Major Air Pollutants Research Articles

Assessment of particulate matter (PM) in ambient air of different settings and its associated health risk in Haripur city, Pakistan.

Particulate matter (PM) is a major air pollutant causing serious health problems. The aim of the present study was to find out concentration of PM in ambient air and its associated health risk in Haripur city, Pakistan. Twenty-three samples were taken at various educational institutes, hospitals, recreational areas and industries in Haripur city. Concentration of PM2.5 (µg/m3) and PM10 (µg/m3) was measured with Youngteng YT-HPC 3000A portable PM counter. The results revealed that values of both PM2.5 and PM10 were above the permissible limits (35 µg/m3 for PM2.5 and 150 µg/m3 for PM10) set by Environmental Protection Agency Pakistan (Pak-EPA) in all the educational institutes, hospitals, recreational areas and industries investigated. Furthermore, significant (p<0.05) variation was found in the concentration of both PM2.5 and PM10 in all the educational institutes, hospitals, recreational areas, and industries studied. The concentration of PM2.5 was positively correlated with the concentration of PM10 in all the sampling sites. Therefore, from 1-14 scale standard of health index, the values of PM2.5 and PM10 exhibited that the ambient air quality of Haripur city Pakistan is under high risk. If the regulatory authorities such as Environmental Protection Agency, Health Department and Local Government monitor PM pollution in different settings of Haripur city, then a decrease can be possible in the pollution level. The remedies that can be taken to overcome the problem of ambient air pollution such as PM are plantation of trees at the sites where there are higher levels of air pollutants and use of masks on personal protection basis along with implementation of pollution control system in industries of Hattar Industrial Estate Haripur city, Pakistan.

Modeling of Improved Sine Trigonometric Single Valued Neutrosophic Information based Air Pollution Prediction Approach

Industrialization and urbanization air is getting polluted due to human activities. CO, NO, C6H6, etc., are the major air pollutants. The focus of air pollutants in ambient air is controlled by the climatological parameters including wind direction, atmospheric speed of wind, temperature, and humidity. Air pollution prediction is a critical sector where machine learning (ML) technique plays a major role. Its main purpose is to tackle and understand the damaging effects of air pollutants on the environment and human health. By using a range of ML techniques such as neural networks, regression, and decision trees, we could analyze historical data on air quality alongside geographical and meteorological factors. This allows us to design model that could detect patterns and predict pollution levels. By taking proactive measures such as providing timely alerts to the public, adjusting controls on emissions, and, implementing strategies to reduce pollution, we can work towards creating healthier and cleaner environments. Embracing the potential of artificial intelligence (AI) in air pollution prediction empowers us to protect the well-being of our communities and make informed decisions. Therefore, this study develops an Improved Sine Trigonometric Single Valued Neutrosophic Information based Air Pollution Prediction (ISTSVNI-APP) approach. The major objective of the ISTSVNI-APP technique is to exploit AI concepts with neutrosophic sets (NS) models for the forecasting of air pollution. To do so, the ISTSVNI-APP technique makes use of min-max normalization as the initial preprocessing step. For predicting air pollution, the ISTSVNI-APP technique uses STSVNI approach. To improve the performance of the ISTSVNI-APP technique, modified crow search algorithm (MCSA) is used for the parameter tuning of the STSVNI system. The performance evaluation of the ISTSVNI-APP method is verified utilizing benchmark dataset. The experimental outcomes stated that the ISTSVNI-APP technique gains better performance in predicting air pollution

Correlation of time trends of air pollutants, greenspaces and tracheal, bronchus and lung cancer incidence and mortality among the adults in United States.

Tracheal, Bronchus, and Lung (TBL) cancer continues to represent the majority of cancer-related incidence and mortality in United States (U.S.). While air pollutants are considered essential risk factors, both global and national average concentrations of major harmful air pollutants have significantly decreased over the decades. Green space may have a beneficial effect on human health. We obtained data on national and state-level burden of TBL cancer, the annual average concentration of main air pollutants, and levels of green spaces in 2007, 2013, and 2019. According to generalized estimating equation (GEE), we examine the associations among incidence and mortality of TBL cancer, air pollutants, and greenspaces, represented by the Normalized Difference Vegetation Index (NDVI) in different age groups with models adjusted with meteorological, and socio-demographic. We observed additional effects of the interaction between the NDVI, Ozone, PM2.5, and other factors, which helped us to interpret and understand our results. Also, we collated states that witnessed net increments in forest coverage and conducted the same analysis separately. In our analysis, the majority of associations between NDVI and air pollutants with TBL cancer remained significantly positive, particularly noticeable among individuals aged 20 to 54. However, our findings did not explore air pollution as a potential mediator between greenspace exposure and TBL cancer. While the associations of PM2.5 with TBL cancer remained positive, the other four pollutants showed positive but statistically insignificant associations. Our interaction analysis yielded that there were positive associations between NDVI and ozone, PM2.5, and tobacco use. Max NDVI acts as a protective factor along with high HDI. Additionally, PM2.5 and HDI also showed a negative association. In 18 states with more forest, NDVI acts as a protective factor along with higher health care coverage, better health status, and participation in physical activities. In the state-level of U.S., the effects of total greenspace with TBL cancer are mixed and could be modified by various socio-economic factors. PM2.5 has a direct correlation with TBL cancer and the effects can be influenced by underlying socioeconomic conditions.

SO3 removal characteristics from flue gas of FCC regeneration process by activated carbon modified red mud adsorbent

Sulfur trioxide (SO3) is one of the major air pollutants in the flue gas of Fluid catalytic cracking (FCC) units during the regeneration process in refineries. However, it is difficult to remove SO3 with the existing desulfurization systems. Nowadays, alkaline adsorbent spraying technology has been a promising method to remove SO3. However, there is no relevant application research in the field of FCC regeneration flue gas. Therefore, in this paper, The SO3 removal characteristics from FCC flue gas by activated carbon modified red mud adsorbent were studied, including modification method, reaction temperature and flue gas components. Combined with characterization analysis of adsorbents, the mechanism of SO3 removal was studied. It was found that when the AC mass ratio is 10% and modification temperature is 400 ℃, the best LBRMAC adsorbent was achieved. When particle size is 0.096–0.125 mm and reaction temperature is 400 ℃, the highest SO3 adsorption efficiency is up to 83.4%. The addition of NO has little influence on the adsorption efficiency of SO3. The addition of SO2 leads to competitive adsorption between SO3 and SO2, resulting in a rapid decline in the removal efficiency of SO3. Meanwhile, the characterization analysis proves that the SO3 adsorption process mainly include chemical kinetics control stage and product layer diffusion stage.

Chemometric Techniques in the Assessment of Ambient Air Quality and Development of Air Quality Index of Coal Mining Complex: A Statistical Approach

This study aims to analyze the regional variation in the source of air pollution, identify the percentage contribution of each pollutant, and distribute the mass contribution of each source category using multivariate analysis. The nine air monitoring sites were successfully divided into three groups using hierarchical agglomerative cluster analysis (HACA) (clusters 1, 2, and 3). The collected meteorological data is non-parametric data for the years 2020–2021 which includes PM2.5, PM10, SO2, NO2, NO, NOx, CO, wind speed, humidity, wind direction, temperature, cloud cover, and surface radiation. The most major air pollution sources were identified using FA. Multiple linear regression (MLR) and principal component regression (PCR) were utilized to create an equation model explaining contaminants' impact in each cluster. However, it was shown that the most important pollutants impacting the value of the air pollutant index (API) are gaseous pollutants (NOx and SO2) and particulate matter (PM10 and PM2.5). Gas and non-gas pollutants have a 65% influence on cluster 1 and meteorological conditions have a 35% effect. Cluster 3 is influenced by 65% particle and non-gas pollutants and 35% weather conditions, compared to Cluster 2 which is 100% affected by gas and particulate pollutants because of its spatial location. This study shows the value of the multivariate modeling technique in minimizing the time and expense associated with monitoring redundant stations and parameters.

Chemometric Techniques in the Assessment of Ambient Air Quality and Development of Air Quality Index of Coal Mining Complex: A Statistical Approach

This study aims to analyze the regional variation in the source of air pollution, identify the percentage contribution of each pollutant, and distribute the mass contribution of each source category using multivariate analysis. The nine air monitoring sites were successfully divided into three groups using hierarchical agglomerative cluster analysis (HACA) (clusters 1, 2, and 3). The collected meteorological data is non-parametric data for the years 2020–2021 which includes PM2.5, PM10, SO2, NO2, NO, NOx, CO, wind speed, humidity, wind direction, temperature, cloud cover, and surface radiation. The most major air pollution sources were identified using FA. Multiple linear regression (MLR) and principal component regression (PCR) were utilized to create an equation model explaining contaminants' impact in each cluster. However, it was shown that the most important pollutants impacting the value of the air pollutant index (API) are gaseous pollutants (NOx and SO2) and particulate matter (PM10 and PM2.5). Gas and non-gas pollutants have a 65% influence on cluster 1 and meteorological conditions have a 35% effect. Cluster 3 is influenced by 65% particle and non-gas pollutants and 35% weather conditions, compared to Cluster 2 which is 100% affected by gas and particulate pollutants because of its spatial location. This study shows the value of the multivariate modeling technique in minimizing the time and expense associated with monitoring redundant stations and parameters.

Chemometric Techniques in the Assessment of Ambient Air Quality and Development of Air Quality Index of Coal Mining Complex: A Statistical Approach

This study aims to analyze the regional variation in the source of air pollution, identify the percentage contribution of each pollutant, and distribute the mass contribution of each source category using multivariate analysis. The nine air monitoring sites were successfully divided into three groups using hierarchical agglomerative cluster analysis (HACA) (clusters 1, 2, and 3). The collected meteorological data is non-parametric data for the years 2020–2021 which includes PM2.5, PM10, SO2, NO2, NO, NOx, CO, wind speed, humidity, wind direction, temperature, cloud cover, and surface radiation. The most major air pollution sources were identified using FA. Multiple linear regression (MLR) and principal component regression (PCR) were utilized to create an equation model explaining contaminants' impact in each cluster. However, it was shown that the most important pollutants impacting the value of the air pollutant index (API) are gaseous pollutants (NOx and SO2) and particulate matter (PM10 and PM2.5). Gas and non-gas pollutants have a 65% influence on cluster 1 and meteorological conditions have a 35% effect. Cluster 3 is influenced by 65% particle and non-gas pollutants and 35% weather conditions, compared to Cluster 2 which is 100% affected by gas and particulate pollutants because of its spatial location. This study shows the value of the multivariate modeling technique in minimizing the time and expense associated with monitoring redundant stations and parameters.

Past and recent changes in the pollution characteristics of PM10 and SO2 in the largest industrial city in South Korea

PM10 and SO2 are major air pollutants in Ulsan, the largest industrial city in South Korea. The purpose of this study was to evaluate the effectiveness of pollution reduction policies by comparing the pollution characteristics of PM10 and SO2 between 2012 and 2020. The concentrations of PM10 and SO2 have substantially decreased over time. Local industry was the primary driver of SO2 levels, whereas various sources were associated with PM10 levels. In 2012 and 2020, the spatial distributions of SO2 were similar, with hot spots around petrochemical and nonferrous industrial complexes, whereas the sources of PM10 differed between the two years. Although industrial emissions of PM10 decreased from 2012 to 2020, the concentrations derived from an air dispersion model were slightly higher in 2020 due to changes in both wind patterns and the strength of the emission sources. In contrast, modeled SO2 levels were higher in 2012 than in 2020 due to greater emissions in 2012. Eastern and northeastern China were identified as potential source areas for the long-range atmospheric transport of both PM10 and SO2, though its influence was significantly lower in 2020. A comparison of the PM10 and SO2 concentrations predicted by a random forest model and the measured concentrations indicates that government policies have been effective in reducing the emissions and ambient air concentrations of both pollutants. In conclusion, this study demonstrates that local industry remains a significant contributor to air pollution in Ulsan despite the effects of governmental policy. Furthermore, as the influence of external sources weakens, there is a pressing need to strengthen the management of local sources to effectively mitigate air pollution in Ulsan.

Particulate matter 10 induces oxidative stress and apoptosis in rhesus macaques skin fibroblast.

Particulate matter (PM) is a major air pollutant that affects human health worldwide. PM can pass through the skin barrier, thus causing skin diseases such as heat rash, allergic reaction, infection, or inflammation. However, only a few studies have been conducted on the cytotoxic effects of PM exposure on large-scale animals. Therefore, herein, we investigated whether and how PM affects rhesus macaque skin fibroblasts. Rhesus macaque skin fibroblasts were treated with various concentrations of PM10 (1, 5, 10, 50, and 100 μg/mL) and incubated for 24, 48, and 72 h. Then, cell viability assay, TUNEL assay, and qRT-PCR were performed on the treated cells. Further, the reactive oxygen species, glutathione, and cathepsin B levels were determined. The MTT assay revealed that PM10 (>50 μg/mL) proportionately reduced the cell proliferation rate. PM10 treatment increased TUNEL-positive cell numbers, following the pro-apoptosis-associated genes (CASP3 and BAX) and tumor suppressor gene TP53 were significantly upregulated. PM10 treatment induced reactive oxidative stress. Cathepsin B intensity was increased, whereas GSH intensity was decreased. The mRNA expression levels of antioxidant enzyme-related genes (CAT, GPX1 and GPX3) were significantly upregulated. Furthermore, PM10 reduced the mitochondrial membrane potential. The mRNA expression of mitochondrial complex genes, such as NDUFA1, NDUFA2, NDUFAC2, NDUFS4, and ATP5H were also significantly upregulated. In conclusion, these results showed that PM10 triggers apoptosis and mitochondrial damage, thus inducing ROS accumulation. These findings provide potential information on the cytotoxic effects of PM10 treatment and help to understand the mechanism of air pollution-induced skin diseases.

Spatial variation of air pollutants by using GIS modelling

&lt;p&gt;Air pollution in Tamil Nadu has become a pressing issue, as various pollutants have surpassed the limits established by the pollution control board. The major air pollutants in the region, namely sulphur dioxide, nitrogen dioxide, and suspended particulate matter, have been identified. This study aims to monitor and analyse the levels of air pollution in Tamil Nadu, with a specific focus on key parameters such as SO2, NO2, PM2.5, PM10, and ozone. By examining the atmospheric levels of these pollutants and their correlation with temperature fluctuations, the impact on human health and respiratory problems can be evaluated. Moreover, the study will assess the contribution of traffic emissions and vehicular pollution to the concentration of PM10 in different areas. Additionally, GIS technology will be utilized to spatially and temporally investigate pollution concentrations and evaluate their effect on the air quality index. By comparing the pollution concentration from thermal power stations and vehicular pollution, the study will identify the sources of pollutants, with a particular emphasis on SO2 and NOx emissions from vehicles and higher levels of suspended particulate matter from thermal stations. The analysis of these pollutants and their sources will provide valuable insights for the implementation of effective pollution control measures and the management of environmental pollution in Tamil Nadu. Given the concerns regarding pollutants exceeding the prescribed limits set by the pollution control board, monitoring air pollution in Tamil Nadu is of utmost importance.&lt;/p&gt;&#x0D;

Short-term exposure to ambient air pollution and injuries due to external causes according to intentions and mechanisms

Although injuries are a leading cause of death and affect the life expectancy of individuals who live with disabilities globally, the potential role of air pollution exposure on injuries due to external causes has received little scientific attention, especially compared with that given to the association of air pollution and non-external causes of morbidity and mortality. We investigated the association between emergency department visits for externally caused injuries and short-term exposure to major ambient air pollutants, with focus on the intentions and mechanisms of injuries. We identified 2,049,855 injured patients in Seoul, South Korea between 2008 and 2016 using the National Emergency Database. Daily short-term exposure to air pollution including particles <10 μm (PM10) and <2.5 μm (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) was estimated based on hourly concentrations. We employed a time-stratified case-crossover study design using a conditional Poisson regression model adjusted for meteorological variables, influenza epidemics, and holidays. Immediate exposure (lag 0) to most pollutants significantly increased the risk of total injuries (PM2.5, 0.42 %; NO2, 0.68 %; SO2, 1.05 %; CO, 0.57 %; O3, 1.86 % per interquartile range increment), and the associations differed according to the intention and mechanism of injury. Unintentional and assault injuries were significantly associated with air pollution exposure, whereas self-harm injuries showed no association. In mechanism-specific analyses, injuries caused by falls, blunt objects, penetration, traffic accidents, machinery, and slips were associated with specific air pollutants, even in the co-pollutant models. The associations were stronger in injured patients aged <15 years, and in males than in their counterparts. Our results suggest that short-term air pollution exposure might play a role in the risk of externally caused injuries and the association may differ depending on the intention and mechanism of injury, which provide important evidence for injury prevention and air quality strategies.

Anthropogenic Drivers of Hourly Air Pollutant Change in an Urban Environment during 2019–2021—A Case Study in Wuhan

Wuhan experienced a noticeable enhancement in air quality from January to April 2020 due to the epidemic lockdown. The improvement was a combined result of anthropogenic emission reduction and meteorological variability. Environmental policymakers are often concerned about the impact of industrial production and human activities on improvements in environmental sustainability. This study split and quantified the impact of anthropogenic emissions on the pollution level changes of six major air pollutants (CO, SO2, NO2, O3, PM10, and PM2.5) for the first half year of 2019 to 2021 in Wuhan with an improved meteorological normalization algorithm. The results show sharp decreases in anthropogenic pollutant loads during 2020, except for O3, with the ranking of NO2 &gt; PM10 &gt; SO2 &gt; CO &gt; PM2.5. The decrease in NO2 emissions caused by humans was more than 50% compared to 2019. The low NO2 led to a decrease in O3 consumption, resulting in high O3 concentrations from February to April 2020 during the city lockdown. Moreover, except O3, the impact of anthropogenic and weather influences on air pollution exhibited opposing effects; that is, meteorology tended to aggravate pollution, while human intervention was conducive to improving air quality, and human factors played the dominant role. Of all six pollutants, O3 is the one that is relatively least subject to anthropogenic emissions. Although concentrations of SO2, NO2, PM10, and PM2.5 rebounded in 2021, none of them were able to return to their pre-lockdown levels, suggesting the epidemic’s continuous inhibition of people’s activities. Compared with 2019 and 2021, the atmospheric oxidation capacity and secondary aerosol formation showed an overall decreasing trend during 2020. This study provides a reference for assessing the effectiveness of anthropogenic emission reduction policies.

Integrated metabolomics and transcriptomics analysis reveals γ-aminobutyric acid enhances the ozone tolerance of wheat by accumulation of flavonoids

Wheat is susceptible to atmospheric ozone (O3) pollution, thus the increasing O3 is a serious threat to wheat production. γ-aminobutyric acid (GABA) is found to play key roles in the tolerance of plants to stress. However, few studies elaborated the function of GABA in response of wheat to O3. Here, we incorporated metabolome and transcriptome data to provide a more comprehensive insight on the role of GABA in enhancing the O3-tolerance of wheat. In our study, there were 31, 23, and 32 differentially accumulated flavonoids in the carbon-filtered air with GABA, elevated O3 with or without GABA treatments compared to the carbon-filtered air treatment, respectively. Elevated O3 triggered the accumulation of dihydroflavone, flavonols, and flavanols. Exogenous GABA enhanced dihydroflavone and dihydroflavonol, and also altered the expression of genes encoding some key enzymes in the flavonoid synthesis pathway. Additionally, GABA stimulated proline accumulation and antioxidant enzyme activities under elevated O3, resulting in the less accumulation of H2O2 and malondialdehyde. Consequently, GABA alleviated the grain yield loss from 19.6% to 9.6% induced by elevated O3. Our study provided comprehensive insight into the role of GABA in the alleviating the detrimental effects of elevated O3 on wheat, and a new avenue to mitigate O3 damage to the productivity of crops.

Impacts of irregular and strategic lockdown on air quality over Indo-Pak Subcontinent: Pre-to-post COVID-19 analysis

The COVID-19 pandemic has received enormous attention worldwide due to its environmental, and societal impacts. After the discovery of this novel virus in December 2019, the governmental bodies have suspended the social and economic activities and declare an unprecedented lockdown emergency. The present work sought to investigate the impact of COVID-19 containment measures on air quality in the region of Indo-Pak subcontinent (IPS). The satellite retrievals of aerosol optical depth (AOD), sulfur dioxide (SO2), nitrogen dioxide (NO2), and Ozone (O3) was used to estimate the spatial-temporal extent of major air pollutants in the region during lock down period (March–May 2020) in comparison to the pre-lockdown period (March–May 2015–2019) and post-lockdown (March–May 2021) across the IPS. From the results, a considerable reduction in air pollutants concentration has been observed during the lockdown phase that in turns significantly improved the regional air quality. Moreover, we have also done comparative analysis among meteorological parameters and ambient air pollutants and observed that meteorological factors were not the main reason for the dramatic reductions of pollutants in the atmosphere. In addition, backward trajectory analysis has been done through the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model to find the path and direction of air particles parcels as large-scale atmospheric circulation is a critical factor in driving pollutant. Following the implementation of strict lockdown, reduction in anthropogenic activities led to a substantial decline in AOD, SO2 and NO2 whereas, a considerable increase in tropospheric O3 has been observed and well supported by other regional studies. Overall, the findings of this study are believed to be a useful addition to the regulatory authorities and will help the policymakers to formulate the air pollution mitigation strategies in a long-term perspective.

An approach to using the AQI components in urban air pollution sources identifying

Problem statement. The fact that the air we breathe is polluted is well known. There are many sources of pollution, especially in big cities. Various sensors are installed to monitor pollutants in the air. One of the global systems for registering the concentration of pollutants in urban air is AQI. Air quality monitors collect data of five major air pollutants – ground-level ozone, particle pollution, carbon monoxide, sulfur dioxide, and nitrogen dioxide, that then convert to the Air Quality Index. And although the sensors themselves are not capable of reducing pollution in the air, based on the data they provide, it is possible to create indicative maps of urban pollution. Such mapping of urban areas will enable authorities to develop and implement plans to improve the most dangerous areas, as is already done in other countries. But there are still no such maps for Kyiv. Study objective is to analyse the air quality index in Kyiv, identify the main source of atmospheric pollution and to visualize urban air pollution. Methodology involves data analysis from 15 sensors of the AQI worldwide network. We analyzed the concentration of 5 main air pollutants contributed to the common Air Quality Index for a certain period in Kyiv and its surroundings. Knowing the exact coordinates of each sensor and the results of their measurements, we drew a map of air pollution in Kyiv using OriginPro 8.1 software and images from the Google maps. Research results. It was determined that the largest contribution to the Air Quality Index is made by the fine particulate matter emissions. We determined that the morning sensor data on the amount of dust in the air is the most informative. It is known transportation is one of the main sources of PM2.5 in the city. Our map clearly shows that the area with the highest AQI value coincides with a major road junction on the north-west outskirts of the city. Thus, atmospheric pollution in Kyiv is mainly determined by the amount of fine dust in the air. Further research will be aimed at identifying the relationship between the amount of PM2.5 in the air and the morphological parameters of indicator plants. Scientific novelty of the research. We showed for the first time that air pollution does not coincide with the official sources of atmospheric pollution given by the Kyiv Bureau of Technical Supervision. We also presented new approach to draw up-to-date, representative, and accurate pollution maps that can be submitted to the representatives of environmental services and other interested parties. Such investigations are of great importance as they can give the opportunity to the government to take real actions on pollutants reducing.

Experimental study of adsorption characteristic of novel pyrazole-based metal-organic frameworks for volatile organic compounds

Volatile organic compounds (VOCs) are the major indoor air pollutants that affect human health, comfort, and working performance. Sorbent material and adsorption technology are considered to be one of the most effective and economical ways to remove VOCs from indoor air. Among many sorbent materials, metal-organic frameworks (MOFs) have received more and more attention because of their superior performance in capturing a variety of compounds. In this work, the adsorption characteristic of a newly synthesized pyrazole-based metal-organic framework (Al-pda) for different VOCs was investigated. First, the single-pass VOC adsorption rates of Al-pda and activated carbon were tested under different low-concentration VOCs mixture, which is in line with the practical application in the indoor environment. The results showed that Al-pda had an especially high adsorption ability for formaldehyde. The single-pass efficiency of formaldehyde adsorption was up to 89% at the beginning of the test and gradually stabilized at 66%. Compared with activated carbon, Al-pda had significantly higher selectivity and adsorption efficiency for the capture of formaldehyde. Then the effect of relative humidity on formaldehyde adsorption was investigated. After increasing the relative humidity from 15% to 65%, Al-pda’s formaldehyde single-pass adsorption rate almost remained at a similar level. Preliminary research showed that Al-pda is a promising material for the selective adsorption of indoor formaldehyde under humid conditions. More work is needed to probe this new MOF’s adsorption mechanism and application method.

Emission Control Routes in Liner Shipping between Korea and Japan

Maritime shipping is considered a major air pollutant that directly affects global warming and climate change. This study aims to design an emission control route (ECR) that can contribute to long-term initiatives along with emission prevention policies. Applying a scenario analysis, this paper has analyzed the impact of ECR implementation on air quality in the shipping route between Korea and Japan. In this study, the ECR is defined as eco-friendly shipping routes that recommend maintaining a specific level of speed while sailing. Based on the navigation trajectories of 55 O/Ds, which were extracted by the automatic identification system (AIS) data of 135 container ships for each port call during one year, the ECR domains were designated. To analyze the introduction impacts, this study employed a scenario analysis based on the current Vessel Speed Reduction Program (VSRP) recommendation of 12 knots or less. The results demonstrated that the introduction of ECR in the liner services between Korea and Japan would increase the average travel time by 10.47–16.98%. However, according to the results of the scenario analysis, the introduction of the ECR can reduce emissions by 25.65% to 39.73%. By suggesting the initial concept of ECR and analyzing the emission reduction effects of its implementation, this study provides useful insights for a balance between economic and environmental performance.

Changes in total volatile organic compound concentration in Seoul subway stations before (2019) and after (2021) the COVID-19 outbreak

Volatile organic compounds (VOCs) are major air pollutants often designated as specific hazardous or toxic. This study analyzed the trends in concentration changes and influencing factors of VOCs in underground subway stations in the Seoul Metro before (2019) and after (2021) the COVID-19 pandemic. A total of 506 samples were collected from 253 stations on lines 1–8 between May 2019 and September 2021. Total VOC concentrations in Seoul Metro increased after the COVID-19 pandemic 3.8 times over. The deeper the underground station platform, the greater the difference in the VOC concentrations between 2019 and 2021, which was positively related. Average VOC concentration was the highest (52.8 µg/m3) at a depth of 25–30 m and the lowest (23.9 µg/m3) at a depth of < 10 m in 2019. In conclusion, excessive disinfection during the COVID-19 pandemic resulted in increased VOC concentrations in the Seoul Metro, especially in the deeper underground stations. Less frequent quarantine disinfection is recommended to improve air quality.

Diesel exhaust particles exposure exacerbates pro-thrombogenic plasma features ex-vivo after cerebral ischemia and accelerates tPA-induced clot-lysis in hypertensive subjects

The combustion of fossil fuels, mainly by diesel engines, generates Diesel Exhaust Particles (DEP) which are the main source of Particulate Matter (PM), a major air pollutant in urban areas. These particles are a risk factor for stroke with 5.6% of cases attributed to PM exposure. Our aim was to evaluate the effect of DEP exposure on clot formation and lysis in the context of stroke. An ex-vivo clot formation and lysis turbidimetric assay has been conducted in human and mouse plasma samples from ischemic stroke or control subjects exposed to DEP or control conditions. Experimental DEP exposure was achieved by nasal instillation in mice, or by ex-vivo exposure in human plasma. Results show consistent pro-thrombogenic features in plasma after human ischemic stroke and mouse cerebral ischemia (distal MCAo), boosted by the presence of DEP. Otherwise, thrombolysis times were increased after ischemia in chronically exposed mice but not in the DEP exposed group. Finally, subjects living in areas with high PM levels presented accelerated thrombolysis compared to those living in low polluted areas. Overall, our results point at a disbalance of the thrombogenic/lytic system in presence of DEP which could impact on ischemic stroke onset, clot size and thrombolytic treatment.

Computational and experimental assessment of health risks of fine particulate matter in Nanjing and Yangzhou, China.

Fine particulate matter (PM2.5) is a major air pollutant in most cities of China, and poses great health risks to local residents. In this study, the health effects of PM2.5 in Nanjing and Yangzhou were compared using computational and experimental methods. The global exposure mortality model (GEMM), including the results of a cohort study in China, was used to estimate the disease-related risks. Premature mortality attributable to PM2.5 exposure were markedly higher in Nanjing than that in Yangzhou at comparable levels of PM2.5 (8191 95% CI, 6975-9994 vs. 6548 95% CI, 5599-8049 in 2015). However, the baseline mortality rate was on a country-level and the age distribution was on a province-level, traditional estimation method could not accurately represent the health burdens of PM2.5 on a city-level. We proposed a refined calculation method which based on the actual deaths of each city and the disease death rates. Conversely, similar concentrations of PM2.5 exposure resulted in higher actual deaths per million population in Yangzhou (1466 95% CI, 1266-1746) than that in Nanjing (1271 95% CI, 1098-1514). Health risks of PM2.5 are associated with the generation of reactive oxygen species, among which hydroxyl radial (·OH) is the most reactive one. We then collected these PM2.5 samples and quantified the induced ·OH. Consistently, average ·OH concentration in 2015 was higher in Yangzhou than that in Nanjing, again indicating that PM2.5 in Yangzhou was more toxic. The combination of computational and experimental methods demonstrated the complex relationship between health risks and PM2.5 concentrations. The refined estimation method could help us better estimate and interpret the risks caused by PM2.5 exposure on a city-level.