Climate change and global warming are not just scientific terms but are significant issues that directly impact our daily lives and the future of our planet. These changes affect everything from the weather patterns we experience to the health of ecosystems that support life on Earth.

Interest in This Topic: The reason for choosing this topic is straightforward. I am deeply concerned about the observable changes in our environment and the challenges they present. By studying climate change and global warming, I aim to contribute to a broader understanding of how these changes are happening, the data that evidences these trends, and what it means for us as a global community. This project seeks to delve into the vast amounts of data available to paint a clearer picture of our changing climate, hoping to foster awareness and action.

Understanding these changes helps us better prepare for and adapt to new environmental conditions, ensuring a sustainable future for all.

The datasets used in this project are derived from the following sources: - NASA’s Global Climate Change: Vital Signs of the Planet - Our World in Data

Datasets

• Global Temperature
• CO2 Concentrations
• Sea Level
• Ice Sheet Mass Balance
• Number of Homeless from Natural Disasters
• Greenhouse Gas Emissions

All data has been used to perform a detailed exploratory analysis to understand the trends and potential impacts of climate variables over time.

Air pollution significantly impacts climate change and global warming. The primary greenhouse gases contributing to this are: - Carbon dioxide (CO2): The major contributor, typically emitted through fossil fuel combustion. - Methane (CH4): Emitted during the production and transport of coal, oil, and natural gas. - Nitrous oxide (N2O): Emitted from agricultural and industrial activities, as well as during combustion of fossil fuels and solid waste.

These gases trap heat in the atmosphere, leading to increased global temperatures and significant climatic alterations. Below is the R code used to analyze the trends of these emissions.

library(ggplot2)
greenhouse_gas <- read.csv("greenhouse-gas-emissions.csv")

ggplot(greenhouse_gas, aes(x = Year, y = Annual.greenhouse.gas.emissions.in.CO..equivalents)) +
  geom_line() +
  labs(title = "Trend of Greenhouse Gas Emissions in the Atmosphere",
       x = "Year",
       y = "CO2, CH4, N2O") +
  theme_minimal() +
  theme(plot.margin = unit(c(1, 1, 1, 1), "cm"),
        axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
        axis.text.y = element_text(size = 8),
        legend.position = "bottom")

Now among these gases, CO2 is around 70 to 80 percent in the greenhouse gases, so its important to see the CO2 concentraion trend

library(ggplot2)
library(dplyr)
co2_conc <- read.csv("global-co2-concentration.csv")
co2_conc <- co2_conc %>%
  filter(!is.na(Rolling.yearly.average.of.the.concentration.of.atmospheric.carbon.dioxide)) %>%
  mutate(Day = as.Date(Day, format = "%Y-%m-%d")) 
if(nrow(co2_conc) > 0) {
  ggplot(co2_conc, aes(x = Day, y = Rolling.yearly.average.of.the.concentration.of.atmospheric.carbon.dioxide)) +
    geom_line(color = "steelblue", linewidth = 1.2, linetype = "solid") +
    labs(title = "Trend of Global CO2 Concentrations",
         subtitle = "Rolling yearly average concentration of CO2",
         x = "Year",
         y = "CO2 Concentration (ppm)") +
    theme_minimal()
} else {
  print("No data available to plot.")
}

Below we visualize the trend of global CO2 concentrations based on the rolling yearly averages. We’ll see the rise in CO2 levels over the years.

Now we combinely call CO2 and greenhouse gases as pollution and it has affected our earth in many bad ways like beacuse of pollution the ice reserves (source of clean water) on th earth are metling, which is causing the sea level to rise causing tsunamis all over the world. Also beacuse of pollution the temperature of Sea and Earth surface is rising. All these things lead to unusual harsh weather conditions, affecting a lot of people all over the world. Their houses are destroyed forcing them to find shelter and making so many people homeless.

ice_sheet <- read.csv("ice-sheet-mass-balance.csv")

ice_sheet$Day <- as.Date(ice_sheet$Day, format = "%Y-%m-%d")

ggplot(ice_sheet, aes(x = Day, 
                      y = `Cumulative.change.in.mass.in.the.ice.sheets..according.to.NASA.JPL`,
                      color = Entity)) +
  geom_line(linewidth = 1) +
  labs(title = "Ice Sheet Mass Balance Trend",
       x = "Date",
       y = "Mass Change (units as per data)") +
  theme_minimal()

library(dplyr)
sea_level <- read.csv("sea-level.csv")

sea_level$Day <- as.Date(sea_level$Day, format = "%Y-%m-%d")
sea_level$Year <- as.numeric(format(sea_level$Day, "%Y"))

sea_level_avg <- sea_level %>%
  group_by(Year) %>%
  summarise(avg_sea_level = mean(`Global.sea.level.as.an.average.of.Church.and.White..2011..and.UHSLC.data`, na.rm = TRUE))

ggplot(sea_level_avg, aes(x = Year, y = avg_sea_level)) +
  geom_line(linewidth = 1, color = "darkgreen") +
  labs(title = "Global Sea Level Rise",
       x = "Year",
       y = "Average Sea Level") +
  theme_minimal()

global_temp <- read.csv("Global temperature.csv")

global_temp$Year <- as.numeric(as.character(global_temp$Year))
global_temp$`Lowess.5.` <- as.numeric(as.character(global_temp$`Lowess.5.`))

ggplot(global_temp, aes(x = Year, y = `Lowess.5.`)) +
  geom_line(linewidth = 1, color = "tomato") +
  labs(title = "Global Temperature Trend",
       x = "Year",
       y = "Temperature (Lowess 5)") +
  theme_minimal()

homeless <- read.csv("number-homeless-from-natural-disasters.csv")

homeless$Year <- as.numeric(as.character(homeless$Year))

ggplot(homeless, aes(x = Year, y = Homeless)) +
  geom_line(linewidth = 1, color = "purple") +
  labs(title = "Number of Homeless People from Natural Disasters",
       x = "Year",
       y = "Number of Homeless People") +
  theme_minimal()