
协变量	F	VER	p	F	VER	p	F	VER	p	F	VER	P	F	VER	p
s(PM_2.5)	18.91	4.99	<0.001	5.29	2.64	0.022	5.17	4.48	<0.001	4.62	3.39	<0.001	1.41	0.96	0.187
s(PM₁₀)	28.74	7.58	<0.001	1.58	0.79	0.126	5.44	4.71	<0.001	2.74	2.01	0.007	4.27	2.91	<0.001
s(BC)	15.62	4.12	<0.001	9.50	4.74	0.002	0.82	0.71	0.438	4.75	3.49	0.029	8.66	5.91	<0.001
s(AP)	80.15	21.14	<0.001	41.70	20.79	<0.001	9.92	8.60	<0.001	24.77	18.21	<0.001	38.98	26.60	<0.001
s(AT)	61.24	16.15	<0.001	28.02	13.97	<0.001	38.21	33.11	<0.001	12.97	9.54	<0.001	7.23	4.93	<0.001
s(RH)	37.60	9.92	<0.001	9.55	4.76	<0.001	4.50	3.90	<0.001	11.88	8.73	<0.001	4.17	2.84	<0.001
s(DP)	31.80	8.39	<0.001	11.82	5.89	<0.001	6.42	5.56	<0.001	12.87	9.46	<0.001	11.34	7.74	<0.001
s(SG)	9.18	2.42	<0.001	11.70	5.83	<0.001	3.18	2.75	0.037	9.60	7.05	<0.001	2.96	2.02	0.003
s(DE)	14.59	3.85	<0.001	10.35	5.16	<0.001	14.10	12.21	<0.001	13.90	10.22	<0.001	18.93	12.92	<0.001
s(VC)	10.46	2.76	<0.001	2.85	1.42	0.037	4.55	3.94	<0.001	0.41	0.30	0.521	4.66	3.18	<0.001
s(DV)	9.21	2.43	<0.001	46.76	23.32	<0.001	12.04	10.43	<0.001	16.31	11.99	<0.001	24.83	16.94	<0.001
s(TV)	42.90	11.31	<0.001	16.16	8.06	<0.001	9.12	7.90	<0.001	18.41	13.53	<0.001	8.44	5.76	<0.001
te(WS,WD)	18.82	4.96	<0.001	5.28	2.63	<0.001	1.96	1.70	0.027	2.83	2.08	<0.001	10.70	7.30	<0.001