Association Between Air Pollution and COVID-19 Pandemic: An Investigation in Mumbai, India

Indoor air pollution and cognitive function among older adults in India: a multiple mediation approach through depression and sleep disorders

BACKGROUND

Studies across multiple countries reveal that depression and sleep disorders can lead to cognitive decline. This study aims to speculate on the effect of different sources of indoor air pollution on cognition and to explore the mediation effect of depression and sleep disorders on cognition when exposed to indoor air pollution. We hypothesize that an older adult experiences higher cognitive decline from indoor pollution when mediated by depression and sleep disorders.

METHODS

We use data from Longitudinal Aging Study in India (LASI), 2017-2018, and employ a multiple mediation model to understand the relationship between indoor air pollution and cognition through sleep disorders and depression while adjusting for possible confounders. Sensitivity analysis was applied to see the effect of different sources of indoor pollution (cooking fuel, indoor smoke products, and secondhand smoke) on cognitive performance.

RESULTS

The effect of three sources of indoor pollutants on cognition increased when combined, indicating stronger cognitive decline. Unclean cooking practices, indoor smoke (from incense sticks and mosquito coils), and secondhand smoke were strongly associated with sleep disorders and depression among older adults. Indoor air pollution was negatively associated with cognitive health (β= -0.38) while positively associated with depression (β= 0.18) and sleep disorders (β= 0.038) acting as mediators. Sensitivity analysis explained 45% variability while adjusting for confounders.

CONCLUSION

The study lays a foundation for future investigations into the nexus of indoor pollution and mental health. It is essential to formulate policies to reduce exposure to varying sources of indoor air pollutants and improve screening for mental health services as a public health priority.

A bibliometric analysis of the impact of COVID-19 social lockdowns on air quality: research trends and future directions

Social lockdowns improved air quality during the COVID-19 pandemic. Governments had previously spent a lot of money addressing air pollution without success. This bibliometric study measured the influence of COVID-19 social lockdowns on air pollution, identified emerging issues, and discussed future perspectives. The researchers examined the contributions of countries, authors, and most productive journals to COVID-19 and air pollution research from January 1, 2020, to September 12, 2022, from the Web of Sciences Core Collection (WoS). The results showed that (a) publications on the COVID-19 pandemic and air pollution were 504 (research articles) with 7495 citations, (b) China ranked first in the number of publications (n = 151; 29.96% of the global output) and was the main country in international cooperation network, followed by India (n = 101; 20.04% of the total articles) and the USA (n = 41; 8.13% of the global output). Air pollution plagues China, India, and the USA, calling for many studies. After a high spike in 2020, research published in 2021 declined in 2022. The author's keywords have focused on "COVID-19," "air pollution," "lockdown," and "PM25." These keywords suggest that research in this area is focused on understanding the health impacts of air pollution, developing policies to address air pollution, and improving air quality monitoring. The COVID-19 social lockdown served as a specified procedure to reduce air pollution in these countries. However, this paper provides practical recommendations for future research and a model for environmental and health scientists to examine the likely impact of COVID-19 social lockdowns on urban air pollution.

Assessing the Effect of the U.S. Vaccination Program on the Coronavirus Positivity Rate With a Multivariate Framework

The factors influencing the incidence of COVID-19, including the impact of the vaccination programs, have been studied in the literature. Most studies focus on one or two factors, without considering their interactions, which is not enough to assess a vaccination program in a statistically robust manner. We examine the impact of the U.S. vaccination program on the SARS-CoV-2 positivity rate while simultaneously considering a large number of factors involved in the spread of the virus and the feedbacks among them. We consider the effects of the following sets of factors: socioeconomic factors, public policy factors, environmental factors, and non-observable factors. A time series Error Correction Model (ECM) was used to estimate the impact of the vaccination program at the national level on the positivity rate. Additionally, state-level ECMs with panel data were combined with machine learning techniques to assess the impact of the program and identify relevant factors to build the best-fitting models. We find that the vaccination program reduced the virus positivity rate. However, the program was partially undermined by a feedback loop in which increased vaccination led to increased mobility. Although some external factors reduced the positivity rate, the emergence of new variants increased the positivity rate. The positivity rate was associated with several forces acting simultaneously in opposite directions such as the number of vaccine doses administered and mobility. The existence of complex interactions, between the factors studied, implies that there is a need to combine different public policies to strengthen the impact of the vaccination program.

Spatial shifting of COVID-19 clusters and disease association with environmental parameters in India: A time series analysis

BACKGROUND

The viability and virulence of COVID-19 are complex in nature. Although the relationship between environmental parameters and COVID-19 is well studied across the globe, in India, such studies are limited. This research aims to explore long-term exposure to weather conditions and the role of air pollution on the infection spread and mortality due to COVID-19 in India.

METHOD

District-level COVID-19 data from April 26, 2020 to July 10, 2021 was used for the study. Environmental determinants such as land surface temperature, relative humidity (RH), Sulphur dioxide (SO₂), Nitrogen dioxide (NO₂), Ozone (O₃), and Aerosol Optical Depth (AOD) were considered for analysis. The bivariate spatial association was used to explore the spatial relationship between Case Fatality Rate (CFR) and these environmental factors. Further, the Bayesian multivariate linear regression model was applied to observe the association between environmental factors and the CFR of COVID-19.

RESULTS

Spatial shifting of COVID-19 cases from Western to Southern and then Eastern parts of India were well observed. The infection rate was highly concentrated in most of the Western and Southern regions of India, while the CFR shows more concentration in Northern India along with Maharashtra. Four main spatial clusters of infection were recognized during the study period. The time-series analysis indicates significantly more CFR with higher AOD, O₃, and NO₂ in India.

CONCLUSIONS

COVID-19 is highly associated with environmental parameters and air pollution in India. The study provides evidence to warrant consideration of environmental parameters in health models to mediate potential solutions. Cleaner air is a must to mitigate COVID-19.

Rising surface ozone due to anthropogenic activities and its impact on COVID-19 related deaths in Delhi, India

The rapidity and global spread of the COVID-19 pandemic have left several vital questions in the research community requiring coordinated investigation and unique perspectives to explore the relationship between the spread of disease and air quality. Previous studies have focused mainly on the relation of particulate matter concentration with COVID-19-related mortalities. In contrast, surficial ozone has not been given much attention as surface ozone is a primary air pollutant and directly impacts the respiratory system of humans. Hence, we analyzed the relationship between surface ozone pollution and COVID-19-related mortalities. In this study, we have analyzed the variability of various atmospheric pollutants (particulate matter (PM_2.5 and PM₁₀), Nitrogen dioxide (NO₂), Carbon monoxide (CO), and Ozone) in the National Capital Region (NCR) of India during 2020-2021 using station data and investigated the relationship of the air-quality parameters with the COVID-19 related deaths. In northern parts of India, the concentration of particulate matter (PM_2.5 and PM₁₀), Nitrogen dioxide (NO₂), Carbon monoxide (CO), and Ozone remain high during the pre- and post-monsoon seasons due to dust loading and crop residue burning (after winter wheat in April & summer rice in November). The westerly wind brings the polluted airmass from western and northwestern parts to Delhi and National Capital Region during April-June and October-November, and meteorological conditions help raise the concentration of these pollutants. Due to long solar hours and high CO concentrations, the ozone concentration is higher from April to June and September. While comparing major air quality parameters with COVID-19-related deaths, we found a good relationship between surface ozone and COVID-19 mortality in Delhi. We also observed a time lag relationship between ozone concentration and mortality in Delhi, so the exposure to Ozone in a large population of Delhi may have augmented the rise of COVID-19-related deaths. The analysis suggested that ozone has a significant relationship with COVID-19 related mortality in Delhi in comparison to other parameters.

Potential hotspot modeling and monitoring of PM2.5 concentration for sustainable environmental health in Maharashtra, India

Modern human civilization has suffered from the disastrous impact of COVID-19, but it teaches us the lesson that the environment can restore its stability without human activity. The Government of India (GOI) has launched many strategies to prevent the situation of COVID-19, including a lockdown that has a great impact on the environment. The present study focuses on the analysis of Particulate Matter 2.5 (PM2.5) concentration levels in pre-locking, lockdown, and unlocking phases across ten major cities of Maharashtra (MH) that were the COVID hotspot of India during the COVID-19 outbreak; phase-wise and year-wise (2018-2020) hotspot analysis, box diagram and line graph methods were used to assess spatial variation in PM2.5 across MH cities. Our study showed that the PM2.5 concentration level was severe at pre-lockdown stage (January-March) and it decreased dramatically at the lockdown stage, later it also increased in its previous position at the unlocking stages, i.e., PM2.5 decreased dramatically (59%) during the lockdown period compared to the pre-lockdown period due to the shutdown of outdoor activities. It returns to its previous position due to the unlocking situation and increases (70%) compared to the lockdown period which illustrated the ups and downs of PM2.5 and ensures the position of different cities in the Air Quality Index (AQI) categories at different times. In the pre-lockdown phase, maximum PM2.5 concentration was in Navi Mumbai (NAV) (358) and Mumbai (MUM) (338), and Pune (PUN) (335) and Nashik NAS (325) subsequently, whereas at the last of the lockdown phase, it becomes Chandrapur (CHN) (82), Nagpur (NAG) (76), and Solapur (SOL) (45) subsequently. Hence, the restoration of the environment during the lockdown phase was temporary rather than permanent. Therefore, our findings propose that several effective policies of government such as relocation of polluting industries, short-term lockdown, odd-even vehicle number, installation of air purifier, and government strict initiatives are needed in making a sustainable environment.

Impact of Short-Term Air Pollution on Respiratory Infections: A Time-Series Analysis of COVID-19 Cases in California during the 2020 Wildfire Season

The 2020 California wildfire season coincided with the peak of the COVID-19 pandemic affecting many counties in California, with impacts on air quality. We quantitatively analyzed the short-term effect of air pollution on COVID-19 transmission using county-level data collected during the 2020 wildfire season. Using time-series methodology, we assessed the relationship between short-term exposure to particulate matter (PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), and Air Quality Index (AQI) on confirmed cases of COVID-19 across 20 counties impacted by wildfires. Our findings indicate that PM2.5, CO, and AQI are positively associated with confirmed COVID-19 cases. This suggests that increased air pollution could worsen the situation of a health crisis such as the COVID-19 pandemic. Health policymakers should make tailored policies to cope with situations that may increase the level of air pollution, especially during a wildfire season.

Novel Evidence Showing the Possible Effect of Environmental Variables on COVID-19 Spread

Coronavirus disease (COVID-19) remains a serious issue, and the role played by meteorological indicators in the process of virus spread has been a topic of academic discussion. Previous studies reached different conclusions due to inconsistent methods, disparate meteorological indicators, and specific time periods or regions. This manuscript is based on seven daily meteorological indicators in the NCEP reanalysis data set and COVID-19 data repository of Johns Hopkins University from 22 January 2020 to 1 June 2021. Results showed that worldwide average temperature and precipitable water (PW) had the strongest correlation (ρ > 0.9, p < 0.001) with the confirmed COVID-19 cases per day from 22 January to 31 August 2020. From 22 January to 31 August 2020, positive correlations were observed between the temperature/PW and confirmed COVID-19 cases/deaths in the northern hemisphere, whereas negative correlations were recorded in the southern hemisphere. From 1 September to 31 December 2020, the opposite results were observed. Correlations were weak throughout the near full year, and weak negative correlations were detected worldwide (|ρ| < 0.4, p ≤ 0.05); the lag time had no obvious effect. As the latitude increased, the temperature and PW of the maximum confirmed COVID-19 cases/deaths per day generally showed a decreasing trend; the 2020-year fitting functions of the response latitude pattern were verified by the 2021 data. Meteorological indicators, although not a decisive factor, may influence the virus spread by affecting the virus survival rates and enthusiasm of human activities. The temperature or PW threshold suitable for the spread of COVID-19 may increase as the latitude decreases.