【小夏的论文笔记】北京住宅渗透细颗粒物调查与建模

Investigation and modeling of the residential infiltration of fine particulate matter in Beijing
北京住宅渗透细颗粒物调查与建模

原文链接：http://www.tandfonline.com/doi/full/10.1080/10962247.2016.1272503


一个比较简单的研究。对比了室内室外硫元素的浓度，通过多元线性回归构建住宅渗透影响因素的模型。发现供暖季住宅渗透率明显高于非供暖季。室外温度、窗户宽度，开窗频率和空调的使用是非供暖季主要的预测因素，可以解释57%的变异；而供暖季唯一的预测因素是室外温度，可以解释18%的变异。

Abstract

The objective of this study was to estimate the residential infiltration factor (Finf) of fine particulate matter (PM2.5) and to develop models to predict PM2.5 Finf in Beijing. Eighty-eight paired indoor–outdoor PM2.5 samples were collected by Teflon filters for seven consecutive days during both non-heating and heating seasons (from a total of 55 families between August, 2013 and February, 2014). The mass concentrations of PM2.5 were measured by gravimetric method, and elemental concentrations of sulfur in filter deposits were determined by energy-dispersive x-ray fluorescence (ED-XRF) spectrometry. PM2.5 Finf was estimated as the indoor/outdoor sulfur ratio. Multiple linear regression was used to construct Finf predicting models. The residential PM2.5 Finf in non-heating season (0.70 ± 0.21, median = 0.78, n = 43) was significantly greater than in heating season (0.54 ± 0.18, median = 0.52, n = 45, p < 0.001). Outdoor temperature, window width, frequency of window opening, and air conditioner use were the most important predictors during non-heating season, which could explain 57% variations across residences, while the outdoor temperature was the only predictor identified in heating season, which could explain 18% variations across residences. The substantial variations of PM2.5 Finf between seasons and among residences found in this study highlight the importance of incorporating Finf into exposure assessment in epidemiological studies of air pollution and human health in Beijing. The Finf predicting models developed in this study hold promise for incorporating PM2.5 Finf into large epidemiology studies, thereby reducing exposure misclassification.

Implications: Failure to consider the differences between indoor and outdoor PM2.5 may contribute to exposure misclassification in epidemiological studies estimating exposure from a central site measurement. This study was conducted in Beijing to investigate residential PM2.5 infiltration factor and to develop a localized predictive model in both nonheating and heating seasons. High variations of PM2.5 infiltration factor between the two seasons and across homes within each season were found, highlighting the importance of including infiltration factor in the assessment of exposure to PM2.5 of outdoor origin in epidemiological studies.


今天排球队冒雨训练站位练了两个小时。本来以为天黑只是因为乌云，结果回来发现今天武汉AQI爆表，想想昨天晚上看到PM2.5到180应该就是征兆了吧。今天没有戴口罩暴露了一天，还做了剧烈运动，感觉自己寿命期望又变短了ORZ。。。