描述达卡市室内空气质量特征并确定影响家庭微环境空气质量的因素

Afsana Yasmin , Imran Ahmed , Maria Haider , Md. Kamal Hossain , Mohammad Abdul Motalib , Md. Shakhaoat Hossain
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引用次数: 0

摘要

在中低收入国家,室内空气污染是导致发病和死亡的一个重要因素,这促使人们对城市环境中影响室内空气污染的因素进行研究。因此,本研究旨在调查室内空气质量,并确定影响室内空气质量的因素,从而帮助有针对性地采取干预措施,降低家庭室内空气质量。这项研究在达卡市进行,涉及 43 个家庭,对 PM2.5 浓度进行了 24 小时的连续监测。研究评估了与家庭特征(即家庭面积和烹饪燃料类型)、通风方式(即开窗时间)和室内活动(即烹饪频率、每餐每日平均烹饪时间、清洁、吸烟以及使用蚊香和喷雾剂)有关的各种因素,以探讨它们对室内空气质量的影响。通过多元线性回归模型,分析了这些因素与室内污染物浓度之间的关系。本次调查记录的 PM2.5 平均浓度比世界卫生组织(WHO)的环境空气污染 24 小时指导水平高出五倍。研究发现,室外空气、住宅面积、烹饪时间和清洁频率等四个因素与室内浓度有显著联系,它们共同解释了室内 PM2.5 水平变化的 64%。室外空气渗透是对室内浓度水平影响最大的预测因素,对室内浓度的影响很大。所确定的因素有助于有针对性地采取干预措施,以降低家庭微环境中的 PM2.5 浓度。
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Characterizing indoor air quality and identifying factors influencing air quality at home microenvironment in Dhaka city
In low- and middle-income countries, indoor air pollution stands as a significant contributor to morbidity and mortality, prompting research into the factors influencing exposure at home in urban environments. Thus, this study was designed to investigate the indoor air quality and identifying factors influencing indoor air quality which help in targeting intervention to reduce indoor home air quality. The study conducted in Dhaka city involved 43 homes, where continuous monitoring of PM2.5 concentration was carried out over a 24-h period. Various factors related to home characteristics (i.e., home area and cooking fuel type), ventilation practices (i.e., duration of window opening), and indoor activities (i.e., cooking frequency, daily average cooking duration per meal, cleaning, smoking and use of mosquito coil and spray) were assessed to explore their impact on indoor air quality. Through the multiple linear regression model, the relationship between the factors and indoor pollutant concentrations was analyzed. The average PM2.5 concentration recorded in this investigation was five time higher in comparison to the World Health Organization's (WHO) 24-h guideline level for ambient air pollution. Four factors including outdoor air, home area, cooking duration and cleaning frequency were found to be significantly linked to indoor concentrations, collectively explaining 64 % of the variability in indoor PM2.5 levels. Outdoor air infiltration emerged as the most influential predictor of indoor levels, contributing significantly to indoor concentrations. The identified factors could assist in targeting interventions to reduce microenvironmental PM2.5 concentration at home.
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