Air pollution is a widespread problem in India. The study focuses on forecasting the air quality index (AQI) using time series modeling techniques for the most polluted area of Dehradun City in Uttarakhand state, India. The train test approach of machine learning and Akaike information criterion (AIC) have been used on the monthly data of five years to select the best auto-regressive model. Using the auto-correlation functions (ACF and PACF) and the seasonality component in the time-series dataset, a seasonal autoregressive moving average (ARMA) model with its minimum AIC has been chosen to forecast the AQI. This model is also validated by comparing its predicted values with the actual values of AQI. The results showed that the seasonal ARMA model of (1, 0, 0)(1, 0, 0)12 could forecast AQI based on a stationary dataset. The research also indicates that the asthma patients of the Himalayan Drugs-ISBT region may experience more health effects, especially in winter, due to poor air quality. The model can be helpful for a scientist and the government to take precautionary measures in advance.

Introduction: Tehran city with the most population, about 4 million cars, million liters of fuels consumption, the presence of polluting industries such as petrochemicals and refineries, thermal power plants, and surrounding industrial towns is considered as one of the most populous and most polluted cities in the world. This study aims to investigate the trend of variation in air quality index in Tehran. Materials and methods: In this descriptive and evaluative study, the air quality data of 7 monitoring stations in 2012 were taken from the Tehran Department of Environment and Tehran Air Quality Control Company(AQCC). The calculation of AQI was done according to the EPA guidelines. Results: According to the results of this study, highest AQI averaging for 2016 (208. 49± 42. 13) and the lowest for 2011 (134. 13± 46. 80). Also observed that during the study period PM2. 5 particles with an average of 71. 59% is the most important factor in increasing the air quality index. Conclusion: It was observed that in the cold seasons of the year, due to the temperature inversion phenomenon in Tehran and the increase in the concentration of pollutants, air quality in most regions of Tehran is in unhealthy conditions, but in other season of the year the air quality is in moderate condition. Among the index pollutants, particulates are the major cause of Tehran’ s air quality decline.

Introduction: This study aimed to calculate and evaluate air quality index (AQI) in Tehran according to the EPA’ s instruction, and evaluate the obtained values. Materials and methods: The study period included three years of 2013, 2015, and 2017 within the six-year period of 2013-2017. The concentrations of six studied pollutants were acquired from Department of Environment (DE) and Air Quality Control Company of Tehran (AQCC). EPA’ s method was applied to calculate AQI values during the study period, selecting the highest AQI value among all of the pollutants and monitors. Results: The three-year average of AQI was about 147. During all the three years, the air quality had never been in a “ good” condition (AQI<50). About 92%, 91%, 87% of AQIs indicated the conditions unhealthy for sensitive groups (100

Background: Air pollution indexes are used to indicate the level, quantity, and quality of air pollution. The air quality index (AQI) and air quality health index (AQHI) have been developed to report their association with human health. Methods: Air quality variables (PM2.5, O3, and NOx) per hour were obtained for March 2021 to March 2022 from three central pollution control centers in Ahvaz, the capital city of Khuzestan province, southwest of Iran. R3-3-4, Minitab-17, and SPSS-19 software were used to analyze the obtained data. Results: In this study, AQHI and AQI were predicted with actual data, forecasting the air quality with two confidence interval percentages of 95 and 80 illustrated for future days. Also, the relationship between AQI and AQHI was determined; hence, this relationship is important for some cities for which the AQHI index is not measurable. AQHI can be determined for each place using AQI values and the obtained relationship. Conclusion: The result indicated that we could also forecast the AQI and AQHI for future days and obtain a new equation for AQHI due to AQI values.

Introduction: The quality of air is becoming progressively worsen day to day. The increasing concentration of air pollution and its associated health effects are rapidly rising in Bangladesh and have drawn attention in recent years. The purpose of the research is to look into the increasing levels of air pollution in Bangladesh, specifically the proportion of Air Quality index (AQI) in four seasons and six districts, monthly mean AQI, the correlation between PM2. 5 and AQI between 2014 and 2019. Materials and methods: The AQI data from six monitoring stations have been collected for research purposes from Continuous Air Monitoring Stations (CAMS). MS Excel 2020 and IBM SPSS V27 were used for the analysis. Results: This study reveals that air quality in six districts of Bangladesh has been declining from 2014 to 2019, with winter and monsoon seasons being the most polluted. Dhaka and Narayanganj were found to have the unhealthiest air quality. There is a strong relationship between Paerticulate Matters (PM2. 5) and AQI, with AQI increasing with the amount of PM2. 5 in all cities. In January, February, March, November, and December, the monthly mean AQI was higher, but in May, June, and July, the mean was lower. The F-values were significant based on seasons and stations. Overall, the study highlights the increasing air pollution and associated health effects in Bangladesh. Conclusion: Air pollution in Bangladesh is a significant issue due to industrialization, urbanization, transportation, and fuel use, resulting in consistently high AQI levels throughout the year except during rainy months.