Predictors of indoor fine particulate matter in infants' bedrooms in Denmark

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 Exposure to Selected Air Pollutants in the Home Environment: A Systematic Review

(1) Background: There is increasing awareness that the quality of the indoor environment affects our health and well-being. Indoor air quality (IAQ) in particular has an impact on multiple health outcomes, including respiratory and cardiovascular illness, allergic symptoms, cancers, and premature mortality. (2) Methods: We carried out a global systematic literature review on indoor exposure to selected air pollutants associated with adverse health effects, and related household characteristics, seasonal influences and occupancy patterns. We screened records from six bibliographic databases: ABI/INFORM, Environment Abstracts, Pollution Abstracts, PubMed, ProQuest Biological and Health Professional, and Scopus. (3) Results: Information on indoor exposure levels and determinants, emission sources, and associated health effects was extracted from 141 studies from 29 countries. The most-studied pollutants were particulate matter (PM_2.5 and PM₁₀); nitrogen dioxide (NO₂); volatile organic compounds (VOCs) including benzene, toluene, xylenes and formaldehyde; and polycyclic aromatic hydrocarbons (PAHs) including naphthalene. Identified indoor PM_2.5 sources include smoking, cooking, heating, use of incense, candles, and insecticides, while cleaning, housework, presence of pets and movement of people were the main sources of coarse particles. Outdoor air is a major PM_2.5 source in rooms with natural ventilation in roadside households. Major sources of NO₂ indoors are unvented gas heaters and cookers. Predictors of indoor NO₂ are ventilation, season, and outdoor NO₂ levels. VOCs are emitted from a wide range of indoor and outdoor sources, including smoking, solvent use, renovations, and household products. Formaldehyde levels are higher in newer houses and in the presence of new furniture, while PAH levels are higher in smoking households. High indoor particulate matter, NO₂ and VOC levels were typically associated with respiratory symptoms, particularly asthma symptoms in children. (4) Conclusions: Household characteristics and occupant activities play a large role in indoor exposure, particularly cigarette smoking for PM_2.5, gas appliances for NO₂, and household products for VOCs and PAHs. Home location near high-traffic-density roads, redecoration, and small house size contribute to high indoor air pollution. In most studies, air exchange rates are negatively associated with indoor air pollution. These findings can inform interventions aiming to improve IAQ in residential properties in a variety of settings.

Epidemiology and Risk Factors of Infection in Early Childhood

BACKGROUND

There is a large, unexplained variation in the frequency of childhood infections. We described incidence and risk factors of infections in early childhood.

METHODS

Simple infections were captured during the first 3 years of life in the Copenhagen Prospective Studies on Asthma in Childhood 2000 birth cohort. Environmental exposures were analyzed by quasi-Poisson regression and sparse principal component analysis.

RESULTS

The 334 children experienced a median of 14 (range 2-43) infectious episodes at ages 0 to 3 years. The overall rate of infections was associated with the number of children in the day care (adjusted incidence rate ratio [aIRR] 1.09 [1.2-1.16]) and the m² per child in the day care (aIRR 0.96 [0.92-0.99]). Upper respiratory infections were also associated with the number of children in the day care (aIRR 1.11 [1.03-1.20]) and the m² per child in the day care (aIRR 0.95 [0.91-0.99]), whereas lower respiratory infections were associated with caesarean section (aIRR 1.49 [1.12-1.99]), maternal smoking (aIRR 1.66 [1.18-2.33]), older siblings (aIRR 1.54 [1.19-2.01]), and the age at entry to day care (aIRR 0.77 [0.65-0.91]). The sparse principal component analysis revealed a risk factor profile driven by tobacco exposure, social circumstances, and domestic pets, but could only be used to explain 8.4% of the infection burden.

CONCLUSIONS

Children experienced around 14 infections during the first 3 years of life, but incidences varied greatly. Environmental exposures only explained a small fraction of the variation, suggesting host factors as major determinants of infectious burden.

A review of biomass burning: Emissions and impacts on air quality, health and climate in China

Biomass burning (BB) is a significant air pollution source, with global, regional and local impacts on air quality, public health and climate. Worldwide an extensive range of studies has been conducted on almost all the aspects of BB, including its specific types, on quantification of emissions and on assessing its various impacts. China is one of the countries where the significance of BB has been recognized, and a lot of research efforts devoted to investigate it, however, so far no systematic reviews were conducted to synthesize the information which has been emerging. Therefore the aim of this work was to comprehensively review most of the studies published on this topic in China, including literature concerning field measurements, laboratory studies and the impacts of BB indoors and outdoors in China. In addition, this review provides insights into the role of wildfire and anthropogenic BB on air quality and health globally. Further, we attempted to provide a basis for formulation of policies and regulations by policy makers in China.

[Indoor air pollution by fine particulate matter in the homes of newborns]

INTRODUCTION

Air pollution by particulate matter (PM) is a major public health problem. In Chile, the study has focused on outdoor air and PM₁₀, rather than indoor air and PM_2.5. Because newborns and infants spend most of their time at home, it is necessary to evaluate the exposure to indoor air pollution in this susceptible population.

OBJECTIVE

To determine concentration of PM_2.5 in the homes of newborns and identify the emission sources of the pollutants.

PATIENTS AND METHOD

The PM_2.5 concentration ([PM_2.5]) was collected over a 24hour period in 207 households. Baseline sociodemographic information and environmental factors (heating, ventilation, smoking and house cleaning), were collected.

RESULTS

The median [PM_2.5] was 107.5μg/m³. Family history of asthma was associated with lower [PM_2.5] (P=.0495). Homes without heating showed a lower median [PM_2.5], 58.6μg/m³, while those using firewood, kerosene, and electricity ranged between 112.5 and 114.9, and coal users' homes reached 162.9μg/m³. Wood using homes had significant differences (P=.0164) in median [PM_2.5] whether the stove had complete combustion (98.2μg/m³) vs. incomplete (112.6μg/m³), or a salamander stove (140.6μg/m³). Cigarette smoking was reported in 8.7% of the households, but was not associated with the [PM_2.5]. Ventilation was associated with a higher median [PM_2.5] (120.6 vs. 99.1μg/m³, P=.0039).

CONCLUSION

We found homes with high [PM_2.5]. Residential wood consumption was almost universal, and it is associated with the [PM_2.5]. Natural ventilation increased MP_2.5, probably due to infiltration from outside.

Relationships between socioeconomic and lifestyle factors and indoor air quality in French dwellings

BACKGROUND

To date, few studies have analyzed the relationships between socioeconomic status (SES) and indoor air quality (IAQ).

OBJECTIVE

The aim of this study was to examine the relationships between socioeconomic and other factors and indoor air pollutant levels in French homes.

METHODS

The indoor air concentrations of thirty chemical, biological and physical parameters were measured over one week in a sample of 567 dwellings representative of the French housing stock between September 2003 and December 2005. Information on SES (household structure, educational attainment, income, and occupation), building characteristics, and occupants' habits and activities (smoking, cooking, cleaning, etc.) were collected through administered questionnaires. Separate stepwise linear regression models were fitted to log-transformed concentrations on SES and other factors. Logistic regression was performed on fungal contamination data.

RESULTS

Households with lower income were more likely to have higher indoor concentrations of formaldehyde, but lower perchloroethylene indoor concentrations. Formaldehyde indoor concentrations were also associated with newly built buildings. Smoking was associated with increasing acetaldehyde and PM2.5 levels and the risk of a positive fungal contamination index. BTEX levels were also associated with occupant density and having an attached garage. The major predictors for fungal contamination were dampness and absolute humidity.

CONCLUSION

These results, obtained from a large sample of dwellings, show for the first time in France the relationships between SES factors and indoor air pollutants, and believe they should be considered alongside occupant activities and building characteristics when study IAQ in homes.

Particulate pollution in different housing types in a UK suburban location

To investigate the levels of particulate pollution in residential built environments measurements of PM(10), PM(2.5), and PM(1) and concentrations were made between 2004 and 2008 in various residencies in a UK suburban location. Measurements were carried out in three different residential settings (Types I, II and III). In type I non-smoking living rooms, the highest 24-hour mean concentrations were found in summer. When smoking took place in type I residences, the concentrations of PM(10), PM(2.5) and PM(1), during the winter were almost double those in summer. In type II houses the concentrations were higher in the houses with open plan kitchens than in those with separate kitchens. In type III houses, mean concentrations were significantly higher in wood heated living rooms than those using central heating. In kitchens, cooking resulted in substantially higher concentrations of particulate matter with levels above those in smoking living rooms in winter. The hourly maximum values of number concentration were considerably higher in smoking rooms than non-smoking ones. Cooking resulted in increased number concentrations, with the average hourly maximum concentration of 179,110 #/cm(3). Particle mass and number emission rates were determined for a number of activities. In kitchens grilling had the highest average number emission rate, followed by boiling and frying. The results clearly highlight the impact of different forms of dwelling and their use and management by occupants on the levels of particulate matter in naturally ventilated residential built environments.

Assessment of indoor air pollution in homes with infants

Infants spend most of their indoor time at home; however, residential air quality is poorly understood. We investigated the air quality of infants’ homes in the New England area of the U.S. Participants (N = 53) were parents of infants (0–6 months) who completed telephone surveys to identify potential pollutant sources in their residence. Carbon monoxide (CO), carbon dioxide (CO₂), particulate matter with aerodynamic diameter ≤0.5 µm (PM_0.5), and total volatile organic compounds (TVOCs) were measured in 10 homes over 4–7 days, and levels were compared with health-based guidelines. Pollutant levels varied substantially across homes and within homes with overall levels for some homes up to 20 times higher than for other homes. Average levels were 0.85 ppm, 663.2 ppm, 18.7 µg/m³, and 1626 µg/m³ for CO, CO₂, PM_0.5, and TVOCs, respectively. CO₂, TVOCs, and PM_0.5 levels exceeded health-based indoor air quality guidelines. Survey results suggest that nursery renovations and related potential pollutant sources may be associated with differences in urbanicity, income, and presence of older children with respiratory ailments, which could potentially confound health studies. While there are no standards for indoor residential air quality, our findings suggest that additional research is needed to assess indoor pollution exposure for infants, which may be a vulnerable population.