87 results found
Morawska L, Zhu T, Liu N, et al., 2021, The state of science on severe air pollution episodes: Quantitative and qualitative analysis., Environ Int, Vol: 156
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.
Wang J, Alli AS, Clark S, et al., 2021, Nitrogen oxides (NO and NO2) pollution in the Accra metropolis: Spatiotemporal patterns and the role of meteorology., Sci Total Environ, Vol: 803
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 1928%. 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.
Lim S, Barratt B, Holliday L, et al., 2021, Characterising professional drivers’ exposure to traffic-related air pollution: Evidence for reduction strategies from in-vehicle personal exposure monitoring, Environment International, Vol: 153, ISSN: 0160-4120
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.
Evangelopoulos D, Chatzidiakou L, Walton H, et al., 2021, Personal exposure to air pollution and respiratory health of COPD patients in London, European Respiratory Journal, Vol: 58, ISSN: 0903-1936
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 spatio-temporal 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, PM10, PM2.5, NO2, NO, CO and O3 at one-minute 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% confidence interval: 8.6-24.6%), 9.4% (5.4-13.6%) and 7.6% (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 particles and any outcome.Our findings suggest that, when considering total personal exposure to air pollutants, mainly the gaseous pollutants affect COPD patients' health.
Liu NM, Miyashita L, Sanak M, et al., 2021, Prostaglandin E-2 and phagocytosis of inhaled particulate matter by airway macrophages in cystic fibrosis, JOURNAL OF CYSTIC FIBROSIS, Vol: 20, Pages: 673-677, ISSN: 1569-1993
Alli AS, Clark S, Hughes AF, et al., 2021, Spatial-temporal patterns of ambient fine particulate matter (PM2.5) and black carbon (BC) pollution in Accra, Environmental Research Letters, Vol: 16, Pages: 1-12, ISSN: 1748-9326
Background: Sub-Saharan Africa (SSA) is rapidly urbanizing, and ambient air pollution has emerged as a major environmental health concern in SSA 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. Methods: 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 change in PM2.5 concentrations. Results: The mean annual PM2.5 across the fixed sites ranged from 26 μg/m3 at a peri-urban site to 40 μg/m3 at commercial, business, and industrial (CBI) areas. CBI areas had the highest PM2.5 levels (mean: 37 μg/m3), followed by high-density residential neighborhoods (mean: 36 μg/m3), while peri-urban areas recorded the lowest (mean: 26 μg/m3). Both PM2.5 and BC levels were highest during the dry dusty Harmattan period (mean PM2.5: 89 μg/m3) compared to non-Harmattan season (mean PM2.5: 23 μg/m3). 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/m3) in mean annual PM2.5 concentrations when compared to measurements in 2006-2007 in Accra. Conclusion: 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
Varaden D, Barratt B, Heather K, et al., 2021, Engaging primary students with the issue of air pollution through citizen science: lessons to be learnt, Journal of Emergent Science
Lim S, Holliday L, Barratt B, et al., 2021, Assessing the exposure and hazard of diesel exhaust in professional drivers: a review of the current state of knowledge, Air Quality, Atmosphere and Health, ISSN: 1873-9318
It is well-established that traffic-related air pollution has a detrimental impact on health. Much of the focus has been on diesel exhaust emissions due to a rapid increase in vehicle numbers and studies finding that this pollutant is carcinogenic. Unsurprisingly, the highest diesel exposures that the general population experiences are during urban daily commutes; however, few studies have considered professional drivers who are chronically exposed to the pollutant due to their work in transport microenvironments. In this narrative review, we address the literature on professional drivers’ exposure to diesel exhaust and advocate that a modern exposure science approach utilised in commuter personal exposure studies is needed. This type of evaluation will provide a more detailed understanding of the time-activity of professional drivers’ exposures which is required to identify specific interventions to reduce their risk to diesel exhaust emissions.
Han Y, Chatzidiakou L, Yan L, et al., 2021, 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, Faraday Discussions, Vol: 226, Pages: 569-583, ISSN: 1359-6640
Measurement of ambient fine particulate matter (PM2.5) 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 exhibited an exponentially declining trend, and showed larger variations when ambient PM2.5 levels < 25 μg m−3. Personal exposure to PM2.5 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 PM2.5 might not be a good proxy to estimate the health effect of exposure to personal PM2.5.
Kenis A, Barratt B, 2021, The role of the media in staging air pollution: The controversy on extreme air pollution along Oxford Street and other debates on poor air quality in London, ENVIRONMENT AND PLANNING C-POLITICS AND SPACE, ISSN: 2399-6544
Liu NM, Miyashita L, Maher BA, et al., 2021, Evidence for the presence of air pollution nanoparticles in placental tissue cells, SCIENCE OF THE TOTAL ENVIRONMENT, Vol: 751, ISSN: 0048-9697
Han Y, Chen W, Chatzidiakou L, et al., 2020, Effects of AIR pollution on cardiopuLmonary disEaSe in urban and peri-urban reSidents in Beijing: protocol for the AIRLESS study, Atmospheric Chemistry and Physics, Vol: 20, Pages: 15775-15792, ISSN: 1680-7316
Beijing, as a representative megacity in China, is experiencing some of the most severe air pollution episodes in the world, and its fast urbanization has led to substantial urban and peri-urban disparities in both health status and air quality. Uncertainties remain regarding the possible causal links between individual air pollutants and health outcomes, with spatial comparative investigations of these links lacking, particularly in developing megacities. In light of this challenge, Effects of AIR pollution on cardiopuLmonary disEaSe in urban and peri-urban reSidents in Beijing (AIRLESS) was initiated, with the aim of addressing the complex issue of relating multi-pollutant exposure to cardiopulmonary outcomes. This paper presents the novel methodological framework employed in the project, namely (1) the deployment of two panel studies from established cohorts in urban and peri-urban Beijing, with different exposure settings regarding pollution levels and diverse sources; (2) the collection of detailed measurements and biomarkers of participants from a nested case (hypertensive) and control (healthy) study setting; (3) the assessment of indoor and personal exposure to multiple gaseous pollutants and particulate matter at unprecedented spatial and temporal resolution with validated novel sensor technologies; (4) the assessment of ambient air pollution levels in a large-scale field campaign, particularly the chemical composition of particulate matter. Preliminary results showed that there is a large difference between ambient and personal air pollution levels, and the differences varied between seasons and locations. These large differences were reflected on the different health responses between the two panels.
Analitis A, Barratt B, Green D, et al., 2020, Prediction of PM2.5 concentrations at the locations of monitoring sites measuring PM10 and NOx, using generalized additive models and machine learning methods: A case study in London, ATMOSPHERIC ENVIRONMENT, Vol: 240, ISSN: 1352-2310
Floyd CN, Shahed F, Ukah F, et al., 2020, Acute blood pressure-lowering effects of nitrogen dioxide exposure from domestic gas cooking via elevation of plasma nitrite concentration in healthy individuals, Circulation Research, Vol: 127, Pages: 847-848, ISSN: 0009-7330
Clark S, Alli AS, Brauer M, et al., 2020, 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, BMJ Open, Vol: 10, ISSN: 2044-6055
Introduction: Air and noise pollution are emerging environmental health hazards in African cities, with potentially complex spatial and temporal patterns. Limited local data is 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 characterize air and noise pollution and their sources at high-resolution within the Greater Accra Metropolitan Area, Ghana. Our design utilizes 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 (e.g. 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 the 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 Accra 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. 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, a
Chatzidiakou L, Krause A, Han Y, et al., 2020, Using low-cost sensor technologies and advanced computational methods to improve dose estimations in health panel studies: results of the AIRLESS project, Journal of Exposure Science and Environmental Epidemiology, Vol: 30, Pages: 981-989, ISSN: 1559-0631
BackgroundAir pollution epidemiology has primarily relied on fixed outdoor air quality monitoring networks and static populations.MethodsTaking 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.ResultsApplying 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.ConclusionsFuture 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.
Dube YP, Nyapwere N, Magee LA, et al., 2020, Interactions between the Physical and Social Environments with Adverse Pregnancy Events Related to Placental Disorders-A Scoping Review, INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, Vol: 17
Frederickson LB, Lim S, Russell HS, et al., 2020, Monitoring Excess Exposure to Air Pollution for Professional Drivers in London Using Low-Cost Sensors, ATMOSPHERE, Vol: 11
Butland BK, Samoli E, Atkinson RW, et al., 2020, Comparing the performance of air pollution models for nitrogen dioxide and ozone in the context of a multilevel epidemiological analysis., Environ Epidemiol, Vol: 4
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.
Samoli E, Butland BK, Rodopoulou S, et al., 2020, The impact of measurement error in modeled ambient particles exposures on health effect estimates in multilevel analysis: A simulation study., Environ Epidemiol, Vol: 4
Various spatiotemporal models have been proposed for predicting ambient particulate exposure for inclusion in epidemiological analyses. We investigated the effect of measurement error in the prediction of particulate matter with diameter <10 µm (PM10) and <2.5 µm (PM2.5) concentrations on the estimation of health effects. Methods: We sampled 1,000 small administrative areas in London, United Kingdom, and simulated the "true" underlying daily exposure surfaces for PM10 and PM2.5 for 2009-2013 incorporating temporal variation and spatial covariance informed by the extensive London monitoring network. We added measurement error assessed by comparing measurements at fixed sites and predictions from spatiotemporal land-use regression (LUR) models; dispersion models; models using satellite data and applying machine learning algorithms; and combinations of these methods through generalized additive models. Two health outcomes were simulated to assess whether the bias varies with the effect size. We applied multilevel Poisson regression to simultaneously model the effect of long- and short-term pollutant exposure. For each scenario, we ran 1,000 simulations to assess measurement error impact on health effect estimation. Results: For long-term exposure to particles, we observed bias toward the null, except for traffic PM2.5 for which only LUR underestimated the effect. For short-term exposure, results were variable between exposure models and bias ranged from -11% (underestimate) to 20% (overestimate) for PM10 and of -20% to 17% for PM2.5. Integration of models performed best in almost all cases. Conclusions: No single exposure model performed optimally across scenarios. In most cases, measurement error resulted in attenuation of the effect estimate.
Whitehouse AL, Mushtaq N, Miyashita L, et al., 2020, Airway dendritic cell maturation in children exposed to air pollution, PLOS ONE, Vol: 15, ISSN: 1932-6203
Magee LA, Strang A, Li L, et al., 2020, The PRECISE (PREgnancy Care Integrating translational Science, Everywhere) database: open-access data collection in maternal and newborn health, REPRODUCTIVE HEALTH, Vol: 17
Daniele MAS, Martinez-Alvarez M, Etyang AK, et al., 2020, The contribution of qualitative research within the PRECISE study in sub-Saharan Africa, REPRODUCTIVE HEALTH, Vol: 17
von Dadelszen P, Flint-O'Kane M, Poston L, et al., 2020, The PRECISE (PREgnancy Care Integrating translational Science, Everywhere) Network's first protocol: deep phenotyping in three sub-Saharan African countries, REPRODUCTIVE HEALTH, Vol: 17
Evangelopoulos D, Katsouyanni K, Keogh RH, et al., 2020, PM2.5 and NO2 exposure errors using proxy measures, including derived personal exposure from outdoor sources: A systematic review and meta-analysis, Environment International, Vol: 137, ISSN: 0160-4120
BACKGROUND: The use of proxy exposure estimates for PM2.5 and NO2 in air pollution studies instead of personal exposures, introduces measurement error, which can produce biased epidemiological effect estimates. Most studies consider total personal exposure as the gold standard. However, when studying the effects of ambient air pollution, personal exposure from outdoor sources is the exposure of interest. OBJECTIVES: We assessed the magnitude and variability of exposure measurement error by conducting a systematic review of the differences between personal exposures from outdoor sources and the corresponding measurements for ambient concentrations in order to increase understanding of the measurement error structures of the pollutants. DATA SOURCES AND ELIGIBILITY CRITERIA: We reviewed the literature (ISI Web of Science, Medline, 2000-2016) for English language studies (in any age group in any location (NO2) or Europe and North America (PM2.5)) that reported repeated measurements over time both for personal and ambient PM2.5 or NO2 concentrations. Only a few studies reported personal exposure from outdoor sources. We also collected data for infiltration factors and time-activity patterns of the individuals in order to estimate personal exposures from outdoor sources in every study. STUDY APPRAISAL AND SYNTHESIS METHODS: Studies using modelled rather than monitored exposures were excluded. Type of personal exposure monitor was assessed. Random effects meta-analysis was conducted to quantify exposure error as the mean difference between "true" and proxy measures. RESULTS: Thirty-two papers for PM2.5 and 24 for NO2 were identified. Outdoor sources were found to contribute 44% (range: 33-55%) of total personal exposure to PM2.5 and 74% (range: 57-88%) to NO2. Overall estimates of personal exposure (24-hour averages) from outdoor sources were 9.3 μg/m3 and 12.0 ppb for PM2.5 and NO2 respectively, while the corresponding difference between these expo
Danesh Yazdi M, Kuang Z, Dimakopoulou K, et al., 2020, Predicting fine particulate matter (PM2.5) in the Greater London area: an ensemble approach using machine learning methods, Remote Sensing, Vol: 12, Pages: 1-18, ISSN: 2072-4292
Estimating air pollution exposure has long been a challenge for environmental health researchers. Technological advances and novel machine learning methods have allowed us to increase the geographic range and accuracy of exposure models, making them a valuable tool in conducting health studies and identifying hotspots of pollution. Here, we have created a prediction model for daily PM2.5 levels in the Greater London area from 1st January 2005 to 31st December 2013 using an ensemble machine learning approach incorporating satellite aerosol optical depth (AOD), land use, and meteorological data. The predictions were made on a 1 km × 1 km scale over 3960 grid cells. The ensemble included predictions from three different machine learners: a random forest (RF), a gradient boosting machine (GBM), and a k-nearest neighbor (KNN) approach. Our ensemble model performed very well, with a ten-fold cross-validated R2 of 0.828. Of the three machine learners, the random forest outperformed the GBM and KNN. Our model was particularly adept at predicting day-to-day changes in PM2.5 levels with an out-of-sample temporal R2 of 0.882. However, its ability to predict spatial variability was weaker, with a R2 of 0.396. We believe this to be due to the smaller spatial variation in pollutant levels in this area.
Bone JN, Pickerill K, Woo Kinshella M-L, et al., 2020, Pregnancy cohorts and biobanking in sub-Saharan Africa: a systematic review, BMJ GLOBAL HEALTH, Vol: 5, ISSN: 2059-7908
Preston GW, Dagnino S, Ponzi E, et al., 2020, Relationships between airborne pollutants, serum albumin adducts and short-term health outcomes in an experimental crossover study, Chemosphere, Vol: 239, ISSN: 1879-1298
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 approximately 143Da) 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.
Introduction: Despite the London Underground (LU) handling on average 2.8 million passenger journeys per day, the characteristics and potential health effects of the elevated concentrations of metal-rich PM2.5 found in this subway system are not well understood. Methods: Spatial monitoring campaigns were carried out to characterise the health-relevant chemical and physical properties of PM2.5 across the LU network, including diurnal and day-to-day variability and spatial distribution (above ground, depth below ground and subway line). Population-weighted station PM2.5 rankings were produced to understand the relative importance of concentrations at different stations and on different lines. Results: The PM2.5 mass in the LU (mean 88 μg m−3, median 28 μg m−3) was greater than at ambient background locations (mean 19 μg m−3, median 14 μg m−3) and roadside environments in central London (mean 22 μg m−3, median 14 μg m−3). Concentrations varied between lines and locations, with the deepest and shallowest submerged lines being the District (median 4 μg m−3) and Victoria (median 361 μg m−3 but up to 885 μg m−3). Broadly in agreement with other subway systems around the world, sampled LU PM2.5 comprised 47% iron oxide, 7% elemental carbon, 11% organic carbon, and 14% metallic and mineral oxides. Although a relationship between line depth and air quality inside the tube trains was evident, there were clear influences relating to the distance from cleaner outside air and the exchange with cabin air when the doors open. The passenger population-weighted exposure analysis demonstrated a method to identify stations that should be prioritised for remediation to improve air quality. Conclusion: PM2.5 concentrations in the LU are many times higher than in other London transport Environments. Failure to include this environment in epidemiological studies of the relationship between PM2.5 and health in
Varaden D, 2019, Developing and testing methods to engage communities in air quality issues: an air pollution case study in London
Exposure to air pollution is a public health concern accountable for numerous health problems and tens of thousands of premature deaths each year in the UK. Despite this evidence, public understanding and awareness of the issue is low in comparison to other public health risks. Improved methods for engaging with the public to communicate this risk are required. Participatory research methods have been used in the air pollution field predominantly in unpublished work. However, there is still a lack of systematic empirical evidence on the feasibility of using this approach with diverse members of the community and on the impact that this approach can have on people’s views and perceptions of air pollution. Bringing together natural and social science techniques, this interdisciplinary PhD research aims to investigate the feasibility and impact of using participatory research interventions which involve the collection of personalised exposure data, with community groups to raise awareness of air pollution and identify potential solutions. Over 500 individuals, belonging to five community groups in London - including a primary school, a senior citizens group, a Chronic Obstructive Pulmonary Disease (COPD) patient group, and a parent and baby group – were recruited to take part in participatory research projects. The projects began with the provision of information on air pollution causes and effects. Subsequently, using portable exposure monitors and GPS watches, a subset of individuals from each group measured their own exposure to air pollution in the course of their normal activities. Each participant received a summary of their own findings and the overall results of the project were shared with all members of the community groups. Participants also included a group of activists and politicians who had taken part in similar projects, but on their own accord. Data on the impact of the participation in the projects were collected using observations, survey
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