PM10 Values Research Articles

Source Apportionment of Air Quality Parameters and Noise Levels in the Industrial Zones of Blantyre City

The increase in industrial activities has raised concerns regarding air quality in urban areas within Malawi. To assess the source apportionment of air quality parameters (AQPs) and noise levels, concentrations of AQPs (CO, TSP, PM 2.5, PM10) and noise levels were monitored at 15 sites in Makata, Limbe, Maselema, Chirimba, and Maone during dry and wet seasons, respectively. Active mobile multi-gas monitors and a Dylos DC1100 PRO Laser Particle Counter (2018 model) were used to monitor AQPs, while Integrated Sound Level Meters were used to measure noise levels. Monitoring and analysis were guided by the World Health Organization (WHO) and Malawi Standards (MS). A Positive Matrix Factorization (PMF) model was used to determine source apportionment of AQPs, and matrix trajectories analysed air mass movement. In the wet season, the average concentration values of CO, TSP, PM10, and PM2.5 were 0.49 ± 0.65 mg/m3, 85.03 ± 62.18 µg/m3, 14.65 ± 8.13 µg/m3, and 11.52 ± 7.19 µg/m3, respectively. Dry season average concentration values increased to 1.31 ± 0.81 mg/m3, 99.86± 30.06 µg/m3, 24.35 ± 9.53 µg/m3, and 18.28 ± 7.14 µg/m3. Noise levels remained below public MS and WHO standards (85 dB). Positive correlations between AQPs and noise levels were observed, strengthening from weak in the dry season to moderately strong in the wet season. PMF analysis identified key factors influencing AQPs accumulation, emphasizing the need for periodic sampling to monitor seasonal pollution trends, considering potential impacts on public health and environmental sustainability. Further studies should look at factors affecting the dynamics of PMF in Blantyre City.

Impacts of PM10 exposure on hospitalization for acute bronchitis in Ankara, Türkiye

The purpose of this study is to investigate the relationship between PM10 exposure and acute bronchitis admissions in Ankara, Türkiye. PM10 data and daily acute bronchitis admissions were obtained in Ankara for 2020-2021. A generalized additive model was applied to estimate the effects of daily PM10 concentration on hospital admissions for acute bronchitis. The Distributed Lag Non-linear Model was utilized to evaluate the lagged effects of PM10 concentration. Additionally, the model was adjusted for stratified analyses according to gender and age groups. Relative risks with corresponding 95% confidence intervals (CIs) were obtained for each 10 μg/m3 increment in PM10 values. A rise of 10 μg/m3 in PM10 concentrations was significantly linked with an elevated risk of acute bronchitis with relative risks observed at lag3 (RR: 1.010, 95% CI: 1.001-1.019) and lag4 (RR: 1.010, 95% CI: 1.002-1.019). There were associations for middle-aged individuals (45 to 64 years), with a 0.5% increase in risk at lag3 (RR: 1.005; 95% CI: 1.001-1.009) and lag4 (RR: 1.005; 95% CI: 1.001-1.010). PM10 exposure could increase the risk of acute bronchitis and better air quality would be beneficial to human health.

Effect of COVID‐19 lockdown on ambient air quality

AbstractThe aim of this study was to evaluate the impact of pandemic‐related lockdown on Turkey's air quality throughout time and space. For this purpose, statistical techniques were used to assess daily particulate matter (PM10), sulfur dioxide (SO2), nitrogen oxides and nitrogen dioxide (NOx and NO2), ozone (O3), and carbon monoxide (CO). The study's findings showed that, while the lockdown improved air quality in terms of air pollutant emissions, the most notable reduction was in NO2 and NOx emissions. When comparing the months prior to the pandemic (November 2019 to January 2020) with the months during the pandemic (November 2020 to January 2021), the declines in NO2 were 20%, 3%, and 0.5%, respectively. NOx emissions decreased by an average of 19% and 5% in November and December, respectively, and increased by an average of 16% in January during the pandemic. When the data for the 33 days of lockdown were compared to the data for the same 33 days the previous year, significant differences were determined at several Clean Air Centers, which were two for PM10, two for SO2, seven for NOx, four for NO2, two for CO, and three for O3, respectively. In this study, pollutant concentrations were found in the following ranges from November 2019 to January 2021: PM10: 3–208 µg m–3, SO2: 1–56 µg m–3, NOx: 6–600 µg m–3, NO2: 4–155 µg m–3, CO: 1–3921 µg m–3, and O3: 2–119 µg m–3. There were days that exceeded the limit values for PM10.

A comprehensive examination of temporal-seasonal variations of PM1.0 and PM2.5 in taiwan before and during the COVID-19 lockdown

COVID-19 has been a significant global concern due to its contagious nature. In May 2021, Taiwan experienced a severe outbreak, leading the government to enforce strict Pandemic Alert Level 3 restrictions in order to curtail its spread. Although previous studies in Taiwan have examined the effects of these measures on air quality, further research is required to compare different time periods and assess the health implications of reducing particulate matter during the Level 3 lockdown. Herein, we analyzed the mass concentrations, chemical compositions, seasonal variations, sources, and potential health risks of PM1.0 and PM2.5 in Central Taiwan before and during the Level 3 lockdown. As a result, coal-fired boilers (47%) and traffic emissions (53%) were identified as the predominant sources of polycyclic aromatic hydrocarbons (PAHs) in PM1.0, while in PM2.5, the dominant sources of PAHs were coal-fired boilers (28%), traffic emissions (50%), and iron and steel sinter plants (22.1%). Before the pandemic, a greater value of 20.9 ± 6.92 μg/m3 was observed for PM2.5, which decreased to 15.3 ± 2.51 μg/m3 during the pandemic due to a reduction in industrial and anthropogenic emissions. Additionally, prior to the pandemic, PM1.0 had a contribution rate of 79% to PM2.5, which changed to 89% during the pandemic. Similarly, BaPeq values in PM2.5 exhibited a comparable trend, with PM1.0 contributing 86% and 65% respectively. In both periods, the OC/EC ratios for PM1.0 and PM2.5 were above 2, due to secondary organic compounds. The incremental lifetime cancer risk (ILCR) of PAHs in PM2.5 decreased by 4.03 × 10-5 during the pandemic, with PM1.0 contributing 73% due to reduced anthropogenic activities.

Estimating PM levels on an underground metro platform by exploring a new model-based factor research

Over recent decades, the adverse impacts of airborne particles on human health have received wide attention. Elevated PM concentrations on underground platforms might pose a significant public health issue within underground metro systems. This study explores the impact of introducing a new type of train on the concentration of airborne particles on an underground metro platform through statistical modelling, analyses interactions between various factors, and estimates air quality on underground platforms after introducing a new type of train. Based on the data from a long-term field measurement, a linear mixed model, the multi-factor interaction model, which is an expansion of a previous multi-factor model, explored the impacts of train operations, passenger flow, urban background PM levels, ventilation, nighttime maintenance work, and their interactions on hourly PM10, PM2.5, and PM1 values on the platform. The model results show a positive correlation between those factors and platform PM10, PM2.5 and PM1 values, with significant interactions among these factors. The new model has a higher estimate quality than the previous model. Based on the combination of the model and measurement results, the levels of underground PM decreased significantly after replacing the old type of trains with new ones.

Decadal trends (2009–2018) in Saharan dust transport at Mt. Martano EMEP station, Italy

Saharan dust advections in the Mediterranean Basin are widely studied and subjected to specific regulations due to their impact on the air quality of European cities. As a natural phenomenon, their contribution to PM should be accurately quantified in order to correctly evaluate their impact in anthropized areas. The identification and quantification of Saharan dust advections should be carried out in rural regional background sites. However, there is a lack of suitable sites in Central and Southern Italy, despite its strategic positioning in the Central Mediterranean area. The rural regional background site of Monte Martano site can fill this gap. This study aims at characterising the 10 years climatology of dust at the Monte Martano (MM) site and integrate it in the wider Mediterranean Basin monitoring network. Monte Martano is barely affected by local emissions, with average PM10 concentration of 11.0 ± 7.4 μg m−3, and a marked seasonality compatible with the evolution of the planetary boundary layer (PBL) during the year. Saharan dust advections contribute for 45.9 days per year on average, with a dust load that accounts for 48% of the background PM10 value. The Saharan dust advections' frequency, duration and intensity were evaluated, and a clear seasonality was detected, with minimum probability in summer and long-lasting events mainly occurring in spring and autumn. The dust load calculation was performed with 3 different methods, the EU guideline method, the modified DIAPASON methodology specifically designed for Central Italy and the DREAM8b model. The comparison between the three methods showed a substantial overestimation of the dust load by the model, while the two experimental methods are in accordance (R = 0.97). In conclusion, the MM site has proven to be suitable for Saharan dust monitoring in Central Italy, providing a new insight in the Central Mediterranean Saharan dust observation network.

Assessment of human and meteorological influences on PM10 concentrations: Insights from machine learning algorithms

Air pollution prediction assists residents in planning activities and avoiding regions with elevated pollution levels. This study employed Random Forest (RF), Extreme Gradient Boosting (XGB), and Histogram-Based Gradient Boosting (HistGB) based models to evaluate PM10 under 10-year average meteorological conditions (2012–2021) for Rocklea monitoring station in Queensland, Australia. The meteorological normalisation method adjusted air pollution data was assessed by removing weather-related effects to focus on the impact of human activities. By comparing observed and meteorologically normalised PM10 values, the impact of weather and human activities was assessed. The performance of these models was evaluated through k-Fold cross-validation, with a particular emphasis on their ability to predict PM10 concentrations accurately. Contrary to initial expectations, the RF model exhibited superior performance compared to XGB and HistGB, demonstrating the highest accuracy and consistency in predicting PM10 levels. This result underscores the effectiveness of the RF model in environmental air quality prediction. A key insight from our analysis was the role of weather-related factors in influencing PM10 levels. While the observed PM10 values showed a general decreasing trend from 2012 to 2021, except for an anomaly in 2019, the meteorologically normalised values remained relatively stable. This finding suggests that weather variations, rather than human activities, have been the more significant influence on the observed trend in PM10 levels over the past decade. The consistent meteorologically normalised values further reinforce the importance of considering natural meteorological variability in air quality studies.

Assessment of particulate matter and particle path trajectory analysis using a HYSPLIT model over Dire Dawa, Ethiopia

This work deals with the assessment of particulate matter (PM1, PM2.5, and PM10) over Dire Dawa during the month of May 2021. In the study, purple sensor (PS) and gravimetric methods (GM) were used. The purple sensor was to provide real-time measurements of PM1.0, PM2.5, and PM10 particulates. The GM instruments were constructed using wood with 1 m height (distance to ground), with flat board on top of which filter papers were placed to collect particulate matter. The difference in filter paper weight before and after sampling was used to calculate the particle masses. By dividing the weight gain of the filter by the amount of air measured, the concentrations of suspended particulate matter in the defined size range were estimated. The mean value of PM10 indicated a good status whereas the mean value of PM2.5 revealed a moderate condition as far as pollution is concerned. The purple sensor detected relatively higher values for PM10 measurement as compared to GM method during the study period. According to the calculated results of the ratio of mass concentration of PM1.0 to PM10, coarse particles were dominant whereas in the ratio of PM2.5 to PM10 both coarse and fine mode particles were equally present during the sampling period. The spatial distribution showed variations depending on the locations where the sampling filter papers were placed. The HYSPLIT backward trajectory analysis indicated various air masses and transport channels during different seasons. The predominant pathways were from both urban and desert origins.

Determining Effective Factors Regarding Weather and Some Types of Air Pollutants in Seasonal Changes of PM10 Concentration Using Tree-Based Algorithms in Yazd City

Introduction: This study was carried out with the aim of determining weather parameters and air pollutants affecting seasonal changes of particulate matter of less than 10 microns (PM10) in Yazd city using Random Forest (RF) and extreme gradient boosting (Xgboost) models.
 Materials and Methods: The required data was obtained from 2018 to 2022. Levene’s test was applied to investigate the significant difference in the variance of PM10 values in 4 different seasons, and Boruta algorithm was used to select the best predictive variables. RF and Xgboost models were trained using two-thirds of the input data and were tested using the remaining data set. Their performance was evaluated based on R2, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Nash–Sutcliffe Model Efficiency Coefficient (NSE).
 Results: The RF showed a higher performance in predicting PM10 in all the study seasons (R2 > 0.85; RMSE < 22). The contribution of dust concentration and relative humidity in spring PM10 changes was more than other variables. For summer, wind direction and ozone were identified as the most important variables affecting PM10 concentration. In the autumn and winter, air pollutants and dust concentration had the greatest effect on PM10, respectively.
 Conclusion: RF model could explain more than 85% of PM10 seasonal variability in Yazd city. It is recommended to use the model to predict the changes of this air pollutant in other regions with similar climatic and environmental conditions. The results can also be useful for providing suitable solutions to reduce PM10 pollution hazards in Yazd city

Atmospheric Air Pollution in Niamey, Niger: Assessment of Particulate Matter From Road Traffic (PM2.5 and PM10)

The aim of this study was to characterise the air based on atmospheric particles in the city of Niamey. To do this, a mass concentration detector type PCE-RCM 11 and station type Davis Pro were used as collection equipment. Particulate matter with diameters of less than 2.5 μm and 10 μm, PM2.5 and PM10, respectively, as well as meteorological parameters, temperature and humidity were determined. The study was carried out over 15 days in 5 districts of Niamey between 7.30 am and 10.10 pm at the main roundabouts in each district. According to the results obtained, the average concentrations of PM2.5 in the air measured ranged from 75.34 μg/m3 to 162.34 μg/m3, whereas the WHO recommends 15 μg/m3 for PM2.5 for daily exposure, which would cause cardiovascular or pulmonary disease in those exposed. The PM10 values recorded ranged from 100.43 μg/m3 to 221.90 μg/m3, well in excess of the WHO standards published in 2021, which recommend 45 μg/m3 for PM10, indicating a probable risk of throat and respiratory disorders. Average temperature values ranged from 23.051 °C ± 0.1 °C to 34.186 °C ± 0.1 °C. The average relative humidity measured ranged from 17.164% ± 1% to 26.910% ± 1%. The results of the correlation analysis showed a strong correlation between PM2.5 and PM10. The latter has a strong positive correlation with temperature. PM2.5 is strongly negatively correlated with relative humidity, while PM10 is moderately negatively correlated with relative humidity. Temperature and humidity show a weak negative correlation with each other. Each of the particles studied is present in the communes of Niamey at the 5 roundabouts representing the study area. It should be confirmed that these particles emitted into the air could have come from urban transport. The correlation circle obtained from the PCA explains the relationship between the variables measured (PM and meteorological parameters). The first two dimensions explain 97.55% of the total information, with 81.62% for dimension 1% and 15.93% for dimension 2.

Aerosol Vertical Structure and Optical Properties during Two Dust and Haze Episodes in a Typical Valley Basin City, Lanzhou of Northwest China

The vertical profiles of aerosol optical properties are vital to clarify their transboundary transport, climate forcing and environmental health influences. Based on synergistic measurements of multiple advanced detection techniques, this study investigated aerosol vertical structure and optical characteristics during two dust and haze events in Lanzhou of northwest China. Dust particles originated from remote deserts traveled eastward at different altitudes and reached Lanzhou on 10 April 2020. The trans-regional aloft (~4.0 km) dust particles were entrained into the ground, and significantly modified aerosol optical properties over Lanzhou. The maximum aerosol extinction coefficient (σ), volumetric depolarization ratio (VDR), optical depth at 500 nm (AOD500), and surface PM10 and PM2.5 concentrations were 0.4~1.5 km−1, 0.15~0.30, 0.5~3.0, 200~590 μg/m3 and 134 μg/m3, respectively, under the heavy dust event, which were 3 to 11 times greater than those at the background level. The corresponding Ångström exponent (AE440–870), fine-mode fraction (FMF) and PM2.5/PM10 values consistently persisted within the ranges of 0.10 to 0.50, 0.20 to 0.50, and 0.20 to 0.50, respectively. These findings implied a prevailing dominance of coarse-mode and irregular non-spherical particles. A severe haze episode stemming from local emissions appeared at Lanzhou from 30 December 2020 to 2 January 2021. The low-altitude transboundary transport aerosols seriously deteriorated the air quality level in Lanzhou, and aerosol loading, surface air pollutants and fine-mode particles strikingly increased during the gradual strengthening of haze process. The maximum AOD500, AE440–870nm, FMF, PM2.5 and PM10 concentrations, and PM2.5/PM10 were 0.65, 1.50, 0.85, 110 μg/m3, 180 μg/m3 and 0.68 on 2 January 2021, respectively, while the corresponding σ and VDR at 0.20–0.80 km height were maintained at 0.68 km−1 and 0.03~0.12, implying that fine-mode and spherical small particles were predominant. The profile of ozone concentration exhibited a prominent two-layer structure (0.60–1.40 km and 0.10–0.30 km), and both concentrations at two heights always remained at high levels (60~72 μg/m3) during the entire haze event. Conversely, surface ozone concentration showed a significant decrease during severe haze period, with the peak value of 20~30 μg/m3, which was much smaller than that before haze pollution (~80 μg/m3 on 30 December). Our results also highlighted that the vertical profile of aerosol extinction coefficient was a good proxy for evaluating mass concentrations of surface particulate matters under uniform mixing layers, which was of great scientific significance for retrieving surface air pollutants in remote desert or ocean regions. These statistics of the aerosol vertical profiles and optical properties under heavy dust and haze events in Lanzhou would contribute to investigate and validate the transboundary transport and radiative forcing of aloft aerosols in the application of climate models or satellite remote sensing.

First temporal distribution model of ambient air pollutants (PM2.5, PM10, and O3) in Yangon City, Myanmar during 2019–2021

Air pollution has emerged as a significant global concern, particularly in urban centers. This study aims to investigate the temporal distribution of air pollutants, including PM2.5, PM10, and O3, utilizing multiple linear regression modeling. Additionally, the research incorporates the calculation of the Air Quality Index (AQI) and Autoregressive Integrated Moving Average (ARIMA) time series modeling to predict the AQI for PM2.5 and PM10. The concentrations and AQI values for PM2.5 ranged from 0 to 93.6 μg/m3 and 0 to 171, respectively, surpassing the Word Health Organization's (WHO) acceptable threshold levels. Similarly, concentrations and AQI values for PM10 ranged from 0.1 to 149.27 μg/m3 and 2–98 μg/m3, respectively, also exceeding WHO standards. Particulate matter pollution exhibited notable peaks during summer and winter. Key meteorological factors, including dew point temperature, relative humidity, and rainfall, showed a significant negative association with all pollutants, while ambient temperature exhibited a significant positive correlation with particulate matter. Multiple linear regression models of particulate matter for winter season demonstrated the highest model performance, explaining most of the variation in particulate matter concentrations. The annual multiple linear regression model for PM2.5 exhibited the most robust performance, explaining 60% of the variation, while the models for PM10 and O3 explained 45% of the variation in their concentrations. Time series modeling projected an increasing trend in the AQI for particulate matter in 2022. The precise and accurate results of this study serve as a valuable reference for developing effective air pollution control strategies and raising awareness of AQI in Myanmar.

Angular momentum and chemical transport by azimuthal magnetorotational instability in radiative stellar interiors

Context. The transport of angular momentum and chemical elements within evolving stars remains poorly understood. Asteroseismic and spectroscopic observations of low-mass main sequence stars and red giants reveal that their radiative cores rotate orders of magnitude slower than classical predictions from stellar evolution models and that the abundances of their surface light elements are too small. Magnetohydrodynamic (MHD) turbulence is considered a primary mechanism to enhance the transport in radiative stellar interiors but its efficiency is still largely uncertain. Aims. We explore the transport of angular momentum and chemical elements due to azimuthal magnetorotational instability, one of the dominant instabilities expected in differentially rotating radiative stellar interiors. Methods. We employed 3D MHD direct numerical simulations in a spherical shell of unstratified and stably stratified flows under the Boussinesq approximation. The background differential rotation was maintained by a volumetric body force. We examined the transport of chemical elements using a passive scalar. Results. We provide evidence of magnetorotational instability for purely azimuthal magnetic fields in the parameter regime expected from local and global linear stability analyses. Without stratification and when the Reynolds number Re and the background azimuthal field strength are large enough, we observed dynamo action driven by the instability at values of the magnetic Prandtl number Pm in the range 0.6 − 1, which is the smallest ever reported in a global setup. When considering stable stratification at Pm = 1, the turbulence is transitional and becomes less homogeneous and isotropic upon increasing buoyancy effects. The transport of angular momentum occurs radially outward and is dominated by the Maxwell stresses when stratification is large enough. We find that the turbulent viscosity decreases when buoyancy effects strengthen and scales with the square root of the ratio of the reference rotation rate Ωa to the Brunt–Väisälä frequency N. The chemical turbulent diffusion coefficient scales with stratification similarly to the turbulent viscosity, but is lower in amplitude so that the transport of chemicals is slower than the one of angular momentum, in agreement with recent stellar evolution models of low-mass stars. Conclusions. We show that the transport induced by azimuthal magnetorotational instability scales somewhat slowly with stratification and may enforce rigid rotations of red giant cores on a timescale of a few thousand years. In agreement with recent stellar evolution models of low-mass stars, the instability transports chemical elements less efficiently than angular momentum.

TEMPORAL ANALYSIS AND PREDICTIVE MODELING OF AMBIENT AIR QUALITY IN HULU LANGAT DISTRICT, SELANGOR, MALAYSIA: A CHEMOMETRIC APPROACH

One of the most important environmental problems facing the globe today is air pollution. The centre area for the local populace is the Hulu Langat district, which borders Kuala Lumpur, the capital. The purpose of this study is to look at how the ambient air quality varies in Hulu Langat, Selangor. The Air Quality Division of the Malaysian Department of Environment provided five years' worth of secondary data on the air quality at Hulu Langat. The database included five primary air pollutant characteristics sulphur dioxide (SO2), carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), and particulate matter with a diameter of 10 microns or less (PM10), in addition to data from the Air Pollutant Index (API). Chemometric analysis was used to examine the results. According to the results, SO2, NO2 and PM10 had the greatest correlations with API readings. A statistical process known as statistical control (SPC) showed that certain PM10 values were over national recommendations and control limits. The artificial neural network method's air quality prediction model demonstrated good accuracy with real data (R2 = 0.9). The results of this investigation indicated a strong correlation between the Hulu Langat air quality data. In order to achieve sustainable environmental practices in the future, it is imperative to engage in extensive collaboration across environmental departments and relevant authorities and engage in continuous monitoring of air quality.

Development of end-to-end low-cost IoT system for densely deployed PM monitoring network: an Indian case study

Particulate matter (PM) is considered the primary contributor to air pollution and has severe implications for general health. PM concentration has high spatial variability and thus needs to be monitored locally. Traditional PM monitoring setups are bulky, expensive, and cannot be scaled for dense deployments. This paper argues for a densely deployed network of IoT-enabled PM monitoring devices using low-cost sensors, specifically focusing on PM10 and PM2.5, the most health-impacting particulates. In this work, 49 devices were deployed in a region of the Indian metropolitan city of Hyderabad, of which 43 devices were developed as part of this work, and six devices were taken off the shelf. The low-cost sensors were calibrated for seasonal variations using a precise reference sensor and were particularly adjusted to accurately measure PM10 and PM2.5 levels. A thorough analysis of data collected for 7 months has been presented to establish the need for dense deployment of PM monitoring devices. Different analyses such as mean, variance, spatial interpolation, and correlation have been employed to generate interesting insights about temporal and seasonal variations of PM10 and PM2.5. In addition, event-driven spatio-temporal analysis is done for PM2.5 and PM10 values to understand the impact of the bursting of firecrackers on the evening of the Diwali festival. A web-based dashboard is designed for real-time data visualization.

Long-Term Exposure to PM10 Air Pollution Exaggerates Progression of Coronary Artery Disease

(1) Background: The increase in cardiovascular risk related to air pollution has been a matter of interest in recent years. The role of particulate matter 2.5 (PM2.5) has been postulated as a possible factor for premature death, including cardiovascular death. The role of long-term exposure to PM10 is less known. The aim of the study was to assess the individual relationship between air pollution in habitation and the development of coronary artery disease. (2) Methods: Out of 227 patients who underwent coronary angiography, 63 (38 men and 25 women) with a mean age of 69 (63–74) years, with nonsignificant atherosclerotic changes at the initial examination, were included in the study. The baseline and repeated coronary angiography were compared to reveal patients with atherosclerotic progression and its relation to demographic and clinical factors and exposure to air pollution in the habitation place. (3) Results: In the performed analysis, we found a significant correlation between Syntax score in de novo lesions and BMI (Spearman’s rho −0.334, p = 0.008). The significant and strong correlation between median annual PM10 values of 20 µg/m3 and at least 25 µg/m3 in air pollution and the risk of de novo coronary disease was noticed (Spearman’s rho = 0.319, p = 0.011 and Spearman’s rho = 0.809, p < 0.001, respectively). (4) Conclusions: There is a positive correlation between long-term exposure to PM10 air pollution and coronary artery disease progression, demonstrated by the increase in Syntax score. The presented analysis revealed increased morbidity at lower PM10 concentrations than generally recommended thresholds. Therefore, further investigations concerning air pollution’s influence on cardiovascular risk should be accompanied by promoting lifestyle changes in the population and revisiting the needs for environmental guidelines.

Using the GlutoPeak Tester in Determining the Quality Characteristics of Some Bread Wheat Varieties

The aim of this study is to determine the physical, chemical and technological, rheological properties of 10 registered bread wheat varieties developed by Bahri Dağdaş International Agricultural Research Institute and their quality status in bread analyzes and GutoPeak analyzes. At the same time, it is aimed to investigate the relationships between physical, chemical, technological, rheological and bread analyzes with GlutoPeak quality parameters and to reveal the potentials of the varieties. In the study, some quality parameters and significance levels between varieties were determined. Also, the results obtained from the GlutoPeak analysis are explained by comparing them with other quality parameters. Protein ratio, wet gluten and Zeleny sedimentation values were found to be highly correlated with GlutoPeak AM, BEM, AGGRE, PM, GPRT, GW and GWA. In addition, it was determined that there was a high correlation between farinograph water absorption and GlutoPeak AGGRE, AM, BEM, GGLT, GPRT, GW, GWA and PM values. Alveograph W value was positively correlated with GlutoPeak AM, BEM, PM, GPRT, GGLT, GW, GWA and AGGRE values and negatively correlated with PMT. The results obtained in terms of the examined characteristics in this study show that some varieties stand out in terms of different quality characteristics. With the results of this study, it was determined that the GlutoPeak device can detect the quality of wheat flour with less sample and in a short time, therefore, GlutoPeak analysis will be useful in variety development and similar studies in bread wheat.

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.

The effects of ambient particulate matter air pollution on platelets and hemostasis.

Elevated ambient pollution exposure is potentially linked to thromboembolism. However, the mechanisms by which particulate matter (PM) interferes with the balance of hemostatic system remain unclear. This study investigates PM-mediated hemostatic changes in individuals across unique seasonal variations of ambient pollution. This prospective study was conducted between February and July 2020 during alterations in ambient pollution in Chiang Mai, Thailand. Blood tests from 30 healthy subjects were assessed at four-week intervals, four times in total. Various coagulation tests, including prothrombin time (PT), activated partial thromboplastin time (aPTT), von Willebrand factor (vWF), platelet count, and platelet functions, were evaluated. A mixed-effects model was used to analyze the impact of high PM2.5 and PM10 on hemostatic parameters. Thirty male subjects with mean age of 38.9 ± 8.2 years, were included. High levels of PM2.5 and PM10 were significantly associated with PT shortening, with no such effect observed in aPTT. PM2.5 and PM10 values also positively correlated with vWF function, while vWF antigen levels remained unchanged. Soluble P-selectin showed a strong positive association with PM2.5 and PM10 levels. Platelet function analysis revealed no correlation with PM values. Short-term exposure to elevated PM2.5 and PM10 concentrations was linked to shortened PT and enhanced vWF function in healthy individuals. Exploring the impact of these changes on clinically relevant thrombosis is crucial. Additional studies on the pathogenesis of pollution-related thrombosis are warranted for maintaining good health.

Impact of Lockdown on Air Quality in the Most Polluted Cities of India.

COVID-19 has become a global pandemic, prompting lockdowns in practically every country. To prevent the spread of the disease, India has enforced a rigorous nationwide lockdown that commenced in March 2020. The lockdown imposed amid the pandemic ensured that most commercial activities and vehicle transportation ceased, resulting in a significant reduction in air pollution levels. The value of air pollutants PM10, PM2.5, NO2, and SO2 from January to May 2020 was obtained from the Indian Central Pollution Control Board. Before lockdown and during lockdown, relative fluctuations in ambient concentrations of four air contaminants were investigated. The Box-Jenkins approach was used to estimate future air pollution data points using time series data analysis. The PM10 level reduced by 61%, 30%, 68%, 37%, and 43% in the selected cities, respectively. Comparison of other pollutant concentrations before and after the lockdown also found a reduction in ambient pollutant concentrations, resulting in improved air quality. Inference of predicted model values to observed values revealed a significant increase in the concentrations of all pollutants. The percentage increases in AQImean from predicted to observed values were 206% in Ghaziabad, 148% in Delhi, 59% in Hyderabad, and 160% in Cochin. The strict lockdown has resulted in a significant drop in air pollutant levels. Upgrading present technologies could help keep pollution to a minimum of 37% under control. The findings would prompt the government to consider how to strictly reduce vehicle and industrial pollution to improve air quality and maintain improved public health.