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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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Song et al. 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.)

Background: Many studies on environment-health associations have emphasized that the selected buffer size (i.e., the scale of the geographic context when exposures are assigned at people's address location) may affect estimated effect sizes. However, there is limited methodological progress in addressing these buffer size-related uncertainties., Aim: We aimed to 1) develop a statistical multi-scale approach to address buffer-related scale effects in cohort studies, and 2) investigate how environment-health associations differ between our multi-scale approach and ad hoc selected buffer sizes., Methods: We used lacunarity analyses to determine the largest meaningful buffer size for multiple high-resolution exposure surfaces (i.e., fine particulate matter [PM 2.5 ], noise, and the normalized difference vegetation index [NDVI]). Exposures were linked to 7.7 million Dutch adults at their home addresses. We assigned exposure estimates based on buffers with fine-grained distance increments until the lacunarity-based upper limit was reached. Bayesian Cox model averaging addressed geographic uncertainties in the estimated exposure effect sizes within the exposure-specific upper buffer limits on mortality. Z-tests assessed statistical differences between averaged effect sizes and those obtained through pre-selected 100, 300, 1200, and 1500 m buffers., Results: The estimated lacunarity curves suggested exposure-specific upper buffer size limits; the largest was for NDVI (960 m), followed by noise (910 m) and PM 2.5 (450 m). We recorded 845,229 deaths over eight years of follow-up. Our multi-scale approach indicated that higher values of NDVI were health-protectively associated with mortality risk (hazard ratio [HR]: 0.917, 95 % confidence interval [CI]: 0.886-0.948). Increased noise exposure was associated with an increased risk of mortality (HR: 1.003, 95 % CI: 1.002-1.003), while PM 2.5 showed null associations (HR:0.998, 95 % CI: 0.997-1.000). Effect sizes of NDVI and noise differed significantly across the averaged and prespecified buffers (p < 0.05)., Conclusions: Geographic uncertainties in residential-based exposure assessments may obscure environment-health associations or risk spurious ones. Our multi-scale approach produced more consistent effect estimates and mitigated contextual uncertainties., 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 © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

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/m 3 ) and transportation land-use areas (2.863 μg/m 3 ) compared to commercial areas. Exposure in the evening is 1.308 μg/m 3 higher than in the afternoon. Working or studying activities are associated with 2.863 μg/m 3 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., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

As public awareness of air quality issues becomes heightened, people's perception of air quality is drawing increasing academic interest. However, data about people's perceived environment need scrutiny before being used in environmental health studies. In this research, we examine the associations between people's perceptions of air quality and their self-reported respiratory health symptoms. Spearman rank correlation coefficients were estimated and the associations were tested at the 95% confidence level. Using data collected from participants in two representative communities in Hong Kong, the results indicate a weak but significant association between people's perceived air quality and their self-reported frequency of respiratory symptoms. However, there are disparities in such an association between different genders, age groups, household income levels, education levels, marital statuses, and geographic contexts. The most striking disparities are between genders and geographic contexts. Multiple significant associations were observed for male participants (correlation coefficients: 0.169~0.205, p -values: 0.021~0.049), while none was observed for female participants. Besides, multiple significant associations were observed in the old town (correlation coefficients: 0.164~0.270, p -values: 0.003~0.048), while none was observed in the new town. The results have significant implications for environmental health research using social media data, whose reliability depends on the association between people's perceived or actual environments and their health outcomes. Since inconsistent associations exist between different groups of people, researchers need to scrutinize social media data before using them in health studies.

Background: Few mobility-based studies have investigated the associations between multiple environmental exposures, including social exposures, and mental health., Objective: To assess how exposure to green space, blue space, noise, air pollution, and crowdedness along people's daily mobility paths are associated with anxiety symptoms., Methods: 358 participants were cross-sectionally tracked with Global Positioning System (GPS)-enabled mobile phones. Anxiety symptoms were measured at baseline using the Generalized Anxiety Disorder-7 (GAD-7) questionnaire. Green space, blue space, noise, and air pollution were assessed based on concentric buffers of 50 m and 100 m around each GPS point. Crowdedness was measured by the number of nearby Bluetooth-enabled devices detected along the mobility paths. Multiple linear regressions with full covariate adjustment were fitted to examine anxiety-environmental exposures associations. Random forest models were applied to explore possible nonlinear associations and exposure interactions., Results: Regression results showed null linear associations between GAD-7 scores and environmental exposures. Random forest models indicated that GAD-7-environment associations varied nonlinearly with exposure levels. We found a negative association between green space and GAD-7 scores only for participants with moderate green space exposure. We observed a positive association between GAD-7 scores and noise levels above 60 dB and air pollution concentrations above 17.2 μg m -3 . Crowdedness was positively associated with GAD-7 scores, but exposure-response functions flattened out with pronounced crowdedness of >7.5. Blue space tended to be positively associated with GAD-7 scores. Random forest models ranked environmental exposures as more important to explain GAD-7 scores than linear models., Conclusions: Our findings indicate possible nonlinear associations between mobility-based environmental exposures and anxiety symptoms. More studies are needed to obtain an in-depth understanding of underlying anxiety-environment mechanisms during daily life., 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 © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

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., PM 1 , PM 2.5 , and PM 10 ) 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.

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 PM 2.5 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., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The impact of Coronavirus Disease 2019 (COVID-19) on cause-specific mortality has been investigated on a global scale. However, less is known about the excess all-cause mortality and air pollution-human activity responses. This study estimated the weekly excess all-cause mortality during COVID-19 and evaluated the impacts of air pollution and human activities on mortality variations during the 10th to 52nd weeks of 2020 among sixteen countries. A SARIMA model was adopted to estimate the mortality benchmark based on short-term mortality during 2015-2019 and calculate excess mortality. A quasi-likelihood Poisson-based GAM model was further applied for air pollution/human activity response evaluation, namely ground-level NO 2 and PM 2.5 and the visit frequencies of parks and workplaces. The findings showed that, compared with COVID-19 mortality (i.e., cause-specific mortality), excess all-cause mortality changed from -26.52% to 373.60% during the 10th to 52nd weeks across the sixteen countries examined, revealing higher excess all-cause mortality than COVID-19 mortality in most countries. For the impact of air pollution and human activities, the average country-level relative risk showed that one unit increase in weekly NO 2 , PM 2.5 , park visits and workplace visits was associated with approximately 1.54% increase and 0.19%, 0.23%, and 0.23% decrease in excess all-cause mortality, respectively. Moreover, compared with the impact on COVID-19 mortality, the relative risks of weekly NO 2 and PM 2.5 were lower, and the relative risks of weekly park and workplace visits were higher for excess all-cause mortality. These results suggest that the estimation based on excess all-cause mortality reduced the potential impact of air pollution and enhanced the influence of human activities compared with the estimation based on COVID-19 mortality.

Air pollution and noise are both ubiquitous environmental stressors that pose great threats to public health. Emerging evidence has noticed the combined health risks caused by the coexistence of traffic-related air pollutants and noise in the residential context. However, less is known about how mobile individuals are simultaneously exposed to multiple sources of air pollution and noise, and thus respond with more acute psychological responses beyond the residence. This study examines the co-exposures to fine particles (PM 2.5 ) and noise across spatiotemporal contexts where the concurrent exposures are jointly associated with momentary psychological stress. An innovative research protocol, including GPS-equipped activity-travel diaries, air pollutant and noise sensors, and ecological momentary assessment, was adopted to collect real-time data from a sample of residents in Beijing, China. The results showed a minor correlation between PM 2.5 and noise exposures after accounting for individual mobility and the spatiotemporal dynamics of these two environmental pollutants. Further, exposure to PM 2.5 was more associated with momentary psychological stress given the insignificant independent effect and the weak moderating effect of noise exposure. Three specific spatiotemporal contexts involving the health risks of co-exposures were delineated, including morning rush hours and traveling by public transits with intensified stress risks caused by combined exposures to air pollution and noise, workplaces with counteracting stress effect of both exposures, and evening time at home with stress-induced air pollution and stress-relieving social noise. In conclusion, the mobility-based and context-aware analysis provides a more nuanced understanding of the associations of co-exposures to environmental pollution and synchronous psychological stress in space and time., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Severe air pollution has become a major risk to human health from a global environmental perspective. It has been recognized that human mobility is an essential component in individual exposure assessment. Activity structure reflects the characteristics of human mobility. Thus, a better understanding of the relationship between human activity structure and individual exposure level is of crucial relevance. This study examines this relationship using a large cell-phone GPS dataset in Wuhan, China. The results indicate that there is a strong linear relationship between people's activity structures and exposures to PM 2 . 5 . Inter-group comparisons based on the four activity structure groups obtained with K-means clustering found that groups with different activity structures do experience different levels of PM 2 . 5 exposure. Furthermore, differences in detailed characteristics of activity structure were also found at different exposure levels at the intra-group level. These results show that people's activity structures do influence their exposure levels. The paper provides a new perspective for understanding individual exposure through human activity structure, which helps move the perspective of research on individual exposure from the semantic of physical location to the semantic of human activity pattern.

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., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Air pollution over China has attracted wide interest from public and academic community. PM 2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM 2.5 concentrations are essential to understand the variability of PM 2.5 and seek methods to control PM 2.5 . Since 2013, the measurement of PM 2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM 2.5 -meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM 2.5 concentrations . We start with an introduction of general meteorological conditions and PM 2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM 2.5 concentrations. Next, major methods used to quantify meteorological influences on PM 2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM 2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM 2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM 2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM 2.5 . The feedback effects of PM 2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM 2.5 pollution are made finally., Competing Interests: Declaration of Competing Interest The authors declared that they havenoconflicts of interest to this work., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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.

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.

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.)

In recent years, severe and persistent air pollution episodes in China have drawn wide public concern. Based on ground monitoring air quality data collected in 2015 in Chinese cities above the prefectural level, this study identifies the spatiotemporal variations of air pollution and its associated driving factors in China using descriptive statistics and geographical detector methods. The results show that the average air pollution ratio and continuous air pollution ratio across Chinese cities in 2015 were 23.1 ± 16.9% and 16.2 ± 14.8%. The highest levels of air pollution ratio and continuous air pollution ratio were observed in northern China, especially in the Bohai Rim region and Xinjiang province, and the lowest levels were found in southern China. The average and maximum levels of continuous air pollution show distinct spatial variations when compared with those of the continuous air pollution ratio. Monthly changes in both air pollution ratio and continuous air pollution ratio have a U-shaped variation, indicating that the highest levels of air pollution occurred in winter and the lowest levels happened in summer. The results of the geographical detector model further reveal that the effect intensity of natural factors on the spatial disparity of the air pollution ratio is greater than that of human-related factors. Specifically, among natural factors, the annual average temperature, land relief, and relative humidity have the greatest and most significant negative effects on the air pollution ratio, whereas human factors such as population density, the number of vehicles, and Gross Domestic Product (GDP) witness the strongest and most significant positive effects on air pollution ratio., 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.)

Using individual-level data collected from two communities in Hong Kong, this study proposes a significant association rule mining method to identify the complex associations between individual socioeconomic characteristics and perceived air pollution in people's daily life. It defines a measure, namely the rule inequality index, to assess the social inequality in perceived exposure to air pollution in both residential and visited neighborhoods. The results indicate that the associations between individual socioeconomic characteristics and perceived air pollution are not always consistent over communities, nor are the value ranges of perceived air pollution. Further, the tendency of different social groups to perceive high levels of air pollution can differ considerably depending on whether they are in their residential or visited neighborhoods. We also find that social groups based on different socioeconomic variables typically experience varying degrees of neighborhood effects on the associated social equalities. Our findings emphasize the importance of considering nonstationary associations and human mobility in research on social inequality related to mobility-dependent environmental exposure. [ABSTRACT FROM AUTHOR]

Urban functional fragmentation plays an important role in assessing Nitrogen Dioxide (NO2) emissions and variations. While the mediated impact of anthropogenic-emission restriction has not been comprehensively discussed, the lockdown response to the novel coronavirus disease 2019 (COVID-19) provides an unprecedented opportunity to meet this goal. This study proposes a new idea to explore the effects of urban functional fragmentation on NO2 variation with anthropogenic-emission restriction in China. First, NO2 variations are quantified by an Autoregressive Integrated Moving Average with external variables-Dynamic Time Warping (SARIMAX-DTW)-based model. Then, urban functional fragmentation indices including industrial/public Edge Density (ED) and Landscape Shape Index (LSI), urban functional Aggregation Index (AI) and Number of Patches (NP) are developed. Finally, the mediated impacts of anthropogenic-emission restriction are assessed by evaluating the fragmentation-NO2 variation association before and during the lockdown during COVID-19. The findings reveal negative effects of industrial ED, public LSI, urban functional AI and NP and positive effects of public ED and industrial LSI on NO2 variation based on the restricted anthropogenic emissions. By comparing the association analysis before and during lockdown, the mediated impact of anthropogenic-emission restriction is revealed to partially increase the effect of industrial ED, industrial LSI, public LSI, urban functional AI and NP and decrease the effect of public ED on NO2 variation. This study provides scientific findings for redesigning the urban environment in related to the urban functional configuration to mitigating the air pollution, ultimately developing sustainable societies. [ABSTRACT FROM AUTHOR]

The neighborhood effect averaging problem (NEAP) can be a serious methodological problem that leads to erroneous assessments when studying mobility-dependent exposures (e.g., air or noise pollution) because people's daily mobility could amplify or attenuate the exposures they experienced in their residential neighborhoods. Specifically, the NEAP suggests that individuals' mobility-based exposures tend toward the mean level of the participants or population of a study area when compared to their residence-based exposures. This research provides an in-depth examination of the NEAP and how the NEAP is associated with people's daily mobility through an assessment of individual exposures to ground-level ozone using the activity-travel diary data of 2,737 individuals collected in the Los Angeles metropolitan statistical area. The results obtained with exploratory analysis (e.g., a scatterplot and histograms) and spatial regression models indicate that the NEAP exists when assessing individual exposures to ozone in the study area. Further, high-income, employed, younger, and male participants (when compared to low-income, nonworking, older, and female participants) are associated with higher levels of neighborhood effect averaging because of their higher levels of daily mobility. Finally, three-dimensional interactive geovisualizations of the space-time paths and hourly ozone exposures of seventy-one selected participants who live in the same neighborhood corroborate the findings obtained from the spatial regression analysis. [ABSTRACT FROM AUTHOR]

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To comprehensively evaluate the effects of the recent " 2+26 " regional strategy for air quality improvement, we compared the variations in PM 2.5 concentrations in Beijing during four pollution episodes with different emission-reduction strategies. The 2+26 strategy implemented in March 2018 led to a mean PM 2.5 concentration 16.43 % lower than that during the pollution episode in March 2013, when no specific emission-reduction measures were in place. The same 2+26 strategy implemented in November 2017 led to a mean PM 2.5 concentration 32.70 % lower than that during the pollution episode in November 2016, when local emission-reduction measures were implemented. The results suggested that the effects of the 2+26 regional emission-reduction measures on PM 2.5 reductions were influenced by a diversity of factors and could differ significantly during specific pollution episodes. Furthermore, we found the proportions of sulfate ions decreased significantly, and nitrate ions were the dominant PM 2.5 components during the two 2+26 orange alert periods. Meanwhile, the relative contribution of coal combustion to PM 2.5 concentrations in Beijing during the pollution episodes in March 2013, November 2016, November 2017 and March 2018 was 40 %, 34 %, 28 % and 11 %, respectively, indicating that the recent "Coal to Gas" project and the contingent 2+26 strategy led to a dramatic decrease in coal combustion in the Beijing–Tianjin–Hebei region. On the other hand, the relative contribution of vehicle exhaust during the 2+26 orange alert periods in November 2017 and March 2018 reached 40 % and 54 %, respectively. The relative contribution of local emissions to PM 2.5 concentrations in Beijing also varied significantly and ranged from 49.46 % to 89.35 % during the four pollution episodes. These results suggested that the 2+26page6880 regional emission-reduction strategy should be implemented with red air pollution alerts during heavy pollution episodes to intentionally reduce the dominant contribution of vehicle exhausts to PM 2.5 concentrations in Beijing, while specific emission-reduction measures should be implemented accordingly for different cities within the 2+26 framework. [ABSTRACT FROM AUTHOR]

This article draws on recent studies to argue that researchers need to be attentive to the limits of the neighborhood effect as conventionally understood. It highlights the complexities of contextual influences and the challenges in accurately representing and measuring individual exposures to those influences. Specifically, it discusses the idiosyncratic and multidimensional nature of contextual effects, the temporal complexities of contextual influences, the frame dependence of exposure measures, selective mobility bias, and publication bias in neighborhood effects research. It also discusses how contextual uncertainties could be mitigated in future research (e.g., through collecting and using high-resolution space-time data and moving toward frame-independent exposure measures with results that are not affected by how data are organized with respect to space and time). [ABSTRACT FROM AUTHOR]

Abstract Commuting is an essential part of urban life. Long commutes have negative impacts on individuals and society, such as stress, loss of productivity, traffic congestion and air pollution. However, researchers often face the challenge of lack of data such as commute distance, duration, departure/arrival time, and origins/destinations in countries such as China. This study uses points of interest (POIs) to estimate employment locations, and implements a gravity-based model to estimate interzonal commuting patterns in central Shanghai, China. The results reveal a "busy corridor" in the west of the central city, especially during the morning peak hours. This pattern corresponds well with reported real-time traffic conditions in Shanghai. Our methodology offers a promising alternative for studying commuting patterns when such data are limited. Highlights • This study uses a gravity-based model to estimate commuting patterns. • It shows that POIs can be used to estimate employment. • Employment in Shanghai has a monocentric structure. • There is a busy commuting corridor in the western part of central Shanghai. • It offers an alternative to study commuting patterns when there are limited data. [ABSTRACT FROM AUTHOR]

• The annual averages of the AQI in the Yangtze River Delta (YRD) region from 2016 to 2018 are 78.6, 74.2, and 69.7, respectively, exhibiting a pattern of year-to-year decrease. • Among the 41 metropolitan areas of the YRD, the correlation coefficients of PM 2.5 across metropolitan areas are between 0.75 and 0.95. PM 2.5 is the primary pollutant in 36 metropolitan areas, accounting for 88% of the pollution in the studied areas. • The study results demonstrate that the six impact factors have positive or negative correlations with AQI with significant spatial differences. The mean values of the regression coefficients indicate that relative humidity has the greatest effect on AQI values, followed by rainfall, temperature, wind speed, air pressure, and normalized difference vegetation index (NDVI); of which relative humidity has a positive correlation with AQI while air pressure has negative correction with AQI in the whole study area. Air pollution is a great concern due to its adverse health effects, and reducing air pollution is a major challenge worldwide. Utilizing the air quality monitoring data of the Yangtze River Delta (YRD) from 2016 to 2018, this paper first investigates the spatiotemporal patterns of the Air Quality Index (AQI), identifies the primary pollutants for each metropolitan area using gray relational analysis (GRA) method, and analyzes the spatial heterogeneity of the effects of six influencing factors on AQI in different metropolitan areas using geographically weighted regression (GWR). The study results show that: (1) The annual averages of the AQI in the YRD region from 2016 to 2018 are 78.6, 74.2, and 69.7, respectively, exhibiting a pattern of year-to-year decrease. (2) Among the 41 metropolitan areas of the YRD, the correlation coefficients of PM 2.5 across metropolitan areas are between 0.75 and 0.95. PM 2.5 is the primary pollutant in 36 metropolitan areas, accounting for 88% of the pollution in the studied areas. (3) The study results demonstrate that the six impact factors have positive or negative correlations with AQI with significant spatial differences. The mean values of the regression coefficients indicate that relative humidity has the greatest effect on AQI values, followed by precipitation, temperature, wind speed, air pressure, and Normalized Difference Vegetation Index (NDVI); of which relative humidity has a negative correlation with AQI while air pressure has positive correction with AQI in the whole study area. Since there is spatial heterogeneity on the degree of influence of each influencing factor on AQI, it is necessary to consider the spatial variability on the degree of influence different from indicators when formulating control countermeasures of air quality, so that the management countermeasures can be more targeted. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease 2019 (COVID-19) has caused significantly changes in worldwide environmental and socioeconomics, especially in the early stage. Previous research has found that air pollution is potentially affected by these unprecedented changes and it affects COVID-19 infections. This study aims to explore the non-linear association between yearly and daily global air pollution and the confirmed cases of COVID-19. The concentrations of tropospheric air pollution (CO, NO2, O3, and SO2) and the daily confirmed cases between 23 January 2020 and 31 May 2020 were collected at the global scale. The yearly discrepancies of air pollutions and daily air pollution are associated with total and daily confirmed cases, respectively, based on the generalized additive model. We observed that there are significant spatially and temporally non-stationary variations between air pollution and confirmed cases of COVID-19. For the yearly assessment, the number of confirmed cases is associated with the positive fluctuation of CO, O3, and SO2 discrepancies, while the increasing NO2 discrepancies leads to the significant peak of confirmed cases variation. For the daily assessment, among the selected countries, positive linear or non-linear relationships are found between CO and SO2 concentrations and the daily confirmed cases, whereas NO2 concentrations are negatively correlated with the daily confirmed cases; variations in the ascending/declining associations are identified from the relationship of the O3-confirmed cases. The findings indicate that the non-linear relationships between global air pollution and the confirmed cases of COVID-19 are varied, which implicates the needs as well as the incorporation of our findings in the risk monitoring of public health on local, regional, and global scales. [ABSTRACT FROM AUTHOR]

• Activity satisfaction and air and noise pollution vary in space and time. • Geographic contexts matter in activity satisfaction. • Perceived environmental exposures are significant covariates of wellbeing. • Perceived air pollution has a greater effect than noise on activity satisfaction. Understanding how mobility-based real-time exposures to environmental pollution influence activity satisfaction has great potential for improving subjective wellbeing. Using real-time data from Global Positioning System trajectories and portable sensors collected in Beijing, this study investigates whether and how individual's real-time and perceived exposures to air and noise pollution at a fine spatio-temporal resolution influence activity satisfaction while controlling for geographic contexts, activity characteristics, socio-demographic attributes, health and life circumstances. The results show that activity satisfaction and air and noise pollution vary greatly in various microenvironments. Perceived air and noise pollution are more significant covariates of activity satisfaction than objectively measured pollution, and air pollution has a greater effect than noise pollution on activity satisfaction. These findings indicate that more granular data should be collected to further examine the relationships among geographic microenvironment, personal exposure and wellbeing, and compact city design is of great potential for improving subjective wellbeing. [ABSTRACT FROM AUTHOR]

Rapid industrialization in China has brought forward serious and harmful atmospheric pollution. In this study, spatial econometric analysis was used to analyze the spatial change and the driving forces behind the industrial sulfur dioxide (SO 2 ) discharge in China from 2001 to 2014. The study found that the amount of industrial SO 2 discharge (ISOD) increased first and then decreased during this period. ISOD intensity witnessed a fluctuating drop. There were large differences among intercity ISOD amount and intensity, which had various spatial patterns. Global Moran's I of ISOD amount and intensity had a tendency to increase on the whole, showed positive spatial autocorrelation, and revealed a more and more remarkable clustered spatial pattern. Local spatial autocorrelation analysis found that the spatial patterns of ISOD amount and intensity changed considerably over space and time. The spatial patterns of ISOD were significantly influenced by the regional differences in land use and environmental policy. The study also found that the driving forces of ISOD in China changed significantly from 2001 to 2014. [ABSTRACT FROM AUTHOR]