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城市空气污染数据可视化分析系统-系统功能
基于大数据的城市空气污染数据分析系统是一套专门针对城市空气质量监测与分析的综合性大数据处理平台，采用Hadoop+Spark分布式大数据框架作为核心技术架构，能够高效处理海量的城市空气污染监测数据。系统采用Python作为主要开发语言，结合Django后端框架构建稳定的服务层，前端采用Vue+ElementUI+Echarts技术栈打造直观友好的数据可视化界面，通过MySQL数据库进行数据持久化存储。系统核心功能涵盖五大分析维度：首先是污染时空分布特征分析，包括不同城市污染水平对比、污染物浓度年际变化趋势分析、季节性变化模式分析、日变化规律分析以及工作日与周末污染差异分析，全方位展现污染物的时空分布规律；其次是污染物关联性分析，通过计算PM2.5、PM10、NO2、SO2、CO、O3等主要污染物之间的相关系数矩阵，深入探究污染物协同变化关系和相互转化机制；第三是气象因素影响分析，系统能够分析温度、湿度、风速、气压、降水、能见度等气象条件对污染物扩散的影响程度；第四是空气质量综合评价，通过AQI等级分布统计、优良率计算等方式全面评估城市空气质量状况；最后是重污染成因分析，识别重污染天气的主要污染物和对应的气象成因。系统充分利用Spark SQL和Pandas进行高效的数据清洗与处理，运用NumPy进行数值计算，确保分析结果的准确性，同时通过Echarts实现丰富的图表展示，为环保部门决策提供科学的数据支撑。
城市空气污染数据可视化分析系统-技术选型
大数据框架：Hadoop+Spark
开发语言：Python
后端框架：Spring+SpringMVC+Mybatis
前端：Vue+ElementUI
详细技术点：Hadoop、Spark
数据库：MySQL
城市空气污染数据可视化分析系统-背景意义
选题背景 近年来，随着中国城镇化进程的快速推进和工业发展的不断加速，城市空气污染问题日益突出，成为影响公众健康和社会可持续发展的重要因素。2024年全国PM2.5浓度为29.3微克/立方米，同比下降2.7%，虽然整体呈现改善趋势，但空气质量管理仍面临严峻挑战。北京市2024年实现了四项主要污染物PM2.5、PM10、NO2、SO2浓度分别下降65.9%、50.0%、57.1%、88.7%，这样的治理成效背后需要大量数据支撑和科学分析。目前全国环境空气质量监测网络已覆盖300多个城市，2014年5月以来的全国范围的到站点的逐时空气质量数据，包括AQI、PM2.5、PM10、SO2、NO2、O3、CO等指标产生了海量的监测数据。然而，传统的数据处理方法在面对如此庞大的数据量时显得力不从心，无法充分挖掘数据中蕴含的污染规律和关联关系，这就迫切需要运用大数据技术来构建更加智能化的空气污染分析系统，为环境治理决策提供科学依据。 选题意义 本课题具有重要的实际应用价值和理论研究意义。从实际应用角度来看，该系统能够帮助环保部门更好地理解城市空气污染的时空分布规律，通过分析不同城市、不同季节、不同时段的污染物浓度变化特征，为制定精准的污染防控措施提供数据支撑。通过深入分析PM2.5、PM10、NO2等污染物之间的关联性以及它们与气象因素的关系，能够帮助相关部门准确识别污染来源，预测污染发展趋势，从而实现从被动治理向主动预防的转变。同时，系统提供的AQI等级分布统计和重污染天气成因分析功能，能够为公众提供更加直观的空气质量信息，提高民众的环保意识。从技术发展角度来看，本课题将Hadoop、Spark等大数据技术与环境监测数据相结合，探索了大数据技术在环境保护领域的创新应用，为相关技术研究提供了实践案例。该系统通过Vue、ElementUI、Echarts等现代前端技术实现数据的可视化展示，使复杂的环境数据分析结果能够以更加直观、易懂的方式呈现，提升了大数据分析在环保领域的实用性和普及性，对推动环保信息化建设具有积极的示范作用。
城市空气污染数据可视化分析系统-演示视频
城市空气污染数据可视化分析系统-演示图片
城市空气污染数据可视化分析系统-代码展示
def analyze_city_pollution_comparison(spark_session):
# 读取空气污染数据
pollution_df = spark_session.read.csv("hdfs://localhost:9000/pollution_data.csv", header=True, inferSchema=True)
pollution_df.createOrReplaceTempView("pollution_data")
# 计算各城市主要污染物年均浓度
city_avg_sql = """
SELECT City,
ROUND(AVG(PM25), 2) as avg_pm25,
ROUND(AVG(PM10), 2) as avg_pm10,
ROUND(AVG(AQI), 2) as avg_aqi,
ROUND(AVG(NO2), 2) as avg_no2,
ROUND(AVG(SO2), 2) as avg_so2,
ROUND(AVG(CO), 2) as avg_co,
ROUND(AVG(O3), 2) as avg_o3
FROM pollution_data
GROUP BY City
ORDER BY avg_pm25 DESC
"""
city_comparison_result = spark_session.sql(city_avg_sql)
# 计算污染等级分布统计
pollution_level_sql = """
SELECT City,
SUM(CASE WHEN AQI <= 50 THEN 1 ELSE 0 END) as excellent_days,
SUM(CASE WHEN AQI > 50 AND AQI <= 100 THEN 1 ELSE 0 END) as good_days,
SUM(CASE WHEN AQI > 100 AND AQI <= 150 THEN 1 ELSE 0 END) as light_pollution_days,
SUM(CASE WHEN AQI > 150 AND AQI <= 200 THEN 1 ELSE 0 END) as moderate_pollution_days,
SUM(CASE WHEN AQI > 200 THEN 1 ELSE 0 END) as heavy_pollution_days,
COUNT(*) as total_days
FROM pollution_data
GROUP BY City
"""
pollution_level_result = spark_session.sql(pollution_level_sql)
# 计算各城市空气质量优良率
air_quality_rate_sql = """
SELECT City,
ROUND((SUM(CASE WHEN AQI <= 100 THEN 1 ELSE 0 END) * 100.0 / COUNT(*)), 2) as good_air_rate
FROM pollution_data
GROUP BY City
ORDER BY good_air_rate DESC
"""
air_quality_rate_result = spark_session.sql(air_quality_rate_sql)
# 保存分析结果
city_comparison_result.toPandas().to_csv("city_pollution_comparison.csv", index=False)
pollution_level_result.toPandas().to_csv("city_pollution_level_distribution.csv", index=False)
air_quality_rate_result.toPandas().to_csv("city_air_quality_rate.csv", index=False)
return city_comparison_result, pollution_level_result, air_quality_rate_result
def analyze_pollutant_correlation(spark_session):
# 读取污染数据进行关联性分析
pollution_df = spark_session.read.csv("hdfs://localhost:9000/pollution_data.csv", header=True, inferSchema=True)
# 转换为Pandas DataFrame进行相关性计算
pandas_df = pollution_df.select("PM25", "PM10", "NO2", "SO2", "CO", "O3").toPandas()
# 计算污染物相关系数矩阵
correlation_matrix = pandas_df.corr(method='pearson').round(3)
correlation_matrix.to_csv("pollutant_correlation_matrix.csv")
# 计算PM2.5与PM10的比值分析
pollution_df.createOrReplaceTempView("pollution_data")
pm_ratio_sql = """
SELECT City, Season,
ROUND(AVG(PM25), 2) as avg_pm25,
ROUND(AVG(PM10), 2) as avg_pm10,
ROUND(AVG(PM25/PM10), 3) as pm25_pm10_ratio,
COUNT(*) as record_count
FROM pollution_data
WHERE PM10 > 0
GROUP BY City, Season
ORDER BY City, Season
"""
pm_ratio_result = spark_session.sql(pm_ratio_sql)
# 分析O3与前体物NO2、CO的关系
o3_precursor_sql = """
SELECT HOUR(datetime) as hour_of_day,
ROUND(AVG(O3), 2) as avg_o3,
ROUND(AVG(NO2), 2) as avg_no2,
ROUND(AVG(CO), 2) as avg_co,
ROUND(AVG(Temperature), 2) as avg_temperature,
COUNT(*) as record_count
FROM pollution_data
GROUP BY HOUR(datetime)
ORDER BY hour_of_day
"""
o3_precursor_result = spark_session.sql(o3_precursor_sql)
# 计算不同污染物浓度等级的分布
pollutant_level_sql = """
SELECT
SUM(CASE WHEN PM25 <= 35 THEN 1 ELSE 0 END) as pm25_good,
SUM(CASE WHEN PM25 > 35 AND PM25 <= 75 THEN 1 ELSE 0 END) as pm25_moderate,
SUM(CASE WHEN PM25 > 75 THEN 1 ELSE 0 END) as pm25_unhealthy,
SUM(CASE WHEN O3 <= 100 THEN 1 ELSE 0 END) as o3_good,
SUM(CASE WHEN O3 > 100 AND O3 <= 160 THEN 1 ELSE 0 END) as o3_moderate,
SUM(CASE WHEN O3 > 160 THEN 1 ELSE 0 END) as o3_unhealthy
FROM pollution_data
"""
pollutant_level_result = spark_session.sql(pollutant_level_sql)
# 保存关联性分析结果
pm_ratio_result.toPandas().to_csv("pm25_pm10_ratio_analysis.csv", index=False)
o3_precursor_result.toPandas().to_csv("o3_precursor_relationship.csv", index=False)
pollutant_level_result.toPandas().to_csv("pollutant_concentration_levels.csv", index=False)
return correlation_matrix, pm_ratio_result, o3_precursor_result
def analyze_meteorological_impact(spark_session):
# 读取包含气象数据的污染数据
weather_pollution_df = spark_session.read.csv("hdfs://localhost:9000/pollution_weather_data.csv", header=True, inferSchema=True)
weather_pollution_df.createOrReplaceTempView("weather_pollution")
# 分析气象因素与PM2.5浓度的关系
weather_pm25_sql = """
SELECT
ROUND(AVG(Temperature), 2) as avg_temperature,
ROUND(AVG(Humidity), 2) as avg_humidity,
ROUND(AVG(Wind_Speed), 2) as avg_wind_speed,
ROUND(AVG(Pressure), 2) as avg_pressure,
ROUND(AVG(Visibility), 2) as avg_visibility,
ROUND(AVG(PM25), 2) as avg_pm25,
CASE
WHEN PM25 <= 35 THEN 'Good'
WHEN PM25 <= 75 THEN 'Moderate'
ELSE 'Unhealthy'
END as pm25_level,
COUNT(*) as record_count
FROM weather_pollution
GROUP BY CASE
WHEN PM25 <= 35 THEN 'Good'
WHEN PM25 <= 75 THEN 'Moderate'
ELSE 'Unhealthy'
END
ORDER BY avg_pm25
"""
weather_pm25_result = spark_session.sql(weather_pm25_sql)
# 分析不同风速等级对污染物扩散的影响
wind_impact_sql = """
SELECT
CASE
WHEN Wind_Speed < 2 THEN 'Low_Wind'
WHEN Wind_Speed >= 2 AND Wind_Speed < 5 THEN 'Moderate_Wind'
ELSE 'High_Wind'
END as wind_level,
ROUND(AVG(PM25), 2) as avg_pm25,
ROUND(AVG(PM10), 2) as avg_pm10,
ROUND(AVG(NO2), 2) as avg_no2,
ROUND(AVG(AQI), 2) as avg_aqi,
COUNT(*) as record_count
FROM weather_pollution
GROUP BY CASE
WHEN Wind_Speed < 2 THEN 'Low_Wind'
WHEN Wind_Speed >= 2 AND Wind_Speed < 5 THEN 'Moderate_Wind'
ELSE 'High_Wind'
END
ORDER BY avg_pm25 DESC
"""
wind_impact_result = spark_session.sql(wind_impact_sql)
# 分析不同天气状况下的污染特征
weather_condition_sql = """
SELECT Weather_Condition,
ROUND(AVG(PM25), 2) as avg_pm25,
ROUND(AVG(PM10), 2) as avg_pm10,
ROUND(AVG(AQI), 2) as avg_aqi,
ROUND(AVG(Visibility), 2) as avg_visibility,
ROUND(AVG(Humidity), 2) as avg_humidity,
COUNT(*) as record_count
FROM weather_pollution
GROUP BY Weather_Condition
ORDER BY avg_pm25 DESC
"""
weather_condition_result = spark_session.sql(weather_condition_sql)
# 分析湿度与能见度的关系
humidity_visibility_sql = """
SELECT
CASE
WHEN Humidity < 50 THEN 'Low_Humidity'
WHEN Humidity >= 50 AND Humidity < 80 THEN 'Moderate_Humidity'
ELSE 'High_Humidity'
END as humidity_level,
ROUND(AVG(Visibility), 2) as avg_visibility,
ROUND(AVG(PM25), 2) as avg_pm25,
ROUND(AVG(Humidity), 2) as avg_humidity,
COUNT(*) as record_count
FROM weather_pollution
GROUP BY CASE
WHEN Humidity < 50 THEN 'Low_Humidity'
WHEN Humidity >= 50 AND Humidity < 80 THEN 'Moderate_Humidity'
ELSE 'High_Humidity'
END
ORDER BY avg_pm25 DESC
"""
humidity_visibility_result = spark_session.sql(humidity_visibility_sql)
# 保存气象影响分析结果
weather_pm25_result.toPandas().to_csv("weather_pm25_correlation.csv", index=False)
wind_impact_result.toPandas().to_csv("wind_pollution_impact.csv", index=False)
weather_condition_result.toPandas().to_csv("weather_condition_pollution.csv", index=False)
humidity_visibility_result.toPandas().to_csv("humidity_visibility_relationship.csv", index=False)
return weather_pm25_result, wind_impact_result, weather_condition_result
城市空气污染数据可视化分析系统-结语
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