UTILISING ATMOSPHERIC SIMULATION AND DATA ASSIMILATION TO IMPROVE THE UNDERSTANDING OF HAZE EMISSIONS IN THE CHINESE REGION.

Abstract

Haze emissions continue to be one of the main difficulties for the environment in China. They are harmful to the environment, people's health, and the economy. The rapid growth of cities, the expansion of industry and the increasing energy demand have all increased fine particulate matter (PM) emissions. This has caused a broad haze to form repeatedly in numerous areas. Even though air quality has improved because of measures to cut down on emissions, haze is still a concern. This indicates how complicated the causes of haze are. Everyone must recognise the strong link between haze emissions and weather conditions since haze episodes continue even if the government tries to make the air cleaner. This study suggests that combining atmospheric models with data assimilation might help to understand how haze emissions change throughout China. The study used satellite observations to address uncertainties in emission inventories and climate data using sophisticated atmospheric models to depict the transport, transformation, and origin of pollutants. A stratified sample of 452 respondents from diverse professional backgrounds was used in the quantitative approach. The SPSS 25 study which includes ANOVA and factor analysis shows strong links between haze severity and weather elements including temperature, humidity, speed of the wind and airflow pressure. Simulation and data assimilation work together to develop fuzzy behaviour models that are more precise and dependable. This allows individuals to grow better at making predictions. This all-encompassing strategy provides Chinese researchers with practical tools to make comprehensive laws that would help lessen the negative consequences of haze emissions on the environment and human health.