Particulate Matter Concentrations Research Articles

A novel approach to the quantitative analysis of the particulate matter in conventional cigarette smoke and heated tobacco product aerosols

The particulate and soluble matter present in aerosols from combustible cigarettes (CCs) and Heated Tobacco Products (HTPs) was collected in liquid water. These liquids, yellowish in the experiments with cigarettes and colourless after using HTPs, were analysed by Laser Diffraction (LD) and by Transmission Electron Microscopy coupled to Energy Dispersive X-ray spectroscopy (TEM-EDX) to study the amount, size, composition, and other features of the particulate matter (PM) present in the collected aerosols.The particulate matter concentration in HTPs samples is below the limit of quantification for LD, and only samples from cigarettes show a particulate matter concentration above such limit. TEM analysis has revealed that the liquid samples (from both, cigarettes and HTPs experiments) contain particulate matter, mainly composed of carbon (C) and oxygen (O), but also of traces of inorganic elements. The TEM electron beam results in the evaporation of the particulate matter derived from HTPs, but not of that derived from cigarettes, highlighting the different nature of the particulate matter in both systems, i.e. liquid particulate matter present in the HTPs aerosols and solid particulate matter in the cigarettes smoke. A protocol for the quantitative comparison of the particulate matter present in aerosols has been applied over sixteen TEM images for each sample, confirming important differences from the point of view of the amount of particulate matter and particle size ranges. Thus, the amount of particulate matter for HTPs aerosol samples is more than one order of magnitude lower than for cigarettes smoke.

Spatial and temporal variation of PM2.5 and the influence of vegetation in the Yangtze River Delta region

With rapid urbanisation in China, PM2.5 has become a limiting factor for the sustainable development of cities. Taking the Yangtze River Delta as the experimental area, this study analysed the spatial and temporal changes of PM2.5 concentrations from 2001 to 2020. It also examined the variations, dispersion, and correlation with NDVI of PM2.5 concentrations in different vegetation zones at different temporal and spatial scales. The results showed that: (1) The PM2.5 concentration in the Yangtze River Delta showed an overall decreasing trend during 2001–2020, and the change was divided into two phases, starting with an increasing phase and entering a decreasing phase after 2013. (2) In terms of spatial distribution, PM2.5 concentrations in the Yangtze River Delta show a pattern of low in the south and high in the north, with the spatial focus shifting to the north over time. There is a concentration of high levels of particulate matter in the Hefei-Nanjing-Wuxi area. (3) The effect of natural vegetation on the reduction and stabilization of atmospheric particulate matter concentration is better than that of artificial vegetation. (4) Needleleaf forests, broadleaf forests, and shrubs in natural vegetation are more capable of reducing and stabilizing atmospheric particulate matter than grasses. The study can provide a reference for regional air pollution control and regional plant system construction.

Air Quality Monitoring with Low-Cost Sensors: A Record of the Increase of PM2.5 during Christmas and New Year’s Eve Celebrations in the City of Queretaro, Mexico

The high concentrations of particulate matter (PM) in the atmosphere have adverse effects on both the environment and human health, as well as on urban and faunal biodiversity. Short-term events, such as the burning of fireworks, attributable to human activity, result in a significant and rapid increase in PM concentration levels within the atmosphere. We present findings derived from observations made using low-cost PM sensors deployed as part of the Environmental Monitoring Network in Querétaro, Mexico. These sensors utilize the Environmental Geo Monitoring Stations (EMGA) device, developed by the Institute of Geosciences at UNAM. The observation period spanned from 10 December 2023 to 10 January 2024, encompassed the Christmas and New Year holidays, and focused on PM2.5 concentrations, and a comparison with a similar period from 10 February 2024 to 10 March 2024 considered a typical activity period in the area. The results reveal two substantial increases in PM2.5 concentrations (from less than 50 µg/m3 to more than 340 µg/m3) over time and spatial distribution during this period, with notable increases observed during the festive season coincident with the Christmas and New Year celebrations. Specifically, prolonged periods exceeding up to 6 h were noted on festivity days. This research offers insight into the effects, trends, and spatial–temporal distribution of pollutants within the city of Queretaro, which may be used as a reference for other cities around the world.

Fungal aerosol and particulate matter in horse stables in Poland

Horses stay in different types of stables; especially during the cold season, they stay inside for most of the day. A stable is also a place where many people spend quite a lot of time either as employees who care for and train horses or as equine enthusiasts. Keeping horses in stables causes their constant exposure to high concentrations of particulate matter (PM) and molds in the air inside these facilities. The study was conducted in Udórz Stud Farm located in the southern region of Poland. It was carried out in two different types of stables: three runners and two box stables. The study continued for 2 years; samples were collected in each season of the year. The following devices were used: a six-stage Andersen-Graseby cascade impactor, the DustTrak™ II Aerosol Monitor 8530. The obtained results allowed for the conclusion that horses kept in box stables are exposed to lower concentrations of molds and yeasts than those kept in runners. Molds dominated in the stable air during humid periods—spring and autumn—while yeasts were more prominent during summer and winter. It was observed that cleaning stables reduces the morphotic elements of fungi in the air, even though it results in a higher level of particulate matter in the stable air. It should be noted that microclimate conditions were optimal for horses practically throughout the whole year.Key points• In stables, there is a high level of air intoxication, both by yeast and by mold fungi• The concentrations of fungi in the air depend on the season and the stable cleaning procedure• The PM concentrations depend on the type of stable

Timescales of Ecological Processes, Settling, and Estuarine Transport to Create Estuarine Turbidity Maxima: An Application of the Peter–Parker Model

The estuarine exchange flow increases the longitudinal dispersion of passive tracers and trap sinking particles, potentially creating an estuarine turbidity maximum (ETM): a localized maximum of suspended particulate matter concentration in an estuary. The ETM can have many implications: dead zones due to increased turbidity or hypoxia from organic matter decomposition, naval navigation challenges, and other water quality problems. Using timescales, we investigate how the interaction between exchange flow and particle sinking leads to ETMs by modeling a sinking tracer in an idealized box model of the Total Exchange Flow (TEF) first developed by Parker MacCready. Results indicate that the balance of particle sinking and vertical mixing is critical to determining ETM size and location. We then focus on the role of ecology in ETM formation through the use of the Peter–Parker Model, a new biophysical model which combines the TEF box model with a Nutrient–Phytoplankton–Zooplankton–Detritus (NPZD) model, the likes of which were first developed by Peter J.S. Franks. Detritus sinking rates similarly influence detritus peak concentration and location (an ETM), but detritus ETMs occur in a different location than the sinking tracer due to the influence of biological factors, which create a time lag of about 1 day. Lastly, we characterize the flow of the models with a dimensionless parameter that compares timescales and summarizes the dynamics of the sinking tracer in ETM formation and that can be used across systems.

Modern Methods and Technical Instruments of Ecological Monitoring of the Estuaries of Small Rivers

At present, river estuaries are constantly being explored; therefore, it is necessary to receive up-todate information on their actual environmental condition, hydrological regime, and geographical features. Therefore, it is important to develop and improve the methods and technical instruments for monitoring river estuaries. The article presents a developed methodology for integrated research of small-river estuaries using new methods and techniques. The article discusses the experience in using modern domestic devices for solving practical problems of integrated water-environmental monitoring with a case study of expeditionary work in estuaries of the Chernaya River (Crimean Peninsula, Sevastopol). The possibilities of the following technical instruments used are described: The GAP-AK-12R CTD (MHI RAS), the Condor biophysical complex (Aquastandard), and the Garmin echo sounder, which make it possible to obtain field data on turbidity, the concentration of suspended particulate matter, speed and direction of currents, the average size of suspended particles, and salinity and temperature of the aquatic environment. The results of applying a new method for determining the flow velocity and water consumption based on video image processing are presented. Application of the developed methodology makes it possible to obtain the information necessary to analyze the hydrological regime and ecological state of small-river estuaries.

Examining effects of air pollution on photovoltaic systems via interpretable random forest model

Renewable energy plays a vital role in power generation and solar photovoltaic systems due to resource availability throughout the year. This work aims to investigate the impact of air pollutants and meteorological parameters on the performance of the photovoltaic systems locally, taking into consideration the advantages of the photovoltaic power potential of the SW part of Romania, where Craiova is located (average solar radiation intensity >1350 kWh/m2/year). This study is based on a one-year dataset provided by a sensor that monitors particulate matter concentrations, volatile organic compounds, dioxide of carbon, ozone, noise, formaldehyde and three climate parameters (temperature, pressure, and relative humidity). The research methodology applies an innovative interpretable random forest model emphasising the implications of air pollution for photovoltaic systems. The proposed machine learning model was trained to predict the particulate matter level in air based on the basic environmental variable measurements. The study presents six random forest models of varying complexity, which reach the accuracy of classification for the selected problem up to 99 %, and applies the Shapley Additive Explanations technique to interpret the decision-making model. The observation regarding the highest concentration of particulate matter occurring during cold months, which typically do not align with peak solar irradiance, underscores the importance of considering various environmental factors in solar energy planning. With its practical implications, this insight offers decision-makers valuable information about the feasibility of optimising solar energy generation despite seasonal variations in air pollution levels, directly addressing their needs and concerns.

The influence of concentration and size on the error of particulate matter detection using charge induction method

Particulate matters generated from waste incineration carry charge due to collision and friction. By using charge induction method, it becomes feasible to detect particulate matter concentration by capturing the electrical signals emitted by particulate matters. In this study, a new charge induction device was constructed and tested. The investigation revealed a linear relationship between the sine wave eigenvalues of the electrical signals and the concentration of particulate matters. The corresponding formulas for peak-to-peak value, root mean square, and standard deviation were calculated, with R2 values greater than 0.98. Additionally, the influence of concentration and size on detection error was studied. The results showed that as the concentration increased or the size decreased, the detection error decreased. Furthermore, the study found that the impact of particulate matter concentration on detection results mitigated that of particulate matter size. The detection device, correlation formulas and influencing factors proposed in this study are expected to provide technical support and theoretical basis for particulate matter detection, offering significant value in the field of air pollution control.

Effects of particulate matter (PM2.5) concentration and components on mortality in chronic kidney disease patients: a nationwide spatial–temporal analysis

Chronic kidney disease (CKD) is a major global public health issue and the leading cause of death in Thailand. This study investigated the spatial–temporal association between PM2.5 and its components (organic carbon, black carbon, dust, sulfate, and sea salt) and CKD mortality in Thailand from 2012 to 2021. The Modern-Era Retrospective analysis for Research and Application version 2 (MERRA-2), a NASA atmospheric satellite model, was assessed for the temporal data of PM2.5 concentration and aerosol components. Spatial resources of 77 provinces were integrated using the Geographical Information System (GIS). Multivariate Poisson regression and Bayesian inference analyses were conducted to explore the effects of PM2.5 on CKD mortality across the provinces. Our analysis included 718,686 CKD-related deaths, resulting in a mortality rate of 1107 cases per 100,000 population where was the highest rate in Northeast region. The average age of the deceased was 72.43 ± 13.10 years, with males comprising 50.46% of the cases. Adjusting for age, sex, underlying diseases, co-morbidities, CKD complications, replacement therapy, population density, and income, each 1 µg/m3 increase in PM2.5, black carbon, dust, sulfate, and organic carbon was significantly associated with increased CKD mortality across 77 provinces. Incidence rate ratios were 1.04 (95% CI 1.03–1.04) for PM2.5, 1.11 (95% CI 1.10–1.13) for black carbon, 1.24 (95% CI 1.22–1.25) for dust, 1.16 (95% CI 1.16–1.17) for sulfate, and 1.05 (95% CI 1.04–1.05) for organic carbon. These findings emphasize the significant impact of PM2.5 on CKD mortality and underscore the need for strategies to reduce PM emissions and manage CKD co-morbidities effectively.

Exploratory Data Analytics of Air Pollutant Data for Air Quality Management Application

Purpose: With escalating concerns over the detrimental impacts of air pollution on public health and the environment, this study embarks on a comprehensive exploration of air quality dynamics, focusing on Ogun state metropolis of Nigeria. The primary objective is to contribute to the burgeoning field of air quality management by harnessing the power of data analytics, exploratory data analysis, and advanced python programming libraries. The study seeks to address critical questions regarding the current state of air quality, major sources of pollution, government interventions, and individual contributions to better air quality. Methodology: The research employs Exploratory Data Analysis to unveil intrinsic patterns and trends within historical air quality datasets extracted from IoT devices located across Ogun state. This initial phase aims to discern key statistical parameters, including mean concentrations, variations, and correlations among various pollutants. Subsequently, the study employs Topic Modeling to extract latent themes and sentiments from qualitative data, specifically focusing on public opinions gathered through digital surveys. Resulting topics provide insights into public perceptions regarding air quality, pollution sources, and the efficacy of governmental interventions. Findings: Experimental result is revealing of the diversity of the dataset. The EDA returned a mean Particulate Matter concentration of approximately 12.65 µg/m³, while the mean Nitric Oxide concentration is approximately 13.56 µg/m³. Notable correlations include a strong positive correlation between PM2.5 and PM10 (0.69), indicating a substantial association between fine and coarse particulate matter. Additionally, there is a noteworthy positive correlation between NO and Nox (0.97), suggesting a high degree of correlation between nitric oxide and nitrogen oxide levels. However, negative correlations are observed, such as the substantial negative correlation between RH (relative humidity) and PM2.5 (-0.46), implying an inverse relationship between humidity and fine particulate matter levels. The study develops an innovative air quality management application. Unique Contribution to Theory, Practice and Policy: The application aims to empower both the public and policymakers with actionable insights, fostering informed decision-making and collaborative efforts towards improving air quality. It is highly recommended that industry experts should continue to imbibe ethical standards in data acquisition and deployment practices.

A novel structure grey prediction model with strong compatibility and its application in forecasting the annual average concentration of particulate matter in Beijing

Beijing has long suffered from the events of fine particulate matter pollution. Consequently, it is of paramount importance to be able to scientifically and reasonably predict the concentration of particulate matter. To this end, a novel prediction model with an adaptive grey generation operator and fractional derivative is proposed. The novel model initially expands the range values for the order of the grey generation operator to enhance data preprocessing capabilities. Meanwhile, the introduction of fractional derivatives enhances the model’s adaptability and overcomes the limitations of traditional grey prediction models that use integer-order differential equations. Subsequently, the fractional derivative and operator orders of the novel model are optimized to find the best parameter combination, thereby enhancing the model's performance. Furthermore, the novel model is compatible with seven other models when its parameters take different values, thus expanding the model’s applicability. The novel model is then applied to simulate and predict the particulate matter concentration in Beijing, achieving a comprehensive mean relative percentage error of only 1.86%, significantly outperforming several similar models(17.56%,17.64%,3.42%). Finally, the paper presents a forecast of the particulate matter concentration in Beijing over the next four years.

The interaction between physical activity and ambient particulate matters on cognitive function among Chinese community-dwelling older adults

BackgroundThe interaction between physical activity (PA) and ambient particulate matters (PMs) on cognition is rarely investigated. Our study aimed to assess the interactions of PA and PMs on cognitive function in older adults. MethodsOur study comprised 3937 Chinese community-dwelling older adults. Cognition was evaluated using the Mini-Mental State Examination. PA information was gathered using the International Physical Activity Questionnaire. The data of PMs were obtained from China High Air Pollutants (CHAP). Linear regressions model and interaction plots were applied to assess and visualize the interaction of PA and PMs on cognition, respectively. Bayesian kernel machine regression (BKMR) method was employed to visualize discernible thresholds for the interaction. ResultsPMs were negatively associated with MMSE scores (PM1: β = −0.40, 95 % CI: −0.58, −0.28; PM2.5: β = −0.46, 95 % CI: −0.64, −0.29; PM10: β = −0.44, 95 % CI: −0.61, −0.26), and PA was positively affiliated with MMSE scores (β = 0.18, 95 % CI: −0.01, 0.38). Interaction plots and BKMR demonstrated that adverse connotations of PMs with MMSE increased with the elevated PA levels, and the positive associations of PA with MMSE scores were attenuated by increased PMs (all Pinteraction < 0.20). Discernible thresholds for the interaction between PMs and PA on MMSE were found. ConclusionsOur findings suggest that PA should not be taken at higher PMs concentrations, and that low level of PA could be performed in PMs polluted environment to improve cognitive function. Further experimental and cohort researches are required to reproduce our discovery.

Traffic-Related Air Pollution and Childhood Asthma—Are the Risks Appropriately Mitigated in Australia?

Childhood asthma is a major health issue in Australia, and traffic emissions play a causative role. Two urban planning policies that impact children’s exposure to traffic emissions are considered in terms of the potential health risks to children in a Melbourne suburb with high truck volumes and hospital attendances for childhood asthma. Firstly, the health impact assessment component of the state planning approval of a major road project, and secondly, local government placement of childcare centres and schools in relation to freight routes. Three sources of air quality monitoring data were examined: (i) a Victorian EPA reference site; (ii) a site with planning approval for development into a childcare centre; and (iii) five sites within the boundary of the West Gate Tunnel Project, an AUD 10 billion road and tunnel project. The Australian Urban Research Infrastructure Network data was utilised to assess distances of childcare centres and schools from major truck routes. A range of cconcentration–response functions for childhood asthma (0–18 years) from international systematic meta-analyses and a smaller Australian cross-sectional study were applied to comparative elevations in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations between the EPA reference monitor (used for project risk assessment) and local roadside data. It was found that comparative elevations in NO2 concentrations were associated with the following risk increases: developing asthma 13%, active asthma 12%, and lifetime asthma 9%. Overall, 41% of childcare centres (n = 51) and 36% of schools (n = 22) were ≤150 m to a high-density truck route. Truck emissions likely make a substantial contribution to childhood asthma outcomes in the project area. This study exemplifies how current practices may not be commensurate with guiding policy objectives of harm minimisation and equitable protection.

Decreasing trends in PM2.5 and BC concentrations observed on central and southwestern Japanese Islands

The interannual trends in fine particulate matter (PM2.5) and black carbon (BC) concentrations at remote stations in central (Noto) and southwestern (Fukue) Japan were investigated using statistical trend analysis and the chemical transport model, Regional Air Quality Model 2 (RAQM2ver3). The concentrations of PM2.5 and BC in Noto and Fukue exhibited seasonal variations and decreased from 2013 to 2020. Higher concentrations of PM2.5 and BC were observed during the spring season (April) in both locations. The PM2.5 concentrations peaked in Fukue approximately one month earlier than in Noto, while the BC concentration peak in Noto occurred one month earlier than that of PM2.5. The total reductions in PM2.5 concentrations in Noto and Fukue were 7.5 ± 5.0 and 3.8 ± 0.95 μg m−3, corresponding to reduction rates 1.1 ± 0.7 and 0.5 ± 0.95 μg m−3year−1, respectively, compared to the concentrations in 2013. The total reduction in BC concentrations in Noto from 2013 to 2019 was estimated to be 0.13 ± 0.1 μg m−3, amounting to 0.018 ± 0.014 μg m−3year−1 reduction rate. The results of the source–receptor relationships analysis suggest that the decreases in PM2.5 and BC concentrations in Fukue and Noto were significantly influenced by reductions in amounts transported from central China (CCHN, 30–40°N), northern China (NCHN, >40°N) and Japan (JPN) after 2015/2016. The decrease in emissions from these three regions accounted for the observed reductions in particulate concentrations.

Assessing the impact of particulate matter and formaldehyde in atmospheric air on public health

Introduction. The urgent task at hand is to establish a causal link between the occurrence of public health risks to the public and the impact of air pollution. To ensure the sanitary and epidemiological well-being of the public, it is important to consider the intake and distribution of pollutants in the atmosphere. In addition to anthropogenic sources of pollution, it is also necessary to take into account natural sources. The purpose of the work is to assess the influence of atmospheric pollution with formaldehyde and particulate matter on public health. Additionally, it aims to propose measures and recommendations to improve the environmental, health, and epidemiological well-being of the population. Materials and methods. Quantitative chemical analyses of particulate matter (PM) and formaldehyde in atmospheric precipitation were performed using the gravimetric and fluorometric methods, respectively. The general trend of annual changes in the content of PM in the atmosphere is shown to be based on the results of a study of atmospheric precipitation and air. Data on morbidity in the population was obtained from annual government reports. Results. A high correlation was found between general morbidity and atmospheric particulate matter for all population groups. However, formaldehyde was not found to be correlated with the general morbidity of the population, except for children, who may develop respiratory diseases under the influence of formaldehyde. Adults are the most vulnerable population to developing circulatory diseases, due to their high susceptibility to atmospheric particulate matter. Limitations. The gradation of particulate matter into specific fractions (PM10, PM2.5) and &lt;0.45 microns was not considered. Conclusion. The level of formaldehyde in the air does not significantly impact the environment or the health of the general population. However, an increase in airborne particulate matter can create an unfavourable environmental condition that poses a threat to public health. Adult populations are particularly vulnerable to these conditions, as they have a high risk of developing cardiovascular system diseases that are highly dependent on the content of particulate matter in the air. To reduce this negative impact, a number of measures need to be implemented including expanding monitoring of atmospheric air quality to monitor particles with sizes of PM10, PM2.5, notifying the public about predictions of particulate matter concentrations; conducting in-depth scientific research on the biomedical impacts of particulate matter on human health.

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.

Optimal ventilation strategies for balancing carbon dioxide and suspended particulate matter concentrations in vehicle cabins

Maintaining safe levels of carbon dioxide (CO2) and particulate matter (PM) in vehicle cabins during driving is essential to ensure driver safety and minimize health risks. However, while the fresh-air ventilation mode reduces the CO2 level, it also allows the ingress of PM pollutants from the external environment and hence increases the PM concentration in the cabin. Thus, effective ventilation strategies are required to optimize the air quality by balancing the average levels of CO2 and suspended PM within the cabin. Accordingly, this study performed computational fluid dynamics simulations to explore the effects of different air-conditioning ventilation modes on the indoor air quality in the cabin of a commercial sports utility vehicle. The simulations commenced by examining the optimal switching times between the recirculation and fresh-air modes that yielded an average CO2 concentration of less than 1000 ppm for various numbers of occupants in the cabin. Further simulations were then performed to investigate the effect of the air filtration efficiency of the in-cabin filter on the PM concentration in the cabin under the optimal ventilation strategies determined for 1–5 occupants. The results indicated that, as the number of occupants increased, the required fresh-air mode usage time also increased. Furthermore, to mitigate the corresponding increase in the PM concentration, a higher filtration efficiency was required. Overall, the results showed that the optimal switching cycles achieved the required indoor air quality within the cabin by successfully balancing the CO2 and suspended PM concentrations.

Investigating the impact of urban-environmental factors on air pollutants: a land use regression model approach and health risk assessment.

The presence of pollutants in the earth's atmosphere has a direct impact on human health and the environment. So that pollutants such as carbon monoxide (CO) and particulate matter (PM) cause respiratory diseases, cough headache, etc. Since the amount of pollutants in the air is related to environmental and urban factors, the aim of the current research is to investigate the relationship between the concentration of CO, PM2.5 and PM10 with urban-environmental factors including land use, wind speed and wind direction, topography, traffic, road network, and population through a Land use regression (LUR) model. The concentrations of CO, PM2.5 and PM10 were measured during four seasons from 26th of March 2022 to 16th of March 2023 at 25 monitoring stations and then the information about pollutant measurement points and Land use data were entered into the ArcGIS software. The annual average concentrations of CO, PM2.5 and PM10 were 0.7ppm, 18.94 and 60.76µg/m3, respectively, in which the values of annual average concentration of CO and PMs were outside the air quality guideline standard. The results of the health risk assessment showed that the hazard quotient values for all three investigated pollutants were lower than 1 and therefore, they were not in adverse conditions in terms of health effects. Among the urban-environmental factors affecting air pollution, the traffic variable is the most important factor affecting the annual LUR model of CO, PM2.5 and PM10, and then the topography variable is the second most effective factor on the annual LUR model of the aforementioned pollutants.

Feeding Habits and Prey Composition of Six Mesopelagic Fish Species from an Isolated Central Mediterranean Basin

Mesopelagic fishes hold an important position in marine food webs, serving as a link between lower trophic levels and top predators and transferring energy from their deep mesopelagic habitat to shallower oceanic layers. Despite their ecological importance, research on mesopelagic fishes’ diet and feeding habits in the Mediterranean Sea is far from thorough. The present work attempts to assess the preying patterns and diet composition of four myctophid (Benthosema glaciale, Ceratoscopelus maderensis, Myctophum punctatum, Notoscopelus elongatus) and two sternoptychid (Argyropelecus hemigymnus, Maurolicus muelleri) species from the Corinthian Gulf (Ionian Sea, Greece), sampled during pelagic trawl surveys in 2018 and 2019. Stomach vacuity was high for myctophids caught during daytime, a pattern which sternoptychids did not follow. Estimated trophic indices revealed high dietary diversity (Shannon’s H’ index) for most investigated species, but a narrow trophic niche breadth (Levins’ normalized Bn index). Copepods and various marine crustaceans were dominant in all diets, classifying them under the zooplanktivorous trophic guild, while A. hemigymnus exhibited high concentrations of particulate organic matter in their stomachs and N. elongatus exhibited consumption of fish. Diet overlap was significant among most studied mesopelagic species, as indicated by Shoener’s S index and confirmed by both the multidimensional scaling ordination and a hierarchical cluster analysis. Information on mesopelagic fishes’ diet composition in this poorly studied part of the Mediterranean is useful in further assessing and parameterizing marine food webs and midwater trophic interactions, as well as in quantifying the ensued energy transfer to top predators of commercial interest or conservation concerns.

Prenatal Exposure to Ambient Air Pollution and Cerebral Palsy

Air pollution is associated with structural brain changes, disruption of neurogenesis, and neurodevelopmental disorders. The association between prenatal exposure to ambient air pollution and risk of cerebral palsy (CP), which is the most common motor disability in childhood, has not been thoroughly investigated. To evaluate the associations between prenatal residential exposure to ambient air pollution and risk of CP among children born at term gestation in a population cohort in Ontario, Canada. Population-based cohort study in Ontario, Canada using linked, province-wide health administrative databases. Participants were singleton full term births (≥37 gestational weeks) born in Ontario hospitals between April 1, 2002, and March 31, 2017. Data were analyzed from January to December 2022. Weekly average concentrations of ambient fine particulate matter with a diameter 2.5 μm (PM2.5) or smaller, nitrogen dioxide (NO2), and ozone (O3) during pregnancy assigned by maternal residence reported at delivery from satellite-based estimates and ground-level monitoring data. CP cases were ascertained by a single inpatient hospitalization diagnosis or at least 2 outpatient diagnoses for children from birth to age 18 years. The present study included 1 587 935 mother-child pairs who reached term gestation, among whom 3170 (0.2%) children were diagnosed with CP. The study population had a mean (SD) maternal age of 30.1 (5.6) years and 811 745 infants (51.1%) were male. A per IQR increase (2.7 μg/m3) in prenatal ambient PM2.5 concentration was associated with a cumulative hazard ratio (CHR) of 1.12 (95% CI, 1.03-1.21) for CP. The CHR in male infants (1.14; 95% CI, 1.02-1.26) was higher compared with the CHR in female infants (1.08; 95% CI, 0.96-1.22). No specific window of susceptibility was found for prenatal PM2.5 exposure and CP in the study population. No associations or windows of susceptibility were found for prenatal NO2 or O3 exposure and CP risk. In this large cohort study of singleton full term births in Canada, prenatal ambient PM2.5 exposure was associated with an increased risk of CP in offspring. Further studies are needed to explore this association and its potential biological pathways, which could advance the identification of environmental risk factors of CP in early life.