城市居民CO2实时暴露特征与家庭个体差异

王玉琼1,李云桂1,2,*,王金泽3,刘蕊嘉4,杜伟5,6

1. 西南科技大学环境与资源学院环境工程系,绵阳 621010 2. 国家卫生健康委员会核技术医学转化重点实验室,绵阳 621000 3. 华东师范大学地理科学学院地理信息科学教育部重点实验室,上海 200241 4. 西华大学食品与生物工程学院,成都 610039 5. 昆明理工大学环境科学与工程学院,昆明 650504 6. 云南省土壤固碳与污染控制重点实验室,昆明 650500

摘要:CO2是室内空气质量的重要指标,然而有关居民个体CO2实时暴露研究鲜有报道。为探究城市居民CO2实时暴露特征与家庭个体差异,本研究以四川省32个城市居民家庭(111人)为研究对象,利用CO2实时监测仪监测居民主要生活微环境(厨房、客厅、卧室、室外、办公室和车内)CO2实时浓度,通过详细问卷调查获得居民24 h活动轨迹,绘制CO2实时暴露曲线、分析家庭个体暴露差异、计算不同微环境对居民CO2暴露贡献以及暴露强度。结果表明,13.5%的受试居民存在CO2过度暴露风险(日均暴露浓度>1 000 mg·L-1);所有受试居民均存在短期CO2高浓度暴露风险,日内暴露于1 000 mg·L-1以上浓度累计时长为5.4~11.2 h。受试居民CO2暴露特征存在显著的家庭个体差异。日均暴露浓度与年龄呈正相关,并且存在性别差异。老年、中年和儿童的日均暴露浓度分别为(781±387)、(709±403)和(693±385) mg·L-1。家庭成员中,男童日均暴露浓度高于女童,而中年和老年群体中女性日均暴露浓度均高于男性,控制卧室、客厅、车内和厨房微环境中的CO2浓度可有效减少居民个体CO2暴露风险。本研究为居民CO2实时暴露风险研究和过高浓度暴露风险防控提供参考数据。

关键词:CO2;实时监测;暴露特征;暴露贡献;暴露强度

针对气候变化的严峻形势,我国提出“双碳”重大战略目标[1],环境CO2浓度的控制得到重点关注。CO2对温室效应的贡献占55%[2],大量CO2排放导致全球气候变暖,造成风暴、海平面上升、农作物质量和数量下降等问题[3-4]。不仅如此,CO2还可能引起一定的健康风险。Robertson[5]指出全球变暖并不是大气中CO2浓度上升最严重的结果,CO2有长期的毒性,会对人类生命造成威胁。Satish等[6]通过研究发现CO2是一种容易被忽视的室内污染物,而不仅仅是室内空气质量的替代指标。CO2暴露浓度增加会对人类健康构成各种直接风险,《室内空气质量标准》(GB/T 18883—2002)[7]将室内CO2日均浓度标准值规定为1 000 mg·L-1。所处环境CO2浓度达到1 000 mg·L-1时,人会感到沉闷、注意力不集中、心悸;达到1 500~2 000 mg·L-1时,人会感到气喘、头痛和眩晕;达到2 000~5 000 mg·L-1时,人会感到头疼、嗜睡和呆滞;达到5 000 mg·L-1及以上时,可能造成缺氧、脑损伤、昏迷甚至死亡[8]。有研究表明,暴露于高浓度CO2(>1 000 mg·L-1)2.5 h会影响人的认知、决策以及问题解决等能力;长期暴露于高浓度CO2会引起炎症、呼吸酸中毒等症状,甚至可能增加肥胖、糖尿病患病风险[5,9-11]。因此,监测个体CO2暴露对健康防护具有重要意义。目前已有大量关于环境CO2浓度水平相关研究[12-13],而关于个体CO2实时暴露研究较少[14-15]

城市居民每天有约70%~90%的时间在室内度过[16],室内CO2浓度水平至关重要。室内暴露又可分为住宅暴露、办公场所暴露和其他室内环境暴露等。除了室内暴露,居民暴露场所还有室外暴露和交通出行暴露[17]。因此,使用总体暴露(耦合暴露环境及暴露时间)来评估相关健康风险更合理[18]。本研究将城市居民主要活动微环境中的CO2实时浓度与活动轨迹相结合,探究其CO2实时暴露特征,量化家庭成员个体暴露差异,确定不同微环境对居民CO2暴露贡献,并计算暴露强度。以此研究结果为参考,可评估城市居民CO2暴露风险、关注存在过度暴露风险的群体以及为减少个体CO2暴露提供建议。

1 材料与方法(Materials and methods)

1.1 志愿者家庭

以城市普通白领居民家庭为研究对象,32个志愿者家庭、共计111位城市居民参与本次CO2暴露实验,包含老年24人(男11、女13),中年58人(男29、女29),儿童29人(男16、女13)。表1为受试志愿者家庭成员的年龄与性别分布。志愿者家庭住宅建筑样式为多层公寓,建筑年龄为10年,与交通干道的距离约为100 m,居住楼层为1层~22层,户型包括两室一厅、三室一厅和四室一厅。所有家庭厨房均采用天然气或电等清洁能源,配有抽油烟系统且烹饪时打开,烹饪次数为一日2次或者3次。

表1 受试志愿者家庭成员的年龄与性别分布
Table 1 Age and gender distribution of tested volunteer family members (单位:人) (Unit: Person)

家庭编号老年男性老年女性中年男性中年女性男童女童合计Family No.Elderly maleElderly femaleMiddle-aged male Middle-aged femaleMale childrenFemale childrenTotal11111 421111431111441125111146111147111148111111691111151011111511111312111313111314111315112161121711131811131911132011111521111142211223111324111325123261111427112281111152911133011114311113321113合计 Total111329291613111

注:受试志愿者中,老年大于55岁,中年35~45岁,儿童7~12岁。
Note: Among tested volunteers, the elderly were more than 55 years old, the middle-aged were 35~45 years old, and the children were 7~12 years old.

1.2 微环境CO2浓度监测

2021年2月在32个家庭住宅厨房、客厅和卧室安装了CO2实时监测仪(攀藤PMS-A003(A)),仪器固定在涨杆约150 cm处,涨杆固定在与墙壁垂直距离约100 cm的位置。除此之外,在办公室(同住宅中仪器的安装位置)、车内副驾驶、社区便利店外都安装了该仪器,以获得居民在工作时、驾乘期间和室外活动时的暴露浓度。仪器采集数据的频率为2 min采集1次(车内CO2仅在驾乘期间监测),采集时间为24 h。

1.3 居民活动轨迹调查

111位志愿者居民填写时间活动模式调查表,记录采集数据期间居民在各微环境的停留时间及活动情况。数据采集完毕并回收设备时,研究人员与志愿者面对面交谈完善调查表。

1.4 数据分析处理

将居民24 h内的暴露环境、暴露时长与监测到的CO2实时浓度相匹配,绘制居民CO2实时暴露曲线。进一步分析数据,探究城市居民CO2暴露特征、家庭个体暴露差异和不同微环境对居民CO2暴露贡献以及暴露强度。

(1)居民在某一特定微环境Ei中的暴露时间占比计算公式为:

(1)

式中:PEi为居民在某一特定微环境Ei中的暴露总时长占监测总时长的百分比;TEi为居民在某一特定微环境Ei中的暴露总时长(min);1440为监测总时长(min)。

(2)某一特定微环境Ei对居民24 h总暴露量贡献比计算公式为:

(2)

式中:CEi为某一特定微环境Ei对居民24 h总暴露量贡献比;t1t2为居民在某一特定微环境中Ei中起始暴露时间、终止暴露时间;CCO2为某一特定微环境中的CO2实时浓度(mg·L-1)。

(3)居民在某一特定微环境Ei中的暴露强度DEi计算公式为:

(3)

2 结果(Results)

2.1 微环境CO2浓度

本研究监测到的32个城市家庭住宅室内CO2日均浓度为(624±346) mg·L-1,低于工业区附近住宅室内日均浓度748 mg·L-1以及使用固体燃料的农村地区住宅室内日均浓度750 mg·L-1 [19-20]。图1为居民主要活动微环境中CO2浓度分布箱线图。住宅室内不同微环境CO2日均浓度分别为:厨房(591±296) mg·L-1,客厅(550±180) mg·L-1,卧室(709±466) mg·L-1。除此之外,室外CO2日均浓度为(307±92) mg·L-1,办公室为(433±142) mg·L-1,车内(驾乘期间)为(1 254±576) mg·L-1。车内CO2浓度最高,有研究发现,在车窗完全关闭、车内有一人的驾驶条件下,车内CO2浓度在30 min后便可达到2 500 mg·L-1 [21];车内有2人时,可达到5 000 mg·L-1 [22]。高浓度CO2易引起驾驶疲劳[23-24],在驾驶时打开车窗,能使车内CO2浓度水平保持在400 mg·L-1左右[21]。室内CO2浓度明显高于室外,室内CO2主要来源是人类呼吸和烹饪,并且前者贡献更大[25-26]

图1 不同微环境CO2日均浓度分布
注:车内浓度指驾乘期间浓度平均值。
Fig. 1 Distributions of daily averaged CO2 concentrations of different microenvironments
Note: The concentration in the family vehicle referred to the average concentration in the driving state.

2.2 CO2日内暴露浓度

为了更好描述城市居民家庭成员CO2暴露个体差异,将受试居民按照年龄、性别分为老年男性、老年女性、中年男性、中年女性、男童和女童6个群体。结合微环境CO2实时浓度与活动轨迹,绘制居民CO2日均暴露浓度频率分布图(图2)。由图2可知,86.5%(96名)的居民CO2日均暴露浓度<1 000 mg·L-1,符合《室内空气质量标准》[7]规定;13.5%(15名)的居民存在过度暴露风险,日均暴露浓度>1 000 mg·L-1。这其中包含4个不同家庭中的2名老年男性、2名老年女性、4名中年男性、4名中年女性和3名男童,其日均暴露浓度为(2 652±647) mg·L-1。从绝对数量上看,中年群体中存在过度暴露风险的人数最多,但从相对比例上看,存在过度暴露风险的老年男性和男童占比相对较大,分别为18.1%和18.8%。而老年女性、中年男性和中年女性所占比例分别为15.4%、13.8%和13.8%。根据调查问卷,存在过度暴露风险的群体在夜间睡眠期间关闭门窗,卧室CO2浓度水平随时间线性增加[27]。有研究发现,CO2浓度每增加100 mg·L-1,睡眠质量下降4.3%[28]。因此居民在夜间休息时,应注意选择性打开门窗降低卧室内CO2浓度[29]

图2 居民CO2日均暴露浓度频率分布
Fig. 2 Daily average CO2 exposure concentration frequency distribution of residents

2.3 短期CO2高浓度暴露风险

各群体于不同浓度CO2中日内暴露时长如图3所示。尽管86.5%的居民日均暴露浓度未超过标准1 000 mg·L-1,但如图3所示所有受试居民都存在短期CO2高浓度(>1 000 mg·L-1)暴露风险,尤其是老年群体暴露于1 000 mg·L-1以上浓度CO2的日内累计时间最长。具体而言,老年男性、老年女性、中年男性、中年女性、男童和女童暴露于1 000 mg·L-1以上浓度CO2日内累计时长分别为11.2、9.4、7.2、6.3、6.5和5.4 h。研究表明,暴露于1 000 mg·L-1浓度CO2中,2.5 h后人的认知、决策和问题解决能力显著下降[6,9]。鉴于老年人认知、决策和问题解决能力均有所下降,其短期CO2高浓度暴露风险应重点关注。此外,本研究也表明使用日均暴露浓度评价CO2暴露风险会隐藏短期CO2高浓度暴露风险。

图3 各群体于不同浓度CO2中日内暴露时长
Fig. 3 Duration of exposure of each group to different concentrations of CO2 within one day

2.4 家庭成员CO2暴露差异

选取1个家庭为代表,耦合微环境CO2实时浓度与居民活动轨迹,绘制家庭成员CO2实时暴露曲线。由图4可知,家庭成员CO2实时暴露呈现出明显的个体差异性,暴露浓度和暴露峰值均存在明显的时空差异。老年男性和老年女性的CO2日均暴露浓度显著高于其他家庭成员,主要归因于夜间卧室内的高浓度暴露。相对而言,CO2暴露风险最低的是儿童,但其暴露峰值仍在卧室。中年男性暴露峰值出现在车内,中年女性暴露峰值则出现在厨房烹饪期间[25]

图4 一个家庭成员CO2实时暴露曲线
Fig. 4 Real-time CO2 exposure curves of all members in one family

为进一步分析家庭成员个体暴露差异,分类计算32个志愿者家庭中各群体日均暴露浓度及在各微环境暴露时间占比(表2)。111个受试居民CO2日均暴露浓度为(719±391) mg·L-1,暴露水平与年龄呈正相关,老年、中年和儿童日均暴露浓度为(781±387)、(709±403)和(693±385) mg·L-1。除了年龄,性别也是CO2暴露的重要影响因素。如表2所示,所有居民在住宅室内日均暴露时间占比最高(≥65.9%),可见室内CO2浓度水平是个体CO2暴露的主要决定因素,这与已有研究结果相似[30-31]。老年群体与儿童在室内暴露时间最长且占比相当,但老年卧室因通风问题CO2浓度通常偏高(图4),再叠加烹饪等高暴露过程导致他们日均暴露浓度高于儿童。老年和中年群体中,女性在住宅室内暴露时间比男性长,尤其是在厨房暴露时长的显著差异导致女性日均暴露浓度高于男性。

表2 各群体CO2日均暴露浓度及日内在各微环境暴露时间占比
Table 2 Daily-averaged exposure concentration of each group and proportion of exposure time in each microenvironment within one day

群体GroupCO2日均暴露浓度/(mg·L-1) Daily average CO2exposure concentration/(mg·L-1)占比/% Proportion/%住宅室内Residential indoor厨房Kitchen客厅Living room卧室Bedroom室外Outdoor办公室Office车内Family vehicle老年男性 Elderly male765±36679.51.736.341.519.80.00.7老年女性 Elderly female797±42386.79.531.645.712.70.00.6中年男性 Middle-aged male698±40865.91.623.640.78.421.93.9中年女性 Middle-aged female720±40483.34.734.144.511.53.71.4男童 Male children745±49179.41.532.645.315.13.02.5女童 Female children632±21388.80.045.143.77.12.91.2

注:儿童随老年和中年一起,有在厨房和办公室的活动轨迹。
Note: Children, along with the elderly and middle-aged, had a track of activities in the kitchen and office.

值得注意的是,本研究数据采集是在寒假期间进行,儿童在住宅室内暴露时间总占比较就学期间增加。与住宅室内相比,教室内因人口密集通常CO2浓度更高(705~6 821 mg·L-1)[32],因此儿童就学期间短期CO2高浓度暴露风险会相应增加。

2.5 微环境暴露贡献比

由图5可知,各微环境对不同群体CO2暴露贡献,即居民在某一特定微环境中CO2暴露量与每日总暴露量的比值。卧室对所有群体暴露贡献最大,平均贡献为54.65%,与已有研究结果一致[33];其次是客厅,平均贡献为30.73%。因此,为降低卧室和客厅CO2暴露风险,卧室和客厅需保持通风,必要时可使用空气净化器[34-35]。厨房、室外、办公室和车内对居民的暴露贡献在不同群体之间呈现出差异性。男性倾向于花更长时间在工作、社交和外出等活动上[36]。相对于其他居民群体,办公室和车内对中年男性暴露贡献更大,室外对老年男性暴露贡献更大。女性是家庭烹饪的主体[36],因此厨房对中年、老年女性的暴露贡献通常更大。总体而言,室内微环境对居民CO2暴露贡献大于室外,卧室和客厅是居民CO2暴露的主要来源。

图5 微环境对居民CO2暴露贡献比
Fig. 5 The contribution ratio of different microenvironments to CO2 exposure of residents

2.6 微环境暴露强度

暴露强度量化了某微环境单位时间对居民CO2暴露贡献比。当值为1时,意味着居民在该微环境中CO2暴露水平与日均暴露水平相当;值>1,说明居民在该环境下暴露水平大于日均暴露水平。不同群体在各微环境中的CO2暴露强度如图6所示,所有居民在卧室的暴露强度均在1以上,平均暴露强度为1.24。在车内平均暴露强度达到2.06,47.6%(占有车内活动轨迹的居民人数比例)的居民在车内暴露强度>2。这表明,相对于其他环境,居民在卧室、车内活动期间CO2暴露风险更高。因此,居民在卧室和车内的CO2暴露值得关注,若不能减少在卧室、车内暴露时间,居民有必要降低这2个微环境中的CO2浓度。

图6 居民在不同微环境中的暴露强度
Fig. 6 The exposure intensity of residents in different microenvironments

3 讨论(Discussion)

就日均暴露浓度而言,86.5%的城市居民不存在CO2过度暴露风险(日均暴露浓度<1 000 mg·L-1);但从个体实时暴露上看,受试群体都存在短期CO2高浓度暴露风险(实时暴露浓度>1 000 mg·L-1)。使用日均暴露浓度评价CO2暴露风险会隐藏短期CO2高浓度暴露风险。家庭成员CO2暴露存在显著个体差异性,日均暴露浓度与年龄呈正相关,并且存在性别差异。男童日均暴露浓度高于女童,而老年和中年群体中女性日均暴露浓度高于男性。卧室、客厅、车内和厨房微环境的暴露值得重点关注,控制其CO2浓度可有效减少居民个体CO2暴露风险(选择性或周期性通风)。尤其应着力减少老年群体在卧室休息和厨房烹饪过程中CO2实时暴露风险。此外,规避不必要的峰值暴露也是降低居民个体CO2暴露风险的重要途径。如在烹饪过程中,不光要考虑通过开窗、开门或开启抽油烟系统改善通风条件,降低烹饪者CO2过度暴露风险[37],还应关注厨房CO2在室内其他微环境中的快速扩散风险[38],并主动规避其他群体(特别是儿童)在厨房内的不必要聚集。

参考文献(References):

[1] 赵姗. “双碳”战略引领绿色发展道路[N]. 中国经济时报, 2021-12-31(1)

[2] Jenkinson D S, Adams D E, Wild A. Model estimates of CO2 emissions from soil in response to global warming [J]. Nature, 1991, 351(6324): 304-306

[3] Delangiz N, Varjovi M B, Lajayer B A, et al. The potential of biotechnology for mitigation of greenhouse gasses effects: Solutions, challenges, and future perspectives [J]. Arabian Journal of Geosciences, 2019, 12(5): 174

[4] 凌定元. 温室效应危害及治理措施[J]. 纳税, 2018(13): 252

[5] Robertson D S. The rise in the atmospheric concentration of carbon dioxide and the effects on human health [J]. Medical Hypotheses, 2001, 56(4): 513-518

[6] Satish U, Mendell M J, Shekhar K, et al. Is CO2 an indoor pollutant? Direct effects of low-to-moderate CO2 concentrations on human decision-making performance [J]. Environmental Health Perspectives, 2012, 120(12): 1671-1677

[7] 国家质量监督检验检疫总局, 卫生部. 室内空气质量标准: GB/T 18883—2002[S]. 北京: 中国标准出版社, 2003

[8] 曹聪霄, 邓辉, 庞锋, 等. 低浓度二氧化碳吸附材料及其再生技术研究[C]//中国化学会.·第一届全国二氧化碳资源化利用学术会议摘要集. 天津: 中国化学会, 2019: 163

[9] Allen J G, MacNaughton P, Satish U, et al. Associations of cognitive function scores with carbon dioxide, ventilation, and volatile organic compound exposures in office workers: A controlled exposure study of green and conventional office environments [J]. Environmental Health Perspectives, 2016, 124(6): 805-812

[10] Zhang J, Pang L P, Cao X D, et al. The effects of elevated carbon dioxide concentration and mental workload on task performance in an enclosed environmental chamber [J]. Building and Environment, 2020, 178: 106938

[11] Wu J D, Weng J T, Xia B, et al. The synergistic effect of PM2.5 and CO2 concentrations on occupant satisfaction and work productivity in a meeting room [J]. International Journal of Environmental Research and Public Health, 2021, 18(8): 4109

[12] Kozielska B, Mainka A, M, et al. Indoor air quality in residential buildings in Upper Silesia, Poland [J]. Building and Environment, 2020, 177: 106914

[13] Gabriel M F, Felgueiras F, Batista R, et al. Indoor environmental quality in households of families with infant twins under 1 year of age living in Porto [J]. Environmental Research, 2021, 198: 110477

[14] Gall E T, Cheung T, Luhung I, et al. Real-time monitoring of personal exposures to carbon dioxide [J]. Building and Environment, 2016, 104: 59-67

[15] González Serrano V, Licina D. Longitudinal assessment of personal air pollution clouds in ten home and office environments [J]. Indoor Air, 2022, 32(2): e12993

[16] 赵彤, 孙江, 刘继凤. 室内空气污染现状及处理方法的探讨[J]. 环境科学与管理, 2011, 36(6): 48-49, 88

Zhao T, Sun J, Liu J F. Discussion on status quo of indoor air pollution and disposal method [J]. Environmental Science and Management, 2011, 36(6): 48-49, 88 (in Chinese)

[17] 王春梅, 陶晶, 李婷, 等. 北京城市居民冬季个体暴露PM2.5中多环芳烃特征与室外的差异研究[J]. 环境与健康杂志, 2021, 38(3): 215-219

Wang C M, Tao J, Li T, et al. Differences on characteristics between personal and outdoor exposure to polycyclic aromatic hydrocarbons in PM2.5 for urban residents in winter in Beijing [J]. Journal of Environment and Health, 2021, 38(3): 215-219 (in Chinese)

[18] Yun X, Shen G F, Shen H Z, et al. Residential solid fuel emissions contribute significantly to air pollution and associated health impacts in China [J]. Science Advances, 2020, 6(44): eaba7621

[19] Mentese S, Tasdibi D. Assessment of residential exposure to volatile organic compounds (VOCs) and carbon dioxide (CO2) [J]. Global Nest Journal, 2017, 19(4): 726-732

[20] Li Y J, Liu X L, Men Y T, et al. Indoor coal combustion for heating exacerbates CO2 exposure approaching harmful levels [J]. Environmental Science &Technology Letters, 2021, 8(10): 861-866

[21] Moreno T, Pacitto A, Fernández A, et al. Vehicle interior air quality conditions when travelling by taxi [J]. Environmental Research, 2019, 172: 529-542

[22] Gun K H, Yu Y H, Yang X P, et al. Carbon dioxide (CO2) concentrations and activated carbon fiber filters in passenger vehicles in urban areas of Jeonju, Korea [J]. Carbon Letters, 2018, 26(1): 74-80

[23] Hudda N, Fruin S A. Carbon dioxide accumulation inside vehicles: The effect of ventilation and driving conditions [J]. Science of the Total Environment, 2018, 610-611: 1448-1456

[24] Magaa V C, Scherz W D, Seepold R, et al. The effects of the driver’s mental state and passenger compartment conditions on driving performance and driving stress [J]. Sensors, 2020, 20(18): 5274

[25] Shubhanka B, Ambade B. A critical comparative study of indoor air pollution from household cooking fuels and its effect on health [J]. Oriental Journal of Chemistry, 2016, 32(1): 473-480

[26] Shen G F, Ainiwaer S, Zhu Y Q, et al. Quantifying source contributions for indoor CO2 and gas pollutants based on the highly resolved sensor data [J]. Environmental Pollution, 2020, 267: 115493

[27] Batog P, Badura M. Dynamic of changes in carbon dioxide concentration in bedrooms [J]. Procedia Engineering, 2013, 57: 175-182

[28] Xiong J, Lan L, Lian Z W, et al. Associations of bedroom temperature and ventilation with sleep quality [J]. Science and Technology for the Built Environment, 2020, 26(9): 1274-1284

[29] Mishra A K, van Ruitenbeek A M, Loomans M G L C, et al. Window/door opening-mediated bedroom ventilation and its impact on sleep quality of healthy, young adults [J]. Indoor Air, 2018, 28(2): 339-351

[30] 王贝贝, 段小丽, 蒋秋静, 等. 我国北方典型地区居民呼吸暴露参数研究[J]. 环境科学研究, 2010, 23(11): 1421-1427

Wang B B, Duan X L, Jiang Q J, et al. Inhalation exposure factors of residents in a typical region in Northern China [J]. Research of Environmental Sciences, 2010, 23(11): 1421-1427 (in Chinese)

[31] Boniardi L, Dons E, Longhi F, et al. Personal exposure to equivalent black carbon in children in Milan, Italy: Time-activity patterns and predictors by season [J]. Environmental Pollution, 2021, 274: 116530

[32] Pegas P N, Alves C A, Evtyugina M G, et al. Indoor air quality in elementary schools of Lisbon in spring [J]. Environmental Geochemistry and Health, 2011, 33(5): 455-468

[33] Almeida-Silva M, Wolterbeek H T, Almeida S M. Elderly exposure to indoor air pollutants [J]. Atmospheric Environment, 2014, 85: 54-63

[34] 蔡来胜, 刘春雁, 刘刚. 活性炭纤维及其在空气净化器中的应用[J]. 上海纺织科技, 2003, 31(4): 10-12

Cai L S, Liu C Y, Liu G. Properties and applications in the air cleaner of activated carbon fiber [J]. Shanghai Textile Science &Technology, 2003, 31(4): 10-12 (in Chinese)

[35] 爱稀奇. 这款空气净化器, 自带绿植吸收二氧化碳[J]. 五金科技, 2018(6): 82-83

[36] Zhong M, Wu C Z, Hunt J D. Gender differences in activity participation, time-of-day and duration choices: New evidence from Calgary [J]. Transportation Planning and Technology, 2012, 35(2): 175-190

[37] Kim H, Kim J S, Lee J, et al. The effect of ventilation on reducing the concentration of hazardous substances in the indoor air of a Korean living environment [J]. Analytical Science &Technology, 2020, 33(1): 49-57

[38] 胡园园, 王志荣, 蒋军成. 自然通风条件下室内CO2扩散浓度变化的数值模拟[J]. 南京工业大学学报(自然科学版), 2012, 34(3): 129-133

Hu Y Y, Wang Z R, Jiang J C. Numerical simulation of indoor concentration change of carbon dioxide dispersion under natural ventilation condition [J]. Journal of Nanjing University of Technology (Natural Science Edition), 2012, 34(3): 129-133 (in Chinese)

Real-time Evaluation of Domestic Exposure to CO2 in Sichuan, China: Space Function-associated Characteristics and Variations among Family Members

Wang Yuqiong1, Li Yungui1,2,*, Wang Jinze3, Liu Ruijia4, Du Wei5,6

1. Department of Environmental Engineering, School of Environment and Resources, Southwest University of Science and Technology, Mianyang 621010, China 2. Key Laboratory of Nuclear Technology and Medical Transformation of the National Health Commission, Mianyang 621000, China 3. Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai 200241, China 4. School of Food and Bioengineering, Xihua University, Chengdu 610039, China 5. School of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650504, China 6. Yunnan Provincial Key Lab of Soil Carbon Sequestration and Pollution Control, Kunming 650500, China

Abstract:CO2 is an important indicator of indoor air quality, and elevated indoor CO2 concentration can pose varied risks to human health. However, the residents’ real-time CO2 exposure remains undefined. To explore the characteristics of real-time CO2 exposure in urban residents and the intra-individual variations of families, real-time monitors were used to collect CO2 concentration in different microenvironments (kitchen, living room, bedroom, outdoor, office and family vehicle) in 32 households in urban community of Sichuan Province. Total 111 residents were involved and the time of their stays in selected spaces were obtained through detailed questionnaires. The real-time CO2 exposure curves were drawn, the relative contribution and intensity of each microenvironment to residents’ CO2 exposure were calculated. The results showed that 13.5% of the residents were at risk of excessive exposure to CO2, whose daily average exposure concentration were higher than the national standard limit of 1 000 mg·L-1. All the tested residents had exposed to short-term high concentration CO2 over a period of 5.4~11.2 h daily. There were significant family and individual differences in the characteristics of CO2 exposure among the subjects. The average daily exposure CO2 concentration was positively correlated with the increase of the residents’ age ((693±385) mg·L-1 in children, (709±403) mg·L-1 in middle-aged adults, and (781±387) mg·L-1 in elderly). Among family members, the average daily exposure level of male children was significantly higher than that of female children, while the exposure level of female adults was higher than that of male adults. It is of great importance to maintain the CO2 level in living room, bedroom, kitchen and family vehicle under the threshold to lower the associated health risks. The study focused on the variations of CO2 exposure among family members and provided health strategies on lowering the CO2 excessive exposure. More public attention should be raised on the CO2 exposure characteristics between male and female at different age, and differentiated public health strategies should be introduced too.

Keywords:carbon dioxide; real-time monitoring; exposure characteristics; exposure contribution; exposure intensity

收稿日期:2022-03-17

录用日期:2022-05-24

文章编号:1673-5897(2023)2-384-11

中图分类号:X171.5

文献标识码:A

基金项目:四川省国际科技创新合作项目(2021YFH0046);国家卫生健康委员会核技术医学转化重点实验室开放课题(2021HYX030)

第一作者:王玉琼(1997—),女,硕士研究生,研究方向为环境健康,E-mail: 2580044085@qq.com

*通信作者(Corresponding author), E-mail: liyungui@swust.edu.cn

DOI: 10.7524/AJE.1673-5897.20220317003

王玉琼, 李云桂, 王金泽, 等. 城市居民CO2实时暴露特征与家庭个体差异[J]. 生态毒理学报,2023, 18(2): 384-394

Wang Y Q, Li Y G, Wang J Z, et al. Real-time evaluation of domestic exposure to CO2 in Sichuan, China: Space function-associated characteristics and variations among family members [J]. Asian Journal of Ecotoxicology, 2023, 18(2): 384-394 (in Chinese)

Received 17 March 2022

accepted 24 May 2022

通信作者简介:李云桂(1983—),女,博士,副教授,主要研究方向为污染控制化学与环境健康。