Low-Cost Source Apportionment (LoCoSA) of air pollution - literature review of the state of the art.

Air pollution is the leading environmental risk to public health demanding urgent action. While air quality monitoring provides crucial insights into pollution levels, source apportionment (SA) is essential for identifying pollution sources and informing targeted solutions. However, SA remains underutilized, primarily due to the high costs of data collection.

Acute particulate matter exposure diminishes executive cognitive functioning after four hours regardless of inhalation pathway.

Recent evidence suggests short-term exposure to particulate matter (PM) air pollution can impact brain function after a delay period. It is unknown whether effects are predominantly due to the olfactory or lung-brain pathways. In this study 26 adults (M = 27.

Using low-cost sensors to assess common air pollution sources across multiple residences.

The rapid development of low-cost sensors provides the opportunity to greatly advance the scope and extent of monitoring of indoor air pollution. In this study, calibrated particle matter (PM) sensors and a non-negative matrix factorisation (NMF) source apportionment technique are used to investigate PM concentrations and source contributions across three households in an urban residential area. The NMF is applied to combined data from all houses to generate source profiles that can be used to understand how PM source characteristics are similar or differ between different households in the same urban area.

Assessing the Polarising Impacts of Low-Traffic Neighbourhoods: A Community Perspective from Birmingham, UK.

Globally, the transport sector is a major contributor to air pollution. Currently, in the UK, vehicle emissions contribute significant amounts of nitrogen oxide (NOx) and particulate matter (PM) pollution in urban areas. Low-emission-zone policies have been used as an intervention to tackle air pollution, and in this context, the UK launched the Low-Traffic Neighbourhood scheme.

Pinpointing sources of pollution using citizen science and hyperlocal low-cost mobile source apportionment.

Currently, methodologies for the identification and apportionment of air pollution sources are not widely applied due to their high cost. We present a new approach, combining mobile measurements from multiple sensors collected from the daily walks of citizen scientists, in a high population density area of Birmingham, UK. The methodology successfully pinpoints the different sources affecting the local air quality in the area using only a handful of measurements.

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

Most fine ambient particulate matter (PM)-based epidemiological models use globalized concentration-response (CR) functions assuming that the toxicity of PM is solely mass-dependent without considering its chemical composition. Although oxidative potential (OP) has emerged as an alternate metric of PM toxicity, the association between PM mass and OP on a large spatial extent has not been investigated. In this study, we evaluate this relationship using 385 PM samples collected from 14 different sites across 4 different continents and using 5 different OP (and cytotoxicity) endpoints.

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.

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.

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.

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 (NO), particulate matter pollution-particles with diameter<2.5 µm (PM) and PM) and incidence of asthma hospital admissions among adults, in Oxford, UK.

Design: Retrospective time-series study.

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.

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.

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 (CO and NO) 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.

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 (PM, PM) from January 2020 to September 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.

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.

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.

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.

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.

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.

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.

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.

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.

Effectiveness of interventions to reduce household air pollution from solid biomass fuels and improve maternal and child health outcomes in low- and middle-income countries: A systematic review and meta-analysis.

Interventions to reduce household air pollution (HAP) are key to reducing associated morbidity and mortality in low- and middle- income countries (LMICs); especially among pregnant women and young children. This systematic review aims to determine the effectiveness of interventions aimed to reduce HAP exposure associated with domestic solid biomass fuel combustion, compared to usual cooking practices, for improving health outcomes in pregnant women and children under five in LMIC settings. A systematic review and meta-analysis was undertaken with searches undertaken in MEDLINE, EMBASE, CENTRAL, GIM, ClinicalTrials.