Occurrence of a "forever chemical" in the atmosphere above pristine Amazon Forest.

Per- and polyfluoroalkyl substances (PFAS), often referred to as "forever chemicals", are a class of man-made, extremely stable chemicals, which are widely used in industrial and commercial applications. Exposure to some PFAS is now known to be detrimental to human health. By virtue of PFAS long residence times, they are widely detected in the environment, including remote locations such as the Arctics, where the origin of the PFAS is poorly understood. It has been suggested that PFAS may be transported through contaminated waters, leading to accumulation in coastal areas, where they can be aerosolised via sea spray, thereby extending their geographical distribution far beyond their original source regions. The aim of this work is to investigate, for the first time, whether "forever chemicals" could be transported to areas considered to be pristine, far from coastal sites. This study was performed at the Amazonian Tall Tower Observatory (ATTO), a unique remote site situated in the middle of the Amazon rainforest, where a restricted PFAS, perfluorooctanoic acid (PFOA), was observed with concentrations reaching up to 2 pg/m3. A clear trend of increasing concentration with sampling height was observed and air masses from the south over Manaus had the highest concentrations. Atmospheric lifetime estimations, removal mechanisms supported by measurements at two heights (320 and 42 m above the rainforest), and concentration spikes indicated a long-range transport of PFOA to pristine Amazon rainforest. Potential sources, including industrial activities in urban areas, were explored, and historical fire management practices considered. This research presents the first measurements of PFAS in the atmosphere of Amazon rainforest. Remarkably, even in this remote natural environment, appreciable levels of PFAS can be detected. This study provides valuable insights into the long-range transport of the anthropogenic "forever chemical" into a remote natural ecosystem and should raise awareness of potential environmental implications.

Spatiotemporal distribution of pollutants and impact of local meteorology on source influence on pollutants' level in a traffic air-shed in Lagos megacity, Nigeria.

Pollution from vehicular emissions is a major cause of poor air quality observed in many urban and semi-urban towns and cities. As such, this study was conducted to assess air quality and the spatiotemporal distribution of vehicular and traffic-related pollutants in several air sheds of Lagos megacity, the economic nerve centre of Nigeria. A setup of low-cost air quality sensors comprising five (5) units was deployed between November 2018 and February 2019 within traffic corridors in the heart of the city. Diurnal variation of pollutants indicated that carbon dioxide (CO2) peaked during the early hours of the day, total oxide (Ox = NO2+O3) peaked at mid-day while carbon monoxide (CO) had two distinct peaks which correspond to morning and evening rush hours. Nitrogen dioxide (NO2) concentration peaked during evening hours. Average concentrations are NO2 (97.1 ± 9.7) ppb, Ox (78.6 ± 27.2) ppb, CO2 (450.1 ± 31.2) ppm, and CO (2285.63 ± 743.7) ppb. Average concentrations of pollutants were above thresholds set by the World Health Organization (WHO) except for NO2 which was within the range permissible limits. The implication of this is that the atmosphere is polluted due to elevated concentrations of airborne pollutants, an indication which is of both health and environmental concern. The air quality index (AQI) indicates that the quality of ambient air varies from good to very unhealthy for Ox, and unhealthy to very unhealthy for CO, while AQI for PM2.5 and PM10 showed hazardous at all the sampling locations except at UNILAG where it is unhealthy for the sensitive group. For all of the sampling sites, conditional bivariate probability function (CBPF) plots show a significant agreement with the location of known pollution sources.

Identifying Patterns and Sources of Fine and Ultrafine Particulate Matter in London Using Mobile Measurements of Lung-Deposited Surface Area.

We performed more than a year of mobile, 1 Hz measurements of lung-deposited surface area (LDSA, the surface area of 20-400 nm diameter particles, deposited in alveolar regions of lungs) and optically assessed fine particulate matter (PM2.5), black carbon (BC), and nitrogen dioxide (NO2) in central London. We spatially correlated these pollutants to two urban emission sources: major roadways and restaurants. We show that optical PM2.5 is an ineffective indicator of tailpipe emissions on major roadways, where we do observe statistically higher LDSA, BC, and NO2. Additionally, we find pollutant hot spots in commercial neighborhoods with more restaurants. A low LDSA (15 µm2 cm-3) occurs in areas with fewer major roadways and restaurants, while the highest LDSA (25 µm2 cm-3) occurs in areas with more of both sources. By isolating areas that are higher in one source than the other, we demonstrate the comparable impacts of traffic and restaurants on LDSA. Ratios of hyperlocal enhancements (ΔLDSA:ΔBC and ΔLDSA:ΔNO2) are higher in commercial neighborhoods than on major roadways, further demonstrating the influence of restaurant emissions on LDSA. We demonstrate the added value of using particle surface in identifying hyperlocal patterns of health-relevant PM components, especially in areas with strong vehicular emissions where the high LDSA does not translate to high PM2.5.

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

BACKGROUND: Air pollution epidemiology has primarily relied on fixed outdoor air quality monitoring networks and static populations. METHODS: Taking advantage of recent advancements in sensor technologies and computational techniques, this paper presents a novel methodological approach that improves dose estimations of multiple air pollutants in large-scale health studies. We show the results of an intensive field campaign that measured personal exposures to gaseous pollutants and particulate matter of a health panel of 251 participants residing in urban and peri-urban Beijing with 60 personal air quality monitors (PAMs). Outdoor air pollution measurements were collected in monitoring stations close to the participants' residential addresses. Based on parameters collected with the PAMs, we developed an advanced computational model that automatically classified time-activity-location patterns of each individual during daily life at high spatial and temporal resolution. RESULTS: Applying this methodological approach in two established cohorts, we found substantial differences between doses estimated from outdoor and personal air quality measurements. The PAM measurements also significantly reduced the correlation between pollutant species often observed in static outdoor measurements, reducing confounding effects. CONCLUSIONS: Future work will utilise these improved dose estimations to investigate the underlying mechanisms of air pollution on cardio-pulmonary health outcomes using detailed medical biomarkers in a way that has not been possible before.

Analysis of the variability of airborne particulate matter with prevailing meteorological conditions across a semi-urban environment using a network of low-cost air quality sensors.

The concentrations of fine and coarse fractions of airborne particulate matter (PM) and meteorological variables (wind speed, wind direction, temperature and relative humidity) were measured at six selected locations in Ile Ife, a prominent university town in Nigeria using a network of low-cost air quality (AQ) sensor units. The objective of the deployment was to collate baseline air quality data and assess the impact of prevailing meteorological conditions on PM concentrations in selected residential communities downwind of an iron smelting facility. The raw data obtained from OPC-N2 of the AQ sensor units was corrected using the RH correction factor developed based k-Kohler theory. This PM (corrected) fast time resolution data (20 s) from the AQ sensor units were used to create daily averages. The overall mean mass concentrations for PM2.5 and PM10 were 213.3, 44.1, 23.8, 27.7, 20.2 and 41.5 µg/m3 and; 439.9, 107.1, 55.0, 72.4, 45.5 and 112.0 µg/m3 for Fasina (Iron-Steel Smelting Factory, ISSF), Modomo, Eleweran, Fire Service, O.A.U. staff quarters and Obafemi Awolowo University Teaching and Research Farm (OAUTRF), respectively. PM concentration and wind speed showed a negative exponential distribution curve with the lowest exponential fit coefficient of determination (R2) values of 0.08 for PM2.5 and 0.03 for PM10 during nighttime periods at Eleweran and Fire service sites, respectively. The relationship between PM concentration and temperature gave a decay curve indicating that higher PM concentrations were observed at lower temperatures. The exponential distribution curve for the relationship between PM concentration and relative humidity (RH) showed that PM concentrations do not vary for RH < 80 % while stronger relationship was noticed with higher PM concentration for RH > 80 % for both day and nighttime. The performances of the MLR model were slightly poor and as such not too reliable for predicting the concentration but useful for improving predictive model accuracy when other variables contributing to the variability of PM is considered. The study concluded that the anthropogenic and industrial activities at the smelting factory contribute significantly to the elevated PM mass concentration measured at the study locations.

Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments.

The inaccurate quantification of personal exposure to air pollution introduces error and bias in health estimations, severely limiting causal inference in epidemiological research worldwide. Rapid advancements in affordable, miniaturised air pollution sensor technologies offer the potential to address this limitation by capturing the high variability of personal exposure during daily life in large-scale studies with unprecedented spatial and temporal resolution. However, concerns remain regarding the suitability of novel sensing technologies for scientific and policy purposes. In this paper we characterise the performance of a portable personal air quality monitor (PAM) that integrates multiple miniaturised sensors for nitrogen oxides (NO x ), carbon monoxide (CO), ozone (O3) and particulate matter (PM) measurements along with temperature, relative humidity, acceleration, noise and GPS sensors. Overall, the air pollution sensors showed high reproducibility (mean R ¯ 2 = 0.93, min-max: 0.80-1.00) and excellent agreement with standard instrumentation (mean R ¯ 2 = 0.82, min-max: 0.54-0.99) in outdoor, indoor and commuting microenvironments across seasons and different geographical settings. An important outcome of this study is that the error of the PAM is significantly smaller than the error introduced when estimating personal exposure based on sparsely distributed outdoor fixed monitoring stations. Hence, novel sensing technologies such as the ones demonstrated here can revolutionise health studies by providing highly resolved reliable exposure metrics at a large scale to investigate the underlying mechanisms of the effects of air pollution on health.

Developing a Relative Humidity Correction for Low-Cost Sensors Measuring Ambient Particulate Matter.

There is increasing concern about the health impacts of ambient Particulate Matter (PM) exposure. Traditional monitoring networks, because of their sparseness, cannot provide sufficient spatial-temporal measurements characteristic of ambient PM. Recent studies have shown portable low-cost devices (e.g., optical particle counters, OPCs) can help address this issue; however, their application under ambient conditions can be affected by high relative humidity (RH) conditions. Here, we show how, by exploiting the measured particle size distribution information rather than PM as has been suggested elsewhere, a correction can be derived which not only significantly improves sensor performance but which also retains fundamental information on particle composition. A particle size distribution⁻based correction algorithm, founded on κ -Köhler theory, was developed to account for the influence of RH on sensor measurements. The application of the correction algorithm, which assumed physically reasonable κ values, resulted in a significant improvement, with the overestimation of PM measurements reduced from a factor of ~5 before correction to 1.05 after correction. We conclude that a correction based on particle size distribution, rather than PM mass, is required to properly account for RH effects and enable low cost optical PM sensors to provide reliable ambient PM measurements.

Abatement of an aircraft exhaust plume using aerodynamic baffles.

The exhaust jet from a departing commercial aircraft will eventually rise buoyantly away from the ground; given the high thrust/power (i.e., momentum/buoyancy) ratio of modern aero-engines, however, this is a slow process, perhaps requiring ∼ 1 min or more. Supported by theoretical and wind tunnel modeling, we have experimented with an array of aerodynamic baffles on the surface behind a set of turbofan engines of 124 kN thrust. Lidar and point sampler measurements show that, as long as the intervention takes place within the zone where the Coanda effect holds the jet to the surface (i.e., within about 70 m in this case), then quite modest surface-mounted baffles can rapidly lift the jet away from the ground. This is of potential benefit in abating both surface concentrations and jet blast downstream. There is also some modest acoustic benefit. By distributing the aerodynamic lift and drag across an array of baffles, each need only be a fraction of the height of a single blast fence.