Time-activity Patterns Research Articles

Personal Exposure to Black Carbon: Impact of Time-Activity Patterns and Environmental Factors on Exposure Levels

Black carbon (BC) is a critical environmental pollutant with significant implications for human health. This study aims to quantify personal exposure to BC by focusing on the influence of microenvironments and time-activity patterns in the Rio Grande Valley (RGV) region of South Texas, USA. Over eight days in March and April 2023, a portable aethalometer and GPS tracking instruments were used to measure BC exposure among a study participant, alongside recording the time spent in various microenvironments. The BC measurements were correlated to the time activity patterns to calculate BC inhaled dosage values thereby elucidating its impact on human health. The mean values for different hours ranged from the lowest,0.26 µg/m³ ± 0.29 at 02:00, to the highest, 1.07 µg/m³ ± 1.40 at 17:00. The study also identified peak BC levels in areas near major roadways and airports. Outdoor activities accounted for 31% - 33% of the BC inhaled dosage on weekends, which was much higher than the 0.10% - 1.42% contribution observed on weekdays. The results also reflect a negative impact of Mexican wildfire activities on black carbon in this border region. This research provides insights into one of the first black carbon measurement studies conducted in the South Texas, U.S.– Tamaulipas State, Mexico border region.

Assessment of screen time and its correlates among adolescents in selected rural areas of Puducherry.

To assess screen time and its correlates among adolescents in selected rural areas of Puducherry. This cross-sectional analytical study was conducted among adolescents aged 10-19 years visiting the Pediatrics and other departments at Sri Lakshmi Narayana Institute of Medical Sciences (SLIMS), Puducherry. The sample size of 164 was determined based on previous studies, with a systematic random sampling technique used for participant selection. Data collection involved a semi-structured questionnaire covering sociodemographic factors, screen time, sleep, dietary, and physical activity patterns. Increased screen time was defined as more than 2 h per day, and adequate physical activity was considered as at least 60 min of moderate-to-vigorous intensity exercises. Statistical analysis was performed using Stata version 17, with associations tested using Chi-square analysis and a significance level set at p<0.05. The mean age of the participants was 18.2 (0.9) years, with 86 % in the 16-19 years age group. Males comprised 53.7 %, and 66.5 % were from rural areas. The mean screen time was 4.4 (1.5) hours per day, with 79.1 % exceeding the recommended limit. Mobile phones were the most frequently used devices (92.7 %), primarily for watching movies (51.8 %) and videos (23.8 %). Increased screen time was significantly associated with younger age (10-15 years), urban residence, and school-going status. Adolescents with increased screen time exhibited lower physical activity and inadequate sleep. Additionally, increased consumption of packaged foods was observed among those with higher screen time. More than three-fourths of the adolescents exceeded the recommended screen time, negatively impacting their physical activity and sleep. These findings underscore the need for targeted interventions to promote healthier lifestyle choices among adolescents in rural Puducherry.

Assessing the degree of personal exposure to PM2.5 in growing cities of Rwanda based on time-activity patterns and microenvironments

Long-term exposure to fine particulate matter (PM2.5) is strongly linked with a wide range of diverse health effects making it a substantial global threat and a critical concern for public health. While studies have been conducted on personal exposure (PE) to PM2.5 in specific environments, contributions of different microenvironments and activities to overall daily PE remain unclear. This study evaluates the degree of PE to PM2.5 in five growing cities of Rwanda based on individual’s time-activity patterns and visited microenvironments. A total of 150 participants were recruited to collect real-time personal and ambient PM2.5 measurements during their routine activities in different outdoor and indoor microenvironments for five consecutive days in dry season. Each participant was an employee in one of the six most prevalent urban economic activities found in their city of residence: workshops, flour mill plants, near road activities, garages, markets, kitchens and motorcycle taxi services. The participant’s day was categorized into three most distinct microenvironments grouped under home, work, and other microenvironments (other MEs). PE to PM2.5 assessed for all participants showed significant variability among types of activities and categories of microenvironments (p < 0.05). The work microenvironment experienced the highest daily mean PM2.5 exposures ranging from 12.67 μg/m3 to 192.64 μg/m3, followed by other MEs ranging from 13.25 μg/m3 to 113.58 μg/m3, while the lowest exposures observed at home microenvironment with concentrations ranging from 11.69 μg/m3 to 72.54 μg/m3 among 7 monitored activities in 5 cities. Exposure contributions and personal-ambient differences were dominated by the work microenvironment, with a daily contribution of flour milling activities reaching up to 51.55 %; and some participants were exposed to maximum PM2.5 concentrations up to 22 times higher than ambient levels while in the kitchen activities. This study highlights the significant effects of daily personal activities and visited microenvironments on personal PM2.5 exposure, and the importance of considering a personal lifestyle in understanding the true personal exposure.

An annual result of outdoor and indoor PM 2.5 analysis in two different building types in Ulaanbaatar, Mongolia.

Air pollution has been a significant environmental and public health concern in Ulaanbaatar, the capital city of Mongolia, for many years. The city experiences severe air pollution, particularly during the winter months. This study investigated the annual trends of outdoor and indoor PM2.5 concentrations at two neighboring sites in Ulaanbaatar: an office and a household, using low-cost sensors. Both locations exhibited similar fluctuations in outdoor PM2.5 concentrations over time, with ambient PM2.5 levels beginning to rise in October and declining in April. During the mid-term of the heating season (November to February), hourly averaged PM2.5 concentrations were exceptionally high, with peak pollution events exceeding ~ 1000µg/m3. Notably, PM2.5 concentrations were elevated during this heating season period. Time-activity patterns showed a decrease in PM2.5 concentrations during the periods of 06:00-08:00 and 14:00-18:00. Furthermore, the study found that the indoor environment could remain safe when windows were well-sealed, even under severe outdoor pollution conditions. Overall, this study provided accurate insights into the annual patterns of PM2.5 concentrations and demonstrated their fluctuations during the heating season when pollution levels were particularly high. The findings offer valuable recommendations for individuals to consider when going outside and for taking actions to improve indoor air quality in Ulaanbaatar.

Neighborhood greenness and long-term physical and psychosocial quality of life among prostate cancer survivors in the Health Professionals Follow-up Study

IntroductionNeighborhood greenness may benefit long-term prostate cancer survivorship by promoting physical activity and social integration, and reducing stress and exposure to air pollution, noise, and extreme temperatures. We examined associations of neighborhood greenness and long-term physical and psychosocial quality of life in prostate cancer survivors in the Health Professionals Follow-up Study. MethodsWe included 1437 individuals diagnosed with non-metastatic prostate cancer between 2008 and 2016 across the United States. Neighborhood greenness within a 1230m buffer of each individual's mailing address was measured using the Landsat satellite image-based Normalized Difference Vegetation Index (NDVI). We fit generalized linear mixed effect models to assess associations of greenness (in quintiles) with longitudinal patient reported outcome measures on prostate cancer-specific physical and psychosocial quality of life, adjusting for time-varying individual- and neighborhood-level demographic factors and clinical factors. ResultsThe greatest symptom burden was in the sexual domain. More than half of survivors reported good memory function and the lack of depressive signs at diagnosis. In fully adjusted models, cumulative average greenness since diagnosis was associated with fewer vitality/hormonal symptoms (highest quintile, Q5, vs lowest quintile, Q1: mean difference: 0.46, 95% confidence interval [CI]: 0.81, −0.12). Other domains of physical quality of life (bowel symptoms, urinary incontinence, urinary irritation, and sexual symptoms) did not differ by greenness overall. Psychosocial quality of life did not differ by greenness overall (Q5 vs Q1, odds ratio [95% CI]: memory function: 1.01 [0.61, 1.73]; lack of depressive signs: 1.10 [0.63, 1.95]; and wellbeing: 1.17 [0.71, 1.91]). ConclusionDuring long-term prostate cancer survivorship, cumulative average 1230m greenness since diagnosis was associated with fewer vitality/hormonal symptoms. Other domains of physical quality of life and psychosocial quality of life did not differ by greenness overall. Limitations included potential non-differential exposure measurement error and NDVI's lack of time-activity pattern.

Assessing the Radon Exposure Variability and Lifetime Health Effects across Indoor Microenvironments and Sub-Populations

To assess the health impacts of radon exposure over a lifetime, in the present study, the annual effective dose (AED) and cumulative excess lifetime cancer risk (ELCR-C) were evaluated by considering various indoor microenvironmental exposures based on age-specific time–activity patterns using Monte Carlo simulations. Significant regional variations in indoor radon concentrations across the Republic of Korea were observed, with the highest levels found in schools and single detached houses. Based on the standard annual total of 8760 h spent indoors and outdoors, the AED varied by age group and dwelling type, with the ELCR-C for single detached houses being approximately 1.36 times higher than that for apartments on average. The present study highlights the importance of comprehensive health risk assessments that consider differences across indoor environments and age groups, indicating that limited evaluations of specific sites or areas may distort actual exposure levels.

On-Device Semi-Supervised Activity Detection: A New Privacy-Aware Personalized Health Monitoring Approach.

This paper presents an on-device semi-supervised human activity detection system that can learn and predict human activity patterns in real time. The clinical objective is to monitor and detect the unhealthy sedentary lifestyle of a user. The proposed semi-supervised learning (SSL) framework uses sparsely labelled user activity events acquired from Inertial Measurement Unit sensors installed as wearable devices. The proposed cluster-based learning model in this approach is trained with data from the same target user, thus preserving data privacy while providing personalized activity detection services. Two different cluster labelling strategies, namely, population-based and distance-based strategies, are employed to achieve the desired classification performance. The proposed system is shown to be highly accurate and computationally efficient for different algorithmic parameters, which is relevant in the context of limited computing resources on typical wearable devices. Extensive experimentation and simulation study have been conducted on multi-user human activity data from the public domain in order to analyze the trade-off between classification accuracy and computation complexity of the proposed learning paradigm with different algorithmic hyper-parameters. With 4.17 h of training time for 8000 activity episodes, the proposed SSL approach consumes at most 20 KB of CPU memory space, while providing a maximum accuracy of 90% and 100% classification rates.

Ambient ultrafine particles exacerbate oxygen desaturation during sleep in patients with chronic obstructive pulmonary disease: New insights into the effect spectrum of ultrafine particles on susceptible populations

The health effects of ultrafine particles (UFPs) are of growing global concern, but the epidemiological evidence remains limited. Sleep-disordered breathing (SDB) characterized by hypoxemia is a prevalent condition linked to many debilitating chronic diseases. However, the role of UFPs in the development of SDB is lacking. Therefore, this prospective panel study was performed to specifically investigate the association of short-term exposure to UFPs with SDB parameters in patients with chronic obstructive pulmonary disease (COPD). Ninety-one COPD patients completed 226 clinical visits in Beijing, China. Personal exposure to ambient UFPs of 0–7 days was estimated based on infiltration factor and time-activity pattern. Real-time monitoring of sleep oxygen saturation, spirometry, respiratory questionnaires and airway inflammation detection were performed at each clinical visit. Generalized estimating equation was used to estimate the effects of UFPs. Exposure to UFPs was significantly associated with increased oxygen desaturation index (ODI) and percent of the time with oxygen saturation below 90 % (T90), with estimates of 21.50 % (95%CI: 6.38 %, 38.76 %) and 18.75 % (95%CI: 2.83 %, 37.14 %), respectively, per 3442 particles/cm3 increment of UFPs at lag 0–3 h. Particularly, UFPs' exposure within 0–7 days was positively associated with the concentration of alveolar nitric oxide (CaNO), and alveolar eosinophilic inflammation measured by CaNO exceeding 5 ppb was associated with 29.63 % and 33.48 % increases in ODI and T90, respectively. In addition, amplified effects on oxygen desaturation were observed in current smokers. Notably, individuals with better lung function and activity tolerance were more affected by ambient UFPs due to longer time spent outdoors. To our knowledge, this is the first study to link UFPs to hypoxemia during sleep and uncover the key role of alveolar eosinophilic inflammation. Our findings provide new insights into the effect spectrum of UFPs and potential environmental and behavioral intervention strategies to protect susceptible populations.

Activity budget and habitat use in the Nicobar long-tailed macaque Macaca fascicularis umbrosus

Time-activity budget and pattern of habitat use are basic tools to understand an animal’s interaction with its environment. We studied a primarily commensal group of long-tailed macaques (Macaca fascicularis umbrosus) with 22 individuals in the Great Nicobar Island of India at Campbell Bay. Data on the frequency and duration of different activities were collected through scan sampling and ad libitum notes. We recorded the geo-coordinates of the visually approximated group center using a handheld GPS receiver (Garmin GPS Map 76csx) every 30 min to assess the ranging patterns. The group spent more time resting, followed by feeding, social behaviours, movement, foraging, and scouting. The time spent on various activities did not vary at different times of the day and showed no seasonal variations. The macaques fed mostly on the cultivated fruits. Although the total home range size was 66ha, the most frequently used area was only 6 ha. The macaques ranged over a larger area in the dry season than in the wet season. All these patterns show that the behaviour of the study group was typical of that of the commensal macaques of other species.

Temporal trend of microenvironmental time-activity patterns of the Seoul population from 2004 to 2022 and its potential impact on exposure assessment.

Time-activity pattern (TAP) is an important parameter for determining personal exposure to environmental pollutants. Changes in TAPs could have significant implications for the alterations in outcomes of exposure assessments. This study aimed to evaluate the Seoul population's long-term change in TAPs, along with variations by sociodemographic group. In 2004, 2009, 2014, and 2019, the Time Use Survey of Statistics Korea collected the TAP information of 4036, 2610, 3337, and 2793 Seoul residents, respectively. In 2022, the TAP information of 4401 Seoul residents was collected for Korean Air Pollutant Exposure (KAPEX) research. The microenvironmental TAP changes in the Seoul population from 2004 to 2022 were assessed based on age, gender, work status, and day type. From 2004 to 2022, Seoul people increasingly spent more time in indoor residences (from 14.8 ± 5.1 h to 15.8 ± 4.5 h) and less time in other indoors (from 7.2 ± 4.5 h to 5.9 ± 4.2 h). Their transit time constantly decreased from 2004 (1.4 ± 1.8 h) to 2022 (1.2 ± 1.3 h), whereas the outdoor time fluctuated throughout the years. From 2004 to 2022, the time of the day spent by Seoul people in residential indoor shifted to later in the morning (2004: 8:30 am; 2022: 9:00 am) and earlier in the evening (2004: 9:30 pm; 2022: 7:00 pm); however, the opposite was true for other indoors (2004: from 8:30 am to 9:30 pm; 2022: from 9:00 am to 7:00 pm) and transits (2004: 7:30-9:30 am and 3:00-8:00 pm; 2022: 7:30-9:00 pm and 5:00-9:00). The time of the day spent in outdoors increased from 2004 to 2019, with a distinct peak observed in 2022 (12:00 pm-2:00 pm). The microenvironmental time trends of adolescents and late-adulthoods differed from those of the other age groups, while those of males differed from females. Also, the microenvironmental time trends of the employed differed from those of the unemployed, and those during weekdays differed from those during weekends. Microenvironmental TAP should be essentially considered to estimate the actual exposure to pollutants. This study demonstrates the Seoul population's long-term changes in TAP throughout the 18 years as the significant parameter in exposure assessment. Notably, the microenvironmental TAPs of Seoul people shifted, with variations across different sociodemographic groups. Previous studies in Korea did not consider the TAP shifts in exposure assessment; this study highlights the importance of aligning TAP data with concurrent environmental pollutant data and emphasizes the need for refined data collection in future exposure assessments.

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

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

Analysis of inequalities in personal exposure to PM2.5: A modelling study for the Greater London school-aged population

Exposure to air pollution can lead to negative health impacts, with children highly susceptible due to their immature immune and lung systems. Childhood exposure may vary by socio-economic status (SES) due to differences in both outdoor and indoor air pollution levels, the latter of which depends on, for example, building quality, overcrowding and occupant behaviours; however, exposure estimates typically rely on the outdoor component only. Quantifying population exposure across SES requires accounting for variations in time-activity patterns, outdoor air pollution concentrations, and concentrations in indoor microenvironments that account for pollution-generating occupant behaviours and building characteristics. Here, we present a model that estimates personal exposure to PM2.5 for ~1.3 million children aged 4–16 years old in the Greater London region from different income groups. The model combines 1) A national time-activity database, which gives the percentage of each group in different residential and non-residential microenvironments throughout a typical day; 2) Distributions of modelled outdoor PM2.5 concentrations; 3) Detailed estimates of domestic indoor concentrations for different housing and occupant typologies from the building physics model, EnergyPlus, and; 4) Non-domestic concentrations derived from a mass-balance approach. The results show differences in personal exposure across socio-economic groups for children, where the median daily exposure across all scenarios (winter/summer and weekends/weekdays) is 17.2 μg/m3 (95%CIs: 12.1 μg/m3–41.2 μg/m3) for children from households in the lowest income quintile versus 14.5 μg/m3 (95%CIs: 11.5 μg/m3 – 27.9 μg/m3) for those in the highest income quintile. Though those from lower-income homes generally fare worse, approximately 57 % of London's school-aged population across all income groups, equivalent to 761,976 children, have a median daily exposure which exceeds guideline 24-h limits set by the World Health Organisation. The findings suggest residential indoor sources of PM2.5 are a large contributor to personal exposure for school children in London. Interventions to reduce indoor exposure in the home (for example, via the maintenance of kitchen extract ventilation and transition to cleaner cooking fuels) should therefore be prioritised along with the continued mitigation of outdoor sources in Greater London.

Planning for heat: Time activity patterns, heat hazards, and exertional heat illnesses among American interscholastic football players

Planning for heat: Time activity patterns, heat hazards, and exertional heat illnesses among American interscholastic football players

Understanding the time-activity pattern to improve the measurement of personal exposure: An exploratory and experimental research

Although ambient fine particulate matter (PM2.5) concentrations and their components are commonly used as proxies for personal exposure monitoring, developing an accurate and cost-effective method to use these proxies for personal exposure measurement continues to pose a significant challenge. Herein, we propose a scenario-based exposure model to precisely estimate personal exposure level of heavy metal(loid)s (HMs) using scenario HMs concentrations and time-activity patterns. Personal exposure levels and ambient pollution levels for PM2.5 and HMs differed significantly with corresponding personal/ambient ratios of approximately 2, and exposure scenarios could narrow the assessment error gap by 26.1–45.4%. Using a scenario-based exposure model, we assessed the associated health risks of a large sample population and identified that the carcinogenic risk of As exceeded 1 × 10−6, while we observed non-carcinogenic risks from As, Cd, Ni, and Mn in personal exposure to PM2.5. We conclude that the scenario-based exposure model is a preferential alternative for monitoring personal exposure compared to ambient concentrations. This method ensures the feasibility of personal exposure monitoring and health risk assessments in large-scale studies.

Wing condition does not negatively impact time budget, enclosure usage, or social bonds in a flock of both full-winged and flight-restrained greater flamingos.

Zoo management techniques for captive birds, such as flight restraint and enclosure type, may affect behavioral performance and are consequently worthy of investigation. Flamingos are amongst the most popular of zoo-housed birds and, as such, research into their captive management and associated behavioral responses are widely applicable to many thousands of individuals. As a highly social species, understanding social bonds and behavior of the individual bird and the flock overall can help inform decisions that support husbandry and population management. In this project, 41 greater flamingos at Bristol Zoo Gardens were observed for 49 days across spring and summer 2013 to assess the following: (i) social associations within the flock, (ii) overall activity patterns, and (iii) distribution of time within specific enclosure zones for both full-winged and flight-restrained birds living in the same enclosure. Results showed that pinioning interacted with age in regard to flamingo time-activity patterns, but wing condition did not significantly influence association patterns, performance of social interactions, or performance of breeding behavior. Social network analysis revealed that associations were nonrandom and flamingos, of either wing condition, displayed different roles within the network. Birds of similar age formed the strongest bonds. Enclosure usage was not even, suggesting that the flamingos favored specific areas of the enclosure during the observation period. This study showed that wing condition does not affect flamingo behavior, social bonds, or space use, and that age and sex have more of an overall influence on what flamingos do, and with whom they chose to do it. Further research should extend this study into other, larger captive flocks to further refine behavioral measures of welfare for these popular zoo birds.

Assessment of children's personal and land use regression model-estimated exposure to NO2 in Springfield, Massachusetts

Ambient nitrogen dioxide (NO2) is derived from tailpipe vehicle emission and is linked with various of health outcomes. Personal exposure monitoring is crucial for accurate assessment of the associated disease risks. This study aimed to evaluate the utility of a wearable air pollutant sampler in determining the personal NO2 exposure of school children for comparison with a model-based personal exposure assessment. We employed cost-effective, wearable passive samplers to directly measure personal exposure of 25 children (aged 12–13 years) in Springfield, MA to NO2 over a five-day period in winter 2018. NO2 levels were additionally measured at 40 outdoor sites in the same region using stationary passive samplers. A land use regression (LUR) model was developed based on the ambient NO2 measures, with a good prediction performance (R2 = 0.72) using road lengths, distance to highway, and institutional land area as predictor variables. Time-weighted averages (TWA), which incorporated the time-activity patterns of participants and LUR-derived estimates in children's primary microenvironments (homes, the school and commute paths), were calculated as an indirect measure of personal NO2 exposure. Results indicated that the conventional residence-based exposure estimate approach, often used in epidemiological studies, differed from the direct personal exposure and could overestimate the personal exposure by up to 109 %. TWA improved personal NO2 exposure estimates by accounting for the time activity patterns of individuals, a difference of 5.4 % ± 34.2 % was found for exposures compared to wristband measurements. Nevertheless, the personal wristband measurements exhibited a large variability due to the potential contributions from indoor and in-vehicle NO2 sources. The findings suggest that exposure to NO2 can be highly personalized based on individual activities and contact with pollutants in specific microenvironments, reaffirming the importance of measuring personal exposure.

Estimating elemental composition of personal PM2.5 by a modeling approach in two megacities, China

Personal exposure to PM2.5, and the elemental composition therein, may vary greatly from ambient measurements at fixed monitoring sites. Here, we characterized the differences between personal, indoor, and outdoor concentrations of PM2.5-bound elements, and predicted personal exposures to 21 PM2.5-bound elements. Personal-indoor-outdoor PM2.5 filter samples were collected for five consecutive days across two seasons from 66 healthy non-smoking retired adults in Beijing (BJ) and Nanjing (NJ), China. Personal element-specific models were developed using liner mixed effects models and evaluated by R2 and root mean square error (RMSE). The mean (SD) concentrations of personal exposures varied by element and city, ranging from 2.5 (1.4) ng/m3 for Ni in BJ to 4271.2 (1614.8) ng/m3 for S in NJ. Personal exposures to PM2.5 and most elements were significantly correlated with both indoor and outdoor (except Ni in BJ) measurements, but frequently exceeded indoor levels and fell below outdoor levels. Indoor and outdoor PM2.5 elemental concentrations were the strongest determinants of most personal elemental exposures, with RM2 ranging from 0.074 to 0.975 for indoor and from 0.078 to 0.917 for outdoor levels, respectively. Home ventilation conditions (especially window opening behavior), time-activity patterns, meteorological factors, household characteristics, and season were also key factors influencing personal exposure levels. The final models accounted for 24.2 %–94.0 % (RMSE: 0.135–0.718) of the variance in personal PM2.5 elemental exposures. By incorporating these crucial determinants, the modeling approach used here can improve PM2.5-bound elemental exposure estimates and better associate compositionally dependent PM2.5 exposures and health risks.

The introduction to air pollution on pregnancy outcome (APPO) study: a multicenter cohort study.

The air pollution on pregnancy outcome (APPO) study is a prospective hospital-based cohort study designed to investigate the maternal and fetal effects of a particulate matter with an aerodynamic below 10 μm (PM10) and PM2.5 (below 2.5 μm) exposure. This study aims to analyze a relationship between particulate matter and adverse pregnancy outcomes and to find related biomarkers and develop management guidelines. About 1,200 pregnant women are recruited for 3 years (from January 2021 to December 2023) from seven university hospitals to investigate the effects of particulate matter on pregnancy complications and adverse pregnancy outcomes. We collect biological samples by 5 mL of maternal venous blood and 15 mL of urine in each trimester of pregnancy, and 5 mL of umbilical cord blood and 2×2×2 cm of placental tissue are collected after delivery. In addition, by applying PM10 and PM2.5 concentration values and time-activity patterns from the time weighted average model, the individual predicted exposure of air pollution for the pregnant women are obtained. The average exposure of PM10 and PM2.5 of the participants in the entire period of pregnancy, was exceeded the World Health Organization air quality guidelines (an annual level, PM10 >15 μg/m3, PM2.5 >5 μg/m3). Moreover, it was revealed that the PM concentration was increasing toward the 3rd trimester of pregnancy. The APPO study will be able to identify the degree of exposure to air pollution in pregnant women and use it as basic data for estimating individual exposure to particulate matter. And the results of the APPO study will facilitate in the development of health management for pregnant women against air pollution.

Does density promote non-motorized travel? The effects of individual space–time activity patterns

Does density promote non-motorized travel? The effects of individual space–time activity patterns

Mapping the personal PM2.5 exposure of China's population using random forest

Ambient monitoring may cause estimation errors, and wearable monitoring is expensive and labor-intensive when assessing PM2.5 personal exposure. Estimation errors have limited the development of exposure science and environmental epidemiology. Thus, we developed a scenario-based exposure (SBE) model that covered 8 outdoor exposure scenarios and 1 indoor scenario with corresponding time-activity patterns in Baoding City. The linear regression analysis of the SBE yielded an R2 value of 0.913 with satisfactory accuracy and reliability. To apply the SBE model to large-scale studies, we predicted time-activity patterns with the random forest model and atmosphere-to-scenario ratios with the linear regression model to obtain the essential parameters of the SBE model; their R2 was 0.65–0.93. The developed model would economize the study expenditure of field sampling for personal PM2.5 and deepen the understanding of the influences of indoor and outdoor factors on personal PM2.5. Using this method, we found that the personal PM2.5 exposure of Chinese residents was 10.50–347.02 μg/m3 in 2020, higher than the atmospheric PM2.5 concentration. Residents in North and Central China, especially the Beijing-Tianjin-Hebei region and the Fen-Wei Plains, had higher personal PM2.5 exposure than those in other areas.