Long-term observations of NO2 using GEMS in China: Validations and regional transport.

In 2019, South Korea launched the Geostationary Environment Monitoring Spectrometer (GEMS) to observe trace gases with an hourly temporal resolution. Compared to previous payloads on polar-orbiting satellites, the GEMS payload has significant advantages in detecting the diurnal variation characteristics of NO2. However, there is still a lack of ground-based validations regarding the overall accuracy of GEMS in the Chinese region. In this study, we conducted a systematic ground validation of GEMS NO2 data in China for the first time. We validated the accuracy of GEMS NO2 data in four typical pollution regions in China, namely the Beijing-Tianjin-Hebei region (JJJ), the Yangtze River Delta region (YRD), the Pearl River Delta region (PRD), and the Sichuan Basin region (SCB), based on MAX-DOAS and CNEMC data. The averaged correlations using the two datasets for validation were 0.81 and 0.57, respectively, indicating a high level of accuracy for the data in China. Using the GEMS seasonal averaged NO2 data, we studied the distribution of NO2 levels in the four regions. We found that the highest NO2 in all four regions occurred during winter with concentrations of 1.84 × 1016 molecules cm-2, 1.59 × 1016 molecules cm-2, 1.58 × 1016 molecules cm-2 and 9.47 × 1015 molecules cm-2, respectively. The distribution of NO2 was closely related to the terrain. Additionally, we observed a significant underestimation issue with TROPOMI, exceeding 30 % in many regions. Based on MAX-DOAS, we investigated the vertical distribution of NO2 in the four regions and found that NO2 was mainly concentrated below 0.5 km. with the HNU station having the lowest concentration, averaging only 2.12 ppb, which was approximately 41 % of the highest concentration recorded at the CQ station. Furthermore, we conducted a study on regional and cross-regional transport using a combination of MAX-DOAS and GEMS data. We found that the transport flux of NO2 could increase by over 500 % within 1 h, making a significant contribution to local NO2 concentrations. The joint observations of GEMS and MAX-DOAS will provide reliable data support for NO2 research and control in China, making a substantial contribution to environmental protection and sustainable development.

Influence of Tourism Economy on Air Quality-An Empirical Analysis Based on Panel Data of 102 Cities in China.

The relationship between regional tourism development and air quality is complex. Although air pollution restricts tourists' willingness to travel, the air pollution produced by tourism and its ancillary industries can also not be ignored. Using the annual panel data of PM2.5 concentration and tourism revenue at the city level, and comprehensively using the Panel VAR model, Geodetector and other analysis methods, we explored the spatio-temporal relationship between the tourism economy and its impact on air quality in China. The main conclusions are as follows: first, the "Kuznets" curve of tourism development and air pollution in mainland China from 2004 to 2016 is generally significant-that is, the tourism economy and air pollution generally show an "inverted U-shaped" relationship. Second, the tourism economy has a positive effect on air pollution in the short term, and this effect is stronger in the eastern region. Third, tourism economy is not the leading factor affecting the change in regional air pollution. GDP and industrial structure are more likely to have the greatest impact on air pollution, and the effect of this "joint force" factor on air pollution is greater than that of other single factors. In the future, the high-quality development of China's tourism economy needs to take environmental protection into consideration, and advocate for low-carbon travel and green tourism.

Machine-learning-based computationally efficient particle size distribution retrieval from bulk optical properties.

Retrieval of particle size distribution from bulk optical properties based on evolutionary algorithms is usually computationally expensive. In this paper, we report an efficient numerical approach to solving the inverse scattering problem by accelerating the calculation of bulk optical properties based on machine learning. With the assumption of spherical particles, the forward scattering by particles is first solved by Mie scattering theory and then approximated by machine learning. The particle swarm optimization algorithm is finally employed to optimize the particle size distribution parameters by minimizing the deviation between the target and simulated bulk optical properties. The accuracies of machine learning and particle swarm optimization are separately investigated. Meanwhile, both monomodal and bimodal size distributions are tested, considering the influences of random noise. Results show that machine learning is capable of accurately predicting the scattering efficiency for a specific size distribution in approximately 0.5 µs on a standalone computer. Therefore, the proposed method has the potential to serve as a powerful tool in real-time particle size measurement due to its advantages of simplicity and high efficiency.