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We have analyzed the surface current characteristics along the coast of the Chinese mainland in autumn and winter by the trajectories of coastal drifters in a previous study (Yang et al., 2021). This paper is a follow-up study focusing on the surface current characteristics in spring. By the trajectories of coastal drifters in April-May 2019, we continue to analyze the characteristics of surface currents. It is indicated that: (1) the surface currents were generally southwestward along the coasts of Zhejiang and Fujian, while they flowed northeastward in the offshore area in April 2019. (2) In May 2019, the surface current in the sea area north of 28°N had a small speed without a fixed direction nearshore, but had larger speed offshore. (3) The surface currents along the Zhejiang coast were variable in April-May 2019, mainly corresponding to the change of wind field. (4) The coastal drifter is a useful instrument to observe circulation systems more precisely in nearshore but not deep ocean. [ABSTRACT FROM AUTHOR]

Surface ozone (O3) is influenced not only by anthropogenic emissions but also by meteorological factors, with wind direction being one of the most overlooked factors. Here, we combine the observational data of both O3 and wind flow to compare the variation in surface O3 with wind direction between coastal and inland regions of Fujian, a province in the southeast coast of China with complicated topography. We further conduct a numerical simulation using a global chemical transport model, GEOS-Chem, to interpret the observational results, explore the linkages between these O3 variations and wind flows, and identify the dominant processes for the occurrence of high O3 that varies with wind flows. The results from the observations over 2015–2021 suggest that, over coastal regions, surface O3 concentrations show a strong dependence on wind flow changes. On average, during the daytime, when southeasterly winds prevail, the mean of O3 concentrations reaches 83.5 μg/m3, which is 5.0 μg/m3 higher than its baseline values (the mean O3 concentrations), while the northwesterly winds tend to reduce surface O3 by 6.4 μg/m3. The positive O3 anomalies with southeasterly wind are higher in the autumn and summer than in the spring and winter. During the nighttime, the onshore northeasterly winds are associated with enhanced O3 levels, likely due to the airmass containing less NO2, alleviating the titration effects. Over inland regions, however, surface O3 variations are less sensitive to wind flow changes. The GEOS-Chem simulations show that the prevailing southeasterly and southwesterly winds lead to the positive anomaly of chemical reactions of O3 over coastal regions, suggesting enhanced photochemical production rates. Furthermore, southeasterly winds also aid in transporting more O3 from the outer regions into the coastal regions of Fujian, which jointly results in elevated surface O3 when southeasterly winds dominates. When affected by wind flows in different directions, the chemical reaction and transport in the inland regions do not exhibit significant differences regarding their impact on O3. This could be one of the reasons for the difference in O3 distribution between coastal and inland regions. This study could help to deepen our understanding of O3 pollution and aid in providing an effective warning of high-O3 episodes. [ABSTRACT FROM AUTHOR]

Based on the observation data from 37 national ambient air automatic monitoring stations and the land use/cover data in Fujian Province, the effects of land use/cover on the annual and seasonal variations in the air quality index (AQI), PM2.5 concentration and PM10 concentration are analyzed. In addition, buffer zones with different radii are established to calculate landscape pattern metrics and the influences of landscape patterns on the AQI, PM2.5 concentration and PM10 concentration at different scales are discussed. The results show that land use/cover types have remarkable impacts on the variations in the atmospheric particulate matter concentrations. The AQI and PM10 concentration are the highest for construction land, and the PM2.5 concentration is the highest for cultivated land. The seasonal variations in all air pollutant concentrations show similar characteristics, i.e., high in spring and winter and low in summer and autumn. Different landscape metrics have diverse effects on atmospheric particulate matter concentrations at different scales. In the buffer zone with a 5000 m radius, the patch number and patch density of forest land are positively correlated with the PM2.5 concentration, while the edge density of construction land has a negative correlation with it, indicating that landscape fragmentation affects the PM2.5 concentration. More fragmented forest land has a weaker effect on the reduction in the PM2.5 concentration, and more fragmented construction land has a weaker effect on the increase in the PM2.5 concentration. Moreover, the seasonal variations in the atmospheric particulate matter concentrations are different under different land use/cover types. Except for autumn, the AQI and PM2.5 concentration are most noticeably affected by forest land in all seasons, showing negative correlations. In autumn, the impacts of cultivated land on the AQI and PM2.5 concentration are more pronounced. The PM10 concentration is substantially affected by forest land in spring and summer and is markedly influenced by construction land in autumn and winter. The analysis of the landscape metrics of forest land and construction land at different scales indicates that the optimal scale is 5000 m for studying the annual average of the AQI and PM10 concentration and is 3000 m for investigating the annual average of the PM2.5 concentration. The optimal scales to research the seasonal variations in the AQI, PM2.5 and PM10 concentrations are 4000–5000 m for forest land and construction land, while the optimal scale is 1000 m for cultivated land to research the AQI and PM2.5 in autumn. This study can provide a scientific basis for the optimization of land use/cover and landscape patterns in Fujian Province, the planning and management of green space and the selection of research scales in the future. [ABSTRACT FROM AUTHOR]