PM10 Particles Research Articles

Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution.

Air pollution constitutes a significant worldwide environmental challenge, presenting threats to both our well-being and the purity of our food supply. This study suggests employing Recurrent Neural Network (RNN) models featuring Long Short-Term Memory (LSTM) units for forecasting PM10 particle levels in multiple locations in Skopje simultaneously over a time span of 1, 6, 12, and 24 hours. Historical air quality measurement data were gathered from various local sensors positioned at different sites in Skopje, along with data on meteorological conditions from publicly available APIs. Various implementations and hyperparameters of several deep learning models were compared. Additionally, an analysis was conducted to assess the influence of urban traffic on air and noise pollution, leveraging the COVID-19 lockdown periods when traffic was virtually non-existent. The outcomes suggest that the proposed models can effectively predict air pollution. From the urban traffic perspective, the findings indicate that car traffic is not the major contributing factor to air pollution.

Patterns of PM10 particles change in the atmospheric air of Ivano-Frankivsk city

Patterns of PM10 particles change in the atmospheric air of Ivano-Frankivsk city

Assessment of Chemical Composition and Health Implications Associated with PM10 Exposure: A Comparative Study of an Urban Road Intersection and a Wood-Burning Vicinity

The chemical composition and potential health risks linked to human exposure to ambient PM10 particles were assessed at the Winneba highway intersection (WHI) and Apam, a wood-burning neighbourhood in Ghana’s Central Region. PM10 particulates were collected using a Gent sampler equipped with a Gast pump and a stacked filter unit. The sampling was done three times a week continuously from July 2022 to June 2023, for 24 h. A smoke stain reflectometer and an Ag-anode X-ray tube spectrometer were used to analyse the samples for black carbon and elemental contents, respectively. The health hazards associated with trace elements were evaluated using the US EPA health risk assessment model. The study’s findings indicated that the amounts of Cr (250.5 ng/m3) and Ni (358.1 ng/m3) detected at Apam surpassed the US EPA threshold limits, as did the values discovered at the WHI, which were 78.63 and 178.2 ng/m3, respectively. PCA indicated that PM10 particles were produced at WHI by soil dust, vehicle exhaust, brake and tyre wear, and two-stroke engines, whereas biomass burning, soil dust, and vehicle exhaust emissions contributed to PM10 at Apam. The non-carcinogenic risks of children’s exposure to trace metals in PM10 via the three exposure pathways were higher compared to adults. The overall cancer risk values for exposure to carcinogenic trace metals from ingestion and dermal contact were more than 10−4, suggesting high cancer risks: 9.49 × 10−4–5.20 × 10−3 for children and 9.03 × 10−4–5.26 × 10−3 for adults. Reducing PM10 and the health concerns it poses requires the use of clean fuel and the adoption of environmentally friendly transportation.

Yearly Assessment of Weather and Air Quality Impact on Respiratory Disease Hospitalizations: Faial Island (Azores) Study

Building upon our previous research conducted at weekly and monthly intervals, this study investigates the yearly dynamics of weather conditions and air quality on respiratory diseases, specifically tailored to Faial Island in the Azores. Expanding our analysis to a yearly basis allows for a more comprehensive understanding of long-term trends and seasonal variations in hospital admissions. Drawing upon extensive daily data spanning from 2008 to 2019, sourced from the Statistics Service of Hospital da Horta, the Meteorological Observatory Príncipe Alberto de Mónaco, and the Air Quality Monitoring Network of the Azores, we examined the differentiated relationships between yearly meteorological variables, such as temperature, dew point, and wind direction, and respiratory health outcomes. Additionally, we explored the persistent influence of air quality indicators, including suspended particulate matter with an aerodynamic diameter of 10 micrometers (µm) or less (PM10), sulfur dioxide (SO<sub>2)</sub>, nitrogen dioxide (NO<sub>2)</sub>, and ozone (O<sub>3</sub>), across different seasons and years. By incorporating yearly data into our analysis, this study aims to provide a comprehensive and nuanced understanding of the interplay between environmental factors and respiratory disease hospitalizations, thereby informing targeted interventions and public health strategies in insular regions. The principal component analysis (PCA) applied to the yearly data reveals interesting correlation patterns between meteorological variables and hospitalizations for respiratory diseases such as asthma, pneumonia, and rhinitis. Hospital admissions were found to increase during periods of lower temperatures and smaller temperature amplitudes, suggesting that prolonged cold conditions may exacerbate symptoms of these diseases. Additionally, atmospheric pollutant concentrations, such as suspended particles PM10, SO<sub>2</sub>, NO<sub>2</sub>, and O<sub>3</sub>, significantly varied across seasons, with a notable influence on the exacerbation of respiratory diseases. In insular regions like the Azores, these findings highlight the importance of targeted public health strategies to mitigate the impact of environmental factors on respiratory health.

Creation and research of numerical models of cyclones with inclined and horizontal inlets

The article considers the challenges of using cyclone devices for cleaning gas emissions from solid fuel combustion in power engineering and industry. With the increasing energy consumption and generation, including the use of coal, the requirements for the efficiency of cleaning emissions from small solid particles of classes PM10 and PM2.5 are also rising. One of the key tasks is to maintain high cleaning efficiency while minimizing energy costs, which is also important for reducing the environmental impact of generation and industrial production. The article considers the design features of currently used cyclones, as well as existing approaches to their classification. The main focus is on modern areas of research related to numerical modeling based on CFD (Computational fluid dynamics) aimed at optimizing the design of separators. It is shown that most studies are carried out for cyclone operating conditions in production cycles, rather than in emission cleaning systems that operate at low suspended matter loads. Using the example of creating numerical models of cyclones with inclined and horizontal inlet pipes (CN-11, SK-CN-34), the article discusses certain features of geometry creation in the SpaceClaim Direct Modeler (SCDM) environment, which ensure the correctness of grid generation and calculations in the future. The results of numerical modeling carried out using the ANSYS Fluent software are presented. The turbulence model (RANS, Reynolds Averaged Navier – Stokes), methods for closing the Navier – Stokes equations (k-ε model with near-wall functions), and methods for calculating the dispersed part of the flow (Euler – Lagrange, DPM) are selected based on the conditions relevant to the systems for cleaning emissions from coal generation. The results indicate that, with the same pressure drop, the efficiency of settling suspended matter with a concentration of 100 mg/m3 or less in an apparatus with an inlet angle of 11° to the horizontal can be higher than that of a cyclone with a horizontal inlet.

Prediction of Jakarta's Air Quality Using a Stacking Framework of CLSTM, CatBoost, SVR, and XGBoost

Air quality prediction, particularly in estimating PM10 particle concentration, is a significant challenge in major cities like Jakarta, which experience high levels of air pollution. This study aims to develop an air quality prediction model using an innovative stacking framework that combines several machine learning algorithms, namely ConvLSTM, CatBoost, SVR, and XGBoost. The methodology employed in this research is an experimental approach, where each model is trained and tested individually before being integrated into the stacking framework. The dataset used was sourced from the Kaggle platform, containing historical air quality data in Jakarta. Performance evaluation was conducted by measuring the Root Mean Squared Error (RMSE) for each model. The results of the study showed that the ConvLSTM model produced an RMSE of 13.5168, CatBoost with an RMSE of 13.4113, and SVR with an RMSE of 14.2725. To improve prediction accuracy, the researchers employed a stacking approach of the four models (ConvLSTM, CatBoost, SVR, and XGBoost), which yielded a much lower RMSE of 0.8093. Thus, this stacking framework has proven to significantly enhance air quality prediction performance, particularly in predicting PM10 concentrations in Jakarta.

Poultry farms as a One health priority for Aspergillus section Fumigati surveillance

Abstract In poultry production environments, particulate matter (PM) is considered one of the harmful air pollutants [2]. Considering potential biological pathogens, the genus Aspergillus in particular, Aspergillus fumigatus, is a widely recognized human and animal pathogen [3]. Hot-humid farm environments could contribute to the proliferation of these fungi [4]. Therefore, poultry production workers, besides animals [4], may be at a higher risk of inhaling fungi spores [4,5]. This study aimed to determine particulate matter (PM) contamination and analyze the prevalence of Aspergillus section Fumigati in air samples from poultry pavilions. Indoor and outdoor air samples were taken in the 1st, 2nd and 3rd weeks of birds growth cycle (n = 58). Air was collected through the impaction method (MAS-100) onto polycarbonate filters, set at 100 L/min, for 5 min. Followed by DNA extraction, and real-time PCR detection of Aspergillus section Fumigati. For PM evaluation, portable direct-reading equipment (Lighthouse, model 3016 IAQ) was used. Particle concentrations were measured (n = 67) at five distinct fractions during 5 min. Considering particles fractions, PM10 and PM5 where prevalent in 1st (58% PM10; 28% PM5), 2nd (63% PM10; 26% PM5) and 3rd (64% PM10; 25% PM5) weeks. These results evidence that coarse particles (between 2.5 and 10 µm) are the most relevant PM present in this setting. Regarding fungal detection, Aspergillus section Fumigati was widespread inside poultry pavilions (62%, 26 out of 42), highlighting this environment has a potential reservoir of microbial pathogens [7]. Furthermore, PM can act as a host of biological fragments [6]. Overall, in addition to molecular analysis, future studies should consider using culture-based methods to assess the viability of pathogenic microorganisms and their infection potential [3]. From a One Health perspective, quantitative and qualitative research is required to fully understand this environment. Key messages • This study evidence that coarse particles (between 2.5 and 10 µm) are the most relevant PM present and Aspergillus section Fumigati is widespread in poultry pavilions. • A One health intervention should be considered in future studies.

Single-stage NBI sampler for PM1 mass and five-stage NBI-NMCI sampler for PM1 mass distribution measurements

In recent years, concerns about the health and environmental risks associated with PM1 particles have increased. However, existing PM1 sampling instruments remain to be improved mainly because their PM1 inlets have particle loading and bounce issues. To address these challenges, PM1 inlets based on the Non-Bouncing Impactor (NBI) technique were developed. These inlets employ vacuum oil-wetted glass fiber filter (GFF) substrates to eliminate particle bounce and incorporate a daily vacuum oil injection to prevent particle loading. The 16.7 L/min PM1 NBI, modified from the PM2.5 M-WINS design with a reduced diameter of the single nozzle, was designed to adapt readily with current standard sampling and monitoring systems. The cut-size (Dpa50) of 0.99 ± 0.02 μm was determined by considering the effects of Reynold number and the ratio of jet-to-plate distance and nozzle diameter. Field tests comparing the 16.7 L/min PM1 NBI sampler to the 9-stage NCTU Micro-orifice Cascade Impactor (NMCI9) revealed small sampling biases, with a mean difference of +0.26 ± 2.28 μg/m3 for PM1 measurements when silicone oil-coated aluminum foil (AF) and GFF-AF were used in the NMCI9. The NBI with the oil-wetted GFF substrate effectively removed particles larger than 1.0 μm, resulting in more accurate PM1 mass concentration measurements. Additionally, the development of a 5-stage NMCI with the 30 L/min PM1 NBI as the first stage enabled detailed measurement of PM1 mass distribution in two modes, which was challenging when measuring the entire mass distribution of ambient aerosols. The distribution of size-dependent water-soluble inorganic ions in PM1 showed dominance of SO42− and NH4+ in PM1 compared to PM2.5. In summary, the PM1 NBI enhances accuracy for long-term atmospheric sampling by addressing particle loading and bounce, making it a more reliable standard sampling instrument.

Влияние интенсивных атмосферных осадков на загрязнение воздуха города Владивостока

Concentrations of the main substances polluting the atmosphere over Vladivostok (sulfur dioxide, carbon monoxide, nitrogen dioxide, and PM2.5, PM10 particles) before, during, and after stormwater solid and liquid precipitation on December 21–23, 2022 and June 8–9, 2023, respectively, are considered. The development of synoptic processes that caused this precipitation, air particle trajectories (using the HYSPLIT model), and the variation pattern of pollutant concentrations recorded using the GMAO satellite monitoring during these periods are analyzed. It is shown that heavy rainfall significantly cleanses the atmosphere from aerosol pollution, and during snowfalls, carbon monoxide is better washed out of the atmosphere, and PM10 and PM2.5 particles almost completely fall on the underlying surface.

ЭМУЛЯЦИЯ ПОКАЗАНИЙ ДАТЧИКОВ КАЧЕСТВА ВОЗДУХА В ГОРОДСКОЙ СРЕДЕ УМНОГО ГОРОДА

Urban air pollution poses a serious threat to human health. To monitor it, both individual sensors and systems are used to assess the concentration of dust particles PM1, PM2.5, PM10 and organic compounds. However, the reliability of the sensor system cannot be 100 percent. From time to time, certain sensors in a distributed system fail. For this reason, it is very useful to emulate their readings based on the readings of the remaining sensors. The work describes a data set and proposes a machine learning model, which, based on the readings of functional sensors and weather conditions at the data collection sites, simulates the readings of a failed sensor. The accuracy of such emulation for certain types of pollution has been assessed (the coefficient of determination ranges from 0.43 to 0.61).

Characterization of Lithium-Ion Battery Fire Emissions—Part 2: Particle Size Distributions and Emission Factors

The lithium-ion battery (LIB) thermal runaway (TR) emits a wide size range of particles with diverse chemical compositions. When inhaled, these particles can cause serious adverse health effects. This study measured the size distributions of particles with diameters less than 10 µm released throughout the TR-driven combustion of cylindrical lithium iron phosphate (LFP) and pouch-style lithium cobalt oxide (LCO) LIB cells. The chemical composition of fine particles (PM2.5) and some acidic gases were also characterized from filter samples. The emission factors of particle number and mass as well as chemical components were calculated. Particle number concentrations were dominated by those smaller than 500 nm with geometric number mean diameters below 130 nm. Mass concentrations were also dominated by smaller particles, with PM1 particles making up 81–95% of the measured PM10 mass. A significant amount of organic and elemental carbon, phosphate, and fluoride was released as PM2.5 constituents. The emission factor of gaseous hydrogen fluoride was 10–81 mg/Wh, posing the most immediate danger to human health. The tested LFP cells had higher emission factors of particles and HF than the LCO cells.

Methodological approaches to the creation of a biological model of smoke in landscape fires

Introduction. Long-term smoke from landscape fires causes various health problems, primarily affecting the respiratory, cardiovascular, and nervous systems. To substantiate safe levels of exposure to smoke from landscape fires, it is necessary to create an adequate biological model for smoke that is close to natural conditions. The aim of the study is to develop and test methodological approaches to creating a biological model for smoke during landscape fires to substantiate safe levels of exposure. Materials and methods. Using the developed biological model, we studied the effects of smoke from landscape fires on white rats. We assessed the functional state of the central nervous system based on rat behaviour and EEG examination results, conducted a morphological study of the cerebral cortex and testes of white rats, and examined the offspring immediately after birth and at puberty. Results. We studied the biological response of white rats exposed to inhalation smoke from landscape fires for different durations. The main criterion indicators of health disorders in the central nervous and reproductive systems of animals were identified. Limitations. The study is limited by the lack of data on safe levels of exposure to smoke from landscape fires on the central nervous system and reproductive toxicity. Conclusion. Testing of the developed method allowed identifying the exposure load of carbon monoxide and ultrafine particles PM2.5, at which changes in the nervous and reproductive systems are minimal. A method for calculating safe levels of exposure for the central nervous and reproductive systems, as well as for the effect on offspring, is proposed. An algorithm for conducting research in studying the effect of smoke from landscape fires on the body has been developed.

An Image-Based Sensor System for Low-Cost Airborne Particle Detection in Citizen Science Air Quality Monitoring.

Air pollution poses significant public health risks, necessitating accurate and efficient monitoring of particulate matter (PM). These organic compounds may be released from natural sources like trees and vegetation, as well as from anthropogenic, or human-made sources including industrial activities and motor vehicle emissions. Therefore, measuring PM concentrations is paramount to understanding people's exposure levels to pollutants. This paper introduces a novel image processing technique utilizing photographs/pictures of Do-it-Yourself (DiY) sensors for the detection and quantification of PM10 particles, enhancing community involvement and data collection accuracy in Citizen Science (CS) projects. A synthetic data generation algorithm was developed to overcome the challenge of data scarcity commonly associated with citizen-based data collection to validate the image processing technique. This algorithm generates images by precisely defining parameters such as image resolution, image dimension, and PM airborne particle density. To ensure these synthetic images mimic real-world conditions, variations like Gaussian noise, focus blur, and white balance adjustments and combinations were introduced, simulating the environmental and technical factors affecting image quality in typical smartphone digital cameras. The detection algorithm for PM10 particles demonstrates robust performance across varying levels of noise, maintaining effectiveness in realistic mobile imaging conditions. Therefore, the methodology retains sufficient accuracy, suggesting its practical applicability for environmental monitoring in diverse real-world conditions using mobile devices.

Study of the kinetics and mechanisms of rare earth elements leaching from end-of-life NdFeB magnets through Hydro-Nd process

With the growing demand for REEs in numerous high-tech and energy transition-related applications, recycling these elements from secondary sources, mainly from end-of-life permanent magnets, has become crucial for responsible sourcing of raw materials from a circular economy. To date, numerous processes have been developed and optimized to allow this recovery to be sustainable from both an economic and environmental point of view. In this context, hydrometallurgy is one of the most promising avenues. Through an innovative hydrometallurgical process (Hydro-Nd), this research investigated the kinetics and mechanisms involved in leaching REEs from end-of-life permanent magnets. This process involves the use of citric acid in the leaching phase. Through systematic experimentation and analysis, the article elucidates key factors influencing leaching kinetics, providing insight into critical mechanisms of the process. In particular, ANOVA was used to study the influence of important factors (temperature, particle size, and agitation) on the recovery yields of the elements of interest. The shrinking core model studied the leaching mechanisms to identify a possible rate-determining step. Thanks to the results obtained, the Arrhenius parameters were estimated for the different heterogeneous reactions involved in the process, and the trend of the time constants versus the average diameter of the solid particles was identified. Also, the results showed that 100 % of Nd, Fe, Dy, B, and Pr from small PM particles (0–40 µm) in 4 h and more than 90 % of the mentioned metals from large particles (1000–1700 µm) within 5 h of leaching time were dissolved by citric acid solution. This research not only contributes to the development of an even more sustainable rare earth recovery process thanks to the optimization of several factors but also clarifies fundamental aspects of leaching kinetics and mechanisms, paving the way for future advances in the recovery of REEs from different sources.

Chemical composition, mutagenicity, and cytotoxicity of urban submicron particulate matter (PM1) in Agra, India

This study, conducted in Agra, India, examined the mass concentrations, chemical compositions, and seasonal variations of submicron particles (PM1). The concentrations of metals, water-soluble inorganic ions (WSIs) including anions (F−, Cl−, NO₃−, SO₄2−) and cations (Ca2+, K+, Mg2+, NH₄+, Na+), organic carbon (OC) and elemental carbon (EC) and polycyclic aromatic hydrocarbons (PAHs) in PM1 extract were determined using Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES), Ion Chromatography, Thermogravimetric Analysis and Gas Chromatography-Mass Spectrometry (GC–MS) respectively. For morphological observation of PM1 particles, Field Emission Scanning Electron Microscopy-Energy Dispersive X-ray spectrometry (FESEM-EDS) was used. The annual average concentration of PM1 was 82.9 ± 33.4 μg/m3, which exceeds the World Health Organisation's (WHO) safe limit for PM2.5 of 5 μg/m3 by a factor of 17. The PM1 mass composition included metals (31 %), WSIs (28 %), OC and EC (9.8 %), and PAHs (0.4 %). Winter recorded the highest PM1 concentration (96.1 ± 25.8 μg/m3), followed by post-monsoon, summer, and monsoon seasons. The average concentration of PAHs was 364.6 ± 226.6 ng/m3. Positive Matrix Factorization (PMF) identified traffic, emissions from biomass/coal and wood combustion, industrial/stationary sources, and secondary aerosols as potential contributors. The Ames test revealed the presence of frameshift mutations and base pair substitutions, especially in winter and post-monsoon. Additionally, PM1 exhibited cytotoxic effects on V-79 cells, with heightened toxicity during winter and prolonged exposure in other seasons. This study underscores the urgent need to address local emission sources and establish regulatory standards for PM1 in urban areas.

Morphology and chemical composition of mineral particles in a special dust storm with high relative humidity in North China

A special dust storm characterized by high PM10 mass concentrations (921.9 ± 632.3 μg m−3) and high relative humidity (RH; 60.1 % ± 11.1 %) was observed on March 22–24, 2023 at a coastal city of North China. Aerosol particles of PM10 were analyzed by a scanning electron microscope coupled with energy dispersive X-ray and an ion chromatograph. The results showed that individual mineral particles were dominated by clay minerals, followed by quartz, feldspar, and carbonate. Bulk water-soluble inorganic ions analysis showed that SO42- mass concentrations varied from 3.7 μg m−3 to 23.3 μg m−3 with an average value of 12.4 μg m−3. However, their mass ratios to PM10 were relatively stable, being 1.15–2.01 % with an average value of 1.49 % ± 0.25 %, similar to the value near the dust sources (Tengger Desert). Although S-containing individual mineral dust varied from 5.2 % to 70.7 %, the average weight ratio of S on individual mineral dust was 2.1 %, much lower than that of non-dust periods (11.0 %). The results suggested limited sulfate formation on mineral dust surfaces even under high RH. In contrast, NO3-, which was very limited in dust sources, varied from 0.21 % to 4.11 % of the total PM10 with an average value of 1.61 % ± 1.07 %. The research highlighted that nitrate formation has exceeded sulfate formation during severe dust storm episodes, which might because the atmospheric compositions in China have changed significantly with a high mass ratio of NO2/SO2 after the implementation of the strict emission control measures.

Performances of two-stage electrostatic precipitator with different types of discharge electrodes of unipolar electrostatic agglomerator

Current-voltage characteristics of electrostatic agglomerator with three types of discharge electrodes, operating as the first stage of a two-stage electrostatic precipitator, and the effects of its operating parameter on the removal efficiency of this system for PM10 particles have been investigated. The results shown that the removal efficiency of the system of this specific design only weakly depends on the supply voltage of the AC kinematic electrostatic agglomerator in the supply voltage range of 18–26 kV. A synergistic effect between the operation of electrostatic agglomerator and electrostatic precipitator was obtained. By this effect, the removal efficiency of the whole system, i.e., the electrostatic agglomerator and electrostatic precipitator operating simultaneously, is higher than that determined as the combination of the removal efficiencies obtained for both of these stages operating alone. The effect of synergy results from the agglomeration of fly ash particles in the electrostatic agglomerator stage that decreases the concentration of small particles and increases their removal efficiency by electrostatic precipitator.

Innovative Air-Preconditioning Method for Accurate Particulate Matter Sensing in Humid Environments.

Smart cities rely on a network of sensors to gather real-time data on various environmental factors, including air quality. This paper addresses the challenges of improving the accuracy of low-cost particulate matter sensors (LCPMSs) which can be compromised by environmental conditions, such as high humidity, which is common in many urban areas. Such weather conditions often lead to the overestimation of particle counts due to hygroscopic particle growth, resulting in a potential public concern, although most of the detected particles consist of just water. The paper presents an innovative design for an indicative air-quality measuring station that integrates the particulate matter sensor with a preconditioning subsystem designed to mitigate the impact of humidity. The preconditioning subsystem works by heating the incoming air, effectively reducing the relative humidity and preventing the hygroscopic growth of particles before they reach the sensor. To validate the effectiveness of this approach, parallel measurements were conducted using both preconditioned and non-preconditioned sensors over a period of 19 weeks. The data were analyzed to compare the performance of the sensors in terms of accuracy for PM1, PM2.5, and PM10 particles. The results demonstrated a significant improvement in measurement accuracy for the preconditioned sensor, especially in environments with high relative humidity. When the conditions were too severe and both sensors started measuring incorrect values, the preconditioned sensor-measured values were closer to the actual values. Also, the period of measuring incorrect values was shorter with the preconditioned sensor. The results suggest that the implementation of air preconditioning subsystems in LCPMSs deployed in smart cities can provide a cost-effective solution to overcome humidity-related inaccuracies, thereby improving the overall quality of measured air pollution data.

Indicators of woody plants and soil in the urbanised environment of the Southern Urals (green zone of Ufa, Russia)

ABSTRACT The study investigates biometric indicators of assimilation and reproductive organs of coniferous and deciduous trees in urban environments: leaf area, inflorescence flower count, stipule length of Tilia cordata Mill. and needle length and weight of Scots pine (Pinus sylvestris L.). Samples of wood, needles, leaves and soil from the planting sites were analysed for 17 chemical elements, including Hazard Class I elements (Arsenic, Lead, Cadmium, Mercury, Selenium) and Hazard Class II elements (Chromium, Cobalt, Nickel, Copper, Molybdenum, Antimony). Tree plantations decreased airborne dust by 30–40% compared to open areas. Concentrations of PM2.5 and PM10 particles showed a direct correlation with busy roads (p < 0.05). In instances of environmental degradation, healthy trees exhibited in needle an increase in biomass and a reduction in weight and length. Consequently, forestry measurements and the impact of the urban environment require specific and individual monitoring of trees, rather than a general assessment of plantations.

Vertical distribution of particulate matter near a national highway and influence of roadside tree canopy: A drone/UAV based study

Measurement of PM near roadways has been of major interest due to high contributions of traffic to the overall PM. This paper presents the results from a study that used an unmanned aerial vehicle (UAV, also known as drones) platform to obtain measurements of PM and meteorological parameters up to 100 m above ground level near an open highway, and to understand the influence of tree canopy on the PM distribution. The study involved 42 flights over a period of two days, conducted adjacent to a national highway in India. The overall mean profile for flights conducted behind the tree canopy shows a decrease in PM mass concentrations with an increase in altitude. However, for the near-road flights, the PM concentrations tend to increase with altitude, followed by a gradual decline for a few flights. For the flights near the road, the results show that the mean values of PM1 and PM2.5 mass concentrations at higher altitudes are 16% and 8% higher than the respective ground level concentrations. On the contrary, the mean PM10 mass concentrations at higher altitudes are 6% lower than that at the ground level. For the flights behind tree canopy, the results are different when compared to the flights near the road. The mean mass concentrations for all 3 p.m., PM2.5 and PM10 particles at higher altitudes are 20%, 19% and 21% lower than the respective ground level measurements. Also, the PM1, PM2.5 and PM10 mass concentrations for the flight behind tree canopy cover are 29 %, 24 % and 11 % lower than the near-road flights respectively. The results also showed the concentrations behind the tree canopy remained low for the entire altitude of ∼100 m.