Urban Air Quality Assessment Research Articles

Exploring dispersion modelling for resuspended pollen particles in a heterogeneous urban environment

ABSTRACT Urban air quality assessment in regard to biogenic emissions such as airborne pollen grains (PGs) is indispensable in the urban environment which significantly affects human health, climates, ecosystems, and energy production. Dispersion and deposition of PGs because of resuspension events (ReSE) are investigated using computational fluid dynamics (CFD). The mechanisms in PGs movement are achieved through the Lagrangian formulation-based discrete-phase model (DPM). The observed pollen grains distribution pattern is highly spatially dependent on heterogeneous urban obstacles as it induces modified characteristics of the wind flow field. Wind-urban structure interplays in PGs concentration variation. The simulated results identify the pollen hot-spot zones, where vortical flow features and low wind speed dominate. This approach offers a detailed understanding of the dispersion patterns of airborne PGs in urban environments because of ReSE with high resolution.

Large-eddy simulation of aerosol concentrations in a realistic urban environment: Model validation and transport mechanism

Air pollution in urban environments exhibits large spatial and temporal variations due to high heterogeneous air flow and emissions. To address the complexity of local air pollutant dynamics, a comprehensive large-eddy simulation using the PALM model system v6.0 was conducted. The distribution of flow and vehicle emitted aerosol particles in a realistic urban environment in Malmö, Sweden, was studied and evaluated against on-site measurements made using portable instrumentation on a spring morning in 2021. The canyon transport mechanisms were investigated, and the convective and turbulent mass-transport rates compared to clarify their role in aerosol transport. The horizontal distribution of aerosols showed acceptable evaluation metrics for both mass and number. Flow and pollutant concentrations were more complex than those in idealized street canyon networks. Vertical turbulent mass-transport rate was found to dominate the mass transport process compared with the convective transport rate, contributing more than 70% of the pollutant transport process. Our findings highlight the necessity of examining various aerosol metric due their distinct dispersion behaviour. This study introduces a comprehensive high-resolution modelling framework that accounts for dynamic meteorological and aerosol background boundary conditions, real-time traffic emission, and detailed building features, offering a robust toll for local urban air quality assessment.

Assessment of urban air quality in Abidjan in terms of trace metals (copper, lead, zinc) by arborescent vegetation: Case of the species Hibiscus tiliaceus rubra.

Urban air quality is closely linked to human activities. Policies aimed at reducing or monitoring this form of pollution therefore have a dual benefit for health and the environment. Biomonitoring of air quality is one possible scientific approach. In this study, the woody species Hibiscus tiliaceus rubra was used as a biomonitor of air quality in the city of Abidjan. The leaves of the woody species were used to quantify the bioaccumulation of three trace metals: copper, lead and zinc. The assessment was carried out on five main road sections: "Adjamé-Cocody", " Adjamé-Yopougon", "Cocody-Bingerville", abobo-Anyama” and " Adjamé-Plateau". The results obtained show that the content of these trace metals varies according to the road. High levels of copper and zinc and very low levels of lead were initially found in the leaves of the species. The Adjamé-Cocody axis and the Adjamé-Yopougon axis recorded the highest levels of lead. The Adjamé-Plateau axis remains the least contaminated, with relatively low levels of lead.

Beyond common urban air quality assessment: Relationship between PM2.5 and black carbon during haze and non-haze periods in Bangkok

The atmospheric pollution burden in Bangkok, Thailand, is likely to be similar to that of other large urban agglomerations. Black carbon (BC) aerosol is one of the key components of PM2.5 (particulate matter with equivalent diameter ≤2.5 μm) pollutants; jointly they adversely affect humans and the environment. This study assessed the BC and PM2.5 ambient concentrations in Bangkok over nine months (November 1, 2020, to July 27, 2021), focusing on seasonal differences during the haze period (Hp; december 2020 to February 2021) and non-haze period (NHP; remaining months). PM2.5 data were measured optically, together with filter sampling of particles at heights of 30, 75, and 110 m above ground level (AGL). A thermal/optical carbon analysis method was used to determine the BC mass concentrations in the collected samples. The results showed that the vertically averaged BC concentration within PM2.5 increased more than 3-fold from 2.82 to 9.09 μg/m3 from the NHP to the HP. The vertical dependence of BC concentration was characterized by an increase in the height AGL in the NHP. In contrast, the opposite trend was observed for the HP. The average values of the PM2.5/BC ratio at all heights AGL showed a seasonal trend, with the BC mass concentration accounting for over 25% of PM2.5 concentration during the peak of the haze season.

An urban air quality assessment based on a meteorological perspective.

An integrated approach to understanding all measured pollutants with multi-discipline in different time scales and understanding the mechanisms hidden under low air quality (AQ) conditions is essential for tackling potential air pollution issues. In this study, the air pollution of Sivas province was analyzed with meteorological and PM2.5 data over six years to assess the city's AQ in terms of PM2.5 pollution and analyze the effect of meteorological factors on it. It was found that the winter period (January-February-November-December) of every year except 2019-which has missing data-is the period with the highest air pollution in the province. In addition, the days exceeding the daily PM2.5 limit values in 2016, 2017, 2020, and 2021 were also seen in the spring and summer months, which inclined the study to focus on additional pollutant sources such as long-range dust transport and road vehicles. The year 2017 has the highest values and was analyzed in detail. Pollution periods with the most increased episodes in 2018 were analyzed with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) and Dust Regional Atmospheric Model (DREAM) models. As a result of the study, the average PM2.5 values in 2017 were 31.66 ± 19.2µg/m3 and a correlation of -0.49 between temperature and PM2.5. As a result of model outputs, it was found that the inversion is intensely observed in the province, which is associated with an increase of PM2.5 during the episodes. Dust transport from northwestern Iraq and northeastern Syria is observed, especially on days with daily average PM2.5 values above 100µg/m3. Additionally, planetary boundary layer (PBL) data analysis with PM pollution revealed a significant negative correlation (r = -0.61). Air pollutants, particularly PM2.5, were found to be higher during lower PBL levels.

Remote acoustic sensing for urban air quality assessment

In this work, sound pressure levels and air quality are measured around near roadways in urban areas. The measured sound pressure level data are compared to the air quality index to investigate the relationship between roadway traffic levels and particulate matter concentration. Additionally, the effects of wind speed and direction are investigated. Of particular interest are the upwind and downwind conditions. The results of this work will be presented and discussed.

Assessment of Urban Air Quality using Geo Spatial Techniques for Sustainable Development: Case of Tirupur, India

<p>Rapid urbanisation, industrialization, and population growth have fostered an urgent need for a green, clean-living environment. Among the many pollutants, gaseous pollutants like NO2, SO2, and particle pollutants like PM 2.5 and PM 10 have a profound negative impact on human health. In this investigation, samples of air pollutants like NO2, SO2, PM 2.5, and PM 10 were taken at ten different sites inside the Tirupur corporation boundaries. For the locations taken into consideration, the Air Quality Index has been calculated using the Central Pollution Control Board's model. Utilizing inverse weighted distance (IDW), these data are interpolated and integrated into a geographic information system (GIS). It was discovered that the level of secondary industries and the density of automobiles had a substantial impact on air quality. Results indicate that PM 10 concentrations in the new bus stop and RakkiyapalayamPirivu are 113.7 mg/m3 and 134.73 mg/m3, respectively, which are comparatively higher than the permitted level specified in IS 5182. The areas with the most vehicular traffic and industry include the new bus stop location and RakkiyapalayamPirivu. Thus, the main sources of pollution in the study region are an increase in automobile traffic and industrial development. A clear indication of the area where the concentration of pollutants is high is provided by integrating the pollutant data with geographic information systems, and this enables local government agencies to conduct in-depth environmental evaluations. This aids planners in their decision-making for sustainable development.</p>

Multivariate Urban Air Quality Assessment of Indoor and Outdoor Environments at Chennai Metropolis in South India

The present study examines indoor and outdoor environmental particulate matter and gaseous pollutants in order to evaluate the urban air quality, the sources and pathways of pollutants, and its impact on Chennai megacity, South India. A total number of 25 air conditioner filter particulate matter samples collected from residential buildings, schools, colleges, commercial shopping malls, and buildings near urban highways were studied for indoor air quality. Similarly, outdoor air quality assessments have been done in various parts of the Chennai metropolis, including the Manali-Industrial area, the Velachery-Residential site, and the Alandur Bus Depot, as well as collected air quality data sets from the Central Pollution Control Board at continuous ambient air quality monitoring stations. The suspended atmospheric particles where the highest concentration (47%) occurred were mostly located in the roadside environments followed by commercial areas (42%), which indicates the increase in air pollution in the roadside areas. Further, environmental magnetism and ecological risk indices were studied from the collected data set. The study predicts that the air pollutants were predominantly from anthropogenic sources, such as vehicle emissions, effluents from power plants, abrasion of tires, steelworks, burning of fossil fuels and construction materials, etc. As a result, the current study suggests 68% of indoor pollutants were from the anthropogenic input, 18% from the pedogenic origin, and 14% from high heavy metal pollution at the sampling sites. This indicates that raising the ventilation rate via mechanical components significantly enhances the indoor air quality. These findings might be valuable in improving urban air quality, reducing traffic-related pollutants, and improving environmental quality.

Assessment of urban air quality from Twitter communication using self-attention network and a multilayer classification model.

Social media platforms are one of the prominent new-age methods used by public for spreading awareness or drawing attention on an issue or concern. This study demonstrates how the twitter responses of public can be used for qualitative monitoring of air pollution in an urban area. Tweets discussing about air quality in Delhi, India, were extracted during 2019–2020 using a machine learning technique based on self-attention network. These tweets were cleaned, sorted, and classified into 3-class quality viz. poor air quality, good air quality, and noise or neutral tweets. The present study used a multilayer classification model with first layer as an embedding layer and second layer as bi-directional long-short term memory (BiLSTM) layer. A method was then devised for estimating PM2.5 concentration from the tweets using ‘spaCy’ similarity analysis of classified tweets and data extracted from Continuous Ambient Air Quality Monitoring Stations (CAAQMS) in Delhi for the study period. The accuracy of this estimation was found to be high (80–99%) for extreme air quality conditions (extremely good or severe) and lower during moderate variations in air quality. Application of this methodology depended on perceivable changes in air quality, twitter engagement, and environmental consciousness among public.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-022-22836-w.

Feasibility study of prescribed burning for crop residues based on urban air quality assessment.

Open field burning of crop residue has been intentionally prohibited due to the undesired air pollution in urban regions. To better balance the urban environment and agricultural activity, this paper conducted a feasibility study of prescribed burning for crop residues based on air quality assessment in urban regions. Firstly, emission inventories were established using the top-down approach based on designed sub-regional fire as prescribed burning. Subsequently, the air qualities in urban regions were simulated by the coupled Weather Research and Forecasting Model-Community Multi-scale Air Quality Model (WRF-CMAQ) covering different sensitivity experiments. Finally, PM2.5 is selected as the main indicator of air quality, and the feasibility was assessed by controlling the factors influencing the diffusion of pollutants from prescribed burning, including burning ratio, meteorological factors (wind speed and direction), distance from burning area and burning duration. It is revealed that prescribed burning would achieve highly efficient disposal of crop residues under the premise of ensuring the air quality in urban regions by controlling the factors. Results in the study can be further exploited for designing burning scheme for crop residue, which is expected to promote a sustainable development of agriculture and urban environment.

Urban Air Quality Assessment by Fusing Spatial and Temporal Data from Multiple Study Sources Using Refined Estimation Methods

In urban environmental management and public health evaluation efforts, there is an urgent need for fine-grained urban air quality monitoring. However, the high price and sparse distribution of air quality monitoring equipment make it difficult to develop effective and comprehensive fine-scale monitoring at the city scale. This has also led to air quality estimation methods based on incomplete monitoring data, which lack the ability to detect urban air quality differences within a neighborhood. To address this problem, this study proposes a refined urban air quality estimation method that fuses multisource spatio-temporal data. Based on the fact that urban air quality is easily affected by social activities, this method integrates meteorological data with urban social activity data to form a comprehensive environmental data set. It uses the spatio-temporal feature extraction model to extract the multi-source spatio-temporal features of the comprehensive environmental data set. Finally, the improved cascade forest algorithm is used to fit the relationship between the multisource spatio-temporal features and the air quality index (AQI) to construct an air quality estimation model, and the model is used to estimate the hourly PM2.5 index in Beijing on a 1 km × 1 km grid. The results show that the estimation model has excellent performance, and its goodness-of-fit (R2) and root mean square error (RMSE) reach 0.961 and 17.47, respectively. This method effectively achieves the assessment of urban air quality differences within a neighborhood and provides a new strategy for preventing information fragmentation and improving the effectiveness of information representation in the data fusion process.

Indoor-outdoor pollutant concentration modelling: a comprehensive urban air quality and exposure assessment

This paper is devoted to the investigation of the relationship between concentrations of traffic-related pollutants at pedestrian level in the street and indoor pollutant concentrations inside different rooms of different floors of a standard building. CFD modelling covering the whole urban environment, including the interior of a target building, is used to explicitly simulate wind flow and pollutant dispersion outdoors and indoors. A wide range of scenarios considering different percentage and location of open windows and different wind directions is investigated. A large variability of indoor pollutant concentrations is found depending on the floor and configuration of the open/closed windows, as well as the wind direction and its incidence angle. In general, indoor pollutant concentrations decrease with floor, but this decrease is different depending on the scenario and the room investigated. For some conditions, indoor concentrations higher than the spatially averaged values in the street (up to a ratio of 1.4) are found in some rooms due to the high pollutant concentrations close to open windows. This behavior may lead, on average, to higher exposure inside the room than outside although, in general, indoor pollutant concentrations are lower than that found in the street at pedestrian level. Results are averaged for all scenarios and rooms being the average ratio between indoor and oudoor concentrations 0.56 ± 0.24, which is in accordance with previous studies in real buildings. This paper opens to a unified approach for the assessment of air quality of the total indoor and outdoor environment.

Optimization of HPC Use for 3D High Resolution Urban Air Quality Assessment and Downstream Services

The number of cities, or parts of cities, where air quality has been computed using the PMSS 3D model now appears to be sufficient to allow assessment and understanding of performance. Two fields of application explain the growing number of sites: the first is the long-term air quality assessment required in urban areas for any building or road project. The geometric complexity found in such areas can justify the use of a 3D approach, as opposed to Gaussian ones. However, these studies have constraining rules that can make the modelling challenging: several scenarios are needed (current, future with project, future without project), the long-term impact implies a long physical time period to be computed, and the spatial extension of the domain can be large in order to cover the traffic impact zone of the project. The second type of application is dedicated to services and, essentially, to forecasting. As for impact assessments, the modelling can be challenging here because of the extension of the domain if the target area is a whole city. Forecast also adds the constraint of time, as results are requested early, and the constraint of robustness. The CPU amount needed to meet all these requirements is important. It is therefore crucial to optimize all possible parts of the modelling chain in order to limit cost and delay. The sites presented in the article have been modelled with PMSS for long periods. This allows feedback to be provided on different topics: (a) daily forecasts offer an opportunity to increase the robustness of the modelling chain; (b) quantitative validation at air quality measurement stations; (c) comparison of annual impact based on a whole year, and based on a sampling list of dates selected thanks to a classification process; (d) large calculation domains with widespread pollutant emissions offer a great opportunity to qualitatively check and improve model results on numerous geometrical configurations; (e) CPU time variations between different sites provide valuable information to select the best parametrizations, to predict the cost of the services, and to design the needed hardware for a new site.

Real-time air quality assessment based on the EM-TOPSIS-PROMETHEE integrated assessment method

The goal of this article is to discuss the application of the EM-TOPSIS-PROMETHEE integrated assessment method in the real-time assessment of urban air quality and to compare it with other methods. By using the web crawler based on Selenium + Python we obtained the concentration of five air pollutants in 150 cities. We imported the data into MATLAB, used the Entropy evaluation method (EM) to get the weight of each air pollutant indicator, then used TOPSIS-PROMETHEE to obtain the score of air quality in each city. This method is more comprehensive than AQI, and it can also be combined with IAQI. Finally, we discussed the advantages and disadvantages of this method.

Time-Determination and Contribution Analysis of Transport, Retention, and Offshore Backflow to Long-Term Sand-Dust Coupling

Continuous on-line observation of particulate matter and PM2.5 chemical composition was conducted from October 15th to November 7th 2019 in East China. During the observation period, a wide range of dust-related processes took place. According to supplementary urban air quality assessment affected by dust (hereafter referred to as supplementary provisions), the observations were divided into four stages including pre-dust event, dust Ⅰ, dust Ⅱ, and post-dust event. The dust Ⅰ stage represented the processes of transportation and retention, while the dust Ⅱ stage represented processes of backflow from the sea and scavenging. The start time of the studied dust event was October 29th 08:00-09:00 based on the supplementary provisions, dust tracers, and air quality models; however, disagreements existed between these data sources with respect to the finishing time. The supplementary provisions could not effectively distinguish backflow dust from sea, and results from different dust tracers were variable. The WRF-CMAQ model simulated dust variation trends well but overestimated short-term suspended dust and backflow dust. PM10, PM2.5, and trace element concentrations were much higher during dust events than during non-dust periods, with highest daily concentrations of (234.8±125.5), (76.8±22.5), and (17.54±10.5) μg·m-3, respectively, which occurred on October 29th. During the dust event, concentration of crustal elements were remarkably high in PM2.5. At the same time, secondary ions (SO42-, NO3-, and NH4+) contributed less to PM2.5 mass concentrations. Four major crustal elements (Al, Si, Ca, and Fe) accounted for 23.5% and 13.7% of the mass concentration of PM2.5 and secondary ions accounted for 24.3% and 41.9% during dust Ⅰ and dust Ⅱ stages, respectively. Based on PMF source apportionment, Ca abundance, PM2.5/PM10 in dust sources, and the reconstruction of crustal material, dust particulates accounted for 43.4%-50.0% of PM2.5 and backflow dust accounted for 19.2%-24.7% of PM2.5.

Urban Air-Quality Assessment and Inferring the Association Between Different Factors: A Comparative Study Among Delhi, Kolkata and Chennai Megacity of India

The continuous increase of air pollution is an emerging environmental issue in the Indian megacities for the last few decades. Rapid and uncontrolled urbanization coupled with population growth, rising vehicle population, and growth of industries has been fuelling this problem. The present study analyzed the trend and pattern of air pollution of three Indian megacities: Delhi, Kolkata, and Chennai in a spatio-temporal frame using a comparative approach. To develop the air pollution scenario, air-quality data have been collected from the Central Pollution Control Board and also the State Pollution Control Board of respective cities. Four major pollutants (PM10, PM2.5, NO2, and SO2) have been selected to develop the air-quality index (AQI) from the period of 2017 to 2020. The meteorological parameters have been used to correlate with AQI. Moreover, exceedance factor has been calculated to analyze the level of pollution in comparison to the national standards. The results demonstrate that Delhi and Kolkata are most affected by air pollution. The seasonal distribution shows that higher concentrations of pollution were found during the winter season. The results of this analysis will be helpful in the assessment of air pollution and to investigate the way out through proper policies.

A very high-resolution assessment and modelling of urban air quality

Abstract. Urban air quality is one of the most prominent environmental concerns for modern city residents and authorities. Accurate monitoring of air quality is difficult due to intrinsic urban landscape heterogeneity and superposition of multiple polluting sources. Existing approaches often do not provide the necessary spatial details and peak concentrations of pollutants, especially at larger distances from monitoring stations. A more advanced integrated approach is needed. This study presents a very high-resolution air quality assessment with the Parallelized Large-Eddy Simulation Model (PALM), capitalising on local measurements. This fully three-dimensional primitive-equation hydrodynamical model resolves both structural details of the complex urban surface and turbulent eddies larger than 10 m in size. We ran a set of 27 meteorological weather scenarios in order to assess the dispersion of pollutants in Bergen, a middle-sized Norwegian city embedded in a coastal valley. This set of scenarios represents typically observed weather conditions with high air pollution from nitrogen dioxide (NO2) and particulate matter (PM2.5). The modelling methodology helped to identify pathways and patterns of air pollution caused by the three main local air pollution sources in the city. These are road vehicle traffic, domestic house heating with wood-burning fireplaces and ships docked in the harbour area next to the city centre. The study produced vulnerability maps, highlighting the most impacted districts for each weather and emission scenario. Overall, the largest contribution to air pollution over inhabited areas in Bergen was caused by road traffic emissions for NO2 and wood-burning fireplaces for PM2.5 pollution. The effect of emission from ships in the port was mostly restricted to the areas close to the harbour and moderate in comparison. However, the results have contributed to implementation of measures to reduce emissions from ships in Bergen harbour, including provision of shore power.

Assessment of Urban Air Quality in Indonesia

ABSTRACT This study assessed the urban air quality in 16 large Indonesian cities on the islands of Java, Sumatra, Kalimantan, Sulawesi, Maluku, and Papua from 2010 till 2017. 24-h samples of airborne particulate matter (PM) in two size fractions, PM2.5 (< 2.5 µm in aerodynamic diameter) and PM2.5-10 (2.5–10 µm in aerodynamic diameter), were collected weekly using a Gent stacked filter unit sampler and then analyzed for their mass concentrations, black carbon (BC) content, and elemental compositions. The majority of the average annual PM2.5 concentrations measured at the Java sites (Bandung, Jakarta, Semarang, and Surabaya) exceeded the Indonesian annual ambient air quality standard (15 µg m–3), although the other tested locations, excluding Pekanbaru and Palangka Raya, exhibited values below the standard. During the forest fire episodes of 2015, the average daily PM2.5 concentrations in Pekanbaru and Palangka Raya rose above the national daily ambient standard (65 µg m–3). The percentage of BC, which is associated with traffic emission and biomass burning, averaged between 15% and 26% (a significant fraction) in the PM2.5. The concentrations of the major elements in the PM2.5, viz., Si, S, K, Fe, Zn, and Pb, varied widely from site to site, although all of the locations displayed enhanced levels of the crustal elements Si and S, which originated from unpaved roads and volcanic eruptions, and vehicle fuel, forest fires, and volcanic emissions, respectively. Significantly higher concentrations of heavy metals (Fe, Zn, and Pb in Surabaya and Pb in Tangerang) were found at the heavily industrialized sites, demonstrating the effect of local industrial emissions on air quality. Our results, which are based on a crucial survey of PM concentrations and compositions in Indonesia, provide a scientific basis for developing and improving various air quality policies in the nation, including an early warning system for severe pollution events.

Urban Air Quality Assessment Using Integrated Artificial Intelligence Algorithms and Geographic Information System Modeling in a Highly Congested Area, Iraq

Pollutant emissions are considered to be a major threat to air quality and human health in urban areas. Therefore, accurate modeling and assessment tools are required. In this study, a model was done by the integration of machine learning algorithms and a geographic information system model. This model included the optimization of the support vector regression model by using the principal component analysis algorithm. Then, the integration of the regression model with spatial analysis modeling via a grid (100 x 100 m) was done in order to generate prediction maps during holidays and workdays in the daytime and at nighttime in a highly congested area in Baghdad city, Iraq. The data used in this study categorized into two categories. The first category is the data acquired through field surveying that includes temperature, humidity, wind speed, wind direction, and traffic flow data (e.g., the number of light and heavy vehicles), as well as carbon monoxide samples by using mobile equipment. The second category is the information derived from geographic information system data, such as land use, road network, and building height. The accuracy of the proposed model is 81%, and the lowest value of root mean square error was 0.067 ppm. The integration between air pollution models and geographic information system techniques could be a promising tool for urban air quality assessment and urban planning. These tools effectively utilized by stakeholders and decision-makers to outline proper plans and strategies to mitigate air pollutants in urban areas.

FeinPhone: Low-cost Smartphone Camera-based 2D Particulate Matter Sensor.

Precise, location-specific fine dust measurement is central for the assessment of urban air quality. Classic measurement approaches require dedicated hardware, of which professional equipment is still prohibitively expensive (>10k$) for dense measurements, and inexpensive sensors do not meet accuracy demands. As a step towards filling this gap, we propose FeinPhone, a phone-based fine dust measurement system that uses camera and flashlight functions that are readily available on today’s off-the-shelf smart phones. We introduce a cost-effective passive hardware add-on together with a novel counting approach based on light-scattering particle sensors. Since our approach features a 2D sensor (the camera) instead of a single photodiode, we can employ it to capture the scatter traces from individual particles rather than just retaining a light intensity sum signal as in simple photometers. This is a more direct way of assessing the particle count, it is robust against side effects, e.g., from camera image compression, and enables gaining information on the size spectrum of the particles. Our proof-of-concept evaluation comparing several FeinPhone sensors with data from a high-quality APS/SMPS (Aerodynamic Particle Sizer/Scanning Mobility Particle Sizer) reference device at the World Calibration Center for Aerosol Physics shows that the collected data shows excellent correlation with the inhalable coarse fraction of fine dust particles (r > 0.9) and can successfully capture its levels under realistic conditions.