2023 CiteScore: 1.9
eISSN: 2476-3071
Editor-in-Chief:
Ramin Nabizadeh Nodehi, Ph.D.
Vol 7 No 3 (2022): Summer 2022
Original Research
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Introduction: Benzene, Toluene, Ethylbenzene, and Xylene (BTEX) as the ozone precursors have been classified as the hazardous air pollutants because of their negative effects on humans. This article presents the results of the first assessment of source identification, spatial distribution and BTEX's OzoneForming Potential (OFP) in Zarand.
Materials and methods: The current study was conducted at 30 geographically separated locations, in Zarand, Kerman, southeastern Iran, during the summer and winter of 2020. BTEX samples were collected using passive samplers and then analyzed using Gas Chromatography-Mass Spectrometry (GC-MS). Spatial variations were surveyed using the Kriging method in GIS.
Results: Total BTEX levels (79.26±26.87 µg/m3) during the summer were greater than their levels in the winter (37.38±29.18 µg/m3). The ranking of BTEX level in all samples followed as: toluene>m,pxylene>oxylene>ethylbenzene>benzene. The overall OFP of 374.79±135.08 µg/m3
in the summer and 172.61±148.81 µg/m3 in the winter were more than 100 µg/m3 as recommended guideline defined by World Health Organization (WHO), with toluene having the highest potential.
Conclusion: According to the results of the present study, BTEX relative abundances in all samples were toluene>m,p-xylene>oxylene>ethylbenzene>benzene. Despite of concerns among inhabitants and
workers, benzene concentration was lower than other studied species. Control measures such as management of fuel use in motor vehicles and industries and development of green space must be adopted to attenuate the level of toluene in the atmosphere in the studied area. -
Introduction: Air pollution is the leading environmental risk factor for health. This study aimed to assess heavy metals in Particulate Matter (PM10, PM2.5) and their health impact assessment for a desert city in Iran, Birjand.
Materials and methods: In this study, the concentrations of PM10 and PM2.5 were measured from September 2019 to March 2020. Measurements were performed once every six days for 24 h using high-volume samplers.
Moreover, health-related effects attributed to the suspended particles were estimated using the AirQ2.2.3.
Results: Mean and standard deviation of PM10 and PM2.5 concentrations were 97.5±38.7 μg/m3
and 36.3±19.1 μg/m3, respectively. The mean metal concentrations in PM2.5 were in the Co> Cd> Ce> V order, while the metal concentrations in PM10 were in the Cd> As> Ce>V order. The lowest and highest number of deaths attributed to PM2.5 per 100,000 persons were related to ischemic heart disease (1.73) and chronic respiratory disease (18.35). The highest number of deaths attributed to PM10 per 100,000 persons was related
to chronic bronchitis in adults (35.74).
Conclusion: This study revealed that particle-based air pollution negatively affects health as caused by heavy metals, whereas further research is required to determine the effects of bacterial and fungal bioaerosols on human health. Monitoring the elemental composition of atmospheric particles can contribute to better air quality management. -
Introduction: Deteriorated air quality in nation like India contributes to the health burden. The AirQ+ is used to estimate short-term and long-term health impact attributable to surface Ozone (O3) in Surat city. Average hourly ozone concentration data and other criteria pollutants retrieved from January 2018 to December 2019 from two monitoring stations (Limbayat and Varachha).
Materials and methods: In this study, the Respiratory Mortality (RM), Cardiovascular Mortality (CM), Total Mortality (TM), Hospital Admissions with Cardiovascular Disease (HACVD), and Hospital Admissions with Respiratory Disease (HARD), as well as Respiratory Mortality-Long-Term (LT-RM) were quantified. Baseline Incidence (BI) data were obtained from literature and Relative Risk (RR) values were referred from World Health Organization (WHO). An annual Sum of Maximum 8 h Ozone means over 35 ppb (SOMO35), 70 µg/m3 , used as a predictor of potential long-term health effects.
Results: More ozone concentration were observed in winter and pre-monsoon than concentration formed in southwest monsoon and post-monsoon seasons. The average of O3 concentration for Limbayat are 71.61 (±0.39) µg/m3 and 29.76 (±1.86) µg/m3 and for Varachha are 61.17 9 (±6.15) µg/m3 , 11.32 (±1.35) µg/m3 during 2018 and 2019, respectively and the obtained cumulative number of cases of death are estimated 136, 45, 172 and 18 persons respectively. Total hospital admission due to cardiovascular and respiratory diseases are found
435, 134, 552 and 58 at Limbayat and Varachha during 2018 and 2019. LTRM is attributed to ozone concentration having the most significant value, 6.8% and 4.62% at Limbayat and Varachha in 2018.
Conclusion: More hospital admissions are found than mortality rates using AirQ+ tool. It can be used to estimate public health in context of mortality and morbidity rates which helps to develop air quality management programs and policy makers to reduce the impact of air pollution on health. -
Introduction: Airborne particles are considered as an important indicator of outdoor air quality. Many health problems have been linked to high concentrations of Particulate Matters (PMs) and their associated microorganisms. The aim of this study, therefore, was to investigate the population of bacteria in PMs in various times and locations.
Materials and methods: The PM samples including PM2.5, PM10 and TotalSuspended Particles (TSP) were taken from 4 different stations of Isfahan city, Iran on February (cold season) and July (warm season), 2019 using an air sampling pump on culture media. The number of bacterial colonies was counted after 48 h growth in the incubator at 37 o C. The PMs concentration and some morphological characteristics of bacteria were also investigated.
Results: The highest number of bacterial colonies was in TSP followed by PM10 and PM2.5. The bacterial populations at two stations in north and east of the region in the warm season were higher than in the cold season, and the respective situation in the other two stations at south and center of the city was reversed, which seems somehow to have been the result of the PMs concentration of difference of pollution sources in various locations and seasons.
Conclusion: This study highlights the importance of PMs pollution especially PM2.5 (i.e. the main factor affecting the air quality of the study area) as the carrier of microbial pollution in the air which could adversely affect human health. -
Introduction: Air pollution in cosmopolitan cities is increasingly becoming unprecedented with attendant effects on human and biophysical attributes.
Materials and methods: The study was carried out at Federal University of Technology (FUTO) and environs in the southeastern Nigeria. Some ambient air quality parameters (CO, CO2 , NO2 , CH4 and noise) were sampled and measured at seven unique locations (OR, ER, FR, EJ, IM, FM and FJ) with multi sampler devices, using air differential technique during the morning, midday and evening periods.
Results: The average pollutant results show increased concentrations at the different locations when compared to Occupational Safety and Health Administration (OSHA) than Federal Ministry of Environment (FMEnv) thresholds (FMEnv>CO2 <OSHA; FMEnv>NO2 <OSHA; FMEnv>CH4<OSHA). However, concentrations of CO and Noise in FR and FJ were relatively higher than concentrations observed in other locations (CO: FR [6.4 mg/m3 ] and FJ [4.2 mg/m3 ]; Noise: FR [96.8 dB] and FJ [95.7 mg/m3 ]) respectively.
Conclusion: The significant increase could be attributed to continuous vehicular emissions and presence of make-shift activities in these locations. However, predictive model suggests that given the meteorological conditions and perceived anthropogenic activities over time, OSHA threshold could be evidently compromised.
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Introduction: Air pollution increases the load of hospitalization cases, especially for those who have respiratory problems. For effective environmental management, this study aims to compare the performance of two classification algorithms in machine learning (logistic regression and naive bayes) and to evaluate the selection of the best algorithm for predicting the air quality class.
Materials and methods: Pollutants data (PM10, SO2 , NO2) have been collected from the Haldwani, Kashipur and Rudrapur regions in Uttarakhand (India). In part I of the study, the Air Quality Index (AQI) is calculated and assigned a class accordingly. In part II, the performance of algorithms is compared, and the air quality class is predicted through the best algorithm. In part III, accuracy is calculated after comparing the predicted class with the actual class. Then, it is compared with the accuracy of our selected algorithm.
Results: The study finds a positive correlation between PM10 and SO2 pollutants. The result shows that the highest accuracy is achieved through logistic regression to predict the air quality class. Further, logistic regression has achieved the same accuracy i.e., 98.70% after comparing predicted values with the actual values.
Conclusion: Logistic regression is the best algorithm to predict the air quality class in the regions of Uttarakhand, where pollutants are being measured in the Government’s hospital. The research also indicates that asthma patients in the Kashipur and Rudrapur regions may experience more health effects dueto moderately polluted air quality; however, the situation is improving during the monsoon season. -
Introduction: Poor hospital Indoor Air Quality (IAQ) may result in various occupational hazards, hospital-acquired infections, and sick hospital syndrome. Air-control measures are vital to reduce airborne biological particle dissemination in hospitals. This study aimed to evaluate the effectiveness of High-Efficiency Particulate Air (HEPA) filters in decreasing indoor fungal pollution in an organ transplantation hospital in Mashhad.
Materials and methods: In this work, 96 specimens were collected from the air of three operating rooms and the Intensive Care Unit (ICU) ward. Sampling was performed using National Institute for Occupational Safety and Health (NIOSH-0800) instructions in two stages before and after using HEPA filters. Fungal density was reported based on the number of colonies per m3(CFU/m3).
Results: According to the results before using HEPA filters, the colony frequency of Aspergillus was 50%, which was the highest among the detected fungi. Penicillium with a frequency of 23% was followed by Aspergillus. After using HEPA filters, the frequency of Aspergillus and Penicillium decreased by 40% and 6% to 10% and 17%, respectively. The mean concentrations of fungi in all three operating rooms and ICU before use and after using HEPA filters were 9.52 and 3.11 (CFU/m3), respectively indicating a reduction of about 67%, which is statistically significant (P≤0.005).
Conclusion: Hence, using these filters is recommended considering the good performance and high efficiency of HEPA filters in reducing fungal contamination and its consequences. -
Introduction: Air pollution from industrial sources is a growing problem increasing the amount of air pollution by emitting various gaseous pollutants such as Nitrogen Oxides (NOx). This study analyzed Nitrogen dioxide (NO2) emissions using American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) from the stacks and flares of a gas refinery in the Middle East.
Materials and methods: The NO2 emissions were measured from the stacks and flare of the refinery (231 samples). The distribution of emissions was investigated over a statistical period of 1 year for an average time of 1 h using the AERMOD dispersion model in an area of 25×25 km2. The predicted concentrations were compared with national and international standards and are plotted for the desired zones.
Results: Comparison of simulation results with national and international clean air standards showed that NO2 emission modeled in all periods of 4 seasons is higher than the standard. Examination of NO2 emission and distribution maps also showed that the maximum concentration of NO2 pollutants occurred in the central parts and the area close to the refinery. The highest maximum concentration of 1-h NO2 was 3744.3716 μg/m3
in summer in the west and south of the refinery. Validation results also showed a high correlation between the predicted and actual results.
Conclusion: The power of resources in emission and distribution, topographic conditions, and meteorological characteristics of the region are three important and influential factors in the distribution of NO2 pollutants. So pollution reduction strategies are needed due to the different types of use, surrounding residential areas, personnel, and people involved in the gas refining company.
