From 512814e452a5547083d08b3858b32e8bc76b860f Mon Sep 17 00:00:00 2001
From: WeathermanTrent <109642752+WeathermanTrent@users.noreply.github.com>
Date: Mon, 1 May 2023 22:30:07 -0500
Subject: [PATCH 01/50] Aerosol Discovery (will need edits and figures)
---
datasets/houston-aod.data.mdx | 116 +++++++++++++
discoveries/houston-aod.discoveries.mdx | 214 ++++++++++++++++++++++++
2 files changed, 330 insertions(+)
create mode 100644 datasets/houston-aod.data.mdx
create mode 100644 discoveries/houston-aod.discoveries.mdx
diff --git a/datasets/houston-aod.data.mdx b/datasets/houston-aod.data.mdx
new file mode 100644
index 000000000..fe4a1e882
--- /dev/null
+++ b/datasets/houston-aod.data.mdx
@@ -0,0 +1,116 @@
+---
+id: houston-aod
+name: "Aerosol"
+description: "The Impact of Aerosol on the Houston, TX Metro"
+media:
+ src: ::file ./co2--dataset-cover.jpg
+ alt: Power plant shooting steam at the sky.
+ author:
+ name: Trent Cowan
+ url: https://unsplash.com/photos/WiZOyYqzUss
+thematics:
+ - air-quality
+ - eis
+layers:
+ - id: aod
+ stacCol: aod
+ name: Aerosol Optical Depth
+ type: raster
+ description: "The monthy AOD on the Houston metro area."
+ initialDatetime: newest
+ zoomExtent:
+ - 0
+ - 20
+ sourceParams:
+ colormap_name: rdylbu_r
+ rescale:
+ - 0.000408
+ - 0.000419
+ legend:
+ type: gradient
+ max: "1.1428571428571428"
+ min: "1000000000000000000000"
+ stops:
+ - "#4575b4"
+ - "#91bfdb"
+ - "#e0f3f8"
+ - "#ffffbf"
+ - "#fee090"
+ - "#fc8d59"
+ - "#d73027"
+ -
+---
+
+
+
+ ## Tracking AOD
+
+ Houston, TX is one of the top ten metropolitan areas in the United States. Though Houston is
+ethnically and racially diverse (Emerson et al. 2012) , it is also one of the US's most economically
+segregated metropolitan areas (Nicholson 2017) . A recent study conducted by Environmental
+Defense Fund (EDF) shows that economic segregation in the Houston metropolitan area leads to
+differences in environmental quality with economically disadvantaged populations being subject
+to higher levels of air pollution (Environmental Defense Fund 2020) .
+
+
+
+
+
+
+ ### Local Scale Changes in AOD across the Houston Metro
+
+ To determine the change in the Aerosol Optical Depth and its patterns in Harris County, TX.
+ U. S. Environmental Protection Agency (EPA) has identified six common air pollutants that
+affect human health. Referred to as criteria pollutants, these are particulate matter, ozone, sulfur
+dioxide, carbon monoxide, nitrogen dioxide, and lead. The EPA monitors and tracks these
+criteria pollutants, and their measured amounts in the air gauge environmental quality with
+respect to air pollution. Of particular concern are fine particulate matter known as PM2.5
+(particles with a diameter of 2.5 micrometers or less) and ozone. Of the two, PM2.5 accounts for
+half of the 200,000 excess deaths in the U.S. caused by air pollution (Thakrar et al., 2020).
+
+
+
+
+
+
+
+
+ **Top row**: Reported country-by-country reductions in fossil fuel use during the most intense periods of the COVID-19 lockdowns in a.) China (early February), b.) Europe (early April) and c.) Northeast U.S. (late April). Brighter blue colors indicate greater reductions.
+ **Bottom row**: observed changes in atmospheric CO2 concentration differences derived from OCO-2 measurements. Blue shades indicate reductions in CO2, while red shades indicate increases relative to the baseline CO2 climatology.
+
+
+
+
+
+
+
+
+ Monthly time series of lower atmospheric CO2 enhancements over Beijing, China for January 2017 through April 2020 derived from GOSAT data. The results for January through April of prior years are shown in blue, while those for 2020 are shown in green.
+
+
+
+ ### AOD Changes over Large Urban Areas
+
+ Scientists use GOSAT data to determine changes in atmospheric CO2 over large urban areas, which experienced the largest changes in economic activity associated with the onset of the COVID-19 pandemic. While OCO-2 is optimized for detecting the subtle, regional-scale changes in CO2, GOSAT has advantages for tracking changes in CO2 emissions over large cities.
+
+ GOSAT observations were analyzed to reveal CO2 concentration enhancements, such as fossil fuel emissions that contribute to higher levels of CO2 lower down in atmosphere over cities, relative to the CO2 concentrations at higher altitudes, which are less affected by city emissions. The figure below shows the CO2 concentration enhancements over Beijing, China, derived from GOSAT observations collected in January through April of each year from 2017 to 2020. The results from earlier years illustrate the amount of month-to-month variability in the observed CO2 enhancements that is typical during this season. However, while the CO2 concentration enhancements vary substantially from month-to-month, they are generally much lower in 2020 than in earlier years.
+
+
+
+
+
+ Further inspection of the Beijing results reveals that all months in 2020 have smaller CO2 enhancements relative to prior years. While this behavior is consistent with reported COVID-19-related reductions in fossil fuel emissions from Beijing, it is important to remember that these results include variations in CO2 concentrations not only from COVID-19 shutdowns, but also from other processes such as photosynthesis and respiration by plants and transport by passing weather systems.
+
+ Similar results were derived for the other cities. Shanghai shows reduced CO2 enhancements from February through April 2020. For New York, CO2 values were higher in January 2020, close to normal for February, and lower in March, as lockdowns were imposed. There is no data for New York in April due to cloud cover. In New Delhi, Mumbai and Dhaka, the story is somewhat more mixed. The CO2 enhancements are smaller or almost the same in February, reflecting the large role of natural processes, such as year-to-year differences in CO2 uptake and release by forests and crops. In March 2020, CO2 enhancements are higher than in earlier years in New Delhi, and lower in Mumbai and Dhaka. The CO2 enhancements decrease across all three cities in April, as lockdowns are implemented. However, these changes are very difficult to attribute to the pandemic because of the large-scale natural CO2 changes seen across India during this season.
+
+
diff --git a/discoveries/houston-aod.discoveries.mdx b/discoveries/houston-aod.discoveries.mdx
new file mode 100644
index 000000000..c63681a34
--- /dev/null
+++ b/discoveries/houston-aod.discoveries.mdx
@@ -0,0 +1,214 @@
+---
+featuredOn:
+ - aerosol
+id: 'houston-aod'
+name: Impact of Multi-Decadal Urban Growth of Houston on Air Pollution
+description: "Air quality and aerosols impact human health across the globe, but higher concentrations in metropolitan areas can contribute to a higher risk to human health."
+media:
+ src: ::file ./air-quality-and-covid-19--discovery-cover.jpg
+ alt: Clear nightsky with crescent moon above the mountains
+ author:
+ name: Trent Cowan
+ url: https://unsplash.com/photos/U-Kty6HxcQc
+pubDate: 2022-05-01
+thematics:
+ - air-quality
+---
+
+
+
+ ## Going through changes
+ ## When governments began implementing shutdowns at the start of the COVID-19 pandemic, scientists wondered how the atmosphere would respond to the sudden change in human behavior.
+ Houston, TX is one of the top ten metropolitan areas in the United States. Though Houston is
+ethnically and racially diverse (Emerson et al. 2012) , it is also one of the US's most economically
+segregated metropolitan areas (Nicholson 2017) . A recent study conducted by Environmental
+Defense Fund (EDF) shows that economic segregation in the Houston metropolitan area leads to
+differences in environmental quality with economically disadvantaged populations being subject
+to higher levels of air pollution (Environmental Defense Fund 2020) .
+U. S. Environmental Protection Agency (EPA) has identified six common air pollutants that
+affect human health. Referred to as criteria pollutants, these are particulate matter, ozone, sulfur
+dioxide, carbon monoxide, nitrogen dioxide, and lead. The EPA monitors and tracks these
+criteria pollutants, and their measured amounts in the air gauge environmental quality with
+respect to air pollution. Of particular concern are fine particulate matter known as PM2.5
+(particles with a diameter of 2.5 micrometers or less) and ozone. Of the two, PM2.5 accounts for
+half of the 200,000 excess deaths in the U.S. caused by air pollution (Thakrar et al., 2020).
+Because of its high population density, Houston has large amounts of vehicular traffic that emits
+many of the harmful criteria pollutants. A variety of industries, including the oil refineries,
+distributed throughout the Houston metropolitan region are also sources of criteria pollutants.
+Periodic intrusion of smoke from fires in Mexico, Central America and the surrounding states is
+another source of criteria pollutants affecting the Houston metropolitan region. The combined
+effect of these pollution sources results in a variegated spatial pattern of air pollution that makes
+it difficult to characterize the environmental quality within the metropolitan region. While the air
+quality-monitoring network in the Houston area is more extensive compared to other cities, it
+may not be not adequate to capture the complex spatial patterns of pollution in this region. A
+recent study using pollution sensors mounted on Google Street View cars (Miller et al. 2020)
+shows that there are fine-scale features that are beyond the detection capability of the existing
+network. While mobile pollution monitoring sensors are able to identify fine-scale patterns, they
+are not available for time periods long enough to detect changes over time. Nor do they provide
+monitoring over the entire metropolitan region.
+In this context, observations from instruments that have been in continuous operation on NASA
+satellites over the last two decades provide valuable insights about particulate pollution affecting
+Houston. The NASA Moderate Resolution Imaging Spectroradiometer (MODIS) has the
+capability to detect and quantify the total amount of particulate matter present within the
+atmospheric column at a given geographic location. The MODIS Multi-Angle Implementation of
+Atmospheric Correction (MAIAC) aerosol optical depth (AOD) product provides such
+quantification as the AOD is an indicator of total amount of particulate matter present in an
+atmospheric column, an area of air stretching from the ground all the way up, over a given
+location.
+[Contributor credits]
+In this discovery, we used the MAIAC AOD product that is available since year 2000 at 1 km
+spacing over a given geographical location to examine spatial patterns of particulate pollution
+over the metropolitan region of Houston. Note that while AOD is not a direct indication of
+
+particulate pollution at the surface, prior studies show that it can serve as a proxy for the same
+(Chudnovsky et al. 2014) . We used the MAIAC AOD to examine spatial patterns of particulate
+pollution by computing decadal averages for the years 2000-2009 and 2010-2019.
+
+[Figure 1, can use slider view]
+While the spatially averaged AOD shows a statistically significant decreasing trend over the
+Houston metropolitan region (Figure 2), the spatial pattern shows local increases.
+[Figure 2]
+Comparison of decadal average AOD maps for the two decades show spatial patterns of increase
+during the second decade that correlates well with urban growth patterns determined using
+satellite observations.
+[Figure 3, can use slider view]
+Urban growth and increase in particulate pollution both occur in the vicinity of the highway
+networks.
+We also used the Center for Disease Control (CDC)/Agency for Toxic Substances and Disease
+Registry (ATSDR) social vulnerability index (SVI) to examine if the increase in particulate
+pollution disproportionately affects economically disadvantaged populations. We determined
+average AOD for census tracts within Houston that fall within different ranges of economic
+vulnerability. We used the percentage of population within a census tract below the 150%
+poverty level given in SVI data to characterize the levels of economic disadvantage. We found
+that the average AOD over census tracts with high levels of poverty (> 15% of the population) to
+be substantially diminished compared to tracts with lower levels of poverty (Figure 3).
+[Figure 3, emphasize the two > 15% lines]
+However, within census tracts with 5-10% of the population below the 150% federal poverty
+level, we found AOD to be significantly higher compared to other regions pointing toward
+higher pollution levels.
+[Figure 3, emphasize the 5-10% line]
+While the burden of particulate air pollution over the Houston metropolitan region has been
+steadily declining over the last two decades, local increases have occurred in the recent years.
+These local increases most affect people in census tracts with 5-10% of the population below the
+150% poverty level. These census tracts are also predominantly located in regions of urban
+growth and near major highways. These findings are in contrast to our prior discovery that
+examined satellite derived land surface temperature (LST) as a proxy to heat stress [link to prior
+discovery]. This prior discovery found that LST and potential for heat stress increases
+monotonically with the level of poverty. However, for air pollution the relationship appears to be
+more complex, with the populations most affected being located in the vicinity of highways.
+
+The findings of this discovery are consistent with recent assessment by the Environmental
+Defense Fund based on air pollution sensors mounted on Google Street View cars (Miller et al.
+2020) which found substantial spatial variability in air pollution and that emissions from mobile
+sources, especially emissions from diesel vehicles, play an important role in polluting residential
+neighborhoods. However, note that while satellite derived AOD is often correlated with surface
+pollution, the strength of this association varies depending upon the location. Further studies are
+needed to confirm how much of surface air pollution is represented in the satellite observed
+patterns of column burdens of pollution.
+
+
+
+
+
+ ## Measuring Air Pollution on the Ground at Airports
+ New research during the pandemic is also looking at how COVID-related travel bans are impacting air quality around airports. Current conditions create a unique opportunity to study airport-related pollutants, especially nitrogen dioxide and formaldehyde. While travel bans and strict regulations around air travel have been in place, air traffic has yet to return to previous levels, and many planes remain grounded.
+
+ They are comparing the on-the-ground sensor information from NASA's [Pandora Project](https://pandora.gsfc.nasa.gov/ "Explore the Pandora Project") with satellite information from TROPOMI. So far, they have found that nitrogen dioxide hotspots in Atlanta shifted from the airport, shown here, to the city center from April-June 2020. By September, however, satellites revealed the airport had reemerged as a dominant nitrogen dioxide emission source.
+
+
+
+
+ Levels in 10¹⁵ molecules cm⁻². Darker colors indicate higher nitrogen dioxide (NO₂) levels associated and more activity. Lighter colors indicate lower levels of NO₂ and less activity.
+
+
+
+
+
+
+
+
+ Levels in 10¹⁵ molecules cm⁻². Darker colors indicate higher nitrogen dioxide (NO₂) levels associated and more activity. Lighter colors indicate lower levels of NO₂ and less activity.
+
+
+
+ They are comparing the on-the-ground sensor information from NASA's [Pandora Project](https://pandora.gsfc.nasa.gov/ "Explore the Pandora Project") with satellite information from TROPOMI. So far, they have found that nitrogen dioxide hotspots in Atlanta shifted from the airport, shown here, to the city center from April-June 2020.
+
+
+
+
+
+ By September, however, satellites revealed the airport had reemerged as a dominant nitrogen dioxide emission source.
+
+
+
+
+ Levels in 10¹⁵ molecules cm⁻². Darker colors indicate higher nitrogen dioxide (NO₂) levels associated and more activity. Lighter colors indicate lower levels of NO₂ and less activity.
+
+
+
+
+
+
+
+
+ Levels in 10¹⁵ molecules cm⁻². Darker colors indicate higher nitrogen dioxide (NO₂) levels associated and more activity. Lighter colors indicate lower levels of NO₂ and less activity.
+
+
+
+ ## Seeing Rebounds in NO2
+
+ After the initial shock of COVID-related shutdowns in the spring, communities worldwide began to reopen and gradually increase mobility. Cars returned to the road, and travel restrictions slowly eased. These resumptions corresponded with relative increases in nitrogen dioxide levels and other air pollutants, as air quality levels began to return to pre-pandemic levels.
+
+ This demonstrates how quickly atmospheric nitrogen dioxide responds to reductions in emissions. They will persist as long as emissions persist and decline rapidly if emissions are reduced.
+
+ NASA scientists will continue to monitor nitrogen dioxide levels and long-term trends around the world. NASA is expected to launch its [Tropospheric Emissions: Monitoring of Pollution (TEMPO)](http://tempo.si.edu/overview.html "Explore the TEMPO instrument") instrument in 2022, which will provide hourly, high-resolution measurements of nitrogen dioxide, ozone, and other air pollutants across North America, improving future air quality forecasts.
+
+ [Explore How COVID-19 Is Affecting Earth's Climate](/covid19/discoveries/climate/climate-change-and-covid "Explore How COVID-19 Is Affecting Earth's Climate")
+
+
\ No newline at end of file
From bd988e961920915c084a747739ab50a3c272d9f8 Mon Sep 17 00:00:00 2001
From: WeathermanTrent <109642752+WeathermanTrent@users.noreply.github.com>
Date: Tue, 2 May 2023 23:43:49 -0500
Subject: [PATCH 02/50] Dataset is updated and should be fully correct. Please
let me know if not. The .jpg images are some of the data images that were
created for this story. Let me know if there is something that is missing or
something that I have done wrong.
---
datasets/Harris_AOD_00_09_county.jpg | Bin 0 -> 1115587 bytes
datasets/Harris_AOD_10_19_county.jpg | Bin 0 -> 1230543 bytes
datasets/aerosol-dataset.data.mdx | 47 ++++++++++++++++++++
datasets/smog-national-geographic-cover.jpg | Bin 0 -> 29428 bytes
4 files changed, 47 insertions(+)
create mode 100644 datasets/Harris_AOD_00_09_county.jpg
create mode 100644 datasets/Harris_AOD_10_19_county.jpg
create mode 100644 datasets/aerosol-dataset.data.mdx
create mode 100644 datasets/smog-national-geographic-cover.jpg
diff --git a/datasets/Harris_AOD_00_09_county.jpg b/datasets/Harris_AOD_00_09_county.jpg
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