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Air quality is one of the most important factors in public health. While outdoor air quality is widely studied, the indoor environment has been less scrutinised, even though time spent indoors is typically much greater than outdoors. The emergence of low-cost sensors can help assess indoor air quality. This study provides a new methodology, utilizing low-cost sensors and source apportionment techniques, to understand the relative importance of indoor and outdoor air pollution sources upon indoor air quality. The methodology is tested with three sensors placed in different rooms inside an exemplar house (bedroom, kitchen and office) and one outdoors. When the family was present, the bedroom had the highest average concentrations for PM2.5 and PM10 (3.9 ± 6.8 ug/m3 and 9.6 ± 12.7 μg/m3 respectively), due to the activities undertaken there and the presence of softer furniture and carpeting. The kitchen, while presenting the lowest PM concentrations for both size ranges (2.8 ± 5.9 ug/m3 and 4.2 ± 6.9 μg/m3 respectively), presented the highest PM spikes, especially during cooking times. Increased ventilation in the office resulted in the highest PM1 concentration (1.6 ± 1.9 μg/m3), highlighting the strong effect of infiltration of outdoor air for the smallest particles. Source apportionment, via positive matrix factorisation (PMF), showed that up to 95 % of the PM1 was found to be of outdoor sources in all the rooms. This effect was reduced as particle size increased, with outdoor sources contributing >65 % of the PM2.5, and up to 50 % of the PM10, depending on the room studied. The new approach to elucidate the contributions of different sources to total indoor air pollution exposure, described in this paper, is easily scalable and translatable to different indoor locations.

• A new methodology is provided for low cost source apportionment of household PM. • The characteristics of different rooms are assessed on their air quality impact. • The key role of outdoor air infiltration into the household is quantified. • Daily PM exposure estimates are provided with reference to the WHO guidelines. • Methodology is easily translatable and scalable to different indoor environments. Air quality is one of the most important factors in public health. While outdoor air quality is widely studied, the indoor environment has been less scrutinised, even though time spent indoors is typically much greater than outdoors. The emergence of low-cost sensors can help assess indoor air quality. This study provides a new methodology, utilizing low-cost sensors and source apportionment techniques, to understand the relative importance of indoor and outdoor air pollution sources upon indoor air quality. The methodology is tested with three sensors placed in different rooms inside an exemplar house (bedroom, kitchen and office) and one outdoors. When the family was present, the bedroom had the highest average concentrations for PM 2.5 and PM 10 (3.9 ± 6.8 ug/m3 and 9.6 ± 12.7 μg/m3 respectively), due to the activities undertaken there and the presence of softer furniture and carpeting. The kitchen, while presenting the lowest PM concentrations for both size ranges (2.8 ± 5.9 ug/m3 and 4.2 ± 6.9 μg/m3 respectively), presented the highest PM spikes, especially during cooking times. Increased ventilation in the office resulted in the highest PM 1 concentration (1.6 ± 1.9 μg/m3), highlighting the strong effect of infiltration of outdoor air for the smallest particles. Source apportionment, via positive matrix factorisation (PMF), showed that up to 95 % of the PM 1 was found to be of outdoor sources in all the rooms. This effect was reduced as particle size increased, with outdoor sources contributing >65 % of the PM 2.5 , and up to 50 % of the PM 10 , depending on the room studied. The new approach to elucidate the contributions of different sources to total indoor air pollution exposure, described in this paper, is easily scalable and translatable to different indoor locations. [ABSTRACT FROM AUTHOR]

Cooking and heating with coal and biomass is the main source of household air pollution in China and a leading contributor to disease burden. As part of a baseline assessment for a household energy intervention program, we enrolled 205 adult women cooking with biomass fuels in Sichuan, China and measured their 48-h personal exposure to fine particulate matter (PM 2.5 ) and carbon monoxide (CO) in winter and summer. We also measured the indoor 48-h PM 2.5 concentrations in their homes and conducted outdoor PM 2.5 measurements during 101 (74) days in summer (winter). Indoor concentrations of CO and nitrogen oxides (NO, NO 2 ) were measured over 48-h in a subset of ~ 80 homes. Women's geometric mean 48-h exposure to PM 2.5 was 80 μg/m 3 (95% CI: 74, 87) in summer and twice as high in winter (169 μg/m 3 (95% CI: 150, 190), with similar seasonal trends for indoor PM 2.5 concentrations (winter: 252 μg/m 3 ; 95% CI: 215, 295; summer: 101 μg/m 3 ; 95% CI: 91, 112). We found a moderately strong relationship between indoor PM 2.5 and CO (r = 0.60, 95% CI: 0.46, 0.72), and a weak correlation between personal PM 2.5 and CO (r = 0.41, 95% CI: − 0.02, 0.71). NO 2 /NO ratios were higher in summer (range: 0.01 to 0.68) than in winter (range: 0 to 0.11), suggesting outdoor formation of NO 2 via reaction of NO with ozone is a more important source of NO 2 than biomass combustion indoors. The predictors of women's personal exposure to PM 2.5 differed by season. In winter, our results show that primary heating with a low-polluting fuel (i.e., electric stove or wood-charcoal) and more frequent kitchen ventilation could reduce personal PM 2.5 exposures. In summer, primary use of a gaseous fuel or electricity for cooking and reducing exposure to outdoor PM 2.5 would likely have the greatest impacts on personal PM 2.5 exposure. [ABSTRACT FROM AUTHOR]

Abstract: Different household insecticide applications via two electric vaporizers emitting transfluthrin were realized in a full-scale experimental room under controlled air exchange rate conditions. On-line high-time resolved measurements of the gas-phase concentrations of the active substance during and immediately after the spreading periods were performed with a High Sensitivity Proton-Transfer-Reaction Mass Spectrometer (HS-PTR-MS). Experimental and modelled data from the ConsExpo 4.0 software were also compared to evaluate the sources of differences. Different application scenarios were also compared. Averaged inhaled concentrations over 1h, 1week, and 5months were estimated to be 8.3, 1.8, and 1.8μg.m−3, respectively. Corresponding margins of exposures range from 1000 to 10,000, claiming for the absence of effect. Dermal and dust ingestion pathways, although roughly estimated, seems being non-negligible. This claims for a more in-depth integrated risk assessment. [Copyright &y& Elsevier]

Abstract: Within-house and within-room spatial temporal variability in PBDE contamination of indoor dust may influence substantially the reliability of human exposure assessments based on single point samples, but have hitherto been little studied. This paper reports concentrations of PBDEs 17, 28, 47, 49, 66, 85, 99, 100, 153, and 154 in indoor dust samples (n=112) from two houses in Birmingham, UK. To evaluate within-house spatial variability, four separate rooms were sampled in house 1 and two separate rooms sampled in house 2. Up to four different 1m2 areas in the same room were sampled to evaluate within-room spatial variability, and for all studied areas, samples were taken for eight consecutive months to evaluate temporal and seasonal variability. Concentrations of ΣPBDEs in individual samples from house 1 varied between 21 and 280ngg−1; while the range of concentrations in house 2 was 20–1000ngg−1. This indicates that where and when a sample is taken in a house can influence substantially the contamination detected. In one room, concentrations of PBDEs in an area located close to putative PBDE sources exceeded substantially those in an area 2m away, with marked differences also observed between two areas in another room. Substantial within-room spatial differences in PBDE concentrations were not discernible in the other rooms studied. Concentrations of PBDEs in the majority of rooms within the same houses were not markedly different between rooms. Nevertheless, large differences were observed between PBDE concentrations detected in two rooms in the same house in both houses studied. In one instance, this is hypothesised to be attributable to the presence of a carpet in one room and bare wooden floor in another, but firm conclusions cannot be drawn. Within-room temporal (month-to-month) variability was substantial (relative standard deviations for ΣPBDEs=15–200%). In some rooms, the introduction and removal of putative sources like a TV and a bed, appeared to exert a discernible influence on PBDE concentrations. PBDE concentrations in spring and summer were not markedly different from those observed in autumn and winter. Possible dilution of PBDE concentrations in dust at higher dust loadings (g dust per m2 floor surface) was investigated in a small number of rooms, but no firm evidence of such dilution was evident. [Copyright &y& Elsevier]

Abstract: Buildings in developed countries are becoming increasingly airtight as a response to stricter energy efficiency requirements. At the same time, changes are occurring to the ways in which household energy is supplied, distributed and used. These changes are having important impacts on exposure to indoor air pollutants in residential buildings and present new challenges for professionals interested in assessing the effects of housing on public health. In many circumstances, models are the most appropriate way with which to examine the potential outcomes of future environmental and/or building interventions and policies. As such, there is a need to consider the current state of indoor air pollution exposure modelling. Various indoor exposure modelling techniques are available, ranging from simple statistical regression and mass-balance approaches, to more complex multizone and computational fluid dynamics tools that have correspondingly large input data requirements. This review demonstrates that there remain challenges which limit the applicability of current models to health impact assessment. However, these issues also present opportunities for better integration of indoor exposure modelling and epidemiology in the future. The final part of the review describes the application of indoor exposure models to health impact assessments, given current knowledge and data, and makes recommendations aimed at improving model predictions in the future. [ABSTRACT FROM AUTHOR]

Abstract: Brominated flame retardants (BFRs) are now ubiquitous contaminants with large reservoirs and high concentrations in buildings. Most of the information documenting BFR levels has been obtained in residences, and other environments that can lead to exposure have received relatively little attention, including offices that contain numerous BFR sources and where individuals spend considerable time. The aim of this study is to characterize BFR concentrations, potential emission sources, and migration pathways in office environments. We measure BFR levels in floor dust, indoor air, ventilation filter dust, and carpets in ten commercial and institutional buildings in Michigan, U.S.A. The median concentration of total BDEs in settled dust was 8754ngg−1, at the upper range of levels previously reported. Especially elevated levels were found in offices in buildings that contained known or likely BFR sources, e.g., computer servers. A trends analysis in a newly constructed building showed remarkable increases in concentrations of BFRs in settled dust and indoor air, and apparent steady-state levels were reached 5 to 8months after building completion, a particularly striking finding given that the building was constructed and furnished several years after the voluntary phase-out of the penta- and octa-mixtures. Airborne particulate matter collected in a building''s HVAC system filters contained PBDEs, including BDE-209, at levels exceeding the concentration of floor dust. In conjunction with estimates of building air flow rates, filter efficiency and other parameters, mass balance calculations for this building were used to estimate the emission rates and reservoirs of PBDEs. The widespread distribution of BFRs found in offices in both new and old buildings suggests the significance of workplace exposures, the need for controls to minimize human exposure, intra-building migration, and environmental releases of these chemicals, and the need for monitoring in new buildings to confirm the effectiveness of the PBDE phase-out. [Copyright &y& Elsevier]