风机盘管加新风系统室内PM2.5污染控制

在对民用建筑通风系统常用过滤器滤料性能测试基础上,建立了常规风机盘管加新风系统空调房间室内颗粒物浓度集总参数模型,讨论了回风过滤段和新风过滤段过滤器效率的设计选型方法。以西安市某空调系统为例,为满足室内PM2.5污染控制标准,基于室外PM2.5浓度"不保证10d"取值计算,结果表明,余压为50~80Pa的机组回风过滤器效率选用G3、G4型过滤器,余压为30~50Pa的机组回风过滤器效率选用初效G2、G3型过滤器,同时,室内设置等效过滤效率的空气净化器,新风选用初效G4加中效F7或F8两级过滤。     

Response of consumer and research grade indoor air quality monitors to residential sources of fine particles

The ability to inexpensively monitor PM_2.5 to identify sources and enable controls would advance residential indoor air quality (IAQ) management. Consumer IAQ monitors incorporating low‐cost optical particle sensors and connections with smart home platforms could provide this service if they reliably detect PM_2.5 in homes. In this study, particles from typical residential sources were generated in a 120 m³ laboratory and time‐concentration profiles were measured with 7 consumer monitors (2‐3 units each), 2 research monitors (Thermo pDR‐1500, MetOne BT‐645), a Grimm Mini Wide‐Range Aerosol Spectrometer (GRM), and a Tapered Element Oscillating Microbalance with Filter Dynamic Measurement System (FDMS), a Federal Equivalent Method for PM_2.5. Sources included recreational combustion (candles, cigarettes, incense), cooking activities, an unfiltered ultrasonic humidifier, and dust. FDMS measurements, filter samples, and known densities were used to adjust the GRM to obtain time‐resolved mass concentrations. Data from the research monitors and 4 of the consumer monitors—AirBeam, AirVisual, Foobot, Purple Air—were time correlated and within a factor of 2 of the estimated mass concentrations for most sources. All 7 of the consumer and both research monitors substantially under‐reported or missed events for which the emitted mass was comprised of particles smaller than 0.3 μm diameter.     

Personal exposure to PM2.5 in Chinese rural households in the Yangtze River Delta

High levels of PM_2.5 exposure and associated health risks are of great concern in rural China. For this study, we used portable PM_2.5 monitors for monitoring concentrations online, recorded personal time‐activity patterns, and analyzed the contribution from different microenvironments in rural areas of the Yangtze River Delta, China. The daily exposure levels of rural participants were 66 μg/m³ (SD 40) in winter and 65 μg/m³ (SD 16) in summer. Indoor exposure levels were usually higher than outdoor levels. The exposure levels during cooking in rural kitchens were 140 μg/m³ (SD 116) in winter and 121 μg/m³ (SD 70) in summer, the highest in all microenvironments. Winter and summer values were 252 μg/m³ (SD 103) and 204 μg/m³ (SD 105), respectively, for rural people using biomass for fuel, much higher than those for rural people using LPG and electricity. By combining PM_2.5concentrations and time spent in different microenvironments, we found that 92% (winter) and 85% (summer) of personal exposure to PM_2.5in rural areas was attributable to indoor microenvironments, of which kitchens accounted for 24% and 27%, respectively. Consequently, more effective policies and measures are needed to replace biomass fuel with LPG or electricity, which would benefit the health of the rural population in China.     

办公建筑中空调形式对室内外PM2.5浓度相关性的影响

室外PM2.5可通过新风及围护结构缝隙渗透至室内,室外PM2.5较高时尤为明显,结果导致室内空气中的PM2.5浓度上升。为了研究空调形式对室内外PM2.5浓度相关性的影响,在2015年夏季对重庆某办公建筑中采用不同空调形式的室内外PM2.5浓度进行了实测。实测结果发现:集中式空调、分体式空调和非空调房间室内外PM2.5浓度比变化范围分别为0.59~0.76、0.47~0.76、0.71~0.91。室内外PM2.5浓度相关性系数的排序为:集中式空调环境(0.94)非空调环境(0.92)分体式空调环境(0.77),研究结果表明,办公建筑的空调形式,对室内外PM2.5浓度的相关性有影响。     

Hospital indoor PM10/PM2.5 and associated trace elements in Guangzhou, China

PM10 and PM2.5 samples were collected in the indoor environments of four hospitals and their adjacent outdoor environments in Guangzhou, China during the summertime. The concentrations of 18 target elements in particles were also quantified. The results showed that indoor PM2.5 levels with an average of 99 microg m(-3) were significantly higher than outdoor PM2.5 standard of 65 microg m(-3) recommended by USEPA [United States Environmental Protection Agency. Office of Air and Radiation, Office of Air Quality Planning and Standards, Fact Sheet. EPA's Revised Particulate Matter Standards, 17, July 1997] and PM2.5 constituted a large fraction of indoor respirable particles (PM10) by an average of 78% in four hospitals. High correlation between PM2.5 and PM10 (R(2) of 0.87 for indoors and 0.90 for outdoors) suggested that PM2.5 and PM10 came from similar particulate emission sources. The indoor particulate levels were correlated with the corresponding outdoors (R(2) of 0.78 for PM2.5 and 0.67 for PM10), demonstrating that outdoor infiltration could lead to direct transportation into indoors. In addition to outdoor infiltration, human activities and ventilation types could also influence indoor particulate levels in four hospitals. Total target elements accounted for 3.18-5.56% of PM2.5 and 4.38-9.20% of PM10 by mass, respectively. Na, Al, Ca, Fe, Mg, Mn and Ti were found in the coarse particles, while K, V, Cr, Ni, Cu, Zn, Cd, Sn, Pb, As and Se existed more in the fine particles. The average indoor concentrations of total elements were lower than those measured outdoors, suggesting that indoor elements originated mainly from outdoor emission sources. Enrichment factors (EF) for trace element were calculated to show that elements of anthropogenic origins (Zn, Pb, As, Se, V, Ni, Cu and Cd) were highly enriched with respect to crustal composition (Al, Fe, Ca, Ti and Mn). Factor analysis was used to identify possible pollution source-types, namely street dust, road traffic and combustion processes.     

Applying indoor and outdoor modeling techniques to estimate individual exposure to PM2.5 from personal GPS profiles and diaries: A pilot study

Impacts of individual behavior on personal exposure to particulate matter (PM) and the associated individual health effects are still not well understood. As outdoor PM concentrations exhibit highly temporal and spatial variations, personal PM exposure depends strongly on individual trajectories and activities. Furthermore, indoor environments deserve special attention due to the large fraction of the day people spend indoors. The indoor PM concentration in turn depends on infiltrated outdoor PM and indoor particle sources, partially caused by the activities of people indoor.We present an approach to estimate PM2.5 exposure levels for individuals based upon existing data sources and models. For this pilot study, six persons kept 24-hour diaries and GPS tracks for at least one working day and one weekend day, providing their daily activity profiles and the associated geographical locations. The survey took place in the city of Münster, Germany in the winter period between October 2006 and January 2007. Environmental PM2.5 exposure was estimated by using two different models for outdoor and indoor concentrations, respectively. For the outdoor distribution, a dispersion model was used and extended by actual ambient fixed site measurements. Indoor concentrations were modeled using a simple mass balance model with the estimated outdoor concentration fraction infiltrated and indoor activities estimated from the diaries. A limited number of three 24-hour indoor measurements series for PM were performed to test the model performance.The resulting average daily exposure of the 14 collected profiles ranged from 21 to 198 µg m^− 3 and showed a high variability over the day as affected by personal behavior. Due to the large contribution of indoor particle sources, the mean 24-hour exposure was in most cases higher than the daily means of the respective outdoor fixed site monitors.This feasibility study is a first step towards a more comprehensive modeling approach for personal exposure, and therefore restricted to limited data resources. In future, this model framework not only could be of use for epidemiological research, but also of public interest. Any individual operating a GPS capable device may become able to obtain an estimate of its personal exposure along its trajectory in time and space. This could provide individuals a new insight into the influence of personal habits on their exposure to air pollution and may result in the adaptation of personal behavior to minimize risks.     

公共建筑室内PM2.5污染控制策略研究

根据公共建筑室内PM2.5污染来源、运动规律,结合室内PM2.5污染控制通风过滤模型,分析了通风换气对降低室内PM2.5污染浓度的影响,给出了空气过滤器过滤效率计算公式及简化选型计算公式,提供了室内PM2.5浓度控制标准要求,结合目前常见的集中空调系统空气过滤器配置工况,通过实例计算,给出了集中空调系统空气过滤器等级组合建议。     

Concurrent assessment of personal,indoor, and outdoor PM2.5 and PM1 levels and source contributions using novel low-cost sensing devices

The intensity, frequency, duration, and contribution of distinct PM_2.5 sources in Asian households have seldom been assessed; these are evaluated in this work with concurrent personal, indoor, and outdoor PM_2.5 and PM₁ monitoring using novel low-cost sensing (LCS) devices, AS-LUNG. GRIMM-comparable observations were acquired by the corrected AS-LUNG readings, with R² up to 0.998. Twenty-six non-smoking healthy adults were recruited in Taiwan in 2018 for 7-day personal, home indoor, and home outdoor PM monitoring. The results showed 5-min PM_2.5 and PM₁ exposures of 11.2 ± 10.9 and 10.5 ± 9.8 µg/m³, respectively. Cooking occurred most frequently; cooking with and without solid fuel contributed to high PM_2.5 increments of 76.5 and 183.8 µg/m³ (1 min), respectively. Incense burning had the highest mean PM_2.5 indoor/outdoor (1.44 ± 1.44) ratios at home and on average the highest 5-min PM_2.5 increments (15.0 µg/m³) to indoor levels, among all single sources. Certain events accounted for 14.0%-39.6% of subjects’ daily exposures. With the high resolution of AS-LUNG data and detailed time-activity diaries, the impacts of sources and ventilations were assessed in detail.     

Estimating long-term time-resolved indoor PM2.5 of outdoor and indoor origin using easily obtainable inputs

To evaluate the separate impacts on human health and establish effective control strategies, it is crucial to estimate the contribution of outdoor infiltration and indoor emission to indoor PM_2.5 in buildings. This study used an algorithm to automatically estimate the long-term time-resolved indoor PM_2.5 of outdoor and indoor origin in real apartments with natural ventilation. The inputs for the algorithm were only the time-resolved indoor/outdoor PM_2.5 concentrations and occupants’ window actions, which were easily obtained from the low-cost sensors. This study first applied the algorithm in an apartment in Tianjin, China. The indoor/outdoor contribution to the gross indoor exposure and time-resolved infiltration factor were automatically estimated using the algorithm. The influence of outdoor PM_2.5 data source and algorithm parameters on the estimated results was analyzed. The algorithm was then applied in four other apartments located in Chongqing, Shenyang, Xi'an, and Urumqi to further demonstrate its feasibility. The results provided indirect evidence, such as the plausible explanations for seasonal and spatial variation, to partially support the success of the algorithm used in real apartments. Through the analysis, this study also identified several further development directions to facilitate the practical applications of the algorithm, such as robust long-term outdoor PM_2.5 monitoring using low-cost light-scattering sensors.     

Analysis of indoor PM2.5 exposure in Asian countries using time use survey

Most household fuels used in Asian countries are solid fuels such as coal and biomass (firewood, crop residue and animal dung). The particulate matter (PM), CO, NOx and SOx produced through the combustion of these fuels inside the residence for cooking and heating has an adverse impact on people's health. PM 2.5 in particular, consisting of particles with an aerodynamic diameter of 2.5 μm or less, penetrates deep into the lungs and causes respiratory system and circulatory system diseases and so on. As a result, the World Health Organization (WHO) established guideline values for this type of particulate matter in 2005. In this study, the authors focused on PM 2.5 and estimated indoor exposure concentrations for PM 2.5 in 15 Asian countries. For each environment used for cooking, eating, heating and illumination in which people are present temporarily (microenvironment), exposure concentrations were estimated for individual cohorts categorized according to sex, age and occupation status. To establish the residence time in each microenvironment for each of the cohorts, data from time use surveys conducted in individual countries were used. China had the highest estimate for average exposure concentration in microenvironment used for cooking at 427.5 μg/m³ , followed by Nepal, Laos and India at 285.2 μg/m³, 266.3 μg/m³ and 205.7 μg/m³ , respectively. The study found that, in each country, the PM2.5 exposure concentration was highest for children and unemployed women between the ages of 35 and 64. The study also found that the exposure concentration for individual cohorts in each country was greatly affected by people's use of time indoors. Because differences in individual daily life activities were reflected in the use of time and linked to an assessment of exposure to indoor air-polluting substances, the study enabled detailed assessment of the impact of exposure.     

A recurrent neural network using historical data to predict time series indoor PM2.5 concentrations for residential buildings

Due to the severe outdoor PM2.5 pollution in China, many people have installed air-cleaning systems in homes. To make the systems run automatically and intelligently, we developed a recurrent neural network (RNN) that uses historical data to predict the future indoor PM2.5 concentration. The RNN architecture includes an autoencoder and a recurrent part. We used data measured in an apartment over the course of an entire year to train and test the RNN. The data include indoor/outdoor PM2.5 concentration, environmental parameters and time of day. By comparing three different input strategies, we found that a strategy employing historical PM2.5 and time of day as inputs performed best. With this strategy, the model can be applied to predict the relatively stable trend of indoor PM2.5 concentration in advance. When the input length is 2 h and the prediction horizon is 30 min, the median prediction error is 8.3 µg/m³ for the whole test set. For times with indoor PM2.5 concentrations between (20,50] µg/m³ and (50,100] µg/m³, the median prediction error is 8.3 and 9.2 µg/m³, respectively. The low prediction error between the ground-truth and predicted values shows that the RNN can predict indoor PM2.5 concentrations with satisfactory performance.     

Real-time air monitoring of occupational exposures to particulate matter among hairdressers in Maryland: A pilot study

Hairdressers are exposed to particulate matter (PM), a known air pollutant linked to adverse health effects. Still, studies on occupational PM exposures in hair salons are sparse. We characterized indoor air PM concentrations in three salons primarily serving an African/African American (AA) clientele, and three Dominican salons primarily serving a Latino clientele. We also assessed the performance of low-cost sensors (uRAD, Flow, AirVisual) by comparing them to high-end sensors (DustTrak) to conduct air monitoring in each salon over 3 days to quantify work shift concentrations of PM_2.5, respirable PM (RPM), and PM₁₀. We observed high spatial and temporal variability in 30-min time-weighted average (TWA) RPM concentrations (0.18–5518 μg/m³). Readings for the uRAD and AirVisual sensors were highly correlated with the DustTrak (R² = 0.90–0.99). RPM 8-hour TWAs ranged from 18 to 383 µg/m³ for AA salons, and 9–2115 µg/m³ for Dominican salons. Upper 95th percentiles of daily RPM exposures ranged from 439 to 2669 µg/m³. The overall range of 30-min TWA PM_2.5 and PM₁₀ concentrations was 0.13–5497 and 0.36-,541 μg/m³, respectively. Findings suggest that hairdressers could be overexposed to RPM during an 8-hour shift. Additional comprehensive monitoring studies are warranted to further characterize temporal and spatial variability of PM exposures in this understudied occupational population.     

Household and behavioral determinants of indoor PM2.5 in a rural solid fuel burning Native American community

Indoor and outdoor concentrations of PM_2.5 were measured for 24 h during heating and non-heating seasons in a rural solid fuel burning Native American community. Household building characteristics were collected during the initial home sampling visit using technician walkthrough questionnaires, and behavioral factors were collected through questionnaires by interviewers. To identify seasonal behavioral factors and household characteristics associated with indoor PM_2.5, data were analyzed separately by heating and non-heating seasons using multivariable regression. Concentrations of PM_2.5 were significantly higher during the heating season (indoor: 36.2 μg/m³; outdoor: 22.1 μg/m³) compared with the non-heating season (indoor: 14.6 μg/m³; outdoor: 9.3 μg/m³). Heating season indoor PM_2.5 was strongly associated with heating fuel type, housing type, indoor pests, use of a climate control unit, number of interior doors, and indoor relative humidity. During the non-heating season, different behavioral and household characteristics were associated with indoor PM_2.5 concentrations (indoor smoking and/or burning incense, opening doors and windows, area of surrounding environment, building size and height, and outdoor PM_2.5). Homes heated with coal and/or wood, or a combination of coal and/or wood with electricity and/or natural gas had elevated indoor PM_2.5 concentrations that exceeded both the EPA ambient standard (35 μg/m³) and the WHO guideline (25 μg/m³).     

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.     

Comparison of next-generation portable pollution monitors to measure exposure to PM2.5 from household air pollution in Puno,Peru

Assessment of personal exposure to PM_2.5 is critical for understanding intervention effectiveness and exposure-response relationships in household air pollution studies. In this pilot study, we compared PM_2.5 concentrations obtained from two next-generation personal exposure monitors (the Enhanced Children MicroPEM or ECM; and the Ultrasonic Personal Air Sampler or UPAS) to those obtained with a traditional Triplex Cyclone and SKC Air Pump (a gravimetric cyclone/pump sampler). We co-located cyclone/pumps with an ECM and UPAS to obtain 24-hour kitchen concentrations and personal exposure measurements. We measured Spearmen correlations and evaluated agreement using the Bland-Altman method. We obtained 215 filters from 72 ECM and 71 UPAS co-locations. Overall, the ECM and the UPAS had similar correlation (ECM ρ = 0.91 vs UPAS ρ = 0.88) and agreement (ECM mean difference of 121.7 µg/m³ vs UPAS mean difference of 93.9 µg/m³) with overlapping confidence intervals when compared against the cyclone/pump. When adjusted for the limit of detection, agreement between the devices and the cyclone/pump was also similar for all samples (ECM mean difference of 68.8 µg/m³ vs UPAS mean difference of 65.4 µg/m³) and personal exposure samples (ECM mean difference of −3.8 µg/m³ vs UPAS mean difference of −12.9 µg/m³). Both the ECM and UPAS produced comparable measurements when compared against a cyclone/pump setup.     

上海市某办公建筑PM_(2.5)浓度分布及影响因素的实测研究

通过对上海市某办公建筑在不同时段和条件下PM2.5等颗粒物浓度的现场测试,得到室内PM2.5浓度分布及变化特性,并分析了影响PM2.5浓度变化的室外颗粒物浓度、门窗开启情况、测试时段、室内人员、吸烟、空调系统、地毯扬尘等因素,探讨了PM2.5与其他粒径颗粒物浓度变化的相关性。实测发现办公楼室内PM2.5浓度在不同时期的变化较大,为了室内工作人员的身体健康,建议在颗粒物污染较严重时期,尽量少开门窗,加强新风过滤处理,在室内发尘较严重的区域,建议同时使用局部净化设备。     

Automating the interpretation of PM2.5 time-resolved measurements using a data-driven approach

The rapid development of automated measurement equipment enables researchers to collect greater quantities of time-resolved data from indoor and outdoor environments. While significant, the interpretation of the resulting data can be a time-consuming effort. This paper introduces an automated process of interpreting PM_2.5 time-resolved data and differentiating PM_2.5 emissions resulting from indoor and outdoor sources. We use Random Forest (RF), a machine learning approach, to study a dataset of 836 indoor emission events that occurred over a 2-week period in 18 apartments in California. In this paper, we show model development and evaluate its performance as the sample size and source vary. We discuss the characteristics of the dataset that tended to help the source identification and why. For example, we show that data from many events and from different apartments are essential for the model to be suitable for analyzing a new separate dataset. We also show that longitudinal data appear to be more helpful than the time frequency of measurements within a given apartment. We use the resulting RF model to analyze PM_2.5 data of an entirely separate dataset collected from 65 new homes in California. The RF model identifies 442 indoor emission events, with only a few misidentifications.     

Effectiveness of portable HEPA air cleaners on reducing indoor PM2.5 and NH3 in an agricultural cohort of children with asthma: A randomized intervention trial

We conducted a randomized trial of portable HEPA air cleaners with pre-filters designed to also reduce NH₃ in non-smoking homes of children age 6-12 with asthma in Yakima Valley (Washington, USA). Participants were recruited through the Yakima Valley Farm Workers Clinic asthma education program. All participants received education on home triggers while intervention families additionally received two HEPA cleaners (child's sleeping area, main living area). Fourteen-day integrated samples of PM_2.5 and NH₃ were measured at baseline and one-year follow-up. We fit ANCOVA models to compare follow-up concentrations in HEPA vs control homes, adjusting for baseline concentrations. Seventy-one households (36 HEPA, 35 control) completed the study. Most were single-family homes, with electric heat and stove, A/C, dogs/cats, and mean (SD) 5.3 (1.8) occupants. In the sleeping area, baseline geometric mean (GSD) PM_2.5 was 10.7 (2.3) μg/m³ (HEPA) vs 11.2 (1.9) μg/m³ (control); in the living area, it was 12.5 (2.3) μg/m³ (HEPA) vs 13.6 (1.9) μg/m³ (control). Baseline sleeping area NH₃ was 62.4 (1.6) μg/m³ (HEPA) vs 65.2 (1.8) μg/m³ (control). At follow-up, HEPA families had 60% (95% CI, 41%-72%; p < .0001) and 42% (19%-58%; p = .002) lower sleeping and living area PM_2.5, respectively, consistent with prior studies. NH₃ reductions were not observed.     

Characterization of indoor air quality in primary schools in Antwerp, Belgium

The indoor air quality of 27 primary schools located in the city centre and suburbs of Antwerp, Belgium, was assessed. The primary aim was to obtain correlations between the various pollutant levels. Indoor:outdoor ratios and the building and classroom characteristics of each school were investigated. This paper presents results on indoor and local outdoor PM2.5 mass concentrations, its elemental composition in terms of K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Br, Pb, Al, Si, S, and Cl, and its black smoke content. In addition, indoor and local outdoor levels of the gases NO2, SO2, O3, and BTEX (benzene, toluene, ethyl benzene, and xylene isomers) were determined. Black smoke, NO2, SO2 and O3, occurred at indoor:outdoor ratios below unity, indicating their significant outdoor sources. No linear correlation was established between indoor and outdoor levels for PM2.5 mass concentrations and BTEX; their indoor:outdoor ratios exceeded unity except for benzene. Classroom PM2.5 occurred with a different elemental composition than local outdoor PM2.5. The re-suspension of dust because of room occupation is probably the main contributor for the I/O ratios higher than 1 reported for elements typically constituting dust particles. Finally, increased benzene concentrations were reported for classrooms located at the lower levels. PRACTICAL IMPLICATIONS: The elevated indoor PM2.5, and BTEX concentrations in primary school classrooms, exceeding the ambient concentrations, raise concerns about possible adverse health effects on susceptible children. This is aggravated by the presence of carpets and in the case of classrooms at lower levels. Analysis of PM2.5's elemental composition indicated a considerable contribution of soil dust to indoor PM2.5 mass. In order to set adequate threshold values and guidelines, detailed information on the health impact of specific PM2.5 composites is needed. The results suggest that local outdoor air concentrations measurements do not provide an accurate estimation of children's personal exposures to the identified air pollutants inside classrooms.     

The analysis of PM2.5 and associated elements and their indoor/outdoor pollution status in an urban area

In this study, elemental composition of PM2.5 and the status of indoor/outdoor pollution were investigated in a commercial building near a roadside area in Daejeon, Korea. A total of 60 parallel PM2.5 samples were collected both on the roof (outdoor) and in an indoor office of a building near a highly congested road during the spring and fall of 2008. The concentrations of 23 elements were analysed from these PM2.5 samples using instrumental neutron activation analysis. PM2.5 levels in indoor environment (47.6 ± 16.5 μg/m(3)) were noticeably higher than the outdoor levels (37.7 ± 17.2 μg/m(3)) with the I/O concentration ratio of 1.37 ± 0.33 [correlation coefficient (r) = 0.89, P < 0.001]. Principal component analysis results coincidently showed the predominance of sources such as soil dust, traffic, oil/coal combustion and road dust for both indoor and outdoor microenvironments. An isolated source in the indoor environment was assigned to environmental tobacco smoke (ETS) with high factor loading of Ce, Cl, I, K, La and Zn. The overall results of our study indicate that the sources of indoor constituents were strongly dependent on outdoor processes except for the ones affected by independent sources such as ETS. PRACTICAL IMPLICATIONS: An improved understanding of the factors affecting the indoor PM2.5 concentration levels can lead to the development of an efficient management strategy to control health risks from exposure to indoor PM2.5 and related toxic components. A comparison of our comprehensive data sets indicated that most indoor PM2.5 and associated elemental species were strongly enriched by indoor source activities along with infiltration of ambient outdoor air for a naturally ventilated building.