Ultrafine, fine, and black carbon particle concentrations in California child-care facilities

Airborne Influenza Virus in Daycare Centers

In this study, we investigated the concentration of airborne influenza virus in daycare centers and influencing factors, such as common cold prevalence, air pollutants, and meteorological factors. A total of 209 air samples were collected from daycare centers in Kaohsiung and the influenza virus was analyzed using real-time quantitative polymerase chain reaction. Air pollutants and metrological factors were measured using real-time monitoring equipment. Winter had the highest positive rates of airborne influenza virus and the highest prevalence of the common cold, followed by summer and autumn. The concentration of CO was significantly positively correlated with airborne influenza virus. Daycare center A, with natural ventilation and air condition systems, had a higher concentration of airborne influenza A virus, airborne fungi, and airborne bacteria, as well as a higher prevalence of the common cold, than daycare center B, with a mechanical ventilation system and air purifiers, while the concentrations of CO2, CO, and UFPs in daycare center A were lower than those in daycare center B. We successfully detected airborne influenza virus in daycare centers, demonstrating that aerosol sampling for influenza can provide novel epidemiological insights and inform the management of influenza in daycare centers.

Assessing and Validating the Ability of Machine Learning to Handle Unrefined Particle Air Pollution Mobile Monitoring Data Randomly, Spatially, and Spatiotemporally

Many epidemiological studies have evaluated the accuracy of machine learning models in predicting levels of particulate number (PN) and black carbon (BC) pollutant concentrations. However, few studies have investigated the ability of machine learning to predict the pollutant concentration with using unrefined mobile measurement data and explore the reliability of the prediction models. Additionally, researchers are moving away from using fixed-site data in favor of using mobile monitoring data in a variety of locations to develop hourly empirical models of particulate air pollution. This study compared the differences between long-term (daily average) and short-term (hourly average and 1 s unrefined data) model performance in three different classes of cross validation: randomly, spatially, and spatially temporally. This study used secondary data describing BC and PN pollutant levels in the rural location of Blacksburg (VA). Our results show that the model based on unrefined data was able to detect the pollutant hot spot areas with similar accuracy compared to the aggregated model. Moreover, the performance was found to improve when temporal data added to the model: the 10-fold MAE for the BC and PN were 0.44 μg/m³ and 3391 pt/cm³, respectively, for the unrefined data (one second data) model. The findings detailed here will add to the literature on the correlation between data (pre)processing and the efficacy of machine learning models in predicting pollution levels while also enhancing our understanding of more reliable validation strategies.

Formation of cluster mode particles (1-3 nm) in preschools

This study is the first study that reports the cluster particle (1-3 nm) formation (CPF) in two modern preschools located in Nur-Sultan city of Kazakhstan from October 28 to November 27, 2019. The average particle number concentration and mode diameter values during major CPF events in Preschool I and Preschool II were found to be 1.90 × 10⁶ (SD 6.43 × 10⁶) particles/cm³ and 1.60 (SD 0.85) nm, and 1.11 × 10⁹ (SD 5.46 × 10⁹) particles/cm³ and 2.16 (SD 1.47) nm, respectively. The ultraviolet PM concentration reached as high as 7 μg/m³ in one of the measurement days. The estimated emission rate in Preschool I for CPF events was 9.57 × 10⁹ (SD 1.92 × 10⁹) particles/min. For Preschool II, the emission rate was 7.25 × 10⁹ (SD 12.4 × 10⁹) particles/min. We identified primary cluster particles (CPs) emitted directly from the sources such as candle burning, and secondary CPs formed as a result of the oxidation of indoor VOCs or smoking VOCs. The secondary CPs are likely to be SOA. Indoor VOCs were mainly emitted during cleaning activities as well as during painting and gluing. Indoor VOCs are the controlling factors in the CPF events. Changes in the training and cleaning programs may result in significant reductions in the exposure of the children to CPs.

The developmental toxicity and transcriptome analyses of zebrafish (Danio rerio) embryos exposed to carbon nanoparticles

Long-term and short-term exposure to carbon nanoparticles (CNPs) can affect fetal development and subsequent adverse outcomes including preterm delivery, intrauterine growth restriction, low birth weight, increased health risk linked to cardiovascular, respiratory and nervous systems in adulthood. The adverse developmental outcomes of CNPs were well known, but the underlying mechanisms remain unresolved. In this study, zebrafish embryos were treated with CNPs of 50,100,200 μg/mL and the toxic effects were observed. Using the RNA-seq analysis approach, we examined the effects of CNPs (200 μg/mL) on gene expression in zebrafish embryos exposed from 4 to 96 h-post-fertilization (hpf). We observed that CNPs-treated embryos exhibited increased malformations and decreased hatching. A total of 236 differentially expressed genes were detected by transcriptome analyses, which were associated with phototransduction, amino acid metabolism, steroid and steroid hormone biosynthesis. Transcriptome results were verified by real-time fluorescence quantitative PCR (RT-qPCR). Our results indicated that CNPs exposure was most likely to lead to differential gene changes in steroid and hormone biosynthesis pathways, thus inducing developmental toxicity such as delayed incubation of zebrafish embryos, increased malformation rate and multiple malformation phenotypes.

Traffic exposure, air pollution and children's physical activity at early childhood education and care

BACKGROUND

A significant number of children attend Early Childhood Education and Care (ECEC). ECEC is an important environment and behaviour setting for young children. Time spent outdoors is positively associated with children's physical activity levels, yet increased time spent physically active outdoors may expose young children to traffic-related air pollution, particularly in ECEC centres located in high traffic areas.

METHODS

This study was part of the Play Spaces and Environments for Children's Physical Activity (PLAYCE) study, Perth, Western Australia. Data from 22 ECEC centres and 478 children were collected. Continuous measures of indoor and outdoor fine particulate matter (PM_2.5) were conducted for 48-72 h in each ECEC. Children wore ActiGraph GT3X + accelerometers to measure their physical activity at ECEC. The total length of high traffic roads within a 300m road network service area buffer around each ECEC was used to identify high and low traffic centres.

RESULTS

Outdoor PM_2.5 concentrations peaked in the afternoon (1pm, 2pm and 6pm) at ECEC centres. Outdoor and indoor PM_2.5 concentrations were significantly higher for centres located in high compared with low traffic areas (both p < 0.05). There was no significant association between a centre being located in a high or low traffic area and the time preschoolers spent outdoors or their physical activity levels.

DISCUSSION

Time periods when air pollution concentrations in ECECs are highest correspond with times when preschoolers are likely to be physically active outdoors. Children's potential exposure to traffic-related air pollutants is occurring during a period of rapid lung development. Given there is no evidence of a safe level of exposure to PM_2.5 or a threshold below which no adverse health effects occur, careful planning should be a consideration to avoid locating ECEC centres in high traffic areas.

Using low cost open-face passive samplers to sample PM concentration and elemental composition in childcare facilities

Effective, low noise and low-cost samplers for airborne particulate matter (PM) in indoor environments are needed. In 15 northern California childcare facilities, we deployed open-faced polycarbonate substrate passive aerosol samplers and analyzed them using computer-controlled scanning electron microscopy to measure indoor PM particle size, number, and elemental concentration. Particle concentrations were compared to PM concentrations measured by filter-based active samplers. The PM mass concentrations measured after open-faced passive sampling were in good agreement with the PM concentrations obtained using co-located active samplers. The measured PM2.5 concentrations inside of the childcare facilities were in the range of 6-31 μg m-3, while the PM10 concentrations were in the range of 17-96 μg m-3. For PM10, 93.3% of the passive sampler results were within ±30% of those determined with the active sampler, and 67% of the PM2.5 results fell within ±30% of the active sampler measurements. The Pearson's correlation coefficient between PM mass concentrations estimated from the open-faced passive aerosol sampler (OPS) versus gravimetric (active sampler) methods was 0.77 for PM2.5 and 0.95 for PM10, respectively, indicating a strong correlation between the passive and active sampler results. PM and elemental composition varied by facility location and interior characteristics of the childcare facilities such as floor type, presence of combustion sources, occupant density, and the level of activity. The rich carbon content in the polycarbonate substrate produced positive bias in the carbon concentration analysis. Based on the carbon concentration, determined using a copper substrate, a correction factor was needed to determine the total carbon concentrations in the sampled PM for the polycarbonate substrate passive sampler. Overall, the results demonstrated that the OPS collected enough aerosol particles in one business day (6-8 hours) to assess PM levels.

Exposure to ultrafine particles in children until 18 years of age: A systematic review

Airborne ultrafine particles (UFP) have been related to adverse health effects, but exposure in vulnerable population groups such as children is still not well understood. We aim to review the scientific literature regarding personal exposure to UFP in different microenvironments in populations until 18 years of age. The bibliographical search was carried out in July 2019 using the online database PubMed and was completed with references in articles found in the search. We selected the studies that used continuous counters and measured UFP levels in both specific microenvironment (houses, schools, transport, etc) and personal exposure. Finally, 32 studies fulfilled the criteria: of these, 10 analyzed personal exposure and 22 examined UFP levels in the microenvironment (especially in schools or nurseries (18/22)) and five in various microenvironments (including dwellings and means of transport, where exposure levels were higher). The characteristics of the microenvironments with the greatest levels of UFP were being close to heavy traffic or near cooking and cleaning activities. This review revealed the wide differences in exposure assessment methodologies that could lead to a lack of uniform and comparable information about the real UFP exposure in children.