Machine learning practice 14-Analysis of the impact of nuclear power plants on population in the context of sewage discharge from the Fukushima nuclear power plant in Japan

Hello everyone, I am Weixue AI, and today I will introduce to you machine learning practice 14-under the background of the sewage discharge from the Fukushima nuclear power plant in Japan, the analysis practice of the nuclear power plant's impact on the population.
Recently, the Japanese government held a meeting of cabinet members and decided to launch the discharge of Fukushima nuclear contaminated water into the sea on August 24, 2023. At 13:00 local time on August 24, 2023, the Fukushima Daiichi Nuclear Power Plant in Japan started to discharge nuclear contaminated water into the sea. The nuclear sewage of the Fukushima Daiichi Nuclear Power Plant contains a variety of radioactive substances. Harmful to the human body, among them, strontium-90 can cause bone tissue sarcoma and leukemia; cesium-137 can cause soft tissue tumors and cancer; iodine-129 can easily cause thyroid cancer; carbon-14 may damage human DNA.

1. Radioactive substances

There are three main types of rays in radioactive substances, which are alpha rays (α), beta rays (β) and gamma rays (γ):
1. Alpha rays ( $Alpha$ rays): Alpha rays are beams of charged particles made up of helium nuclei. Since they contain two protons and two neutrons, they have a positive charge. Alpha rays have weak penetrating ability, and generally can only penetrate a few centimeters of air or a few microns of solids, so alpha rays usually cannot pass through thin materials such as the human body or paper. However, if ingested or inhaled internally, it may cause greater harm to the human body.

2. Beta rays ( $Beta$ rays): Beta rays are particle beams composed of charged high-speed electrons or positrons. Electron rays are called$b^{-}$ rays, while positron rays are called$b^{+}$ rays. Beta rays are more penetrating than alpha rays and can penetrate air and some thinner solid substances. However, beta rays still have relatively limited penetration and can be effectively blocked with proper shielding.

3. Gamma rays ( $gamma$ rays): Gamma rays are high-energy electromagnetic radiation, similar to X-rays. Unlike alpha and beta rays, gamma rays don't carry any charges or particles and are therefore not affected by electric or magnetic fields. Gamma rays are very penetrating and can penetrate most common substances, including human tissue. To effectively shield against gamma rays, thicker lead, concrete or other dense materials are usually required.

2. Nuclear reactions of three kinds of rays

Here is an example of a typical nuclear reaction equation for three rays:

1. Alpha rays ( $a$ ) reaction equation:
$$\begin{array}{c}{}_Z^{A}X{\to }_{Z-2}^{A-4}Y{+}_2^4\end{array}$$

Here $X$ represents the starting element,$Y$ represents the generated element,${}_Z^A$Indicates that the atomic number is $Z$ , the mass number is$A$ 's nucleus.

2. Beta rays ( $b$ ) reaction equation:
$$\begin{array}{c}{}_Z^{A}X{\to }_{Z+1}^{A}Y+e^{-}+\overline{n_e}\end{array}$$

Here $X$ represents the starting element,$Y$ represents the generated element,${}_Z^A$Indicates that the atomic number is $Z$ , the mass number is$A$ 's nucleus. $e^{-}$ represents negative electrons (electrons),$\overline{n_e}$stands for antineutrinos.

3. Gamma rays ( $\gamma$) 反应方程：
$$\begin{array}{c}{}_Z^A{X}^{\ast }{\to }_Z^{A}X+\end{array}$$

Here $X^{\ast }$ represents the nucleus of the excited state,$X$ represents the core of the ground state,$\gamma$ denotes gamma rays.

3. Data loading of nuclear power plants

Data download address: Link: https://pan.baidu.com/s/1wz5L2ykpjUNlKs2icTWkNg?pwd=2j0r
Extraction code: 2j0r

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

df = pd.read_csv('nuclear.csv', delimiter=',')

countries_shortNames = [['UNITED STATES OF AMERICA', 'USA'], \
                        ['RUSSIAN FEDERATION', 'RUSSIA'], \
                        ['IRAN, ISLAMIC REPUBLIC OF', 'IRAN'], \
                        ['KOREA, REPUBLIC OF', 'SOUTH KOREA'], \
                        ['TAIWAN, CHINA', 'CHINA']]
for shortName in countries_shortNames:
    df = df.replace(shortName[0], shortName[1])

3. World distribution of nuclear power plants

import folium
import matplotlib.cm as cm
import matplotlib.colors as colors

latitude, longitude = 40, 10.0
map_world_NPP = folium.Map(location=[latitude, longitude], zoom_start=2)

viridis = cm.get_cmap('viridis', df['NumReactor'].max())
colors_array = viridis(np.arange(df['NumReactor'].min() - 1, df['NumReactor'].max()))
rainbow = [colors.rgb2hex(i) for i in colors_array]

for nReactor, lat, lng, borough, neighborhood in zip(df['NumReactor'].astype(int), df['Latitude'].astype(float),
                                                     df['Longitude'].astype(float), df['Plant'], df['NumReactor']):
    label = '{}, {}'.format(neighborhood, borough)
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=3,
        popup=label,
        color=rainbow[nReactor - 1],
        fill=True,
        fill_color=rainbow[nReactor - 1],
        fill_opacity=0.5).add_to(map_world_NPP)

# 在地图上显示
map_world_NPP.save('world_map.html')  # 保存为 HTML 文件
# 然后打开world_map.html 文件 可以看到

4. Comparison of the 20 countries with the most nuclear reactors

countries = df['Country'].unique()
df_count_reactor = [[i, df[df['Country'] == i]['NumReactor'].sum(), df[df['Country'] == i]['Region'].iloc[0]] for i in
                    countries]
df_count_reactor = pd.DataFrame(df_count_reactor, columns=['Country', 'NumReactor', 'Region'])
df_count_reactor = df_count_reactor.set_index('Country').sort_values(by='NumReactor', ascending=False)[:20]
ax = df_count_reactor.plot(kind='bar', stacked=True, figsize=(10, 3),
                           title='The 20 Countries With The Most Nuclear Reactors in 2010')
ax.set_ylim((0, 150))
for p in ax.patches:
    ax.annotate(str(p.get_height()), xy=(p.get_x(), p.get_height() + 2))
df_count_reactor['Country'] = df_count_reactor.index
sns.set(rc={
    
    'figure.figsize': (11.7, 8.27)})
sns.set_style("whitegrid")
plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
ax = sns.barplot(x="NumReactor", y="Country", hue="Region", data=df_count_reactor, dodge=False, orient='h')
ax.set_title('2010年拥有最多核反应堆的20个国家', fontsize=16)
ax.set_xlabel('Reactors', fontsize=16)
ax.set_ylabel('')
ax.legend(fontsize='14')

plt.show()

V. Analysis of the population exposed to nuclear power plants

def getMostExposedNPP(Exposedradius):
    df_pop_sort = df.sort_values(by=str('p10_' + str(Exposedradius)), ascending=False)[:10]
    df_pop_sort['Country'] = df_pop_sort['Plant'] + ',\n' + df_pop_sort['Country']
    df_pop_sort = df_pop_sort.set_index('Country')
    df_pop_sort = df_pop_sort.rename(
        columns={
    
    str('p90_' + str(Exposedradius)): '1990', str('p00_' + str(Exposedradius)): '2000',
                 str('p10_' + str(Exposedradius)): '2010'})
    df_pop_sort = df_pop_sort[['1990', '2000', '2010']] / 1E6
    ax = df_pop_sort.plot(kind='bar', stacked=False, figsize=(10, 4))
    ax.set_ylabel('Population Exposure in millions', size=14)
    ax.set_title(
        'Location of nuclear power plants \n with the most exposed population \n within ' + Exposedradius + ' km radius',
        size=16)
    print(df_pop_sort['2010'])

getMostExposedNPP('30')


latitude, longitude = 40, 10.0
map_world_NPP = folium.Figure(width=100, height=100)
map_world_NPP = folium.Map(location=[latitude, longitude], zoom_start=2)

for nReactor, lat, lng, borough, neighborhood in zip(df['NumReactor'].astype(int), df['Latitude'].astype(float),
                                                     df['Longitude'].astype(float), df['Plant'], df['NumReactor']):
    label = '{}, {}'.format(neighborhood, borough)
    label = folium.Popup(label, parse_html=True)
    folium.Circle(
        [lat, lng],
        radius=30000,
        popup=label,
        color='grey',
        fill=True,
        fill_color='grey',
        fill_opacity=0.5).add_to(map_world_NPP)

Exposedradius = '30'
df_sort = df.sort_values(by=str('p10_' + str(Exposedradius)), ascending=False)[:10]

for nReactor, lat, lng, borough, neighborhood in zip(df_sort['NumReactor'].astype(int),
                                                     df_sort['Latitude'].astype(float),
                                                     df_sort['Longitude'].astype(float), df_sort['Plant'],
                                                     df_sort['NumReactor']):
    label = '{}, {}'.format(neighborhood, borough)
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=5,
        popup=label,
        color='red',
        fill=True,
        fill_color='red',
        fill_opacity=0.25).add_to(map_world_NPP)

for nReactor, lat, lng, borough, neighborhood in zip(df_sort['NumReactor'].astype(int),
                                                     df_sort['Latitude'].astype(float),
                                                     df_sort['Longitude'].astype(float), df_sort['Plant'],
                                                     df_sort['NumReactor']):
    label = '{}, {}'.format(neighborhood, borough)
    label = folium.Popup(label, parse_html=True)
    folium.Circle(
        [lat, lng],
        radius=30000,
        popup=label,
        color='red',
        fill=True,
        fill_color='red',
        fill_opacity=0.25).add_to(map_world_NPP)
# 在地图上显示
map_world_NPP.save('world_map2.html')  # 保存为 HTML 文件

6. Summary

If the nuclear power plant is close to a densely populated area, the discharge of nuclear contaminated water into the sea may have some serious impacts on the surrounding population:

1. Health risks: Radioactive substances pose a potential threat to human health. If nuclear contaminated water is discharged into the ocean, it may enter the human food supply chain through the marine food chain, thereby increasing the risk of ingesting radioactive substances in food. Improper exposure or ingestion of these substances can lead to chronic diseases such as cancer and other health problems associated with radioactive materials.

2. Social and psychological impact: Nuclear accidents may cause social and psychological stress and anxiety. Residents living near the Fukushima nuclear power plant may face forced evacuations, loss of homes, and instability in their lives, which pose challenges to their mental health and social resilience.

3. Economic impact: The nuclear accident has had a continuous impact on the local economy. The accident at the nuclear power plant led to extensive shutdowns and evacuations, severely affecting the livelihoods and economic activities of local residents and businesses. In addition, the nuclear accident has negatively affected local industries such as tourism, agriculture and fisheries, further exacerbating economic difficulties.