Table 2.
The summarized effects of COVID-19 prevalence on the environmental factors.
| Risk factors | Risk measure | Effects of COVID-19 outbreak | methodological basis | orientation and effort of the study | References | |
|---|---|---|---|---|---|---|
| Atmosphere | PM10, PM2.5, NO2, CO | 10–43% | Decreased air pollution | Data obtained from a network of air quality monitoring stations across 22 different cities in India for the past four years (2017–2020) for the time period of March 16th to April 14th. | Determined the effect of restricted emissions during COVID-19 on air quality in India | Sharma et al. (2020) |
| CO | 97.3–207.0% | Increased air pollution | The hourly concentrations from 35 monitoring stations were obtained from China's National Environmental Monitoring Centre and meteorological data were collected from the Meteorological Information Comprehensive Analysis and Process System (MICAPS) of the Chinese Meteorological Administration so modeling was carry out by HYSPLIT, PSCF and CWT. | modeling the effect of COVID-19 on air quality in 3 regions of China | Zhao et al. (2020b) | |
| 50–58% | Decreased air pollution | Data from the eight air quality stations located in various points in the city of Naples was collected | Analysis of Air Quality during the COVID-19 Pandemic Lockdown in Naples | (Sannino et al.) | ||
| 20% | Decreased air pollution | For the CO data Atmospheric Infrared Sounder (AIRS) introduces the grating spectrometer aboard AQUA satellite, launched on May 4, 2002 was used. | Compare the concentrations of atmospheric pollutants in the period before the lockdown and during the implementation of preventive control measures COVID-19. By focusing on East China | (Filonchyk et al.) | ||
| 36.2% | Decreased air pollution | Data were obtained from the platform: http://www.aqistudy.cn/. | Air Quality Index, Indicatory Air Pollutants and Impact of COVID-19 Event on the Air Quality near Central China | Xu et al. (2020a) | ||
| NO2 | 4.5–89.5% | Increased air pollution | The hourly concentrations from 35 monitoring stations were obtained from China's National Environmental Monitoring Centre and meteorological data were collected from the Meteorological Information Comprehensive Analysis and Process System (MICAPS) of the Chinese Meteorological Administration so modeling was carry out by HYSPLIT, PSCF and CWT. | The hourly concentrations from 35 monitoring stations were obtained from China's National Environmental Monitoring Centre and meteorological data were collected from the Meteorological Information Comprehensive Analysis and Process System (MICAPS) of the Chinese Meteorological Administration so modeling was carry out by HYSPLIT, PSCF and CWT. | (Zhao et al., 2020b) | |
| Nearly 50% | Decreased air pollution | The data was collected in two phases, 15 days before the lockdown (i.e., March 10th–March 24th) and 15 days after the lockdown (25th March–April 8th, 2020) Implementation from these targeted cities. The daily air quality data was obtained from the data repository maintained by Central Pollution Control Board (CPCB) under the Ministry of Environment, Forest and Climate Change, India (https://app.cpcbccr.com/AQI_India/). | Assess the changes in air quality parameters during the implementation of the lockdown measures in the four major metropolitan cities of India, viz., Delhi, Mumbai, Kolkata and Chennai for a one-month period (15 days before lockdown and 15 days after the implementation of lockdown). | (Bedi et al., 2020) | ||
| 20–40% | Decreased air pollution | The space-based air quality measurements from the National Aeronautics and Space Administration (NASA) Ozone Monitoring Instrument (OMI) onboard the Aura satellite and the TROPOspheric Monitoring Instrument (TROPOMI) onboard the European Space Agency's (ESA) Sentinel-5 Precursor (Sentinel-5P) satellite were used for assessing the spatial and temporal evolution of tropospheric NO2 throughout California during the pre- and post-initiation of COVID-19 containment measures in the state. | Investigate the impact of COVID-19 Containment Measures on Air Pollution in California | Naeger and Murphy (2020) | ||
| 30% | Decreased air pollution | Nitrogen dioxide (NO2) emission data obtained from Ozone Monitoring Instrument (OMI) on board the AURA satellite launched in 2004 as part of the NASA EOS (Earth Observation System) was used | Compare the concentrations of atmospheric pollutants in the period before the lockdown and during the implementation of preventive control measures COVID-19. By focusing on East China | (Filonchyk et al.) | ||
| SO2 | Negligible | Increased air pollution | Data obtained from a network of air quality monitoring stations across 22 different cities in India for the past four years (2017–2020) for the time period of March 16th to April 14th. | Determined the effect of restricted emissions during COVID-19 on air quality in India | Sharma et al. (2020) | |
| 16.9–33.9% | Increased air pollution | The hourly concentrations from 35 monitoring stations were obtained from China's National Environmental Monitoring Centre and meteorological data were collected from the Meteorological Information Comprehensive Analysis and Process System (MICAPS) of the Chinese Meteorological Administration so modeling was carry out by HYSPLIT, PSCF and CWT. | The hourly concentrations from 35 monitoring stations were obtained from China's National Environmental Monitoring Centre and meteorological data were collected from the Meteorological Information Comprehensive Analysis and Process System (MICAPS) of the Chinese Meteorological Administration so modeling was carry out by HYSPLIT, PSCF and CWT. | Zhao et al. (2020b) | ||
| 70% | Decreased air pollution | Data from the eight air quality stations located in various points in the city of Naples was collected | Analysis of Air Quality during the COVID-19 Pandemic Lockdown in Naples | (Sannino et al.) | ||
| 52.5% | Decreased air pollution | Data were obtained from the platform: http://www.aqistudy.cn/. | Air Quality Index, Indicatory Air Pollutants and Impact of COVID-19 Event on the Air Quality near Central China | Xu et al. (2020a) | ||
| O3 | 17% | Increased air pollution | Data obtained from a network of air quality monitoring stations across 22 different cities in India for the past four years (2017–2020) for the time period of March 16th to April 14th. | Determined the effect of restricted emissions during COVID-19 on air quality in India | Sharma et al. (2020) | |
| 16.4–33.9% | Decreased air pollution | The hourly concentrations from 35 monitoring stations were obtained from China's National Environmental Monitoring Centre and meteorological data were collected from the Meteorological Information Comprehensive Analysis and Process System (MICAPS) of the Chinese Meteorological Administration so modeling was carry out by HYSPLIT, PSCF and CWT. | The hourly concentrations from 35 monitoring stations were obtained from China's National Environmental Monitoring Centre and meteorological data were collected from the Meteorological Information Comprehensive Analysis and Process System (MICAPS) of the Chinese Meteorological Administration so modeling was carry out by HYSPLIT, PSCF and CWT. | Zhao et al. (2020b) | ||
| PM2.5 | 20.5% | Increase air pollution | Tehran Air Quality Control Company data in two years | Compare the concentrations of ambient air PM10 and PM2.5 in Tehran during the SARS-CoV-2 outbreak and over the same period of last year. | (Faridi et al.) | |
| Nearly 50% | Decreased air pollution | The data was collected in two phases, 15 days before the lockdown (i.e., March 10th–March 24th) and 15 days after the lockdown (25th March–April 8th, 2020) Implementation from these targeted cities. The daily air quality data was obtained from the data repository maintained by Central Pollution Control Board (CPCB) under the Ministry of Environment, Forest and Climate Change, India (https://app.cpcbccr.com/AQI_India/). | Assess the changes in air quality parameters during the implementation of the lockdown measures in the four major metropolitan cities of India, viz., Delhi, Mumbai, Kolkata and Chennai for a one-month period (15 days before lockdown and 15 days after the implementation of lockdown). | Bedi et al. (2020) | ||
| 46.5% | Decreased air pollution | Data were obtained from the platform: http://www.aqistudy.cn/. | Air Quality Index, Indicatory Air Pollutants and Impact of COVID-19 Event on the Air Quality near Central China | Xu et al. (2020a) | ||
| PM10 | 15.7% | Increase air pollution | Compared Tehran Air Quality Control Company data | Compare the concentrations of ambient air PM10 and PM2.5 in Tehran during the SARS-CoV-2 outbreak and over the same period of last year. | (Faridi et al.) | |
| Nearly 50% | Decreased air pollution | The data was collected in two phases, 15 days before the lockdown (i.e., March 10th–March 24th) and 15 days after the lockdown (25th March–April 8th, 2020) Implementation from these targeted cities. The daily air quality data was obtained from the data repository maintained by Central Pollution Control Board (CPCB) under the Ministry of Environment, Forest and Climate Change, India (https://app.cpcbccr.com/AQI_India/). | Assess the changes in air quality parameters during the implementation of the lockdown measures in the four major metropolitan cities of India, viz., Delhi, Mumbai, Kolkata and Chennai for a one-month period (15 days before lockdown and 15 days after the implementation of lockdown). | Bedi et al. (2020) | ||
| 48.9% | Decreased air pollution | Data were obtained from the platform: http://www.aqistudy.cn/. | Air Quality Index, Indicatory Air Pollutants and Impact of COVID-19 Event on the Air Quality near Central China | Xu et al. (2020a) | ||
| Water | Water quality | decreased 15.9% of suspended particulate matter | Improved water quality | The suspended particulate matter was measured by the red band (655 nm) | Investigate the effect of COVID-19 lockdown on surface water quality | Yunus et al. (2020) |
| The BOD and COD values reduced by 42.83% and 39.25%, respectively, Faecal Coliform declined by over 40%. | Improved water quality | Water quality data was obtained from the Delhi Pollution Control Committee (DPCC) for nine monitoring stations. The time period primarily examined is from January to April of 2020. The data obtained for 6 January, 13 February and 13 March 2020, was taken to represent the pre-lockdown state while that of 6 April and 14 April 2020, as indicative of the water quality status during the lockdown phase. | Examining the Yamuna's water quality at Delhi during the COVID-19 lockdown period | Patel et al. (2020) | ||
| Clean beaches | The lack of tourists | Decreasing beaches solids and improved seaside water quality | Positive and negative indirect effects of COVID-19 on the environment are presented and compared with the period of time which COVID-19 is not exist. | Presentation Indirect effects of COVID-19 on the environment | Zambrano-Monserrate et al. (2020) | |
| Wastewater | Disinfectants | To prevent the new coronavirus from spreading | The excess of chlorine in the water is harmful for human | Positive and negative indirect effects of COVID-19 on the environment are presented and compared with the period of time which COVID-19 is not exist. | Presentation Indirect effects of COVID-19 on the environment | Zambrano-Monserrate et al. (2020) |
| To prevent the new coronavirus from spreading | Increased disinfectants using | Authors comparison the usage of disinfectants by economic and feasible factors | Investigate the amount and type of disinfection technology of hospital wastes and wastewater | Wang et al. (2020e) | ||
| Wastewater disinfection process | Amount of liquid chlorine, sodium hypochlorite, chlorine dioxide that discharge to sewage | Killing viruses and viruses gens so effluent can discharge for agricultural | Authors comparison the usage of disinfectants by economic and feasible factors | Investigate the amount and type of disinfection technology of hospital wastes and wastewater | Wang et al. (2020e) | |
| Solid waste | Medical waste | – | Medical waste increased | effects of COVID-19 on the hospital waste production was presented by available data | determined the present and future plastic waste, energy and environmental footprints related to COVID-19 | Klemeš et al. (2020) |
| MSW management | – | Decreased MSW management | effects of COVID-19 on the municipal waste production Was investigated using existing data and information from the past | Determine the COVID-19 pandemic on municipal solid waste management | Kulkarni and Anantharama (2020) |