Quantifying human exposure to air pollution--moving from static monitoring to spatio-temporally resolved personal exposure assessment

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.

Assessment of personal exposure to environmentally persistent free radicals in airborne particulate matter

Environmentally persistent free radicals (EPFRs) are a type of emerging contaminants. The EPFR species in airborne particulate matter are similar to carcinogenic tar paramagnetic species in cigarettes that can cause DNA damage. However, understanding on daily EPFR exposure levels and risks are lacking currently. We used personal aerosol exposure monitors worn by volunteers to assess EPFR exposure in a spatio-temporal, non-static manner. Daily individual exposure to EPFRs for urban residents in Beijing, China ranged from 1.11 × 10¹⁷ to 7.42 × 10¹⁷ spins/m³ during the heating period (winter) and from 4.79 × 10¹⁴ to 7.76 × 10¹⁶ spins/m³ during the non-heating period (summer). Carbon-centered radicals were dominant in winter, while oxygen-centered radicals were dominant in summer because of higher atmospheric oxidizing capacity contributing to oxidation reactions. Coal combustion in winter is a key influencing factor in EPFR exposure levels. An intuitional assessment was used to evaluate the inhalation risks of EPFRs by converting their concentrations in inhaled particulate matter to equivalents in cigarettes smoked. The assessment concluded that one urban resident may, on average, inhale the equivalent of 46 cigarettes per day in EPFRs. The health risks of these free radicals, especially during winter, should be researched in depth.

Day time, night time, over time: geographic and temporal uncertainty when linking event and contextual data

BACKGROUND

The growth of geolocated data has opened the door to a wealth of new research opportunities in the health fields. One avenue of particular interest is the relationship between the spaces where people spend time and their health outcomes. This research model typically intersects individual data collected on a specific cohort with publicly available socioeconomic or environmental aggregate data. In spatial terms: individuals are represented as points on map at a particular time, and context is represented as polygons containing aggregated or modeled data from sampled observations. Uncertainty abounds in these kinds of complex representations.

METHODS

We present four sensitivity analysis approaches that interrogate the stability of spatial and temporal relationships between point and polygon data. Positional accuracy assesses the significance of assigning the point to the correct polygon. Neighborhood size investigates how the size of the context assumed to be relevant impacts observed results. Life course considers the impact of variation in contextual effects over time. Time of day recognizes that most people occupy different spaces throughout the day, and that exposure is not simply a function residential location. We use eight years of point data from a longitudinal study of children living in rural Pennsylvania and North Carolina and eight years of air pollution and population data presented at 0.5 mile (0.805 km) grid cells. We first identify the challenges faced for research attempting to match individual outcomes to contextual effects, then present methods for estimating the effect this uncertainty could introduce into an analysis and finally contextualize these measures as part of a larger framework on uncertainty analysis.

RESULTS

Spatial and temporal uncertainty is highly variable across the children within our cohort and the population in general. For our test datasets, we find greater uncertainty over the life course than in positional accuracy and neighborhood size. Time of day uncertainty is relatively low for these children.

CONCLUSIONS

Spatial and temporal uncertainty should be considered for each individual in a study since the magnitude can vary considerably across observations. The underlying assumptions driving the source data play an important role in the level of measured uncertainty.

Spatiotemporal data mining: a survey on challenges and open problems

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

Difference in ambient-personal exposure to PM2.5 and its inflammatory effect in local residents in urban and peri-urban Beijing, China: results of the AIRLESS project

Measurement of ambient fine particulate matter (PM_2.5) is often used as a proxy of personal exposure in epidemiological studies. However, the difference between personal and ambient exposure, and whether it biases the estimates of health effects remain unknown. Based on an epidemiological study (AIRLESS) and simultaneously launched intensive monitoring campaigns (APHH), we quantified and compared the personal and ambient exposure to PM_2.5 and the related health impact among residents in Beijing, China. In total, 123 urban and 128 peri-urban non-smoking participants were recruited from two well-established cohorts in Beijing. During winter 2016 and summer 2017, each participant was instructed to carry a validated personal air monitor (PAM) to measure PM_2.5 concentration at high spatiotemporal resolution for seven consecutive days in each season. Multiple inflammatory biomarkers were measured, including exhaled NO, blood monocytes counts and C-reactive protein. Linear mixed-effect models were used for the associations between exposure and health outcomes with adjustment for confounders. The average level of daily personal exposure to PM_2.5 was consistently lower than using corresponding ambient concentration, and the difference is greater during the winter. The personal to ambient (P/A) ratio of exposure to PM_2.5 exhibited an exponentially declining trend, and showed larger variations when ambient PM_2.5 levels < 25 μg m^-3. Personal exposure to PM_2.5 was significantly associated with the increase in respiratory and systemic inflammatory biomarkers; however, the associations were weaker or became insignificant when ambient concentrations were used. Exposure to ambient PM_2.5 might not be a good proxy to estimate the health effect of exposure to personal PM_2.5.

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

The impact of individuals' mobility on the degree of error in estimates of exposure to ambient PM2.5 concentrations is increasingly reported in the literature. However, the degree to which accounting for mobility reduces error likely varies as a function of two related factors-individuals' routine travel patterns and the local variations of air pollution fields. We investigated whether individuals' routine travel patterns moderate the impact of mobility on individual long-term exposure assessment. Here, we have used real-world time-activity data collected from 2013 participants in Erie/Niagara counties, New York, USA, matched with daily PM2.5 predictions obtained from two spatial exposure models. We further examined the role of the spatiotemporal representation of ambient PM2.5 as a second moderator in the relationship between an individual's mobility and the exposure measurement error using a random effect model. We found that the effect of mobility on the long-term exposure estimates was significant, but that this effect was modified by individuals' routine travel patterns. Further, this effect modification was pronounced when the local variations of ambient PM2.5 concentrations were captured from multiple sources of air pollution data ('a multi-sourced exposure model'). In contrast, the mobility effect and its modification were not detected when ambient PM2.5 concentration was estimated solely from sparse monitoring data ('a single-sourced exposure model'). This study showed that there was a significant association between individuals' mobility and the long-term exposure measurement error. However, the effect could be modified by individuals' routine travel patterns and the error-prone representation of spatiotemporal variability of PM2.5.

Chemical characterisation of particulate matter in urban transport modes

Traffic is a main source of air pollutants in urban areas and consequently daily peak exposures tend to occur during commuting. Personal exposure to particulate matter (PM) was monitored while cycling and travelling by bus, car and metro along an assigned route in Lisbon (Portugal), focusing on PM2.5 and PM10 (PM with aerodynamic diameter <2.5 and 10 µm, respectively) mass concentrations and their chemical composition. In vehicles, the indoor-outdoor interplay was also evaluated. The PM2.5 mean concentrations were 28 ± 5, 31 ± 9, 34 ± 9 and 38 ± 21 µg/m³ for bus, bicycle, car and metro modes, respectively. Black carbon concentrations when travelling by car were 1.4 to 2.0 times higher than in the other transport modes due to the closer proximity to exhaust emissions. There are marked differences in PM chemical composition depending on transport mode. In particular, Fe was the most abundant component of metro PM, derived from abrasion of rail-wheel-brake interfaces. Enhanced concentrations of Zn and Cu in cars and buses were related with brake and tyre wear particles, which can penetrate into the vehicles. In the motorised transport modes, Fe, Zn, Cu, Ni and K were correlated, evidencing their common traffic-related source. On average, the highest inhaled dose of PM2.5 was observed while cycling (55 µg), and the lowest in car travels (17 µg). Cyclists inhaled higher doses of PM2.5 due to both higher inhalation rates and longer journey times, with a clear enrichment in mineral elements. The presented results evidence the importance of considering the transport mode in exposure assessment studies.

Association between exposure to air pollution during intrauterine life and cephalic circumference of the newborn

It has been observed that air pollution can affect newborn health due to the negative effects of pollutants on pregnancy development. However, few studies have evaluated the impact of maternal exposure to urban air pollution on head circumference (HC) at birth. Reduced head growth during pregnancy may be associated with neurocognitive deficits in childhood. The objectives of this study were to evaluate the association between maternal exposure to air pollution and HC at birth and to provide context with a systematic review to investigate this association. This was a prospective study of low-risk pregnant women living in São Paulo, Brazil. Exposure to pollutants, namely, nitrogen dioxide (NO₂) and ozone (O₃), was measured during each trimester using passive personal samplers. We measured newborn HC until 24 h after birth. We used multiple linear regression models to evaluate the association between pollutants and HC while controlling for known determinants of pregnancy. To perform the systematic review, four different electronic databases were searched through November 2018: CENTRAL, EMBASE, LILACS, and MEDLINE. We selected longitudinal or transversal designs associating air pollution and HC at birth. Two reviewers evaluated the inclusion criteria and risk of bias and extracted data from the included papers. Thirteen studies were selected for the systematic review. We evaluated 391 patients, and we did not observe a significant association between air pollution and HC. Regarding the systematic review, 13 studies were selected for the systematic review, 8 studies showed an inverse association between maternal exposure to pollutants and HC, 4 showed no association, and one observed a direct association. In the city of São Paulo, maternal exposure to pollutants was not significantly associated with HC at birth. The systematic review suggested an inverse association between air pollution and HC at birth.

The MEDEA childhood asthma study design for mitigation of desert dust health effects: implementation of novel methods for assessment of air pollution exposure and lessons learned

BACKGROUND

Desert dust events in Mediterranean countries, originating mostly from the Sahara and Arabian deserts, have been linked to climate change and are associated with significant increase in mortality and hospital admissions from respiratory causes. The MEDEA clinical intervention study in children with asthma is funded by EU LIFE+ program to evaluate the efficacy of recommendations aiming to reduce exposure to desert dust and related health effects.

METHODS

This paper describes the design, methods, and challenges of the MEDEA childhood asthma study, which is performed in two highly exposed regions of the Eastern Mediterranean: Cyprus and Greece-Crete. Eligible children are recruited using screening surveys performed at primary schools and are randomized to three parallel intervention groups: a) no intervention for desert dust events, b) interventions for outdoor exposure reduction, and c) interventions for both outdoor and indoor exposure reduction. At baseline visits, participants are enrolled on MEDena® Health-Hub, which communicates, alerts and provides exposure reduction recommendations in anticipation of desert dust events. MEDEA employs novel environmental epidemiology and telemedicine methods including wearable GPS, actigraphy, health parameters sensors as well as indoor and outdoor air pollution samplers to assess study participants' compliance to recommendations, air pollutant exposures in homes and schools, and disease related clinical outcomes.

DISCUSSION

The MEDEA study evaluates, for the first time, interventions aiming to reduce desert dust exposure and implement novel telemedicine methods in assessing clinical outcomes and personal compliance to recommendations. In Cyprus and Crete, during the first study period (February-May 2019), a total of 91 children participated in the trial while for the second study period (February-May 2020), another 120 children completed data collection. Recruitment for the third study period (February-May 2021) is underway. In this paper, we also present the unique challenges faced during the implementation of novel methodologies to reduce air pollution exposure in children. Engagement of families of asthmatic children, schools and local communities, is critical. Successful study completion will provide the knowledge for informed decision-making both at national and international level for mitigating the health effects of desert dust events in South-Eastern Europe.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT03503812 , April 20, 2018.

Community-Based Measurements Reveal Unseen Differences during Air Pollution Episodes

Short-term exposure to fine particulate matter (PM_2.5) pollution is linked to numerous adverse health effects. Pollution episodes, such as wildfires, can lead to substantial increases in PM_2.5 levels. However, sparse regulatory measurements provide an incomplete understanding of pollution gradients. Here, we demonstrate an infrastructure that integrates community-based measurements from a network of low-cost PM_2.5 sensors with rigorous calibration and a Gaussian process model to understand neighborhood-scale PM_2.5 concentrations during three pollution episodes (July 4, 2018, fireworks; July 5 and 6, 2018, wildfire; Jan 3-7, 2019, persistent cold air pool, PCAP). The firework/wildfire events included 118 sensors in 84 locations, while the PCAP event included 218 sensors in 138 locations. The model results accurately predict reference measurements during the fireworks (n: 16, hourly root-mean-square error, RMSE, 12.3-21.5 μg/m³, n(normalized)RMSE: 14.9-24%), the wildfire (n: 46, RMSE: 2.6-4.0 μg/m³; nRMSE: 13.1-22.9%), and the PCAP (n: 96, RMSE: 4.9-5.7 μg/m³; nRMSE: 20.2-21.3%). They also revealed dramatic geospatial differences in PM_2.5 concentrations that are not apparent when only considering government measurements or viewing the US Environmental Protection Agency's AirNow visualizations. Complementing the PM_2.5 estimates and visualizations are highly resolved uncertainty maps. Together, these results illustrate the potential for low-cost sensor networks that combined with a data-fusion algorithm and appropriate calibration and training can dynamically and with improved accuracy estimate PM_2.5 concentrations during pollution episodes. These highly resolved uncertainty estimates can provide a much-needed strategy to communicate uncertainty to end users.

Assessing personal exposure using Agent Based Modelling informed by sensors technology

Technology innovations create possibilities to capture exposure-related data at a great depth and breadth. Considering, though, the substantial hurdles involved in collecting individual data for whole populations, this study introduces a first approach of simulating human movement and interaction behaviour, using Agent Based Modelling (ABM). A city scale ABM was developed for urban Thessaloniki, Greece that feeds into population-based exposure assessment without imposing prior bias, basing its estimations onto emerging properties of the behaviour of the computerised autonomous decision makers (agents) that compose the city-system. Population statistics, road and buildings networks data were transformed into human, road and building agents, respectively. Survey outputs with time-use patterns were associated with human agent rules, aiming to model representative to real-world behaviours. Moreover, time-geography of exposure data, derived from a local sensors campaign, was used to inform and enhance the model. As a prevalence of an agent-specific decision-making, virtual individuals of different sociodemographic backgrounds express different spatiotemporal behaviours and their trajectories are coupled with spatially resolved pollution levels. Personal exposure was evaluated by assigning PM concentrations to human agents based on coordinates, type of location and intensity of encountered activities. Study results indicated that PM2.5 inhalation adjusted exposure between housemates can differ by 56.5% whereas exposure between two neighbours can vary by as much as 87%, due to the prevalence of different behaviours. This study provides details of a new methodology that permits the cost-effective construction of refined time-activity diaries and daily exposure profiles, taking into account different microenvironments and sociodemographic characteristics. The proposed method leads to a refined exposure assessment model, addressing effectively vulnerable subgroups of population. It can be used for evaluating the probable impacts of different public health policies prior to implementation reducing, therefore, the time and expense required to identify efficient measures.

Low-cost sensors for measuring airborne particulate matter: Field evaluation and calibration at a South-Eastern European site

Low-cost sensors are useful tools for the collection of air quality data, augmenting the existing regulatory monitoring networks and providing an unprecedented opportunity to increase their spatial coverage. This study presents a calibration process of a low-cost PM sensor (PurpleAir PA-II, PAir) in ambient conditions in the city of Patras, Greece, during 18 months of 2017-2018. The hourly PM₁ and PM_2.5 measurements using the original sensor values were reasonably well correlated (R² = 0.82 for PM₁ and R² = 0.56 for PM_2.5) with the reference instrument, but with a high mean bias and root mean square error. There was a small improvement of around 10% for the daily averages. For PM_1-2.5 (particles with diameters between 1 and 2.5 μm), PM_2.5-10 (diameters between 2.5 and 10 μm) and PM₁₀, the performance of the low-cost sensors was poor in this area with R² < 0.37 in all cases. The response of the PAir sensor for PM₁ and PM_2.5 changed significantly compared to the reference instrument during periods with high dust (or other coarse particle) concentrations. These periods were excluded and a simple linear calibration was then developed for the rest of the fine PM measurements. A method for the identification of these high dust periods based on regional model predictions is proposed. This calibration reduces the relative mean error for hourly PM₁ to 19% (1.1 μg m^-3) and for PM_2.5 to 18% (1.1 μg m^-3). The corresponding root mean square errors are 25% (1.4 μg m^-3) for hourly PM₁ and 25% (1.6 μg m^-3) for PM_2.5. The biases of the corrected values are, as expected, practically zero. Surprisingly, the relative humidity had a negligible effect on fine PM measurements of the PAir in this location and for the conditions of the study.

Efficient Classification of Environmental Sounds through Multiple Features Aggregation and Data Enhancement Techniques for Spectrogram Images

Over the past few years, the study of environmental sound classification (ESC) has become very popular due to the intricate nature of environmental sounds. This paper reports our study on employing various acoustic features aggregation and data enhancement approaches for the effective classification of environmental sounds. The proposed data augmentation techniques are mixtures of the reinforcement, aggregation, and combination of distinct acoustics features. These features are known as spectrogram image features (SIFs) and retrieved by different audio feature extraction techniques. All audio features used in this manuscript are categorized into two groups: one with general features and the other with Mel filter bank-based acoustic features. Two novel and innovative features based on the logarithmic scale of the Mel spectrogram (Mel), Log (Log-Mel) and Log (Log (Log-Mel)) denoted as L2M and L3M are introduced in this paper. In our study, three prevailing ESC benchmark datasets, ESC-10, ESC-50, and Urbansound8k (Us8k) are used. Most of the audio clips in these datasets are not fully acquired with sound and include silence parts. Therefore, silence trimming is implemented as one of the pre-processing techniques. The training is conducted by using the transfer learning model DenseNet-161, which is further fine-tuned with individual optimal learning rates based on the discriminative learning technique. The proposed methodologies attain state-of-the-art outcomes for all used ESC datasets, i.e., 99.22% for ESC-10, 98.52% for ESC-50, and 97.98% for Us8k. This work also considers real-time audio data to evaluate the performance and efficiency of the proposed techniques. The implemented approaches also have competitive results on real-time audio data.

Dynamic classification of personal microenvironments using a suite of wearable, low-cost sensors

Human exposure to air pollution is associated with increased risk of morbidity and mortality. However, personal air pollution exposures can vary substantially depending on an individual's daily activity patterns and air quality within their residence and workplace. This work developed and validated an adaptive buffer size (ABS) algorithm capable of dynamically classifying an individual's time spent in predefined microenvironments using data from global positioning systems (GPS), motion sensors, temperature sensors, and light sensors. Twenty-two participants in Fort Collins, CO were recruited to carry a personal air sampler for a 48-h period. The personal sampler was retrofitted with a GPS and a pushbutton to complement the existing sensor measurements (temperature, motion, light). The pushbutton was used in conjunction with a traditional time-activity diary to note when the participant was located at "home", "work", or within an "other" microenvironment. The ABS algorithm predicted the amount of time spent in each microenvironment with a median accuracy of 99.1%, 98.9%, and 97.5% for the "home", "work", and "other" microenvironments. The ability to classify microenvironments dynamically in real time can enable the development of new sampling and measurement technologies that classify personal exposure by microenvironment.

Influence of methodology on the estimation of the particle surface area dose received by a population in all-day activities

In everyday life, people are exposed to different concentrations of airborne particles depending on the microenvironment where they perform their different activities. Such exposure can lead to high sub-micron particle doses. The received dose depends on particle concentration to which people are exposed (typically expressed in terms of number or surface area), time spent in each activity or microenvironment (time activity pattern) and amount of air inhaled (inhalation rate). To estimate an actual value of the received dose, all these parameters should be measured under real-life conditions; in fact, the concentrations should be measured on a personal scale (i.e. through a direct exposure assessment), whereas time activity patterns and inhalation rates specific to the activity performed should be considered. The difficulties in obtaining direct measurements of these parameters usually lead to adopt time activity patterns and inhalation rates already available in scientific literature for typical populations, and local outdoor particle concentrations measured with fixed monitoring stations and extrapolated for all the other microenvironments. To overcome these limitations, we propose a full-field method for estimating the received dose of a population sample, in which all the parameters (concentration levels, time activity patterns and inhalation rates) are measured under real-life conditions (also including the inhalation rates, that were evaluated on the basis of the measured heart rates). Specifically, 34 volunteers were continuously monitored for seven days and the data of sub-micron particle concentrations, activities performed, and inhalation rates were recorded. The received dose was calculated with the proposed method and compared with those obtained from different simplified methodologies that consider typical data of particle concentrations, time activity patterns and inhalation rates obtained from literature. The results show that, depending on the methodology used, the differences in the received daily dose can be significant, with a general underestimation of the most simplified method.

Using low-cost sensor technologies and advanced computational methods to improve dose estimations in health panel studies: results of the AIRLESS project

BACKGROUND

Air pollution epidemiology has primarily relied on fixed outdoor air quality monitoring networks and static populations.

METHODS

Taking advantage of recent advancements in sensor technologies and computational techniques, this paper presents a novel methodological approach that improves dose estimations of multiple air pollutants in large-scale health studies. We show the results of an intensive field campaign that measured personal exposures to gaseous pollutants and particulate matter of a health panel of 251 participants residing in urban and peri-urban Beijing with 60 personal air quality monitors (PAMs). Outdoor air pollution measurements were collected in monitoring stations close to the participants' residential addresses. Based on parameters collected with the PAMs, we developed an advanced computational model that automatically classified time-activity-location patterns of each individual during daily life at high spatial and temporal resolution.

RESULTS

Applying this methodological approach in two established cohorts, we found substantial differences between doses estimated from outdoor and personal air quality measurements. The PAM measurements also significantly reduced the correlation between pollutant species often observed in static outdoor measurements, reducing confounding effects.

CONCLUSIONS

Future work will utilise these improved dose estimations to investigate the underlying mechanisms of air pollution on cardio-pulmonary health outcomes using detailed medical biomarkers in a way that has not been possible before.

Environmental Health Surveillance System for a Population Using Advanced Exposure Assessment

Human exposure to air pollution is a major public health concern. Environmental policymakers have been implementing various strategies to reduce exposure, including the 10th-day-no-driving system. To assess exposure of an entire population of a community in a highly polluted area, pollutant concentrations in microenvironments and population time–activity patterns are required. To date, population exposure to air pollutants has been assessed using air monitoring data from fixed atmospheric monitoring stations, atmospheric dispersion modeling, or spatial interpolation techniques for pollutant concentrations. This is coupled with census data, administrative registers, and data on the patterns of the time-based activities at the individual scale. Recent technologies such as sensors, the Internet of Things (IoT), communications technology, and artificial intelligence enable the accurate evaluation of air pollution exposure for a population in an environmental health context. In this study, the latest trends in published papers on the assessment of population exposure to air pollution were reviewed. Subsequently, this study proposes a methodology that will enable policymakers to develop an environmental health surveillance system that evaluates the distribution of air pollution exposure for a population within a target area and establish countermeasures based on advanced exposure assessment.

Monitoring Excess Exposure to Air Pollution for Professional Drivers in London Using Low-Cost Sensors

In this pilot study, low-cost air pollution sensor nodes were fitted in waste removal trucks, hospital vans and taxis to record drivers’ exposure to air pollution in Central London. Particulate matter (PM 2.5 and PM 10 ), CO 2 , NO 2 , temperature and humidity were recorded in real-time with nodes containing low-cost sensors, an electrochemical gas sensor for NO 2 , an optical particle counter for PM 2.5 and PM 10 and a non-dispersive infrared (NDIR) sensor for CO 2 , temperature and relative humidity. An intervention using a pollution filter to trap PM and NO 2 was also evaluated. The measurements were compared with urban background and roadside monitoring stations at Honor Oak Park and Marylebone Road, respectively. The vehicle records show PM and NO 2 concentrations similar to Marylebone Road and a higher NO 2 -to-PM ratio than at Honor Oak Park. Drivers are exposed to elevated pollution levels relative to Honor Oak Park: 1.72 μ g m − 3 , 1.92 μ g m − 3 and 58.38 ppb for PM 2.5 , PM 10 , and NO 2 , respectively. The CO 2 levels ranged from 410 to over 4000 ppm. There is a significant difference in average concentrations of PM 2.5 and PM 10 between the vehicle types and a non-significant difference in the average concentrations measured with and without the pollution filter within the sectors. In conclusion, drivers face elevated air pollution exposure as part of their jobs.

Patterns of distributive environmental inequity under different PM2.5 air pollution scenarios for Salt Lake County public schools

Previous studies have cataloged social disparities in air pollution exposure in US public schools with respect to race/ethnicity and socioeconomic status. These studies rely upon chronic, averaged measures of air pollution, which fosters a static conception of exposure disparities. This paper examines PM_2.5 exposure disparities in Salt Lake County (SLC), Utah public schools under three different PM_2.5 scenarios-relatively clean air, a moderate winter persistent cold air pool (PCAP), and a major winter PCAP-with respect to race/ethnicity, economic deprivation, student age, and school type. We pair demographic data for SLC schools (n = 174) with modelled PM_2.5 values, obtained from a distributed network of sensors placed through a community-university partnership. Results from generalized estimating equations controlling for school district clustering and other covariates reveal that patterns of social inequality vary under different PM_2.5 pollution scenarios. Charter schools and schools serving economically deprived students experienced disproportionate exposure during relatively clean air and moderate PM_2.5 PCAP conditions, but those inequalities attenuated under major PCAP conditions. Schools with higher proportions of racial/ethnic minority students were unequally exposed under all PM_2.5 pollution scenarios, reflecting the robustness of racial/ethnic disparities in exposure. The findings speak to the need for policy changes to protect school-aged children from environmental harm in SLC and elsewhere.

Personal exposure to particulate matter in peri-urban India: predictors and association with ambient concentration at residence

Scalable exposure assessment approaches that capture personal exposure to particles for purposes of epidemiology are currently limited, but valuable, particularly in low-/middle-income countries where sources of personal exposure are often distinct from those of ambient concentrations. We measured 2 × 24-h integrated personal exposure to PM_2.5 and black carbon in two seasons in 402 participants living in peri-urban South India. Means (sd) of PM_2.5 personal exposure were 55.1(82.8) µg/m³ for men and 58.5(58.8) µg/m³ for women; corresponding figures for black carbon were 4.6(7.0) µg/m³ and 6.1(9.6) µg/m³. Most variability in personal exposure was within participant (intra-class correlation ~20%). Personal exposure measurements were not correlated (R_spearman < 0.2) with annual ambient concentration at residence modeled by land-use regression; no subgroup with moderate or good agreement could be identified (weighted kappa ≤ 0.3 in all subgroups). We developed models to predict personal exposure in men and women separately, based on time-invariant characteristics collected at baseline (individual, household, and general time-activity) using forward stepwise model building with mixed models. Models for women included cooking activities and household socio-economic position, while models for men included smoking and occupation. Models performed moderately in terms of between-participant variance explained (38-53%) and correlations between predictions and measurements (R_spearman: 0.30-0.50). More detailed, time-varying time-activity data did not substantially improve the performance of the models. Our results demonstrate the feasibility of predicting personal exposure in support of epidemiological studies investigating long-term particulate matter exposure in settings characterized by solid fuel use and high occupational exposure to particles.

Inferring Respiratory Minute Volume from Wrist Motion

Exposure to air pollutants poses major health risk for patients with chronic pulmonary diseases such as asthma, bronchitis, and emphysema. Such risk can be mitigated by continuous exposure tracking. The effective dose of exposure is directly proportional to the respiratory minute volume, aka minute ventilation (VE). Till date, the clinical standard for measuring VE is Spirometry, a highly invasive and cumbersome modality, which is not suitable for continuous day-to-day use. This paper presents a novel non-invasive method toward continuous assessment of VE using a wrist-mount wearable motion sensor. Data from 25 healthy subjects were collected while they performed ambulatory and sedentary activities and physical exercises. Noise and artifacts of the motion signal are removed and the processed signal is used to extract explanatory features. The features are used to train and evaluate multiple regression models, among which, the probabilistic Gaussian process regression achieves the best performance in inferring VE from the wearable motion signal. The effects of inter- and intra-personal variations are explored to demonstrate the potential of the proposed method for continuously monitoring pollutant exposure risk in respiratory health applications.

Personal exposure to particulate air pollution and vascular damage in peri-urban South India

OBJECTIVE

Air pollution is a leading preventable risk factor for cardiovascular diseases. Previous studies mostly relied on concentrations at residence, which might not represent personal exposure. Personal air pollution exposure has a greater variability compared with levels of ambient air pollution, facilitating evaluation of exposure-response functions and vascular pathophysiology. We aimed to evaluate the association between predicted annual personal exposure to PM2.5 and black carbon (BC) and three vascular damage markers in peri-urban South India.

METHODS

We analyzed the third wave of the APCAPS cohort (2010-2012), which recruited participants from 28 villages. We used predicted personal exposure to PM2.5 and BC derived from 610 participant-days of 24 h average gravimetric PM2.5 and BC measurements and predictors related to usual time-activity. Outcomes included carotid intima-media thickness (CIMT), carotid-femoral pulse wave velocity (cf-PWV) and augmentation index (AIx). We fit linear mixed models, adjusting for potential confounders and accounting for the clustered data structure. We evaluated nonlinear associations using generalized additive mixed models.

RESULTS

Of the 3017 participants (mean age 38 years), 1453 (48%) were women. The average PM2.5 exposure was 51 µg/m3 (range 13-85) for men, and 61 µg/m3 (range 40-120) for women, while the average BC was 4 µg/m3 (range 3-7) for men and 8 µg/m3 (range 3-22) for women. A 10 μg/m3 increase of PM2.5 was positively associated with CIMT (0.026 mm, 95% CI 0.014, 0.037), cf-PWV (0.069 m/s, 95% CI 0.008, 0.131) and AIx (0.8%, 95% CI 0.3, 1.3) among men. The exposure-response function for PM2.5 and AIx among men showed non-linearity, particularly within the exposure range dominated by tobacco smoking and occupational exposures. Both PM2.5 and BC were positively associated with AIx among women (0.6%, 95% CI 0.2, 1.0, per 10 μg/m3 PM2.5; 0.5%, 95% CI 0.1, 0.8, per 2 μg/m3 BC).

CONCLUSIONS

Personal exposure to particulate matter was associated with vascular damage in a peri-urban population in South India. Personal exposure to particulate matter appears to have gender-specific effects on the type of vascular damage, potentially reflecting differences in sources of personal exposure by gender.

Personal strategies to minimise effects of air pollution on respiratory health: advice for providers, patients and the public

As global awareness of air pollution rises, so does the imperative to provide evidence-based recommendations for strategies to mitigate its impact. While public policy has a central role in reducing air pollution, exposure can also be reduced by personal choices. Qualified evidence supports limiting physical exertion outdoors on high air pollution days and near air pollution sources, reducing near-roadway exposure while commuting, utilising air quality alert systems to plan activities, and wearing facemasks in prescribed circumstances. Other strategies include avoiding cooking with solid fuels, ventilating and isolating cooking areas, and using portable air cleaners fitted with high-efficiency particulate air filters. We detail recommendations to assist providers and public health officials when advising patients and the public regarding personal-level strategies to mitigate risk imposed by air pollution, while recognising that well-designed prospective studies are urgently needed to better establish and validate interventions that benefit respiratory health in this context.

Biomass cooking carbon monoxide levels in commercial canteens in Kigali, Rwanda

Carbon monoxide (CO) is harmful to human health, yet there is limited evidence concerning emissions associated with biomass fuel cooking in occupational settings. Real-time 48-hour monitoring of CO concentrations at breathing height, was undertaken in staff and student kitchen and serving areas of two commercial canteens. We characterized two diurnal CO peaks coinciding with cooking activities. Peak CO concentrations of 255.5 ppm and 1-hour average of 76.3 ppm (IQR: 57.8-109.0 ppm) were observed in the student kitchen; the staff kitchen levels were 208.5 ppm, and 76.3 ppm (IQR: 52.5-114.0 ppm), respectively. High magnitude CO concentrations (8-hour average: 40.7 ppm SD: 40.0 ppm) which exceed World Health Organisation (WHO) Indoor Air Quality standards were observed. Further investigation of personal exposure and health impacts among kitchen staff is required, to inform interventions in this setting.

Association between microenvironment air quality and cardiovascular health outcomes

Exposure to fine particulate matter (PM_2.5) is associated with cardiovascular disease risk. To date, there are few studies on short-term PM_2.5 exposure in different microenvironments and its impact on immediate health effects, particularly in the Southeast Asia region. This study assessed PM_2.5 concentrations in different microenvironments in a densely populated city in the tropics using low-cost personal PM_2.5 sensors as well as their associations with acute cardiovascular health outcomes. A total of 49 adult participants affiliated with the National University of Singapore (NUS) community were recruited. Personal low-cost sensors were used to measure PM_2.5 concentrations between September 2017 and March 2019. Demographic information and time-activity patterns were collected using questionnaires. Wilcoxon pairwise comparisons were used to determine statistical differences between PM_2.5 exposures at 18 different microenvironments. Generalized Estimating Equations (GEE) models were used to assess the association between PM_2.5 exposure and blood pressure as well as heart rate. All models were adjusted for age, sex, body mass index, physical activity, temperature, duration of exposure, and baseline cardiovascular parameters. Significant differences in PM_2.5 concentrations were observed across different microenvironments. Air-conditioned offices and tertiary teaching spaces had the lowest (median = 13.1 μg/m³) and hawker centres had the highest (median = 32.0 μg/m³) PM_2.5 concentrations. Significant positive associations between PM_2.5 exposure and heart rate (β = 0.40, p = 4.6 × 10^-5) as well as diastolic blood pressure (β = 0.16, p = 0.0077) were also observed. Short-term exposure to PM_2.5 was significantly associated with higher heart rate and blood pressure. Further work is needed to investigate the variations within each type of microenvironment and expand the study to other sub-populations such as the elderly and children.

The evaluation and optimization of calibration methods for low-cost particulate matter sensors: Inter-comparison between fixed and mobile methods

With the development of the air pollution control, the low-cost sensors are widely used in air quality monitoring, while the data quality of these sensors is always the most concern for users. In this study, data from nine air monitoring stations with standard PM instruments were used as reference and compared with the data of mobile and fixed PM sensors in Jinan, the capital city of Shandong Province, China. Data quality of PM sensors was checked by the cross-comparison among standard method, fixed and mobile sensors. And the impacts of relative humidity and size distribution (PM_2.5/PM₁₀) on the performance of PM sensors were evaluated as well. To optimize the calibration method for both fixed and mobile PM sensors, a two-step model was designed, in which the RH and PM_2.5/PM₁₀ ratio were both used as input parameters. We firstly calibrated the sensors with five independent models, and then all the calibrated data were linearly fitted by the LR-final model. In comparison with standard instruments, the LR-final model increased the R² values of the PM_2.5 and PM₁₀ measured by fixed sensors from 0.89 and 0.79 to 0.98 and 0.97, respectively. The R² values of PM_2.5 and PM₁₀ measured by the mobile sensors both increased to 0.99 from 0.79 and 0.62. Overall, the two-step calibration model appeared to be a promising approach to solve the poor performance of low-cost sensors.

Short term seasonal effects of airborne fungal spores on lung function in a panel study of schoolchildren residing in informal settlements of the Western Cape of South Africa

BACKGROUND

The individual effects of biological constituents of particulate matter (PM) such as fungal spores, on lung function in children are not well known. This study investigated the seasonal short-term effect of daily variation in Alternaria and Cladosporium fungal spores on lung function in schoolchildren.

METHODS

This panel study evaluated 313 schoolchildren in informal settlements of the Western Cape of South Africa, exposed to spores of two commonly encountered fungi, Alternaria and Cladosporium species. The children provided forced-expiratory volume in 1-s (FEV₁) and peak-expiratory flow (PEF) measurements thrice daily for two consecutive school-weeks in summer and winter. Daily PM₁₀ levels, from a stationary ambient air quality monitor and fungal spore levels using spore traps were measured in each study area throughout the year. The effects of Alternaria and Cladosporium spores, on lung function were analysed for lag periods up to five-days, adjusting-for PM₁₀, other pollen exposures, study area, and other host and meteorological factors. Same-day exposure-response curves were computed for both fungal species.

RESULTS

There was more variability in Alternaria spores level with noticeable peaks in summer. There were consistent lag-effects for Alternaria on PEF compared to Cladosporium, with the largest PEF deficit observed in winter (mean deficit: 13.78 L/min, 95%CI: 24.34 to -3.23 L/min) per 10spores/m³ increase in Alternaria spores on lag day-2. Although there were no observable lag-effects for Alternaria and Cladosporium on FEV₁, same-day effects of Cladosporium spores on FEV₁ was present across both seasons. Threshold effects of Alternaria on both PEF and FEV₁ deficits were apparent at levels of 100 spores/m³, but could not be explored for Cladosporium beyond the levels observed during the study.

CONCLUSION

The study provides evidence for the independent effects of daily exposure to ambient fungal spores of Alternaria and Cladosporium on lung function deficits, more especially in winter for PEF.

Influence of travel behaviour and daily mobility on exposure to traffic-related air pollution

This study evaluates the daily exposure of urban residents across various commuting modes and destinations by intersecting data from a travel survey with exposure surfaces for ultrafine particles and black carbon, in Toronto, Canada. We demonstrate that exposure misclassification is bound to arise when we approximate daily exposure with the concentration at the home location. We also identify potential inequities in the distribution of exposure to traffic-related air pollution whereby those who are mostly responsible for the generation of traffic-related air pollution (drivers and passengers) are exposed the least while active commuters and transit riders, are exposed the most.

Application of an advanced spatiotemporal model for PM2.5 prediction in Jiangsu Province, China

Either long- or short-term of fine particle (PM_2.5) exposure is associated with adverse health effects especially for children. Primary school students spend much time in schools whereas PM_2.5 prediction for such fine-scale places remains a demanding task, let alone a combined prediction with high temporal resolution. The study aimed to estimate PM_2.5 levels of different time scales for primary schools in Jiangsu Province, China. Hourly PM_2.5 measurements within the academic year (Sept. 2016-June 2017) were collected from 72 routine monitoring sites. Together with PM_2.5 emission inventory and dozens of geographic variables, an advanced spatiotemporal land use regression (LUR) model was employed to estimate PM_2.5 concentrations of biweekly, seasonal and academic year levels in Jiangsu Province at 2457 primary school locations. 10-fold cross-validation verified high prediction ability with squared correlations R_CV² of 0.72 for temporal and 0.71 for spatial changes. PM_2.5 levels in primary schools in Nanjing and Nantong were >10% higher than that of the corresponding cities while pollution levels in primary schools in Xuzhou were >20% lower. For 10 out of the 13 cities in Jiangsu, PM_2.5 levels for primary schools surpassed 70 μg/m³ in winter. Schools in Lianyungang, Zhenjiang and Huai'an suffered the most. This study demonstrated the fine-scale prediction ability of the novel spatiotemporal LUR model, as well as the potential and necessity to apply it in epidemiological studies. It also verified the emergency of pollution control for primary schools from cities such as Lianyungang and Zhenjiang.

Modelling Cyclists’ Multi-Exposure to Air and Noise Pollution with Low-Cost Sensors—The Case of Paris

Cyclists are particularly exposed to air and noise pollution because of their higher ventilation rate and their proximity to traffic. However, few studies have investigated their multi-exposure and have taken into account its real complexity in building statistical models (nonlinearity, pseudo replication, autocorrelation, etc.). We propose here to model cyclists’ exposure to air and noise pollution simultaneously in Paris (France). Specifically, the purpose of this study is to develop a methodology based on an extensive mobile data collection using low-cost sensors to determine which factors of the urban micro-scale environment contribute to cyclists’ multi-exposure and to what extent. To this end, we developed a conceptual framework to define cyclists’ multi-exposure and applied it to a multivariate generalized additive model with mixed effects and temporal autocorrelation. The results show that it is possible to reduce cyclists’ multi-exposure by adapting the planning and development practices of cycling infrastructure, and that this reduction can be substantial for noise exposure.

Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution

Airborne particulate matter (PM) exposure has been identified as a key environmental risk factor, associated especially with diseases of the respiratory and cardiovascular system and with almost 9 million premature deaths per year. Low-cost optical sensors for PM measurement are desirable for monitoring exposure closer to the personal level and particularly suited for developing spatiotemporally dense city sensor networks. However, questions remain over the accuracy and reliability of the data they produce, particularly regarding the influence of environmental parameters such as humidity and temperature, and with varying PM sources and concentration profiles. In this study, eight units each of five different models of commercially available low-cost optical PM sensors (40 individual sensors in total) were tested under controlled laboratory conditions, against higher-grade instruments for: lower limit of detection, response time, responses to sharp pollution spikes lasting <1 min , and the impact of differing humidity and PM source. All sensors detected the spikes generated with a varied range of performances depending on the model and presenting different sensitivity mainly to sources of pollution and to size distributions with a lesser impact of humidity. The sensitivity to particle size distribution indicates that the sensors may provide additional information to PM mass concentrations. It is concluded that improved performance in field monitoring campaigns, including tracking sources of pollution, could be achieved by using a combination of some of the different models to take advantage of the additional information made available by their differential response.

Roof cavity dust as an exposure proxy for extreme air pollution events

Understanding exposure to air pollution during extreme events such as fire emergencies is critical for assessing their potential health impacts. However, air pollution emergencies often affect places without a network of air quality monitoring and characterising exposure retrospectively is methodologically challenging due to the complex behaviour of smoke and other air pollutants. Here we test the potential of roof cavity (attic) dust to act as a robust household-level exposure proxy, using a major air pollution event associated with a coal mine fire in the Latrobe Valley, Australia, as an illustrative study. To assess the relationship between roof cavity dust composition and mine fire exposure, we analysed the elemental and polycyclic aromatic hydrocarbon composition of roof cavity dust (<150μm) from 39 homes along a gradient of exposure to the mine fire plume. These homes were grouped into 12 zones along this exposure gradient: eight zones across Morwell, where mine fire impacts were greatest, and four in other Latrobe Valley towns at increasing distance from the fire. We identified two elements-barium and magnesium-as 'chemical markers' that show a clear and theoretically grounded relationship with the brown coal mine fire plume exposure. This relationship is robust to the influence of plausible confounders and contrasts with other, non-mine fire related elements, which showed distinct and varied distributional patterns. We conclude that targeted components of roof cavity dust can be a useful empirical marker of household exposure to severe air pollution events and their use could support epidemiological studies by providing spatially-resolved exposure estimates post-event.

Integrating Modes of Transport in a Dynamic Modelling Approach to Evaluate Population Exposure to Ambient NO2 and PM2.5 Pollution in Urban Areas

To evaluate the effectiveness of alternative policies and measures to reduce air pollution effects on urban citizen's health, population exposure assessments are needed. Due to road traffic emissions being a major source of emissions and exposure in European cities, it is necessary to account for differentiated transport environments in population dynamics for exposure studies. In this study, we applied a modelling system to evaluate population exposure in the urban area of Hamburg in 2016. The modeling system consists of an urban-scale chemistry transport model to account for ambient air pollutant concentrations and a dynamic time-microenvironment-activity (TMA) approach, which accounts for population dynamics in different environments as well as for infiltration of outdoor to indoor air pollution. We integrated different modes of transport in the TMA approach to improve population exposure assessments in transport environments. The newly developed approach reports 12% more total exposure to NO2 and 19% more to PM2.5 compared with exposure estimates based on residential addresses. During the time people spend in different transport environments, the in-car environment contributes with 40% and 33% to the annual sum of exposure to NO2 and PM2.5, in the walking environment with 26% and 30%, in the cycling environment with 15% and 17% and other environments (buses, subway, suburban, and regional trains) with less than 10% respectively. The relative contribution of road traffic emissions to population exposure is highest in the in-car environment (57% for NO2 and 15% for PM2.5). Results for population-weighted exposure revealed exposure to PM2.5 concentrations above the WHO AQG limit value in the cycling environment. Uncertainties for the exposure contributions arising from emissions and infiltration from outdoor to indoor pollutant concentrations range from -12% to +7% for NO2 and PM2.5. The developed "dynamic transport approach" is integrated in a computationally efficient exposure model, which is generally applicable in European urban areas. The presented methodology is promoted for use in urban mobility planning, e.g., to investigate on policy-driven changes in modal split and their combined effect on emissions, population activity and population exposure.

Identifying predictors of personal exposure to air temperature in peri-urban India

Characterizing personal exposure to air temperature is critical to understanding exposure measurement error in epidemiologic studies using fixed-site exposure data and to identify strategies to protect public health. To date, no study evaluating personal air temperature in the general population has been conducted in a low-and-middle income country. We used data from the CHAI study consisting of 50 adults monitored in up to six non-consecutive 24 h sessions in peri-urban south India. We quantified the agreement and association between fixed-site ambient and personal air temperature, and identified predictors of personal air temperature based on housing assessment, self-reported, GPS, remote sensing, and wearable camera data. Mean (SD) daytime (6 am-10 pm) average personal air temperature was 31.2 (2.6) °C and mean nighttime (10 pm-6 am) average temperature was 28.8 (2.8) °C. Agreement between average personal air and fixed-site ambient temperatures was limited, especially at night when personal air temperatures were underestimated by fixed-site temperatures (MBE = -5.6 °C). The proportion of average personal nighttime temperature variability explained by ambient fixed-site temperatures was moderate (R²_mar = 0.39); daytime associations were stronger for women (R²_mar = 0.51) than for men (R²_mar = 0.3). Other predictors of average nighttime personal air temperature included residential altitude, ceiling height, and household income. Predictors of average daytime personal air temperature included roof materials, GPS-tracked altitude, time working in agriculture (for women), and time travelling (for men). No biomass cooking, urban heat island, or greenspace effects were identified. R²_mar between ambient fixed-site and personal air temperature indicate that ambient fixed-site temperature is only a moderately useful proxy of personal air temperature in the context of peri-urban India. Our findings suggest that people living in houses at lower altitude, with lower ceiling height and asbestos roofing sheets might be more vulnerable to heat. We also identified households with higher income, women working in agriculture and men with long commutes as disproportionately exposed to high temperatures.

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.

Perceptions and experiences of outdoor occupational workers using digital devices for geospatial biometeorological monitoring

Wearable devices have the potential to track and monitor a wide range of biometeorological conditions (e.g., temperature, UV, air quality) and health outcomes (e.g., mental stress, physical activity, physiologic strain, and cognitive impairments). These sensors provide the potential for personalized environmental exposure information that can be harnessed for at-risk populations. Personalized environmental exposure information is of particular importance for populations that are continuously exposed to hazardous environmental conditions or with underlying health conditions. Yet, for these devices to be effective, individuals must be willing to monitor their health and, if prompted, adhere to warnings or notifications. To date, no study has examined the perceptions and use of digital devices and wearable sensors among vulnerable outdoor working populations. This study evaluated digital device use and perceptions among a population of groundworkers in three diverse geographic sites in the southeastern USA (Boone, NC, Raleigh, NC, and Starkville, MS). Our results demonstrate that biometeorological health interventions should focus on smartphone technology as a platform for monitoring environmental exposure and associated health outcomes. It was encouraging to find that those study participants were very likely to wear sensors and utilize global positioning system technology despite potential privacy issues. In addition, 3 out of 4 workers indicated that they would change their behavior if given a personalized heat preventive warning. Future development of wearable sensors and smartphone applications should integrate personalized weather warnings and ensure privacy to facilitate the use of these technologies among vulnerable populations.

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 PM_2.5 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 PM_2.5 from outdoor to indoor. The approach is tested comprehensively by modelling ambient air concentrations of PM_2.5 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 PM_2.5 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 PM_2.5 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.

Particle exposure and inhaled dose while commuting in Lisbon

While commuting, individuals are exposed to high concentrations of urban air pollutants that can lead to adverse health effects. This study aims to assess commuters' exposure to particulate matter (PM) when travelling by car, bicycle, metro and bus in Lisbon. Mass concentrations of PM_2.5 and PM₁₀ were higher in the metro. On the other hand, the highest BC and PN_0.01-1 average concentrations were found in car and bus mode, respectively. In cars, the outdoor concentrations and the type of ventilation appeared to affect the indoor concentrations. In fact, the use of ventilation led to a decrease of PM_2.5 and PM₁₀ concentrations and to an increase of BC concentrations. The highest inhaled doses were mostly observed in bicycle journeys, due to the longest travel periods combined with enhanced physical activity and, consequently, highest inhalation rates.

Novel Approaches to Air Pollution Exposure and Clinical Outcomes Assessment in Environmental Health Studies

An accurate assessment of pollutants’ exposure and precise evaluation of the clinical outcomes pose two major challenges to the contemporary environmental health research. The common methods for exposure assessment are based on residential addresses and are prone to many biases. Pollution levels are defined based on monitoring stations that are sparsely distributed and frequently distanced far from residential addresses. In addition, the degree of an association between outdoor and indoor air pollution levels is not fully elucidated, making the exposure assessment all the more inaccurate. Clinical outcomes’ assessment, on the other hand, mostly relies on the access to medical records from hospital admissions and outpatients’ visits in clinics. This method differentiates by health care seeking behavior and is therefore, problematic in evaluation of an onset, duration, and severity of an outcome. In the current paper, we review a number of novel solutions aimed to mitigate the aforementioned biases. First, a hybrid satellite-based modeling approach provides daily continuous spatiotemporal estimations with improved spatial resolution of 1 × 1 km2 and 200 × 200 m2 grid, and thus allows a more accurate exposure assessment. Utilizing low-cost air pollution sensors allowing a direct measurement of indoor air pollution levels can further validate these models. Furthermore, the real temporal-spatial activity can be assessed by GPS tracking devices within the individuals’ smartphones. A widespread use of smart devices can help with obtaining objective measurements of some of the clinical outcomes such as vital signs and glucose levels. Finally, human biomonitoring can be efficiently done at a population level, providing accurate estimates of in-vivo absorbed pollutants and allowing for the evaluation of body responses, by biomarkers examination. We suggest that the adoption of these novel methods will change the research paradigm heavily relying on ecological methodology and support development of the new clinical practices preventing adverse environmental effects on human health.

Personal exposure to fine particulate matter (PM2.5) of pregnant women during three trimesters in rural Yunnan of China

Little is known about fine particulate matter (PM_2.5) exposure among pregnant women in rural China. This study aims to characterize exposure to PM_2.5 among pregnant women in rural China, and investigate potential risk factors of personal exposure to PM_2.5. The data were obtained from a birth cohort study that enrolled 606 pregnant women in Xuanwei, a county known for its high rates of lung cancer. The personal exposure to PM_2.5 was measured using small portable particulate monitors during each trimester of pregnancy. Participants were interviewed using structured questionnaires that sought information on risk factors of PM_2.5 exposure. The daily exposure to PM_2.5 among the pregnant women ranged from 19.68 to 97.08 μg/m³ (median = 26.08). Exposure to PM_2.5 was higher in winter and autumn than other seasons (p < 0.05); higher during the day than during the night (p < 0.001); and greater during cooking hours than during the rest of the day (p < 0.001). Using a mixed effects model, domestic solid fuel for cooking (β = 1.75, p < 0.001), winter and autumn (β = 2.96, p < 0.001), cooking ≥ once per day (β = 1.58, p < 0.05), heating with coal (β = 1.69, p < 0.001), secondhand smoke exposure (β = 1.59, p < 0.001) and township 1(β = 2.39, p < 0.001) were identified as risk factors for personal exposure to PM_2.5 of pregnant women throughout pregnancy. Indirect effects of season and township factors on personal PM_2.5 exposure were mediated by heating, cooking and domestic fuel using. In conclusion, PM_2.5 levels in Xuanwei exceeded WHO guidelines. Seasonal and township factors and individual behaviors like domestic solid fuel using for cooking, heating with coal and secondhand smoke exposure are associated with higher personal PM_2.5 exposure among pregnant women in rural China.

Relationships between airborne pollutants, serum albumin adducts and short-term health outcomes in an experimental crossover study

Exposure to air pollution can have both short-term and long-term effects on health. However, the relationships between specific pollutants and their effects can be obscured by characteristics of both the pollution and the exposed population. One way of elucidating the relationships is to link exposures and internal changes at the level of the individual. To this end, we combined personal exposure monitoring (59 individuals, Oxford Street II crossover study) with mass-spectrometry-based analyses of putative serum albumin adducts (fixed-step selected reaction monitoring). We attempted to infer adducts' identities using data from another, higher-resolution mass spectrometry method, and were able to detect a semi-synthetic standard with both methods. A generalised least squares regression method was used to test for associations between amounts of adducts and pollution measures (ambient concentrations of nitrogen dioxide and particulate matter), and between amounts of adducts and short-term health outcomes (measures of lung health and arterial stiffness). Amounts of some putative adducts (e.g., one with a positive mass shift of ∼143 Da) were associated with exposure to pollution (11 associations), and amounts of other adducts were associated with health outcomes (eight associations). Adducts did not appear to provide a link between exposures and short-term health outcomes.

Extensive evaluation and classification of low-cost dust sensors in laboratory using a newly developed test method

An extensive evaluation of low-cost dust sensors was performed using an exponentially decaying particle concentration. A total of 264 sensors including 27 sensors with light-emitting diodes (LEDs) and 237 sensors with laser lighting sources were tested. Those tested sensors were classified into 4 groups based on the deviation from the reference data obtained by a reference instrument. The response linearities of all the tested samples for PM1 , PM2.5 , and PM10 were in excellent agreement with the reference instrument, except a few samples. For the measurements of PM1 and PM2.5 , the lighting source, that is, LED or laser, did not show any significant difference in overall sensor performance. However, LED-based sensors did not perform well for PM10 measurements. The 32, 24, and 16% of all the tested sensors for PM1 , PM2.5 , and PM10 measurement, respectively, are in the category of Class 1 (reference instrument reading ± 20%) requirement. The performance of the low-cost dust sensors for PM10 measurement was relatively less satisfactory.

Mobile-Platform Measurement of Air Pollutant Concentrations in California: Performance Assessment, Statistical Methods for Evaluating Spatial Variations, and Spatial Representativeness

Mobile platform measurements provide new opportunities for characterizing spatial variations of air pollution within urban areas, identifying emission sources, and enhancing knowledge of atmospheric processes. The Aclima, Inc. mobile measurement and data acquisition platform was used to equip four Google Street View cars with research-grade instruments, two of which were available for the duration of this study. On-road measurements of air quality were made during a series of sampling campaigns between May 2016 and September 2017 at high (i.e., 1-second [s]) temporal and spatial resolution at several California locations: Los Angeles, San Francisco, and the northern San Joaquin Valley (including non-urban roads and the cities of Tracy, Stockton, Manteca, Merced, Modesto, and Turlock). The results demonstrate that the approach is effective for quantifying spatial variations of air pollutant concentrations over measurement periods as short as two weeks. Measurement accuracy and precision are evaluated using results of weekly performance checks and periodic audits conducted through the sampler inlets, which show that research instruments located within stationary vehicles are capable of reliably measuring nitric oxide (NO), nitrogen dioxide (NO₂), ozone (O₃), methane (CH₄) black carbon (BC), and particle number (PN) concentration with bias and precision ranging from <10 % for gases to <25 % for BC and PN at 1-s time resolution. The quality of the mobile measurements in the ambient environment is examined by comparisons with data from an adjacent (< 9 m) stationary regulatory air quality monitoring site and by paired collocated vehicle comparisons, both stationary and driving. The mobile measurements indicate that U.S. EPA classifications of two Los Angeles stationary regulatory monitors' scales of representation are appropriate. Paired time-synchronous mobile measurements are used to characterize the spatial scales of concentration variations when vehicles were separated by <1 to 10 kilometers (km). A data analysis approach is developed to characterize spatial variations while limiting the confounding influence of diurnal variability. The approach is illustrated using data from San Francisco, revealing 1-km scale differences in mean NO₂ and O₃ concentrations up to 117 % and 46 %, respectively, of mean values during a two-week sampling period. In San Francisco and Los Angeles, spatial variations up to factors of 6 to 8 occur at sampling scales of 100 - 300m, corresponding to 1-minute averages.

Mobile monitoring of air and noise pollution in Philadelphia neighborhoods during summer 2017

Mobile monitoring is a useful approach for measuring intra-urban variation of air pollution in urban environments. In this study, we used a mobile monitoring approach to study the spatial-temporal variability of air and noise pollution in urban neighborhoods of Philadelphia. During summer 2017, we used portable instruments to measure PM_2.5, black carbon (BC), and noise levels along 5 km paths in four residential neighborhoods (Tioga, Mill Creek, Chestnut Hill, and Northern Liberties) and one commercial district (Center City) in Philadelphia, Pennsylvania, USA. A total of 62 sets of measurements were made at three different times of day (during morning rush hour, mid-afternoon, and during afternoon rush hour) from June 5 to July 7, 2017. Spatially, there was a significant difference in PM_2.5 concentrations among the four residential neighborhoods. Overall, the Chestnut Hill neighborhood had the highest PM_2.5 concentrations (13.25 ± 6.89 μg/m³), followed by Tioga (9.58 ± 4.83 μg/m³), Northern Liberties (7.02 ± 4.17 μg/m³), and Mill Creek (3.9 ± 4.5 μg/m³). There was temporal variability of pollutants depending on the neighborhood; Northern Liberties demonstrated the highest temporal variability in these data. The highest PM_2.5 (18.86 ± 3.17 mg/m³) was measured in the Chestnut Hill neighborhood during mid-afternoon. Mean PM_2.5, BC, and noise levels based on mobile measurements at Philadelphia during summer 2017 were 8.41 ± 4.31 μg/m³, 0.99 ± 0.44 μg C/m³, and 62.01 ± 3.20 dBA, respectively. Environmental noise showed the highest temporal variation of the monitored components for 3 time periods. In general, tree cover showed a weak and inconclusive association with particulate pollution levels.

Schoolchildren's personal exposure to ultrafine particles in and near Accra, Ghana

Exposure to air pollution is a significant health risk, and children who are exposed to it are likely to have lifelong consequences. Ultrafine particles (UFPs) are emitted by all combustion sources, and can be used as a proxy for the presence of combustion products. The present study, the first of its kind to be conducted in Africa, assessed schoolchildren's exposure to UFPs, and apportioned their daily exposure to seven different microenvironments that they inhabited on a typical school day. The personal exposure of 61 pupils attending three junior high schools was measured for 24 h each using wearable monitors over a period of 10 weeks. Two of the schools were located in suburbs of Accra and the third in Berekuso, a nearby rural community. The results of our study revealed the complex nature of children's UFP exposure and its overall high to very high levels, significantly influenced by the locality (suburb) of residence and the type of activities in which the children were engaged. The mean (±standard error) daily exposure to UFPs (cm^-3) was6.9×10⁴(±6.8×10³),4.9(±1.0)×10⁴ and 1.6×10⁴±1.9×10³for pupils attending the Ashia Mills, Faith Baptist and Berekuso Basic Schools, respectively. Pupils attending the schools in urban Accra received higher exposure than those attending the school in the rural environment of Berekuso. The highest mean microenvironmental exposure was registered in the Home other microenvironment in an urban school and in Bedroom in another urban school and the rural school. The high exposure in Home other was due to pupils conducting trash burning and encountering environmental tobacco smoke, and the high exposure in Bedroom microenvironment was due to the burning of mosquito coils at night to prevent malaria. The principal sources that heightened exposure to UFPs were emissions from cooking (using firewood and charcoal), vehicular traffic and combustion of biomass and trash. All pupils recorded the highest exposure intensity in the Kitchen microenvironment.

Development of a calibration chamber to evaluate the performance of low-cost particulate matter sensors

Low-cost particulate matter (PM) air quality sensors are becoming widely available and are being increasingly deployed in ambient and home/workplace environments due to their low cost, compactness, and ability to provide more highly resolved spatiotemporal PM concentrations. However, the PM data from these sensors are often of questionable quality, and the sensors need to be characterized individually for the environmental conditions under which they will be making measurements. In this study, we designed and assessed a cost-effective (∼$700) calibration chamber capable of continuously providing a uniform PM concentration simultaneously to multiple low-cost PM sensors and robust calibration relationships that are independent of sensor position. The chamber was designed and evaluated with a Computational Fluid Dynamics (CFD) model and a rigorous experimental protocol. We then used this new chamber to calibrate 242 Plantower PMS 3003 sensors from two production lots (Batches I and II) with two aerosol types: ammonium nitrate (for Batches I and II) and alumina oxide (for Batch I). Our CFD models and experiments demonstrated that the chamber is capable of providing uniform PM concentration to 8 PM sensors at once within 6% error and with excellent reliability (intraclass correlation coefficient > 0.771). The study identified two malfunctioning sensors and showed that the remaining sensors had high linear correlations with a DustTrak monitor that was calibrated for each aerosol type (R2 > 0.978). Finally, the results revealed statistically significant differences between the responses of Batches I and II sensors to the same aerosol (P-value<0.001) and the Batch I sensors to the two different aerosol types (P-value<0.001). This chamber design and evaluation protocol can provide a useful tool for those interested in systematic laboratory characterization of low-cost PM sensors.

Dynamic Estimation of Individual Exposure Levels to Air Pollution Using Trajectories Reconstructed from Mobile Phone Data

The spatiotemporal variability in air pollutant concentrations raises challenges in linking air pollution exposure to individual health outcomes. Thus, understanding the spatiotemporal patterns of human mobility plays an important role in air pollution epidemiology and health studies. With the advantages of massive users, wide spatial coverage and passive acquisition capability, mobile phone data have become an emerging data source for compiling exposure estimates. However, compared with air pollution monitoring data, the temporal granularity of mobile phone data is not high enough, which limits the performance of individual exposure estimation. To mitigate this problem, we present a novel method of estimating dynamic individual air pollution exposure levels using trajectories reconstructed from mobile phone data. Using the city of Shanghai as a case study, we compared three different types of exposure estimates using (1) reconstructed mobile phone trajectories, (2) recorded mobile phone trajectories, and (3) residential locations. The results demonstrate the necessity of trajectory reconstruction in exposure and health risk assessment. Additionally, we measure the potential health effects of air pollution from both individual and geographical perspectives. This helped reveal the temporal variations in individual exposures and the spatial distribution of residential areas with high exposure levels. The proposed method allows us to perform large-area and long-term exposure estimations for a large number of residents at a high spatiotemporal resolution, which helps support policy-driven environmental actions and reduce potential health risks.

Ambient air pollution and lipid profile: Systematic review and meta-analysis

Ambient air pollution (AAP) is recognized a cardiovascular risk factor and lipid profile dysregulation seems to be one of the potential mediators involved. However, results from epidemiologic research on the association between exposure to AAP and altered lipid profile have been inconsistent. This study aims to systematically review and meta-analyse epidemiologic evidence on the association between exposure to ambient air pollutants (particulate matter, nitrogen oxides, sulphur dioxide, ozone, carbon monoxide, back carbon) and lipid profile parameters (Total cholesterol; High-Density Lipoprotein Cholesterol; Low-Density Lipoprotein Cholesterol; TG-Triglycerides) or dyslipidaemia. Systematic electronic literature search was performed in PubMed, Web of Science and Scopus databases (last search on 24th May 2019) using keywords related to the exposure (ambient air pollutants) and to the outcomes (lipid profile parameters/dyslipidaemia). Qualitative and quantitative information of the studies were extracted and fixed or random-effects models were used to obtain a pooled effect estimate per each pollutant/outcome combination. 22 studies were qualitatively analysed and, from those, 3 studies were quantitatively analysed. Particulate matters were the most studied pollutants and a considerable heterogeneity in air pollution assessment methods and outcomes definitions was detected. Age, obesity related measures, tobacco consumption, sex and socioeconomic factors were the most frequent considered variables for confounding adjustment in the models. In a long-term exposure scenario, we found a 3.14% (1.36%-4.95%) increase in TG levels per 10 μg/m³ PM₁₀ increment and a 4.24% (1.37%-7.19%) increase in TG levels per 10 μg/m³ NO₂ increment. No significant associations were detected for the remaining pollutant/outcome combinations. Despite the few studies included in the meta-analysis, our study suggests some epidemiologic evidence supporting the association between PM₁₀ and NO₂ exposures and increased TG levels. Due to the very low level of evidence, more studies are needed to clarify the role of lipid profile dysregulation as a mediator on the AAP adverse cardiovascular effects.