PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)

PM2.5 Air Pollution

Air pollution remains one of the most critical environmental issues affecting global health and ecosystems. Among the various pollutants, particulate matter, especially PM2.5, is particularly concerning due to its small size, which allows it to penetrate deep into the lungs and even enter the bloodstream. PM2.5 refers to particulate matter that is 2.5 micrometers in diameter or smaller, originating from various sources including vehicle emissions, industrial discharges, and natural occurrences such as wildfires. The mean annual exposure to PM2.5, measured in micrograms per cubic meter, serves as an essential indicator of air quality and environmental health.

The importance of monitoring PM2.5 levels cannot be overstated. High exposure to PM2.5 is linked to various adverse health effects, including respiratory and cardiovascular diseases, and is a significant factor in premature mortality. In 2020, the global median exposure to PM2.5 was reported at 20.03 micrograms per cubic meter. This figure reflects the exposure levels that affect millions of individuals worldwide, impacting public health, economic productivity, and quality of life.

Understanding PM2.5 levels in conjunction with other indicators, such as general air quality indices and health statistics, helps policymakers and researchers to assess the effectiveness of environmental regulations. Moreover, PM2.5 exposure is closely related to other environmental health concerns like nitrogen dioxide (NO2) and sulfur dioxide (SO2) levels, which further complicate air pollution management. For instance, cities with heightened traffic often experience elevated levels of both PM2.5 and NO2, exacerbating respiratory conditions among residents.

Several factors influence PM2.5 levels and their variations across different regions. Industrial activities, urban density, and energy production methods are significant contributors. Countries with heavy reliance on coal for energy, such as Niger and Qatar, often report higher PM2.5 levels due to emissions from power plants and manufacturing. The top five areas with the highest PM2.5 exposures in 2020 illustrate this trend: Niger at 85.12 micrograms per cubic meter, Qatar at 75.66, Mauritania at 70.82, Senegal at 63.74, and Bahrain at 58.5. The high values in these nations are indicative of their reliance on fossil fuel-based industrial processes, inadequate regulatory measures, and the inherent challenges of monitoring and enforcing air quality standards in developing regions.

On the contrary, some countries show remarkably low PM2.5 levels, such as Finland with 4.9 micrograms per cubic meter, Iceland at 5.11, and Sweden at 5.64. These nations have taken proactive measures to implement stricter emissions regulations, promote clean energy technologies, and reduce the burning of fossil fuels. Their approaches demonstrate that effective policies can significantly enhance air quality and protect public health.

Strategies to improve PM2.5 levels typically involve adopting cleaner technologies, increasing energy efficiency, and enhancing public transportation systems. Transitioning from coal and other fossil fuels to renewable energy sources, such as wind and solar, is critical to reducing PM2.5 emissions. Additionally, promoting electric vehicles and improving fuel standards can play an essential role in decreasing pollutant levels, especially in densely populated urban centers.

Community awareness and participation are also crucial in combatting air pollution. Public campaigns to reduce emissions from vehicles and the implementation of green spaces in urban planning can lead to a substantial reduction in PM2.5 levels. For example, cities that have prioritized the development of parks, trees, and vegetation not only improve aesthetics but also facilitate higher air quality through natural filtration processes.

However, challenges remain. Many developing nations often lack the financial resources and infrastructure to implement comprehensive air quality management strategies. Furthermore, there can be a lack of political will to enforce existing regulations or change energy production methods due to economic pressures or reliance on traditional industries. The disparity between countries that successfully maintain low PM2.5 levels and those struggling with elevated concentrations underscores the need for international cooperation in addressing air pollution rather than treating it solely as a national issue.

An analysis of historical data reveals a gradual decline in global PM2.5 levels over the past three decades, indicating progress in air quality management efforts. The world values show a decrease from 39.66 micrograms per cubic meter in 1990 to 31.32 micrograms in 2020, demonstrating a collective movement toward cleaner air. However, this progress must be accelerated, especially given the increasing urbanization and energy demands from a growing global population.

In conclusion, PM2.5 air pollution is a critical health and environmental indicator that requires urgent attention. The significant variation in exposure levels across different geographic areas highlights the necessity for tailored approaches to air quality management. Through concerted efforts in policy implementation, technological advancement, and public engagement, the global community can continue to make strides toward cleaner air and healthier societies for all.

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'EN.ATM.PM25.MC.M3'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)
                    
                

                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'EN.ATM.PM25.MC.M3'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))