PM2.5 Population Exposure in New Delhi Using a Probabilistic Simulation Framework

Exploring particle concentrations and inside-to-outside ratios in vehicles: A real-time road test study

In transportation microenvironments, humans exposed to particulate matter (PM) inside vehicles can experience higher levels of daily exposure. To make inside-vehicle PM exposure measurements more feasible and easy under real driving conditions, and to quantify the relationship between the concentrations and influencing factors, we assessed PM1, PM2.5, and PM10. levels. Additionally, we collected key influencing factors to develop predictive models. The measurements of PM1, PM2.5, and PM10 concentrations showed that the ventilation setting was a significant influencing factor. The concentrations decreased significantly under the recirculation setting (RC) compared to the outside air setting (OA). The inside-to-outside (I/O) ratios of PM were 1.69 to 1.93-fold higher than those of RC under OA conditions. However, a substantial reduction in the I/O ratios was observed when RC was employed. Although both the concentrations and I/O ratios exhibited significant differences, they demonstrated strong potential relationships. PM2.5 I/O ratios accounted for over 85 % of the variation in the PM1 and PM10 I/O ratios. The developed models for the I/O ratios of PM accounted for >40 and 60 % of the variation in the measured I/O ratios for RC and OA, respectively. We used the vehicle age, vehicle interior volume, speed, cabin temperature, cabin humidity, and their higher-order terms as predictive variables. It is important to note that the influential predictive feature importance differed under RC and OA, and considering the vehicle characteristics between vehicles of the same type may be necessary when using RC. Overall, these findings indicate that the inside-vehicle PM exposure can be measured more easily under real driving conditions by considering the key influencing factors and utilizing the developed predictive models.

Spatio-Temporal Variation-Induced Group Disparity of Intra-Urban NO2 Exposure

Previous studies on exposure disparity have focused more on spatial variation but ignored the temporal variation of air pollution; thus, it is necessary to explore group disparity in terms of spatio-temporal variation to assist policy-making regarding public health. This study employed the dynamic land use regression (LUR) model and mobile phone signal data to illustrate the variation features of group disparity in Shanghai. The results showed that NO₂ exposure followed a bimodal, diurnal variation pattern and remained at a high level on weekdays but decreased on weekends. The most critical at-risk areas were within the central city in areas with a high population density. Moreover, women and the elderly proved to be more exposed to NO₂ pollution in Shanghai. Furthermore, the results of this study showed that it is vital to focus on land-use planning, transportation improvement programs, and population agglomeration to attenuate exposure inequality.

Analysis of the spatio-temporal network of air pollution in the Yangtze River Delta urban agglomeration, China

The complex correlation between regions caused by the externality of air pollution increases the difficulty of its governance. Therefore, analysis of the spatio-temporal network of air pollution (STN-AP) holds great significance for the cross-regional coordinated governance of air pollution. Although the spatio-temporal distribution of air pollution has been analyzed, the structural characteristics of the STN-AP still need to be clarified. The STN-AP in the Yangtze River Delta urban agglomeration (YRDUA) is constructed based on the improved gravity model and is visualized by UCINET with data from 2012 to 2019. Then, its overall-individual-clustering characteristics are analyzed through social network analysis (SNA) method. The results show that the STN-AP in the YRDUA was overall stable, and the correlation level gradually improved. The centrality of every individual city is different in the STN-AP, which reveals the different state of their interactive mechanism. The STN-AP could be subdivided into the receptive block, overflow block, bidirectional block and intermediary block. Shanghai, Suzhou, Hangzhou and Wuxi could be key cities with an all above degree centrality, betweenness centrality and closeness centrality and located in the overflow block of the STN-AP. This showed that these cities had a greater impact on the STN-AP and caused a more pronounced air pollution spillovers. The influencing factors of the spatial correlation of air pollution are further determined through the quadratic assignment procedure (QAP) method. Among all factors, geographical proximity has the strongest impact and deserves to be paid attention in order to prevent the cross-regional overflow of air pollution. Furthermore, several suggestions are proposed to promote coordinated governance of air pollution in the YRDUA.

Environmental exposure during travel: A research review and suggestions forward

Daily travel through the urban fabric exposes urban dwellers to a range of environmental conditions that may have an impact on their health and wellbeing. Knowledge about exposures during travel, their associations with travel behavior, and their social and health outcomes are still limited. In our review, we aim to explain how the current environmental exposure research addresses the interactions between human and environmental systems during travel through their spatial, temporal and contextual dimensions. Based on the 104 selected studies, we identify significant recent advances in addressing the spatiotemporal dynamics of exposure during travel. However, the conceptual and methodological framework for understanding the role of multiple environmental exposures in travel environments is still in an early phase, and the health and wellbeing impacts at individual or population level are not well known. Further research with greater geographical balance is needed to fill the gaps in the empirical evidence, and linking environmental exposures during travel with the causal health and wellbeing outcomes. These advancements can enable evidence-based urban and transport planning to take the next step in advancing urban livability.

Airborne Particulates Affect Corneal Homeostasis and Immunity

Purpose

To determine the effects of airborne particulate matter (PM) <2.5 µm in vitro and on the normal and Pseudomonas aeruginosa (PA)-infected cornea.

Methods

An MTT viability assay tested the effects of PM_2.5 on mouse corneal epithelial cells (MCEC) and human corneal epithelial cells (HCET). MCEC were tested for reactive oxygen species using a 2′,7′-dichlorodihydrofluorescein assay; RT-PCR determined mRNA levels of inflammatory and oxidative stress markers in MCEC (HMGB1, toll-like receptor 2, IL-1β, CXCL2, GPX1, GPX2, GR1, superoxide dismutase 2, and heme oxygenase 1) and HCET (high mobility group box 1, CXCL2, and IL-1β). C57BL/6 mice also were infected and after 6 hours, the PM_2.5 was topically applied. Disease was graded by clinical score and evaluated by histology, plate count, myeloperoxidase assay, RT-PCR, ELISA, and Western blot.

Results

After PM_2.5 (25–200 µg/mL), 80% to 90% of MCEC and HCET were viable and PM exposure increased reactive oxygen species in MCEC and mRNA expression levels for inflammatory and oxidative stress markers in mouse and human cells. In vivo, the cornea of PA+PM_2.5 exposed mice exhibited earlier perforation over PA alone (confirmed histologically). In cornea, plate counts were increased after PA+PM_2.5, whereas myeloperoxidase activity was significantly increased after PA+PM_2.5 over other groups. The mRNA levels for several proinflammatory and oxidative stress markers were increased in the cornea in the PA+PM_2.5 over other groups; protein levels were elevated for high mobility group box 1, but not toll-like receptor 4 or glutathione reductase 1. Uninfected corneas treated with PM_2.5 did not differ from normal.

Conclusions

PM_2.5 triggers reactive oxygen species, upregulates mRNA levels of oxidative stress, inflammatory markers, and high mobility group box 1 protein, contributing to perforation in PA-infected corneas.

Characterization of a High PM2.5 Exposure Group in Seoul Using the Korea Simulation Exposure Model for PM2.5 (KoSEM-PM) Based on Time⁻Activity Patterns and Microenvironmental Measurements

The Korea Simulation Exposure Model for fine particulate matter (PM_2.5) (KoSEM-PM) was developed to estimate population PM_2.5 exposure in Korea. The data were acquired based on 59,945 min of the actual microenvironmental PM_2.5 measurements and on the time–activity patterns of 8072 residents of Seoul. The aims of the study were to estimate daily PM_2.5 exposure of Seoul population, and to determine the characteristics of a high exposure group. KoSEM-PM estimated population exposures by applying the PM_2.5 distribution to the matching time–activity patterns at 10-min intervals. The mean personal PM_2.5 exposure level of the surveyed subjects in Seoul was 26.0 ± 2.7 µg/m³ (range: 21.0–40.2 µg/m³) in summer. Factors significantly associated with high exposure included day of the week, age, industry sector, job type, and working hours. Individuals surveyed on Saturdays were more likely to be in the high exposure group than those surveyed on weekdays and Sundays. Younger, non-office-working individuals with longer working hours were more likely to be in the high exposure group. KoSEM-PM could be a useful tool to estimate population exposure levels to other region in Korea; to expand its use, microenvironmental measurements are required for other region in Korea.

Chemical composition of outdoor and indoor PM2.5 collected during haze events: Transformations and modified source contributions resulting from outdoor-to-indoor transport

Changes in the chemical constitution and sources of ambient PM_2.5 following the infiltration of air into indoor environments were investigated. We collected PM_2.5 samples from air inside and outside 31 rooms in Beijing residences during hazy episodes. We calculated the indoor-to-outdoor ratios and the correction (kⁱ ) of each infiltration factor for each chemical component of PM_2.5 to determine the effects of infiltrative behavior. The outdoor and indoor mass concentrations of PM_2.5 during the sampling period were 70-460 and 10-315 μg/m³ , respectively. Differences in the average indoor-to-outdoor ratios of PM_2.5 mass and each component (mean value ± standard deviation: PM_2.5 mass = 0.53 ± 0.26, organic matter = 0.75 ± 0.34, elemental carbon = 0.62 ± 0.31, trace elements = 0.62 ± 0.26, SO 4 2 - = 0.67 ± 0.32 , NH 4 + = 0.53 ± 0.54 , NO 3 - = 0.45 ± 0.36 , Cl^- = 0.37 ± 0.35, and crustal dust = 0.30 ± 0.19) may be attributed to size distribution, chemical properties, temperature, and humidity. The positive matrix factorization model was applied to calculate the source contributions to equivalent population exposure (Indoor concentration·Indoor time fraction + Outdoor concentration·Outdoor time fraction). The contributions of fossil fuel combustion, secondary source, vehicle exhaust, and mixed dust to the equivalent PM_2.5 population source exposure were 37%, 24%, 22%, and 17%, respectively.

Integrating travel behavior with land use regression to estimate dynamic air pollution exposure in Hong Kong

BACKGROUND

Epidemiological studies typically use subjects' residential address to estimate individuals' air pollution exposure. However, in reality this exposure is rarely static as people move from home to work/study locations and commute during the day. Integrating mobility and time-activity data may reduce errors and biases, thereby improving estimates of health risks.

OBJECTIVES

To incorporate land use regression with movement and building infiltration data to estimate time-weighted air pollution exposures stratified by age, sex, and employment status for population subgroups in Hong Kong.

METHODS

A large population-representative survey (N = 89,385) was used to characterize travel behavior, and derive time-activity pattern for each subject. Infiltration factors calculated from indoor/outdoor monitoring campaigns were used to estimate micro-environmental concentrations. We evaluated dynamic and static (residential location-only) exposures in a staged modeling approach to quantify effects of each component.

RESULTS

Higher levels of exposures were found for working adults and students due to increased mobility. Compared to subjects aged 65 or older, exposures to PM_2.5, BC, and NO₂ were 13%, 39% and 14% higher, respectively for subjects aged below 18, and 3%, 18% and 11% higher, respectively for working adults. Exposures of females were approximately 4% lower than those of males. Dynamic exposures were around 20% lower than ambient exposures at residential addresses.

CONCLUSIONS

The incorporation of infiltration and mobility increased heterogeneity in population exposure and allowed identification of highly exposed groups. The use of ambient concentrations may lead to exposure misclassification which introduces bias, resulting in lower effect estimates than 'true' exposures.

Characterization of PM2.5 in Delhi: role and impact of secondary aerosol, burning of biomass, and municipal solid waste and crustal matter

Delhi is one among the highly air polluted cities in the world. Absence of causal relationship between emitting sources of PM_2.5 and their impact has resulted in inadequate actions. This research combines a set of innovative and state-of-the-art analytical techniques to establish relative predominance of PM_2.5 sources. Air quality sampling at six sites in summer and winter for 40 days (at each site) showed alarmingly high PM_2.5 concentrations (340 ± 135 μg/m³). The collected PM_2.5 was subjected to chemical speciation including ions, metals, organic and elemental carbons which followed application of chemical mass balance technique for source apportionment. The source apportionment results showed that secondary aerosols, biomass burning (BMB), vehicles, fugitive dust, coal and fly ash, and municipal solid waste burning were the important sources. It was observed that secondary aerosol and crustal matter accounted for over 50% of mass. The PM_2.5 levels were not solely result of emissions from Delhi; it is a larger regional problem caused by contiguous urban agglomerations. It was argued that emission reduction of precursors of secondary aerosol, SO₂, NO_x, and volatile organic compounds, which are unabated, is essential. A substantial reduction in BMB and suspension of crustal dust is equally important to ensure compliance with air quality standards.

Characterization of PM2.5 exposure concentration in transport microenvironments using portable monitors

Recently, portable monitors have been increasingly used to quantify air pollutant concentrations at high spatiotemporal resolution. A sampling campaign was conducted to measure the fine particulate matter (PM_2.5) and carbon monoxide (CO) exposure concentrations in transport microenvironments (TMEs) in Hong Kong in January and June 2015 using TSI DustTrak and Q-Trak portable monitors. The objectives were to: (1) calibrate DustTrak and Q-Trak; (2) evaluate variability between seasons and microenvironments; (3) estimate indoor/outdoor relationships; and (4) determine minimum sample size. Calibration equations, obtained through side-by-side measurement against stationary reference methods in winter and summer, were applied to correct the measured PM_2.5 data set. In general, PM_2.5 concentrations in all TMEs were significantly higher in winter than in summer. The mean PM_2.5 concentration in winter was lower for underground sections of the Mass Transit Railway (MTR) metro system (31 μg/m³) than for other TMEs, whereas in summer TMEs had mean PM_2.5 concentrations in the range of 10-15 μg/m³, with above-ground MTR train as an exception, at 23 μg/m³. PM_2.5 concentrations measured in TMEs were strongly correlated with nearby air quality monitoring stations (AQMSs) measurements in winter, but in summer there was little correlation. The minimum sample size estimates varied more among TMEs in summer versus winter because of the differences in PM_2.5 concentration distributions related to changes in ambient PM_2.5 concentrations and ventilation practices. This study provides a feasible protocol on the calibration and application of portable monitors in TME air quality measurement and develops a method for estimating minimum sample size.

Exposure to particulate matter in India: A synthesis of findings and future directions

Air pollution poses a critical threat to human health with ambient and household air pollution identified as key health risks in India. While there are many studies investigating concentration, composition, and health effects of air pollution, investigators are only beginning to focus on estimating or measuring personal exposure. Further, the relevance of exposures studies from the developed countries in developing countries is uncertain. This review summarizes existing research on exposure to particulate matter (PM) in India, identifies gaps and offers recommendations for future research. There are a limited number of studies focused on exposure to PM and/or associated health effects in India, but it is evident that levels of exposure are much higher than those reported in developed countries. Most studies have focused on coarse aerosols, with a few studies on fine aerosols. Additionally, most studies have focused on a handful of cities, and there are many unknowns in terms of ambient levels of PM as well as personal exposure. Given the high mortality burden associated with air pollution exposure in India, a deeper understanding of ambient pollutant levels as well as source strengths is crucial, both in urban and rural areas. Further, the attention needs to expand beyond the handful large cities that have been studied in detail.