| Literature DB >> 33549623 |
Shakeel Ahmad Bhat1, Omar Bashir2, Muhammad Bilal3, Aamir Ishaq4, Mehraj U Din Dar4, Rohitashw Kumar1, Rouf Ahmad Bhat5, Farooq Sher6.
Abstract
The outbreak of COVID-19 pandemic has emerged as a major challenge from human health perspective. The alarming exponential increase in the transmission and fatality rates related to this disease has brought the world to a halt so as to cope up with its stern consequences. This has led to the imposition of lockdown across the globe to prevent the further spread of this disease. This lock down brought about drastic impacts at social and economic fronts. However, it also posed some positive impacts on environment as well particularly in the context of air quality due to reduction in concentrations of particulate matter (PM), NO2 and CO across the major cities of the globe as indicated by several research organizations. In China, Italy, France and Spain, there were about 20-30% reduction in NO2 emission while in USA 30% reduction in NO2 emission were observed. Compared to previous year, there was 11.4% improvement in the air quality in China. Drastic reductions in NO (-77.3%), NO2 (-54.3%) and CO (-64.8%) (negative sign indicating a decline) concentrations were observed in Brazil during partial lockdown compared to the five year monthly mean. In India there were about -51.84, -53.11, -17.97, -52.68, -30.35, 0.78 and -12.33% reduction in the concentration of PM10, PM2.5, SO2, NO2, CO, O3 and NH3 respectively. This article highlights the impact of lockdown on the environment and also discusses the pre and post lockdown air pollution scenario across major cities of the world. Several aspect of environment such as air, water, noise pollution and waste management during, pre and post lockdown scenario were studied and evaluated comprehensively. This research would therefore serve as a guide to environmentalist, administrators and frontline warriors for fighting our the way to beat this deadly disease and minimize its long term implications on health and environment.Entities:
Keywords: Air quality index; Climate change; Environmental impacts; Global pandemic; Outbreak; Waste management and Covid-19
Year: 2021 PMID: 33549623 PMCID: PMC7860963 DOI: 10.1016/j.envres.2021.110839
Source DB: PubMed Journal: Environ Res ISSN: 0013-9351 Impact factor: 6.498
Fig. 1The trend of 24 h mean concentrations; (a) PM10, (b) PM2.5, (c) SO2, (d) NO2, (g) NH3 and (h) NAQI and 8 h mean daily maxima of (e) CO and (f) O3 between 3rd of March and 14th April 2020 (On 24th March 2020 the lockdown commenced) in NCT Delhi, India (Mahato et al., 2020).
Average concentrations and variations of criterion pollutants in NCT Delhi, India from 2 March to21 March 2020 (before lockdown) and 25 March to 14 April 2020 (during lockdown).
| Pollutants | Before lockdown | During lockdown | Overall variation | Percentage | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| NCT Delhi Avg. | Industrial location Avg. | Transport location Avg. | Residential and other location Avg. | NCT Delhi Avg. | Industrial location Avg. | Transport location Avg. | Residential and other location Avg. | Net | (%) | |
| PM10 | 176.07 | 190.74 | 195.77 | 160.48 | 84.79 | 91.25 | 90.11 | 76.48 | −91.28 | −51.85 |
| PM 2.5 | 80.51 | 88.05 | 94.83 | 72.67 | 37.75 | 39.67 | 44.23 | 31.09 | −42.76 | −53.11 |
| SO2 | 16.08 | 15.48 | 14.56 | 14.17 | 13.19 | 14.07 | 12.53 | 11.20 | −2.89 | −17.97 |
| NO2 | 42.59 | 34.81 | 47.35 | 48.75 | 20.16 | 18.80 | 23.38 | 18.79 | −22.44 | −52.68 |
| CO | 1.03 | 1.33 | 1.13 | 1.01 | 0.72 | 1.04 | 0.71 | 0.64 | −0.31 | −30.35 |
| O3 | 34.05 | 26.37 | 35.07 | 37.36 | 34.32 | 31.00 | 38.87 | 37.97 | 0.27 | 0.78 |
| NH3 | 33.93 | 38.43 | 38.02 | 30.66 | 29.75 | 35.84 | 33.06 | 25.97 | −4.18 | −12.33 |
| NAQI | 185.99 | 196.38 | 215.29 | 174.78 | 72.64 | 92.45 | 87.29 | 79.80 | −113.36 | −60.95 |
Fig. 2NAQI change at NCT Delhi from 3 March to 14 April 2020 (Mahato et al., 2020).
Analysis of current air quality work during COVID-19 across different regions of the world.
| Issues addressed/Major outcomes | Region | Reference |
|---|---|---|
| Restricted human activities have contributed to air quality deterioration and reduced excessive associated risk. | Major cities of India | |
| Deficient air quality regions are related to high mortality rates. | China | |
| In several cities, lockout led to an increase in the quality of air and a decrease in premature death. | China | ( |
| The air pollution and Covid-19 infection relationship is considerable. | China | |
| Decrease of primary pollutants (like NOx) during lockdown offers compensation for increased secondary pollutants (like O3). | China | |
| Adverse weather conditions overwhelmed emissions reductions. | China | |
| Significant decrease in most contaminants and increased concentrations of ozone during the lockdown. | Spain | |
| Accelerated Covid-19 transmissions are mainly using “air pollution -to -human transmission” rather than “human -to -human transmission”. | Italy | |
| Mean temperature, minimum temperature and air quality were strongly related to Covid-19 outbreak. | New York, USA | |
| After four days of lockdown in Delhi, about 40–50% enhancement in the quality of air is identified. | Delhi, India | |
| The Covid-19 pandemic lock-down caused the quality of air in many cities around the world to increase and water pollution to decrease in some parts of the world. | Major cities of the world | |
| The AQI and concentrations of PM2.5 were decreased by 25% within weeks. | China | ( |
| Reducing concentrations of PM2.5 and NO2 in China, Germany, Spain, France and Italy. | Major cities of the world | |
| A sudden drop in CO2 emission profile in the atmosphere. Decreased percentage of CO2 ranged from 24.56 to 45.37 at Deshbandhu park and Sealdah station respectively. | Kolkata, India | |
| Reduction in the concentration of major air contaminants PM2.5, PM10, NO2 and CO in most cities around the world. | Major cities of the world | |
| A decrease in concentrations of major air pollutants. | Cities across the globe | |
| Observable air pollution reduction following lockdown. | Cities across the globe |
Fig. 3Comparison of air quality before the COVID-19 pandemic and after the lockout in several largest cities of the world; (a) New Delhi, India, (b) Beijing, China, (c) Paris, France and (d) New York, USA (Saadat et al., 2020).
Acquisition of data of NO2 emissions across various regions of the world.
| Agency | Location | Reduction (%) | Satellite | Source |
|---|---|---|---|---|
| NASA and ESA | Wuhan | 30 | Aura and Sentinel-5P | (NASA 2020) |
| ESA | China | 20 to 30 | Sentinel-5P | (ESA 2020) |
| ESA | Europe | 20 to 30 | Sentinel-5P | (ESA 2020) |
| NASA | USA | 30 | Aura | (NASA 2020) |
| ESA | Italy | 20 to 30 | Sentinel-5P | (ESA 2020) |
| ESA | France | 20 to 30 | Sentinel-5P | (ESA 2020) |
| ESA | Spain | 20 to 30 | Sentinel-5P | (ESA 2020) |
Fig. 4NO2 emissions in Wuhan (China) during the year 2019 and 2020 (NASA 2020).
Fig. 5ER correlated with pollutant parameters in different regions of India; PM2.5, PM10, O3, NO2, SO2 and CO. ERs are shown separately during the current year and previous 3 years (2017–2019) during the study period (Sharma et al., 2020).
Model results using MFB and expected concentration shifts in the worst meteorological case in comparison to the base case at the Delhi-NCR examination sites.
| Station | MFB | Change (%) |
|---|---|---|
| Najafgarh | −0.10 | −54.33 |
| Narela | −0.70 | −40.40 |
| Okhla Phase-2 | −0.40 | 12.97 |
| Lodhi Road | 0.00 | 28.19 |
| Mandir Marg | −0.10 | 21.75 |
| MDC National Stadium | 0.10 | 33.29 |
| North Campus, DU | 0.80 | −23.03 |
| NSIT Dwarka | 0.30 | −43.13 |
| CRRI Mathura Road | −0.90 | 17.62 |
| ITO | 0.40 | 154.64 |
| IGI Airport (T3) | −0.40 | −52.73 |
| IHBAS, Dilshad Garden | −0.20 | 104.51 |
| JLN Stadium | −0.90 | 04.04 |
| Burari Crossing | −0.60 | 105.27 |
| Punjabi Bagh | 0.10 | −21.10 |
| Pusa | −0.40 | 29.37 |
| R K Puram | 0.10 | −47.06 |
| Sonia Vihar | −0.30 | 31.20 |
| Vivek Vihar | −0.40 | 36.65 |
| Wazirpur | 0.60 | 268.75 |
| Anand Vihar | −0.30 | 64.30 |
| Ashok Viha | −0.10 | 71.32 |
| Rohini | 0.10 | −48.35 |
| Shadipur | −0.40 | 12.31 |
Fig. 6Ben Morris posted pictures of the beach masks on the Soko Islands, Hong Kong (Kalina and Tilley, 2020).