High Concentrations Of PM2 Research Articles

Variability and health impact of air pollutants and bioaerosols in multi-functional indoor environments with mechanical ventilation system

The condition of indoor environments significantly impacts human daily life and health, where exposure to harmful substances affects the resilience of occupants. To understand the dynamics of indoor air quality and its implications for occupant health thus this study evaluated air pollutant concentration (TVOC, NO2, O3, CO, CO2, PM10, PM2.5, and PM1) and bioaerosol in mechanically ventilated offices, laboratories, corridors, a library and a mechanical workshop. Simultaneous measurements of all air pollutants were performed for 24 h, including non-working hours, while bioaerosol was sampled for fungal identification and quantified via the colony counting method. High concentrations of PM10, PM2.5, PM1, O3 and bioaerosol were found in the mechanical workshop, where these pollutants were strongly related to humidity (p < 0.05) and indoor source contributions. Other air pollutants such as TVOC had the highest concentrations in the office areas during working hours with an average concentration of 361 ± 14.8 μg m−3, while for CO, the highest concentration was observed in the laboratories for both working hours (442 ± 26.0 ppb) and 24 h average concentration (438 ± 22.1 ppb). Dominant species identified in bioaerosol were Aspergillus sp., Geotrichum sp., Fusarium sp., and Penicillium sp., where the exposure to bioaerosol was found to be of low risk to all workers as the hazard quotient (HQ) was <1 for all multi-functional indoor environments. The results from this study suggest that indoor air pollutants and bioaerosol concentrations have a variety of characteristics and source contributions, especially from human activities and indoor sources.

Application of Doppler sodar in short-term forecasting of PM10 concentration in the air in Krakow (Poland)

Abstract. This paper describes an attempt to use data obtained from sodar (sound detection and ranging) for short-term forecasting of PM10 concentration levels in Krakow. Krakow is one of the most polluted cities in central Europe (CE) in terms of PM10 concentration. This is due to the high municipal emissions. Thanks to intensive corrective actions taken by the city authorities, these are being effectively eliminated, but the unfavourable topographic location of the city limits natural ventilation. The article describes all these conditions, focusing on presenting the method of short-term correction of air quality for the time needed to take quick corrective actions by the city authorities in the event of anticipated exceedances of the permissible values. Based on several years of measurements of the physical properties of the atmosphere with sodar, the authors of the paper suggest that sodar data could be considered for operational use to generate short-term predictions. The proposed method is based on the use of the spectrum, i.e. the set of amplitudes of signals returning to the sodar receiver from the reflection of a single-frequency sound transmission and its characteristic properties depending on the physical state of the atmosphere. Similar spectra were parameterised with a single numerical value using statistical methods. It was found that, in some cases preceding high concentrations of PM10, the spectral parameters had similar values. This made it possible to develop a forecasting method for such concentrations by using data mining to search for conditions in historical data closest to the state of the atmosphere at the time of forecasting. In this part of the study, data from 2017–2018 were used. In the next step, three methods of using the sodar data developed for PM10 prediction were proposed, comparing them with the method without sodar use. The study results were tested on independent material using data concerning the episodes of high concentrations of pollutants from October 2021 to March 2022 in Krakow. The findings were considered to be encouraging, also taking into account the speed and low cost of preparing the forecast.

Modeling and Predicting PM2.5 Concentration at a Construction Site Using the Artificial Intelligence

Introduction. High concentration of PM2.5 has the adverse effect on people's health. According to the evaluations made by the researchers, the impact of the particulate matter from the construction dust emissions resulted in 18% of deaths from respiratory diseases. Due to the growth of construction production volume and consequent increase of dust emission volumes, there arises the need to expand the scope of using the end-to-end technologies, namely the artificial intelligence technologies, for predicting the fine-dispersed dust particles PM2.5 concentration in dust emissions at the construction site.Materials and methods. To achieve this goal, the measurements of PM2.5 concentration at the construction site were carried out using the Handheld 3016 IAQ particle counter in the period from July 1 to July 6, 2023 taking into account the meteorological characteristics of the territory, which then became the input data for modelling the forecast of dust pollution concentration using such algorithms as ARIMA, EMA, XGBoost, etc., and the ensemble models that included the above machine learning algorithms. The efficiency of using these technologies for predicting was determined by comparing the results of the forecast and the field measurements data.Results. A correlation analysis was performed using the Modeltime program, which determined the relationship between PM2.5 concentration and meteorological variables. Autocorrelation was performed using Pearson correlation. At the first stage, four one-dimensional models based on the artificial intelligence were evaluated to determine the accuracy of mean concentration forecast. The next step was to evaluate the capacity of predicting the mean PM2.5 concentration using the multidimensional models that took into account the relationships between the independent and dependent variables. At the final stage of the research, three most efficient predictive models were included to test the ensemble model.Discussion and conclusion. The reliable predictive models can be the useful tools for understanding the concentration impact factors. In the present research, seven machine learning algorithms were used to predict the concentration of PM2.5. The research, as a whole, presents the evidences of the integrated modeling method efficiency for predicting the air pollution.

Predicting particulate matter, nitrogen dioxide, and ozone across Great Britain with high spatiotemporal resolution based on random forest models

In Great Britain, limited studies have employed machine learning methods to predict air pollution especially ozone (O3) with high spatiotemporal resolution. This study aimed to address this gap by developing random forest models for four key pollutants (fine and inhalable particulate matter [PM2.5 and PM10], nitrogen dioxide [NO2] and O3) by integrating multiple-source predictors at a daily level and 1-km resolution. The out-of-bag R2 (root mean squared error, RMSE) between predictions from models and measurements from monitoring stations in 2006–2013 was 0.85 (3.63 μg/m3) for PM2.5, 0.77 (6.00 μg/m3) for PM10, 0.85 (9.71 μg/m3) for NO2, and 0.85 (9.39 μg/m3) for maximum daily 8-h average (MDA8) O3 at daily level, and the predicting accuracy was higher at monthly and annual level. The high-resolution predictions captured characterized spatiotemporal patterns of the four pollutants. Higher concentrations of PM2.5, PM10, and NO2 were distributed in densely populated southern regions of Great Britain while O3 showed an inverse spatial pattern in general, which could not be fully depicted by monitoring stations. Therefore, predictions produced in this study could improve exposure assessment with less exposure misclassification and flexible exposure windows for future epidemiological studies to investigate the impact of air pollution across Great Britain.

Spatial Distributions of Gaseous Air Pollutants Including Particulate Matter in the Narsingdi City of Dhaka Division

ABSTRACT This study summarizes the concentration of major gaseous air pollutants in Narsingdi city of Dhaka division. To accomplish this study, we investigate fifteen sampling stations (Velanagar, Railway Station, Boro Bazar, Ghoradia, Brahmondi, Shaheprotap, Launch Terminal, Satirpara, Bus Terminal, Silmandi, Gangpar Bridge, Panchdona, Shekherchar and Anandi) of the study area. In this study we measured six gaseous air pollutantstogether with PM2.5 and PM10 during the month September, 2022 using air quality meter Aeroqual (Series500). Highest concentration of PM2.5 was detected 58 µg/m3 at Boro Bazar whereas highest concentration ofPM10 was 165 µg/m3 was at Anandi of the study area where emission from vehile, construction activities and waste burning are predominant. Moreover, in this study we found statistically significant correlation with CH4 and CO2 (r = 0.679, p &gt; 0.01), PM2.5 and PM10 (r = 0.630, p &gt; 0.05) indicating their sources of emission might be similar including fossil fuel burning in vehicles, industrial emissions and road dust. Furthermore, we calculated AQI value based on PM2.5 concentration and highest AQI (152) value was observed in Boro Bazarof the study area followed by Bus Terminal (129), Gangpar Bridge (117), Anandi (112), Ghoradia (102), Brahmondi (89), Panchdona (84), Satirpara (83), Shaherpotap (82), Silmandi (80), Railway Station (78), Madhabdi (78), Shekherchar (76), Launch Terminal (76), Velanagar (59) and were very much compatiblewith US consulate, Bangladesh published data.

Variations of PM2.5 and Health Risk Assessment in the City of Mashhad During 2010-2018

Background: Air pollution stands as a significant environmental threat impacting human health across the globe, encompassing both developed and developing nations. This study aimed to survey the temporal variations of PM2.5 and gauge its potential health effects in the city of Mashhad over the period of 2010-2018. Methods: This cross-sectional study was carried out among the residents of Mashhad city. PM2.5 concentration data spanning the years 2008-2019 were collected and subjected to analysis using Excel and AirQ software. Results: The results indicate that the highest concentration of PM2.5 was associated with the year 2010, while the lowest concentration was observed in 2015. The analytical findings demonstrated that for each 10 µg/m3 increase in PM2.5 concentrations, the relative risk for total mortality increased by 10.47%. Furthermore, based on the Air Quality Index (AQI) results, 2010 exhibited the most adverse condition in terms of PM2.5 concentrations in Mashhad city. Conclusion: In general, long-term exposure to ambient PM2.5 significantly contributed to mortality in the megacity of Mashhad. As air pollution is a modifiable risk factor, it is advisable to implement sustainable control policies to protect public health.

Associations between PM2.5, ambient heat exposure and congenital hydronephrosis in southeastern China.

This research aims to analyze how exposure to fine particulate matter (PM2.5) and ambient heat during pregnancy increases the risk of congenital hydronephrosis (CH) in newborns. A case-control study was conducted to investigate the relationship between exposure to PM2.5 and ambient heat during pregnancy and the occurrence of CH in newborns. The study, which was conducted from 2015 to 2020, included 409 infants with CH as the case group and 409 infants without any abnormalities as the control group. Using spatial remote sensing technology, the exposure of each pregnant mother to PM2.5 concentration was meticulously mapped. Additionally, data on the ambient temperature of exposure for each participant were also collected. A logistics regression model was used to calculate the influence of exposure to PM2.5 and ambient heat on the occurrence of CH. Stratified analysis and interaction analysis were used to study the interaction between ambient heat exposure and PM2.5 on the occurrence of CH. At the 6th week of gestation, exposure to PM2.5 may increase the risk of CH. For every 10 μg/m3 increase in PM2.5 exposure, the risk of CH increased by 2% (95%CI = 0.98, 1.05) at a p-value of >0.05, indicating that there was no significant relationship between the results. Exposure to intense heat at 6th and 7th weeks of gestation increased the risk of CH. Specifically, for every 1°C increase in heat exposure, the risk of CH in offspring increased by 21% (95%CI = 1.04, 1.41) during the 6th week and 13% during the 7th week (95%CI = 1.02, 1.24). At 5th and 6th weeks of gestation, the relative excess risk due to interaction (RERI) was greater than 0 at the 50th percentile (22.58°C), 75th percentile (27.25°C), and 90th percentile (29.13°C) of daily maximum temperature (Tmax) distribution, indicating that the risk of CH was higher when exposed to both ambient heat and PM2.5 at the same time compared to exposure to a single risk factor. Exposure to higher levels of PM2.5 and ambient heat during pregnancy increases the risk of CH in infants. There was a positive interaction between exposure to intense heat and high concentration of PM2.5 on the occurrence of CH.

Non-optimum temperatures modified the associations between PM2.5 and its components and hospitalizations for chronic kidney disease in China

Epidemiological evidence concerning whether exposure to fine particulate matter (PM2.5) and its components was linked to an elevated risk of hospitalizations for chronic kidney disease (CKD) remains insufficient. Moreover, it remains unclear whether ambient temperatures have potential modification effects on PM2.5's impacts. In the current study, we collected a nationwide sample of 2,259,240 hospitalization records for CKD in 260 Chinese cities. The associations between air pollutants and CKD hospitalizations were determined by the space-time-stratified case-crossover design. We further assessed the effects of PM2.5 and its components in three temperature strata [i.e., lowest (<25th), medium (25–75th), and highest (>75th)]. The findings demonstrated the significant and monotonic associations between risk of CKD hospitalizations and exposure to PM2.5, black carbon (BC), sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), and organic matter (OM). For instance, along with an interquartile range increment in PM2.5 (29.47 μg/m3), the relative risks (RR) were 1.016 (95 % confidence interval [CI]:1.012–1.019) at lag 0–4 days. Higher ambient temperature significantly exacerbated the estimated impact of PM2.5 and its components on CKD hospitalizations, while significantly stronger associations were also observed at lower temperature for SO42− and NO3−. The modification effects of non-optimum temperatures varied among different etiologies of CKD and geographic regions. This study provides insights into the joint kidney health effects of climate change and air pollution. These findings highlight the necessity of protection measures against high concentration of PM2.5 and non-optimum temperatures for the vulnerable populations.

Black Carbon and Particulate Matter Concentrations: Air Pollution Levels in Rio de Janeiro, Brazil

This research explores the concentration of black carbon (BC) in particulate matter (PM10 and PM2.5) from ten monitoring stations in the Metropolitan Region of Rio de Janeiro (MRRJ), Bonsucesso (BS), Botafogo (BOT), Copacabana (COP), Gavea (GAV), Gericino (GER), Lagoa (LAG), Recreio dos Bandeirantes (REC), Santa Cruz (SC), Castelo (CAS) and Urca (URC), covering a range of pollution sources (vehicular, industrial, and residential). PM samples were collected using filter units every week from January 2018 to December 2019. Results revealed high concentrations of PM10 in BS (86 ± 22 µg m-3) and PM2.5 in REC (30 ± 11 µg m-3). Likewise, both monitoring stations exceeded the international limits. In 2019, BC in PM10 decreased in the following order: BS &gt; CAS &gt; GER &gt; BOT &gt; SC &gt; GAV. For 2018, BC in PM2.5 decreased as follows REC &gt; LAG &gt; SC, while 2019 REC &gt; GAV &gt; LAG &gt; COP &gt; URC. REC and BS have industrial and commercial activities and intense vehicular traffic. During the period of study, average BC concentrations in PM10 and PM2.5 were 3.3 ± 1.5 and 1.9 ± 0.70 µg m-3, respectively. These findings indicate that BC concentrations should be monitored and regulated in locations with high levels of traffic-related air pollution for offering new insights and guiding efforts to minimize emissions and enhance public health.

Identifying source apportionment of atmospheric particulate matter and gaseous pollutants using receptor models : A case study of Bengaluru, India

The data of Particulate matter PMs (PM2.5, PM10) and Gaseous Pollutants such as carbon monoxide (CO), methane (CH4), oxides of nitrogen (NOx: NO and NO2), non-methane hydrocarbons (NMHCs), sulfur dioxide (SO2), along with ammonia (NH3) at five different locations across Bengaluru from 1st January, 2017 to 20th March, 2018 were collected. The primary objective of this research work is to identify the sources of atmospheric particulate matter and gaseous pollutants using receptor models in Bengaluru, India. To execute this, receptor models, namely Conditional Bivariate Probability Function (CBPF) and Concentrated Weighted Trajectory (CWT) Analysis, are applied. Conditional Bivariate Probability Function (CBPF) shows that, annually, the maximum concentrations of PMs over receptor sites were detected during low wind speed (&lt; 2 knots) along the north-east direction specifying that the long-range transport does not play an essential role in the transportation of higher concentrations of PM and their primary source region may be localized. Concentrated Weighted Trajectory (CWT) analysis shows that, seasonally, the highest air mass contribution of about 37% was noticed in summer, whereas the lowest was in the post-monsoon season (13%). The significant contribution of PM2.5 transported from long distances was during monsoon, and in the case of PM10, it was in summer. The study suggests that the long-range transport of PMs and gaseous Pollutants was not vital and was observed to be localized.

Association between ambient temperature, PM2.5 and tuberculosis in Northwest China

ABSTRACT Existing evidence suggested that the risk of tuberculosis (TB) infection was associated to the variations in temperature and PM2.5. A total of 9,111 cases of TB were reported in Ningxia Hui Autonomous Region, China from 2013 to 2015 on a daily basis, and 57.2% of them were male. The TB risk was more prominent for a lower temperature in males (RR of 1.724, 95% CI: 1.241, 2.394), the aged over 64 years (RR of 2.241, 95% CI: 1.554, 3.231), and the high mobility occupation subpopulation (RR of 2.758, 95% CI: 1.745, 4.359). High concentration of PM2.5 showed a short-term effect and was only associated with an increased risk in the early stages of exposure for the female, and aged 36–64 years group. There were 15.06% (1370 cases) of cases of TB may be attributable to the temperature, and 2.94% (268 cases) may be attributable to the increase of PM2.5 exposures. Low temperatures may be associated with significantly increase in the risk of TB, and high PM2.5 concentrations have a short-term association on increasing the risk of TB. Strengthening the monitoring and regular prevention and control of high risk groups will provide scientific guidance to reduce the incidence of TB.

Clinical Impact of Ambient Particulate Matters on Dry Eye Disease

Purpose: Dry eye disease (DED) has become a common ocular surface disorder in the recent decades, which can be influenced by environmental factors. This study aimed to examine the clinical association between DED and ambient air pollutants.Methods: A total of 216 patients (18 patients per each month) diagnosed with DED were enrolled in this retrospective study. Ocular surface disease index (OSDI), tear film breakup time (TFBUT), fluorescein corneal staining (FCS), and Schirmer’s test 1 (ST1) were examined. The monthly concentrations of air pollutants in 2018 were obtained from the data released by the Korean Ministry of Environment. Based on the mean of PM&lt;sub&gt;10&lt;/sub&gt; and PM&lt;sub&gt;2.5&lt;/sub&gt;, subgroup with exposure to low (L–PM) or high concentration of PM (H–PM) was analyzed.Results: Patients had moderate DED as high OSDI, low TFBUT, increased FSC, and decreased ST1. During the year 2018, monthly variation of PM showed that May to October was the period of L-PM and January to April and November to December was the period of H-PM. In subgroup analysis, patients in H-PM showed significantly higher OSDI, FCS, and lower ST1 compared to in L-PM. However, there was no significant difference in TFBUT between subgroups.Conclusions: DED was more severe in the period of H-PM than L-PM even though there was no difference in tear film instability between both periods. Ambient inflammatory factors including PM might deteriorate ocular surface as well as tear secretion, resulting in aggravating DED.

Study of PM2.5 and PM10 Concentrations Near the Plating Plant Using Automatic Monitoring Stations

The content of PM2.5 and PM10 in the air at the Dalpribor enterprise in Vladivostok (Russian Federation) was studied. The distribution of galvanic aerosol particles in the area adjacent to the enterprise was studied. The highest concentrations of PM2.5 and PM10 particles were recorded inside the electroplating shop. A significant contribution (exceeding 10 %) of electroplating enterprises to the overall urban air pollution was revealed. It was emphasized that, in general, the measured average concentration of PM2.5 and PM10 particles does not exceed the MAC values.

Comparison of PM10 emission flux of two fugitive area sources based on the real-time flux monitoring results

Due to a high concentration of particulate matter (PM10), the Korean Peninsula experienced its first poor air quality event of the year between November 19 and November 26, 2021. This study analyzes the reasons behind the occurrence of high-concentration PM10, using the real-time PM10 fugitive emission fluxes and meteorological data measured at two landfills for fly ash of coal-fired power plants located on the west coast. The real-time fugitive emission fluxes of PM10 were estimated at two different locations by a flux-gradient technique based on the eddy covariance method. The measurement results show a weak correlation between PM10 and various meteorological factors in the two places when PM10 levels are low. However, high PM10 concentrations were found to be strongly associated with the relative humidity of site A and the friction velocity of site B, respectively. High emission fluxes were observed at both sites under elevated temperature, high humidity, low wind speed, low frictional velocity, and atmospheric instability. The variation in weather patterns witnessed during periods of high PM10 concentrations in the two locations indicates that the causes of PM10 accumulation are different. The study demonstrates that the gradient-flux method's real-time measurement of fugitive emissions can explain the origin of high PM10 levels and provide essential data to efficiently regulate PM10.

Effects of personal exposure to the oxidative potential of PM2.5 on oxidative stress biomarkers in pregnant women

Oxidative stress is a prominent pathway for the health effects associated with fine particulate matter (PM2.5) exposure. Oxidative potential (OP) of PM has been associated to several health endpoints, but studies on its impact on biomarkers of oxidative stress remains insufficient. 300 pregnant women from the SEPAGES cohort (France) carried personal PM2.5 samplers for a week and OP was measured using ascorbic acid (AA) and dithiothreitol (DTT) assays, and normalized by 1) PM2.5 mass (OPm) and 2) sampled air volume (OPv). A pool of three urine spots collected on the 7th day of PM sampling was analyzed for biomarkers, namely 8-hydroxy-2-deoxyguanosine (8-OHdG), malondialdehyde (MDA) and 8-isoprostaglandin-F2α (8-isoPGF2α). Associations were investigated using adjusted multiple linear regressions. OP effects were additionally investigated by stratifying by median PM2.5 concentration (14 μg m−3). In the main models, no association was observed with 8-isoPGF2α, nor MDA. An interquartile range (IQR) increase in OPmAA exposure was associated with increased 8-OHdG (percent change: 6.2 %; 95 % CI: 0.2 % to 12.6 %). In the stratified analysis, exposure to OPmAA was associated with 8-OHdG for participants exposed to low levels of PM2.5 (percent change: 11.4 %; 95 % CI: 3.3 % to 20.1 %), but not for those exposed to high levels (percent change: −1.0 %; 95 % CI: −10.6 % to 9.6 %). Associations for OPmDTT also followed a similar pattern (p-values for OPmAA-PM and OPmDTT-PM interaction terms were 0.12 and 0.11, respectively). Overall, our findings suggest that OPmAA may be associated with increased DNA oxidative damage. This association was not observed with PM2.5 mass concentration exposure. The effects of OPmAA in 8-OHdG tended to be stronger at lower (below median) vs. higher concentrations of PM2.5. Further epidemiological, toxicological and aerosol research are needed to further investigate the OPmAA effects on 8-OHdG and the potential modifying effect of PM mass concentration on this association.

Variability of ambient particulate matter loading at Henties Bay, Namibia

The Namibian coast is one of the areas of international interest for aerosol studies. This is due to the region’s importance for the global radiation budget because of the presence of a semi-permanent stratocumulus cloud along the coast. Aerosol particles may scatter/absorb radiation and directly influence how long clouds last by modifying their properties. This is all dependent on the particles’ chemical and physical properties because of the sources they were emitted from. In this study, we identified and investigated episodes of high (HAE) and low (LAE) PM concentrations and the meteorology that may favour their occurrence. Here, we investigated PM2.5 (particles with an aerodynamic diameter of 2.5 µm or less) and PM10 (particles with an aerodynamic diameter of 10 µm or less) at Henties Bay, Namibia. Daily aerosol measurements were taken with E-samplers between 15 and 29 July 2019. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to investigate the long-range atmospheric transport of air masses that reached Henties Bay. The study found that during HAEs, the average PM2.5 concentration was 28.40 ± 18.10 µg/m3 and the average PM10 concentration was 68.20 ± 44.3 µg/m3. In contrast, during LAEs, the average PM2.5 concentration was 13.3 ± 9.52 µg/m3 and the average PM10 concentration was 30.00 ± 23.00 µg/m3. In both fractions, there was an observed dominant contribution from marine sources.

Heavy metal deposition and parameter change of soft contact lenses by exposure to particulate matter

BackgroundParticulate matter (PM) is known to contain heavy metals and be harmful to the tissues and organs of the human body including the eyes. As such, in this study, the deposition of heavy metals from PM on soft contact lenses was examined, and changes in the lens parameters were further investigated.MethodsSix types of soft contact lenses were exposed to captured PM10 for eight hours. The central thickness, water content, refractive power, and oxygen transmissibility of each contact lens were measured after analyzing the amounts of six heavy metals adsorbed on the contact lenses.ResultsLead, manganese, barium, arsenic, vanadium, and cadmium were detected in the captured PM, and only lead was adsorbed on all soft contact lenses except senofilcon C. The largest deposition was 23.21 ± 0.70 (10− 3)µg/lens of the lead on lotrafilcon B. The oxygen transmissibility of nelfilcon A exhibited statistically significant changes, however, it was within the ISO standard tolerance. Nevertheless, changes in the central thickness, water content, and refractive power of each soft contact lens were not statistically significant.ConclusionsThis study revealed that a considerable amount of lead in PM10 was adsorbed on soft contact lenses. Amongst lens parameters, only oxygen transmissibility changed significantly. Thus, wearing soft contact lenses under high PM10 concentration might affect the physiology of the eyes.

Investigating PM2.5 toxicity in highly polluted urban and industrial areas in the Middle East: human health risk assessment and spatial distribution

Exposure to particulate matter (PM) can be considered as a factor affecting human health. The aim of this study was to investigate the concentration of PM2.5 and heavy metals and their influence on survival of A549 human lung cells in exposure to PM2.5 breathing air of Ahvaz city. In order to assess the levels of PM2.5 and heavy metals, air samples were collected from 14 sampling stations positioned across Ahvaz city during both winter and summer seasons. The concentration of heavy metals was determined using ICP OES. Next, the MTT assay [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] was employed to ascertain the survival rate of A549 cells. The findings from this research demonstrated that average PM2.5 of the study period was (149.5 μg/m3). Also, the average concentration of PM2.5 in the urban area in winter and summer was (153.3- and 106.9 μg/m3) and in the industrial area this parameter was (191.6 and 158.3 μg/m3). The average concentration of metals (ng/m3) of urban areas against industrial, Al (493 vs. 485), Fe (536 vs. 612), Cu (198 vs. 212), Ni (128 vs. 129), Cr (48.5 vs. 54), Cd (118 vs. 124), Mn (120 vs. 119), As (51 vs. 67), Hg (37 vs. 50), Zn (302 vs. 332) and Pb (266 vs. 351) were obtained. The results of the MTT assay showed that the highest percentage of cell survival according to the exposure concentration was 25 > 50 > 100 > 200. Also, the lowest percentage of survival (58.8%) was observed in the winter season and in industrial areas with a concentration of 200 μg/ml. The carcinogenic risk assessment of heavy metals indicated that except for Cr, whose carcinogenicity was 1.32E−03, other metals were in the safe range (10–4–10–6) for human health. The high concentration of PM2.5 and heavy metals can increase respiratory and cardiovascular diseases and reduce the public health level of Ahvaz citizens.

Spatial and Temporal Variation of Particulate Matter (PM10 and PM2.5) and Its Health Effects during the Haze Event in Malaysia

This study aims to assess and compare levels of particulate matter (PM10 and PM2.5) in urban and industrial areas in Malaysia during haze episodes, which typically occur in the south west monsoon season. The high concentrations of atmospheric particles are mainly due to pollution from neighbouring countries. Daily PM concentrations were analysed for urban and industrial areas including Alor Setar, Tasek, Shah Alam, Klang, Bandaraya Melaka, Larkin, Balok Baru, and Kuala Terengganu in 2018 and 2019. The analysis employed spatiotemporal to examine how PM levels were distributed. The data summary revealed that PM levels in all study areas were right-skewed, indicating the occurrence of high particulate events. Significant peaks in PM concentrations during haze events were consistently observed between June and October, encompassing the south west monsoon and inter-monsoon periods. The study on acute respiratory illnesses primarily focused on Selangor. Analysis revealed that Klang had the highest mean number of inpatient cases for acute exacerbation of bronchial asthma (AEBA) and acute exacerbation of chronic obstructive pulmonary disease (AECOPD) with values of 260.500 and 185.170, respectively. Similarly, for outpatient cases of AEBA and AECOPD, Klang had the highest average values of 41.67 and 14.00, respectively. Shah Alam and Sungai Buloh did not show a significant increase in cases during periods of biomass burning. The statistical analysis concluded that higher concentrations of PM were associated with increased hospital admissions, particularly from June to September, as shown in the bar diagram. Haze episodes were associated with more healthcare utilization due to haze-related respiratory illnesses, seen in higher inpatient and outpatient visits (p &lt; 0.05). However, seasonal variability had minimal impact on healthcare utilization. These findings offer a comprehensive assessment of PM levels during historic haze episodes, providing valuable insights for authorities to develop policies and guidelines for effective monitoring and mitigation of the negative impacts of haze events.

High-speed shear-induced micro regenerated cellulose materials preparation: A promising solution for highly efficient and low pressure drop air filtration using agricultural waste

Cellulose has attracted much attention in application of air filtration for the excellent ability of degradability and biocompatibility. However, limited by the strength of natural structure, air filters based on native cellulose usually have high pressure drop (ΔP) or thick volume. In this study, an efficient air filter was produced with low ΔP (6.8 Pa) using a low amount of dry material (9 ∼ 12 mg/cm3) through simple preparation process with high-speed shear induced. Analysis showed the chains of regenerated cellulose (RC) were stretched and arranged orderly due to the shear induced during the entire coagulation process, letting to an increasing of crystalline of RC. Furthermore, RC filter exhibited a high PM2.5 capture efficiency (over 99.65 %) with high-quality factor (0.853 Pa−1) under high PM concentration conditions. This work gave a novel sight on the potential of air filtration produced by agricultural waste bioresources furthermore, offered an interesting and simple avenue for daily respiratory protective media.