Atmospheric Pollution Research Articles

Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application

The problem of ground-level ozone (O3) pollution has become a global environmental challenge with far-reaching impacts on public health and ecosystems. Effective control of ozone pollution still faces complex challenges from factors such as complex precursor interactions, variable meteorological conditions and atmospheric chemical processes. To address this problem, a convolutional neural network (CNN) model combining the improved particle swarm optimization (IPSO) algorithm and SHAP analysis, called SHAP-IPSO-CNN, is developed in this study, aiming to reveal the key factors affecting ground-level ozone pollution and their interaction mechanisms. Firstly, an atmospheric dispersion model is utilized to predict the distribution concentration of VOCs emitted by enterprises in the park at the target monitoring stations based on the ozone generation mechanism. Then three mainstream machine learning models are compared for SHAP analysis to obtain the significance results of relevant features. Finally, the IPSO algorithm is combined with SHAP analysis to dynamically adjust the training features to optimize the performance of the CNN model. The model integrates atmospheric pollutants and related meteorological data to explore the nonlinear influence relationship of ozone formation in depth. The performance of the model is validated by the comprehensive evaluation indexes of R2, MAE and RMSE, and the results show that the present model outperforms the IPSO-CNN and SHAP-PSO-CNN models with the performance indexes of R2 of 0.9492, MAE of 0.0061 mg/m3 and RMSE of 0.0084 mg/m3. This study not only advances the understanding of ozone pollution formation mechanisms, but also provides an assessment of the impact of VOCs emissions from enterprises in the park, which provides empirical support for environmental management.

ПРОСТРАНСТВЕННЫЙ АНАЛИЗ ЗАГРЯЗНЕНИЯ ВОЗДУШНОГО БАССЕЙНА ЗАПАДНО-КАЗАХСТАНСКОЙ ОБЛАСТИ

The article is devoted to spatial analysis of atmospheric pollutions in the West Kazakhstan region. Changes are observed in the structure of air pollution, which is associated with an increase in production, the share of transport, and the construction of new industries. However, in terms of types of economic activity, the main share belongs to industry. It has been established that the main pollutants of the atmosphere are the oil and gas, energy sectors, as well as transport. Research shows that the regions of the territorial distribution of industrial facilities and the region of social activity (Burli, Baiterek, Uralsk) have the greatest anthropogenic impact on the atmosphere. Purification of atmospheric air from harmful impurities, primarily solid particles, is partially carried out only in these zones, while in other areas of the region there are no treatment plants. The above-mentioned northern areas of the industrial zone of the region are characterized by high values of calculated air pollution coefficients. In contrast, in the Akzhaik, Karatobinsky, Zhanibek and Bokeyordinsky districts, the indicated indicators are minimal, which indicates a significant inequality in the distribution of atmospheric pollution. The completed work is the basis for an in-depth analysis of the assessment of atmospheric air quality in the industrial-agrarian center.

A five-year Study Using Sentinel-5P Data Observing Seasonal Dynamics and Long-term Trends of Atmospheric Pollutants

Air pollution is an escalating concern for both environmental sustainability and public health, exacerbated by urbanization and industrial growth. In Saudi Arabia, pollutants primarily from industrial activities and vehicle emissions present significant health hazards. This study utilizes data from the Sentinel-5P satellite to analyze the variations in Carbon Monoxide (CO), Nitrogen Dioxide (NO₂), Sulfur Dioxide (SO₂), and Particulate Matter (PM2.5) over a five-year period, from January 2019 to December 2023. The data was processed using Google Earth Engine (GEE) to produce monthly and seasonal averages, while ArcGIS Pro was used to map trends and spatial distribution. The results reveal distinct seasonal fluctuations in pollution levels, with CO peaking between March-May and July-September but showing an overall decline. NO₂ and SO₂ exhibit seasonal highs with slight upward trends, likely linked to industrial output and traffic emissions. PM2.5, the most harmful pollutant to human health, consistently surpasses World Health Organization (WHO) limits, especially during high-emission periods. These findings underscore the urgency of adopting targeted measures to mitigate pollution during critical times and safeguard public health. The seasonal spikes, particularly in industrial and densely populated regions, highlight the need for improved policies and technologies to effectively monitor and manage air quality

Studies on the Electrochemical Behavior of Sulfite on Incoloy 800 in a Neutral Environment

Avoiding atmospheric pollution with sulfur dioxide is generally achieved by its absorption from combustion gases in alkaline solutions and conversion to sulfites. Afterwards, sulfites can be transformed into neutral and environmentally safe chemicals by oxidation to sulfates. The oxidation of sulfites to sulfates can also be carried out in a cell in which the fuel will be sulfite ions. In this way, in addition to the beneficial effect of neutralizing large quantities of sulfite waste, electrical energy is also obtained. This is one of the reasons why study of the anodic oxidation of sulfite to sulfate on various electrode materials was necessary. Given the sensitivity of electrode materials in the presence of sulfur compounds, in our research we approached the study of sulfite oxidation on the Incoloy 800 anode in neutral solution (1 mol L−1 Na2SO4). In this research, the results obtained in the study of the kinetic parameters of the anodic process as a function of the sulfite concentration (10−1, 0.5, and 1 mol L−1), using linear voltammetry, are presented. The appreciable values of the exchange current density (3.4, 3.0, and 2.6 A m−2) show that Incoloy 800 has a significant catalytic effect in the anodic oxidation of sulfite. Chronoamperometric studies have shown that the anodic oxidation of sulfite is controlled by the mass transfer of sulfite ions from the bulk solution to the electrode surface. According to the chronocoulometric diagrams, it can be appreciated that, up to anodic potentials of +1.50 V, sulfite oxidation occurs on the electrode, while at more positive potentials, the oxygen evolution reaction is the main process. Electrochemical impedance data provide evidence of a chemical reaction coupled with electron transfer, which was modeled using a Gerischer impedance. At high sulfite concentrations, the charge transfer resistance (Rct) decreases by a factor of 10, indicating that the sulfite oxidation reaction is fast at sufficiently positive potentials. On the other hand, the passivation tendency of stainless steels upon anodic polarization gives them a high corrosion resistance, so that Incoloy 800 can be a viable option as an anode material for sulfite/oxygen (air) fuel cells.

Comparative Foliar Atmospheric Mercury Accumulation across Functional Types in Temperate Trees.

Vegetation assimilation of atmospheric gaseous elemental mercury (GEM) represents the largest dry deposition pathway in global terrestrial ecosystems. This study investigated Hg accumulation mechanisms in deciduous broadleaves and evergreen needles, focusing on how ecophysiological strategies─reflected by δ13C, δ18O, leaf mass per area, and leaf dry matter content-mediated Hg accumulation. Results showed that deciduous leaves exhibited higher total Hg (THg) concentrations and accumulation rates (THgrate), which were 85.3 ± 17.7 and 110.0 ± 0.3% higher than those in evergreen needles. The two tree types exhibited distinct ecophysiological strategies: deciduous broadleaves, with higher stomatal conductance and photosynthetic rates, rapidly adjust stomata to changes in meteorological and pollutant factors, playing a key role in controlling THgrate. In contrast, evergreen needles featured stable stomatal control, highlighting the direct positive effect of GEM on their THgrate. Precipitation and wind speed negatively influenced foliar THgrate. Correlations between PM2.5, NO2, and THgrate in evergreen needles suggested synergistic patterns between atmospheric Hg and pollutants. This study underscores distinct GEM accumulation mechanisms across tree functional types and emphasizes the importance of species-specific foliar ecophysiological strategies. An empirical model linking THgrate with ecophysiological, meteorological, and atmospheric pollution factors was provided, contributing to the refinement of foliar Hg accumulation models.

Summer urban synergistic effects of anthropogenic pollutants and low-molecular-weight biogenic volatile organic compounds on secondary organic aerosol presented by PM1.

Biogenic volatile organic compounds (BVOCs) are emitted by urban vegetation and can interact with anthropogenic pollutants to generate secondary organic aerosols (SOA) that are atmospheric pollutants in urban environments. In urban forests, SOA comprise up to 90% of all fine aerosols (particulate matter smaller than 1μm [PM1]) in the summer. PM1 can greatly affect urban air quality and public health. The formation of SOA is affected by both environmental conditions and the presence of light BVOCs-predominantly isoprene, pentene, butene, and 1,3-butadiene. These factors exhibit complex interactions and nonlinear relationships. In this study, high-frequency field observations were conducted in two urban forest sites in Shanghai during the summers of 2022 and 2023. Data were collected regarding the concentrations of light BVOCs; SOA; and the anthropogenic pollutants NOx, O3, and SO2, as well as solar radiation, temperature, and humidity. A model was developed to identify the synergistic effects of anthropogenic pollutants, meteorological factors, and BVOCs on SOA concentrations. Increases in short-term SOA concentrations were most strongly correlated with O3, which had a synergistic effect alongside NOx. The empirical analysis indicated that 0.144-0.585μg/m3 SOA is produced per μg/m3 of urban BVOCs but can be augmented by 0.072-0.491μg/m3 in the presence of O3, NOx, and SO2. However, long-term feedback mechanisms in urban forests contribute to the maintenance of stable SOA concentrations. The field data and models in this study provide a scientific basis for regulating atmospheric pollutants in urban forests under real-world conditions and offer intuitive and straightforward solutions for managing complex urban air pollution. Synopsis: Anthropogenic pollutants NOx, O3, and SO2 boost BVOCs' SOA production in summer urban forests. Short-term, pollutants are restrictive factors in SOA generation, but long-term forest SOA production exhibits negative feedback.

New Developments and Future Prospects in the Solar Water Splitting

Hydrogen is the material with the highest energy density. Therefore, we can consider it the fuel of the future. Methods of obtaining hydrogen in recent years have become the most important area of scientific research. Hydrogen production using solar energy is very important due to the absence of atmospheric pollution and environmental protection. In this article, we consider methods of obtaining hydrogen by water splitting on components using solar energy. With this goal, we consider a hydrogen fuel cell principle of operation and various methods for hydrogen production. The main attention is offered to the solar-powered water splitting driven by a photoelectrode reaction. We consider such methods as photoelectrochemical water splitting, photovoltaic electrolysis, and application plasmon-enhanced solar cells for the water splitting. The paper highlights advantages and disadvantages of different methods. According to our analysis, the further progress in the hydrogen production is based on application of nanotechnologies and plasmonic effects, which promise increasing of the water splitting efficiency. Advances in nanotechnology, including plasmon-enhanced materials and multi-junction photovoltaic cells, offer novel routes to higher efficiency and lower costs.

Unleashing the potential of geostationary satellite observations in air quality forecasting through artificial intelligence techniques

Abstract. Air quality forecasting plays a critical role in mitigating air pollution. However, current physics-based air pollution predictions encounter challenges in accuracy and spatiotemporal resolution due to limitations in the understanding of atmospheric physical mechanisms, observational constraints, and computational capacity. The world's first geostationary satellite UV–Vis spectrometer, i.e., the Geostationary Environment Monitoring Spectrometer (GEMS), offers hourly measurements of atmospheric trace gas pollutants at high spatial resolution over East Asia. In this study, we successfully incorporate geostationary satellite observations into a neural network model (GeoNet) to forecast full-coverage surface nitrogen dioxide (NO2) concentrations over eastern China at 4 h intervals for the next 24 h. GeoNet leverages spatiotemporal series of satellite NO2 observations to capture the intricate relationships among air quality, meteorology, and emissions in both temporal and spatial domains. Evaluation against ground-based measurements demonstrates that GeoNet accurately predicts diurnal variations and spatial distribution details of next-day NO2 pollution, yielding a coefficient of determination of 0.68 and a root mean square of error of 12.31 µg m−3, significantly surpassing traditional air quality model forecasts. The model's interpretability reveals that geostationary satellite observations notably improve NO2 forecast capability more than other input features, especially over polluted regions. Our findings demonstrate the significant potential of geostationary satellite observations in artificial-intelligence-based air quality forecasting, with implications for early warning of air pollution events and human health exposure.

Minimizing the Environmental Impact of Aircraft Engines with the Use of Sustainable Aviation Fuel (SAF) and Hydrogen

Adverse climate change has forced a deeper reflection on the scale of pollution related to human activity, including in the aviation industry. As a result, fundamental questions have arisen about the characteristics of these pollutants, the mechanisms of their formation and potential strategies for reducing them. This paper provides a comprehensive overview of key technical solutions to minimize the environmental impact of aircraft engines. The solutions presented range from fuel innovations to advanced design changes and drive concepts. Particular attention was paid to sustainable aviation fuels (SAFs), which are currently an important element of the environmental strategy regulated by the European Union. It also discusses the potential use of hydrogen as a potential alternative fuel to replace traditional aviation fuels in the long term. The analysis in the article made it possible to characterize in detail possible modifications in the functioning of aircraft engines, based both on the current state of technical knowledge and on the anticipated directions of its development, which has not been a frequent issue in comprehensive research so far. The analysis shows that the type of raw material used to create SAF has a strong impact on its physical and chemical parameters and the degree of greenhouse gas emissions. This necessitates a broader analysis of the legitimacy of using a given type of fuel from the SAF group in the direction of selected air operations and areas with a higher risk of severe atmospheric pollution. These results provide the basis for further research into sustainable solutions in the aviation sector that can contribute to significantly reducing its impact on climate change.

The perils of open landfill: a study on environmental risk assessment in Dharamshala, Himachal Pradesh, India.

Improper and unscientific management of municipal solid waste (MSW) landfill sites has increasingly become a pressing environmental issue especially in the mountainous regions worldwide. In view of this, an attempt was made to assess the detrimental effects of MSW landfill on the natural water sources at Dharamshala, Himachal Pradesh. Further, the suitability of potential landfill site and dispersion of pollutant air masses were stipulated using Arc GIS and HYSPLIT model. The findings show a discernible increase in electrical conductivity (323-858 μS/cm) and total dissolved solids (1086-1144mg/kg levels) during sampling period. The results exhibited a notable increasing trend in the mean concentrations of heavy metals viz. As (0.13mg/kg and 0.10mg/kg), Hg (0.52mg/kg and 0.65mg/kg), Pb (0.10mg/kg and 0.06mg/kg), Zn (30.40mg/kg and 0.22mg/kg), Cd (0.46mg/kg and 0.04mg/kg), Cr (0.10mg/kg and 0.05mg/kg), Ni (0.28mg/kg and 0.10mg/kg), Mn (24.40mg/kg and 0.35mg/kg) and Fe (1.81mg/kg and 0.96mg/kg) during monsoon and post monsoon. High HPI values were observed at the sampling location near to landfill drain (9060), followed by spring site (7338). However, most of sampling points consistently exceeding the critical HPI value, across all seasons, indicated a severe level of heavy metal pollution, where sampling sites near to landfill drain pose significant environmental health risks of 63%. An overwhelming 93% population in vicinity of MSW site expressed their concern that the current landfill site poses substantial threat to their health and livestock. Furthermore, the obtained forward trajectories showed the downhill dispersion of polluted air arising from solid waste burning. A continuous monitoring of landfill leachate dynamics, atmospheric pollutants due to burning of waste and their potential impact on regional climate followed by appropriate adaptation strategies will be a promising step towards a sustainable future for the Indian Himalayan Region (IHR).

Integration of Stepwise Multiple Linear Regression and Artificial Neural Network Models for Predicting Surface Ozone Concentration in Iraq

Surface ozone (O3) is a significant atmospheric pollutant with adverse effects on both human health and ecosystems. The study aims to construct an integrated model for predicting surface O3 concentrations using stepwise multiple linear regression (SR) and artificial neural network (ANN) models. Additionally, it seeks to assess the impact of air pollutants and meteorological factors on surface O3 levels. Data on O3 levels, meteorological factors including temperature (T2m), total precipitation (TP), boundary layer height (BLH), surface pressure (SP), 10-meter v-component of wind (V10m), and 10-meter u-component of wind (U10m), as well as air pollutants such as (NO2, CO, SO2, PM2.5, and PM10), were analyzed to identify the most significant predictors. The results revealed that the final SR model explained R 2 (0.824,0.865, and 0.719) of the variance in the surface O3 concentration. The model was statistically significant (F-value = 509.057, 629.070, and 231.103), (p value = .000, .000, and.000) for Mosul Baghdad and Basrah respectively. Also, the results of SR model indicate that the primary factors influencing O3 concentration are the significant predictors identified in the resulting predictive equations. The adjusted R 2 values for the performance of the SR and ANN models are as follows: for the SR model, the adjusted R 2 ranged from 0.722 to 0.866, while for the ANN model, it ranged from 0.847 to 0.885 across the three provinces. For the ANN model, various network structures were tested, and the results indicated that the optimal performance was achieved with 5 nodes in the hidden layer and the tanh activation function. The adjusted R 2 values were 0.859, 0.894, and 0.832 for Mosul, Baghdad, and Basrah, respectively. ANN models outperform SR in capturing nonlinear relationships, demonstrating stronger predictive abilities for complex and nonlinear datasets, with higher R 2 and lower RMSE and MAE values.

Seasonal trend analysis and influence of meteorological factors on the concentration of atmospheric pollutants in an urban area

Introduction: Air pollution is a growing concern in Greater Noida, Uttar Pradesh, where meteorological factors play a major role. As urbanization accelerates, the region has not yet thoroughly examined the impact of factors like temperature, humidity, wind direction, and speed on pollutants like Particulate Matters (PM2.5, PM10), Nitrogen dioxide (NO2), and Sulfur dioxide (SO2) Materials and methods: To explore this issue, a 12-month study (January to December 2023) was conducted to analyze the relationships between these meteorological factors and pollutant levels. In the present study, pollutant movement was tracked over time. Pearson correlation was used to correlate air pollution concentrations with weather conditions. Results: The study revealed that the average concentrations of PM , PM , 10 2.5 and NO2 were 912.54 µg/m³, 257.42 µg/m³, and 65.98 µg/m³, respectively. In contrast, SO2 had an average concentration of 26.85 µg/m³. Conclusion: The study indicates that particulate matter and temperature are strongly negatively correlated, while wind speed shows a weak positive correlation with particulate matter. Pollution levels and relative humidity also display a negative correlation, as do particulate matter concentrations and humidity. However, gaseous pollutants exhibit little to no significant correlation with these weather conditions

A Review of Laboratory Studies on the Heterogeneous Chemistry of NO2: Mechanisms and Uptake Kinetics.

NO2 is a significant primary atmospheric pollutant that plays a key role in atmospheric chemistry. It serves as a crucial precursor to photochemical smog, acid rain, and secondary particulate matter and is instrumental in determining the atmospheric oxidation capacity. In this review, we focus on the heterogeneous chemistry of NO2, which has been demonstrated to significantly influence the sources and sinks of various nitrogen-containing species through field measurements and model simulations. We provide a comprehensive summary of laboratory studies investigating the reaction mechanisms and uptake kinetics of NO2 in heterogeneous reactions. NO2 can undergo disproportionation reactions on atmospheric particles. For instance, it may hydrolyze on wetted surfaces to form HONO and HNO3, produce nitrate and NO on mineral dust, or generate nitrate and NOCl on sea salt. Additionally, NO2 can be reduced to HONO on soot and Fe-bearing minerals or photocatalytically reduced to HONO and NO on photosensitive components. Furthermore, NO2 can be photo-oxidized to nitrate or N2O5 on illuminated TiO2. In addition, the synergistic effect of the heterogeneous reactions between NO2 and SO2 is discussed. The uptake coefficients of NO2 on typical particles and the factors influencing these coefficients are also summarized. Finally, based on the current insufficient understanding of heterogeneous reactions of NO2, we propose prospects for future research.

The establishment of blue-sky index: how to quantify the aerosol optical impacts on sky color

Abstract Color of the sky serves as a direct indicator of haze, while also exerting substantial influence on human physiological and psychological well-being, as well as the overall environmental scenery. However, quantifying color changes is quite challenging. This research develops a quantitative approach for evaluating the influence of atmospheric aerosol pollution on the appearance of the sky. We first calculated the influence of atmospheric aerosols on the sky’s diffuse radiation, which is the source of sky color. Then, we mapped the diffuse radiation onto a perceptually uniform color space to quantify alterations in sky color. The results reveal that the relationship between aerosol concentration and sky color change follows a logarithmic-like distribution: the higher the concentration, the weaker the change in sky color per unit increase in aerosol concentration. Consequently, during the early phases of air pollution control implementation, there may be minimal observable changes in the color of the sky. However, as these measures are further enforced, a discernible bluing effect on the sky will become evident. Based on the quantification of the change of sky color, a blue-sky index (BSI) was established. We find that concurrent with improvements in air quality resulting from emission reduction measures, the BSI has significantly decreased in recent years in China. This trend indicates an increase in the frequency of blue-sky occurrences.

Biomagnetic monitoring of urban atmospheric pollution: A review of magnetic signatures from different types of plants.

Biomagnetic monitoring has rapidly emerged as a valuable tool in urban atmospheric pollution (UAP) assessment due to its high spatial resolution, complementing traditional monitoring systems. This review systematically elucidates the principles of plant dust retention and the factors influencing it, while also reviewing the advancements in global research on UAP monitoring through the magnetic properties of various plant species. We provide a comprehensive analysis of the current applications of biomagnetic monitoring in UAP and identify critical challenges, including species-specific monitoring discrepancies, complex pollution sources, and non-standardized sample preparation methods. Additionally, we propose future research directions to address these challenges and enhance the efficacy of biomagnetic monitoring in UAP.

The Relationship between Noise Pollution and Depression and Implications for Healthy Aging: A Spatial Analysis Using Routinely Collected Primary Care Data.

Environmental noise is a significant public health concern, ranking among the top environmental risks to citizens' health and quality of life. Despite extensive research on atmospheric pollution's impact on mental health, spatial studies on noise pollution effects are lacking. This study fills this gap by exploring the association between noise pollution and depression in England, with a focus on localised patterns based on area deprivation. Depression prevalence, defined as the percentage of patients with a recorded depression diagnosis, was calculated for small areas within Cheshire and Merseyside ICS using the Quality and Outcomes Framework Indicators dataset for 2019. Strategic noise mapping for rail and road noise (Lden) was used to measure 24-h annual average noise levels, with adjustments for evening and night periods. The English Index of Multiple Deprivation (IMD) was employed to represent neighborhood deprivation. Geographically weighted regression and generalised structural equation spatial modeling (GSESM) assessed the relationships between transportation noise, depression prevalence, and IMD at the Lower Super Output Area level. The study found that while transportation noise had a low direct effect on depression levels, it significantly mediated other factors associated with depression. Notably, GSESM showed that health deprivation and disability were strongly linked (0.62) to depression through the indirect effect of noise, especially where transportation noise exceeds 55dB on a 24-h basis. Understanding these variations is crucial for developing noise mitigation strategies. This research offers new insights into noise, deprivation, and mental health, supporting targeted interventions to improve quality of life and address health inequalities.

Titanium Dioxide and Photocatalytic CO2 Reduction: A Detailed Review of the Current Status and Future Prospects

A significant amount of carbon dioxide is released into the atmosphere as a result of the extensive usage of fossil fuels. The photocatalytic reduction and conversion of CO2 under visible light into alternative renewable solar fuels or other oxygenated products (methane, formaldehyde, methanol, and formic acid) are practical and efficient methods for reducing atmospheric carbon pollution. Functional materials containing titanium dioxide (TiO2) have attracted significant interest for the photocatalytic reduction of CO2. In this direction, many studies have been conducted in recent years, especially on solar energy harvesting and the charge separation, adsorption, activation, and reduction of CO2 on enhanced TiO2. Recent studies have shown that brookite TiO2 (BT) was the most active photocatalyst, followed by rutile and anatase. Therefore, this study aims to review in detail the recent advances in the development of selective and active catalysts for photocatalytic CO2 reduction using titanium dioxide with a brookite structure. This review evaluates the most common methods for obtaining BT. It discusses various engineered strategies, including doping with metallic or non-metallic heteroatoms, addition of a co-catalyst, formation of heterojunctions, and other algorithms to improve the CO2 reduction mechanism. The influence of another phase and crystal facets on the photocatalytic CO2 reduction reaction are discussed in detail. The problems associated with BT-based photocatalysts, namely, their modest visible-light absorption, slow interfacial charge separation, and poor surface catalytic dynamics, are further discussed, along with possible solutions. To stimulate additional research in this field, the difficulties and potential advantages of photocatalytic CO2 conversion methods are discussed.

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.

Mitigating the Atmospheric Pollutant Injuries on Pear Trees Grown near the Freeways Via Application of Various Anti-Stress Compounds to Ameliorate Fruit Quality and Storability

Mitigating the Atmospheric Pollutant Injuries on Pear Trees Grown near the Freeways Via Application of Various Anti-Stress Compounds to Ameliorate Fruit Quality and Storability

Monitoring the trends of carbon monoxide and tropospheric formaldehyde in Edo State using Sentinel-5P and Google Earth Engine from 2018 to 2023.

This research was carried out to assess the concentrations of carbon monoxide (CO) and formaldehyde (HCHO) in Edo State, Southern Nigeria, using remote sensing data. A secondary data collection method was used for the assessment, and the levels of CO and HCHO were extracted annually from Google Earth Engine using information from Sentinel-5-P satellite data (COPERNISCUS/S5P/NRTI/L3_) and processed using ArcMap, Google Earth Engine, and Microsoft Excel to determine the levels of CO and HCHO in the study area from 2018 to 2023. The geometry of the study location is highlighted, saved and run, and a raster imagery file of the study area is generated after the task has been completed with a 'projection and extent' in the Geographic Tagged Image File Format (.tiff) and downloaded from the Google Drive and saved into folders, imported into the ArcMap for data processing and Excel worksheet for analysis. The raster data were collected annually for each pollutant with the 'filterDate = year-01-01; year-12-31'. Results showed that the annual mean concentrations of CO ranged from '4.67 × 10-2mol/m2' to '5.34 × 10-2mol/m2'. The maximum concentration was found in the year 2018 and the minimum was found in the year 2023, a relatively high concentration of CO may lead to the formation of carboxyhaemoglobin which decreases the capacity of the blood to transport oxygen causing lung cancer, heart problems, respiratory conditions and damage to other organs. While the annual mean concentrations of HCHO ranged from '1.89 × 10-4mol/m2' to '2.18 × 10-4mol/m2', the maximum concentration was found in the year 2021 and the minimum was found in the year 2019, increasing concentration of HCHO may be due to biomass burning and the combustion of methane (CH4 gas), and can cause nasopharyngeal cancer in humans. Based on the result of this study, constant monitoring of the air quality and atmospheric pollutants to ensure early detection of a decrease or increase in the concentration of atmospheric pollutants, implementation of air pollution control policies, spatial data collection, air quality modelling, hotspot identification and source distribution using the geographic information system (GIS), promotion of cleaner technologies, including the use of low-emission vehicles and renewable energy sources, public awareness and education on the impact of atmospheric pollutants and the human contributions to the increasing production of atmospheric pollutants are highly recommended.