Assessment of air pollution and air quality perception mismatch using mobility-based real-time exposure.

Air pollution poses a threat to human health. Public perceptions of air pollution are important for individual self-protection and policy-making. Given the uncertainty faced by residence-based exposure (RB) measurements, this study measures individuals' real-time mobility-based (MB) exposures and perceptions of air pollution by considering people's daily movement. It explores how contextual uncertainties may influence the disparities in perceived air quality by taking into account RB and MB environmental factors. In addition, we explore factors that are related to the mismatch between people's perceived air quality and actual air pollution exposure. Using K-means clustering to divide the PM2.5 values into two groups, a mismatch happens when the perceived air quality is poor but the air pollution level is lower than 15.536µg/m3 and when the perceived air quality is good but the air pollution level is higher than 15.608µg/m3. The results show that there is a mismatch between air pollution exposure and perception of air pollution. People with low income are exposed to higher air pollution. Unemployed people and people with more serious mental health symptoms (e.g., depression) have a higher chance of accurately assessing air pollution (e.g., perceiving air quality as poor when air pollution levels are high). Older people and those with a higher MB open space density tend to underestimate air pollution. Students tend to perceive air quality as good. People who are surrounded by higher MB transportation land-use density and green space density tend to perceive air quality as poor. The results can help policymakers to increase public awareness of high air pollution areas, and consider the health effects of landscapes during planning.

Air pollution perception bias: Mismatch between air pollution exposure and perception of air quality in real-time contexts.

Air pollution perception biases hinder the public's awareness of actual air quality. Past studies that examined the association and mismatch between actual and perceived air quality neglected individuals' dynamic exposure and their activity, travel, spatial, temporal, and social contexts. Using data collected with real-time air pollutant sensors and ecological momentary assessment (EMA), this study investigated the association and mismatch between momentary air pollution exposure and perceived air quality. It also examined how activity type, travel mode, spatial and temporal contexts, and social factors contribute to this disparity. The results show that exposure to air pollution is significantly higher in residential areas (1.777 µg/m3) and transportation land-use areas (2.863 µg/m3) compared to commercial areas. Exposure in the evening is 1.308 µg/m3 higher than in the afternoon. Working or studying activities are associated with 2.863 µg/m3 lower exposure, and individuals perceive air quality as good when working or studying and in residential areas. Conversely, individuals assess air quality as poor in railway travel contexts and being accompanied by friends. This study also reveals the nonstationary association between air pollution exposure and perceived air quality. The odds of underestimating air pollution are 1.8-2.7 times as high as that in residential areas and 2.1 to 2.6 times that in transportation land-use areas when compared to commercial areas. Implementing targeted mitigation measures in these contexts can enhance public awareness of air pollution.

Examining individual-level tri-exposure to greenspace and air/noise pollution using individual-level GPS-based real-time sensing data.

OBJECTIVE: 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. METHOD: 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. RESULTS: 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. CONCLUSION: 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.

Assessing the spatial distribution of and inequality in 15-minute PCR test site accessibility in Beijing and Guangzhou, China.

China has been planning to construct SARS-CoV-2 antigen testing sites within a 15-min walk in most major cities to timely identify asymptomatic cases and stop the transmission of COVID-19. However, little is known about the spatial distribution of 15-min accessibility to PCR test sites. In this study, we analyze the spatial distribution of and inequality in 15-min accessibility to PCR test sites in two major Chinese cities (Beijing and Guangzhou) based on the cumulative-opportunity model. The results indicate that the current distribution of 15-min accessibility to PCR test sites is satisfactory when normal commuting is not disrupted. However, disruptions of normal commuting (e.g., due to work-from-home restrictions) can negatively influence 15-min accessibility to PCR test sites and increase its inequality. Our study provides policymakers with up-to-date knowledge about the spatial distribution of 15-min accessibility to PCR test sites, identifies the disadvantaged neighborhoods in terms of test site accessibility, and highlights the changes in accessibility and inequality because of travel disruptions.

COVID-19 infection rate but not severity is associated with availability of greenness in the United States.

Human exposure to greenness is associated with COVID-19 prevalence and severity, but most relevant research has focused on the relationships between greenness and COVID-19 infection rates. In contrast, relatively little is known about the associations between greenness and COVID-19 hospitalizations and deaths, which are important for risk assessment, resource allocation, and intervention strategies. Moreover, it is unclear whether greenness could help reduce health inequities by offering more benefits to disadvantaged populations. Here, we estimated the associations between availability of greenness (expressed as population-density-weighted normalized difference vegetation index) and COVID-19 outcomes across the urban-rural continuum gradient in the United States using generalized additive models with a negative binomial distribution. We aggregated individual COVID-19 records at the county level, which includes 3,040 counties for COVID-19 case infection rates, 1,397 counties for case hospitalization rates, and 1,305 counties for case fatality rates. Our area-level ecological study suggests that although availability of greenness shows null relationships with COVID-19 case hospitalization and fatality rates, COVID-19 infection rate is statistically significant and negatively associated with more greenness availability. When performing stratified analyses by different sociodemographic groups, availability of greenness shows stronger negative associations for men than for women, and for adults than for the elderly. This indicates that greenness might have greater health benefits for the former than the latter, and thus has limited effects for ameliorating COVID-19 related inequity. The revealed greenness-COVID-19 links across different space, time and sociodemographic groups provide working hypotheses for the targeted design of nature-based interventions and greening policies to benefit human well-being and reduce health inequity. This has important implications for the post-pandemic recovery and future public health crises.

Assessment of Doubly Disadvantaged Neighborhoods by Healthy Living Environment Exposure.

Good access to greenspace and healthy food has commonly been found to be positively associated with health outcomes, despite some studies finding no significant relationship between them. Examining inequalities in accessing greenspace and healthy food among different disadvantaged neighborhoods can help reveal the disadvantaged races/ethnicities in cities with a high level of residential segregation (i.e., population of the same race/ethnicity concentrated in the same neighborhoods). However, existing studies have mostly focused on measuring the inequalities in accessing either greenspace or healthy food alone, which can lead to the inaccurate depiction of disadvantaged neighborhoods in healthy living environments. Therefore, this paper aims at improving the assessment of doubly disadvantaged neighborhoods by considering accessibility to both greenspace and healthy food in the City of Chicago. Our results show that black-majority neighborhoods are the most doubly disadvantaged in terms of exposure to healthy living environments. This study can help guide policymakers to divert more resources towards the improvement of the urban environment for the most doubly disadvantaged neighborhoods.

Field Evaluation and Calibration of Low-Cost Air Pollution Sensors for Environmental Exposure Research.

This paper seeks to evaluate and calibrate data collected by low-cost particulate matter (PM) sensors in different environments and using different aggregated temporal units (i.e., 5-s, 1-min, 10-min, 30 min intervals). We first collected PM concentrations (i.e., PM1, PM2.5, and PM10) data in five different environments (i.e., indoor and outdoor of an office building, a train platform and lobby of a subway station, and a seaside location) in Hong Kong, using five AirBeam2 sensors as the low-cost sensors and a TSI DustTrak DRX Aerosol Monitor 8533 as the reference sensor. By comparing the collected PM concentrations, we found high linearity and correlation between the data reported by the AirBeam2 sensors in different environments. Furthermore, the results suggest that the accuracy and bias of the PM data reported by the AirBeam2 sensors are affected by rainy weather and environments with high humidity and a high level of hygroscopic salts (i.e., a seaside location). In addition, increasing the aggregation level of the temporal units (i.e., from 5-s to 30 min intervals) increases the correlation between the PM concentrations obtained by the AirBeam2 sensors, while it does not significantly improve the accuracy and bias of the data. Lastly, our results indicate that using a machine learning model (i.e., random forest) for the calibration of PM concentrations collected on sunny days generates better results than those obtained with multiple linear models. These findings have important implications for researchers when designing environmental exposure studies based on low-cost PM sensors.

Mobility-based environmental justice: Understanding housing disparity in real-time exposure to air pollution and momentary psychological stress in Beijing, China.

Environmental justice is a crucial environmental and social problem. Previous research in the cities of developed countries has found that ethnic minorities and low-income people were disproportionately exposed to the residential environment with more serious environmental risks. This study proposed a transition from the residence-based perspective to a mobility-based and context-aware approach to reinterpret environmental justice with a focus on the air pollution issue in urban China. A novel research protocol combining geographic ecological momentary assessment and portable air pollutant sensors was developed to collect and analyze real-time data of air pollution exposure and psychological stress for residents living in the same residential neighborhood of Beijing, China. The results show that residents of different types of housing were exposed to varying PM2.5 concentrations although they experienced similar levels of air pollution in their residential neighborhoods. Residents of public low-rent housing were the disadvantaged group because of their limited mobility, exposure to serious air pollution at home, and insensitive stress responses to air pollution. These findings not only uncover the mobility-based environmental justice issue in the context of government-led and egalitarianism-pursuing urban China, but also provide references for the residential mix policy on how to narrow the disparity in environmental pollution exposure from the perspective of human mobility.

Observed inequality in urban greenspace exposure in China.

Given the important role of green environments playing in healthy cities, the inequality in urban greenspace exposure has aroused growing attentions. However, few comparative studies are available to quantify this phenomenon for cities with different population sizes across a country, especially for those in the developing world. Besides, commonly used inequality measures are always hindered by the conceptual simplification without accounting for human mobility in greenspace exposure assessments. To fill this knowledge gap, we leverage multi-source geospatial big data and a modified assessment framework to evaluate the inequality in urban greenspace exposure for 303 cities in China. Our findings reveal that the majority of Chinese cities are facing high inequality in greenspace exposure, with 207 cities having a Gini index larger than 0.6. Driven by the spatiotemporal variability of human distribution, the magnitude of inequality varies over different times of the day. We also find that exposure inequality is correlated with low greenspace provision with a statistical significance (p-value < 0.05). The inadequate provision may result from various factors, such as dry cold climate and urbanization patterns. Our study provides evidence and insights for central and local governments in China to implement more effective and sustainable greening programs adjusted to different local circumstances and incorporate the public participatory engagement to achieve a real balance between greenspace supply and demand for developing healthy cities.

An Integrated Individual Environmental Exposure Assessment System for Real-Time Mobile Sensing in Environmental Health Studies.

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.

Assessment of sociodemographic disparities in environmental exposure might be erroneous due to neighborhood effect averaging: Implications for environmental inequality research.

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.

Geographic Ecological Momentary Assessment (GEMA) of environmental noise annoyance: the influence of activity context and the daily acoustic environment.

BACKGROUND: Noise annoyance is considered to be the most widespread and recognized health effect of environmental noise. Previous research is mostly based on the static study of residential environmental noise, but few studies have focused on the effects of noise exposure in different activity contexts on real-time annoyance. The two deficiency are that they neglect the influence of activity context besides residence and fail to reflect the difference of time-scale effect of noise influence. METHODS: Using portable noise and air sensors, GPS-equipped mobile phones, questionnaire survey, and geographic ecological momentary assessment (GEMA), this paper measured the environmental noise and real-time noise annoyance of participants at different activity places. Hierarchical logistic regression models were used to examine the effects of environmental noise on people's real-time annoyance. The paper further considered the influence of the geographic context of the activity places and daily acoustic environment on participants' real-time annoyance. Further, a nonlinear regression model was constructed using Random Forest to further examine the nonlinear relationship between environmental noise and real-time annoyance. RESULTS: The results showed that: (1) the average cumulative equivalent sound level during was 55 dB (A) when the participants responded to the EMA surveys; (2) Only the temperature of activity places had an influence on momentary annoyance and the higher the temperature, the more likely participants were annoyed; (3) Participants with higher perception of noise pollution in residential communities were more likely to be annoyed. However, participants with higher daily exposure to noise were less likely to feel annoyed; (4) The threshold value of the effect of noise on real-time annoyance was 58 dB (A) to 78 dB (A). CONCLUSIONS: These findings can guide the development of urban planning and environmental noise standards and also provide a reference for noise barrier requirements for different activity places.

Understanding the relationships among individual-based momentary measured noise, perceived noise, and psychological stress: A geographic ecological momentary assessment (GEMA) approach.

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.

Understanding Racial Disparities in Exposure to Traffic-Related Air Pollution: Considering the Spatiotemporal Dynamics of Population Distribution.

This study investigates the effect of spatiotemporal distributions of racial groups on disparities in exposure to traffic-related air pollution by considering people's daily movement patterns. Due to human mobility, a residential neighborhood does not fully represent the true geographic context in which people experience racial segregation and unequal exposure to air pollution. Using travel-activity survey data containing individuals' activity locations and time spent at each location, this study measures segregation levels that an individual might experience during the daytime and nighttime, estimates personal exposure by integrating hourly pollution maps and the survey data, and examines the association between daytime/nighttime segregation and exposure levels. The proximity of each activity location to major roads is also evaluated to further examine the unequal exposure. The results reveal that people are more integrated for work in high-traffic areas, which contributes to similarly high levels of exposure for all racial groups during the daytime. However, white people benefit from living in suburbs/exurbs away from busy roads. The finding suggests that policies for building an extensive and equitable public transit system should be implemented together with the policies for residential mixes among racial groups to reduce everyone's exposure to traffic-related air pollution and achieve environmental justice.

The impacts of urbanization on fine particulate matter (PM2.5) concentrations: Empirical evidence from 135 countries worldwide.

Few attempts have been made to systematically investigate the impacts of urbanization on PM2.5 concentrations in countries at different stages of economic development. In this study, a broad concept of urbanization that considers the transformations in the urban economy and the transport sector induced by urbanization is proposed to investigate the influence of urbanization on national PM2.5 concentrations for underdeveloped, developing and developed countries during 1998-2014. The results indicate that urbanization has a significant relationship with PM2.5 concentrations, but the magnitude of its influence varies among groups of countries with different development levels. First, the positive response of PM2.5 concentrations to increased urbanization and transport-related emissions in underdeveloped countries are noticeably stronger than that in developing and developed countries. Second, for developing countries, urbanization, transport-related emissions and industrialization all have a significant positive effect on national PM2.5 concentrations increase, although their impacts are unexpectedly smaller than those in the other groups of countries. Finally, increasing urbanization and the decrease in CO2 emissions from manufacturing industry appear to reduce national average PM2.5 concentrations in developed countries, while the decrease in transport-related CO2 emission is likely to cause the rise in national average PM2.5 concentrations.

An Analytical Framework for Integrating the Spatiotemporal Dynamics of Environmental Context and Individual Mobility in Exposure Assessment: A Study on the Relationship between Food Environment Exposures and Body Weight.

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.

Evaluating the Accessibility of Healthcare Facilities Using an Integrated Catchment Area Approach.

Accessibility is a major method for evaluating the distribution of service facilities and identifying areas in shortage of service. Traditional accessibility methods, however, are largely model-based and do not consider the actual utilization of services, which may lead to results that are different from those obtained when people's actual behaviors are taken into account. Based on taxi GPS trajectory data, this paper proposed a novel integrated catchment area (ICA) that integrates actual human travel behavior to evaluate the accessibility to healthcare facilities in Shenzhen, China, using the enhanced two-step floating catchment area (E2SFCA) method. This method is called the E2SFCA-ICA method. First, access probability is proposed to depict the probability of visiting a healthcare facility. Then, integrated access probability (IAP), which integrates model-based access probability (MAP) and data-based access probability (DAP), is presented. Under the constraint of IAP, ICA is generated and divided into distinct subzones. Finally, the ICA and subzones are incorporated into the E2SFCA method to evaluate the accessibility of the top-tier hospitals in Shenzhen, China. The results show that the ICA not only reduces the differences between model-based catchment areas and data-based catchment areas, but also distinguishes the core catchment area, stable catchment area, uncertain catchment area and remote catchment area of healthcare facilities. The study also found that the accessibility of Shenzhen's top-tier hospitals obtained with traditional catchment areas tends to be overestimated and more unequally distributed in space when compared to the accessibility obtained with integrated catchment areas.

The Neighborhood Effect Averaging Problem (NEAP): An Elusive Confounder of the Neighborhood Effect.

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.

A Multilevel Analysis of Perceived Noise Pollution, Geographic Contexts and Mental Health in Beijing.

With rapid urbanization and increase in car ownership, ambient noise pollution resulting from diversified sources (e.g., road traffic, railway, commercial services) has become a severe environmental problem in the populated areas in China. However, research on the spatial variation of noise pollution and its potential effects on urban residents' mental health has to date been quite scarce in developing countries like China. Using a health survey conducted in Beijing in 2017, we for the first time investigated the spatial distributions of multiple noise pollution perceived by residents in Beijing, including road traffic noise, railway (or subway) noise, commercial noise, and housing renovation (or construction) noise. Our results indicate that there is geographic variability in noise pollution at the neighborhood scale, and road traffic and housing renovation/construction are the principal sources of noise pollution in Beijing. We then employed Bayesian multilevel logistic models to examine the associations between diversified noise pollution and urban residents' mental health symptoms, including anxiety, stress, fatigue, headache, and sleep disturbance, while controlling for a wide range of confounding factors such as socio-demographics, objective built environment characteristics, social environment and geographic context. The results show that perceived higher noise-pollution exposure is significantly associated with worse mental health, while physical environment variables seem to contribute little to variations in self-reported mental disorders, except for proximity to the main road. Social factors or socio-demographic attributes, such as age and income, are significant covariates of urban residents' mental health, while the social environment (i.e., community attachment) and housing satisfaction are significantly correlated with anxiety and stress. This study provides empirical evidence on the noise-health relationships in the Chinese context and sheds light on the policy implications for environmental pollution mitigation and healthy city development in China.

An Innovative Context-Based Crystal-Growth Activity Space Method for Environmental Exposure Assessment: A Study Using GIS and GPS Trajectory Data Collected in Chicago.

Scholars in the fields of health geography, urban planning, and transportation studies have long attempted to understand the relationships among human movement, environmental context, and accessibility. One fundamental question for this research area is how to measure individual activity space, which is an indicator of where and how people have contact with their social and physical environments. Conventionally, standard deviational ellipses, road network buffers, minimum convex polygons, and kernel density surfaces have been used to represent people's activity space, but they all have shortcomings. Inconsistent findings of the effects of environmental exposures on health behaviors/outcomes suggest that the reliability of existing studies may be affected by the uncertain geographic context problem (UGCoP). This paper proposes the context-based crystal-growth activity space as an innovative method for generating individual activity space based on both GPS trajectories and the environmental context. This method not only considers people's actual daily activity patterns based on GPS tracks but also takes into account the environmental context which either constrains or encourages people's daily activity. Using GPS trajectory data collected in Chicago, the results indicate that the proposed new method generates more reasonable activity space when compared to other existing methods. This can help mitigate the UGCoP in environmental health studies.