严寒地区农村老年人住宅室内空气质量研究

严寒地区农村住宅冬季多采用独立敞开式小锅炉供暖,容易引发室内空气污染.为探究采暖期严寒地区农村老年人住宅的室内空气污染程度,本研究以齐齐哈尔市为例,在2017年1月和2019年1月分别实测了10户和8户农村老年人住宅室内的CO2、PM2.5、CO、NOx、VOCs、SVOC等污染物浓度,并用数学模糊法评价了室内的空气质...     

不同住所室内PM2.5的浓度变化规律及温湿度对室内颗粒物影响的初探

为深入探究不同住所室内颗粒物PM_(2.5)的浓度水平随室外颗粒物及室内污染源的变化规律,于2017年1月-2018年1月对北京城区4户居民住宅、2个学生宿舍和1户农村住宅的室内PM_(1.0)、PM_(2.5)及PM_(10)浓度、室外PM_(2.5)浓度及室内外温湿度变化进行了逐时测试,并对测试分析结果进行了差异性检验。结果表明:①城市住户室内污染源对室内PM_(2.5)浓度的影响程度为:吸烟烹饪清洁。农户污染源对室内PM_(2.5)的影响顺序为:燃煤燃烧薪柴吸烟清洁人员活动。②当住所门窗关闭室外雾霾开始或结束后,室内PM_(2.5)浓度的升高或降低均延迟于室外PM_(2.5)的变化。③城市住宅与学生宿舍的平均I/O小于1,农村住户的平均I/O大于1,且不同住所之间的I/O差异性显著(P0.05)。④住户相对湿度在10%～50%时(冬季采暖),室内颗粒物PM_(1.0)/PM_(2.5)及PM_(2.5)/PM_(10)的比值随相对湿度增加而增加,室内细颗粒PM_(2.5)的主要占比为细微颗粒物PM_(1.0);住户相对湿度在50%～80%时(秋季实测),室内颗粒物PM_(1.0)、PM_(2.5)和PM_(10)平均浓度随相对湿度增加而下降。⑥Spearman相关分析得到室内外温差与室内PM_(10)浓度和PM_(2.5)浓度呈现显著性负相关,与室内PM_(1.0)浓度无明显相关性。研究成果可为室内颗粒物控制提供理论依据并对改善住所环境和保护人体健康具有重要意义。     

上海地区不同通风方式住宅的室内环境实测分析

本文以上海地区13户住宅为对象,长期监测室内的温湿度,CO_2浓度,PM2.5浓度以及开关窗行为。调查住宅通风换气,舒适度情况以及PM2.5现状水平,探究不同通风方式对室内环境的影响。研究发现:住宅室内CO_2浓度在冬季最高,中位数水平未超过国家标准1000 ppm。相比于机械通风,自然通风住宅存在通风不足现象。开窗可增大换气次数,且可显著降低室内CO_2水平。同时,过渡季节室内的温湿度均满足舒适度要求,全年室内PM2.5浓度优良率达到93.4%。上海地区冬季室外污染时,建议住宅采用短时开窗+净化器辅助的通风方式,其他时间在保证人员热舒适情况下,建议住宅采用自然通风方式,且增大夜间开窗时长。     

温度控制与污染物浓度控制相独立的通风空调系统

为解决传统通风空调系统温度控制与污染物浓度控制需求风量不匹配的问题,提出了一种温度控制与污染物浓度控制相独立的通风空调系统结构和控制方法。通过室内负荷计算及PM2.5浓度计算,对比分析了北京、上海、深圳地区在保证室内温湿度需求时典型办公室内的PM2.5浓度水平。结果表明,该通风空调系统可以同时满足室内温度和PM2.5浓度的控制要求,尤其在室外气候温和且大气污染较重时期对室内环境的保障效果更为明显。以北京地区2018年为例计算,在室内温度相同情况下,采用该通风空调系统室内PM2.5浓度全面达标,而采用传统通风空调系统则有10.3%的时间超标。     

北京市某办公建筑夏冬季室内外PM_(2.5)浓度变化特征

为了把握雾霾天气大气环境细颗粒物PM2.5浓度变化对室内环境的影响规律,项目组先后于2013年6月~8月(夏季)和2013年12月~2014年2月(冬季)对北京地区一办公建筑室内外细颗粒物(PM2.5)质量浓度及I/O比值变化规律进行了实时监测。实测结果表明:1)在建筑外窗关闭、室内无其他污染源且机械通风系统关闭条件下,夏、冬季室内外PM2.5质量浓度的日变化规律均为夜间高白天低,周变化规律为周一~周五呈逐渐上升趋势;2)冬季各月的室内外PM2.5质量浓度水平均高于夏季各月的,对应的室内外PM2.5质量浓度I/O比值也是冬季高于夏季;3)室外风速和空气相对湿度与室内外PM2.5质量浓度存在明显的负相关,而室外空气温度与室内外PM2.5质量浓度水平的变化相关性不明显。     

蒸发冷却技术对室内颗粒物浓度的影响

对冬季雾霾天和夏季高温天气利用蒸发式冷风扇及窗式蒸发冷却空调器进行了实验,分析了直接蒸发冷却通风空调技术对室内颗粒物浓度的影响;对比了2种不同条件下室内颗粒物的净化效率,得出夏季选用窗式蒸发冷却空调器与通风结合,对室内PM2.5净化效率为52.1%,对PM10净化效率为62.9%,对TSP净化效率为72%的结论;提出了一种与蒸发冷却技术相结合的空调方案。     

厦门住宅夏季空调运行实测及其相关分析

本文采用实测方法对厦门4户住宅夏季空调室温变化进行记录,统计分析了空调启用室温、空调同时使用及室内人员作息情况。结果表明:厦门住户拥有空调数2~3台,以3台为多;拥有活动电扇1台;空调器都安装在客厅和卧室,一般是在室内气温高于28℃后才启用,室内气温低于28℃时,可以利用自然通风实现室内热舒适,室内气温在28℃~30℃时,自然通风可使室内热环境达到可接受水平;客厅空调一般在白天和晚间使用,主卧室和次卧室空调一般用于白天午休和夜间休息,客厅、主卧、次卧空调都存在同时使用情况,进行空调能耗模拟时,考虑空调同时使用系数,客厅4人,主卧2人,次卧1人是合适的。     

农村住宅夏季自然通风设计研究

利用室内自然通风系统来通风降温,取代过去单纯依靠空调送风的局面,是近年来室内通风的发展方向,自然通风能够为室内提供充足的新鲜空气,并且在夏季起到降低室内温度的作用,减少空调的使用。该文以扎兰屯某农村住宅为例,展示了通过数值模拟改进建筑方案的过程,提出了数值模拟指导设计的新方法。     

住宅室内PM2.5净化状况的实测研究

为认识住宅室内PM2.5实际的通风净化状况,对北京地区15户住宅客厅和卧室的室内PM2.5浓度、空气净化器和机械新风系统的运行功率及外窗开关状态进行了连续测试记录。基于不同室外PM2.5污染等级下室内PM2.5质量浓度低于35μg/m~3的相对时长,提出了室内PM2.5净化时间达标率的概念,由此分析了室外PM2.5污染等级、房间通风净化方式等对室内PM2.5净化时间达标率的影响。结果表明:现有以开窗率和净化开启率描述的通风净化策略并不理想;对于室外PM2.5污染等级为轻度至重度的情况,通过优化通风净化策略可改善室内PM2.5环境。     

住宅新风系统PM2.5控制效果分析

针对影响住宅室内PM2.5的室外源,分析住宅机械通风系统影响室内PM2.5浓度的风量、过滤效率和室内气流组织等主要因素,针对主要影响因素,提出适用于住宅的室内PM2.5控制技术,有PM2.5过滤功能的户式新风系统,进而提出其主要指标要求(换气次数、过滤效率、室内PM2.5控制指标等)、系统形式、送/回风口设置原则等技术要求。针对住宅PM2.5控制系统,建立数学模型,确定计算输入条件,进行理论计算,分析室内PM2.5控制效果。根据理论计算结果可知,当围护结构气密性较好时,采用有PM2.5过滤功能的户式新风系统可有效控制室内PM2.5浓度。     

Indoor air quality for poor families: new evidence from Bangladesh

Poor households in Bangladesh depend heavily on wood, dung and other biomass fuels for cooking. This paper provides a detailed analysis of the implications for indoor air pollution (IAP), drawing on new 24-h monitoring data for respirable airborne particulates (PM10). A stratified sample of 236 households was selected in Dhaka and Narayanganj, with a particular focus on fuel use, cooking locations, structural materials, ventilation practices, and other potential determinants of exposure to IAP. At each household, PM10 concentrations in the kitchen and living room were monitored for a 24-h period during December, 2003-February, 2004. Concentrations of 300 microg/m3 or greater are common in our sample, implying widespread exposure to a serious health hazard. A regression analysis for these 236 households was then conducted to explore the relationships between PM10 concentrations, fuel choices and a large set of variables that describe household cooking and ventilation practices, structure characteristics and building materials. As expected, our econometric results indicate that fuel choice significantly affects indoor pollution levels: natural gas and kerosene are significantly cleaner than biomass fuels. However, household-specific factors apparently matter more than fuel choice in determining PM10 concentrations. In some biomass-burning households, concentrations are scarcely higher than in households that use natural gas. Our results suggest that cross-household variation is strongly affected by structural arrangements: cooking locations, construction materials, and ventilation practices. A large variation in PM10 was also found during the 24-h cycle within households. For example, within the 'dirtiest' firewood-using household in our sample, readings over the 24-h cycle vary from 68 to 4864 microg/m3. Such variation occurs because houses can recycle air very quickly in Bangladesh. After the midday meal, when ventilation is common, air quality in many houses goes from very dirty to reasonably clean within an hour. Rapid change also occurs within households: diffusion of pollution from kitchens to living areas is nearly instantaneous in many cases, regardless of internal space configuration, and living-area concentrations are almost always in the same range as kitchen concentrations. By implication, exposure to dangerous indoor pollution levels is not confined to cooking areas. To assess the broader implications for poor Bangladeshi households, we extrapolate our regression results to representative 600 household samples from rural, peri-urban and urban areas in six regions: Rangpur in the north-west, Sylhet in the north-east, Rajshahi and Jessore in the west, Faridpur in the center, and Cox's Bazar in the south-east. Our results indicate great geographic variation, even for households in the same per capita income group. This variation reflects local differences in fuel use and, more significantly, construction practices that affect ventilation. For households with per capita income 相似文献   

Fine particle number and mass concentration measurements in urban Indian households

Fine particle number concentration (D(p)>10 nm, cm(-3)), mass concentrations (approximation of PM(2.5), microg m(-3)) and indoor/outdoor number concentration ratio (I/O) measurements have been conducted for the first time in 11 urban households in India, 2002. The results indicate remarkable high indoor number and mass concentrations and I/O number concentration ratios caused by cooking. Besides cooking stoves that used liquefied petroleum gas (LPG) or kerosene as the main fuel, high indoor concentrations can be explained by poor ventilation systems. Particle number concentrations of more than 300,000 cm(-3) and mass concentrations of more than 1000 microg m(-3) were detected in some cases. When the number and mass concentrations during cooking times were statistically compared, a correlation coefficient r>0.50 was observed in 63% of the households. Some households used other fuels like wood and dung cakes along with the main fuel, but also other living activities influenced the concentrations. In some areas, outdoor combustion processes had a negative impact on indoor air quality. The maximum concentrations observed in most cases were due to indoor combustion sources. Reduction of exposure risk and health effects caused by poor indoor air in urban Indian households is possible by improving indoor ventilation and reducing penetration of outdoor particles.     

上海地区冬季住宅室内外颗粒物浓度的相关性

对上海市某住宅建筑室内外PM10、PM2.5、PM1的浓度进行了测量,研究了最小通风量(外门窗关闭)条件下3种天气时颗粒浓度随时间变化的规律以及相关性,分析了颗粒物浓度与环境温湿度参数之间的关系。研究结果显示,测试期间,室内外空气中细颗粒(PM 2.5)占可吸入颗粒(PM 10)浓度比例分别达65%和87%以上;无明显室内源时,I/O比值小于1且随粒径减小而减小;室内外颗粒浓度相关性与粒径大小有关系,PM1、PM2.5的浓度相关性大于PM10。研究还表明,颗粒物浓度的关联性与天气状况有关系,多云、雨天和阴天时浓度关联性有显著差别;颗粒物的浓度受到室内外温湿度的影响,且受天气状况影响而呈现复杂性。     

Simulating indoor concentrations of NO(2) and PM(2.5) in multifamily housing for use in health-based intervention modeling

Residents of low-income multifamily housing can have elevated exposures to multiple environmental pollutants known to influence asthma. Simulation models can characterize the health implications of changing indoor concentrations, but quantifying the influence of interventions on concentrations is challenging given complex airflow and source characteristics. In this study, we simulated concentrations in a prototype multifamily building using CONTAM, a multizone airflow and contaminant transport program. Contaminants modeled included PM(2.5) and NO(2) , and parameters included stove use, presence and operability of exhaust fans, smoking, unit level, and building leakiness. We developed regression models to explain variability in CONTAM outputs for individual sources, in a manner that could be utilized in simulation modeling of health outcomes. To evaluate our models, we generated a database of 1000 simulated households with characteristics consistent with Boston public housing developments and residents and compared the predicted levels of NO(2) and PM(2.5) and their correlates with the literature. Our analyses demonstrated that CONTAM outputs could be readily explained by available parameters (R(2) between 0.89 and 0.98 across models), but that one-compartment box models would mischaracterize concentrations and source contributions. Our study quantifies the key drivers for indoor concentrations in multifamily housing and helps to identify opportunities for interventions. PRACTICAL IMPLICATIONS: Many low-income urban asthmatics live in multifamily housing that may be amenable to ventilation-related interventions such as weatherization or air sealing, wall and ceiling hole repairs, and exhaust fan installation or repair, but such interventions must be designed carefully given their cost and their offsetting effects on energy savings as well as indoor and outdoor pollutants. We developed models to take into account the complex behavior of airflow patterns in multifamily buildings, which can be used to identify and evaluate environmental and non-environmental interventions targeting indoor air pollutants which can trigger asthma exacerbations.     

Hospital indoor respirable particles and carbonaceous composition

Indoor air PM2.5 and PM10 samples were collected at the different types of indoor enviornment in the four hospitals and their adjacent outdoor environments in Guangzhou, China, during the summertime. The objectives of this study were (1) to characterize the indoor PM concentrations and associated carbonaceous species in hospitals, (2) to investigate the potential indoor sources and (3) to reconstruct carbonaceous composition in PM. Additionally, regression analysis was made to evaluate effect of outdoor sources to indoor PM levels and comparison was made between I/O levels in different types of indoor environment to evaluate effects of human activities and ventilation types to indoor PM levels.     

Statistical analyses on summer energy consumption characteristics of residential buildings in some cities of China

The purposes of this paper are to analyse energy consumption characteristics and to find out influence factors of residential energy consumption in summer in typical cities of China. The investigated residences were located in seven cities of five architecture thermotechnical design zones. Questionnaire surveys revealed housing unit characteristics, household characteristics, the possession and utilization of domestic energy consuming appliances and indoor thermal environment in summer. Energy consumption analyses show that summer energy consumption amounts in different cities bear distinct regional characteristics: the household amounts of electricity use are largest in Hongkong, and the values are smaller but still at a high level in Beijing, Shanghai and Changsha, and at the smallest level in Kunming, Harbin and Urumqi, while the difference in gas use is small among these cities. Influence factor analyses show that city locations, housing unit characteristics, the utilization of space coolers and water heaters, household characteristics, and subjective evaluation of indoor thermal environment all contribute to the residential energy consumption in summer when taking all the families in the seven cities as the sample collectivity, while detail analyses for separate cities shows each city has its own characteristics. In Shanghai, the satisfaction rate of thermal environment, the possession and operation of air conditioners and housing unit characteristics greatly affect the summer energy consumption, but the electrical fan is judged as the non-influence factor, while in Urumqi, the possession and operation of electrical fans and the categories of water heaters have remarkable effect, and the influence of housing unit characteristics is also distinct, but the number of air conditioners and their usage contribute little to energy use due to the cool climate.     

Measuring the exposure of infants and children to indoor air pollution from biomass fuels in The Gambia

Indoor air pollution (IAP) from biomass fuels contains high concentrations of health damaging pollutants and is associated with an increased risk of childhood pneumonia. We aimed to design an exposure measurement component for a matched case-control study of IAP as a risk factor for pneumonia and severe pneumonia in infants and children in The Gambia. We conducted co-located simultaneous area measurement of carbon monoxide (CO) and particles with aerodynamic diameter <2.5 microm (PM(2.5)) in 13 households for 48 h each. CO was measured using a passive integrated monitor and PM(2.5) using a continuous monitor. In three of the 13 households, we also measured continuous PM(2.5) concentration for 2 weeks in the cooking, sleeping, and playing areas. We used gravimetric PM(2.5) samples as the reference to correct the continuous PM(2.5) for instrument measurement error. Forty-eight hour CO and PM(2.5) concentrations in the cooking area had a correlation coefficient of 0.80. Average 48-h CO and PM(2.5) concentrations in the cooking area were 3.8 +/- 3.9 ppm and 361 +/- 312 microg/m3, respectively. The average 48-h CO exposure was 1.5 +/- 1.6 ppm for children and 2.4 +/- 1.9 ppm for mothers. PM(2.5) exposure was an estimated 219 microg/m3 for children and 275 microg/m3 for their mothers. The continuous PM(2.5) concentration had peaks in all households representing the morning, midday, and evening cooking periods, with the largest peak corresponding to midday. The results are used to provide specific recommendations for measuring the exposure of infants and children in an epidemiological study. PRACTICAL IMPLICATIONS: Measuring personal particulate matter (PM) exposure of young children in epidemiological studies is hindered by the absence of small personal monitors. Simultaneous measurement of PM and carbon monoxide suggests that a combination of methods may be needed for measuring children's PM exposure in areas where household biomass combustion is the primary source of indoor air pollution. Children's PM exposure in biomass burning homes in The Gambia is substantially higher than concentrations in the world's most polluted cities.     

Housing characteristics and indoor air quality in households of Alaska Native children with chronic lung conditions

Alaska Native children experience high rates of respiratory infections and conditions. Household crowding, indoor smoke, lack of piped water, and poverty have been associated with respiratory infections. We describe the baseline household characteristics of children with severe or chronic lung disease participating in a 2012–2015 indoor air study. We monitored indoor PM2.5, CO₂, relative humidity %, temperature, and VOCs and interviewed caregivers about children's respiratory symptoms. We evaluated the association between reported children's respiratory symptoms and indoor air quality indicators using multiple logistic regression analysis. Compared with general US households, study households were more likely overcrowded 73% (62%–82%) vs 3.2% (3.1%–3.3%); had higher woodstove use as primary heat source 16% (9%–25%) vs 2.1% (2.0%–2.2%); and higher proportion of children in a household with a smoker 49% (38%–60%) vs 26.2% (25.5%–26.8%). Median PM2.5 was 33 μg/m³. Median CO₂ was 1401 ppm. VOCs were detectable in all homes. VOCs, smoker, primary wood heat, and PM2.5>25 μg/m³ were associated with higher risk for cough between colds; VOCs were associated with higher risk for wheeze between colds and asthma diagnosis. High indoor air pollutant levels were associated with respiratory symptoms in household children, likely related to overcrowding, poor ventilation, woodstove use, and tobacco smoke.     

Particulate exposure and size distribution from wood burning stoves in Costa Rica

Biomass fuel is the most common energy source for cooking and space heating in developing countries. Biomass fuel combustion causes high levels of indoor air pollutants including particulates and other combustion by-products. We measured indoor air quality in 23 houses with a wood burning stove in rural residential areas of Costa Rica. Daily PM2.5, PM10 and CO concentrations, and particle size distribution were simultaneously measured in the kitchen. When a wood burning stove was used during the monitoring period, average daily PM2.5 and PM10 concentrations were 44 and 132 microg/m3, respectively. Average CO concentrations were between 0.5 and 3.3 ppm. All houses had a particle size distribution of either one or two peaks at around 0.7 and 2.5 microm aerodynamic diameters. The particulate levels increased rapidly during cooking and decreased quickly after cooking. The maximum peak particulate levels ranged from 310 to 8170 microg/m3 for PM2.5 and from 500 to 18900 microg/m3 for PM10 in all houses. Although the 24-h particulate levels in this study are lower than the National Ambient Air Quality Standards of PM2.5 and PM10, it is important to note that people, especially women and children, are exposed to extremely high levels of particulates during cooking.     

Indoor air quality investigation at air-conditioned and non-air-conditioned markets in Hong Kong

To characterize indoor air quality at the markets in Hong Kong, three non-air-conditioned and two air-conditioned markets were selected for this study. The indoor air pollutants measured included PM(10) (particulate matters with aerodynamic diameter less than 10 microm), total bacteria count (TBC), carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO(2)) and sulfur dioxide (SO(2)). The indoor and outdoor concentrations of these target air pollutants at these markets were measured and compared. The effects of air conditioning, temperature/relative humidity variation and different stalls on the indoor air quality were also investigated. The results indicated that all of the average indoor concentrations of PM(10), TBC, CO and NO(2) at the markets were below the Hong Kong Indoor Air Quality Objectives (HKIAQO) standards with a few exceptions for PM(10) and TBC. The elevated PM(10) concentrations at Hung Hom, Ngau Tau Kok and Wan Chai markets were probably due to the air filtration of outdoor airborne particulates emitted from vehicular exhaust, whereas high concentrations of airborne bacteria at Sai Ying Pun and Tin Shing markets were linked to the use of air conditioning. Correlation analysis demonstrated that indoor bacteria concentrations were correlated with temperature and relative humidity. The operation of air conditioning did not significantly reduce the levels of air pollutants at the markets. However, the higher indoor/outdoor ratios demonstrated that the operation of air conditioning had influence on the levels of bacteria at the markets. It was found that average PM(10) concentration at poultry stalls was higher than the HKIAQO standard of 180 microg/m(3), and was over two times that measured at vegetable, fish and meat stalls. Furthermore, the concentration of airborne bacteria at the poultry stalls was as high as 1031 CFU/m(3), which was above the HKIAQO standard of 1000 CFU/m(3). The bacteria levels at other three stalls were all below the HKIAQO standard. Statistical analysis indicated that there were no significant differences among the four stalls for CO, NO(x) and SO(2).