Inter-continental variability in the relationship of oxidative potential and cytotoxicity with PM2.5 mass.

Most fine ambient particulate matter (PM2.5)-based epidemiological models use globalized concentration-response (CR) functions assuming that the toxicity of PM2.5 is solely mass-dependent without considering its chemical composition. Although oxidative potential (OP) has emerged as an alternate metric of PM2.5 toxicity, the association between PM2.5 mass and OP on a large spatial extent has not been investigated. In this study, we evaluate this relationship using 385 PM2.5 samples collected from 14 different sites across 4 different continents and using 5 different OP (and cytotoxicity) endpoints. Our results show that the relationship between PM2.5 mass vs. OP (and cytotoxicity) is largely non-linear due to significant differences in the intrinsic toxicity, resulting from a spatially heterogeneous chemical composition of PM2.5. These results emphasize the need to develop localized CR functions incorporating other measures of PM2.5 properties (e.g., OP) to better predict the PM2.5-attributed health burdens.

Local spatiotemporal dynamics of particulate matter and oak pollen measured by machine learning aided optical particle counters.

Conventional techniques for monitoring pollen currently have significant limitations in terms of labour, cost and the spatiotemporal resolution that can be achieved. Pollen monitoring networks across the world are generally sparse and are not able to fully represent the detailed characteristics of airborne pollen. There are few studies that observe concentrations on a local scale, and even fewer that do so in ecologically rich rural areas and close to emitting sources. Better understanding of these would be relevant to occupational risk assessments for public health, as well as ecology, biodiversity, and climate. We present a study using low-cost optical particle counters (OPCs) and the application of machine learning models to monitor particulate matter and pollen within a mature oak forest in the UK. We characterise the observed oak pollen concentrations, first during an OPC colocation period (6 days) for calibration purposes, then for a period (36 days) when the OPCs were distributed on an observational tower at different heights through the canopy. We assess the efficacy and usefulness of this method and discuss directions for future development, including the requirements for training data. The results show promise, with the derived pollen concentrations following the expected diurnal trends and interactions with meteorological variables. Quercus pollen concentrations appeared greatest when measured at the canopy height of the forest (20-30 m). Quercus pollen concentrations were lowest at the greatest measurement height that is above the canopy (40 m), which is congruent with previous studies of background pollen in urban environments. The attenuation of pollen concentrations as sources are depleted is also observed across the season and at different heights, with some evidence that the pollen concentrations persist later at the lowest level beneath the canopy (10 m) where catkins mature latest in the season compared to higher catkins.

HIPTox-Hazard Identification Platform to Assess the Health Impacts from Indoor and Outdoor Air Pollutant Exposures, through Mechanistic Toxicology: A Single-Centre Double-Blind Human Exposure Trial Protocol.

Over the past decade, our understanding of the impact of air pollution on short- and long-term population health has advanced considerably, focusing on adverse effects on cardiovascular and respiratory systems. There is, however, increasing evidence that air pollution exposures affect cognitive function, particularly in susceptible groups. Our study seeks to assess and hazard rank the cognitive effects of prevalent indoor and outdoor pollutants through a single-centre investigation on the cognitive functioning of healthy human volunteers aged 50 and above with a familial predisposition to dementia. Participants will all undertake five sequential controlled exposures. The sources of the air pollution exposures are wood smoke, diesel exhaust, cleaning products, and cooking emissions, with clean air serving as the control. Pre- and post-exposure spirometry, nasal lavage, blood sampling, and cognitive assessments will be performed. Repeated testing pre and post exposure to controlled levels of pollutants will allow for the identification of acute changes in functioning as well as the detection of peripheral markers of neuroinflammation and neuronal toxicity. This comprehensive approach enables the identification of the most hazardous components in indoor and outdoor air pollutants and further understanding of the pathways contributing to neurodegenerative diseases. The results of this project have the potential to facilitate greater refinement in policy, emphasizing health-relevant pollutants and providing details to aid mitigation against pollutant-associated health risks.

Mapping urban mobility using vehicle telematics to understand driving behaviour.

Telematics data, primarily collected from on-board vehicle devices (OBDs), has been utilised in this study to generate a thorough understanding of driving behaviour. The urban case study area is the large metropolitan region of the West Midlands, UK, but the approach is generalizable and translatable to other global urban regions. The new approach of GeoSpatial and Temporal Mapping of Urban Mobility (GeoSTMUM) is used to convert telematics data into driving metrics, including the relative time the vehicle fleet spends idling, cruising, accelerating, and decelerating. The telematics data is also used to parameterize driving volatility and aggressiveness, which are key factors within road safety, which is a global issue. Two approaches to defining aggressive driving are applied and assessed, they are vehicle jerk (the second derivative of vehicle speed), and the profile of speed versus acceleration/deceleration. The telematics-based approach has a very high spatial resolution (15-150 m) and temporal resolution (2 h), which can be used to develop more accurate driving cycles. The approach allows for the determination of road segments with the highest potential for aggressive driving and highlights where additional safety measures could beneficially be adopted. Results highlight the strong correlation between vehicle road occupancy and aggressive driving.

Impacts of ambient air quality on acute asthma hospital admissions during the COVID-19 pandemic in Oxford City, UK: a time-series study.

OBJECTIVES: The study aims to investigate the short-term associations between exposure to ambient air pollution (nitrogen dioxide (NO2), particulate matter pollution-particles with diameter<2.5 µm (PM2.5) and PM10) and incidence of asthma hospital admissions among adults, in Oxford, UK. DESIGN: Retrospective time-series study. SETTING: Oxford City (postcode areas OX1-OX4), UK. PARTICIPANTS: Adult population living within the postcode areas OX1-OX4 in Oxford, UK from 1 January 2015 to 31 December 2021. PRIMARY AND SECONDARY OUTCOME MEASURES: Hourly NO2, PM2.5 and PM10 concentrations and meteorological data for the period 1 January 2015 to 31 December 2020 were analysed and used as exposures. We used Poisson linear regression analysis to identify independent associations between air pollutant concentrations and asthma admissions rate among the adult study population, using both single (NO2, PM2.5, PM10) and multipollutant (NO2 and PM2.5, NO2 and PM10) models, where they adjustment for temperature and relative humidity. RESULTS: The overall 5-year average asthma admissions rate was 78 per 100 000 population during the study period. The annual average rate decreased to 46 per 100 000 population during 2020 (incidence rate ratio 0.58, 95% CI 0.42 to 0.81, p<0.001) compared to the prepandemic years (2015-2019). In single-pollutant analysis, we observed a significantly increased risk of asthma admission associated with each 1 µg/m3 increase in monthly concentrations of NO2 4% (95% CI 1.009% to 1.072%), PM2.5 3% (95% CI 1.006% to 1.052%) and PM10 1.8% (95% CI 0.999% to 1.038%). However, in the multipollutant regression model, the effect of each individual pollutant was attenuated. CONCLUSIONS: Ambient NO2 and PM2.5 air pollution exposure increased the risk of asthma admissions in this urban setting. Improvements in air quality during COVID-19 lockdown periods may have contributed to a substantially reduced acute asthma disease burden. Large-scale measures to improve air quality have potential to protect vulnerable people living with chronic asthma in urban areas.

Traffic Noise Assessment Using Intelligent Acoustic Sensors (Traffic Ear) and Vehicle Telematics Data.

Here, we introduce Traffic Ear, an acoustic sensor pack that determines the engine noise of each passing vehicle without interrupting traffic flow. The device consists of an array of microphones combined with a computer vision camera. The class and speed of passing vehicles were estimated using sound wave analysis, image processing, and machine learning algorithms. We compared the traffic composition estimated with the Traffic Ear sensor with that recorded using an automatic number plate recognition (ANPR) camera and found a high level of agreement between the two approaches for determining the vehicle type and fuel, with uncertainties of 1-4%. We also developed a new bottom-up assessment approach that used the noise analysis provided by the Traffic Ear sensor along with the extensively detailed urban mobility maps that were produced using the geospatial and temporal mapping of urban mobility (GeoSTMUM) approach. It was applied to vehicles travelling on roads in the West Midlands region of the UK. The results showed that the reduction in traffic engine noise over the whole of the study road was over 8% during rush hours, while the weekday-weekend effect had a deterioration effect of almost half. Traffic noise factors (dB/m) on a per-vehicle basis were almost always higher on motorways compared the other roads studied.

Machine learning methods for low-cost pollen monitoring - Model optimisation and interpretability.

Pollen is a major issue globally, causing as much as 40 % of the population to suffer from hay fever and other allergic conditions. Current techniques for monitoring pollen are either laborious and slow, or expensive, thus alternative methods are needed to provide timely and more localised information on airborne pollen concentrations. We have demonstrated previously that low-cost Optical Particle Counter (OPC) sensors can be used to estimate pollen concentrations when machine learning methods are used to process the data and learn the relationships between OPC output data and conventionally measured pollen concentrations. This study demonstrates how methodical hyperparameter tuning can be employed to significantly improve model performance. We present the results of a range of models based on tuned hyperparameter configurations trained to predict Poaceae (Barnhart), Quercus (L.), Betula (L.), Pinus (L.) and total pollen concentrations. The results achieved here are a significant improvement on results we previously reported: the average R2 scores for the total pollen models have at least doubled compared to using previous parameter settings. Furthermore, we employ the explainable Artificial Intelligence (XAI) technique, SHAP, to interpret the models and understand how each of the input features (i.e. particle sizes) affect the estimated output concentration for each pollen type. In particular, we found that Quercus pollen has a strong positive correlation with particles of optical diameter 1.7-2.3 µm, which distinguishes it from other pollen types such as Poaceae and may suggest that type-specific subpollen particles are present in this size range. There is much further work to be done, especially in training and testing models on data obtained across different environments to evaluate the extent of generalisability. Nevertheless, this work demonstrates the potential this method can offer for low-cost monitoring of pollen and the valuable insight we can gain from what the model has learned.

Telematics data for geospatial and temporal mapping of urban mobility: Fuel consumption, and air pollutant and climate-forcing emissions of passenger cars.

In this study, we use the approach of geospatial and temporal (GeoST) mapping of urban mobility to evaluate the speed-time-acceleration profile (dynamic status) of passenger cars. We then use a pre-developed model, fleet composition and real-world emission factor (EF) datasets to translate vehicles dynamics status into real-urban fuel consumption (FC) and exhaustive (CO2 and NOx) emissions with high spatial (15 m) and temporal (2 h) resolutions. Road transport in the West Midlands, UK, for 2016 and 2018 is the spatial and temporal scope of this study. Our approach enables the analysis of the influence of factors such as road slope, non-rush/rush hour and weed days/weekends effects on the characteristics of the transport environment. The results show that real-urban NOx EFs reduced by more than 14 % for 2016-18. This can be attributed to the increasing contribution of Euro 6 vehicles by 63 %, and the increasing contribution of diesel vehicles by 13 %. However, the variations in the real-urban FC and CO2 EFs are less significant (±2 %). We found that the FC estimated for driving under the NEDC (National European Driving Cycle) is a qualified benchmark for evaluating real-urban FCs. Considering the role of road slope increases the estimated real-urban FC, and NOx, and CO2 EFs by a weighted average of 4.8 %, 3.9 %, and 3.0 %, respectively. Time of travel (non-rush/rush hour or weed days/weekends) has a profound effect on vehicle fuel consumption and related emissions, with EFs increasing in more free-flowing conditions.

The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK.

Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities with arising impacts upon urban air quality. To date, these air quality changes associated with lockdown measures have typically been assessed using limited city-level regulatory monitoring data, however, low-cost air quality sensors provide capabilities to assess changes across multiple locations at higher spatial-temporal resolution, thereby generating insights relevant for future air quality interventions. The aim of this study was to utilise high-spatial resolution air quality information utilising data arising from a validated (using a random forest field calibration) network of 15 low-cost air quality sensors within Oxford, UK to monitor the impacts of multiple COVID-19 public heath restrictions upon particulate matter concentrations (PM10, PM2.5) from January 2020 to September 2021. Measurements of PM10 and PM2.5 particle size fractions both within and between site locations are compared to a pre-pandemic related public health restrictions baseline. While average peak concentrations of PM10 and PM2.5 were reduced by 9-10 µg/m3 below typical peak levels experienced in recent years, mean daily PM10 and PM2.5 concentrations were only ∼1 µg/m3 lower and there was marked temporal (as restrictions were added and removed) and spatial variability (across the 15-sensor network) in these observations. Across the 15-sensor network we observed a small local impact from traffic related emission sources upon particle concentrations near traffic-oriented sensors with higher average and peak concentrations as well as greater dynamic range, compared to more intermediate and background orientated sensor locations. The greater dynamic range in concentrations is indicative of exposure to more variable emission sources, such as road transport emissions. Our findings highlight the great potential for low-cost sensor technology to identify highly localised changes in pollutant concentrations as a consequence of changes in behaviour (in this case influenced by COVID-19 restrictions), generating insights into non-traffic contributions to PM emissions in this setting. It is evident that additional non-traffic related measures would be required in Oxford to reduce the PM10 and PM2.5 levels to within WHO health-based guidelines and to achieve compliance with PM2.5 targets developed under the Environment Act 2021.

Monitoring and apportioning sources of indoor air quality using low-cost particulate matter sensors.

Air quality is one of the most important factors in public health. While outdoor air quality is widely studied, the indoor environment has been less scrutinised, even though time spent indoors is typically much greater than outdoors. The emergence of low-cost sensors can help assess indoor air quality. This study provides a new methodology, utilizing low-cost sensors and source apportionment techniques, to understand the relative importance of indoor and outdoor air pollution sources upon indoor air quality. The methodology is tested with three sensors placed in different rooms inside an exemplar house (bedroom, kitchen and office) and one outdoors. When the family was present, the bedroom had the highest average concentrations for PM2.5 and PM10 (3.9 ± 6.8 ug/m3 and 9.6 ± 12.7 µg/m3 respectively), due to the activities undertaken there and the presence of softer furniture and carpeting. The kitchen, while presenting the lowest PM concentrations for both size ranges (2.8 ± 5.9 ug/m3 and 4.2 ± 6.9 µg/m3 respectively), presented the highest PM spikes, especially during cooking times. Increased ventilation in the office resulted in the highest PM1 concentration (1.6 ± 1.9 µg/m3), highlighting the strong effect of infiltration of outdoor air for the smallest particles. Source apportionment, via positive matrix factorisation (PMF), showed that up to 95 % of the PM1 was found to be of outdoor sources in all the rooms. This effect was reduced as particle size increased, with outdoor sources contributing >65 % of the PM2.5, and up to 50 % of the PM10, depending on the room studied. The new approach to elucidate the contributions of different sources to total indoor air pollution exposure, described in this paper, is easily scalable and translatable to different indoor locations.

Impact assessment of a raw coal ban on maternal and child health outcomes in Ulaanbaatar: a protocol for an interrupted time series study.

INTRODUCTION: Despite a decade of policy actions, Ulaanbaatar's residents continue to be exposed to extreme levels of air pollution, a major public health concern, especially for vulnerable populations such as pregnant women and children. In May 2019, the Mongolian government implemented a raw coal ban (RCB), prohibiting distribution and use of raw coal in households and small businesses in Ulaanbaatar. Here, we present the protocol for an interrupted time series (ITS; a strong quasi-experimental study design for public health interventions) that aims to assess the effectiveness of this coal ban policy on environmental (air quality) and health (maternal and child) outcomes. METHODS AND ANALYSIS: Routinely collected data on pregnancy and child respiratory health outcomes between 2016 and 2022 in Ulaanbaatar will be collected retrospectively from the four main hospitals providing maternal and/or paediatric care as well as the National Statistics Office. Hospital admissions data for childhood diarrhoea, an unrelated outcome to air pollution exposure, will be collected to control for unknown or unmeasured coinciding events. Retrospective air pollution data will be collected from the district weather stations and the US Embassy. An ITS analysis will be conducted to determine the RCB intervention impact on these outcomes. Prior to the ITS, we have proposed an impact model based on a framework of five key factors, which were identified through literature search and qualitative research to potentially influence the intervention impact assessment. ETHICS AND DISSEMINATION: Ethical approval has been obtained via the Ministry of Health, Mongolia (No.445) and University of Birmingham (ERN_21-1403). To inform relevant stakeholders of our findings, key results will be disseminated on both (inter)national and population levels through publications, scientific conferences and community briefings. These findings are aimed to provide evidence for decision-making in coal pollution mitigation strategies in Mongolia and similar settings throughout the world.

Limited impact of diesel particle filters on road traffic emissions of ultrafine particles.

Diesel engines are a major contributor to emissions of both Black Carbon (BC) and ultrafine particles. Analysis of data from the only roadside monitoring site in Europe with a continuous dataset for size-segregated particle number count (Marylebone Road, London) from 2010 to 2021 reveals that the growing number of vehicles fitted with a Diesel Oxidation Catalyst (DOC) and Diesel Particle Filter (DPF) has been very effective in controlling the emissions of solid particles and hence BC, but that there has been little change in the liquid mode (<30 nm) particles, and that concentrations of ultrafine particles (<100 nm) still well exceed the threshold for "high" concentrations (>104 cm-3 /24-hour mean) defined by WHO. BC declined by 81% between 2014 and 2021, but the ultrafine particle (<100 nm) count declined by only 26%. Consequently, in locations worldwide with heavy diesel traffic, concentrations of ultrafine particles are likely to remain "high" for the foreseeable future unless more effective abatement technologies are implemented.

Constructing a pollen proxy from low-cost Optical Particle Counter (OPC) data processed with Neural Networks and Random Forests.

Pollen allergies affect a significant proportion of the global population, and this is expected to worsen in years to come. There is demand for the development of automated pollen monitoring systems. Low-cost Optical Particle Counters (OPCs) measure particulate matter and have attractive advantages of real-time high time resolution data and affordable costs. This study asks whether low-cost OPC sensors can be used for meaningful monitoring of airborne pollen. We employ a variety of methods, including supervised machine learning techniques, to construct pollen proxies from hourly-average OPC data and evaluate their performance, holding out 40 % of observations to test the proxies. The most successful methods are supervised machine learning Neural Network (NN) and Random Forest (RF) methods, trained from pollen concentrations collected from a Hirst-type sampler. These perform significantly better than using a simple particle size-filtered proxy or a Positive Matrix Factorisation (PMF) source apportionment pollen proxy. Twelve NN and RF models were developed to construct a pollen proxy, each varying by model type, input features and target variable. The results show that such models can construct useful information on pollen from OPC data. The best metrics achieved (Spearman correlation coefficient = 0.85, coefficient of determination = 0.67) were for the NN model constructing a Poaceae (grass) pollen proxy, based on particle size information, temperature, and relative humidity. Ability to distinguish high pollen events was evaluated using F1 Scores, a score reflecting the fraction of true positives with respect to false positives and false negatives, with promising results (F1 ≤ 0.83). Model-constructed proxies demonstrated the ability to follow monthly and diurnal trends in pollen. We discuss the suitability of OPCs for monitoring pollen and offer advice for future progress. We demonstrate an attractive alternative for automated pollen monitoring that could provide valuable and timely information to the benefit of pollen allergy sufferers.

Towards European automatic bioaerosol monitoring: Comparison of 9 automatic pollen observational instruments with classic Hirst-type traps.

To benefit allergy patients and the medical practitioners, pollen information should be available in both a reliable and timely manner; the latter is only recently possible due to automatic monitoring. To evaluate the performance of all currently available automatic instruments, an international intercomparison campaign was jointly organised by the EUMETNET AutoPollen Programme and the ADOPT COST Action in Munich, Germany (March-July 2021). The automatic systems (hardware plus identification algorithms) were compared with manual Hirst-type traps. Measurements were aggregated into 3-hourly or daily values to allow comparison across all devices. We report results for total pollen as well as for Betula, Fraxinus, Poaceae, and Quercus, for all instruments that provided these data. The results for daily averages compared better with Hirst observations than the 3-hourly values. For total pollen, there was a considerable spread among systems, with some reaching R2 > 0.6 (3 h) and R2 > 0.75 (daily) compared with Hirst-type traps, whilst other systems were not suitable to sample total pollen efficiently (R2 < 0.3). For individual pollen types, results similar to the Hirst were frequently shown by a small group of systems. For Betula, almost all systems performed well (R2 > 0.75 for 9 systems for 3-hourly data). Results for Fraxinus and Quercus were not as good for most systems, while for Poaceae (with some exceptions), the performance was weakest. For all pollen types and for most measurement systems, false positive classifications were observed outside of the main pollen season. Different algorithms applied to the same device also showed different results, highlighting the importance of this aspect of the measurement system. Overall, given the 30 % error on daily concentrations that is currently accepted for Hirst-type traps, several automatic systems are currently capable of being used operationally to provide real-time observations at high temporal resolutions. They provide distinct advantages compared to the manual Hirst-type measurements.

Detecting high emitting vehicle subsets using emission remote sensing systems.

It is often assumed that a small proportion of a given vehicle fleet produces a disproportionate amount of air pollution emissions. If true, policy actions to target the highly polluting section of the fleet could lead to significant improvements in air quality. In this paper, high-emitter vehicle subsets are defined and their contributions to the total fleet emission are assessed. A new approach, using enrichment factor in cumulative Pareto analysis is proposed for detecting high emitter vehicle subsets within the vehicle fleet. A large dataset (over 94,000 remote-sensing measurements) from five UK-based EDAR (emission detecting and reporting system) field campaigns for the years 2016-17 is used as the test data. In addition to discussions about the high emitter screening criteria, the data analysis procedure and future issues of implementation are discussed. The results show different high emitter trends dependent on the pollutant investigated, and the vehicle type investigated. For example, the analysis indicates that 23 % and 51 % of petrol and diesel cars were responsible for 80 % of NO emissions within that subset of the fleet, respectively. Overall, the contributions of vehicles that account for 80 % of total fleet emissions usually reduce with EURO class improvement, with the subset fleet emissions becoming more homogenous. The high emitter constituent was more noticeable for pollutant PM compared with the other gaseous pollutants, and it was also more prominent for petrol cars when compared to diesel ones.

Carbon monoxide levels in households using coal-briquette fuelled stoves exceed WHO air quality guidelines in Ulaanbaatar, Mongolia.

In 2019, a domestic raw coal ban (RCB) was introduced in Ulaanbaatar, Mongolia. Coal-briquettes have since been promoted in Ger district households, however implications for carbon monoxide (CO) exposure remains uncertain. We obtained 48-hour indoor CO concentrations in 23 Ger district households and compared these to 10 raw-coal households. Information on household characteristics, fuel use behaviour and stove venting practices was collected by survey. Mean 48-hour CO concentrations in coal-briquette households was 6.1 ppm (range 1.5-35.8 ppm) with no signfiicant differences by household, stove or venting factors. Peak time-weighted average CO concentrations exceeded WHO Indoor Air Quality guidelines in 9 (39%) households; with all surpassing the 8-hour guideline (>8.6 ppm); 3(13%) the 24-hour guideline (>6 ppm) and 2(9%) the 1-hour guideline (>30 ppm). Median CO levels were significantly lower in coal-briquette compared to raw coal households (p = 0.049). Indoor CO reduction was associated with RCB implementation although hazardous levels persist in this setting.

Mitigating the impact of air pollution on dementia and brain health: Setting the policy agenda.

BACKGROUND: Emerging research suggests exposure to high levels of air pollution at critical points in the life-course is detrimental to brain health, including cognitive decline and dementia. Social determinants play a significant role, including socio-economic deprivation, environmental factors and heightened health and social inequalities. Policies have been proposed more generally, but their benefits for brain health have yet to be fully explored. OBJECTIVE AND METHODS: Over the course of two years, we worked as a consortium of 20+ academics in a participatory and consensus method to develop the first policy agenda for mitigating air pollution's impact on brain health and dementia, including an umbrella review and engaging 11 stakeholder organisations. RESULTS: We identified three policy domains and 14 priority areas. Research and Funding included: (1) embracing a complexities of place approach that (2) highlights vulnerable populations; (3) details the impact of ambient PM2.5 on brain health, including current and historical high-resolution exposure models; (4) emphasises the importance of indoor air pollution; (5) catalogues the multiple pathways to disease for brain health and dementia, including those most at risk; (6) embraces a life course perspective; and (7) radically rethinks funding. Education and Awareness included: (8) making this unrecognised public health issue known; (9) developing educational products; (10) attaching air pollution and brain health to existing strategies and campaigns; and (11) providing publicly available monitoring, assessment and screening tools. Policy Evaluation included: (12) conducting complex systems evaluation; (13) engaging in co-production; and (14) evaluating air quality policies for their brain health benefits. CONCLUSION: Given the pressing issues of brain health, dementia and air pollution, setting a policy agenda is crucial. Policy needs to be matched by scientific evidence and appropriate guidelines, including bespoke strategies to optimise impact and mitigate unintended consequences. The agenda provided here is the first step toward such a plan.

Amateur runners more influenced than elite runners by temperature and air pollution during the UK's Great North Run half marathon.

The short- and long-term impacts of air pollution on human health are well documented and include cardiovascular, neurological, immune system and developmental damage. Additionally, the irritant qualities of air pollutants can cause respiratory and cardiovascular distress. This can be heightened during exercise and especially so for those with respiratory conditions such as asthma. Meteorological conditions have also been shown to adversely impact athletic performance; but research has mostly examined the impact of pollution and meteorology on marathon times or running under laboratory settings. This study focuses on the half marathon distance (13.1 miles/21.1 km) and utilises the Great North Run held in Newcastle-upon-Tyne, England, between 2006 and 2019. Local meteorological (temperature, relative humidity, heat index and wind speed) and air quality (ozone, nitrogen dioxide and PM2.5) data is used in conjunction with finishing times of the quickest and slowest amateur participants, along with the elite field, to determine the extent to which each group is influenced in real-world conditions. Results show that increased temperatures, heat index and ozone concentrations are significantly detrimental to amateur half marathon performances. The elite field meanwhile is influenced by higher ozone concentrations. It is thought that the increased exposure time to the environmental conditions contributes to this greater decrease in performance for the slowest participants. For elite athletes that are performing closer to their maximal capacity (VO2 max), the higher ozone concentrations likely results in respiratory irritation and decreased performance. Nitrogen dioxide and PM2.5 pollution showed no significant relationship with finishing times. These results provide additional insight into the environmental effects on exercise, which is particularly important under the increasing effects climate change and regional air pollution. This study can be used to inform event organisation and start times for both mass participation and major elite events with the aim to reduce heat- and pollution-related incidents.

Cooking outdoors or with cleaner fuels does not increase malarial risk in children under 5 years: a cross-sectional study of 17 sub-Saharan African countries.

BACKGROUND: Smoke from solid biomass cooking is often stated to reduce household mosquito levels and, therefore, malarial transmission. However, household air pollution (HAP) from solid biomass cooking is estimated to be responsible for 1.67 times more deaths in children aged under 5 years compared to malaria globally. This cross-sectional study investigates the association between malaria and (i) cleaner fuel usage; (ii) wood compared to charcoal fuel; and, (iii) household cooking location, among children aged under 5 years in sub-Saharan Africa (SSA). METHODS: Population-based data was obtained from Demographic and Health Surveys (DHS) for 85,263 children within 17 malaria-endemic sub-Saharan countries who were who were tested for malaria with a malarial rapid diagnostic test (RDT) or microscopy. To assess the independent association between malarial diagnosis (positive, negative), fuel type and cooking location (outdoor, indoor, attached to house), multivariable logistic regression was used, controlling for individual, household and contextual confounding factors. RESULTS: Household use of solid biomass fuels and kerosene cooking fuels was associated with a 57% increase in the odds ratio of malarial infection after adjusting for confounding factors (RDT adjusted odds ratio (AOR):1.57 [1.30-1.91]; Microscopy AOR: 1.58 [1.23-2.04]) compared to cooking with cleaner fuels. A similar effect was observed when comparing wood to charcoal among solid biomass fuel users (RDT AOR: 1.77 [1.54-2.04]; Microscopy AOR: 1.21 [1.08-1.37]). Cooking in a separate building was associated with a 26% reduction in the odds of malarial infection (RDT AOR: 0.74 [0.66-0.83]; Microscopy AOR: 0.75 [0.67-0.84]) compared to indoor cooking; however no association was observed with outdoor cooking. Similar effects were observed within a sub-analysis of malarial mesoendemic areas only. CONCLUSION: Cleaner fuels and outdoor cooking practices associated with reduced smoke exposure were not observed to have an adverse effect upon malarial infection among children under 5 years in SSA. Further mixed-methods research will be required to further strengthen the evidence base concerning this risk paradigm and to support appropriate public health messaging in this context.

Mie scattering from optically levitated mixed sulfuric acid-silica core-shell aerosols: observation of core-shell morphology for atmospheric science.

Sulfuric acid is shown to form a core-shell particle on a micron-sized, optically-trapped spherical silica bead. The refractive indices of the silica and sulfuric acid, along with the shell thickness and bead radius were determined by reproducing Mie scattered optical white light as a function of wavelength in Mie spectroscopy. Micron-sized silica aerosols (silica beads were used as a proxy for atmospheric silica minerals) were levitated in a mist of sulfuric acid particles; continuous collection of Mie spectra throughout the collision of sulfuric acid aerosols with the optically trapped silica aerosol demonstrated that the resulting aerosol particle had a core-shell morphology. Contrastingly, the collision of aqueous sulfuric acid aerosols with optically trapped polystyrene aerosol resulted in a partially coated system. The light scattering from the optically levitated aerosols was successfully modelled to determine the diameter of the core aerosol (±0.003 µm), the shell thickness (±0.0003 µm) and the refractive index (±0.007). The experiment demonstrated that the presence of a thin film rapidly changed the light scattering of the original aerosol. When a 1.964 µm diameter silica aerosol was covered with a film of sulfuric acid 0.287 µm thick, the wavelength dependent Mie peak positions resembled sulfuric acid. Thus mineral aerosol advected into the stratosphere would likely be coated with sulfuric acid, with a core-shell morphology, and its light scattering properties would be effectively indistinguishable from a homogenous sulfuric acid aerosol if the film thickness was greater than a few 100 s of nm for UV-visible wavelengths.