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The paper aims to quantify the changes in the pollutants level in the ambient air of Madhya Pradesh during COVID-19 induced 21 days lockdown. The data of 16 continuous ambient air quality monitoring stations (CAAQMS) for six pollutants, namely PM2.5, PM10, SO2, NOx, CO, and Ozone for pre-lockdown (1–21 March 2020) and lockdown (25 March-14 April 2020) period was compiled and appropriate statistical analysis was done. The result showed a reduction of 13%, 20%, 37%, 39% and 25% in the ambient air level of PM2.5, PM10, SO2, NOx and CO, respectively, during the lockdown. No such declining trend was observed in the ambient air ozone level. Ambient air ozone level changed between −22 and +30% compared to pre-lockdown concentrations. The mean ambient air ozone concentration was reduced at the residential/commercial stations (−5%), but was increased at the industrial stations (+19%) concentration. [ABSTRACT FROM AUTHOR]

To reduce the pollution from the atmosphere or polluted cities like the capital city Delhi of India, use of artificial rain is a solution. In this paper, we have proposed and analyzed a nonlinear mathematical model to reduce the pollution level by rain making. In the proposed model five variables are considered, namely; (i) number density of water vapor, (ii) number density of cloud drops, (iii) number density of raindrops, (iv) cumulative concentration of aerosols, and (v) concentration of pollutant particles suspended in the region of consideration. The effect of environmental fluctuations has been studied with the help of Lyapunov functionals. The model is analyzed in the presence of white noise and proved that if rain persists, the pollutants can be totally washed out. It has been observed that the environmental disturbances are not much favorable in such experiments as the presence of environmental disturbance may destabilize the system. It is found that to remove pollutants completely, it is necessary to prevent the formation of pollutants. The simulation is performed to support the analytical findings. [ABSTRACT FROM AUTHOR]

The rapid urbanization and industrialization in many parts of the world have made air pollution a global public health problem. A study conducted by the Swiss organization IQAir indicated that 22 of the top 30 most polluted cities in the world are in India. This creates the problem of air pollution, which is very relevant to India as well. Exposure to air pollutants has both acute (short-term) and chronic (long-term) impacts on health. Among the major air pollutants, particulate matter 2.5 (PM2.5) is the most harmful, and its long-term exposure can impair lung functions. Pollutant concentrations vary temporally and are dependent on the local meteorology and emissions at a given geographic location. PM2.5 forecasting models have the potential to develop strategies for evaluating and alerting the public regarding expected hazardous levels of air pollution. Accurate measurement and forecasting of pollutant concentrations are critical for assessing air quality and making informed strategic decisions. Recently, data-driven machine learning algorithms for PM2.5 forecasting have received a lot of attention. In this work, a spatiotemporal analysis of air quality was first performed for Hyderabad, indicating that average PM2.5 concentrations during the winter were 68% higher than those during the summer. Following that, PM2.5 modelling was done using three different techniques: multilinear regression, K-nearest neighbours (KNN), and histogram-based gradient boost (HGBoost). Among these, the HGBoost regression model, which used both pollution and meteorological data as inputs, outperformed the other two techniques. During testing, the model acquired an amazing R² value of 0.859, suggesting a significant connection with the actual data. Additionally, the model exhibited a minimum Mean Absolute Error (MAE) of 5.717 μg/m³ and a Root Mean Square Error (RMSE) of 7.647 μg/m³, further confirming its accuracy in predicting PM2.5 concentrations. In our investigation, we discovered that the HGBoost3 model beat other PM2.5 modelling models by having the lowest error and the highest R² value. This study made a substantial addition by incorporating the spatiotemporal relationship between air pollutants and meteorological variables in predicting air quality. This method has the potential to improve the creation of more precise air pollution forecast models. [ABSTRACT FROM AUTHOR]

There is extensive evidence linking air pollution exposure to physical health. Less is known about the mental health impacts of poor air quality, especially in developing countries. We use data from India and estimate the causal impact of air pollution exposure on feeling sad, experiencing cognitive difficulties, and feeling unable to control and cope with important things in life. We instrument for air pollution exposure using the annual number of nighttime thermal inversions and show that air pollution exposure in the previous calendar year significantly worsens mental health in the current year. We examine potential mechanisms and find that air pollution exposure negatively impacts self-reported physical health, worsens respiratory conditions, and increases the likelihood of experiencing sleeping difficulties. Accounting for mental health impacts of pollution exposure is critical to accurately estimating the true health costs of air pollution and designing optimal environmental policy. [ABSTRACT FROM AUTHOR]

Gaseous pollutants serve as precursors of ionic particles in the atmospheric environment, thus their control is necessitated along with the particulate with special reference to soluble ions. The present study was undertaken to assess the effect of Particulate Matter (PM10) associated Water-Soluble Ions (WSI) on the ambient air concentration. In total 134 airborne particulate matter (PM10) samples were collected from 11 stations from January to December in and around Gandhidham, Gujarat, India. Monthly sampling was carried out by the Respirable Dust Sampler (RDS) on 24 hr basis. The samples were subjected to chemical analyses for estimating water-soluble ions. The PM10 concentration in the analyzed samples ranged from 29 to 401 µg/m3. The amount of Na+, Ca2+ and K+ varied from 0.04 to 8.43 µg/m3, Below Detection Level (BDL) to 12.25 µg/m3, and 0.09 to 3.49 µg/m3, respectively following flame photometer method. The anions SO 2-, NO - and Cl- ranged from 0.31 to 3.68 µg/m3, 0.39 to 32.47 µg/m3, and 4.03 to 54.08 µg/m3, respectively. Higher concentrations were reported for all the ions during winter. From the results, it is evident that secondary anthropogenic aerosols, natural mineral aerosols and mixed sea salt contributed to higher ionic concentrations in the air. [ABSTRACT FROM AUTHOR]

The present study aims to assess the air quality status in the central library of Indian Institute of Technology Roorkee, India. Pollutants concentrations (i.e. PM10, PM2.5, PM1 and TVOC) and comfort parameters i.e. CO2, temperature and relative humidity were monitored across all floors of the library. Air quality was found to vary significantly (P < 0.05) among the different floors of the library. The average concentration of PM10, PM2.5 and PM1 was found to be highest at the first floor. On the other hand, the highest concentration of TVOC (51.7 ± 30 ppb) and CO2 (838.4 ± 99 ppm) was observed at the ground floor. Pollutant concentration was higher in the morning hours. The indoor pollutants were found positively correlated with each other except relative humidity. Indoor to outdoor ratio for PM1, TVOC and CO2 was found to be greater than 1, which indicate a substantial contribution from indoor sources. Exceedance of WHO guidelines was observed for the daily average PM2.5 concentration. Abbreviation: IAQ: indoor air quality; ASHRAE: American Society of Heating, Refrigerating, and Air-Conditioning Engineers; WHO: World Health Organization; PM: particulate matter; VOC: volatile organic carbon; CO2: carbon dioxide; TVOC: Total volatile organic compound; RH: relative humidity; HVAC: heating ventilation and air-conditioning; PID: Photo Ionization Detector; PTFE: Polytetrafluoroethylene; NDIR: Non-dispersive infra-red. [ABSTRACT FROM AUTHOR]

Research studies connecting air pollution and its effect on health are much needed by the policymakers in India. Generalized additive model (GAM) was applied to quantify the effect of air pollutants on hospital visits related to respiratory diseases for an Indian city, Guwahati. A total of 6335 patients visited the only medical college and hospital in the city for respiratory diseases during 2016-2018. The multi-pollutant GAM models indicate the existence of synergistic effects of particulate matter less than 2.5 µm in diameter (PM2.5), nitrogen dioxide (NO2), and carbon monoxide (CO) on hospital visits of the patients due to respiratory diseases. The ambient PM2.5 concentrations have a strong link with hospital visits in all 6 single lag days. However, the effect of CO on hospital visits due to respiratory diseases was more robust. The percent change of hospital visits associated with each 1 unit increase in CO concentrations at lag 1 day was 15.7% (RR = 1.157; 95% CI 0.929, 1.442), which was enhanced to as high as 40.8% (RR = 1.408; 1.130,1.755) at lag3. The rise of each 10 units of concentrations of PM2.5 and 1 unit of CO at cumulative lag0-6 day was associated with the percentage change in hospital visits of 6.67% and 62.36% respectively. Single-pollutant GAM models indicate that the risk percentage of PM2.5 causing respiratory disease increased in combination with other pollutants. [ABSTRACT FROM AUTHOR]