高效过滤器干预对控制哮喘儿童家庭颗粒物浓度的有效性研究
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摘要
哮喘是儿童最常见的慢性呼吸道疾病之一,对儿童健康造成了极大的损害。室内空气中的颗粒物及烟草烟雾是儿童哮喘病恶化的重要环境诱因,欧美国家采用高效过滤器的空气净化机作为控制颗粒物的主要技术手段,但对颗粒物浓度控制的有效性研究、过滤系统性能的评价体系目前尚不完善；改善过滤器干预性能的措施仍处于理论研究阶段。我国对哮喘病的研究主要集中在室外颗粒物的影响,室内颗粒物研究主要在颗粒物浓度采集的实验分析和模型预测,鲜有研究从流行病学的角度提出有效的干预控制方法和技术手段。针对以上问题,本课题开展了以下研究工作：
(1)通过实验研究分析室内空气环境中的颗粒物在不同干预条件下的差异。将干预条件分为：有/无高效过滤器、高效过滤器干预前/后、是/否存在空调、是/否存在烟草烟雾、短/长期。将126户参与家庭随机分成三组：对照组,社区保健工作者的随访；标准组,随访及高效过滤器；加强组：随访、高效过滤器及窗式空调。在完成房屋现场勘察及看护者调研后,对哮喘儿童卧室内进行了高效过滤器干预下的颗粒物浓度采样、检测及实验分析。采样时间包括基线周和季节性回访周(共346周),主要参数为：颗粒物日平均克重、颗粒物计数浓度(0.3-1.0μm和1.0-5.0μm粒径段)、二氧化碳浓度、温度、相对湿度、高效过滤器使用率；实验分析烟草烟雾痕量气体、计算换气次数。采用单盲法对采样周及采样周区间高效过滤器的使用进行监测。结果表明：无论是在短期或是长期干预下,颗粒物浓度在不同干预组、干预前后和不同季节均有显著差异；烟草烟雾对颗粒物浓度具有显著贡献,平均为12-15μgm-3,与干预时间、干预组无关。
(2)研究采用颗粒物净化模型预测高效过滤器干预特性。采用颗粒物质量平衡原理分析给出了颗粒物净化模型,用于分析高效过滤器对颗粒物的干预净化性能及主要影响因子。采用颗粒物净化模型的预测结果与高效过滤器干预对颗粒物平均净化作用的实验结果相近,均在55-95%。采用变量相对敏感性分析得到控制颗粒物浓度的主要影响因子为：高效过滤器风量、房间体积和换气次数。
(3)结合现场勘查所得房屋基本特性及看护者调研,研究分析了不同干预组的颗粒物浓度和高效过滤器使用模式。研究结果表明：高效过滤器长期干预对颗粒物的平均净化作用为50%,原因在于季节、环境烟草对颗粒物浓度具有显著影响；高效过滤器使用率是干预研究中控制颗粒物浓度的核心影响因子。经济原因和噪声是高效过滤器使用率较低的主要因素。
(4)研究采用理论分析法建立颗粒物干预模型预测污染源在不同位置的颗粒物浓度分布及主要影响因子。采用了双区域干预模型,对污染源分别在客厅、儿童卧室及室外环境中的颗粒物浓度进行分析计算。结果表明：换气次数与季节、房屋/卧室体积、中央空调、是否存在吸烟者及其数量、二氧化碳和有机挥发物浓度、清洁和打扫等显著相关。影响颗粒物浓度分布的主要影响因子为：污染源源强、高效过滤器风量、室外颗粒物浓度、室外气流、区域体积、沉降系数、区域间气流。
(5)研究采用广义估计方程法评估高效过滤器干预对控制颗粒物浓度的有效性及其主要影响因子。研究采用三个模型进行评估：标准组干预和加强组干预的有效性；标准组干预和加强组干预的区别；中央空调对标准组干预和加强组干预中高效过滤器性能的影响。结果表明：高效过滤器干预可有效地控制室内颗粒物浓度；中央空调和卧室空调的使用对高效过滤器干预有效性无显著影响。给出颗粒物浓度的计算方法,其主要影响因子包括室外颗粒物浓度、季节、换气次数、烟草烟雾、高效过滤器使用率、儿童及宠物的数量、温度、相对湿度等,且与参与者行为及供热、通风系统密切相关。
本课题研究了高效过滤器干预对控制哮喘儿童家庭颗粒物浓度的有效性,给出了颗粒物浓度的计算方法、主要影响因子及影响程度。采用大样品分析得到了具有相对代表性及可靠性的结果,为高效过滤器干预的流行病控制研究提供理论依据和应用指导,也为我国儿童哮喘控制干预研究提供研究思路和发展方向。
Asthma is the most common chronic disease of childhood, bringing significant burden on child's health status. Indoor environmental exposures such as airborne particulate matter (PM) and environmental tobacco smoke (ETS) play a substantial role in asthma aggravation. Free-standing HEPA filters have been widely used for indoor PM control, but few studies have assessed the effectiveness of filters, and factors affecting filter performance. The assessment of promising approaches to reduction of indoor PM by air filter and further exploration of causal pathways in which children's asthma related health still needs to be improved. In China, studies have focused on the influence of outdoor PM concentrations on asthma symptoms; indoor PM studies are mainly about measurements and simulations on its concentrations and distributions. Strategies such as the use of air filter to reduce children's exposure to the airborne PM as a part of an epidemiological study have rarely been conducted. This study was motivated by the need to understand the key factors in effectiveness of interventions using free-standing HEPA filters for indoor PM control in households of children with asthma. This research includes the following:
(1) Characterize indoor PM by the presence, if any, of HEPA filters, interventions, air conditioner, ETS exposure, short/long term though filed studies and experiments.126 low income households of children with asthma were recruited and randomized into one of three groups:a control group receiving only community health worker (CHW) home education visits (n=37); the standard intervention group receiving a free-standing HEPA filter (HEPA filter), and CHW visits (n=47); and an enhanced intervention group receiving the HEPA filter, the CHW visits, plus an air conditioner (n=42). After a household inspection and child's caregiver survey, each household received a baseline visit and seasonal visits (346 weeks in all). The measurements included PM, particle number counts (PNCs) in 0.3-1.0μm and 1.0-5.0μm dia size ranges, carbon dioxide (CO2), temperature, relative humidity and HEPA usage, ETS tracers, and air exchange rates (AERs). Participants were blinded to the filter use measurements during both sampling week and inter-season periods. The results showed that the PM concentrations differed significantly by HEPA filter intervention, groups and seasons; the ETS contributions to PM exposures remained about the same in different groups and intervention periods, by an average of 12-15μg m-3.
(2) Evaluate the effectiveness of HEPA filters in reducing PM concentrations and factors affecting the performance by simulation models. The results showed that PM removal rates were quite similar to rates found in the field, with a range of 55-95%; filter air flow, room volume and AERs were the key parameters affecting PM removal.
(3) Characterize the PM levels over the study period, understand the patterns of filter use and gather information from home characteristics and caregiver survey and the reasons for why persons might not use filters from a focus group. During weeks when air quality was monitored, filters reduced PM levels in the child's bedroom by an average of 50%. PM concentrations showed seasonal effects in different groups and differed before and after filter deployment, and ETS contributed PM levels significantly. Filter usage is a key factor in exposure and epidemiological studies using filter interventions. Cost and noisy were main reasons for low usage.
(4) Characterize AERs and inter-zonal air flows, explore the sensitivity of mixing and transport on localized sources in residences, and the effectiveness of HEPA filter and factors affecting the PM distributions. AERs in homes and children's bedrooms showed significant seasonal differences and statistically significant correlated with house/bedroom volume, central air conditioner, any smokers indoors, number of smokers, number of bedrooms, CO2 and VOC concentrations. Two zone models showed that PM concentrations depended on emission rates, filter air flow, air flows from outdoor, volume, deposition rates and interzonal air flows.
(5) Assess the effectiveness of interventions and identify the determining factors. General estimating equation (GEE) models were used to evaluate the effects of standard and enhanced interventions, differences between standard and enhanced interventions, and investigate whether central air conditioners altered effects of filters and distinguish effects of the standard and enhanced interventions. The results showed that HEPA filters could dramatically reduce indoor PM levels. Filter effectiveness was maintained in homes with central air conditioning; installation of a room air conditioner and filter in the bedroom in the enhanced intervention group did not yield improvements beyond that obtained by providing the filter alone. The effectiveness of HEPA filters depends strongly on outdoor PM levels, season, AERs, ETS, filter usage, the number of children and pets, temperature and relative humidity, as well as occupant behavior and the heating and ventilation system.
This study has assessed the effectiveness of interventions using free-standing HEPA filters for indoor PM control in households of asthmatic children and identified the determining factors. Our approach and sample size obtained representative and robust results, which not only have helped to guide the development of active interventions but also be helpful and useful in research to improve asthma health status for children in China.
(1)通过实验研究分析室内空气环境中的颗粒物在不同干预条件下的差异。将干预条件分为：有/无高效过滤器、高效过滤器干预前/后、是/否存在空调、是/否存在烟草烟雾、短/长期。将126户参与家庭随机分成三组：对照组,社区保健工作者的随访；标准组,随访及高效过滤器；加强组：随访、高效过滤器及窗式空调。在完成房屋现场勘察及看护者调研后,对哮喘儿童卧室内进行了高效过滤器干预下的颗粒物浓度采样、检测及实验分析。采样时间包括基线周和季节性回访周(共346周),主要参数为：颗粒物日平均克重、颗粒物计数浓度(0.3-1.0μm和1.0-5.0μm粒径段)、二氧化碳浓度、温度、相对湿度、高效过滤器使用率；实验分析烟草烟雾痕量气体、计算换气次数。采用单盲法对采样周及采样周区间高效过滤器的使用进行监测。结果表明：无论是在短期或是长期干预下,颗粒物浓度在不同干预组、干预前后和不同季节均有显著差异；烟草烟雾对颗粒物浓度具有显著贡献,平均为12-15μgm-3,与干预时间、干预组无关。
(2)研究采用颗粒物净化模型预测高效过滤器干预特性。采用颗粒物质量平衡原理分析给出了颗粒物净化模型,用于分析高效过滤器对颗粒物的干预净化性能及主要影响因子。采用颗粒物净化模型的预测结果与高效过滤器干预对颗粒物平均净化作用的实验结果相近,均在55-95%。采用变量相对敏感性分析得到控制颗粒物浓度的主要影响因子为：高效过滤器风量、房间体积和换气次数。
(3)结合现场勘查所得房屋基本特性及看护者调研,研究分析了不同干预组的颗粒物浓度和高效过滤器使用模式。研究结果表明：高效过滤器长期干预对颗粒物的平均净化作用为50%,原因在于季节、环境烟草对颗粒物浓度具有显著影响；高效过滤器使用率是干预研究中控制颗粒物浓度的核心影响因子。经济原因和噪声是高效过滤器使用率较低的主要因素。
(4)研究采用理论分析法建立颗粒物干预模型预测污染源在不同位置的颗粒物浓度分布及主要影响因子。采用了双区域干预模型,对污染源分别在客厅、儿童卧室及室外环境中的颗粒物浓度进行分析计算。结果表明：换气次数与季节、房屋/卧室体积、中央空调、是否存在吸烟者及其数量、二氧化碳和有机挥发物浓度、清洁和打扫等显著相关。影响颗粒物浓度分布的主要影响因子为：污染源源强、高效过滤器风量、室外颗粒物浓度、室外气流、区域体积、沉降系数、区域间气流。
(5)研究采用广义估计方程法评估高效过滤器干预对控制颗粒物浓度的有效性及其主要影响因子。研究采用三个模型进行评估：标准组干预和加强组干预的有效性；标准组干预和加强组干预的区别；中央空调对标准组干预和加强组干预中高效过滤器性能的影响。结果表明：高效过滤器干预可有效地控制室内颗粒物浓度；中央空调和卧室空调的使用对高效过滤器干预有效性无显著影响。给出颗粒物浓度的计算方法,其主要影响因子包括室外颗粒物浓度、季节、换气次数、烟草烟雾、高效过滤器使用率、儿童及宠物的数量、温度、相对湿度等,且与参与者行为及供热、通风系统密切相关。
本课题研究了高效过滤器干预对控制哮喘儿童家庭颗粒物浓度的有效性,给出了颗粒物浓度的计算方法、主要影响因子及影响程度。采用大样品分析得到了具有相对代表性及可靠性的结果,为高效过滤器干预的流行病控制研究提供理论依据和应用指导,也为我国儿童哮喘控制干预研究提供研究思路和发展方向。
Asthma is the most common chronic disease of childhood, bringing significant burden on child's health status. Indoor environmental exposures such as airborne particulate matter (PM) and environmental tobacco smoke (ETS) play a substantial role in asthma aggravation. Free-standing HEPA filters have been widely used for indoor PM control, but few studies have assessed the effectiveness of filters, and factors affecting filter performance. The assessment of promising approaches to reduction of indoor PM by air filter and further exploration of causal pathways in which children's asthma related health still needs to be improved. In China, studies have focused on the influence of outdoor PM concentrations on asthma symptoms; indoor PM studies are mainly about measurements and simulations on its concentrations and distributions. Strategies such as the use of air filter to reduce children's exposure to the airborne PM as a part of an epidemiological study have rarely been conducted. This study was motivated by the need to understand the key factors in effectiveness of interventions using free-standing HEPA filters for indoor PM control in households of children with asthma. This research includes the following:
(1) Characterize indoor PM by the presence, if any, of HEPA filters, interventions, air conditioner, ETS exposure, short/long term though filed studies and experiments.126 low income households of children with asthma were recruited and randomized into one of three groups:a control group receiving only community health worker (CHW) home education visits (n=37); the standard intervention group receiving a free-standing HEPA filter (HEPA filter), and CHW visits (n=47); and an enhanced intervention group receiving the HEPA filter, the CHW visits, plus an air conditioner (n=42). After a household inspection and child's caregiver survey, each household received a baseline visit and seasonal visits (346 weeks in all). The measurements included PM, particle number counts (PNCs) in 0.3-1.0μm and 1.0-5.0μm dia size ranges, carbon dioxide (CO2), temperature, relative humidity and HEPA usage, ETS tracers, and air exchange rates (AERs). Participants were blinded to the filter use measurements during both sampling week and inter-season periods. The results showed that the PM concentrations differed significantly by HEPA filter intervention, groups and seasons; the ETS contributions to PM exposures remained about the same in different groups and intervention periods, by an average of 12-15μg m-3.
(2) Evaluate the effectiveness of HEPA filters in reducing PM concentrations and factors affecting the performance by simulation models. The results showed that PM removal rates were quite similar to rates found in the field, with a range of 55-95%; filter air flow, room volume and AERs were the key parameters affecting PM removal.
(3) Characterize the PM levels over the study period, understand the patterns of filter use and gather information from home characteristics and caregiver survey and the reasons for why persons might not use filters from a focus group. During weeks when air quality was monitored, filters reduced PM levels in the child's bedroom by an average of 50%. PM concentrations showed seasonal effects in different groups and differed before and after filter deployment, and ETS contributed PM levels significantly. Filter usage is a key factor in exposure and epidemiological studies using filter interventions. Cost and noisy were main reasons for low usage.
(4) Characterize AERs and inter-zonal air flows, explore the sensitivity of mixing and transport on localized sources in residences, and the effectiveness of HEPA filter and factors affecting the PM distributions. AERs in homes and children's bedrooms showed significant seasonal differences and statistically significant correlated with house/bedroom volume, central air conditioner, any smokers indoors, number of smokers, number of bedrooms, CO2 and VOC concentrations. Two zone models showed that PM concentrations depended on emission rates, filter air flow, air flows from outdoor, volume, deposition rates and interzonal air flows.
(5) Assess the effectiveness of interventions and identify the determining factors. General estimating equation (GEE) models were used to evaluate the effects of standard and enhanced interventions, differences between standard and enhanced interventions, and investigate whether central air conditioners altered effects of filters and distinguish effects of the standard and enhanced interventions. The results showed that HEPA filters could dramatically reduce indoor PM levels. Filter effectiveness was maintained in homes with central air conditioning; installation of a room air conditioner and filter in the bedroom in the enhanced intervention group did not yield improvements beyond that obtained by providing the filter alone. The effectiveness of HEPA filters depends strongly on outdoor PM levels, season, AERs, ETS, filter usage, the number of children and pets, temperature and relative humidity, as well as occupant behavior and the heating and ventilation system.
This study has assessed the effectiveness of interventions using free-standing HEPA filters for indoor PM control in households of asthmatic children and identified the determining factors. Our approach and sample size obtained representative and robust results, which not only have helped to guide the development of active interventions but also be helpful and useful in research to improve asthma health status for children in China.
引文
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