Particulate Matter Monitoring Research Articles

IOT Based Air Quality Monitoring Device

The air quality in the past years has fallen dramatically, creating serious global environmental and health concerns. This project talks about the development of an Internet of Things-based air quality monitoring device that aims to continuously measure and report on certain air quality parameters. This system utilises an Arduino microcontroller as a base for several sensors, including humidity and temperature sensors, particulate matter monitoring sensors, and gas sensors like MQ135. The data uploaded is to a cloud server in real-time on the basis of the present modules of wireless connectivity for easy and remote accessibility to monitor. Apart from that, this technology may be used in the widescale environmental monitoring network while making information easily accessible to the respective user. With the inclusion of IoT, real-time alerts concerning pollutant level can go a long way in enhancing awareness and can help in taking informed decisions that will reduce air pollution. This research solution addresses the critical need in air quality monitoring since it is cheap, scalable, and efficient. Keywords - IoT (Internet of Things), Air Quality Monitoring, Arduino, Sensors, Pollution Detection, Real-time Data, Environmental Monitoring

Exploring the cooking energy biomass and its impact on women's health and quality of life in rural households: a micro-environmental study from West Bengal in India.

Exposure to household air pollutants has become a significant environmental health concern in developing nations. This study aimed to understand the growing energy consumption within households, particularly for cooking, and its impact on women's health in rural areas. We conducted real-time monitoring of ambient particulate matter (PM2.5) and carbon monoxide (CO) levels in 61 rural kitchens in the Medinipur and Jhargram districts of West Bengal, India. Additionally, we calculated the Standard Living Index (SLI) based on socio-demographic factors from 540 households. Our analyses using ANOVA and chi-square methods revealed significant effects of cooking fuel types and locations on various health indicators among women. Eye irritation was prevalent across all fuel types, followed by shortness of breath (33%), coughing (22%), and dizziness (21%). Alarmingly, nearly half (48%) of children under five consistently accompanied their mothers during cooking, exposing them to health risks. Indoor air pollution, particularly from traditional fuels like fuelwood, cow dung cakes, and leaves, poses a grave threat to families. These fuels emit considerable amounts of PM2.5 and CO, with levels reaching as high as 565µg/m3 and 12.5ppm, respectively, leading to respiratory and cardiovascular complications. Clean cooking fuel users, such as those using LPG, reported improved quality of life scores across physical, psychological, social, and environmental categories. This study highlights the urgent need to transition to cleaner cooking fuels to mitigate adverse health effects on women and children in rural households.

Visualization of particulate matters in air using lensless microscopy

Abstract. Air quality is crucial for health, yet monitoring airborne particulate matter (PM), particularly PM2.5, remains challenging due to pollution and widespread distribution. PM2.5, with diameters less than 2.5 micrometers, are known to have significant health implications. Traditional methods of PMs detection and analysis are often time-consuming or require complex optical systems. This study explores the use of lensless microscopy as an innovative and cost-effective alternative for the visualization and measurement of PMs. Utilizing computational imaging techniques, lensless microscopy captures high-resolution images without the need for traditional lenses, facilitating rapid and accurate analysis of air quality. We describe the implementation of lensless imaging to detect and quantify PM2.5 particles in the air, highlighting the use of the single light wavelength and the computational reconstruction method. Our experiments demonstrated that this approach could effectively capture fine details of PMs, achieving resolutions comparable to traditional microscopy. This validates the potential of lensless microscopy as a practical tool for real-time air quality monitoring. By adopting lensless microscopy, we offer a more efficient and economical method for the continuous monitoring of PMs, which can significantly contribute to environmental health studies and pollution control efforts. According to specific needs, simply replacing the flow rate of the air pump can adapt to the PM measurement speed requirements in different environments. This research introduces a novel approach to air quality assessment, providing a promising solution for the rapid and detailed analysis of airborne particulate matter while at an affordable cost.

A wearable real-time particulate monitor demonstrates that soaking hay reduces dust exposure.

Affordable particulate matter (PM) monitors suitable for use on horses will facilitate the evaluation of PM mitigation methods and improve the management of equine asthma. Calibrate a real-time wearable PM monitor (Black Beauty [BB]) and compare the PM exposures of horses fed dry or soaked hay. Laboratory calibration; complete cross-over feed trial. Side-by-side sampling with BB monitors and tapered element oscillating microbalances (TEOMs) was performed under varying concentrations of PM from alfalfa hay. Linear regression was used to derive a calibration formula for each unit based on TEOM PM measurements. Precision was evaluated by calculating the coefficient of variation and pairwise correlation coefficients between three BB monitors. PM exposure was measured at the breathing zone of 10 horses for 8 h after they were fed dry or soaked hay. Repeated measures generalised linear models were constructed to determine the effect of hay treatment and measurement duration (initial 20-min vs. 8-h) upon exposure to PM with diameters smaller than or equal to 10 μm (PM10) and 2.5 μm (PM2.5). BB monitor PM2.5 and PM10 measurements were linearly correlated with TEOM data (coefficient of determination r2 > 0.85 and r2 > 0.90 respectively), but underestimated PM2.5 mass concentrations by a factor of 4 and PM10 concentrations by a factor of 44. Measures from the three BB monitors had a coefficient of variation <15% and pairwise r > 0.98. Feeding soaked hay significantly reduced average PM2.5 exposures (20-min: dry: 160 μg/m3, soaked: 53 μg/m3, p < 0.0001; 8-h: dry: 76 μg/m3, soaked: 31 μg/m3, p = 0.0008) and PM10 exposures (20-min: dry: 2829 μg/m3, soaked: 970 μg/m3, p < 0.0001; 8-h: dry: 1581 μg/m3, soaked: 488 μg/m3, p = 0.008). No health outcome measures were collected. With appropriate corrections, the BB monitor can be used to estimate horse PM exposure. While 20-min measurements yielded higher estimates of exposure than 8-h measurements, both intervals demonstrate that soaking hay reduces PM exposures by more than 50%.

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.

Estimation of human airway deposition of size-segregated particulate and their health impact in monsoon and post-monsoon season in semi-urban area of northern India

Numerous studies have shown the negative association between Particulate Matter (PM) exposure and health impact, understanding this risks associated with PM exposure for generating appropriate risk-control strategies, an accurate evaluation of PM concentration and respiratory deposition is necessary. So, the present study aims to describe the temporal and diurnal variation of Particulate Matter and Black Carbon concentrations, as well as their health risks. Real-time monitoring of PM and BC was conducted near a national highway (NH-19) in the semi-urban area. Our findings indicated that the washout effect played its role in reducing the PM level in monsoon season while meteorological parameters, local anthropogenic activities, and stubble-burning influenced the high PM concentration in the post-monsoon season. The current study also estimates the particle deposition in the human respiratory tract using Multiple Path Dosimetry Model (MPPD) for understanding the health effects of particle inhalation. The results revealed that different seasons exhibit different deposition patterns. For example, during the monsoon season, the trend was PM10>PM2.5>PM1.0 while in post-monsoon season PM2.5 >PM10 > PM1.0 respectively. The findings indicate that small particles easily deposited deeper into the lungs have a significant impact on various age groups, particularly children, who are the most affected. Furthermore, the health risks of PM and BC exceeded the safer limit (HQ = 1) and ELCR≥10−6, suggesting prolonged inhalation exposure to particulate could lead to cancer. The present study will give an insight into designing interventions and policies to control PM pollution in smaller cities, where a significant portion of the population resides.

The Deployment Modeling of Low-Cost Sensors for Urban Particulate Matter Monitoring: A Case Study for PM2.5 Monitoring in Tehran City

The Deployment Modeling of Low-Cost Sensors for Urban Particulate Matter Monitoring: A Case Study for PM2.5 Monitoring in Tehran City

Monitoring and Ensuring Worker Health in Controlled Environments Using Economical Particle Sensors.

Nowadays, indoor air quality monitoring has become an issue of great importance, especially in industrial spaces and laboratories where materials are handled that may release particles into the air that are harmful to health. This study focuses on the monitoring of air quality and particle concentration using low-cost sensors (LCSs). To carry out this work, particulate matter (PM) monitoring sensors were used, in controlled conditions, specifically focusing on particle classifications with PM2.5 and PM10 diameters: the Nova SDS011, the Sensirion SEN54, the DFRobot SEN0460, and the Sensirion SPS30, for which an adapted environmental chamber was built, and gaged using the Temtop M2000 2nd as a reference sensor (SRef). The main objective was to preliminarily assess the performance of the sensors, to select the most suitable ones for future research and their possible use in different work environments. The monitoring of PM2.5 and PM10 particles is essential to ensure the health of workers and avoid possible illnesses. This study is based on the comparison of the selected LCS with the SRef and the results of the comparison based on statistics. The results showed variations in the precision and accuracy of the LCS as opposed to the SRef. Additionally, it was found that the Sensirion SEN54 was the most suitable and valuable tool to be used to maintain a safe working environment and would contribute significantly to the protection of the workers' health.

Wearable Sensor Node for Safety Improvement in Workplaces: Technology Assessment in a Simulated Environment.

Personal protective equipment (PPE) has been universally recognized for its role in protecting workers from injuries and illnesses. Smart PPE integrates Internet of Things (IoT) technologies to enable continuous monitoring of workers and their surrounding environment, preventing undesirable events, facilitating rapid emergency response, and informing rescuers of potential hazards. This work presents a smart PPE system with a sensor node architecture designed to monitor workers and their surroundings. The sensor node is equipped with various sensors and communication capabilities, enabling the monitoring of specific gases (VOC, CO2, CO, O2), particulate matter (PM), temperature, humidity, positional information, audio signals, and body gestures. The system utilizes artificial intelligence algorithms to recognize patterns in worker activity that could lead to risky situations. Gas tests were conducted in a special chamber, positioning capabilities were tested indoors and outdoors, and the remaining sensors were tested in a simulated laboratory environment. This paper presents the sensor node architecture and the results of tests on target risky scenarios. The sensor node performed well in all situations, correctly signaling all cases that could lead to risky situations.

Calibration of CAMS PM2.5 data over Hungary: a machine learning approach

Air pollution is a major environmental problem, and reliable monitoring of particulate matter (PM) concentrations is critical for assessing its impact on human health and the environment. The Copernicus Atmosphere Monitoring Service (CAMS) offers vital data on PM2.5 concentrations by applying a worldwide modelling system. This study compares in situ PM2.5 measurements and raw CAMS data at 0.1° × 0.1° resolutions for 2019 and 2020 in Hungary. It proposes a calibration method to improve the accuracy of CAMS PM2.5 data at the scale of air monitoring stations. In the study, the accuracy of the raw CAMS PM2.5 data is assessed based on the chosen air quality stations. Then, to improve the precision, we employed machine learning algorithms (LightGBM, Random Forest (RF), and Multiple Linear Regression (MLR)) for calibration. Initial assessment of the raw CAMS PM2.5 data showed positive hourly Spearman correlation coefficient values (SR between 0.64 and 0.87 for the 14 air quality stations used), indicating a positive relationship between the datasets but a systemic underestimation. Our findings highlight LightGBM as the most effective method, consistently demonstrating elevated correlation SR and coefficient of determination R2 values reaching up to 0.95 and 0.93, respectively, and very good RSR (Root mean square error ratio) and NSE (Nash-Sutcliffe Efficiency) values (lower than 0.5 and higher than 0.75 for RSR and NSE, respectively). In contrast, RF yields mixed results, and MLR exhibits variable performance. By correcting underestimation and lowering modelling biases, the calibrated PM2.5 data better matches ground-based observations, which can be promising for using the obtained model for accurate estimation at individual air monitoring stations.

Data Evaluation of a Low-Cost Sensor Network for Atmospheric Particulate Matter Monitoring in 15 Municipalities in Serbia.

Conventional air quality monitoring networks typically tend to be sparse over areas of interest. Because of the high cost of establishing such monitoring systems, some areas are often completely left out of regulatory monitoring networks. Recently, a new paradigm in monitoring has emerged that utilizes low-cost air pollution sensors, thus making it possible to reduce the knowledge gap in air pollution levels for areas not covered by regulatory monitoring networks and increase the spatial resolution of monitoring in others. The benefits of such networks for the community are almost self-evident since information about the level of air pollution can be transmitted in real time and the data can be analysed immediately over the wider area. However, the accuracy and reliability of newly produced data must also be taken into account in order to be able to correctly interpret the results. In this study, we analyse particulate matter pollution data from a large network of low-cost particulate matter monitors that was deployed and placed in outdoor spaces in schools in central and western Serbia under the Schools for Better Air Quality UNICEF pilot initiative in the period from April 2022 to June 2023. The network consisted of 129 devices in 15 municipalities, with 11 of the municipalities having such extensive real-time measurements of particulate matter concentration for the first time. The analysis showed that the maximum concentrations of PM2.5 and PM10 were in the winter months (heating season), while during the summer months (non-heating season), the concentrations were several times lower. Also, in some municipalities, the maximum values and number of daily exceedances of PM10 (50 μg/m3) were much higher than in the others because of diversity and differences in the low-cost sensor sampling sites. The particulate matter mass daily concentrations obtained by low-cost sensors were analysed and also classified according to the European AQI (air quality index) applied to low-cost sensor data. This study confirmed that the large network of low-cost air pollution sensors can be useful in providing real-time information and warnings about higher pollution days and episodes, particularly in situations where there is a lack of local or national regulatory monitoring stations in the area.

Designing a low-cost wireless sensor network for particulate matter monitoring: Implementation, calibration, and field-test

Poor air quality can provoke severe impacts on health, necessitating environmental monitoring of atmospheric particulate matter (PM) to assess potential threats to human well-being. However, traditional continuous air quality monitoring systems are often costly and time-consuming in data treatment. Lately, there is a growing trend towards the use of low-cost wireless PM sensors, providing more detailed information than standard systems. This paper presents a system designed to measure air quality, specifically, a wireless sensor network composed of a distributed sensor network linked to a cloud system. The proposed system can efficiently measure air quality as it is cost-effective, small-sized, and consumes little power. Sensor nodes based on low-power long range (LoRa) motes transmit field measurement data to the cloud via a gateway, and a cloud computing system is implemented to store, monitor, process, and visualise the data. Advanced techniques were included in our cloud for data processing and analysis to optimise the detection of PM. Laboratory and field tests in the historic Riotinto mine validate the system's viability, offering real-time air quality information for nearby populations. Once calibrated, sensors demonstrate high accuracy, presenting mean error of −0.3% and low deviation (R2 = 0.96) when compared to regulatory systems for both low (<10 μgPM10/m3) and hazardous concentrations (300 μgPM10/m3), which makes them perfect as early warning systems for atmospheric pollution in mining.

Evaluation of atmospheric particulate matter pollution characteristics in Shanghai based on biomagnetic monitoring technology

Atmospheric particulate matter (PM) pollution is one of the world's most serious environmental challenges, and it poses a significant threat to environmental quality and human health. Biomagnetic monitoring of PM has great potential to improve spatial resolution and provide alternative indicators for large area measurements, with respect and complementary to standard air quality monitoring stations. In this study, 160 samples of evergreen plant leaves were collected from park green spaces within five different functional areas of Shanghai. Magnetic properties were investigated to understand the extent and nature of particulate pollution and the possible sources, and to assess the suitability of various plant leaves for urban particulate pollution monitoring. The results showed that magnetic particles of the plant leaf-adherent PM were predominantly composed of pseudo-single domain (PSD) and multi-domain (MD) ferrimagnetic particles. Magnolia grandiflora, as a large evergreen arbor with robust PM retention capabilities, proved to be a more suitable candidate for monitoring urban particulate pollution compared to Osmanthus fragrans, a small evergreen arbor, and Aucuba japonica Thunb. var. variegata and Photinia serratifolia, evergreen shrubs. Meanwhile, there were significant differences in the spatial distribution of the magnetic particle content and heavy metal enrichment of the samples, mainly showing regional variations of industrial area > traffic area > commercial area > residential area > clean area. Additionally, the combination with the results of scanning electron microscopy, shows that industrial production (metal smelting, coal burning), transport and other activities are the main sources of particulate pollution. Plant leaves can be used as an effective tool for urban particulate pollution monitoring and assessment of atmospheric particulate pollution characteristics, and the technique provided useful information on particle size, mineralogy and possible sources.

Peaks, sources, and immediate health impacts of PM2.5 and PM1 exposure in Indonesia and Taiwan with microsensors.

Microsensors have been used for the high-resolution particulate matter (PM) monitoring. This study applies PM and health microsensors with the objective of assessing the peak exposure, sources, and immediate health impacts of PM2.5 and PM1 in two Asian countries. Exposure assessment and health evaluation were carried out for 50 subjects in 2018 and 2019 in Bandung, Indonesia and for 55 subjects in 2019 and 2020 in Kaohsiung, Taiwan. Calibrated AS-LUNG sets and medical-certified RootiRx® sensors were used to assess PM and heart-rate variability (HRV), respectively. Overall, the 5-min mean exposure of PM2.5 and PM1 was 30.4 ± 20.0 and 27.0 ± 15.7 µg/m3 in Indonesia and 14.9 ± 11.2 and 13.9 ± 9.8 µg/m3 in Taiwan, respectively. The maximum 5-min peak PM2.5 and PM1 exposures were 473.6 and 154.0 µg/m3 in Indonesia and 467.4 and 217.7 µg/m3 in Taiwan, respectively. Community factories and mosquito coil burning are the two most important exposure sources, resulting in, on average, 4.73 and 5.82 µg/m3 higher PM2.5 exposure increments for Indonesian subjects and 10.1 and 9.82 µg/m3 higher PM2.5 exposure for Taiwanese subjects compared to non-exposure periods, respectively. Moreover, agricultural waste burning and incense burning were another two important exposure sources, but only in Taiwan. Furthermore, 5-min PM2.5 and PM1 exposure had statistically significantly immediate impacts on the HRV indices and heart rates of all subjects in Taiwan and the scooter subjects in Indonesia with generalized additive mixed models. The HRV change for a 10 µg/m3 increase in PM2.5 and PM1 ranged from -0.9% to -2.5% except for ratio of low-high frequency, with greater impacts associated with PM1 than PM2.5 in both countries. This work highlights the ability of microsensors to capture high peaks of PM2.5 and PM1, to identify exposure sources through the integration of activity records, and to assess immediate changes in heart rate variability for a panel of approximately 50 subjects in Indonesia and Taiwan. This study stands out as one of the few to demonstrate the immediate health impacts of peak PM, complementing to the short-term (days or weeks) or long-term effects (months or longer) assessed in most epidemiological studies. The technology/methodology employed offer great potential for researchers in the resource-limited countries with high PM2.5 and PM1 levels.

Multi-Criteria Optimization of a Laboratory Top-Lit Updraft Gasifier in Order to Reduce Greenhouse Gases and Particulate Matter Emissions

Air pollution from combustion processes is harming human health and the environment. To mitigate this, one needs to adopt cleaner energy production methods, in particular, to optimize combustion systems in order to minimize pollutants and increase efficiency. Flue gas analysis and particulate matter (PM) monitoring, starting from the prototype phase, is crucial to minimize and regulate pollutant emissions. This article analyses the emissions of pollutants and particulate matter from a combustion test gasifier working on the Top-Lit Updraft (TLUD) principle in order to optimize functionality and reduce exhaust emissions. Three experiments were performed in which the primary (gasification) air flow rate (GA) was kept constant at 25 L/min, and the secondary (combustion) air flow rate (CA) was adjusted to obtain a CA/GA ratio of 2 (50 L/min), 3 (75 L/min), and 4 (100 L/min) respectively. Based on a multi-criterial analysis, the optimal CA/GA ratio for TLUD combustion is 3, offering a well-rounded performance in output temperatures, PM and greenhouse gases (GHG) emissions, and efficiency, while the CA/GA ratio of 4 has good PM and GHG emissions performance but lower efficiency, and the CA/GA ratio of 2 is the least favorable due to its poor performance in output temperatures, PM and GHG emissions.

Particulate matter (pm10) monitoring in the United Arab Emirates using a satellite remote sensing based model

Particulate matter (PM) is one of the major factors causing air pollution, which is considered a concern for human health. Hence, measuring and monitoring the concentrations of these particles is essential. In this study, the main objective is to develop a remote sensing based PM10 monitoring model for the United Arab Emirates (UAE) using Landsat 8 imagery. Landsat 8 images acquired during the four-year period from 2016 to 2022 were obtained and used along with PM10 data collected at 41 ground monitoring stations corresponding to the acquisition of the satellite data (data from 30 stations used for model development 11 stations were used for model testing). The Landsat 8 data was obtained from the United States Geological Survey (USGS) Core Science Systems in the form of Digital Numbers (DNs). The DNs of the four optical bands of Landsat 8 were then converted to top of the atmosphere reflectance (TOA) through radiometric processing, and then used to estimate the Aerosol Optical Thickness. A spectral PM10 model was then developed through regression analysis, correlating AOT to PM10 values obtained at the ground stations. The model provided an R-squared value of 65% and a Root Mean Square Error (RMSE) of 12.55 µg/m3. The results suggest that the developed model is robust in estimating PM10 values and can therefore be used for satellite-based monitoring at any location in the UAE.

Calibration Methods for Low-Cost Particulate Matter Sensors Considering Seasonal Variability.

Many countries use low-cost sensors for high-resolution monitoring of particulate matter (PM2.5 and PM10) to manage public health. To enhance the accuracy of low-cost sensors, studies have been conducted to calibrate them considering environmental variables. Previous studies have considered various variables to calibrate seasonal variations in the PM concentration but have limitations in properly accounting for seasonal variability. This study considered the meridian altitude to account for seasonal variations in the PM concentration. In the PM10 calibration, we considered the calibrated PM2.5 as a subset of PM10. To validate the proposed methodology, we used the feedforward neural network, support vector machine, generalized additive model, and stepwise linear regression algorithms to analyze the results for different combinations of input variables. The inclusion of the meridian altitude enhanced the accuracy and explanatory power of the calibration model. For PM2.5, the combination of relative humidity, temperature, and meridian altitude yielded the best performance, with an average R2 of 0.93 and root mean square error of 5.6 µg/m3. For PM10, the average mean absolute percentage error decreased from 27.41% to 18.55% when considering the meridian altitude and further decreased to 15.35% when calibrated PM2.5 was added.

Analysis of measurement uncertainty for verification/calibration of particulate matter measuring instruments using the gravimetric method

As demand for particulate matter (PM) monitoring increases for scientific and regulatory purposes, the need for an accurate calibration process for these instruments will also increase. This article focuses on studying the factors that cause measurement uncertainty during the calibration process with the calibration facility (ManDust) for reference gravimetric instruments. Measurement uncertainty in this article included type A and type B uncertainties. Type A uncertainty mostly included repeatability and reproducibility while volume uncertainty predominately contributed to type B uncertainty. To clearly examine how these factors contributed to the combined uncertainty, the calibration process was carried out at a high concentration range from 0.4 to 1.6 mg m−3. In general, the measurement uncertainty ranged from 1.95% to 6.65%. Further analysis showed that type A uncertainty contributed more to the total uncertainty than type B. In particular, uncertainty from volume calculation and measurement contributed over 80% of type B uncertainty. During the PM filter weighing process, the uncertainty during the conditioning process contributed the most to type B uncertainty. This article also conducted a sensitivity analysis and found that besides volume measurement, the filter weighing procedure was also sensitive to changes even though it only contributed less than 5% of type B uncertainty. Using high-quality devices and careful handling during these two procedures should be emphasized to reduce the uncertainty of the calibration process.

Particulate Matter Pollution and Health Impacts in Six North African Countries

This paper addresses the issue of outdoor particulate matter pollution in six North African countries-Algeria, Egypt, Libya, Morocco, Sudan, and Tunisia. Major particulate matter sources include various industries and transportation systems, dust storms, and sandstorms. It has been shown that there is a need to increase public awareness and participation and enhance capacities regarding ground-based monitoring of particulate matter, using satellite data, and assessing the health impacts of exposure to particulate matter. There is also a need for improved legislation and better governance regarding its implementation and enforcement. If possible, examples of potential impacts of particulate matter pollution on the Gross National Product (GNP) for various economic and health sectors will be reported. As outdoor particulate matter pollution is one of the major sources of loss to the GNP, air quality management needs to be strengthened.

Evaluation of the effects of dust pollution on specific plant species near and around the marble mining site in Rajasthan, India.

Airborne particles (dust pollution) pose a significant threat to both human and plant populations. Plant leaves act as crucial biofilters, capturing significant amounts of air pollution; this characteristic offers a valuable tool to measure local pollution levels and assess individual plant species' ability to intercept and mitigate harmful dust particles. The present study was carried out to asses the effect of responses of various plant species to dust pollution near and around the marble mining site comprising residential site, highway area, and Central University of Rajasthan as control. The anticipated pollution index, air pollution tolerance index (APTI), dust absorption capacity, metal accumulation index (MAI), and biochemical factors were used to evaluate plant responses. Azadirachta indica A. Juss. demonstrated the highest (29.0) and Vachellia nilotica L. showed lowest (5.6) APTI, respectively. A. indica showed maximum MAI values in comparison to other plant species situated at residential site. Additionally, monitoring of particulate matter (PM10) observed to highest at highway, followed by mining, residential, and control sites. Overall A. indica representing highest APTI and effective dust capturing capacity at all sites could serve as potential pollution sinks. V. nilotica, with its very low APTI, can be marked as biomonitoring tool for detecting dust pollution.