PM10 Ratio Research Articles

Influence of climatic conditions on particulate emissions in mixed traffic conditions across various pavement types

Non-exhaust emissions containing particulate matter (PM), as indicated by PM2.5 and PM10, poses significant health, environmental, and climatic risks. This study investigates the impact of temperature and humidity on PM concentrations across different pavement types (asphalt and cement concrete) under mixed traffic conditions. Utilizing a sensor based portable air quality monitor, air quality data were collected over multiple seasons, encompassing winter, pre-monsoon, monsoon, and post-monsoon periods. The results reveal a strong correlation between PM levels and meteorological conditions, with higher temperatures and lower humidity contributing to elevated PM concentrations, particularly during the pre-monsoon season. Conversely, monsoon rains effectively reduce PM levels. The study also highlights the variations in the PM2.5/PM10 ratio between pavement types, with cement concrete showing a consistently higher ratio, indicating a greater prevalence of fine particulates. These findings underscore the necessity of season-specific air quality management strategies, considering the influence of both pavement type and environmental factors on PM emissions.

Comprehensive analysis of particulate matter, gaseous pollutants, and microbiological contamination in an international chain supermarket

Indoor environmental quality is of utmost importance since urban populations spend a large proportion of their life in confined spaces. Supermarkets offer a wide range of products and services that are prone to emitting several air pollutants. This study aimed to perform a comprehensive characterisation of the indoor and outdoor air quality in a multinational supermarket, encompassing not only criteria parameters but also unregulated pollutants of concern. Monitoring included measurements of comfort parameters, CO2, multiple gaseous pollutants, particulate matter (PM10) and bioburden. PM10, volatile organic compounds (VOCs) and carbonyls were subject to chemical speciation. Globally, the supermarket presented CO2, VOCs, and PM10 values below the limits imposed by international regulations. The PM10 concentration in the supermarket was 33.5 ± 23.2 μg/m3, and the indoor-to-outdoor PM10 ratio was 1.76. Carbonaceous constituents represented PM10 mass fractions of 21.6% indoors and 15.3% outdoors. Due to the use of stainless-steel utensils, flour and fermentation processes, the bakery proved to be a pollution hotspot, presenting the highest concentrations of PM10 (73.1 ± 9.16 μg/m3), PM10-bound elements (S, Cl, K, Ca, Ti, and Cr) and acetaldehyde (42.7 μg/m3). The maximum tetrachloroethylene level (130 μg/m3) was obtained in the cleaning products section. The highest values of colony-forming units of bacteria and fungi were recorded in the bakery, and fruit and vegetable section. The most prevalent fungal species was Penicillium sp., corresponding to 56.9% of the total colonies. In addition, other fungal species/sections with toxicological or pathogenic potential were detected (Aspergillus sections Aspergilli, Circumdati, Flavi, Mucor and Fusarium sp.).

Sufficient sleep and physical activity can relieve the effects of long-term exposure to particulate matter on depressive symptoms among 0.31 million children and adolescents from 103 counties in China.

Although long-term exposures to air pollutants have been linked to mental disorders, existing studies remain limited and inconsistent. We investigated the relationship between exposure to particulate matter (PM) and depressive symptoms, as well as the potential role of sleep duration and physical activity. Using the surveillance data (2019 to 2022) of common diseases and risk factors among 312,390 students aged 10-25years, logistic regression, generalized liner model (GLM) and restricted cubic spline (RCS) were employed to investigate the relationship between long-term exposure to PM and depressive symptoms. Significant associations were found between PM1 (OR=1.21, 95% CI: 1.12-1.32), PM2.5 (OR=1.24, 95% CI: 1.19-1.38), and PM10 (OR=1.87, 95% CI: 1.69-2.07) and increased risks of depressive symptoms. Sleep duration and physical activity relieved these associations. The odds ratios (ORs) of PM1, PM2.5, and PM10 on depressive symptoms were lower in group with sufficient sleep (1.02 vs. 1.49, 1.20 vs. 1.80, 2.15 vs. 2.23), lower in group with high level MVPA (1.13 vs. 1.48, 1.14 vs. 1.58, 1.85 vs. 2.38), and lower in group with high level outdoor activity (1.19 vs. 1.55, 1.23 vs. 1.63, 1.83 vs. 2.72). Conclusions about causality remain speculative because of the cross-sectional design. Sufficient sleep duration and outdoor activity may mitigate the decline in mental health among adults in developing countries caused by long-term exposure to PM. This contribution enhanced our understanding of the mechanisms linking air pollution to mental health.

Enhancing indoor PM2.5 predictions based on land use and indoor environmental factors by applying machine learning and spatial modeling approaches

The presence of fine particulate matter (PM2.5) indoors constitutes a significant component of overall PM2.5 exposure, as individuals spend 90% of their time indoors; however, personal monitoring for large cohorts is often impractical. In light of this, this study seeks to employ a novel geospatial artificial intelligence (Geo-AI) coupled with machine learning (ML) approaches to develop indoor PM2.5 models. Multiple predictor variables were collected from 102 residential households, including meteorological data; elevation; land use; indoor environmental factors including human activities, building characteristics, infiltration factors, and real-time measurements; and various other factors. Geo-AI, which integrates land use regression, inverse distance weighting, and ML algorithms, was utilized to construct outdoor PM2.5 and PM10 estimates for residential households. The most influential variables were identified via correlation analysis and stepwise regression. Three ML methods, namely support vector machine, multiple linear regression, and multilayer perceptron (MLP) were used to estimate indoor PM2.5 concentration. Then, MLP was employed to blend three ML algorithms. The resulting model demonstrated commendable performance, achieving a 10-fold cross-validation R2 of 0.92 and a root mean square error of 2.3 μg/m3 for indoor PM2.5 estimations. Notably, the combination of Geo-AI and ensembled ML models in this study outperformed all other individual models. In addition, the present study pointed out the most influential factors for indoor PM2.5 model were outdoor PM2.5, PM2.5/PM10 ratio, sampling month, infiltration factor, located near factory, cleaning frequency, number of door entrance linked with outdoor, and wall material. Further exploration of diverse ensemble model formats to integrate estimates from different models could enhance overall performance. Consequently, the potential applications of this model extend to estimating real individual exposure to PM2.5 for further epidemiological research. Moreover, the model offers valuable insights for efficient indoor air quality management and control strategies.

A probabilistic framework for identifying anomalies in urban air quality data.

Just as the value of crude oil is unlocked through refining, the true potential of air quality data is realized through systematic processing, analysis, and application. This refined data is critical for making informed decisions that may protect health and the environment. Perhaps ground-based air quality monitoring data often face quality control issues, notably outliers. The outliers in air quality data are reported as error and event-based. The error-based outliers are due to instrument failure, self-calibration, sensor drift over time, and the event based focused on the sudden change in meteorological conditions. The event-based outliers are meaningful while error-based outliers are noise that needs to be eliminated and replaced post-detection. In this study, we address error-based outlier detection in air quality data, particularly targeting particulate pollutants (PM2.5 and PM10) across various monitoring sites in Delhi. Our research specifically examines data from sites with less than 5% missing values and identifies four distinct types of error-based outliers: extreme values due to measurement errors, consecutive constant readings and low variance due to instrument malfunction, periodic outliers from self-calibration exceptions, and anomalies in the PM2.5/PM10 ratio indicative of issues with the instruments' dryer unit. We developed a robust methodology for outlier detection by fitting a non-linear filter to the data, calculating residuals between observed and predicted values, and then assessing these residuals using a standardized Z-score to determine their probability. Outliers are flagged based on a probability threshold established through sensitivity testing. This approach helps distinguish normal data points from suspicious ones, ensuring the refined quality of data necessary for accurate air quality modeling. This method is essential for improving the reliability of statistical and machine learning models that depend on high-quality environmental data.

Two distinct early Paleozoic subduction zones in the eastern Central Asian Orogenic Belt: Evidence of subduction recycling and arc evolution

This paper presents zircon U–Pb dating and Hf isotopic, and whole-rock geochemical and Sr–Nd isotopic data for early Paleozoic igneous rocks as well as zircon U–Pb dating for the sedimentary rocks of the Songnen Massif within the eastern Central Asian Orogenic Belt (CAOB), aiming to reveal arc magma evolution process. The zircon U–Pb dating results of the siltstone of the Xiaojingou Formation yielded 450 Ma, 471 Ma, 502 Ma, 773 Ma, 818 Ma and 949 Ma age peaks, as well as multi-episodic Paleo-proterozoic ages (2486-1612 Ma), implying its deposition time is younger than ∼ 450 Ma. The zircon U–Pb dating results reveal that the Songnen Massif exists three episodes of volcanic rocks of the middle Cambrian (502 Ma), Middle Ordovician (453-449 Ma) and the Silurian (435-420 Ma). The middle Cambrian basalts are low-K tholeiitic to calc-alkaline series, weakly depleted εHf(t) and εNd(t) values. They were derived from the weakly depleted mantle metasomatized by slab fluid in an immature island arc setting. The Middle Ordovician to Silurian volcanic rocks show SiO2 of 49.4–58.1 wt%, plotting into high-K calc-alkaline series, which are also featured by higher Th/Nd and (Hf/Sm)PM ratios, together with moderate depleted εNd(t) values, suggesting their derivation of the depleted mantle wedge metasomatized by sediment-derived melt. In contrast, coeval early Paleozoic volcanic rocks in the eastern Xing’an Massif with the MORB-like εNd(t) values are characterized by an origin of the depleted mantle wedge and the distinct interaction of oceanic crust melt and mantle peridotite. Combined with the variation of calculated crust thickness, we suggested that there was a continental arc in the eastern Songnen Massif, which developed from immaturity to maturity during middle Cambrian to Silurian, while the early Paleozoic island arc in the eastern Xing’an Massif is characterized by ocean ridge subduction process.

Assessing the spatial transferability of calibration models across a low-cost sensors network

Low-cost sensor networks (LCSNs) are expanding worldwide to gather high spatiotemporal resolution data due to their economic feasibility and compact size. The reliability of LCS-recorded data is limited due to their calibration dependencies in the field. Previous studies have focused on the development of LCS calibration models by co-location with the regulatory monitoring stations. However, it is challenging to calibrate LCS in the field for countries with limited infrastructure for air quality monitoring, pointing towards the need for transferable calibration models. Only a few studies have addressed this challenge and provide no information on the factors that may affect the performance of transferable calibration models. Here, we examined the spatial transferability of the calibration models developed using machine learning (ML) algorithms for an LCSN with twenty-two (22) sites in NCT-Delhi. The site-specific calibration models performed well at each site with high R2 and significantly low RMSE values. These models were transferred to the other sites, and the effect of distance between the sites (D), source composition, PM ratios, and particle size distribution (PSD) on the transferability of calibration models was investigated. The models developed at the Mundka (S10) and Punjabi Bagh (S16) sites complied with the evaluation criterion (R2 ≥ 0.70) for each site, irrespective of the distance between the sites. Furthermore, PM ratios reported by the LCSs did not significantly differ across sites, suggesting that the PMS algorithm provides a proxy of the size-resolved mass fractions. Evaluation of the PSD at different sites supported our findings. We also introduced the concept of selecting representative locations for LCS co-location by computing transferability scores using k-means clustering and presented a reference map for NCT-Delhi for developing scalable calibration models.

Morphology, aspect ratio, and surface elemental composition of primary aerosol particles at urban region of India.

The PM2.5 and PM10 particles were characterized in terms of morphology (size and shape) and surface elemental composition at two different (traffic and industrial) locations in urban region of India and further linked to different morphological defining parameters. The overall PM2.5 and PM10 showed significant daily variability indicating higher PM10 as compared to PM2.5. PM2.5/PM10 ratio was found to be 0.58 ± 0.10 indicating the abundance of PM2.5. Soot aggregates, aluminosilicates, and brochosomes particles were classified based on morphology, aspect ratio (AR), and surface elemental composition of single particles. The linear regression analysis indicates the significant correlation between area equivalent (Daeq) and feret diameter (Dfd) (R2 0.86-0.98). Higher aspect ratio (1.48 ± 0.87-1.43 ± 0.50) was noted at traffic site as compared to industrial site (1.33 ± 0.58-1.29 ± 0.30), while circularity showed the opposite trend. Fractal dimension (Df) of soot aggregates estimated by the soot parameters method (SPM) were found to be 1.70, 1.72, and 1.88, mainly attributed to vehicular emissions, biomass, and industrial emission/coal burning, respectively. This further inferred that freshly emitted soot particles exhibited lacey in nature with spherical shape (Df 1.70) at traffic site, while at industrial location, they were different with compact shapes (Df 1.88) due to particle aging processes. This study inferred the synoptic changes in mass, chemical characteristics, and morphology of aerosol particles which provide the new insights into individual atmospheric particle and their dynamic nature.

Unveiling respiratory health risks: PCDD/Fs adhering to particulate matter (PM10 and PM2.5) in Bien Hoa City, Dong Nai, Vietnam

This study investigates the concentrations of atmospheric aerosol particles (PM, including PM2.5 and PM10), as well as the presence and distribution of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in ambient air in Bien Hoa City (Dong Nai province, Vietnam) during the dry and rainy seasons in 2023. The concentrations of 7 PCDDs and 10 PCDFs congeners in PM10 or PM2.5 were determined using gas chromatography-mass spectrometry (GC-MS). The results show that the concentration of PM10 and PM2.5 were 54.7 ± 9.4 µg/m3, 65.3 ± 7.6 µg/m3, and 39.6 ± 7.9 µg/m3, 58.9 ± 12.6 µg/m3, in the rainy season and dry season, respectively. This reveals that while only PM2.5 concentration levels in the dry seasonexceed national ambient air quality standards, but all of PM2.5 concentration levels consistently exceeded WHO guidelines. The study highlights the dominance of PM2.5 and its adverse health implications, as evidenced by the PM2.5/PM10 ratio (0.74 and 0.85 for the rainy and dry seasons, respectively) and its association with worse air quality. Analysis of PCDD/Fs concentrations in PM samples demonstrates their comparable levels in both seasons, with OCDD being the most abundant species. Furthermore, the study estimates the toxic equivalency (TEQ) of PCDD/Fs and assesses health risks, revealing a potentially high cancer risk from exposure to these pollutants, particularly PM2.5 under WHO2005-TEQ. Several PCDD/Fs- WHO2005-TEQ congeners exhibit higher concentration levels in PM2.5 compared to PM10, suggesting differential particulate matter dynamics and potential environmental implications. These findings underscore the urgent need for mitigation strategies to alleviate air pollution and reduce associated health risks in the dioxin hotspot of Vietnam.

Performance Assessment of Two Low-Cost PM2.5 and PM10 Monitoring Networks in the Padana Plain (Italy).

Two low-cost (LC) monitoring networks, PurpleAir (instrumented by Plantower PMS5003 sensors) and AirQino (Novasense SDS011), were assessed in monitoring PM2.5 and PM10 daily concentrations in the Padana Plain (Northern Italy). A total of 19 LC stations for PM2.5 and 20 for PM10 concentrations were compared vs. regulatory-grade stations during a full "heating season" (15 October 2022-15 April 2023). Both LC sensor networks showed higher accuracy in fitting the magnitude of PM10 than PM2.5 reference observations, while lower accuracy was shown in terms of RMSE, MAE and R2. AirQino stations under-estimated both PM2.5 and PM10 reference concentrations (MB = -4.8 and -2.9 μg/m3, respectively), while PurpleAir stations over-estimated PM2.5 concentrations (MB = +5.4 μg/m3) and slightly under-estimated PM10 concentrations (MB = -0.4 μg/m3). PurpleAir stations were finer than AirQino at capturing the time variation of both PM2.5 and PM10 daily concentrations (R2 = 0.68-0.75 vs. 0.59-0.61). LC sensors from both monitoring networks failed to capture the magnitude and dynamics of the PM2.5/PM10 ratio, confirming their well-known issues in correctly discriminating the size of individual particles. These findings suggest the need for further efforts in the implementation of mass conversion algorithms within LC units to improve the tuning of PM2.5 vs. PM10 outputs.

Temporal Variation and Sources of Particulate Matter in Kannur: Insights from a Coastal City in South India

In this study, particulate matter (PM) variation has been analyzed to address the dearth of scientific research on air pollution in a coastal city from south India. Mean PM2.5 and PM10 concentrations were 61.6 ± 26.4 and 87.3 ± 35.4 μg/m3. The highest (lowest) seasonal concentration was observed during winter (monsoon). Increase in PM from September to February is attributed to low relative humidity, biomass burning in nearby paddy fields, and continental air mass from the east and north-easterly direction. The mean PM2.5/PM10 ratio was 0.71, with the seasonal ratio ranging between 0.64 (summer) and 0.76 (monsoon), indicating the dominance of anthropogenic PM fraction. PM exhibited typical characteristic of the urban region, i.e., a two-peak diurnal trend during all seasons except monsoon. PM10 (PM2.5) concentrations decreased by ∼7–24.2% (∼11–21%) on the weekends, with the highest reduction in winter seasons. Regarding correlation analysis among PM, CO, and ozone; a negative correlation between PM10 and O3 and a positive correlation between PM and CO was observed. Wind speed and PM10 showed a positive quadratic linear, whereas wind speed and PM2.5 showed a negative linear relationship. The primary sources of particulate matter in Kannur town are motor vehicle emissions, dust from construction work in surrounding zones, and nearby industrial units.

Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods.

The aim was to evaluate and optimize the performance of sensor monitors in measuring PM2.5 and PM10 under typical emission scenarios both indoors and outdoors. Parallel measurements and comparisons of PM2.5 and PM10 were carried out between sensor monitors and standard instruments in typical indoor (2 months) and outdoor environments (1 year) in Shanghai, respectively. The optimized validation model was determined by comparing six machining learning models, adjusting for meteorological and related factors. The intra- and inter-device variation, measurement accuracy, and stability of sensor monitors were calculated and compared before and after validation. Indoor particles were measured in a range of 0.8-370.7 μg/m3 and 1.9-465.2 μg/m3 for PM2.5 and PM10, respectively, while the outdoor ones were in the ranges of 1.0-211.0 μg/m3 and 0.0-493.0 μg/m3, correspondingly. Compared to machine learning models including multivariate linear model (ML), K-nearest neighbor model (KNN), support vector machine model (SVM), decision tree model (DT), and neural network model (MLP), the random forest (RF) model showed the best validation after adjusting for temperature, relative humidity (RH), PM2.5/PM10 ratios, and measurement time lengths (months) for both PM2.5 and PM10, in indoor (R2: 0.97 and 0.91, root-mean-square error (RMSE) of 1.91 μg/m3 and 4.56 μg/m3, respectively) and outdoor environments (R2: 0.90 and 0.80, RMSE of 5.61 μg/m3 and 17.54 μg/m3, respectively), respectively. Sensor monitors could provide reliable measurements of PM2.5 and PM10 with high accuracy and acceptable inter and intra-device consistency under typical indoor and outdoor scenarios after validation by RF model. Adjusting for both climate factors and the ratio of PM2.5/PM10 could improve the validation performance.

Vertical distribution characteristics and potential sources of atmospheric pollutants in the North China Plain basing on the MAX-DOAS measurement

The mechanism for the generation of atmospheric pollution sources can be further investigated through the examination of atmospheric evolution and diffusion characteristics. The authors of this study conducted a 3-month MAX-DOAS (multi-axis differential optical absorption spectroscopy) vertical observation in Shijiazhuang City, North China Plain, in the summer of 2020 in response to the long-standing air pollution issues in the region. The vertical distribution profiles of aerosol, NO2, HCHO, and CHOCHO were generated, and the inversion findings showed good agreement with the TROPOMI (tropospheric monitoring instrument) satellite remote sensing validation, demonstrating the validity and accuracy of the observations. The near-surface boundary layer is home to the majority of the NO2, HCHO, and CHOCHO species. The species’ daytime evolution trends varied, with the highest NO2 peaks occurring in the morning and evening commute, the highest HCHO peaks occurring in the morning at 10:00 a.m., and CHOCHO's concentration during the day declined. Two minor aerosol pollution processes took place in Shijiazhuang City during the summer observation period. The elevated concentrations of NO2, CO, and the PM2.5/PM10 ratio during the pollution processes suggest that anthropogenic emissions, particularly biomass burning, were responsible for the large number of fine particles generated during the pollution events. Based on the examination of pollutant concentration profiles and meteorological data, it was determined that local emissions and north wind transport were the primary causes of Shijiazhuang's high NO2 values. Meanwhile, the southern region of Shijiazhuang was primarily responsible for the majority of the potential sources of atmospheric HCHO, and local emissions were also a major factor affecting the high CHOCHO values. Shijiazhuang's local near-surface volatile organic compounds (VOCs) are mostly caused by human emissions, although biomass burning and its regional transportation have a greater influence on the middle and upper boundary layers. This study systematically sorted the evolution characteristics and potential sources of pollutants in Shijiazhuang City during the summer based on the joint observations of various pollutants, including NO2, HCHO, and CHOCHO. These results can be used to support the development of appropriate policies for the prevention and control of pollutants in the Shijiazhuang local area of the North China Plain.

Temporal variation of the PM2.5/PM10 ratio and its association with meteorological factors in a South American megacity: Metropolitan Area of Lima-Callao, Peru.

The Metropolitan Area of Lima-Callao (MALC) is a South American megacity that has suffered a serious deterioration in air quality due to high levels of particulate matter (PM2.5 and PM10). Studies on the behavior of the PM2.5/PM10 ratio and its temporal variability in relation to meteorological parameters are still very limited. The objective of this study was to analyze the temporal trends of the PM2.5/PM10 ratio, its temporal variability, and its association with meteorological variables over a period of 5years (2015-2019). For this, the Theil-Sen estimator, bivariate polar plots, and correlation analysis were used. The regions of highest mean concentrations of PM2.5 and PM10 were identified at eastern Lima (ATE station-41.2µg/m3) and southern Lima (VMT station-126.7µg/m3), respectively. The lowest concentrations were recorded in downtown Lima (CDM station-16.8µg/m3 and 34.0µg/m3, respectively). The highest average PM2.5/PM10 ratio was found at the CDM station (0.55) and the lowest at the VMT station (0.27), indicating a predominance of emissions from the vehicular fleet within central Lima and a greater emission of coarse particles by resuspension in southern Lima. The temporal progression of the ratio of PM2.5/PM10 showed positive and highly significant trends in northern and central Lima with values of 0.03 and 0.1 units of PM2.5/PM10 per year, respectively. In the southern region of Lima, the trend was also significant, showcasing a value of 0.02 units of PM2.5/PM10 per year. At the hourly and monthly level, the PM2.5/PM10 ratio presented a negative and significant correlation with wind speed and air temperature, and a positive and significant correlation with relative humidity. These findings offer insights into identifying the sources of PM pollution and are useful for implementing regulations to reduce air emissions considering both anthropogenic sources and meteorological dispersion patterns.

Personal exposure monitoring of fine and coarse particulate matter using exposure assessment models for elderly residents in Hong Kong

This study investigated the determinants of personal exposures (PE) to coarse (PM2.5-10) and fine particulate matter (PM2.5) for elderly communities in Hong Kong. The mean PE PM2.5 and PM2.5-10 were 23.6 ± 10.8 and 13.5 ± 22.1 μg/m3, respectively during the sampling period. Approximately 76% of study subjects presented statistically significant differences between PE and ambient origin for PM2.5 compared to approximately 56% for PM2.5-10, possibly due to the coarse-size particles being more influenced by similar sources (road dust and construction dust emissions) compared to the PM2.5 particles. Individual PE to ambient (P/A) ratios for PM2.5 all exceeded unity (≥1), suggesting the dominant influences of non-ambient particles contributed towards total PE values. There were about 80% individual P/A ratios (≤1) for PM2.5-10, implying possible effective infiltration prevention of larger size particulate matter particles leading to dominant influences from the outdoor sources. The higher concentration of NO3− and SO42− in PM2.5-10 compared to PM2.5 suggests possible heterogeneous reactions of alkaline minerals leading to the formation of NO3− and SO42− in PM2.5-10 particles. The PE and ambient OC/EC ratios in PM2.5 (8.8 ± 3.3 and 10.4 ± 22.4, respectively) and in PM2.5-10 (6.0 ± 1.9 and 3.0 ± 1.1, respectively) suggest possible secondary formed OC from surrounding rural areas. Heterogeneous distributions (COD >0.2) between the PE and ambient concentrations were found for both the PM2.5 and PM2.5-10 samples. The calibration coefficient as the association between personal and surrogate exposure measure of PE to PM2.5 (0.84) was higher than PM2.5-10 (0.52). The findings further confirm that local sources were the dominant contributor to the coarse particles and these coefficients can potentially be used to estimate different PE to PM2.5 and PM2.5-10 conditions. A comprehensive understanding of the PE to determinants in coarse particles is essential to further reduce potential exposure misclassification.

New insights from seasonal and weekly evolutions of aerosol absorption properties and their association with PM2.5 and NO2 concentrations at a central Mediterranean site

Aerosol absorption parameters were investigated in this study using the measurements performed by an aethalometer during a monitoring campaign from December 2021 to July 2022 in a suburban area in south-eastern Italy. The aerosol absorption coefficient was compared to both PM2.5 and NO2 concentrations from two nearby stations (a rural and an urban site). Different seasonal evolutions were identified for these extensive parameters, even if they also showed a common feature because all these parameters presented a higher variability in winter due to the highly variable meteorological conditions during that season. Intensive parameters like the aerosol absorption Ångström exponent and the PM2.5/PM10 ratio were also investigated showing the effects of wood burning from domestic heating and of vehicular traffic, especially both in the urban and in the suburban site. Then, the weekly evolution of both selected extensive and intensive parameters was also analyzed to identify potential impacts due to the weekly cycle of human activities. Most of the selected parameters presented a significant increase starting from Tuesday to Friday and then they generally decreased during the weekends due to the relevant reduction of the human activities. The selection of different types of monitoring sites (urban, suburban, and rural) and temporal scales (seasonal and weekly) has been proved for a proper characterization of the aerosol absorption properties at the monitored area due to its geographical location at the center of the Mediterranean area.

Assessment of Heavy Metal Contamination in Dust in Vilnius Schools: Source Identification, Pollution Levels, and Potential Health Risks for Children.

The main objective of this study is to thoroughly evaluate the diversity and sources of heavy metals in the school environment. Specifically, this study examines the presence of heavy metals in the dust found and collected from 24 schools in Vilnius. Employing hierarchical cluster analysis, principal component analysis, and positive matrix factorization, we identified combustion-related activities as primary contributors to elevated metal concentrations, notably zinc, scandium, and copper, with PM2.5/PM10 ratios indicating a combustion source. They reveal significant differences in the levels of elements such as arsenic (4.55-69.96 mg/kg), copper (51.28-395.37 mg/kg), zinc, and lead, which are affected by both local environmental factors and human activities. Elevated pollution levels were found in certain school environments, indicating environmental degradation. Pollution assessment and specific element pairings' strong positive correlations suggested shared origins or deposition processes. While this study primarily assesses non-carcinogenic risks to children based on a health risk assessment model, it acknowledges the well-documented carcinogenic potential of substances such as lead and arsenic. The research emphasizes the immediate necessity for efficient pollution management in educational environments, as indicated by the elevated hazard index for substances such as lead and arsenic, which present non-carcinogenic risks to children. This research offers important insights into the composition and origins of dust pollution in schools. It also promotes the need for broader geographic sampling and prolonged data collection to improve our understanding of pollution sources, alongside advocating for actionable strategies such as environmental management and policy reforms to effectively reduce exposure risks in educational settings. Furthermore, it aims to develop specific strategies to safeguard the health of students in Vilnius and similar urban areas.

Assessment of particulate matter and particle path trajectory analysis using a HYSPLIT model over Dire Dawa, Ethiopia

This work deals with the assessment of particulate matter (PM1, PM2.5, and PM10) over Dire Dawa during the month of May 2021. In the study, purple sensor (PS) and gravimetric methods (GM) were used. The purple sensor was to provide real-time measurements of PM1.0, PM2.5, and PM10 particulates. The GM instruments were constructed using wood with 1 m height (distance to ground), with flat board on top of which filter papers were placed to collect particulate matter. The difference in filter paper weight before and after sampling was used to calculate the particle masses. By dividing the weight gain of the filter by the amount of air measured, the concentrations of suspended particulate matter in the defined size range were estimated. The mean value of PM10 indicated a good status whereas the mean value of PM2.5 revealed a moderate condition as far as pollution is concerned. The purple sensor detected relatively higher values for PM10 measurement as compared to GM method during the study period. According to the calculated results of the ratio of mass concentration of PM1.0 to PM10, coarse particles were dominant whereas in the ratio of PM2.5 to PM10 both coarse and fine mode particles were equally present during the sampling period. The spatial distribution showed variations depending on the locations where the sampling filter papers were placed. The HYSPLIT backward trajectory analysis indicated various air masses and transport channels during different seasons. The predominant pathways were from both urban and desert origins.

Footprints of COVID-19 on PM2.5/PM10 Ratio in a Brazilian Tropical Metropolis

Footprints of COVID-19 on PM2.5/PM10 Ratio in a Brazilian Tropical Metropolis

Status of air quality in Rajshahi metropolitan area, Bangladesh

Rajshahi is well-known as a model city for clean air in Bangladesh but in recent time air pollution is increasing in metropolitan areas. This study aims to examine the concentration of Particulate Matters (PM₁, PM₂.₅, and PM₁₀) in the Rajshahi metropolitan area during March 2021, with a focus on distinct land use categories. The research was carried out in 40 different sites within the Rajshahi metropolitan area, utilizing a portable device for measuring air quality. It was found that the average concentrations of PM₂.₅ and PM₁₀ were 56.41 and 72.63 µg/m³ respectively, which is 2.25 and 1.45-fold than World Health Organizations (WHO) Standards. According to this study, the three locations with the highest levels of PM₂.₅ pollution were the Mohanonda project gate (105.750 µg/m³), Textile Mill (79.750 µg/m³), and Sarkar Cold Storage (71.750 µg/m³). The concentration of PM₂.₅ in the most polluted location was found to be 1.63 times than the standard level set by Department of Environment (DoE) and 4.23 times higher than the WHO standard. Furthermore, it was calculated that the average PM₂.₅/PM₁₀ ratio was 77.70% and the average PM₁/PM₂.₅ ratio was 60.16%. It also revealed that temperature is negatively and humidity is positively correlated with PM₁, PM₂.₅ and PM₁₀, which means any change in the concentration of PM₁, PM₂.₅ and PM₁₀ can cause a significant change in the percentage of humidity in the same direction, for humidity the change would be in opposite direction. The concentration of PM₂.₅ in ambient air with respect to land use decreases as follows: industrial area > village area > road intersection area > sensitive area > mixed area > commercial area > residential area.