Wind energy, as a vital renewable energy source, also plays a significant role in reducing carbon emissions and mitigating climate change. It is therefore of utmost necessity to evaluate ocean wind energy resources for electricity generation and environmental management. Ocean wind distribution around the globe can be obtained from satellite observations to compensate for limited in situ measurements. However, previous studies have largely ignored uncertainties in ocean wind energy resources assessment with multiple satellite data. It is against this background that the current study compares mean wind speeds (MWS) and wind power densities (WPD) retrieved from scatterometers (QuikSCAT, ASCAT) and radiometers (WindSAT) and their different combinations with National Data Buoy Center (NDBC) buoy measurements at heights of 10 m and 100 m (wind turbine hub height) above sea level. Our results show an improvement in the accuracy of wind resources estimation with the use of multiple satellite observations. This has implications for the acquisition of reliable data on ocean wind energy in support of management policies.

Understanding the response of tropospheric ozone (O₃) to nitrogen dioxide (NO₂) change is important for local O₃ control. The relationship between O₃ and NO₂ at county scale in China has been extensively studied using models, but there is a lack of results from direct measurements. In this study, we used measurements of O₃, NO₂ and meteorological conditions from a dense network in the Yangtze River Delta (YRD), and satellite observed formaldehyde (HCHO) and NO₂ column densities for the analysis of O₃ variabilities and its relationship to NO₂. As a result, severe O₃ pollution occurred mainly in Shanghai city, southern Jiangsu and northern Zhejiang provinces in YRD during April–September. In addition, meteorological conditions could explain 54% the diurnal O₃ variation over YRD. During April–September 2015–2021, O₃ showed a significant positive relationship (r = 0.61 ± 0.10) with NO₂ after removing the impact from meteorological conditions. However, the relationship could be reversed with NO₂ concentration change. Our result suggested that the controllable O₃ related to NO₂ change is up to 100 μg·m⁻³ in megacities over Shanghai and northern Zhejiang province. The O₃ is much more sensitive to the NO₂ reduction in megacities than surrounding areas. Our results evaluate the different impacts of NO₂ changes on O₃ formation, which provides explanation for the simultaneously alleviated O₃ pollution and reduced NO₂ in 2020 in Shanghai and northern Zhejiang, as well as the increased O₃ in most counties before 2019 with reduced NO₂ during October–March. The driving mechanism as revealed from this study for O₃ and NO₂ will be valuable for the O₃ abatement through NO₂ reduction at sub-county scale over YRD in China.

In the context of global warming, how to measure summer climate suitability at a local scale is important for meteorological services. Considering meteorological and ecological conditions, body comfort, and the atmospheric environment, an assessment method for summer climate suitability for Zhejiang Province is proposed. In this paper, a summer suitable index (SSI) for Zhejiang is calculated, including four secondary indices: a summer cool index (SCI), a comfort days index (CDI), a good air days index (GADI) and a vegetation cover index (VCI). Using a local evaluation criterion, summer climate suitable areas are distinguished objectively according to the SSI. The results show that especially suitable regions account for 4.97% of Zhejiang Province, very suitable regions account for 22.2%, suitable regions account for 39.58%, and general regions account for 33.25%. The summer climate suitable areas are located mainly in high mountains and hills and coastal island areas while plain areas cannot be considered a suitable destination for summer tourism. By comparison and discussion, the SSI is demonstrated to capture summer climate suitability well. In contrast to a fixed evaluation index, benchmark values obtained for the SSI depend on the local climate and the index is straightforward to apply.