Nationwide CoViD-19 lockdown impact on air quality in India

This paper discusses the comparative results of surface and satellite measurements made during the Phase1 (25 March to 14 April), Phase2 (15 April to 3 May) and Phase3 (3 May to 17May) of Covid-19 imposed lockdown periods of 2020 and those of the same locations and periods during 2019 over India. These comparative analyses are performed for Indian states and Tier 1 megacities where economic activities have been severely affected with the nationwide lockdown. The focus is on changes in the surface concentration of sulfur dioxide (SO2), carbon monoxide (CO), PM2.5 and PM10, Ozone (O3), Nitrogen dioxide (NO2) and retrieved columnar NO2 from TROPOMI and Aerosol Optical Depth (AOD) from MODIS satellite. Surface concentrations of PM2.5 were reduced by 30.59%, 31.64% and 37.06%, PM10 by 40.64%, 44.95% and 46.58%, SO2 by 16.73%, 12.13% and 6.71%, columnar NO2 by 46.34%, 45.82% and 39.58% and CO by 45.08%, 41.51% and 60.45% during lockdown periods of Phase1, Phase2 and Phase3 respectively as compared to those of 2019 periods over India. During 1st phase of lockdown, model simulated PM2.5 shows overestimations to those of observed PM2.5 mass concentrations. The model underestimates the PM2.5 to those of without reduction before lockdown and 1st phase of lockdown periods. The reduction in emissions of PM2.5, PM10, CO and columnar NO2 are discussed with the surface transportation mobility maps during the study periods. Reduction in the emissions based on the observed reduction in the surface mobility data, the model showed excellent skills in capturing the observed PM2.5 concentrations. Nevertheless, during the 1st & 3rd phases of lockdown periods AOD reduced by 5 to 40%. Surface O3 was increased by 1.52% and 5.91% during 1st and 3rd Phases of lockdown periods respectively, while decreased by -8.29% during 2nd Phase of lockdown period.

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Observing the silent world under COVID-19 with a comprehensive impact analysis based on human mobility

Scientific Reports ◽

10.1038/s41598-021-94060-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Shaobin Wang ◽

Yun Tong ◽

Yupeng Fan ◽

Haimeng Liu ◽

Jun Wu ◽

...

Keyword(s):

Impact Analysis ◽

Human Mobility ◽

Economic Activities ◽

Underdeveloped Countries ◽

Mobility Data ◽

Long Run ◽

Level Of Economic Development ◽

Human World ◽

Dynamic Relationships ◽

The Impact

AbstractSince spring 2020, the human world seems to be exceptionally silent due to mobility reduction caused by the COVID-19 pandemic. To better measure the real-time decline of human mobility and changes in socio-economic activities in a timely manner, we constructed a silent index (SI) based on Google’s mobility data. We systematically investigated the relations between SI, new COVID-19 cases, government policy, and the level of economic development. Results showed a drastic impact of the COVID-19 pandemic on increasing SI. The impact of COVID-19 on human mobility varied significantly by country and place. Bi-directional dynamic relationships between SI and the new COVID-19 cases were detected, with a lagging period of one to two weeks. The travel restriction and social policies could immediately affect SI in one week; however, could not effectively sustain in the long run. SI may reflect the disturbing impact of disasters or catastrophic events on the activities related to the global or national economy. Underdeveloped countries are more affected by the COVID-19 pandemic.

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Long-Term Exposure to Fine and Coarse Particulate Matter and COVID-19 Incidence and Mortality Rate in Chile during 2020

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18147409 ◽

2021 ◽

Vol 18 (14) ◽

pp. 7409

Author(s):

Macarena Valdés Salgado ◽

Pamela Smith ◽

Mariel Opazo ◽

Nicolás Huneeus

Keyword(s):

Mortality Rate ◽

Incidence Rate ◽

Air Pollutants ◽

Negative Binomial ◽

Transport Model ◽

Surface Concentration ◽

Mortality Rates ◽

Incidence And Mortality ◽

Pm2.5 And Pm10

Background: Several countries have documented the relationship between long-term exposure to air pollutants and epidemiological indicators of the COVID-19 pandemic, such as incidence and mortality. This study aims to explore the association between air pollutants, such as PM2.5 and PM10, and the incidence and mortality rates of COVID-19 during 2020. Methods: The incidence and mortality rates were estimated using the COVID-19 cases and deaths from the Chilean Ministry of Science, and the population size was obtained from the Chilean Institute of Statistics. A chemistry transport model was used to estimate the annual mean surface concentration of PM2.5 and PM10 in a period before the current pandemic. Negative binomial regressions were used to associate the epidemiological information with pollutant concentrations while considering demographic and social confounders. Results: For each microgram per cubic meter, the incidence rate increased by 1.3% regarding PM2.5 and 0.9% regarding PM10. There was no statistically significant relationship between the COVID-19 mortality rate and PM2.5 or PM10. Conclusions: The adjusted regression models showed that the COVID-19 incidence rate was significantly associated with chronic exposure to PM2.5 and PM10, even after adjusting for other variables.

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COVID-19 lockdowns cause global air pollution declines

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2006853117 ◽

2020 ◽

Vol 117 (32) ◽

pp. 18984-18990 ◽

Cited By ~ 16

Author(s):

Zander S. Venter ◽

Kristin Aunan ◽

Sourangsu Chowdhury ◽

Jos Lelieveld

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Fine Particulate Matter ◽

Ground Level ◽

Air Pollutant ◽

Pollutant Emissions ◽

Economic Activities ◽

Long Distance ◽

Mobility Data ◽

Fine Particulate

The lockdown response to coronavirus disease 2019 (COVID-19) has caused an unprecedented reduction in global economic and transport activity. We test the hypothesis that this has reduced tropospheric and ground-level air pollution concentrations, using satellite data and a network of >10,000 air quality stations. After accounting for the effects of meteorological variability, we find declines in the population-weighted concentration of ground-level nitrogen dioxide (NO2: 60% with 95% CI 48 to 72%), and fine particulate matter (PM2.5: 31%; 95% CI: 17 to 45%), with marginal increases in ozone (O3: 4%; 95% CI: −2 to 10%) in 34 countries during lockdown dates up until 15 May. Except for ozone, satellite measurements of the troposphere indicate much smaller reductions, highlighting the spatial variability of pollutant anomalies attributable to complex NOxchemistry and long-distance transport of fine particulate matter with a diameter less than 2.5 µm (PM2.5). By leveraging Google and Apple mobility data, we find empirical evidence for a link between global vehicle transportation declines and the reduction of ambient NO2exposure. While the state of global lockdown is not sustainable, these findings allude to the potential for mitigating public health risk by reducing “business as usual” air pollutant emissions from economic activities. Explore trends here:https://nina.earthengine.app/view/lockdown-pollution.

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Abrupt decline in tropospheric nitrogen dioxide over China after the outbreak of COVID-19

Science Advances ◽

10.1126/sciadv.abc2992 ◽

2020 ◽

Vol 6 (28) ◽

pp. eabc2992 ◽

Cited By ~ 19

Author(s):

Fei Liu ◽

Aaron Page ◽

Sarah A. Strode ◽

Yasuko Yoshida ◽

Sungyeon Choi ◽

...

Keyword(s):

Nitrogen Dioxide ◽

Fuel Consumption ◽

Fossil Fuel ◽

Economic Impacts ◽

Economic Consequences ◽

Economic Activities ◽

Satellite Measurements ◽

Vertical Column ◽

Policy Interventions ◽

Fossil Fuel Consumption

China’s policy interventions to reduce the spread of the coronavirus disease 2019 have environmental and economic impacts. Tropospheric nitrogen dioxide indicates economic activities, as nitrogen dioxide is primarily emitted from fossil fuel consumption. Satellite measurements show a 48% drop in tropospheric nitrogen dioxide vertical column densities from the 20 days averaged before the 2020 Lunar New Year to the 20 days averaged after. This decline is 21 ± 5% larger than that from 2015 to 2019. We relate this reduction to two of the government’s actions: the announcement of the first report in each province and the date of a province’s lockdown. Both actions are associated with nearly the same magnitude of reductions. Our analysis offers insights into the unintended environmental and economic consequences through reduced economic activities.

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Study of sporadic E layers based on GPS radio occultation measurements and digisonde data over the Brazilian region

Annales Geophysicae ◽

10.5194/angeo-36-587-2018 ◽

2018 ◽

Vol 36 (2) ◽

pp. 587-593 ◽

Cited By ~ 7

Author(s):

Laysa C. A. Resende ◽

Christina Arras ◽

Inez S. Batista ◽

Clezio M. Denardini ◽

Thainá O. Bertollotto ◽

...

Keyword(s):

Radio Occultation ◽

Signal To Noise Ratio ◽

Satellite Measurements ◽

Low Latitude ◽

Middle Latitudes ◽

Sporadic E Layers ◽

Sporadic E ◽

Comparative Results ◽

Layer Development ◽

Good Agreement

Abstract. This work presents new results about sporadic E-layers (Es layers) using GPS (global positioning system) radio occultation (RO) measurements obtained from the FORMOSAT-3/COSMIC satellites and digisonde data. The RO profiles are used to study the Es layer occurrence as well as its intensity of the signal-to-noise ratio (SNR) of the 50 Hz GPS L1 signal. The methodology was applied to identify the Es layer on RO measurements over Cachoeira Paulista, a low-latitude station in the Brazilian region, in which the Es layer development is not driven tidal winds only as it is at middle latitudes. The coincident events were analyzed using the RO technique and ionosonde observations during the year 2014 to 2016. We used the electron density obtained using the blanketing frequency parameter (fbEs) and the Es layer height (h'Es) acquired from the ionograms to validate the satellite measurements. The comparative results show that the Es layer characteristics extracted from the RO measurements are in good agreement with the Es layer parameters from the digisonde. Keywords. Ionosphere (ionosphere–atmosphere interactions)

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Spatial Variation in Particulate Emission Resulting from Animal Farming in Poland

Agriculture ◽

10.3390/agriculture11020168 ◽

2021 ◽

Vol 11 (2) ◽

pp. 168

Author(s):

Monika Roman ◽

Kamil Roman ◽

Michał Roman

Keyword(s):

Spatial Variation ◽

Data Bank ◽

Particulate Emissions ◽

Local Data ◽

Particulate Emission ◽

Animal Farming ◽

Tier 1 ◽

Negative Effect ◽

Pm2.5 And Pm10 ◽

Adequate Method

The article presents a spatial variation in particulate emission from animal farming in Poland. In addition, this paper estimates the PM2.5 and PM10 particulate emissions. The data on respective emissions sources have been acquired from the Central Statistics Authority (GUS) of 2019 (Local Data Bank). The emissions of PM2.5 and PM10 particulates were estimated from the structure of the emissions sources covered in the “EEA/EMEP Emission Inventory Guidebook” following the Tier 1 method. The research shows that, in Poland, the biggest share in particulate emission is found for poultry and cattle farming, which are the emissions of 5.5 and 3 m kg of particulates annually all across Poland, respectively. The highest pollution with PM2.5 resulting from animal farming was recorded for the Podlaskie (0.19 kg/ha), Wielkopolskie (0.16 kg/ha), and Mazowieckie (0.14 kg/ha) provinces, whereas the highest pollution with PM10 was recorded for the Wielkopolskie province (0.83 kg/ha). The key sources of particulate emission indicated in the study facilitate adapting the adequate method to reduce the particulate emissions in respective provinces. It is essential, especially due to the negative effect of particulates on human health.

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A TRANSPARENT, OPEN-SOURCE SIRD MODEL FOR COVID19 DEATH PROJECTIONS IN INDIA

10.1101/2020.06.02.20119917 ◽

2020 ◽

Author(s):

Ananye Agarwal ◽

Utkarsh Tyagi

Keyword(s):

Open Source ◽

First Principles ◽

Policy Decisions ◽

Indian States ◽

Social Distancing ◽

Death Toll ◽

Mobility Data ◽

Novel Method

AbstractAs India emerges from the lockdown with ever higher COVID19 case counts and a mounting death toll, reliable projections of case numbers and deaths counts are critical in informing policy decisions. We examine various existing models and their shortcomings. Given the amount of uncertainty surrounding the disease we choose a simple SIRD model with minimal assumptions enabling us to make robust predictions. We employ publicly available mobility data from Google to estimate social distancing covariates which influence how fast the disease spreads. We further present a novel method for estimating the uncertainty in our predictions based on first principles. To demonstrate, we fit our model to three regions (Spain, Italy, NYC) where the peak has passed and obtain predictions for the Indian states of Delhi and Maharashtra where the peak is desperately awaited.

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Using Light-at-Night (LAN) Satellite Data for Identifying Clusters of Economic Activities in Europe

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-1403-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 1403-1409

Author(s):

N. A. Rybnikova ◽

B. A. Portnov

Keyword(s):

Missing Data ◽

Statistical Models ◽

Economic Activities ◽

Satellite Measurements ◽

Light At Night ◽

Multivariate Statistical ◽

Development Policies ◽

Economic Forces ◽

Satellite Sensors ◽

Sufficient Degree

Enterprises organized in clusters are often efficient in stimulating urban development, productivity and profit outflows. Identifying clusters of economic activities (EAs) thus becomes an important step in devising regional development policies, aimed at facilitating regional economic development. However, a major problem with cluster identification stems from limited reporting of specific EAs by individual countries and administrative entities. Even Eurostat, which maintains most advances regional databases, provides data for less than 50% of all regional subdivisions of the 3<sup>rd</sup> tier of the Nomenclature of Territorial Units for Statistics (NUTS3). Such poor reporting impedes identification of EA clusters and economic forces behind them. In this study, we test a possibility that missing data on geographic concentrations of EAs can be reconstructed using Light-at-Night (LAN) satellite measurements, and that such reconstructed data can then be used for the identification of EA clusters. As we hypothesize, LAN, captured by satellite sensors, is characterized by different intensity, depending on its source – production facilities, services, etc., – and this information can be used for EA identification. The study was carried out in three stages. First, using nighttime satellite images, we determined what types of EAs can be identified, with a sufficient degree of accuracy, by LAN they emit. Second, we calculated multivariate statistical models, linking EAs concentrations with LAN intensities and several locational and development attributes of NUTS3 regions in Europe. Next, using the obtained statistical models, we restored missing data on EAs across NUTS3 regions in Europe and identified clusters of EAs, using spatial analysis tools.

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Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data

Scientific Reports ◽

10.1038/s41598-021-94696-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Santi García-Cremades ◽

Juan Morales-García ◽

Rocío Hernández-Sanjaime ◽

Raquel Martínez-España ◽

Andrés Bueno-Crespo ◽

...

Keyword(s):

Health System ◽

Herd Immunity ◽

Multivariate Model ◽

Main Criterion ◽

Economic Activities ◽

Medium Term ◽

Policy Makers ◽

Mobility Data ◽

The Impact

AbstractWe are witnessing the dramatic consequences of the COVID-19 pandemic which, unfortunately, go beyond the impact on the health system. Until herd immunity is achieved with vaccines, the only available mechanisms for controlling the pandemic are quarantines, perimeter closures and social distancing with the aim of reducing mobility. Governments only apply these measures for a reduced period, since they involve the closure of economic activities such as tourism, cultural activities, or nightlife. The main criterion for establishing these measures and planning socioeconomic subsidies is the evolution of infections. However, the collapse of the health system and the unpredictability of human behavior, among others, make it difficult to predict this evolution in the short to medium term. This article evaluates different models for the early prediction of the evolution of the COVID-19 pandemic to create a decision support system for policy-makers. We consider a wide branch of models including artificial neural networks such as LSTM and GRU and statistically based models such as autoregressive (AR) or ARIMA. Moreover, several consensus strategies to ensemble all models into one system are proposed to obtain better results in this uncertain environment. Finally, a multivariate model that includes mobility data provided by Google is proposed to better forecast trend changes in the 14-day CI. A real case study in Spain is evaluated, providing very accurate results for the prediction of 14-day CI in scenarios with and without trend changes, reaching 0.93 $$R^2$$ R 2 , 4.16 RMSE and 1.08 MAE.

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THE IMPACT OF THE LOCKDOWN ON AIR QUALITY IN RESULT OF COVID-19 PANDEMIC OVER HUBEI PROVINCE, CHINA

Environment & Ecosystem science ◽

10.26480/ees.01.2021.15.22 ◽

2021 ◽

Vol 5 (1) ◽

pp. 15-22

Author(s):

Faisal Mumtaz ◽

Yu Tao ◽

Barjeece Bashir ◽

Hamid Faiz ◽

Mariam Kareem ◽

...

Keyword(s):

Air Quality ◽

Anthropogenic Activities ◽

Viral Disease ◽

Hubei Province ◽

Economic Activities ◽

High Emission ◽

Negative Impacts ◽

Novel Coronavirus ◽

Pm2.5 And Pm10 ◽

The Impact

The novel coronavirus (COVID-19) infectious respiratory disease becomes a global pandemic in few weeks from its start in December 2019 to early 2020. Various countries across the world including China went to lockdown and several caution were implemented to reduce the further spread of this infectious disease. Wuhan (China) was the first city to impose the lockdown for controlling the impact of COVID-19. The lockdown unexpectedly gives the scientific community a chance to investigate the influence of the human activity on air pollution in real world scenario. The present study attempted to investigate the impact of lockdown during the ongoing viral disease on the changes of fine particulate matters and some unhealthy gases i.e. PM2.5, PM10, SO2, CO, O3, AQI and NO2 over Hubei province of China, by using ground station data and TROPOMI satellite data. The air pollutants were compared as, (i) pre COVID-19 period (i.e. October-December 2019), (ii) throughout the lockdown in Hubei province (i.e. January 2020-March 2020) and Post lockdown duration (i.e. April 2020-June 2020). Results clearly showed that air quality was not secured due to high emission of CO, SO2, NO2, O3, PM2.5, and PM10 on Pre COVID-19 times, but under the lockdown continuously decrease in NO2 from (54 mg/cm3 to 26 mg/cm3), SO2 (10.5 mg/cm3 to 7.77 mg/cm3) PM2.5 (49.22 mg/cm3 to 44.34 mg/cm3), PM10 concentrations (80.83 mg/cm3 to 57.04 mg/cm3) and AQI (72.95 mg/cm3 to 49.64 mg/cm3) has been observed. Because lockdown shuts all anthropogenic activities like industrial work, traffic vehicles and various socio-economic activities, which developed a healthy change on air quality. Emission of unhealthy gases and particulates were quite clear during the lockdown but again increase after finishing the lockdown period. However, we don’t support the lockdown as a measure for the betterment of air quality as this has severely posed negative impacts on the socio-economic processes and progress, but changes in human behavior of using industries and vehicles can help us to improve the air quality.

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