Sources Of Particulate Matter Research Articles

Human sperm mitochondrial DNA copy numbers and deletion rates: comparing persons living in two urban industrial agglomerations differing in sources of air pollution

Persons living in industrial environments are exposed to levels of air pollution that can affect their health and fertility. The Czech capital city, Prague, and the Ostrava industrial agglomeration differ in their major sources of air pollution. In Prague, heavy traffic produces high levels of nitrogen oxides throughout the year. In the Ostrava region, an iron industry and local heating are sources of particulate matter (PM) and benzo[a]pyrene (B[a]P), especially in the winter. We evaluated the effects of air pollution on human sperm mitochondrial DNA (mtDNA). Using real-time PCR, we analysed sperm mtDNA copy number and deletion rate in Prague city policemen in two seasons (spring and autumn) and compared the results with those from Ostrava. In Prague, the sperm mtDNA deletion rate was significantly higher in autumn than in spring, which is the opposite of the results from Ostrava. The sperm mtDNA copy number did not show any seasonal differences in either of the cities; it was correlated negatively with sperm concentration, motility, and viability, and with sperm chromatin integrity (assessed with the Sperm Chromatin Structure Assay). The comparison between the two cities showed that the sperm mtDNA deletion rate in spring and the sperm mtDNA copy number in autumn were significantly lower in Prague vs. Ostrava. Our study supports the hypothesis that sperm mtDNA deletion rate is affected by the composition of air pollution. Sperm mtDNA abundance is closely associated with chromatin damage and standard semen characteristics.

Biomass-burning sources control ambient particulate matter, but traffic and industrial sources control volatile organic compound (VOC) emissions and secondary-pollutant formation during extreme pollution events in Delhi

Abstract. Volatile organic compounds (VOCs) and particulate matter (PM) are major constituents of smog. Delhi experiences severe smog during the post-monsoon season, but a quantitative understanding of VOCs and PM sources is still lacking. Here, we conduct a source apportionment study for VOCs and PM using a recent (2022), high-quality dataset of 111 VOCs, PM2.5, and PM10 in a positive matrix factorization (PMF) model. Contrasts between clean monsoon air and polluted post-monsoon air, VOC source fingerprints, and molecular tracers enabled us to differentiate paddy residue burning from other biomass-burning sources, which had previously been impossible. Burning of fresh paddy residue, as well as residential heating and waste burning, contributed the most to observed PM10 levels (25 % and 23 %, respectively) and PM2.5 levels (23 % and 24 %, respectively), followed by heavy-duty vehicles fuelled by compressed natural gas (CNG), with a PM10 contribution of 15 % and a PM2.5 contribution of 11 %. For ambient VOCs, ozone formation potential, and secondary-organic-aerosol (SOA) formation potential, the top sources were petrol four-wheelers (20 %, 25 %, and 30 %, respectively), petrol two-wheelers (14 %, 12 %, and 20 %, respectively), industrial emissions (12 %, 14 %, and 15 %, respectively), solid-fuel-based cooking (10 %, 10 %, and 8 %, respectively), and road construction (8 %, 6 %, and 9 %, respectively). Emission inventories tended to overestimate residential biofuel emissions at least by a factor of 2 relative to the PMF output. The major source of PM pollution was regional biomass burning, while traffic and industries governed VOC emissions and secondary-pollutant formation. Our novel source apportionment method even quantitatively resolved similar biomass and fossil fuel sources, offering insights into both VOC and PM sources affecting extreme pollution events. This approach represents a notable advancement compared to current source apportionment approaches, and it could be of great relevance for future studies in other polluted cities and regions of the world with complex source mixtures.

Localizing particulate matter sources in indoor environments with weak airflow: An experimental study using swarm intelligence methods

In indoor environments with weak airflow, advective transport diminishes while turbulent dispersion prevails. The absence of usable airflow data and the unpredictability of turbulence-led pollutant dispersion pose significant challenges to mobile robots in localizing pollution sources. This study advances the field by shifting the focus from gases to particulate matter (PM) in such environments. An initial detailed examination of airflow dynamics and PM behavior under weak airflow conditions laid the foundation for extensive 3D source localization experiments using two methodologies: the Whale Optimization Algorithm (WOA_3D) and Particle Swarm Optimization (PSO_3D), executed through a specialized multi-robot system. Evaluation across 11 distinct scenarios and 165 trials showcased the methods’ adaptability to different source heights, PM sizes, and the transition from gaseous to particulate pollutants. WOA_3D notably excelled in localizing PM2.5 sources at 1.35 m heights, achieving a 73.3% success rate and proving consistently effective across various elevations. However, its efficacy waned with larger PM sizes, and it was less effective in transitioning from ethanol vapor to PM source localization. These results highlight the importance of addressing PM settling to improve PM localization strategies, effectively combining insights into PM behavior’s complexities with methodological progress.

Methods and applications for quantitative assessment of uncertainty in atmospheric particulate matter source profiles

Particulate Matter (PM), a primary atmospheric pollutant, is characterized by diverse emission sources and complex influencing factors. Particulate matter source profiles (PM-SPs) reported in different studies exhibited significant variability, and their uncertainty is not well documented, highlighting the urgent need for further research. To scientifically evaluate PM-SPs, this study begins with the construction process of PM-SPs, identifying key stages (sampling, weighing, and chemical analysis) that influence their uncertainty. The uncertainty components at each stage, as well as the comprehensive uncertainty, are then quantitatively assessed, resulting in the development of an assessment method that quantifies the uncertainty of PM-SPs. The industrial PM-SPs are evaluated as a case study, which indicate this full-chain method can quantitatively identify the uncertainties introduced at various stages. The sampling uncertainties are mostly below 40%, primarily resulting from errors in repeat sampling. The weighing uncertainties are generally minor, while the impact of the chemical analysis process varies significantly across different components. Among the fifty-three evaluated PM-SPs, 80% have average comprehensive uncertainties below 40%, with only five profiles exhibiting relatively high uncertainties. In practical applications, the uncertainty assessment of PM-SPs should be analyzed on a case-by-case basis, considering the importance of different components, with special attention given to marker components.

Impact of anthropogenic and natural constituents on particulate matter in oasis cities on the southern margin of the Taklimakan Desert based on MERRA-2 and multi-site ground observation

Dust storms are associated with large amounts of particulate matter (PM) that can have adverse effects on health and the environment. The contribution of natural dust to atmospheric PM levels represents a scientific challenge, especially in areas with close proximity to dust sources. To improve our knowledge in this area, we collected 300 PM filter-samples across five oasis cities on the southern edge of the Taklimakan Desert in 2016, and applied the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) data to quantify the contribution of natural dust to PM on sand dust and non-sand dust days. Research has shown that the particle size distribution of mineral dust in Taklimakan dust aerosols was relatively uniform. On sand dust days, CaSO4 and Na2SO4 were directly emitted from the surface, leading to higher sulfate concentrations in PM. While the increase in the proportions of organic carbon - OC1 and OC2 was attributed to the mixed anthropogenic emissions. On non-sand dust days, SO42− concentration was significantly affected by emissions from anthropogenic sources. The automotive composite emissions and coal combustion were the main sources of elemental carbon - EC1. Based on the results of MERRA-2 reanalysis data, natural dust constituted 53 % and 70 % of the total PM2.5 and PM10, respectively. In this study, we have quantified the contribution of natural dust under different weather conditions and identifying potential sources of PM in oasis cities. This study provides support for the assessment of natural dust and PM prevention in oasis cities.

Characterization of carbonaceous components and PAHs on ultrafine particles in Phnom Penh, Cambodia.

This study investigated seasonal fluctuations in particulate matter (PM) concentrations, including carbon and polycyclic aromatic hydrocarbon (PAH) components, in Phnom Penh, Cambodia, focusing on ultrafine particles (UFPs or ≤ 100nm). UFP levels were notably higher during the dry season, averaging 23.73 ± 3.7µg/m3 compared to 19.64 ± 3.4µg/m3 in the wet season, attributed to increased emissions from vehicles and agricultural burning. In contrast, lower concentrations during the wet season were due to scavenging effect of rain. When compared to other Southeast Asian cities, UFP levels in Phnom Penh were significantly higher during the dry season, surpassing those in cities like Bangkok and Kuala Lumpur. Seasonal variations in carbonaceous components showed higher elemental carbon (EC) and total carbon (TC) during the dry season, with EC/TC ratios suggesting substantial influence from vehicular emissions and biomass burning. PAH analysis revealed seasonal disparities, with higher concentrations of benzo[b]fluoranthene (BbF) and benzo[k]fluoranthene (BkF) during the wet season, whereas fluoranthene (Flu) and pyrene (Pyr) were consistently present, indicating diverse PAH sources. The Flu/(Flu + Pyr) ratios, indicative of biomass burning, were higher in the dry season. Correlations between PAHs and carbon components confirmed combustion as a significant source of PAHs, aligning with global trends. This emphasizes the need to address distinct PM sources during various season in Phnom Penh.

Exposure to ultrafine particles, black carbon and particulate matter during commute in a suburb from the Yangtze River Delta, China

This study evaluated the exposure of commuters to particulate matter (PM), black carbon (BC), and ultrafine particulate matter (UFP) concentrations in five different traffic microenvironments (bike, bus, taxi, electric motorbike, and subway) under various conditions, such as regular and dust days, weekdays and weekends, and peak and off-peak commute hours in Nanjing, China. In the morning peak hours, bicycle commuters were exposed to the highest PM concentration, those on buses to the highest BC concentration (3.4 ± 3.0 μg/m3), and those on buses and taxis to the highest UFP number concentration. Throughout the rides, the commuter on the subway had the lowest mean exposure concentration of PM, BC, and UFP number. Bicycle commuters (16.25 μg/km) had the highest total inhaled dose of PM2.5, followed by e-bikers (1.98 μg/km), bus riders (1.28 μg/km), taxi riders (0.69 μg/km), and subway users (0.45 μg/km). Weekday PM2.5 exposure levels (115 μg/m3) and BC exposure levels (1.8 μg/m3) were higher than weekend levels (170% and 158%, respectively). The mean exposure concentration of PM10 (1299 ± 1286 μg/m3) in the evening peak of dust days was 803% and 994% higher than the mean concentrations on weekdays and holidays, respectively. PM2.5, PM10, and BC concentrations on dust days were significantly higher than those on weekdays and holidays. The overall mean ratios of PM1/PM2.5 and PM2.5/PM10 were 0.47 and 0.36, respectively, and the five commuting microenvironments had higher proportions of 2.5–10 μm and 1–2.5 μm particles in PM10 and PM2.5, respectively. Reducing the sources of particulate matter in the corresponding particle size ranges could lower exposure levels.

Black carbon and particle lung-deposited surface area in residential wood combustion emissions: Effects of an electrostatic precipitator and photochemical aging

Residential wood combustion (RWC) remains a significant global source of particulate matter (PM) emissions with adverse impacts on regional air quality, climate, and human health. The lung-deposited surface area (LDSA) and equivalent black carbon (eBC) concentrations have emerged as important metrics to assess particulate pollution. In this study we estimated combustion phase-dependent emission factors of LDSA for alveolar, tracheobronchial, and head-airway regions of human lungs and explored the relationships between eBC and LDSA in fresh and photochemically aged RWC emissions. Photochemical aging was simulated in an oxidative flow reactor at OH• exposures equivalent to 1.4 or 3.4 days in the atmosphere. Further, the efficiency of a small-scale electrostatic precipitator (ESP) for reducing LDSA and eBC from the wood stove was determined. For fresh emission eBC correlated extremely well with LDSA, but the correlation decreased after aging. Soot-dominated flaming phase showed the highest eBC dependency of LDSA whereas for ignition and char burning phases non-BC particles contributed strongly the LDSA. Deposition to the alveolar region contributed around 60 % of the total lung-deposition. The ESP was found as an effective method to mitigate particulate mass, LDSA, as well as eBC emissions from wood stoves, as they were reduced on average by 72%, 71%, and 69%, respectively. The reduction efficiencies, however, consistently dropped over the span of an experiment, especially for eBC. Further, the ESP was found to increase the sub-30 nm ultrafine particle number emissions, with implications for LDSA. The results of this study can be used for assessing the contribution of RWC to LDSA concentrations in ambient air.

A novel approach for large-scale characterization of residential cooking-generated PM with computer vision and low-cost sensors

Cooking is one of the major sources of indoor particulate matter (PM), which poses significant health risks and is a severe health hazard. Current studies lack an economical and effective analytical framework for quantifying inhalable particles (PM10) and fine particulate matter (PM2.5) from residential cooking activities on a large scale under real-world scenarios. This study bridges this gap by employing computer vision (CV) technology and readily available sensors. We collected data over a month in real-world settings, including cooking videos and air quality data (indoor PM10, PM2.5, CO2, temperature, relative humidity, and outdoor PM10 and PM2.5 concentrations). To classify high-emission (pan-frying, stir-frying, deep-frying) and low-emission (stewing, steaming, boiling, non-cooking) activities, we developed and validated a robust CV model named “Cooking-I3D.” This model leverages a pre-trained Two-Stream Inflated 3D ConvNet (I3D) architecture. We then assessed the efficacy of the CV-predicted cooking method in PM characterization using a first-order multivariate autoregressive model, controlling for environmental factors. The Cooking-I3D model achieved exceptional performance, boasting an accuracy of 95 % and an Area Under the Curve (AUC) of 0.98. Our results indicate that a single 6-minute high-emission cooking event triggers a 21–25 % increase in indoor PM concentrations and a 23–24 % increase in the indoor/outdoor ratio, with relative errors in these estimates ranging from 10 to 21 %. This innovative method offers a powerful tool for long-term assessment of cooking-related indoor air pollution and facilitates precision exposure assessment in human health studies.

Criteria, Greenhouse Gas, and Hazardous Air Pollutant Emissions Factors from Residential Cordwood and Pellet Stoves Using an Integrated Duty Cycle Test Protocol.

Air pollution from residential wood heating (RWH) presents challenges at the intersection of climate and public health. With a revised National Ambient Air Quality Standard (NAAQS, at 9 μg/m3) for particulate matter (PM) in the United States (U.S.), the Environmental Protection Agency (EPA) will likely classify new non-attainment areas due primarily to emissions from RWH. Agencies will use emissions factors (EFs) to develop attainment strategies. Many will rely on EPA modeling platforms based on data from the National Emissions Inventory (NEI). The NEI uses RWH EFs based on data from mid-1990's in-situ studies and a speciation profile from a 2001 study of fireplace emissions. The NEI does not include greenhouse gas (GHG) emissions for this sector, which plays a key role when assessing climate reduction strategies for the buildings sector. Here, we tested seven wood stoves to determine EFs, representing various vintages and control technologies, using a novel test method that reflects in-use operational settings called the Integrated Duty Cycle. The study measured multiple pollutants concurrently: criteria pollutants (particulate matter [PM], CO, and NOx), nonmethane total hydrocarbons (NMTHCs), GHGs, black carbon (eBC), brown carbon (BrC), and multiple hazardous air pollutants (HAPs). We found no significant difference in PM EFs between uncertified and non-catalytic stove technologies. RWH EF results from this study exceeded 2020 NEI RWH EFs for NMTHC and multiple HAPs. Applying our study's EFs to the 2020 NEI suggests that RWH, compared to all other sources, ranks as the 2nd largest source category of formaldehyde; the 3rd largest of benzene, 1,3-butadiene, and acrolein; and the 4th largest of Pb emissions. RWH also emits more methane compared to natural gas or oil residential heating, raising questions about substitution of wood as a climate neutral heating fuel. However, compared to uncertified stoves, pellet stove EFs (except toxic metals) were significantly lower (p < 0.01). In summary, RWH appears to be an underestimated source of PM (non-catalytic technology), methane, NMTHC, toxic metals, and other HAPs, which has important implications for climate and public health policy in the U.S. and globally.

Airborne particulate matter integral assessment in Magdalena department, Colombia: Patterns, health impact, and policy management

The relevance of atmospheric particulate matter (PM) to health and the environment is widely known. Long-term studies are necessary for understanding current and future trends in air quality management. This study aimed to assess the long-term PM concentration in the Magdalena department (Colombia). It focused on the following aspects: i) spatiotemporal patterns, ii) correlation with meteorology, iii) compliance with standards, iv) temporal trends over time, v) impact on health, and vi) impact of policy management. Fifteen stations from 2003 to 2021 were analyzed. Spearman-Rho and Mann-Kendall methods were used to correlate concentration with meteorology. The temporal and five-year moving trends were determined, and the trend magnitude was calculated using Teil-Sen. Acute respiratory infection odd ratios and risk of cancer associated with PM concentration were used to assess the impact on health. The study found that the maximum PM10 concentration was 194.5 μg/m3, and the minimum was 3 μg/m3. In all stations, a negative correlation was observed between PM10 and atmospheric water content, while the wind speed and temperature showed a positive correlation. The global trends indicated an increasing value, with five fluctuations in five-year moving trends, consistent with PM sources and socio-economic behavior. PM concentrations were found to comply with national standard; however, the results showed a potential impact on population health. The management regulation had a limited impact on increasing concentration. Considering that national regulations tend to converge towards WHO standards, the study area must create a management program to ensure compliance.

Coupling Imports of Dissolved Inorganic Nitrogen and Particulate Organic Matter by Aquaculture Sewage to Zhangjiang Estuary, Southeastern China

Estuary ecosystems serve as crucial connectors between terrestrial and marine environments, thus playing vital roles in maintaining the ecological balance of coastal marine ecosystems. In recent years, the eutrophication in estuaries caused by aquaculture sewage has been revealed, highlighting the necessity to understand its influence on the nutrient conditions and carbon storage of estuaries. In this study, δ15N and δ18O were used to indicate the contribution of aquaculture-derived sewage to dissolved inorganic nitrogen in Zhangjiang Estuary, and δ13C and C:N ratio were used to reveal its effects on the particulate organic matter. The major results are as follows: (1) Aquaculture water contributed 62~86% and 60~100% of the total nitrate and ammonium in Zhangjiang Estuary, respectively, and the drainage periods of the cultured species has a great influence on the content and composition of dissolved inorganic nitrogen. (2) Aquaculture water was also the major source of particulate organic matter (24~33% of the total content) here, most of which may be derived from crab ponds. (3) The imports of nutrients by aquaculture water may potentially regulate particulate organic matter in Zhangjiang Estuary by promoting the growth of phytoplankton and zooplankton. Our study revealed the coupling effects of aquaculture activities on the nitrogen and carbon storage in an estuarine ecosystem. It also indicates that isotopes may be efficient in the monitoring of a coastal environment, which may further aid the management of inshore cultivation.

Air pollution generated in an industrial region: Effect on the cardiovascular health of humans and damage caused to a plant species, Piper gaudichaudianum (Piperaceae), used for biomonitoring

Atmospheric pollution due to anthropogenic activities is a complex mixture of gasses and particulate matter (PM) that is currently one of the main causes of premature death in the world. Similarly, it is also capable of directly interfering with plant species by reducing their photosynthetic capacity and growth and killing cells. This work is about an observational study conducted in a region with two industries: a mine and an automobile parts manufacturer. Mining rocks is a source of PM in the air like that caused by other industrial activities. Twenty-five people that work or live in the industrial region cited (area A) and 25 people that live further away (area B) were selected to evaluate their vital signs and conduct a transthoracic echocardiogram. Leaves of Piper gaudichaudianum (Piperaceae), a native plant species, were also collected in both areas and evaluated in a laboratory. The PM accumulated on the leaves was evaluated using scanning electron microscopy (SEM) and inductively coupled plasma-optical emission spectrometry (ICP-OES). A statistical difference (P < 0.05) was verified for the levels of systolic blood pressure (SBP), diastolic blood pressure (DBP), and left ventricular mass index by echocardiography; the values were greater in people in area A. For the plant analysis, there was a statistical difference for all characters evaluated, chlorophyll levels, fresh mass, dry mass and leaf area were reduced, and thickness was greater in area A (P < 0.001). The PM analysis revealed a predominance of silicon, iron, and aluminum chemical elements. The present study suggests that particulate matter pollution is harmful to both humans and the flora.

Estimating black carbon levels using machine learning models in high-concentration regions

Black carbon (BC) is emitted into the atmosphere during combustion processes, often in conjunction with emissions such as nitrogen oxides (NOx) and ozone (O3), which are also by-products of combustion. In highly polluted regions, combustion processes are one of the main sources of aerosols and particulate matter (PM) concentrations, which affect the radiative budget. Despite the high relevance of this air pollution metric, BC monitoring is quite expensive in terms of instrumentation and of maintenance and servicing. With the aim to provide tools to estimate BC while minimising instrumentation costs, we use machine learning approaches to estimate BC from air pollution and meteorological parameters (NOx, O3, PM2.5, relative humidity (RH), and solar radiation (SR)) from currently available networks. We assess the effectiveness of various machine learning models, such as random forest (RF), support vector regression (SVR), and multilayer perceptron (MLP) artificial neural network, for predicting black carbon (BC) mass concentrations in areas with high BC levels such as Northern Indian cities (Delhi and Agra), across different seasons. The results demonstrate comparable effectiveness among the models, with the multilayer perceptron (MLP) showing the most promising results. In addition, the comparability between estimated and monitored BC concentrations was high. In Delhi, the MLP shows high correlations between measured and modelled concentrations during winter (R2: 0.85) and post-monsoon (R2: 0.83) seasons, and notable metrics in the pre-monsoon (R2: 0.72). The results from Agra are consistent with those from Delhi, highlighting the consistency of the neural network's performance. These results highlight the usefulness of machine learning, particularly MLP, as a valuable tool for predicting BC concentrations. This approach provides critical new opportunities for urban air quality management and mitigation strategies and may be especially valuable for megacities in medium- and low-income regions.

Tracing sources of inorganic suspended particulate matter in the Great Barrier Reef lagoon, Australia

Water clarity on the inshore Great Barrier Reef (GBR) is greatly influenced by terrestrial runoff of suspended particulate matter (SPM). Catchment sediment tracing studies often do not extend into the marine environment, preventing the analysis of preferential marine transport. This study employs novel collection and sediment tracing techniques to examine the transport of the terrigenous ‘mineral’ component of plume SPM within the GBR lagoon for two flood events. Utilising geochemical, radionuclide and clay mineral analysis, we trace terrigenous mineral sediments > 100 km from the river mouth. We show that the SPM geochemistry is highly influenced by particle-size fractionation, desorption, and dilution within the plume, rendering traditional tracing methods unviable. However, the ratios of rare earth elements (REE) to thorium (Th) provide stable tracers of mineral SPM transported across the catchment to marine continuum and allow the identification of discrete catchment sources for each flood event. Plume sediment radionuclides are also stable and consistent with sub-surface erosion sources.

Quantifying the effect of driving restrictions on fine particulate matter concentrations with WRF-Chem model: A case study in Kaifeng, China

One of the significant sources of urban air particulate matter (PM2.5 and PM10) has been found to be vehicle exhaust emission. Vehicle restriction measures have been taken to alleviate particulate matter pollution in the world. Here, we quantified the impact of vehicle restriction measures on the PM2.5 concentrations using the Weather Research and Forecasting model with Chemistry (WRF-Chem) in Kaifeng, China. This study set up three different scenarios to simulate the spatiotemporal changes of PM2.5 concentrations in four seasons in 2020. They were no emission reduction (S0), the restriction of two tailing license plate numbers (S1), and the restriction of odd–even tailing numbers of license plates (S2), respectively. In general, the odd–even license plate restriction policy has proven to be more effective than restricting two specific license plate numbers. In scenario S1, the range of PM2.5 concentrations reduction in Kaifeng City was between 0.2% and 1.28%. In scenario S2, the range of PM2.5 concentrations reduction was between 0.98% and 14.42%. The results showed that the effectiveness of vehicle restriction measures varies from season to season, and the effect in summer and winter is better than that in spring and autumn. We suggest that it may be difficult to reduce the PM2.5 concentrations to a great extent if simply reducing the traffic exhaust emissions through motor vehicle restrictions, on the contrary, would lead to the increase of O3 concentration, bringing some other effects. This study can serve as a foundation for the rational development of motor vehicle control strategies and help advance the ongoing improvement of air quality.

Unveiling the optimal regression model for source apportionment of the oxidative potential of PM10

Abstract. The capacity of particulate matter (PM) to generate reactive oxygen species (ROS) in vivo leading to oxidative stress is thought to be a main pathway in the health effects of PM inhalation. Exogenous ROS from PM can be assessed by acellular oxidative potential (OP) measurements as a proxy of the induction of oxidative stress in the lungs. Here, we investigate the importance of OP apportionment methods for OP distribution by PM10 sources in different types of environments. PM10 sources derived from receptor models (e.g., EPA positive matrix factorization (EPA PMF)) are coupled with regression models expressing the associations between PM10 sources and PM10 OP measured by ascorbic acid (OPAA) and dithiothreitol assay (OPDTT). These relationships are compared for eight regression techniques: ordinary least squares, weighted least squares, positive least squares, Ridge, Lasso, generalized linear model, random forest, and multilayer perceptron. The models are evaluated on 1 year of PM10 samples and chemical analyses at each of six sites of different typologies in France to assess the possible impact of PM source variability on PM10 OP apportionment. PM10 source-specific OPDTT and OPAA and out-of-sample apportionment accuracy vary substantially by model, highlighting the importance of model selection according to the datasets. Recommendations for the selection of the most accurate model are provided, encompassing considerations such as multicollinearity and homoscedasticity.

Levels and health risk assessments of particulate matter and inorganic gaseous pollutants in urban and industrial areas of Hawassa city, Ethiopia

Air pollution is a global public health concern due to its harmful health effects. However, there is scarce data on concentrations and sources of inorganic gaseous pollutants (NO2, CO, and SO2) and particulate matter (PM2.5 and PM10) in Ethiopia, particularly Hawassa City. Thus, the goal of this research is to determine the indoor and outdoor concentrations of PM2.5, PM10, NO2, CO, and SO2 in urban and industrial areas of Hawassa City, Ethiopia, and to evaluate potential health concerns. A portable gas monitor device (HoldPeak Laser PM meter, HP 5800D) was used to measure the levels of PM10 and PM2.5. The Aeroqual Series 500 Portable Air Quality Monitor (Aeroqual Ltd., New Zealand) was used to measure the concentrations of NO2, CO, and SO2. The results of this study showed that the average concentrations of PM2.5, PM10, and NO2 ranged from 8.8 to 310.7, 20.1–515.8, and 40.0–123.7 μg/m3, respectively, during the dry season. In the wet season, the ranges for PM2.5, PM10, NO2, and CO levels were 17.2–117.4, 24.3–167.2, 31.8–111.3, and 77–33312 μg/m3, respectively. The wide range of variations in the concentrations of PM2.5, PM10, NO2 and CO variations may be related to spatial and temporal factors such as sampling locations and seasonal changes. In both the wet and dry seasons, the hazard quotient (HQ) for PM2.5 and PM10 was greater than one, suggesting a non-carcinogenic effect. The PM2.5 excess lifetime cancer risk (ELCR) ranged from 0.1 to 0.7, which is greater than the recommended values by the WHO (ranging from 1 × 10−5 to 1 × 10−6) and the USEPA (less than 1 × 10−6). The HQ and ELCR values imply a considerable health risk for the general population.

Associations between Indoor and Outdoor Size-Resolved Particulate Matter in Urban Beijing: Chemical Compositions, Sources, and Health Risks

Ventilation may lead to a deterioration in indoor air quality in urban environments located close to roads. Understanding the differences in the chemical compositions of size-resolved particulate matter (PM) in indoor air and outdoor air could aid in assessing the health impacts of air in these settings and establishing relevant regulation policies. In this study, indoor and outdoor size-resolved PM was collected from an office in Beijing in summer (between 5 and 25 July 2020) and winter (between 5 and 31 January 2021). Its chemical components, including sulfate, nitrate, ammonium, chlorine, organic matter (OM), elemental carbon (EC), crustal materials (CM), and heavy metals (HM), were analyzed. The mean levels of indoor and outdoor PM2.1 and PM9 were found to be much higher than those in the guidelines for PM2.5 and PM10 outlined by the National Ambient Air Quality Standard. Moreover, the levels of PM2.1 and PM2.1–9 mass were higher outdoors than they were indoors. The size distributions of mass concentrations were shown to be bimodal, peaking at 0.43–0.65 μm and 4.7–5.8 μm, respectively. The most abundant chemicals were OM, nitrate, and sulfate for PM2.1 and OM, CM, and nitrate for PM2.1–9. We found higher percentages of sulfate, nitrate, ammonium, EC, and HM in smaller-size fractions of PM. Additionally, positive matrix factorization showed that biomass burning, secondary inorganic aerosol, coal combustion, dust, traffic, and industrial pollution were the main sources of PM during the study period. The greatest non-carcinogenic and carcinogenic hazards were found at 0.43–0.65 μm in summer and 2.1–3.3 μm in winter. Our results indicate that size-resolved PM of ambient origin may infiltrate buildings near roads to varying degrees, resulting in negative health effects.

Advancing Methodologies for Investigating PM2.5 Removal Using Green Wall System.

Combustion processes are the primary source of fine particulate matter in indoor air. Since the 1970s, plants have been extensively studied for their potential to reduce indoor air pollution. Leaves can retain particles on their surfaces, influenced by factors such as wax content and the presence of hairs. This study introduces an innovative experimental approach using metal oxide particles in an office-like environment to evaluate the depolluting effect of plant walls. Two plant walls were installed in a controlled room, housing three plant species: Aglaonema commutatum 'Silver Bay', Dracaena fragrans, and Epipremnum aureum. Metal oxide particles were introduced via a compressed air blower positioned between the two walls. The concentration of these particles was monitored using PM2.5 sensors, and the deposition of iron (Fe) on the leaves was quantified through Inductively Coupled Plasma Mass Spectrometry (ICP-MS). This novel methodology effectively demonstrated the utility of both real-time sensors and ICP-MS in quantifying airborne particle concentrations and leaf deposition, respectively. The results revealed that Dracaena fragrans had a 44% higher Fe particle retention rate compared to the control (wallpaper). However, further validation through methodological replication is necessary to confirm the reproducibility of these findings.