Particular Matter Research Articles

Forecasting the Exceedances of PM2.5 in an Urban Area

Particular matter (PM) constitutes one of the major air pollutants. Human exposure to fine PM (PM with a median diameter less than or equal to 2.5 μm, PM2.5) has many negative and diverse outcomes for human health, such as respiratory mortality, lung cancer, etc. Accurate air-quality forecasting on a regional scale enables local agencies to design and apply appropriate policies (e.g., meet specific emissions limitations) to tackle the problem of air pollution. Under this framework, low-cost sensors have recently emerged as a valuable tool, facilitating the spatiotemporal monitoring of air pollution on a local scale. In this study, we present a deep learning approach (long short-term memory, LSTM) to forecast the intra-day air pollution exceedances across urban and suburban areas. The PM2.5 data used in this study were collected from 12 well-calibrated low-cost sensors (Purple Air) located in the greater area of the Municipality of Thermi in Thessaloniki, Greece. The LSTM-based methodology implements PM2.5 data as well as auxiliary data, meteorological variables from the Copernicus Atmosphere Monitoring Service (CAMS), which is operated by ECMWF, and time variables related to local emissions to enhance the air pollution forecasting performance. The accuracy of the model forecasts reported adequate results, revealing a correlation coefficient between the measured PM2.5 and the LSTM forecast data ranging between 0.67 and 0.94 for all time horizons, with a decreasing trend as the time horizon increases. Regarding air pollution exceedances, the LSTM forecasting system can correctly capture more than 70.0% of the air pollution exceedance events in the study region. The latter findings highlight the model’s capabilities to correctly detect possible WHO threshold exceedances and provide valuable information regarding local air quality.

Investigation of the effect of different structure parameters and operating factors on the integrated exhaust aftertreatment system for diesel engines and parameter importance analysis

With diesel emission regulations growing more stringent, it is important to study the effect of different structure parameters and operating factors on the integrated aftertreatment system consisting of diesel oxidation catalyst (DOC), catalytic diesel particulate filter (CDPF) and selective catalytic reduction (SCR) during the design and optimization of the aftertreatment system. In this paper, a simulation model capable of exploring the interaction between the aftertreatment components was developed based on some relevant reaction mechanism, and the model accuracy was verified well using the experiment data. Then the effect of different parameters on the overall performance of the aftertreatment system was analyzed qualitatively. Finally, the importance score of each parameter was determined quantitatively based on Latin Hypercubic Sampling method and Random Forests algorithm. The results showed that larger diameter or larger cell density in DOC could improve the aftertreatment performance effectively. The increase in the wall thickness of each component had a negative impact on the aftertreatment system, except for CDPF wall thickness, which improved the filtration efficiency for particular matter (PM). When total aftertreatment length was fixed, high conversion efficiencies for carbon monoxide (CO) and total hydrocarbons (THC) were distributed in the region with large DOC length ratio, and NOx conversion efficiency was affected simultaneously by the length ratio of DOC, CDPF and SCR. An increase in CDPF length ratio and a decrease in DOC and SCR length ratios could contribute to the reduction of total pressure drop. Finally, Random Forests algorithm provided a good identification of the importance score of each parameter. DOC length and diameter played a dominant role in CO and THC conversion. The aftertreatment diameter had higher effect on NOx conversion efficiency than SCR length and PM filtration efficiency was mainly affected by DOC and CDPF length. Based on these qualitative and quantitative results, a design strategy for the integrated aftertreatment system was proposed, which provided some ideas for improving aftertreatment performance.

Impact of air pollution from different sources on sperm DNA methylation

ABSTRACT Environmental exposure is associated with increased incidence of respiratory and cardiovascular diseases and reduced fertility. Exposure to air pollution can influence gene expression through epigenetic mechanisms. In this study, we analysed gene-specific CpG methylation in spermatozoa of city policemen occupationally exposed to air pollution in two Czech cities differing by sources and composition of the air pollution. In Prague, the pollution is mainly formed by NO2 from heavy traffic. Ostrava is a hotspot of industrial air pollution with high concentrations of particular matter (PM) and benzo[a]pyrene (B[a]P). We performed genome-wide methylation sequencing using the SureSelectXT Human Methyl-Seq system (Agilent Technologies) and next-generation sequencing to reveal differentially methylated CpG sites and regions. We identified differential methylation in the region chr5:662169 – 663376 annotated to genes CEP72 and TPPP. The region was then analysed in sperm DNA from 117 policemen using targeted methylation sequencing, which proved its hypermethylation in sperm of Ostrava policemen.

The effect of braking conditions on the particular matter emissions and brake squeal

Particular matter (PM) and squeal noise emitted from disc brakes are essential research subjects in the automotive industry. This study investigates the effect of braking conditions on the PM emissions and brake squeal of low metallic (LM), semi-metallic (SM) and non-asbestos organic (NAO) brake friction materials (BFMs) with a pin-on-disc tester. Braking conditions significantly affect the total number concentration (TNC) for all three BFMs. Generally, the geometric mean diameter (GMD) for NAO BFM is smaller than that for SM and LM BFMs. Moreover, the effect of braking conditions on the GMD for SM and LM BFMs is higher than that for NAO BFM. The speed has a more significant effect on GMD than contact load. The braking conditions can significantly affect the PMC. SM and LM BFMs exhibit larger particle mass concentrations (PMCs) than NAO BFM in most cases. All three BFMs show comparable A-weighted sound pressure levels (SPLs). The influence of braking conditions on brake squeal for SM and LM BFMs is stronger than that for NAO BFM.

Dust soiling effects on decentralized solar in West Africa

West Africa’s proximity to the Sahara Desert can cause photovoltaic (PV) systems to experience significant power generation reductions when particulates settle onto solar panel surfaces in a phenomenon known as soiling. Previous studies have created models to estimate PV soiling globally, but these models have several limitations in the region due to the accuracy of the input particular matter (PM) concentration values, the accuracy of methods for computing power loss, and the regional use of decentralized solar systems. Here we develop and apply a methodology for the development of a soiling estimation model based on the Coello framework, and evaluate it when using input PM concentration values from both a reanalysis dataset (MERRA-2) and a dataset derived from satellite-observations (WUSTL). This model reduces error in soiling-related power loss estimates by approximately 50% when compared to the unadjusted Coello approach. Using this model, we find soiling losses can exceed 50% in the most greatly affected locations during the Harmattan season and these power losses can vary considerably each year (between 26 and 53% in Niamey, Niger). To support PV system design, we develop a function that relates maximum soiling losses to annual cleaning frequencies in major cities. Finally, we find if decentralized solar is used to meet electricity access goals as projected, PV cleaning costs could sum up to $1Bn (USD) annually in sub-Saharan Africa by 2030.

A novel gradient structured nanofiber and silver nanowire composite membrane for multifunctional air Filters, oil water Separation, and health monitoring flexible wearable devices.

Particular matter (PM), oily wastewater, and microorganisms (e.g., bacteria) have caused serious environmental, health, and safety issues. However, the development of multifunctional filtration materials to address these problems remains a great challenge. Here, we present a series of gradient structured air filters by simply spray coating poly(vinyl alcohol-co-ethylene) (PVA-co-PE) nanofibers on nylon mesh, followed by silver nanowires (AgNWs). Interestingly, it is found that the ANF-6 air filter is an anisotropic Janus membrane with asymmetric wettability and translucency. The as-prepared ANF-6 air filter exhibits excellent water vapor transmission rate (4447.92 ± 184.78g/(m2. d)), PM filtration (96.42 ± 0.64% for PM0.3), photothermal (79.6°C under 1sun in 150s), thermal insulation, antibacterial, and oil water separation. Additionally, the obtained ANF-6 air filter was impregnated with carbon black (CB) dispersion and served as flexible pressure sensors to monitor human respiration rate (17 times/min) and wrist pulse rate (80 times/min). The gradient structured PVA-co-PE nanofibers and AgNWs network provides excellent air filtration, oil water separation, and sensitivity performance for the sensors. These results provide a new scheme for designing multifunctional filtration materials and wearable pressure sensors in the application of air filtration, oil water separation, and wearable electronics for monitoring human health.

Distribution patterns and influencing factors of population exposure risk to particulate matters based on cell phone signaling data

In this study, spatial-temporal characteristics of particular matter (PM) exposure risk in Shenyang were analyzed with landscape patterns using data from land use, cell phone signaling, and PM mobile monitoring. Pollution surfaces were established with geographically weighted regression models and impact factors analysis was implemented by boosted regression tree models. The results showed that weekdays and weekends had different spatial distributions of PM, and the exposure risk was lower on weekends. High exposure risks of PM10 were concentrated in the first ring zone (76.53 people·m−2·μg·m−3) and residential-commercial land (292.34 people·m−2·μg·m−3). Exposure risks of PM2.5 were most affected by residential-commercial land and fourth-class (relative contribution: 59.69 and 8.88, respectively). However, the exposure risks of PM10 were more influenced by first-class roads (relative contribution: 2.01). The results indicated that independent modeling analysis of different types of PM and periods contribute to more detailed studies of spatial-temporal variation of PM. For human activity studies, cell phone signaling data can effectively distinguish spatial-temporal distribution characteristics of the population on weekdays and weekends. Multi-source big data combined with mobile monitoring and model simulations were used to make population exposure risk studies more accessible, real-time, and cost-effective for sustainable urban planning and development.

Long-term effect of submicronic particulate matter (PM1) and intermodal particulate matter (PM1-2.5) on incident dyslipidemia in China: A nationwide 5-year cohort study

BackgroundThere is insufficient evidence of associations between incident dyslipidemia with PM1 (submicronic particulate matter) and PM1-2.5 (intermodal particulate matter) in the middle-aged and elderly. We aimed to determine the long-term effects of PM1 and PM1-2.5 on incident dyslipidemia respectively. MethodsWe studied 6976 individuals aged ≥45 from the China Health and Retirement Longitudinal Study from 2013 to 2018. The concentrations of particular matter (PM) for every individual's address were evaluated using a satellite-based spatiotemporal model. Dyslipidemia was evaluated by self-reported. The generalized linear mixed model was applied to quantify the correlations between PM and incident dyslipidemia. ResultsAfter a 5-year follow-up, 333 (4.77%) participants developed dyslipidemia. Per 10 μg/m³ uptick in four-year average concentrations of PMs (PM1 and PM1-2.5) corresponded to 1.11 [95% confidence interval (CI): 1.01–1.23)] and 1.23 (95% CI: 1.06–1.43) fold risks of incident dyslipidemia. Nonlinear exposure-response curves were observed between PM and incident dyslipidemia. The effect size of PM1 on incident dyslipidemia was slightly higher in males [1.14 (95% CI: 0.98–1.32) vs. 1.04 (95% CI: 0.89–1.21)], the elderly [1.23 (95% CI: 1.04–1.45) vs. 1.03 (95% CI: 0.91–1.17)], people with less than primary school education [1.12 (95% CI: 0.94–1.33) vs. 1.08 (95% CI: 0.94–1.23)], and solid cooking fuel users [1.17 (95% CI: 1.00–1.36) vs. 1.06 (95% CI: 0.93–1.21)], however, the difference was not statistically significant (Z = −0.82, P = 0.413; Z = −1.66, P = 0.097; Z = 0.32, P = 0.752; Z = −0.89, P = 0.372). ConclusionsLong-term exposure to PM1 and PM1-2.5 were linked with an increased morbidity of dyslipidemia in the middle-aged and elderly population. Males, the elderly, and solid cooking fuel users had higher risk. Further studies would be warranted to establish an accurate reference value of PM to mitigate growing dyslipidemia.

Field evidence for enhanced generation of reactive oxygen species in atmospheric aerosol containing quinoline components

Organic constituents of atmospheric particular matter (PM) contribute significantly to prompt generation of reactive oxygen species (ROS) in particle phase, which is often quantified in terms of its oxidative potential (OP). In this study, we used complementary methods of the advanced molecular characterization and the OP quantitation to explore potential of different organic species to enhance ROS generation in ambient aerosol samples. We used seven PM2.5 samples collected in the Midwest US, and extracted them sequentially into a set of solvents with different polarity: first non-polar hexane (HEX), then moderately polar dichloromethane (DCM) and finally polar deionized (DI) water. The DI extracts were further separated using solid phase extraction to isolate water-soluble organic carbon (WSOC) from inorganic ions. The OP of obtained extracts was analyzed using dithiothreitol (DTT) assay and reported here in terms of the DTT consumption rate (called DTT activity). The molecular composition of the same extracts was analyzed using a tandem platform of high-performance liquid chromatography (HPLC) coupled with a photodiode array detector (PDA) and a high-resolution mass spectrometer (HRMS). Highest DTT activities were observed in WSOC fractions (>1.1 μM min−1) followed by DCM (0.75–1.1 μM min−1) and HEX (<0.75 μM min−1) fractions, respectively. These results were complemented by the HPLC-PDA-HRMS molecular characterization data indicating numerous redox-active polar organics detected in the WSOC fractions. In particular, chemical component with elemental formula of C11H11N - attributed to dimethyl quinoline (DMQ) - was found systematically across all DTT-active fractions. The unprotonated nitrogen atom in the pyridine ring of quinolines facilitates electron transfer in the redox reactions, catalyzing generation of ROS in the quinoline containing samples. This is the first study that unambiguously identifies quinoline as a promoter of ROS generation determined within complex mixtures of ambient organic aerosols.

Effect of portable air purifier on indoor air quality: reduced exposure to particulate matter and health risk assessment.

We sought to investigate the impact of air purifiers in the removal of particular matter (PM)10, PM2.5, PM1, and particle number concentration (PNC) in the indoor air of dormitories located at Iran's largest medical university, Tehran University of Medical Sciences. Twelve rooms were selected and randomly assigned to two rooms: sham air purifier system deployed room (SR) and true air purifier system deployed room (TR). All study samples were drawn simultaneously from assigned rooms using portable GRIMM dust monitors for 24h. The PM monitors of air were positioned in the middle of each room next to the air purifier at the height of the breathing zone (1.5m in height). The mean PM10, PM2.5, PM1, and PNC removal efficiency in rooms with and without a smoker were measured to be 40.7 vs 83.8%, 31.2 vs 78.4%, 29.9 vs 72.3%, and 44.3 vs 75.6%, respectively. The results showed that smoking is an important influencing factor on the indoor air quality; smoking lowered the removal efficiency of PM10, PM2.5, PM1, and PNC by 43%, 47%, 43%, and 31%, respectively. An air purifier could decline the PM10 and PM2.5 even lower than the WHO 24-h guideline level in non-smoker rooms. This study revealed that using household air purifiers in rooms with smokers and non-smokers significantly reduces the non-carcinogenic risks of exposure to PM10 and PM2.5.

The impact of thermal status on emissions of a non-road diesel engine equipped with aftertreatment system under transient operation

The current study investigates the influence of hot and cold start on the emissions of a non-road diesel engine with an after-treatment system. The emissions were studied by experimental measurements on an engine during non-road transient operating conditions. These results indicate that the emissions of cold start are much higher than that of the hot start. The conversion efficiency of particular matter (PM) is only 33%, which is 31% lower than that of the hot start. Besides, the conversion efficiency of PM is weak under the cold start, and high exhaust flow, and the phenomenon of blow-off is prone to happen. The conversion efficiencies of NOx, HC, and CO are 86%, 79% and 63%, respectively, which differs from the hot-start conversion efficiency by 10%–16%. And their differences are mainly reflected in the early stage (0–600 s). Overall, the exhaust temperature and flow are important factors that affect the conversion efficiency of the after-treatment system, and the specific process was discussed in this paper. These provide enlightenment/direction for the emission reduction of non-road diesel engines.

Poly(lactic acid)/artificially cultured diatom frustules nanofibrous membranes with fast and controllable degradation rates for air filtration.

The worldwide pandemic, coronavirus COVID-19, has been posing a serious threat to the global economy and security in last 2 years. The monthly consumption and subsequent discarding of 129 billion masks (equivalent to 645,000 tons) pose a serious detrimental impact on environmental sustainability. In this study, we report a novel type of nanofibrous membranes (NFMs) with supreme filtration performance and controllable degradation rates, which are mainly composed of polylactic acid (PLA) and artificially cultured diatom frustules (DFs). In this way, the filtration efficiency of particular matter (PM) and the pressure drop were significantly improved in the prepared PLA/DFs NFMs as compared with the neat PLA NFM. In specific, with incorporation of 5% DFs into fibers, PM0.3 removal with a filtration efficiency of over 99% and a pressure drop of 109 Pa were achieved with a membrane thickness of only 0.1 mm. Moreover, the yield strength and crystallinity degree of the PLA/DFs5 NFMs were sharply increased from 1.88 Mpa and 26.37% to 2.72 Mpa and 30.02%. Besides those unique characters, the PLA/DFs5 presented excellent degradability, accompanied by the degradation of 38% in 0.01 M sodium hydroxide solution after 7 days and approximately 100% in natural condition after 42 days, respectively. Meanwhile, the environmentally friendly raw materials of the composite polylactic acid and artificially cultured diatom frustules could be extracted from corn-derived biomass and artificially cultivated diatoms, ensuring the conformance to carbon neutrality and promising applications in personal protection.Graphical abstract Supplementary informationThe online version contains supplementary material available at 10.1007/s42114-022-00474-7.

Urinary CC16, a potential indicator of lung integrity and inflammation, increases in children after short-term exposure to PM2.5/PM10 and is driven by the CC16 38GG genotype

Particular matter (PM) exposure is a big hazard for public health, especially for children. Serum CC16 is a well-known biomarker of respiratory health. Urinary CC16 (U-CC16) can be a noninvasive alternative, albeit requiring adequate adjustment for renal handling. Moreover, the SNP CC16 G38A influences CC16 levels. This study aimed to monitor the effect of short-term PM exposure on CC16 levels, measured noninvasively in schoolchildren, using an integrative approach. We used a selection of urine and buccal DNA samples from 86 children stored in an existing biobank. Using a multiple reaction monitoring method, we measured U-CC16, as well as RBP4 (retinol binding protein 4) and β2M (beta-2-microglobulin), required for adjustment. Buccal DNA samples were used for CC16 G38A genotyping. Linear mixed-effects models were used to find relevant associations between U-CC16 and previously obtained data from recent daily PM ≤ 2.5 or 10 μm exposure (PM2.5, PM10) modeled at the child's residence. Our study showed that exposure to low PM at the child's residence (median levels 18.9 μg/m³ (PM2.5) and 23.6 μg/m³ (PM10)) one day before sampling had an effect on the covariates-adjusted U-CC16 levels. This effect was dependent on the CC16 G38A genotype, due to its strong interaction with the association between PM levels and covariates-adjusted U-CC16 (P = 0.024 (PM2.5); P = 0.061 (PM10)). Only children carrying the 38GG genotype showed an increase of covariates-adjusted U-CC16, measured 24h after exposure, with increasing PM2.5 and PM10 (β = 0.332; 95% CI: 0.110 to 0.554 and β = 0.372; 95% CI: 0.101 to 0.643, respectively). To the best of our knowledge, this is the first study using an integrative approach to investigate short-term PM exposure of children, using urine to detect early signs of pulmonary damage, and taking into account important determinants such as the genetic background and adequate adjustment of the measured biomarker in urine.

The Relationship between PM2.5 and PM10 in Central Italy: Application of Machine Learning Model to Segregate Anthropogenic from Natural Sources

Particular Matter (PM) data are the most used for the assessment of air quality, but it is also useful to monitor VOC and CO. The health impact of PM increases with decreasing aerodynamic dimensions, therefore most of the monitoring is aimed at PM10 (fraction of PM with aerodynamic dimensions smaller than 10 µm) and PM2.5 (fraction with aerodynamic dimensions lower than 2.5 µm). Generally, anthropogenic emissions contribute mainly to PM2.5 levels, whereas natural sources can largely affect PM10 concentrations. PM2.5/PM10 ratio can be used as a proxy of the origin (anthropogenic vs natural) of the PM, providing a useful indication about the main sources of PM that characterizes a specific geographical or urban setting. This paper presents the results of the analysis of continuous measurements of PM10 and PM2.5 concentrations at eight stations of the regional air quality monitoring network in Abruzzo (Central Italy), in the period 2017–2018. The application of models based on machine learning technique shows that PM2.5/PM10 ratio can be used to classify PM emissions and to know the nature of the emission source (natural and anthropogenic), under determinate conditions, and properly taking into account the meteorological parameters.

COVID-19 strict lockdown impact on urban air quality and atmospheric temperature in four megacities of India

COVID-19 pandemic has forced to lockdown entire India starting from 24th March 2020 to 14th April 2020 (first phase), extended up to 3rd May 2020 (second phase), and further extended up to 17th May 2020 (third phase) with limited relaxation in non-hotspot areas. This strict lockdown has severely curtailed human activity across India. Here, aerosol concentrations of particular matters (PM) i.e., PM10, PM2.5, carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), ammonia (NH3) and ozone (O3), and associated temperature fluctuation in four megacities (Delhi, Mumbai, Kolkata, and Chennai) from different regions of India were investigated. In this pandemic period, air temperature of Delhi, Kolkata, Mumbai and Chennai has decreased about 3 °C, 2.5 °C, 2 °C and 2 °C respectively. Compared to previous years and pre-lockdown period, air pollutants level and aerosol concentration (−41.91%, −37.13%, −54.94% and −46.79% respectively for Delhi, Mumbai, Kolkata and Chennai) in these four megacities has improved drastically during this lockdown period. Emission of PM2.5 has experienced the highest decrease in these megacities, which directly shows the positive impact of restricted vehicular movement. Restricted emissions produce encouraging results in terms of urban air quality and temperature, which may encourage policymakers to consider it in terms of environmental sustainability.

Variation Characteristics and Potential Sources of the Mt. Haituo Aerosol Chemical Composition in Different Pollution Processes During Winter in Beijing, China

In order to investigate the chemical composition and source apportionment of aerosols during winter in the Beijing-Tianjin-Heibei region, the particular matter (PM) and aerosol chemical composition at Mt. Haituo were observed by using a GRIMM 180, a single-particle soot photometer (SP2), and a high-resolution time-of-flight aerosol mass spectrometer (HR-TOF-AMS) from December 28, 2020 to February 3, 2021. Combining these observations with meteorological data and the HYSPLIT model, we calculated the potential source contribution factor (PSCF) and concentration weighted trajectory (CWT) and analyzed the temporal evolution and potential sources apportionment of PM and aerosol chemical composition under different pollution processes. The results showed that the dust storm process mainly affected PM10 and PM2.5 in Mt. Haituo during the winter and had a small impact on PM1; by contrast, haze pollution mainly affected PM1. Chemical components of aerosol accounted for 85.0% and 73.4% of PM1 on clean and haze days, respectively, but only 47.4% of PM1 in dust storm processes. NO3- was the chemical component with the largest mass concentration in haze, accounting for 25.2% of PM1; black carbon (BC) had the largest mass concentration on clean and dust storm days, accounting for 24.1% and 12.8% of PM1, respectively. The median diameters of BC were 209.7, 207.5, and 204.7 nm on clean, dust storm, and haze days, respectively. Dp/Dc was 2.15 in haze pollution, which was 1.38 and 1.39 times that on dust storm and clean days, respectively. Diurnal variations in PM and aerosol chemical components were different during the different processes. PM10 and PM2.5had high mass concentrations at night and low mass concentrations during the daytime on clean and dust storm days and had a unimodal distribution with a peak at 14:00 in haze. Diurnal variations in chemical composition had a unimodal distribution on clean days and a bimodal distribution on dust storm and haze days. The chemical compositions of the BC coating layer were different under different processes. The coating layers of BC were mainly NH4NO3, (NH4)2SO4, and organic matter on the clean, dust storm, and haze days, respectively. The distribution of potential sources of PM1 and its chemical components were different under different processes. The high-value area of the potential sources was mainly concentrated in the Beijing-Baoding-Shijiazhuang-Yangquan area in the southwestern portion of the site during dust storms and was mainly concentrated in Yanqing, Huailai, and Changping in the areas around the site during haze.

Effect of ambient air pollutants PM2.5 and PM10 on COVID-19 incidence and mortality: observational study.

Environmental pollution is a leading global challenge affecting weather conditions and causing severe environmental, social, and public health problems. This study explores the impact of ambient air pollutants, particular matter (PM), PM2.5 and PM10 on morbidity and mortality of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) globally. In this study, initially 492 research articles were identified through seven search engines containing Web of Science, Medline, PubMed, EMBASE, Scopus, WHO COVID-19 literature, and Ovid databases by consuming keywords "Environmental pollution, Ambient air pollutants, particulate matter, PM2.5, PM10, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), and COVID-19 pandemic". Finally, 26 original research publications were included for the analysis, and the remaining were excluded. These studies were originated from the United States of America (9), China (5), Italy (3), India (1), worldwide (1), and one study each from England, Spain, Canada, Saudi Arabia, Singapore, Japan, and Iran. The analysis based on a worldwide dataset assembled the information from the global literature from December 2019 to September 30, 2021. The assessment for the various regions to a global extent was based on air pollutants and COVID-19 cases and deaths. Twenty- six studies met the selection criteria representing almost all over the world. Based on the synthesis of scientific studies, it was identified that PM2.5 and PM10 are associated with 15.08%, 11.44 increased COVID-19 cases, and 9.26% and 0.75% COVID-19 deaths, respectively. The evidence indicates that PM2.5 and PM10 can affect COVID-19 epidemiology in various geographical regions.The findings established an association and a possible causal link between increasing ambient air pollutants, particulate matter PM2.5 and PM10 with increased incidence and mortality of COVID-19. The global health authorities must take strict preventive measures to minimize air pollution and combat such challenging and threatening COVID-19 pandemic globally.

Effect of Covid-19 on NO2 and particular matter (PM) concentrations and reaffirmation of the need to use biofuels in the world

On 23 January 2020, the world saw the first coronavirus lockdown come into force in Wuhan, China in an effort to stop the spread of the illness. This lockdown set the precedent for similar measures in other cities across the country, putting a halt to daily activities including industry and traffic. Factories and other industries were shut down and people were confined to their homes. Similar measures were then put in place worldwide in the following weeks and months. As a result, a significant reduction in air pollutants across the world was detected by satellites. This included reduced emissions of particular matter (PM) and nitrogen dioxide. The aim of this work is to investigate the effects of COVID-19 pandemics on NO2 and particular matter (PM) concentrations during lockdown, to answer the question of whether this crisis should reduce the development of biofuels or with regard to the reduction and end of COVID-19 restrictions we need to pay more attention to the effects and importance of biofuels on human health. The lockdown reduced the amount of PM in different countries of the world by 9 to 63% compared to the time of corona lock. Its distribution also decreased by 20–30% in European countries and up to 70% in some Asian countries. But wait, you should not be happy. Because these changes have taken place at a time when the world is facing a crisis and governments are forced to shut down businesses and impose locks. The use of biofuels can also reduce the emission of these air pollutants. Corona conditions once again reminded of the need to use these fuels. The condition of the corona is also temporary, and with the discovery of the corona vaccine and the start of vaccination around the world, these locks will be reduced and air pollutants will return to the time of the previous corona. Therefore, the need to use renewable and sustainable energy such as biofuels becomes apparent.

PM2.5-induced inflammation and myocardial cell injury in rats.

Numerous studies show association of particular matter (PM) in air pollution with cardiovascular dysfunction, and increased morbidity and mortality. The main mechanisms of this adverse effect involve increasing oxidative stress, inflammatory responses, and genotoxicity. Several recent studies investigated the ability of PM2.5 to cause myocardial injury in animal models using various methods, such as intratracheal instillation, intraperitoneal injection or tail vein injection. The purpose of this study is to explore the PM2.5-induced myocardial inflammatory reaction in rats through the new technology of multi-functional aerosol concentration and enrichment system. Thirty Wistar rats were divided into two groups, 15 in each group. In the exposure group, PM2.5 multi-functional aerosol concentration and enrichment system was used for PM2.5 online oral and nasal exposure (5 times a week, 4 hours exposure, for the duration of 3 months). Histopathological examination of the left ventricular myocardial tissue of both groups was done using hematoxylin and eosin (H&E) staining. Ultrastructural changes of the heart specimens were assessed using electron microscopy. The levels of CRP and ICAM-1 were detected by immunohistochemistry. Compared with the control group, myocardial tissue of the exposure group exhibited edema, widened myocardial space and infiltration of inflammatory cells. There was nuclear pyknosis, mitochondrial membrane and spinal fusion, rough endoplasmic reticulum expansion, degranulation and cell swelling in the exposed group. The area of CRP positive staining in the exposed group was 3.7-fold higher than that in the control group (p < 0.05), and the ICAM-1 positive staining area of the exposed group was 12-fold higher than that of the control group (p < 0.05). Prolonged exposure to PM2.5 inhalation promotes significant upregulation of ICAM-1 and CRP expression in myocardial tissues, ultrastructural alterations in myocardial cells, and influx of inflammatory cells.

Revealing Soil and Tree Leaves Deposited Particulate Matter PTE Relationship and Potential Sources in Urban Environment.

Trees play a pivotal role in improving urban environmental quality and provide several ecosystem services including the removal of pollutants from the air, such as particular matter (PM) and potentially toxic elements (PTE). Therefore, understanding the tree PM and PTE capturing potential, also in connection with plant species, is of great concern, especially in urban areas. This study aims to reveal the link between the elemental composition of PM deposited on tree leaves and soils PTE contents, as well as to rank the PM capturing efficiency of 10 different tree species growing under the impact of urban environments. This also allowed us to test the efficiency of PM deposited on tree leaves as a PTE biomonitoring and pollution source identification tool, in the two biggest urban areas of Armenia. Indeed, high contents of PTE are detected in both soil- and leaf-deposited PM from sites characterized by the presence of localized and active pollution sources (i.e., industrial unites, high traffic, etc.), which are identified by specific tracers (such as Mo, Cu, Zn, Pb, and Cd). Among the studied tree species, the highest PM amount per unit leaf area is observed for Platanus orientalis, but elm species are also identified as promising canditates to be considered for their PM removing potential, and need to be included in future more details studies.