Ambient Particles Research Articles

Source Apportionment of Ambient Fine Particle Size Distribution Using Positive Matrix Factorisation and Conditional Bivariate Probability Function in a Coastal Urban Area

Source Apportionment of Ambient Fine Particle Size Distribution Using Positive Matrix Factorisation and Conditional Bivariate Probability Function in a Coastal Urban Area

Label-Free Identification of Carbonaceous Particles Using Nonlinear Optical Microscopy.

The adverse health effects of ambient carbonaceous particles (CPs) such as carbon black (CB), black carbon (BC), and brown carbon (BrC) are becoming more evident and depend on their composition and emission source. Therefore, identifying and quantifying these particles in biological samples are important to better understand their toxicity. Here, we report the development of a nonlinear optical approach for the identification of CPs such as CB and BrC using imaging conditions compatible with biomedical samples. The unique visible light fingerprint of CB and BrC nanoparticles (NPs) upon illumination with a femtosecond (fs) pulsed laser at 1300 nm excitation wavelength is an effective approach for their identification in their biological context. The emission from spectral features of these CPs was investigated with time-domain fluorescence lifetime imaging (FLIM) to further support their identification. This study is performed for different types of CPs embedded in agarose gel as well as in in vitro mammalian cells. The unique nonlinear emissive behavior of CP NPs used for their label-free identification is further complementary with fluorophores typically used for specific staining of biological samples thus providing the relevant bio-context.

54 Impact of Carbon Nanoparticle–Triggered Gammaherpesvirus Reactivation on Lung Immunity and Diseases

Abstract Inhalation of environmental particles and herpesvirus infection are ubiquitous in our society. Herpesviruses persist lifelong in the host in a latent state, which can be exited by stress-induced reactivation with production of lytic virus. Our previous investigations show that pulmonary exposure to carbon nanoparticles (CNP) triggers reactivation of latent murine gammaherpesvirus 68 (MHV-68) in mouse lungs, mainly localizing to CD11b+ infiltrating macrophages and dependent on p38 MAPK signaling. Furthermore, we observed that a two-time repeated CNP administration to latently infected mice resulted in alveolar damage leading to progressive emphysema. However, the underlying adverse mechanisms causing alveolar damage and the corresponding cell communication upon CNP exposure in latently infected lungs is still unclear. To investigate this, we developed a 3D lung organoid culture from primary murine epithelial cells in combination with persistently MHV-68 infected macrophages. This model enables us to detect reactivation and cytokine release upon CNP exposure, and to gain insights into the cellular communication of epithelial cells and macrophages, to understand the mechanisms underlying reactivation-triggered damage. In parallel, adverse effects of this second hit scenario on the alveolar epithelium and subsequent disease development get investigated in vivo. Latently infected mice are exposed to CNP up to 5 times, and virus reactivation and further epithelial damage is analyzed. By uncovering molecular mechanisms underlying the adverse effects and elucidating the communication occurring in vitro and in vivo we want to identify potential targets to prevent exacerbation of lung diseases caused by these two omnipresent factors, herpesvirus infection and ambient particle exposure.

98 Inhalation Exposure to Traffic-Related Air Pollution Accelerates Alzheimer’s Disease-like Pathology in a Murine Model

Abstract A growing number of epidemiological studies has linked exposure to traffic-related air pollution (TRAP) to neurological and neurodegenerative diseases. In a previous study, we showed that subchronic inhalation exposure to diesel engine exhaust, as a model of TRAP, aggravates amyloid-β plaque formation and motor function impairment in the 5xFAD transgenic mouse model of Alzheimer’s disease (AD). To further strengthen the epidemiological association between TRAP and AD we exposed 5xFAD and wildtype littermate mice for 5 h/day, 5 days/week during 2 or 4 consecutive weeks at a representative urban traffic-dominated location to: (I) concentrated ambient particles, (II) particle filtered ambient air or (III) clean air (control). Our study revealed that repeated inhalation exposure to TRAP accelerates plaque formation in the 5xFAD mice. Data also indicated that the gaseous fraction of the air pollutant mixture probably plays only a minor role in this effect. The major outcome of this study substantiates the role of air pollution in AD and suggests that long-term exposure to traffic-related air pollution particles is a risk factor for this debilitating disease. Acknowledgements: The work leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 814978 (TUBE) and a cross-border grant awarded by the Alzheimer Forschung Initiative (AFI, Germany) and Alzheimer Nederland.

Morphology and phase state of PM2.5 in urban and coastal-rural areas during summer

The morphology and phase state are critical physical properties of aerosol particles. However, studies related to the analysis of these properties primarily focus on laboratory experiments, and studies on real aerosol particles are limited. Herein, fine particulate matter (PM2.5) filter samples were obtained to investigate and compare the morphology and phase state of ambient aerosol particles in South Korea. The PM2.5 samples were collected in the summer of June 2021 from two different environments: Seoul (urban) and Seosan (coastal-rural). Optical microscopy was combined with the poke-and-flow technique to determine the morphology and phase state of the PM2.5 as a function of relative humidity (RH) at 293 ± 1 K. At both sites, the PM2.5 droplets, which were extracted in purified water, showed a multiphase nature that was dependent on the RH and chemical composition. Based on the results and ambient average RH in Seoul, most of the PM2.5 was observed in a liquid state on polluted days under an inorganic-dominant condition, but in a semisolid state on clean days under an organic carbon-rich condition. In Seosan, the PM2.5 predominantly existed in a liquid state, due to the high RH caused by proximity to the Yellow Sea. Our study provides fundamental physical properties of PM2.5 for both urban and coastal-rural environments. The results have strong applications for atmospheric chemistry and predicting particle size distributions.

Kinetics of macroautophagy in lung adenocarcinoma (A549) cells exposed to nanoparticles

Macroautophagy is an intracellular process helping to maintain normal cellular function. Dysregulation of autophagy is recognized in certain diseases, but it is unknown how maintenance of cellular homeostasis might be affected by the kinetics of autophagy in response to various stimuli. In this study, we measured autophagy kinetics in A549 cells exposed to nanoparticles (NP) (including ambient ultrafine particles (UFP)) and/or Rapamycin. A549 cells were apically exposed (t=0-24hrs) in four experimental settings: (1) control exposure only to culture fluid; (2) exposure to polystyrene NP (PNP), UFP or Rapamycin; (3) concurrent exposure to PNP and Rapamycin or UFP and Rapamycin; and, (4) sequential (in both orders) exposure to a first agent (PNP, UFP or Rapamycin) at t=0-5hrs, followed by exposure to an additional agent (Rapamycin after PNP (or UFP) and PNP (or UFP) after Rapamycin) at t=5-24hrs. Autophagic activity was assessed using fluorescence intensity of DAPRed (a marker for autophagosomes and autolysosomes; 0.1 μM; Dojindo; Rockville, MD) or BacMam RFP-GFP-LC3B (Thermo Fisher; Waltham, MA) in the presence or absence of 40 μM chloroquine measured over the entire cell volume of live single cells using confocal laser scanning microscopy. Autophagic flux was calculated as the difference of observed fluorescence intensity of DAPRed or RFP-GFP-LC3B in the presence and absence of chloroquine. Autophagic flux increased in all experimental modalities, including both single agent and double agent exposures, and reached steady state in all cases ~2 times control from ~8 to 24 hrs, suggesting the presence of an upper limit to autophagic activity. Rapamycin exposure alone induced a rapid rise in autophagic flux, peaking at ~3hrs post exposure, followed by a lowered steady state autophagic flux for up to 24hrs. Exposure to PNP or UFP alone also increased autophagic flux, gradually reaching steady state at ~10-24hrs. During concurrent double exposure to Rapamycin and PNP or UFP at t=0, the steady state level of autophagic flux remained comparable to that observed with single exposures to Rapamycin, PNP or UFP for up to 24 hrs. Under sequential exposure conditions, the steady state level of autophagic flux did not exceed the steady state level observed in single or concurrent exposures. In summary, exposure to nanoparticles activates macroautophagy in a time-dependent manner in A549 cells. Autophagic activity demonstrated comparable steady state levels after exposure to single or dual stimuli, indicating an effective upper limit of autophagic flux in A549 cells under our experimental conditions. These findings suggest that environmental stressors might exert their harmful effects, at least in part, by limiting available autophagic capacity, thereby making NP-exposed cells more susceptible to secondary injury due to autophagic overload. Funding: NIH; WRMPPF This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Forces, electric fields and currents at the subsolar martian MPB: MAVEN observations and multifluid MHD simulation

The Martian MPB (Magnetic Pileup Boundary) is a key boundary in the Mars/Solar Wind interaction as it is here that part of the momentum and energy from the solar wind plasma are transferred to the planetary plasma. Since this interaction is for the most part collisionless, the transfer is mediated by electric and magnetic fields. The acceleration processes and the interaction of particles with electromagnetic fields operate at spatial scales determined by the ambient particle populations. In particular, in regions with sizes of the order of the ion inertial length (ion scales), the Hall electric field is expected to be dominant.In the present work we combine data from the MAVEN spacecraft along one orbit around Mars and multifluid MHD simulation results to study the role of electric fields, currents and forces at the MPB at ion scales. In particular, we find that the current densities deduced from MAVEN data (J ∼ 238 nA/m2) of the same order as the values obtained in the simulation (J ∼ 56-156 nA/m2) and that the Hall electric force points sunward in both cases. In addition, we find that in the subsolar MPB current layer the Hall electric field (∼3.2 mV/m) dominates over the solar wind convective electric field (∼0.4 mV/m) and electron pressure gradient (∼0.8 mV/m). These values are consistent with previous results suggesting that the MPB thickness is of the order of the solar wind proton inertial length and support the idea that non ideal terms in Ohm’s law must be considered when analysing the dynamics of particles around plasma boundaries with ion scale thicknesses.

Using particle-resolved aerosol model simulations to guide the interpretations of cloud condensation nuclei experimental data

Ambient aerosol particles can undergo dynamic mixing processes as they coagulate with particles from other air masses and emission sources. Therefore, aerosols exist in a spectrum, from externally mixed to internally mixed. The mixing state of aerosols can affect their ability to uptake water (hygroscopicity) and their cloud condensation nuclei (CCN) activity, modifying their contribution to the planet’s total radiative budget. However, current water-uptake measurement methods may not be able to capture the complex mixing state. In this research, the dynamic mixing process was simulated by the particle-resolved aerosol model PartMC and also created by experiments in a laminar flow mixing tube. The mixing evolution of ammonium sulfate and sucrose binary mixtures were observed along with the changes in their water uptake properties expressed as the single hygroscopicity parameter, κ. The use of a mixing simulation in conjunction with experiments allow for better identification of the particle mixing state and the particle water uptake and show that no one kappa value can capture the complexity of mixing across the mixed particle size distribution. In other words, the PartMC simulations can be used as a guiding tool to interpret a system’s mixing state based on its experimental droplet activation spectra. This work demonstrates the importance of the integration and use of mixing models to aid in mixing state determination and hygroscopicity measurements of mixed systems.Copyright © 2023 American Association for Aerosol Research

Interactions between long-term ambient particle exposures and lifestyle on the prevalence of hypertension and diabetes: insight from a large community-based survey

IntroductionEvidence on the interaction of lifestyle and long-term ambient particle (PM) exposure on the prevalence of hypertension, diabetes, particularly their combined condition is limited. We investigate the associations between PM...

Optimization of the QuEChERS extraction method to determine Polycyclic Aromatic Hydrocarbons (PAHs) in powder aerosol particles collected by cyclone

Conventional methods for collecting and extracting aerosol particles are challenging in accurately characterizing particle-bound polycyclic aromatic hydrocarbons (PAHs) contamination and conducting cellular exposure studies. Therefore, developing direct aerosol sample collection and simple pretreatment analytical methods is necessary. This study describes the optimization of the QuEChERS (quick, easy, cheap, effective, rugged, and safe) extraction method for the determination of PAHs in powder-form environmental standard materials (SRM1944) and proves it is also effective for analyzing PAHs in powder aerosol particle samples collected by cyclone. In this study, we used an I-optimal design response surface method combined with a desirability function to investigate the extraction effect of PAHs under different combinations of QuEChERS extraction factors, including extraction solvent, salt, and clean-up reagent. The optimized conditions of the method were determined using the 2-factor interaction (2FI) model combined with boundary conditions, resulting in acetonitrile/dichloromethane (7:1, v/v), Na2SO4/NaCl (1:1, w/w), and primary and secondary amine (PSA) as extraction solvents, extraction salts, and clean-up reagent, respectively. The optimized method showed good recoveries of PAHs reaching 76%–118% and 85%–121% for SRM1944 and standard solution. The results for cyclone-collected ambient powder aerosol particles extracted by the optimized QuEChERS illustrated that this improved procedure has good PAHs extraction efficiency as the traditional Soxhlet method, while reducing hazardous organic solvents usage, experimental cost (per mg sample) by 97% and sample pretreatment time. Overall, this combination of “powder aerosol particle collection and modified QuEChERS extraction” provides new insight on aerosol sample collection, extraction, and in vitro/in vivo exposure studies.

In-cloud scavenging of chemically segregated particle types by individual particle observation

While aerosol plays a significant role in the formation of cloud, the in-cloud scavenging of chemically segregated particle types were still insufficiently investigated and the controlling factors remain ambiguous. Several field observations were carried out during 2018–2021 at Mt. Tianjing (1690 m a.s.l.) in southern China, based on ground-based counterflow virtual impactor (GCVI)-single particle aerosol mass spectrometer (SPAMS), to investigate the in-cloud scavenging (activation) of various particle types, including black carbon (BC), organic-containing particles (OC), mineral dust (Dust), metal-containing particles (Metal), potassium-rich particles (K-rich) and sea salt (SS). GCVI was used to sample cloud droplet residual particles (Res) while PM2.5 inlet sampled interstitial particles (Inter) during cloud events and ambient particles (Amb) on sunny days, respectively. Different types of particles were recognized based on spectral characteristics obtained by the SPAMS. Based on the coupled GCVI-SPAMS measurements, the number-based scavenging efficiencies (NSEs) of various particle types could be quantified. Besides, the influence of liquid water content (LWC), PM2.5, particle size, and mixing state on the NSEs were discussed. The NSE of SS was the highest (32.4%) while OC showed the lowest NSE (9.1%). Other particle types shared similar NSEs with all the detected particles, with an average of ∼20–25%. Apart from OC, NSEs of other types of particles increased with the increase of LWC and decreased with the increase of the concentration of PM2.5, suggesting that numerous PM2.5 suppressed particles entering cloud droplets via competing for water vapor. The annual variations of NSEs were generally in accordance with LWC and PM2.5, reflecting the certain role of these environment conditions. The NSEs typically increased with particle size in a range of 0.2–2.0 μm, reflecting the dominant nucleation mechanism during in-cloud scavenging. Particles mixed with sulfate and nitrate were easier to be scavenged than those coated with organic compounds, e.g., the discrepancy reached 11.8% for the K-rich particles.

Decoherence from long-range forces in atom interferometry

Atom interferometers provide a powerful means of realizing quantum coherent systems with increasingly macroscopic extent in space and time. These systems provide an opportunity for a variety of novel tests of fundamental physics, including ultralight dark matter searches and tests of modifications of gravity, using long drop times and microgravity environments. However, as experiments operate with longer periods of free fall and become sensitive to smaller background effects, key questions start to emerge about the fundamental limits to future atom interferometry experiments. We study the effects on atomic coherence from hard-to-screen backgrounds due to baths of ambient particles with long-range forces, such as gravitating baths and charged cosmic rays. Our approach---working in the Heisenberg picture for the atomic motion---makes proper inclusion of the experimental apparatus feasible and clearly shows how to handle long-range forces. We find that these potential backgrounds are likely negligible for the next generation of interferometers, as aggressive estimates for the gravitational decoherence from a background bath of dark matter particles gives a decoherence timescale on the order of years.

Kinetics of autophagic activity in nanoparticle-exposed lung adenocarcinoma (A549) cells.

Autophagy, a homeostatic mechanism, is crucial in maintaining normal cellular function. Although dysregulation of autophagic processes is recognized in certain diseases, it is unknown how maintenance of cellular homeostasis might be affected by the kinetics of autophagic activity in response to various stimuli. In this study, we assessed those kinetics in lung adenocarcinoma (A549) cells in response to exposure to nanoparticles (NP) and/or Rapamycin. Since NP are known to induce autophagy, we wished to determine if this phenomenon could be a driver of the harmful effects seen in lung tissues exposed to air pollution. A549 cells were loaded with a fluorescent marker (DAPRed) that labels autophagosomes and autolysosomes. Autophagic activity was assessed based on the fluorescence intensity of DAPRed measured over the entire cell volume of live single cells using confocal laser scanning microscopy (CLSM). Autophagic activity over time was determined during exposure of A549 cells to single agents (50 nM Rapamycin; 80 μg/mL, 20 nm carboxylated polystyrene NP (PNP); or, 1 μg/mL ambient ultrafine particles (UFP) (<180 nm)), or double agents (Rapamycin + PNP or Rapamycin + UFP; concomitant and sequential), known to stimulate autophagy. Autophagic activity increased in all experimental modalities, including both single agent and double agent exposures, and reached a steady state in all cases ~2 times control from ~8 to 24 hrs, suggesting the presence of an upper limit to autophagic capacity. These results are consistent with the hypothesis that environmental stressors might exert their harmful effects, at least in part, by limiting available autophagic response to additional stimulation, thereby making nanoparticle-exposed cells more susceptible to secondary injury due to autophagic overload.

Premature deaths related to urban air pollution in Poland

In the present study, we conducted a risk assessment of urban population exposure to ambient particles (PM2.5, PM10), nitrogen dioxides (NO2), and tropospheric ozone (O3) in six Polish urban areas over the period 2015–2020. The main goals were to: i) evaluate the number of premature deaths attributed to ambient air pollutants, and to ii) assess the effectiveness of emissions control policies in reducing urban population exposure to air pollution, and thus related health effects. For that, we used air quality data from 15 urban monitoring stations and city-specific health data (relative risk values and baseline incidence). For all cities, the annual PM2.5, PM10, and NO2 mean concentrations exceeded the 2021 World Health Organization Air Quality Guidelines in 2015–2020. In all cities, the annual PM2.5 (- 0.5% to - 6.1% year−1), PM10 (- 1.6 to - 8.1% year−1), and NO2 (- 1.3% to - 4.5% year−1) mean concentrations declined over time, while O3 levels rose (+1.0% to + 4.2% year−1). Exposure to ambient PM2.5, PM10, NO2, and O3 has substantially contributed to mortality in the six studied cities: a total of 6700 deaths for non-accidental causes, 2310 deaths for cardiovascular diseases, and 500 deaths for respiratory diseases were reported. Between 2015 and 2020, the number of annual PM2.5-, PM10-, and NO2-related premature deaths decreased (respectively, by - 8.4, - 6.7, and - 2.1 deaths per 106 inhabitants) in line with cleaner air. The rising O3 levels led to an increase in annual O3-related premature deaths (+1.3 deaths per 106 inhabitants) over time. A major concern is related to the overrun of exposure metrics compared to the target values for human health. To date, human health is mainly threatened by PM2.5 and O3, particularly in Southern Poland.

A novel method for the quantitative assessment of the fitted containment efficiency of face coverings.

Face masks reduce disease transmission by protecting the wearer from inhaled pathogens and reducing the emission of infectious aerosols. Although methods quantifying efficiency for wearer protection are established, current methods for assessing face mask containment efficiency rely on measurement of a low concentration of aerosols emitted from an infected or noninfected individual. A small port enabled the introduction of 0.05 µm sodium chloride particles at a constant rate behind the mask worn by a study participant. A condensation particle counter monitored ambient particle numbers 60 cm in front of the participant over 3-minute periods of rest, speaking, and coughing. The containment efficiency (%) for each mask and procedure was calculated as follows: 100 × (1 - average ambient concentration with face covering worn/average ambient concentration with a sham face covering in place). The protection efficiency (%) was also measured using previously published methods. The probability of transmission (%) from infected to uninfected (a function of both the containment efficiency and the protection efficiency) was calculated as follows: {1 - (containment efficiency/100)}×{1 - (protection efficiency/100)}×100. The average containment efficiencies for each mask over all procedures and repeated measures were 94.6%, 60.9%, 38.8%, and 43.2%, respectively, for the N95 mask, the KN95 mask, the procedure face mask, and the gaiter. The corresponding protection efficiencies for each mask were 99.0%, 63.7%, 45.3%, and 24.2%, respectively. For example, the transmission probability for 1 infected and 1 uninfected individual in close proximity was ∼14.2% for KN95 masks, compared to 36%-39% when only 1 individual wore a KN95 mask. Overall, we detected a good correlation between the protection and containment that a face covering afforded to a wearer.

Quantifying the effects of urban fabric and vegetation combination pattern to mitigate particle pollution in near-road areas using machine learning

There are many intertwined factors such as urban fabric, vegetation combination, and meteorological conditions that influence the spatial distribution of fine particles (PM2.5, aerodynamic diameter less than 2.5 μm) in near-road urban areas, and it is difficult yet important to disentangle them. Identifying the relative importance of those factors could facilitate designing urban fabric and roadside vegetation combination patterns to mitigate the negative impacts of particles. This study first proposes an effective and reliable integrated method of the ENVI-met microclimate model and interpretable machine learning to quantify the multifactorial effects. Data from ENVI-met models with different built environments and meteorological conditions are used to train and test the machine learning (ML) models. All ML models show high accuracy in predicting PM2.5 concentrations with R2 high than 0.87. Results indicate that height level and study area are the two most important features, explaining 22.8% and 20.4% of the variation in PM2.5 concentration, respectively. The feature pairwise interactions account for 45.8% of ambient particle concentrations. The combination of trees and hedges in urban fabrics with longer building length than width could mitigate PM2.5 concentrations in residential areas. The integrated methods in this study could be extrapolated to future research to perform source identification analysis.

Impact of Ambient Ultrafine Particles on Cause-Specific Mortality in Three German Cities.

Rationale: Exposure to ambient air pollution has been associated with adverse effects on morbidity and mortality. However, the evidence for ultrafine particles (UFPs; 10-100 nm) based on epidemiological studies remains scarce and inconsistent. Objectives: We examined associations between short-term exposures to UFPs and total particle number concentrations (PNCs; 10-800 nm) and cause-specific mortality in three German cities: Dresden, Leipzig, and Augsburg. Methods: We obtained daily counts of natural, cardiovascular, and respiratory mortality between 2010 and 2017. UFPs and PNCs were measured at six sites, and measurements of fine particulate matter (PM2.5; ⩽2.5 μm in aerodynamic diameter) and nitrogen dioxide were collected from routine monitoring. We applied station-specific confounder-adjusted Poisson regression models. We investigated air pollutant effects at aggregated lags (0-1, 2-4, 5-7, and 0-7 d after UFP exposure) and used a novel multilevel meta-analytical method to pool the results. Additionally, we assessed interdependencies between pollutants using two-pollutant models. Measurements and Main Results: For respiratory mortality, we found a delayed increase in relative risk of 4.46% (95% confidence interval, 1.52 to 7.48%) per 3,223-particles/cm3 increment 5-7 days after UFP exposure. Effects for PNCs showed smaller but comparable estimates consistent with the observation that the smallest UFP fractions showed the largest effects. No clear associations were found for cardiovascular or natural mortality. UFP effects were independent of PM2.5 in two-pollutant models. Conclusions: We found delayed effects for respiratory mortality within 1 week after exposure to UFPs and PNCs but no associations for natural or cardiovascular mortality. This finding adds to the evidence on the independent health effects of UFPs.

High-resolution patterns and inequalities in ambient fine particle mass (PM2.5) and black carbon (BC) in the Greater Accra Metropolis, Ghana

Growing cities in sub-Saharan Africa (SSA) experience high levels of ambient air pollution. However, sparse long-term city-wide air pollution exposure data limits policy mitigation efforts and assessment of the health and climate effects. In the first study of its kind in West Africa, we developed high resolution spatiotemporal land use regression (LUR) models to map fine particulate matter (PM2.5) and black carbon (BC) concentrations in the Greater Accra Metropolitan Area (GAMA), one of the fastest sprawling metropolises in SSA. We conducted a one-year measurement campaign covering 146 sites and combined these data with geospatial and meteorological predictors to develop separate Harmattan and non-Harmattan season PM2.5 and BC models at 100 m resolution. The final models were selected with a forward stepwise procedure and performance was evaluated with 10-fold cross-validation. Model predictions were overlayed with the most recent census data to estimate the population distribution of exposure and socioeconomic inequalities in exposure at the census enumeration area level. The fixed effects components of the models explained 48–69 % and 63–71 % of the variance in PM2.5 and BC concentrations, respectively. Spatial variables related to road traffic and vegetation explained the most variability in the non-Harmattan models, while temporal variables were dominant in the Harmattan models. The entire GAMA population is exposed to PM2.5 levels above the World Health Organization guideline, including even the Interim Target 3 (15 μg/m3), with the highest exposures in poorer neighborhoods. The models can be used to support air pollution mitigation policies, health, and climate impact assessments. The measurement and modelling approach used in this study can be adapted to other African cities to bridge the air pollution data gap in the region.

Assessing the impact of aircraft arrival on ambient ultrafine particle number concentrations in near-airport communities in Boston, Massachusetts

Aircraft emissions contribute to overall ambient air pollution, including ultrafine particle (UFP) concentrations. However, accurately ascertaining aviation contributions to UFP is challenging due to high spatiotemporal variability along with intermittent aviation emissions. The objective of this study was to evaluate the impact of arrival aircraft on particle number concentration (PNC), a proxy for UFP, across six study sites 3–17 km from a major arrival aircraft flight path into Boston Logan International Airport by utilizing real-time aircraft activity and meteorological data. Ambient PNC at all monitoring sites was similar at the median but had greater variation at the 95th and 99th percentiles with more than two-fold increases in PNC observed at sites closer to the airport. PNC was elevated during the hours with high aircraft activity with sites closest to the airport exhibiting stronger signals when downwind from the airport. Regression models indicated that the number of arrival aircraft per hour was associated with measured PNC at all six sites, with a maximum contribution of 50% of total PNC at a monitor 3 km from the airport during hours with arrival activity on the flight path of interest (26% across all hours). Our findings suggest strong but intermittent contributions from arrival aircraft to ambient PNC in communities near airports.

Estimating PM2.5 concentrations in a central region of China using a three-stage model

ABSTRACT Owing to uneven environmental monitoring site distribution, there are significant spatial data gaps for concentrations of ambient fine particles with diameters ≤ 2.5 µm (PM2.5) obtained using traditional monitoring methods. Satellite products are an alternative data source for locations where monitoring sites are unavailable. The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product has been widely used in PM2.5 assessment for years; however, it has obvious data gaps in winter. Here, the Visible Infrared Imaging Radiometer Suite (VIIRS) AOD was applied to supplement MODIS AOD data to obtain a fused AOD dataset. A three-stage model consisting of a corrected AOD model, mixed effects model, and geographically weighted regression model was developed and used with meteorological and vegetation factors to estimate PM2.5. Results showed overall model fitting by cross-validation (CV) with an R2 of 0.92, mean absolute error of 5.72 µg/m3, and root mean square error of 7.15 µg/m3. The combination of MODIS AOD and VIIRS AOD was a suitable method for enhancing AOD coverage. The CV R2 value of the three-stage model (0.92) was higher than that of the two-stage model (0.9). Hence, the three-stage model could achieve a better fit in estimating PM2.5 on a regional scale.