NIHR HPRU in Environmental Exposures and Health, Imperial College London, UK; Environmental Research Group, MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.
BIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China; China National Environmental Monitoring Centre, Beijing 100012, China.
Sci Total Environ. 2022 Nov 1;845:157249. doi: 10.1016/j.scitotenv.2022.157249. Epub 2022 Jul 9.
Limited number of projects have attempted to partition and quantify indoor- and outdoor-generated PM (PM and PM) where strong indoor sources (e.g., solid fuel, tobacco smoke, or kerosene) exist. This study aimed to apply and refine a previous recursive model used to derive infiltration efficiency (F) to additionally partition pollution concentrations into indoor and outdoor origins within residences challenged by elevated ambient and indoor combustion-related sources. During the winter of 2016 and summer of 2017 we collected residential measurements in 72 homes in urban and peri-urban Beijing, 12 of which had additional paired residential outdoor measurements during the summer season. Local ambient measurements were collected throughout. We then compared the calculated PM and using (i) outdoor and (ii) ambient measurements as model inputs. The results from outdoor and ambient measurements were not significantly different, which suggests that ambient measurements can be used as a model input for pollution origin partitioning when paired outdoor measurements are not available. From the results calculated using ambient measurements, the mean percentage contribution of indoor-generated PM was 19 % (σ = 22 %), and 7 % (11 %) of the total indoor PM for peri-urban and urban homes respectively during the winter; and 18 % (18 %) and 6 % (10 %) of the total indoor PM during the summer. Partitioning pollution into PM and PM is important to allow investigation of distinct associations between health outcomes and particulate mixes, often with different physiochemical composition and toxicity. It will also inform targeted interventions that impact indoor and outdoor sources of pollution (e.g., domestic fuel switching vs. power generation), which are typically radically different in design and implementation.
有限数量的项目试图将室内和室外生成的 PM（PM 和 PM）进行分区和量化，其中存在强烈的室内源（例如，固体燃料、烟草烟雾或煤油）。本研究旨在应用和改进先前用于推导渗透率（F）的递归模型，以将污染浓度进一步分为住宅内的室内和室外来源，这些住宅受到升高的环境和室内燃烧源的影响。在 2016 年冬季和 2017 年夏季，我们在北京城区和近郊的 72 户住宅中进行了住宅测量，其中 12 户在夏季还进行了额外的住宅外配对测量。在整个过程中都采集了当地环境测量数据。然后，我们将计算出的 PM 和使用（i）室外和（ii）环境测量数据作为模型输入进行了比较。室外和环境测量数据的结果没有显著差异，这表明当无法获得配对的室外测量数据时，环境测量数据可作为污染来源分区的模型输入。根据使用环境测量数据计算得出的结果，在冬季，城乡住宅的室内生成 PM 的平均贡献率分别为 19%（σ=22%）和 7%（11%）；在夏季，这一比例分别为 18%（18%）和 6%（10%）。将污染分为 PM 和 PM 很重要，因为这可以允许研究健康结果与颗粒物混合物之间的不同关联，这些颗粒物混合物通常具有不同的物理化学组成和毒性。它还将为影响室内和室外污染源的有针对性的干预措施提供信息（例如，家用燃料转换与发电），这些干预措施在设计和实施方面通常有很大的不同。
