Estimate Population Exposure Research Articles

Tobacco and tobacco branding in films most popular in the UK from 2009 to 2017

BackgroundExposure to tobacco content in films is a cause of smoking uptake in young people. In an earlier study, we reported that tobacco content occurred in 70% of UK box...

Establishing and extending EPHT capacity in England: governance and operational aspects

Within Public Health England (PHE), an Environmental Public Health Tracking (EPHT) programme was established that includes several activities on surveillance of environmental hazards, exposures and health outcomes. We aim to account for key aspects of EPHT governance and operations in England. We describe the process for selecting topics to be included in EPHT and managing its data. This core infrastructure of EPHT within PHE has delivered capacity to support activities on ongoing concerns regarding hazardous pollutants and chemicals in drinking water, land, food and air, as well as linking with novel interpretative frameworks derived from research programmes in collaboration with external research groups. The English EPHT programme has adopted an approach providing common governance for disparate themes, this includes a EPHT board accountable to PHE’s executive via an environmental hazards programme board, a strategic advisory group, stakeholder consultations, and PHE working groups with the remit to establish surveillance structures and functions appropriate to specific information needs. This process has produced activities on population exposure estimation of arsenic in private water supplies, the burden of disease of carbon monoxide poisoning, lead exposure in children, fluoridation monitoring, and guidance for investigating non-infectious disease clusters from potential environmental causes. Recent developments include national systems for enhanced air pollution exposure surveillance and access to weather data for public health use. Studies of distribution of vectors, climate variability and infectious disease, coastal changes and toxin-producing algae, social factors affecting use of green spaces and related health benefits, all illustrate novel interpretative frameworks that were first tested and documented in a research setting, and having been co-designed and co-produced with public health agencies, their relevance for EPHT can more easily be considered. In conclusion, EPHT in England is a programme that enables both activities on well-known environmental factors, and the capacity to integrate emerging concerns.

A Bayesian Method for Population-wide Cardiotoxicity Hazard and Risk Characterization Using an In Vitro Human Model

Human induced pluripotent stem cell (iPSC)-derived cardiomyocytes are an established model for testing potential chemical hazards. Interindividual variability in toxicodynamic sensitivity has also been demonstrated in vitro; however, quantitative characterization of the population-wide variability has not been fully explored. We sought to develop a method to address this gap by combining a population-based iPSC-derived cardiomyocyte model with Bayesian concentration-response modeling. A total of 136 compounds, including 54 pharmaceuticals and 82 environmental chemicals, were tested in iPSC-derived cardiomyocytes from 43 nondiseased humans. Hierarchical Bayesian population concentration-response modeling was conducted for 5 phenotypes reflecting cardiomyocyte function or viability. Toxicodynamic variability was quantified through the derivation of chemical- and phenotype-specific variability factors. Toxicokinetic modeling was used for probabilistic in vitro-to-in vivo extrapolation to derive population-wide margins of safety for pharmaceuticals and margins of exposure for environmental chemicals. Pharmaceuticals were found to be active across all phenotypes. Over half of tested environmental chemicals showed activity in at least one phenotype, most commonly positive chronotropy. Toxicodynamic variability factor estimates for the functional phenotypes were greater than those for cell viability, usually exceeding the generally assumed default of approximately 3. Population variability-based margins of safety for pharmaceuticals were correctly predicted to be relatively narrow, including some below 10; however, margins of exposure for environmental chemicals, based on population exposure estimates, generally exceeded 1000, suggesting they pose little risk at current general population exposures even to sensitive subpopulations. Overall, this study demonstrates how a high-throughput, human population-based, in vitro-in silico model can be used to characterize toxicodynamic population variability in cardiotoxic risk.

The Likelihood of Adverse Pregnancy Outcomes and Genetic Disease (Transgenerational Effects) from Exposure to Radioactive Fallout from the 1945 Trinity Atomic Bomb Test.

The potential health consequences of the Trinity nuclear weapon test of 16 July 1945 at Alamogordo, New Mexico, are challenging to assess. Population data are available for mortality but not for cancer incidence for New Mexico residents for the first 25 y after the test, and the estimates of radiation dose to the nearby population are lower than the cumulative dose received from ubiquitous natural background radiation. Despite the estimates of low population exposures, it is believed by some that cancer rates in counties near the Trinity test site (located in Socorro County) are elevated compared with other locations across the state. Further, there is a concern about adverse pregnancy outcomes and genetic diseases (transgenerational or heritable effects) related to population exposure to fallout radiation. The possibility of an intergenerational effect has long been a concern of exposed populations, e.g., Japanese atomic bomb survivors, survivors of childhood and adolescent cancer, radiation workers, and environmentally exposed groups. In this paper, the likelihood of discernible transgenerational effects is discounted because (1) in all large-scale comprehensive studies of exposed populations, no heritable genetic effects have been demonstrated in children of exposed parents; (2) the distribution of estimated doses from Trinity is much lower than in other studied populations where no transgenerational effects have been observed; and (3) there is no evidence of increased cancer rates among the scientific, military, and professional participants at the Trinity test and at other nuclear weapons tests who received much higher doses than New Mexico residents living downwind of the Trinity site.

Absorbed doses in bricks and TL-dosimeters due to anthropogenic and natural environmental radiation sources

Radiation doses accumulated in ceramic or brick and assessed by thermoluminescence (TL) or optically stimulated luminescence (OSL) measurements are commonly used as a source of independent dosimetric information, helpful for validation of retrospective estimates of population exposures to anthropogenic radiation sources. This work systematically evaluates contributions to the cumulative dose in brick samples located at different heights in a wall from anthropogenic and natural radiation sources and provides data for quantification of the natural background component of the total dose in the brick derived in TL/OSL-measurements. Al- and Cu-cased thermoluminescence dosimeters (TLD) based on Al2O3:C are used to measure contemporary doses in brick walls as benchmarks and for validation of the dose reconstruction procedures. Correspondingly, doses in TLD and in brick in the same locations and under the same irradiation conditions are obtained by Monte Carlo simulations of radiation transport in realistic geometries. The data obtained in the simulations indicate that energy response of Al-cased dosimeters agrees better with the energy response of the brick than the response of Cu-cased TLD. The dosimetric data and relationships between doses in TLD and in brick are systematically derived for different locations in the wall and above the ground and used in other dose reconstruction studies with luminescence techniques.

Estimation of future global population exposure to heatwaves—based on the heat stress index

Estimation of future global population exposure to heatwaves—based on the heat stress index

Use of public water supply fluoride concentration as an indicator of population exposure to fluoride in England 1995\u20132015

Public health monitoring of Community Water Fluoridation (CWF) schemes requires estimates of exposure to fluoride in public water supplies (PWS). We aimed to use routine data to estimate population exposure to PWS-fluoride in England and to determine whether PWS-fluoride exposure from 2005 to 2015 could be used as a proxy for exposure for 1995–2004, when fluoride concentration data that could be linked to population health data were unavailable. We calculated annual mean water supply zone PWS-fluoride concentrations from monitoring data for 1995–2015, stratified by fluoridation scheme-flagging. We allocated annual 2005–2015 mean PWS-fluoride concentrations to small area boundaries to describe population exposure within five concentration categories (< 0.1 to ≥ 0.7 mg/L). We compared zone-level 1995–2004 and 2005–2015 mean PWS-fluoride concentrations using Spearman correlation. Most (72%) of the population received PWS with < 0.2 mg/L fluoride and 10% with ≥ 0.7 mg/L. Fluoride concentrations in 1995–2004 and 2005–2015 were similar (median 0.11 mg/L (lower quartile–upper quartile (LQ–UQ) 0.06–0.17) and 0.11 mg/L (LQ–UQ 0.07–0.17), respectively) and highly correlated (coefficient 0.93) if un-fluoridated but differed (1995–2004 median 0.78 mg/L (LQ–UQ 0.59–0.92); 2005–2015 0.84 mg/L (LQ–UQ 0.72–0.95)) and correlated weakly (coefficient 0.31) if fluoridated. Fluoride concentrations in 2005–2015 approximate those in 1995–2004 but with a greater risk of misclassification in fluoridation schemes.

Big spatial data for urban and environmental sustainability

ABSTRACT Eighty percent of big data are associated with spatial information, and thus are Big Spatial Data (BSD). BSD provides new and great opportunities to rework problems in urban and environmental sustainability with advanced BSD analytics. To fully leverage the advantages of BSD, it is integrated with conventional data (e.g. remote sensing images) and improved methods are developed. This paper introduces four case studies: (1) Detection of polycentric urban structures; (2) Evaluation of urban vibrancy; (3) Estimation of population exposure to PM2.5; and (4) Urban land-use classification via deep learning. The results provide evidence that integrated methods can harness the advantages of both traditional data and BSD. Meanwhile, they can also improve the effectiveness of big data itself. Finally, this study makes three key recommendations for the development of BSD with regards to data fusion, data and predicting analytics, and theoretical modeling.

Effect of parking lanes on assessing the impact of road traffic noise on building façades

The use of strategic noise maps as a means for estimating population exposure to environmental noise and defining action plans to mitigate its effects on human health has become a reality since the publication of the European Noise Directive. In this context, it is known that some differences can be found between the values obtained for sound indicators through simulation and measurements due to different causes. One of these factors is the presence of elements in urban environments not currently considered in calculation methods but certainly present in validation measurements. This paper presents an assessment of the acoustic shielding effect due to parked vehicles on urban streets using computational methods. First of all, a process of validation of the software model by means of different simulation methods and in situ measurements was carried out. Then, a study was developed varying different variables related to urban planning and noise modelling, as well as considering different typologies of real streets according to a categorisation method. Broadband results show that this shielding effect can be significant in common configurations in urban environments, even to receiver heights of 4 m considered as a reference in strategic noise maps. The magnitude of this effect varied depending on the distances between the building façade, parked vehicles and sound sources, as well as the receiver height. Differences up to 4 dBA in sound levels were found in several configurations between situations without and with cars parked at 4 m, although in some specific cases it reached up to 8 dBA. Therefore, results of this study indicated that parking lane shielding effect should be considered in calculations and validation measurements for strategic noise maps in order to obtain an adequate estimation of population exposure to road traffic noise.

A global dataset and model of earthquake-induced landslide fatalities

In this study, we present a newly developed, comprehensive dataset of 196 historical earthquakes, including 127 events with known landslide fatality counts, which we use to examine the impact of earthquake-induced landslides. Using this dataset, we assess the potential impact of seismically induced landslides on affected populations, based on the exposure of population to expected landslide occurrence. We calculate predicted landslide probabilities out of a global statistical landslide model in the area surrounding each earthquake using USGS’s ShakeMap model of strong ground motion. We identify estimated population exposure by comparing the predicted probability grid with a time-corrected global population dataset to determine a predicted landslide exposure index. We compare these values to the number of actual fatalities for 91 training events and use these comparisons, together with the United Nations Human Development Index as a vulnerability proxy, to develop a multiple regression model that can potentially be used to provide order-of-magnitude estimates of human impact of earthquake-induced landslides. We observe a significant positive correlation between predicted and observed fatalities, but with very high variability in fatality rates for similar exposure levels, suggesting that other factors (e.g., landslide type and density, the effect of urbanization on population exposure) should be used to improve this estimate. We also demonstrate that these estimates of landslide impacts can be used in a deterministic mode, in collaboration with use of scenario earthquakes, to assess the potential impacts of anticipated future events. With further development, these landslide scenarios can be used by vulnerable communities to improve land use planning, structural design, and emergency response in landslide-prone areas.

Innovative Methods of Air Pollution Exposure Assessment for Environmental Safety

Abstract Indignation from air pollution is an early signal to instinctive health issues in human. Assessment of human exposure to pollutants for the epidemiological studies will be one of the subsidiary objectives of data analytics for precision medicine forecast for the individuals in the industrially polluted region. The objective of the research determines whether to estimate personal air pollution exposure or population exposure. The exposure can be long term and short term. It includes indoor and outdoor. Direct methods like personal monitoring by using smart phones, air pollution sensors are accurate but quite expensive for huge populations. Indirect methods incorporate information gained from questionnaires accompanied by measured pollutants from the monitoring sites. Lean on the nature of analysis, the assessment pattern may vary. This paper discusses most popular air pollution assessment methods applied in the epidemiological studies related to air pollution. Recent trends in epidemiological studies led to the development of air pollution exposure assessment models that rely on satellite measurement of pollutants. Usage of geographic information system for exposure assessment demands spatial analysis. Mobile applications and sensor technology equipment for personal exposure assessment are promising methods in this study. The articles discussed in this study also observes the method of measurement of each pollutant exposure to individuals by geocoding the addresses with the average pollutant concentration measured at fixed site stations.

Estimating exposure to fine particulate matter emissions from vehicle traffic: Exposure misclassification and daily activity patterns in a large, sprawling region

Vehicle traffic is responsible for a significant portion of toxic air pollution in urban areas that has been linked to a wide range of adverse health outcomes. Most vehicle air quality analyses used for transportation planning and health effect studies estimate exposure from the measured or modeled concentration of an air pollutant at a person's home. This study evaluates exposure to fine particulate matter from vehicle traffic and the magnitude and cause of exposure misclassification that result from not accounting for population mobility during the day in a large, sprawling region. We develop a dynamic exposure model by integrating activity-based travel demand, vehicle emission, and air dispersion models to evaluate the magnitude, components and spatial patterns of vehicle exposure misclassification in the Atlanta, Georgia metropolitan area. Overall, we find that population exposure estimates increase by 51% when population mobility is accounted for. Errors are much larger in suburban and rural areas where exposure is underestimated while exposure may be overestimated near high volume roadways and in the urban core. Exposure while at work and traveling account for much of the error. We find much larger errors than prior studies, all of which have focused on more compact urban regions. Since many people spend a large part of their day away from their homes and vehicle emissions are known to create "hotspots" along roadways, home-based exposure is unlikely to be a robust estimator of a person's actual exposure. Accounting for population mobility in vehicle emission exposure studies may reveal more effective mitigation strategies, important differences in exposure between population groups with different travel patterns, and reduce exposure misclassification in health studies.

An approach to predict population exposure to ambient air PM2.5 concentrations and its dependence on population activity for the megacity London

A comprehensive modelling approach has been developed to predict population exposure to the ambient air PM2.5 concentrations in different microenvironments in London. The modelling approach integrates air pollution dispersion and exposure assessment, including treatment of the locations and time activity of the population in three microenvironments, namely, residential, work and transport, based on national demographic information. The approach also includes differences between urban centre and suburban areas of London by taking account of the population movements and the infiltration of PM2.5 from outdoor to indoor. The approach is tested comprehensively by modelling ambient air concentrations of PM2.5 at street scale for the year 2008, including both regional and urban contributions. Model analysis of the exposure in the three microenvironments shows that most of the total exposure, 85%, occurred at home and work microenvironments and 15% in the transport microenvironment. However, the annual population weighted mean (PWM) concentrations of PM2.5 for London in transport microenvironments were almost twice as high (corresponding to 13–20 μg/m3) as those for home and work environments (7–12 μg/m3). Analysis has shown that the PWM PM2.5 concentrations in central London were almost 20% higher than in the surrounding suburban areas. Moreover, the population exposure in the central London per unit area was almost three times higher than that in suburban regions. The exposure resulting from all activities, including outdoor to indoor infiltration, was about 20% higher, when compared with the corresponding value obtained assuming inside home exposure for all times. The exposure assessment methodology used in this study predicted approximately over one quarter (−28%) lower population exposure, compared with using simply outdoor concentrations at residential locations. An important implication of this study is that for estimating population exposure, one needs to consider the population movements, and the infiltration of pollution from outdoors to indoors.

Day-of-week patterns for ultrafine particulate matter components at four sites in California

Ultrafine particulate matter (PM0.1) samples were collected during three-day averaging periods over an annual cycle at four sites across California: Los Angeles, East Oakland, San Pablo, and Fresno. PM0.1 samples were analyzed for thermal carbon fractions using a thermal-optical carbon analyzer and trace elements using ICP-MS. A statistical analysis of the day-of-week trends for 15 PM0.1 components reveals location-specific patterns along with important general trends for UFP concentrations. PM0.1 elemental carbon (EC) concentrations are elevated in the middle of the week consistent with increased activity for diesel engines during this time period. Just as significantly, PM0.1 organic carbon (OC) concentrations do not exhibit a day-of-week pattern. Since OC accounts for the majority of the PM0.1 total mass, the lack of a day-of-week pattern for PM0.1 OC suggests that diesel engines do not dominate total PM0.1 mass in California. A paired t-test constructed using measurements on weekends compared to measurements immediately preceding or immediately following the weekend showed that PM0.1 potassium (K) and rubidium (Rb) concentrations were elevated on weekends. This pattern is consistent with increased biomass combustion on weekends (a previously unknown exposure pattern for UFPs). The two closest sampling locations (East Oakland and San Pablo) had the greatest number of UFP components with identical weekly trends, but even at these locations only 4 out of 15 components displayed the exact same day-of-week profiles. Less similarity was observed in measured day-of-week profiles for UFP components at other sites. The heterogeneity between sites suggests that the details of UFP concentrations at each location reflect the mixture of sources immediately adjacent to that site. By extension, individual neighborhoods across California will each experience unique day-of-week concentration profiles for UFP components that reflect the surrounding sources. These factors must be considered when estimating population exposure to ultrafine particles during future health effects studies.

Improved Estimates of Population Exposure in Low-Elevation Coastal Zones of China.

With sea level predicted to rise and the frequency and intensity of coastal flooding expected to increase due to climate change, high-resolution gridded population datasets have been extensively used to estimate the size of vulnerable populations in low-elevation coastal zones (LECZ). China is the most populous country, and populations in its LECZ grew rapidly due to urbanization and remarkable economic growth in coastal areas. In assessing the potential impacts of coastal hazards, the spatial distribution of population exposure in China’s LECZ should be examined. In this study, we propose a combination of multisource remote sensing images, point-of-interest data, and machine learning methods to improve the performance of population disaggregation in coastal China. The resulting population grid map of coastal China for the reference year 2010, with a spatial resolution of 100 × 100 m, is presented and validated. Then, we analyze the distribution of population in LECZ by overlaying the new gridded population data and LECZ footprints. Results showed that the total population exposed in China’s LECZ in 2010 was 158.2 million (random forest prediction) and 160.6 million (Cubist prediction), which account for 12.17% and 12.36% of the national population, respectively. This study also showed the considerable potential in combining geospatial big data for high-resolution population estimation.

High spatial resolution health risk assessment of road traffic noise on ischemic heart disease deaths for Melbourne, Australia in 2011

TPS 781: Health effects of noise, Exhibition Hall, Ground floor, August 27, 2019, 3:00 PM - 4:30 PM Introduction: Road traffic noise pollution increases risk of mortality from ischemic heart disease (IHD). Noise pollution is highly localized, so high resolution mapping of the exposure surface is a key concern. For health impact assessments optimally the data on exposure and health outcomes would both be available at small-area level. However, in Australia IHD deaths data are only easily accessed at low resolution, aggregated to local government areas (LGA, mean ~130,000 people). Our method utilizes low-resolution health data with high spatial resolution noise mapping for producing health risk maps at the mesh-block level (MB, ~75 people). Methods: We used a noise contour map for Melbourne, Australia from 2011 based on a spatial predictive model to estimate population exposures at the MB level. We used the non-linear exposure–response function for traffic noise and IHD as recommended by the World Health Organization to calculate attributable fractions of deaths for MBs. LGA-level IHD rates were then downscaled to MBs using publicly available data to estimate attributable numbers. Results: Average noise exposures for MBs was 35 dB and peaked at 72 dB. Approximately 5% of the population was exposed to traffic noise above the threshold of 55 dB. Attributable rates of IHD deaths due to noise were generally very low but in some MBs was as high as 5-10 per 100,000 and in extreme cases can represent more than 8% of the total expected burden of IHD death. Conclusion: Road traffic noise is a public health concern because it increases risk for IHD death. We present a method that does not require high resolution health data and can be used with high resolution exposure data to provide estimates of health risk. This method could be applied in a wide range of other environmental health settings.

Exposure Beyond Mass

PDS 76: Source specific outdoor air pollution studies, Exhibition Hall (PDS), Ground floor, August 26, 2019, 4:30 PM - 5:30 PM Background: Most epidemiological studies of health effects of atmospheric particulate matter use conventional filter measurements. These measurements quantify total mass concentration but may oversimplify air pollution exposure estimates as they are blind to many critical physiochemical properties of atmospheric particles. These properties likely have larger exposure variability than particulate matter mass. Objectives: This study is designed to measure and predict the spatial variations of particulate matter exposure using state-of-the-art single-particle mass spectrometry to resolve detailed physicochemical properties, namely number, size, source and chemical mixing state of individual particles in a populous urban area to study their population exposure variability and the contrasts with mass exposure. Methods: We performed mobile sampling using an advanced single-particle mass spectrometer to measure the within-city variability of number concentration of source-resolved 50 – 1000 nm particles and particle mixing state in Pittsburgh, PA, USA. We built land use regression (LUR) models to estimate their spatial patterns. We coupled demographic data to estimate population exposure. Results: Particle number concentration has a much larger spatial variability (factor of ~5) than mass concentration (factor of 2) within the city. Fresh primary particles emitted from traffic and cooking drive the exposure variability on the number concentration basis, but the mass exposure is dominated by aged background particles composed of secondary material. In addition, we show that people exposed to elevated number concentrations of atmospheric particles are also exposed to more externally mixed, primary particles. Conclusions: There is a large intra-city spatial heterogeneity of exposure to traffic and cooking particles on the number basis. Such heterogeneity is not captured by conventional mass exposure measurements. In addition, the resulting strong spatial patterns of particle chemical mixing state may potentially be significant when evaluating health effects of particulate matter exposure.

Co-occurrence, possible origin, and health-risk assessment of arsenic and fluoride in drinking water sources in Mexico: Geographical data visualization

Arsenic and fluoride in drinking water present a significant challenge to public health worldwide. In this study, we analyze the results of one of the largest surveys of drinking water quality in Mexico: 14,058 samples from 3951 sites, collected between January and December 2017. We use these data to identify the distribution and possible origin of arsenic and fluoride in drinking water throughout the country, and to estimate the associated health burden. The highest concentrations appear in alluvial aquifers in arid northern Mexico, where high-silica volcanic rock likely releases both arsenic and fluoride to the groundwater. We find fluoride contamination to be significantly correlated with aridity (Pearson correlation = −0.45, p = 0.0105), and also find a significant difference in fluoride concentrations between arid and humid states (Welch's t-test, p = 0.004). We estimate population exposure by assigning to each town in Mexico the average concentration of any sampling sites within 5 km. Our results show that 56% of the Mexican population lives within 5 km of a sampling site, 3.05 million people are exposed to fluoride above the reference dosage of 0.06 mg/(kg ∗ day), 8.81 million people are exposed to arsenic above the limit of 10 μg/L, and an additional 13,070 lifetime cases of cancer are expected from this arsenic exposure alone. This burden of disease is concentrated in the arid states of north-central Mexico.

Residential Wood Combustion in Finland: PM2.5 Emissions and Health Impacts with and without Abatement Measures.

Exposure to fine particles in ambient air has been estimated to be one of the leading environmental health risks in Finland. Residential wood combustion is the largest domestic source of fine particles, and there is increasing political interest in finding feasible measures to reduce those emissions. In this paper, we present the PM2.5 emissions from residential wood combustion in Finland, as well as the resulting concentrations. We used population-weighed concentrations in a 250 × 250 m grid as population exposure estimates, with which we calculated the disease burden of the emissions. Compared to a projected baseline scenario, we studied the effect of chosen reduction measures in several abatement scenarios. In 2015, the resulting annual average concentrations were between 0.5 and 2 µg/m3 in the proximity of most cities, and disease burden attributable to residential wood combustion was estimated to be 3400 disability-adjusted life years (DALY) and 200 deaths. Disease burden decreased by 8% in the 2030 baseline scenario and by an additional 63% in the maximum feasible reduction scenario. Informational campaigns and improvement of the sauna stove stock were assessed to be the most feasible abatement measures to be implemented in national air quality policies.

A spatio-temporally weighted hybrid model to improve estimates of personal PM2.5 exposure: Incorporating big data from multiple data sources

An accurate estimation of population exposure to particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) is crucial to hazard assessment and epidemiology. This study integrated annual data from 1146 in-home air monitors, air quality monitoring network, public applications, and traffic smart cards to determine the pattern of PM2.5 concentrations and activities in different microenvironments (including outdoors, indoors, subways, buses, and cars). By combining massive amounts of signaling data from cell phones, this study applied a spatio-temporally weighted model to improve the estimation of PM2.5 exposure. Using Shanghai as a case study, the annual average indoor PM2.5 concentration was estimated to be 29.3 ± 27.1 μg/m3 (n = 365), with an average infiltration factor of 0.63. The spatio-temporally weighted PM2.5 exposure was estimated to be 32.1 ± 13.9 μg/m3 (n = 365), with indoor PM2.5 contributing the most (85.1%), followed by outdoor (7.6%), bus (3.7%), subway (3.1%), and car (0.5%). However, considering that outdoor PM2.5 makes a significant contribution to indoor PM2.5, outdoor PM2.5 was responsible for most of the exposure in Shanghai. A heatmap of PM2.5 exposure indicated that the inner-city exposure index was significantly higher than that of the outskirts city, which demonstrated that the importance of spatial differences in population exposure estimation.