Correlating the trends of COVID-19 spread and air quality during lockdowns in Tier-I and Tier-II cities of India—lessons learnt and futuristic strategies

Abstract

The present study focuses on the impact of early imposed lockdowns and following unlocking phases on the status of air quality in six Tier-I and nine Tier-II cities of India as compared to the pre-lockdown measures. Furthermore, the study highlights the possible correlation of air quality index (AQI) with the initial trend of COVID-19 issues including the vaccination cases. Based on the statistical data analysis, we observed that the long-term averages for representing the short-term pre-lockdown conditions can impose a healing effect to the observed anomalies in air pollution data. However, the reduction in air pollution during the imposed lockdown series was only a phenomenal consequence, and the trends started reversing during the later phases of partial unlocking, where the correlation showed reversing trends. Being a yearly averaged parameter, the marginal reductions in the exceedance factor (EF) alone could not dictate air quality compared to the AQI. As there is incoherent variability in the pollutant distributions among the cities during various phases of the study, the trend analysis served as a preferable criterion to choose the preferred sources of variations. Based on the results, the correlation analysis revealed that air quality expressed in terms of AQI can act as an important precursor to estimate the critical phase of COVID-19 spread and the effectiveness of various control measures taken during each phase. Based on our proposed ranking, Kolkata and Patna are ranked first in the Tier-I and Tier-II cities respectively according to their responsiveness to the various institutionalized restrictions in terms of air quality parameters.

Lockdown Amid COVID-19 Ascendancy over Ambient Particulate Matter Pollution Anomaly

“…According to AQI, during the first quarter of each year, shown in Figure 3 , the AQI in 2020 improved significantly in most cities compared to pre-COVID (2019) time; however, most of the metropolises regained poor AQI scores in 2021. Similar trends were observed in other lockdown-impacted AQI assessment studies [ 105 , 106 ]. The greatest percentage decrease in PM 2.5 and PM 10 in 2020 compared to 2019 was seen in places such as Stockholm and Abu Dhabi (−40.05% and −40.13%), while the greatest increases were seen in Ankara and Buenos Aires (+37.97% and +16.95%, respectively).…”

Assessment of Aerosol Optical Depth over Indian Subcontinent during COVID-19 lockdown (March–May 2020)

“…However, few positive anomaly patches were seen in Maharashtra, Madhya Pradesh, Karnataka, Telangana, Chhattisgarh, Odisha and West Bengal which may be due to some transport activities. Similar results are being obtained by few researchers while assessing the AOD and air quality which are fully influenced by the shutdown and lockdown phases in India (Soni, 2021;Sharma et al, 2020;Gautham, 2020;Vasudevan et al, 2021;Satya, 2021). The percentage change of AOD level during different phases of lockdown period with respect to the long-term mean across various regions of India is summarized in Table 1.…”

Predictive modeling and analysis of air quality – Visualizing before and during COVID-19 scenarios

“…The credibility of the forecasts made by the prediction models is to be examined as pollution data evolves. Moreover, forecasts of AQI values made available in public domain during COVID-19 lockdown showed high deviation (forecast error) from actual due to reduced economic activities ( Vasudevan et al, 2021 ), Therefore, there is a need to develop event-based prediction models for accurate AQI forecasts. Clustering and prediction models using machine learning together could yield multi-pollutant profiling and forecasts of AQI which could help in planning environmental missions, controlling emissions and restoring environmental health.…”