Predicting Indoor Concentrations of Black Carbon in Residential Environments

Implications of fuel poverty for indoor black carbon concentrations from space heating

Despite the global transition towards cleaner energy sources observed over the last decade, disparities in access persist worldwide. The dependence on biomass for household heating exacerbates fuel poverty, as economically vulnerable households face challenges in obtaining certified firewood and often resort to using contaminated biomass as a substitute, either partially or completely. We examined black carbon (BC) particle concentrations -a marker for combustion- during wood stove operation through a five-day case study in a typical Chilean household. BC increased rapidly following the ignition of the stove, with the combustion of dry Eucalyptus globulus logs yielding a substantially lower peak (5.29 μg/m3) than when using unclean biomass: 35.75 μg/m3 with demolition wood and painted furniture, and 87.11 μg/m3 with the addition of a blend of particleboard with polystyrene foam. During the latter two events, BC particles remained indoors for about 20 h before the concentrations reverted to pre-spike levels. The slow decay in BC concentrations was further influenced by the infiltration of outdoor air. The mean indoor BC concentrations were comparable to or even exceeded those observed on busy roads in major cities worldwide. These results highlight the risks associated with limited access to clean fuels for indoor heating, alongside inadequate insulation. This study sheds light on the problem of fuel poverty and its adverse effects on health and well-being.

Breathing zone pollutant levels are associated with asthma exacerbations in high-risk children

Background

Indoor and outdoor air pollution levels are associated with poor asthma outcomes in children. However, few studies have evaluated whether breathing zone pollutant levels associate with asthma outcomes.

Objective

Determine breathing zone exposure levels of NO 2 , O 3 , total PM 10 and PM 10 constituents among children with exacerbation-prone asthma, and examine correspondence with in-home and community measurements and associations with outcomes.

Methods

We assessed children's personal breathing zone exposures using wearable monitors. Personal exposures were compared to in-home and community measurements and tested for association with lung function, asthma control, and asthma exacerbations.

Results

81 children completed 219 monitoring sessions. Correlations between personal and community levels of PM 10 , NO 2 , and O 3 were poor, whereas personal PM 10 and NO 2 levels correlated with in-home measurements. However, in-home monitoring underdetected brown carbon (Personal:79%, Home:36.8%) and ETS (Personal:83.7%, Home:4.1%) personal exposures, and detected black carbon in participants without these personal exposures (Personal: 26.5%, Home: 96%). Personal exposures were not associated with lung function or asthma control. Children experiencing an asthma exacerbation within 60 days of personal exposure monitoring had 1.98, 2.21 and 2.04 times higher brown carbon (p<0.001), ETS (p=0.007), and endotoxin (p=0.012), respectively. These outcomes were not associated with community or in-home exposure levels.

Conclusions

Monitoring pollutant levels in the breathing zone is essential to understand how exposures influence asthma outcomes, as agreement between personal and in-home monitors is limited. Inhaled exposure to PM 10 constituents modifies asthma exacerbation risk, suggesting efforts to limit these exposures among high-risk children may decrease their asthma burden.

CLINICAL IMPLICATIONS

In-home and community monitoring of environmental pollutants may underestimate personal exposures. Levels of inhaled exposure to PM 10 constituents appear to strongly influence asthma exacerbation risk. Therefore, efforts should be made to mitigate these exposures.

CAPSULE SUMMARY

Leveraging wearable, breathing-zone monitors, we show exposures to inhaled pollutants are poorly proxied by in-home and community monitors, among children with exacerbation-prone asthma. Inhaled exposure to multiple PM 10 constituents is associated with asthma exacerbation risk.

NO2, BC and PM Exposure of Participants in the Polluscope Autumn 2019 Campaign in the Paris Region

The Polluscope project aims to better understand the personal exposure to air pollutants in the Paris region. This article is based on one campaign from the project, which was conducted in the autumn of 2019 and involved 63 participants equipped with portable sensors (i.e., NO₂, BC and PM) for one week. After a phase of data curation, analyses were performed on the results from all participants, as well as on individual participants' data for case studies. A machine learning algorithm was used to allocate the data to different environments (e.g., transportation, indoor, home, office, and outdoor). The results of the campaign showed that the participants' exposure to air pollutants depended very much on their lifestyle and the sources of pollution that may be present in the vicinity. Individuals' use of transportation was found to be associated with higher levels of pollutants, even when the time spent on transport was relatively short. In contrast, homes and offices were environments with the lowest concentrations of pollutants. However, some activities performed in indoor air (e.g., cooking) also showed a high levels of pollution over a relatively short period.

A review of critical residential buildings parameters and activities when investigating indoor air quality and pollutants

Indoor air in residential dwellings can contain a variety of chemicals, sometimes present at concentrations or in combinations which can have a negative impact on human health. Indoor Air Quality (IAQ) surveys are often required to characterize human exposure or to investigate IAQ concerns and complaints. Such surveys should include sufficient contextual information to elucidate sources, pathways, and the magnitude of exposures. The aim of this review was to investigate and describe the parameters that affect IAQ in residential dwellings: building location, layout, and ventilation, finishing materials, occupant activities, and occupant demography. About 180 peer-reviewed articles, published from 01/2013 to 09/2021 (plus some important earlier publications), were reviewed. The importance of the building parameters largely depends on the study objectives and whether the focus is on a specific pollutant or to assess health risk. When considering classical pollutants such as particulate matter (PM) or volatile organic compounds (VOCs), the building parameters can have a significant impact on IAQ, and detailed information of these parameters needs to be reported in each study. Research gaps and suggestions for the future studies together with recommendation of where measurements should be done are also provided.

The Effects of Indoor Air Filter on Reductions in PM2.5 Associated Health Risks of Respiratory Function in Mouse

This study aimed to assess whether protective measures could reduce the health risks of air pollution in mice living in the chambers situated at a suburban site in Beijing. The living chambers of mice were divided into four groups: male mice with and without the high-efficiency particulate air (HEPA) filter (male group A and group B), as well as female mice with and without the HEPA filter (female group A and group B). The experiment was carried out from 1 December 2017 to 31 May 2018. Parameters of respiratory function during periods of clean air and air pollution were determined for all groups to evaluate the role of the indoor air filter (i.e., HEPA) in protection against respiratory health risks in mice. Significant differences in minute volumes were observed in male and female groups with versus without the HEPA. Additionally, respiratory health parameters including respiratory rate, duration of breaking, expiratory time, and relaxation time exhibited differences in female groups with HEPA versus without HEPA. Levels of inflammatory factors in the lungs were measured for all groups after 6months of exposure. Greater mean levels of IL-6 and TNF-α were found in the male groups without HEPA than in those with HEPA. Higher average concentrations of IL-6, T-AOC, SOD, GSH-Px, LDH, TNF-α, and TGF-β1 were found in the female group without HEPA than those without HEPA. Our study has proved the effective protection provided by indoor air filters (i.e., HEPA filters) in reducing respiratory health risks associated with PM2.5.

Bayesian Proxy Modelling for Estimating Black Carbon Concentrations using White-Box and Black-Box Models

Black carbon (BC) is an important component of particulate matter (PM) in urban environments. BC is typically emitted from gas and diesel engines, coal-fired power plants, and other sources that burn fossil fuel. In contrast to PM, BC measurements are not always available on a large scale due to the operational cost and complexity of the instrumentation. Therefore, it is advantageous to develop a mathematical model for estimating the quantity of BC in the air, termed a BC proxy, to enable widening of spatial air pollution mapping. This article presents the development of BC proxies based on a Bayesian framework using measurements of PM concentrations and size distributions from 10 to 10,000 nm from a recent mobile air pollution study across several areas of Jordan. Bayesian methods using informative priors can naturally prevent over-fitting in the modelling process and the methods generate a confidence interval around the prediction, thus the estimated BC concentration can be directly quantified and assessed. In particular, two types of models are developed based on their transparency and interpretability, referred to as white-box and black-box models. The proposed methods are tested on extensive data sets obtained from the measurement campaign in Jordan. In this study, black-box models perform slightly better due to their model complexity. Nevertheless, the results demonstrate that the performance of both models does not differ significantly. In practice, white-box models are relatively more convenient to be deployed, the methods are well understood by scientists, and the models can be used to better understand key relationships.

Machine learning and statistical models for predicting indoor air quality

Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically based mechanistic models or statistical models that are driven by measured data. In comparison with mechanistic models mostly used in unoccupied or scenario-based environments, statistical models have great potential to explore IAQ captured in large measurement campaigns or in real occupied environments. The present study carried out the first literature review of the use of statistical models to predict IAQ. The most commonly used statistical modeling methods were reviewed and their strengths and weaknesses discussed. Thirty-seven publications, in which statistical models were applied to predict IAQ, were identified. These studies were all published in the past decade, indicating the emergence of the awareness and application of machine learning and statistical modeling in the field of IAQ. The concentrations of indoor particulate matter (PM_2.5 and PM₁₀ ) were the most frequently studied parameters, followed by carbon dioxide and radon. The most popular statistical models applied to IAQ were artificial neural networks, multiple linear regression, partial least squares, and decision trees.