Aerosol Spectrometer Research Articles

Detection of a biological aerosol using optical particle counters

So far, detection and quantification of bio-aerosols requires genotypic or phenotypic identification of every single particle. Successful use of optical particle counters, as a time- and cost-saving alternative, has not been reported to date, indicating the need for further method development. Previously, such studies have focused on pollen. Here we report on a laboratory test of commercially available low-cost optical particle counters from Alphasense (OPC-N3 and OPC-R2) for the quantification of a known fungal aerosol (Botrytis cinerea). Aerosols were quantified using a Grimm Portable Aerosol Spectrometer (11E). Our measurements reveal that the low-cost sensors almost correctly detect the relative particle size distribution of the spores, as compared with the Grimm reference instrument, and potentially can be used in environmental detection of fungal aerosols.

Seasonal and Spatial Variations in Particulate Matter, Black Carbon and Metals in Delhi, India’s Megacity

This study explores the spatial patterns of particulate matter (PM) in the megacity of Delhi. A GRIMM aerosol spectrometer is used to analyze different aerodynamic diameters (PM10, PM2.5, PM1.0), inhalable, thoracic, and alveolic particles, and black carbon (BC) at six prominent locations in Delhi during summer and winter. Additionally, metals (Pb, Fe, Ca, Al, Zn), along with silicon and sulfur, are analyzed using an ED-XRF spectrometer over the sampling locations during the summer season. The sampling site data are interpolated using the Kriging method to generate spatial maps to explore the air pollution problem in Delhi. East Delhi is observed to be the most polluted site, while Guru Gobind Singh Indraprastha University (GGSIPU) is the least polluted site. We further observe a high correlation between Al-Fe, Al-Ca, Zn-Pb, Ca-Fe, and S-Zn, indicating their common source of emission. Aerosols are also found to be highly enriched with metals like Al, S, Fe, Zn, and Pb, suggesting strong anthropogenic sources of these metals. Construction activities, resuspended dust, an increased number of vehicles, faulty agricultural practices, and soil could be recognized as major sources of the particulate concentration in an urban area like Delhi.

Applicability of Aerosol Spectrometers for Control of the Efficiency of Automatic Artificial Pollination in Agricultural Biotechnology and Analysis of Contamination in Plant Quarantine Assessment

Applicability of Aerosol Spectrometers for Control of the Efficiency of Automatic Artificial Pollination in Agricultural Biotechnology and Analysis of Contamination in Plant Quarantine Assessment

The Cloud Indicator: A novel algorithm for automatic detection and classification of clouds using airborne in situ observations

Airborne in situ observations of aerosols and clouds provide relevant data sets crucial to improving the understanding of cloud microphysical processes and reducing uncertainties connected to future climate predictions. However, the selection and classification of cloud sequences in such data sets can be very time-consuming if done manually, and criteria used by the community are numerous and prone to misclassification, especially when coarse aerosol particles (> 1 μm diameter) are present.In this study, we present the Cloud Indicator, a novel algorithm that automatically detects and classifies measurement periods inside clouds. The Cloud Indicator was developed using data from three international airborne field campaigns, including ATom (Atmospheric Tomography; 2016–2018), A-LIFE (Absorbing aerosol layers in a changing climate: aging, lifetime and dynamics; 2017), and FIREX-AQ (Fire Influence on Regional to Global Environments Experiment and Air Quality; 2019). The algorithm utilizes size distribution measurements, combined with measurements of relative humidity and temperature, to automatically detect flight sequences in clouds and classify the cloud type. As an additional criterion for the Cloud Indicator, we established the cloud-aerosol volume factor fCA to ensure a precise and robust distinction between clouds and aerosol layers such as mineral dust or biomass burning thereby reducing misclassifications. The Cloud Indicator algorithm was developed with data from a second-generation Cloud, Aerosol, and Precipitation Spectrometer (CAPS) which was calibrated with a novel calibration procedure – introduced in this study - that results in a refractive index independent calibration and works also in the field. However, the Cloud Indicator algorithm is not restricted specifically to the CAPS and thus allows its application to a variety of in situ instruments that measure coarse aerosol and cloud particle size.Case studies from ATom and A-LIFE demonstrate the ability of the Cloud Indicator to precisely screen (airborne) in situ data sets for clouds. The algorithm separates the data set into cloud-free periods, Aerosol-Cloud Transition Regime (ACTR), liquid clouds, clouds in the Mixed-Phase Temperature Regime (MPTR), and cirrus clouds. The unique ability of the Cloud Indicator to successfully differentiate between layers of enhanced coarse-mode aerosol concentrations and clouds is demonstrated by measurements in a complex mixture of clouds embedded into a mineral dust layer during the A-LIFE. The empirically derived parameter thresholds of the Cloud Indicator are in good agreement with values in the literature.

62°S Witnesses the Transition of Boundary Layer Marine Aerosol Pattern Over the Southern Ocean (50°S–68°S, 63°E–150°E) During the Spring and Summer: Results From MARCUS (I)

AbstractThe Atmospheric Radiation Measurement Mobile Facility‐2 was installed onboard the research vessel Aurora Australis to measure aerosol properties during the 2017–2018 Measurement of Aerosols, Radiation, and CloUds over the pristine Southern ocean (MARCUS) Experiment, providing unique data on aerosols latitudinal and seasonal variation, including south of 60°S where previous observations are scarce. Data from a Cloud Condensation Nuclei (CCN) counter and Ultra‐High‐Sensitivity Aerosol Spectrometer show that both the number concentration (NCCN) and size distribution of CCN‐active aerosols, with diameters (D) between 60 nm < D < 1,000 nm are different over the North Southern Ocean (NSO) (50°S–60°S) and the South Southern Ocean (SSO) (62°S–68°S). The average NSO NCCN at 0.2% and 0.5% supersaturation were 28% and 49% less than that over the SSO. This increase of CCN over the SSO is caused by the increase of aerosols with 60 nm < D < 200 nm, consistent with calculations of Aerosol Scattering Angstrom Exponents derived from a nephelometer. Aerosol hygroscopicity growth factor measured by the Hygroscopic Tandem Differential Mobility Analyzer stayed close to 1.41 for aerosols with 50 nm < D < 250 nm over the SSO, but increased from 1.30 to 1.67 over the NSO, indicating different chemical compositions. Both CCN and Ice Nucleating Particles (INPs) showed a stronger variation with season than with latitude. The variation of heat‐labile and presumably proteinacous INPs suggests an increase of ice nucleating‐active microbes in summer.

Fine Particle Formation and Dependence of Wet Deposition on Precipitation Intensity in an Urban Area

ABSTRACT In order to understand the characteristics of the change of aerosol concentration in an urban area and the impact of meteorological conditions on aerosol concentration in urban areas, particle concentration and size distribution were measured using an aerosol particle counter and an aerosol spectrometer in Hefei, China in spring 2017. Meteorological data, such as temperature, relative humidity, wind speed, wind direction, and shortwave radiation, were measured on a meteorological tower. The concentration of coarse mode (d > 2.5 μm, d is particle diameter), accumulation mode (0.25 μm<d < 2.5 μm), and ultrafine (Aitken and nucleation mode, d < 0.25 μm) particles under different meteorological conditions were recorded. Results show that, approximately 10–20 min after the solar downward shortwave radiation decreases (rises), the ultrafine particles (UFPs) concentration decreases (rises). Diurnal change in radiation causes daily change in the concentration of UFPs, and when the relative humidity was low, UFP generation easily occurs. On wet deposition, the concentration of coarse mode particles is significantly reduced during precipitation, and the concentration of accumulation mode particles varies during heavy precipitation and slight precipitation. Because of the combined action of wet deposition and hygroscopic growth, the concentration of accumulation mode particles first increases and then decreases. And during the slight precipitation, the concentration only increases with the relative humidity.

Investigation of the Influence of Data Collection and Analysis Procedures on Aerosol Filtration Model Development

Fibrous air filters are common devices used to remove airborne particles. Their performance is typically measured through their resistance to airflow and captured particle mass. Models describing the evolution of filter performance have been heavily researched; however, the need for improvement remains. Experimental work is irreplaceable in the development of high-fidelity models, yet the estimation of necessary variables is not trivial and may be influenced by selected measurement instruments and analysis methodologies. Therefore, the purpose of this work is to propose a framework to investigate the response of common aerosol measurement instruments, their corresponding analysis methodologies, and the application of their data. A Scanning Mobility Particle Sizer (SMPS) and Laser Aerosol Spectrometer (LAS) were selected for consideration, and their recorded data were compared against baseline measurements. The results of the experiments indicated that the SMPS and LAS yielded a ratio of estimated mass concentrations to the baseline mass concentrations of approximately 1.175 and 0.749, respectively. Regarding the SMPS, it was suggested that the measurable size range, application of a coverage factor, dynamic shape factor, and association between the curve fits and histograms were influential in the final estimates. For the LAS, the application of a curve fit, its association to the histograms, and the selection of the sampling periods were influential. Considering the results, the impact of these factors may not be considered negligible and may skew reproducibility between studies and fossilize confounding factors. Therefore, the proposed methodologies are useful in addressing potential errors in data collection and analysis.

High-concentration measurements with optical aerosol spectrometers by signal fluctuation analysis

Optical aerosol measurement systems can be classified into single-particle counting techniques and ensemble techniques. In single-particle counting instruments, such as optical aerosol spectrometers, each particle is analyzed individually. This allows for the accurate measurement of the number concentration and the size distribution of the measured aerosol. Current aerosol spectrometers suffer from a limited concentration range due to coincidence errors. In contrast, ensemble techniques, such as photometers, are suitable for high-concentration measurements. One major drawback of such instruments is that they only provide information about one aerosol property, which is typically the mass concentration. A new ensemble technique for the simultaneous measurement of two aerosol properties, namely particle size and concentration, was developed and experimentally verified. The results show that the median particle size (D50) of a monodisperse aerosol can be determined with a deviation of about 10 %, and the concentration with a deviation of about 30 %. With individual instrument calibration, the accuracy can be further improved to approximately 5 % with respect to particle size and 10 % with respect to concentration. The method may be either implemented as a standalone measurement device or, as an add-on for e. g. optical particle spectrometers in the form of a high-concentration mode.

Comparison of aerosol spectrometers : accounting for evaporation and sampling losses

Measuring aerosol size distribution with precision is critical to understand the transmission of pathogens causing respiratory illnesses and to identify risk mitigation strategies. It is however a challenging task as the size of pathogen-carrying particles evolves over time due to evaporation. Although measurement techniques well established in the field of aerosol science are often used to characterize bioaerosols, their performance is seldom assessed with respect to evaporation and deposition in sampling lines. Four instruments providing aerosol size distribution were compared using oil and water-based particles. They each rely on different measurement principles: phase doppler anemometry, light scattering, electrical mobility and aerodynamic impaction. Size distributions of oil-based particles showed consistency across different measurement instruments, but significant discrepancies arose for water-based particles undergoing evaporation. These larger differences result from both evaporation and particle deposition in transit between the sampling point and the measurement inside the instrument. Phase doppler anemometry was best suited for precise size distribution measurement, as it eliminates the need for a sampling line, thereby preventing particle loss or evaporation during transit. With this instrument as a reference, empirical correction factors for evaporation and deposition were derived from dimensionless numbers and experimental data, enabling quantitative assessment of bioaerosol size distribution using different instruments. To obtain the size distribution at the source of the aerosol generation, complete drying of a salt solution was performed. Using the complete drying technique and accounting for losses, sampling instruments can reliably provide this critical information and allow for thorough risk assessment in the context of airborne transmission.

Size Distribution as a Forensic Tool to Source Apportion Ambient PM

The particulate matter (PM) emitted from multiple sources contribute to air pollution near highways, affecting, mainly the commuters on road, dwellers and shopkeepers nearby. In this study, the size distribution of the PM near a highway, obtained using real time laser aerosol spectrometer, was used to apportion the contributions from vehicular traffic and road dust. Data on vehicle count and type were obtained for the selected highway stretch. The size distribution of emission from petrol and diesel vehicles and the size distribution of re-suspended dust were obtained through independent studies. Size based distribution of PM in the study area contributed by vehicles, were modelled using AERMOD using the vehicle emissions and vehicle count data. Aggregation of the modelled concentrations of PM of various size ranges with the contribution from re-suspended dust gave size based concentration distributions closely matching with that of PM in ambient air. The size based PM analyses showed that in the study area, 47.36%, 15.74%, and 15.46% of PM2.5, were contributed by diesel vehicles, petrol vehicles and suspended dust, respectively. The corresponding contributions to PM10 were, 14.42%, 10.59% and 68.35%. The emission studies on vehicles showed diesel vehicles contributing significantly higher concentration of PM to ambient air than petrol vehicles, with condition of vehicle influencing the amount of PM emitted. The use of particle size distribution pattern for preliminary source apportionment, demonstrated in this study, has promising environmental forensic applications as it provides a cheap and fast corroborative evidence for responsibility allocation.

Evaluation of four ground-based retrievals of cloud droplet number concentration in marine stratocumulus with aircraft in situ measurements

Abstract. Cloud droplet number concentration (Nd) is crucial for understanding aerosol–cloud interactions (ACI) and associated radiative effects. We present evaluations of four ground-based Nd retrievals based on comprehensive datasets from the Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The Nd retrieval methods use ARM ENA observatory ground-based remote sensing observations from a micropulse lidar, Raman lidar, cloud radar, and the ARM NDROP (Droplet Number Concentration) value-added product (VAP), all of which also retrieve cloud effective radius (re). The retrievals are compared against aircraft measurements from the fast cloud droplet probe (FCDP) and the cloud and aerosol spectrometer (CAS) obtained from low-level marine boundary layer clouds on 12 flight days during summer and winter seasons. Additionally, the in situ measurements are used to validate the assumptions and characterizations used in the retrieval algorithms. Statistical comparisons of the probability distribution function (PDF) of the Nd and cloud re retrievals with aircraft measurements demonstrate that these retrievals align well with in situ measurements for overcast clouds, but they may substantially differ for broken clouds or clouds with low liquid water path (LWP). The retrievals are applied to 4 years of ground-based remote sensing measurements of overcast marine boundary layer clouds at the ARM ENA observatory to find that Nd (re) values exhibit seasonal variations, with higher (lower) values during the summer season and lower (higher) values during the winter season. The ensemble of various retrievals using different measurements and retrieval algorithms such as those in this paper can help to quantify Nd retrieval uncertainties and identify reliable Nd retrieval scenarios. Of the retrieval methods, we recommend using the micropulse lidar-based method. This method has good agreement with in situ measurements, less sensitivity to issues arising from precipitation and low cloud LWP and/or optical depth, and broad applicability by functioning for both daytime and nighttime conditions.

Satellite-based, top-down approach for the adjustment of aerosol precursor emissions over East Asia: the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product and the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol optical depth (AOD) data fusion product and its proxy

Abstract. In response to the need for an up-to-date emissions inventory and the recent achievement of geostationary observations afforded by the Geostationary Environment Monitoring Spectrometer (GEMS) and its sister instruments, this study aims to establish a top-down approach for adjusting aerosol precursor emissions over East Asia. This study involves a series of the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product, the GEMS aerosol optical depth (AOD) data fusion product and its proxy product, and chemical transport model (CTM)-based inverse modeling techniques. We begin by sequentially adjusting bottom-up estimates of nitrogen oxides (NOx) and primary particulate matter (PM) emissions, both of which significantly contribute to aerosol loadings over East Asia to reduce model biases in AOD simulations during the year 2019. While the model initially underestimates AOD by 50.73 % on average, the sequential emissions adjustments that led to overall increases in the amounts of NOx emissions by 122.79 % and of primary PM emissions by 76.68 % and 114.63 % (single- and multiple-instrument-derived emissions adjustments, respectively) reduce the extents of AOD underestimation to 33.84 % and 19.60 %, respectively. We consider the outperformance of the model using the emissions constrained by the data fusion product to be the result of the improvement in the quantity of available data. Taking advantage of the data fusion product, we perform sequential emissions adjustments during the spring of 2022, the period during which the substantial reductions in anthropogenic emissions took place accompanied by the COVID-19 pandemic lockdowns over highly industrialized and urbanized regions in China. While the model initially overestimates surface PM2.5 concentrations by 47.58 % and 20.60 % in the North China Plain (NCP) region and South Korea (hereafter referred to as Korea), the sequential emissions adjustments that led to overall decreases in NOx and primary PM emissions by 7.84 % and 9.03 %, respectively, substantially reduce the extents of PM2.5 underestimation to 19.58 % and 6.81 %, respectively. These findings indicate that the series of emissions adjustments, supported by the TROPOMI and GEMS-involved data fusion products, performed in this study are generally effective at reducing model biases in simulations of aerosol loading over East Asia; in particular, the model performance tends to improve to a greater extent on the condition that spatiotemporally more continuous and frequent observational references are used to capture variations in bottom-up estimates of emissions. In addition to reconfirming the close association between aerosol precursor emissions and AOD as well as surface PM2.5 concentrations, the findings of this study could provide a useful basis for how to most effectively exploit multisource top-down information for capturing highly varying anthropogenic emissions.

Performance evaluation of the Alphasense OPC-N3 and Plantower PMS5003 sensor in measuring dust events in the Salt Lake Valley, Utah

Abstract. As the changing climate expands the extent of arid and semi-arid lands, the number of, severity of, and health effects associated with dust events are likely to increase. However, regulatory measurements capable of capturing dust (PM10, particulate matter smaller than 10 µm in diameter) are sparse, sparser than measurements of PM2.5 (PM smaller than 2.5 µm in diameter). Although low-cost sensors could supplement regulatory monitors, as numerous studies have shown for PM2.5 concentrations, most of these sensors are not effective at measuring PM10 despite claims by sensor manufacturers. This study focuses on the Salt Lake Valley, adjacent to the Great Salt Lake, which recently reached historic lows exposing 1865 km2 of dry lake bed. It evaluated the field performance of the Plantower PMS5003, a common low-cost PM sensor, and the Alphasense OPC-N3, a promising candidate for low-cost measurement of PM10, against a federal equivalent method (FEM, beta attenuation) and research measurements (GRIMM aerosol spectrometer model 1.109) at three different locations. During a month-long field study that included five dust events in the Salt Lake Valley with PM10 concentrations reaching 311 µg m−3, the OPC-N3 exhibited strong correlation with FEM PM10 measurements (R2 = 0.865, RMSE = 12.4 µg m−3) and GRIMM (R2 = 0.937, RMSE = 17.7 µg m−3). The PMS exhibited poor to moderate correlations (R2 &lt; 0.49, RMSE = 33–45 µg m−3) with reference or research monitors and severely underestimated the PM10 concentrations (slope &lt; 0.099) for PM10. We also evaluated a PM-ratio-based correction method to improve the estimated PM10 concentration from PMSs. After applying this method, PMS PM10 concentrations correlated reasonably well with FEM measurements (R2 &gt; 0.63) and GRIMM measurements (R2 &gt; 0.76), and the RMSE decreased to 15–25 µg m−3. Our results suggest that it may be possible to obtain better resolved spatial estimates of PM10 concentration using a combination of PMSs (often publicly available in communities) and measurements of PM2.5 and PM10, such as those provided by FEMs, research-grade instrumentation, or the OPC-N3.

The Structural Influence of Airborne Particles on their Toxicity

This paper studies human exposure to microplastics in real-life situations. The concentration of the polyamide nano/microplastics in the proximity of textile workplaces was made by the device Laser Aerosol Spectrometer. The descriptive statistical analysis was elaborated for: variables TSC (total concentration µg/m3), PM10 (µg/m3), PM2.5 (µg/m3), PM1 (µg/m3) and TC (total number of particles, 1/l). The histograms of each variable highlight the asymmetry of the distribution, with the predominance of frequencies or variables; representation of level indicators (average, median) and dispersion-box-plot graphs were obtained. Collection of the particle made with TECORA-SKYPORT PM-HV and GilAirPlus pumps, by using Quartz and Nucleopore filters with different diameters highlighted that the highest mass (683µg) was collected by using GilAir Plus pump and Quartz filter 37mm. A strong correlation was obtained between the variable: "Collected mass/ Air concentration": r = 0997775 ÷ 0,999477 and "Collected mass/ Air volume", r = -0,97473 ÷ 0.80064. The prediction of the value of the collected mass as a function of the diameter of the filters, particle concentration, airflow, and total air volume was made by obtaining the regression equations. Optic microscope analyses highlighted the dimension of microfibres (50% are <10µm) and SEM the dimension: 25% < 68.47 nm and the spherical shape of particles. FT-IR, µRaman and TG-DSC analyses showed the presence of PA particles in the collected particles.

Artificial neural network based coincidence correction for optical aerosol spectrometers

Many applications such as filter testing, healthcare, quality monitoring, and environmental measurements require precise aerosol quantification by optical aerosol spectrometers. This type of measurement equipment is capable of in-situ measurements and provides easy access to the size distribution of the particles. Due to the coincidence error, optical aerosol spectrometers are limited to applications with relatively low concentrations. At high concentrations, the counting efficiency is reduced, while the size distribution is shifted towards larger particles. In 1984 Raasch and Umhauer proposed an analytical correction method for the size distribution. Although the approach is easy to implement, it has some disadvantages. In this work, an alternative correction method for the size distribution is presented, which is based on neural networks. The performance of both correction methods is evaluated on the cumulative distribution of raw detector voltages. The relative error of the median, as well as an error integral over the whole distribution is used as a measure. The neural network-based method gives a correction result that shows approximately half the relative median error, and a third of the error integral compared to the method of Raasch and Umhauer, for high concentrations.

Velocity Dependence of Submicron Ice Grain Rebound, Sticking, Particle Fragmentation, and Impact Ionization up to 2.4 km/s

The impact dynamics of ice grains on surfaces as a function of velocity, including particle breakup and impact ionization, are of intrinsic interest and are critical for the design of future probes to study the ice grain plume around Enceladus. Measurements of the scattering dynamics of ∼700 nm diameter pure water ice grains upon 0.2–2.4 km/s impact with a metal target are reported here. Production of these Enceladus plume grain analogues and their subsequent acceleration to controlled final velocities were performed with an aerosol impact spectrometer. The particle impact and various impact behaviors, including rebound, sticking, particle fragmentation, and impact ionization, were characterized as a function of velocity with an angle-resolved image-charge particle detector. The probability of rebound, sticking, and particle fragmentation was the highest below 400 m/s, between 400 and 800 m/s, and above 800 m/s, respectively. Impact ionization was also observed for impact velocities above 1000 m/s.

Гомогенное зародышеобразование при лазерно-плазмохимическом синтезе твердых покрытий SiCN из гексаметилдисилазана

Using a differential aerosol spectrometer (DSA), a study was made of the homogeneous formation of solid phase nuclei in the process of laser-plasmochemical deposition of SiCN from hexamethyldisilazane vapor in a flow of plasma-forming argon gas. It was found that the characteristic size of SiCN nanoparticles in the gas phase is in the range of 20–120 nm and depends on the HMDS vapor concentration and the total argon flow rate. The studies performed have shown that the formation of a layer of silicon carbonitride in laser plasma-chemical deposition proceeds with the formation of nuclei in the gas phase, and upon their collision with the substrate and activation by laser plasma, it leads to the formation of a solid nanostructured coating.

Airborne dust pollution due to mechanised scaling in underground metalliferous mine cross-cut drive under buoyancy-driven airflow

ABSTRACT Nowadays, mechanised scaling is extensively used in fully mechanised underground metalliferous mines (FMUMM) for removing potentially dangerous loose rocks from the mine roof and side walls. This study measures and analyses the generation and dispersion of airborne dust due to mechanised scaling operation by scaler in cross-cut (X-cut) drive of an underground mine using real-time aerosol spectrometers. The airborne dust is analysed in terms of the concentration of various size particles in total airborne dust (TAD). The analyses of different dust sizes, viz. ≤20 μm, ≤15 μm, ≤10 μm, ≤5 μm and ≤1 μm revealed that the per cent proportion of ≤1 μm and ≤10 μm varies from 2.6 to 22.4% and 60.5 to 88.3%, respectively, in the cross-cut. Moreover, the concentration proportions of alveolic and thoracic dusts in TAD during the 20-min retention time are assessed. This study can aid in developing safe re-entry periods and efficient dust control appliances.

WRF‐CHEM Simulations of Unhealthy PM10 Concentrations During Four Dust Events in Senegal

AbstractWinter season Saharan dust aerosols are transported into West Africa and pose as a natural hazard with individual dust events significantly reducing air quality. We simulate four multi‐day dust events using the Weather, Research and Forecasting Chemistry (WRF‐CHEM) in Senegal during December 2016, December 2017, February 2019, and March 2019. We use Geospatial Informational System mapping to estimate PM10 concentrations for the 14 administrative districts within Senegal. Saharan dust events are evaluated using surface PM10 concentrations at Dakar, Senegal, Moderate Resolution Imaging Spectrometer Aerosol Optical Depth, Aerosol Robotic Network, and the WRF‐CHEM model to evaluate dust concentrations across Senegal. We show that a north to south pressure gradient associated with the southward movement of the Azores high into North Africa are responsible for dust generation and transport into Senegal during 2016 and 2017. Conversely, Saharan depressions moving across the Sahara Desert are responsible for the two high‐impact dust events in February and March of 2019. Based on our analysis, the most severe of the four dust events impacting Senegal occurred in December 2017, with hazardous PM10 concentrations simulated for 7 days affecting an estimated 86% of Senegal's population. Furthermore, the February 2019 dust event occurred during the presidential elections, potentially exposing many voters to poor air quality. A network of surface dust observations and dust forecasts are critical for protecting the Sahelian public from dangerous dust events. Together, they allow for preventative measures to minimize harmful PM10 concentration exposure, especially for children under 5, the elderly, and those with existing respiratory and cardiovascular diseases.

Variations in black carbon and particulate matters (PM1, PM2.5 and PM10) during firecrackers bursting episodes and biomass burning: A case study during the Diwali festival

&#x0D; &#x0D; &#x0D; &#x0D; Introduction: The Black Carbon (BC) and Particulate Matters (PMs) was measured in the Diwali festival during 3-7 November, 2021. Diwali is being one of the main festivals of India accompanied by lighting and firecrackers bursting.&#x0D; Materials and methods: BC concentration was measured by aerosol black carbon low-cost sensor/detector and PM1, PM2.5 and PM10 mass concentration as well as number concentration was measured using Grimm Aerosol Spectrometer at Agra, India.&#x0D; Results: Average mass of PM1 (&lt;1 μm), PM2.5 ( &lt;2.5 μm) and PM10 ( &lt;10 μm) were 218.32±56.82 μg/m3, 261.56±74.38 μg/m3 and 380.40±67.78 μg/ m3 respectively in Diwali day (2021). The observed concentration of PM was much higher than National Ambient Air Quality Standards (NAAQS) and World Health Organization (WHO) standard limits. Diurnal peaks for all the pollutants (PM1, PM2.5 and PM10) were perceived in the evening time which was due to festival celebration events. Particle Size number Distribution (PSND) showed one major peak in the particle &lt;1 μm size range. BC mass concentration was found to be 16.28±5.62 μg/m3 in Diwali days. BC/PMs ratio decreases as the PM size increases due to the heavy load of PM2.5.&#x0D; Conclusion: BC and PM concentration was significantly amplified on Diwali due to firecrackers bursting and candles and biomass burning which was responsible for the poor air quality. Particle Mass Size Distribution (PSMD) showed multiple peaks while PSND showed one major peak in fine mode. Higher PM1/PM2.5 indicated the abundance of fine particles as compared to coarse. BC concentration was increased by 20 % on Diwali day due to firecracker bursting, other burning materials and more vehicles movement.&#x0D; &#x0D; &#x0D; &#x0D;