dor_id: 4119337

506.#.#.a: Público

590.#.#.d: Los artículos enviados a la revista "Atmósfera", se juzgan por medio de un proceso de revisión por pares

510.0.#.a: Consejo Nacional de Ciencia y Tecnología (CONACyT); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Scientific Electronic Library Online (SciELO); SCOPUS, Web Of Science (WoS); SCImago Journal Rank (SJR)

561.#.#.u: https://www.atmosfera.unam.mx/

650.#.4.x: Físico Matemáticas y Ciencias de la Tierra

336.#.#.b: article

336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://www.revistascca.unam.mx/atm/index.php/atm/index

351.#.#.b: Atmósfera

351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

590.#.#.c: Open Journal Systems (OJS)

270.#.#.d: MX

270.1.#.d: México

590.#.#.b: Concentrador

883.#.#.u: https://revistas.unam.mx/catalogo/

883.#.#.a: Revistas UNAM

590.#.#.a: Coordinación de Difusión Cultural

883.#.#.1: https://www.publicaciones.unam.mx/

883.#.#.q: Dirección General de Publicaciones y Fomento Editorial

850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: https://www.revistascca.unam.mx/atm/index.php/atm/article/view/ATM.2019.32.04.05/46656

100.1.#.a: Shao, Feng; Wu, Haitang; Li, Guo; Sun, Fengbin; Yu, Lu; Zhang, Yinke; Dong, Li; Bao, Zhiyi

524.#.#.a: Shao, Feng, et al. (2019). PM. Atmósfera; Vol. 32 No. 4, 2019; 323-336. Recuperado de https://repositorio.unam.mx/contenidos/4119337

720.#.#.a: National Forestry and Grassland Administration (NFGA)National Natural Science Foundation of China (NSFC)

245.1.0.a: PM

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM

264.#.0.c: 2019

264.#.1.c: 2019-09-30

653.#.#.a: PM2.5; idle zone of an expressway toll station; greenbelt; meteorological factors; structure of plant community; traffic flow; correlation analysis

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC 4.0 Internacional, https://creativecommons.org/licenses/by-nc/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico editora@atmosfera.unam.mx

884.#.#.k: https://www.revistascca.unam.mx/atm/index.php/atm/article/view/ATM.2019.32.04.05

001.#.#.#: 022.oai:ojs.pkp.sfu.ca:article/52601

041.#.7.h: eng

520.3.#.a: Expressways in China are developing rapidly, as is traffic pollution, which is one of the major sources of urban pollution. In this study, we chose the greenbelt in the idle zone near the Lin’an toll station along the Hang Rui expressway as our sampling area. Five points in the sampling area along Qianjin road were marked vertically at distances of 0, 15, 30, 45 and 60 m to monitor concentrations of PM2.5 and learn the varying patterns of these concentrations and influencing factors. The results showed that in spring (March, April and May), the average PM2.5 concentrations in the greenbelt were 32.56 ± 22.51, 77.71 ± 32.11 and 64.15 ± 29.00 μg m–3, respectively. The ranking of concentrations at different monitoring points in the same period was 0 > 15 > 60 > 30 > 45 m. The average concentrations in winter (November and December 2017, and February 2018) were 33.56 ± 9.34, 60.78 ± 17.67 and 124.71 ± 43.19 μg m–3, respectively. However, the ranking of concentrations at different monitoring points in the same period revealed some differences. Except at 0 m, the concentrations of PM2.5 in the other four positions were higher in winter than in spring. The reduction rate at 45 m reached its maximum in both spring and winter. PM2.5 concentrations were significantly correlated with meteorological factors, the structure of the plant community and traffic flow. PM2.5 concentrations were negatively correlated with temperature, positively correlated with relative humidity and was not significantly correlated with wind speed. The correlations of PM2.5 concentrations with the canopy density and degree of porosity differed greatly due to different seasons, and concentrations were significantly correlated with the amount of traffic flow, especially when there were large trucks.

773.1.#.t: Atmósfera; Vol. 32 No. 4 (2019); 323-336

773.1.#.o: https://www.revistascca.unam.mx/atm/index.php/atm/index

046.#.#.j: 2021-10-20 00:00:00.000000

022.#.#.a: ISSN electrónico: 2395-8812; ISSN impreso: 0187-6236

310.#.#.a: Trimestral

300.#.#.a: Páginas: 323-336

264.#.1.b: Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM

doi: https://doi.org/10.20937/ATM.2019.32.04.05

handle: 6e8a44d5ce9b5342

harvesting_date: 2023-06-20 16:00:00.0

856.#.0.q: application/pdf

file_creation_date: 2019-11-14 16:53:43.0

file_modification_date: 2019-11-14 16:53:46.0

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last_modified: 2023-06-20 16:00:00

license_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode.es

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