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Ambient nitrogen dioxide (NO2) not only has adverse health effects on humans but also contributes to the production of two major secondary atmospheric pollutants, ozone (O3) and fine particulate matter (PM2.5). In this study, surface NO2 concentrations in East Asia from 2001 to 2016 were estimated by combining an ensemble backpropagation neural network method, satellite NO2 column data, and reanalysis data. The estimated monthly and annual mean NO2 concentrations were well‐correlated with the observations, with R (correlation coefficient) values of 0.89 and 0.91, respectively. Our results indicate that the NO2 concentrations in most areas of East Asia peaked during 2011–2013. The NO2 concentrations in autumn and winter, especially in the eastern and northern parts of China, were much higher than those in summer. In terms of population NO2 exposure, over 25 million South Korea residents (∼45% of the population) were exposed to NO2 concentrations higher than the 2005 World Health Organization's annual standard (40 μg/m3, ∼22 ppbv at 25°C) in 2015. In contrast, the entire populations of some developing countries, such as Vietnam, Cambodia, Myanmar, the Philippines, and Lao PDR, were exposed to NO2 concentrations less than 14 ppbv. Based on the estimation, NO2‐related asthma cases in East Asia increased by 1.37% annually from 2001 to 2015, reaching 139,000 cases (95% confidence interval: 37,400–263,400) in 2015. NOx emission inventories vary from country to country in East Asia; thus, more targeted NOx emission‐control policies are urgently required. Plain Language Summary: As one of the major atmospheric pollutants, NO2 contributes to the formation of several secondary atmospheric pollutants and the development of respiratory diseases. Satellite NO2 data and meteorological factors are commonly used to retrieve surface NO2 concentrations. Several regression methods have been adopted to construct models to estimate NO2 pollution concentrations; however, limitations such as low resolution and relatively large bias hinder the performance of these simulations. In this study, we applied a machine learning method to estimate surface NO2 concentrations over East Asia, improving the estimation accuracy and resolution compared with previous models. The spatial distribution pattern revealed that NO2 concentrations were relatively high in most territories in South Korea and the North China Plain. Based on the estimated NO2 concentrations, population NO2 exposure and NO2‐related asthma incidence were analyzed for each country. Residents of South Korea and China were exposed to higher NO2 concentrations than those in other countries, and NO2‐related asthma incidence increased by 1.37% annually in East Asia from 2001 to 2015. Therefore, more stringent NOx emission‐control policies are urgently needed in East Asian countries. Key Points: An ensemble machine learning method with satellite NO2 and reanalysis data was adopted to estimate surface NO2 concentration in East AsiaThe populations of South Korea and North China were exposed to the highest NO2 levels, which peaked in 2010 and decreased slightly in 2015NO2‐related asthma incidence increased by 1.37% annually from 2001 to 2015 in East Asia, reaching 139,000 cases in 2015 [ABSTRACT FROM AUTHOR]

As the concentrations of primary components of fine particulate matter (PM 2.5) have substantially decreased, the contribution of secondary inorganic aerosols to PM 2.5 pollution has become more prominent. Therefore, understanding the variations in and characteristics of secondary inorganic aerosols is vital to further reducing PM 2.5 concentrations in the future. In this study, an ensemble back-propagation neural network model was built by combining 3D numerical models, observation data, and machine learning methods, to estimate the concentrations of secondary inorganic aerosols (SO2- 4 , NO- 3 , and NH+ 4) across the Greater Bay Area (GBA) in 2005 and 2015. The ensemble model provided a better estimation than the 3D numerical air quality model, with higher correlation coefficients (approximately 0.85) and lower root mean square errors. The model revealed that the concentrations of the SO2- 4 , NO- 3 , and NH+ 4 decreased by 1.91, 0.20, and 0.49 μg/m3, respectively, from 2005 to 2015. To investigate the oxidation and acidy of sulfate, the sulfur oxidation ratio (SOR), degree of sulfate neutralization (DSN), and particle neutralization ratio (PNR) were calculated and analyzed for 2005 and 2015 across the GBA region. The SOR slightly increased in summer, but decreased in other seasons in 2015, indicating the overall weaker sulfate chemical formation due to sulfur emission control measures. The increasing DSN and PNR indicated that more sulfate was neutralized due to reduced sulfur emission and increased ammonia availability. Our study suggests that more effort is needed to control ammonia emission to further reduce the concentrations of SO2- 4 , NO- 3 , and NH+ 4 across the GBA region in the future. [Display omitted] • Secondary inorganic aerosols were much better estimated by the ensemble BPNN model. • The annual concentration of sulfate over the GBA decreased by 31% from 2005 to 2015. • The decreased SOR indicated the weaker sulfate formation due to sulfur emission control. • The increasing DSN and PNR reflected the decreased aerosol acidity over the GBA region. [ABSTRACT FROM AUTHOR]

PM 2.5 pollution has adverse health effects on humans. Urbanization and long-term meteorological variations play important roles in influencing the PM 2.5 concentration and its associated health effects. Our results indicate that the urbanization process can enhance the PM 2.5 concentration globally. The PM 2.5 -caused mortality density (deaths/100 km2) is also positively correlated with the urbanization degree in both developed and developing countries. The results from machine learning technique revealed that the meteorology-driven variation in PM 2.5 -caused health burden has increased with the increase in the urbanization degree from 1980 to 2018, suggesting that residents living in urban areas are more vulnerable to experiencing unfavorable meteorological conditions (e.g. low wind speed and planetary boundary layer height). The maximum difference in PM 2.5 -caused mortality due to the variation in annual meteorological conditions (between 2013 and 1986) was 270 600 (196 800–317 900). Our findings indicate an urgent need to understand the driving force behind the appearance of unfavorable meteorological situations and propose suitable climate mitigation measures. Image 1 • BPNN has the capability to link the PM 2.5 with emission and meteorology. • PM 2.5 rises by over 100% when the urban fraction increases from 0% to 80–100%. • Death difference related to PM 2.5 in various meteorological fields exceeded 200 000. • The PM 2.5 related health burdens in urban are more sensitive to climate change. [ABSTRACT FROM AUTHOR]