Effect of energy renovation and occupants' activities on airborne particle concentrations in Swedish rental apartments

Pedestrian flow-environmental pollutants interactions and health risks to residents in high-occupancy public areas of apartment buildings

The current interaction of pedestrian flow and environmental pollutants in high-occupancy public areas of apartment and the risks of residents being exposed to environmental pollutants are issues that are often overlooked but urgently need to be addressed. In this study, we provide a comprehensive of pedestrian flow-environmental pollutants interactions and health risks to residents in first-floor public areas of apartment with high-occupancy. The main findings indicate that under closed management conditions, there is a significant increase in TVOC and noise levels during the peak periods of nighttime pedestrian flow. In the correlation analysis, the significant impact of time granularity selection in clarifying the correlation between pedestrian flow and environmental pollutants has been highlighted, with larger time granularities generally showing stronger correlations, while finer time granularities may help identify specific risks in areas directly connected to the external environment. There is a significant correlation exists between pedestrian flow and environmental pollutants (TVOC, ozone, and noise), with higher concentrations of these pollutants observed during peak pedestrian flow periods, thereby increasing the risk of residents being exposed to adverse environmental conditions. To mitigate the risks associated with TVOC pollution and noise exposure, it is crucial to maintain proper ventilation, avoid conducting cleaning or maintenance activities during peak hours, and implement noise-reducing measures, such as distancing noise sources from residential areas or installing soundproofing barriers. Additionally, the study identifies total volatile organic compounds originating from property maintenance activities and clarifies their dispersion patterns, emphasizing the importance of developing robust, standardized maintenance protocols for indoor environmental quality assurance. This research can improve the environmental sustainability of apartment buildings and provide a theoretical basis for the development of environmental health strategies for high-occupancy public areas of apartment buildings.

Construction and evaluation of hourly average indoor PM2.5 concentration prediction models based on multiple types of places

Background

People usually spend most of their time indoors, so indoor fine particulate matter (PM2.5) concentrations are crucial for refining individual PM2.5 exposure evaluation. The development of indoor PM2.5 concentration prediction models is essential for the health risk assessment of PM2.5 in epidemiological studies involving large populations.

Methods

In this study, based on the monitoring data of multiple types of places, the classical multiple linear regression (MLR) method and random forest regression (RFR) algorithm of machine learning were used to develop hourly average indoor PM2.5 concentration prediction models. Indoor PM2.5 concentration data, which included 11,712 records from five types of places, were obtained by on-site monitoring. Moreover, the potential predictor variable data were derived from outdoor monitoring stations and meteorological databases. A ten-fold cross-validation was conducted to examine the performance of all proposed models.

Results

The final predictor variables incorporated in the MLR model were outdoor PM2.5 concentration, type of place, season, wind direction, surface wind speed, hour, precipitation, air pressure, and relative humidity. The ten-fold cross-validation results indicated that both models constructed had good predictive performance, with the determination coefficients (R2) of RFR and MLR were 72.20 and 60.35%, respectively. Generally, the RFR model had better predictive performance than the MLR model (RFR model developed using the same predictor variables as the MLR model, R2 = 71.86%). In terms of predictors, the importance results of predictor variables for both types of models suggested that outdoor PM2.5 concentration, type of place, season, hour, wind direction, and surface wind speed were the most important predictor variables.

Conclusion

In this research, hourly average indoor PM2.5 concentration prediction models based on multiple types of places were developed for the first time. Both the MLR and RFR models based on easily accessible indicators displayed promising predictive performance, in which the machine learning domain RFR model outperformed the classical MLR model, and this result suggests the potential application of RFR algorithms for indoor air pollutant concentration prediction.

Indoor PM2.5 from occupied residences in Sweden caused higher inflammation in mice compared to outdoor PM2.5

We spend most of our time indoors; however, little is known about the effects of exposure to aerosol particles indoors. We aimed to determine differences in relative toxicity and physicochemical properties of PM_2.5 collected simultaneously indoors (PM_{2.5 INDOOR} ) and outdoors (PM_{2.5 OUTDOOR} ) in 15 occupied homes in southern Sweden. Collected particles were extracted from filters, pooled (indoor and outdoor separately), and characterized for chemical composition and endotoxins before being tested for toxicity in mice via intratracheal instillation. Various endpoints including lung inflammation, genotoxicity, and acute-phase response in lung and liver were assessed 1, 3, and 28 days post-exposure. Chemical composition of particles used in toxicological assessment was compared to particles analyzed without extraction. Time-resolved particle mass and number concentrations were monitored. PM_{2.5 INDOOR} showed higher relative concentrations (μg mg^-1 ) of metals, PAHs, and endotoxins compared to PM_{2.5 OUTDOOR} . These differences may be linked to PM_{2.5 INDOOR} causing significantly higher lung inflammation and lung acute-phase response 1 day post-exposure compared to PM_{2.5 OUTDOOR} and vehicle controls, respectively. None of the tested materials caused genotoxicity. PM_{2.5 INDOOR} displayed higher relative toxicity than PM_{2.5 OUTDOOR} under the studied conditions, that is, wintertime with reduced air exchange rates, high influence of indoor sources, and relatively low outdoor concentrations of PM. Reducing PM_{2.5 INDOOR} exposure requires reduction of both infiltration from outdoors and indoor-generated particles.

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.

Characterization of fine particulate matter from indoor cooking with solid biomass fuels

Household burning of solid biomass fuels emits pollution particles that are a huge health risk factor, especially in low-income countries (LICs) such as those in Sub-Saharan Africa. In epidemiological studies, indoor exposure is often more challenging to assess than outdoor exposure. Laboratory studies of solid biomass fuels, performed under real-life conditions, are an important path toward improved exposure assessments. Using on- and offline measurement techniques, particulate matter (PM) from the most commonly used solid biomass fuels (charcoal, wood, dung, and crops residue) was characterized in laboratory settings using a way of burning the fuels and an air exchange rate that is representative of real-world settings in low-income countries. All the fuels generated emissions that resulted in concentrations which by far exceed both the annual and the 24-hour-average WHO guidelines for healthy air. Fuels with lower energy density, such as dung, emitted orders of magnitude more than, for example, charcoal. The vast majority of the emitted particles were smaller than 300 nm, indicating high deposition in the alveoli tract. The chemical composition of the indoor pollution changes over time, with organic particle emissions often peaking early in the stove operation. The chemical composition of the emitted PM is different for different biomass fuels, which is important to consider both in toxicological studies and in source apportionment efforts. For example, dung and wood yield higher organic aerosol emissions, and for dung, nitrogen content in the organic PM fraction is higher than for the other fuels. We show that aerosol mass spectrometry can be used to differentiate stove-related emissions from fuel, accelerant, and incense. We argue that further emission studies, targeting, for example, vehicles relevant for LICs and trash burning, coupled with field observations of chemical composition, would advance our understanding of air pollution in LIC. We believe this to be a necessary step for improved air quality policy.

Associations of Early-Life Exposure to Submicron Particulate Matter With Childhood Asthma and Wheeze in China

IMPORTANCE

Exposure to particulate matter (PM) has been associated with childhood asthma and wheeze. However, the specific associations between asthma and PM with an aerodynamic equivalent diameter of 1 μm or less (ie, PM1), which is a contributor to PM2.5 and potentially more toxic than PM2.5, remain unclear.

OBJECTIVE

To investigate the association of early-life (prenatal and first year) exposure to size-segregated PM, including PM1, PM1-2.5, PM2.5, PM2.5-10, and PM10, with childhood asthma and wheeze.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study was based on a questionnaire administered between June 2019 and June 2020 to caregivers of children aged 3 to 6 years in 7 Chinese cities (Wuhan, Changsha, Taiyuan, Nanjing, Shanghai, Chongqing, and Urumqi) as the second phase of the China, Children, Homes, Health study.

EXPOSURES

Exposure to PM1, PM1-2.5, PM2.5, PM2.5-10, and PM10 during the prenatal period and first year of life.

MAIN OUTCOMES AND MEASURES

The main outcomes were caregiver-reported childhood asthma and wheeze. A machine learning-based space-time model was applied to estimate early-life PM1, PM2.5, and PM10 exposure at 1 × 1-km resolution. Concentrations of PM1-2.5 and PM2.5-10 were calculated by subtracting PM1 from PM2.5 and PM2.5 from PM10, respectively. Multilevel (city and child) logistic regression models were applied to assess associations.

RESULTS

Of 29 418 children whose caregivers completed the survey (15 320 boys [52.1%]; mean [SD] age, 4.9 [0.9] years), 2524 (8.6%) ever had wheeze and 1161 (3.9%) were diagnosed with asthma. Among all children, 18 514 (62.9%) were breastfed for more than 6 months and 787 (2.7%) had parental history of atopy. A total of 22 250 children (75.6%) had a mother with an educational level of university or above. Of the 25 422 children for whom information about cigarette smoking exposure was collected, 576 (2.3%) had a mother who was a current or former smoker during pregnancy and 7525 (29.7%) had passive household cigarette smoke exposure in early life. Early-life PM1, PM2.5, and PM10 exposure were significantly associated with increased risk of childhood asthma, with higher estimates per 10-μg/m3 increase in PM1 (OR, 1.55; 95% CI, 1.27-1.89) than in PM2.5 (OR, 1.14; 95% CI, 1.03-1.26) and PM10 (OR, 1.11; 95% CI, 1.02-1.20). No association was observed between asthma and PM1-2.5 exposure, suggesting that PM1 rather than PM1-2.5 contributed to the association between PM2.5 and childhood asthma. There were significant associations between childhood wheeze and early-life PM1 exposure (OR, 1.23; 95% CI, 1.07-1.41) and PM2.5 exposure (OR, 1.08; 95% CI, 1.01-1.16) per 10-μg/m3 increase in PM1 and PM2.5, respectively.

CONCLUSIONS AND RELEVANCE

In this cross-sectional study, higher estimates were observed for the association between PM with smaller particles, such as PM1, vs PM with larger particles and childhood asthma. The results suggest that the association between PM2.5 and childhood asthma was mainly attributable to PM1.