The longitudinal associations between ambient air pollution exposure and dementia in the UK: results from the cognitive function and ageing study II and Wales.

BACKGROUND: Air pollution has been recognised as a potential risk factor for dementia. Yet recent epidemiological research shows mixed evidence. The aim of this study is to investigate the longitudinal associations between ambient air pollution exposure and dementia in older people across five urban and rural areas in the UK. METHODS: This study was based on two population-based cohort studies of 11329 people aged ≥ 65 in the Cognitive Function and Ageing Study II (2008-2011) and Wales (2011-2013). An algorithmic diagnosis method was used to identify dementia cases. Annual concentrations of four air pollutants (NO2, O3, PM10, PM2.5) were modelled for the year 2012 and linked via the participants' postcodes. Multistate modelling was used to examine the effects of exposure to air pollutants on incident dementia incorporating death and adjusting for sociodemographic factors and area deprivation. A random-effect meta-analysis was carried out to summarise results from the current and nine existing cohort studies. RESULTS: Higher exposure levels of NO2 (HR: 1.04; 95% CI: 0.94, 1.14), O3 (HR: 0.90; 95% CI: 0.70, 1.15), PM10 (HR: 1.17; 95% CI: 0.86, 1.58), PM2.5 (HR: 1.41; 95% CI: 0.71, 2.79) were not strongly associated with dementia in the two UK-based cohorts. Inconsistent directions and strengths of the associations were observed across the two cohorts, five areas, and nine existing studies. CONCLUSIONS: In contrast to the literature, this study did not find clear associations between air pollution and dementia. Future research needs to investigate how methodological and contextual factors can affect evidence in this field and clarity the influence of air pollution exposure on cognitive health over the lifecourse.

Characterising sources of PM2·5 exposure for school children with asthma: a personal exposure study across six cities in sub-Saharan Africa.

BACKGROUND: Air pollution is the second largest risk to health in Africa, and children with asthma are particularly susceptible to its effects. Yet, there is a scarcity of air pollution exposure data from cities in sub-Saharan Africa. We aimed to identify potential exposure reduction strategies for school children with asthma living in urban areas in sub-Saharan Africa. METHODS: This personal exposure study was part of the Achieving Control of Asthma in Children in Africa (ACACIA) project. Personal exposure to particulate matter (PM) was monitored in school children in six cities in sub-Saharan Africa (Blantyre, Malawi; Durban, South Africa; Harare, Zimbabwe; Kumasi, Ghana; Lagos, Nigeria; and Moshi, Tanzania). Participants were selected if they were aged 12-16 years and had symptoms of asthma. Monitoring was conducted between June 21, and Nov 26, 2021, from Monday morning (approximately 1000 h) to Friday morning (approximately 1000 h), by use of a bespoke backpack with a small air pollution monitoring unit with an inbuilt Global Positioning System (GPS) data logger. Children filled in a questionnaire detailing potential sources of air pollution during monitoring and exposures were tagged into three different microenvironments (school, commute, and home) with GPS coordinates. Mixed-effects models were used to identify the most important determinants of children's PM2·5 (PM <2·5 µm in diameter) exposure. FINDINGS: 330 children were recruited across 43 schools; of these, 297 had valid monitoring data, and 1109 days of valid data were analysed. Only 227 (20%) of 1109 days monitored were lower than the current WHO 24 h PM2·5 exposure health guideline of 15 µg/m3. Children in Blantyre had the highest PM2·5 exposure (median 41·8 µg/m3), whereas children in Durban (16·0 µg/m3) and Kumasi (17·9 µg/m3) recorded the lowest exposures. Children had significantly higher PM2·5 exposures at school than at home in Kumasi (median 19·6 µg/m3vs 14·2 µg/m3), Lagos (32·0 µg/m3vs 18·0 µg/m3), and Moshi (33·1 µg/m3vs 23·6 µg/m3), while children in the other three cities monitored had significantly higher PM2·5 exposures at home and while commuting than at school (median 48·0 µg/m3 and 43·2 µg/m3vs 32·3 µg/m3 in Blantyre, 20·9 µg/m3 and 16·3 µg/m3vs 11·9 µg/m3 in Durban, and 22·7 µg/m3 and 25·4 µg/m3vs 16·4 µg/m3 in Harare). The mixed-effects model highlighted the following determinants for higher PM2·5 exposure: presence of smokers at home (23·0% higher exposure, 95% CI 10·8-36·4), use of coal or wood for cooking (27·1%, 3·9-56·3), and kerosene lamps for lighting (30·2%, 9·1-55·2). By contrast, 37·2% (95% CI 22·9-48·2) lower PM2·5 exposures were found for children who went to schools with paved grounds compared with those whose school grounds were covered with loose dirt. INTERPRETATION: Our study suggests that the most effective changes to reduce PM2·5 exposures in these cities would be to provide paving in school grounds, increase the use of clean fuel for cooking and light in homes, and discourage smoking within homes. The most efficient way to improve air quality in these cities would require tailored interventions to prioritise different exposure-reduction policies in different cities. FUNDING: UK National Institute for Health and Care Research.

Early detection of COPD patients' symptoms with personal environmental sensors: a remote sensing framework using probabilistic latent component analysis with linear dynamic systems.

In this study, we present a cohort study involving 106 COPD patients using portable environmental sensor nodes with attached air pollution sensors and activity-related sensors, as well as daily symptom records and peak flow measurements to monitor patients' activity and personal exposure to air pollution. This is the first study which attempts to predict COPD symptoms based on personal air pollution exposure. We developed a system that can detect COPD patients' symptoms one day in advance of symptoms appearing. We proposed using the Probabilistic Latent Component Analysis (PLCA) model based on 3-dimensional and 4-dimensional spectral dictionary tensors for personalised and population monitoring, respectively. The model is combined with Linear Dynamic Systems (LDS) to track the patients' symptoms. We compared the performance of PLCA and PLCA-LDS models against Random Forest models in the identification of COPD patients' symptoms, since tree-based classifiers were used for remote monitoring of COPD patients in the literature. We found that there was a significant difference between the classifiers, symptoms and the personalised versus population factors. Our results show that the proposed PLCA-LDS-3D model outperformed the PLCA and the RF models between 4 and 20% on average. When we used only air pollutants as input, the PLCA-LDS-3D forecasting results in personalised and population models were 48.67 and 36.33% accuracy for worsening of lung capacity and 38.67 and 19% accuracy for exacerbation of COPD patients' symptoms, respectively. We have shown that indicators of the quality of an individual's environment, specifically air pollutants, are as good predictors of the worsening of respiratory symptoms in COPD patients as a direct measurement.

Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution.

BACKGROUND: Air pollution epidemiology has primarily relied on measurements from fixed outdoor air quality monitoring stations to derive population-scale exposure. Characterisation of individual time-activity-location patterns is critical for accurate estimations of personal exposure and dose because pollutant concentrations and inhalation rates vary significantly by location and activity. METHODS: We developed and evaluated an automated model to classify major exposure-related microenvironments (home, work, other static, in-transit) and separated them into indoor and outdoor locations, sleeping activity and five modes of transport (walking, cycling, car, bus, metro/train) with multidisciplinary methods from the fields of movement ecology and artificial intelligence. As input parameters, we used GPS coordinates, accelerometry, and noise, collected at 1 min intervals with a validated Personal Air quality Monitor (PAM) carried by 35 volunteers for one week each. The model classifications were then evaluated against manual time-activity logs kept by participants. RESULTS: Overall, the model performed reliably in classifying home, work, and other indoor microenvironments (F1-score>0.70) but only moderately well for sleeping and visits to outdoor microenvironments (F1-score=0.57 and 0.3 respectively). Random forest approaches performed very well in classifying modes of transport (F1-score>0.91). We found that the performance of the automated methods significantly surpassed those of manual logs. CONCLUSIONS: Automated models for time-activity classification can markedly improve exposure metrics. Such models can be developed in many programming languages, and if well formulated can have general applicability in large-scale health studies, providing a comprehensive picture of environmental health risks during daily life with readily gathered parameters from smartphone technologies.

Partitioning indoor-generated and outdoor-generated PM2.5 from real-time residential measurements in urban and peri-urban Beijing.

Limited number of projects have attempted to partition and quantify indoor- and outdoor-generated PM2.5 (PM2.5ig and PM2.5og) where strong indoor sources (e.g., solid fuel, tobacco smoke, or kerosene) exist. This study aimed to apply and refine a previous recursive model used to derive infiltration efficiency (Finf) to additionally partition pollution concentrations into indoor and outdoor origins within residences challenged by elevated ambient and indoor combustion-related sources. During the winter of 2016 and summer of 2017 we collected residential measurements in 72 homes in urban and peri-urban Beijing, 12 of which had additional paired residential outdoor measurements during the summer season. Local ambient measurements were collected throughout. We then compared the calculated PM2.5ig and using (i) outdoor and (ii) ambient measurements as model inputs. The results from outdoor and ambient measurements were not significantly different, which suggests that ambient measurements can be used as a model input for pollution origin partitioning when paired outdoor measurements are not available. From the results calculated using ambient measurements, the mean percentage contribution of indoor-generated PM2.5 was 19 % (σ = 22 %), and 7 % (11 %) of the total indoor PM2.5 for peri-urban and urban homes respectively during the winter; and 18 % (18 %) and 6 % (10 %) of the total indoor PM2.5 during the summer. Partitioning pollution into PM2.5ig and PM2.5og is important to allow investigation of distinct associations between health outcomes and particulate mixes, often with different physiochemical composition and toxicity. It will also inform targeted interventions that impact indoor and outdoor sources of pollution (e.g., domestic fuel switching vs. power generation), which are typically radically different in design and implementation.

Development and Evaluation of Spatio-Temporal Air Pollution Exposure Models and Their Combinations in the Greater London Area, UK.

Land use regression (LUR) and dispersion/chemical transport models (D/CTMs) are frequently applied to predict exposure to air pollution concentrations at a fine scale for use in epidemiological studies. Moreover, the use of satellite aerosol optical depth data has been a key predictor especially for particulate matter pollution and when studying large populations. Within the STEAM project we present a hybrid spatio-temporal modeling framework by (a) incorporating predictions from dispersion modeling of nitrogen dioxide (NO2), ozone (O3) and particulate matter with an aerodynamic diameter equal or less than 10 µm (PM10) and less than 2.5 µm (PM2.5) into a spatio-temporal LUR model; and (b) combining the predictions LUR and dispersion modeling and additionally, only for PM2.5, from an ensemble machine learning approach using a generalized additive model (GAM). We used air pollution measurements from 2009 to 2013 from 62 fixed monitoring sites for O3, 115 for particles and up to 130 for NO2, obtained from the dense network in the Greater London Area, UK. We assessed all models following a 10-fold cross validation (10-fold CV) procedure. The hybrid models performed better compared to separate LUR models. Incorporation of the dispersion estimates in the LUR models as a predictor, improved the LUR model fit: CV-R2 increased to 0.76 from 0.71 for NO2, to 0.79 from 0.57 for PM10, to 0.81 to 0.66 for PM2.5 and to 0.75 from 0.62 for O3. The CV-R2 obtained from the hybrid GAM framework was also increased compared to separate LUR models (CV-R2 = 0.80 for NO2, 0.76 for PM10, 0.79 for PM2.5 and 0.75 for O3). Our study supports the combined use of different air pollution exposure assessment methods in a single modeling framework to improve the accuracy of spatio-temporal predictions for subsequent use in epidemiological studies.

Comparing human exposure to fine particulate matter in low and high-income countries: A systematic review of studies measuring personal PM2.5 exposure.

BACKGROUND: Due to the adverse health effects of air pollution, researchers have advocated for personal exposure measurements whereby individuals carry portable monitors in order to better characterise and understand the sources of people's pollution exposure. OBJECTIVES: The aim of this systematic review is to assess the differences in the magnitude and sources of personal PM2.5 exposures experienced between countries at contrasting levels of income. METHODS: This review summarised studies that measured participants personal exposure by carrying a PM2.5 monitor throughout their typical day. Personal PM2.5 exposures were summarised to indicate the distribution of exposures measured within each country income category (based on low (LIC), lower-middle (LMIC), upper-middle (UMIC), and high (HIC) income countries) and between different groups (i.e. gender, age, urban or rural residents). RESULTS: From the 2259 search results, there were 140 studies that met our criteria. Overall, personal PM2.5 exposures in HICs were lower compared to other countries, with UMICs exposures being slightly lower than exposures measured in LMICs or LICs. 34% of measured groups in HICs reported below the ambient World Health Organisation 24-h PM2.5 guideline of 15 µg/m3, compared to only 1% of UMICs and 0% of LMICs and LICs. There was no difference between rural and urban participant exposures in HICs, but there were noticeably higher exposures recorded in rural areas compared to urban areas in non-HICs, due to significant household sources of PM2.5 in rural locations. In HICs, studies reported that secondhand smoke, ambient pollution infiltrating indoors, and traffic emissions were the dominant contributors to personal exposures. While, in non-HICs, household cooking and heating with biomass and coal were reported as the most important sources. CONCLUSION: This review revealed a growing literature of personal PM2.5 exposure studies, which highlighted a large variability in exposures recorded and severe inequalities in geographical and social population subgroups.

What you see is what you breathe? Estimating air pollution spatial variation using street level imagery.

High spatial resolution information on urban air pollution levels is unavailable in many areas globally, partially due to high input data needs of existing estimation approaches. Here we introduce a computer vision method to estimate annual means for air pollution levels from street level images. We used annual mean estimates of NO2 and PM2.5 concentrations from locally calibrated models as labels from London, New York, and Vancouver to allow for compilation of a sufficiently large dataset (~250k images for each city). Our experimental setup is designed to quantify intra and intercity transferability of image-based model estimates. Performances were high and comparable to traditional land-use regression (LUR) and dispersion models when training and testing on images from the same city (R2 values between 0.51 and 0.95 when validated on data from ground monitoring stations). Like LUR models, transferability of models between cities in different geographies is more difficult. Specifically, transferability between the three cities i.e., London, New York, and Vancouver, which have similar pollution source profiles were moderately successful (R2 values between zero and 0.67). Comparatively, performances when transferring models trained on these cities with very different source profiles i.e., Accra in Ghana and Hong Kong were lower (R2 between zero and 0.21) suggesting the need for local calibration with local calibration using additional measurement data from cities that share similar source profiles.

Nitrogen oxides (NO and NO2) pollution in the Accra metropolis: Spatiotemporal patterns and the role of meteorology.

Economic and urban development in sub-Saharan Africa (SSA) may be shifting the dominant air pollution sources in cities from biomass to road traffic. Considered as a marker for traffic-related air pollution in cities, we conducted a city-wide measurement of NOx levels in the Accra Metropolis and examined their spatiotemporal patterns in relation to land use and meteorological factors. Between April 2019 to June 2020, we collected weekly integrated NOx (n = 428) and NO2 (n = 472) samples at 10 fixed (year-long) and 124 rotating (week-long) sites. Data from the same time of year were compared to a previous study (2006) to assess changes in NO2 concentrations. NO and NO2 concentrations were highest in commercial/business/industrial (66 and 76 µg/m3, respectively) and high-density residential areas (47 and 59 µg/m3, respectively), compared with peri-urban locations. We observed annual means of 68 and 70 µg/m3 for NO and NO2, and a clear seasonal variation, with the mean NO2 of 63 µg/m3 (non-Harmattan) increased by 25-56% to 87 µg/m3 (Harmattan) across different site types. The NO2/NOx ratio was also elevated by 19-28%. Both NO and NO2 levels were associated with indicators of road traffic emissions (e.g. distance to major roads), but not with community biomass use (e.g. wood and charcoal). We found strong correlations between both NO2 and NO2/NOx and mixing layer depth, incident solar radiation and water vapor mixing ratio. These findings represent an increase of 25-180% when compared to a small study conducted in two high-density residential neighborhoods in Accra in 2006. Road traffic may be replacing community biomass use (major source of fine particulate matter) as the prominent source of air pollution in Accra, with policy implication for growing cities in SSA.

Identifying trends in ultrafine particle infiltration and carbon dioxide ventilation in 92 vehicle models.

There has been ongoing research aimed at reducing pollution concentrations in vehicles due to the high exposure which occurs in this setting. These studies have found using recirculate (RC) settings substantially reduces in-cabin traffic-related pollution concentrations but possibly leads to an adverse accumulation of carbon dioxide (CO2) from driver respiration. The aim of this study was to highlight how vehicle models and ventilation settings affect in-cabin concentrations to ultrafine particles (UFP) and CO2 in real-world conditions. We assessed the ability of different vehicles to balance reductions in UFP against the build-up of in-cabin CO2 concentrations by measuring these pollutants concurrently both inside and outside the vehicle to derive an in/out ratio. When ventilation settings were set to RC, UFP concentrations inside the vehicles (median: 3205 pt./cm3) were 86% lower compared to outside air (OA) (23,496 pt./cm3) across a 30-min real-world driving route. However, CO2 concentrations demonstrated a rapid linear increase under RC settings, at times exceeding 2500 ppm. These concentrations have previously been associated with decreased cognitive performance. Our study did not find an effect of gasoline fuelled vehicles affecting in-cabin UFP levels compared to hybrid or electric vehicles, suggesting that self-pollution was not an issue. We also found that certain vehicle models were better at reducing both in-cabin UFP and CO2 concentrations. The results suggest that under RC settings in/out CO2 ratios are largely determined by the leakiness of the vehicle cabin, whereas in/out UFP ratios are primarily determined by the efficacy of the in-built air filter in the vehicles ventilation system.

The state of science on severe air pollution episodes: Quantitative and qualitative analysis.

Severe episodic air pollution blankets entire cities and regions and have a profound impact on humans and their activities. We compiled daily fine particle (PM2.5) data from 100 cities in five continents, investigated the trends of number, frequency, and duration of pollution episodes, and compared these with the baseline trend in air pollution. We showed that the factors contributing to these events are complex; however, long-term measures to abate emissions from all anthropogenic sources at all times is also the most efficient way to reduce the occurrence of severe air pollution events. In the short term, accurate forecasting systems of such events based on the meteorological conditions favouring their occurrence, together with effective emergency mitigation of anthropogenic sources, may lessen their magnitude and/or duration. However, there is no clear way of preventing events caused by natural sources affected by climate change, such as wildfires and desert dust outbreaks.

Spatial-temporal patterns of ambient fine particulate matter (PM2.5) and black carbon (BC) pollution in Accra.

Sub-Saharan Africa (SSA) is rapidly urbanizing, and ambient air pollution has emerged as a major environmental health concern in growing cities. Yet, effective air quality management is hindered by limited data. We deployed robust, low-cost and low-power devices in a large-scale measurement campaign and characterized within-city variations in fine particulate matter (PM2.5) and black carbon (BC) pollution in Accra, Ghana. Between April 2019 and June 2020, we measured weekly gravimetric (filter-based) and minute-by-minute PM2.5 concentrations at 146 unique locations, comprising of 10 fixed (∼1 year) and 136 rotating (7 day) sites covering a range of land-use and source influences. Filters were weighed for mass, and light absorbance (10-5m-1) of the filters was used as proxy for BC concentration. Year-long data at four fixed sites that were monitored in a previous study (2006-2007) were compared to assess changes in PM2.5 concentrations. The mean annual PM2.5 across the fixed sites ranged from 26 µg m-3 at a peri-urban site to 43 µg m-3 at a commercial, business, and industrial (CBI) site. CBI areas had the highest PM2.5 levels (mean: 37 µg m-3), followed by high-density residential neighborhoods (mean: 36 µg m-3), while peri-urban areas recorded the lowest (mean: 26 µg m-3). Both PM2.5 and BC levels were highest during the dry dusty Harmattan period (mean PM2.5: 89 µg m-3) compared to non-Harmattan season (mean PM2.5: 23 µg m-3). PM2.5 at all sites peaked at dawn and dusk, coinciding with morning and evening heavy traffic. We found about a 50% reduction (71 vs 37 µg m-3) in mean annual PM2.5 concentrations when compared to measurements in 2006-2007 in Accra. Ambient PM2.5 concentrations in Accra may have plateaued at levels lower than those seen in large Asian megacities. However, levels are still 2- to 4-fold higher than the WHO guideline. Effective and equitable policies are needed to reduce pollution levels and protect public health.

"I am an air quality scientist"- Using citizen science to characterise school children's exposure to air pollution.

Children are particularly vulnerable to the harmful effects of air pollution. To tackle this issue and implement effective strategies to reduce child exposure, it is important to understand how children are exposed to this risk. This study followed a citizen science approach to air pollution monitoring, aiming to characterise school children's exposure to air pollution and to analyse how a citizen science approach to data collection could contribute to and enhance the research process. 258 children across five London primary schools attended air pollution education sessions and measured air pollution for a week using backpacks with built-in air quality sensors. Children received a summary of the results, advice and information on how to reduce exposure to air pollution. Data on the impact of the approach on the school community were collected using surveys and focus groups with children and their parents and interviews with the teachers involved. The unique data set obtained permitted us to map different routes and modes of transport used by the children and quantify different exposure levels. We identified that, on average, children were exposed to higher levels of air pollution when travelling to and from school, particularly during the morning journey where air pollution levels were on average 52% higher than exposures at school. Children who walked to and from school through busy main roads were exposed to 33% higher levels of air pollution than those who travelled through back streets. The findings from this study showed that using a citizen science approach to data collection, where children are actively involved in the research process, not only facilitated the gathering of a large data set by encouraging participation and stimulating adherence with the study protocol, but also increased children's awareness of air pollution, encouraging them to adopt positive behaviour changes to reduce their exposure.

Characterising professional drivers' exposure to traffic-related air pollution: Evidence for reduction strategies from in-vehicle personal exposure monitoring.

Professional drivers working in congested urban areas are required to work near harmful traffic related pollutants for extended periods, representing a significant, but understudied occupational risk. This study collected personal black carbon (BC) exposures for 141 drivers across seven sectors in London. The aim of the study was to assess the magnitude and the primary determinants of their exposure, leading to the formulation of targeted exposure reduction strategies for the occupation. Each participant's personal BC exposures were continuously measured using real-time monitors for 96 h, incorporating four shifts per participant. 'At work' BC exposures (3.1 ± 3.5 µg/m3) were 2.6 times higher compared to when 'not at work' (1.2 ± 0.7 µg/m3). Workers spent 19% of their time 'at work driving', however this activity contributed 36% of total BC exposure, highlighting the disproportionate effect driving had on their daily exposure. Taxi drivers experienced the highest BC exposures due to the time they spent working in congested central London, while emergency services had the lowest. Spikes in exposure were observed while driving and were at times greater than 100 µg/m3. The most significant determinants of drivers' exposures were driving in tunnels, congestion, location, day of week and time of shift. Driving with closed windows significantly reduced exposures and is a simple behaviour change drivers could implement. Our results highlight strategies by which employers and local policy makers can reduce professional drivers' exposure to traffic-related air pollution.

Personal exposure to air pollution and respiratory health of COPD patients in London.

Previous studies have investigated the effects of air pollution on chronic obstructive pulmonary disease (COPD) patients using either fixed-site measurements or a limited number of personal measurements, usually for one pollutant and a short time period. These limitations may introduce bias and distort the epidemiological associations as they do not account for all the potential sources or the temporal variability of pollution.We used detailed information on individuals' exposure to various pollutants measured at fine spatiotemporal scale to obtain more reliable effect estimates. A panel of 115 patients was followed up for an average continuous period of 128 days carrying a personal monitor specifically designed for this project that measured temperature, nitrogen dioxide (NO2), ozone (O3), nitric oxide (NO), carbon monoxide (CO), and particulate matter with aerodynamic diameter <2.5 and <10 µm at 1-min time resolution. Each patient recorded daily information on respiratory symptoms and measured peak expiratory flow (PEF). A pulmonologist combined related data to define a binary variable denoting an "exacerbation". The exposure-response associations were assessed with mixed effects models.We found that gaseous pollutants were associated with a deterioration in patients' health. We observed an increase of 16.4% (95% CI 8.6-24.6%), 9.4% (95% CI 5.4-13.6%) and 7.6% (95% CI 3.0-12.4%) in the odds of exacerbation for an interquartile range increase in NO2, NO and CO, respectively. Similar results were obtained for cough and sputum. O3 was found to have adverse associations with PEF and breathlessness. No association was observed between particulate matter and any outcome.Our findings suggest that, when considering total personal exposure to air pollutants, mainly the gaseous pollutants affect COPD patients' health.

Prostaglandin E2 and phagocytosis of inhaled particulate matter by airway macrophages in cystic fibrosis.

BACKGROUND: Exposure to particulate matter (PM) air pollution is associated with adverse health outcomes in children with cystic fibrosis (CF). Airway macrophages (AM) phagocytose and retain inhaled PM in vivo, and the area of carbon in AM reflects both inhaled PM dose and phagocytic function. Since airway prostaglandin-E2 (PGE2) is increased in CF, and PGE2 suppresses AM phagocytosis, we sought evidence for PGE2-mediated suppression of AM phagocytosis of inhaled carbonaceous PM in CF. METHODS: After informed consent, urine was obtained from 20 controls and 24 CF children. In the subgroup of older children, at least one induced sputum was done in 20 controls and 19 CF children. Urinary tetranor PGEM, the major metabolite of PGE2, and sputum PGE2 were measured by mass spectrometry. The area of carbon in AM was determined by image analysis. Exposure to PM was assessed by modelling and personal monitoring. The effect of either PGE2 or CF sputum supernatant on phagocytosis of diesel exhaust particle (DEP) by AM was assessed in vitro. Data were analysed by t-test. RESULTS: Both urinary tetranor PGEM (P<0.05), and sputum PGE2 (P<0.05) were increased in CF . Despite no difference in PM exposure between groups, the area of phagocytosed carbon by AM was decreased in children with CF (P<0.01). PGE2 suppressed phagocytosis of DEP by AM from both controls and CF (P<0.0001). CF sputum supernatant suppressed phagocytosis of DEP by AM (P<0.0001) in a PGE2-dependent manner. CONCLUSION: Increased PGE2 in the CF airway suppresses phagocytosis of inhaled PM by AM.

Evidence for the presence of air pollution nanoparticles in placental tissue cells.

Inhaled particulate matter (PM) from combustion- and friction-sourced air pollution adversely affects organs distant from the lung. A putative mechanism for the remote effect of inhaled PM is that ultrafine, nano-sized fraction (<100 nm) translocates across the air-tissue barrier, directly interacting with phagocytic tissue cells. Although PM is reported in other tissues, whether it is phagocytosed by non-respiratory tissue resident cells is unclear. Using the placenta as an accessible organ for phagocytic cells, we sought to seek evidence for air pollution-derived PM in tissue resident phagocytes. Macrophage-enriched placental cells (MEPCs) were isolated, and examined by light and electron microscopy. MEPC carbon was assessed by image analysis (mean µm2/1000 cells); particle composition and numbers were investigated using magnetic analyses and energy dispersive X-ray spectroscopy. MEPCs phagocytic capacity was assessed by culture with diesel exhaust PM in vitro. Fifteen placentas were analysed. Black inclusions morphologically compatible with inhaled PM were identified within MEPCs from all samples (mean ± SEM carbon loading, 1000 MEPCs/participant of 0.004 ± 0.001 µm2). High resolution scanning/transmission electron microscopy revealed abundant nano-sized particle aggregates within MEPCs. MEPC PM was predominantly carbonaceous but also co-associated with a range of trace metals, indicative of high temperature (i.e. exogenous) generation. MEPCs contained readily-measurable amounts of iron-rich, ferrimagnetic particles, in concentrations/particle number concentrations ranging, respectively, from 8 to 50 ng/g and 10 to 60.107 magnetic particles/g (wet wt) MEPCs. Extracted MEPCs (n = 20/ placenta) were phagocytic for PM since all cells showed increased carbon area after culture with diesel PM in vitro (mean ± SEM increase 7.55 ± 1.26 µm2 carbon PM). These findings demonstrate that inhaled, metal-bearing, air pollution-derived PM can not only translocate to distant organs, but is taken up by tissue resident phagocytes in vivo. The human placenta, and hence probably the fetus, thus appears to be a target for such particles.

High-resolution spatiotemporal measurement of air and environmental noise pollution in Sub-Saharan African cities: Pathways to Equitable Health Cities Study protocol for Accra, Ghana.

INTRODUCTION: Air and noise pollution are emerging environmental health hazards in African cities, with potentially complex spatial and temporal patterns. Limited local data are a barrier to the formulation and evaluation of policies to reduce air and noise pollution. METHODS AND ANALYSIS: We designed a year-long measurement campaign to characterise air and noise pollution and their sources at high-resolution within the Greater Accra Metropolitan Area (GAMA), Ghana. Our design uses a combination of fixed (year-long, n=10) and rotating (week-long, n =~130) sites, selected to represent a range of land uses and source influences (eg, background, road traffic, commercial, industrial and residential areas, and various neighbourhood socioeconomic classes). We will collect data on fine particulate matter (PM2.5), nitrogen oxides (NOx), weather variables, sound (noise level and audio) along with street-level time-lapse images. We deploy low-cost, low-power, lightweight monitoring devices that are robust, socially unobtrusive, and able to function in Sub-Saharan African (SSA) climate. We will use state-of-the-art methods, including spatial statistics, deep/machine learning, and processed-based emissions modelling, to capture highly resolved temporal and spatial variations in pollution levels across the GAMA and to identify their potential sources. This protocol can serve as a prototype for other SSA cities. ETHICS AND DISSEMINATION: This environmental study was deemed exempt from full ethics review at Imperial College London and the University of Massachusetts Amherst; it was approved by the University of Ghana Ethics Committee (ECH 149/18-19). This protocol is designed to be implementable in SSA cities to map environmental pollution to inform urban planning decisions to reduce health harming exposures to air and noise pollution. It will be disseminated through local stakeholder engagement (public and private sectors), peer-reviewed publications, contribution to policy documents, media, and conference presentations.

Interactions between the Physical and Social Environments with Adverse Pregnancy Events Related to Placental Disorders-A Scoping Review.

BACKGROUND: Due to different social and physical environments across Africa, understanding how these environments differ in interacting with placental disorders will play an important role in developing effective interventions. METHODS: A scoping review was conducted, to identify current knowledge on interactions between the physical and social environment and the incidence of placental disease in Africa. RESULTS: Heavy metals were said to be harmful when environmental concentrations are beyond critical limits. Education level, maternal age, attendance of antenatal care and parity were the most investigated social determinants. CONCLUSIONS: More evidence is needed to determine the relationships between the environment and placental function in Africa. The results show that understanding the nature of the relationship between social determinants of health (SDH) and placental health outcomes plays a pivotal role in understanding the risk in the heterogenous communities in Africa.

Comparing the performance of air pollution models for nitrogen dioxide and ozone in the context of a multilevel epidemiological analysis.

Using modeled air pollutant predictions as exposure variables in epidemiological analyses can produce bias in health effect estimation. We used statistical simulation to estimate these biases and compare different air pollution models for London. METHODS: Our simulations were based on a sample of 1,000 small geographical areas within London, United Kingdom. "True" pollutant data (daily mean nitrogen dioxide [NO2] and ozone [O3]) were simulated to include spatio-temporal variation and spatial covariance. All-cause mortality and cardiovascular hospital admissions were simulated from "true" pollution data using prespecified effect parameters for short and long-term exposure within a multilevel Poisson model. We compared: land use regression (LUR) models, dispersion models, LUR models including dispersion output as a spline (hybrid1), and generalized additive models combining splines in LUR and dispersion outputs (hybrid2). Validation datasets (model versus fixed-site monitor) were used to define simulation scenarios. RESULTS: For the LUR models, bias estimates ranged from -56% to +7% for short-term exposure and -98% to -68% for long-term exposure and for the dispersion models from -33% to -15% and -52% to +0.5%, respectively. Hybrid1 provided little if any additional benefit, but hybrid2 appeared optimal in terms of bias estimates for short-term (-17% to +11%) and long-term (-28% to +11%) exposure and in preserving coverage probability and statistical power. CONCLUSIONS: Although exposure error can produce substantial negative bias (i.e., towards the null), combining outputs from different air pollution modeling approaches may reduce bias in health effect estimation leading to improved impact evaluation of abatement policies.