The relationship between greenspace and personal exposure to PM2.5 during walking trips in Delhi, India

Residential greenness, respiratory symptoms and lung function in children, adolescents and adults with asthma: A cross-sectional study

BACKGROUND

We hypothesize that green areas within cities affect the respiratory symptoms of individuals with asthma, but this effect may not be the same for all age groups.

OBJECTIVE

Evaluate whether there is an association between the percentage of green area close to the residence and asthma outcomes, stratified by age group.

METHODS

We included individuals with asthma from all 42 public health facilities in a Brazilian municipality with 400,000 inhabitants. Two independent researchers, blinded to clinical information regarding asthma, measured the extent of green area around the residence of study volunteers using satellite images. The primary outcome was the severity of respiratory symptoms. The secondary outcome was the presence of airway obstruction in the spirometry test carried out at the study visit. Adjusted regression analyzes evaluated whether the percentage of green area close to the residence was associated with asthma outcomes.

RESULTS

In children-adolescents (n = 322), greater density of green area was associated with a greater frequency of uncontrolled asthma symptoms [OR 1.25, 95CI (1.02-1.54)]. In adults (n = 966), more greenness was associated with a lower frequency of uncontrolled asthma symptoms [OR 0.90, 95CI (0.81-0.99)] and a lower frequency of airway obstruction [OR 0.86, 95CI (0.78-0.96)].

CONCLUSIONS

The extent of green areas close to the residence is associated with asthma morbidity, but the direction of this association differs between child-adolescents and adults. Studies need to clarify why the effect of vegetation cover on asthma symptoms differs between children-adolescents and adults.

Spatial and seasonal association study between P M 2.5 and related contributing factors in India

Global environmental pollution and rapid climate change have become a serious matter of concern. Remarkable spatial and seasonal variations have been observed due to rapid industrialization, urbanization, different festive occasions, etc. Among all the existing pollutants, the fine airborne particlesPM 2.5 (with aerodynamic equivalent diameter ≤ 2.5 μ m ) andPM 10 (with aerodynamic equivalent diameter ≤ 10 μ m ) are associated with chronic diseases. This leads to carry out the study regarding the varying relationship betweenPM 2.5 and other associated factors so that its concentration level might be under control. Existing literature has explored the geographical association between the pollutants and a few other important factors. To address this problem, the present study aims to explore the wide spatio-temporal relationships between the particulate matter (PM 2.5 ) with the other associated factors (e.g., socio-demographic, meteorological factors, and air pollutants). For this analysis, the geographically weighted regression (GWR) model with different kernels (viz. Gaussian and Bisquare kernels) and the ordinary least squares (OLS) model have been carried out to analyze the same from the perspective of the four major seasons (i.e., autumn, winter, summer, and monsoon) in different districts of India. It may be inferred from the results that the local model (i.e., GWR model with Bisquare kernel) captures the spatial heterogeneity in a better way and their performances have been compared in terms ofR 2 values ( > 0.99 in all cases) and corrected Akaike information criterion (AIC c ) (maximum value - 618.69 and minimum value - 896.88 ). It has been revealed that there is a strong negative impact between forest coverage and PM pollution in northern India during the major seasons. The same has been found in Delhi, Haryana, and a few districts of Rajasthan during the 1-year cycle (October 2022-September 2023). It has also been found that PM concentration levels become high over the specified period with the temperature drop in Delhi, Uttar Pradesh, etc. Moreover, a strong positive association is visible in PM pollution level with the total population.

Spatial and temporal determinants of particulate matter peak exposures during pregnancy and early postpartum

Background

Fine particulate matter (PM2.5) exposure is an important environmental risk for maternal and children's health, with peak exposures especially those derived from primary combustion hypothesized to pose greater risk. Identifying PM2.5 peaks and their contributions to personal exposure remains challenging. This study measured personal PM2.5 exposure, characterized primary combustion peaks, and investigated their determinants during and after pregnancy and among Hispanic women in Los Angeles, CA.

Methods

Continuous personal PM2.5 exposure, Global Positioning System geolocation, and ecological momentary assessment surveys were collected from 63 women for 4 consecutive days in their 1st trimester, 3rd trimester and 4-6 months postpartum. Based on the shape of PM2.5 time-series, primary combustion peaks were identified, characterized (number, duration, area under the curve [AUC]), and linked with locations they occurred in. Zero-inflated generalized mixed-effect models were used to examine the spatial and temporal determinants of PM2.5 peak exposures.

Results

A total of 490 PM2.5 peaks were identified from 618 person-days of monitoring. Spending an additional minute at parks and open spaces was related to smaller (AUC decreased 3.1 %, 95 % CI: 1.5 %-4.6 %) and shorter (duration decreased 1.7 %, 0.5 %-2.9 %) PM2.5 peak exposure. An additional minute in vehicular trips also related to smaller and shorter peak exposure (AUC and duration decreased 2.5 %, 1.2 %-3.7 % and 1.8 %, 1.0 %-2.6 %, respectively). However, an additional minute at industrial locations was associated with greater number (3.6 %, 2.0 %-5.2 %), AUC (1.6 %, 0.1 %-3.2 %) and duration (1.0 %, 0.0 %-2.1 %) of personal PM2.5 peak exposure.

Conclusions

This study demonstrates the potential to statistically identify exposure to primary combustion PM2.5 peaks and understand their determinants from personal monitoring data. Results suggest that visits to parks and open spaces may minimize PM2.5 peak exposures, while visiting industrial locations may increase them in and around pregnancy.

Association between outdoor jogging behavior and PM2.5 exposure: Evidence from massive GPS trajectory data in Beijing

Outdoor jogging is one of the most popular practised exercises worldwide, providing various benefits for health and wellbeing. However, PM2.5 exposure risks of jogging behaviors were rarely explored. This study aims to investigate the association between jogging behavior and PM2.5 exposure with big data. PM2.5 exposure concentration and dose inhalation of individuals were calculated by integrating hourly PM2.5 concentration data and jogging GPS trajectory recorded by a sports app during 2015 in Beijing, after which relationships between jogging behaviors and PM2.5 exposure were unpacked using statistics analysis and structural equation modelling. Experimental results on massive jogging trajectories show that: (1) the average jogging PM2.5 exposure concentration is 60.43 μg/m3, and female joggers inhaled significantly less air pollution dose (19.70 μg) than men (24.91 μg). (2) There exist significant spatiotemporal disparities in jogging exposure to PM2.5. Joggings in the city center, in the morning, on weekdays and in autumn and winter seasons were exposed to higher pollution concentrations. (3) Jogging behavior characteristics, especially distance, activity space size, duration and rotation, were systematically associated with PM2.5 exposure across space and time. (4) The role of gender directly shaped joggers' dose inhalation of PM2.5 pollution and indirectly via duration, timing choice and distance. (5) The effects of weather conditions on joggers' exposure to PM2.5 are mainly via direct effects, whereas the direct impacts of precipitation and wind speed are mitigated by indirect effects stemming from jogging behavior patterns. Our findings provide insights for personal guidance and policy intervention for the sake of promoting physical activity and reducing PM2.5 exposure.

Associations between community green view index and fine particulate matter from Airboxes

Urban greenery can help to improve air quality, reduce health risks and create healthy livable urban communities. This study aimed to explore the role of urban greenery in reducing air pollution at the community level in Tainan City, Taiwan, using air quality sensors and street-view imagery. We also collected the number of road trees around each air quality sensor site and identified the species that were best at absorbing PM2.5. Three greenness metrics were used to assess community greenery in this study: two Normalized Difference Vegetation Indices (NDVI) from different satellites and the Green View Index (GVI) from Google Street View (GSV) images. Land-use Regression (LUR) was used for statistical analysis. The results showed that a higher GVI within a 500 m buffer was significantly associated with decreased PM2.5. Neither NDVI metrics within a 500 m circular buffer were significantly associated with decreased PM2.5. Evergreen trees were significantly associated with lower ambient PM2.5, compared with deciduous and semi-deciduous trees. Because localized changes in air quality profoundly affect public health and environmental equity, our findings provide evidence for future urban community greenspace planning and its beneficial impacts on reducing air pollution.

EAACI guidelines on environmental science in allergic diseases and asthma - Leveraging artificial intelligence and machine learning to develop a causality model in exposomics

Allergic diseases and asthma are intrinsically linked to the environment we live in and to patterns of exposure. The integrated approach to understanding the effects of exposures on the immune system includes the ongoing collection of large-scale and complex data. This requires sophisticated methods to take full advantage of what this data can offer. Here we discuss the progress and further promise of applying artificial intelligence and machine-learning approaches to help unlock the power of complex environmental data sets toward providing causality models of exposure and intervention. We discuss a range of relevant machine-learning paradigms and models including the way such models are trained and validated together with examples of machine learning applied to allergic disease in the context of specific environmental exposures as well as attempts to tie these environmental data streams to the full representative exposome. We also discuss the promise of artificial intelligence in personalized medicine and the methodological approaches to healthcare with the final AI to improve public health.

Integrated assessment of personal monitor applications for evaluating exposure to urban stressors: A scoping review

Urban stressors pose a health risk, and individual-level assessments provide necessary and fine-grained insight into exposure. An ever-increasing amount of research literature on individual-level exposure to urban stressors using data collected with personal monitors, has called for an integrated assessment approach to identify trends, gaps and needs, and provide recommendations for future research. To this end, a scoping review of the respective literature was performed, as part of the H2020 URBANOME project. Moreover, three specific aims were identified: (i) determine current state of research, (ii) analyse literature according with a waterfall methodological framework and identify gaps and needs, and (iii) provide recommendations for more integrated, inclusive and robust approaches. Knowledge and gaps were extracted based on a systematic approach, e.g., data extraction questionnaires, as well as through the expertise of the researchers performing the review. The findings were assessed through a waterfall methodology of delineating projects into four phases. Studies described in the papers vary in their scope, with most assessing exposure in a single macro domain, though a trend of moving towards multi-domain assessment is evident. Simultaneous measurements of multiple stressors are not common, and papers predominantly assess exposure to air pollution. As urban environments become more diverse, stakeholders from different groups are included in the study designs. Most frequently (per the quadruple helix model), civil society/NGO groups are involved, followed by government and policymakers, while business or private sector stakeholders are less frequently represented. Participants in general function as data collectors and are rarely involved in other phases of the research. While more active involvement is not necessary, more collaborative approaches show higher engagement and motivation of participants to alter their lifestyles based on the research results. The identified trends, gaps and needs can aid future exposure research and provide recommendations on addressing different urban communities and stakeholders.

Spatial interpolation of regional PM2.5 concentrations in China during COVID-19 incorporating multivariate data

During specific periods when the PM_2.5 variation pattern is unusual, such as during the coronavirus disease 2019 (COVID-19) outbreak, epidemic PM_2.5 regional interpolation models have been relatively little investigated, and little consideration has been given to the residuals of optimized models and changes in model interpolation accuracy for the PM_2.5 concentration under the influence of epidemic phenomena. Therefore, this paper mainly introduces four interpolation methods (kriging, empirical Bayesian kriging, tensor spline function and complete regular spline function), constructs geographically weighted regression (GWR) models of the PM_2.5 concentration in Chinese regions for the periods from January-June 2019 and January-June 2020 by considering multiple factors, and optimizes the GWR regression residuals using these four interpolation methods, thus achieving the purpose of enhancing the model accuracy. The PM_2.5 concentrations in many regions of China showed a downward trend during the same period before and after the COVID-19 outbreak. Atmospheric pollutants, meteorological factors, elevation, zenith wet delay (ZWD), normalized difference vegetation index (NDVI) and population maintained a certain relationship with the PM_2.5 concentration in terms of linear spatial relationships, which could explain why the PM_2.5 concentration changed to a certain extent. By evaluating the model accuracy from two perspectives, i.e., the overall interpolation effect and the validation set interpolation effect, the results showed that all four interpolation methods could improve the numerical accuracy of GWR to different degrees, among which the tensor spline function and the fully regular spline function achieved the most stable effect on the correction of GWR residuals, followed by kriging and empirical Bayesian kriging.