Concentration Levels Of Pollutants Research Articles

Air Quality-Driven Traffic Management Using High-Resolution Urban Climate Modeling Coupled with a Large Traffic Simulation

This study presents a framework for integrating traffic simulation with high-resolution air pollution modeling to design adaptive traffic management policies aimed at reducing urban air pollution. Building on prior work that establishes the coupling of the MATSim traffic model with the PALM-4U urban climate model, this second part focuses on implementing a feedback loop to inform traffic management decisions based on simulated air pollution concentration levels. The research explores how traffic volumes and atmospheric conditions, such as boundary layer dynamics, influence air quality throughout the day. In an artificial case study of Berlin, a time-based toll is introduced, aimed at mitigating concentration peaks in the morning hours. The toll scheme is tested in two simulation scenarios and evaluated regarding the effectiveness of reducing air pollution levels, particularly NO2 during the morning hours. The case study results serve to illustrate the framework’s capabilities and highlight the potential of integrating traffic and environmental models for adaptive policy design. The presented approach provides a model for responsive urban traffic management, effectively aligning transportation policies with environmental goals to improve air quality in urban settings.

Machine learning identifies routine blood tests as accurate predictive measures of pollution-dependent poor cognitive function.

Several modifiable risk factors for dementia and related neurodegenerative diseases have been identified including education level, socio-economic status, and environmental exposures - however, how these population-level risks relate to individual risk remains elusive. To address this, we assess over 450 potential risk factors in one deeply clinically and demographically phenotyped cohort using random forest classifiers to determine predictive markers of poor cognitive function. This study aims to understand early risk factors for dementia by identifying predictors of poor cognitive performance amongst a comprehensive battery of imaging, blood, atmospheric pollutant and socio-economic measures. Random forest modelling was used to determine significant predictors of poor cognitive performance in a cohort of 324 individuals (age 61.6 ± 4.8 years; 150 males, 174 females) without extant neurological disease. 457 features were assessed including brain imaging measures of volume and iron deposition, blood measures of anaemia, inflammation, and heavy metal levels, social deprivation indicators and atmospheric pollution exposure. Routinely assessed markers of anaemia including mean corpuscular haemoglobin concentration were identified as robust predictors of poor general cognition, where both extremes (low and high) were associated with poor cognitive performance. The strongest, most consistent predictors of poor cognitive performance were environmental measures of atmospheric pollution, in particular, lead, carbon monoxide, and particulate matter. Feature analysis demonstrated a significant negative relationship between low mean corpuscular haemoglobin concentration and high levels of atmospheric pollutants highlighting the potential of routinely assessed blood tests as a predictive measure of pollution-dependent cognitive functioning, at an individual level. Taken together, these data demonstrate how routine, inexpensive medical testing and local authority initiatives could help to identify and protect at-risk individuals. These findings highlight the potential to identify individuals for targeted, cost effective medical and social interventions to improve population cognitive health.

Outdoor air pollution exposure and the risk of type 2 diabetes mellitus: A systematic umbrella review and meta-analysis.

The association between different air pollutants and Type 2 Diabetes Mellitus (T2DM) is a growing topic of interest in public health research. This umbrella review and meta-analysis aimed to consolidate current literature on the association between various outdoor air pollutants and T2DM. Subgroups and dose-response relationships were also analyzed to further quantify the association, especially by the factors such as the type of pollutants, duration of exposure, and geographical variation, etc. A thorough literature search of three databases revealed a total of 71 records for umbrella review and 1524 records for meta-analysis where 8 studies were included in the final review of umbrella review and 46 studies for meta-analysis. The evaluation of the study's quality in umbrella review and meta-analysis were conducted using the AMSTAR 2 criteria and the Newcastle-Ottawa Scale (NOS), respectively. Exposure to Particulate Matter (PM) 2.5, PM10, Nitrogen dioxides (NO2) and Ozone (O3) were significantly associated with the risk of T2DM [OR=1.12 (95% Confidence Interval (CI): 1.09, 1.15), 1.12 (95% CI: 1.06, 1.18), 1.09 (95%CI: 1.07, 1.12), 1.05 (95%CI: 1.03, 1.08), respectively] and subgroup analysis further revealed that PM2.5, PM10, and NO2 associations were confounded by factors such as ages, study design, regions of exposure and air pollution concentration levels. Lastly, only exposure to PM10 had a significant dose-response relationship with the risk of T2DM (p-value=0.000). These findings further emphasized the need for standardized methods in conducting air pollution research and additional research on other air pollutants to further explore the relationships between these air pollutants and T2DM.

DIURNAL EFFECT OF PM10 AND NOX ON CHRONIC OBSTRUCTIVE PULMONARY DISEASE AND ASTHMA IN ABUJA NIGERIA

Introduction: Air pollution is emerging as a crucial risk factor for respiratory health problems like Chronic Obstructive Pulmonary Disease (COPD) and asthma in developing countries, including Nigeria where air pollutant concentrations are elevated. In these countries, urban and peri-urban areas like Abuja bear the bulk of the problem. Nevertheless, data on the health effects of air pollution and pollutants are limited. This study aimed to critically examine the health effects of particulate matter less than 10 μg/m3 (PM10), and nitrogen dioxide (NO2) in Abuja FTC, Nigeria. Methodology: The diurnal effect of PM10, and NO2 concentrations was used to examine COPD and asthma patients due to exposure to these pollutants from November 2015 to December 2018. PM10 and NO2 air concentrations were monitored by the Nigerian Metrological Agency throughout the study. The 402 participants recruited were part of a major study which examined the effect of photochemical smog on COPD and asthma patients. For this study, participants completed the Medical Research Council (MRC) dyspnoea questionnaire; lung function tests was performed to determine the airway obstructive level in association with the pollutant at varying concentrations. Results: The 24-hour mean of PM10 was 296.7µg/m3, and the NO2 1-hour mean was 253.1µg/m3, which are both higher than the WHO set thresholds. The diurnal variation of PM10 varied from an average of 149.5µg/m3 in the morning to 345.3µg/m3 during the afternoon (dry season) and 108.5µg/m3 to 250.4µg/m3 (wet season). The PM10 increase was significantly associated with decreased forced expiration volume (FEV)1 and forced vital capacity (FVC) in the participants (-786, P=.000); with a moderate significant association between NOx and FVC (-.582, P= .018). A significant association was also observed between PM10 with Dyspnoea (-.786, P=.000). When we stratified for gender, it was observed that women had a higher significance P=.001 and for every 10 μg/m3 increase in NO2 and PM10 concentration above the WHO recommended 24hr thresholds, the relative risk for developing respiratory symptoms was 1.09 (95% CI: 1.07 to 1.05) and 1.06 (95% CI: 1.01 to 1.10), respectively. Conclusion/Recommendation: There is high level of pollutant concentration with strong association with COPD and asthma symptoms in FCT Abuja. Immediate policies and actions are needed to reduce pollutants from various sources including from transport and energy manufacturing facilities.

The effectiveness of ozonation treatment in reducing ph, turbidity, bod, cod, and toc in pharmaceutical industrial wastewater

Abstract The pharmaceutical industry’s fabrication of pharmaceutical products releases byproducts that have the potential to negatively impact the environment. Indicators of contaminants include characteristics such as COD, BOD, TOC, pH, and turbidity that are within acceptable standards of excellence. The objective of this study was to look at fluctuations in the concentrations of pharmaceutical wastewater substances parameters before and following treatment with the ozonation method, as well as to assess the ozonation procedure’s performance on pharmaceutical wastewater contaminants parameters. The ozonation methods, which altered the O3 retention period, served as the independent variable in this study. The dependent variables applied were pH optimalization, and the removal efficiency of turbidity, BOD, COD, and TOC. Controlling variables involve the starting pollutant concentration, pressure and temperature levels, and equipment conditions. To determine the ideal time variation for the ozonation process, many variations will be evaluated. However, several parameters (e.g. pH, BOD, and TOC) were not reducing with the designed time variations. Nevertheless, this approach was effective in reducing turbidity levels by 48.5% and COD levels by 55.4% over 15 minutes.

Modeling Land Use and Land Cover Changes and Its Atmospheric Pollutant Concentration in the Coal Mining Area of Ramgarh District of Jharkhand, India, Using Multi‐Layer Perceptron Neural Networks (MLPNN)

ABSTRACTLand use refers to anthropogenic phenomena in the natural environment; humans utilize land resources for their developmental activities. On the other hand, the ecosystems of land use and land cover alter the natural world—the artificial infrastructure leads toward a busted concrete jungle instead of a green footprint. The global green footprint is continually shrinking owing to overutilization of natural resources. The present research examines the land use pattern that changes from 1990 to 2021 and projected projections for 2041 and 2061 in the Ramgarh District. The study also focuses on how artificial modifications alter the concentration level of pollutants in the atmosphere. The Landsat data utilized for 1990, 2000, 2011, and 2021 were incorporated into the LULC map using supervised classification and for analysis of future predictions for 2041 and 2061 using an ANN‐based on MLPNNs (multi‐layer perceptron neural networks) for Ramgarh District. It also focuses on the trend and patterns of atmospheric pollutants from data using NASA‐GIOVANNI MERRA‐2. The current study reveals that in 1990, water bodies, coal mining, vegetation, built‐up, agriculture, and barren land were 3.01%, 2.24%, 54.07%, 3.64%, 36.85%, and 0.18 %. However, in 2021, water bodies decreased to 1.61%, vegetation to 45.47%, barren land to 0.65%, and an increasing tendency was observed in built‐up areas to 6.65%, coal mining to 2.43%, and farmland to 43.19%. A significant trend in atmospheric pollutants, such as CO2, SO2, SO4, NO2, and dust, is observed in the Ramgarh district. The importance of this study is to attain the maximum level of environmental sustainability; it would also encourage the local level planning fitted during the extraction of natural resources.

A predictive model for the estimation of industrial [formula omitted] emissions for IoT-based devices

The paper is devoted to solving the problem, associated with increasing the forecasting accuracy of pollutant concentration level based on PM2.5 dust. The LANN model (LANN – Lagrange powered Artificial Neuron Network), proposed in the paper, allows you to take into account the mutual influence of pollution sources on the concentration level at monitoring points, which, on the one hand, allows to manage the volume of emissions from industrial enterprises in such a way as to avoid penalties and, on the other hand, allows the regulatory agencies to determine points for environmental control, at which permissible standards will be most likely exceeded. The suggested solution makes it possible to use low-cost IoT-based air quality monitoring devices, which were primarily considered not applicable due to their low accuracy and exposure to the influence of short-term stochastic factors; besides, no models were available, which would be capable to provide the necessary accuracy and forecast horizon on the base of IoT data. At the same time, the obtained model has low requirements for computing resources compared to dispersion models and is dependent on the accuracy of weather information; besides, the model allows you to take into account trends and obtain better forecast values in terms of accuracy than the models, that use regression methods, neural network models and ML models, stochastic models and their ensembles. The application of the model improves well-known pollutant dispersion estimation calculation techniques through the use of measurement data flow and dynamic adaptation to changing conditions. The model was trained on the dataset over the period 2022–2023 collected from 5 monitoring devices, which were installed in the catchment area of the group, incl. 13 industrial points of PM2.5 pollutant sources.

Evaluating the impacts of printing operations on indoor air quality in a printing press

PurposeBuilding operations and human activities indoors continuously affect air quality, contaminating the air and sometimes exceeding permissible limits which can be health threatening either in the short or long time. This implies a need for strict awareness and compliance with air quality standards, particularly in workplaces prone to air contaminants emissions. This study aims to evaluate printing-related pollutant concentrations and their effects on indoor air quality (IAQ). The study investigated a printing press's total volatile organic compounds (TVOC), particulate matter (PM), carbon monoxide and carbon dioxide emissions.Design/methodology/approachThis study used mainly an experimental research design supported by physical assessment by identifying the major printing-related pollutants, assessing the existing situation and measuring pollutant concentration levels using literature reviews, walkthrough inspections and experiments, respectively. The measurements were conducted in two scenarios: with and without printing activities, and the results were compared with relevant standards and guidelines.FindingsThe outcomes indicate that TVOC concentration reaches 120 ppb during printing and binding activities, exceeding the 75 ppb acceptable limit based on the time-weighted average. The PM2.5 concentrations reach 49 µg/m3 and PM10 up to 150 µg/m3, exceeding acceptable levels given by the US Environmental Protection Agency (EPA), which are 35 µg/m3 and 150 µg/m3 for PM2.5 and PM10, respectively. These high concentrations of TVOC and PM indicate a significant risk to the health of building occupants, particularly those with respiratory conditions. PM concentrations do not exceed permissible levels when no printing or bookbinding occurs, suggesting that printing-related activities can contribute to elevated TVOC and PM concentrations.Social implicationsThe social implication of the study lies in its ability to promote awareness among workers and improve their well-being which in turn relates to productivity. The study outcome could also encourage businesses to adopt more responsible environmental and social practices as part of corporate social responsibility practices.Originality/valueThe study's findings, which highlight the need for improved ventilation in printing halls, have the potential to significantly benefit building system designers, facility managers, policymakers and decision-makers. By providing information and theoretical support, the research can help integrate policies that regulate IAQ by reducing pollutant concentrations. This protects workers' health and helps update and enforce stricter IAQ regulations for industrial operations.

Occurrence, Bioaccumulation, and Risk Assessment of Organophosphate Esters in Rivers Receiving Different Effluents.

Organophosphate esters (OPEs), as alternatives to brominated flame retardants, are extensively used in both production and daily life, with their environmental contamination and toxic effects being a concern. This study investigated the concentration levels, bioaccumulation, and ecological effects of OPEs in five different effluent-receiving rivers. The results demonstrate that the concentration range of Σ13OPEs across the five rivers was between 142.23 and 304.56 ng/L (mean: 193.50 ng/L). The highest pollution levels of OPEs were found in rivers receiving airport and industrial wastewater, followed by agricultural wastewater, mixed wastewater, and domestic wastewater. Tris(2-chloroisopropyl) phosphate (TCPP), triethyl phosphate (TEP), and tricresyl phosphate (TCrP) were identified as the main pollutants. The accumulation concentrations of OPEs in fish ranged from 54.0 to 1080.88 ng/g dw, with the highest bioaccumulation found in Pelteobagrus fulvidraco, followed by Carassius auratus and Misgurnus anguillicaudatus. The brain was the primary organ of accumulation, followed by the liver, gills, intestine, and muscle. Tri-n-propyl phosphate (TPeP) and TEP exhibited the highest bioconcentration, with log BAF values exceeding three. The bioaccumulation of OPEs was influenced by pollutant concentration levels, hydrophobic properties, and biological metabolism. Ecological risk assessment revealed that the cumulative risk values of Σ13OPEs ranged from 0.025 to 16.76, with TCrP being the major contributor. It posed a medium-low risk to algae but a high risk to crustaceans and fish.

Enhancing air pollution prediction: A neural transfer learning approach across different air pollutants

Air pollution stands out as one of the most alarming environmental challenges. It poses significant risks to human health and the environment. Accurate forecasting of air pollutant concentration levels is crucial for effective air quality management and timely implementation of mitigation strategies. In this paper, the transfer learning technique is investigated using the artificial neural network (ANN), also called multi-layer perception (MLP), to transfer knowledge across different air pollutants forecasting, and therefore, to generalize over a large set of air pollutants in the same air monitoring station. By leveraging the knowledge learned from a source forecasting task, transfer learning allows us to reduce the data requirements, speed up the training of the models, and enhance the predictive performance for different air pollutants for the target forecasting task. We present a comprehensive analysis of the transfer learning across different air pollutants in the same air monitoring station on a large dataset of air quality measurements. Our results demonstrate that transfer learning significantly improves forecasting accuracy with fewer fine-tuning data, particularly when limited labeled data is available for the target task. The findings of this study contribute to the advancement of air pollution forecasting methodologies, facilitating better decision-making processes and proactive air quality management.

Evaluation of Deep Learning Models for Predicting the Concentration of Air Pollutants in Urban Environments

Air pollution is an issue of great concern globally due to the risks to the health of humanity, animals, and ecosystems. On the one hand, air quality monitoring systems allow for determining the concentration level of air pollutants and health risks through an air quality index (AQI). On the other hand, accurate future predictions of air pollutant concentration levels can provide valuable information for data-driven decision-making to reduce health risks from short- and long-term exposure when indicators exceed permissible limits. In this paper, five deep learning architectures are evaluated to predict the concentration of particulate matter pollutants (in their fractions PM2.5 and PM10) and carbon monoxide (CO) in consecutive hours. The proposed prediction models are based on recurrent neural networks (RNNs), long short-term memory (LSTM), vanilla LSTM, Stacked LSTM, Bi-LSTM, and encoder–decoder LSTM networks. Moreover, a methodology is presented to guide the construction of the prediction model, encompassing raw data processing, model design and optimization, and neural network training, testing, and evaluation. The results underscore the precision and reliability of the Stacked LSTM model in predicting the hourly concentration level for PM2.5, with an RMSE of 3.4538 μg/m3. Similarly, the encoder–decoder LSTM model accurately predicts the concentration level for PM10 and CO, with an RMSE of 3.2606 μg/m3 and 2.1510 ppm, respectively. These evaluations, with their minimal differences in error metrics and coefficient of determination, validate the effectiveness and superiority of the deep learning models over other reference models, instilling confidence in their potential.

Real-time IoT-powered AI system for monitoring and forecasting of air pollution in industrial environment

Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH3, CO, NO2, CH4, CO2, SO2, O3, PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings.

Impacts of daily household activities on indoor particulate and NO2 concentrations; a case study from oxford UK

This study explores indoor air pollutant (PM1, PM2.5 and NO2) concentrations over a 15-week period during the COVID-19 pandemic in a typical suburban household in Oxford, UK. A multi-room intensive monitoring study was conducted in a single dwelling using 10 air quality sensors measuring real-time pollutant concentrations at 10 second intervals to assess temporal and spatial variability in PM1, PM2.5 and NO2 concentrations, identify pollution-prone areas, and investigate the impact of residents' activities on indoor air quality. Significant spatial variations in PM concentrations were observed within the study dwelling, with highest hourly concentrations (769.0 & 300.9 μg m−3 for PM2.5, and PM1, respectively) observed in the upstairs study room, which had poor ventilation. Cooking activities were identified as a major contributor to indoor particulate pollution, with peak concentrations aligning with cooking events. Indoor NO2 levels were typically higher than outdoor levels, particularly in the kitchen where a gas-cooking appliance was used. There was no significant association observed between outdoor and indoor PM concentrations; however, a clear correlation was evident between kitchen PM emissions and indoor levels. Similarly, outdoor NO2 had a limited influence on indoor air quality compared to kitchen activities. Indoor sources were found to dominate for both PM and NO2, with higher Indoor/Outdoor (I/O) ratios observed in the upstairs bedroom and the kitchen. Overall, our findings highlight the contribution of indoor air pollutant sources and domestic activities to indoor air pollution exposure, notably during the COVID-19 pandemic when people were typically spending more time in domestic settings. Our novel findings, which suggest high levels of pollutant concentrations in upstairs (first floor) rooms, underscore the necessity for targeted interventions. These interventions include the implementation of source control measures, effective ventilation strategies and occupant education for behaviour change, all aimed at improving indoor air quality and promoting healthier living environments.

Impact of indoor air pollutant concentration levels on the health of press operators of printing facilities in Zaria Metropolis, Nigeria

Impact of indoor air pollutant concentration levels on the health of press operators of printing facilities in Zaria Metropolis, Nigeria

The interactive effects of temperature and air quality on productivity

ABSTRACT This paper estimates the effects of carbon monoxide, sulphur dioxide, nitrogen dioxide, ozone, and particulate matter on the productivity at a Midwestern U.S.A. factory using real-time data from 1983–89. We confirm that elevated concentrations of the five pollutants significantly reduces productivity, with ozone and carbon monoxide having the largest effects. We also find that models including only one pollutant may yield biased marginal effects, with the largest bias in ozone, sulphur dioxide, and nitrogen dioxide. Additionally, omitting ozone leads to a substantial bias in the estimation of the productivity effects of temperature. This suggests that both temperature and ozone must be accounted for. Finally, we find that high temperatures meaningfully modify the marginal effects of pollutants, except for sulphur dioxide. Our results are found at pollution concentration levels that are lower than the current EPA standards.

A Short-Term Air Pollutant Concentration Forecasting Method Based on a Hybrid Neural Network and Metaheuristic Optimization Algorithms

In the contemporary era, global air quality has been adversely affected by technological progress, urban development, population expansion, and the proliferation of industries and power plants. Recognizing the urgency of addressing air pollution consequences, the prediction of the concentration levels of air pollutants has become crucial. This study focuses on the short-term prediction of nitrogen dioxide (NO2) and sulfur dioxide (SO2), prominent air pollutants emitted by the Kerman Combined Cycle Power Plant, from May to September 2019. The proposed method utilizes a new two-step feature selection (FS) process, a hybrid neural network (HNN), and the Coot optimization algorithm (COOT). This combination of FS and COOT selects the most relevant input features while eliminating redundant ones, leading to improved prediction accuracy. The application of HNN for training further enhances the accuracy significantly. To assess the model’s performance, two datasets, including real data from two different parts of Combined Cycle Power Plant in Kerman, Iran, from 1 May 2019 to 30 September 2019 (namely dataset A and B), are utilized. Subsequently, mean square error (MSE), mean absolute error (MAE), root mean square deviation (RMSE), and mean absolute percentage error (MAPE) were employed to obtain the accuracy of FS-HNN-COOT. Experimental results showed MSE of FS-HNN-COOT for NO2 ranged from 0.002 to 0.005, MAE from 0.016 to 0.0492, RMSE from 0.0142 to 0.0736, and MAEP from 4.21% to 8.69%. Also, MSE, MAE, RMSE, and MAPE ranged from 0.0001 to 0.0137, 0.0108 to 0.0908, 0.0137 to 0.1173, and 9.03% to 15.93%, respectively, for SO2.

Health risk assessment in sediment cores: A preliminary study in Erdek and Bandırma Bays, Türkiye

The contents of heavy metals in the core sediments of Erdek and Bandırma Bays were investigated to determine the pollution level, metal distribution and the associated human risks. It is very important to assess the concentration level of metal pollutants and health risk to adults and children. Therefore, metal analysis, geoaccumulation index (Igeo) contamination degree (Cdeg), modified contamination degree (mCdeg), toxic unit (TU), toxic risk index (TRI), modified hazard quotient (mHQ), and human risk assessment were carried out. The measured metals in the sediments were in the range of Al: 0.71–6.5%, Fe: 0.73–6.7%, Mn:100–923 mg/kg, As: 0.5–28 mg/kg, Co: 0.1–21 mg/kg, Cr: 31–595 mg/kg, Cu: 0.1–58 mg/kg, Ni: 11–196 mg/kg, Pb: 3–72 mg/kg, V: 19–159 mg/kg, Zn: 6–255 mg/kg, Hg: 0.03–1 mg/kg. Based on the Igeo, the mean values were < 0, except for Hg in cores MD72 (0.1), BD1 (0.9), and ER1 (0.2), which means that both bays are “unpolluted to moderately contaminated” with Hg. The mean mCdeg values of Erdek and Bandırma Bays sediments were very low contaminated with selected metals. The mean values of ΣTUs values for the cores (BK1, MD72, BD1 and ER1) ranged from 2.8 to 1.0, 4.9–0.5, 5.8–0.5, and 2.9–0.7, respectively. The mHQ-Hg and -Ni values indicated moderate to considerable significant contamination to living organisms, while it was for -Zn, -Cu, and –Pb indicated low to very low contamination to living organisms. There was no toxic risk to the marine ecosystem according to the TRI value (<5). Based on the human risk assessment using to the Hazard Quotient (HQ) that recommended by the US EPA, there are no adverse health effects by ingestion and dermal contact. Moreover, there are no non-carcinogenic and carcinogenic harm to adults and children. The results of this study will serve as a preliminary baseline human health risk assessment for Bandırma and Erdek Bays.

Comprehensive analysis of air pollution and the influence of meteorological factors: a case study of adiyaman province.

Adıyaman, a city recently affected by an earthquake, is facing significant air pollution challenges due to both anthropogenic activities and natural events. The sources of air pollution have been investigated using meteorological variables. Elevated southerly winds, especially prominent in spring and autumn, significantly contribute to dust transport, leading to a decline in local air quality as detected by the HYSPLIT model. Furthermore, using Suomi-NPP Thermal Anomaly satellite product, it is detected and analyzed for crop burning activities. Agricultural practices, including stubble burning, contribute to the exacerbation of PM10 pollution during the summer months, particularly when coupled with winds from all directions except the north. In fall and winter months, heating is identified as the primary cause of pollution. The city center located north of the station is the dominant source of pollution throughout all seasons. The study established the connection between air pollutants and meteorological variables. Furthermore, the Spearman correlation coefficients reveal associations between PM10 and SO2, indicating moderate positive correlations under pressure conditions (r = 0.35, 0.52). Conversely, a negative correlation is observed with windspeed (r = -0.35, -0.50), and temperature also exhibits a negative correlation (r = -0.39, -0.54). During atmospheric conditions with high pressure, PM10 and SO2 concentrations are respectively 41.2% and 117.2% higher. Furthermore, pollutant concentration levels are 29.2% and 53.3% higher on days with low winds. Last, practical strategies for mitigating air pollution have been thoroughly discussed and proposed. It is imperative that decision-makers engaged in city planning and renovation give careful consideration to the profound impact of air pollution on both public health and the environment, particularly in the aftermath of a recent major earthquake.

Variability in molecular composition and optical absorption of atmospheric brown carbon aerosols in two contrasting urban areas of China

Atmospheric brown carbon (BrC) aerosols were investigated at two urban sites in southern (Hefei) and northern (Shijiazhuang) China during summer and winter of 2019–2020 to explore regional variability in their compositional and optical properties. Organic matter in ambient PM2.5 samples were characterized at molecular level using ultrahigh performance liquid chromatography coupled with a diode array detector and an Orbitrap mass spectrometer. Although the molecular composition of organic aerosols varied substantially over different ambient environments, they were mainly composed by CHO and CHON species in positive ionization mode while CHO and CHOS species in negative mode. The mass absorption coefficients of BrC aerosols at wavelength range 250–450 nm were relatively higher for winter samples in both cities and for Shijiazhuang samples in both seasons, partly attributed to the higher concentration levels of anthropogenic air pollutants in these environments. The absorption Ångström exponents further revealed that BrC aerosols in winter seasons and in Shijiazhuang had a greater capacity of absorption at shorter wavelengths. A total of 26 BrC species with strong absorption were unambiguously identified from different environments, which mainly consisted of CHO, CHON, and CHN species and had higher degrees of unsaturation and lower degrees of oxidation. The presence and abundance of these BrC species varied dynamically across the seasons and cities, with a greater number of species presented in the winter of Shijiazhuang. The BrC species together contributed 12–26 % in the total absorbance of light-absorbing organic components at 250–450 nm. This study highlights the regional differences in BrC properties influenced by the sources and atmospheric processes, which should be taken into account to assess their climate impacts.

Modeling SO2 dispersion from future eruptions in the Auckland Volcanic Field, New Zealand

Auckland city (pop. 1.7 M) is Aotearoa New Zealand’s largest city and an important economic hub. The city is built upon the active intraplate basaltic Auckland Volcanic Field (AVF). An AVF eruption would cause considerable impacts. An important component of volcanic risk management is assessing the likely volcanic hazards to help inform emergency planning and other preparedness activities. Previous volcanic hazard assessments for the AVF, particularly those for emergency planning scenarios, have modeled multiple volcanic hazards including lava flows, pyroclastic density currents, ballistic projectiles and tephra fall. Despite volcanic gas being an important and impactful hazard from intraplate basaltic field eruptions, there has been limited consideration of volcanic gas in AVF hazard assessment to date. This project is one of the first to quantitatively assess potential volcanic gas hazards for an explosive eruption scenario. For basaltic volcanism, sulfur dioxide (SO2) gas is typically the most consequential volcanic gas emitted. The aim of this exploratory study was to model SO2 dispersion from a high impact eruption during weather conditions conducive to high ground level pollutant concentrations. Since ground level SO2 concentrations are influenced by complex wind patterns resulting from interactions of locally driven flow circulations and topographically influenced weather, we modeled SO2 dispersion using the HYSPLIT model, a state-of-the art hybrid Eulerian and Lagrangian dispersion model widely used for volcanic gases, using high-resolution meteorological forcing fields given by the Weather Research and Forecasting (WRF) model.Modeled air parcel trajectories and ground level SO2 concentrations illustrate the effect of the converging sea breeze winds on SO2 dispersion. Under worst-case dispersion conditions, extensive areas of up to hundreds of square kilometers to the north and northwest of the eruption location would exceed New Zealand short-term (24 h) air quality standards and guidelines for SO2, indicating heightened health risks to downwind communities. Using this numerical modeling approach, this work presents a methodology for future applications to other AVF eruption scenarios, with a wider range of meteorological conditions that can help in exploring consequences for health services such as anticipated emergency department respiratory admissions.