Fine Dust: Improving Prediction Models

Image courtesy of Wikimedia Commons.

For those living in China, the simple act of stepping outside poses a major health risk. With each breath, people’s lungs are filled with loads of tiny dust particles known as fine particulate matter (PM2.5). After breathing this air day after day, people are likely to experience throat and lung irritation. Medical conditions such as asthma and heart disease are often worsened by long-term exposure to air pollution.  

Researchers at the Nanjing University of Information Science and Technology have discovered that there are more of these particles in the air than previously predicted. The existing Weather Research and Forecasting model underestimated sulfate concentrations by eighty-one percent while overestimating nitrate by 184 percent and ammonium by fifty-seven percent. 

By taking the amount of water in clouds into account, researcher Tong Sha developed a model that more accurately portrayed the sulfate, nitrate, and ammonium concentrations in the air. Because much of the sulfate in the air ends up in clouds, cloud water content had to be analyzed. Supplementing the model with satellite observations of factors such as cloud cover, therefore, created more precise predictions to reflect atmospheric observations.

This new model will aid in creating better strategies to improve air quality. “When the fog appears, we should adhere to the emission control strategies and reduce the sharp increase of PM2.5 concentration after the fog dissipation,” Tong Sha said. Factory emissions should be reduced for a significant period of time following foggy days, since the high water content in clouds can retain more particulate matter.

This study will serve to reduce the impact of normalized mean bias, a type of bias in which people minimize environmental threats. By creating a more accurate model for studying fine particulate matter, this research will help combat air pollution.