Indoor Air Pollution from Residential Stoves: Examining the Flooding of Particulate Matter into Homes during Real-World Use

Development and Validation of Low-Cost Indoor Air Quality Monitoring System for Swine Buildings

The optimal indoor environment is associated with comfortable temperatures along with favorable indoor air quality. One of the air pollutants, particulate matter (PM), is potentially harmful to animals and humans. Most farms have monitoring systems to identify other hazardous gases rather than PM due to the sensor cost. In recent decades, the application of environmental monitoring systems based on Internet of Things (IoT) devices that incorporate low-cost sensors has elevated extensively. The current study develops a low-cost air quality monitoring system for swine buildings based on Raspberry Pi single-board computers along with a sensor array. The system collects data using 11 types of environmental variables along with temperature, humidity, CO2, light, pressure, and different types of gases, namely PM1, PM2.5, and PM10. The system is designed with a central web server that provides real-time data visualization and data availability through the Internet. It was tested in actual pig barns to ensure stability and functionality. In addition, there was a collocation test conducted by placing the system in two different pig barns to validate the sensor data. The Wilcoxon rank sum test demonstrates that there are no significant differences between the two sensor datasets, as all variables have a p-value greater than 0.05. However, except for carbon monoxide (CO), none of the variables exhibit correlation exceeding 0.5 with PM concentrations. Overall, a scalable, portable, non-complex, low-cost air quality monitoring system was successfully developed within a cost of USD 94.

Indoor PM from residential coal combustion: Levels, chemical composition, and toxicity

Indoor air quality is crucial for human health due to the significant time people spend at home, and it is mainly affected by internal sources such as solid fuel combustion for heating. This study investigated the indoor air quality and health implications associated with residential coal burning covering gaseous pollutants (CO, CO2 and total volatile organic compounds), particulate matter, and toxicity. The PM10 chemical composition was obtained by ICP-MS/OES (elements), ion chromatography (water-soluble ions) and thermal-optical analysis (organic and elemental carbon). During coal combustion, PM10 levels were higher (up to 8.8 times) than background levels and the indoor-to-outdoor ratios were, on average, greater than unity, confirming the existence of a significant indoor source. The chemical characterisation of PM10 revealed increased concentrations of organic carbon and elemental carbon during coal combustion as well as arsenic, cadmium and lead. Carcinogenic risks associated with exposure to arsenic exceeded safety thresholds. Indoor air quality fluctuated during the study, with varying toxicity levels assessed using the Aliivibrio fischeri bioluminescence inhibition assay. These findings underscore the importance of mitigating indoor air pollution associated with coal burning and highlight the potential health risks from long-term exposure. Effective interventions are needed to improve indoor air quality and reduce health risks in coal-burning households.

The dark satanic mills: Evaluating patterns of health in England during the industrial revolution

OBJECTIVE

this research seeks to investigate the impact the industrial revolution had on the population of England.

MATERIALS

Pre-existing skeletal data from 1154 pre-Industrial (1066-1700 CE) and 4157 industrial (1700-1905) skeletons from 21 cemeteries (N = 5411).

METHODS

Context number, sex, age-at-death, stature and presence/absence of selected pathological conditions were collated. The data were compared using chi square, Kolmogorov-Smirnov, t-tests and logistic regression (α = 0.01).

RESULTS

There was a statistically significant increase in cribra orbitalia, periosteal reactions, rib lesions, fractures, rickets, osteoporosis, osteoarthritis, enamel hypoplasia, dental caries and periapical lesions in the industrial period. Osteomyelitis decreased from the pre-industrial to industrial period.

CONCLUSION

Our results confirm the industrial revolution had a significant negative impact on human health, however the prevalence of TB, treponemal disease, maxillary sinusitis, osteomalacia, scurvy, gout and DISH did not change, suggesting these diseases were not impacted by the change in environmental conditions.

SIGNIFICANCE

This is the largest study of health in the industrial revolution that includes non-adults and adults and considers age-at-death alongside disease status to date. This data supports the hypothesis that the rise of industry was associated with a significant decline in general health, but not an increase in all pathological conditions.

Exposure to fine particulate matter (PM2.5) from non-tobacco sources in homes within high-income countries: a systematic review

The health impacts associated with exposure to elevated concentrations of fine particulate matter (PM_2.5) are well recognised. There is a substantial number of studies characterising PM_2.5 concentrations outdoors, as well as in homes within low- and middle-income countries. In high-income countries (HICs), there is a sizeable literature on indoor PM_2.5 relating to smoking, but the evidence on exposure to PM_2.5 generated from non-tobacco sources in homes is sparse. This is especially relevant as people living in HICs spend the majority of their time at home, and in the northern hemisphere households often have low air exchange rates for energy efficiency. This review identified 49 studies that described indoor PM_2.5 concentrations generated from a variety of common household sources in real-life home settings in HICs. These included wood/solid fuel burning appliances, cooking, candles, incense, cleaning and humidifiers. The reported concentrations varied widely, both between sources and within groups of the same source. The burning of solid fuels was found to generate the highest indoor PM_2.5 concentrations. On occasion, other sources were also reported to be responsible for high PM_2.5 concentrations; however, this was only in a few select examples. This review also highlights the many inconsistencies in the ways data are collected and reported. The variable methods of measurement and reporting make comparison and interpretation of data difficult. There is a need for standardisation of methods and agreed contextual data to make household PM_2.5 data more useful in epidemiological studies and aid comparison of the impact of different interventions and policies.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11869-022-01288-8.

Development of an Integrated Platform to Assess the Physicochemical and Toxicological Properties of Wood Combustion Particulate Matter

Wood burning contributes to indoor and ambient particulate matter (PM) pollution and has been associated with increased morbidity and mortality. Here, we present an integrated methodology that allows to generate, sample, and characterize wood smoke derived from different moisture contents and representative combustion conditions using pine wood as a model. Flaming, smoldering, and incomplete combustion were assessed for low-moisture pine, whereas both low-moisture pine and high-moisture pine were investigated under flaming conditions. Real-time monitoring of carbon monoxide, volatile organic compounds, and aerosol number concentration/size in wood smoke was performed. The PM was size-fractionated, sampled, and characterized for elemental/organic carbon, organic functional groups, and inorganic elements. Bioactivity of PM was assessed by measuring the sterile alpha motif (SAM) pointed domain containing ETS (E-twenty-six) transcription factor (SPDEF) gene promoter activity in human embryonic kidney 293 (HEK-293T) cells, a biomarker for mucin gene expression. Findings showed that moisture content and combustion condition significantly affected the organic and inorganic elemental composition of PM0.1 as well as its bioactivity. Also, for a given moisture and combustion scenario, PM chemistry and bioactivity differed considerably with PM size. Importantly, PM0.1 from flaming combustion of low-moisture pine contained the highest abundance of the oxygenated saturated aliphatic functional group [H-C-O] and was also biologically most potent in stimulating SPDEF promoter activity, suggesting the role of organic compounds such as carbohydrates and sugar alcohols (that contain [H-C-O]) in driving mucus-related respiratory outcomes. Our platform enables further well-controlled parametric studies using a combination of in vitro and in vivo approaches to link wood burning parameters with acute and chronic inhalation health effects of wood smoke.

Health impacts of PM2.5 originating from residential wood combustion in four nordic cities

Background

Residential wood combustion (RWC) is one of the largest sources of fine particles (PM_2.5) in the Nordic cities. The current study aims to calculate the related health effects in four studied city areas in Sweden, Finland, Norway, and Denmark.

Methods

Health impact assessment (HIA) was employed as the methodology to quantify the health burden. Firstly, the RWC induced annual average PM_2.5 concentrations from local sources were estimated with air pollution dispersion modelling. Secondly, the baseline mortality rates were retrieved from the national health registers. Thirdly, the concentration-response function from a previous epidemiological study was applied. For the health impact calculations, the WHO-developed tool AirQ + was used.

Results

Amongst the studied city areas, the local RWC induced PM_2.5 concentration was lowest in the Helsinki Metropolitan Area (population-weighted annual average concentration 0.46 µg m^− 3) and highest in Oslo (2.77 µg m^− 3). Each year, particulate matter attributed to RWC caused around 19 premature deaths in Umeå (95% CI: 8–29), 85 in the Helsinki Metropolitan Area (95% CI: 35–129), 78 in Copenhagen (95% CI: 33–118), and 232 premature deaths in Oslo (95% CI: 97–346). The average loss of life years per premature death case was approximately ten years; however, in the whole population, this reflects on average a decrease in life expectancy by 0.25 (0.10–0.36) years. In terms of the relative contributions in cities, life expectancy will be decreased by 0.10 (95% CI: 0.05–0.16), 0.18 (95% CI: 0.07–0.28), 0.22 (95% CI: 0.09–0.33) and 0.63 (95% CI: 0.26–0.96) years in the Helsinki Metropolitan Area, Umeå, Copenhagen and Oslo respectively. The number of years of life lost was lowest in Umeå (172, 95% CI: 71–260) and highest in Oslo (2458, 95% CI: 1033–3669).

Conclusions

All four Nordic city areas have a substantial amount of domestic heating, and RWC is one of the most significant sources of PM_2.5. This implicates a substantial predicted impact on public health in terms of premature mortality. Thus, several public health measures are needed to reduce the RWC emissions.

Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring

With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental results showed that calibration with AH has better performance as compared with RH. The experimental results showed the selection and calibration techniques that can be used in designing similar LCS based monitoring systems.

Lung-deposited dose of particulate matter from residential exposure to smoke from wood burning

Residential settings are of utmost importance for human exposure, as it is where people spend most of their time. Residential wood combustion is a widespread practice known as a source of indoor particulate matter (PM). Nevertheless, research on the risks of exposure associated with this source is scarce, and a better understanding of respiratory deposition of smoke particles is needed. The dosimetry model ExDoM2 was applied to determine the deposited dose of inhalable particulate matter (PM₁₀) from residential biomass combustion in the human respiratory tract (HRT) of adults and children. The dose was estimated using PM₁₀ exposure concentrations obtained from a field campaign carried out in two households during the operation of an open fireplace and a woodstove. Simultaneously, PM₁₀ levels were monitored outside to investigate the outdoor dose in a rural area strongly impacted by biomass burning emissions. Indoors, the 8-h average PM₁₀ concentrations ranged from 88.3 to 489 μg m^-3 and from 69.4 to 122 μg m^-3 for the operation of the fireplace and the woodstove, respectively, while outdoor average PM₁₀ concentrations ranged from 17.3 to 94.2 μg m^-3. The highest amount of the deposited particles was recorded in the extrathoracic region (68-79%), whereas the deposition was much lower in the tracheobronchial tree (5-6%) and alveolar-interstitial region (16-21%). The total dose received while using the fireplace was more than twofold the one received in the room with a woodstove and more than 10 times higher than in the absence of the source. Overall, indoor doses were higher than the ones received by a subject exposed outdoors, especially at the alveolar-interstitial region. After 24 h of exposure, it was estimated that approximately 35 to 37% of the particles deposited in the HRT were transferred to the gastrointestinal tract, while approximately 2.0-2.5% were absorbed into the blood. The results from exposure and dose of indoor particles gathered in this work suggest that homeowners should be encouraged to upgrade the wood burning technology to reduce the PM levels inside their residences. This study also provides biologically relevant results on the lung deposition of particles from residential biomass burning that can be used as a reference for future research.