Quantitative association analysis between PM2.5 concentration and factors on industry, energy, agriculture, and transportation

Abstract

Rapid urbanization is causing serious PM2.5 (particulate matter ≤2.5 μm) pollution in China. However, the impacts of human activities (including industrial production, energy production, agriculture, and transportation) on PM2.5 concentrations have not been thoroughly studied. In this study, we obtained a regression formula for PM2.5 concentration based on more than 1 million PM2.5 recorded values and data from meteorology, industrial production, energy production, agriculture, and transportation for 31 provinces of mainland China between January 2013 and May 2017. We used stepwise regression to process 49 factors that influence PM2.5 concentration, and obtained the 10 primary influencing factors. Data of PM2.5 concentration and 10 factors from June to December, 2017 was used to verify the robustness of the model. Excluding meteorological factors, production of natural gas, industrial boilers, and ore production have the highest association with PM2.5 concentration, while nuclear power generation is the most positive factor in decreasing PM2.5 concentration. Tianjin, Beijing, and Hebei provinces are the most vulnerable to high PM2.5 concentrations caused by industrial production, energy production, agriculture, and transportation (IEAT).

A new approach to evaluate regional inequity determined by PM2.5 emissions and concentrations

“…According to previous studies, wind speed, humidity, rainfall and temperature can significantly affect PM 2.5 concentrations. First, a negative correlation was found between wind speed and PM 2.5 widely across China; higher wind speed can reduce the concentrations ( Stortini et al, 2009 ; Zhang et al, 2018 ). The coastline regions, where there are better atmospheric diffusion conditions, benefit from emissions transportation in the atmosphere, which reduces regional pollution.…”

“…This emission source structure is generally similar to the PM 2.5 emission data from the MEIC in Asia ( Kurokawa et al, 2013 ; Li et al, 2017 ), indicating that our analysis is credible. Thus, PM 2.5 emission reduction should be focused on these sectors, and effective measures should be strictly implemented, such as using clean energy to replace coal-burning power plants ( Zhang et al, 2018 ), strengthening industrial and vehicle emission standards, closing small and polluting factories, and upgrading industrial boilers ( Zhang et al, 2019 ; Wang et al, 2020 ).…”

“…Additionally, most of China's developed regions are located along the coastline; along with the influence of domestic trade, the advantages for these provinces were expanded. Studies have found that temperature is negatively correlated with PM 2.5 concentrations; an increase in temperature will help reduce PM 2.5 emissions ( Zhang et al, 2016b , 2018 ). Another important factor is rainfall; annual accumulative rainfall was also found to be negatively correlated with the PM 2.5 emissions ( Ozbek et al, 2016 ; Chen et al, 2020 ).…”

“…Long-term exposure to PM 2.5 has been consistently linked to cardiovascular and respiratory diseases, such as asthma, tracheitis and even lung cancer ( Kioumourtzoglou et al, 2016 ; Zhang et al, 2016a ). In 2013, China implemented the Air Pollution Prevention and Control Action Plan, which aimed to reduce PM 2.5 concentrations by 25% by 2017 compared with the level in 2012 ( Huang et al, 2014 ; Zhang et al, 2018 ). China made great efforts to meet this goal, such as restrictions on road traffic and construction dust, strengthening emission standards for main industries, and closing many factories that caused serious pollution.…”

“…Once emitted, the emissions will be influenced by various factors and transported in the atmosphere; therefore, nearby or even faraway regions will also be affected. By considering the main meteorological factors, many models, such as the WRF-Chem model, the GEOS-Chem model, and the CMAQ model, have been developed and used to simulate PM 2.5 transportation and concentrations ( Chemel et al, 2014 ; Lee et al, 2017 ; Zhang et al, 2018 ). However, to reduce regional concentrations, natural factors are unnecessary or difficult to control, and emissions control remains the most effective approach.…”