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Background: Limited studies have assessed the impact of residential greenness exposure on allergic rhinitis in adults, and its relationship with ambient air pollutants remains unknown., Objective: To investigate the association of residential greenness with allergic rhinitis incidence and explore the mediation effects of ambient air pollutants in adults using data from a prospective cohort study in UK Biobank., Methods: Greenness was defined as the area-weighted mean of greenness coverage based on the land used data from the Generalized Land Use Database for England (GLUD) 2005 in the UK Biobank. Multiple Cox proportional hazard models and a generalized additive model incorporating restricted cubic splines were used to model the potential nonlinear effect of residential greenness on allergic rhinitis incidence and the potential mediation effect of ambient air pollutants., Results: Among the 281,699 subjects included in the analysis, 3260 allergic rhinitis incident cases occurred after a median follow-up of 14 years. With per 10% increase in residential greenness at a 300-m buffer, a 2.5% (95% CI: 1.0%, 4.0%) decreased risk of allergic rhinitis was observed. An L-shaped, non-linear dose-response relationship was indicated with a threshold of 54.9% greenness above which no excess allergic rhinitis risk was seen. PM 10 partially mediated the relationship between greenness and allergic rhinitis incidence with a mediation effect of 26.9% (95% CI: 12.6%, 41.2%). A similar pattern of association was found at 1000-m buffer size., Conclusion: This study demonstrates a significant beneficial effect of residential greenness on reducing allergic rhinitis incidence. Greenness may erase air pollutants and mitigate the rhinitis risk from air pollution., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Greenspaces are crucial for enhancing mental and physical health. Recent research has shifted from static methods of assessing exposure to greenspaces, based on fixed locations, to dynamic approaches that account for individual mobility. These dynamic evaluations utilize advanced technologies like GPS tracking and remote sensing to provide more precise exposure estimates. However, little work has been conducted to compare dynamic and static exposure assessments and the effect of individual mobility on these evaluations. This study delves into how greenspaces around homes and workplaces, along with mobility patterns, affect dynamic greenspace exposure in Hong Kong. Data was collected from 787 participants in four communities in Hong Kong using GPS, portable sensors, and surveys. Using multiple statistical tests, our study revealed significant variations in participants' daily mobility patterns across socio-demographic and temporal factors. Further, using linear mixed-effects models, we identified complex and statistically significant interactions between participants' static greenspace exposure and their mobility patterns. Our findings suggest that individual mobility patterns significantly modify the relationship between static and dynamic greenspace exposure and play a critical role in explaining socio-demographic and temporal context differences in the relationship between static and dynamic greenspace exposure., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Advanced nighttime light (NTL) remote sensing techniques enable the large-scope epidemiological investigations of people's exposure to outdoor artificial light at night (ALAN) and its health effects. However, multiple uncertainties remain in the measurements of people's exposure to outdoor ALAN, including the representations of outdoor ALAN, the contextual settings of exposure measurements, and measurement approaches. Non-exposed but included outdoor ALAN and causally irrelevant outdoor ALAN may manifest as contextual errors, and these uncertain contextual errors may lead to biased measurements and erroneous interpretations when modeling people's health outcomes. In this study, we systematically investigated outdoor ALAN exposure measurements in different geographic contexts using either residence-based or mobility-oriented measurements, different spatial scales, and multiple NTL data sources. Based on the GPS data collected from 208 participants in Hong Kong, outdoor ALAN exposures were measured from NTL imagery at 10 m, 130 m, and 500 m spatial resolutions using in-situ methods or 100 m, 300 m, and 500 m buffer zone averaging. Descriptive analysis, multiple t-tests, and logistic regression were employed to examine the differences between outdoor ALAN exposure measurements using various contextual settings and their effects on modeling people's overall health. Our results confirmed that different contextual settings may lead to significantly different outdoor ALAN exposure measurements. Our results also confirmed that contextual errors may lead to erroneous conclusions when using improper contextual settings to model people's overall health. Consequentially, we suggest measuring people's exposure to outdoor ALAN using the mobility-oriented approach, NTL representation with the high spatial resolution, and a very small buffer zone as a contextual unit to derive outdoor ALAN exposure. This study articulates essential methodological issues induced by uncertainties in outdoor ALAN exposure measurements and can provide essential implications and suggestions for a broad scope of studies that need accurate outdoor ALAN exposure measurements., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Liu, Kwan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Annoyance is a major health burden induced by environmental noise. However, our understanding of the health impacts of noise is seriously undermined by the fixed contextual unit and limited sound characteristics (e.g., the sound level only) used in noise exposure assessments as well as the stationarity assumption made for exposure-response relationships. To address these limitations, we analyze the complex and dynamic relationships between personal momentary noise annoyance and real-time noise exposure in various activity microenvironments and times of day, taking into account individual mobility, multiple sound characteristics and nonstationary relationships. Using real-time mobile sensing, we collected individual data of momentary noise annoyance, real-time noise exposure as well as daily activities and travels in Hong Kong. A new sound characteristic, namely sound increment, is defined to capture the sudden increase in sound level over time and is used along with the sound level to achieve a multi-faceted assessment of personal real-time noise exposure at the moment of annoyance responses. Further, the complex noise exposure-annoyance relationships are learned using logistic regression and random forest models while controlling the effects of daily activity microenvironments, individual sociodemographic attributes and temporal contexts. The results indicate that the effects of the real-time sound level and sound increment on personal momentary noise annoyance are nonlinear, despite the overall significant and positive impacts, and different sound characteristics can produce a joint effect on annoyance. We also find that the daily activity microenvironments and individual sociodemographic attributes can affect noise annoyance and its relationship with different sound characteristics to varying degrees. Due to the temporal changes in daily activities and travels, the noise exposure-annoyance relationships can also vary over different times of the day. These findings can inform both local governments and residents with scientific evidence to promote the creation of acoustically comfortable living environments., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Research on the associations between environmental exposures and mental health has attracted considerable attention. Most studies to date have mainly estimated environmental health effects based on static geographic contexts (e.g., residential neighborhoods, administrative units), ignoring the dynamic nature of individual spatiotemporal behavior, which may lead to unreliable results. To address this limitation, this study collects survey data from 1003 adults in Guangzhou, China. Then, it delineates dynamic geographic context to capture individual daily activity and travel and assesses individual exposure to environmental factors derived from the home buffer (HB) and the time-weighted activity and travel buffer (TATB). Finally, multiple linear regression models are used in this paper to examine and compare the relationships between individual environmental exposure and mental health based on the HB and TATB. The results of this study indicate that there are great differences in individual environmental exposure levels based on the HB and TATB. The explanatory power of the environmental factors obtained from the TATB on mental health is greater than that derived from the HB. Specifically, exposures to some environmental factors (i.e., green space coverage, blue space coverage, fitness facility density, and recreational facility density) derived from the TATB have mental health-promoting effects, while exposures to the other environmental factors (i.e., public transit station density) have mental health-constraining effects. These findings enrich our knowledge of spatiotemporal behavior and the effects of the dynamic contextual environment on mental health, as well as provide valuable implications for urban planning and public health service., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The effects of environmental exposure on human health have been widely explored by scholars in health geography for decades. However, recent advances in geospatial technologies, especially the development of mobile approaches to collecting real-time and high-resolution individual data, have enabled sophisticated methods for assessing people's environmental exposure. This study proposes an individual environmental exposure assessment system (IEEAS) that integrates objective real-time monitoring devices and subjective sensing tools to provide a composite way for individual-based environmental exposure data collection. With field test data collected in Chicago and Beijing, we illustrate and discuss the advantages of the proposed IEEAS and the composite analysis that could be applied. Data collected with the proposed IEEAS yield relatively accurate measurements of individual exposure in a composite way, and offer new opportunities for developing more sophisticated ways to measure individual environmental exposure. With the capability to consider both the variations in environmental risks and human mobility in high spatial and temporal resolutions, the IEEAS also helps mitigate some uncertainties in environmental exposure assessment and thus enables a better understanding of the relationship between individual environmental exposure and health outcomes.

An increasing number of studies have observed that ignoring individual exposures to non-residential environments in people's daily life may result in misleading findings in research on environmental exposure. This issue was recognized as the neighborhood effect averaging problem (NEAP). This study examines ethnic segregation and exposure through the perspective of NEAP. Focusing on Xining, China, it compares the Hui ethnic minorities and the Han majorities. Using 2010 census data and activity diary data collected in 2013, the study found that NEAP exists when examining ethnic exposure. Respondents who live in highly mixed neighborhoods (with high exposures to the other ethnic group) experience lower activity-space exposures because they tend to conduct their daily activities in ethnically less mixed areas outside their home neighborhoods (which are more segregated). By contrast, respondents who live in highly segregated neighborhoods (with low exposures to the other ethnic group) tend to have higher exposures in their activity locations outside their home neighborhoods (which are less segregated). Therefore, taking into account individuals' daily activities in non-residential contexts in the assessment of environmental exposure will likely lead to an overall tendency towards the mean exposure. Using Tobit models, we further found that specific types of activity places, especially workplaces and parks, contribute to NEAP. Ignoring individual exposures in people's activity places will most likely result in misleading findings in the measurement of environmental exposure, including ethnic exposure.

It has been widely acknowledged that air pollution has a considerable adverse impact on people's health. Disadvantaged groups such as low-income people are often found to experience greater negative effects of environmental pollution. Thus, improving the accuracy of air pollution exposure assessment might be essential to policy-making. Recently, the neighborhood effect averaging problem (NEAP) has been identified as a specific form of possible bias when assessing individual exposure to air pollution and its health impacts. In this paper, we assessed the real-time air pollution exposure and residential-based exposure of 106 participants in a high-pollution community in Beijing, China. The study found that: (1) there are significant differences between the two assessments; (2) most participants experienced the NEAP and could lower their exposure by their daily mobility; (3) three vulnerable groups with low daily mobility and could not avoid the high pollution in their residential neighborhoods were identified as exceptions to this: low-income people who have low levels of daily mobility and limited travel outside their residential neighborhoods, blue-collar workers who spend long hours at low-end workplaces, and elderly people who face many household constraints. Public policies thus need to focus on the hidden environmental injustice revealed by the NEAP in order to improve the well-being of these environmentally vulnerable groups.

In past studies, individual environmental exposures were largely measured in a static manner. In this study, we develop and implement an analytical framework that dynamically represents environmental context (the environmental context cube) and effectively integrates individual daily movement (individual space-time tunnel) for accurately deriving individual environmental exposures (the environmental context exposure index). The framework is applied to examine the relationship between food environment exposures and the overweight status of 46 participants using data collected with global positioning systems (GPS) in Columbus, Ohio, and binary logistic regression models. The results indicate that the proposed framework generates more reliable measurements of individual food environment exposures when compared to other widely used methods. Taking into account the complex spatial and temporal dynamics of individual environmental exposures, the proposed framework also helps to mitigate the uncertain geographic context problem (UGCoP). It can be used in other environmental health studies concerning environmental influences on a wide range of health behaviors and outcomes.

Ignoring people's daily mobility and exposures to nonresidential contexts may lead to erroneous results in epidemiological studies of people's exposures to and the health impact of environmental factors. This paper identifies and describes a phenomenon called neighborhood effect averaging, which may significantly confound the neighborhood effect as a result of such neglect when examining the health impact of mobility-dependent exposures (e.g., air pollution). Several recent studies that provide strong evidence for the neighborhood effect averaging problem (NEAP) are discussed. The paper concludes that, due to the observed attenuation of the neighborhood effect associated with people's daily mobility, increasing the mobility of those who live in disadvantaged neighborhoods may be helpful for improving their health outcomes.

Air pollution, being especially severe in the fast-growing developing world, continues to post a threat to public health. Yet, few studies are capable of quantifying well how different groups of people in different places experience different levels of air pollution at the global scale. In this paper, we use worldwide Chinese as a lens to quantify the spatiotemporal variations and geographic differences in PM 2.5 exposures using unprecedented mobile phone big data and air pollution records. The results show that Chinese in South and East Asia suffer relatively serious PM 2.5 exposures, where the Chinese in China have the highest PM 2.5 exposures (52.8 μg/m 3 /year), which is fourfold higher than the exposures in the United States (10.7 μg/m 3 /year). Overall, the Chinese in Asian cities (35.5 μg/m 3 /year) experienced the most serious PM 2.5 exposures when compared with the Chinese in the cities of other continents. These results, partly presented as a spatiotemporally explicit map of PM 2.5 exposures for worldwide Chinese, help researchers and governments to consider how to address the effects of air pollution on public health with respect to different population groups and geographic locations., (Copyright © 2018. Published by Elsevier Ltd.)

Many environmental justice studies have sought to examine the effect of residential segregation on unequal exposure to environmental factors among different social groups, but little is known about how segregation in non-residential contexts affects such disparity. Based on a review of the relevant literature, this paper discusses the limitations of traditional residence-based approaches in examining the association between socioeconomic or racial/ethnic segregation and unequal environmental exposure in environmental justice research. It emphasizes that future research needs to go beyond residential segregation by considering the full spectrum of segregation experienced by people in various geographic and temporal contexts of everyday life. Along with this comprehensive understanding of segregation, the paper also highlights the importance of assessing environmental exposure at a high spatiotemporal resolution in environmental justice research. The successful integration of a comprehensive concept of segregation, high-resolution data and fine-grained spatiotemporal approaches to assessing segregation and environmental exposure would provide more nuanced and robust findings on the associations between segregation and disparities in environmental exposure and their health impacts. Moreover, it would also contribute to significantly expanding the scope of environmental justice research., Competing Interests: The authors declare no conflict of interest.

This study aims to empirically demonstrate the necessity to consider both the spatiotemporal variability of air pollution and individual daily movement patterns in exposure and health risk assessment. It compares four different types of exposure estimates generated by using (1) individual movement data and hourly air pollution concentrations; (2) individual movement data and daily average air pollution data; (3) residential location and hourly pollution levels; and (4) residential location and daily average pollution data. These four estimates are significantly different, which supports the argument that ignoring the spatiotemporal variability of environmental risk factors and human mobility may lead to misleading results in exposure assessment. Additionally, three-dimensional (3D) geovisualization presented in the paper shows how person-specific space-time context is generated by the interactions between air pollution and an individual, and how the different individualized contexts place individuals at different levels of health risk., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Temporal contexts are essential for the derivation of causally relevant environmental exposure in environmental and public health studies. We argue that the proper temporal contexts need further emphasis in the mobility-oriented research paradigm and articulate this issue using a study in Hong Kong. We use people's exposure to outdoor artificial light at night (ALAN) and green space as two essential examples. We recruited 208 participants from two representative communities in Hong Kong and derived their mobility-oriented environmental exposures from high-resolution remote sensing data and Google Street View imagery. We employed one-standard-deviational ellipses to quantitatively represent participants' activity spaces, and we further used participants' seven-day Global Positioning System trajectories to derive their spatiotemporally weighted exposures to green space and outdoor ALAN in different temporal contexts. Multiple t tests were used to examine the disparities in activity spaces and measured exposures in different temporal contexts, and these exposure measurements were then used to predict people's health outcomes. We found that people's activity spaces are significantly different in size between day and night, for both weekdays and weekends, and in both geographic contexts. We further observed that improper temporal contexts could lead to significantly different environmental exposure levels and causally irrelevant modeling results. [ABSTRACT FROM AUTHOR]

Based on different conceptualizations and measures of individual-level environmental exposure, this study examines how the uncertain geographic context problem (UGCoP) and the neighborhood effect averaging problem (NEAP) might affect the assessment of COVID-19 risk. Using the COVID-19 data on an open-access government Web site and the individual-level activity data of sixty confirmed COVID-19 cases (infected persons) in Hong Kong, we first represent COVID-19 risk environments using case-based and venues-based high-risk locations. The COVID-19 risk of each of the sixty selected cases is then evaluated by three approaches based on their exposures to the case-based or venues-based risk environments: the mobility-based approach, the residence-based approach, and the activity space–based approach. The results indicate that the UGCoP and the NEAP exist in the assessment of COVID-19 risk, which has significant implications: Ecological COVID-19 studies need to address the uncertainties due to the UGCoP and the NEAP by considering people's daily mobility. Otherwise, ignoring peoples' daily mobility and its interactions with complex and dynamic COVID-19 risk environments could lead to misleading results and misinform government nonpharmaceutical intervention measures. [ABSTRACT FROM AUTHOR]

• This study uses geo-narrative analysis to examine individuals' sound exposures and perceptions during their daily mobilities. • Daily mobilities are understood as actual movements and potentials for movements shaped by space–time constraints. • Data were collected using a mixed-method approach in a suburban community in Beijing, China. • Individuals' daily mobilities influence their sound exposures, psychological responses, and coping strategies differently. People's exposure to high levels of sound leads to psychological annoyance and stress. This study seeks to understand and conceptualize how human daily mobilities influence urban residents' sound exposure and psychological health involving annoyance and stress in their everyday lives. Human daily mobilities are understood as people's daily movement patterns and potentials for movements. Using a mixed-method approach, data were collected from 32 participants in a mixed-housing community in suburban Beijing in China. Each participant carried a portable sound sensor and a GPS tracker and recorded activity-travel diaries for a weekday and a weekend day to track their daily space–time trajectories, measured sound levels, and details of each daily activity and trip. Further, in-depth interviews were conducted to understand participants' perceptions of urban sounds and psychological health during their daily movements. Geo-narrative analysis was used to integrate these multiple data sources and gain an in-depth understanding of participants' daily mobilities, individual sound exposures, and perceptions of sound and psychological health. This exploratory study finds that urban residents are exposed to different sound levels and sources in multiple urban geographic contexts in the course of their day. Further, this study identifies how socially vulnerable and susceptible residents encounter difficulties in escaping from high levels of sound in different urban contexts due to their space–time constraints. This study not only enriches the knowledge of how human daily mobilities can influence individual-based sound exposure and psychological health. It also highlights the need to provide better sonic environments for socially vulnerable and susceptible groups in cities. [ABSTRACT FROM AUTHOR]

People's exposure to green space is a critical link between urban green space and urban residents' health. Since green space may affect human health through multiple pathways regarding diverse human health outcomes, the measurement of people's exposure to green space must be tailored to concrete study contexts and research questions. In this scoping review, we systematically categorized the available green space representations and metrics in the last two decades that can be used to derive people's exposure to green space regarding different research topics. A three-phase systematic review was conducted after a generalized search of relevant research articles from the three most-used publication databases, namely Scopus, the Web of Science, and PubMed. We identified 260 research articles that particularly discuss green space representations and metrics. We further developed a multi-pathway framework to articulate the complicated context issues in green space studies. We categorized the most relevant green space representations and metrics into five groups, including green space indices, the delineation, inventory, and usage of green space, the spatiotemporal evolution of green space, the attributes and components of green space, and the green space landscape and fragmentation. Finally, we discussed the inter-conversion between different green space representations and metrics, the "mobility-turn" in green space studies and how it may affect the derivation of people's exposure to green space, and other potential methodological issues in measuring people's exposure to green space. Our scoping review provides the most comprehensive framework and categories for deriving people's exposure to green space to date, which may strongly support a broad range of studies that concern green space's health effects. • We systematically categorized green space representations and metrics in the last two decades. • A three-phase systematic review was conducted after a generalized search of relevant articles. • We developed a multi-pathway framework to articulate the complicated context issues in green space studies. • We categorized the most relevant green space representations and metrics into 5 groups. • We discussed how the mobility turn may affect the derivation of people's exposure to green space. [ABSTRACT FROM AUTHOR]

Individuals are often exposed to multiple environmental factors simultaneously. Understanding their joint effects is essential for developing effective public health policies. However, there has been a lack of research examining individuals' concurrent exposures to multiple environmental factors during people's daily mobility. To address this gap, this study investigated the relationships between and geographic patterns of individual exposures to air pollution (PM 2.5), noise and greenspace using individual-level real-time GPS and mobile sensing data collected in outdoor environments. The findings indicate that the relationships between individual exposures to air pollution, noise and greenspace vary across different value ranges of exposures. The study also reveals that people's concurrent exposures to multiple environmental factors exhibit spatial nonstationary and strong clustering patterns. These results highlight the importance of considering spatial nonstationary and spatial heterogeneity of environmental exposures in understanding the relationships between multiple exposures in environmental health research. • We investigate the geographic patterns of individual exposures to air pollution, noise and greenspace. • We use real-time GPS data, mobile sensing data, and a mobility-based framework. • The relationships between individuals' concurrent exposures vary across different value ranges of exposures. • People's concurrent exposures exhibit spatial nonstationary and strong clustering patterns. • It is necessary to consider spatial nonstationary and spatial heterogeneity in concurrent environmental exposures. [ABSTRACT FROM AUTHOR]

Although exposure to air/noise pollution and greenspace has been found to significantly affect people's physical and mental health outcomes, there is still a lack of knowledge on what built-environment and socioeconomic factors are significantly associated with people's tri-exposure to air/noise pollution and greenspace. This study analyzes the associations between built-environment and socioeconomic factors and the tri-exposure to greenspace and air/noise pollution in Hong Kong. Based on individual-level activity data, real-time GPS trajectories, and exposure data collected by portable sensors as well as remote sensing satellite imagery, we employ multinomial logistic regression to determine the socioeconomic and built-environment factors that are significantly associated with the type of participants' tri-exposure at the grid cell level. The results show that higher transit nodal accessibility, building density, building height and land-use mix are significantly associated with a higher likelihood of being disadvantaged in terms of tri-exposure to air/noise pollution and greenspace. While more advantageous tri-exposures are significantly related to higher median monthly household income and sky view factor. Old high-rise high-density neighborhoods are more likely to be triply disadvantaged with low greenspace exposure but high air pollution and noise pollution exposure. The findings provide policymakers with critical reference in terms of addressing the inequalities in the tri-exposure outcomes. • This study used individual-level real-time GPS/exposure data and satellite imagery. • It examined individuals' tri-exposure to greenspace and air/noise pollution. • It analyzed the associations between built-environment and socioeconomic factors and tri-exposures. • Disadvantaged tri-exposures are associated with higher transit nodal accessibility, building density/height and land-use mix. • Advantageous tri-exposures are related to higher household income and sky view factor. [ABSTRACT FROM AUTHOR]

Few studies have used individual-level data to explore the association between COVID-19 risk with multiple environmental exposures and housing conditions. Using individual-level data collected with GPS-tracking smartphones, mobile air-pollutant and noise sensors, an activity-travel diary, and a questionnaire from two typical neighborhoods in a dense and well-developed city (i.e., Hong Kong), this study seeks to examine 1) the associations between multiple environmental exposures (i.e., different types of greenspace, PM 2.5 , and noise) and housing conditions (i.e., housing types, ownership, and overcrowding) with individuals' COVID-19 risk both in residential neighborhoods and along daily mobility trajectories; 2) which social groups are disadvantaged in COVID-19 risk through the perspective of the neighborhood effect averaging problem (NEAP). Using separate multiple linear regression and logistical regression models, we found a significant negative association between COVID-19 risk with greenspace (i.e., NDVI) both in residential areas and along people's daily mobility trajectories. Meanwhile, we also found that high open space and recreational land exposure and poor housing conditions were positively associated with COVID-19 risk in high-risk neighborhoods, and noise exposure was positively associated with COVID-19 risk in low-risk neighborhoods. Further, people with work places in high-risk areas and poor housing conditions were disadvantaged in COVID-19 risk. • We examined the associations between multiple environmental exposures, housing conditions, and individuals' COVID-19 risk. • We identified people's disadvantages in COVID-19 risk through the neighborhood effect averaging problem (NEAP). • Greenspace (i.e., NDVI) was negatively associated with COVID-19 risk. • Open space and recreational land, noise, and housing conditions were positively associated with COVID-19 risk. • People with workplaces in high-risk areas and poor housing conditions were disadvantaged in COVID-19 risk. [ABSTRACT FROM AUTHOR]

The neighborhood effect averaging problem (NEAP) is a major methodological problem that might affect the accuracy of assessments of individual exposure to mobility-dependent environmental factors (e.g., air/noise pollution, green/blue spaces, or healthy food environments). Focusing on outdoor ground-level ozone as a major air pollutant, this paper examines the NEAP in the evaluation of sociodemographic disparities in people's air pollution exposures in Los Angeles using one-day activity-travel diary data of 3790 individuals. It addresses two questions: (1) How does the NEAP affect the evaluation of sociodemographic disparities in people's air pollution exposures? (2) Which social groups with high residence-based exposures do not experience neighborhood effect averaging? The results of our spatial regression models indicate that assessments of sociodemographic disparities in people's outdoor ground-level ozone exposures might be erroneous when people's daily mobility is ignored because of the different manifestations of neighborhood effect averaging for different social/racial groups. The results of our spatial autologistic regression model reveal that non-workers (e.g., the unemployed, homemakers, the retired, and students) do not experience downward averaging : they have significantly lower odds of experiencing downward averaging that could have attenuated their high exposures experienced in their residential neighborhoods while traveling to other neighborhoods (thus, being doubly disadvantaged). Therefore, to avoid erroneous conclusions in environmental inequality research and ineffective public policies, it would be critical to take the NEAP into account in future studies of sociodemographic disparities related to mobility-dependent environmental factors. • Ignoring human mobility may lead to the neighborhood effect averaging problem (NEAP). • This study examined how the NEAP affects assessments of sociodemographic disparities in people's ozone exposures. • We found that these assessments might be erroneous when people's daily mobility is ignored. • Non-workers constitute the doubly disadvantaged groups that do not experience downward averaging. • Studies on exposures to mobility-dependent environmental factors should consider the NEAP. [ABSTRACT FROM AUTHOR]

To investigate the association between physical activity (including active travel modes) and environmental factors, much research has estimated contextual influences based on zones or areas delineated with buffer analysis. However, few studies to date have examined the effects of different buffer sizes on estimates of individuals' dynamic exposures along their daily trips recorded as GPS trajectories. Thus, using a 7-day GPS dataset collected in the Chicago Regional Household Travel Inventory (CRHTI) Survey, this study addresses the methodological issue of how the associations between environmental contexts and active travel modes (ATMs) as a subset of physical activity vary with GPS-based buffer size. The results indicate that buffer size influences such associations and the significance levels of the seven environmental factors selected as predictors. Further, the findings on the effects of buffer size on such associations and the significance levels are clearly different between the ATMs of walking and biking. Such evidence of the existence of buffer-size effects for multiple environmental factors not only confirms the importance of the uncertain geographic context problem (UGCoP) but provides a resounding cautionary note to all future research on human mobility involving individuals' GPS trajectories, including studies on physical activity and travel behaviors, especially on the reliable estimation of individual exposures to environmental factors and their health outcomes. [ABSTRACT FROM AUTHOR]

Research on environmental exposure and its impacts on people's mood has attracted increasing attention. Most studies focus on the spatiality of geographic contexts, but they neglect the influence of temporality in the relationships between environments and mood. To this end, a survey was conducted in January 2019 in Guangzhou, China, and measured data (micro-environments, built environments, EMA records, GPS trajectories, and activity logs) covering a weekday were collected from 125 participants. Then, multiple linear regression models are employed to examine and compare the associations between environments and mood based on three possible types of temporal responses (cumulative response, momentary response, and time-lagged response). The results indicate that there are great differences in environmental mood effects based on different types of temporal responses. Specifically, (i) for three types of temporal responses, exposure to PM 2.5 and noise have mood-blunting effects, whereas exposure to green spaces has mood-augmenting effects. (ii) For two types of temporal responses, higher temperature (in winter) may positively influence individual mood based on cumulative and time-lagged response, and higher POI density can positively affect mood based on cumulative and momentary response. (iii) Relative humidity may not have time-related effects on mood. Although all three types of temporal responses are observed in this study, the most significant manifestation is momentary response. These findings not only enrich theoretical research on environmental mood effects and temporality, but also inform the practice of more refined and humanistic urban planning, environmental governance, and public services. • Temporality of geographic contexts should be emphasized in environmental effects research. • All three types of temporal responses exist and have a role in affecting mood. • Momentary response is the most significant manifestation rather than cumulative and time-lagged response. • Various environmental factors have diverse time-related effects on mood. [ABSTRACT FROM AUTHOR]

This study aims to understand how the relationship between individual-based noise exposure and psychological stress is influenced by perceived noise and context. Using geographic ecological momentary assessment, along with activity-travel diaries, GPS tracking, and portable noise sensors, this study collected real-time data of individuals' daily movement, noise exposure, and self-reported noise perception and psychological stress. Structural equation modeling was used to examine the direct and indirect pathways among context, momentary measured noise, perceived noise, and psychological stress. The study finds that momentary measured noise influences psychological stress through the mediating effect of perceived noise. Further, different activity and travel, social, and temporal contexts significantly influence people's momentary measured noise, perceived noise, and psychological stress. These findings advance our understanding of specific contexts, individual-based objectively measured and subjectively perceived environmental exposures, and their effects on psychological health at a high spatiotemporal resolution. [ABSTRACT FROM AUTHOR]

Understanding the difference of greenspace in different urban areas is a critical requirement for maintaining urban natural environment and lessening environmental inequality. However, how urban expansion impacts on people's exposure to ambient green environments has been limitedly addressed. Here we integrated multi-source geospatial big data including mobile-phone location-based service (LBS) data, Sentinel-2, and nighttime light satellite imageries to quantitatively estimate changes in people's exposure to green environments for 290 cities in China from 1992 to 2015. Results showed that the urban expansion process directly led to differences in green environments between old and new urban areas. These differences were not only observed by the green coverage rate but also captured using a dynamic assessment of people's exposure to greenspace. For most of China's large cities, people could enjoy more greenspace in new urban areas than the old ones. A significant day-to-night variation of people's exposure to greenspace was identified between old and new urban areas. Our results also revealed that urbanization did bring some positive effects to improve green environments for cities located in harsh natural conditions (e.g., semiarid/arid and desert regions). • Old and new urban areas were compared in term of dynamic greenspace exposure. • Urban boundaries in 1992 and 2015 were extracted based on NTL and LU data. • LBS data were used to characterize spatiotemporal distribution of population. • Ubiquitous differences in greenspace exposure were found between old and new urban areas. • Urbanization did not always negatively impact on the urban green environment. [ABSTRACT FROM AUTHOR]

Physical inactivity among children is on the rise. Active transport to school (ATS), namely walking and cycling there, adds to children's activity level. Little is known about how exposures along actual routes influence children's transport behavior. This study examined how natural and built environments influence mode choice among Dutch children aged 6-11 years. 623 school trips were tracked with global positioning system. Natural and built environmental exposures were determined by means of a geographic information system and their associations with children's active/passive mode choice were analyzed using mixed models. The actual commuted distance is inversely associated with ATS when only personal, traffic safety, and weather features are considered. When the model is adjusted for urban environments, the results are reversed and distance is no longer significant, whereas well-connected streets and cycling lanes are positively associated with ATS. Neither green space nor weather is significant. As distance is not apparent as a constraining travel determinant when moving through urban landscapes, planning authorities should support children's ATS by providing well-designed cities. [ABSTRACT FROM AUTHOR]