This study analyzed the differences in ozone (O₃) sensitivity in four different urban areas in China from February 2019 to January 2020 based on data on various near-surface pollutants from passive multi-axis differential optical absorption spectroscopy (MAX-DOAS) sites and nearby China National Environmental Monitoring Center (CNEMC) sites. Across the four cities, the nitrogen dioxide (NO₂) and formaldehyde (HCHO) concentrations varied seasonally. Xianghe consistently displayed the lowest NO₂ levels, suggesting reduced emissions compared to other cities. Guangzhou, a city with a robust economy and a high level of vehicle ownership, exhibited higher concentrations in spring. Summer brought elevated HCHO levels in Guangzhou, Xianghe, and Shenyang due to intensified photochemical processes. Autumn and winter showed higher HCHO concentrations in Guangzhou and Xianghe compared to Lanzhou and Shenyang. Overall, Guangzhou recorded the highest annual averages, due to its developed economy, while Xianghe’s lower NO₂ levels were offset by the elevated HCHO due to higher O₃ values. The analysis delved into primary and secondary HCHO sources across seasons and used carbon monoxide (CO) and O₃ data. Xianghe showcased the dominance of secondary sources in summer and autumn, while Lanzhou was characterized by primary dominance throughout the year. Shenyang mirrored Xianghe’s evolution due to industrial emissions. In Guangzhou, due to the high levels of vehicular traffic and sunlight conditions, secondary sources predominantly influenced HCHO concentrations. These findings highlight the interplay between primary and secondary emissions in diverse urban settings. This study explored O₃ sensitivity variations across seasons. Xianghe exhibited a balanced distribution among volatile organic compound (VOC)-limited conditions, nitrogen oxide (NO_x)-limited conditions, and transitional influences. Lanzhou was mainly affected by VOC-limited conditions in winter and NO_x-limited conditions in other seasons. Shenyang’s sensitivity varied with the seasons and was primarily influenced by transitions between VOCs and NO_x in autumn and NO_x-limited conditions otherwise. Guangzhou experienced varied influences. During periods of high O₃ pollution, all regions were affected by NO_x-limited conditions, indicating the necessity of NO_x monitoring in these areas, especially during summer in all regions and during autumn in Xianghe and Guangzhou.

Objective: This study aims to evaluate the effects of dietary supplement of rubber seed oil on production performance, egg quality, and yolk fatty acid composition in laying hens during a 16-week feeding trial period.Methods: Forty-eight 25-week-old laying hens of Hy-Line Brown were randomly divided into three groups. Each group comprised four replicates and each replicate had four birds. Rubber seed oil was incorporated into a corn-soybean meal basal diet by 3.5% (group I), 4.5% (group II), or 0 (control group) and equivalent nutrition was supplied for the test groups and the control group. The performance related values were determined using standard or well established methods.Results: No significant difference was found in the production performance, the egg quality, the composition of saturated fatty acids, and the content of cholesterol and monounsaturated fatty acids in the yolk within the three groups. Interestingly, both test groups achieved a significantly higher content of linoleic acid, α-linolenic acid, eicosapentaenoic acid, and docosahexaenoic acid and a significantly lower content of arachidonic acid (p<0.05) compared with the control group. With the increased level of dietary rubber seed oil, there was an increasing trend in the content of n-6 polyunsaturated fatty acids (PUFA), n-3 PUFA and total PUFA, but a declining trend in the n-6/n-3 ratio.Conclusion: These results demonstrate that the rubber seed oil supplemented diet effectively improved the total PUFA content in eggs without impairing the layers’ production performance and the egg quality.

The vertical profile of PM_2.5 is important for understanding its secondary formation, transport, and deposition at high altitudes; it also provides important data support for studying the causes and sources of PM_2.5 near the ground. Based on machine learning methods, this study fully utilized simultaneous Multi-Axis Differential Optical Absorption Spectroscopy measurements of multiple air pollutants in the atmosphere and employed the measured vertical profiles of aerosol extinction—as well as the vertical profiles of precursors such as NO₂ and SO₂—to evaluate the vertical distribution of PM_2.5 concentration. Three machine learning models (eXtreme Gradient Boosting, Random Forest, and back-propagation neural network) were evaluated using Multi-Axis Differential Optical Absorption Spectroscopy instruments in four typical cities in China: Beijing, Lanzhou, Guangzhou, and Hefei. According to the comparison between estimated PM_2.5 and in situ measurements on the ground surface in the four cities, the eXtreme Gradient Boosting model has the best estimation performance, with the Pearson correlation coefficient reaching 0.91. In addition, the in situ instrument mounted on the meteorological observation tower in Beijing was used to validate the estimated PM_2.5 profile, and the Pearson correlation coefficient at each height was greater than 0.7. The average PM_2.5 vertical profiles in the four typical cities all show an exponential pattern. In Beijing and Guangzhou, PM_2.5 can diffuse to high altitudes between 500 and 1000 m; in Lanzhou, it can diffuse to around 1500 m, while it is primarily distributed between the near surface and 500 m in Hefei. Based on the vertical distribution of PM_2.5 mass concentration in Beijing, a high-altitude PM_2.5 pollutant transport event was identified from January 19th to 21st, 2021, which was not detected by ground-based in situ instruments. During this process, PM_2.5 was transported from the 200 to 1500 m altitude level and then sank to the near surface, causing the concentration on the ground surface to continuously increase. The sinking process contributes to approximately 7% of the ground surface PM_2.5 every hour.

Urban air pollution has become a regional environmental problem. In order to explore whether island areas were affected by the urban development of surrounding areas, in this paper, we systematically study the vertical distribution characteristics of atmospheric components, meteorological drivers, potential pollution sources, and the population health risks of fine particulate matter in island cities in China. The vertical profiles of three atmospheric pollutants (aerosols, NO₂, and HCHO) in the lower troposphere of Huaniao Island in the East China Sea (ECS) were obtained using ground-based multi-axial differential optical absorption spectroscopy (MAX-DOAS). The results show that the aerosol extinction coefficients, NO₂, and HCHO were primarily distributed at altitudes below 1 km, and the atmospheric pollutants in Zhoushan were obviously affected by high-altitude transfer. The main meteorological driving factors of aerosols, NO₂, and HCHO were different at different altitudes. The key factor contributing to the high column concentrations of NO₂ and HCHO in the upper air (greater than 400 m) was the transport of pollutants brought about by changes in wind speed. By exploring the main potential sources of atmospheric pollutants, it was found that the main sources of aerosols, NO₂, and HCHO are coastal cities in the Yangtze River Delta, including southeast Zhejiang Province, southeast Fujian Province, Shanghai, ECS, and the Yellow Sea. Compared with aerosols and HCHO, local primary emissions are an important source of NO₂, which are mainly related to industrial activities in Zhoushan Port. In addition, using the expose-response function model, the number of attributable cases of PM_2.5 air pollution in Zhoushan City in 2019 accounted for 6.58% of the total population. This study enriches our understanding of the vertical distribution characteristics of atmospheric composition and health risk assessment on Chinese islands.