LC
L. Chu
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1
The outbreak of the coronavirus disease 19 (Covid-19) has posed a worldwide threat to human beings, economic activities, and society. Enforced lockdowns for limiting the spread of Covid-19 virus also substantially reduce air pollutant emissions from vehicle traffic, industrial plants, etc. The lockdown restrictions have brought beneficial environmental implications, such as improvement of air quality. Previous studies recorded the reduction of air pollutants during the short-term lockdown in some cities and areas in Indian, China, and the U.S. [1-5]. While some studies argue that the improvement of air quality is not due to lockdown, but season influence or temporary change by coincidence [6]. Therefore, there is not enough evidence that the improvement of air quality is mainly due to reduced human activities. It is beneficial to answer this question by investigating and comparing the air pollution changes within countries with multi waves of pandemic timelines and different lockdown measures.
Our research chose Germany and the Netherlands to investigate the air pollutant changes during their multiple lockdowns. Both of the two countries have gone through several pandemic waves while their imposed strategies are different, ranging from lockdown light, partial lockdown, full lockdown, to curfew in different stages of the pandemic. Our research investigates changes in air quality during their multiple pandemic waves and compares seasonal and monthly changes with the historical records (pre-pandemic) from ground stations to analyze the anomalies. During the pandemic period, the research will compare the disparities of air quality improvement with the several pandemic waves among mega urban agglomerations within the two countries. For the pre-pandemic period, this research analyzes the anomaly in comparison with the historical records with the air quality index.
In particular, we adopt the datasets produced by a space-borne air pollution sensor TROPOMI on the Sentinel-5P satellite, provided in Google Earth Engine data catalog. We process the data and extract information about air pollution, including CO, NO2, SO2, O3, and CH4 for analyzing the air pollutant composition changes during the several pandemic waves. First, the decline values of air pollutant composition will be calculated and analyzed between the pandemic waves to prove the different changes following every wave in main urban areas within the two countries. Second, by aggregating the air pollutant concentrations from the satellite-based air pollution data into monthly, seasonal, and annual data and comparing them with corresponding historical records from ground stations at the same periods of pre-pandemic time, the anomalies will be calculated and analyzed to illustrate the improvement of air quality because of pandemic lockdowns at the country level. The historical record data will be collected from the air quality index based on the ground station measurements. Third, the disparities of air pollutant reduction during the pandemic will be also analyzed between the Netherlands and Germany, considering their different lockdown strategies.
The result will provide strong evidence on the air quality improvement due to the reduction of human activities during lockdown periods and highlight the influence of anthropogenic activities on air pollution. The resulting information will provide information to policymakers concerning emission control and sustainable urban development.
Keywords: Air quality changes, lockdowns, pre-pandemic, Google Earth Engine
Reference:
1.Parida, B.R., et al., Impact of COVID-19 induced lockdown on land surface temperature, aerosol, and urban heat in Europe and North America. Sustainable Cities and Society, 2021. 75: p. 103336.
2.Naqvi, H.R., et al., Improved air quality and associated mortalities in India under COVID-19 lockdown. Environmental Pollution, 2021. 268: p. 115691.
3.Berman, J.D. and K. Ebisu, Changes in U.S. air pollution during the COVID-19 pandemic. Science of The Total Environment, 2020. 739: p. 139864.
4.Sahani, N., S.K. Goswami, and A. Saha, The impact of COVID-19 induced lockdown on the changes of air quality and land surface temperature in Kolkata city, India. Spatial Information Research, 2021. 29(4): p. 519-534.
5.Li, L., et al., Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: An insight into the impact of human activity pattern changes on air pollution variation. Science of The Total Environment, 2020. 732: p. 139282.
6.Etchie, T.O., et al., Season, not lockdown, improved air quality during COVID-19 State of Emergency in Nigeria. Science of The Total Environment, 2021. 768: p. 145187.
...
The outbreak of the coronavirus disease 19 (Covid-19) has posed a worldwide threat to human beings, economic activities, and society. Enforced lockdowns for limiting the spread of Covid-19 virus also substantially reduce air pollutant emissions from vehicle traffic, industrial plants, etc. The lockdown restrictions have brought beneficial environmental implications, such as improvement of air quality. Previous studies recorded the reduction of air pollutants during the short-term lockdown in some cities and areas in Indian, China, and the U.S. [1-5]. While some studies argue that the improvement of air quality is not due to lockdown, but season influence or temporary change by coincidence [6]. Therefore, there is not enough evidence that the improvement of air quality is mainly due to reduced human activities. It is beneficial to answer this question by investigating and comparing the air pollution changes within countries with multi waves of pandemic timelines and different lockdown measures.
Our research chose Germany and the Netherlands to investigate the air pollutant changes during their multiple lockdowns. Both of the two countries have gone through several pandemic waves while their imposed strategies are different, ranging from lockdown light, partial lockdown, full lockdown, to curfew in different stages of the pandemic. Our research investigates changes in air quality during their multiple pandemic waves and compares seasonal and monthly changes with the historical records (pre-pandemic) from ground stations to analyze the anomalies. During the pandemic period, the research will compare the disparities of air quality improvement with the several pandemic waves among mega urban agglomerations within the two countries. For the pre-pandemic period, this research analyzes the anomaly in comparison with the historical records with the air quality index.
In particular, we adopt the datasets produced by a space-borne air pollution sensor TROPOMI on the Sentinel-5P satellite, provided in Google Earth Engine data catalog. We process the data and extract information about air pollution, including CO, NO2, SO2, O3, and CH4 for analyzing the air pollutant composition changes during the several pandemic waves. First, the decline values of air pollutant composition will be calculated and analyzed between the pandemic waves to prove the different changes following every wave in main urban areas within the two countries. Second, by aggregating the air pollutant concentrations from the satellite-based air pollution data into monthly, seasonal, and annual data and comparing them with corresponding historical records from ground stations at the same periods of pre-pandemic time, the anomalies will be calculated and analyzed to illustrate the improvement of air quality because of pandemic lockdowns at the country level. The historical record data will be collected from the air quality index based on the ground station measurements. Third, the disparities of air pollutant reduction during the pandemic will be also analyzed between the Netherlands and Germany, considering their different lockdown strategies.
The result will provide strong evidence on the air quality improvement due to the reduction of human activities during lockdown periods and highlight the influence of anthropogenic activities on air pollution. The resulting information will provide information to policymakers concerning emission control and sustainable urban development.
Keywords: Air quality changes, lockdowns, pre-pandemic, Google Earth Engine
Reference:
1.Parida, B.R., et al., Impact of COVID-19 induced lockdown on land surface temperature, aerosol, and urban heat in Europe and North America. Sustainable Cities and Society, 2021. 75: p. 103336.
2.Naqvi, H.R., et al., Improved air quality and associated mortalities in India under COVID-19 lockdown. Environmental Pollution, 2021. 268: p. 115691.
3.Berman, J.D. and K. Ebisu, Changes in U.S. air pollution during the COVID-19 pandemic. Science of The Total Environment, 2020. 739: p. 139864.
4.Sahani, N., S.K. Goswami, and A. Saha, The impact of COVID-19 induced lockdown on the changes of air quality and land surface temperature in Kolkata city, India. Spatial Information Research, 2021. 29(4): p. 519-534.
5.Li, L., et al., Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: An insight into the impact of human activity pattern changes on air pollution variation. Science of The Total Environment, 2020. 732: p. 139282.
6.Etchie, T.O., et al., Season, not lockdown, improved air quality during COVID-19 State of Emergency in Nigeria. Science of The Total Environment, 2021. 768: p. 145187.
Poster
(2022)
-
Lixia Chu, Jeroen Nelen, Lukas Höller, Hülya Lasch, Dirk Schubert, Carola Hein, Christoph Lofi
Long-term exposure to ambient air pollution is one of the main public health concerns worldwide. Exposure to air pollution is highly related to a range of diseases including respiratory and cardiovascular diseases, such as lung cancers, asthma, diabetes, irregular heartbeat, stroke and obesity [1-3]. The outbreak of the pathogenic agent of coronavirus disease 19 (Covid-19) has led to a large number of deaths worldwide, and previous studies have pointed out how the long-term exposure to air pollution may have an impact on its high death rate [4]. Moreover, the hospitalization rate and infected population numbers are central indicators for lock-down policy-making, indicating whether the local medical system is able to handle the increasing infected population number through its available intensive care facilities. In fact, predicting hospitalization is vital for authorities and policymakers. We hereby hypothesize that high air pollutants concentration leads to a rise in the hospitalization rate under the influence of Covid-19 outbreaks. We attempt to predict such hospitalization numbers for past data by means of a task-specific optimized machine learning model, after we integrate social, economic, cultural, and other environmental features in future with an ongoing project we are conducting. While such a prediction model cannot directly be used for predicting the future development of the pandemic, analysing it still gives valuable insights on the influence of various environmental features had on it in the past.Air pollution is a mixture of a large number of chemical compounds such as CO2, CO, NOx, SO2, O3, heavy metals, and respirable particulate matter (PM2.5 and PM10); the main sources of such pollutants are identified as vehicle traffic, heating systems, and industrial plants [5]. Previous studies focused on the relationships between the variables of pandemic with the air pollutants information. Among all the air pollutants, NO2 and respirable articulate matter are highly related to the pandemic variables [6-8]. In our research, we extract the air pollutants information (CO, NO2, CH4, SO2) from the Sentinel-5P TROPOMI sensor, and integrate it with open-access data on Covid-19 features (mortality, infection rate, intensive care rate, etc). The air pollutant data is processed from the Sentinel-5P data catalog provided in Google Earth Engine. We therefore aim to ascertain the relationships between hospitalization and air pollutants concentration with the incidence of Covid-19. In particular, our ultimate research purpose is to develop a machine learning model to uncover the relationships between a mixture of features derived from air pollutants and Covid-19 related information, at municipality scales in Germany and the Netherlands. The relationships provide important clues on understanding how air pollution may affect on hospitalization rate and other features of Covid-19, through the evidence of potential low hospitalization or low mortality with better air quality. The output will deliver key information regarding public health effects and control of emission in Germany and the Netherlands.
Specifically, on a temporal scale, we aggregated daily Covid-19 data and four air pollutant measures into weekly measures. On a spatial scale, the air pollutants were aggregated based on each municipality in Germany and the Netherlands to match the Covid-19 features. A choice of machine learning models were trained and evaluated on historical data (from March of 2020 to Oct of 2021), using features comprising weekly hospitalizations, death rate, and infected rate, tropospheric NO2 concentration, CO, SO2, CH4 concentrations. In addition, a post-processing analysis using machine-learning explainability methodologies was carried out to mine potential relationships between hospitalization attributes and specific air pollution concentration features. By processing municipalities as separate spatial entities, the results are intended to highlight hospitalization disparities and pollutants’ effect diversities among different geographic areas.
By highlighting the relationships between air pollutant concentrations and incidence of Covid-19 with the hospitalization rate, and illustrating the hospitalization disparities among municipalities, our results provide key information regarding policymaking on urban emission control and public health at municipality level. When integrating other Covid-related features, our models could offer support to policymakers on effective lock-down decisions and health system management.
Keywords: Air pollutant, Covid-19, supervised machine learning models, Google Earth Engine.
Reference
1. Bernstein, J.A., et al., Health effects of air pollution. Journal of allergy and clinical immunology, 2004. 114(5): p. 1116-1123.
2. Brunekreef, B. and S.T. Holgate, Air pollution and health. The lancet, 2002. 360(9341): p. 1233-1242.
3. Strak, M., et al., Long-term exposure to particulate matter, NO2 and the oxidative potential of particulates and diabetes prevalence in a large national health survey. Environment international, 2017. 108: p. 228-236.
4. Ogen, Y., Assessing nitrogen dioxide (NO2) levels as a contributing factor to coronavirus (COVID-19) fatality. Science of The Total Environment, 2020. 726: p. 138605.
5. Vineis, P., et al., Air pollution and risk of lung cancer in a prospective study in Europe. International Journal of Cancer, 2006. 119(1): p. 169-174.
6. Gautam, S., COVID-19: air pollution remains low as people stay at home. Air Quality, Atmosphere & Health, 2020. 13: p. 853-857.
7. Vîrghileanu, M., et al., Nitrogen Dioxide (NO2) Pollution monitoring with Sentinel-5P satellite imagery over Europe during the coronavirus pandemic outbreak. Remote Sensing, 2020. 12(21): p. 3575.
8. Omrani, H., et al., Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France. Data in Brief, 2020. 28: p. 105089.
...
Long-term exposure to ambient air pollution is one of the main public health concerns worldwide. Exposure to air pollution is highly related to a range of diseases including respiratory and cardiovascular diseases, such as lung cancers, asthma, diabetes, irregular heartbeat, stroke and obesity [1-3]. The outbreak of the pathogenic agent of coronavirus disease 19 (Covid-19) has led to a large number of deaths worldwide, and previous studies have pointed out how the long-term exposure to air pollution may have an impact on its high death rate [4]. Moreover, the hospitalization rate and infected population numbers are central indicators for lock-down policy-making, indicating whether the local medical system is able to handle the increasing infected population number through its available intensive care facilities. In fact, predicting hospitalization is vital for authorities and policymakers. We hereby hypothesize that high air pollutants concentration leads to a rise in the hospitalization rate under the influence of Covid-19 outbreaks. We attempt to predict such hospitalization numbers for past data by means of a task-specific optimized machine learning model, after we integrate social, economic, cultural, and other environmental features in future with an ongoing project we are conducting. While such a prediction model cannot directly be used for predicting the future development of the pandemic, analysing it still gives valuable insights on the influence of various environmental features had on it in the past.Air pollution is a mixture of a large number of chemical compounds such as CO2, CO, NOx, SO2, O3, heavy metals, and respirable particulate matter (PM2.5 and PM10); the main sources of such pollutants are identified as vehicle traffic, heating systems, and industrial plants [5]. Previous studies focused on the relationships between the variables of pandemic with the air pollutants information. Among all the air pollutants, NO2 and respirable articulate matter are highly related to the pandemic variables [6-8]. In our research, we extract the air pollutants information (CO, NO2, CH4, SO2) from the Sentinel-5P TROPOMI sensor, and integrate it with open-access data on Covid-19 features (mortality, infection rate, intensive care rate, etc). The air pollutant data is processed from the Sentinel-5P data catalog provided in Google Earth Engine. We therefore aim to ascertain the relationships between hospitalization and air pollutants concentration with the incidence of Covid-19. In particular, our ultimate research purpose is to develop a machine learning model to uncover the relationships between a mixture of features derived from air pollutants and Covid-19 related information, at municipality scales in Germany and the Netherlands. The relationships provide important clues on understanding how air pollution may affect on hospitalization rate and other features of Covid-19, through the evidence of potential low hospitalization or low mortality with better air quality. The output will deliver key information regarding public health effects and control of emission in Germany and the Netherlands.
Specifically, on a temporal scale, we aggregated daily Covid-19 data and four air pollutant measures into weekly measures. On a spatial scale, the air pollutants were aggregated based on each municipality in Germany and the Netherlands to match the Covid-19 features. A choice of machine learning models were trained and evaluated on historical data (from March of 2020 to Oct of 2021), using features comprising weekly hospitalizations, death rate, and infected rate, tropospheric NO2 concentration, CO, SO2, CH4 concentrations. In addition, a post-processing analysis using machine-learning explainability methodologies was carried out to mine potential relationships between hospitalization attributes and specific air pollution concentration features. By processing municipalities as separate spatial entities, the results are intended to highlight hospitalization disparities and pollutants’ effect diversities among different geographic areas.
By highlighting the relationships between air pollutant concentrations and incidence of Covid-19 with the hospitalization rate, and illustrating the hospitalization disparities among municipalities, our results provide key information regarding policymaking on urban emission control and public health at municipality level. When integrating other Covid-related features, our models could offer support to policymakers on effective lock-down decisions and health system management.
Keywords: Air pollutant, Covid-19, supervised machine learning models, Google Earth Engine.
Reference
1. Bernstein, J.A., et al., Health effects of air pollution. Journal of allergy and clinical immunology, 2004. 114(5): p. 1116-1123.
2. Brunekreef, B. and S.T. Holgate, Air pollution and health. The lancet, 2002. 360(9341): p. 1233-1242.
3. Strak, M., et al., Long-term exposure to particulate matter, NO2 and the oxidative potential of particulates and diabetes prevalence in a large national health survey. Environment international, 2017. 108: p. 228-236.
4. Ogen, Y., Assessing nitrogen dioxide (NO2) levels as a contributing factor to coronavirus (COVID-19) fatality. Science of The Total Environment, 2020. 726: p. 138605.
5. Vineis, P., et al., Air pollution and risk of lung cancer in a prospective study in Europe. International Journal of Cancer, 2006. 119(1): p. 169-174.
6. Gautam, S., COVID-19: air pollution remains low as people stay at home. Air Quality, Atmosphere & Health, 2020. 13: p. 853-857.
7. Vîrghileanu, M., et al., Nitrogen Dioxide (NO2) Pollution monitoring with Sentinel-5P satellite imagery over Europe during the coronavirus pandemic outbreak. Remote Sensing, 2020. 12(21): p. 3575.
8. Omrani, H., et al., Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France. Data in Brief, 2020. 28: p. 105089.