The Impact of Individual Mobility on Long-Term Exposure to Ambient PM2.5: Assessing Effect Modification by Travel Patterns and Spatial Variability of PM2.5

Assessing potential benefits of visits to neighborhoods with higher tree canopy coverage using mobility data: Associations with cardiovascular health outcomes in twenty US metropolitan areas

BACKGROUND

Research on health benefits due to exposure to green space, such as tree canopy coverage, has predominantly focused on canopy coverage in home neighborhoods. Yet exposures to tree canopy coverage in other spaces visited during the week or on weekends outside the home neighborhoods remains largely unexplored.

OBJECTIVES

We examined whether differences in coverage levels of tree canopy in neighborhoods visited compared to home neighborhoods was associated with lower prevalence of coronary heart disease (CHD) and stroke, adjusting for exposure to home canopy coverage. We further investigated if the associations varied across levels of home canopy coverage, and if they were more pronounced on weekdays or weekends.

METHODS

We used 2018 mobile phone data from the twenty largest U.S. Metropolitan Statistical Areas (MSAs). For each home census tract, we derived a weighted tree canopy coverage exposure from all visited tracts based on the proportion of visits to other tracts by home tract residents. We subtracted home canopy coverage from the weighted canopy coverage in each of the visited tracts to calculate tract-specific differences. We evaluated associations between differences in tree canopy coverage and prevalence of CHD and stroke via spatial error models, adjusting for tract-level home canopy coverage, MSA, socioeconomic and built environment characteristics.

RESULTS

For every ten-percentage-point increase in tree canopy coverage in visited tracts relative to home tracts, there was a 0.32-0.34% decrease in stroke prevalence. Association with CHD prevalence was not observed after adjusting for spatial autocorrelation. Variations between weekdays and weekends were minimal. The difference in tree canopy coverage was associated with CHD prevalence only for home tracts with low tree canopy coverage, while the difference was associated with stroke prevalence across home tracts with low, moderate, and high tree canopy coverage, with diminishing effect size.

DISCUSSION

This study identified that greater tree canopy coverage in visited neighborhoods relative to home neighborhoods was associated with lower stroke prevalence, and associations varied across home neighborhoods with different tree canopy coverage levels. It emphasized the need to factor in the neighborhood mobility networks in urban planning initiatives to promote cardiovascular health.

Examining the geographical distribution of air pollution disparities across different racial and ethnic groups: Incorporating workplace addresses

BACKGROUND

Most previous studies on air pollution exposure disparities among racial and ethnic groups in the US have been limited to residence-based exposure and have given little consideration to population mobility and spatial patterns of residences, workplaces, and air pollution. This study aimed to examine air pollution exposure disparities by racial and ethnic groups while explicitly accounting for both the work-related activity of the population and localized spatial patterns of residential segregation, clustering of workplaces, and variability of air pollutant concentration.

METHOD

In the present study, we assessed population-level exposure to air pollution using tabulated residence and workplace addresses of formally employed workers from LEHD Origin-Destination Employment Statistics (LODES) data at the census tract level across eight Metropolitan Statistical Areas (MSAs). Combined with annual-averaged predictions for three air pollutants (PM2.5, NO2, O3), we investigated racial and ethnic disparities in air pollution exposures at home and workplaces using pooled (i.e., across eight MSAs) and regional (i.e., with each MSA) data.

RESULTS

We found that non-White groups consistently had the highest levels of exposure to all three air pollutants, at both their residential and workplace locations. Narrower exposure disparities were found at workplaces than residences across all three air pollutants in the pooled estimates, due to substantially lower workplace segregation than residential segregation. We also observed that racial disparities in air pollution exposure and the effect of considering work-related activity in the exposure assessment varied by region, due to both the levels and patterns of segregation in the environments where people spend their time and the local heterogeneity of air pollutants.

CONCLUSIONS

The results indicated that accounting for workplace activity illuminates important variation between home- and workplace-based air pollution exposure among racial and ethnic groups, especially in the case of NO2. Our findings suggest that consideration of both activity patterns and place-based exposure is important to improve our understanding of population-level air pollution exposure disparities, and consequently to health disparities that are closely linked to air pollution exposure.

Association of ambient PM2.5 concentration with tuberculosis reactivation diseases-an integrated spatio-temporal analysis

Objectives

While the plausible role of ambient particulate matter (PM)2.5 exposure in tuberculosis (TB) reactivation has been inferred from in vitro experiments, epidemiologic evidence is lacking. We examined the relationship between ambient PM2.5 concentration and pulmonary TB (PTB) in an intermediate TB endemicity city dominated by reactivation diseases.

Methods

Spatio-temporal analyses were performed on TB notification data and satellite-based annual mean PM2.5 concentration in Hong Kong. A total of 52,623 PTB cases from 2005-2018 were mapped to over 400 subdistrict units. PTB standardized notification ratio by population subgroups (elderly aged ≥65, middle-aged 50-64, and young adults aged 15-49) was calculated and correlated with ambient PM2.5 concentration.

Results

Significant associations were detected between high ambient PM2.5 concentration and increased PTB among the elderly. Such associations were stable to the adjustment for socio-economic factors and other criteria pollutants. Unstable patterns of association between PM2.5 and PTB risk were observed in the middle-aged population and young adults, for which the observed associations were confounded by other criteria pollutants.

Conclusion

With elderly PTB almost exclusively attributable to reactivation, our findings suggested that increased TB reactivations have occurred in association with high ambient PM2.5 exposure, lending support to preventive measures that minimize PM2.5-related TB reactivation.

Comparison of static and dynamic exposures to air pollution, noise, and greenness among seniors living in compact-city environments

GPS technology and tracking study designs have gained popularity as a tool to go beyond the limitations of static exposure assessments based on the subject's residence. These dynamic exposure assessment methods offer high potential upside in terms of accuracy but also disadvantages in terms of cost, sample sizes, and types of data generated. Because of that, with our study we aim to understand in which cases researchers need to use GPS-based methods to guarantee the necessary accuracy in exposure assessment. With a sample of 113 seniors living in Barcelona (Spain) we compare their estimated daily exposures to air pollution (PM2.5, PM10, NO2), noise (dB), and greenness (NDVI) using static and dynamic exposure assessment techniques. Results indicate that significant differences between static and dynamic exposure assessments are only present in selected exposures, and would thus suggest that static assessments using the place of residence would provide accurate-enough values across a number of exposures in the case of seniors. Our models for Barcelona's seniors suggest that dynamic exposure would only be required in the case of exposure to smaller particulate matter (PM2.5) and exposure to noise levels. The study signals to the need to consider both the mobility patterns and the built environment context when deciding between static or dynamic measures of exposure assessment.

Imputation of missing time-activity data with long-term gaps: A multi-scale residual CNN-LSTM network model

Despite the increasing availability and spatial granularity of individuals' time-activity (TA) data, the missing data problem, particularly long-term gaps, remains as a major limitation of TA data as a primary source of human mobility studies. In the present study, we propose a two-step imputation method to address the missing TA data with long-term gaps, based on both efficient representation of TA patterns and high regularity in TA data. The method consists of two steps: (1) the continuous bag-of-words word2vec model to convert daily TA sequences into a low-dimensional numerical representation to reduce complexity; (2) a multi-scale residual Convolutional Neural Network (CNN)-stacked Long Short-Term Memory (LSTM) model to capture multi-scale temporal dependencies across historical observations and to predict the missing TAs. We evaluated the performance of the proposed imputation method using the mobile phone-based TA data collected from 180 individuals in western New York, USA, from October 2016 to May 2017, with a 10-fold out-of-sample cross-validation method. We found that the proposed imputation method achieved excellent performance with 84% prediction accuracy, which led us to conclude that the proposed imputation method was successful at reconstructing the sequence, duration, and spatial extent of activities from incomplete TA data. We believe that the proposed imputation method can be applied to impute incomplete TA data with relatively long-term gaps with high accuracy.

Structural Differences of PM2.5 Spatial Correlation Networks in Ten Metropolitan Areas of China

The cross-impact of environmental pollution among cities has been reported in more research works recently. To implement the coordinated control of environmental pollution, it is necessary to explore the structural characteristics and influencing factors of the PM2.5 spatial correlation network from the perspective of the metropolitan area. This paper utilized the gravity model to construct the PM2.5 spatial correlation network of ten metropolitan areas in China from 2019 to 2020. After analyzing the overall characteristics and node characteristics of each spatial correlation network based on the social network analysis (SNA) method, the quadratic assignment procedure (QAP) regression analysis method was used to explore the influence mechanism of each driving factor. Patent granted differences, as a new indicator, were also considered during the above. The results showed that: (1) In the overall network characteristics, the network density of Chengdu and the other three metropolitan areas displayed a downward trend in two years, and the network density of Wuhan and Chengdu was the lowest. The network density and network grade of Hangzhou and the other four metropolitan areas were high and stable, and the network structure of each metropolitan area was unstable. (2) From the perspective of the node characteristics, the PM2.5 spatial correlation network all performed trends of centralization and marginalization. Beijing-Tianjin-Hebei and South Central Liaoning were “multi-core” metropolitan areas, and the other eight were “single-core” metropolitan areas. (3) The analysis results of QAP regression illustrated that the top three influencing factors of the six metropolitan areas were geographical locational relationship, the secondary industrial proportion differences, respectively, and patent granted differences, and the other metropolitan areas had no dominant influencing factors.

Using GPS-enabled mobile phones to evaluate the associations between human mobility changes and the onset of influenza illness

Due to the challenges in data collection, there are few studies examining how individuals' routine mobility patterns change when they experience influenza-like symptoms (ILS). In the present study, we aimed to assess the association between changes in routine mobility and ILS using mobile phone-based GPS traces and self-reported surveys from 1,155 participants over the 2016-2017 influenza season. We used a set of mobility metrics to capture individuals' routine mobility patterns and matched their weekly ILS survey responses. For a statistical analysis, we used a time-stratified case-crossover analysis and conducted a stratified analysis to examine if such associations are moderated by demographic and socioeconomic factors, such as age, gender, occupational status, neighborhood poverty and education levels, and work type. We found that statistically significant associations existed between reduced routine mobility patterns and the experience of ILS. Results also indicated that the association between reduced mobility and ILS was significant only for female and for participants with high socioeconomic status. Our findings offered an improved understanding of ILS-associated mobility changes at the individual level and suggest the potential of individual mobility data for influenza surveillance.

The influence of outdoor PM2.5 concentration at workplace on nonaccidental mortality estimates in a Canadian census-based cohort

Background

Associations between mortality and exposure to ambient air pollution are usually explored using concentrations of residential outdoor fine particulate matter (PM_2.5) to estimate individual exposure. Such studies all have an important limitation in that they do not capture data on individual mobility throughout the day to areas where concentrations may be substantially different, leading to possible exposure misclassification. We examine the possible role of outdoor PM_2.5 concentrations at work for a large population-based mortality cohort.

Methods

Using the 2001 Canadian Census Health and Environment Cohort (CanCHEC), we created a time-weighted average that incorporates employment hours worked in the past week and outdoor PM_2.5 concentration at work and home. We used a Cox proportional hazard model with a 15-year follow-up (2001 to 2016) to explore whether inclusion of workplace estimates had an impact on hazard ratios for mortality for this cohort.

Results

Hazard ratios relying on outdoor PM_2.5 concentration at home were not significantly different from those using a time-weighted estimate, for the full cohort, nor for those who commute to a regular workplace. When exploring cohort subgroups according to neighborhood type and commute distance, there was a notable but insignificant change in risk of nonaccidental death for those living in car-oriented neighborhoods, and with commutes greater than 10 km.

Conclusions

Risk analyses performed with large cohorts in low-pollution environments do not seem to be biased if relying solely on outdoor PM_2.5 concentrations at home to estimate exposure.

Short-term PM2.5 and cardiovascular admissions in NY State: assessing sensitivity to exposure model choice

BACKGROUND

Air pollution health studies have been increasingly using prediction models for exposure assessment even in areas without monitoring stations. To date, most studies have assumed that a single exposure model is correct, but estimated effects may be sensitive to the choice of exposure model.

METHODS

We obtained county-level daily cardiovascular (CVD) admissions from the New York (NY) Statewide Planning and Resources Cooperative System (SPARCS) and four sets of fine particulate matter (PM_2.5) spatio-temporal predictions (2002-2012). We employed overdispersed Poisson models to investigate the relationship between daily PM_2.5 and CVD, adjusting for potential confounders, separately for each state-wide PM_2.5 dataset.

RESULTS

For all PM_2.5 datasets, we observed positive associations between PM_2.5 and CVD. Across the modeled exposure estimates, effect estimates ranged from 0.23% (95%CI: -0.06, 0.53%) to 0.88% (95%CI: 0.68, 1.08%) per 10 µg/m³ increase in daily PM_2.5. We observed the highest estimates using monitored concentrations 0.96% (95%CI: 0.62, 1.30%) for the subset of counties where these data were available.

CONCLUSIONS

Effect estimates varied by a factor of almost four across methods to model exposures, likely due to varying degrees of exposure measurement error. Nonetheless, we observed a consistently harmful association between PM_2.5 and CVD admissions, regardless of model choice.