PM10 Monitoring Research Articles

Comparing Large-Eddy Simulation and Gaussian Plume Model to Sensor Measurements of an Urban Smoke Plume

The fast prediction of the extent and impact of accidental air pollution releases is important to enable a quick and informed response, especially in cities. Despite this importance, only a small number of case studies are available studying the dispersion of air pollutants from fires in a short distance (O(1 km)) in urban areas. While monitoring pollution levels in Southampton, UK, using low-cost sensors, a fire broke out from an outbuilding containing roughly 3000 reels of highly flammable cine nitrate film and movie equipment, which resulted in high values of PM2.5 being measured by the sensors approximately 1500 m downstream of the fire site. This provided a unique opportunity to evaluate urban air pollution dispersion models using observed data for PM2.5 and the meteorological conditions. Two numerical approaches were used to simulate the plume from the transient fire: a high-fidelity computational fluid dynamics model with large-eddy simulation (LES) embedded in the open-source package OpenFOAM, and a lower-fidelity Gaussian plume model implemented in a commercial software package: the Atmospheric Dispersion Modeling System (ADMS). Both numerical models were able to quantitatively reproduce consistent spatial and temporal profiles of the PM2.5 concentration at approximately 1500 m downstream of the fire site. Considering the unavoidable large uncertainties, a comparison between the sensor measurements and the numerical predictions was carried out, leading to an approximate estimation of the emission rate, temperature, and the start and duration of the fire. The estimation of the fire start time was consistent with the local authority report. The LES data showed that the fire lasted for at least 80 min at an emission rate of 50 g/s of PM2.5. The emission was significantly greater than a ‘normal’ house fire reported in the literature, suggesting the crucial importance of the emission estimation and monitoring of PM2.5 concentration in such incidents. Finally, we discuss the advantages and limitations of the two numerical approaches, aiming to suggest the selection of fast-response numerical models at various compromised levels of accuracy, efficiency and cost.

Effect of geographical parameters on PM10 pollution in European landscapes: a machine learning algorithm-based analysis

BackgroundPM10, comprising particles with diameters of 10 µm or less, has been identified as a significant environmental pollutant associated with adverse health outcomes in European cities. Understanding the temporal variation of the relationship between PM10 and geographical parameters is crucial for sustainable land use planning and air quality management in European landscapes. This study utilizes Conditional Inference Forest modeling and partial correlation to examine the impact of geographical factors on monthly average concentrations of PM10 in European suburban and urban landscapes during heating and cooling periods. The investigation focuses on two buffer zones (1000 m and 3000 m circle radiuses) surrounding 1216 European air quality monitoring stations.ResultsResults reveal importance and significant correlations between various geographical variables (soil texture, land use, transportation network, and meteorological) and PM10 quality on a continental scale. In suburban landscapes, soil texture, temperature, roads, and rail density play pivotal roles, while meteorological variables, particularly monthly average temperature and wind speed, dominate in urban landscapes. Urban sites exhibit higher R-squared values during both cooling (0.41) and heating periods (0.61) compared to suburban sites (cooling period R-squared: 0.39; heating period: R-squared: 0.51), indicating better predictive performance likely attributed to the less heterogeneous land use patterns surrounding urban PM10 monitoring sites.ConclusionThe study underscores the importance of investigating spatial and temporal dynamics of geographical factors for accurate PM10 air quality prediction models in European urban and suburban landscapes. These findings provide valuable insights for policymakers, urban planners, and environmental scientists, guiding efforts toward sustainable and healthier urban environments.

Environmental injustice in the air quality for digital platform delivery workers in Bogotá, Colombia, 2021

Air quality is a matter of interest for public health due to its rapid deterioration in low- and middle-income countries and the effects of polluted air on the health of populations. To explore the air quality conditions in which digital platform delivery workers carry out their work, evaluating the localities of Kennedy and Usaquén in Bogotá, 2021. We developed a mixed parallel convergent study based on four sources of information: 1) Ethnographic observation in five commercial locations of the two localities; 2) Monitoring of PM10 and PM2.5 in 56 delivery routes using a low-cost sensor; 3) Daily logs of the routes to support the device data interpretation, and 4) A semi-structured interview applied to the drivers to explore their danger perception during the routes. We identified elements causing environmental injustice among digital platform delivery workers between the two study locations. The routes made by the delivery drivers in the locality of Kennedy registered higher concentrations of PM10 and PM2.5, compared to the values observed in Usaquén. The sources of air pollution identified by the delivery drivers through ethnographic observation and the router logbook showed the worst parameters in Kennedy. We evidenced that air quality, urban equipment, road infrastructure, mobile sources, and geospatial location are elements that mark the presence of environmental injustice for the digital platform delivery drivers in the studied localities. To reduce this inequity, it is necessary for digital delivery platforms and the district government to implement strategies that reduce the exposure and emission of air pollutants to protect the health of digital platform delivery workers.

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.

Assessing the Impact of Spatial and Temporal Variability in Fine Particulate Matter Pollution on Respiratory Health Outcomes in Asthma and COPD Patients.

Ambient air pollution's health impacts are well documented, yet the domestic environment remains underexplored. We aimed to compare indoor versus outdoor (I/O) air quality and estimate the association between indoor/ambient fine particulate matter (PM2.5) exposure and lung function in asthma and chronic obstructive pulmonary disease (COPD) patients. The study involved 24 h monitoring of PM2.5 levels indoors and outdoors, daily peak expiratory flow (PEF), and biweekly symptoms collection from five patients with asthma and COPD (average age of 50 years, 40% male) over a whole year. Data analysis was performed with linear mixed effect models for PEF and generalized estimating equations (GEE) for exacerbations. More than 5 million PM2.5 exposure and meteorological data were collected, demonstrating significant I/O PM2.5 ratio variability with an average ratio of 2.20 (±2.10). Identified indoor PM2.5 sources included tobacco use, open fireplaces, and cooking, resulting in average indoor PM2.5 concentrations of 63.89 μg/m3 (±68.41), significantly exceeding revised World Health Organization (WHO) guidelines. Analysis indicated a correlation between ambient PM2.5 levels and decreased PEF over 0-to-3-day lag, with autumn indoor exposure significantly impacting PEF and wheezing. The study underscores the need to incorporate domestic air quality into public health research and policy-making. A personalized approach is required depending on the living conditions, taking into account the exposure to particulate pollution.

Natural and anthropogenic impact on the microclimate and particulate matter in the UNESCO show cave

Particulate matter concentrations (PM10, PM2.5, PM1) and microclimatic parameters (air temperature, CO2) were monitored in the Škocjan Caves (Slovenia). The effects of tourist visits on the PM concentrations and the cave’s microclimate are immediate and direct, but these values normalise relatively quickly. The results showed seasonal, diurnal, and spatial differences in all parameters studied. Due to the higher number of visitors, their influence on the cave’s microclimate and PM10 and PM2.5 concentrations is greater in summer than in winter. The measured PM1 levels depend on the ventilation in the cave, as air transport plays an important role in their introduction into the cave. PM consists of minerals of natural origin resulting from the re-suspension of cave sediments due to strong air currents generated by the opening of the doors to tourists and their walks. The second most common influence is the anthropogenic phases originating from maintenance work in the cave, electronic devices, cave lighting and emissions from outside the cave (aerosols from the polluted Reka River, industry, traffic, gypsum waste disposal). In order to upgrade the sustainable use of the UNESCO-listed Škocjan Caves for tourism, we propose regular monitoring of PM and a detailed characterization of individual PMs and their sources, in addition to monitoring of the cave’s microclimate and biology.

Bioavailable fractions of heavy metals in the road dust during infrastructure construction at urban Coimbatore and its potential health implications, India.

Over the past two decades the Global South is witnessing unprecedented economic transformation and Asian Cities in particular have a remarkable upsurge. Coimbatore, an industrial city in Southern India with an estimated population of 2 million (in 2022) is witnessing a rapid transition in terms of infrastructure development. In this context, the present study attempts to assess the particulate matter (PM10 and PM2.5) emissions at road network construction sites and the heavy metal fractionation in the road dust/sediment samples with a core focus to quantify the bioavailable fraction of metals (Fe, Cu, Cr, Cd, Pb and Ni) and its source apportionment in the road side dust/sediment samples. About 60 composite road dust/sediment samples were collected for heavy metal fractionation analysis in the six arterial roads that undergo core developments like construction of road over bridges, additional road incorporation and street expansions. PM monitoring revealed that 24h average PM2.5 (47µg/m3) and PM10 (69µg/m3) concentrations at many construction sites exceeded 24h average recommended by WHO guidelines [PM2.5 (15µg/m3) and PM10 (45µg/m3), respectively]. The bioavailable fractions of Fe, Cu, Cr and Cd are notably higher in the roadside sediment samples at road construction sites. Health Risk assessment, such as carcinogenic risks (Children-4.41 × 10-2, Adult-3.598 × 10-6) and non-carcinogenic risks, inferred substantial risks at high intensity construction sites with statistical analyses, including PCA and cluster analysis, indicating considerable anthropogenic influences in the heavy metal fractions.

Machine learning models application for spatiotemporal patterns of particulate matter prediction and forecasting over Morocco in north of Africa

Atmospheric air pollution exposure raises morbidity and mortality rates and is a major cause of the world's illness burden. In this context, we explored spatial and temporal trends in particulate matter PM10 from 2003 to 2020 over Morocco to assess air pollution exposure. We use the capabilities of ML models to study PM10 trends using 26 predictor variables, including meteorological parameters, volatile organic compounds, atmospheric oxidants, and aerosol optical depth data from the Copernicus Atmosphere Monitoring Service (CAMS). For this purpose, three ML models were built: Multiple Linear Regression (MLR), Random Forest Regression (RFR), and Generalized Additive Model (GAM). To match and optimize these models, a set of ML algorithms has been coupled with each model. The results show all these models are highly accurate in predicting and forecasting PM10 total column trends. Cross-validation showed that GAM had better prediction ability for the PM10 total column with R2 = 0.994 and a very low root mean squared error (RMSE) not exceeding 0.046 × 10−16 kg/m2. The GAM model showed much higher predictive ability and lower bias than the other models. This finding can be explained by the advantages of GAMs, including their ability to capture complex and non-linear patterns in the data, making them particularly useful when relationships are not easily represented by linear models. This study has presented a comprehensive methodology for predicting the spatiotemporal variability of PM10. The proposed methodology holds potential applicability across all regions, facilitating the generation of high-resolution PM10 monitoring and the establishment of systems for the early detection of air pollution incidents in Morocco. Furthermore, the developed models exhibit versatility, enabling their application for estimating future trends of individual pollutants or making real-time predictions of air quality levels. This research contributes to advancing the understanding and proactive management of air quality in the context of Morocco, offering valuable insights for pollution control efforts.

Assessing the Nonlinear Relationship between Land Cover Change and PM10 Concentration Change in China

Inhalable particulate matter (PM10) is a major air pollutant that has significant impacts on environmental climate and human health. Land-cover change is also a key factor influencing changes in atmospheric pollution. Changes in land-cover types can lead to changes in the sources and sinks of air pollutants, thus affecting the spatial distribution of PM10, which poses a threat to human health. Therefore, exploring the relationship between PM10 concentration change and land-cover change is of great significance. In this study, we constructed an extreme randomized trees model (ET) based on ground PM10 monitoring data, satellite-based aerosol optical depth (AOD) data, and auxiliary data including meteorological, vegetation, and population data to retrieve ground-level PM10 concentrations across China. The coefficient of determination (R2), the mean absolute error (MAE), and the root mean square error (RMSE) of the model were 0.878, 5.742 μg/m3, and 8.826 μg/m3, respectively. Based on this, we analyzed the spatio-temporal distribution of PM10 concentrations in China from 2015 to 2021. High PM10 values were mainly observed in the desert areas of northwestern China and the Beijing–Tianjin–Hebei urban agglomeration. The majority of China showed a significant decrease in PM10 concentrations. Additionally, we also analyzed the nonlinear response mechanism of the PM10 concentration change to land-cover change. The PM10 concentration is sensitive to forest and barren land change. Therefore, strengthening the protection of forests and desertification control can significantly reduce air pollution. Attention should also be paid to emission management in agricultural activities and urbanization processes.

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.

Potential of low-cost PM monitoring sensors to fill monitoring gaps in areas of Sub-Saharan Africa

Recent advances in low-cost (LC) sensor technology fostered their deployment in low-income and undersampled countries such as Sub-Saharan Africa (SSA) regions, affected by the highest particulate matter (PM) concentrations and population exposure. The present study is the first addressed in Niamey, Niger, and focuses on assessing LC sensor data and global reanalysis products. Three LC PM2.5 and PM10 monitoring stations were deployed and successfully operated across ∼8 months at different (urban, suburban and rural) locations.Observed PM2.5 and PM10 concentrations revealed consistent patterns, higher during the dry Harmattan season, while appreciably lower during the humid monsoon season. In Niamey, PM2.5 mean concentrations (6.1–20.1 μg/m3) were similar to those observed over higher-income countries, confirming the hypothesis of strictly depending on urbanisation, and thus on anthropogenic activities. Conversely, PM10 concentrations (55.3–142.8 μg/m3) were remarkably higher than most of those measured elsewhere worldwide, and predominantly constituted (81–89%) by coarse fraction. PM10 origin, inferred by backtrajectory analysis, was mainly natural (Saharan dust) during the Harmattan season, and both natural and anthropogenic during the monsoon season.Low-resolution gridded estimations by the Copernicus Atmosphere Monitoring Service (CAMS) were not capable of adequately resolving the spatial variability of PM2.5 and PM10 observations in Niamey, further highlighting the importance of sensor network data to improve air quality knowledge. To tackle the harmful effects of Saharan dust on population, and create robust datasets integrated with gridded products in this challenging region, effort should be put toward creation of trans-national integrated monitoring networks based on LC sensors across SSA.

PM2.5 and O3 in an Enclosed Basin, the Guanzhong Basin of Northern China: Insights into Distributions, Appointment Sources, and Transport Pathways

Aerosol samples (PM2.5) were collected in Xi’an (XN) from 11 August to 11 September 2021 and in Qinling (QL) from 14 July to 24 August 2021, respectively. In addition, ozone (O3) data were collected in order to investigate the characteristics and source areas of PM2.5 and O3 in the Guanzhong Basin (GB). The concentrations of PM2.5, organic carbon (OC), and elemental carbon (EC) in XN (53.40 ± 17.42, 4.61 ± 2.41, and 0.78 ± 0.60 μg m−3, respectively) were higher than those in QL (27.57 ± 8.27, 4.23 ± 1.37, and 0.67 ± 0.53 μg m−3, respectively) in summer. Total water-soluble ions (TWSIIs) accounted for 19.40% and 39.37% of the PM2.5 concentrations in XN and QL, respectively. O3 concentrations in summer were 102.44 ± 35.08 μg m−3 and 47.95 ± 21.63 μg m−3 in XN and QL, respectively, and they showed a significant correlation with Ox. The positive matrix factorization (PMF) model identified three main sources in XN and QL, including coal combustion source (COB), secondary aerosol (SA), and dust sources (DUSs). The potential source contribution function (PSCF) and a concentration weight trajectory (CWT) model with back-trajectory analysis showed that Inner Mongolia, the interior of Shaanxi, and nearby areas to the southwest were the sources and source areas of carbonaceous matter in XN and QL. The results of this study can contribute to the development of prevention and control policies and guidelines for PM2.5 and O3 in the GB. Furthermore, long-term and sustainable measuring and monitoring of PM2.5 and O3 are necessary, which is of great significance for studying climate change and the sustainable development of the environment.

Physio-chemical analysis of pollutants in residential micro-environment through continuous monitoring and settled dust characterization

The concern about indoor air quality in various micro-environments has been on the rise as buildings are vulnerable to an array of contaminants generated from various sources. The study aims to investigate and highlight the pollutant sources and composition in middle-income residential spaces through a case study approach. With large migration from small towns and saturation of megacities, the middle-income population in tier-2 cities in India is on the rise, raising concern about the well-being of the dense urban population. It involves a careful selection of typical MIG housing to identify major pollution sources from outdoor and indoor environments. The focus lies in the findings stemming from continuous air quality monitoring of PM10, PM2.5, SO2, NO2, HCHO, TVOC, CO and C6H6 and an in-depth physio-chemical analysis facilitated by Fourier transform infrared (FTIR), Analytical Scanning Electron Microscope (A-SEM) and energy-dispersive X-ray (EDX) spectroscopy of settled household dust. Analysis unveiled indoor PM2.5 concentrations of 21.93 µg/m3, exceeding WHO standards, whereas, TVOC concentrations were higher indoors than outdoors. Stark disparities between indoor and outdoor dust compositions and sources were also observed. Despite limitations, it provides a portrait of middle-income housing conditions, offering insights for interventions and future research on indoor air quality challenges.

Representativeness of the US EPA PM monitoring site locations to the US population: implications for air pollution prediction modeling.

Air pollution prediction modeling establishes relationships between measurements and geographical and meteorological characteristics to infer concentrations at locations without measurements. Since air pollution monitors are limited in number, predictions may be generated for locations different than those used to train the model. The epidemiologic impacts of this potential mismatch hinge on whether the population resides in areas well-represented by monitoring sites. Here we quantify the fraction of the population with geographical characteristics not reflected by the 2000, 2010, and 2020 EPA PM2.5 and PM10 regulatory sites. We evaluated this measure nationwide, regionally, and by race. Nationally, the networks were very representative of the population experience; however, there was less overlap regionally and in regions stratified by race. This suggests that sub-national exposure modeling should carefully consider the representativeness of monitors for their populations. It also highlights that exposure models often borrow information from distal places to predict full population exposure.

Elemental analysis of hourly collected air filters with X-ray fluorescence under grazing incidence.

The X-ray fluorescence under grazing incidence condition (XRF-UGI) was applied for the direct analysis of aerosol filters. Particulate matter less than 2.5 microns (PM2.5) was collected hourly on polytetrafluoroethylene filters using a continuous PM monitor with a virtual impactor method. Although the sampling mass is in trace amounts of 5-30μg, the metallic contents, such as V, Cr, Mn, Fe, Zn, and Pb, can be measured at sub-ng m-3 detection limits. The effects of the non-uniformity and poor flatness of the PM filters were discussed with regard to the measurement repeatability. The relationship between the XRF-UGI intensities and the mass concentrations obtained via conventional X-ray fluorescence (XRF) analysis was confirmed using the fundamental parameter method. Finally, quantification was successfully demonstrated using the XRF-UGI results with the relative sensitivity factors.

APPLICATION OF DOMESTIC GAS ANALYZERS FOR MEASURING PM CONCENTRATION IN ATMOSPHERIC AIR

Environmental monitoring is one of the key elements to ensure the most effective security measures in various areas of life. The most famous technology in this field is the gas detection system within the framework of atmospheric air monitoring. It serves not only for an early warning program, but also for a constant check of the state of the atmosphere in certain areas. In the guidelines of the World Health Organization (WHO), updated in 2005, solid particles (dust or suspended substances) are among the main indicators of atmospheric air quality. According to the requirements of Directive 2008/50/EC of the European Parliament and the Council of 05.21.2008 on atmospheric air quality and cleaner air for Europe Ukraine should establish zones and agglomerations according to the degree of atmospheric air pollution throughout its territory, as well as the procedure for their review. In general, the goal set by the Directive is extremely difficult for Ukraine ‑ to create a European monitoring system with modern equipment, which is a reliable tool for the development and implementation of environmental policy, on the remains of the air pollution control network that was created back in Soviet times. Nowadays gas sensors can be divided into two categories: conductive metal oxides and electrochemical, with the latter gaining more recognition over the last decade as the future standard component in any gas sensor. The paper presents the complete concept of an electrochemical gas sensor, including the basic science and basic electronic engineering that go into the device. The main focus is on a specific type of amperometric sensors as the most promising electrochemical sensor on the Ukrainian market. That is why the scientific novelty of the article lies in the fact that for the first time, on the basis of analysis and theoretical generalizations, it is proposed to create a program for the production of devices specifically for continuous automatic monitoring of atmospheric air quality. The practical value of the article lies in the systematization of scientific ideas regarding the monitoring of PM, which should become the basis for initial measures aimed at reducing emissions, which will increase the efficiency of management decision-making and improve the efficiency of the DSMD at all levels.

Indoor environmental conditions of selected shopping malls in Nigeria: A comparative study of microclimatic conditions, noise levels, and microbial burdens

The activities of people and equipment used within shopping malls are major factors that contribute to air pollution and increased sound levels, thereby affecting indoor environmental quality and the well-being of mall operators. This study assessed indoor environmental quality through microbial characterization and measurement of environmental conditions present in selected shopping malls. Investigations were conducted at three shopping malls in Ibadan selected through convenience sampling technique. Environmental parameters such as noise level, relative humidity, temperature, PM2.5 levels, total volatile organic compound (TVOC) levels, microbial characterization, and quantity were determined. Microclimatic parameters (temperature and relative humidity) were measured using a 4-in-1 Precision Gold N09AQ multi-tester. Culturable airborne microbes were collected using the settle plate technique. PM2.5 and TVOC levels were measured using a Thermo Scientific MIE pDR-1500 PM monitor and sf200-TVOC meter respectively. Two bacteria species and five fungi species were isolated across the malls. The noise levels ranged from 61.27 to 81.20 dB. The mean temperatures (highest mean of 33.44 ± 1.42 °C), PM2.5 (highest mean of 114.06 ± 25.64 μg/m3), and TVOC (highest mean of 55.21 ± 8.28 ppm) concentrations were higher than the permissible limits stipulated by the WHO guidelines and NESREA standard limits across all the selected malls. A positive correlation was found to exist between particulate matter and TVOC (r = 0.174, p = 0.004). The total bacteria count was generally high with the highest mean of 1965.33 ± 368.56 CFU/m3, while the total fungi count was generally low with the highest mean of 579.82 ± 51.55 CFU/m3. Bacillus spp. and Candida spp. were found to the consistent from all sample points across the three malls. The bacteria isolated are Gram-positive bacteria associated with human skin which suggests a high rate of indoor pollution from humans. In conclusion, this research has demonstrated the necessity to monitor noise levels and indoor air quality in malls. Also, there is need for government policies to improve indoor air quality which must be enforced and regulated, especially within shopping malls.

Effects of land use patterns on PM10 concentrations in urban and suburban areas. A European scale analysis

Landscape structure, the spatial characteristic of land cover, is a key factor in air quality. In this study, we investigate the connection between different land-use category areas taken from the 2018 Urban Atlas and the European Environmental Agency's monthly average coarse particulate matter (PM10) concentrations. We identified the main PM10 emission-source land-use categories that restrict the transmission of PM10 and therefore reduce PM10 concentrations in urban and suburban areas. According to our results, water, forest and urban park areas inside differently sized buffer zones surrounding PM10 monitoring stations have an obvious clearing effect on PM10 concentrations throughout almost all seasons, while there is a positive correlation between areas of vacant land, railways and mine, dump and construction sites and monthly PM10 concentrations. In addition, the strong effect of the correlation between built-up areas, industrial areas and roads and monthly average PM10 concentrations changes seasonally. Air pollution from motor vehicles shows a significant positive statistical relationship with PM10 concentrations only in the summer, but in contrast, during the heating (cold) period, because of motor vehicle driving (secondary) winds, traffic corridors have a significant decreasing effect on PM10 concentrations. By understanding the effect of different land use/land cover patterns on PM10 concentrations and variability, we can derive precise spatial urban planning strategies that are adaptable for health care.

Quantifying the contributions of natural and anthropogenic dust sources in Shanxi Province, northern China

Dust storms have direct or indirect impacts on climate change and human health. Identifying and quantifying natural/anthropogenic dust sources can facilitate effective prevention and control of dust events. Based on surface real-time PM10 monitoring data, satellite remote sensing and the HYSPLIT model, this study determined the specific timing, coverage and sources of dust events in Shanxi Province, Northern China. Thus, a composite fingerprinting technique was established to quantify potential dust sources and dust contributions of single dust events. The dust oxidation model was validated, indicating that the composite fingerprinting technique was well suited to the study region. The results show that natural dust sources (67%) contributed more to the study region than anthropogenic dust sources. They were mainly from the northwest and north of the study region. Particularly, the contributions of Taiyuan (TY) and Linfen (LF) accounted for the largest (82%) and smallest (55%) proportions, respectively, both exceeding 50%. Anthropogenic dust sources contributed 33%, mainly from the east and south of the study region. The contribution of anthropogenic dust sources increased in the study region from north to south. In terms of potential dust sources, the Tengger Desert and Badain Jaran Desert (TDBD) contributed the most (26%), followed by the Otindag Sandy Land (OL) (22%). The Taklimakan Desert (TD) contributed the least (2%). The Middle Farmland region of the Hexi Corridor (HMF) in the west (15%) had the largest proportion of anthropogenic dust sources. Differences in the regional contribution of potential dust sources mainly resulted from winter winds, surface drought severity and particle size. At an insignificant distance from the study region, the contribution of potential dust sources was larger in the west than in the east and increased from south to north overall. These methods and findings can contribute to improving the ecological environment in Northern China.

A mobile car monitoring system as a supplementary tool for air quality monitoring in urban and rural environments: the case study from Poland

Living in healthy environment should be regarded as a primary human right and not a privilege rendered to chosen ones. For that reason, a national air monitoring grid should be as extensive as possible. Unfortunately, small towns and villages, which are also exposed to air pollution episodes are not commonly covered by monitoring grid. Fixed air monitoring systems have their limitations, which can be overcome by e.g., properly validated, reliable but cheaper mobile monitoring systems. The aim of this study was to assess the use of a car mobile PM10 monitoring system to study ambient air quality in rural communities surrounding Kraków, not covered by fixed monitoring grid. A monitoring dataset was collected during 6 winter car campaigns conducted between December 2021 and March 2022. Except for providing multiple monitoring data, the main goal pertain to methodological aspect of the mobile system, including its validation, indicating its advantages, limitations as well as providing recommendations for the future mobile studies. Our car mobile monitoring system was useful in identifying a local hot-spots with good spatial and temporal resolution, thus giving the local government decision-making tool for taking appropriate action in places not included in national monitoring grid.