Journal of Air Pollution and Health
Volume:6 Issue: 3, Summer 2021

The estimation of air pollution level is well indicated by Air Quality Index (AQI), which tells how unhealthy the ambient air is and how polluted it can become in near future. Hence, the predictions or modeling of AQI is always of greater concern among researchers and this present study aims to develop such a model for forecasting the AQI.

A combination of Artificial Neural Network (ANN) and Fuzzy logic (FL) system, called Adaptive Neuro-Fuzzy Inference System (ANFIS) have been considered for model development. Daily air quality data (PM2.5 and PM10) and meteorological data (temperature and humidity) over a period of March 2020 to March 2021 were used as the input data and AQI as the output variable for the ANFIS model. The performances of models were evaluated based on Root Mean Square Error (RMSE), Regression coefficient (R2) and Average Absolute Relative Deviation (AARD).

A total of 100 datasets is split into training (70), testing (15) and simulation (15). Gaussian and Constant membership functions were employed for classifications and the final index consisted of 81 inference (IF/THEN) rules. The ANFIS Simulation result shows an R2 and RMSE value of 0.9872 and 0.0287 respectively.

According to the results from this study, ANFIS based AQI is a comprehensive tool for classification of air quality and it is inclined to produce accurate results. Therefore, local authorities in air quality assessment and management schemes can apply these reliable and suitable results.

The indoor environment of dental clinics may endanger dental patients and personnel and due to a great variety of air pollutants throughout the usual dental operation. The purpose of the present cross-sectional study was the evaluation of Indoor Air Quality (IAQ) and factors affecting it in a dentistry faculty of Arak University of Medical Sciences.

The IAQ of five dental active wards and the patient waiting room was evaluated. The concentrations of Total Volatile Organic Compounds (TVOC), CO2, particulate matter, and bioaerosols were measured.

The TVOCs concentration in sampling locations ranged between 817 to 3670 μg/m3 during dental work and exceeded the Leadership in Energy and Environmental Design (LEED) guideline in all sampling locations. The highest values of Particulate Matter (PM) for PM10, PM2.5, and PM1 were observed in the periodontics ward, while the lowest values were observed in the endodontics ward. The PM2.5 concentrations exceeded the WHO limit in periodontics and pediatric wards. TVOC levels had a significant positive correlation with temperature (r=0.374, p<0.01) and RH (r=0.265, p<0.05). The predominant bacterial genus of the patient waiting area was Bacillus (36%), while the dominant bacterial genus of the other sampling site was Micrococcus spp. Penicillium (35.5%) and Cladosporium (28%) were the predominant fungi detected.

Controlling of airborne particles is to be standardized by the infection control actions of dental clinics and improved ventilation capacity in the air conditioning system was suggested for reducing VOCs and PM concentrations.

The entry of dust particles into water areas, which has increased sharply in recent years, causes a lot of environmental damage. The Persian Gulf and the Gulf of Oman are among the water areas that are covered with dust many times of the year.

In this study, a severe dust from July 27 to 31, 2018 is analyzed, in which a large part of the Persian Gulf, Oman Sea and the western part of the Indian Ocean was involved. To study this phenomenon from different perspectives, satellite products, visibility from synoptic stations and synoptic maps were analyzed and the output of two numerical dust models of United States National Aeronautics and Space Administration-Goddard Earth Observing System (NASA-GEOS) and Dust Regional Atmospheric Model with 8 categories-Monitoring Atmospheric Composition and Climate (DREAM8-MACC) were examined. To qualitative and quantitative evaluate of the model outputs, the Aerosol Optical Depth (AOD) of TERRA/MODIS was used.

Satellite imagery shows that in this case study, parts of the Persian Gulf and the Sea of Oman were affected by dust, and on July 30, dust particles entered the western half of the Indian Ocean. Comparison of model outputs with satellite data resulted that both models underestimate the AOD values, especially over water, and do not show well the entrance of dust particles into the eastern part of the Persian Gulf, the Gulf of Oman and the western half of the Indian Ocean.

Qualitative and quantitative comparison of AOD output of the two models with satellite data showed that the NASA-GEOS model had better performance and its output correlation with observational data was higher.

Worldwide coronavirus created is a major problem for human health, food security, economy and many more. World Health Organisation (WHO) named this virus COVID-19. This virus is first detected in Wuhan, China in December 2019 and after that, it’s spreading over the world. Lockdown is healing the environmental condition because major Indian metropolitan cities are recovered from different pollutants. This study is to identify the air quality trend before, during and after the lockdown in Siliguri city, the third-largest city of West Bengal and this city is also a commercial and transportation hub.

The air quality data have been derived from West Bengal Pollution Control Board (WBPCB) and proceed in MS-Office and ArcGIS 10.4. The air pollutant and week air quality data have been used for monitoring the environmental situation.

In this study, results show that around 70%-90% of air quality is increased during strict lockdown but again air quality is decreased after lockdown gradually. The weekly air quality graph significantly changes during lockdown but after lockdown, the graph was increased. The highest air quality shows 347 before lockdown but during lockdown it’s decreased 25 on 23-24 May 2020. After lockdown public transport, industrial area and small scale industries are reopened and again the air quality increased. The highest air quality shows 353 on 14 January 2021 during unlock 8.0.

This pandemic taught how anthropogenic activates, like urbanization, population pressure and industrial works were endangering the environment and some caution is essential for future livelihood.

Air quality improvement was an unparalleled environmental consequence of the Covid-19 global crisis in many regions. Numerous researches have been conducted on the influence of national quarantines on air pollution and the relationship between the abundance of infected cases and mortality caused by this pandemic with air pollutants; however, these investigations are limited in Iran. The present study aims to investigate the correlation between Covid-19 cases and air pollution from a statistical viewpoint in order to evaluate the performance of multiple national lockdowns from February 2020 to August 2021 through measuring changes in air pollutants in the 31 provinces of Iran.

We applied a remote sensing method by employing Sentinel-5P satellite data to analyze changes in PM2.5, CO, and O3 during the three public quarantine periods and their two months earlier.

We recognized a considerable positive correlation between PM2.5 and the infected cases (r=0.63, p=0.001) and victims (r=0.41, p=0.001). Moreover, we compared the efficiency of lockdowns and supposed lockdown 2 (November-December 2020) as an only effective quarantine due to a dramatic reduction in PM2.5 (21.2%), CO (0.8%), the infected cases (48.7%), and victims (66.9%) in comparison to the average of its next two months.

Governments should handle the outbreak of Covid-19 by implementing efficient quarantines, as well as environmental conservation strategies.

Azandarian industrial zone with about 40 active silica crushing units is one of the largest industrial area in Hamadan province, Iran.

In this study, the personal exposure of workers in the activated silica crushing units was measured. Assessing the risk of mortality due to exposure to Respirable Crystalline Silica (RCS) in the workplace was then estimated through measuring the personnel exposure in accordance with the National Institute for Occupational Safety and Health (NIOSH) 7601 method. Moreover, the mortality rate of lung cancer and risk of mortality due to exposure to RCS were estimated.

Based on the results, the average exposure of employees to RCS in the crushing units was in the range of 1.70 -0.14 mg/m3. As observed, the lowest and highest exposure was obtained for the admission unit and sandstone, respectively. In general, it can be inferred that in all studied occupation positions, the exposure level was higher than the recommended standard (0.25 mg/m3). As can be seen, the carcinogenic risk level for the exposed workers was in the range 2-26/1000. The results of risk assessment showed that the highest risk level was related to the stamping machine operator unit and the lowest was related to the administrative unit.

Therefore, the workers working in high-risk units such as stamping machine operator and stone separation operator are more likely to suffer from adverse health complications such as silicosis, lung cancer and other respiratory complications.