Air Pollution Dynamics in Shanghai : Trends from 2014 to 2024

Air pollution remains a critical environmental and public health challenge in Shanghai, driven by seasonal variations in criteria pollutants such as PM2.5, PM10, SO2, NO2, O3, and CO. This study examines monthly and long-term trends in air quality using data from 2014 to 2024, highlighting the influence of anthropogenic emissions, and regulatory measures. The results indicate that particulate matter (PM2.5 and PM10) and NO2 peak during winter due to increased coal-based heating, industrial activities, and atmospheric stagnation, while O3 concentrations rise in summer due to enhanced photochemical reactions. SO2 and CO levels remain relatively stable throughout the year, reflecting the success of emission control policies. The Air Quality Index (AQI) closely aligns with PM concentrations, with the highest pollution levels occurring in winter and the lowest in autumn. Many pollutants, particularly PM2.5 and NO2, exceed World Health Organization (WHO) guidelines, posing significant health risks. The findings underscore the urgent need for season-specific mitigation strategies, including stricter winter emission controls and summer ozone management policies. Enhancing air quality requires comprehensive approaches such as transitioning to clean energy, improving industrial emissions standards, and expanding public transportation infrastructure.

Authors

Institutions

Publication Details

Journal
International Journal of Scientific Research in Science and Technology
Published
2025-04-02
DOI
https://doi.org/10.32628/ijsrst25122241
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Air Pollution Dynamics in Shanghai : Trends from 2014 to 2024

Anbu Clemensis Johnson
International Journal of Scientific Research in Science and Technology
Air Quality Monitoring and Forecasting
article

Air Pollution Dynamics in Shanghai : Trends from 2014 to 2024

Anbu Clemensis Johnson
article en

Abstract

Air pollution remains a critical environmental and public health challenge in Shanghai, driven by seasonal variations in criteria pollutants such as PM2.5, PM10, SO2, NO2, O3, and CO. This study examines monthly and long-term trends in air quality using data from 2014 to 2024, highlighting the influence of anthropogenic emissions, and regulatory measures. The results indicate that particulate matter (PM2.5 and PM10) and NO2 peak during winter due to increased coal-based heating, industrial activities, and atmospheric stagnation, while O3 concentrations rise in summer due to enhanced photochemical reactions. SO2 and CO levels remain relatively stable throughout the year, reflecting the success of emission control policies. The Air Quality Index (AQI) closely aligns with PM concentrations, with the highest pollution levels occurring in winter and the lowest in autumn. Many pollutants, particularly PM2.5 and NO2, exceed World Health Organization (WHO) guidelines, posing significant health risks. The findings underscore the urgent need for season-specific mitigation strategies, including stricter winter emission controls and summer ozone management policies. Enhancing air quality requires comprehensive approaches such as transitioning to clean energy, improving industrial emissions standards, and expanding public transportation infrastructure.

International Journal of Scientific Research in Science and TechnologyVol. 12(2)
Nanjing University of Information Science and Technology (CN)
Openalex Percentile: Top 94%
Air Quality Monitoring and Forecasting
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.