Spatiotemporal Variations of Indoor PM2.5 Concentrations in Nanjing, China
Spatiotemporal Variations of Indoor PM2.5 Concentrations in Nanjing, China
复制标题
中国南京市室内PM2.5浓度时空变化
DOI：
10.3390/ijerph16010144
复制
发表时间：
2019-01
影响因子：
--
通讯作者：
Wang Jinnan
中科院分区：
文献类型：
--
作者：
Shao Zhijuan;Yin Xiangjun;Bi Jun;Ma Zongwei;Wang Jinnan
室内细颗粒物（PM2.5）很重要，因为人们大部分时间都在室内。然而，在一个城市的室内PM2.5浓度的时空变化的知识是有限的。在这项研究中，室内PM2. 5水平的时空分布在南京，中国是模拟的多区气流和污染物传输程序（CONTAM），基于地理分布的住宅，人类活动，和室外PM2. 5浓度。验证了CONTAM模型的准确性，模型模拟和测量之间具有良好的一致性（r = 0.940，N = 110）。两种不同的方案被认为是研究建筑性能和影响的居住者行为。当考虑室内活动时，在情景下观察到较高的PM2.5浓度。室内PM2.5水平存在季节性变化，冬季浓度最高，夏季浓度最低。建筑特征对室内PM2.5浓度的空间分布有显著影响，多层住宅比高层住宅更容易受到室外PM2.5的渗透。估算了南京市PM2.5的总暴露量。如果不考虑室内暴露，则会高估16.67%，这将导致健康影响评估的偏差。
Indoor fine particulate matter (PM2.5) is important since people spend most of their time indoors. However, knowledge of the spatiotemporal variations of indoor PM2.5 concentrations within a city is limited. In this study, the spatiotemporal distributions of indoor PM2.5 levels in Nanjing, China were modeled by the multizone airflow and contaminant transport program (CONTAM), based on the geographically distributed residences, human activities, and outdoor PM2.5 concentrations. The accuracy of the CONTAM model was verified, with a good agreement between the model simulations and measurements (r = 0.940, N = 110). Two different scenarios were considered to examine the building performance and influence of occupant behaviors. Higher PM2.5 concentrations were observed under the scenario when indoor activities were considered. Seasonal variability was observed in indoor PM2.5 levels, with the highest concentrations occurring in the winter and the lowest occurring in the summer. Building characteristics have a significant effect on the spatial distribution of indoor PM2.5 concentrations, with multistory residences being more vulnerable to outdoor PM2.5 infiltration than high-rise residences. The overall population exposure to PM2.5 in Nanjing was estimated. It would be overestimated by 16.67% if indoor exposure was not taken into account, which would lead to a bias in the health impacts assessment.
登录
查看更多内容
影响因子：
10.4
作者：
Burnett RT;Pope CA 3rd;Ezzati M;Olives C;Lim SS;Mehta S;Shin HH;Singh G;Hubbell B;Brauer M;Anderson HR;Smith KR;Balmes JR;Bruce NG;Kan H;Laden F;Prüss-Ustün A;Turner MC;Gapstur SM;Diver WR;Cohen A
通讯作者：
Cohen A
影响因子：
4.9
作者：
Atkinson RW;Carey IM;Kent AJ;van Staa TP;Anderson HR;Cook DG
通讯作者：
Cook DG
影响因子：
5.8
作者：
MacNeill, M.;Kearney, J.;Wheeler, A. J.
通讯作者：
Wheeler, A. J.
影响因子：
5.8
作者：
J. Taylor;C. Shrubsole;M. Davies;P. Biddulph;P. Das;I. Hamilton;S. Vardoulakis;A. Mavrogianni;B. Jones;E. Oikonomou
通讯作者：
J. Taylor;C. Shrubsole;M. Davies;P. Biddulph;P. Das;I. Hamilton;S. Vardoulakis;A. Mavrogianni;B. Jones;E. Oikonomou
DOI：
--
发表时间：
2011-04
期刊：
--
影响因子：
--
作者：
Who Europe
通讯作者：
Who Europe