Sources of indoor air pollution in New York City residences of asthmatic children

The effects of urban green space and road proximity to indoor traffic-related PM2.5, NO2, and BC exposure in inner-city schools

BACKGROUND

Since there are known adverse health impacts of traffic-related air pollution, while at the same time there are potential health benefits from greenness, it is important to examine more closely the impacts of these factors on indoor air quality in urban schools.

OBJECTIVE

This study investigates the association of road proximity and urban greenness to indoor traffic-related fine particulate matter (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) in inner-city schools.

METHODS

PM2.5, NO2, and BC were measured indoors at 74 schools and outdoors at a central urban over a 10-year period. Seasonal urban greenness was estimated using the Normalized Difference Vegetation Index (NDVI) with 270 and 1230 m buffers. The associations between indoor traffic-related air pollution and road proximity and greenness were investigated with mixed-effects models.

RESULTS

The analysis showed linear decays of indoor traffic-related PM2.5, NO2, and BC by 60%, 35%, and 22%, respectively for schools located at a greater distance from major roads. The results further showed that surrounding school greenness at 270 m buffer was significantly associated (p < 0.05) with lower indoor traffic-related PM2.5: -0.068 (95% CI: -0.124, -0.013), NO2: -0.139 (95% CI: -0.185, -0.092), and BC: -0.060 (95% CI: -0.115, -0.005). These associations were stronger for surrounding greenness at a greater distance from the schools (buffer 1230 m) PM2.5: -0.101 (95% CI: -0.156, -0.046) NO2: -0.122 (95% CI: -0.169, -0.075) BC: -0.080 (95% CI: -0.136, -0.026). These inverse associations were stronger after fully adjusting for regional pollution and meteorological conditions.

IMPACT STATEMENT

More than 90% of children under the age of 15 worldwide are exposed to elevated air pollution levels exceeding the WHO's guidelines. The study investigates the impact that urban infrastructure and greenness, in particular green areas and road proximity, have on indoor exposures to traffic-related PM2.5, NO2, and BC in inner-city schools. By examining a 10-year period the study provides insights for air quality management, into how road proximity and greenness at different buffers from the school locations can affect indoor exposure.

Sources of personal PM2.5 exposure during pregnancy in the MADRES cohort

BACKGROUND

Personal exposure to fine particulate matter (PM2.5) is impacted by different sources each with different chemical composition. Determining these sources is important for reducing personal exposure and its health risks especially during pregnancy.

OBJECTIVE

Identify main sources and their contributions to the personal PM2.5 exposure in 213 women in the 3rd trimester of pregnancy in Los Angeles, CA.

METHODS

We measured 48-hr integrated personal PM2.5 exposure and analyzed filters for PM2.5 mass, elemental composition, and optical carbon fractions. We used the EPA Positive Matrix Factorization (PMF) model to resolve and quantify the major sources of personal PM2.5 exposure. We then investigated bivariate relationships between sources, time-activity patterns, and environmental exposures in activity spaces and residential neighborhoods to further understand sources.

RESULTS

Mean personal PM2.5 mass concentration was 22.3 (SD = 16.6) μg/m3. Twenty-five species and PM2.5 mass were used in PMF with a final R2 of 0.48. We identified six sources (with major species in profiles and % contribution to PM2.5 mass) as follows: secondhand smoking (SHS) (brown carbon, environmental tobacco smoke; 65.3%), fuel oil (nickel, vanadium; 11.7%), crustal (aluminum, calcium, silicon; 11.5%), fresh sea salt (sodium, chlorine; 4.7%), aged sea salt (sodium, magnesium, sulfur; 4.3%), and traffic (black carbon, zinc; 2.6%). SHS was significantly greater in apartments compared to houses. Crustal source was correlated with more occupants in the household. Aged sea salt increased with temperature and outdoor ozone, while fresh sea salt was highest on days with westerly winds from the Pacific Ocean. Traffic was positively correlated with ambient NO2 and traffic-related NOx at residence. Overall, 76.8% of personal PM2.5 mass came from indoor or personal compared to outdoor sources.

IMPACT

We conducted source apportionment of personal PM2.5 samples in pregnancy in Los Angeles, CA. Among identified sources, secondhand smoking contributed the most to the personal exposure. In addition, traffic, crustal, fuel oil, fresh and aged sea salt sources were also identified as main sources. Traffic sources contained markers of combustion and non-exhaust wear emissions. Crustal source was correlated with more occupants in the household. Aged sea salt source increased with temperature and outdoor ozone and fresh sea salt source was highest on days with westerly winds from the Pacific Ocean.

Advances in photocatalytic NO oxidation by Z-scheme heterojunctions

The fast development of urbanisation and industrialisation has led to a rise in nitrogen oxide (NOx) emissions, specifically nitric oxide (NO). One effective method for reducing the harmful effects of this dangerous air pollutant on both human health and the environment is the photocatalytic oxidation of NO. Z-scheme heterojunctions enhance incident light utilisation and increase photocatalytic activity, eventually leading to better NO oxidation performance by encouraging the effective separation of charges and migration. A comprehensive discussion of Z-scheme-based heterojunctions is provided in this review paper, with a focus on their applications in the photocatalytic oxidation of NO. Significant progress has been made in the fabrication of efficient photocatalytic devices in recent years, with Z-scheme-based heterojunctions proving to be particularly successful. The review looks into the various methodologies used to create Z-scheme-based heterojunctions as well as photocatalytic NO oxidation mechanisms. Recent studies on photocatalysts employing Z-scheme heterojunctions for the photocatalytic oxidation of NO are also discussed. The possibilities for new opportunities as well as the present challenges, barriers, advances, and solutions have been emphasized.

Source-based health risk assessment of heavy metal contamination in soil: a case study from a polymetallic mining region in Southeastern Hubei, Central China

To conduct a precise health risk assessment of heavy metals (HMs) in soil, it is imperative to ascertain the primary sources of potential health risks. In this study, we conducted comprehensive measurements of HMs, specifically focusing on the accumulation of Cu, Cd, Sb, Zn, and Pb in local soil, which may pose threats to environmental quality. To achieve our objective, we employed a method that combines positive matrix factorization with a health risk assessment model to quantify the health risks associated with specific sources. The results obtained from the geo-accumulation index indicate that the majority of HMs found in the local soil are influenced by anthropogenic activities. Among these sources, local industrial-related activities contributed the largest proportion of HMs to the soil at 34.7%, followed by natural sources at 28.7%, mining and metallurgy-related activities at 28.2%, and traffic-related activities at 8.40%. Although the non-carcinogenic and carcinogenic risks associated with individual HMs were found to be below safety thresholds, the cumulative health risks stemming from total HMs exceeded safety limits for children. Moreover, the unacceptable health risks for children originating from industrial-related activities, natural sources, and mining and metallurgy-related activities were primarily concentrated in proximity to mining sites and industrial areas within the local region. This investigation furnishes valuable insights that can aid governmental authorities in formulating precise control policies to mitigate health threats posed by soils in polymetallic mining areas.

Moss-bag technique as an approach to monitor elemental concentration indoors

The moss-bag technique has been used for many decades to monitor outdoor pollution. More recently, however, the method has been used to monitor indoor air pollution (IAP), as humans spend the majority of their time indoors. The purpose of the research conducted was to evaluate indoor air pollution using active moss biomonitoring. Pleurozium schreberi moss bags were exposed for two seasons (summer and winter), hanging over tile stoves and coal stoves. The selected elements: Al, Cu, Cd, Co, Pb, Zn, V, Ba, Cr, Fe, Mn, Sr, P, Ni, and S were determined by Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES) and, for Hg, by a direct mercury analyzer. The study found the exposure season affected the concentrations of selected elements in 62.5% of cases, and their source was identified. The average concentrations of Co, Ba, Cr, and Sr were higher, and statistically significant, in winter, after a 12-week exposure period of the mosses, regardless of the type of heating or cooking stove owned. The higher phosphorus concentrations obtained in summer indicate physiological stress caused by unfavorable winter exposure conditions. In the future, the number of species used to assess indoor air pollution should be increased and the range of pollutants expanded, along with the identification of their sources, taking residents' lifestyles into account.

Personal exposure to fine particulate matter (PM2.5) and self-reported asthma-related health

PM2.5 (fine particulate matter ≤2.5 μm in diameter) is a key pollutant that can produce acute asthma exacerbations and longer-term deterioration of respiratory health. Individual exposure to PM2.5 is unique and varies across microenvironments. Low-cost sensors (LCS) can collect data at a spatiotemporal resolution previously unattainable, allowing the study of exposures across microenvironments. The aim of this study is to investigate the acute effects of personal exposure to PM2.5 on self-reported asthma-related health. Twenty-eight non-smoking adults with asthma living in Scotland collected PM2.5 personal exposure data using LCS. Measurements were made at a 2-min time resolution for a period of 7 days as participants conducted their typical daily routines. Concurrently, participants were asked to keep a detailed time-activity diary, logging their activities and microenvironments, along with hourly information on their respiratory health and medication use. Health outcomes were modelled as a function of hourly PM2.5 concentration (plus 1- and 2-h lag) using generalized mixed-effects models adjusted for temperature and relative humidity. Personal exposures to PM2.5 varied across microenvironments, with the largest average microenvironmental exposure observed in private residences (11.5 ± 48.6 μg/m3) and lowest in the work microenvironment (2.9 ± 11.3 μg/m3). The most frequently reported asthma symptoms, wheezing, chest tightness and cough, were reported on 3.4%, 1.6% and 1.6% of participant-hours, respectively. The odds of reporting asthma symptoms increased per interquartile range (IQR) in PM2.5 exposure (odds ratio (OR) 1.29, 95% CI 1.07-1.54) for same-hour exposure. Despite this, no association was observed between reliever inhaler use (non-routine, non-exercise related) and PM2.5 exposure (OR 1.02, 95% CI 0.71-1.48). Current air quality monitoring practices are inadequate to detect acute asthma symptom prevalence resulting from PM2.5 exposure; to detect these requires high-resolution air quality data and health information collected in situ. Personal exposure monitoring could have significant implications for asthma self-management and clinical practice.

The impact of the air pollution on health in New York City

New York City is attempting to find a solution to an issue that many states and cities face: how to minimize air pollution so that it has fewer negative impacts on human health. Despite having the highest population in the United States (US), New York City typically has reasonably clean air. As the City and State of New York have worked to reduce emissions from local and regional sources, the air quality in New York City has improved during the past few decades. Despite these advancements, air pollution still poses a severe hazard to the health of everyone living in New York's environment. Various diseases including respiratory, circulatory, neurological, gastrointestinal, and urinary illnesses, which can be fatal, are intimately associated with air pollution. This review article will concentrate on how air pollution affects respiratory diseases such as asthma in children. In addition to analyzing the severe effects of air pollution on the vulnerable population, this review article will highlight the health repercussions of air pollution on New York City and its residents. furthermore, we argue for potential ideas and discoveries while also putting up a policy option to lower air pollution.

Pulmonary, inflammatory, and oxidative effects of indoor nitrogen dioxide in patients with COPD

Introduction

Indoor nitrogen dioxide (NO2) sources include gas heating, cooking, and infiltration from outdoors. Associations with pulmonary function, systemic inflammation, and oxidative stress in patients with chronic obstructive pulmonary disease (COPD) are uncertain.

Methods

We recruited 144 COPD patients at the VA Boston Healthcare System between 2012 and 2017. In-home NO2 was measured using an Ogawa passive sampling badge for a week seasonally followed by measuring plasma biomarkers of systemic inflammation (C-reactive protein [CRP] and interleukin-6 [IL-6]), urinary oxidative stress biomarkers (8-hydroxy-2'deoxyguanosine [8-OHdG] and malondialdehyde [MDA]), and pre- and postbronchodilator spirometry. Linear mixed effects regression with a random intercept for each subject was used to assess associations with weekly NO2. Effect modification by COPD severity and by body mass index (BMI) was examined using multiplicative interaction terms and stratum-specific effect estimates.

Results

Median (25%ile, 75%ile) concentration of indoor NO2 was 6.8 (4.4, 11.2) ppb. There were no associations observed between NO2 with CRP, 8-OHdG, or MDA. Although the confidence intervals were wide, there was a reduction in prebronchodilator FEV1 and FVC among participants with more severe COPD (FEV1: -17.36 mL; -58.35, 23.60 and FVC: -28.22 mL; -91.49, 35.07) that was greater than in patients with less severe COPD (FEV1: -1.64 mL; -24.80, 21.57 and FVC: -6.22 mL; -42.16, 29.71). In participants with a BMI <30, there was a reduction in FEV1 and FVC.

Conclusions

Low-level indoor NO2 was not associated with systemic inflammation or oxidative stress. There was a suggestive association with reduced lung function among patients with more severe COPD and among patients with a lower BMI.

Ecological study on household air pollution exposure and prevalent chronic disease in the elderly

Older people spend most of their time indoors. Limited evidence demonstrates that exposure to indoor air pollutants might be related to chronic complications. This study aimed to estimate the correlation between household air pollution (HAP)'s long-term exposure and the prevalence of elevated hypertension, diabetes mellitus (DM), obesity, and low-density lipoprotein (LDL) cholesterol. From the Global Burden disease dataset, we extracted HAP, hypertension, DM, body mass index, and LDL cholesterol data from Iran from 1990 to 2019 to males and females in people over 50 years. We present APC and AAPC and their confidence intervals using Joinpoint Software statistical software. R software examined the correlation between HAP and hypertension, DM2, Obesity, and high LDL cholesterol. Our finding showed a significant and positive correlation between HAP exposure and prevalence of high low-density lipoprotein cholesterol (p ≤ 0.001, r = 0.70), high systolic blood pressure (p ≤ 0.001, r = 0.63), and high body mass index (p ≤ 0.001, r = 0.57), and DM2 (p ≤ 0.001, r = 0.38). The analysis results also illustrated a positive correlation between indoor air pollution and smoking (p ≤ 0.001, r = 0.92). HAP exposure might be a risk factor for elevated blood pressure, DM, obesity, and LDL cholesterol and, consequently, more serious health problems. According to our results, smoking is one of the sources of HAP. However, ecological studies cannot fully support causal relationships, and this article deals only with Iran. Our findings should be corroborated in personal exposure and biomonitoring approach studies.

Chemical exposure and alveolar macrophages responses: 'the role of pulmonary defense mechanism in inhalation injuries'

Epidemiological and clinical studies have indicated an association between particulate matter (PM) exposure and acute and chronic pulmonary inflammation, which may be registered as increased mortality and morbidity. Despite the increasing evidence, the pathophysiology mechanism of these PMs is still not fully characterised. Pulmonary alveolar macrophages (PAMs), as a predominant cell in the lung, play a critically important role in these pathological mechanisms. Toxin exposure triggers events associated with macrophage activation, including oxidative stress, acute damage, tissue disruption, remodelling and fibrosis. Targeting macrophage may potentially be employed to treat these types of lung inflammation without affecting the natural immune response to bacterial infections. Biological toxins, their sources of exposure, physical and other properties, and their effects on the individuals are summarised in this article. Inhaled particulates from air pollution and toxic gases containing chemicals can interact with alveolar epithelial cells and immune cells in the airways. PAMs can sense ambient pollutants and be stimulated, triggering cellular signalling pathways. These cells are highly adaptable and can change their function and phenotype in response to inhaled agents. PAMs also have the ability to polarise and undergo plasticity in response to tissue damage, while maintaining resistance to exposure to inhaled agents.

Health Impact Assessment of Short-Term Exposure to Particulate Matter (PM10) in Northern Thailand

In northern Thailand, in recent decades, particulate pollution from the burning of biomass has become a serious issue with toxicological implications for human health, especially during the winter months of January to April. The purpose of this study was to explore short-term exposure to particulate matter (PM₁₀) in northern Thailand. The high PM₁₀ concentration in 2012 was used as a case study. We used the EPA's Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) for the health impact assessment, along with ground-based measurement data. The annual average observed PM₁₀ concentration was in the range of 43-61 μg/m³, with a maximum observed PM₁₀ concentration of 300 μg/m³ in March. We then assessed the impacts of PM₁₀ exposure in northern Thailand. When the PM₁₀ concentration was reduced to 120 μg/m³, the undesirable effects on respiratory mortality decreased by 5%-11%. When the concentration of PM₁₀ was reduced to 45 μg/m³, the deleterious effects on respiratory mortality decreased by 11-30%. In conclusion, adherence to the WHO-AQG, particularly for PM₁₀ (45 μg/m³), tends to result in considerable reductions in respiratory disease mortality in northern Thailand.

Improved indoor air quality during desert dust storms: The impact of the MEDEA exposure-reduction strategies

Desert dust storms (DDS) are natural events that impact not only populations close to the emission sources but also populations many kilometers away. Countries located across the main dust sources, including countries in the Eastern Mediterranean, are highly affected by DDS. In addition, climate change is expanding arid areas exacerbating DDS events. Currently, there are no intervention measures with proven, quantified exposure reduction to desert dust particles. As part of the wider "MEDEA" project, co-funded by LIFE 2016 Programme, we examined the effectiveness of an indoor exposure-reduction intervention (i.e., decrease home ventilation during DDS events and continuous use of air purifier during DDS and non-DDS days) across homes and/or classrooms of schoolchildren with asthma and adults with atrial fibrillation in Cyprus and Crete-Greece. Participants were randomized to a control or intervention groups, including an indoor intervention group with exposure reduction measures and the use of air purifiers. Particle sampling, PM₁₀ and PM_2.5, was conducted in participants' homes and/or classrooms, between 2019 and 2022, during DDS-free weeks and during DDS days for as long as the event lasted. In indoor and outdoor PM₁₀ and PM_2.5 samples, mass and content in main and trace elements was determined. Indoor PM_2.5 and PM₁₀ mass concentrations, adjusting for premise type and dust conditions, were significantly lower in the indoor intervention group compared to the control group (PM_{2.5-intervention}/PM_2.5-control = 0.57, 95% CI: 0.47, 0.70; PM_{10-intervention}/PM_10-control = 0.59, 95% CI: 0.49, 0.71). In addition, the PM_2.5 and PM₁₀ particles of outdoor origin were significantly lower in the intervention vs. the control group (PM_2.5 infiltration intervention-to-control ratio: 0.49, 95% CI: 0.42, 0.58; PM₁₀ infiltration intervention-to-control ratio: 0.68, 95% CI: 0.52, 0.89). Our findings suggest that the use of air purifiers alongside decreased ventilation measures is an effective protective measure that reduces significantly indoor exposure to particles during DDS and non-DDS in high-risk population groups.

Using Sensor Data to Identify Factors Affecting Internal Air Quality within 279 Lower Income Households in Cornwall, South West of England

(1) Background: Poor air quality affects health and causes premature death and disease. Outdoor air quality has received significant attention, but there has been less focus on indoor air quality and what drives levels of diverse pollutants in the home, such as particulate matter, and the impact this has on health; (2) Methods: This study conducts analysis of cross-sectional data from the Smartline project. Analyses of data from 279 social housing properties with indoor sensor data were used to assess multiple factors that could impact levels of particulate matter. T-Tests and Anova tests were used to explore associations between elevated PM2.5 and building, household and smoking and vaping characteristics. Binary logistic regression was used to test the association between elevated particulate matter and self-reported health; (3) Results: Of the multiple potential drivers of the particulate matter investigated, smoking and vaping were significantly associated with mean PM2.5. Following multivariate analysis, only smoking remained significantly associated with higher mean concentrations. Properties in which <15 cigarettes/day were smoked were predicted to have PM2.5 concentrations 9.06 µg/m3 higher (95% CI 6.4, 12.82, p ≤ 0.001) than those in which residents were non-smokers and 11.82 µg/m3 higher (95% CI 7.67, 18.19, p ≤ 0.001) where >15 cigarettes were smoked; (4) Conclusions: A total of 25% of social housing properties in this study experienced levels of indoor PM greater than WHO guideline levels for ambient air pollution. Although there are many factors that impact air quality, in this study the main driver was smoking. This highlights the importance of targeting smoking in indoor environments in future smoking cessation and control policy and practice and of understanding how pollutants interact in the home environment. There is also a need for further research into the impact on indoor air quality of vaping, particularly due to the rise in use and uncertainty of its long-term impact.

Disparities in Air Pollutants Across Racial, Ethnic, and Poverty Groups at US Public Schools

We investigate socioeconomic disparities in air quality at public schools in the contiguous US using high resolution estimates of fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) concentrations. We find that schools with higher proportions of people of color (POC) and students eligible for the federal free or reduced lunch program, a proxy for poverty level, are associated with higher pollutant concentrations. For example, we find that the median annual NO₂ concentration for White students, nationally, was 7.7 ppbv, compared to 9.2 ppbv for Black and African American students. Statewide and regional disparities in pollutant concentrations across racial, ethnic, and poverty groups are consistent with nationwide results, where elevated NO₂ concentrations were associated with schools with higher proportions of POC and higher levels of poverty. Similar, though smaller, differences were found in PM_2.5 across racial and ethnic groups in most states. Racial, ethnic, and economic segregation across the rural-urban divide is likely an important factor in pollution disparities at US public schools. We identify distinct regional patterns of disparities, highlighting differences between California, New York, and Florida. Finally, we highlight that disparities exist not only across urban and non-urban lines but also within urban environments.

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.

The impact of GPS-derived activity spaces on personal PM2.5 exposures in the MADRES cohort

BACKGROUND

In-utero exposure to particulate matter with aerodynamic diameter less than 2.5 μm (PM_2.5) is associated with low birth weight and health risks later in life. Pregnant women are mobile and locations they spend time in contribute to their personal PM_2.5 exposures. Therefore, it is important to understand how mobility and exposures encountered within activity spaces contribute to personal PM_2.5 exposures during pregnancy.

METHODS

We collected 48-h integrated personal PM_2.5 samples and continuous geolocation (GPS) data for 213 predominantly Hispanic/Latina pregnant women in their 3^rd trimester in Los Angeles, CA. We also collected questionnaires and modeled outdoor air pollution and meteorology in their residential neighborhood. We calculated three GPS-derived activity space measures of exposure to road networks, greenness (NDVI), parks, traffic volume, walkability, and outdoor PM_2.5 and temperature. We used bivariate analyses to screen variables (GPS-extracted exposures in activity spaces, individual characteristics, and residential neighborhood exposures) based on their relationship with personal, 48-h integrated PM_2.5 concentrations. We then built a generalized linear model to explain the variability in personal PM_2.5 exposure and identify key contributing factors.

RESULTS

Indoor PM_2.5 sources, parity, and home ventilation were significantly associated with personal exposure. Activity-space based exposure to roads was associated with significantly higher personal PM_2.5 exposure, while greenness was associated with lower personal PM_2.5 exposure (β = -3.09 μg/m³ per SD increase in NDVI, p-value = 0.018). The contribution of outdoor PM_2.5 to personal exposure was positive but relatively lower (β = 2.05 μg/m³ per SD increase, p-value = 0.016) than exposures in activity spaces and the indoor environment. The final model explained 34% of the variability in personal PM_2.5 concentrations.

CONCLUSIONS

Our findings highlight the importance of activity spaces and the indoor environment on personal PM_2.5 exposures of pregnant women living in Los Angeles, CA. This work also showcases the multiple, complex factors that contribute to total personal PM_2.5 exposure.

Partitioning indoor-generated and outdoor-generated PM2.5 from real-time residential measurements in urban and peri-urban Beijing

Limited number of projects have attempted to partition and quantify indoor- and outdoor-generated PM_2.5 (PM_2.5^ig and PM_2.5^og) where strong indoor sources (e.g., solid fuel, tobacco smoke, or kerosene) exist. This study aimed to apply and refine a previous recursive model used to derive infiltration efficiency (F_inf) to additionally partition pollution concentrations into indoor and outdoor origins within residences challenged by elevated ambient and indoor combustion-related sources. During the winter of 2016 and summer of 2017 we collected residential measurements in 72 homes in urban and peri-urban Beijing, 12 of which had additional paired residential outdoor measurements during the summer season. Local ambient measurements were collected throughout. We then compared the calculated PM_2.5^ig and using (i) outdoor and (ii) ambient measurements as model inputs. The results from outdoor and ambient measurements were not significantly different, which suggests that ambient measurements can be used as a model input for pollution origin partitioning when paired outdoor measurements are not available. From the results calculated using ambient measurements, the mean percentage contribution of indoor-generated PM_2.5 was 19 % (σ = 22 %), and 7 % (11 %) of the total indoor PM_2.5 for peri-urban and urban homes respectively during the winter; and 18 % (18 %) and 6 % (10 %) of the total indoor PM_2.5 during the summer. Partitioning pollution into PM_2.5^ig and PM_2.5^og is important to allow investigation of distinct associations between health outcomes and particulate mixes, often with different physiochemical composition and toxicity. It will also inform targeted interventions that impact indoor and outdoor sources of pollution (e.g., domestic fuel switching vs. power generation), which are typically radically different in design and implementation.

In-utero personal exposure to PM2.5 impacted by indoor and outdoor sources and birthweight in the MADRES cohort

BACKGROUND

In-utero exposure to outdoor particulate matter with aerodynamic diameter less than 2.5 μm (PM_2.5) is linked with low birthweight. However, previous results are mixed, likely due to measurement error introduced by estimating personal exposure from ambient data. This study investigated the effect of total personal PM_2.5 exposure on birthweight and whether it differed when it was more heavily impacted by sources of indoor vs outdoor origin in the MADRES cohort study.

METHODS

Personal PM_2.5 exposure was measured in 205 pregnant women in the 3rd trimester using 48 h integrated, filter-based sampling. Linear regression was used to test the association between personal PM_2.5 exposure and birthweight, adjusting for key covariates. Interactions of PM_2.5 with variables representing indoor sources of PM_2.5, home ventilation, or time spent indoors tested whether the effect of total PM_2.5 on birthweight varied when it was more impacted by sources of indoor vs outdoor origin.

RESULTS

In a sample of largely Hispanic (81%) pregnant women, total personal PM_2.5 was not significantly associated with birthweight (β = 38.6 per 1SD increase in PM_2.5; 95% CI:-21.1, 98.2). This association however, differed by home type (single family home: 156.9 (26.9, 287.0), 2-4 attached units:-16.6 (-111.9, 78.7), 5+ units:-62.6 (-184.9, 59.6), missing: 145.4 (-4.1, 294.9), interaction p = 0.028) and by household air conditioner use (none of the time: -27.6 (-101.5, 46.3) vs. some of the time: 139.9 (42.9, 237.0), interaction p = 0.008) Additionally, the effect of personal PM_2.5 on birthweight varied by time spent indoors (none or little of the time: - 45.1 (-208.3, 118.1) vs. most or all of the time: 57.1 (-7.3, 121.6), interaction p = 0.255).

CONCLUSIONS

While no significant association between total personal PM_2.5 exposure and birthweight was found, there was evidence that multi-unit housing (vs. single-family homes), candle and/or incense smoke, and greater outdoor source contributions to personal PM_2.5 were more strongly associated with lower birthweight.

Functional Kriging for Spatiotemporal Modeling of Nitrogen Dioxide in a Middle Eastern Megacity

Long-term hour-specific air pollution exposure estimates have rarely been of interest in epidemiological research. However, this can be relevant for studies that aim to estimate the residential exposure for the hours that subjects mostly spend time there, or for those hours that they may work in another location. Here, we developed a model by spatially predicting the long-term diurnal curves of nitrogen dioxide (NO2) in Tehran, Iran, one of the most polluted and populated megacities in the Middle East. We used the statistical framework of functional data analysis (FDA) including ordinary kriging for functional data (OKFD) and functional analysis of variance (fANOVA) for modeling. The long-term NO2 diurnal curves had two distinct maxima and minima. The absolute minimum value of the city average was 40.6 ppb (around 4:00 p.m.) and the absolute maximum value was 52.0 ppb (around 10:00 p.m.). The OKFD showed the concentrations, the diurnal maximum/minimum values, and their corresponding occurring times varied across the city. The fANOVA highlighted that the effect of population density on the NO2 concentrations is not constant and depends on time within the diurnal period. The provided estimation of long-term hour-specific maps can inform future epidemiological studies to use the long-term mean for specific hour(s) of the day. Moreover, the demonstrated FDA framework can be used as a set of flexible statistical methods.

Human Health, Environmental Quality and Governance Quality: Novel Findings and Implications From Human Health Perspective

Human health and wellbeing are intimately linked to the state of the environment. The current study emphasizes the role of environmental quality, government policies, and human health. This paper provides a detailed literature review of existing findings regarding our key variables of interest. The results argue that the implications of poor government policies and environmental pollution for rising economic development have led to poor environmental quality and health issues for humans. Based on earlier investigations, the present study reviewed the state-of-the-art review and determined innovative insights for outdoor and indoor environment difficulties. This study provides a detailed review of human health, environmental quality, and governance quality. In addition, the study conducts an empirical analysis using the annual data of low-income countries from 1996 to 2020. Government actions and health systems must be modified immediately to address these rising concerns successfully. The report offers policy recommendations for addressing health, governance, and environmental change mitigation issues, all of which are directly or indirectly related to the study. This article presents an overview of environmental change's health impacts and explores how health hazards may be reduced or eliminated through effective adaptation strategies.

A Mobile App for Children With Asthma to Monitor Indoor Air Quality (AirBuddy): Development and Usability Study

Background

Indoor air quality is an important environmental factor that triggers and exacerbates asthma, the most common chronic disease in children. A mobile app to monitor indoor air quality could help occupants keep their indoor air quality clean. However, no app is available that allows children to monitor and improve their indoor air quality.

Objective

Previously, we conducted a series of user-centered design studies to identify user needs and design requirements toward creating a mobile app that helps children with asthma to engage in monitoring and improving indoor air quality as part of their asthma management. Based on the findings from these studies, we created AirBuddy, a child-friendly app that visualizes air quality indoors and outdoors.

Methods

This paper reports on the findings from a field deployment with 7 pediatric asthma patients, where we evaluated AirBuddy’s usability and usefulness in real-world settings by conducting weekly semistructured interviews for 8 weeks.

Results

All participants positively responded to the usefulness and usability of AirBuddy, which we believe is thanks to the iterative, user-centered design approach that allowed us to identify and address potential usability issues early on and throughout the design process.

Conclusions

This project contributes to the field of mHealth app design for children by demonstrating how a user-centered design process can lead to the development of digital devices that are more acceptable and relevant to target users’ needs.

The Content of Heavy Metals in Cigarettes and the Impact of Their Leachates on the Aquatic Ecosystem

Smoked cigarettes and butts are the most common kind of litter around the world. The buildup of these litters has badly polluted local water bodies and their compartments, and the cumulative effect of many cigarette butts scattered in a centralized location may pose a serious hazard to living species. To understand how heavy metals are leached out into the aquatic ecosystem, researchers must analyse the behavior of the materials that make up cigarettes. Using atomic absorption spectrometry, this study evaluated the content of several metals (such as Cd, Cu, Fe, Pb, Sn, Zn, and Hg) leached from various brands of unsmoked and smoked cigarettes and cigarette butts. The findings revealed that heavy metal is more prevalent in butte. These findings indicate that cigarette litter is a major source of metal contamination in the aquatic ecosystem and that apparent leaching may increase the risk of toxicity to aquatic organisms.

Factors Influencing Classroom Exposures to Fine Particles, Black Carbon, and Nitrogen Dioxide in Inner-City Schools and Their Implications for Indoor Air Quality

BACKGROUND

School classrooms, where students spend the majority of their time during the day, are the second most important indoor microenvironment for children.

OBJECTIVE

We investigated factors influencing classroom exposures to fine particulate matter (PM2.5), black carbon (BC), and nitrogen dioxide (NO2) in urban schools in the northeast United States.

METHODS

Over the period of 10 y (2008-2013; 2015-2019) measurements were conducted in 309 classrooms of 74 inner-city schools during fall, winter, and spring of the academic period. The data were analyzed using adaptive mixed-effects least absolute shrinkage and selection operator (LASSO) regression models. The LASSO variables included meteorological-, school-, and classroom-based covariates.

RESULTS

LASSO identified 10, 10, and 11 significant factors (p<0.05) that were associated with indoor PM2.5, BC, and NO2 exposures, respectively. The overall variability explained by these models was R2=0.679, 0.687, and 0.621 for PM2.5, BC, and NO2, respectively. Of the model's explained variability, outdoor air pollution was the most important predictor, accounting for 53.9%, 63.4%, and 34.1% of the indoor PM2.5, BC, and NO2 concentrations. School-based predictors included furnace servicing, presence of a basement, annual income, building type, building year of construction, number of classrooms, number of students, and type of ventilation that, in combination, explained 18.6%, 26.1%, and 34.2% of PM2.5, BC, and NO2 levels, whereas classroom-based predictors included classroom floor level, classroom proximity to cafeteria, number of windows, frequency of cleaning, and windows facing the bus area and jointly explained 24.0%, 4.2%, and 29.3% of PM2.5, BC, and NO2 concentrations, respectively.

DISCUSSION

The adaptive LASSO technique identified significant regional-, school-, and classroom-based factors influencing classroom air pollutant levels and provided robust estimates that could potentially inform targeted interventions aiming at improving children's health and well-being during their early years of development. https://doi.org/10.1289/EHP10007.

Exploring Children’s Engagement in Monitoring Indoor Air Quality: Longitudinal Study

Background

Indoor air pollution is harmful to everyone, but children are of particular concern, as they are more vulnerable to its adverse health effects from air pollutants. Although mobile technology is increasingly being designed to support monitoring and improving air quality indoors, little attention has been paid to its use by and for children. Previously, we created inAirKids, a child-friendly device to promote children’s engagement with monitoring indoor air quality through a participatory design process. The next step is to evaluate its usability in the real world.

Objective

The aim of this study is to investigate how inAirKids affects children’s understanding of and engagement with indoor air quality through a longitudinal field deployment study.

Methods

We deployed inAirKids in the homes of 9 children aged between 6 and 7 years, and investigated their use for up to 16 weeks by conducting semistructured, biweekly interviews.

Results

The results show that participants promptly engaged with inAirKids but quickly lost interest in it owing to the lack of engaging factors to sustain engagement. In addition, we identified 2 design considerations that can foster sustained engagement of children with monitoring indoor air quality: design interactivity for engaging in continuity and corporate hands-on activities as part of indoor air quality monitoring for experiential learning.

Conclusions

Our findings shed light on the potential to promote the engagement of children in indoor air quality as well as considerations for designing a child-friendly digital device. To the best of our knowledge, this is the first longitudinal field deployment to investigate how to engage children in monitoring indoor air quality.

Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: A comprehensive review and outlook

A viral outbreak is a global challenge that affects public health and safety. The coronavirus disease 2019 (COVID-19) has been spreading globally, affecting millions of people worldwide, and led to significant loss of lives and deterioration of the global economy. The current adverse effects caused by the COVID-19 pandemic demands finding new detection methods for future viral outbreaks. The environment's transmission pathways include and are not limited to air, surface water, and wastewater environments. The wastewater surveillance, known as wastewater-based epidemiology (WBE), can potentially monitor viral outbreaks and provide a complementary clinical testing method. Another investigated outbreak surveillance technique that has not been yet implemented in a sufficient number of studies is the surveillance of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in the air. Artificial intelligence (AI) and its related machine learning (ML) and deep learning (DL) technologies are currently emerging techniques for detecting viral outbreaks using global data. To date, there are no reports that illustrate the potential of using WBE with AI to detect viral outbreaks. This study investigates the transmission pathways of SARS-CoV-2 in the environment and provides current updates on the surveillance of viral outbreaks using WBE, viral air sampling, and AI. It also proposes a novel framework based on an ensemble of ML and DL algorithms to provide a beneficial supportive tool for decision-makers. The framework exploits available data from reliable sources to discover meaningful insights and knowledge that allows researchers and practitioners to build efficient methods and protocols that accurately monitor and detect viral outbreaks. The proposed framework could provide early detection of viruses, forecast risk maps and vulnerable areas, and estimate the number of infected citizens.

Biomarkers for the adverse effects on respiratory system health associated with atmospheric particulate matter exposure

Large amounts of epidemiological evidence have confirmed the atmospheric particulate matter (PM_2.5) exposure was positively correlated with the morbidity and mortality of respiratory diseases. Nevertheless, its pathogenesis remains incompletely understood, probably resulting from the activation of oxidative stress, inflammation, altered genetic and epigenetic modifications in the lung upon PM_2.5 exposure. Currently, biomarker investigations have been widely used in epidemiological and toxicological studies, which may help in understanding the biologic mechanisms underlying PM_2.5-elicited adverse health outcomes. Here, the emerging biomarkers to indicate PM_2.5-respiratory system interactions were summarized, primarily related to oxidative stress (ROS, MDA, GSH, etc.), inflammation (Interleukins, FE_NO, CC16, etc.), DNA damage (8-OHdG, γH2AX, OGG1) and also epigenetic modulation (DNA methylation, histone modification, microRNAs). The identified biomarkers shed light on PM_2.5-elicited inflammation, fibrogenesis and carcinogenesis, thus may favor more precise interventions in public health. It is worth noting that some inconsistent findings may possibly relate to the inter-study differentials in the airborne PM_2.5 sample, exposure mode and targeted subjects, as well as methodological issues. Further research, particularly by -omics technique to identify novel, specific biomarkers, is warranted to illuminate the causal relationship between PM_2.5 pollution and deleterious lung outcomes.

Inter- and Intra-Individual Variability of Personal Health Risk of Combined Particle and Gaseous Pollutants across Selected Urban Microenvironments

Exposure surrogates, such as air quality measured at a fixed-site monitor (FSM) or residence, are typically used for health estimates. However, people spend various amounts of time in different microenvironments, including the home, office, outdoors and in transit, where they are exposed to different magnitudes of particle and gaseous air pollutants. Health risks caused by air pollution exposure differ among individuals due to differences in activity, microenvironmental concentration, as well as the toxicity of pollutants. We evaluated individual and combined added health risks (AR) of exposure to PM_2.5, NO_2, and O₃ for 21 participants in their daily life based on real-world personal exposure measurements. Exposure errors from using surrogates were quantified. Inter- and intra-individual variability in health risks and key contributors in variations were investigated using linear mixed-effects models and correlation analysis, respectively. Substantial errors were found between personal exposure concentrations and ambient concentrations when using air quality measurements at either FSM or the residence location. The mean exposure errors based on the measurements taken at either the FSM or residence as exposure surrogates was higher for NO₂ than PM_2.5, because of the larger spatial variability in NO₂ concentrations in urban areas. The daily time-integrated AR for the combined PM_2.5, NO₂, and O₃ (TIAR_combine) ranged by a factor of 2.5 among participants and by a factor up to 2.5 for a given person across measured days. Inter- and intra-individual variability in TIAR_combine is almost equally important. Several factors were identified to be significantly correlated with daily TIAR_combine, with the top five factors, including PM_2.5, NO₂ and O₃ concentrations at ‘home indoor’, O₃ concentrations at ‘office indoor’ and ambient PM_2.5 concentrations. The results on the contributors of variability in the daily TIAR_combine could help in targeting interventions to reduce daily health damage related to air pollutants.

Selection of metric for indoor-outdoor source apportionment of metals in PM2.5 : mg/kg versus ng/m3

Trends in the elemental composition of fine particulate matter (PM_2.5 ) collected from indoor, outdoor, and personal microenvironments were investigated using two metrics: ng/m³ and mg/kg. Pearson correlations that were positive using one metric commonly disappeared or flipped to become negative when the other metric was applied to the same dataset. For example, the correlation between Mo and S in the outdoor microenvironment was positive using ng/m³ (p < 0.05) but negative using mg/kg (p < 0.05). In general, elemental concentrations (mg/kg) within PM_2.5 decreased significantly (p < 0.05) as PM_2.5 concentrations (µg/m³ ) increased-a dilution effect that was observed in all microenvironments and seasons. An exception was S: in the outdoor microenvironment, the correlation between wt% S and PM_2.5  flipped from negative in the winter (p < 0.01) to positive (p < 0.01) in the summer, whereas in the indoor microenvironment, this correlation was negative year-round (p < 0.05). Correlation analyses using mg/kg indicated that elemental associations may arise from Fe-Mn oxyhydroxide sorption processes that occur as particles age, with or without the presence of a common anthropogenic source. Application of mass-normalized concentration metrics (mg/kg or wt%), enabled by careful gravimetric analysis, revealed new evidence of the importance of indoor sources of elements in PM_2.5 .

Multi-elemental profile and enviromagnetic analysis of moss transplants exposed indoors and outdoors in Italy and Belgium

Air pollution represents one of the major concerns worldwide, fueled by the increasing urbanization and related PM production worsening air quality in open air as well as in confined environments. In the present work, exposure to atmospheric metal pollution was investigated in 20 paired indoor (I)-outdoor (O) sites located in two urban areas of Italy and Belgium, by chemical (ICP-MS) and magnetic (saturation isothermal remanent magnetization, SIRM) analyses of Hypnum cupressiforme moss exposed in bags. After 12 weeks, the elemental profiles of the moss material exposed in the two countries largely overlapped, except for some elements which specifically accumulated in Belgium (Ag, As, Cd, Mo, Pb and Sb) and in Italy (Ca, Mg, Co, Cr, Sr, Ti and U). Element concentrations were higher in moss exposed outdoors, with the Italian sites mostly showing a terrigenous footprint, and the Belgian sites mostly affected by elements of environmental concern (e.g., As, Pb, Sb). The Indoor/Outdoor ratios (mostly lower than 0.75) indicated indoor pollution as strongly affected by outdoor pollution, although specific elements could be of indoor origin or magnified in indoor environments (e.g., Al, Ag, Cd and Co). In line with the chemical analysis, the SIRM signal was significantly higher in outdoor than indoor moss material. A positive, significant correlation was observed between SIRM and several accumulated elements indicating SIRM analysis as a powerful tool to predict the level of metal pollution. Moss bags were confirmed as a useful and versatile tool to highlight metal contamination even in confined environments, an essential prerogative in the perspective of the evaluation of the total exposure risk for humans to these pollutants.

Effectiveness of indoor air purification intervention in improving cardiovascular health: A systematic review and meta-analysis of randomized controlled trials

Indoor air purifiers are increasingly marketed for their health benefits, but their cardiovascular effects remain unclear. We systematically reviewed and meta-analysed randomized controlled trials (RCTs) on the cardiovascular effects of indoor air purification interventions in humans of all ages. We searched Embase, Medline, PubMed, and Web of Science from inception to 22 August 2020. Fourteen cross-over RCTs (18 publications) were included. Systolic blood pressure (SBP) was significantly reduced after intervention (-2.28 (95% CI: -3.92, -0.64) mmHg). There were tendencies of reductions in diastolic blood pressure (-0.35 [-1.52, 0.83] mmHg), pulse pressure (PP) (-0.86 [-2.07, 0.34] mmHg), C-reactive protein (-0.23 [-0.63, 0.18] mg/L), and improvement in reactive hyperaemia index (RHI) (0.10 [-0.04, 0.24]) after indoor air purification, although the effects were not statistically significant. However, when restricting the analyses to RCTs using physical-type purifiers only, significant improvements in PP (-1.56 [-2.98, -0.15] mmHg) and RHI (0.13 [0.01, 0.25]) were observed. This study found potential evidence on the short-term cardiovascular benefits of using indoor air purifiers, especially for SBP, PP and RHI. However, under the Grading of Recommendations Assessment, Development and Evaluation framework, the overall certainty of evidence was very low, which discourage unsubstantiated claims on the cardiovascular benefits of air purifiers. We have also identified several key methodological limitations, including small sample size, short duration of intervention, and the lack of wash-out period. Further RCTs with larger sample size and longer follow-up duration are needed to clarify the cardiovascular benefits of air purification interventions.

Real-time molecular characterization of air pollutants in a Hong Kong residence: Implication of indoor source emissions and heterogeneous chemistry

Due to the high health risks associated with indoor air pollutants and long-term exposure, indoor air quality has received increasing attention. In this study, we put emphasis on the molecular composition, source emissions, and chemical aging of air pollutants in a residence with designed activities mimicking ordinary Hong Kong homes. More than 150 air pollutants were detected at molecular level, 87 of which were quantified at a time resolution of not less than 1 hour. The indoor-to-outdoor ratios were higher than 1 for most of the primary air pollutants, due to emissions of indoor activities and indoor backgrounds (especially for aldehydes). In contrast, many secondary air pollutants exhibited higher concentrations in outdoor air. Painting ranked first in aldehyde emissions, which also caused great enhancement of aromatics. Incense burning had the highest emissions of particle-phase organics, with vanillic acid and syringic acid as markers. The other noteworthy fingerprints enabled by online measurements included linoleic acid, cholesterol, and oleic acid for cooking, 2,5-dimethylfuran, stigmasterol, iso-/anteiso-alkanes, and fructose isomers for smoking, C₂₈ -C₃₄ even n-alkanes for candle burning, and monoterpenes for the use of air freshener, cleaning agents, and camphor oil. We showed clear evidence of chemical aging of cooking emissions, giving a hint of indoor heterogeneous chemistry. This study highlights the value of organic molecules measured at high time resolutions in enhancing our knowledge on indoor air quality.

Psychophysiological Response According to the Greenness Index of Subway Station Space

This study proposed a plan for implementing a pleasant and healthy indoor landscape in subway station space. To this end, it established a 3D landscape model of the subway interior by reviewing previous studies on indoor landscape and the greenness index of indoor spaces. Moreover, it investigated and analyzed psychophysiological responses of users to environmental indoor landscape design in subway station space. Subway stations were classified as underground subway stations and ground subway stations according to the presence of natural light inflow. The greenness index of indoor spaces was also divided into four types of 0%, 10%, 15%, and 20%. Through this process, eight 3D landscape models of the subway interior were implemented. In addition, this study investigated psychophysiological responses of 60 male and female adults in their 20 s and 30 s using the models implemented. The investigation result was analyzed based on a frequency analysis, the χ² test, T-test, one-way analysis of variance, and multidimensional scaling, which were performed in SPSS Statistics 25. The results of this study can be summarized as follows. First, physiological responses of research subjects were analyzed based on their prefrontal α wave asymmetric values. The analytic result showed that the environment where interior landscape was adopted produced more positive effects than the environment where interior landscape was not adopted. Second, psychological responses of research subjects were examined based on their greenness index preference, awareness of interior landscape area, attention restoration effect, and space images. The analytic result indicated that, among eight 3D landscape models of the subway interior, they preferred the model with the greenness index of 15% for underground subway stations. In addition, they preferred the model with the greenness index of 10% the most for ground subway stations.

Sources of indoor PM2.5 gross α and β activities measured in 340 homes

Particle radioactivity (PR) exposure has been linked to adverse health effects. PR refers to the presence of α- and β-emitting radioisotopes attached to fine particulate matter (PM_2.5). This study investigated sources contributing to indoor PM_2.5 gross α- and β-radioactivity levels. We measured activity from long-lived radon progeny radionuclides from archived PM_2.5 samples collected in 340 homes in Massachusetts during the period 2006-2010. We analyzed the data using linear mixed effects models and positive matrix factorization (PMF) analysis. Indoor PM_2.5 gross α-activity levels were correlated with sulfur (S), iron (Fe), bromine (Br), vanadium (V), sodium (Na), lead (Pb), potassium (K), calcium (Ca), silicon (Si), zinc (Zn), arsenic (As), titanium (Ti), radon (²²²Rn) and black carbon (BC) concentrations (p <0.05). Indoor PM_2.5 β-activity was correlated with S, As, antimony (Sb), Pb, Br and BC. We identified four indoor PM_2.5 sources: outdoor air pollution (62%), salt aerosol source (14%), fireworks and environmental tobacco smoke (7%) and indoor mixed dust (17%). Outdoor air pollution was the most significant contributor to indoor PM_2.5 α- and β-activity levels. The contributions of this source were during the summer months and when windows were open. Indoor mixed dust was also found to contribute to PM_2.5 α-activity. PM_2.5 α-activity was further associated with radon during winter months, showing radon's important role as an indoor source of ionizing radiation.

Automating the interpretation of PM2.5 time-resolved measurements using a data-driven approach

The rapid development of automated measurement equipment enables researchers to collect greater quantities of time-resolved data from indoor and outdoor environments. While significant, the interpretation of the resulting data can be a time-consuming effort. This paper introduces an automated process of interpreting PM_2.5 time-resolved data and differentiating PM_2.5 emissions resulting from indoor and outdoor sources. We use Random Forest (RF), a machine learning approach, to study a dataset of 836 indoor emission events that occurred over a 2-week period in 18 apartments in California. In this paper, we show model development and evaluate its performance as the sample size and source vary. We discuss the characteristics of the dataset that tended to help the source identification and why. For example, we show that data from many events and from different apartments are essential for the model to be suitable for analyzing a new separate dataset. We also show that longitudinal data appear to be more helpful than the time frequency of measurements within a given apartment. We use the resulting RF model to analyze PM_2.5 data of an entirely separate dataset collected from 65 new homes in California. The RF model identifies 442 indoor emission events, with only a few misidentifications.

The use of indoor plant as an alternative strategy to improve indoor air quality in Indonesia

Indoor air pollution marked with decreased air quality below the set standard. The quality of indoor air is determined by ambient air quality as well as by a harmful substance resulting from the household activity. Indoor air pollution may cause several problems such as sick building syndrome, chronic obstructive pulmonary disease (COPD), asthma, lung cancer, and is responsible for nearly two million death in developing countries. One of the interesting research topics to overcome the indoor air pollution problem is the application of indoor plants. Although there are no established criteria to specify the best indoor plant, several studies have revealed the capability of a particular indoor plant to remove the harmful substances. This paper summarizes important information about indoor air pollution and provides the evidence-based insight of indoor plant usefulness as an alternative way for indoor air remediation.

A hybrid model for forecasting of particulate matter concentrations based on multiscale characterization and machine learning techniques

Accurate prediction of particulate matter (PM) using time series data is a challenging task. The recent advancements in sensor technology, computing devices, nonlinear computational tools, and machine learning (ML) approaches provide new opportunities for robust prediction of PM concentrations. In this study, we develop a hybrid model for forecasting PM₁₀ and PM_2.5 based on the multiscale characterization and ML techniques. At first, we use the empirical mode decomposition (EMD) algorithm for multiscale characterization of PM₁₀ and PM_2.5 by decomposing the original time series into numerous intrinsic mode functions (IMFs). Different individual ML algorithms such as random forest (RF), support vector regressor (SVR), k-nearest neighbors (kNN), feed forward neural network (FFNN), and AdaBoost are then used to develop EMD-ML models. The air quality time series data from Masfalah air station Makkah, Saudi Arabia are utilized for validating the EMD-ML models, and results are compared with non-hybrid ML models. The PMs (PM₁₀ and PM_2.5) concentrations data of Dehli, India are also utilized for validating the EMD-ML models. The performance of each model is evaluated using root mean square error (RMSE) and mean absolute error (MAE). The average bias in the predictive model is estimated using mean bias error (MBE). Obtained results reveal that EMD-FFNN model provides the lowest error rate for both PM₁₀ (RMSE = 12.25 and MAE = 7.43) and PM_2.5 (RMSE = 4.81 and MAE = 3.02) using Misfalah, Makkah data whereas EMD-kNN model provides the lowest error rate for PM₁₀ (RMSE = 20.56 and MAE = 12.87) and EMD-AdaBoost provides the lowest error rate for PM_2.5 (RMSE = 15.29 and MAE = 9.45) using Dehli, India data. The findings also reveal that EMD-ML models can be effectively used in forecasting PM mass concentrations and to develop rapid air quality warning systems.

Measurement of the gross alpha activity of the fine fractions of road dust and near-roadway ambient particle matter

Traffic-related air pollution, including direct exhaust emissions and road dust (RD), impacts individuals living near busy roads. We recently conducted a study to investigate the sources and composition of tailpipe and non-tailpipe traffic emissions, where we collected and analyzed samples of ambient air fine particulate matter (PM_2.5) and fine RD (RD_2.5) at different distances from major roadways. We analyzed a subset of the samples, including those collected at the roadside and local background, for their alpha activity level. Subsequently, we investigated whether there is a distance-related decay in the alpha activity in RD_2.5 or PM_2.5 similar to those observed for traffic-related species in PM_2.5 and RD_2.5. We found that the alpha activity of ambient air PM_2.5 (Bq/mg) was more than an order of magnitude higher than the activity level of the corresponding RD_2.5 sample, suggesting that PM_2.5 may be more toxic than RD_2.5. Using mixed-effects regression models, we found that ambient PM_2.5 alpha activity was significantly higher during the cold months than during warm months, and that the background was higher than the roadside (though not significantly). In contrast, the RD_2.5 alpha activity was significantly higher at the background site compared to the roadside but was not significantly affected by season. In addition to sampling position, both Zn and elemental carbon (EC) were significant predictors of RD_2.5 alpha activity. In addition, the roadside RD_2.5 activity levels were found to be higher at highways as compared to secondary roads. While traffic-related emissions do not appear to be significant sources of either ambient PM_2.5 or RD_2.5 alpha activity, the RD_2.5 results suggest that traffic-related particles may contribute to RD_2.5 alpha-activity. Implications: Many studies have reported the effects of traffic-related particulate matter (PM) on human health, and there is growing interest in the health effects of exposure to environmental PM alpha activity. This is the first study to report on the alpha activity of road dust (RD) or near-roadway ambient PM. We found that the alpha activity of ambient PM is twenty times higher than RD, suggesting that ambient PM may be more toxic. In PM and RD, the alpha activities were higher at background sites than at the roadside, indicating that traffic-related emissions are not a significant source of particulate radioactivity.

Effects of Air Purifiers on Patients with Allergic Rhinitis: a Multicenter, Randomized, Double-Blind, and Placebo-Controlled Study

PURPOSE

Exposure to particulate matter (PM) is a well-known risk factor in the triggering and exacerbation of allergic airway disease. Indoor environments, where people spend most of their time, are of utmost importance. To assess the effects of air purifiers [equipped with high-efficiency particulate air (HEPA) filters] on allergic rhinitis (AR) in adult patients, we performed a multicenter, randomized, double-blind, and placebo-controlled study.

MATERIALS AND METHODS

Patients with house dust mite (HDM)-induced AR were randomly assigned to either active or mockup (placebo) air-purification groups. Two air purifiers (placed in living room and bedroom) were operated for 6 weeks in each home environment. The primary study endpoint was to achieve improvement in AR symptoms and medication scores. Secondary endpoints were to achieve improvement in the quality of life (QoL) and visual analog scale (VAS) scores, as well as in the indoor (bedroom and living room) concentrations of PM_2.5 and PM₁₀.

RESULTS

After 6 weeks of air purifier use, medication scores improved significantly in the active (vs. placebo) group, although subjective measures (symptoms, VAS, and QoL scores) did not differ. Bedroom PM_2.5 concentrations initially exceeded living room or outdoor levels, but declined (by up to 51.8%) following active purifier operation. Concentrations of PM_2.5 in living room and PM₁₀ in bedroom and living room were also significantly reduced through active purification.

CONCLUSION

The use of air purifiers with HEPA filters significantly reduced medication requirements for patients with HDM-induced AR and significantly lowered indoor PM_2.5 concentrations, regardless of room placement. Active intervention to reduce household air pollutants may help improve allergic airway disease (clinicaltrials.gov NCT03313453).

Impact of School Location on Children's Air Pollution Exposure

The role of school location in children's air pollution exposure and ability to actively commute is a growing policy issue. Well-documented health impacts associated with near-roadway exposures have led school districts to consider school sites in cleaner air quality environments requiring school bus transportation. We analyze children's traffic-related air pollution exposure across an average Detroit school day to assess whether the benefits of reduced air pollution exposure at cleaner school sites are eroded by the need to transport students by bus or private vehicle. We simulated two school attendance scenarios using modeled hourly pollutant concentrations over the school day to understand how air pollution exposure may vary by school location and commute mode. We found that busing children from a high-traffic neighborhood to a school 19 km away in a low-traffic environment resulted in average daily exposures 2 to 3 times higher than children walking to a local school. Health benefits of siting schools away from high-volume roadways may be diminished by pollution exposure during bus commutes. School districts cannot simply select sites with low levels of air pollution, but must carefully analyze tradeoffs between location, transportation, and pollution exposure.

Dust Events and Indoor Air Quality in Residential Homes in Kuwait

Kuwait is a developed Middle Eastern country that is impacted by frequent dust storms from regional and/or remote deserts. The effectiveness of keeping homes tightly closed during these events to reduce dust exposures was assessed using indoor and outdoor particle samples at 10 residences within the metropolitan Kuwait City area. Specifically, this study compared indoor and outdoor levels of black carbon and 19 trace elements (Na, Mg, Al, Si, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Br, Sr, and Zr) during dust and non-dust events and found that particle penetration efficiencies were lower during dust storm events (less than 20–30%) than during non-dust storm events (40–60%). Coarse particles had lower penetration efficiency compared to fine particles, which is due to differences in infiltration rates and settling velocities between these two size fractions. Our findings suggest that increasing home insulation could be an effective strategy to reduce indoor exposure to crustal particles from dust storm events in residential houses of Kuwait City.

Workflow for Comparison of Chemical and Biological Metrics of Filter Collected PM2.5

There is limited understanding of adverse health effect associations with chemical constituents of fine particulate matter (PM2.5) as well as the underlying mechanisms. We outlined a workflow to assess metrics, beyond concentration, using household and personal PM2.5 filter samples collected in India as a proof of concept for future large-scale studies. Oxidative potential, chemical composition (polycyclic aromatic hydrocarbons and elements), and bioactivity (developmental exposures in zebrafish) were determined. Significant differences were observed in all metrics between personal and household PM2.5 samples. This work established methods to characterize multiple metrics of PM2.5 to ultimately support the identification of more health-relevant metrics than concentration.

Residential wood stove use and indoor exposure to PM2.5 and its components in Northern New England

BACKGROUND

Residential wood stove use has become more prevalent in high-income countries, but only limited data exist on indoor exposure to PM_2.5 and its components.

METHODS

From 2014 to 2016, we collected 7-day indoor air samples in 137 homes of pregnant women in Northern New England, using a micro-environmental monitor. We examined associations of wood stove use with PM_2.5 mass and its components [black carbon (BC), organic and elemental carbon and their fractions, and trace elements], adjusted for sampling season, community wood stove use, and indoor activities. We examined impact of stove age, EPA-certification, and wood moisture on indoor pollutants.

RESULTS

Median (IQR) household PM_2.5 was 6.65 (5.02) µg/m³ and BC was 0.23 (0.20) µg/m³. Thirty percent of homes used a wood stove during monitoring. In homes with versus without a stove, PM_2.5 was 20.6% higher [although 95% confidence intervals (-10.6, 62.6) included the null] and BC was 61.5% higher (95% CI: 11.6, 133.6). Elemental carbon (total and fractions 3 and 4), potassium, calcium, and chloride were also higher in homes with a stove. Older stoves, non-EPA-certified stoves, and wet or mixed (versus dry) wood were associated with higher pollutant concentrations, especially BC.

CONCLUSIONS

Homes with wood stoves, particularly those that were older and non-EPA-certified or burning wet wood had higher concentrations of indoor air combustion-related pollutants.

Indoor PM2.5, tobacco smoking and chronic lung diseases: A narrative review

The lung is one of the most important organs exposed to environmental agents. People spend approximately 90% of their time indoors, and risks to health may thus be greater from exposure to poor air quality indoors than outdoors. Multiple indoor pollutants have been linked to chronic respiratory diseases. Environmental tobacco smoke (ETS) is known as an important source of multiple pollutants, especially in indoor environments. Indoor PM_2.5 (particulate matter with aerodynamic diameter < 2.5 μm) was reported to be the most reliable marker of the presence of tobacco smoke. Recent studies have demonstrated that PM_2.5 is closely correlated with chronic lung diseases. In this paper, we reviewed the relationship of tobacco smoking and indoor PM_2.5 and the mechanism that underpin the link of tobacco smoke, indoor PM_2.5 and chronic lung diseases.

Viruses and non-allergen environmental triggers in asthma

Asthma is a complex inflammatory disease with many triggers. The best understood asthma inflammatory pathways involve signals characterized by peripheral eosinophilia and elevated immunoglobulin E levels (called T2-high or allergic asthma), though other asthma phenotypes exist (eg, T2-low or non-allergic asthma, eosinophilic or neutrophilic-predominant). Common triggers that lead to poor asthma control and exacerbations include respiratory viruses, aeroallergens, house dust, molds, and other organic and inorganic substances. Increasingly recognized non-allergen triggers include tobacco smoke, small particulate matter (eg, PM2.5), and volatile organic compounds. The interaction between respiratory viruses and non-allergen asthma triggers is not well understood, though it is likely a connection exists which may lead to asthma development and/or exacerbations. In this paper we describe common respiratory viruses and non-allergen triggers associated with asthma. In addition, we aim to show the possible interactions, and potential synergy, between viruses and non-allergen triggers. Finally, we introduce a new clinical approach that collects exhaled breath condensates to identify metabolomics associated with viruses and non-allergen triggers that may promote the early management of asthma symptoms.

Assisted Deposition of PM2.5 from Indoor Air by Ornamental Potted Plants

This study clarifies whether vegetation can promote the decrease of indoor PM2.5 concentration. The indoor PM2.5 concentration in two periods of 2013 in Wuhan city was simulated by cigarette burning in a series of sealed chambers. Six common indoor potted plants were selected as samples to investigate the effect of plants on PM2.5 decline. The effects of potted plants on PM2.5 decline were analyzed from three aspects: plant species, leaf characteristics and relative humidity. The results show that the presence of potted plants accelerated the decline of PM2.5. The additional removal rates (excluding gravity sedimentation of PM2.5 itself) for Aloe vera and Epipremnum aureum were 5.2% and 30% respectively, when the initial PM2.5 concentration was around 200 μg/m3. The corresponding values were 0% and 17.2%, respectively, when the initial PM2.5 was around 300 μg/m3. Epipremnum aureum was the optimum potted plant for PM2.5 sedimentation, due to its rough and groove leaf surface, highest LAI (leaf area index, 2.27), and strong humidifying capacity (i.e., can promote chamber humidity to 65% in 30–60 minutes.). Actual indoor studies have also confirmed that a certain amount of Epipremnum aureum can promote the decrease of indoor PM2.5. This paper provides insights on reducing the concentration of fine particulate matter by indoor greening efforts.

Indoor particulate matter and lung function in children

People generally spend more time indoors than outdoors resulting in a higher proportion of exposure to particulate matter (PM) occurring indoors. Consequently, indoor PM levels, in contrast to outdoor PM levels, may have a stronger relationship with lung function. To test this hypothesis, indoor and outdoor PM_2.5 and fungal spore data were simultaneously collected from the homes of forty-four asthmatic children aged 10-16 years. An optical absorption technique was utilized on the collected PM_2.5 mass to obtain concentrations of black carbon (BC) and ultraviolet light absorbing particulate matter, (UVPM; a marker of light absorbing PM_2.5 emitted from smoldering organics). Enrolled children completed spirometry after environmental measurements were made. Given the high correlation between PM_2.5, BC, and UVPM, principal component analysis was used to obtain uncorrelated summaries of the measured PM. Separate linear mixed-effect models were developed to estimate the association between principal components of the PM variables and spirometry values, as well as the uncorrelated original PM variables and spirometry values. A one-unit increase in the first principal component variable representing indoor PM (predominantly composed of UVPM and PM_2.5) was associated with 4.1% decrease (99% CI = -6.9, -1.4) in FEV₁/FVC ratio. 11.3 μg/m³ increase in indoor UVPM was associated with 6.4% and 14.7% decrease (99% CI = -10.4, -2.4 and 99% CI = -26.3, -2.9, respectively) in percent predicted FEV₁/FVC ratio and FEF_25-75 respectively. Additionally, 17.7 μg/m³ increase in indoor PM_2.5 was associated with 6.1% and 12.9% decrease (99% CI = -10.2, -1.9 and 99% CI = -24.9, -1.0, respectively) in percent predicted FEV₁/FVC ratio and FEF_25-75, respectively. Outdoor PM, indoor BC, and indoor fungal spores were not significantly associated with lung function. The results indicate that indoor PM is more strongly associated with lung function in children with asthma as compared with outdoor PM.

Predicting Indoor Concentrations of Black Carbon in Residential Environments

Black carbon (BC) is a descriptive term that refers to light-absorbing particulate matter (PM) produced by incomplete combustion and is often used as a surrogate for traffic-related air pollution. Exposure to BC has been linked to adverse health effects. Penetration of ambient BC is typically the primary source of indoor BC in the developed world. Other sources of indoor BC include biomass and kerosene stoves, lit candles, and charring food during cooking. Home characteristics can influence the levels of indoor BC. As people spend most of their time indoors, human exposure to BC can be associated to a large extent with indoor environments. At the same time, due to the cost of environmental monitoring, it is often not feasible to directly measure BC inside multiple individual homes in large-scale population-based studies. Thus, a predictive model for indoor BC is needed to support risk assessment in public health. In this study, home characteristics and occupant activities that potentially modify indoor levels of BC were documented in 23 homes, and indoor and outdoor BC concentrations were measured twice. The homes were located in the Cincinnati-Kentucky-Indiana tristate region and measurements occurred from September 2015 through August 2017. A linear mixed-effect model was developed to predict BC concentration in residential environments. The measured outdoor BC concentrations and the documented home characteristics were utilized as predictors of indoor BC concentrations. After the model was developed, a leave-one-out cross-validation algorithm was deployed to assess the predictive accuracy of the output. The following home characteristics and occupant activities significantly modified the concentration of indoor BC: outdoor BC, lit candles and electrostatic or high efficiency particulate air (HEPA) filters in heating, ventilation and air conditioning (HVAC) systems. Predicted indoor BC concentrations explained 78% of the variability in the measured indoor BC concentrations. The data show that outdoor BC combined with home characteristics can be used to predict indoor BC levels with reasonable accuracy.

Effectiveness of a portable air cleaner in removing aerosol particles in homes close to highways

Outdoor traffic-related airborne particles can infiltrate a building and adversely affect the indoor air quality. Limited information is available on the effectiveness of high efficiency particulate air (HEPA) filtration of traffic-related particles. Here, we investigated the effectiveness of portable HEPA air cleaners in reducing indoor concentrations of traffic-related and other aerosols, including black carbon (BC), PM2.5 , ultraviolet absorbing particulate matter (UVPM) (a marker of tobacco smoke), and fungal spores. This intervention study consisted of a placebo-controlled cross-over design, in which a HEPA cleaner and a placebo "dummy" were placed in homes for 4-weeks each, with 48-hour air sampling conducted prior to and during the end of each treatment period. The concentrations measured for BC, PM2.5 , UVPM, and fungal spores were significantly reduced following HEPA filtration, but not following the dummy period. The indoor fraction of BC/PM2.5 was significantly reduced due to the HEPA cleaner, indicating that black carbon was particularly impacted by HEPA filtration. This study demonstrates that HEPA air purification can result in a significant reduction of traffic-related and other aerosols in diverse residential settings.

Short-term effects of airport-associated ultrafine particle exposure on lung function and inflammation in adults with asthma

BACKGROUND

Exposure to ultrafine particles (UFP, particles with aerodynamic diameter < 100 nm) is associated with reduced lung function and airway inflammation in individuals with asthma. Recently, elevated UFP number concentrations (PN) from aircraft landing and takeoff activity were identified downwind of the Los Angeles International Airport (LAX) but little is known about the health impacts of airport-related UFP exposure.

METHODS

We conducted a randomized crossover study of 22 non-smoking adults with mild to moderate asthma in Nov-Dec 2014 and May-Jul 2015 to investigate short-term effects of exposure to LAX airport-related UFPs. Participants conducted scripted, mild walking activity on two occasions in public parks inside (exposure) and outside (control) of the high UFP zone. Spirometry, multiple flow exhaled nitric oxide, and circulating inflammatory cytokines were measured before and after exposure. Personal UFP PN and lung deposited surface area (LDSA) and stationary UFP PN, black carbon (BC), particle-bound PAHs (PB-PAH), ozone (O3), carbon dioxide (CO2) and particulate matter (PM2.5) mass were measured. Source apportionment analysis was conducted to distinguish aircraft from roadway traffic related UFP sources. Health models investigated within-subject changes in outcomes as a function of pollutants and source factors.

RESULTS

A high two-hour walking period average contrast of ~34,000 particles·cm-3 was achieved with mean (std) PN concentrations of 53,342 (25,529) and 19,557 (11,131) particles·cm-3 and mean (std) particle size of 28.7 (9.5) and 33.2 (11.5) at the exposure and control site, respectively. Principal components analysis differentiated airport UFPs (PN), roadway traffic (BC, PB-PAH), PM mass (PM2.5, PM10), and secondary photochemistry (O3) sources. A standard deviation increase in the 'Airport UFPs' factor was significantly associated with IL-6, a circulating marker of inflammation (single-pollutant model: 0.21, 95% CI = 0.08-0.34; multi-pollutant model: 0.18, 0.04-0.32). The 'Traffic' factor was significantly associated with lower Forced Expiratory Volume in 1 s (FEV1) (single-pollutant model: -1.52, -2.28 to -0.77) and elevated sTNFrII (single-pollutant model: 36.47; 6.03-66.91; multi-pollutant model: 64.38; 6.30-122.46). No consistent associations were observed with exhaled nitric oxide.

CONCLUSIONS

To our knowledge, our study is the first to demonstrate increased acute systemic inflammation following exposure to airport-related UFPs. Health effects associated with roadway traffic exposure were distinct. This study emphasizes the importance of multi-pollutant measurements and modeling techniques to disentangle sources of UFPs contributing to the complex urban air pollution mixture and to evaluate population health risks.