Embedded Files
Reading time :- Overview - 14min / Focused - 20min
Introduction
Introduction
In this chapter, we examined how the quality of the air changed and have been changing during the Covid-19 pandemic. We used data from different sources and platforms and compared the quality of air before and during the Covid-19. Here we took greenhouse gas concentration, tropospheric NO2 fluctuations and PM2.5 concentration of some major cities in central and southern Asia into consideration.
Procedure
Procedure
First, we collected air-quality-related data from Earth Observing Dashboard and world air quality reports from Greenpeace (2018,2019,2020). We examined air quality under the above-mentioned 3 data categories.
Then we created a database of all the data and started analyzing them.
During the analysis, we categorized the data in each section as data before Covid-19 and data during the Covid-19. (As a comparison)
After categorizing the data we took the average of each of the three data types (greenhouse gas concentration, NO2 fluctuations, PM2.5 concentration) from each city and found out how the air quality changed during the Covid-19.
Finally, we graphed the data and created the output.
Inputs
Inputs
01.What are greenhouse gases?
Greenhouse gases are the gases that contribute to the greenhouse effect. They help heat up the planet, making this place a habitable zone for life to thrive. But due to the over emission of greenhouse gases from factories and industries the global temperature has been increasing. This effect is what is commonly known as global warming and it is the main reason for increasing the number of natural disasters around the globe during recent years.
Carbon dioxide, Methane, Nitrous Oxide and water vapor are some common greenhouse gases in the atmosphere.
02.What is tropospheric NO2 gas?
Nitrogen dioxide (NO2) is a member of the Nitrogen oxides group, which plays a key role in tropospheric chemistry regulating the level of ozone by maintaining the oxidizing capacity in the troposphere.
By the way, NO2 can cause irritation of the eyes, nose and throat and when inhaled might cause lung irritations and decreased lung function. In areas with higher levels of Nitrogen Dioxide, a greater chance of asthma attacks and an increase in hospital stays because of respiratory complaints are observed.
03.What is PM2.5?
PM2.5 is defined as ambient airborne particulates that measure up to 2.5 microns in size. Among criteria pollutants commonly measured in real-time, fine particulate matter (PM2.5) is currently understood to be the most harmful to human health due to its prevalence and far-reaching health risks.
Exposure to PM2.5 has been linked to negative health effects like cardiovascular disease, respiratory illness and premature mortality. The microscopic size of PM2.5 allows these particles to be absorbed deep into the bloodstream upon inhalation, potentially causing far-reaching health effects like asthma, lung cancer and heart disease.
PM2.5 exposure has also been associated with low birth weight, increased acute respiratory infections and stroke. These particles include a range of chemical makeups and come from a range of sources. The most common human-made sources include fossil-fuel-powered motor vehicles, power generation, industrial activity, agriculture and biomass burning.
The WHO PM2.5 guideline stipulates that PM2.5 not exceed 10 μg/m3 annual mean or 25 μg/m3 24-hour mean; and that PM10 not exceed 20 μg/m3 annual mean or 50 μg/m3 24-hour mean. For ozone (O3), the guidelines suggest values no higher than 100 μg/m3 for an 8-hour mean.
WHO PM2.5 Target : 10 µg/m³
What can we decide from this data?
What can we decide from this data?
The main sources of greenhouse gases are human activities such as burning fossil fuels, deforestation, factories and industries. Therefore from the fluctuation of the greenhouse gas concentrations, we can determine how human activities have been decreased or increased from time to time in different regions.
From NO2 and PM2.5 we can detect how the air pollution of the region changes over time in different regions. Usually, these pollutants are released from vehicles, fossil fuel burning and factories. Usually, air pollution in urbanized and industrialized areas is high. Therefore from the fluctuations of air pollution, we can determine the fluctuations of human activities.
How does these data relate to our challenge?
How does these data relate to our challenge?
Sometimes you might wonder how this greenhouse gas relates to our challenge. Well, there are several cases we thought of.
The main cause is that most of the greenhouse-gas-concentrated areas are fully or partially urbanized areas. Therefore one potential assumption we can take from this is to find an area that doesn't show that many fluctuations in the greenhouse gases and continuously have been showing low greenhouse emissions in recent years. That can be either a less industrialized rural area or an unpopulated area.
By observing population distribution maps we can simply identify areas with a comparative population that follows the above conditions. The area we found in such a way might be a population consisting of low-income families, tribal or indigenous people.
The same process can be applied in the case of NO2 and PM2.5 (Air pollution) as well.
What are the limitations?
What are the limitations?
Even though we have a clear approach for finding places where low-income families, tribal and indigenous people live, there are lots of errors and limitations of data.
One limitation why we cannot achieve this is, there is no reasonable data for every city,town and country. There is only data for a few major cities.
Since there is no highly accurate data for each small town, we cannot identify the low-income people living in industrialized urban and suburban areas.
Outputs
Outputs
i) Greenhouse gas concentration (From GOSAT)
Greenhouse gas fluctuations from 2016 to 2020 of six cities over the time (Mumbai, New Delhi, Dhaka, Shanghai, Tokyo, Beijing)
Comparison of Average greenhouse gas concentration of each city before Covid-19 and during Covid-19.
Observation from Greenhouse gas fluctuations:-
Average decrease rate due to the pandemic situation = 10.12%
ii) Tropospheric NO2 gas concentration (From ESA TROPOMI)
Asian Region
Tropospheric NO2 gas fluctuations from 2018 May to 2020 May of four cities over the time (Mumbai, New Delhi, Tokyo, Beijing)
Comparison of Average Tropospheric NO2 gas concentration of each city before Covid-19 and during Covid-19.
Observation from Tropospheric NO2 fluctuations of Asian Region:-
Average decrease rate due to the pandemic situation = 4.77%
Sri Lanka
Comparison of Average Tropospheric NO2 gas concentration of Sri Lanka before Covid-19 and during Covid-19.
Observation from Tropospheric NO2 fluctuations of Sri Lanka:-
Average decrease rate due to the pandemic situation = 17.65%
iii) PM2.5 concentration visualized using US AQI (From the annual report by Greenpeace Organization)
The US AQI translates daily pollutant concentrations into 6 categories ranging from “good” (green) to “hazardous” (maroon). This system has been adopted because it is widely used. However, adverse health effects can occur at any level of PM2.5 exposure, including those labeled as “good” by the US AQI.
The report by Greenpeace Organization uses the US AQI to visualize the PM2.5 levels that exceed the WHO target.
PM2.5 concentration fluctuations comparison from 2018 to 2020 of Asian countries year-wise
Observations from PM2.5 consideration report :-
There is an eventual decrease of average PM2.5 per µg/m3 from 2018 to 2020.
PM2.5 values of regional countries from 2018 to 2020
So what we can decide from the data analysis done so far…
As every cloud has a silver lining Covid-19 also has a good side. That is the decrease in air pollution.
As we see in our data whole world has faced this good possibility. However, Covid-19 taught us to rethink about our environment not only the air but also water, soil etc. So we must think about what we learnt and we must keep them in mind and act according to that.
Anyway, when we look at air quality data we can assume that the air quality was getting better than before because the factories were closed due to lock-downs and high restrictive time periods. So we can make an assumption that the enclosing of factories has directly been affected on the communities of low income, because most of the factory labours, especially in Sri Lanka, who work according to a daily basis salary are low-income people.
But the problem is we do not have enough data resources to talk much about Sri Lanka's Covid-19 impact on air quality. So we analyzed the data of our neighbour countries so we can give our prediction about Sri Lanka according to those changes.
Recommended
Page updated
Google Sites
Report abuse