From 44e3dad36bf76209abfd9b6736f6904fa8a5d9bb Mon Sep 17 00:00:00 2001 From: AparicioSF <33723271+AparicioSF@users.noreply.github.com> Date: Wed, 11 Dec 2024 10:18:49 +0100 Subject: [PATCH] Update eodashMarkdown_HUNGA_ERUPTION_2.md (#2702) * Update eodashMarkdown_HUNGA_ERUPTION_2.md * Update eodashMarkdown_EXTREME_POLLUTION_2.md updates based on review of authors * Update eodashMarkdown_EXTREME_POLLUTION_2.md * Update eodashMarkdown_EXTREME_POLLUTION_2.md * Update stories.json Correct name of author of Extreme Pollution story * Update eodashMarkdown_EXTREME_POLLUTION_2.md More updates to the story - inclusion of an animation requested by the authors. * Update eodashMarkdown_HUNGA_ERUPTION_2.md Updated with ALOS-PALSAR2 images where you can actually see the disappearance of the the island - as requested by the authros. * Update eodashMarkdown_EXTREME_POLLUTION_2.md Add video animation as requested by the authors. * Update eodashMarkdown_EXTREME_POLLUTION_2.md Removing placeholder map --- .../eodashMarkdown_EXTREME_POLLUTION_2.md | 154 ++++++++++-------- .../eodashMarkdown_HUNGA_ERUPTION_2.md | 50 +++--- app/src/config/stories.json | 2 +- 3 files changed, 117 insertions(+), 89 deletions(-) diff --git a/app/public/data/storytelling-md/eodashMarkdown_EXTREME_POLLUTION_2.md b/app/public/data/storytelling-md/eodashMarkdown_EXTREME_POLLUTION_2.md index d47dc4040b..faa3400664 100644 --- a/app/public/data/storytelling-md/eodashMarkdown_EXTREME_POLLUTION_2.md +++ b/app/public/data/storytelling-md/eodashMarkdown_EXTREME_POLLUTION_2.md @@ -2,49 +2,64 @@ ###### *This story is based on results from the [3rd Earth System Science Challenge]( https://sciencehub.esa.int/2024/05/09/3rd-earth-system-science-challenge/) organised and hosted by ESA's ESRIN Science Hub in February 2024* -The research presented in this story was developed in the frame of the Earth System Science Challenge organised by the European Space Agency and hosted at ESRIN’s Science Hub in February 2024. The scope of this challenge was to identify the days on which severe air pollution episodes occured in northern India and Pakistan, using the percentile technique applied on time series of carbon monoxide (CO) concentrations measured by Copernicus Sentinel-5p TROPOMI. The method was implemented on the [DeepESDL platform](https://earthsystemdatalab.net) by a team of PhD students from Sorbonne Université. The data and code are made openly available. +The research presented in this story was developed in the frame of the [Earth System Science Challenge](https://sciencehub.esa.int/2024/05/09/3rd-earth-system-science-challenge/) organized by the European Space Agency (ESA) and hosted at ESRIN’s Science Hub in February 2024. The scope of this challenge was to identify the days on which severe air pollution episodes occurred in northern India and Pakistan, using the percentile technique applied on time series of carbon monoxide (CO) concentrations measured by Copernicus Sentinel-5P TROPOMI. The method was implemented on the [DeepESDL platform](https://earthsystemdatalab.net) by a team of PhD students from Sorbonne Université. The data and code are made openly available. ## Air Pollution and Health -Air pollution is a real concern for human health, as poor air quality may lead to breathing difficulties, cardiovascular disease, or cancer. According to the World Health Organization (WHO), "outdoor air pollution is estimated to have caused 4.2 million premature deaths worldwide in 2019". "Some 89% of those premature deaths occurred in low- and middle-income countries, and the greatest number in the WHO South-East Asia and Western Pacific Regions." (WHO 2024) +Air pollution is a real concern for human health, as poor air quality may lead to breathing difficulties, cardiovascular disease, or cancer. According to the World Health Organization (WHO), "outdoor air pollution is estimated to have caused 4.2 million premature deaths worldwide in 2019". "Some 89% of those premature deaths occurred in low- and middle-income countries, and the greatest number in the WHO South-East Asia and Western Pacific Regions." [WHO, 2024] -The region along the Himalayas in Northern India and Pakistan, also know as the Indo-Gangetic Plain (IGP), is a highly populated region of intense agricultural and industrial activities. The region frequently experiences severe air pollution episodes, putting the local population at risk, as documented and reported by the national and international press (Le Monde 2023), (India Today 2022). Understanding the formation of pollution episodes in this region is vital to help the government establish laws limiting pollutant emissions, and thus enable the local population to live in a healthy environment. +The region along the Himalayas, encompassing Pakistan, Northern India and Bangladesh, also known as the Indo-Gangetic Plain (IGP), is a highly populated region of intense agricultural and industrial activities. The IGP frequently experiences severe air pollution episodes, putting the local population at risk, as documented and reported by the national and international press [India Today, 2022; Le Monde, 2023]. Understanding the formation of air pollution episodes in this region is vital to help the government establish laws mitigating pollutant emissions, and thus enable the local population to live in a healthy environment. -The following map shows the population density for 2020, provided by the Center for International Earth Science Information Network - CIESIN - Columbia University. Darker shades indicate higher density, with values ranging from 1-10.000 persons/km2. +The following map shows the population density for 2020, provided by the Center for International Earth Science Information Network - CIESIN - Columbia University. Darker shades indicate higher density, with values ranging from 1-10.000 persons +/km2. ## ## -## Earth Observations -Agencies such as ESA, NASA and JAXA have Earth-observing satellites whose instruments observe air pollutants around the world. Missions such as NASA's Aura Satellite carrying the [Ozone Monitoring Instrument (OMI)](https://www.earthdata.nasa.gov/learn/find-data/near-real-time/omi) or ESA's Sentinel-5p carrying the [TROPOspheric Monitoring Instrument (TROPOMI)](https://www.tropomi.eu/) provide essential data that is used to study the impact of air pollution on human health and agriculture. +## Earth Observations for air quality monitoring +Agencies such as ESA, NASA and JAXA have Earth observation satellites with instruments dedicated for monitoring atmospheric chemistry. Satellite missions such as NASA's Aura, carrying the [Ozone Monitoring Instrument (OMI)](https://www.earthdata.nasa.gov/learn/find-data/near-real-time/omi), or ESA's Sentinel-5P, equipped of the [TROPOspheric Monitoring Instrument (TROPOMI)](https://www.tropomi.eu/) provide essential data used to study the impact of air pollution on human health and agriculture. -Measurable air pollutants include: +Space instruments can numerous measure gaseous pollutants, such as : -* **Particulate Matter (PM)**: Unhealthy particulate matter are suspended microscopic liquid or solid particles (such as dust or black carbon) in the atmosphere, with a diameter of less than 10 micrometers (able to pass through the throat and nose to enter the lungs). (ECMWF Air Pollution) -* **Nitrogen Dioxide (NO2)**: NO2 is produced by natural and anthropogenic sources. Globally, the main source of NO2 is fossil fuel combustion. Thus, coal- and gas-fired power plants and automobiles are the main sources.(NASA Air Pollution) -* **Carbon Monoxide (CO)**: CO is a colorless, odorless gas that can be harmful when inhaled in large amounts. CO is released when something is burned. The greatest sources of CO to outdoor air are vehicles or machinery that burn fossil fuels (EPA 2024) -* **Ozone (O3)**: Breathing ground-level ozone can also result in a number of health effects. O3 also has a negative impact on plants, reducing crop yields. (EPA) -* **Sulfur Dioxide (So2)**: Sulfur dioxide (SO2) is a colorless, reactive air pollutant with a strong odor and is unhealthy to breathe. The main sources of SO2 emissions are from fossil fuel combustion and natural volcanic activity. +* **Nitrogen Dioxide (NO2 )** : NO2 is emitted during combustion processes. While it can be produced naturally in lesser quantities, by lightning for instance, its main sources are mainly anthropogenic, such as gas-fired power plants and automobiles [NASA Air Pollution]. +* **Carbon Monoxide (CO)** : CO is a colorless and odorless gas that can be harmful when inhaled in large quantities. It is released during incomplete combustion processes, with the largest sources in outdoor air being any anthropogenic activities that burn fossil fuel (thermal vehicles, industries, coal mines …) as well as biomass burning [UCAR Air Quality, CO]. +* **Ozone (O3)** : At Earth’ surface, O3 is a short-time pollutant that has a negative impact on human health and vegetation. It does not have direct emission sources : it is formed by the interaction of sunlight with volatile organic compounds (VOCs), including methane, and nitrogen oxides (NOx), such as NO₂ [UCAR Air Quality, O3]. +* **Sulfur Dioxide (SO₂)** : SO₂ is a reactive pollutant with a strong odor that affects the respiratory system, impairs lung function, and causes eye irritation. It is primarily emitted from fossil fuel combustion at the surface level [UCAR Air Quality, sulfur oxides]. + +Some of these gaseous pollutants (NO₂, SO₂) are precursors of Particulate Matter (PM). PM are aerosols, which are defined as suspended microscopic liquid or solid particles (such as dust or black carbon) in the atmosphere with a diameter of less than 10 micrometers. The smaller the size of particulate matter, the more hazardous it is to human health, as it can more easily become lodged in the lungs, for instance [US EPA]. Instruments like MODIS, onboard NASA’s Terra and Aqua satellites and TROPOMI can measure the optical properties of aerosols, from which we can derive information about their size and general composition (Aerosols Optical Depth and Aerosol Index). + + +## Method and datasets +In this challenge, the authors aimed at determining the number of extreme CO pollution episodes for 2023 in 3 major cities in the IGP region (Lahore in Pakistan, New Delhi and Lucknow in India). +CO is a trace gas that is often studied in the field of air quality, as it is a good tracer of pollution due to its long lifespan (from a few weeks to a few months, depending on the season and latitude), which enables it to be transported over long distances. CO is mainly emitted during incomplete combustion processes (anthropogenic activities such as heating, cooking, industrial activities and vegetation fires). +CO concentrations are retrieved from TROPOMI (Sentinel-5P) measurements. With its swath width of ~2600 km and a global coverage of 1 day, TROPOMI is a valuable instrument for daily monitoring of CO worldwide [TROPOMI.eu]. -In this challenge, the authors aimed at studying the number of extreme air pollution episodes for the year of 2023 for one pollutant in 3 major cities of the IGP region in India. The pollutant studied was carbon monoxide (CO) measured by TROPOMI. The TROPOMI instrument onboard of Copernicus Sentinel-5P. has a global coverage of 1 day, which can help us to study the daily variation of CO anywhere on the globe. (TROPOMI.eu).
TROPOMI Instrument. Source: ESA
-## Data and Method -The study focuses on 3 densely populated cities in the region of interest: Lahore in Pakistan, New Delhi and Lucknow in India. The analysis was done for 2023, but the same study can be carried out for earlier years. +To determine the number of extreme pollution episodes experienced by the IGP during 2023, the team used the percentile method, which was applied to the daily time series of CO concentrations, which were calculated within a rectangular area of 0.8° in both longitude and latitude (~80 km x 90 km) around each of the three cities of interest. +The percentile method is a technique used to identify outliers or extreme values by setting a specific percentile threshold. The process involves several steps: first, defining the percentile thresholds (such as 90%, 95%, and 99%); then calculating the corresponding threshold values; and finally, identifying any values that exceed these thresholds as outliers or extreme values. + +After identifying the days of extreme CO pollution, the study team selected a pollution event that occurred at the very end of October 2023 and evolved during November. + +
+ + +CO data on EO Dashboard +
-Carbon Monoxide (CO) is a trace gas, naturally present in the atmosphere and mainly emitted by incomplete combustion processes (anthropogenic activities such as heating, cooking, industrial activities or vegetation fires). This gas is often studied in the field of air quality, as it is a good tracer of pollution due to its long lifespan (from a few weeks to a few months, depending on the season and latitude), which enables it to be transported over long distances. -After identifying the days on which there is a pollution episode, the study team choose one event to explain its formation and evolution over time, using: +They tried to understand the formation of this extreme air pollution episode and to analyze its evolution over time. Several datasets were used for this study, such as : +* **Active Fires from Visible Infrared Imaging Radiometer Suite (VIIRS)**, embarked on NASA’s Suomi-NPP satellite. One of VIIRS's main missions is to monitor fires from space. Indeed, With its spatial resolution of 375m and swath width of ~3000 km, VIIRS can detect small fires worldwide. Suomi-NPP passes twice a day at 01:30 a.m. (nighttime) and 01:30 p.m. (daytime). Daytime and nighttime VIIRS data were used here to locate the fires near in the IGP region, from October to November 2023. -1. **Data that identify sources of CO: Active Fires from VIIRS-SNPP**. -The animation below shows the location of fires detected by Visible Infrared Imaging Radiometer Suite, or VIIRS during the month of October and November 2023 in the IGP. The VIIRS instrument flies on the Joint Polar Satellite System’s Suomi-NPP and NOAA-20 polar-orbiting satellites (NASA VIIRS). This imager has a spatial resolution of 375m and a swath width of 3000, which helps to monitor small fires around the world. This study used day and night time data, which allowed to show the location of fires detected by VIIRS during the month of October and November 2023 in the IGP. The number of fires increased over this period, which could explain the rise of CO concentrations. +From the animation below, VIIRS detected numerous fire spots in an area located close to Lahore and New Delhi. We note that the number of fires detected increased at the very end of October 2023, explaining the observed rise of CO concentrations in the IGP region. -We see that the number of fires increases over this period, explaining the observed rise of CO. The VIIRS Active Fires data has some limitations: it give only a hint on the fire location and not their lifetime and their size (i.e., a small temporary fire is counted in the same way as a large fire lasting over time), and is based on optical data which is affected by clouds.
-Explore [MODIS active fire data on EO Dashboard over the IGP]( https://www.eodashboard.org/explore?indicator=Modis_SNPP_2023&x=8415682.56522&y=3510441.28382&z=4.93607). -2. **Meteorological data horizontal winds at 100m from the ERA5 reanalysis** +Indeed, local farmers have been known to burn agricultural waste at this time of year, as an economical and quicker way of getting rid of waste and thus preparing for the next crop [Sembhi et al. 2020]. +However, the VIIRS Active Fires data has some limitations: it only gives us a hint on the fire location and not their lifetime and their size (i.e., a small temporary fire is counted in the same way as a large fire lasting over time). Moreover, because of VIIRS’ optical properties, fires can be missed in the presence of clouds or smoke. -The following map shows the horizontal wind from ERA5 hourly data provided by the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Hersbach 2023). Values range from [-4, 4] m/s. Blue shades indicate lower values. -## -## +* **Horizontal wind speed and direction at 100m from ERA5 reanalysis, provided by the Copernicus Climate Change Service (C3S)**. Analyzing surface winds is essential for understanding CO transport during the pollution episode. For this study, daily averages of the horizontal winds were calculated in the Pakistan timezone (UTC+05). +
+ + +Evolution of horizontal winds at 100m from mid-October to mid-November 2023 +
+ + + +In this animation, we can see that winds are weak during this period. We note a change in wind direction which occurred around October 30: winds shifted from south-easterly to westerly. Later, the winds shifted back to south-easterly (around November 4th). ## For each city a rectangle of -0.4 to 0.4° of longitude and -0.4 to 0.4° latitude was generated (from the given coordinates of the chosen city in latitude and longitude) which corresponds to -39.8 to 39.8km in longitude and to -44.5 to 44.5km in latitude. Then the computed time series of each day is the average value of all CO concentration values measured by TROPOMI within that rectangle (with a resolution of 0.025°). The percentile method is a strategy utilized to recognize outliers or extreme values based upon a defined percent limit. It involves calculating the threshold values based on percentiles and the steps are to first determine the percentage threshold (in this case 90%, 95%, and 99%), then calculate the threshold values, and then identify outliers and extreme values above this threshold. -### Daily CO variation in 2023 +### Evolution of daily CO concentrations in 2023 in the IGP region -The following figures display the time series of daily concentration of CO in Lahore, New Delhi and Lucknow. In each of the figures, the yellow line indicates the 90% percentile extreme, the blue one, the 95% percentile extreme, and the green one, the 99% percentile extreme. The points above these lines are the extreme events resulted form the percentile technique. What can be noticed here is that the extreme events seem to happen at the same time for the 3 cities especially for 99% percentile extremes (at the end of October-November). +The following figures show the daily time series of CO concentrations in Lahore, New Delhi, and Lucknow. In each figure, the yellow line represents the 90th percentile threshold, the blue line indicates the 95th percentile threshold, and the green, the 99th percentile threshold. Any data points above these lines are considered extreme events, as identified by the percentile method. Notably, these extreme events appear to occur simultaneously across all three cities, particularly for the 99th percentile threshold, during the period from late October to November. +We can also observe that the number of days with extreme CO pollution was nearly the same for all three cities, indicating a potential common source of CO in these areas. It is important to note that the total number of days in 2023 for which we have a CO concentration value is not 365, as some measurements from TROPOMI were unavailable due to the presence of clouds. -Furthermore, when these extreme episodes were quantified, the number of days which are considered extremes were almost the same for the 3 cities so there might be a correlation between the extreme pollution events in the 3 cities. We must note that the total number of days in 2023 is not 365 since for some days we do not have measurements because of clouds or other factors. -| City | Total Number of Days in 2023 | Number of Days ≥ 90% | Number of Days ≥ 95% | Number of Days ≥ 99% | +| City | Number of measurement days | Number of Days ≥ 90% | Number of Days ≥ 95% | Number of Days ≥ 99% | |------------|-----------------------------|----------------------|----------------------|----------------------| | Lahore | 341 | 34 | 17 | 4 | | New Delhi | 341 | 34 | 17 | 4 | @@ -80,12 +104,12 @@ Furthermore, when these extreme episodes were quantified, the number of days whi The table indicates the number of days which can be considered as extremes (for 90%, 95%, and 99%). We notice that these number of days are almost the same for the 3 cities, indicating a potential correlation between the extreme pollution events in the 3 cities. Note that the total number of days in 2023 is not 365 since for some days we do not have measurements because of clouds or other factors. -## CO Variation +## Daily CO concentrations ### -#### Lahore -* **Map**: CO concentration measured on 2023-11-09 [[view full time series](https://www.eodashboard.org/explore?indicator=N1_CO&x=0&y=-1224599.44035&z=2.35425)] -* **Chart**: CO daily variation for 2023 +#### Evolution of daily CO concentrations in Lahore, 2023 +* **Map**: Daily timeseries of CO concentration in Lahore in 2023 [[view full time series](https://www.eodashboard.org/explore?indicator=N1_CO&x=0&y=-1224599.44035&z=2.35425)] +* **Chart**: Daily timeseries of CO concentration in Lahore in 2023
@@ -93,17 +117,16 @@ Furthermore, when these extreme episodes were quantified, the number of days whi CO daily variation in 2023 for Lahore
-A first sharp increase in carbon monoxide concentration can be observed at the end of October. The emissions seem to be spontaneous, suggesting they can be linked to unusual antropogenic activities or vegetation fires. - -A second peak in CO was detected in Lahore on 11/07. This can be explained by the fact that wind speed was very low in the city and the region of the fires: CO then accumulated again, further increasing the CO concentration, which was already high due to the accumulation around 10/30; ​ +A sharp increase in CO concentrations was first observed at the end of October 2023 in Lahore. The sudden rise in CO concentrations around October 30th is explained by agricultural waste burning practiced in the region which is very close to Lahore, as we can see on the animation of Active Fires data from VIIRS. Furthermore, weak winds at the very end of October (see animation of wind speed and direction from ERA5 reanalysis) and the high mountainous terrain surrounding the IGP (the Hindu Kush, to the west and the Himalayas, to the North) helped to contain CO in the region, thus explaining the high CO concentrations detected by TROPOMI. +A second peak in CO concentrations was detected in Lahore on November 7th. This is attributed to the low wind speeds in the city and the surrounding fire-affected regions, causing CO to accumulate further. This accumulation led to a rise in CO levels, which were already elevated due to the earlier buildup around October 30th; ​ ### -#### New Delhi -* **Map**: CO concentration measured on 2023-11-08 [[view full time series](https://www.eodashboard.org/explore?indicator=N1_CO&x=0&y=-1224599.44035&z=2.35425)] -* **Chart**: CO daily variation for 2023 +#### Evolution of daily CO concentrations in New Delhi, 2023 +* **Map**: Daily timeseries of CO concentration in New Delhi in 2023 [[view full time series](https://www.eodashboard.org/explore?indicator=N1_CO&x=0&y=-1224599.44035&z=2.35425)] +* **Chart**: Daily timeseries of CO concentration in New Delhi in 2023 -Similar to Lahore, the peak observed in New Delhi on 11/04 indicates spontaneous emissions, potentially from fires. Once CO had accumulated, the wind generally blew towards the southeast from where the fires were detected. Being the closest city to the fires (in the southeast direction), New Delhi experiences the first peak in CO concentration. +The CO peak observed in New Delhi on November 4th is explained by the sudden CO emissions from fires set to burn crop residues, but also by the winds. Indeed, once the CO accumulated around October 30th due to weak winds in IGP, high levels of CO were then transported along the Himalayas as winds generally blew southeastward from the area where the fires were detected by VIIRS, around November 3rd. As the closest city to the fires in this direction, New Delhi is the first city in India (among the cities of interest) to experience the extreme CO pollution in 2023.
@@ -111,11 +134,11 @@ Similar to Lahore, the peak observed in New Delhi on 11/04 indicates spontaneous
### -#### Lucknow -* **Map**: CO concentration measured on 2023-11-10 [[view full time series](https://www.eodashboard.org/explore?indicator=N1_CO&x=0&y=-1224599.44035&z=2.35425)] -* **Chart**: CO daily variation for 2023 +#### Evolution of daily CO concentrations in Lucknow, 2023 +* **Map**: Daily timeseries of CO concentration in Lucknow in 2023 [[view full time series](https://www.eodashboard.org/explore?indicator=N1_CO&x=0&y=-1224599.44035&z=2.35425)] +* **Chart**: : Daily timeseries of CO concentration in Lucknow in 2023 -The last peak in CO was detected in Lucknow. This city is far from the region where agricultural waste was burned, yet it is impacted by these episodes of extreme pollution. So, the presence of fires may not be the only contributor to this pollution event, another parameter must be taken into account, especially local meteorology. +Even though Lucknow is the farthest city from the region where agricultural waste is burned, it also experienced high levels of CO at the beginning of November 2023. This is mainly explained by the wind direction and speed. From the end of October to the beginning of November, wind speed was low, which favored the buildup of CO in the region. When the winds blew again towards the southeast, the accumulated CO was transported to the east of the IGP, which explains the high CO concentrations detected in Lucknow around November 10th.
@@ -139,13 +162,13 @@ In general, wind speed was low during this period. However, it was also noticed -## Conclusions -* Severe air pollution episodes can be monitored with satellite observations. -* Using multiple variables (fires and winds) can help us to describe the formation and the evolution of a severe air pollution episode. +## Conclusion +* This study demonstrates the potential of using data from satellite instruments and reanalysis combined to understand the formation and progression of severe air pollution episodes in the Indo-Gangetic Plain. Indeed, using multiple datasets, such as CO concentration, fire location and wind speed and direction can help us to describe the formation and the evolution of a severe air pollution episode. * IGP experienced extreme CO pollution episodes in October/November 2023 due to the burning of agricultural waste by farmers but also due to stable meteorological conditions (low wind speed), favoring its accumulation along the Himalayas. * Limits and perspectives of the study: * Explaining pollution episodes can be complex as they depend on multiple factors (atmospheric chemistry, local meteorology, and local emissions). Therefore, it is necessary to work with various instruments, including those from space. However, space instruments may struggle to detect gases due to clouds or smoke emitted by fires, for example. As a result, there were many days in the time series where there were no CO concentrations. For this reason, pollution episodes for certain days may have been missed. To address this issue, it is important to work with local measurements from air quality stations. - * The IGP is a highly polluted region characterized by mixtures of gaseous pollutants and aerosols (such as fine particles, known as PM2.5). CO is one among many pollutants emitted during vegetation fires. In the case of the November 2023 air pollution episode, it was interesting to study this molecule, as the cause of the November 2023 smog was agricultural waste burning. However, for other pollution episodes (such as those occurring in summer), it might be more interesting to study tropospheric ozone, as it is predominantly produced under strong sunlight conditions. Additionally, studying ammonia, a precursor of fine particles, is also important because hot weather leads to high ammonia emissions, thereby promoting smog formation. Furthermore, TROPOMI also measures NO2 and SO2, two other precursor gases of PM2.5, offering the opportunity to track their evolution throughout the year to determine days with smog events. + * The IGP is a highly polluted region characterized by mixtures of gaseous pollutants and aerosols (such as fine particles, known as PM2.5). CO is one among many pollutants emitted during vegetation fires. In the case of the November 2023 air pollution episode, it was interesting to study this molecule, as the cause of this smog was agricultural waste burning. However, for other pollution episodes (such as those occurring in summer), it might be more interesting to study tropospheric ozone, as it is predominantly produced under strong sunlight conditions. + ## Open Science @@ -163,23 +186,24 @@ The analysis was carried out on the [ESA DeepESDL (Deep Earth System Data Lab)]( ### References 1. World Health Organization. (n.d.). Ambient (outdoor) air quality and health. [WHO 2024](https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health). Accessed February 29, 2024. -2. Le Monde, Carole Dieterich, 2023, November 18 [New Delhi's air pollution crisis is poisoning millions of children every winter](https://www.lemonde.fr/en/environment/article/2023/11/18/new-delhi-s-air-pollution-crisis-is-poisoning-millions-of-children-every-winter_6265386_114.html) -3. India Today, Kumar Kunal. 2022, June 3, [Delhi's new normal: Air pollution not just in winter. India Today]( https://www.indiatoday.in/diu/story/delhi-new-normal-air-pollution-not-just-in-winter-1958072-2022-06-03) -4. Sembhi et al. 2020 Environ. Res. Lett. 15 104067, [DOI 10.1088/1748-9326/aba714](https://iopscience.iop.org/article/10.1088/1748-9326/aba714) -5. V.P. Kanawade, A.K. Srivastava, K. Ram, E. Asmi, V. Vakkari, V.K. Soni, V. Varaprasad, C. Sarangi, What caused severe air pollution episode of November 2016 in New Delhi?Atmospheric Environment, Volume 222, -2020, 117125, ISSN 1352-2310, [https://doi.org/10.1016/j.atmosenv.2019.117125](https://doi.org/10.1016/j.atmosenv.2019.117125). -(https://www.sciencedirect.com/science/article/pii/S1352231019307642) -6. Li, Ainong et al. “A geo-spatial database about the eco-environment and its key issues in South Asia.” Big Earth Data 2 (2018): 298 - 319, [https://doi.org/10.1080/20964471.2018.1548053](https://doi.org/10.1080/20964471.2018.1548053) -7. [NASA Air Pollution](https://airquality.gsfc.nasa.gov/) -8. [EPA O3 2024](https://www.epa.gov/ozone-pollution-and-your-patients-health/health-effects-ozone-general-population) -9. [EPA CO 2024](https://www.epa.gov/co-pollution/basic-information-about-carbon-monoxide-co-outdoor-air-pollution) -10. [ECMWF Air Pollution](https://stories.ecmwf.int/tracking-air-pollution/index.html) -11. [Copernicus Sentinel-5p]( https://sentinel.esa.int/web/sentinel/copernicus/sentinel-5p) -12. [TROPOMI.eu](https://www.tropomi.eu/data-products/carbon-monoxide) -13. [NASA VIIRS](https://www.earthdata.nasa.gov/sensors/viirs) -14. [MODIS Fire Detections](https://radiantearth.github.io/stac-browser/#/external/eurodatacube.github.io/eodash-catalog/trilateral/Modis_SNPP_2023/Modis_SNPP_2023/collection.json) -15. Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (Accessed on 02-M07-2024) -16. Story Cover image: NASA image courtesy Jeff Schmaltz, MODIS Rapid Response Team. Caption: NASA/Goddard, Lynn, Jenner, source: [https://www.eurekalert.org/multimedia/575396](https://www.eurekalert.org/multimedia/575396) +2. India Today, Kumar Kunal. 2022, June 3, [Delhi's new normal: Air pollution not just in winter. India Today]( https://www.indiatoday.in/diu/story/delhi-new-normal-air-pollution-not-just-in-winter-1958072-2022-06-03) +3. Le Monde, Carole Dieterich, 2023, November 18 [New Delhi's air pollution crisis is poisoning millions of children every winter](https://www.lemonde.fr/en/environment/article/2023/11/18/new-delhi-s-air-pollution-crisis-is-poisoning-millions-of-children-every-winter_6265386_114.html) +4. [NASA Air Pollution](https://airquality.gsfc.nasa.gov/) +5. [UCAR Air Quality (CO)](https://scied.ucar.edu/learning-zone/air-quality/carbon-monoxide) +6. [UCAR Air Quality (O3)](https://scied.ucar.edu/learning-zone/air-quality/ozone) +7. [UCAR Air Quality (sulfur oxides)](https://scied.ucar.edu/learning-zone/air-quality/sulfur-oxides) +8. [US EPA Particulate Matter](https://www.epa.gov/pm-pollution/particulate-matter-pm-basics) +9. [Copernicus Sentinel-5p](https://sentinel.esa.int/web/sentinel/copernicus/sentinel-5p) +10. [TROPOMI CO Total Column](https://www.tropomi.eu/data-products/carbon-monoxide) +11. [NASA VIIRS](https://www.earthdata.nasa.gov/data/instruments/viirs) +12. MODIS Fire Detections +13. Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (Accessed on 02-M07-2024) +14. Story Cover image: NASA image courtesy Jeff Schmaltz, MODIS Rapid Response Team. Caption: NASA/Goddard, Lynn, Jenner, source: https://www.eurekalert.org/multimedia/575396 +Sources used for the analysis of the pollution episode +15. Sembhi et al. 2020 Environ. Res. Lett. 15 104067, DOI 10.1088/1748-9326/aba714. [Source](https://iopscience.iop.org/article/10.1088/1748-9326/aba714) +16. V.P. Kanawade, A.K. Srivastava, K. Ram, E. Asmi, V. Vakkari, V.K. Soni, V. Varaprasad, C. Sarangi, What caused severe air pollution episode of November 2016 in New Delhi?Atmospheric Environment, Volume 222, 2020, 117125, ISSN 1352-2310, https://doi.org/10.1016/j.atmosenv.2019.117125. [Source](https://www.sciencedirect.com/science/article/pii/S1352231019307642) +17. Li, Ainong et al. “A geo-spatial database about the eco-environment and its key issues in South Asia.” Big Earth Data 2 (2018): 298 - 319, https://doi.org/10.1080/20964471.2018.1548053. [Source](https://doi.org/10.1080/20964471.2018.1548053) + diff --git a/app/public/data/storytelling-md/eodashMarkdown_HUNGA_ERUPTION_2.md b/app/public/data/storytelling-md/eodashMarkdown_HUNGA_ERUPTION_2.md index 4fed9f5630..8d90dc460f 100644 --- a/app/public/data/storytelling-md/eodashMarkdown_HUNGA_ERUPTION_2.md +++ b/app/public/data/storytelling-md/eodashMarkdown_HUNGA_ERUPTION_2.md @@ -9,25 +9,25 @@ The research presented in this story was developed in the frame of the Earth Sys ### #### Volcanoes and Climate -Extreme events such as megafires or stratospheric volcanic eruptions can have a profound radiative impact on a planetary scale by significantly affecting the global temperatures. A famous example of this process is the eruption of Pinatubo in 1991 which caused a drop of approximately 0.6°C in the average global temperature in the next 15 months following the eruption eruption (Parker et al., 1996). +Extreme events such as megafires or stratospheric volcanic eruptions can have a profound radiative impact on a planetary scale by significantly affecting the global temperatures. A famous example of this process is the eruption of Pinatubo in 1991 which caused a drop of approximately 0.6°C in the average global temperature in the next 15 months following the eruption (Parker et al., 1996). ### -#### The Hunga Tonga erruption of 2022 -This story focuses on the recent catastrophic volcanic eruption of Hunga Tonga - Hunga H'apai Volcano in January 2022. Radiative studies are underway to determine the long-term radiative impact of Hunga, following the study by Sellitto et al. (2022) which found a net warming of the climate system (Sellitto et al., 2022) at the top of atmosphere for 15 days after the eruption due to the water vapor in the plume. Previous research observed that the presence of sulfate aerosols formed in the wake of major eruptions tends to cool the climate, but the Hunga erruption offers the opportunity to study, for the first time in the satellite era, the radiative effect of a consequent disturbance in water vapour. +#### The Hunga eruption of 2022 +This story focuses on the recent catastrophic eruption of Hunga volcano in January 2022 characterized by an exceptional injection of water vapour into the stratosphere. Previous research observed that the presence of sulfate aerosols formed in the wake of major eruptions tends to cool the climate, but the Hunga eruption offers the opportunity to study, for the first time in the satellite era, the radiative effect of a stratospheric consequent disturbance in water vapour. Radiative studies are underway to determine the long-term radiative impact of Hunga, following the study by Sellitto et al. (2022) which found a net warming of the climate system at the top of atmosphere for 15 days after the eruption due to the water vapor in the plume. -Map information: Hunga Tonga - Hunga Ha'apai Volcano, source: EOxCloudless 2021 -Sentinel-2 cloudless by EOX IT Services GmBH; contains modified Copernicus Sentinel data 2021. Overlay data: © OpenStreetMap contributors, made with Natural Earth, rendering © EOX +Map information: Hunga volcano, source: EOxCloudless 2021 -Sentinel-2 cloudless by EOX IT Services GmBH; contains modified Copernicus Sentinel data 2021. Overlay data: © OpenStreetMap contributors, made with Natural Earth, rendering © EOX ## Observing the eruption -Satellite observations from NASA, ESA and JAXA can provide essential and complementary data about volcanic activity, helping us understand the full cycle, from the processes that move and store magma beneath volcanoes, to how eruptions occur and what are their impacts. +Satellite observations from NASA, ESA and JAXA provide essential and complementary data about volcanic activity. For the Hunga submarine eruption, they help us to understand how the eruption occurs and what impact it has. In the case of terrestrial volcanoes, they are also able to monitor the processes that move and store magma beneath volcanoes before the eruption. -The 2022 eruption of the Hunga Tonga-Hunga Ha‘apai underwater volcano was the biggest reported erruption globally in 30 years. After the eruption on January 15th at 4:15 UTC, the plume reached a maximum height of 58 km within 30 minutes (Carr et al.,2022). While most of the plume remained between 26 and 34 km, it began moving towards western longitudes, injecting approximately 0.4–0.5 Tg of SO2 into the atmosphere (Carn et al., 2022) and an unprecedented amount of water vapor into the stratosphere, causing an instantaneous 10% increase in stratospheric water content (Millán et al., 2022). +The 2022 eruption of the Hunga Tonga-Hunga Ha‘apai underwater volcano was the biggest reported eruption in 30 years. After the eruption on January 15th at 4:15 UTC, the plume reached a maximum overshooting height of about 56 km within 30 minutes (Carr et al., 2022). Located between 26 and 34 km, the main plume began moving towards western longitudes, injecting an estimated amount between 0.5 and 1.0 Tg of SO2 (Carn et al., 2022; Sellitto et al., 2024) and about 140 Tg of water vapor into the stratosphere, causing an instantaneous 10% increase in stratospheric water content (Millán et al., 2022).
-Himawari-8 satellite images of the 15 January 2022 eruption of Hunga Tonga-Hunga Haʻapai. +Himawari-8 satellite images of the 15 January 2022 Hunga eruption. Animation produced by the Japan Meteorological Agency (https://www.jma.go.jp/jma/kishou/info/coment.html). Legal notice (http://www.jma.go.jp/jma/en/copyright.html). Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/).
@@ -36,7 +36,7 @@ Animation produced by the Japan Meteorological Agency (https://www.jma.go.jp/jma ### #### SO2 Plume -Observations from several satellites such as ESA’s TROPOspheric Monitoring Instrument, TROPOMI onboard the Copernicus Sentinel-5p showed enhanced levels of stratospheric sulfur dioxide (SO2). The map illustrates the SO2 concentration observed by Sentinel-5p TROPOMI. Note that in this map the SO2 from potential anthropogenic sources has not been filtered out. The Copernicus Sentinel-5P SO2 measurements are those retrieved assuming SO2 at an altitude of 7km and explicitly filtering for pixels where a volcanic source is most likely (sulfurdioxide_detection_flag > 0) and where the solar zenith angle is within limits (SZA < 70°). +Observations from several satellites such as ESA’s TROPOspheric Monitoring Instrument, TROPOMI onboard the Copernicus Sentinel-5p showed enhanced levels of stratospheric sulfur dioxide (SO2). The map illustrates the SO2 concentration observed by Sentinel-5p TROPOMI. Note that in this map the SO2 from potential anthropogenic sources has not been filtered out. The Copernicus Sentinel-5P SO2 measurements are those retrieved assuming SO2 at an altitude of 7 km and explicitly selecting pixels where a volcanic source is most likely (sulfurdioxide_detection_flag > 0) and where the solar zenith angle is within limits (SZA < 70°). Map information: SO2 plume observed by Copernicus Sentinel-5p TROPOMI on 17 of January 2022 @@ -46,22 +46,22 @@ The eruption caused significant damages to Tonga and neighbouring countries in t This map shows the Hunga Tonga - Hunga Ha'apai island before the eruption observed by the Copernicus Sentinel-2 on 08 December 2021. ### -#### Dissapearance of the island +#### Disappearance of the island This map shows the Hunga Tonga - Hunga Ha'apai island after the eruption observed by the Copernicus Sentinel-2 on 27 January 2022. ### -In the context of this disaster, [Advanced Land Observing Satellite-2 “DAICHI-2” (ALOS-2)]( https://global.jaxa.jp/projects/sat/alos2/) PALSAR-2 synthetic aperture radar provided emergency observations due to its capability of imaging under clouds and plumes. The images below made available on the [Sentinel-Asia website](https://sentinel-asia.org/EO/2022/article20220115TO.html) show the main island of Tonga before (2020/03/07) and after (2022/01/22) the eruption observed by JAXA’s ALOS-2 PALSAR-2. +In the context of this disaster, [Advanced Land Observing Satellite-2 “DAICHI-2” (ALOS-2)]( https://global.jaxa.jp/projects/sat/alos2/) PALSAR-2 synthetic aperture radar provided emergency observations due to its capability of imaging under clouds and plumes. The images below made available on the [Sentinel-Asia website](https://sentinel-asia.org/EO/2022/article20220115TO.html) show the main island of Tonga before (2020/04/07) and after (2022/01/27) the eruption observed by JAXA’s ALOS-2 PALSAR-2. - + - + ## Data and Methods This section presents the method to track the plume of the Hunga erruption that was developed by the team of students during the Science Hub Challenge. -In order to precisely monitor the movement of the sulfur dioxide plume from the Hung a erruption, the students used sulfate aerosol data (Siddans et al., 2022) produced by RAL (Rutherford Appleton Laboratory) with co-located satellite data from by IASI (Infrared Atmospheric Sounding Interferometer), AMSU (Advanced Microwave Sounding Unit) and MHS(Microwave Humidity Sounder) on MetOp-B spacecraft. They were interested in the optical properties of the components of the plume, especially the sulfate aerosols optical depth (SAOD). The injected SO2 was rapidly converted into sulfate aerosols thanks to the abundant presence of water vapor in the stratosphere (Legras et al., 2022). The SAOD measurements allowed them to understand where the plume is located and its displacement. In order to track the plume they tracked the sulfate aerosols within the plume. +In order to precisely monitor the motion of the aerosol plume from the Hunga erruption, the students used sulfate aerosol data (Siddans et al., 2022) produced by RAL (Rutherford Appleton Laboratory) with co-located satellite data from by IASI (Infrared Atmospheric Sounding Interferometer), AMSU (Advanced Microwave Sounding Unit) and MHS (Microwave Humidity Sounder) on MetOp-B spacecraft. They were interested in the retrieved optical properties of the components of the plume, especially the sulfate aerosols optical depth (SAOD). The injected SO2 was rapidly converted into sulfate aerosols thanks to the abundant presence of water vapor in the stratosphere (Zhu et al., 2022; Legras et al., 2022). The SAOD measurements allowed them to understand where the plume is located and its displacement. In order to track the plume they tracked the sulfate aerosols within the plume. However, because of the satellite geometry, particularly the swath wide, the gaps in their datasets made it hard to follow efficiently the movement of the aerosols. @@ -72,9 +72,9 @@ Visualisation of the plume between 13/01/2022 and 29/01/2022 (no interpolation) The students’ idea was to use temporal interpolation techniques to be able to fill in those missing data. To do so they constructed their research following these four steps: 1. Selection of consecutive time series data of days before, during and after the event. -2. Introduction of temporal gaps of different periods -3. Interpolation using different algorithms: namely linear regression and second-degree polynomial regression* methods to fill in the temporal gaps -4. Comparison of interpolation methods +2. Introduction of temporal gaps of different periods. +3. Interpolation using different algorithms: namely linear regression and second-degree polynomial regression methods to fill in the temporal gaps. +4. Comparison of interpolation methods. Starting from the selection of time series data, consecutive time series data were selected by choosing a single pixel within the aerosol plume, ensuring it has no temporal gaps, and retrieving data from a random pixel within the plume. Temporal gaps of various durations were then introduced by randomly removing points from the dataset. Subsequently, different interpolation algorithms, such as linear regression and second-degree polynomial regression, were applied to fill these gaps, followed by a comparison of their effectiveness. The chosen interpolation method was then adapted to the dataset, creating linear interpolation functions based on SAOD and date time series. The linear interpolation function for one pixel involved looping over the time series, addressing gaps where both adjacent points or a single point are missing, and implementing interpolation based on general cases and boundary conditions. @@ -90,17 +90,16 @@ To reproduce this experiment, the data and code are made openly available. Visualisation of the plume between 13/01/2022 and 31/01/2022 (after interpolation) ## Conclusions -In this work, the students concluded that relevant interpolation is possible with linear regression, and that gap filling by interpolation allows to improve the precision of the evolution of the plume. This strenghtens the evaluation of the radiative impact of the sulfates (especially for satellite tracks). The method still has a few weaknesses at this stage. From a technical point of view the team suggests further improvements by implementing a 2nd degree interpolation with more points and develop the function to handle 3 or maybe more consecutive gaps, as well as potentially implementing a shift to take into account the rapid horizontal displacement of the plume with the wind angular rotation speed from ERA5 reanalysis. +In this work, the students concluded that relevant interpolation is possible with linear regression, and that gap filling by interpolation allows to improve the tracking of the plume. This strenghtens the evaluation of the radiative impact of the sulfates (especially for satellite tracks). The method still has a few weaknesses at this stage as discontinuities have not been removed from images. From a technical point of view the team suggests further improvements by implementing a 2nd degree interpolation with more points and develop the function to handle 3 or maybe more consecutive gaps, as well as potentially implementing transport shift, using the wind data from ERA5 reanalysis, to take into account the rapid motion of the plume in longitude. In the context of climate change, monitoring and understanding the impacts of such extremes is essential for adaptation and mitigation. In fact, studies of stratospheric volcanic eruptions and their long term radiative impacts can provide important results for geoengineering. With the warming climate, solutions such as injecting highly diffusive particles such as sulfate aerosols directly into the stratosphere are being explored to limit rising temperatures. In this context, stratospheric volcanic eruptions provide important real-world case studies to see the impact of gases or particles injected directly at high altitude. -#### Precursors to underwater volcanic eruptions +### Submarine eruption and discolored seawater +Observations from JAXA’s Global Change Observation Mission – Climate “SHIKISAI” (GCOM-C) offered information about the presence of discolored seawater. It is originated by the reaction of hot water caused by volcanic activity with seawater, suggesting enhancement of volcanic activity. Read more about this on the JAXA website. -Satellites can provide essential information about volcanic activtiy long before eruptions occur. +#### Precursors to volcanic eruptions -Observations from JAXA’s [Global Change Observation Mission – Climate “SHIKISAI” (GCOM-C)]( https://global.jaxa.jp/projects/sat/gcom_c/) offered information about precursor processes such as the presence of discolored seawater originated by the reaction of hot water caused by volcanic activity with seawater, suggesting enhancement of volcanic activity. Read more about this on the [JAXA website]( https://earth.jaxa.jp/en/earthview/2022/01/20/6701/index.html). - -Other precursor information about volcanic activity comes from below the Earth’s crust. Understanding of the natural processes such as the buildup in the mantle supports the development of methods for better characterisation and prediction of eruptions. Satellite data from GOCE – ESA’s gravity mission – provided essential information to improve our understanding of the processes beneath the Hunga Tonga-Hunga Ha‘apai. [ESA’s Science for Society 3D Earth project](https://eo4society.esa.int/projects/stse-3d-earth/) developed a model of the lithosphere combining different satellite data, such as gravity data from ESA’s GOCE mission, with in-situ observations, which showed differences in temperature, or thermal structures, indicating that the Tonga volcano was due to erupt at some point. Read more about this on the [ESA website]( https://www.esa.int/Applications/Observing_the_Earth/FutureEO/GOCE/Deep_down_temperature_shifts_give_rise_to_eruptions). +Satellites provide information about volcanic activity long before eruptions occur. Precursor information about volcanic activity comes from below the Earth’s crust. Understanding of the natural processes such as the buildup in the mantle supports the development of methods for better characterisation and prediction of eruptions. Satellite data from GOCE – ESA’s gravity mission – provided essential information to improve our understanding of the processes beneath the Hunga volcano. [ESA’s Science for Society 3D Earth project](https://eo4society.esa.int/projects/stse-3d-earth/) developed a model of the lithosphere combining different satellite data, such as gravity data from ESA’s GOCE mission, with in-situ observations, which showed differences in temperature, or thermal structures, indicating that the Hunga was due to erupt at some point without knowing exactly when. Read more about this on the [ESA website]( https://www.esa.int/Applications/Observing_the_Earth/FutureEO/GOCE/Deep_down_temperature_shifts_give_rise_to_eruptions).
@@ -112,13 +111,16 @@ Other precursor information about volcanic activity comes from below the Earth ## Open Science -The analysis was carried out on the [ESA DeepESDL (Deep Earth System Data Lab)](https://earthsystemdatalab.net ). For research purposes, ESA is offering this resources under a sponsorship scheme through the Network of Resources. +The analysis was carried out on the [ESA DeepESDL (Deep Earth System Data Lab)](https://earthsystemdatalab.net ). For research purposes, ESA is offering these resources under a sponsorship scheme through the Network of Resources. * [DeepESDL website](https://earthsystemdatalab.net) * [Network of Resources website](https://nor-discover.org/en/portfolio/) * [Apply for sponsorsed access to DeepESDL](https://portfolio.nor-discover.org/?textSearch=DeepESDL) * [RAL Dataset](https://zenodo.org/records/7102472) * [Jupyter Notebook](https://github.com/eurodatacube/eodash-assets/blob/main/stories/ScienceHub-Challenge-February-2024/hunga-tonga/5_OpenChallengeNotebook-Hunga-Ch3_Duchamp_Barton_Baldazo.ipynb) (*Note that the polynomial regression is incomplete. However, you are encouraged to expand the notebook to include your own implementation.)* +### Acknowledgements +We thank Bernard Legras and Pasquale Sellitto for scientific discussions, Martin Reinhardt for methodological suggestions and the RAL science team for providing access to the data. + ### References 1. Carn, S. A., Krotkov, N. A., Fisher, B. L., & Li, C. (2022). Out of the blue: Volcanic SO2 emissions during the 2021–2022 eruptions of Hunga Tonga—Hunga Ha'apai (Tonga). Frontiers in Earth Science, 10, 976962. https://doi.org/10.3389/feart.2022.976962 @@ -128,6 +130,8 @@ The analysis was carried out on the [ESA DeepESDL (Deep Earth System Data Lab)]( 5. Legras, B., Duchamp, C., Sellitto, P., Podglajen, A., Carboni, E., Siddans, R., et al. (2022). The evolution and dynamics of the Hunga Tonga–Hunga Ha'apai sulfate aerosol plume in the stratosphere. Atmospheric Chemistry and Physics, 22(22), 14957–14970. https://doi.org/10.5194/acp-22-14957-2022 6. Parker, D.E., Wilson, H., Jones, P.D., Christy, J.R. & Folland, C.K. (1996). The Impact of Mount Pinatubo on World-Wide Temperatures. Int. J. Climatol., 16: 487-497. https://doi.org/10.1002/(SICI)1097-0088(199605)16:5<487::AID-JOC39>3.0.CO;2-J 7. Sellitto, P., Podglajen, A., Belhadji, R., Boichu, M., Carboni, E., Cuesta, J., Duchamp, C., et al. (2022). The unexpected radiative impact of the Hunga Tonga eruption of 15th January 2022. Communications Earth & Environment, 3(1), 288. https://doi.org/10.1038/s43247-022-00618-z +8. Sellitto, P., Siddans, R., Belhadji, R.,Carboni, E., Legras, B., Podglajen, A., Duchamp, C., et al. (2024). Observing the SO2 and sulfate aerosol plumes from the 2022 Hunga eruption with the Infrared Atmospheric Sounding Interferometer (IASI). Geophysical Research Letters, 51, e2023GL105565. https://doi.org/10.1029/2023GL105565 +9. Zhu, Y., Bardeen, C. G., Tilmes, S., Mills, M. J., Wang, X., Harvey, V. L., et al. (2022). Perturbations in stratospheric aerosol evolution due to the water-rich plume of the 2022 Hunga-Tonga eruption. Communications Earth & Environment, 3(1), 248. https://doi.org/10.1038/s43247-022-00580-w diff --git a/app/src/config/stories.json b/app/src/config/stories.json index 50c43ab1cd..b84baa2229 100644 --- a/app/src/config/stories.json +++ b/app/src/config/stories.json @@ -126,7 +126,7 @@ "air-pollution-extremes": { "storyMarkdown": "./data/storytelling-md/eodashMarkdown_EXTREME_POLLUTION_2.md", "title": "Extreme air pollution episodes in Northern India and Pakistan", - "subtitle": "Authors: Selviga Sinnathamby, Natacha Kaminski, Ellie Zoghbi - Sorbonne Universite, Paris, France", + "subtitle": "Authors: Selviga Sinnathamby, Natacha Kaminski, Elie Zoghbi - Sorbonne Universite, Paris, France", "image": "https://raw.githubusercontent.com/eurodatacube/eodash-assets/main/stories/ScienceHub-Challenge-February-2024/AirPollutionIndia/cover-pakistan.jpg", "imagePlaceholder": "https://raw.githubusercontent.com/eurodatacube/eodash-assets/main/stories/ScienceHub-Challenge-February-2024/AirPollutionIndia/cover-pakistan-small.jpg", "logoAlternative": "https://raw.githubusercontent.com/eurodatacube/eodash-assets/main/stories/ScienceHub-Challenge-February-2024/sorbonne-logos-white.png"